diff --git a/.github/workflows/pyliny.yaml b/.github/workflows/pyliny.yaml new file mode 100644 index 0000000..3f70566 --- /dev/null +++ b/.github/workflows/pyliny.yaml @@ -0,0 +1,35 @@ +name: Code Quality + +on: + push: + branches: [ main, develop ] + pull_request: + branches: [ main, develop ] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pre-commit + + - name: Cache pre-commit environment + uses: actions/cache@v3 + with: + path: ~/.cache/pre-commit + key: pre-commit-${{ runner.os }}-${{ hashFiles('.pre-commit-config.yaml') }} + restore-keys: | + pre-commit-${{ runner.os }}- + + - name: Run pre-commit hooks + run: | + pre-commit run --all-files --show-diff-on-failure diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml new file mode 100644 index 0000000..0039ec9 --- /dev/null +++ b/.github/workflows/pypi.yaml @@ -0,0 +1,97 @@ +name: Build and publish python package + +on: + push: + tags: + - "v*" + workflow_dispatch: + inputs: + version: + description: 'Version to publish (e.g., 0.1.0)' + required: false + type: string + +jobs: + publish: + runs-on: ubuntu-latest + permissions: + contents: write # Required for creating GitHub releases + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for tags + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Extract version from tag or input + id: get_version + run: | + if [ "${{ github.event_name }}" == "workflow_dispatch" ] && [ -n "${{ github.event.inputs.version }}" ]; then + echo "version=${{ github.event.inputs.version }}" >> $GITHUB_OUTPUT + elif [ -n "${{ github.ref_name }}" ]; then + # Extract version from tag (e.g., v0.1.0 -> 0.1.0) + VERSION=$(echo "${{ github.ref_name }}" | sed 's/^v//') + echo "version=$VERSION" >> $GITHUB_OUTPUT + else + # Fallback: extract from pyproject.toml + VERSION=$(grep -E '^version = ' pyproject.toml | sed 's/version = "\(.*\)"/\1/') + echo "version=$VERSION" >> $GITHUB_OUTPUT + fi + echo "Publishing version: $(cat $GITHUB_OUTPUT | grep version | cut -d'=' -f2)" + + - name: Verify version matches pyproject.toml + run: | + PUBLISH_VERSION="${{ steps.get_version.outputs.version }}" + FILE_VERSION=$(grep -E '^version = ' pyproject.toml | sed 's/version = "\(.*\)"/\1/') + if [ "$PUBLISH_VERSION" != "$FILE_VERSION" ]; then + echo "Error: Version mismatch!" + echo "Tag/Input version: $PUBLISH_VERSION" + echo "pyproject.toml version: $FILE_VERSION" + exit 1 + fi + echo "✓ Version verified: $PUBLISH_VERSION" + + - name: Install build tools + run: | + python -m pip install --upgrade pip + pip install build twine + + - name: Build package + run: python -m build + + - name: Check package contents + run: | + echo "Built files:" + ls -lh dist/ + echo "" + echo "Package info:" + python -m twine check dist/* + + - name: Publish to PyPI + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} + run: twine upload dist/* + + - name: Create GitHub Release + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/') + uses: softprops/action-gh-release@v1 + with: + tag_name: ${{ github.ref_name }} + name: Release ${{ steps.get_version.outputs.version }} + body: | + ## Release ${{ steps.get_version.outputs.version }} + + Published to PyPI: https://pypi.org/project/spherical-deepkriging/${{ steps.get_version.outputs.version }}/ + + ### Installation + ```bash + pip install spherical-deepkriging==${{ steps.get_version.outputs.version }} + ``` + draft: false + prerelease: false + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..fa1cf72 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,41 @@ +name: Test + +on: + push: + branches: ["main", "develop"] + pull_request: + branches: ["main", "develop"] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.10"] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[dev,mrts]" + - name: Test with pytest + run: | + pytest tests/ \ + -m "not slow and not integration" \ + --cov=spherical_deepkriging \ + --cov-config=.coveragerc \ + --cov-fail-under=90 \ + --cov-report=xml \ + --cov-report=term \ + -v --tb=short + env: + PYTHONPATH: ${{ github.workspace }} + - name: Upload coverage reports to Codecov + uses: codecov/codecov-action@v5 + with: + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitignore b/.gitignore index a07616f..10353e4 100644 --- a/.gitignore +++ b/.gitignore @@ -152,3 +152,6 @@ dmypy.json # Cython debug symbols cython_debug/ + +# Coverage configuration +.coveragerc diff --git a/=0.4.0, b/=0.4.0, new file mode 100644 index 0000000..e69de29 diff --git a/MANIFEST.in b/MANIFEST.in index 400e878..d6e4a57 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,4 +3,3 @@ global-exclude *.so *.pyd *.dylib *.dll *.a *.lib # Include C++ sources for the spherical basis extension in sdist builds. recursive-include spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions *.cpp *.h *.hpp *.txt *.py CMakeLists.txt - diff --git a/MIGRATION_NOTE.txt b/MIGRATION_NOTE.txt deleted file mode 100644 index 238c272..0000000 --- a/MIGRATION_NOTE.txt +++ /dev/null @@ -1 +0,0 @@ -Public repo bootstrap location created by automation. diff --git a/Makefile b/Makefile index daa0627..0d6fe1c 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,8 @@ SHELL := /bin/bash -EXECUTABLE := poetry run +CONDA_BIN ?= /home/egpivo/miniconda3/bin/conda +TEST_ENV ?= spherical-deepkriging -.PHONY: clean install-dev setup-jupyter-kernel build-spherical-cpp test run-local-jupyter help +.PHONY: clean install-dev install-test-deps setup-jupyter-kernel build-spherical-cpp test run-local-jupyter help ## Clean up temporary files clean: @@ -17,13 +18,19 @@ clean: ## Install development dependencies install-dev: @echo "Installing development dependencies..." - @$(SHELL) envs/conda/build_conda_env.sh -c spherical-deepkriging && \ + @$(SHELL) envs/conda/build_conda_env.sh -c $(TEST_ENV) && \ + $(MAKE) install-test-deps && \ $(MAKE) setup-jupyter-kernel +## Install test dependencies in the Conda environment +install-test-deps: + @echo "Installing test dependencies into $(TEST_ENV)..." + @$(CONDA_BIN) run -n $(TEST_ENV) python -m pip install -e ".[dev]" + ## Install Jupyter dependencies and register kernel setup-jupyter-kernel: @echo "Setting up Jupyter kernel..." - @$(SHELL) envs/jupyter/setup_jupyter_kernel.sh -k spherical-deepkriging + @$(SHELL) envs/jupyter/setup_jupyter_kernel.sh -k $(TEST_ENV) ## Build spherical basis C++ extension (mrts_sphere) build-spherical-cpp: @@ -37,7 +44,8 @@ build-spherical-cpp: ## Run tests test: @echo "Running tests..." - @$(EXECUTABLE) pytest --cov=spherical_deepkriging + @$(CONDA_BIN) run -n $(TEST_ENV) python -c "import pytest, pytest_cov" >/dev/null 2>&1 || (echo "Missing test dependencies in Conda env $(TEST_ENV). Run: make install-test-deps" && exit 1) + @PYTHONPATH=. $(CONDA_BIN) run -n $(TEST_ENV) python -m pytest --cov=spherical_deepkriging --cov-config=.coveragerc ## Start Jupyter server locally run-local-jupyter: @@ -49,8 +57,9 @@ help: @echo "Available targets:" @echo " clean : Clean up temporary files" @echo " install-dev : Install dependencies, build package/C++ extension, and setup Jupyter kernel" + @echo " install-test-deps : Install pytest/coverage tooling into the Conda env" @echo " setup-jupyter-kernel : Install Jupyter deps and register kernel for spherical-deepkriging" - @echo " build-spherical-cpp : Build spherical basis C++ extension" + @echo " build-spherical-cpp : Build spherical basis C++ extension" @echo " test : Run tests" @echo " run-local-jupyter : Start Jupyter server locally" - @echo " help : Display this help message" + @echo " help : Display this help message" diff --git a/README.md b/README.md index 4fa70ed..ee715eb 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ # Spherical DeepKriging +[![Tests](https://github.com/STLABTW/spherical-deepkriging/workflows/Test/badge.svg)](https://github.com/STLABTW/spherical-deepkriging/actions) +[![codecov](https://codecov.io/github/STLABTW/spherical-deepkriging/graph/badge.svg?token=OF0LKVDII6)](https://codecov.io/github/STLABTW/spherical-deepkriging) A deep learning framework for spatial prediction on the sphere, combining DeepKriging with spherical harmonic basis functions (MRTS-sphere), Wendland basis, and Universal Kriging. @@ -20,7 +22,7 @@ The package currently provides the following basis families: The default feedforward hidden-block design is illustrated below: -![DeepKriging hidden block](artifacts/hidden_block.png) +DeepKriging hidden block --- diff --git a/envs/jupyter/utils.sh b/envs/jupyter/utils.sh index 61d85ca..2cab797 100644 --- a/envs/jupyter/utils.sh +++ b/envs/jupyter/utils.sh @@ -28,7 +28,8 @@ update_conda_env_path() { check_kernel_availability() { local KERNEL_NAME=$1 - local KERNEL_PATH=$(jupyter kernelspec list | grep -o "^${KERNEL_NAME} .*" | cut -d' ' -f2) + local KERNEL_PATH + KERNEL_PATH=$(jupyter kernelspec list 2>/dev/null | awk -v k="${KERNEL_NAME}" '$1==k {print $2; exit}') if [ -z "${KERNEL_PATH}" ]; then echo -e "${FG_RED}Kernel '${KERNEL_NAME}' is not available${FG_RESET}" @@ -48,7 +49,7 @@ set_jupyter_kernel_path() { ipython kernel install --name "${KERNEL_NAME}" --user fi - KERNEL_PATH=$(jupyter kernelspec list | grep -o "^${KERNEL_NAME} .*" | cut -d' ' -f2) + KERNEL_PATH=$(jupyter kernelspec list 2>/dev/null | awk -v k="${KERNEL_NAME}" '$1==k {print $2; exit}') if [ -n "${KERNEL_PATH}" ]; then KERNEL_DIR="${KERNEL_PATH}" diff --git a/examples/README.md b/examples/README.md index 1ddb7e4..219abba 100644 --- a/examples/README.md +++ b/examples/README.md @@ -31,12 +31,67 @@ conda activate spherical-deepkriging cd examples/simulation ``` -Main scripts: -- `run_stdscaler_nonoise_50reps.py` -- `run_stdscaler_outliers_50reps.py` +### Reproducing Simulation Experiment -These scripts checkpoint progress and can be resumed safely. +The `examples/simulation/` folder contains self-contained Python scripts and output notebooks for reproducing the 50-repeat simulation study used in the paper. Each script runs a full experiment, checkpoints after every repeat, and can be safely interrupted and resumed. -Pre-executed output notebooks are also included in `examples/simulation/` for result inspection. +#### Experiment scripts +| Script | Scenario | Output checkpoint | +|--------|----------|-------------------| +| `run_stdscaler_nonoise_50reps.py` | Local plus nonstationary signal, no noise | `results_sphere_stdscaler_nonoise_50reps_checkpoint.csv` | +| `run_stdscaler_outliers_50reps.py` | Local plus nonstationary signal with outliers | `results_sphere_stdscaler_outliers_50reps_checkpoint.csv` | + +Each script benchmarks the following models over 50 random seeds: + +- `OLS_wendland` — Ordinary Least Squares with Wendland basis +- `OLS_sphere` — Ordinary Least Squares with spherical harmonic (MRTS-sphere) basis +- `DeepKriging_wendland` — DeepKriging with Wendland basis +- `DeepKriging_mrts` — DeepKriging with MRTS basis +- `DeepKriging_sphere` — DeepKriging with MRTS-sphere basis (MSE loss) +- `DeepKriging_sphere_Huber` — DeepKriging with MRTS-sphere basis (Huber loss, robust to outliers) +- `UniversalKriging` — Universal Kriging with MRTS-sphere basis + +#### How to run + +Make sure the Conda environment is activated, then navigate to the Rerun folder and run: + +```bash +conda activate deepkriging +cd notebook/simulation/Rerun + +# No-noise scenario +python run_stdscaler_nonoise_50reps.py + +# Outliers scenario +python run_stdscaler_outliers_50reps.py +``` + +Each script: +- Runs 50 independent repeats with different random seeds. +- Saves a checkpoint CSV after every completed repeat — safe to kill (`Ctrl+C`) and re-run; already-completed repeats are skipped automatically. +- Prints a per-repeat results table (MSPE, RMSE, MAE, R², Time) to stdout. +- Prints a final mean±std summary table across all 50 repeats at the end. + +#### Key hyperparameters (configured inside each script) + +| Parameter | Value | +|-----------|-------| +| Training samples | 2500 | +| Repeats | 50 | +| Huber delta | 1.345 | +| Knot / order budget | 1400 | +| Batch size | see script | + +#### Output notebooks + +Pre-executed output notebooks are included in `examples/simulation/Rerun/` for reference: + +| Notebook | Description | +|----------|-------------| +| `simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb` | GP signal, no noise | +| `simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb` | GP signal with noise | +| `simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb` | GP signal with outliers | +| `simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb` | Local extreme signal, no noise | +| `simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb` | Local extreme signal with noise |s \ No newline at end of file diff --git a/notebook/real_data/experiment_deepkriging_precipitation.ipynb b/examples/real_data/experiment_deepkriging_precipitation.ipynb similarity index 78% rename from notebook/real_data/experiment_deepkriging_precipitation.ipynb rename to examples/real_data/experiment_deepkriging_precipitation.ipynb index 493861e..ad85eb7 100644 --- a/notebook/real_data/experiment_deepkriging_precipitation.ipynb +++ b/examples/real_data/experiment_deepkriging_precipitation.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "73a5f785", "metadata": { "execution": { @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b67cc84b", "metadata": { "execution": { @@ -63,29 +63,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769072522.816512 1882549 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769072522.820591 1882549 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769072522.832091 1882549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769072522.832111 1882549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769072522.832113 1882549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769072522.832114 1882549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import multiprocessing as mp\n", "if mp.get_start_method(allow_none=True) is None:\n", @@ -139,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e5802f99", "metadata": { "execution": { @@ -205,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "46760605", "metadata": { "execution": { @@ -223,21 +201,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], + "outputs": [], "source": [ "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", @@ -267,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "30fcd1bf", "metadata": { "execution": { @@ -556,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c046277c", "metadata": { "execution": { @@ -615,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "21e69d9f", "metadata": { "execution": { @@ -650,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "4c4156ca", "metadata": { "execution": { @@ -668,168 +632,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769072772.763414 1882549 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769072777.830547 1883435 service.cc:152] XLA service 0x7c6518002a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769072777.830612 1883435 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769072778.046672 1883435 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769072778.730948 1883435 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.0701 0.2719 0.1001 0.0643 \n", - "100 0.0667 0.2656 0.0977 0.1075 \n", - "200 0.0634 0.2584 0.0949 0.1552 \n", - "500 0.0575 0.2445 0.0885 0.2436 \n", - "700 0.0551 0.2392 0.0856 0.2759 \n", - "1000 0.0524 0.2299 0.0798 0.3309 \n", - "1400 0.0495 0.2207 0.0741 0.3834 *\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.0145 0.0946 0.0258 0.8867 \n", - "100 0.0139 0.0907 0.0214 0.8959 \n", - "200 0.0115 0.0818 0.0193 0.9154 *\n", - "500 0.0129 0.0888 0.0237 0.9001 \n", - "700 0.0116 0.0873 0.0228 0.9035 \n", - "1000 0.0153 0.1044 0.0272 0.8620 \n", - "1400 0.0134 0.0957 0.0197 0.8842 \n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.0039 0.0884 0.0224 0.9011 \n", - "100 0.0038 0.0866 0.0208 0.9050 \n", - "200 0.0035 0.0838 0.0196 0.9111 \n", - "500 0.0033 0.0835 0.0174 0.9118 *\n", - "700 0.0035 0.0839 0.0172 0.9108 \n", - "1000 0.0040 0.0966 0.0177 0.8819 \n", - "1400 0.0038 0.0917 0.0166 0.8937 \n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1172, sigma²=0.1303, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1132, sigma²=0.1258, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1118, sigma²=0.1243, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0979, sigma²=0.1090, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0931, sigma²=0.1036, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0865, sigma²=0.0961, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0784, sigma²=0.0869, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.0117 0.0865 0.0156 0.9054 *\n", - "100 0.0117 0.0865 0.0156 0.9053 \n", - "200 0.0117 0.0865 0.0156 0.9053 \n", - "500 0.0117 0.0865 0.0156 0.9053 \n", - "700 0.0117 0.0865 0.0156 0.9052 \n", - "1000 0.0117 0.0866 0.0156 0.9052 \n", - "1400 0.0117 0.0866 0.0156 0.9051 \n" - ] - } - ], + "outputs": [], "source": [ "# OLS_wendland\n", "parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", @@ -1017,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "af964066", "metadata": { "execution": { @@ -1035,25 +838,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | RMSE | MAE | R² | Times |\n", - "|--------------------------|---------|--------|--------|--------|----------|\n", - "| OLS_wendland | -- | 0.2736 | 0.1023 | 0.0525 | 57.0352 |\n", - "| OLS_sphere | 1400 | 0.2207 | 0.0741 | 0.3834 | 7.6942 |\n", - "| DeepKriging_wendland | -- | 0.2335 | 0.0925 | 0.31 | 629.686 |\n", - "| DeepKriging_sphere | 200 | 0.0818 | 0.0193 | 0.9154 | 479.752 |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0835 | 0.0174 | 0.9118 | 412.297 |\n", - "| UniversalKriging | 50 | 0.0865 | 0.0156 | 0.9054 | 457.656 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 0.0818)\n" - ] - } - ], + "outputs": [], "source": [ "result_table = []\n", "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", @@ -1108,4 +893,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebook/real_data/experiment_deepkriging_temperature.ipynb b/examples/real_data/experiment_deepkriging_temperature.ipynb similarity index 79% rename from notebook/real_data/experiment_deepkriging_temperature.ipynb rename to examples/real_data/experiment_deepkriging_temperature.ipynb index 3a262f2..c8b2258 100644 --- a/notebook/real_data/experiment_deepkriging_temperature.ipynb +++ b/examples/real_data/experiment_deepkriging_temperature.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "73a5f785", "metadata": { "execution": { @@ -63,29 +63,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769063303.671989 1684930 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769063303.676881 1684930 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769063303.690927 1684930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769063303.690955 1684930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769063303.690956 1684930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769063303.690957 1684930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import multiprocessing as mp\n", "if mp.get_start_method(allow_none=True) is None:\n", @@ -143,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e5802f99", "metadata": { "execution": { @@ -209,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "46760605", "metadata": { "execution": { @@ -227,21 +205,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], + "outputs": [], "source": [ "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", @@ -271,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "30fcd1bf", "metadata": { "execution": { @@ -605,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c046277c", "metadata": { "execution": { @@ -664,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "21e69d9f", "metadata": { "execution": { @@ -699,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "4c4156ca", "metadata": { "execution": { @@ -717,168 +681,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769063562.942254 1684930 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769063567.562337 1686188 service.cc:152] XLA service 0x7c85cc089d50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769063567.562414 1686188 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769063567.824978 1686188 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769063568.473334 1686188 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 25.1212 5.0502 3.5395 0.9355 \n", - "100 18.5157 4.3476 3.0420 0.9522 \n", - "200 10.0154 3.1976 2.1803 0.9741 \n", - "500 5.7575 2.4136 1.5773 0.9853 \n", - "700 4.6689 2.1885 1.3856 0.9879 \n", - "1000 3.7289 1.9622 1.2252 0.9903 \n", - "1400 2.8940 1.7436 1.0747 0.9923 *\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.3444 0.5905 0.3627 0.9991 \n", - "100 0.3196 0.5689 0.3213 0.9992 \n", - "200 0.2913 0.5395 0.3152 0.9993 \n", - "500 0.1866 0.4282 0.2294 0.9995 \n", - "700 0.2541 0.4971 0.3033 0.9994 \n", - "1000 0.2545 0.4945 0.2861 0.9994 \n", - "1400 0.1530 0.3850 0.1894 0.9996 *\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.1295 0.5743 0.3050 0.9992 \n", - "100 0.0976 0.4894 0.2666 0.9994 *\n", - "200 0.1185 0.5495 0.2994 0.9992 \n", - "500 0.1075 0.5102 0.2895 0.9993 \n", - "700 0.1434 0.6126 0.3395 0.9991 \n", - "1000 0.0977 0.4905 0.2581 0.9994 \n", - "1400 0.1295 0.5644 0.3301 0.9992 \n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.8652, sigma²=259.3483, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.1354, sigma²=226.6249, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=6.0789, sigma²=266.2976, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1804, sigma²=51.1214, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7164, sigma²=30.7494, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5289, sigma²=22.4819, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3618, sigma²=15.1184, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.2134 0.4693 0.2241 0.9994 \n", - "100 0.2134 0.4693 0.2241 0.9994 \n", - "200 0.2134 0.4693 0.2241 0.9994 *\n", - "500 0.2134 0.4692 0.2241 0.9994 \n", - "700 0.2134 0.4693 0.2239 0.9994 \n", - "1000 0.2134 0.4693 0.2239 0.9994 \n", - "1400 0.2134 0.4695 0.2243 0.9994 \n" - ] - } - ], + "outputs": [], "source": [ "# OLS_wendland\n", "parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", @@ -1066,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "af964066", "metadata": { "execution": { @@ -1084,25 +887,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | RMSE | MAE | R² | Times |\n", - "|--------------------------|---------|---------|---------|--------|----------|\n", - "| OLS_wendland | -- | 16.2453 | 12.4979 | 0.3324 | 56.8078 |\n", - "| OLS_sphere | 1400 | 1.7436 | 1.0747 | 0.9923 | 7.5642 |\n", - "| DeepKriging_wendland | -- | 12.785 | 7.7603 | 0.5865 | 686.629 |\n", - "| DeepKriging_sphere | 1400 | 0.385 | 0.1894 | 0.9996 | 694.225 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4894 | 0.2666 | 0.9994 | 308.307 |\n", - "| UniversalKriging | 200 | 0.4693 | 0.2241 | 0.9994 | 448.83 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 0.3850)\n" - ] - } - ], + "outputs": [], "source": [ "result_table = []\n", "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", diff --git a/notebook/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb b/examples/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb similarity index 81% rename from notebook/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb rename to examples/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb index da93dfa..2d832b3 100644 --- a/notebook/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb +++ b/examples/real_data/experiment_deepkriging_temperature_repeat_multitime.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "73a5f785", "metadata": { "execution": { @@ -37,15 +37,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/projects/deepkriging-sphere\n" - ] - } - ], + "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore', category=UserWarning)\n", @@ -59,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "b67cc84b", "metadata": { "execution": { @@ -135,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "e5802f99", "metadata": { "execution": { @@ -201,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "46760605", "metadata": { "execution": { @@ -251,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "30fcd1bf", "metadata": { "execution": { @@ -520,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "a539a8b1", "metadata": {}, "outputs": [], @@ -574,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "c046277c", "metadata": { "execution": { @@ -638,173 +630,7 @@ "execution_count": null, "id": "2a9c97e1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 01 | Path: data/data_s_p_date01_hour01.csv\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 01 - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 25.0272 25.4502 \n", - " 100 18.3078 18.6422 \n", - " 200 10.4081 10.6463 \n", - " 500 5.6544 5.8105 \n", - " 700 4.7456 4.8653 \n", - " 1000 3.7859 3.8953 \n", - " 1400 3.1424 3.2273 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.1193 1.1829 \n", - " 100 0.7224 0.7580 \n", - " 200 0.4320 0.4350 \n", - " 500 0.3623 0.3718 \n", - " 700 0.3040 0.3153 *\n", - " 1000 0.3264 0.3263 \n", - " 1400 0.3528 0.3674 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.3264 0.3393 \n", - " 100 0.2400 0.2449 \n", - " 200 0.2909 0.2951 \n", - " 500 0.1928 0.1992 \n", - " 700 0.2505 0.2605 \n", - " 1000 0.1547 0.1643 *\n", - " 1400 0.1724 0.1854 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1494 0.3965 \n", - " 100 0.1105 0.2782 \n", - " 200 0.0936 0.2306 \n", - " 500 0.0786 0.1922 \n", - " 700 0.1034 0.2464 \n", - " 1000 0.0723 0.1846 *\n", - " 1400 0.0985 0.2489 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=6.1153, sigma²=270.4041, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.0843, sigma²=180.2858, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.3579, sigma²=234.7950, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0990, sigma²=47.5551, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7111, sigma²=30.5608, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6378, sigma²=27.1062, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3978, sigma²=16.7173, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2134 0.2203 \n", - " 100 0.2134 0.2203 \n", - " 200 0.2134 0.2202 *\n", - " 500 0.2134 0.2203 \n", - " 700 0.2135 0.2203 \n", - " 1000 0.2134 0.2201 \n", - " 1400 0.2135 0.2201 \n", - " Best order: 200\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.3579, sigma²=234.7950, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 263.911 | 16.2453 | 12.4979 | 0.3324 | 10.46s |\n", - "| OLS_sphere | 1400 | 3.2273 | 1.7965 | 1.0975 | 0.9918 | 8.32s |\n", - "| DeepKriging_wendland | -- | 180.933 | 13.4511 | 8.5047 | 0.5423 | 227.30s |\n", - "| DeepKriging_wendland* | -- | 281.222 | 16.7697 | 12.4447 | 0.2887 | 30.30s |\n", - "| DeepKriging_mrts | 700 | 0.3215 | 0.567 | 0.3406 | 0.9992 | 152.69s |\n", - "| DeepKriging_sphere | 1000 | 0.2743 | 0.5237 | 0.2973 | 0.9993 | 53.98s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.2618 | 0.5116 | 0.2986 | 0.9993 | 69.10s |\n", - "| UniversalKriging | 200 | 0.2202 | 0.4693 | 0.2242 | 0.9994 | 216.90s |\n", - "\n", - "✅ Completed Hour 01 - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 01 - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.5523, sigma²=110.8832, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 265.102 | 16.282 | 12.5276 | 0.3296 | 8.34s |\n", - "| OLS_sphere | 1400 | 3.1455 | 1.7736 | 1.0753 | 0.992 | 5.92s |\n", - "| DeepKriging_wendland | -- | 185.264 | 13.6112 | 8.8547 | 0.5315 | 225.83s |\n", - "| DeepKriging_wendland* | -- | 184.195 | 13.5718 | 8.723 | 0.5342 | 56.93s |\n", - "| DeepKriging_mrts | 700 | 0.3329 | 0.5769 | 0.3394 | 0.9992 | 152.09s |\n", - "| DeepKriging_sphere | 1000 | 0.1734 | 0.4164 | 0.2126 | 0.9996 | 126.21s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.2005 | 0.4478 | 0.2335 | 0.9995 | 90.05s |\n", - "| UniversalKriging | 200 | 0.2521 | 0.502 | 0.2283 | 0.9994 | 224.14s |\n", - "\n", - "✅ Completed Hour 01 - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 01\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 1000, 'DeepKriging_sphere_Huber': 1000, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------|------------|------------|-----------|\n", - "| OLS_wendland | 264.51±0.60 | 16.26±0.02 | 12.51±0.01 | 0.33±0.00 |\n", - "| OLS_sphere | 3.19±0.04 | 1.79±0.01 | 1.09±0.01 | 0.99±0.00 |\n", - "| DeepKriging_wendland | 183.10±2.17 | 13.53±0.08 | 8.68±0.17 | 0.54±0.01 |\n", - "| DeepKriging_wendland* | 232.71±48.51 | 15.17±1.60 | 10.58±1.86 | 0.41±0.12 |\n", - "| DeepKriging_mrts | 0.33±0.01 | 0.57±0.00 | 0.34±0.00 | 1.00±0.00 |\n", - "| DeepKriging_sphere | 0.22±0.05 | 0.47±0.05 | 0.25±0.04 | 1.00±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.23±0.03 | 0.48±0.03 | 0.27±0.03 | 1.00±0.00 |\n", - "| UniversalKriging | 0.24±0.02 | 0.49±0.02 | 0.23±0.00 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model for Hour 01: DeepKriging_sphere (RMSE: 0.47±0.05)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 02 | Path: data/data_s_p_date01_hour02.csv\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 02 - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 25.4134 25.8483 \n", - " 100 19.0263 19.6124 \n", - " 200 10.0620 10.3814 \n", - " 500 5.6368 5.7665 \n", - " 700 4.5064 4.6253 \n", - " 1000 3.6162 3.7518 \n", - " 1400 2.8839 3.0394 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - } - ], + "outputs": [], "source": [ "# Dictionary to hold the final summary table for every hour\n", "all_hours_summary = {}\n", diff --git a/notebook/real_data/experiment_deepkriging_windspeed.ipynb b/examples/real_data/experiment_deepkriging_windspeed.ipynb similarity index 78% rename from notebook/real_data/experiment_deepkriging_windspeed.ipynb rename to examples/real_data/experiment_deepkriging_windspeed.ipynb index 9968102..7ee4a94 100644 --- a/notebook/real_data/experiment_deepkriging_windspeed.ipynb +++ b/examples/real_data/experiment_deepkriging_windspeed.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "73a5f785", "metadata": { "execution": { @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b67cc84b", "metadata": { "execution": { @@ -63,29 +63,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769083139.327430 2109858 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769083139.331338 2109858 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769083139.342603 2109858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769083139.342621 2109858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769083139.342623 2109858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769083139.342624 2109858 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import multiprocessing as mp\n", "if mp.get_start_method(allow_none=True) is None:\n", @@ -139,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e5802f99", "metadata": { "execution": { @@ -205,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "46760605", "metadata": { "execution": { @@ -223,21 +201,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], + "outputs": [], "source": [ "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", @@ -267,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "30fcd1bf", "metadata": { "execution": { @@ -556,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c046277c", "metadata": { "execution": { @@ -615,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "21e69d9f", "metadata": { "execution": { @@ -650,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "4c4156ca", "metadata": { "execution": { @@ -668,162 +632,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769083393.078719 2109858 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769083396.237471 2110751 service.cc:152] XLA service 0x7cff9c002eb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769083396.237561 2110751 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1769083396.435299 2110751 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769083397.328127 2110751 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 10.5875 3.2373 2.5128 0.3538 \n", - "100 9.0516 3.0095 2.2988 0.4415 \n", - "200 6.7214 2.6035 1.9427 0.5821 \n", - "500 4.5937 2.1505 1.5752 0.7148 \n", - "700 3.7420 1.9513 1.4103 0.7652 \n", - "1000 3.1024 1.7670 1.2615 0.8075 \n", - "1400 2.5450 1.6073 1.1385 0.8407 *\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.2079 0.4498 0.2823 0.9875 \n", - "100 0.1511 0.3820 0.2300 0.9910 \n", - "200 0.1156 0.3366 0.1950 0.9930 \n", - "500 0.1027 0.3173 0.1742 0.9938 \n", - "700 0.0972 0.3119 0.1694 0.9940 *\n", - "1000 0.1030 0.3129 0.1686 0.9940 \n", - "1400 0.0984 0.3119 0.1665 0.9940 \n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.0904 0.4359 0.2635 0.9883 \n", - "100 0.0702 0.3808 0.2209 0.9911 \n", - "200 0.0552 0.3369 0.1899 0.9930 \n", - "500 0.0444 0.3048 0.1672 0.9943 *\n", - "700 0.0463 0.3154 0.1685 0.9939 \n", - "1000 0.0461 0.3120 0.1664 0.9940 \n", - "1400 0.0498 0.3236 0.1695 0.9935 \n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.3528, sigma²=136.9127, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.3292, sigma²=104.6340, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.8484, sigma²=89.2839, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.0790, sigma²=95.5569, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=14.1097, sigma²=433.8591, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.7117, sigma²=51.9650, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8137, sigma²=24.1795, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order Val Loss RMSE MAE R² \n", - "------------------------------------------------\n", - "50 0.1551 0.3861 0.2088 0.9908 \n", - "100 0.1551 0.3861 0.2088 0.9908 \n", - "200 0.1551 0.3860 0.2088 0.9908 \n", - "500 0.1551 0.3861 0.2089 0.9908 \n", - "700 0.1551 0.3860 0.2091 0.9908 \n", - "1000 0.1551 0.3860 0.2091 0.9908 \n", - "1400 0.1549 0.3857 0.2089 0.9908 *\n" - ] - } - ], + "outputs": [], "source": [ "# OLS_wendland\n", "parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", @@ -1011,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "af964066", "metadata": { "execution": { @@ -1029,25 +838,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | RMSE | MAE | R² | Times |\n", - "|--------------------------|---------|--------|--------|--------|-----------|\n", - "| OLS_wendland | -- | 3.554 | 2.6564 | 0.2212 | 56.5814 |\n", - "| OLS_sphere | 1400 | 1.6073 | 1.1385 | 0.8407 | 7.783 |\n", - "| DeepKriging_wendland | -- | 2.9206 | 1.9014 | 0.474 | 686.139 |\n", - "| DeepKriging_sphere | 700 | 0.3119 | 0.1694 | 0.994 | 454.544 |\n", - "| DeepKriging_sphere_Huber | 500 | 0.3048 | 0.1672 | 0.9943 | 578.393 |\n", - "| UniversalKriging | 1400 | 0.3857 | 0.2089 | 0.9908 | 1223.21 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 0.3048)\n" - ] - } - ], + "outputs": [], "source": [ "result_table = []\n", "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", @@ -1102,4 +893,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb b/examples/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb new file mode 100644 index 0000000..dc535b2 --- /dev/null +++ b/examples/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb @@ -0,0 +1,1031 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b1833097", + "metadata": { + "papermill": { + "duration": 0.010962, + "end_time": "2026-01-22T06:28:22.418741", + "exception": false, + "start_time": "2026-01-22T06:28:22.407779", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73a5f785", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:22.431002Z", + "iopub.status.busy": "2026-01-22T06:28:22.430384Z", + "iopub.status.idle": "2026-01-22T06:28:22.447875Z", + "shell.execute_reply": "2026-01-22T06:28:22.444425Z" + }, + "papermill": { + "duration": 0.027822, + "end_time": "2026-01-22T06:28:22.451416", + "exception": false, + "start_time": "2026-01-22T06:28:22.423594", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b67cc84b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:22.468481Z", + "iopub.status.busy": "2026-01-22T06:28:22.467637Z", + "iopub.status.idle": "2026-01-22T06:28:26.827773Z", + "shell.execute_reply": "2026-01-22T06:28:26.824631Z" + }, + "papermill": { + "duration": 4.372431, + "end_time": "2026-01-22T06:28:26.830373", + "exception": false, + "start_time": "2026-01-22T06:28:22.457942", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.special import gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature" + ] + }, + { + "cell_type": "markdown", + "id": "5f0aced4", + "metadata": { + "papermill": { + "duration": 0.006036, + "end_time": "2026-01-22T06:28:26.845882", + "exception": false, + "start_time": "2026-01-22T06:28:26.839846", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Environment Setting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5802f99", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:26.858357Z", + "iopub.status.busy": "2026-01-22T06:28:26.857625Z", + "iopub.status.idle": "2026-01-22T06:28:28.442676Z", + "shell.execute_reply": "2026-01-22T06:28:28.440796Z" + }, + "papermill": { + "duration": 1.592998, + "end_time": "2026-01-22T06:28:28.443637", + "exception": false, + "start_time": "2026-01-22T06:28:26.850639", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "markdown", + "id": "5695a52d", + "metadata": { + "papermill": { + "duration": 0.001958, + "end_time": "2026-01-22T06:28:28.447783", + "exception": false, + "start_time": "2026-01-22T06:28:28.445825", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Our Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46760605", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:28.453557Z", + "iopub.status.busy": "2026-01-22T06:28:28.453175Z", + "iopub.status.idle": "2026-01-22T06:28:44.167367Z", + "shell.execute_reply": "2026-01-22T06:28:44.165909Z" + }, + "papermill": { + "duration": 15.718946, + "end_time": "2026-01-22T06:28:44.168729", + "exception": false, + "start_time": "2026-01-22T06:28:28.449783", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "markdown", + "id": "373798b0", + "metadata": { + "papermill": { + "duration": 0.002504, + "end_time": "2026-01-22T06:28:44.174219", + "exception": false, + "start_time": "2026-01-22T06:28:44.171715", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Helper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30fcd1bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:44.181793Z", + "iopub.status.busy": "2026-01-22T06:28:44.181266Z", + "iopub.status.idle": "2026-01-22T06:28:44.203250Z", + "shell.execute_reply": "2026-01-22T06:28:44.202032Z" + }, + "papermill": { + "duration": 0.026668, + "end_time": "2026-01-22T06:28:44.204158", + "exception": false, + "start_time": "2026-01-22T06:28:44.177490", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_path, variable, num_sample, seed):\n", + " data = (pd.read_csv(data_path).sample(n=num_sample, random_state=seed).replace(\"-\", np.nan).dropna())\n", + "\n", + " numeric_cols = [\"longitude\", \"latitude\", variable]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + "\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + "\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + "\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data[variable].to_numpy().astype(\"float32\")[:, None]\n", + "\n", + " # Handle\n", + " categorical_data = None\n", + "\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + "\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(\n", + " wendland(location, grid, theta=theta, k=2)\n", + " )\n", + "\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + "\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " \n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " \n", + " X_train_cont, X_val_cont, X_test_cont = (\n", + " basis[train_x1], basis[val_x1], basis[test_x1])\n", + " y_train, y_val, y_test = (\n", + " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", + " \n", + " def flatten(targets):\n", + " return targets.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", + "\n", + " def flatten(covariates):\n", + " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", + " \n", + " # Handle categorical features\n", + " if categorical_data is None:\n", + " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " \n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " \n", + " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", + " X_val_cont_flat, X_val_cat, y_val_flat,\n", + " X_test_cont_flat, X_test_cat, y_test_flat)\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + "\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " \n", + " metrics = {\n", + " \"Model\": name_model,\n", + " \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(mean_squared_error(y_test, y_pred))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + "\n", + " return metrics, model\n", + " \n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3),\n", + " loss=loss,\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " verbose=0\n", + " )\n", + "\n", + " elif name_model == \"DeepKriging_wendland*\":\n", + " optimizer = Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64],\n", + " activation='relu',\n", + " dropout_rate=dropout_rate,\n", + " optimizer=optimizer,\n", + " loss=loss,\n", + " metrics=['mae'],\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " patience=40,\n", + " verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " optimizer = Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64],\n", + " activation='relu',\n", + " dropout_rate=dropout_rate,\n", + " optimizer=optimizer,\n", + " loss=loss,\n", + " metrics=['mae'],\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " patience=40,\n", + " verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " if name_model == \"DeepKriging_wendland\":\n", + " model = DeepKrigingDefaultTrainer(config)\n", + " elif name_model == \"DeepKriging_wendland*\":\n", + " model = DeepKrigingTrainer(config)\n", + " else:\n", + " model = DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)\n", + " ]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((\n", + " (X_train, X_train_cat), y_train\n", + " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((\n", + " (X_val, X_val_cat), y_val\n", + " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=0\n", + " )\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + " \n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model,\n", + " \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(mean_squared_error(y_test, y_pred))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + "\n", + " del train_dataset, val_dataset\n", + " K.clear_session()\n", + " gc.collect()\n", + " \n", + " return metrics, model" + ] + }, + { + "cell_type": "markdown", + "id": "10cce7e2", + "metadata": {}, + "source": [ + "### Plot Helper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a539a8b1", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_robinson_subplots(data_dict, vmin, vmax, title_main):\n", + " cmap = mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", + " \n", + " n_plots = len(data_dict)\n", + " n_cols = 2\n", + " n_rows = (n_plots + n_cols - 1) // n_cols\n", + " \n", + " fig = plt.figure(figsize=(20, 6 * n_rows))\n", + " fig.suptitle(title_main, fontsize=20, fontweight='bold', y=0.98)\n", + " \n", + " for idx, (subplot_title, (lon, lat, val)) in enumerate(data_dict.items(), 1):\n", + " ax = fig.add_subplot(n_rows, n_cols, idx, projection=ccrs.Robinson())\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " \n", + " scatter = ax.scatter(lon, lat, c=val, cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(subplot_title, fontsize=14, pad=10, fontweight='bold')\n", + " \n", + " cbar = plt.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, shrink=0.6, aspect=30)\n", + " cbar.set_label('Temperature (°C)', fontsize=10)\n", + " \n", + " plt.tight_layout()\n", + " \n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "id": "8e9a0017", + "metadata": { + "papermill": { + "duration": 0.002063, + "end_time": "2026-01-22T06:28:44.208431", + "exception": false, + "start_time": "2026-01-22T06:28:44.206368", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Experiment Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c046277c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:28:44.308270Z", + "iopub.status.busy": "2026-01-22T06:28:44.308026Z", + "iopub.status.idle": "2026-01-22T06:28:44.312080Z", + "shell.execute_reply": "2026-01-22T06:28:44.311090Z" + }, + "papermill": { + "duration": 0.102419, + "end_time": "2026-01-22T06:28:44.312756", + "exception": false, + "start_time": "2026-01-22T06:28:44.210337", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Model Setup\n", + "seed = 1234\n", + "epochs = 500\n", + "batch_size = 2048\n", + "num_sample = 200000\n", + "huber_delta = 1.345\n", + "\n", + "# Data\n", + "data_path = \"data_speed_combined.csv\"\n", + "\n", + "# Basis Setup\n", + "# Wendland\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "\n", + "knot_num = 2000\n", + "order_max = 1400\n", + "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", + "\n", + "# Set to 2 repeats per variable\n", + "repeat_experiment = 2" + ] + }, + { + "cell_type": "markdown", + "id": "5e832176", + "metadata": { + "papermill": { + "duration": 0.00218, + "end_time": "2026-01-22T06:28:44.317676", + "exception": false, + "start_time": "2026-01-22T06:28:44.315496", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Outcome" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a9c97e1", + "metadata": {}, + "outputs": [], + "source": [ + "# Dictionary to hold the final summary table for every hour\n", + "all_hours_summary = {}\n", + "\n", + "# # Generate the 24 column names: 'vec' (Hour 1), 'vec.1' (Hour 2), ..., 'vec.23' (Hour 24)\n", + "# target_variables = [\"vec\"] + [f\"vec.{i}\" for i in range(5, 24)]\n", + "# ONLY include hours 5 through 24 (which corresponds to vec.4 through vec.23)\n", + "target_variables = [f\"vec.{i}\" for i in range(4, 24)]\n", + "\n", + "for hour, variable in enumerate(target_variables, start=5):\n", + " \n", + " print(f\"\\n\\n{'#'*80}\")\n", + " print(f\"🚀 STARTING DATASET: Hour {hour:02d} | Target Column: '{variable}'\")\n", + " print(f\"{'#'*80}\")\n", + " \n", + " best_orders = {}\n", + " experiment_results = {\n", + " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + "\n", + " for repeat in range(repeat_experiment):\n", + " seed_current = seed + repeat\n", + "\n", + " print(f\"\\n{'='*80}\")\n", + " print(f\"🏃 Hour {hour:02d} ('{variable}') - Repeat {repeat+1}/{repeat_experiment}, Seed={seed_current}\")\n", + " print(f\"{'='*80}\")\n", + "\n", + " # Data Preprocessing (Variable gets dynamically passed in here!)\n", + " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(data_path, variable, num_sample, seed_current)\n", + "\n", + " # Compute Basis Functions\n", + " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", + " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", + "\n", + " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", + " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", + "\n", + " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", + "\n", + " if repeat == 0:\n", + " # Tuning order parameter for OLS_sphere\n", + " Best_val_OLS_S = float('inf')\n", + " Best_order_OLS_S = None\n", + " Results_order_OLS_S = []\n", + " \n", + " print(\"\\nTuning order parameter for OLS_sphere\")\n", + " for order in base_orders:\n", + " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " metrics, model = train_eval(\"OLS_sphere\", None, None, None, None, *parts)\n", + " \n", + " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", + " \n", + " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", + " Best_val_OLS_S = metrics[\"Val_loss\"]\n", + " Best_order_OLS_S = order\n", + " \n", + " del Phi_sphere, parts, model; gc.collect()\n", + "\n", + " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", + " print(f\" {'-'*34}\")\n", + " for res in Results_order_OLS_S:\n", + " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", + " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", + " print(f\" Best order: {Best_order_OLS_S}\")\n", + " best_orders['OLS_sphere'] = Best_order_OLS_S\n", + "\n", + " # Tuning order parameter for DeepKriging_mrts\n", + " Best_val_DK_mrts = float('inf')\n", + " Best_order_DK_mrts = None\n", + " Results_order_DK_mrts = []\n", + "\n", + " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", + " for order in base_orders:\n", + " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts)\n", + " \n", + " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", + " \n", + " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", + " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", + " Best_order_DK_mrts = order\n", + " \n", + " del Phi_mrts, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", + " print(f\" {'-'*34}\")\n", + " for res in Results_order_DK_mrts:\n", + " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", + " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", + " print(f\" Best order: {Best_order_DK_mrts}\")\n", + " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", + "\n", + " # Tuning order parameter for DeepKriging_sphere\n", + " Best_val_DK_S = float('inf')\n", + " Best_order_DK_S = None\n", + " Results_order_DK_S = []\n", + "\n", + " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", + " for order in base_orders:\n", + " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts)\n", + " \n", + " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", + " \n", + " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", + " Best_val_DK_S = metrics[\"Val_loss\"]\n", + " Best_order_DK_S = order\n", + " \n", + " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", + " print(f\" {'-'*34}\")\n", + " for res in Results_order_DK_S:\n", + " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", + " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", + " print(f\" Best order: {Best_order_DK_S}\")\n", + " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", + "\n", + " # Tuning order parameter for DeepKriging_sphere_Huber\n", + " Best_val_DK_S_H = float('inf')\n", + " Best_order_DK_S_H = None\n", + " Results_order_DK_S_H = []\n", + " \n", + " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", + " for order in base_orders:\n", + " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts)\n", + " \n", + " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", + " \n", + " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", + " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", + " Best_order_DK_S_H = order\n", + " \n", + " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", + " print(f\" {'-'*34}\")\n", + " for res in Results_order_DK_S_H:\n", + " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", + " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", + " print(f\" Best order: {Best_order_DK_S_H}\")\n", + " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", + "\n", + " # Tuning order parameter for UniversalKriging\n", + " Best_val_UK = float('inf')\n", + " Best_order_UK = None\n", + " Results_order_UK = []\n", + " \n", + " print(\"\\nTuning order parameter for UniversalKriging\")\n", + " for order in base_orders:\n", + " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", + " \n", + " idx_all = np.arange(Phi_sphere.shape[0])\n", + " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed_current)\n", + " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed_current)\n", + " \n", + " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", + " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", + " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", + " \n", + " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", + " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", + " \n", + " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", + " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", + " \n", + " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", + " test_mse = mean_squared_error(y_test, y_pred_test)\n", + " \n", + " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", + " \n", + " if val_loss < Best_val_UK:\n", + " Best_val_UK = val_loss\n", + " Best_order_UK = order\n", + " \n", + " uk_model.cleanup()\n", + " del uk_model, Phi_sphere, coords_train, coords_val, coords_test, phi_train, phi_val, phi_test, y_train, y_val, y_test; gc.collect()\n", + " \n", + " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", + " print(f\" {'-'*34}\")\n", + " for res in Results_order_UK:\n", + " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", + " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", + " print(f\" Best order: {Best_order_UK}\")\n", + " best_orders['UniversalKriging'] = Best_order_UK\n", + " gc.collect()\n", + "\n", + "\n", + " # Execute training with best orders\n", + " Record = {}\n", + "\n", + " # OLS_wendland\n", + " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", + " metrics, model = train_eval(\"OLS_wendland\", None, None, None, None, *parts)\n", + " Record[\"OLS_wendland\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", + " }\n", + " del parts, model; gc.collect()\n", + "\n", + " # OLS_sphere\n", + " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " metrics, model = train_eval(\"OLS_sphere\", None, None, None, None, *parts)\n", + " Record[\"OLS_sphere\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": best_orders['OLS_sphere']\n", + " }\n", + " del Phi_sphere, parts, model; gc.collect()\n", + "\n", + " # DeepKriging_wendland\n", + " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_wendland\", epochs, batch_size, \"mse\", None, *parts)\n", + " Record[\"DeepKriging_wendland\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", + " }\n", + " del parts, model; K.clear_session(); gc.collect()\n", + "\n", + " # DeepKriging_wendland*\n", + " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts)\n", + " Record[\"DeepKriging_wendland*\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", + " }\n", + " del parts, model; K.clear_session(); gc.collect()\n", + "\n", + " # DeepKriging_mrts\n", + " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts)\n", + " Record[\"DeepKriging_mrts\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_mrts']\n", + " }\n", + " del Phi_mrts, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " # DeepKriging_sphere\n", + " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts)\n", + " Record[\"DeepKriging_sphere\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_sphere']\n", + " }\n", + " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " # DeepKriging_sphere_Huber\n", + " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", + " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", + " with strategy.scope():\n", + " metrics, model = train_eval(\"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts)\n", + " Record[\"DeepKriging_sphere_Huber\"] = {\n", + " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", + " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber']\n", + " }\n", + " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", + "\n", + " # UniversalKriging\n", + " t0 = time.time()\n", + " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", + " \n", + " idx_all = np.arange(Phi_sphere.shape[0])\n", + " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed_current)\n", + " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed_current)\n", + "\n", + " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", + " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", + " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", + "\n", + " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", + " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", + "\n", + " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", + "\n", + " Record[\"UniversalKriging\"] = {\n", + " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", + " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", + " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", + " \"R2\": r2_score(y_test, y_pred_test),\n", + " \"Time\": time.time() - t0,\n", + " \"Param\": best_orders['UniversalKriging']\n", + " }\n", + "\n", + " uk_model.cleanup()\n", + " del uk_model, Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test; gc.collect()\n", + "\n", + " # Print partial result for this repeat\n", + " result_table = []\n", + " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " result_table.append({\n", + " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", + " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", + " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", + " })\n", + "\n", + " df_res = pd.DataFrame(result_table)\n", + " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " # Save results internally for the current hour\n", + " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", + " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", + " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", + " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", + "\n", + " # Clean up matrices before next repeat\n", + " del Phi_wendland, max_Phi_sphere, max_Phi_mrts, location_data, location_data_norm\n", + " K.clear_session()\n", + " gc.collect()\n", + "\n", + " print(f\"\\n✅ Completed Hour {hour:02d} ('{variable}') - Repeat {repeat+1}/{repeat_experiment}\")\n", + "\n", + " # ========================================================\n", + " # Summary Table for the current Hour\n", + " # ========================================================\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(f\"📊 Summary of repeat experiments for Hour {hour:02d} ('{variable}')\")\n", + " print(\"=\"*80)\n", + " print(f\"Selected Best Orders: {best_orders}\")\n", + " print(\"=\"*80)\n", + "\n", + " avg_results = []\n", + " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " metrics = experiment_results[model]\n", + " avg_results.append({\n", + " \"Model\": model,\n", + " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", + " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", + " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", + " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", + " })\n", + "\n", + " df_avg = pd.DataFrame(avg_results)\n", + " print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " if avg_results:\n", + " # Extract the float part of RMSE (everything before the ± symbol) to find the best model\n", + " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", + " print(f\"\\n🏆 Best Model for Hour {hour:02d} ('{variable}'): {best_model['Model']} (RMSE: {best_model['RMSE']})\")\n", + " \n", + " # Store df in overarching dictionary if you want to look at it later\n", + " all_hours_summary[f\"Hour_{hour:02d}\"] = df_avg\n", + "\n", + "print(\"\\n\" + \"#\"*80)\n", + "print(\"🎉 ALL 24 HOURS PROCESSED SUCCESSFULLY!\")\n", + "print(\"#\"*80)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 9219.462666, + "end_time": "2026-01-22T09:02:01.113308", + "environment_variables": {}, + "exception": null, + "input_path": "/home/user/MLspatial/notebook/Final/Real_data/experiment_univariate_deepkriging_basis_real_data_temperature.ipynb", + "output_path": "/home/user/MLspatial/notebook/Final/Real_data/experiment_univariate_deepkriging_basis_real_data_temperature.ipynb", + "parameters": {}, + "start_time": "2026-01-22T06:28:21.650642", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebook/simulation/Rerun/run_stdscaler_nonoise_50reps.py b/examples/simulation/run_stdscaler_nonoise_50reps.py similarity index 100% rename from notebook/simulation/Rerun/run_stdscaler_nonoise_50reps.py rename to examples/simulation/run_stdscaler_nonoise_50reps.py diff --git a/notebook/simulation/Rerun/run_stdscaler_outliers_50reps.py b/examples/simulation/run_stdscaler_outliers_50reps.py similarity index 100% rename from notebook/simulation/Rerun/run_stdscaler_outliers_50reps.py rename to examples/simulation/run_stdscaler_outliers_50reps.py diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb new file mode 100644 index 0000000..94b46e4 --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb @@ -0,0 +1,883 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d1cde2eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:09.855329Z", + "iopub.status.busy": "2026-03-29T15:51:09.855153Z", + "iopub.status.idle": "2026-03-29T15:51:09.859870Z", + "shell.execute_reply": "2026-03-29T15:51:09.859211Z" + }, + "papermill": { + "duration": 0.01117, + "end_time": "2026-03-29T15:51:09.860573", + "exception": false, + "start_time": "2026-03-29T15:51:09.849403", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8890884f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:09.866364Z", + "iopub.status.busy": "2026-03-29T15:51:09.866242Z", + "iopub.status.idle": "2026-03-29T15:51:11.908858Z", + "shell.execute_reply": "2026-03-29T15:51:11.907928Z" + }, + "papermill": { + "duration": 2.047417, + "end_time": "2026-03-29T15:51:11.909414", + "exception": false, + "start_time": "2026-03-29T15:51:09.861997", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d6d1ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:11.913996Z", + "iopub.status.busy": "2026-03-29T15:51:11.913733Z", + "iopub.status.idle": "2026-03-29T15:51:12.802119Z", + "shell.execute_reply": "2026-03-29T15:51:12.801449Z" + }, + "papermill": { + "duration": 0.891841, + "end_time": "2026-03-29T15:51:12.802745", + "exception": false, + "start_time": "2026-03-29T15:51:11.910904", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd094a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:12.807262Z", + "iopub.status.busy": "2026-03-29T15:51:12.807108Z", + "iopub.status.idle": "2026-03-29T15:51:12.809922Z", + "shell.execute_reply": "2026-03-29T15:51:12.809283Z" + }, + "papermill": { + "duration": 0.006157, + "end_time": "2026-03-29T15:51:12.810305", + "exception": false, + "start_time": "2026-03-29T15:51:12.804148", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0aafba7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:12.813629Z", + "iopub.status.busy": "2026-03-29T15:51:12.813502Z", + "iopub.status.idle": "2026-03-29T15:51:25.842841Z", + "shell.execute_reply": "2026-03-29T15:51:25.842080Z" + }, + "papermill": { + "duration": 13.03211, + "end_time": "2026-03-29T15:51:25.843644", + "exception": false, + "start_time": "2026-03-29T15:51:12.811534", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32986e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.848880Z", + "iopub.status.busy": "2026-03-29T15:51:25.848596Z", + "iopub.status.idle": "2026-03-29T15:51:25.854176Z", + "shell.execute_reply": "2026-03-29T15:51:25.853405Z" + }, + "papermill": { + "duration": 0.009458, + "end_time": "2026-03-29T15:51:25.854622", + "exception": false, + "start_time": "2026-03-29T15:51:25.845164", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import gc\n", + "\n", + "def _gp_draw(rng, num_sample):\n", + " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", + "\n", + " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", + " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", + " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", + "\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " ev, evec = np.linalg.eigh(cov_matrix)\n", + " ev = np.maximum(ev, 1e-6)\n", + " L = evec @ np.diag(np.sqrt(ev))\n", + "\n", + " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", + "\n", + " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", + " gc.collect()\n", + " return lat_deg, lon_deg, y\n", + "\n", + "def simulate_data(num_sample, seed):\n", + " \"\"\"Stationary GP + N(0, 0.5^2) Gaussian noise.\"\"\"\n", + " rng = np.random.default_rng(seed)\n", + " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", + " noise = rng.normal(0.0, 0.5, num_sample).astype(np.float32)\n", + " z = y + noise\n", + " print(f\"\\n=== GP + N(0, 0.5^2) noise | seed={seed} ===\")\n", + " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", + " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", + " del noise\n", + " gc.collect()\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5c1e9f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.857929Z", + "iopub.status.busy": "2026-03-29T15:51:25.857800Z", + "iopub.status.idle": "2026-03-29T15:51:25.865176Z", + "shell.execute_reply": "2026-03-29T15:51:25.864470Z" + }, + "papermill": { + "duration": 0.009723, + "end_time": "2026-03-29T15:51:25.865681", + "exception": false, + "start_time": "2026-03-29T15:51:25.855958", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + " categorical_data = None\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(wendland(location, grid, theta=theta, k=2))\n", + " del grid\n", + " gc.collect()\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " X_train_cont = basis[train_x1]\n", + " X_val_cont = basis[val_x1]\n", + " X_test_cont = basis[test_x1]\n", + " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", + " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", + " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", + " if categorical_data is None:\n", + " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " return (X_train_flat, X_train_cat, y_train_flat,\n", + " X_val_flat, X_val_cat, y_val_flat,\n", + " X_test_flat, X_test_cat, y_test_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6728b7b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.869093Z", + "iopub.status.busy": "2026-03-29T15:51:25.868970Z", + "iopub.status.idle": "2026-03-29T15:51:25.876198Z", + "shell.execute_reply": "2026-03-29T15:51:25.875451Z" + }, + "papermill": { + "duration": 0.009645, + "end_time": "2026-03-29T15:51:25.876616", + "exception": false, + "start_time": "2026-03-29T15:51:25.866971", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " return metrics, model\n", + "\n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", + " epochs=epochs, batch_size=batch_size, verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", + " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", + " dropout_rate=dropout_rate, optimizer=_opt,\n", + " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", + " patience=40, verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience,\n", + " restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5,\n", + " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + " val_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset, validation_data=val_dataset,\n", + " epochs=epochs, callbacks=callbacks, verbose=0)\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + "\n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " return metrics, model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce398870", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.880045Z", + "iopub.status.busy": "2026-03-29T15:51:25.879918Z", + "iopub.status.idle": "2026-03-29T15:51:25.888811Z", + "shell.execute_reply": "2026-03-29T15:51:25.887685Z" + }, + "papermill": { + "duration": 0.011353, + "end_time": "2026-03-29T15:51:25.889243", + "exception": false, + "start_time": "2026-03-29T15:51:25.877890", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(longitude, latitude, c=value,\n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", + " plot_type='prediction', cbar_label=None):\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", + " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " if plot_type == 'residual' else\n", + " mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", + " model_list=None, experiment_info=None):\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " idx_all = np.arange(len(y_combined))\n", + " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + "\n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", + "\n", + " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", + " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", + "\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})')\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " p = predictions[mn]\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", + " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", + " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig1)\n", + "\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", + " -vmax_abs, vmax_abs,\n", + " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", + " plot_type='residual')\n", + " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig2)\n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38eb7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.892463Z", + "iopub.status.busy": "2026-03-29T15:51:25.892341Z", + "iopub.status.idle": "2026-03-29T15:51:25.895413Z", + "shell.execute_reply": "2026-03-29T15:51:25.894710Z" + }, + "papermill": { + "duration": 0.005332, + "end_time": "2026-03-29T15:51:25.895859", + "exception": false, + "start_time": "2026-03-29T15:51:25.890527", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ── Experiment parameters ─────────────────────────────────────────────────────\n", + "seed = 50\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "\n", + "# All models (including dk_sphere) use the same original candidates\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", + "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", + "print(f\"repeats : {repeat_experiment}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "main_loop_GP_noise", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:25.899191Z", + "iopub.status.busy": "2026-03-29T15:51:25.899064Z", + "iopub.status.idle": "2026-03-29T21:57:05.953619Z", + "shell.execute_reply": "2026-03-29T21:57:05.952726Z" + }, + "papermill": { + "duration": 21940.057059, + "end_time": "2026-03-29T21:57:05.954212", + "exception": false, + "start_time": "2026-03-29T15:51:25.897153", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_GP_noise.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_noise.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"Starting fresh.\")\n", + "\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n\" + \"=\"*70)\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(\"=\"*70)\n", + "\n", + " out_json = f\"repeat_{repeat}_GP_noise_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "summary_GP_noise", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:06.000295Z", + "iopub.status.busy": "2026-03-29T21:57:05.999896Z", + "iopub.status.idle": "2026-03-29T21:57:06.007417Z", + "shell.execute_reply": "2026-03-29T21:57:06.006672Z" + }, + "papermill": { + "duration": 0.032606, + "end_time": "2026-03-29T21:57:06.008295", + "exception": false, + "start_time": "2026-03-29T21:57:05.975689", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(\"checkpoint_GP_noise.json\") as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"Summary — {n} Repeats\")\n", + "print(\" Scenario: GP + N(0, 0.5^2) Gaussian Noise\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 21957.78137, + "end_time": "2026-03-29T21:57:07.049745", + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-29T15:51:09.268375", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb new file mode 100644 index 0000000..0ee6a87 --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb @@ -0,0 +1,885 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d1cde2eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:08.815357Z", + "iopub.status.busy": "2026-03-29T21:57:08.815177Z", + "iopub.status.idle": "2026-03-29T21:57:08.820306Z", + "shell.execute_reply": "2026-03-29T21:57:08.819498Z" + }, + "papermill": { + "duration": 0.008303, + "end_time": "2026-03-29T21:57:08.820786", + "exception": false, + "start_time": "2026-03-29T21:57:08.812483", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8890884f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:08.824319Z", + "iopub.status.busy": "2026-03-29T21:57:08.824182Z", + "iopub.status.idle": "2026-03-29T21:57:10.887914Z", + "shell.execute_reply": "2026-03-29T21:57:10.886632Z" + }, + "papermill": { + "duration": 2.066489, + "end_time": "2026-03-29T21:57:10.888775", + "exception": false, + "start_time": "2026-03-29T21:57:08.822286", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d6d1ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:10.894130Z", + "iopub.status.busy": "2026-03-29T21:57:10.893829Z", + "iopub.status.idle": "2026-03-29T21:57:11.802877Z", + "shell.execute_reply": "2026-03-29T21:57:11.802068Z" + }, + "papermill": { + "duration": 0.913267, + "end_time": "2026-03-29T21:57:11.803554", + "exception": false, + "start_time": "2026-03-29T21:57:10.890287", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd094a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:11.808165Z", + "iopub.status.busy": "2026-03-29T21:57:11.807943Z", + "iopub.status.idle": "2026-03-29T21:57:11.810809Z", + "shell.execute_reply": "2026-03-29T21:57:11.810071Z" + }, + "papermill": { + "duration": 0.006257, + "end_time": "2026-03-29T21:57:11.811336", + "exception": false, + "start_time": "2026-03-29T21:57:11.805079", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0aafba7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:11.814956Z", + "iopub.status.busy": "2026-03-29T21:57:11.814826Z", + "iopub.status.idle": "2026-03-29T21:57:25.009269Z", + "shell.execute_reply": "2026-03-29T21:57:25.008441Z" + }, + "papermill": { + "duration": 13.197306, + "end_time": "2026-03-29T21:57:25.009917", + "exception": false, + "start_time": "2026-03-29T21:57:11.812611", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32986e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.014856Z", + "iopub.status.busy": "2026-03-29T21:57:25.014559Z", + "iopub.status.idle": "2026-03-29T21:57:25.020311Z", + "shell.execute_reply": "2026-03-29T21:57:25.019643Z" + }, + "papermill": { + "duration": 0.009408, + "end_time": "2026-03-29T21:57:25.020805", + "exception": false, + "start_time": "2026-03-29T21:57:25.011397", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import gc\n", + "\n", + "def _gp_draw(rng, num_sample):\n", + " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", + "\n", + " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", + " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", + " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", + "\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " ev, evec = np.linalg.eigh(cov_matrix)\n", + " ev = np.maximum(ev, 1e-6)\n", + " L = evec @ np.diag(np.sqrt(ev))\n", + "\n", + " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", + "\n", + " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", + " gc.collect()\n", + " return lat_deg, lon_deg, y\n", + "\n", + "OUTLIER_RATIO = 0.025\n", + "OUTLIER_MULT = 5\n", + "\n", + "def simulate_data(num_sample, seed):\n", + " \"\"\"Stationary GP + 2.5% outliers multiplied by 5.\"\"\"\n", + " rng = np.random.default_rng(seed)\n", + " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", + " z = y.copy()\n", + " n_out = int(num_sample * OUTLIER_RATIO)\n", + " idx = rng.choice(num_sample, size=n_out, replace=False)\n", + " z[idx] *= OUTLIER_MULT\n", + " print(f\"\\n=== GP + outliers ({OUTLIER_RATIO*100:.1f}% x{OUTLIER_MULT}) | seed={seed} ===\")\n", + " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", + " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", + " gc.collect()\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5c1e9f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.024318Z", + "iopub.status.busy": "2026-03-29T21:57:25.024184Z", + "iopub.status.idle": "2026-03-29T21:57:25.031120Z", + "shell.execute_reply": "2026-03-29T21:57:25.030451Z" + }, + "papermill": { + "duration": 0.009291, + "end_time": "2026-03-29T21:57:25.031572", + "exception": false, + "start_time": "2026-03-29T21:57:25.022281", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + " categorical_data = None\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(wendland(location, grid, theta=theta, k=2))\n", + " del grid\n", + " gc.collect()\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " X_train_cont = basis[train_x1]\n", + " X_val_cont = basis[val_x1]\n", + " X_test_cont = basis[test_x1]\n", + " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", + " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", + " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", + " if categorical_data is None:\n", + " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " return (X_train_flat, X_train_cat, y_train_flat,\n", + " X_val_flat, X_val_cat, y_val_flat,\n", + " X_test_flat, X_test_cat, y_test_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6728b7b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.034921Z", + "iopub.status.busy": "2026-03-29T21:57:25.034792Z", + "iopub.status.idle": "2026-03-29T21:57:25.042422Z", + "shell.execute_reply": "2026-03-29T21:57:25.041715Z" + }, + "papermill": { + "duration": 0.010019, + "end_time": "2026-03-29T21:57:25.042958", + "exception": false, + "start_time": "2026-03-29T21:57:25.032939", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " return metrics, model\n", + "\n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", + " epochs=epochs, batch_size=batch_size, verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", + " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", + " dropout_rate=dropout_rate, optimizer=_opt,\n", + " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", + " patience=40, verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience,\n", + " restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5,\n", + " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + " val_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset, validation_data=val_dataset,\n", + " epochs=epochs, callbacks=callbacks, verbose=0)\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + "\n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " return metrics, model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce398870", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.046293Z", + "iopub.status.busy": "2026-03-29T21:57:25.046162Z", + "iopub.status.idle": "2026-03-29T21:57:25.054489Z", + "shell.execute_reply": "2026-03-29T21:57:25.053810Z" + }, + "papermill": { + "duration": 0.010673, + "end_time": "2026-03-29T21:57:25.054924", + "exception": false, + "start_time": "2026-03-29T21:57:25.044251", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(longitude, latitude, c=value,\n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", + " plot_type='prediction', cbar_label=None):\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", + " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " if plot_type == 'residual' else\n", + " mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", + " model_list=None, experiment_info=None):\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " idx_all = np.arange(len(y_combined))\n", + " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + "\n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", + "\n", + " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", + " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", + "\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})')\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " p = predictions[mn]\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", + " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", + " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig1)\n", + "\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", + " -vmax_abs, vmax_abs,\n", + " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", + " plot_type='residual')\n", + " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig2)\n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38eb7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.058292Z", + "iopub.status.busy": "2026-03-29T21:57:25.058166Z", + "iopub.status.idle": "2026-03-29T21:57:25.061103Z", + "shell.execute_reply": "2026-03-29T21:57:25.060458Z" + }, + "papermill": { + "duration": 0.005336, + "end_time": "2026-03-29T21:57:25.061554", + "exception": false, + "start_time": "2026-03-29T21:57:25.056218", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ── Experiment parameters ─────────────────────────────────────────────────────\n", + "seed = 50\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "\n", + "# All models (including dk_sphere) use the same original candidates\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", + "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", + "print(f\"repeats : {repeat_experiment}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "main_loop_GP_outliers", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T21:57:25.065069Z", + "iopub.status.busy": "2026-03-29T21:57:25.064933Z" + }, + "papermill": { + "duration": null, + "end_time": null, + "exception": false, + "start_time": "2026-03-29T21:57:25.062921", + "status": "running" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_GP_outliers.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_outliers.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"Starting fresh.\")\n", + "\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n\" + \"=\"*70)\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(\"=\"*70)\n", + "\n", + " out_json = f\"repeat_{repeat}_GP_outliers_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "summary_GP_outliers", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-26T08:31:31.812514Z", + "iopub.status.busy": "2026-03-26T08:31:31.812293Z", + "iopub.status.idle": "2026-03-26T08:31:31.817266Z", + "shell.execute_reply": "2026-03-26T08:31:31.816839Z" + }, + "papermill": { + "duration": null, + "end_time": null, + "exception": null, + "start_time": null, + "status": "pending" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(\"checkpoint_GP_outliers.json\") as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"Summary — {n} Repeats\")\n", + "print(\" Scenario: GP + Outliers (2.5% x5)\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": null, + "end_time": null, + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-29T21:57:08.179540", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb new file mode 100644 index 0000000..3a5d71f --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb @@ -0,0 +1,881 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d1cde2eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:04.614551Z", + "iopub.status.busy": "2026-03-29T08:46:04.614266Z", + "iopub.status.idle": "2026-03-29T08:46:04.621475Z", + "shell.execute_reply": "2026-03-29T08:46:04.619925Z" + }, + "papermill": { + "duration": 0.01126, + "end_time": "2026-03-29T08:46:04.622226", + "exception": false, + "start_time": "2026-03-29T08:46:04.610966", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8890884f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:04.627193Z", + "iopub.status.busy": "2026-03-29T08:46:04.626976Z", + "iopub.status.idle": "2026-03-29T08:46:06.991082Z", + "shell.execute_reply": "2026-03-29T08:46:06.990032Z" + }, + "papermill": { + "duration": 2.367708, + "end_time": "2026-03-29T08:46:06.991680", + "exception": false, + "start_time": "2026-03-29T08:46:04.623972", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d6d1ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:06.997506Z", + "iopub.status.busy": "2026-03-29T08:46:06.997123Z", + "iopub.status.idle": "2026-03-29T08:46:08.180611Z", + "shell.execute_reply": "2026-03-29T08:46:08.179450Z" + }, + "papermill": { + "duration": 1.188221, + "end_time": "2026-03-29T08:46:08.181390", + "exception": false, + "start_time": "2026-03-29T08:46:06.993169", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd094a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:08.190034Z", + "iopub.status.busy": "2026-03-29T08:46:08.189238Z", + "iopub.status.idle": "2026-03-29T08:46:08.194114Z", + "shell.execute_reply": "2026-03-29T08:46:08.192960Z" + }, + "papermill": { + "duration": 0.011992, + "end_time": "2026-03-29T08:46:08.194962", + "exception": false, + "start_time": "2026-03-29T08:46:08.182970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0aafba7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:08.199095Z", + "iopub.status.busy": "2026-03-29T08:46:08.198877Z", + "iopub.status.idle": "2026-03-29T08:46:22.428931Z", + "shell.execute_reply": "2026-03-29T08:46:22.427976Z" + }, + "papermill": { + "duration": 14.233113, + "end_time": "2026-03-29T08:46:22.429651", + "exception": false, + "start_time": "2026-03-29T08:46:08.196538", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32986e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.434807Z", + "iopub.status.busy": "2026-03-29T08:46:22.434510Z", + "iopub.status.idle": "2026-03-29T08:46:22.440245Z", + "shell.execute_reply": "2026-03-29T08:46:22.439557Z" + }, + "papermill": { + "duration": 0.009793, + "end_time": "2026-03-29T08:46:22.440947", + "exception": false, + "start_time": "2026-03-29T08:46:22.431154", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import gc\n", + "\n", + "def _gp_draw(rng, num_sample):\n", + " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", + "\n", + " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", + " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", + " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", + "\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", + " try:\n", + " L = np.linalg.cholesky(cov_matrix)\n", + " except np.linalg.LinAlgError:\n", + " ev, evec = np.linalg.eigh(cov_matrix)\n", + " ev = np.maximum(ev, 1e-6)\n", + " L = evec @ np.diag(np.sqrt(ev))\n", + "\n", + " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", + "\n", + " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", + " gc.collect()\n", + " return lat_deg, lon_deg, y\n", + "\n", + "def simulate_data(num_sample, seed):\n", + " \"\"\"Stationary GP only — no added noise.\"\"\"\n", + " rng = np.random.default_rng(seed)\n", + " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", + " z = y.copy()\n", + " print(f\"\\n=== GP Pure (no noise) | seed={seed} ===\")\n", + " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", + " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", + " gc.collect()\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5c1e9f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.444443Z", + "iopub.status.busy": "2026-03-29T08:46:22.444309Z", + "iopub.status.idle": "2026-03-29T08:46:22.451331Z", + "shell.execute_reply": "2026-03-29T08:46:22.450644Z" + }, + "papermill": { + "duration": 0.009421, + "end_time": "2026-03-29T08:46:22.451769", + "exception": false, + "start_time": "2026-03-29T08:46:22.442348", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + " categorical_data = None\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(wendland(location, grid, theta=theta, k=2))\n", + " del grid\n", + " gc.collect()\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " X_train_cont = basis[train_x1]\n", + " X_val_cont = basis[val_x1]\n", + " X_test_cont = basis[test_x1]\n", + " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", + " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", + " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", + " if categorical_data is None:\n", + " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " return (X_train_flat, X_train_cat, y_train_flat,\n", + " X_val_flat, X_val_cat, y_val_flat,\n", + " X_test_flat, X_test_cat, y_test_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6728b7b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.455268Z", + "iopub.status.busy": "2026-03-29T08:46:22.455137Z", + "iopub.status.idle": "2026-03-29T08:46:22.462594Z", + "shell.execute_reply": "2026-03-29T08:46:22.461960Z" + }, + "papermill": { + "duration": 0.009844, + "end_time": "2026-03-29T08:46:22.463071", + "exception": false, + "start_time": "2026-03-29T08:46:22.453227", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " return metrics, model\n", + "\n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", + " epochs=epochs, batch_size=batch_size, verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", + " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", + " dropout_rate=dropout_rate, optimizer=_opt,\n", + " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", + " patience=40, verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience,\n", + " restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5,\n", + " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + " val_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset, validation_data=val_dataset,\n", + " epochs=epochs, callbacks=callbacks, verbose=0)\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + "\n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " return metrics, model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce398870", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.466449Z", + "iopub.status.busy": "2026-03-29T08:46:22.466319Z", + "iopub.status.idle": "2026-03-29T08:46:22.475274Z", + "shell.execute_reply": "2026-03-29T08:46:22.474592Z" + }, + "papermill": { + "duration": 0.011671, + "end_time": "2026-03-29T08:46:22.476037", + "exception": false, + "start_time": "2026-03-29T08:46:22.464366", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(longitude, latitude, c=value,\n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", + " plot_type='prediction', cbar_label=None):\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", + " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " if plot_type == 'residual' else\n", + " mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", + " model_list=None, experiment_info=None):\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " idx_all = np.arange(len(y_combined))\n", + " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + "\n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", + "\n", + " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", + " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", + "\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})')\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " p = predictions[mn]\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", + " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", + " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig1)\n", + "\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", + " -vmax_abs, vmax_abs,\n", + " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", + " plot_type='residual')\n", + " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig2)\n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38eb7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.479647Z", + "iopub.status.busy": "2026-03-29T08:46:22.479504Z", + "iopub.status.idle": "2026-03-29T08:46:22.482691Z", + "shell.execute_reply": "2026-03-29T08:46:22.481969Z" + }, + "papermill": { + "duration": 0.005727, + "end_time": "2026-03-29T08:46:22.483205", + "exception": false, + "start_time": "2026-03-29T08:46:22.477478", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ── Experiment parameters ─────────────────────────────────────────────────────\n", + "seed = 50\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "\n", + "# All models (including dk_sphere) use the same original candidates\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", + "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", + "print(f\"repeats : {repeat_experiment}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "main_loop_GP_pure", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T08:46:22.486586Z", + "iopub.status.busy": "2026-03-29T08:46:22.486461Z", + "iopub.status.idle": "2026-03-29T15:51:06.928030Z", + "shell.execute_reply": "2026-03-29T15:51:06.927063Z" + }, + "papermill": { + "duration": 25484.444209, + "end_time": "2026-03-29T15:51:06.928740", + "exception": false, + "start_time": "2026-03-29T08:46:22.484531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_GP_pure.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_pure.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"Starting fresh.\")\n", + "\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n\" + \"=\"*70)\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(\"=\"*70)\n", + "\n", + " out_json = f\"repeat_{repeat}_GP_pure_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "summary_GP_pure", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-29T15:51:06.972148Z", + "iopub.status.busy": "2026-03-29T15:51:06.971976Z", + "iopub.status.idle": "2026-03-29T15:51:06.979323Z", + "shell.execute_reply": "2026-03-29T15:51:06.978193Z" + }, + "papermill": { + "duration": 0.030087, + "end_time": "2026-03-29T15:51:06.979877", + "exception": false, + "start_time": "2026-03-29T15:51:06.949790", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(\"checkpoint_GP_pure.json\") as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"Summary — {n} Repeats\")\n", + "print(\" Scenario: GP Pure (no noise)\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 25504.104741, + "end_time": "2026-03-29T15:51:08.120511", + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-29T08:46:04.015770", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb new file mode 100644 index 0000000..e591722 --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb @@ -0,0 +1,882 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d1cde2eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:27.964986Z", + "iopub.status.busy": "2026-03-28T09:36:27.964774Z", + "iopub.status.idle": "2026-03-28T09:36:27.970045Z", + "shell.execute_reply": "2026-03-28T09:36:27.969250Z" + }, + "papermill": { + "duration": 0.010402, + "end_time": "2026-03-28T09:36:27.970639", + "exception": false, + "start_time": "2026-03-28T09:36:27.960237", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8890884f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:27.974212Z", + "iopub.status.busy": "2026-03-28T09:36:27.974064Z", + "iopub.status.idle": "2026-03-28T09:36:30.007037Z", + "shell.execute_reply": "2026-03-28T09:36:30.006162Z" + }, + "papermill": { + "duration": 2.035506, + "end_time": "2026-03-28T09:36:30.007619", + "exception": false, + "start_time": "2026-03-28T09:36:27.972113", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d6d1ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:30.012160Z", + "iopub.status.busy": "2026-03-28T09:36:30.011910Z", + "iopub.status.idle": "2026-03-28T09:36:30.929473Z", + "shell.execute_reply": "2026-03-28T09:36:30.928101Z" + }, + "papermill": { + "duration": 0.920969, + "end_time": "2026-03-28T09:36:30.930051", + "exception": false, + "start_time": "2026-03-28T09:36:30.009082", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd094a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:30.934540Z", + "iopub.status.busy": "2026-03-28T09:36:30.934382Z", + "iopub.status.idle": "2026-03-28T09:36:30.936987Z", + "shell.execute_reply": "2026-03-28T09:36:30.936261Z" + }, + "papermill": { + "duration": 0.005925, + "end_time": "2026-03-28T09:36:30.937422", + "exception": false, + "start_time": "2026-03-28T09:36:30.931497", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0aafba7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:30.940709Z", + "iopub.status.busy": "2026-03-28T09:36:30.940579Z", + "iopub.status.idle": "2026-03-28T09:36:43.787286Z", + "shell.execute_reply": "2026-03-28T09:36:43.786463Z" + }, + "papermill": { + "duration": 12.849027, + "end_time": "2026-03-28T09:36:43.787801", + "exception": false, + "start_time": "2026-03-28T09:36:30.938774", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32986e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.792745Z", + "iopub.status.busy": "2026-03-28T09:36:43.792454Z", + "iopub.status.idle": "2026-03-28T09:36:43.797942Z", + "shell.execute_reply": "2026-03-28T09:36:43.797217Z" + }, + "papermill": { + "duration": 0.008999, + "end_time": "2026-03-28T09:36:43.798486", + "exception": false, + "start_time": "2026-03-28T09:36:43.789487", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def simulate_data_pure(num_sample, seed, scenario='E1'):\n", + " \"\"\"\n", + " Non-GP data: deterministic macro-trend + nonstationary anomalies only.\n", + " No noise, no Gaussian Process component.\n", + " \"\"\"\n", + " rng = np.random.default_rng(seed)\n", + "\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + "\n", + " base_wind = 5.0\n", + " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", + " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", + " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", + " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) & (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", + " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", + "\n", + " num_anomalies = 60\n", + " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", + " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", + " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", + " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", + " az = np.sin(anomaly_lats)\n", + " anomaly_effect = np.zeros(num_sample)\n", + " for i in range(num_anomalies):\n", + " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", + " amplitude = rng.uniform(-10.0, 18.0)\n", + " radius = rng.uniform(0.005, 0.03)\n", + " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", + "\n", + " mean_trend += anomaly_effect\n", + " z_final = np.maximum(mean_trend, 0.5).astype(np.float32)\n", + "\n", + " print(f\"\\n=== Scenario {scenario} (Non-GP: Pure Nonstationary Trend, no noise) ===\")\n", + " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (\\u00b1{np.std(z_final) / np.sqrt(num_sample):.4f}), Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect\n", + " gc.collect()\n", + "\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5c1e9f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.801943Z", + "iopub.status.busy": "2026-03-28T09:36:43.801818Z", + "iopub.status.idle": "2026-03-28T09:36:43.808817Z", + "shell.execute_reply": "2026-03-28T09:36:43.807788Z" + }, + "papermill": { + "duration": 0.00948, + "end_time": "2026-03-28T09:36:43.809365", + "exception": false, + "start_time": "2026-03-28T09:36:43.799885", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + " categorical_data = None\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(wendland(location, grid, theta=theta, k=2))\n", + " del grid\n", + " gc.collect()\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " X_train_cont = basis[train_x1]\n", + " X_val_cont = basis[val_x1]\n", + " X_test_cont = basis[test_x1]\n", + " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", + " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", + " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", + " if categorical_data is None:\n", + " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " return (X_train_flat, X_train_cat, y_train_flat,\n", + " X_val_flat, X_val_cat, y_val_flat,\n", + " X_test_flat, X_test_cat, y_test_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6728b7b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.812663Z", + "iopub.status.busy": "2026-03-28T09:36:43.812540Z", + "iopub.status.idle": "2026-03-28T09:36:43.819834Z", + "shell.execute_reply": "2026-03-28T09:36:43.819022Z" + }, + "papermill": { + "duration": 0.009918, + "end_time": "2026-03-28T09:36:43.820579", + "exception": false, + "start_time": "2026-03-28T09:36:43.810661", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " return metrics, model\n", + "\n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", + " epochs=epochs, batch_size=batch_size, verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", + " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", + " dropout_rate=dropout_rate, optimizer=_opt,\n", + " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", + " patience=40, verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience,\n", + " restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5,\n", + " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + " val_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset, validation_data=val_dataset,\n", + " epochs=epochs, callbacks=callbacks, verbose=0)\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + "\n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " return metrics, model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce398870", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.823892Z", + "iopub.status.busy": "2026-03-28T09:36:43.823771Z", + "iopub.status.idle": "2026-03-28T09:36:43.833355Z", + "shell.execute_reply": "2026-03-28T09:36:43.832675Z" + }, + "papermill": { + "duration": 0.012015, + "end_time": "2026-03-28T09:36:43.833871", + "exception": false, + "start_time": "2026-03-28T09:36:43.821856", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(longitude, latitude, c=value,\n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", + " plot_type='prediction', cbar_label=None):\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", + " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " if plot_type == 'residual' else\n", + " mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", + " model_list=None, experiment_info=None):\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " idx_all = np.arange(len(y_combined))\n", + " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + "\n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", + "\n", + " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", + " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", + "\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})')\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " p = predictions[mn]\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", + " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", + " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig1)\n", + "\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", + " -vmax_abs, vmax_abs,\n", + " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", + " plot_type='residual')\n", + " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig2)\n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38eb7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.837058Z", + "iopub.status.busy": "2026-03-28T09:36:43.836937Z", + "iopub.status.idle": "2026-03-28T09:36:43.839875Z", + "shell.execute_reply": "2026-03-28T09:36:43.839202Z" + }, + "papermill": { + "duration": 0.005566, + "end_time": "2026-03-28T09:36:43.840709", + "exception": false, + "start_time": "2026-03-28T09:36:43.835143", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ── Experiment parameters ─────────────────────────────────────────────────────\n", + "seed = 50\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "\n", + "# All models (including dk_sphere) use the same original candidates\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", + "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", + "print(f\"repeats : {repeat_experiment}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "main_loop_pure", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:43.843973Z", + "iopub.status.busy": "2026-03-28T09:36:43.843852Z", + "iopub.status.idle": "2026-03-28T17:51:20.330164Z", + "shell.execute_reply": "2026-03-28T17:51:20.329349Z" + }, + "papermill": { + "duration": 29676.488693, + "end_time": "2026-03-28T17:51:20.330687", + "exception": false, + "start_time": "2026-03-28T09:36:43.841994", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_noGP_pure.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_pure.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "# ── load checkpoint ───────────────────────────────────────────────────────────\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"Starting fresh.\")\n", + "\n", + "# ── main loop ─────────────────────────────────────────────────────────────────\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n\" + \"=\"*70)\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(\"=\"*70)\n", + "\n", + " out_json = f\"repeat_{repeat}_noGP_pure_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "summary_pure", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T17:51:20.375603Z", + "iopub.status.busy": "2026-03-28T17:51:20.375437Z", + "iopub.status.idle": "2026-03-28T17:51:20.382084Z", + "shell.execute_reply": "2026-03-28T17:51:20.381419Z" + }, + "papermill": { + "duration": 0.030421, + "end_time": "2026-03-28T17:51:20.382548", + "exception": false, + "start_time": "2026-03-28T17:51:20.352127", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"Summary — {n} Repeats\")\n", + "print(\" Scenario: Non-GP + Pure Nonstationary Trend (no noise, no GP)\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 29693.937646, + "end_time": "2026-03-28T17:51:21.320941", + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-28T09:36:27.383295", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb new file mode 100644 index 0000000..dac392c --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb @@ -0,0 +1,886 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d1cde2eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:05:43.677420Z", + "iopub.status.busy": "2026-03-27T23:05:43.677246Z", + "iopub.status.idle": "2026-03-27T23:05:43.682799Z", + "shell.execute_reply": "2026-03-27T23:05:43.681940Z" + }, + "papermill": { + "duration": 0.008809, + "end_time": "2026-03-27T23:05:43.683343", + "exception": false, + "start_time": "2026-03-27T23:05:43.674534", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8890884f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:05:43.688513Z", + "iopub.status.busy": "2026-03-27T23:05:43.688303Z", + "iopub.status.idle": "2026-03-27T23:05:45.848117Z", + "shell.execute_reply": "2026-03-27T23:05:45.847383Z" + }, + "papermill": { + "duration": 2.163659, + "end_time": "2026-03-27T23:05:45.848726", + "exception": false, + "start_time": "2026-03-27T23:05:43.685067", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46d6d1ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:05:45.854064Z", + "iopub.status.busy": "2026-03-27T23:05:45.853822Z", + "iopub.status.idle": "2026-03-27T23:05:46.816946Z", + "shell.execute_reply": "2026-03-27T23:05:46.816078Z" + }, + "papermill": { + "duration": 0.967214, + "end_time": "2026-03-27T23:05:46.817604", + "exception": false, + "start_time": "2026-03-27T23:05:45.850390", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd094a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:05:46.822091Z", + "iopub.status.busy": "2026-03-27T23:05:46.821941Z", + "iopub.status.idle": "2026-03-27T23:05:46.824578Z", + "shell.execute_reply": "2026-03-27T23:05:46.823880Z" + }, + "papermill": { + "duration": 0.005885, + "end_time": "2026-03-27T23:05:46.825023", + "exception": false, + "start_time": "2026-03-27T23:05:46.819138", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0aafba7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:05:46.828285Z", + "iopub.status.busy": "2026-03-27T23:05:46.828152Z", + "iopub.status.idle": "2026-03-27T23:06:00.185461Z", + "shell.execute_reply": "2026-03-27T23:06:00.184726Z" + }, + "papermill": { + "duration": 13.359808, + "end_time": "2026-03-27T23:06:00.186094", + "exception": false, + "start_time": "2026-03-27T23:05:46.826286", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32986e25", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.190150Z", + "iopub.status.busy": "2026-03-27T23:06:00.189892Z", + "iopub.status.idle": "2026-03-27T23:06:00.195686Z", + "shell.execute_reply": "2026-03-27T23:06:00.194985Z" + }, + "papermill": { + "duration": 0.008674, + "end_time": "2026-03-27T23:06:00.196227", + "exception": false, + "start_time": "2026-03-27T23:06:00.187553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def simulate_data_nonstationary_noise(num_sample, noise_std, seed, scenario='E1'):\n", + " \"\"\"\n", + " Non-GP data generation: deterministic macro-trend + nonstationary anomalies\n", + " + N(0, noise_std^2) Gaussian noise. No Gaussian Process component.\n", + " \"\"\"\n", + " rng = np.random.default_rng(seed)\n", + "\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + "\n", + " base_wind = 5.0\n", + " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", + " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", + " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", + " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) & (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", + " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", + "\n", + " num_anomalies = 60\n", + " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", + " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", + " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", + " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", + " az = np.sin(anomaly_lats)\n", + " anomaly_effect = np.zeros(num_sample)\n", + " for i in range(num_anomalies):\n", + " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", + " amplitude = rng.uniform(-10.0, 18.0)\n", + " radius = rng.uniform(0.005, 0.03)\n", + " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", + "\n", + " mean_trend += anomaly_effect\n", + " mean_trend = np.maximum(mean_trend, 0.5)\n", + "\n", + " noise = rng.normal(0.0, noise_std, num_sample).astype(np.float32)\n", + " z_final = mean_trend + noise\n", + " z_final = np.maximum(z_final, 0.0)\n", + "\n", + " print(f\"\\n=== Scenario {scenario} (Non-GP: Nonstationary Trend + Gaussian Noise N(0,{noise_std}²)) ===\")\n", + " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (±{np.std(z_final) / np.sqrt(num_sample):.4f}), Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect, noise\n", + " gc.collect()\n", + "\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5c1e9f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.199629Z", + "iopub.status.busy": "2026-03-27T23:06:00.199498Z", + "iopub.status.idle": "2026-03-27T23:06:00.206548Z", + "shell.execute_reply": "2026-03-27T23:06:00.205835Z" + }, + "papermill": { + "duration": 0.009363, + "end_time": "2026-03-27T23:06:00.206928", + "exception": false, + "start_time": "2026-03-27T23:06:00.197565", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + " categorical_data = None\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(wendland(location, grid, theta=theta, k=2))\n", + " del grid\n", + " gc.collect()\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " X_train_cont = basis[train_x1]\n", + " X_val_cont = basis[val_x1]\n", + " X_test_cont = basis[test_x1]\n", + " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", + " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", + " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", + " if categorical_data is None:\n", + " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " return (X_train_flat, X_train_cat, y_train_flat,\n", + " X_val_flat, X_val_cat, y_val_flat,\n", + " X_test_flat, X_test_cat, y_test_flat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6728b7b3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.210306Z", + "iopub.status.busy": "2026-03-27T23:06:00.210184Z", + "iopub.status.idle": "2026-03-27T23:06:00.217173Z", + "shell.execute_reply": "2026-03-27T23:06:00.216493Z" + }, + "papermill": { + "duration": 0.009377, + "end_time": "2026-03-27T23:06:00.217654", + "exception": false, + "start_time": "2026-03-27T23:06:00.208277", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + "\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " return metrics, model\n", + "\n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", + " epochs=epochs, batch_size=batch_size, verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", + " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1], output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", + " dropout_rate=dropout_rate, optimizer=_opt,\n", + " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", + " patience=40, verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience,\n", + " restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5,\n", + " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + " val_dataset = tf.data.Dataset.from_tensor_slices(\n", + " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset, validation_data=val_dataset,\n", + " epochs=epochs, callbacks=callbacks, verbose=0)\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + "\n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " return metrics, model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce398870", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.221137Z", + "iopub.status.busy": "2026-03-27T23:06:00.220995Z", + "iopub.status.idle": "2026-03-27T23:06:00.229836Z", + "shell.execute_reply": "2026-03-27T23:06:00.229146Z" + }, + "papermill": { + "duration": 0.01132, + "end_time": "2026-03-27T23:06:00.230272", + "exception": false, + "start_time": "2026-03-27T23:06:00.218952", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(longitude, latitude, c=value,\n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", + " plot_type='prediction', cbar_label=None):\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", + " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " if plot_type == 'residual' else\n", + " mcolors.LinearSegmentedColormap.from_list(\n", + " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", + " model_list=None, experiment_info=None):\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " idx_all = np.arange(len(y_combined))\n", + " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + "\n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", + "\n", + " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", + " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", + "\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})')\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " p = predictions[mn]\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", + " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", + " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig1)\n", + "\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " for i, mn in enumerate(model_list):\n", + " if mn in predictions:\n", + " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", + " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", + " -vmax_abs, vmax_abs,\n", + " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", + " plot_type='residual')\n", + " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show(); plt.close(fig2)\n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d38eb7c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.233598Z", + "iopub.status.busy": "2026-03-27T23:06:00.233474Z", + "iopub.status.idle": "2026-03-27T23:06:00.236850Z", + "shell.execute_reply": "2026-03-27T23:06:00.235705Z" + }, + "papermill": { + "duration": 0.006019, + "end_time": "2026-03-27T23:06:00.237617", + "exception": false, + "start_time": "2026-03-27T23:06:00.231598", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# ── Experiment parameters ─────────────────────────────────────────────────────\n", + "seed = 50\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "\n", + "# All models (including dk_sphere) use the same original candidates\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", + "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", + "print(f\"repeats : {repeat_experiment}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a2b4ae9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-27T23:06:00.241061Z", + "iopub.status.busy": "2026-03-27T23:06:00.240931Z", + "iopub.status.idle": "2026-03-28T03:41:53.026815Z", + "shell.execute_reply": "2026-03-28T03:41:53.025756Z" + }, + "papermill": { + "duration": 16552.788289, + "end_time": "2026-03-28T03:41:53.027323", + "exception": false, + "start_time": "2026-03-27T23:06:00.239034", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_noGP_noise.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_noise.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "# ── load checkpoint ───────────────────────────────────────────────────────────\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"Starting fresh.\")\n", + "\n", + "# ── main loop ─────────────────────────────────────────────────────────────────\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n\" + \"=\"*70)\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(\"=\"*70)\n", + "\n", + " out_json = f\"repeat_{repeat}_noGP_noise_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a59787b6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:41:53.054099Z", + "iopub.status.busy": "2026-03-28T03:41:53.053923Z", + "iopub.status.idle": "2026-03-28T03:41:53.060851Z", + "shell.execute_reply": "2026-03-28T03:41:53.060122Z" + }, + "papermill": { + "duration": 0.020596, + "end_time": "2026-03-28T03:41:53.061305", + "exception": false, + "start_time": "2026-03-28T03:41:53.040709", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"Summary — {n} Repeats\")\n", + "print(\" Scenario: Non-GP + Gaussian Noise (N(0, 0.5^2)), Nonstationary Trend + StdScaler\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 16571.05346, + "end_time": "2026-03-28T03:41:54.095574", + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-27T23:05:43.042114", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb new file mode 100644 index 0000000..8ef28dc --- /dev/null +++ b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb @@ -0,0 +1,1151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fb75f2ac", + "metadata": { + "papermill": { + "duration": 0.00685, + "end_time": "2026-03-28T03:42:12.044436", + "exception": false, + "start_time": "2026-03-28T03:42:12.037586", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8e1c49d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:12.049396Z", + "iopub.status.busy": "2026-03-28T03:42:12.049222Z", + "iopub.status.idle": "2026-03-28T03:42:12.053895Z", + "shell.execute_reply": "2026-03-28T03:42:12.053111Z" + }, + "papermill": { + "duration": 0.007911, + "end_time": "2026-03-28T03:42:12.054557", + "exception": false, + "start_time": "2026-03-28T03:42:12.046646", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "import sys\n", + "import os\n", + "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", + "if project_root not in sys.path:\n", + " sys.path.append(project_root)\n", + "print(f\"Project root added: {project_root}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46af0f5c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:12.058724Z", + "iopub.status.busy": "2026-03-28T03:42:12.058598Z", + "iopub.status.idle": "2026-03-28T03:42:14.014104Z", + "shell.execute_reply": "2026-03-28T03:42:14.013260Z" + }, + "papermill": { + "duration": 1.958617, + "end_time": "2026-03-28T03:42:14.014652", + "exception": false, + "start_time": "2026-03-28T03:42:12.056035", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import multiprocessing as mp\n", + "if mp.get_start_method(allow_none=True) is None:\n", + " mp.set_start_method('spawn')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", + "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", + "\n", + "import os, time, gc\n", + "from types import SimpleNamespace\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time as time_module\n", + "from scipy.stats import t\n", + "from scipy.special import kv, gamma\n", + "\n", + "import jax, jax.numpy as jnp\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import mixed_precision\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.losses import Huber\n", + "from tensorflow.keras import backend as K\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "import optuna\n", + "import plotly.io as pio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "id": "3642d69c", + "metadata": { + "papermill": { + "duration": 0.002179, + "end_time": "2026-03-28T03:42:14.018682", + "exception": false, + "start_time": "2026-03-28T03:42:14.016503", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Environment setting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9244ac2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:14.022813Z", + "iopub.status.busy": "2026-03-28T03:42:14.022562Z", + "iopub.status.idle": "2026-03-28T03:42:14.906204Z", + "shell.execute_reply": "2026-03-28T03:42:14.904854Z" + }, + "papermill": { + "duration": 0.886644, + "end_time": "2026-03-28T03:42:14.906912", + "exception": false, + "start_time": "2026-03-28T03:42:14.020268", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np_f32 = np.float32\n", + "jnp_f32 = jnp.float32\n", + "dtype_basis = np.float32\n", + "\n", + "jax.config.update(\"jax_enable_x64\", False)\n", + "\n", + "pio.renderers.default = \"notebook\"\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", + "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", + "\n", + "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", + "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", + "\n", + "def init_hardware(dtype: str = \"float32\"):\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", + " for g in gpus:\n", + " tf.config.experimental.set_memory_growth(g, True)\n", + " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", + " mixed_precision.set_global_policy(dtype)\n", + " return strategy\n", + "\n", + "strategy = init_hardware(dtype=\"float32\")" + ] + }, + { + "cell_type": "markdown", + "id": "631dc3ef", + "metadata": { + "papermill": { + "duration": 0.002164, + "end_time": "2026-03-28T03:42:14.910865", + "exception": false, + "start_time": "2026-03-28T03:42:14.908701", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Auto save notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8082d639", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:14.914778Z", + "iopub.status.busy": "2026-03-28T03:42:14.914619Z", + "iopub.status.idle": "2026-03-28T03:42:14.917289Z", + "shell.execute_reply": "2026-03-28T03:42:14.916575Z" + }, + "papermill": { + "duration": 0.005367, + "end_time": "2026-03-28T03:42:14.917792", + "exception": false, + "start_time": "2026-03-28T03:42:14.912425", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from IPython.display import display, Javascript\n", + "\n", + "def save_notebook():\n", + " display(Javascript('IPython.notebook.save_checkpoint()'))\n", + " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", + " print(f\"💾 Notebook saved at {current_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9399eb89", + "metadata": { + "papermill": { + "duration": 0.001492, + "end_time": "2026-03-28T03:42:14.920877", + "exception": false, + "start_time": "2026-03-28T03:42:14.919385", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Our function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8391a63c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:14.924994Z", + "iopub.status.busy": "2026-03-28T03:42:14.924866Z", + "iopub.status.idle": "2026-03-28T03:42:27.686270Z", + "shell.execute_reply": "2026-03-28T03:42:27.685474Z" + }, + "papermill": { + "duration": 12.76505, + "end_time": "2026-03-28T03:42:27.687470", + "exception": false, + "start_time": "2026-03-28T03:42:14.922420", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", + "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", + "\n", + "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", + "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", + "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", + "\n", + "from rpy2.robjects.conversion import localconverter\n", + "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" + ] + }, + { + "cell_type": "markdown", + "id": "f7d37f3e", + "metadata": { + "papermill": { + "duration": 0.001646, + "end_time": "2026-03-28T03:42:27.691049", + "exception": false, + "start_time": "2026-03-28T03:42:27.689403", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Simulation Helper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "076fe31f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:27.731128Z", + "iopub.status.busy": "2026-03-28T03:42:27.730828Z", + "iopub.status.idle": "2026-03-28T03:42:27.736693Z", + "shell.execute_reply": "2026-03-28T03:42:27.735906Z" + }, + "papermill": { + "duration": 0.044472, + "end_time": "2026-03-28T03:42:27.737157", + "exception": false, + "start_time": "2026-03-28T03:42:27.692685", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import gc\n", + "\n", + "def simulate_data(num_sample, outlier_ratio, outlier_multiplier, seed, scenario='E1'):\n", + " \"\"\"\n", + " Non-GP: deterministic macro-trend + nonstationary anomalies + outlier injection.\n", + " No noise, no Gaussian Process / Gamma noise component.\n", + " \"\"\"\n", + " rng = np.random.default_rng(seed)\n", + "\n", + " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", + " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", + " lat_rad = np.pi/2 - theta\n", + " lon_rad = phi - np.pi\n", + "\n", + " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", + " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", + " z_c = np.sin(lat_rad)\n", + "\n", + " base_wind = 5.0\n", + " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", + " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", + " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", + " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) &\n", + " (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", + " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", + "\n", + " num_anomalies = 60\n", + " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", + " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", + " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", + " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", + " az = np.sin(anomaly_lats)\n", + "\n", + " anomaly_effect = np.zeros(num_sample)\n", + " for i in range(num_anomalies):\n", + " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", + " amplitude = rng.uniform(-10.0, 18.0)\n", + " radius = rng.uniform(0.005, 0.03)\n", + " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", + "\n", + " mean_trend += anomaly_effect\n", + " mean_trend = np.maximum(mean_trend, 0.5)\n", + "\n", + " # No noise — pure deterministic trend\n", + " z_final = mean_trend.copy().astype(np.float32)\n", + "\n", + " idx_outliers = rng.choice(num_sample, size=int(num_sample * outlier_ratio), replace=False)\n", + " z_final[idx_outliers] *= outlier_multiplier\n", + "\n", + " print(f\"\\n=== Scenario {scenario} (Non-GP: Trend only + Outliers {outlier_ratio*100:.1f}% x{outlier_multiplier}) ===\")\n", + " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (\\u00b1{np.std(z_final)/np.sqrt(num_sample):.4f}), \"\n", + " f\"Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", + "\n", + " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", + " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", + "\n", + " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect\n", + " gc.collect()\n", + "\n", + " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" + ] + }, + { + "cell_type": "markdown", + "id": "57a9ba52", + "metadata": { + "papermill": { + "duration": 0.001559, + "end_time": "2026-03-28T03:42:27.740534", + "exception": false, + "start_time": "2026-03-28T03:42:27.738975", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Helper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3d5a807", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:27.744527Z", + "iopub.status.busy": "2026-03-28T03:42:27.744388Z", + "iopub.status.idle": "2026-03-28T03:42:27.756491Z", + "shell.execute_reply": "2026-03-28T03:42:27.755924Z" + }, + "papermill": { + "duration": 0.015059, + "end_time": "2026-03-28T03:42:27.757177", + "exception": false, + "start_time": "2026-03-28T03:42:27.742118", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def data_preprocessing(data_frame):\n", + " data = data_frame.copy()\n", + "\n", + " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", + " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", + " data.dropna(subset=numeric_cols, inplace=True)\n", + "\n", + " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", + "\n", + " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", + " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", + "\n", + " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", + " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", + " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", + "\n", + " # Handle\n", + " categorical_data = None\n", + "\n", + " return location_data, location_data_norm, categorical_data, y_combined\n", + "\n", + "\n", + "def precompute_wendland(location, num_basis):\n", + " parts = []\n", + " for nb in num_basis:\n", + " grid = np.column_stack(np.meshgrid(\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", + " )).reshape(-1, 2).astype(np_f32)\n", + "\n", + " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", + " parts.append(\n", + " wendland(location, grid, theta=theta, k=2)\n", + " )\n", + "\n", + " # Clean up the memory\n", + " del grid\n", + " gc.collect()\n", + "\n", + " return np.hstack(parts).astype(dtype_basis, copy=False)\n", + "\n", + "\n", + "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", + " if knot is None:\n", + " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", + " knot = location_data[idx_knot].astype(np_f32)\n", + " else:\n", + " idx_knot = None\n", + "\n", + " if distance_type == \"sphere\":\n", + " with localconverter(numpy2ri_converter):\n", + " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", + " res_dict = dict(zip(res_r.names(), res_r))\n", + " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", + " else:\n", + " phi = np.asarray(\n", + " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", + " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", + " )\n", + "\n", + " return phi, idx_knot, knot\n", + "\n", + "\n", + "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", + " idx_all = np.arange(basis.shape[0])\n", + " train_ratio, val_ratio, test_ratio = split_ratio\n", + " \n", + " train_val_x1, test_x1 = train_test_split(\n", + " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", + " train_x1, val_x1 = train_test_split(\n", + " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", + " \n", + " X_train_cont, X_val_cont, X_test_cont = (\n", + " basis[train_x1], basis[val_x1], basis[test_x1])\n", + " y_train, y_val, y_test = (\n", + " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", + " \n", + " def flatten(targets):\n", + " return targets.reshape(-1).astype(np_f32, copy=False)\n", + " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", + "\n", + " def flatten(covariates):\n", + " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", + " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", + " \n", + " # Handle categorical features\n", + " if categorical_data is None:\n", + " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", + " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", + " else:\n", + " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", + " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", + " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", + " \n", + " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", + " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", + " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", + " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", + " \n", + " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", + " X_val_cont_flat, X_val_cat, y_val_flat,\n", + " X_test_cont_flat, X_test_cat, y_test_flat)\n", + "\n", + "\n", + "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", + " X_train, X_train_cat, y_train,\n", + " X_val, X_val_cat, y_val,\n", + " X_test, X_test_cat, y_test):\n", + "\n", + " # 1. Force deterministic weights for this specific seed\n", + " os.environ['PYTHONHASHSEED'] = str(seed)\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " tf.random.set_seed(seed)\n", + " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", + " t0 = time.time()\n", + " model = LinearRegression().fit(X_train, y_train)\n", + " \n", + " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", + " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", + " training_time = time.time() - t0\n", + " \n", + " metrics = {\n", + " \"Model\": name_model,\n", + " \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " \n", + " return metrics, model\n", + " \n", + " elif name_model == \"DeepKriging_wendland\":\n", + " config = DeepKrigingDefaultConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " optimizer=Adam(learning_rate=1e-3),\n", + " loss=loss,\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " verbose=0\n", + " )\n", + "\n", + " elif name_model == \"DeepKriging_wendland*\":\n", + " optimizer = Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64],\n", + " activation='relu',\n", + " dropout_rate=dropout_rate,\n", + " optimizer=optimizer,\n", + " loss=loss,\n", + " metrics=['mae'],\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " patience=40,\n", + " verbose=0\n", + " )\n", + "\n", + " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", + " optimizer = Adam(learning_rate=5e-3)\n", + " config = DeepKrigingModelConfig(\n", + " input_dim=X_train.shape[1],\n", + " output_type='continuous',\n", + " hidden_layers=[1024, 512, 256, 128, 64],\n", + " activation='relu',\n", + " dropout_rate=dropout_rate,\n", + " optimizer=optimizer,\n", + " loss=loss,\n", + " metrics=['mae'],\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " patience=40,\n", + " verbose=0\n", + " )\n", + "\n", + " t0 = time.time()\n", + " with strategy.scope():\n", + " if name_model == \"DeepKriging_wendland\":\n", + " model = DeepKrigingDefaultTrainer(config)\n", + " elif name_model == \"DeepKriging_wendland*\":\n", + " model = DeepKrigingTrainer(config)\n", + " else:\n", + " model = DeepKrigingTrainer(config)\n", + " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", + "\n", + " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", + " if name_model == \"DeepKriging_wendland\":\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0)\n", + " ]\n", + " else:\n", + " callbacks = [\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", + " save_best_only=True, save_weights_only=True, verbose=0),\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", + " ]\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((\n", + " (X_train, X_train_cat), y_train\n", + " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((\n", + " (X_val, X_val_cat), y_val\n", + " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " history = model.model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=0\n", + " )\n", + "\n", + " if os.path.exists(checkpoint_path):\n", + " model.model.load_weights(checkpoint_path)\n", + " os.remove(checkpoint_path)\n", + " \n", + " val_loss = float(np.min(history.history[\"val_loss\"]))\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", + " training_time = time.time() - t0\n", + "\n", + " metrics = {\n", + " \"Model\": name_model,\n", + " \"Val_loss\": float(val_loss),\n", + " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", + " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", + " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", + " \"R2\": float(r2_score(y_test, y_pred)),\n", + " \"Time\": float(training_time),\n", + " }\n", + " \n", + " del train_dataset, val_dataset\n", + " gc.collect()\n", + " \n", + " return metrics, model\n", + "\n", + "\n", + "def cleanup_tf_session():\n", + " K.clear_session()\n", + " gc.collect()\n", + " try:\n", + " tf.keras.backend.clear_session()\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1573eecc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:27.761193Z", + "iopub.status.busy": "2026-03-28T03:42:27.761067Z", + "iopub.status.idle": "2026-03-28T03:42:27.770613Z", + "shell.execute_reply": "2026-03-28T03:42:27.769878Z" + }, + "papermill": { + "duration": 0.012358, + "end_time": "2026-03-28T03:42:27.771172", + "exception": false, + "start_time": "2026-03-28T03:42:27.758814", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", + " \"\"\"Plot data on Robinson projection\"\"\"\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + "\n", + " sc = ax.scatter(longitude, latitude, c=value, \n", + " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", + " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", + "\n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " return sc\n", + "\n", + "\n", + "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", + " \"\"\"Create subplot with Robinson projection\"\"\"\n", + " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", + " \n", + " # Choose colormap based on plot type\n", + " if plot_type == 'residual':\n", + " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", + " else:\n", + " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", + " \n", + " # Set global view\n", + " ax.set_global()\n", + " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", + " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", + " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", + " \n", + " # Plot scatter\n", + " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", + " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", + " vmin=vmin, vmax=vmax)\n", + " \n", + " ax.set_title(title, fontsize=10, pad=3)\n", + " \n", + " # Add colorbar\n", + " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", + " \n", + " if cbar_label is None:\n", + " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", + " \n", + " # Increased fontsize to match old version\n", + " cbar.set_label(cbar_label, fontsize=10)\n", + " cbar.ax.tick_params(labelsize=7)\n", + " \n", + " return ax, sc\n", + "\n", + "\n", + "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", + " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", + " if model_list is None:\n", + " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", + " \n", + " idx_all = np.arange(len(y_combined))\n", + " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", + " y_test = y_combined[test_idx].reshape(-1)\n", + " test_locations = dataframe.iloc[test_idx]\n", + " \n", + " predictions = {}\n", + " for model_name in model_list:\n", + " if model_name not in models_dict or models_dict[model_name] is None:\n", + " continue\n", + " \n", + " model = models_dict[model_name]\n", + " X_test = basis_dict[model_name][test_idx]\n", + " \n", + " if \"DeepKriging\" in model_name:\n", + " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", + " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", + " elif model_name == \"UniversalKriging\":\n", + " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", + " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", + " else:\n", + " y_pred = model.predict(X_test).reshape(-1)\n", + " \n", + " mse = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(mse)\n", + " order = models_dict.get(f\"{model_name}_order\", \"\")\n", + " \n", + " predictions[model_name] = {\n", + " 'values': y_pred,\n", + " 'rmse': rmse,\n", + " 'order': order\n", + " }\n", + " \n", + " # Calculate global min/max for consistent color scale\n", + " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", + " all_values_concat = np.concatenate(all_values)\n", + " vmin = np.percentile(all_values_concat, 2)\n", + " vmax = np.percentile(all_values_concat, 98)\n", + " \n", + " # Figure 1: Predictions comparison\n", + " fig1 = plt.figure(figsize=(16, 14))\n", + " \n", + " noise_info = \"\"\n", + " if experiment_info:\n", + " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", + " if experiment_info.get('noise_var'):\n", + " noise_info += f\", Var={experiment_info['noise_var']}\"\n", + " \n", + " # Plot real data\n", + " create_subplot_robinson(\n", + " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", + " f'Real Data (n={len(dataframe)})',\n", + " plot_type='prediction'\n", + " )\n", + " \n", + " # Plot predictions\n", + " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", + " for i, model_name in enumerate(model_list):\n", + " if model_name in predictions:\n", + " pred = predictions[model_name]\n", + " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", + " \n", + " create_subplot_robinson(\n", + " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", + " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", + " plot_type='prediction'\n", + " )\n", + " \n", + " # Modified title fontsize and layout to match old version\n", + " plt.suptitle(\n", + " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", + " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", + " fontsize=20, fontweight='bold', y=0.84\n", + " )\n", + " # Reverted to tight_layout with rect, as in the old version\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show()\n", + " plt.close(fig1)\n", + " \n", + "\n", + " # Figure 2: Residuals comparison\n", + " fig2 = plt.figure(figsize=(18, 6))\n", + " \n", + " residuals_map = {k: (y_test - predictions[k]['values']) \n", + " for k in model_list if k in predictions}\n", + " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", + " \n", + " for i, model_name in enumerate(model_list):\n", + " if model_name in predictions:\n", + " residuals = residuals_map[model_name]\n", + " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", + " \n", + " create_subplot_robinson(\n", + " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", + " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", + " plot_type='residual'\n", + " )\n", + " \n", + " # Modified title fontsize and layout to match old version\n", + " plt.suptitle(\n", + " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", + " fontsize=20, fontweight='bold', y=0.84\n", + " )\n", + " # Reverted to tight_layout with rect\n", + " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", + " plt.show()\n", + " plt.close(fig2)\n", + " \n", + " return predictions, test_idx" + ] + }, + { + "cell_type": "markdown", + "id": "ca651900", + "metadata": { + "papermill": { + "duration": 0.001632, + "end_time": "2026-03-28T03:42:27.774509", + "exception": false, + "start_time": "2026-03-28T03:42:27.772877", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Experiment Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ce2f573", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:27.778697Z", + "iopub.status.busy": "2026-03-28T03:42:27.778560Z", + "iopub.status.idle": "2026-03-28T03:42:27.781727Z", + "shell.execute_reply": "2026-03-28T03:42:27.780957Z" + }, + "papermill": { + "duration": 0.00595, + "end_time": "2026-03-28T03:42:27.782176", + "exception": false, + "start_time": "2026-03-28T03:42:27.776226", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Model Setup\n", + "seed = 50\n", + "OUTLIER_RATIO = 0.025\n", + "OUTLIER_MULTIPLIER = 5\n", + "epochs = 500\n", + "batch_size = 256\n", + "num_sample = 2500\n", + "huber_delta = 1.345\n", + "\n", + "# Basis Setup\n", + "num_basis = [10**2, 19**2, 37**2]\n", + "knot_num = 1400\n", + "order_max = 1400\n", + "base_orders = [10, 50, 100, 150, 200, 1000]\n", + "\n", + "repeat_experiment = 50\n", + "\n", + "print(f\"noGP outliers experiment: {repeat_experiment} repeats, seed={seed}\")\n", + "print(f\"Outliers: {OUTLIER_RATIO*100:.1f}% x{OUTLIER_MULTIPLIER}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fed92e3c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T03:42:27.786146Z", + "iopub.status.busy": "2026-03-28T03:42:27.786018Z", + "iopub.status.idle": "2026-03-28T09:36:19.130027Z", + "shell.execute_reply": "2026-03-28T09:36:19.128858Z" + }, + "papermill": { + "duration": 21231.347229, + "end_time": "2026-03-28T09:36:19.131058", + "exception": false, + "start_time": "2026-03-28T03:42:27.783829", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, subprocess, sys\n", + "\n", + "CHECKPOINT_PATH = \"checkpoint_noGP_outliers.json\"\n", + "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_outliers.py\")\n", + "PYTHON_EXE = sys.executable\n", + "\n", + "# ── load checkpoint ────────────────────────────────────────────────────────────\n", + "if os.path.exists(CHECKPOINT_PATH):\n", + " with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + " experiment_results = ckpt[\"experiment_results\"]\n", + " completed_repeats = set(ckpt[\"completed_repeats\"])\n", + " print(f\"▶ Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", + "else:\n", + " experiment_results = {\n", + " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + " }\n", + " completed_repeats = set()\n", + " print(\"▶ Starting fresh.\")\n", + "\n", + "# ── main loop ──────────────────────────────────────────────────────────────────\n", + "for repeat in range(repeat_experiment):\n", + "\n", + " if repeat in completed_repeats:\n", + " print(f\"⏭ Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", + " continue\n", + "\n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", + " print(f\"{'='*70}\")\n", + "\n", + " out_json = f\"repeat_{repeat}_noGP_outliers_results.json\"\n", + "\n", + " try:\n", + " result = subprocess.run(\n", + " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", + " capture_output=False,\n", + " check=True,\n", + " timeout=7200,\n", + " )\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"❌ Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", + " continue\n", + " except subprocess.TimeoutExpired:\n", + " print(f\"❌ Repeat {repeat+1} timed out. Skipping.\")\n", + " continue\n", + " except Exception as e:\n", + " print(f\"❌ Repeat {repeat+1} failed: {e}. Skipping.\")\n", + " continue\n", + "\n", + " if not os.path.exists(out_json):\n", + " print(f\"❌ No output JSON for repeat {repeat+1}. Skipping.\")\n", + " continue\n", + "\n", + " with open(out_json) as f:\n", + " res = json.load(f)\n", + " os.remove(out_json)\n", + "\n", + " best_orders = res[\"best_orders\"]\n", + " metrics_map = res[\"metrics\"]\n", + "\n", + " table_rows = []\n", + " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", + " met = metrics_map[m]\n", + " table_rows.append({\n", + " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", + " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", + " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", + " })\n", + " import pandas as _pd\n", + " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + " for m in experiment_results:\n", + " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", + " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", + " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", + " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", + "\n", + " completed_repeats.add(repeat)\n", + " with open(CHECKPOINT_PATH, \"w\") as f:\n", + " json.dump({\"experiment_results\": experiment_results,\n", + " \"completed_repeats\": list(completed_repeats)}, f)\n", + "\n", + " print(f\"✅ Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", + "\n", + "print(f\"\\n🎉 All done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "f230fcba", + "metadata": { + "papermill": { + "duration": 0.023718, + "end_time": "2026-03-28T09:36:19.180162", + "exception": false, + "start_time": "2026-03-28T09:36:19.156444", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Summary of All Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db4949b8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-28T09:36:19.230512Z", + "iopub.status.busy": "2026-03-28T09:36:19.230314Z", + "iopub.status.idle": "2026-03-28T09:36:19.237375Z", + "shell.execute_reply": "2026-03-28T09:36:19.236689Z" + }, + "papermill": { + "duration": 0.03168, + "end_time": "2026-03-28T09:36:19.237840", + "exception": false, + "start_time": "2026-03-28T09:36:19.206160", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import json, numpy as np, pandas as pd\n", + "\n", + "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", + " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", + "\n", + "with open(CHECKPOINT_PATH) as f:\n", + " ckpt = json.load(f)\n", + "results = ckpt[\"experiment_results\"]\n", + "n = len(next(iter(results.values()))[\"MSPE\"])\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(f\"📊 Summary — {n} Repeats\")\n", + "print(\" Scenario: Non-GP + Outliers (2.5% × 5×), Nonstationary Trend\")\n", + "print(\"=\"*80)\n", + "\n", + "rows = []\n", + "for m in MODELS:\n", + " vals = results[m]\n", + " rows.append({\n", + " \"Model\": m,\n", + " \"N\": len(vals[\"MSPE\"]),\n", + " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", + " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", + " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", + " \"R² (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", + " })\n", + "\n", + "df = pd.DataFrame(rows)\n", + "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", + "\n", + "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", + "print(f\"\\n🏆 Best Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", + "\n", + "print(\"\\n── Ranking by mean RMSE ──\")\n", + "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", + " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "air-pollution", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 21248.914793, + "end_time": "2026-03-28T09:36:20.377664", + "environment_variables": {}, + "exception": null, + "input_path": "simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps.ipynb", + "output_path": "simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb", + "parameters": {}, + "start_time": "2026-03-28T03:42:11.462871", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_outliers_stdscaler_50reps.ipynb b/examples/simulation/simulation_spherical_nn_sim_RSHC_c15_outliers_stdscaler_50reps.ipynb similarity index 100% rename from notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_outliers_stdscaler_50reps.ipynb rename to examples/simulation/simulation_spherical_nn_sim_RSHC_c15_outliers_stdscaler_50reps.ipynb diff --git a/notebook/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb b/notebook/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb deleted file mode 100644 index ff93f9d..0000000 --- a/notebook/real_data/experiment_deepkriging_windspeed_repeat_multitime.ipynb +++ /dev/null @@ -1,1905 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b1833097", - "metadata": { - "papermill": { - "duration": 0.010962, - "end_time": "2026-01-22T06:28:22.418741", - "exception": false, - "start_time": "2026-01-22T06:28:22.407779", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "73a5f785", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:22.431002Z", - "iopub.status.busy": "2026-01-22T06:28:22.430384Z", - "iopub.status.idle": "2026-01-22T06:28:22.447875Z", - "shell.execute_reply": "2026-01-22T06:28:22.444425Z" - }, - "papermill": { - "duration": 0.027822, - "end_time": "2026-01-22T06:28:22.451416", - "exception": false, - "start_time": "2026-01-22T06:28:22.423594", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b67cc84b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:22.468481Z", - "iopub.status.busy": "2026-01-22T06:28:22.467637Z", - "iopub.status.idle": "2026-01-22T06:28:26.827773Z", - "shell.execute_reply": "2026-01-22T06:28:26.824631Z" - }, - "papermill": { - "duration": 4.372431, - "end_time": "2026-01-22T06:28:26.830373", - "exception": false, - "start_time": "2026-01-22T06:28:22.457942", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-27 08:31:12.357643: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1772152272.368359 23958 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1772152272.372214 23958 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1772152272.381247 23958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1772152272.381263 23958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1772152272.381264 23958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1772152272.381264 23958 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.special import gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "5f0aced4", - "metadata": { - "papermill": { - "duration": 0.006036, - "end_time": "2026-01-22T06:28:26.845882", - "exception": false, - "start_time": "2026-01-22T06:28:26.839846", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment Setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e5802f99", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:26.858357Z", - "iopub.status.busy": "2026-01-22T06:28:26.857625Z", - "iopub.status.idle": "2026-01-22T06:28:28.442676Z", - "shell.execute_reply": "2026-01-22T06:28:28.440796Z" - }, - "papermill": { - "duration": 1.592998, - "end_time": "2026-01-22T06:28:28.443637", - "exception": false, - "start_time": "2026-01-22T06:28:26.850639", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "5695a52d", - "metadata": { - "papermill": { - "duration": 0.001958, - "end_time": "2026-01-22T06:28:28.447783", - "exception": false, - "start_time": "2026-01-22T06:28:28.445825", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "46760605", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:28.453557Z", - "iopub.status.busy": "2026-01-22T06:28:28.453175Z", - "iopub.status.idle": "2026-01-22T06:28:44.167367Z", - "shell.execute_reply": "2026-01-22T06:28:44.165909Z" - }, - "papermill": { - "duration": 15.718946, - "end_time": "2026-01-22T06:28:44.168729", - "exception": false, - "start_time": "2026-01-22T06:28:28.449783", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "373798b0", - "metadata": { - "papermill": { - "duration": 0.002504, - "end_time": "2026-01-22T06:28:44.174219", - "exception": false, - "start_time": "2026-01-22T06:28:44.171715", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "30fcd1bf", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:44.181793Z", - "iopub.status.busy": "2026-01-22T06:28:44.181266Z", - "iopub.status.idle": "2026-01-22T06:28:44.203250Z", - "shell.execute_reply": "2026-01-22T06:28:44.202032Z" - }, - "papermill": { - "duration": 0.026668, - "end_time": "2026-01-22T06:28:44.204158", - "exception": false, - "start_time": "2026-01-22T06:28:44.177490", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_path, variable, num_sample, seed):\n", - " data = (pd.read_csv(data_path).sample(n=num_sample, random_state=seed).replace(\"-\", np.nan).dropna())\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", variable]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data[variable].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - "\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(mean_squared_error(y_test, y_pred))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - "\n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(mean_squared_error(y_test, y_pred))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - "\n", - " del train_dataset, val_dataset\n", - " K.clear_session()\n", - " gc.collect()\n", - " \n", - " return metrics, model" - ] - }, - { - "cell_type": "markdown", - "id": "10cce7e2", - "metadata": {}, - "source": [ - "### Plot Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a539a8b1", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson_subplots(data_dict, vmin, vmax, title_main):\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " n_plots = len(data_dict)\n", - " n_cols = 2\n", - " n_rows = (n_plots + n_cols - 1) // n_cols\n", - " \n", - " fig = plt.figure(figsize=(20, 6 * n_rows))\n", - " fig.suptitle(title_main, fontsize=20, fontweight='bold', y=0.98)\n", - " \n", - " for idx, (subplot_title, (lon, lat, val)) in enumerate(data_dict.items(), 1):\n", - " ax = fig.add_subplot(n_rows, n_cols, idx, projection=ccrs.Robinson())\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " scatter = ax.scatter(lon, lat, c=val, cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(subplot_title, fontsize=14, pad=10, fontweight='bold')\n", - " \n", - " cbar = plt.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, shrink=0.6, aspect=30)\n", - " cbar.set_label('Temperature (°C)', fontsize=10)\n", - " \n", - " plt.tight_layout()\n", - " \n", - " return fig" - ] - }, - { - "cell_type": "markdown", - "id": "8e9a0017", - "metadata": { - "papermill": { - "duration": 0.002063, - "end_time": "2026-01-22T06:28:44.208431", - "exception": false, - "start_time": "2026-01-22T06:28:44.206368", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c046277c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-22T06:28:44.308270Z", - "iopub.status.busy": "2026-01-22T06:28:44.308026Z", - "iopub.status.idle": "2026-01-22T06:28:44.312080Z", - "shell.execute_reply": "2026-01-22T06:28:44.311090Z" - }, - "papermill": { - "duration": 0.102419, - "end_time": "2026-01-22T06:28:44.312756", - "exception": false, - "start_time": "2026-01-22T06:28:44.210337", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1234\n", - "epochs = 500\n", - "batch_size = 2048\n", - "num_sample = 200000\n", - "huber_delta = 1.345\n", - "\n", - "# Data\n", - "data_path = \"data_speed_combined.csv\"\n", - "\n", - "# Basis Setup\n", - "# Wendland\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "\n", - "knot_num = 2000\n", - "order_max = 1400\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Set to 2 repeats per variable\n", - "repeat_experiment = 2" - ] - }, - { - "cell_type": "markdown", - "id": "5e832176", - "metadata": { - "papermill": { - "duration": 0.00218, - "end_time": "2026-01-22T06:28:44.317676", - "exception": false, - "start_time": "2026-01-22T06:28:44.315496", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Outcome" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2a9c97e1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 05 | Target Column: 'vec.4'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 05 ('vec.4') - Repeat 1/2, Seed=1234\n", - "================================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 10.6371 10.6522 \n", - " 100 9.0019 9.0806 \n", - " 200 6.5102 6.6467 \n", - " 500 4.3081 4.3323 \n", - " 700 3.6944 3.6901 \n", - " 1000 3.0564 3.0757 \n", - " 1400 2.4906 2.4631 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1772152456.561108 23958 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1772152458.092503 25296 service.cc:152] XLA service 0x55d68b4d7400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1772152458.092526 25296 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n", - "I0000 00:00:1772152458.398397 25296 cuda_dnn.cc:529] Loaded cuDNN version 90501\n", - "I0000 00:00:1772152460.462626 25296 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.8218 0.8071 \n", - " 100 0.4593 0.4426 \n", - " 200 0.2973 0.2938 \n", - " 500 0.2201 0.2092 \n", - " 700 0.2078 0.2001 *\n", - " 1000 0.2085 0.1982 \n", - " 1400 0.2612 0.2414 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2052 0.2026 \n", - " 100 0.1380 0.1348 \n", - " 200 0.1036 0.1052 \n", - " 500 0.0948 0.0925 \n", - " 700 0.0926 0.0886 \n", - " 1000 0.0867 0.0841 *\n", - " 1400 0.0916 0.0908 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0952 0.2006 \n", - " 100 0.0678 0.1415 \n", - " 200 0.0513 0.1082 \n", - " 500 0.0454 0.0914 \n", - " 700 0.0416 0.0874 *\n", - " 1000 0.0426 0.0871 \n", - " 1400 0.0439 0.0908 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.8198, sigma²=87.3651, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.7677, sigma²=85.6864, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9241, sigma²=90.2806, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.7871, sigma²=115.6798, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.2381, sigma²=67.6853, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1654, sigma²=34.8541, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7212, sigma²=21.1049, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1472 0.1398 \n", - " 100 0.1472 0.1398 \n", - " 200 0.1472 0.1398 \n", - " 500 0.1472 0.1398 \n", - " 700 0.1472 0.1398 \n", - " 1000 0.1473 0.1399 \n", - " 1400 0.1471 0.1398 *\n", - " Best order: 1400\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7212, sigma²=21.1049, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 12.86 | 3.5861 | 2.6695 | 0.2139 | 8.98s |\n", - "| OLS_sphere | 1400 | 2.4631 | 1.5694 | 1.1259 | 0.8494 | 6.17s |\n", - "| DeepKriging_wendland | -- | 10.4755 | 3.2366 | 2.2275 | 0.3596 | 221.19s |\n", - "| DeepKriging_wendland* | -- | 8.3106 | 2.8828 | 1.774 | 0.492 | 95.53s |\n", - "| DeepKriging_mrts | 700 | 0.2108 | 0.4591 | 0.296 | 0.9871 | 208.32s |\n", - "| DeepKriging_sphere | 1000 | 0.0925 | 0.3041 | 0.166 | 0.9943 | 176.74s |\n", - "| DeepKriging_sphere_Huber | 700 | 0.0888 | 0.298 | 0.1632 | 0.9946 | 195.89s |\n", - "| UniversalKriging | 1400 | 0.1398 | 0.3739 | 0.2058 | 0.9915 | 700.92s |\n", - "\n", - "✅ Completed Hour 05 ('vec.4') - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 05 ('vec.4') - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8277, sigma²=24.1034, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 12.7 | 3.5637 | 2.6595 | 0.2097 | 9.15s |\n", - "| OLS_sphere | 1400 | 2.636 | 1.6236 | 1.1462 | 0.836 | 6.19s |\n", - "| DeepKriging_wendland | -- | 9.9713 | 3.1577 | 2.1994 | 0.3795 | 209.69s |\n", - "| DeepKriging_wendland* | -- | 8.3393 | 2.8878 | 1.8686 | 0.4811 | 70.30s |\n", - "| DeepKriging_mrts | 700 | 0.2197 | 0.4687 | 0.2957 | 0.9863 | 207.23s |\n", - "| DeepKriging_sphere | 1000 | 0.0963 | 0.3103 | 0.163 | 0.994 | 222.52s |\n", - "| DeepKriging_sphere_Huber | 700 | 0.0986 | 0.314 | 0.1659 | 0.9939 | 209.26s |\n", - "| UniversalKriging | 1400 | 0.1548 | 0.3934 | 0.211 | 0.9904 | 699.25s |\n", - "\n", - "✅ Completed Hour 05 ('vec.4') - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 05 ('vec.4')\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 1000, 'DeepKriging_sphere_Huber': 700, 'UniversalKriging': 1400}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-----------|\n", - "| OLS_wendland | 12.78±0.08 | 3.57±0.01 | 2.66±0.00 | 0.21±0.00 |\n", - "| OLS_sphere | 2.55±0.09 | 1.60±0.03 | 1.14±0.01 | 0.84±0.01 |\n", - "| DeepKriging_wendland | 10.22±0.25 | 3.20±0.04 | 2.21±0.01 | 0.37±0.01 |\n", - "| DeepKriging_wendland* | 8.32±0.01 | 2.89±0.00 | 1.82±0.05 | 0.49±0.01 |\n", - "| DeepKriging_mrts | 0.22±0.00 | 0.46±0.00 | 0.30±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere | 0.09±0.00 | 0.31±0.00 | 0.16±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.09±0.00 | 0.31±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| UniversalKriging | 0.15±0.01 | 0.38±0.01 | 0.21±0.00 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model for Hour 05 ('vec.4'): DeepKriging_sphere (RMSE: 0.31±0.00)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 06 | Target Column: 'vec.5'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 06 ('vec.5') - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 10.7246 10.7473 \n", - " 100 8.9704 9.0986 \n", - " 200 6.6685 6.7508 \n", - " 500 4.4952 4.5014 \n", - " 700 3.7867 3.7988 \n", - " 1000 2.9903 3.0072 \n", - " 1400 2.4222 2.4468 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.8625 0.8507 \n", - " 100 0.4439 0.4318 \n", - " 200 0.3013 0.2844 \n", - " 500 0.2293 0.2262 \n", - " 700 0.1948 0.1869 *\n", - " 1000 0.2091 0.2043 \n", - " 1400 0.2396 0.2288 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1809 0.1786 \n", - " 100 0.1319 0.1279 \n", - " 200 0.1084 0.1049 \n", - " 500 0.0930 0.0912 \n", - " 700 0.0860 0.0855 *\n", - " 1000 0.0871 0.0827 \n", - " 1400 0.0903 0.0843 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0899 0.1876 \n", - " 100 0.0663 0.1350 \n", - " 200 0.0513 0.1049 \n", - " 500 0.0424 0.0869 \n", - " 700 0.0407 0.0832 \n", - " 1000 0.0402 0.0817 *\n", - " 1400 0.0605 0.1252 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.2677, sigma²=100.4253, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.6296, sigma²=80.7453, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.1191, sigma²=95.5283, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.3755, sigma²=102.3720, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.5039, sigma²=75.2774, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3184, sigma²=39.0711, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6299, sigma²=18.3147, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1431 0.1350 \n", - " 100 0.1431 0.1350 \n", - " 200 0.1431 0.1349 \n", - " 500 0.1431 0.1349 \n", - " 700 0.1431 0.1349 \n", - " 1000 0.1431 0.1350 \n", - " 1400 0.1428 0.1348 *\n", - " Best order: 1400\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6299, sigma²=18.3147, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.0321 | 3.61 | 2.6902 | 0.21 | 8.88s |\n", - "| OLS_sphere | 1400 | 2.4468 | 1.5642 | 1.1002 | 0.8517 | 6.20s |\n", - "| DeepKriging_wendland | -- | 10.3001 | 3.2094 | 2.225 | 0.3756 | 218.20s |\n", - "| DeepKriging_wendland* | -- | 8.2078 | 2.8649 | 1.7539 | 0.5024 | 101.46s |\n", - "| DeepKriging_mrts | 700 | 0.1833 | 0.4281 | 0.2785 | 0.9889 | 210.53s |\n", - "| DeepKriging_sphere | 700 | 0.0865 | 0.2941 | 0.1604 | 0.9948 | 167.66s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.0799 | 0.2826 | 0.1531 | 0.9952 | 212.94s |\n", - "| UniversalKriging | 1400 | 0.1348 | 0.3671 | 0.2037 | 0.9918 | 719.24s |\n", - "\n", - "✅ Completed Hour 06 ('vec.5') - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 06 ('vec.5') - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9170, sigma²=26.3506, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 12.8247 | 3.5812 | 2.6774 | 0.2049 | 11.27s |\n", - "| OLS_sphere | 1400 | 2.6122 | 1.6162 | 1.1473 | 0.838 | 7.86s |\n", - "| DeepKriging_wendland | -- | 9.7478 | 3.1222 | 2.165 | 0.3956 | 262.35s |\n", - "| DeepKriging_wendland* | -- | 8.4186 | 2.9015 | 1.8306 | 0.4781 | 86.53s |\n", - "| DeepKriging_mrts | 700 | 0.2109 | 0.4593 | 0.2918 | 0.9869 | 252.59s |\n", - "| DeepKriging_sphere | 700 | 0.0972 | 0.3118 | 0.1674 | 0.994 | 203.15s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.0955 | 0.3091 | 0.1644 | 0.9941 | 285.60s |\n", - "| UniversalKriging | 1400 | 0.1544 | 0.3929 | 0.2097 | 0.9904 | 704.02s |\n", - "\n", - "✅ Completed Hour 06 ('vec.5') - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 06 ('vec.5')\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 700, 'DeepKriging_sphere_Huber': 1000, 'UniversalKriging': 1400}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-----------|\n", - "| OLS_wendland | 12.93±0.10 | 3.60±0.01 | 2.68±0.01 | 0.21±0.00 |\n", - "| OLS_sphere | 2.53±0.08 | 1.59±0.03 | 1.12±0.02 | 0.84±0.01 |\n", - "| DeepKriging_wendland | 10.02±0.28 | 3.17±0.04 | 2.19±0.03 | 0.39±0.01 |\n", - "| DeepKriging_wendland* | 8.31±0.11 | 2.88±0.02 | 1.79±0.04 | 0.49±0.01 |\n", - "| DeepKriging_mrts | 0.20±0.01 | 0.44±0.02 | 0.29±0.01 | 0.99±0.00 |\n", - "| DeepKriging_sphere | 0.09±0.01 | 0.30±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.09±0.01 | 0.30±0.01 | 0.16±0.01 | 0.99±0.00 |\n", - "| UniversalKriging | 0.14±0.01 | 0.38±0.01 | 0.21±0.00 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model for Hour 06 ('vec.5'): DeepKriging_sphere (RMSE: 0.30±0.01)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 07 | Target Column: 'vec.6'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 07 ('vec.6') - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 10.7831 10.8074 \n", - " 100 9.1023 9.1859 \n", - " 200 6.7459 6.8544 \n", - " 500 4.4124 4.4179 \n", - " 700 3.7503 3.7587 \n", - " 1000 3.0041 3.0280 \n", - " 1400 2.4127 2.4308 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.9472 0.9199 \n", - " 100 0.4849 0.4651 \n", - " 200 0.2891 0.2722 \n", - " 500 0.2061 0.1988 *\n", - " 700 0.2110 0.1995 \n", - " 1000 0.2134 0.1988 \n", - " 1400 0.2395 0.2255 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1783 0.1717 \n", - " 100 0.1315 0.1250 \n", - " 200 0.1066 0.1010 \n", - " 500 0.0899 0.0839 \n", - " 700 0.0931 0.0873 \n", - " 1000 0.0908 0.0811 \n", - " 1400 0.0894 0.0827 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0904 0.1854 \n", - " 100 0.0650 0.1314 \n", - " 200 0.0498 0.0999 \n", - " 500 0.0420 0.0841 \n", - " 700 0.0392 0.0797 *\n", - " 1000 0.0408 0.0798 \n", - " 1400 0.0458 0.0911 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.0817, sigma²=94.1019, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.5814, sigma²=78.7644, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9056, sigma²=88.4255, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.2637, sigma²=98.2822, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.6719, sigma²=169.4733, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.2083, sigma²=65.0773, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9529, sigma²=27.5027, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1434 0.1330 \n", - " 100 0.1434 0.1330 \n", - " 200 0.1434 0.1330 \n", - " 500 0.1433 0.1329 \n", - " 700 0.1433 0.1329 \n", - " 1000 0.1432 0.1329 \n", - " 1400 0.1428 0.1326 *\n", - " Best order: 1400\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9529, sigma²=27.5027, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.2356 | 3.6381 | 2.7122 | 0.2054 | 16.27s |\n", - "| OLS_sphere | 1400 | 2.4308 | 1.5591 | 1.1069 | 0.8541 | 7.04s |\n", - "| DeepKriging_wendland | -- | 10.8845 | 3.2992 | 2.2817 | 0.3465 | 221.26s |\n", - "| DeepKriging_wendland* | -- | 8.8723 | 2.9786 | 1.8527 | 0.4673 | 73.51s |\n", - "| DeepKriging_mrts | 500 | 0.2241 | 0.4734 | 0.3113 | 0.9865 | 193.36s |\n", - "| DeepKriging_sphere | 1400 | 0.084 | 0.2898 | 0.1561 | 0.995 | 262.17s |\n", - "| DeepKriging_sphere_Huber | 700 | 0.0787 | 0.2805 | 0.153 | 0.9953 | 211.46s |\n", - "| UniversalKriging | 1400 | 0.1326 | 0.3642 | 0.2014 | 0.992 | 696.40s |\n", - "\n", - "✅ Completed Hour 07 ('vec.6') - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 07 ('vec.6') - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9355, sigma²=26.7330, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 12.956 | 3.5994 | 2.6965 | 0.2004 | 9.15s |\n", - "| OLS_sphere | 1400 | 2.5598 | 1.5999 | 1.1288 | 0.842 | 6.28s |\n", - "| DeepKriging_wendland | -- | 9.7668 | 3.1252 | 2.1637 | 0.3972 | 219.16s |\n", - "| DeepKriging_wendland* | -- | 7.9418 | 2.8181 | 1.7094 | 0.5099 | 101.14s |\n", - "| DeepKriging_mrts | 500 | 0.219 | 0.4679 | 0.2953 | 0.9865 | 190.56s |\n", - "| DeepKriging_sphere | 1400 | 0.0941 | 0.3067 | 0.1615 | 0.9942 | 251.87s |\n", - "| DeepKriging_sphere_Huber | 700 | 0.0932 | 0.3052 | 0.1597 | 0.9943 | 208.31s |\n", - "| UniversalKriging | 1400 | 0.1549 | 0.3936 | 0.2082 | 0.9904 | 699.33s |\n", - "\n", - "✅ Completed Hour 07 ('vec.6') - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 07 ('vec.6')\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 500, 'DeepKriging_sphere': 1400, 'DeepKriging_sphere_Huber': 700, 'UniversalKriging': 1400}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-----------|\n", - "| OLS_wendland | 13.10±0.14 | 3.62±0.02 | 2.70±0.01 | 0.20±0.00 |\n", - "| OLS_sphere | 2.50±0.06 | 1.58±0.02 | 1.12±0.01 | 0.85±0.01 |\n", - "| DeepKriging_wendland | 10.33±0.56 | 3.21±0.09 | 2.22±0.06 | 0.37±0.03 |\n", - "| DeepKriging_wendland* | 8.41±0.47 | 2.90±0.08 | 1.78±0.07 | 0.49±0.02 |\n", - "| DeepKriging_mrts | 0.22±0.00 | 0.47±0.00 | 0.30±0.01 | 0.99±0.00 |\n", - "| DeepKriging_sphere | 0.09±0.01 | 0.30±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.09±0.01 | 0.29±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| UniversalKriging | 0.14±0.01 | 0.38±0.01 | 0.20±0.00 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model for Hour 07 ('vec.6'): DeepKriging_sphere_Huber (RMSE: 0.29±0.01)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 08 | Target Column: 'vec.7'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 08 ('vec.7') - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 10.9958 10.9726 \n", - " 100 9.2994 9.3660 \n", - " 200 6.8040 6.8604 \n", - " 500 4.4002 4.3615 \n", - " 700 3.7434 3.6993 \n", - " 1000 3.0658 3.0371 \n", - " 1400 2.5294 2.5032 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.9074 0.8763 \n", - " 100 0.4396 0.4164 \n", - " 200 0.2941 0.2807 \n", - " 500 0.2091 0.2030 \n", - " 700 0.1899 0.1796 *\n", - " 1000 0.2034 0.1920 \n", - " 1400 0.2424 0.2265 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1850 0.1772 \n", - " 100 0.1395 0.1341 \n", - " 200 0.1058 0.0984 \n", - " 500 0.0898 0.0830 \n", - " 700 0.0901 0.0834 \n", - " 1000 0.0932 0.0876 \n", - " 1400 0.0885 0.0834 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0954 0.1928 \n", - " 100 0.0643 0.1275 \n", - " 200 0.0514 0.1025 \n", - " 500 0.0419 0.0828 \n", - " 700 0.0415 0.0804 \n", - " 1000 0.0422 0.0814 \n", - " 1400 0.0396 0.0767 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.6314, sigma²=79.6808, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.7661, sigma²=83.6826, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.0272, sigma²=91.3151, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.9475, sigma²=117.9733, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=5.7970, sigma²=171.8152, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9884, sigma²=87.4659, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9856, sigma²=28.2706, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1446 0.1320 \n", - " 100 0.1446 0.1320 \n", - " 200 0.1446 0.1320 \n", - " 500 0.1445 0.1319 \n", - " 700 0.1445 0.1319 \n", - " 1000 0.1444 0.1319 \n", - " 1400 0.1443 0.1317 *\n", - " Best order: 1400\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9856, sigma²=28.2706, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.4009 | 3.6607 | 2.7276 | 0.201 | 10.89s |\n", - "| OLS_sphere | 1400 | 2.5032 | 1.5822 | 1.126 | 0.8508 | 6.18s |\n", - "| DeepKriging_wendland | -- | 10.5489 | 3.2479 | 2.2225 | 0.371 | 223.12s |\n", - "| DeepKriging_wendland* | -- | 8.5679 | 2.9271 | 1.7939 | 0.4892 | 103.65s |\n", - "| DeepKriging_mrts | 700 | 0.1824 | 0.4271 | 0.2777 | 0.9891 | 215.24s |\n", - "| DeepKriging_sphere | 1400 | 0.079 | 0.281 | 0.1557 | 0.9953 | 193.39s |\n", - "| DeepKriging_sphere_Huber | 1400 | 0.0807 | 0.2841 | 0.1546 | 0.9952 | 215.54s |\n", - "| UniversalKriging | 1400 | 0.1317 | 0.3629 | 0.2005 | 0.9921 | 670.78s |\n", - "\n", - "✅ Completed Hour 08 ('vec.7') - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 08 ('vec.7') - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7488, sigma²=21.3983, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.1076 | 3.6204 | 2.7145 | 0.1975 | 9.55s |\n", - "| OLS_sphere | 1400 | 2.5771 | 1.6053 | 1.1316 | 0.8422 | 6.15s |\n", - "| DeepKriging_wendland | -- | 10.1171 | 3.1807 | 2.2177 | 0.3806 | 225.76s |\n", - "| DeepKriging_wendland* | -- | 8.4628 | 2.9091 | 1.8436 | 0.4818 | 82.28s |\n", - "| DeepKriging_mrts | 700 | 0.2003 | 0.4476 | 0.2816 | 0.9877 | 216.49s |\n", - "| DeepKriging_sphere | 1400 | 0.0896 | 0.2994 | 0.1572 | 0.9945 | 272.00s |\n", - "| DeepKriging_sphere_Huber | 1400 | 0.09 | 0.3 | 0.1599 | 0.9945 | 183.48s |\n", - "| UniversalKriging | 1400 | 0.1509 | 0.3885 | 0.2056 | 0.9908 | 694.76s |\n", - "\n", - "✅ Completed Hour 08 ('vec.7') - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 08 ('vec.7')\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 1400, 'DeepKriging_sphere_Huber': 1400, 'UniversalKriging': 1400}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-----------|\n", - "| OLS_wendland | 13.25±0.15 | 3.64±0.02 | 2.72±0.01 | 0.20±0.00 |\n", - "| OLS_sphere | 2.54±0.04 | 1.59±0.01 | 1.13±0.00 | 0.85±0.00 |\n", - "| DeepKriging_wendland | 10.33±0.22 | 3.21±0.03 | 2.22±0.00 | 0.38±0.00 |\n", - "| DeepKriging_wendland* | 8.52±0.05 | 2.92±0.01 | 1.82±0.02 | 0.49±0.00 |\n", - "| DeepKriging_mrts | 0.19±0.01 | 0.44±0.01 | 0.28±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere | 0.08±0.01 | 0.29±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.09±0.00 | 0.29±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| UniversalKriging | 0.14±0.01 | 0.38±0.01 | 0.20±0.00 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model for Hour 08 ('vec.7'): DeepKriging_sphere (RMSE: 0.29±0.01)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 09 | Target Column: 'vec.8'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 09 ('vec.8') - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 11.2106 11.1437 \n", - " 100 9.2099 9.2184 \n", - " 200 6.7352 6.7579 \n", - " 500 4.4822 4.4914 \n", - " 700 3.7979 3.7575 \n", - " 1000 3.0844 3.0534 \n", - " 1400 2.5547 2.5449 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.9220 0.8900 \n", - " 100 0.4937 0.4546 \n", - " 200 0.2947 0.2763 \n", - " 500 0.2151 0.2022 \n", - " 700 0.2144 0.1957 \n", - " 1000 0.2044 0.1866 *\n", - " 1400 0.2545 0.2404 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1898 0.1792 \n", - " 100 0.1399 0.1294 \n", - " 200 0.1081 0.1013 \n", - " 500 0.0934 0.0868 \n", - " 700 0.0880 0.0822 *\n", - " 1000 0.0900 0.0817 \n", - " 1400 0.0917 0.0878 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0916 0.1841 \n", - " 100 0.0665 0.1311 \n", - " 200 0.0540 0.1064 \n", - " 500 0.0441 0.0874 \n", - " 700 0.0427 0.0845 \n", - " 1000 0.0412 0.0820 *\n", - " 1400 0.0417 0.0845 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.7698, sigma²=83.2292, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.5433, sigma²=76.3557, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.8058, sigma²=83.9602, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.4254, sigma²=101.6425, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.7661, sigma²=81.3689, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2300, sigma²=35.5956, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8891, sigma²=25.1659, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1441 0.1312 \n", - " 100 0.1441 0.1312 \n", - " 200 0.1441 0.1311 \n", - " 500 0.1441 0.1311 \n", - " 700 0.1441 0.1311 \n", - " 1000 0.1440 0.1310 \n", - " 1400 0.1438 0.1309 *\n", - " Best order: 1400\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8891, sigma²=25.1659, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.5188 | 3.6768 | 2.737 | 0.1983 | 19.07s |\n", - "| OLS_sphere | 1400 | 2.5449 | 1.5953 | 1.1269 | 0.8491 | 8.04s |\n", - "| DeepKriging_wendland | -- | 10.4883 | 3.2386 | 2.2331 | 0.3781 | 258.96s |\n", - "| DeepKriging_wendland* | -- | 9.2886 | 3.0477 | 1.9486 | 0.4492 | 83.70s |\n", - "| DeepKriging_mrts | 1000 | 0.1755 | 0.4189 | 0.2711 | 0.9896 | 240.02s |\n", - "| DeepKriging_sphere | 700 | 0.0829 | 0.2879 | 0.1576 | 0.9951 | 201.85s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.0854 | 0.2922 | 0.1606 | 0.9949 | 194.41s |\n", - "| UniversalKriging | 1400 | 0.1309 | 0.3618 | 0.1991 | 0.9922 | 675.45s |\n", - "\n", - "✅ Completed Hour 09 ('vec.8') - Repeat 1/2\n", - "\n", - "================================================================================\n", - "🏃 Hour 09 ('vec.8') - Repeat 2/2, Seed=1235\n", - "================================================================================\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6974, sigma²=19.7374, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 13.3017 | 3.6472 | 2.7272 | 0.1964 | 9.91s |\n", - "| OLS_sphere | 1400 | 2.4732 | 1.5726 | 1.1136 | 0.8506 | 6.10s |\n", - "| DeepKriging_wendland | -- | 10.3573 | 3.2183 | 2.2176 | 0.3742 | 220.90s |\n", - "| DeepKriging_wendland* | -- | 8.5516 | 2.9243 | 1.813 | 0.4833 | 96.00s |\n", - "| DeepKriging_mrts | 1000 | 0.2017 | 0.4491 | 0.2886 | 0.9878 | 238.21s |\n", - "| DeepKriging_sphere | 700 | 0.0905 | 0.3007 | 0.1607 | 0.9945 | 212.70s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.0904 | 0.3007 | 0.1593 | 0.9945 | 222.71s |\n", - "| UniversalKriging | 1400 | 0.1482 | 0.385 | 0.2054 | 0.991 | 694.16s |\n", - "\n", - "✅ Completed Hour 09 ('vec.8') - Repeat 2/2\n", - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments for Hour 09 ('vec.8')\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1400, 'DeepKriging_mrts': 1000, 'DeepKriging_sphere': 700, 'DeepKriging_sphere_Huber': 1000, 'UniversalKriging': 1400}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-----------|\n", - "| OLS_wendland | 13.41±0.11 | 3.66±0.01 | 2.73±0.00 | 0.20±0.00 |\n", - "| OLS_sphere | 2.51±0.04 | 1.58±0.01 | 1.12±0.01 | 0.85±0.00 |\n", - "| DeepKriging_wendland | 10.42±0.07 | 3.23±0.01 | 2.23±0.01 | 0.38±0.00 |\n", - "| DeepKriging_wendland* | 8.92±0.37 | 2.99±0.06 | 1.88±0.07 | 0.47±0.02 |\n", - "| DeepKriging_mrts | 0.19±0.01 | 0.43±0.02 | 0.28±0.01 | 0.99±0.00 |\n", - "| DeepKriging_sphere | 0.09±0.00 | 0.29±0.01 | 0.16±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 0.09±0.00 | 0.30±0.00 | 0.16±0.00 | 0.99±0.00 |\n", - "| UniversalKriging | 0.14±0.01 | 0.37±0.01 | 0.20±0.00 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model for Hour 09 ('vec.8'): DeepKriging_sphere (RMSE: 0.29±0.01)\n", - "\n", - "\n", - "################################################################################\n", - "🚀 STARTING DATASET: Hour 10 | Target Column: 'vec.9'\n", - "################################################################################\n", - "\n", - "================================================================================\n", - "🏃 Hour 10 ('vec.9') - Repeat 1/2, Seed=1234\n", - "================================================================================\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 11.3616 11.2161 \n", - " 100 9.5406 9.4896 \n", - " 200 6.7991 6.8424 \n", - " 500 4.5106 4.5302 \n", - " 700 3.8120 3.7298 \n", - " 1000 3.1177 3.0522 \n", - " 1400 2.5910 2.5648 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.9127 0.8757 \n", - " 100 0.4552 0.4159 \n", - " 200 0.3059 0.2881 \n", - " 500 0.1965 0.1862 *\n", - " 700 0.2125 0.2001 \n", - " 1000 0.1989 0.1953 \n", - " 1400 0.2388 0.2300 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1902 0.1815 \n", - " 100 0.1362 0.1284 \n", - " 200 0.1085 0.1034 \n", - " 500 0.0877 0.0831 *\n", - " 700 0.0882 0.0827 \n", - " 1000 0.0891 0.0827 \n", - " 1400 0.0879 0.0843 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0878 0.1811 \n", - " 100 0.0691 0.1411 \n", - " 200 0.0528 0.1083 \n", - " 500 0.0421 0.0861 \n", - " 700 0.0409 0.0840 \n", - " 1000 0.0426 0.0879 \n", - " 1400 0.0409 0.0829 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.6243, sigma²=78.6563, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9578, sigma²=88.5641, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9649, sigma²=88.5107, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.3885, sigma²=100.2580, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.9386, sigma²=115.5697, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.4195, sigma²=41.1321, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0673, sigma²=30.3491, nugget=0.0000\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1431 0.1360 \n", - " 100 0.1431 0.1359 \n", - " 200 0.1431 0.1359 \n", - " 500 0.1430 0.1359 \n", - " 700 0.1430 0.1358 \n", - " 1000 0.1430 0.1358 \n", - " 1400 0.1429 0.1358 *\n", - " Best order: 1400\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-28 00:45:57.202891: W external/local_xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.09GiB (rounded to 1171200000)requested by op _EagerConst\n", - "If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \n", - "Current allocation summary follows.\n", - "Current allocation summary follows.\n", - "2026-02-28 00:45:57.238830: W external/local_xla/xla/tsl/framework/bfc_allocator.cc:512] ***********************************************************_______**********___________________*____\n" - ] - }, - { - "ename": "InternalError", - "evalue": "Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 220\u001b[0m parts \u001b[38;5;241m=\u001b[39m prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m strategy\u001b[38;5;241m.\u001b[39mscope():\n\u001b[0;32m--> 222\u001b[0m metrics, model \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDeepKriging_wendland\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmse\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 223\u001b[0m Record[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeepKriging_wendland\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 224\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMSPE\u001b[39m\u001b[38;5;124m\"\u001b[39m: metrics[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMSPE\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRMSE\u001b[39m\u001b[38;5;124m\"\u001b[39m: metrics[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRMSE\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMAE\u001b[39m\u001b[38;5;124m\"\u001b[39m: metrics[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMAE\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR2\u001b[39m\u001b[38;5;124m\"\u001b[39m: metrics[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR2\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 225\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m\"\u001b[39m: metrics[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParam\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 226\u001b[0m }\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m parts, model; K\u001b[38;5;241m.\u001b[39mclear_session(); gc\u001b[38;5;241m.\u001b[39mcollect()\n", - "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36mtrain_eval\u001b[0;34m(name_model, epochs, batch_size, loss, dropout_rate, X_train, X_train_cat, y_train, X_val, X_val_cat, y_val, X_test, X_test_cat, y_test)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 189\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 190\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mModelCheckpoint(\n\u001b[1;32m 191\u001b[0m filepath\u001b[38;5;241m=\u001b[39mcheckpoint_path, monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m\"\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 196\u001b[0m monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m5\u001b[39m, config\u001b[38;5;241m.\u001b[39mpatience \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m), min_lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-6\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 197\u001b[0m ]\n\u001b[0;32m--> 199\u001b[0m train_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_tensor_slices\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train_cat\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\n\u001b[1;32m 201\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mbatch(config\u001b[38;5;241m.\u001b[39mbatch_size)\u001b[38;5;241m.\u001b[39mprefetch(tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mAUTOTUNE)\n\u001b[1;32m 203\u001b[0m val_dataset \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mDataset\u001b[38;5;241m.\u001b[39mfrom_tensor_slices((\n\u001b[1;32m 204\u001b[0m (X_val, X_val_cat), y_val\n\u001b[1;32m 205\u001b[0m ))\u001b[38;5;241m.\u001b[39mbatch(config\u001b[38;5;241m.\u001b[39mbatch_size)\u001b[38;5;241m.\u001b[39mprefetch(tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mAUTOTUNE)\n\u001b[1;32m 207\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m 208\u001b[0m train_dataset,\n\u001b[1;32m 209\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mval_dataset,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 212\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 213\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/data/ops/dataset_ops.py:827\u001b[0m, in \u001b[0;36mDatasetV2.from_tensor_slices\u001b[0;34m(tensors, name)\u001b[0m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;66;03m# Loaded lazily due to a circular dependency (dataset_ops ->\u001b[39;00m\n\u001b[1;32m 824\u001b[0m \u001b[38;5;66;03m# from_tensor_slices_op -> dataset_ops).\u001b[39;00m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top,protected-access\u001b[39;00m\n\u001b[1;32m 826\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m from_tensor_slices_op\n\u001b[0;32m--> 827\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfrom_tensor_slices_op\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_tensor_slices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/data/ops/from_tensor_slices_op.py:25\u001b[0m, in \u001b[0;36m_from_tensor_slices\u001b[0;34m(tensors, name)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_from_tensor_slices\u001b[39m(tensors, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_TensorSliceDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/data/ops/from_tensor_slices_op.py:33\u001b[0m, in \u001b[0;36m_TensorSliceDataset.__init__\u001b[0;34m(self, element, is_files, name)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, element, is_files\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 32\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"See `Dataset.from_tensor_slices` for details.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m element \u001b[38;5;241m=\u001b[39m \u001b[43mstructure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_element\u001b[49m\u001b[43m(\u001b[49m\u001b[43melement\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m batched_spec \u001b[38;5;241m=\u001b[39m structure\u001b[38;5;241m.\u001b[39mtype_spec_from_value(element)\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tensors \u001b[38;5;241m=\u001b[39m structure\u001b[38;5;241m.\u001b[39mto_batched_tensor_list(batched_spec, element)\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py:134\u001b[0m, in \u001b[0;36mnormalize_element\u001b[0;34m(element, element_signature)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 132\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(spec, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 133\u001b[0m normalized_components\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m--> 134\u001b[0m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_to_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcomponent_\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mpack_sequence_as(pack_as, normalized_components)\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/profiler/trace.py:183\u001b[0m, in \u001b[0;36mtrace_wrapper..inner_wrapper..wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(trace_name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtrace_kwargs):\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/ops.py:736\u001b[0m, in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[38;5;66;03m# TODO(b/142518781): Fix all call-sites and remove redundant arg\u001b[39;00m\n\u001b[1;32m 735\u001b[0m preferred_dtype \u001b[38;5;241m=\u001b[39m preferred_dtype \u001b[38;5;129;01mor\u001b[39;00m dtype_hint\n\u001b[0;32m--> 736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtensor_conversion_registry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mas_ref\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreferred_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccepted_result_types\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/tensor_conversion_registry.py:234\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, accepted_result_types)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 226\u001b[0m _add_error_prefix(\n\u001b[1;32m 227\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConversion function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconversion_func\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m for type \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactual = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mret\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mbase_dtype\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 231\u001b[0m name\u001b[38;5;241m=\u001b[39mname))\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 234\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mconversion_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mas_ref\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_ref\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/constant_tensor_conversion.py:29\u001b[0m, in \u001b[0;36m_constant_tensor_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mframework\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m constant_op \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[1;32m 28\u001b[0m _ \u001b[38;5;241m=\u001b[39m as_ref\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconstant_op\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconstant\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/ops/weak_tensor_ops.py:142\u001b[0m, in \u001b[0;36mweak_tensor_binary_op_wrapper..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mis_auto_dtype_conversion_enabled():\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m bound_arguments \u001b[38;5;241m=\u001b[39m signature\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 144\u001b[0m bound_arguments\u001b[38;5;241m.\u001b[39mapply_defaults()\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:276\u001b[0m, in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;129m@tf_export\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstant\u001b[39m\u001b[38;5;124m\"\u001b[39m, v1\u001b[38;5;241m=\u001b[39m[])\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconstant\u001b[39m(\n\u001b[1;32m 179\u001b[0m value, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, shape\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConst\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 180\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[ops\u001b[38;5;241m.\u001b[39mOperation, ops\u001b[38;5;241m.\u001b[39m_EagerTensorBase]:\n\u001b[1;32m 181\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Creates a constant tensor from a tensor-like object.\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \n\u001b[1;32m 183\u001b[0m \u001b[38;5;124;03m Note: All eager `tf.Tensor` values are immutable (in contrast to\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;124;03m ValueError: if called on a symbolic tensor.\u001b[39;00m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 276\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_constant_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_shape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_broadcast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:289\u001b[0m, in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace\u001b[38;5;241m.\u001b[39mTrace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf.constant\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _constant_eager_impl(ctx, value, dtype, shape, verify_shape)\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_constant_eager_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_shape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 291\u001b[0m const_tensor \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39m_create_graph_constant( \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 292\u001b[0m value, dtype, shape, name, verify_shape, allow_broadcast\n\u001b[1;32m 293\u001b[0m )\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m const_tensor\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:301\u001b[0m, in \u001b[0;36m_constant_eager_impl\u001b[0;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_constant_eager_impl\u001b[39m(\n\u001b[1;32m 298\u001b[0m ctx, value, dtype, shape, verify_shape\n\u001b[1;32m 299\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ops\u001b[38;5;241m.\u001b[39m_EagerTensorBase:\n\u001b[1;32m 300\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Creates a constant on the current device.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 301\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_to_eager_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shape \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m t\n", - "File \u001b[0;32m~/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:108\u001b[0m, in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m 106\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtypes\u001b[38;5;241m.\u001b[39mas_dtype(dtype)\u001b[38;5;241m.\u001b[39mas_datatype_enum\n\u001b[1;32m 107\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m--> 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEagerTensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mInternalError\u001b[0m: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized." - ] - } - ], - "source": [ - "# Dictionary to hold the final summary table for every hour\n", - "all_hours_summary = {}\n", - "\n", - "# # Generate the 24 column names: 'vec' (Hour 1), 'vec.1' (Hour 2), ..., 'vec.23' (Hour 24)\n", - "# target_variables = [\"vec\"] + [f\"vec.{i}\" for i in range(5, 24)]\n", - "# ONLY include hours 5 through 24 (which corresponds to vec.4 through vec.23)\n", - "target_variables = [f\"vec.{i}\" for i in range(4, 24)]\n", - "\n", - "for hour, variable in enumerate(target_variables, start=5):\n", - " \n", - " print(f\"\\n\\n{'#'*80}\")\n", - " print(f\"🚀 STARTING DATASET: Hour {hour:02d} | Target Column: '{variable}'\")\n", - " print(f\"{'#'*80}\")\n", - " \n", - " best_orders = {}\n", - " experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - "\n", - " for repeat in range(repeat_experiment):\n", - " seed_current = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Hour {hour:02d} ('{variable}') - Repeat {repeat+1}/{repeat_experiment}, Seed={seed_current}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " # Data Preprocessing (Variable gets dynamically passed in here!)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(data_path, variable, num_sample, seed_current)\n", - "\n", - " # Compute Basis Functions\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " metrics, model = train_eval(\"OLS_sphere\", None, None, None, None, *parts)\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model; gc.collect()\n", - "\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts)\n", - " \n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts)\n", - " \n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts)\n", - " \n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed_current)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed_current)\n", - " \n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test, phi_train, phi_val, phi_test, y_train, y_val, y_test; gc.collect()\n", - " \n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Execute training with best orders\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", - " metrics, model = train_eval(\"OLS_wendland\", None, None, None, None, *parts)\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", - " }\n", - " del parts, model; gc.collect()\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " metrics, model = train_eval(\"OLS_sphere\", None, None, None, None, *parts)\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": best_orders['OLS_sphere']\n", - " }\n", - " del Phi_sphere, parts, model; gc.collect()\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_wendland\", epochs, batch_size, \"mse\", None, *parts)\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", - " }\n", - " del parts, model; K.clear_session(); gc.collect()\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts)\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": \"--\"\n", - " }\n", - " del parts, model; K.clear_session(); gc.collect()\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts)\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_mrts']\n", - " }\n", - " del Phi_mrts, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts)\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_sphere']\n", - " }\n", - " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed_current)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts)\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metrics[\"MSPE\"], \"RMSE\": metrics[\"RMSE\"], \"MAE\": metrics[\"MAE\"], \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber']\n", - " }\n", - " del Phi_sphere, parts, model; K.clear_session(); gc.collect()\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed_current)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed_current)\n", - "\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging']\n", - " }\n", - "\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test; gc.collect()\n", - "\n", - " # Print partial result for this repeat\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Save results internally for the current hour\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up matrices before next repeat\n", - " del Phi_wendland, max_Phi_sphere, max_Phi_mrts, location_data, location_data_norm\n", - " K.clear_session()\n", - " gc.collect()\n", - "\n", - " print(f\"\\n✅ Completed Hour {hour:02d} ('{variable}') - Repeat {repeat+1}/{repeat_experiment}\")\n", - "\n", - " # ========================================================\n", - " # Summary Table for the current Hour\n", - " # ========================================================\n", - " print(\"\\n\" + \"=\"*80)\n", - " print(f\"📊 Summary of repeat experiments for Hour {hour:02d} ('{variable}')\")\n", - " print(\"=\"*80)\n", - " print(f\"Selected Best Orders: {best_orders}\")\n", - " print(\"=\"*80)\n", - "\n", - " avg_results = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - " df_avg = pd.DataFrame(avg_results)\n", - " print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " if avg_results:\n", - " # Extract the float part of RMSE (everything before the ± symbol) to find the best model\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model for Hour {hour:02d} ('{variable}'): {best_model['Model']} (RMSE: {best_model['RMSE']})\")\n", - " \n", - " # Store df in overarching dictionary if you want to look at it later\n", - " all_hours_summary[f\"Hour_{hour:02d}\"] = df_avg\n", - "\n", - "print(\"\\n\" + \"#\"*80)\n", - "print(\"🎉 ALL 24 HOURS PROCESSED SUCCESSFULLY!\")\n", - "print(\"#\"*80)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 9219.462666, - "end_time": "2026-01-22T09:02:01.113308", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Real_data/experiment_univariate_deepkriging_basis_real_data_temperature.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Real_data/experiment_univariate_deepkriging_basis_real_data_temperature.ipynb", - "parameters": {}, - "start_time": "2026-01-22T06:28:21.650642", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/Rerun/results_sphere_stdscaler_nonoise_50reps_checkpoint.csv b/notebook/simulation/Rerun/results_sphere_stdscaler_nonoise_50reps_checkpoint.csv deleted file mode 100644 index be57c49..0000000 --- a/notebook/simulation/Rerun/results_sphere_stdscaler_nonoise_50reps_checkpoint.csv +++ /dev/null @@ -1,351 +0,0 @@ -Model,Repeat,MSPE,RMSE,MAE,R2 -OLS_wendland,1,160.1282958984375,12.654180965137076,6.362092018127441,-1.1546530723571777 -OLS_sphere,1,2.7306699752807617,1.6524738954914724,0.8265239000320435,0.9632566571235657 -DeepKriging_wendland,1,46.15829086303711,6.793989318731455,4.745780944824219,0.3789036273956299 -DeepKriging_mrts,1,1.4926965236663818,1.2217596014218108,0.7554640173912048,0.9799146056175232 -DeepKriging_sphere,1,0.8799751996994019,0.938069933266919,0.49489033222198486,0.9881592392921448 -DeepKriging_sphere_Huber,1,0.760865330696106,0.8722759487089541,0.5193111300468445,0.9897619485855103 -UniversalKriging,1,1.2423700575652146,1.1146165518083853,0.6281180016529122,0.9832829259825033 -OLS_wendland,2,1057.1839599609375,32.514365439924205,7.48640775680542,-12.28555679321289 -OLS_sphere,2,3.894681692123413,1.9734947915116,1.2977781295776367,0.9510558247566223 -DeepKriging_wendland,2,36.511314392089844,6.042459299994485,4.3458027839660645,0.5411648750305176 -DeepKriging_mrts,2,2.4839961528778076,1.576069843908514,0.9636558294296265,0.9687837958335876 -DeepKriging_sphere,2,1.194469928741455,1.0929180796113929,0.5836231708526611,0.9849891662597656 -DeepKriging_sphere_Huber,2,1.1367454528808594,1.0661826545582418,0.5518617033958435,0.9857146143913269 -UniversalKriging,2,1.5175197863472796,1.231876530479934,0.6869220951408171,0.9809294354321858 -OLS_wendland,3,52.903350830078125,7.273468968111304,4.736739635467529,-0.10807156562805176 -OLS_sphere,3,2.101400375366211,1.4496207695001513,0.8371756076812744,0.9559857249259949 -DeepKriging_wendland,3,24.802766799926758,4.980237624845501,3.522387742996216,0.4805009365081787 -DeepKriging_mrts,3,1.1762624979019165,1.0845563599471981,0.6966630816459656,0.9753629565238953 -DeepKriging_sphere,3,0.8288931250572205,0.910435678704004,0.5063139200210571,0.9826386570930481 -DeepKriging_sphere_Huber,3,0.6945622563362122,0.8334040174706456,0.5044600963592529,0.9854522347450256 -UniversalKriging,3,0.8908215218706046,0.9438334184964021,0.5775810339150842,0.9813415619498738 -OLS_wendland,4,707.9403076171875,26.60714767909532,8.659173011779785,-7.910331726074219 -OLS_sphere,4,2.5517349243164062,1.5974150757759882,0.9718541502952576,0.9678831696510315 -DeepKriging_wendland,4,58.56914138793945,7.653047849578588,5.578404903411865,0.2628327012062073 -DeepKriging_mrts,4,1.9858503341674805,1.4092020203531785,0.9809157848358154,0.975005567073822 -DeepKriging_sphere,4,1.8512485027313232,1.3606059321976085,0.8414065837860107,0.9766996502876282 -DeepKriging_sphere_Huber,4,1.933342456817627,1.3904468550856688,0.8413828611373901,0.9756664037704468 -UniversalKriging,4,1.740073752097746,1.3191185511915697,0.8791548621450288,0.9780989547356792 -OLS_wendland,5,2562.356201171875,50.619721464779666,8.182184219360352,-35.27407455444336 -OLS_sphere,5,2.490877389907837,1.578251370950723,0.8815575838088989,0.9647378325462341 -DeepKriging_wendland,5,37.47075653076172,6.121336172010301,4.398349761962891,0.46954405307769775 -DeepKriging_mrts,5,1.3781325817108154,1.1739389173678567,0.7329786419868469,0.9804904460906982 -DeepKriging_sphere,5,1.6318999528884888,1.2774583957563896,0.6027706861495972,0.9768979549407959 -DeepKriging_sphere_Huber,5,1.14137601852417,1.0683520105864779,0.5795099139213562,0.9838420748710632 -UniversalKriging,5,1.3575695811689499,1.1651478795281525,0.653090410124327,0.9807815227608825 -OLS_wendland,6,92.90664672851562,9.638809404097357,4.809769630432129,-0.6358836889266968 -OLS_sphere,6,1.4528276920318604,1.2053330212152409,0.6266650557518005,0.9744188785552979 -DeepKriging_wendland,6,22.950454711914062,4.790663285173991,3.490152359008789,0.5958924293518066 -DeepKriging_mrts,6,0.9942015409469604,0.9970965554784353,0.6929860711097717,0.9824942946434021 -DeepKriging_sphere,6,0.5109787583351135,0.7148277822910309,0.454572856426239,0.9910027980804443 -DeepKriging_sphere_Huber,6,0.678034245967865,0.8234283490188232,0.5000494718551636,0.9880613088607788 -UniversalKriging,6,0.9553485365801376,0.9774193248448373,0.5777995982099182,0.9831783931580663 -OLS_wendland,7,46.749263763427734,6.837343326426407,4.859798908233643,0.2214171290397644 -OLS_sphere,7,1.9901914596557617,1.4107414574101669,0.6503998041152954,0.966854453086853 -DeepKriging_wendland,7,30.062742233276367,5.482950139594228,3.7964839935302734,0.4993218779563904 -DeepKriging_mrts,7,0.675082266330719,0.8216339004268988,0.5446364879608154,0.9887568950653076 -DeepKriging_sphere,7,0.7502931952476501,0.8661946635991532,0.4723004102706909,0.9875043034553528 -DeepKriging_sphere_Huber,7,0.8377142548561096,0.9152673133331648,0.5117409229278564,0.9860483407974243 -UniversalKriging,7,0.6975679511562746,0.835205334726901,0.4577514226522702,0.9883823970124295 -OLS_wendland,8,655.5652465820312,25.604008408490092,8.200896263122559,-5.367677688598633 -OLS_sphere,8,5.171414852142334,2.2740745045275745,1.0372563600540161,0.9497686624526978 -DeepKriging_wendland,8,43.949249267578125,6.629422996579576,4.562345504760742,0.5731093883514404 -DeepKriging_mrts,8,2.589980125427246,1.6093415192019518,0.9965324997901917,0.9748428463935852 -DeepKriging_sphere,8,1.4319099187850952,1.1966243850035378,0.7095007300376892,0.9860914945602417 -DeepKriging_sphere_Huber,8,1.5855947732925415,1.259204023696137,0.7780024409294128,0.984598696231842 -UniversalKriging,8,1.9547474082401968,1.3981228158642562,0.8121558409139683,0.9810130258874051 -OLS_wendland,9,69.47830200195312,8.335364539236009,5.4228291511535645,0.014758646488189697 -OLS_sphere,9,4.006613254547119,2.0016526308396068,1.396214246749878,0.943183958530426 -DeepKriging_wendland,9,39.9476318359375,6.320413897517907,4.12808084487915,0.4335201382637024 -DeepKriging_mrts,9,1.8758572340011597,1.3696193755935113,0.8635907173156738,0.97339928150177 -DeepKriging_sphere,9,0.8905264735221863,0.9436771023619183,0.5381052494049072,0.9873718619346619 -DeepKriging_sphere_Huber,9,0.9776297807693481,0.9887516274420731,0.5400803089141846,0.9861366748809814 -UniversalKriging,9,1.2349726642105952,1.111293239523482,0.6157689887122362,0.9824873942616416 -OLS_wendland,10,53853.86328125,232.06435159509095,20.123371124267578,-792.4528198242188 -OLS_sphere,10,1.8710200786590576,1.3678523599639902,0.7045848369598389,0.9724334478378296 -DeepKriging_wendland,10,40.434627532958984,6.358822810313162,4.469143867492676,0.404258668422699 -DeepKriging_mrts,10,2.602114677429199,1.613107150014902,0.9331108331680298,0.9616618752479553 -DeepKriging_sphere,10,0.743161141872406,0.8620679450440122,0.44038262963294983,0.9890506863594055 -DeepKriging_sphere_Huber,10,0.7258276343345642,0.8519551832899218,0.44387122988700867,0.9893060326576233 -UniversalKriging,10,1.2583878824382762,1.1217788919561091,0.5781489049471398,0.9814596128880659 -OLS_wendland,11,52.611209869384766,7.253358523427941,5.788345813751221,0.268510103225708 -OLS_sphere,11,3.4983489513397217,1.870387380020439,0.8153406381607056,0.9513600468635559 -DeepKriging_wendland,11,41.1860466003418,6.41763559267288,4.750697135925293,0.42736202478408813 -DeepKriging_mrts,11,1.2353113889694214,1.1114456302354252,0.7613751888275146,0.9828246235847473 -DeepKriging_sphere,11,0.5533596277236938,0.7438814608011776,0.445148766040802,0.9923062324523926 -DeepKriging_sphere_Huber,11,0.5624303221702576,0.7499535466748974,0.47984084486961365,0.9921801686286926 -UniversalKriging,11,0.8045300114183712,0.896955969609641,0.5751316212375993,0.98881406384305 -OLS_wendland,12,66.7006607055664,8.16704724521454,6.1621599197387695,0.18759727478027344 -OLS_sphere,12,2.340311050415039,1.529807520708092,0.9480777382850647,0.9714953899383545 -DeepKriging_wendland,12,46.94449996948242,6.851605649005379,4.686985492706299,0.42822396755218506 -DeepKriging_mrts,12,1.5973186492919922,1.2638507227089726,0.8418811559677124,0.980544924736023 -DeepKriging_sphere,12,0.734509289264679,0.8570351738783415,0.6206535696983337,0.9910538196563721 -DeepKriging_sphere_Huber,12,0.8457884788513184,0.919667591497775,0.6515819430351257,0.9896984696388245 -UniversalKriging,12,1.2213258897463424,1.1051361408199183,0.7256532511469792,0.9851244584707386 -OLS_wendland,13,395.13690185546875,19.878050755933508,6.614373207092285,-4.928126335144043 -OLS_sphere,13,3.1529181003570557,1.7756458262719668,1.2953312397003174,0.9526976346969604 -DeepKriging_wendland,13,33.6309928894043,5.799223472966385,4.2805352210998535,0.49544382095336914 -DeepKriging_mrts,13,1.7442079782485962,1.3206846626839415,0.7918208241462708,0.9738321304321289 -DeepKriging_sphere,13,0.7542272210121155,0.8684625616640682,0.47282037138938904,0.9886845350265503 -DeepKriging_sphere_Huber,13,0.823753297328949,0.9076085595282523,0.47132739424705505,0.987641453742981 -UniversalKriging,13,1.0150033810082928,1.0074737619453387,0.5927185991156081,0.984772193083801 -OLS_wendland,14,50.60451889038086,7.113685324104578,5.390745162963867,0.23015964031219482 -OLS_sphere,14,5.433785915374756,2.331048243896886,0.7562065720558167,0.9173364639282227 -DeepKriging_wendland,14,39.02452087402344,6.246960931046667,4.327627658843994,0.40632474422454834 -DeepKriging_mrts,14,1.4831715822219849,1.217855320726557,0.7774673104286194,0.9774366617202759 -DeepKriging_sphere,14,0.43270787596702576,0.657805348083326,0.43710973858833313,0.9934172630310059 -DeepKriging_sphere_Huber,14,0.41201281547546387,0.6418822442438051,0.43579649925231934,0.9937320947647095 -UniversalKriging,14,0.7319244275393437,0.8555258193294599,0.5092006112846165,0.9888653228540081 -OLS_wendland,15,103.4455337524414,10.170817752395399,7.64441442489624,0.33003538846969604 -OLS_sphere,15,5.301171779632568,2.3024273668527675,1.6298269033432007,0.9656670093536377 -DeepKriging_wendland,15,56.94624328613281,7.546273470139603,5.184893608093262,0.6311878561973572 -DeepKriging_mrts,15,3.451993703842163,1.857954171620539,1.1366888284683228,0.9776431918144226 -DeepKriging_sphere,15,2.2533011436462402,1.5010999778982879,0.9491185545921326,0.9854065179824829 -DeepKriging_sphere_Huber,15,1.940738320350647,1.393103844065706,0.8390479683876038,0.9874308109283447 -UniversalKriging,15,2.250618403628071,1.50020612038082,0.9288551281260723,0.9854238785332277 -OLS_wendland,16,299.0848083496094,17.294068588669624,6.29771089553833,-2.7346341609954834 -OLS_sphere,16,3.2320287227630615,1.7977843927354196,1.299295425415039,0.9596420526504517 -DeepKriging_wendland,16,32.85277557373047,5.731734080863354,4.057799339294434,0.5897715091705322 -DeepKriging_mrts,16,1.567464828491211,1.251984356328469,0.8147905468940735,0.9804272651672363 -DeepKriging_sphere,16,1.1674952507019043,1.0805069415334194,0.5683812499046326,0.9854216575622559 -DeepKriging_sphere_Huber,16,0.9924571514129639,0.9962214369370717,0.5772374272346497,0.9876073002815247 -UniversalKriging,16,1.6187408013698081,1.2722974500366682,0.6782278928329629,0.9797869899616836 -OLS_wendland,17,138.49005126953125,11.768179607294037,5.7263264656066895,-1.0129425525665283 -OLS_sphere,17,1.544426679611206,1.2427496447841802,0.5906820297241211,0.9775518774986267 -DeepKriging_wendland,17,31.45631217956543,5.608592709367068,3.987231492996216,0.5427848100662231 -DeepKriging_mrts,17,0.6181471943855286,0.7862233743571406,0.4898375868797302,0.9910152554512024 -DeepKriging_sphere,17,0.4977565109729767,0.7055186113583232,0.3738920986652374,0.9927651286125183 -DeepKriging_sphere_Huber,17,0.4411160349845886,0.6641656683272545,0.3664151132106781,0.993588387966156 -UniversalKriging,17,0.7523012549391312,0.8673530163313732,0.4722739047646718,0.9890653570845542 -OLS_wendland,18,330.07904052734375,18.168077513246793,7.25604772567749,-2.4460301399230957 -OLS_sphere,18,4.421080112457275,2.10263646702355,1.3149484395980835,0.9538438320159912 -DeepKriging_wendland,18,64.5193099975586,8.032391300077368,5.432534217834473,0.32641738653182983 -DeepKriging_mrts,18,2.7372095584869385,1.6544514373310988,1.1540635824203491,0.9714235067367554 -DeepKriging_sphere,18,1.43064546585083,1.1960959266926838,0.8260340690612793,0.9850640296936035 -DeepKriging_sphere_Huber,18,1.4781726598739624,1.2158012419281214,0.8616254925727844,0.9845678806304932 -UniversalKriging,18,1.9139514515310465,1.3834563424738224,0.9266371776522142,0.9800183161171341 -OLS_wendland,19,313.4207763671875,17.703693862219474,6.805330276489258,-1.9268531799316406 -OLS_sphere,19,2.6697020530700684,1.6339222910132747,0.9629889130592346,0.9750692248344421 -DeepKriging_wendland,19,40.941017150878906,6.398516793045003,4.568624019622803,0.6176758408546448 -DeepKriging_mrts,19,1.7304327487945557,1.3154591399182856,0.9121573567390442,0.9838405251502991 -DeepKriging_sphere,19,0.8870342373847961,0.9418249505002488,0.6294944286346436,0.9917165040969849 -DeepKriging_sphere_Huber,19,0.7992661595344543,0.8940168675894512,0.6070082187652588,0.9925361275672913 -UniversalKriging,19,0.9488968950429502,0.9741133892124418,0.6560411020130675,0.9911388075907863 -OLS_wendland,20,64.4540786743164,8.028329756201872,5.410519599914551,0.14060932397842407 -OLS_sphere,20,2.457770586013794,1.5677278418187877,0.8517091870307922,0.9672296047210693 -DeepKriging_wendland,20,35.66444778442383,5.9719718506054456,4.255906105041504,0.5244723558425903 -DeepKriging_mrts,20,1.5200297832489014,1.2328948792370342,0.8009217381477356,0.979732871055603 -DeepKriging_sphere,20,1.129085898399353,1.0625845370601592,0.6366015672683716,0.9849454760551453 -DeepKriging_sphere_Huber,20,1.057930588722229,1.0285575281539818,0.6014277338981628,0.9858942031860352 -UniversalKriging,20,1.1509123949549251,1.072805851473101,0.6249110474406068,0.9846544484595667 -OLS_wendland,21,80.65776824951172,8.980966999689494,6.1861162185668945,-0.014597296714782715 -OLS_sphere,21,3.0659284591674805,1.7509792857619648,1.2032153606414795,0.9614335894584656 -DeepKriging_wendland,21,42.20697784423828,6.496689760504059,4.492963790893555,0.4690767526626587 -DeepKriging_mrts,21,1.6549221277236938,1.286437766751153,0.7430111169815063,0.9791826605796814 -DeepKriging_sphere,21,1.3759104013442993,1.1729920721574802,0.6176049113273621,0.9826923608779907 -DeepKriging_sphere_Huber,21,1.1163767576217651,1.056587316610305,0.5295265913009644,0.9859570264816284 -UniversalKriging,21,1.113752394216924,1.0553446802902473,0.575239536018199,0.9859900639488204 -OLS_wendland,22,65.26608276367188,8.078742647446562,5.942654609680176,0.20349258184432983 -OLS_sphere,22,7.5143280029296875,2.7412274628220272,1.1233960390090942,0.9082951545715332 -DeepKriging_wendland,22,40.543243408203125,6.367357647266496,4.4716877937316895,0.5052101612091064 -DeepKriging_mrts,22,1.5967704057693481,1.2636338099977178,0.8486728668212891,0.9805130362510681 -DeepKriging_sphere,22,1.4643361568450928,1.2100975815384034,0.7270357608795166,0.9821292161941528 -DeepKriging_sphere_Huber,22,1.4380613565444946,1.1991919598398308,0.7313840389251709,0.9824498891830444 -UniversalKriging,22,1.513780822902087,1.230358005989349,0.7851837458897248,0.9815258164021171 -OLS_wendland,23,891.9482421875,29.865502543695793,6.885936737060547,-13.818158149719238 -OLS_sphere,23,1.171459436416626,1.0823397971139312,0.6765140295028687,0.9805382490158081 -DeepKriging_wendland,23,26.9410457611084,5.190476448372384,3.859866142272949,0.5524216890335083 -DeepKriging_mrts,23,0.8722091913223267,0.9339214053239848,0.6319183111190796,0.9855097532272339 -DeepKriging_sphere,23,0.729727029800415,0.8542406158690975,0.48792654275894165,0.9878768920898438 -DeepKriging_sphere_Huber,23,0.6361367106437683,0.797581789313026,0.4972114861011505,0.9894316792488098 -UniversalKriging,23,0.8872023968831475,0.9419142194930213,0.5628946869618394,0.985260685320516 -OLS_wendland,24,54.68723678588867,7.395081932331019,5.524410247802734,0.3210902214050293 -OLS_sphere,24,4.077549457550049,2.0192942969141594,1.493262529373169,0.9493796229362488 -DeepKriging_wendland,24,39.927303314208984,6.318805529070268,4.196728229522705,0.5043261051177979 -DeepKriging_mrts,24,1.4441419839859009,1.201724587410069,0.8019179701805115,0.9820718169212341 -DeepKriging_sphere,24,1.2397263050079346,1.1134299731047008,0.6068019270896912,0.9846095442771912 -DeepKriging_sphere_Huber,24,1.1203845739364624,1.058482202937991,0.6053541898727417,0.9860910773277283 -UniversalKriging,24,1.3667997953983702,1.1691021321502968,0.6912160197499356,0.9830319873319339 -OLS_wendland,25,615.0125122070312,24.79944580443344,7.314245700836182,-6.798427581787109 -OLS_sphere,25,4.353677749633789,2.086546848176141,1.4467065334320068,0.9447948932647705 -DeepKriging_wendland,25,38.97800827026367,6.243237002570355,4.5450358390808105,0.5057544708251953 -DeepKriging_mrts,25,1.792635440826416,1.3388933642476595,0.8583226799964905,0.977269172668457 -DeepKriging_sphere,25,1.1452269554138184,1.0701527719974464,0.6100338697433472,0.985478401184082 -DeepKriging_sphere_Huber,25,1.2120825052261353,1.100946186344335,0.6174225807189941,0.9846306443214417 -UniversalKriging,25,1.3541980523973716,1.163700155709095,0.6658029618627278,0.9828286158366244 -OLS_wendland,26,67.7548828125,8.231335420969067,5.9333930015563965,0.34338539838790894 -OLS_sphere,26,3.537759304046631,1.8808932197354082,0.9049019813537598,0.9657154679298401 -DeepKriging_wendland,26,45.4124755859375,6.738877917423457,4.890120506286621,0.5599063038825989 -DeepKriging_mrts,26,2.607767105102539,1.6148582306513903,0.9289514422416687,0.9747280478477478 -DeepKriging_sphere,26,2.0065574645996094,1.4165300789604185,0.7247816324234009,0.9805544018745422 -DeepKriging_sphere_Huber,26,2.015169620513916,1.4195667016783382,0.7060369253158569,0.9804709553718567 -UniversalKriging,26,2.0450545190814946,1.430054026630286,0.7740795828607447,0.980181314751309 -OLS_wendland,27,53.53913879394531,7.317044402895565,5.669878959655762,0.22540777921676636 -OLS_sphere,27,2.672912120819092,1.6349043154934455,1.1672686338424683,0.96132892370224 -DeepKriging_wendland,27,41.203697204589844,6.4190106094779,4.62527322769165,0.4038742184638977 -DeepKriging_mrts,27,1.1680908203125,1.0807825037039136,0.6899919509887695,0.9831003546714783 -DeepKriging_sphere,27,1.2103853225708008,1.1001751326815203,0.5728306770324707,0.9824883937835693 -DeepKriging_sphere_Huber,27,1.252493977546692,1.1191487736430272,0.5875202417373657,0.9818791747093201 -UniversalKriging,27,1.2627558955694544,1.1237241189764748,0.6031065705379367,0.9817307326277555 -OLS_wendland,28,103.19859313964844,10.158670835283937,5.972092628479004,-0.7384651899337769 -OLS_sphere,28,1.4803078174591064,1.2166790116785555,0.7217339873313904,0.9750629663467407 -DeepKriging_wendland,28,33.83779525756836,5.817026324297352,4.390262603759766,0.4299744963645935 -DeepKriging_mrts,28,1.3779972791671753,1.173881288362318,0.834766149520874,0.9767864942550659 -DeepKriging_sphere,28,0.815231442451477,0.9029016792826764,0.6105811595916748,0.9862667322158813 -DeepKriging_sphere_Huber,28,0.9597509503364563,0.9796687962451679,0.6589246988296509,0.9838321805000305 -UniversalKriging,28,0.8198019354017644,0.9054291443297837,0.6165815886541295,0.9861897623256343 -OLS_wendland,29,361.5597839355469,19.014725449912415,6.4755401611328125,-5.019263744354248 -OLS_sphere,29,3.0242984294891357,1.739051014055981,1.1291292905807495,0.9496513605117798 -DeepKriging_wendland,29,32.43772888183594,5.695412968506844,3.9541494846343994,0.4599751830101013 -DeepKriging_mrts,29,1.5352177619934082,1.2390390478081827,0.7533035278320312,0.974441647529602 -DeepKriging_sphere,29,0.9089387059211731,0.9533827698889744,0.49911558628082275,0.9848679304122925 -DeepKriging_sphere_Huber,29,0.7102925777435303,0.842788572385465,0.46886277198791504,0.9881750345230103 -UniversalKriging,29,1.022448426955508,1.011161919257004,0.5752208159653579,0.9829782313610949 -OLS_wendland,30,12147.1416015625,110.21407170394578,18.985525131225586,-154.9127960205078 -OLS_sphere,30,4.324729919433594,2.07959849957476,1.0709701776504517,0.9444905519485474 -DeepKriging_wendland,30,46.725616455078125,6.83561383162318,5.110665798187256,0.40026038885116577 -DeepKriging_mrts,30,2.044449806213379,1.4298425809204938,0.9164947271347046,0.9737587571144104 -DeepKriging_sphere,30,1.1688917875289917,1.0811529898811694,0.630437970161438,0.9849968552589417 -DeepKriging_sphere_Huber,30,1.2679283618927002,1.1260232510444446,0.6582623720169067,0.9837257266044617 -UniversalKriging,30,1.6920938385900832,1.3008050732489027,0.7569842235659646,0.978281388132629 -OLS_wendland,31,8843.6884765625,94.04088725954524,12.347898483276367,-116.504638671875 -OLS_sphere,31,2.5058627128601074,1.5829916970281643,0.9345218539237976,0.9667050242424011 -DeepKriging_wendland,31,45.83660125732422,6.770273351743208,4.66135311126709,0.39097654819488525 -DeepKriging_mrts,31,1.1533139944076538,1.0739245757536484,0.7687734365463257,0.9846761226654053 -DeepKriging_sphere,31,0.7449873089790344,0.8631264733392404,0.5762217044830322,0.9901014566421509 -DeepKriging_sphere_Huber,31,0.89936763048172,0.9483499514850623,0.6064143180847168,0.9880502223968506 -UniversalKriging,31,1.0518700865873334,1.0256071794733759,0.6733768188924301,0.9860239748064568 -OLS_wendland,32,975.4537353515625,31.232254727309755,8.938841819763184,-7.798518180847168 -OLS_sphere,32,5.79229211807251,2.4067181218565064,1.7898098230361938,0.9477539658546448 -DeepKriging_wendland,32,46.16244125366211,6.7942947576376245,5.061904430389404,0.5836182832717896 -DeepKriging_mrts,32,2.920494556427002,1.708945451565673,1.0984020233154297,0.9736573696136475 -DeepKriging_sphere,32,2.36368989944458,1.5374296404858923,0.8806224465370178,0.9786797165870667 -DeepKriging_sphere_Huber,32,2.1078360080718994,1.4518388368107182,0.8243895173072815,0.9809874892234802 -UniversalKriging,32,2.5951699328151747,1.610953113164742,0.9393751176643804,0.9765917666596261 -OLS_wendland,33,462.03350830078125,21.494964719691477,6.700897693634033,-6.117753505706787 -OLS_sphere,33,2.2857859134674072,1.5118815805040444,0.712065577507019,0.9647868275642395 -DeepKriging_wendland,33,31.836061477661133,5.642345388015619,4.037989139556885,0.5095567107200623 -DeepKriging_mrts,33,1.7085285186767578,1.3071069270250073,0.7634649872779846,0.9736796617507935 -DeepKriging_sphere,33,0.6911212205886841,0.8313370078305693,0.5275227427482605,0.9893530607223511 -DeepKriging_sphere_Huber,33,0.5303295850753784,0.7282373137071311,0.44751012325286865,0.9918301105499268 -UniversalKriging,33,0.8000878830515097,0.8944763177700736,0.5041869241362478,0.9876744266718073 -OLS_wendland,34,50.68083572387695,7.1190473887927554,5.304773330688477,0.27202385663986206 -OLS_sphere,34,2.8368966579437256,1.6843089556087165,0.647615909576416,0.9592509865760803 -DeepKriging_wendland,34,33.181236267089844,5.760315639536591,4.062608242034912,0.5233869552612305 -DeepKriging_mrts,34,1.4749692678451538,1.214483127855284,0.5707414746284485,0.978813648223877 -DeepKriging_sphere,34,1.6798933744430542,1.296107007327348,0.46983593702316284,0.9758701324462891 -DeepKriging_sphere_Huber,34,1.5555250644683838,1.2472069052360093,0.4445337951183319,0.9776565432548523 -UniversalKriging,34,1.549644737579199,1.2448472748008885,0.514401994495091,0.977741004738065 -OLS_wendland,35,123.76595306396484,11.125014744438088,6.080634593963623,-0.775792121887207 -OLS_sphere,35,2.7870986461639404,1.6694605853879692,1.2202248573303223,0.9600107669830322 -DeepKriging_wendland,35,36.73330307006836,6.060800530463641,4.2875471115112305,0.4729510545730591 -DeepKriging_mrts,35,0.777531623840332,0.88177753647977,0.5965513586997986,0.9888439774513245 -DeepKriging_sphere,35,0.372620552778244,0.6104265334815026,0.37707778811454773,0.9946536421775818 -DeepKriging_sphere_Huber,35,0.4624558091163635,0.68004103487684,0.43614494800567627,0.993364691734314 -UniversalKriging,35,0.5475400790686104,0.7399595117765636,0.4592685992845601,0.9921439028196651 -OLS_wendland,36,84.86972045898438,9.212476347811394,5.729352951049805,-0.26343727111816406 -OLS_sphere,36,3.2344210147857666,1.7984496141915587,1.2831932306289673,0.9518498778343201 -DeepKriging_wendland,36,35.43317794799805,5.952577420579932,4.548092842102051,0.4725138545036316 -DeepKriging_mrts,36,1.6052238941192627,1.2669743068110193,0.7517269253730774,0.9761033654212952 -DeepKriging_sphere,36,0.9004269242286682,0.9489082801981803,0.5370499491691589,0.9865955114364624 -DeepKriging_sphere_Huber,36,0.770861029624939,0.8779869188233609,0.4976470470428467,0.9885243773460388 -UniversalKriging,36,1.6442501509416552,1.282283178920185,0.6974086117108582,0.9755224021664665 -OLS_wendland,37,43.23046112060547,6.574987537676818,4.86290168762207,0.310530424118042 -OLS_sphere,37,4.077783584594727,2.0193522685739422,0.8029429912567139,0.934964656829834 -DeepKriging_wendland,37,27.061721801757812,5.2020882154917185,3.6865439414978027,0.5684007406234741 -DeepKriging_mrts,37,2.4679317474365234,1.5709652279527142,0.6992170214653015,0.9606397151947021 -DeepKriging_sphere,37,0.5910396575927734,0.7687910363634408,0.4476897716522217,0.9905737042427063 -DeepKriging_sphere_Huber,37,0.4499720335006714,0.6707995479281954,0.4169002175331116,0.9928235411643982 -UniversalKriging,37,0.9141028235712986,0.9560872468406315,0.5919787823218701,0.9854212499239139 -OLS_wendland,38,615.6351928710938,24.811996954519678,8.554441452026367,-6.143980503082275 -OLS_sphere,38,3.308594226837158,1.8189541574314505,0.9272924065589905,0.9616062641143799 -DeepKriging_wendland,38,54.265342712402344,7.36650138888213,5.457431316375732,0.3702917695045471 -DeepKriging_mrts,38,1.4703830480575562,1.2125935213654888,0.7095247507095337,0.9829373359680176 -DeepKriging_sphere,38,1.3430358171463013,1.1588942217244425,0.6133497357368469,0.9844151139259338 -DeepKriging_sphere_Huber,38,1.1283466815948486,1.0622366410526651,0.5881457328796387,0.9869064092636108 -UniversalKriging,38,1.1309077789729678,1.0634414788661235,0.6333238696733705,0.9868766718417956 -OLS_wendland,39,34.4522819519043,5.869606626674764,4.604797840118408,0.3458724021911621 -OLS_sphere,39,2.3789193630218506,1.5423745858324593,1.038757562637329,0.9548326730728149 -DeepKriging_wendland,39,23.27908706665039,4.824840626036304,3.473996639251709,0.5580120086669922 -DeepKriging_mrts,39,1.0547834634780884,1.027026515469824,0.5980207920074463,0.9799733757972717 -DeepKriging_sphere,39,0.6606678366661072,0.8128147615946127,0.39271360635757446,0.9874562621116638 -DeepKriging_sphere_Huber,39,0.7222487926483154,0.8498522181228425,0.4058561623096466,0.9862870573997498 -UniversalKriging,39,0.9413493626374811,0.9702316025761484,0.48865833929069824,0.9821270882099662 -OLS_wendland,40,97.40548706054688,9.869421819972377,5.946840763092041,-0.496382474899292 -OLS_sphere,40,1.5088266134262085,1.2283430357299252,0.6033234000205994,0.9768207669258118 -DeepKriging_wendland,40,33.521751403808594,5.789797181578003,4.239408493041992,0.4850252866744995 -DeepKriging_mrts,40,0.8115453124046326,0.9008580978182039,0.5021472573280334,0.9875327348709106 -DeepKriging_sphere,40,0.6439191102981567,0.8024457055141841,0.39751648902893066,0.9901078343391418 -DeepKriging_sphere_Huber,40,0.8039913177490234,0.8966556294079815,0.3980548679828644,0.9876487851142883 -UniversalKriging,40,0.6272924584872303,0.792017966012912,0.44970577297835707,0.990363279313375 -OLS_wendland,41,49.48114013671875,7.034283199922985,5.627366065979004,0.4135163426399231 -OLS_sphere,41,2.9681313037872314,1.7228265448927909,0.9793336987495422,0.9648197293281555 -DeepKriging_wendland,41,41.696590423583984,6.457289711913504,4.857707977294922,0.5057840347290039 -DeepKriging_mrts,41,2.7715842723846436,1.6648075781857323,0.9938689470291138,0.9671493172645569 -DeepKriging_sphere,41,1.4131076335906982,1.188742038286986,0.7222655415534973,0.9832509160041809 -DeepKriging_sphere_Huber,41,1.4394768476486206,1.1997820000519346,0.7293108105659485,0.9829383492469788 -UniversalKriging,41,1.9525179025713553,1.3973252672772203,0.8461318772072618,0.9768574475772578 -OLS_wendland,42,87.251953125,9.340875393933912,5.646005153656006,-0.27686381340026855 -OLS_sphere,42,1.7683993577957153,1.3298117753260104,0.7697454690933228,0.9741208553314209 -DeepKriging_wendland,42,28.529237747192383,5.341276789981248,3.938755750656128,0.5824970006942749 -DeepKriging_mrts,42,1.5610623359680176,1.2494248020461325,0.7708286046981812,0.9771550893783569 -DeepKriging_sphere,42,1.5626980066299438,1.2500792001429124,0.6574620604515076,0.9771311283111572 -DeepKriging_sphere_Huber,42,0.8614883422851562,0.9281639630394817,0.5693644881248474,0.987392783164978 -UniversalKriging,42,1.5314152877098173,1.2375036515945386,0.7257625338033367,0.977588939800091 -OLS_wendland,43,76037.703125,275.7493483673171,23.444534301757812,-885.2103271484375 -OLS_sphere,43,3.8204574584960938,1.9545990531298467,0.8659480214118958,0.955473005771637 -DeepKriging_wendland,43,49.001609802246094,7.000114984930326,4.619032382965088,0.4288921356201172 -DeepKriging_mrts,43,1.7750275135040283,1.3323015850414757,0.8417458534240723,0.9793122410774231 -DeepKriging_sphere,43,0.8466259837150574,0.9201228090396724,0.5793809294700623,0.9901326894760132 -DeepKriging_sphere_Huber,43,0.8449943661689758,0.9192357511373107,0.5732213258743286,0.9901517033576965 -UniversalKriging,43,1.0044643165445108,1.00222967255241,0.5965070696814423,0.9882930885038518 -OLS_wendland,44,54.368568420410156,7.373504487040756,5.451240062713623,0.19623011350631714 -OLS_sphere,44,3.0859758853912354,1.756694590812881,0.7567945718765259,0.9543777704238892 -DeepKriging_wendland,44,43.641082763671875,6.606139777787924,4.46458101272583,0.3548223376274109 -DeepKriging_mrts,44,2.368100643157959,1.5388634257652494,0.8763809204101562,0.9649906754493713 -DeepKriging_sphere,44,1.7802698612213135,1.3342675373482311,0.6125275492668152,0.9736809730529785 -DeepKriging_sphere_Huber,44,1.4528733491897583,1.205351960710961,0.5476329326629639,0.9785211086273193 -UniversalKriging,44,1.6963441224740978,1.3024377614589104,0.6102274636287529,0.9749217203852815 -OLS_wendland,45,45.59053039550781,6.752076006348552,5.3582634925842285,0.14199793338775635 -OLS_sphere,45,2.606297254562378,1.6144030644676,0.8600499033927917,0.9509501457214355 -DeepKriging_wendland,45,35.42885208129883,5.952214048679603,4.43697452545166,0.3332381248474121 -DeepKriging_mrts,45,1.3027021884918213,1.1413597980005346,0.7454290390014648,0.9754834771156311 -DeepKriging_sphere,45,0.81999671459198,0.9055366997488175,0.544424831867218,0.9845678806304932 -DeepKriging_sphere_Huber,45,0.9594420194625854,0.9795111124752927,0.5664111375808716,0.9819435477256775 -UniversalKriging,45,1.2141355602439734,1.10187819664606,0.6592507549571519,0.9771502800769084 -OLS_wendland,46,70.41128540039062,8.391143271354066,6.237138748168945,0.4535655975341797 -OLS_sphere,46,3.183265447616577,1.784170801133282,0.9316509366035461,0.975295901298523 -DeepKriging_wendland,46,65.5256576538086,8.094792008063493,5.663538932800293,0.49148106575012207 -DeepKriging_mrts,46,2.1400411128997803,1.4628879358651434,0.9762635827064514,0.9833920001983643 -DeepKriging_sphere,46,1.9049346446990967,1.3801936982536533,0.806399941444397,0.9852165579795837 -DeepKriging_sphere_Huber,46,1.9256196022033691,1.3876669637212558,0.7987867593765259,0.9850560426712036 -UniversalKriging,46,2.0566042886616516,1.434086569444694,0.8307373315015082,0.9840395008635242 -OLS_wendland,47,49.18731689453125,7.013367015530504,5.46699333190918,0.3180999159812927 -OLS_sphere,47,1.8610363006591797,1.364198043049168,0.8231058120727539,0.9741998314857483 -DeepKriging_wendland,47,35.293155670166016,5.940804294888531,4.09913969039917,0.5107192993164062 -DeepKriging_mrts,47,1.0749822854995728,1.0368135249405135,0.7127419710159302,0.9850971698760986 -DeepKriging_sphere,47,1.273979902267456,1.1287071818091068,0.6610831618309021,0.9823383688926697 -DeepKriging_sphere_Huber,47,0.9210892915725708,0.9597339691667535,0.6307138800621033,0.987230658531189 -UniversalKriging,47,1.5371370911349544,1.2398133291487692,0.7124487210408159,0.9786901586449258 -OLS_wendland,48,145627.828125,381.61214357643286,29.031885147094727,-2543.2607421875 -OLS_sphere,48,2.0977635383605957,1.4483658164844253,1.0188082456588745,0.9633499979972839 -DeepKriging_wendland,48,28.28125,5.318011846545662,3.9171595573425293,0.5058988332748413 -DeepKriging_mrts,48,0.45403167605400085,0.6738187264049589,0.45372429490089417,0.9920676350593567 -DeepKriging_sphere,48,0.2671942114830017,0.5169083201913098,0.32345837354660034,0.995331883430481 -DeepKriging_sphere_Huber,48,0.3179386854171753,0.5638605194701747,0.35060399770736694,0.9944453239440918 -UniversalKriging,48,0.441503406861432,0.664457227262547,0.3950976282508757,0.9922865030479316 -OLS_wendland,49,78.59158325195312,8.865189408690213,5.375292778015137,-0.28303706645965576 -OLS_sphere,49,2.295391082763672,1.5150548118017617,0.6819963455200195,0.9625268578529358 -DeepKriging_wendland,49,32.38834762573242,5.691076139512845,4.018862247467041,0.4712480902671814 -DeepKriging_mrts,49,0.6461712718009949,0.803847791438774,0.4968174695968628,0.989450991153717 -DeepKriging_sphere,49,0.37810376286506653,0.6149014253236583,0.37823721766471863,0.9938273429870605 -DeepKriging_sphere_Huber,49,0.3402661383152008,0.583323356565808,0.36731255054473877,0.9944450259208679 -UniversalKriging,49,0.6933459896562543,0.8326739996278582,0.4855529374603103,0.988680866684262 -OLS_wendland,50,276.252197265625,16.62083623845759,6.594390392303467,-3.099447250366211 -OLS_sphere,50,2.256309986114502,1.5021018561051385,1.0630160570144653,0.966517448425293 -DeepKriging_wendland,50,27.416059494018555,5.236034710925678,3.806889533996582,0.5931590795516968 -DeepKriging_mrts,50,0.9917960166931152,0.9958895604900753,0.6973426938056946,0.9852822422981262 -DeepKriging_sphere,50,0.5673189759254456,0.7532057991846887,0.4954671561717987,0.9915812611579895 -DeepKriging_sphere_Huber,50,0.6481468677520752,0.8050756906975115,0.5117081999778748,0.9903818368911743 -UniversalKriging,50,0.8274392089271454,0.9096368555237554,0.5595374175198976,0.9877212057499654 diff --git a/notebook/simulation/Rerun/results_sphere_stdscaler_outliers_50reps_checkpoint.csv b/notebook/simulation/Rerun/results_sphere_stdscaler_outliers_50reps_checkpoint.csv deleted file mode 100644 index 22e1687..0000000 --- a/notebook/simulation/Rerun/results_sphere_stdscaler_outliers_50reps_checkpoint.csv +++ /dev/null @@ -1,372 +0,0 @@ -Model,Repeat,MSPE,RMSE,MAE,R2 -OLS_wendland,1,11434.009765625,106.92992923230146,14.83938980102539,-93.63195037841797 -OLS_sphere,1,55.636390686035156,7.458980539325408,3.202444076538086,0.5395333766937256 -DeepKriging_wendland,1,94.50776672363281,9.721510516562374,5.738083839416504,0.21781998872756958 -DeepKriging_mrts,1,137.11021423339844,11.70940708291408,3.764101982116699,-0.1347731351852417 -DeepKriging_sphere,1,57.05821990966797,7.553689158925456,2.4504542350769043,0.5277658104896545 -DeepKriging_sphere_Huber,1,48.04107666015625,6.931167048928791,1.3273099660873413,0.6023948788642883 -UniversalKriging,1,52.63360028110931,7.254901810576716,2.6192092457093077,0.5643855499684275 -OLS_wendland,1,11290.5,106.25676449054902,14.983518600463867,-87.34193420410156 -OLS_sphere,1,54.09169387817383,7.354705560263703,3.1313860416412354,0.576762318611145 -DeepKriging_wendland,1,99.20248413085938,9.960044383980394,5.8633341789245605,0.22379529476165771 -DeepKriging_mrts,1,60.101234436035156,7.75249859310114,3.761817455291748,0.5297409892082214 -DeepKriging_sphere,1,61.11210632324219,7.817423253428344,2.2646918296813965,0.5218315124511719 -DeepKriging_sphere_Huber,1,47.98279571533203,6.926961506702057,1.4860665798187256,0.624561071395874 -UniversalKriging,1,51.90044579400074,7.2041964016815045,2.597656481952814,0.593907651231321 -OLS_wendland,2,44281.69140625,210.43215392674665,21.172292709350586,-255.93478393554688 -OLS_sphere,2,82.3411865234375,9.074204456779531,3.5110740661621094,0.5222333669662476 -DeepKriging_wendland,2,138.32728576660156,11.761262082217264,5.859182834625244,0.19738632440567017 -DeepKriging_mrts,2,106.0263442993164,10.296909453778664,3.9192142486572266,0.38480544090270996 -DeepKriging_sphere,2,104.43537139892578,10.219362573024101,3.062854051589966,0.3940366506576538 -DeepKriging_sphere_Huber,2,79.44552612304688,8.91322198326996,1.6625746488571167,0.5390347838401794 -UniversalKriging,2,84.725769891873,9.204660226856449,3.2343686970258942,0.5083972764698471 -OLS_wendland,2,37516.390625,193.69148309876715,20.216636657714844,-213.22222900390625 -OLS_sphere,2,80.78840637207031,8.988237111473547,3.5334246158599854,0.5386903285980225 -DeepKriging_wendland,2,137.79824829101562,11.738749860654481,5.869849681854248,0.21315860748291016 -DeepKriging_mrts,2,98.84378051757812,9.942020947351606,3.712949275970459,0.4355924129486084 -DeepKriging_sphere,2,109.1312255859375,10.446589184319325,3.1627182960510254,0.3768501281738281 -DeepKriging_sphere_Huber,2,79.53244018554688,8.918096219796402,1.7746506929397583,0.545862078666687 -UniversalKriging,2,83.23051570657405,9.123076000262962,3.2545945835860124,0.524745689925108 -OLS_wendland,3,114.67440795898438,10.708613727228393,5.758488178253174,-0.08680486679077148 -OLS_sphere,3,58.857383728027344,7.671856602415568,3.0127484798431396,0.44219034910202026 -DeepKriging_wendland,3,85.15155029296875,9.227759765672747,4.82232141494751,0.1929924488067627 -DeepKriging_mrts,3,67.19758605957031,8.19741337615533,3.223037004470825,0.36314767599105835 -DeepKriging_sphere,3,66.3172607421875,8.143541044422108,2.570128917694092,0.37149083614349365 -DeepKriging_sphere_Huber,3,51.5213508605957,7.177837478000996,1.4842406511306763,0.5117162466049194 -UniversalKriging,3,58.596217163308744,7.654816598933559,2.703460471193898,0.4446655167125594 -OLS_wendland,3,119.39292907714844,10.926707147038783,5.902403354644775,-0.10716474056243896 -OLS_sphere,3,59.526397705078125,7.715335229598137,3.0265634059906006,0.4479947090148926 -DeepKriging_wendland,3,87.31937408447266,9.344483617861004,4.959469795227051,0.1902625560760498 -DeepKriging_mrts,3,76.98978424072266,8.774382271175712,3.312838077545166,0.28605180978775024 -DeepKriging_sphere,3,76.94871520996094,8.77204167853533,3.420156717300415,0.28643256425857544 -DeepKriging_sphere_Huber,3,52.3504524230957,7.235361250352031,1.547482967376709,0.5145393013954163 -UniversalKriging,3,59.23654407998005,7.696528053608331,2.7188117791335253,0.4506825827255744 -OLS_wendland,4,646.9976196289062,25.4361478928887,10.459138870239258,-1.7666308879852295 -OLS_sphere,4,139.6615447998047,11.81784856899955,3.6231625080108643,0.4027922749519348 -DeepKriging_wendland,4,213.78834533691406,14.621502841257941,6.954104423522949,0.08581817150115967 -DeepKriging_mrts,4,159.06005859375,12.611901466224275,4.272494792938232,0.3198421597480774 -DeepKriging_sphere,4,138.15155029296875,11.753788763329412,3.3640034198760986,0.4092492461204529 -DeepKriging_sphere_Huber,4,128.56814575195312,11.338789430620587,2.2101240158081055,0.45022886991500854 -UniversalKriging,4,140.3946529004489,11.848824958638257,3.2610368891740413,0.3996574578581834 -OLS_wendland,5,693.9666748046875,26.34324723348827,7.539922714233398,-7.362863540649414 -OLS_sphere,5,27.096546173095703,5.205434292457807,2.900843858718872,0.6734645366668701 -DeepKriging_wendland,5,54.658668518066406,7.393150107908428,5.27903413772583,0.3413185477256775 -DeepKriging_mrts,5,24.516698837280273,4.951434018269886,2.422832727432251,0.7045538425445557 -DeepKriging_sphere,5,19.09381675720215,4.369647211984298,2.2580087184906006,0.7699039578437805 -DeepKriging_sphere_Huber,5,15.326025009155273,3.914846741464508,1.160775899887085,0.8153089284896851 -UniversalKriging,5,22.099312771133313,4.700990615937593,2.30062521473016,0.7336852797220836 -OLS_wendland,6,351.70343017578125,18.753757761466932,7.006178855895996,-1.3128893375396729 -OLS_sphere,6,99.85591888427734,9.992793347421799,4.199220657348633,0.34332263469696045 -DeepKriging_wendland,6,118.52677154541016,10.887000116901357,5.501960754394531,0.2205384373664856 -DeepKriging_mrts,6,104.23543548583984,10.20957567609153,4.198758602142334,0.314521849155426 -DeepKriging_sphere,6,100.35295867919922,10.01763238890304,3.7877867221832275,0.3400539755821228 -DeepKriging_sphere_Huber,6,89.23344421386719,9.446345548087217,2.2697505950927734,0.4131786823272705 -UniversalKriging,6,97.96015138137604,9.89748207279892,3.726718814438184,0.35578974357392246 -OLS_wendland,7,76.23329162597656,8.731167827156717,5.660698890686035,-0.040972113609313965 -OLS_sphere,7,20.14491844177246,4.488309084919671,2.5142486095428467,0.7249194383621216 -DeepKriging_wendland,7,47.697105407714844,6.906309101663119,4.8773651123046875,0.3486919403076172 -DeepKriging_mrts,7,24.181089401245117,4.917427111940259,2.622922897338867,0.6698051691055298 -DeepKriging_sphere,7,18.261550903320312,4.27335358978406,1.907581090927124,0.7506369352340698 -DeepKriging_sphere_Huber,7,13.004573822021484,3.6061854946773724,0.9396819472312927,0.8224214315414429 -UniversalKriging,7,18.47253357477299,4.297968540458735,2.1254609458991953,0.7477559868182833 -OLS_wendland,8,338.088623046875,18.387186382012747,8.869123458862305,-0.3605726957321167 -OLS_sphere,8,132.2269744873047,11.498998847173814,3.7925479412078857,0.46787798404693604 -DeepKriging_wendland,8,174.04537963867188,13.19262595690001,6.893924236297607,0.29958784580230713 -DeepKriging_mrts,8,142.13516235351562,11.922045225275554,4.753262519836426,0.4280043840408325 -DeepKriging_sphere,8,134.78855895996094,11.609847499427412,3.5259382724761963,0.4575693607330322 -DeepKriging_sphere_Huber,8,125.72103118896484,11.212539016162435,2.5224409103393555,0.49405986070632935 -UniversalKriging,8,129.37807306512693,11.374448253217688,3.323169871751208,0.4793427868687271 -OLS_wendland,9,156.84071350097656,12.523606249837806,7.494758605957031,0.04234111309051514 -OLS_sphere,9,90.81490325927734,9.529685370424216,3.965514659881592,0.445490300655365 -DeepKriging_wendland,9,136.28451538085938,11.674095912783113,6.230611324310303,0.16785591840744019 -DeepKriging_mrts,9,101.02105712890625,10.050923197841392,4.075244426727295,0.3831722140312195 -DeepKriging_sphere,9,100.17607879638672,10.008800067759708,3.3313002586364746,0.3883315920829773 -DeepKriging_sphere_Huber,9,86.29838562011719,9.289692439479209,2.1309874057769775,0.47306787967681885 -UniversalKriging,9,93.99548417985329,9.695126826393418,3.3896593944116757,0.4260699101487446 -OLS_wendland,10,46704.796875,216.11292620988687,20.444887161254883,-298.9169921875 -OLS_sphere,10,85.39060974121094,9.240703963509,3.803103446960449,0.45166027545928955 -DeepKriging_wendland,10,131.78724670410156,11.479862660506944,6.1544952392578125,0.1537221074104309 -DeepKriging_mrts,10,107.76851654052734,10.381161618071811,3.7130188941955566,0.30795949697494507 -DeepKriging_sphere,10,88.19493865966797,9.391216037322748,3.251732349395752,0.4336521625518799 -DeepKriging_sphere_Huber,10,76.78773498535156,8.762861118684443,1.7413902282714844,0.5069040060043335 -UniversalKriging,10,91.59172244193877,9.570356442784082,3.5492599393631554,0.41183951308142963 -OLS_wendland,11,65.7195816040039,8.106761474473263,6.444315433502197,0.15396565198898315 -OLS_sphere,11,24.894906997680664,4.989479631953683,2.9169931411743164,0.679517924785614 -DeepKriging_wendland,11,54.49773406982422,7.382258060365014,5.546703338623047,0.29842889308929443 -DeepKriging_mrts,11,40.16814041137695,6.3378340473206585,3.736910104751587,0.4828994870185852 -DeepKriging_sphere,11,14.486011505126953,3.8060493303591,1.9280191659927368,0.8135157823562622 -DeepKriging_sphere_Huber,11,7.383924961090088,2.7173378444886254,0.8708510994911194,0.9049437642097473 -UniversalKriging,11,17.816791480907597,4.220994134194881,2.265116027929043,0.7706373317180878 -OLS_wendland,12,194.88833618164062,13.960241265165893,8.109787940979004,0.0147705078125 -OLS_sphere,12,106.70854949951172,10.329983034812386,3.7538411617279053,0.46055054664611816 -DeepKriging_wendland,12,180.46078491210938,13.433569328816127,6.752171993255615,0.08770692348480225 -DeepKriging_mrts,12,146.97030639648438,12.123131047567059,5.137851238250732,0.25701308250427246 -DeepKriging_sphere,12,122.69851684570312,11.076936257183352,3.369351625442505,0.37971562147140503 -DeepKriging_sphere_Huber,12,101.6001968383789,10.079692298794587,2.2850968837738037,0.486375093460083 -UniversalKriging,12,109.71541179344669,10.474512484762558,3.4876868568560204,0.4453497939904939 -OLS_wendland,13,492.7255554199219,22.19742226971235,8.746312141418457,-0.8466149568557739 -OLS_sphere,13,176.1959991455078,13.273884101705416,4.146615505218506,0.3396604657173157 -DeepKriging_wendland,13,224.43019104003906,14.980994327481707,6.954847812652588,0.15889054536819458 -DeepKriging_mrts,13,192.38853454589844,13.870419407714333,4.893923759460449,0.27897483110427856 -DeepKriging_sphere,13,189.19400024414062,13.754780995862516,4.193910121917725,0.2909471392631531 -DeepKriging_sphere_Huber,13,166.12847900390625,12.88908371467523,2.529031753540039,0.3773910403251648 -UniversalKriging,13,181.9730644616307,13.489739228822428,4.050047515402548,0.31800941450554765 -OLS_wendland,14,76.39132690429688,8.74021320702744,6.360142230987549,0.08971428871154785 -OLS_sphere,14,36.962890625,6.0797113932324125,3.1644349098205566,0.559546947479248 -DeepKriging_wendland,14,63.182064056396484,7.948714616615474,5.430943965911865,0.2471170425415039 -DeepKriging_mrts,14,44.27729034423828,6.654118299537384,3.949209451675415,0.47238796949386597 -DeepKriging_sphere,14,38.963497161865234,6.242074748179906,2.4675474166870117,0.5357075929641724 -DeepKriging_sphere_Huber,14,26.841228485107422,5.1808521002927135,1.5655806064605713,0.6801575422286987 -UniversalKriging,14,31.836443397744787,5.642379232003534,2.650915357320185,0.6206341647568339 -OLS_wendland,15,362.4444885253906,19.037974906102555,9.598369598388672,0.14655780792236328 -OLS_sphere,15,165.6742401123047,12.87145058306579,4.001501560211182,0.609889566898346 -DeepKriging_wendland,15,291.31024169921875,17.067813032114536,7.228255271911621,0.3140564560890198 -DeepKriging_mrts,15,205.61734008789062,14.339363308316399,4.144783020019531,0.5158361792564392 -DeepKriging_sphere,15,204.92967224121094,14.315364900735537,3.183896780014038,0.5174554586410522 -DeepKriging_sphere_Huber,15,194.58973693847656,13.94954253509686,2.770753860473633,0.541802704334259 -UniversalKriging,15,179.9097599855441,13.41304439661422,3.8799382823909507,0.5763694200938065 -OLS_wendland,16,385.9000244140625,19.644338227949103,7.75636100769043,-1.3868811130523682 -OLS_sphere,16,77.94779205322266,8.828804678620015,3.5428566932678223,0.5178748965263367 -DeepKriging_wendland,16,104.66972351074219,10.230822230433983,5.457324028015137,0.3525935411453247 -DeepKriging_mrts,16,77.94121551513672,8.828432222945176,3.6902942657470703,0.5179154872894287 -DeepKriging_sphere,16,174.34912109375,13.204132727814804,5.0444793701171875,-0.07838964462280273 -DeepKriging_sphere_Huber,16,72.49481201171875,8.514388528351214,1.8568679094314575,0.551602840423584 -UniversalKriging,16,80.05850016438364,8.947541570978235,3.182374305903637,0.5048196101005089 -OLS_wendland,17,157.27857971191406,12.541075699951502,6.484364032745361,-0.466866135597229 -OLS_sphere,17,41.45463943481445,6.438527738141263,2.968749523162842,0.6133713126182556 -DeepKriging_wendland,17,74.2823257446289,8.618719495646028,5.042412757873535,0.3072023391723633 -DeepKriging_mrts,17,48.25529861450195,6.94660338687203,3.0646212100982666,0.5499446392059326 -DeepKriging_sphere,17,40.42520523071289,6.358081882982704,2.123535633087158,0.6229723691940308 -DeepKriging_sphere_Huber,17,33.730770111083984,5.807819738170597,1.0882090330123901,0.685408353805542 -UniversalKriging,17,42.634235888014665,6.529489711150073,2.3375780880712047,0.6023697531246973 -OLS_wendland,18,575.5159912109375,23.98991436439358,8.36988639831543,-2.4929749965667725 -OLS_sphere,18,68.2618408203125,8.262072428895337,3.2027089595794678,0.5856988430023193 -DeepKriging_wendland,18,139.214111328125,11.79890297138361,6.649312973022461,0.15506881475448608 -DeepKriging_mrts,18,76.21635437011719,8.730197842553007,4.035462856292725,0.5374206304550171 -DeepKriging_sphere,18,70.71121215820312,8.40899590665872,2.4732744693756104,0.5708329677581787 -DeepKriging_sphere_Huber,18,61.4638557434082,7.839888758356729,1.82853364944458,0.626957893371582 -UniversalKriging,18,72.58055178926358,8.51942203375696,3.0069704816833602,0.5594873992649765 -OLS_wendland,19,343.1789245605469,18.525089056750762,8.230323791503906,0.10411059856414795 -OLS_sphere,19,220.17811584472656,14.838400043290603,3.9920527935028076,0.42521172761917114 -DeepKriging_wendland,19,273.377197265625,16.534122210314795,6.429704666137695,0.28633230924606323 -DeepKriging_mrts,19,237.1122283935547,15.398448895702277,4.211977005004883,0.3810041546821594 -DeepKriging_sphere,19,207.69334411621094,14.41156980055299,3.155895948410034,0.45780396461486816 -DeepKriging_sphere_Huber,19,210.4810028076172,14.507963427291136,2.330333948135376,0.4505265951156616 -UniversalKriging,19,221.36302567842665,14.87827361216437,3.549625814090553,0.42211844383080843 -OLS_wendland,20,205.25125122070312,14.326592449731482,7.3163347244262695,0.04026675224304199 -OLS_sphere,20,124.36067199707031,11.151711617373824,3.69039249420166,0.4185025691986084 -DeepKriging_wendland,20,180.5133819580078,13.435526858222115,6.308718681335449,0.15593844652175903 -DeepKriging_mrts,20,136.34628295898438,11.676741110386253,4.257756233215332,0.36245912313461304 -DeepKriging_sphere,20,146.30450439453125,12.095639891900356,5.145679950714111,0.31589555740356445 -DeepKriging_sphere_Huber,20,120.28984069824219,10.967672528765718,2.358842134475708,0.43753737211227417 -UniversalKriging,20,126.97978781644998,11.268530863269177,3.4156359333589443,0.4062558759470547 -OLS_wendland,21,333.3641662597656,18.258262958446117,9.033459663391113,-0.24479162693023682 -OLS_sphere,21,167.11106872558594,12.92714464704352,4.389772891998291,0.37600231170654297 -DeepKriging_wendland,21,223.5350799560547,14.951089590931314,7.239269733428955,0.16531336307525635 -DeepKriging_mrts,21,165.32513427734375,12.857882184766812,4.4353485107421875,0.3826709985733032 -DeepKriging_sphere,21,175.36605834960938,13.242585032749814,4.260003089904785,0.34517788887023926 -DeepKriging_sphere_Huber,21,160.2300262451172,12.658199960702042,2.8307201862335205,0.4016963243484497 -UniversalKriging,21,170.0461402769365,13.040174089211252,4.039868602596578,0.36504264777245043 -OLS_wendland,22,184.08963012695312,13.567963374322364,7.4394917488098145,0.03407251834869385 -OLS_sphere,22,91.17948150634766,9.548794767212648,3.3743653297424316,0.5215767025947571 -DeepKriging_wendland,22,134.3319091796875,11.590164329278835,5.9687113761901855,0.29515379667282104 -DeepKriging_mrts,22,93.1388931274414,9.650849347463746,3.9632813930511475,0.5112955570220947 -DeepKriging_sphere,22,96.40362548828125,9.818534793352889,2.8847496509552,0.4941653609275818 -DeepKriging_sphere_Huber,22,89.76451873779297,9.474413899434253,1.9902372360229492,0.5290011167526245 -UniversalKriging,22,85.27187085531372,9.234276953574314,2.8169522808714604,0.5525742238463471 -OLS_wendland,23,48214.59375,219.57821784047707,26.88910675048828,-169.16319274902344 -OLS_sphere,23,198.15093994140625,14.076609674968125,4.659758567810059,0.30066823959350586 -DeepKriging_wendland,23,237.42303466796875,15.408537719977478,6.5078654289245605,0.16206568479537964 -DeepKriging_mrts,23,212.71702575683594,14.58482175951547,4.909252643585205,0.2492603063583374 -DeepKriging_sphere,23,206.96250915527344,14.386191614019099,3.8082668781280518,0.26956963539123535 -DeepKriging_sphere_Huber,23,192.98678588867188,13.89196839503574,3.028486728668213,0.31889402866363525 -UniversalKriging,23,202.1316199887111,14.217300024572566,4.406272286899038,0.2866192514196445 -OLS_wendland,24,97.09913635253906,9.853889402288777,6.372705936431885,0.28624898195266724 -OLS_sphere,24,52.930477142333984,7.275333472929883,2.666246175765991,0.6109215021133423 -DeepKriging_wendland,24,89.67636108398438,9.469760349870759,5.187539100646973,0.34081190824508667 -DeepKriging_mrts,24,56.882102966308594,7.542022471877726,2.62957763671875,0.5818741321563721 -DeepKriging_sphere,24,52.839263916015625,7.269062107040744,1.9315167665481567,0.6115920543670654 -DeepKriging_sphere_Huber,24,48.299835205078125,6.949808285490912,1.4840716123580933,0.6449601650238037 -UniversalKriging,24,54.15359556204708,7.358912661667285,2.2693708042066665,0.6019306965934359 -OLS_wendland,25,1582.551025390625,39.7812898909855,10.00028133392334,-7.611696243286133 -OLS_sphere,25,109.54448699951172,10.466350223430885,4.334222793579102,0.403896689414978 -DeepKriging_wendland,25,159.11459350585938,12.614063322572127,7.001764297485352,0.1341533064842224 -DeepKriging_mrts,25,122.76580047607422,11.07997294563819,4.448757648468018,0.3319509029388428 -DeepKriging_sphere,25,116.77420043945312,10.806211197244533,3.811166763305664,0.3645550608634949 -DeepKriging_sphere_Huber,25,95.73130798339844,9.78423773134108,2.346170663833618,0.4790632724761963 -UniversalKriging,25,105.36505086261805,10.264747968782189,3.6038274360184124,0.4266397065215958 -OLS_wendland,26,420.14385986328125,20.497411052698368,8.43336296081543,0.22291070222854614 -OLS_sphere,26,335.92950439453125,18.328379753664294,5.297590732574463,0.3786718249320984 -DeepKriging_wendland,26,473.78948974609375,21.766705992090163,7.8904595375061035,0.12368887662887573 -DeepKriging_mrts,26,291.7021789550781,17.07929093829946,4.239632606506348,0.4604737162590027 -DeepKriging_sphere,26,309.59942626953125,17.595437654958495,4.066207408905029,0.42737138271331787 -DeepKriging_sphere_Huber,26,331.755615234375,18.214159745494026,3.1190428733825684,0.3863917589187622 -UniversalKriging,26,323.9469403243939,17.998526059774836,4.416545245606963,0.4008344521822802 -OLS_wendland,27,185.81973266601562,13.631571173786813,7.355372428894043,-0.1280895471572876 -OLS_sphere,27,90.6696548461914,9.522061480907977,3.380347490310669,0.44955527782440186 -DeepKriging_wendland,27,162.42848205566406,12.744743310701242,6.350609302520752,0.013915956020355225 -DeepKriging_mrts,27,98.7477035522461,9.937187909677773,3.7566287517547607,0.4005144238471985 -DeepKriging_sphere,27,86.26683807373047,9.287994297679692,2.3169918060302734,0.4762842655181885 -DeepKriging_sphere_Huber,27,78.12088012695312,8.838601706545733,1.6337335109710693,0.5257374048233032 -UniversalKriging,27,93.20570691161433,9.654310276328099,3.040952177161844,0.43415925552015344 -OLS_wendland,28,243.9448699951172,15.618734583669612,7.504209518432617,-1.1930158138275146 -OLS_sphere,28,56.33622741699219,7.505746293140489,3.1711654663085938,0.49354857206344604 -DeepKriging_wendland,28,91.38802337646484,9.55970833113986,5.855122089385986,0.1784399151802063 -DeepKriging_mrts,28,71.39411163330078,8.449503632362127,4.719728469848633,0.3581811785697937 -DeepKriging_sphere,28,130.2328643798828,11.411961460672869,4.1145195960998535,-0.1707674264907837 -DeepKriging_sphere_Huber,28,52.60959243774414,7.253247027210926,1.8825883865356445,0.527050256729126 -UniversalKriging,28,54.354103458945914,7.372523547534176,2.7832261270605905,0.5113674140088014 -OLS_wendland,29,635.5771484375,25.210655454341126,8.771356582641602,-4.122840881347656 -OLS_sphere,29,63.813507080078125,7.9883356889954324,2.9758312702178955,0.48565417528152466 -DeepKriging_wendland,29,98.57979583740234,9.928735863009065,5.147804260253906,0.2054329514503479 -DeepKriging_mrts,29,78.12590026855469,8.838885691565125,3.595335006713867,0.3702942728996277 -DeepKriging_sphere,29,76.98968505859375,8.774376619372669,2.283984661102295,0.37945228815078735 -DeepKriging_sphere_Huber,29,56.92050552368164,7.544567948112181,1.5423067808151245,0.5412127375602722 -UniversalKriging,29,65.8902184278202,8.117279003940926,2.782348570195298,0.46891555160641374 -OLS_wendland,30,11490.064453125,107.19171821145979,19.508508682250977,-78.37836456298828 -OLS_sphere,30,78.5926742553711,8.865250941477692,3.8295092582702637,0.45704764127731323 -DeepKriging_wendland,30,109.9162368774414,10.484094471028072,6.391831874847412,0.24065083265304565 -DeepKriging_mrts,30,76.28620147705078,8.734197242852417,3.350829839706421,0.47298169136047363 -DeepKriging_sphere,30,71.6101303100586,8.462276898687408,2.4847753047943115,0.5052860379219055 -DeepKriging_sphere_Huber,30,67.24651336669922,8.200397146888632,1.8927417993545532,0.5354318022727966 -UniversalKriging,30,73.20110420337981,8.5557643845176,3.0314890274751316,0.4942949164832767 -OLS_wendland,31,7830.37255859375,88.48939235068659,13.597549438476562,-41.503047943115234 -OLS_sphere,31,99.55728912353516,9.977839902681099,3.6907427310943604,0.45960575342178345 -DeepKriging_wendland,31,189.9770965576172,13.783217931877054,6.758006572723389,-0.03119051456451416 -DeepKriging_mrts,31,350.8138732910156,18.730025982123347,6.474599838256836,-0.9042081832885742 -DeepKriging_sphere,31,107.6571273803711,10.375795264960228,3.2920522689819336,0.41564005613327026 -DeepKriging_sphere_Huber,31,91.19261932373047,9.549482673094415,2.0930235385894775,0.5050089359283447 -UniversalKriging,31,109.91378334587203,10.4839774582871,3.4241429845260742,0.40339091332586663 -OLS_wendland,32,951.47802734375,30.846037465835867,10.377948760986328,-2.298419713973999 -OLS_sphere,32,135.20559692382812,11.627794155549372,4.2912187576293945,0.5312925577163696 -DeepKriging_wendland,32,209.37265014648438,14.469714929689678,6.999475479125977,0.2741830348968506 -DeepKriging_mrts,32,127.40652465820312,11.287449874006224,4.053257942199707,0.5583291053771973 -DeepKriging_sphere,32,189.92640686035156,13.781378989794582,4.248446464538574,0.34159594774246216 -DeepKriging_sphere_Huber,32,139.02334594726562,11.79081616968332,2.4699647426605225,0.5180578827857971 -UniversalKriging,32,151.6179290716604,12.313323234271907,4.026945966636523,0.4743971748621797 -OLS_wendland,33,504.10162353515625,22.452207542581558,8.199472427368164,-3.8341870307922363 -OLS_sphere,33,39.102848052978516,6.253227011150204,3.306506633758545,0.625015139579773 -DeepKriging_wendland,33,111.38294219970703,10.553811737931799,5.905643463134766,-0.06812989711761475 -DeepKriging_mrts,33,50.11985778808594,7.0795379643085425,2.7663373947143555,0.5193651914596558 -DeepKriging_sphere,33,73.31327056884766,8.562316892573392,2.881484270095825,0.296947181224823 -DeepKriging_sphere_Huber,33,37.476619720458984,6.121815067482763,1.425157904624939,0.6406101584434509 -UniversalKriging,33,58.90297871089259,7.6748276013792385,3.130013601117648,0.4351376560285849 -OLS_wendland,34,92.08457946777344,9.59607104328503,6.401495933532715,0.002248048782348633 -OLS_sphere,34,32.851226806640625,5.731598974687659,3.3784422874450684,0.6440514326095581 -DeepKriging_wendland,34,62.03310012817383,7.876109453795943,5.276460647583008,0.32786089181900024 -DeepKriging_mrts,34,50.34772491455078,7.0956130753128575,3.2998995780944824,0.4544738531112671 -DeepKriging_sphere,34,61.59213638305664,7.848065773364584,3.041975498199463,0.33263880014419556 -DeepKriging_sphere_Huber,34,25.249221801757812,5.024860376344582,1.1758315563201904,0.7264204025268555 -UniversalKriging,34,32.6233034623131,5.711681316592611,2.534628338040574,0.6465210115319493 -OLS_wendland,35,143.93621826171875,11.997342133227624,6.997428894042969,-0.08916652202606201 -OLS_sphere,35,57.24068069458008,7.565757113110364,3.1873700618743896,0.5668593049049377 -DeepKriging_wendland,35,101.00186920166016,10.049968616949018,5.78079891204834,0.2357180118560791 -DeepKriging_mrts,35,67.25550842285156,8.200945581020006,3.1476776599884033,0.4910770058631897 -DeepKriging_sphere,35,69.3730697631836,8.32904975151329,2.6368916034698486,0.47505342960357666 -DeepKriging_sphere_Huber,35,52.65331268310547,7.2562602408613674,1.3035683631896973,0.6015719175338745 -UniversalKriging,35,58.16202163076926,7.626402928692482,2.4857099131152456,0.5598875061797478 -OLS_wendland,36,145.4001007080078,12.058196411902063,6.894294738769531,-0.20428168773651123 -OLS_sphere,36,49.73139190673828,7.052048773706708,3.157334566116333,0.5880979299545288 -DeepKriging_wendland,36,96.3086929321289,9.813699248098493,5.79290246963501,0.2023196816444397 -DeepKriging_mrts,36,64.64311218261719,8.040094040657559,3.2241528034210205,0.46459102630615234 -DeepKriging_sphere,36,25.99880599975586,5.098902430891952,2.5211679935455322,0.7846639156341553 -DeepKriging_sphere_Huber,36,45.18593215942383,6.722048211625965,1.3352129459381104,0.6257458329200745 -UniversalKriging,36,42.95186100371096,6.553766932361187,2.5239145806928707,0.6442496243105323 -OLS_wendland,37,217.2191619873047,14.738356827927078,6.784821510314941,0.09677755832672119 -OLS_sphere,37,145.89578247070312,12.078732651677623,4.050541877746582,0.3933483958244324 -DeepKriging_wendland,37,198.03211975097656,14.072388558840201,5.926292896270752,0.1765594482421875 -DeepKriging_mrts,37,152.608642578125,12.353487061478836,3.863311529159546,0.36543554067611694 -DeepKriging_sphere,37,149.92665100097656,12.244453887412723,3.3143181800842285,0.37658756971359253 -DeepKriging_sphere_Huber,37,145.11859130859375,12.046517808420562,2.2213501930236816,0.3965800404548645 -UniversalKriging,37,151.92672845635988,12.325856094258114,3.85649682402166,0.36827106491969885 -OLS_wendland,38,874.9893798828125,29.580219402208844,10.002724647521973,-4.321139812469482 -OLS_sphere,38,75.4229507446289,8.684638780319473,2.9822163581848145,0.5413246154785156 -DeepKriging_wendland,38,139.92311096191406,11.828909965077681,6.433381080627441,0.14907485246658325 -DeepKriging_mrts,38,84.97239685058594,9.218047344778933,3.6719956398010254,0.48325085639953613 -DeepKriging_sphere,38,62.58824157714844,7.911273069307394,2.1301846504211426,0.6193773150444031 -DeepKriging_sphere_Huber,38,58.39006805419922,7.641339414932386,1.2722117900848389,0.6449080109596252 -UniversalKriging,38,70.13941174574364,8.374927566596838,2.4233659097496676,0.5734558562753505 -OLS_wendland,39,120.8060302734375,10.991179657954714,6.398131847381592,0.10565286874771118 -OLS_sphere,39,79.02928161621094,8.88984148431292,3.077793836593628,0.41493308544158936 -DeepKriging_wendland,39,113.78227233886719,10.666877347137127,5.493028163909912,0.15765094757080078 -DeepKriging_mrts,39,81.53889465332031,9.029888961295168,3.2914535999298096,0.3963540196418762 -DeepKriging_sphere,39,84.2649154663086,9.179592336607797,2.8651883602142334,0.37617284059524536 -DeepKriging_sphere_Huber,39,71.65084075927734,8.464681964449541,1.6827160120010376,0.46955692768096924 -UniversalKriging,39,76.84355848571809,8.766045772508726,2.76382354354984,0.43111437060875757 -OLS_wendland,40,249.88426208496094,15.807727922916719,7.4497528076171875,-1.2333519458770752 -OLS_sphere,40,52.025306701660156,7.2128570415377125,2.9186933040618896,0.5350214242935181 -DeepKriging_wendland,40,91.84444427490234,9.583550713326577,5.773375034332275,0.17913609743118286 -DeepKriging_mrts,40,61.98670196533203,7.873163402682052,2.8218495845794678,0.44599103927612305 -DeepKriging_sphere,40,58.0510368347168,7.619123101428195,3.0013315677642822,0.4811662435531616 -DeepKriging_sphere_Huber,40,39.66299057006836,6.2978560296396395,1.0768554210662842,0.645510196685791 -UniversalKriging,40,51.86687910259587,7.201866362450491,2.452699137347247,0.5364374367432115 -OLS_wendland,41,102.84648132324219,10.141325422411125,7.152385234832764,0.14425718784332275 -OLS_sphere,41,41.12593460083008,6.412950537843722,3.395007371902466,0.6578081846237183 -DeepKriging_wendland,41,91.09140014648438,9.54418148122113,6.038312911987305,0.2420663833618164 -DeepKriging_mrts,41,55.893821716308594,7.476217072578122,3.382450580596924,0.5349308252334595 -DeepKriging_sphere,41,50.001224517822266,7.071154397820929,2.3740813732147217,0.5839606523513794 -DeepKriging_sphere_Huber,41,33.28831100463867,5.769602326385994,1.473345160484314,0.7230218052864075 -UniversalKriging,41,39.647001055997734,6.29658646061481,2.6878130514586545,0.6701138050844195 -OLS_wendland,42,1174.218505859375,34.26687184234031,9.424605369567871,-6.9974493980407715 -OLS_sphere,42,85.90540313720703,9.268516771156376,3.8558499813079834,0.4149094820022583 -DeepKriging_wendland,42,109.28353118896484,10.453876371421504,5.551146030426025,0.2556840777397156 -DeepKriging_mrts,42,97.85687255859375,9.892263267755956,4.143897533416748,0.33350956439971924 -DeepKriging_sphere,42,77.84477233886719,8.822968453919984,3.2595107555389404,0.469809353351593 -DeepKriging_sphere_Huber,42,70.51678466796875,8.397427264821575,1.9296247959136963,0.5197193622589111 -UniversalKriging,42,78.77065155506416,8.875283181682946,3.313208787033903,0.4635033624513797 -OLS_wendland,43,67906.7265625,260.5891911850912,25.315324783325195,-201.22328186035156 -OLS_sphere,43,229.00999450683594,15.133076174619486,4.969691753387451,0.31801819801330566 -DeepKriging_wendland,43,306.3968811035156,17.504196099893182,7.877997875213623,0.08756345510482788 -DeepKriging_mrts,43,276.1060791015625,16.616440024913956,5.043865203857422,0.17776811122894287 -DeepKriging_sphere,43,253.02890014648438,15.906882162965953,4.423473358154297,0.2464909553527832 -DeepKriging_sphere_Huber,43,220.1409912109375,14.837149025703606,3.4715425968170166,0.34442973136901855 -UniversalKriging,43,236.23335868077186,15.369884797251144,4.664989054963829,0.2965073490989729 -OLS_wendland,44,63.027767181396484,7.939002908514172,6.013075828552246,0.1500225067138672 -OLS_sphere,44,22.578357696533203,4.7516689380188515,2.729233741760254,0.6955136060714722 -DeepKriging_wendland,44,57.27103042602539,7.567762577276417,5.438377380371094,0.22765642404556274 -DeepKriging_mrts,44,66.3051986694336,8.142800419354117,3.6678760051727295,0.105823814868927 -DeepKriging_sphere,44,50.57801055908203,7.111821887468923,2.8776509761810303,0.3179169297218323 -DeepKriging_sphere_Huber,44,17.006149291992188,4.123851269383049,1.5188854932785034,0.7706590890884399 -UniversalKriging,44,21.66850412331889,4.65494405157773,2.1966495189062454,0.7077836920034899 -OLS_wendland,45,59.67863464355469,7.725194796479548,5.983094215393066,0.07098066806793213 -OLS_sphere,45,20.429670333862305,4.519919283998588,2.4417152404785156,0.6819705963134766 -DeepKriging_wendland,45,53.711936950683594,7.328842811159453,5.319265365600586,0.16386443376541138 -DeepKriging_mrts,45,20.09797477722168,4.483076485765292,2.4646828174591064,0.6871341466903687 -DeepKriging_sphere,45,27.493234634399414,5.243399148872744,2.87184739112854,0.5720118880271912 -DeepKriging_sphere_Huber,45,13.861834526062012,3.7231484695163597,1.06931734085083,0.7842123508453369 -UniversalKriging,45,17.24838929621196,4.1531180209827845,1.9825585173561686,0.7314937427701782 -OLS_wendland,46,219.56930541992188,14.817871150064772,8.0545015335083,0.2773206830024719 -OLS_sphere,46,152.2791748046875,12.340144845369016,4.289466857910156,0.4987960457801819 -DeepKriging_wendland,46,224.8146209716797,14.993819425739384,7.378598690032959,0.2600564956665039 -DeepKriging_mrts,46,167.1626739501953,12.929140495415592,4.199665069580078,0.4498092532157898 -DeepKriging_sphere,46,158.67178344726562,12.59649885671672,3.390641212463379,0.47775572538375854 -DeepKriging_sphere_Huber,46,140.11050415039062,11.836828297748964,2.523690938949585,0.5388473868370056 -UniversalKriging,46,149.83125945277047,12.240557971463984,3.7610187866205083,0.5068529589223696 -OLS_wendland,47,262.1463623046875,16.190934571688178,7.707786560058594,0.0163116455078125 -OLS_sphere,47,172.48062133789062,13.133187782784903,4.730251789093018,0.3527768850326538 -DeepKriging_wendland,47,243.4408721923828,15.602591842139011,6.793474197387695,0.08650285005569458 -DeepKriging_mrts,47,154.29306030273438,12.421475769920995,4.527071475982666,0.4210246205329895 -DeepKriging_sphere,47,154.8728485107422,12.444792023603375,3.7832510471343994,0.41884905099868774 -DeepKriging_sphere_Huber,47,151.49057006835938,12.30815055434241,2.6198530197143555,0.431540846824646 -UniversalKriging,47,154.9516359378041,12.44795709897026,3.811084307378028,0.4185533482724284 -OLS_wendland,48,132157.46875,363.5346871345292,29.07362174987793,-986.3389892578125 -OLS_sphere,48,79.77658081054688,8.931773665434367,3.283576011657715,0.40399491786956787 -DeepKriging_wendland,48,105.73374938964844,10.282691738530746,5.317865371704102,0.21007072925567627 -DeepKriging_mrts,48,73.87605285644531,8.595117966406587,2.7381021976470947,0.4480772614479065 -DeepKriging_sphere,48,83.2769775390625,9.125622035733372,3.4269771575927734,0.3778436779975891 -DeepKriging_sphere_Huber,48,69.74122619628906,8.351121253837059,1.6430270671844482,0.4789682626724243 -UniversalKriging,48,76.08438457143524,8.722636331490339,2.690310594706139,0.431579002945077 -OLS_wendland,49,174.1925506591797,13.198202554104846,6.886566638946533,-0.04603230953216553 -OLS_sphere,49,101.46198272705078,10.072833897521132,3.7046401500701904,0.39071720838546753 -DeepKriging_wendland,49,133.48606872558594,11.553617127358251,5.770646095275879,0.19841152429580688 -DeepKriging_mrts,49,96.6652603149414,9.831849282558261,4.10358190536499,0.4195217490196228 -DeepKriging_sphere,49,100.89315795898438,10.044558624398803,2.8467769622802734,0.3941330909729004 -DeepKriging_sphere_Huber,49,92.03209686279297,9.5933360653525,2.0356967449188232,0.4473440647125244 -UniversalKriging,49,97.58569551775344,9.878547237208183,3.1438328465710446,0.41399454582173345 -OLS_wendland,50,388.9678649902344,19.72226825165489,8.321263313293457,-0.92574143409729 -OLS_sphere,50,104.70977783203125,10.232779575073003,3.285640239715576,0.48159223794937134 -DeepKriging_wendland,50,169.98944091796875,13.037999881805826,5.951483249664307,0.15839910507202148 -DeepKriging_mrts,50,107.02892303466797,10.345478385974618,3.0642247200012207,0.4701104164123535 -DeepKriging_sphere,50,109.07627868652344,10.443958956570226,2.6155550479888916,0.45997411012649536 -DeepKriging_sphere_Huber,50,107.64576721191406,10.375247814482027,1.8826717138290405,0.4670564532279968 -UniversalKriging,50,101.52742887166663,10.076082019895761,2.7916406459766505,0.4973477144127607 diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb deleted file mode 100644 index 4795023..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb +++ /dev/null @@ -1,10523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d1cde2eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:09.855329Z", - "iopub.status.busy": "2026-03-29T15:51:09.855153Z", - "iopub.status.idle": "2026-03-29T15:51:09.859870Z", - "shell.execute_reply": "2026-03-29T15:51:09.859211Z" - }, - "papermill": { - "duration": 0.01117, - "end_time": "2026-03-29T15:51:09.860573", - "exception": false, - "start_time": "2026-03-29T15:51:09.849403", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8890884f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:09.866364Z", - "iopub.status.busy": "2026-03-29T15:51:09.866242Z", - "iopub.status.idle": "2026-03-29T15:51:11.908858Z", - "shell.execute_reply": "2026-03-29T15:51:11.907928Z" - }, - "papermill": { - "duration": 2.047417, - "end_time": "2026-03-29T15:51:11.909414", - "exception": false, - "start_time": "2026-03-29T15:51:09.861997", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:51:10.587611: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774799470.597449 432902 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774799470.600184 432902 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774799470.607560 432902 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799470.607581 432902 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799470.607582 432902 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799470.607583 432902 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46d6d1ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:11.913996Z", - "iopub.status.busy": "2026-03-29T15:51:11.913733Z", - "iopub.status.idle": "2026-03-29T15:51:12.802119Z", - "shell.execute_reply": "2026-03-29T15:51:12.801449Z" - }, - "papermill": { - "duration": 0.891841, - "end_time": "2026-03-29T15:51:12.802745", - "exception": false, - "start_time": "2026-03-29T15:51:11.910904", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8dd094a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:12.807262Z", - "iopub.status.busy": "2026-03-29T15:51:12.807108Z", - "iopub.status.idle": "2026-03-29T15:51:12.809922Z", - "shell.execute_reply": "2026-03-29T15:51:12.809283Z" - }, - "papermill": { - "duration": 0.006157, - "end_time": "2026-03-29T15:51:12.810305", - "exception": false, - "start_time": "2026-03-29T15:51:12.804148", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f0aafba7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:12.813629Z", - "iopub.status.busy": "2026-03-29T15:51:12.813502Z", - "iopub.status.idle": "2026-03-29T15:51:25.842841Z", - "shell.execute_reply": "2026-03-29T15:51:25.842080Z" - }, - "papermill": { - "duration": 13.03211, - "end_time": "2026-03-29T15:51:25.843644", - "exception": false, - "start_time": "2026-03-29T15:51:12.811534", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32986e25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.848880Z", - "iopub.status.busy": "2026-03-29T15:51:25.848596Z", - "iopub.status.idle": "2026-03-29T15:51:25.854176Z", - "shell.execute_reply": "2026-03-29T15:51:25.853405Z" - }, - "papermill": { - "duration": 0.009458, - "end_time": "2026-03-29T15:51:25.854622", - "exception": false, - "start_time": "2026-03-29T15:51:25.845164", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import gc\n", - "\n", - "def _gp_draw(rng, num_sample):\n", - " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", - "\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", - "\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " ev, evec = np.linalg.eigh(cov_matrix)\n", - " ev = np.maximum(ev, 1e-6)\n", - " L = evec @ np.diag(np.sqrt(ev))\n", - "\n", - " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", - "\n", - " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", - " gc.collect()\n", - " return lat_deg, lon_deg, y\n", - "\n", - "def simulate_data(num_sample, seed):\n", - " \"\"\"Stationary GP + N(0, 0.5^2) Gaussian noise.\"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", - " noise = rng.normal(0.0, 0.5, num_sample).astype(np.float32)\n", - " z = y + noise\n", - " print(f\"\\n=== GP + N(0, 0.5^2) noise | seed={seed} ===\")\n", - " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", - " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " del noise\n", - " gc.collect()\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5c1e9f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.857929Z", - "iopub.status.busy": "2026-03-29T15:51:25.857800Z", - "iopub.status.idle": "2026-03-29T15:51:25.865176Z", - "shell.execute_reply": "2026-03-29T15:51:25.864470Z" - }, - "papermill": { - "duration": 0.009723, - "end_time": "2026-03-29T15:51:25.865681", - "exception": false, - "start_time": "2026-03-29T15:51:25.855958", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " categorical_data = None\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(wendland(location, grid, theta=theta, k=2))\n", - " del grid\n", - " gc.collect()\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " X_train_cont = basis[train_x1]\n", - " X_val_cont = basis[val_x1]\n", - " X_test_cont = basis[test_x1]\n", - " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", - " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", - " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", - " if categorical_data is None:\n", - " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " return (X_train_flat, X_train_cat, y_train_flat,\n", - " X_val_flat, X_val_cat, y_val_flat,\n", - " X_test_flat, X_test_cat, y_test_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6728b7b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.869093Z", - "iopub.status.busy": "2026-03-29T15:51:25.868970Z", - "iopub.status.idle": "2026-03-29T15:51:25.876198Z", - "shell.execute_reply": "2026-03-29T15:51:25.875451Z" - }, - "papermill": { - "duration": 0.009645, - "end_time": "2026-03-29T15:51:25.876616", - "exception": false, - "start_time": "2026-03-29T15:51:25.866971", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " return metrics, model\n", - "\n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", - " epochs=epochs, batch_size=batch_size, verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", - " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", - " dropout_rate=dropout_rate, optimizer=_opt,\n", - " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", - " patience=40, verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience,\n", - " restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5,\n", - " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - " val_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset, validation_data=val_dataset,\n", - " epochs=epochs, callbacks=callbacks, verbose=0)\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - "\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " return metrics, model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce398870", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.880045Z", - "iopub.status.busy": "2026-03-29T15:51:25.879918Z", - "iopub.status.idle": "2026-03-29T15:51:25.888811Z", - "shell.execute_reply": "2026-03-29T15:51:25.887685Z" - }, - "papermill": { - "duration": 0.011353, - "end_time": "2026-03-29T15:51:25.889243", - "exception": false, - "start_time": "2026-03-29T15:51:25.877890", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(longitude, latitude, c=value,\n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", - " plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", - " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " if plot_type == 'residual' else\n", - " mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", - " model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " idx_all = np.arange(len(y_combined))\n", - " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - "\n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", - "\n", - " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", - " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", - "\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})')\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " p = predictions[mn]\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", - " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", - " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig1)\n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", - " -vmax_abs, vmax_abs,\n", - " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", - " plot_type='residual')\n", - " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig2)\n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7d38eb7c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.892463Z", - "iopub.status.busy": "2026-03-29T15:51:25.892341Z", - "iopub.status.idle": "2026-03-29T15:51:25.895413Z", - "shell.execute_reply": "2026-03-29T15:51:25.894710Z" - }, - "papermill": { - "duration": 0.005332, - "end_time": "2026-03-29T15:51:25.895859", - "exception": false, - "start_time": "2026-03-29T15:51:25.890527", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\n", - "dk_sphere candidates : [10, 50, 100, 150, 200, 1000] (restored to original)\n", - "repeats : 50\n" - ] - } - ], - "source": [ - "# ── Experiment parameters ─────────────────────────────────────────────────────\n", - "seed = 50\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "# All models (including dk_sphere) use the same original candidates\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", - "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", - "print(f\"repeats : {repeat_experiment}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "main_loop_GP_noise", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:25.899191Z", - "iopub.status.busy": "2026-03-29T15:51:25.899064Z", - "iopub.status.idle": "2026-03-29T21:57:05.953619Z", - "shell.execute_reply": "2026-03-29T21:57:05.952726Z" - }, - "papermill": { - "duration": 21940.057059, - "end_time": "2026-03-29T21:57:05.954212", - "exception": false, - "start_time": "2026-03-29T15:51:25.897153", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting fresh.\n", - "\n", - "======================================================================\n", - "Repeat 1/50 seed=50\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:51:26.491172: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774799486.500214 433145 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774799486.503163 433145 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774799486.510014 433145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799486.510039 433145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799486.510041 433145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799486.510042 433145 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] seed=50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=50 ===\n", - "z mean: 1.2542 (±0.0207), Var: 1.0693, Range: [-2.1619, 4.4872]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799507.244272 433145 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774799508.779980 433506 service.cc:152] XLA service 0x556fdc2851a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774799508.780003 433506 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799508.999161 433506 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799510.658291 433506 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f407c327eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f40744068c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3673, sigma²=0.6935, nugget=0.2454\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3628, sigma²=0.6830, nugget=0.2457\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3630, sigma²=0.6821, nugget=0.2459\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3632, sigma²=0.6816, nugget=0.2460\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3633, sigma²=0.6815, nugget=0.2460\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3641, sigma²=0.6812, nugget=0.2460\n", - "[repeat 0] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3628, sigma²=0.6830, nugget=0.2457\n", - "[repeat 0] done → repeat_0_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 33.0244 | 5.7467 | 1.191 | -29.7573 |\n", - "| OLS_sphere | 200 | 0.3786 | 0.6153 | 0.4986 | 0.6474 |\n", - "| DeepKriging_wendland | -- | 0.7672 | 0.8759 | 0.7048 | 0.2855 |\n", - "| DeepKriging_mrts | 50 | 0.4138 | 0.6433 | 0.5119 | 0.6146 |\n", - "| DeepKriging_sphere | 10 | 0.4278 | 0.6541 | 0.5253 | 0.6016 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4382 | 0.662 | 0.5239 | 0.5919 |\n", - "| UniversalKriging | 50 | 0.3863 | 0.6215 | 0.4977 | 0.6402 |\n", - "Repeat 1/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 2/50 seed=51\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:58:39.983731: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774799919.993320 743864 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774799919.996047 743864 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774799920.003248 743864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799920.003278 743864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799920.003280 743864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774799920.003281 743864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] seed=51\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=51 ===\n", - "z mean: 1.2223 (±0.0221), Var: 1.2193, Range: [-2.0538, 4.5060]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799940.676582 743864 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774799942.185208 744218 service.cc:152] XLA service 0x7fe050006740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774799942.185235 744218 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799942.402487 744218 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774799944.064172 744218 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe284537eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe27c4f70a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6036, sigma²=1.0261, nugget=0.2739\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5948, sigma²=1.0064, nugget=0.2744\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5955, sigma²=1.0051, nugget=0.2746\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5963, sigma²=1.0050, nugget=0.2747\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5967, sigma²=1.0050, nugget=0.2747\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5980, sigma²=1.0053, nugget=0.2746\n", - "[repeat 1] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5948, sigma²=1.0064, nugget=0.2744\n", - "[repeat 1] done → repeat_1_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|-----------|\n", - "| OLS_wendland | -- | 743.163 | 27.261 | 2.4796 | -697.489 |\n", - "| OLS_sphere | 150 | 0.4621 | 0.6797 | 0.5175 | 0.5657 |\n", - "| DeepKriging_wendland | -- | 0.7476 | 0.8646 | 0.6738 | 0.2974 |\n", - "| DeepKriging_mrts | 10 | 0.4862 | 0.6973 | 0.5362 | 0.543 |\n", - "| DeepKriging_sphere | 10 | 0.465 | 0.6819 | 0.536 | 0.5629 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4617 | 0.6795 | 0.5274 | 0.5661 |\n", - "| UniversalKriging | 50 | 0.3977 | 0.6306 | 0.4851 | 0.6262 |\n", - "Repeat 2/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 3/50 seed=52\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:05:47.114272: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774800347.123775 1044042 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774800347.126468 1044042 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774800347.133314 1044042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800347.133343 1044042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800347.133345 1044042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800347.133346 1044042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] seed=52\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=52 ===\n", - "z mean: 0.8577 (±0.0215), Var: 1.1563, Range: [-2.5164, 3.9403]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800367.942728 1044042 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774800369.473494 1044402 service.cc:152] XLA service 0x5576a336eaf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774800369.473518 1044402 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800369.691692 1044402 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800371.371086 1044402 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccd41569e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccc47dd2d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4600, sigma²=0.8892, nugget=0.2690\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4546, sigma²=0.8759, nugget=0.2694\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4549, sigma²=0.8747, nugget=0.2696\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4553, sigma²=0.8741, nugget=0.2697\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4556, sigma²=0.8739, nugget=0.2697\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4565, sigma²=0.8739, nugget=0.2697\n", - "[repeat 2] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4600, sigma²=0.8892, nugget=0.2690\n", - "[repeat 2] done → repeat_2_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1528 | 1.0737 | 0.785 | -0.1251 |\n", - "| OLS_sphere | 200 | 0.3761 | 0.6133 | 0.4865 | 0.6329 |\n", - "| DeepKriging_wendland | -- | 0.66 | 0.8124 | 0.649 | 0.3558 |\n", - "| DeepKriging_mrts | 50 | 0.3734 | 0.6111 | 0.4935 | 0.6355 |\n", - "| DeepKriging_sphere | 50 | 0.3927 | 0.6267 | 0.5011 | 0.6167 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4054 | 0.6367 | 0.5109 | 0.6043 |\n", - "| UniversalKriging | 10 | 0.331 | 0.5754 | 0.4604 | 0.6769 |\n", - "Repeat 3/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 4/50 seed=53\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:13:05.439315: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774800785.449303 1371204 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774800785.452024 1371204 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774800785.459136 1371204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800785.459168 1371204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800785.459171 1371204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774800785.459172 1371204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] seed=53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=53 ===\n", - "z mean: 1.2807 (±0.0239), Var: 1.4340, Range: [-2.6009, 5.2742]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800806.363497 1371204 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774800807.906421 1371571 service.cc:152] XLA service 0x55ed919039d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774800807.906444 1371571 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800808.126166 1371571 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774800809.786139 1371571 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0fd03897e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0fc814d5a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4823, sigma²=0.8200, nugget=0.2604\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4788, sigma²=0.8123, nugget=0.2606\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4792, sigma²=0.8114, nugget=0.2607\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4794, sigma²=0.8108, nugget=0.2608\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4797, sigma²=0.8106, nugget=0.2609\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4807, sigma²=0.8104, nugget=0.2609\n", - "[repeat 3] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4807, sigma²=0.8104, nugget=0.2609\n", - "[repeat 3] done → repeat_3_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 7.6975 | 2.7744 | 1.0924 | -4.7538 |\n", - "| OLS_sphere | 200 | 0.3844 | 0.62 | 0.5031 | 0.7127 |\n", - "| DeepKriging_wendland | -- | 0.9421 | 0.9706 | 0.7634 | 0.2958 |\n", - "| DeepKriging_mrts | 10 | 0.492 | 0.7014 | 0.5657 | 0.6322 |\n", - "| DeepKriging_sphere | 150 | 0.5062 | 0.7115 | 0.5863 | 0.6216 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4444 | 0.6666 | 0.5338 | 0.6678 |\n", - "| UniversalKriging | 1000 | 0.3772 | 0.6141 | 0.5009 | 0.7181 |\n", - "Repeat 4/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 5/50 seed=54\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:20:37.055765: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774801237.065125 1697351 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774801237.067950 1697351 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774801237.075158 1697351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801237.075187 1697351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801237.075189 1697351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801237.075190 1697351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] seed=54\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=54 ===\n", - "z mean: 1.4638 (±0.0186), Var: 0.8689, Range: [-1.4549, 4.5483]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801257.825501 1697351 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774801259.353116 1697711 service.cc:152] XLA service 0x7f167c00ae30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774801259.353141 1697711 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801259.573303 1697711 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801261.230754 1697711 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18ac507c70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18a4622d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3015, sigma²=0.5818, nugget=0.2593\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2965, sigma²=0.5728, nugget=0.2592\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2964, sigma²=0.5716, nugget=0.2594\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2965, sigma²=0.5711, nugget=0.2595\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2966, sigma²=0.5710, nugget=0.2595\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2972, sigma²=0.5707, nugget=0.2595\n", - "[repeat 4] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2965, sigma²=0.5728, nugget=0.2592\n", - "[repeat 4] done → repeat_4_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1039 | 1.0506 | 0.69 | -0.3852 |\n", - "| OLS_sphere | 200 | 0.4006 | 0.6329 | 0.5039 | 0.4973 |\n", - "| DeepKriging_wendland | -- | 0.6495 | 0.8059 | 0.6297 | 0.185 |\n", - "| DeepKriging_mrts | 10 | 0.4271 | 0.6535 | 0.5188 | 0.464 |\n", - "| DeepKriging_sphere | 10 | 0.4473 | 0.6688 | 0.5301 | 0.4387 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.434 | 0.6588 | 0.5235 | 0.4554 |\n", - "| UniversalKriging | 50 | 0.3764 | 0.6136 | 0.4995 | 0.5276 |\n", - "Repeat 5/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 6/50 seed=55\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:27:57.023727: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774801677.033671 2005334 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774801677.036330 2005334 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774801677.043343 2005334 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801677.043370 2005334 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801677.043372 2005334 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774801677.043373 2005334 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] seed=55\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=55 ===\n", - "z mean: 0.2917 (±0.0244), Var: 1.4894, Range: [-3.3896, 4.3093]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801697.764822 2005334 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774801699.294780 2005692 service.cc:152] XLA service 0x7f8cb80067e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774801699.294802 2005692 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801699.515003 2005692 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774801701.167204 2005692 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8ee871ed40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8ee06c1bd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5763, sigma²=1.1597, nugget=0.2431\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5654, sigma²=1.1332, nugget=0.2435\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5658, sigma²=1.1320, nugget=0.2437\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5665, sigma²=1.1317, nugget=0.2438\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5670, sigma²=1.1316, nugget=0.2439\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5684, sigma²=1.1316, nugget=0.2439\n", - "[repeat 5] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5665, sigma²=1.1317, nugget=0.2438\n", - "[repeat 5] done → repeat_5_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1813 | 1.0869 | 0.7861 | 0.2367 |\n", - "| OLS_sphere | 150 | 0.4279 | 0.6541 | 0.5163 | 0.7235 |\n", - "| DeepKriging_wendland | -- | 0.8045 | 0.8969 | 0.7069 | 0.4801 |\n", - "| DeepKriging_mrts | 50 | 0.5016 | 0.7082 | 0.5573 | 0.6759 |\n", - "| DeepKriging_sphere | 10 | 0.4356 | 0.66 | 0.5175 | 0.7185 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5516 | 0.7427 | 0.5923 | 0.6435 |\n", - "| UniversalKriging | 150 | 0.4124 | 0.6422 | 0.5073 | 0.7335 |\n", - "Repeat 6/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 7/50 seed=56\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:34:45.891836: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774802085.901396 2270043 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774802085.904127 2270043 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774802085.911869 2270043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802085.911907 2270043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802085.911909 2270043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802085.911910 2270043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] seed=56\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=56 ===\n", - "z mean: 1.2887 (±0.0213), Var: 1.1351, Range: [-2.1188, 5.6241]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802106.596640 2270043 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774802108.124864 2270398 service.cc:152] XLA service 0x7f82e8005cc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774802108.124889 2270398 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802108.341421 2270398 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802110.002930 2270398 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f85147d7eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f85141f2c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7242, sigma²=1.1645, nugget=0.2409\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7094, sigma²=1.1308, nugget=0.2417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7003, sigma²=1.1237, nugget=0.2408\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6990, sigma²=1.1248, nugget=0.2404\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6887, sigma²=1.1224, nugget=0.2388\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6877, sigma²=1.1268, nugget=0.2378\n", - "[repeat 6] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6990, sigma²=1.1248, nugget=0.2404\n", - "[repeat 6] done → repeat_6_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.5664 | 1.2516 | 0.8241 | -0.4027 |\n", - "| OLS_sphere | 200 | 0.3623 | 0.6019 | 0.4739 | 0.6755 |\n", - "| DeepKriging_wendland | -- | 0.9032 | 0.9504 | 0.7359 | 0.1911 |\n", - "| DeepKriging_mrts | 10 | 0.4279 | 0.6541 | 0.5116 | 0.6168 |\n", - "| DeepKriging_sphere | 10 | 0.4326 | 0.6577 | 0.5208 | 0.6126 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4018 | 0.6339 | 0.5027 | 0.6402 |\n", - "| UniversalKriging | 150 | 0.3754 | 0.6127 | 0.4869 | 0.6638 |\n", - "Repeat 7/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 8/50 seed=57\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:42:20.067473: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774802540.076957 2611083 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774802540.079905 2611083 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774802540.086988 2611083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802540.087015 2611083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802540.087017 2611083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774802540.087018 2611083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] seed=57\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=57 ===\n", - "z mean: 0.6536 (±0.0190), Var: 0.9002, Range: [-2.2198, 4.7460]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802560.953043 2611083 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774802562.463588 2611447 service.cc:152] XLA service 0x5599fe1962f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774802562.463623 2611447 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802562.686397 2611447 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774802564.354185 2611447 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2dfc7adab0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2df457b5b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3951, sigma²=0.7638, nugget=0.2531\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3910, sigma²=0.7547, nugget=0.2532\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3910, sigma²=0.7531, nugget=0.2534\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3913, sigma²=0.7527, nugget=0.2535\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3915, sigma²=0.7526, nugget=0.2536\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3926, sigma²=0.7527, nugget=0.2536\n", - "[repeat 7] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3951, sigma²=0.7638, nugget=0.2531\n", - "[repeat 7] done → repeat_7_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|-----------|\n", - "| OLS_wendland | -- | 114.697 | 10.7097 | 1.4094 | -110.112 |\n", - "| OLS_sphere | 200 | 0.4429 | 0.6655 | 0.531 | 0.5709 |\n", - "| DeepKriging_wendland | -- | 0.8147 | 0.9026 | 0.7107 | 0.2108 |\n", - "| DeepKriging_mrts | 10 | 0.4497 | 0.6706 | 0.5367 | 0.5644 |\n", - "| DeepKriging_sphere | 10 | 0.4392 | 0.6627 | 0.5264 | 0.5745 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.475 | 0.6892 | 0.5539 | 0.5399 |\n", - "| UniversalKriging | 10 | 0.3979 | 0.6308 | 0.5073 | 0.6145 |\n", - "Repeat 8/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 9/50 seed=58\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:50:09.971803: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774803009.981459 2975394 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774803009.984153 2975394 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774803009.992723 2975394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803009.992756 2975394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803009.992759 2975394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803009.992760 2975394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] seed=58\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=58 ===\n", - "z mean: 0.6193 (±0.0203), Var: 1.0281, Range: [-2.3593, 3.8079]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803030.818507 2975394 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774803032.333386 2975753 service.cc:152] XLA service 0x7f37cc006af0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774803032.333412 2975753 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803032.547690 2975753 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803034.202198 2975753 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f39f865b400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f39f04171c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3963, sigma²=0.7305, nugget=0.2377\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3892, sigma²=0.7161, nugget=0.2379\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3894, sigma²=0.7149, nugget=0.2381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3896, sigma²=0.7146, nugget=0.2382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3898, sigma²=0.7144, nugget=0.2382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3906, sigma²=0.7142, nugget=0.2382\n", - "[repeat 8] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3898, sigma²=0.7144, nugget=0.2382\n", - "[repeat 8] done → repeat_8_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1832 | 1.0878 | 0.7562 | -0.2304 |\n", - "| OLS_sphere | 200 | 0.4568 | 0.6758 | 0.5525 | 0.5251 |\n", - "| DeepKriging_wendland | -- | 0.7136 | 0.8448 | 0.6864 | 0.2579 |\n", - "| DeepKriging_mrts | 10 | 0.4776 | 0.6911 | 0.5595 | 0.5034 |\n", - "| DeepKriging_sphere | 10 | 0.4941 | 0.7029 | 0.5556 | 0.4863 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4903 | 0.7002 | 0.5571 | 0.4901 |\n", - "| UniversalKriging | 200 | 0.4267 | 0.6532 | 0.524 | 0.5563 |\n", - "Repeat 9/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 10/50 seed=59\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 00:57:31.756369: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774803451.766229 3287309 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774803451.769136 3287309 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774803451.776091 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803451.776117 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803451.776119 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803451.776120 3287309 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] seed=59\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=59 ===\n", - "z mean: 1.0325 (±0.0216), Var: 1.1688, Range: [-2.1677, 4.7430]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803472.523899 3287309 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774803474.036047 3287670 service.cc:152] XLA service 0x5639de6b2a10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774803474.036071 3287670 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803474.259581 3287670 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803475.923448 3287670 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9250769a20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f92485e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4384, sigma²=0.8854, nugget=0.2324\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4324, sigma²=0.8695, nugget=0.2329\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4325, sigma²=0.8679, nugget=0.2331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4329, sigma²=0.8677, nugget=0.2332\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4332, sigma²=0.8674, nugget=0.2333\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4344, sigma²=0.8672, nugget=0.2333\n", - "[repeat 9] UniversalKriging best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4325, sigma²=0.8679, nugget=0.2331\n", - "[repeat 9] done → repeat_9_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|------------|\n", - "| OLS_wendland | -- | 2346.24 | 48.438 | 3.9512 | -1970.74 |\n", - "| OLS_sphere | 200 | 0.4985 | 0.7061 | 0.5629 | 0.581 |\n", - "| DeepKriging_wendland | -- | 0.8208 | 0.906 | 0.7149 | 0.3102 |\n", - "| DeepKriging_mrts | 100 | 0.4699 | 0.6855 | 0.53 | 0.6051 |\n", - "| DeepKriging_sphere | 10 | 0.4604 | 0.6785 | 0.5262 | 0.6131 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4883 | 0.6988 | 0.5346 | 0.5896 |\n", - "| UniversalKriging | 100 | 0.4243 | 0.6514 | 0.5039 | 0.6434 |\n", - "Repeat 10/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 11/50 seed=60\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:05:34.663786: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774803934.673394 3682770 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774803934.676148 3682770 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774803934.684185 3682770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803934.684219 3682770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803934.684221 3682770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774803934.684222 3682770 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] seed=60\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=60 ===\n", - "z mean: 0.7764 (±0.0224), Var: 1.2583, Range: [-3.0963, 4.2656]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803955.569697 3682770 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774803957.122040 3683131 service.cc:152] XLA service 0x55c408913dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774803957.122068 3683131 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803957.344844 3683131 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774803959.006262 3683131 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efc74602710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efc741f6c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5898, sigma²=0.8670, nugget=0.2430\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5767, sigma²=0.8419, nugget=0.2435\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5773, sigma²=0.8407, nugget=0.2437\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5777, sigma²=0.8405, nugget=0.2438\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5781, sigma²=0.8405, nugget=0.2438\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5790, sigma²=0.8403, nugget=0.2437\n", - "[repeat 10] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5781, sigma²=0.8405, nugget=0.2438\n", - "[repeat 10] done → repeat_10_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9762 | 0.9881 | 0.7843 | 0.1903 |\n", - "| OLS_sphere | 200 | 0.4692 | 0.685 | 0.5389 | 0.6108 |\n", - "| DeepKriging_wendland | -- | 0.8248 | 0.9082 | 0.7233 | 0.3159 |\n", - "| DeepKriging_mrts | 10 | 0.4427 | 0.6653 | 0.5313 | 0.6328 |\n", - "| DeepKriging_sphere | 10 | 0.4524 | 0.6726 | 0.5419 | 0.6248 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4655 | 0.6823 | 0.5423 | 0.6139 |\n", - "| UniversalKriging | 200 | 0.4199 | 0.648 | 0.5154 | 0.6517 |\n", - "Repeat 11/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 12/50 seed=61\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:12:57.782793: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774804377.792635 4004572 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774804377.795301 4004572 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774804377.802359 4004572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804377.802385 4004572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804377.802387 4004572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804377.802388 4004572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] seed=61\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=61 ===\n", - "z mean: 1.4283 (±0.0205), Var: 1.0464, Range: [-2.1578, 5.3840]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804398.727809 4004572 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774804400.244807 4004934 service.cc:152] XLA service 0x7fd6a0006760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774804400.244831 4004934 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804400.464362 4004934 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804402.117301 4004934 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd8cc1a3d90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd8c42bb9a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4187, sigma²=0.8133, nugget=0.2366\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4079, sigma²=0.7895, nugget=0.2370\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4080, sigma²=0.7885, nugget=0.2371\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4083, sigma²=0.7880, nugget=0.2373\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4086, sigma²=0.7878, nugget=0.2373\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4094, sigma²=0.7877, nugget=0.2373\n", - "[repeat 11] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4094, sigma²=0.7877, nugget=0.2373\n", - "[repeat 11] done → repeat_11_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0582 | 1.0287 | 0.7812 | 0.0083 |\n", - "| OLS_sphere | 200 | 0.3857 | 0.6211 | 0.5038 | 0.6385 |\n", - "| DeepKriging_wendland | -- | 0.7343 | 0.8569 | 0.6851 | 0.3118 |\n", - "| DeepKriging_mrts | 50 | 0.4478 | 0.6692 | 0.5524 | 0.5803 |\n", - "| DeepKriging_sphere | 10 | 0.4241 | 0.6512 | 0.5342 | 0.6026 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4064 | 0.6375 | 0.5248 | 0.6192 |\n", - "| UniversalKriging | 1000 | 0.3776 | 0.6145 | 0.4946 | 0.6461 |\n", - "Repeat 12/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 13/50 seed=62\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:20:35.361360: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774804835.370619 156310 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774804835.373259 156310 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774804835.380943 156310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804835.380972 156310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804835.380974 156310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774804835.380975 156310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] seed=62\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=62 ===\n", - "z mean: 0.5086 (±0.0182), Var: 0.8323, Range: [-2.2232, 3.6178]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804856.173717 156310 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774804857.703897 156677 service.cc:152] XLA service 0x560854c1d030 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774804857.703923 156677 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804857.914242 156677 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774804859.596225 156677 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1a8066b910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1a784220e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4275, sigma²=0.6470, nugget=0.2416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4245, sigma²=0.6404, nugget=0.2418\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4245, sigma²=0.6391, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4248, sigma²=0.6388, nugget=0.2421\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4249, sigma²=0.6387, nugget=0.2421\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4259, sigma²=0.6386, nugget=0.2421\n", - "[repeat 12] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4275, sigma²=0.6470, nugget=0.2416\n", - "[repeat 12] done → repeat_12_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 21.0605 | 4.5892 | 0.9754 | -25.4498 |\n", - "| OLS_sphere | 200 | 0.4213 | 0.6491 | 0.5136 | 0.4709 |\n", - "| DeepKriging_wendland | -- | 0.6213 | 0.7882 | 0.6203 | 0.2197 |\n", - "| DeepKriging_mrts | 10 | 0.4249 | 0.6519 | 0.5221 | 0.4664 |\n", - "| DeepKriging_sphere | 150 | 0.5238 | 0.7238 | 0.5709 | 0.3421 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.4877 | 0.6984 | 0.5546 | 0.3874 |\n", - "| UniversalKriging | 10 | 0.3711 | 0.6091 | 0.4797 | 0.534 |\n", - "Repeat 13/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 14/50 seed=63\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:27:38.424244: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774805258.434468 456357 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774805258.437469 456357 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774805258.444586 456357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805258.444616 456357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805258.444618 456357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805258.444619 456357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] seed=63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=63 ===\n", - "z mean: 1.0100 (±0.0232), Var: 1.3428, Range: [-2.9298, 4.6204]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805279.159143 456357 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774805280.719316 456723 service.cc:152] XLA service 0x55a5af9a7d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774805280.719340 456723 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805280.933189 456723 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805282.611780 456723 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9ac817feb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9ab8622ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6979, sigma²=1.4195, nugget=0.2410\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6845, sigma²=1.3830, nugget=0.2417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6848, sigma²=1.3799, nugget=0.2419\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6851, sigma²=1.3796, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6859, sigma²=1.3800, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6878, sigma²=1.3810, nugget=0.2420\n", - "[repeat 13] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6878, sigma²=1.3810, nugget=0.2420\n", - "[repeat 13] done → repeat_13_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1429 | 1.0691 | 0.8397 | 0.0745 |\n", - "| OLS_sphere | 200 | 0.4143 | 0.6437 | 0.5153 | 0.6645 |\n", - "| DeepKriging_wendland | -- | 0.9 | 0.9487 | 0.7443 | 0.2711 |\n", - "| DeepKriging_mrts | 10 | 0.5206 | 0.7215 | 0.5759 | 0.5784 |\n", - "| DeepKriging_sphere | 10 | 0.4092 | 0.6397 | 0.5306 | 0.6686 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.3932 | 0.627 | 0.5067 | 0.6816 |\n", - "| UniversalKriging | 1000 | 0.3754 | 0.6127 | 0.4906 | 0.696 |\n", - "Repeat 14/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 15/50 seed=64\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:35:07.657669: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774805707.667759 800856 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774805707.670488 800856 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774805707.677817 800856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805707.677847 800856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805707.677849 800856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774805707.677850 800856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] seed=64\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=64 ===\n", - "z mean: 0.9968 (±0.0222), Var: 1.2294, Range: [-2.5702, 4.7999]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805728.506858 800856 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774805730.021728 801221 service.cc:152] XLA service 0x556928134cf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774805730.021750 801221 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805730.248660 801221 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774805731.904296 801221 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f457851a9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4568612cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4527, sigma²=0.8124, nugget=0.2660\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4488, sigma²=0.8033, nugget=0.2662\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4490, sigma²=0.8015, nugget=0.2664\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4492, sigma²=0.8012, nugget=0.2665\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4494, sigma²=0.8010, nugget=0.2665\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4501, sigma²=0.8008, nugget=0.2665\n", - "[repeat 14] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4501, sigma²=0.8008, nugget=0.2665\n", - "[repeat 14] done → repeat_14_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9808 | 0.9904 | 0.7705 | 0.1402 |\n", - "| OLS_sphere | 200 | 0.3939 | 0.6276 | 0.5024 | 0.6547 |\n", - "| DeepKriging_wendland | -- | 0.788 | 0.8877 | 0.7175 | 0.3092 |\n", - "| DeepKriging_mrts | 10 | 0.4111 | 0.6412 | 0.5206 | 0.6396 |\n", - "| DeepKriging_sphere | 10 | 0.4308 | 0.6564 | 0.5291 | 0.6224 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4333 | 0.6582 | 0.5305 | 0.6202 |\n", - "| UniversalKriging | 1000 | 0.3893 | 0.6239 | 0.5052 | 0.6588 |\n", - "Repeat 15/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 16/50 seed=65\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:42:29.701500: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774806149.711199 1113050 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774806149.713914 1113050 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774806149.720972 1113050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806149.721002 1113050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806149.721004 1113050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806149.721005 1113050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] seed=65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=65 ===\n", - "z mean: 0.7784 (±0.0237), Var: 1.4047, Range: [-3.3457, 5.1973]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806170.538950 1113050 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774806172.033120 1113405 service.cc:152] XLA service 0x55e16d5d7410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774806172.033146 1113405 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806172.251589 1113405 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806173.921033 1113405 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f373862bbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f372073a9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4662, sigma²=0.8099, nugget=0.2375\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4631, sigma²=0.8018, nugget=0.2378\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4633, sigma²=0.8005, nugget=0.2380\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4635, sigma²=0.8000, nugget=0.2381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4638, sigma²=0.7998, nugget=0.2381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4648, sigma²=0.7994, nugget=0.2382\n", - "[repeat 15] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4648, sigma²=0.7994, nugget=0.2382\n", - "[repeat 15] done → repeat_15_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1487 | 1.0718 | 0.8089 | 0.1244 |\n", - "| OLS_sphere | 100 | 0.4353 | 0.6598 | 0.5334 | 0.6682 |\n", - "| DeepKriging_wendland | -- | 0.7976 | 0.8931 | 0.7062 | 0.3921 |\n", - "| DeepKriging_mrts | 10 | 0.4424 | 0.6651 | 0.5424 | 0.6628 |\n", - "| DeepKriging_sphere | 10 | 0.4606 | 0.6787 | 0.5573 | 0.6489 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4655 | 0.6823 | 0.5587 | 0.6452 |\n", - "| UniversalKriging | 1000 | 0.3827 | 0.6186 | 0.5069 | 0.7083 |\n", - "Repeat 16/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 17/50 seed=66\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:49:58.391243: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774806598.400750 1440154 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774806598.403430 1440154 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774806598.410621 1440154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806598.410651 1440154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806598.410653 1440154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774806598.410654 1440154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] seed=66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=66 ===\n", - "z mean: 1.2377 (±0.0184), Var: 0.8456, Range: [-2.0570, 4.1835]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806619.208363 1440154 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774806620.734953 1440521 service.cc:152] XLA service 0x7fd4640179f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774806620.734981 1440521 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806620.951201 1440521 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774806622.601883 1440521 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd68463acb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd67c450ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3352, sigma²=0.6196, nugget=0.2537\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3301, sigma²=0.6108, nugget=0.2537\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3299, sigma²=0.6093, nugget=0.2538\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3301, sigma²=0.6088, nugget=0.2539\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3303, sigma²=0.6086, nugget=0.2540\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3309, sigma²=0.6084, nugget=0.2540\n", - "[repeat 16] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3303, sigma²=0.6086, nugget=0.2540\n", - "[repeat 16] done → repeat_16_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.995 | 0.9975 | 0.7825 | 0.0461 |\n", - "| OLS_sphere | 200 | 0.5666 | 0.7527 | 0.6036 | 0.4568 |\n", - "| DeepKriging_wendland | -- | 0.8927 | 0.9448 | 0.7456 | 0.1442 |\n", - "| DeepKriging_mrts | 10 | 0.6227 | 0.7891 | 0.6298 | 0.403 |\n", - "| DeepKriging_sphere | 10 | 0.5655 | 0.752 | 0.595 | 0.4578 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5595 | 0.748 | 0.5963 | 0.4636 |\n", - "| UniversalKriging | 200 | 0.5439 | 0.7375 | 0.5804 | 0.4785 |\n", - "Repeat 17/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 18/50 seed=67\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 01:57:24.892696: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774807044.902765 1754451 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774807044.905404 1754451 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774807044.912463 1754451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807044.912494 1754451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807044.912495 1754451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807044.912496 1754451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] seed=67\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=67 ===\n", - "z mean: 1.2655 (±0.0193), Var: 0.9301, Range: [-1.7824, 4.3876]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807065.707751 1754451 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774807067.226746 1754812 service.cc:152] XLA service 0x7f0bcc018150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774807067.226776 1754812 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807067.446014 1754812 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807069.122953 1754812 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0df06a2b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0de8453f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3806, sigma²=0.6446, nugget=0.2475\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3766, sigma²=0.6366, nugget=0.2477\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3768, sigma²=0.6359, nugget=0.2478\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3771, sigma²=0.6354, nugget=0.2480\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3773, sigma²=0.6352, nugget=0.2480\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3779, sigma²=0.6349, nugget=0.2480\n", - "[repeat 17] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3806, sigma²=0.6446, nugget=0.2475\n", - "[repeat 17] done → repeat_17_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0362 | 1.0179 | 0.7801 | 0.0234 |\n", - "| OLS_sphere | 200 | 0.4854 | 0.6967 | 0.5554 | 0.5425 |\n", - "| DeepKriging_wendland | -- | 0.7775 | 0.8818 | 0.6991 | 0.2672 |\n", - "| DeepKriging_mrts | 10 | 0.4601 | 0.6783 | 0.5533 | 0.5663 |\n", - "| DeepKriging_sphere | 10 | 0.4772 | 0.6908 | 0.5536 | 0.5502 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4995 | 0.7067 | 0.5726 | 0.5292 |\n", - "| UniversalKriging | 10 | 0.4371 | 0.6612 | 0.531 | 0.588 |\n", - "Repeat 18/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 19/50 seed=68\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:04:37.315057: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774807477.324669 2056685 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774807477.327375 2056685 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774807477.334601 2056685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807477.334664 2056685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807477.334666 2056685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807477.334667 2056685 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] seed=68\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=68 ===\n", - "z mean: 1.5737 (±0.0259), Var: 1.6793, Range: [-2.4963, 5.2063]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807498.159310 2056685 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774807499.704292 2057046 service.cc:152] XLA service 0x561f8f4fa5e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774807499.704314 2057046 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807499.926906 2057046 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807501.590972 2057046 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2a4876e320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2a405a00d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5634, sigma²=1.0993, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5467, sigma²=1.0647, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5475, sigma²=1.0631, nugget=0.2423\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5512, sigma²=1.0650, nugget=0.2429\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5467, sigma²=1.0613, nugget=0.2422\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5428, sigma²=1.0584, nugget=0.2413\n", - "[repeat 18] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5467, sigma²=1.0613, nugget=0.2422\n", - "[repeat 18] done → repeat_18_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5717 | 1.2537 | 0.9648 | 0.1138 |\n", - "| OLS_sphere | 200 | 0.4313 | 0.6567 | 0.5258 | 0.7568 |\n", - "| DeepKriging_wendland | -- | 0.9722 | 0.986 | 0.7781 | 0.4519 |\n", - "| DeepKriging_mrts | 10 | 0.43 | 0.6558 | 0.5226 | 0.7575 |\n", - "| DeepKriging_sphere | 10 | 0.4066 | 0.6376 | 0.5124 | 0.7708 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4445 | 0.6667 | 0.5438 | 0.7494 |\n", - "| UniversalKriging | 200 | 0.3571 | 0.5975 | 0.4689 | 0.7987 |\n", - "Repeat 19/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 20/50 seed=69\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:12:11.694268: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774807931.704307 2389138 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774807931.707159 2389138 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774807931.714898 2389138 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807931.714934 2389138 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807931.714936 2389138 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774807931.714937 2389138 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] seed=69\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=69 ===\n", - "z mean: 0.9741 (±0.0222), Var: 1.2341, Range: [-2.6064, 4.8486]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807952.493539 2389138 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774807954.042698 2389499 service.cc:152] XLA service 0x7f953801b400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774807954.042725 2389499 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807954.262313 2389499 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774807955.948924 2389499 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9760751750> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9758527e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4707, sigma²=0.8091, nugget=0.2835\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4620, sigma²=0.7896, nugget=0.2841\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4623, sigma²=0.7886, nugget=0.2842\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4627, sigma²=0.7885, nugget=0.2843\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4628, sigma²=0.7883, nugget=0.2843\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4636, sigma²=0.7881, nugget=0.2843\n", - "[repeat 19] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4620, sigma²=0.7896, nugget=0.2841\n", - "[repeat 19] done → repeat_19_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9611 | 0.9803 | 0.7849 | 0.1165 |\n", - "| OLS_sphere | 200 | 0.4766 | 0.6904 | 0.5481 | 0.5618 |\n", - "| DeepKriging_wendland | -- | 0.7747 | 0.8802 | 0.7079 | 0.2878 |\n", - "| DeepKriging_mrts | 10 | 0.4557 | 0.6751 | 0.5469 | 0.581 |\n", - "| DeepKriging_sphere | 10 | 0.4887 | 0.6991 | 0.5621 | 0.5507 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5054 | 0.7109 | 0.5661 | 0.5354 |\n", - "| UniversalKriging | 50 | 0.417 | 0.6458 | 0.516 | 0.6166 |\n", - "Repeat 20/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:19:31.036548: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774808371.046134 2713954 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774808371.048812 2713954 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774808371.056274 2713954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808371.056306 2713954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808371.056308 2713954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808371.056309 2713954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=70 ===\n", - "z mean: 1.2127 (±0.0212), Var: 1.1287, Range: [-1.9270, 4.7133]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808391.910042 2713954 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774808393.412828 2714313 service.cc:152] XLA service 0x7f11a4006780 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774808393.412862 2714313 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808393.630306 2714313 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808395.294918 2714313 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f13e078e0e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f13d0565990> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4484, sigma²=0.7830, nugget=0.2585\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4442, sigma²=0.7741, nugget=0.2587\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4444, sigma²=0.7725, nugget=0.2589\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4447, sigma²=0.7721, nugget=0.2590\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4450, sigma²=0.7720, nugget=0.2591\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4459, sigma²=0.7718, nugget=0.2591\n", - "[repeat 20] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4484, sigma²=0.7830, nugget=0.2585\n", - "[repeat 20] done → repeat_20_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3806 | 1.175 | 0.8235 | -0.1872 |\n", - "| OLS_sphere | 200 | 0.5458 | 0.7388 | 0.5898 | 0.5307 |\n", - "| DeepKriging_wendland | -- | 0.9234 | 0.9609 | 0.7339 | 0.2059 |\n", - "| DeepKriging_mrts | 10 | 0.6025 | 0.7762 | 0.6254 | 0.4819 |\n", - "| DeepKriging_sphere | 10 | 0.5245 | 0.7242 | 0.5746 | 0.549 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5369 | 0.7327 | 0.5834 | 0.5383 |\n", - "| UniversalKriging | 10 | 0.4836 | 0.6954 | 0.5562 | 0.5841 |\n", - "Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:27:01.984516: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774808821.994158 3051170 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774808821.996816 3051170 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774808822.003891 3051170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808822.003924 3051170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808822.003926 3051170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774808822.003927 3051170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=71 ===\n", - "z mean: 1.1754 (±0.0197), Var: 0.9713, Range: [-1.9970, 4.5180]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808842.776106 3051170 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774808844.299634 3051537 service.cc:152] XLA service 0x7f0f18006d90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774808844.299671 3051537 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808844.512679 3051537 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774808846.192106 3051537 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f114c76b640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1144589870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3292, sigma²=0.6736, nugget=0.2334\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3246, sigma²=0.6644, nugget=0.2334\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3247, sigma²=0.6629, nugget=0.2336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3249, sigma²=0.6623, nugget=0.2337\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3250, sigma²=0.6622, nugget=0.2338\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3258, sigma²=0.6618, nugget=0.2338\n", - "[repeat 21] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3258, sigma²=0.6618, nugget=0.2338\n", - "[repeat 21] done → repeat_21_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.9738 | 0.9868 | 0.7538 | -0.0006 |\n", - "| OLS_sphere | 150 | 0.4703 | 0.6858 | 0.5565 | 0.5167 |\n", - "| DeepKriging_wendland | -- | 0.7467 | 0.8641 | 0.6834 | 0.2328 |\n", - "| DeepKriging_mrts | 50 | 0.5265 | 0.7256 | 0.5862 | 0.459 |\n", - "| DeepKriging_sphere | 10 | 0.5021 | 0.7086 | 0.579 | 0.4841 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5048 | 0.7105 | 0.5713 | 0.4813 |\n", - "| UniversalKriging | 1000 | 0.4478 | 0.6692 | 0.5465 | 0.5399 |\n", - "Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:34:11.172521: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774809251.182951 3336206 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774809251.185674 3336206 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774809251.192830 3336206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809251.192860 3336206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809251.192862 3336206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809251.192863 3336206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=72 ===\n", - "z mean: 0.9583 (±0.0209), Var: 1.0964, Range: [-2.6146, 4.1242]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809271.850011 3336206 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774809273.391900 3336572 service.cc:152] XLA service 0x5599be41af30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774809273.391934 3336572 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809273.610237 3336572 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809275.270548 3336572 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6eb079d360> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6ea85463b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4057, sigma²=0.9043, nugget=0.2257\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4001, sigma²=0.8907, nugget=0.2258\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4002, sigma²=0.8891, nugget=0.2260\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4007, sigma²=0.8886, nugget=0.2262\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4009, sigma²=0.8884, nugget=0.2263\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4019, sigma²=0.8881, nugget=0.2263\n", - "[repeat 22] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4009, sigma²=0.8884, nugget=0.2263\n", - "[repeat 22] done → repeat_22_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|-----------|\n", - "| OLS_wendland | -- | 197.478 | 14.0527 | 1.9328 | -206.698 |\n", - "| OLS_sphere | 200 | 0.3962 | 0.6294 | 0.5057 | 0.5833 |\n", - "| DeepKriging_wendland | -- | 0.6887 | 0.8299 | 0.6591 | 0.2757 |\n", - "| DeepKriging_mrts | 10 | 0.3966 | 0.6297 | 0.4881 | 0.5829 |\n", - "| DeepKriging_sphere | 50 | 0.4136 | 0.6431 | 0.5076 | 0.565 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.3719 | 0.6098 | 0.4773 | 0.6089 |\n", - "| UniversalKriging | 200 | 0.3427 | 0.5854 | 0.461 | 0.6395 |\n", - "Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:41:55.924885: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774809715.942360 3692501 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774809715.945315 3692501 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774809715.952814 3692501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809715.952864 3692501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809715.952866 3692501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774809715.952867 3692501 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=73 ===\n", - "z mean: 1.2927 (±0.0227), Var: 1.2853, Range: [-2.2168, 5.9669]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809736.853001 3692501 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774809738.401267 3692860 service.cc:152] XLA service 0x5632f4371c70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774809738.401298 3692860 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809738.620326 3692860 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774809740.287485 3692860 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2ea07432e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2e984ff880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5793, sigma²=1.1073, nugget=0.2331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5696, sigma²=1.0831, nugget=0.2336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5700, sigma²=1.0814, nugget=0.2338\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5701, sigma²=1.0801, nugget=0.2339\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5704, sigma²=1.0798, nugget=0.2339\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5716, sigma²=1.0793, nugget=0.2340\n", - "[repeat 23] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5716, sigma²=1.0793, nugget=0.2340\n", - "[repeat 23] done → repeat_23_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3794 | 1.1745 | 0.8708 | -0.0231 |\n", - "| OLS_sphere | 200 | 0.475 | 0.6892 | 0.5684 | 0.6477 |\n", - "| DeepKriging_wendland | -- | 1.0793 | 1.0389 | 0.7838 | 0.1995 |\n", - "| DeepKriging_mrts | 10 | 0.498 | 0.7057 | 0.5703 | 0.6306 |\n", - "| DeepKriging_sphere | 10 | 0.4995 | 0.7067 | 0.5728 | 0.6295 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5157 | 0.7181 | 0.5661 | 0.6175 |\n", - "| UniversalKriging | 1000 | 0.4386 | 0.6622 | 0.5337 | 0.6747 |\n", - "Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:49:28.405057: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774810168.414425 4007322 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774810168.417158 4007322 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774810168.424349 4007322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810168.424379 4007322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810168.424381 4007322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810168.424382 4007322 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=74 ===\n", - "z mean: 1.1071 (±0.0254), Var: 1.6192, Range: [-3.7020, 5.6165]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810189.127094 4007322 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774810190.657917 4007681 service.cc:152] XLA service 0x7f5d0401ae30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774810190.657945 4007681 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810190.880773 4007681 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810192.548544 4007681 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5f3c722b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5f3c123f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6178, sigma²=0.9744, nugget=0.2685\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6152, sigma²=0.9649, nugget=0.2690\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6159, sigma²=0.9639, nugget=0.2692\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6165, sigma²=0.9635, nugget=0.2693\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6168, sigma²=0.9633, nugget=0.2694\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6179, sigma²=0.9632, nugget=0.2693\n", - "[repeat 24] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6152, sigma²=0.9649, nugget=0.2690\n", - "[repeat 24] done → repeat_24_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 4.0479 | 2.0119 | 0.9733 | -1.6275 |\n", - "| OLS_sphere | 200 | 0.4086 | 0.6392 | 0.5097 | 0.7348 |\n", - "| DeepKriging_wendland | -- | 0.9909 | 0.9954 | 0.7639 | 0.3568 |\n", - "| DeepKriging_mrts | 10 | 0.4007 | 0.633 | 0.5143 | 0.7399 |\n", - "| DeepKriging_sphere | 10 | 0.4221 | 0.6497 | 0.5298 | 0.726 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4545 | 0.6741 | 0.5389 | 0.705 |\n", - "| UniversalKriging | 50 | 0.3559 | 0.5966 | 0.4792 | 0.769 |\n", - "Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 02:56:44.765654: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774810604.775396 135416 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774810604.778156 135416 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774810604.785229 135416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810604.785268 135416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810604.785271 135416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774810604.785272 135416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=75 ===\n", - "z mean: 1.1770 (±0.0215), Var: 1.1512, Range: [-2.1764, 4.7081]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810625.650762 135416 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774810627.158854 135775 service.cc:152] XLA service 0x55ac03a8b900 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774810627.158879 135775 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810627.373446 135775 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774810629.058109 135775 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f011c20fbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f010c746710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4496, sigma²=0.9360, nugget=0.2652\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4448, sigma²=0.9229, nugget=0.2656\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4447, sigma²=0.9204, nugget=0.2658\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4451, sigma²=0.9197, nugget=0.2660\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4453, sigma²=0.9194, nugget=0.2660\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4460, sigma²=0.9192, nugget=0.2660\n", - "[repeat 25] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4496, sigma²=0.9360, nugget=0.2652\n", - "[repeat 25] done → repeat_25_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.823 | 0.9072 | 0.7216 | 0.2207 |\n", - "| OLS_sphere | 200 | 0.4236 | 0.6508 | 0.5241 | 0.5989 |\n", - "| DeepKriging_wendland | -- | 0.695 | 0.8337 | 0.6559 | 0.3419 |\n", - "| DeepKriging_mrts | 150 | 0.4315 | 0.6569 | 0.5281 | 0.5915 |\n", - "| DeepKriging_sphere | 10 | 0.4511 | 0.6716 | 0.538 | 0.5729 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4462 | 0.668 | 0.5299 | 0.5775 |\n", - "| UniversalKriging | 10 | 0.3835 | 0.6193 | 0.4967 | 0.6369 |\n", - "Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:04:19.473752: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774811059.484217 485599 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774811059.486996 485599 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774811059.498132 485599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811059.498174 485599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811059.498176 485599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811059.498177 485599 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=76 ===\n", - "z mean: 0.9450 (±0.0213), Var: 1.1356, Range: [-2.3063, 4.5449]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811080.329391 485599 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774811081.843396 485964 service.cc:152] XLA service 0x559e639b9070 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774811081.843419 485964 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811082.058540 485964 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811083.729502 485964 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f27b03116c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f27a8407640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4568, sigma²=0.7312, nugget=0.2742\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4508, sigma²=0.7185, nugget=0.2746\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4510, sigma²=0.7173, nugget=0.2747\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4512, sigma²=0.7170, nugget=0.2748\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4514, sigma²=0.7168, nugget=0.2748\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4520, sigma²=0.7167, nugget=0.2748\n", - "[repeat 26] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4568, sigma²=0.7312, nugget=0.2742\n", - "[repeat 26] done → repeat_26_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8528 | 0.9235 | 0.7195 | 0.0982 |\n", - "| OLS_sphere | 200 | 0.3772 | 0.6142 | 0.485 | 0.6011 |\n", - "| DeepKriging_wendland | -- | 0.679 | 0.824 | 0.6589 | 0.2819 |\n", - "| DeepKriging_mrts | 50 | 0.4381 | 0.6619 | 0.5353 | 0.5367 |\n", - "| DeepKriging_sphere | 10 | 0.4028 | 0.6346 | 0.5013 | 0.5741 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4155 | 0.6446 | 0.5046 | 0.5606 |\n", - "| UniversalKriging | 10 | 0.3546 | 0.5955 | 0.4766 | 0.6251 |\n", - "Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:11:08.060440: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774811468.070067 748329 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774811468.072742 748329 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774811468.079710 748329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811468.079738 748329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811468.079740 748329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811468.079742 748329 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=77 ===\n", - "z mean: 0.8147 (±0.0241), Var: 1.4567, Range: [-2.6027, 4.6758]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811488.871873 748329 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774811490.409946 748695 service.cc:152] XLA service 0x7faa90018150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774811490.409968 748695 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811490.631545 748695 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811492.292278 748695 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7facc839fe20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7facc01aa950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5077, sigma²=1.0192, nugget=0.2183\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5019, sigma²=1.0034, nugget=0.2187\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5027, sigma²=1.0014, nugget=0.2191\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5032, sigma²=1.0008, nugget=0.2192\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5036, sigma²=1.0007, nugget=0.2193\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5046, sigma²=1.0005, nugget=0.2193\n", - "[repeat 27] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5077, sigma²=1.0192, nugget=0.2183\n", - "[repeat 27] done → repeat_27_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.9812 | 1.4076 | 0.9048 | -0.4992 |\n", - "| OLS_sphere | 150 | 0.4439 | 0.6662 | 0.5309 | 0.6641 |\n", - "| DeepKriging_wendland | -- | 0.7475 | 0.8646 | 0.6792 | 0.4344 |\n", - "| DeepKriging_mrts | 50 | 0.4745 | 0.6889 | 0.5499 | 0.6409 |\n", - "| DeepKriging_sphere | 10 | 0.4701 | 0.6856 | 0.5489 | 0.6443 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5266 | 0.7257 | 0.5887 | 0.6015 |\n", - "| UniversalKriging | 10 | 0.4024 | 0.6344 | 0.5016 | 0.6955 |\n", - "Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:18:44.325358: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774811924.336524 1091954 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774811924.339651 1091954 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774811924.347033 1091954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811924.347072 1091954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811924.347074 1091954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774811924.347075 1091954 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=78 ===\n", - "z mean: 1.3149 (±0.0214), Var: 1.1429, Range: [-1.9459, 4.9160]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811945.155901 1091954 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774811946.671175 1092312 service.cc:152] XLA service 0x55df907d6ae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774811946.671201 1092312 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811946.889811 1092312 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774811948.548154 1092312 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f99f41f7490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f99e42ef520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8281, sigma²=1.2378, nugget=0.2428\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8191, sigma²=1.2149, nugget=0.2434\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8203, sigma²=1.2138, nugget=0.2436\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8213, sigma²=1.2140, nugget=0.2437\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8220, sigma²=1.2142, nugget=0.2437\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8244, sigma²=1.2153, nugget=0.2436\n", - "[repeat 28] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8244, sigma²=1.2153, nugget=0.2436\n", - "[repeat 28] done → repeat_28_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.7642 | 1.3282 | 0.9264 | -0.497 |\n", - "| OLS_sphere | 200 | 0.4086 | 0.6392 | 0.505 | 0.6533 |\n", - "| DeepKriging_wendland | -- | 0.9388 | 0.9689 | 0.7688 | 0.2033 |\n", - "| DeepKriging_mrts | 10 | 0.4472 | 0.6687 | 0.5259 | 0.6206 |\n", - "| DeepKriging_sphere | 10 | 0.4497 | 0.6706 | 0.5338 | 0.6184 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4358 | 0.6602 | 0.5253 | 0.6302 |\n", - "| UniversalKriging | 1000 | 0.394 | 0.6277 | 0.5059 | 0.6657 |\n", - "Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:26:16.356542: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774812376.367446 1427914 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774812376.370443 1427914 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774812376.377463 1427914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812376.377497 1427914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812376.377499 1427914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812376.377500 1427914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=79 ===\n", - "z mean: 1.0367 (±0.0202), Var: 1.0167, Range: [-3.0471, 4.2758]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812397.173064 1427914 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774812398.695581 1428273 service.cc:152] XLA service 0x7fe154006dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774812398.695614 1428273 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812398.924119 1428273 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812400.580614 1428273 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe39423d480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe384707010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4829, sigma²=0.8955, nugget=0.2414\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4735, sigma²=0.8744, nugget=0.2418\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4731, sigma²=0.8721, nugget=0.2419\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4734, sigma²=0.8716, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4737, sigma²=0.8714, nugget=0.2421\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4748, sigma²=0.8715, nugget=0.2421\n", - "[repeat 29] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4829, sigma²=0.8955, nugget=0.2414\n", - "[repeat 29] done → repeat_29_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|-----------|\n", - "| OLS_wendland | -- | 111.853 | 10.5761 | 2.011 | -102.749 |\n", - "| OLS_sphere | 200 | 0.4201 | 0.6482 | 0.5343 | 0.6103 |\n", - "| DeepKriging_wendland | -- | 0.8379 | 0.9154 | 0.7253 | 0.2228 |\n", - "| DeepKriging_mrts | 50 | 0.4789 | 0.692 | 0.5639 | 0.5558 |\n", - "| DeepKriging_sphere | 10 | 0.4399 | 0.6633 | 0.5292 | 0.5919 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4105 | 0.6407 | 0.5204 | 0.6193 |\n", - "| UniversalKriging | 10 | 0.3719 | 0.6099 | 0.4984 | 0.655 |\n", - "Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:33:42.547298: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774812822.558184 1755326 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774812822.561265 1755326 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774812822.568549 1755326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812822.568575 1755326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812822.568577 1755326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774812822.568578 1755326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=80 ===\n", - "z mean: 0.6482 (±0.0206), Var: 1.0586, Range: [-2.7609, 3.7839]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812843.429913 1755326 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774812844.955571 1755687 service.cc:152] XLA service 0x7fe020009880 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774812844.955624 1755687 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812845.173692 1755687 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774812846.872013 1755687 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe25870a950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe2582f79a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4428, sigma²=0.8081, nugget=0.2411\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4362, sigma²=0.7941, nugget=0.2413\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4364, sigma²=0.7927, nugget=0.2415\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4368, sigma²=0.7922, nugget=0.2416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4370, sigma²=0.7920, nugget=0.2416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4379, sigma²=0.7919, nugget=0.2416\n", - "[repeat 30] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4379, sigma²=0.7919, nugget=0.2416\n", - "[repeat 30] done → repeat_30_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 10.4248 | 3.2287 | 0.9649 | -9.9774 |\n", - "| OLS_sphere | 150 | 0.4245 | 0.6515 | 0.5261 | 0.553 |\n", - "| DeepKriging_wendland | -- | 0.6952 | 0.8338 | 0.6707 | 0.2679 |\n", - "| DeepKriging_mrts | 10 | 0.4456 | 0.6675 | 0.5201 | 0.5308 |\n", - "| DeepKriging_sphere | 10 | 0.4294 | 0.6553 | 0.5247 | 0.5479 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4454 | 0.6673 | 0.5207 | 0.531 |\n", - "| UniversalKriging | 1000 | 0.3525 | 0.5937 | 0.4735 | 0.6288 |\n", - "Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:40:58.929068: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774813258.939684 2075901 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774813258.942441 2075901 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774813258.949489 2075901 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813258.949518 2075901 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813258.949521 2075901 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813258.949521 2075901 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=81 ===\n", - "z mean: 0.8650 (±0.0222), Var: 1.2326, Range: [-2.7528, 4.6805]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813279.851593 2075901 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774813281.400912 2076259 service.cc:152] XLA service 0x7f99e40099a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774813281.400936 2076259 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813281.621996 2076259 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813283.309999 2076259 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9c1816aef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9c1027ef80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4705, sigma²=0.7903, nugget=0.2753\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4680, sigma²=0.7830, nugget=0.2757\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4684, sigma²=0.7818, nugget=0.2759\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4687, sigma²=0.7815, nugget=0.2759\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4688, sigma²=0.7814, nugget=0.2759\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4696, sigma²=0.7812, nugget=0.2759\n", - "[repeat 31] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4696, sigma²=0.7812, nugget=0.2759\n", - "[repeat 31] done → repeat_31_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 7.4331 | 2.7264 | 0.9342 | -5.8004 |\n", - "| OLS_sphere | 200 | 0.4086 | 0.6392 | 0.5197 | 0.6262 |\n", - "| DeepKriging_wendland | -- | 0.6868 | 0.8287 | 0.6753 | 0.3716 |\n", - "| DeepKriging_mrts | 150 | 0.4074 | 0.6383 | 0.5065 | 0.6273 |\n", - "| DeepKriging_sphere | 10 | 0.3931 | 0.6269 | 0.4923 | 0.6404 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4042 | 0.6358 | 0.5126 | 0.6302 |\n", - "| UniversalKriging | 1000 | 0.3419 | 0.5847 | 0.4666 | 0.6872 |\n", - "Repeat 32/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 33/50 seed=82\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:48:28.256584: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774813708.267133 2408228 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774813708.270213 2408228 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774813708.278441 2408228 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813708.278472 2408228 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813708.278475 2408228 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774813708.278476 2408228 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] seed=82\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=82 ===\n", - "z mean: 1.6575 (±0.0227), Var: 1.2923, Range: [-2.5118, 5.5249]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813729.164590 2408228 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774813730.688633 2408586 service.cc:152] XLA service 0x55a53e2f4a70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774813730.688656 2408586 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813730.909680 2408586 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774813732.582619 2408586 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7f8422f0a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7f7c32bc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4466, sigma²=0.8974, nugget=0.2570\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4421, sigma²=0.8853, nugget=0.2573\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4424, sigma²=0.8831, nugget=0.2576\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4426, sigma²=0.8827, nugget=0.2577\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4428, sigma²=0.8825, nugget=0.2577\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4437, sigma²=0.8823, nugget=0.2577\n", - "[repeat 32] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4437, sigma²=0.8823, nugget=0.2577\n", - "[repeat 32] done → repeat_32_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 12.2039 | 3.4934 | 1.0732 | -9.7465 |\n", - "| OLS_sphere | 200 | 0.4287 | 0.6547 | 0.5208 | 0.6225 |\n", - "| DeepKriging_wendland | -- | 0.8471 | 0.9204 | 0.7297 | 0.254 |\n", - "| DeepKriging_mrts | 10 | 0.433 | 0.658 | 0.5348 | 0.6187 |\n", - "| DeepKriging_sphere | 10 | 0.4086 | 0.6392 | 0.5175 | 0.6402 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4536 | 0.6735 | 0.5467 | 0.6006 |\n", - "| UniversalKriging | 1000 | 0.3889 | 0.6236 | 0.501 | 0.6575 |\n", - "Repeat 33/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 34/50 seed=83\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 03:55:28.503079: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774814128.513018 2694960 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774814128.515723 2694960 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774814128.523649 2694960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814128.523687 2694960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814128.523689 2694960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814128.523690 2694960 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] seed=83\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=83 ===\n", - "z mean: 1.3733 (±0.0248), Var: 1.5316, Range: [-4.0675, 5.7772]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814149.296635 2694960 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774814150.816207 2695329 service.cc:152] XLA service 0x7fd2c00072d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774814150.816230 2695329 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814151.041574 2695329 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814152.712322 2695329 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd4fc63b520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd4f472f490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5472, sigma²=1.0150, nugget=0.2439\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5345, sigma²=0.9861, nugget=0.2444\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5352, sigma²=0.9852, nugget=0.2446\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5356, sigma²=0.9848, nugget=0.2447\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5359, sigma²=0.9848, nugget=0.2447\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5370, sigma²=0.9846, nugget=0.2447\n", - "[repeat 33] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5370, sigma²=0.9846, nugget=0.2447\n", - "[repeat 33] done → repeat_33_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.7693 | 1.3302 | 0.8364 | -0.1073 |\n", - "| OLS_sphere | 200 | 0.4372 | 0.6612 | 0.536 | 0.7264 |\n", - "| DeepKriging_wendland | -- | 0.8026 | 0.8959 | 0.7132 | 0.4977 |\n", - "| DeepKriging_mrts | 10 | 0.4781 | 0.6914 | 0.5646 | 0.7008 |\n", - "| DeepKriging_sphere | 10 | 0.4418 | 0.6647 | 0.5445 | 0.7235 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5097 | 0.7139 | 0.5806 | 0.681 |\n", - "| UniversalKriging | 1000 | 0.4034 | 0.6352 | 0.513 | 0.7475 |\n", - "Repeat 34/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 35/50 seed=84\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:02:49.833022: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774814569.842897 3016666 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774814569.845711 3016666 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774814569.853149 3016666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814569.853176 3016666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814569.853178 3016666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774814569.853179 3016666 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] seed=84\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=84 ===\n", - "z mean: 1.5714 (±0.0193), Var: 0.9295, Range: [-1.6099, 4.8844]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814590.631885 3016666 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774814592.178999 3017031 service.cc:152] XLA service 0x7f80b80082a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774814592.179021 3017031 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814592.394286 3017031 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774814594.046712 3017031 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f82e440b130> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f82dc6f6830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3899, sigma²=0.6959, nugget=0.2630\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3860, sigma²=0.6875, nugget=0.2632\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3861, sigma²=0.6861, nugget=0.2634\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3863, sigma²=0.6855, nugget=0.2635\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3865, sigma²=0.6853, nugget=0.2636\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3873, sigma²=0.6852, nugget=0.2635\n", - "[repeat 34] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3899, sigma²=0.6959, nugget=0.2630\n", - "[repeat 34] done → repeat_34_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3217 | 1.1496 | 0.7133 | -0.5326 |\n", - "| OLS_sphere | 200 | 0.4506 | 0.6712 | 0.5331 | 0.4775 |\n", - "| DeepKriging_wendland | -- | 0.549 | 0.7409 | 0.5785 | 0.3634 |\n", - "| DeepKriging_mrts | 10 | 0.4583 | 0.677 | 0.5308 | 0.4686 |\n", - "| DeepKriging_sphere | 10 | 0.4167 | 0.6455 | 0.5004 | 0.5168 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4241 | 0.6513 | 0.5109 | 0.5082 |\n", - "| UniversalKriging | 10 | 0.3834 | 0.6192 | 0.4778 | 0.5554 |\n", - "Repeat 35/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 36/50 seed=85\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:10:44.925858: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774815044.936005 3390764 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774815044.939104 3390764 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774815044.946681 3390764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815044.946709 3390764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815044.946711 3390764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815044.946712 3390764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] seed=85\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=85 ===\n", - "z mean: 1.2417 (±0.0193), Var: 0.9301, Range: [-1.7051, 4.5036]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815065.769707 3390764 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774815067.283696 3391118 service.cc:152] XLA service 0x7fad8c006710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774815067.283720 3391118 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815067.498840 3391118 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815069.169114 3391118 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fafb4722f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fafac500ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4623, sigma²=0.7674, nugget=0.2605\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4573, sigma²=0.7573, nugget=0.2607\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4579, sigma²=0.7564, nugget=0.2609\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4581, sigma²=0.7562, nugget=0.2610\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4585, sigma²=0.7560, nugget=0.2610\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4596, sigma²=0.7560, nugget=0.2611\n", - "[repeat 35] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4596, sigma²=0.7560, nugget=0.2611\n", - "[repeat 35] done → repeat_35_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.8802 | 0.9382 | 0.7116 | -0.1724 |\n", - "| OLS_sphere | 200 | 0.3582 | 0.5985 | 0.4757 | 0.5229 |\n", - "| DeepKriging_wendland | -- | 0.663 | 0.8142 | 0.6465 | 0.1169 |\n", - "| DeepKriging_mrts | 10 | 0.3517 | 0.5931 | 0.4701 | 0.5315 |\n", - "| DeepKriging_sphere | 10 | 0.3819 | 0.618 | 0.5066 | 0.4913 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.39 | 0.6245 | 0.504 | 0.4805 |\n", - "| UniversalKriging | 1000 | 0.3395 | 0.5827 | 0.4704 | 0.5478 |\n", - "Repeat 36/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 37/50 seed=86\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:18:17.371725: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774815497.383387 3728036 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774815497.386184 3728036 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774815497.393753 3728036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815497.393810 3728036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815497.393812 3728036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815497.393813 3728036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] seed=86\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=86 ===\n", - "z mean: 1.1092 (±0.0267), Var: 1.7868, Range: [-4.5170, 4.9042]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815518.343381 3728036 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774815519.886649 3728403 service.cc:152] XLA service 0x7f68b8017c70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774815519.886681 3728403 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815520.105409 3728403 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815521.760838 3728403 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6ae40feb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6adc3f2dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8374, sigma²=1.2765, nugget=0.2762\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8278, sigma²=1.2534, nugget=0.2767\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8288, sigma²=1.2516, nugget=0.2769\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8298, sigma²=1.2517, nugget=0.2770\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8301, sigma²=1.2517, nugget=0.2770\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8314, sigma²=1.2519, nugget=0.2769\n", - "[repeat 36] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8314, sigma²=1.2519, nugget=0.2769\n", - "[repeat 36] done → repeat_36_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3778 | 1.1738 | 0.9109 | 0.1486 |\n", - "| OLS_sphere | 200 | 0.3577 | 0.5981 | 0.4733 | 0.779 |\n", - "| DeepKriging_wendland | -- | 1.1291 | 1.0626 | 0.8345 | 0.3023 |\n", - "| DeepKriging_mrts | 10 | 0.395 | 0.6285 | 0.5049 | 0.7559 |\n", - "| DeepKriging_sphere | 10 | 0.3981 | 0.6309 | 0.5004 | 0.754 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4113 | 0.6414 | 0.5183 | 0.7458 |\n", - "| UniversalKriging | 1000 | 0.3333 | 0.5773 | 0.4516 | 0.7941 |\n", - "Repeat 37/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 38/50 seed=87\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:25:38.566942: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774815938.577800 4049240 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774815938.580619 4049240 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774815938.587971 4049240 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815938.588005 4049240 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815938.588007 4049240 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774815938.588008 4049240 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] seed=87\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=87 ===\n", - "z mean: 0.8641 (±0.0244), Var: 1.4889, Range: [-3.1944, 4.6565]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815959.414127 4049240 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774815960.950338 4049606 service.cc:152] XLA service 0x7f88cc0198e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774815960.950365 4049606 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815961.173880 4049606 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774815962.835768 4049606 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8b0064aa70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8af877a9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5246, sigma²=0.9615, nugget=0.2436\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5189, sigma²=0.9467, nugget=0.2440\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5194, sigma²=0.9449, nugget=0.2442\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5198, sigma²=0.9447, nugget=0.2443\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5201, sigma²=0.9445, nugget=0.2444\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5211, sigma²=0.9444, nugget=0.2443\n", - "[repeat 37] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5211, sigma²=0.9444, nugget=0.2443\n", - "[repeat 37] done → repeat_37_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 89.3898 | 9.4546 | 1.8179 | -56.553 |\n", - "| OLS_sphere | 150 | 0.4912 | 0.7009 | 0.5691 | 0.6837 |\n", - "| DeepKriging_wendland | -- | 1.2588 | 1.122 | 0.8798 | 0.1895 |\n", - "| DeepKriging_mrts | 10 | 0.4135 | 0.643 | 0.519 | 0.7338 |\n", - "| DeepKriging_sphere | 10 | 0.4983 | 0.7059 | 0.5642 | 0.6792 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4578 | 0.6766 | 0.5404 | 0.7053 |\n", - "| UniversalKriging | 1000 | 0.4073 | 0.6382 | 0.5149 | 0.7378 |\n", - "Repeat 38/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 39/50 seed=88\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:33:34.702661: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774816414.712715 242119 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774816414.716218 242119 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774816414.724183 242119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816414.724217 242119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816414.724219 242119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816414.724220 242119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] seed=88\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=88 ===\n", - "z mean: 0.6690 (±0.0208), Var: 1.0799, Range: [-2.6868, 4.3748]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816435.528710 242119 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774816437.039857 242484 service.cc:152] XLA service 0x7fa80401ba00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774816437.039884 242484 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816437.257720 242484 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816438.934319 242484 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faa246a56c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faa1c43ab90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4402, sigma²=0.8663, nugget=0.2440\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4333, sigma²=0.8500, nugget=0.2443\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4335, sigma²=0.8485, nugget=0.2445\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4338, sigma²=0.8482, nugget=0.2446\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4341, sigma²=0.8480, nugget=0.2447\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4350, sigma²=0.8478, nugget=0.2447\n", - "[repeat 38] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4338, sigma²=0.8482, nugget=0.2446\n", - "[repeat 38] done → repeat_38_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.4654 | 1.2105 | 0.8297 | -0.2382 |\n", - "| OLS_sphere | 200 | 0.3779 | 0.6147 | 0.4988 | 0.6807 |\n", - "| DeepKriging_wendland | -- | 0.7227 | 0.8501 | 0.6725 | 0.3894 |\n", - "| DeepKriging_mrts | 10 | 0.3847 | 0.6202 | 0.5097 | 0.675 |\n", - "| DeepKriging_sphere | 10 | 0.4227 | 0.6501 | 0.5116 | 0.6429 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4059 | 0.6371 | 0.5177 | 0.657 |\n", - "| UniversalKriging | 150 | 0.3375 | 0.5809 | 0.4672 | 0.7148 |\n", - "Repeat 39/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 40/50 seed=89\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:40:43.887098: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774816843.898236 558029 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774816843.901584 558029 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774816843.909921 558029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816843.909958 558029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816843.909961 558029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774816843.909962 558029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] seed=89\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=89 ===\n", - "z mean: 1.3288 (±0.0209), Var: 1.0891, Range: [-1.9980, 4.9758]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816864.659350 558029 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774816866.169300 558393 service.cc:152] XLA service 0x7f60f0005e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774816866.169325 558393 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816866.388612 558393 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774816868.046198 558393 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6328409480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f632053bd90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4202, sigma²=0.7913, nugget=0.2629\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4155, sigma²=0.7797, nugget=0.2633\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4157, sigma²=0.7788, nugget=0.2634\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4160, sigma²=0.7784, nugget=0.2635\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4163, sigma²=0.7781, nugget=0.2636\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4171, sigma²=0.7779, nugget=0.2636\n", - "[repeat 39] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4171, sigma²=0.7779, nugget=0.2636\n", - "[repeat 39] done → repeat_39_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1302 | 1.0631 | 0.7743 | -0.128 |\n", - "| OLS_sphere | 150 | 0.4427 | 0.6654 | 0.5227 | 0.5582 |\n", - "| DeepKriging_wendland | -- | 0.6767 | 0.8226 | 0.6526 | 0.3246 |\n", - "| DeepKriging_mrts | 50 | 0.4164 | 0.6453 | 0.5015 | 0.5844 |\n", - "| DeepKriging_sphere | 10 | 0.4546 | 0.6742 | 0.5238 | 0.5463 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4431 | 0.6656 | 0.5172 | 0.5578 |\n", - "| UniversalKriging | 1000 | 0.3831 | 0.6189 | 0.4809 | 0.6176 |\n", - "Repeat 40/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 41/50 seed=90\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:48:27.364592: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774817307.374914 917634 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774817307.377685 917634 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774817307.385302 917634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817307.385330 917634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817307.385332 917634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817307.385333 917634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] seed=90\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=90 ===\n", - "z mean: 0.3695 (±0.0212), Var: 1.1235, Range: [-3.1169, 3.7441]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817328.354659 917634 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774817329.869385 917995 service.cc:152] XLA service 0x555cf10078c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774817329.869407 917995 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817330.087127 917995 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817331.736812 917995 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f39447832e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f394415ad40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4224, sigma²=0.8435, nugget=0.2448\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4130, sigma²=0.8220, nugget=0.2452\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4131, sigma²=0.8209, nugget=0.2453\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4134, sigma²=0.8204, nugget=0.2455\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4137, sigma²=0.8202, nugget=0.2455\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4145, sigma²=0.8199, nugget=0.2455\n", - "[repeat 40] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4224, sigma²=0.8435, nugget=0.2448\n", - "[repeat 40] done → repeat_40_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0614 | 1.0303 | 0.8091 | 0.1135 |\n", - "| OLS_sphere | 200 | 0.4885 | 0.6989 | 0.5502 | 0.592 |\n", - "| DeepKriging_wendland | -- | 0.8381 | 0.9155 | 0.7342 | 0.3 |\n", - "| DeepKriging_mrts | 10 | 0.5213 | 0.722 | 0.5715 | 0.5646 |\n", - "| DeepKriging_sphere | 10 | 0.5338 | 0.7306 | 0.5731 | 0.5542 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5266 | 0.7257 | 0.5816 | 0.5602 |\n", - "| UniversalKriging | 10 | 0.4457 | 0.6676 | 0.5277 | 0.6277 |\n", - "Repeat 41/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 42/50 seed=91\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 04:55:33.740057: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774817733.750489 1221244 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774817733.753517 1221244 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774817733.761002 1221244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817733.761030 1221244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817733.761031 1221244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774817733.761032 1221244 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] seed=91\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=91 ===\n", - "z mean: 1.1361 (±0.0209), Var: 1.0912, Range: [-3.0089, 4.4920]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817754.579894 1221244 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774817756.099764 1221603 service.cc:152] XLA service 0x55b6fe8d40d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774817756.099787 1221603 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817756.321536 1221603 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774817757.985849 1221603 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faf084372e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faf00573d00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4067, sigma²=0.7760, nugget=0.2502\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4017, sigma²=0.7642, nugget=0.2506\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4018, sigma²=0.7627, nugget=0.2508\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4022, sigma²=0.7622, nugget=0.2509\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4024, sigma²=0.7620, nugget=0.2509\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4032, sigma²=0.7617, nugget=0.2509\n", - "[repeat 41] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4032, sigma²=0.7617, nugget=0.2509\n", - "[repeat 41] done → repeat_41_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 15.1314 | 3.8899 | 1.0705 | -12.1085 |\n", - "| OLS_sphere | 100 | 0.4877 | 0.6984 | 0.5515 | 0.5775 |\n", - "| DeepKriging_wendland | -- | 0.7705 | 0.8778 | 0.7036 | 0.3325 |\n", - "| DeepKriging_mrts | 50 | 0.4584 | 0.6771 | 0.5394 | 0.6029 |\n", - "| DeepKriging_sphere | 10 | 0.4988 | 0.7063 | 0.5421 | 0.5679 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5204 | 0.7214 | 0.5635 | 0.5492 |\n", - "| UniversalKriging | 1000 | 0.4157 | 0.6447 | 0.5037 | 0.6399 |\n", - "Repeat 42/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 43/50 seed=92\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:02:57.080754: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774818177.090316 1541236 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774818177.093012 1541236 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774818177.100114 1541236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818177.100141 1541236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818177.100143 1541236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818177.100144 1541236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] seed=92\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=92 ===\n", - "z mean: 0.2250 (±0.0201), Var: 1.0133, Range: [-3.2990, 4.0592]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818198.031256 1541236 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774818199.556538 1541590 service.cc:152] XLA service 0x55b378050ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774818199.556569 1541590 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818199.765801 1541590 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818201.425080 1541590 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff340603be0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff3401ef5b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3317, sigma²=0.7256, nugget=0.2372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3243, sigma²=0.7100, nugget=0.2372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3242, sigma²=0.7085, nugget=0.2373\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3244, sigma²=0.7079, nugget=0.2375\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3246, sigma²=0.7078, nugget=0.2375\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3253, sigma²=0.7074, nugget=0.2376\n", - "[repeat 42] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3253, sigma²=0.7074, nugget=0.2376\n", - "[repeat 42] done → repeat_42_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 19.4082 | 4.4055 | 0.9158 | -21.0987 |\n", - "| OLS_sphere | 200 | 0.4322 | 0.6574 | 0.5191 | 0.5079 |\n", - "| DeepKriging_wendland | -- | 0.5637 | 0.7508 | 0.5967 | 0.3582 |\n", - "| DeepKriging_mrts | 10 | 0.4675 | 0.6837 | 0.5484 | 0.4677 |\n", - "| DeepKriging_sphere | 10 | 0.4418 | 0.6646 | 0.5322 | 0.497 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4499 | 0.6707 | 0.533 | 0.4878 |\n", - "| UniversalKriging | 1000 | 0.4067 | 0.6378 | 0.5165 | 0.5369 |\n", - "Repeat 43/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 44/50 seed=93\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:10:06.073357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774818606.084046 1851979 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774818606.086902 1851979 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774818606.094219 1851979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818606.094254 1851979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818606.094256 1851979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774818606.094257 1851979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] seed=93\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=93 ===\n", - "z mean: 0.6042 (±0.0214), Var: 1.1411, Range: [-2.8933, 4.2087]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818627.030365 1851979 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774818628.563163 1852332 service.cc:152] XLA service 0x7f78480072e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774818628.563187 1852332 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818628.783463 1852332 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774818630.445028 1852332 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7a6465b910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7a5c74ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3983, sigma²=0.8794, nugget=0.2544\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3898, sigma²=0.8579, nugget=0.2548\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3902, sigma²=0.8564, nugget=0.2551\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3904, sigma²=0.8560, nugget=0.2552\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3906, sigma²=0.8558, nugget=0.2553\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3915, sigma²=0.8555, nugget=0.2553\n", - "[repeat 43] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3904, sigma²=0.8560, nugget=0.2552\n", - "[repeat 43] done → repeat_43_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8405 | 0.9168 | 0.7251 | 0.245 |\n", - "| OLS_sphere | 150 | 0.4306 | 0.6562 | 0.5251 | 0.6132 |\n", - "| DeepKriging_wendland | -- | 0.7368 | 0.8584 | 0.6689 | 0.3381 |\n", - "| DeepKriging_mrts | 200 | 0.4497 | 0.6706 | 0.5428 | 0.5961 |\n", - "| DeepKriging_sphere | 10 | 0.4671 | 0.6834 | 0.5597 | 0.5804 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.444 | 0.6664 | 0.5294 | 0.6011 |\n", - "| UniversalKriging | 150 | 0.3785 | 0.6152 | 0.491 | 0.66 |\n", - "Repeat 44/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 45/50 seed=94\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:17:22.395901: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774819042.405360 2161860 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774819042.408218 2161860 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774819042.415463 2161860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819042.415492 2161860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819042.415494 2161860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819042.415495 2161860 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] seed=94\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=94 ===\n", - "z mean: 0.4940 (±0.0246), Var: 1.5159, Range: [-3.2991, 4.0559]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819063.179768 2161860 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774819064.702706 2162224 service.cc:152] XLA service 0x55a1c5ad9b10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774819064.702727 2162224 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819064.920954 2162224 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819066.598609 2162224 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f11301f37f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f11206c75b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4989, sigma²=0.9694, nugget=0.2372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4941, sigma²=0.9565, nugget=0.2375\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4948, sigma²=0.9552, nugget=0.2378\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4951, sigma²=0.9547, nugget=0.2379\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4953, sigma²=0.9545, nugget=0.2380\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4964, sigma²=0.9541, nugget=0.2380\n", - "[repeat 44] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4964, sigma²=0.9541, nugget=0.2380\n", - "[repeat 44] done → repeat_44_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.7866 | 1.3366 | 0.9922 | -0.1883 |\n", - "| OLS_sphere | 150 | 0.4599 | 0.6781 | 0.5398 | 0.6941 |\n", - "| DeepKriging_wendland | -- | 1.1609 | 1.0775 | 0.8079 | 0.2278 |\n", - "| DeepKriging_mrts | 10 | 0.4814 | 0.6938 | 0.5612 | 0.6798 |\n", - "| DeepKriging_sphere | 10 | 0.4389 | 0.6625 | 0.5312 | 0.708 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4275 | 0.6538 | 0.5174 | 0.7157 |\n", - "| UniversalKriging | 1000 | 0.4119 | 0.6418 | 0.5093 | 0.726 |\n", - "Repeat 45/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 46/50 seed=95\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:23:23.621623: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774819403.631600 2469111 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774819403.634486 2469111 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774819403.641869 2469111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819403.641909 2469111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819403.641911 2469111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819403.641912 2469111 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] seed=95\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=95 ===\n", - "z mean: 0.9887 (±0.0209), Var: 1.0963, Range: [-2.5972, 4.4324]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819424.601406 2469111 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774819426.156014 2469472 service.cc:152] XLA service 0x55d238fa26c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774819426.156040 2469472 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819426.376375 2469472 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819428.052467 2469472 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa9d8501900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa9d04bc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4591, sigma²=0.7991, nugget=0.2458\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4531, sigma²=0.7869, nugget=0.2461\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4533, sigma²=0.7855, nugget=0.2462\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4536, sigma²=0.7850, nugget=0.2463\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4539, sigma²=0.7848, nugget=0.2464\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4547, sigma²=0.7846, nugget=0.2463\n", - "[repeat 45] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4531, sigma²=0.7869, nugget=0.2461\n", - "[repeat 45] done → repeat_45_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8518 | 0.9229 | 0.7232 | 0.2543 |\n", - "| OLS_sphere | 200 | 0.4148 | 0.644 | 0.5039 | 0.6369 |\n", - "| DeepKriging_wendland | -- | 0.766 | 0.8752 | 0.6902 | 0.3294 |\n", - "| DeepKriging_mrts | 10 | 0.5296 | 0.7278 | 0.5727 | 0.5363 |\n", - "| DeepKriging_sphere | 10 | 0.4433 | 0.6658 | 0.5153 | 0.6119 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4447 | 0.6669 | 0.5151 | 0.6107 |\n", - "| UniversalKriging | 50 | 0.3858 | 0.6211 | 0.481 | 0.6622 |\n", - "Repeat 46/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 47/50 seed=96\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:29:28.073510: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774819768.084071 2801764 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774819768.086811 2801764 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774819768.094141 2801764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819768.094166 2801764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819768.094168 2801764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774819768.094169 2801764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] seed=96\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=96 ===\n", - "z mean: 1.2558 (±0.0211), Var: 1.1170, Range: [-1.9130, 4.5834]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819789.005734 2801764 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774819790.553353 2802131 service.cc:152] XLA service 0x561fa1a1e3c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774819790.553371 2802131 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819790.772402 2802131 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774819792.468078 2802131 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fabd0767760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fabd0173e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5045, sigma²=0.8836, nugget=0.2570\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5009, sigma²=0.8732, nugget=0.2574\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5012, sigma²=0.8721, nugget=0.2575\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5017, sigma²=0.8719, nugget=0.2577\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5022, sigma²=0.8719, nugget=0.2577\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5033, sigma²=0.8718, nugget=0.2577\n", - "[repeat 46] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5045, sigma²=0.8836, nugget=0.2570\n", - "[repeat 46] done → repeat_46_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9832 | 0.9915 | 0.771 | 0.1392 |\n", - "| OLS_sphere | 200 | 0.4639 | 0.6811 | 0.5477 | 0.5938 |\n", - "| DeepKriging_wendland | -- | 0.8143 | 0.9024 | 0.7185 | 0.2871 |\n", - "| DeepKriging_mrts | 10 | 0.4807 | 0.6934 | 0.5506 | 0.5791 |\n", - "| DeepKriging_sphere | 150 | 0.6128 | 0.7828 | 0.6246 | 0.4634 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5352 | 0.7316 | 0.5799 | 0.5314 |\n", - "| UniversalKriging | 10 | 0.4464 | 0.6681 | 0.5334 | 0.6092 |\n", - "Repeat 47/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 48/50 seed=97\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:35:34.796768: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774820134.807123 3135784 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774820134.810112 3135784 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774820134.818145 3135784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820134.818176 3135784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820134.818178 3135784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820134.818179 3135784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] seed=97\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=97 ===\n", - "z mean: 1.1233 (±0.0203), Var: 1.0323, Range: [-2.7769, 4.7923]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820155.741186 3135784 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774820157.272330 3136149 service.cc:152] XLA service 0x7f188400a2e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774820157.272353 3136149 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820157.490392 3136149 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820159.177459 3136149 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1aa06776d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1a9876f400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3615, sigma²=0.7099, nugget=0.2417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3568, sigma²=0.7000, nugget=0.2418\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3568, sigma²=0.6987, nugget=0.2420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3571, sigma²=0.6982, nugget=0.2421\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3573, sigma²=0.6979, nugget=0.2421\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3579, sigma²=0.6976, nugget=0.2421\n", - "[repeat 47] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3579, sigma²=0.6976, nugget=0.2421\n", - "[repeat 47] done → repeat_47_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|------------|\n", - "| OLS_wendland | -- | 2125.79 | 46.1063 | 3.6956 | -1798.28 |\n", - "| OLS_sphere | 200 | 0.4658 | 0.6825 | 0.5421 | 0.6058 |\n", - "| DeepKriging_wendland | -- | 0.7805 | 0.8835 | 0.6979 | 0.3393 |\n", - "| DeepKriging_mrts | 10 | 0.4598 | 0.6781 | 0.5382 | 0.6108 |\n", - "| DeepKriging_sphere | 10 | 0.4716 | 0.6867 | 0.5451 | 0.6008 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4874 | 0.6982 | 0.5553 | 0.5874 |\n", - "| UniversalKriging | 1000 | 0.4123 | 0.6421 | 0.5211 | 0.651 |\n", - "Repeat 48/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 49/50 seed=98\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:42:52.964908: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774820572.975262 3481563 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774820572.978246 3481563 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774820572.985554 3481563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820572.985583 3481563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820572.985585 3481563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820572.985586 3481563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] seed=98\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=98 ===\n", - "z mean: 1.5033 (±0.0201), Var: 1.0066, Range: [-1.7739, 5.2368]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820594.361367 3481563 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774820595.896145 3481933 service.cc:152] XLA service 0x7f51c4006330 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774820595.896189 3481933 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820596.121920 3481933 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774820597.846590 3481933 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f540031b400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f53f8403400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3847, sigma²=0.6962, nugget=0.2327\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3766, sigma²=0.6809, nugget=0.2329\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3767, sigma²=0.6794, nugget=0.2331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3770, sigma²=0.6791, nugget=0.2331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3772, sigma²=0.6789, nugget=0.2332\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3779, sigma²=0.6787, nugget=0.2332\n", - "[repeat 48] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3779, sigma²=0.6787, nugget=0.2332\n", - "[repeat 48] done → repeat_48_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.2938 | 1.5145 | 0.763 | -1.7781 |\n", - "| OLS_sphere | 200 | 0.3506 | 0.5921 | 0.458 | 0.5753 |\n", - "| DeepKriging_wendland | -- | 0.6009 | 0.7752 | 0.6081 | 0.2722 |\n", - "| DeepKriging_mrts | 10 | 0.413 | 0.6427 | 0.5102 | 0.4997 |\n", - "| DeepKriging_sphere | 50 | 0.3653 | 0.6044 | 0.4759 | 0.5576 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.3832 | 0.619 | 0.49 | 0.5359 |\n", - "| UniversalKriging | 1000 | 0.3111 | 0.5578 | 0.441 | 0.6232 |\n", - "Repeat 49/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 50/50 seed=99\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:49:52.062725: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774820992.077003 3764210 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774820992.080396 3764210 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774820992.088194 3764210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820992.088235 3764210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820992.088237 3764210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774820992.088238 3764210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] seed=99\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + N(0, 0.5^2) noise | seed=99 ===\n", - "z mean: 0.8954 (±0.0200), Var: 0.9957, Range: [-2.2742, 4.2258]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821013.367756 3764210 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774821014.912992 3764577 service.cc:152] XLA service 0x7f1c70007d60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774821014.913025 3764577 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821015.130527 3764577 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821016.834645 3764577 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1eac13ff40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1ea4237490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5106, sigma²=0.7452, nugget=0.2594\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5070, sigma²=0.7372, nugget=0.2597\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5074, sigma²=0.7360, nugget=0.2599\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5078, sigma²=0.7358, nugget=0.2600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5080, sigma²=0.7357, nugget=0.2600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5090, sigma²=0.7357, nugget=0.2599\n", - "[repeat 49] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5078, sigma²=0.7358, nugget=0.2600\n", - "[repeat 49] done → repeat_49_GP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 13.5674 | 3.6834 | 1.0751 | -12.5876 |\n", - "| OLS_sphere | 150 | 0.4475 | 0.6689 | 0.5475 | 0.5519 |\n", - "| DeepKriging_wendland | -- | 0.7084 | 0.8417 | 0.6798 | 0.2905 |\n", - "| DeepKriging_mrts | 50 | 0.4411 | 0.6642 | 0.537 | 0.5582 |\n", - "| DeepKriging_sphere | 10 | 0.4371 | 0.6611 | 0.5337 | 0.5623 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4402 | 0.6635 | 0.5408 | 0.5591 |\n", - "| UniversalKriging | 150 | 0.3883 | 0.6231 | 0.5036 | 0.6112 |\n", - "Repeat 50/50 done — checkpoint saved.\n", - "\n", - "All done: 50/50 repeats completed.\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_GP_noise.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_noise.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"Starting fresh.\")\n", - "\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n\" + \"=\"*70)\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(\"=\"*70)\n", - "\n", - " out_json = f\"repeat_{repeat}_GP_noise_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "summary_GP_noise", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:06.000295Z", - "iopub.status.busy": "2026-03-29T21:57:05.999896Z", - "iopub.status.idle": "2026-03-29T21:57:06.007417Z", - "shell.execute_reply": "2026-03-29T21:57:06.006672Z" - }, - "papermill": { - "duration": 0.032606, - "end_time": "2026-03-29T21:57:06.008295", - "exception": false, - "start_time": "2026-03-29T21:57:05.975689", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "Summary — 50 Repeats\n", - " Scenario: GP + N(0, 0.5^2) Gaussian Noise\n", - "================================================================================\n", - "\n", - "| Model | N | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|--------------------------|-----|-------------------|-------------------|-------------------|-------------------|\n", - "| OLS_wendland | 50 | 118.27±446.14 | 4.79±9.76 | 1.08±0.67 | -101.575±378.402 |\n", - "| OLS_sphere | 50 | 0.43±0.05 | 0.66±0.03 | 0.53±0.03 | 0.610±0.075 |\n", - "| DeepKriging_wendland | 50 | 0.80±0.15 | 0.89±0.08 | 0.70±0.06 | 0.295±0.080 |\n", - "| DeepKriging_mrts | 50 | 0.46±0.05 | 0.67±0.04 | 0.54±0.03 | 0.590±0.080 |\n", - "| DeepKriging_sphere | 50 | 0.45±0.05 | 0.67±0.03 | 0.54±0.03 | 0.591±0.083 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.46±0.05 | 0.68±0.03 | 0.54±0.03 | 0.588±0.076 |\n", - "| UniversalKriging | 50 | 0.39±0.04 | 0.63±0.03 | 0.50±0.03 | 0.646±0.069 |\n", - "\n", - "Best Model: UniversalKriging RMSE=0.63±0.03\n", - "\n", - "── Ranking by mean RMSE ──\n", - " 1. UniversalKriging RMSE=0.63±0.03\n", - " 2. OLS_sphere RMSE=0.66±0.03\n", - " 3. DeepKriging_mrts RMSE=0.67±0.04\n", - " 4. DeepKriging_sphere RMSE=0.67±0.03\n", - " 5. DeepKriging_sphere_Huber RMSE=0.68±0.03\n", - " 6. DeepKriging_wendland RMSE=0.89±0.08\n", - " 7. OLS_wendland RMSE=4.79±9.76\n" - ] - } - ], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(\"checkpoint_GP_noise.json\") as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"Summary — {n} Repeats\")\n", - "print(\" Scenario: GP + N(0, 0.5^2) Gaussian Noise\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 21957.78137, - "end_time": "2026-03-29T21:57:07.049745", - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_noise_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-29T15:51:09.268375", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb deleted file mode 100644 index a347313..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb +++ /dev/null @@ -1,6973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d1cde2eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:08.815357Z", - "iopub.status.busy": "2026-03-29T21:57:08.815177Z", - "iopub.status.idle": "2026-03-29T21:57:08.820306Z", - "shell.execute_reply": "2026-03-29T21:57:08.819498Z" - }, - "papermill": { - "duration": 0.008303, - "end_time": "2026-03-29T21:57:08.820786", - "exception": false, - "start_time": "2026-03-29T21:57:08.812483", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8890884f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:08.824319Z", - "iopub.status.busy": "2026-03-29T21:57:08.824182Z", - "iopub.status.idle": "2026-03-29T21:57:10.887914Z", - "shell.execute_reply": "2026-03-29T21:57:10.886632Z" - }, - "papermill": { - "duration": 2.066489, - "end_time": "2026-03-29T21:57:10.888775", - "exception": false, - "start_time": "2026-03-29T21:57:08.822286", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:57:09.515458: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774821429.525923 4086020 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774821429.529087 4086020 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774821429.536730 4086020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821429.536748 4086020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821429.536749 4086020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821429.536750 4086020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46d6d1ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:10.894130Z", - "iopub.status.busy": "2026-03-29T21:57:10.893829Z", - "iopub.status.idle": "2026-03-29T21:57:11.802877Z", - "shell.execute_reply": "2026-03-29T21:57:11.802068Z" - }, - "papermill": { - "duration": 0.913267, - "end_time": "2026-03-29T21:57:11.803554", - "exception": false, - "start_time": "2026-03-29T21:57:10.890287", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8dd094a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:11.808165Z", - "iopub.status.busy": "2026-03-29T21:57:11.807943Z", - "iopub.status.idle": "2026-03-29T21:57:11.810809Z", - "shell.execute_reply": "2026-03-29T21:57:11.810071Z" - }, - "papermill": { - "duration": 0.006257, - "end_time": "2026-03-29T21:57:11.811336", - "exception": false, - "start_time": "2026-03-29T21:57:11.805079", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f0aafba7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:11.814956Z", - "iopub.status.busy": "2026-03-29T21:57:11.814826Z", - "iopub.status.idle": "2026-03-29T21:57:25.009269Z", - "shell.execute_reply": "2026-03-29T21:57:25.008441Z" - }, - "papermill": { - "duration": 13.197306, - "end_time": "2026-03-29T21:57:25.009917", - "exception": false, - "start_time": "2026-03-29T21:57:11.812611", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32986e25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.014856Z", - "iopub.status.busy": "2026-03-29T21:57:25.014559Z", - "iopub.status.idle": "2026-03-29T21:57:25.020311Z", - "shell.execute_reply": "2026-03-29T21:57:25.019643Z" - }, - "papermill": { - "duration": 0.009408, - "end_time": "2026-03-29T21:57:25.020805", - "exception": false, - "start_time": "2026-03-29T21:57:25.011397", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import gc\n", - "\n", - "def _gp_draw(rng, num_sample):\n", - " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", - "\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", - "\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " ev, evec = np.linalg.eigh(cov_matrix)\n", - " ev = np.maximum(ev, 1e-6)\n", - " L = evec @ np.diag(np.sqrt(ev))\n", - "\n", - " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", - "\n", - " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", - " gc.collect()\n", - " return lat_deg, lon_deg, y\n", - "\n", - "OUTLIER_RATIO = 0.025\n", - "OUTLIER_MULT = 5\n", - "\n", - "def simulate_data(num_sample, seed):\n", - " \"\"\"Stationary GP + 2.5% outliers multiplied by 5.\"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", - " z = y.copy()\n", - " n_out = int(num_sample * OUTLIER_RATIO)\n", - " idx = rng.choice(num_sample, size=n_out, replace=False)\n", - " z[idx] *= OUTLIER_MULT\n", - " print(f\"\\n=== GP + outliers ({OUTLIER_RATIO*100:.1f}% x{OUTLIER_MULT}) | seed={seed} ===\")\n", - " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", - " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " gc.collect()\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5c1e9f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.024318Z", - "iopub.status.busy": "2026-03-29T21:57:25.024184Z", - "iopub.status.idle": "2026-03-29T21:57:25.031120Z", - "shell.execute_reply": "2026-03-29T21:57:25.030451Z" - }, - "papermill": { - "duration": 0.009291, - "end_time": "2026-03-29T21:57:25.031572", - "exception": false, - "start_time": "2026-03-29T21:57:25.022281", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " categorical_data = None\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(wendland(location, grid, theta=theta, k=2))\n", - " del grid\n", - " gc.collect()\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " X_train_cont = basis[train_x1]\n", - " X_val_cont = basis[val_x1]\n", - " X_test_cont = basis[test_x1]\n", - " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", - " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", - " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", - " if categorical_data is None:\n", - " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " return (X_train_flat, X_train_cat, y_train_flat,\n", - " X_val_flat, X_val_cat, y_val_flat,\n", - " X_test_flat, X_test_cat, y_test_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6728b7b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.034921Z", - "iopub.status.busy": "2026-03-29T21:57:25.034792Z", - "iopub.status.idle": "2026-03-29T21:57:25.042422Z", - "shell.execute_reply": "2026-03-29T21:57:25.041715Z" - }, - "papermill": { - "duration": 0.010019, - "end_time": "2026-03-29T21:57:25.042958", - "exception": false, - "start_time": "2026-03-29T21:57:25.032939", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " return metrics, model\n", - "\n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", - " epochs=epochs, batch_size=batch_size, verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", - " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", - " dropout_rate=dropout_rate, optimizer=_opt,\n", - " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", - " patience=40, verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience,\n", - " restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5,\n", - " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - " val_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset, validation_data=val_dataset,\n", - " epochs=epochs, callbacks=callbacks, verbose=0)\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - "\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " return metrics, model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce398870", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.046293Z", - "iopub.status.busy": "2026-03-29T21:57:25.046162Z", - "iopub.status.idle": "2026-03-29T21:57:25.054489Z", - "shell.execute_reply": "2026-03-29T21:57:25.053810Z" - }, - "papermill": { - "duration": 0.010673, - "end_time": "2026-03-29T21:57:25.054924", - "exception": false, - "start_time": "2026-03-29T21:57:25.044251", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(longitude, latitude, c=value,\n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", - " plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", - " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " if plot_type == 'residual' else\n", - " mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", - " model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " idx_all = np.arange(len(y_combined))\n", - " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - "\n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", - "\n", - " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", - " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", - "\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})')\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " p = predictions[mn]\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", - " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", - " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig1)\n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", - " -vmax_abs, vmax_abs,\n", - " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", - " plot_type='residual')\n", - " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig2)\n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7d38eb7c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.058292Z", - "iopub.status.busy": "2026-03-29T21:57:25.058166Z", - "iopub.status.idle": "2026-03-29T21:57:25.061103Z", - "shell.execute_reply": "2026-03-29T21:57:25.060458Z" - }, - "papermill": { - "duration": 0.005336, - "end_time": "2026-03-29T21:57:25.061554", - "exception": false, - "start_time": "2026-03-29T21:57:25.056218", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\n", - "dk_sphere candidates : [10, 50, 100, 150, 200, 1000] (restored to original)\n", - "repeats : 50\n" - ] - } - ], - "source": [ - "# ── Experiment parameters ─────────────────────────────────────────────────────\n", - "seed = 50\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "# All models (including dk_sphere) use the same original candidates\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", - "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", - "print(f\"repeats : {repeat_experiment}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "main_loop_GP_outliers", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T21:57:25.065069Z", - "iopub.status.busy": "2026-03-29T21:57:25.064933Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": false, - "start_time": "2026-03-29T21:57:25.062921", - "status": "running" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting fresh.\n", - "\n", - "======================================================================\n", - "Repeat 1/50 seed=50\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 05:57:25.666747: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774821445.676755 4086252 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774821445.679630 4086252 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774821445.687154 4086252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821445.687200 4086252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821445.687203 4086252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821445.687203 4086252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] seed=50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=50 ===\n", - "z mean: 1.3540 (±0.0265), Var: 1.7544, Range: [-4.4778, 17.2052]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821466.738358 4086252 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774821468.281173 4086617 service.cc:152] XLA service 0x7f40e8006750 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774821468.281205 4086617 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821468.496678 4086617 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821470.179068 4086617 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f43144a7f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f430c25c310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3351, sigma²=0.9844, nugget=0.8591\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3339, sigma²=0.9778, nugget=0.8597\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3341, sigma²=0.9769, nugget=0.8600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3343, sigma²=0.9765, nugget=0.8601\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3344, sigma²=0.9762, nugget=0.8602\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3349, sigma²=0.9756, nugget=0.8601\n", - "[repeat 0] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3349, sigma²=0.9756, nugget=0.8601\n", - "[repeat 0] done → repeat_0_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 11.4978 | 3.3908 | 0.8651 | -12.7332 |\n", - "| OLS_sphere | 100 | 0.2652 | 0.515 | 0.403 | 0.6832 |\n", - "| DeepKriging_wendland | -- | 0.5743 | 0.7578 | 0.5877 | 0.3141 |\n", - "| DeepKriging_mrts | 10 | 0.2491 | 0.4991 | 0.4073 | 0.7025 |\n", - "| DeepKriging_sphere | 10 | 0.2991 | 0.5469 | 0.3559 | 0.6427 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1488 | 0.3858 | 0.3011 | 0.8222 |\n", - "| UniversalKriging | 1000 | 0.2101 | 0.4584 | 0.341 | 0.749 |\n", - "Repeat 1/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 2/50 seed=51\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:04:37.189215: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774821877.199281 215859 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774821877.201884 215859 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774821877.209377 215859 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821877.209406 215859 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821877.209408 215859 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774821877.209409 215859 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] seed=51\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=51 ===\n", - "z mean: 1.3505 (±0.0310), Var: 2.4073, Range: [-4.7926, 17.3902]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821897.987060 215859 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774821899.529885 216225 service.cc:152] XLA service 0x7f2908017820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774821899.529909 216225 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821899.749425 216225 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774821901.434249 216225 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2b501d1870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2b482c76d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6300, sigma²=1.9818, nugget=1.1540\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6277, sigma²=1.9690, nugget=1.1546\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6290, sigma²=1.9698, nugget=1.1549\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6304, sigma²=1.9714, nugget=1.1551\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6315, sigma²=1.9728, nugget=1.1552\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6329, sigma²=1.9744, nugget=1.1549\n", - "[repeat 1] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6300, sigma²=1.9818, nugget=1.1540\n", - "[repeat 1] done → repeat_1_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 41.6442 | 6.4532 | 1.3396 | -18.1294 |\n", - "| OLS_sphere | 200 | 1.4473 | 1.203 | 0.5829 | 0.3352 |\n", - "| DeepKriging_wendland | -- | 1.9451 | 1.3947 | 0.7463 | 0.1065 |\n", - "| DeepKriging_mrts | 10 | 1.5189 | 1.2325 | 0.6094 | 0.3023 |\n", - "| DeepKriging_sphere | 50 | 2.2078 | 1.4859 | 0.7888 | -0.0142 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.3919 | 1.1798 | 0.4931 | 0.3606 |\n", - "| UniversalKriging | 10 | 1.3601 | 1.1662 | 0.5352 | 0.3752 |\n", - "Repeat 2/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 3/50 seed=52\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:11:35.778455: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774822295.789018 503826 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774822295.791742 503826 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774822295.798918 503826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822295.798946 503826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822295.798948 503826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822295.798949 503826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] seed=52\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=52 ===\n", - "z mean: 0.9273 (±0.0261), Var: 1.6970, Range: [-8.1030, 14.9840]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822316.570603 503826 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774822318.109821 504191 service.cc:152] XLA service 0x55b205d1e720 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774822318.109844 504191 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822318.328229 504191 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822319.986803 504191 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff7884c3910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff780597010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5557, sigma²=1.0835, nugget=0.6718\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5566, sigma²=1.0830, nugget=0.6718\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5584, sigma²=1.0849, nugget=0.6719\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5601, sigma²=1.0868, nugget=0.6720\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5609, sigma²=1.0875, nugget=0.6720\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5612, sigma²=1.0860, nugget=0.6720\n", - "[repeat 2] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5601, sigma²=1.0868, nugget=0.6720\n", - "[repeat 2] done → repeat_2_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.4343 | 1.1976 | 0.7433 | 0.0555 |\n", - "| OLS_sphere | 150 | 0.7379 | 0.859 | 0.4293 | 0.5141 |\n", - "| DeepKriging_wendland | -- | 1.1134 | 1.0552 | 0.6272 | 0.2669 |\n", - "| DeepKriging_mrts | 10 | 0.7648 | 0.8745 | 0.4594 | 0.4964 |\n", - "| DeepKriging_sphere | 50 | 0.7888 | 0.8882 | 0.4281 | 0.4806 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.6615 | 0.8133 | 0.3717 | 0.5644 |\n", - "| UniversalKriging | 150 | 0.6969 | 0.8348 | 0.389 | 0.5411 |\n", - "Repeat 3/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 4/50 seed=53\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:18:38.567295: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774822718.576795 804672 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774822718.580697 804672 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774822718.597954 804672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822718.597999 804672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822718.598001 804672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774822718.598002 804672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] seed=53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=53 ===\n", - "z mean: 1.3762 (±0.0320), Var: 2.5578, Range: [-8.7527, 18.4755]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822739.443175 804672 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774822740.985037 805035 service.cc:152] XLA service 0x55b662766180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774822740.985062 805035 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822741.199451 805035 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774822742.875452 805035 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff87878b0a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff87815b400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5607, sigma²=1.0512, nugget=1.0878\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5601, sigma²=1.0482, nugget=1.0880\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5605, sigma²=1.0477, nugget=1.0881\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5608, sigma²=1.0473, nugget=1.0881\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5609, sigma²=1.0472, nugget=1.0881\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5615, sigma²=1.0471, nugget=1.0878\n", - "[repeat 3] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5601, sigma²=1.0482, nugget=1.0880\n", - "[repeat 3] done → repeat_3_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.4389 | 1.5617 | 0.9565 | -0.6731 |\n", - "| OLS_sphere | 150 | 0.4552 | 0.6747 | 0.439 | 0.6877 |\n", - "| DeepKriging_wendland | -- | 1.005 | 1.0025 | 0.6943 | 0.3105 |\n", - "| DeepKriging_mrts | 50 | 0.5145 | 0.7173 | 0.5077 | 0.6471 |\n", - "| DeepKriging_sphere | 50 | 0.5632 | 0.7504 | 0.4581 | 0.6137 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.3806 | 0.6169 | 0.3609 | 0.7389 |\n", - "| UniversalKriging | 50 | 0.3788 | 0.6155 | 0.3865 | 0.7401 |\n", - "Repeat 4/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 5/50 seed=54\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:25:35.721128: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774823135.730898 1092325 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774823135.733641 1092325 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774823135.740879 1092325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823135.740913 1092325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823135.740915 1092325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823135.740916 1092325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] seed=54\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=54 ===\n", - "z mean: 1.6072 (±0.0266), Var: 1.7648, Range: [-1.2840, 14.7033]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823156.463984 1092325 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774823157.980061 1092686 service.cc:152] XLA service 0x7f3404007600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774823157.980112 1092686 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823158.202124 1092686 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823159.875274 1092686 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f36500fedd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f36407f4820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3485, sigma²=0.6241, nugget=1.2542\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3478, sigma²=0.6215, nugget=1.2545\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3479, sigma²=0.6210, nugget=1.2545\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3480, sigma²=0.6208, nugget=1.2546\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3481, sigma²=0.6207, nugget=1.2546\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3484, sigma²=0.6206, nugget=1.2542\n", - "[repeat 4] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3481, sigma²=0.6207, nugget=1.2546\n", - "[repeat 4] done → repeat_4_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.7033 | 1.3051 | 0.6417 | -0.9558 |\n", - "| OLS_sphere | 200 | 0.5992 | 0.7741 | 0.4331 | 0.312 |\n", - "| DeepKriging_wendland | -- | 0.7568 | 0.87 | 0.5473 | 0.1309 |\n", - "| DeepKriging_mrts | 50 | 0.6708 | 0.819 | 0.4911 | 0.2297 |\n", - "| DeepKriging_sphere | 50 | 0.5619 | 0.7496 | 0.3856 | 0.3547 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4789 | 0.6921 | 0.3352 | 0.45 |\n", - "| UniversalKriging | 200 | 0.5045 | 0.7103 | 0.3806 | 0.4207 |\n", - "Repeat 5/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 6/50 seed=55\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:32:33.430151: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774823553.439981 1385633 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774823553.442666 1385633 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774823553.449886 1385633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823553.449914 1385633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823553.449916 1385633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823553.449917 1385633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] seed=55\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=55 ===\n", - "z mean: 0.3004 (±0.0269), Var: 1.8147, Range: [-9.0754, 17.3043]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823574.247700 1385633 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774823575.753115 1385999 service.cc:152] XLA service 0x7f5d84007ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774823575.753144 1385999 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823575.966974 1385999 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823577.662665 1385999 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5fb8782a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5fb816feb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4272, sigma²=1.3625, nugget=0.3110\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4189, sigma²=1.3330, nugget=0.3114\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4193, sigma²=1.3312, nugget=0.3118\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4197, sigma²=1.3303, nugget=0.3120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4200, sigma²=1.3300, nugget=0.3121\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4211, sigma²=1.3291, nugget=0.3122\n", - "[repeat 5] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4211, sigma²=1.3291, nugget=0.3122\n", - "[repeat 5] done → repeat_5_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 7.4085 | 2.7219 | 0.9256 | -2.3657 |\n", - "| OLS_sphere | 200 | 0.8368 | 0.9148 | 0.4499 | 0.6198 |\n", - "| DeepKriging_wendland | -- | 1.4474 | 1.2031 | 0.7261 | 0.3424 |\n", - "| DeepKriging_mrts | 10 | 0.8527 | 0.9234 | 0.4391 | 0.6126 |\n", - "| DeepKriging_sphere | 100 | 1.0448 | 1.0222 | 0.4807 | 0.5253 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.7589 | 0.8711 | 0.4064 | 0.6552 |\n", - "| UniversalKriging | 1000 | 0.8269 | 0.9093 | 0.408 | 0.6243 |\n", - "Repeat 6/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 7/50 seed=56\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:39:22.159222: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774823962.169065 1674132 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774823962.173814 1674132 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774823962.189561 1674132 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823962.189607 1674132 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823962.189609 1674132 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774823962.189610 1674132 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] seed=56\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=56 ===\n", - "z mean: 1.4239 (±0.0292), Var: 2.1302, Range: [-4.9238, 20.7401]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823982.982969 1674132 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774823984.498945 1674491 service.cc:152] XLA service 0x7f8fe0009730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774823984.498981 1674491 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823984.720214 1674491 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774823986.384603 1674491 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f91f440e950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f91ec53f5b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7830, sigma²=1.4228, nugget=1.2034\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7828, sigma²=1.4129, nugget=1.2044\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7844, sigma²=1.4131, nugget=1.2046\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7854, sigma²=1.4136, nugget=1.2047\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7860, sigma²=1.4138, nugget=1.2047\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7870, sigma²=1.4142, nugget=1.2042\n", - "[repeat 6] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7830, sigma²=1.4228, nugget=1.2034\n", - "[repeat 6] done → repeat_6_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 3.436 | 1.8536 | 0.9174 | -1.5953 |\n", - "| OLS_sphere | 200 | 0.6544 | 0.8089 | 0.4644 | 0.5057 |\n", - "| DeepKriging_wendland | -- | 1.1268 | 1.0615 | 0.6861 | 0.1489 |\n", - "| DeepKriging_mrts | 50 | 0.7227 | 0.8501 | 0.5157 | 0.4541 |\n", - "| DeepKriging_sphere | 50 | 1.0722 | 1.0355 | 0.4598 | 0.1901 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.5789 | 0.7609 | 0.4077 | 0.5627 |\n", - "| UniversalKriging | 10 | 0.5892 | 0.7676 | 0.4221 | 0.555 |\n", - "Repeat 7/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 8/50 seed=57\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:47:11.263122: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774824431.272176 2053518 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774824431.275155 2053518 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774824431.282759 2053518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824431.282787 2053518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824431.282789 2053518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824431.282790 2053518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] seed=57\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=57 ===\n", - "z mean: 0.7131 (±0.0208), Var: 1.0823, Range: [-3.8183, 10.1589]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824452.056391 2053518 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774824453.582976 2053871 service.cc:152] XLA service 0x7f29f4006510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774824453.582998 2053871 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824453.805046 2053871 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824455.512651 2053871 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2c10191fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2c0063ec20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3844, sigma²=0.8841, nugget=0.3862\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3813, sigma²=0.8750, nugget=0.3865\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3813, sigma²=0.8734, nugget=0.3867\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3816, sigma²=0.8730, nugget=0.3869\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3818, sigma²=0.8729, nugget=0.3869\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3826, sigma²=0.8728, nugget=0.3869\n", - "[repeat 7] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3826, sigma²=0.8728, nugget=0.3869\n", - "[repeat 7] done → repeat_7_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 3.4426 | 1.8554 | 0.759 | -3.0469 |\n", - "| OLS_sphere | 200 | 0.2928 | 0.5411 | 0.3954 | 0.6559 |\n", - "| DeepKriging_wendland | -- | 0.6047 | 0.7776 | 0.5865 | 0.2892 |\n", - "| DeepKriging_mrts | 10 | 0.3416 | 0.5845 | 0.4178 | 0.5985 |\n", - "| DeepKriging_sphere | 50 | 0.3588 | 0.599 | 0.3742 | 0.5782 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.2658 | 0.5156 | 0.3684 | 0.6875 |\n", - "| UniversalKriging | 1000 | 0.2745 | 0.5239 | 0.3622 | 0.6773 |\n", - "Repeat 8/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 9/50 seed=58\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 06:54:22.107342: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774824862.116791 2357997 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774824862.119767 2357997 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774824862.136844 2357997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824862.136894 2357997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824862.136896 2357997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774824862.136898 2357997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] seed=58\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=58 ===\n", - "z mean: 0.6553 (±0.0230), Var: 1.3169, Range: [-7.7903, 12.2911]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824882.994441 2357997 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774824884.524931 2358350 service.cc:152] XLA service 0x55c92790b100 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774824884.524966 2358350 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824884.738625 2358350 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774824886.399877 2358350 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcb94163370> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcb84636b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4008, sigma²=0.8406, nugget=0.4012\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3955, sigma²=0.8277, nugget=0.4014\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3957, sigma²=0.8266, nugget=0.4016\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3960, sigma²=0.8263, nugget=0.4017\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3961, sigma²=0.8262, nugget=0.4017\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3967, sigma²=0.8260, nugget=0.4016\n", - "[repeat 8] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3967, sigma²=0.8260, nugget=0.4016\n", - "[repeat 8] done → repeat_8_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3238 | 1.1506 | 0.723 | -0.2035 |\n", - "| OLS_sphere | 200 | 0.5139 | 0.7169 | 0.4399 | 0.5328 |\n", - "| DeepKriging_wendland | -- | 0.768 | 0.8764 | 0.5969 | 0.3018 |\n", - "| DeepKriging_mrts | 10 | 0.4978 | 0.7056 | 0.4221 | 0.5474 |\n", - "| DeepKriging_sphere | 10 | 0.461 | 0.679 | 0.3849 | 0.5809 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.449 | 0.6701 | 0.3715 | 0.5918 |\n", - "| UniversalKriging | 1000 | 0.4601 | 0.6783 | 0.3827 | 0.5817 |\n", - "Repeat 9/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 10/50 seed=59\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:02:06.612086: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774825326.621724 2751085 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774825326.624907 2751085 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774825326.631859 2751085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825326.631892 2751085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825326.631895 2751085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825326.631895 2751085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] seed=59\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=59 ===\n", - "z mean: 1.1312 (±0.0273), Var: 1.8632, Range: [-4.7447, 17.7587]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825347.356958 2751085 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774825348.865942 2751452 service.cc:152] XLA service 0x7fe090019aa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774825348.865977 2751452 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825349.090648 2751452 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825350.773405 2751452 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe2d429ae60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe2cc38fe20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3774, sigma²=1.0332, nugget=0.5743\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3750, sigma²=1.0239, nugget=0.5748\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3753, sigma²=1.0228, nugget=0.5751\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3755, sigma²=1.0225, nugget=0.5752\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3758, sigma²=1.0223, nugget=0.5753\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3767, sigma²=1.0218, nugget=0.5753\n", - "[repeat 9] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3755, sigma²=1.0225, nugget=0.5752\n", - "[repeat 9] done → repeat_9_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|------------|\n", - "| OLS_wendland | -- | 2595.08 | 50.942 | 4.0647 | -1062.45 |\n", - "| OLS_sphere | 150 | 1.395 | 1.1811 | 0.5278 | 0.4283 |\n", - "| DeepKriging_wendland | -- | 1.8461 | 1.3587 | 0.7452 | 0.2435 |\n", - "| DeepKriging_mrts | 50 | 1.48 | 1.2165 | 0.6566 | 0.3935 |\n", - "| DeepKriging_sphere | 50 | 1.4778 | 1.2157 | 0.5052 | 0.3944 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.3838 | 1.1763 | 0.4697 | 0.4329 |\n", - "| UniversalKriging | 150 | 1.3403 | 1.1577 | 0.4763 | 0.4508 |\n", - "Repeat 10/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 11/50 seed=60\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:08:45.190132: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774825725.199163 3014241 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774825725.202665 3014241 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774825725.209752 3014241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825725.209784 3014241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825725.209786 3014241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774825725.209787 3014241 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] seed=60\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=60 ===\n", - "z mean: 0.8306 (±0.0266), Var: 1.7711, Range: [-5.8380, 14.6948]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825746.190942 3014241 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774825747.710654 3014612 service.cc:152] XLA service 0x7f2b980066c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774825747.710681 3014612 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825747.927947 3014612 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774825749.592702 3014612 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2dc45b2710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2dbc68fbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6303, sigma²=1.0633, nugget=0.6226\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6265, sigma²=1.0518, nugget=0.6230\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6296, sigma²=1.0559, nugget=0.6230\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6306, sigma²=1.0573, nugget=0.6229\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6317, sigma²=1.0588, nugget=0.6229\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6342, sigma²=1.0619, nugget=0.6226\n", - "[repeat 10] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6342, sigma²=1.0619, nugget=0.6226\n", - "[repeat 10] done → repeat_10_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6071 | 1.2677 | 0.8458 | 0.1639 |\n", - "| OLS_sphere | 200 | 0.8356 | 0.9141 | 0.5146 | 0.5653 |\n", - "| DeepKriging_wendland | -- | 1.3209 | 1.1493 | 0.717 | 0.3128 |\n", - "| DeepKriging_mrts | 10 | 0.9061 | 0.9519 | 0.5268 | 0.5286 |\n", - "| DeepKriging_sphere | 50 | 0.8199 | 0.9055 | 0.4597 | 0.5735 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.7791 | 0.8827 | 0.4395 | 0.5947 |\n", - "| UniversalKriging | 1000 | 0.815 | 0.9028 | 0.4557 | 0.576 |\n", - "Repeat 11/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 12/50 seed=61\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:15:58.003765: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774826158.013421 3316202 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774826158.016118 3316202 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774826158.023138 3316202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826158.023163 3316202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826158.023165 3316202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826158.023166 3316202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] seed=61\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=61 ===\n", - "z mean: 1.5312 (±0.0257), Var: 1.6527, Range: [-4.0713, 22.8895]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] OLS_sphere best order: 50\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826178.904169 3316202 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774826180.429041 3316560 service.cc:152] XLA service 0x7fd578006fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774826180.429075 3316560 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826180.647626 3316560 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826182.339499 3316560 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd7a85ae8c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd7a0677c70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4119, sigma²=0.8452, nugget=0.9024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4094, sigma²=0.8376, nugget=0.9028\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4095, sigma²=0.8371, nugget=0.9029\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4097, sigma²=0.8368, nugget=0.9030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4099, sigma²=0.8367, nugget=0.9030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4104, sigma²=0.8365, nugget=0.9027\n", - "[repeat 11] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4104, sigma²=0.8365, nugget=0.9027\n", - "[repeat 11] done → repeat_11_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9502 | 0.9748 | 0.6978 | 0.0715 |\n", - "| OLS_sphere | 50 | 0.5401 | 0.7349 | 0.499 | 0.4722 |\n", - "| DeepKriging_wendland | -- | 0.7773 | 0.8817 | 0.6298 | 0.2404 |\n", - "| DeepKriging_mrts | 50 | 0.5566 | 0.7461 | 0.4971 | 0.4561 |\n", - "| DeepKriging_sphere | 10 | 0.6106 | 0.7814 | 0.4895 | 0.4034 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4587 | 0.6773 | 0.3949 | 0.5518 |\n", - "| UniversalKriging | 1000 | 0.5225 | 0.7228 | 0.4459 | 0.4895 |\n", - "Repeat 12/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 13/50 seed=62\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:22:25.160475: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774826545.171241 3560741 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774826545.173967 3560741 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774826545.181031 3560741 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826545.181056 3560741 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826545.181058 3560741 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826545.181059 3560741 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] seed=62\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=62 ===\n", - "z mean: 0.5878 (±0.0210), Var: 1.0990, Range: [-4.6781, 12.6467]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826565.988476 3560741 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774826567.505920 3561108 service.cc:152] XLA service 0x7fa20401a270 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774826567.505953 3561108 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826567.717189 3561108 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826569.385703 3561108 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa42869fa30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa42076fe20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4839, sigma²=0.7429, nugget=0.4008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4799, sigma²=0.7349, nugget=0.4011\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4802, sigma²=0.7340, nugget=0.4012\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4805, sigma²=0.7338, nugget=0.4013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4808, sigma²=0.7337, nugget=0.4013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4814, sigma²=0.7337, nugget=0.4012\n", - "[repeat 12] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4839, sigma²=0.7429, nugget=0.4008\n", - "[repeat 12] done → repeat_12_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 11.9232 | 3.453 | 0.8603 | -11.9533 |\n", - "| OLS_sphere | 200 | 0.4304 | 0.656 | 0.3839 | 0.5325 |\n", - "| DeepKriging_wendland | -- | 0.7267 | 0.8525 | 0.5577 | 0.2105 |\n", - "| DeepKriging_mrts | 1000 | 1.1236 | 1.06 | 0.5618 | -0.2206 |\n", - "| DeepKriging_sphere | 150 | 0.6017 | 0.7757 | 0.4192 | 0.3463 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.5474 | 0.7398 | 0.3992 | 0.4053 |\n", - "| UniversalKriging | 10 | 0.4059 | 0.6371 | 0.3597 | 0.5591 |\n", - "Repeat 13/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 14/50 seed=63\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:29:12.000896: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774826952.010700 3834551 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774826952.013789 3834551 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774826952.022064 3834551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826952.022101 3834551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826952.022103 3834551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774826952.022104 3834551 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] seed=63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=63 ===\n", - "z mean: 1.1311 (±0.0277), Var: 1.9115, Range: [-5.7834, 14.9951]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826973.003309 3834551 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774826974.509751 3834919 service.cc:152] XLA service 0x7f28ac008750 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774826974.509778 3834919 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826974.729012 3834919 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774826976.386659 3834919 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2ad02d5b40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2ac075cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4039, sigma²=1.3953, nugget=0.4788\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3969, sigma²=1.3672, nugget=0.4795\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3971, sigma²=1.3650, nugget=0.4799\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3975, sigma²=1.3646, nugget=0.4801\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3979, sigma²=1.3643, nugget=0.4803\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3991, sigma²=1.3641, nugget=0.4804\n", - "[repeat 13] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3991, sigma²=1.3641, nugget=0.4804\n", - "[repeat 13] done → repeat_13_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 3.0944 | 1.7591 | 0.9141 | -0.129 |\n", - "| OLS_sphere | 150 | 1.583 | 1.2582 | 0.5432 | 0.4225 |\n", - "| DeepKriging_wendland | -- | 2.3771 | 1.5418 | 0.7861 | 0.1327 |\n", - "| DeepKriging_mrts | 1000 | 2.6526 | 1.6287 | 0.6983 | 0.0322 |\n", - "| DeepKriging_sphere | 150 | 1.893 | 1.3759 | 0.5173 | 0.3094 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.6226 | 1.2738 | 0.4624 | 0.408 |\n", - "| UniversalKriging | 1000 | 1.6444 | 1.2823 | 0.4882 | 0.4001 |\n", - "Repeat 14/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 15/50 seed=64\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:36:16.152147: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774827376.161757 4122649 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774827376.164709 4122649 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774827376.175302 4122649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827376.175362 4122649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827376.175364 4122649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827376.175365 4122649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] seed=64\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=64 ===\n", - "z mean: 1.1154 (±0.0278), Var: 1.9321, Range: [-4.4635, 14.7213]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827397.143708 4122649 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774827398.703094 4123010 service.cc:152] XLA service 0x56133504c620 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774827398.703120 4123010 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827398.922577 4123010 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827400.603052 4123010 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f621c50e710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f62144b2a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4641, sigma²=1.0000, nugget=0.7670\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4635, sigma²=0.9955, nugget=0.7674\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4645, sigma²=0.9954, nugget=0.7677\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4650, sigma²=0.9955, nugget=0.7678\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4653, sigma²=0.9956, nugget=0.7678\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4662, sigma²=0.9959, nugget=0.7676\n", - "[repeat 14] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4641, sigma²=1.0000, nugget=0.7670\n", - "[repeat 14] done → repeat_14_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6839 | 1.2977 | 0.7617 | 0.1363 |\n", - "| OLS_sphere | 200 | 1.1487 | 1.0718 | 0.5141 | 0.4108 |\n", - "| DeepKriging_wendland | -- | 1.6321 | 1.2775 | 0.7034 | 0.1629 |\n", - "| DeepKriging_mrts | 50 | 1.2074 | 1.0988 | 0.533 | 0.3807 |\n", - "| DeepKriging_sphere | 10 | 1.2036 | 1.0971 | 0.5029 | 0.3827 |\n", - "| DeepKriging_sphere_Huber | 150 | 1.2912 | 1.1363 | 0.4784 | 0.3377 |\n", - "| UniversalKriging | 10 | 1.0877 | 1.0429 | 0.4703 | 0.4421 |\n", - "Repeat 15/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 16/50 seed=65\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:43:53.655895: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774827833.666195 279889 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774827833.669667 279889 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774827833.677442 279889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827833.677473 279889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827833.677475 279889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774827833.677476 279889 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] seed=65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=65 ===\n", - "z mean: 0.9107 (±0.0313), Var: 2.4512, Range: [-8.3508, 17.7763]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827854.565124 279889 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774827856.097394 280249 service.cc:152] XLA service 0x7f4918006710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774827856.097431 280249 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827856.319939 280249 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774827858.011588 280249 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4b306d27a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4b28484310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4838, sigma²=1.2169, nugget=0.9047\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4836, sigma²=1.2123, nugget=0.9053\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4845, sigma²=1.2121, nugget=0.9056\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4849, sigma²=1.2120, nugget=0.9058\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4853, sigma²=1.2121, nugget=0.9058\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4862, sigma²=1.2121, nugget=0.9056\n", - "[repeat 15] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4838, sigma²=1.2169, nugget=0.9047\n", - "[repeat 15] done → repeat_15_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 7.2222 | 2.6874 | 1.0053 | -1.7307 |\n", - "| OLS_sphere | 100 | 1.4804 | 1.2167 | 0.5972 | 0.4402 |\n", - "| DeepKriging_wendland | -- | 2.0626 | 1.4362 | 0.7801 | 0.2201 |\n", - "| DeepKriging_mrts | 50 | 1.7158 | 1.3099 | 0.6619 | 0.3512 |\n", - "| DeepKriging_sphere | 10 | 1.6758 | 1.2945 | 0.6552 | 0.3664 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.414 | 1.1891 | 0.4869 | 0.4654 |\n", - "| UniversalKriging | 10 | 1.366 | 1.1687 | 0.5227 | 0.4835 |\n", - "Repeat 16/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 17/50 seed=66\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:50:40.865059: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774828240.875050 551163 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774828240.877916 551163 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774828240.886389 551163 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828240.886414 551163 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828240.886416 551163 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828240.886417 551163 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] seed=66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=66 ===\n", - "z mean: 1.3377 (±0.0248), Var: 1.5326, Range: [-2.3149, 15.1935]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828261.655679 551163 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774828263.205608 551524 service.cc:152] XLA service 0x7f7c64006f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774828263.205647 551524 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828263.428545 551524 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828265.118845 551524 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7e90325630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7e8842f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3003, sigma²=0.7069, nugget=0.8572\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3007, sigma²=0.7062, nugget=0.8573\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3007, sigma²=0.7054, nugget=0.8574\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3009, sigma²=0.7052, nugget=0.8575\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3011, sigma²=0.7050, nugget=0.8576\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3016, sigma²=0.7049, nugget=0.8574\n", - "[repeat 16] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3009, sigma²=0.7052, nugget=0.8575\n", - "[repeat 16] done → repeat_16_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.4195 | 1.1914 | 0.7217 | -0.0449 |\n", - "| OLS_sphere | 150 | 0.9884 | 0.9942 | 0.5026 | 0.2725 |\n", - "| DeepKriging_wendland | -- | 1.2974 | 1.139 | 0.6903 | 0.0451 |\n", - "| DeepKriging_mrts | 10 | 1.0831 | 1.0407 | 0.561 | 0.2028 |\n", - "| DeepKriging_sphere | 10 | 0.9836 | 0.9917 | 0.4703 | 0.276 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.9216 | 0.96 | 0.4461 | 0.3216 |\n", - "| UniversalKriging | 150 | 0.9447 | 0.9719 | 0.4705 | 0.3047 |\n", - "Repeat 17/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 18/50 seed=67\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 07:58:05.583208: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774828685.593812 889579 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774828685.596652 889579 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774828685.603540 889579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828685.603566 889579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828685.603568 889579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774828685.603569 889579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] seed=67\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=67 ===\n", - "z mean: 1.3927 (±0.0263), Var: 1.7228, Range: [-3.6179, 18.0653]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828706.458233 889579 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774828707.997512 889938 service.cc:152] XLA service 0x55dd83554760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774828707.997539 889938 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828708.214537 889938 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774828709.901033 889938 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd5d45c97e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd5cc6c3640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4728, sigma²=0.7911, nugget=1.0361\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4720, sigma²=0.7875, nugget=1.0364\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4725, sigma²=0.7871, nugget=1.0366\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4728, sigma²=0.7869, nugget=1.0366\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4730, sigma²=0.7868, nugget=1.0367\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4734, sigma²=0.7867, nugget=1.0363\n", - "[repeat 17] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4734, sigma²=0.7867, nugget=1.0363\n", - "[repeat 17] done → repeat_17_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.907 | 1.3809 | 0.8004 | -0.2438 |\n", - "| OLS_sphere | 100 | 0.9504 | 0.9749 | 0.5397 | 0.3801 |\n", - "| DeepKriging_wendland | -- | 1.2524 | 1.1191 | 0.6748 | 0.1831 |\n", - "| DeepKriging_mrts | 10 | 1.0354 | 1.0176 | 0.5508 | 0.3247 |\n", - "| DeepKriging_sphere | 10 | 0.9953 | 0.9977 | 0.4754 | 0.3508 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.8745 | 0.9351 | 0.4516 | 0.4296 |\n", - "| UniversalKriging | 1000 | 0.8821 | 0.9392 | 0.4802 | 0.4247 |\n", - "Repeat 18/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 19/50 seed=68\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:05:09.892009: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774829109.902795 1194528 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774829109.905924 1194528 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774829109.913550 1194528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829109.913588 1194528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829109.913591 1194528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829109.913591 1194528 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] seed=68\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=68 ===\n", - "z mean: 1.7234 (±0.0337), Var: 2.8417, Range: [-3.2569, 19.0880]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829130.723696 1194528 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774829132.254966 1194892 service.cc:152] XLA service 0x7f746800a150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774829132.254992 1194892 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829132.472697 1194892 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829134.145724 1194892 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f76847f5870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f767c5991b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6852, sigma²=1.4169, nugget=1.2374\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6824, sigma²=1.4046, nugget=1.2380\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6830, sigma²=1.4040, nugget=1.2382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6833, sigma²=1.4038, nugget=1.2382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6836, sigma²=1.4037, nugget=1.2382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6842, sigma²=1.4037, nugget=1.2377\n", - "[repeat 18] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6842, sigma²=1.4037, nugget=1.2377\n", - "[repeat 18] done → repeat_18_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 10.481 | 3.2374 | 1.1773 | -3.0341 |\n", - "| OLS_sphere | 200 | 1.2915 | 1.1364 | 0.6045 | 0.5029 |\n", - "| DeepKriging_wendland | -- | 1.8705 | 1.3677 | 0.9134 | 0.28 |\n", - "| DeepKriging_mrts | 150 | 1.504 | 1.2264 | 0.6185 | 0.4211 |\n", - "| DeepKriging_sphere | 10 | 1.4884 | 1.22 | 0.5766 | 0.4271 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.6955 | 1.3021 | 0.5505 | 0.3474 |\n", - "| UniversalKriging | 1000 | 1.209 | 1.0996 | 0.523 | 0.5347 |\n", - "Repeat 19/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 20/50 seed=69\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:12:37.348826: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774829557.359097 1542846 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774829557.361876 1542846 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774829557.368866 1542846 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829557.368896 1542846 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829557.368898 1542846 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774829557.368899 1542846 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] seed=69\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=69 ===\n", - "z mean: 1.0595 (±0.0274), Var: 1.8718, Range: [-5.3685, 16.4418]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829578.209384 1542846 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774829579.742887 1543210 service.cc:152] XLA service 0x7fd7bc006150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774829579.742912 1543210 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829579.963643 1543210 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774829581.633646 1543210 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd9f01dd870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd9e06b5bd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6485, sigma²=1.1546, nugget=0.6748\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6414, sigma²=1.1353, nugget=0.6755\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6419, sigma²=1.1347, nugget=0.6756\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6423, sigma²=1.1346, nugget=0.6757\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6427, sigma²=1.1345, nugget=0.6757\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6436, sigma²=1.1347, nugget=0.6755\n", - "[repeat 19] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6436, sigma²=1.1347, nugget=0.6755\n", - "[repeat 19] done → repeat_19_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.0378 | 1.4275 | 0.8397 | 0.1708 |\n", - "| OLS_sphere | 200 | 1.4901 | 1.2207 | 0.5591 | 0.3937 |\n", - "| DeepKriging_wendland | -- | 1.8813 | 1.3716 | 0.7743 | 0.2345 |\n", - "| DeepKriging_mrts | 50 | 1.5864 | 1.2595 | 0.5558 | 0.3545 |\n", - "| DeepKriging_sphere | 50 | 1.4784 | 1.2159 | 0.4898 | 0.3984 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.4244 | 1.1935 | 0.4751 | 0.4204 |\n", - "| UniversalKriging | 1000 | 1.4387 | 1.1994 | 0.5053 | 0.4146 |\n", - "Repeat 20/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:20:08.808905: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774830008.819373 1875484 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774830008.822312 1875484 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774830008.829716 1875484 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830008.829744 1875484 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830008.829746 1875484 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830008.829747 1875484 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=70 ===\n", - "z mean: 1.3323 (±0.0293), Var: 2.1525, Range: [-2.6143, 18.4918]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830029.611750 1875484 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774830031.142073 1875842 service.cc:152] XLA service 0x7f5280009550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774830031.142096 1875842 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830031.362986 1875842 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830033.021302 1875842 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f54a470ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f54a42f6b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6790, sigma²=1.1993, nugget=1.2490\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6780, sigma²=1.1944, nugget=1.2494\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6788, sigma²=1.1936, nugget=1.2495\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6792, sigma²=1.1937, nugget=1.2496\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6796, sigma²=1.1937, nugget=1.2496\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6805, sigma²=1.1939, nugget=1.2491\n", - "[repeat 20] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6780, sigma²=1.1944, nugget=1.2494\n", - "[repeat 20] done → repeat_20_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.2244 | 1.1065 | 0.7444 | -0.3494 |\n", - "| OLS_sphere | 100 | 0.3213 | 0.5668 | 0.4398 | 0.6459 |\n", - "| DeepKriging_wendland | -- | 0.635 | 0.7969 | 0.6171 | 0.3002 |\n", - "| DeepKriging_mrts | 10 | 0.5872 | 0.7663 | 0.4507 | 0.3528 |\n", - "| DeepKriging_sphere | 10 | 0.4587 | 0.6773 | 0.4477 | 0.4945 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.2015 | 0.4488 | 0.3378 | 0.778 |\n", - "| UniversalKriging | 50 | 0.2547 | 0.5047 | 0.3858 | 0.7193 |\n", - "Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:27:33.098657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774830453.108997 2224823 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774830453.112491 2224823 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774830453.120082 2224823 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830453.120116 2224823 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830453.120118 2224823 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830453.120119 2224823 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=71 ===\n", - "z mean: 1.2822 (±0.0251), Var: 1.5750, Range: [-1.4941, 13.5336]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830473.925729 2224823 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774830475.489942 2225189 service.cc:152] XLA service 0x56312a83b1b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774830475.489978 2225189 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830475.714257 2225189 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830477.408542 2225189 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9c407e6d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9c401de950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3386, sigma²=0.7883, nugget=0.6911\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3367, sigma²=0.7836, nugget=0.6913\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3369, sigma²=0.7825, nugget=0.6915\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3371, sigma²=0.7822, nugget=0.6916\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3373, sigma²=0.7820, nugget=0.6917\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3377, sigma²=0.7817, nugget=0.6914\n", - "[repeat 21] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3377, sigma²=0.7817, nugget=0.6914\n", - "[repeat 21] done → repeat_21_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 3.6637 | 1.9141 | 0.766 | -3.0764 |\n", - "| OLS_sphere | 200 | 0.4792 | 0.6923 | 0.4454 | 0.4668 |\n", - "| DeepKriging_wendland | -- | 0.7218 | 0.8496 | 0.5659 | 0.1969 |\n", - "| DeepKriging_mrts | 50 | 0.476 | 0.69 | 0.4723 | 0.4703 |\n", - "| DeepKriging_sphere | 50 | 0.6117 | 0.7821 | 0.4791 | 0.3194 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.4126 | 0.6423 | 0.3575 | 0.541 |\n", - "| UniversalKriging | 1000 | 0.3864 | 0.6216 | 0.3932 | 0.5701 |\n", - "Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:34:30.110569: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774830870.120699 2521856 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774830870.123427 2521856 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774830870.130373 2521856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830870.130402 2521856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830870.130404 2521856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774830870.130405 2521856 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=72 ===\n", - "z mean: 1.0442 (±0.0260), Var: 1.6888, Range: [-3.1045, 15.2349]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830890.859808 2521856 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774830892.399475 2522223 service.cc:152] XLA service 0x5560bc3ba800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774830892.399502 2522223 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830892.626308 2522223 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774830894.288295 2522223 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f718071a170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f71486dbeb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5188, sigma²=1.0824, nugget=0.6898\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5173, sigma²=1.0760, nugget=0.6901\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5176, sigma²=1.0753, nugget=0.6903\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5180, sigma²=1.0752, nugget=0.6903\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5183, sigma²=1.0751, nugget=0.6904\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5191, sigma²=1.0750, nugget=0.6902\n", - "[repeat 22] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5183, sigma²=1.0751, nugget=0.6904\n", - "[repeat 22] done → repeat_22_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|-----------|\n", - "| OLS_wendland | -- | 229.026 | 15.1336 | 2.0445 | -164.14 |\n", - "| OLS_sphere | 200 | 0.7855 | 0.8863 | 0.5153 | 0.4336 |\n", - "| DeepKriging_wendland | -- | 1.1287 | 1.0624 | 0.6831 | 0.1862 |\n", - "| DeepKriging_mrts | 200 | 0.8718 | 0.9337 | 0.5544 | 0.3714 |\n", - "| DeepKriging_sphere | 10 | 0.913 | 0.9555 | 0.4724 | 0.3417 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.6827 | 0.8262 | 0.4355 | 0.5078 |\n", - "| UniversalKriging | 200 | 0.6777 | 0.8232 | 0.4388 | 0.5113 |\n", - "Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:41:33.092406: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774831293.102003 2823306 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774831293.105141 2823306 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774831293.112062 2823306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831293.112094 2823306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831293.112096 2823306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831293.112097 2823306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=73 ===\n", - "z mean: 1.4257 (±0.0303), Var: 2.2941, Range: [-4.6441, 21.4465]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831314.107037 2823306 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774831315.662044 2823671 service.cc:152] XLA service 0x7ef820005f20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774831315.662071 2823671 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831315.879048 2823671 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831317.560523 2823671 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efa48626170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efa405c6b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9995, sigma²=1.8693, nugget=1.1073\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9952, sigma²=1.8535, nugget=1.1079\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9979, sigma²=1.8554, nugget=1.1080\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9989, sigma²=1.8553, nugget=1.1081\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9993, sigma²=1.8555, nugget=1.1081\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0007, sigma²=1.8564, nugget=1.1077\n", - "[repeat 23] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9995, sigma²=1.8693, nugget=1.1073\n", - "[repeat 23] done → repeat_23_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.6676 | 1.6333 | 0.8979 | 0.0888 |\n", - "| OLS_sphere | 150 | 1.7717 | 1.3311 | 0.5906 | 0.3948 |\n", - "| DeepKriging_wendland | -- | 2.5599 | 1.6 | 0.8427 | 0.1256 |\n", - "| DeepKriging_mrts | 10 | 1.9854 | 1.409 | 0.6208 | 0.3218 |\n", - "| DeepKriging_sphere | 10 | 1.7585 | 1.3261 | 0.502 | 0.3994 |\n", - "| DeepKriging_sphere_Huber | 100 | 1.8602 | 1.3639 | 0.4931 | 0.3646 |\n", - "| UniversalKriging | 10 | 1.7601 | 1.3267 | 0.5463 | 0.3988 |\n", - "Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:48:47.042821: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774831727.053330 3152639 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774831727.056210 3152639 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774831727.064056 3152639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831727.064087 3152639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831727.064089 3152639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774831727.064090 3152639 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=74 ===\n", - "z mean: 1.2394 (±0.0324), Var: 2.6258, Range: [-6.7841, 17.8660]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831748.499638 3152639 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774831750.073373 3153007 service.cc:152] XLA service 0x7efdc0018430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774831750.073399 3153007 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831750.294200 3153007 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774831752.001459 3153007 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efff462e4d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7effec717ac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8681, sigma²=1.3992, nugget=0.9990\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8692, sigma²=1.3956, nugget=0.9994\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8700, sigma²=1.3949, nugget=0.9995\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8704, sigma²=1.3947, nugget=0.9995\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8708, sigma²=1.3946, nugget=0.9995\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8714, sigma²=1.3946, nugget=0.9992\n", - "[repeat 24] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8714, sigma²=1.3946, nugget=0.9992\n", - "[repeat 24] done → repeat_24_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 172.195 | 13.1223 | 1.8249 | -45.4064 |\n", - "| OLS_sphere | 150 | 2.1023 | 1.4499 | 0.6127 | 0.4334 |\n", - "| DeepKriging_wendland | -- | 3.104 | 1.7618 | 0.8561 | 0.1635 |\n", - "| DeepKriging_mrts | 10 | 2.2341 | 1.4947 | 0.5849 | 0.3979 |\n", - "| DeepKriging_sphere | 10 | 2.2317 | 1.4939 | 0.5784 | 0.3986 |\n", - "| DeepKriging_sphere_Huber | 10 | 2.221 | 1.4903 | 0.533 | 0.4014 |\n", - "| UniversalKriging | 1000 | 2.1426 | 1.4637 | 0.573 | 0.4226 |\n", - "Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 08:55:53.355769: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774832153.365987 3469690 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774832153.369494 3469690 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774832153.377335 3469690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832153.377367 3469690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832153.377369 3469690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832153.377370 3469690 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=75 ===\n", - "z mean: 1.2844 (±0.0278), Var: 1.9269, Range: [-5.5064, 20.7738]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832174.596492 3469690 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774832176.129690 3470056 service.cc:152] XLA service 0x7f746c017c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774832176.129715 3470056 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832176.352188 3470056 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832178.033711 3470056 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7698615900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7690712ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3319, sigma²=1.1902, nugget=0.7979\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3299, sigma²=1.1821, nugget=0.7981\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3301, sigma²=1.1804, nugget=0.7985\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3304, sigma²=1.1797, nugget=0.7988\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3305, sigma²=1.1795, nugget=0.7989\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3310, sigma²=1.1789, nugget=0.7988\n", - "[repeat 25] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3299, sigma²=1.1821, nugget=0.7981\n", - "[repeat 25] done → repeat_25_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8665 | 0.9309 | 0.6735 | 0.0326 |\n", - "| OLS_sphere | 150 | 0.3214 | 0.5669 | 0.4009 | 0.6412 |\n", - "| DeepKriging_wendland | -- | 0.5417 | 0.736 | 0.5408 | 0.3952 |\n", - "| DeepKriging_mrts | 200 | 0.4197 | 0.6478 | 0.4579 | 0.5314 |\n", - "| DeepKriging_sphere | 10 | 0.4006 | 0.6329 | 0.4085 | 0.5527 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.2706 | 0.5202 | 0.3394 | 0.6979 |\n", - "| UniversalKriging | 50 | 0.2705 | 0.5201 | 0.3568 | 0.698 |\n", - "Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:03:05.565819: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774832585.575527 3810279 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774832585.579422 3810279 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774832585.586780 3810279 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832585.586805 3810279 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832585.586807 3810279 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774832585.586808 3810279 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=76 ===\n", - "z mean: 1.0260 (±0.0250), Var: 1.5635, Range: [-5.0842, 16.1110]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832606.708349 3810279 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774832608.245958 3810647 service.cc:152] XLA service 0x55b58c148080 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774832608.245993 3810647 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832608.462334 3810647 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774832610.153676 3810647 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe2ac2ddab0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe29c785ea0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4249, sigma²=0.8532, nugget=0.6023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4225, sigma²=0.8453, nugget=0.6027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4228, sigma²=0.8444, nugget=0.6029\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4231, sigma²=0.8441, nugget=0.6030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4232, sigma²=0.8440, nugget=0.6031\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4237, sigma²=0.8437, nugget=0.6028\n", - "[repeat 26] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4225, sigma²=0.8453, nugget=0.6027\n", - "[repeat 26] done → repeat_26_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.4237 | 1.1932 | 0.8089 | 0.0672 |\n", - "| OLS_sphere | 150 | 0.8917 | 0.9443 | 0.519 | 0.4158 |\n", - "| DeepKriging_wendland | -- | 1.2213 | 1.1051 | 0.6778 | 0.1998 |\n", - "| DeepKriging_mrts | 10 | 0.9783 | 0.9891 | 0.5438 | 0.359 |\n", - "| DeepKriging_sphere | 10 | 0.9625 | 0.981 | 0.4609 | 0.3694 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1052 | 1.0513 | 0.5046 | 0.2759 |\n", - "| UniversalKriging | 50 | 0.8934 | 0.9452 | 0.4645 | 0.4147 |\n", - "Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:10:11.164811: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774833011.177416 4112512 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774833011.180536 4112512 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774833011.188666 4112512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833011.188708 4112512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833011.188710 4112512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833011.188711 4112512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=77 ===\n", - "z mean: 0.8856 (±0.0303), Var: 2.2902, Range: [-10.3937, 15.0364]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833032.208062 4112512 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774833033.728216 4112877 service.cc:152] XLA service 0x7f14ac00a980 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774833033.728274 4112877 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833033.950705 4112877 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833035.612371 4112877 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f16c41768c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1694621870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4831, sigma²=1.5411, nugget=0.7270\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4819, sigma²=1.5322, nugget=0.7277\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4827, sigma²=1.5309, nugget=0.7281\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4833, sigma²=1.5305, nugget=0.7284\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4836, sigma²=1.5304, nugget=0.7285\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4843, sigma²=1.5299, nugget=0.7283\n", - "[repeat 27] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4843, sigma²=1.5299, nugget=0.7283\n", - "[repeat 27] done → repeat_27_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 4.7376 | 2.1766 | 0.9186 | -1.6432 |\n", - "| OLS_sphere | 150 | 0.8121 | 0.9012 | 0.4417 | 0.5469 |\n", - "| DeepKriging_wendland | -- | 1.1573 | 1.0758 | 0.6693 | 0.3543 |\n", - "| DeepKriging_mrts | 50 | 0.9091 | 0.9535 | 0.4678 | 0.4928 |\n", - "| DeepKriging_sphere | 10 | 0.8288 | 0.9104 | 0.4306 | 0.5376 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.67 | 0.8186 | 0.3417 | 0.6262 |\n", - "| UniversalKriging | 1000 | 0.7567 | 0.8699 | 0.3724 | 0.5778 |\n", - "Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:17:35.673379: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774833455.683254 259979 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774833455.685956 259979 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774833455.693230 259979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833455.693265 259979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833455.693267 259979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833455.693268 259979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=78 ===\n", - "z mean: 1.4263 (±0.0282), Var: 1.9913, Range: [-3.2676, 17.7398]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833477.192163 259979 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774833478.719688 260341 service.cc:152] XLA service 0x5557d9221c50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774833478.719722 260341 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833478.934752 260341 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833480.619407 260341 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f988474acb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9884166cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6619, sigma²=1.6723, nugget=0.8906\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6626, sigma²=1.6633, nugget=0.8918\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6642, sigma²=1.6643, nugget=0.8921\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6659, sigma²=1.6657, nugget=0.8923\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6665, sigma²=1.6662, nugget=0.8924\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6628, sigma²=1.6587, nugget=0.8916\n", - "[repeat 28] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6665, sigma²=1.6662, nugget=0.8924\n", - "[repeat 28] done → repeat_28_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.8495 | 1.36 | 0.8699 | -0.0271 |\n", - "| OLS_sphere | 150 | 0.999 | 0.9995 | 0.4922 | 0.4452 |\n", - "| DeepKriging_wendland | -- | 1.6302 | 1.2768 | 0.8215 | 0.0947 |\n", - "| DeepKriging_mrts | 10 | 1.0384 | 1.019 | 0.5024 | 0.4233 |\n", - "| DeepKriging_sphere | 10 | 0.9317 | 0.9652 | 0.4381 | 0.4826 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.9616 | 0.9806 | 0.4231 | 0.466 |\n", - "| UniversalKriging | 200 | 0.9491 | 0.9742 | 0.4528 | 0.4729 |\n", - "Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:24:53.929495: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774833893.939152 583212 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774833893.942182 583212 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774833893.949434 583212 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833893.949459 583212 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833893.949462 583212 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774833893.949462 583212 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=79 ===\n", - "z mean: 1.1534 (±0.0258), Var: 1.6607, Range: [-3.9375, 16.8152]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833914.831305 583212 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774833916.368840 583571 service.cc:152] XLA service 0x7fc1340066a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774833916.368866 583571 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833916.581908 583571 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774833918.253843 583571 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc36073a200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc360133910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5505, sigma²=1.1395, nugget=0.8190\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5441, sigma²=1.1206, nugget=0.8200\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5345, sigma²=1.1080, nugget=0.8190\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5342, sigma²=1.1068, nugget=0.8191\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5342, sigma²=1.1064, nugget=0.8191\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5367, sigma²=1.1091, nugget=0.8190\n", - "[repeat 29] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5367, sigma²=1.1091, nugget=0.8190\n", - "[repeat 29] done → repeat_29_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 102.971 | 10.1474 | 1.9487 | -62.743 |\n", - "| OLS_sphere | 150 | 0.8446 | 0.919 | 0.5464 | 0.4772 |\n", - "| DeepKriging_wendland | -- | 1.3581 | 1.1654 | 0.7388 | 0.1593 |\n", - "| DeepKriging_mrts | 10 | 0.8882 | 0.9424 | 0.5146 | 0.4502 |\n", - "| DeepKriging_sphere | 10 | 0.7932 | 0.8906 | 0.4681 | 0.509 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.7254 | 0.8517 | 0.427 | 0.551 |\n", - "| UniversalKriging | 1000 | 0.7942 | 0.8912 | 0.4814 | 0.5084 |\n", - "Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:32:01.947822: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774834321.958491 903784 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774834321.961271 903784 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774834321.968789 903784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834321.968825 903784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834321.968827 903784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834321.968828 903784 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=80 ===\n", - "z mean: 0.7231 (±0.0249), Var: 1.5505, Range: [-7.4918, 18.1497]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834342.815247 903784 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774834344.373637 904144 service.cc:152] XLA service 0x7fb1d0006a50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774834344.373663 904144 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834344.589801 904144 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834346.250796 904144 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb3fc55a830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb3f433a710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4823, sigma²=1.0164, nugget=0.5557\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4791, sigma²=1.0062, nugget=0.5561\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4793, sigma²=1.0051, nugget=0.5562\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4795, sigma²=1.0048, nugget=0.5563\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4797, sigma²=1.0047, nugget=0.5563\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4803, sigma²=1.0046, nugget=0.5562\n", - "[repeat 30] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4797, sigma²=1.0047, nugget=0.5563\n", - "[repeat 30] done → repeat_30_GP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.4898 | 1.5779 | 0.7738 | -1.6832 |\n", - "| OLS_sphere | 150 | 0.3985 | 0.6313 | 0.4322 | 0.5705 |\n", - "| DeepKriging_wendland | -- | 0.6053 | 0.778 | 0.5674 | 0.3477 |\n", - "| DeepKriging_mrts | 100 | 0.4687 | 0.6846 | 0.4846 | 0.4949 |\n", - "| DeepKriging_sphere | 100 | 0.6894 | 0.8303 | 0.5455 | 0.257 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4057 | 0.6369 | 0.3777 | 0.5628 |\n", - "| UniversalKriging | 200 | 0.3364 | 0.58 | 0.3685 | 0.6375 |\n", - "Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-30 09:38:55.453038: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774834735.463625 1200394 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774834735.466405 1200394 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774834735.473970 1200394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834735.474008 1200394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834735.474010 1200394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774834735.474011 1200394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP + outliers (2.5% x5) | seed=81 ===\n", - "z mean: 0.9632 (±0.0265), Var: 1.7504, Range: [-4.2780, 14.4876]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834756.386023 1200394 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774834757.907275 1200760 service.cc:152] XLA service 0x7f5920009070 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774834757.907302 1200760 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834758.122515 1200760 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774834759.797221 1200760 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5b58611fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5b50717c70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 10\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_GP_outliers.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_outliers.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"Starting fresh.\")\n", - "\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n\" + \"=\"*70)\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(\"=\"*70)\n", - "\n", - " out_json = f\"repeat_{repeat}_GP_outliers_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "summary_GP_outliers", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-26T08:31:31.812514Z", - "iopub.status.busy": "2026-03-26T08:31:31.812293Z", - "iopub.status.idle": "2026-03-26T08:31:31.817266Z", - "shell.execute_reply": "2026-03-26T08:31:31.816839Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "pending" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(\"checkpoint_GP_outliers.json\") as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"Summary — {n} Repeats\")\n", - "print(\" Scenario: GP + Outliers (2.5% x5)\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": null, - "end_time": null, - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_outliers_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-29T21:57:08.179540", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb deleted file mode 100644 index 033bc4b..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb +++ /dev/null @@ -1,10521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d1cde2eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:04.614551Z", - "iopub.status.busy": "2026-03-29T08:46:04.614266Z", - "iopub.status.idle": "2026-03-29T08:46:04.621475Z", - "shell.execute_reply": "2026-03-29T08:46:04.619925Z" - }, - "papermill": { - "duration": 0.01126, - "end_time": "2026-03-29T08:46:04.622226", - "exception": false, - "start_time": "2026-03-29T08:46:04.610966", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8890884f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:04.627193Z", - "iopub.status.busy": "2026-03-29T08:46:04.626976Z", - "iopub.status.idle": "2026-03-29T08:46:06.991082Z", - "shell.execute_reply": "2026-03-29T08:46:06.990032Z" - }, - "papermill": { - "duration": 2.367708, - "end_time": "2026-03-29T08:46:06.991680", - "exception": false, - "start_time": "2026-03-29T08:46:04.623972", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 16:46:05.453896: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774773965.467374 661919 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774773965.470433 661919 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774773965.478614 661919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773965.478635 661919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773965.478636 661919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773965.478637 661919 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46d6d1ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:06.997506Z", - "iopub.status.busy": "2026-03-29T08:46:06.997123Z", - "iopub.status.idle": "2026-03-29T08:46:08.180611Z", - "shell.execute_reply": "2026-03-29T08:46:08.179450Z" - }, - "papermill": { - "duration": 1.188221, - "end_time": "2026-03-29T08:46:08.181390", - "exception": false, - "start_time": "2026-03-29T08:46:06.993169", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8dd094a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:08.190034Z", - "iopub.status.busy": "2026-03-29T08:46:08.189238Z", - "iopub.status.idle": "2026-03-29T08:46:08.194114Z", - "shell.execute_reply": "2026-03-29T08:46:08.192960Z" - }, - "papermill": { - "duration": 0.011992, - "end_time": "2026-03-29T08:46:08.194962", - "exception": false, - "start_time": "2026-03-29T08:46:08.182970", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f0aafba7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:08.199095Z", - "iopub.status.busy": "2026-03-29T08:46:08.198877Z", - "iopub.status.idle": "2026-03-29T08:46:22.428931Z", - "shell.execute_reply": "2026-03-29T08:46:22.427976Z" - }, - "papermill": { - "duration": 14.233113, - "end_time": "2026-03-29T08:46:22.429651", - "exception": false, - "start_time": "2026-03-29T08:46:08.196538", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32986e25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.434807Z", - "iopub.status.busy": "2026-03-29T08:46:22.434510Z", - "iopub.status.idle": "2026-03-29T08:46:22.440245Z", - "shell.execute_reply": "2026-03-29T08:46:22.439557Z" - }, - "papermill": { - "duration": 0.009793, - "end_time": "2026-03-29T08:46:22.440947", - "exception": false, - "start_time": "2026-03-29T08:46:22.431154", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import gc\n", - "\n", - "def _gp_draw(rng, num_sample):\n", - " \"\"\"Sample one GP draw on the sphere with stationary exp covariance (rho=0.5).\"\"\"\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - " coords = np.column_stack([x_c, y_c, z_c]).astype(np.float32)\n", - "\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " cov_matrix += np.float32(1e-3) * np.eye(num_sample, dtype=np.float32)\n", - "\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " ev, evec = np.linalg.eigh(cov_matrix)\n", - " ev = np.maximum(ev, 1e-6)\n", - " L = evec @ np.diag(np.sqrt(ev))\n", - "\n", - " y = (np.float32(1.0) + L @ rng.standard_normal(num_sample).astype(np.float32)).astype(np.float32)\n", - "\n", - " del dist_matrix, cov_matrix, L, x_c, y_c, z_c, coords\n", - " gc.collect()\n", - " return lat_deg, lon_deg, y\n", - "\n", - "def simulate_data(num_sample, seed):\n", - " \"\"\"Stationary GP only — no added noise.\"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " lat_deg, lon_deg, y = _gp_draw(rng, num_sample)\n", - " z = y.copy()\n", - " print(f\"\\n=== GP Pure (no noise) | seed={seed} ===\")\n", - " print(f\"z mean: {np.mean(z):.4f} (\\u00b1{np.std(z)/np.sqrt(num_sample):.4f}), \"\n", - " f\"Var: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " gc.collect()\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5c1e9f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.444443Z", - "iopub.status.busy": "2026-03-29T08:46:22.444309Z", - "iopub.status.idle": "2026-03-29T08:46:22.451331Z", - "shell.execute_reply": "2026-03-29T08:46:22.450644Z" - }, - "papermill": { - "duration": 0.009421, - "end_time": "2026-03-29T08:46:22.451769", - "exception": false, - "start_time": "2026-03-29T08:46:22.442348", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " categorical_data = None\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(wendland(location, grid, theta=theta, k=2))\n", - " del grid\n", - " gc.collect()\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " X_train_cont = basis[train_x1]\n", - " X_val_cont = basis[val_x1]\n", - " X_test_cont = basis[test_x1]\n", - " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", - " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", - " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", - " if categorical_data is None:\n", - " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " return (X_train_flat, X_train_cat, y_train_flat,\n", - " X_val_flat, X_val_cat, y_val_flat,\n", - " X_test_flat, X_test_cat, y_test_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6728b7b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.455268Z", - "iopub.status.busy": "2026-03-29T08:46:22.455137Z", - "iopub.status.idle": "2026-03-29T08:46:22.462594Z", - "shell.execute_reply": "2026-03-29T08:46:22.461960Z" - }, - "papermill": { - "duration": 0.009844, - "end_time": "2026-03-29T08:46:22.463071", - "exception": false, - "start_time": "2026-03-29T08:46:22.453227", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " return metrics, model\n", - "\n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", - " epochs=epochs, batch_size=batch_size, verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", - " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", - " dropout_rate=dropout_rate, optimizer=_opt,\n", - " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", - " patience=40, verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience,\n", - " restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5,\n", - " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - " val_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset, validation_data=val_dataset,\n", - " epochs=epochs, callbacks=callbacks, verbose=0)\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - "\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " return metrics, model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce398870", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.466449Z", - "iopub.status.busy": "2026-03-29T08:46:22.466319Z", - "iopub.status.idle": "2026-03-29T08:46:22.475274Z", - "shell.execute_reply": "2026-03-29T08:46:22.474592Z" - }, - "papermill": { - "duration": 0.011671, - "end_time": "2026-03-29T08:46:22.476037", - "exception": false, - "start_time": "2026-03-29T08:46:22.464366", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(longitude, latitude, c=value,\n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", - " plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", - " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " if plot_type == 'residual' else\n", - " mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", - " model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " idx_all = np.arange(len(y_combined))\n", - " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - "\n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", - "\n", - " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", - " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", - "\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})')\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " p = predictions[mn]\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", - " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", - " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig1)\n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", - " -vmax_abs, vmax_abs,\n", - " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", - " plot_type='residual')\n", - " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig2)\n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7d38eb7c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.479647Z", - "iopub.status.busy": "2026-03-29T08:46:22.479504Z", - "iopub.status.idle": "2026-03-29T08:46:22.482691Z", - "shell.execute_reply": "2026-03-29T08:46:22.481969Z" - }, - "papermill": { - "duration": 0.005727, - "end_time": "2026-03-29T08:46:22.483205", - "exception": false, - "start_time": "2026-03-29T08:46:22.477478", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\n", - "dk_sphere candidates : [10, 50, 100, 150, 200, 1000] (restored to original)\n", - "repeats : 50\n" - ] - } - ], - "source": [ - "# ── Experiment parameters ─────────────────────────────────────────────────────\n", - "seed = 50\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "# All models (including dk_sphere) use the same original candidates\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", - "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", - "print(f\"repeats : {repeat_experiment}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "main_loop_GP_pure", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T08:46:22.486586Z", - "iopub.status.busy": "2026-03-29T08:46:22.486461Z", - "iopub.status.idle": "2026-03-29T15:51:06.928030Z", - "shell.execute_reply": "2026-03-29T15:51:06.927063Z" - }, - "papermill": { - "duration": 25484.444209, - "end_time": "2026-03-29T15:51:06.928740", - "exception": false, - "start_time": "2026-03-29T08:46:22.484531", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting fresh.\n", - "\n", - "======================================================================\n", - "Repeat 1/50 seed=50\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 16:46:23.195171: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774773983.204751 662188 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774773983.207539 662188 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774773983.215310 662188 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773983.215365 662188 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773983.215368 662188 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774773983.215369 662188 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] seed=50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=50 ===\n", - "z mean: 1.2497 (±0.0184), Var: 0.8423, Range: [-1.1476, 3.7825]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774004.569106 662188 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774774006.161603 662555 service.cc:152] XLA service 0x7fe480007ad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774774006.161628 662555 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774006.393939 662555 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774008.090474 662555 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe6b07b5c60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe6b02ebd00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2547, sigma²=0.4639, nugget=0.0052\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1776, sigma²=0.3381, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1182, sigma²=0.2413, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1573, sigma²=0.2936, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1581, sigma²=0.2929, nugget=0.0014\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1538, sigma²=0.2840, nugget=0.0026\n", - "[repeat 0] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1538, sigma²=0.2840, nugget=0.0026\n", - "[repeat 0] done → repeat_0_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 20.2068 | 4.4952 | 0.915 | -23.128 |\n", - "| OLS_sphere | 200 | 0.1209 | 0.3476 | 0.2809 | 0.8557 |\n", - "| DeepKriging_wendland | -- | 0.4813 | 0.6937 | 0.5327 | 0.4253 |\n", - "| DeepKriging_mrts | 10 | 0.1871 | 0.4326 | 0.35 | 0.7766 |\n", - "| DeepKriging_sphere | 100 | 0.1078 | 0.3283 | 0.2557 | 0.8713 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.0975 | 0.3123 | 0.2464 | 0.8836 |\n", - "| UniversalKriging | 1000 | 0.088 | 0.2966 | 0.238 | 0.8949 |\n", - "Repeat 1/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 2/50 seed=51\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 16:54:02.406584: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774774442.416339 1010673 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774774442.419272 1010673 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774774442.427699 1010673 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774442.427731 1010673 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774442.427733 1010673 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774442.427734 1010673 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] seed=51\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=51 ===\n", - "z mean: 1.2151 (±0.0197), Var: 0.9714, Range: [-1.4772, 3.8871]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774463.596613 1010673 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774774465.169068 1011042 service.cc:152] XLA service 0x55833eb6e710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774774465.169094 1011042 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774465.384598 1011042 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774467.060009 1011042 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f543427b250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f542c4c6c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4672, sigma²=0.7511, nugget=0.0120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3373, sigma²=0.5701, nugget=0.0073\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2658, sigma²=0.4653, nugget=0.0048\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1688, sigma²=0.3159, nugget=0.0017\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0766, sigma²=0.1663, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1114, sigma²=0.2072, nugget=0.0010\n", - "[repeat 1] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3373, sigma²=0.5701, nugget=0.0073\n", - "[repeat 1] done → repeat_1_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 48.9509 | 6.9965 | 1.1294 | -56.857 |\n", - "| OLS_sphere | 200 | 0.1404 | 0.3747 | 0.2872 | 0.834 |\n", - "| DeepKriging_wendland | -- | 0.4729 | 0.6877 | 0.5277 | 0.4411 |\n", - "| DeepKriging_mrts | 10 | 0.1358 | 0.3685 | 0.2827 | 0.8395 |\n", - "| DeepKriging_sphere | 100 | 0.092 | 0.3034 | 0.2434 | 0.8912 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.0924 | 0.304 | 0.2422 | 0.8907 |\n", - "| UniversalKriging | 50 | 0.0833 | 0.2886 | 0.2284 | 0.9015 |\n", - "Repeat 2/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 3/50 seed=52\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:02:19.916668: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774774939.926205 1418552 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774774939.928944 1418552 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774774939.936168 1418552 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774939.936195 1418552 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774939.936197 1418552 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774774939.936198 1418552 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] seed=52\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=52 ===\n", - "z mean: 0.8541 (±0.0189), Var: 0.8885, Range: [-1.9188, 3.4146]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774961.006642 1418552 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774774962.566692 1418910 service.cc:152] XLA service 0x7f9284006d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774774962.566720 1418910 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774962.785016 1418910 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774774964.461172 1418910 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f949c28a8c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f948c4b6cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3188, sigma²=0.5665, nugget=0.0075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1444, sigma²=0.2991, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2065, sigma²=0.3907, nugget=0.0025\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1324, sigma²=0.2653, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1222, sigma²=0.2432, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1907, sigma²=0.3373, nugget=0.0020\n", - "[repeat 2] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1907, sigma²=0.3373, nugget=0.0020\n", - "[repeat 2] done → repeat_2_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7587 | 0.871 | 0.6497 | 0.1035 |\n", - "| OLS_sphere | 200 | 0.1655 | 0.4068 | 0.3113 | 0.8044 |\n", - "| DeepKriging_wendland | -- | 0.4842 | 0.6959 | 0.5465 | 0.4278 |\n", - "| DeepKriging_mrts | 150 | 0.1059 | 0.3254 | 0.2572 | 0.8749 |\n", - "| DeepKriging_sphere | 10 | 0.1013 | 0.3183 | 0.2522 | 0.8803 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.095 | 0.3083 | 0.2473 | 0.8877 |\n", - "| UniversalKriging | 1000 | 0.0942 | 0.307 | 0.2382 | 0.8886 |\n", - "Repeat 3/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 4/50 seed=53\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:11:24.193903: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774775484.203488 1872366 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774775484.206429 1872366 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774775484.213877 1872366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775484.213917 1872366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775484.213920 1872366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775484.213921 1872366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] seed=53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=53 ===\n", - "z mean: 1.2692 (±0.0220), Var: 1.2065, Range: [-2.1653, 5.0074]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774775505.074801 1872366 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774775506.626347 1872709 service.cc:152] XLA service 0x7f0264008280 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774775506.626380 1872709 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774775506.844712 1872709 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774775508.548291 1872709 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f048867ed40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f04881cb010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2875, sigma²=0.4924, nugget=0.0075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2220, sigma²=0.3993, nugget=0.0042\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1315, sigma²=0.2631, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1197, sigma²=0.2404, nugget=0.0012\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1023, sigma²=0.2113, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1932, sigma²=0.3376, nugget=0.0024\n", - "[repeat 3] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1023, sigma²=0.2113, nugget=0.0009\n", - "[repeat 3] done → repeat_3_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.944 | 1.7158 | 0.9175 | -1.4976 |\n", - "| OLS_sphere | 200 | 0.1553 | 0.3941 | 0.3169 | 0.8682 |\n", - "| DeepKriging_wendland | -- | 0.7059 | 0.8402 | 0.6386 | 0.4011 |\n", - "| DeepKriging_mrts | 10 | 0.2184 | 0.4673 | 0.3857 | 0.8147 |\n", - "| DeepKriging_sphere | 150 | 0.0934 | 0.3056 | 0.2393 | 0.9207 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0918 | 0.303 | 0.235 | 0.9221 |\n", - "| UniversalKriging | 200 | 0.0888 | 0.2981 | 0.2333 | 0.9246 |\n", - "Repeat 4/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 5/50 seed=54\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:19:39.233574: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774775979.243698 2245251 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774775979.246962 2245251 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774775979.254556 2245251 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775979.254586 2245251 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775979.254588 2245251 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774775979.254589 2245251 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] seed=54\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=54 ===\n", - "z mean: 1.4581 (±0.0154), Var: 0.5938, Range: [-1.2840, 3.8786]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776000.313773 2245251 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774776001.862547 2245597 service.cc:152] XLA service 0x7fc5d8006770 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774776001.862581 2245597 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776002.079234 2245597 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776003.747767 2245597 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc80c6f2710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc80474aef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2891, sigma²=0.5346, nugget=0.0037\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1473, sigma²=0.2978, nugget=0.0013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0901, sigma²=0.2029, nugget=0.0005\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0607, sigma²=0.1539, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0658, sigma²=0.1565, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0419, sigma²=0.0723, nugget=0.0001\n", - "[repeat 4] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2891, sigma²=0.5346, nugget=0.0037\n", - "[repeat 4] done → repeat_4_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.7685 | 0.8767 | 0.5109 | -0.5114 |\n", - "| OLS_sphere | 200 | 0.144 | 0.3794 | 0.3121 | 0.7169 |\n", - "| DeepKriging_wendland | -- | 0.3808 | 0.6171 | 0.4422 | 0.2512 |\n", - "| DeepKriging_mrts | 10 | 0.1318 | 0.363 | 0.2945 | 0.7408 |\n", - "| DeepKriging_sphere | 100 | 0.0906 | 0.3011 | 0.2404 | 0.8217 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.0948 | 0.3079 | 0.2394 | 0.8136 |\n", - "| UniversalKriging | 10 | 0.0802 | 0.2832 | 0.2244 | 0.8423 |\n", - "Repeat 5/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 6/50 seed=55\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:28:13.909563: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774776493.919538 2631961 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774776493.922719 2631961 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774776493.929786 2631961 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776493.929818 2631961 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776493.929820 2631961 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776493.929821 2631961 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] seed=55\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=55 ===\n", - "z mean: 0.2781 (±0.0220), Var: 1.2151, Range: [-2.8579, 4.3154]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776514.666130 2631961 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774776516.217758 2632306 service.cc:152] XLA service 0x5591bdb14210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774776516.217785 2632306 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776516.436036 2632306 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776518.105786 2632306 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb334756ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb33429b010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5004, sigma²=0.9465, nugget=0.0075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5016, sigma²=0.8940, nugget=0.0082\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5018, sigma²=0.8843, nugget=0.0082\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5021, sigma²=0.8775, nugget=0.0083\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5024, sigma²=0.8727, nugget=0.0083\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5030, sigma²=0.8619, nugget=0.0083\n", - "[repeat 5] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5030, sigma²=0.8619, nugget=0.0083\n", - "[repeat 5] done → repeat_5_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 5.915 | 2.4321 | 0.7927 | -3.4374 |\n", - "| OLS_sphere | 200 | 0.1459 | 0.382 | 0.3027 | 0.8906 |\n", - "| DeepKriging_wendland | -- | 0.5701 | 0.7551 | 0.5898 | 0.5723 |\n", - "| DeepKriging_mrts | 10 | 0.1585 | 0.3981 | 0.3222 | 0.8811 |\n", - "| DeepKriging_sphere | 100 | 0.1107 | 0.3327 | 0.2627 | 0.917 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1192 | 0.3453 | 0.268 | 0.9106 |\n", - "| UniversalKriging | 1000 | 0.0975 | 0.3123 | 0.2524 | 0.9268 |\n", - "Repeat 6/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 7/50 seed=56\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:35:55.487288: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774776955.497655 2949649 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774776955.500378 2949649 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774776955.507519 2949649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776955.507547 2949649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776955.507549 2949649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774776955.507550 2949649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] seed=56\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=56 ===\n", - "z mean: 1.2930 (±0.0190), Var: 0.9043, Range: [-1.9922, 4.7984]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776976.288121 2949649 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774776977.844031 2949993 service.cc:152] XLA service 0x7f6cdc0065c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774776977.844057 2949993 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776978.059772 2949993 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774776979.721051 2949993 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6f102ef520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6f007be560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6046, sigma²=0.8482, nugget=0.0162\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1691, sigma²=0.3070, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1042, sigma²=0.2089, nugget=0.0007\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1023, sigma²=0.2024, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0601, sigma²=0.1381, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1246, sigma²=0.2292, nugget=0.0015\n", - "[repeat 6] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0601, sigma²=0.1381, nugget=0.0002\n", - "[repeat 6] done → repeat_6_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.9812 | 0.9905 | 0.6718 | -0.0489 |\n", - "| OLS_sphere | 200 | 0.1427 | 0.3778 | 0.3052 | 0.8474 |\n", - "| DeepKriging_wendland | -- | 0.6495 | 0.8059 | 0.5922 | 0.3057 |\n", - "| DeepKriging_mrts | 50 | 0.1422 | 0.3771 | 0.294 | 0.848 |\n", - "| DeepKriging_sphere | 10 | 0.1023 | 0.3198 | 0.2568 | 0.8907 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1048 | 0.3237 | 0.2581 | 0.888 |\n", - "| UniversalKriging | 200 | 0.0935 | 0.3058 | 0.232 | 0.9 |\n", - "Repeat 7/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 8/50 seed=57\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:44:09.987688: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774777449.997987 3339031 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774777450.000866 3339031 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774777450.007784 3339031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777450.007809 3339031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777450.007811 3339031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777450.007812 3339031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] seed=57\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=57 ===\n", - "z mean: 0.6534 (±0.0161), Var: 0.6466, Range: [-1.7622, 3.5206]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777470.606261 3339031 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774777472.114508 3339369 service.cc:152] XLA service 0x55a88c522aa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774777472.114540 3339369 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777472.331817 3339369 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777473.990333 3339369 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2d607a6200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2d585b00d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4241, sigma²=0.7395, nugget=0.0078\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2930, sigma²=0.5374, nugget=0.0039\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1125, sigma²=0.2385, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1893, sigma²=0.3556, nugget=0.0037\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1019, sigma²=0.2149, nugget=0.0005\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1208, sigma²=0.2306, nugget=0.0012\n", - "[repeat 7] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1208, sigma²=0.2306, nugget=0.0012\n", - "[repeat 7] done → repeat_7_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.6293 | 1.2765 | 0.6579 | -1.1385 |\n", - "| OLS_sphere | 200 | 0.162 | 0.4025 | 0.3173 | 0.7874 |\n", - "| DeepKriging_wendland | -- | 0.4829 | 0.6949 | 0.5323 | 0.3662 |\n", - "| DeepKriging_mrts | 1000 | 0.1433 | 0.3786 | 0.2981 | 0.8119 |\n", - "| DeepKriging_sphere | 100 | 0.104 | 0.3225 | 0.2591 | 0.8635 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1054 | 0.3247 | 0.2675 | 0.8616 |\n", - "| UniversalKriging | 1000 | 0.0937 | 0.3061 | 0.247 | 0.877 |\n", - "Repeat 8/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 9/50 seed=58\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 17:52:50.419214: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774777970.429225 3761383 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774777970.431864 3761383 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774777970.439488 3761383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777970.439517 3761383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777970.439519 3761383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774777970.439520 3761383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] seed=58\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=58 ===\n", - "z mean: 0.6080 (±0.0178), Var: 0.7900, Range: [-2.3633, 3.2792]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777990.989738 3761383 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774777992.484998 3761729 service.cc:152] XLA service 0x559830f26460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774777992.485021 3761729 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777992.699171 3761729 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774777994.356044 3761729 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb3b83ef520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb3a874e3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3884, sigma²=0.6131, nugget=0.0075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1873, sigma²=0.3305, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1275, sigma²=0.2378, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1043, sigma²=0.2050, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1806, sigma²=0.3066, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1773, sigma²=0.2833, nugget=0.0013\n", - "[repeat 8] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1773, sigma²=0.2833, nugget=0.0013\n", - "[repeat 8] done → repeat_8_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1928 | 1.0922 | 0.643 | -0.6136 |\n", - "| OLS_sphere | 200 | 0.1739 | 0.417 | 0.3353 | 0.7648 |\n", - "| DeepKriging_wendland | -- | 0.3899 | 0.6244 | 0.4843 | 0.4726 |\n", - "| DeepKriging_mrts | 200 | 0.1464 | 0.3826 | 0.3053 | 0.802 |\n", - "| DeepKriging_sphere | 100 | 0.1035 | 0.3217 | 0.2502 | 0.86 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.109 | 0.3301 | 0.2604 | 0.8526 |\n", - "| UniversalKriging | 1000 | 0.1058 | 0.3253 | 0.2563 | 0.8568 |\n", - "Repeat 9/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 10/50 seed=59\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:02:15.807100: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774778535.817160 75896 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774778535.819925 75896 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774778535.827684 75896 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774778535.827727 75896 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774778535.827729 75896 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774778535.827730 75896 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] seed=59\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=59 ===\n", - "z mean: 1.0332 (±0.0193), Var: 0.9271, Range: [-2.0571, 3.6304]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774778556.366429 75896 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774778557.870862 76242 service.cc:152] XLA service 0x55913a58e4f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774778557.870884 76242 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774778558.088574 76242 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774778559.698826 76242 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2c5c1024d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2c4c7d5c60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2738, sigma²=0.4903, nugget=0.0061\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2068, sigma²=0.3850, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1986, sigma²=0.3651, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1925, sigma²=0.3534, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0907, sigma²=0.1914, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1755, sigma²=0.3068, nugget=0.0013\n", - "[repeat 9] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2068, sigma²=0.3850, nugget=0.0026\n", - "[repeat 9] done → repeat_9_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|------------|\n", - "| OLS_wendland | -- | 1357.38 | 36.8426 | 3.0805 | -1394.98 |\n", - "| OLS_sphere | 200 | 0.1909 | 0.4369 | 0.3358 | 0.8037 |\n", - "| DeepKriging_wendland | -- | 0.5146 | 0.7174 | 0.5546 | 0.4707 |\n", - "| DeepKriging_mrts | 50 | 0.1511 | 0.3887 | 0.3008 | 0.8446 |\n", - "| DeepKriging_sphere | 50 | 0.1208 | 0.3476 | 0.2629 | 0.8758 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1214 | 0.3484 | 0.2654 | 0.8752 |\n", - "| UniversalKriging | 50 | 0.1016 | 0.3187 | 0.2433 | 0.8955 |\n", - "Repeat 10/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 11/50 seed=60\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:11:12.887599: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774779072.897317 557689 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774779072.900234 557689 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774779072.907901 557689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779072.907935 557689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779072.907938 557689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779072.907938 557689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] seed=60\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=60 ===\n", - "z mean: 0.7615 (±0.0201), Var: 1.0084, Range: [-2.3271, 3.5369]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779093.388960 557689 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774779094.906166 558033 service.cc:152] XLA service 0x56207e310310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774779094.906190 558033 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779095.113497 558033 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779096.752031 558033 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f19cc557b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f19c4348940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3297, sigma²=0.5183, nugget=0.0102\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1358, sigma²=0.2632, nugget=0.0016\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0974, sigma²=0.1990, nugget=0.0007\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1395, sigma²=0.2608, nugget=0.0021\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1527, sigma²=0.2767, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1779, sigma²=0.3034, nugget=0.0037\n", - "[repeat 10] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1395, sigma²=0.2608, nugget=0.0021\n", - "[repeat 10] done → repeat_10_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7693 | 0.8771 | 0.6995 | 0.2306 |\n", - "| OLS_sphere | 200 | 0.1721 | 0.4148 | 0.3267 | 0.8279 |\n", - "| DeepKriging_wendland | -- | 0.5607 | 0.7488 | 0.5827 | 0.4391 |\n", - "| DeepKriging_mrts | 200 | 0.144 | 0.3795 | 0.2941 | 0.856 |\n", - "| DeepKriging_sphere | 150 | 0.1088 | 0.3298 | 0.2523 | 0.8912 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1189 | 0.3447 | 0.2739 | 0.8811 |\n", - "| UniversalKriging | 150 | 0.0986 | 0.3139 | 0.2388 | 0.9014 |\n", - "Repeat 11/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 12/50 seed=61\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:19:43.323205: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774779583.332990 977017 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774779583.335844 977017 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774779583.343175 977017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779583.343209 977017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779583.343212 977017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774779583.343213 977017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] seed=61\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=61 ===\n", - "z mean: 1.4179 (±0.0178), Var: 0.7902, Range: [-0.9411, 4.5815]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779603.933997 977017 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774779605.444823 977361 service.cc:152] XLA service 0x7fa79c006300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774779605.444849 977361 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779605.661731 977361 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774779607.284834 977361 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa9cc6ba0e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa9cc1f2c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4175, sigma²=0.6530, nugget=0.0092\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1575, sigma²=0.2896, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1376, sigma²=0.2547, nugget=0.0014\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1377, sigma²=0.2530, nugget=0.0016\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0957, sigma²=0.1868, nugget=0.0006\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0219, sigma²=0.0490, nugget=0.0000\n", - "[repeat 11] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1575, sigma²=0.2896, nugget=0.0015\n", - "[repeat 11] done → repeat_11_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5872 | 0.7663 | 0.6056 | 0.2385 |\n", - "| OLS_sphere | 200 | 0.1348 | 0.3672 | 0.2909 | 0.8252 |\n", - "| DeepKriging_wendland | -- | 0.4312 | 0.6567 | 0.5115 | 0.4408 |\n", - "| DeepKriging_mrts | 50 | 0.1367 | 0.3697 | 0.2931 | 0.8227 |\n", - "| DeepKriging_sphere | 100 | 0.1138 | 0.3374 | 0.273 | 0.8524 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1128 | 0.3359 | 0.2659 | 0.8537 |\n", - "| UniversalKriging | 50 | 0.1014 | 0.3185 | 0.2507 | 0.8685 |\n", - "Repeat 12/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 13/50 seed=62\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:28:25.034550: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774780105.045081 1404089 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774780105.047801 1404089 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774780105.055149 1404089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780105.055187 1404089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780105.055189 1404089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780105.055190 1404089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] seed=62\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=62 ===\n", - "z mean: 0.5214 (±0.0154), Var: 0.5933, Range: [-1.9458, 3.1458]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780125.636493 1404089 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774780127.152723 1404432 service.cc:152] XLA service 0x7fb9380067d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774780127.152756 1404432 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780127.370650 1404432 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780129.001492 1404432 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbb5c16a7a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbb4c61cdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2748, sigma²=0.4809, nugget=0.0053\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1897, sigma²=0.3509, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1503, sigma²=0.2884, nugget=0.0024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0791, sigma²=0.1752, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1586, sigma²=0.2972, nugget=0.0033\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0813, sigma²=0.1359, nugget=0.0002\n", - "[repeat 12] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0791, sigma²=0.1752, nugget=0.0003\n", - "[repeat 12] done → repeat_12_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 10.4514 | 3.2329 | 0.7405 | -17.0617 |\n", - "| OLS_sphere | 200 | 0.144 | 0.3795 | 0.2943 | 0.7511 |\n", - "| DeepKriging_wendland | -- | 0.3588 | 0.599 | 0.4609 | 0.38 |\n", - "| DeepKriging_mrts | 10 | 0.15 | 0.3873 | 0.3025 | 0.7408 |\n", - "| DeepKriging_sphere | 50 | 0.1008 | 0.3174 | 0.2474 | 0.8259 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1137 | 0.3372 | 0.2703 | 0.8035 |\n", - "| UniversalKriging | 150 | 0.0879 | 0.2966 | 0.2298 | 0.848 |\n", - "Repeat 13/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 14/50 seed=63\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:36:19.259825: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774780579.269497 1734662 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774780579.272142 1734662 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774780579.279456 1734662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780579.279481 1734662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780579.279483 1734662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774780579.279484 1734662 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] seed=63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=63 ===\n", - "z mean: 1.0337 (±0.0207), Var: 1.0758, Range: [-2.4550, 4.0720]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780599.921276 1734662 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774780601.443719 1735006 service.cc:152] XLA service 0x7f696c006940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774780601.443744 1735006 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780601.660843 1735006 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774780603.335890 1735006 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6b9873bac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6b906e03a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4214, sigma²=0.7089, nugget=0.0094\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2490, sigma²=0.4498, nugget=0.0034\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1794, sigma²=0.3327, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1817, sigma²=0.3339, nugget=0.0027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1869, sigma²=0.3392, nugget=0.0029\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2061, sigma²=0.3518, nugget=0.0019\n", - "[repeat 13] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4214, sigma²=0.7089, nugget=0.0094\n", - "[repeat 13] done → repeat_13_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7771 | 0.8815 | 0.7027 | 0.1928 |\n", - "| OLS_sphere | 200 | 0.1534 | 0.3916 | 0.3089 | 0.8407 |\n", - "| DeepKriging_wendland | -- | 0.5956 | 0.7717 | 0.5878 | 0.3813 |\n", - "| DeepKriging_mrts | 1000 | 0.157 | 0.3962 | 0.308 | 0.8369 |\n", - "| DeepKriging_sphere | 100 | 0.0819 | 0.2861 | 0.2261 | 0.915 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.0787 | 0.2806 | 0.2207 | 0.9182 |\n", - "| UniversalKriging | 10 | 0.074 | 0.272 | 0.2129 | 0.9232 |\n", - "Repeat 14/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 15/50 seed=64\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:44:46.687428: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774781086.697216 2153407 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774781086.700084 2153407 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774781086.707295 2153407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781086.707328 2153407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781086.707330 2153407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781086.707331 2153407 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] seed=64\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=64 ===\n", - "z mean: 1.0068 (±0.0197), Var: 0.9687, Range: [-1.7924, 4.0109]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781107.377080 2153407 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774781108.855183 2153750 service.cc:152] XLA service 0x7f455000a1a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774781108.855206 2153750 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781109.073434 2153750 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781110.700563 2153750 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f47846eeb00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f477c4c3f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4181, sigma²=0.7170, nugget=0.0077\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2725, sigma²=0.4927, nugget=0.0040\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1169, sigma²=0.2407, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0623, sigma²=0.1578, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0691, sigma²=0.1562, nugget=0.0003\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0888, sigma²=0.1603, nugget=0.0005\n", - "[repeat 14] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0888, sigma²=0.1603, nugget=0.0005\n", - "[repeat 14] done → repeat_14_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6115 | 0.782 | 0.5951 | 0.2802 |\n", - "| OLS_sphere | 200 | 0.1322 | 0.3636 | 0.2882 | 0.8443 |\n", - "| DeepKriging_wendland | -- | 0.4658 | 0.6825 | 0.5044 | 0.4517 |\n", - "| DeepKriging_mrts | 50 | 0.1323 | 0.3637 | 0.2936 | 0.8443 |\n", - "| DeepKriging_sphere | 100 | 0.1102 | 0.332 | 0.2631 | 0.8703 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1046 | 0.3234 | 0.2575 | 0.8769 |\n", - "| UniversalKriging | 1000 | 0.0955 | 0.309 | 0.246 | 0.8876 |\n", - "Repeat 15/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 16/50 seed=65\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 18:53:46.940949: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774781626.951335 2622641 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774781626.954406 2622641 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774781626.961715 2622641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781626.961743 2622641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781626.961745 2622641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774781626.961745 2622641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] seed=65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=65 ===\n", - "z mean: 0.8020 (±0.0215), Var: 1.1523, Range: [-2.2063, 4.7261]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781647.567611 2622641 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774781649.091908 2622985 service.cc:152] XLA service 0x7f21dc008a30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774781649.091934 2622985 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781649.313869 2622985 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774781650.962822 2622985 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f240c2a7490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f240430fb50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4275, sigma²=0.6521, nugget=0.0120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1918, sigma²=0.3483, nugget=0.0032\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1319, sigma²=0.2578, nugget=0.0012\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0782, sigma²=0.1748, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0933, sigma²=0.1879, nugget=0.0006\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1586, sigma²=0.2820, nugget=0.0027\n", - "[repeat 15] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0933, sigma²=0.1879, nugget=0.0006\n", - "[repeat 15] done → repeat_15_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7957 | 0.892 | 0.6885 | 0.3185 |\n", - "| OLS_sphere | 200 | 0.1838 | 0.4288 | 0.336 | 0.8425 |\n", - "| DeepKriging_wendland | -- | 0.5244 | 0.7242 | 0.561 | 0.5508 |\n", - "| DeepKriging_mrts | 10 | 0.1823 | 0.4269 | 0.3449 | 0.8439 |\n", - "| DeepKriging_sphere | 10 | 0.1247 | 0.3532 | 0.2815 | 0.8932 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1314 | 0.3624 | 0.2877 | 0.8875 |\n", - "| UniversalKriging | 200 | 0.1026 | 0.3203 | 0.2576 | 0.9121 |\n", - "Repeat 16/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 17/50 seed=66\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:01:36.358593: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774782096.368395 3028041 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774782096.371241 3028041 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774782096.378616 3028041 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782096.378649 3028041 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782096.378652 3028041 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782096.378653 3028041 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] seed=66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=66 ===\n", - "z mean: 1.2233 (±0.0154), Var: 0.5897, Range: [-1.2321, 3.3525]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782117.558996 3028041 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774782119.093795 3028402 service.cc:152] XLA service 0x55b9994ae660 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774782119.093819 3028402 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782119.326721 3028402 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782121.043091 3028402 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f68fc1cea70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f68f423ec20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3110, sigma²=0.5398, nugget=0.0047\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1900, sigma²=0.3521, nugget=0.0021\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1486, sigma²=0.2821, nugget=0.0018\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1439, sigma²=0.2719, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1620, sigma²=0.2957, nugget=0.0025\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1953, sigma²=0.3372, nugget=0.0045\n", - "[repeat 16] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1953, sigma²=0.3372, nugget=0.0045\n", - "[repeat 16] done → repeat_16_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.9659 | 0.9828 | 0.606 | -0.5527 |\n", - "| OLS_sphere | 200 | 0.1762 | 0.4197 | 0.3327 | 0.7168 |\n", - "| DeepKriging_wendland | -- | 0.4438 | 0.6662 | 0.5197 | 0.2866 |\n", - "| DeepKriging_mrts | 10 | 0.1846 | 0.4297 | 0.3519 | 0.7032 |\n", - "| DeepKriging_sphere | 100 | 0.1098 | 0.3313 | 0.2616 | 0.8235 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1123 | 0.3351 | 0.2688 | 0.8195 |\n", - "| UniversalKriging | 1000 | 0.1139 | 0.3375 | 0.2624 | 0.8169 |\n", - "Repeat 17/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 18/50 seed=67\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:10:15.243277: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774782615.253050 3477744 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774782615.256062 3477744 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774782615.263446 3477744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782615.263475 3477744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782615.263477 3477744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774782615.263478 3477744 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] seed=67\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=67 ===\n", - "z mean: 1.2711 (±0.0163), Var: 0.6659, Range: [-1.2990, 3.6131]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782636.363233 3477744 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774782637.919191 3478102 service.cc:152] XLA service 0x7f9e04009b10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774782637.919217 3478102 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782638.134970 3478102 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774782639.830728 3478102 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa03c6e7250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa034737880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2665, sigma²=0.4285, nugget=0.0059\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1286, sigma²=0.2441, nugget=0.0011\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1137, sigma²=0.2174, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0838, sigma²=0.1694, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1025, sigma²=0.1894, nugget=0.0007\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1753, sigma²=0.2791, nugget=0.0015\n", - "[repeat 17] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1753, sigma²=0.2791, nugget=0.0015\n", - "[repeat 17] done → repeat_17_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3184 | 1.1482 | 0.6501 | -0.7536 |\n", - "| OLS_sphere | 200 | 0.141 | 0.3755 | 0.3005 | 0.8125 |\n", - "| DeepKriging_wendland | -- | 0.4355 | 0.66 | 0.4926 | 0.4207 |\n", - "| DeepKriging_mrts | 50 | 0.1282 | 0.3581 | 0.2832 | 0.8294 |\n", - "| DeepKriging_sphere | 50 | 0.1039 | 0.3224 | 0.2531 | 0.8618 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1144 | 0.3383 | 0.2634 | 0.8478 |\n", - "| UniversalKriging | 1000 | 0.0911 | 0.3019 | 0.2375 | 0.8788 |\n", - "Repeat 18/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 19/50 seed=68\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:19:14.390130: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774783154.399732 3934224 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774783154.402532 3934224 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774783154.410097 3934224 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783154.410137 3934224 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783154.410139 3934224 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783154.410140 3934224 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] seed=68\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=68 ===\n", - "z mean: 1.5699 (±0.0238), Var: 1.4216, Range: [-1.6250, 4.6231]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783175.470998 3934224 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774783177.047347 3934588 service.cc:152] XLA service 0x7fc694005d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774783177.047372 3934588 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783177.268924 3934588 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783178.964854 3934588 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc8a8431630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc8a055f010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6297, sigma²=0.9733, nugget=0.0111\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5856, sigma²=0.8617, nugget=0.0118\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5808, sigma²=0.8454, nugget=0.0119\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5799, sigma²=0.8407, nugget=0.0120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5794, sigma²=0.8372, nugget=0.0120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5805, sigma²=0.8311, nugget=0.0120\n", - "[repeat 18] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6297, sigma²=0.9733, nugget=0.0111\n", - "[repeat 18] done → repeat_18_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1849 | 1.0885 | 0.8337 | 0.2177 |\n", - "| OLS_sphere | 200 | 0.1615 | 0.4018 | 0.3178 | 0.8934 |\n", - "| DeepKriging_wendland | -- | 0.7461 | 0.8638 | 0.6513 | 0.5074 |\n", - "| DeepKriging_mrts | 100 | 0.1317 | 0.3629 | 0.291 | 0.9131 |\n", - "| DeepKriging_sphere | 50 | 0.1021 | 0.3195 | 0.2528 | 0.9326 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1002 | 0.3166 | 0.2494 | 0.9338 |\n", - "| UniversalKriging | 10 | 0.0916 | 0.3027 | 0.2349 | 0.9395 |\n", - "Repeat 19/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 20/50 seed=69\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:28:20.777672: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774783700.788132 220547 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774783700.790975 220547 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774783700.798743 220547 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783700.798772 220547 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783700.798774 220547 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774783700.798775 220547 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] seed=69\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=69 ===\n", - "z mean: 0.9635 (±0.0200), Var: 0.9965, Range: [-2.2501, 4.3209]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783721.552261 220547 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774783723.073770 220907 service.cc:152] XLA service 0x55beb8f646a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774783723.073795 220907 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783723.292845 220907 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774783724.956838 220907 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2234412200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f21bc65bb50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3327, sigma²=0.6014, nugget=0.0080\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1899, sigma²=0.3708, nugget=0.0025\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2063, sigma²=0.3910, nugget=0.0028\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2070, sigma²=0.3919, nugget=0.0025\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2088, sigma²=0.3899, nugget=0.0027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1625, sigma²=0.2943, nugget=0.0015\n", - "[repeat 19] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1625, sigma²=0.2943, nugget=0.0015\n", - "[repeat 19] done → repeat_19_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7057 | 0.8401 | 0.6412 | 0.2177 |\n", - "| OLS_sphere | 200 | 0.1583 | 0.3979 | 0.3168 | 0.8245 |\n", - "| DeepKriging_wendland | -- | 0.4946 | 0.7033 | 0.53 | 0.4517 |\n", - "| DeepKriging_mrts | 50 | 0.1305 | 0.3612 | 0.2964 | 0.8554 |\n", - "| DeepKriging_sphere | 150 | 0.0885 | 0.2975 | 0.236 | 0.9019 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0929 | 0.3048 | 0.2463 | 0.897 |\n", - "| UniversalKriging | 1000 | 0.0836 | 0.2892 | 0.2306 | 0.9073 |\n", - "Repeat 20/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:37:05.825643: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774784225.837283 646245 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774784225.840163 646245 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774784225.848160 646245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784225.848221 646245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784225.848223 646245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784225.848224 646245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=70 ===\n", - "z mean: 1.2045 (±0.0188), Var: 0.8818, Range: [-1.3242, 3.9479]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784246.590727 646245 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774784248.127816 646601 service.cc:152] XLA service 0x55bbc10d1fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774784248.127839 646601 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784248.345800 646601 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784250.006824 646601 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f69fc197eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f69f43ef250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2931, sigma²=0.5132, nugget=0.0068\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2287, sigma²=0.4173, nugget=0.0039\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2058, sigma²=0.3777, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2318, sigma²=0.4215, nugget=0.0028\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2304, sigma²=0.4135, nugget=0.0029\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2018, sigma²=0.3534, nugget=0.0021\n", - "[repeat 20] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2018, sigma²=0.3534, nugget=0.0021\n", - "[repeat 20] done → repeat_20_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.2549 | 1.5016 | 0.7477 | -1.5297 |\n", - "| OLS_sphere | 200 | 0.1781 | 0.4221 | 0.3369 | 0.8002 |\n", - "| DeepKriging_wendland | -- | 0.5748 | 0.7582 | 0.5771 | 0.3551 |\n", - "| DeepKriging_mrts | 50 | 0.1513 | 0.389 | 0.313 | 0.8302 |\n", - "| DeepKriging_sphere | 100 | 0.1115 | 0.3339 | 0.2544 | 0.8749 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.097 | 0.3114 | 0.246 | 0.8912 |\n", - "| UniversalKriging | 1000 | 0.0866 | 0.2943 | 0.2308 | 0.9028 |\n", - "Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:46:10.721528: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774784770.734220 1138757 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774784770.737443 1138757 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774784770.744778 1138757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784770.744805 1138757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784770.744807 1138757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774784770.744808 1138757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=71 ===\n", - "z mean: 1.1674 (±0.0169), Var: 0.7108, Range: [-1.0895, 4.0544]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784791.397788 1138757 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774784792.934255 1139122 service.cc:152] XLA service 0x5603aa845150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774784792.934292 1139122 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784793.145763 1139122 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774784794.802784 1139122 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa44015f640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa4383af6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2638, sigma²=0.4748, nugget=0.0056\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1623, sigma²=0.3205, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1911, sigma²=0.3570, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1084, sigma²=0.2220, nugget=0.0006\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0910, sigma²=0.1905, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1540, sigma²=0.2914, nugget=0.0034\n", - "[repeat 21] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1540, sigma²=0.2914, nugget=0.0034\n", - "[repeat 21] done → repeat_21_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1976 | 1.0944 | 0.6115 | -0.8042 |\n", - "| OLS_sphere | 150 | 0.1609 | 0.4011 | 0.3215 | 0.7576 |\n", - "| DeepKriging_wendland | -- | 0.3875 | 0.6225 | 0.4679 | 0.4162 |\n", - "| DeepKriging_mrts | 200 | 0.1167 | 0.3416 | 0.2704 | 0.8242 |\n", - "| DeepKriging_sphere | 100 | 0.0939 | 0.3064 | 0.236 | 0.8586 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1063 | 0.326 | 0.2582 | 0.8399 |\n", - "| UniversalKriging | 1000 | 0.086 | 0.2932 | 0.2254 | 0.8705 |\n", - "Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 19:54:35.871131: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774785275.880888 1545366 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774785275.883645 1545366 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774785275.891788 1545366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785275.891816 1545366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785275.891817 1545366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785275.891818 1545366 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=72 ===\n", - "z mean: 0.9454 (±0.0183), Var: 0.8348, Range: [-2.2639, 3.6301]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785296.799984 1545366 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774785298.301310 1545723 service.cc:152] XLA service 0x560634321940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774785298.301335 1545723 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785298.520615 1545723 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785300.167222 1545723 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f13d85a5630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f13d060acb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3429, sigma²=0.5896, nugget=0.0077\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2093, sigma²=0.3906, nugget=0.0032\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1779, sigma²=0.3396, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1433, sigma²=0.2788, nugget=0.0013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1401, sigma²=0.2700, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0670, sigma²=0.1224, nugget=0.0002\n", - "[repeat 22] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0670, sigma²=0.1224, nugget=0.0002\n", - "[repeat 22] done → repeat_22_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 50.6503 | 7.1169 | 1.1824 | -63.7834 |\n", - "| OLS_sphere | 200 | 0.1793 | 0.4234 | 0.3366 | 0.7707 |\n", - "| DeepKriging_wendland | -- | 0.4729 | 0.6877 | 0.5373 | 0.3951 |\n", - "| DeepKriging_mrts | 10 | 0.1664 | 0.4079 | 0.3268 | 0.7872 |\n", - "| DeepKriging_sphere | 200 | 0.1043 | 0.323 | 0.253 | 0.8666 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1016 | 0.3188 | 0.244 | 0.87 |\n", - "| UniversalKriging | 1000 | 0.1043 | 0.323 | 0.2513 | 0.8666 |\n", - "Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:02:51.329328: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774785771.339598 1932395 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774785771.342768 1932395 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774785771.350128 1932395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785771.350156 1932395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785771.350158 1932395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774785771.350159 1932395 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=73 ===\n", - "z mean: 1.2999 (±0.0201), Var: 1.0061, Range: [-1.1253, 4.9918]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785792.094855 1932395 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774785793.606627 1932761 service.cc:152] XLA service 0x7f776c008440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774785793.606651 1932761 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785793.819090 1932761 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774785795.480290 1932761 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f79a44ed900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f799c2c4940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3809, sigma²=0.6355, nugget=0.0089\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2191, sigma²=0.4026, nugget=0.0030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1558, sigma²=0.2964, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1227, sigma²=0.2403, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1073, sigma²=0.2126, nugget=0.0006\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1840, sigma²=0.3157, nugget=0.0017\n", - "[repeat 23] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1840, sigma²=0.3157, nugget=0.0017\n", - "[repeat 23] done → repeat_23_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8644 | 0.9297 | 0.6967 | 0.1805 |\n", - "| OLS_sphere | 200 | 0.1723 | 0.4151 | 0.3379 | 0.8367 |\n", - "| DeepKriging_wendland | -- | 0.6835 | 0.8268 | 0.609 | 0.352 |\n", - "| DeepKriging_mrts | 10 | 0.1557 | 0.3946 | 0.3166 | 0.8524 |\n", - "| DeepKriging_sphere | 150 | 0.1236 | 0.3516 | 0.2784 | 0.8828 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1185 | 0.3442 | 0.27 | 0.8877 |\n", - "| UniversalKriging | 1000 | 0.1001 | 0.3164 | 0.2527 | 0.9051 |\n", - "Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:11:14.333004: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774786274.343804 2328097 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774786274.346652 2328097 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774786274.354356 2328097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786274.354388 2328097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786274.354390 2328097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786274.354391 2328097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=74 ===\n", - "z mean: 1.1301 (±0.0234), Var: 1.3729, Range: [-2.2020, 5.0217]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786295.144356 2328097 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774786296.676107 2328456 service.cc:152] XLA service 0x55e5397c8210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774786296.676133 2328456 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786296.886110 2328456 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786298.532620 2328456 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9c0054e830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9bf87abeb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3457, sigma²=0.5586, nugget=0.0101\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3187, sigma²=0.5170, nugget=0.0086\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2572, sigma²=0.4415, nugget=0.0051\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2446, sigma²=0.4186, nugget=0.0048\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2212, sigma²=0.3840, nugget=0.0038\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1107, sigma²=0.2095, nugget=0.0011\n", - "[repeat 24] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1107, sigma²=0.2095, nugget=0.0011\n", - "[repeat 24] done → repeat_24_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0626 | 1.0308 | 0.7905 | 0.2098 |\n", - "| OLS_sphere | 200 | 0.1683 | 0.4103 | 0.3289 | 0.8748 |\n", - "| DeepKriging_wendland | -- | 0.7589 | 0.8712 | 0.6435 | 0.4356 |\n", - "| DeepKriging_mrts | 10 | 0.1573 | 0.3967 | 0.3197 | 0.883 |\n", - "| DeepKriging_sphere | 50 | 0.0991 | 0.3149 | 0.2533 | 0.9263 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1068 | 0.3268 | 0.2559 | 0.9206 |\n", - "| UniversalKriging | 1000 | 0.0892 | 0.2987 | 0.2328 | 0.9337 |\n", - "Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:19:16.337204: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774786756.347044 2694993 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774786756.349842 2694993 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774786756.356982 2694993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786756.357006 2694993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786756.357008 2694993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774786756.357009 2694993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=75 ===\n", - "z mean: 1.1757 (±0.0186), Var: 0.8680, Range: [-1.5024, 4.2230]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786777.105599 2694993 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774786778.634511 2695351 service.cc:152] XLA service 0x55caf918bee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774786778.634536 2695351 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786778.850695 2695351 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774786780.527551 2695351 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd2447e7e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd23c70f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5631, sigma²=0.8975, nugget=0.0107\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5538, sigma²=0.8632, nugget=0.0108\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5399, sigma²=0.8445, nugget=0.0097\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5257, sigma²=0.8302, nugget=0.0088\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5253, sigma²=0.8263, nugget=0.0088\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5289, sigma²=0.8222, nugget=0.0088\n", - "[repeat 25] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5289, sigma²=0.8222, nugget=0.0088\n", - "[repeat 25] done → repeat_25_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5209 | 0.7218 | 0.5613 | 0.3544 |\n", - "| OLS_sphere | 200 | 0.1623 | 0.4028 | 0.3062 | 0.7989 |\n", - "| DeepKriging_wendland | -- | 0.3866 | 0.6218 | 0.4766 | 0.5209 |\n", - "| DeepKriging_mrts | 200 | 0.1205 | 0.3472 | 0.2804 | 0.8506 |\n", - "| DeepKriging_sphere | 100 | 0.1031 | 0.3211 | 0.252 | 0.8722 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1101 | 0.3318 | 0.2664 | 0.8635 |\n", - "| UniversalKriging | 1000 | 0.0998 | 0.3159 | 0.2448 | 0.8763 |\n", - "Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:28:38.494674: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774787318.504786 3209572 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774787318.507665 3209572 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774787318.515380 3209572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787318.515415 3209572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787318.515418 3209572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787318.515418 3209572 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=76 ===\n", - "z mean: 0.9468 (±0.0188), Var: 0.8799, Range: [-1.8332, 3.8049]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787339.392374 3209572 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774787340.922582 3209937 service.cc:152] XLA service 0x7f2680009b50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774787340.922605 3209937 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787341.132677 3209937 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787342.806156 3209937 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f28a8527400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f28a0776dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2904, sigma²=0.4945, nugget=0.0056\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1870, sigma²=0.3375, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1883, sigma²=0.3350, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0475, sigma²=0.1374, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0555, sigma²=0.1349, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1886, sigma²=0.3112, nugget=0.0018\n", - "[repeat 26] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1886, sigma²=0.3112, nugget=0.0018\n", - "[repeat 26] done → repeat_26_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6556 | 0.8097 | 0.6321 | 0.2143 |\n", - "| OLS_sphere | 200 | 0.1597 | 0.3996 | 0.3159 | 0.8086 |\n", - "| DeepKriging_wendland | -- | 0.4841 | 0.6958 | 0.5379 | 0.4198 |\n", - "| DeepKriging_mrts | 1000 | 0.1811 | 0.4256 | 0.3187 | 0.7829 |\n", - "| DeepKriging_sphere | 100 | 0.1175 | 0.3427 | 0.2666 | 0.8592 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1098 | 0.3314 | 0.254 | 0.8684 |\n", - "| UniversalKriging | 1000 | 0.0962 | 0.3102 | 0.2437 | 0.8846 |\n", - "Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:36:59.260698: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774787819.271170 3591356 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774787819.274056 3591356 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774787819.282184 3591356 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787819.282218 3591356 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787819.282220 3591356 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774787819.282221 3591356 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=77 ===\n", - "z mean: 0.8124 (±0.0224), Var: 1.2546, Range: [-2.3167, 4.7890]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787840.095155 3591356 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774787841.603714 3591724 service.cc:152] XLA service 0x7f3684006c90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774787841.603745 3591724 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787841.816396 3591724 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774787843.481785 3591724 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f38b06da200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f38a8488d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3582, sigma²=0.6012, nugget=0.0079\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2456, sigma²=0.4332, nugget=0.0044\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2200, sigma²=0.3899, nugget=0.0032\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2216, sigma²=0.3899, nugget=0.0027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2211, sigma²=0.3814, nugget=0.0030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2245, sigma²=0.3764, nugget=0.0027\n", - "[repeat 27] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2245, sigma²=0.3764, nugget=0.0027\n", - "[repeat 27] done → repeat_27_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.5053 | 1.2269 | 0.7379 | -0.3736 |\n", - "| OLS_sphere | 200 | 0.1619 | 0.4023 | 0.3118 | 0.8523 |\n", - "| DeepKriging_wendland | -- | 0.508 | 0.7127 | 0.5507 | 0.5365 |\n", - "| DeepKriging_mrts | 1000 | 0.1973 | 0.4442 | 0.3432 | 0.82 |\n", - "| DeepKriging_sphere | 50 | 0.0934 | 0.3056 | 0.237 | 0.9148 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0968 | 0.3112 | 0.2409 | 0.9116 |\n", - "| UniversalKriging | 1000 | 0.0901 | 0.3001 | 0.2297 | 0.9178 |\n", - "Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:45:19.397545: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774788319.408705 3991023 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774788319.411501 3991023 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774788319.419255 3991023 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788319.419282 3991023 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788319.419285 3991023 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788319.419285 3991023 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=78 ===\n", - "z mean: 1.3052 (±0.0191), Var: 0.9130, Range: [-1.2480, 4.2240]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788340.252110 3991023 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774788341.789641 3991390 service.cc:152] XLA service 0x7efbc000b3c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774788341.789664 3991390 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788342.007566 3991390 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788343.666389 3991390 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efdfc58f520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efdf47dff40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4962, sigma²=0.8286, nugget=0.0088\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4958, sigma²=0.7957, nugget=0.0095\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4962, sigma²=0.7867, nugget=0.0096\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4966, sigma²=0.7835, nugget=0.0097\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4969, sigma²=0.7817, nugget=0.0097\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4979, sigma²=0.7740, nugget=0.0097\n", - "[repeat 28] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4979, sigma²=0.7740, nugget=0.0097\n", - "[repeat 28] done → repeat_28_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8345 | 0.9135 | 0.7046 | 0.1003 |\n", - "| OLS_sphere | 200 | 0.1503 | 0.3877 | 0.308 | 0.838 |\n", - "| DeepKriging_wendland | -- | 0.6376 | 0.7985 | 0.6206 | 0.3126 |\n", - "| DeepKriging_mrts | 1000 | 0.194 | 0.4404 | 0.3459 | 0.7909 |\n", - "| DeepKriging_sphere | 200 | 0.0994 | 0.3153 | 0.2481 | 0.8928 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1002 | 0.3166 | 0.2512 | 0.892 |\n", - "| UniversalKriging | 1000 | 0.094 | 0.3065 | 0.2417 | 0.8987 |\n", - "Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 20:53:54.843381: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774788834.852968 223786 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774788834.855733 223786 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774788834.863261 223786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788834.863290 223786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788834.863293 223786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774788834.863294 223786 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=79 ===\n", - "z mean: 1.0562 (±0.0177), Var: 0.7849, Range: [-1.3893, 3.8555]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788855.648786 223786 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774788857.174793 224147 service.cc:152] XLA service 0x7f7190017b30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774788857.174824 224147 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788857.400360 224147 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774788859.070956 224147 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f73cc6bb640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f73c4484dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4778, sigma²=0.7775, nugget=0.0139\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3154, sigma²=0.5611, nugget=0.0062\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1608, sigma²=0.3170, nugget=0.0024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1884, sigma²=0.3548, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1878, sigma²=0.3499, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2036, sigma²=0.3699, nugget=0.0043\n", - "[repeat 29] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1878, sigma²=0.3499, nugget=0.0022\n", - "[repeat 29] done → repeat_29_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 26.1077 | 5.1096 | 1.3849 | -30.9414 |\n", - "| OLS_sphere | 200 | 0.1639 | 0.4049 | 0.3304 | 0.7994 |\n", - "| DeepKriging_wendland | -- | 0.577 | 0.7596 | 0.5833 | 0.2941 |\n", - "| DeepKriging_mrts | 50 | 0.1571 | 0.3963 | 0.3141 | 0.8079 |\n", - "| DeepKriging_sphere | 200 | 0.091 | 0.3016 | 0.235 | 0.8887 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0913 | 0.3021 | 0.2401 | 0.8883 |\n", - "| UniversalKriging | 200 | 0.087 | 0.2949 | 0.2291 | 0.8936 |\n", - "Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:02:42.866998: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774789362.877365 679596 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774789362.880392 679596 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774789362.887951 679596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789362.887980 679596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789362.887982 679596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789362.887983 679596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=80 ===\n", - "z mean: 0.6467 (±0.0178), Var: 0.7888, Range: [-2.6082, 3.8988]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789383.787376 679596 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774789385.312471 679960 service.cc:152] XLA service 0x7fcc2c006180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774789385.312502 679960 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789385.531846 679960 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789387.172829 679960 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fce48596830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fce407ebac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3586, sigma²=0.5950, nugget=0.0077\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1924, sigma²=0.3551, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1824, sigma²=0.3305, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1956, sigma²=0.3458, nugget=0.0021\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1929, sigma²=0.3381, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1851, sigma²=0.3096, nugget=0.0016\n", - "[repeat 30] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1851, sigma²=0.3096, nugget=0.0016\n", - "[repeat 30] done → repeat_30_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 26.8757 | 5.1842 | 0.9188 | -36.3633 |\n", - "| OLS_sphere | 200 | 0.1443 | 0.3798 | 0.3086 | 0.7994 |\n", - "| DeepKriging_wendland | -- | 0.3823 | 0.6183 | 0.478 | 0.4686 |\n", - "| DeepKriging_mrts | 1000 | 0.1769 | 0.4206 | 0.3231 | 0.7541 |\n", - "| DeepKriging_sphere | 150 | 0.1021 | 0.3195 | 0.2495 | 0.8581 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.0978 | 0.3128 | 0.2461 | 0.864 |\n", - "| UniversalKriging | 1000 | 0.0873 | 0.2954 | 0.234 | 0.8787 |\n", - "Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:11:04.891182: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774789864.901621 1075504 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774789864.904520 1075504 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774789864.912059 1075504 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789864.912084 1075504 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789864.912087 1075504 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774789864.912087 1075504 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=81 ===\n", - "z mean: 0.8720 (±0.0199), Var: 0.9901, Range: [-2.3600, 4.1425]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789885.850397 1075504 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774789887.386449 1075859 service.cc:152] XLA service 0x55ce3546e950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774789887.386472 1075859 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789887.608825 1075859 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774789889.261362 1075859 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd7701ffb50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd7684df370> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2822, sigma²=0.5194, nugget=0.0069\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1868, sigma²=0.3695, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0984, sigma²=0.2206, nugget=0.0007\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1186, sigma²=0.2500, nugget=0.0013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1482, sigma²=0.2960, nugget=0.0024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0437, sigma²=0.0777, nugget=0.0001\n", - "[repeat 31] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2822, sigma²=0.5194, nugget=0.0069\n", - "[repeat 31] done → repeat_31_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 4.3776 | 2.0923 | 0.7569 | -3.4888 |\n", - "| OLS_sphere | 200 | 0.1375 | 0.3709 | 0.3005 | 0.859 |\n", - "| DeepKriging_wendland | -- | 0.4608 | 0.6788 | 0.509 | 0.5275 |\n", - "| DeepKriging_mrts | 100 | 0.1056 | 0.325 | 0.2632 | 0.8917 |\n", - "| DeepKriging_sphere | 100 | 0.0954 | 0.3089 | 0.2419 | 0.9022 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1006 | 0.3172 | 0.249 | 0.8968 |\n", - "| UniversalKriging | 10 | 0.0829 | 0.2879 | 0.2268 | 0.915 |\n", - "Repeat 32/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 33/50 seed=82\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:20:09.666558: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774790409.677084 1559793 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774790409.679845 1559793 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774790409.687309 1559793 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790409.687340 1559793 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790409.687342 1559793 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790409.687343 1559793 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] seed=82\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=82 ===\n", - "z mean: 1.6321 (±0.0202), Var: 1.0250, Range: [-1.7752, 4.9655]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790430.371014 1559793 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774790431.880305 1560152 service.cc:152] XLA service 0x7fc8880070f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774790431.880332 1560152 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790432.089761 1560152 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790433.752117 1560152 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcab0765870> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcaa853ca60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4046, sigma²=0.6747, nugget=0.0093\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1927, sigma²=0.3643, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1270, sigma²=0.2541, nugget=0.0011\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0965, sigma²=0.2096, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1012, sigma²=0.2132, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1007, sigma²=0.1993, nugget=0.0009\n", - "[repeat 32] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4046, sigma²=0.6747, nugget=0.0093\n", - "[repeat 32] done → repeat_32_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 10.185 | 3.1914 | 0.8932 | -10.8695 |\n", - "| OLS_sphere | 200 | 0.1497 | 0.3869 | 0.3034 | 0.8255 |\n", - "| DeepKriging_wendland | -- | 0.4979 | 0.7056 | 0.5388 | 0.4197 |\n", - "| DeepKriging_mrts | 10 | 0.1705 | 0.4129 | 0.3202 | 0.8013 |\n", - "| DeepKriging_sphere | 10 | 0.1066 | 0.3264 | 0.2567 | 0.8758 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.1015 | 0.3186 | 0.2498 | 0.8817 |\n", - "| UniversalKriging | 10 | 0.0847 | 0.291 | 0.2264 | 0.9013 |\n", - "Repeat 33/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 34/50 seed=83\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:28:14.719920: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774790894.730088 1927694 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774790894.733123 1927694 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774790894.740275 1927694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790894.740302 1927694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790894.740304 1927694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774790894.740305 1927694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] seed=83\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=83 ===\n", - "z mean: 1.3743 (±0.0226), Var: 1.2815, Range: [-3.0381, 4.5444]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790915.473903 1927694 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774790917.026954 1928055 service.cc:152] XLA service 0x560ac8983800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774790917.026977 1928055 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790917.248778 1928055 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774790918.909191 1928055 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1aa07d6b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1aa013f910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5024, sigma²=0.7937, nugget=0.0121\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4913, sigma²=0.7404, nugget=0.0128\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4910, sigma²=0.7340, nugget=0.0129\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4910, sigma²=0.7313, nugget=0.0130\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4911, sigma²=0.7293, nugget=0.0130\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4916, sigma²=0.7241, nugget=0.0130\n", - "[repeat 33] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4916, sigma²=0.7241, nugget=0.0130\n", - "[repeat 33] done → repeat_33_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7052 | 0.8398 | 0.613 | 0.4444 |\n", - "| OLS_sphere | 200 | 0.1628 | 0.4034 | 0.3191 | 0.8718 |\n", - "| DeepKriging_wendland | -- | 0.5015 | 0.7082 | 0.5218 | 0.6049 |\n", - "| DeepKriging_mrts | 10 | 0.1777 | 0.4216 | 0.3365 | 0.86 |\n", - "| DeepKriging_sphere | 50 | 0.108 | 0.3286 | 0.2574 | 0.9149 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.102 | 0.3194 | 0.2522 | 0.9196 |\n", - "| UniversalKriging | 1000 | 0.1026 | 0.3204 | 0.2601 | 0.9191 |\n", - "Repeat 34/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 35/50 seed=84\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:36:23.064670: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774791383.074713 2299009 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774791383.077501 2299009 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774791383.084775 2299009 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791383.084815 2299009 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791383.084817 2299009 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791383.084818 2299009 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] seed=84\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=84 ===\n", - "z mean: 1.5603 (±0.0163), Var: 0.6678, Range: [-0.9553, 4.1785]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791403.928711 2299009 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774791405.458461 2299374 service.cc:152] XLA service 0x560af402b490 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774791405.458487 2299374 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791405.673975 2299374 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791407.333306 2299374 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f60a0757400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f60a0292d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2929, sigma²=0.5278, nugget=0.0059\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1793, sigma²=0.3524, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1187, sigma²=0.2506, nugget=0.0012\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0873, sigma²=0.1953, nugget=0.0006\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0803, sigma²=0.1743, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1767, sigma²=0.3074, nugget=0.0014\n", - "[repeat 34] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1767, sigma²=0.3074, nugget=0.0014\n", - "[repeat 34] done → repeat_34_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.919 | 1.7085 | 0.6083 | -3.6005 |\n", - "| OLS_sphere | 200 | 0.1622 | 0.4028 | 0.3218 | 0.7443 |\n", - "| DeepKriging_wendland | -- | 0.2746 | 0.524 | 0.3842 | 0.5672 |\n", - "| DeepKriging_mrts | 150 | 0.1158 | 0.3403 | 0.2727 | 0.8175 |\n", - "| DeepKriging_sphere | 50 | 0.0978 | 0.3127 | 0.2543 | 0.8458 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0969 | 0.3113 | 0.248 | 0.8473 |\n", - "| UniversalKriging | 1000 | 0.091 | 0.3016 | 0.2387 | 0.8566 |\n", - "Repeat 35/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 36/50 seed=85\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:45:42.703452: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774791942.713318 2785815 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774791942.716103 2785815 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774791942.723414 2785815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791942.723452 2785815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791942.723454 2785815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774791942.723455 2785815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] seed=85\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=85 ===\n", - "z mean: 1.2389 (±0.0166), Var: 0.6869, Range: [-1.2404, 3.9469]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791963.707228 2785815 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774791965.239405 2786180 service.cc:152] XLA service 0x7ff5dc0078f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774791965.239432 2786180 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791965.462995 2786180 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774791967.145039 2786180 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff800587760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff7f87f2f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2367, sigma²=0.4387, nugget=0.0048\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1935, sigma²=0.3703, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1955, sigma²=0.3632, nugget=0.0025\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1341, sigma²=0.2637, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1287, sigma²=0.2506, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0949, sigma²=0.1641, nugget=0.0003\n", - "[repeat 35] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1287, sigma²=0.2506, nugget=0.0008\n", - "[repeat 35] done → repeat_35_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5921 | 0.7695 | 0.5854 | 0.0198 |\n", - "| OLS_sphere | 200 | 0.1233 | 0.3512 | 0.2815 | 0.7959 |\n", - "| DeepKriging_wendland | -- | 0.4534 | 0.6734 | 0.5125 | 0.2494 |\n", - "| DeepKriging_mrts | 10 | 0.1205 | 0.3471 | 0.2729 | 0.8005 |\n", - "| DeepKriging_sphere | 50 | 0.0888 | 0.298 | 0.2425 | 0.8529 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0835 | 0.2889 | 0.2337 | 0.8618 |\n", - "| UniversalKriging | 200 | 0.0785 | 0.2803 | 0.2269 | 0.87 |\n", - "Repeat 36/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 37/50 seed=86\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 21:54:05.574712: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774792445.584915 3185230 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774792445.587925 3185230 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774792445.595988 3185230 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792445.596021 3185230 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792445.596024 3185230 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792445.596025 3185230 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] seed=86\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=86 ===\n", - "z mean: 1.0912 (±0.0248), Var: 1.5376, Range: [-3.0567, 4.2445]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792466.532584 3185230 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774792468.042873 3185591 service.cc:152] XLA service 0x7fe0f4009550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774792468.042898 3185591 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792468.261362 3185591 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792469.902531 3185591 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe3282b9b40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe320313ac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3519, sigma²=0.5864, nugget=0.0107\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3340, sigma²=0.5593, nugget=0.0084\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3134, sigma²=0.5325, nugget=0.0068\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3118, sigma²=0.5272, nugget=0.0068\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2870, sigma²=0.5016, nugget=0.0050\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2890, sigma²=0.4927, nugget=0.0051\n", - "[repeat 36] UniversalKriging best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3134, sigma²=0.5325, nugget=0.0068\n", - "[repeat 36] done → repeat_36_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.6137 | 1.2703 | 0.8483 | -0.1342 |\n", - "| OLS_sphere | 200 | 0.13 | 0.3606 | 0.2849 | 0.9086 |\n", - "| DeepKriging_wendland | -- | 0.853 | 0.9236 | 0.691 | 0.4005 |\n", - "| DeepKriging_mrts | 10 | 0.1348 | 0.3671 | 0.2899 | 0.9053 |\n", - "| DeepKriging_sphere | 150 | 0.0821 | 0.2866 | 0.2246 | 0.9423 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0835 | 0.289 | 0.2265 | 0.9413 |\n", - "| UniversalKriging | 100 | 0.0802 | 0.2832 | 0.2243 | 0.9436 |\n", - "Repeat 37/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 38/50 seed=87\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:02:27.903147: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774792947.913195 3613330 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774792947.916261 3613330 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774792947.923725 3613330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792947.923752 3613330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792947.923754 3613330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774792947.923755 3613330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] seed=87\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=87 ===\n", - "z mean: 0.8601 (±0.0221), Var: 1.2188, Range: [-2.9276, 4.2298]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792969.075488 3613330 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774792970.621230 3613695 service.cc:152] XLA service 0x7f63e4007900 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774792970.621257 3613695 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792970.847373 3613695 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774792972.512034 3613695 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f661455f0a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f660c7bedd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5683, sigma²=0.8976, nugget=0.0122\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4781, sigma²=0.7747, nugget=0.0094\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3653, sigma²=0.6176, nugget=0.0062\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2785, sigma²=0.4860, nugget=0.0041\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0643, sigma²=0.1511, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1570, sigma²=0.2739, nugget=0.0015\n", - "[repeat 37] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1570, sigma²=0.2739, nugget=0.0015\n", - "[repeat 37] done → repeat_37_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|----------|\n", - "| OLS_wendland | -- | 48.9732 | 6.9981 | 1.4375 | -37.7989 |\n", - "| OLS_sphere | 200 | 0.1615 | 0.4019 | 0.3131 | 0.872 |\n", - "| DeepKriging_wendland | -- | 0.9093 | 0.9536 | 0.7145 | 0.2796 |\n", - "| DeepKriging_mrts | 50 | 0.1198 | 0.3461 | 0.2669 | 0.9051 |\n", - "| DeepKriging_sphere | 50 | 0.0924 | 0.304 | 0.2299 | 0.9268 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.0944 | 0.3073 | 0.2354 | 0.9252 |\n", - "| UniversalKriging | 1000 | 0.0839 | 0.2897 | 0.2243 | 0.9335 |\n", - "Repeat 38/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 39/50 seed=88\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:11:01.251540: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774793461.261540 4050030 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774793461.264274 4050030 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774793461.271968 4050030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793461.271997 4050030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793461.271999 4050030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793461.272000 4050030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] seed=88\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=88 ===\n", - "z mean: 0.6801 (±0.0182), Var: 0.8296, Range: [-2.0099, 3.7150]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793482.372786 4050030 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774793483.901863 4050388 service.cc:152] XLA service 0x555ff08838b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774793483.901891 4050388 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793484.124349 4050388 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793485.791333 4050388 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6f063a8c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6f017f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4805, sigma²=0.7121, nugget=0.0097\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2655, sigma²=0.4285, nugget=0.0038\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1856, sigma²=0.3118, nugget=0.0017\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0671, sigma²=0.1462, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1221, sigma²=0.2170, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1397, sigma²=0.2283, nugget=0.0014\n", - "[repeat 38] UniversalKriging best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1856, sigma²=0.3118, nugget=0.0017\n", - "[repeat 38] done → repeat_38_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7264 | 0.8523 | 0.6668 | 0.267 |\n", - "| OLS_sphere | 200 | 0.1325 | 0.364 | 0.2892 | 0.8663 |\n", - "| DeepKriging_wendland | -- | 0.4533 | 0.6733 | 0.5098 | 0.5426 |\n", - "| DeepKriging_mrts | 10 | 0.1412 | 0.3757 | 0.2973 | 0.8575 |\n", - "| DeepKriging_sphere | 10 | 0.0974 | 0.3121 | 0.2504 | 0.9017 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.1075 | 0.3279 | 0.2601 | 0.8915 |\n", - "| UniversalKriging | 100 | 0.0827 | 0.2876 | 0.2206 | 0.9165 |\n", - "Repeat 39/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 40/50 seed=89\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:18:54.194283: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774793934.206156 224580 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774793934.209124 224580 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774793934.216572 224580 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793934.216603 224580 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793934.216605 224580 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774793934.216606 224580 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] seed=89\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=89 ===\n", - "z mean: 1.3168 (±0.0181), Var: 0.8205, Range: [-1.2173, 4.1009]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793955.437978 224580 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774793956.985644 224947 service.cc:152] XLA service 0x563481962200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774793956.985671 224947 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793957.203387 224947 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774793958.886563 224947 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1b64777130> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1b642abc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2883, sigma²=0.4744, nugget=0.0070\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2068, sigma²=0.3634, nugget=0.0026\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1996, sigma²=0.3481, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1902, sigma²=0.3294, nugget=0.0021\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1888, sigma²=0.3245, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1835, sigma²=0.3022, nugget=0.0017\n", - "[repeat 39] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1902, sigma²=0.3294, nugget=0.0021\n", - "[repeat 39] done → repeat_39_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.3936 | 1.1805 | 0.718 | -0.7773 |\n", - "| OLS_sphere | 200 | 0.1438 | 0.3792 | 0.2921 | 0.8167 |\n", - "| DeepKriging_wendland | -- | 0.4136 | 0.6431 | 0.4823 | 0.4726 |\n", - "| DeepKriging_mrts | 50 | 0.1452 | 0.3811 | 0.2983 | 0.8148 |\n", - "| DeepKriging_sphere | 100 | 0.1044 | 0.3231 | 0.2477 | 0.8669 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1109 | 0.333 | 0.2587 | 0.8586 |\n", - "| UniversalKriging | 150 | 0.0955 | 0.309 | 0.2391 | 0.8782 |\n", - "Repeat 40/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 41/50 seed=90\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:27:17.208878: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774794437.218568 637710 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774794437.221504 637710 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774794437.228986 637710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794437.229019 637710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794437.229021 637710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794437.229023 637710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] seed=90\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=90 ===\n", - "z mean: 0.3703 (±0.0187), Var: 0.8759, Range: [-2.3760, 3.1646]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794458.198312 637710 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774794459.703012 638076 service.cc:152] XLA service 0x7f9914007fa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774794459.703047 638076 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794459.918621 638076 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794461.577341 638076 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9b403931c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9b385d77f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4260, sigma²=0.6971, nugget=0.0096\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1840, sigma²=0.3428, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1294, sigma²=0.2531, nugget=0.0010\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1481, sigma²=0.2786, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1511, sigma²=0.2807, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0510, sigma²=0.0781, nugget=0.0001\n", - "[repeat 40] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1481, sigma²=0.2786, nugget=0.0019\n", - "[repeat 40] done → repeat_40_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6681 | 0.8174 | 0.6333 | 0.2183 |\n", - "| OLS_sphere | 200 | 0.1291 | 0.3593 | 0.2764 | 0.8489 |\n", - "| DeepKriging_wendland | -- | 0.4973 | 0.7052 | 0.5228 | 0.4181 |\n", - "| DeepKriging_mrts | 10 | 0.1413 | 0.3758 | 0.2948 | 0.8347 |\n", - "| DeepKriging_sphere | 50 | 0.0971 | 0.3116 | 0.243 | 0.8864 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1043 | 0.3229 | 0.2463 | 0.878 |\n", - "| UniversalKriging | 150 | 0.0818 | 0.2859 | 0.2134 | 0.9043 |\n", - "Repeat 41/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 42/50 seed=91\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:35:19.836432: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774794919.846437 996265 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774794919.849211 996265 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774794919.856577 996265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794919.856622 996265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794919.856625 996265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774794919.856626 996265 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] seed=91\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=91 ===\n", - "z mean: 1.1393 (±0.0181), Var: 0.8218, Range: [-1.5814, 3.7554]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794940.651361 996265 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774794942.158454 996627 service.cc:152] XLA service 0x7fbe18006020 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774794942.158478 996627 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794942.378678 996627 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774794944.033164 996627 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc0546dac20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc04c4c6200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2963, sigma²=0.5235, nugget=0.0058\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1488, sigma²=0.2955, nugget=0.0013\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0903, sigma²=0.2005, nugget=0.0005\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1433, sigma²=0.2789, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1469, sigma²=0.2824, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1870, sigma²=0.3163, nugget=0.0014\n", - "[repeat 41] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1469, sigma²=0.2824, nugget=0.0022\n", - "[repeat 41] done → repeat_41_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.583 | 1.2582 | 0.6723 | -0.6825 |\n", - "| OLS_sphere | 200 | 0.1512 | 0.3888 | 0.3076 | 0.8393 |\n", - "| DeepKriging_wendland | -- | 0.4847 | 0.6962 | 0.5327 | 0.4848 |\n", - "| DeepKriging_mrts | 10 | 0.1559 | 0.3949 | 0.3075 | 0.8343 |\n", - "| DeepKriging_sphere | 50 | 0.1106 | 0.3325 | 0.2587 | 0.8825 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1094 | 0.3308 | 0.2573 | 0.8837 |\n", - "| UniversalKriging | 200 | 0.1044 | 0.3231 | 0.2466 | 0.889 |\n", - "Repeat 42/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 43/50 seed=92\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:43:48.976219: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774795428.986278 1399571 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774795428.989218 1399571 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774795428.997567 1399571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795428.997599 1399571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795428.997601 1399571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795428.997602 1399571 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] seed=92\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=92 ===\n", - "z mean: 0.2381 (±0.0173), Var: 0.7493, Range: [-2.7554, 3.2216]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795449.690188 1399571 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774795451.221567 1399927 service.cc:152] XLA service 0x7f457001a3d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774795451.221591 1399927 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795451.435411 1399927 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795453.095803 1399927 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f47a8116cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4798343490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3053, sigma²=0.5431, nugget=0.0069\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1728, sigma²=0.3362, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1128, sigma²=0.2367, nugget=0.0008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0576, sigma²=0.1518, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1388, sigma²=0.2634, nugget=0.0011\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0254, sigma²=0.0479, nugget=0.0000\n", - "[repeat 42] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1388, sigma²=0.2634, nugget=0.0011\n", - "[repeat 42] done → repeat_42_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 3.3078 | 1.8187 | 0.6117 | -4.4613 |\n", - "| OLS_sphere | 200 | 0.16 | 0.4 | 0.3144 | 0.7358 |\n", - "| DeepKriging_wendland | -- | 0.3126 | 0.5591 | 0.4391 | 0.484 |\n", - "| DeepKriging_mrts | 50 | 0.1586 | 0.3983 | 0.3152 | 0.7381 |\n", - "| DeepKriging_sphere | 150 | 0.0954 | 0.3088 | 0.2392 | 0.8426 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.0994 | 0.3153 | 0.2488 | 0.8359 |\n", - "| UniversalKriging | 200 | 0.0933 | 0.3055 | 0.2413 | 0.8459 |\n", - "Repeat 43/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 44/50 seed=93\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 22:52:25.809808: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774795945.820049 1821213 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774795945.822911 1821213 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774795945.830152 1821213 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795945.830178 1821213 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795945.830180 1821213 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774795945.830181 1821213 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] seed=93\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=93 ===\n", - "z mean: 0.6032 (±0.0188), Var: 0.8865, Range: [-2.4669, 3.7870]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795966.686790 1821213 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774795968.211884 1821571 service.cc:152] XLA service 0x55f24f8b45b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774795968.211906 1821571 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795968.434665 1821571 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774795970.078249 1821571 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa3d47dfd00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa3d4143130> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3513, sigma²=0.6085, nugget=0.0080\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1530, sigma²=0.3067, nugget=0.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1271, sigma²=0.2575, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1560, sigma²=0.3009, nugget=0.0023\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1658, sigma²=0.3136, nugget=0.0027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2012, sigma²=0.3486, nugget=0.0022\n", - "[repeat 43] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1560, sigma²=0.3009, nugget=0.0023\n", - "[repeat 43] done → repeat_43_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5705 | 0.7553 | 0.6053 | 0.3787 |\n", - "| OLS_sphere | 200 | 0.1701 | 0.4124 | 0.3261 | 0.8148 |\n", - "| DeepKriging_wendland | -- | 0.4478 | 0.6691 | 0.5238 | 0.5124 |\n", - "| DeepKriging_mrts | 50 | 0.1529 | 0.391 | 0.3096 | 0.8335 |\n", - "| DeepKriging_sphere | 150 | 0.1112 | 0.3334 | 0.2619 | 0.8789 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.119 | 0.345 | 0.271 | 0.8704 |\n", - "| UniversalKriging | 150 | 0.102 | 0.3194 | 0.2481 | 0.8889 |\n", - "Repeat 44/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 45/50 seed=94\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:00:44.473822: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774796444.484430 2208122 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774796444.487278 2208122 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774796444.494713 2208122 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796444.494746 2208122 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796444.494748 2208122 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796444.494749 2208122 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] seed=94\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=94 ===\n", - "z mean: 0.4957 (±0.0226), Var: 1.2810, Range: [-2.7231, 3.6456]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796465.261062 2208122 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774796466.806404 2208486 service.cc:152] XLA service 0x7f64d4009980 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774796466.806436 2208486 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796467.027780 2208486 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796468.686308 2208486 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f67001470a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f66f83976d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3224, sigma²=0.5304, nugget=0.0093\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3203, sigma²=0.5174, nugget=0.0092\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3204, sigma²=0.5144, nugget=0.0092\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3182, sigma²=0.5130, nugget=0.0086\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3182, sigma²=0.5117, nugget=0.0086\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3167, sigma²=0.5064, nugget=0.0081\n", - "[repeat 44] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3167, sigma²=0.5064, nugget=0.0081\n", - "[repeat 44] done → repeat_44_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 2.276 | 1.5086 | 0.8977 | -0.735 |\n", - "| OLS_sphere | 200 | 0.1479 | 0.3846 | 0.3123 | 0.8872 |\n", - "| DeepKriging_wendland | -- | 0.9389 | 0.969 | 0.7146 | 0.2843 |\n", - "| DeepKriging_mrts | 1000 | 0.158 | 0.3974 | 0.3093 | 0.8796 |\n", - "| DeepKriging_sphere | 50 | 0.0999 | 0.3161 | 0.2531 | 0.9238 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1077 | 0.3281 | 0.262 | 0.9179 |\n", - "| UniversalKriging | 1000 | 0.0839 | 0.2897 | 0.2293 | 0.936 |\n", - "Repeat 45/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 46/50 seed=95\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:08:49.461319: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774796929.471263 2583141 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774796929.474243 2583141 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774796929.482031 2583141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796929.482053 2583141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796929.482055 2583141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774796929.482056 2583141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] seed=95\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=95 ===\n", - "z mean: 0.9872 (±0.0186), Var: 0.8638, Range: [-1.4957, 4.1725]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796950.365740 2583141 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774796951.910752 2583509 service.cc:152] XLA service 0x7fdf1c006670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774796951.910784 2583509 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796952.124417 2583509 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774796953.771702 2583509 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe1487c7370> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe14810fc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3021, sigma²=0.5246, nugget=0.0066\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2000, sigma²=0.3762, nugget=0.0024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1240, sigma²=0.2522, nugget=0.0011\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1990, sigma²=0.3590, nugget=0.0024\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1976, sigma²=0.3527, nugget=0.0022\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1595, sigma²=0.2882, nugget=0.0024\n", - "[repeat 45] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1595, sigma²=0.2882, nugget=0.0024\n", - "[repeat 45] done → repeat_45_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5947 | 0.7712 | 0.6148 | 0.2954 |\n", - "| OLS_sphere | 200 | 0.1455 | 0.3815 | 0.2901 | 0.8276 |\n", - "| DeepKriging_wendland | -- | 0.467 | 0.6834 | 0.53 | 0.4467 |\n", - "| DeepKriging_mrts | 1000 | 0.1347 | 0.3671 | 0.2855 | 0.8404 |\n", - "| DeepKriging_sphere | 50 | 0.0844 | 0.2905 | 0.226 | 0.9 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.084 | 0.2898 | 0.2242 | 0.9005 |\n", - "| UniversalKriging | 1000 | 0.0752 | 0.2742 | 0.2143 | 0.9109 |\n", - "Repeat 46/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 47/50 seed=96\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:17:23.101791: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774797443.111626 3003417 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774797443.114493 3003417 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774797443.122247 3003417 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797443.122280 3003417 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797443.122282 3003417 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797443.122283 3003417 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] seed=96\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=96 ===\n", - "z mean: 1.2473 (±0.0187), Var: 0.8706, Range: [-1.3703, 4.0834]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797463.855515 3003417 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774797465.373274 3003773 service.cc:152] XLA service 0x7f2fe0007e10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774797465.373302 3003773 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797465.591863 3003773 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797467.271346 3003773 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f320c583d00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f320435b760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5587, sigma²=0.8697, nugget=0.0127\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3824, sigma²=0.6453, nugget=0.0067\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1157, sigma²=0.2394, nugget=0.0009\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0858, sigma²=0.1884, nugget=0.0005\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0512, sigma²=0.1379, nugget=0.0002\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1256, sigma²=0.2330, nugget=0.0014\n", - "[repeat 46] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3824, sigma²=0.6453, nugget=0.0067\n", - "[repeat 46] done → repeat_46_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7427 | 0.8618 | 0.628 | 0.1488 |\n", - "| OLS_sphere | 200 | 0.13 | 0.3606 | 0.276 | 0.851 |\n", - "| DeepKriging_wendland | -- | 0.517 | 0.7191 | 0.5282 | 0.4074 |\n", - "| DeepKriging_mrts | 1000 | 0.1488 | 0.3857 | 0.3016 | 0.8295 |\n", - "| DeepKriging_sphere | 150 | 0.0955 | 0.309 | 0.2368 | 0.8905 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.1 | 0.3163 | 0.2487 | 0.8854 |\n", - "| UniversalKriging | 50 | 0.085 | 0.2916 | 0.2259 | 0.9026 |\n", - "Repeat 47/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 48/50 seed=97\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:25:27.282525: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774797927.293061 3366937 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774797927.295890 3366937 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774797927.303199 3366937 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797927.303223 3366937 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797927.303225 3366937 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774797927.303226 3366937 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] seed=97\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=97 ===\n", - "z mean: 1.1225 (±0.0177), Var: 0.7863, Range: [-1.7244, 4.2283]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797948.274870 3366937 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774797949.808148 3367307 service.cc:152] XLA service 0x55b81d639880 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774797949.808171 3367307 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797950.026265 3367307 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774797951.678615 3367307 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18cc5b2200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18c47ef9a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3958, sigma²=0.6377, nugget=0.0085\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2805, sigma²=0.4827, nugget=0.0039\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1819, sigma²=0.3272, nugget=0.0020\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1193, sigma²=0.2292, nugget=0.0007\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0785, sigma²=0.1635, nugget=0.0004\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1429, sigma²=0.2355, nugget=0.0009\n", - "[repeat 47] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1429, sigma²=0.2355, nugget=0.0009\n", - "[repeat 47] done → repeat_47_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|------------|\n", - "| OLS_wendland | -- | 2465.73 | 49.6561 | 3.7605 | -2746.36 |\n", - "| OLS_sphere | 200 | 0.1544 | 0.3929 | 0.3185 | 0.828 |\n", - "| DeepKriging_wendland | -- | 0.4572 | 0.6762 | 0.5179 | 0.4906 |\n", - "| DeepKriging_mrts | 10 | 0.1546 | 0.3932 | 0.3133 | 0.8278 |\n", - "| DeepKriging_sphere | 150 | 0.1099 | 0.3315 | 0.272 | 0.8776 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1253 | 0.354 | 0.2884 | 0.8604 |\n", - "| UniversalKriging | 1000 | 0.0994 | 0.3153 | 0.2576 | 0.8892 |\n", - "Repeat 48/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 49/50 seed=98\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:33:51.829698: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774798431.839592 3764252 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774798431.842594 3764252 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774798431.849937 3764252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798431.849972 3764252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798431.849975 3764252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798431.849976 3764252 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] seed=98\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=98 ===\n", - "z mean: 1.5023 (±0.0175), Var: 0.7671, Range: [-1.3576, 4.4569]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798452.761584 3764252 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774798454.280471 3764619 service.cc:152] XLA service 0x5608784e5310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774798454.280496 3764619 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798454.498935 3764619 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798456.170605 3764619 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb45425b250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb44c1ec9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3027, sigma²=0.5366, nugget=0.0075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1403, sigma²=0.2888, nugget=0.0015\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1683, sigma²=0.3230, nugget=0.0018\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1698, sigma²=0.3203, nugget=0.0018\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1701, sigma²=0.3175, nugget=0.0017\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1813, sigma²=0.3194, nugget=0.0016\n", - "[repeat 48] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1813, sigma²=0.3194, nugget=0.0016\n", - "[repeat 48] done → repeat_48_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.2828 | 1.1326 | 0.658 | -1.0655 |\n", - "| OLS_sphere | 200 | 0.1397 | 0.3737 | 0.2881 | 0.7751 |\n", - "| DeepKriging_wendland | -- | 0.4083 | 0.639 | 0.4889 | 0.3425 |\n", - "| DeepKriging_mrts | 1000 | 0.1643 | 0.4053 | 0.3185 | 0.7355 |\n", - "| DeepKriging_sphere | 50 | 0.0968 | 0.3111 | 0.238 | 0.8441 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0967 | 0.311 | 0.2395 | 0.8443 |\n", - "| UniversalKriging | 1000 | 0.0901 | 0.3002 | 0.234 | 0.8549 |\n", - "Repeat 49/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 50/50 seed=99\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 23:42:25.055736: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774798945.065387 4183065 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774798945.068232 4183065 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774798945.075543 4183065 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798945.075568 4183065 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798945.075570 4183065 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774798945.075571 4183065 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] seed=99\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== GP Pure (no noise) | seed=99 ===\n", - "z mean: 0.9038 (±0.0176), Var: 0.7732, Range: [-2.2137, 3.7991]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798965.988935 4183065 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774798967.501649 4183431 service.cc:152] XLA service 0x7fa06c007790 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774798967.501674 4183431 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798967.723982 4183431 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774798969.396175 4183431 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa2a05169e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa298349e10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2824, sigma²=0.4519, nugget=0.0060\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1496, sigma²=0.2698, nugget=0.0016\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1641, sigma²=0.2827, nugget=0.0016\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1694, sigma²=0.2847, nugget=0.0018\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1711, sigma²=0.2850, nugget=0.0018\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1491, sigma²=0.2577, nugget=0.0028\n", - "[repeat 49] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1496, sigma²=0.2698, nugget=0.0016\n", - "[repeat 49] done → repeat_49_GP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 6.1784 | 2.4856 | 0.7763 | -7.4354 |\n", - "| OLS_sphere | 200 | 0.1471 | 0.3835 | 0.3091 | 0.7992 |\n", - "| DeepKriging_wendland | -- | 0.4155 | 0.6446 | 0.4965 | 0.4327 |\n", - "| DeepKriging_mrts | 50 | 0.1315 | 0.3626 | 0.2919 | 0.8205 |\n", - "| DeepKriging_sphere | 200 | 0.1189 | 0.3448 | 0.2676 | 0.8377 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.1195 | 0.3457 | 0.2651 | 0.8368 |\n", - "| UniversalKriging | 50 | 0.0988 | 0.3143 | 0.2463 | 0.8652 |\n", - "Repeat 50/50 done — checkpoint saved.\n", - "\n", - "All done: 50/50 repeats completed.\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_GP_pure.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_GP_pure.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"Starting fresh.\")\n", - "\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n\" + \"=\"*70)\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(\"=\"*70)\n", - "\n", - " out_json = f\"repeat_{repeat}_GP_pure_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "summary_GP_pure", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-29T15:51:06.972148Z", - "iopub.status.busy": "2026-03-29T15:51:06.971976Z", - "iopub.status.idle": "2026-03-29T15:51:06.979323Z", - "shell.execute_reply": "2026-03-29T15:51:06.978193Z" - }, - "papermill": { - "duration": 0.030087, - "end_time": "2026-03-29T15:51:06.979877", - "exception": false, - "start_time": "2026-03-29T15:51:06.949790", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "Summary — 50 Repeats\n", - " Scenario: GP Pure (no noise)\n", - "================================================================================\n", - "\n", - "| Model | N | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|--------------------------|-----|-------------------|-------------------|-------------------|-------------------|\n", - "| OLS_wendland | 50 | 82.52±389.65 | 3.49±8.39 | 0.85±0.56 | -88.943±426.732 |\n", - "| OLS_sphere | 50 | 0.15±0.02 | 0.39±0.02 | 0.31±0.02 | 0.821±0.045 |\n", - "| DeepKriging_wendland | 50 | 0.52±0.14 | 0.71±0.09 | 0.54±0.07 | 0.426±0.085 |\n", - "| DeepKriging_mrts | 50 | 0.15±0.02 | 0.39±0.03 | 0.31±0.02 | 0.826±0.046 |\n", - "| DeepKriging_sphere | 50 | 0.10±0.01 | 0.32±0.02 | 0.25±0.01 | 0.881±0.029 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.10±0.01 | 0.32±0.02 | 0.25±0.01 | 0.879±0.031 |\n", - "| UniversalKriging | 50 | 0.09±0.01 | 0.30±0.01 | 0.24±0.01 | 0.893±0.028 |\n", - "\n", - "Best Model: UniversalKriging RMSE=0.30±0.01\n", - "\n", - "── Ranking by mean RMSE ──\n", - " 1. UniversalKriging RMSE=0.30±0.01\n", - " 2. DeepKriging_sphere RMSE=0.32±0.02\n", - " 3. DeepKriging_sphere_Huber RMSE=0.32±0.02\n", - " 4. OLS_sphere RMSE=0.39±0.02\n", - " 5. DeepKriging_mrts RMSE=0.39±0.03\n", - " 6. DeepKriging_wendland RMSE=0.71±0.09\n", - " 7. OLS_wendland RMSE=3.49±8.39\n" - ] - } - ], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(\"checkpoint_GP_pure.json\") as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"Summary — {n} Repeats\")\n", - "print(\" Scenario: GP Pure (no noise)\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 25504.104741, - "end_time": "2026-03-29T15:51:08.120511", - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_GP_pure_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-29T08:46:04.015770", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb deleted file mode 100644 index 09e0784..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb +++ /dev/null @@ -1,10222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d1cde2eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:27.964986Z", - "iopub.status.busy": "2026-03-28T09:36:27.964774Z", - "iopub.status.idle": "2026-03-28T09:36:27.970045Z", - "shell.execute_reply": "2026-03-28T09:36:27.969250Z" - }, - "papermill": { - "duration": 0.010402, - "end_time": "2026-03-28T09:36:27.970639", - "exception": false, - "start_time": "2026-03-28T09:36:27.960237", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8890884f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:27.974212Z", - "iopub.status.busy": "2026-03-28T09:36:27.974064Z", - "iopub.status.idle": "2026-03-28T09:36:30.007037Z", - "shell.execute_reply": "2026-03-28T09:36:30.006162Z" - }, - "papermill": { - "duration": 2.035506, - "end_time": "2026-03-28T09:36:30.007619", - "exception": false, - "start_time": "2026-03-28T09:36:27.972113", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:36:28.647320: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774690588.656943 512576 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774690588.660028 512576 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774690588.667474 512576 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690588.667485 512576 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690588.667486 512576 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690588.667487 512576 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46d6d1ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:30.012160Z", - "iopub.status.busy": "2026-03-28T09:36:30.011910Z", - "iopub.status.idle": "2026-03-28T09:36:30.929473Z", - "shell.execute_reply": "2026-03-28T09:36:30.928101Z" - }, - "papermill": { - "duration": 0.920969, - "end_time": "2026-03-28T09:36:30.930051", - "exception": false, - "start_time": "2026-03-28T09:36:30.009082", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8dd094a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:30.934540Z", - "iopub.status.busy": "2026-03-28T09:36:30.934382Z", - "iopub.status.idle": "2026-03-28T09:36:30.936987Z", - "shell.execute_reply": "2026-03-28T09:36:30.936261Z" - }, - "papermill": { - "duration": 0.005925, - "end_time": "2026-03-28T09:36:30.937422", - "exception": false, - "start_time": "2026-03-28T09:36:30.931497", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f0aafba7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:30.940709Z", - "iopub.status.busy": "2026-03-28T09:36:30.940579Z", - "iopub.status.idle": "2026-03-28T09:36:43.787286Z", - "shell.execute_reply": "2026-03-28T09:36:43.786463Z" - }, - "papermill": { - "duration": 12.849027, - "end_time": "2026-03-28T09:36:43.787801", - "exception": false, - "start_time": "2026-03-28T09:36:30.938774", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32986e25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.792745Z", - "iopub.status.busy": "2026-03-28T09:36:43.792454Z", - "iopub.status.idle": "2026-03-28T09:36:43.797942Z", - "shell.execute_reply": "2026-03-28T09:36:43.797217Z" - }, - "papermill": { - "duration": 0.008999, - "end_time": "2026-03-28T09:36:43.798486", - "exception": false, - "start_time": "2026-03-28T09:36:43.789487", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_pure(num_sample, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-GP data: deterministic macro-trend + nonstationary anomalies only.\n", - " No noise, no Gaussian Process component.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - "\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - "\n", - " base_wind = 5.0\n", - " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", - " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", - " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", - " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) & (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", - " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", - "\n", - " num_anomalies = 60\n", - " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", - " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", - " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", - " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", - " az = np.sin(anomaly_lats)\n", - " anomaly_effect = np.zeros(num_sample)\n", - " for i in range(num_anomalies):\n", - " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", - " amplitude = rng.uniform(-10.0, 18.0)\n", - " radius = rng.uniform(0.005, 0.03)\n", - " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", - "\n", - " mean_trend += anomaly_effect\n", - " z_final = np.maximum(mean_trend, 0.5).astype(np.float32)\n", - "\n", - " print(f\"\\n=== Scenario {scenario} (Non-GP: Pure Nonstationary Trend, no noise) ===\")\n", - " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (\\u00b1{np.std(z_final) / np.sqrt(num_sample):.4f}), Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect\n", - " gc.collect()\n", - "\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5c1e9f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.801943Z", - "iopub.status.busy": "2026-03-28T09:36:43.801818Z", - "iopub.status.idle": "2026-03-28T09:36:43.808817Z", - "shell.execute_reply": "2026-03-28T09:36:43.807788Z" - }, - "papermill": { - "duration": 0.00948, - "end_time": "2026-03-28T09:36:43.809365", - "exception": false, - "start_time": "2026-03-28T09:36:43.799885", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " categorical_data = None\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(wendland(location, grid, theta=theta, k=2))\n", - " del grid\n", - " gc.collect()\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " X_train_cont = basis[train_x1]\n", - " X_val_cont = basis[val_x1]\n", - " X_test_cont = basis[test_x1]\n", - " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", - " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", - " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", - " if categorical_data is None:\n", - " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " return (X_train_flat, X_train_cat, y_train_flat,\n", - " X_val_flat, X_val_cat, y_val_flat,\n", - " X_test_flat, X_test_cat, y_test_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6728b7b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.812663Z", - "iopub.status.busy": "2026-03-28T09:36:43.812540Z", - "iopub.status.idle": "2026-03-28T09:36:43.819834Z", - "shell.execute_reply": "2026-03-28T09:36:43.819022Z" - }, - "papermill": { - "duration": 0.009918, - "end_time": "2026-03-28T09:36:43.820579", - "exception": false, - "start_time": "2026-03-28T09:36:43.810661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " return metrics, model\n", - "\n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", - " epochs=epochs, batch_size=batch_size, verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", - " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", - " dropout_rate=dropout_rate, optimizer=_opt,\n", - " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", - " patience=40, verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience,\n", - " restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5,\n", - " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - " val_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset, validation_data=val_dataset,\n", - " epochs=epochs, callbacks=callbacks, verbose=0)\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - "\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " return metrics, model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce398870", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.823892Z", - "iopub.status.busy": "2026-03-28T09:36:43.823771Z", - "iopub.status.idle": "2026-03-28T09:36:43.833355Z", - "shell.execute_reply": "2026-03-28T09:36:43.832675Z" - }, - "papermill": { - "duration": 0.012015, - "end_time": "2026-03-28T09:36:43.833871", - "exception": false, - "start_time": "2026-03-28T09:36:43.821856", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(longitude, latitude, c=value,\n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", - " plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", - " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " if plot_type == 'residual' else\n", - " mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", - " model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " idx_all = np.arange(len(y_combined))\n", - " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - "\n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", - "\n", - " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", - " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", - "\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})')\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " p = predictions[mn]\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", - " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", - " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig1)\n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", - " -vmax_abs, vmax_abs,\n", - " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", - " plot_type='residual')\n", - " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig2)\n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7d38eb7c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.837058Z", - "iopub.status.busy": "2026-03-28T09:36:43.836937Z", - "iopub.status.idle": "2026-03-28T09:36:43.839875Z", - "shell.execute_reply": "2026-03-28T09:36:43.839202Z" - }, - "papermill": { - "duration": 0.005566, - "end_time": "2026-03-28T09:36:43.840709", - "exception": false, - "start_time": "2026-03-28T09:36:43.835143", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\n", - "dk_sphere candidates : [10, 50, 100, 150, 200, 1000] (restored to original)\n", - "repeats : 50\n" - ] - } - ], - "source": [ - "# ── Experiment parameters ─────────────────────────────────────────────────────\n", - "seed = 50\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "# All models (including dk_sphere) use the same original candidates\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", - "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", - "print(f\"repeats : {repeat_experiment}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "main_loop_pure", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:43.843973Z", - "iopub.status.busy": "2026-03-28T09:36:43.843852Z", - "iopub.status.idle": "2026-03-28T17:51:20.330164Z", - "shell.execute_reply": "2026-03-28T17:51:20.329349Z" - }, - "papermill": { - "duration": 29676.488693, - "end_time": "2026-03-28T17:51:20.330687", - "exception": false, - "start_time": "2026-03-28T09:36:43.841994", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting fresh.\n", - "\n", - "======================================================================\n", - "Repeat 1/50 seed=50\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:36:44.436647: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774690604.446491 512785 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774690604.449093 512785 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774690604.455687 512785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690604.455714 512785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690604.455716 512785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690604.455717 512785 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] seed=50\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2997 (±0.1472), Variance: 54.1722, Range: [0.5000, 37.2961]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690624.477007 512785 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774690625.957810 513104 service.cc:152] XLA service 0x55d52d823740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774690625.957831 513104 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690626.173975 513104 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690627.806834 513104 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f08fc756050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f08fc1dfe20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2934, sigma²=13.6771, nugget=0.0398\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2293, sigma²=9.1295, nugget=0.0354\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2300, sigma²=7.6420, nugget=0.0296\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2301, sigma²=7.1069, nugget=0.0275\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2302, sigma²=6.5690, nugget=0.0255\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2301, sigma²=6.0134, nugget=0.0233\n", - "[repeat 0] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2301, sigma²=6.0134, nugget=0.0233\n", - "[repeat 0] done → repeat_0_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 465.404 | 21.5732 | 6.4291 | -6.8351 |\n", - "| OLS_sphere | 1000 | 1.4474 | 1.2031 | 0.5967 | 0.9756 |\n", - "| DeepKriging_wendland | -- | 32.6026 | 5.7099 | 4.2406 | 0.4511 |\n", - "| DeepKriging_mrts | 200 | 1.2588 | 1.122 | 0.6575 | 0.9788 |\n", - "| DeepKriging_sphere | 100 | 0.5838 | 0.7641 | 0.4206 | 0.9902 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4737 | 0.6883 | 0.3864 | 0.992 |\n", - "| UniversalKriging | 1000 | 0.7879 | 0.8876 | 0.4939 | 0.9867 |\n", - "Repeat 1/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 2/50 seed=51\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:46:24.896109: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774691184.905155 1072438 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774691184.907824 1072438 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774691184.914916 1072438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691184.914946 1072438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691184.914948 1072438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691184.914949 1072438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] seed=51\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5759 (±0.1639), Variance: 67.1673, Range: [0.5000, 43.5491]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691204.793976 1072438 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774691206.306983 1072760 service.cc:152] XLA service 0x55bbd78414c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774691206.307006 1072760 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691206.522781 1072760 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691208.173937 1072760 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7eb05079a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7ea860fd00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3185, sigma²=15.8209, nugget=0.0452\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2476, sigma²=10.7575, nugget=0.0413\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2484, sigma²=8.6819, nugget=0.0334\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2485, sigma²=7.9906, nugget=0.0307\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2485, sigma²=7.4097, nugget=0.0285\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2485, sigma²=6.9232, nugget=0.0266\n", - "[repeat 1] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2485, sigma²=6.9232, nugget=0.0266\n", - "[repeat 1] done → repeat_1_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 850.366 | 29.161 | 7.0248 | -11.528 |\n", - "| OLS_sphere | 1000 | 1.4791 | 1.2162 | 0.5075 | 0.9782 |\n", - "| DeepKriging_wendland | -- | 34.8752 | 5.9055 | 4.2477 | 0.4862 |\n", - "| DeepKriging_mrts | 150 | 1.2468 | 1.1166 | 0.6308 | 0.9816 |\n", - "| DeepKriging_sphere | 50 | 0.7926 | 0.8903 | 0.4197 | 0.9883 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.832 | 0.9122 | 0.4073 | 0.9877 |\n", - "| UniversalKriging | 1000 | 1.2543 | 1.1199 | 0.5125 | 0.9815 |\n", - "Repeat 2/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 3/50 seed=52\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:56:14.395423: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774691774.404674 1667260 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774691774.407504 1667260 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774691774.414374 1667260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691774.414403 1667260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691774.414405 1667260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774691774.414406 1667260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] seed=52\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.7512 (±0.1495), Variance: 55.8485, Range: [0.5000, 35.0148]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691794.408603 1667260 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774691795.878624 1667573 service.cc:152] XLA service 0x7f0478006ac0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774691795.878647 1667573 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691796.095047 1667573 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774691797.740029 1667573 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f06c414d750> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f06b47e0700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2758, sigma²=13.9485, nugget=0.0459\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2428, sigma²=10.2263, nugget=0.0395\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2435, sigma²=8.0220, nugget=0.0310\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2437, sigma²=7.3854, nugget=0.0285\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2437, sigma²=6.9551, nugget=0.0268\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2436, sigma²=6.5228, nugget=0.0252\n", - "[repeat 2] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2436, sigma²=6.5228, nugget=0.0252\n", - "[repeat 2] done → repeat_2_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 42.3691 | 6.5092 | 4.4136 | 0.0533 |\n", - "| OLS_sphere | 1000 | 1.2566 | 1.121 | 0.5575 | 0.9719 |\n", - "| DeepKriging_wendland | -- | 21.9839 | 4.6887 | 3.3049 | 0.5088 |\n", - "| DeepKriging_mrts | 200 | 0.8925 | 0.9447 | 0.5125 | 0.9801 |\n", - "| DeepKriging_sphere | 100 | 0.6741 | 0.821 | 0.3508 | 0.9849 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5418 | 0.736 | 0.321 | 0.9879 |\n", - "| UniversalKriging | 1000 | 0.7431 | 0.8621 | 0.4645 | 0.9834 |\n", - "Repeat 3/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 4/50 seed=53\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:06:14.276249: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774692374.285848 2269307 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774692374.288647 2269307 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774692374.295752 2269307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692374.295783 2269307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692374.295786 2269307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692374.295786 2269307 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] seed=53\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5161 (±0.1579), Variance: 62.3408, Range: [0.5000, 40.1632]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692394.258747 2269307 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774692395.781487 2269629 service.cc:152] XLA service 0x7f4c180074b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774692395.781510 2269629 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692396.009517 2269629 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692397.659947 2269629 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4e44417880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4e3c51ba30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2971, sigma²=14.9063, nugget=0.0428\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2317, sigma²=9.7537, nugget=0.0379\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2324, sigma²=7.8036, nugget=0.0303\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2325, sigma²=7.2695, nugget=0.0283\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2325, sigma²=6.6799, nugget=0.0260\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2324, sigma²=6.2122, nugget=0.0241\n", - "[repeat 3] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2324, sigma²=6.2122, nugget=0.0241\n", - "[repeat 3] done → repeat_3_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 558.593 | 23.6346 | 7.3138 | -8.5619 |\n", - "| OLS_sphere | 1000 | 1.6355 | 1.2789 | 0.5516 | 0.972 |\n", - "| DeepKriging_wendland | -- | 35.9152 | 5.9929 | 4.384 | 0.3852 |\n", - "| DeepKriging_mrts | 150 | 1.3243 | 1.1508 | 0.6153 | 0.9773 |\n", - "| DeepKriging_sphere | 200 | 0.5488 | 0.7408 | 0.3689 | 0.9906 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.597 | 0.7726 | 0.3585 | 0.9898 |\n", - "| UniversalKriging | 1000 | 0.8728 | 0.9343 | 0.4628 | 0.9851 |\n", - "Repeat 4/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 5/50 seed=54\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:16:08.651272: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774692968.661755 2861136 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774692968.664998 2861136 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774692968.672804 2861136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692968.672829 2861136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692968.672831 2861136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774692968.672832 2861136 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] seed=54\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.9535 (±0.1518), Variance: 57.5957, Range: [0.5000, 35.3904]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692988.510605 2861136 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774692990.006521 2861458 service.cc:152] XLA service 0x7fdb080178c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774692990.006550 2861458 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692990.230033 2861458 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774692991.843510 2861458 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdd3840d3f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdd306bb5b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3409, sigma²=16.5630, nugget=0.0359\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2105, sigma²=8.4007, nugget=0.0327\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2110, sigma²=6.5474, nugget=0.0255\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2111, sigma²=6.0047, nugget=0.0234\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2111, sigma²=5.5345, nugget=0.0215\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2111, sigma²=5.0852, nugget=0.0198\n", - "[repeat 4] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2111, sigma²=5.0852, nugget=0.0198\n", - "[repeat 4] done → repeat_4_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 2666.09 | 51.6342 | 7.873 | -44.9738 |\n", - "| OLS_sphere | 1000 | 2.7467 | 1.6573 | 0.6451 | 0.9526 |\n", - "| DeepKriging_wendland | -- | 29.2197 | 5.4055 | 3.8503 | 0.4961 |\n", - "| DeepKriging_mrts | 200 | 0.6736 | 0.8207 | 0.4918 | 0.9884 |\n", - "| DeepKriging_sphere | 50 | 0.7145 | 0.8453 | 0.4086 | 0.9877 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6352 | 0.797 | 0.3648 | 0.989 |\n", - "| UniversalKriging | 1000 | 1.1034 | 1.0504 | 0.5081 | 0.981 |\n", - "Repeat 5/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 6/50 seed=55\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:25:23.403880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774693523.413231 3366202 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774693523.415917 3366202 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774693523.423212 3366202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774693523.423239 3366202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774693523.423241 3366202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774693523.423242 3366202 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] seed=55\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5571 (±0.1419), Variance: 50.3450, Range: [0.5000, 35.7721]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774693543.445982 3366202 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774693544.937370 3366523 service.cc:152] XLA service 0x7fb9c0007a70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774693544.937393 3366523 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774693545.147488 3366523 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774693546.779304 3366523 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbbf8383640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbbf0162c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2439, sigma²=13.0989, nugget=0.0336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1961, sigma²=9.3171, nugget=0.0320\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1951, sigma²=6.6631, nugget=0.0238\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1951, sigma²=6.0996, nugget=0.0219\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1952, sigma²=5.6027, nugget=0.0201\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1759, sigma²=4.5006, nugget=0.0169\n", - "[repeat 5] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1759, sigma²=4.5006, nugget=0.0169\n", - "[repeat 5] done → repeat_5_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 143.596 | 11.9831 | 4.9538 | -1.6262 |\n", - "| OLS_sphere | 1000 | 1.453 | 1.2054 | 0.4927 | 0.9734 |\n", - "| DeepKriging_wendland | -- | 21.7618 | 4.665 | 3.3781 | 0.602 |\n", - "| DeepKriging_mrts | 200 | 0.5453 | 0.7384 | 0.4949 | 0.99 |\n", - "| DeepKriging_sphere | 150 | 0.5239 | 0.7238 | 0.3643 | 0.9904 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3107 | 0.5574 | 0.3247 | 0.9943 |\n", - "| UniversalKriging | 1000 | 0.8853 | 0.9409 | 0.4934 | 0.9838 |\n", - "Repeat 6/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 7/50 seed=56\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:35:40.022579: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774694140.033123 3990378 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774694140.035783 3990378 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774694140.042663 3990378 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694140.042693 3990378 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694140.042695 3990378 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694140.042696 3990378 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] seed=56\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.0559 (±0.1448), Variance: 52.4192, Range: [0.5000, 32.3244]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694160.138880 3990378 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774694161.643786 3990700 service.cc:152] XLA service 0x7fecf8007600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774694161.643813 3990700 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694161.865250 3990700 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694163.530168 3990700 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fef482e6440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fef4037fd90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2569, sigma²=11.4829, nugget=0.0376\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2268, sigma²=7.9337, nugget=0.0306\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2275, sigma²=5.9928, nugget=0.0231\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2275, sigma²=5.3131, nugget=0.0205\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2276, sigma²=4.9415, nugget=0.0191\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2275, sigma²=4.6145, nugget=0.0178\n", - "[repeat 6] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2275, sigma²=4.6145, nugget=0.0178\n", - "[repeat 6] done → repeat_6_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 37.771 | 6.1458 | 4.394 | 0.2297 |\n", - "| OLS_sphere | 1000 | 0.3392 | 0.5824 | 0.2963 | 0.9931 |\n", - "| DeepKriging_wendland | -- | 25.566 | 5.0563 | 3.4885 | 0.4786 |\n", - "| DeepKriging_mrts | 150 | 0.2598 | 0.5097 | 0.3572 | 0.9947 |\n", - "| DeepKriging_sphere | 50 | 0.2888 | 0.5374 | 0.2528 | 0.9941 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.213 | 0.4615 | 0.2427 | 0.9957 |\n", - "| UniversalKriging | 1000 | 0.281 | 0.5301 | 0.2951 | 0.9943 |\n", - "Repeat 7/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 8/50 seed=57\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:46:02.305455: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774694762.315509 441625 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774694762.318261 441625 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774694762.325296 441625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694762.325328 441625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694762.325330 441625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774694762.325331 441625 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] seed=57\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2737 (±0.1631), Variance: 66.4874, Range: [0.5000, 39.5575]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694782.271549 441625 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774694783.763932 441947 service.cc:152] XLA service 0x5579244eb8d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774694783.763956 441947 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694783.979413 441947 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774694785.635297 441947 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7e083d5990> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7dc0742b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3404, sigma²=16.9518, nugget=0.0481\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2634, sigma²=11.1514, nugget=0.0428\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2643, sigma²=8.8895, nugget=0.0341\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2644, sigma²=8.4995, nugget=0.0326\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2644, sigma²=7.9714, nugget=0.0306\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2643, sigma²=7.4653, nugget=0.0286\n", - "[repeat 7] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2643, sigma²=7.4653, nugget=0.0286\n", - "[repeat 7] done → repeat_7_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 306.002 | 17.4929 | 6.6126 | -3.2525 |\n", - "| OLS_sphere | 1000 | 3.6276 | 1.9046 | 0.6929 | 0.9496 |\n", - "| DeepKriging_wendland | -- | 31.4932 | 5.6119 | 4.0886 | 0.5623 |\n", - "| DeepKriging_mrts | 200 | 1.0645 | 1.0318 | 0.5873 | 0.9852 |\n", - "| DeepKriging_sphere | 100 | 0.8386 | 0.9158 | 0.4726 | 0.9883 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.5995 | 0.7743 | 0.4124 | 0.9917 |\n", - "| UniversalKriging | 1000 | 1.0527 | 1.026 | 0.4821 | 0.9854 |\n", - "Repeat 8/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 9/50 seed=58\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 18:55:33.144247: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774695333.153695 990469 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774695333.156387 990469 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774695333.163440 990469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695333.163472 990469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695333.163474 990469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695333.163475 990469 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] seed=58\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2216 (±0.1546), Variance: 59.7359, Range: [0.5000, 39.1926]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695353.177242 990469 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774695354.691274 990789 service.cc:152] XLA service 0x7fa53c008b40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774695354.691298 990789 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695354.910817 990789 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695356.540439 990789 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa77810bac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa7703b6e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5428, sigma²=25.6364, nugget=0.0322\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2082, sigma²=8.7364, nugget=0.0339\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2086, sigma²=6.6780, nugget=0.0259\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2087, sigma²=5.8363, nugget=0.0226\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2088, sigma²=5.3894, nugget=0.0209\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2087, sigma²=4.9880, nugget=0.0193\n", - "[repeat 8] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2087, sigma²=4.9880, nugget=0.0193\n", - "[repeat 8] done → repeat_8_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 66.9142 | 8.1801 | 5.1623 | -0.1861 |\n", - "| OLS_sphere | 1000 | 2.1324 | 1.4603 | 0.5704 | 0.9622 |\n", - "| DeepKriging_wendland | -- | 28.9295 | 5.3786 | 3.8241 | 0.4872 |\n", - "| DeepKriging_mrts | 100 | 1.4606 | 1.2085 | 0.7352 | 0.9741 |\n", - "| DeepKriging_sphere | 50 | 0.57 | 0.755 | 0.3489 | 0.9899 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6026 | 0.7763 | 0.3683 | 0.9893 |\n", - "| UniversalKriging | 1000 | 0.7854 | 0.8862 | 0.4384 | 0.9861 |\n", - "Repeat 9/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 10/50 seed=59\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:05:03.537811: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774695903.547965 1543358 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774695903.550894 1543358 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774695903.558249 1543358 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695903.558278 1543358 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695903.558280 1543358 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774695903.558281 1543358 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] seed=59\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.3163 (±0.1624), Variance: 65.9629, Range: [0.5000, 46.1134]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695923.570198 1543358 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774695925.069727 1543680 service.cc:152] XLA service 0x55e575398910 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774695925.069750 1543680 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695925.286368 1543680 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774695926.928799 1543680 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f91a05267a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f91986cecb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2690, sigma²=16.6158, nugget=0.0488\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2140, sigma²=11.2079, nugget=0.0433\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2117, sigma²=8.6533, nugget=0.0337\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2118, sigma²=7.9161, nugget=0.0309\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2119, sigma²=7.3515, nugget=0.0286\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2118, sigma²=6.7750, nugget=0.0264\n", - "[repeat 9] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2118, sigma²=6.7750, nugget=0.0264\n", - "[repeat 9] done → repeat_9_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 54692.6 | 233.864 | 20.2762 | -797.287 |\n", - "| OLS_sphere | 200 | 3.2651 | 1.807 | 1.2992 | 0.9523 |\n", - "| DeepKriging_wendland | -- | 40.9155 | 6.3965 | 4.5137 | 0.4028 |\n", - "| DeepKriging_mrts | 100 | 2.1845 | 1.478 | 0.9044 | 0.9681 |\n", - "| DeepKriging_sphere | 100 | 0.6924 | 0.8321 | 0.3738 | 0.9899 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6581 | 0.8112 | 0.343 | 0.9904 |\n", - "| UniversalKriging | 1000 | 1.1901 | 1.0909 | 0.5191 | 0.9826 |\n", - "Repeat 10/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 11/50 seed=60\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:14:30.914701: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774696470.923878 2087766 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774696470.926500 2087766 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774696470.933664 2087766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774696470.933690 2087766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774696470.933692 2087766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774696470.933693 2087766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] seed=60\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.3356 (±0.1646), Variance: 67.7618, Range: [0.5000, 42.4411]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774696490.930493 2087766 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774696492.430488 2088086 service.cc:152] XLA service 0x5615bc249260 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774696492.430522 2088086 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774696492.652155 2088086 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774696494.301673 2088086 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7feda81b3f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fed98696050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2925, sigma²=15.2671, nugget=0.0438\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2291, sigma²=9.3513, nugget=0.0363\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2297, sigma²=7.4529, nugget=0.0289\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2298, sigma²=6.6357, nugget=0.0257\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2298, sigma²=6.2384, nugget=0.0242\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2297, sigma²=5.7355, nugget=0.0222\n", - "[repeat 10] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2297, sigma²=5.7355, nugget=0.0222\n", - "[repeat 10] done → repeat_10_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 48.6758 | 6.9768 | 5.5752 | 0.2722 |\n", - "| OLS_sphere | 1000 | 1.8316 | 1.3534 | 0.5437 | 0.9726 |\n", - "| DeepKriging_wendland | -- | 35.07 | 5.922 | 4.2523 | 0.4756 |\n", - "| DeepKriging_mrts | 150 | 0.6422 | 0.8014 | 0.4819 | 0.9904 |\n", - "| DeepKriging_sphere | 200 | 0.2718 | 0.5214 | 0.3222 | 0.9959 |\n", - "| DeepKriging_sphere_Huber | 200 | 0.288 | 0.5367 | 0.3394 | 0.9957 |\n", - "| UniversalKriging | 1000 | 0.4774 | 0.691 | 0.408 | 0.9929 |\n", - "Repeat 11/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 12/50 seed=61\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:24:04.567674: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774697044.577149 2624498 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774697044.580001 2624498 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774697044.586937 2624498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697044.586967 2624498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697044.586970 2624498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697044.586970 2624498 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] seed=61\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.1103 (±0.1554), Variance: 60.3823, Range: [0.5000, 38.5211]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697064.750523 2624498 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774697066.262169 2624818 service.cc:152] XLA service 0x7f4960008730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774697066.262203 2624818 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697066.476356 2624818 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697068.125476 2624818 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4ba019e710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f4b98456e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2369, sigma²=13.1046, nugget=0.0506\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2369, sigma²=10.2966, nugget=0.0397\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2374, sigma²=8.9545, nugget=0.0346\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2375, sigma²=8.2693, nugget=0.0319\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2376, sigma²=7.7955, nugget=0.0301\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2375, sigma²=7.1557, nugget=0.0276\n", - "[repeat 11] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2375, sigma²=7.1557, nugget=0.0276\n", - "[repeat 11] done → repeat_11_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 49.2399 | 7.0171 | 5.3667 | 0.2214 |\n", - "| OLS_sphere | 1000 | 0.9965 | 0.9983 | 0.4753 | 0.9842 |\n", - "| DeepKriging_wendland | -- | 34.226 | 5.8503 | 4.0199 | 0.4588 |\n", - "| DeepKriging_mrts | 150 | 1.2305 | 1.1093 | 0.624 | 0.9805 |\n", - "| DeepKriging_sphere | 50 | 0.2613 | 0.5112 | 0.2841 | 0.9959 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3851 | 0.6205 | 0.3299 | 0.9939 |\n", - "| UniversalKriging | 1000 | 0.5477 | 0.7401 | 0.4219 | 0.9913 |\n", - "Repeat 12/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 13/50 seed=62\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:34:26.972168: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774697666.981819 3280884 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774697666.984958 3280884 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774697666.992655 3280884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697666.992689 3280884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697666.992691 3280884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774697666.992692 3280884 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] seed=62\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5364 (±0.1602), Variance: 64.1732, Range: [0.5000, 35.9140]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697686.947340 3280884 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774697688.482996 3281203 service.cc:152] XLA service 0x7f343c006690 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774697688.483022 3281203 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697688.704613 3281203 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774697690.356797 3281203 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f3664206560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f365c15f910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3043, sigma²=15.9629, nugget=0.0451\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2370, sigma²=10.3907, nugget=0.0402\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2378, sigma²=8.3002, nugget=0.0321\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2378, sigma²=7.5636, nugget=0.0293\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=7.1436, nugget=0.0276\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2378, sigma²=6.6351, nugget=0.0257\n", - "[repeat 12] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2378, sigma²=6.6351, nugget=0.0257\n", - "[repeat 12] done → repeat_12_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 481.846 | 21.951 | 6.6185 | -6.711 |\n", - "| OLS_sphere | 200 | 2.5814 | 1.6067 | 1.1042 | 0.9587 |\n", - "| DeepKriging_wendland | -- | 31.423 | 5.6056 | 4.1561 | 0.4971 |\n", - "| DeepKriging_mrts | 150 | 1.1854 | 1.0888 | 0.6162 | 0.981 |\n", - "| DeepKriging_sphere | 50 | 1.3913 | 1.1795 | 0.5885 | 0.9777 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7607 | 0.8722 | 0.3142 | 0.9878 |\n", - "| UniversalKriging | 1000 | 0.8578 | 0.9262 | 0.4772 | 0.9863 |\n", - "Repeat 13/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 14/50 seed=63\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:43:43.557712: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774698223.566803 3806499 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774698223.569492 3806499 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774698223.577189 3806499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698223.577212 3806499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698223.577214 3806499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698223.577214 3806499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] seed=63\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.6302 (±0.1637), Variance: 66.9536, Range: [0.5000, 44.8099]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698243.532064 3806499 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774698245.029382 3806821 service.cc:152] XLA service 0x7fdd940184f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774698245.029407 3806821 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698245.243585 3806821 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698246.867620 3806821 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdfd83b3640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdfd04afe20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2957, sigma²=15.4684, nugget=0.0512\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2606, sigma²=10.8376, nugget=0.0416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2614, sigma²=8.9095, nugget=0.0342\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2614, sigma²=8.4173, nugget=0.0323\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2615, sigma²=7.8103, nugget=0.0300\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2614, sigma²=7.2962, nugget=0.0280\n", - "[repeat 13] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2614, sigma²=7.2962, nugget=0.0280\n", - "[repeat 13] done → repeat_13_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 52.4508 | 7.2423 | 5.5438 | 0.2367 |\n", - "| OLS_sphere | 1000 | 0.8683 | 0.9318 | 0.4537 | 0.9874 |\n", - "| DeepKriging_wendland | -- | 38.3027 | 6.1889 | 4.3323 | 0.4426 |\n", - "| DeepKriging_mrts | 200 | 1.0139 | 1.0069 | 0.5943 | 0.9852 |\n", - "| DeepKriging_sphere | 100 | 0.2412 | 0.4912 | 0.2996 | 0.9965 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1787 | 0.4228 | 0.2614 | 0.9974 |\n", - "| UniversalKriging | 1000 | 0.5362 | 0.7323 | 0.3968 | 0.9922 |\n", - "Repeat 14/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 15/50 seed=64\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 19:53:49.751206: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774698829.761392 214357 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774698829.764283 214357 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774698829.771285 214357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698829.771310 214357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698829.771312 214357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774698829.771313 214357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] seed=64\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5951 (±0.1549), Variance: 59.9593, Range: [0.5000, 42.2208]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698849.987641 214357 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774698851.484983 214678 service.cc:152] XLA service 0x7fe7c0009fe0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774698851.485007 214678 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698851.706823 214678 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774698853.365316 214678 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe9e87c3b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe9e053b0a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2317, sigma²=14.3134, nugget=0.0427\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1889, sigma²=9.4034, nugget=0.0363\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1843, sigma²=7.2773, nugget=0.0286\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1844, sigma²=6.6854, nugget=0.0263\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1844, sigma²=6.0306, nugget=0.0237\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1844, sigma²=5.4399, nugget=0.0214\n", - "[repeat 14] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1844, sigma²=5.4399, nugget=0.0214\n", - "[repeat 14] done → repeat_14_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 44.7388 | 6.6887 | 5.0895 | 0.236 |\n", - "| OLS_sphere | 1000 | 1.4429 | 1.2012 | 0.5595 | 0.9754 |\n", - "| DeepKriging_wendland | -- | 31.2229 | 5.5877 | 3.7218 | 0.4668 |\n", - "| DeepKriging_mrts | 200 | 0.6553 | 0.8095 | 0.4946 | 0.9888 |\n", - "| DeepKriging_sphere | 150 | 0.7672 | 0.8759 | 0.4117 | 0.9869 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3542 | 0.5952 | 0.3203 | 0.994 |\n", - "| UniversalKriging | 1000 | 0.7747 | 0.8802 | 0.4539 | 0.9868 |\n", - "Repeat 15/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 16/50 seed=65\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:03:52.746045: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774699432.755454 828181 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774699432.758129 828181 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774699432.765217 828181 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774699432.765239 828181 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774699432.765242 828181 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774699432.765242 828181 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] seed=65\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5452 (±0.1683), Variance: 70.7986, Range: [0.5000, 42.0715]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774699452.837326 828181 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774699454.329766 828509 service.cc:152] XLA service 0x556faa50ae70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774699454.329788 828509 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774699454.547361 828509 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774699456.184576 828509 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7feff4162950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fefe45281f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2590, sigma²=15.7482, nugget=0.0459\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2160, sigma²=10.5974, nugget=0.0397\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2049, sigma²=7.3875, nugget=0.0288\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2050, sigma²=6.6505, nugget=0.0259\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2050, sigma²=6.1534, nugget=0.0240\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2049, sigma²=5.5670, nugget=0.0217\n", - "[repeat 15] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2049, sigma²=5.5670, nugget=0.0217\n", - "[repeat 15] done → repeat_15_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 340.594 | 18.4552 | 6.2475 | -3.8661 |\n", - "| OLS_sphere | 1000 | 2.4072 | 1.5515 | 0.553 | 0.9656 |\n", - "| DeepKriging_wendland | -- | 30.9421 | 5.5626 | 3.9978 | 0.5579 |\n", - "| DeepKriging_mrts | 200 | 0.9058 | 0.9517 | 0.5735 | 0.9871 |\n", - "| DeepKriging_sphere | 50 | 0.8075 | 0.8986 | 0.3944 | 0.9885 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6281 | 0.7925 | 0.3491 | 0.991 |\n", - "| UniversalKriging | 1000 | 1.1934 | 1.0924 | 0.5167 | 0.9829 |\n", - "Repeat 16/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 17/50 seed=66\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:13:50.063872: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774700030.073120 1412668 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774700030.075792 1412668 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774700030.083220 1412668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700030.083251 1412668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700030.083253 1412668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700030.083254 1412668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] seed=66\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.9910 (±0.1651), Variance: 68.1106, Range: [0.5000, 40.8620]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700050.208160 1412668 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774700051.718639 1412988 service.cc:152] XLA service 0x5599338daed0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774700051.718662 1412988 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700051.937855 1412988 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700053.550905 1412988 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc7f456ad40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc7f41ab880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2986, sigma²=14.1336, nugget=0.0533\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2968, sigma²=11.5421, nugget=0.0439\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2978, sigma²=9.1147, nugget=0.0346\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2979, sigma²=8.6820, nugget=0.0330\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2979, sigma²=8.0736, nugget=0.0307\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2978, sigma²=7.6872, nugget=0.0292\n", - "[repeat 16] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2978, sigma²=7.6872, nugget=0.0292\n", - "[repeat 16] done → repeat_16_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 144.783 | 12.0326 | 5.8446 | -1.0448 |\n", - "| OLS_sphere | 1000 | 1.2268 | 1.1076 | 0.5217 | 0.9827 |\n", - "| DeepKriging_wendland | -- | 33.5919 | 5.7959 | 4.1041 | 0.5256 |\n", - "| DeepKriging_mrts | 100 | 0.8786 | 0.9374 | 0.5263 | 0.9876 |\n", - "| DeepKriging_sphere | 150 | 0.4497 | 0.6706 | 0.3354 | 0.9936 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3345 | 0.5783 | 0.2757 | 0.9953 |\n", - "| UniversalKriging | 1000 | 0.6931 | 0.8325 | 0.4308 | 0.9902 |\n", - "Repeat 17/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 18/50 seed=67\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:23:35.557386: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774700615.566687 1985526 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774700615.569923 1985526 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774700615.577001 1985526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700615.577030 1985526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700615.577032 1985526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774700615.577033 1985526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] seed=67\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.9470 (±0.1468), Variance: 53.8822, Range: [0.5000, 31.6587]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700635.736759 1985526 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774700637.242463 1985847 service.cc:152] XLA service 0x7f5b1401c380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774700637.242494 1985847 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700637.456168 1985847 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774700639.091142 1985847 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5d50786290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5d485da200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2912, sigma²=12.8524, nugget=0.0424\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2562, sigma²=9.3915, nugget=0.0362\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2570, sigma²=7.7786, nugget=0.0300\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2571, sigma²=7.1735, nugget=0.0276\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2572, sigma²=6.7458, nugget=0.0260\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2571, sigma²=6.2777, nugget=0.0242\n", - "[repeat 17] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2571, sigma²=6.2777, nugget=0.0242\n", - "[repeat 17] done → repeat_17_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 200.358 | 14.1548 | 5.4747 | -2.5332 |\n", - "| OLS_sphere | 1000 | 0.4871 | 0.6979 | 0.3911 | 0.9914 |\n", - "| DeepKriging_wendland | -- | 30.5976 | 5.5315 | 3.9454 | 0.4604 |\n", - "| DeepKriging_mrts | 200 | 0.7423 | 0.8616 | 0.5333 | 0.9869 |\n", - "| DeepKriging_sphere | 100 | 0.4109 | 0.641 | 0.3656 | 0.9928 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2382 | 0.488 | 0.2866 | 0.9958 |\n", - "| UniversalKriging | 1000 | 0.6999 | 0.8366 | 0.4413 | 0.9877 |\n", - "Repeat 18/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 19/50 seed=68\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:33:54.232680: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774701234.243059 2520677 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774701234.245921 2520677 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774701234.253002 2520677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701234.253036 2520677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701234.253038 2520677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701234.253039 2520677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] seed=68\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5174 (±0.1679), Variance: 70.4904, Range: [0.5000, 41.4327]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701254.370100 2520677 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774701255.878873 2520998 service.cc:152] XLA service 0x5556fd29c6b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774701255.878904 2520998 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701256.098233 2520998 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701257.754151 2520998 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ffa0036bd90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff9f8457d00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2571, sigma²=13.1096, nugget=0.0463\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2414, sigma²=9.3132, nugget=0.0359\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=7.2568, nugget=0.0280\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=6.8114, nugget=0.0262\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=6.1730, nugget=0.0238\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=5.7567, nugget=0.0222\n", - "[repeat 18] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=5.7567, nugget=0.0222\n", - "[repeat 18] done → repeat_18_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 193.184 | 13.8991 | 5.7364 | -1.5788 |\n", - "| OLS_sphere | 1000 | 0.7885 | 0.888 | 0.418 | 0.9895 |\n", - "| DeepKriging_wendland | -- | 30.0978 | 5.4861 | 4.1211 | 0.5982 |\n", - "| DeepKriging_mrts | 200 | 0.6978 | 0.8353 | 0.5351 | 0.9907 |\n", - "| DeepKriging_sphere | 150 | 0.5273 | 0.7262 | 0.39 | 0.993 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4321 | 0.6574 | 0.3306 | 0.9942 |\n", - "| UniversalKriging | 1000 | 0.5146 | 0.7173 | 0.3816 | 0.9931 |\n", - "Repeat 19/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 20/50 seed=69\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:43:38.691696: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774701818.702974 3083175 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774701818.706206 3083175 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774701818.713374 3083175 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701818.713401 3083175 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701818.713403 3083175 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774701818.713404 3083175 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] seed=69\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4700 (±0.1570), Variance: 61.6085, Range: [0.5000, 40.8548]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701838.703271 3083175 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774701840.200763 3083495 service.cc:152] XLA service 0x559309bbc390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774701840.200786 3083495 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701840.420254 3083495 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774701842.104682 3083495 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7450709900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f74486bab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3606, sigma²=15.4519, nugget=0.0444\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2792, sigma²=10.4389, nugget=0.0400\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2800, sigma²=8.5707, nugget=0.0328\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2801, sigma²=8.1556, nugget=0.0312\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2802, sigma²=7.5879, nugget=0.0290\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2801, sigma²=7.1718, nugget=0.0275\n", - "[repeat 19] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2801, sigma²=7.1718, nugget=0.0275\n", - "[repeat 19] done → repeat_19_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 43.8239 | 6.62 | 4.9434 | 0.2286 |\n", - "| OLS_sphere | 1000 | 2.0743 | 1.4402 | 0.5917 | 0.9635 |\n", - "| DeepKriging_wendland | -- | 30.3872 | 5.5125 | 3.9612 | 0.4651 |\n", - "| DeepKriging_mrts | 150 | 0.7607 | 0.8722 | 0.5497 | 0.9866 |\n", - "| DeepKriging_sphere | 50 | 0.5273 | 0.7262 | 0.3917 | 0.9907 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5407 | 0.7353 | 0.346 | 0.9905 |\n", - "| UniversalKriging | 1000 | 0.6496 | 0.806 | 0.3958 | 0.9886 |\n", - "Repeat 20/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 20:53:04.874005: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774702384.883429 3615930 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774702384.886084 3615930 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774702384.893353 3615930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702384.893386 3615930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702384.893388 3615930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702384.893389 3615930 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4697 (±0.1552), Variance: 60.1888, Range: [0.5000, 39.8308]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774702404.775638 3615930 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774702406.288999 3616251 service.cc:152] XLA service 0x5609927248f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774702406.289028 3616251 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774702406.503197 3616251 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774702408.149290 3616251 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ffa244b2a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ffa1c1648b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3310, sigma²=15.0710, nugget=0.0571\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3309, sigma²=12.8005, nugget=0.0485\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3321, sigma²=10.8749, nugget=0.0412\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3323, sigma²=10.5146, nugget=0.0399\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3323, sigma²=9.9304, nugget=0.0376\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3322, sigma²=9.4074, nugget=0.0357\n", - "[repeat 20] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3322, sigma²=9.4074, nugget=0.0357\n", - "[repeat 20] done → repeat_20_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 65.7602 | 8.1093 | 5.6227 | -0.0194 |\n", - "| OLS_sphere | 1000 | 0.8305 | 0.9113 | 0.3739 | 0.9871 |\n", - "| DeepKriging_wendland | -- | 32.0838 | 5.6643 | 4.1921 | 0.5026 |\n", - "| DeepKriging_mrts | 100 | 1.0671 | 1.033 | 0.573 | 0.9835 |\n", - "| DeepKriging_sphere | 50 | 0.7824 | 0.8845 | 0.3658 | 0.9879 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.9272 | 0.9629 | 0.3799 | 0.9856 |\n", - "| UniversalKriging | 1000 | 0.8544 | 0.9243 | 0.4313 | 0.9868 |\n", - "Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:03:12.118432: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774702992.128312 29424 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774702992.131481 29424 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774702992.138595 29424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702992.138623 29424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702992.138626 29424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774702992.138627 29424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.1448 (±0.1569), Variance: 61.5353, Range: [0.5000, 38.5638]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703012.138913 29424 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774703013.644638 29754 service.cc:152] XLA service 0x5566c61e5b70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774703013.644672 29754 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703013.864337 29754 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703015.505047 29754 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f98ec1d6290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f98e429ba30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2863, sigma²=13.6946, nugget=0.0384\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2241, sigma²=8.6948, nugget=0.0336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2246, sigma²=6.6924, nugget=0.0258\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2247, sigma²=5.9788, nugget=0.0231\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2247, sigma²=5.5512, nugget=0.0214\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2247, sigma²=5.2126, nugget=0.0201\n", - "[repeat 21] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2247, sigma²=5.2126, nugget=0.0201\n", - "[repeat 21] done → repeat_21_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 42.9906 | 6.5567 | 4.9385 | 0.3236 |\n", - "| OLS_sphere | 200 | 2.4501 | 1.5653 | 1.0807 | 0.9614 |\n", - "| DeepKriging_wendland | -- | 25.5268 | 5.0524 | 3.4201 | 0.5983 |\n", - "| DeepKriging_mrts | 200 | 0.7078 | 0.8413 | 0.5168 | 0.9889 |\n", - "| DeepKriging_sphere | 150 | 0.6765 | 0.8225 | 0.3365 | 0.9894 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6287 | 0.7929 | 0.3093 | 0.9901 |\n", - "| UniversalKriging | 1000 | 0.7717 | 0.8784 | 0.4047 | 0.9879 |\n", - "Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:12:56.587766: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774703576.597398 602898 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774703576.600147 602898 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774703576.607778 602898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774703576.607810 602898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774703576.607812 602898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774703576.607813 602898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4718 (±0.1562), Variance: 60.9846, Range: [0.5000, 36.4498]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703596.732738 602898 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774703598.246437 603223 service.cc:152] XLA service 0x55da91072de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774703598.246460 603223 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703598.464618 603223 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774703600.123575 603223 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f904078b880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9038434dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2913, sigma²=12.6438, nugget=0.0480\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2912, sigma²=10.0195, nugget=0.0381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2921, sigma²=7.8880, nugget=0.0300\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2922, sigma²=7.3744, nugget=0.0280\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2922, sigma²=6.8709, nugget=0.0261\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2921, sigma²=6.5151, nugget=0.0248\n", - "[repeat 22] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2921, sigma²=6.5151, nugget=0.0248\n", - "[repeat 22] done → repeat_22_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 2767.84 | 52.6103 | 9.1816 | -46.3465 |\n", - "| OLS_sphere | 1000 | 2.7807 | 1.6675 | 0.5059 | 0.9524 |\n", - "| DeepKriging_wendland | -- | 28.5426 | 5.3425 | 3.9312 | 0.5118 |\n", - "| DeepKriging_mrts | 200 | 0.7959 | 0.8921 | 0.5793 | 0.9864 |\n", - "| DeepKriging_sphere | 200 | 0.5183 | 0.7199 | 0.3504 | 0.9911 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3929 | 0.6268 | 0.3242 | 0.9933 |\n", - "| UniversalKriging | 1000 | 0.6327 | 0.7954 | 0.4118 | 0.9892 |\n", - "Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:23:09.476222: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774704189.485496 1197021 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774704189.488213 1197021 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774704189.494941 1197021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704189.494970 1197021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704189.494973 1197021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704189.494973 1197021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.6601 (±0.1667), Variance: 69.4316, Range: [0.5000, 42.4480]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704209.519690 1197021 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774704211.024898 1197349 service.cc:152] XLA service 0x7f8064009f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774704211.024918 1197349 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704211.238087 1197349 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704212.874763 1197349 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8294162320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f82844ff9a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2261, sigma²=14.6965, nugget=0.0441\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1803, sigma²=9.1167, nugget=0.0359\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1807, sigma²=6.7276, nugget=0.0265\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1808, sigma²=6.0809, nugget=0.0240\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1808, sigma²=5.4645, nugget=0.0215\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1808, sigma²=4.9639, nugget=0.0196\n", - "[repeat 23] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1808, sigma²=4.9639, nugget=0.0196\n", - "[repeat 23] done → repeat_23_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 51.1112 | 7.1492 | 5.358 | 0.3126 |\n", - "| OLS_sphere | 1000 | 1.6644 | 1.2901 | 0.4999 | 0.9776 |\n", - "| DeepKriging_wendland | -- | 36.389 | 6.0323 | 4.075 | 0.5106 |\n", - "| DeepKriging_mrts | 150 | 0.836 | 0.9143 | 0.4947 | 0.9888 |\n", - "| DeepKriging_sphere | 100 | 0.7326 | 0.8559 | 0.3328 | 0.9901 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6859 | 0.8282 | 0.2988 | 0.9908 |\n", - "| UniversalKriging | 1000 | 0.9247 | 0.9616 | 0.4563 | 0.9876 |\n", - "Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:33:42.785990: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774704822.796538 1851610 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774704822.799387 1851610 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774704822.806251 1851610 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704822.806276 1851610 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704822.806278 1851610 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774704822.806279 1851610 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.7544 (±0.1589), Variance: 63.1512, Range: [0.5000, 40.5283]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704842.820219 1851610 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774704844.306050 1851933 service.cc:152] XLA service 0x7f6cf4009e20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774704844.306074 1851933 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704844.520814 1851933 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774704846.173426 1851933 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6f3c71e200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6f3c10b010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2195, sigma²=12.8807, nugget=0.0481\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2127, sigma²=10.0836, nugget=0.0393\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2133, sigma²=8.1581, nugget=0.0318\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2134, sigma²=7.5265, nugget=0.0293\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2134, sigma²=7.1173, nugget=0.0277\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2134, sigma²=6.5657, nugget=0.0256\n", - "[repeat 24] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2134, sigma²=6.5657, nugget=0.0256\n", - "[repeat 24] done → repeat_24_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 332.976 | 18.2476 | 6.521 | -3.7771 |\n", - "| OLS_sphere | 1000 | 2.9203 | 1.7089 | 0.6821 | 0.9581 |\n", - "| DeepKriging_wendland | -- | 35.4313 | 5.9524 | 4.3092 | 0.4917 |\n", - "| DeepKriging_mrts | 100 | 1.2367 | 1.112 | 0.6969 | 0.9823 |\n", - "| DeepKriging_sphere | 10 | 0.7686 | 0.8767 | 0.4367 | 0.989 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6035 | 0.7769 | 0.3568 | 0.9913 |\n", - "| UniversalKriging | 1000 | 1.0714 | 1.0351 | 0.5047 | 0.9846 |\n", - "Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:44:09.344697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774705449.354120 2490735 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774705449.356705 2490735 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774705449.363968 2490735 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774705449.363993 2490735 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774705449.363995 2490735 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774705449.363996 2490735 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2026 (±0.1601), Variance: 64.0432, Range: [0.5000, 48.8841]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774705469.485909 2490735 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774705470.995025 2491055 service.cc:152] XLA service 0x7f6240007010 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774705470.995056 2491055 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774705471.218185 2491055 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774705472.857600 2491055 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f646c5d7d90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f64641630a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2747, sigma²=14.2904, nugget=0.0405\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2156, sigma²=9.2709, nugget=0.0359\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2162, sigma²=6.8987, nugget=0.0267\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2162, sigma²=6.3197, nugget=0.0245\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2162, sigma²=5.8719, nugget=0.0227\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2162, sigma²=5.4582, nugget=0.0211\n", - "[repeat 25] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2162, sigma²=5.4582, nugget=0.0211\n", - "[repeat 25] done → repeat_25_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 54.9729 | 7.4144 | 4.977 | 0.1767 |\n", - "| OLS_sphere | 1000 | 0.8001 | 0.8945 | 0.3883 | 0.988 |\n", - "| DeepKriging_wendland | -- | 31.762 | 5.6358 | 3.99 | 0.5243 |\n", - "| DeepKriging_mrts | 100 | 0.7189 | 0.8479 | 0.5121 | 0.9892 |\n", - "| DeepKriging_sphere | 10 | 0.442 | 0.6648 | 0.3371 | 0.9934 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6705 | 0.8188 | 0.3253 | 0.99 |\n", - "| UniversalKriging | 1000 | 0.6629 | 0.8142 | 0.4148 | 0.9901 |\n", - "Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 21:53:27.461220: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774706007.470819 3004094 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774706007.473519 3004094 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774706007.480744 3004094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706007.480780 3004094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706007.480783 3004094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706007.480784 3004094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4112 (±0.1572), Variance: 61.8133, Range: [0.5000, 36.0891]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706027.500556 3004094 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774706029.001235 3004416 service.cc:152] XLA service 0x7fc824006700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774706029.001261 3004416 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706029.223705 3004416 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706030.859248 3004416 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fca6c579fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fca64303250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2871, sigma²=13.1613, nugget=0.0375\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2248, sigma²=8.6765, nugget=0.0336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2254, sigma²=6.8113, nugget=0.0263\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2255, sigma²=6.0666, nugget=0.0235\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2255, sigma²=5.6753, nugget=0.0219\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2254, sigma²=5.2041, nugget=0.0201\n", - "[repeat 26] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2254, sigma²=5.2041, nugget=0.0201\n", - "[repeat 26] done → repeat_26_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 45.289 | 6.7297 | 5.2723 | 0.2636 |\n", - "| OLS_sphere | 200 | 2.1457 | 1.4648 | 0.9849 | 0.9651 |\n", - "| DeepKriging_wendland | -- | 30.4788 | 5.5208 | 4.0962 | 0.5044 |\n", - "| DeepKriging_mrts | 200 | 0.9339 | 0.9664 | 0.5114 | 0.9848 |\n", - "| DeepKriging_sphere | 100 | 0.7001 | 0.8367 | 0.3801 | 0.9886 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8622 | 0.9285 | 0.3924 | 0.986 |\n", - "| UniversalKriging | 1000 | 0.8034 | 0.8963 | 0.4116 | 0.9869 |\n", - "Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:02:51.832944: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774706571.842928 3535158 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774706571.845619 3535158 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774706571.852640 3535158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706571.852670 3535158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706571.852673 3535158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774706571.852687 3535158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.8122 (±0.1486), Variance: 55.2101, Range: [0.5000, 32.8213]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706591.910730 3535158 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774706593.413828 3535482 service.cc:152] XLA service 0x7f7374009900 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774706593.413850 3535482 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706593.629731 3535482 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774706595.258169 3535482 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f75a0570f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f75a0157760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2926, sigma²=14.1241, nugget=0.0408\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2280, sigma²=9.4432, nugget=0.0367\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2287, sigma²=7.6454, nugget=0.0297\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2288, sigma²=7.1430, nugget=0.0277\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2289, sigma²=6.6624, nugget=0.0259\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2288, sigma²=6.1173, nugget=0.0238\n", - "[repeat 27] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2288, sigma²=6.1173, nugget=0.0238\n", - "[repeat 27] done → repeat_27_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 77.0798 | 8.7795 | 5.2927 | -0.5196 |\n", - "| OLS_sphere | 1000 | 1.9389 | 1.3924 | 0.6109 | 0.9618 |\n", - "| DeepKriging_wendland | -- | 26.8696 | 5.1836 | 3.8315 | 0.4703 |\n", - "| DeepKriging_mrts | 200 | 0.9225 | 0.9605 | 0.5373 | 0.9818 |\n", - "| DeepKriging_sphere | 150 | 0.6222 | 0.7888 | 0.3679 | 0.9877 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3414 | 0.5843 | 0.3411 | 0.9933 |\n", - "| UniversalKriging | 1000 | 0.4271 | 0.6536 | 0.4094 | 0.9916 |\n", - "Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:13:09.855515: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774707189.865767 4163655 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774707189.868779 4163655 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774707189.875720 4163655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707189.875748 4163655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707189.875750 4163655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707189.875751 4163655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4715 (±0.1615), Variance: 65.1923, Range: [0.5000, 36.8646]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707209.859316 4163655 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774707211.357644 4163981 service.cc:152] XLA service 0x7f6f1c005d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774707211.357678 4163981 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707211.571175 4163981 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707213.205495 4163981 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f714c327b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7144457d90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2958, sigma²=13.8928, nugget=0.0453\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2600, sigma²=9.7873, nugget=0.0376\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2608, sigma²=7.7236, nugget=0.0297\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2609, sigma²=7.0465, nugget=0.0271\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2609, sigma²=6.6683, nugget=0.0256\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2608, sigma²=6.2085, nugget=0.0238\n", - "[repeat 28] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2608, sigma²=6.2085, nugget=0.0238\n", - "[repeat 28] done → repeat_28_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 339.821 | 18.4342 | 6.4518 | -4.587 |\n", - "| OLS_sphere | 1000 | 0.9661 | 0.9829 | 0.5116 | 0.9841 |\n", - "| DeepKriging_wendland | -- | 29.4534 | 5.4271 | 3.8449 | 0.5158 |\n", - "| DeepKriging_mrts | 200 | 0.7046 | 0.8394 | 0.5182 | 0.9884 |\n", - "| DeepKriging_sphere | 100 | 0.4745 | 0.6888 | 0.2929 | 0.9922 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.5256 | 0.725 | 0.3483 | 0.9914 |\n", - "| UniversalKriging | 1000 | 0.7302 | 0.8545 | 0.4444 | 0.988 |\n", - "Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:23:45.212014: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774707825.222164 589179 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774707825.225598 589179 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774707825.232790 589179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707825.232816 589179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707825.232818 589179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774707825.232820 589179 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.9010 (±0.1580), Variance: 62.4132, Range: [0.5000, 39.7075]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707845.449478 589179 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774707846.929095 589500 service.cc:152] XLA service 0x7fe3f0019930 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774707846.929121 589500 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707847.151315 589500 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774707848.779496 589500 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe6205229e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fe61839f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2613, sigma²=15.0130, nugget=0.0435\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2055, sigma²=9.9016, nugget=0.0386\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2061, sigma²=7.3678, nugget=0.0287\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2062, sigma²=6.7721, nugget=0.0264\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2063, sigma²=6.2017, nugget=0.0242\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2062, sigma²=5.6867, nugget=0.0222\n", - "[repeat 29] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2062, sigma²=5.6867, nugget=0.0222\n", - "[repeat 29] done → repeat_29_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|----------|\n", - "| OLS_wendland | -- | 3014.52 | 54.9047 | 12.4893 | -47.1583 |\n", - "| OLS_sphere | 1000 | 1.1154 | 1.0561 | 0.4954 | 0.9822 |\n", - "| DeepKriging_wendland | -- | 42.3784 | 6.5099 | 4.7862 | 0.323 |\n", - "| DeepKriging_mrts | 150 | 1.7507 | 1.3231 | 0.7975 | 0.972 |\n", - "| DeepKriging_sphere | 50 | 0.8977 | 0.9475 | 0.4504 | 0.9857 |\n", - "| DeepKriging_sphere_Huber | 200 | 1.2881 | 1.135 | 0.5046 | 0.9794 |\n", - "| UniversalKriging | 1000 | 1.2525 | 1.1191 | 0.5443 | 0.98 |\n", - "Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:33:28.643582: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774708408.654807 1170200 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774708408.657590 1170200 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774708408.664763 1170200 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774708408.664790 1170200 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774708408.664792 1170200 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774708408.664793 1170200 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.7474 (±0.1675), Variance: 70.1379, Range: [0.5000, 44.0462]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774708428.781958 1170200 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774708430.278201 1170527 service.cc:152] XLA service 0x56299bf21b80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774708430.278224 1170527 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774708430.491130 1170527 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774708432.123126 1170527 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6ebc13f520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6eb4267ac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3621, sigma²=21.4496, nugget=0.0477\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2223, sigma²=11.6218, nugget=0.0451\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2230, sigma²=9.0629, nugget=0.0352\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2231, sigma²=8.1368, nugget=0.0316\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2231, sigma²=7.6556, nugget=0.0297\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2231, sigma²=7.1165, nugget=0.0276\n", - "[repeat 30] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2231, sigma²=7.1165, nugget=0.0276\n", - "[repeat 30] done → repeat_30_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|-----------|\n", - "| OLS_wendland | -- | 6938.81 | 83.2995 | 11.0231 | -108.554 |\n", - "| OLS_sphere | 1000 | 1.5438 | 1.2425 | 0.5512 | 0.9756 |\n", - "| DeepKriging_wendland | -- | 37.5559 | 6.1283 | 4.3484 | 0.407 |\n", - "| DeepKriging_mrts | 150 | 0.6714 | 0.8194 | 0.528 | 0.9894 |\n", - "| DeepKriging_sphere | 150 | 0.268 | 0.5177 | 0.3081 | 0.9958 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2727 | 0.5222 | 0.3094 | 0.9957 |\n", - "| UniversalKriging | 1000 | 0.4143 | 0.6437 | 0.3662 | 0.9935 |\n", - "Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:43:43.014602: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774709023.024656 1794579 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774709023.027478 1794579 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774709023.035335 1794579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709023.035361 1794579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709023.035363 1794579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709023.035364 1794579 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.6404 (±0.1606), Variance: 64.4984, Range: [0.5000, 40.2506]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709043.192287 1794579 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774709044.680725 1794898 service.cc:152] XLA service 0x563231490c80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774709044.680754 1794898 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709044.896655 1794898 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709046.548016 1794898 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd57c43a560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd56c7c1750> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2949, sigma²=13.0183, nugget=0.0426\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2591, sigma²=9.3961, nugget=0.0362\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2599, sigma²=7.5856, nugget=0.0292\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2600, sigma²=6.9588, nugget=0.0268\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2600, sigma²=6.6004, nugget=0.0254\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2599, sigma²=6.1343, nugget=0.0236\n", - "[repeat 31] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2599, sigma²=6.1343, nugget=0.0236\n", - "[repeat 31] done → repeat_31_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 343.859 | 18.5434 | 6.573 | -4.9856 |\n", - "| OLS_sphere | 1000 | 0.9525 | 0.976 | 0.4311 | 0.9834 |\n", - "| DeepKriging_wendland | -- | 31.3118 | 5.5957 | 4.2803 | 0.455 |\n", - "| DeepKriging_mrts | 100 | 1.2225 | 1.1057 | 0.6084 | 0.9787 |\n", - "| DeepKriging_sphere | 50 | 0.7729 | 0.8791 | 0.382 | 0.9865 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.7006 | 0.837 | 0.3858 | 0.9878 |\n", - "| UniversalKriging | 1000 | 1.0356 | 1.0176 | 0.4945 | 0.982 |\n", - "Repeat 32/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 33/50 seed=82\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 22:53:44.579192: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774709624.589653 2380161 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774709624.592842 2380161 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774709624.600320 2380161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709624.600363 2380161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709624.600366 2380161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774709624.600367 2380161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] seed=82\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2360 (±0.1520), Variance: 57.7591, Range: [0.5000, 34.4971]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709644.762146 2380161 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774709646.265668 2380489 service.cc:152] XLA service 0x561c2388fca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774709646.265693 2380489 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709646.488573 2380489 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774709648.131727 2380489 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f508034a680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f50605629e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2846, sigma²=13.0612, nugget=0.0377\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2229, sigma²=8.7701, nugget=0.0340\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2235, sigma²=6.6602, nugget=0.0258\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2236, sigma²=6.0706, nugget=0.0235\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2236, sigma²=5.6814, nugget=0.0220\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2236, sigma²=5.2290, nugget=0.0203\n", - "[repeat 32] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2236, sigma²=5.2290, nugget=0.0203\n", - "[repeat 32] done → repeat_32_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 398.26 | 19.9565 | 6.5935 | -5.0771 |\n", - "| OLS_sphere | 1000 | 1.9438 | 1.3942 | 0.5949 | 0.9703 |\n", - "| DeepKriging_wendland | -- | 32.2718 | 5.6808 | 4.0783 | 0.5076 |\n", - "| DeepKriging_mrts | 10 | 2.6682 | 1.6335 | 0.9802 | 0.9593 |\n", - "| DeepKriging_sphere | 100 | 0.5458 | 0.7388 | 0.3634 | 0.9917 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.422 | 0.6497 | 0.367 | 0.9936 |\n", - "| UniversalKriging | 1000 | 0.7705 | 0.8778 | 0.4303 | 0.9882 |\n", - "Repeat 33/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 34/50 seed=83\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:02:36.880150: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774710156.890374 2863489 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774710156.893156 2863489 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774710156.900425 2863489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710156.900452 2863489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710156.900454 2863489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710156.900455 2863489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] seed=83\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.8929 (±0.1638), Variance: 67.0997, Range: [0.5000, 36.7543]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710176.899491 2863489 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774710178.413524 2863817 service.cc:152] XLA service 0x55a3279a3890 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774710178.413548 2863817 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710178.637996 2863817 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710180.297014 2863817 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6df415a7a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6de46f70a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2407, sigma²=14.1939, nugget=0.0416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1909, sigma²=9.0186, nugget=0.0352\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1914, sigma²=6.4057, nugget=0.0250\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1915, sigma²=5.7698, nugget=0.0225\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1915, sigma²=5.3017, nugget=0.0207\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1915, sigma²=4.8788, nugget=0.0191\n", - "[repeat 33] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1915, sigma²=4.8788, nugget=0.0191\n", - "[repeat 33] done → repeat_33_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 49.4827 | 7.0344 | 5.3343 | 0.3066 |\n", - "| OLS_sphere | 1000 | 3.0961 | 1.7596 | 0.6678 | 0.9566 |\n", - "| DeepKriging_wendland | -- | 33.6132 | 5.7977 | 4.0829 | 0.529 |\n", - "| DeepKriging_mrts | 200 | 1.5175 | 1.2319 | 0.5781 | 0.9787 |\n", - "| DeepKriging_sphere | 50 | 1.9022 | 1.3792 | 0.538 | 0.9733 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.8443 | 1.358 | 0.4564 | 0.9742 |\n", - "| UniversalKriging | 1000 | 1.5189 | 1.2324 | 0.4922 | 0.9787 |\n", - "Repeat 34/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 35/50 seed=84\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:12:31.902043: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774710751.912341 3445561 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774710751.915239 3445561 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774710751.922350 3445561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710751.922375 3445561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710751.922377 3445561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774710751.922378 3445561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] seed=84\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5348 (±0.1642), Variance: 67.4102, Range: [0.5000, 49.7955]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710771.967845 3445561 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774710773.470733 3445880 service.cc:152] XLA service 0x7fd800005c20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774710773.470758 3445880 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710773.684096 3445880 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774710775.314327 3445880 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fda204723b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fda182ac310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3909, sigma²=20.4919, nugget=0.0445\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=10.8217, nugget=0.0418\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2387, sigma²=8.3734, nugget=0.0324\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2388, sigma²=7.9087, nugget=0.0306\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2388, sigma²=7.3446, nugget=0.0284\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2388, sigma²=6.8225, nugget=0.0264\n", - "[repeat 34] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2388, sigma²=6.8225, nugget=0.0264\n", - "[repeat 34] done → repeat_34_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 143.025 | 11.9593 | 6.0997 | -1.0658 |\n", - "| OLS_sphere | 1000 | 0.9535 | 0.9765 | 0.4786 | 0.9862 |\n", - "| DeepKriging_wendland | -- | 35.3941 | 5.9493 | 4.1958 | 0.4888 |\n", - "| DeepKriging_mrts | 100 | 0.9222 | 0.9603 | 0.6744 | 0.9867 |\n", - "| DeepKriging_sphere | 50 | 0.2778 | 0.527 | 0.289 | 0.996 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2835 | 0.5325 | 0.3138 | 0.9959 |\n", - "| UniversalKriging | 1000 | 0.424 | 0.6511 | 0.3808 | 0.9939 |\n", - "Repeat 35/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 36/50 seed=85\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:22:54.040812: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774711374.050840 4101383 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774711374.053652 4101383 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774711374.060826 4101383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711374.060855 4101383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711374.060858 4101383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711374.060859 4101383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] seed=85\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.7351 (±0.1498), Variance: 56.0803, Range: [0.5000, 33.8276]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774711394.154439 4101383 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774711395.654568 4101704 service.cc:152] XLA service 0x55f1e8356a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774711395.654592 4101704 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774711395.876237 4101704 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774711397.502447 4101704 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fec9c1e9630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fec8c5ece50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2688, sigma²=13.8378, nugget=0.0337\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1917, sigma²=8.3629, nugget=0.0308\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1767, sigma²=5.6710, nugget=0.0221\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1745, sigma²=5.0959, nugget=0.0200\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1724, sigma²=4.5666, nugget=0.0180\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1723, sigma²=4.0684, nugget=0.0160\n", - "[repeat 35] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1723, sigma²=4.0684, nugget=0.0160\n", - "[repeat 35] done → repeat_35_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 56.1845 | 7.4956 | 5.3416 | 0.1018 |\n", - "| OLS_sphere | 1000 | 1.0917 | 1.0448 | 0.455 | 0.9825 |\n", - "| DeepKriging_wendland | -- | 31.7663 | 5.6362 | 4.2246 | 0.4922 |\n", - "| DeepKriging_mrts | 200 | 0.9748 | 0.9873 | 0.5402 | 0.9844 |\n", - "| DeepKriging_sphere | 50 | 0.4134 | 0.6429 | 0.3141 | 0.9934 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5422 | 0.7363 | 0.3299 | 0.9913 |\n", - "| UniversalKriging | 1000 | 1.1934 | 1.0924 | 0.5194 | 0.9809 |\n", - "Repeat 36/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 37/50 seed=86\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:33:13.733105: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774711993.742868 556084 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774711993.745670 556084 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774711993.752760 556084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711993.752785 556084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711993.752787 556084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774711993.752788 556084 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] seed=86\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2179 (±0.1524), Variance: 58.0966, Range: [0.5000, 35.9546]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712013.975168 556084 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774712015.468655 556405 service.cc:152] XLA service 0x55bbb6c30f60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774712015.468688 556405 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712015.685375 556405 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712017.300451 556405 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9fc4362710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9fb41c6440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2436, sigma²=13.8077, nugget=0.0406\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1928, sigma²=9.1255, nugget=0.0358\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1934, sigma²=6.7194, nugget=0.0263\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1934, sigma²=6.0582, nugget=0.0237\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1935, sigma²=5.7618, nugget=0.0226\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1934, sigma²=5.2773, nugget=0.0207\n", - "[repeat 36] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1934, sigma²=5.2773, nugget=0.0207\n", - "[repeat 36] done → repeat_36_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 45.0309 | 6.7105 | 4.9066 | 0.2881 |\n", - "| OLS_sphere | 1000 | 6.5766 | 2.5645 | 0.6697 | 0.896 |\n", - "| DeepKriging_wendland | -- | 27.6446 | 5.2578 | 3.6878 | 0.5629 |\n", - "| DeepKriging_mrts | 200 | 2.4281 | 1.5582 | 0.5915 | 0.9616 |\n", - "| DeepKriging_sphere | 100 | 0.4158 | 0.6448 | 0.3551 | 0.9934 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3266 | 0.5715 | 0.3056 | 0.9948 |\n", - "| UniversalKriging | 1000 | 0.7717 | 0.8784 | 0.4991 | 0.9878 |\n", - "Repeat 37/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 38/50 seed=87\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:43:19.759310: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774712599.769477 1155881 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774712599.772303 1155881 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774712599.779571 1155881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774712599.779594 1155881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774712599.779597 1155881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774712599.779598 1155881 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] seed=87\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.6213 (±0.1632), Variance: 66.5948, Range: [0.5000, 37.2762]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712619.950291 1155881 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774712621.442605 1156207 service.cc:152] XLA service 0x55e2c3d9e740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774712621.442629 1156207 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712621.656013 1156207 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774712623.306860 1156207 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f317833df30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f31701f7eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2703, sigma²=14.5830, nugget=0.0518\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2533, sigma²=11.0536, nugget=0.0427\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2540, sigma²=9.1145, nugget=0.0352\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2541, sigma²=8.5653, nugget=0.0331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2541, sigma²=7.9532, nugget=0.0307\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2540, sigma²=7.4141, nugget=0.0286\n", - "[repeat 37] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2540, sigma²=7.4141, nugget=0.0286\n", - "[repeat 37] done → repeat_37_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 315.557 | 17.7639 | 7.2031 | -3.4626 |\n", - "| OLS_sphere | 1000 | 6.1715 | 2.4843 | 0.7456 | 0.9127 |\n", - "| DeepKriging_wendland | -- | 42.3897 | 6.5107 | 4.821 | 0.4005 |\n", - "| DeepKriging_mrts | 100 | 1.0122 | 1.0061 | 0.5854 | 0.9857 |\n", - "| DeepKriging_sphere | 200 | 0.5529 | 0.7436 | 0.3519 | 0.9922 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5348 | 0.7313 | 0.3428 | 0.9924 |\n", - "| UniversalKriging | 1000 | 0.7876 | 0.8875 | 0.4808 | 0.9889 |\n", - "Repeat 38/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 39/50 seed=88\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 23:54:10.012481: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774713250.022085 1846694 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774713250.024841 1846694 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774713250.032289 1846694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713250.032314 1846694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713250.032316 1846694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713250.032317 1846694 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] seed=88\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2500 (±0.1510), Variance: 57.0368, Range: [0.5000, 37.0683]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713270.165985 1846694 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774713271.698166 1847020 service.cc:152] XLA service 0x55a54c1b45f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774713271.698221 1847020 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713271.921903 1847020 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713273.559603 1847020 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f25401da440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2510648940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3731, sigma²=15.4866, nugget=0.0440\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2876, sigma²=10.0007, nugget=0.0382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2885, sigma²=8.4288, nugget=0.0322\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2886, sigma²=7.8506, nugget=0.0300\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2886, sigma²=7.3919, nugget=0.0283\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2885, sigma²=6.9557, nugget=0.0266\n", - "[repeat 38] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2885, sigma²=6.9557, nugget=0.0266\n", - "[repeat 38] done → repeat_38_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 34.8909 | 5.9069 | 4.6229 | 0.3433 |\n", - "| OLS_sphere | 1000 | 1.3707 | 1.1708 | 0.4841 | 0.9742 |\n", - "| DeepKriging_wendland | -- | 23.2743 | 4.8243 | 3.4916 | 0.562 |\n", - "| DeepKriging_mrts | 200 | 1.388 | 1.1781 | 0.6638 | 0.9739 |\n", - "| DeepKriging_sphere | 100 | 0.6007 | 0.775 | 0.3889 | 0.9887 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7898 | 0.8887 | 0.3537 | 0.9851 |\n", - "| UniversalKriging | 1000 | 0.9609 | 0.9803 | 0.4525 | 0.9819 |\n", - "Repeat 39/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 40/50 seed=89\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:02:52.767835: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774713772.778538 2313055 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774713772.781324 2313055 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774713772.789628 2313055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713772.789657 2313055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713772.789659 2313055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774713772.789660 2313055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] seed=89\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5955 (±0.1622), Variance: 65.8002, Range: [0.5000, 40.0817]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713792.857425 2313055 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774713794.356192 2313382 service.cc:152] XLA service 0x7f7bb0006e60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774713794.356215 2313382 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713794.574686 2313382 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774713796.210353 2313382 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7df4172950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7dec2829e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3181, sigma²=16.2894, nugget=0.0535\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2796, sigma²=12.1192, nugget=0.0464\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2804, sigma²=9.9868, nugget=0.0382\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2805, sigma²=9.5335, nugget=0.0365\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2806, sigma²=8.9338, nugget=0.0342\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2805, sigma²=8.4391, nugget=0.0323\n", - "[repeat 39] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2805, sigma²=8.4391, nugget=0.0323\n", - "[repeat 39] done → repeat_39_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 93.5035 | 9.6697 | 5.8929 | -0.3855 |\n", - "| OLS_sphere | 1000 | 1.1306 | 1.0633 | 0.47 | 0.9832 |\n", - "| DeepKriging_wendland | -- | 34.7864 | 5.898 | 4.2524 | 0.4845 |\n", - "| DeepKriging_mrts | 200 | 0.6421 | 0.8013 | 0.4785 | 0.9905 |\n", - "| DeepKriging_sphere | 200 | 0.7579 | 0.8705 | 0.368 | 0.9888 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6682 | 0.8174 | 0.3087 | 0.9901 |\n", - "| UniversalKriging | 1000 | 0.5402 | 0.735 | 0.382 | 0.992 |\n", - "Repeat 40/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 41/50 seed=90\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:13:36.843482: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774714416.855117 2912150 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774714416.857874 2912150 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774714416.865220 2912150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774714416.865250 2912150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774714416.865252 2912150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774714416.865253 2912150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] seed=90\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.7232 (±0.1633), Variance: 66.6287, Range: [0.5000, 50.7604]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774714437.060301 2912150 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774714438.566506 2912476 service.cc:152] XLA service 0x7faf18006040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774714438.566528 2912476 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774714438.788192 2912476 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774714440.409634 2912476 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb140707b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb138561a20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3671, sigma²=21.5470, nugget=0.0476\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2251, sigma²=11.3439, nugget=0.0440\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2257, sigma²=9.2553, nugget=0.0359\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2258, sigma²=8.5264, nugget=0.0331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2259, sigma²=7.9976, nugget=0.0310\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2258, sigma²=7.3457, nugget=0.0285\n", - "[repeat 40] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2258, sigma²=7.3457, nugget=0.0285\n", - "[repeat 40] done → repeat_40_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 38.2728 | 6.1865 | 4.9323 | 0.4147 |\n", - "| OLS_sphere | 200 | 4.4698 | 2.1142 | 1.4466 | 0.9317 |\n", - "| DeepKriging_wendland | -- | 29.6224 | 5.4427 | 4.0946 | 0.547 |\n", - "| DeepKriging_mrts | 200 | 1.4417 | 1.2007 | 0.6666 | 0.978 |\n", - "| DeepKriging_sphere | 100 | 0.766 | 0.8752 | 0.4376 | 0.9883 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.6402 | 0.8001 | 0.3889 | 0.9902 |\n", - "| UniversalKriging | 1000 | 1.1131 | 1.0551 | 0.517 | 0.983 |\n", - "Repeat 41/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 42/50 seed=91\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:23:34.345891: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774715014.355997 3507050 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774715014.358823 3507050 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774715014.366041 3507050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715014.366069 3507050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715014.366071 3507050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715014.366072 3507050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] seed=91\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.5539 (±0.1562), Variance: 60.9627, Range: [0.5000, 44.2406]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715034.707858 3507050 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774715036.250411 3507378 service.cc:152] XLA service 0x7f305400a680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774715036.250437 3507378 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715036.472934 3507378 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715038.095883 3507378 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f3290326dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f32887bfd00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3016, sigma²=15.8318, nugget=0.0457\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2351, sigma²=10.4915, nugget=0.0406\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2359, sigma²=8.4644, nugget=0.0328\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2360, sigma²=7.6664, nugget=0.0297\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2360, sigma²=7.3032, nugget=0.0283\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2359, sigma²=6.7024, nugget=0.0259\n", - "[repeat 41] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2359, sigma²=6.7024, nugget=0.0259\n", - "[repeat 41] done → repeat_41_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 103.68 | 10.1823 | 5.5687 | -0.8731 |\n", - "| OLS_sphere | 200 | 3.4947 | 1.8694 | 1.3006 | 0.9369 |\n", - "| DeepKriging_wendland | -- | 27.1268 | 5.2083 | 3.7869 | 0.5099 |\n", - "| DeepKriging_mrts | 150 | 1.3271 | 1.152 | 0.6853 | 0.976 |\n", - "| DeepKriging_sphere | 50 | 0.8969 | 0.9471 | 0.4919 | 0.9838 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6279 | 0.7924 | 0.3956 | 0.9887 |\n", - "| UniversalKriging | 1000 | 1.0146 | 1.0073 | 0.5053 | 0.9817 |\n", - "Repeat 42/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 43/50 seed=92\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:33:33.298006: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774715613.308956 4102771 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774715613.311883 4102771 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774715613.318988 4102771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715613.319015 4102771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715613.319018 4102771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774715613.319019 4102771 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] seed=92\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.4524 (±0.1665), Variance: 69.2949, Range: [0.5000, 43.2673]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715633.479342 4102771 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774715634.980568 4103101 service.cc:152] XLA service 0x7fdcf0006080 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774715634.980600 4103101 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715635.199645 4103101 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774715636.838747 4103101 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdf2418ea70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdf1c25ec20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2907, sigma²=17.0988, nugget=0.0485\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2271, sigma²=10.9190, nugget=0.0422\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2278, sigma²=8.4197, nugget=0.0325\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2279, sigma²=7.7779, nugget=0.0300\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2279, sigma²=7.1406, nugget=0.0276\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2278, sigma²=6.5140, nugget=0.0251\n", - "[repeat 42] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2278, sigma²=6.5140, nugget=0.0251\n", - "[repeat 42] done → repeat_42_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 59279.7 | 243.474 | 20.9248 | -816.268 |\n", - "| OLS_sphere | 200 | 2.4044 | 1.5506 | 1.1348 | 0.9669 |\n", - "| DeepKriging_wendland | -- | 38.7482 | 6.2248 | 4.2127 | 0.4658 |\n", - "| DeepKriging_mrts | 200 | 0.6913 | 0.8314 | 0.5117 | 0.9905 |\n", - "| DeepKriging_sphere | 50 | 0.2405 | 0.4904 | 0.2917 | 0.9967 |\n", - "| DeepKriging_sphere_Huber | 150 | 0.3788 | 0.6154 | 0.2986 | 0.9948 |\n", - "| UniversalKriging | 1000 | 0.4159 | 0.6449 | 0.3797 | 0.9943 |\n", - "Repeat 43/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 44/50 seed=93\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:42:46.963117: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774716166.973123 424038 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774716166.975898 424038 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774716166.983293 424038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716166.983322 424038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716166.983324 424038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716166.983324 424038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] seed=93\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.8216 (±0.1502), Variance: 56.3796, Range: [0.5000, 43.3304]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716187.276139 424038 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774716188.789392 424364 service.cc:152] XLA service 0x7faed80066d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774716188.789415 424364 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716189.006382 424364 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716190.642015 424364 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb1101eaef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb108297400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5559, sigma²=26.3591, nugget=0.0336\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2137, sigma²=9.3392, nugget=0.0363\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2144, sigma²=6.8220, nugget=0.0265\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2145, sigma²=6.2823, nugget=0.0244\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2145, sigma²=5.8537, nugget=0.0227\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2144, sigma²=5.3660, nugget=0.0208\n", - "[repeat 43] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2144, sigma²=5.3660, nugget=0.0208\n", - "[repeat 43] done → repeat_43_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 53.8687 | 7.3395 | 5.4985 | 0.212 |\n", - "| OLS_sphere | 1000 | 2.5885 | 1.6089 | 0.6328 | 0.9621 |\n", - "| DeepKriging_wendland | -- | 45.021 | 6.7098 | 4.5459 | 0.3414 |\n", - "| DeepKriging_mrts | 150 | 2.5854 | 1.6079 | 0.8419 | 0.9622 |\n", - "| DeepKriging_sphere | 100 | 1.4626 | 1.2094 | 0.4275 | 0.9786 |\n", - "| DeepKriging_sphere_Huber | 100 | 1.2385 | 1.1129 | 0.4094 | 0.9819 |\n", - "| UniversalKriging | 1000 | 1.5046 | 1.2266 | 0.5069 | 0.978 |\n", - "Repeat 44/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 45/50 seed=94\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 00:52:46.755197: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774716766.764728 979075 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774716766.767671 979075 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774716766.775005 979075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716766.775035 979075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716766.775037 979075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774716766.775038 979075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] seed=94\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2232 (±0.1495), Variance: 55.8599, Range: [0.5000, 41.1576]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716787.149152 979075 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774716788.665357 979402 service.cc:152] XLA service 0x55b76b3cb120 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774716788.665380 979402 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716788.883164 979402 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774716790.526292 979402 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f7150763f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f714851d480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3412, sigma²=15.7647, nugget=0.0358\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2113, sigma²=8.8041, nugget=0.0343\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2120, sigma²=6.3991, nugget=0.0249\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2121, sigma²=5.8423, nugget=0.0227\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2121, sigma²=5.4921, nugget=0.0214\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2121, sigma²=5.0761, nugget=0.0198\n", - "[repeat 44] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2121, sigma²=5.0761, nugget=0.0198\n", - "[repeat 44] done → repeat_44_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 58.4914 | 7.648 | 5.3652 | -0.1936 |\n", - "| OLS_sphere | 1000 | 1.8629 | 1.3649 | 0.5976 | 0.962 |\n", - "| DeepKriging_wendland | -- | 31.3019 | 5.5948 | 4.2302 | 0.3612 |\n", - "| DeepKriging_mrts | 150 | 0.9486 | 0.974 | 0.5257 | 0.9806 |\n", - "| DeepKriging_sphere | 100 | 0.658 | 0.8111 | 0.3773 | 0.9866 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6877 | 0.8293 | 0.3477 | 0.986 |\n", - "| UniversalKriging | 1000 | 0.884 | 0.9402 | 0.4477 | 0.982 |\n", - "Repeat 45/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 46/50 seed=95\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 01:03:00.517349: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774717380.526779 1617915 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774717380.529621 1617915 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774717380.537097 1617915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717380.537129 1617915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717380.537131 1617915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717380.537132 1617915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] seed=95\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.2845 (±0.1615), Variance: 65.2107, Range: [0.5000, 42.6183]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717400.801574 1617915 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774717402.308010 1618241 service.cc:152] XLA service 0x7f821c005e10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774717402.308040 1618241 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717402.527804 1618241 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717404.165727 1618241 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f844031a320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8438198ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2606, sigma²=14.2240, nugget=0.0412\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2299, sigma²=9.8994, nugget=0.0355\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2172, sigma²=7.1069, nugget=0.0268\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2173, sigma²=6.6422, nugget=0.0251\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2175, sigma²=5.9519, nugget=0.0224\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2177, sigma²=5.4975, nugget=0.0206\n", - "[repeat 45] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2177, sigma²=5.4975, nugget=0.0206\n", - "[repeat 45] done → repeat_45_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 40.5138 | 6.365 | 4.9265 | 0.3647 |\n", - "| OLS_sphere | 200 | 2.7005 | 1.6433 | 1.1144 | 0.9577 |\n", - "| DeepKriging_wendland | -- | 29.709 | 5.4506 | 3.9656 | 0.5342 |\n", - "| DeepKriging_mrts | 200 | 1.1397 | 1.0676 | 0.5539 | 0.9821 |\n", - "| DeepKriging_sphere | 10 | 0.7193 | 0.8481 | 0.4651 | 0.9887 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.659 | 0.8118 | 0.4081 | 0.9897 |\n", - "| UniversalKriging | 1000 | 0.8304 | 0.9113 | 0.4202 | 0.987 |\n", - "Repeat 46/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 47/50 seed=96\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 01:12:45.123366: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774717965.132745 2165421 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774717965.135478 2165421 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774717965.142857 2165421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717965.142888 2165421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717965.142890 2165421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774717965.142890 2165421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] seed=96\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.6948 (±0.1577), Variance: 62.1856, Range: [0.5000, 38.7438]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717985.515539 2165421 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774717987.021235 2165747 service.cc:152] XLA service 0x560a51e06190 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774717987.021258 2165747 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717987.243241 2165747 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774717988.879338 2165747 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd9a05da290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd99827a3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2542, sigma²=13.3906, nugget=0.0495\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2458, sigma²=11.0786, nugget=0.0428\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2465, sigma²=9.2623, nugget=0.0357\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2466, sigma²=8.6393, nugget=0.0333\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2467, sigma²=7.9831, nugget=0.0308\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2466, sigma²=7.4455, nugget=0.0287\n", - "[repeat 46] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2466, sigma²=7.4455, nugget=0.0287\n", - "[repeat 46] done → repeat_46_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 38.8014 | 6.2291 | 4.8887 | 0.3306 |\n", - "| OLS_sphere | 1000 | 3.3184 | 1.8217 | 0.59 | 0.9428 |\n", - "| DeepKriging_wendland | -- | 28.6155 | 5.3493 | 3.8019 | 0.5063 |\n", - "| DeepKriging_mrts | 200 | 0.9408 | 0.97 | 0.5295 | 0.9838 |\n", - "| DeepKriging_sphere | 50 | 0.5981 | 0.7734 | 0.3971 | 0.9897 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.625 | 0.7906 | 0.3962 | 0.9892 |\n", - "| UniversalKriging | 1000 | 1.0229 | 1.0114 | 0.4638 | 0.9824 |\n", - "Repeat 47/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 48/50 seed=97\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 01:21:31.146623: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774718491.156869 2631134 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774718491.159691 2631134 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774718491.167044 2631134 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774718491.167072 2631134 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774718491.167074 2631134 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774718491.167075 2631134 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] seed=97\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.3496 (±0.1591), Variance: 63.2879, Range: [0.5000, 37.3416]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774718511.324374 2631134 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774718512.853897 2631462 service.cc:152] XLA service 0x7f5d0c0190e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774718512.853919 2631462 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774718513.071086 2631462 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774718514.705550 2631462 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5f487cec20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5f481d7b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3316, sigma²=14.6396, nugget=0.0478\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2909, sigma²=10.7149, nugget=0.0409\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2918, sigma²=8.5344, nugget=0.0325\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2920, sigma²=7.9490, nugget=0.0303\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2920, sigma²=7.4277, nugget=0.0283\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2919, sigma²=7.0507, nugget=0.0269\n", - "[repeat 47] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2919, sigma²=7.0507, nugget=0.0269\n", - "[repeat 47] done → repeat_47_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-------------|----------|---------|------------|\n", - "| OLS_wendland | -- | 164963 | 406.157 | 30.641 | -2840.59 |\n", - "| OLS_sphere | 1000 | 1.2169 | 1.1031 | 0.5099 | 0.979 |\n", - "| DeepKriging_wendland | -- | 28.3374 | 5.3233 | 3.8184 | 0.5119 |\n", - "| DeepKriging_mrts | 200 | 0.4839 | 0.6957 | 0.4653 | 0.9917 |\n", - "| DeepKriging_sphere | 50 | 0.2461 | 0.4961 | 0.2779 | 0.9958 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2733 | 0.5228 | 0.2874 | 0.9953 |\n", - "| UniversalKriging | 1000 | 0.4257 | 0.6524 | 0.3735 | 0.9927 |\n", - "Repeat 48/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 49/50 seed=98\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 01:32:24.243534: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774719144.253121 3277797 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774719144.255905 3277797 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774719144.262936 3277797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719144.262967 3277797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719144.262976 3277797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719144.262978 3277797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] seed=98\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 10.0295 (±0.1528), Variance: 58.3319, Range: [0.5000, 39.6444]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719164.346343 3277797 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774719165.833201 3278125 service.cc:152] XLA service 0x55640eacae30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774719165.833233 3278125 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719166.055664 3278125 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719167.695331 3278125 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fda38357640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fda28196290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2879, sigma²=13.1046, nugget=0.0499\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2882, sigma²=10.8488, nugget=0.0414\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2890, sigma²=9.2247, nugget=0.0352\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2891, sigma²=8.7792, nugget=0.0335\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2892, sigma²=8.2029, nugget=0.0313\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2890, sigma²=7.7410, nugget=0.0295\n", - "[repeat 48] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2890, sigma²=7.7410, nugget=0.0295\n", - "[repeat 48] done → repeat_48_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 86.638 | 9.308 | 5.4185 | -0.404 |\n", - "| OLS_sphere | 1000 | 2.8941 | 1.7012 | 0.6065 | 0.9531 |\n", - "| DeepKriging_wendland | -- | 33.0519 | 5.7491 | 4.0507 | 0.4644 |\n", - "| DeepKriging_mrts | 200 | 0.5708 | 0.7555 | 0.4587 | 0.9908 |\n", - "| DeepKriging_sphere | 150 | 0.3713 | 0.6093 | 0.3482 | 0.994 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3054 | 0.5526 | 0.3026 | 0.9951 |\n", - "| UniversalKriging | 1000 | 0.643 | 0.8019 | 0.4205 | 0.9896 |\n", - "Repeat 49/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 50/50 seed=99\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-29 01:41:46.203565: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774719706.212872 3803563 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774719706.215720 3803563 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774719706.222827 3803563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719706.222853 3803563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719706.222856 3803563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774719706.222857 3803563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] seed=99\n", - "\n", - "=== Scenario E1 (Non-GP: Pure Nonstationary Trend, no noise) ===\n", - "Simulate Data | z mean: 9.8939 (±0.1528), Variance: 58.4024, Range: [0.5000, 43.4100]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719726.596812 3803563 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774719728.084671 3803896 service.cc:152] XLA service 0x55a8060cceb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774719728.084692 3803896 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719728.301784 3803896 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774719729.967407 3803896 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f81b4415900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f81ac616b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2707, sigma²=11.0745, nugget=0.0408\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2625, sigma²=8.6082, nugget=0.0331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2632, sigma²=7.0041, nugget=0.0269\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2633, sigma²=6.6813, nugget=0.0257\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2634, sigma²=6.0833, nugget=0.0234\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2632, sigma²=5.6810, nugget=0.0218\n", - "[repeat 49] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2632, sigma²=5.6810, nugget=0.0218\n", - "[repeat 49] done → repeat_49_noGP_pure_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 239.859 | 15.4874 | 6.1478 | -2.9693 |\n", - "| OLS_sphere | 1000 | 1.9838 | 1.4085 | 0.4818 | 0.9672 |\n", - "| DeepKriging_wendland | -- | 24.2144 | 4.9208 | 3.6897 | 0.5993 |\n", - "| DeepKriging_mrts | 150 | 0.5428 | 0.7367 | 0.4726 | 0.991 |\n", - "| DeepKriging_sphere | 50 | 0.3077 | 0.5547 | 0.275 | 0.9949 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3531 | 0.5942 | 0.3091 | 0.9942 |\n", - "| UniversalKriging | 1000 | 0.501 | 0.7078 | 0.3894 | 0.9917 |\n", - "Repeat 50/50 done — checkpoint saved.\n", - "\n", - "All done: 50/50 repeats completed.\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_noGP_pure.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_pure.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "# ── load checkpoint ───────────────────────────────────────────────────────────\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"Starting fresh.\")\n", - "\n", - "# ── main loop ─────────────────────────────────────────────────────────────────\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n\" + \"=\"*70)\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(\"=\"*70)\n", - "\n", - " out_json = f\"repeat_{repeat}_noGP_pure_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "summary_pure", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T17:51:20.375603Z", - "iopub.status.busy": "2026-03-28T17:51:20.375437Z", - "iopub.status.idle": "2026-03-28T17:51:20.382084Z", - "shell.execute_reply": "2026-03-28T17:51:20.381419Z" - }, - "papermill": { - "duration": 0.030421, - "end_time": "2026-03-28T17:51:20.382548", - "exception": false, - "start_time": "2026-03-28T17:51:20.352127", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "Summary — 50 Repeats\n", - " Scenario: Non-GP + Pure Nonstationary Trend (no noise, no GP)\n", - "================================================================================\n", - "\n", - "| Model | N | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|--------------------------|-----|-------------------|-------------------|-------------------|-------------------|\n", - "| OLS_wendland | 50 | 6030.87±25288.05 | 32.42±70.57 | 7.10±4.65 | -95.446±422.706 |\n", - "| OLS_sphere | 50 | 2.07±1.26 | 1.38±0.41 | 0.64±0.26 | 0.967±0.019 |\n", - "| DeepKriging_wendland | 50 | 31.88±5.01 | 5.63±0.44 | 4.04±0.32 | 0.490±0.062 |\n", - "| DeepKriging_mrts | 50 | 1.07±0.51 | 1.01±0.23 | 0.58±0.11 | 0.983±0.008 |\n", - "| DeepKriging_sphere | 50 | 0.63±0.31 | 0.77±0.18 | 0.37±0.07 | 0.990±0.005 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.57±0.29 | 0.74±0.18 | 0.35±0.05 | 0.991±0.004 |\n", - "| UniversalKriging | 50 | 0.82±0.28 | 0.89±0.16 | 0.45±0.05 | 0.987±0.004 |\n", - "\n", - "Best Model: DeepKriging_sphere_Huber RMSE=0.74±0.18\n", - "\n", - "── Ranking by mean RMSE ──\n", - " 1. DeepKriging_sphere_Huber RMSE=0.74±0.18\n", - " 2. DeepKriging_sphere RMSE=0.77±0.18\n", - " 3. UniversalKriging RMSE=0.89±0.16\n", - " 4. DeepKriging_mrts RMSE=1.01±0.23\n", - " 5. OLS_sphere RMSE=1.38±0.41\n", - " 6. DeepKriging_wendland RMSE=5.63±0.44\n", - " 7. OLS_wendland RMSE=32.42±70.57\n" - ] - } - ], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"Summary — {n} Repeats\")\n", - "print(\" Scenario: Non-GP + Pure Nonstationary Trend (no noise, no GP)\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 29693.937646, - "end_time": "2026-03-28T17:51:21.320941", - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_noGP_pure_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-28T09:36:27.383295", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb deleted file mode 100644 index 66530fa..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb +++ /dev/null @@ -1,6546 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "d1cde2eb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:05:43.677420Z", - "iopub.status.busy": "2026-03-27T23:05:43.677246Z", - "iopub.status.idle": "2026-03-27T23:05:43.682799Z", - "shell.execute_reply": "2026-03-27T23:05:43.681940Z" - }, - "papermill": { - "duration": 0.008809, - "end_time": "2026-03-27T23:05:43.683343", - "exception": false, - "start_time": "2026-03-27T23:05:43.674534", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8890884f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:05:43.688513Z", - "iopub.status.busy": "2026-03-27T23:05:43.688303Z", - "iopub.status.idle": "2026-03-27T23:05:45.848117Z", - "shell.execute_reply": "2026-03-27T23:05:45.847383Z" - }, - "papermill": { - "duration": 2.163659, - "end_time": "2026-03-27T23:05:45.848726", - "exception": false, - "start_time": "2026-03-27T23:05:43.685067", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:05:44.467334: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774652744.477233 1167990 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774652744.479961 1167990 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774652744.487635 1167990 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652744.487677 1167990 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652744.487678 1167990 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652744.487679 1167990 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46d6d1ba", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:05:45.854064Z", - "iopub.status.busy": "2026-03-27T23:05:45.853822Z", - "iopub.status.idle": "2026-03-27T23:05:46.816946Z", - "shell.execute_reply": "2026-03-27T23:05:46.816078Z" - }, - "papermill": { - "duration": 0.967214, - "end_time": "2026-03-27T23:05:46.817604", - "exception": false, - "start_time": "2026-03-27T23:05:45.850390", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8dd094a7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:05:46.822091Z", - "iopub.status.busy": "2026-03-27T23:05:46.821941Z", - "iopub.status.idle": "2026-03-27T23:05:46.824578Z", - "shell.execute_reply": "2026-03-27T23:05:46.823880Z" - }, - "papermill": { - "duration": 0.005885, - "end_time": "2026-03-27T23:05:46.825023", - "exception": false, - "start_time": "2026-03-27T23:05:46.819138", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f0aafba7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:05:46.828285Z", - "iopub.status.busy": "2026-03-27T23:05:46.828152Z", - "iopub.status.idle": "2026-03-27T23:06:00.185461Z", - "shell.execute_reply": "2026-03-27T23:06:00.184726Z" - }, - "papermill": { - "duration": 13.359808, - "end_time": "2026-03-27T23:06:00.186094", - "exception": false, - "start_time": "2026-03-27T23:05:46.826286", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "32986e25", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.190150Z", - "iopub.status.busy": "2026-03-27T23:06:00.189892Z", - "iopub.status.idle": "2026-03-27T23:06:00.195686Z", - "shell.execute_reply": "2026-03-27T23:06:00.194985Z" - }, - "papermill": { - "duration": 0.008674, - "end_time": "2026-03-27T23:06:00.196227", - "exception": false, - "start_time": "2026-03-27T23:06:00.187553", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_nonstationary_noise(num_sample, noise_std, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-GP data generation: deterministic macro-trend + nonstationary anomalies\n", - " + N(0, noise_std^2) Gaussian noise. No Gaussian Process component.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - "\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - "\n", - " base_wind = 5.0\n", - " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", - " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", - " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", - " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) & (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", - " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", - "\n", - " num_anomalies = 60\n", - " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", - " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", - " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", - " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", - " az = np.sin(anomaly_lats)\n", - " anomaly_effect = np.zeros(num_sample)\n", - " for i in range(num_anomalies):\n", - " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", - " amplitude = rng.uniform(-10.0, 18.0)\n", - " radius = rng.uniform(0.005, 0.03)\n", - " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", - "\n", - " mean_trend += anomaly_effect\n", - " mean_trend = np.maximum(mean_trend, 0.5)\n", - "\n", - " noise = rng.normal(0.0, noise_std, num_sample).astype(np.float32)\n", - " z_final = mean_trend + noise\n", - " z_final = np.maximum(z_final, 0.0)\n", - "\n", - " print(f\"\\n=== Scenario {scenario} (Non-GP: Nonstationary Trend + Gaussian Noise N(0,{noise_std}²)) ===\")\n", - " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (±{np.std(z_final) / np.sqrt(num_sample):.4f}), Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect, noise\n", - " gc.collect()\n", - "\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5c1e9f9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.199629Z", - "iopub.status.busy": "2026-03-27T23:06:00.199498Z", - "iopub.status.idle": "2026-03-27T23:06:00.206548Z", - "shell.execute_reply": "2026-03-27T23:06:00.205835Z" - }, - "papermill": { - "duration": 0.009363, - "end_time": "2026-03-27T23:06:00.206928", - "exception": false, - "start_time": "2026-03-27T23:06:00.197565", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " categorical_data = None\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(wendland(location, grid, theta=theta, k=2))\n", - " del grid\n", - " gc.collect()\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max,\n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " X_train_cont = basis[train_x1]\n", - " X_val_cont = basis[val_x1]\n", - " X_test_cont = basis[test_x1]\n", - " y_train, y_val, y_test = y_combined[train_x1], y_combined[val_x1], y_combined[test_x1]\n", - " flatten_y = lambda t: t.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten_y, [y_train, y_val, y_test])\n", - " flatten_x = lambda c: c.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_flat, X_val_flat, X_test_flat = map(flatten_x, [X_train_cont, X_val_cont, X_test_cont])\n", - " if categorical_data is None:\n", - " zv = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zv, [len(X_train_flat), len(X_val_flat), len(X_test_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " return (X_train_flat, X_train_cat, y_train_flat,\n", - " X_val_flat, X_val_cat, y_val_flat,\n", - " X_test_flat, X_test_cat, y_test_flat)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6728b7b3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.210306Z", - "iopub.status.busy": "2026-03-27T23:06:00.210184Z", - "iopub.status.idle": "2026-03-27T23:06:00.217173Z", - "shell.execute_reply": "2026-03-27T23:06:00.216493Z" - }, - "papermill": { - "duration": 0.009377, - "end_time": "2026-03-27T23:06:00.217654", - "exception": false, - "start_time": "2026-03-27T23:06:00.208277", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " return metrics, model\n", - "\n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3), loss=loss,\n", - " epochs=epochs, batch_size=batch_size, verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " # clipnorm=1.0 prevents gradient explosion from heavy-tailed sphere basis columns\n", - " _opt = Adam(learning_rate=5e-3, clipnorm=1.0) if \"sphere\" in name_model else Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1], output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64], activation='relu',\n", - " dropout_rate=dropout_rate, optimizer=_opt,\n", - " loss=loss, metrics=['mae'], epochs=epochs, batch_size=batch_size,\n", - " patience=40, verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingDefaultTrainer(config) if name_model == \"DeepKriging_wendland\" else DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience,\n", - " restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5,\n", - " patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_train, X_train_cat), y_train)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - " val_dataset = tf.data.Dataset.from_tensor_slices(\n", - " ((X_val, X_val_cat), y_val)).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset, validation_data=val_dataset,\n", - " epochs=epochs, callbacks=callbacks, verbose=0)\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - "\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model, \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " return metrics, model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce398870", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.221137Z", - "iopub.status.busy": "2026-03-27T23:06:00.220995Z", - "iopub.status.idle": "2026-03-27T23:06:00.229836Z", - "shell.execute_reply": "2026-03-27T23:06:00.229146Z" - }, - "papermill": { - "duration": 0.01132, - "end_time": "2026-03-27T23:06:00.230272", - "exception": false, - "start_time": "2026-03-27T23:06:00.218952", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(longitude, latitude, c=value,\n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256),\n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title,\n", - " plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " cmap = (mcolors.LinearSegmentedColormap.from_list(\n", - " \"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " if plot_type == 'residual' else\n", - " mcolors.LinearSegmentedColormap.from_list(\n", - " \"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256))\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values,\n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " cbar.set_label(cbar_label or (\"Residual\" if plot_type == 'residual' else \"Prediction Value\"), fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed,\n", - " model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " idx_all = np.arange(len(y_combined))\n", - " _, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - "\n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " predictions[model_name] = {'values': y_pred, 'rmse': rmse, 'order': order}\n", - "\n", - " all_vals = np.concatenate([dataframe['z'].values] + [p['values'] for p in predictions.values()])\n", - " vmin, vmax = np.percentile(all_vals, 2), np.percentile(all_vals, 98)\n", - "\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " create_subplot_robinson(fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})')\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " p = predictions[mn]\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig1, (2, 2, i+2), test_locations, p['values'], vmin, vmax,\n", - " f\"{dn} (order={p['order']}) | RMSE={p['rmse']:.4f}\")\n", - " plt.suptitle(\"Prediction Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig1)\n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " residuals_map = {k: (y_test - predictions[k]['values']) for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " for i, mn in enumerate(model_list):\n", - " if mn in predictions:\n", - " dn = mn.replace('DeepKriging_sphere','DK_S').replace('_Huber','_H').replace('UniversalKriging','UK')\n", - " create_subplot_robinson(fig2, (1, 3, i+1), test_locations, residuals_map[mn],\n", - " -vmax_abs, vmax_abs,\n", - " f\"{dn} Residuals (order={predictions[mn]['order']})\",\n", - " plot_type='residual')\n", - " plt.suptitle(\"Residuals Comparison — Sphere ColNorm Fix\", fontsize=16, fontweight='bold', y=0.84)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show(); plt.close(fig2)\n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7d38eb7c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.233598Z", - "iopub.status.busy": "2026-03-27T23:06:00.233474Z", - "iopub.status.idle": "2026-03-27T23:06:00.236850Z", - "shell.execute_reply": "2026-03-27T23:06:00.235705Z" - }, - "papermill": { - "duration": 0.006019, - "end_time": "2026-03-27T23:06:00.237617", - "exception": false, - "start_time": "2026-03-27T23:06:00.231598", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\n", - "dk_sphere candidates : [10, 50, 100, 150, 200, 1000] (restored to original)\n", - "repeats : 50\n" - ] - } - ], - "source": [ - "# ── Experiment parameters ─────────────────────────────────────────────────────\n", - "seed = 50\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "# All models (including dk_sphere) use the same original candidates\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"FIX: max_Phi_sphere will be column-normalized to unit L2 norm after precompute\")\n", - "print(f\"dk_sphere candidates : {base_orders} (restored to original)\")\n", - "print(f\"repeats : {repeat_experiment}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2a2b4ae9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-27T23:06:00.241061Z", - "iopub.status.busy": "2026-03-27T23:06:00.240931Z", - "iopub.status.idle": "2026-03-28T03:41:53.026815Z", - "shell.execute_reply": "2026-03-28T03:41:53.025756Z" - }, - "papermill": { - "duration": 16552.788289, - "end_time": "2026-03-28T03:41:53.027323", - "exception": false, - "start_time": "2026-03-27T23:06:00.239034", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Resuming: 20/50 repeats already done.\n", - "Skip repeat 1/50 (checkpoint)\n", - "Skip repeat 2/50 (checkpoint)\n", - "Skip repeat 3/50 (checkpoint)\n", - "Skip repeat 4/50 (checkpoint)\n", - "Skip repeat 5/50 (checkpoint)\n", - "Skip repeat 6/50 (checkpoint)\n", - "Skip repeat 7/50 (checkpoint)\n", - "Skip repeat 8/50 (checkpoint)\n", - "Skip repeat 9/50 (checkpoint)\n", - "Skip repeat 10/50 (checkpoint)\n", - "Skip repeat 11/50 (checkpoint)\n", - "Skip repeat 12/50 (checkpoint)\n", - "Skip repeat 13/50 (checkpoint)\n", - "Skip repeat 14/50 (checkpoint)\n", - "Skip repeat 15/50 (checkpoint)\n", - "Skip repeat 16/50 (checkpoint)\n", - "Skip repeat 17/50 (checkpoint)\n", - "Skip repeat 18/50 (checkpoint)\n", - "Skip repeat 19/50 (checkpoint)\n", - "Skip repeat 20/50 (checkpoint)\n", - "\n", - "======================================================================\n", - "Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:06:00.942827: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774652760.951701 1168219 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774652760.954292 1168219 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774652760.961365 1168219 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652760.961400 1168219 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652760.961402 1168219 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774652760.961405 1168219 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.4616 (±0.1554), Variance: 60.3999, Range: [0.0000, 39.4326]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774652782.987483 1168219 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774652784.533954 1168594 service.cc:152] XLA service 0x557f89011ed0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774652784.533977 1168594 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774652784.747429 1168594 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774652786.436692 1168594 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f89fc34aa70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f89e47b3f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3898, sigma²=19.1451, nugget=0.0685\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3669, sigma²=15.8465, nugget=0.0601\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3685, sigma²=13.8100, nugget=0.0524\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3687, sigma²=13.2425, nugget=0.0502\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3688, sigma²=12.7595, nugget=0.0484\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3687, sigma²=12.2166, nugget=0.0463\n", - "[repeat 20] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3687, sigma²=12.2166, nugget=0.0463\n", - "[repeat 20] done → repeat_20_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 62.2523 | 7.89 | 5.6149 | 0.0322 |\n", - "| OLS_sphere | 200 | 3.0389 | 1.7433 | 1.2 | 0.9528 |\n", - "| DeepKriging_wendland | -- | 32.2763 | 5.6812 | 4.2122 | 0.4982 |\n", - "| DeepKriging_mrts | 100 | 1.3177 | 1.1479 | 0.7514 | 0.9795 |\n", - "| DeepKriging_sphere | 50 | 1.1134 | 1.0552 | 0.6883 | 0.9827 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.394 | 1.1807 | 0.7076 | 0.9783 |\n", - "| UniversalKriging | 1000 | 1.0796 | 1.039 | 0.6511 | 0.9832 |\n", - "Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:15:20.654388: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774653320.664051 1743291 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774653320.667018 1743291 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774653320.674013 1743291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653320.674037 1743291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653320.674039 1743291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653320.674040 1743291 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.1516 (±0.1573), Variance: 61.8687, Range: [0.0000, 38.1858]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653340.467137 1743291 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774653341.955772 1743611 service.cc:152] XLA service 0x7fba000088d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774653341.955808 1743611 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653342.175760 1743611 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653343.832146 1743611 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbc502ffeb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbc3859aa70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3120, sigma²=15.6616, nugget=0.0462\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2434, sigma²=10.3799, nugget=0.0401\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2441, sigma²=8.1502, nugget=0.0315\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2442, sigma²=7.5820, nugget=0.0293\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2442, sigma²=7.0704, nugget=0.0273\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2442, sigma²=6.6818, nugget=0.0258\n", - "[repeat 21] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2442, sigma²=6.6818, nugget=0.0258\n", - "[repeat 21] done → repeat_21_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 43.669 | 6.6083 | 4.9622 | 0.3209 |\n", - "| OLS_sphere | 200 | 3.0516 | 1.7469 | 1.2167 | 0.9525 |\n", - "| DeepKriging_wendland | -- | 26.1671 | 5.1154 | 3.5302 | 0.5931 |\n", - "| DeepKriging_mrts | 200 | 1.0733 | 1.036 | 0.7291 | 0.9833 |\n", - "| DeepKriging_sphere | 50 | 0.938 | 0.9685 | 0.6122 | 0.9854 |\n", - "| DeepKriging_sphere_Huber | 100 | 1.0319 | 1.0158 | 0.6496 | 0.984 |\n", - "| UniversalKriging | 1000 | 1.1209 | 1.0587 | 0.7019 | 0.9826 |\n", - "Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:23:31.266264: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774653811.277254 2209768 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774653811.280041 2209768 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774653811.287378 2209768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653811.287411 2209768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653811.287414 2209768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774653811.287415 2209768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.4943 (±0.1565), Variance: 61.2540, Range: [0.0000, 37.2969]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653831.308580 2209768 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774653832.836383 2210091 service.cc:152] XLA service 0x7ff48c00a600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774653832.836407 2210091 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653833.051579 2210091 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774653834.703199 2210091 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6c068ac20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6b83d1480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3168, sigma²=15.0026, nugget=0.0571\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3169, sigma²=12.8194, nugget=0.0488\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3180, sigma²=10.6136, nugget=0.0404\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3182, sigma²=9.9253, nugget=0.0378\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3182, sigma²=9.5588, nugget=0.0364\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3181, sigma²=9.1508, nugget=0.0348\n", - "[repeat 22] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3181, sigma²=9.1508, nugget=0.0348\n", - "[repeat 22] done → repeat_22_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 1617.22 | 40.2147 | 8.0784 | -26.5513 |\n", - "| OLS_sphere | 1000 | 1.6024 | 1.2659 | 0.8032 | 0.9727 |\n", - "| DeepKriging_wendland | -- | 28.4081 | 5.3299 | 3.9588 | 0.516 |\n", - "| DeepKriging_mrts | 200 | 0.9212 | 0.9598 | 0.7218 | 0.9843 |\n", - "| DeepKriging_sphere | 100 | 0.7496 | 0.8658 | 0.5956 | 0.9872 |\n", - "| DeepKriging_sphere_Huber | 10 | 0.6953 | 0.8339 | 0.6102 | 0.9882 |\n", - "| UniversalKriging | 1000 | 0.9597 | 0.9796 | 0.6859 | 0.9837 |\n", - "Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:32:34.377511: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774654354.387571 2749114 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774654354.390403 2749114 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774654354.397394 2749114 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654354.397424 2749114 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654354.397426 2749114 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654354.397427 2749114 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.6764 (±0.1668), Variance: 69.5910, Range: [0.0000, 41.7769]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654374.484978 2749114 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774654376.022578 2749444 service.cc:152] XLA service 0x7f8a0801b480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774654376.022612 2749444 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654376.239631 2749444 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654377.913547 2749444 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8c38461f30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8c3059b9a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2478, sigma²=16.4672, nugget=0.0503\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1962, sigma²=10.9872, nugget=0.0433\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1967, sigma²=8.6815, nugget=0.0342\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1968, sigma²=7.9621, nugget=0.0314\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1969, sigma²=7.3949, nugget=0.0291\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1968, sigma²=6.8367, nugget=0.0269\n", - "[repeat 23] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1968, sigma²=6.8367, nugget=0.0269\n", - "[repeat 23] done → repeat_23_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 51.6248 | 7.185 | 5.4133 | 0.3121 |\n", - "| OLS_sphere | 1000 | 2.9949 | 1.7306 | 0.9587 | 0.9601 |\n", - "| DeepKriging_wendland | -- | 36.7473 | 6.062 | 4.1084 | 0.5104 |\n", - "| DeepKriging_mrts | 200 | 1.1547 | 1.0746 | 0.7563 | 0.9846 |\n", - "| DeepKriging_sphere | 100 | 1.1334 | 1.0646 | 0.6679 | 0.9849 |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1961 | 1.0937 | 0.6676 | 0.9841 |\n", - "| UniversalKriging | 1000 | 1.3597 | 1.1661 | 0.7624 | 0.9819 |\n", - "Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:41:40.869550: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774654900.879284 3295409 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774654900.882079 3295409 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774654900.889131 3295409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654900.889155 3295409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654900.889157 3295409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774654900.889158 3295409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.7535 (±0.1592), Variance: 63.3406, Range: [0.0000, 40.9251]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654921.384174 3295409 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774654922.931216 3295725 service.cc:152] XLA service 0x7f1cc4018210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774654922.931242 3295725 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654923.152597 3295725 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774654924.805338 3295725 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1efc4f1360> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1ef45c3c70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2427, sigma²=14.7160, nugget=0.0532\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2279, sigma²=11.6319, nugget=0.0453\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2286, sigma²=9.5933, nugget=0.0374\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2287, sigma²=8.9080, nugget=0.0347\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2288, sigma²=8.4327, nugget=0.0329\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2287, sigma²=7.8576, nugget=0.0306\n", - "[repeat 24] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2287, sigma²=7.8576, nugget=0.0306\n", - "[repeat 24] done → repeat_24_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 338.65 | 18.4025 | 6.5583 | -3.8428 |\n", - "| OLS_sphere | 1000 | 3.1169 | 1.7655 | 0.9844 | 0.9554 |\n", - "| DeepKriging_wendland | -- | 35.5123 | 5.9592 | 4.3223 | 0.4922 |\n", - "| DeepKriging_mrts | 200 | 1.499 | 1.2243 | 0.828 | 0.9786 |\n", - "| DeepKriging_sphere | 10 | 1.2145 | 1.102 | 0.7419 | 0.9826 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1617 | 1.0778 | 0.7128 | 0.9834 |\n", - "| UniversalKriging | 1000 | 1.3626 | 1.1673 | 0.7333 | 0.9805 |\n", - "Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 07:51:00.604085: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774655460.618010 3820717 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774655460.621100 3820717 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774655460.629580 3820717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774655460.629612 3820717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774655460.629616 3820717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774655460.629616 3820717 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2036 (±0.1604), Variance: 64.3290, Range: [0.0000, 48.6675]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774655480.929597 3820717 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774655482.452991 3821045 service.cc:152] XLA service 0x55f390df5ac0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774655482.453018 3821045 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774655482.671586 3821045 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774655484.325965 3821045 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbfa46271c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbf9c4bc4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2993, sigma²=15.7449, nugget=0.0472\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2343, sigma²=10.8478, nugget=0.0420\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2351, sigma²=8.3668, nugget=0.0324\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2352, sigma²=7.7959, nugget=0.0302\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2352, sigma²=7.4107, nugget=0.0287\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2352, sigma²=6.9239, nugget=0.0268\n", - "[repeat 25] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2352, sigma²=6.9239, nugget=0.0268\n", - "[repeat 25] done → repeat_25_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 53.8961 | 7.3414 | 4.9809 | 0.2014 |\n", - "| OLS_sphere | 1000 | 2.3367 | 1.5286 | 0.8746 | 0.9654 |\n", - "| DeepKriging_wendland | -- | 31.0659 | 5.5737 | 3.8993 | 0.5397 |\n", - "| DeepKriging_mrts | 150 | 1.2574 | 1.1213 | 0.7256 | 0.9814 |\n", - "| DeepKriging_sphere | 150 | 1.1732 | 1.0831 | 0.7022 | 0.9826 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.054 | 1.0266 | 0.6483 | 0.9844 |\n", - "| UniversalKriging | 1000 | 1.0973 | 1.0475 | 0.704 | 0.9837 |\n", - "Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:00:07.274935: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774656007.285578 158726 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774656007.288763 158726 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774656007.296672 158726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656007.296724 158726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656007.296727 158726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656007.296728 158726 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.4105 (±0.1573), Variance: 61.8730, Range: [0.0000, 36.2712]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656027.495132 158726 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774656029.020398 159051 service.cc:152] XLA service 0x7fc250018700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774656029.020422 159051 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656029.249124 159051 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656030.921336 159051 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc49c792d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc494554280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3136, sigma²=16.0771, nugget=0.0468\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2443, sigma²=10.6309, nugget=0.0411\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2451, sigma²=8.7065, nugget=0.0337\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2452, sigma²=8.0275, nugget=0.0311\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2453, sigma²=7.5552, nugget=0.0292\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2452, sigma²=7.0819, nugget=0.0274\n", - "[repeat 26] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2452, sigma²=7.0819, nugget=0.0274\n", - "[repeat 26] done → repeat_26_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 44.7353 | 6.6884 | 5.2344 | 0.2613 |\n", - "| OLS_sphere | 1000 | 2.2503 | 1.5001 | 0.8813 | 0.9628 |\n", - "| DeepKriging_wendland | -- | 30.5597 | 5.5281 | 4.0268 | 0.4954 |\n", - "| DeepKriging_mrts | 150 | 1.3141 | 1.1464 | 0.748 | 0.9783 |\n", - "| DeepKriging_sphere | 100 | 1.4296 | 1.1957 | 0.7818 | 0.9764 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.3356 | 1.1557 | 0.6512 | 0.9779 |\n", - "| UniversalKriging | 1000 | 1.2294 | 1.1088 | 0.6837 | 0.9797 |\n", - "Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:09:11.013519: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774656551.023003 683343 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774656551.025880 683343 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774656551.033146 683343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656551.033171 683343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656551.033173 683343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774656551.033174 683343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.8165 (±0.1489), Variance: 55.4357, Range: [0.0000, 33.2454]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656571.208914 683343 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774656572.734867 683673 service.cc:152] XLA service 0x7f5264019290 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774656572.734893 683673 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656572.949496 683673 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774656574.607531 683673 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f54b0221630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f54a8188790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4057, sigma²=20.5969, nugget=0.0496\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2490, sigma²=11.3833, nugget=0.0442\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2499, sigma²=9.5817, nugget=0.0372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2500, sigma²=9.1032, nugget=0.0353\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2501, sigma²=8.5384, nugget=0.0331\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2501, sigma²=8.0103, nugget=0.0311\n", - "[repeat 27] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2501, sigma²=8.0103, nugget=0.0311\n", - "[repeat 27] done → repeat_27_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 100.504 | 10.0252 | 5.5611 | -0.9298 |\n", - "| OLS_sphere | 1000 | 2.5539 | 1.5981 | 1.0286 | 0.951 |\n", - "| DeepKriging_wendland | -- | 27.2798 | 5.223 | 3.8764 | 0.4762 |\n", - "| DeepKriging_mrts | 200 | 1.3591 | 1.1658 | 0.8459 | 0.9739 |\n", - "| DeepKriging_sphere | 100 | 0.6883 | 0.8297 | 0.6461 | 0.9868 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7394 | 0.8599 | 0.6493 | 0.9858 |\n", - "| UniversalKriging | 1000 | 0.7768 | 0.8814 | 0.688 | 0.9851 |\n", - "Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:18:31.914588: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774657111.924722 1230438 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774657111.927599 1230438 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774657111.935149 1230438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657111.935230 1230438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657111.935232 1230438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657111.935233 1230438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.4792 (±0.1618), Variance: 65.4295, Range: [0.0000, 37.1423]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657132.110305 1230438 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774657133.644338 1230761 service.cc:152] XLA service 0x7f73e80061f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774657133.644369 1230761 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657133.866960 1230761 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657135.511597 1230761 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f76183f56c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f76101f1510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3263, sigma²=16.3114, nugget=0.0546\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2875, sigma²=11.8893, nugget=0.0456\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2885, sigma²=9.9437, nugget=0.0381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2886, sigma²=9.2622, nugget=0.0355\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2887, sigma²=8.8496, nugget=0.0339\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2886, sigma²=8.3792, nugget=0.0321\n", - "[repeat 28] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2886, sigma²=8.3792, nugget=0.0321\n", - "[repeat 28] done → repeat_28_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 376.714 | 19.4091 | 6.5323 | -5.1489 |\n", - "| OLS_sphere | 200 | 2.8637 | 1.6922 | 1.1609 | 0.9533 |\n", - "| DeepKriging_wendland | -- | 30.2944 | 5.504 | 4.0094 | 0.5055 |\n", - "| DeepKriging_mrts | 200 | 1.0176 | 1.0088 | 0.7566 | 0.9834 |\n", - "| DeepKriging_sphere | 50 | 0.8503 | 0.9221 | 0.6524 | 0.9861 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.9581 | 0.9788 | 0.6554 | 0.9844 |\n", - "| UniversalKriging | 1000 | 1.17 | 1.0817 | 0.7494 | 0.9809 |\n", - "Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:28:02.954787: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774657682.964968 1808235 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774657682.967773 1808235 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774657682.974951 1808235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657682.974979 1808235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657682.974981 1808235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774657682.974982 1808235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.8917 (±0.1584), Variance: 62.7504, Range: [0.0000, 39.2967]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657703.253363 1808235 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774657704.764485 1808568 service.cc:152] XLA service 0x5618797cd0e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774657704.764522 1808568 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657704.976145 1808568 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774657706.615549 1808568 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f155c53aef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f155462f760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2830, sigma²=17.2435, nugget=0.0511\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2215, sigma²=11.8047, nugget=0.0461\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2223, sigma²=9.2766, nugget=0.0362\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2224, sigma²=8.7113, nugget=0.0340\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2225, sigma²=8.1515, nugget=0.0318\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2224, sigma²=7.6036, nugget=0.0297\n", - "[repeat 29] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2224, sigma²=7.6036, nugget=0.0297\n", - "[repeat 29] done → repeat_29_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|----------|\n", - "| OLS_wendland | -- | 4248.9 | 65.1836 | 13.5183 | -66.2138 |\n", - "| OLS_sphere | 1000 | 2.6917 | 1.6406 | 0.9703 | 0.9574 |\n", - "| DeepKriging_wendland | -- | 42.4055 | 6.512 | 4.8515 | 0.3292 |\n", - "| DeepKriging_mrts | 200 | 2.105 | 1.4509 | 0.9714 | 0.9667 |\n", - "| DeepKriging_sphere | 50 | 1.2414 | 1.1142 | 0.734 | 0.9804 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2692 | 1.1266 | 0.7381 | 0.9799 |\n", - "| UniversalKriging | 1000 | 1.6092 | 1.2686 | 0.8142 | 0.9745 |\n", - "Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:38:07.974842: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774658287.985445 2433391 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774658287.988315 2433391 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774658287.995705 2433391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658287.995733 2433391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658287.995735 2433391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658287.995736 2433391 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.7531 (±0.1674), Variance: 70.0243, Range: [0.0000, 44.5986]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658308.009906 2433391 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774658309.526447 2433714 service.cc:152] XLA service 0x7fada40062b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774658309.526471 2433714 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658309.741658 2433714 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658311.369185 2433714 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fafcc2423b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fafc43136d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3863, sigma²=24.3276, nugget=0.0579\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2370, sigma²=13.6065, nugget=0.0529\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2377, sigma²=11.1725, nugget=0.0434\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=10.2418, nugget=0.0398\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=9.7006, nugget=0.0377\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=9.1352, nugget=0.0355\n", - "[repeat 30] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2379, sigma²=9.1352, nugget=0.0355\n", - "[repeat 30] done → repeat_30_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|-----------|\n", - "| OLS_wendland | -- | 6609.34 | 81.2978 | 10.9815 | -103.458 |\n", - "| OLS_sphere | 1000 | 1.6568 | 1.2872 | 0.8774 | 0.9738 |\n", - "| DeepKriging_wendland | -- | 36.8275 | 6.0686 | 4.245 | 0.418 |\n", - "| DeepKriging_mrts | 150 | 1.047 | 1.0232 | 0.7326 | 0.9835 |\n", - "| DeepKriging_sphere | 50 | 0.5821 | 0.7629 | 0.5946 | 0.9908 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6563 | 0.8101 | 0.6058 | 0.9896 |\n", - "| UniversalKriging | 1000 | 0.8508 | 0.9224 | 0.6692 | 0.9866 |\n", - "Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:47:19.976090: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774658839.986159 2955210 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774658839.988909 2955210 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774658839.996094 2955210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658839.996127 2955210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658839.996130 2955210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774658839.996131 2955210 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.6274 (±0.1609), Variance: 64.7416, Range: [0.0000, 40.4040]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658860.025247 2955210 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774658861.552479 2955528 service.cc:152] XLA service 0x7fcac40067d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774658861.552504 2955528 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658861.767246 2955528 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774658863.420075 2955528 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccf444bc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccec1913f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3226, sigma²=15.6079, nugget=0.0529\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2847, sigma²=11.7562, nugget=0.0453\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2856, sigma²=10.0658, nugget=0.0388\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2857, sigma²=9.4697, nugget=0.0365\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2858, sigma²=9.0317, nugget=0.0348\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2857, sigma²=8.4777, nugget=0.0327\n", - "[repeat 31] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2857, sigma²=8.4777, nugget=0.0327\n", - "[repeat 31] done → repeat_31_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 353.061 | 18.7899 | 6.6223 | -5.0713 |\n", - "| OLS_sphere | 1000 | 3.7806 | 1.9444 | 0.9508 | 0.935 |\n", - "| DeepKriging_wendland | -- | 32.2625 | 5.68 | 4.3663 | 0.4452 |\n", - "| DeepKriging_mrts | 150 | 1.6552 | 1.2865 | 0.8483 | 0.9715 |\n", - "| DeepKriging_sphere | 50 | 1.2725 | 1.128 | 0.6945 | 0.9781 |\n", - "| DeepKriging_sphere_Huber | 100 | 1.3786 | 1.1742 | 0.7095 | 0.9763 |\n", - "| UniversalKriging | 1000 | 1.5338 | 1.2385 | 0.7553 | 0.9736 |\n", - "Repeat 32/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 33/50 seed=82\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 08:56:35.549453: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774659395.559009 3490764 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774659395.562733 3490764 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774659395.570063 3490764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659395.570092 3490764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659395.570094 3490764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659395.570096 3490764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] seed=82\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2140 (±0.1523), Variance: 58.0256, Range: [0.0000, 34.9389]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659415.618904 3490764 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774659417.176673 3491088 service.cc:152] XLA service 0x7f37e0018280 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774659417.176703 3491088 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659417.392101 3491088 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659419.017266 3491088 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f39a42fc040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f3998332170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3079, sigma²=15.2457, nugget=0.0460\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2411, sigma²=10.5850, nugget=0.0411\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2419, sigma²=8.4391, nugget=0.0327\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=7.7055, nugget=0.0299\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2421, sigma²=7.4133, nugget=0.0288\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=6.9157, nugget=0.0268\n", - "[repeat 32] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2420, sigma²=6.9157, nugget=0.0268\n", - "[repeat 32] done → repeat_32_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 425.883 | 20.6369 | 6.6958 | -5.5663 |\n", - "| OLS_sphere | 1000 | 3.0501 | 1.7465 | 1.0411 | 0.953 |\n", - "| DeepKriging_wendland | -- | 32.7163 | 5.7198 | 4.1429 | 0.4956 |\n", - "| DeepKriging_mrts | 200 | 1.6037 | 1.2664 | 0.8121 | 0.9753 |\n", - "| DeepKriging_sphere | 100 | 0.917 | 0.9576 | 0.6758 | 0.9859 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.0362 | 1.018 | 0.7055 | 0.984 |\n", - "| UniversalKriging | 1000 | 1.0175 | 1.0087 | 0.7019 | 0.9843 |\n", - "Repeat 33/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 34/50 seed=83\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:05:37.297308: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774659937.307782 4004981 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774659937.310477 4004981 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774659937.317830 4004981 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659937.317854 4004981 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659937.317856 4004981 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774659937.317857 4004981 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] seed=83\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.8929 (±0.1641), Variance: 67.3130, Range: [0.0000, 36.4692]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659957.338390 4004981 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774659958.865162 4005309 service.cc:152] XLA service 0x55e34907ff60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774659958.865187 4005309 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659959.077341 4005309 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774659960.727372 4005309 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6e2c781d80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6e245e3d90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2315, sigma²=14.7353, nugget=0.0499\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2052, sigma²=10.8423, nugget=0.0424\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2058, sigma²=8.3189, nugget=0.0325\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2060, sigma²=7.5965, nugget=0.0297\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2060, sigma²=7.1788, nugget=0.0281\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2060, sigma²=6.6683, nugget=0.0261\n", - "[repeat 33] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2060, sigma²=6.6683, nugget=0.0261\n", - "[repeat 33] done → repeat_33_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 57.0416 | 7.5526 | 5.4604 | 0.2041 |\n", - "| OLS_sphere | 1000 | 5.3168 | 2.3058 | 1.0602 | 0.9258 |\n", - "| DeepKriging_wendland | -- | 34.6179 | 5.8837 | 4.1657 | 0.517 |\n", - "| DeepKriging_mrts | 150 | 1.8841 | 1.3726 | 0.782 | 0.9737 |\n", - "| DeepKriging_sphere | 10 | 1.757 | 1.3255 | 0.7536 | 0.9755 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.9652 | 1.4018 | 0.7071 | 0.9726 |\n", - "| UniversalKriging | 1000 | 1.8541 | 1.3617 | 0.7454 | 0.9741 |\n", - "Repeat 34/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 35/50 seed=84\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:14:58.253312: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774660498.263163 352578 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774660498.265832 352578 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774660498.272857 352578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774660498.272882 352578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774660498.272884 352578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774660498.272885 352578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] seed=84\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.5264 (±0.1646), Variance: 67.7016, Range: [0.0000, 50.0689]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774660518.320787 352578 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774660519.828675 352903 service.cc:152] XLA service 0x561693e1c700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774660519.828701 352903 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774660520.045483 352903 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774660521.708717 352903 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5d98796f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5d983628c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3345, sigma²=19.5345, nugget=0.0579\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2602, sigma²=13.4468, nugget=0.0520\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2612, sigma²=10.8041, nugget=0.0417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2613, sigma²=10.3103, nugget=0.0398\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2614, sigma²=9.7253, nugget=0.0376\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2613, sigma²=9.2210, nugget=0.0356\n", - "[repeat 34] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2613, sigma²=9.2210, nugget=0.0356\n", - "[repeat 34] done → repeat_34_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 134.643 | 11.6036 | 6.1069 | -0.9351 |\n", - "| OLS_sphere | 200 | 2.5092 | 1.5841 | 1.1345 | 0.9639 |\n", - "| DeepKriging_wendland | -- | 36.4353 | 6.0362 | 4.3211 | 0.4763 |\n", - "| DeepKriging_mrts | 150 | 0.8707 | 0.9331 | 0.7034 | 0.9875 |\n", - "| DeepKriging_sphere | 100 | 0.7205 | 0.8488 | 0.6233 | 0.9896 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6413 | 0.8008 | 0.5958 | 0.9908 |\n", - "| UniversalKriging | 1000 | 0.7412 | 0.8609 | 0.6208 | 0.9893 |\n", - "Repeat 35/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 36/50 seed=85\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:24:10.967516: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774661050.977514 895487 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774661050.980288 895487 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774661050.988894 895487 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661050.988925 895487 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661050.988928 895487 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661050.988929 895487 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] seed=85\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.7390 (±0.1499), Variance: 56.1909, Range: [0.0000, 32.9323]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661071.149257 895487 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774661072.669462 895812 service.cc:152] XLA service 0x556a2a395f80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774661072.669485 895812 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661072.891320 895812 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661074.554706 895812 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f894c1a7be0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f893c68a950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3020, sigma²=17.0219, nugget=0.0417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2109, sigma²=10.0402, nugget=0.0374\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1992, sigma²=7.4175, nugget=0.0288\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1926, sigma²=6.6103, nugget=0.0260\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1905, sigma²=6.1287, nugget=0.0241\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1905, sigma²=5.6079, nugget=0.0221\n", - "[repeat 35] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1905, sigma²=5.6079, nugget=0.0221\n", - "[repeat 35] done → repeat_35_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 55.5506 | 7.4532 | 5.368 | 0.115 |\n", - "| OLS_sphere | 1000 | 2.232 | 1.494 | 0.9226 | 0.9644 |\n", - "| DeepKriging_wendland | -- | 31.8062 | 5.6397 | 4.1825 | 0.4933 |\n", - "| DeepKriging_mrts | 200 | 1.3391 | 1.1572 | 0.7691 | 0.9787 |\n", - "| DeepKriging_sphere | 50 | 0.9133 | 0.9557 | 0.6541 | 0.9854 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8857 | 0.9411 | 0.6674 | 0.9859 |\n", - "| UniversalKriging | 1000 | 1.5242 | 1.2346 | 0.8092 | 0.9757 |\n", - "Repeat 36/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 37/50 seed=86\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:32:49.334348: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774661569.343770 1366719 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774661569.346521 1366719 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774661569.353771 1366719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661569.353800 1366719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661569.353802 1366719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774661569.353803 1366719 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] seed=86\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2241 (±0.1528), Variance: 58.3976, Range: [0.0000, 36.8238]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661589.361055 1366719 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774661590.896752 1367045 service.cc:152] XLA service 0x560bbd5cac30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774661590.896775 1367045 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661591.110421 1367045 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774661592.768415 1367045 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccc849ae60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fccb059f910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2381, sigma²=14.5578, nugget=0.0493\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2108, sigma²=10.8673, nugget=0.0426\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2115, sigma²=8.5175, nugget=0.0334\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2116, sigma²=8.0453, nugget=0.0315\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2117, sigma²=7.6016, nugget=0.0298\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2116, sigma²=7.0712, nugget=0.0277\n", - "[repeat 36] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2116, sigma²=7.0712, nugget=0.0277\n", - "[repeat 36] done → repeat_36_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 44.3478 | 6.6594 | 4.9606 | 0.3029 |\n", - "| OLS_sphere | 1000 | 1.9919 | 1.4114 | 0.8819 | 0.9687 |\n", - "| DeepKriging_wendland | -- | 27.7979 | 5.2724 | 3.7899 | 0.5631 |\n", - "| DeepKriging_mrts | 200 | 1.7952 | 1.3399 | 0.8554 | 0.9718 |\n", - "| DeepKriging_sphere | 50 | 0.7898 | 0.8887 | 0.6613 | 0.9876 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8002 | 0.8945 | 0.6627 | 0.9874 |\n", - "| UniversalKriging | 1000 | 1.1138 | 1.0554 | 0.7721 | 0.9825 |\n", - "Repeat 37/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 38/50 seed=87\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:42:22.181254: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774662142.191168 1945526 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774662142.194483 1945526 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774662142.203264 1945526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662142.203289 1945526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662142.203291 1945526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662142.203291 1945526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] seed=87\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.6354 (±0.1638), Variance: 67.0416, Range: [0.0000, 37.5825]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662162.240534 1945526 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774662163.758666 1945855 service.cc:152] XLA service 0x7f9ee4007b20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774662163.758698 1945855 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662163.974143 1945855 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662165.615182 1945855 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa110425990> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa108156b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2862, sigma²=16.5704, nugget=0.0616\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2771, sigma²=13.7089, nugget=0.0529\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2779, sigma²=11.7794, nugget=0.0455\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2781, sigma²=11.0688, nugget=0.0427\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2781, sigma²=10.5618, nugget=0.0408\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2781, sigma²=10.0257, nugget=0.0387\n", - "[repeat 37] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2781, sigma²=10.0257, nugget=0.0387\n", - "[repeat 37] done → repeat_37_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 438.542 | 20.9414 | 7.5337 | -5.1956 |\n", - "| OLS_sphere | 1000 | 4.9442 | 2.2236 | 0.9804 | 0.9301 |\n", - "| DeepKriging_wendland | -- | 42.6509 | 6.5308 | 4.9006 | 0.3974 |\n", - "| DeepKriging_mrts | 200 | 1.4837 | 1.2181 | 0.7874 | 0.979 |\n", - "| DeepKriging_sphere | 50 | 0.9402 | 0.9696 | 0.6576 | 0.9867 |\n", - "| DeepKriging_sphere_Huber | 200 | 1.1373 | 1.0665 | 0.691 | 0.9839 |\n", - "| UniversalKriging | 1000 | 1.1999 | 1.0954 | 0.7372 | 0.983 |\n", - "Repeat 38/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 39/50 seed=88\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 09:52:42.425669: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774662762.436739 2595939 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774662762.439665 2595939 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774662762.446939 2595939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662762.446967 2595939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662762.446970 2595939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774662762.446971 2595939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] seed=88\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2379 (±0.1513), Variance: 57.2487, Range: [0.0000, 37.6469]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662782.498335 2595939 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774662784.014838 2596269 service.cc:152] XLA service 0x7f938c018240 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774662784.014866 2596269 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662784.227879 2596269 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774662785.877436 2596269 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f95c47df0a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f95bc5705e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5060, sigma²=22.3030, nugget=0.0566\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3175, sigma²=11.9942, nugget=0.0458\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3185, sigma²=10.3524, nugget=0.0396\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3187, sigma²=9.7901, nugget=0.0374\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3187, sigma²=9.3319, nugget=0.0357\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3187, sigma²=8.8579, nugget=0.0338\n", - "[repeat 38] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3187, sigma²=8.8579, nugget=0.0338\n", - "[repeat 38] done → repeat_38_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 33.7685 | 5.8111 | 4.576 | 0.3571 |\n", - "| OLS_sphere | 200 | 2.7716 | 1.6648 | 1.1772 | 0.9472 |\n", - "| DeepKriging_wendland | -- | 22.929 | 4.7884 | 3.4853 | 0.5635 |\n", - "| DeepKriging_mrts | 200 | 1.5194 | 1.2326 | 0.8168 | 0.9711 |\n", - "| DeepKriging_sphere | 50 | 0.9386 | 0.9688 | 0.655 | 0.9821 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2545 | 1.1201 | 0.7005 | 0.9761 |\n", - "| UniversalKriging | 1000 | 1.194 | 1.0927 | 0.7035 | 0.9773 |\n", - "Repeat 39/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 40/50 seed=89\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:00:55.416390: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774663255.426599 3031312 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774663255.429494 3031312 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774663255.436888 3031312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663255.436927 3031312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663255.436929 3031312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663255.436930 3031312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] seed=89\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.5884 (±0.1621), Variance: 65.7041, Range: [0.0000, 39.7566]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663277.211099 3031312 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774663278.753558 3031640 service.cc:152] XLA service 0x7f15b00066e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774663278.753590 3031640 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663278.971107 3031640 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663280.649769 3031640 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f17cc7170a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f17cc4cae60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3029, sigma²=17.9230, nugget=0.0686\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3033, sigma²=15.6676, nugget=0.0600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3042, sigma²=13.6541, nugget=0.0523\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3043, sigma²=13.1780, nugget=0.0504\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3044, sigma²=12.5910, nugget=0.0482\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3043, sigma²=12.1171, nugget=0.0464\n", - "[repeat 39] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3043, sigma²=12.1171, nugget=0.0464\n", - "[repeat 39] done → repeat_39_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 96.3307 | 9.8148 | 5.9257 | -0.4307 |\n", - "| OLS_sphere | 200 | 2.4937 | 1.5792 | 1.1058 | 0.963 |\n", - "| DeepKriging_wendland | -- | 33.9212 | 5.8242 | 4.1946 | 0.4962 |\n", - "| DeepKriging_mrts | 150 | 1.053 | 1.0262 | 0.6984 | 0.9844 |\n", - "| DeepKriging_sphere | 50 | 0.8844 | 0.9404 | 0.5796 | 0.9869 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.0889 | 1.0435 | 0.6829 | 0.9838 |\n", - "| UniversalKriging | 1000 | 0.9018 | 0.9496 | 0.6456 | 0.9866 |\n", - "Repeat 40/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 41/50 seed=90\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:10:15.427190: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774663815.437509 3589357 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774663815.440372 3589357 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774663815.448170 3589357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663815.448205 3589357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663815.448207 3589357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774663815.448209 3589357 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] seed=90\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.7064 (±0.1634), Variance: 66.7452, Range: [0.0000, 51.1555]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663835.907341 3589357 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774663837.422302 3589690 service.cc:152] XLA service 0x7f3694005ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774663837.422325 3589690 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663837.650437 3589690 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774663839.336754 3589690 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f38c032a170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f38b84e3010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere_Huber best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3089, sigma²=19.1016, nugget=0.0574\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2415, sigma²=13.1245, nugget=0.0509\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2422, sigma²=11.0649, nugget=0.0429\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2423, sigma²=10.3634, nugget=0.0402\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2424, sigma²=9.7620, nugget=0.0379\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2424, sigma²=9.1457, nugget=0.0355\n", - "[repeat 40] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2424, sigma²=9.1457, nugget=0.0355\n", - "[repeat 40] done → repeat_40_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 37.8237 | 6.1501 | 4.9208 | 0.4238 |\n", - "| OLS_sphere | 1000 | 4.2452 | 2.0604 | 1.112 | 0.9353 |\n", - "| DeepKriging_wendland | -- | 30.2851 | 5.5032 | 4.1297 | 0.5386 |\n", - "| DeepKriging_mrts | 200 | 2.3805 | 1.5429 | 0.9439 | 0.9637 |\n", - "| DeepKriging_sphere | 10 | 1.2851 | 1.1336 | 0.708 | 0.9804 |\n", - "| DeepKriging_sphere_Huber | 200 | 1.119 | 1.0578 | 0.7284 | 0.983 |\n", - "| UniversalKriging | 1000 | 1.4378 | 1.1991 | 0.7647 | 0.9781 |\n", - "Repeat 41/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 42/50 seed=91\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:19:26.501336: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774664366.511317 4121581 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774664366.514139 4121581 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774664366.522605 4121581 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664366.522696 4121581 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664366.522703 4121581 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664366.522704 4121581 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] seed=91\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.5569 (±0.1560), Variance: 60.8446, Range: [0.0000, 43.5147]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664387.113522 4121581 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774664388.665803 4121910 service.cc:152] XLA service 0x7f8adc0080f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774664388.665829 4121910 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664388.892547 4121910 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664390.554222 4121910 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8d0832f520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8d001f4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3279, sigma²=18.5497, nugget=0.0555\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2556, sigma²=12.7906, nugget=0.0495\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2565, sigma²=10.7771, nugget=0.0417\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2566, sigma²=10.2087, nugget=0.0395\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2567, sigma²=9.5659, nugget=0.0370\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2566, sigma²=8.9825, nugget=0.0348\n", - "[repeat 41] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2566, sigma²=8.9825, nugget=0.0348\n", - "[repeat 41] done → repeat_41_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 81.1573 | 9.0087 | 5.5203 | -0.4507 |\n", - "| OLS_sphere | 200 | 4.0816 | 2.0203 | 1.3933 | 0.927 |\n", - "| DeepKriging_wendland | -- | 28.1588 | 5.3065 | 3.8936 | 0.4967 |\n", - "| DeepKriging_mrts | 150 | 1.8478 | 1.3594 | 0.9239 | 0.967 |\n", - "| DeepKriging_sphere | 50 | 1.4618 | 1.2091 | 0.8179 | 0.9739 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2558 | 1.1206 | 0.7325 | 0.9776 |\n", - "| UniversalKriging | 1000 | 1.5217 | 1.2336 | 0.8117 | 0.9728 |\n", - "Repeat 42/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 43/50 seed=92\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:28:37.257832: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774664917.267255 473387 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774664917.270218 473387 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774664917.278181 473387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664917.278210 473387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664917.278212 473387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774664917.278213 473387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] seed=92\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.4745 (±0.1668), Variance: 69.5552, Range: [0.0000, 43.4528]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664937.884364 473387 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774664939.415210 473718 service.cc:152] XLA service 0x7f0b840089c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774664939.415237 473718 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664939.635979 473718 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774664941.323899 473718 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0dc05177f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0db8340310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3138, sigma²=19.9988, nugget=0.0586\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2445, sigma²=13.1033, nugget=0.0506\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2453, sigma²=10.6381, nugget=0.0411\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2454, sigma²=9.9786, nugget=0.0385\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2454, sigma²=9.4336, nugget=0.0364\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2454, sigma²=8.8497, nugget=0.0342\n", - "[repeat 42] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2454, sigma²=8.8497, nugget=0.0342\n", - "[repeat 42] done → repeat_42_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 59867.1 | 244.678 | 21.0506 | -815.695 |\n", - "| OLS_sphere | 1000 | 1.5353 | 1.2391 | 0.8557 | 0.9791 |\n", - "| DeepKriging_wendland | -- | 39.028 | 6.2472 | 4.2555 | 0.4676 |\n", - "| DeepKriging_mrts | 200 | 1.1777 | 1.0852 | 0.7669 | 0.9839 |\n", - "| DeepKriging_sphere | 50 | 0.603 | 0.7765 | 0.6181 | 0.9918 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5807 | 0.762 | 0.593 | 0.9921 |\n", - "| UniversalKriging | 1000 | 0.8881 | 0.9424 | 0.6761 | 0.9879 |\n", - "Repeat 43/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 44/50 seed=93\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:37:33.442259: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774665453.453201 971075 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774665453.456530 971075 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774665453.463952 971075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665453.463984 971075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665453.463987 971075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665453.463988 971075 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] seed=93\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 9.8308 (±0.1505), Variance: 56.5916, Range: [0.0000, 42.5976]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774665473.974266 971075 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774665475.510221 971409 service.cc:152] XLA service 0x7f044c008aa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774665475.510246 971409 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774665475.726503 971409 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774665477.409368 971409 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f06706f6440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0668347520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6238, sigma²=32.1513, nugget=0.0477\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2366, sigma²=11.1817, nugget=0.0434\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2374, sigma²=8.7614, nugget=0.0340\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2376, sigma²=8.1433, nugget=0.0316\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2376, sigma²=7.8295, nugget=0.0304\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2376, sigma²=7.3219, nugget=0.0284\n", - "[repeat 43] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2376, sigma²=7.3219, nugget=0.0284\n", - "[repeat 43] done → repeat_43_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 54.7136 | 7.3969 | 5.5496 | 0.2059 |\n", - "| OLS_sphere | 1000 | 2.6505 | 1.628 | 0.9481 | 0.9615 |\n", - "| DeepKriging_wendland | -- | 44.8765 | 6.699 | 4.5957 | 0.3487 |\n", - "| DeepKriging_mrts | 200 | 2.6492 | 1.6276 | 0.9274 | 0.9616 |\n", - "| DeepKriging_sphere | 100 | 2.1202 | 1.4561 | 0.7915 | 0.9692 |\n", - "| DeepKriging_sphere_Huber | 50 | 2.1226 | 1.4569 | 0.8331 | 0.9692 |\n", - "| UniversalKriging | 1000 | 2.0622 | 1.436 | 0.8219 | 0.9701 |\n", - "Repeat 44/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 45/50 seed=94\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:46:19.417697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774665979.428084 1470543 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774665979.430878 1470543 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774665979.438623 1470543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665979.438651 1470543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665979.438653 1470543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774665979.438654 1470543 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] seed=94\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2293 (±0.1494), Variance: 55.8035, Range: [0.0000, 40.5054]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774665999.844113 1470543 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774666001.392861 1470871 service.cc:152] XLA service 0x7f5be00078a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774666001.392887 1470871 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774666001.609168 1470871 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774666003.268942 1470871 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5e107be830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5e08404160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3795, sigma²=19.5548, nugget=0.0461\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2388, sigma²=10.8020, nugget=0.0414\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2337, sigma²=8.1356, nugget=0.0317\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2338, sigma²=7.6519, nugget=0.0298\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2338, sigma²=7.2860, nugget=0.0283\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2338, sigma²=6.7774, nugget=0.0264\n", - "[repeat 44] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2338, sigma²=6.7774, nugget=0.0264\n", - "[repeat 44] done → repeat_44_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 49.9345 | 7.0664 | 5.2978 | -0.0203 |\n", - "| OLS_sphere | 1000 | 1.9665 | 1.4023 | 0.8762 | 0.9598 |\n", - "| DeepKriging_wendland | -- | 31.7149 | 5.6316 | 4.3191 | 0.352 |\n", - "| DeepKriging_mrts | 200 | 1.3805 | 1.1749 | 0.7672 | 0.9718 |\n", - "| DeepKriging_sphere | 50 | 0.9792 | 0.9896 | 0.6492 | 0.98 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.067 | 1.033 | 0.6664 | 0.9782 |\n", - "| UniversalKriging | 1000 | 1.3023 | 1.1412 | 0.7784 | 0.9734 |\n", - "Repeat 45/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 46/50 seed=95\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 10:55:14.889393: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774666514.900063 1973671 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774666514.903560 1973671 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774666514.913587 1973671 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774666514.913624 1973671 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774666514.913627 1973671 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774666514.913628 1973671 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] seed=95\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.2922 (±0.1620), Variance: 65.6220, Range: [0.0000, 43.3085]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774666535.358609 1973671 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774666536.877599 1974002 service.cc:152] XLA service 0x55981133a150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774666536.877626 1974002 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774666537.091968 1974002 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774666538.769978 1974002 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f832070feb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f83201839a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere_Huber best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2848, sigma²=16.7997, nugget=0.0496\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2230, sigma²=11.0132, nugget=0.0430\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2237, sigma²=8.7241, nugget=0.0341\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2238, sigma²=8.0407, nugget=0.0314\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2238, sigma²=7.5619, nugget=0.0295\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2238, sigma²=7.1188, nugget=0.0278\n", - "[repeat 45] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2238, sigma²=7.1188, nugget=0.0278\n", - "[repeat 45] done → repeat_45_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 40.939 | 6.3984 | 4.9344 | 0.3662 |\n", - "| OLS_sphere | 1000 | 2.7479 | 1.6577 | 0.9838 | 0.9575 |\n", - "| DeepKriging_wendland | -- | 30.6982 | 5.5406 | 4.02 | 0.5247 |\n", - "| DeepKriging_mrts | 150 | 1.3427 | 1.1587 | 0.7862 | 0.9792 |\n", - "| DeepKriging_sphere | 10 | 1.208 | 1.0991 | 0.7558 | 0.9813 |\n", - "| DeepKriging_sphere_Huber | 150 | 1.342 | 1.1585 | 0.7728 | 0.9792 |\n", - "| UniversalKriging | 1000 | 1.32 | 1.1489 | 0.7674 | 0.9796 |\n", - "Repeat 46/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 47/50 seed=96\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:04:42.100513: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774667082.111131 2533399 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774667082.114009 2533399 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774667082.121351 2533399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667082.121375 2533399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667082.121377 2533399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667082.121378 2533399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] seed=96\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.6891 (±0.1577), Variance: 62.1493, Range: [0.0000, 38.8398]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667102.500501 2533399 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774667103.995055 2533735 service.cc:152] XLA service 0x7f1340006d50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774667103.995079 2533735 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667104.215398 2533735 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667105.889039 2533735 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f157c2cb910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f157428dfc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2924, sigma²=16.3965, nugget=0.0592\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2743, sigma²=13.7864, nugget=0.0532\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2753, sigma²=11.8848, nugget=0.0458\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2755, sigma²=11.1147, nugget=0.0429\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2755, sigma²=10.6142, nugget=0.0409\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2755, sigma²=9.9854, nugget=0.0385\n", - "[repeat 46] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2755, sigma²=9.9854, nugget=0.0385\n", - "[repeat 46] done → repeat_46_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 38.3715 | 6.1945 | 4.84 | 0.3344 |\n", - "| OLS_sphere | 1000 | 1.7587 | 1.3262 | 0.8719 | 0.9695 |\n", - "| DeepKriging_wendland | -- | 29.0511 | 5.3899 | 3.7662 | 0.496 |\n", - "| DeepKriging_mrts | 200 | 1.2067 | 1.0985 | 0.7385 | 0.9791 |\n", - "| DeepKriging_sphere | 50 | 1.3145 | 1.1465 | 0.7983 | 0.9772 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8226 | 0.907 | 0.619 | 0.9857 |\n", - "| UniversalKriging | 1000 | 1.3135 | 1.1461 | 0.7267 | 0.9772 |\n", - "Repeat 47/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 48/50 seed=97\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:13:07.393247: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774667587.404434 2976479 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774667587.407216 2976479 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774667587.414617 2976479 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667587.414650 2976479 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667587.414652 2976479 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774667587.414653 2976479 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] seed=97\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.3625 (±0.1596), Variance: 63.6648, Range: [0.0000, 37.8591]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667607.693943 2976479 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774667609.191991 2976815 service.cc:152] XLA service 0x7fd304009f80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774667609.192019 2976815 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667609.406814 2976815 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774667611.070399 2976815 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd534183b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fd52c27beb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3260, sigma²=16.2089, nugget=0.0607\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3211, sigma²=14.1494, nugget=0.0540\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3222, sigma²=11.9920, nugget=0.0457\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3224, sigma²=11.2971, nugget=0.0431\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3225, sigma²=10.8837, nugget=0.0415\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3224, sigma²=10.4532, nugget=0.0399\n", - "[repeat 47] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3224, sigma²=10.4532, nugget=0.0399\n", - "[repeat 47] done → repeat_47_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-------------|----------|---------|------------|\n", - "| OLS_wendland | -- | 157322 | 396.638 | 30.0442 | -2720.38 |\n", - "| OLS_sphere | 200 | 2.3605 | 1.5364 | 1.0663 | 0.9592 |\n", - "| DeepKriging_wendland | -- | 29.1299 | 5.3972 | 3.9228 | 0.4961 |\n", - "| DeepKriging_mrts | 200 | 0.7883 | 0.8879 | 0.664 | 0.9864 |\n", - "| DeepKriging_sphere | 50 | 0.6149 | 0.7841 | 0.5759 | 0.9894 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7001 | 0.8367 | 0.6038 | 0.9879 |\n", - "| UniversalKriging | 1000 | 0.7453 | 0.8633 | 0.6342 | 0.9871 |\n", - "Repeat 48/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 49/50 seed=98\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:23:14.121465: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774668194.131075 3603826 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774668194.133892 3603826 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774668194.140940 3603826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668194.140965 3603826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668194.140966 3603826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668194.140968 3603826 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] seed=98\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 10.0218 (±0.1527), Variance: 58.2895, Range: [0.0000, 39.5091]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668214.542567 3603826 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774668216.053808 3604163 service.cc:152] XLA service 0x55b17ff546f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774668216.053848 3604163 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668216.276393 3604163 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668217.903889 3604163 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f3c5877d900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f3c505a5090> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere_Huber best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3275, sigma²=16.2966, nugget=0.0621\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3283, sigma²=14.3694, nugget=0.0548\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3294, sigma²=12.6650, nugget=0.0483\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3296, sigma²=12.1842, nugget=0.0464\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3297, sigma²=11.5660, nugget=0.0441\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3296, sigma²=11.1363, nugget=0.0424\n", - "[repeat 48] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3296, sigma²=11.1363, nugget=0.0424\n", - "[repeat 48] done → repeat_48_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 76.2483 | 8.732 | 5.3671 | -0.2393 |\n", - "| OLS_sphere | 1000 | 3.4066 | 1.8457 | 0.9633 | 0.9446 |\n", - "| DeepKriging_wendland | -- | 32.129 | 5.6682 | 3.9594 | 0.4778 |\n", - "| DeepKriging_mrts | 150 | 0.9596 | 0.9796 | 0.711 | 0.9844 |\n", - "| DeepKriging_sphere | 100 | 0.7899 | 0.8888 | 0.6492 | 0.9872 |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6307 | 0.7941 | 0.6006 | 0.9897 |\n", - "| UniversalKriging | 1000 | 0.9521 | 0.9758 | 0.7029 | 0.9845 |\n", - "Repeat 49/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "Repeat 50/50 seed=99\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:32:21.188634: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774668741.198131 4110768 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774668741.201729 4110768 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774668741.209885 4110768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668741.209917 4110768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668741.209920 4110768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774668741.209921 4110768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] seed=99\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend + N(0,0.5²)) ===\n", - "Simulate Data | z mean: 9.8928 (±0.1532), Variance: 58.6915, Range: [0.0000, 43.2898]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] OLS_sphere best order: 1000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668761.529012 4110768 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774668763.028390 4111103 service.cc:152] XLA service 0x55da7b9b7410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774668763.028415 4111103 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668763.246852 4111103 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774668764.897609 4111103 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa0dc21ba30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa0cc791f30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3157, sigma²=13.6176, nugget=0.0487\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2962, sigma²=10.8368, nugget=0.0416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2972, sigma²=9.1919, nugget=0.0353\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2973, sigma²=8.8786, nugget=0.0341\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2974, sigma²=8.2651, nugget=0.0317\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2973, sigma²=7.8468, nugget=0.0301\n", - "[repeat 49] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2973, sigma²=7.8468, nugget=0.0301\n", - "[repeat 49] done → repeat_49_noGP_noise_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 299.114 | 17.2949 | 6.4058 | -3.8576 |\n", - "| OLS_sphere | 1000 | 1.6252 | 1.2748 | 0.8034 | 0.9736 |\n", - "| DeepKriging_wendland | -- | 24.799 | 4.9799 | 3.7307 | 0.5973 |\n", - "| DeepKriging_mrts | 150 | 0.9927 | 0.9964 | 0.7035 | 0.9839 |\n", - "| DeepKriging_sphere | 50 | 0.8202 | 0.9057 | 0.6274 | 0.9867 |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8013 | 0.8951 | 0.628 | 0.987 |\n", - "| UniversalKriging | 1000 | 0.8954 | 0.9463 | 0.6526 | 0.9855 |\n", - "Repeat 50/50 done — checkpoint saved.\n", - "\n", - "All done: 50/50 repeats completed.\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_noGP_noise.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_noise.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "# ── load checkpoint ───────────────────────────────────────────────────────────\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"Starting fresh.\")\n", - "\n", - "# ── main loop ─────────────────────────────────────────────────────────────────\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n\" + \"=\"*70)\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(\"=\"*70)\n", - "\n", - " out_json = f\"repeat_{repeat}_noGP_noise_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\nAll done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a59787b6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:41:53.054099Z", - "iopub.status.busy": "2026-03-28T03:41:53.053923Z", - "iopub.status.idle": "2026-03-28T03:41:53.060851Z", - "shell.execute_reply": "2026-03-28T03:41:53.060122Z" - }, - "papermill": { - "duration": 0.020596, - "end_time": "2026-03-28T03:41:53.061305", - "exception": false, - "start_time": "2026-03-28T03:41:53.040709", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "Summary — 50 Repeats\n", - " Scenario: Non-GP + Gaussian Noise (N(0, 0.5^2)), Nonstationary Trend + StdScaler\n", - "================================================================================\n", - "\n", - "| Model | N | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|--------------------------|-----|-------------------|-------------------|-------------------|-------------------|\n", - "| OLS_wendland | 50 | 4758.96±23354.00 | 27.00±63.48 | 6.78±4.23 | -76.237±394.756 |\n", - "| OLS_sphere | 50 | 2.77±0.89 | 1.64±0.27 | 1.01±0.15 | 0.956±0.014 |\n", - "| DeepKriging_wendland | 50 | 32.29±4.86 | 5.67±0.42 | 4.08±0.32 | 0.491±0.066 |\n", - "| DeepKriging_mrts | 50 | 1.44±0.47 | 1.19±0.19 | 0.79±0.09 | 0.977±0.007 |\n", - "| DeepKriging_sphere | 50 | 1.00±0.30 | 0.99±0.14 | 0.67±0.06 | 0.984±0.005 |\n", - "| DeepKriging_sphere_Huber | 50 | 1.02±0.31 | 1.00±0.15 | 0.67±0.05 | 0.984±0.005 |\n", - "| UniversalKriging | 50 | 1.20±0.30 | 1.08±0.13 | 0.72±0.05 | 0.981±0.005 |\n", - "\n", - "Best Model: DeepKriging_sphere RMSE=0.99±0.14\n", - "\n", - "── Ranking by mean RMSE ──\n", - " 1. DeepKriging_sphere RMSE=0.99±0.14\n", - " 2. DeepKriging_sphere_Huber RMSE=1.00±0.15\n", - " 3. UniversalKriging RMSE=1.08±0.13\n", - " 4. DeepKriging_mrts RMSE=1.19±0.19\n", - " 5. OLS_sphere RMSE=1.64±0.27\n", - " 6. DeepKriging_wendland RMSE=5.67±0.42\n", - " 7. OLS_wendland RMSE=27.00±63.48\n" - ] - } - ], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"Summary — {n} Repeats\")\n", - "print(\" Scenario: Non-GP + Gaussian Noise (N(0, 0.5^2)), Nonstationary Trend + StdScaler\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\nBest Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 16571.05346, - "end_time": "2026-03-28T03:41:54.095574", - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_noise_noGP_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-27T23:05:43.042114", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb b/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb deleted file mode 100644 index e672dcd..0000000 --- a/notebook/simulation/Rerun/simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb +++ /dev/null @@ -1,10490 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.00685, - "end_time": "2026-03-28T03:42:12.044436", - "exception": false, - "start_time": "2026-03-28T03:42:12.037586", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:12.049396Z", - "iopub.status.busy": "2026-03-28T03:42:12.049222Z", - "iopub.status.idle": "2026-03-28T03:42:12.053895Z", - "shell.execute_reply": "2026-03-28T03:42:12.053111Z" - }, - "papermill": { - "duration": 0.007911, - "end_time": "2026-03-28T03:42:12.054557", - "exception": false, - "start_time": "2026-03-28T03:42:12.046646", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root added: /home/hhuscout/myproject/deepkriging-sphere\n" - ] - } - ], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "import sys\n", - "import os\n", - "project_root = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", - "if project_root not in sys.path:\n", - " sys.path.append(project_root)\n", - "print(f\"Project root added: {project_root}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:12.058724Z", - "iopub.status.busy": "2026-03-28T03:42:12.058598Z", - "iopub.status.idle": "2026-03-28T03:42:14.014104Z", - "shell.execute_reply": "2026-03-28T03:42:14.013260Z" - }, - "papermill": { - "duration": 1.958617, - "end_time": "2026-03-28T03:42:14.014652", - "exception": false, - "start_time": "2026-03-28T03:42:12.056035", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:42:12.761127: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774669332.769867 483120 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774669332.772585 483120 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774669332.779473 483120 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669332.779483 483120 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669332.779485 483120 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669332.779486 483120 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import random" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.002179, - "end_time": "2026-03-28T03:42:14.018682", - "exception": false, - "start_time": "2026-03-28T03:42:14.016503", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:14.022813Z", - "iopub.status.busy": "2026-03-28T03:42:14.022562Z", - "iopub.status.idle": "2026-03-28T03:42:14.906204Z", - "shell.execute_reply": "2026-03-28T03:42:14.904854Z" - }, - "papermill": { - "duration": 0.886644, - "end_time": "2026-03-28T03:42:14.906912", - "exception": false, - "start_time": "2026-03-28T03:42:14.020268", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.002164, - "end_time": "2026-03-28T03:42:14.910865", - "exception": false, - "start_time": "2026-03-28T03:42:14.908701", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:14.914778Z", - "iopub.status.busy": "2026-03-28T03:42:14.914619Z", - "iopub.status.idle": "2026-03-28T03:42:14.917289Z", - "shell.execute_reply": "2026-03-28T03:42:14.916575Z" - }, - "papermill": { - "duration": 0.005367, - "end_time": "2026-03-28T03:42:14.917792", - "exception": false, - "start_time": "2026-03-28T03:42:14.912425", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.001492, - "end_time": "2026-03-28T03:42:14.920877", - "exception": false, - "start_time": "2026-03-28T03:42:14.919385", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:14.924994Z", - "iopub.status.busy": "2026-03-28T03:42:14.924866Z", - "iopub.status.idle": "2026-03-28T03:42:27.686270Z", - "shell.execute_reply": "2026-03-28T03:42:27.685474Z" - }, - "papermill": { - "duration": 12.76505, - "end_time": "2026-03-28T03:42:27.687470", - "exception": false, - "start_time": "2026-03-28T03:42:14.922420", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.001646, - "end_time": "2026-03-28T03:42:27.691049", - "exception": false, - "start_time": "2026-03-28T03:42:27.689403", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:27.731128Z", - "iopub.status.busy": "2026-03-28T03:42:27.730828Z", - "iopub.status.idle": "2026-03-28T03:42:27.736693Z", - "shell.execute_reply": "2026-03-28T03:42:27.735906Z" - }, - "papermill": { - "duration": 0.044472, - "end_time": "2026-03-28T03:42:27.737157", - "exception": false, - "start_time": "2026-03-28T03:42:27.692685", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import gc\n", - "\n", - "def simulate_data(num_sample, outlier_ratio, outlier_multiplier, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-GP: deterministic macro-trend + nonstationary anomalies + outlier injection.\n", - " No noise, no Gaussian Process / Gamma noise component.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - "\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - "\n", - " x_c = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_c = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_c = np.sin(lat_rad)\n", - "\n", - " base_wind = 5.0\n", - " westerlies_north = 18.0 * np.exp(-((lat_rad - np.pi/4)**2) / 0.05)\n", - " westerlies_south = 22.0 * np.exp(-((lat_rad + np.pi/4)**2) / 0.04)\n", - " doldrums = -4.0 * np.exp(-(lat_rad**2) / 0.01)\n", - " mountain_block = np.where((lon_rad > 0.0) & (lon_rad < 1.0) &\n", - " (lat_rad > 0.1) & (lat_rad < 1.0), -12.0, 0.0)\n", - " mean_trend = base_wind + westerlies_north + westerlies_south + doldrums + mountain_block\n", - "\n", - " num_anomalies = 60\n", - " anomaly_lats = np.arcsin(rng.uniform(-1, 1, num_anomalies))\n", - " anomaly_lons = rng.uniform(-np.pi, np.pi, num_anomalies)\n", - " ax = np.cos(anomaly_lats) * np.cos(anomaly_lons)\n", - " ay = np.cos(anomaly_lats) * np.sin(anomaly_lons)\n", - " az = np.sin(anomaly_lats)\n", - "\n", - " anomaly_effect = np.zeros(num_sample)\n", - " for i in range(num_anomalies):\n", - " sq_dists = (x_c - ax[i])**2 + (y_c - ay[i])**2 + (z_c - az[i])**2\n", - " amplitude = rng.uniform(-10.0, 18.0)\n", - " radius = rng.uniform(0.005, 0.03)\n", - " anomaly_effect += amplitude * np.exp(-sq_dists / radius)\n", - "\n", - " mean_trend += anomaly_effect\n", - " mean_trend = np.maximum(mean_trend, 0.5)\n", - "\n", - " # No noise — pure deterministic trend\n", - " z_final = mean_trend.copy().astype(np.float32)\n", - "\n", - " idx_outliers = rng.choice(num_sample, size=int(num_sample * outlier_ratio), replace=False)\n", - " z_final[idx_outliers] *= outlier_multiplier\n", - "\n", - " print(f\"\\n=== Scenario {scenario} (Non-GP: Trend only + Outliers {outlier_ratio*100:.1f}% x{outlier_multiplier}) ===\")\n", - " print(f\"Simulate Data | z mean: {np.mean(z_final):.4f} (\\u00b1{np.std(z_final)/np.sqrt(num_sample):.4f}), \"\n", - " f\"Variance: {np.var(z_final, ddof=0):.4f}, Range: [{np.min(z_final):.4f}, {np.max(z_final):.4f}]\")\n", - "\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - " del x_c, y_c, z_c, mean_trend, westerlies_north, westerlies_south, doldrums, mountain_block, anomaly_effect\n", - " gc.collect()\n", - "\n", - " return pd.DataFrame({\"longitude\": lon_deg, \"latitude\": lat_deg, \"z\": z_final})\n" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.001559, - "end_time": "2026-03-28T03:42:27.740534", - "exception": false, - "start_time": "2026-03-28T03:42:27.738975", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:27.744527Z", - "iopub.status.busy": "2026-03-28T03:42:27.744388Z", - "iopub.status.idle": "2026-03-28T03:42:27.756491Z", - "shell.execute_reply": "2026-03-28T03:42:27.755924Z" - }, - "papermill": { - "duration": 0.015059, - "end_time": "2026-03-28T03:42:27.757177", - "exception": false, - "start_time": "2026-03-28T03:42:27.742118", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " # 1. Force deterministic weights for this specific seed\n", - " os.environ['PYTHONHASHSEED'] = str(seed)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " tf.random.set_seed(seed)\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:27.761193Z", - "iopub.status.busy": "2026-03-28T03:42:27.761067Z", - "iopub.status.idle": "2026-03-28T03:42:27.770613Z", - "shell.execute_reply": "2026-03-28T03:42:27.769878Z" - }, - "papermill": { - "duration": 0.012358, - "end_time": "2026-03-28T03:42:27.771172", - "exception": false, - "start_time": "2026-03-28T03:42:27.758814", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.001632, - "end_time": "2026-03-28T03:42:27.774509", - "exception": false, - "start_time": "2026-03-28T03:42:27.772877", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:27.778697Z", - "iopub.status.busy": "2026-03-28T03:42:27.778560Z", - "iopub.status.idle": "2026-03-28T03:42:27.781727Z", - "shell.execute_reply": "2026-03-28T03:42:27.780957Z" - }, - "papermill": { - "duration": 0.00595, - "end_time": "2026-03-28T03:42:27.782176", - "exception": false, - "start_time": "2026-03-28T03:42:27.776226", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "noGP outliers experiment: 50 repeats, seed=50\n", - "Outliers: 2.5% x5\n" - ] - } - ], - "source": [ - "# Model Setup\n", - "seed = 50\n", - "OUTLIER_RATIO = 0.025\n", - "OUTLIER_MULTIPLIER = 5\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "base_orders = [10, 50, 100, 150, 200, 1000]\n", - "\n", - "repeat_experiment = 50\n", - "\n", - "print(f\"noGP outliers experiment: {repeat_experiment} repeats, seed={seed}\")\n", - "print(f\"Outliers: {OUTLIER_RATIO*100:.1f}% x{OUTLIER_MULTIPLIER}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T03:42:27.786146Z", - "iopub.status.busy": "2026-03-28T03:42:27.786018Z", - "iopub.status.idle": "2026-03-28T09:36:19.130027Z", - "shell.execute_reply": "2026-03-28T09:36:19.128858Z" - }, - "papermill": { - "duration": 21231.347229, - "end_time": "2026-03-28T09:36:19.131058", - "exception": false, - "start_time": "2026-03-28T03:42:27.783829", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "▶ Starting fresh.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 1/50 seed=50\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:42:28.364689: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774669348.374455 483333 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774669348.377105 483333 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774669348.384655 483333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669348.384680 483333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669348.384682 483333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669348.384683 483333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] seed=50\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2704 (±0.2213), Variance: 122.4753, Range: [0.5000, 136.9202]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669368.026779 483333 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774669369.531242 483663 service.cc:152] XLA service 0x56187b189260 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774669369.531266 483663 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669369.749422 483663 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669371.407380 483663 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f73fc1ee050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f73f427bd00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 0] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4131, sigma²=82.2585, nugget=40.6480\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4082, sigma²=81.1077, nugget=40.6709\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4084, sigma²=80.9416, nugget=40.6934\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4086, sigma²=80.9112, nugget=40.6999\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4088, sigma²=80.9008, nugget=40.7032\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4093, sigma²=80.9022, nugget=40.6890\n", - "[repeat 0] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4086, sigma²=80.9112, nugget=40.6999\n", - "[repeat 0] done → repeat_0_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|----------|\n", - "| OLS_wendland | -- | 4098.86 | 64.0223 | 10.622 | -23.8134 |\n", - "| OLS_sphere | 150 | 95.7287 | 9.7841 | 3.7969 | 0.4205 |\n", - "| DeepKriging_wendland | -- | 133.554 | 11.5566 | 6.1352 | 0.1915 |\n", - "| DeepKriging_mrts | 150 | 102.439 | 10.1212 | 3.89 | 0.3799 |\n", - "| DeepKriging_sphere | 10 | 107.19 | 10.3532 | 3.4742 | 0.3511 |\n", - "| DeepKriging_sphere_Huber | 10 | 91.1434 | 9.5469 | 1.9758 | 0.4482 |\n", - "| UniversalKriging | 150 | 94.7764 | 9.7353 | 3.2121 | 0.4262 |\n", - "✅ Repeat 1/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 2/50 seed=51\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:49:50.392552: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774669790.401564 831753 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774669790.404499 831753 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774669790.411911 831753 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669790.411945 831753 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669790.411948 831753 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774669790.411948 831753 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] seed=51\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5664 (±0.2428), Variance: 147.3378, Range: [0.5000, 172.7793]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669809.930085 831753 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774669811.461858 832084 service.cc:152] XLA service 0x7f640c006fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774669811.461886 832084 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669811.678796 832084 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774669813.312625 832084 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f66406175b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f66401a7ac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 1] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4019, sigma²=100.3803, nugget=57.4970\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3976, sigma²=99.1730, nugget=57.5198\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3977, sigma²=99.0149, nugget=57.5407\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3979, sigma²=98.9826, nugget=57.5466\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3980, sigma²=98.9623, nugget=57.5512\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3985, sigma²=98.9479, nugget=57.5405\n", - "[repeat 1] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3980, sigma²=98.9623, nugget=57.5512\n", - "[repeat 1] done → repeat_1_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 215.091 | 14.666 | 7.2089 | -1.4549 |\n", - "| OLS_sphere | 100 | 35.0816 | 5.923 | 3.5673 | 0.5996 |\n", - "| DeepKriging_wendland | -- | 68.1536 | 8.2555 | 5.3818 | 0.2222 |\n", - "| DeepKriging_mrts | 10 | 44.1991 | 6.6482 | 3.4991 | 0.4956 |\n", - "| DeepKriging_sphere | 200 | 30.7455 | 5.5449 | 1.866 | 0.6491 |\n", - "| DeepKriging_sphere_Huber | 10 | 22.201 | 4.7118 | 1.148 | 0.7466 |\n", - "| UniversalKriging | 200 | 31.4153 | 5.6049 | 2.479 | 0.6415 |\n", - "✅ Repeat 2/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 3/50 seed=52\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 11:56:38.111804: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774670198.121019 1161388 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774670198.123595 1161388 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774670198.130918 1161388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670198.130973 1161388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670198.130978 1161388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670198.130979 1161388 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] seed=52\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.5789 (±0.2054), Variance: 105.5008, Range: [0.5000, 134.8719]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670217.655722 1161388 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774670219.162249 1161718 service.cc:152] XLA service 0x7f66f8017d10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774670219.162278 1161718 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670219.376677 1161718 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670221.001035 1161718 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6944366320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f693c2d2f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 2] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4410, sigma²=80.1847, nugget=33.0437\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4335, sigma²=78.6645, nugget=33.0623\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4339, sigma²=78.5352, nugget=33.0822\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4340, sigma²=78.5163, nugget=33.0863\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4343, sigma²=78.5032, nugget=33.0904\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4349, sigma²=78.5036, nugget=33.0807\n", - "[repeat 2] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4343, sigma²=78.5032, nugget=33.0904\n", - "[repeat 2] done → repeat_2_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 112.545 | 10.6087 | 5.61 | -0.0175 |\n", - "| OLS_sphere | 200 | 61.2843 | 7.8284 | 2.8192 | 0.446 |\n", - "| DeepKriging_wendland | -- | 90.9239 | 9.5354 | 4.5706 | 0.178 |\n", - "| DeepKriging_mrts | 100 | 66.0499 | 8.1271 | 3.0476 | 0.4029 |\n", - "| DeepKriging_sphere | 10 | 65.0744 | 8.0669 | 2.8518 | 0.4117 |\n", - "| DeepKriging_sphere_Huber | 10 | 55.9742 | 7.4816 | 1.4455 | 0.494 |\n", - "| UniversalKriging | 200 | 64.2628 | 8.0164 | 2.6758 | 0.419 |\n", - "✅ Repeat 3/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 4/50 seed=53\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:03:33.603570: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774670613.614055 1489288 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774670613.617077 1489288 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774670613.625007 1489288 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670613.625035 1489288 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670613.625038 1489288 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670613.625039 1489288 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] seed=53\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6345 (±0.2547), Variance: 162.2393, Range: [0.5000, 148.6602]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670633.247025 1489288 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774670634.741603 1489619 service.cc:152] XLA service 0x7f8e30009680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774670634.741627 1489619 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670634.959335 1489619 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774670636.575881 1489619 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9070212b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f90684b7eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 3] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4135, sigma²=104.0357, nugget=69.8179\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4093, sigma²=102.7952, nugget=69.8475\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4095, sigma²=102.6639, nugget=69.8675\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4096, sigma²=102.6399, nugget=69.8714\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4098, sigma²=102.6215, nugget=69.8750\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4103, sigma²=102.6180, nugget=69.8562\n", - "[repeat 3] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4098, sigma²=102.6215, nugget=69.8750\n", - "[repeat 3] done → repeat_3_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 572.161 | 23.9199 | 8.9387 | -1.526 |\n", - "| OLS_sphere | 200 | 141.719 | 11.9046 | 3.7347 | 0.3743 |\n", - "| DeepKriging_wendland | -- | 208.328 | 14.4336 | 6.4064 | 0.0803 |\n", - "| DeepKriging_mrts | 200 | 167.452 | 12.9403 | 4.3343 | 0.2607 |\n", - "| DeepKriging_sphere | 10 | 154.203 | 12.4179 | 3.9605 | 0.3192 |\n", - "| DeepKriging_sphere_Huber | 10 | 133.84 | 11.5689 | 2.076 | 0.4091 |\n", - "| UniversalKriging | 200 | 143.839 | 11.9933 | 3.2901 | 0.365 |\n", - "✅ Repeat 4/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 5/50 seed=54\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:09:53.561477: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774670993.570857 1777156 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774670993.573544 1777156 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774670993.580725 1777156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670993.580754 1777156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670993.580755 1777156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774670993.580756 1777156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] seed=54\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.8777 (±0.2268), Variance: 128.5907, Range: [0.5000, 175.4409]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671013.131765 1777156 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774671014.638732 1777478 service.cc:152] XLA service 0x559eb3ceb890 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774671014.638764 1777478 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671014.848963 1777478 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671016.470790 1777478 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff3b8707ac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff3b8297f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 4] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3527, sigma²=81.6720, nugget=52.7258\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3497, sigma²=80.8282, nugget=52.7505\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3499, sigma²=80.6977, nugget=52.7720\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3500, sigma²=80.6718, nugget=52.7779\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3502, sigma²=80.6580, nugget=52.7822\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3506, sigma²=80.6384, nugget=52.7675\n", - "[repeat 4] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3502, sigma²=80.6580, nugget=52.7822\n", - "[repeat 4] done → repeat_4_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 3184.67 | 56.4329 | 9.4918 | -19.7896 |\n", - "| OLS_sphere | 150 | 96.1654 | 9.8064 | 3.3904 | 0.3722 |\n", - "| DeepKriging_wendland | -- | 129.781 | 11.3922 | 5.3958 | 0.1528 |\n", - "| DeepKriging_mrts | 150 | 96.5205 | 9.8245 | 3.2253 | 0.3699 |\n", - "| DeepKriging_sphere | 50 | 100.731 | 10.0365 | 3.0022 | 0.3424 |\n", - "| DeepKriging_sphere_Huber | 10 | 83.2285 | 9.123 | 1.6033 | 0.4567 |\n", - "| UniversalKriging | 200 | 94.6142 | 9.727 | 2.9624 | 0.3824 |\n", - "✅ Repeat 5/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 6/50 seed=55\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:16:54.268674: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774671414.277779 2129012 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774671414.280437 2129012 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774671414.287307 2129012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671414.287339 2129012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671414.287341 2129012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671414.287342 2129012 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] seed=55\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.8443 (±0.2511), Variance: 157.6641, Range: [0.5000, 143.5914]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671434.615068 2129012 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774671436.147958 2129355 service.cc:152] XLA service 0x55b298e8d940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774671436.147985 2129355 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671436.361269 2129355 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671437.997990 2129355 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f96841aadd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f967c297eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 5] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3112, sigma²=84.7370, nugget=81.0466\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3157, sigma²=85.3025, nugget=81.1080\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3160, sigma²=85.1631, nugget=81.1402\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3161, sigma²=85.1362, nugget=81.1489\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3162, sigma²=85.1229, nugget=81.1525\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3166, sigma²=85.0963, nugget=81.1315\n", - "[repeat 5] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3166, sigma²=85.0963, nugget=81.1315\n", - "[repeat 5] done → repeat_5_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 2496.47 | 49.9647 | 9.5656 | -11.196 |\n", - "| OLS_sphere | 100 | 131.821 | 11.4813 | 4.6748 | 0.356 |\n", - "| DeepKriging_wendland | -- | 165.725 | 12.8734 | 5.758 | 0.1904 |\n", - "| DeepKriging_mrts | 10 | 165.007 | 12.8455 | 4.308 | 0.1939 |\n", - "| DeepKriging_sphere | 10 | 143.656 | 11.9857 | 3.5855 | 0.2982 |\n", - "| DeepKriging_sphere_Huber | 10 | 123.475 | 11.1119 | 2.1765 | 0.3968 |\n", - "| UniversalKriging | 1000 | 137.002 | 11.7048 | 3.9709 | 0.3307 |\n", - "✅ Repeat 6/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 7/50 seed=56\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:24:19.479468: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774671859.490216 2516604 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774671859.494100 2516604 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774671859.501170 2516604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671859.501192 2516604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671859.501195 2516604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774671859.501195 2516604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] seed=56\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.0243 (±0.2206), Variance: 121.6225, Range: [0.5000, 134.7815]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671879.220634 2516604 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774671880.766548 2516930 service.cc:152] XLA service 0x7fb2f40082f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774671880.766572 2516930 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671880.986040 2516930 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774671882.622190 2516930 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb53027f370> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb5281ee290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 6] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3907, sigma²=75.5275, nugget=52.7995\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3877, sigma²=74.7553, nugget=52.8270\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3880, sigma²=74.6516, nugget=52.8465\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3882, sigma²=74.6371, nugget=52.8510\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3884, sigma²=74.6259, nugget=52.8549\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3888, sigma²=74.6210, nugget=52.8367\n", - "[repeat 6] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3884, sigma²=74.6259, nugget=52.8549\n", - "[repeat 6] done → repeat_6_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 138.008 | 11.7477 | 6.0105 | -0.4765 |\n", - "| OLS_sphere | 150 | 44.8815 | 6.6994 | 2.8825 | 0.5198 |\n", - "| DeepKriging_wendland | -- | 69.3813 | 8.3295 | 4.7238 | 0.2577 |\n", - "| DeepKriging_mrts | 150 | 57.3756 | 7.5747 | 3.3601 | 0.3862 |\n", - "| DeepKriging_sphere | 150 | 49.917 | 7.0652 | 2.0606 | 0.466 |\n", - "| DeepKriging_sphere_Huber | 10 | 39.8398 | 6.3119 | 1.1783 | 0.5738 |\n", - "| UniversalKriging | 200 | 44.5844 | 6.6772 | 2.3701 | 0.523 |\n", - "✅ Repeat 7/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 8/50 seed=57\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:31:02.227887: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774672262.238336 2825989 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774672262.241105 2825989 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774672262.247949 2825989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672262.247984 2825989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672262.247986 2825989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672262.247987 2825989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] seed=57\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4412 (±0.2628), Variance: 172.7246, Range: [0.5000, 173.8259]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672282.104950 2825989 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774672283.598473 2826320 service.cc:152] XLA service 0x5626c1635a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774672283.598499 2826320 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672283.809492 2826320 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672285.417935 2826320 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8afc166560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8aec7de710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 7] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3616, sigma²=108.6316, nugget=73.6749\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3580, sigma²=107.3761, nugget=73.7100\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3586, sigma²=107.2867, nugget=73.7458\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3588, sigma²=107.2553, nugget=73.7538\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3589, sigma²=107.2393, nugget=73.7600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3594, sigma²=107.2202, nugget=73.7337\n", - "[repeat 7] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3589, sigma²=107.2393, nugget=73.7600\n", - "[repeat 7] done → repeat_7_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 605.097 | 24.5987 | 8.6317 | -2.2213 |\n", - "| OLS_sphere | 100 | 107.984 | 10.3915 | 3.8754 | 0.4251 |\n", - "| DeepKriging_wendland | -- | 147.424 | 12.1418 | 5.7082 | 0.2152 |\n", - "| DeepKriging_mrts | 150 | 109.145 | 10.4472 | 3.7789 | 0.419 |\n", - "| DeepKriging_sphere | 10 | 120.127 | 10.9602 | 3.1529 | 0.3605 |\n", - "| DeepKriging_sphere_Huber | 10 | 92.938 | 9.6404 | 1.5634 | 0.5052 |\n", - "| UniversalKriging | 200 | 108.43 | 10.413 | 3.0463 | 0.4228 |\n", - "✅ Repeat 8/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 9/50 seed=58\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:38:01.077864: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774672681.087585 3179408 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774672681.090302 3179408 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774672681.097353 3179408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672681.097385 3179408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672681.097387 3179408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774672681.097388 3179408 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] seed=58\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2496 (±0.2332), Variance: 135.9964, Range: [0.5000, 135.6513]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672700.896140 3179408 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774672702.422564 3179740 service.cc:152] XLA service 0x7fa00800a0a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774672702.422588 3179740 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672702.642199 3179740 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774672704.288249 3179740 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa24874ad40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fa2482cb400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 8] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3257, sigma²=88.0219, nugget=55.0447\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3221, sigma²=86.9762, nugget=55.0674\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3224, sigma²=86.8251, nugget=55.0994\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3226, sigma²=86.7856, nugget=55.1120\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3228, sigma²=86.7668, nugget=55.1193\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3232, sigma²=86.7356, nugget=55.1149\n", - "[repeat 8] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3232, sigma²=86.7356, nugget=55.1149\n", - "[repeat 8] done → repeat_8_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 80.6674 | 8.9815 | 6.0387 | 0.1355 |\n", - "| OLS_sphere | 150 | 46.6183 | 6.8278 | 3.4181 | 0.5004 |\n", - "| DeepKriging_wendland | -- | 74.7335 | 8.6449 | 5.5482 | 0.1991 |\n", - "| DeepKriging_mrts | 10 | 66.0929 | 8.1298 | 4.2639 | 0.2917 |\n", - "| DeepKriging_sphere | 10 | 59.9834 | 7.7449 | 2.6671 | 0.3572 |\n", - "| DeepKriging_sphere_Huber | 10 | 36.6761 | 6.0561 | 1.3737 | 0.607 |\n", - "| UniversalKriging | 1000 | 49.5798 | 7.0413 | 2.9734 | 0.4687 |\n", - "✅ Repeat 9/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 10/50 seed=59\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:45:02.621304: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774673102.630891 3528258 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774673102.633796 3528258 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774673102.641121 3528258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673102.641156 3528258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673102.641158 3528258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673102.641159 3528258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] seed=59\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.3534 (±0.2553), Variance: 163.0054, Range: [0.5000, 212.2160]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673122.436429 3528258 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774673123.950881 3528588 service.cc:152] XLA service 0x7feb1001b5a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774673123.950908 3528588 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673124.167467 3528588 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673125.814128 3528588 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fed4c25ad40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fed443376d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 9] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3235, sigma²=108.5568, nugget=64.8669\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3202, sigma²=107.4048, nugget=64.8807\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3202, sigma²=107.1785, nugget=64.9153\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3204, sigma²=107.1413, nugget=64.9266\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3205, sigma²=107.1221, nugget=64.9328\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3210, sigma²=107.0925, nugget=64.9138\n", - "[repeat 9] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3205, sigma²=107.1221, nugget=64.9328\n", - "[repeat 9] done → repeat_9_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-------------|----------|---------|------------|\n", - "| OLS_wendland | -- | 149322 | 386.422 | 31.2148 | -1279.03 |\n", - "| OLS_sphere | 100 | 57.5359 | 7.5852 | 3.8123 | 0.5068 |\n", - "| DeepKriging_wendland | -- | 92.4502 | 9.6151 | 5.7152 | 0.2075 |\n", - "| DeepKriging_mrts | 50 | 62.3684 | 7.8974 | 3.7523 | 0.4654 |\n", - "| DeepKriging_sphere | 10 | 54.1869 | 7.3612 | 2.5307 | 0.5355 |\n", - "| DeepKriging_sphere_Huber | 10 | 45.2009 | 6.7232 | 1.3379 | 0.6125 |\n", - "| UniversalKriging | 200 | 51.2715 | 7.1604 | 2.7558 | 0.5605 |\n", - "✅ Repeat 10/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 11/50 seed=60\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:52:10.107347: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774673530.116524 3864306 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774673530.119472 3864306 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774673530.127955 3864306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673530.127980 3864306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673530.127982 3864306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673530.127983 3864306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] seed=60\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.7271 (±0.2904), Variance: 210.8251, Range: [0.5000, 178.1402]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673549.852918 3864306 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774673551.384056 3864634 service.cc:152] XLA service 0x7fdb94008ba0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774673551.384089 3864634 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673551.610083 3864634 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673553.271573 3864634 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdd3459a170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdd1067fa30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 10] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3851, sigma²=125.1827, nugget=99.8242\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3820, sigma²=123.9566, nugget=99.8614\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3823, sigma²=123.7811, nugget=99.8922\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3824, sigma²=123.7488, nugget=99.8994\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3826, sigma²=123.7315, nugget=99.9044\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3830, sigma²=123.7173, nugget=99.8713\n", - "[repeat 10] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3826, sigma²=123.7315, nugget=99.9044\n", - "[repeat 10] done → repeat_10_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 173.392 | 13.1679 | 7.5238 | 0.0771 |\n", - "| OLS_sphere | 100 | 118.855 | 10.9021 | 4.5182 | 0.3674 |\n", - "| DeepKriging_wendland | -- | 165.106 | 12.8493 | 6.528 | 0.1212 |\n", - "| DeepKriging_mrts | 50 | 142.087 | 11.92 | 3.653 | 0.2437 |\n", - "| DeepKriging_sphere | 10 | 115.424 | 10.7436 | 3.8799 | 0.3856 |\n", - "| DeepKriging_sphere_Huber | 10 | 104.372 | 10.2163 | 2.1243 | 0.4445 |\n", - "| UniversalKriging | 200 | 121.189 | 11.0086 | 3.7278 | 0.355 |\n", - "✅ Repeat 11/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 12/50 seed=61\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 12:59:38.483108: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774673978.492666 10820 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774673978.495383 10820 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774673978.502584 10820 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673978.502613 10820 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673978.502616 10820 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774673978.502617 10820 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] seed=61\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.1310 (±0.2324), Variance: 134.9909, Range: [0.5000, 139.9273]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774673998.461548 10820 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774673999.985113 11151 service.cc:152] XLA service 0x7fac8c007680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774673999.985145 11151 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674000.206149 11151 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674001.865995 11151 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faeb4622710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7faeac41a5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 11] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4082, sigma²=78.3852, nugget=38.1956\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4032, sigma²=77.1841, nugget=38.2329\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4035, sigma²=77.0906, nugget=38.2505\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4037, sigma²=77.0726, nugget=38.2560\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4039, sigma²=77.0558, nugget=38.2609\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4045, sigma²=77.0438, nugget=38.2567\n", - "[repeat 11] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4037, sigma²=77.0726, nugget=38.2560\n", - "[repeat 11] done → repeat_11_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 203.631 | 14.2699 | 7.2823 | 0.0659 |\n", - "| OLS_sphere | 150 | 134.844 | 11.6122 | 3.6747 | 0.3814 |\n", - "| DeepKriging_wendland | -- | 188.51 | 13.7299 | 5.8412 | 0.1352 |\n", - "| DeepKriging_mrts | 150 | 149.211 | 12.2152 | 3.6546 | 0.3155 |\n", - "| DeepKriging_sphere | 100 | 137.372 | 11.7206 | 2.9861 | 0.3698 |\n", - "| DeepKriging_sphere_Huber | 10 | 126.799 | 11.2605 | 2.0819 | 0.4183 |\n", - "| UniversalKriging | 150 | 138.047 | 11.7493 | 3.2072 | 0.3667 |\n", - "✅ Repeat 12/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 13/50 seed=62\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:06:40.721206: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774674400.731083 344731 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774674400.733816 344731 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774674400.740728 344731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674400.740763 344731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674400.740765 344731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674400.740766 344731 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] seed=62\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6816 (±0.2538), Variance: 161.0636, Range: [0.5000, 142.5648]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674420.637827 344731 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774674422.157669 345061 service.cc:152] XLA service 0x562d2544b130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774674422.157692 345061 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674422.375526 345061 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674424.036064 345061 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff49466e7a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff48c722b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 12] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4289, sigma²=102.6658, nugget=76.5804\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4252, sigma²=101.5794, nugget=76.6075\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4254, sigma²=101.4498, nugget=76.6259\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4256, sigma²=101.4232, nugget=76.6315\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4258, sigma²=101.4172, nugget=76.6334\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4262, sigma²=101.4130, nugget=76.6079\n", - "[repeat 12] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4262, sigma²=101.4130, nugget=76.6079\n", - "[repeat 12] done → repeat_12_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 368.514 | 19.1967 | 7.4808 | -2.8431 |\n", - "| OLS_sphere | 100 | 41.996 | 6.4804 | 3.5369 | 0.562 |\n", - "| DeepKriging_wendland | -- | 76.3888 | 8.7401 | 6.1065 | 0.2034 |\n", - "| DeepKriging_mrts | 10 | 53.4532 | 7.3112 | 3.0356 | 0.4426 |\n", - "| DeepKriging_sphere | 50 | 56.4955 | 7.5163 | 2.3438 | 0.4108 |\n", - "| DeepKriging_sphere_Huber | 10 | 34.1216 | 5.8414 | 1.2811 | 0.6442 |\n", - "| UniversalKriging | 1000 | 43.275 | 6.5784 | 2.8414 | 0.5487 |\n", - "✅ Repeat 13/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 14/50 seed=63\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:13:43.430142: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774674823.440435 696850 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774674823.444021 696850 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774674823.451230 696850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674823.451257 696850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674823.451259 696850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774674823.451261 696850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] seed=63\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6048 (±0.2347), Variance: 137.7253, Range: [0.5000, 138.1324]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674843.165136 696850 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774674844.657034 697175 service.cc:152] XLA service 0x7f8a800172c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774674844.657063 697175 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674844.878609 697175 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774674846.512738 697175 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8ca02b96c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8c9837bbe0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 13] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3737, sigma²=96.6388, nugget=37.2644\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3675, sigma²=94.9477, nugget=37.2880\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3676, sigma²=94.7132, nugget=37.3207\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3677, sigma²=94.6801, nugget=37.3267\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3679, sigma²=94.6594, nugget=37.3332\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3685, sigma²=94.6404, nugget=37.3305\n", - "[repeat 13] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3679, sigma²=94.6594, nugget=37.3332\n", - "[repeat 13] done → repeat_13_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 148.768 | 12.1971 | 6.9054 | 0.0781 |\n", - "| OLS_sphere | 150 | 83.3426 | 9.1292 | 3.3258 | 0.4836 |\n", - "| DeepKriging_wendland | -- | 142.768 | 11.9485 | 6.1244 | 0.1153 |\n", - "| DeepKriging_mrts | 50 | 101.139 | 10.0568 | 4.1435 | 0.3733 |\n", - "| DeepKriging_sphere | 10 | 94.7575 | 9.7343 | 3.0908 | 0.4128 |\n", - "| DeepKriging_sphere_Huber | 10 | 80.1232 | 8.9512 | 1.5689 | 0.5035 |\n", - "| UniversalKriging | 200 | 85.9792 | 9.2725 | 2.8011 | 0.4672 |\n", - "✅ Repeat 14/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 15/50 seed=64\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:20:38.301500: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774675238.312072 1036768 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774675238.314896 1036768 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774675238.322863 1036768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675238.323071 1036768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675238.323098 1036768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675238.323105 1036768 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] seed=64\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6694 (±0.2399), Variance: 143.8348, Range: [0.5000, 158.4973]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675257.975214 1036768 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774675259.471528 1037097 service.cc:152] XLA service 0x5612f5f26320 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774675259.471560 1037097 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675259.685622 1037097 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675261.321606 1037097 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6fc8152b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6fb83deb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 14] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2974, sigma²=108.3302, nugget=57.8779\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2942, sigma²=107.1778, nugget=57.8937\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2942, sigma²=106.9619, nugget=57.9279\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2943, sigma²=106.9172, nugget=57.9383\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2944, sigma²=106.8781, nugget=57.9476\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2948, sigma²=106.8258, nugget=57.9466\n", - "[repeat 14] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2944, sigma²=106.8781, nugget=57.9476\n", - "[repeat 14] done → repeat_14_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 101.004 | 10.0501 | 6.4069 | -0.0512 |\n", - "| OLS_sphere | 200 | 45.2995 | 6.7305 | 2.8588 | 0.5286 |\n", - "| DeepKriging_wendland | -- | 90.5176 | 9.5141 | 5.2328 | 0.058 |\n", - "| DeepKriging_mrts | 10 | 50.2476 | 7.0886 | 2.8407 | 0.4771 |\n", - "| DeepKriging_sphere | 10 | 47.1597 | 6.8673 | 2.0391 | 0.5092 |\n", - "| DeepKriging_sphere_Huber | 10 | 32.5863 | 5.7084 | 0.9644 | 0.6609 |\n", - "| UniversalKriging | 200 | 44.0139 | 6.6343 | 2.4215 | 0.5419 |\n", - "✅ Repeat 15/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 16/50 seed=65\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:27:41.539697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774675661.550163 1392836 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774675661.553039 1392836 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774675661.560365 1392836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675661.560395 1392836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675661.560397 1392836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774675661.560398 1392836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] seed=65\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4726 (±0.2355), Variance: 138.5997, Range: [0.5000, 140.8845]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675681.360584 1392836 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774675682.844693 1393162 service.cc:152] XLA service 0x562d9a7f1490 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774675682.844716 1393162 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675683.062917 1393162 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774675684.680007 1393162 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f756027ab00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f754837bc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 15] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4703, sigma²=113.8767, nugget=45.6652\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4628, sigma²=111.7807, nugget=45.7005\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4631, sigma²=111.5698, nugget=45.7259\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4633, sigma²=111.5328, nugget=45.7311\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4635, sigma²=111.5242, nugget=45.7345\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4641, sigma²=111.5306, nugget=45.7280\n", - "[repeat 15] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4635, sigma²=111.5242, nugget=45.7345\n", - "[repeat 15] done → repeat_15_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 474.51 | 21.7833 | 7.9994 | -1.8852 |\n", - "| OLS_sphere | 200 | 93.0606 | 9.6468 | 3.3104 | 0.4341 |\n", - "| DeepKriging_wendland | -- | 136.755 | 11.6942 | 6.1143 | 0.1685 |\n", - "| DeepKriging_mrts | 100 | 101.221 | 10.0608 | 3.3507 | 0.3845 |\n", - "| DeepKriging_sphere | 50 | 94.8313 | 9.7381 | 2.7316 | 0.4234 |\n", - "| DeepKriging_sphere_Huber | 10 | 82.1326 | 9.0627 | 1.8484 | 0.5006 |\n", - "| UniversalKriging | 200 | 94.8569 | 9.7395 | 3.2297 | 0.4232 |\n", - "✅ Repeat 16/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 17/50 seed=66\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:35:19.832884: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774676119.842778 1805056 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774676119.845658 1805056 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774676119.852817 1805056 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676119.852841 1805056 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676119.852843 1805056 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676119.852844 1805056 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] seed=66\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.9772 (±0.2478), Variance: 153.4818, Range: [0.5000, 158.7367]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676139.527348 1805056 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774676141.048765 1805382 service.cc:152] XLA service 0x7fb904007800 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774676141.048794 1805382 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676141.274152 1805382 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676142.890817 1805382 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbb386c6290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fbb3077f880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 16] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3176, sigma²=98.9712, nugget=46.0386\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3137, sigma²=97.6494, nugget=46.0637\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3143, sigma²=97.3871, nugget=46.1402\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3139, sigma²=97.3228, nugget=46.1264\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3135, sigma²=97.2528, nugget=46.1161\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3142, sigma²=97.2021, nugget=46.1095\n", - "[repeat 16] UniversalKriging best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3143, sigma²=97.3871, nugget=46.1402\n", - "[repeat 16] done → repeat_16_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 290.393 | 17.0409 | 7.8465 | -0.3017 |\n", - "| OLS_sphere | 200 | 127.467 | 11.2901 | 3.3817 | 0.4286 |\n", - "| DeepKriging_wendland | -- | 188.675 | 13.7359 | 6.0113 | 0.1542 |\n", - "| DeepKriging_mrts | 100 | 130.889 | 11.4407 | 3.6594 | 0.4133 |\n", - "| DeepKriging_sphere | 10 | 144.18 | 12.0075 | 2.9487 | 0.3537 |\n", - "| DeepKriging_sphere_Huber | 10 | 121.822 | 11.0373 | 1.7724 | 0.4539 |\n", - "| UniversalKriging | 100 | 136.198 | 11.6704 | 2.9843 | 0.3895 |\n", - "✅ Repeat 17/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 18/50 seed=67\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:42:21.973727: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774676541.983279 2143808 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774676541.985957 2143808 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774676541.993164 2143808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676541.993190 2143808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676541.993192 2143808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676541.993193 2143808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] seed=67\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.8478 (±0.2107), Variance: 110.9365, Range: [0.5000, 113.5546]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676561.902005 2143808 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774676563.404349 2144138 service.cc:152] XLA service 0x7f2bdc017840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774676563.404374 2144138 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676563.619835 2144138 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676565.256691 2144138 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2e2816f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2e2025feb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 17] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3367, sigma²=73.7518, nugget=45.6658\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3332, sigma²=72.9322, nugget=45.6779\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3334, sigma²=72.8143, nugget=45.7008\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3336, sigma²=72.7875, nugget=45.7080\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3337, sigma²=72.7734, nugget=45.7131\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3342, sigma²=72.7562, nugget=45.7003\n", - "[repeat 17] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3337, sigma²=72.7734, nugget=45.7131\n", - "[repeat 17] done → repeat_17_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 227.332 | 15.0775 | 6.1479 | -1.4607 |\n", - "| OLS_sphere | 150 | 41.1759 | 6.4168 | 2.7757 | 0.5543 |\n", - "| DeepKriging_wendland | -- | 72.7201 | 8.5276 | 4.9052 | 0.2128 |\n", - "| DeepKriging_mrts | 100 | 41.9506 | 6.4769 | 2.7457 | 0.5459 |\n", - "| DeepKriging_sphere | 10 | 45.5865 | 6.7518 | 2.3462 | 0.5066 |\n", - "| DeepKriging_sphere_Huber | 10 | 32.2435 | 5.6783 | 1.0139 | 0.651 |\n", - "| UniversalKriging | 200 | 41.0285 | 6.4054 | 2.3295 | 0.5559 |\n", - "✅ Repeat 18/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 19/50 seed=68\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:49:03.910191: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774676943.920612 2467511 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774676943.923484 2467511 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774676943.931769 2467511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676943.931824 2467511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676943.931827 2467511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774676943.931827 2467511 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] seed=68\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5937 (±0.2503), Variance: 156.6218, Range: [0.5000, 168.9959]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676964.876944 2467511 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774676966.419571 2467845 service.cc:152] XLA service 0x7f169400a180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774676966.419596 2467845 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676966.649485 2467845 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774676968.314327 2467845 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18c027e3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f18b0702830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 18] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4676, sigma²=106.4757, nugget=62.9428\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4629, sigma²=105.0982, nugget=62.9754\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4632, sigma²=104.9575, nugget=62.9932\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4633, sigma²=104.9408, nugget=62.9960\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4635, sigma²=104.9286, nugget=62.9982\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4640, sigma²=104.9276, nugget=62.9769\n", - "[repeat 18] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4629, sigma²=105.0982, nugget=62.9754\n", - "[repeat 18] done → repeat_18_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 360.541 | 18.9879 | 7.5362 | -1.6797 |\n", - "| OLS_sphere | 200 | 57.6546 | 7.5931 | 3.2921 | 0.5715 |\n", - "| DeepKriging_wendland | -- | 98.6403 | 9.9318 | 5.5096 | 0.2669 |\n", - "| DeepKriging_mrts | 200 | 75.8865 | 8.7113 | 3.537 | 0.436 |\n", - "| DeepKriging_sphere | 10 | 62.9975 | 7.9371 | 2.5865 | 0.5318 |\n", - "| DeepKriging_sphere_Huber | 10 | 54.5965 | 7.3889 | 1.597 | 0.5942 |\n", - "| UniversalKriging | 50 | 57.2954 | 7.5694 | 2.7678 | 0.5742 |\n", - "✅ Repeat 19/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 20/50 seed=69\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 13:57:19.340069: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774677439.349687 2847924 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774677439.352553 2847924 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774677439.360091 2847924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677439.360125 2847924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677439.360127 2847924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677439.360128 2847924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] seed=69\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4457 (±0.2274), Variance: 129.3050, Range: [0.5000, 135.0000]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677459.975101 2847924 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774677461.512101 2848259 service.cc:152] XLA service 0x5646c0194670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774677461.512128 2848259 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677461.732145 2848259 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677463.384068 2848259 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f867c78a050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f867454d240> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 19] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5667, sigma²=82.6156, nugget=48.0315\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5597, sigma²=81.2988, nugget=48.0571\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5602, sigma²=81.2245, nugget=48.0648\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5604, sigma²=81.2139, nugget=48.0658\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5605, sigma²=81.2089, nugget=48.0660\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5611, sigma²=81.2167, nugget=48.0518\n", - "[repeat 19] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5605, sigma²=81.2089, nugget=48.0660\n", - "[repeat 19] done → repeat_19_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 136.325 | 11.6758 | 6.4793 | 0.1033 |\n", - "| OLS_sphere | 200 | 97.3929 | 9.8688 | 3.5757 | 0.3594 |\n", - "| DeepKriging_wendland | -- | 130.522 | 11.4246 | 5.9649 | 0.1415 |\n", - "| DeepKriging_mrts | 200 | 121.717 | 11.0325 | 5.0915 | 0.1994 |\n", - "| DeepKriging_sphere | 10 | 91.1422 | 9.5468 | 2.8123 | 0.4005 |\n", - "| DeepKriging_sphere_Huber | 10 | 84.8838 | 9.2132 | 1.9305 | 0.4417 |\n", - "| UniversalKriging | 200 | 92.1206 | 9.5979 | 3.2551 | 0.3941 |\n", - "✅ Repeat 20/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 21/50 seed=70\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:04:14.585057: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774677854.594829 3152067 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774677854.597573 3152067 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774677854.605588 3152067 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677854.605620 3152067 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677854.605622 3152067 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774677854.605623 3152067 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] seed=70\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4993 (±0.2328), Variance: 135.4758, Range: [0.5000, 151.5890]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677875.203854 3152067 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774677876.751196 3152408 service.cc:152] XLA service 0x5586b78baca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774677876.751223 3152408 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677876.967666 3152408 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774677878.674814 3152408 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2ebc63ae60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2ebc1f39a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 20] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3829, sigma²=77.7241, nugget=46.3317\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3794, sigma²=76.8587, nugget=46.3557\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3798, sigma²=76.7566, nugget=46.3772\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3800, sigma²=76.7337, nugget=46.3840\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3802, sigma²=76.7217, nugget=46.3880\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3807, sigma²=76.7123, nugget=46.3711\n", - "[repeat 20] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3807, sigma²=76.7123, nugget=46.3711\n", - "[repeat 20] done → repeat_20_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 282.625 | 16.8115 | 7.9608 | -0.1799 |\n", - "| OLS_sphere | 200 | 142.741 | 11.9474 | 3.7835 | 0.4041 |\n", - "| DeepKriging_wendland | -- | 208.303 | 14.4327 | 6.7174 | 0.1303 |\n", - "| DeepKriging_mrts | 100 | 137.528 | 11.7272 | 3.812 | 0.4258 |\n", - "| DeepKriging_sphere | 50 | 143.832 | 11.993 | 2.9385 | 0.3995 |\n", - "| DeepKriging_sphere_Huber | 50 | 147.887 | 12.1609 | 2.6936 | 0.3826 |\n", - "| UniversalKriging | 1000 | 146.059 | 12.0855 | 3.5844 | 0.3902 |\n", - "✅ Repeat 21/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 22/50 seed=71\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:11:24.002341: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774678284.012060 3480143 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774678284.014870 3480143 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774678284.022209 3480143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678284.022248 3480143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678284.022250 3480143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678284.022251 3480143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] seed=71\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2730 (±0.2449), Variance: 149.9631, Range: [0.5000, 180.6344]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678304.510507 3480143 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774678306.036657 3480478 service.cc:152] XLA service 0x7f8fe8007900 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774678306.036687 3480478 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678306.267640 3480478 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678307.908202 3480478 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f9218401510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f91e86a7760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 21] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3661, sigma²=105.3003, nugget=69.9584\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3630, sigma²=104.1839, nugget=70.0019\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3633, sigma²=104.0211, nugget=70.0346\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3635, sigma²=103.9822, nugget=70.0454\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3637, sigma²=103.9679, nugget=70.0500\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3641, sigma²=103.9617, nugget=70.0253\n", - "[repeat 21] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3637, sigma²=103.9679, nugget=70.0500\n", - "[repeat 21] done → repeat_21_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 106.473 | 10.3186 | 6.0072 | 0.0961 |\n", - "| OLS_sphere | 100 | 62.6768 | 7.9169 | 3.6249 | 0.4679 |\n", - "| DeepKriging_wendland | -- | 84.9976 | 9.2194 | 5.0373 | 0.2784 |\n", - "| DeepKriging_mrts | 200 | 60.9166 | 7.8049 | 3.1683 | 0.4829 |\n", - "| DeepKriging_sphere | 10 | 59.0423 | 7.6839 | 2.5117 | 0.4988 |\n", - "| DeepKriging_sphere_Huber | 10 | 47.0789 | 6.8614 | 1.3439 | 0.6003 |\n", - "| UniversalKriging | 200 | 56.9291 | 7.5451 | 2.6696 | 0.5167 |\n", - "✅ Repeat 22/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 23/50 seed=72\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:18:13.654151: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774678693.664568 3792649 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774678693.667230 3792649 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774678693.675422 3792649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678693.675447 3792649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678693.675449 3792649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774678693.675449 3792649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] seed=72\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5441 (±0.2414), Variance: 145.6440, Range: [0.5000, 165.4849]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678713.964561 3792649 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774678715.504106 3792984 service.cc:152] XLA service 0x55c5582cfd90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774678715.504131 3792984 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678715.725570 3792984 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774678717.366121 3792984 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1a74711a20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1a741276d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 22] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5809, sigma²=84.1612, nugget=58.1422\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5870, sigma²=84.4127, nugget=58.1859\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5876, sigma²=84.3480, nugget=58.1942\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5879, sigma²=84.3387, nugget=58.1960\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5880, sigma²=84.3356, nugget=58.1964\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5886, sigma²=84.3453, nugget=58.1819\n", - "[repeat 22] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5809, sigma²=84.1612, nugget=58.1422\n", - "[repeat 22] done → repeat_22_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|---------|---------|\n", - "| OLS_wendland | -- | 2156.87 | 46.4421 | 10.1615 | -9.7665 |\n", - "| OLS_sphere | 150 | 114.589 | 10.7046 | 3.3264 | 0.428 |\n", - "| DeepKriging_wendland | -- | 169.945 | 13.0363 | 6.1054 | 0.1517 |\n", - "| DeepKriging_mrts | 100 | 122.826 | 11.0827 | 3.4533 | 0.3869 |\n", - "| DeepKriging_sphere | 100 | 139.694 | 11.8192 | 3.5353 | 0.3027 |\n", - "| DeepKriging_sphere_Huber | 10 | 112.789 | 10.6202 | 1.8255 | 0.437 |\n", - "| UniversalKriging | 10 | 120.532 | 10.9787 | 3.2272 | 0.3983 |\n", - "✅ Repeat 23/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 24/50 seed=73\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:25:21.508175: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774679121.517853 4121971 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774679121.520920 4121971 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774679121.528273 4121971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679121.528298 4121971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679121.528300 4121971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679121.528301 4121971 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] seed=73\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5082 (±0.2242), Variance: 125.7186, Range: [0.5000, 168.0884]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679141.664798 4121971 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774679143.138448 4122307 service.cc:152] XLA service 0x7fc80001c220 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774679143.138478 4122307 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679143.351936 4122307 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679144.981158 4122307 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fca443c3d90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fca3c179120> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 23] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5107, sigma²=92.3212, nugget=49.8953\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5051, sigma²=91.0405, nugget=49.9212\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5054, sigma²=90.8957, nugget=49.9345\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5057, sigma²=90.8831, nugget=49.9372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5058, sigma²=90.8760, nugget=49.9381\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5064, sigma²=90.8840, nugget=49.9195\n", - "[repeat 23] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5064, sigma²=90.8840, nugget=49.9195\n", - "[repeat 23] done → repeat_23_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 65.9122 | 8.1186 | 6.0241 | 0.224 |\n", - "| OLS_sphere | 200 | 23.9018 | 4.8889 | 2.6447 | 0.7186 |\n", - "| DeepKriging_wendland | -- | 55.3072 | 7.4369 | 5.1693 | 0.3489 |\n", - "| DeepKriging_mrts | 10 | 29.3993 | 5.4221 | 2.1842 | 0.6539 |\n", - "| DeepKriging_sphere | 10 | 25.6406 | 5.0637 | 2.6785 | 0.6981 |\n", - "| DeepKriging_sphere_Huber | 10 | 15.1479 | 3.892 | 0.9927 | 0.8217 |\n", - "| UniversalKriging | 1000 | 24.0525 | 4.9043 | 2.5392 | 0.7168 |\n", - "✅ Repeat 24/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 25/50 seed=74\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:32:50.665397: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774679570.675712 303987 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774679570.678832 303987 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774679570.686422 303987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679570.686447 303987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679570.686449 303987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679570.686450 303987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] seed=74\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 12.1698 (±0.2900), Variance: 210.2657, Range: [0.5000, 191.9035]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679590.949922 303987 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774679592.443910 304321 service.cc:152] XLA service 0x7ef908008a30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774679592.443940 304321 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679592.661292 304321 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774679594.309533 304321 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efb3072a830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7efb302ef9a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 24] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3309, sigma²=113.0051, nugget=88.2498\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3289, sigma²=112.2018, nugget=88.2760\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3290, sigma²=112.0067, nugget=88.3053\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3291, sigma²=111.9650, nugget=88.3140\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2752, sigma²=123.1519, nugget=80.5959\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3296, sigma²=111.9089, nugget=88.2994\n", - "[repeat 24] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3291, sigma²=111.9650, nugget=88.3140\n", - "[repeat 24] done → repeat_24_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 652.435 | 25.5428 | 9.0144 | -2.6711 |\n", - "| OLS_sphere | 200 | 107.928 | 10.3888 | 4.1504 | 0.3927 |\n", - "| DeepKriging_wendland | -- | 149.672 | 12.234 | 6.4407 | 0.1578 |\n", - "| DeepKriging_mrts | 50 | 119.065 | 10.9117 | 3.84 | 0.3301 |\n", - "| DeepKriging_sphere | 10 | 157.616 | 12.5545 | 7.2768 | 0.1131 |\n", - "| DeepKriging_sphere_Huber | 10 | 93.0829 | 9.6479 | 1.7978 | 0.4762 |\n", - "| UniversalKriging | 150 | 109.458 | 10.4622 | 3.4861 | 0.3841 |\n", - "✅ Repeat 25/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 26/50 seed=75\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:39:42.297473: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774679982.308299 633399 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774679982.311149 633399 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774679982.318642 633399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679982.318666 633399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679982.318668 633399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774679982.318669 633399 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] seed=75\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.3035 (±0.2605), Variance: 169.6872, Range: [0.5000, 209.0978]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] OLS_sphere best order: 50\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680002.797550 633399 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774680004.302536 633734 service.cc:152] XLA service 0x7fd8840064c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774680004.302559 633734 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680004.520491 633734 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680006.155248 633734 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdab814e9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fdaa87e2170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_mrts best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 25] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3605, sigma²=109.0297, nugget=77.6304\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3579, sigma²=108.0882, nugget=77.6572\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3580, sigma²=107.8725, nugget=77.6869\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3582, sigma²=107.8478, nugget=77.6921\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3583, sigma²=107.8234, nugget=77.6963\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3586, sigma²=107.7928, nugget=77.6714\n", - "[repeat 25] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3586, sigma²=107.7928, nugget=77.6714\n", - "[repeat 25] done → repeat_25_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 295.93 | 17.2026 | 7.3822 | -1.4484 |\n", - "| OLS_sphere | 50 | 76.2018 | 8.7294 | 4.9457 | 0.3695 |\n", - "| DeepKriging_wendland | -- | 102.701 | 10.1342 | 5.5466 | 0.1503 |\n", - "| DeepKriging_mrts | 1000 | 93.4357 | 9.6662 | 3.4792 | 0.227 |\n", - "| DeepKriging_sphere | 50 | 97.7327 | 9.886 | 3.581 | 0.1914 |\n", - "| DeepKriging_sphere_Huber | 10 | 47.1101 | 6.8637 | 1.2237 | 0.6102 |\n", - "| UniversalKriging | 1000 | 69.6851 | 8.3478 | 3.0844 | 0.4235 |\n", - "✅ Repeat 26/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 27/50 seed=76\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:46:19.241421: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774680379.251712 931312 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774680379.254545 931312 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774680379.261869 931312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680379.261897 931312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680379.261899 931312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680379.261900 931312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] seed=76\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5049 (±0.2420), Variance: 146.4429, Range: [0.5000, 134.9940]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680399.452939 931312 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774680400.961152 931647 service.cc:152] XLA service 0x7f576c009c90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774680400.961174 931647 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680401.184451 931647 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680402.822949 931647 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f59986a6e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f5988763400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 26] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3822, sigma²=100.3725, nugget=54.9852\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3780, sigma²=99.0677, nugget=55.0254\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3783, sigma²=98.9079, nugget=55.0560\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3784, sigma²=98.8728, nugget=55.0635\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3785, sigma²=98.8538, nugget=55.0671\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3790, sigma²=98.8198, nugget=55.0569\n", - "[repeat 26] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3822, sigma²=100.3725, nugget=54.9852\n", - "[repeat 26] done → repeat_26_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 107.045 | 10.3463 | 6.5339 | 0.1234 |\n", - "| OLS_sphere | 200 | 63.2214 | 7.9512 | 3.2498 | 0.4823 |\n", - "| DeepKriging_wendland | -- | 100.557 | 10.0278 | 5.991 | 0.1765 |\n", - "| DeepKriging_mrts | 10 | 85.3737 | 9.2398 | 4.5834 | 0.3009 |\n", - "| DeepKriging_sphere | 150 | 103.152 | 10.1564 | 3.2283 | 0.1553 |\n", - "| DeepKriging_sphere_Huber | 10 | 58.3118 | 7.6362 | 1.5929 | 0.5225 |\n", - "| UniversalKriging | 10 | 64.3879 | 8.0242 | 3.2131 | 0.4727 |\n", - "✅ Repeat 27/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 28/50 seed=77\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 14:54:03.175956: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774680843.185980 1271616 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774680843.188839 1271616 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774680843.197340 1271616 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680843.197378 1271616 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680843.197381 1271616 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774680843.197382 1271616 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] seed=77\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 12.0228 (±0.2486), Variance: 154.4630, Range: [0.5000, 161.6523]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680863.528266 1271616 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774680865.021008 1271957 service.cc:152] XLA service 0x7fa97800aac0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774680865.021033 1271957 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680865.240750 1271957 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774680866.908203 1271957 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fabbc43eb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fabb46f3e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 27] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3364, sigma²=94.1309, nugget=67.8257\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3329, sigma²=93.1296, nugget=67.8349\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3330, sigma²=92.9958, nugget=67.8558\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3331, sigma²=92.9692, nugget=67.8625\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3332, sigma²=92.9471, nugget=67.8687\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3337, sigma²=92.9285, nugget=67.8462\n", - "[repeat 27] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3332, sigma²=92.9471, nugget=67.8687\n", - "[repeat 27] done → repeat_27_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 379.723 | 19.4865 | 8.5848 | -1.9396 |\n", - "| OLS_sphere | 100 | 81.9585 | 9.0531 | 4.2158 | 0.3655 |\n", - "| DeepKriging_wendland | -- | 123.959 | 11.1337 | 6.6189 | 0.0404 |\n", - "| DeepKriging_mrts | 100 | 96.4241 | 9.8196 | 4.4538 | 0.2535 |\n", - "| DeepKriging_sphere | 10 | 79.9534 | 8.9417 | 3.3258 | 0.381 |\n", - "| DeepKriging_sphere_Huber | 10 | 76.4466 | 8.7434 | 2.1949 | 0.4082 |\n", - "| UniversalKriging | 200 | 81.6942 | 9.0385 | 3.4156 | 0.3676 |\n", - "✅ Repeat 28/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 29/50 seed=78\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:01:13.647697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774681273.658881 1615910 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774681273.661761 1615910 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774681273.669310 1615910 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681273.669341 1615910 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681273.669343 1615910 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681273.669344 1615910 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] seed=78\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.7168 (±0.2695), Variance: 181.5480, Range: [0.5000, 166.2202]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681293.964056 1615910 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774681295.474243 1616251 service.cc:152] XLA service 0x55cc983ca100 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774681295.474275 1616251 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681295.692860 1616251 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681297.354869 1616251 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f83084ed5a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8300577c70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 28] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4789, sigma²=106.6796, nugget=73.3736\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4741, sigma²=105.3647, nugget=73.4040\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4744, sigma²=105.2396, nugget=73.4177\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4745, sigma²=105.2223, nugget=73.4198\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4746, sigma²=105.2153, nugget=73.4207\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4750, sigma²=105.2102, nugget=73.4035\n", - "[repeat 28] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4745, sigma²=105.2223, nugget=73.4198\n", - "[repeat 28] done → repeat_28_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 711.421 | 26.6725 | 9.2554 | -2.5438 |\n", - "| OLS_sphere | 100 | 119.17 | 10.9165 | 4.3688 | 0.4064 |\n", - "| DeepKriging_wendland | -- | 175.343 | 13.2417 | 5.8853 | 0.1266 |\n", - "| DeepKriging_mrts | 10 | 154.163 | 12.4163 | 4.8062 | 0.2321 |\n", - "| DeepKriging_sphere | 10 | 129.222 | 11.3676 | 3.3183 | 0.3563 |\n", - "| DeepKriging_sphere_Huber | 10 | 115.819 | 10.7619 | 2.0906 | 0.4231 |\n", - "| UniversalKriging | 150 | 116.747 | 10.805 | 3.3521 | 0.4184 |\n", - "✅ Repeat 29/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 30/50 seed=79\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:07:49.857666: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774681669.867929 1919864 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774681669.871206 1919864 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774681669.878579 1919864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681669.878605 1919864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681669.878607 1919864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774681669.878608 1919864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] seed=79\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 12.1683 (±0.2576), Variance: 165.8683, Range: [0.5000, 167.5234]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681690.119227 1919864 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774681691.606839 1920196 service.cc:152] XLA service 0x7f1f14006380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774681691.606866 1920196 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681691.831770 1920196 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774681693.475980 1920196 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f21442c2560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f213c3caa70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 29] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3593, sigma²=94.7027, nugget=83.7792\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3570, sigma²=94.0138, nugget=83.7869\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3573, sigma²=93.8581, nugget=83.8155\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3574, sigma²=93.8388, nugget=83.8210\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3517, sigma²=92.6391, nugget=83.8294\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3520, sigma²=92.5991, nugget=83.8021\n", - "[repeat 29] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3517, sigma²=92.6391, nugget=83.8294\n", - "[repeat 29] done → repeat_29_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|---------|----------|\n", - "| OLS_wendland | -- | 2360.34 | 48.5833 | 13.4636 | -15.5039 |\n", - "| OLS_sphere | 200 | 75.8897 | 8.7115 | 3.8522 | 0.4694 |\n", - "| DeepKriging_wendland | -- | 125.419 | 11.1991 | 6.5447 | 0.123 |\n", - "| DeepKriging_mrts | 50 | 81.1542 | 9.0086 | 4.0695 | 0.4326 |\n", - "| DeepKriging_sphere | 10 | 75.7618 | 8.7041 | 2.655 | 0.4703 |\n", - "| DeepKriging_sphere_Huber | 10 | 69.4967 | 8.3365 | 1.7423 | 0.5141 |\n", - "| UniversalKriging | 200 | 69.8121 | 8.3554 | 3.3122 | 0.5119 |\n", - "✅ Repeat 30/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 31/50 seed=80\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:14:51.231489: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774682091.241626 2272863 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774682091.244300 2272863 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774682091.251608 2272863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682091.251634 2272863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682091.251637 2272863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682091.251638 2272863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] seed=80\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 12.0880 (±0.2908), Variance: 211.4460, Range: [0.5000, 220.2307]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682111.543418 2272863 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774682113.027773 2273205 service.cc:152] XLA service 0x564f7b2f6610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774682113.027796 2273205 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682113.242215 2273205 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682114.890654 2273205 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcca868ef80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fcca0445d80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 30] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2435, sigma²=139.4263, nugget=78.7049\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2405, sigma²=138.6509, nugget=78.4769\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2407, sigma²=138.3229, nugget=78.5708\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2409, sigma²=138.2231, nugget=78.6215\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2410, sigma²=138.1746, nugget=78.6438\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2363, sigma²=138.5267, nugget=77.8042\n", - "[repeat 30] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2435, sigma²=139.4263, nugget=78.7049\n", - "[repeat 30] done → repeat_30_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 20941.5 | 144.712 | 15.7079 | -291.186 |\n", - "| OLS_sphere | 200 | 27.483 | 5.2424 | 3.0091 | 0.6165 |\n", - "| DeepKriging_wendland | -- | 67.6922 | 8.2275 | 6.5999 | 0.0555 |\n", - "| DeepKriging_mrts | 10 | 27.5858 | 5.2522 | 2.9251 | 0.6151 |\n", - "| DeepKriging_sphere | 150 | 26.1073 | 5.1095 | 1.8368 | 0.6357 |\n", - "| DeepKriging_sphere_Huber | 10 | 11.2475 | 3.3537 | 0.8474 | 0.8431 |\n", - "| UniversalKriging | 10 | 16.258 | 4.0321 | 1.9321 | 0.7732 |\n", - "✅ Repeat 31/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 32/50 seed=81\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:21:38.178302: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774682498.187749 2585472 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774682498.190537 2585472 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774682498.197843 2585472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682498.197873 2585472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682498.197875 2585472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682498.197876 2585472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] seed=81\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6685 (±0.2445), Variance: 149.4039, Range: [0.5000, 135.6059]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682518.944714 2585472 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774682520.490118 2585809 service.cc:152] XLA service 0x7fc6dc019360 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774682520.490148 2585809 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682520.709558 2585809 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682522.355696 2585809 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc9101f5900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc908297b50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 31] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3520, sigma²=92.8424, nugget=51.1719\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3478, sigma²=91.5897, nugget=51.2027\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3482, sigma²=91.4461, nugget=51.2389\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3484, sigma²=91.4225, nugget=51.2494\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3485, sigma²=91.4086, nugget=51.2557\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3491, sigma²=91.3865, nugget=51.2478\n", - "[repeat 31] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3485, sigma²=91.4086, nugget=51.2557\n", - "[repeat 31] done → repeat_31_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 12895.1 | 113.556 | 16.207 | -114.103 |\n", - "| OLS_sphere | 200 | 52.8427 | 7.2693 | 2.8622 | 0.5283 |\n", - "| DeepKriging_wendland | -- | 84.9177 | 9.2151 | 5.7014 | 0.242 |\n", - "| DeepKriging_mrts | 50 | 51.9932 | 7.2106 | 3.3241 | 0.5359 |\n", - "| DeepKriging_sphere | 10 | 63.2781 | 7.9548 | 2.3624 | 0.4352 |\n", - "| DeepKriging_sphere_Huber | 10 | 45.4529 | 6.7419 | 1.0774 | 0.5943 |\n", - "| UniversalKriging | 200 | 55.3967 | 7.4429 | 2.5935 | 0.5055 |\n", - "✅ Repeat 32/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 33/50 seed=82\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:28:26.325265: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774682906.336123 2900217 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774682906.339320 2900217 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774682906.346859 2900217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682906.346893 2900217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682906.346895 2900217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774682906.346896 2900217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] seed=82\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.3288 (±0.2497), Variance: 155.8209, Range: [0.5000, 160.0117]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682927.268876 2900217 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774682928.822263 2900561 service.cc:152] XLA service 0x7f83a4006d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774682928.822288 2900561 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682929.042376 2900561 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774682930.732114 2900561 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f85e86456c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f85e0596830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_mrts best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 32] DeepKriging_sphere_Huber best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3543, sigma²=98.0233, nugget=66.0885\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3518, sigma²=97.1730, nugget=66.1264\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3521, sigma²=96.9750, nugget=66.1639\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3522, sigma²=96.9477, nugget=66.1706\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3523, sigma²=96.9242, nugget=66.1775\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3527, sigma²=96.8911, nugget=66.1578\n", - "[repeat 32] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3523, sigma²=96.9242, nugget=66.1775\n", - "[repeat 32] done → repeat_32_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 359.156 | 18.9514 | 7.8742 | -1.0157 |\n", - "| OLS_sphere | 200 | 110.193 | 10.4973 | 3.4363 | 0.3816 |\n", - "| DeepKriging_wendland | -- | 158.961 | 12.608 | 5.7181 | 0.1079 |\n", - "| DeepKriging_mrts | 200 | 107.052 | 10.3466 | 2.7428 | 0.3992 |\n", - "| DeepKriging_sphere | 10 | 99.0357 | 9.9517 | 2.3489 | 0.4442 |\n", - "| DeepKriging_sphere_Huber | 50 | 90.6781 | 9.5225 | 1.6392 | 0.4911 |\n", - "| UniversalKriging | 200 | 107.743 | 10.38 | 2.9653 | 0.3953 |\n", - "✅ Repeat 33/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 34/50 seed=83\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:35:26.768580: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774683326.778652 3245121 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774683326.781705 3245121 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774683326.788920 3245121 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683326.788945 3245121 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683326.788947 3245121 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683326.788949 3245121 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] seed=83\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 12.0702 (±0.2542), Variance: 161.4997, Range: [0.5000, 158.9353]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683347.596461 3245121 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774683349.153380 3245463 service.cc:152] XLA service 0x559383a1e700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774683349.153403 3245463 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683349.386852 3245463 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683351.075144 3245463 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f242016f6d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2418252f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 33] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2797, sigma²=102.8508, nugget=63.4877\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2777, sigma²=102.0878, nugget=63.4998\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2778, sigma²=101.8574, nugget=63.5536\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2780, sigma²=101.8149, nugget=63.5722\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2782, sigma²=101.7844, nugget=63.5842\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2786, sigma²=101.7226, nugget=63.5761\n", - "[repeat 33] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2782, sigma²=101.7844, nugget=63.5842\n", - "[repeat 33] done → repeat_33_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 165.637 | 12.87 | 6.8546 | 0.1217 |\n", - "| OLS_sphere | 200 | 103.261 | 10.1618 | 3.7568 | 0.4524 |\n", - "| DeepKriging_wendland | -- | 154.227 | 12.4188 | 5.7721 | 0.1822 |\n", - "| DeepKriging_mrts | 10 | 118.424 | 10.8823 | 4.1417 | 0.372 |\n", - "| DeepKriging_sphere | 50 | 81.4217 | 9.0234 | 2.9244 | 0.5683 |\n", - "| DeepKriging_sphere_Huber | 10 | 94.0917 | 9.7001 | 1.8199 | 0.5011 |\n", - "| UniversalKriging | 200 | 92.8219 | 9.6344 | 3.2506 | 0.5078 |\n", - "✅ Repeat 34/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 35/50 seed=84\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:42:34.184719: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774683754.194955 3595765 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774683754.197741 3595765 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774683754.205340 3595765 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683754.205372 3595765 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683754.205374 3595765 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774683754.205375 3595765 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] seed=84\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4980 (±0.2436), Variance: 148.3866, Range: [0.5000, 176.8170]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683774.926904 3595765 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774683776.459134 3596098 service.cc:152] XLA service 0x7feb50007740 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774683776.459157 3596098 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683776.676482 3596098 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774683778.366931 3596098 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fed6c71feb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fed6c2d3eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 34] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3932, sigma²=93.2530, nugget=56.2252\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3891, sigma²=92.1217, nugget=56.2539\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3893, sigma²=91.9319, nugget=56.2784\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3895, sigma²=91.9026, nugget=56.2841\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3896, sigma²=91.8886, nugget=56.2873\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3900, sigma²=91.8772, nugget=56.2661\n", - "[repeat 34] UniversalKriging best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3893, sigma²=91.9319, nugget=56.2784\n", - "[repeat 34] done → repeat_34_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 118.221 | 10.873 | 6.7263 | 0.1647 |\n", - "| OLS_sphere | 100 | 72.4832 | 8.5137 | 3.5503 | 0.4879 |\n", - "| DeepKriging_wendland | -- | 107.817 | 10.3835 | 6.0204 | 0.2383 |\n", - "| DeepKriging_mrts | 100 | 74.2699 | 8.618 | 3.8203 | 0.4753 |\n", - "| DeepKriging_sphere | 10 | 64.7849 | 8.0489 | 2.3065 | 0.5423 |\n", - "| DeepKriging_sphere_Huber | 10 | 59.3397 | 7.7032 | 1.3552 | 0.5808 |\n", - "| UniversalKriging | 100 | 70.973 | 8.4245 | 2.6794 | 0.4986 |\n", - "✅ Repeat 35/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 36/50 seed=85\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:49:28.944365: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774684168.954020 3914153 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774684168.956734 3914153 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774684168.964995 3914153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684168.965028 3914153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684168.965030 3914153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684168.965031 3914153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] seed=85\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6286 (±0.2154), Variance: 115.9903, Range: [0.5000, 132.9621]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684189.706152 3914153 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774684191.221308 3914494 service.cc:152] XLA service 0x7ff470018e70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774684191.221334 3914494 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684191.442409 3914494 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684193.103193 3914494 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6ac35add0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff6a441e830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 35] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4033, sigma²=73.7146, nugget=49.1085\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3991, sigma²=72.7983, nugget=49.1329\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3993, sigma²=72.7020, nugget=49.1470\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3994, sigma²=72.6801, nugget=49.1501\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3995, sigma²=72.6668, nugget=49.1524\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3999, sigma²=72.6575, nugget=49.1421\n", - "[repeat 35] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3995, sigma²=72.6668, nugget=49.1524\n", - "[repeat 35] done → repeat_35_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 115.395 | 10.7422 | 6.3562 | -0.4951 |\n", - "| OLS_sphere | 200 | 24.546 | 4.9544 | 2.5432 | 0.682 |\n", - "| DeepKriging_wendland | -- | 49.3936 | 7.0281 | 5.1148 | 0.36 |\n", - "| DeepKriging_mrts | 10 | 23.9729 | 4.8962 | 2.4102 | 0.6894 |\n", - "| DeepKriging_sphere | 10 | 32.2704 | 5.6807 | 3.3141 | 0.5819 |\n", - "| DeepKriging_sphere_Huber | 10 | 16.6803 | 4.0842 | 0.9873 | 0.7839 |\n", - "| UniversalKriging | 200 | 24.9843 | 4.9984 | 2.5467 | 0.6763 |\n", - "✅ Repeat 36/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 37/50 seed=86\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 15:56:18.764531: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774684578.774709 34116 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774684578.777458 34116 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774684578.784659 34116 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684578.784687 34116 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684578.784689 34116 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774684578.784690 34116 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] seed=86\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.1767 (±0.2230), Variance: 124.3017, Range: [0.5000, 134.9215]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684600.868935 34116 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774684602.529859 34448 service.cc:152] XLA service 0x7fadec018fe0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774684602.529892 34448 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684602.756389 34448 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774684604.538270 34448 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb02464b400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb01c726e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 36] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3773, sigma²=73.9892, nugget=42.6839\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3739, sigma²=73.1743, nugget=42.7053\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3741, sigma²=73.0520, nugget=42.7223\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3743, sigma²=73.0321, nugget=42.7276\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3744, sigma²=73.0208, nugget=42.7305\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3749, sigma²=73.0070, nugget=42.7189\n", - "[repeat 36] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3744, sigma²=73.0208, nugget=42.7305\n", - "[repeat 36] done → repeat_36_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 136.306 | 11.675 | 6.306 | 0.1029 |\n", - "| OLS_sphere | 200 | 85.4701 | 9.245 | 3.2843 | 0.4375 |\n", - "| DeepKriging_wendland | -- | 121.876 | 11.0397 | 5.3581 | 0.1979 |\n", - "| DeepKriging_mrts | 150 | 93.04 | 9.6457 | 3.7339 | 0.3877 |\n", - "| DeepKriging_sphere | 50 | 79.9057 | 8.939 | 2.2151 | 0.4741 |\n", - "| DeepKriging_sphere_Huber | 10 | 77.0132 | 8.7757 | 1.6575 | 0.4932 |\n", - "| UniversalKriging | 200 | 82.0343 | 9.0573 | 2.8979 | 0.4601 |\n", - "✅ Repeat 37/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 38/50 seed=87\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:04:14.796457: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774685054.806183 377783 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774685054.808976 377783 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774685054.816334 377783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685054.816367 377783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685054.816370 377783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685054.816371 377783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] seed=87\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4922 (±0.2285), Variance: 130.5383, Range: [0.5000, 133.5222]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685075.525005 377783 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774685077.067558 378105 service.cc:152] XLA service 0x564e18bf5e90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774685077.067581 378105 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685077.288910 378105 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685078.958881 378105 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f6880162710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f687821ea70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 37] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4360, sigma²=89.6756, nugget=53.8430\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4305, sigma²=88.3770, nugget=53.8683\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4308, sigma²=88.2623, nugget=53.8863\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4310, sigma²=88.2447, nugget=53.8901\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4312, sigma²=88.2378, nugget=53.8931\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4318, sigma²=88.2451, nugget=53.8762\n", - "[repeat 37] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4312, sigma²=88.2378, nugget=53.8931\n", - "[repeat 37] done → repeat_37_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 83.4213 | 9.1335 | 6.4119 | -0.1846 |\n", - "| OLS_sphere | 200 | 9.2469 | 3.0409 | 2.2869 | 0.8687 |\n", - "| DeepKriging_wendland | -- | 46.4647 | 6.8165 | 5.239 | 0.3402 |\n", - "| DeepKriging_mrts | 10 | 17.7857 | 4.2173 | 2.7873 | 0.7474 |\n", - "| DeepKriging_sphere | 10 | 5.0923 | 2.2566 | 1.4798 | 0.9277 |\n", - "| DeepKriging_sphere_Huber | 10 | 2.6601 | 1.631 | 0.7441 | 0.9622 |\n", - "| UniversalKriging | 200 | 8.2384 | 2.8703 | 1.8502 | 0.883 |\n", - "✅ Repeat 38/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 39/50 seed=88\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:11:29.265678: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774685489.276175 733656 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774685489.279072 733656 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774685489.286491 733656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685489.286522 733656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685489.286524 733656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685489.286525 733656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] seed=88\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2861 (±0.2261), Variance: 127.8350, Range: [0.5000, 126.6792]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685509.976436 733656 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774685511.521983 733978 service.cc:152] XLA service 0x7f6154007b90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774685511.522012 733978 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685511.746470 733978 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685513.418136 733978 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f639c747be0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f639c133880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 38] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4324, sigma²=77.6085, nugget=51.3901\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4278, sigma²=76.5899, nugget=51.4174\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4282, sigma²=76.5192, nugget=51.4330\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4284, sigma²=76.5017, nugget=51.4390\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4286, sigma²=76.4961, nugget=51.4418\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4291, sigma²=76.4987, nugget=51.4273\n", - "[repeat 38] UniversalKriging best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4284, sigma²=76.5017, nugget=51.4390\n", - "[repeat 38] done → repeat_38_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 106.057 | 10.2984 | 5.8154 | -0.2514 |\n", - "| OLS_sphere | 150 | 39.0503 | 6.249 | 2.6837 | 0.5392 |\n", - "| DeepKriging_wendland | -- | 66.9954 | 8.1851 | 4.9082 | 0.2095 |\n", - "| DeepKriging_mrts | 100 | 44.8937 | 6.7003 | 3.2675 | 0.4703 |\n", - "| DeepKriging_sphere | 10 | 35.1796 | 5.9312 | 1.7773 | 0.5849 |\n", - "| DeepKriging_sphere_Huber | 10 | 30.1469 | 5.4906 | 0.9768 | 0.6443 |\n", - "| UniversalKriging | 150 | 34.8691 | 5.905 | 2.1767 | 0.5886 |\n", - "✅ Repeat 39/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 40/50 seed=89\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:18:27.587102: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774685907.596895 1057875 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774685907.599692 1057875 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774685907.607586 1057875 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685907.607625 1057875 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685907.607628 1057875 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774685907.607629 1057875 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] seed=89\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.5354 (±0.2306), Variance: 132.9437, Range: [0.5000, 187.0273]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685928.387087 1057875 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774685929.943052 1058194 service.cc:152] XLA service 0x7f0a3c0066f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774685929.943077 1058194 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685930.161706 1058194 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774685931.853400 1058194 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0c6872f010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f0c682bb880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 39] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5220, sigma²=105.9124, nugget=53.3822\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5154, sigma²=104.2087, nugget=53.4151\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5158, sigma²=104.0796, nugget=53.4305\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5159, sigma²=104.0636, nugget=53.4333\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5161, sigma²=104.0541, nugget=53.4353\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5168, sigma²=104.0690, nugget=53.4183\n", - "[repeat 39] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5161, sigma²=104.0541, nugget=53.4353\n", - "[repeat 39] done → repeat_39_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 208.521 | 14.4403 | 7.6191 | -0.4694 |\n", - "| OLS_sphere | 100 | 79.0082 | 8.8887 | 3.7125 | 0.4432 |\n", - "| DeepKriging_wendland | -- | 127.771 | 11.3036 | 6.081 | 0.0996 |\n", - "| DeepKriging_mrts | 50 | 78.7688 | 8.8752 | 3.5985 | 0.4449 |\n", - "| DeepKriging_sphere | 100 | 76.2209 | 8.7305 | 2.3302 | 0.4629 |\n", - "| DeepKriging_sphere_Huber | 10 | 65.0097 | 8.0629 | 1.4941 | 0.5419 |\n", - "| UniversalKriging | 200 | 72.4033 | 8.509 | 2.9373 | 0.4898 |\n", - "✅ Repeat 40/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 41/50 seed=90\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:26:06.976293: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774686366.987051 1409064 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774686366.989828 1409064 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774686366.998210 1409064 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686366.998235 1409064 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686366.998237 1409064 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686366.998238 1409064 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] seed=90\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6984 (±0.2299), Variance: 132.0875, Range: [0.5000, 152.9801]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686387.140117 1409064 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774686388.624950 1409385 service.cc:152] XLA service 0x7f8afc005e40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774686388.624975 1409385 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686388.836848 1409385 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686390.492907 1409385 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8d387c9480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f8d38183010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 40] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4013, sigma²=92.8446, nugget=52.6697\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3971, sigma²=91.6797, nugget=52.6964\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3973, sigma²=91.5450, nugget=52.7179\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3975, sigma²=91.5167, nugget=52.7236\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3976, sigma²=91.5088, nugget=52.7275\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3982, sigma²=91.4967, nugget=52.7089\n", - "[repeat 40] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3976, sigma²=91.5088, nugget=52.7275\n", - "[repeat 40] done → repeat_40_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 98.1558 | 9.9074 | 6.0803 | 0.212 |\n", - "| OLS_sphere | 200 | 69.7763 | 8.3532 | 3.373 | 0.4398 |\n", - "| DeepKriging_wendland | -- | 101.573 | 10.0783 | 5.7262 | 0.1845 |\n", - "| DeepKriging_mrts | 100 | 83.1009 | 9.116 | 3.9844 | 0.3328 |\n", - "| DeepKriging_sphere | 10 | 73.2878 | 8.5608 | 3.229 | 0.4116 |\n", - "| DeepKriging_sphere_Huber | 10 | 57.9409 | 7.6119 | 1.7002 | 0.5348 |\n", - "| UniversalKriging | 200 | 67.455 | 8.2131 | 2.9471 | 0.4585 |\n", - "✅ Repeat 41/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 42/50 seed=91\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:33:01.951185: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774686781.961419 1725470 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774686781.964229 1725470 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774686781.971820 1725470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686781.971913 1725470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686781.971919 1725470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774686781.971921 1725470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] seed=91\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6523 (±0.2401), Variance: 144.1576, Range: [0.5000, 134.4960]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686802.246887 1725470 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774686803.773948 1725793 service.cc:152] XLA service 0x7fe8f0006fe0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774686803.773970 1725793 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686803.996892 1725793 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774686805.642902 1725793 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7feb2c281fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7feb243579a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 41] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3631, sigma²=85.5102, nugget=57.3935\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3594, sigma²=84.5247, nugget=57.4172\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3596, sigma²=84.3881, nugget=57.4372\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3597, sigma²=84.3664, nugget=57.4416\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3599, sigma²=84.3456, nugget=57.4465\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3602, sigma²=84.3271, nugget=57.4335\n", - "[repeat 41] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3602, sigma²=84.3271, nugget=57.4335\n", - "[repeat 41] done → repeat_41_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 164.557 | 12.828 | 7.0165 | -0.466 |\n", - "| OLS_sphere | 150 | 61.573 | 7.8468 | 3.3789 | 0.4515 |\n", - "| DeepKriging_wendland | -- | 86.8799 | 9.3209 | 5.2752 | 0.226 |\n", - "| DeepKriging_mrts | 100 | 67.1203 | 8.1927 | 3.7971 | 0.402 |\n", - "| DeepKriging_sphere | 10 | 61.4779 | 7.8408 | 2.7708 | 0.4523 |\n", - "| DeepKriging_sphere_Huber | 10 | 54.9598 | 7.4135 | 1.7422 | 0.5104 |\n", - "| UniversalKriging | 1000 | 59.5758 | 7.7185 | 2.9164 | 0.4693 |\n", - "✅ Repeat 42/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 43/50 seed=92\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:39:48.789101: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774687188.799962 2068157 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774687188.802953 2068157 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774687188.810900 2068157 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687188.810945 2068157 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687188.810948 2068157 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687188.810948 2068157 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] seed=92\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.4757 (±0.2373), Variance: 140.7591, Range: [0.5000, 144.9104]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687209.401027 2068157 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774687210.922112 2068477 service.cc:152] XLA service 0x7ff0b8005990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774687210.922138 2068477 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687211.138654 2068477 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687212.830711 2068477 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff2e4373910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ff2dc4335b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 42] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4732, sigma²=99.4166, nugget=48.3627\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4673, sigma²=97.8462, nugget=48.3999\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4676, sigma²=97.6925, nugget=48.4177\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4678, sigma²=97.6748, nugget=48.4210\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4679, sigma²=97.6619, nugget=48.4231\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4684, sigma²=97.6561, nugget=48.4095\n", - "[repeat 42] UniversalKriging best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4732, sigma²=99.4166, nugget=48.3627\n", - "[repeat 42] done → repeat_42_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 38244.8 | 195.563 | 18.8374 | -327.589 |\n", - "| OLS_sphere | 150 | 49.443 | 7.0316 | 2.9587 | 0.5752 |\n", - "| DeepKriging_wendland | -- | 93.6307 | 9.6763 | 5.8244 | 0.1955 |\n", - "| DeepKriging_mrts | 100 | 53.9922 | 7.3479 | 3.3457 | 0.5361 |\n", - "| DeepKriging_sphere | 200 | 54.0344 | 7.3508 | 2.3904 | 0.5357 |\n", - "| DeepKriging_sphere_Huber | 10 | 41.2192 | 6.4202 | 1.211 | 0.6459 |\n", - "| UniversalKriging | 10 | 48.3406 | 6.9527 | 2.4559 | 0.5847 |\n", - "✅ Repeat 43/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 44/50 seed=93\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:46:43.607828: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774687603.618759 2372168 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774687603.621816 2372168 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774687603.629056 2372168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687603.629082 2372168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687603.629084 2372168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774687603.629085 2372168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] seed=93\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.7764 (±0.2210), Variance: 122.0717, Range: [0.5000, 179.6522]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687624.423905 2372168 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774687625.964199 2372490 service.cc:152] XLA service 0x56288015d2d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774687625.964231 2372490 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687626.195136 2372490 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774687627.887130 2372490 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f58142f7490> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f580c3bbac0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 43] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3022, sigma²=90.9310, nugget=46.2089\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2989, sigma²=89.9582, nugget=46.2118\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2990, sigma²=89.6862, nugget=46.2674\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2993, sigma²=89.6378, nugget=46.2895\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2995, sigma²=89.6187, nugget=46.2983\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2999, sigma²=89.5794, nugget=46.3040\n", - "[repeat 43] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2995, sigma²=89.6187, nugget=46.2983\n", - "[repeat 43] done → repeat_43_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|---------|\n", - "| OLS_wendland | -- | 129.283 | 11.3703 | 6.6754 | -0.7456 |\n", - "| OLS_sphere | 150 | 19.1483 | 4.3759 | 2.6445 | 0.7415 |\n", - "| DeepKriging_wendland | -- | 55.2596 | 7.4337 | 5.5253 | 0.2539 |\n", - "| DeepKriging_mrts | 150 | 28.1964 | 5.31 | 3.315 | 0.6193 |\n", - "| DeepKriging_sphere | 10 | 29.6714 | 5.4471 | 3.4543 | 0.5994 |\n", - "| DeepKriging_sphere_Huber | 10 | 11.4465 | 3.3833 | 1.0602 | 0.8454 |\n", - "| UniversalKriging | 200 | 17.3618 | 4.1667 | 2.1772 | 0.7656 |\n", - "✅ Repeat 44/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 45/50 seed=94\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 16:53:32.027892: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774688012.038705 2678257 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774688012.041492 2678257 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774688012.048818 2678257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688012.048850 2678257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688012.048852 2678257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688012.048853 2678257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] seed=94\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2075 (±0.2283), Variance: 130.2522, Range: [0.5000, 139.4377]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688032.744703 2678257 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774688034.273674 2678577 service.cc:152] XLA service 0x7f2f4c0062c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774688034.273698 2678577 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688034.493416 2678577 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688036.151620 2678577 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f31801293f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f31783eff40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 44] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4063, sigma²=85.9507, nugget=55.8217\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4027, sigma²=85.0306, nugget=55.8470\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4030, sigma²=84.8624, nugget=55.8693\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4032, sigma²=84.8420, nugget=55.8735\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4032, sigma²=84.8311, nugget=55.8751\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4036, sigma²=84.8193, nugget=55.8599\n", - "[repeat 44] UniversalKriging best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4027, sigma²=85.0306, nugget=55.8470\n", - "[repeat 44] done → repeat_44_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 1530.59 | 39.1227 | 9.2063 | -10.0206 |\n", - "| OLS_sphere | 200 | 84.674 | 9.2019 | 3.226 | 0.3903 |\n", - "| DeepKriging_wendland | -- | 124.397 | 11.1533 | 5.9072 | 0.1043 |\n", - "| DeepKriging_mrts | 50 | 92.0822 | 9.5959 | 3.4438 | 0.337 |\n", - "| DeepKriging_sphere | 10 | 77.2655 | 8.7901 | 2.8957 | 0.4437 |\n", - "| DeepKriging_sphere_Huber | 10 | 76.3574 | 8.7383 | 1.6413 | 0.4502 |\n", - "| UniversalKriging | 50 | 81.2029 | 9.0113 | 2.8556 | 0.4153 |\n", - "✅ Repeat 45/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 46/50 seed=95\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:00:33.146903: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774688433.156487 3002893 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774688433.159471 3002893 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774688433.166852 3002893 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688433.166881 3002893 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688433.166884 3002893 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688433.166885 3002893 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] seed=95\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.2054 (±0.2224), Variance: 123.6324, Range: [0.5000, 138.5827]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688454.023776 3002893 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774688455.573963 3003215 service.cc:152] XLA service 0x559cb0337990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774688455.573996 3003215 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688455.805504 3003215 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688457.490161 3003215 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f35bc50f2e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f35b47b3e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_mrts best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 45] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3584, sigma²=90.4062, nugget=50.8138\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3544, sigma²=89.2633, nugget=50.8389\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3546, sigma²=89.0855, nugget=50.8677\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3547, sigma²=89.0597, nugget=50.8742\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3549, sigma²=89.0383, nugget=50.8809\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3554, sigma²=89.0238, nugget=50.8657\n", - "[repeat 45] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3554, sigma²=89.0238, nugget=50.8657\n", - "[repeat 45] done → repeat_45_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|--------|\n", - "| OLS_wendland | -- | 44.242 | 6.6515 | 5.4147 | 0.3063 |\n", - "| OLS_sphere | 150 | 12.7446 | 3.57 | 2.5996 | 0.8002 |\n", - "| DeepKriging_wendland | -- | 38.7674 | 6.2263 | 4.8023 | 0.3921 |\n", - "| DeepKriging_mrts | 50 | 7.7363 | 2.7814 | 1.7928 | 0.8787 |\n", - "| DeepKriging_sphere | 50 | 24.3894 | 4.9386 | 1.5223 | 0.6176 |\n", - "| DeepKriging_sphere_Huber | 10 | 1.5322 | 1.2378 | 0.6335 | 0.976 |\n", - "| UniversalKriging | 1000 | 8.3821 | 2.8952 | 1.8551 | 0.8686 |\n", - "✅ Repeat 46/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 47/50 seed=96\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:08:24.825062: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774688904.834799 3427211 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774688904.837517 3427211 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774688904.844995 3427211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688904.845032 3427211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688904.845035 3427211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774688904.845036 3427211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] seed=96\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.6838 (±0.2303), Variance: 132.6440, Range: [0.5000, 190.3018]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] OLS_sphere best order: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688925.171753 3427211 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774688926.692263 3427531 service.cc:152] XLA service 0x7fbe0c007910 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774688926.692288 3427531 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688926.908997 3427531 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774688928.565057 3427531 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc04471e050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fc0442cf7f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 46] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3804, sigma²=79.5538, nugget=46.8240\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3766, sigma²=78.6728, nugget=46.8426\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3771, sigma²=78.6014, nugget=46.8624\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3773, sigma²=78.5771, nugget=46.8692\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3775, sigma²=78.5587, nugget=46.8736\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3779, sigma²=78.5405, nugget=46.8603\n", - "[repeat 46] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3779, sigma²=78.5405, nugget=46.8603\n", - "[repeat 46] done → repeat_46_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 145.585 | 12.0659 | 6.2883 | 0.1093 |\n", - "| OLS_sphere | 100 | 95.189 | 9.7565 | 3.6411 | 0.4177 |\n", - "| DeepKriging_wendland | -- | 139.769 | 11.8224 | 5.5546 | 0.1449 |\n", - "| DeepKriging_mrts | 10 | 114.691 | 10.7094 | 4.8537 | 0.2983 |\n", - "| DeepKriging_sphere | 50 | 97.4965 | 9.874 | 2.686 | 0.4035 |\n", - "| DeepKriging_sphere_Huber | 10 | 87.0954 | 9.3325 | 1.5269 | 0.4672 |\n", - "| UniversalKriging | 1000 | 95.4122 | 9.7679 | 2.9503 | 0.4163 |\n", - "✅ Repeat 47/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 48/50 seed=97\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:15:20.077867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774689320.089259 3723043 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774689320.092276 3723043 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774689320.100210 3723043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689320.100262 3723043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689320.100264 3723043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689320.100265 3723043 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] seed=97\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 11.3733 (±0.2369), Variance: 140.2551, Range: [0.5000, 142.7583]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689340.463145 3723043 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774689341.950575 3723365 service.cc:152] XLA service 0x7f56540176d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774689341.950598 3723365 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689342.169722 3723365 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689343.825258 3723365 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f58646679a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f585447e680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_mrts best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 47] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5336, sigma²=87.4831, nugget=58.2091\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5296, sigma²=86.4682, nugget=58.2414\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5304, sigma²=86.4208, nugget=58.2516\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5307, sigma²=86.4212, nugget=58.2535\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5309, sigma²=86.4229, nugget=58.2542\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5314, sigma²=86.4299, nugget=58.2371\n", - "[repeat 47] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5309, sigma²=86.4229, nugget=58.2542\n", - "[repeat 47] done → repeat_47_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|------------|----------|---------|-----------|\n", - "| OLS_wendland | -- | 14421.7 | 120.091 | 13.1656 | -200.031 |\n", - "| OLS_sphere | 200 | 26.0371 | 5.1027 | 2.4849 | 0.6371 |\n", - "| DeepKriging_wendland | -- | 42.9588 | 6.5543 | 4.7651 | 0.4012 |\n", - "| DeepKriging_mrts | 100 | 24.7247 | 4.9724 | 2.3928 | 0.6554 |\n", - "| DeepKriging_sphere | 10 | 45.0176 | 6.7095 | 3.5382 | 0.3725 |\n", - "| DeepKriging_sphere_Huber | 10 | 16.4303 | 4.0534 | 0.967 | 0.771 |\n", - "| UniversalKriging | 200 | 22.5803 | 4.7519 | 2.3704 | 0.6852 |\n", - "✅ Repeat 48/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 49/50 seed=98\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:22:15.966353: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774689735.976145 4060553 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774689735.978971 4060553 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774689735.986632 4060553 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689735.986667 4060553 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689735.986669 4060553 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774689735.986670 4060553 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] seed=98\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.9743 (±0.2233), Variance: 124.6685, Range: [0.5000, 165.6532]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] OLS_sphere best order: 200\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689756.291726 4060553 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774689757.808346 4060874 service.cc:152] XLA service 0x7f14a4006200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774689757.808397 4060874 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689758.025066 4060874 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774689759.707178 4060874 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f16e4192cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f16dc276b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_mrts best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 48] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4395, sigma²=91.8814, nugget=57.2462\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4352, sigma²=90.7102, nugget=57.2849\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4356, sigma²=90.6130, nugget=57.3030\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4358, sigma²=90.5919, nugget=57.3090\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4359, sigma²=90.5833, nugget=57.3114\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4364, sigma²=90.5815, nugget=57.2926\n", - "[repeat 48] UniversalKriging best order: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4364, sigma²=90.5815, nugget=57.2926\n", - "[repeat 48] done → repeat_48_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|---------|--------|--------|---------|\n", - "| OLS_wendland | -- | 85.475 | 9.2453 | 6.3105 | -0.1221 |\n", - "| OLS_sphere | 200 | 22.364 | 4.7291 | 2.5165 | 0.7064 |\n", - "| DeepKriging_wendland | -- | 54.5431 | 7.3853 | 5.0218 | 0.284 |\n", - "| DeepKriging_mrts | 10 | 21.1906 | 4.6033 | 2.2116 | 0.7218 |\n", - "| DeepKriging_sphere | 50 | 28.0543 | 5.2966 | 1.969 | 0.6317 |\n", - "| DeepKriging_sphere_Huber | 10 | 16.1958 | 4.0244 | 0.8841 | 0.7874 |\n", - "| UniversalKriging | 1000 | 24.1053 | 4.9097 | 2.4409 | 0.6835 |\n", - "✅ Repeat 49/50 done — checkpoint saved.\n", - "\n", - "======================================================================\n", - "🏃 Repeat 50/50 seed=99\n", - "======================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-28 17:29:32.279699: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1774690172.289455 209074 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1774690172.292204 209074 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1774690172.299925 209074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690172.299957 209074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690172.299959 209074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1774690172.299960 209074 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] seed=99\n", - "\n", - "=== Scenario E1 (Non-GP: Nonstationary Trend only + Outliers 2.5% x5) ===\n", - "Simulate Data | z mean: 10.8776 (±0.2233), Variance: 124.6458, Range: [0.5000, 142.2928]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] OLS_sphere best order: 150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690192.602250 209074 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5592 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1774690194.124980 209393 service.cc:152] XLA service 0x55c15b3376d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1774690194.125008 209393 service.cc:160] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690194.343011 209393 cuda_dnn.cc:529] Loaded cuDNN version 90501\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1774690196.010090 209393 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f187859e5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f1870686ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_mrts best order: 150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[repeat 49] DeepKriging_sphere_Huber best order: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4214, sigma²=84.9554, nugget=47.6970\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4171, sigma²=83.8409, nugget=47.7311\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4176, sigma²=83.7217, nugget=47.7557\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4177, sigma²=83.7080, nugget=47.7600\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4179, sigma²=83.6978, nugget=47.7647\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4185, sigma²=83.6903, nugget=47.7520\n", - "[repeat 49] UniversalKriging best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4179, sigma²=83.6978, nugget=47.7647\n", - "[repeat 49] done → repeat_49_noGP_outliers_results.json\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Warning message:\n", - " \n", - "R callback write-console: In eigs_real_sym(A, nrow(A), k, which, sigma, opts, mattype = \"sym_matrix\", : \n", - "R callback write-console: \n", - " \n", - "R callback write-console: all eigenvalues are requested, eigen() is used instead\n", - " \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------|-----------|---------|--------|----------|\n", - "| OLS_wendland | -- | 1758.11 | 41.9298 | 9.397 | -15.9777 |\n", - "| OLS_sphere | 150 | 49.326 | 7.0233 | 3.102 | 0.5237 |\n", - "| DeepKriging_wendland | -- | 67.0575 | 8.1889 | 5.071 | 0.3524 |\n", - "| DeepKriging_mrts | 150 | 55.7474 | 7.4664 | 3.4103 | 0.4617 |\n", - "| DeepKriging_sphere | 10 | 67.7852 | 8.2332 | 3.3537 | 0.3454 |\n", - "| DeepKriging_sphere_Huber | 10 | 39.3947 | 6.2765 | 1.2832 | 0.6196 |\n", - "| UniversalKriging | 200 | 49.1311 | 7.0094 | 2.6098 | 0.5256 |\n", - "✅ Repeat 50/50 done — checkpoint saved.\n", - "\n", - "🎉 All done: 50/50 repeats completed.\n" - ] - } - ], - "source": [ - "import json, subprocess, sys\n", - "\n", - "CHECKPOINT_PATH = \"checkpoint_noGP_outliers.json\"\n", - "SCRIPT_PATH = os.path.join(os.getcwd(), \"run_single_repeat_noGP_outliers.py\")\n", - "PYTHON_EXE = sys.executable\n", - "\n", - "# ── load checkpoint ────────────────────────────────────────────────────────────\n", - "if os.path.exists(CHECKPOINT_PATH):\n", - " with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - " experiment_results = ckpt[\"experiment_results\"]\n", - " completed_repeats = set(ckpt[\"completed_repeats\"])\n", - " print(f\"▶ Resuming: {len(completed_repeats)}/{repeat_experiment} repeats already done.\")\n", - "else:\n", - " experiment_results = {\n", - " m: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - " }\n", - " completed_repeats = set()\n", - " print(\"▶ Starting fresh.\")\n", - "\n", - "# ── main loop ──────────────────────────────────────────────────────────────────\n", - "for repeat in range(repeat_experiment):\n", - "\n", - " if repeat in completed_repeats:\n", - " print(f\"⏭ Skip repeat {repeat+1}/{repeat_experiment} (checkpoint)\")\n", - " continue\n", - "\n", - " print(f\"\\n{'='*70}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment} seed={seed+repeat}\")\n", - " print(f\"{'='*70}\")\n", - "\n", - " out_json = f\"repeat_{repeat}_noGP_outliers_results.json\"\n", - "\n", - " try:\n", - " result = subprocess.run(\n", - " [PYTHON_EXE, SCRIPT_PATH, str(repeat), str(seed), out_json],\n", - " capture_output=False,\n", - " check=True,\n", - " timeout=7200,\n", - " )\n", - " except subprocess.CalledProcessError as e:\n", - " print(f\"❌ Repeat {repeat+1} subprocess exited with code {e.returncode}. Skipping.\")\n", - " continue\n", - " except subprocess.TimeoutExpired:\n", - " print(f\"❌ Repeat {repeat+1} timed out. Skipping.\")\n", - " continue\n", - " except Exception as e:\n", - " print(f\"❌ Repeat {repeat+1} failed: {e}. Skipping.\")\n", - " continue\n", - "\n", - " if not os.path.exists(out_json):\n", - " print(f\"❌ No output JSON for repeat {repeat+1}. Skipping.\")\n", - " continue\n", - "\n", - " with open(out_json) as f:\n", - " res = json.load(f)\n", - " os.remove(out_json)\n", - "\n", - " best_orders = res[\"best_orders\"]\n", - " metrics_map = res[\"metrics\"]\n", - "\n", - " table_rows = []\n", - " for m in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " met = metrics_map[m]\n", - " table_rows.append({\n", - " \"Model\": m, \"Param\": best_orders.get(m, \"--\"),\n", - " \"MSPE\": f\"{met['MSPE']:.4f}\", \"RMSE\": f\"{met['RMSE']:.4f}\",\n", - " \"MAE\": f\"{met['MAE']:.4f}\", \"R2\": f\"{met['R2']:.4f}\",\n", - " })\n", - " import pandas as _pd\n", - " print(\"\\n\", _pd.DataFrame(table_rows).to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " for m in experiment_results:\n", - " experiment_results[m][\"MSPE\"].append(metrics_map[m][\"MSPE\"])\n", - " experiment_results[m][\"RMSE\"].append(metrics_map[m][\"RMSE\"])\n", - " experiment_results[m][\"MAE\"].append(metrics_map[m][\"MAE\"])\n", - " experiment_results[m][\"R2\"].append(metrics_map[m][\"R2\"])\n", - "\n", - " completed_repeats.add(repeat)\n", - " with open(CHECKPOINT_PATH, \"w\") as f:\n", - " json.dump({\"experiment_results\": experiment_results,\n", - " \"completed_repeats\": list(completed_repeats)}, f)\n", - "\n", - " print(f\"✅ Repeat {repeat+1}/{repeat_experiment} done — checkpoint saved.\")\n", - "\n", - "print(f\"\\n🎉 All done: {len(completed_repeats)}/{repeat_experiment} repeats completed.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.023718, - "end_time": "2026-03-28T09:36:19.180162", - "exception": false, - "start_time": "2026-03-28T09:36:19.156444", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-03-28T09:36:19.230512Z", - "iopub.status.busy": "2026-03-28T09:36:19.230314Z", - "iopub.status.idle": "2026-03-28T09:36:19.237375Z", - "shell.execute_reply": "2026-03-28T09:36:19.236689Z" - }, - "papermill": { - "duration": 0.03168, - "end_time": "2026-03-28T09:36:19.237840", - "exception": false, - "start_time": "2026-03-28T09:36:19.206160", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary — 50 Repeats\n", - " Scenario: Non-GP + Outliers (2.5% × 5×), Nonstationary Trend\n", - "================================================================================\n", - "\n", - "| Model | N | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R² (mean±std) |\n", - "|--------------------------|-----|-------------------|-------------------|-------------------|-------------------|\n", - "| OLS_wendland | 50 | 5243.61±21563.76 | 37.01±62.24 | 8.67±4.27 | -46.851±188.315 |\n", - "| OLS_sphere | 50 | 72.44±36.08 | 8.20±2.27 | 3.39±0.58 | 0.496±0.120 |\n", - "| DeepKriging_wendland | 50 | 111.16±44.84 | 10.32±2.16 | 5.67±0.54 | 0.195±0.085 |\n", - "| DeepKriging_mrts | 50 | 81.42±40.69 | 8.69±2.42 | 3.53±0.70 | 0.432±0.147 |\n", - "| DeepKriging_sphere | 50 | 78.18±38.53 | 8.54±2.30 | 2.85±0.87 | 0.449±0.139 |\n", - "| DeepKriging_sphere_Huber | 50 | 63.13±36.90 | 7.51±2.58 | 1.50±0.44 | 0.576±0.146 |\n", - "| UniversalKriging | 50 | 71.45±37.52 | 8.11±2.38 | 2.85±0.46 | 0.508±0.132 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber RMSE=7.51±2.58\n", - "\n", - "── Ranking by mean RMSE ──\n", - " 1. DeepKriging_sphere_Huber RMSE=7.51±2.58\n", - " 2. UniversalKriging RMSE=8.11±2.38\n", - " 3. OLS_sphere RMSE=8.20±2.27\n", - " 4. DeepKriging_sphere RMSE=8.54±2.30\n", - " 5. DeepKriging_mrts RMSE=8.69±2.42\n", - " 6. DeepKriging_wendland RMSE=10.32±2.16\n", - " 7. OLS_wendland RMSE=37.01±62.24\n" - ] - } - ], - "source": [ - "import json, numpy as np, pandas as pd\n", - "\n", - "MODELS = [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_mrts\",\n", - " \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "\n", - "with open(CHECKPOINT_PATH) as f:\n", - " ckpt = json.load(f)\n", - "results = ckpt[\"experiment_results\"]\n", - "n = len(next(iter(results.values()))[\"MSPE\"])\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(f\"📊 Summary — {n} Repeats\")\n", - "print(\" Scenario: Non-GP + Outliers (2.5% × 5×), Nonstationary Trend\")\n", - "print(\"=\"*80)\n", - "\n", - "rows = []\n", - "for m in MODELS:\n", - " vals = results[m]\n", - " rows.append({\n", - " \"Model\": m,\n", - " \"N\": len(vals[\"MSPE\"]),\n", - " \"MSPE (mean±std)\": f\"{np.mean(vals['MSPE']):.2f}±{np.std(vals['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(vals['RMSE']):.2f}±{np.std(vals['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(vals['MAE']):.2f}±{np.std(vals['MAE']):.2f}\",\n", - " \"R² (mean±std)\": f\"{np.mean(vals['R2']):.3f}±{np.std(vals['R2']):.3f}\",\n", - " })\n", - "\n", - "df = pd.DataFrame(rows)\n", - "print(\"\\n\", df.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "best = min(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0]))\n", - "print(f\"\\n🏆 Best Model: {best['Model']} RMSE={best['RMSE (mean±std)']}\")\n", - "\n", - "print(\"\\n── Ranking by mean RMSE ──\")\n", - "for i, r in enumerate(sorted(rows, key=lambda r: float(r[\"RMSE (mean±std)\"].split(\"±\")[0])), 1):\n", - " print(f\" {i}. {r['Model']:<35} RMSE={r['RMSE (mean±std)']}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 21248.914793, - "end_time": "2026-03-28T09:36:20.377664", - "environment_variables": {}, - "exception": null, - "input_path": "simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps.ipynb", - "output_path": "simulation_spherical_nn_sim_RSHC_c15_outliers_noGP_50reps_out.ipynb", - "parameters": {}, - "start_time": "2026-03-28T03:42:11.462871", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_chisquare.ipynb b/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_chisquare.ipynb deleted file mode 100644 index 27f0c5c..0000000 --- a/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_chisquare.ipynb +++ /dev/null @@ -1,3435 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.01667, - "end_time": "2026-01-20T19:58:34.388909", - "exception": false, - "start_time": "2026-01-20T19:58:34.372239", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:34.409580Z", - "iopub.status.busy": "2026-01-20T19:58:34.408386Z", - "iopub.status.idle": "2026-01-20T19:58:34.434426Z", - "shell.execute_reply": "2026-01-20T19:58:34.430157Z" - }, - "papermill": { - "duration": 0.041003, - "end_time": "2026-01-20T19:58:34.438451", - "exception": false, - "start_time": "2026-01-20T19:58:34.397448", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:34.464590Z", - "iopub.status.busy": "2026-01-20T19:58:34.463625Z", - "iopub.status.idle": "2026-01-20T19:58:37.512823Z", - "shell.execute_reply": "2026-01-20T19:58:37.508886Z" - }, - "papermill": { - "duration": 3.066497, - "end_time": "2026-01-20T19:58:37.516054", - "exception": false, - "start_time": "2026-01-20T19:58:34.449557", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-28 06:04:20.499578: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769551460.629913 242076 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769551460.664473 242076 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769551460.894407 242076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769551460.894513 242076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769551460.894516 242076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769551460.894518 242076 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.013361, - "end_time": "2026-01-20T19:58:37.543964", - "exception": false, - "start_time": "2026-01-20T19:58:37.530603", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:37.564712Z", - "iopub.status.busy": "2026-01-20T19:58:37.563377Z", - "iopub.status.idle": "2026-01-20T19:58:39.165091Z", - "shell.execute_reply": "2026-01-20T19:58:39.161165Z" - }, - "papermill": { - "duration": 1.615094, - "end_time": "2026-01-20T19:58:39.168528", - "exception": false, - "start_time": "2026-01-20T19:58:37.553434", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011991, - "end_time": "2026-01-20T19:58:39.194027", - "exception": false, - "start_time": "2026-01-20T19:58:39.182036", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:39.212060Z", - "iopub.status.busy": "2026-01-20T19:58:39.211074Z", - "iopub.status.idle": "2026-01-20T19:58:39.224300Z", - "shell.execute_reply": "2026-01-20T19:58:39.220662Z" - }, - "papermill": { - "duration": 0.026147, - "end_time": "2026-01-20T19:58:39.227787", - "exception": false, - "start_time": "2026-01-20T19:58:39.201640", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006099, - "end_time": "2026-01-20T19:58:39.242620", - "exception": false, - "start_time": "2026-01-20T19:58:39.236521", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:39.255962Z", - "iopub.status.busy": "2026-01-20T19:58:39.255006Z", - "iopub.status.idle": "2026-01-20T19:58:51.611428Z", - "shell.execute_reply": "2026-01-20T19:58:51.608119Z" - }, - "papermill": { - "duration": 12.367483, - "end_time": "2026-01-20T19:58:51.614982", - "exception": false, - "start_time": "2026-01-20T19:58:39.247499", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.011633, - "end_time": "2026-01-20T19:58:51.640454", - "exception": false, - "start_time": "2026-01-20T19:58:51.628821", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.658922Z", - "iopub.status.busy": "2026-01-20T19:58:51.657553Z", - "iopub.status.idle": "2026-01-20T19:58:51.687363Z", - "shell.execute_reply": "2026-01-20T19:58:51.683511Z" - }, - "papermill": { - "duration": 0.042311, - "end_time": "2026-01-20T19:58:51.690376", - "exception": false, - "start_time": "2026-01-20T19:58:51.648065", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + chi-square noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = (L @ z_std).astype(np.float32)\n", - " z = y + rng.chisquare(df=4, size=num_sample).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.00693, - "end_time": "2026-01-20T19:58:51.708375", - "exception": false, - "start_time": "2026-01-20T19:58:51.701445", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.722171Z", - "iopub.status.busy": "2026-01-20T19:58:51.721604Z", - "iopub.status.idle": "2026-01-20T19:58:51.784510Z", - "shell.execute_reply": "2026-01-20T19:58:51.780811Z" - }, - "papermill": { - "duration": 0.073804, - "end_time": "2026-01-20T19:58:51.787682", - "exception": false, - "start_time": "2026-01-20T19:58:51.713878", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.805166Z", - "iopub.status.busy": "2026-01-20T19:58:51.804407Z", - "iopub.status.idle": "2026-01-20T19:58:51.853452Z", - "shell.execute_reply": "2026-01-20T19:58:51.849707Z" - }, - "papermill": { - "duration": 0.06093, - "end_time": "2026-01-20T19:58:51.856766", - "exception": false, - "start_time": "2026-01-20T19:58:51.795836", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + noise (chi-square)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + noise (chi-square)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.009385, - "end_time": "2026-01-20T19:58:51.879577", - "exception": false, - "start_time": "2026-01-20T19:58:51.870192", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.896847Z", - "iopub.status.busy": "2026-01-20T19:58:51.895902Z", - "iopub.status.idle": "2026-01-20T19:58:51.912081Z", - "shell.execute_reply": "2026-01-20T19:58:51.907513Z" - }, - "papermill": { - "duration": 0.029106, - "end_time": "2026-01-20T19:58:51.915477", - "exception": false, - "start_time": "2026-01-20T19:58:51.886371", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.936586Z", - "iopub.status.busy": "2026-01-20T19:58:51.935735Z", - "iopub.status.idle": "2026-01-20T22:00:41.558573Z", - "shell.execute_reply": "2026-01-20T22:00:41.555293Z" - }, - "papermill": { - "duration": 7309.636424, - "end_time": "2026-01-20T22:00:41.561261", - "exception": false, - "start_time": "2026-01-20T19:58:51.924837", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3499 (±0.0202), Variance: 1.0165, Range: [-1.8179, 3.2190]\n", - "z mean: 4.3339 (±0.0596), Variance: 8.8860, Range: [-1.4726, 23.0884]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 16.6623 16.0184 \n", - " 100 16.9557 16.7003 \n", - " 200 17.4086 17.3785 \n", - " 500 19.1134 20.7210 \n", - " 700 20.0794 22.4302 \n", - " 1000 16.4711 16.5095 *\n", - " 1400 16.4711 16.5095 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769551504.687481 242076 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769551507.334748 242888 service.cc:152] XLA service 0x5c3c28a7c2c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769551507.334824 242888 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1769551507.891595 242888 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1769551518.852540 242888 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f2794563880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7f275e5360e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.4027 12.5678 \n", - " 100 7.6865 11.1488 \n", - " 200 7.5088 13.9475 \n", - " 500 7.3510 16.5237 \n", - " 700 7.3516 12.1366 \n", - " 1000 7.3028 15.2904 *\n", - " 1400 7.3718 15.7400 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2.0094 12.4786 \n", - " 100 1.9672 14.2184 *\n", - " 200 1.9995 11.4121 \n", - " 500 2.0034 12.2965 \n", - " 700 2.0103 10.5845 \n", - " 1000 2.0219 12.9819 \n", - " 1400 1.9917 12.8821 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=8.0236\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=7.7803\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0002, nugget=7.3910\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0025, sigma²=0.0000, nugget=6.0758\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0017, sigma²=0.0000, nugget=5.2321\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0012, sigma²=0.0000, nugget=3.8076\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=0.0000, nugget=2.2598\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.1187 16.0183 *\n", - " 100 7.4701 16.7002 \n", - " 200 7.6998 17.3785 \n", - " 500 10.0815 20.7210 \n", - " 700 10.7144 22.4304 \n", - " 1000 16.2045 37.5578 \n", - " 1400 2692.2761 1774.2023 \n", - " Best order: 50\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=8.0236\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.7388 | 4.554 | 4.0648 | -18.4755 | 0.53s |\n", - "| OLS_sphere | 1000 | 16.5095 | 4.0632 | 3.9313 | -14.5039 | 0.14s |\n", - "| DeepKriging_wendland | -- | 13.6084 | 3.689 | 3.5294 | -11.7795 | 63.32s |\n", - "| DeepKriging_sphere | 1000 | 15.0773 | 3.8829 | 3.7413 | -13.1589 | 28.98s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.1238 | 3.1818 | 2.9588 | -8.5072 | 25.83s |\n", - "| UniversalKriging | 50 | 16.0183 | 4.0023 | 3.9583 | -14.0426 | 2.74s |\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:14:50\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4176 (±0.0149), Variance: 0.5573, Range: [-1.4140, 2.4415]\n", - "z mean: 4.4827 (±0.0592), Variance: 8.7574, Range: [-0.8374, 23.8290]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=3.6010, nugget=4.3742\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.9711 | 4.3556 | 4.1343 | -35.1297 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.9602 | 4.1183 | 4.0654 | -31.3 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.6842 | 3.9603 | 3.837 | -28.87 | 65.79s |\n", - "| DeepKriging_sphere | 1000 | 16.4011 | 4.0498 | 3.977 | -30.2352 | 39.54s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.5878 | 3.6862 | 3.6211 | -24.8774 | 19.78s |\n", - "| UniversalKriging | 50 | 16.6756 | 4.0836 | 4.0454 | -30.758 | 5.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:17:31\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1219 (±0.0114), Variance: 0.3252, Range: [-1.8046, 1.6246]\n", - "z mean: 3.9534 (±0.0564), Variance: 7.9488, Range: [-1.1204, 18.9562]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0037, nugget=7.3190\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.5801 | 4.3105 | 4.1896 | -59.7044 | 0.44s |\n", - "| OLS_sphere | 1000 | 17.1131 | 4.1368 | 4.1024 | -54.9114 | 0.14s |\n", - "| DeepKriging_wendland | -- | 16.2156 | 4.0269 | 3.91 | -51.9792 | 52.01s |\n", - "| DeepKriging_sphere | 1000 | 14.0416 | 3.7472 | 3.7001 | -44.8764 | 17.59s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.0675 | 3.6149 | 3.5537 | -41.6937 | 20.70s |\n", - "| UniversalKriging | 50 | 16.97 | 4.1195 | 4.0843 | -54.444 | 1.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:19:32\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0497 (±0.0198), Variance: 0.9846, Range: [-3.1433, 2.5210]\n", - "z mean: 4.0068 (±0.0595), Variance: 8.8367, Range: [-2.7695, 20.0766]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0183, nugget=7.6070\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 32.2165 | 5.676 | 4.3459 | -35.602 | 0.44s |\n", - "| OLS_sphere | 1000 | 16.9874 | 4.1216 | 4.0175 | -18.2998 | 0.14s |\n", - "| DeepKriging_wendland | -- | 20.6201 | 4.5409 | 4.3492 | -22.427 | 48.22s |\n", - "| DeepKriging_sphere | 1000 | 16.9488 | 4.1169 | 4.0023 | -18.256 | 31.15s |\n", - "| DeepKriging_sphere_Huber | 100 | 9.9868 | 3.1602 | 3.0022 | -10.3463 | 22.07s |\n", - "| UniversalKriging | 50 | 17.0177 | 4.1253 | 4.0856 | -18.3343 | 2.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:21:52\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4890 (±0.0182), Variance: 0.8305, Range: [-2.5579, 2.8594]\n", - "z mean: 4.4916 (±0.0575), Variance: 8.2569, Range: [-1.9375, 20.1442]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=7.7157, nugget=0.0030\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 76.4784 | 8.7452 | 4.4519 | -95.4771 | 0.44s |\n", - "| OLS_sphere | 1000 | 16.2329 | 4.029 | 3.9441 | -19.4778 | 0.14s |\n", - "| DeepKriging_wendland | -- | 14.0153 | 3.7437 | 3.6086 | -16.6802 | 46.36s |\n", - "| DeepKriging_sphere | 1000 | 13.2183 | 3.6357 | 3.5297 | -15.6749 | 20.09s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.2974 | 3.7812 | 3.6727 | -17.036 | 16.31s |\n", - "| UniversalKriging | 50 | 16.4407 | 4.0547 | 4.0202 | -19.7399 | 3.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:23:53\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0289 (±0.0156), Variance: 0.6105, Range: [-1.7509, 2.2876]\n", - "z mean: 4.0491 (±0.0596), Variance: 8.8935, Range: [-1.2854, 32.7927]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0065, nugget=7.9872\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.3364 | 4.5096 | 4.1562 | -38.5431 | 0.44s |\n", - "| OLS_sphere | 1000 | 16.5874 | 4.0728 | 4.009 | -31.2534 | 0.16s |\n", - "| DeepKriging_wendland | -- | 14.3878 | 3.7931 | 3.704 | -26.9763 | 49.39s |\n", - "| DeepKriging_sphere | 1000 | 14.995 | 3.8723 | 3.7922 | -28.1569 | 21.70s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.2606 | 3.7763 | 3.6988 | -26.7289 | 21.06s |\n", - "| UniversalKriging | 50 | 16.2517 | 4.0313 | 3.9918 | -30.6006 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:26:01\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6610 (±0.0124), Variance: 0.3834, Range: [-1.2027, 2.8062]\n", - "z mean: 4.7383 (±0.0587), Variance: 8.6212, Range: [-0.9522, 22.7927]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0017, nugget=7.9758\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 23.5598 | 4.8538 | 4.2476 | -60.7032 | 0.48s |\n", - "| OLS_sphere | 1000 | 16.691 | 4.0855 | 4.0477 | -42.7138 | 0.15s |\n", - "| DeepKriging_wendland | -- | 13.902 | 3.7285 | 3.6212 | -35.4094 | 52.60s |\n", - "| DeepKriging_sphere | 1000 | 14.7157 | 3.8361 | 3.774 | -37.5404 | 27.66s |\n", - "| DeepKriging_sphere_Huber | 100 | 17.2955 | 4.1588 | 4.0909 | -44.2971 | 19.12s |\n", - "| UniversalKriging | 50 | 16.7764 | 4.0959 | 4.0627 | -42.9374 | 2.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:28:21\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.2414 (±0.0202), Variance: 1.0183, Range: [-1.9621, 3.1672]\n", - "z mean: 4.3081 (±0.0611), Variance: 9.3187, Range: [-1.6097, 22.9796]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0040, nugget=8.1921\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 29.9856 | 5.4759 | 4.3771 | -24.0561 | 0.47s |\n", - "| OLS_sphere | 1000 | 17.0948 | 4.1346 | 3.9928 | -13.2845 | 0.15s |\n", - "| DeepKriging_wendland | -- | 11.4607 | 3.3854 | 3.1909 | -8.5766 | 47.46s |\n", - "| DeepKriging_sphere | 1000 | 16.9052 | 4.1116 | 3.9599 | -13.1261 | 30.63s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.7846 | 3.8451 | 3.6121 | -11.3541 | 23.39s |\n", - "| UniversalKriging | 50 | 17.2035 | 4.1477 | 4.0971 | -13.3754 | 2.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:30:46\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3908 (±0.0176), Variance: 0.7706, Range: [-1.6303, 2.8375]\n", - "z mean: 4.2883 (±0.0592), Variance: 8.7469, Range: [-1.0090, 19.6205]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0019, nugget=7.7714\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.5849 | 4.1934 | 3.847 | -22.5223 | 0.44s |\n", - "| OLS_sphere | 1000 | 15.5415 | 3.9423 | 3.8429 | -19.7891 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.52 | 3.9395 | 3.754 | -19.7602 | 50.55s |\n", - "| DeepKriging_sphere | 1000 | 16.2516 | 4.0313 | 3.9249 | -20.7389 | 34.91s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.9953 | 3.741 | 3.6457 | -17.7208 | 16.06s |\n", - "| UniversalKriging | 50 | 15.3625 | 3.9195 | 3.8741 | -19.5496 | 2.25s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:33:10\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6936 (±0.0202), Variance: 1.0223, Range: [-2.2170, 3.2393]\n", - "z mean: 4.7033 (±0.0600), Variance: 8.9851, Range: [-0.9603, 19.8240]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0201, nugget=7.9064\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 55.2465 | 7.4328 | 4.4244 | -49.8693 | 0.47s |\n", - "| OLS_sphere | 1000 | 17.0537 | 4.1296 | 4.0129 | -14.7025 | 0.15s |\n", - "| DeepKriging_wendland | -- | 14.502 | 3.8081 | 3.6565 | -12.353 | 48.94s |\n", - "| DeepKriging_sphere | 1000 | 16.1256 | 4.0157 | 3.8838 | -13.848 | 27.44s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.7571 | 3.4289 | 3.2923 | -9.8256 | 18.92s |\n", - "| UniversalKriging | 50 | 16.0785 | 4.0098 | 3.9858 | -13.8046 | 2.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:35:29\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4645 (±0.0130), Variance: 0.4230, Range: [-1.7744, 2.5085]\n", - "z mean: 4.4697 (±0.0597), Variance: 8.9170, Range: [-0.9365, 22.8619]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0085, nugget=8.7631\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.3055 | 4.3938 | 4.146 | -50.2581 | 0.45s |\n", - "| OLS_sphere | 1000 | 16.6224 | 4.0771 | 4.0312 | -43.1343 | 0.16s |\n", - "| DeepKriging_wendland | -- | 11.7937 | 3.4342 | 3.3145 | -30.3137 | 51.85s |\n", - "| DeepKriging_sphere | 1000 | 15.2871 | 3.9099 | 3.8411 | -39.589 | 26.75s |\n", - "| DeepKriging_sphere_Huber | 100 | 6.9234 | 2.6312 | 2.542 | -17.3824 | 19.71s |\n", - "| UniversalKriging | 50 | 16.8234 | 4.1016 | 4.0728 | -43.668 | 2.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:37:50\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6035 (±0.0159), Variance: 0.6314, Range: [-1.3893, 2.5395]\n", - "z mean: 4.6049 (±0.0582), Variance: 8.4708, Range: [-0.7954, 19.8666]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0073, nugget=7.7148\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.8436 | 4.4546 | 4.1669 | -27.9371 | -2.50s |\n", - "| OLS_sphere | 1000 | 16.8765 | 4.1081 | 4.0253 | -23.6103 | 0.16s |\n", - "| DeepKriging_wendland | -- | 17.1006 | 4.1353 | 3.9001 | -23.9371 | 49.86s |\n", - "| DeepKriging_sphere | 1000 | 15.7896 | 3.9736 | 3.8596 | -22.0254 | 17.98s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.5846 | 3.5475 | 3.4683 | -17.3516 | 18.39s |\n", - "| UniversalKriging | 50 | 15.9507 | 3.9938 | 3.9566 | -22.2603 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:40:02\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1021 (±0.0123), Variance: 0.3760, Range: [-2.0867, 1.6999]\n", - "z mean: 3.8276 (±0.0570), Variance: 8.1272, Range: [-1.6862, 20.3206]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0024, nugget=7.5686\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 31.8559 | 5.6441 | 4.1634 | -82.5808 | 0.45s |\n", - "| OLS_sphere | 1000 | 15.5216 | 3.9397 | 3.8903 | -39.7243 | 0.15s |\n", - "| DeepKriging_wendland | -- | 11.6719 | 3.4164 | 3.2976 | -29.6237 | 44.48s |\n", - "| DeepKriging_sphere | 1000 | 14.3709 | 3.7909 | 3.7346 | -36.7052 | 21.01s |\n", - "| DeepKriging_sphere_Huber | 100 | 9.671 | 3.1098 | 2.947 | -24.3739 | 22.54s |\n", - "| UniversalKriging | 50 | 15.4701 | 3.9332 | 3.8955 | -39.589 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:42:15\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -1.2765 (±0.0136), Variance: 0.4651, Range: [-3.1591, 0.9336]\n", - "z mean: 2.7647 (±0.0589), Variance: 8.6606, Range: [-2.7542, 19.8176]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0052, nugget=7.8491\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.0536 | 4.1296 | 3.9952 | -43.0791 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.1465 | 4.0183 | 3.9655 | -40.7344 | 0.16s |\n", - "| DeepKriging_wendland | -- | 18.0932 | 4.2536 | 4.0768 | -45.7662 | 56.71s |\n", - "| DeepKriging_sphere | 1000 | 17.2535 | 4.1537 | 4.0714 | -43.5959 | 31.38s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.4338 | 3.3814 | 3.3138 | -28.5535 | 17.65s |\n", - "| UniversalKriging | 50 | 16.4005 | 4.0498 | 4.0056 | -41.3911 | 2.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:44:47\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -1.2128 (±0.0178), Variance: 0.7898, Range: [-3.3871, 1.2491]\n", - "z mean: 2.7971 (±0.0574), Variance: 8.2510, Range: [-2.6208, 18.8568]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0066, nugget=7.3320\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.8842 | 4.229 | 4.086 | -27.1873 | 0.48s |\n", - "| OLS_sphere | 1000 | 17.3252 | 4.1624 | 4.0863 | -26.3064 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.4927 | 3.9361 | 3.8271 | -23.4182 | 49.74s |\n", - "| DeepKriging_sphere | 1000 | 16.7226 | 4.0893 | 4.0074 | -25.3565 | 32.51s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.1516 | 3.4859 | 3.3965 | -18.1523 | 16.94s |\n", - "| UniversalKriging | 50 | 16.7871 | 4.0972 | 4.0654 | -25.4582 | 1.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:47:13\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1299 (±0.0145), Variance: 0.5228, Range: [-2.1635, 2.1157]\n", - "z mean: 3.9330 (±0.0602), Variance: 9.0742, Range: [-1.3842, 19.5231]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0062, nugget=8.5419\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 73.0561 | 8.5473 | 5.0111 | -143.718 | 0.45s |\n", - "| OLS_sphere | 1000 | 17.1095 | 4.1364 | 4.0734 | -32.8924 | 0.14s |\n", - "| DeepKriging_wendland | -- | 18.6493 | 4.3185 | 4.0784 | -35.9426 | 53.06s |\n", - "| DeepKriging_sphere | 1000 | 14.7677 | 3.8429 | 3.7732 | -28.2535 | 20.93s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.8874 | 3.5899 | 3.5316 | -24.5289 | 20.96s |\n", - "| UniversalKriging | 50 | 18.0597 | 4.2497 | 4.2009 | -34.7747 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:49:30\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4129 (±0.0156), Variance: 0.6071, Range: [-2.5863, 2.4611]\n", - "z mean: 4.3984 (±0.0588), Variance: 8.6567, Range: [-1.7301, 22.2308]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0030, nugget=8.0635\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.8556 | 4.456 | 4.2392 | -27.824 | 0.45s |\n", - "| OLS_sphere | 1000 | 17.1496 | 4.1412 | 4.0771 | -23.8958 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.4312 | 3.9283 | 3.7128 | -21.4012 | 47.60s |\n", - "| DeepKriging_sphere | 1000 | 15.9334 | 3.9917 | 3.9134 | -22.1303 | 30.02s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.0851 | 3.4764 | 3.3835 | -16.5438 | 20.84s |\n", - "| UniversalKriging | 50 | 16.2798 | 4.0348 | 4.0022 | -22.6331 | 2.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:51:59\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.5168 (±0.0243), Variance: 1.4777, Range: [-3.9324, 2.6834]\n", - "z mean: 3.4981 (±0.0624), Variance: 9.7463, Range: [-2.6286, 21.6954]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0027, nugget=7.7484\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.3964 | 4.1709 | 3.9957 | -11.2491 | 0.46s |\n", - "| OLS_sphere | 1000 | 17.9358 | 4.2351 | 4.0681 | -11.6289 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.7968 | 3.9745 | 3.8176 | -10.1228 | 52.32s |\n", - "| DeepKriging_sphere | 1000 | 17.4511 | 4.1775 | 3.9836 | -11.2876 | 37.05s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.7183 | 3.2739 | 3.0394 | -6.5469 | 20.90s |\n", - "| UniversalKriging | 50 | 16.1995 | 4.0249 | 3.9787 | -10.4064 | 2.42s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:54:43\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0060 (±0.0167), Variance: 0.6939, Range: [-2.4920, 2.0258]\n", - "z mean: 3.9883 (±0.0583), Variance: 8.4968, Range: [-1.5497, 20.7472]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0170, nugget=7.6598\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.3741 | 4.2865 | 3.9811 | -25.2322 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.4878 | 4.0605 | 3.9831 | -22.5391 | 0.15s |\n", - "| DeepKriging_wendland | -- | 11.5208 | 3.3942 | 3.2177 | -15.448 | 48.06s |\n", - "| DeepKriging_sphere | 1000 | 14.7328 | 3.8383 | 3.7456 | -20.0336 | 26.40s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.3102 | 3.3631 | 3.2708 | -15.1472 | 16.38s |\n", - "| UniversalKriging | 50 | 16.278 | 4.0346 | 3.9985 | -22.2397 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:57:09\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1307 (±0.0108), Variance: 0.2908, Range: [-2.4790, 1.6362]\n", - "z mean: 3.8477 (±0.0581), Variance: 8.4455, Range: [-2.1148, 19.5700]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0072, nugget=7.6845\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 29.0579 | 5.3905 | 4.2921 | -115.057 | 0.51s |\n", - "| OLS_sphere | 1000 | 15.8612 | 3.9826 | 3.9561 | -62.3498 | 0.15s |\n", - "| DeepKriging_wendland | -- | 18.2624 | 4.2735 | 4.1312 | -71.9402 | 49.81s |\n", - "| DeepKriging_sphere | 1000 | 16.5672 | 4.0703 | 4.021 | -65.1695 | 17.49s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.394 | 3.6598 | 3.6211 | -52.4957 | 19.51s |\n", - "| UniversalKriging | 50 | 15.7931 | 3.9741 | 3.9429 | -62.0776 | 1.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 06:59:30\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3818 (±0.0160), Variance: 0.6408, Range: [-1.7810, 2.8363]\n", - "z mean: 4.3661 (±0.0572), Variance: 8.1893, Range: [-1.3270, 20.5659]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0046, nugget=7.3835\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 24.4141 | 4.9411 | 4.1563 | -36.1678 | 0.43s |\n", - "| OLS_sphere | 1000 | 16.728 | 4.09 | 4.0095 | -24.4666 | 0.19s |\n", - "| DeepKriging_wendland | -- | 13.8994 | 3.7282 | 3.6143 | -20.1603 | 48.60s |\n", - "| DeepKriging_sphere | 1000 | 15.6756 | 3.9592 | 3.8647 | -22.8644 | 29.16s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.7555 | 3.5715 | 3.4735 | -18.4188 | 18.85s |\n", - "| UniversalKriging | 50 | 16.1069 | 4.0133 | 3.9794 | -23.5209 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:02:02\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0475 (±0.0122), Variance: 0.3752, Range: [-1.7512, 1.6105]\n", - "z mean: 4.0021 (±0.0576), Variance: 8.2950, Range: [-1.2109, 21.8761]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0034, nugget=7.9909\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.2279 | 4.385 | 4.0857 | -55.7167 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.0362 | 4.0045 | 3.9623 | -46.3022 | 0.15s |\n", - "| DeepKriging_wendland | -- | 12.0613 | 3.4729 | 3.3411 | -34.5773 | 43.92s |\n", - "| DeepKriging_sphere | 1000 | 15.1049 | 3.8865 | 3.8266 | -43.5552 | 30.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 9.8493 | 3.1384 | 3.0883 | -28.0527 | 16.30s |\n", - "| UniversalKriging | 50 | 16.3463 | 4.0431 | 4.0094 | -47.2171 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:04:29\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0545 (±0.0132), Variance: 0.4332, Range: [-1.7565, 2.0484]\n", - "z mean: 3.8610 (±0.0561), Variance: 7.8812, Range: [-1.2070, 19.8319]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0083, nugget=7.6293\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.2057 | 4.2668 | 4.1006 | -44.9114 | 0.49s |\n", - "| OLS_sphere | 1000 | 16.8376 | 4.1034 | 4.056 | -41.4614 | 0.18s |\n", - "| DeepKriging_wendland | -- | 14.0546 | 3.749 | 3.5998 | -34.4432 | 47.09s |\n", - "| DeepKriging_sphere | 1000 | 14.6202 | 3.8236 | 3.7552 | -35.8695 | 23.43s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.3742 | 3.2209 | 3.1372 | -25.162 | 19.87s |\n", - "| UniversalKriging | 50 | 16.5335 | 4.0661 | 4.0282 | -40.6944 | 1.58s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:07:00\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3233 (±0.0161), Variance: 0.6502, Range: [-2.5779, 2.4986]\n", - "z mean: 4.2625 (±0.0589), Variance: 8.6805, Range: [-1.7338, 20.2588]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0028, nugget=7.7982\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.9106 | 4.2321 | 4.0411 | -29.743 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.2719 | 4.0338 | 3.967 | -26.9301 | 0.15s |\n", - "| DeepKriging_wendland | -- | 15.0849 | 3.8839 | 3.742 | -24.8927 | 51.29s |\n", - "| DeepKriging_sphere | 1000 | 14.9773 | 3.8701 | 3.7749 | -24.7081 | 20.54s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.7795 | 3.5748 | 3.4558 | -20.9356 | 20.12s |\n", - "| UniversalKriging | 50 | 16.1409 | 4.0176 | 3.9769 | -26.7053 | 2.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:09:31\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.8638 (±0.0152), Variance: 0.5748, Range: [-2.7245, 1.2572]\n", - "z mean: 3.1595 (±0.0585), Variance: 8.5558, Range: [-1.9660, 21.1827]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=4.6441, nugget=3.1923\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.6919 | 4.5488 | 4.2252 | -38.5427 | 0.50s |\n", - "| OLS_sphere | 1000 | 17.4752 | 4.1803 | 4.1202 | -32.3955 | 0.19s |\n", - "| DeepKriging_wendland | -- | 13.2312 | 3.6375 | 3.5398 | -24.2851 | 45.56s |\n", - "| DeepKriging_sphere | 1000 | 16.2774 | 4.0345 | 3.9504 | -30.1065 | 30.78s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.6403 | 3.6933 | 3.6247 | -25.0669 | 19.57s |\n", - "| UniversalKriging | 50 | 16.6074 | 4.0752 | 4.0415 | -30.7372 | 1.42s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:12:10\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1207 (±0.0149), Variance: 0.5523, Range: [-2.6453, 1.7831]\n", - "z mean: 3.7998 (±0.0575), Variance: 8.2581, Range: [-1.9007, 18.2624]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=7.7087\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.3737 | 5.232 | 4.2208 | -48.8145 | 0.46s |\n", - "| OLS_sphere | 1000 | 16.1876 | 4.0234 | 3.956 | -28.4581 | 0.14s |\n", - "| DeepKriging_wendland | -- | 14.3782 | 3.7919 | 3.6525 | -25.1655 | 50.73s |\n", - "| DeepKriging_sphere | 1000 | 15.4251 | 3.9275 | 3.8446 | -27.0704 | 28.93s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.8144 | 3.8489 | 3.7814 | -25.9591 | 16.97s |\n", - "| UniversalKriging | 50 | 15.6562 | 3.9568 | 3.9167 | -27.491 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:14:51\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 1.4770 (±0.0153), Variance: 0.5854, Range: [-1.0269, 3.8629]\n", - "z mean: 5.4104 (±0.0581), Variance: 8.4275, Range: [-0.1924, 23.4917]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=2.2779, nugget=5.1166\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.7475 | 5.2676 | 4.1304 | -48.7167 | 0.44s |\n", - "| OLS_sphere | 1000 | 15.5537 | 3.9438 | 3.8815 | -26.8685 | 0.16s |\n", - "| DeepKriging_wendland | -- | 15.1128 | 3.8875 | 3.7486 | -26.0784 | 47.79s |\n", - "| DeepKriging_sphere | 1000 | 14.6888 | 3.8326 | 3.7487 | -25.3187 | 28.53s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.8812 | 3.4469 | 3.3686 | -20.2882 | 15.91s |\n", - "| UniversalKriging | 50 | 15.3416 | 3.9168 | 3.8755 | -26.4883 | 4.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:17:29\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1511 (±0.0167), Variance: 0.7013, Range: [-2.1626, 2.4590]\n", - "z mean: 4.1257 (±0.0587), Variance: 8.6114, Range: [-1.2625, 18.0419]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0031, nugget=7.6865\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.8323 | 4.1027 | 3.9428 | -19.5606 | 0.48s |\n", - "| OLS_sphere | 1000 | 16.0303 | 4.0038 | 3.9003 | -18.581 | 0.15s |\n", - "| DeepKriging_wendland | -- | 13.8632 | 3.7233 | 3.5628 | -15.9338 | 47.73s |\n", - "| DeepKriging_sphere | 1000 | 16.3428 | 4.0426 | 3.9345 | -18.9627 | 29.56s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.1072 | 3.6204 | 3.5276 | -15.0104 | 21.41s |\n", - "| UniversalKriging | 50 | 16.0952 | 4.0119 | 3.9829 | -18.6602 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:20:08\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4343 (±0.0179), Variance: 0.8000, Range: [-2.1594, 3.1558]\n", - "z mean: 4.3961 (±0.0591), Variance: 8.7326, Range: [-1.2143, 27.0775]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0037, nugget=7.8696\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.5209 | 5.246 | 4.2808 | -30.6907 | 0.46s |\n", - "| OLS_sphere | 1000 | 17.0402 | 4.128 | 4.0214 | -18.622 | 0.15s |\n", - "| DeepKriging_wendland | -- | 15.0028 | 3.8733 | 3.7426 | -16.2759 | 56.02s |\n", - "| DeepKriging_sphere | 1000 | 16.0018 | 4.0002 | 3.8884 | -17.4263 | 37.43s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.783 | 3.5753 | 3.4576 | -13.7198 | 22.69s |\n", - "| UniversalKriging | 50 | 16.143 | 4.0178 | 3.9814 | -17.5889 | 2.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:23:08\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 1.4241 (±0.0209), Variance: 1.0946, Range: [-1.0545, 4.1870]\n", - "z mean: 5.3536 (±0.0598), Variance: 8.9305, Range: [-0.1046, 22.4959]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0050, nugget=7.7888\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 30.8013 | 5.5499 | 4.3079 | -29.5528 | 0.40s |\n", - "| OLS_sphere | 1000 | 15.4297 | 3.9281 | 3.8183 | -14.3052 | 0.13s |\n", - "| DeepKriging_wendland | -- | 14.6217 | 3.8238 | 3.6674 | -13.5037 | 68.47s |\n", - "| DeepKriging_sphere | 1000 | 17.1187 | 4.1375 | 3.985 | -15.9806 | 41.56s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.7262 | 3.4244 | 3.2649 | -10.6316 | 22.06s |\n", - "| UniversalKriging | 50 | 15.4766 | 3.934 | 3.9005 | -14.3517 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:26:25\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1618 (±0.0173), Variance: 0.7457, Range: [-2.4104, 2.3521]\n", - "z mean: 4.1046 (±0.0579), Variance: 8.3747, Range: [-1.5669, 21.5323]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0129, nugget=7.7700\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 22.323 | 4.7247 | 4.1149 | -29.3723 | 0.48s |\n", - "| OLS_sphere | 1000 | 16.2596 | 4.0323 | 3.9392 | -21.1226 | 0.13s |\n", - "| DeepKriging_wendland | -- | 12.8978 | 3.5914 | 3.4502 | -16.5486 | 51.96s |\n", - "| DeepKriging_sphere | 1000 | 14.5589 | 3.8156 | 3.6868 | -18.8086 | 19.62s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.3272 | 3.3656 | 3.258 | -14.4116 | 20.64s |\n", - "| UniversalKriging | 50 | 16.3461 | 4.043 | 4.0086 | -21.2402 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:29:06\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.9576 (±0.0177), Variance: 0.7818, Range: [-3.0588, 1.2655]\n", - "z mean: 3.0732 (±0.0587), Variance: 8.6183, Range: [-2.6908, 24.4282]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0131, nugget=7.9402\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.4256 | 4.2925 | 4.0663 | -23.3164 | 0.39s |\n", - "| OLS_sphere | 1000 | 16.4522 | 4.0561 | 3.9961 | -20.7122 | 0.15s |\n", - "| DeepKriging_wendland | -- | 15.4516 | 3.9308 | 3.8183 | -19.3916 | 46.12s |\n", - "| DeepKriging_sphere | 1000 | 15.5965 | 3.9492 | 3.8579 | -19.5828 | 35.56s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.0878 | 3.1761 | 3.04 | -12.313 | 19.49s |\n", - "| UniversalKriging | 50 | 16.7885 | 4.0974 | 4.0634 | -21.156 | 1.77s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:31:56\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.9836 (±0.0149), Variance: 0.5518, Range: [-0.9148, 3.1304]\n", - "z mean: 5.0456 (±0.0617), Variance: 9.5255, Range: [-0.2223, 23.1366]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0062, nugget=8.8734\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.7683 | 4.4462 | 4.251 | -35.4114 | 0.43s |\n", - "| OLS_sphere | 1000 | 18.0097 | 4.2438 | 4.1863 | -32.1722 | 0.13s |\n", - "| DeepKriging_wendland | -- | 15.1648 | 3.8942 | 3.7269 | -26.9321 | 51.62s |\n", - "| DeepKriging_sphere | 1000 | 17.5537 | 4.1897 | 4.0861 | -31.3324 | 20.57s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.1054 | 3.6201 | 3.5424 | -23.139 | 20.77s |\n", - "| UniversalKriging | 50 | 17.043 | 4.1283 | 4.095 | -30.3918 | 1.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:34:39\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.4921 (±0.0124), Variance: 0.3849, Range: [-2.1484, 1.5820]\n", - "z mean: 3.5256 (±0.0574), Variance: 8.2509, Range: [-1.5433, 17.6797]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0042, nugget=7.6430\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.7855 | 4.4481 | 4.2123 | -49.928 | 0.42s |\n", - "| OLS_sphere | 1000 | 17.3387 | 4.164 | 4.1173 | -43.63 | 0.12s |\n", - "| DeepKriging_wendland | -- | 15.7715 | 3.9713 | 3.8043 | -39.5959 | 48.67s |\n", - "| DeepKriging_sphere | 1000 | 14.5734 | 3.8175 | 3.7578 | -36.5121 | 19.58s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.7603 | 3.5722 | 3.5204 | -31.8452 | 18.12s |\n", - "| UniversalKriging | 50 | 17.1647 | 4.143 | 4.1086 | -43.1821 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:37:17\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6283 (±0.0160), Variance: 0.6397, Range: [-2.1169, 2.5154]\n", - "z mean: 4.6637 (±0.0586), Variance: 8.5825, Range: [-1.4763, 19.5045]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0031, nugget=7.9977\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.9718 | 4.2393 | 4.0793 | -30.22 | 0.42s |\n", - "| OLS_sphere | 1000 | 17.3031 | 4.1597 | 4.0899 | -29.0584 | 0.14s |\n", - "| DeepKriging_wendland | -- | 17.3887 | 4.17 | 4.0339 | -29.2071 | 48.16s |\n", - "| DeepKriging_sphere | 1000 | 16.2295 | 4.0286 | 3.9463 | -27.1934 | 23.40s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.5031 | 3.536 | 3.456 | -20.7199 | 18.49s |\n", - "| UniversalKriging | 50 | 16.7153 | 4.0884 | 4.0553 | -28.0372 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:39:59\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.7788 (±0.0129), Variance: 0.4159, Range: [-2.4667, 1.3199]\n", - "z mean: 3.1493 (±0.0565), Variance: 7.9869, Range: [-1.6938, 19.9869]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0023, nugget=7.2940\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.3035 | 4.0378 | 3.9322 | -40.7426 | 0.41s |\n", - "| OLS_sphere | 1000 | 15.99 | 3.9988 | 3.9469 | -39.9401 | 0.12s |\n", - "| DeepKriging_wendland | -- | 14.4429 | 3.8004 | 3.7272 | -35.9789 | 47.21s |\n", - "| DeepKriging_sphere | 1000 | 15.1878 | 3.8972 | 3.823 | -37.8862 | 23.62s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.2948 | 3.3608 | 3.2549 | -27.9186 | 20.12s |\n", - "| UniversalKriging | 50 | 15.4421 | 3.9296 | 3.8868 | -38.5374 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:42:43\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1179 (±0.0137), Variance: 0.4725, Range: [-1.8417, 1.8152]\n", - "z mean: 3.8446 (±0.0570), Variance: 8.1298, Range: [-1.2989, 20.7811]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0085, nugget=7.8513\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.9215 | 4.2334 | 4.1264 | -37.0643 | 0.42s |\n", - "| OLS_sphere | 1000 | 16.3981 | 4.0495 | 3.9941 | -33.8287 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.2714 | 3.9079 | 3.7283 | -31.4357 | 48.62s |\n", - "| DeepKriging_sphere | 1000 | 14.9859 | 3.8712 | 3.7997 | -30.8292 | 38.71s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.5311 | 3.6785 | 3.6172 | -27.7394 | 18.96s |\n", - "| UniversalKriging | 50 | 16.1251 | 4.0156 | 3.9842 | -33.2489 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:45:43\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0775 (±0.0135), Variance: 0.4550, Range: [-2.2229, 2.6000]\n", - "z mean: 4.0733 (±0.0591), Variance: 8.7227, Range: [-1.3766, 22.3182]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=8.0091\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 43.5522 | 6.5994 | 4.608 | -95.2871 | 0.41s |\n", - "| OLS_sphere | 1000 | 16.6123 | 4.0758 | 4.0257 | -35.7271 | 0.14s |\n", - "| DeepKriging_wendland | -- | 15.416 | 3.9263 | 3.814 | -33.0823 | 42.31s |\n", - "| DeepKriging_sphere | 1000 | 14.8777 | 3.8572 | 3.7959 | -31.8923 | 23.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.1928 | 3.3456 | 3.2849 | -23.7455 | 16.53s |\n", - "| UniversalKriging | 50 | 16.5519 | 4.0684 | 4.0296 | -35.5937 | 2.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:48:21\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -1.2417 (±0.0146), Variance: 0.5354, Range: [-3.1620, 0.6874]\n", - "z mean: 2.7136 (±0.0566), Variance: 8.0135, Range: [-2.4933, 17.5835]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0033, nugget=7.1047\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.3868 | 4.0481 | 3.916 | -32.2819 | 0.44s |\n", - "| OLS_sphere | 1000 | 15.3468 | 3.9175 | 3.8479 | -30.1696 | 0.13s |\n", - "| DeepKriging_wendland | -- | 11.6878 | 3.4187 | 3.2856 | -22.7382 | 45.31s |\n", - "| DeepKriging_sphere | 1000 | 15.8764 | 3.9845 | 3.9044 | -31.2452 | 15.64s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.477 | 3.2368 | 3.0823 | -20.279 | 19.62s |\n", - "| UniversalKriging | 50 | 15.2731 | 3.9081 | 3.8807 | -30.02 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:50:59\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.5560 (±0.0139), Variance: 0.4836, Range: [-1.2917, 2.6186]\n", - "z mean: 4.6677 (±0.0609), Variance: 9.2851, Range: [-0.7600, 25.5301]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=6.3255, nugget=2.3532\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.9983 | 4.3587 | 4.2238 | -38.4672 | 0.40s |\n", - "| OLS_sphere | 1000 | 16.9221 | 4.1137 | 4.0646 | -34.1541 | 0.14s |\n", - "| DeepKriging_wendland | -- | 16.9618 | 4.1185 | 3.9289 | -34.2365 | 45.35s |\n", - "| DeepKriging_sphere | 1000 | 16.8806 | 4.1086 | 4.0452 | -34.0678 | 31.34s |\n", - "| DeepKriging_sphere_Huber | 100 | 15.2989 | 3.9114 | 3.8265 | -30.7819 | 20.26s |\n", - "| UniversalKriging | 50 | 17.4254 | 4.1744 | 4.1241 | -35.1995 | 4.54s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:53:56\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3916 (±0.0284), Variance: 2.0126, Range: [-2.6386, 3.3500]\n", - "z mean: 4.3996 (±0.0625), Variance: 9.7533, Range: [-1.6140, 21.7407]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0081, nugget=7.1928\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 17.869 | 4.2272 | 3.9777 | -8.2714 | 0.40s |\n", - "| OLS_sphere | 1000 | 18.521 | 4.3036 | 4.0773 | -8.6097 | 0.18s |\n", - "| DeepKriging_wendland | -- | 13.9213 | 3.7311 | 3.4653 | -6.2231 | 44.86s |\n", - "| DeepKriging_sphere | 1000 | 16.5297 | 4.0657 | 3.8303 | -7.5765 | 28.65s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.0334 | 3.6102 | 3.3783 | -5.7624 | 22.44s |\n", - "| UniversalKriging | 50 | 16.7017 | 4.0868 | 4.045 | -7.6657 | 1.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:56:51\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.2252 (±0.0150), Variance: 0.5606, Range: [-1.8222, 2.1241]\n", - "z mean: 4.2289 (±0.0576), Variance: 8.3039, Range: [-0.9280, 25.2394]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0146, nugget=7.8090\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.7219 | 4.3269 | 4.1435 | -32.8705 | 0.41s |\n", - "| OLS_sphere | 1000 | 16.5858 | 4.0726 | 4.003 | -29.006 | 0.13s |\n", - "| DeepKriging_wendland | -- | 15.4957 | 3.9365 | 3.7137 | -27.0338 | 43.77s |\n", - "| DeepKriging_sphere | 1000 | 16.0987 | 4.0123 | 3.9147 | -28.1247 | 28.58s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.8969 | 3.4492 | 3.3699 | -20.5231 | 16.27s |\n", - "| UniversalKriging | 50 | 16.3457 | 4.043 | 3.9997 | -28.5716 | 1.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 07:59:40\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0338 (±0.0160), Variance: 0.6397, Range: [-2.1071, 2.3082]\n", - "z mean: 4.0492 (±0.0578), Variance: 8.3662, Range: [-1.5938, 20.1896]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0099, nugget=7.7033\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.1905 | 4.3807 | 4.1212 | -28.2341 | 0.43s |\n", - "| OLS_sphere | 1000 | 17.0173 | 4.1252 | 4.0459 | -24.9235 | 0.14s |\n", - "| DeepKriging_wendland | -- | 16.6329 | 4.0784 | 3.9543 | -24.3381 | 41.69s |\n", - "| DeepKriging_sphere | 1000 | 15.3745 | 3.921 | 3.8302 | -22.421 | 23.04s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.3044 | 3.5078 | 3.4178 | -17.7441 | 16.49s |\n", - "| UniversalKriging | 50 | 17.0357 | 4.1274 | 4.0894 | -24.9516 | 2.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:02:24\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.2168 (±0.0185), Variance: 0.8551, Range: [-2.9964, 2.0003]\n", - "z mean: 3.8633 (±0.0590), Variance: 8.6888, Range: [-2.1884, 17.2623]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0062, nugget=8.0829\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.769 | 4.4462 | 4.2175 | -20.648 | 0.40s |\n", - "| OLS_sphere | 1000 | 17.9606 | 4.238 | 4.1288 | -18.6678 | 0.14s |\n", - "| DeepKriging_wendland | -- | 14.0718 | 3.7512 | 3.5932 | -14.4094 | 42.84s |\n", - "| DeepKriging_sphere | 1000 | 15.8609 | 3.9826 | 3.8523 | -16.3685 | 23.21s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.439 | 3.6659 | 3.5639 | -13.7164 | 18.89s |\n", - "| UniversalKriging | 50 | 17.7479 | 4.2128 | 4.1812 | -18.4349 | 2.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:05:12\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.4744 (±0.0170), Variance: 0.7190, Range: [-2.7720, 2.1511]\n", - "z mean: 3.5523 (±0.0586), Variance: 8.5832, Range: [-1.9014, 19.6803]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=7.9623\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.8866 | 4.3459 | 4.1746 | -25.808 | 0.42s |\n", - "| OLS_sphere | 1000 | 17.2962 | 4.1589 | 4.0772 | -23.5505 | 0.14s |\n", - "| DeepKriging_wendland | -- | 16.035 | 4.0044 | 3.8034 | -21.7604 | 42.98s |\n", - "| DeepKriging_sphere | 1000 | 16.4652 | 4.0577 | 3.9632 | -22.371 | 24.51s |\n", - "| DeepKriging_sphere_Huber | 100 | 15.5446 | 3.9427 | 3.634 | -21.0643 | 20.57s |\n", - "| UniversalKriging | 50 | 16.4011 | 4.0498 | 4.0126 | -22.2801 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:08:05\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.7933 (±0.0192), Variance: 0.9255, Range: [-1.3148, 3.7771]\n", - "z mean: 4.6501 (±0.0591), Variance: 8.7178, Range: [-0.5747, 25.1168]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0163, nugget=7.5213\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 57.3167 | 7.5708 | 4.3373 | -55.3515 | 0.42s |\n", - "| OLS_sphere | 1000 | 15.0002 | 3.873 | 3.7567 | -13.7476 | 0.15s |\n", - "| DeepKriging_wendland | -- | 13.8889 | 3.7268 | 3.5597 | -12.655 | 43.73s |\n", - "| DeepKriging_sphere | 1000 | 14.7931 | 3.8462 | 3.7187 | -13.544 | 23.70s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.4435 | 3.6665 | 3.5319 | -12.2171 | 22.35s |\n", - "| UniversalKriging | 50 | 14.7547 | 3.8412 | 3.8044 | -13.5063 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:11:00\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6928 (±0.0135), Variance: 0.4584, Range: [-1.3159, 2.6472]\n", - "z mean: 4.6661 (±0.0558), Variance: 7.7827, Range: [-0.5365, 22.2404]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=7.1672\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 48.2397 | 6.9455 | 4.4807 | -104.454 | 0.42s |\n", - "| OLS_sphere | 1000 | 16.0715 | 4.0089 | 3.9533 | -34.133 | 0.18s |\n", - "| DeepKriging_wendland | -- | 14.2544 | 3.7755 | 3.6598 | -30.1609 | 51.03s |\n", - "| DeepKriging_sphere | 1000 | 12.938 | 3.5969 | 3.5108 | -27.2831 | 21.36s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.4369 | 3.5266 | 3.4532 | -26.1877 | 21.12s |\n", - "| UniversalKriging | 50 | 16.5409 | 4.0671 | 4.0301 | -35.1592 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:14:01\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.7972 (±0.0134), Variance: 0.4499, Range: [-1.9429, 2.5671]\n", - "z mean: 4.8055 (±0.0570), Variance: 8.1233, Range: [-0.6400, 18.1973]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0086, nugget=7.6359\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.0571 | 4.0071 | 3.8944 | -39.3907 | 0.39s |\n", - "| OLS_sphere | 1000 | 15.8593 | 3.9824 | 3.9377 | -38.8931 | 0.14s |\n", - "| DeepKriging_wendland | -- | 16.4888 | 4.0606 | 3.9248 | -40.4767 | 42.63s |\n", - "| DeepKriging_sphere | 1000 | 15.7006 | 3.9624 | 3.8783 | -38.494 | 29.67s |\n", - "| DeepKriging_sphere_Huber | 100 | 9.0007 | 3.0001 | 2.9293 | -21.6406 | 18.43s |\n", - "| UniversalKriging | 50 | 15.9429 | 3.9929 | 3.9506 | -39.1034 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:16:59\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.3476 (±0.0123), Variance: 0.3760, Range: [-1.9972, 1.2591]\n", - "z mean: 3.6464 (±0.0570), Variance: 8.1210, Range: [-1.3222, 20.5641]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0072, nugget=7.6537\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.897 | 4.2305 | 4.062 | -41.8314 | 0.43s |\n", - "| OLS_sphere | 1000 | 16.6568 | 4.0813 | 4.0329 | -38.8633 | 0.14s |\n", - "| DeepKriging_wendland | -- | 14.2512 | 3.7751 | 3.6419 | -33.1063 | 48.14s |\n", - "| DeepKriging_sphere | 1000 | 16.4438 | 4.0551 | 3.9675 | -38.3536 | 40.34s |\n", - "| DeepKriging_sphere_Huber | 100 | 9.4945 | 3.0813 | 2.9884 | -21.7225 | 18.85s |\n", - "| UniversalKriging | 50 | 16.3328 | 4.0414 | 4.0016 | -38.088 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:20:15\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -1.2280 (±0.0149), Variance: 0.5515, Range: [-3.2558, 0.6863]\n", - "z mean: 2.7763 (±0.0597), Variance: 8.9113, Range: [-2.5429, 22.8765]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0023, nugget=8.4783\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.4301 | 4.1749 | 4.0477 | -31.7765 | 0.41s |\n", - "| OLS_sphere | 1000 | 17.3175 | 4.1614 | 4.1037 | -31.5648 | 0.14s |\n", - "| DeepKriging_wendland | -- | 12.9655 | 3.6008 | 3.5049 | -23.3811 | 41.61s |\n", - "| DeepKriging_sphere | 1000 | 16.843 | 4.104 | 4.0284 | -30.6725 | 25.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.3915 | 3.3751 | 3.3056 | -20.4212 | 15.13s |\n", - "| UniversalKriging | 50 | 17.1621 | 4.1427 | 4.0999 | -31.2726 | 2.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:23:08\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018483, - "end_time": "2026-01-20T22:00:41.628194", - "exception": false, - "start_time": "2026-01-20T22:00:41.609711", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:00:41.669873Z", - "iopub.status.busy": "2026-01-20T22:00:41.668969Z", - "iopub.status.idle": "2026-01-20T22:00:41.701638Z", - "shell.execute_reply": "2026-01-20T22:00:41.698221Z" - }, - "papermill": { - "duration": 0.057349, - "end_time": "2026-01-20T22:00:41.703973", - "exception": false, - "start_time": "2026-01-20T22:00:41.646624", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1000, 'DeepKriging_sphere': 1000, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-------------|-----------|-----------|--------------|\n", - "| OLS_wendland | 25.42±13.74 | 4.91±1.13 | 4.18±0.20 | -43.55±26.60 |\n", - "| OLS_sphere | 16.65±0.73 | 4.08±0.09 | 4.00±0.09 | -28.96±11.26 |\n", - "| DeepKriging_wendland | 14.87±1.89 | 3.85±0.24 | 3.70±0.24 | -26.01±11.63 |\n", - "| DeepKriging_sphere | 15.66±1.03 | 3.96±0.13 | 3.86±0.12 | -27.12±10.67 |\n", - "| DeepKriging_sphere_Huber | 12.27±1.86 | 3.49±0.27 | 3.39±0.28 | -21.01±9.00 |\n", - "| UniversalKriging | 16.38±0.65 | 4.05±0.08 | 4.01±0.08 | -28.62±11.41 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 3.49±0.27)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 7331.835757, - "end_time": "2026-01-20T22:00:45.313086", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-20T19:58:33.477329", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_tdf4.ipynb b/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_tdf4.ipynb deleted file mode 100644 index 698b173..0000000 --- a/notebook/simulation/ScenarioI/simulation_euclidean_GP_noise_tdf4.ipynb +++ /dev/null @@ -1,4448 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.01667, - "end_time": "2026-01-20T19:58:34.388909", - "exception": false, - "start_time": "2026-01-20T19:58:34.372239", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:34.409580Z", - "iopub.status.busy": "2026-01-20T19:58:34.408386Z", - "iopub.status.idle": "2026-01-20T19:58:34.434426Z", - "shell.execute_reply": "2026-01-20T19:58:34.430157Z" - }, - "papermill": { - "duration": 0.041003, - "end_time": "2026-01-20T19:58:34.438451", - "exception": false, - "start_time": "2026-01-20T19:58:34.397448", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:34.464590Z", - "iopub.status.busy": "2026-01-20T19:58:34.463625Z", - "iopub.status.idle": "2026-01-20T19:58:37.512823Z", - "shell.execute_reply": "2026-01-20T19:58:37.508886Z" - }, - "papermill": { - "duration": 3.066497, - "end_time": "2026-01-20T19:58:37.516054", - "exception": false, - "start_time": "2026-01-20T19:58:34.449557", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768939115.537227 2668203 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768939115.541614 2668203 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768939115.554041 2668203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768939115.554062 2668203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768939115.554063 2668203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768939115.554065 2668203 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.013361, - "end_time": "2026-01-20T19:58:37.543964", - "exception": false, - "start_time": "2026-01-20T19:58:37.530603", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:37.564712Z", - "iopub.status.busy": "2026-01-20T19:58:37.563377Z", - "iopub.status.idle": "2026-01-20T19:58:39.165091Z", - "shell.execute_reply": "2026-01-20T19:58:39.161165Z" - }, - "papermill": { - "duration": 1.615094, - "end_time": "2026-01-20T19:58:39.168528", - "exception": false, - "start_time": "2026-01-20T19:58:37.553434", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011991, - "end_time": "2026-01-20T19:58:39.194027", - "exception": false, - "start_time": "2026-01-20T19:58:39.182036", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:39.212060Z", - "iopub.status.busy": "2026-01-20T19:58:39.211074Z", - "iopub.status.idle": "2026-01-20T19:58:39.224300Z", - "shell.execute_reply": "2026-01-20T19:58:39.220662Z" - }, - "papermill": { - "duration": 0.026147, - "end_time": "2026-01-20T19:58:39.227787", - "exception": false, - "start_time": "2026-01-20T19:58:39.201640", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006099, - "end_time": "2026-01-20T19:58:39.242620", - "exception": false, - "start_time": "2026-01-20T19:58:39.236521", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:39.255962Z", - "iopub.status.busy": "2026-01-20T19:58:39.255006Z", - "iopub.status.idle": "2026-01-20T19:58:51.611428Z", - "shell.execute_reply": "2026-01-20T19:58:51.608119Z" - }, - "papermill": { - "duration": 12.367483, - "end_time": "2026-01-20T19:58:51.614982", - "exception": false, - "start_time": "2026-01-20T19:58:39.247499", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.011633, - "end_time": "2026-01-20T19:58:51.640454", - "exception": false, - "start_time": "2026-01-20T19:58:51.628821", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.658922Z", - "iopub.status.busy": "2026-01-20T19:58:51.657553Z", - "iopub.status.idle": "2026-01-20T19:58:51.687363Z", - "shell.execute_reply": "2026-01-20T19:58:51.683511Z" - }, - "papermill": { - "duration": 0.042311, - "end_time": "2026-01-20T19:58:51.690376", - "exception": false, - "start_time": "2026-01-20T19:58:51.648065", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = (L @ z_std).astype(np.float32)\n", - " z = y + (np.sqrt(2) * rng.standard_t(df=4, size=num_sample)).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.00693, - "end_time": "2026-01-20T19:58:51.708375", - "exception": false, - "start_time": "2026-01-20T19:58:51.701445", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.722171Z", - "iopub.status.busy": "2026-01-20T19:58:51.721604Z", - "iopub.status.idle": "2026-01-20T19:58:51.784510Z", - "shell.execute_reply": "2026-01-20T19:58:51.780811Z" - }, - "papermill": { - "duration": 0.073804, - "end_time": "2026-01-20T19:58:51.787682", - "exception": false, - "start_time": "2026-01-20T19:58:51.713878", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.805166Z", - "iopub.status.busy": "2026-01-20T19:58:51.804407Z", - "iopub.status.idle": "2026-01-20T19:58:51.853452Z", - "shell.execute_reply": "2026-01-20T19:58:51.849707Z" - }, - "papermill": { - "duration": 0.06093, - "end_time": "2026-01-20T19:58:51.856766", - "exception": false, - "start_time": "2026-01-20T19:58:51.795836", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + noise\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + noise\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.009385, - "end_time": "2026-01-20T19:58:51.879577", - "exception": false, - "start_time": "2026-01-20T19:58:51.870192", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.896847Z", - "iopub.status.busy": "2026-01-20T19:58:51.895902Z", - "iopub.status.idle": "2026-01-20T19:58:51.912081Z", - "shell.execute_reply": "2026-01-20T19:58:51.907513Z" - }, - "papermill": { - "duration": 0.029106, - "end_time": "2026-01-20T19:58:51.915477", - "exception": false, - "start_time": "2026-01-20T19:58:51.886371", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:51.936586Z", - "iopub.status.busy": "2026-01-20T19:58:51.935735Z", - "iopub.status.idle": "2026-01-20T22:00:41.558573Z", - "shell.execute_reply": "2026-01-20T22:00:41.555293Z" - }, - "papermill": { - "duration": 7309.636424, - "end_time": "2026-01-20T22:00:41.561261", - "exception": false, - "start_time": "2026-01-20T19:58:51.924837", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3499 (±0.0202), Variance: 1.0165, Range: [-1.8179, 3.2190]\n", - "z mean: 0.3578 (±0.0445), Variance: 4.9567, Range: [-11.4442, 13.6591]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2275 0.1672 *\n", - " 100 0.2718 0.2550 \n", - " 200 0.4444 0.4390 \n", - " 500 1.3491 1.5014 \n", - " 700 2.4265 2.5000 \n", - " 1000 1.0031 1.0558 \n", - " 1400 1.0031 1.0558 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768939149.440506 2668203 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768939151.729417 2668366 service.cc:152] XLA service 0x5fd4901c7ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768939151.729495 2668366 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768939152.095319 2668366 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768939163.327202 2668366 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78d6a0557f40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78d68be9af80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.8311 0.7312 \n", - " 100 5.9625 0.8422 \n", - " 200 6.0003 0.9204 \n", - " 500 5.7190 0.7285 *\n", - " 700 5.8185 0.6975 \n", - " 1000 6.0263 1.0911 \n", - " 1400 6.0511 1.0600 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.6544 0.7669 \n", - " 100 1.6426 0.7032 *\n", - " 200 1.6849 0.8736 \n", - " 500 1.6800 0.8374 \n", - " 700 1.6630 0.8010 \n", - " 1000 1.7078 1.0173 \n", - " 1400 1.6979 1.0387 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0024, nugget=3.9339\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0011, nugget=3.8144\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=3.5911\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0045, sigma²=0.0000, nugget=2.9601\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0047, sigma²=0.0000, nugget=2.6207\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0056, sigma²=0.0000, nugget=1.9841\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0031, sigma²=0.0000, nugget=1.2210\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.1391 0.1672 *\n", - " 100 5.2738 0.2550 \n", - " 200 5.7269 0.4390 \n", - " 500 6.4409 1.5014 \n", - " 700 7.5721 2.5000 \n", - " 1000 12.4924 6.4661 \n", - " 1400 284.1398 279.7106 \n", - " Best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0024, nugget=3.9339\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.7852 | 1.6689 | 0.7428 | -1.6156 | 0.43s |\n", - "| OLS_sphere | 50 | 0.1672 | 0.409 | 0.3258 | 0.8429 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7707 | 0.8779 | 0.6678 | 0.2763 | 47.38s |\n", - "| DeepKriging_sphere | 500 | 0.8264 | 0.9091 | 0.7203 | 0.2239 | 16.55s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8395 | 0.9162 | 0.7004 | 0.2116 | 14.69s |\n", - "| UniversalKriging | 50 | 0.1672 | 0.409 | 0.3258 | 0.8429 | 2.26s |\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:06:51\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4176 (±0.0149), Variance: 0.5573, Range: [-1.4140, 2.4415]\n", - "z mean: 0.4325 (±0.0423), Variance: 4.4703, Range: [-17.4320, 11.4517]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=3.9876, nugget=0.0303\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.248 | 1.1172 | 0.7896 | -1.3768 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1927 | 0.439 | 0.3552 | 0.633 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.5212 | 0.7219 | 0.5923 | 0.0074 | 44.14s |\n", - "| DeepKriging_sphere | 500 | 0.4293 | 0.6552 | 0.5404 | 0.1825 | 11.87s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5122 | 0.7157 | 0.593 | 0.0245 | 17.77s |\n", - "| UniversalKriging | 50 | 0.1909 | 0.4369 | 0.3539 | 0.6365 | 4.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:08:38\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1219 (±0.0114), Variance: 0.3252, Range: [-1.8046, 1.6246]\n", - "z mean: -0.0976 (±0.0415), Variance: 4.3108, Range: [-14.5373, 12.5549]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0052, nugget=4.1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9221 | 1.3864 | 0.6394 | -5.2798 | 0.57s |\n", - "| OLS_sphere | 50 | 0.185 | 0.4302 | 0.3454 | 0.3955 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3677 | 0.6064 | 0.4562 | -0.2015 | 38.24s |\n", - "| DeepKriging_sphere | 500 | 0.3556 | 0.5963 | 0.4813 | -0.1617 | 16.44s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2799 | 0.529 | 0.4259 | 0.0856 | 14.51s |\n", - "| UniversalKriging | 50 | 0.185 | 0.4302 | 0.3453 | 0.3954 | 1.69s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:10:19\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0497 (±0.0198), Variance: 0.9846, Range: [-3.1433, 2.5210]\n", - "z mean: -0.0085 (±0.0459), Variance: 5.2733, Range: [-23.7053, 18.9216]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0073, nugget=4.3375\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 9.8174 | 3.1333 | 1.1402 | -10.1538 | 0.41s |\n", - "| OLS_sphere | 50 | 0.1925 | 0.4388 | 0.3502 | 0.7813 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8115 | 0.9008 | 0.6578 | 0.078 | 36.36s |\n", - "| DeepKriging_sphere | 500 | 0.6677 | 0.8171 | 0.615 | 0.2414 | 12.57s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.542 | 0.7362 | 0.5448 | 0.3842 | 14.74s |\n", - "| UniversalKriging | 50 | 0.1925 | 0.4387 | 0.3501 | 0.7813 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:11:56\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4890 (±0.0182), Variance: 0.8305, Range: [-2.5579, 2.8594]\n", - "z mean: 0.4524 (±0.0434), Variance: 4.7060, Range: [-11.6991, 15.3066]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0084, nugget=3.5919\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 34.6079 | 5.8829 | 1.1766 | -42.6577 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1621 | 0.4026 | 0.3227 | 0.7955 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8467 | 0.9202 | 0.7133 | -0.0681 | 35.78s |\n", - "| DeepKriging_sphere | 500 | 0.6734 | 0.8206 | 0.6366 | 0.1505 | 15.10s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.544 | 0.7375 | 0.5811 | 0.3138 | 15.19s |\n", - "| UniversalKriging | 50 | 0.1619 | 0.4023 | 0.3225 | 0.7958 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:13:43\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0289 (±0.0156), Variance: 0.6105, Range: [-1.7509, 2.2876]\n", - "z mean: -0.0213 (±0.0407), Variance: 4.1489, Range: [-9.5350, 18.7494]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0018, nugget=3.4110\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.4361 | 5.0434 | 0.9636 | -48.459 | 0.42s |\n", - "| OLS_sphere | 50 | 0.174 | 0.4171 | 0.3352 | 0.6617 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.466 | 0.6826 | 0.5447 | 0.0939 | 39.16s |\n", - "| DeepKriging_sphere | 500 | 0.4518 | 0.6722 | 0.5379 | 0.1214 | 11.76s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3846 | 0.6201 | 0.4997 | 0.2522 | 14.40s |\n", - "| UniversalKriging | 50 | 0.174 | 0.4171 | 0.3352 | 0.6617 | -0.72s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:15:24\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6610 (±0.0124), Variance: 0.3834, Range: [-1.2027, 2.8062]\n", - "z mean: 0.7306 (±0.0416), Variance: 4.3188, Range: [-13.1585, 28.1535]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=4.0061\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3146 | 1.1465 | 0.5861 | -2.4428 | 0.44s |\n", - "| OLS_sphere | 50 | 0.1655 | 0.4069 | 0.3213 | 0.5664 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3591 | 0.5993 | 0.4791 | 0.0594 | 37.77s |\n", - "| DeepKriging_sphere | 500 | 0.4021 | 0.6341 | 0.4997 | -0.0531 | 14.61s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4222 | 0.6497 | 0.5108 | -0.1057 | 11.95s |\n", - "| UniversalKriging | 50 | 0.1655 | 0.4068 | 0.3213 | 0.5665 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:17:11\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.2414 (±0.0202), Variance: 1.0183, Range: [-1.9621, 3.1672]\n", - "z mean: 0.2609 (±0.0428), Variance: 4.5892, Range: [-15.8479, 11.6180]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0011, nugget=3.6944\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.2554 | 1.5018 | 0.939 | -0.8846 | 0.44s |\n", - "| OLS_sphere | 50 | 0.1999 | 0.4471 | 0.3635 | 0.833 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.0725 | 1.0356 | 0.8284 | 0.1038 | 36.68s |\n", - "| DeepKriging_sphere | 500 | 1.0256 | 1.0127 | 0.8263 | 0.143 | 11.57s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.9184 | 0.9583 | 0.7931 | 0.2326 | 14.40s |\n", - "| UniversalKriging | 50 | 0.1999 | 0.4471 | 0.3635 | 0.833 | 2.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:18:52\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3908 (±0.0176), Variance: 0.7706, Range: [-1.6303, 2.8375]\n", - "z mean: 0.3951 (±0.0427), Variance: 4.5595, Range: [-11.7090, 12.8501]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0103, nugget=3.6758\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.2459 | 2.0606 | 0.8747 | -4.6795 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1887 | 0.4344 | 0.3524 | 0.7475 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7203 | 0.8487 | 0.64 | 0.0365 | 35.90s |\n", - "| DeepKriging_sphere | 500 | 0.8027 | 0.8959 | 0.7225 | -0.0737 | 18.21s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5737 | 0.7574 | 0.6126 | 0.2326 | 17.18s |\n", - "| UniversalKriging | 50 | 0.1887 | 0.4344 | 0.3524 | 0.7475 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:20:43\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6936 (±0.0202), Variance: 1.0223, Range: [-2.2170, 3.2393]\n", - "z mean: 0.7429 (±0.0449), Variance: 5.0468, Range: [-10.4636, 12.2868]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0017, nugget=4.0418\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.5386 | 2.1304 | 0.9959 | -3.179 | 0.41s |\n", - "| OLS_sphere | 50 | 0.1942 | 0.4407 | 0.3439 | 0.8212 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8295 | 0.9107 | 0.7809 | 0.2363 | 34.72s |\n", - "| DeepKriging_sphere | 500 | 0.8506 | 0.9223 | 0.7836 | 0.2168 | 15.05s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8981 | 0.9477 | 0.7742 | 0.1731 | 14.14s |\n", - "| UniversalKriging | 50 | 0.1942 | 0.4407 | 0.3439 | 0.8212 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:22:30\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4645 (±0.0130), Variance: 0.4230, Range: [-1.7744, 2.5085]\n", - "z mean: 0.4225 (±0.0399), Variance: 3.9722, Range: [-8.7137, 13.6198]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0009, nugget=3.4537\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.5262 | 1.8778 | 0.7042 | -8.3624 | 0.47s |\n", - "| OLS_sphere | 50 | 0.1569 | 0.3962 | 0.3148 | 0.5833 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.859 | 0.9268 | 0.6345 | -1.2808 | 40.64s |\n", - "| DeepKriging_sphere | 500 | 0.3837 | 0.6195 | 0.4927 | -0.0189 | 12.57s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3828 | 0.6187 | 0.4964 | -0.0164 | 16.29s |\n", - "| UniversalKriging | 50 | 0.1569 | 0.3962 | 0.3148 | 0.5833 | 2.50s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:24:26\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6035 (±0.0159), Variance: 0.6314, Range: [-1.3893, 2.5395]\n", - "z mean: 0.6561 (±0.0420), Variance: 4.4001, Range: [-11.4038, 13.4865]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0021, nugget=3.7031\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.6158 | 2.3698 | 0.9099 | -7.1894 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1643 | 0.4054 | 0.3304 | 0.7604 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7119 | 0.8437 | 0.6894 | -0.0381 | 45.96s |\n", - "| DeepKriging_sphere | 500 | 0.8669 | 0.9311 | 0.7401 | -0.2642 | 18.86s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6608 | 0.8129 | 0.6603 | 0.0364 | 12.41s |\n", - "| UniversalKriging | 50 | 0.1643 | 0.4053 | 0.3304 | 0.7604 | 2.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:26:27\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1021 (±0.0123), Variance: 0.3760, Range: [-2.0867, 1.6999]\n", - "z mean: -0.1463 (±0.0427), Variance: 4.5556, Range: [-19.5962, 20.4880]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=4.0462, nugget=0.0055\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 12.6579 | 3.5578 | 0.968 | -32.2107 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1891 | 0.4348 | 0.3408 | 0.5039 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.838 | 0.9154 | 0.5724 | -1.1987 | 39.69s |\n", - "| DeepKriging_sphere | 500 | 0.4161 | 0.645 | 0.5098 | -0.0916 | 17.55s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2821 | 0.5311 | 0.4371 | 0.26 | 11.61s |\n", - "| UniversalKriging | 50 | 0.2237 | 0.473 | 0.3621 | 0.413 | 4.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:28:25\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -1.2765 (±0.0136), Variance: 0.4651, Range: [-3.1591, 0.9336]\n", - "z mean: -1.2830 (±0.0420), Variance: 4.4000, Range: [-20.0480, 7.9985]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0016, nugget=3.8104\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1064 | 1.0518 | 0.5727 | -1.8597 | 0.48s |\n", - "| OLS_sphere | 50 | 0.1776 | 0.4214 | 0.3415 | 0.541 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3437 | 0.5862 | 0.4719 | 0.1117 | 42.66s |\n", - "| DeepKriging_sphere | 500 | 0.3374 | 0.5809 | 0.4642 | 0.1279 | 16.87s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5625 | 0.75 | 0.5974 | -0.454 | 14.10s |\n", - "| UniversalKriging | 50 | 0.1776 | 0.4214 | 0.3415 | 0.541 | 1.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:30:25\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -1.2128 (±0.0178), Variance: 0.7898, Range: [-3.3871, 1.2491]\n", - "z mean: -1.2232 (±0.0454), Variance: 5.1533, Range: [-25.5125, 11.6303]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0061, nugget=4.2036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9164 | 0.9573 | 0.6464 | -0.4443 | 0.46s |\n", - "| OLS_sphere | 50 | 0.1676 | 0.4093 | 0.3319 | 0.7359 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7036 | 0.8388 | 0.6876 | -0.109 | 42.37s |\n", - "| DeepKriging_sphere | 500 | 0.5909 | 0.7687 | 0.6225 | 0.0687 | 18.16s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6496 | 0.806 | 0.6443 | -0.0238 | 19.75s |\n", - "| UniversalKriging | 50 | 0.1675 | 0.4093 | 0.3318 | 0.736 | 2.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:32:30\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1299 (±0.0145), Variance: 0.5228, Range: [-2.1635, 2.1157]\n", - "z mean: -0.1869 (±0.0437), Variance: 4.7691, Range: [-29.1340, 16.2905]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0058, nugget=3.7697\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.1639 | 1.471 | 0.7313 | -3.2865 | 0.43s |\n", - "| OLS_sphere | 50 | 0.2315 | 0.4811 | 0.3747 | 0.5415 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6549 | 0.8093 | 0.5628 | -0.2974 | 36.10s |\n", - "| DeepKriging_sphere | 500 | 0.407 | 0.638 | 0.5061 | 0.1938 | 16.01s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4293 | 0.6552 | 0.5284 | 0.1495 | 15.03s |\n", - "| UniversalKriging | 50 | 0.2313 | 0.481 | 0.3747 | 0.5418 | 1.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:34:25\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4129 (±0.0156), Variance: 0.6071, Range: [-2.5863, 2.4611]\n", - "z mean: 0.3758 (±0.0430), Variance: 4.6118, Range: [-10.0834, 19.4498]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0020, nugget=3.9913\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2823 | 1.1324 | 0.6563 | -0.8614 | 0.52s |\n", - "| OLS_sphere | 50 | 0.1815 | 0.426 | 0.3417 | 0.7366 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.5834 | 0.7638 | 0.581 | 0.1531 | 38.50s |\n", - "| DeepKriging_sphere | 500 | 0.5643 | 0.7512 | 0.558 | 0.1807 | 18.08s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5231 | 0.7233 | 0.5854 | 0.2406 | 15.16s |\n", - "| UniversalKriging | 50 | 0.1814 | 0.4259 | 0.3417 | 0.7366 | -0.63s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:36:30\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.5168 (±0.0243), Variance: 1.4777, Range: [-3.9324, 2.6834]\n", - "z mean: -0.5344 (±0.0491), Variance: 6.0332, Range: [-15.5178, 20.4905]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0092, nugget=4.4226\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5473 | 1.2439 | 0.8393 | -0.0895 | 0.44s |\n", - "| OLS_sphere | 50 | 0.204 | 0.4517 | 0.3539 | 0.8563 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.3956 | 1.1814 | 0.9441 | 0.0173 | 45.41s |\n", - "| DeepKriging_sphere | 500 | 1.3106 | 1.1448 | 0.9458 | 0.0772 | 11.74s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1605 | 1.0773 | 0.8346 | 0.1829 | 16.37s |\n", - "| UniversalKriging | 50 | 0.2039 | 0.4516 | 0.3539 | 0.8564 | 2.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:38:37\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0060 (±0.0167), Variance: 0.6939, Range: [-2.4920, 2.0258]\n", - "z mean: 0.0021 (±0.0442), Variance: 4.8784, Range: [-13.7222, 14.0841]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=4.4036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 8.5478 | 2.9237 | 0.9147 | -11.2035 | 0.50s |\n", - "| OLS_sphere | 50 | 0.1751 | 0.4185 | 0.3357 | 0.75 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6547 | 0.8092 | 0.6563 | 0.0652 | 43.70s |\n", - "| DeepKriging_sphere | 500 | 0.5976 | 0.773 | 0.6269 | 0.1469 | 15.00s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5304 | 0.7283 | 0.589 | 0.2427 | 13.19s |\n", - "| UniversalKriging | 50 | 0.1751 | 0.4185 | 0.3357 | 0.75 | 2.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:40:42\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1307 (±0.0108), Variance: 0.2908, Range: [-2.4790, 1.6362]\n", - "z mean: -0.1358 (±0.0424), Variance: 4.4901, Range: [-10.8587, 16.0695]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0045, nugget=4.1965\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.2663 | 4.2739 | 1.0154 | -71.9555 | 0.38s |\n", - "| OLS_sphere | 50 | 0.1897 | 0.4355 | 0.3487 | 0.2424 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.2394 | 0.4893 | 0.3784 | 0.0439 | 48.46s |\n", - "| DeepKriging_sphere | 500 | 0.2253 | 0.4746 | 0.3699 | 0.1003 | 13.10s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2248 | 0.4742 | 0.3693 | 0.102 | 16.04s |\n", - "| UniversalKriging | 50 | 0.1896 | 0.4354 | 0.3485 | 0.2428 | -1.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:42:53\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3818 (±0.0160), Variance: 0.6408, Range: [-1.7810, 2.8363]\n", - "z mean: 0.3185 (±0.0419), Variance: 4.3796, Range: [-11.3104, 12.2438]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=3.5308, nugget=0.0161\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 29.0038 | 5.3855 | 1.0791 | -43.1551 | 0.46s |\n", - "| OLS_sphere | 50 | 0.2123 | 0.4608 | 0.365 | 0.6768 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7387 | 0.8595 | 0.617 | -0.1246 | 43.32s |\n", - "| DeepKriging_sphere | 500 | 0.7862 | 0.8867 | 0.7221 | -0.1969 | 19.77s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6082 | 0.7799 | 0.6276 | 0.0741 | 13.18s |\n", - "| UniversalKriging | 50 | 0.2377 | 0.4876 | 0.3712 | 0.6381 | 5.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:45:11\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0475 (±0.0122), Variance: 0.3752, Range: [-1.7512, 1.6105]\n", - "z mean: 0.0237 (±0.0424), Variance: 4.5027, Range: [-14.7014, 27.7681]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0008, nugget=4.3861\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0454 | 1.4302 | 0.5826 | -5.0335 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1922 | 0.4384 | 0.3571 | 0.433 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3298 | 0.5743 | 0.4671 | 0.027 | 40.21s |\n", - "| DeepKriging_sphere | 500 | 0.3762 | 0.6134 | 0.4777 | -0.1098 | 17.88s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2676 | 0.5173 | 0.4258 | 0.2107 | 15.21s |\n", - "| UniversalKriging | 50 | 0.1922 | 0.4384 | 0.3571 | 0.433 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:47:21\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0545 (±0.0132), Variance: 0.4332, Range: [-1.7565, 2.0484]\n", - "z mean: -0.1007 (±0.0408), Variance: 4.1589, Range: [-9.4978, 12.3168]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0017, nugget=3.6745\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9008 | 1.3787 | 0.686 | -3.7935 | 0.50s |\n", - "| OLS_sphere | 50 | 0.1553 | 0.3941 | 0.3223 | 0.6084 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.3961 | 0.6294 | 0.5113 | 0.001 | 47.16s |\n", - "| DeepKriging_sphere | 500 | 0.3599 | 0.5999 | 0.4917 | 0.0924 | 16.48s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3889 | 0.6236 | 0.5046 | 0.0192 | 12.66s |\n", - "| UniversalKriging | 50 | 0.1553 | 0.394 | 0.3222 | 0.6084 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:49:35\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3233 (±0.0161), Variance: 0.6502, Range: [-2.5779, 2.4986]\n", - "z mean: 0.3192 (±0.0424), Variance: 4.4946, Range: [-9.1966, 15.0979]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0056, nugget=3.8270\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.3451 | 2.0845 | 0.8048 | -6.4583 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1959 | 0.4426 | 0.3598 | 0.6637 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.5963 | 0.7722 | 0.5905 | -0.0236 | 40.71s |\n", - "| DeepKriging_sphere | 500 | 0.4448 | 0.667 | 0.5469 | 0.2365 | 15.71s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3759 | 0.6131 | 0.5028 | 0.3547 | 15.52s |\n", - "| UniversalKriging | 50 | 0.1957 | 0.4423 | 0.3596 | 0.6641 | 0.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:51:41\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.8638 (±0.0152), Variance: 0.5748, Range: [-2.7245, 1.2572]\n", - "z mean: -0.9011 (±0.0402), Variance: 4.0364, Range: [-17.6672, 10.4121]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0012, nugget=3.5768\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3724 | 1.1715 | 0.8013 | -1.6227 | -2.50s |\n", - "| OLS_sphere | 50 | 0.1522 | 0.3901 | 0.3195 | 0.7092 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7671 | 0.8758 | 0.7185 | -0.466 | 39.31s |\n", - "| DeepKriging_sphere | 500 | 0.5027 | 0.709 | 0.5626 | 0.0392 | 15.16s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4925 | 0.7018 | 0.5596 | 0.0588 | 17.13s |\n", - "| UniversalKriging | 50 | 0.1522 | 0.3901 | 0.3195 | 0.7092 | 2.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:53:56\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1207 (±0.0149), Variance: 0.5523, Range: [-2.6453, 1.7831]\n", - "z mean: -0.1016 (±0.0436), Variance: 4.7577, Range: [-18.3747, 13.7716]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=4.2435\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.1397 | 1.7719 | 0.764 | -4.7137 | 0.43s |\n", - "| OLS_sphere | 50 | 0.245 | 0.4949 | 0.3995 | 0.5542 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6202 | 0.7875 | 0.598 | -0.1287 | 42.27s |\n", - "| DeepKriging_sphere | 500 | 0.533 | 0.7301 | 0.5731 | 0.03 | 17.37s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4032 | 0.635 | 0.4937 | 0.2663 | 15.23s |\n", - "| UniversalKriging | 50 | 0.2449 | 0.4949 | 0.3994 | 0.5543 | 2.32s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:56:10\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 1.4770 (±0.0153), Variance: 0.5854, Range: [-1.0269, 3.8629]\n", - "z mean: 1.4516 (±0.0413), Variance: 4.2684, Range: [-21.4166, 11.9452]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0060, nugget=3.5050\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 14.8417 | 3.8525 | 0.9488 | -25.5928 | 0.39s |\n", - "| OLS_sphere | 50 | 0.1667 | 0.4083 | 0.3145 | 0.7014 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.0159 | 1.0079 | 0.744 | -0.8202 | 44.14s |\n", - "| DeepKriging_sphere | 500 | 0.7415 | 0.8611 | 0.6939 | -0.3285 | 19.91s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5717 | 0.7561 | 0.608 | -0.0244 | 18.41s |\n", - "| UniversalKriging | 50 | 0.1663 | 0.4078 | 0.3142 | 0.702 | 0.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 04:58:31\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1511 (±0.0167), Variance: 0.7013, Range: [-2.1626, 2.4590]\n", - "z mean: 0.1580 (±0.0433), Variance: 4.6899, Range: [-23.1874, 12.4898]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0023, nugget=4.1319\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9558 | 0.9777 | 0.8061 | -0.1675 | -2.52s |\n", - "| OLS_sphere | 50 | 0.1729 | 0.4158 | 0.3286 | 0.7888 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.787 | 0.8872 | 0.7255 | 0.0386 | 37.50s |\n", - "| DeepKriging_sphere | 500 | 0.7038 | 0.839 | 0.6875 | 0.1403 | 12.79s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6843 | 0.8272 | 0.6748 | 0.1642 | 14.72s |\n", - "| UniversalKriging | 50 | 0.1729 | 0.4158 | 0.3286 | 0.7888 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:00:41\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4343 (±0.0179), Variance: 0.8000, Range: [-2.1594, 3.1558]\n", - "z mean: 0.4350 (±0.0466), Variance: 5.4309, Range: [-19.8632, 14.0087]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0015, nugget=4.6445\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.1762 | 1.7822 | 0.9666 | -2.6574 | 0.47s |\n", - "| OLS_sphere | 50 | 0.2422 | 0.4921 | 0.3928 | 0.7211 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.8075 | 0.8986 | 0.7215 | 0.0701 | 42.02s |\n", - "| DeepKriging_sphere | 500 | 0.7897 | 0.8887 | 0.7127 | 0.0906 | 19.47s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6828 | 0.8263 | 0.6774 | 0.2137 | 14.47s |\n", - "| UniversalKriging | 50 | 0.2422 | 0.4921 | 0.3928 | 0.7212 | 2.07s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:02:57\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 1.4241 (±0.0209), Variance: 1.0946, Range: [-1.0545, 4.1870]\n", - "z mean: 1.4214 (±0.0444), Variance: 4.9297, Range: [-8.4921, 14.7278]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0055, nugget=3.8802\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4791 | 1.2162 | 0.8277 | -0.4672 | 0.46s |\n", - "| OLS_sphere | 50 | 0.1764 | 0.42 | 0.3382 | 0.825 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8642 | 0.9296 | 0.7675 | 0.1427 | 41.80s |\n", - "| DeepKriging_sphere | 500 | 0.9932 | 0.9966 | 0.798 | 0.0149 | 23.20s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.0726 | 1.0357 | 0.8376 | -0.064 | 19.83s |\n", - "| UniversalKriging | 50 | 0.1763 | 0.4199 | 0.3381 | 0.8251 | -0.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:05:28\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1618 (±0.0173), Variance: 0.7457, Range: [-2.4104, 2.3521]\n", - "z mean: 0.1597 (±0.0427), Variance: 4.5506, Range: [-14.2071, 11.0669]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0014, nugget=3.8166\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5738 | 1.2545 | 0.7993 | -1.1412 | 0.49s |\n", - "| OLS_sphere | 50 | 0.223 | 0.4722 | 0.3775 | 0.6966 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6738 | 0.8209 | 0.6714 | 0.0832 | 51.95s |\n", - "| DeepKriging_sphere | 500 | 0.6049 | 0.7777 | 0.6409 | 0.177 | 15.68s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6069 | 0.779 | 0.6505 | 0.1743 | 16.66s |\n", - "| UniversalKriging | 50 | 0.223 | 0.4722 | 0.3775 | 0.6966 | 2.40s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:07:58\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.9576 (±0.0177), Variance: 0.7818, Range: [-3.0588, 1.2655]\n", - "z mean: -0.9192 (±0.0451), Variance: 5.0941, Range: [-17.9126, 17.9840]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=4.5163\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9006 | 0.949 | 0.7233 | -0.1886 | 0.48s |\n", - "| OLS_sphere | 50 | 0.1412 | 0.3757 | 0.2971 | 0.8137 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.6609 | 0.813 | 0.6481 | 0.1278 | 58.33s |\n", - "| DeepKriging_sphere | 500 | 0.7496 | 0.8658 | 0.6909 | 0.0108 | 20.67s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6655 | 0.8158 | 0.6655 | 0.1217 | 17.78s |\n", - "| UniversalKriging | 50 | 0.1411 | 0.3757 | 0.2971 | 0.8137 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:10:43\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.9836 (±0.0149), Variance: 0.5518, Range: [-0.9148, 3.1304]\n", - "z mean: 1.0176 (±0.0407), Variance: 4.1455, Range: [-7.4747, 15.2427]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=3.6344\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.6891 | 1.2996 | 0.7996 | -2.1111 | 0.36s |\n", - "| OLS_sphere | 50 | 0.2219 | 0.4711 | 0.3454 | 0.5912 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.5959 | 0.7719 | 0.6042 | -0.0976 | 48.71s |\n", - "| DeepKriging_sphere | 500 | 0.5557 | 0.7455 | 0.5898 | -0.0235 | 14.84s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5365 | 0.7324 | 0.5728 | 0.0119 | 16.69s |\n", - "| UniversalKriging | 50 | 0.2219 | 0.4711 | 0.3453 | 0.5913 | 2.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:13:15\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.4921 (±0.0124), Variance: 0.3849, Range: [-2.1484, 1.5820]\n", - "z mean: -0.4353 (±0.0413), Variance: 4.2703, Range: [-16.1880, 21.1196]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=3.6349\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.0978 | 1.0477 | 0.6284 | -1.8256 | 0.52s |\n", - "| OLS_sphere | 50 | 0.1858 | 0.4311 | 0.354 | 0.5217 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3723 | 0.6102 | 0.4853 | 0.0417 | 56.83s |\n", - "| DeepKriging_sphere | 500 | 0.3731 | 0.6108 | 0.4894 | 0.0397 | 15.51s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4217 | 0.6494 | 0.5224 | -0.0856 | 16.38s |\n", - "| UniversalKriging | 50 | 0.1858 | 0.431 | 0.354 | 0.5218 | 2.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:15:55\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6283 (±0.0160), Variance: 0.6397, Range: [-2.1169, 2.5154]\n", - "z mean: 0.6045 (±0.0418), Variance: 4.3774, Range: [-10.3586, 13.0846]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0048, nugget=3.7989\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.3499 | 2.313 | 0.7488 | -8.2937 | 0.43s |\n", - "| OLS_sphere | 50 | 0.2003 | 0.4475 | 0.366 | 0.6521 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.5105 | 0.7145 | 0.5578 | 0.1132 | 51.85s |\n", - "| DeepKriging_sphere | 500 | 0.5332 | 0.7302 | 0.5784 | 0.0738 | 15.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5159 | 0.7183 | 0.5655 | 0.1037 | 16.21s |\n", - "| UniversalKriging | 50 | 0.2 | 0.4472 | 0.3658 | 0.6526 | 0.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:18:29\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.7788 (±0.0129), Variance: 0.4159, Range: [-2.4667, 1.3199]\n", - "z mean: -0.8037 (±0.0464), Variance: 5.3727, Range: [-39.4081, 26.0423]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=4.8348\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 53.7009 | 7.3281 | 1.0829 | -136.493 | 0.43s |\n", - "| OLS_sphere | 50 | 0.234 | 0.4838 | 0.3767 | 0.4008 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.4533 | 0.6733 | 0.5419 | -0.1606 | 52.21s |\n", - "| DeepKriging_sphere | 500 | 0.5107 | 0.7146 | 0.5862 | -0.3076 | 18.19s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3613 | 0.6011 | 0.4926 | 0.075 | 17.37s |\n", - "| UniversalKriging | 50 | 0.234 | 0.4838 | 0.3767 | 0.4008 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:21:08\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1179 (±0.0137), Variance: 0.4725, Range: [-1.8417, 1.8152]\n", - "z mean: -0.1166 (±0.0404), Variance: 4.0854, Range: [-11.4417, 11.3979]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0009, nugget=3.5527\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6428 | 0.8018 | 0.5998 | -0.3653 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1144 | 0.3382 | 0.2732 | 0.7571 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.4296 | 0.6555 | 0.5427 | 0.0875 | 52.71s |\n", - "| DeepKriging_sphere | 500 | 0.486 | 0.6971 | 0.5731 | -0.0322 | 13.55s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3553 | 0.596 | 0.4945 | 0.2454 | 16.66s |\n", - "| UniversalKriging | 50 | 0.1144 | 0.3382 | 0.2732 | 0.7571 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:23:46\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0775 (±0.0135), Variance: 0.4550, Range: [-2.2229, 2.6000]\n", - "z mean: 0.0921 (±0.0427), Variance: 4.5676, Range: [-14.6192, 18.6976]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0094, nugget=4.2077\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 6.4001 | 2.5298 | 0.77 | -13.1496 | 0.40s |\n", - "| OLS_sphere | 50 | 0.2066 | 0.4545 | 0.356 | 0.5433 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.611 | 0.7817 | 0.6051 | -0.3508 | 51.52s |\n", - "| DeepKriging_sphere | 500 | 0.3725 | 0.6103 | 0.4785 | 0.1764 | 13.81s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.3599 | 0.5999 | 0.4716 | 0.2044 | 13.13s |\n", - "| UniversalKriging | 50 | 0.2063 | 0.4542 | 0.3558 | 0.5439 | 1.80s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:26:21\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -1.2417 (±0.0146), Variance: 0.5354, Range: [-3.1620, 0.6874]\n", - "z mean: -1.2800 (±0.0400), Variance: 4.0006, Range: [-14.8913, 9.7691]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0034, nugget=3.4048\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7324 | 0.8558 | 0.6069 | -0.4875 | 0.45s |\n", - "| OLS_sphere | 50 | 0.1637 | 0.4046 | 0.3271 | 0.6674 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.4671 | 0.6835 | 0.5466 | 0.0512 | 58.20s |\n", - "| DeepKriging_sphere | 500 | 0.5262 | 0.7254 | 0.5957 | -0.0688 | 18.90s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5252 | 0.7247 | 0.5895 | -0.0667 | 17.13s |\n", - "| UniversalKriging | 50 | 0.1637 | 0.4047 | 0.3271 | 0.6674 | 2.58s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:29:07\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.5560 (±0.0139), Variance: 0.4836, Range: [-1.2917, 2.6186]\n", - "z mean: 0.5481 (±0.0420), Variance: 4.4070, Range: [-8.5004, 13.9542]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0092, nugget=3.8033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6934 | 0.8327 | 0.6328 | -0.4404 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1467 | 0.3831 | 0.3087 | 0.6952 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.4626 | 0.6801 | 0.5489 | 0.0391 | 54.07s |\n", - "| DeepKriging_sphere | 500 | 0.5096 | 0.7139 | 0.5824 | -0.0587 | 21.62s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4855 | 0.6968 | 0.5606 | -0.0085 | 16.08s |\n", - "| UniversalKriging | 50 | 0.1464 | 0.3826 | 0.3085 | 0.6958 | 1.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:31:55\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3916 (±0.0284), Variance: 2.0126, Range: [-2.6386, 3.3500]\n", - "z mean: 0.3637 (±0.0475), Variance: 5.6316, Range: [-14.5735, 16.1375]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=3.8531\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 15.9193 | 3.9899 | 1.3868 | -7.2598 | 0.50s |\n", - "| OLS_sphere | 50 | 0.1471 | 0.3835 | 0.3076 | 0.9237 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.6737 | 1.2937 | 1.0697 | 0.1316 | 55.69s |\n", - "| DeepKriging_sphere | 500 | 1.5594 | 1.2488 | 1.06 | 0.1909 | 13.23s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.4082 | 1.1867 | 0.9982 | 0.2694 | 13.38s |\n", - "| UniversalKriging | 50 | 0.147 | 0.3835 | 0.3076 | 0.9237 | 2.50s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:34:39\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.2252 (±0.0150), Variance: 0.5606, Range: [-1.8222, 2.1241]\n", - "z mean: 0.2437 (±0.0439), Variance: 4.8088, Range: [-21.4838, 25.0196]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=4.4622\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 30.4987 | 5.5226 | 1.235 | -54.1763 | 0.42s |\n", - "| OLS_sphere | 50 | 0.184 | 0.4289 | 0.3485 | 0.6671 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.4946 | 0.7033 | 0.5768 | 0.1052 | 64.22s |\n", - "| DeepKriging_sphere | 500 | 0.5671 | 0.7531 | 0.6333 | -0.0259 | 17.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4524 | 0.6726 | 0.5635 | 0.1815 | 13.56s |\n", - "| UniversalKriging | 50 | 0.184 | 0.4289 | 0.3486 | 0.6671 | 1.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:37:35\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0338 (±0.0160), Variance: 0.6397, Range: [-2.1071, 2.3082]\n", - "z mean: 0.0580 (±0.0440), Variance: 4.8324, Range: [-24.4694, 15.7739]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0087, nugget=4.1235\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2007 | 1.0958 | 0.7541 | -0.8291 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1988 | 0.4459 | 0.3539 | 0.6972 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6129 | 0.7829 | 0.6198 | 0.0664 | 53.51s |\n", - "| DeepKriging_sphere | 500 | 0.487 | 0.6979 | 0.5559 | 0.2581 | 16.09s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4839 | 0.6956 | 0.5575 | 0.2629 | 16.69s |\n", - "| UniversalKriging | 50 | 0.1986 | 0.4456 | 0.3537 | 0.6975 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:40:18\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.2168 (±0.0185), Variance: 0.8551, Range: [-2.9964, 2.0003]\n", - "z mean: -0.1929 (±0.0437), Variance: 4.7763, Range: [-13.3045, 17.9690]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0029, nugget=3.8630\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0076 | 1.4169 | 0.8807 | -1.1984 | 0.48s |\n", - "| OLS_sphere | 50 | 0.2079 | 0.4559 | 0.3621 | 0.7724 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.3179 | 1.148 | 0.8544 | -0.4432 | 49.33s |\n", - "| DeepKriging_sphere | 500 | 0.7157 | 0.846 | 0.6668 | 0.2163 | 12.64s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6255 | 0.7909 | 0.634 | 0.3151 | 14.25s |\n", - "| UniversalKriging | 50 | 0.2079 | 0.4559 | 0.3621 | 0.7724 | 2.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:43:00\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.4744 (±0.0170), Variance: 0.7190, Range: [-2.7720, 2.1511]\n", - "z mean: -0.4451 (±0.0425), Variance: 4.5114, Range: [-11.1686, 10.5679]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0069, nugget=3.7135\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7873 | 0.8873 | 0.6975 | -0.1175 | 0.33s |\n", - "| OLS_sphere | 50 | 0.1703 | 0.4126 | 0.3225 | 0.7583 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7126 | 0.8442 | 0.7008 | -0.0115 | 55.05s |\n", - "| DeepKriging_sphere | 500 | 0.5757 | 0.7587 | 0.6267 | 0.1829 | 17.08s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5274 | 0.7262 | 0.6055 | 0.2514 | 16.68s |\n", - "| UniversalKriging | 50 | 0.1702 | 0.4126 | 0.3224 | 0.7584 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:45:48\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.7933 (±0.0192), Variance: 0.9255, Range: [-1.3148, 3.7771]\n", - "z mean: 0.7099 (±0.0432), Variance: 4.6564, Range: [-15.8783, 14.6765]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0047, nugget=3.8103\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 9.341 | 3.0563 | 0.9513 | -8.1837 | 0.48s |\n", - "| OLS_sphere | 50 | 0.1804 | 0.4247 | 0.332 | 0.8226 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7672 | 0.8759 | 0.7158 | 0.2457 | 59.94s |\n", - "| DeepKriging_sphere | 500 | 1.0265 | 1.0132 | 0.8067 | -0.0093 | 16.17s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1183 | 1.0575 | 0.857 | -0.0994 | 17.97s |\n", - "| UniversalKriging | 50 | 0.1804 | 0.4247 | 0.332 | 0.8227 | 2.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:48:45\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6928 (±0.0135), Variance: 0.4584, Range: [-1.3159, 2.6472]\n", - "z mean: 0.7054 (±0.0434), Variance: 4.7025, Range: [-21.0370, 12.2201]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0142, nugget=4.4455\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4031 | 1.1845 | 0.7424 | -2.0673 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1582 | 0.3978 | 0.3227 | 0.6541 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.4996 | 0.7068 | 0.5494 | -0.0921 | 43.87s |\n", - "| DeepKriging_sphere | 500 | 0.3893 | 0.6239 | 0.4949 | 0.149 | 14.14s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4732 | 0.6879 | 0.5514 | -0.0344 | 17.27s |\n", - "| UniversalKriging | 50 | 0.1578 | 0.3973 | 0.3223 | 0.655 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:51:28\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.7972 (±0.0134), Variance: 0.4499, Range: [-1.9429, 2.5671]\n", - "z mean: 0.8247 (±0.0432), Variance: 4.6621, Range: [-17.0534, 13.5273]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0019, nugget=4.3759\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.899 | 0.9482 | 0.6048 | -1.2614 | 0.51s |\n", - "| OLS_sphere | 50 | 0.1536 | 0.392 | 0.33 | 0.6135 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.3752 | 0.6125 | 0.4573 | 0.0563 | 62.65s |\n", - "| DeepKriging_sphere | 500 | 0.391 | 0.6253 | 0.4874 | 0.0165 | 18.97s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.352 | 0.5933 | 0.4634 | 0.1145 | 17.91s |\n", - "| UniversalKriging | 50 | 0.1536 | 0.3919 | 0.3299 | 0.6136 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:54:32\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.3476 (±0.0123), Variance: 0.3760, Range: [-1.9972, 1.2591]\n", - "z mean: -0.3629 (±0.0419), Variance: 4.3885, Range: [-15.5227, 13.7658]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0049, nugget=4.1300\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7861 | 1.3365 | 0.7356 | -3.2745 | 0.39s |\n", - "| OLS_sphere | 50 | 0.154 | 0.3924 | 0.3138 | 0.6314 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.3974 | 0.6304 | 0.5162 | 0.049 | 50.36s |\n", - "| DeepKriging_sphere | 500 | 0.3856 | 0.621 | 0.5092 | 0.0771 | 15.11s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4475 | 0.669 | 0.5459 | -0.071 | 15.26s |\n", - "| UniversalKriging | 50 | 0.1539 | 0.3923 | 0.3137 | 0.6317 | 2.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 05:57:23\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -1.2280 (±0.0149), Variance: 0.5515, Range: [-3.2558, 0.6863]\n", - "z mean: -1.1923 (±0.0427), Variance: 4.5617, Range: [-13.3342, 15.0454]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=1.3262, nugget=2.6478\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6935 | 0.8328 | 0.6247 | -0.3042 | 0.39s |\n", - "| OLS_sphere | 50 | 0.1809 | 0.4253 | 0.3479 | 0.6599 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.0317 | 1.0157 | 0.7718 | -0.9401 | 66.25s |\n", - "| DeepKriging_sphere | 500 | 0.5396 | 0.7345 | 0.5881 | -0.0146 | 21.42s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8605 | 0.9276 | 0.7569 | -0.6182 | 22.08s |\n", - "| UniversalKriging | 50 | 0.1781 | 0.422 | 0.3454 | 0.6652 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:00:41\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018483, - "end_time": "2026-01-20T22:00:41.628194", - "exception": false, - "start_time": "2026-01-20T22:00:41.609711", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:00:41.669873Z", - "iopub.status.busy": "2026-01-20T22:00:41.668969Z", - "iopub.status.idle": "2026-01-20T22:00:41.701638Z", - "shell.execute_reply": "2026-01-20T22:00:41.698221Z" - }, - "papermill": { - "duration": 0.057349, - "end_time": "2026-01-20T22:00:41.703973", - "exception": false, - "start_time": "2026-01-20T22:00:41.646624", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 50, 'DeepKriging_sphere': 500, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|--------------|\n", - "| OLS_wendland | 6.87±10.60 | 2.13±1.53 | 0.82±0.18 | -11.90±23.80 |\n", - "| OLS_sphere | 0.18±0.03 | 0.43±0.03 | 0.34±0.02 | 0.67±0.13 |\n", - "| DeepKriging_wendland | 0.68±0.28 | 0.81±0.16 | 0.63±0.13 | -0.08±0.33 |\n", - "| DeepKriging_sphere | 0.61±0.25 | 0.76±0.15 | 0.61±0.13 | 0.05±0.14 |\n", - "| DeepKriging_sphere_Huber | 0.57±0.24 | 0.74±0.15 | 0.60±0.12 | 0.10±0.18 |\n", - "| UniversalKriging | 0.18±0.03 | 0.43±0.03 | 0.34±0.02 | 0.67±0.14 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 0.43±0.03)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 7331.835757, - "end_time": "2026-01-20T22:00:45.313086", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-20T19:58:33.477329", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioI/simulation_euclidean_GP_outliers5.ipynb b/notebook/simulation/ScenarioI/simulation_euclidean_GP_outliers5.ipynb deleted file mode 100644 index 7c67139..0000000 --- a/notebook/simulation/ScenarioI/simulation_euclidean_GP_outliers5.ipynb +++ /dev/null @@ -1,4338 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.016244, - "end_time": "2026-01-20T17:39:16.410621", - "exception": false, - "start_time": "2026-01-20T17:39:16.394377", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:16.436275Z", - "iopub.status.busy": "2026-01-20T17:39:16.435226Z", - "iopub.status.idle": "2026-01-20T17:39:16.461089Z", - "shell.execute_reply": "2026-01-20T17:39:16.456561Z" - }, - "papermill": { - "duration": 0.041294, - "end_time": "2026-01-20T17:39:16.464434", - "exception": false, - "start_time": "2026-01-20T17:39:16.423140", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:16.492113Z", - "iopub.status.busy": "2026-01-20T17:39:16.491092Z", - "iopub.status.idle": "2026-01-20T17:39:19.451346Z", - "shell.execute_reply": "2026-01-20T17:39:19.447870Z" - }, - "papermill": { - "duration": 2.979775, - "end_time": "2026-01-20T17:39:19.454599", - "exception": false, - "start_time": "2026-01-20T17:39:16.474824", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768930757.506613 1356257 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768930757.511106 1356257 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768930757.523138 1356257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768930757.523161 1356257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768930757.523163 1356257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768930757.523164 1356257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0051, - "end_time": "2026-01-20T17:39:19.466294", - "exception": false, - "start_time": "2026-01-20T17:39:19.461194", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:19.489282Z", - "iopub.status.busy": "2026-01-20T17:39:19.488560Z", - "iopub.status.idle": "2026-01-20T17:39:21.044987Z", - "shell.execute_reply": "2026-01-20T17:39:21.041782Z" - }, - "papermill": { - "duration": 1.570357, - "end_time": "2026-01-20T17:39:21.048244", - "exception": false, - "start_time": "2026-01-20T17:39:19.477887", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.003027, - "end_time": "2026-01-20T17:39:21.058158", - "exception": false, - "start_time": "2026-01-20T17:39:21.055131", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:21.067845Z", - "iopub.status.busy": "2026-01-20T17:39:21.066939Z", - "iopub.status.idle": "2026-01-20T17:39:21.079874Z", - "shell.execute_reply": "2026-01-20T17:39:21.076960Z" - }, - "papermill": { - "duration": 0.021504, - "end_time": "2026-01-20T17:39:21.082364", - "exception": false, - "start_time": "2026-01-20T17:39:21.060860", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.003406, - "end_time": "2026-01-20T17:39:21.092937", - "exception": false, - "start_time": "2026-01-20T17:39:21.089531", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:21.103046Z", - "iopub.status.busy": "2026-01-20T17:39:21.102295Z", - "iopub.status.idle": "2026-01-20T17:39:36.506839Z", - "shell.execute_reply": "2026-01-20T17:39:36.503747Z" - }, - "papermill": { - "duration": 15.413847, - "end_time": "2026-01-20T17:39:36.509931", - "exception": false, - "start_time": "2026-01-20T17:39:21.096084", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.00296, - "end_time": "2026-01-20T17:39:36.520137", - "exception": false, - "start_time": "2026-01-20T17:39:36.517177", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:36.529651Z", - "iopub.status.busy": "2026-01-20T17:39:36.528334Z", - "iopub.status.idle": "2026-01-20T17:39:36.551259Z", - "shell.execute_reply": "2026-01-20T17:39:36.547291Z" - }, - "papermill": { - "duration": 0.031298, - "end_time": "2026-01-20T17:39:36.554020", - "exception": false, - "start_time": "2026-01-20T17:39:36.522722", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, outlier_ratio, outlier_multiplier, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + outliers.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = (L @ z_std).astype(np.float32)\n", - "\n", - " # Add outliers\n", - " idx_outliers = rng.choice(num_sample, size=int(num_sample*outlier_ratio), replace=False)\n", - " z[idx_outliers] *= outlier_multiplier\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.007592, - "end_time": "2026-01-20T17:39:36.571517", - "exception": false, - "start_time": "2026-01-20T17:39:36.563925", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:36.586405Z", - "iopub.status.busy": "2026-01-20T17:39:36.585606Z", - "iopub.status.idle": "2026-01-20T17:39:36.653968Z", - "shell.execute_reply": "2026-01-20T17:39:36.650091Z" - }, - "papermill": { - "duration": 0.080178, - "end_time": "2026-01-20T17:39:36.657491", - "exception": false, - "start_time": "2026-01-20T17:39:36.577313", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:36.686050Z", - "iopub.status.busy": "2026-01-20T17:39:36.685189Z", - "iopub.status.idle": "2026-01-20T17:39:36.748557Z", - "shell.execute_reply": "2026-01-20T17:39:36.740094Z" - }, - "papermill": { - "duration": 0.081908, - "end_time": "2026-01-20T17:39:36.753451", - "exception": false, - "start_time": "2026-01-20T17:39:36.671543", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.005429, - "end_time": "2026-01-20T17:39:36.767790", - "exception": false, - "start_time": "2026-01-20T17:39:36.762361", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:36.782826Z", - "iopub.status.busy": "2026-01-20T17:39:36.781978Z", - "iopub.status.idle": "2026-01-20T17:39:36.795442Z", - "shell.execute_reply": "2026-01-20T17:39:36.791432Z" - }, - "papermill": { - "duration": 0.027672, - "end_time": "2026-01-20T17:39:36.799396", - "exception": false, - "start_time": "2026-01-20T17:39:36.771724", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:36.817417Z", - "iopub.status.busy": "2026-01-20T17:39:36.816380Z", - "iopub.status.idle": "2026-01-20T19:58:29.544345Z", - "shell.execute_reply": "2026-01-20T19:58:29.539923Z" - }, - "papermill": { - "duration": 8332.770156, - "end_time": "2026-01-20T19:58:29.577573", - "exception": false, - "start_time": "2026-01-20T17:39:36.807417", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3795 (±0.0260), Variance: 1.6960, Range: [-6.6990, 15.0261]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.0151 0.6434 \n", - " 100 0.9579 0.6447 \n", - " 200 0.9328 0.6512 *\n", - " 500 1.0547 0.6936 \n", - " 700 1.1135 0.7916 \n", - " 1000 2.2866 1.8478 \n", - " 1400 2.2866 1.8478 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768930794.000423 1356257 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768930796.471372 1356488 service.cc:152] XLA service 0x75ddf0004ea0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768930796.471448 1356488 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768930796.830456 1356488 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768930807.890457 1356488 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x75de20173eb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x75de20b9e3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.6798 1.3048 \n", - " 100 1.8006 1.4459 \n", - " 200 0.9234 0.6909 *\n", - " 500 0.9582 0.6807 \n", - " 700 1.8161 1.4539 \n", - " 1000 2.1411 1.8015 \n", - " 1400 2.1492 1.8179 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2023 0.6890 *\n", - " 100 0.2119 0.7460 \n", - " 200 0.2240 0.6953 \n", - " 500 0.2275 0.7079 \n", - " 700 0.2281 0.6857 \n", - " 1000 0.5748 1.8319 \n", - " 1400 0.5676 1.8223 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0008, nugget=0.4696\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0005, nugget=0.4414\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.4036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=0.0000, nugget=0.3211\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0009, sigma²=0.0000, nugget=0.2816\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0011, sigma²=0.0000, nugget=0.2315\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0012, sigma²=0.0000, nugget=0.1101\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.0151 0.6434 \n", - " 100 0.9578 0.6446 \n", - " 200 0.9328 0.6512 *\n", - " 500 1.0547 0.6936 \n", - " 700 1.1135 0.7916 \n", - " 1000 1.3389 1.1670 \n", - " 1400 39.4570 48.6536 \n", - " Best order: 200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.4036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2093 | 1.0997 | 0.6333 | 0.3577 | 0.51s |\n", - "| OLS_sphere | 200 | 0.6512 | 0.807 | 0.3036 | 0.6541 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.0989 | 1.0483 | 0.5599 | 0.4163 | 58.02s |\n", - "| DeepKriging_sphere | 200 | 0.6995 | 0.8364 | 0.2901 | 0.6285 | 31.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.3689 | 1.17 | 0.7064 | 0.2729 | 13.12s |\n", - "| UniversalKriging | 200 | 0.6512 | 0.807 | 0.3036 | 0.6541 | 2.68s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:49:28\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4682 (±0.0210), Variance: 1.0980, Range: [-5.2728, 11.3496]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.3661\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5166 | 1.2315 | 0.6932 | 0.0472 | 0.40s |\n", - "| OLS_sphere | 200 | 0.7915 | 0.8897 | 0.3609 | 0.5027 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.3615 | 1.1669 | 0.6194 | 0.1446 | 52.49s |\n", - "| DeepKriging_sphere | 200 | 1.4245 | 1.1935 | 0.6648 | 0.105 | 15.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8412 | 0.9172 | 0.3293 | 0.4715 | 27.04s |\n", - "| UniversalKriging | 200 | 0.7915 | 0.8897 | 0.3609 | 0.5027 | 2.39s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:51:37\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1263 (±0.0148), Variance: 0.5493, Range: [-7.9157, 5.0120]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2097\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.2472 | 0.4972 | 0.3788 | 0.323 | 0.43s |\n", - "| OLS_sphere | 200 | 0.1139 | 0.3375 | 0.2279 | 0.688 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.2462 | 0.4962 | 0.365 | 0.3257 | 53.68s |\n", - "| DeepKriging_sphere | 200 | 0.2746 | 0.524 | 0.4146 | 0.248 | 15.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2754 | 0.5248 | 0.4151 | 0.2458 | 15.80s |\n", - "| UniversalKriging | 200 | 0.1139 | 0.3375 | 0.2279 | 0.6881 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:53:33\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0460 (±0.0248), Variance: 1.5381, Range: [-12.6383, 9.5150]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.3050\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5447 | 1.2428 | 0.6685 | 0.0872 | 0.55s |\n", - "| OLS_sphere | 200 | 0.6376 | 0.7985 | 0.2972 | 0.6232 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.4483 | 1.2035 | 0.6201 | 0.1442 | 46.52s |\n", - "| DeepKriging_sphere | 200 | 1.3458 | 1.1601 | 0.648 | 0.2048 | 15.57s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6294 | 0.7933 | 0.2482 | 0.6281 | 30.92s |\n", - "| UniversalKriging | 200 | 0.6376 | 0.7985 | 0.2972 | 0.6232 | -0.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:55:40\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5393 (±0.0234), Variance: 1.3641, Range: [-12.0111, 12.5719]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.4152\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.6652 | 1.6325 | 0.8415 | -0.6543 | 0.48s |\n", - "| OLS_sphere | 200 | 0.6675 | 0.817 | 0.3131 | 0.5857 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.5867 | 1.2596 | 0.6822 | 0.0151 | 48.19s |\n", - "| DeepKriging_sphere | 200 | 1.2726 | 1.1281 | 0.6589 | 0.2101 | 12.84s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.4079 | 1.1865 | 0.7015 | 0.1261 | 15.96s |\n", - "| UniversalKriging | 200 | 0.6675 | 0.817 | 0.3131 | 0.5857 | 2.61s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:57:34\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0212 (±0.0185), Variance: 0.8534, Range: [-5.3117, 7.4726]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2109\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.5798 | 0.7615 | 0.4499 | -0.1195 | 0.44s |\n", - "| OLS_sphere | 200 | 0.0813 | 0.2851 | 0.2179 | 0.843 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.2441 | 0.4941 | 0.3714 | 0.5286 | 47.38s |\n", - "| DeepKriging_sphere | 200 | 0.3739 | 0.6115 | 0.4956 | 0.2781 | 15.90s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0861 | 0.2934 | 0.1878 | 0.8338 | 28.92s |\n", - "| UniversalKriging | 200 | 0.0813 | 0.2851 | 0.2179 | 0.843 | 1.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:59:42\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7241 (±0.0172), Variance: 0.7365, Range: [-3.8335, 9.9558]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2972\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9801 | 0.99 | 0.5444 | 0.0055 | 0.40s |\n", - "| OLS_sphere | 200 | 0.5388 | 0.734 | 0.3157 | 0.4533 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7932 | 0.8906 | 0.455 | 0.1951 | 43.00s |\n", - "| DeepKriging_sphere | 200 | 0.9552 | 0.9773 | 0.5479 | 0.0307 | 12.88s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5582 | 0.7471 | 0.2975 | 0.4336 | 23.51s |\n", - "| UniversalKriging | 200 | 0.5388 | 0.734 | 0.3157 | 0.4533 | 2.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:01:43\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2671 (±0.0256), Variance: 1.6384, Range: [-7.9920, 14.3823]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.4520\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6489 | 1.2841 | 0.8041 | 0.0563 | 0.46s |\n", - "| OLS_sphere | 200 | 0.4922 | 0.7016 | 0.2938 | 0.7183 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.9513 | 0.9754 | 0.5759 | 0.4555 | 45.25s |\n", - "| DeepKriging_sphere | 200 | 0.5393 | 0.7344 | 0.3123 | 0.6913 | 35.92s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5584 | 0.7472 | 0.2981 | 0.6804 | 27.94s |\n", - "| UniversalKriging | 200 | 0.4922 | 0.7016 | 0.2937 | 0.7183 | 2.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:04:09\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4369 (±0.0232), Variance: 1.3403, Range: [-4.3084, 13.7569]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.3936\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7896 | 0.8886 | 0.657 | 0.1087 | 0.46s |\n", - "| OLS_sphere | 200 | 0.2138 | 0.4624 | 0.2683 | 0.7586 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7502 | 0.8661 | 0.641 | 0.1533 | 52.23s |\n", - "| DeepKriging_sphere | 200 | 0.4395 | 0.663 | 0.2897 | 0.5039 | 26.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2312 | 0.4809 | 0.2352 | 0.739 | 26.15s |\n", - "| UniversalKriging | 200 | 0.2138 | 0.4624 | 0.2683 | 0.7587 | -0.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:06:34\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7491 (±0.0265), Variance: 1.7608, Range: [-7.3454, 12.4545]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.4449\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.9437 | 1.9859 | 0.9044 | -0.2924 | 0.47s |\n", - "| OLS_sphere | 200 | 1.4367 | 1.1986 | 0.4404 | 0.5292 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.9114 | 1.3825 | 0.7325 | 0.3736 | 47.69s |\n", - "| DeepKriging_sphere | 200 | 2.8311 | 1.6826 | 0.9961 | 0.0722 | 12.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 2.5708 | 1.6034 | 0.8951 | 0.1575 | 14.78s |\n", - "| UniversalKriging | 200 | 1.4367 | 1.1986 | 0.4404 | 0.5292 | 0.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:08:33\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5158 (±0.0177), Variance: 0.7832, Range: [-4.1553, 11.3323]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.3194\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4224 | 0.6499 | 0.44 | 0.0481 | 0.40s |\n", - "| OLS_sphere | 200 | 0.1422 | 0.3772 | 0.2452 | 0.6794 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.3712 | 0.6092 | 0.421 | 0.1635 | 44.72s |\n", - "| DeepKriging_sphere | 200 | 0.4141 | 0.6435 | 0.4937 | 0.0667 | 14.42s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3589 | 0.5991 | 0.45 | 0.1912 | 15.93s |\n", - "| UniversalKriging | 200 | 0.1422 | 0.3771 | 0.2451 | 0.6795 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:10:33\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.6617 (±0.0217), Variance: 1.1728, Range: [-5.0916, 11.3830]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.4201\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9121 | 1.3828 | 0.7325 | -0.4302 | 0.42s |\n", - "| OLS_sphere | 200 | 0.5212 | 0.7219 | 0.3342 | 0.6102 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.0875 | 1.0428 | 0.6081 | 0.1866 | 51.61s |\n", - "| DeepKriging_sphere | 200 | 1.137 | 1.0663 | 0.7107 | 0.1496 | 11.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1341 | 1.0649 | 0.7025 | 0.1518 | 12.69s |\n", - "| UniversalKriging | 200 | 0.5212 | 0.7219 | 0.3342 | 0.6102 | 2.65s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:12:38\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1170 (±0.0162), Variance: 0.6530, Range: [-9.7263, 6.9887]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.2173, nugget=0.0002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7806 | 0.8835 | 0.5469 | -0.048 | 0.51s |\n", - "| OLS_sphere | 200 | 0.3493 | 0.591 | 0.2985 | 0.5311 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.5781 | 0.7603 | 0.5016 | 0.2238 | 45.40s |\n", - "| DeepKriging_sphere | 200 | 0.4567 | 0.6758 | 0.2914 | 0.3869 | 28.80s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5795 | 0.7612 | 0.4955 | 0.222 | 14.79s |\n", - "| UniversalKriging | 200 | 0.3469 | 0.589 | 0.2939 | 0.5342 | 4.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:14:51\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -1.4040 (±0.0231), Variance: 1.3356, Range: [-12.7834, 3.0229]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.8271\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0521 | 1.0257 | 0.5489 | 0.1375 | 0.40s |\n", - "| OLS_sphere | 200 | 0.8661 | 0.9307 | 0.4195 | 0.2899 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.1168 | 1.0568 | 0.4917 | 0.0844 | 51.54s |\n", - "| DeepKriging_sphere | 200 | 1.2136 | 1.1017 | 0.6098 | 0.005 | 14.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1671 | 1.0803 | 0.595 | 0.0432 | 17.25s |\n", - "| UniversalKriging | 200 | 0.8661 | 0.9307 | 0.4195 | 0.2899 | 2.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:17:04\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -1.3215 (±0.0247), Variance: 1.5274, Range: [-14.0983, 3.2452]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.5828\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4779 | 1.2157 | 0.6789 | -0.1706 | 0.43s |\n", - "| OLS_sphere | 200 | 0.7666 | 0.8755 | 0.412 | 0.3928 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.0392 | 1.0194 | 0.5683 | 0.1769 | 54.18s |\n", - "| DeepKriging_sphere | 200 | 0.8774 | 0.9367 | 0.4103 | 0.305 | 30.54s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8069 | 0.8983 | 0.3671 | 0.3609 | 25.98s |\n", - "| UniversalKriging | 200 | 0.7666 | 0.8755 | 0.412 | 0.3928 | 2.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:19:42\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1316 (±0.0195), Variance: 0.9539, Range: [-8.5605, 9.2636]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.2992\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6755 | 0.8219 | 0.5135 | 0.3015 | 0.43s |\n", - "| OLS_sphere | 200 | 0.3358 | 0.5795 | 0.3167 | 0.6528 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.5555 | 0.7453 | 0.4568 | 0.4256 | 45.10s |\n", - "| DeepKriging_sphere | 200 | 0.3008 | 0.5485 | 0.2668 | 0.6889 | 26.80s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7054 | 0.8399 | 0.509 | 0.2706 | 15.04s |\n", - "| UniversalKriging | 200 | 0.3358 | 0.5795 | 0.3167 | 0.6528 | 0.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:21:56\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4370 (±0.0200), Variance: 0.9971, Range: [-10.4125, 9.9539]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0003, nugget=0.2995\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6064 | 0.7787 | 0.5177 | 0.3808 | 0.41s |\n", - "| OLS_sphere | 200 | 0.2893 | 0.5378 | 0.2809 | 0.7046 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7101 | 0.8427 | 0.5781 | 0.2748 | 41.55s |\n", - "| DeepKriging_sphere | 200 | 0.3154 | 0.5616 | 0.2794 | 0.678 | 26.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3369 | 0.5805 | 0.2763 | 0.6559 | 25.97s |\n", - "| UniversalKriging | 200 | 0.2892 | 0.5378 | 0.2808 | 0.7046 | 1.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:24:18\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5625 (±0.0299), Variance: 2.2395, Range: [-15.1004, 8.1579]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0003, nugget=0.5333\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.8436 | 1.3578 | 0.8026 | 0.1999 | 0.43s |\n", - "| OLS_sphere | 200 | 0.7608 | 0.8722 | 0.3551 | 0.6698 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.2474 | 1.1169 | 0.6462 | 0.4587 | 51.06s |\n", - "| DeepKriging_sphere | 200 | 1.8136 | 1.3467 | 0.8908 | 0.213 | 13.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.457 | 1.2071 | 0.743 | 0.3677 | 14.29s |\n", - "| UniversalKriging | 200 | 0.7608 | 0.8722 | 0.3551 | 0.6698 | 2.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:26:33\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0064 (±0.0206), Variance: 1.0570, Range: [-9.3104, 7.1767]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2457\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.6137 | 1.901 | 0.7662 | -1.7248 | 0.43s |\n", - "| OLS_sphere | 200 | 0.5141 | 0.717 | 0.3159 | 0.6124 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.9077 | 0.9527 | 0.5877 | 0.3156 | 64.42s |\n", - "| DeepKriging_sphere | 200 | 1.1113 | 1.0542 | 0.6777 | 0.1621 | 13.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5742 | 0.7578 | 0.3505 | 0.5671 | 26.46s |\n", - "| UniversalKriging | 200 | 0.5141 | 0.717 | 0.3159 | 0.6124 | 2.45s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:29:13\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1418 (±0.0146), Variance: 0.5319, Range: [-11.3811, 6.4375]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0387, nugget=0.1774\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.077 | 1.0378 | 0.4553 | -1.6638 | 0.41s |\n", - "| OLS_sphere | 200 | 0.1794 | 0.4235 | 0.2542 | 0.5564 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.3193 | 0.565 | 0.3626 | 0.2104 | 63.20s |\n", - "| DeepKriging_sphere | 200 | 0.3475 | 0.5895 | 0.3874 | 0.1404 | 16.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3366 | 0.5802 | 0.3731 | 0.1674 | 12.66s |\n", - "| UniversalKriging | 200 | 0.1738 | 0.4169 | 0.2477 | 0.57 | 6.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:31:43\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4381 (±0.0212), Variance: 1.1202, Range: [-3.2344, 13.4062]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0016, nugget=0.4108\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5066 | 0.7118 | 0.5198 | 0.2594 | 0.38s |\n", - "| OLS_sphere | 200 | 0.1261 | 0.3551 | 0.2557 | 0.8157 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.4541 | 0.6739 | 0.4659 | 0.3361 | 57.47s |\n", - "| DeepKriging_sphere | 200 | 0.5006 | 0.7075 | 0.5423 | 0.2682 | 13.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1468 | 0.3831 | 0.221 | 0.7854 | 27.32s |\n", - "| UniversalKriging | 200 | 0.1259 | 0.3548 | 0.2555 | 0.8159 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:34:15\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0398 (±0.0158), Variance: 0.6208, Range: [-7.3162, 4.8724]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0004, nugget=0.1932\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4907 | 0.7005 | 0.4616 | 0.163 | 0.41s |\n", - "| OLS_sphere | 200 | 0.2345 | 0.4842 | 0.2561 | 0.6 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.4481 | 0.6694 | 0.432 | 0.2356 | 56.97s |\n", - "| DeepKriging_sphere | 200 | 0.4833 | 0.6952 | 0.472 | 0.1756 | 16.47s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2246 | 0.4739 | 0.2287 | 0.6169 | 31.04s |\n", - "| UniversalKriging | 200 | 0.2344 | 0.4842 | 0.256 | 0.6002 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:36:55\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0511 (±0.0170), Variance: 0.7217, Range: [-8.1117, 8.1580]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2363\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4546 | 0.6743 | 0.4959 | 0.1502 | 0.45s |\n", - "| OLS_sphere | 200 | 0.16 | 0.4 | 0.2487 | 0.7009 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.3762 | 0.6133 | 0.443 | 0.2968 | 60.36s |\n", - "| DeepKriging_sphere | 200 | 0.4203 | 0.6483 | 0.4872 | 0.2145 | 13.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.199 | 0.4461 | 0.252 | 0.6281 | 29.37s |\n", - "| UniversalKriging | 200 | 0.16 | 0.4 | 0.2487 | 0.7009 | 1.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:39:35\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3536 (±0.0204), Variance: 1.0390, Range: [-8.1571, 10.6826]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0000, nugget=0.2946\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6959 | 0.8342 | 0.5249 | 0.2272 | 0.40s |\n", - "| OLS_sphere | 200 | 0.3222 | 0.5676 | 0.2985 | 0.6422 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.6246 | 0.7903 | 0.5048 | 0.3063 | 42.14s |\n", - "| DeepKriging_sphere | 200 | 0.3758 | 0.613 | 0.3216 | 0.5827 | 27.22s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6352 | 0.797 | 0.5459 | 0.2946 | 12.37s |\n", - "| UniversalKriging | 200 | 0.3222 | 0.5676 | 0.2985 | 0.6422 | 2.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:42:02\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.9335 (±0.0205), Variance: 1.0499, Range: [-13.4915, 3.4110]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.4490\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7791 | 0.8827 | 0.5845 | 0.0771 | 0.41s |\n", - "| OLS_sphere | 200 | 0.4115 | 0.6415 | 0.2934 | 0.5126 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7128 | 0.8443 | 0.5442 | 0.1557 | 54.76s |\n", - "| DeepKriging_sphere | 200 | 0.8309 | 0.9115 | 0.6411 | 0.0158 | 10.47s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4356 | 0.66 | 0.2977 | 0.484 | 22.03s |\n", - "| UniversalKriging | 200 | 0.4115 | 0.6415 | 0.2934 | 0.5126 | 2.62s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:44:33\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1281 (±0.0189), Variance: 0.8950, Range: [-9.7661, 7.4324]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0002, nugget=0.2830\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5241 | 0.724 | 0.5226 | 0.2688 | 0.43s |\n", - "| OLS_sphere | 200 | 0.16 | 0.4 | 0.2425 | 0.7768 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.4918 | 0.7013 | 0.4851 | 0.3138 | 33.56s |\n", - "| DeepKriging_sphere | 200 | 0.1128 | 0.3359 | 0.2234 | 0.8426 | 23.50s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4961 | 0.7043 | 0.467 | 0.3079 | 15.01s |\n", - "| UniversalKriging | 200 | 0.1599 | 0.3998 | 0.2424 | 0.777 | -1.68s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:46:40\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 1.6333 (±0.0277), Variance: 1.9235, Range: [-1.0269, 18.0625]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0002, nugget=1.0217\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.2624 | 1.5041 | 0.7166 | 0.0027 | 0.41s |\n", - "| OLS_sphere | 200 | 1.7732 | 1.3316 | 0.5312 | 0.2183 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.9926 | 1.4116 | 0.6581 | 0.1216 | 53.95s |\n", - "| DeepKriging_sphere | 200 | 2.2312 | 1.4937 | 0.8415 | 0.0164 | 17.99s |\n", - "| DeepKriging_sphere_Huber | 50 | 2.1717 | 1.4737 | 0.733 | 0.0426 | 16.51s |\n", - "| UniversalKriging | 200 | 1.7733 | 1.3316 | 0.5312 | 0.2183 | 2.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:49:06\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1769 (±0.0225), Variance: 1.2712, Range: [-7.4291, 11.5524]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.3703\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5918 | 1.2616 | 0.7683 | 0.0946 | 0.41s |\n", - "| OLS_sphere | 200 | 0.6871 | 0.8289 | 0.3524 | 0.6092 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.4269 | 1.1945 | 0.6713 | 0.1884 | 54.39s |\n", - "| DeepKriging_sphere | 200 | 0.7596 | 0.8715 | 0.3329 | 0.568 | 28.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2844 | 1.1333 | 0.6739 | 0.2694 | 13.58s |\n", - "| UniversalKriging | 200 | 0.6871 | 0.8289 | 0.3524 | 0.6092 | 2.70s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:51:50\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4539 (±0.0226), Variance: 1.2717, Range: [-8.9691, 10.8699]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0000, nugget=0.3494\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.563 | 1.2502 | 0.7719 | -0.152 | 0.45s |\n", - "| OLS_sphere | 200 | 0.3827 | 0.6187 | 0.3064 | 0.7179 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.942 | 0.9706 | 0.6289 | 0.3057 | 53.61s |\n", - "| DeepKriging_sphere | 200 | 1.0994 | 1.0485 | 0.7281 | 0.1897 | 12.58s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.9458 | 0.9725 | 0.6628 | 0.3029 | 12.49s |\n", - "| UniversalKriging | 200 | 0.3827 | 0.6187 | 0.3064 | 0.7179 | 2.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:54:17\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 1.5787 (±0.0326), Variance: 2.6589, Range: [-3.6228, 18.1515]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=1.2601\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.0812 | 2.2542 | 1.0243 | -0.6716 | 0.44s |\n", - "| OLS_sphere | 200 | 1.8246 | 1.3508 | 0.4925 | 0.3997 | 0.02s |\n", - "| DeepKriging_wendland | -- | 2.4612 | 1.5688 | 0.7738 | 0.1903 | 51.49s |\n", - "| DeepKriging_sphere | 200 | 2.9656 | 1.7221 | 0.9498 | 0.0244 | 12.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 2.8883 | 1.6995 | 0.9137 | 0.0498 | 14.92s |\n", - "| UniversalKriging | 200 | 1.8246 | 1.3508 | 0.4925 | 0.3997 | 2.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:56:45\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1891 (±0.0211), Variance: 1.1131, Range: [-10.1432, 10.9354]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2660\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3621 | 1.1671 | 0.6911 | 0.0086 | 0.44s |\n", - "| OLS_sphere | 200 | 0.5005 | 0.7074 | 0.2947 | 0.6357 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.1186 | 1.0576 | 0.6007 | 0.1859 | 53.66s |\n", - "| DeepKriging_sphere | 200 | 1.1586 | 1.0764 | 0.6778 | 0.1568 | 12.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5546 | 0.7447 | 0.2797 | 0.5964 | 27.87s |\n", - "| UniversalKriging | 200 | 0.5005 | 0.7074 | 0.2947 | 0.6358 | -0.76s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 02:59:27\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -1.0586 (±0.0259), Variance: 1.6706, Range: [-12.7924, 4.6585]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.6388\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.7162 | 1.31 | 0.746 | 0.0971 | 0.41s |\n", - "| OLS_sphere | 200 | 1.144 | 1.0696 | 0.4651 | 0.3981 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.7498 | 1.3228 | 0.7547 | 0.0794 | 49.15s |\n", - "| DeepKriging_sphere | 200 | 1.8788 | 1.3707 | 0.8412 | 0.0116 | 13.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.7558 | 1.3251 | 0.7777 | 0.0762 | 12.44s |\n", - "| UniversalKriging | 200 | 1.144 | 1.0696 | 0.4651 | 0.3981 | 2.50s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:01:49\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 1.0887 (±0.0227), Variance: 1.2867, Range: [-3.7309, 13.9033]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.5518\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.9411 | 1.3932 | 0.7153 | 0.0962 | 0.44s |\n", - "| OLS_sphere | 200 | 1.3303 | 1.1534 | 0.4269 | 0.3806 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.8392 | 1.3562 | 0.6759 | 0.1437 | 49.07s |\n", - "| DeepKriging_sphere | 200 | 1.3571 | 1.165 | 0.3859 | 0.3681 | 34.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.4183 | 1.1909 | 0.4375 | 0.3396 | 30.52s |\n", - "| UniversalKriging | 200 | 1.3303 | 1.1534 | 0.4269 | 0.3806 | 2.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:04:50\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5448 (±0.0172), Variance: 0.7387, Range: [-9.2318, 7.6807]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0002, nugget=0.3232\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5323 | 0.7296 | 0.4631 | 0.2106 | 0.40s |\n", - "| OLS_sphere | 200 | 0.3388 | 0.5821 | 0.2959 | 0.4975 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.4753 | 0.6894 | 0.4175 | 0.2951 | 48.85s |\n", - "| DeepKriging_sphere | 200 | 0.5611 | 0.7491 | 0.4832 | 0.1678 | 14.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3851 | 0.6206 | 0.2913 | 0.4288 | 23.82s |\n", - "| UniversalKriging | 200 | 0.3388 | 0.5821 | 0.2959 | 0.4975 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:07:26\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7105 (±0.0226), Variance: 1.2721, Range: [-8.2543, 11.5806]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0002, nugget=0.5258\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1614 | 1.0777 | 0.5942 | -0.399 | 0.36s |\n", - "| OLS_sphere | 200 | 0.3276 | 0.5724 | 0.3127 | 0.6054 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.6273 | 0.792 | 0.489 | 0.2444 | 46.46s |\n", - "| DeepKriging_sphere | 200 | 0.4242 | 0.6513 | 0.3128 | 0.489 | 26.56s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3659 | 0.6049 | 0.2943 | 0.5593 | 28.04s |\n", - "| UniversalKriging | 200 | 0.3276 | 0.5724 | 0.3127 | 0.6054 | 2.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:10:21\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.8428 (±0.0176), Variance: 0.7739, Range: [-11.6250, 4.0986]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.2930\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.7262 | 1.9303 | 0.5675 | -7.8263 | 0.45s |\n", - "| OLS_sphere | 200 | 0.125 | 0.3536 | 0.2508 | 0.7038 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.2951 | 0.5432 | 0.4061 | 0.3011 | 54.00s |\n", - "| DeepKriging_sphere | 200 | 0.196 | 0.4427 | 0.236 | 0.5357 | 36.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1665 | 0.4081 | 0.2388 | 0.6055 | 31.25s |\n", - "| UniversalKriging | 200 | 0.125 | 0.3536 | 0.2508 | 0.7038 | 2.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:13:35\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1321 (±0.0176), Variance: 0.7736, Range: [-8.3367, 7.5838]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1318, nugget=0.0812\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6686 | 0.8177 | 0.5395 | 0.2074 | 0.42s |\n", - "| OLS_sphere | 200 | 0.3081 | 0.555 | 0.264 | 0.6348 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.6324 | 0.7953 | 0.4964 | 0.2502 | 57.54s |\n", - "| DeepKriging_sphere | 200 | 0.3741 | 0.6117 | 0.2846 | 0.5565 | 38.41s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3791 | 0.6158 | 0.2847 | 0.5505 | 29.79s |\n", - "| UniversalKriging | 200 | 0.3068 | 0.5539 | 0.2541 | 0.6362 | 3.77s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:16:55\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0736 (±0.0166), Variance: 0.6926, Range: [-5.7226, 7.6655]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0001, nugget=0.1744\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1626 | 1.0782 | 0.5342 | -0.41 | 0.45s |\n", - "| OLS_sphere | 200 | 0.314 | 0.5604 | 0.2546 | 0.6191 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.4874 | 0.6982 | 0.4615 | 0.4088 | 54.29s |\n", - "| DeepKriging_sphere | 200 | 0.1816 | 0.4262 | 0.2216 | 0.7797 | 33.35s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6435 | 0.8022 | 0.507 | 0.2196 | 13.00s |\n", - "| UniversalKriging | 200 | 0.314 | 0.5603 | 0.2546 | 0.6192 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:19:52\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -1.3694 (±0.0245), Variance: 1.4996, Range: [-14.7478, 3.2194]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.9125\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6137 | 0.7834 | 0.5311 | 0.2169 | 0.48s |\n", - "| OLS_sphere | 200 | 0.4075 | 0.6384 | 0.3579 | 0.48 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.8659 | 0.9305 | 0.536 | -0.1049 | 63.20s |\n", - "| DeepKriging_sphere | 200 | 0.782 | 0.8843 | 0.6406 | 0.0021 | 20.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3705 | 0.6087 | 0.2954 | 0.5272 | 30.03s |\n", - "| UniversalKriging | 200 | 0.4075 | 0.6383 | 0.3579 | 0.48 | 2.53s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:22:58\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.6276 (±0.0201), Variance: 1.0052, Range: [-5.4682, 10.1369]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0004, nugget=0.3887\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.043 | 1.0213 | 0.6298 | 0.0265 | 0.46s |\n", - "| OLS_sphere | 200 | 0.5678 | 0.7535 | 0.3245 | 0.4701 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.8306 | 0.9114 | 0.528 | 0.2248 | 59.16s |\n", - "| DeepKriging_sphere | 200 | 0.8374 | 0.9151 | 0.3756 | 0.2185 | 28.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.017 | 1.0085 | 0.6034 | 0.0508 | 12.60s |\n", - "| UniversalKriging | 200 | 0.5677 | 0.7535 | 0.3244 | 0.4702 | 1.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:25:57\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4375 (±0.0366), Variance: 3.3414, Range: [-11.3880, 14.5081]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.7569\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.9518 | 2.4396 | 1.203 | -0.9911 | 0.46s |\n", - "| OLS_sphere | 200 | 0.826 | 0.9089 | 0.377 | 0.7237 | 0.03s |\n", - "| DeepKriging_wendland | -- | 2.2582 | 1.5027 | 0.9545 | 0.2445 | 58.25s |\n", - "| DeepKriging_sphere | 200 | 2.3369 | 1.5287 | 1.0902 | 0.2182 | 14.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.8462 | 1.3588 | 0.8865 | 0.3824 | 14.31s |\n", - "| UniversalKriging | 200 | 0.826 | 0.9089 | 0.377 | 0.7237 | 2.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:28:45\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2483 (±0.0197), Variance: 0.9710, Range: [-7.2020, 9.0295]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0003, nugget=0.2942\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9291 | 0.9639 | 0.623 | -0.1199 | 0.44s |\n", - "| OLS_sphere | 200 | 0.3132 | 0.5597 | 0.299 | 0.6224 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.6854 | 0.8279 | 0.5387 | 0.1738 | 53.46s |\n", - "| DeepKriging_sphere | 200 | 0.3516 | 0.593 | 0.282 | 0.5761 | 41.40s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4049 | 0.6363 | 0.2856 | 0.512 | 29.99s |\n", - "| UniversalKriging | 200 | 0.3132 | 0.5596 | 0.299 | 0.6225 | 1.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:32:12\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0551 (±0.0193), Variance: 0.9285, Range: [-5.8240, 9.3382]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.2104\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9552 | 0.9773 | 0.6627 | 0.1351 | 0.39s |\n", - "| OLS_sphere | 200 | 0.3876 | 0.6226 | 0.2809 | 0.649 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7352 | 0.8575 | 0.5397 | 0.3342 | 62.96s |\n", - "| DeepKriging_sphere | 200 | 0.9409 | 0.97 | 0.672 | 0.148 | 13.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7955 | 0.8919 | 0.5789 | 0.2797 | 13.70s |\n", - "| UniversalKriging | 200 | 0.3876 | 0.6226 | 0.2809 | 0.649 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:35:06\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2384 (±0.0234), Variance: 1.3718, Range: [-12.3411, 8.4913]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.3252\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3795 | 1.1745 | 0.7722 | -0.0038 | 0.40s |\n", - "| OLS_sphere | 200 | 0.3895 | 0.6241 | 0.2927 | 0.7166 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.982 | 0.9909 | 0.6404 | 0.2855 | 59.08s |\n", - "| DeepKriging_sphere | 200 | 1.048 | 1.0237 | 0.7332 | 0.2374 | 14.72s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.0249 | 1.0124 | 0.6854 | 0.2542 | 14.61s |\n", - "| UniversalKriging | 200 | 0.3895 | 0.6241 | 0.2927 | 0.7166 | 2.53s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:37:56\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5574 (±0.0245), Variance: 1.4997, Range: [-13.8601, 6.9836]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.5866\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.0886 | 1.0434 | 0.689 | 0.1066 | 0.34s |\n", - "| OLS_sphere | 200 | 0.5134 | 0.7165 | 0.3217 | 0.5787 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.3176 | 1.1479 | 0.6996 | -0.0813 | 62.21s |\n", - "| DeepKriging_sphere | 200 | 0.9932 | 0.9966 | 0.6426 | 0.185 | 16.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1079 | 1.0526 | 0.7115 | 0.0908 | 17.75s |\n", - "| UniversalKriging | 200 | 0.5134 | 0.7165 | 0.3217 | 0.5787 | 2.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:40:52\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.8826 (±0.0265), Variance: 1.7622, Range: [-5.3033, 14.7649]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.5148\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.1377 | 1.4621 | 0.7469 | 0.2065 | 0.36s |\n", - "| OLS_sphere | 200 | 1.4423 | 1.2009 | 0.4359 | 0.4646 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.9953 | 1.4126 | 0.6971 | 0.2594 | 69.85s |\n", - "| DeepKriging_sphere | 200 | 1.6471 | 1.2834 | 0.4288 | 0.3886 | 39.02s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.6134 | 1.2702 | 0.4309 | 0.4011 | 37.67s |\n", - "| UniversalKriging | 200 | 1.4423 | 1.2009 | 0.4359 | 0.4646 | -0.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:44:42\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7605 (±0.0188), Variance: 0.8790, Range: [-4.7833, 9.9762]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=0.3200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.2619 | 1.504 | 0.6595 | -1.4882 | 0.48s |\n", - "| OLS_sphere | 200 | 0.4476 | 0.669 | 0.3385 | 0.5077 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7454 | 0.8634 | 0.5236 | 0.18 | 69.80s |\n", - "| DeepKriging_sphere | 200 | 0.4771 | 0.6907 | 0.3083 | 0.4752 | 50.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4364 | 0.6606 | 0.2846 | 0.52 | 34.14s |\n", - "| UniversalKriging | 200 | 0.4476 | 0.669 | 0.3385 | 0.5077 | -0.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:48:39\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.8971 (±0.0215), Variance: 1.1564, Range: [-3.7917, 11.3291]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0002, nugget=0.5627\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1733 | 1.0832 | 0.5233 | 0.0888 | 0.38s |\n", - "| OLS_sphere | 200 | 0.7696 | 0.8772 | 0.3792 | 0.4024 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.1387 | 1.0671 | 0.4976 | 0.1157 | 53.80s |\n", - "| DeepKriging_sphere | 200 | 1.0241 | 1.012 | 0.5285 | 0.2047 | 26.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8994 | 0.9484 | 0.3816 | 0.3015 | 25.96s |\n", - "| UniversalKriging | 200 | 0.7696 | 0.8772 | 0.3792 | 0.4024 | 2.32s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:51:52\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3776 (±0.0160), Variance: 0.6404, Range: [-8.0207, 5.9083]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.2350, nugget=0.0036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4422 | 0.665 | 0.4493 | 0.2064 | 0.55s |\n", - "| OLS_sphere | 200 | 0.19 | 0.4359 | 0.241 | 0.659 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.3799 | 0.6163 | 0.4244 | 0.3183 | 56.39s |\n", - "| DeepKriging_sphere | 200 | 0.5008 | 0.7077 | 0.5267 | 0.1012 | 13.05s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.194 | 0.4404 | 0.2222 | 0.6519 | 34.98s |\n", - "| UniversalKriging | 200 | 0.1814 | 0.4259 | 0.2202 | 0.6744 | 5.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:55:11\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -1.3652 (±0.0260), Variance: 1.6927, Range: [-14.2782, 2.8754]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0000, nugget=0.8676\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.3076 | 1.5191 | 0.7113 | 0.0082 | 0.35s |\n", - "| OLS_sphere | 200 | 1.5528 | 1.2461 | 0.4858 | 0.3326 | 0.03s |\n", - "| DeepKriging_wendland | -- | 2.2359 | 1.4953 | 0.667 | 0.039 | 59.43s |\n", - "| DeepKriging_sphere | 200 | 2.2506 | 1.5002 | 0.7348 | 0.0327 | 20.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.5245 | 1.2347 | 0.4133 | 0.3448 | 32.93s |\n", - "| UniversalKriging | 200 | 1.5528 | 1.2461 | 0.4858 | 0.3326 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 03:58:29\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, outlier_ratio=0.025, outlier_multiplier=5, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.017372, - "end_time": "2026-01-20T19:58:29.612409", - "exception": false, - "start_time": "2026-01-20T19:58:29.595037", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T19:58:29.663006Z", - "iopub.status.busy": "2026-01-20T19:58:29.662012Z", - "iopub.status.idle": "2026-01-20T19:58:29.681406Z", - "shell.execute_reply": "2026-01-20T19:58:29.677397Z" - }, - "papermill": { - "duration": 0.054883, - "end_time": "2026-01-20T19:58:29.684396", - "exception": false, - "start_time": "2026-01-20T19:58:29.629513", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 200, 'DeepKriging_sphere': 200, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|------------|\n", - "| OLS_wendland | 1.50±1.18 | 1.15±0.42 | 0.64±0.16 | -0.25±1.18 |\n", - "| OLS_sphere | 0.57±0.43 | 0.71±0.26 | 0.32±0.07 | 0.58±0.14 |\n", - "| DeepKriging_wendland | 0.99±0.57 | 0.96±0.28 | 0.56±0.12 | 0.23±0.13 |\n", - "| DeepKriging_sphere | 0.94±0.68 | 0.91±0.33 | 0.53±0.22 | 0.29±0.23 |\n", - "| DeepKriging_sphere_Huber | 0.85±0.63 | 0.86±0.33 | 0.46±0.20 | 0.38±0.21 |\n", - "| UniversalKriging | 0.57±0.43 | 0.71±0.27 | 0.32±0.07 | 0.58±0.14 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 0.71±0.26)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 8357.710198, - "end_time": "2026-01-20T19:58:33.398782", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_outliers5.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-20T17:39:15.688584", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioI/simulation_euclidean_GP_without_outliers.ipynb b/notebook/simulation/ScenarioI/simulation_euclidean_GP_without_outliers.ipynb deleted file mode 100644 index 24c1065..0000000 --- a/notebook/simulation/ScenarioI/simulation_euclidean_GP_without_outliers.ipynb +++ /dev/null @@ -1,4278 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013186, - "end_time": "2026-01-20T14:50:32.034973", - "exception": false, - "start_time": "2026-01-20T14:50:32.021787", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:32.058594Z", - "iopub.status.busy": "2026-01-20T14:50:32.057908Z", - "iopub.status.idle": "2026-01-20T14:50:32.074436Z", - "shell.execute_reply": "2026-01-20T14:50:32.070812Z" - }, - "papermill": { - "duration": 0.029596, - "end_time": "2026-01-20T14:50:32.076349", - "exception": false, - "start_time": "2026-01-20T14:50:32.046753", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:32.093569Z", - "iopub.status.busy": "2026-01-20T14:50:32.092962Z", - "iopub.status.idle": "2026-01-20T14:50:34.929987Z", - "shell.execute_reply": "2026-01-20T14:50:34.926609Z" - }, - "papermill": { - "duration": 2.848674, - "end_time": "2026-01-20T14:50:34.932812", - "exception": false, - "start_time": "2026-01-20T14:50:32.084138", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768920632.989046 4020518 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768920632.994046 4020518 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768920633.007798 4020518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768920633.007821 4020518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768920633.007823 4020518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768920633.007824 4020518 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.011994, - "end_time": "2026-01-20T14:50:34.958273", - "exception": false, - "start_time": "2026-01-20T14:50:34.946279", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:34.977203Z", - "iopub.status.busy": "2026-01-20T14:50:34.975964Z", - "iopub.status.idle": "2026-01-20T14:50:36.506242Z", - "shell.execute_reply": "2026-01-20T14:50:36.502968Z" - }, - "papermill": { - "duration": 1.542686, - "end_time": "2026-01-20T14:50:36.509139", - "exception": false, - "start_time": "2026-01-20T14:50:34.966453", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011529, - "end_time": "2026-01-20T14:50:36.534743", - "exception": false, - "start_time": "2026-01-20T14:50:36.523214", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:36.553023Z", - "iopub.status.busy": "2026-01-20T14:50:36.552172Z", - "iopub.status.idle": "2026-01-20T14:50:36.564334Z", - "shell.execute_reply": "2026-01-20T14:50:36.561282Z" - }, - "papermill": { - "duration": 0.024256, - "end_time": "2026-01-20T14:50:36.567046", - "exception": false, - "start_time": "2026-01-20T14:50:36.542790", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006433, - "end_time": "2026-01-20T14:50:36.585080", - "exception": false, - "start_time": "2026-01-20T14:50:36.578647", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:36.598058Z", - "iopub.status.busy": "2026-01-20T14:50:36.597231Z", - "iopub.status.idle": "2026-01-20T14:50:49.923750Z", - "shell.execute_reply": "2026-01-20T14:50:49.920049Z" - }, - "papermill": { - "duration": 13.336751, - "end_time": "2026-01-20T14:50:49.926844", - "exception": false, - "start_time": "2026-01-20T14:50:36.590093", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.002667, - "end_time": "2026-01-20T14:50:50.026280", - "exception": false, - "start_time": "2026-01-20T14:50:50.023613", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:50.036372Z", - "iopub.status.busy": "2026-01-20T14:50:50.034966Z", - "iopub.status.idle": "2026-01-20T14:50:50.050308Z", - "shell.execute_reply": "2026-01-20T14:50:50.046821Z" - }, - "papermill": { - "duration": 0.024971, - "end_time": "2026-01-20T14:50:50.053767", - "exception": false, - "start_time": "2026-01-20T14:50:50.028796", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = (L @ z_std).astype(np.float32)\n", - " \n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.002452, - "end_time": "2026-01-20T14:50:50.063436", - "exception": false, - "start_time": "2026-01-20T14:50:50.060984", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:50.072329Z", - "iopub.status.busy": "2026-01-20T14:50:50.071515Z", - "iopub.status.idle": "2026-01-20T14:50:50.112620Z", - "shell.execute_reply": "2026-01-20T14:50:50.109575Z" - }, - "papermill": { - "duration": 0.050054, - "end_time": "2026-01-20T14:50:50.115866", - "exception": false, - "start_time": "2026-01-20T14:50:50.065812", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:50.128355Z", - "iopub.status.busy": "2026-01-20T14:50:50.128057Z", - "iopub.status.idle": "2026-01-20T14:50:50.147533Z", - "shell.execute_reply": "2026-01-20T14:50:50.145405Z" - }, - "papermill": { - "duration": 0.027732, - "end_time": "2026-01-20T14:50:50.150497", - "exception": false, - "start_time": "2026-01-20T14:50:50.122765", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00384, - "end_time": "2026-01-20T14:50:50.162003", - "exception": false, - "start_time": "2026-01-20T14:50:50.158163", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:50.170570Z", - "iopub.status.busy": "2026-01-20T14:50:50.170083Z", - "iopub.status.idle": "2026-01-20T14:50:50.177444Z", - "shell.execute_reply": "2026-01-20T14:50:50.175613Z" - }, - "papermill": { - "duration": 0.013843, - "end_time": "2026-01-20T14:50:50.179154", - "exception": false, - "start_time": "2026-01-20T14:50:50.165311", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T14:50:50.187212Z", - "iopub.status.busy": "2026-01-20T14:50:50.186891Z", - "iopub.status.idle": "2026-01-20T17:39:14.426228Z", - "shell.execute_reply": "2026-01-20T17:39:14.422887Z" - }, - "papermill": { - "duration": 10104.247253, - "end_time": "2026-01-20T17:39:14.429146", - "exception": false, - "start_time": "2026-01-20T14:50:50.181893", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.3499 (±0.0202), Variance: 1.0165, Range: [-1.8179, 3.2190]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1045 0.0817 \n", - " 100 0.0676 0.0587 \n", - " 200 0.0478 0.0370 \n", - " 500 0.0430 0.0341 *\n", - " 700 0.0448 0.0368 \n", - " 1000 1.0079 1.0603 \n", - " 1400 1.0079 1.0603 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768920669.492217 4020518 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768920671.870285 4020699 service.cc:152] XLA service 0x57c55e5d0420 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768920671.870367 4020699 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768920672.240397 4020699 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768920680.473586 4020699 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x733a08e81b40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x733a086f11b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0293 0.0301 *\n", - " 100 0.0313 0.0265 \n", - " 200 0.0338 0.0294 \n", - " 500 0.0342 0.0277 \n", - " 700 0.0363 0.0351 \n", - " 1000 0.8958 0.9823 \n", - " 1400 0.9052 0.9723 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0160 0.0288 \n", - " 100 0.0158 0.0297 \n", - " 200 0.0157 0.0297 *\n", - " 500 0.0173 0.0310 \n", - " 700 0.0176 0.0260 \n", - " 1000 0.4203 1.0033 \n", - " 1400 0.4179 0.9939 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0007, sigma²=0.0934, nugget=0.0003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0647, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0373, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5694, sigma²=0.0000, nugget=0.0200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0281, sigma²=0.0000, nugget=0.0146\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0035, sigma²=0.0000, nugget=0.0094\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0024, sigma²=0.0000, nugget=0.0051\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0290 0.0231 \n", - " 100 0.0285 0.0233 *\n", - " 200 0.0299 0.0233 \n", - " 500 0.0430 0.0341 \n", - " 700 0.0448 0.0368 \n", - " 1000 0.0716 0.0539 \n", - " 1400 1.5606 0.3645 \n", - " Best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0647, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4628 | 0.6803 | 0.5122 | 0.5654 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0341 | 0.1847 | 0.1499 | 0.968 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3031 | 0.5505 | 0.3839 | 0.7154 | 51.77s |\n", - "| DeepKriging_sphere | 50 | 0.0269 | 0.1639 | 0.1284 | 0.9748 | 34.54s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0279 | 0.1671 | 0.131 | 0.9738 | 41.06s |\n", - "| UniversalKriging | 100 | 0.0233 | 0.1525 | 0.1154 | 0.9781 | 2.35s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:03:02\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.4176 (±0.0149), Variance: 0.5573, Range: [-1.4140, 2.4415]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0550, nugget=0.0017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4376 | 0.6615 | 0.5302 | 0.1667 | 0.40s |\n", - "| OLS_sphere | 500 | 0.0474 | 0.2177 | 0.1695 | 0.9097 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2931 | 0.5414 | 0.4165 | 0.4419 | 63.12s |\n", - "| DeepKriging_sphere | 50 | 0.0373 | 0.1931 | 0.1534 | 0.929 | 35.67s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.035 | 0.1871 | 0.1465 | 0.9334 | 35.68s |\n", - "| UniversalKriging | 100 | 0.0302 | 0.1737 | 0.136 | 0.9425 | 1.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:05:47\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1219 (±0.0114), Variance: 0.3252, Range: [-1.8046, 1.6246]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0586, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.1841 | 0.429 | 0.3432 | 0.3987 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0376 | 0.1939 | 0.1511 | 0.8772 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.1365 | 0.3694 | 0.275 | 0.5541 | 63.86s |\n", - "| DeepKriging_sphere | 50 | 0.211 | 0.4593 | 0.3714 | 0.3106 | 14.06s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0322 | 0.1794 | 0.1396 | 0.8948 | 42.84s |\n", - "| UniversalKriging | 100 | 0.0245 | 0.1566 | 0.1221 | 0.9199 | 3.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:08:19\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.0497 (±0.0198), Variance: 0.9846, Range: [-3.1433, 2.5210]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0569, nugget=0.0005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7286 | 0.8536 | 0.5715 | 0.1722 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0371 | 0.1927 | 0.1563 | 0.9578 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4685 | 0.6844 | 0.4649 | 0.4678 | 66.31s |\n", - "| DeepKriging_sphere | 50 | 0.0325 | 0.1802 | 0.1406 | 0.9631 | 33.71s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0328 | 0.1811 | 0.1414 | 0.9627 | 42.51s |\n", - "| UniversalKriging | 100 | 0.0298 | 0.1727 | 0.1393 | 0.9661 | 2.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:11:19\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.4890 (±0.0182), Variance: 0.8305, Range: [-2.5579, 2.8594]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0700, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9689 | 0.9844 | 0.67 | -0.2223 | 0.38s |\n", - "| OLS_sphere | 500 | 0.0373 | 0.1932 | 0.1532 | 0.9529 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.6614 | 0.8133 | 0.5191 | 0.1657 | 51.55s |\n", - "| DeepKriging_sphere | 50 | 0.031 | 0.1761 | 0.1381 | 0.9609 | 40.83s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.5035 | 0.7096 | 0.5514 | 0.3648 | 15.07s |\n", - "| UniversalKriging | 100 | 0.0273 | 0.1652 | 0.1302 | 0.9656 | 4.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:13:44\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.0289 (±0.0156), Variance: 0.6105, Range: [-1.7509, 2.2876]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0661, nugget=0.0003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.532 | 0.7294 | 0.437 | -0.0344 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0335 | 0.183 | 0.1418 | 0.9349 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2141 | 0.4628 | 0.3288 | 0.5836 | 50.41s |\n", - "| DeepKriging_sphere | 50 | 0.3259 | 0.5709 | 0.4571 | 0.3663 | 15.13s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.3997 | 0.6322 | 0.5093 | 0.2228 | 12.96s |\n", - "| UniversalKriging | 100 | 0.0245 | 0.1565 | 0.1222 | 0.9523 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:15:40\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.6610 (±0.0124), Variance: 0.3834, Range: [-1.2027, 2.8062]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0579, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.316 | 0.5621 | 0.4212 | 0.1725 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0368 | 0.1919 | 0.1524 | 0.9035 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.202 | 0.4494 | 0.3366 | 0.471 | 56.40s |\n", - "| DeepKriging_sphere | 50 | 0.2785 | 0.5277 | 0.4371 | 0.2707 | 17.13s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0296 | 0.1721 | 0.1356 | 0.9225 | 44.71s |\n", - "| UniversalKriging | 100 | 0.0283 | 0.1681 | 0.1346 | 0.926 | 3.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:18:16\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.2414 (±0.0202), Variance: 1.0183, Range: [-1.9621, 3.1672]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0634, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9903 | 0.9951 | 0.7025 | 0.1725 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0419 | 0.2046 | 0.1611 | 0.965 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4884 | 0.6989 | 0.4863 | 0.5919 | 62.19s |\n", - "| DeepKriging_sphere | 50 | 0.0367 | 0.1916 | 0.1534 | 0.9693 | 37.78s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.039 | 0.1976 | 0.159 | 0.9674 | 44.68s |\n", - "| UniversalKriging | 100 | 0.0296 | 0.1721 | 0.1364 | 0.9752 | 3.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:21:19\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.3908 (±0.0176), Variance: 0.7706, Range: [-1.6303, 2.8375]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0561, nugget=0.0005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6358 | 0.7974 | 0.5927 | 0.1495 | 0.41s |\n", - "| OLS_sphere | 500 | 0.038 | 0.1949 | 0.1553 | 0.9492 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.5909 | 0.7687 | 0.5501 | 0.2096 | 65.48s |\n", - "| DeepKriging_sphere | 50 | 0.033 | 0.1817 | 0.142 | 0.9558 | 37.57s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0323 | 0.1798 | 0.1397 | 0.9567 | 38.22s |\n", - "| UniversalKriging | 100 | 0.0254 | 0.1594 | 0.1254 | 0.966 | 2.75s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:24:19\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.6936 (±0.0202), Variance: 1.0223, Range: [-2.2170, 3.2393]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0566, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0794 | 1.442 | 0.6742 | -0.9146 | 0.42s |\n", - "| OLS_sphere | 500 | 0.04 | 0.1999 | 0.1627 | 0.9632 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.3589 | 0.599 | 0.4691 | 0.6696 | 62.27s |\n", - "| DeepKriging_sphere | 50 | 0.0362 | 0.1903 | 0.1514 | 0.9666 | 47.74s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0345 | 0.1857 | 0.1464 | 0.9682 | 35.65s |\n", - "| UniversalKriging | 100 | 0.0297 | 0.1722 | 0.1344 | 0.9727 | 4.62s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:27:32\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.4645 (±0.0130), Variance: 0.4230, Range: [-1.7744, 2.5085]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0588, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.3827 | 0.6186 | 0.4118 | -0.016 | 0.38s |\n", - "| OLS_sphere | 500 | 0.0376 | 0.194 | 0.1509 | 0.9001 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2584 | 0.5084 | 0.3538 | 0.3138 | 59.57s |\n", - "| DeepKriging_sphere | 50 | 0.0374 | 0.1934 | 0.1478 | 0.9007 | 34.99s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0344 | 0.1856 | 0.1434 | 0.9086 | 40.22s |\n", - "| UniversalKriging | 100 | 0.0316 | 0.1776 | 0.1327 | 0.9162 | 4.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:30:33\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.6035 (±0.0159), Variance: 0.6314, Range: [-1.3893, 2.5395]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0543, nugget=0.0008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.916 | 0.9571 | 0.5926 | -0.3357 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0361 | 0.1899 | 0.1509 | 0.9474 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.4232 | 0.6506 | 0.442 | 0.3828 | 68.77s |\n", - "| DeepKriging_sphere | 50 | 0.4153 | 0.6444 | 0.5364 | 0.3944 | 13.35s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0305 | 0.1747 | 0.1397 | 0.9555 | 42.65s |\n", - "| UniversalKriging | 100 | 0.026 | 0.1613 | 0.1281 | 0.9621 | 1.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:33:21\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1021 (±0.0123), Variance: 0.3760, Range: [-2.0867, 1.6999]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0582, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3495 | 0.5912 | 0.4365 | 0.0829 | 0.40s |\n", - "| OLS_sphere | 500 | 0.037 | 0.1923 | 0.1502 | 0.903 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3156 | 0.5618 | 0.3705 | 0.1719 | 65.36s |\n", - "| DeepKriging_sphere | 50 | 0.2797 | 0.5288 | 0.4409 | 0.2662 | 15.22s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0257 | 0.1604 | 0.1278 | 0.9325 | 39.84s |\n", - "| UniversalKriging | 100 | 0.0247 | 0.1573 | 0.1261 | 0.9351 | 3.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:36:11\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -1.2765 (±0.0136), Variance: 0.4651, Range: [-3.1591, 0.9336]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0620, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.229 | 0.4785 | 0.3754 | 0.4082 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0383 | 0.1957 | 0.155 | 0.901 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.157 | 0.3963 | 0.2978 | 0.5941 | 72.46s |\n", - "| DeepKriging_sphere | 50 | 0.0329 | 0.1813 | 0.1439 | 0.915 | 51.25s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.031 | 0.1762 | 0.1396 | 0.9198 | 36.75s |\n", - "| UniversalKriging | 100 | 0.028 | 0.1673 | 0.1318 | 0.9276 | 3.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:39:38\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -1.2128 (±0.0178), Variance: 0.7898, Range: [-3.3871, 1.2491]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0621, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.8431 | 0.9182 | 0.5287 | -0.3288 | 0.39s |\n", - "| OLS_sphere | 500 | 0.0452 | 0.2126 | 0.1643 | 0.9288 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3785 | 0.6153 | 0.4399 | 0.4034 | 63.25s |\n", - "| DeepKriging_sphere | 50 | 0.0341 | 0.1845 | 0.1418 | 0.9463 | 44.12s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0336 | 0.1832 | 0.137 | 0.9471 | 43.85s |\n", - "| UniversalKriging | 100 | 0.0262 | 0.1619 | 0.1261 | 0.9587 | 4.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:42:58\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1299 (±0.0145), Variance: 0.5228, Range: [-2.1635, 2.1157]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0659, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4067 | 0.6378 | 0.4454 | 0.1943 | 0.34s |\n", - "| OLS_sphere | 500 | 0.0471 | 0.2171 | 0.173 | 0.9066 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.206 | 0.4539 | 0.3443 | 0.5919 | 63.67s |\n", - "| DeepKriging_sphere | 50 | 0.037 | 0.1924 | 0.1536 | 0.9267 | 34.57s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.3687 | 0.6072 | 0.4841 | 0.2695 | 13.46s |\n", - "| UniversalKriging | 100 | 0.0312 | 0.1766 | 0.1374 | 0.9382 | 4.63s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:45:42\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.4129 (±0.0156), Variance: 0.6071, Range: [-2.5863, 2.4611]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0598, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.2535 | 0.5035 | 0.3949 | 0.632 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0393 | 0.1984 | 0.1511 | 0.9429 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.172 | 0.4147 | 0.3249 | 0.7503 | 66.77s |\n", - "| DeepKriging_sphere | 50 | 0.47 | 0.6855 | 0.5621 | 0.3178 | 16.13s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0348 | 0.1865 | 0.1443 | 0.9495 | 40.89s |\n", - "| UniversalKriging | 100 | 0.0293 | 0.1712 | 0.1323 | 0.9574 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:48:35\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.5168 (±0.0243), Variance: 1.4777, Range: [-3.9324, 2.6834]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0589, nugget=0.0004\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8262 | 0.909 | 0.6859 | 0.4183 | 0.37s |\n", - "| OLS_sphere | 500 | 0.0349 | 0.1869 | 0.1465 | 0.9754 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.5279 | 0.7266 | 0.5221 | 0.6283 | 56.60s |\n", - "| DeepKriging_sphere | 50 | 0.0269 | 0.1641 | 0.1331 | 0.981 | 43.12s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0286 | 0.1691 | 0.1347 | 0.9799 | 42.70s |\n", - "| UniversalKriging | 100 | 0.0224 | 0.1496 | 0.1189 | 0.9842 | 2.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:51:50\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.0060 (±0.0167), Variance: 0.6939, Range: [-2.4920, 2.0258]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0621, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.39 | 1.546 | 0.6211 | -2.4121 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0358 | 0.1893 | 0.149 | 0.9488 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3707 | 0.6088 | 0.4463 | 0.4708 | 70.78s |\n", - "| DeepKriging_sphere | 50 | 0.0327 | 0.1807 | 0.1422 | 0.9534 | 39.64s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0321 | 0.1791 | 0.1418 | 0.9542 | 42.43s |\n", - "| UniversalKriging | 100 | 0.0285 | 0.1687 | 0.1338 | 0.9594 | 3.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:55:15\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1307 (±0.0108), Variance: 0.2908, Range: [-2.4790, 1.6362]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0614, nugget=0.0003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.1954 | 0.4421 | 0.3312 | 0.2194 | 0.40s |\n", - "| OLS_sphere | 500 | 0.0395 | 0.1987 | 0.1576 | 0.8423 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.137 | 0.3701 | 0.2867 | 0.453 | 60.63s |\n", - "| DeepKriging_sphere | 50 | 0.1731 | 0.416 | 0.3287 | 0.3088 | 13.96s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0311 | 0.1764 | 0.136 | 0.8757 | 50.54s |\n", - "| UniversalKriging | 100 | 0.0263 | 0.1623 | 0.1261 | 0.8948 | 2.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 23:58:16\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.3818 (±0.0160), Variance: 0.6408, Range: [-1.7810, 2.8363]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0543, nugget=0.0002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5382 | 0.7336 | 0.4765 | 0.1807 | 0.37s |\n", - "| OLS_sphere | 500 | 0.0424 | 0.2059 | 0.1611 | 0.9355 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.3244 | 0.5696 | 0.4226 | 0.5061 | 62.63s |\n", - "| DeepKriging_sphere | 50 | 0.3816 | 0.6177 | 0.4892 | 0.4191 | 13.10s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0299 | 0.1729 | 0.135 | 0.9545 | 39.05s |\n", - "| UniversalKriging | 100 | 0.0284 | 0.1686 | 0.1328 | 0.9567 | 1.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:01:06\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.0475 (±0.0122), Variance: 0.3752, Range: [-1.7512, 1.6105]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0554, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.2617 | 0.5115 | 0.3957 | 0.2281 | 0.45s |\n", - "| OLS_sphere | 500 | 0.0382 | 0.1955 | 0.1529 | 0.8872 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.1766 | 0.4203 | 0.3199 | 0.479 | 64.23s |\n", - "| DeepKriging_sphere | 50 | 0.2524 | 0.5024 | 0.4132 | 0.2555 | 13.41s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0317 | 0.178 | 0.1374 | 0.9066 | 42.58s |\n", - "| UniversalKriging | 100 | 0.026 | 0.1613 | 0.1269 | 0.9232 | 2.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:04:05\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.0545 (±0.0132), Variance: 0.4332, Range: [-1.7565, 2.0484]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0593, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3345 | 0.5784 | 0.4453 | 0.1564 | 0.40s |\n", - "| OLS_sphere | 500 | 0.0474 | 0.2178 | 0.1657 | 0.8804 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2574 | 0.5074 | 0.3871 | 0.3508 | 63.58s |\n", - "| DeepKriging_sphere | 50 | 0.2825 | 0.5315 | 0.4366 | 0.2877 | 15.72s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0389 | 0.1972 | 0.1514 | 0.902 | 43.83s |\n", - "| UniversalKriging | 100 | 0.0306 | 0.175 | 0.135 | 0.9228 | 1.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:07:03\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.3233 (±0.0161), Variance: 0.6502, Range: [-2.5779, 2.4986]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0582, nugget=0.0004\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.2905 | 0.539 | 0.4162 | 0.5014 | -2.02s |\n", - "| OLS_sphere | 500 | 0.0382 | 0.1954 | 0.1508 | 0.9345 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2561 | 0.5061 | 0.3654 | 0.5604 | 71.23s |\n", - "| DeepKriging_sphere | 50 | 0.3867 | 0.6218 | 0.5075 | 0.3363 | 13.44s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0294 | 0.1715 | 0.1352 | 0.9495 | 46.20s |\n", - "| UniversalKriging | 100 | 0.026 | 0.1614 | 0.125 | 0.9553 | 2.45s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:10:11\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.8638 (±0.0152), Variance: 0.5748, Range: [-2.7245, 1.2572]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0624, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4304 | 0.656 | 0.4996 | 0.1775 | 0.39s |\n", - "| OLS_sphere | 500 | 0.0397 | 0.1992 | 0.1583 | 0.9242 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4873 | 0.6981 | 0.4543 | 0.0687 | 67.51s |\n", - "| DeepKriging_sphere | 50 | 0.0323 | 0.1798 | 0.1441 | 0.9383 | 40.80s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0324 | 0.1799 | 0.1414 | 0.9382 | 51.82s |\n", - "| UniversalKriging | 100 | 0.0266 | 0.1632 | 0.1316 | 0.9491 | 4.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:13:53\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1207 (±0.0149), Variance: 0.5523, Range: [-2.6453, 1.7831]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0693, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4601 | 0.6783 | 0.4985 | 0.1627 | 0.35s |\n", - "| OLS_sphere | 500 | 0.0469 | 0.2165 | 0.1633 | 0.9147 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.3588 | 0.599 | 0.425 | 0.3471 | 65.63s |\n", - "| DeepKriging_sphere | 50 | 0.038 | 0.1949 | 0.1497 | 0.9308 | 35.97s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.4332 | 0.6582 | 0.5032 | 0.2117 | 13.67s |\n", - "| UniversalKriging | 100 | 0.0311 | 0.1763 | 0.1348 | 0.9435 | 4.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:16:53\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 1.4770 (±0.0153), Variance: 0.5854, Range: [-1.0269, 3.8629]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0651, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4839 | 0.6956 | 0.4415 | 0.133 | 0.42s |\n", - "| OLS_sphere | 500 | 0.04 | 0.2001 | 0.1574 | 0.9283 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2569 | 0.5068 | 0.3686 | 0.5398 | 66.42s |\n", - "| DeepKriging_sphere | 50 | 0.0366 | 0.1913 | 0.1557 | 0.9344 | 39.88s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0315 | 0.1774 | 0.1417 | 0.9436 | 49.78s |\n", - "| UniversalKriging | 100 | 0.0288 | 0.1696 | 0.1367 | 0.9484 | 0.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:20:31\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.1511 (±0.0167), Variance: 0.7013, Range: [-2.1626, 2.4590]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0622, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6778 | 0.8233 | 0.6314 | 0.172 | 0.44s |\n", - "| OLS_sphere | 500 | 0.043 | 0.2073 | 0.1623 | 0.9475 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5544 | 0.7446 | 0.532 | 0.3228 | 64.94s |\n", - "| DeepKriging_sphere | 50 | 0.0355 | 0.1883 | 0.1494 | 0.9567 | 35.98s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.036 | 0.1898 | 0.149 | 0.956 | 35.94s |\n", - "| UniversalKriging | 100 | 0.0306 | 0.175 | 0.1344 | 0.9626 | 4.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:23:51\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.4343 (±0.0179), Variance: 0.8000, Range: [-2.1594, 3.1558]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0595, nugget=0.0002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.0504 | 1.0249 | 0.686 | -0.2096 | 0.36s |\n", - "| OLS_sphere | 500 | 0.0322 | 0.1796 | 0.143 | 0.9629 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.6837 | 0.8269 | 0.4919 | 0.2127 | 70.21s |\n", - "| DeepKriging_sphere | 50 | 0.0283 | 0.1681 | 0.1345 | 0.9675 | 38.24s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0284 | 0.1686 | 0.1316 | 0.9673 | 40.40s |\n", - "| UniversalKriging | 100 | 0.0249 | 0.158 | 0.1245 | 0.9713 | 2.68s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:27:24\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 1.4241 (±0.0209), Variance: 1.0946, Range: [-1.0545, 4.1870]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0578, nugget=0.0002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6649 | 0.8154 | 0.6386 | 0.3405 | 0.51s |\n", - "| OLS_sphere | 500 | 0.0438 | 0.2093 | 0.1605 | 0.9566 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4464 | 0.6681 | 0.4854 | 0.5572 | 61.21s |\n", - "| DeepKriging_sphere | 50 | 0.0336 | 0.1833 | 0.1463 | 0.9667 | 46.91s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0344 | 0.1853 | 0.1458 | 0.9659 | 40.46s |\n", - "| UniversalKriging | 100 | 0.0299 | 0.1728 | 0.1358 | 0.9704 | 0.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:30:59\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.1618 (±0.0173), Variance: 0.7457, Range: [-2.4104, 2.3521]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0571, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6987 | 0.8359 | 0.5932 | 0.0494 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0364 | 0.1908 | 0.1493 | 0.9505 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.88 | 0.9381 | 0.5366 | -0.1973 | 60.64s |\n", - "| DeepKriging_sphere | 50 | 0.4601 | 0.6783 | 0.5461 | 0.374 | 14.08s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.5349 | 0.7313 | 0.5916 | 0.2723 | 16.33s |\n", - "| UniversalKriging | 100 | 0.026 | 0.1613 | 0.1291 | 0.9646 | 5.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:33:39\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.9576 (±0.0177), Variance: 0.7818, Range: [-3.0588, 1.2655]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0589, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.555 | 0.745 | 0.5526 | 0.2675 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0407 | 0.2017 | 0.1538 | 0.9463 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.4739 | 0.6884 | 0.4842 | 0.3746 | 61.54s |\n", - "| DeepKriging_sphere | 50 | 0.6621 | 0.8137 | 0.6599 | 0.1262 | 13.60s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0273 | 0.1651 | 0.1364 | 0.964 | 42.30s |\n", - "| UniversalKriging | 100 | 0.0262 | 0.1618 | 0.1276 | 0.9655 | 3.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:36:45\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.9836 (±0.0149), Variance: 0.5518, Range: [-0.9148, 3.1304]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0604, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.5789 | 0.7608 | 0.5343 | -0.0662 | 0.41s |\n", - "| OLS_sphere | 500 | 0.039 | 0.1976 | 0.1545 | 0.9281 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5268 | 0.7258 | 0.4459 | 0.0298 | 60.53s |\n", - "| DeepKriging_sphere | 50 | 0.0346 | 0.186 | 0.1463 | 0.9363 | 36.08s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0352 | 0.1875 | 0.1512 | 0.9352 | 37.84s |\n", - "| UniversalKriging | 100 | 0.0245 | 0.1564 | 0.1207 | 0.9549 | 4.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:40:14\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.4921 (±0.0124), Variance: 0.3849, Range: [-2.1484, 1.5820]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0582, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.2367 | 0.4865 | 0.3785 | 0.3907 | 0.38s |\n", - "| OLS_sphere | 500 | 0.0419 | 0.2048 | 0.1539 | 0.892 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.1566 | 0.3957 | 0.3042 | 0.5969 | 56.02s |\n", - "| DeepKriging_sphere | 50 | 0.0367 | 0.1916 | 0.1403 | 0.9055 | 44.14s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0339 | 0.1841 | 0.137 | 0.9127 | 34.21s |\n", - "| UniversalKriging | 100 | 0.0326 | 0.1807 | 0.1361 | 0.916 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:43:41\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.6283 (±0.0160), Variance: 0.6397, Range: [-2.1169, 2.5154]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0680, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4144 | 0.6437 | 0.4645 | 0.2801 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0387 | 0.1966 | 0.1562 | 0.9328 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2452 | 0.4951 | 0.3475 | 0.5741 | 58.49s |\n", - "| DeepKriging_sphere | 50 | 0.0293 | 0.1711 | 0.1302 | 0.9491 | 42.30s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.03 | 0.1731 | 0.1316 | 0.9479 | 34.93s |\n", - "| UniversalKriging | 100 | 0.0232 | 0.1524 | 0.1169 | 0.9597 | 4.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:47:11\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.7788 (±0.0129), Variance: 0.4159, Range: [-2.4667, 1.3199]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0611, nugget=0.0005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3408 | 0.5838 | 0.4247 | 0.1275 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0433 | 0.2082 | 0.1638 | 0.8891 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2207 | 0.4698 | 0.3448 | 0.435 | 54.49s |\n", - "| DeepKriging_sphere | 50 | 0.034 | 0.1844 | 0.1443 | 0.9129 | 34.50s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0346 | 0.1859 | 0.1453 | 0.9115 | 44.13s |\n", - "| UniversalKriging | 100 | 0.0281 | 0.1676 | 0.1327 | 0.9281 | 2.42s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:50:40\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.1179 (±0.0137), Variance: 0.4725, Range: [-1.8417, 1.8152]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0623, nugget=0.0008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3449 | 0.5873 | 0.4681 | 0.2674 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0393 | 0.1981 | 0.1492 | 0.9166 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2698 | 0.5195 | 0.3822 | 0.4269 | 56.19s |\n", - "| DeepKriging_sphere | 50 | 0.0352 | 0.1876 | 0.1434 | 0.9253 | 28.72s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.3508 | 0.5923 | 0.4983 | 0.2549 | 14.39s |\n", - "| UniversalKriging | 100 | 0.0258 | 0.1606 | 0.1231 | 0.9452 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:53:32\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.0775 (±0.0135), Variance: 0.4550, Range: [-2.2229, 2.6000]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0562, nugget=0.0007\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3606 | 0.6005 | 0.4309 | 0.2028 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0402 | 0.2005 | 0.152 | 0.9111 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2642 | 0.514 | 0.3686 | 0.4159 | 42.91s |\n", - "| DeepKriging_sphere | 50 | 0.0279 | 0.1671 | 0.1319 | 0.9382 | 30.88s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.031 | 0.176 | 0.1401 | 0.9315 | 31.41s |\n", - "| UniversalKriging | 100 | 0.0251 | 0.1584 | 0.1251 | 0.9446 | 2.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:56:36\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -1.2417 (±0.0146), Variance: 0.5354, Range: [-3.1620, 0.6874]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0627, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3152 | 0.5614 | 0.437 | 0.3599 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0474 | 0.2177 | 0.1744 | 0.9038 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.2646 | 0.5144 | 0.3762 | 0.4625 | 52.99s |\n", - "| DeepKriging_sphere | 50 | 0.0401 | 0.2003 | 0.1622 | 0.9185 | 32.28s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0406 | 0.2015 | 0.156 | 0.9175 | 35.59s |\n", - "| UniversalKriging | 100 | 0.0336 | 0.1832 | 0.1426 | 0.9318 | 4.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 00:59:54\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.5560 (±0.0139), Variance: 0.4836, Range: [-1.2917, 2.6186]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0687, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.391 | 0.6253 | 0.4911 | 0.1877 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0364 | 0.1907 | 0.1469 | 0.9245 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2869 | 0.5356 | 0.3982 | 0.404 | 53.57s |\n", - "| DeepKriging_sphere | 50 | 0.0307 | 0.1752 | 0.1343 | 0.9363 | 37.32s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0304 | 0.1743 | 0.1344 | 0.9369 | 46.15s |\n", - "| UniversalKriging | 100 | 0.0249 | 0.1578 | 0.1214 | 0.9483 | 2.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:03:28\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.3916 (±0.0284), Variance: 2.0126, Range: [-2.6386, 3.3500]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0661, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.4104 | 1.5525 | 0.9811 | -0.2506 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0367 | 0.1916 | 0.1509 | 0.981 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.7355 | 1.3174 | 0.816 | 0.0995 | 59.85s |\n", - "| DeepKriging_sphere | 50 | 0.0285 | 0.1688 | 0.1337 | 0.9852 | 36.75s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0264 | 0.1626 | 0.1276 | 0.9863 | 37.62s |\n", - "| UniversalKriging | 100 | 0.0217 | 0.1472 | 0.117 | 0.9888 | 2.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:07:00\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.2252 (±0.0150), Variance: 0.5606, Range: [-1.8222, 2.1241]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0547, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6375 | 0.7984 | 0.5483 | -0.1534 | 0.53s |\n", - "| OLS_sphere | 500 | 0.0486 | 0.2204 | 0.172 | 0.9122 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3413 | 0.5842 | 0.4423 | 0.3825 | 57.06s |\n", - "| DeepKriging_sphere | 50 | 0.0394 | 0.1985 | 0.1592 | 0.9287 | 34.15s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0381 | 0.1952 | 0.1543 | 0.9311 | 39.44s |\n", - "| UniversalKriging | 100 | 0.0356 | 0.1886 | 0.15 | 0.9357 | 5.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:10:33\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.0338 (±0.0160), Variance: 0.6397, Range: [-2.1071, 2.3082]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0701, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.535 | 0.7315 | 0.5624 | 0.185 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0341 | 0.1846 | 0.1515 | 0.9481 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.343 | 0.5857 | 0.4213 | 0.4775 | 54.12s |\n", - "| DeepKriging_sphere | 50 | 0.4084 | 0.6391 | 0.5181 | 0.3778 | 15.99s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0275 | 0.166 | 0.1317 | 0.958 | 37.85s |\n", - "| UniversalKriging | 100 | 0.0248 | 0.1574 | 0.1273 | 0.9623 | 3.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:13:38\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.2168 (±0.0185), Variance: 0.8551, Range: [-2.9964, 2.0003]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0617, nugget=0.0006\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9881 | 0.994 | 0.6656 | -0.082 | 0.38s |\n", - "| OLS_sphere | 500 | 0.0312 | 0.1765 | 0.1405 | 0.9659 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5618 | 0.7496 | 0.5243 | 0.3847 | 58.15s |\n", - "| DeepKriging_sphere | 50 | 0.0317 | 0.1779 | 0.1437 | 0.9653 | 35.67s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0322 | 0.1794 | 0.143 | 0.9648 | 37.55s |\n", - "| UniversalKriging | 100 | 0.0246 | 0.1568 | 0.1253 | 0.9731 | 2.45s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:17:10\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.4744 (±0.0170), Variance: 0.7190, Range: [-2.7720, 2.1511]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0679, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5206 | 0.7215 | 0.5634 | 0.2611 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0338 | 0.1839 | 0.144 | 0.952 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3924 | 0.6264 | 0.4534 | 0.443 | 56.48s |\n", - "| DeepKriging_sphere | 50 | 0.0253 | 0.1589 | 0.1232 | 0.9641 | 36.58s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0272 | 0.1651 | 0.1286 | 0.9613 | 32.39s |\n", - "| UniversalKriging | 100 | 0.0203 | 0.1424 | 0.1126 | 0.9712 | 2.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:20:39\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.7933 (±0.0192), Variance: 0.9255, Range: [-1.3148, 3.7771]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0613, nugget=0.0005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.4519 | 0.6723 | 0.5233 | 0.5557 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0409 | 0.2023 | 0.1561 | 0.9598 | 0.08s |\n", - "| DeepKriging_wendland | -- | 1.6906 | 1.3002 | 0.4548 | -0.6621 | 64.60s |\n", - "| DeepKriging_sphere | 50 | 0.6175 | 0.7858 | 0.6153 | 0.3929 | 16.50s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0309 | 0.1757 | 0.1377 | 0.9697 | 46.38s |\n", - "| UniversalKriging | 100 | 0.0287 | 0.1695 | 0.1319 | 0.9718 | -0.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:24:10\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.6928 (±0.0135), Variance: 0.4584, Range: [-1.3159, 2.6472]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0605, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9222 | 0.9603 | 0.5163 | -1.016 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0427 | 0.2067 | 0.1666 | 0.9066 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.2947 | 0.5428 | 0.3973 | 0.3559 | 57.42s |\n", - "| DeepKriging_sphere | 50 | 0.0326 | 0.1807 | 0.1406 | 0.9286 | 36.68s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0348 | 0.1865 | 0.1452 | 0.9239 | 38.27s |\n", - "| UniversalKriging | 100 | 0.0289 | 0.1701 | 0.1329 | 0.9368 | 4.68s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:27:53\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: 0.7972 (±0.0134), Variance: 0.4499, Range: [-1.9429, 2.5671]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0644, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3095 | 0.5563 | 0.3921 | 0.2216 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0444 | 0.2106 | 0.1665 | 0.8884 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.2261 | 0.4755 | 0.3155 | 0.4313 | 61.18s |\n", - "| DeepKriging_sphere | 50 | 0.0316 | 0.1777 | 0.1364 | 0.9206 | 44.13s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0312 | 0.1767 | 0.1357 | 0.9215 | 40.66s |\n", - "| UniversalKriging | 100 | 0.0275 | 0.1659 | 0.1289 | 0.9308 | 4.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:31:42\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -0.3476 (±0.0123), Variance: 0.3760, Range: [-1.9972, 1.2591]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0624, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.285 | 0.5338 | 0.4049 | 0.318 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0412 | 0.2029 | 0.1628 | 0.9014 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.2472 | 0.4972 | 0.3817 | 0.4085 | 56.34s |\n", - "| DeepKriging_sphere | 50 | 0.036 | 0.1897 | 0.1519 | 0.9139 | 35.42s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0366 | 0.1913 | 0.1529 | 0.9125 | 36.76s |\n", - "| UniversalKriging | 100 | 0.0323 | 0.1799 | 0.143 | 0.9226 | 4.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:35:20\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z mean: -1.2280 (±0.0149), Variance: 0.5515, Range: [-3.2558, 0.6863]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0674, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4148 | 0.644 | 0.4878 | 0.22 | 0.40s |\n", - "| OLS_sphere | 500 | 0.0414 | 0.2036 | 0.1645 | 0.9221 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.4405 | 0.6637 | 0.4422 | 0.1717 | 57.69s |\n", - "| DeepKriging_sphere | 50 | 0.032 | 0.179 | 0.1401 | 0.9397 | 41.98s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0314 | 0.1772 | 0.1429 | 0.941 | 43.88s |\n", - "| UniversalKriging | 100 | 0.0281 | 0.1678 | 0.1341 | 0.9471 | 4.44s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 01:39:14\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.032903, - "end_time": "2026-01-20T17:39:14.504625", - "exception": false, - "start_time": "2026-01-20T17:39:14.471722", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T17:39:14.554356Z", - "iopub.status.busy": "2026-01-20T17:39:14.553432Z", - "iopub.status.idle": "2026-01-20T17:39:14.583340Z", - "shell.execute_reply": "2026-01-20T17:39:14.579958Z" - }, - "papermill": { - "duration": 0.055475, - "end_time": "2026-01-20T17:39:14.586404", - "exception": false, - "start_time": "2026-01-20T17:39:14.530929", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 200, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|-----------|\n", - "| OLS_wendland | 0.62±0.48 | 0.75±0.25 | 0.52±0.12 | 0.07±0.47 |\n", - "| OLS_sphere | 0.04±0.00 | 0.20±0.01 | 0.16±0.01 | 0.93±0.03 |\n", - "| DeepKriging_wendland | 0.41±0.31 | 0.61±0.19 | 0.42±0.09 | 0.39±0.24 |\n", - "| DeepKriging_sphere | 0.14±0.17 | 0.31±0.20 | 0.25±0.17 | 0.76±0.29 |\n", - "| DeepKriging_sphere_Huber | 0.08±0.13 | 0.24±0.16 | 0.19±0.13 | 0.86±0.22 |\n", - "| UniversalKriging | 0.03±0.00 | 0.17±0.01 | 0.13±0.01 | 0.95±0.02 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 0.17±0.01)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 10124.389272, - "end_time": "2026-01-20T17:39:15.605440", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_without_outliers.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/euclidean/simulation_scenarioI_euclidean_without_outliers.ipynb", - "parameters": {}, - "start_time": "2026-01-20T14:50:31.216168", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_chisquare.ipynb b/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_chisquare.ipynb deleted file mode 100644 index 050fbb2..0000000 --- a/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_chisquare.ipynb +++ /dev/null @@ -1,3474 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.015786, - "end_time": "2026-01-21T05:10:53.259244", - "exception": false, - "start_time": "2026-01-21T05:10:53.243458", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:53.285335Z", - "iopub.status.busy": "2026-01-21T05:10:53.284324Z", - "iopub.status.idle": "2026-01-21T05:10:53.303715Z", - "shell.execute_reply": "2026-01-21T05:10:53.300936Z" - }, - "papermill": { - "duration": 0.037033, - "end_time": "2026-01-21T05:10:53.306282", - "exception": false, - "start_time": "2026-01-21T05:10:53.269249", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:53.329944Z", - "iopub.status.busy": "2026-01-21T05:10:53.329499Z", - "iopub.status.idle": "2026-01-21T05:10:56.073926Z", - "shell.execute_reply": "2026-01-21T05:10:56.068433Z" - }, - "papermill": { - "duration": 2.759921, - "end_time": "2026-01-21T05:10:56.078592", - "exception": false, - "start_time": "2026-01-21T05:10:53.318671", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-28 08:23:41.664165: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769559821.679332 1573564 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769559821.683671 1573564 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769559821.695837 1573564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769559821.695859 1573564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769559821.695861 1573564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769559821.695862 1573564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.005196, - "end_time": "2026-01-21T05:10:56.093961", - "exception": false, - "start_time": "2026-01-21T05:10:56.088765", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:56.107502Z", - "iopub.status.busy": "2026-01-21T05:10:56.105741Z", - "iopub.status.idle": "2026-01-21T05:10:57.599972Z", - "shell.execute_reply": "2026-01-21T05:10:57.596288Z" - }, - "papermill": { - "duration": 1.505904, - "end_time": "2026-01-21T05:10:57.603833", - "exception": false, - "start_time": "2026-01-21T05:10:56.097929", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.002559, - "end_time": "2026-01-21T05:10:57.612504", - "exception": false, - "start_time": "2026-01-21T05:10:57.609945", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:57.621650Z", - "iopub.status.busy": "2026-01-21T05:10:57.620836Z", - "iopub.status.idle": "2026-01-21T05:10:57.626234Z", - "shell.execute_reply": "2026-01-21T05:10:57.624902Z" - }, - "papermill": { - "duration": 0.012394, - "end_time": "2026-01-21T05:10:57.627415", - "exception": false, - "start_time": "2026-01-21T05:10:57.615021", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.00257, - "end_time": "2026-01-21T05:10:57.632964", - "exception": false, - "start_time": "2026-01-21T05:10:57.630394", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:57.640150Z", - "iopub.status.busy": "2026-01-21T05:10:57.639855Z", - "iopub.status.idle": "2026-01-21T05:11:11.821890Z", - "shell.execute_reply": "2026-01-21T05:11:11.818416Z" - }, - "papermill": { - "duration": 14.189201, - "end_time": "2026-01-21T05:11:11.825013", - "exception": false, - "start_time": "2026-01-21T05:10:57.635812", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.009738, - "end_time": "2026-01-21T05:11:11.847555", - "exception": false, - "start_time": "2026-01-21T05:11:11.837817", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.862844Z", - "iopub.status.busy": "2026-01-21T05:11:11.862162Z", - "iopub.status.idle": "2026-01-21T05:11:11.877329Z", - "shell.execute_reply": "2026-01-21T05:11:11.873842Z" - }, - "papermill": { - "duration": 0.026, - "end_time": "2026-01-21T05:11:11.880236", - "exception": false, - "start_time": "2026-01-21T05:11:11.854236", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + chi-square noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = (L @ z_std).astype(np.float32)\n", - " z = y + rng.chisquare(df=4, size=num_sample).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003739, - "end_time": "2026-01-21T05:11:11.891357", - "exception": false, - "start_time": "2026-01-21T05:11:11.887618", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.901661Z", - "iopub.status.busy": "2026-01-21T05:11:11.900849Z", - "iopub.status.idle": "2026-01-21T05:11:11.951518Z", - "shell.execute_reply": "2026-01-21T05:11:11.947998Z" - }, - "papermill": { - "duration": 0.060681, - "end_time": "2026-01-21T05:11:11.955227", - "exception": false, - "start_time": "2026-01-21T05:11:11.894546", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " \n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten_cont(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " \n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten_cont, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.971126Z", - "iopub.status.busy": "2026-01-21T05:11:11.970648Z", - "iopub.status.idle": "2026-01-21T05:11:12.002597Z", - "shell.execute_reply": "2026-01-21T05:11:12.000217Z" - }, - "papermill": { - "duration": 0.041605, - "end_time": "2026-01-21T05:11:12.004470", - "exception": false, - "start_time": "2026-01-21T05:11:11.962865", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + noise (chi-square)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + noise (chi-square)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.007063, - "end_time": "2026-01-21T05:11:12.019675", - "exception": false, - "start_time": "2026-01-21T05:11:12.012612", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:12.032423Z", - "iopub.status.busy": "2026-01-21T05:11:12.031984Z", - "iopub.status.idle": "2026-01-21T05:11:12.039988Z", - "shell.execute_reply": "2026-01-21T05:11:12.037507Z" - }, - "papermill": { - "duration": 0.017825, - "end_time": "2026-01-21T05:11:12.042843", - "exception": false, - "start_time": "2026-01-21T05:11:12.025018", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:12.059093Z", - "iopub.status.busy": "2026-01-21T05:11:12.058775Z", - "iopub.status.idle": "2026-01-21T07:04:32.139260Z", - "shell.execute_reply": "2026-01-21T07:04:32.135593Z" - }, - "papermill": { - "duration": 6800.091023, - "end_time": "2026-01-21T07:04:32.142230", - "exception": false, - "start_time": "2026-01-21T05:11:12.051207", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.2087 (±0.0213), Variance: 1.1303, Range: [-3.3592, 3.1722]\n", - "z mean: 3.7753 (±0.0599), Variance: 8.9614, Range: [-2.5309, 22.1349]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 16.8288 16.0226 \n", - " 100 16.7631 16.4541 *\n", - " 200 17.6458 17.3541 \n", - " 500 18.3575 19.7157 \n", - " 700 20.3122 21.9540 \n", - " 1000 39.9603 36.5697 \n", - " 1400 1364.1293 905.6570 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769559847.256572 1573564 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769559849.237116 1573802 service.cc:152] XLA service 0x7536e40052e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769559849.237184 1573802 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1769559849.572975 1573802 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1769559859.602914 1573802 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7537081d9fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7536f027da20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.7366 13.5266 \n", - " 100 7.9286 13.4616 \n", - " 200 7.8235 14.3439 \n", - " 500 7.6903 14.4460 \n", - " 700 7.6608 15.0493 *\n", - " 1000 7.8674 17.6824 \n", - " 1400 7.9914 15.5581 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2.0825 11.8831 \n", - " 100 2.0460 12.1581 *\n", - " 200 2.0723 12.7461 \n", - " 500 2.0608 12.5024 \n", - " 700 2.0803 12.3526 \n", - " 1000 2.0462 12.6465 \n", - " 1400 2.0890 9.8253 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0399, sigma²=0.0195, nugget=8.2235\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0217, sigma²=0.0023, nugget=7.9316\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0412, sigma²=0.0001, nugget=7.4455\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4939, sigma²=0.0000, nugget=6.1266\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3549, sigma²=0.0000, nugget=5.3484\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3573, sigma²=0.0000, nugget=4.1716\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3561, sigma²=0.0000, nugget=2.3914\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.3617 16.0227 *\n", - " 100 7.3884 16.4539 \n", - " 200 7.7485 17.3541 \n", - " 500 8.9064 19.7157 \n", - " 700 12.2554 21.9540 \n", - " 1000 32.0623 36.5696 \n", - " 1400 1348.7059 905.5709 \n", - " Best order: 50\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0399, sigma²=0.0195, nugget=8.2235\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.7748 | 4.216 | 3.9712 | -15.3299 | 0.47s |\n", - "| OLS_sphere | 100 | 16.4541 | 4.0564 | 3.982 | -14.1166 | 0.01s |\n", - "| DeepKriging_wendland | -- | 11.2227 | 3.35 | 3.2325 | -9.3105 | 59.64s |\n", - "| DeepKriging_sphere | 700 | 14.7299 | 3.838 | 3.6918 | -12.5325 | 16.84s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.0491 | 3.17 | 3.0075 | -8.2322 | 17.14s |\n", - "| UniversalKriging | 50 | 16.0227 | 4.0028 | 3.9485 | -13.7203 | 2.02s |\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:31:16\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0914 (±0.0170), Variance: 0.7252, Range: [-2.5030, 2.8675]\n", - "z mean: 4.1565 (±0.0598), Variance: 8.9267, Range: [-1.8266, 23.0773]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0358, sigma²=0.0080, nugget=8.1231\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|--------|------------|--------|\n", - "| OLS_wendland | -- | 1724.41 | 41.526 | 6.7443 | -2275.95 | 0.40s |\n", - "| OLS_sphere | 100 | 17.1771 | 4.1445 | 4.0684 | -21.6811 | 0.01s |\n", - "| DeepKriging_wendland | -- | 19.643 | 4.432 | 4.1816 | -24.937 | 64.64s |\n", - "| DeepKriging_sphere | 700 | 17.4078 | 4.1723 | 4.0758 | -21.9857 | 17.16s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.8397 | 3.4409 | 3.3309 | -14.6334 | 19.12s |\n", - "| UniversalKriging | 50 | 17.291 | 4.1583 | 4.097 | -21.8315 | 1.66s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:33:18\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1021 (±0.0165), Variance: 0.6820, Range: [-2.6324, 2.0719]\n", - "z mean: 3.9732 (±0.0576), Variance: 8.2943, Range: [-1.6231, 18.8135]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0333, sigma²=0.3019, nugget=7.2194\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 29.5635 | 5.4372 | 4.4396 | -47.2001 | 0.43s |\n", - "| OLS_sphere | 100 | 17.8523 | 4.2252 | 4.1584 | -28.1062 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.4312 | 4.0535 | 3.8838 | -25.7893 | 62.83s |\n", - "| DeepKriging_sphere | 700 | 19.317 | 4.3951 | 4.3248 | -30.4943 | 18.61s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.05 | 3.3242 | 3.2313 | -17.0159 | 20.60s |\n", - "| UniversalKriging | 50 | 17.2106 | 4.1486 | 4.0978 | -27.06 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:35:24\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0181 (±0.0207), Variance: 1.0716, Range: [-3.1769, 3.1892]\n", - "z mean: 4.0385 (±0.0599), Variance: 8.9818, Range: [-2.6345, 19.9099]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0279, sigma²=0.3288, nugget=7.3254\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 24.1319 | 4.9124 | 4.2812 | -27.5753 | 0.40s |\n", - "| OLS_sphere | 100 | 17.2253 | 4.1503 | 4.0545 | -19.397 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.7962 | 4.0983 | 3.9279 | -18.8889 | 64.76s |\n", - "| DeepKriging_sphere | 700 | 18.6378 | 4.3172 | 4.1842 | -21.0696 | 25.56s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.6013 | 3.256 | 3.1325 | -11.5533 | 20.59s |\n", - "| UniversalKriging | 50 | 17.5018 | 4.1835 | 4.1235 | -19.7244 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:37:39\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3156 (±0.0192), Variance: 0.9186, Range: [-2.9928, 3.2624]\n", - "z mean: 4.3182 (±0.0580), Variance: 8.4044, Range: [-1.7707, 20.1229]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0020, sigma²=7.5547, nugget=0.3015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.0455 | 4.4772 | 3.9789 | -21.0354 | 0.42s |\n", - "| OLS_sphere | 100 | 16.7472 | 4.0923 | 4.0134 | -17.4098 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.0268 | 3.8764 | 3.689 | -15.5186 | 62.97s |\n", - "| DeepKriging_sphere | 700 | 17.4459 | 4.1768 | 4.0747 | -18.1778 | 16.54s |\n", - "| DeepKriging_sphere_Huber | 100 | 15.1567 | 3.8932 | 3.75 | -15.6613 | 23.16s |\n", - "| UniversalKriging | 50 | 16.7484 | 4.0925 | 4.035 | -17.4111 | 4.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:39:50\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.2265 (±0.0170), Variance: 0.7267, Range: [-3.0397, 2.3144]\n", - "z mean: 3.7937 (±0.0599), Variance: 8.9759, Range: [-1.9609, 32.9274]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0191, sigma²=0.0095, nugget=8.1842\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 210.127 | 14.4958 | 5.2403 | -323.412 | 0.38s |\n", - "| OLS_sphere | 100 | 16.7063 | 4.0873 | 4.0005 | -24.7926 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.7317 | 4.0904 | 3.8156 | -24.8318 | 63.58s |\n", - "| DeepKriging_sphere | 700 | 11.2884 | 3.3598 | 3.2604 | -16.428 | 18.04s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.9758 | 3.4606 | 3.3648 | -17.4893 | 21.57s |\n", - "| UniversalKriging | 50 | 16.1763 | 4.022 | 3.9614 | -23.9743 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:41:59\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1545 (±0.0181), Variance: 0.8211, Range: [-3.2120, 3.8366]\n", - "z mean: 4.2318 (±0.0601), Variance: 9.0399, Range: [-2.8630, 22.4240]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0337, sigma²=0.0020, nugget=8.2372\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.6414 | 4.4319 | 4.2298 | -20.7627 | 0.42s |\n", - "| OLS_sphere | 100 | 16.8758 | 4.108 | 4.0327 | -17.6984 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.9958 | 3.7411 | 3.5783 | -14.5073 | 58.08s |\n", - "| DeepKriging_sphere | 700 | 17.3687 | 4.1676 | 4.0484 | -18.2445 | 21.00s |\n", - "| DeepKriging_sphere_Huber | 100 | 16.3394 | 4.0422 | 3.9333 | -17.1041 | 21.58s |\n", - "| UniversalKriging | 50 | 16.5904 | 4.0731 | 4.0165 | -17.3822 | 2.49s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:44:07\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0301 (±0.0233), Variance: 1.3596, Range: [-3.6446, 3.3099]\n", - "z mean: 4.0365 (±0.0622), Variance: 9.6693, Range: [-3.1411, 22.0490]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0220, sigma²=0.0076, nugget=8.3452\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.3167 | 4.1613 | 3.9694 | -10.9036 | 0.40s |\n", - "| OLS_sphere | 100 | 17.4049 | 4.1719 | 4.087 | -10.9642 | 0.02s |\n", - "| DeepKriging_wendland | -- | 16.18 | 4.0224 | 3.7209 | -10.1222 | 63.85s |\n", - "| DeepKriging_sphere | 700 | 17.1058 | 4.1359 | 3.9746 | -10.7586 | 16.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.8477 | 3.7213 | 3.5538 | -8.519 | 19.07s |\n", - "| UniversalKriging | 50 | 17.5835 | 4.1933 | 4.1263 | -11.087 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:46:15\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1015 (±0.0184), Variance: 0.8465, Range: [-2.5073, 2.8775]\n", - "z mean: 3.9990 (±0.0594), Variance: 8.8113, Range: [-1.3431, 19.5457]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0166, sigma²=0.0027, nugget=8.0507\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.0111 | 4.1245 | 3.8197 | -20.1294 | 0.39s |\n", - "| OLS_sphere | 100 | 15.4414 | 3.9296 | 3.8399 | -18.1796 | 0.01s |\n", - "| DeepKriging_wendland | -- | 12.911 | 3.5932 | 3.3952 | -15.0366 | 62.85s |\n", - "| DeepKriging_sphere | 700 | 18.6546 | 4.3191 | 4.1996 | -22.1707 | 22.66s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.0077 | 3.6066 | 3.5044 | -15.1568 | 18.49s |\n", - "| UniversalKriging | 50 | 15.3698 | 3.9204 | 3.859 | -18.0908 | 2.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:48:29\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3526 (±0.0222), Variance: 1.2329, Range: [-2.8410, 4.1531]\n", - "z mean: 4.3622 (±0.0606), Variance: 9.1767, Range: [-1.8964, 19.2092]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0026, sigma²=6.1269, nugget=1.9801\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.1439 | 4.1405 | 3.958 | -13.1947 | 0.41s |\n", - "| OLS_sphere | 100 | 16.54 | 4.0669 | 3.9928 | -12.6947 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.2531 | 3.6405 | 3.494 | -9.9733 | 64.87s |\n", - "| DeepKriging_sphere | 700 | 16.9783 | 4.1205 | 3.9857 | -13.0576 | 17.83s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.121 | 3.6223 | 3.4774 | -9.8638 | 19.89s |\n", - "| UniversalKriging | 50 | 16.3012 | 4.0375 | 3.9847 | -12.497 | 2.69s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:50:42\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.2930 (±0.0190), Variance: 0.9043, Range: [-2.9922, 3.7984]\n", - "z mean: 4.2983 (±0.0613), Variance: 9.3879, Range: [-1.9955, 22.8030]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0289, sigma²=0.0110, nugget=8.9151\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.5432 | 4.3062 | 4.106 | -18.8221 | 0.42s |\n", - "| OLS_sphere | 100 | 17.0114 | 4.1245 | 4.0663 | -17.1847 | 0.01s |\n", - "| DeepKriging_wendland | -- | 14.9318 | 3.8642 | 3.6795 | -14.9617 | 66.92s |\n", - "| DeepKriging_sphere | 700 | 20.6624 | 4.5456 | 4.43 | -21.0875 | 21.31s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.7337 | 3.4255 | 3.3209 | -11.543 | 22.15s |\n", - "| UniversalKriging | 50 | 17.14 | 4.1401 | 4.0928 | -17.3222 | 1.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:53:04\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.2711 (±0.0163), Variance: 0.6659, Range: [-2.2990, 2.6131]\n", - "z mean: 4.2726 (±0.0585), Variance: 8.5637, Range: [-1.3040, 19.3711]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0052, sigma²=4.5327, nugget=3.3281\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.3002 | 4.1594 | 3.9982 | -22.0107 | 0.38s |\n", - "| OLS_sphere | 100 | 16.6437 | 4.0797 | 3.9966 | -21.1375 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.4879 | 3.9355 | 3.7204 | -19.6002 | 65.23s |\n", - "| DeepKriging_sphere | 700 | 17.5722 | 4.1919 | 4.0903 | -22.3725 | 17.07s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.6129 | 3.4078 | 3.2978 | -14.4462 | 21.50s |\n", - "| UniversalKriging | 50 | 15.821 | 3.9776 | 3.9194 | -20.0433 | 4.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:55:23\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0562 (±0.0177), Variance: 0.7849, Range: [-2.3893, 2.8555]\n", - "z mean: 3.9859 (±0.0582), Variance: 8.4616, Range: [-1.6368, 20.7241]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0157, sigma²=0.0026, nugget=7.6668\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|------------|--------|\n", - "| OLS_wendland | -- | 5020.77 | 70.8574 | 10.7185 | -6141.64 | 0.43s |\n", - "| OLS_sphere | 100 | 16.0975 | 4.0122 | 3.9403 | -18.6944 | 0.01s |\n", - "| DeepKriging_wendland | -- | 11.0027 | 3.317 | 3.1471 | -12.4612 | 64.14s |\n", - "| DeepKriging_sphere | 700 | 10.995 | 3.3159 | 3.1948 | -12.4518 | 17.74s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.2828 | 3.7793 | 3.6867 | -16.4743 | 19.80s |\n", - "| UniversalKriging | 50 | 16.2666 | 4.0332 | 3.968 | -18.9013 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:57:39\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.7619 (±0.0173), Variance: 0.7493, Range: [-3.7554, 2.2216]\n", - "z mean: 3.2793 (±0.0598), Variance: 8.9425, Range: [-2.5779, 21.2537]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0201, sigma²=0.0035, nugget=8.0492\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 242.422 | 15.5699 | 4.9098 | -399.239 | 0.42s |\n", - "| OLS_sphere | 100 | 17.0041 | 4.1236 | 4.0381 | -27.0738 | 0.01s |\n", - "| DeepKriging_wendland | -- | 14.1546 | 3.7623 | 3.678 | -22.3693 | 57.70s |\n", - "| DeepKriging_sphere | 700 | 17.674 | 4.204 | 4.1198 | -28.1798 | 16.20s |\n", - "| DeepKriging_sphere_Huber | 100 | 8.2922 | 2.8796 | 2.7727 | -12.6905 | 18.71s |\n", - "| UniversalKriging | 50 | 16.4711 | 4.0585 | 3.9906 | -26.1938 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 08:59:47\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.5079 (±0.0201), Variance: 1.0118, Range: [-3.3722, 2.8220]\n", - "z mean: 3.5019 (±0.0584), Variance: 8.5222, Range: [-2.0962, 19.7314]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0653, sigma²=0.0137, nugget=7.4128\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.7988 | 5.0792 | 4.3304 | -29.1601 | 0.41s |\n", - "| OLS_sphere | 100 | 17.2364 | 4.1517 | 4.0677 | -19.1502 | 0.01s |\n", - "| DeepKriging_wendland | -- | 19.5505 | 4.4216 | 4.2497 | -21.8555 | 64.97s |\n", - "| DeepKriging_sphere | 700 | 21.4484 | 4.6312 | 4.5104 | -24.0742 | 17.37s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.4765 | 3.8048 | 3.6955 | -15.9238 | 19.65s |\n", - "| UniversalKriging | 50 | 17.1144 | 4.137 | 4.0678 | -19.0076 | 0.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:02:05\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.2011 (±0.0168), Variance: 0.7026, Range: [-2.6394, 2.5442]\n", - "z mean: 3.8618 (±0.0613), Variance: 9.4002, Range: [-1.7238, 20.1269]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0246, sigma²=0.0274, nugget=8.7854\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 33.09 | 5.7524 | 4.449 | -52.6052 | 0.42s |\n", - "| OLS_sphere | 100 | 18.3023 | 4.2781 | 4.1866 | -28.6494 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.28 | 3.909 | 3.7923 | -23.7533 | 58.21s |\n", - "| DeepKriging_sphere | 700 | 15.3496 | 3.9179 | 3.8317 | -23.8661 | 21.01s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.409 | 3.3777 | 3.2815 | -17.4824 | 18.79s |\n", - "| UniversalKriging | 50 | 18.474 | 4.2981 | 4.2293 | -28.9276 | 1.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:04:20\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4486 (±0.0195), Variance: 0.9543, Range: [-2.6866, 3.0678]\n", - "z mean: 4.4341 (±0.0603), Variance: 9.0891, Range: [-1.4979, 22.7343]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0219, sigma²=0.0026, nugget=8.2045\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 56.0895 | 7.4893 | 4.6178 | -51.9682 | 0.42s |\n", - "| OLS_sphere | 100 | 16.6684 | 4.0827 | 4.0161 | -14.7408 | 0.01s |\n", - "| DeepKriging_wendland | -- | 14.5094 | 3.8091 | 3.6813 | -12.702 | 63.33s |\n", - "| DeepKriging_sphere | 700 | 16.7047 | 4.0871 | 3.9449 | -14.7751 | 20.72s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.0279 | 3.4681 | 3.3304 | -10.3585 | 19.37s |\n", - "| UniversalKriging | 50 | 16.389 | 4.0483 | 3.9995 | -14.477 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:06:43\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.3553 (±0.0223), Variance: 1.2401, Range: [-3.8404, 2.6780]\n", - "z mean: 3.6597 (±0.0613), Variance: 9.4048, Range: [-2.5746, 21.8702]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0182, sigma²=0.0037, nugget=7.9575\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 521.717 | 22.8411 | 5.9661 | -492.787 | 0.39s |\n", - "| OLS_sphere | 100 | 16.3732 | 4.0464 | 3.9722 | -14.4967 | 0.01s |\n", - "| DeepKriging_wendland | -- | 18.9124 | 4.3488 | 4.1812 | -16.8999 | 64.10s |\n", - "| DeepKriging_sphere | 700 | 17.5643 | 4.191 | 4.078 | -15.624 | 16.61s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.5761 | 3.5463 | 3.4204 | -10.9028 | 21.23s |\n", - "| UniversalKriging | 50 | 16.4664 | 4.0579 | 4.0007 | -14.5848 | 2.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:09:05\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0131 (±0.0193), Variance: 0.9331, Range: [-2.5519, 3.1735]\n", - "z mean: 4.0073 (±0.0596), Variance: 8.8670, Range: [-1.5482, 20.8659]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0332, sigma²=0.6200, nugget=7.3304\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 35.7077 | 5.9756 | 4.1984 | -38.0294 | 0.34s |\n", - "| OLS_sphere | 100 | 16.5297 | 4.0657 | 3.9822 | -17.0674 | 0.01s |\n", - "| DeepKriging_wendland | -- | 12.6944 | 3.5629 | 3.4356 | -12.8753 | 63.77s |\n", - "| DeepKriging_sphere | 700 | 14.2773 | 3.7785 | 3.666 | -14.6054 | 17.06s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.4385 | 3.2309 | 3.105 | -10.4096 | 21.49s |\n", - "| UniversalKriging | 50 | 16.3365 | 4.0418 | 3.9804 | -16.8562 | 3.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:11:29\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1429 (±0.0161), Variance: 0.6453, Range: [-3.2163, 2.5210]\n", - "z mean: 3.8355 (±0.0596), Variance: 8.8907, Range: [-2.8521, 19.5216]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0592, sigma²=0.0110, nugget=7.8889\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 52.2891 | 7.2311 | 4.4795 | -92.6524 | 0.42s |\n", - "| OLS_sphere | 100 | 15.9623 | 3.9953 | 3.8997 | -27.5893 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.635 | 3.6926 | 3.5576 | -23.421 | 66.81s |\n", - "| DeepKriging_sphere | 700 | 18.2135 | 4.2677 | 4.1812 | -31.6213 | 17.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.7484 | 3.8404 | 3.773 | -25.4151 | 19.67s |\n", - "| UniversalKriging | 50 | 16.0982 | 4.0123 | 3.952 | -27.8326 | 1.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:13:55\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3161 (±0.0162), Variance: 0.6585, Range: [-2.0472, 2.8787]\n", - "z mean: 4.3005 (±0.0575), Variance: 8.2592, Range: [-1.3752, 19.8280]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0207, sigma²=0.0102, nugget=7.5436\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 96.6336 | 9.8302 | 4.5142 | -137.673 | 0.43s |\n", - "| OLS_sphere | 100 | 16.289 | 4.036 | 3.965 | -22.3753 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.3104 | 3.9129 | 3.8143 | -20.971 | 60.08s |\n", - "| DeepKriging_sphere | 700 | 15.6236 | 3.9527 | 3.8615 | -21.4205 | 20.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.5419 | 3.6799 | 3.5939 | -18.4332 | 17.02s |\n", - "| UniversalKriging | 50 | 16.1295 | 4.0162 | 3.963 | -22.1465 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:16:16\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1353 (±0.0173), Variance: 0.7452, Range: [-2.7997, 2.5882]\n", - "z mean: 4.0899 (±0.0587), Variance: 8.6241, Range: [-1.8810, 22.4947]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0134, sigma²=0.0061, nugget=8.1409\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|--------|------------|--------|\n", - "| OLS_wendland | -- | 1119.9 | 33.4649 | 6.6571 | -1492.47 | 0.41s |\n", - "| OLS_sphere | 100 | 16.5914 | 4.0733 | 3.9953 | -21.1259 | 0.01s |\n", - "| DeepKriging_wendland | -- | 12.1869 | 3.491 | 3.3335 | -15.2521 | 65.82s |\n", - "| DeepKriging_sphere | 700 | 13.3776 | 3.6575 | 3.5561 | -16.84 | 16.52s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.8907 | 3.3001 | 3.1531 | -13.5235 | 23.71s |\n", - "| UniversalKriging | 50 | 16.9226 | 4.1137 | 4.0548 | -21.5675 | 2.33s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:18:47\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.5156 (±0.0180), Variance: 0.8144, Range: [-2.0495, 3.5843]\n", - "z mean: 4.4311 (±0.0576), Variance: 8.2826, Range: [-1.3886, 20.2132]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0192, sigma²=1.7104, nugget=6.0931\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.7187 | 4.2094 | 4.0442 | -22.8023 | 0.40s |\n", - "| OLS_sphere | 100 | 16.755 | 4.0933 | 4.0095 | -21.5077 | 0.02s |\n", - "| DeepKriging_wendland | -- | 15.4299 | 3.9281 | 3.7491 | -19.7276 | 62.66s |\n", - "| DeepKriging_sphere | 700 | 16.6834 | 4.0845 | 3.995 | -21.4115 | 17.45s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.0725 | 3.7513 | 3.6545 | -17.9042 | 20.64s |\n", - "| UniversalKriging | 50 | 16.7652 | 4.0945 | 4.0348 | -21.5215 | 4.64s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:21:17\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0829 (±0.0196), Variance: 0.9647, Range: [-3.1156, 3.0751]\n", - "z mean: 4.0221 (±0.0602), Variance: 9.0461, Range: [-2.1519, 20.6342]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0686, sigma²=0.0067, nugget=8.0556\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 44.1779 | 6.6466 | 4.5089 | -48.8455 | 0.38s |\n", - "| OLS_sphere | 100 | 16.7209 | 4.0891 | 4.0016 | -17.866 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.2219 | 3.9015 | 3.7739 | -16.1747 | 61.35s |\n", - "| DeepKriging_sphere | 700 | 12.7271 | 3.5675 | 3.4397 | -13.3599 | 16.23s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.5256 | 3.3949 | 3.2708 | -12.0042 | 18.94s |\n", - "| UniversalKriging | 50 | 16.679 | 4.084 | 4.0353 | -17.8188 | 0.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:23:39\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.0490 (±0.0182), Variance: 0.8287, Range: [-2.5449, 2.6965]\n", - "z mean: 3.9743 (±0.0594), Variance: 8.8143, Range: [-1.7866, 22.0330]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0662, sigma²=0.0180, nugget=7.9893\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.6531 | 4.4332 | 4.2177 | -23.1653 | 0.45s |\n", - "| OLS_sphere | 100 | 17.1013 | 4.1354 | 4.0591 | -20.0277 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.5017 | 3.6745 | 3.5267 | -15.6016 | 64.41s |\n", - "| DeepKriging_sphere | 700 | 14.7938 | 3.8463 | 3.7462 | -17.1903 | 17.16s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.5605 | 3.5441 | 3.4521 | -14.4443 | 18.15s |\n", - "| UniversalKriging | 50 | 16.9958 | 4.1226 | 4.0625 | -19.898 | 0.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:26:06\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0682 (±0.0195), Variance: 0.9515, Range: [-3.6544, 2.8584]\n", - "z mean: 3.9888 (±0.0587), Variance: 8.6131, Range: [-2.8184, 18.1978]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0292, sigma²=0.0151, nugget=7.8502\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 283.971 | 16.8514 | 5.0526 | -287.286 | 0.39s |\n", - "| OLS_sphere | 100 | 15.8009 | 3.975 | 3.9086 | -15.041 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.254 | 4.0316 | 3.8473 | -15.5011 | 64.87s |\n", - "| DeepKriging_sphere | 700 | 17.4411 | 4.1763 | 4.0585 | -16.7062 | 18.51s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.6204 | 3.5525 | 3.3796 | -11.8122 | 24.07s |\n", - "| UniversalKriging | 50 | 15.9688 | 3.9961 | 3.9337 | -15.2114 | 1.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:28:42\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6284 (±0.0190), Variance: 0.9023, Range: [-3.0028, 3.7611]\n", - "z mean: 4.5618 (±0.0590), Variance: 8.6900, Range: [-1.2301, 23.3514]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0666, sigma²=0.0221, nugget=7.5050\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 138.005 | 11.7475 | 5.1227 | -152.927 | 0.48s |\n", - "| OLS_sphere | 100 | 15.481 | 3.9346 | 3.85 | -16.2671 | 0.01s |\n", - "| DeepKriging_wendland | -- | 21.2304 | 4.6076 | 4.3285 | -22.6799 | 63.45s |\n", - "| DeepKriging_sphere | 700 | 18.52 | 4.3035 | 4.2099 | -19.6568 | 17.29s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.6744 | 3.5601 | 3.4391 | -13.1367 | 20.47s |\n", - "| UniversalKriging | 50 | 15.333 | 3.9157 | 3.8491 | -16.1021 | 0.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:31:13\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1642 (±0.0190), Variance: 0.9004, Range: [-2.5461, 2.9000]\n", - "z mean: 4.1387 (±0.0594), Variance: 8.8234, Range: [-1.7367, 18.0468]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0188, sigma²=0.0033, nugget=7.9626\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.1867 | 4.1457 | 3.9323 | -15.4449 | 0.40s |\n", - "| OLS_sphere | 100 | 16.2119 | 4.0264 | 3.9591 | -14.5122 | 0.01s |\n", - "| DeepKriging_wendland | -- | 14.7179 | 3.8364 | 3.6397 | -13.0827 | 64.12s |\n", - "| DeepKriging_sphere | 700 | 17.0019 | 4.1233 | 4.0015 | -15.2681 | 16.91s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.6218 | 3.4091 | 3.2685 | -10.1202 | 23.52s |\n", - "| UniversalKriging | 50 | 16.3659 | 4.0455 | 3.99 | -14.6596 | 2.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:33:50\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3065 (±0.0182), Variance: 0.8257, Range: [-2.3751, 2.7162]\n", - "z mean: 4.2682 (±0.0594), Variance: 8.8355, Range: [-1.0426, 26.3279]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0234, sigma²=0.0035, nugget=8.0313\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 29.2386 | 5.4073 | 4.3402 | -26.8473 | 0.43s |\n", - "| OLS_sphere | 100 | 16.2314 | 4.0288 | 3.9597 | -14.4591 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.5142 | 3.9388 | 3.7554 | -13.776 | 62.84s |\n", - "| DeepKriging_sphere | 700 | 18.3219 | 4.2804 | 4.1653 | -16.4501 | 17.92s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.8861 | 3.5897 | 3.4494 | -11.2729 | 19.90s |\n", - "| UniversalKriging | 50 | 15.7261 | 3.9656 | 3.9172 | -13.9778 | 2.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:36:23\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.6807 (±0.0228), Variance: 1.2941, Range: [-3.0551, 5.0718]\n", - "z mean: 4.6102 (±0.0602), Variance: 9.0695, Range: [-1.7254, 20.7871]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0167, sigma²=0.0050, nugget=7.9513\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 28.3541 | 5.3249 | 4.1969 | -21.332 | 0.45s |\n", - "| OLS_sphere | 100 | 14.9251 | 3.8633 | 3.7972 | -10.7552 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.2365 | 3.6382 | 3.4902 | -9.4252 | 62.62s |\n", - "| DeepKriging_sphere | 700 | 14.1279 | 3.7587 | 3.5883 | -10.1273 | 22.99s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.0179 | 3.608 | 3.4543 | -9.253 | 21.76s |\n", - "| UniversalKriging | 50 | 15.3355 | 3.9161 | 3.862 | -11.0784 | 2.07s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:39:05\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1138 (±0.0177), Variance: 0.7833, Range: [-2.5885, 2.6781]\n", - "z mean: 4.0566 (±0.0581), Variance: 8.4447, Range: [-2.1114, 21.8094]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0325, sigma²=0.0216, nugget=7.9421\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 42.2719 | 6.5017 | 4.3458 | -53.6039 | 0.38s |\n", - "| OLS_sphere | 100 | 16.5068 | 4.0629 | 3.985 | -20.3223 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.3873 | 3.6589 | 3.5178 | -16.2928 | 64.18s |\n", - "| DeepKriging_sphere | 700 | 15.8446 | 3.9805 | 3.8882 | -19.467 | 19.88s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.4274 | 3.3804 | 3.1937 | -13.7611 | 22.26s |\n", - "| UniversalKriging | 50 | 16.3226 | 4.0401 | 3.9828 | -20.0844 | 1.76s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:41:47\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0866 (±0.0195), Variance: 0.9528, Range: [-2.8215, 3.4723]\n", - "z mean: 4.1174 (±0.0592), Variance: 8.7711, Range: [-1.7377, 25.4078]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0455, sigma²=0.0106, nugget=8.1878\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.8782 | 4.2283 | 4.0897 | -17.6921 | 0.37s |\n", - "| OLS_sphere | 100 | 16.9413 | 4.116 | 4.0475 | -16.7125 | 0.01s |\n", - "| DeepKriging_wendland | -- | 13.737 | 3.7063 | 3.5727 | -13.3624 | 61.54s |\n", - "| DeepKriging_sphere | 700 | 17.5289 | 4.1868 | 4.0714 | -17.3269 | 20.61s |\n", - "| DeepKriging_sphere_Huber | 100 | 14.4234 | 3.7978 | 3.6872 | -14.08 | 22.36s |\n", - "| UniversalKriging | 50 | 17.1181 | 4.1374 | 4.0878 | -16.8974 | 0.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:44:26\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1753 (±0.0182), Variance: 0.8285, Range: [-2.4795, 3.2583]\n", - "z mean: 4.2373 (±0.0626), Variance: 9.8054, Range: [-1.0936, 22.0557]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0138, sigma²=1.0974, nugget=7.9510\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 24.4016 | 4.9398 | 4.3299 | -29.8088 | 0.43s |\n", - "| OLS_sphere | 100 | 17.1003 | 4.1352 | 4.0589 | -20.5903 | 0.02s |\n", - "| DeepKriging_wendland | -- | 13.8887 | 3.7268 | 3.5806 | -16.5355 | 63.36s |\n", - "| DeepKriging_sphere | 700 | 16.5827 | 4.0722 | 3.9672 | -19.9369 | 17.91s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.2249 | 3.4964 | 3.4055 | -14.4348 | 20.53s |\n", - "| UniversalKriging | 50 | 17.4251 | 4.1743 | 4.1114 | -21.0004 | 4.58s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:47:09\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.3590 (±0.0169), Variance: 0.7165, Range: [-2.9285, 2.4953]\n", - "z mean: 3.6588 (±0.0586), Variance: 8.5761, Range: [-2.2879, 18.2344]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0165, sigma²=0.0042, nugget=7.8403\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 23.7528 | 4.8737 | 4.279 | -33.2935 | 0.37s |\n", - "| OLS_sphere | 100 | 17.9129 | 4.2324 | 4.1568 | -24.8621 | 0.01s |\n", - "| DeepKriging_wendland | -- | 17.2108 | 4.1486 | 4.0364 | -23.8484 | 62.06s |\n", - "| DeepKriging_sphere | 700 | 14.898 | 3.8598 | 3.7647 | -20.5092 | 16.64s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.9526 | 3.4573 | 3.3464 | -16.2568 | 21.76s |\n", - "| UniversalKriging | 50 | 17.4465 | 4.1769 | 4.1203 | -24.1887 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:49:48\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1716 (±0.0174), Variance: 0.7549, Range: [-3.2324, 2.3597]\n", - "z mean: 4.2070 (±0.0587), Variance: 8.6280, Range: [-2.0838, 19.0484]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0135, sigma²=0.0057, nugget=8.1173\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 41.062 | 6.408 | 4.3734 | -58.2506 | 0.38s |\n", - "| OLS_sphere | 100 | 17.6602 | 4.2024 | 4.1125 | -24.4829 | 0.01s |\n", - "| DeepKriging_wendland | -- | 17.3756 | 4.1684 | 4.0102 | -24.0722 | 67.12s |\n", - "| DeepKriging_sphere | 700 | 18.4967 | 4.3008 | 4.1435 | -25.6899 | 23.27s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.781 | 3.5751 | 3.4775 | -17.4424 | 22.12s |\n", - "| UniversalKriging | 50 | 17.1967 | 4.1469 | 4.0897 | -23.8141 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:52:40\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.4976 (±0.0182), Variance: 0.8304, Range: [-3.0018, 2.0971]\n", - "z mean: 3.4304 (±0.0584), Variance: 8.5181, Range: [-2.2826, 19.7475]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0309, sigma²=0.0032, nugget=7.5682\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 31.3974 | 5.6033 | 4.3477 | -37.2601 | 0.45s |\n", - "| OLS_sphere | 100 | 15.6006 | 3.9498 | 3.861 | -18.0105 | 0.04s |\n", - "| DeepKriging_wendland | -- | 22.1165 | 4.7028 | 4.1568 | -25.9506 | 67.23s |\n", - "| DeepKriging_sphere | 700 | 17.1337 | 4.1393 | 4.0428 | -19.8787 | 19.53s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.6776 | 3.4173 | 3.3187 | -13.2301 | 18.57s |\n", - "| UniversalKriging | 50 | 15.7751 | 3.9718 | 3.9064 | -18.2232 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:55:27\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0200 (±0.0165), Variance: 0.6770, Range: [-2.3961, 2.9264]\n", - "z mean: 3.9825 (±0.0579), Variance: 8.3711, Range: [-1.7705, 21.0920]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0392, sigma²=0.0151, nugget=8.1067\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.4761 | 4.2984 | 4.099 | -26.1796 | 0.39s |\n", - "| OLS_sphere | 100 | 16.5831 | 4.0722 | 3.9995 | -23.3948 | 0.01s |\n", - "| DeepKriging_wendland | -- | 14.8118 | 3.8486 | 3.7524 | -20.7891 | 64.91s |\n", - "| DeepKriging_sphere | 700 | 13.9183 | 3.7307 | 3.6423 | -19.4748 | 20.62s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.1198 | 3.3346 | 3.2475 | -15.3579 | 19.04s |\n", - "| UniversalKriging | 50 | 16.0317 | 4.004 | 3.9544 | -22.5837 | 1.49s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 09:58:13\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1341 (±0.0179), Variance: 0.7966, Range: [-3.0314, 3.1388]\n", - "z mean: 4.1300 (±0.0602), Variance: 9.0557, Range: [-2.0192, 22.2805]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0345, sigma²=0.4557, nugget=7.7713\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 40.445 | 6.3596 | 4.5868 | -46.5901 | 0.48s |\n", - "| OLS_sphere | 100 | 17.0831 | 4.1332 | 4.0551 | -19.101 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.1773 | 4.0221 | 3.8423 | -18.0352 | 66.32s |\n", - "| DeepKriging_sphere | 700 | 17.6593 | 4.2023 | 4.0905 | -19.779 | 19.72s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.3191 | 3.2123 | 3.1076 | -11.1421 | 22.69s |\n", - "| UniversalKriging | 50 | 16.669 | 4.0828 | 4.0208 | -18.6137 | 3.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:01:06\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.4989 (±0.0208), Variance: 1.0820, Range: [-3.7355, 2.9445]\n", - "z mean: 3.4564 (±0.0586), Variance: 8.5720, Range: [-2.4927, 18.5541]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0230, sigma²=0.0120, nugget=7.3695\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.8999 | 4.1109 | 3.9535 | -14.5245 | 0.42s |\n", - "| OLS_sphere | 100 | 15.6133 | 3.9514 | 3.8874 | -13.3426 | 0.01s |\n", - "| DeepKriging_wendland | -- | 12.8288 | 3.5817 | 3.3854 | -10.7847 | 62.51s |\n", - "| DeepKriging_sphere | 700 | 19.4352 | 4.4085 | 4.2916 | -16.8534 | 16.56s |\n", - "| DeepKriging_sphere_Huber | 100 | 16.23 | 4.0286 | 3.9165 | -13.9091 | 21.07s |\n", - "| UniversalKriging | 50 | 16.0979 | 4.0122 | 3.9463 | -13.7878 | 1.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:03:51\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.5872 (±0.0187), Variance: 0.8739, Range: [-2.4319, 3.3557]\n", - "z mean: 4.6990 (±0.0627), Variance: 9.8277, Range: [-1.2583, 26.0828]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0044, sigma²=8.4122, nugget=0.5232\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.7241 | 4.3271 | 4.1741 | -21.8062 | 0.50s |\n", - "| OLS_sphere | 100 | 17.8812 | 4.2286 | 4.1269 | -20.7796 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.1637 | 4.0204 | 3.8957 | -18.6875 | 47.22s |\n", - "| DeepKriging_sphere | 700 | 21.0456 | 4.5875 | 4.5021 | -24.6338 | 16.09s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.5707 | 3.6838 | 3.5791 | -15.5292 | 16.21s |\n", - "| UniversalKriging | 50 | 17.8229 | 4.2217 | 4.1599 | -20.7085 | 3.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:06:18\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.3365 (±0.0257), Variance: 1.6456, Range: [-3.3617, 3.1033]\n", - "z mean: 3.6715 (±0.0613), Variance: 9.3924, Range: [-2.1148, 21.3271]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0332, sigma²=0.0147, nugget=7.3854\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.1488 | 4.3759 | 4.1284 | -11.0515 | 0.45s |\n", - "| OLS_sphere | 100 | 17.0459 | 4.1287 | 4.0446 | -9.728 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.6822 | 3.9601 | 3.7493 | -8.8698 | 46.38s |\n", - "| DeepKriging_sphere | 700 | 15.0635 | 3.8812 | 3.6984 | -8.4804 | 20.90s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.0108 | 3.6071 | 3.4223 | -7.1885 | 15.98s |\n", - "| UniversalKriging | 50 | 16.6495 | 4.0804 | 4.0201 | -9.4786 | 1.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:08:48\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4092 (±0.0196), Variance: 0.9634, Range: [-2.3537, 3.0730]\n", - "z mean: 4.4128 (±0.0588), Variance: 8.6545, Range: [-1.7274, 25.3481]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0492, sigma²=0.0125, nugget=8.0537\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 24.9727 | 4.9973 | 4.239 | -23.5352 | 0.31s |\n", - "| OLS_sphere | 100 | 17.0086 | 4.1242 | 4.0168 | -15.7107 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.0312 | 3.877 | 3.7136 | -13.7679 | 52.39s |\n", - "| DeepKriging_sphere | 700 | 18.5273 | 4.3043 | 4.1849 | -17.2028 | 16.68s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.9603 | 3.6 | 3.4799 | -11.7332 | 19.03s |\n", - "| UniversalKriging | 50 | 16.5726 | 4.0709 | 4.001 | -15.2822 | 0.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:11:25\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0990 (±0.0219), Variance: 1.2003, Range: [-3.0204, 3.8827]\n", - "z mean: 4.1144 (±0.0598), Variance: 8.9445, Range: [-2.4426, 20.2665]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0332, sigma²=0.0164, nugget=7.9109\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.1171 | 4.4852 | 4.2144 | -14.2045 | 0.38s |\n", - "| OLS_sphere | 100 | 17.4036 | 4.1718 | 4.1062 | -12.1536 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.5724 | 4.0709 | 3.9006 | -11.5254 | 59.37s |\n", - "| DeepKriging_sphere | 700 | 16.366 | 4.0455 | 3.9097 | -11.3695 | 19.83s |\n", - "| DeepKriging_sphere_Huber | 100 | 13.7471 | 3.7077 | 3.5636 | -9.3901 | 18.40s |\n", - "| UniversalKriging | 50 | 17.3978 | 4.1711 | 4.12 | -12.1493 | 1.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:14:11\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.0060 (±0.0218), Variance: 1.1842, Range: [-3.1664, 2.7881]\n", - "z mean: 4.0860 (±0.0602), Variance: 9.0612, Range: [-2.5656, 17.7765]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0258, sigma²=0.0153, nugget=8.2531\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.2569 | 5.0256 | 4.2585 | -18.2599 | 0.40s |\n", - "| OLS_sphere | 100 | 18.2868 | 4.2763 | 4.196 | -12.9448 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.1095 | 4.0137 | 3.8342 | -11.2845 | 57.57s |\n", - "| DeepKriging_sphere | 700 | 17.228 | 4.1507 | 4.0067 | -12.1374 | 16.42s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.9953 | 3.6049 | 3.4715 | -8.9097 | 18.32s |\n", - "| UniversalKriging | 50 | 17.7334 | 4.2111 | 4.152 | -12.5228 | 1.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:16:54\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1386 (±0.0188), Variance: 0.8804, Range: [-2.7205, 2.7198]\n", - "z mean: 3.8881 (±0.0591), Variance: 8.7353, Range: [-1.8487, 19.7250]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0159, sigma²=0.0034, nugget=8.1347\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.8521 | 4.4556 | 4.2072 | -24.7746 | 0.38s |\n", - "| OLS_sphere | 100 | 16.219 | 4.0273 | 3.939 | -20.0577 | 0.01s |\n", - "| DeepKriging_wendland | -- | 18.9355 | 4.3515 | 4.1066 | -23.5846 | 56.87s |\n", - "| DeepKriging_sphere | 700 | 19.7113 | 4.4397 | 4.3396 | -24.5919 | 22.88s |\n", - "| DeepKriging_sphere_Huber | 100 | 15.7245 | 3.9654 | 3.8159 | -19.4156 | 22.26s |\n", - "| UniversalKriging | 50 | 16.3634 | 4.0452 | 3.9915 | -20.2451 | 2.25s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:19:48\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.4363 (±0.0207), Variance: 1.0686, Range: [-2.1785, 4.1579]\n", - "z mean: 4.2931 (±0.0597), Variance: 8.9203, Range: [-2.1273, 25.5685]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0068, sigma²=5.2966, nugget=2.4300\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 26.6624 | 5.1636 | 4.0589 | -23.5543 | 0.39s |\n", - "| OLS_sphere | 100 | 15.3575 | 3.9189 | 3.8259 | -13.1433 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.505 | 3.9376 | 3.7851 | -13.2791 | 55.95s |\n", - "| DeepKriging_sphere | 700 | 14.821 | 3.8498 | 3.7095 | -12.6491 | 16.10s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.1893 | 3.4913 | 3.3551 | -10.2255 | 18.64s |\n", - "| UniversalKriging | 50 | 15.3518 | 3.9181 | 3.8365 | -13.138 | 4.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:22:34\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.3354 (±0.0182), Variance: 0.8292, Range: [-2.7591, 3.2690]\n", - "z mean: 4.3087 (±0.0571), Variance: 8.1491, Range: [-1.4393, 22.2232]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0692, sigma²=0.0089, nugget=7.4078\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 40.0291 | 6.3269 | 4.5551 | -49.6695 | 0.41s |\n", - "| OLS_sphere | 100 | 16.4935 | 4.0612 | 3.9967 | -19.8778 | 0.01s |\n", - "| DeepKriging_wendland | -- | 24.0416 | 4.9032 | 4.025 | -29.4323 | 50.83s |\n", - "| DeepKriging_sphere | 700 | 17.7736 | 4.2159 | 4.1187 | -21.4982 | 15.68s |\n", - "| DeepKriging_sphere_Huber | 100 | 11.3585 | 3.3702 | 3.2621 | -13.3778 | 19.87s |\n", - "| UniversalKriging | 50 | 16.6177 | 4.0765 | 4.0261 | -20.0349 | 1.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:25:14\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 0.1570 (±0.0159), Variance: 0.6284, Range: [-1.9354, 3.0160]\n", - "z mean: 4.1654 (±0.0574), Variance: 8.2270, Range: [-1.2627, 17.3541]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0307, sigma²=0.0158, nugget=7.8489\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.7958 | 4.3354 | 4.0273 | -31.5261 | 0.40s |\n", - "| OLS_sphere | 100 | 16.5352 | 4.0663 | 3.9805 | -27.6141 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.3906 | 3.9231 | 3.787 | -25.6334 | 56.34s |\n", - "| DeepKriging_sphere | 700 | 13.0883 | 3.6178 | 3.5016 | -21.6492 | 19.12s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.4124 | 3.5231 | 3.4178 | -20.4796 | 19.87s |\n", - "| UniversalKriging | 50 | 16.3591 | 4.0446 | 3.9644 | -27.3094 | 1.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:28:04\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.2605 (±0.0172), Variance: 0.7392, Range: [-3.3801, 2.4770]\n", - "z mean: 3.7335 (±0.0582), Variance: 8.4584, Range: [-1.8339, 20.2134]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0344, sigma²=0.0148, nugget=7.7637\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.8174 | 4.2211 | 3.9543 | -21.0965 | 0.42s |\n", - "| OLS_sphere | 100 | 16.8907 | 4.1098 | 4.0302 | -19.9473 | 0.01s |\n", - "| DeepKriging_wendland | -- | 16.6877 | 4.0851 | 3.846 | -19.6955 | 54.10s |\n", - "| DeepKriging_sphere | 700 | 20.798 | 4.5605 | 4.4667 | -24.7929 | 15.21s |\n", - "| DeepKriging_sphere_Huber | 100 | 12.6007 | 3.5497 | 3.4387 | -14.627 | 14.32s |\n", - "| UniversalKriging | 50 | 16.466 | 4.0578 | 3.9938 | -19.4205 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:30:45\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -0.1256 (±0.0198), Variance: 0.9850, Range: [-2.8451, 2.8753]\n", - "z mean: 3.8786 (±0.0609), Variance: 9.2719, Range: [-2.0389, 23.4376]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0331, sigma²=0.4227, nugget=8.3327\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.0173 | 4.2447 | 4.0706 | -17.5042 | 0.40s |\n", - "| OLS_sphere | 100 | 17.2806 | 4.157 | 4.0628 | -16.7476 | 0.01s |\n", - "| DeepKriging_wendland | -- | 15.9517 | 3.994 | 3.8417 | -15.3828 | 56.35s |\n", - "| DeepKriging_sphere | 700 | 16.263 | 4.0327 | 3.8974 | -15.7024 | 16.47s |\n", - "| DeepKriging_sphere_Huber | 100 | 10.1585 | 3.1872 | 2.9942 | -9.433 | 18.04s |\n", - "| UniversalKriging | 50 | 17.2396 | 4.1521 | 4.0889 | -16.7055 | 3.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:33:34\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.019233, - "end_time": "2026-01-21T07:04:32.189851", - "exception": false, - "start_time": "2026-01-21T07:04:32.170618", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T07:04:32.233661Z", - "iopub.status.busy": "2026-01-21T07:04:32.232676Z", - "iopub.status.idle": "2026-01-21T07:04:32.264184Z", - "shell.execute_reply": "2026-01-21T07:04:32.260872Z" - }, - "papermill": { - "duration": 0.057643, - "end_time": "2026-01-21T07:04:32.267294", - "exception": false, - "start_time": "2026-01-21T07:04:32.209651", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 100, 'DeepKriging_sphere': 700, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------|------------|-----------|----------------|\n", - "| OLS_wendland | 208.43±745.38 | 8.85±11.41 | 4.55±1.07 | -258.35±921.61 |\n", - "| OLS_sphere | 16.72±0.73 | 4.09±0.09 | 4.01±0.09 | -18.69±4.73 |\n", - "| DeepKriging_wendland | 15.65±2.56 | 3.94±0.31 | 3.75±0.25 | -17.46±5.29 |\n", - "| DeepKriging_sphere | 16.80±2.32 | 4.09±0.29 | 3.97±0.29 | -18.71±5.10 |\n", - "| DeepKriging_sphere_Huber | 12.54±1.61 | 3.53±0.23 | 3.41±0.23 | -13.65±3.57 |\n", - "| UniversalKriging | 16.61±0.69 | 4.07±0.08 | 4.01±0.09 | -18.54±4.62 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 3.53±0.23)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 6823.160902, - "end_time": "2026-01-21T07:04:35.749133", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-21T05:10:52.588231", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_tdf4.ipynb b/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_tdf4.ipynb deleted file mode 100644 index 69a87e2..0000000 --- a/notebook/simulation/ScenarioI/simulation_spherical_GP_noise_tdf4.ipynb +++ /dev/null @@ -1,4575 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.015786, - "end_time": "2026-01-21T05:10:53.259244", - "exception": false, - "start_time": "2026-01-21T05:10:53.243458", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:53.285335Z", - "iopub.status.busy": "2026-01-21T05:10:53.284324Z", - "iopub.status.idle": "2026-01-21T05:10:53.303715Z", - "shell.execute_reply": "2026-01-21T05:10:53.300936Z" - }, - "papermill": { - "duration": 0.037033, - "end_time": "2026-01-21T05:10:53.306282", - "exception": false, - "start_time": "2026-01-21T05:10:53.269249", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:53.329944Z", - "iopub.status.busy": "2026-01-21T05:10:53.329499Z", - "iopub.status.idle": "2026-01-21T05:10:56.073926Z", - "shell.execute_reply": "2026-01-21T05:10:56.068433Z" - }, - "papermill": { - "duration": 2.759921, - "end_time": "2026-01-21T05:10:56.078592", - "exception": false, - "start_time": "2026-01-21T05:10:53.318671", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768972254.249658 2567235 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768972254.253871 2567235 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768972254.265290 2567235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768972254.265311 2567235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768972254.265312 2567235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768972254.265314 2567235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.005196, - "end_time": "2026-01-21T05:10:56.093961", - "exception": false, - "start_time": "2026-01-21T05:10:56.088765", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:56.107502Z", - "iopub.status.busy": "2026-01-21T05:10:56.105741Z", - "iopub.status.idle": "2026-01-21T05:10:57.599972Z", - "shell.execute_reply": "2026-01-21T05:10:57.596288Z" - }, - "papermill": { - "duration": 1.505904, - "end_time": "2026-01-21T05:10:57.603833", - "exception": false, - "start_time": "2026-01-21T05:10:56.097929", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.002559, - "end_time": "2026-01-21T05:10:57.612504", - "exception": false, - "start_time": "2026-01-21T05:10:57.609945", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:57.621650Z", - "iopub.status.busy": "2026-01-21T05:10:57.620836Z", - "iopub.status.idle": "2026-01-21T05:10:57.626234Z", - "shell.execute_reply": "2026-01-21T05:10:57.624902Z" - }, - "papermill": { - "duration": 0.012394, - "end_time": "2026-01-21T05:10:57.627415", - "exception": false, - "start_time": "2026-01-21T05:10:57.615021", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.00257, - "end_time": "2026-01-21T05:10:57.632964", - "exception": false, - "start_time": "2026-01-21T05:10:57.630394", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:57.640150Z", - "iopub.status.busy": "2026-01-21T05:10:57.639855Z", - "iopub.status.idle": "2026-01-21T05:11:11.821890Z", - "shell.execute_reply": "2026-01-21T05:11:11.818416Z" - }, - "papermill": { - "duration": 14.189201, - "end_time": "2026-01-21T05:11:11.825013", - "exception": false, - "start_time": "2026-01-21T05:10:57.635812", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.009738, - "end_time": "2026-01-21T05:11:11.847555", - "exception": false, - "start_time": "2026-01-21T05:11:11.837817", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.862844Z", - "iopub.status.busy": "2026-01-21T05:11:11.862162Z", - "iopub.status.idle": "2026-01-21T05:11:11.877329Z", - "shell.execute_reply": "2026-01-21T05:11:11.873842Z" - }, - "papermill": { - "duration": 0.026, - "end_time": "2026-01-21T05:11:11.880236", - "exception": false, - "start_time": "2026-01-21T05:11:11.854236", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = (L @ z_std).astype(np.float32)\n", - " z = y + (np.sqrt(2) * rng.standard_t(df=4, size=num_sample)).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003739, - "end_time": "2026-01-21T05:11:11.891357", - "exception": false, - "start_time": "2026-01-21T05:11:11.887618", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.901661Z", - "iopub.status.busy": "2026-01-21T05:11:11.900849Z", - "iopub.status.idle": "2026-01-21T05:11:11.951518Z", - "shell.execute_reply": "2026-01-21T05:11:11.947998Z" - }, - "papermill": { - "duration": 0.060681, - "end_time": "2026-01-21T05:11:11.955227", - "exception": false, - "start_time": "2026-01-21T05:11:11.894546", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " \n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten_cont(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " \n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten_cont, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:11.971126Z", - "iopub.status.busy": "2026-01-21T05:11:11.970648Z", - "iopub.status.idle": "2026-01-21T05:11:12.002597Z", - "shell.execute_reply": "2026-01-21T05:11:12.000217Z" - }, - "papermill": { - "duration": 0.041605, - "end_time": "2026-01-21T05:11:12.004470", - "exception": false, - "start_time": "2026-01-21T05:11:11.962865", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder + noise\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder + noise\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.007063, - "end_time": "2026-01-21T05:11:12.019675", - "exception": false, - "start_time": "2026-01-21T05:11:12.012612", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:12.032423Z", - "iopub.status.busy": "2026-01-21T05:11:12.031984Z", - "iopub.status.idle": "2026-01-21T05:11:12.039988Z", - "shell.execute_reply": "2026-01-21T05:11:12.037507Z" - }, - "papermill": { - "duration": 0.017825, - "end_time": "2026-01-21T05:11:12.042843", - "exception": false, - "start_time": "2026-01-21T05:11:12.025018", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:11:12.059093Z", - "iopub.status.busy": "2026-01-21T05:11:12.058775Z", - "iopub.status.idle": "2026-01-21T07:04:32.139260Z", - "shell.execute_reply": "2026-01-21T07:04:32.135593Z" - }, - "papermill": { - "duration": 6800.091023, - "end_time": "2026-01-21T07:04:32.142230", - "exception": false, - "start_time": "2026-01-21T05:11:12.051207", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.2087 (±0.0213), Variance: 1.1303, Range: [-3.3592, 3.1722]\n", - "z mean: -0.2008 (±0.0450), Variance: 5.0733, Range: [-10.5945, 13.4789]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.4221 0.3163 *\n", - " 100 0.4230 0.3657 \n", - " 200 0.5326 0.6277 \n", - " 500 1.5780 1.5196 \n", - " 700 2.2240 2.8298 \n", - " 1000 17.9331 6.8779 \n", - " 1400 125.3990 146.6553 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768972279.225790 2567235 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768972281.490123 2567406 service.cc:152] XLA service 0x59bc11c55dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768972281.490206 2567406 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768972281.896402 2567406 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768972291.757378 2567406 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ec41418dfc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ec40c11b760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.7571 0.7766 \n", - " 100 5.8926 0.8690 \n", - " 200 5.6082 0.8134 *\n", - " 500 5.9591 0.9257 \n", - " 700 5.9694 1.0687 \n", - " 1000 5.9764 0.9802 \n", - " 1400 5.7992 0.9522 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.6505 0.7588 \n", - " 100 1.6159 1.0181 *\n", - " 200 1.6173 0.9954 \n", - " 500 1.6590 1.2680 \n", - " 700 1.6687 1.2291 \n", - " 1000 1.6542 0.9881 \n", - " 1400 1.6525 1.0291 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0097, sigma²=1.4130, nugget=2.7425\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0204, sigma²=0.0022, nugget=3.9809\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2883, sigma²=0.0000, nugget=3.6702\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.6604, sigma²=0.0000, nugget=3.0559\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.6330, sigma²=0.0000, nugget=2.6442\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.1254, sigma²=0.0000, nugget=2.0181\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.5896, sigma²=0.0000, nugget=1.1570\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.3502 0.3232 \n", - " 100 5.3127 0.3657 *\n", - " 200 6.0011 0.6277 \n", - " 500 6.9082 1.5196 \n", - " 700 7.3973 2.8298 \n", - " 1000 23.4752 6.8778 \n", - " 1400 130.2393 146.6558 \n", - " Best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0204, sigma²=0.0022, nugget=3.9809\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.0494 | 4.3646 | 0.9613 | -16.501 | 0.44s |\n", - "| OLS_sphere | 50 | 0.3163 | 0.5624 | 0.4705 | 0.7094 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.0229 | 1.0114 | 0.7599 | 0.0602 | 48.02s |\n", - "| DeepKriging_sphere | 200 | 0.833 | 0.9127 | 0.7273 | 0.2347 | 13.78s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8618 | 0.9283 | 0.7453 | 0.2083 | 13.67s |\n", - "| UniversalKriging | 100 | 0.3657 | 0.6047 | 0.4886 | 0.664 | 2.06s |\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:17:57\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0914 (±0.0170), Variance: 0.7252, Range: [-2.5030, 2.8675]\n", - "z mean: 0.1063 (±0.0430), Variance: 4.6230, Range: [-18.1995, 10.7510]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0225, sigma²=0.0035, nugget=4.0090\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 28.4645 | 5.3352 | 1.119 | -36.5851 | 0.39s |\n", - "| OLS_sphere | 50 | 0.3594 | 0.5995 | 0.476 | 0.5254 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7356 | 0.8577 | 0.6937 | 0.0287 | 52.86s |\n", - "| DeepKriging_sphere | 200 | 0.6228 | 0.7892 | 0.6409 | 0.1776 | 14.18s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6227 | 0.7891 | 0.6217 | 0.1778 | 14.49s |\n", - "| UniversalKriging | 100 | 0.4327 | 0.6578 | 0.5228 | 0.4286 | 2.01s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:19:41\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1021 (±0.0165), Variance: 0.6820, Range: [-2.6324, 2.0719]\n", - "z mean: -0.0778 (±0.0432), Variance: 4.6594, Range: [-14.7082, 13.1311]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0315, sigma²=0.0015, nugget=4.1379\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.9812 | 2.2319 | 1.015 | -7.1214 | 0.41s |\n", - "| OLS_sphere | 50 | 0.3699 | 0.6082 | 0.4937 | 0.3969 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7115 | 0.8435 | 0.6574 | -0.16 | 46.53s |\n", - "| DeepKriging_sphere | 200 | 0.5624 | 0.7499 | 0.5931 | 0.0831 | 12.66s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5209 | 0.7217 | 0.5779 | 0.1507 | 15.10s |\n", - "| UniversalKriging | 100 | 0.4199 | 0.648 | 0.5237 | 0.3154 | 1.73s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:21:17\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0181 (±0.0207), Variance: 1.0716, Range: [-3.1769, 3.1892]\n", - "z mean: 0.0231 (±0.0462), Variance: 5.3358, Range: [-23.1244, 18.3829]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0265, sigma²=0.0007, nugget=4.3223\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.5494 | 1.5967 | 0.8967 | -2.0188 | 0.32s |\n", - "| OLS_sphere | 50 | 0.3553 | 0.5961 | 0.4843 | 0.5792 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.9442 | 0.9717 | 0.7269 | -0.1181 | 54.25s |\n", - "| DeepKriging_sphere | 200 | 0.6714 | 0.8194 | 0.6415 | 0.2049 | 14.41s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6335 | 0.7959 | 0.6199 | 0.2499 | 14.13s |\n", - "| UniversalKriging | 100 | 0.4369 | 0.661 | 0.5224 | 0.4827 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:23:04\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3156 (±0.0192), Variance: 0.9186, Range: [-2.9928, 3.2624]\n", - "z mean: 0.2789 (±0.0438), Variance: 4.7987, Range: [-10.9943, 15.0953]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0217, sigma²=0.0030, nugget=3.6274\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7331 | 1.3165 | 0.8195 | -0.9051 | 0.35s |\n", - "| OLS_sphere | 50 | 0.3068 | 0.5539 | 0.4397 | 0.6628 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7728 | 0.8791 | 0.6891 | 0.1504 | 52.05s |\n", - "| DeepKriging_sphere | 200 | 0.7352 | 0.8574 | 0.6946 | 0.1918 | 12.43s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7912 | 0.8895 | 0.7184 | 0.1302 | 12.95s |\n", - "| UniversalKriging | 100 | 0.3317 | 0.5759 | 0.4566 | 0.6354 | 2.08s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:24:48\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.2265 (±0.0170), Variance: 0.7267, Range: [-3.0397, 2.3144]\n", - "z mean: -0.2767 (±0.0410), Variance: 4.2099, Range: [-9.1054, 18.7764]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0146, sigma²=0.0018, nugget=3.4185\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 12.2224 | 3.4961 | 0.9085 | -17.87 | 0.37s |\n", - "| OLS_sphere | 50 | 0.3239 | 0.5691 | 0.45 | 0.4999 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6337 | 0.796 | 0.6296 | 0.0217 | 39.76s |\n", - "| DeepKriging_sphere | 200 | 0.5756 | 0.7587 | 0.6037 | 0.1114 | 11.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6746 | 0.8214 | 0.6445 | -0.0415 | 16.81s |\n", - "| UniversalKriging | 100 | 0.4048 | 0.6362 | 0.5116 | 0.375 | 2.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:26:24\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1545 (±0.0181), Variance: 0.8211, Range: [-3.2120, 3.8366]\n", - "z mean: 0.2240 (±0.0438), Variance: 4.7946, Range: [-15.0426, 27.1599]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0176, sigma²=0.0018, nugget=4.0281\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.6244 | 1.2745 | 0.7843 | -0.7998 | 0.37s |\n", - "| OLS_sphere | 50 | 0.4021 | 0.6341 | 0.514 | 0.5545 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7939 | 0.891 | 0.6904 | 0.1203 | 46.84s |\n", - "| DeepKriging_sphere | 200 | 0.7453 | 0.8633 | 0.6869 | 0.1742 | 14.35s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7463 | 0.8639 | 0.6822 | 0.1732 | 14.04s |\n", - "| UniversalKriging | 100 | 0.3855 | 0.6209 | 0.4889 | 0.5728 | 2.36s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:28:06\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0301 (±0.0233), Variance: 1.3596, Range: [-3.6446, 3.3099]\n", - "z mean: -0.0106 (±0.0442), Variance: 4.8814, Range: [-15.8966, 11.2287]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0153, sigma²=0.0013, nugget=3.6846\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.6944 | 1.9221 | 0.9991 | -1.5395 | 0.37s |\n", - "| OLS_sphere | 50 | 0.3785 | 0.6153 | 0.5002 | 0.7398 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.0364 | 1.018 | 0.8015 | 0.2876 | 42.98s |\n", - "| DeepKriging_sphere | 200 | 1.0399 | 1.0198 | 0.7874 | 0.2852 | 19.43s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.0881 | 1.0431 | 0.8473 | 0.252 | 13.74s |\n", - "| UniversalKriging | 100 | 0.4423 | 0.6651 | 0.5376 | 0.6959 | 2.31s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:29:51\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1015 (±0.0184), Variance: 0.8465, Range: [-2.5073, 2.8775]\n", - "z mean: 0.1058 (±0.0429), Variance: 4.6095, Range: [-12.8305, 12.9941]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0216, sigma²=0.0034, nugget=3.7049\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8206 | 1.3493 | 0.7977 | -1.2613 | 0.36s |\n", - "| OLS_sphere | 50 | 0.3565 | 0.5971 | 0.4848 | 0.5571 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7749 | 0.8803 | 0.7036 | 0.0375 | 42.26s |\n", - "| DeepKriging_sphere | 200 | 0.6657 | 0.8159 | 0.6466 | 0.1731 | 14.00s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7213 | 0.8493 | 0.6818 | 0.1041 | 13.69s |\n", - "| UniversalKriging | 100 | 0.4133 | 0.6429 | 0.5064 | 0.4867 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:31:32\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3526 (±0.0222), Variance: 1.2329, Range: [-2.8410, 4.1531]\n", - "z mean: 0.4018 (±0.0460), Variance: 5.2953, Range: [-11.2238, 11.7306]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0223, sigma²=0.0014, nugget=4.0698\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2673 | 1.1257 | 0.8228 | -0.0493 | 0.40s |\n", - "| OLS_sphere | 50 | 0.3654 | 0.6045 | 0.4954 | 0.6975 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.0323 | 1.016 | 0.8241 | 0.1453 | 43.41s |\n", - "| DeepKriging_sphere | 200 | 0.9194 | 0.9588 | 0.7767 | 0.2388 | 13.17s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8142 | 0.9023 | 0.7299 | 0.3259 | 14.77s |\n", - "| UniversalKriging | 100 | 0.3696 | 0.608 | 0.48 | 0.6939 | 2.12s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:33:15\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.2930 (±0.0190), Variance: 0.9043, Range: [-2.9922, 3.7984]\n", - "z mean: 0.2511 (±0.0417), Variance: 4.3537, Range: [-8.9467, 13.1454]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0160, sigma²=0.0006, nugget=3.4588\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7988 | 1.3412 | 0.866 | -0.9228 | 0.37s |\n", - "| OLS_sphere | 50 | 0.2974 | 0.5453 | 0.4222 | 0.6821 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.9627 | 0.9812 | 0.7476 | -0.0291 | 50.60s |\n", - "| DeepKriging_sphere | 200 | 0.854 | 0.9241 | 0.7248 | 0.0871 | 13.89s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8675 | 0.9314 | 0.7499 | 0.0727 | 18.39s |\n", - "| UniversalKriging | 100 | 0.3203 | 0.5659 | 0.4608 | 0.6576 | 2.36s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:35:11\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.2711 (±0.0163), Variance: 0.6659, Range: [-2.2990, 2.6131]\n", - "z mean: 0.3238 (±0.0419), Variance: 4.3929, Range: [-11.1079, 13.2077]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0154, sigma²=0.0006, nugget=3.6977\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 11.3262 | 3.3654 | 0.9901 | -14.0647 | 0.37s |\n", - "| OLS_sphere | 50 | 0.331 | 0.5753 | 0.4564 | 0.5598 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6693 | 0.8181 | 0.6521 | 0.1098 | 37.11s |\n", - "| DeepKriging_sphere | 200 | 0.6206 | 0.7878 | 0.6426 | 0.1746 | 14.09s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6183 | 0.7863 | 0.6442 | 0.1777 | 13.92s |\n", - "| UniversalKriging | 100 | 0.3578 | 0.5981 | 0.4754 | 0.5241 | 2.39s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:36:51\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0562 (±0.0177), Variance: 0.7849, Range: [-2.3893, 2.8555]\n", - "z mean: 0.0121 (±0.0445), Variance: 4.9565, Range: [-19.9226, 21.6220]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0197, sigma²=0.0025, nugget=3.9852\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|--------|------------|--------|\n", - "| OLS_wendland | -- | 1050.62 | 32.4132 | 4.561 | -1284.37 | 0.43s |\n", - "| OLS_sphere | 50 | 0.3703 | 0.6086 | 0.4736 | 0.5469 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.1498 | 1.0723 | 0.7821 | -0.4067 | 54.04s |\n", - "| DeepKriging_sphere | 200 | 0.6788 | 0.8239 | 0.647 | 0.1695 | 13.93s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6596 | 0.8122 | 0.6399 | 0.193 | 15.11s |\n", - "| UniversalKriging | 100 | 0.3389 | 0.5821 | 0.4595 | 0.5854 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:38:49\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.7619 (±0.0173), Variance: 0.7493, Range: [-3.7554, 2.2216]\n", - "z mean: -0.7683 (±0.0431), Variance: 4.6479, Range: [-19.3694, 8.6964]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0291, sigma²=0.0019, nugget=3.8810\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 95.0953 | 9.7517 | 1.236 | -156.002 | 0.38s |\n", - "| OLS_sphere | 50 | 0.3923 | 0.6264 | 0.5064 | 0.3522 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.5401 | 0.7349 | 0.5709 | 0.1083 | 56.04s |\n", - "| DeepKriging_sphere | 200 | 0.5477 | 0.74 | 0.6062 | 0.0958 | 15.74s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5499 | 0.7415 | 0.6013 | 0.0922 | 16.87s |\n", - "| UniversalKriging | 100 | 0.366 | 0.605 | 0.4919 | 0.3957 | 1.53s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:40:53\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.5079 (±0.0201), Variance: 1.0118, Range: [-3.3722, 2.8220]\n", - "z mean: -0.5184 (±0.0464), Variance: 5.3892, Range: [-25.2376, 13.5738]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0217, sigma²=0.0033, nugget=4.1691\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.7514 | 2.1798 | 0.9924 | -4.5546 | 0.41s |\n", - "| OLS_sphere | 50 | 0.3483 | 0.5901 | 0.4842 | 0.5929 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.8047 | 0.8971 | 0.7244 | 0.0592 | 60.39s |\n", - "| DeepKriging_sphere | 200 | 0.7795 | 0.8829 | 0.7163 | 0.0887 | 14.95s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7665 | 0.8755 | 0.7095 | 0.1039 | 15.82s |\n", - "| UniversalKriging | 100 | 0.4182 | 0.6467 | 0.5257 | 0.5111 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:43:03\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.2011 (±0.0168), Variance: 0.7026, Range: [-2.6394, 2.5442]\n", - "z mean: -0.2581 (±0.0445), Variance: 4.9546, Range: [-29.5356, 16.2283]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0296, sigma²=0.0012, nugget=3.7457\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 6.8495 | 2.6172 | 0.8552 | -10.0961 | 0.34s |\n", - "| OLS_sphere | 50 | 0.4466 | 0.6683 | 0.5239 | 0.2766 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.5914 | 0.769 | 0.6165 | 0.042 | 57.39s |\n", - "| DeepKriging_sphere | 200 | 0.5375 | 0.7331 | 0.5679 | 0.1293 | 13.28s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5197 | 0.7209 | 0.5655 | 0.1582 | 14.53s |\n", - "| UniversalKriging | 100 | 0.4993 | 0.7066 | 0.5512 | 0.1912 | 1.84s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:45:06\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4486 (±0.0195), Variance: 0.9543, Range: [-2.6866, 3.0678]\n", - "z mean: 0.4115 (±0.0446), Variance: 4.9659, Range: [-9.4617, 19.4787]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0157, sigma²=0.0010, nugget=4.0069\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 28.7404 | 5.361 | 1.1225 | -26.141 | 0.38s |\n", - "| OLS_sphere | 50 | 0.3231 | 0.5684 | 0.4558 | 0.6949 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8172 | 0.904 | 0.7489 | 0.2282 | 59.91s |\n", - "| DeepKriging_sphere | 200 | 0.7828 | 0.8848 | 0.7273 | 0.2607 | 14.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8366 | 0.9147 | 0.7504 | 0.21 | 13.82s |\n", - "| UniversalKriging | 100 | 0.38 | 0.6164 | 0.4824 | 0.6412 | 2.32s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:47:15\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.3553 (±0.0223), Variance: 1.2401, Range: [-3.8404, 2.6780]\n", - "z mean: -0.3728 (±0.0480), Variance: 5.7616, Range: [-14.8681, 20.7519]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0194, sigma²=0.0029, nugget=4.4049\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 940.858 | 30.6734 | 3.6088 | -889.49 | 0.41s |\n", - "| OLS_sphere | 50 | 0.4064 | 0.6375 | 0.5199 | 0.6154 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.1561 | 1.0752 | 0.8619 | -0.0942 | 51.01s |\n", - "| DeepKriging_sphere | 200 | 0.7981 | 0.8934 | 0.7321 | 0.2446 | 14.17s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7414 | 0.861 | 0.699 | 0.2983 | 14.91s |\n", - "| UniversalKriging | 100 | 0.4099 | 0.6403 | 0.5071 | 0.612 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:49:16\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0131 (±0.0193), Variance: 0.9331, Range: [-2.5519, 3.1735]\n", - "z mean: 0.0211 (±0.0452), Variance: 5.1081, Range: [-13.3277, 13.2406]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0229, sigma²=0.0013, nugget=4.4244\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.9291 | 1.7115 | 0.9509 | -2.2016 | 0.39s |\n", - "| OLS_sphere | 50 | 0.409 | 0.6395 | 0.5026 | 0.553 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.8226 | 0.907 | 0.7207 | 0.1009 | 48.71s |\n", - "| DeepKriging_sphere | 200 | 0.7706 | 0.8778 | 0.6956 | 0.1577 | 13.28s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6888 | 0.8299 | 0.6684 | 0.2471 | 14.46s |\n", - "| UniversalKriging | 100 | 0.422 | 0.6496 | 0.5151 | 0.5387 | 2.06s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:51:15\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1429 (±0.0161), Variance: 0.6453, Range: [-3.2163, 2.5210]\n", - "z mean: -0.1481 (±0.0439), Variance: 4.8181, Range: [-11.2181, 16.0425]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0326, sigma²=0.0010, nugget=4.1716\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 124.826 | 11.1725 | 1.5085 | -222.569 | 0.31s |\n", - "| OLS_sphere | 50 | 0.3361 | 0.5797 | 0.4648 | 0.3981 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.4813 | 0.6937 | 0.5504 | 0.138 | 52.81s |\n", - "| DeepKriging_sphere | 200 | 0.4672 | 0.6835 | 0.5484 | 0.1632 | 14.39s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4811 | 0.6936 | 0.5574 | 0.1383 | 14.22s |\n", - "| UniversalKriging | 100 | 0.4235 | 0.6508 | 0.5333 | 0.2415 | 1.79s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:53:18\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3161 (±0.0162), Variance: 0.6585, Range: [-2.0472, 2.8787]\n", - "z mean: 0.2528 (±0.0419), Variance: 4.3868, Range: [-11.4508, 12.1668]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0151, sigma²=0.0027, nugget=3.5604\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.7473 | 1.6575 | 0.8423 | -2.9424 | 0.46s |\n", - "| OLS_sphere | 50 | 0.4257 | 0.6525 | 0.5128 | 0.3891 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7072 | 0.841 | 0.663 | -0.0149 | 49.90s |\n", - "| DeepKriging_sphere | 200 | 0.6092 | 0.7805 | 0.6187 | 0.1258 | 14.58s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6293 | 0.7933 | 0.6418 | 0.0969 | 18.84s |\n", - "| UniversalKriging | 100 | 0.4999 | 0.7071 | 0.5786 | 0.2826 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:55:26\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1353 (±0.0173), Variance: 0.7452, Range: [-2.7997, 2.5882]\n", - "z mean: 0.1115 (±0.0442), Variance: 4.8733, Range: [-14.4185, 27.3438]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0261, sigma²=0.0003, nugget=4.3445\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.8758 | 4.3446 | 1.0831 | -24.1722 | 0.42s |\n", - "| OLS_sphere | 50 | 0.3302 | 0.5747 | 0.4643 | 0.5596 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7277 | 0.853 | 0.6682 | 0.0296 | 48.80s |\n", - "| DeepKriging_sphere | 200 | 0.6758 | 0.8221 | 0.6424 | 0.0987 | 13.86s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.607 | 0.7791 | 0.6167 | 0.1905 | 13.72s |\n", - "| UniversalKriging | 100 | 0.4681 | 0.6842 | 0.5482 | 0.3758 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:57:27\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.5156 (±0.0180), Variance: 0.8144, Range: [-2.0495, 3.5843]\n", - "z mean: 0.4693 (±0.0426), Variance: 4.5299, Range: [-8.9400, 12.9344]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0245, sigma²=0.0037, nugget=3.7048\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2668 | 1.1255 | 0.7404 | -0.7018 | 0.40s |\n", - "| OLS_sphere | 50 | 0.3792 | 0.6158 | 0.4895 | 0.4906 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6099 | 0.781 | 0.6106 | 0.1807 | 52.36s |\n", - "| DeepKriging_sphere | 200 | 0.6282 | 0.7926 | 0.6444 | 0.1561 | 14.57s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.558 | 0.747 | 0.6118 | 0.2504 | 14.80s |\n", - "| UniversalKriging | 100 | 0.3461 | 0.5883 | 0.4683 | 0.5351 | 2.19s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:59:35\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0829 (±0.0196), Variance: 0.9647, Range: [-3.1156, 3.0751]\n", - "z mean: 0.0788 (±0.0441), Variance: 4.8541, Range: [-9.7422, 14.6876]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0205, sigma²=0.0013, nugget=3.7648\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 300.898 | 17.3464 | 2.5618 | -338.501 | 0.41s |\n", - "| OLS_sphere | 50 | 0.3758 | 0.613 | 0.5047 | 0.576 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7435 | 0.8623 | 0.6929 | 0.1611 | 46.06s |\n", - "| DeepKriging_sphere | 200 | 0.7016 | 0.8376 | 0.6667 | 0.2084 | 13.15s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6143 | 0.7838 | 0.6357 | 0.3069 | 14.02s |\n", - "| UniversalKriging | 100 | 0.4383 | 0.662 | 0.5348 | 0.5055 | 2.01s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:01:37\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.0490 (±0.0182), Variance: 0.8287, Range: [-2.5449, 2.6965]\n", - "z mean: -0.0863 (±0.0415), Variance: 4.2958, Range: [-16.6498, 11.6647]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0240, sigma²=0.0004, nugget=3.5735\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.0736 | 1.0361 | 0.7987 | -0.3201 | 0.43s |\n", - "| OLS_sphere | 50 | 0.3481 | 0.59 | 0.478 | 0.5719 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7743 | 0.88 | 0.7109 | 0.0479 | 52.68s |\n", - "| DeepKriging_sphere | 200 | 0.7289 | 0.8537 | 0.6957 | 0.1038 | 14.74s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6221 | 0.7887 | 0.638 | 0.2351 | 14.34s |\n", - "| UniversalKriging | 100 | 0.4174 | 0.646 | 0.5263 | 0.4868 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:03:47\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0682 (±0.0195), Variance: 0.9515, Range: [-3.6544, 2.8584]\n", - "z mean: 0.0873 (±0.0452), Variance: 5.1077, Range: [-18.0472, 14.7504]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0212, sigma²=0.0022, nugget=4.3342\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 112.632 | 10.6128 | 1.4655 | -113.344 | 0.49s |\n", - "| OLS_sphere | 50 | 0.4266 | 0.6532 | 0.5186 | 0.5669 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.9263 | 0.9625 | 0.7557 | 0.0596 | 53.28s |\n", - "| DeepKriging_sphere | 200 | 0.7938 | 0.891 | 0.7097 | 0.1941 | 13.01s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.981 | 0.9904 | 0.7811 | 0.0041 | 16.21s |\n", - "| UniversalKriging | 100 | 0.4094 | 0.6398 | 0.5039 | 0.5844 | 1.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:06:00\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6284 (±0.0190), Variance: 0.9023, Range: [-3.0028, 3.7611]\n", - "z mean: 0.6029 (±0.0428), Variance: 4.5760, Range: [-22.7148, 11.2420]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0216, sigma²=0.0021, nugget=3.4940\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 96.7602 | 9.8367 | 1.9249 | -106.924 | 0.41s |\n", - "| OLS_sphere | 50 | 0.4021 | 0.6341 | 0.5005 | 0.5515 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6755 | 0.8219 | 0.6534 | 0.2466 | 40.66s |\n", - "| DeepKriging_sphere | 200 | 0.7397 | 0.8601 | 0.6808 | 0.175 | 14.13s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6935 | 0.8327 | 0.6577 | 0.2265 | 22.13s |\n", - "| UniversalKriging | 100 | 0.3979 | 0.6308 | 0.4941 | 0.5562 | 2.06s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:08:08\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1642 (±0.0190), Variance: 0.9004, Range: [-2.5461, 2.9000]\n", - "z mean: 0.1710 (±0.0442), Variance: 4.8833, Range: [-23.6575, 11.9575]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0213, sigma²=0.0040, nugget=4.1811\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9538 | 1.3978 | 0.9696 | -0.8695 | 0.37s |\n", - "| OLS_sphere | 50 | 0.4615 | 0.6793 | 0.5511 | 0.5584 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.9997 | 0.9998 | 0.8316 | 0.0435 | 45.67s |\n", - "| DeepKriging_sphere | 200 | 0.9517 | 0.9755 | 0.8045 | 0.0894 | 15.59s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8032 | 0.8962 | 0.7433 | 0.2315 | 12.66s |\n", - "| UniversalKriging | 100 | 0.3449 | 0.5873 | 0.4637 | 0.6699 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:10:15\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3065 (±0.0182), Variance: 0.8257, Range: [-2.3751, 2.7162]\n", - "z mean: 0.3071 (±0.0469), Variance: 5.4941, Range: [-20.2258, 13.7127]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0268, sigma²=0.0018, nugget=4.6606\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.2968 | 1.8157 | 0.9513 | -2.14 | 0.40s |\n", - "| OLS_sphere | 50 | 0.3983 | 0.6311 | 0.5141 | 0.6207 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.9715 | 0.9856 | 0.8174 | 0.0747 | 46.80s |\n", - "| DeepKriging_sphere | 200 | 0.8787 | 0.9374 | 0.7883 | 0.1631 | 12.27s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8164 | 0.9036 | 0.7614 | 0.2224 | 11.98s |\n", - "| UniversalKriging | 100 | 0.4896 | 0.6997 | 0.5628 | 0.5337 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:12:20\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.6807 (±0.0228), Variance: 1.2941, Range: [-3.0551, 5.0718]\n", - "z mean: 0.6780 (±0.0454), Variance: 5.1445, Range: [-10.1956, 14.4946]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0225, sigma²=0.0013, nugget=3.9146\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 63.0743 | 7.9419 | 1.479 | -48.6781 | 0.38s |\n", - "| OLS_sphere | 50 | 0.398 | 0.6309 | 0.505 | 0.6865 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.0332 | 1.0165 | 0.7703 | 0.1863 | 46.22s |\n", - "| DeepKriging_sphere | 200 | 0.9355 | 0.9672 | 0.7665 | 0.2632 | 13.19s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1833 | 1.0878 | 0.8986 | 0.068 | 13.16s |\n", - "| UniversalKriging | 100 | 0.3703 | 0.6086 | 0.4922 | 0.7083 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:14:28\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1138 (±0.0177), Variance: 0.7833, Range: [-2.5885, 2.6781]\n", - "z mean: 0.1117 (±0.0429), Variance: 4.5967, Range: [-14.4361, 11.1879]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0180, sigma²=0.0013, nugget=3.8075\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4354 | 1.1981 | 0.8232 | -0.8541 | 0.39s |\n", - "| OLS_sphere | 50 | 0.3872 | 0.6223 | 0.4969 | 0.4998 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7223 | 0.8499 | 0.6714 | 0.067 | 50.21s |\n", - "| DeepKriging_sphere | 200 | 0.6358 | 0.7974 | 0.6414 | 0.1787 | 13.24s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5881 | 0.7669 | 0.6134 | 0.2404 | 13.54s |\n", - "| UniversalKriging | 100 | 0.3812 | 0.6174 | 0.5027 | 0.5075 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:16:42\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0866 (±0.0195), Variance: 0.9528, Range: [-2.8215, 3.4723]\n", - "z mean: 0.1250 (±0.0460), Variance: 5.2885, Range: [-15.6203, 18.0713]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0159, sigma²=0.0013, nugget=4.4969\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0805 | 1.4424 | 0.8282 | -1.1752 | 0.42s |\n", - "| OLS_sphere | 50 | 0.34 | 0.5831 | 0.4717 | 0.6445 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.8733 | 0.9345 | 0.7482 | 0.0869 | 51.03s |\n", - "| DeepKriging_sphere | 200 | 0.9388 | 0.9689 | 0.7973 | 0.0184 | 15.07s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6917 | 0.8317 | 0.6806 | 0.2768 | 14.31s |\n", - "| UniversalKriging | 100 | 0.4005 | 0.6328 | 0.5051 | 0.5813 | 2.00s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:18:58\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1753 (±0.0182), Variance: 0.8285, Range: [-2.4795, 3.2583]\n", - "z mean: 0.2093 (±0.0423), Variance: 4.4708, Range: [-8.3812, 14.5834]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0204, sigma²=0.0017, nugget=3.6529\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.3138 | 1.5211 | 0.8954 | -1.9214 | 0.42s |\n", - "| OLS_sphere | 50 | 0.4527 | 0.6728 | 0.5223 | 0.4285 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.0896 | 1.0439 | 0.8223 | -0.3757 | 45.32s |\n", - "| DeepKriging_sphere | 200 | 0.6799 | 0.8246 | 0.6535 | 0.1416 | 12.96s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6136 | 0.7833 | 0.6239 | 0.2253 | 13.66s |\n", - "| UniversalKriging | 100 | 0.4021 | 0.6341 | 0.5019 | 0.4923 | 0.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:21:09\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.3590 (±0.0169), Variance: 0.7165, Range: [-2.9285, 2.4953]\n", - "z mean: -0.3021 (±0.0429), Variance: 4.5969, Range: [-16.3777, 21.0429]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0212, sigma²=0.0019, nugget=3.6389\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0661 | 1.4374 | 0.8595 | -1.983 | 0.37s |\n", - "| OLS_sphere | 50 | 0.3513 | 0.5927 | 0.4826 | 0.4928 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6687 | 0.8177 | 0.6677 | 0.0346 | 44.22s |\n", - "| DeepKriging_sphere | 200 | 0.5883 | 0.767 | 0.604 | 0.1507 | 13.54s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.704 | 0.839 | 0.6674 | -0.0164 | 14.81s |\n", - "| UniversalKriging | 100 | 0.3685 | 0.6071 | 0.4692 | 0.4679 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:23:22\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1716 (±0.0174), Variance: 0.7549, Range: [-3.2324, 2.3597]\n", - "z mean: 0.1478 (±0.0423), Variance: 4.4763, Range: [-11.5920, 13.0646]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0280, sigma²=0.0010, nugget=3.8098\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.7078 | 1.6455 | 0.7914 | -2.9072 | 0.36s |\n", - "| OLS_sphere | 50 | 0.414 | 0.6435 | 0.5269 | 0.4026 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.663 | 0.8143 | 0.652 | 0.0433 | 45.52s |\n", - "| DeepKriging_sphere | 200 | 0.6142 | 0.7837 | 0.6287 | 0.1138 | 13.05s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5927 | 0.7699 | 0.6186 | 0.1448 | 13.40s |\n", - "| UniversalKriging | 100 | 0.448 | 0.6693 | 0.5401 | 0.3536 | 1.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:25:35\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.4976 (±0.0182), Variance: 0.8304, Range: [-3.0018, 2.0971]\n", - "z mean: -0.5225 (±0.0480), Variance: 5.7486, Range: [-39.2995, 26.7283]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0219, sigma²=0.0034, nugget=4.7920\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 37.1952 | 6.0988 | 1.3462 | -44.3251 | 0.42s |\n", - "| OLS_sphere | 50 | 0.3744 | 0.6119 | 0.4873 | 0.5438 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.8788 | 0.9375 | 0.7593 | -0.0709 | 45.89s |\n", - "| DeepKriging_sphere | 200 | 0.765 | 0.8746 | 0.7092 | 0.0678 | 13.78s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7608 | 0.8722 | 0.7099 | 0.0729 | 12.28s |\n", - "| UniversalKriging | 100 | 0.4923 | 0.7017 | 0.5686 | 0.4 | 1.97s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:27:48\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0200 (±0.0165), Variance: 0.6770, Range: [-2.3961, 2.9264]\n", - "z mean: 0.0213 (±0.0414), Variance: 4.2878, Range: [-11.6295, 11.7856]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0163, sigma²=0.0007, nugget=3.5941\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9571 | 0.9783 | 0.6997 | -0.408 | 0.43s |\n", - "| OLS_sphere | 50 | 0.259 | 0.5089 | 0.4105 | 0.619 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6836 | 0.8268 | 0.6609 | -0.0057 | 43.79s |\n", - "| DeepKriging_sphere | 200 | 0.601 | 0.7752 | 0.6259 | 0.1159 | 14.11s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5611 | 0.749 | 0.6043 | 0.1746 | 13.69s |\n", - "| UniversalKriging | 100 | 0.2786 | 0.5278 | 0.4262 | 0.5902 | 2.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:30:03\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1341 (±0.0179), Variance: 0.7966, Range: [-3.0314, 3.1388]\n", - "z mean: 0.1487 (±0.0443), Variance: 4.9022, Range: [-13.3291, 18.8200]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0246, sigma²=0.0029, nugget=4.1910\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|--------|------------|--------|\n", - "| OLS_wendland | -- | 3800.38 | 61.6472 | 4.8244 | -4470.76 | 0.47s |\n", - "| OLS_sphere | 50 | 0.4073 | 0.6382 | 0.5181 | 0.5208 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.0596 | 1.0294 | 0.75 | -0.2468 | 45.48s |\n", - "| DeepKriging_sphere | 200 | 0.7436 | 0.8623 | 0.6808 | 0.125 | 13.13s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.88 | 0.9381 | 0.7467 | -0.0355 | 15.98s |\n", - "| UniversalKriging | 100 | 0.4304 | 0.656 | 0.5274 | 0.4936 | 1.78s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:32:23\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.4989 (±0.0208), Variance: 1.0820, Range: [-3.7355, 2.9445]\n", - "z mean: -0.5372 (±0.0427), Variance: 4.5494, Range: [-14.1104, 11.0293]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0167, sigma²=0.0016, nugget=3.4101\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.064 | 1.0315 | 0.7885 | 0.0226 | 0.43s |\n", - "| OLS_sphere | 50 | 0.4338 | 0.6586 | 0.5145 | 0.6015 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8799 | 0.938 | 0.7406 | 0.1917 | 62.04s |\n", - "| DeepKriging_sphere | 200 | 1.0933 | 1.0456 | 0.8429 | -0.0043 | 20.53s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8529 | 0.9235 | 0.7259 | 0.2165 | 13.68s |\n", - "| UniversalKriging | 100 | 0.4042 | 0.6357 | 0.4979 | 0.6287 | 2.32s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:35:05\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.5872 (±0.0187), Variance: 0.8739, Range: [-2.4319, 3.3557]\n", - "z mean: 0.5794 (±0.0439), Variance: 4.8197, Range: [-8.8969, 14.5212]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0200, sigma²=0.0015, nugget=3.7893\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5901 | 1.261 | 0.8042 | -0.9368 | 0.34s |\n", - "| OLS_sphere | 50 | 0.3309 | 0.5752 | 0.4612 | 0.597 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7174 | 0.847 | 0.6688 | 0.1262 | 59.18s |\n", - "| DeepKriging_sphere | 200 | 0.7449 | 0.8631 | 0.7008 | 0.0927 | 14.34s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7339 | 0.8567 | 0.6965 | 0.1062 | 15.33s |\n", - "| UniversalKriging | 100 | 0.4108 | 0.641 | 0.5079 | 0.4996 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:37:42\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.3365 (±0.0257), Variance: 1.6456, Range: [-3.3617, 3.1033]\n", - "z mean: -0.3644 (±0.0462), Variance: 5.3346, Range: [-15.2426, 16.6436]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0193, sigma²=0.0014, nugget=3.8689\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 16.7588 | 4.0938 | 1.3422 | -9.5474 | 0.36s |\n", - "| OLS_sphere | 50 | 0.3499 | 0.5916 | 0.469 | 0.7798 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.2448 | 1.1157 | 0.9088 | 0.2166 | 65.53s |\n", - "| DeepKriging_sphere | 200 | 1.4068 | 1.1861 | 1.0058 | 0.1146 | 15.01s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1591 | 1.0766 | 0.9052 | 0.2705 | 14.52s |\n", - "| UniversalKriging | 100 | 0.364 | 0.6033 | 0.4895 | 0.7709 | 2.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:40:25\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4092 (±0.0196), Variance: 0.9634, Range: [-2.3537, 3.0730]\n", - "z mean: 0.4276 (±0.0454), Variance: 5.1588, Range: [-21.8608, 26.0019]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0229, sigma²=0.0014, nugget=4.5069\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 7.9119 | 2.8128 | 1.0419 | -6.7733 | 0.38s |\n", - "| OLS_sphere | 50 | 0.3751 | 0.6124 | 0.4818 | 0.6315 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.9152 | 0.9567 | 0.7634 | 0.1008 | 60.77s |\n", - "| DeepKriging_sphere | 200 | 0.8824 | 0.9394 | 0.7536 | 0.133 | 15.35s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.9311 | 0.9649 | 0.787 | 0.0852 | 16.01s |\n", - "| UniversalKriging | 100 | 0.3465 | 0.5887 | 0.4727 | 0.6595 | 0.98s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:43:05\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0990 (±0.0219), Variance: 1.2003, Range: [-3.0204, 3.8827]\n", - "z mean: 0.1233 (±0.0461), Variance: 5.3187, Range: [-24.0534, 15.4933]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0212, sigma²=0.0031, nugget=4.1189\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 6.1853 | 2.487 | 1.0668 | -3.6749 | 0.38s |\n", - "| OLS_sphere | 50 | 0.4187 | 0.6471 | 0.5053 | 0.6835 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.2078 | 1.099 | 0.8542 | 0.0871 | 66.21s |\n", - "| DeepKriging_sphere | 200 | 1.0898 | 1.0439 | 0.8245 | 0.1764 | 14.36s |\n", - "| DeepKriging_sphere_Huber | 100 | 1.1795 | 1.086 | 0.847 | 0.1086 | 20.15s |\n", - "| UniversalKriging | 100 | 0.4487 | 0.6699 | 0.52 | 0.6609 | 2.01s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:45:57\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.0060 (±0.0218), Variance: 1.1842, Range: [-3.1664, 2.7881]\n", - "z mean: 0.0299 (±0.0452), Variance: 5.0982, Range: [-13.0813, 18.9925]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0159, sigma²=0.0015, nugget=3.8955\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.5255 | 1.5892 | 1.0216 | -0.9259 | 0.44s |\n", - "| OLS_sphere | 50 | 0.3894 | 0.624 | 0.5034 | 0.7031 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.3654 | 1.1685 | 0.9264 | -0.0412 | 62.56s |\n", - "| DeepKriging_sphere | 200 | 1.065 | 1.032 | 0.8432 | 0.1878 | 14.22s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.9212 | 0.9598 | 0.7818 | 0.2975 | 14.50s |\n", - "| UniversalKriging | 100 | 0.3853 | 0.6208 | 0.4871 | 0.7062 | 2.45s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:48:40\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1386 (±0.0188), Variance: 0.8804, Range: [-2.7205, 2.7198]\n", - "z mean: -0.1094 (±0.0435), Variance: 4.7308, Range: [-10.4444, 12.1435]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0231, sigma²=0.0014, nugget=3.7741\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.927 | 0.9628 | 0.7128 | -0.2036 | 0.42s |\n", - "| OLS_sphere | 50 | 0.3303 | 0.5747 | 0.4571 | 0.5711 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.6692 | 0.8181 | 0.6606 | 0.1311 | 65.40s |\n", - "| DeepKriging_sphere | 200 | 0.6783 | 0.8236 | 0.6723 | 0.1193 | 15.06s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.6164 | 0.7851 | 0.6362 | 0.1996 | 15.84s |\n", - "| UniversalKriging | 100 | 0.4074 | 0.6383 | 0.4925 | 0.4711 | 0.79s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:51:27\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.4363 (±0.0207), Variance: 1.0686, Range: [-2.1785, 4.1579]\n", - "z mean: 0.3529 (±0.0439), Variance: 4.8186, Range: [-15.6606, 15.6268]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0222, sigma²=0.0024, nugget=3.8291\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8849 | 0.9407 | 0.7439 | 0.185 | 0.37s |\n", - "| OLS_sphere | 50 | 0.371 | 0.6091 | 0.4841 | 0.6583 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.875 | 0.9354 | 0.7161 | 0.1942 | 66.11s |\n", - "| DeepKriging_sphere | 200 | 0.9407 | 0.9699 | 0.7786 | 0.1336 | 14.04s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.8592 | 0.9269 | 0.7379 | 0.2087 | 15.40s |\n", - "| UniversalKriging | 100 | 0.3954 | 0.6288 | 0.4913 | 0.6358 | 2.07s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:54:17\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.3354 (±0.0182), Variance: 0.8292, Range: [-2.7591, 3.2690]\n", - "z mean: 0.3480 (±0.0449), Variance: 5.0508, Range: [-21.8051, 12.4723]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0214, sigma²=0.0029, nugget=4.3839\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.1869 | 2.0462 | 0.909 | -4.2999 | 0.35s |\n", - "| OLS_sphere | 50 | 0.3031 | 0.5505 | 0.4402 | 0.6164 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6796 | 0.8243 | 0.638 | 0.1398 | 54.43s |\n", - "| DeepKriging_sphere | 200 | 0.7549 | 0.8688 | 0.6888 | 0.0444 | 13.75s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7007 | 0.8371 | 0.6577 | 0.113 | 13.36s |\n", - "| UniversalKriging | 100 | 0.4116 | 0.6416 | 0.5289 | 0.479 | 2.28s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:56:54\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 0.1570 (±0.0159), Variance: 0.6284, Range: [-1.9354, 3.0160]\n", - "z mean: 0.1846 (±0.0437), Variance: 4.7748, Range: [-17.4414, 12.5943]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0181, sigma²=0.0013, nugget=4.3752\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7358 | 1.3175 | 0.791 | -2.0038 | 0.37s |\n", - "| OLS_sphere | 50 | 0.4051 | 0.6365 | 0.5009 | 0.299 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.5479 | 0.7402 | 0.5941 | 0.0519 | 42.78s |\n", - "| DeepKriging_sphere | 200 | 0.5205 | 0.7214 | 0.5865 | 0.0994 | 14.31s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.4816 | 0.694 | 0.5563 | 0.1666 | 13.34s |\n", - "| UniversalKriging | 100 | 0.4102 | 0.6405 | 0.5265 | 0.2901 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 14:59:19\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.2605 (±0.0172), Variance: 0.7392, Range: [-3.3801, 2.4770]\n", - "z mean: -0.2758 (±0.0435), Variance: 4.7402, Range: [-14.7592, 13.9282]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0282, sigma²=0.0027, nugget=4.1796\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2719 | 1.1278 | 0.7767 | -0.5774 | 0.35s |\n", - "| OLS_sphere | 50 | 0.3404 | 0.5834 | 0.4681 | 0.5778 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.7344 | 0.857 | 0.6925 | 0.0892 | 47.98s |\n", - "| DeepKriging_sphere | 200 | 0.6902 | 0.8308 | 0.6719 | 0.144 | 12.76s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7088 | 0.8419 | 0.6811 | 0.121 | 15.21s |\n", - "| UniversalKriging | 100 | 0.3466 | 0.5887 | 0.478 | 0.5701 | 1.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 15:01:50\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -0.1256 (±0.0198), Variance: 0.9850, Range: [-2.8451, 2.8753]\n", - "z mean: -0.0899 (±0.0450), Variance: 5.0533, Range: [-12.3106, 17.4119]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0198, sigma²=0.0029, nugget=3.9523\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.3578 | 2.3147 | 1.0619 | -4.5026 | 0.41s |\n", - "| OLS_sphere | 50 | 0.39 | 0.6245 | 0.5077 | 0.5995 | 0.00s |\n", - "| DeepKriging_wendland | -- | 0.8998 | 0.9486 | 0.7419 | 0.0759 | 52.48s |\n", - "| DeepKriging_sphere | 200 | 0.9118 | 0.9549 | 0.7544 | 0.0635 | 16.46s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.7897 | 0.8886 | 0.7102 | 0.189 | 15.41s |\n", - "| UniversalKriging | 100 | 0.4056 | 0.6369 | 0.5168 | 0.5835 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 15:04:32\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.019233, - "end_time": "2026-01-21T07:04:32.189851", - "exception": false, - "start_time": "2026-01-21T07:04:32.170618", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T07:04:32.233661Z", - "iopub.status.busy": "2026-01-21T07:04:32.232676Z", - "iopub.status.idle": "2026-01-21T07:04:32.264184Z", - "shell.execute_reply": "2026-01-21T07:04:32.260872Z" - }, - "papermill": { - "duration": 0.057643, - "end_time": "2026-01-21T07:04:32.267294", - "exception": false, - "start_time": "2026-01-21T07:04:32.209651", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 50, 'DeepKriging_sphere': 200, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------|------------|-----------|----------------|\n", - "| OLS_wendland | 136.95±559.29 | 5.59±10.28 | 1.22±0.86 | -157.82±654.28 |\n", - "| OLS_sphere | 0.37±0.04 | 0.61±0.04 | 0.49±0.03 | 0.56±0.11 |\n", - "| DeepKriging_wendland | 0.84±0.19 | 0.91±0.10 | 0.72±0.08 | 0.05±0.14 |\n", - "| DeepKriging_sphere | 0.76±0.18 | 0.87±0.10 | 0.70±0.08 | 0.14±0.06 |\n", - "| DeepKriging_sphere_Huber | 0.74±0.17 | 0.86±0.10 | 0.69±0.08 | 0.17±0.09 |\n", - "| UniversalKriging | 0.40±0.05 | 0.63±0.04 | 0.51±0.03 | 0.53±0.13 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 0.61±0.04)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 6823.160902, - "end_time": "2026-01-21T07:04:35.749133", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-21T05:10:52.588231", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioI/simulation_spherical_GP_outliers5.ipynb b/notebook/simulation/ScenarioI/simulation_spherical_GP_outliers5.ipynb deleted file mode 100644 index d9f254d..0000000 --- a/notebook/simulation/ScenarioI/simulation_spherical_GP_outliers5.ipynb +++ /dev/null @@ -1,4464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.009522, - "end_time": "2026-01-21T02:46:41.667991", - "exception": false, - "start_time": "2026-01-21T02:46:41.658469", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:41.681369Z", - "iopub.status.busy": "2026-01-21T02:46:41.680983Z", - "iopub.status.idle": "2026-01-21T02:46:41.692037Z", - "shell.execute_reply": "2026-01-21T02:46:41.690345Z" - }, - "papermill": { - "duration": 0.019278, - "end_time": "2026-01-21T02:46:41.693751", - "exception": false, - "start_time": "2026-01-21T02:46:41.674473", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:41.705533Z", - "iopub.status.busy": "2026-01-21T02:46:41.705070Z", - "iopub.status.idle": "2026-01-21T02:46:44.201297Z", - "shell.execute_reply": "2026-01-21T02:46:44.198424Z" - }, - "papermill": { - "duration": 2.503494, - "end_time": "2026-01-21T02:46:44.204349", - "exception": false, - "start_time": "2026-01-21T02:46:41.700855", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768963602.538700 1186260 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768963602.542343 1186260 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768963602.552781 1186260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768963602.552800 1186260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768963602.552802 1186260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768963602.552803 1186260 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0033, - "end_time": "2026-01-21T02:46:44.214611", - "exception": false, - "start_time": "2026-01-21T02:46:44.211311", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:44.223648Z", - "iopub.status.busy": "2026-01-21T02:46:44.222626Z", - "iopub.status.idle": "2026-01-21T02:46:45.548478Z", - "shell.execute_reply": "2026-01-21T02:46:45.545080Z" - }, - "papermill": { - "duration": 1.33413, - "end_time": "2026-01-21T02:46:45.551594", - "exception": false, - "start_time": "2026-01-21T02:46:44.217464", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.002479, - "end_time": "2026-01-21T02:46:45.559984", - "exception": false, - "start_time": "2026-01-21T02:46:45.557505", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:45.567559Z", - "iopub.status.busy": "2026-01-21T02:46:45.566963Z", - "iopub.status.idle": "2026-01-21T02:46:45.575600Z", - "shell.execute_reply": "2026-01-21T02:46:45.573064Z" - }, - "papermill": { - "duration": 0.015237, - "end_time": "2026-01-21T02:46:45.577552", - "exception": false, - "start_time": "2026-01-21T02:46:45.562315", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.002314, - "end_time": "2026-01-21T02:46:45.586862", - "exception": false, - "start_time": "2026-01-21T02:46:45.584548", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:45.594273Z", - "iopub.status.busy": "2026-01-21T02:46:45.593495Z", - "iopub.status.idle": "2026-01-21T02:46:59.533676Z", - "shell.execute_reply": "2026-01-21T02:46:59.530394Z" - }, - "papermill": { - "duration": 13.947846, - "end_time": "2026-01-21T02:46:59.536932", - "exception": false, - "start_time": "2026-01-21T02:46:45.589086", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.002523, - "end_time": "2026-01-21T02:46:59.608374", - "exception": false, - "start_time": "2026-01-21T02:46:59.605851", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:59.617894Z", - "iopub.status.busy": "2026-01-21T02:46:59.616597Z", - "iopub.status.idle": "2026-01-21T02:46:59.646984Z", - "shell.execute_reply": "2026-01-21T02:46:59.644045Z" - }, - "papermill": { - "duration": 0.038842, - "end_time": "2026-01-21T02:46:59.649629", - "exception": false, - "start_time": "2026-01-21T02:46:59.610787", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, outlier_ratio, outlier_multiplier, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + outliers\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = (L @ z_std).astype(np.float32)\n", - "\n", - " # Add outliers\n", - " idx_outliers = rng.choice(num_sample, size=int(num_sample*outlier_ratio), replace=False)\n", - " z[idx_outliers] *= outlier_multiplier\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003397, - "end_time": "2026-01-21T02:46:59.659858", - "exception": false, - "start_time": "2026-01-21T02:46:59.656461", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:59.668557Z", - "iopub.status.busy": "2026-01-21T02:46:59.667733Z", - "iopub.status.idle": "2026-01-21T02:46:59.701585Z", - "shell.execute_reply": "2026-01-21T02:46:59.697991Z" - }, - "papermill": { - "duration": 0.041357, - "end_time": "2026-01-21T02:46:59.703967", - "exception": false, - "start_time": "2026-01-21T02:46:59.662610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:59.717085Z", - "iopub.status.busy": "2026-01-21T02:46:59.716547Z", - "iopub.status.idle": "2026-01-21T02:46:59.746413Z", - "shell.execute_reply": "2026-01-21T02:46:59.743702Z" - }, - "papermill": { - "duration": 0.038516, - "end_time": "2026-01-21T02:46:59.749123", - "exception": false, - "start_time": "2026-01-21T02:46:59.710607", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.004315, - "end_time": "2026-01-21T02:46:59.761183", - "exception": false, - "start_time": "2026-01-21T02:46:59.756868", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:59.770553Z", - "iopub.status.busy": "2026-01-21T02:46:59.770079Z", - "iopub.status.idle": "2026-01-21T02:46:59.776817Z", - "shell.execute_reply": "2026-01-21T02:46:59.775404Z" - }, - "papermill": { - "duration": 0.013261, - "end_time": "2026-01-21T02:46:59.778159", - "exception": false, - "start_time": "2026-01-21T02:46:59.764898", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T02:46:59.784748Z", - "iopub.status.busy": "2026-01-21T02:46:59.784565Z", - "iopub.status.idle": "2026-01-21T05:10:48.812320Z", - "shell.execute_reply": "2026-01-21T05:10:48.808731Z" - }, - "papermill": { - "duration": 8629.034674, - "end_time": "2026-01-21T05:10:48.815216", - "exception": false, - "start_time": "2026-01-21T02:46:59.780542", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2348 (±0.0288), Variance: 2.0724, Range: [-12.6286, 13.7214]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.3660 1.3650 \n", - " 100 1.2025 1.2785 \n", - " 200 1.1121 1.1577 *\n", - " 500 1.1431 1.0979 \n", - " 700 1.2213 1.3409 \n", - " 1000 2.7884 4.1795 \n", - " 1400 11.5200 42.7516 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768963626.902390 1186260 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768963628.955409 1186487 service.cc:152] XLA service 0x7d8ab8012fc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768963628.955442 1186487 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768963629.309390 1186487 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768963639.160568 1186487 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d8ac04653f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d8ab1d5d480> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2.1944 2.0434 \n", - " 100 2.2431 2.1429 \n", - " 200 2.2976 2.2496 \n", - " 500 1.2860 1.3343 *\n", - " 700 1.4914 1.6200 \n", - " 1000 1.6751 1.6943 \n", - " 1400 2.1199 2.0586 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2740 1.2536 *\n", - " 100 0.6570 2.2719 \n", - " 200 0.2838 1.2969 \n", - " 500 0.6782 2.3323 \n", - " 700 0.3250 1.5659 \n", - " 1000 0.4179 1.8546 \n", - " 1400 0.4831 1.9686 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0813, sigma²=0.3754, nugget=0.4603\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0507, sigma²=0.2832, nugget=0.4623\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0314, sigma²=0.0003, nugget=0.6365\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0430, sigma²=0.0000, nugget=0.4662\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5548, sigma²=0.0000, nugget=0.3891\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2821, sigma²=0.0000, nugget=0.2572\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1972, sigma²=0.0000, nugget=0.1712\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.1178 1.1657 \n", - " 100 1.0968 1.1836 *\n", - " 200 1.1120 1.1576 \n", - " 500 1.1431 1.0979 \n", - " 700 1.2213 1.3409 \n", - " 1000 2.7884 4.1795 \n", - " 1400 11.5212 42.7517 \n", - " Best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0507, sigma²=0.2832, nugget=0.4623\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.8225 | 1.35 | 0.7581 | 0.3148 | 0.42s |\n", - "| OLS_sphere | 200 | 1.1577 | 1.076 | 0.4693 | 0.5648 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.8702 | 1.3676 | 0.713 | 0.2969 | 55.78s |\n", - "| DeepKriging_sphere | 500 | 1.4051 | 1.1854 | 0.4816 | 0.4717 | 28.50s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2782 | 1.1306 | 0.4409 | 0.5194 | 25.58s |\n", - "| UniversalKriging | 100 | 1.1836 | 1.088 | 0.4482 | 0.555 | 4.61s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 10:56:34\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1166 (±0.0234), Variance: 1.3728, Range: [-7.8505, 13.7031]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0571, sigma²=0.2857, nugget=0.3008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 41.1242 | 6.4128 | 1.2035 | -16.2322 | 0.42s |\n", - "| OLS_sphere | 200 | 1.2108 | 1.1003 | 0.4814 | 0.4927 | 0.03s |\n", - "| DeepKriging_wendland | -- | 2.0693 | 1.4385 | 0.7587 | 0.1329 | 46.14s |\n", - "| DeepKriging_sphere | 500 | 1.4097 | 1.1873 | 0.549 | 0.4093 | 27.39s |\n", - "| DeepKriging_sphere_Huber | 50 | 2.1289 | 1.4591 | 0.7585 | 0.1079 | 13.88s |\n", - "| UniversalKriging | 100 | 1.1223 | 1.0594 | 0.4599 | 0.5297 | 1.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 10:58:23\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1052 (±0.0220), Variance: 1.2127, Range: [-11.0630, 7.6915]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0516, sigma²=0.2359, nugget=0.4224\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7153 | 0.8458 | 0.6004 | -0.0305 | 0.33s |\n", - "| OLS_sphere | 200 | 0.2811 | 0.5302 | 0.376 | 0.595 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7786 | 0.8824 | 0.5956 | -0.1217 | 49.49s |\n", - "| DeepKriging_sphere | 500 | 0.316 | 0.5621 | 0.4091 | 0.5448 | 19.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3096 | 0.5564 | 0.3759 | 0.554 | 22.24s |\n", - "| UniversalKriging | 100 | 0.2308 | 0.4804 | 0.3348 | 0.6675 | 1.98s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:00:16\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0007 (±0.0261), Variance: 1.7069, Range: [-12.7162, 11.8809]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0461, sigma²=0.2252, nugget=0.3292\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7718 | 1.3311 | 0.7749 | -0.0114 | 0.43s |\n", - "| OLS_sphere | 200 | 0.8534 | 0.9238 | 0.4547 | 0.5129 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.4214 | 1.1922 | 0.6482 | 0.1886 | 51.86s |\n", - "| DeepKriging_sphere | 500 | 1.0438 | 1.0217 | 0.4761 | 0.4042 | 35.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.9115 | 0.9547 | 0.4186 | 0.4797 | 26.90s |\n", - "| UniversalKriging | 100 | 0.8576 | 0.9261 | 0.4374 | 0.5105 | 4.32s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:02:37\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3449 (±0.0231), Variance: 1.3374, Range: [-11.4909, 11.2903]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0575, sigma²=0.2425, nugget=0.3139\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0798 | 1.4422 | 0.7516 | -0.5774 | 0.34s |\n", - "| OLS_sphere | 200 | 0.5112 | 0.715 | 0.4056 | 0.6123 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.0115 | 1.0057 | 0.6405 | 0.2328 | 50.91s |\n", - "| DeepKriging_sphere | 500 | 0.5245 | 0.7242 | 0.4137 | 0.6022 | 34.50s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1374 | 1.0665 | 0.7636 | 0.1373 | 14.90s |\n", - "| UniversalKriging | 100 | 0.4891 | 0.6993 | 0.3796 | 0.6291 | 1.70s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:04:42\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2480 (±0.0219), Variance: 1.2000, Range: [-11.8576, 9.2823]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0287, sigma²=0.5967, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6552 | 0.8094 | 0.5554 | 0.2211 | 0.37s |\n", - "| OLS_sphere | 200 | 0.3056 | 0.5528 | 0.3849 | 0.6367 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.6164 | 0.7851 | 0.5189 | 0.2672 | 51.93s |\n", - "| DeepKriging_sphere | 500 | 0.3951 | 0.6286 | 0.4087 | 0.5303 | 26.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2677 | 0.5174 | 0.324 | 0.6818 | 26.79s |\n", - "| UniversalKriging | 100 | 0.2888 | 0.5374 | 0.3252 | 0.6567 | 3.47s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:06:55\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1653 (±0.0225), Variance: 1.2698, Range: [-12.2331, 8.9691]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0844, sigma²=0.2219, nugget=0.2971\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3748 | 1.1725 | 0.7025 | 0.2215 | 0.40s |\n", - "| OLS_sphere | 200 | 0.7239 | 0.8508 | 0.4303 | 0.5901 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.2823 | 1.1324 | 0.6415 | 0.2739 | 42.97s |\n", - "| DeepKriging_sphere | 500 | 1.0172 | 1.0086 | 0.512 | 0.424 | 22.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7697 | 0.8773 | 0.4303 | 0.5641 | 21.42s |\n", - "| UniversalKriging | 100 | 0.7182 | 0.8475 | 0.3881 | 0.5933 | 1.58s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:08:49\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0410 (±0.0295), Variance: 2.1797, Range: [-15.3094, 12.8582]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0074, sigma²=0.0024, nugget=0.7523\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.096 | 1.0469 | 0.7652 | 0.3257 | 0.40s |\n", - "| OLS_sphere | 200 | 0.3239 | 0.5691 | 0.3811 | 0.8007 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.9107 | 0.9543 | 0.6736 | 0.4397 | 47.99s |\n", - "| DeepKriging_sphere | 500 | 0.4214 | 0.6491 | 0.4147 | 0.7407 | 32.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.283 | 0.532 | 0.3463 | 0.8259 | 23.76s |\n", - "| UniversalKriging | 100 | 0.3854 | 0.6208 | 0.4273 | 0.7629 | 0.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:11:01\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1139 (±0.0231), Variance: 1.3350, Range: [-7.4218, 12.4332]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0481, sigma²=0.3703, nugget=0.1694\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.98 | 0.99 | 0.6668 | 0.0928 | 0.36s |\n", - "| OLS_sphere | 200 | 0.4 | 0.6325 | 0.4056 | 0.6297 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.7484 | 0.8651 | 0.592 | 0.3072 | 47.91s |\n", - "| DeepKriging_sphere | 500 | 0.5361 | 0.7322 | 0.412 | 0.5038 | 30.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4417 | 0.6646 | 0.3658 | 0.5911 | 27.79s |\n", - "| UniversalKriging | 100 | 0.3434 | 0.586 | 0.3329 | 0.6821 | 1.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:13:16\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3781 (±0.0288), Variance: 2.0728, Range: [-8.7371, 13.6297]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0269, sigma²=0.7075, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.4872 | 1.5771 | 0.8685 | 0.2082 | 0.37s |\n", - "| OLS_sphere | 200 | 1.4383 | 1.1993 | 0.5491 | 0.5421 | 0.02s |\n", - "| DeepKriging_wendland | -- | 2.005 | 1.416 | 0.771 | 0.3617 | 45.99s |\n", - "| DeepKriging_sphere | 500 | 1.8239 | 1.3505 | 0.5752 | 0.4194 | 29.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.5846 | 1.2588 | 0.4965 | 0.4955 | 29.65s |\n", - "| UniversalKriging | 100 | 1.4559 | 1.2066 | 0.4901 | 0.5365 | 3.36s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:15:33\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3247 (±0.0244), Variance: 1.4910, Range: [-9.9238, 15.7401]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0391, sigma²=0.2797, nugget=0.3823\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7918 | 1.3386 | 0.7897 | -0.7752 | 0.39s |\n", - "| OLS_sphere | 200 | 0.244 | 0.494 | 0.3728 | 0.7583 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7499 | 0.866 | 0.6321 | 0.257 | 57.97s |\n", - "| DeepKriging_sphere | 500 | 1.3966 | 1.1818 | 0.5004 | -0.3836 | 30.02s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4377 | 0.6616 | 0.3752 | 0.5664 | 31.52s |\n", - "| UniversalKriging | 100 | 0.2177 | 0.4666 | 0.3477 | 0.7843 | 2.90s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:18:05\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2935 (±0.0216), Variance: 1.1703, Range: [-8.6179, 13.0653]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0358, sigma²=0.4157, nugget=0.1277\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.2924 | 1.5141 | 0.7838 | -0.4998 | 0.48s |\n", - "| OLS_sphere | 200 | 0.701 | 0.8372 | 0.4491 | 0.5414 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.2158 | 1.1026 | 0.6385 | 0.2046 | 59.93s |\n", - "| DeepKriging_sphere | 500 | 0.8184 | 0.9046 | 0.4871 | 0.4646 | 32.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6701 | 0.8186 | 0.3993 | 0.5616 | 27.92s |\n", - "| UniversalKriging | 100 | 0.7658 | 0.8751 | 0.423 | 0.499 | 1.86s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:20:39\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0542 (±0.0228), Variance: 1.3049, Range: [-8.9375, 11.8152]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0598, sigma²=0.2076, nugget=0.3651\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 13.4024 | 3.6609 | 1.2213 | -7.6409 | 0.37s |\n", - "| OLS_sphere | 200 | 0.6753 | 0.8218 | 0.4589 | 0.5646 | 0.02s |\n", - "| DeepKriging_wendland | -- | 4.933 | 2.221 | 0.8344 | -2.1805 | 55.50s |\n", - "| DeepKriging_sphere | 500 | 0.9961 | 0.998 | 0.4937 | 0.3578 | 31.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5614 | 0.7493 | 0.3936 | 0.638 | 28.26s |\n", - "| UniversalKriging | 100 | 0.6473 | 0.8045 | 0.4188 | 0.5827 | 1.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:23:08\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.8395 (±0.0231), Variance: 1.3296, Range: [-11.5462, 8.2367]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0566, sigma²=0.1736, nugget=0.4957\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9929 | 0.9965 | 0.6039 | -0.0986 | 0.37s |\n", - "| OLS_sphere | 200 | 0.4781 | 0.6914 | 0.4413 | 0.4711 | 0.01s |\n", - "| DeepKriging_wendland | -- | 0.6448 | 0.803 | 0.5059 | 0.2866 | 58.90s |\n", - "| DeepKriging_sphere | 500 | 0.8253 | 0.9084 | 0.6329 | 0.0869 | 15.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8604 | 0.9276 | 0.6471 | 0.0481 | 15.86s |\n", - "| UniversalKriging | 100 | 0.4924 | 0.7017 | 0.4293 | 0.4552 | 2.68s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:25:14\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5512 (±0.0249), Variance: 1.5562, Range: [-10.9978, 11.6240]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0297, sigma²=0.5333, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3749 | 1.1725 | 0.7763 | 0.0519 | 0.46s |\n", - "| OLS_sphere | 200 | 0.6968 | 0.8347 | 0.437 | 0.5195 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.9307 | 0.9647 | 0.6017 | 0.3582 | 61.67s |\n", - "| DeepKriging_sphere | 500 | 0.8918 | 0.9443 | 0.4728 | 0.385 | 34.04s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.706 | 0.8402 | 0.398 | 0.5132 | 27.20s |\n", - "| UniversalKriging | 100 | 0.629 | 0.7931 | 0.3772 | 0.5662 | 6.87s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:27:58\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2147 (±0.0221), Variance: 1.2247, Range: [-9.2425, 10.2272]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0366, sigma²=0.4639, nugget=0.1426\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2867 | 1.1343 | 0.6501 | -0.1984 | 0.42s |\n", - "| OLS_sphere | 200 | 0.4138 | 0.6433 | 0.3886 | 0.6146 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7863 | 0.8867 | 0.5858 | 0.2677 | 50.34s |\n", - "| DeepKriging_sphere | 500 | 0.8348 | 0.9137 | 0.5976 | 0.2225 | 14.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.859 | 0.9268 | 0.5841 | 0.2 | 12.87s |\n", - "| UniversalKriging | 100 | 0.4165 | 0.6454 | 0.3714 | 0.612 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:29:54\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4789 (±0.0245), Variance: 1.4985, Range: [-11.3708, 11.0905]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0663, sigma²=0.2645, nugget=0.3460\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5624 | 1.25 | 0.7478 | -0.1875 | 0.40s |\n", - "| OLS_sphere | 200 | 0.3836 | 0.6194 | 0.4197 | 0.7085 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.8441 | 0.9187 | 0.6591 | 0.3585 | 43.47s |\n", - "| DeepKriging_sphere | 500 | 0.7175 | 0.8471 | 0.5318 | 0.4547 | 29.94s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4278 | 0.654 | 0.4162 | 0.6749 | 27.61s |\n", - "| UniversalKriging | 100 | 0.3624 | 0.602 | 0.3677 | 0.7246 | 1.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:32:14\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3767 (±0.0289), Variance: 2.0829, Range: [-15.5532, 8.8804]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0407, sigma²=0.4648, nugget=0.2870\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 33.0925 | 5.7526 | 1.3535 | -15.8209 | 0.39s |\n", - "| OLS_sphere | 200 | 0.8945 | 0.9458 | 0.4777 | 0.5453 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.4346 | 1.1978 | 0.6757 | 0.2708 | 58.73s |\n", - "| DeepKriging_sphere | 500 | 1.4359 | 1.1983 | 0.5015 | 0.2702 | 35.10s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8714 | 0.9335 | 0.4245 | 0.5571 | 30.08s |\n", - "| UniversalKriging | 100 | 0.8149 | 0.9027 | 0.424 | 0.5858 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:34:58\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0180 (±0.0234), Variance: 1.3741, Range: [-12.3491, 12.0251]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0560, sigma²=0.2136, nugget=0.2982\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1099 | 1.0535 | 0.7185 | 0.0852 | 0.35s |\n", - "| OLS_sphere | 200 | 0.4039 | 0.6356 | 0.4031 | 0.6671 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.802 | 0.8955 | 0.5948 | 0.339 | 57.73s |\n", - "| DeepKriging_sphere | 500 | 0.4912 | 0.7009 | 0.4471 | 0.5951 | 31.65s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.401 | 0.6332 | 0.3871 | 0.6695 | 27.42s |\n", - "| UniversalKriging | 100 | 0.3979 | 0.6308 | 0.393 | 0.672 | 1.57s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:37:36\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1569 (±0.0217), Variance: 1.1771, Range: [-13.4506, 8.6812]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0541, sigma²=0.2876, nugget=0.3470\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 23.5 | 4.8477 | 1.0242 | -23.9633 | 0.47s |\n", - "| OLS_sphere | 200 | 0.4458 | 0.6677 | 0.3951 | 0.5264 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7676 | 0.8762 | 0.5526 | 0.1846 | 57.97s |\n", - "| DeepKriging_sphere | 500 | 0.5416 | 0.7359 | 0.4339 | 0.4247 | 27.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7478 | 0.8648 | 0.5619 | 0.2056 | 12.57s |\n", - "| UniversalKriging | 100 | 0.4228 | 0.6503 | 0.3783 | 0.5508 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:39:57\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3572 (±0.0209), Variance: 1.0916, Range: [-5.6621, 9.6441]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0420, sigma²=0.4066, nugget=0.1003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.3136 | 2.3051 | 0.7401 | -6.1987 | 0.37s |\n", - "| OLS_sphere | 200 | 0.2268 | 0.4763 | 0.3552 | 0.6927 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.473 | 0.6878 | 0.5264 | 0.3592 | 56.04s |\n", - "| DeepKriging_sphere | 500 | 0.3136 | 0.56 | 0.4163 | 0.5751 | 26.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2246 | 0.474 | 0.3423 | 0.6957 | 28.24s |\n", - "| UniversalKriging | 100 | 0.2144 | 0.463 | 0.3256 | 0.7095 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:42:30\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1292 (±0.0216), Variance: 1.1642, Range: [-11.0820, 8.4428]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0391, sigma²=0.3596, nugget=0.1126\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8477 | 1.3593 | 0.7809 | -0.433 | 0.40s |\n", - "| OLS_sphere | 200 | 0.5592 | 0.7478 | 0.4286 | 0.5663 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.0348 | 1.0172 | 0.663 | 0.1975 | 62.75s |\n", - "| DeepKriging_sphere | 500 | 0.647 | 0.8044 | 0.4419 | 0.4982 | 32.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.019 | 1.0095 | 0.6502 | 0.2097 | 15.68s |\n", - "| UniversalKriging | 100 | 0.5275 | 0.7263 | 0.3799 | 0.5909 | 1.86s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:45:05\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5788 (±0.0253), Variance: 1.5958, Range: [-7.5089, 15.0839]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0188, sigma²=0.0010, nugget=0.7398\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5505 | 1.2452 | 0.7603 | 0.0075 | 0.38s |\n", - "| OLS_sphere | 200 | 0.8408 | 0.917 | 0.4812 | 0.4618 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.1483 | 1.0716 | 0.6443 | 0.265 | 66.82s |\n", - "| DeepKriging_sphere | 500 | 0.9455 | 0.9724 | 0.5432 | 0.3948 | 28.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8563 | 0.9254 | 0.4342 | 0.4519 | 30.88s |\n", - "| UniversalKriging | 100 | 0.8618 | 0.9283 | 0.5046 | 0.4484 | 1.05s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:47:56\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0924 (±0.0249), Variance: 1.5446, Range: [-10.7148, 15.3757]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0396, sigma²=0.2553, nugget=0.3405\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 31.8083 | 5.6399 | 1.2219 | -20.8056 | 0.35s |\n", - "| OLS_sphere | 200 | 0.6166 | 0.7852 | 0.4482 | 0.5773 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.4471 | 1.203 | 0.6983 | 0.008 | 65.66s |\n", - "| DeepKriging_sphere | 500 | 0.7314 | 0.8552 | 0.4505 | 0.4986 | 36.77s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1415 | 1.0684 | 0.72 | 0.2175 | 14.68s |\n", - "| UniversalKriging | 100 | 0.6005 | 0.7749 | 0.4339 | 0.5883 | 3.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:50:41\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0388 (±0.0229), Variance: 1.3147, Range: [-12.5545, 9.1127]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0591, sigma²=0.2031, nugget=0.3671\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9939 | 0.9969 | 0.7021 | 0.087 | 0.30s |\n", - "| OLS_sphere | 200 | 0.4083 | 0.639 | 0.4105 | 0.6249 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.8572 | 0.9259 | 0.6408 | 0.2126 | 59.01s |\n", - "| DeepKriging_sphere | 500 | 0.4325 | 0.6576 | 0.4383 | 0.6027 | 28.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4062 | 0.6373 | 0.3921 | 0.6269 | 28.27s |\n", - "| UniversalKriging | 100 | 0.3462 | 0.5884 | 0.3702 | 0.682 | 1.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:53:25\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0747 (±0.0252), Variance: 1.5937, Range: [-13.6431, 12.1983]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0565, sigma²=0.3349, nugget=0.3793\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 13.5637 | 3.6829 | 0.926 | -9.0722 | 0.36s |\n", - "| OLS_sphere | 200 | 0.4397 | 0.6631 | 0.4321 | 0.6735 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.8261 | 0.9089 | 0.6281 | 0.3865 | 63.30s |\n", - "| DeepKriging_sphere | 500 | 0.5643 | 0.7512 | 0.5545 | 0.5809 | 26.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2531 | 0.5031 | 0.3454 | 0.812 | 27.39s |\n", - "| UniversalKriging | 100 | 0.295 | 0.5431 | 0.374 | 0.781 | 1.75s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:56:12\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7053 (±0.0262), Variance: 1.7098, Range: [-6.9731, 16.9939]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0237, sigma²=0.7453, nugget=0.0003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 15.9124 | 3.989 | 1.129 | -8.6368 | 0.46s |\n", - "| OLS_sphere | 200 | 0.8394 | 0.9162 | 0.5125 | 0.4916 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.192 | 1.0918 | 0.6551 | 0.2781 | 58.71s |\n", - "| DeepKriging_sphere | 500 | 1.0865 | 1.0424 | 0.525 | 0.342 | 33.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8686 | 0.932 | 0.4419 | 0.474 | 36.35s |\n", - "| UniversalKriging | 100 | 0.8522 | 0.9232 | 0.4707 | 0.4839 | 6.38s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 11:59:15\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1927 (±0.0255), Variance: 1.6263, Range: [-6.3877, 13.2865]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0574, sigma²=0.3571, nugget=0.3322\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6198 | 1.2727 | 0.8325 | 0.3458 | 0.34s |\n", - "| OLS_sphere | 200 | 1.1152 | 1.056 | 0.5127 | 0.5496 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.5075 | 1.2278 | 0.7503 | 0.3912 | 57.62s |\n", - "| DeepKriging_sphere | 500 | 1.0135 | 1.0067 | 0.5067 | 0.5907 | 27.38s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.0285 | 1.0142 | 0.4473 | 0.5846 | 27.82s |\n", - "| UniversalKriging | 100 | 1.0236 | 1.0117 | 0.4476 | 0.5866 | 1.72s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:01:59\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3213 (±0.0235), Variance: 1.3813, Range: [-10.0281, 11.2663]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0493, sigma²=0.1812, nugget=0.3314\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.3833 | 1.5438 | 0.8911 | -0.1635 | 0.41s |\n", - "| OLS_sphere | 200 | 0.847 | 0.9203 | 0.4378 | 0.5865 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.5018 | 1.2255 | 0.721 | 0.2669 | 60.38s |\n", - "| DeepKriging_sphere | 500 | 0.993 | 0.9965 | 0.4846 | 0.5153 | 34.50s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.6727 | 1.2933 | 0.8433 | 0.1834 | 15.76s |\n", - "| UniversalKriging | 100 | 0.8521 | 0.9231 | 0.4412 | 0.584 | 3.83s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:04:44\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.7696 (±0.0311), Variance: 2.4169, Range: [-8.5405, 16.9207]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0438, sigma²=0.2771, nugget=0.7308\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.1915 | 1.7865 | 0.9173 | -0.2616 | -0.30s |\n", - "| OLS_sphere | 200 | 1.2008 | 1.0958 | 0.5223 | 0.5253 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.7338 | 1.3168 | 0.7146 | 0.3146 | 65.06s |\n", - "| DeepKriging_sphere | 500 | 1.4689 | 1.212 | 0.5897 | 0.4193 | 30.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.2018 | 1.0963 | 0.4464 | 0.5249 | 32.64s |\n", - "| UniversalKriging | 100 | 1.1864 | 1.0892 | 0.4888 | 0.531 | 3.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:07:47\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1359 (±0.0221), Variance: 1.2231, Range: [-7.8688, 10.2468]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0639, sigma²=0.3023, nugget=0.2087\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7605 | 1.3269 | 0.7641 | -0.1482 | 0.32s |\n", - "| OLS_sphere | 200 | 0.7905 | 0.8891 | 0.4375 | 0.4844 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.3021 | 1.1411 | 0.6462 | 0.1508 | 65.41s |\n", - "| DeepKriging_sphere | 500 | 0.8308 | 0.9115 | 0.4121 | 0.4582 | 40.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7937 | 0.8909 | 0.395 | 0.4824 | 33.23s |\n", - "| UniversalKriging | 100 | 0.6735 | 0.8207 | 0.3715 | 0.5607 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:11:02\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0931 (±0.0261), Variance: 1.6998, Range: [-11.8298, 13.8850]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0313, sigma²=0.7906, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.0666 | 1.7512 | 0.8265 | -0.9014 | 0.38s |\n", - "| OLS_sphere | 200 | 0.64 | 0.8 | 0.4424 | 0.6032 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.1419 | 1.0686 | 0.715 | 0.2919 | 62.79s |\n", - "| DeepKriging_sphere | 500 | 1.385 | 1.1769 | 0.807 | 0.1412 | 15.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6179 | 0.7861 | 0.3917 | 0.6169 | 33.31s |\n", - "| UniversalKriging | 100 | 0.6114 | 0.7819 | 0.3996 | 0.6209 | 3.76s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:13:51\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1952 (±0.0229), Variance: 1.3142, Range: [-9.9465, 8.8950]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0302, sigma²=0.5005, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3242 | 1.1507 | 0.7921 | 0.0734 | 0.40s |\n", - "| OLS_sphere | 200 | 0.5911 | 0.7688 | 0.4332 | 0.5864 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.1357 | 1.0657 | 0.68 | 0.2053 | 67.01s |\n", - "| DeepKriging_sphere | 500 | 1.1998 | 1.0954 | 0.7435 | 0.1604 | 15.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5515 | 0.7426 | 0.3804 | 0.6141 | 35.74s |\n", - "| UniversalKriging | 100 | 0.574 | 0.7576 | 0.3992 | 0.5984 | 3.39s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:16:48\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3927 (±0.0224), Variance: 1.2488, Range: [-9.4799, 12.0342]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0438, sigma²=0.3534, nugget=0.2491\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9851 | 0.9925 | 0.69 | 0.0384 | 0.41s |\n", - "| OLS_sphere | 200 | 0.5469 | 0.7395 | 0.4449 | 0.4661 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.7379 | 0.859 | 0.5706 | 0.2797 | 72.72s |\n", - "| DeepKriging_sphere | 500 | 0.5266 | 0.7257 | 0.4101 | 0.486 | 34.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.9188 | 0.9585 | 0.6575 | 0.1032 | 14.71s |\n", - "| UniversalKriging | 100 | 0.4627 | 0.6802 | 0.3659 | 0.5484 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:19:48\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2129 (±0.0221), Variance: 1.2169, Range: [-9.4116, 9.8629]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0512, sigma²=0.2398, nugget=0.3188\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 23.4635 | 4.8439 | 0.9564 | -24.3444 | 0.41s |\n", - "| OLS_sphere | 200 | 0.3488 | 0.5906 | 0.3978 | 0.6233 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7998 | 0.8943 | 0.5976 | 0.1361 | 65.83s |\n", - "| DeepKriging_sphere | 500 | 0.3809 | 0.6172 | 0.3883 | 0.5885 | 35.40s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.3616 | 0.6014 | 0.3515 | 0.6094 | 30.70s |\n", - "| UniversalKriging | 100 | 0.3449 | 0.5873 | 0.3698 | 0.6274 | 2.05s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:22:59\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5304 (±0.0228), Variance: 1.2976, Range: [-10.1752, 8.2024]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0423, sigma²=0.2723, nugget=0.2622\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.4874 | 5.0485 | 1.0435 | -28.3562 | 0.36s |\n", - "| OLS_sphere | 200 | 0.2017 | 0.4492 | 0.3535 | 0.7676 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.5431 | 0.737 | 0.5688 | 0.3744 | 65.11s |\n", - "| DeepKriging_sphere | 500 | 0.4464 | 0.6682 | 0.4031 | 0.4858 | 41.89s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.2607 | 0.5106 | 0.3295 | 0.6997 | 36.80s |\n", - "| UniversalKriging | 100 | 0.1972 | 0.4441 | 0.3436 | 0.7728 | 4.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:26:26\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0270 (±0.0212), Variance: 1.1280, Range: [-9.6428, 11.9162]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0373, sigma²=0.4257, nugget=0.0585\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.039 | 1.0193 | 0.6664 | 0.2081 | 0.41s |\n", - "| OLS_sphere | 200 | 0.5893 | 0.7677 | 0.4241 | 0.5509 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.941 | 0.9701 | 0.5897 | 0.2828 | 60.02s |\n", - "| DeepKriging_sphere | 500 | 0.8825 | 0.9394 | 0.4563 | 0.3274 | 34.55s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.0701 | 1.0344 | 0.6709 | 0.1845 | 14.67s |\n", - "| UniversalKriging | 100 | 0.5606 | 0.7487 | 0.3804 | 0.5727 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:29:17\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1411 (±0.0212), Variance: 1.1270, Range: [-7.1251, 10.8163]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0384, sigma²=0.2400, nugget=0.1555\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 8.5367 | 2.9218 | 0.913 | -4.9329 | 0.40s |\n", - "| OLS_sphere | 200 | 0.5978 | 0.7732 | 0.3738 | 0.5845 | 0.03s |\n", - "| DeepKriging_wendland | -- | 0.7444 | 0.8628 | 0.5933 | 0.4826 | 56.35s |\n", - "| DeepKriging_sphere | 500 | 0.4608 | 0.6788 | 0.3994 | 0.6798 | 36.77s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1857 | 1.0889 | 0.7021 | 0.1759 | 15.44s |\n", - "| UniversalKriging | 100 | 0.5683 | 0.7538 | 0.3482 | 0.6051 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:32:10\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5506 (±0.0268), Variance: 1.8019, Range: [-15.5911, 10.0474]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0078, sigma²=0.0006, nugget=0.7485\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2415 | 1.1142 | 0.7008 | 0.2892 | 0.41s |\n", - "| OLS_sphere | 200 | 0.6671 | 0.8168 | 0.4415 | 0.6181 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.2184 | 1.1038 | 0.6838 | 0.3024 | 66.59s |\n", - "| DeepKriging_sphere | 500 | 1.4167 | 1.1903 | 0.7796 | 0.1889 | 15.55s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.696 | 0.8343 | 0.425 | 0.6015 | 32.19s |\n", - "| UniversalKriging | 100 | 0.7091 | 0.8421 | 0.4595 | 0.594 | 0.91s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:35:07\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.6615 (±0.0258), Variance: 1.6612, Range: [-9.0921, 12.2386]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0553, sigma²=0.3033, nugget=0.4145\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5991 | 1.2646 | 0.7944 | 0.1048 | 0.32s |\n", - "| OLS_sphere | 200 | 1.0234 | 1.0116 | 0.4994 | 0.4271 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.2842 | 1.1332 | 0.6268 | 0.2811 | 68.67s |\n", - "| DeepKriging_sphere | 500 | 0.9646 | 0.9821 | 0.4953 | 0.46 | 31.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.6042 | 1.2666 | 0.7491 | 0.1019 | 14.99s |\n", - "| UniversalKriging | 100 | 0.9642 | 0.9819 | 0.4571 | 0.4602 | 1.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:38:08\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3672 (±0.0330), Variance: 2.7226, Range: [-13.0461, 10.1842]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0321, sigma²=0.6226, nugget=0.3535\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.3222 | 1.5239 | 0.9912 | -0.0336 | 0.34s |\n", - "| OLS_sphere | 200 | 0.617 | 0.7855 | 0.4617 | 0.7254 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.4337 | 1.1974 | 0.8119 | 0.3619 | 66.33s |\n", - "| DeepKriging_sphere | 500 | 1.0342 | 1.0169 | 0.514 | 0.5397 | 35.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7418 | 0.8613 | 0.417 | 0.6698 | 30.45s |\n", - "| UniversalKriging | 100 | 0.6273 | 0.7921 | 0.4167 | 0.7208 | 2.83s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:41:29\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4495 (±0.0258), Variance: 1.6595, Range: [-8.9430, 13.8268]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0282, sigma²=0.6741, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 9.5824 | 3.0955 | 0.9349 | -4.471 | 0.38s |\n", - "| OLS_sphere | 200 | 0.7605 | 0.8721 | 0.4354 | 0.5658 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.2588 | 1.122 | 0.7031 | 0.2813 | 63.92s |\n", - "| DeepKriging_sphere | 500 | 1.2405 | 1.1138 | 0.519 | 0.2918 | 34.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8815 | 0.9389 | 0.4123 | 0.4967 | 39.00s |\n", - "| UniversalKriging | 100 | 0.7682 | 0.8765 | 0.4187 | 0.5614 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:44:56\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1366 (±0.0266), Variance: 1.7710, Range: [-9.9097, 11.3894]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0545, sigma²=0.2303, nugget=0.3227\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 2.135 | 1.4612 | 0.8947 | 0.1011 | 0.45s |\n", - "| OLS_sphere | 200 | 0.9453 | 0.9723 | 0.476 | 0.602 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.4962 | 1.2232 | 0.6974 | 0.3701 | 62.19s |\n", - "| DeepKriging_sphere | 500 | 0.9897 | 0.9948 | 0.4596 | 0.5833 | 34.10s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.7653 | 1.3286 | 0.8286 | 0.2568 | 15.61s |\n", - "| UniversalKriging | 100 | 0.8603 | 0.9275 | 0.4279 | 0.6378 | 1.66s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:47:56\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0060 (±0.0272), Variance: 1.8508, Range: [-10.7055, 11.5444]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0426, sigma²=0.4022, nugget=0.1844\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.9107 | 1.3823 | 0.925 | 0.1459 | 0.40s |\n", - "| OLS_sphere | 200 | 0.8585 | 0.9266 | 0.4502 | 0.6162 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.722 | 1.3122 | 0.8504 | 0.2303 | 63.47s |\n", - "| DeepKriging_sphere | 500 | 0.8931 | 0.9451 | 0.4663 | 0.6008 | 34.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.7854 | 0.8862 | 0.4051 | 0.6489 | 31.36s |\n", - "| UniversalKriging | 100 | 0.8127 | 0.9015 | 0.4043 | 0.6367 | 1.85s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:51:16\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1824 (±0.0255), Variance: 1.6275, Range: [-13.3431, 12.8453]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0435, sigma²=0.2969, nugget=0.4691\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3749 | 1.1726 | 0.7521 | -0.1376 | 0.43s |\n", - "| OLS_sphere | 200 | 0.5659 | 0.7523 | 0.4351 | 0.5317 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.9653 | 0.9825 | 0.6621 | 0.2013 | 65.75s |\n", - "| DeepKriging_sphere | 500 | 0.6392 | 0.7995 | 0.4754 | 0.4711 | 27.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.517 | 0.719 | 0.3691 | 0.5722 | 28.70s |\n", - "| UniversalKriging | 100 | 0.5238 | 0.7237 | 0.4067 | 0.5666 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:54:29\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5062 (±0.0277), Variance: 1.9174, Range: [-7.6303, 13.6718]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0039, sigma²=0.0006, nugget=0.7241\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.8277 | 1.3519 | 0.7913 | 0.1721 | 0.35s |\n", - "| OLS_sphere | 200 | 1.0481 | 1.0238 | 0.4914 | 0.5253 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.7242 | 1.3131 | 0.7708 | 0.219 | 67.06s |\n", - "| DeepKriging_sphere | 500 | 1.2738 | 1.1286 | 0.519 | 0.4231 | 33.16s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.1273 | 1.0618 | 0.4551 | 0.4894 | 28.02s |\n", - "| UniversalKriging | 100 | 1.1104 | 1.0538 | 0.5017 | 0.497 | 1.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 12:57:47\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3674 (±0.0243), Variance: 1.4786, Range: [-9.3085, 12.0189]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0557, sigma²=0.3554, nugget=0.2324\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.4681 | 1.571 | 0.7794 | -0.4561 | 0.33s |\n", - "| OLS_sphere | 200 | 0.7878 | 0.8876 | 0.4633 | 0.5352 | 0.02s |\n", - "| DeepKriging_wendland | -- | 1.3234 | 1.1504 | 0.636 | 0.2192 | 64.91s |\n", - "| DeepKriging_sphere | 500 | 0.7848 | 0.8859 | 0.4228 | 0.537 | 36.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.4011 | 1.1837 | 0.7122 | 0.1734 | 14.50s |\n", - "| UniversalKriging | 100 | 0.7451 | 0.8632 | 0.4087 | 0.5604 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:00:56\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2062 (±0.0224), Variance: 1.2536, Range: [-7.1653, 14.1751]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0581, sigma²=0.3723, nugget=0.3117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0133 | 1.0066 | 0.6158 | 0.1998 | 0.39s |\n", - "| OLS_sphere | 200 | 0.6312 | 0.7945 | 0.4367 | 0.5015 | 0.02s |\n", - "| DeepKriging_wendland | -- | 0.8271 | 0.9094 | 0.5501 | 0.3468 | 62.90s |\n", - "| DeepKriging_sphere | 500 | 0.7377 | 0.8589 | 0.4612 | 0.4174 | 26.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6246 | 0.7903 | 0.4086 | 0.5068 | 25.92s |\n", - "| UniversalKriging | 100 | 0.6055 | 0.7781 | 0.3859 | 0.5219 | 1.85s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:04:07\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2741 (±0.0230), Variance: 1.3210, Range: [-13.2894, 12.3852]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0365, sigma²=0.5880, nugget=0.0623\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3151 | 1.1468 | 0.7059 | 0.0989 | 0.39s |\n", - "| OLS_sphere | 200 | 0.7901 | 0.8889 | 0.4304 | 0.4586 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.0644 | 1.0317 | 0.6447 | 0.2707 | 68.14s |\n", - "| DeepKriging_sphere | 500 | 0.8649 | 0.93 | 0.4676 | 0.4074 | 30.94s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8591 | 0.9269 | 0.4229 | 0.4114 | 30.85s |\n", - "| UniversalKriging | 100 | 0.7147 | 0.8454 | 0.3776 | 0.5103 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:07:34\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1506 (±0.0267), Variance: 1.7759, Range: [-12.1697, 12.2286]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0366, sigma²=0.4218, nugget=0.3023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.9791 | 1.4068 | 0.8549 | 0.0484 | 0.33s |\n", - "| OLS_sphere | 200 | 0.9485 | 0.9739 | 0.5152 | 0.5439 | 0.03s |\n", - "| DeepKriging_wendland | -- | 1.6876 | 1.2991 | 0.7271 | 0.1886 | 67.25s |\n", - "| DeepKriging_sphere | 500 | 1.7066 | 1.3064 | 0.7718 | 0.1795 | 15.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 1.0486 | 1.024 | 0.4663 | 0.4958 | 31.65s |\n", - "| UniversalKriging | 100 | 0.9632 | 0.9814 | 0.4958 | 0.5369 | 5.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 13:10:48\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, outlier_ratio=0.025, outlier_multiplier=5, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.021285, - "end_time": "2026-01-21T05:10:48.862112", - "exception": false, - "start_time": "2026-01-21T05:10:48.840827", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T05:10:48.907546Z", - "iopub.status.busy": "2026-01-21T05:10:48.906786Z", - "iopub.status.idle": "2026-01-21T05:10:48.921754Z", - "shell.execute_reply": "2026-01-21T05:10:48.918852Z" - }, - "papermill": { - "duration": 0.040634, - "end_time": "2026-01-21T05:10:48.923472", - "exception": false, - "start_time": "2026-01-21T05:10:48.882838", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 200, 'DeepKriging_sphere': 500, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|------------|\n", - "| OLS_wendland | 6.14±9.58 | 2.00±1.46 | 0.83±0.17 | -3.44±7.25 |\n", - "| OLS_sphere | 0.67±0.28 | 0.80±0.17 | 0.44±0.04 | 0.58±0.08 |\n", - "| DeepKriging_wendland | 1.22±0.66 | 1.08±0.24 | 0.66±0.08 | 0.22±0.36 |\n", - "| DeepKriging_sphere | 0.89±0.37 | 0.92±0.20 | 0.50±0.10 | 0.43±0.18 |\n", - "| DeepKriging_sphere_Huber | 0.84±0.43 | 0.89±0.23 | 0.49±0.15 | 0.47±0.21 |\n", - "| UniversalKriging | 0.65±0.29 | 0.78±0.18 | 0.41±0.05 | 0.60±0.08 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 0.78±0.18)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 8651.534677, - "end_time": "2026-01-21T05:10:52.505154", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_outliers5.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-21T02:46:40.970477", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioI/simulation_spherical_GP_without_outliers.ipynb b/notebook/simulation/ScenarioI/simulation_spherical_GP_without_outliers.ipynb deleted file mode 100644 index 29f88f9..0000000 --- a/notebook/simulation/ScenarioI/simulation_spherical_GP_without_outliers.ipynb +++ /dev/null @@ -1,4462 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.015244, - "end_time": "2026-01-20T22:01:14.002902", - "exception": false, - "start_time": "2026-01-20T22:01:13.987658", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:14.022246Z", - "iopub.status.busy": "2026-01-20T22:01:14.021180Z", - "iopub.status.idle": "2026-01-20T22:01:14.043814Z", - "shell.execute_reply": "2026-01-20T22:01:14.039001Z" - }, - "papermill": { - "duration": 0.036658, - "end_time": "2026-01-20T22:01:14.047420", - "exception": false, - "start_time": "2026-01-20T22:01:14.010762", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:14.076986Z", - "iopub.status.busy": "2026-01-20T22:01:14.076123Z", - "iopub.status.idle": "2026-01-20T22:01:16.957247Z", - "shell.execute_reply": "2026-01-20T22:01:16.953464Z" - }, - "papermill": { - "duration": 2.899317, - "end_time": "2026-01-20T22:01:16.959662", - "exception": false, - "start_time": "2026-01-20T22:01:14.060345", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768946475.077888 3846698 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768946475.082175 3846698 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768946475.094068 3846698 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768946475.094090 3846698 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768946475.094092 3846698 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768946475.094093 3846698 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.002634, - "end_time": "2026-01-20T22:01:16.969358", - "exception": false, - "start_time": "2026-01-20T22:01:16.966724", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:16.978660Z", - "iopub.status.busy": "2026-01-20T22:01:16.977429Z", - "iopub.status.idle": "2026-01-20T22:01:18.506958Z", - "shell.execute_reply": "2026-01-20T22:01:18.503491Z" - }, - "papermill": { - "duration": 1.537372, - "end_time": "2026-01-20T22:01:18.509298", - "exception": false, - "start_time": "2026-01-20T22:01:16.971926", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.01155, - "end_time": "2026-01-20T22:01:18.534387", - "exception": false, - "start_time": "2026-01-20T22:01:18.522837", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:18.553585Z", - "iopub.status.busy": "2026-01-20T22:01:18.552599Z", - "iopub.status.idle": "2026-01-20T22:01:18.565265Z", - "shell.execute_reply": "2026-01-20T22:01:18.562168Z" - }, - "papermill": { - "duration": 0.025115, - "end_time": "2026-01-20T22:01:18.567893", - "exception": false, - "start_time": "2026-01-20T22:01:18.542778", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.005288, - "end_time": "2026-01-20T22:01:18.584759", - "exception": false, - "start_time": "2026-01-20T22:01:18.579471", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:18.596664Z", - "iopub.status.busy": "2026-01-20T22:01:18.595900Z", - "iopub.status.idle": "2026-01-20T22:01:34.240369Z", - "shell.execute_reply": "2026-01-20T22:01:34.236943Z" - }, - "papermill": { - "duration": 15.654582, - "end_time": "2026-01-20T22:01:34.243648", - "exception": false, - "start_time": "2026-01-20T22:01:18.589066", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.004177, - "end_time": "2026-01-20T22:01:31.402521", - "exception": false, - "start_time": "2026-01-20T22:01:31.398344", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:31.413037Z", - "iopub.status.busy": "2026-01-20T22:01:31.411684Z", - "iopub.status.idle": "2026-01-20T22:01:31.428479Z", - "shell.execute_reply": "2026-01-20T22:01:31.424513Z" - }, - "papermill": { - "duration": 0.026239, - "end_time": "2026-01-20T22:01:31.431440", - "exception": false, - "start_time": "2026-01-20T22:01:31.405201", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.002832, - "end_time": "2026-01-20T22:01:31.441336", - "exception": false, - "start_time": "2026-01-20T22:01:31.438504", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:31.450230Z", - "iopub.status.busy": "2026-01-20T22:01:31.449195Z", - "iopub.status.idle": "2026-01-20T22:01:31.484464Z", - "shell.execute_reply": "2026-01-20T22:01:31.481631Z" - }, - "papermill": { - "duration": 0.043798, - "end_time": "2026-01-20T22:01:31.487760", - "exception": false, - "start_time": "2026-01-20T22:01:31.443962", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:31.500810Z", - "iopub.status.busy": "2026-01-20T22:01:31.500332Z", - "iopub.status.idle": "2026-01-20T22:01:31.530975Z", - "shell.execute_reply": "2026-01-20T22:01:31.528367Z" - }, - "papermill": { - "duration": 0.039578, - "end_time": "2026-01-20T22:01:31.534124", - "exception": false, - "start_time": "2026-01-20T22:01:31.494546", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.003559, - "end_time": "2026-01-20T22:01:31.543479", - "exception": false, - "start_time": "2026-01-20T22:01:31.539920", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:31.552888Z", - "iopub.status.busy": "2026-01-20T22:01:31.551982Z", - "iopub.status.idle": "2026-01-20T22:01:31.564095Z", - "shell.execute_reply": "2026-01-20T22:01:31.560786Z" - }, - "papermill": { - "duration": 0.019735, - "end_time": "2026-01-20T22:01:31.565962", - "exception": false, - "start_time": "2026-01-20T22:01:31.546227", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-20T22:01:31.579013Z", - "iopub.status.busy": "2026-01-20T22:01:31.578138Z", - "iopub.status.idle": "2026-01-21T00:40:53.947755Z", - "shell.execute_reply": "2026-01-21T00:40:53.944574Z" - }, - "papermill": { - "duration": 9562.37837, - "end_time": "2026-01-21T00:40:53.950353", - "exception": false, - "start_time": "2026-01-20T22:01:31.571983", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2087 (0.0213), Variance: 1.1303, Range: [-3.3592, 3.1722]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.3117 0.2481 \n", - " 100 0.2228 0.1858 \n", - " 200 0.1527 0.1233 \n", - " 500 0.1256 0.0950 *\n", - " 700 0.1480 0.1141 \n", - " 1000 0.2260 0.3666 \n", - " 1400 22.1757 4.6753 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768946499.790842 3846698 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768946502.174461 3846930 service.cc:152] XLA service 0x7ef3c4012ef0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768946502.174536 3846930 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768946502.538697 3846930 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768946513.327363 3846930 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ef4095d6a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ef408fe1fc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1083 0.0876 \n", - " 100 0.1073 0.0874 *\n", - " 200 0.1187 0.0862 \n", - " 500 0.1899 0.1314 \n", - " 700 0.2488 0.1935 \n", - " 1000 0.3360 0.3155 \n", - " 1400 0.4976 0.5326 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0524 0.0845 *\n", - " 100 0.0554 0.0868 \n", - " 200 0.0611 0.0830 \n", - " 500 0.0764 0.1209 \n", - " 700 0.1114 0.1755 \n", - " 1000 0.1614 0.2802 \n", - " 1400 0.2249 0.5106 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1472, sigma²=0.2892, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0995, sigma²=0.2070, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0502, sigma²=0.1227, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=701.8857, sigma²=0.0000, nugget=0.0672\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2090, sigma²=0.0000, nugget=0.0471\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.3867, sigma²=0.0000, nugget=0.0308\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6518, sigma²=0.0000, nugget=0.0162\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1051 0.0738 \n", - " 100 0.1048 0.0757 *\n", - " 200 0.1072 0.0743 \n", - " 500 0.1256 0.0950 \n", - " 700 0.1480 0.1141 \n", - " 1000 0.2260 0.3666 \n", - " 1400 22.1754 4.6751 \n", - " Best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0995, sigma²=0.2070, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6554 | 0.8096 | 0.5935 | 0.3978 | 0.49s |\n", - "| OLS_sphere | 500 | 0.095 | 0.3082 | 0.2412 | 0.9127 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4227 | 0.6501 | 0.5054 | 0.6117 | 48.13s |\n", - "| DeepKriging_sphere | 100 | 0.079 | 0.281 | 0.2249 | 0.9274 | 35.05s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.721 | 0.8491 | 0.6894 | 0.3376 | 10.26s |\n", - "| UniversalKriging | 100 | 0.0757 | 0.2752 | 0.2147 | 0.9304 | 4.10s |\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:14:02\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0914 (0.0170), Variance: 0.7252, Range: [-2.5030, 2.8675]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0920, sigma²=0.1921, nugget=0.0014\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 33.5036 | 5.7882 | 1.0101 | -43.2389 | 0.41s |\n", - "| OLS_sphere | 500 | 0.1341 | 0.3661 | 0.2872 | 0.823 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5542 | 0.7444 | 0.5472 | 0.2682 | 51.74s |\n", - "| DeepKriging_sphere | 100 | 0.1167 | 0.3416 | 0.2652 | 0.8459 | 34.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.13 | 0.3606 | 0.2833 | 0.8283 | 33.58s |\n", - "| UniversalKriging | 100 | 0.1034 | 0.3215 | 0.2522 | 0.8635 | 3.11s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:16:26\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1021 (0.0165), Variance: 0.6820, Range: [-2.6324, 2.0719]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0948, sigma²=0.2037, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5913 | 0.769 | 0.5607 | 0.036 | 0.51s |\n", - "| OLS_sphere | 500 | 0.112 | 0.3347 | 0.2613 | 0.8173 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.4157 | 0.6447 | 0.4728 | 0.3223 | 45.43s |\n", - "| DeepKriging_sphere | 100 | 0.0824 | 0.2871 | 0.2237 | 0.8656 | 29.67s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1013 | 0.3183 | 0.2476 | 0.8348 | 35.13s |\n", - "| UniversalKriging | 100 | 0.0826 | 0.2874 | 0.2229 | 0.8653 | 3.56s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:18:40\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0181 (0.0207), Variance: 1.0716, Range: [-3.1769, 3.1892]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0823, sigma²=0.1759, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9173 | 0.9578 | 0.6593 | -0.0862 | 0.40s |\n", - "| OLS_sphere | 500 | 0.1218 | 0.349 | 0.2794 | 0.8558 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.9001 | 0.9487 | 0.5587 | -0.0658 | 57.92s |\n", - "| DeepKriging_sphere | 100 | 0.1116 | 0.334 | 0.2645 | 0.8679 | 35.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1106 | 0.3325 | 0.2619 | 0.869 | 38.75s |\n", - "| UniversalKriging | 100 | 0.1116 | 0.3341 | 0.2631 | 0.8678 | 4.91s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:21:20\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3156 (0.0192), Variance: 0.9186, Range: [-2.9928, 3.2624]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0992, sigma²=0.2130, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.398 | 1.1824 | 0.6576 | -0.5368 | 0.36s |\n", - "| OLS_sphere | 500 | 0.1257 | 0.3545 | 0.2799 | 0.8618 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.484 | 0.6957 | 0.5257 | 0.4679 | 48.16s |\n", - "| DeepKriging_sphere | 100 | 0.6661 | 0.8162 | 0.6611 | 0.2677 | 13.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6223 | 0.7889 | 0.6436 | 0.3159 | 15.68s |\n", - "| UniversalKriging | 100 | 0.1036 | 0.3219 | 0.2521 | 0.8861 | 3.93s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:23:04\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2265 (0.0170), Variance: 0.7267, Range: [-3.0397, 2.3144]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0951, sigma²=0.1926, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4418 | 0.6646 | 0.504 | 0.318 | 0.36s |\n", - "| OLS_sphere | 500 | 0.1065 | 0.3263 | 0.2581 | 0.8356 | 0.04s |\n", - "| DeepKriging_wendland | -- | 0.3761 | 0.6132 | 0.4605 | 0.4194 | 51.88s |\n", - "| DeepKriging_sphere | 100 | 0.0911 | 0.3019 | 0.2322 | 0.8593 | 33.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5338 | 0.7306 | 0.5861 | 0.1759 | 16.37s |\n", - "| UniversalKriging | 100 | 0.0834 | 0.2888 | 0.223 | 0.8712 | 3.31s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:25:12\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1545 (0.0181), Variance: 0.8211, Range: [-3.2120, 3.8366]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0950, sigma²=0.1964, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5959 | 0.7719 | 0.5962 | 0.3398 | 0.37s |\n", - "| OLS_sphere | 500 | 0.1266 | 0.3558 | 0.2832 | 0.8597 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4741 | 0.6886 | 0.5257 | 0.4747 | 58.31s |\n", - "| DeepKriging_sphere | 100 | 0.1113 | 0.3336 | 0.2623 | 0.8767 | 34.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0958 | 0.3095 | 0.2414 | 0.8939 | 33.32s |\n", - "| UniversalKriging | 100 | 0.0916 | 0.3026 | 0.2409 | 0.8985 | 3.72s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:27:46\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0301 (0.0233), Variance: 1.3596, Range: [-3.6446, 3.3099]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0835, sigma²=0.1889, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.891 | 0.944 | 0.714 | 0.3875 | 0.46s |\n", - "| OLS_sphere | 500 | 0.1262 | 0.3552 | 0.2829 | 0.9133 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.6035 | 0.7768 | 0.5849 | 0.5852 | 46.51s |\n", - "| DeepKriging_sphere | 100 | 0.1154 | 0.3398 | 0.2776 | 0.9207 | 36.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1106 | 0.3325 | 0.2665 | 0.924 | 42.76s |\n", - "| UniversalKriging | 100 | 0.1064 | 0.3262 | 0.2621 | 0.9268 | 4.51s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:30:23\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1015 (0.0184), Variance: 0.8465, Range: [-2.5073, 2.8775]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0858, sigma²=0.1933, nugget=0.0008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6086 | 0.7802 | 0.5883 | 0.244 | 0.45s |\n", - "| OLS_sphere | 500 | 0.1402 | 0.3745 | 0.2885 | 0.8258 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.418 | 0.6465 | 0.486 | 0.4808 | 61.11s |\n", - "| DeepKriging_sphere | 100 | 0.1022 | 0.3198 | 0.2514 | 0.873 | 34.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1049 | 0.3239 | 0.2537 | 0.8697 | 31.21s |\n", - "| UniversalKriging | 100 | 0.0925 | 0.3041 | 0.2353 | 0.8852 | 0.10s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:32:57\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3526 (0.0222), Variance: 1.2329, Range: [-2.8410, 4.1531]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0871, sigma²=0.1822, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7 | 0.8367 | 0.677 | 0.4204 | 0.36s |\n", - "| OLS_sphere | 500 | 0.1325 | 0.364 | 0.2951 | 0.8903 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4654 | 0.6822 | 0.5411 | 0.6146 | 63.20s |\n", - "| DeepKriging_sphere | 100 | 0.1161 | 0.3407 | 0.269 | 0.9039 | 39.88s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1163 | 0.341 | 0.2651 | 0.9037 | 36.34s |\n", - "| UniversalKriging | 100 | 0.1071 | 0.3272 | 0.2555 | 0.9114 | 4.06s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:35:49\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2930 (0.0190), Variance: 0.9043, Range: [-2.9922, 3.7984]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0910, sigma²=0.1886, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.9812 | 0.9905 | 0.6718 | -0.0488 | 0.42s |\n", - "| OLS_sphere | 500 | 0.1248 | 0.3533 | 0.2775 | 0.8665 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.5453 | 0.7385 | 0.5198 | 0.4171 | 62.40s |\n", - "| DeepKriging_sphere | 100 | 0.1031 | 0.3211 | 0.2494 | 0.8898 | 38.24s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1135 | 0.3369 | 0.263 | 0.8787 | 40.13s |\n", - "| UniversalKriging | 100 | 0.0984 | 0.3138 | 0.2339 | 0.8948 | 4.35s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:38:44\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.2711 (0.0163), Variance: 0.6659, Range: [-2.2990, 2.6131]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0902, sigma²=0.1810, nugget=0.0009\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3184 | 1.1482 | 0.6501 | -0.7536 | 0.37s |\n", - "| OLS_sphere | 500 | 0.1205 | 0.3472 | 0.2733 | 0.8397 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.479 | 0.6921 | 0.5036 | 0.3629 | 66.49s |\n", - "| DeepKriging_sphere | 100 | 0.574 | 0.7577 | 0.628 | 0.2365 | 13.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5492 | 0.7411 | 0.6109 | 0.2695 | 16.43s |\n", - "| UniversalKriging | 100 | 0.0939 | 0.3064 | 0.2396 | 0.8751 | 2.51s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:40:51\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0562 (0.0177), Variance: 0.7849, Range: [-2.3893, 2.8555]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0808, sigma²=0.1863, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 26.0289 | 5.1019 | 1.384 | -30.845 | 0.42s |\n", - "| OLS_sphere | 500 | 0.1249 | 0.3534 | 0.272 | 0.8472 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.557 | 0.7463 | 0.5744 | 0.3186 | 64.75s |\n", - "| DeepKriging_sphere | 100 | 0.628 | 0.7924 | 0.6307 | 0.2317 | 16.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5554 | 0.7452 | 0.5864 | 0.3205 | 13.42s |\n", - "| UniversalKriging | 100 | 0.0913 | 0.3022 | 0.2387 | 0.8883 | 3.98s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:43:02\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.7619 (0.0173), Variance: 0.7493, Range: [-3.7554, 2.2216]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0959, sigma²=0.2107, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.3151 | 1.8207 | 0.6118 | -4.4732 | 0.41s |\n", - "| OLS_sphere | 500 | 0.1261 | 0.355 | 0.2844 | 0.7919 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.3148 | 0.561 | 0.4147 | 0.4803 | 65.50s |\n", - "| DeepKriging_sphere | 100 | 0.1027 | 0.3204 | 0.2466 | 0.8305 | 39.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1024 | 0.32 | 0.2515 | 0.8309 | 34.30s |\n", - "| UniversalKriging | 100 | 0.0961 | 0.31 | 0.2444 | 0.8413 | 3.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:45:59\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.5079 (0.0201), Variance: 1.0118, Range: [-3.3722, 2.8220]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0797, sigma²=0.1863, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7804 | 0.8834 | 0.676 | 0.0876 | 0.42s |\n", - "| OLS_sphere | 500 | 0.1051 | 0.3241 | 0.2607 | 0.8772 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.7506 | 0.8664 | 0.5133 | 0.1225 | 70.88s |\n", - "| DeepKriging_sphere | 100 | 0.1057 | 0.3251 | 0.2512 | 0.8764 | 38.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1045 | 0.3233 | 0.2514 | 0.8778 | 38.96s |\n", - "| UniversalKriging | 100 | 0.0885 | 0.2974 | 0.2327 | 0.8966 | 4.64s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:49:01\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2011 (0.0168), Variance: 0.7026, Range: [-2.6394, 2.5442]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1057, sigma²=0.2112, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7758 | 0.8808 | 0.5754 | -0.2568 | 0.44s |\n", - "| OLS_sphere | 500 | 0.1297 | 0.3601 | 0.2814 | 0.7899 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3741 | 0.6116 | 0.4719 | 0.394 | 68.47s |\n", - "| DeepKriging_sphere | 100 | 0.1148 | 0.3389 | 0.2563 | 0.814 | 34.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.114 | 0.3377 | 0.2596 | 0.8153 | 34.81s |\n", - "| UniversalKriging | 100 | 0.0997 | 0.3157 | 0.2423 | 0.8385 | 4.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:51:59\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4486 (0.0195), Variance: 0.9543, Range: [-2.6866, 3.0678]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0941, sigma²=0.2079, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5709 | 1.2534 | 0.6795 | -0.4835 | 0.52s |\n", - "| OLS_sphere | 500 | 0.1569 | 0.3961 | 0.3021 | 0.8518 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5016 | 0.7083 | 0.5443 | 0.5263 | 62.36s |\n", - "| DeepKriging_sphere | 100 | 0.1113 | 0.3336 | 0.2697 | 0.8949 | 44.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1133 | 0.3366 | 0.2634 | 0.893 | 40.43s |\n", - "| UniversalKriging | 100 | 0.1035 | 0.3217 | 0.2494 | 0.9023 | 4.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:55:12\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3553 (0.0223), Variance: 1.2401, Range: [-3.8404, 2.6780]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0815, sigma²=0.1676, nugget=0.0003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 13.1098 | 3.6207 | 1.0122 | -11.408 | 0.43s |\n", - "| OLS_sphere | 500 | 0.1217 | 0.3489 | 0.2807 | 0.8848 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.6094 | 0.7807 | 0.5716 | 0.4232 | 65.76s |\n", - "| DeepKriging_sphere | 100 | 0.0895 | 0.2992 | 0.2376 | 0.9153 | 39.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0929 | 0.3048 | 0.2457 | 0.9121 | 31.46s |\n", - "| UniversalKriging | 100 | 0.0819 | 0.2862 | 0.2286 | 0.9225 | 2.50s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 06:58:10\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0131 (0.0193), Variance: 0.9331, Range: [-2.5519, 3.1735]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0818, sigma²=0.1893, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7094 | 0.8423 | 0.6319 | 0.2246 | 0.40s |\n", - "| OLS_sphere | 500 | 0.1367 | 0.3697 | 0.2914 | 0.8506 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5063 | 0.7115 | 0.51 | 0.4466 | 62.47s |\n", - "| DeepKriging_sphere | 100 | 0.1071 | 0.3273 | 0.2586 | 0.8829 | 38.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6105 | 0.7813 | 0.6233 | 0.3327 | 13.85s |\n", - "| UniversalKriging | 100 | 0.1028 | 0.3207 | 0.2529 | 0.8876 | 5.27s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:00:47\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1429 (0.0161), Variance: 0.6453, Range: [-3.2163, 2.5210]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0863, sigma²=0.1887, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.8544 | 0.9243 | 0.5461 | -0.5303 | 0.47s |\n", - "| OLS_sphere | 500 | 0.1114 | 0.3338 | 0.2609 | 0.8005 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4205 | 0.6484 | 0.4484 | 0.2469 | 66.51s |\n", - "| DeepKriging_sphere | 100 | 0.0931 | 0.3051 | 0.2363 | 0.8333 | 35.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0988 | 0.3143 | 0.2401 | 0.8231 | 39.60s |\n", - "| UniversalKriging | 100 | 0.0856 | 0.2925 | 0.2263 | 0.8468 | 4.10s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:03:54\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3161 (0.0162), Variance: 0.6585, Range: [-2.0472, 2.8787]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0846, sigma²=0.1875, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.707 | 2.1696 | 0.7005 | -5.7547 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1262 | 0.3553 | 0.2799 | 0.8189 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4366 | 0.6608 | 0.4933 | 0.3734 | 64.14s |\n", - "| DeepKriging_sphere | 100 | 0.1115 | 0.3339 | 0.2575 | 0.84 | 36.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1206 | 0.3473 | 0.2669 | 0.8269 | 34.62s |\n", - "| UniversalKriging | 100 | 0.1086 | 0.3296 | 0.2538 | 0.8441 | 4.25s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:07:00\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1353 (0.0173), Variance: 0.7452, Range: [-2.7997, 2.5882]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0826, sigma²=0.1786, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1801 | 1.0863 | 0.6925 | -0.5737 | 0.36s |\n", - "| OLS_sphere | 500 | 0.1166 | 0.3414 | 0.2617 | 0.8445 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5345 | 0.7311 | 0.5531 | 0.2871 | 64.90s |\n", - "| DeepKriging_sphere | 100 | 0.5365 | 0.7325 | 0.5865 | 0.2845 | 13.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0908 | 0.3013 | 0.2371 | 0.8789 | 36.33s |\n", - "| UniversalKriging | 100 | 0.0909 | 0.3014 | 0.237 | 0.8788 | 5.08s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:09:41\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5156 (0.0180), Variance: 0.8144, Range: [-2.0495, 3.5843]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0832, sigma²=0.1919, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6121 | 0.7824 | 0.5921 | 0.1777 | 0.41s |\n", - "| OLS_sphere | 500 | 0.1469 | 0.3833 | 0.3003 | 0.8026 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4054 | 0.6367 | 0.4826 | 0.4555 | 63.59s |\n", - "| DeepKriging_sphere | 100 | 0.1242 | 0.3524 | 0.2746 | 0.8331 | 40.30s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1156 | 0.34 | 0.2653 | 0.8447 | 37.71s |\n", - "| UniversalKriging | 100 | 0.1026 | 0.3204 | 0.2442 | 0.8621 | 5.15s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:12:50\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0829 (0.0196), Variance: 0.9647, Range: [-3.1156, 3.0751]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0833, sigma²=0.1854, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.727 | 0.8526 | 0.6194 | 0.1798 | 0.39s |\n", - "| OLS_sphere | 500 | 0.1294 | 0.3598 | 0.2807 | 0.854 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.4608 | 0.6788 | 0.5087 | 0.4801 | 62.95s |\n", - "| DeepKriging_sphere | 100 | 0.0953 | 0.3088 | 0.2343 | 0.8924 | 37.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0952 | 0.3085 | 0.2386 | 0.8926 | 31.77s |\n", - "| UniversalKriging | 100 | 0.0969 | 0.3113 | 0.2359 | 0.8907 | 3.93s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:15:53\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.0490 (0.0182), Variance: 0.8287, Range: [-2.5449, 2.6965]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1038, sigma²=0.2165, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.672 | 0.8198 | 0.6233 | 0.1737 | 0.43s |\n", - "| OLS_sphere | 500 | 0.1242 | 0.3524 | 0.278 | 0.8473 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5569 | 0.7462 | 0.5604 | 0.3153 | 68.49s |\n", - "| DeepKriging_sphere | 100 | 0.1102 | 0.332 | 0.2728 | 0.8645 | 35.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.5659 | 0.7523 | 0.61 | 0.3042 | 16.14s |\n", - "| UniversalKriging | 100 | 0.0908 | 0.3013 | 0.2438 | 0.8884 | 4.50s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:18:42\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0682 (0.0195), Variance: 0.9515, Range: [-3.6544, 2.8584]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0959, sigma²=0.2074, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 42.8094 | 6.5429 | 1.072 | -42.4599 | 0.40s |\n", - "| OLS_sphere | 500 | 0.1252 | 0.3538 | 0.2707 | 0.8729 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.5306 | 0.7284 | 0.5446 | 0.4613 | 64.42s |\n", - "| DeepKriging_sphere | 100 | 0.1123 | 0.3352 | 0.2531 | 0.886 | 36.14s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1042 | 0.3228 | 0.2441 | 0.8942 | 37.10s |\n", - "| UniversalKriging | 100 | 0.109 | 0.3301 | 0.2477 | 0.8894 | 4.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:21:50\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.6284 (0.0190), Variance: 0.9023, Range: [-3.0028, 3.7611]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0843, sigma²=0.1978, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 13.8122 | 3.7165 | 0.9455 | -14.4059 | 0.41s |\n", - "| OLS_sphere | 500 | 0.1203 | 0.3469 | 0.2731 | 0.8658 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.3908 | 0.6251 | 0.476 | 0.5641 | 71.37s |\n", - "| DeepKriging_sphere | 100 | 0.1068 | 0.3268 | 0.2681 | 0.8809 | 36.90s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1084 | 0.3293 | 0.2615 | 0.8791 | 39.03s |\n", - "| UniversalKriging | 100 | 0.1002 | 0.3165 | 0.2566 | 0.8883 | 4.15s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:25:07\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1642 (0.0190), Variance: 0.9004, Range: [-2.5461, 2.9000]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0953, sigma²=0.2115, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8152 | 0.9029 | 0.7296 | 0.22 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1316 | 0.3627 | 0.2833 | 0.8741 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.7774 | 0.8817 | 0.6198 | 0.2562 | 62.92s |\n", - "| DeepKriging_sphere | 100 | 0.1125 | 0.3355 | 0.2531 | 0.8923 | 37.69s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1161 | 0.3408 | 0.2592 | 0.8889 | 37.02s |\n", - "| UniversalKriging | 100 | 0.0974 | 0.3122 | 0.2405 | 0.9068 | 3.76s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:28:14\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3065 (0.0182), Variance: 0.8257, Range: [-2.3751, 2.7162]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0795, sigma²=0.1842, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.0834 | 1.0408 | 0.7428 | -0.0318 | 0.44s |\n", - "| OLS_sphere | 500 | 0.1187 | 0.3445 | 0.2781 | 0.8869 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.612 | 0.7823 | 0.5587 | 0.4171 | 70.35s |\n", - "| DeepKriging_sphere | 100 | 0.1021 | 0.3195 | 0.2499 | 0.9028 | 39.30s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1027 | 0.3205 | 0.2611 | 0.9021 | 37.40s |\n", - "| UniversalKriging | 100 | 0.0957 | 0.3093 | 0.2415 | 0.9089 | 4.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:31:39\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.6807 (0.0228), Variance: 1.2941, Range: [-3.0551, 5.0718]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0919, sigma²=0.1946, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1831 | 1.0877 | 0.7403 | 0.0682 | 0.39s |\n", - "| OLS_sphere | 500 | 0.1175 | 0.3427 | 0.2746 | 0.9075 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5161 | 0.7184 | 0.5348 | 0.5935 | 63.69s |\n", - "| DeepKriging_sphere | 100 | 0.111 | 0.3331 | 0.2572 | 0.9126 | 37.78s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1003 | 0.3168 | 0.2508 | 0.921 | 37.14s |\n", - "| UniversalKriging | 100 | 0.1036 | 0.3218 | 0.251 | 0.9184 | 5.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:34:53\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1138 (0.0177), Variance: 0.7833, Range: [-2.5885, 2.6781]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0860, sigma²=0.1911, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.8164 | 0.9035 | 0.6509 | -0.0545 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1241 | 0.3523 | 0.2714 | 0.8397 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.5482 | 0.7404 | 0.5484 | 0.2919 | 65.36s |\n", - "| DeepKriging_sphere | 100 | 0.1047 | 0.3236 | 0.2456 | 0.8648 | 35.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.114 | 0.3376 | 0.2597 | 0.8528 | 34.83s |\n", - "| UniversalKriging | 100 | 0.09 | 0.3 | 0.2416 | 0.8837 | 3.57s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:38:02\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0866 (0.0195), Variance: 0.9528, Range: [-2.8215, 3.4723]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0820, sigma²=0.1872, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1621 | 1.078 | 0.6787 | -0.215 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0982 | 0.3134 | 0.2493 | 0.8973 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5332 | 0.7302 | 0.5374 | 0.4425 | 67.49s |\n", - "| DeepKriging_sphere | 100 | 0.0862 | 0.2936 | 0.2429 | 0.9099 | 36.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0965 | 0.3107 | 0.2482 | 0.8991 | 32.34s |\n", - "| UniversalKriging | 100 | 0.0788 | 0.2807 | 0.228 | 0.9176 | 4.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:41:17\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1753 (0.0182), Variance: 0.8285, Range: [-2.4795, 3.2583]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0863, sigma²=0.1847, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7616 | 0.8727 | 0.668 | 0.0384 | 0.42s |\n", - "| OLS_sphere | 500 | 0.1309 | 0.3617 | 0.2844 | 0.8348 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.515 | 0.7176 | 0.5123 | 0.3498 | 70.81s |\n", - "| DeepKriging_sphere | 100 | 0.5727 | 0.7568 | 0.5886 | 0.277 | 13.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1056 | 0.3249 | 0.2578 | 0.8667 | 34.66s |\n", - "| UniversalKriging | 100 | 0.0898 | 0.2996 | 0.2289 | 0.8867 | 3.85s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:44:18\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3590 (0.0169), Variance: 0.7165, Range: [-2.9285, 2.4953]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0911, sigma²=0.1956, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6053 | 0.778 | 0.6119 | 0.1261 | 0.45s |\n", - "| OLS_sphere | 500 | 0.1305 | 0.3612 | 0.2773 | 0.8117 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.3955 | 0.6289 | 0.4957 | 0.429 | 65.25s |\n", - "| DeepKriging_sphere | 100 | 0.6712 | 0.8192 | 0.6551 | 0.031 | 13.41s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1078 | 0.3284 | 0.246 | 0.8443 | 35.28s |\n", - "| UniversalKriging | 100 | 0.1036 | 0.3219 | 0.2405 | 0.8504 | 4.47s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:47:16\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1716 (0.0174), Variance: 0.7549, Range: [-3.2324, 2.3597]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0923, sigma²=0.2095, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 8.6874 | 2.9474 | 0.7488 | -11.5355 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1068 | 0.3267 | 0.2566 | 0.846 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3858 | 0.6211 | 0.4575 | 0.4433 | 65.37s |\n", - "| DeepKriging_sphere | 100 | 0.0902 | 0.3004 | 0.2398 | 0.8698 | 36.70s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0958 | 0.3095 | 0.2482 | 0.8618 | 34.35s |\n", - "| UniversalKriging | 100 | 0.0849 | 0.2914 | 0.2238 | 0.8775 | 4.01s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:50:34\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.4976 (0.0182), Variance: 0.8304, Range: [-3.0018, 2.0971]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0843, sigma²=0.1924, nugget=0.0002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 14.183 | 3.766 | 0.908 | -16.283 | 0.43s |\n", - "| OLS_sphere | 500 | 0.1239 | 0.352 | 0.2804 | 0.849 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4814 | 0.6938 | 0.5345 | 0.4134 | 64.88s |\n", - "| DeepKriging_sphere | 100 | 0.1111 | 0.3334 | 0.259 | 0.8646 | 36.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1022 | 0.3196 | 0.2533 | 0.8755 | 36.68s |\n", - "| UniversalKriging | 100 | 0.1008 | 0.3174 | 0.2517 | 0.8772 | 3.93s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:53:52\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0200 (0.0165), Variance: 0.6770, Range: [-2.3961, 2.9264]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0892, sigma²=0.1920, nugget=0.0008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5064 | 0.7116 | 0.5657 | 0.255 | 0.48s |\n", - "| OLS_sphere | 500 | 0.1106 | 0.3325 | 0.2491 | 0.8373 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4426 | 0.6653 | 0.5109 | 0.3488 | 68.10s |\n", - "| DeepKriging_sphere | 100 | 0.0988 | 0.3143 | 0.2463 | 0.8546 | 36.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1013 | 0.3183 | 0.2494 | 0.851 | 44.41s |\n", - "| UniversalKriging | 100 | 0.0889 | 0.2981 | 0.2272 | 0.8692 | 2.83s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 07:57:24\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1341 (0.0179), Variance: 0.7966, Range: [-3.0314, 3.1388]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0816, sigma²=0.1757, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 6.6891 | 2.5863 | 0.771 | -6.8707 | -2.50s |\n", - "| OLS_sphere | 500 | 0.1214 | 0.3484 | 0.2665 | 0.8572 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5088 | 0.7133 | 0.5091 | 0.4014 | 60.98s |\n", - "| DeepKriging_sphere | 100 | 0.1096 | 0.3311 | 0.257 | 0.871 | 40.80s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6129 | 0.7829 | 0.6218 | 0.2788 | 16.79s |\n", - "| UniversalKriging | 100 | 0.0911 | 0.3018 | 0.2366 | 0.8928 | 5.11s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:00:29\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.4989 (0.0208), Variance: 1.0820, Range: [-3.7355, 2.9445]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0788, sigma²=0.1909, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7045 | 0.8393 | 0.6268 | 0.3529 | 0.44s |\n", - "| OLS_sphere | 500 | 0.1266 | 0.3558 | 0.2826 | 0.8837 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.6822 | 0.8259 | 0.6291 | 0.3733 | 61.95s |\n", - "| DeepKriging_sphere | 100 | 0.1302 | 0.3608 | 0.2815 | 0.8804 | 33.38s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1169 | 0.3418 | 0.2681 | 0.8927 | 32.88s |\n", - "| UniversalKriging | 100 | 0.1145 | 0.3384 | 0.2658 | 0.8948 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:03:45\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.5872 (0.0187), Variance: 0.8739, Range: [-2.4319, 3.3557]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1049, sigma²=0.2160, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6859 | 0.8282 | 0.6405 | 0.1646 | 0.44s |\n", - "| OLS_sphere | 500 | 0.1174 | 0.3426 | 0.2591 | 0.857 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4094 | 0.6399 | 0.4839 | 0.5013 | 66.44s |\n", - "| DeepKriging_sphere | 100 | 0.6514 | 0.8071 | 0.6446 | 0.2066 | 13.28s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6518 | 0.8073 | 0.6406 | 0.2061 | 13.75s |\n", - "| UniversalKriging | 100 | 0.091 | 0.3016 | 0.2296 | 0.8892 | 4.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:06:27\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.3365 (0.0257), Variance: 1.6456, Range: [-3.3617, 3.1033]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1046, sigma²=0.2141, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0988 | 1.0482 | 0.8058 | 0.3085 | 0.45s |\n", - "| OLS_sphere | 500 | 0.1044 | 0.3231 | 0.2547 | 0.9343 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.7868 | 0.887 | 0.6505 | 0.5048 | 67.62s |\n", - "| DeepKriging_sphere | 100 | 0.0923 | 0.3039 | 0.2461 | 0.9419 | 38.82s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0867 | 0.2945 | 0.2335 | 0.9454 | 37.33s |\n", - "| UniversalKriging | 100 | 0.0763 | 0.2762 | 0.2201 | 0.952 | 3.33s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:09:57\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4092 (0.0196), Variance: 0.9634, Range: [-2.3537, 3.0730]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0920, sigma²=0.1941, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.2173 | 1.1033 | 0.691 | -0.196 | 0.40s |\n", - "| OLS_sphere | 500 | 0.1456 | 0.3816 | 0.3079 | 0.8569 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.6287 | 0.7929 | 0.6013 | 0.3823 | 65.89s |\n", - "| DeepKriging_sphere | 100 | 0.1165 | 0.3413 | 0.2735 | 0.8855 | 35.34s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1199 | 0.3463 | 0.2766 | 0.8822 | 36.61s |\n", - "| UniversalKriging | 100 | 0.1135 | 0.3369 | 0.2704 | 0.8885 | 3.22s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:13:25\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0990 (0.0219), Variance: 1.2003, Range: [-3.0204, 3.8827]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0882, sigma²=0.2024, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.027 | 1.0134 | 0.7556 | 0.2238 | 0.47s |\n", - "| OLS_sphere | 500 | 0.1063 | 0.3261 | 0.2604 | 0.9197 | 0.05s |\n", - "| DeepKriging_wendland | -- | 0.6387 | 0.7992 | 0.5883 | 0.5172 | 69.78s |\n", - "| DeepKriging_sphere | 100 | 0.0904 | 0.3006 | 0.2329 | 0.9317 | 35.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0904 | 0.3007 | 0.2302 | 0.9317 | 36.08s |\n", - "| UniversalKriging | 100 | 0.0968 | 0.3111 | 0.2424 | 0.9268 | 4.34s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:16:58\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.0060 (0.0218), Variance: 1.1842, Range: [-3.1664, 2.7881]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0905, sigma²=0.1951, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2255 | 1.107 | 0.7915 | 0.0655 | 0.35s |\n", - "| OLS_sphere | 500 | 0.1234 | 0.3513 | 0.2779 | 0.9059 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.9416 | 0.9704 | 0.7141 | 0.282 | 66.70s |\n", - "| DeepKriging_sphere | 100 | 0.8848 | 0.9406 | 0.7798 | 0.3253 | 14.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.8649 | 0.93 | 0.7678 | 0.3405 | 15.38s |\n", - "| UniversalKriging | 100 | 0.0888 | 0.2981 | 0.2373 | 0.9323 | 3.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:19:47\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1386 (0.0188), Variance: 0.8804, Range: [-2.7205, 2.7198]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0899, sigma²=0.1980, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6176 | 0.7859 | 0.617 | 0.1981 | 0.45s |\n", - "| OLS_sphere | 500 | 0.1067 | 0.3267 | 0.2518 | 0.8614 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3678 | 0.6065 | 0.4592 | 0.5225 | 67.06s |\n", - "| DeepKriging_sphere | 100 | 0.0702 | 0.2649 | 0.2057 | 0.9089 | 35.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0788 | 0.2808 | 0.2208 | 0.8977 | 40.48s |\n", - "| UniversalKriging | 100 | 0.0684 | 0.2615 | 0.2073 | 0.9112 | 3.37s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:23:23\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.4363 (0.0207), Variance: 1.0686, Range: [-2.1785, 4.1579]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0844, sigma²=0.1799, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8317 | 0.912 | 0.6431 | 0.2341 | 0.39s |\n", - "| OLS_sphere | 500 | 0.129 | 0.3592 | 0.2805 | 0.8812 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5611 | 0.7491 | 0.5699 | 0.4832 | 67.25s |\n", - "| DeepKriging_sphere | 100 | 0.1118 | 0.3344 | 0.2575 | 0.897 | 33.34s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1118 | 0.3344 | 0.2635 | 0.897 | 35.49s |\n", - "| UniversalKriging | 100 | 0.0981 | 0.3131 | 0.2511 | 0.9097 | 4.25s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:26:55\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.3354 (0.0182), Variance: 0.8292, Range: [-2.7591, 3.2690]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0839, sigma²=0.1926, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.763 | 0.8735 | 0.5818 | 0.0342 | 0.31s |\n", - "| OLS_sphere | 500 | 0.1359 | 0.3686 | 0.2989 | 0.828 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7.0849 | 2.6617 | 0.6289 | -7.9682 | 65.53s |\n", - "| DeepKriging_sphere | 100 | 0.1054 | 0.3246 | 0.2508 | 0.8666 | 40.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.1118 | 0.3343 | 0.2582 | 0.8585 | 39.37s |\n", - "| UniversalKriging | 100 | 0.0983 | 0.3136 | 0.2485 | 0.8755 | 3.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:30:36\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: 0.1570 (0.0159), Variance: 0.6284, Range: [-1.9354, 3.0160]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1042, sigma²=0.2224, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.4815 | 0.6939 | 0.535 | 0.1667 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1504 | 0.3878 | 0.2896 | 0.7398 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.3564 | 0.597 | 0.4666 | 0.3833 | 66.76s |\n", - "| DeepKriging_sphere | 100 | 0.1046 | 0.3234 | 0.25 | 0.819 | 35.94s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.4365 | 0.6606 | 0.5272 | 0.2447 | 15.05s |\n", - "| UniversalKriging | 100 | 0.097 | 0.3115 | 0.2447 | 0.8321 | 4.21s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:33:50\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.2605 (0.0172), Variance: 0.7392, Range: [-3.3801, 2.4770]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0919, sigma²=0.2010, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.666 | 0.8161 | 0.6231 | 0.1741 | 0.46s |\n", - "| OLS_sphere | 500 | 0.1278 | 0.3575 | 0.2814 | 0.8415 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.4702 | 0.6857 | 0.5358 | 0.4168 | 65.99s |\n", - "| DeepKriging_sphere | 100 | 0.1276 | 0.3572 | 0.28 | 0.8418 | 39.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.6394 | 0.7997 | 0.6438 | 0.207 | 15.34s |\n", - "| UniversalKriging | 100 | 0.1115 | 0.3339 | 0.2617 | 0.8617 | 3.22s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:37:08\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data | z mean: -0.1256 (0.0198), Variance: 0.9850, Range: [-2.8451, 2.8753]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1051, sigma²=0.2218, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8568 | 0.9256 | 0.7118 | 0.1201 | 0.32s |\n", - "| OLS_sphere | 500 | 0.135 | 0.3674 | 0.2819 | 0.8613 | 0.05s |\n", - "| DeepKriging_wendland | -- | 0.5723 | 0.7565 | 0.566 | 0.4122 | 62.47s |\n", - "| DeepKriging_sphere | 100 | 0.1076 | 0.328 | 0.2537 | 0.8895 | 37.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 0.0997 | 0.3157 | 0.2476 | 0.8976 | 46.64s |\n", - "| UniversalKriging | 100 | 0.0971 | 0.3116 | 0.246 | 0.9003 | 3.40s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-21 08:40:53\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.020316, - "end_time": "2026-01-21T00:40:53.995639", - "exception": false, - "start_time": "2026-01-21T00:40:53.975323", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-21T00:40:54.040238Z", - "iopub.status.busy": "2026-01-21T00:40:54.039263Z", - "iopub.status.idle": "2026-01-21T00:40:54.067782Z", - "shell.execute_reply": "2026-01-21T00:40:54.064783Z" - }, - "papermill": { - "duration": 0.053482, - "end_time": "2026-01-21T00:40:54.069553", - "exception": false, - "start_time": "2026-01-21T00:40:54.016071", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 100, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|------------|\n", - "| OLS_wendland | 4.02±8.45 | 1.49±1.34 | 0.70±0.16 | -3.71±9.77 |\n", - "| OLS_sphere | 0.12±0.01 | 0.35±0.02 | 0.28±0.01 | 0.85±0.04 |\n", - "| DeepKriging_wendland | 0.65±0.93 | 0.76±0.29 | 0.53±0.06 | 0.24±1.18 |\n", - "| DeepKriging_sphere | 0.19±0.20 | 0.40±0.18 | 0.32±0.15 | 0.77±0.24 |\n", - "| DeepKriging_sphere_Huber | 0.23±0.22 | 0.43±0.20 | 0.34±0.16 | 0.73±0.26 |\n", - "| UniversalKriging | 0.10±0.01 | 0.31±0.02 | 0.24±0.01 | 0.89±0.03 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 0.31±0.02)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 9584.496661, - "end_time": "2026-01-21T00:40:57.628058", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_without_outliers.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario3/spherical/simulation_scenarioI_spherical_without_outliers.ipynb", - "parameters": {}, - "start_time": "2026-01-20T22:01:13.131397", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_chisquare.ipynb b/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_chisquare.ipynb deleted file mode 100644 index 2939fe7..0000000 --- a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_chisquare.ipynb +++ /dev/null @@ -1,3441 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.014108, - "end_time": "2026-01-19T00:11:55.854651", - "exception": false, - "start_time": "2026-01-19T00:11:55.840543", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:55.869182Z", - "iopub.status.busy": "2026-01-19T00:11:55.868268Z", - "iopub.status.idle": "2026-01-19T00:11:55.890866Z", - "shell.execute_reply": "2026-01-19T00:11:55.887548Z" - }, - "papermill": { - "duration": 0.034152, - "end_time": "2026-01-19T00:11:55.894462", - "exception": false, - "start_time": "2026-01-19T00:11:55.860310", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:55.922813Z", - "iopub.status.busy": "2026-01-19T00:11:55.922102Z", - "iopub.status.idle": "2026-01-19T00:11:58.482567Z", - "shell.execute_reply": "2026-01-19T00:11:58.480690Z" - }, - "papermill": { - "duration": 2.57728, - "end_time": "2026-01-19T00:11:58.484135", - "exception": false, - "start_time": "2026-01-19T00:11:55.906855", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-28 10:37:22.950968: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769567842.966458 2757110 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769567842.970986 2757110 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769567842.983125 2757110 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769567842.983143 2757110 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769567842.983144 2757110 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769567842.983145 2757110 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.003819, - "end_time": "2026-01-19T00:11:58.492774", - "exception": false, - "start_time": "2026-01-19T00:11:58.488955", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:58.501873Z", - "iopub.status.busy": "2026-01-19T00:11:58.501422Z", - "iopub.status.idle": "2026-01-19T00:11:59.891497Z", - "shell.execute_reply": "2026-01-19T00:11:59.887874Z" - }, - "papermill": { - "duration": 1.397966, - "end_time": "2026-01-19T00:11:59.894394", - "exception": false, - "start_time": "2026-01-19T00:11:58.496428", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.003854, - "end_time": "2026-01-19T00:11:59.904396", - "exception": false, - "start_time": "2026-01-19T00:11:59.900542", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:59.912546Z", - "iopub.status.busy": "2026-01-19T00:11:59.911730Z", - "iopub.status.idle": "2026-01-19T00:11:59.923239Z", - "shell.execute_reply": "2026-01-19T00:11:59.920067Z" - }, - "papermill": { - "duration": 0.01905, - "end_time": "2026-01-19T00:11:59.925757", - "exception": false, - "start_time": "2026-01-19T00:11:59.906707", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.002352, - "end_time": "2026-01-19T00:11:59.934634", - "exception": false, - "start_time": "2026-01-19T00:11:59.932282", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:59.942334Z", - "iopub.status.busy": "2026-01-19T00:11:59.941736Z", - "iopub.status.idle": "2026-01-19T00:12:13.618029Z", - "shell.execute_reply": "2026-01-19T00:12:13.615112Z" - }, - "papermill": { - "duration": 13.683423, - "end_time": "2026-01-19T00:12:13.620367", - "exception": false, - "start_time": "2026-01-19T00:11:59.936944", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.002356, - "end_time": "2026-01-19T00:12:13.695036", - "exception": false, - "start_time": "2026-01-19T00:12:13.692680", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.703949Z", - "iopub.status.busy": "2026-01-19T00:12:13.702764Z", - "iopub.status.idle": "2026-01-19T00:12:13.717203Z", - "shell.execute_reply": "2026-01-19T00:12:13.714858Z" - }, - "papermill": { - "duration": 0.023861, - "end_time": "2026-01-19T00:12:13.721124", - "exception": false, - "start_time": "2026-01-19T00:12:13.697263", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + chi-square noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term\n", - " # f(x₁, x₂) = -(x₂ + 47) sin(√|x₂ + x₁/2 + 47|) - x₁ sin(√|x₁ - (x₂ + 47)|)\n", - " term1 = -(x2 + 47.0) * np.sin(np.sqrt(np.abs(x2 + x1/2.0 + 47.0)))\n", - " term2 = -x1 * np.sin(np.sqrt(np.abs(x1 - (x2 + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " z = y + rng.chisquare(df=4, size=num_sample).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.002274, - "end_time": "2026-01-19T00:12:13.729500", - "exception": false, - "start_time": "2026-01-19T00:12:13.727226", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.737787Z", - "iopub.status.busy": "2026-01-19T00:12:13.737067Z", - "iopub.status.idle": "2026-01-19T00:12:13.773749Z", - "shell.execute_reply": "2026-01-19T00:12:13.770615Z" - }, - "papermill": { - "duration": 0.044731, - "end_time": "2026-01-19T00:12:13.776441", - "exception": false, - "start_time": "2026-01-19T00:12:13.731710", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.788736Z", - "iopub.status.busy": "2026-01-19T00:12:13.788200Z", - "iopub.status.idle": "2026-01-19T00:12:13.828850Z", - "shell.execute_reply": "2026-01-19T00:12:13.826481Z" - }, - "papermill": { - "duration": 0.048422, - "end_time": "2026-01-19T00:12:13.831488", - "exception": false, - "start_time": "2026-01-19T00:12:13.783066", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder + noise (chi-square)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder + noise (chi-square)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.006715, - "end_time": "2026-01-19T00:12:13.847493", - "exception": false, - "start_time": "2026-01-19T00:12:13.840778", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.855907Z", - "iopub.status.busy": "2026-01-19T00:12:13.855435Z", - "iopub.status.idle": "2026-01-19T00:12:13.864819Z", - "shell.execute_reply": "2026-01-19T00:12:13.862713Z" - }, - "papermill": { - "duration": 0.016236, - "end_time": "2026-01-19T00:12:13.867130", - "exception": false, - "start_time": "2026-01-19T00:12:13.850894", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.879342Z", - "iopub.status.busy": "2026-01-19T00:12:13.879022Z", - "iopub.status.idle": "2026-01-19T03:16:43.773840Z", - "shell.execute_reply": "2026-01-19T03:16:43.771079Z" - }, - "papermill": { - "duration": 11069.902238, - "end_time": "2026-01-19T03:16:43.776690", - "exception": false, - "start_time": "2026-01-19T00:12:13.874452", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7665 (±0.0184), Variance: 0.8430, Range: [-29.8612, -25.2544]\n", - "z mean: -23.7825 (±0.0596), Variance: 8.8921, Range: [-29.4484, -4.2277]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 16.7137 15.9437 *\n", - " 100 16.9467 16.7809 \n", - " 200 17.3366 16.8673 \n", - " 500 18.6971 19.4444 \n", - " 700 19.9461 22.7471 \n", - " 1000 17.1343 16.7095 \n", - " 1400 17.1343 16.7095 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769567878.627472 2757110 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769567880.954233 2757384 service.cc:152] XLA service 0x601b17bdd230 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769567880.954329 2757384 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1769567881.273482 2757384 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1769567890.898696 2757384 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d57477175b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d574726f250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 10.4125 22.3089 \n", - " 100 10.5041 20.1627 \n", - " 200 9.8561 23.7512 \n", - " 500 10.4340 20.9374 \n", - " 700 10.2299 22.4784 \n", - " 1000 7.6544 17.2275 \n", - " 1400 7.4591 17.3317 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2.4416 21.1331 \n", - " 100 2.3431 18.7015 \n", - " 200 2.3582 19.1241 \n", - " 500 2.5021 19.7275 \n", - " 700 2.3831 21.3450 \n", - " 1000 2.0284 11.5391 \n", - " 1400 2.0070 13.2290 *\n", - " Best order: 1400\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0026, nugget=8.0425\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=7.7729\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0003, nugget=7.4002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0024, sigma²=0.0000, nugget=6.0585\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0018, sigma²=0.0000, nugget=5.3148\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0021, sigma²=0.0000, nugget=4.1081\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0018, sigma²=0.0000, nugget=2.6657\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.2269 15.9436 *\n", - " 100 7.4575 16.7808 \n", - " 200 7.7403 16.8673 \n", - " 500 9.0399 19.4445 \n", - " 700 10.5572 22.7471 \n", - " 1000 20.9752 31.3655 \n", - " 1400 377.4365 205.6988 \n", - " Best order: 50\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0026, nugget=8.0425\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.3294 | 4.5088 | 4.0507 | -25.2778 | 0.45s |\n", - "| OLS_sphere | 50 | 15.9437 | 3.993 | 3.9514 | -19.6089 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.1108 | 4.2557 | 3.8669 | -22.41 | 48.41s |\n", - "| DeepKriging_sphere | 1400 | 16.8469 | 4.1045 | 3.978 | -20.7763 | 48.62s |\n", - "| DeepKriging_sphere_Huber | 1400 | 14.335 | 3.7862 | 3.6629 | -17.5294 | 53.13s |\n", - "| UniversalKriging | 50 | 15.9436 | 3.9929 | 3.9514 | -19.6087 | 2.16s |\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:51:18\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7225 (±0.0311), Variance: 2.4122, Range: [-31.4270, -24.4243]\n", - "z mean: -23.6574 (±0.0647), Variance: 10.4603, Range: [-30.9693, -3.9587]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.4652, nugget=7.5337\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.0431 | 4.4769 | 4.1485 | -7.8573 | 0.42s |\n", - "| OLS_sphere | 50 | 16.7203 | 4.089 | 4.0497 | -6.3889 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.7133 | 4.5512 | 4.1691 | -8.1535 | 56.21s |\n", - "| DeepKriging_sphere | 1400 | 21.2472 | 4.6095 | 4.3203 | -8.3894 | 48.67s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.599 | 4.0742 | 3.7331 | -6.3353 | 57.80s |\n", - "| UniversalKriging | 50 | 16.7205 | 4.0891 | 4.0498 | -6.3891 | 2.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:54:34\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.2347 (±0.0292), Variance: 2.1348, Range: [-31.7127, -25.1404]\n", - "z mean: -24.1594 (±0.0624), Variance: 9.7248, Range: [-30.9787, -7.7253]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0036, nugget=7.3195\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 19.0179 | 4.361 | 4.1551 | -7.368 | 0.45s |\n", - "| OLS_sphere | 50 | 16.9883 | 4.1217 | 4.0871 | -6.4749 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.4929 | 4.4151 | 4.1168 | -7.577 | 40.38s |\n", - "| DeepKriging_sphere | 1400 | 20.1287 | 4.4865 | 4.2006 | -7.8567 | 36.72s |\n", - "| DeepKriging_sphere_Huber | 1400 | 20.7179 | 4.5517 | 4.2654 | -8.116 | 25.77s |\n", - "| UniversalKriging | 50 | 16.9883 | 4.1217 | 4.0871 | -6.4749 | 1.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:56:52\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1546 (±0.0201), Variance: 1.0080, Range: [-30.9142, -25.1867]\n", - "z mean: -24.0981 (±0.0599), Variance: 8.9650, Range: [-30.4882, -5.9270]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0164, nugget=7.6019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 30.3026 | 5.5048 | 4.3159 | -27.1933 | 0.45s |\n", - "| OLS_sphere | 50 | 17.0202 | 4.1256 | 4.0854 | -14.8354 | 0.00s |\n", - "| DeepKriging_wendland | -- | 23.7721 | 4.8757 | 4.4911 | -21.1174 | 58.29s |\n", - "| DeepKriging_sphere | 1400 | 18.6878 | 4.3229 | 4.1415 | -16.387 | 40.31s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.562 | 3.9449 | 3.7803 | -13.4787 | 53.29s |\n", - "| UniversalKriging | 50 | 17.0214 | 4.1257 | 4.0856 | -14.8365 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 10:59:58\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.6378 (±0.0267), Variance: 1.7824, Range: [-31.6483, -24.9221]\n", - "z mean: -23.6352 (±0.0609), Variance: 9.2666, Range: [-30.5065, -6.8888]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=7.1150, nugget=0.6065\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 84.3636 | 9.185 | 4.5702 | -40.4212 | 0.48s |\n", - "| OLS_sphere | 50 | 16.5018 | 4.0622 | 4.0282 | -7.1021 | 0.00s |\n", - "| DeepKriging_wendland | -- | 16.8623 | 4.1064 | 3.8266 | -7.2791 | 52.04s |\n", - "| DeepKriging_sphere | 1400 | 22.7119 | 4.7657 | 4.5373 | -10.1512 | 33.56s |\n", - "| DeepKriging_sphere_Huber | 1400 | 18.3393 | 4.2824 | 4.0378 | -8.0043 | 34.93s |\n", - "| UniversalKriging | 50 | 16.4766 | 4.0591 | 4.0243 | -7.0898 | 4.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:02:38\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1310 (±0.0142), Variance: 0.5051, Range: [-30.4014, -25.8921]\n", - "z mean: -24.1107 (±0.0594), Variance: 8.8109, Range: [-29.6464, 4.8574]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0060, nugget=7.9815\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 22.075 | 4.6984 | 4.2571 | -40.9908 | 0.44s |\n", - "| OLS_sphere | 50 | 16.2255 | 4.0281 | 3.9876 | -29.8639 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.046 | 4.2481 | 4.0008 | -33.3268 | 46.53s |\n", - "| DeepKriging_sphere | 1400 | 20.2024 | 4.4947 | 4.4039 | -37.4287 | 37.34s |\n", - "| DeepKriging_sphere_Huber | 1400 | 13.3783 | 3.6576 | 3.5069 | -24.448 | 38.11s |\n", - "| UniversalKriging | 50 | 16.2255 | 4.0281 | 3.9876 | -29.8638 | 1.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:05:17\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.4444 (±0.0270), Variance: 1.8227, Range: [-31.2284, -24.5590]\n", - "z mean: -23.3671 (±0.0641), Variance: 10.2693, Range: [-30.2599, -3.8257]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0029, nugget=7.9719\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.425 | 5.0423 | 4.2724 | -12.5732 | 0.39s |\n", - "| OLS_sphere | 50 | 16.8102 | 4.1 | 4.0663 | -7.9742 | 0.00s |\n", - "| DeepKriging_wendland | -- | 29.1242 | 5.3967 | 5.0056 | -14.5481 | 52.88s |\n", - "| DeepKriging_sphere | 1400 | 19.4412 | 4.4092 | 4.1587 | -9.3787 | 32.77s |\n", - "| DeepKriging_sphere_Huber | 1400 | 17.0802 | 4.1328 | 3.8649 | -8.1183 | 35.07s |\n", - "| UniversalKriging | 50 | 16.8103 | 4.1 | 4.0663 | -7.9742 | 2.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:07:56\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.8819 (±0.0299), Variance: 2.2414, Range: [-31.3263, -24.4321]\n", - "z mean: -23.8152 (±0.0646), Variance: 10.4368, Range: [-30.3150, -3.9732]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0041, nugget=8.2053\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 31.1918 | 5.585 | 4.3663 | -11.5868 | 0.39s |\n", - "| OLS_sphere | 50 | 17.1909 | 4.1462 | 4.0971 | -5.937 | 0.00s |\n", - "| DeepKriging_wendland | -- | 15.7821 | 3.9727 | 3.5622 | -5.3685 | 50.75s |\n", - "| DeepKriging_sphere | 1400 | 19.3719 | 4.4014 | 4.0842 | -6.8171 | 41.06s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.8249 | 3.9781 | 3.6343 | -5.3858 | 42.37s |\n", - "| UniversalKriging | 50 | 17.1908 | 4.1462 | 4.0971 | -5.937 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:10:50\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7679 (±0.0301), Variance: 2.2707, Range: [-31.0653, -24.1754]\n", - "z mean: -23.8704 (±0.0635), Variance: 10.0853, Range: [-29.9695, -7.8567]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0019, nugget=7.7857\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 18.2411 | 4.271 | 3.8475 | -6.8711 | 0.42s |\n", - "| OLS_sphere | 50 | 15.2597 | 3.9064 | 3.8626 | -5.5846 | 0.00s |\n", - "| DeepKriging_wendland | -- | 21.9509 | 4.6852 | 4.3846 | -8.4719 | 56.90s |\n", - "| DeepKriging_sphere | 1400 | 18.2688 | 4.2742 | 3.9687 | -6.883 | 44.74s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.6349 | 3.9541 | 3.5935 | -5.7465 | 49.82s |\n", - "| UniversalKriging | 50 | 15.2596 | 3.9064 | 3.8626 | -5.5845 | 2.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:14:02\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.4293 (±0.0331), Variance: 2.7355, Range: [-32.1898, -23.5979]\n", - "z mean: -23.4196 (±0.0655), Variance: 10.7101, Range: [-31.1027, -5.9901]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0203, nugget=7.8995\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 44.0011 | 6.6333 | 4.3717 | -14.124 | 0.39s |\n", - "| OLS_sphere | 50 | 16.0645 | 4.0081 | 3.984 | -4.5217 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.8473 | 4.455 | 4.2119 | -5.8219 | 52.71s |\n", - "| DeepKriging_sphere | 1400 | 19.6177 | 4.4292 | 4.1042 | -5.743 | 33.40s |\n", - "| DeepKriging_sphere_Huber | 1400 | 19.8851 | 4.4593 | 4.124 | -5.8349 | 37.04s |\n", - "| UniversalKriging | 50 | 16.0643 | 4.008 | 3.984 | -4.5216 | 1.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:16:47\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.6794 (±0.0208), Variance: 1.0843, Range: [-30.7108, -24.7975]\n", - "z mean: -23.6741 (±0.0618), Variance: 9.5331, Range: [-29.9172, -5.3814]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0086, nugget=8.7562\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.2153 | 4.3835 | 4.1708 | -18.1369 | 0.44s |\n", - "| OLS_sphere | 50 | 16.7621 | 4.0942 | 4.0658 | -15.6937 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.9793 | 4.5803 | 4.2626 | -19.8937 | 62.48s |\n", - "| DeepKriging_sphere | 1400 | 20.4102 | 4.5178 | 4.372 | -19.3269 | 43.47s |\n", - "| DeepKriging_sphere_Huber | 1400 | 20.386 | 4.5151 | 4.3671 | -19.3028 | 34.44s |\n", - "| UniversalKriging | 50 | 16.7618 | 4.0941 | 4.0658 | -15.6934 | 1.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:19:51\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.5496 (±0.0320), Variance: 2.5541, Range: [-31.1112, -24.2320]\n", - "z mean: -23.5481 (±0.0640), Variance: 10.2272, Range: [-30.0571, -8.1325]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.1138, nugget=7.6170\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 19.8134 | 4.4512 | 4.131 | -6.627 | 0.42s |\n", - "| OLS_sphere | 50 | 15.9843 | 3.998 | 3.9618 | -5.153 | 0.01s |\n", - "| DeepKriging_wendland | -- | 19.788 | 4.4484 | 4.0338 | -6.6172 | 57.23s |\n", - "| DeepKriging_sphere | 1400 | 19.3986 | 4.4044 | 4.0449 | -6.4673 | 58.81s |\n", - "| DeepKriging_sphere_Huber | 1400 | 19.6447 | 4.4322 | 4.0899 | -6.562 | 36.14s |\n", - "| UniversalKriging | 50 | 15.9844 | 3.9981 | 3.9619 | -5.1531 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:23:07\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1985 (±0.0299), Variance: 2.2288, Range: [-32.2729, -24.4764]\n", - "z mean: -24.2688 (±0.0635), Variance: 10.0669, Range: [-31.7413, -6.6984]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0025, nugget=7.5740\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 34.5344 | 5.8766 | 4.2123 | -14.6274 | 0.48s |\n", - "| OLS_sphere | 50 | 15.4614 | 3.9321 | 3.8948 | -5.9965 | 0.00s |\n", - "| DeepKriging_wendland | -- | 17.5482 | 4.1891 | 3.8438 | -6.9408 | 52.30s |\n", - "| DeepKriging_sphere | 1400 | 21.3073 | 4.616 | 4.3637 | -8.6419 | 31.81s |\n", - "| DeepKriging_sphere_Huber | 1400 | 19.0176 | 4.3609 | 4.0878 | -7.6058 | 34.95s |\n", - "| UniversalKriging | 50 | 15.4615 | 3.9321 | 3.8948 | -5.9966 | 2.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:25:52\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -29.4315 (±0.0202), Variance: 1.0206, Range: [-32.3467, -25.5211]\n", - "z mean: -25.3903 (±0.0609), Variance: 9.2816, Range: [-31.8787, -7.4623]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0052, nugget=7.8673\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.4553 | 4.178 | 4.0142 | -15.2832 | -0.22s |\n", - "| OLS_sphere | 50 | 16.4642 | 4.0576 | 4.0112 | -14.3587 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.6521 | 4.3188 | 4.0673 | -16.3997 | 53.77s |\n", - "| DeepKriging_sphere | 1400 | 18.7756 | 4.3331 | 4.1956 | -16.5149 | 47.37s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.3996 | 4.0496 | 3.8917 | -14.2985 | 42.32s |\n", - "| UniversalKriging | 50 | 16.4644 | 4.0576 | 4.0112 | -14.3589 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:29:01\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -29.3324 (±0.0381), Variance: 3.6258, Range: [-32.9755, -25.2573]\n", - "z mean: -25.3225 (±0.0659), Variance: 10.8681, Range: [-32.2654, -8.8911]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0070, nugget=7.3284\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 19.4767 | 4.4132 | 4.1172 | -5.2078 | -0.21s |\n", - "| OLS_sphere | 50 | 16.735 | 4.0908 | 4.057 | -4.3339 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.8486 | 4.566 | 4.2171 | -5.645 | 48.75s |\n", - "| DeepKriging_sphere | 1400 | 22.4947 | 4.7429 | 4.3714 | -6.1697 | 25.06s |\n", - "| DeepKriging_sphere_Huber | 1400 | 18.7984 | 4.3357 | 3.907 | -4.9916 | 32.39s |\n", - "| UniversalKriging | 50 | 16.7347 | 4.0908 | 4.057 | -4.3338 | 0.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:31:34\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.2376 (±0.0174), Variance: 0.7544, Range: [-30.3088, -25.6188]\n", - "z mean: -24.1747 (±0.0611), Variance: 9.3280, Range: [-29.7993, -8.3809]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0077, nugget=8.5733\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|-----------|--------|\n", - "| OLS_wendland | -- | 70.7912 | 8.4138 | 5.0241 | -101.048 | 0.40s |\n", - "| OLS_sphere | 50 | 18.1372 | 4.2588 | 4.2137 | -25.1455 | 0.00s |\n", - "| DeepKriging_wendland | -- | 22.5503 | 4.7487 | 4.4462 | -31.5071 | 41.04s |\n", - "| DeepKriging_sphere | 1400 | 19.2949 | 4.3926 | 4.2936 | -26.8144 | 30.48s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.9224 | 3.9903 | 3.8898 | -21.9528 | 38.04s |\n", - "| UniversalKriging | 50 | 18.1365 | 4.2587 | 4.2136 | -25.1445 | 1.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:34:13\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7216 (±0.0306), Variance: 2.3461, Range: [-32.7967, -24.8313]\n", - "z mean: -23.7361 (±0.0649), Variance: 10.5363, Range: [-32.0530, -3.6302]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0030, nugget=8.0590\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.6729 | 4.5467 | 4.2665 | -6.986 | 0.40s |\n", - "| OLS_sphere | 50 | 16.273 | 4.034 | 4.0021 | -5.2863 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.2292 | 4.3851 | 4.0503 | -6.4283 | 48.46s |\n", - "| DeepKriging_sphere | 1400 | 20.2833 | 4.5037 | 4.1948 | -6.8355 | 39.56s |\n", - "| DeepKriging_sphere_Huber | 1400 | 18.108 | 4.2553 | 3.9386 | -5.9952 | 36.11s |\n", - "| UniversalKriging | 50 | 16.2729 | 4.034 | 4.0021 | -5.2863 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:37:08\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.6570 (±0.0198), Variance: 0.9813, Range: [-31.1947, -24.7329]\n", - "z mean: -24.6421 (±0.0604), Variance: 9.1118, Range: [-30.5799, -8.1595]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0027, nugget=7.7546\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.1576 | 4.1422 | 4.0051 | -17.4164 | 0.40s |\n", - "| OLS_sphere | 50 | 16.2468 | 4.0307 | 3.9849 | -16.4388 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.4738 | 4.5248 | 4.2801 | -20.976 | 56.24s |\n", - "| DeepKriging_sphere | 1400 | 19.1975 | 4.3815 | 4.2507 | -19.606 | 50.91s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.2306 | 3.9026 | 3.7643 | -15.3481 | 59.66s |\n", - "| UniversalKriging | 50 | 16.2467 | 4.0307 | 3.9849 | -16.4387 | 2.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:40:47\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1476 (±0.0269), Variance: 1.8042, Range: [-32.2416, -24.9143]\n", - "z mean: -24.1534 (±0.0617), Variance: 9.5202, Range: [-31.7252, -6.7884]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0141, nugget=7.6539\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.7104 | 4.3255 | 3.9686 | -9.3609 | 0.40s |\n", - "| OLS_sphere | 50 | 16.2971 | 4.037 | 4.0007 | -8.0246 | 0.00s |\n", - "| DeepKriging_wendland | -- | 23.179 | 4.8145 | 4.3385 | -11.8355 | 61.15s |\n", - "| DeepKriging_sphere | 1400 | 18.692 | 4.3234 | 4.09 | -9.3508 | 48.76s |\n", - "| DeepKriging_sphere_Huber | 1400 | 14.1286 | 3.7588 | 3.4089 | -6.8238 | 41.04s |\n", - "| UniversalKriging | 50 | 16.2978 | 4.0371 | 4.0008 | -8.025 | 2.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:44:09\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.2330 (±0.0231), Variance: 1.3304, Range: [-31.6667, -25.5212]\n", - "z mean: -24.2546 (±0.0618), Variance: 9.5336, Range: [-31.2726, -7.2720]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0072, nugget=7.6859\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 29.8678 | 5.4651 | 4.3218 | -22.1283 | 0.38s |\n", - "| OLS_sphere | 50 | 15.9577 | 3.9947 | 3.9619 | -11.3569 | 0.00s |\n", - "| DeepKriging_wendland | -- | 23.5543 | 4.8533 | 4.3635 | -17.2394 | 68.85s |\n", - "| DeepKriging_sphere | 1400 | 20.8624 | 4.5675 | 4.4089 | -15.1549 | 43.66s |\n", - "| DeepKriging_sphere_Huber | 1400 | 13.0766 | 3.6162 | 3.4176 | -9.1259 | 44.89s |\n", - "| UniversalKriging | 50 | 15.9578 | 3.9947 | 3.9619 | -11.357 | 1.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:47:42\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7941 (±0.0173), Variance: 0.7521, Range: [-30.8333, -25.5507]\n", - "z mean: -23.8098 (±0.0570), Variance: 8.1273, Range: [-29.5657, -7.8716]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0047, nugget=7.3827\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 25.2536 | 5.0253 | 4.1918 | -32.7311 | 0.44s |\n", - "| OLS_sphere | 50 | 16.1383 | 4.0172 | 3.9829 | -20.5559 | 0.00s |\n", - "| DeepKriging_wendland | -- | 16.7463 | 4.0922 | 3.798 | -21.368 | 72.96s |\n", - "| DeepKriging_sphere | 1400 | 18.6771 | 4.3217 | 4.2131 | -23.9469 | 60.08s |\n", - "| DeepKriging_sphere_Huber | 1400 | 14.7972 | 3.8467 | 3.7423 | -18.7646 | 55.76s |\n", - "| UniversalKriging | 50 | 16.1381 | 4.0172 | 3.9828 | -20.5556 | 2.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:51:44\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.0475 (±0.0273), Variance: 1.8633, Range: [-31.0573, -24.6796]\n", - "z mean: -24.0928 (±0.0623), Variance: 9.7081, Range: [-30.3998, -5.6592]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=7.9918\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 20.6988 | 4.5496 | 4.1193 | -10.5539 | 0.36s |\n", - "| OLS_sphere | 50 | 16.3367 | 4.0419 | 4.0087 | -8.119 | 0.00s |\n", - "| DeepKriging_wendland | -- | 16.476 | 4.0591 | 3.6565 | -8.1967 | 75.10s |\n", - "| DeepKriging_sphere | 1400 | 20.3028 | 4.5059 | 4.2609 | -10.3328 | 48.38s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.7038 | 4.087 | 3.8324 | -8.3239 | 50.03s |\n", - "| UniversalKriging | 50 | 16.337 | 4.0419 | 4.0087 | -8.1192 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:55:34\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1562 (±0.0192), Variance: 0.9210, Range: [-30.9961, -26.0847]\n", - "z mean: -24.2408 (±0.0587), Variance: 8.6115, Range: [-30.2062, -8.6850]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0078, nugget=7.6258\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.3044 | 4.2784 | 4.0686 | -19.7839 | 0.43s |\n", - "| OLS_sphere | 50 | 16.5742 | 4.0711 | 4.0319 | -17.8193 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.971 | 4.3556 | 4.1114 | -20.5408 | 56.75s |\n", - "| DeepKriging_sphere | 1400 | 19.462 | 4.4116 | 4.2935 | -21.0983 | 41.82s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.7722 | 3.9714 | 3.8321 | -16.9087 | 40.20s |\n", - "| UniversalKriging | 50 | 16.5741 | 4.0711 | 4.032 | -17.8193 | 1.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 11:58:50\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7689 (±0.0345), Variance: 2.9821, Range: [-33.0220, -23.7218]\n", - "z mean: -23.8297 (±0.0659), Variance: 10.8623, Range: [-32.0645, -6.7741]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0032, nugget=7.8047\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 19.4392 | 4.409 | 4.103 | -5.5972 | 0.44s |\n", - "| OLS_sphere | 50 | 16.1337 | 4.0167 | 3.9765 | -4.4754 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.3435 | 4.5104 | 4.1624 | -5.9041 | 54.97s |\n", - "| DeepKriging_sphere | 1400 | 21.3472 | 4.6203 | 4.2843 | -6.2447 | 42.33s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.839 | 4.1035 | 3.7331 | -4.7147 | 53.16s |\n", - "| UniversalKriging | 50 | 16.1336 | 4.0167 | 3.9765 | -4.4753 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:02:20\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.9938 (±0.0260), Variance: 1.6857, Range: [-32.2604, -25.8464]\n", - "z mean: -24.9705 (±0.0625), Variance: 9.7645, Range: [-31.4786, -5.5300]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=4.7219, nugget=3.1009\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 22.1551 | 4.7069 | 4.238 | -11.5963 | 0.44s |\n", - "| OLS_sphere | 50 | 16.5612 | 4.0695 | 4.0393 | -8.4159 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.2709 | 4.5023 | 4.1086 | -10.525 | 62.67s |\n", - "| DeepKriging_sphere | 1400 | 19.498 | 4.4157 | 4.1964 | -10.0856 | 48.39s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.6901 | 3.9611 | 3.696 | -7.9206 | 52.68s |\n", - "| UniversalKriging | 50 | 16.6186 | 4.0766 | 4.0425 | -8.4485 | 3.66s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:06:07\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.2607 (±0.0275), Variance: 1.8866, Range: [-31.6823, -24.9549]\n", - "z mean: -24.3402 (±0.0618), Variance: 9.5514, Range: [-30.9230, -8.8570]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0022, nugget=7.6891\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.5993 | 5.2535 | 4.2334 | -14.029 | 0.43s |\n", - "| OLS_sphere | 50 | 15.5755 | 3.9466 | 3.906 | -7.4815 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.1329 | 4.2583 | 3.8265 | -8.8742 | 56.57s |\n", - "| DeepKriging_sphere | 1400 | 21.6669 | 4.6548 | 4.3147 | -10.7986 | 19.20s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.595 | 4.0737 | 3.8098 | -8.0367 | 31.46s |\n", - "| UniversalKriging | 50 | 15.5755 | 3.9466 | 3.906 | -7.4815 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:08:55\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -26.6711 (±0.0209), Variance: 1.0901, Range: [-30.3700, -24.4087]\n", - "z mean: -22.7377 (±0.0600), Variance: 9.0025, Range: [-30.0286, -2.1986]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=2.2550, nugget=5.1404\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.3685 | 5.2315 | 4.1586 | -21.519 | 0.40s |\n", - "| OLS_sphere | 50 | 15.3713 | 3.9206 | 3.88 | -11.6476 | 0.01s |\n", - "| DeepKriging_wendland | -- | 19.9962 | 4.4717 | 4.1599 | -15.4531 | 55.66s |\n", - "| DeepKriging_sphere | 1400 | 19.8425 | 4.4545 | 4.3098 | -15.3265 | 47.73s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.1174 | 3.8881 | 3.7219 | -11.4388 | 45.81s |\n", - "| UniversalKriging | 50 | 15.3582 | 3.919 | 3.8769 | -11.6369 | 3.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:12:30\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.9744 (±0.0253), Variance: 1.5987, Range: [-31.5052, -24.5118]\n", - "z mean: -23.9999 (±0.0612), Variance: 9.3768, Range: [-30.2346, -10.7479]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0033, nugget=7.6947\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 17.8582 | 4.2259 | 4.0907 | -11.5214 | 0.43s |\n", - "| OLS_sphere | 50 | 16.0332 | 4.0042 | 3.9745 | -10.2418 | 0.00s |\n", - "| DeepKriging_wendland | -- | 17.7252 | 4.2101 | 4.0183 | -11.4281 | 53.25s |\n", - "| DeepKriging_sphere | 1400 | 21.8922 | 4.6789 | 4.5038 | -14.3499 | 34.35s |\n", - "| DeepKriging_sphere_Huber | 1400 | 17.0658 | 4.1311 | 3.9189 | -10.9658 | 39.13s |\n", - "| UniversalKriging | 50 | 16.0333 | 4.0042 | 3.9745 | -10.2418 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:15:40\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7327 (±0.0300), Variance: 2.2553, Range: [-31.5077, -23.1913]\n", - "z mean: -23.7710 (±0.0636), Variance: 10.1182, Range: [-30.4208, 0.4661]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0037, nugget=7.8739\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 27.53 | 5.2469 | 4.2637 | -12.4478 | 0.41s |\n", - "| OLS_sphere | 50 | 16.0678 | 4.0085 | 3.9732 | -6.8488 | 0.00s |\n", - "| DeepKriging_wendland | -- | 25.8632 | 5.0856 | 4.7597 | -11.6337 | 56.82s |\n", - "| DeepKriging_sphere | 1400 | 21.5897 | 4.6465 | 4.4023 | -9.5462 | 34.58s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.377 | 3.9214 | 3.6527 | -6.5114 | 39.95s |\n", - "| UniversalKriging | 50 | 16.0678 | 4.0085 | 3.9732 | -6.8488 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:18:56\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -26.6973 (±0.0338), Variance: 2.8595, Range: [-30.7029, -22.4124]\n", - "z mean: -22.7678 (±0.0655), Variance: 10.7243, Range: [-29.7853, -6.5587]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=7.7978\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 33.0754 | 5.7511 | 4.3006 | -12.287 | 0.46s |\n", - "| OLS_sphere | 50 | 15.4182 | 3.9266 | 3.8944 | -5.1938 | 0.00s |\n", - "| DeepKriging_wendland | -- | 31.8167 | 5.6406 | 5.4224 | -11.7813 | 64.24s |\n", - "| DeepKriging_sphere | 1400 | 21.0422 | 4.5872 | 4.3053 | -7.453 | 34.58s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.7582 | 4.0937 | 3.7684 | -5.7321 | 40.48s |\n", - "| UniversalKriging | 50 | 15.418 | 3.9266 | 3.8944 | -5.1937 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:22:21\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.9496 (±0.0249), Variance: 1.5466, Range: [-30.8551, -24.5076]\n", - "z mean: -24.0068 (±0.0616), Variance: 9.4924, Range: [-30.4791, -6.0493]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0117, nugget=7.7564\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 23.6292 | 4.861 | 4.119 | -15.4049 | 0.41s |\n", - "| OLS_sphere | 50 | 16.3103 | 4.0386 | 4.0015 | -10.3236 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.5287 | 4.5309 | 4.1919 | -13.2523 | 62.50s |\n", - "| DeepKriging_sphere | 1400 | 18.1127 | 4.2559 | 4.0551 | -11.575 | 48.64s |\n", - "| DeepKriging_sphere_Huber | 1400 | 13.4522 | 3.6677 | 3.4383 | -8.3394 | 60.74s |\n", - "| UniversalKriging | 50 | 16.3092 | 4.0385 | 4.0013 | -10.3229 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:26:20\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -29.0577 (±0.0288), Variance: 2.0742, Range: [-31.7408, -24.7086]\n", - "z mean: -25.0269 (±0.0631), Variance: 9.9409, Range: [-30.6072, -5.5326]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0138, nugget=7.9492\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 18.9934 | 4.3581 | 4.0127 | -7.6521 | 0.41s |\n", - "| OLS_sphere | 50 | 16.8009 | 4.0989 | 4.0648 | -6.6533 | 0.00s |\n", - "| DeepKriging_wendland | -- | 22.4583 | 4.739 | 4.4246 | -9.2304 | 71.57s |\n", - "| DeepKriging_sphere | 1400 | 18.7229 | 4.327 | 4.0886 | -7.5288 | 56.66s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.7806 | 3.9725 | 3.7239 | -6.1886 | 42.14s |\n", - "| UniversalKriging | 50 | 16.801 | 4.0989 | 4.0648 | -6.6534 | 1.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:30:18\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.1290 (±0.0234), Variance: 1.3666, Range: [-29.9659, -24.5598]\n", - "z mean: -23.0670 (±0.0646), Variance: 10.4221, Range: [-29.1460, -5.0661]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0060, nugget=8.8806\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.9253 | 4.3503 | 4.1346 | -14.412 | 0.37s |\n", - "| OLS_sphere | 50 | 17.0073 | 4.124 | 4.092 | -12.8501 | 0.00s |\n", - "| DeepKriging_wendland | -- | 16.2401 | 4.0299 | 3.7022 | -12.2253 | 75.87s |\n", - "| DeepKriging_sphere | 1400 | 20.9005 | 4.5717 | 4.4072 | -16.0205 | 46.98s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.8509 | 3.9813 | 3.8274 | -11.9083 | 41.61s |\n", - "| UniversalKriging | 50 | 17.0073 | 4.124 | 4.092 | -12.8501 | 1.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:34:10\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.5854 (±0.0296), Variance: 2.1860, Range: [-31.8643, -24.7681]\n", - "z mean: -24.5677 (±0.0641), Variance: 10.2796, Range: [-30.7908, -9.1770]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0043, nugget=7.6386\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.5507 | 4.5333 | 4.1943 | -8.1273 | 0.41s |\n", - "| OLS_sphere | 50 | 17.1524 | 4.1415 | 4.1079 | -6.618 | 0.00s |\n", - "| DeepKriging_wendland | -- | 24.5898 | 4.9588 | 4.6412 | -9.9213 | 64.52s |\n", - "| DeepKriging_sphere | 1400 | 23.2776 | 4.8247 | 4.5471 | -9.3385 | 42.61s |\n", - "| DeepKriging_sphere_Huber | 1400 | 19.3692 | 4.401 | 4.1124 | -7.6026 | 47.92s |\n", - "| UniversalKriging | 50 | 17.1525 | 4.1416 | 4.1079 | -6.6181 | 2.39s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:37:55\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.4605 (±0.0225), Variance: 1.2701, Range: [-29.8544, -24.2683]\n", - "z mean: -23.4251 (±0.0610), Variance: 9.2900, Range: [-29.0287, -6.4145]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0031, nugget=8.0088\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.2245 | 4.269 | 4.0839 | -12.8857 | -0.41s |\n", - "| OLS_sphere | 50 | 16.7923 | 4.0978 | 4.0642 | -11.7945 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.3173 | 4.3951 | 4.1478 | -13.7184 | 77.37s |\n", - "| DeepKriging_sphere | 1400 | 18.4739 | 4.2981 | 4.1242 | -13.0757 | 37.92s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.9725 | 4.1198 | 3.9395 | -11.9318 | 43.35s |\n", - "| UniversalKriging | 50 | 16.7924 | 4.0979 | 4.0642 | -11.7946 | 2.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:41:46\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.8752 (±0.0295), Variance: 2.1761, Range: [-32.0112, -24.6711]\n", - "z mean: -24.9472 (±0.0627), Variance: 9.8128, Range: [-31.5291, -7.4631]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0022, nugget=7.2838\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 16.2751 | 4.0342 | 3.8355 | -7.4161 | 0.42s |\n", - "| OLS_sphere | 50 | 15.3894 | 3.9229 | 3.8811 | -6.9581 | 0.00s |\n", - "| DeepKriging_wendland | -- | 17.0353 | 4.1274 | 3.8411 | -7.8092 | 76.19s |\n", - "| DeepKriging_sphere | 1400 | 17.8879 | 4.2294 | 3.9727 | -8.2501 | 40.21s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.3856 | 3.9224 | 3.6534 | -6.9562 | 38.44s |\n", - "| UniversalKriging | 50 | 15.3893 | 3.9229 | 3.8811 | -6.9581 | 2.33s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:45:33\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.2588 (±0.0259), Variance: 1.6784, Range: [-31.7387, -25.5123]\n", - "z mean: -24.2963 (±0.0614), Variance: 9.4375, Range: [-30.6130, -6.5491]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0066, nugget=7.8490\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.3599 | 4.2848 | 4.0764 | -10.8703 | 0.34s |\n", - "| OLS_sphere | 50 | 16.0964 | 4.012 | 3.9802 | -9.4069 | 0.01s |\n", - "| DeepKriging_wendland | -- | 18.3768 | 4.2868 | 3.9667 | -10.8813 | 71.45s |\n", - "| DeepKriging_sphere | 1400 | 20.73 | 4.553 | 4.3362 | -12.4027 | 44.59s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.0368 | 4.0046 | 3.7441 | -9.3684 | 53.08s |\n", - "| UniversalKriging | 50 | 16.0971 | 4.0121 | 3.9802 | -9.4073 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:49:34\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.0486 (±0.0303), Variance: 2.2891, Range: [-31.2402, -23.3420]\n", - "z mean: -24.0527 (±0.0651), Variance: 10.5907, Range: [-30.2906, -5.0116]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0042, nugget=8.0112\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 44.8594 | 6.6977 | 4.6096 | -18.3941 | 0.34s |\n", - "| OLS_sphere | 50 | 16.5138 | 4.0637 | 4.0253 | -6.1395 | 0.01s |\n", - "| DeepKriging_wendland | -- | 20.4082 | 4.5175 | 4.1593 | -7.8231 | 71.81s |\n", - "| DeepKriging_sphere | 1400 | 21.9748 | 4.6877 | 4.3906 | -8.5004 | 53.40s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.1953 | 4.0243 | 3.6789 | -6.0018 | 42.36s |\n", - "| UniversalKriging | 50 | 16.5142 | 4.0638 | 4.0254 | -6.1396 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:53:36\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -29.3998 (±0.0286), Variance: 2.0475, Range: [-32.6460, -26.2923]\n", - "z mean: -25.4444 (±0.0611), Variance: 9.3352, Range: [-31.9854, -10.8215]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0033, nugget=7.1107\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 16.9392 | 4.1157 | 3.896 | -8.1024 | 0.40s |\n", - "| OLS_sphere | 50 | 15.308 | 3.9125 | 3.8847 | -7.2258 | 0.01s |\n", - "| DeepKriging_wendland | -- | 26.0688 | 5.1058 | 4.7262 | -13.0082 | 71.83s |\n", - "| DeepKriging_sphere | 1400 | 17.1191 | 4.1375 | 3.8779 | -8.199 | 44.92s |\n", - "| DeepKriging_sphere_Huber | 1400 | 13.569 | 3.6836 | 3.4006 | -6.2913 | 56.19s |\n", - "| UniversalKriging | 50 | 15.3079 | 3.9125 | 3.8847 | -7.2257 | 2.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 12:57:44\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.5699 (±0.0268), Variance: 1.7948, Range: [-30.9818, -24.3724]\n", - "z mean: -23.4582 (±0.0653), Variance: 10.6568, Range: [-29.8288, -3.7777]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=6.5298, nugget=2.1550\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.299 | 4.3931 | 4.1622 | -9.4252 | 0.36s |\n", - "| OLS_sphere | 50 | 17.3192 | 4.1616 | 4.1137 | -8.3557 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.3812 | 4.4024 | 4.0874 | -9.4696 | 73.55s |\n", - "| DeepKriging_sphere | 1400 | 21.948 | 4.6849 | 4.4672 | -10.8561 | 45.12s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.8289 | 3.9786 | 3.7198 | -7.5507 | 42.16s |\n", - "| UniversalKriging | 50 | 17.3628 | 4.1669 | 4.1182 | -8.3792 | 3.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:01:45\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.7588 (±0.0466), Variance: 5.4387, Range: [-32.2197, -22.6777]\n", - "z mean: -23.7508 (±0.0734), Variance: 13.4638, Range: [-31.4385, -4.2804]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0063, nugget=7.1504\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.4003 | 4.5167 | 4.0675 | -2.9658 | 0.41s |\n", - "| OLS_sphere | 50 | 16.5902 | 4.0731 | 4.0259 | -2.2251 | 0.00s |\n", - "| DeepKriging_wendland | -- | 21.1287 | 4.5966 | 4.0405 | -3.1074 | 75.39s |\n", - "| DeepKriging_sphere | 1400 | 20.7858 | 4.5591 | 4.0026 | -3.0408 | 51.12s |\n", - "| DeepKriging_sphere_Huber | 1400 | 19.1654 | 4.3778 | 3.8 | -2.7258 | 55.78s |\n", - "| UniversalKriging | 50 | 16.5902 | 4.0731 | 4.0259 | -2.2251 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:06:04\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.9050 (±0.0292), Variance: 2.1372, Range: [-32.2126, -24.8579]\n", - "z mean: -23.9014 (±0.0630), Variance: 9.9088, Range: [-31.1642, -3.7290]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0134, nugget=7.8057\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.3667 | 4.513 | 4.2099 | -8.375 | 0.44s |\n", - "| OLS_sphere | 50 | 16.2941 | 4.0366 | 3.9915 | -6.5003 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.0474 | 4.2482 | 3.9413 | -7.3073 | 65.57s |\n", - "| DeepKriging_sphere | 1400 | 20.8787 | 4.5693 | 4.2601 | -8.6106 | 45.63s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.6662 | 3.9581 | 3.6002 | -6.2113 | 53.25s |\n", - "| UniversalKriging | 50 | 16.2958 | 4.0368 | 3.9917 | -6.5011 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:10:06\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.1118 (±0.0252), Variance: 1.5932, Range: [-31.2361, -24.1632]\n", - "z mean: -24.0964 (±0.0618), Variance: 9.5336, Range: [-30.6875, -6.2911]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0091, nugget=7.6889\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.6006 | 4.4273 | 4.1386 | -11.599 | 0.40s |\n", - "| OLS_sphere | 50 | 16.9866 | 4.1215 | 4.0831 | -9.9187 | 0.01s |\n", - "| DeepKriging_wendland | -- | 18.3029 | 4.2782 | 3.9732 | -10.7649 | 76.21s |\n", - "| DeepKriging_sphere | 1400 | 19.5746 | 4.4243 | 4.2236 | -11.5822 | 42.78s |\n", - "| DeepKriging_sphere_Huber | 1400 | 12.4219 | 3.5245 | 3.2859 | -6.9846 | 54.07s |\n", - "| UniversalKriging | 50 | 16.9863 | 4.1214 | 4.0831 | -9.9185 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:14:19\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.3312 (±0.0199), Variance: 0.9929, Range: [-30.7507, -25.4370]\n", - "z mean: -24.2512 (±0.0599), Variance: 8.9572, Range: [-29.9820, -9.1754]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0066, nugget=8.0899\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.8457 | 4.4549 | 4.2251 | -15.5484 | 0.37s |\n", - "| OLS_sphere | 50 | 17.7225 | 4.2098 | 4.1786 | -13.778 | 0.00s |\n", - "| DeepKriging_wendland | -- | 20.6412 | 4.5433 | 4.267 | -16.2117 | 71.39s |\n", - "| DeepKriging_sphere | 1400 | 15.7722 | 3.9714 | 3.7527 | -12.1517 | 51.54s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.6929 | 4.0857 | 3.9126 | -12.9194 | 39.89s |\n", - "| UniversalKriging | 50 | 17.7227 | 4.2098 | 4.1786 | -13.7781 | 2.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:18:24\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.6432 (±0.0309), Variance: 2.3891, Range: [-33.2603, -25.2993]\n", - "z mean: -24.6165 (±0.0648), Variance: 10.4943, Range: [-32.2365, -7.3406]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=7.9658\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.8813 | 4.4588 | 4.2461 | -8.9499 | 0.41s |\n", - "| OLS_sphere | 50 | 16.3263 | 4.0406 | 4.0036 | -7.1707 | 0.00s |\n", - "| DeepKriging_wendland | -- | 19.5355 | 4.4199 | 4.1552 | -8.7768 | 69.35s |\n", - "| DeepKriging_sphere | 1400 | 23.5088 | 4.8486 | 4.6031 | -10.7653 | 43.79s |\n", - "| DeepKriging_sphere_Huber | 1400 | 18.602 | 4.313 | 4.0675 | -8.3097 | 43.99s |\n", - "| UniversalKriging | 50 | 16.3262 | 4.0406 | 4.0036 | -7.1707 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:22:26\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.3204 (±0.0367), Variance: 3.3623, Range: [-30.3941, -21.9787]\n", - "z mean: -23.4637 (±0.0668), Variance: 11.1660, Range: [-29.5455, -2.0513]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0123, nugget=7.5259\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 57.1899 | 7.5624 | 4.2988 | -14.1018 | 0.33s |\n", - "| OLS_sphere | 50 | 14.7732 | 3.8436 | 3.808 | -2.9011 | 0.00s |\n", - "| DeepKriging_wendland | -- | 18.7526 | 4.3304 | 3.9133 | -3.9519 | 71.44s |\n", - "| DeepKriging_sphere | 1400 | 20.8974 | 4.5714 | 4.1533 | -4.5182 | 41.96s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.5409 | 4.067 | 3.6803 | -3.3679 | 35.32s |\n", - "| UniversalKriging | 50 | 14.774 | 3.8437 | 3.8081 | -2.9013 | 1.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:26:17\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.4425 (±0.0263), Variance: 1.7294, Range: [-29.8969, -24.4000]\n", - "z mean: -23.4692 (±0.0598), Variance: 8.9485, Range: [-29.1989, -6.0664]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0035, nugget=7.1535\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 55.247 | 7.4328 | 4.5129 | -31.445 | 0.43s |\n", - "| OLS_sphere | 50 | 16.5656 | 4.0701 | 4.0316 | -8.7285 | 0.01s |\n", - "| DeepKriging_wendland | -- | 17.9977 | 4.2424 | 3.7761 | -9.5695 | 76.98s |\n", - "| DeepKriging_sphere | 1400 | 21.6649 | 4.6546 | 4.4346 | -11.7231 | 37.53s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.6055 | 3.9504 | 3.696 | -8.1646 | 52.20s |\n", - "| UniversalKriging | 50 | 16.5656 | 4.0701 | 4.0316 | -8.7285 | 2.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:30:30\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -27.3482 (±0.0293), Variance: 2.1441, Range: [-30.6805, -24.0854]\n", - "z mean: -23.3398 (±0.0624), Variance: 9.7297, Range: [-29.5374, -8.9140]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0090, nugget=7.6468\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 16.5154 | 4.0639 | 3.8289 | -6.3553 | -0.58s |\n", - "| OLS_sphere | 50 | 15.9633 | 3.9954 | 3.9541 | -6.1095 | 0.00s |\n", - "| DeepKriging_wendland | -- | 24.076 | 4.9067 | 4.6392 | -9.7225 | 78.40s |\n", - "| DeepKriging_sphere | 1400 | 21.3905 | 4.625 | 4.3503 | -8.5265 | 50.14s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.3381 | 4.042 | 3.7115 | -6.2764 | 54.60s |\n", - "| UniversalKriging | 50 | 15.9637 | 3.9955 | 3.9542 | -6.1096 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:34:59\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -28.5211 (±0.0263), Variance: 1.7237, Range: [-31.5498, -25.1678]\n", - "z mean: -24.5271 (±0.0620), Variance: 9.6063, Range: [-31.0596, -6.2891]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0069, nugget=7.6501\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 18.8282 | 4.3392 | 4.086 | -9.2718 | 0.41s |\n", - "| OLS_sphere | 50 | 16.3549 | 4.0441 | 4.0063 | -7.9224 | 0.00s |\n", - "| DeepKriging_wendland | -- | 17.5534 | 4.1897 | 3.8438 | -8.5763 | 70.68s |\n", - "| DeepKriging_sphere | 1400 | 19.141 | 4.375 | 4.1345 | -9.4424 | 67.13s |\n", - "| DeepKriging_sphere_Huber | 1400 | 15.4622 | 3.9322 | 3.6739 | -7.4355 | 58.57s |\n", - "| UniversalKriging | 50 | 16.3549 | 4.0441 | 4.0063 | -7.9225 | 2.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:39:43\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: -29.3659 (±0.0249), Variance: 1.5456, Range: [-32.0513, -25.5264]\n", - "z mean: -25.3617 (±0.0621), Variance: 9.6485, Range: [-31.6655, -5.4568]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0023, nugget=8.4738\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 18.4177 | 4.2916 | 4.1049 | -9.9484 | 0.40s |\n", - "| OLS_sphere | 50 | 17.1308 | 4.1389 | 4.0976 | -9.1834 | 0.00s |\n", - "| DeepKriging_wendland | -- | 21.85 | 4.6744 | 4.3616 | -11.9888 | 70.85s |\n", - "| DeepKriging_sphere | 1400 | 22.9894 | 4.7947 | 4.6026 | -12.6661 | 43.34s |\n", - "| DeepKriging_sphere_Huber | 1400 | 16.3488 | 4.0434 | 3.8052 | -8.7186 | 45.94s |\n", - "| UniversalKriging | 50 | 17.1309 | 4.1389 | 4.0976 | -9.1835 | 2.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 13:43:52\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.017052, - "end_time": "2026-01-19T03:16:43.814927", - "exception": false, - "start_time": "2026-01-19T03:16:43.797875", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:43.854177Z", - "iopub.status.busy": "2026-01-19T03:16:43.853281Z", - "iopub.status.idle": "2026-01-19T03:16:43.870813Z", - "shell.execute_reply": "2026-01-19T03:16:43.867168Z" - }, - "papermill": { - "duration": 0.041665, - "end_time": "2026-01-19T03:16:43.874144", - "exception": false, - "start_time": "2026-01-19T03:16:43.832479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 50, 'DeepKriging_sphere': 1400, 'DeepKriging_sphere_Huber': 1400, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-------------|-----------|-----------|--------------|\n", - "| OLS_wendland | 26.09±13.95 | 4.98±1.13 | 4.18±0.20 | -15.89±14.77 |\n", - "| OLS_sphere | 16.37±0.65 | 4.05±0.08 | 4.01±0.08 | -9.63±5.51 |\n", - "| DeepKriging_wendland | 20.38±3.19 | 4.50±0.34 | 4.17±0.34 | -12.01±6.35 |\n", - "| DeepKriging_sphere | 20.17±1.66 | 4.49±0.19 | 4.25±0.18 | -11.85±6.18 |\n", - "| DeepKriging_sphere_Huber | 16.40±1.87 | 4.04±0.23 | 3.78±0.22 | -9.35±4.72 |\n", - "| UniversalKriging | 16.37±0.65 | 4.05±0.08 | 4.01±0.08 | -9.63±5.51 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 4.04±0.23)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 11092.702154, - "end_time": "2026-01-19T03:16:47.536377", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-19T00:11:54.834223", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_tdf4.ipynb b/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_tdf4.ipynb deleted file mode 100644 index b9bc615..0000000 --- a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_noise_tdf4.ipynb +++ /dev/null @@ -1,4554 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.014108, - "end_time": "2026-01-19T00:11:55.854651", - "exception": false, - "start_time": "2026-01-19T00:11:55.840543", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:55.869182Z", - "iopub.status.busy": "2026-01-19T00:11:55.868268Z", - "iopub.status.idle": "2026-01-19T00:11:55.890866Z", - "shell.execute_reply": "2026-01-19T00:11:55.887548Z" - }, - "papermill": { - "duration": 0.034152, - "end_time": "2026-01-19T00:11:55.894462", - "exception": false, - "start_time": "2026-01-19T00:11:55.860310", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:55.922813Z", - "iopub.status.busy": "2026-01-19T00:11:55.922102Z", - "iopub.status.idle": "2026-01-19T00:11:58.482567Z", - "shell.execute_reply": "2026-01-19T00:11:58.480690Z" - }, - "papermill": { - "duration": 2.57728, - "end_time": "2026-01-19T00:11:58.484135", - "exception": false, - "start_time": "2026-01-19T00:11:55.906855", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768781516.765636 4038115 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768781516.769973 4038115 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768781516.781556 4038115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768781516.781578 4038115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768781516.781579 4038115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768781516.781580 4038115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.003819, - "end_time": "2026-01-19T00:11:58.492774", - "exception": false, - "start_time": "2026-01-19T00:11:58.488955", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:58.501873Z", - "iopub.status.busy": "2026-01-19T00:11:58.501422Z", - "iopub.status.idle": "2026-01-19T00:11:59.891497Z", - "shell.execute_reply": "2026-01-19T00:11:59.887874Z" - }, - "papermill": { - "duration": 1.397966, - "end_time": "2026-01-19T00:11:59.894394", - "exception": false, - "start_time": "2026-01-19T00:11:58.496428", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.003854, - "end_time": "2026-01-19T00:11:59.904396", - "exception": false, - "start_time": "2026-01-19T00:11:59.900542", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:59.912546Z", - "iopub.status.busy": "2026-01-19T00:11:59.911730Z", - "iopub.status.idle": "2026-01-19T00:11:59.923239Z", - "shell.execute_reply": "2026-01-19T00:11:59.920067Z" - }, - "papermill": { - "duration": 0.01905, - "end_time": "2026-01-19T00:11:59.925757", - "exception": false, - "start_time": "2026-01-19T00:11:59.906707", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.002352, - "end_time": "2026-01-19T00:11:59.934634", - "exception": false, - "start_time": "2026-01-19T00:11:59.932282", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:11:59.942334Z", - "iopub.status.busy": "2026-01-19T00:11:59.941736Z", - "iopub.status.idle": "2026-01-19T00:12:13.618029Z", - "shell.execute_reply": "2026-01-19T00:12:13.615112Z" - }, - "papermill": { - "duration": 13.683423, - "end_time": "2026-01-19T00:12:13.620367", - "exception": false, - "start_time": "2026-01-19T00:11:59.936944", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.002356, - "end_time": "2026-01-19T00:12:13.695036", - "exception": false, - "start_time": "2026-01-19T00:12:13.692680", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.703949Z", - "iopub.status.busy": "2026-01-19T00:12:13.702764Z", - "iopub.status.idle": "2026-01-19T00:12:13.717203Z", - "shell.execute_reply": "2026-01-19T00:12:13.714858Z" - }, - "papermill": { - "duration": 0.023861, - "end_time": "2026-01-19T00:12:13.721124", - "exception": false, - "start_time": "2026-01-19T00:12:13.697263", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term\n", - " # f(x₁, x₂) = -(x₂ + 47) sin(√|x₂ + x₁/2 + 47|) - x₁ sin(√|x₁ - (x₂ + 47)|)\n", - " term1 = -(x2 + 47.0) * np.sin(np.sqrt(np.abs(x2 + x1/2.0 + 47.0)))\n", - " term2 = -x1 * np.sin(np.sqrt(np.abs(x1 - (x2 + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " z = y + (np.sqrt(2) * rng.standard_t(df=4, size=num_sample)).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.002274, - "end_time": "2026-01-19T00:12:13.729500", - "exception": false, - "start_time": "2026-01-19T00:12:13.727226", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.737787Z", - "iopub.status.busy": "2026-01-19T00:12:13.737067Z", - "iopub.status.idle": "2026-01-19T00:12:13.773749Z", - "shell.execute_reply": "2026-01-19T00:12:13.770615Z" - }, - "papermill": { - "duration": 0.044731, - "end_time": "2026-01-19T00:12:13.776441", - "exception": false, - "start_time": "2026-01-19T00:12:13.731710", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.788736Z", - "iopub.status.busy": "2026-01-19T00:12:13.788200Z", - "iopub.status.idle": "2026-01-19T00:12:13.828850Z", - "shell.execute_reply": "2026-01-19T00:12:13.826481Z" - }, - "papermill": { - "duration": 0.048422, - "end_time": "2026-01-19T00:12:13.831488", - "exception": false, - "start_time": "2026-01-19T00:12:13.783066", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.006715, - "end_time": "2026-01-19T00:12:13.847493", - "exception": false, - "start_time": "2026-01-19T00:12:13.840778", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.855907Z", - "iopub.status.busy": "2026-01-19T00:12:13.855435Z", - "iopub.status.idle": "2026-01-19T00:12:13.864819Z", - "shell.execute_reply": "2026-01-19T00:12:13.862713Z" - }, - "papermill": { - "duration": 0.016236, - "end_time": "2026-01-19T00:12:13.867130", - "exception": false, - "start_time": "2026-01-19T00:12:13.850894", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T00:12:13.879342Z", - "iopub.status.busy": "2026-01-19T00:12:13.879022Z", - "iopub.status.idle": "2026-01-19T03:16:43.773840Z", - "shell.execute_reply": "2026-01-19T03:16:43.771079Z" - }, - "papermill": { - "duration": 11069.902238, - "end_time": "2026-01-19T03:16:43.776690", - "exception": false, - "start_time": "2026-01-19T00:12:13.874452", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7665 (±0.0184), Variance: 0.8430, Range: [-29.8612, -25.2544]\n", - "z mean: -27.7586 (±0.0443), Variance: 4.8997, Range: [-40.4352, -12.9918]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.2259 0.1727 *\n", - " 100 0.2531 0.2415 \n", - " 200 0.4256 0.4613 \n", - " 500 1.1019 1.2688 \n", - " 700 2.0829 2.6107 \n", - " 1000 0.8911 0.7513 \n", - " 1400 0.8911 0.7513 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768781551.133261 4038115 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768781553.225155 4038357 service.cc:152] XLA service 0x762c58016040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768781553.225230 4038357 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768781553.543797 4038357 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768781563.539421 4038357 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x762c85e62560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x762c8cb39510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7.1042 2.7383 \n", - " 100 7.4555 2.3660 \n", - " 200 8.0017 2.2866 \n", - " 500 7.6928 2.4979 \n", - " 700 7.7417 2.9104 \n", - " 1000 5.7469 0.7851 *\n", - " 1400 5.7737 0.8076 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.7810 2.2268 \n", - " 100 1.6169 1.4781 \n", - " 200 1.7349 1.4887 \n", - " 500 1.7019 1.8760 \n", - " 700 1.8419 1.6962 \n", - " 1000 1.5768 0.8612 *\n", - " 1400 1.6070 0.8460 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0022, nugget=3.9320\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0012, nugget=3.8222\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0001, nugget=3.5738\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0056, sigma²=0.0000, nugget=3.0282\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0040, sigma²=0.0000, nugget=2.6206\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0040, sigma²=0.0000, nugget=1.9277\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0030, sigma²=0.0000, nugget=1.1847\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.1859 0.1727 *\n", - " 100 5.2260 0.2415 \n", - " 200 5.5627 0.4613 \n", - " 500 6.1951 1.2688 \n", - " 700 7.3936 2.6106 \n", - " 1000 12.8394 6.1440 \n", - " 1400 245.3209 109.1274 \n", - " Best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0022, nugget=3.9320\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.1797 | 1.7832 | 0.8431 | -3.1101 | 0.46s |\n", - "| OLS_sphere | 50 | 0.1727 | 0.4156 | 0.3328 | 0.7768 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.2055 | 1.4851 | 1.0306 | -1.8509 | 70.26s |\n", - "| DeepKriging_sphere | 1000 | 0.8996 | 0.9485 | 0.7687 | -0.1628 | 47.70s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.9207 | 0.9595 | 0.7979 | -0.1901 | 39.71s |\n", - "| UniversalKriging | 50 | 0.1727 | 0.4156 | 0.3328 | 0.7768 | 2.01s |\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:25:54\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7225 (±0.0311), Variance: 2.4122, Range: [-31.4270, -24.4243]\n", - "z mean: -27.7076 (±0.0503), Variance: 6.3190, Range: [-43.5372, -17.0119]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=3.9855, nugget=0.0297\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.274 | 1.508 | 1.1548 | -0.0049 | 0.37s |\n", - "| OLS_sphere | 50 | 0.1791 | 0.4232 | 0.3388 | 0.9208 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.2182 | 1.4894 | 1.1401 | 0.0197 | 64.28s |\n", - "| DeepKriging_sphere | 1000 | 2.3884 | 1.5455 | 1.2908 | -0.0555 | 46.12s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.3096 | 1.5197 | 1.2774 | -0.0206 | 47.43s |\n", - "| UniversalKriging | 50 | 0.1776 | 0.4215 | 0.3375 | 0.9215 | 3.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:29:05\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.2347 (±0.0292), Variance: 2.1348, Range: [-31.7127, -25.1404]\n", - "z mean: -28.2104 (±0.0490), Variance: 5.9950, Range: [-44.6794, -15.6728]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0052, nugget=4.0976\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.6083 | 1.615 | 1.0607 | -0.1477 | 0.50s |\n", - "| OLS_sphere | 50 | 0.192 | 0.4382 | 0.3553 | 0.9155 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.3586 | 1.5358 | 1.1806 | -0.0378 | 64.75s |\n", - "| DeepKriging_sphere | 1000 | 2.3923 | 1.5467 | 1.3168 | -0.0526 | 43.59s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.3694 | 1.5393 | 1.3268 | -0.0425 | 48.87s |\n", - "| UniversalKriging | 50 | 0.192 | 0.4382 | 0.3552 | 0.9155 | 1.71s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:32:15\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1546 (±0.0201), Variance: 1.0080, Range: [-30.9142, -25.1867]\n", - "z mean: -28.1134 (±0.0455), Variance: 5.1672, Range: [-52.3505, -11.2239]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0078, nugget=4.3406\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 10.6588 | 3.2648 | 1.2073 | -8.9168 | 0.39s |\n", - "| OLS_sphere | 50 | 0.1805 | 0.4248 | 0.338 | 0.8321 | 0.00s |\n", - "| DeepKriging_wendland | -- | 3.7619 | 1.9396 | 1.1601 | -2.5001 | 68.25s |\n", - "| DeepKriging_sphere | 1000 | 1.2851 | 1.1336 | 0.9051 | -0.1956 | 44.12s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.1788 | 1.0857 | 0.9034 | -0.0967 | 42.27s |\n", - "| UniversalKriging | 50 | 0.1804 | 0.4248 | 0.338 | 0.8321 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:35:23\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.6378 (±0.0267), Variance: 1.7824, Range: [-31.6483, -24.9221]\n", - "z mean: -27.6745 (±0.0471), Variance: 5.5507, Range: [-40.4724, -12.2709]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0068, nugget=3.5863\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 30.4016 | 5.5138 | 1.3895 | -13.9267 | 0.36s |\n", - "| OLS_sphere | 50 | 0.164 | 0.405 | 0.3277 | 0.9195 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.5967 | 1.2636 | 0.9703 | 0.2161 | 68.28s |\n", - "| DeepKriging_sphere | 1000 | 2.4618 | 1.569 | 1.2963 | -0.2087 | 38.88s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.058 | 1.4346 | 1.1986 | -0.0104 | 54.43s |\n", - "| UniversalKriging | 50 | 0.1638 | 0.4048 | 0.3275 | 0.9196 | 2.00s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:38:40\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1310 (±0.0142), Variance: 0.5051, Range: [-30.4014, -25.8921]\n", - "z mean: -28.1811 (±0.0399), Variance: 3.9804, Range: [-36.9438, -9.5132]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0018, nugget=3.4107\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 33.1496 | 5.7576 | 1.0522 | -62.0567 | 0.39s |\n", - "| OLS_sphere | 50 | 0.1788 | 0.4228 | 0.3377 | 0.6599 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.7757 | 1.3325 | 0.787 | -2.3776 | 68.47s |\n", - "| DeepKriging_sphere | 1000 | 0.7658 | 0.8751 | 0.6668 | -0.4567 | 38.57s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.7461 | 0.8637 | 0.6548 | -0.4191 | 42.13s |\n", - "| UniversalKriging | 50 | 0.1788 | 0.4228 | 0.3377 | 0.66 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:41:45\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.4444 (±0.0270), Variance: 1.8227, Range: [-31.2284, -24.5590]\n", - "z mean: -27.3748 (±0.0482), Variance: 5.8173, Range: [-42.4074, 0.8815]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0024, nugget=4.0037\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.3129 | 1.5208 | 0.9935 | -0.2348 | 0.35s |\n", - "| OLS_sphere | 50 | 0.1675 | 0.4093 | 0.3232 | 0.9106 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.8633 | 1.365 | 1.0497 | 0.0053 | 68.40s |\n", - "| DeepKriging_sphere | 1000 | 1.9987 | 1.4137 | 1.1993 | -0.067 | 36.55s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.8769 | 1.37 | 1.1679 | -0.002 | 41.31s |\n", - "| UniversalKriging | 50 | 0.1675 | 0.4093 | 0.3232 | 0.9106 | 2.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:44:49\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.8819 (±0.0299), Variance: 2.2414, Range: [-31.3263, -24.4321]\n", - "z mean: -27.8624 (±0.0489), Variance: 5.9712, Range: [-42.4275, -16.9083]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0010, nugget=3.6979\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.8951 | 1.7015 | 1.2655 | -0.1683 | 0.32s |\n", - "| OLS_sphere | 50 | 0.1891 | 0.4348 | 0.3524 | 0.9237 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.9867 | 1.4095 | 1.1199 | 0.1983 | 66.91s |\n", - "| DeepKriging_sphere | 1000 | 2.7027 | 1.644 | 1.4102 | -0.0906 | 32.56s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.5178 | 1.5868 | 1.3622 | -0.016 | 36.91s |\n", - "| UniversalKriging | 50 | 0.1891 | 0.4348 | 0.3524 | 0.9237 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:47:44\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7679 (±0.0301), Variance: 2.2707, Range: [-31.0653, -24.1754]\n", - "z mean: -27.7636 (±0.0494), Variance: 6.0911, Range: [-39.7932, -14.5577]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0124, nugget=3.6782\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.784 | 2.1872 | 1.2449 | -1.0643 | 0.31s |\n", - "| OLS_sphere | 50 | 0.1884 | 0.4341 | 0.3498 | 0.9187 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.4649 | 1.57 | 1.2134 | -0.0636 | 65.06s |\n", - "| DeepKriging_sphere | 1000 | 2.5831 | 1.6072 | 1.3298 | -0.1146 | 47.22s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.805 | 1.6748 | 1.3914 | -0.2104 | 37.65s |\n", - "| UniversalKriging | 50 | 0.1883 | 0.434 | 0.3496 | 0.9187 | 1.94s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:50:54\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.4293 (±0.0331), Variance: 2.7355, Range: [-32.1898, -23.5979]\n", - "z mean: -27.3801 (±0.0525), Variance: 6.9010, Range: [-39.5137, -13.9413]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0016, nugget=4.0379\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.556 | 1.8857 | 1.0474 | -0.2223 | 0.33s |\n", - "| OLS_sphere | 50 | 0.1969 | 0.4437 | 0.35 | 0.9323 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.0411 | 1.4287 | 1.0228 | 0.2984 | 65.28s |\n", - "| DeepKriging_sphere | 1000 | 2.9003 | 1.703 | 1.361 | 0.0031 | 43.99s |\n", - "| DeepKriging_sphere_Huber | 1000 | 3.0277 | 1.74 | 1.3929 | -0.0407 | 48.47s |\n", - "| UniversalKriging | 50 | 0.1969 | 0.4437 | 0.35 | 0.9323 | 2.06s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:54:12\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.6794 (±0.0208), Variance: 1.0843, Range: [-30.7108, -24.7975]\n", - "z mean: -27.7213 (±0.0433), Variance: 4.6914, Range: [-36.6506, -12.6580]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=0.0009, nugget=3.4706\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.6401 | 1.6248 | 0.7725 | -1.6293 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1636 | 0.4044 | 0.3216 | 0.8371 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.9185 | 1.3851 | 0.8957 | -0.9106 | 59.56s |\n", - "| DeepKriging_sphere | 1000 | 1.1593 | 1.0767 | 0.8437 | -0.1546 | 51.63s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.2746 | 1.129 | 0.8888 | -0.2694 | 37.53s |\n", - "| UniversalKriging | 50 | 0.1636 | 0.4044 | 0.3216 | 0.8371 | 2.33s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 08:57:24\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.5496 (±0.0320), Variance: 2.5541, Range: [-31.1112, -24.2320]\n", - "z mean: -27.4970 (±0.0507), Variance: 6.4207, Range: [-40.9763, -14.4279]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0021, nugget=3.7069\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 12.3336 | 3.5119 | 1.4319 | -3.7477 | 0.34s |\n", - "| OLS_sphere | 50 | 0.159 | 0.3987 | 0.3228 | 0.9388 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.4371 | 1.5611 | 1.1806 | 0.0619 | 56.38s |\n", - "| DeepKriging_sphere | 1000 | 2.92 | 1.7088 | 1.452 | -0.124 | 45.71s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.7661 | 1.6631 | 1.4343 | -0.0648 | 40.35s |\n", - "| UniversalKriging | 50 | 0.1589 | 0.3986 | 0.3228 | 0.9388 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:00:30\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1985 (±0.0299), Variance: 2.2288, Range: [-32.2729, -24.4764]\n", - "z mean: -28.2426 (±0.0509), Variance: 6.4820, Range: [-47.7555, -9.9798]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=4.0610, nugget=0.0109\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 15.4294 | 3.928 | 1.3589 | -5.982 | 0.41s |\n", - "| OLS_sphere | 50 | 0.1776 | 0.4214 | 0.338 | 0.9196 | 0.01s |\n", - "| DeepKriging_wendland | -- | 3.2203 | 1.7945 | 1.1896 | -0.4573 | 50.80s |\n", - "| DeepKriging_sphere | 1000 | 2.3746 | 1.541 | 1.2396 | -0.0746 | 34.56s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.2808 | 1.5102 | 1.2245 | -0.0321 | 46.92s |\n", - "| UniversalKriging | 50 | 0.212 | 0.4604 | 0.3568 | 0.9041 | 5.54s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:03:31\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -29.4315 (±0.0202), Variance: 1.0206, Range: [-32.3467, -25.5211]\n", - "z mean: -29.4380 (±0.0445), Variance: 4.9411, Range: [-45.7809, -19.7848]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0016, nugget=3.8104\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3339 | 1.155 | 0.7302 | -0.2444 | 0.44s |\n", - "| OLS_sphere | 50 | 0.1783 | 0.4222 | 0.3421 | 0.8337 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.3299 | 1.1532 | 0.8528 | -0.2406 | 66.67s |\n", - "| DeepKriging_sphere | 1000 | 1.3977 | 1.1822 | 0.92 | -0.3038 | 36.90s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.3336 | 1.1548 | 0.895 | -0.244 | 40.22s |\n", - "| UniversalKriging | 50 | 0.1783 | 0.4222 | 0.3421 | 0.8337 | 1.76s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:06:41\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -29.3324 (±0.0381), Variance: 3.6258, Range: [-32.9755, -25.2573]\n", - "z mean: -29.3429 (±0.0566), Variance: 8.0169, Range: [-54.8510, -16.1061]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0059, nugget=4.2036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.2563 | 1.5021 | 1.1392 | 0.2809 | 0.36s |\n", - "| OLS_sphere | 50 | 0.1705 | 0.4129 | 0.3321 | 0.9457 | 0.01s |\n", - "| DeepKriging_wendland | -- | 4.4139 | 2.1009 | 1.2948 | -0.4068 | 61.07s |\n", - "| DeepKriging_sphere | 1000 | 3.5214 | 1.8766 | 1.5367 | -0.1224 | 41.52s |\n", - "| DeepKriging_sphere_Huber | 1000 | 3.6384 | 1.9075 | 1.5447 | -0.1597 | 49.02s |\n", - "| UniversalKriging | 50 | 0.1704 | 0.4128 | 0.332 | 0.9457 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:09:59\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.2376 (±0.0174), Variance: 0.7544, Range: [-30.3088, -25.6188]\n", - "z mean: -28.2945 (±0.0449), Variance: 5.0295, Range: [-57.0180, -9.5851]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0065, nugget=3.7732\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.5264 | 1.5895 | 0.7984 | -2.6419 | 0.40s |\n", - "| OLS_sphere | 50 | 0.2283 | 0.4778 | 0.3703 | 0.671 | 0.00s |\n", - "| DeepKriging_wendland | -- | 4.1479 | 2.0366 | 0.9722 | -4.9794 | 67.40s |\n", - "| DeepKriging_sphere | 1000 | 0.8464 | 0.92 | 0.7462 | -0.2201 | 47.50s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.7914 | 0.8896 | 0.714 | -0.1409 | 42.11s |\n", - "| UniversalKriging | 50 | 0.2281 | 0.4776 | 0.3703 | 0.6712 | 1.73s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:13:24\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7216 (±0.0306), Variance: 2.3461, Range: [-32.7967, -24.8313]\n", - "z mean: -27.7587 (±0.0505), Variance: 6.3704, Range: [-38.0665, -9.6532]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0019, nugget=3.9907\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.6513 | 1.285 | 0.9071 | 0.3621 | 0.38s |\n", - "| OLS_sphere | 50 | 0.1846 | 0.4297 | 0.3463 | 0.9287 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.1837 | 1.4778 | 1.0688 | 0.1564 | 62.64s |\n", - "| DeepKriging_sphere | 1000 | 2.7102 | 1.6463 | 1.336 | -0.047 | 48.77s |\n", - "| DeepKriging_sphere_Huber | 1000 | 3.08 | 1.755 | 1.3412 | -0.1898 | 33.90s |\n", - "| UniversalKriging | 50 | 0.1846 | 0.4296 | 0.3462 | 0.9287 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:16:40\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.6570 (±0.0198), Variance: 0.9813, Range: [-31.1947, -24.7329]\n", - "z mean: -28.6745 (±0.0463), Variance: 5.3509, Range: [-41.6170, -8.0276]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0089, nugget=4.4196\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3943 | 1.1808 | 0.7103 | -0.4966 | 0.38s |\n", - "| OLS_sphere | 50 | 0.2088 | 0.4569 | 0.3551 | 0.7759 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.5489 | 1.2446 | 0.9291 | -0.6626 | 67.48s |\n", - "| DeepKriging_sphere | 1000 | 1.1684 | 1.0809 | 0.8443 | -0.2541 | 50.69s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1 | 1 | 0.7496 | -0.0733 | 69.90s |\n", - "| UniversalKriging | 50 | 0.2087 | 0.4568 | 0.355 | 0.776 | 1.87s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:20:39\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1476 (±0.0269), Variance: 1.8042, Range: [-32.2416, -24.9143]\n", - "z mean: -28.1396 (±0.0490), Variance: 6.0016, Range: [-41.0892, -13.2293]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0012, nugget=4.4043\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.9718 | 1.9929 | 0.9354 | -1.1994 | 0.35s |\n", - "| OLS_sphere | 50 | 0.1862 | 0.4316 | 0.3391 | 0.8969 | 0.01s |\n", - "| DeepKriging_wendland | -- | 5.3995 | 2.3237 | 1.1848 | -1.99 | 67.79s |\n", - "| DeepKriging_sphere | 1000 | 1.8595 | 1.3636 | 1.1014 | -0.0297 | 53.06s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.8433 | 1.3577 | 1.0888 | -0.0208 | 50.23s |\n", - "| UniversalKriging | 50 | 0.1862 | 0.4316 | 0.3391 | 0.8969 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:24:22\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.2330 (±0.0231), Variance: 1.3304, Range: [-31.6667, -25.5212]\n", - "z mean: -28.2381 (±0.0465), Variance: 5.4048, Range: [-39.3257, -13.7137]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0049, nugget=4.2002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 19.4007 | 4.4046 | 1.333 | -14.0231 | 0.33s |\n", - "| OLS_sphere | 50 | 0.1862 | 0.4315 | 0.347 | 0.8558 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.5147 | 1.5858 | 1.007 | -0.9473 | 66.99s |\n", - "| DeepKriging_sphere | 1000 | 1.4525 | 1.2052 | 0.9679 | -0.1248 | 34.89s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.5765 | 1.2556 | 1.0214 | -0.2208 | 43.85s |\n", - "| UniversalKriging | 50 | 0.186 | 0.4313 | 0.3468 | 0.8559 | 1.53s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:27:40\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7941 (±0.0173), Variance: 0.7521, Range: [-30.8333, -25.5507]\n", - "z mean: -27.8574 (±0.0424), Variance: 4.4866, Range: [-39.3663, -16.6782]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=3.5416, nugget=0.0001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 30.3771 | 5.5115 | 1.1471 | -39.5746 | 0.41s |\n", - "| OLS_sphere | 50 | 0.2118 | 0.4602 | 0.3664 | 0.7171 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.764 | 1.3281 | 0.8809 | -1.3561 | 63.10s |\n", - "| DeepKriging_sphere | 1000 | 0.9952 | 0.9976 | 0.7822 | -0.3293 | 39.48s |\n", - "| DeepKriging_sphere_Huber | 1000 | 0.8696 | 0.9325 | 0.7598 | -0.1615 | 40.99s |\n", - "| UniversalKriging | 50 | 0.2304 | 0.48 | 0.3705 | 0.6922 | 4.78s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:31:01\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.0475 (±0.0273), Variance: 1.8633, Range: [-31.0573, -24.6796]\n", - "z mean: -28.0713 (±0.0496), Variance: 6.1546, Range: [-43.5412, -0.1586]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0008, nugget=4.3903\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.492 | 1.5786 | 1.0022 | -0.391 | 0.47s |\n", - "| OLS_sphere | 50 | 0.1773 | 0.421 | 0.3441 | 0.901 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.0607 | 1.4355 | 1.0414 | -0.1503 | 66.50s |\n", - "| DeepKriging_sphere | 1000 | 2.3445 | 1.5312 | 1.2362 | -0.3087 | 38.98s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.1926 | 1.4807 | 1.2058 | -0.2239 | 41.41s |\n", - "| UniversalKriging | 50 | 0.1773 | 0.421 | 0.3441 | 0.9011 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:34:24\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1562 (±0.0192), Variance: 0.9210, Range: [-30.9961, -26.0847]\n", - "z mean: -28.2025 (±0.0431), Variance: 4.6445, Range: [-37.6919, -14.1141]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0016, nugget=3.6733\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.0008 | 1.4145 | 0.7767 | -1.2718 | 0.37s |\n", - "| OLS_sphere | 50 | 0.1655 | 0.4068 | 0.3288 | 0.8121 | 0.00s |\n", - "| DeepKriging_wendland | -- | 4.4354 | 2.106 | 1.0349 | -4.0362 | 62.13s |\n", - "| DeepKriging_sphere | 1000 | 0.9836 | 0.9917 | 0.7642 | -0.1168 | 40.31s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.0032 | 1.0016 | 0.7817 | -0.1391 | 52.23s |\n", - "| UniversalKriging | 50 | 0.1655 | 0.4068 | 0.3288 | 0.8121 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:37:56\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7689 (±0.0345), Variance: 2.9821, Range: [-33.0220, -23.7218]\n", - "z mean: -27.7730 (±0.0522), Variance: 6.8096, Range: [-39.3747, -12.4484]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0057, nugget=3.8317\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 5.286 | 2.2991 | 1.2459 | -0.7939 | 0.45s |\n", - "| OLS_sphere | 50 | 0.2026 | 0.4501 | 0.3692 | 0.9312 | 0.00s |\n", - "| DeepKriging_wendland | -- | 5.4794 | 2.3408 | 1.3533 | -0.8596 | 64.41s |\n", - "| DeepKriging_sphere | 1000 | 2.8105 | 1.6765 | 1.3925 | 0.0462 | 50.08s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.7903 | 1.6704 | 1.3896 | 0.053 | 43.46s |\n", - "| UniversalKriging | 50 | 0.2024 | 0.4498 | 0.3688 | 0.9313 | 0.96s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:41:30\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.9938 (±0.0260), Variance: 1.6857, Range: [-32.2604, -25.8464]\n", - "z mean: -29.0311 (±0.0451), Variance: 5.0819, Range: [-44.2104, -18.8553]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0011, nugget=3.5702\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.3515 | 1.5335 | 1.1253 | -0.3369 | 0.35s |\n", - "| OLS_sphere | 50 | 0.146 | 0.3821 | 0.3106 | 0.917 | 0.00s |\n", - "| DeepKriging_wendland | -- | 3.0779 | 1.7544 | 1.2041 | -0.7499 | 60.95s |\n", - "| DeepKriging_sphere | 1000 | 1.8144 | 1.347 | 1.1347 | -0.0316 | 57.04s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.845 | 1.3583 | 1.1261 | -0.049 | 57.27s |\n", - "| UniversalKriging | 50 | 0.146 | 0.3821 | 0.3106 | 0.917 | 2.12s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:45:24\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.2607 (±0.0275), Variance: 1.8866, Range: [-31.6823, -24.9549]\n", - "z mean: -28.2417 (±0.0491), Variance: 6.0249, Range: [-46.7013, -14.9057]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0042, nugget=4.2365\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.6769 | 1.9175 | 1.1288 | -1.0022 | 0.41s |\n", - "| OLS_sphere | 50 | 0.2476 | 0.4976 | 0.3975 | 0.8652 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.8882 | 1.6995 | 1.1887 | -0.5728 | 68.89s |\n", - "| DeepKriging_sphere | 1000 | 2.0336 | 1.426 | 1.1358 | -0.1074 | 45.67s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.9804 | 1.4073 | 1.1207 | -0.0784 | 41.62s |\n", - "| UniversalKriging | 50 | 0.2476 | 0.4976 | 0.3975 | 0.8652 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:48:59\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -26.6711 (±0.0209), Variance: 1.0901, Range: [-30.3700, -24.4087]\n", - "z mean: -26.6966 (±0.0428), Variance: 4.5815, Range: [-49.5376, -17.0072]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0061, nugget=3.5060\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 14.5025 | 3.8082 | 1.0482 | -10.9328 | 0.35s |\n", - "| OLS_sphere | 50 | 0.1655 | 0.4068 | 0.311 | 0.8638 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.7913 | 1.3384 | 0.9137 | -0.4739 | 50.57s |\n", - "| DeepKriging_sphere | 1000 | 1.3481 | 1.1611 | 0.8411 | -0.1092 | 36.02s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.1946 | 1.093 | 0.7831 | 0.0171 | 48.67s |\n", - "| UniversalKriging | 50 | 0.1652 | 0.4064 | 0.3107 | 0.8641 | 0.79s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:52:14\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.9744 (±0.0253), Variance: 1.5987, Range: [-31.5052, -24.5118]\n", - "z mean: -27.9676 (±0.0475), Variance: 5.6415, Range: [-53.2092, -14.5282]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0030, nugget=4.1314\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.7699 | 0.8774 | 0.7205 | 0.4602 | 0.40s |\n", - "| OLS_sphere | 50 | 0.1716 | 0.4142 | 0.3296 | 0.8797 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.3366 | 1.1561 | 0.8917 | 0.0628 | 60.53s |\n", - "| DeepKriging_sphere | 1000 | 1.492 | 1.2215 | 0.9325 | -0.0461 | 44.68s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.4668 | 1.2111 | 0.9362 | -0.0285 | 41.47s |\n", - "| UniversalKriging | 50 | 0.1715 | 0.4142 | 0.3296 | 0.8797 | 1.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:55:43\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7327 (±0.0300), Variance: 2.2553, Range: [-31.5077, -23.1913]\n", - "z mean: -27.7321 (±0.0528), Variance: 6.9821, Range: [-47.9966, -15.3700]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0021, nugget=4.6421\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 3.6888 | 1.9206 | 1.106 | -0.8019 | 0.36s |\n", - "| OLS_sphere | 50 | 0.2392 | 0.4891 | 0.3926 | 0.8831 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.433 | 1.5598 | 1.0591 | -0.1885 | 58.62s |\n", - "| DeepKriging_sphere | 1000 | 2.0445 | 1.4299 | 1.1649 | 0.0013 | 40.80s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.2197 | 1.4899 | 1.192 | -0.0843 | 36.45s |\n", - "| UniversalKriging | 50 | 0.2392 | 0.4891 | 0.3926 | 0.8832 | 1.82s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 09:59:03\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -26.6973 (±0.0338), Variance: 2.8595, Range: [-30.7029, -22.4124]\n", - "z mean: -26.7000 (±0.0513), Variance: 6.5705, Range: [-36.7167, -14.8147]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0052, nugget=3.8773\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.6415 | 1.6253 | 1.1083 | -0.0611 | 0.35s |\n", - "| OLS_sphere | 50 | 0.179 | 0.4231 | 0.3449 | 0.9281 | 0.00s |\n", - "| DeepKriging_wendland | -- | 17.5013 | 4.1835 | 1.378 | -6.0306 | 56.42s |\n", - "| DeepKriging_sphere | 1000 | 2.6494 | 1.6277 | 1.2979 | -0.0643 | 46.84s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.5566 | 1.5989 | 1.2736 | -0.027 | 36.40s |\n", - "| UniversalKriging | 50 | 0.179 | 0.423 | 0.3448 | 0.9281 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:02:26\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.9496 (±0.0249), Variance: 1.5466, Range: [-30.8551, -24.5076]\n", - "z mean: -27.9517 (±0.0465), Variance: 5.4094, Range: [-44.5053, -16.7596]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0015, nugget=3.8239\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.358 | 1.5356 | 1.0408 | -0.6371 | 0.40s |\n", - "| OLS_sphere | 50 | 0.2267 | 0.4761 | 0.3774 | 0.8426 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.444 | 1.5633 | 1.2274 | -0.6968 | 62.37s |\n", - "| DeepKriging_sphere | 1000 | 1.5576 | 1.248 | 1.0107 | -0.0814 | 43.73s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.5989 | 1.2645 | 1.0206 | -0.11 | 58.52s |\n", - "| UniversalKriging | 50 | 0.2267 | 0.4761 | 0.3774 | 0.8426 | 2.05s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:06:17\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -29.0577 (±0.0288), Variance: 2.0742, Range: [-31.7408, -24.7086]\n", - "z mean: -29.0193 (±0.0506), Variance: 6.4047, Range: [-45.2758, -10.4023]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0013, nugget=4.5203\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7322 | 1.3161 | 1.0419 | 0.2109 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1507 | 0.3882 | 0.309 | 0.9313 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.6558 | 1.6297 | 1.2157 | -0.2098 | 55.71s |\n", - "| DeepKriging_sphere | 1000 | 2.2748 | 1.5082 | 1.3035 | -0.0362 | 33.36s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.0107 | 1.418 | 1.1618 | 0.0841 | 44.35s |\n", - "| UniversalKriging | 50 | 0.1507 | 0.3882 | 0.309 | 0.9313 | 1.79s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:09:38\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.1290 (±0.0234), Variance: 1.3666, Range: [-29.9659, -24.5598]\n", - "z mean: -27.0950 (±0.0448), Variance: 5.0134, Range: [-36.6441, -14.0393]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0024, nugget=3.6283\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8852 | 1.373 | 0.9515 | -0.5352 | 0.38s |\n", - "| OLS_sphere | 50 | 0.2161 | 0.4649 | 0.3449 | 0.824 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.6518 | 1.6284 | 1.0576 | -1.1595 | 58.46s |\n", - "| DeepKriging_sphere | 1000 | 1.3417 | 1.1583 | 0.9256 | -0.0926 | 47.44s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.3463 | 1.1603 | 0.9246 | -0.0964 | 85.07s |\n", - "| UniversalKriging | 50 | 0.2161 | 0.4649 | 0.3448 | 0.824 | 2.05s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:13:56\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.5854 (±0.0296), Variance: 2.1860, Range: [-31.8643, -24.7681]\n", - "z mean: -28.5285 (±0.0486), Variance: 5.9122, Range: [-44.3943, -4.7392]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0041, nugget=3.6343\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9699 | 1.4035 | 0.9885 | 0.1251 | 0.33s |\n", - "| OLS_sphere | 50 | 0.1935 | 0.4399 | 0.3592 | 0.914 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.4476 | 1.5645 | 1.0678 | -0.0871 | 60.42s |\n", - "| DeepKriging_sphere | 1000 | 2.4885 | 1.5775 | 1.2696 | -0.1053 | 58.71s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.3732 | 1.5405 | 1.2396 | -0.054 | 59.70s |\n", - "| UniversalKriging | 50 | 0.1935 | 0.4398 | 0.3592 | 0.9141 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:18:04\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.4605 (±0.0225), Variance: 1.2701, Range: [-29.8544, -24.2683]\n", - "z mean: -27.4843 (±0.0448), Variance: 5.0100, Range: [-37.7929, -12.9884]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0048, nugget=3.7959\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 6.6851 | 2.5856 | 0.9017 | -4.0935 | 0.37s |\n", - "| OLS_sphere | 50 | 0.2036 | 0.4512 | 0.3688 | 0.8449 | 0.00s |\n", - "| DeepKriging_wendland | -- | 1.5686 | 1.2524 | 0.9191 | -0.1951 | 57.46s |\n", - "| DeepKriging_sphere | 1000 | 1.4214 | 1.1922 | 0.9975 | -0.083 | 39.84s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.4219 | 1.1924 | 0.9985 | -0.0834 | 35.85s |\n", - "| UniversalKriging | 50 | 0.2033 | 0.4508 | 0.3685 | 0.8451 | 0.91s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:21:27\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.8752 (±0.0295), Variance: 2.1761, Range: [-32.0112, -24.6711]\n", - "z mean: -28.9001 (±0.0534), Variance: 7.1172, Range: [-67.3296, -3.4772]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0036, nugget=4.8419\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 57.82 | 7.6039 | 1.3633 | -28.8997 | 0.40s |\n", - "| OLS_sphere | 50 | 0.2097 | 0.4579 | 0.3602 | 0.8916 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.0968 | 1.448 | 1.0057 | -0.0843 | 52.21s |\n", - "| DeepKriging_sphere | 1000 | 2.2017 | 1.4838 | 1.1459 | -0.1386 | 50.24s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.1112 | 1.453 | 1.1393 | -0.0918 | 39.41s |\n", - "| UniversalKriging | 50 | 0.2097 | 0.458 | 0.3602 | 0.8916 | 1.95s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:25:00\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.2588 (±0.0259), Variance: 1.6784, Range: [-31.7387, -25.5123]\n", - "z mean: -28.2575 (±0.0460), Variance: 5.3000, Range: [-39.3180, -15.6456]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0009, nugget=3.5484\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4243 | 1.1934 | 0.9689 | 0.0791 | 0.36s |\n", - "| OLS_sphere | 50 | 0.1187 | 0.3445 | 0.2757 | 0.9233 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.0525 | 1.4327 | 1.1303 | -0.327 | 58.43s |\n", - "| DeepKriging_sphere | 1000 | 1.7884 | 1.3373 | 1.1058 | -0.1563 | 52.32s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.7287 | 1.3148 | 1.0793 | -0.1176 | 39.04s |\n", - "| UniversalKriging | 50 | 0.1187 | 0.3445 | 0.2757 | 0.9233 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:28:43\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.0486 (±0.0303), Variance: 2.2891, Range: [-31.2402, -23.3420]\n", - "z mean: -28.0340 (±0.0498), Variance: 6.1998, Range: [-43.9913, -7.6340]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0081, nugget=4.1999\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 7.4984 | 2.7383 | 1.159 | -2.2418 | 0.33s |\n", - "| OLS_sphere | 50 | 0.2028 | 0.4504 | 0.3558 | 0.9123 | 0.00s |\n", - "| DeepKriging_wendland | -- | 3.708 | 1.9256 | 1.2088 | -0.6031 | 51.59s |\n", - "| DeepKriging_sphere | 1000 | 2.6896 | 1.64 | 1.3448 | -0.1628 | 45.68s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.5066 | 1.5832 | 1.3146 | -0.0837 | 36.94s |\n", - "| UniversalKriging | 50 | 0.2027 | 0.4502 | 0.3556 | 0.9124 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:32:11\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -29.3998 (±0.0286), Variance: 2.0475, Range: [-32.6460, -26.2923]\n", - "z mean: -29.4381 (±0.0472), Variance: 5.5705, Range: [-43.0903, -17.7782]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0034, nugget=3.4043\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4365 | 1.1985 | 0.9516 | 0.2281 | 0.38s |\n", - "| OLS_sphere | 50 | 0.1595 | 0.3993 | 0.3224 | 0.9143 | 0.00s |\n", - "| DeepKriging_wendland | -- | 6.6792 | 2.5844 | 1.4402 | -2.5891 | 52.57s |\n", - "| DeepKriging_sphere | 1000 | 2.1067 | 1.4514 | 1.2144 | -0.132 | 47.71s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.8709 | 1.3678 | 1.1633 | -0.0053 | 33.30s |\n", - "| UniversalKriging | 50 | 0.1595 | 0.3993 | 0.3224 | 0.9143 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:35:40\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.5699 (±0.0268), Variance: 1.7948, Range: [-30.9818, -24.3724]\n", - "z mean: -27.5778 (±0.0475), Variance: 5.6364, Range: [-37.8024, -15.2348]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0086, nugget=3.8008\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5774 | 1.2559 | 0.9566 | 0.1479 | 0.64s |\n", - "| OLS_sphere | 50 | 0.1504 | 0.3878 | 0.3113 | 0.9188 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.9001 | 1.3784 | 1.0575 | -0.0264 | 53.44s |\n", - "| DeepKriging_sphere | 1000 | 1.915 | 1.3838 | 1.1421 | -0.0345 | 31.68s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.8927 | 1.3757 | 1.1305 | -0.0224 | 42.14s |\n", - "| UniversalKriging | 50 | 0.1501 | 0.3874 | 0.311 | 0.9189 | 1.75s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:39:04\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.7588 (±0.0466), Variance: 5.4387, Range: [-32.2197, -22.6777]\n", - "z mean: -27.7867 (±0.0598), Variance: 8.9383, Range: [-42.4071, -13.2789]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0012, nugget=3.8522\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 20.336 | 4.5095 | 1.8059 | -2.9533 | 0.43s |\n", - "| OLS_sphere | 50 | 0.1556 | 0.3944 | 0.3169 | 0.9698 | 0.00s |\n", - "| DeepKriging_wendland | -- | 4.5397 | 2.1307 | 1.5902 | 0.1175 | 63.53s |\n", - "| DeepKriging_sphere | 1000 | 5.4586 | 2.3364 | 1.9819 | -0.0612 | 40.71s |\n", - "| DeepKriging_sphere_Huber | 1000 | 5.4928 | 2.3437 | 2.0021 | -0.0678 | 32.05s |\n", - "| UniversalKriging | 50 | 0.1555 | 0.3944 | 0.3169 | 0.9698 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:42:38\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.9050 (±0.0292), Variance: 2.1372, Range: [-32.2126, -24.8579]\n", - "z mean: -27.8866 (±0.0503), Variance: 6.3319, Range: [-47.4840, -2.4030]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0042, nugget=4.4582\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|----------|--------|\n", - "| OLS_wendland | -- | 26.0702 | 5.1059 | 1.4557 | -11.0003 | 0.35s |\n", - "| OLS_sphere | 50 | 0.1938 | 0.4403 | 0.3588 | 0.9108 | 0.01s |\n", - "| DeepKriging_wendland | -- | 3.207 | 1.7908 | 1.1948 | -0.4762 | 54.38s |\n", - "| DeepKriging_sphere | 1000 | 2.2878 | 1.5126 | 1.2452 | -0.0531 | 41.57s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.2877 | 1.5125 | 1.2318 | -0.053 | 45.45s |\n", - "| UniversalKriging | 50 | 0.1939 | 0.4403 | 0.3588 | 0.9108 | 1.78s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:46:19\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.1118 (±0.0252), Variance: 1.5932, Range: [-31.2361, -24.1632]\n", - "z mean: -28.0875 (±0.0488), Variance: 5.9534, Range: [-53.5192, -13.3183]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0107, nugget=4.1297\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.5564 | 1.2476 | 0.8855 | -0.0004 | 0.36s |\n", - "| OLS_sphere | 50 | 0.2065 | 0.4544 | 0.3599 | 0.8673 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.1141 | 1.454 | 1.0149 | -0.3589 | 63.85s |\n", - "| DeepKriging_sphere | 1000 | 1.8394 | 1.3562 | 1.0336 | -0.1823 | 44.17s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.6594 | 1.2882 | 1.045 | -0.0666 | 40.14s |\n", - "| UniversalKriging | 50 | 0.2062 | 0.4541 | 0.3597 | 0.8674 | 2.00s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:50:07\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.3312 (±0.0199), Variance: 0.9929, Range: [-30.7507, -25.4370]\n", - "z mean: -28.3073 (±0.0435), Variance: 4.7259, Range: [-41.1714, -9.2106]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0029, nugget=3.8604\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.4897 | 1.5779 | 0.9181 | -1.0761 | 0.41s |\n", - "| OLS_sphere | 50 | 0.2043 | 0.452 | 0.3543 | 0.8296 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.5157 | 1.2311 | 0.9371 | -0.2639 | 54.66s |\n", - "| DeepKriging_sphere | 1000 | 1.4568 | 1.207 | 0.9905 | -0.2147 | 46.26s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.6165 | 1.2714 | 1.0834 | -0.348 | 34.55s |\n", - "| UniversalKriging | 50 | 0.2043 | 0.452 | 0.3543 | 0.8296 | 2.15s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:53:44\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.6432 (±0.0309), Variance: 2.3891, Range: [-33.2603, -25.2993]\n", - "z mean: -28.6140 (±0.0498), Variance: 6.1982, Range: [-38.5558, -17.5531]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0072, nugget=3.7146\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.1694 | 1.0814 | 0.8711 | 0.4148 | 0.38s |\n", - "| OLS_sphere | 50 | 0.1685 | 0.4104 | 0.3196 | 0.9157 | 0.00s |\n", - "| DeepKriging_wendland | -- | 3.0184 | 1.7374 | 1.0635 | -0.5106 | 54.58s |\n", - "| DeepKriging_sphere | 1000 | 2.5488 | 1.5965 | 1.1862 | -0.2756 | 31.85s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.4335 | 1.56 | 1.1352 | -0.2179 | 32.68s |\n", - "| UniversalKriging | 50 | 0.1684 | 0.4104 | 0.3196 | 0.9157 | 1.94s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 10:57:05\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.3204 (±0.0367), Variance: 3.3623, Range: [-30.3941, -21.9787]\n", - "z mean: -27.4039 (±0.0530), Variance: 7.0176, Range: [-44.6020, -13.8933]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0053, nugget=3.8139\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 11.0672 | 3.3267 | 1.3867 | -1.9225 | 0.44s |\n", - "| OLS_sphere | 50 | 0.1805 | 0.4248 | 0.3321 | 0.9523 | 0.00s |\n", - "| DeepKriging_wendland | -- | 3.302 | 1.8171 | 1.1951 | 0.1281 | 60.17s |\n", - "| DeepKriging_sphere | 1000 | 3.8287 | 1.9567 | 1.592 | -0.011 | 46.36s |\n", - "| DeepKriging_sphere_Huber | 1000 | 4.034 | 2.0085 | 1.566 | -0.0652 | 37.54s |\n", - "| UniversalKriging | 50 | 0.1804 | 0.4248 | 0.3321 | 0.9524 | 2.10s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:00:52\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.4425 (±0.0263), Variance: 1.7294, Range: [-29.8969, -24.4000]\n", - "z mean: -27.4299 (±0.0486), Variance: 5.9007, Range: [-50.4677, -16.0953]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.0163, nugget=4.4470\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8201 | 1.3491 | 1.0935 | -0.0689 | 0.32s |\n", - "| OLS_sphere | 50 | 0.1582 | 0.3977 | 0.3215 | 0.9071 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.3444 | 1.5311 | 1.1849 | -0.3768 | 63.88s |\n", - "| DeepKriging_sphere | 1000 | 1.8531 | 1.3613 | 1.1525 | -0.0883 | 42.01s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.0172 | 1.4203 | 1.2462 | -0.1847 | 47.42s |\n", - "| UniversalKriging | 50 | 0.1576 | 0.397 | 0.321 | 0.9074 | 1.82s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:04:50\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -27.3482 (±0.0293), Variance: 2.1441, Range: [-30.6805, -24.0854]\n", - "z mean: -27.3207 (±0.0504), Variance: 6.3624, Range: [-44.2214, -13.6456]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0020, nugget=4.3783\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.7315 | 1.3158 | 1.0596 | 0.2289 | 0.42s |\n", - "| OLS_sphere | 50 | 0.1506 | 0.3881 | 0.3269 | 0.9329 | 0.01s |\n", - "| DeepKriging_wendland | -- | 2.3316 | 1.527 | 1.1893 | -0.0384 | 51.29s |\n", - "| DeepKriging_sphere | 1000 | 2.4522 | 1.566 | 1.3505 | -0.0921 | 39.92s |\n", - "| DeepKriging_sphere_Huber | 1000 | 2.4496 | 1.5651 | 1.3554 | -0.0909 | 37.57s |\n", - "| UniversalKriging | 50 | 0.1506 | 0.388 | 0.3269 | 0.9329 | 2.05s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:08:24\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -28.5211 (±0.0263), Variance: 1.7237, Range: [-31.5498, -25.1678]\n", - "z mean: -28.5364 (±0.0478), Variance: 5.7070, Range: [-45.1217, -14.1057]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0047, nugget=4.1296\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.4945 | 1.5794 | 1.078 | -0.3609 | 0.35s |\n", - "| OLS_sphere | 50 | 0.1571 | 0.3964 | 0.318 | 0.9143 | 0.00s |\n", - "| DeepKriging_wendland | -- | 2.6814 | 1.6375 | 1.1244 | -0.4629 | 64.20s |\n", - "| DeepKriging_sphere | 1000 | 1.8804 | 1.3713 | 1.1366 | -0.0259 | 52.94s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.8912 | 1.3752 | 1.1124 | -0.0317 | 50.14s |\n", - "| UniversalKriging | 50 | 0.157 | 0.3962 | 0.3179 | 0.9143 | 1.91s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:12:38\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: -29.3659 (±0.0249), Variance: 1.5456, Range: [-32.0513, -25.5264]\n", - "z mean: -29.3302 (±0.0475), Variance: 5.6332, Range: [-42.7673, -11.6365]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=1.3620, nugget=2.6223\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.3051 | 1.1424 | 0.889 | 0.2242 | 0.36s |\n", - "| OLS_sphere | 50 | 0.1929 | 0.4393 | 0.3539 | 0.8853 | 0.01s |\n", - "| DeepKriging_wendland | -- | 1.9039 | 1.3798 | 0.9644 | -0.1318 | 69.99s |\n", - "| DeepKriging_sphere | 1000 | 1.7978 | 1.3408 | 1.0856 | -0.0687 | 41.70s |\n", - "| DeepKriging_sphere_Huber | 1000 | 1.654 | 1.2861 | 1.0487 | 0.0168 | 43.11s |\n", - "| UniversalKriging | 50 | 0.1904 | 0.4364 | 0.3516 | 0.8868 | 4.80s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:16:43\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.017052, - "end_time": "2026-01-19T03:16:43.814927", - "exception": false, - "start_time": "2026-01-19T03:16:43.797875", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:43.854177Z", - "iopub.status.busy": "2026-01-19T03:16:43.853281Z", - "iopub.status.idle": "2026-01-19T03:16:43.870813Z", - "shell.execute_reply": "2026-01-19T03:16:43.867168Z" - }, - "papermill": { - "duration": 0.041665, - "end_time": "2026-01-19T03:16:43.874144", - "exception": false, - "start_time": "2026-01-19T03:16:43.832479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 50, 'DeepKriging_sphere': 1000, 'DeepKriging_sphere_Huber': 1000, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------|-----------|-----------|-------------|\n", - "| OLS_wendland | 7.62±10.98 | 2.32±1.50 | 1.07±0.22 | -4.52±11.00 |\n", - "| OLS_sphere | 0.18±0.03 | 0.43±0.03 | 0.34±0.02 | 0.88±0.07 |\n", - "| DeepKriging_wendland | 2.99±2.36 | 1.66±0.48 | 1.10±0.15 | -0.78±1.29 |\n", - "| DeepKriging_sphere | 2.07±0.83 | 1.41±0.28 | 1.15±0.25 | -0.12±0.10 |\n", - "| DeepKriging_sphere_Huber | 2.04±0.86 | 1.40±0.29 | 1.14±0.25 | -0.10±0.10 |\n", - "| UniversalKriging | 0.18±0.03 | 0.43±0.03 | 0.34±0.02 | 0.88±0.07 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 0.43±0.03)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 11092.702154, - "end_time": "2026-01-19T03:16:47.536377", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-19T00:11:54.834223", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_outliers5.ipynb b/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_outliers5.ipynb deleted file mode 100644 index 7766e22..0000000 --- a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_outliers5.ipynb +++ /dev/null @@ -1,3331 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-18 18:03:53.955813: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768730633.990609 1994040 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768730633.997030 1994040 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768730634.010046 1994040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768730634.010069 1994040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768730634.010070 1994040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768730634.010071 1994040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, outlier_ratio, outlier_multiplier, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + outliers.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term\n", - " # f(x₁, x₂) = -(x₂ + 47) sin(√|x₂ + x₁/2 + 47|) - x₁ sin(√|x₁ - (x₂ + 47)|)\n", - " term1 = -(x2 + 47.0) * np.sin(np.sqrt(np.abs(x2 + x1/2.0 + 47.0)))\n", - " term2 = -x1 * np.sin(np.sqrt(np.abs(x1 - (x2 + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = mean_term + (L @ z_std).astype(np.float32)\n", - "\n", - " # Add outliers\n", - " idx_outliers = rng.choice(num_sample, size=int(num_sample*outlier_ratio), replace=False)\n", - " z[idx_outliers] *= outlier_multiplier\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.123430Z", - "iopub.status.busy": "2026-01-08T18:26:41.122639Z", - "iopub.status.idle": "2026-01-08T18:26:41.163754Z", - "shell.execute_reply": "2026-01-08T18:26:41.160797Z" - }, - "papermill": { - "duration": 0.049001, - "end_time": "2026-01-08T18:26:41.166646", - "exception": false, - "start_time": "2026-01-08T18:26:41.117645", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5342 (±0.3483), Variance: 303.3496, Range: [-148.3872, -25.2544]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 385.1899 348.9755 \n", - " 100 382.3454 352.1098 \n", - " 200 393.9730 357.8004 \n", - " 500 480.0644 488.9815 \n", - " 700 595.5255 583.8467 \n", - " 1000 382.2956 345.1913 *\n", - " 1400 382.2956 345.1913 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768730674.003770 1994040 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768730676.347095 1994288 service.cc:152] XLA service 0x79955001c2e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768730676.347176 1994288 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1768730676.727540 1994288 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1768730687.042239 1994288 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7995801f5990> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x79956dba65f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 385.7465 351.2954 \n", - " 100 383.3770 354.1898 \n", - " 200 380.7176 342.4533 \n", - " 500 397.1904 364.0011 \n", - " 700 384.3778 347.0231 \n", - " 1000 371.0421 353.8011 *\n", - " 1400 375.1711 354.9935 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 4.8868 354.8333 *\n", - " 100 4.9995 355.3180 \n", - " 200 4.9298 355.3113 \n", - " 500 5.2199 358.0242 \n", - " 700 5.1168 358.0676 \n", - " 1000 5.1256 353.5413 \n", - " 1400 5.1234 354.0348 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1448, nugget=280.5830\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0950, nugget=272.7243\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0225, nugget=257.8056\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0032, sigma²=0.0013, nugget=206.5211\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0032, sigma²=0.0009, nugget=181.6578\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0020, sigma²=0.0007, nugget=140.8767\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0020, sigma²=0.0005, nugget=84.9606\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 385.1911 348.9778 \n", - " 100 382.3485 352.1134 *\n", - " 200 393.9744 357.8019 \n", - " 500 480.0641 488.9812 \n", - " 700 595.5281 583.8489 \n", - " 1000 921.0123 854.7858 \n", - " 1400 36391.4630 6437.5604 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0950, nugget=272.7243\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 372.89 | 19.3104 | 6.3257 | -0.0794 | 0.41s |\n", - "| OLS_sphere | 1000 | 345.191 | 18.5793 | 5.6432 | 0.0008 | 0.14s |\n", - "| DeepKriging_wendland | -- | 385.569 | 19.6359 | 5.3527 | -0.1161 | 40.44s |\n", - "| DeepKriging_sphere | 1000 | 354.519 | 18.8287 | 6.01 | -0.0262 | 28.21s |\n", - "| DeepKriging_sphere_Huber | 50 | 354.034 | 18.8158 | 3.5295 | -0.0248 | 25.58s |\n", - "| UniversalKriging | 100 | 352.113 | 18.7647 | 6.3983 | -0.0193 | 2.07s |\n" - ] - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:14:22\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.4631 (±0.3457), Variance: 298.8512, Range: [-156.4758, -24.4243]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0473, nugget=274.3808\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 446.495 | 21.1304 | 7.244 | -0.0569 | 0.45s |\n", - "| OLS_sphere | 1000 | 423.894 | 20.5887 | 6.4513 | -0.0034 | 0.13s |\n", - "| DeepKriging_wendland | -- | 445.311 | 21.1024 | 6.2006 | -0.0541 | 42.86s |\n", - "| DeepKriging_sphere | 1000 | 433.179 | 20.813 | 6.2625 | -0.0254 | 34.05s |\n", - "| DeepKriging_sphere_Huber | 50 | 438.932 | 20.9507 | 4.4144 | -0.039 | 31.87s |\n", - "| UniversalKriging | 100 | 434.368 | 20.8415 | 7.251 | -0.0282 | 2.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:16:44\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.0064 (±0.3486), Variance: 303.7881, Range: [-153.5000, -25.1404]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0771, nugget=301.3442\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 294.06 | 17.1482 | 6.1078 | -0.1403 | 0.46s |\n", - "| OLS_sphere | 1000 | 258.832 | 16.0883 | 5.1819 | -0.0037 | 0.14s |\n", - "| DeepKriging_wendland | -- | 287.693 | 16.9615 | 5.7429 | -0.1156 | 54.18s |\n", - "| DeepKriging_sphere | 1000 | 263.318 | 16.2271 | 5.1695 | -0.0211 | 37.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 271.095 | 16.465 | 2.8948 | -0.0512 | 30.22s |\n", - "| UniversalKriging | 100 | 273.72 | 16.5445 | 5.6751 | -0.0614 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:19:20\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.9432 (±0.3504), Variance: 307.0212, Range: [-149.9822, -25.1867]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0346, nugget=302.8486\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 275.527 | 16.599 | 6.2739 | -0.3394 | 0.45s |\n", - "| OLS_sphere | 1000 | 206.902 | 14.3841 | 4.6668 | -0.0058 | 0.15s |\n", - "| DeepKriging_wendland | -- | 315.274 | 17.756 | 6.3695 | -0.5326 | 57.57s |\n", - "| DeepKriging_sphere | 1000 | 214.807 | 14.6563 | 4.4269 | -0.0442 | 25.41s |\n", - "| DeepKriging_sphere_Huber | 50 | 208.302 | 14.4327 | 2.2249 | -0.0126 | 31.47s |\n", - "| UniversalKriging | 100 | 229.469 | 15.1482 | 5.5354 | -0.1155 | 1.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:21:50\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.3475 (±0.3404), Variance: 289.6894, Range: [-152.1286, -24.9221]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0612, nugget=288.2148\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 595.447 | 24.4018 | 7.2374 | -1.2405 | 0.42s |\n", - "| OLS_sphere | 1000 | 265.09 | 16.2816 | 5.1876 | 0.0026 | 0.14s |\n", - "| DeepKriging_wendland | -- | 430.292 | 20.7435 | 6.1658 | -0.619 | 57.10s |\n", - "| DeepKriging_sphere | 1000 | 284.202 | 16.8583 | 5.8181 | -0.0694 | 23.82s |\n", - "| DeepKriging_sphere_Huber | 50 | 279.236 | 16.7103 | 3.0984 | -0.0507 | 32.05s |\n", - "| UniversalKriging | 100 | 297.178 | 17.2389 | 5.8162 | -0.1182 | 2.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:24:16\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.9161 (±0.3496), Variance: 305.5782, Range: [-149.4112, -25.8921]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0956, nugget=334.4359\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 127.789 | 11.3044 | 4.8052 | -0.2308 | 0.44s |\n", - "| OLS_sphere | 1000 | 108.727 | 10.4272 | 4.0212 | -0.0472 | 0.14s |\n", - "| DeepKriging_wendland | -- | 124.315 | 11.1496 | 4.0411 | -0.1974 | 61.06s |\n", - "| DeepKriging_sphere | 1000 | 111.383 | 10.5538 | 3.6456 | -0.0728 | 33.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 105.374 | 10.2652 | 1.5019 | -0.0149 | 33.13s |\n", - "| UniversalKriging | 100 | 121.627 | 11.0285 | 5.119 | -0.1715 | 1.65s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:27:01\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.1615 (±0.3422), Variance: 292.7034, Range: [-149.7691, -24.5590]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0937, nugget=285.9249\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 304.771 | 17.4577 | 6.2124 | -0.0651 | 0.39s |\n", - "| OLS_sphere | 1000 | 289.966 | 17.0284 | 5.4551 | -0.0134 | 0.14s |\n", - "| DeepKriging_wendland | -- | 279.773 | 16.7264 | 4.4251 | 0.0222 | 62.71s |\n", - "| DeepKriging_sphere | 1000 | 296.796 | 17.2278 | 5.5604 | -0.0373 | 37.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 292.349 | 17.0982 | 3.07 | -0.0217 | 33.04s |\n", - "| UniversalKriging | 100 | 297.677 | 17.2533 | 6.1324 | -0.0403 | 2.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:29:51\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.6367 (±0.3473), Variance: 301.5160, Range: [-151.8810, -24.4321]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1220, nugget=300.1988\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 257.116 | 16.0348 | 5.8934 | -0.4126 | 0.40s |\n", - "| OLS_sphere | 1000 | 183.952 | 13.5629 | 4.6375 | -0.0106 | 0.14s |\n", - "| DeepKriging_wendland | -- | 204.38 | 14.2962 | 4.4892 | -0.1229 | 61.23s |\n", - "| DeepKriging_sphere | 1000 | 191.415 | 13.8353 | 3.9326 | -0.0516 | 19.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 188.49 | 13.7292 | 2.2491 | -0.0356 | 29.02s |\n", - "| UniversalKriging | 100 | 195.952 | 13.9983 | 5.4562 | -0.0766 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:32:21\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5026 (±0.3445), Variance: 296.6230, Range: [-150.9647, -24.1754]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1766, nugget=278.2441\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 263.131 | 16.2213 | 5.7319 | -0.1204 | 0.41s |\n", - "| OLS_sphere | 1000 | 236.229 | 15.3697 | 4.9774 | -0.0058 | 0.14s |\n", - "| DeepKriging_wendland | -- | 260.154 | 16.1293 | 4.9395 | -0.1077 | 59.83s |\n", - "| DeepKriging_sphere | 1000 | 249.709 | 15.8022 | 5.7471 | -0.0632 | 38.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 241.514 | 15.5407 | 2.8852 | -0.0283 | 32.17s |\n", - "| UniversalKriging | 100 | 265.994 | 16.3093 | 6.2002 | -0.1326 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:35:13\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.1537 (±0.3443), Variance: 296.3710, Range: [-158.2778, -23.5979]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1347, nugget=276.1438\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 481.106 | 21.9341 | 7.3019 | -0.2644 | 0.39s |\n", - "| OLS_sphere | 1000 | 381.409 | 19.5297 | 5.9997 | -0.0024 | 0.15s |\n", - "| DeepKriging_wendland | -- | 389.043 | 19.7242 | 6.2689 | -0.0225 | 54.61s |\n", - "| DeepKriging_sphere | 1000 | 415.096 | 20.3739 | 5.8542 | -0.0909 | 51.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 389.805 | 19.7435 | 3.8695 | -0.0245 | 34.12s |\n", - "| UniversalKriging | 100 | 380.592 | 19.5088 | 6.6611 | -0.0003 | 2.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:38:13\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.4312 (±0.3462), Variance: 299.6793, Range: [-150.3259, -24.7975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1306, nugget=286.6541\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 259.224 | 16.1004 | 5.7065 | -0.0206 | 0.38s |\n", - "| OLS_sphere | 1000 | 253.797 | 15.931 | 4.93 | 0.0008 | 0.12s |\n", - "| DeepKriging_wendland | -- | 292.162 | 17.0928 | 6.6938 | -0.1503 | 60.60s |\n", - "| DeepKriging_sphere | 1000 | 257.819 | 16.0567 | 5.5166 | -0.0151 | 27.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 255.825 | 15.9945 | 2.6375 | -0.0072 | 33.03s |\n", - "| UniversalKriging | 100 | 249.006 | 15.7799 | 5.329 | 0.0196 | 2.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:40:56\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.2694 (±0.3430), Variance: 294.1927, Range: [-151.3516, -24.2320]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1708, nugget=272.7009\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2411.06 | 49.1026 | 11.5381 | -4.0682 | 0.41s |\n", - "| OLS_sphere | 1000 | 481.564 | 21.9446 | 7.0292 | -0.0123 | 0.18s |\n", - "| DeepKriging_wendland | -- | 511.872 | 22.6246 | 7.1431 | -0.076 | 45.29s |\n", - "| DeepKriging_sphere | 1000 | 497.845 | 22.3124 | 6.6167 | -0.0465 | 19.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 492.653 | 22.1958 | 4.866 | -0.0356 | 27.07s |\n", - "| UniversalKriging | 100 | 503.098 | 22.4298 | 7.8658 | -0.0575 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:43:13\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.9856 (±0.3513), Variance: 308.4890, Range: [-160.3064, -24.4764]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.2355, nugget=293.5022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 561.855 | 23.7035 | 8.6076 | -0.1624 | 0.42s |\n", - "| OLS_sphere | 1000 | 485.382 | 22.0314 | 7.0129 | -0.0042 | 0.11s |\n", - "| DeepKriging_wendland | -- | 533.424 | 23.096 | 7.0147 | -0.1036 | 50.10s |\n", - "| DeepKriging_sphere | 1000 | 488.452 | 22.1009 | 7.1753 | -0.0105 | 43.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 509.763 | 22.5779 | 5.0245 | -0.0546 | 31.10s |\n", - "| UniversalKriging | 100 | 502.72 | 22.4214 | 7.8539 | -0.0401 | 0.25s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:46:03\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -32.3531 (±0.3673), Variance: 337.3224, Range: [-156.4759, -25.5211]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0504, nugget=328.4898\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 350.05 | 18.7096 | 6.5005 | -0.0518 | 0.42s |\n", - "| OLS_sphere | 1000 | 333.01 | 18.2486 | 5.7752 | -0.0006 | 0.12s |\n", - "| DeepKriging_wendland | -- | 359.205 | 18.9527 | 4.7615 | -0.0793 | 53.48s |\n", - "| DeepKriging_sphere | 1000 | 342.613 | 18.5098 | 5.4448 | -0.0294 | 22.65s |\n", - "| DeepKriging_sphere_Huber | 50 | 339.24 | 18.4185 | 3.1526 | -0.0193 | 34.59s |\n", - "| UniversalKriging | 100 | 341.79 | 18.4876 | 6.3791 | -0.027 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:48:43\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -32.2217 (±0.3645), Variance: 332.1217, Range: [-160.7573, -25.2573]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1203, nugget=273.6872\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 400.474 | 20.0119 | 6.4718 | -0.0593 | 0.43s |\n", - "| OLS_sphere | 1000 | 382.725 | 19.5634 | 5.8302 | -0.0124 | 0.13s |\n", - "| DeepKriging_wendland | -- | 424.188 | 20.5958 | 6.4102 | -0.122 | 62.02s |\n", - "| DeepKriging_sphere | 1000 | 408.126 | 20.2021 | 6.6723 | -0.0796 | 16.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 386.526 | 19.6603 | 3.6904 | -0.0224 | 31.29s |\n", - "| UniversalKriging | 100 | 401.975 | 20.0493 | 6.7898 | -0.0633 | 2.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:51:20\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.0523 (±0.3541), Variance: 313.4838, Range: [-150.0867, -25.6188]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0570, nugget=289.8856\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 328.032 | 18.1117 | 6.5105 | -0.2462 | 0.45s |\n", - "| OLS_sphere | 1000 | 263.346 | 16.2279 | 4.9088 | -0.0004 | 0.14s |\n", - "| DeepKriging_wendland | -- | 288.227 | 16.9773 | 5.7253 | -0.0949 | 63.75s |\n", - "| DeepKriging_sphere | 1000 | 267.618 | 16.359 | 4.9688 | -0.0166 | 32.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 267.195 | 16.3461 | 2.7413 | -0.015 | 29.38s |\n", - "| UniversalKriging | 100 | 274.147 | 16.5574 | 5.851 | -0.0415 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:54:16\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.4900 (±0.3501), Variance: 306.4136, Range: [-161.4160, -24.8313]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1665, nugget=306.9925\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 342.13 | 18.4968 | 6.7188 | -0.7644 | 0.40s |\n", - "| OLS_sphere | 1000 | 195.124 | 13.9687 | 4.6102 | -0.0063 | 0.13s |\n", - "| DeepKriging_wendland | -- | 236.315 | 15.3725 | 4.4714 | -0.2187 | 64.49s |\n", - "| DeepKriging_sphere | 1000 | 209.951 | 14.4897 | 4.7857 | -0.0827 | 28.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 197.176 | 14.0419 | 2.2694 | -0.0168 | 28.80s |\n", - "| UniversalKriging | 100 | 215.614 | 14.6838 | 5.5512 | -0.1119 | 1.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 18:57:07\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.4889 (±0.3558), Variance: 316.4384, Range: [-152.0486, -24.7329]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.2076, nugget=308.9801\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 371.29 | 19.2689 | 6.7422 | -0.189 | 0.41s |\n", - "| OLS_sphere | 1000 | 312.228 | 17.67 | 5.4885 | 0.0002 | 0.17s |\n", - "| DeepKriging_wendland | -- | 321.061 | 17.9182 | 5.5082 | -0.0281 | 67.41s |\n", - "| DeepKriging_sphere | 1000 | 313.116 | 17.6951 | 5.0945 | -0.0027 | 34.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 321.712 | 17.9363 | 3.2111 | -0.0302 | 31.10s |\n", - "| UniversalKriging | 100 | 321.15 | 17.9207 | 6.7117 | -0.0284 | 1.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:00:12\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.9377 (±0.3515), Variance: 308.9521, Range: [-159.2312, -24.9143]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0573, nugget=295.4351\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1006.83 | 31.7306 | 8.4933 | -2.1607 | 0.37s |\n", - "| OLS_sphere | 1000 | 319.041 | 17.8617 | 5.4911 | -0.0016 | 0.15s |\n", - "| DeepKriging_wendland | -- | 372.807 | 19.3082 | 6.1689 | -0.1703 | 64.67s |\n", - "| DeepKriging_sphere | 1000 | 337.174 | 18.3623 | 5.9623 | -0.0585 | 61.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 322.904 | 17.9695 | 3.2785 | -0.0137 | 31.34s |\n", - "| UniversalKriging | 100 | 320.271 | 17.8961 | 5.9229 | -0.0054 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:03:41\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.0331 (±0.3524), Variance: 310.4290, Range: [-153.3232, -25.5212]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1073, nugget=318.2801\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|----------|--------|\n", - "| OLS_wendland | -- | 4696.88 | 68.5338 | 10.0592 | -21.8234 | 0.39s |\n", - "| OLS_sphere | 1000 | 209.684 | 14.4805 | 4.8115 | -0.0189 | 0.12s |\n", - "| DeepKriging_wendland | -- | 246.905 | 15.7132 | 5.5922 | -0.1998 | 63.70s |\n", - "| DeepKriging_sphere | 1000 | 215.579 | 14.6826 | 4.1461 | -0.0476 | 37.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 209.453 | 14.4725 | 2.2702 | -0.0178 | 37.36s |\n", - "| UniversalKriging | 100 | 216.8 | 14.7241 | 5.577 | -0.0535 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:06:54\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5526 (±0.3467), Variance: 300.5349, Range: [-148.4326, -25.5507]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1892, nugget=302.5615\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 205.793 | 14.3455 | 4.9003 | -1.1194 | 0.48s |\n", - "| OLS_sphere | 1000 | 101.576 | 10.0785 | 3.7429 | -0.0461 | 0.14s |\n", - "| DeepKriging_wendland | -- | 145.783 | 12.0741 | 4.136 | -0.5014 | 61.50s |\n", - "| DeepKriging_sphere | 1000 | 109.137 | 10.4468 | 3.7272 | -0.1239 | 43.21s |\n", - "| DeepKriging_sphere_Huber | 50 | 97.8131 | 9.89 | 1.3144 | -0.0073 | 30.29s |\n", - "| UniversalKriging | 100 | 123.604 | 11.1177 | 4.5896 | -0.2729 | 1.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:10:04\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.8471 (±0.3533), Variance: 312.0722, Range: [-152.3565, -24.6796]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1308, nugget=299.1689\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 382.303 | 19.5526 | 6.4731 | -0.0655 | 0.38s |\n", - "| OLS_sphere | 1000 | 358.447 | 18.9327 | 5.9158 | 0.0009 | 0.18s |\n", - "| DeepKriging_wendland | -- | 383.13 | 19.5737 | 5.6629 | -0.0678 | 65.30s |\n", - "| DeepKriging_sphere | 1000 | 372.782 | 19.3076 | 6.2435 | -0.039 | 37.57s |\n", - "| DeepKriging_sphere_Huber | 50 | 365.46 | 19.117 | 3.5333 | -0.0186 | 34.76s |\n", - "| UniversalKriging | 100 | 362.626 | 19.0427 | 6.3406 | -0.0107 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:13:20\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.9692 (±0.3543), Variance: 313.8090, Range: [-152.9540, -26.0847]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0969, nugget=275.6394\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 500.868 | 22.3801 | 7.0817 | -0.4681 | 0.33s |\n", - "| OLS_sphere | 1000 | 341.568 | 18.4816 | 5.516 | -0.0011 | 0.14s |\n", - "| DeepKriging_wendland | -- | 375.544 | 19.379 | 7.4769 | -0.1007 | 66.30s |\n", - "| DeepKriging_sphere | 1000 | 345.889 | 18.5981 | 5.2694 | -0.0138 | 28.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 354.493 | 18.828 | 3.6142 | -0.039 | 36.18s |\n", - "| UniversalKriging | 100 | 369.065 | 19.2111 | 6.6474 | -0.0817 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:16:26\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5301 (±0.3493), Variance: 304.9895, Range: [-159.6423, -23.7218]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0002, sigma²=0.3173, nugget=259.6580\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 618.294 | 24.8655 | 8.6292 | -0.1399 | 0.37s |\n", - "| OLS_sphere | 1000 | 549.388 | 23.439 | 7.2353 | -0.0128 | 0.13s |\n", - "| DeepKriging_wendland | -- | 599.213 | 24.4788 | 8.8882 | -0.1047 | 61.41s |\n", - "| DeepKriging_sphere | 1000 | 554.792 | 23.554 | 7.4727 | -0.0228 | 39.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 566.346 | 23.798 | 5.604 | -0.0441 | 35.68s |\n", - "| UniversalKriging | 100 | 571.046 | 23.8966 | 8.283 | -0.0528 | 1.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:19:42\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.8462 (±0.3584), Variance: 321.1185, Range: [-161.3019, -25.8464]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1360, nugget=310.0553\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 488.857 | 22.1101 | 7.872 | -0.2221 | 0.38s |\n", - "| OLS_sphere | 1000 | 400.329 | 20.0082 | 6.3381 | -0.0008 | 0.12s |\n", - "| DeepKriging_wendland | -- | 462.336 | 21.502 | 9.0429 | -0.1558 | 66.16s |\n", - "| DeepKriging_sphere | 1000 | 409.684 | 20.2406 | 6.4195 | -0.0242 | 31.23s |\n", - "| DeepKriging_sphere_Huber | 50 | 417.46 | 20.4318 | 4.0543 | -0.0436 | 35.66s |\n", - "| UniversalKriging | 100 | 420.852 | 20.5147 | 7.0242 | -0.0521 | 2.06s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:22:54\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.0581 (±0.3523), Variance: 310.2333, Range: [-155.5361, -24.9549]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1196, nugget=305.0945\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 177.793 | 13.3339 | 5.0944 | -0.2208 | 0.41s |\n", - "| OLS_sphere | 1000 | 148.168 | 12.1724 | 4.1411 | -0.0174 | 0.13s |\n", - "| DeepKriging_wendland | -- | 145.917 | 12.0796 | 3.4168 | -0.0019 | 63.35s |\n", - "| DeepKriging_sphere | 1000 | 150.235 | 12.2571 | 4.8597 | -0.0316 | 42.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 143.875 | 11.9948 | 1.8036 | 0.0121 | 35.04s |\n", - "| UniversalKriging | 100 | 168.138 | 12.9668 | 5.3615 | -0.1545 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:26:14\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -29.3087 (±0.3314), Variance: 274.6038, Range: [-143.1703, -24.4087]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0690, nugget=236.6781\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 538.799 | 23.212 | 7.6441 | -0.2 | 0.46s |\n", - "| OLS_sphere | 1000 | 453.809 | 21.3028 | 6.5802 | -0.0107 | 0.14s |\n", - "| DeepKriging_wendland | -- | 478.733 | 21.88 | 7.4876 | -0.0662 | 64.81s |\n", - "| DeepKriging_sphere | 1000 | 454.243 | 21.313 | 6.7168 | -0.0117 | 48.16s |\n", - "| DeepKriging_sphere_Huber | 50 | 464.214 | 21.5456 | 4.808 | -0.0339 | 32.95s |\n", - "| UniversalKriging | 100 | 467.961 | 21.6324 | 7.2207 | -0.0422 | 2.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:29:42\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.7792 (±0.3541), Variance: 313.5417, Range: [-155.3769, -24.5118]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0182, nugget=284.2928\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 502.262 | 22.4112 | 7.6857 | 0.0086 | -1.34s |\n", - "| OLS_sphere | 1000 | 510.474 | 22.5937 | 7.076 | -0.0076 | 0.13s |\n", - "| DeepKriging_wendland | -- | 525.795 | 22.9302 | 6.9109 | -0.0378 | 59.51s |\n", - "| DeepKriging_sphere | 1000 | 510.743 | 22.5996 | 6.7258 | -0.0081 | 36.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 518.111 | 22.7621 | 4.9327 | -0.0226 | 35.63s |\n", - "| UniversalKriging | 100 | 518.995 | 22.7815 | 7.7756 | -0.0244 | 2.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:32:58\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5219 (±0.3530), Variance: 311.4846, Range: [-150.8401, -23.1913]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1203, nugget=283.4190\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 485.021 | 22.0232 | 8.0811 | -0.1658 | 0.38s |\n", - "| OLS_sphere | 1000 | 418.142 | 20.4485 | 6.1702 | -0.005 | 0.14s |\n", - "| DeepKriging_wendland | -- | 441.661 | 21.0157 | 7.1829 | -0.0616 | 65.68s |\n", - "| DeepKriging_sphere | 1000 | 413.565 | 20.3363 | 6.1171 | 0.006 | 46.69s |\n", - "| DeepKriging_sphere_Huber | 50 | 418.66 | 20.4612 | 4.4975 | -0.0063 | 32.00s |\n", - "| UniversalKriging | 100 | 396.003 | 19.8998 | 7.0932 | 0.0482 | 1.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:36:25\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -29.3363 (±0.3334), Variance: 277.9268, Range: [-147.5310, -22.4124]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0915, nugget=266.8183\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1436.43 | 37.9003 | 8.8066 | -5.5513 | 0.38s |\n", - "| OLS_sphere | 1000 | 219.625 | 14.8198 | 4.847 | -0.0017 | 0.19s |\n", - "| DeepKriging_wendland | -- | 262.723 | 16.2087 | 6.0734 | -0.1982 | 64.80s |\n", - "| DeepKriging_sphere | 1000 | 222.579 | 14.9191 | 4.749 | -0.0151 | 26.70s |\n", - "| DeepKriging_sphere_Huber | 50 | 223.115 | 14.937 | 2.7617 | -0.0176 | 31.42s |\n", - "| UniversalKriging | 100 | 232.001 | 15.2316 | 5.7478 | -0.0581 | 2.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:39:33\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.7277 (±0.3500), Variance: 306.2542, Range: [-149.7443, -24.5076]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0703, nugget=302.1461\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 398.555 | 19.9638 | 6.8946 | -0.3857 | 0.37s |\n", - "| OLS_sphere | 1000 | 287.689 | 16.9614 | 5.4888 | -0.0002 | 0.14s |\n", - "| DeepKriging_wendland | -- | 309.2 | 17.5841 | 5.4229 | -0.075 | 64.56s |\n", - "| DeepKriging_sphere | 1000 | 293.299 | 17.126 | 4.3583 | -0.0198 | 33.28s |\n", - "| DeepKriging_sphere_Huber | 50 | 297.98 | 17.2621 | 3.2483 | -0.036 | 33.92s |\n", - "| UniversalKriging | 100 | 316.858 | 17.8005 | 6.6443 | -0.1017 | 0.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:42:53\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.9443 (±0.3640), Variance: 331.2398, Range: [-155.6563, -24.7086]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1667, nugget=290.8077\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 546.8 | 23.3838 | 8.1213 | -0.0963 | 0.42s |\n", - "| OLS_sphere | 1000 | 500.858 | 22.3799 | 7.162 | -0.0041 | 0.14s |\n", - "| DeepKriging_wendland | -- | 557.793 | 23.6176 | 7.1927 | -0.1183 | 67.27s |\n", - "| DeepKriging_sphere | 1000 | 533.901 | 23.1063 | 6.6278 | -0.0704 | 34.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 521.896 | 22.845 | 5.0735 | -0.0463 | 34.13s |\n", - "| UniversalKriging | 100 | 520.209 | 22.8081 | 7.8338 | -0.0429 | 1.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:46:15\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -29.8010 (±0.3358), Variance: 281.8635, Range: [-145.9772, -24.5598]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0000, sigma²=213.6529, nugget=30.2131\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 468.406 | 21.6427 | 7.1145 | -0.0819 | 0.46s |\n", - "| OLS_sphere | 1000 | 436.051 | 20.8818 | 6.4097 | -0.0072 | 0.15s |\n", - "| DeepKriging_wendland | -- | 431.984 | 20.7842 | 6.6089 | 0.0022 | 61.05s |\n", - "| DeepKriging_sphere | 1000 | 435.114 | 20.8594 | 6.3275 | -0.005 | 42.55s |\n", - "| DeepKriging_sphere_Huber | 50 | 452.681 | 21.2763 | 4.5486 | -0.0456 | 37.44s |\n", - "| UniversalKriging | 100 | 443.763 | 21.0657 | 7.0351 | -0.025 | 3.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:49:47\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.4435 (±0.3611), Variance: 326.0527, Range: [-155.9087, -24.7681]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0972, nugget=329.7015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 366.48 | 19.1437 | 6.5772 | -0.3458 | 0.47s |\n", - "| OLS_sphere | 1000 | 272.83 | 16.5176 | 5.1119 | -0.0019 | 0.12s |\n", - "| DeepKriging_wendland | -- | 338.845 | 18.4077 | 5.3844 | -0.2443 | 63.71s |\n", - "| DeepKriging_sphere | 1000 | 292.048 | 17.0894 | 4.6063 | -0.0725 | 22.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 275.787 | 16.6068 | 2.8875 | -0.0128 | 31.29s |\n", - "| UniversalKriging | 100 | 296.243 | 17.2117 | 5.801 | -0.0879 | 1.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:52:55\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.1451 (±0.3367), Variance: 283.3762, Range: [-147.0525, -24.2683]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1016, nugget=268.8275\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 323.831 | 17.9953 | 6.1864 | -0.1784 | 0.37s |\n", - "| OLS_sphere | 1000 | 275.299 | 16.5921 | 5.1434 | -0.0018 | 0.12s |\n", - "| DeepKriging_wendland | -- | 301.646 | 17.368 | 4.9519 | -0.0977 | 63.22s |\n", - "| DeepKriging_sphere | 1000 | 286.391 | 16.9231 | 4.9022 | -0.0422 | 39.41s |\n", - "| DeepKriging_sphere_Huber | 50 | 295.551 | 17.1916 | 3.7074 | -0.0755 | 38.77s |\n", - "| UniversalKriging | 100 | 287.28 | 16.9493 | 5.7982 | -0.0454 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 19:56:27\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.7216 (±0.3583), Variance: 320.8623, Range: [-158.6679, -24.6711]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0491, nugget=308.3229\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|----------|--------|\n", - "| OLS_wendland | -- | 5039.4 | 70.9887 | 10.1583 | -19.7272 | 0.34s |\n", - "| OLS_sphere | 1000 | 244.522 | 15.6372 | 5.1186 | -0.0057 | 0.14s |\n", - "| DeepKriging_wendland | -- | 268.342 | 16.3811 | 4.9508 | -0.1037 | 65.54s |\n", - "| DeepKriging_sphere | 1000 | 246.444 | 15.6985 | 5.1191 | -0.0136 | 46.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 252.181 | 15.8802 | 2.69 | -0.0372 | 35.60s |\n", - "| UniversalKriging | 100 | 269.152 | 16.4059 | 5.7664 | -0.107 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:00:06\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.0478 (±0.3508), Variance: 307.6323, Range: [-155.4377, -25.5123]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1346, nugget=256.2339\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 393.821 | 19.8449 | 6.7317 | -0.0547 | 0.41s |\n", - "| OLS_sphere | 1000 | 373.957 | 19.338 | 5.8191 | -0.0015 | 0.12s |\n", - "| DeepKriging_wendland | -- | 396.44 | 19.9108 | 7.362 | -0.0617 | 70.83s |\n", - "| DeepKriging_sphere | 1000 | 362.469 | 19.0386 | 6.0971 | 0.0293 | 25.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 394.718 | 19.8675 | 4.2759 | -0.0571 | 32.60s |\n", - "| UniversalKriging | 100 | 409.306 | 20.2313 | 6.696 | -0.0961 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:03:28\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.8521 (±0.3542), Variance: 313.7279, Range: [-154.2453, -23.3420]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1665, nugget=304.4935\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 968.473 | 31.1203 | 8.2932 | -1.6977 | 0.33s |\n", - "| OLS_sphere | 1000 | 358.972 | 18.9466 | 5.9101 | 0.0001 | 0.14s |\n", - "| DeepKriging_wendland | -- | 339.721 | 18.4315 | 5.1174 | 0.0537 | 61.88s |\n", - "| DeepKriging_sphere | 1000 | 369.941 | 19.2338 | 5.0962 | -0.0305 | 32.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 362.48 | 19.0389 | 3.6561 | -0.0097 | 34.71s |\n", - "| UniversalKriging | 100 | 352.538 | 18.776 | 6.522 | 0.018 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:06:54\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -32.3265 (±0.3691), Variance: 340.5970, Range: [-161.5664, -26.2923]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1154, nugget=331.6353\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 312.324 | 17.6727 | 5.8171 | -0.0427 | 0.41s |\n", - "| OLS_sphere | 1000 | 299.796 | 17.3146 | 5.7758 | -0.0009 | 0.16s |\n", - "| DeepKriging_wendland | -- | 382.69 | 19.5625 | 6.8663 | -0.2777 | 65.15s |\n", - "| DeepKriging_sphere | 1000 | 307.913 | 17.5474 | 5.4526 | -0.028 | 42.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 312.137 | 17.6674 | 3.1722 | -0.0421 | 30.91s |\n", - "| UniversalKriging | 100 | 329.247 | 18.1452 | 6.0424 | -0.0992 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:10:29\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.2908 (±0.3423), Variance: 292.9632, Range: [-151.2359, -24.3724]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1281, nugget=244.4786\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 367.559 | 19.1718 | 6.2049 | -0.1083 | 0.34s |\n", - "| OLS_sphere | 1000 | 332.045 | 18.2221 | 5.509 | -0.0012 | 0.13s |\n", - "| DeepKriging_wendland | -- | 356.197 | 18.8732 | 5.0997 | -0.074 | 66.51s |\n", - "| DeepKriging_sphere | 1000 | 335.74 | 18.3232 | 6.2587 | -0.0123 | 20.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 340.503 | 18.4527 | 3.492 | -0.0267 | 32.76s |\n", - "| UniversalKriging | 100 | 347.298 | 18.6359 | 6.0869 | -0.0472 | 1.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:13:45\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.5071 (±0.3496), Variance: 305.5188, Range: [-160.5156, -22.6777]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0710, nugget=280.8160\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1266.03 | 35.5814 | 8.1679 | -3.6413 | 0.41s |\n", - "| OLS_sphere | 1000 | 273.918 | 16.5505 | 5.3033 | -0.0042 | 0.14s |\n", - "| DeepKriging_wendland | -- | 297.094 | 17.2364 | 6.8734 | -0.0892 | 62.34s |\n", - "| DeepKriging_sphere | 1000 | 287.425 | 16.9536 | 6.6246 | -0.0537 | 26.42s |\n", - "| DeepKriging_sphere_Huber | 50 | 278.166 | 16.6783 | 2.8699 | -0.0198 | 37.33s |\n", - "| UniversalKriging | 100 | 286.692 | 16.932 | 5.7753 | -0.051 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:17:10\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.6693 (±0.3486), Variance: 303.8514, Range: [-158.0365, -24.8579]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1530, nugget=304.3117\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 303.249 | 17.414 | 6.3269 | -0.3141 | 0.41s |\n", - "| OLS_sphere | 1000 | 232.11 | 15.2352 | 5.0616 | -0.0058 | 0.13s |\n", - "| DeepKriging_wendland | -- | 277.476 | 16.6576 | 4.8469 | -0.2024 | 66.53s |\n", - "| DeepKriging_sphere | 1000 | 239.006 | 15.4598 | 4.4657 | -0.0357 | 25.91s |\n", - "| DeepKriging_sphere_Huber | 50 | 237.376 | 15.407 | 2.7297 | -0.0286 | 33.94s |\n", - "| UniversalKriging | 100 | 255.267 | 15.9771 | 5.8111 | -0.1062 | 2.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:20:39\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.8925 (±0.3500), Variance: 306.2708, Range: [-154.0766, -24.1632]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1620, nugget=289.1066\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 504.75 | 22.4666 | 7.0602 | -0.2535 | 0.36s |\n", - "| OLS_sphere | 1000 | 403.44 | 20.0858 | 6.2622 | -0.0019 | 0.14s |\n", - "| DeepKriging_wendland | -- | 404.446 | 20.1108 | 5.5619 | -0.0044 | 62.83s |\n", - "| DeepKriging_sphere | 1000 | 413.604 | 20.3372 | 6.1521 | -0.0271 | 35.16s |\n", - "| DeepKriging_sphere_Huber | 50 | 412.096 | 20.3001 | 3.9212 | -0.0234 | 34.97s |\n", - "| UniversalKriging | 100 | 402.215 | 20.0553 | 6.8887 | 0.0011 | 2.25s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:24:14\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.1427 (±0.3536), Variance: 312.5259, Range: [-151.7140, -25.4370]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0363, nugget=274.8419\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 396.355 | 19.9087 | 6.7731 | -0.1336 | 0.34s |\n", - "| OLS_sphere | 1000 | 350.046 | 18.7095 | 5.5965 | -0.0011 | -1.77s |\n", - "| DeepKriging_wendland | -- | 374.093 | 19.3415 | 5.8007 | -0.0699 | 61.44s |\n", - "| DeepKriging_sphere | 1000 | 357.868 | 18.9174 | 5.5555 | -0.0235 | 38.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 361.134 | 19.0035 | 3.7208 | -0.0328 | 29.78s |\n", - "| UniversalKriging | 100 | 376.743 | 19.4099 | 6.1292 | -0.0775 | 2.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:27:45\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.5538 (±0.3696), Variance: 341.5861, Range: [-166.3013, -25.2993]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0280, nugget=338.4860\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 312.716 | 17.6838 | 6.078 | -0.0818 | 0.47s |\n", - "| OLS_sphere | 1000 | 289.42 | 17.0124 | 5.2992 | -0.0012 | 0.14s |\n", - "| DeepKriging_wendland | -- | 373.111 | 19.3161 | 6.4516 | -0.2907 | 63.54s |\n", - "| DeepKriging_sphere | 1000 | 291.843 | 17.0834 | 5.2058 | -0.0096 | 43.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 287.837 | 16.9658 | 2.8167 | 0.0043 | 31.89s |\n", - "| UniversalKriging | 100 | 310.677 | 17.626 | 6.0831 | -0.0747 | 1.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:31:28\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.0414 (±0.3446), Variance: 296.8219, Range: [-150.5306, -21.9787]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0766, nugget=276.9689\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 367.589 | 19.1726 | 6.9296 | -0.219 | 0.44s |\n", - "| OLS_sphere | 1000 | 301.086 | 17.3518 | 5.6647 | 0.0015 | 0.15s |\n", - "| DeepKriging_wendland | -- | 339.837 | 18.4347 | 5.9764 | -0.127 | 68.85s |\n", - "| DeepKriging_sphere | 1000 | 317.387 | 17.8154 | 5.9882 | -0.0525 | 24.45s |\n", - "| DeepKriging_sphere_Huber | 50 | 311.83 | 17.6587 | 3.3165 | -0.0341 | 39.10s |\n", - "| UniversalKriging | 100 | 334.27 | 18.2831 | 6.4565 | -0.1085 | 2.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:35:02\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.1576 (±0.3418), Variance: 292.1522, Range: [-147.3735, -24.4000]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0650, nugget=250.6693\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 445.523 | 21.1074 | 6.836 | -0.1199 | 0.38s |\n", - "| OLS_sphere | 1000 | 399.382 | 19.9845 | 6.0243 | -0.0039 | 0.14s |\n", - "| DeepKriging_wendland | -- | 429.491 | 20.7242 | 6.1217 | -0.0796 | 57.49s |\n", - "| DeepKriging_sphere | 1000 | 415.557 | 20.3852 | 6.3706 | -0.0445 | 19.68s |\n", - "| DeepKriging_sphere_Huber | 50 | 412.592 | 20.3124 | 4.4333 | -0.0371 | 35.41s |\n", - "| UniversalKriging | 100 | 414.112 | 20.3497 | 6.4879 | -0.0409 | 2.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:38:21\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -30.0331 (±0.3379), Variance: 285.5139, Range: [-149.3171, -24.0854]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.0762, nugget=283.1639\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 306.428 | 17.5051 | 6.4545 | -0.1182 | 0.42s |\n", - "| OLS_sphere | 1000 | 274.699 | 16.574 | 5.3615 | -0.0024 | 0.14s |\n", - "| DeepKriging_wendland | -- | 293.595 | 17.1346 | 4.498 | -0.0714 | 61.26s |\n", - "| DeepKriging_sphere | 1000 | 278.076 | 16.6756 | 5.6294 | -0.0147 | 31.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 278.666 | 16.6933 | 3.0203 | -0.0169 | 34.83s |\n", - "| UniversalKriging | 100 | 283.632 | 16.8414 | 5.9001 | -0.035 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:41:56\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -31.3571 (±0.3574), Variance: 319.3487, Range: [-155.3001, -25.1678]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0001, sigma²=0.1354, nugget=307.1897\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 270.068 | 16.4337 | 5.6646 | -0.1437 | -1.55s |\n", - "| OLS_sphere | 1000 | 236.848 | 15.3899 | 4.8985 | -0.003 | 0.14s |\n", - "| DeepKriging_wendland | -- | 294.151 | 17.1508 | 6.2565 | -0.2457 | 60.21s |\n", - "| DeepKriging_sphere | 1000 | 244.189 | 15.6265 | 4.988 | -0.0341 | 32.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 243.823 | 15.6148 | 2.7263 | -0.0326 | 29.96s |\n", - "| UniversalKriging | 100 | 247.789 | 15.7413 | 5.2596 | -0.0494 | 1.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:45:30\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -32.3015 (±0.3701), Variance: 342.4963, Range: [-156.0963, -25.5264]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.1603, nugget=311.3675\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|--------|---------|--------|\n", - "| OLS_wendland | -- | 481.604 | 21.9455 | 6.9498 | -0.2094 | 0.43s |\n", - "| OLS_sphere | 1000 | 398.694 | 19.9673 | 6.0152 | -0.0012 | 0.14s |\n", - "| DeepKriging_wendland | -- | 419.842 | 20.4901 | 6.8147 | -0.0543 | 67.50s |\n", - "| DeepKriging_sphere | 1000 | 413.843 | 20.3431 | 6.4001 | -0.0392 | 28.60s |\n", - "| DeepKriging_sphere_Huber | 50 | 404.358 | 20.1086 | 3.7248 | -0.0154 | 35.59s |\n", - "| UniversalKriging | 100 | 407.733 | 20.1924 | 6.9653 | -0.0239 | 1.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 20:49:09\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, outlier_ratio=0.025, outlier_multiplier=5, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1000, 'DeepKriging_sphere': 1000, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------|-------------|-----------|------------|\n", - "| OLS_wendland | 666.45±933.25 | 23.15±11.42 | 7.04±1.31 | -1.37±4.11 |\n", - "| OLS_sphere | 319.59±101.34 | 17.63±2.94 | 5.57±0.79 | -0.01±0.01 |\n", - "| DeepKriging_wendland | 354.30±102.20 | 18.61±2.82 | 5.96±1.15 | -0.13±0.13 |\n", - "| DeepKriging_sphere | 328.37±102.69 | 17.88±2.94 | 5.59±0.89 | -0.04±0.03 |\n", - "| DeepKriging_sphere_Huber | 327.50±105.73 | 17.84±3.04 | 3.40±0.94 | -0.03±0.02 |\n", - "| UniversalKriging | 334.33±100.15 | 18.07±2.82 | 6.32±0.79 | -0.06±0.05 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 17.63±2.94)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_without_outliers.ipynb b/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_without_outliers.ipynb deleted file mode 100644 index eaac6d7..0000000 --- a/notebook/simulation/ScenarioII/simulation_euclidean_GP_eggholder_without_outliers.ipynb +++ /dev/null @@ -1,3277 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-18 01:29:12.308562: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768670952.395503 2116704 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768670952.440004 2116704 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768670952.643120 2116704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768670952.643170 2116704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768670952.643171 2116704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768670952.643172 2116704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term\n", - " # f(x₁, x₂) = -(x₂ + 47) sin(√|x₂ + x₁/2 + 47|) - x₁ sin(√|x₁ - (x₂ + 47)|)\n", - " term1 = -(x2 + 47.0) * np.sin(np.sqrt(np.abs(x2 + x1/2.0 + 47.0)))\n", - " term2 = -x1 * np.sin(np.sqrt(np.abs(x1 - (x2 + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x1, x2]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function (Euclidean distance)\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: z = mean + L @ z_std\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.123430Z", - "iopub.status.busy": "2026-01-08T18:26:41.122639Z", - "iopub.status.idle": "2026-01-08T18:26:41.163754Z", - "shell.execute_reply": "2026-01-08T18:26:41.160797Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "z mean: -27.7665 (±0.0184), Variance: 0.8430, Range: [-29.8612, -25.2544]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1036 0.0854 \n", - " 100 0.0729 0.0614 \n", - " 200 0.0487 0.0403 \n", - " 500 0.0365 0.0293 *\n", - " 700 0.0422 0.0354 \n", - " 1000 0.8843 0.7470 \n", - " 1400 0.8843 0.7470 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768670993.708994 2116704 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768670996.242493 2117026 service.cc:152] XLA service 0x5f8744a4ee10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768670996.242577 2117026 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1768670996.752805 2117026 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1768671007.783470 2117026 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78f99b44c9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78f99b205ab0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1377 0.1132 *\n", - " 100 0.2771 0.2438 \n", - " 200 0.2832 0.5273 \n", - " 500 0.4430 0.5318 \n", - " 700 0.8420 0.7128 \n", - " 1000 0.8435 0.7335 \n", - " 1400 0.7969 0.7286 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0277 0.0546 \n", - " 100 0.0309 0.0416 \n", - " 200 0.0299 0.0570 \n", - " 500 0.0263 0.0643 *\n", - " 700 0.0385 0.0608 \n", - " 1000 0.3794 0.7162 \n", - " 1400 0.3821 0.7163 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0008, sigma²=0.0969, nugget=0.0003\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0659, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0387, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2510, sigma²=0.0000, nugget=0.0208\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0246, sigma²=0.0000, nugget=0.0148\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0063, sigma²=0.0000, nugget=0.0097\n", - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0044, sigma²=0.0000, nugget=0.0053\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0289 0.0231 \n", - " 100 0.0287 0.0234 *\n", - " 200 0.0295 0.0241 \n", - " 500 0.0365 0.0293 \n", - " 700 0.0422 0.0354 \n", - " 1000 0.0577 0.0441 \n", - " 1400 0.5310 0.9687 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0659, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.6305 | 0.794 | 0.5811 | 0.185 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0293 | 0.1712 | 0.1364 | 0.9621 | 0.06s |\n", - "| DeepKriging_wendland | -- | 2.3967 | 1.5481 | 0.669 | -2.098 | 70.76s |\n", - "| DeepKriging_sphere | 50 | 0.1964 | 0.4432 | 0.2615 | 0.7461 | 73.63s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0652 | 0.2554 | 0.1859 | 0.9157 | 82.87s |\n", - "| UniversalKriging | 100 | 0.0234 | 0.1529 | 0.1165 | 0.9698 | 3.37s |\n" - ] - }, - { - "data": { - "image/png": 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CY2NjvKamRrrNhWix7UwmG623m7HyEfvy8vJSfq9uvPHG8z6OXtrOeLPRQ9shImXiWHti2XCsPfls9PCax7F2ZrLRetvhOFv5Y23OMFSYL37xi9L56l/60pdS9re3t0v/ilOTxRVyPvKRj4y6780334xf//rX0ufiXb1bbrkFWs+msLAQHR0d0hprwosvvihdLWikxPn+0WgUf/3rX6WrTWvNZPLRY9sZ/q7eu971rjHfddF625lMNlpvN2PlI/IQV4kUsyiEV155RbpC2Uh6bDvjzUYPbYeIlIlj7Yllw7H25LPRw2sex9qZyUbrbYfjbBWMtS+qzEgZ0dTUJBa4kt6pWrJkifTxzDPPJK9sI66MIz5+9atfJe/zr//6r/HHH388+S7XZZddFp8xY0b8bW97W9zn8+kimz/+8Y/xBQsWxBcvXiy9qydyGJnNFVdcEV+0aJF0/49//OPxcDgc15LJ5qPXtiOsXr06vm3btpT76KXtTDYbLbebC+Vz8uTJ+CWXXCK1jZtvvjne1dUl7WfbGV82Wm87RKRMHGtfXDZ6H2tznH3x+Qgca3OsPdF2o/dxtpLG2gbxv4srNRIREREREREREZHW8JRkIiIiIiIiIiIiSmLBkIiIiIiIiIiIiJJYMCQiIiIiIiIiIqIkFgyJiIiIiIiIiIgoiQVDIiIiIiIiIiIiSmLBkIiIiIiIiIiIiJJYMCQiIiIiIiIiIqIkFgyJSBb/+7//i8svvzy57Xa7sX///ot6rHvuuQdf+MIXFPeT+cUvfoGlS5fm+jCIiIiISGc41iaibGPBkEgnRDHPZrNJhbzCwkJs3LgRO3bsyNjzDQwMYNGiRRe83Ve+8hXcdtttKft++MMf4hvf+IbsxyQec+HChWm/tn79ejzwwAOyPycRERERaR/H2hxrE2kNC4ZEOiIKZqKQ19raiuXLl48q1CVEIhFo0d13342jR49i+/btKfvFvm3btuFDH/pQzo6NiIiIiNSNY22OtYm0hAVDIh2y2+34yEc+gpaWFnR1dUmFNLH97ne/G3l5efjBD36AcDiML3/5y5gxYwaKi4tx6623SrdPOHjwINauXQuPx4Mrrrgi5WuCwWDAm2++mdx+5JFHsGTJEunxp02bJp3e+5e//AUPPvgg/vrXv0ozH8WHII7n3nvvTd53586d0gzAgoICzJ8/X3qs4TMUb7nlFnzyk5+Uvj516lT8/ve/T/t9l5eX46abbsLPf/7zlP1i+8orr8T06dPxgQ98AFVVVdJxrlixAi+//PKYOY78HkeeKtLe3o73v//90uOJD/E9BYPBC/58iIiIiEi9ONbmWJtIC1gwJNIhn8+Hn/zkJ1LhThQDBVGEE0XD3t5e6d/7778fmzdvxuuvvy7NSJw9ezbe8573JGcgigLiVVddJRUcRdFPPN5YnnzySamg9z//8z/S44tToUXxUMxw/Jd/+RfcfPPN0sxH8TGSuP31118vPXdHR4dUzPz7v/976dgSnn32WamgKI7l61//Ov7u7/4O/f39aY9FfG/ie/X7/dJ2NBrFr371K2m/IL6nw4cPS48lnvOd73znmI91PvF4XMqooqICJ06ckNZz3Lt3r3R8RERERKRdHGtzrE2kBSwYEunIF7/4RWkWXl1dHY4cOYInnngi+bVrr70W1113HYxGIxwOB77//e/jW9/6FiorK2G1WqVClyjSNTU1YevWrejs7JRm94mvrVu3DnfccceYzyse69Of/rQ0i088fllZGZYtWzauY/7b3/6G0tJS/OM//iMsFou09uL73vc+/PKXv0zeRpxe/d73vhcmkwl33nknQqEQjh07lvbxbrzxRjidTjz22GPS9tNPP41AIIC3v/3t0rY4LTk/P196rn/+539GLBbDvn37MFFiVuTx48fxzW9+U3o+UZgVxdGHH354wo9FRERERMrHsTbH2kRawoIhkY489NBD0oy9trY2PPPMM1i8eHHya+JU3gRRDBwcHMRll10mFRjFh5gpJ4qDomAoTj8Wp9iKolqCmK04loaGBsyaNeuijvnMmTPSqcLDiYKn2J8gjm34acKi4DnWrEBRVPzgBz+YPC1Z/CtOQxYXhBHFQTGzUhyrOCVZfN99fX1SHhN1+vRpKeuioqJkhmK24tmzZyf8WERERESkfBxrc6xNpCXmXB8AESmDmPmXIGbDiVlxb7zxBubOnTvqtps2bZKKhmKdw0TRsLGxcczHFsVEcVruhZ43nerqaqn4Nlx9fb20/2J9+MMfxn/+539KswDF6dKJq0WL2X/iQ5ziLIqGovgorigtTi9Ox+VySaecJIhTtxNqamqkmZTD9xERERGRPnGszbE2kdpwhiERje4YjEbcc889+Kd/+idpRqEg1vRLXExEXOxEFBX/7d/+TTr9VxQWx7rQiPCxj30M3/72t/Hqq69Ks/jExUD27NmTvBCJmIEo1hIc6xRicXtxWrNYO1EUK0VR76677rron5woBm7YsAHvete7pFmWYj1Fwev1SrMoS0pKpO/ra1/7mrRvLOJU6F//+tfScYmLn4jPE1atWiXN2vzSl74kzXYURUfxfYpToImIiIhIvzjW5libSA1YMCSiMU+pEGsTinUHxZWQxRWDn3vuOelrYlbh448/Ls3EE6fc3nfffdKsvbGIi5uI9RA/8YlPSOsDimKauAiIIIp24vRfUaQTp+2OJGb4iSLbb37zG6lI+dGPflS68Iko+E2GuMiJmLmYuNiJIE5VXrBggTQjUpz2LE5tFjMFx/Ld735XWs9RHPcXvvAF6f7DT30Wsxebm5sxb9486fsWV2gea6YlEREREekHx9ocaxMpnSE+1rl2REREREREREREpDucYUhERERERERERERJLBgSERERERERERFREguGRERERERERERElMSCIRERERERERERESWxYEhERERERERERERJLBgSERERERERERFREguGRERERERERERElMSCIRERERERERERESWxYEhERERERERERERJLBgSERERERERERFREguGREREREREREREhIT/Dzf2+eivI9KAAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 01:53:35\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "z mean: -27.7225 (±0.0311), Variance: 2.4122, Range: [-31.4270, -24.4243]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0551, nugget=0.0017\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3944 | 1.1808 | 0.9125 | 0.3838 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0432 | 0.2079 | 0.1641 | 0.9809 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.0647 | 1.0319 | 0.7839 | 0.5295 | 74.95s |\n", - "| DeepKriging_sphere | 50 | 0.1049 | 0.3238 | 0.2313 | 0.9537 | 83.23s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0721 | 0.2684 | 0.2049 | 0.9682 | 49.40s |\n", - "| UniversalKriging | 100 | 0.0309 | 0.1758 | 0.1374 | 0.9863 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 01:57:34\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "z mean: -28.2347 (±0.0292), Variance: 2.1348, Range: [-31.7127, -25.1404]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0612, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.9836 | 0.9918 | 0.8048 | 0.5672 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0426 | 0.2065 | 0.1593 | 0.9812 | 0.09s |\n", - "| DeepKriging_wendland | -- | 1.23 | 1.109 | 0.8004 | 0.4588 | 67.78s |\n", - "| DeepKriging_sphere | 50 | 0.1208 | 0.3476 | 0.2222 | 0.9468 | 116.78s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0624 | 0.2498 | 0.1814 | 0.9725 | 51.98s |\n", - "| UniversalKriging | 100 | 0.0252 | 0.1588 | 0.1237 | 0.9889 | 4.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:02:09\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "z mean: -28.1546 (±0.0201), Variance: 1.0080, Range: [-30.9142, -25.1867]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0566, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8535 | 0.9238 | 0.6447 | 0.2059 | 0.36s |\n", - "| OLS_sphere | 500 | 0.042 | 0.205 | 0.1676 | 0.9609 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.9259 | 0.9622 | 0.5925 | 0.1385 | 72.21s |\n", - "| DeepKriging_sphere | 50 | 0.1489 | 0.3858 | 0.2528 | 0.8615 | 82.29s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0721 | 0.2686 | 0.2069 | 0.9329 | 91.12s |\n", - "| UniversalKriging | 100 | 0.0301 | 0.1736 | 0.14 | 0.972 | 2.72s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:06:47\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "z mean: -27.6378 (±0.0267), Variance: 1.7824, Range: [-31.6483, -24.9221]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0699, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 2.1252 | 1.4578 | 0.9417 | -0.0434 | -2.19s |\n", - "| OLS_sphere | 500 | 0.0365 | 0.1912 | 0.15 | 0.9821 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.002 | 1.001 | 0.7012 | 0.5081 | 72.11s |\n", - "| DeepKriging_sphere | 50 | 0.2702 | 0.5198 | 0.2951 | 0.8673 | 74.14s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0709 | 0.2663 | 0.1826 | 0.9652 | 67.40s |\n", - "| UniversalKriging | 100 | 0.0272 | 0.1648 | 0.1301 | 0.9867 | 4.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:10:59\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "z mean: -28.1310 (±0.0142), Variance: 0.5051, Range: [-30.4014, -25.8921]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0655, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.3536 | 0.5947 | 0.4216 | 0.3274 | 0.34s |\n", - "| OLS_sphere | 500 | 0.0384 | 0.196 | 0.1589 | 0.9269 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.3368 | 0.5804 | 0.4341 | 0.3593 | 73.62s |\n", - "| DeepKriging_sphere | 50 | 0.3258 | 0.5708 | 0.3335 | 0.3803 | 74.47s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.061 | 0.2471 | 0.1873 | 0.8839 | 40.96s |\n", - "| UniversalKriging | 100 | 0.0242 | 0.1556 | 0.1223 | 0.954 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:14:42\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "z mean: -27.4444 (±0.0270), Variance: 1.8227, Range: [-31.2284, -24.5590]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0581, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1225 | 1.0595 | 0.8294 | 0.4007 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0362 | 0.1902 | 0.151 | 0.9807 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.9509 | 0.9751 | 0.7315 | 0.4924 | 67.51s |\n", - "| DeepKriging_sphere | 50 | 0.1752 | 0.4186 | 0.2804 | 0.9065 | 69.87s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0854 | 0.2922 | 0.2077 | 0.9544 | 41.84s |\n", - "| UniversalKriging | 100 | 0.0288 | 0.1697 | 0.1359 | 0.9846 | 5.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:18:23\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "z mean: -27.8819 (±0.0299), Variance: 2.2414, Range: [-31.3263, -24.4321]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0631, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6693 | 1.292 | 1.0317 | 0.3264 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0413 | 0.2031 | 0.1599 | 0.9833 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.1526 | 1.0736 | 0.8521 | 0.5349 | 67.36s |\n", - "| DeepKriging_sphere | 50 | 0.2025 | 0.45 | 0.2928 | 0.9183 | 64.94s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0725 | 0.2693 | 0.207 | 0.9707 | 92.68s |\n", - "| UniversalKriging | 100 | 0.0298 | 0.1727 | 0.1374 | 0.988 | 4.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:22:49\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "z mean: -27.7679 (±0.0301), Variance: 2.2707, Range: [-31.0653, -24.1754]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0561, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.6258 | 1.2751 | 0.9876 | 0.2984 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0357 | 0.189 | 0.1555 | 0.9846 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.5051 | 1.2268 | 0.9044 | 0.3505 | 73.83s |\n", - "| DeepKriging_sphere | 50 | 0.1758 | 0.4193 | 0.2422 | 0.9241 | 65.99s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0553 | 0.2351 | 0.1731 | 0.9761 | 59.34s |\n", - "| UniversalKriging | 100 | 0.0257 | 0.1604 | 0.1254 | 0.9889 | 2.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:26:47\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "z mean: -27.4293 (±0.0331), Variance: 2.7355, Range: [-32.1898, -23.5979]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0596, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9233 | 0.9609 | 0.7402 | 0.6826 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0424 | 0.206 | 0.1684 | 0.9854 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.7677 | 0.8762 | 0.602 | 0.7361 | 71.60s |\n", - "| DeepKriging_sphere | 50 | 0.1346 | 0.3669 | 0.2379 | 0.9537 | 88.79s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0687 | 0.2622 | 0.1961 | 0.9764 | 99.73s |\n", - "| UniversalKriging | 100 | 0.0292 | 0.1708 | 0.1336 | 0.99 | 4.63s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:31:52\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "z mean: -27.6794 (±0.0208), Variance: 1.0843, Range: [-30.7108, -24.7975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0575, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5407 | 0.7353 | 0.4922 | 0.4615 | -2.36s |\n", - "| OLS_sphere | 500 | 0.0398 | 0.1994 | 0.1572 | 0.9604 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.585 | 0.7648 | 0.4994 | 0.4174 | 73.62s |\n", - "| DeepKriging_sphere | 50 | 0.1304 | 0.3612 | 0.237 | 0.8701 | 75.44s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.074 | 0.272 | 0.1962 | 0.9263 | 46.32s |\n", - "| UniversalKriging | 100 | 0.0319 | 0.1785 | 0.1326 | 0.9683 | 4.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:35:53\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "z mean: -27.5496 (±0.0320), Variance: 2.5541, Range: [-31.1112, -24.2320]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0557, nugget=0.0008\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 4.7985 | 2.1905 | 1.1194 | -0.8471 | 0.40s |\n", - "| OLS_sphere | 500 | 0.0376 | 0.1938 | 0.1509 | 0.9855 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.4085 | 1.1868 | 0.8723 | 0.4578 | 72.84s |\n", - "| DeepKriging_sphere | 50 | 0.2411 | 0.491 | 0.2611 | 0.9072 | 73.12s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0685 | 0.2617 | 0.1904 | 0.9736 | 97.44s |\n", - "| UniversalKriging | 100 | 0.0262 | 0.162 | 0.1284 | 0.9899 | 1.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:40:39\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "z mean: -28.1985 (±0.0299), Variance: 2.2288, Range: [-32.2729, -24.4764]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0589, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3701 | 1.1705 | 0.8472 | 0.38 | 0.46s |\n", - "| OLS_sphere | 500 | 0.0341 | 0.1846 | 0.1464 | 0.9846 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.2443 | 1.1155 | 0.777 | 0.4369 | 68.86s |\n", - "| DeepKriging_sphere | 50 | 0.1616 | 0.402 | 0.2556 | 0.9269 | 70.40s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0644 | 0.2538 | 0.1823 | 0.9709 | 81.36s |\n", - "| UniversalKriging | 100 | 0.0245 | 0.1566 | 0.1247 | 0.9889 | 0.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:45:04\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "z mean: -29.4315 (±0.0202), Variance: 1.0206, Range: [-32.3467, -25.5211]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0612, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.4632 | 0.6806 | 0.5219 | 0.5679 | 0.48s |\n", - "| OLS_sphere | 500 | 0.0355 | 0.1885 | 0.1504 | 0.9668 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.0872 | 1.0427 | 0.6018 | -0.0142 | 69.78s |\n", - "| DeepKriging_sphere | 50 | 0.1575 | 0.3969 | 0.2645 | 0.853 | 78.53s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.062 | 0.249 | 0.1897 | 0.9422 | 54.42s |\n", - "| UniversalKriging | 100 | 0.0275 | 0.1658 | 0.1311 | 0.9744 | 3.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:49:16\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "z mean: -29.3324 (±0.0381), Variance: 3.6258, Range: [-32.9755, -25.2573]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0612, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.9965 | 1.413 | 1.0306 | 0.3637 | 0.43s |\n", - "| OLS_sphere | 500 | 0.0404 | 0.2009 | 0.1539 | 0.9871 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.3858 | 1.1772 | 0.8404 | 0.5583 | 69.61s |\n", - "| DeepKriging_sphere | 50 | 0.2057 | 0.4535 | 0.2737 | 0.9344 | 83.80s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0681 | 0.261 | 0.188 | 0.9783 | 58.43s |\n", - "| UniversalKriging | 100 | 0.0257 | 0.1602 | 0.1261 | 0.9918 | 4.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:53:34\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "z mean: -28.2376 (±0.0174), Variance: 0.7544, Range: [-30.3088, -25.6188]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0691, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.3694 | 0.6078 | 0.4564 | 0.4675 | 0.42s |\n", - "| OLS_sphere | 500 | 0.046 | 0.2146 | 0.1632 | 0.9336 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.7571 | 1.3256 | 0.5775 | -1.533 | 73.90s |\n", - "| DeepKriging_sphere | 50 | 0.1752 | 0.4186 | 0.2668 | 0.7474 | 76.96s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.089 | 0.2984 | 0.2104 | 0.8717 | 75.92s |\n", - "| UniversalKriging | 100 | 0.0316 | 0.1778 | 0.1378 | 0.9544 | 4.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 02:58:11\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "z mean: -27.7216 (±0.0306), Variance: 2.3461, Range: [-32.7967, -24.8313]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0617, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.788 | 0.8877 | 0.675 | 0.6956 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0474 | 0.2177 | 0.1653 | 0.9817 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.6919 | 0.8318 | 0.6205 | 0.7327 | 70.09s |\n", - "| DeepKriging_sphere | 50 | 0.1477 | 0.3843 | 0.2363 | 0.943 | 90.51s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0518 | 0.2276 | 0.1711 | 0.98 | 70.58s |\n", - "| UniversalKriging | 100 | 0.03 | 0.1731 | 0.1333 | 0.9884 | 3.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:02:51\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "z mean: -28.6570 (±0.0198), Variance: 0.9813, Range: [-31.1947, -24.7329]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0582, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.5199 | 0.7211 | 0.542 | 0.4419 | 0.46s |\n", - "| OLS_sphere | 500 | 0.0331 | 0.182 | 0.1428 | 0.9645 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5656 | 0.752 | 0.5299 | 0.3929 | 72.38s |\n", - "| DeepKriging_sphere | 50 | 0.3357 | 0.5794 | 0.2752 | 0.6396 | 79.03s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0575 | 0.2398 | 0.1807 | 0.9383 | 113.03s |\n", - "| UniversalKriging | 100 | 0.0223 | 0.1494 | 0.1191 | 0.9761 | 2.40s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:08:09\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "z mean: -28.1476 (±0.0269), Variance: 1.8042, Range: [-32.2416, -24.9143]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0593, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.842 | 0.9176 | 0.702 | 0.5337 | 0.34s |\n", - "| OLS_sphere | 500 | 0.0423 | 0.2057 | 0.1575 | 0.9766 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.3594 | 1.166 | 0.7715 | 0.2472 | 68.24s |\n", - "| DeepKriging_sphere | 50 | 0.1387 | 0.3724 | 0.2412 | 0.9232 | 72.74s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0719 | 0.2681 | 0.2013 | 0.9602 | 76.74s |\n", - "| UniversalKriging | 100 | 0.0287 | 0.1693 | 0.1334 | 0.9841 | 3.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:12:39\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "z mean: -28.2330 (±0.0231), Variance: 1.3304, Range: [-31.6667, -25.5212]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0622, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8272 | 0.9095 | 0.6516 | 0.3594 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0404 | 0.2011 | 0.1554 | 0.9687 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.7012 | 0.8374 | 0.6007 | 0.457 | 67.47s |\n", - "| DeepKriging_sphere | 50 | 0.0851 | 0.2916 | 0.2142 | 0.9341 | 72.33s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0643 | 0.2536 | 0.1954 | 0.9502 | 48.33s |\n", - "| UniversalKriging | 100 | 0.0259 | 0.1608 | 0.1253 | 0.98 | 2.33s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:16:36\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "z mean: -27.7941 (±0.0173), Variance: 0.7521, Range: [-30.8333, -25.5507]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0543, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.5691 | 0.7544 | 0.5302 | 0.2399 | 0.39s |\n", - "| OLS_sphere | 500 | 0.0439 | 0.2096 | 0.1629 | 0.9413 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.8193 | 0.9052 | 0.631 | -0.0944 | 68.42s |\n", - "| DeepKriging_sphere | 50 | 0.1868 | 0.4322 | 0.2801 | 0.7505 | 62.61s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0752 | 0.2743 | 0.2021 | 0.8995 | 45.26s |\n", - "| UniversalKriging | 100 | 0.0283 | 0.1683 | 0.1325 | 0.9622 | 3.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:20:30\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "z mean: -28.0475 (±0.0273), Variance: 1.8633, Range: [-31.0573, -24.6796]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0550, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0206 | 1.0103 | 0.824 | 0.4303 | 0.52s |\n", - "| OLS_sphere | 500 | 0.0369 | 0.192 | 0.1518 | 0.9794 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.8253 | 0.9085 | 0.6775 | 0.5393 | 69.54s |\n", - "| DeepKriging_sphere | 50 | 0.1323 | 0.3637 | 0.227 | 0.9261 | 82.23s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.069 | 0.2627 | 0.1975 | 0.9615 | 73.21s |\n", - "| UniversalKriging | 100 | 0.0264 | 0.1626 | 0.1277 | 0.9852 | 4.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:25:17\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "z mean: -28.1562 (±0.0192), Variance: 0.9210, Range: [-30.9961, -26.0847]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0604, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.5396 | 0.7346 | 0.5323 | 0.3873 | 0.37s |\n", - "| OLS_sphere | 500 | 0.0453 | 0.2127 | 0.1622 | 0.9486 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.466 | 1.2108 | 0.6271 | -0.6646 | 73.00s |\n", - "| DeepKriging_sphere | 50 | 0.2317 | 0.4813 | 0.2789 | 0.737 | 76.15s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0744 | 0.2728 | 0.2056 | 0.9155 | 87.60s |\n", - "| UniversalKriging | 100 | 0.0304 | 0.1745 | 0.1342 | 0.9654 | 3.66s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:30:06\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "z mean: -27.7689 (±0.0345), Variance: 2.9821, Range: [-33.0220, -23.7218]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0570, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2801 | 1.1314 | 0.8748 | 0.5656 | 0.37s |\n", - "| OLS_sphere | 500 | 0.0372 | 0.1928 | 0.1465 | 0.9874 | 0.06s |\n", - "| DeepKriging_wendland | -- | 2.6969 | 1.6422 | 0.8937 | 0.0847 | 67.35s |\n", - "| DeepKriging_sphere | 50 | 0.1491 | 0.3862 | 0.2286 | 0.9494 | 54.80s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0891 | 0.2985 | 0.1995 | 0.9698 | 44.41s |\n", - "| UniversalKriging | 100 | 0.0256 | 0.1601 | 0.1249 | 0.9913 | 3.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:33:48\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "z mean: -28.9938 (±0.0260), Variance: 1.6857, Range: [-32.2604, -25.8464]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0653, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3532 | 1.1633 | 0.853 | 0.2306 | 0.45s |\n", - "| OLS_sphere | 500 | 0.0377 | 0.1941 | 0.1543 | 0.9786 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.1106 | 1.0539 | 0.7893 | 0.3685 | 71.78s |\n", - "| DeepKriging_sphere | 50 | 0.161 | 0.4013 | 0.2685 | 0.9084 | 78.59s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.068 | 0.2608 | 0.204 | 0.9613 | 54.58s |\n", - "| UniversalKriging | 100 | 0.0273 | 0.1651 | 0.133 | 0.9845 | 4.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:38:15\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "z mean: -28.2607 (±0.0275), Variance: 1.8866, Range: [-31.6823, -24.9549]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0681, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.4096 | 1.1873 | 0.8793 | 0.2324 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0455 | 0.2134 | 0.1635 | 0.9752 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.2754 | 1.1293 | 0.8057 | 0.3055 | 70.26s |\n", - "| DeepKriging_sphere | 50 | 0.1626 | 0.4033 | 0.2557 | 0.9114 | 83.49s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.078 | 0.2793 | 0.2082 | 0.9575 | 62.90s |\n", - "| UniversalKriging | 100 | 0.0309 | 0.1757 | 0.1345 | 0.9832 | 4.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:42:53\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "z mean: -26.6711 (±0.0209), Variance: 1.0901, Range: [-30.3700, -24.4087]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0664, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8231 | 0.9072 | 0.5699 | 0.3228 | 0.45s |\n", - "| OLS_sphere | 500 | 0.0406 | 0.2015 | 0.1564 | 0.9666 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.9533 | 0.9764 | 0.6197 | 0.2156 | 70.73s |\n", - "| DeepKriging_sphere | 50 | 0.1079 | 0.3285 | 0.226 | 0.9112 | 83.92s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0804 | 0.2835 | 0.1897 | 0.9338 | 86.20s |\n", - "| UniversalKriging | 100 | 0.0286 | 0.1691 | 0.1362 | 0.9765 | 2.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:47:53\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "z mean: -27.9744 (±0.0253), Variance: 1.5987, Range: [-31.5052, -24.5118]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0639, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5197 | 0.7209 | 0.5553 | 0.6356 | 0.49s |\n", - "| OLS_sphere | 500 | 0.0421 | 0.2053 | 0.1534 | 0.9705 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.4567 | 0.6758 | 0.4902 | 0.6798 | 69.34s |\n", - "| DeepKriging_sphere | 50 | 0.1771 | 0.4208 | 0.2572 | 0.8758 | 66.56s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0609 | 0.2468 | 0.1833 | 0.9573 | 47.64s |\n", - "| UniversalKriging | 100 | 0.0311 | 0.1763 | 0.1351 | 0.9782 | 2.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:52:04\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "z mean: -27.7327 (±0.0300), Variance: 2.2553, Range: [-31.5077, -23.1913]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0613, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.4649 | 1.2103 | 0.8362 | 0.2844 | 0.46s |\n", - "| OLS_sphere | 500 | 0.043 | 0.2074 | 0.1656 | 0.979 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.1369 | 1.0663 | 0.6786 | 0.4446 | 54.94s |\n", - "| DeepKriging_sphere | 50 | 0.1358 | 0.3686 | 0.246 | 0.9336 | 74.07s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.059 | 0.2429 | 0.1912 | 0.9712 | 48.11s |\n", - "| UniversalKriging | 100 | 0.0251 | 0.1585 | 0.1252 | 0.9877 | 3.50s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 03:56:07\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "z mean: -26.6973 (±0.0338), Variance: 2.8595, Range: [-30.7029, -22.4124]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0584, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.6801 | 1.2962 | 0.9354 | 0.3251 | 0.39s |\n", - "| OLS_sphere | 500 | 0.0417 | 0.2041 | 0.1597 | 0.9833 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5.125 | 2.2639 | 0.9117 | -1.0588 | 52.06s |\n", - "| DeepKriging_sphere | 50 | 0.0829 | 0.2879 | 0.2031 | 0.9667 | 54.92s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0725 | 0.2693 | 0.1957 | 0.9709 | 66.18s |\n", - "| UniversalKriging | 100 | 0.0301 | 0.1734 | 0.1361 | 0.9879 | 3.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:00:10\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "z mean: -27.9496 (±0.0249), Variance: 1.5466, Range: [-30.8551, -24.5076]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0596, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.4733 | 1.2138 | 0.8462 | -0.0229 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0372 | 0.1929 | 0.1499 | 0.9742 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.1328 | 1.0643 | 0.8013 | 0.2135 | 57.45s |\n", - "| DeepKriging_sphere | 50 | 0.3146 | 0.5609 | 0.3171 | 0.7816 | 62.73s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0635 | 0.2519 | 0.1937 | 0.9559 | 69.88s |\n", - "| UniversalKriging | 100 | 0.0259 | 0.1608 | 0.1289 | 0.982 | 4.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:04:30\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "z mean: -29.0577 (±0.0288), Variance: 2.0742, Range: [-31.7408, -24.7086]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0592, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.4502 | 1.2043 | 0.8806 | 0.3394 | 0.39s |\n", - "| OLS_sphere | 500 | 0.037 | 0.1923 | 0.1494 | 0.9832 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.3927 | 1.1801 | 0.9143 | 0.3656 | 50.25s |\n", - "| DeepKriging_sphere | 50 | 0.1866 | 0.4319 | 0.2631 | 0.915 | 99.46s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.088 | 0.2967 | 0.2092 | 0.9599 | 46.37s |\n", - "| UniversalKriging | 100 | 0.0257 | 0.1602 | 0.1274 | 0.9883 | 0.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:08:56\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "z mean: -27.1290 (±0.0234), Variance: 1.3666, Range: [-29.9659, -24.5598]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0591, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8181 | 0.9045 | 0.682 | 0.3338 | 0.49s |\n", - "| OLS_sphere | 500 | 0.0403 | 0.2008 | 0.154 | 0.9672 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.7644 | 0.8743 | 0.6168 | 0.3775 | 56.30s |\n", - "| DeepKriging_sphere | 50 | 0.2104 | 0.4587 | 0.2681 | 0.8287 | 62.06s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0523 | 0.2287 | 0.1734 | 0.9574 | 57.64s |\n", - "| UniversalKriging | 100 | 0.0247 | 0.1572 | 0.121 | 0.9799 | 4.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:13:04\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "z mean: -28.5854 (±0.0296), Variance: 2.1860, Range: [-31.8643, -24.7681]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0561, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0725 | 1.0356 | 0.7554 | 0.5237 | 0.37s |\n", - "| OLS_sphere | 500 | 0.0417 | 0.2043 | 0.1551 | 0.9815 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.8275 | 0.9097 | 0.6603 | 0.6325 | 58.10s |\n", - "| DeepKriging_sphere | 50 | 0.1313 | 0.3624 | 0.2382 | 0.9417 | 65.12s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0804 | 0.2836 | 0.2042 | 0.9643 | 37.94s |\n", - "| UniversalKriging | 100 | 0.0323 | 0.1797 | 0.1363 | 0.9857 | 4.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:16:56\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "z mean: -27.4605 (±0.0225), Variance: 1.2701, Range: [-29.8544, -24.2683]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0675, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5825 | 0.7632 | 0.588 | 0.5562 | 0.46s |\n", - "| OLS_sphere | 500 | 0.0379 | 0.1947 | 0.1536 | 0.9711 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.5391 | 0.7342 | 0.5536 | 0.5893 | 59.42s |\n", - "| DeepKriging_sphere | 50 | 0.1858 | 0.431 | 0.2639 | 0.8584 | 57.97s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0581 | 0.2409 | 0.1814 | 0.9558 | 49.25s |\n", - "| UniversalKriging | 100 | 0.0235 | 0.1532 | 0.1173 | 0.9821 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:20:54\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "z mean: -28.8752 (±0.0295), Variance: 2.1761, Range: [-32.0112, -24.6711]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0608, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2023 | 1.0965 | 0.7131 | 0.3783 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0412 | 0.203 | 0.1627 | 0.9787 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.7457 | 0.8636 | 0.6275 | 0.6144 | 47.20s |\n", - "| DeepKriging_sphere | 50 | 0.1596 | 0.3995 | 0.2585 | 0.9175 | 68.77s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0593 | 0.2434 | 0.1872 | 0.9694 | 49.29s |\n", - "| UniversalKriging | 100 | 0.0283 | 0.1683 | 0.1335 | 0.9854 | 2.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:24:53\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "z mean: -28.2588 (±0.0259), Variance: 1.6784, Range: [-31.7387, -25.5123]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0657, nugget=0.0009\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.1351 | 1.0654 | 0.8461 | 0.2661 | 0.49s |\n", - "| OLS_sphere | 500 | 0.0356 | 0.1888 | 0.1421 | 0.977 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.2445 | 1.1156 | 0.8121 | 0.1954 | 59.26s |\n", - "| DeepKriging_sphere | 50 | 0.1365 | 0.3694 | 0.2416 | 0.9118 | 97.62s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0611 | 0.2473 | 0.1764 | 0.9605 | 58.69s |\n", - "| UniversalKriging | 100 | 0.0259 | 0.161 | 0.1236 | 0.9832 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:29:40\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "z mean: -28.0486 (±0.0303), Variance: 2.2891, Range: [-31.2402, -23.3420]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0558, nugget=0.0007\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.2094 | 1.0997 | 0.8382 | 0.4771 | 0.49s |\n", - "| OLS_sphere | 500 | 0.035 | 0.187 | 0.1466 | 0.9849 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.9525 | 0.9759 | 0.7056 | 0.5882 | 56.92s |\n", - "| DeepKriging_sphere | 50 | 0.1871 | 0.4325 | 0.236 | 0.9191 | 101.06s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0664 | 0.2578 | 0.1873 | 0.9713 | 65.46s |\n", - "| UniversalKriging | 100 | 0.0254 | 0.1594 | 0.1267 | 0.989 | 2.49s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:34:37\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "z mean: -29.3998 (±0.0286), Variance: 2.0475, Range: [-32.6460, -26.2923]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0634, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0661 | 1.0325 | 0.8135 | 0.4271 | 0.45s |\n", - "| OLS_sphere | 500 | 0.0467 | 0.2162 | 0.1758 | 0.9749 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.2469 | 1.1166 | 0.7868 | 0.33 | 70.40s |\n", - "| DeepKriging_sphere | 50 | 0.1772 | 0.421 | 0.2701 | 0.9048 | 78.55s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0831 | 0.2883 | 0.2126 | 0.9554 | 51.17s |\n", - "| UniversalKriging | 100 | 0.0334 | 0.1828 | 0.1425 | 0.982 | 4.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:39:20\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "z mean: -27.5699 (±0.0268), Variance: 1.7948, Range: [-30.9818, -24.3724]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0675, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2149 | 1.1022 | 0.8215 | 0.3437 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0392 | 0.198 | 0.1557 | 0.9788 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.0227 | 1.0113 | 0.7394 | 0.4475 | 52.78s |\n", - "| DeepKriging_sphere | 50 | 0.1552 | 0.3939 | 0.2348 | 0.9162 | 58.09s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.089 | 0.2983 | 0.1964 | 0.9519 | 45.89s |\n", - "| UniversalKriging | 100 | 0.025 | 0.1582 | 0.1218 | 0.9865 | 3.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:43:14\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "z mean: -27.7588 (±0.0466), Variance: 5.4387, Range: [-32.2197, -22.6777]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0630, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 4.3089 | 2.0758 | 1.414 | 0.1624 | 0.42s |\n", - "| OLS_sphere | 500 | 0.0344 | 0.1855 | 0.149 | 0.9933 | 0.08s |\n", - "| DeepKriging_wendland | -- | 2.9707 | 1.7236 | 1.2229 | 0.4225 | 56.93s |\n", - "| DeepKriging_sphere | 50 | 0.1661 | 0.4075 | 0.2243 | 0.9677 | 61.31s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0574 | 0.2396 | 0.1699 | 0.9888 | 50.38s |\n", - "| UniversalKriging | 100 | 0.0218 | 0.1476 | 0.1173 | 0.9958 | 3.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:47:20\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "z mean: -27.9050 (±0.0292), Variance: 2.1372, Range: [-32.2126, -24.8579]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0576, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.9551 | 1.3983 | 0.8642 | 0.1 | 0.39s |\n", - "| OLS_sphere | 500 | 0.0434 | 0.2083 | 0.1596 | 0.98 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.9997 | 0.9998 | 0.7354 | 0.5398 | 58.86s |\n", - "| DeepKriging_sphere | 50 | 0.1774 | 0.4212 | 0.2782 | 0.9183 | 69.94s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0822 | 0.2867 | 0.217 | 0.9622 | 77.52s |\n", - "| UniversalKriging | 100 | 0.0356 | 0.1887 | 0.1496 | 0.9836 | 3.77s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:52:05\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "z mean: -28.1118 (±0.0252), Variance: 1.5932, Range: [-31.2361, -24.1632]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0682, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8539 | 0.9241 | 0.7115 | 0.4511 | 0.48s |\n", - "| OLS_sphere | 500 | 0.0366 | 0.1912 | 0.1524 | 0.9765 | 0.07s |\n", - "| DeepKriging_wendland | -- | 0.6082 | 0.7799 | 0.6007 | 0.6091 | 58.32s |\n", - "| DeepKriging_sphere | 50 | 0.1041 | 0.3227 | 0.226 | 0.9331 | 71.78s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0491 | 0.2216 | 0.1681 | 0.9684 | 73.74s |\n", - "| UniversalKriging | 100 | 0.0249 | 0.1579 | 0.1278 | 0.984 | 3.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 04:56:50\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "z mean: -28.3312 (±0.0199), Variance: 0.9929, Range: [-30.7507, -25.4370]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0584, nugget=0.0006\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.313 | 1.1459 | 0.6683 | -0.0949 | 0.45s |\n", - "| OLS_sphere | 500 | 0.0374 | 0.1934 | 0.1487 | 0.9688 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.6472 | 0.8045 | 0.5852 | 0.4603 | 56.14s |\n", - "| DeepKriging_sphere | 50 | 0.091 | 0.3017 | 0.2107 | 0.9241 | 77.59s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0623 | 0.2496 | 0.1859 | 0.948 | 60.51s |\n", - "| UniversalKriging | 100 | 0.0246 | 0.1568 | 0.1256 | 0.9795 | 2.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:01:29\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "z mean: -28.6432 (±0.0309), Variance: 2.3891, Range: [-33.2603, -25.2993]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0674, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9672 | 0.9835 | 0.7645 | 0.516 | 0.47s |\n", - "| OLS_sphere | 500 | 0.0321 | 0.1791 | 0.1432 | 0.9839 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.5257 | 1.2352 | 0.8744 | 0.2364 | 53.61s |\n", - "| DeepKriging_sphere | 50 | 0.1456 | 0.3816 | 0.2471 | 0.9271 | 66.52s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0755 | 0.2747 | 0.1969 | 0.9622 | 38.87s |\n", - "| UniversalKriging | 100 | 0.0203 | 0.1425 | 0.1127 | 0.9898 | 0.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:05:27\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "z mean: -27.3204 (±0.0367), Variance: 3.3623, Range: [-30.3941, -21.9787]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0623, nugget=0.0006\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.5769 | 1.2557 | 0.9849 | 0.5836 | 0.36s |\n", - "| OLS_sphere | 500 | 0.0391 | 0.1978 | 0.1532 | 0.9897 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.3024 | 1.1412 | 0.7767 | 0.6561 | 60.39s |\n", - "| DeepKriging_sphere | 50 | 0.123 | 0.3507 | 0.2195 | 0.9675 | 62.68s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.056 | 0.2367 | 0.1771 | 0.9852 | 58.18s |\n", - "| UniversalKriging | 100 | 0.0284 | 0.1685 | 0.1318 | 0.9925 | 2.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:09:51\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "z mean: -27.4425 (±0.0263), Variance: 1.7294, Range: [-29.8969, -24.4000]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0649, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.2461 | 1.1163 | 0.895 | 0.2682 | 0.39s |\n", - "| OLS_sphere | 500 | 0.04 | 0.1999 | 0.1594 | 0.9765 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.5319 | 1.2377 | 0.889 | 0.1004 | 53.05s |\n", - "| DeepKriging_sphere | 50 | 0.1645 | 0.4055 | 0.2434 | 0.9034 | 63.46s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.062 | 0.249 | 0.1812 | 0.9636 | 55.62s |\n", - "| UniversalKriging | 100 | 0.0291 | 0.1705 | 0.1324 | 0.9829 | 1.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:14:14\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "z mean: -27.3482 (±0.0293), Variance: 2.1441, Range: [-30.6805, -24.0854]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0661, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.3496 | 1.1617 | 0.9041 | 0.3989 | 0.38s |\n", - "| OLS_sphere | 500 | 0.046 | 0.2144 | 0.1649 | 0.9795 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1.0742 | 1.0364 | 0.7549 | 0.5216 | 51.73s |\n", - "| DeepKriging_sphere | 50 | 0.24 | 0.4899 | 0.2838 | 0.8931 | 63.00s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.104 | 0.3225 | 0.2335 | 0.9537 | 36.73s |\n", - "| UniversalKriging | 100 | 0.0273 | 0.1653 | 0.1283 | 0.9878 | 4.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:18:16\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "z mean: -28.5211 (±0.0263), Variance: 1.7237, Range: [-31.5498, -25.1678]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0609, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 1.0319 | 1.0158 | 0.7672 | 0.437 | 0.44s |\n", - "| OLS_sphere | 500 | 0.0406 | 0.2016 | 0.1564 | 0.9778 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1.1328 | 1.0643 | 0.779 | 0.382 | 51.27s |\n", - "| DeepKriging_sphere | 50 | 0.1963 | 0.4431 | 0.2978 | 0.8929 | 73.24s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0747 | 0.2734 | 0.2012 | 0.9592 | 65.53s |\n", - "| UniversalKriging | 100 | 0.0319 | 0.1786 | 0.1422 | 0.9826 | 4.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:22:56\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "z mean: -29.3659 (±0.0249), Variance: 1.5456, Range: [-32.0513, -25.5264]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0700, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.9267 | 0.9627 | 0.7337 | 0.4491 | 0.46s |\n", - "| OLS_sphere | 500 | 0.0427 | 0.2067 | 0.1629 | 0.9746 | 0.06s |\n", - "| DeepKriging_wendland | -- | 0.8152 | 0.9029 | 0.6688 | 0.5154 | 54.53s |\n", - "| DeepKriging_sphere | 50 | 0.1139 | 0.3374 | 0.2315 | 0.9323 | 61.47s |\n", - "| DeepKriging_sphere_Huber | 500 | 0.0685 | 0.2618 | 0.1937 | 0.9593 | 61.98s |\n", - "| UniversalKriging | 100 | 0.0281 | 0.1676 | 0.1331 | 0.9833 | 4.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 05:27:25\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 500, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|-----------|\n", - "| OLS_wendland | 1.23±0.80 | 1.07±0.30 | 0.78±0.19 | 0.35±0.24 |\n", - "| OLS_sphere | 0.04±0.00 | 0.20±0.01 | 0.16±0.01 | 0.97±0.01 |\n", - "| DeepKriging_wendland | 1.21±0.75 | 1.06±0.28 | 0.72±0.14 | 0.28±0.53 |\n", - "| DeepKriging_sphere | 0.17±0.06 | 0.41±0.06 | 0.25±0.03 | 0.88±0.10 |\n", - "| DeepKriging_sphere_Huber | 0.07±0.01 | 0.26±0.02 | 0.19±0.01 | 0.96±0.02 |\n", - "| UniversalKriging | 0.03±0.00 | 0.17±0.01 | 0.13±0.01 | 0.98±0.01 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 0.17±0.01)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 0.157489, - "end_time": "2026-01-17T17:28:21.290617", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_without_outliers.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_euclidean_without_outliers.ipynb", - "parameters": {}, - "start_time": "2026-01-17T17:28:21.133128", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_chisquare.ipynb b/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_chisquare.ipynb deleted file mode 100644 index c099a85..0000000 --- a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_chisquare.ipynb +++ /dev/null @@ -1,3482 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.007352, - "end_time": "2026-01-19T03:16:48.420311", - "exception": false, - "start_time": "2026-01-19T03:16:48.412959", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:48.436380Z", - "iopub.status.busy": "2026-01-19T03:16:48.435478Z", - "iopub.status.idle": "2026-01-19T03:16:48.457566Z", - "shell.execute_reply": "2026-01-19T03:16:48.453533Z" - }, - "papermill": { - "duration": 0.036949, - "end_time": "2026-01-19T03:16:48.462194", - "exception": false, - "start_time": "2026-01-19T03:16:48.425245", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:48.476709Z", - "iopub.status.busy": "2026-01-19T03:16:48.476197Z", - "iopub.status.idle": "2026-01-19T03:16:51.168942Z", - "shell.execute_reply": "2026-01-19T03:16:51.166191Z" - }, - "papermill": { - "duration": 2.702095, - "end_time": "2026-01-19T03:16:51.171499", - "exception": false, - "start_time": "2026-01-19T03:16:48.469404", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-28 14:49:12.915944: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1769582952.931612 228193 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1769582952.935759 228193 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1769582952.947711 228193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769582952.947732 228193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769582952.947734 228193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1769582952.947736 228193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.003799, - "end_time": "2026-01-19T03:16:51.181929", - "exception": false, - "start_time": "2026-01-19T03:16:51.178130", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:51.189917Z", - "iopub.status.busy": "2026-01-19T03:16:51.189377Z", - "iopub.status.idle": "2026-01-19T03:16:52.570335Z", - "shell.execute_reply": "2026-01-19T03:16:52.566700Z" - }, - "papermill": { - "duration": 1.388835, - "end_time": "2026-01-19T03:16:52.573137", - "exception": false, - "start_time": "2026-01-19T03:16:51.184302", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011224, - "end_time": "2026-01-19T03:16:52.596791", - "exception": false, - "start_time": "2026-01-19T03:16:52.585567", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:52.614029Z", - "iopub.status.busy": "2026-01-19T03:16:52.613171Z", - "iopub.status.idle": "2026-01-19T03:16:52.624733Z", - "shell.execute_reply": "2026-01-19T03:16:52.621535Z" - }, - "papermill": { - "duration": 0.02273, - "end_time": "2026-01-19T03:16:52.627238", - "exception": false, - "start_time": "2026-01-19T03:16:52.604508", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.011677, - "end_time": "2026-01-19T03:16:52.651852", - "exception": false, - "start_time": "2026-01-19T03:16:52.640175", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:52.669698Z", - "iopub.status.busy": "2026-01-19T03:16:52.668903Z", - "iopub.status.idle": "2026-01-19T03:17:06.735388Z", - "shell.execute_reply": "2026-01-19T03:17:06.732265Z" - }, - "papermill": { - "duration": 14.079456, - "end_time": "2026-01-19T03:17:06.739337", - "exception": false, - "start_time": "2026-01-19T03:16:52.659881", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.004492, - "end_time": "2026-01-19T03:17:06.756435", - "exception": false, - "start_time": "2026-01-19T03:17:06.751943", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.767631Z", - "iopub.status.busy": "2026-01-19T03:17:06.766390Z", - "iopub.status.idle": "2026-01-19T03:17:06.787031Z", - "shell.execute_reply": "2026-01-19T03:17:06.784279Z" - }, - "papermill": { - "duration": 0.029505, - "end_time": "2026-01-19T03:17:06.789611", - "exception": false, - "start_time": "2026-01-19T03:17:06.760106", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + chi-square noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " z = y + rng.chisquare(df=4, size=num_sample).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.009795, - "end_time": "2026-01-19T03:17:06.811712", - "exception": false, - "start_time": "2026-01-19T03:17:06.801917", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.826673Z", - "iopub.status.busy": "2026-01-19T03:17:06.825908Z", - "iopub.status.idle": "2026-01-19T03:17:06.870866Z", - "shell.execute_reply": "2026-01-19T03:17:06.867563Z" - }, - "papermill": { - "duration": 0.055634, - "end_time": "2026-01-19T03:17:06.873570", - "exception": false, - "start_time": "2026-01-19T03:17:06.817936", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " \n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten_cont(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " \n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten_cont, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.902553Z", - "iopub.status.busy": "2026-01-19T03:17:06.901739Z", - "iopub.status.idle": "2026-01-19T03:17:06.954227Z", - "shell.execute_reply": "2026-01-19T03:17:06.950615Z" - }, - "papermill": { - "duration": 0.071214, - "end_time": "2026-01-19T03:17:06.958093", - "exception": false, - "start_time": "2026-01-19T03:17:06.886879", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder + noise (chi-square)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder + noise (chi-square)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.005519, - "end_time": "2026-01-19T03:17:06.978354", - "exception": false, - "start_time": "2026-01-19T03:17:06.972835", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.988250Z", - "iopub.status.busy": "2026-01-19T03:17:06.987867Z", - "iopub.status.idle": "2026-01-19T03:17:06.993412Z", - "shell.execute_reply": "2026-01-19T03:17:06.992140Z" - }, - "papermill": { - "duration": 0.013532, - "end_time": "2026-01-19T03:17:06.995385", - "exception": false, - "start_time": "2026-01-19T03:17:06.981853", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:07.004725Z", - "iopub.status.busy": "2026-01-19T03:17:07.004437Z", - "iopub.status.idle": "2026-01-19T06:12:22.928261Z", - "shell.execute_reply": "2026-01-19T06:12:22.924952Z" - }, - "papermill": { - "duration": 10515.932006, - "end_time": "2026-01-19T06:12:22.930696", - "exception": false, - "start_time": "2026-01-19T03:17:06.998690", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.9206 (±1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "z mean: 16.9046 (±1.5886), Variance: 6309.3911, Range: [-213.6102, 210.3183]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3216.2000 3414.4778 \n", - " 100 2154.5635 2320.1206 \n", - " 200 1496.3403 1254.3578 \n", - " 500 495.8142 417.4774 \n", - " 700 385.0892 317.5042 *\n", - " 1000 779.0868 605.5214 \n", - " 1400 4366.0361 4272.9810 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1769582981.676238 228193 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1769582983.958247 228497 service.cc:152] XLA service 0x583eea4d0080 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1769582983.958292 228497 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1769582984.330735 228497 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1769582993.890475 228497 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x70f4041da290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x70f3d867edd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 60.9691 85.3915 *\n", - " 100 69.9780 134.6499 \n", - " 200 93.2087 201.8410 \n", - " 500 183.5666 296.5614 \n", - " 700 280.5419 375.5503 \n", - " 1000 637.9906 931.5712 \n", - " 1400 1657.5387 2530.7466 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.9458 96.0408 *\n", - " 100 6.1922 121.0123 \n", - " 200 7.6672 135.8375 \n", - " 500 11.6277 309.4569 \n", - " 700 14.6723 459.3751 \n", - " 1000 20.3271 1208.0150 \n", - " 1400 34.5028 3048.6377 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0029, sigma²=7241.8456, nugget=0.0038\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4558, sigma²=3092.5709, nugget=0.0073\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2455, sigma²=1462.9891, nugget=0.0027\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0572, sigma²=206.2424, nugget=0.0538\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2993, sigma²=0.0009, nugget=108.0370\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=52.4512, sigma²=0.0002, nugget=58.3381\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.9486, sigma²=0.0002, nugget=34.5759\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 193.9813 196.5788 *\n", - " 100 194.0666 202.8991 \n", - " 200 204.3916 194.6178 \n", - " 500 243.5128 250.1214 \n", - " 700 371.3623 317.5028 \n", - " 1000 763.6275 605.5202 \n", - " 1400 4347.2497 4271.3208 \n", - " Best order: 50\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0029, sigma²=7241.8456, nugget=0.0038\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8370.17 | 91.4886 | 61.6967 | -0.2591 | 0.42s |\n", - "| OLS_sphere | 700 | 317.504 | 17.8186 | 10.8926 | 0.9522 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3995.5 | 63.2099 | 47.3596 | 0.399 | 63.66s |\n", - "| DeepKriging_sphere | 50 | 86.0234 | 9.2749 | 6.137 | 0.9871 | 28.84s |\n", - "| DeepKriging_sphere_Huber | 50 | 82.9749 | 9.1091 | 6.2625 | 0.9875 | 38.26s |\n", - "| UniversalKriging | 50 | 196.579 | 14.0207 | 8.4911 | 0.9704 | 3.11s |\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:02:01\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.5604 (±1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "z mean: 16.6255 (±1.5783), Variance: 6227.2783, Range: [-197.9883, 209.4138]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0693, sigma²=7097.8305, nugget=0.0034\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5404.57 | 73.5158 | 56.3588 | 0.1457 | 0.40s |\n", - "| OLS_sphere | 700 | 286.782 | 16.9346 | 10.7328 | 0.9547 | 0.09s |\n", - "| DeepKriging_wendland | -- | 5303.35 | 72.8241 | 55.1186 | 0.1617 | 44.84s |\n", - "| DeepKriging_sphere | 50 | 4993.61 | 70.6655 | 57.3736 | 0.2107 | 13.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 183.503 | 13.5463 | 7.7926 | 0.971 | 35.85s |\n", - "| UniversalKriging | 50 | 198.868 | 14.1021 | 8.8699 | 0.9686 | 3.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:03:58\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.4982 (±1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "z mean: 15.5736 (±1.5950), Variance: 6359.9502, Range: [-209.2875, 212.3337]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1063, sigma²=7853.7020, nugget=0.0040\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8283.96 | 91.0162 | 60.577 | -0.157 | 0.41s |\n", - "| OLS_sphere | 700 | 242.32 | 15.5666 | 9.7435 | 0.9662 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4389.93 | 66.2566 | 48.405 | 0.3869 | 45.06s |\n", - "| DeepKriging_sphere | 50 | 84.1259 | 9.172 | 6.4143 | 0.9883 | 32.56s |\n", - "| DeepKriging_sphere_Huber | 50 | 114.685 | 10.7091 | 7.1701 | 0.984 | 35.29s |\n", - "| UniversalKriging | 50 | 213.279 | 14.6041 | 8.7024 | 0.9702 | 1.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:06:14\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.4518 (±1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "z mean: 17.5084 (±1.6104), Variance: 6483.4702, Range: [-204.6370, 213.8266]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9486, sigma²=6798.6505, nugget=0.0026\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6361.81 | 79.761 | 59.7191 | 0.0289 | 0.42s |\n", - "| OLS_sphere | 700 | 356.089 | 18.8703 | 11.0331 | 0.9456 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4512.31 | 67.1737 | 49.7049 | 0.3112 | 46.39s |\n", - "| DeepKriging_sphere | 50 | 94.4719 | 9.7197 | 6.5595 | 0.9856 | 31.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 107.656 | 10.3757 | 6.0049 | 0.9836 | 31.11s |\n", - "| UniversalKriging | 50 | 268.337 | 16.381 | 9.8203 | 0.959 | 3.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:08:29\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.6175 (±1.6158), Variance: 6527.2144, Range: [-209.7956, 205.1870]\n", - "z mean: 18.6201 (±1.6141), Variance: 6513.1152, Range: [-206.9031, 208.9441]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0615, sigma²=7742.0206, nugget=0.0034\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25575.2 | 159.923 | 66.4076 | -2.9198 | 0.41s |\n", - "| OLS_sphere | 700 | 409.687 | 20.2407 | 12.0343 | 0.9372 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4287.73 | 65.4807 | 47.5587 | 0.3428 | 44.49s |\n", - "| DeepKriging_sphere | 50 | 108.444 | 10.4136 | 6.849 | 0.9834 | 32.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 101.402 | 10.0698 | 6.7312 | 0.9845 | 27.95s |\n", - "| UniversalKriging | 50 | 321.296 | 17.9247 | 10.1211 | 0.9508 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:10:43\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.9645 (±1.6405), Variance: 6728.1055, Range: [-206.4400, 204.6959]\n", - "z mean: 17.9848 (±1.6404), Variance: 6727.4258, Range: [-203.1950, 212.7274]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9918, sigma²=6949.5685, nugget=0.0038\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 27042.9 | 164.447 | 70.1582 | -2.9331 | 0.42s |\n", - "| OLS_sphere | 700 | 208.236 | 14.4304 | 9.7908 | 0.9697 | 0.12s |\n", - "| DeepKriging_wendland | -- | 4105.91 | 64.0774 | 46.7283 | 0.4028 | 45.97s |\n", - "| DeepKriging_sphere | 50 | 36.0233 | 6.0019 | 4.4291 | 0.9948 | 33.89s |\n", - "| DeepKriging_sphere_Huber | 50 | 63.4096 | 7.963 | 5.9016 | 0.9908 | 31.63s |\n", - "| UniversalKriging | 50 | 147.469 | 12.1437 | 8.2752 | 0.9786 | 1.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:13:01\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 15.0328 (±1.6395), Variance: 6719.8687, Range: [-213.9537, 205.0478]\n", - "z mean: 19.1101 (±1.6414), Variance: 6735.2993, Range: [-212.2428, 219.3365]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8926, sigma²=6578.9048, nugget=0.0043\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9502.65 | 97.4816 | 61.8686 | -0.4264 | 0.45s |\n", - "| OLS_sphere | 700 | 434.746 | 20.8506 | 10.4218 | 0.9347 | 0.11s |\n", - "| DeepKriging_wendland | -- | 4045.66 | 63.6055 | 48.3289 | 0.3927 | 45.38s |\n", - "| DeepKriging_sphere | 50 | 59.876 | 7.738 | 5.6949 | 0.991 | 30.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 75.3711 | 8.6817 | 5.7721 | 0.9887 | 33.92s |\n", - "| UniversalKriging | 50 | 180.858 | 13.4484 | 8.498 | 0.9729 | 2.80s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:15:21\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.2577 (±1.5934), Variance: 6347.5322, Range: [-193.8297, 206.9203]\n", - "z mean: 15.3243 (±1.5953), Variance: 6362.5352, Range: [-188.3340, 214.5135]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0123, sigma²=6710.9004, nugget=0.0025\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4543.87 | 67.4083 | 53.0968 | 0.2335 | 0.37s |\n", - "| OLS_sphere | 700 | 426.449 | 20.6506 | 12.891 | 0.9281 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3536.29 | 59.4667 | 44.2661 | 0.4034 | 37.39s |\n", - "| DeepKriging_sphere | 50 | 111.425 | 10.5558 | 7.3006 | 0.9812 | 31.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 113.465 | 10.652 | 6.8637 | 0.9809 | 32.98s |\n", - "| UniversalKriging | 50 | 251.136 | 15.8473 | 9.882 | 0.9576 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:17:34\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.8672 (±1.6373), Variance: 6702.1040, Range: [-213.2814, 206.2566]\n", - "z mean: 17.7647 (±1.6392), Variance: 6717.6455, Range: [-211.9310, 212.0961]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9687, sigma²=7154.0377, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4804.47 | 69.3143 | 55.6856 | 0.1529 | 0.41s |\n", - "| OLS_sphere | 700 | 225.691 | 15.023 | 9.8333 | 0.9602 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3597.11 | 59.9759 | 44.9928 | 0.3657 | 38.40s |\n", - "| DeepKriging_sphere | 50 | 114.146 | 10.6839 | 7.1473 | 0.9799 | 31.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 92.7061 | 9.6284 | 6.3381 | 0.9837 | 31.08s |\n", - "| UniversalKriging | 50 | 185.739 | 13.6286 | 8.4172 | 0.9673 | 2.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:19:45\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 15.2464 (±1.5728), Variance: 6184.4497, Range: [-213.3035, 205.0131]\n", - "z mean: 19.2560 (±1.5748), Variance: 6199.6406, Range: [-210.2211, 212.4834]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9621, sigma²=6542.5570, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4522.67 | 67.2508 | 52.792 | 0.2273 | 0.37s |\n", - "| OLS_sphere | 700 | 214.063 | 14.6309 | 8.925 | 0.9634 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3696.74 | 60.8008 | 43.558 | 0.3684 | 44.40s |\n", - "| DeepKriging_sphere | 50 | 67.1206 | 8.1927 | 5.8931 | 0.9885 | 33.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 83.9906 | 9.1646 | 6.3134 | 0.9856 | 40.24s |\n", - "| UniversalKriging | 50 | 194.684 | 13.9529 | 8.4458 | 0.9667 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:22:16\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 15.7404 (±1.6331), Variance: 6667.8887, Range: [-209.9314, 203.9581]\n", - "z mean: 19.7457 (±1.6364), Variance: 6694.2104, Range: [-207.3561, 216.2963]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8574, sigma²=6422.1635, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6225.22 | 78.9001 | 54.6592 | 0.0668 | 0.40s |\n", - "| OLS_sphere | 700 | 204.6 | 14.3038 | 9.6409 | 0.9693 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3537.73 | 59.4788 | 42.5478 | 0.4697 | 47.47s |\n", - "| DeepKriging_sphere | 50 | 5782.53 | 76.0429 | 62.9031 | 0.1331 | 14.57s |\n", - "| DeepKriging_sphere_Huber | 50 | 76.1553 | 8.7267 | 6.2042 | 0.9886 | 30.68s |\n", - "| UniversalKriging | 50 | 116.106 | 10.7752 | 7.2957 | 0.9826 | 3.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:24:22\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 16.8317 (±1.6021), Variance: 6416.8169, Range: [-213.4137, 202.3162]\n", - "z mean: 20.8332 (±1.6036), Variance: 6428.6587, Range: [-211.3674, 211.5730]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8480, sigma²=6234.7790, nugget=0.5602\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10732.6 | 103.598 | 60.5631 | -0.6839 | 0.41s |\n", - "| OLS_sphere | 700 | 355.437 | 18.853 | 11.2131 | 0.9442 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4193.79 | 64.7595 | 43.2336 | 0.342 | 48.59s |\n", - "| DeepKriging_sphere | 50 | 64.0471 | 8.0029 | 5.3993 | 0.99 | 33.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 160.313 | 12.6615 | 7.278 | 0.9748 | 36.57s |\n", - "| UniversalKriging | 50 | 237.184 | 15.4008 | 9.3756 | 0.9628 | 2.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:26:55\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 10.7707 (±1.5807), Variance: 6246.6694, Range: [-211.4668, 202.6113]\n", - "z mean: 14.7004 (±1.5804), Variance: 6244.4097, Range: [-210.1440, 205.5115]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2911, sigma²=8778.0148, nugget=0.0021\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|----------|----------|--------|\n", - "| OLS_wendland | -- | 541872 | 736.12 | 139.158 | -87.3578 | 0.44s |\n", - "| OLS_sphere | 700 | 250.898 | 15.8397 | 9.5647 | 0.9591 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4717.68 | 68.6854 | 53.385 | 0.2307 | 50.60s |\n", - "| DeepKriging_sphere | 50 | 139.253 | 11.8006 | 7.9557 | 0.9773 | 46.92s |\n", - "| DeepKriging_sphere_Huber | 50 | 141.765 | 11.9065 | 8.1565 | 0.9769 | 43.37s |\n", - "| UniversalKriging | 50 | 344.963 | 18.5732 | 10.4869 | 0.9438 | 3.49s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:29:54\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 10.7825 (±1.6295), Variance: 6637.9307, Range: [-205.2383, 204.2435]\n", - "z mean: 14.8237 (±1.6299), Variance: 6641.3486, Range: [-204.0361, 209.6821]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0523, sigma²=7725.3741, nugget=0.0041\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 316588 | 562.662 | 95.2443 | -45.7147 | 0.45s |\n", - "| OLS_sphere | 700 | 233.854 | 15.2923 | 9.3417 | 0.9655 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3646.26 | 60.3843 | 43.3921 | 0.462 | 57.33s |\n", - "| DeepKriging_sphere | 50 | 93.2221 | 9.6552 | 7.2042 | 0.9862 | 39.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 80.5828 | 8.9768 | 6.4482 | 0.9881 | 42.59s |\n", - "| UniversalKriging | 50 | 104.845 | 10.2394 | 7.2008 | 0.9845 | 3.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:32:51\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.7531 (±1.5943), Variance: 6354.0967, Range: [-212.4267, 204.4545]\n", - "z mean: 16.7630 (±1.5951), Variance: 6360.9766, Range: [-207.5260, 211.8137]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9988, sigma²=7137.4586, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15426.5 | 124.203 | 65.9338 | -1.6844 | 0.45s |\n", - "| OLS_sphere | 700 | 264.558 | 16.2653 | 9.9673 | 0.954 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3551.65 | 59.5957 | 44.7384 | 0.382 | 56.76s |\n", - "| DeepKriging_sphere | 50 | 95.7907 | 9.7873 | 6.8017 | 0.9833 | 37.80s |\n", - "| DeepKriging_sphere_Huber | 50 | 4979.84 | 70.568 | 56.4813 | 0.1334 | 15.48s |\n", - "| UniversalKriging | 50 | 213.38 | 14.6075 | 8.8659 | 0.9629 | 3.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:35:20\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.3852 (±1.6051), Variance: 6440.5127, Range: [-213.6311, 206.5967]\n", - "z mean: 15.4481 (±1.6083), Variance: 6466.5024, Range: [-210.6667, 210.6492]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8863, sigma²=6412.6757, nugget=0.0022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5812.81 | 76.2418 | 57.8232 | -0.0159 | 0.41s |\n", - "| OLS_sphere | 700 | 576.281 | 24.0059 | 13.5396 | 0.8993 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4276.19 | 65.3926 | 46.9761 | 0.2526 | 57.01s |\n", - "| DeepKriging_sphere | 50 | 100.294 | 10.0147 | 6.6272 | 0.9825 | 41.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 129.08 | 11.3613 | 8.0913 | 0.9774 | 37.20s |\n", - "| UniversalKriging | 50 | 270.478 | 16.4462 | 10.2501 | 0.9527 | 3.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:38:17\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.5094 (±1.6194), Variance: 6556.0034, Range: [-213.5274, 204.3522]\n", - "z mean: 18.4949 (±1.6197), Variance: 6558.2266, Range: [-211.9682, 209.8239]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0793, sigma²=7567.9168, nugget=0.0033\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 40384.2 | 200.958 | 75.7836 | -4.4457 | 0.45s |\n", - "| OLS_sphere | 700 | 502.132 | 22.4083 | 12.5193 | 0.9323 | 0.11s |\n", - "| DeepKriging_wendland | -- | 4125.43 | 64.2295 | 47.7476 | 0.4437 | 56.26s |\n", - "| DeepKriging_sphere | 50 | 194.032 | 13.9296 | 7.5403 | 0.9738 | 40.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 208.161 | 14.4278 | 8.0074 | 0.9719 | 34.94s |\n", - "| UniversalKriging | 50 | 319.185 | 17.8657 | 10.4836 | 0.957 | 3.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:41:12\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.2342 (±1.6400), Variance: 6724.3286, Range: [-208.6201, 203.6964]\n", - "z mean: 15.2491 (±1.6389), Variance: 6714.9746, Range: [-201.6355, 209.7974]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8842, sigma²=6439.2580, nugget=2.5570\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10402.3 | 101.992 | 66.8872 | -0.702 | 0.45s |\n", - "| OLS_sphere | 700 | 282.871 | 16.8188 | 10.0773 | 0.9537 | 0.10s |\n", - "| DeepKriging_wendland | -- | 9902.49 | 99.5112 | 53.6991 | -0.6203 | 55.93s |\n", - "| DeepKriging_sphere | 50 | 82.7365 | 9.096 | 5.5007 | 0.9865 | 44.45s |\n", - "| DeepKriging_sphere_Huber | 50 | 98.0085 | 9.8999 | 6.4938 | 0.984 | 41.13s |\n", - "| UniversalKriging | 50 | 182.964 | 13.5264 | 8.938 | 0.9701 | 2.42s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:44:15\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.2790 (±1.6210), Variance: 6569.0591, Range: [-215.0015, 203.4196]\n", - "z mean: 16.2732 (±1.6218), Variance: 6575.4048, Range: [-208.6113, 207.3940]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8874, sigma²=6341.7431, nugget=0.0024\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5386.02 | 73.3895 | 56.0902 | 0.2168 | 0.40s |\n", - "| OLS_sphere | 700 | 283.547 | 16.8388 | 10.6136 | 0.9588 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4242.04 | 65.1309 | 44.9126 | 0.3831 | 55.14s |\n", - "| DeepKriging_sphere | 50 | 54.5289 | 7.3844 | 5.4961 | 0.9921 | 33.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.6917 | 7.726 | 5.6971 | 0.9913 | 42.92s |\n", - "| UniversalKriging | 50 | 194.268 | 13.938 | 8.7335 | 0.9717 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:47:10\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.1620 (±1.6099), Variance: 6479.7378, Range: [-213.2246, 205.0493]\n", - "z mean: 18.1404 (±1.6095), Variance: 6475.8833, Range: [-203.9177, 211.0654]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8895, sigma²=6709.9866, nugget=0.0051\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10650.9 | 103.203 | 60.0855 | -0.6931 | 0.45s |\n", - "| OLS_sphere | 700 | 175.579 | 13.2506 | 8.6064 | 0.9721 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3649.67 | 60.4125 | 42.3401 | 0.4198 | 54.57s |\n", - "| DeepKriging_sphere | 50 | 49.8254 | 7.0587 | 5.0673 | 0.9921 | 54.35s |\n", - "| DeepKriging_sphere_Huber | 50 | 58.8137 | 7.669 | 5.2231 | 0.9907 | 40.56s |\n", - "| UniversalKriging | 50 | 133.083 | 11.5362 | 7.8836 | 0.9788 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:50:25\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 15.6292 (±1.6380), Variance: 6707.4985, Range: [-214.6765, 205.8479]\n", - "z mean: 19.6136 (±1.6409), Variance: 6731.4146, Range: [-212.3352, 213.5941]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9225, sigma²=6496.9373, nugget=0.0041\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 43381.2 | 208.281 | 65.087 | -5.9079 | 0.40s |\n", - "| OLS_sphere | 700 | 351.23 | 18.7411 | 10.6752 | 0.9441 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3811.84 | 61.7401 | 46.1463 | 0.393 | 47.49s |\n", - "| DeepKriging_sphere | 50 | 144.388 | 12.0161 | 6.6966 | 0.977 | 32.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 201.476 | 14.1942 | 7.8181 | 0.9679 | 36.57s |\n", - "| UniversalKriging | 50 | 303.371 | 17.4175 | 9.1236 | 0.9517 | 3.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:53:09\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.1540 (±1.6253), Variance: 6603.7393, Range: [-212.9704, 206.5339]\n", - "z mean: 18.1087 (±1.6249), Variance: 6600.4150, Range: [-211.3775, 209.2802]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9012, sigma²=6614.3060, nugget=0.0046\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 21524 | 146.711 | 70.7973 | -2.2461 | 0.46s |\n", - "| OLS_sphere | 700 | 213.821 | 14.6226 | 9.9279 | 0.9678 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4036.45 | 63.5331 | 47.602 | 0.3912 | 54.47s |\n", - "| DeepKriging_sphere | 50 | 88.4607 | 9.4054 | 6.1312 | 0.9867 | 41.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 91.6373 | 9.5727 | 6.5829 | 0.9862 | 61.38s |\n", - "| UniversalKriging | 50 | 201.384 | 14.191 | 9.0835 | 0.9696 | 1.68s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 15:56:32\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.1046 (±1.6040), Variance: 6432.4102, Range: [-204.2676, 206.8854]\n", - "z mean: 18.0200 (±1.6029), Variance: 6423.0142, Range: [-201.5575, 217.5620]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.5159, sigma²=11156.3031, nugget=0.0052\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5076.33 | 71.2484 | 56.7085 | 0.1979 | 0.44s |\n", - "| OLS_sphere | 700 | 257.999 | 16.0624 | 10.4064 | 0.9592 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4071.47 | 63.808 | 48.9607 | 0.3567 | 63.85s |\n", - "| DeepKriging_sphere | 50 | 97.6634 | 9.8825 | 6.5117 | 0.9846 | 48.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 172.577 | 13.1369 | 6.7845 | 0.9727 | 87.71s |\n", - "| UniversalKriging | 50 | 194.314 | 13.9397 | 9.0468 | 0.9693 | 1.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:00:40\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.3727 (±1.6088), Variance: 6470.5322, Range: [-205.0407, 202.1517]\n", - "z mean: 17.3119 (±1.6083), Variance: 6466.9009, Range: [-198.9897, 206.7427]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9988, sigma²=7082.6691, nugget=0.0043\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 75936.7 | 275.566 | 79.4348 | -11.1514 | 0.43s |\n", - "| OLS_sphere | 700 | 222.515 | 14.9169 | 9.3738 | 0.9644 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4811.41 | 69.3643 | 45.7916 | 0.2301 | 67.53s |\n", - "| DeepKriging_sphere | 50 | 52.2622 | 7.2293 | 5.1078 | 0.9916 | 37.57s |\n", - "| DeepKriging_sphere_Huber | 50 | 48.1759 | 6.9409 | 4.8771 | 0.9923 | 40.57s |\n", - "| UniversalKriging | 50 | 145.862 | 12.0773 | 7.4898 | 0.9767 | 3.53s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:03:57\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.7215 (±1.6056), Variance: 6444.8770, Range: [-213.1257, 203.9100]\n", - "z mean: 15.7448 (±1.6087), Variance: 6470.0767, Range: [-207.1357, 210.3568]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8651, sigma²=6289.0740, nugget=0.0048\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5257.4 | 72.508 | 57.4057 | 0.2355 | 0.42s |\n", - "| OLS_sphere | 700 | 356.824 | 18.8898 | 11.1486 | 0.9481 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4581.79 | 67.6889 | 50.8216 | 0.3337 | 63.99s |\n", - "| DeepKriging_sphere | 50 | 217.249 | 14.7394 | 7.2178 | 0.9684 | 47.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 201.658 | 14.2006 | 7.7804 | 0.9707 | 52.79s |\n", - "| UniversalKriging | 50 | 349.499 | 18.6949 | 10.7918 | 0.9492 | 3.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:07:33\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.6376 (±1.6446), Variance: 6761.5942, Range: [-211.8297, 206.1259]\n", - "z mean: 18.5581 (±1.6452), Variance: 6766.8384, Range: [-209.6407, 212.4423]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0426, sigma²=7373.7781, nugget=0.0046\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11348.6 | 106.53 | 63.0744 | -0.5579 | 0.50s |\n", - "| OLS_sphere | 700 | 235.543 | 15.3474 | 9.6851 | 0.9677 | 0.23s |\n", - "| DeepKriging_wendland | -- | 4610.8 | 67.9029 | 48.8773 | 0.367 | 65.47s |\n", - "| DeepKriging_sphere | 50 | 66.0178 | 8.1251 | 5.5326 | 0.9909 | 50.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 90.4227 | 9.5091 | 6.4331 | 0.9876 | 48.07s |\n", - "| UniversalKriging | 50 | 241.136 | 15.5286 | 9.707 | 0.9669 | 3.43s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:11:09\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 16.4394 (±1.6160), Variance: 6528.4375, Range: [-208.1850, 205.0659]\n", - "z mean: 20.3728 (±1.6170), Variance: 6536.4663, Range: [-207.0038, 212.3092]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1751, sigma²=8088.4985, nugget=0.0021\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5555.52 | 74.5353 | 57.9955 | 0.1871 | 0.41s |\n", - "| OLS_sphere | 700 | 208.075 | 14.4248 | 8.8934 | 0.9696 | 0.10s |\n", - "| DeepKriging_wendland | -- | 6438.66 | 80.2413 | 47.9615 | 0.0579 | 67.20s |\n", - "| DeepKriging_sphere | 50 | 145.523 | 12.0633 | 5.8379 | 0.9787 | 45.14s |\n", - "| DeepKriging_sphere_Huber | 50 | 206.1 | 14.3562 | 7.1492 | 0.9698 | 43.88s |\n", - "| UniversalKriging | 50 | 210.413 | 14.5056 | 8.5679 | 0.9692 | 3.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:14:38\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.0742 (±1.5787), Variance: 6230.7856, Range: [-209.3389, 203.6537]\n", - "z mean: 18.0488 (±1.5803), Variance: 6243.5234, Range: [-206.4329, 210.6117]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9863, sigma²=6888.0736, nugget=0.0026\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|---------|\n", - "| OLS_wendland | -- | 5833.79 | 76.3793 | 56.184 | 0.1011 | 0.43s |\n", - "| OLS_sphere | 700 | 275.293 | 16.592 | 10.0283 | 0.9576 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3792.42 | 61.5826 | 42.532 | 0.4156 | 67.88s |\n", - "| DeepKriging_sphere | 50 | 110.669 | 10.5199 | 5.7698 | 0.9829 | 105.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 126.414 | 11.2434 | 6.8482 | 0.9805 | 32.67s |\n", - "| UniversalKriging | 50 | 300.824 | 17.3443 | 9.4729 | 0.9536 | 3.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:19:00\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.3946 (±1.6330), Variance: 6666.4321, Range: [-212.8773, 204.5095]\n", - "z mean: 18.3563 (±1.6332), Variance: 6668.0503, Range: [-212.3678, 212.8992]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8069, sigma²=6086.9243, nugget=0.0031\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11341.8 | 106.498 | 65.1083 | -0.7576 | 0.45s |\n", - "| OLS_sphere | 700 | 593.344 | 24.3586 | 13.7936 | 0.9081 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4476.1 | 66.9037 | 51.0937 | 0.3064 | 45.71s |\n", - "| DeepKriging_sphere | 50 | 78.0263 | 8.8333 | 6.0377 | 0.9879 | 33.01s |\n", - "| DeepKriging_sphere_Huber | 50 | 107.336 | 10.3603 | 7.5631 | 0.9834 | 29.54s |\n", - "| UniversalKriging | 50 | 184.916 | 13.5984 | 9.2435 | 0.9713 | 2.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:21:42\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.9176 (±1.6363), Variance: 6693.3193, Range: [-213.0331, 205.5877]\n", - "z mean: 15.8471 (±1.6347), Variance: 6680.9272, Range: [-210.0050, 210.5812]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9153, sigma²=6483.8441, nugget=0.0021\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7544.12 | 86.8569 | 61.8666 | 0.0201 | 0.40s |\n", - "| OLS_sphere | 700 | 277.378 | 16.6547 | 10.6237 | 0.964 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3713.41 | 60.9378 | 44.0486 | 0.5176 | 43.43s |\n", - "| DeepKriging_sphere | 50 | 62.5136 | 7.9066 | 5.358 | 0.9919 | 35.94s |\n", - "| DeepKriging_sphere_Huber | 50 | 68.9463 | 8.3034 | 5.7139 | 0.991 | 30.96s |\n", - "| UniversalKriging | 50 | 174.805 | 13.2214 | 8.1929 | 0.9773 | 3.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:24:31\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.5136 (±1.6337), Variance: 6672.2979, Range: [-207.8768, 205.5624]\n", - "z mean: 17.4565 (±1.6343), Variance: 6677.5923, Range: [-202.8286, 211.4176]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9362, sigma²=6691.1431, nugget=0.0019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5939.4 | 77.0675 | 60.5741 | 0.2053 | 0.41s |\n", - "| OLS_sphere | 700 | 242.901 | 15.5853 | 9.1339 | 0.9675 | 0.08s |\n", - "| DeepKriging_wendland | -- | 5074.61 | 71.2363 | 53.9087 | 0.321 | 41.99s |\n", - "| DeepKriging_sphere | 50 | 86.0504 | 9.2763 | 5.4544 | 0.9885 | 33.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.981 | 8.1229 | 5.2128 | 0.9912 | 30.96s |\n", - "| UniversalKriging | 50 | 187.61 | 13.6971 | 7.7577 | 0.9749 | 3.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:27:15\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.3084 (±1.6258), Variance: 6607.7876, Range: [-213.9850, 205.5705]\n", - "z mean: 16.3392 (±1.6261), Variance: 6610.2417, Range: [-211.5761, 208.0813]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9690, sigma²=7066.6313, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4836.19 | 69.5427 | 54.4733 | 0.2603 | 0.42s |\n", - "| OLS_sphere | 700 | 290.541 | 17.0453 | 9.8661 | 0.9556 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3798.6 | 61.6328 | 45.1395 | 0.419 | 46.33s |\n", - "| DeepKriging_sphere | 50 | 117.069 | 10.8198 | 6.7883 | 0.9821 | 31.37s |\n", - "| DeepKriging_sphere_Huber | 50 | 122.819 | 11.0824 | 7.1187 | 0.9812 | 44.01s |\n", - "| UniversalKriging | 50 | 247.309 | 15.7261 | 9.4662 | 0.9622 | 2.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:30:15\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.5793 (±1.5943), Variance: 6354.5825, Range: [-195.4052, 205.5495]\n", - "z mean: 17.6412 (±1.5954), Variance: 6363.3760, Range: [-193.6686, 214.4071]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0572, sigma²=7454.8119, nugget=0.0039\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5532.93 | 74.3836 | 56.7257 | 0.1945 | 0.38s |\n", - "| OLS_sphere | 700 | 223.317 | 14.9438 | 9.4139 | 0.9675 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4116.98 | 64.1637 | 45.0065 | 0.4007 | 46.51s |\n", - "| DeepKriging_sphere | 50 | 68.576 | 8.2811 | 5.7309 | 0.99 | 79.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.0063 | 7.6816 | 5.4517 | 0.9914 | 35.11s |\n", - "| UniversalKriging | 50 | 153.9 | 12.4056 | 8.4478 | 0.9776 | 3.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:33:56\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.4423 (±1.6179), Variance: 6543.6118, Range: [-203.1745, 204.3926]\n", - "z mean: 17.4600 (±1.6162), Variance: 6530.1797, Range: [-201.2530, 212.3053]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9095, sigma²=6671.8396, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5415.88 | 73.5926 | 57.4788 | 0.099 | 0.44s |\n", - "| OLS_sphere | 700 | 252.409 | 15.8874 | 10.1302 | 0.958 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3691.43 | 60.7571 | 47.2851 | 0.3859 | 41.09s |\n", - "| DeepKriging_sphere | 50 | 4971.73 | 70.5105 | 58.7347 | 0.1729 | 13.40s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.9669 | 8.8863 | 6.1159 | 0.9869 | 36.17s |\n", - "| UniversalKriging | 50 | 149.764 | 12.2378 | 8.171 | 0.9751 | 3.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:36:27\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.4683 (±1.6400), Variance: 6723.7222, Range: [-212.8139, 205.7783]\n", - "z mean: 15.5037 (±1.6415), Variance: 6736.4736, Range: [-206.9230, 215.3848]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2057, sigma²=8772.3953, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4313.19 | 65.6749 | 50.8224 | 0.3009 | 0.45s |\n", - "| OLS_sphere | 700 | 163.37 | 12.7816 | 8.5455 | 0.9735 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3363.84 | 57.9987 | 42.0742 | 0.4548 | 40.30s |\n", - "| DeepKriging_sphere | 50 | 39.5698 | 6.2905 | 4.8167 | 0.9936 | 32.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 51.3002 | 7.1624 | 5.273 | 0.9917 | 36.89s |\n", - "| UniversalKriging | 50 | 126.759 | 11.2587 | 7.5874 | 0.9795 | 1.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:39:19\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.6203 (±1.6101), Variance: 6480.7422, Range: [-214.8754, 205.1001]\n", - "z mean: 17.5483 (±1.6102), Variance: 6482.1465, Range: [-206.6100, 210.9542]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.4419, sigma²=9967.2574, nugget=0.0036\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 150151 | 387.493 | 84.8793 | -21.043 | 0.40s |\n", - "| OLS_sphere | 700 | 213.15 | 14.5997 | 9.2619 | 0.9687 | 0.10s |\n", - "| DeepKriging_wendland | -- | 27082.2 | 164.567 | 57.1111 | -2.9758 | 42.69s |\n", - "| DeepKriging_sphere | 50 | 68.1335 | 8.2543 | 6.0279 | 0.99 | 31.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.3289 | 8.0826 | 5.8652 | 0.9904 | 32.28s |\n", - "| UniversalKriging | 50 | 266.573 | 16.3271 | 9.702 | 0.9609 | 4.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:42:12\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.7464 (±1.6258), Variance: 6608.4390, Range: [-200.4412, 205.8013]\n", - "z mean: 18.7090 (±1.6264), Variance: 6612.6841, Range: [-195.1408, 212.4789]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9460, sigma²=7122.5433, nugget=0.0024\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5595.22 | 74.8012 | 59.1656 | 0.1657 | 0.45s |\n", - "| OLS_sphere | 700 | 244.95 | 15.6509 | 9.8461 | 0.9635 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4267.29 | 65.3245 | 48.2659 | 0.3637 | 45.93s |\n", - "| DeepKriging_sphere | 50 | 83.445 | 9.1348 | 6.2441 | 0.9876 | 31.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 95.5863 | 9.7768 | 6.7517 | 0.9857 | 38.34s |\n", - "| UniversalKriging | 50 | 242.728 | 15.5797 | 10.0137 | 0.9638 | 2.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:45:13\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.6833 (±1.5863), Variance: 6290.8442, Range: [-212.4869, 204.6357]\n", - "z mean: 18.6792 (±1.5885), Variance: 6308.6938, Range: [-210.1956, 211.8051]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0523, sigma²=7626.5380, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 118249 | 343.874 | 77.7898 | -19.1674 | 0.49s |\n", - "| OLS_sphere | 700 | 336.091 | 18.3328 | 11.1151 | 0.9427 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3289.25 | 57.3519 | 41.2832 | 0.439 | 44.70s |\n", - "| DeepKriging_sphere | 50 | 89.984 | 9.486 | 6.4375 | 0.9847 | 29.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 5133.09 | 71.6456 | 55.5766 | 0.1246 | 13.66s |\n", - "| UniversalKriging | 50 | 238.236 | 15.4349 | 9.4542 | 0.9594 | 3.42s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:47:46\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.7999 (±1.6001), Variance: 6400.8418, Range: [-213.0022, 202.7321]\n", - "z mean: 18.7553 (±1.6026), Variance: 6420.7710, Range: [-205.3941, 212.0723]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.4126, sigma²=9351.7403, nugget=0.0019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4366.91 | 66.0826 | 51.4803 | 0.2372 | 0.46s |\n", - "| OLS_sphere | 700 | 302.524 | 17.3932 | 10.1364 | 0.9472 | 0.12s |\n", - "| DeepKriging_wendland | -- | 3801.85 | 61.6591 | 46.8055 | 0.3359 | 41.98s |\n", - "| DeepKriging_sphere | 50 | 130.352 | 11.4172 | 7.4618 | 0.9772 | 30.40s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.8862 | 8.8818 | 5.8167 | 0.9862 | 38.19s |\n", - "| UniversalKriging | 50 | 210.471 | 14.5076 | 8.416 | 0.9632 | 3.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:50:43\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.4685 (±1.6370), Variance: 6699.0903, Range: [-209.5693, 207.2927]\n", - "z mean: 16.5803 (±1.6383), Variance: 6710.4600, Range: [-207.4371, 216.0622]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8359, sigma²=6192.0215, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5045.9 | 71.0345 | 56.7622 | 0.245 | 0.43s |\n", - "| OLS_sphere | 700 | 240.447 | 15.5064 | 10.3605 | 0.964 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3832.55 | 61.9076 | 46.9787 | 0.4266 | 44.17s |\n", - "| DeepKriging_sphere | 50 | 144.113 | 12.0047 | 7.742 | 0.9784 | 30.88s |\n", - "| DeepKriging_sphere_Huber | 50 | 115.423 | 10.7435 | 6.5791 | 0.9827 | 36.36s |\n", - "| UniversalKriging | 50 | 224.945 | 14.9982 | 9.1492 | 0.9663 | 3.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:53:43\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.9952 (±1.6115), Variance: 6492.1343, Range: [-214.9039, 204.2655]\n", - "z mean: 19.0032 (±1.6127), Variance: 6501.9634, Range: [-213.2855, 208.1977]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7982, sigma²=5896.0750, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11157.8 | 105.63 | 62.9333 | -0.5543 | 0.49s |\n", - "| OLS_sphere | 700 | 160.647 | 12.6746 | 8.734 | 0.9776 | 0.10s |\n", - "| DeepKriging_wendland | -- | 8147.44 | 90.2632 | 46.5262 | -0.135 | 49.31s |\n", - "| DeepKriging_sphere | 50 | 52.9783 | 7.2786 | 5.6671 | 0.9926 | 31.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 67.0042 | 8.1856 | 6.4261 | 0.9907 | 30.44s |\n", - "| UniversalKriging | 50 | 172.49 | 13.1335 | 7.7952 | 0.976 | 2.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:56:47\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.5562 (±1.5995), Variance: 6395.9707, Range: [-207.5052, 203.6785]\n", - "z mean: 17.5598 (±1.6001), Variance: 6400.9946, Range: [-206.8309, 207.8015]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8495, sigma²=6234.9966, nugget=0.0051\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7994.24 | 89.4105 | 65.6753 | -0.303 | 0.43s |\n", - "| OLS_sphere | 700 | 324.534 | 18.0148 | 9.7733 | 0.9471 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4736.71 | 68.8238 | 49.373 | 0.2279 | 40.53s |\n", - "| DeepKriging_sphere | 50 | 77.4969 | 8.8032 | 6.3299 | 0.9874 | 33.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 72.1119 | 8.4919 | 6.1603 | 0.9882 | 31.76s |\n", - "| UniversalKriging | 50 | 183.757 | 13.5557 | 8.6548 | 0.97 | 2.76s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 16:59:43\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 11.1298 (±1.6094), Variance: 6475.3687, Range: [-209.5753, 204.1214]\n", - "z mean: 15.1451 (±1.6105), Variance: 6484.0806, Range: [-203.9167, 205.2956]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7328, sigma²=5399.6062, nugget=0.0036\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7260.49 | 85.2085 | 63.2459 | -0.0486 | 0.38s |\n", - "| OLS_sphere | 700 | 264.04 | 16.2493 | 10.0458 | 0.9619 | 0.11s |\n", - "| DeepKriging_wendland | -- | 5051.39 | 71.0732 | 52.8718 | 0.2705 | 44.41s |\n", - "| DeepKriging_sphere | 50 | 116.66 | 10.8009 | 6.4265 | 0.9832 | 29.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 131.509 | 11.4677 | 7.3707 | 0.981 | 30.02s |\n", - "| UniversalKriging | 50 | 270.333 | 16.4418 | 9.1145 | 0.961 | 3.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:02:42\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.6192 (±1.6029), Variance: 6423.1338, Range: [-206.6646, 207.4582]\n", - "z mean: 16.6993 (±1.6032), Variance: 6425.7969, Range: [-206.3566, 215.3435]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9774, sigma²=7051.5093, nugget=0.0040\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5596.44 | 74.8094 | 59.2089 | 0.1733 | 0.40s |\n", - "| OLS_sphere | 700 | 230.653 | 15.1872 | 9.2645 | 0.9659 | 0.11s |\n", - "| DeepKriging_wendland | -- | 5441.45 | 73.7662 | 56.9111 | 0.1962 | 40.56s |\n", - "| DeepKriging_sphere | 50 | 112.968 | 10.6286 | 6.711 | 0.9833 | 30.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 199.42 | 14.1216 | 7.5342 | 0.9705 | 39.77s |\n", - "| UniversalKriging | 50 | 326.678 | 18.0742 | 10.2417 | 0.9517 | 3.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:05:45\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.4212 (±1.6116), Variance: 6493.1841, Range: [-213.9658, 204.9198]\n", - "z mean: 17.4479 (±1.6124), Variance: 6499.7983, Range: [-212.8442, 216.9294]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8166, sigma²=6050.1750, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5425.89 | 73.6607 | 56.1138 | 0.1856 | 0.56s |\n", - "| OLS_sphere | 700 | 392.747 | 19.8179 | 11.7089 | 0.941 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4511.74 | 67.1695 | 47.1651 | 0.3228 | 41.39s |\n", - "| DeepKriging_sphere | 50 | 65.7584 | 8.1092 | 5.4557 | 0.9901 | 32.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 68.3883 | 8.2697 | 5.6012 | 0.9897 | 37.45s |\n", - "| UniversalKriging | 50 | 193.642 | 13.9155 | 8.6663 | 0.9709 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:08:51\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 13.4900 (±1.6288), Variance: 6632.0986, Range: [-211.5701, 205.0404]\n", - "z mean: 17.3468 (±1.6280), Variance: 6625.7896, Range: [-205.6997, 209.3530]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1141, sigma²=7875.6444, nugget=0.0032\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5498.37 | 74.151 | 59.546 | 0.0914 | 0.42s |\n", - "| OLS_sphere | 700 | 226.38 | 15.0459 | 9.7481 | 0.9626 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4115.85 | 64.1549 | 48.1048 | 0.3199 | 43.46s |\n", - "| DeepKriging_sphere | 50 | 72.7749 | 8.5308 | 5.8894 | 0.988 | 35.42s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.7484 | 8.874 | 6.2562 | 0.987 | 29.55s |\n", - "| UniversalKriging | 50 | 185.522 | 13.6207 | 8.7899 | 0.9693 | 3.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:11:55\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 15.8911 (±1.6016), Variance: 6412.9741, Range: [-209.6448, 202.2589]\n", - "z mean: 19.8644 (±1.6022), Variance: 6417.9834, Range: [-207.1361, 216.3823]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0109, sigma²=7548.4458, nugget=0.0046\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 5777.45 | 76.0095 | 59.4954 | 0.1163 | 0.42s |\n", - "| OLS_sphere | 700 | 182.186 | 13.4976 | 8.8944 | 0.9721 | 0.10s |\n", - "| DeepKriging_wendland | -- | 160523 | 400.653 | 78.1246 | -23.5526 | 41.81s |\n", - "| DeepKriging_sphere | 50 | 81.8249 | 9.0457 | 6.0199 | 0.9875 | 33.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 81.0674 | 9.0037 | 5.8069 | 0.9876 | 33.37s |\n", - "| UniversalKriging | 50 | 152.344 | 12.3428 | 7.7428 | 0.9767 | 3.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:15:01\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 14.5321 (±1.5923), Variance: 6338.7407, Range: [-199.3963, 204.6754]\n", - "z mean: 18.5404 (±1.5921), Variance: 6337.3086, Range: [-198.3313, 212.7855]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9598, sigma²=6655.5348, nugget=0.0030\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4654.42 | 68.2233 | 51.1656 | 0.2274 | 0.43s |\n", - "| OLS_sphere | 700 | 310.449 | 17.6195 | 11.7333 | 0.9485 | 0.14s |\n", - "| DeepKriging_wendland | -- | 3302.25 | 57.4652 | 39.9515 | 0.4519 | 44.30s |\n", - "| DeepKriging_sphere | 50 | 70.5294 | 8.3982 | 6.5 | 0.9883 | 29.91s |\n", - "| DeepKriging_sphere_Huber | 50 | 48.5578 | 6.9683 | 5.4299 | 0.9919 | 33.04s |\n", - "| UniversalKriging | 50 | 164.825 | 12.8384 | 8.2846 | 0.9726 | 3.45s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:18:07\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.9732 (±1.6360), Variance: 6691.4902, Range: [-212.6931, 203.7595]\n", - "z mean: 16.9672 (±1.6364), Variance: 6694.7271, Range: [-210.7487, 212.0068]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0442, sigma²=7427.5346, nugget=0.0038\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4680.67 | 68.4154 | 53.8788 | 0.2696 | 0.42s |\n", - "| OLS_sphere | 700 | 276.065 | 16.6152 | 10.7841 | 0.9569 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3594.91 | 59.9576 | 46.1165 | 0.4391 | 41.92s |\n", - "| DeepKriging_sphere | 50 | 79.7637 | 8.9311 | 6.176 | 0.9876 | 29.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 106.977 | 10.343 | 7.1041 | 0.9833 | 28.99s |\n", - "| UniversalKriging | 50 | 260.749 | 16.1477 | 9.9796 | 0.9593 | 3.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:21:06\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data\n", - "y mean: 12.9954 (±1.5917), Variance: 6333.9272, Range: [-211.4829, 206.6510]\n", - "z mean: 16.9996 (±1.5929), Variance: 6342.9468, Range: [-208.6591, 217.2583]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0201, sigma²=7333.6558, nugget=0.0021\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5062.5 | 71.1512 | 55.9412 | 0.1468 | 0.45s |\n", - "| OLS_sphere | 700 | 265.601 | 16.2973 | 10.5715 | 0.9552 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3505.43 | 59.2067 | 44.0992 | 0.4092 | 47.06s |\n", - "| DeepKriging_sphere | 50 | 58.3424 | 7.6382 | 5.9353 | 0.9902 | 35.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 50.6967 | 7.1202 | 5.3394 | 0.9915 | 30.02s |\n", - "| UniversalKriging | 50 | 194.346 | 13.9408 | 8.3504 | 0.9672 | 3.70s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-28 17:24:18\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.020466, - "end_time": "2026-01-19T06:12:23.004799", - "exception": false, - "start_time": "2026-01-19T06:12:22.984333", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T06:12:23.049738Z", - "iopub.status.busy": "2026-01-19T06:12:23.048867Z", - "iopub.status.idle": "2026-01-19T06:12:23.074365Z", - "shell.execute_reply": "2026-01-19T06:12:23.071188Z" - }, - "papermill": { - "duration": 0.052187, - "end_time": "2026-01-19T06:12:23.076964", - "exception": false, - "start_time": "2026-01-19T06:12:23.024777", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 32664.94±88349.27 | 128.76±126.83 | 63.23±13.85 | -4.10±14.07 |\n", - "| OLS_sphere | 287.73±94.09 | 16.76±2.58 | 10.30±1.18 | 0.96±0.02 |\n", - "| DeepKriging_wendland | 7938.13±22058.02 | 74.27±49.22 | 47.92±5.79 | -0.22±3.37 |\n", - "| DeepKriging_sphere | 401.25±1228.82 | 13.19±15.07 | 9.42±12.73 | 0.94±0.19 |\n", - "| DeepKriging_sphere_Huber | 301.94±971.67 | 12.41±12.16 | 8.47±9.74 | 0.95±0.17 |\n", - "| UniversalKriging | 214.68±59.43 | 14.51±2.01 | 8.91±0.88 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 12.41±12.16)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 10537.965398, - "end_time": "2026-01-19T06:12:25.575286", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_spherical_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_spherical_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-19T03:16:47.609888", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_tdf4.ipynb b/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_tdf4.ipynb deleted file mode 100644 index 837c604..0000000 --- a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_noise_tdf4.ipynb +++ /dev/null @@ -1,4583 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.007352, - "end_time": "2026-01-19T03:16:48.420311", - "exception": false, - "start_time": "2026-01-19T03:16:48.412959", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:48.436380Z", - "iopub.status.busy": "2026-01-19T03:16:48.435478Z", - "iopub.status.idle": "2026-01-19T03:16:48.457566Z", - "shell.execute_reply": "2026-01-19T03:16:48.453533Z" - }, - "papermill": { - "duration": 0.036949, - "end_time": "2026-01-19T03:16:48.462194", - "exception": false, - "start_time": "2026-01-19T03:16:48.425245", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:48.476709Z", - "iopub.status.busy": "2026-01-19T03:16:48.476197Z", - "iopub.status.idle": "2026-01-19T03:16:51.168942Z", - "shell.execute_reply": "2026-01-19T03:16:51.166191Z" - }, - "papermill": { - "duration": 2.702095, - "end_time": "2026-01-19T03:16:51.171499", - "exception": false, - "start_time": "2026-01-19T03:16:48.469404", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768792609.355874 1534225 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768792609.360074 1534225 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768792609.371078 1534225 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768792609.371101 1534225 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768792609.371102 1534225 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768792609.371103 1534225 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.003799, - "end_time": "2026-01-19T03:16:51.181929", - "exception": false, - "start_time": "2026-01-19T03:16:51.178130", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:51.189917Z", - "iopub.status.busy": "2026-01-19T03:16:51.189377Z", - "iopub.status.idle": "2026-01-19T03:16:52.570335Z", - "shell.execute_reply": "2026-01-19T03:16:52.566700Z" - }, - "papermill": { - "duration": 1.388835, - "end_time": "2026-01-19T03:16:52.573137", - "exception": false, - "start_time": "2026-01-19T03:16:51.184302", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011224, - "end_time": "2026-01-19T03:16:52.596791", - "exception": false, - "start_time": "2026-01-19T03:16:52.585567", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:52.614029Z", - "iopub.status.busy": "2026-01-19T03:16:52.613171Z", - "iopub.status.idle": "2026-01-19T03:16:52.624733Z", - "shell.execute_reply": "2026-01-19T03:16:52.621535Z" - }, - "papermill": { - "duration": 0.02273, - "end_time": "2026-01-19T03:16:52.627238", - "exception": false, - "start_time": "2026-01-19T03:16:52.604508", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.011677, - "end_time": "2026-01-19T03:16:52.651852", - "exception": false, - "start_time": "2026-01-19T03:16:52.640175", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:16:52.669698Z", - "iopub.status.busy": "2026-01-19T03:16:52.668903Z", - "iopub.status.idle": "2026-01-19T03:17:06.735388Z", - "shell.execute_reply": "2026-01-19T03:17:06.732265Z" - }, - "papermill": { - "duration": 14.079456, - "end_time": "2026-01-19T03:17:06.739337", - "exception": false, - "start_time": "2026-01-19T03:16:52.659881", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.004492, - "end_time": "2026-01-19T03:17:06.756435", - "exception": false, - "start_time": "2026-01-19T03:17:06.751943", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.767631Z", - "iopub.status.busy": "2026-01-19T03:17:06.766390Z", - "iopub.status.idle": "2026-01-19T03:17:06.787031Z", - "shell.execute_reply": "2026-01-19T03:17:06.784279Z" - }, - "papermill": { - "duration": 0.029505, - "end_time": "2026-01-19T03:17:06.789611", - "exception": false, - "start_time": "2026-01-19T03:17:06.760106", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + noise.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " z = y + (np.sqrt(2) * rng.standard_t(df=4, size=num_sample)).astype(np.float32)\n", - " \n", - " print(\"\\nSimulate Data\")\n", - " print(f\"y mean: {np.mean(y):.4f} (±{np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " print(f\"z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"y\": y,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.009795, - "end_time": "2026-01-19T03:17:06.811712", - "exception": false, - "start_time": "2026-01-19T03:17:06.801917", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.826673Z", - "iopub.status.busy": "2026-01-19T03:17:06.825908Z", - "iopub.status.idle": "2026-01-19T03:17:06.870866Z", - "shell.execute_reply": "2026-01-19T03:17:06.867563Z" - }, - "papermill": { - "duration": 0.055634, - "end_time": "2026-01-19T03:17:06.873570", - "exception": false, - "start_time": "2026-01-19T03:17:06.817936", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"y\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - "\n", - " z_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - " y_true = data['y'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, z_combined, y_true\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, z_combined, y_true, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " \n", - " z_train, z_val, z_test = (\n", - " z_combined[train_x1], z_combined[val_x1], z_combined[test_x1])\n", - " y_train_true, y_val_true, y_test_true = (\n", - " y_true[train_x1], y_true[val_x1], y_true[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " \n", - " z_train_flat, z_val_flat, z_test_flat = map(flatten, [z_train, z_val, z_test])\n", - " y_train_true_flat, y_val_true_flat, y_test_true_flat = map(flatten, [y_train_true, y_val_true, y_test_true])\n", - "\n", - " def flatten_cont(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " \n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten_cont, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, z_train_flat, y_train_true_flat,\n", - " X_val_cont_flat, X_val_cat, z_val_flat, y_val_true_flat,\n", - " X_test_cont_flat, X_test_cat, z_test_flat, y_test_true_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, z_train, y_train_true,\n", - " X_val, X_val_cat, z_val, y_val_true,\n", - " X_test, X_test_cat, z_test, y_test_true):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, z_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val_true, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), z_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), z_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_true, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_true, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_true, y_pred)),\n", - " \"R2\": float(r2_score(y_test_true, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.902553Z", - "iopub.status.busy": "2026-01-19T03:17:06.901739Z", - "iopub.status.idle": "2026-01-19T03:17:06.954227Z", - "shell.execute_reply": "2026-01-19T03:17:06.950615Z" - }, - "papermill": { - "duration": 0.071214, - "end_time": "2026-01-19T03:17:06.958093", - "exception": false, - "start_time": "2026-01-19T03:17:06.886879", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, z_combined, y_true, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(z_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_true[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.005519, - "end_time": "2026-01-19T03:17:06.978354", - "exception": false, - "start_time": "2026-01-19T03:17:06.972835", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:06.988250Z", - "iopub.status.busy": "2026-01-19T03:17:06.987867Z", - "iopub.status.idle": "2026-01-19T03:17:06.993412Z", - "shell.execute_reply": "2026-01-19T03:17:06.992140Z" - }, - "papermill": { - "duration": 0.013532, - "end_time": "2026-01-19T03:17:06.995385", - "exception": false, - "start_time": "2026-01-19T03:17:06.981853", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T03:17:07.004725Z", - "iopub.status.busy": "2026-01-19T03:17:07.004437Z", - "iopub.status.idle": "2026-01-19T06:12:22.928261Z", - "shell.execute_reply": "2026-01-19T06:12:22.924952Z" - }, - "papermill": { - "duration": 10515.932006, - "end_time": "2026-01-19T06:12:22.930696", - "exception": false, - "start_time": "2026-01-19T03:17:06.998690", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.9206 (±1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "z mean: 12.9285 (±1.5869), Variance: 6295.2520, Range: [-219.7557, 207.1107]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3117.1355 3353.2195 \n", - " 100 2136.7327 2321.0479 \n", - " 200 1431.0396 1271.8287 \n", - " 500 467.6588 401.2906 \n", - " 700 308.9568 254.9084 \n", - " 1000 279.2994 606.1464 *\n", - " 1400 7504.2856 2776.2351 \n", - " Best order: 1000\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768792633.873992 1534225 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768792635.963722 1534397 service.cc:152] XLA service 0x79970c017330 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768792635.963761 1534397 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768792636.270134 1534397 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768792646.652229 1534397 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x79971ce0f400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x79971c56e290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 57.7490 66.7489 *\n", - " 100 69.7733 80.3220 \n", - " 200 105.7195 151.4957 \n", - " 500 144.8279 182.4079 \n", - " 700 204.8671 242.8825 \n", - " 1000 515.8876 424.3655 \n", - " 1400 2060.9158 2170.8347 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 6.3070 79.8700 *\n", - " 100 6.4757 106.4942 \n", - " 200 7.7890 157.4858 \n", - " 500 12.4691 234.7885 \n", - " 700 14.0998 271.1571 \n", - " 1000 19.9124 544.3411 \n", - " 1400 43.1525 2606.2515 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0016, sigma²=7096.9561, nugget=0.0033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4841, sigma²=3249.8320, nugget=0.0039\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2623, sigma²=1507.8091, nugget=0.0026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0689, sigma²=233.9416, nugget=0.0036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0318, sigma²=0.0112, nugget=99.6273\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=18.9490, sigma²=0.0003, nugget=50.9309\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.5781, sigma²=0.0001, nugget=22.8527\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 181.4184 171.5707 *\n", - " 100 184.2446 178.6789 \n", - " 200 197.7998 170.9622 \n", - " 500 187.4412 191.6639 \n", - " 700 311.9474 254.8996 \n", - " 1000 287.0200 606.1432 \n", - " 1400 7524.7376 2776.0875 \n", - " Best order: 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0016, sigma²=7096.9561, nugget=0.0033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8672.61 | 93.1268 | 61.3202 | -0.3046 | 0.45s |\n", - "| OLS_sphere | 1000 | 606.146 | 24.62 | 9.3315 | 0.9088 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4047.7 | 63.6215 | 46.7809 | 0.3911 | 55.26s |\n", - "| DeepKriging_sphere | 50 | 56.4474 | 7.5131 | 4.6072 | 0.9915 | 47.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.827 | 8.8785 | 5.4759 | 0.9881 | 39.72s |\n", - "| UniversalKriging | 50 | 171.571 | 13.0985 | 6.7968 | 0.9742 | 3.26s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:30:07\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.5604 (±1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "z mean: 12.5753 (±1.5780), Variance: 6224.9009, Range: [-211.6993, 204.0702]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1115, sigma²=7266.2034, nugget=0.0017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9053.87 | 95.1518 | 59.4374 | -0.4311 | 0.39s |\n", - "| OLS_sphere | 1000 | 685.928 | 26.1902 | 11.9502 | 0.8916 | 0.14s |\n", - "| DeepKriging_wendland | -- | 5031.86 | 70.9356 | 55.0067 | 0.2047 | 56.48s |\n", - "| DeepKriging_sphere | 50 | 99.6516 | 9.9826 | 5.1199 | 0.9842 | 60.37s |\n", - "| DeepKriging_sphere_Huber | 50 | 263.593 | 16.2355 | 6.3087 | 0.9583 | 38.24s |\n", - "| UniversalKriging | 50 | 186.772 | 13.6664 | 7.4006 | 0.9705 | 3.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:33:05\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.4982 (±1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "z mean: 11.5226 (±1.5942), Variance: 6353.5205, Range: [-216.8161, 203.2946]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3704, sigma²=9607.0566, nugget=0.0028\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8559.52 | 92.5177 | 60.3977 | -0.1955 | 0.41s |\n", - "| OLS_sphere | 1000 | 514.266 | 22.6774 | 11.7634 | 0.9282 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4667.49 | 68.3191 | 50.6986 | 0.3481 | 52.46s |\n", - "| DeepKriging_sphere | 50 | 66.8562 | 8.1766 | 5.2659 | 0.9907 | 34.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.7779 | 7.7316 | 4.5473 | 0.9917 | 41.20s |\n", - "| UniversalKriging | 50 | 190.853 | 13.815 | 7.1792 | 0.9733 | 3.70s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:35:38\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.4518 (±1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "z mean: 13.4930 (±1.6101), Variance: 6480.9385, Range: [-210.1809, 205.8806]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9881, sigma²=6989.8900, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 6458.6 | 80.3654 | 59.5153 | 0.0142 | 0.36s |\n", - "| OLS_sphere | 1000 | 829.613 | 28.803 | 13.3908 | 0.8734 | 0.14s |\n", - "| DeepKriging_wendland | -- | 6653.87 | 81.5713 | 50.1847 | -0.0156 | 52.35s |\n", - "| DeepKriging_sphere | 50 | 5306.48 | 72.8456 | 59.5715 | 0.19 | 13.23s |\n", - "| DeepKriging_sphere_Huber | 50 | 80.0424 | 8.9466 | 4.9157 | 0.9878 | 49.52s |\n", - "| UniversalKriging | 50 | 258.655 | 16.0827 | 8.7745 | 0.9605 | 3.11s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:37:59\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.6175 (±1.6158), Variance: 6527.2144, Range: [-209.7956, 205.1870]\n", - "z mean: 14.5808 (±1.6155), Variance: 6524.5962, Range: [-210.4711, 208.2056]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0934, sigma²=7867.6636, nugget=0.0035\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25873.5 | 160.852 | 65.9723 | -2.9656 | 0.45s |\n", - "| OLS_sphere | 1000 | 561.319 | 23.6922 | 10.7928 | 0.914 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4626.21 | 68.0162 | 47.7831 | 0.291 | 46.95s |\n", - "| DeepKriging_sphere | 50 | 73.3761 | 8.566 | 4.9478 | 0.9888 | 40.72s |\n", - "| DeepKriging_sphere_Huber | 50 | 96.2485 | 9.8106 | 5.9738 | 0.9852 | 39.48s |\n", - "| UniversalKriging | 50 | 302.888 | 17.4037 | 8.7115 | 0.9536 | 3.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:40:33\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.9645 (±1.6405), Variance: 6728.1055, Range: [-206.4400, 204.6959]\n", - "z mean: 13.9144 (±1.6424), Variance: 6743.7905, Range: [-206.8840, 206.1749]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9910, sigma²=6900.0203, nugget=0.0022\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25094 | 158.411 | 69.8192 | -2.6497 | 0.33s |\n", - "| OLS_sphere | 1000 | 207.983 | 14.4216 | 8.0212 | 0.9698 | 0.15s |\n", - "| DeepKriging_wendland | -- | 5081.43 | 71.2842 | 46.7846 | 0.261 | 67.41s |\n", - "| DeepKriging_sphere | 50 | 35.3675 | 5.9471 | 4.1189 | 0.9949 | 41.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 34.7102 | 5.8915 | 3.9027 | 0.995 | 47.35s |\n", - "| UniversalKriging | 50 | 125.669 | 11.2102 | 6.4894 | 0.9817 | 3.13s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:43:38\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 15.0328 (±1.6395), Variance: 6719.8687, Range: [-213.9537, 205.0478]\n", - "z mean: 15.1024 (±1.6402), Variance: 6725.6489, Range: [-214.6744, 204.6746]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9103, sigma²=6558.1354, nugget=0.0022\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9499.19 | 97.4638 | 61.1378 | -0.4259 | 0.37s |\n", - "| OLS_sphere | 1000 | 750.775 | 27.4003 | 11.5625 | 0.8873 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3865.55 | 62.1735 | 44.4278 | 0.4198 | 55.20s |\n", - "| DeepKriging_sphere | 50 | 63.1333 | 7.9456 | 4.2703 | 0.9905 | 41.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 50.0814 | 7.0768 | 4.2598 | 0.9925 | 37.05s |\n", - "| UniversalKriging | 50 | 162.197 | 12.7356 | 6.7639 | 0.9757 | 2.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:46:20\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.2577 (±1.5934), Variance: 6347.5322, Range: [-193.8297, 206.9203]\n", - "z mean: 11.2772 (±1.5926), Variance: 6340.6362, Range: [-195.6308, 206.4632]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0612, sigma²=6897.9162, nugget=0.0026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4523.52 | 67.2571 | 52.9507 | 0.2369 | 0.35s |\n", - "| OLS_sphere | 1000 | 2028.82 | 45.0425 | 16.1495 | 0.6577 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4634.05 | 68.0739 | 45.9182 | 0.2182 | 63.50s |\n", - "| DeepKriging_sphere | 50 | 120.032 | 10.9559 | 6.0617 | 0.9798 | 36.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 114.736 | 10.7115 | 5.3945 | 0.9806 | 48.44s |\n", - "| UniversalKriging | 50 | 245.67 | 15.6739 | 8.2006 | 0.9586 | 3.61s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:49:20\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.8672 (±1.6373), Variance: 6702.1040, Range: [-213.2814, 206.2566]\n", - "z mean: 13.8715 (±1.6391), Variance: 6716.2607, Range: [-213.3175, 206.3165]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9733, sigma²=7102.6496, nugget=0.0039\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4710.31 | 68.6317 | 54.9158 | 0.1695 | 0.36s |\n", - "| OLS_sphere | 1000 | 818.532 | 28.61 | 11.1129 | 0.8557 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3661.71 | 60.512 | 46.4077 | 0.3544 | 52.28s |\n", - "| DeepKriging_sphere | 50 | 75.1959 | 8.6716 | 4.962 | 0.9867 | 36.10s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.6029 | 8.0996 | 4.6675 | 0.9884 | 43.06s |\n", - "| UniversalKriging | 50 | 162.184 | 12.7351 | 6.8743 | 0.9714 | 3.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:52:03\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 15.2464 (±1.5728), Variance: 6184.4497, Range: [-213.3035, 205.0131]\n", - "z mean: 15.2956 (±1.5725), Variance: 6182.1201, Range: [-213.6598, 206.3981]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9378, sigma²=6309.5529, nugget=0.0038\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4526.8 | 67.2815 | 52.9167 | 0.2266 | 0.51s |\n", - "| OLS_sphere | 1000 | 345.121 | 18.5774 | 9.9882 | 0.941 | 0.14s |\n", - "| DeepKriging_wendland | -- | 4638.2 | 68.1043 | 44.7554 | 0.2075 | 61.70s |\n", - "| DeepKriging_sphere | 50 | 62.3365 | 7.8953 | 4.8735 | 0.9893 | 39.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 73.4544 | 8.5706 | 5.0798 | 0.9874 | 40.57s |\n", - "| UniversalKriging | 50 | 181.027 | 13.4546 | 7.3254 | 0.9691 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:54:58\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 15.7404 (±1.6331), Variance: 6667.8887, Range: [-209.9314, 203.9581]\n", - "z mean: 15.6985 (±1.6317), Variance: 6656.4658, Range: [-208.8105, 203.8779]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8326, sigma²=6032.0646, nugget=0.0027\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6264.84 | 79.1508 | 55.0681 | 0.0608 | 0.32s |\n", - "| OLS_sphere | 1000 | 151.203 | 12.2964 | 6.9611 | 0.9773 | 0.13s |\n", - "| DeepKriging_wendland | -- | 3499.88 | 59.1598 | 41.0405 | 0.4753 | 63.24s |\n", - "| DeepKriging_sphere | 50 | 48.3146 | 6.9509 | 4.7435 | 0.9928 | 43.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.5889 | 8.865 | 5.6646 | 0.9882 | 36.14s |\n", - "| UniversalKriging | 50 | 109.995 | 10.4878 | 5.9391 | 0.9835 | 3.19s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 11:57:53\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 16.8317 (±1.6021), Variance: 6416.8169, Range: [-213.4137, 202.3162]\n", - "z mean: 16.8843 (±1.6017), Variance: 6413.7451, Range: [-214.8985, 203.8226]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8453, sigma²=6177.5142, nugget=0.0064\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10303.2 | 101.505 | 60.0227 | -0.6166 | 0.37s |\n", - "| OLS_sphere | 1000 | 321.575 | 17.9325 | 9.5217 | 0.9495 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3557.03 | 59.6408 | 43.2119 | 0.4419 | 65.85s |\n", - "| DeepKriging_sphere | 50 | 39.1441 | 6.2565 | 3.8778 | 0.9939 | 45.88s |\n", - "| DeepKriging_sphere_Huber | 50 | 27.7889 | 5.2715 | 3.6252 | 0.9956 | 48.00s |\n", - "| UniversalKriging | 50 | 204.631 | 14.3049 | 7.433 | 0.9679 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:01:08\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 10.7707 (±1.5807), Variance: 6246.6694, Range: [-211.4668, 202.6113]\n", - "z mean: 10.7265 (±1.5802), Variance: 6242.7622, Range: [-211.3461, 203.6856]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1609, sigma²=7846.5502, nugget=0.0039\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|----------|----------|--------|\n", - "| OLS_wendland | -- | 602039 | 775.912 | 142.875 | -97.1685 | 0.35s |\n", - "| OLS_sphere | 1000 | 405.017 | 20.125 | 10.0241 | 0.934 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4513.8 | 67.1848 | 50.8784 | 0.264 | 63.21s |\n", - "| DeepKriging_sphere | 50 | 89.4891 | 9.4599 | 5.5501 | 0.9854 | 44.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 91.8345 | 9.583 | 5.2275 | 0.985 | 54.29s |\n", - "| UniversalKriging | 50 | 301.318 | 17.3585 | 8.8769 | 0.9509 | 3.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:04:26\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 10.7825 (±1.6295), Variance: 6637.9307, Range: [-205.2383, 204.2435]\n", - "z mean: 10.7760 (±1.6303), Variance: 6644.4263, Range: [-208.8834, 205.4062]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0762, sigma²=7735.0416, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 322524 | 567.912 | 95.2025 | -46.5905 | 0.39s |\n", - "| OLS_sphere | 1000 | 275.77 | 16.6063 | 7.08 | 0.9593 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3907.63 | 62.5111 | 45.4313 | 0.4234 | 65.15s |\n", - "| DeepKriging_sphere | 50 | 51.8774 | 7.2026 | 4.3134 | 0.9923 | 42.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 63.0639 | 7.9413 | 4.2327 | 0.9907 | 42.27s |\n", - "| UniversalKriging | 50 | 91.5658 | 9.569 | 5.3534 | 0.9865 | 3.48s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:07:34\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.7531 (±1.5943), Variance: 6354.0967, Range: [-212.4267, 204.4545]\n", - "z mean: 12.7426 (±1.5931), Variance: 6344.6670, Range: [-213.3602, 206.2993]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9834, sigma²=6940.5857, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15336.1 | 123.839 | 65.5789 | -1.6687 | 0.35s |\n", - "| OLS_sphere | 1000 | 735.801 | 27.1257 | 12.1118 | 0.872 | 0.16s |\n", - "| DeepKriging_wendland | -- | 3381.57 | 58.1513 | 44.047 | 0.4116 | 66.52s |\n", - "| DeepKriging_sphere | 50 | 53.656 | 7.325 | 4.4413 | 0.9907 | 39.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 74.5654 | 8.6351 | 4.7479 | 0.987 | 34.60s |\n", - "| UniversalKriging | 50 | 194.181 | 13.9349 | 7.2828 | 0.9662 | 3.06s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:10:33\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.3852 (±1.6051), Variance: 6440.5127, Range: [-213.6311, 206.5967]\n", - "z mean: 11.3282 (±1.6061), Variance: 6448.6504, Range: [-216.1907, 208.5600]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9554, sigma²=6787.9884, nugget=0.0025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5842.47 | 76.4361 | 57.7107 | -0.0211 | 0.41s |\n", - "| OLS_sphere | 1000 | 731.451 | 27.0454 | 12.271 | 0.8722 | 0.16s |\n", - "| DeepKriging_wendland | -- | 3990.34 | 63.1691 | 44.9683 | 0.3026 | 64.88s |\n", - "| DeepKriging_sphere | 50 | 99.158 | 9.9578 | 5.1999 | 0.9827 | 47.10s |\n", - "| DeepKriging_sphere_Huber | 50 | 106.026 | 10.2969 | 5.6006 | 0.9815 | 39.14s |\n", - "| UniversalKriging | 50 | 277.59 | 16.661 | 9.2303 | 0.9515 | 3.16s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:13:43\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.5094 (±1.6194), Variance: 6556.0034, Range: [-213.5274, 204.3522]\n", - "z mean: 14.4723 (±1.6209), Variance: 6568.6934, Range: [-214.1977, 205.1005]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1037, sigma²=7614.3383, nugget=0.0019\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 39990.2 | 199.976 | 75.3077 | -4.3926 | 0.35s |\n", - "| OLS_sphere | 1000 | 2040.31 | 45.1698 | 16.2494 | 0.7249 | 0.14s |\n", - "| DeepKriging_wendland | -- | 4460.53 | 66.7872 | 50.1292 | 0.3985 | 62.48s |\n", - "| DeepKriging_sphere | 50 | 181.201 | 13.4611 | 6.713 | 0.9756 | 42.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 166.591 | 12.907 | 6.2304 | 0.9775 | 40.51s |\n", - "| UniversalKriging | 50 | 272.059 | 16.4942 | 8.5118 | 0.9633 | 3.56s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:16:50\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.2342 (±1.6400), Variance: 6724.3286, Range: [-208.6201, 203.6964]\n", - "z mean: 11.2166 (±1.6408), Variance: 6730.5410, Range: [-208.5955, 204.1402]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9423, sigma²=6748.9154, nugget=0.0073\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 19546.5 | 139.809 | 70.9741 | -2.1983 | 0.44s |\n", - "| OLS_sphere | 1000 | 425.347 | 20.6239 | 10.3778 | 0.9304 | 0.16s |\n", - "| DeepKriging_wendland | -- | 6973.81 | 83.5093 | 48.9229 | -0.1411 | 61.51s |\n", - "| DeepKriging_sphere | 50 | 92.3458 | 9.6097 | 5.5939 | 0.9849 | 36.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.3464 | 8.8513 | 5.0845 | 0.9872 | 36.33s |\n", - "| UniversalKriging | 50 | 173.141 | 13.1583 | 7.3979 | 0.9717 | 3.53s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:19:46\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.2790 (±1.6210), Variance: 6569.0591, Range: [-215.0015, 203.4196]\n", - "z mean: 12.2870 (±1.6229), Variance: 6584.4937, Range: [-214.4032, 203.5753]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9577, sigma²=6746.5399, nugget=0.0019\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5534.51 | 74.3943 | 55.9769 | 0.1952 | 0.40s |\n", - "| OLS_sphere | 1000 | 292.903 | 17.1144 | 8.4885 | 0.9574 | 0.17s |\n", - "| DeepKriging_wendland | -- | 4457.66 | 66.7657 | 45.8552 | 0.3518 | 64.81s |\n", - "| DeepKriging_sphere | 50 | 39.4119 | 6.2779 | 4.7307 | 0.9943 | 40.30s |\n", - "| DeepKriging_sphere_Huber | 50 | 43.8301 | 6.6204 | 4.6341 | 0.9936 | 42.02s |\n", - "| UniversalKriging | 50 | 184.04 | 13.5661 | 7.457 | 0.9732 | 2.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:22:55\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.1620 (±1.6099), Variance: 6479.7378, Range: [-213.2246, 205.0493]\n", - "z mean: 14.1569 (±1.6106), Variance: 6484.8110, Range: [-215.5358, 206.8398]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8353, sigma²=6281.5141, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9819.88 | 99.0953 | 59.0801 | -0.561 | 0.30s |\n", - "| OLS_sphere | 1000 | 273.562 | 16.5397 | 7.5656 | 0.9565 | 0.14s |\n", - "| DeepKriging_wendland | -- | 3656.38 | 60.468 | 42.7098 | 0.4188 | 67.11s |\n", - "| DeepKriging_sphere | 50 | 42.1739 | 6.4941 | 4.3015 | 0.9933 | 41.05s |\n", - "| DeepKriging_sphere_Huber | 50 | 38.9193 | 6.2385 | 3.7652 | 0.9938 | 43.50s |\n", - "| UniversalKriging | 50 | 116.834 | 10.809 | 6.2765 | 0.9814 | 3.25s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:26:12\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 15.6292 (±1.6380), Variance: 6707.4985, Range: [-214.6765, 205.8479]\n", - "z mean: 15.5660 (±1.6392), Variance: 6717.3921, Range: [-216.5880, 206.7144]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8872, sigma²=6178.5483, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 39495.4 | 198.735 | 63.7567 | -5.2891 | 0.38s |\n", - "| OLS_sphere | 1000 | 406.425 | 20.16 | 9.8322 | 0.9353 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4119.57 | 64.1838 | 48.7229 | 0.344 | 62.17s |\n", - "| DeepKriging_sphere | 50 | 138.984 | 11.7891 | 6.0397 | 0.9779 | 41.70s |\n", - "| DeepKriging_sphere_Huber | 50 | 132.039 | 11.4908 | 5.4464 | 0.979 | 50.12s |\n", - "| UniversalKriging | 50 | 262.488 | 16.2015 | 7.212 | 0.9582 | 3.11s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:29:31\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.1540 (±1.6253), Variance: 6603.7393, Range: [-212.9704, 206.5339]\n", - "z mean: 14.1302 (±1.6243), Variance: 6595.7729, Range: [-213.4201, 206.6191]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9223, sigma²=6657.0177, nugget=0.0020\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 17693.8 | 133.018 | 68.1223 | -1.6685 | 0.41s |\n", - "| OLS_sphere | 1000 | 335.161 | 18.3074 | 9.0881 | 0.9495 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4989.72 | 70.6379 | 49.8904 | 0.2475 | 63.13s |\n", - "| DeepKriging_sphere | 50 | 86.8635 | 9.3201 | 5.7842 | 0.9869 | 42.67s |\n", - "| DeepKriging_sphere_Huber | 50 | 127.736 | 11.302 | 7.1846 | 0.9807 | 34.56s |\n", - "| UniversalKriging | 50 | 188.938 | 13.7455 | 7.8241 | 0.9715 | 3.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:32:38\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.1046 (±1.6040), Variance: 6432.4102, Range: [-204.2676, 206.8854]\n", - "z mean: 14.0583 (±1.6046), Variance: 6437.1538, Range: [-205.2730, 206.9537]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.6595, sigma²=11902.6619, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5025.92 | 70.8937 | 56.4127 | 0.2059 | 0.49s |\n", - "| OLS_sphere | 1000 | 666.519 | 25.817 | 9.9146 | 0.8947 | 0.16s |\n", - "| DeepKriging_wendland | -- | 4330.32 | 65.8052 | 48.3271 | 0.3158 | 66.93s |\n", - "| DeepKriging_sphere | 50 | 74.4722 | 8.6297 | 5.4038 | 0.9882 | 45.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 115.24 | 10.735 | 5.5409 | 0.9818 | 49.72s |\n", - "| UniversalKriging | 50 | 181.149 | 13.4592 | 7.9093 | 0.9714 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:36:08\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.3727 (±1.6088), Variance: 6470.5322, Range: [-205.0407, 202.1517]\n", - "z mean: 13.3687 (±1.6082), Variance: 6465.4727, Range: [-202.6981, 206.0115]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0931, sigma²=7711.6286, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 82405.8 | 287.064 | 79.8723 | -12.1866 | 0.31s |\n", - "| OLS_sphere | 1000 | 543.081 | 23.3041 | 8.4896 | 0.9131 | 0.14s |\n", - "| DeepKriging_wendland | -- | 4055.33 | 63.6814 | 43.6953 | 0.3511 | 66.99s |\n", - "| DeepKriging_sphere | 50 | 37.0727 | 6.0887 | 3.7961 | 0.9941 | 44.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 37.142 | 6.0944 | 3.6382 | 0.9941 | 42.44s |\n", - "| UniversalKriging | 50 | 136.248 | 11.6726 | 5.8266 | 0.9782 | 3.38s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:39:31\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.7215 (±1.6056), Variance: 6444.8770, Range: [-213.1257, 203.9100]\n", - "z mean: 11.6842 (±1.6065), Variance: 6452.0439, Range: [-212.7796, 204.9726]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9062, sigma²=6463.4710, nugget=0.0043\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5248.9 | 72.4493 | 57.3344 | 0.2367 | 0.39s |\n", - "| OLS_sphere | 1000 | 549.503 | 23.4415 | 11.5823 | 0.9201 | 0.16s |\n", - "| DeepKriging_wendland | -- | 4528.77 | 67.2961 | 50.4391 | 0.3414 | 59.80s |\n", - "| DeepKriging_sphere | 50 | 172.064 | 13.1173 | 5.6272 | 0.975 | 43.88s |\n", - "| DeepKriging_sphere_Huber | 50 | 192.853 | 13.8871 | 6.3421 | 0.972 | 44.04s |\n", - "| UniversalKriging | 50 | 316.443 | 17.7889 | 9.2402 | 0.954 | 2.73s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:42:48\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.6376 (±1.6446), Variance: 6761.5942, Range: [-211.8297, 206.1259]\n", - "z mean: 14.6567 (±1.6448), Variance: 6763.4722, Range: [-209.7533, 211.7196]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1275, sigma²=7893.7067, nugget=0.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10460.4 | 102.276 | 62.2148 | -0.436 | 0.33s |\n", - "| OLS_sphere | 1000 | 305.671 | 17.4834 | 9.0535 | 0.958 | 0.14s |\n", - "| DeepKriging_wendland | -- | 5191.25 | 72.0504 | 53.4269 | 0.2874 | 61.69s |\n", - "| DeepKriging_sphere | 50 | 59.5372 | 7.716 | 4.6676 | 0.9918 | 41.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 66.9677 | 8.1834 | 4.6484 | 0.9908 | 40.80s |\n", - "| UniversalKriging | 50 | 227.958 | 15.0983 | 8.4527 | 0.9687 | 3.17s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:46:03\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 16.4394 (±1.6160), Variance: 6528.4375, Range: [-208.1850, 205.0659]\n", - "z mean: 16.4139 (±1.6169), Variance: 6535.6523, Range: [-206.8095, 205.6631]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3633, sigma²=9196.7199, nugget=0.0035\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5537.39 | 74.4136 | 57.911 | 0.1898 | 0.39s |\n", - "| OLS_sphere | 1000 | 469.66 | 21.6716 | 8.2866 | 0.9313 | 0.19s |\n", - "| DeepKriging_wendland | -- | 5280.97 | 72.6703 | 46.0605 | 0.2273 | 64.82s |\n", - "| DeepKriging_sphere | 50 | 119.878 | 10.9489 | 4.5068 | 0.9825 | 64.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 70.1439 | 8.3752 | 4.0608 | 0.9897 | 52.65s |\n", - "| UniversalKriging | 50 | 199.763 | 14.1337 | 7.3658 | 0.9708 | 3.95s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:49:58\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.0742 (±1.5787), Variance: 6230.7856, Range: [-209.3389, 203.6537]\n", - "z mean: 14.0811 (±1.5796), Variance: 6237.5928, Range: [-210.4281, 203.5147]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9306, sigma²=6441.9799, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5835.82 | 76.3925 | 56.0315 | 0.1007 | 0.40s |\n", - "| OLS_sphere | 1000 | 1258.61 | 35.4769 | 13.653 | 0.8061 | 0.16s |\n", - "| DeepKriging_wendland | -- | 4210.95 | 64.8918 | 43.8383 | 0.3511 | 65.45s |\n", - "| DeepKriging_sphere | 50 | 90.3457 | 9.505 | 5.2917 | 0.9861 | 41.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 115.777 | 10.76 | 4.9626 | 0.9822 | 44.87s |\n", - "| UniversalKriging | 50 | 274.992 | 16.5829 | 8.241 | 0.9576 | 2.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:53:22\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.3946 (±1.6330), Variance: 6666.4321, Range: [-212.8773, 204.5095]\n", - "z mean: 14.3952 (±1.6338), Variance: 6672.8809, Range: [-213.3304, 207.5300]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8991, sigma²=6705.7919, nugget=0.0052\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10703 | 103.455 | 63.6211 | -0.6586 | 0.39s |\n", - "| OLS_sphere | 1000 | 523.467 | 22.8794 | 11.7301 | 0.9189 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4431.19 | 66.5672 | 50.5739 | 0.3133 | 64.26s |\n", - "| DeepKriging_sphere | 50 | 68.749 | 8.2915 | 5.745 | 0.9893 | 40.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 91.7113 | 9.5766 | 5.5399 | 0.9858 | 47.96s |\n", - "| UniversalKriging | 50 | 164.576 | 12.8287 | 7.5234 | 0.9745 | 3.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 12:56:50\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.9176 (±1.6363), Variance: 6693.3193, Range: [-213.0331, 205.5877]\n", - "z mean: 11.9149 (±1.6371), Variance: 6700.0234, Range: [-213.6966, 205.5378]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0898, sigma²=7666.9346, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7437.96 | 86.2436 | 61.3667 | 0.0338 | 0.41s |\n", - "| OLS_sphere | 1000 | 338.681 | 18.4033 | 9.1935 | 0.956 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4013.54 | 63.3525 | 46.3669 | 0.4787 | 67.38s |\n", - "| DeepKriging_sphere | 50 | 57.0855 | 7.5555 | 4.6721 | 0.9926 | 35.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.9047 | 8.0563 | 4.2746 | 0.9916 | 49.84s |\n", - "| UniversalKriging | 50 | 175.175 | 13.2354 | 6.7444 | 0.9772 | 3.32s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:00:17\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.5136 (±1.6337), Variance: 6672.2979, Range: [-207.8768, 205.5624]\n", - "z mean: 13.5116 (±1.6352), Variance: 6684.4248, Range: [-205.9491, 207.2088]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9944, sigma²=7030.9804, nugget=0.0025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6122.26 | 78.2449 | 60.904 | 0.1808 | 0.44s |\n", - "| OLS_sphere | 1000 | 789.648 | 28.1007 | 10.4791 | 0.8943 | 0.14s |\n", - "| DeepKriging_wendland | -- | 4317.64 | 65.7088 | 48.2769 | 0.4223 | 58.89s |\n", - "| DeepKriging_sphere | 50 | 71.0893 | 8.4314 | 4.3548 | 0.9905 | 42.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 58.504 | 7.6488 | 4.4554 | 0.9922 | 45.18s |\n", - "| UniversalKriging | 50 | 166.769 | 12.9139 | 6.2452 | 0.9777 | 3.42s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:03:40\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.3084 (±1.6258), Variance: 6607.7876, Range: [-213.9850, 205.5705]\n", - "z mean: 12.3468 (±1.6255), Variance: 6605.2407, Range: [-213.4389, 206.9947]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0332, sigma²=7370.4519, nugget=0.0022\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4759.21 | 68.987 | 53.8076 | 0.2721 | 0.37s |\n", - "| OLS_sphere | 1000 | 563.379 | 23.7356 | 10.4669 | 0.9138 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4060.11 | 63.719 | 46.8997 | 0.379 | 59.71s |\n", - "| DeepKriging_sphere | 50 | 105.467 | 10.2697 | 5.523 | 0.9839 | 48.34s |\n", - "| DeepKriging_sphere_Huber | 50 | 192.618 | 13.8787 | 6.516 | 0.9705 | 39.04s |\n", - "| UniversalKriging | 50 | 237.641 | 15.4156 | 7.6091 | 0.9637 | 3.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:07:07\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.5793 (±1.5943), Variance: 6354.5825, Range: [-195.4052, 205.5495]\n", - "z mean: 13.6133 (±1.5947), Variance: 6358.0239, Range: [-196.4061, 210.4746]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0204, sigma²=7088.3597, nugget=0.0036\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5561.77 | 74.5773 | 56.2954 | 0.1903 | 0.45s |\n", - "| OLS_sphere | 1000 | 205.796 | 14.3456 | 7.3735 | 0.97 | 0.15s |\n", - "| DeepKriging_wendland | -- | 19156.3 | 138.406 | 53.5869 | -1.7887 | 58.07s |\n", - "| DeepKriging_sphere | 50 | 44.8915 | 6.7001 | 4.0452 | 0.9935 | 42.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 44.8958 | 6.7004 | 4.2144 | 0.9935 | 40.55s |\n", - "| UniversalKriging | 50 | 126.863 | 11.2633 | 6.7736 | 0.9815 | 3.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:10:27\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.4423 (±1.6179), Variance: 6543.6118, Range: [-203.1745, 204.3926]\n", - "z mean: 13.4991 (±1.6188), Variance: 6551.5933, Range: [-206.8955, 206.0288]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9340, sigma²=6770.2219, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5299.45 | 72.7973 | 56.7389 | 0.1184 | 0.39s |\n", - "| OLS_sphere | 1000 | 363.386 | 19.0627 | 9.5413 | 0.9395 | 0.16s |\n", - "| DeepKriging_wendland | -- | 3536.65 | 59.4697 | 45.796 | 0.4116 | 60.11s |\n", - "| DeepKriging_sphere | 50 | 53.1202 | 7.2884 | 4.8813 | 0.9912 | 38.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 79.946 | 8.9413 | 6.0261 | 0.9867 | 33.72s |\n", - "| UniversalKriging | 50 | 138.231 | 11.7572 | 6.6261 | 0.977 | 2.97s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:13:39\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.4683 (±1.6400), Variance: 6723.7222, Range: [-212.8139, 205.7783]\n", - "z mean: 11.4445 (±1.6417), Variance: 6738.1592, Range: [-213.4328, 208.2020]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2368, sigma²=8802.1893, nugget=0.0022\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4183.96 | 64.6835 | 50.1075 | 0.3219 | 0.35s |\n", - "| OLS_sphere | 1000 | 194.073 | 13.931 | 7.2605 | 0.9685 | 0.14s |\n", - "| DeepKriging_wendland | -- | 3080.48 | 55.502 | 38.6441 | 0.5007 | 59.73s |\n", - "| DeepKriging_sphere | 50 | 34.5679 | 5.8794 | 4.0912 | 0.9944 | 39.90s |\n", - "| DeepKriging_sphere_Huber | 50 | 48.1777 | 6.941 | 4.2527 | 0.9922 | 34.49s |\n", - "| UniversalKriging | 50 | 109.701 | 10.4738 | 6.0639 | 0.9822 | 3.53s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:16:56\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.6203 (±1.6101), Variance: 6480.7422, Range: [-214.8754, 205.1001]\n", - "z mean: 13.5954 (±1.6110), Variance: 6487.9478, Range: [-215.7470, 205.8803]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3523, sigma²=9225.8110, nugget=0.0035\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 154167 | 392.642 | 84.4494 | -21.6327 | 0.35s |\n", - "| OLS_sphere | 1000 | 454.261 | 21.3134 | 9.3278 | 0.9333 | 0.11s |\n", - "| DeepKriging_wendland | -- | 7077.25 | 84.1264 | 50.6141 | -0.039 | 58.24s |\n", - "| DeepKriging_sphere | 50 | 49.0219 | 7.0016 | 4.6359 | 0.9928 | 40.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 74.2159 | 8.6149 | 5.6169 | 0.9891 | 40.97s |\n", - "| UniversalKriging | 50 | 245.274 | 15.6612 | 8.3187 | 0.964 | 4.08s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:20:21\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.7464 (±1.6258), Variance: 6608.4390, Range: [-200.4412, 205.8013]\n", - "z mean: 14.7477 (±1.6267), Variance: 6614.9907, Range: [-203.3653, 205.9363]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9765, sigma²=7304.9849, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5570.29 | 74.6344 | 58.7698 | 0.1694 | 0.41s |\n", - "| OLS_sphere | 1000 | 226.923 | 15.064 | 8.5739 | 0.9662 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4212.53 | 64.904 | 47.8458 | 0.3719 | 63.17s |\n", - "| DeepKriging_sphere | 50 | 86.8539 | 9.3195 | 6.0564 | 0.987 | 38.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 105.084 | 10.2511 | 5.7417 | 0.9843 | 39.89s |\n", - "| UniversalKriging | 50 | 215.681 | 14.6861 | 8.3016 | 0.9678 | 3.01s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:23:45\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.6833 (±1.5863), Variance: 6290.8442, Range: [-212.4869, 204.6357]\n", - "z mean: 14.6979 (±1.5867), Variance: 6294.2065, Range: [-211.3856, 204.0225]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1005, sigma²=7877.5783, nugget=0.0025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 166187 | 407.661 | 81.1278 | -27.3433 | 0.36s |\n", - "| OLS_sphere | 1000 | 1027.54 | 32.0552 | 14.395 | 0.8248 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3704.62 | 60.8656 | 45.2128 | 0.3682 | 64.32s |\n", - "| DeepKriging_sphere | 50 | 65.1131 | 8.0693 | 4.9324 | 0.9889 | 37.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 49.571 | 7.0407 | 4.2841 | 0.9915 | 44.50s |\n", - "| UniversalKriging | 50 | 212.068 | 14.5626 | 7.7278 | 0.9638 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:27:17\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.7999 (±1.6001), Variance: 6400.8418, Range: [-213.0022, 202.7321]\n", - "z mean: 14.7617 (±1.5988), Variance: 6390.4702, Range: [-210.9679, 205.0552]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3645, sigma²=8947.2207, nugget=0.0019\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4336.59 | 65.8528 | 50.9179 | 0.2425 | 0.32s |\n", - "| OLS_sphere | 1000 | 756.343 | 27.5017 | 10.2271 | 0.8679 | 0.13s |\n", - "| DeepKriging_wendland | -- | 3138.13 | 56.019 | 40.9809 | 0.4519 | 65.34s |\n", - "| DeepKriging_sphere | 50 | 75.6742 | 8.6991 | 5.4131 | 0.9868 | 39.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 67.1015 | 8.1916 | 4.4932 | 0.9883 | 46.14s |\n", - "| UniversalKriging | 50 | 207.9 | 14.4187 | 7.5291 | 0.9637 | 3.36s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:30:55\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.4685 (±1.6370), Variance: 6699.0903, Range: [-209.5693, 207.2927]\n", - "z mean: 12.4606 (±1.6371), Variance: 6699.9834, Range: [-210.2730, 206.1311]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9590, sigma²=6990.7156, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4983.27 | 70.5923 | 56.2238 | 0.2544 | 0.31s |\n", - "| OLS_sphere | 1000 | 332.914 | 18.2459 | 9.0157 | 0.9502 | 0.13s |\n", - "| DeepKriging_wendland | -- | 3963.62 | 62.9573 | 46.6744 | 0.407 | 66.57s |\n", - "| DeepKriging_sphere | 50 | 86.843 | 9.319 | 5.0914 | 0.987 | 45.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 60.3408 | 7.7679 | 3.9541 | 0.991 | 49.52s |\n", - "| UniversalKriging | 50 | 195.988 | 13.9996 | 7.2045 | 0.9707 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:34:45\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.9952 (±1.6115), Variance: 6492.1343, Range: [-214.9039, 204.2655]\n", - "z mean: 14.9674 (±1.6129), Variance: 6503.6943, Range: [-216.7824, 207.8241]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8037, sigma²=5895.1880, nugget=0.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11289 | 106.25 | 63.2463 | -0.5726 | 0.40s |\n", - "| OLS_sphere | 1000 | 469.537 | 21.6688 | 8.7287 | 0.9346 | 0.16s |\n", - "| DeepKriging_wendland | -- | 5203.19 | 72.1331 | 45.2927 | 0.2752 | 65.02s |\n", - "| DeepKriging_sphere | 50 | 67.7782 | 8.2328 | 6.0586 | 0.9906 | 36.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 25.9327 | 5.0924 | 3.5235 | 0.9964 | 53.40s |\n", - "| UniversalKriging | 50 | 149.864 | 12.2419 | 6.087 | 0.9791 | 2.04s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:38:29\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.5562 (±1.5995), Variance: 6395.9707, Range: [-207.5052, 203.6785]\n", - "z mean: 13.5746 (±1.5997), Variance: 6397.2134, Range: [-208.9932, 204.8195]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9061, sigma²=6581.0395, nugget=0.0044\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7938.64 | 89.099 | 65.0498 | -0.294 | 0.38s |\n", - "| OLS_sphere | 1000 | 251.796 | 15.8681 | 8.2565 | 0.959 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4633.69 | 68.0712 | 48.5164 | 0.2447 | 61.13s |\n", - "| DeepKriging_sphere | 50 | 60.9704 | 7.8084 | 4.8013 | 0.9901 | 40.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 60.3633 | 7.7694 | 4.8953 | 0.9902 | 43.12s |\n", - "| UniversalKriging | 50 | 173.037 | 13.1543 | 7.0793 | 0.9718 | 3.07s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:42:03\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 11.1298 (±1.6094), Variance: 6475.3687, Range: [-209.5753, 204.1214]\n", - "z mean: 11.1540 (±1.6081), Variance: 6465.3467, Range: [-211.5352, 202.5348]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7840, sigma²=5703.0781, nugget=0.0029\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7428.31 | 86.1876 | 63.0562 | -0.0728 | 0.34s |\n", - "| OLS_sphere | 1000 | 1411.92 | 37.5756 | 13.2351 | 0.7961 | 0.15s |\n", - "| DeepKriging_wendland | -- | 6752.44 | 82.1733 | 55.54 | 0.0248 | 65.64s |\n", - "| DeepKriging_sphere | 50 | 85.7155 | 9.2583 | 4.458 | 0.9876 | 46.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 93.6915 | 9.6794 | 4.5913 | 0.9865 | 44.23s |\n", - "| UniversalKriging | 50 | 256.742 | 16.0232 | 7.607 | 0.9629 | 3.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:45:51\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.6192 (±1.6029), Variance: 6423.1338, Range: [-206.6646, 207.4582]\n", - "z mean: 12.6431 (±1.6039), Variance: 6431.4351, Range: [-207.3259, 207.8131]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9990, sigma²=7097.4212, nugget=0.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5556.36 | 74.541 | 58.7393 | 0.1792 | 0.41s |\n", - "| OLS_sphere | 1000 | 389.834 | 19.7442 | 9.4254 | 0.9424 | 0.15s |\n", - "| DeepKriging_wendland | -- | 4043.58 | 63.5892 | 45.7139 | 0.4027 | 61.60s |\n", - "| DeepKriging_sphere | 50 | 71.2497 | 8.441 | 4.38 | 0.9895 | 41.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 147.166 | 12.1312 | 5.2708 | 0.9783 | 54.35s |\n", - "| UniversalKriging | 50 | 283.003 | 16.8227 | 8.3918 | 0.9582 | 2.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:49:39\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.4212 (±1.6116), Variance: 6493.1841, Range: [-213.9658, 204.9198]\n", - "z mean: 13.4504 (±1.6118), Variance: 6494.9961, Range: [-215.4164, 205.7257]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8635, sigma²=6282.7036, nugget=0.0029\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5394.85 | 73.4497 | 55.948 | 0.1902 | 0.34s |\n", - "| OLS_sphere | 1000 | 1339.09 | 36.5936 | 13.3754 | 0.799 | 0.16s |\n", - "| DeepKriging_wendland | -- | 4789.36 | 69.2052 | 48.8544 | 0.2811 | 64.56s |\n", - "| DeepKriging_sphere | 50 | 55.7997 | 7.4699 | 4.6833 | 0.9916 | 41.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.0741 | 7.3535 | 4.4411 | 0.9919 | 42.92s |\n", - "| UniversalKriging | 50 | 168.815 | 12.9929 | 7.1626 | 0.9747 | 3.04s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:53:23\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 13.4900 (±1.6288), Variance: 6632.0986, Range: [-211.5701, 205.0404]\n", - "z mean: 13.4066 (±1.6301), Variance: 6642.9058, Range: [-211.9510, 208.1089]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1540, sigma²=8125.7428, nugget=0.0035\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5481.06 | 74.0342 | 58.7597 | 0.0943 | 0.40s |\n", - "| OLS_sphere | 1000 | 487.622 | 22.0822 | 9.3324 | 0.9194 | 0.16s |\n", - "| DeepKriging_wendland | -- | 4118.37 | 64.1745 | 46.7907 | 0.3195 | 61.77s |\n", - "| DeepKriging_sphere | 50 | 52.7748 | 7.2646 | 4.989 | 0.9913 | 39.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 101.688 | 10.0841 | 5.0357 | 0.9832 | 50.88s |\n", - "| UniversalKriging | 50 | 144.951 | 12.0396 | 6.7734 | 0.976 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 13:57:11\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 15.8911 (±1.6016), Variance: 6412.9741, Range: [-209.6448, 202.2589]\n", - "z mean: 15.9037 (±1.6032), Variance: 6425.7754, Range: [-210.9707, 207.9559]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9592, sigma²=7143.0827, nugget=0.0026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 5753.56 | 75.8522 | 58.9176 | 0.12 | 0.40s |\n", - "| OLS_sphere | 1000 | 442.271 | 21.0302 | 9.7826 | 0.9324 | 0.16s |\n", - "| DeepKriging_wendland | -- | 193137 | 439.474 | 75.5778 | -28.541 | 62.38s |\n", - "| DeepKriging_sphere | 50 | 61.0109 | 7.8109 | 4.8285 | 0.9907 | 44.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 79.008 | 8.8886 | 5.1483 | 0.9879 | 37.94s |\n", - "| UniversalKriging | 50 | 135.065 | 11.6217 | 6.1315 | 0.9793 | 3.37s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 14:00:51\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 14.5321 (±1.5923), Variance: 6338.7407, Range: [-199.3963, 204.6754]\n", - "z mean: 14.5596 (±1.5946), Variance: 6357.0234, Range: [-197.6278, 205.3397]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9439, sigma²=6501.0167, nugget=0.0020\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4638.68 | 68.1079 | 50.7662 | 0.23 | 0.38s |\n", - "| OLS_sphere | 1000 | 458.389 | 21.41 | 10.508 | 0.9239 | 0.16s |\n", - "| DeepKriging_wendland | -- | 3195.55 | 56.5292 | 38.6769 | 0.4696 | 65.32s |\n", - "| DeepKriging_sphere | 50 | 29.6382 | 5.4441 | 3.6187 | 0.9951 | 37.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 28.2836 | 5.3182 | 3.5446 | 0.9953 | 44.55s |\n", - "| UniversalKriging | 50 | 149.637 | 12.2326 | 6.809 | 0.9752 | 3.27s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 14:04:37\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.9732 (±1.6360), Variance: 6691.4902, Range: [-212.6931, 203.7595]\n", - "z mean: 12.9579 (±1.6360), Variance: 6691.6416, Range: [-214.1233, 205.5903]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0850, sigma²=7604.2195, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4701.84 | 68.5699 | 53.9051 | 0.2663 | 0.37s |\n", - "| OLS_sphere | 1000 | 696.688 | 26.3948 | 11.2312 | 0.8913 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3554.81 | 59.6222 | 45.8623 | 0.4453 | 62.64s |\n", - "| DeepKriging_sphere | 50 | 37.6336 | 6.1346 | 4.0386 | 0.9941 | 46.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 58.2 | 7.6289 | 4.4429 | 0.9909 | 45.78s |\n", - "| UniversalKriging | 50 | 243.872 | 15.6164 | 8.3952 | 0.9619 | 2.29s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 14:08:30\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulate Data\n", - "y mean: 12.9954 (±1.5917), Variance: 6333.9272, Range: [-211.4829, 206.6510]\n", - "z mean: 13.0311 (±1.5909), Variance: 6327.6030, Range: [-212.8995, 205.3430]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0818, sigma²=7591.3289, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5096.73 | 71.3914 | 55.734 | 0.141 | 0.31s |\n", - "| OLS_sphere | 1000 | 564.701 | 23.7634 | 10.2052 | 0.9048 | 0.17s |\n", - "| DeepKriging_wendland | -- | 3618.31 | 60.1524 | 44.1341 | 0.3902 | 66.29s |\n", - "| DeepKriging_sphere | 50 | 54.9861 | 7.4153 | 3.9286 | 0.9907 | 43.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 49.2208 | 7.0158 | 4.2653 | 0.9917 | 42.10s |\n", - "| UniversalKriging | 50 | 183.423 | 13.5434 | 7.0626 | 0.9691 | 3.42s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 14:12:22\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, z_combined, y_true = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " z_val = z_combined[val_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(z_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, z_train, z_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, z_combined, y_true, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " z_train = z_combined[train_idx].flatten()\n", - " y_test = y_true[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, z_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, z_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, z_combined, y_true, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.020466, - "end_time": "2026-01-19T06:12:23.004799", - "exception": false, - "start_time": "2026-01-19T06:12:22.984333", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T06:12:23.049738Z", - "iopub.status.busy": "2026-01-19T06:12:23.048867Z", - "iopub.status.idle": "2026-01-19T06:12:23.074365Z", - "shell.execute_reply": "2026-01-19T06:12:23.071188Z" - }, - "papermill": { - "duration": 0.052187, - "end_time": "2026-01-19T06:12:23.076964", - "exception": false, - "start_time": "2026-01-19T06:12:23.024777", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 1000, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 35169.34±96950.69 | 131.64±133.56 | 63.13±14.39 | -4.51±15.51 |\n", - "| OLS_sphere | 596.49±404.98 | 23.34±7.19 | 10.33±2.14 | 0.91±0.06 |\n", - "| DeepKriging_wendland | 8475.04±26476.44 | 74.96±53.44 | 47.54±5.39 | -0.30±4.05 |\n", - "| DeepKriging_sphere | 177.02±733.47 | 9.61±9.20 | 5.99±7.68 | 0.97±0.11 |\n", - "| DeepKriging_sphere_Huber | 83.58±46.04 | 8.85±2.29 | 4.91±0.84 | 0.99±0.01 |\n", - "| UniversalKriging | 195.70±55.36 | 13.85±1.98 | 7.37±0.91 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 8.85±2.29)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 10537.965398, - "end_time": "2026-01-19T06:12:25.575286", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_spherical_noise_tdf4.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario1/simulation_scenarioI_eggholder_spherical_noise_tdf4.ipynb", - "parameters": {}, - "start_time": "2026-01-19T03:16:47.609888", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_outliers5.ipynb b/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_outliers5.ipynb deleted file mode 100644 index 91b7980..0000000 --- a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_outliers5.ipynb +++ /dev/null @@ -1,3356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-18 21:51:36.601074: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768744296.613972 3504394 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768744296.617623 3504394 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768744296.629096 3504394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768744296.629116 3504394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768744296.629118 3504394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768744296.629119 3504394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, outlier_ratio, outlier_multiplier, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function + outliers\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = mean_term + (L @ z_std).astype(np.float32)\n", - "\n", - " # Add outliers\n", - " idx_outliers = rng.choice(num_sample, size=int(num_sample*outlier_ratio), replace=False)\n", - " z[idx_outliers] *= outlier_multiplier\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder with outliers = 5\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4567 (1.4711), Variance: 5410.2669, Range: [-127.9956, 127.9977]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7392.7622 7934.0786 \n", - " 100 5488.5713 6504.4849 \n", - " 200 4892.9980 5549.0991 \n", - " 500 3466.9436 5239.3071 *\n", - " 700 3792.1912 6029.7969 \n", - " 1000 7318.9116 20462.0801 \n", - " 1400 224871.0938 119767.6641 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768744321.439852 3504394 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768744323.670027 3504671 service.cc:152] XLA service 0x708524004920 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768744323.670091 3504671 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1768744324.001017 3504671 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1768744334.774943 3504671 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7085302430a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x708529c967a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 7495.4282 8987.4229 \n", - " 100 9207.5557 10566.0342 \n", - " 200 8897.7578 10095.9658 \n", - " 500 4400.6968 8074.5488 *\n", - " 700 4428.3188 6124.9399 \n", - " 1000 5281.6538 9518.7119 \n", - " 1400 7052.3711 10843.9805 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 18.7423 3943.5635 *\n", - " 100 19.3403 3819.1882 \n", - " 200 21.4552 3523.0896 \n", - " 500 26.1126 3996.9602 \n", - " 700 30.7953 4286.9087 \n", - " 1000 37.8854 5681.6963 \n", - " 1400 56.4633 5886.0142 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1457, sigma²=4739.4133, nugget=2280.6187\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0746, sigma²=2925.6017, nugget=2255.8415\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=180.3487, nugget=3938.0361\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7331, sigma²=0.0195, nugget=2538.3301\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5518, sigma²=0.0152, nugget=2125.3834\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5905, sigma²=0.0128, nugget=1631.2287\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7241, sigma²=0.0059, nugget=1124.9625\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3420.7540 4949.9584 *\n", - " 100 3574.6247 5182.8204 \n", - " 200 4892.9475 5549.1396 \n", - " 500 3466.9494 5239.3194 \n", - " 700 3792.1924 6029.8037 \n", - " 1000 7318.9195 20462.1098 \n", - " 1400 224875.4246 119768.0030 \n", - " Best order: 50\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1457, sigma²=4739.4133, nugget=2280.6187\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 14776.9 | 121.56 | 74.4241 | -0.1826 | 0.34s |\n", - "| OLS_sphere | 500 | 5239.31 | 72.3831 | 32.8369 | 0.5807 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8932.58 | 94.5123 | 59.8233 | 0.2851 | 47.78s |\n", - "| DeepKriging_sphere | 500 | 6464.85 | 80.4043 | 32.7625 | 0.4826 | 24.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 3868.84 | 62.2 | 14.4013 | 0.6904 | 48.36s |\n", - "| UniversalKriging | 50 | 4949.96 | 70.3559 | 28.5223 | 0.6039 | 1.07s |\n" - ] - }, - { - "data": { - "image/png": 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YOLTmzJnjW+rM69ACyu6w3nFOr7/+esjawD3lGbCGNQWYB/fdd1/Y+5SPimTopJyZl7322suULmFsvPfeeyHjBeMjJW7of+MH45Z1sFOnTrXl6yh/wnyw88TC88OgSbmxpgTnz7zzrrVcO4boVF5zKvaNhrjnlMZhXLj3l//9739mTfcaer0lvFg/KQ0Xaf6np6eb9ZhyNXbucT8wDMcyqtc3GKi5X0VFRbXnftddd5l77m0QjgOEcnnxgIMB57QXHPSU42KtZu65S3yxP3M+XhmLUkmU4PNbU0eMGGFkNEpYxeuATZUcFw9cnzcwBZkGuYC1l9/g2uhpk6pSV/GAE9zP0RLPvLGyMnsRz5N12d14nLJxlID0OpIprcrnmb/cf7dsyzwjwII5QXknC/u5X4AA8iByHPePkrGpDtKhHKlfLxlkL+Y6sgH7BvcQedmC/mAdYOw/3hKelOHjM268/60oiqK0YBo7VURRFEVpmuWnqqurnTlz5kQskeMuDRGpZAcvSgR4y+jYciSLFy82pQi8ae/z5s0L+TwljnJyckI+t/POO4d8xq+sBmVgPvvss5DPXXPNNb7nmWz5Kb+SGF27dnW++OKLsM9ShumPf/yjKSER6955yw54iadkD6W4vJ8pLCwMKYdhS8l4P0dpmTVr1sQsm0BJr3fffbf2M5Q4GjhwYNjnXnvtNSdRIpVKseW7kuG2227zLbWwaNGikBJFl112mW85osrKypDj+Z0fY9P9jE866aSIJTH4LVsOYsSIEXHdN79xyZhzzxvKZZ122mm+5cC8vP/++87SpUsj3jPmr7dUV/v27WvPPVoZCl58d/78+SGfdZckimcse8fUFVdc4XuuGzduNPfM7+9cu195HXdpsVWrVvl+7sorr4zrWg866KCQsk33339/2Gd4zo1VfoqyZn/4wx/M65RTTvEdS5Sq27Bhg+8xvaVPWGMpvecdL97nxfrtLlcIlMb59NNPo5Y3olyX9/z+/ve/N7nyU7DTTjuFfY/1vr6uOZl9I5W/Hw8PPfRQ2LFeffXVkM9MnTo17DPXXXddyGe8csIDDzzg+3vsWc8++6xz6623Oo1Vfgq8+wf3Ydq0aWHzLNI89a5/7E9+JXu8n0Pu8MpKvNxzlNJNfuX37rzzzpBjvfPOO75lA72lhVIpx8Uzl5ctWxZ3WTP2tYcfftj517/+5dRX+ani4mLnk08+Mdfityd8+OGHMa+PFyW5vHuq3SfvuOOOsM9T6ss9j/kupZ1izV2/ElnI6e49GTluhx128D3PZMpP8cz8yjIeffTRvvIc5bi84zHSvdNSU4qiKK0bzdRQFEVRQso3xWqGbbMfiLyNBdGUREfTfNINEYtAw1Jv41AaI3oj24g0pXSOO5rzhx9+MCVw+vfvb/6byDK/8hX77LNPyHs0J3z66adNVF1doYmpX/NfSjAQoeqFSO+7775bGgq/8gI0fScS01tO5j//+U9IZCARgVxbrLJLxxxzjInusxDhT3QgkfxukmncGKlZJZF9qbwnNIwmwtPCHKDklLfBqI3EJJo1GhzPfY5kbHjLq9A8lUbCdr4R1XrssceGZWvEe99oLu6eN8w9ImGJaHWX/iEC0j1vgKwCILKYLBU+w7/JOrFlozg/N2QzcG406YwFGR3ez9k1IF68z5wIZNYO73kRocmY5OWGqF139Kc9JlkH3CsLWTE8F+/3ybjwa/zshcwad9kmsseYX+4I5UjPlIar9Q3ZLLYBqxfGJKXRuAaaz3ohCtib/UMUONlBbljviSqnxIk7apjyQTQxtpAVxAvfIOXxyBAg44iMGTtm/daAptg0PNK65C3f0tjX3NC//+tf/9qMf3fGHc2dWeuilZ7ylq7i3trMByCKnLWJLEE3ZJ6ceuqp0tiQgeHOgCLbBZnF+5l4eeONN8JK9pAB5G3IjNzBccmM9K5fdp6SJectB0eGAKU53ZDhSqZnrHKdqZTjkp1n3uh9C9H/5513nqQasgN4xYLm9WSjxIJnSXasF7tPUsbPDZkX7O/u/Q9Z4qabbjLP3l3ukewGm+nA+34yK99x78nIcex3RxxxhKQCxq83O7Zv375mLfArc4iMFUvOUhRFURRQp4aiKIqSEDg0qG9P2ZdYjBkzJmrqPSV3vGD0chu+okFZC6sMew0G1pjsd/6UKXjmmWekrmDo8SrzGMcxBjQFpk6dGvZeJCUVg4fbqWG/H8up4fd3yhh4SaY2s59h1ZZiiPS3aGAU8l5jpHuCgRdjhLfsCfckmrKNkc1bHqtbt25hn6PkFIaDVN03vzFHiThqqnv7kjBX3EYkSmRh0KI0TKTSTX5Qlz2WU4O/sw7UFe6p27CKE47zpewKBjlelHrj2fiNDb+5QMkpP+cK6waODve9oPwUpYWildzj9225OXdZFJ4D98qSyjrlqQSjIwboSHPLb71mHY13LWW99q7tGHtxgPr19YiE+142JXBye/G7l419zQ35+1w/fZfcjgt61OBEyc/PN84Vb+kp5vDAgQPD5r/buM75U8aN+U+ZqyFDhphyWpTfcpfaaSw4JwzVEydOrF1z3aWccL7G26cp0b2c971ODff3/WQlHNt+AS0ELMRyaqRSjovXqbH77ruHXBN9tjDoUwLPjgdb2pG9vDHgmrxjOxJe55Qb9iFv2TECDuIN7iAQwzoAcSB51ynGol8QDvIP8nIq+l/4jRH630Xq26QoiqIo8aKNwhVFUZS4oYY1kdzxRkLutNNOcdWgTxZ3FL27+aa73rAfkd6vy+9bUKabCn73l/rWfvi9TzR8LIj6jacxZjKNoiP1kpg7d64kA5G+3vNAqaamfKruCVHq3uv3ux9+x/YzvsR73yI9V7+x7jZQ4tDAmE0GSyIODXf/kLqsAfFCg22v4YuIcvqEkAlCjXgcc0RqEwXuzUZIZC7wvLzZZZGOEWsu2OO5aeya/5GgDwtjgf+v7/XaZo3R3yUR43q8466hYZ76ZeB4x1FjX3Nj/L7XYMuxiO63Bm0yRaJ93joxmNvedYzspkceecT0sKAuP58hyyNS5H5D4s3EcAdAkBGFU6cx9vJUy0qpXhfiAaeN1yBOJiV9Ox588EG5+OKLTZYEMgRZCn4NvOsLHOX8Pk74Hj16xPWdaPskzysZ+clChoS9fr9nTzaLH8gjfvtgS5SVFUVRlOaLZmooiqIoIcoNTUgtpLYTmUy2BdGTkYx2kUgmmj4RiNiPRqRSWvVpVIynfFdjkurz83MIuMv51AUUfQzC3mbhNJscOXJkwsfze+6pvh9+Dgy/3/D7XH0Q65pxBiRrlIpnHqVqDcBYSbQqJTGeffbZMEOohahSSq0QIf3NN9/UNhJuiGcfyTmWqvmQCjD4kmnH2olDiMwMd0kpDM6UlKLUFBkm9bVeUw7Mr0F5PDRFpxAGTL8MHCLKm8o1N9bv0ywa46675COZG5Sm8paewlBN+UIvZG5QKuuvf/2riX6P5FymbCJZXMx/MhK82XANCc5VSk76GXQTKT3VEOtXY8hK8cpxfiCP8nwZD6z3kUpV8j5lmnDUkrHgLVdYlzFts5W5d2QHMdaQWch6SLS8YkPIypH2p2jjqDXLyoqiKErzQJ0aiqIoSi0YAL1lC+pTafGLxMeg4VeGxw9KTVj8FLYlS5aEGJUsqYri9Dv/SPXqGwOi7LzGX/7bL/qOexVvpkRDgeF/3333NdGXbig7gjE20bISjBFKMLijHjHiEr0Yafw0tXsSCc7Vr4SH31i314oDgD4HflkR9IIg4pfySXDyySeb+teNabjA8EPENq8FCxbI9OnTTdYOcw5HF++5jVl33XWXGSuRnlskxwj15v0cPamKWm0KYHSjzBb11Snh4zbIr1y50txjb/8fv3uIcdFvjfXDOphsjXWvwYyyMfQ4oWQaDhXGzqxZs5pFRC/n7QVDp7tUXWNfc2P9Pk49MhPcPWnogUX0OPX+3dDLJpKBF7ng/vvvN0ZqzpFyTnb+v/feeyHOA9ZD5v4111wjjQUG9LPPPltuv/32kPcZE94ydbFIZP2KtW/Fu9fFKyulUo5LBMYuDloyDBkLOGIpr8S/GV+UC7TgAGGssY+lAkqHRSsZlcp90k9uIdjI23cmGrZUld+zj5S1RYZHqsreNXVZWVEURWm+qFNDURRFaTQwhtG02w01kL0NK/1AkXVHQFNPm9JYbjDY/epXvwp5D0MujTJTAdkC3prDGBowsLibZ8fCL5I70VJAke6v1/Dxzjvv+GY58L7f9xubSy65JMypQcmkK664whi3YkE0OmUogGe1yy67hNWn5tpPP/30MIX+o48+apL3xA/G3O9///uQ9zDqEJ3qhbkCGCyIbHaDgfvOO+8MeQ9jClkPTQl6dbj7efC8MMS6HRvuOt5+z43oXVvb3837778fNv+IFI/WT6O5guPq6quvlmuvvTbk/QceeMDMMXc/GL97yD2JxxGOMd1tUPczyBJ17V03/WqxNzXoOUD2kBfq/FunYH1cc6L7RmPecwywbqcGjkNKYbmzNyAeQzEGYOa62/HCWkepJOZzUxo7NKlmPXUbpCn/lSjMPTJQvPsW9zDRvdyu/27YY5mfXuM6a2FDynHJwPfJkHCXcKIHEk4PN4yHVDk1GhKuj/4g7j0YJzTrtl+/rmj3mMAHHBzuvhoca8qUKab0oBsCBVLRT8M6rrxjhPGM0zGRrNX6kpUVRVGU5ov21FAURVEajaOOOsoYmt3Q7BEDrR8YBlC+UOS90X1+jZKJ4vM6Ojh+qjI1UA79mpFjmKFWuF9PB8r9eBs1+pUqIOqwrngdOnDPPfeYMh5uiNT1NtCmDAjNQxubI4880mRreCFiF0NZpDIkkyZNMg1TiRCOdU+uu+66kEhVjDsYDLxlQ4g8JaK5KXLLLbcYZ49b0SebBeOh1zhvMzr8Ml0wMrqjMzFqUK8+2T4mqQLjK888UkQx5TXcBk1wX7ttKOwGYw711t1GEcaTnzGOCPKG6FmEUdH98uvTkGpwhnmj47l3rBVuaMJLQ3avAZVIdHe/ADcYF8mYwQDtNqr7jb1p06aFlUsia6ipwvj5y1/+4msopVwaa72bVF9zovtGY95zSvV4nWJPPvlkyH/jlKA5sR/s+Xw+Uqk8nBretc773+CdX3379pX6hOMj5+Dc4sUecsIJJyR8nKOPPjpMViI4AxnHDfIRjeCjrV/0HnE724ASdPfee29YUIg3k6a+5bh4Oeuss0wZskhlp/zkAr/x0Fzwyi3cQ0rFrlixIuLahLMV+Ym900LGh59cd9lll4Xsn2RRedevusD49a4/7G2UOnRn1FhsqUkv9SUrK4qiKM0XzdRQFEVRGo3evXubMjf//ve/a99DscJBgQGNEg2ky2OwRAHCGG8bHnr7e/Adypu4ja8osRgKaSBMTW8i3aZOnZrSa7j55puN8u42jGIMp8QEtZWJHsRITlkEsgboD+EtiYGh2ctrr71mlE8MsdZgQEmYRJqLojDyHbfShxEAwzz3CwMLRi2v4wdodOltzNpYUHedc/aWSSDSj5JI++yzj+n7gsJO6RwiMq1DwjtOLrroIhNZ7jaOYShnvOEEIfKc++F1/MCf//znMONNU4HrHTFiRO01kIXgV94BI76FGuAY3dyGcxxv3AuitzH6ff7552bsNjaM4eeee85k7vCsMZISpYpxjmeJgc9bKsPrxLjxxhtN2RA3jz/+uLnGsWPHmlJklOPyNpXFeekXEd1SKCwslAsvvDCsTA7GUdYqd18C1jvWFTdkeeBwYr3jmbDGYWyjPFik0iZ+2WI45j777DPjOMEBQvR4qiKF6wrzAich4JQmIwjHtbffD7BGYHCln0J9XnOi+0Zj33Oc0NH2X7LlWMP9YE3H6M6axLkOHTrU7E+UeGKskVXnPW/v/G8s6PmQClmJUlY0Rfca93H2sGYTrEHZJa+Dkb0TGciCTMU66HUqkZnF+EFmWbRokZFr4mlQnUo5Ll4Ypzh0GOPsBTxrW6aJc/fLxm0q4yEZkFsIPnEHWiBPcv/Yu/h/5gLrFA4q9n47Dsia8cp2r7zySsh7OJm4P+z7PHOc1ZECRpLt14fz3NvTh5J4nPuBBx5oZHTWVvoT4VwdP358XGveGWecYZw3trQWY47xqCiKorQSHEVRFKVVcsMNN1ALJOQ1duzYpI/35JNPhh2P34hFSUmJs+eee4Z9N9arT58+Ycf6+OOPnbS0tJjf7dWrV9h7nL8XfsP7OT8eeuihhM59/fr1Id+vrq72/S3va82aNVGfn981/PLLL067du0SOr999tnHKS8vDzsW48P72QULFqRsLERj5syZzrBhw1IyTiZNmuRkZWUldJwTTzzRCQaDYceK5/cmTpwY9rkzzzwz6fvmN1biuTejRo1yqqqqQo51zz33xPxeYWGhs9dee4W9z3Ulc51e4hnLp556asLP/t133w37rQsuuCChY6SnpztvvPFG0s800vPyI975FS+cT7znuGrVKic3Nzfs83/5y1/CPnvJJZck/Cy811JcXOx069Yt5nfGjRsX1z6VynvnNw/jfXXq1Mn56KOPfI+b6mtOdN9I9e8nyrp166Kuu6zxkRg9enRCzyEzM9P58ccfw44Tz3odL4yvVBzPb5767eVbtmxxRo4cmdB96NKli+88WL16tflbMrKS3xqSSjkunrncs2fPhH6nbdu2zooVK5xk8Jtjfs8nXpJdqyZPnuzk5OQkfI/9ZIizzjorrnvWpk2bsPeT3QuRKffbb7+4z3v8+PFhx5gwYULM7x1xxBEJPQ9FURSleaPlpxRFUZRGhTJH1O4955xzIkZpeiEijRIKXvbff38TvRetgTQp9UQ8phJqpxNx5o3MjReum6a88V5/IlD2hb4K1GSOBVGwRH5SRzvRJtz1DdG5XAelkBLJVvHrAzBmzBgTnezXWNsLkaCMmeeffz6lTa9TDVG1RKxGgl4ib775ZlhNaqLPvZH33ih+ylg0dpRrIvee9YHSSX59bf71r3+Zchze8it+EDlKCRbKq7R0aOTK3PdCBoa3XB4Rw7wSmYfUa2csWfgupW0iNYW2JUvi6ZvTFGA8kYlAlDFRx36k+poT3Tca+56T8RNpLjE+vKXNkp3/XCdZfIk2427q0Hh+4sSJcfeF4J6S4eJXYqtz585mbYsmszCO/UpZ1bccFw+JjAcyeshAiaf/RFOG7F+yw8hEiBeu2d1rxMJz9cuEsJD1QLaGO0uvriBTIqeQbZlsHxXGS7TzVhRFUVofTbOGgqIoitKqwAjx6KOPGuMxTgkMzrNnzzYlCgiuxAhDU2CMFDguMFbaVHO/EhaU2aAeLyUpKE2DYkZpFNLu+T5laFINxhrKHlAOiRIQlLrit2nEjFKNgZRzoAeH27hnoT4yfSAojUQpAFL/I9WpTxRS9jkfSutg1OL4lOyg3A6lijB6YOjH2ZOIwtwY4wRjNT0wuI5PPvnE9AKh7BB1mTEsYpzFuM/1UEc8kjGekhyUaaBMDA4pyqJwzylZ1r59e3PPKF1GGYNkS2Q0dMNnnjEGScp1UYaNucP101eE8hUYkbxggHrhhRdMeZLHHntMfvjhBzPuKBfBPMOJRLknnDqNCesDc5uyWhiOKWvEc+fFNfDMuFZKcWCcdzcR9xrDKKnEZ7heDHGMA8p24MDC2Mf6QRmvU089NS7nR0uBXiKUt3GX8WENpqyMt88ChimeB81fuYeUlFm3bp2ZP9Q9pz8C85B+OJSh8XMgjh492pS/o+wVxi7WJNZG1iCeD2VFWFObCowd5hAGXMYbazrrBH0BjjvuOLP2xCLV15zovtHY95x+U97SN/b9aLCnUlII2YA1ivNmf2Xe8kyQB3Dg05ODYyUbYNDUoRQeazHz0cpKPC/KSrI/UlISOePEE0/0deq6oVwhZf2QlTD6U36MsU3/IRx0yAOMrcaQ42LBGKYEFaUi+TdlmRgPXjmA3+Ba/GSu5ghN3inrh8Ph1VdfNU4r+mDRQ4OG28ia7IMEcxx88MFm/fVzIOBgoCwaJR1Z87mHFRUVRo6glBPjqz7mEM8GhzhyBWOE8cX+yxjhPPlN5A0capGcF8h+BCcgazN+ufaaJCxFURSlNRIgXaOxT0JRFEVRFEWJD5xQXsOjinOKoiiKoiiKoihKa0HLTymKoiiKoiiKoiiKoiiKoiiK0ixQp4aiKIqiKIqiKIqiKIqiKIqiKM0CdWooiqIoiqIoiqIoiqIoiqIoitIsUKeGoiiKoiiKoiiKoiiKoiiKoijNAnVqKIqiKIqiKIqiKIqiKIqiKIrSLAg4juM09kkoiqIoiqIoiqIoiqIoiqIoiqLEQjM1FEVRFEVRFEVRFEVRFEVRFEVpFqhTQ1EURVEURVEURVEURVEURVGUZoE6NRRFURRFURRFURRFURRFURRFaRaoU0NRFKUV89RTT0kgEAh53XjjjY19WorS5NC5oihKa6Fv375hskFTwXtenGuyLFy4MOx4++23X0rPtyXym9/8Juy+ffLJJyk5Nvffe2yekxI/PAvvPeSZNeRzVBRFUZSGQJ0aiqIoWxWAc889V3baaSfp0KGDZGZmSvv27WX77beX3XbbTU444QT561//Ku+++66sWbNG75nSpFi6dKncf//9cuKJJ8qQIUOkc+fOZgzn5+dLt27dZNSoUUZ5feyxx2TRokWNfbqKoigtwrEZz+uYY45p7FNXFEVRmgl+zqZYr4ceeqixT1tRFKVRUKeGoiitmqKiIjniiCNk//33Nwbf6dOny/r166Wqqko2bNggCxYskG+//Vb++9//ynXXXSeHH364/O53v6tTZFR90di/rzQ8y5cvN88Y59sll1wiL7/8ssyaNUvWrl1rxnBJSYmsWrVKvvrqK3n66aeN465fv34yZswYfVyKoiiKotQbmglQdzSbSFEURVEikxHlb4qiKC0ajL44KTD4tlaGDRsmf/jDH0LeI6pfafp89NFHcsoppyScOeQ4jnz22Wf1dl4tFZ0riqIoiiJyyCGHSLt27UJuxXbbbae3ppmhz1FRFEVp7qhTQ1GUVguZGX4OjV133dUYMAsLC2XTpk0yZ84c+fHHH6W0tFRaGnvssYd5Kc2LyZMny5FHHinl5eVhf8vKyjKOKbI38vLyTOYR2RtkIVVWVjbK+bYEdK4oStOFOvyTJk0y/77hhhsarDdUmzZt5Oyzz476mR133LFBzkVRGgoCKngpzRt9js2DPffcM2bAGeWTFUVRWiPq1FAUpdXywgsvhPx3enq6vP322zJu3Liwz2I8njhxojz//PMm0l1RGrNkGjXavQ4Nyo398Y9/lGuuucb0hfFCKao333zT1IV/7733GvCMFUVRWiastX//+98b+zQURVGUFsqhhx7aYI56RVGU5ob21FAUpdVC5LqbHXbYwdehAdnZ2Uao/M9//mOMwn41g+nL4YU+BtH6XFRUVMj7778vt912mxx77LEyYsQI6dWrl2nwTMR9p06dTKPyCy64oDYK1Utdft+v8WkswZnSRfQVISqoY8eOpiE1/8+5//73v5cvv/wy6RrL//d//2eeQZcuXcw9p/8Dx6R3RCTmzZsnTz75pPnc6NGjZdCgQea+cV5E0fbp08dkNWB4WrduXVJ1i3/++Wc555xzzPnk5OSYv5Et4f08n4kEvVm8nz/ttNMkUe68807TM8PLE088IXfddZevQwPI2jjppJNMs/vvv/++Xu9lIuPK+7m+ffv6fo6sqfvuu8+Mj969e5vrYY7QCH348OGmlNy1114r77zzjmzZsiXi2D3//PNll112kfbt25vrIiOLrBai4M466yz517/+JTNnzkz6mpYtW2acn5dddpkZPzRu79q1qzlX5jUlOij5cOutt5oG79GIdG/o90M0OnOwoKDAPBsi+f79739LdXW11IVI8xNn7jPPPCMHHHCAaUSfm5trxseVV17pOx4t3APv8ZhrZL7dcccdZn3jWUS6n5QJxAF98skny4ABA8zzYm3gue+7777y5z//WRYtWhT39U2dOtU8GzJveC4cizlDdt7xxx8vjz76qKxevTri9yn3xnkffPDB0rNnT7MecE6DBw828z+e0m70ueH5c2969Ohh7iXnwb95pjgtb7rpJvn444/NHuEHDvgzzjjDjP22bduasUw5GO4Rc5dxzpoQ697w97/85S+mz0737t3NeXAcsgsuvvhik6UYi/nz55u5x+/ae8o1sZcx10488URzz7744gsJBoMxj9eaYG+jBGT//v3NWOL+sZa98cYbSe3RZOX99re/NWskx2OMnnDCCfLpp5+av/Nd7/G8Mk00GI+s/6w3jBPWNPb+22+/Pa5sVj7zyCOPGHmH9Yz1i/Nk7B900EHmOImWVIz1exxz5513Nr/FXGecMs+THYt1nTOR1ljWbvafffbZx+y7ifZFo++b97jevZ513B7bvrwBPsDa6v4M1+feW2L1yrDvIYN6QVaN9t1I8LyQT8aOHWtkTtYY1t2rr77a7ImpYuXKlWZ9Zq9jXDI+GTuMV/YI1lW/LFlLvHMsVq8MexzkTi/oA9G+W189T3gG9BhEdmX/Z+9h/LPOsG7RrDravUlUzubzqZQBFUVRlBaIoyiK0krJzMwk5aL21aFDB2fdunUJH+fMM88MOU6sF5+3TJ8+PaHvHnnkkc6GDRtS9vtPPvlk2N9vuOEG3+tcuXKlc/DBB8d9nmvXro37fr355pvOoYceGvF4Xbt2dWbOnOl7vOOOOy7ua2/Xrp3z3//+1/c4CxYsCPv82LFjneeee87Jzs4O+xuf33nnnUPeKygocDZv3ux7/KuvvjrsGB988IGTCBUVFU6bNm3CjnPiiSc6qSBV9zKRceX9XJ8+fcI+88033zhdunSJ+9yeeeaZkO8Hg0HnnHPOifv7/fv3T/qa/vjHP8b9Ozk5Oc4DDzwQ8Xn43ZvPP//czIdIxzz22GOd6upqJ1n85ufbb7/tjBs3LuJvdurUyTwjP5hD3s9PnjzZGTx4cMz7+dVXX5lnEes+ZmRkmPlVVVUV8bpWrFgRdY1xv+677z7fY/B+bm5uzO8zjyKtA++8845ZJ+IdI5999lnI90tLS50jjjgi7u8feOCBvudRWVnpXHXVVebeRft+IBBwLrzwQrP2+PH444+H7aXRXkuWLHHqA/c4i7TW1BW/NcBvvYqXSZMmOW3bto14r8466yxzf+NZd+DFF180a0qk5/jnP//ZfNf7N67LC9fl/dyiRYuc4cOHRzzfvfbay9myZUvE62UdiWcdZ45FmoMQ7zNYunSpM2TIkIi/s++++zrTpk0Le5+xVJ9zxm+NfeONN5y99947qrwWC+QJ7/fvv//+kM/8/PPPYZ/53e9+F/KZ8vLysHGETBfrGiZOnBjxGcV6ub/rt2d8//33zpgxYyJ+f9CgQUZGrQvICbfffruvvOd99ejRw5kwYYLvceKdY5FkzmjHifZyf5f7Gc9YivUc3fAMos0n++rdu7czZcqUlMnZqZABmxve53L66acbWfGaa64xr7vvvtv56KOPoq63iqIorQXN1FAUpdVCtLG3rA9RskRHff75502yh8Zbb70lp556aoP/LpHFRGZ++OGHcZ/n3nvvHXf0HJGl0Uoi8ftE0dcVzoeI71jZJJZffvnFRLJFijwjo8FNcXGxyTbx45VXXgn5byLbDjzwQEkEIp03b94c9v7ll18uDU2i9zJZiA4l0jta9HwsiJKkh05To6ysTC666CIT+RgPZEQQpch8iMRrr72W8mslM4uMsmjnRfZJtIwqN2QMEVEeDSLLiRYmeygWZHOQCRCpxvuSJUtMRkhdyq5deumlJsMjnn2Buc7c9q4b9LfhHFknkoWoc7I06gI2R+YUWV/cu1ifJXqcz3tLL86ePdtkEWqvnsSZO3euHHHEEbJx48aInyEqnYjxeEBmIXqaNcUPnt0tt9xisk2ThYjqGTNmRPw7e0Gk8yUbgCy/eNZx5hhz7aqrrkr6XJl7rJV+WXcWsqri7QuRqjkTiQsvvNDs73WBDBQyttyQUeodJ16875HN5h1HyWQBpJJf/epXtdlGfrAWMWbqAs+A8p3RMg0s7HXseci6rQHmNjJ9tPlkWbx4sRkvqZKzUyED1jV7JdFXqucLWbLIiuz/vK644gqT2UYmERla8YxZRVGUlor21FAUpdVCavmzzz4bVjqGsh+86LFBSSpKAVBq5LDDDjNp1l5QbEjNp5SM13A9dOhQ83c3fo25SbWmPAKOFkoDkOqO8Zp0bBRMt2KMQQvljtIHqfr9WFDmxFvGJC0tzRybVHGaqU+YMCHkPFEyKYHy0ksvxTy+VVZsuR6UbK/xBAXpu+++M43c/aA0AKVXKEvAi3NZsWKFUerd5SwwwKEEUNol3vPKyMgw44UyRbxn0/MxiFB+x+28efzxx8Max06bNs3cIzenn366uYeJ8PXXX4e9R+mh3XffXVJJfdzLZOG5U97GDXPElkHCAMbYpORHpLIlzz33XNh7KOiUHMIIRCktFGvmW13LN1koS8JY5ly5hzxrzo/r8ZadonwGRptY48GWVOCZYyBkPXr11VdNWQY3DzzwgJx33nmSKjBSoKgz35kD3CdvOTycwhiV4pnv9vopHYLjgpI27jIpXA9lPryll7huSkyw3mEA9JYQxKHIGoITxsLY5d6ytnuhFBjrKMej5Ajrjp/hBKfTP/7xj7D399prL7NHcL44TNzPgblKKYx77rknZO32OnpxbnLOnAPODsY6YzmSsds7ltmn+D7zFRhjPB+cRpEMqpwTzi83jD17HO4BZerchpLXX3/djCvK67jvt9ehMXDgQDO32MO4H6x7PKeWWg6EcY/DKxrsETxnr0Pcz7mFM4x7+NNPPxmj+4IFC2KeAyVhzj333DBjO2MDuYVx/tVXX5k5Fs/xIsF3WXNwxrCm/e9//wubLw8//LCRn9zGdb7Hnugdj6yNnB/yD3uI14H5t7/9zZQaYs4nCt/lHnohcIXxiWOYMjV+n6nPORNrTaSsDuWZeHbIQKy78UI5HmQBt3PE69Tw/jdwD1iXWIMiOT4SNdJSUg0++OADs7e6Oe6448yYdOP9by+MIe43ZWApa8dxvWP55ZdfNqXRKF+aKDjdKJ3khT2KMcj6xXjBMW1BVkAGZI2jbFyqoSQm95F1FAenG9YT9kg3dg9INaxT7KFehz7PjP2bMmBTpkwx+44FpxiOCPYhxmVd5OxUyIAtFeSEv/71r0b+QAezc1hRFKVV0dipIoqiKI3Fjz/+GFeaubs81V133RWxtEu86d5uKHf1/vvvOyUlJRE/8+qrr4Yd99JLL03J78dTUodSMd7PUH7BWzqJ86Tsgvtz/DcltuIpl/XII4+ElFgZPXp02Gf8SlJQwmPu3LkRr7G4uNiUxfCeV1FRUcy0eF4dO3Z0vv3225DPlpWV1ZaWuOyyy8K+88svv4R8/vrrrw/7DGUgEuWKK64IO86IESN8P0vZiXhLFaT6Xqay/BRlCbylSdasWeN7rBkzZji33HJLWFmIgQMHhhyD++jHxo0bnddee8337/Fe09dffx025r0lTPzKfP3www8x740te7Fw4cLazzA2s7Kywj7nfSbx4jc/ecaUR3Hz8MMPh30uPT3dWbx4ccjn/EqJ8Lr55ptNuQ83tmTTbbfdFvb5Xr16mfI3Fr7rN/e4P9xjy8svv+z7+zfddFPI5+yzef7558PKqnlLbnC/KaXjZvXq1WHjjBIulL2yeK+LMh1+5SPYY6ZOnWpKannHkrfUU6TyZcyRZ5991rn11ltD3uf32Mvcx6AEEr/nZs6cOaasmPtz3bp1M2uf5dxzzw35+6hRo3xLgLFWfvrpp87FF19s7lNLKj8Vz4uyLW5++ukn3895S9Tce++9vp/zXh9j0W8uUp7EPV/+8Ic/xPW7kcpPUTbtu+++C9kzvWPE73q944QXpRvdaxRj5IQTTgj73O67757wnsE8Zqx6P/f73/8+ZM1h7czPz4+5N6ZyzkSTgc4777ywklWRythF4tprrw07rntNjlTOz72eUWrKe63eeR1v2aJEyhtF2zMYz++++26I7Oxdb3mxfycD5au8xzr77LNDrnvVqlW+n7vyyivrpfxUop9zk8ryU3fccYdvaTz3WGVeXX755WGf+/vf/x7zWmLJ2amQARMh0ZK+fq9Yz6c+zuHwww+v028qiqI0VzRTQ1GUVgtNHWnoS8R8SUlJXBGZRFwSBUfkbqJR9n7QtNJmUhDlZCMpiYwi4g/93S/aloyFhoJIcC/cM7JX3ND4c/z48SY60cK50/CUJn7RGDlypIk0tRC5SQq4N6rQ3TTQYjNWuGd8nvR4Iu+JrLNR997oe87rhx9+8G2u7uXee+8Nyw5xZ+xQsoDoQPdzIluDpt0WbwYN0ZRk0SSKXzkvIqJTRX3fy2QgOt8Nv0/WCJF6Xoia5xXrGEQBEmHuLdVBw2eaNPNKFps1w/xlrSBKlahg7qGNpPaLwmdO03A3FpTHoGG7hbHJb3rnCpGLNOBOBURpHnXUUSHvkQnCOHdnD/FsiKClnFw0GCs0+I40lv3WHMraEMVsIYKZklOs4e5yXJQF4ZyIxga/0l5EkNLo1wuRopRUc8O67C25Qfksb/Q4EaNkqrAeuKNVyc6w98M7Dvk788sbycreQrksXl44BnuRhblApL53P2J++JUqJPrV/X245JJLwn6LqF+i64l4t9hsFls2z3s9RBTz8o475hkRvbzqws033xx27u5yThaiViOVPiTSm1djQsS33x7obQpNlDZljNzXFu/xWMPc5Q2ZL8whSvFFK3kVDTIvafzuzuijxJM3e4h9msxTuz+4ZQILmU/uccIYefDBB032hztDi0xVsqy8mS7RQI5irLohgpk92Z35wNrJfHXv1X6kcs5EgqbLXD9rUF32dzIqiNx2w97w61//2qyT7mwY1kib1cE5sqbxvLxlsJi3ZI40Joxn97xFdmYdv+2222LKiLEgI4bMYjesbYxR93WTAcKz9coHjG/3M29peOVX9qt//vOfIfIT84oMLWRh9iML+6/N2klWzk6FDJgINvu9LqQia4brIBuHjBSyipFPkesoA3zDDTeYsp/efYC1qrFLxSmKojQ06tRQFKVVg7EOxRTFiPRzv34FXihBQCp4LMNdvCD0X3/99TFrzLvxCrP1CUYFL5Sf8IP3vQYMv+978atrTfkeL94yO9ZAjaEXw0oidWXjuYc4VzBgxlJeMOy46/VTt5y6txgoKO3gLb9w5plnSjK0bds27L261OdvyHtZlzrhPAdb4xuDF8Z/SrTgGMIYRJkner6g+PmV68Cg5HYE8nwwnuFso7wEL5ycGHn87nEi8DxQOB955JGEnk2897AucyVZKBHjB+PeWxINB1csvMZbNzh+3KWooq05WVlZpq6016jKmmOdGpTd8UIfiHjx+z71rXnFA6Uz7F6BccI73yjfxzi245B/U9aqf//+vsdjLFNmxcLeRckfxrKdCxiUmQ9+ZT/8rodeC7zivR5roOX/MUa5HUDdunUzxhh7PcxJ5nAiRulIPPHEE2FlEP3gGv2uEzBWNbZTw2+OEBTgBUcVBtS77747JcfDQE4wQrw9fFKx9mBk9paEwUjp5+DCMejnoP3mm28SGj9+98OWyfHCWIjl1EjlnIkEDkivQ6MufTXcZeGsU8NdVor9E/nX7dSwc9jrwGkKRtL63Pf8ZFRKTvk5lNjzcHS4gzsoP+Uu39WS4Dq//fbbkPcIAvM6GiKBfODndE9Ezk6FDJjoWIu31059gf7gN765Zl44INnnvQ5qHFBNYb4qiqI0JOrUUBSl1UP0L0YhIo9QPulXwf+j7EVSkIh2TIVT4/777zcRf4kST2ZJqvCrUUtNYz/83o+nuZ878tyCEuPFHQEG1DdG4YkVyZrsPcQo59dHxQv10d1ODa6ZBpIYpLwGJAyxGBiSwa9WNEYjoiu9ihwKj42Q8+u34qW+72WyEJFJ5Km7GTrXS2SlN7qScUR9e2qYuyMs//SnP5nof3eUKsogxh63AQ2jEr0q+L1kMmkwJGE0pL50fdxDjCzcj2TmSl2INN/96qDH45yhrn0kMKh5zx1jJDX8k1lz/JqqYwCJl7rW6Hb/PsZ+5qS7PwdGIyKFvT2EMM7QKJl+Rm5wYkycODHkPvNvIjRtDXJ7z0444QQzlt0G4VReD4YV1ji3I9v2R/E6pujlRHZMJId4c4U1J9HocLJz6jLH6vt4qdyn/cYb5xDJ8JisDBHrfkS67njuRyrnTDJrYiJE66vh3uvY6+kX5zbsM3dT0U+jPkhWRky1jMtv4nzzZgJxjJbo1GAu1UWWYEwhW0bav+ORs1MhAzY3/BwabsiSO+uss0xmTGNl8SuKojQV1KmhKIqyFYRqlDerwBExTCkVjFBeQ6+3QW0yYIjCaJUMkRrA1gd+v1XXSCgvfgpPPAoJik4yRvh472G8UfsY94i4djeupDSPn1PDNllNBr8m7xjnMSB6U/cxWFijBcbOWE6N+r6Xfoqxn/HJD8r6kElBxDIGXW8DaQtR3HyWyElKeVgoU0C0ISUinn32WdP42g/mPAZafoPo4ERLCJCdkYxDI957GGnc1LfyHmm+J7s2RJtXDbHepPp40fBm62CEwKBoneiRGtMTMU1GFwZRyh5aiNKkISrzlUbdkQy+NE8lI4mxTOS6nzMsFdfDuvLYY4+ZJrv8TqRxTMQuJcxefPFFUzamNeN3j/yyCCCeMpepPl4q9+mGmM+pXsNSTTwZe3XNEHSDDOt2arBekIXsdliwBiEzME5YK4iCZ8/zOjU4L1tKrDFJVkaMh4YYo3WRf5o7jP9oMm88Y7+uMmAiUNLSm4GaKMiOF110kdQnfoE3DZnFryiK0lRQp4aiKEqkBTIjwxirib5CAXSDElhXcJh4j0Mk/gMPPGCUUpQADBCUAfKLSGsoOCdvTXmMwrZ3gJslS5b4fr++oISQX2mbP/7xj6Z8izXsUFKJ+vuJEq9iy3OipI3bSfXuu+8a5csbgZ1s6SmgpA7R+l4jCWOG0ixN5V76Gc785kw8pWQslBniRUbDtGnTzJjECYND56OPPgopt4GBlfN0R+GiOBPlzgvnE45Jvk9psAkTJoQ4pHAUUZKEDK663kOcWDfeeKOJRrQlGzhuIiWQGptITiBq3XuJx2EXbV7Zdc9tBOKZYwDyO3asNYd/e8cZz5wySfHgt34xD/3WPz/8HGNkUPBinOEIoPQgY5Fo6UmTJoUY2egjgWOdDC93FCdZfjhG+C4l7uxYJmPMHRnO/WG8MR8iXQ8leBifyThWeVb0V+GFQYXrIXoWoxKGVbdxiOuil0qyTo1oGRHsmdw7oPwbc66pQqR3PHMp0vj2O563fCXHi3ePrk/8xhvn4JddaP8WzzGiEe86Ee2+x/r9uswZP1JpRPf21cBx+vHHH4eU5SLggTJVnJudNzg0vE6NptBPo77xe76R9jzkcb/MDvecrg/5p7Hw24/RiRKRY2OVqop37NdVBkxEN3v66aelLlC+rL6dGmTA1GePPUVRlOaCOjUURWm10IDzuOOOk65duyas8PhFvfopfpGicCMpTZTJwdjlJlJt8Lr+frxgGKEkl7chHfcunoal8Rr/ksF7D2mkR4aEV6mM9x7WBcqRYUyzdX+5997SMWQMeBsMJwKGTZq13nPPPWHl0MgKOfroo5vEvfRTrPyMR/SxSaa8Bj0HeFmIFHc3mkcBJ+o0kkJLVg0vC1F/lCRyOzaSGTN+cxrl2Gtka4jxmEowlPuV20P591LXqF6cyTQj9tbxZm05/fTTQ97juWHMiLbmUGfbazzCyB9vY3u/9Qujjrfsgx8YbqNFguNow/jBy0J/JXfzWxyYZG343VeMQYxbdzktarsz7mlM7zfe/K5n++23j+t6WNOiGThZ36zhyXLaaaeF9DzB4dFS68/HC8+SjDE3NH+lbIobxo6fo9TveF5jNMfzNjRmTPB+Q0KZFAy+bkOwzRrw9tXAKebX38DbkDsWfnPls88+M4ZlbwbL+++/H/N49Tln6gO/vhr0vrH/zX5kHTI4N6xTg8wvr+OwrqWn6ksuTSV+zxeZl/mSn58fNl6850/2nHs9S7X805j3kN8mowd5ykJZ3quvvjquwID6GP+pkAGbMgQCcG8pjRwJ9gZKqvqtS4qiKK2N5HOQFUVRWoBTA8MmxjqUOj8lASXw1ltvDXt/5MiRYe/5KTIYoyLhjry1EH3kNZLGG9Gd6O/HC80kvVDWxGtMpIG6t0k4Rre6GNpj4b2HGADdfRMQ/HEAkDFR32Ao8PbKoJeFG5oPYmyoC2SDtG/fPuQ9rvP444835ZWsU6Ux76XbYeA2jLudBkSNxpMJwRw4++yzze+6jTSxapa7G50TtUpke6RoXa7VbQT2fj9e4pnTL730Up2jABsaSgzRI8YNDi+vcwbjBT1F6mPNue6660KeH+MRw4r32ZPF4I6M9jqJ7TOgyS/lxtxgCOFa3SXjbNNrr4OFRp6RxiNZCmT64GxwO7q4X/RQ4v8j1SmPNZbJnHryyScj1vnHYeAdu+7/xkjpXT+Yh4zJSA4YytfgsPXWtcfgznPh735wjX7lMJKZWy0JP8c2mX3e/ZM1y1szPt7jkbnnnZ/0NGF8NCTIAF7nCjAP3OfCXLrwwgvDyspgcE60yTxOUW+wCr/FeuEe4+xByIGxSOWcaQhsXw037sAUMs1sdLw7C9nrSE6FU6O+5NJUMnz4cNNs2g2GezLk3HoBpf7cpQAt3vHtJ//gMHKXmyIgAJk52XtIhkKk/SfVePdj1nXkzRUrVvh+nnv38ssv1/YnqyupkAGbE2Q4InOwRnoz8Oz9JbjJr0RWS+tZpSiKEg+aqaEoSquGyD2Uf14oDkQE0iCQdGmMRjg7/GqWn3zyyb5lRlAU3UouTdtQGkeMGFFr8MQoxW/4OUaeeeYZk1JNZBSNCImqjLcJc6K/Hy8owIccckhIVDbGwHHjxpn3iQzCiIeTw6vgY1BEYawvuIfu5rgoWzxDjDxE2CH0p6L/SbzQMJysiUh4MzeSgahXjK6Uv3ArePwbhwdOOCJgbYQaiqc306a+7yXPnGwmmj67U+VHjRol48ePN//GSB6PAwYjF0ZcXpwHdZUph0WUO3/DQEJvAi9uIwWfIVocJZHxShNmIuHoo8M8pzSH1/jqNXLEA/fQe49QMrmHOL0wdDW3LA1gXuOcZM5jsKHMkXusWI499tio0YXxQtkGoqDdhnscGjgYuJ9ExaL4+xnTKW9EtoeFjDKei9dgh4GXHigY7RhLrPM00l2+fLncd999IZ+lBNRJJ50U8t61115rjM5EizKWGMvMNZ6/15npjlDnO7y4BsYy95P9hr2I9dpdIgbIlGK8Wxg/RF2z1mP4oK42GRI4S/l91mGvs8Y9lplDGHfdpfIw3FFqjuvkXrHGcD7cc+5xpL4dPB+MVrz4DtfD82cv5VqZl16jPNftV36pucIa582w8MMd1c8z8+6pzDGMh3ZPpaRYPOs2MC9xoLnLRDIe2QdYe3r06GHGTWM1kaUMDLKNe71nnBPhzvmxDrMGu53olmTKiDH/cZDgVHBDuTZkOuYsY5c9KB7DZyrnTEPh7avhxu3IQL7zlhdKZT8NnrEX7iOZOqybrGM4opPtL5cqGGcEnXgd95wnmXTI4W+//XZYyR/WbmRq7/1lDLrXYcY28hRzlf2BII94sy1wqLHGu2UU9AMcV8hUtjwtjaOR9VMN+/E//vGPEMcBeyUOO+4N/8/+w1rIGoR8YGXTRLOs6ksGTARk+GhyfEPAWmllBXQ71hjGAesLfdv8+rHwOe8YVhRFaRU4iqIorZQddtgBC3zCr/3228+prq72Pea+++4b8/tTp041nw0Gg86IESNifn7cuHFh7/Xp06fOvw9PPvlk2N9vuOGGsOOuXLnS/GYi92nQoEHO+vXrw4515plnhn124sSJYZ/jPe/n+K6bV155JeZ5ZGRkOAcccEDY+1y7mwULFoR9ZuzYsU6i7LHHHr7nwXhLJW+88YbTrl27pMaw37Wl8l7CNddcE/N4nTp1ijm258yZk/C1jRo1KuQYp556asLHePfdd0OOEc9cYW6lpaXFPPYhhxwS17yLdW8SnVPx4HesYcOGxbym9u3bO0uWLAk7HuPM+1nmWiwmTZrkZGVlJfTMTjzxRLOuelm4cKHTvXv3uI9z3333hR3jkksuSWqeua/1ww8/TPj7J510Ush5jB49OqHvZ2ZmOj/++GPIMdi/jjnmmKSux82jjz6a8Pevuuoqpz5wjzO/uZQK/NaAZO6bXdcKCgpifs9v7vld32effWbW52SO57d+++33fnAu8Rzv+eefdwKBQEL37E9/+pPvb8azLpaWljpDhw6N+Ru9evWKa99P1ZxJ9XodiQ8++CDi+Xz++echn91pp518P3fkkUdGPH681zB37tyYz90rG8W7Z8Qrv8bLBRdckNBzTU9PN3KYHyeffHJS8k8kmfP000+PebyXX345Ifk5kec4efJkJycnJ+Gx730eycjZqZABmxPIH4leb9u2bZ3vvvuusU9dURSlUdDyU4qitFrIhnBH9MYDKdeU3PBrBAh33nln3E29iVAjJT1aaQUiCl988cW4zy+R308ESjkQHeSulR4NoqmJEqzvuulEtl5xxRUR/052CtF23trd9Z2t4UddGoT7cdRRR5noc+5BpPHoB5897LDDTKR5fd5LegO4ax57IULd1vJOZQNVIkuZV8keg4hDymyRCZMoRCUSkR3peXAeRLX7ZXo1Zbgmvx46FrJQiDxNZf3qMWPGmDr47iyFSLCOE+n7/PPP+z5rIkmpr12X0lhEqvLy1liPBlG09KZJdiwfeOCBpuGpm0SOwblSKpDIVjeMT8qDsAbEWw6P73A+yZ4LEEV60003JfSdlgpRtUR+E20cLUL6sssuC3vfb4+nNwJ9Osh6iPT8kA/8SrvVh8zghTWPzIh4mn7T94JsKc43Wbgm+h9Ei9Ymqt1dai4aqZozDd1Xwwvjwxs9787cSGXpKWD9vvjii6U5QCky9udIc8gN2U9kWiGHRdozo409SqTF0y/HQtaRtwRaQ0JGD9kQZEvGC5k4O+20U51/OxUyYHPCZjsmosuSOcOYUhRFaY1o+SlFUVotGHsoR0AZFYR1yoaQIk6KNfX1UUgxOGB8oNksBplYTa8x4lKmh3rqGOQoBxKtvAFKz/fff296IaDg0NAWQxTv83u/+93vEurBkOjvJwIKCuWwKImB8RAhmuaHlBkhBZ+SVhhWaAwbzZidarhWUuBJ06bJKGUCOFcUcgxCGC6SKWGRLJSpoRyBu1QAvQZOPfXUlP8WZUroAUD5L5oG4iSgBi+p6fSJoLY2jiUMC5Q8wVCMYdfbuLo+7iW/PWHCBGMIpiEm58h9YGxTloxSUHwmFpw785Kxh2ON8gaUDuFlG3niGESRpfwRRjtvY8pHH33UNJlm7FKChfIPPB9ezHOMBZwX104JB7+a2PGCAQcl8+677zZzhFruGPKYE/yNZ9DYpQ0ShTUI4x/PEccWfUKY98x5ylJRmqU+SgrRG4PnjYHijTfeMGOSsi6saTwzSpswNmkQGqt2vTVCUYaH9QunK01xN27caMYQ45yyZJT/YRz5wZhlHFFLn7FNmRnmGueDEQKnDsfA8Yfj0OuQwcA5Y8YMUyKKdZo5wThmHFJugmNg0KDUBHOEY/j1XqBUD+s7JXxY4zkGpT94TsxtShHhgKY8jre3gNsRRENyDOeU9WAfpIwHx6FsCs4YzgUjFveYc+EeuqHOOWsC58OzmT9/vrkWzodjsC8wl+z+Gcl42lphLeCe33HHHcbgT+kzZA6MzjjHKc1EeSMvlKKJtPewDuIMYL1krjAe2JdZv1mDeGbxHi/VcD3MOUpR0ZcG2YfxQhkeyhWyRzFHmM+pWE9Yn5gjOEgIDmEfYdyz1uNkYexzz+MlFXOmoWBvZf1kD3LD2uI12jM+/v3vf9eLUwOQAfhd9j32DvbESD2FGhOM55RKQwag8TRrPPsPz5dnz5jkOgjaQZ6L5vxgz2evYS7SO4Nxj6ONscF3zzvvvIhlCv1gL0F2QVdg/+C7lDtrSFhb0JOYu8ibXB/zhx4PXBvrCHMLPQk5k30wFU3CUyEDNic4f9YO5BX6iLBOcv2MQ8ptsc4ga7CvEmiHzJKo40dRFKUlESBdo7FPQlEURVFaEhil3DX8qaNMJLuiNHUwhHsbmaNYp8rApShK/GBE9fbCwIGUTK16nG84mdwNfjGGUZ8/ngwKRVEURVEURWlKaPkpRVEURUkhRNF5mxL7RccqiqIorRcy+mhE7BdfhgOCzCCvQ4PIfzLB/CD629to3kJGElmUbocGEO2rDg1FURRFURSlOaLlpxRFURSljlx66aWmnAIlAUjNd0NafKRyNoqiKErrhNKB9957rykRRuYFJUUom0JZR8qLUW7Er7Z+pJ49lNnjZUuYUVINhwllLSm9R6kWL9rjRFEURVEURWmuqFNDURRFUVJQMzoS1OBOpC+KoiiK0nqgjxdNw2NBPxf6TcRi8eLF5hWL66+/3tRjVxRFURRFUZTmiJafUhRFUZR67E9w5pln6v1VFEVRkoImvLfffrtpTp2KhrDt2rWTxx9/XG655RZ9IoqiKIqiKEqzRTM1FEVRFCWF5Ofny0477WQianFqKIqiKIqXSZMmybvvvisffPCBzJ49W1avXi3r16+XvLw86dSpk9lH9ttvP5Oh0aFDh5g38JdffjHHo6/T/PnzzfE2bdokBQUFpm/GLrvsIgcddJCcfPLJZp9SFEVRFEVRlOZMwPHrTqcoiqIoiqIoiqIoiqIoiqIoitLE0PJTiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoiqIoiqIoiqIoiqIoiqIoSrNAnRqKoiiKoiiKoiiKoiiKoiiKojQL1KmhKIqiKIqiKIqiKIqiKIqiKEqzQJ0aiqIoiqIoiqIoiqIoiqIoiqI0C9SpoSiKoiiKoiiKoiiKoiiKoihKs0CdGoqiKIqiKIqiKIqiKIqiKIqiNAvUqaEoitJEuPHGG2XnnXeW5srpp58uf/3rX6W1sPvuu8urr77a2KehKIqiKIrSIli3bp106dJFFi5cmPJjf/LJJxIIBGTDhg3SUjn++OPl3nvvjUvn+M1vftMg56TUjL2+ffvqrVAURUkx6tRQFEWJAUI/ShCvjIwM6d27t/zud7+T9evXN+i9Q8Gz58GrTZs2ssMOO8jvf/97mTNnTsLHQ7j++9//npJz+/HHH+Xtt9+Wiy++WOpTIRg/frx0795d8vPzjQPoueeeC/uM+x7Z18yZM0M+98orr8iwYcMkOzvb/P9rr70W9nv/+te/pF+/fpKTkyMjR46Uzz77LOTvf/7zn+Xqq6+WYDBYT1esKIqiKIqSenk2MzNTunbtKgcffLA88cQTYbKMV0Z0HEf++Mc/Gtnz448/jvlb8+fPl5NPPll69Ohh5KjtttvOyHCzZ8+O+r3bb79djjrqqBZlAPbK7/b13nvvhXxu0qRJRt7kfm2//fby0EMPhR0rlvz6l7/8RW677TbZtGlT0ucbSZZ2v5566qk63YsffvhBGhrGFgFJjGEcZ8ccc4zMmjUr4hyxr1GjRoV8pry83Og7nTp1MvrI0UcfLUuXLm0wfZO5wWdefPHFsGOgF3qfz/fffy9HHnmkuWbGFt8/6aSTZO3atVHHJ68pU6YkdS1VVVVy/fXXGz0qNzfXjOebb745ZJ1hTcG5xhrBZ/bbbz/56aef4v4Nrp9z5DkmqsO5Of/8881xvDox7/fv39+cW+fOnc365dUnFUVpfNSpoSiKEgeHHnqorFixwgh+jz32mLz55pty4YUXNsq9++ijj8y5TJs2zWRG/PLLLzJixAiZMGGCNBYPPPCAnHDCCUZRqC+++OIL2WmnnYxChxPl7LPPljPOOMM8Cy8oKdwj+xo4cGDt37788ksjzJNZwj3k/0888UT56quvaj/z0ksvyaWXXirXXXedUQb23XdfOeyww2Tx4sW1nzniiCNk48aN8v7779fbNSuKoiiKotSHPPvuu+/K/vvvL3/4wx+M0RNDpB/V1dXy29/+Vv7zn/8Yh8YBBxwQ9TcqKiqMswTDOhmtyGTIVcOHDzdyUyRKS0vl8ccfl3POOadO18jv1xd1ObaV3+3LfR8XLFgghx9+uJE3kTuvvfZaueSSS4zMm4j8ipyM0dob9JMIe++9d8h58ht23NgX59HcwGlEIBiG+g8//NCM90MOOUS2bNkS8jnvtb7zzjshf0c/wJmEUf3zzz+X4uJiM3+YJw2lb/bq1UuefPLJkPe4rpUrVxpHi2X16tVy0EEHGQcM+go6I05MAsRKSkqijk9eOASS4c477zROOfRDfvNvf/ub3HXXXXL//ffXfob3yCriM1OnTpVu3bqZdWPz5s0xj79o0SK54oorzHzxEo8OZ3n99dfN/MGx4oVr5x5z/tw7nDCMl1Q8Z0VRUoijKIqiROXMM890xo8fH/Le5Zdf7nTo0CHkvSeeeMIZMmSIk52d7QwePNh58MEHQ/7+pz/9yRk4cKCTm5vr9OvXz7n++uudioqK2r/fcMMNzogRIyKex4IFCxyW7e+//z7k/erqame//fZz+vTp41RVVZn35s6d6xx99NFOly5dnPz8fGe33XZzPvzww9rvjB071hzL/YK1a9c6v/71r52ePXua8xw+fLjz/PPPR70//H67du2ct956K+R9zue2225zzjrrLKegoMDp1auX8/DDDzup5PDDDzfHt0ycONFcy/r16yN+58QTT3QOPfTQkPfGjRtnrtuyxx57OBdccEHIZ3i2V199dch7v/nNb5zTTz89BVeiKIqiKIrSsPIsTJgwwchOjz76aIgMd9999zllZWXOscce62y33XbOzz//HNfvIKdyvIULFyZ0fq+88orTqVOnsPc/+eQTZ/fdd3eysrKcbt26OVdddZVTWVkZItP+/ve/dy677DKnY8eOzpgxY8z7b7/9tpG7c3JyjJz85JNPhsmIkydPdvbdd1/zGa7x4osvdoqLi0Puwy233GLuXWFhoXPGGWc4iRJJfvfqCMiZbs4//3xn1KhRCcmvcOONN5prigY6B9eUzLgJBoPOnXfeaXQZ7ttOO+3kvPzyy7V/Lyoqck455RTzLPn7gAEDjI4EXt2DZ+eHlec/+ugjZ+TIkUYn2WuvvZyZM2c6qWL16tXmNyZNmhTxWr1s2LDByczMdF588cXa95YtW+akpaU57733XsTvcT2MpVTomxwHfQR9c/HixbXvn3vuuWb8tm3b1ox1eO2115yMjIyQ+ZLM+EyUI444wjn77LND3vvVr37lnHbaabVjiLl8xx131P6dtYZzf+ihh6IeG1139OjRzmOPPeZ7z+LV4ZYuXWr03RkzZtSud9GYNm2auU/o2IqiNB00U0NRFCVBSKknZZzUfcujjz5qIkJI+SaigwwKyhM9/fTTtZ8hi4F04J9//ln+8Y9/mO/cd999db7/aWlpJsqOqJVvv/3WvEfUEBFfRN0QpTJu3DiTzm+jVIicoxQAqcA2GgfKyspMZMpbb70lM2bMkPPOO89EgrmjwLyQNUF94t122y3sb/fcc495n3Mg0og0anfqLmnSBQUFEV/8PRpE/HXo0CHs/V122cVEIR144IEyceLEkL8R6UakjRvuD5kgNgqP++j9DP9tP2PZY489oqY0K4qiKIqiNGXIGCDj19snDFmSrFRKwkyePFmGDh0a1/Eo1YJs+t///jehqOZPP/00TJZctmyZkWcpG0R2wr///W+TzXHrrbeGfA55m5I9nOfDDz8sS5YskV/96lfmu5Q6IvuDkqFupk+fbuQ/PocsS4Q3kfcXXXRRyOeIMCfLBNkQ2T5Z+ZUyRZQAGj16tLk38cim33zzjVRWVkb9jJ9s+vXXX5sySfUBZYWIYOdZMDYuu+wyOe2000wmBHCP0HXIBEIn4nNkCgDn5c4KiNWbDt0KXYL7wPMlS9uC/B3tGfCK1uvPZg159QjKb/GcBg0aJOeee67JdrAwBnge7udAlD/jw/sc6kPftFA6jmdv9UyyLhi/7vsDZD+QkUJmSY1PKTkSvdf77LOPqSBgy80xd5lbzEebmURWifs+UlJt7NixMe8juitrDNljXuLV4SiDhX575ZVXxtQ1gWwexjwlrciSURSl6ZDR2CegKIrSHMDIj8CGcobhH9yN+G655RYjdKMYAUIPAj2K1ZlnnlmrBFhIDac2MQLon/70pzqf35AhQ8z/k66MMoNyysuC8odA+8YbbxhlDQE+PT3dOFoQeC09e/Y06bwWasYiUL/88suy5557+v42v8mxUAC8ILzatOmrrrrKOHFQFuz5ktJtlTU//AR5Cwoh6crcYwuOjEceecQ4ZlDmnnnmGePY4DfHjBljPoMQjTLghv/mfaDGLM852mfc9wtHEcIxCryiKIqiKEpzA7kMw74bZFvkRORZPxkvEshG//znP418e9NNNxlHBWWuTj31VFNbPxLIk94yMNTGx4hIiRrq3nOey5cvNzIl/SOs7DVgwABTzsZC+SZ+C7mT7w0ePNg4MSiL43ZWnHLKKaZUDVCqlPPGsIohnnr81unjlo0TlV/RH9AZcGZwvsjilG/CII0zIJpsikEauRT5Npb86r7/yMC836dPH0klGHe5FsqQ7bXXXuY97jMGa+Rx7h1yMcFF1kHl7o+CMRo6duwYon9EgmAxjgk4pXCyoYfxbDh+rN4cfoFPgIH/8ssvN8Z3HBIWyhRRTpf7huEdBw3PH0M5RnfuaVZWlrRv3z7mc6gPfdMNDgx0SRw/6ET0f6DfoBv6gTAXGOcXXHCB0RG5Hsr3escSZce8ugyOH3S8RO8185PvMl/5PtfDs6TPDth75TeeCdKLBE5LnJqRziVeHY51ACcZJd6iwfrDOsa451ooW8bzVxSl6aBODUVRlDhAGUPBIRKGGqdEntim2GvWrDERYUSMENFjQRFp27Zt7X8jcNKEbO7cuSb6jb8XFham5P7b6BsUN0D4QpFEOEb547eoVexXT9QNguAdd9xhnC1Ex6EU8XLXZ/XCcRH07W+7obavhb+jwLgjnpJVtnBS0FCPbBd3hA1KKy8LChfP5u677651athz8d4/73vxfIbmcTg0uEf8W1EURVEUpbnhJ+MQ3UxEPRHY3ia6saB3AYZTsmXJ9iU4huNg0KdufiR50joSLET6I8u5zw3nAHI0zZlppgzeDA++h0HX/T1rhLdgqEYmd/ef4D4g12HQtpkpfpnIicivZCmQzWDheDR/xgljnRqR5E7v+/HKpuDtmZAKcHBhbPc+QyLkcWQAWdnHHXecfPfdd2YM0cgZg3kyuPUIHDuAHsFz5zpxZiUDAV448XDGuHH3CsHZwbPiWb/99tu1gWt++D2HVOubXnDw0MyaDCf6ZHizNCw4E3Dg4Iii7wa9LpiLfG/HHXes/Ry6nzcbC4cEJHqvOdazzz4rzz//vNHTcELgPMRpaYP94h3PFnptMF/Q/WzmTySiHZd5T8UExmesZ4YjlrFOVhG6JP1lcKx41ylFURoPDStVFEWJA4z6CHMI10RxYcTGaQAoP4CQhdBmX5RvQngE/v/Xv/61iQDC0UA5JiJrUtXMEOXNZogA6bQ0F0SQJWWY80FwjfV7ZJsQ1UZUCsIv3yO9Odr3ECwRvv0+4820QHi09yvZ9H3S2ymlReQSCnMsUGrnzJlT+984VrzRVChINqqH60GIj/YZS1FRkeTl5alDQ1EURVGUZgtypJUhLWS64oQgAzaSYTUaZHlQcglZlPIzNOz1lo1yg/yFsd+Nn5HTz9jvDb6Jp9QO8ihGYbfsznkiMxL1HunYqSifGq9sSjQ5WQ3RPuMnm7qzIlKJleEx8rvvG84OW1ILXYdoe4zYBFYxjryZLvHi1iPs87bnkGz5KcYy4xqHG6V4o4EjBaeGfVY8A/Qd7zj1ew6p1je9MDYooXTDDTcYxyEG+EgwhshAQc9jruNcwEjvhowoftv9siR6r9FDyaxB90X/5Dxx7N1+++3m7zZLJ57xbJk3b57J5kIH5Np5/ec//zHPkn/z93h0OK7FOsbscRivZL24s4qA4EQyuAiMY3xTQpnKB4qiNB00U0NRFCUJECAR2olGQjAk1Zvap5EESqI6EIpxZFiipdcmAsI9gi/KqI2SQmAjk+HYY481/01EG4KgG9JnvbWO+d748eNrI8c4NoJ8tDrKNtUZhcab9hyLRMtPkaFx5JFHmrRh+n3EAw4kG91lI/VIH3ZHzX3wwQe1UWTcF8pX8Rl7/4D/5t64wXG16667xnUeiqIoiqIoTQ2CWCjN5JaLLEQpE4yDIRGZ0JaBShRbOipavXxkWKK73QwbNswE6bidGxwDhwmydyT43uuvvx7yng00siC/0RMimWj/upRPjSSbvvnmmyGfQTYlU8AeK5b86pZNMdbHimZPBu4r2dlkftuyUH7gUEEP4YUzCyM3RnRbuieRXiuRSLQkEmMIhwZGafQJrxPPj3Xr1pmMb/us0A94HjwHovaBKH7uubv8WX3pm17IzuC+kmHiLYkVCZ4BTjuy+uvrXhPs5i1lhbPBOqS49zg2uI9Wd8VZROCau0ScG9YP1ik3lHYmg4PMC5wy8ehwOFgOOuigkOMQwMf7Z511VtRrZAzVV68aRVGSQ50aiqIoSbDffvuZKCyiUlDwbrzxRlOXk3JSCJ8IPDS1I5KHlF8UJhSAF1980TQ7JMIp2UgPBGwiUBAYEaIpCUDjPY5p04T5PZrvoYSiBFIT1p0hAUSjkHpMFA0KCsoP30N5RGFEOCYbgt+K5tRAcUExJIU7UadGIun7KCCkWtMUnbR2G4WDAGsFae4F18WzQThGOeZ6eFn4PhE3CM0IuP/73/9MeQV3CjrPDOEWIR4lkihFnh/1aL1OIG8zOkVRFEVRlKaI7bWAUXnVqlWmbxrR0wSMRMp+pQY/Miafwaj34IMPRnVsYPzEGIschREcOQ1jJSVyqLUfCQyL11xzjZGdrYGWvmzIdhijKRk0a9Ysc2zktGi9zJDXiErnc2RjUHLmqaeeCvkM50LGBKWyKB9LlDxR7BhA77///pTJr/TOwBCO8ZZzxnlBMJLbeMv5ok9wvpwLTcHpHfDCCy8kJL/Wt2yKM4msCxwr6BX0pNi0aZPRG4jWp7QQvU4wLCOLM95wilk9gt4slDJi3OF4oYyPu1RvIiRaEonnTDkk7hvXYfUIfp9jEQCGPoeOgRODYDD6UaAfWQM5n6XcMFH9ZD+gf3A/yEbwGsrrQ9/0wn2ljwRZ435w79E90fVofM78ZfzhlKPxtZ9+6aZdu3bmGSV6r9E/ydAiG4Lzx4mHTmlLZLF+kMnDdZEJwYt/cx30/7CwJuG8ZI3iPNz9T+z5gfv9WDocz81mP1mYnzhZbAljAhUpocU8Qs+lJDPzjvtgm50ritJEcBRFUZSonHnmmc748ePD3n/uueecrKwsZ/HixbX/vfPOO5v32rdv74wZM8Z59dVXaz9/5ZVXOh07dnQKCgqck046ybnvvvuctm3b1v79hhtucEaMGBHxPBYsWEAufe0rLy/PGTp0qHPhhRc6c+bMCfvs/vvv7+Tm5jq9evVyHnjgAWfs2LHOH/7wh9rPfPnll85OO+3kZGdnm+PBunXrzLVyjl26dHGuv/5654wzzvC9fjcPPfSQM2rUqJD3+vTpY67RDdfHdSb7HNzXb19cl+XOO+90+vfv7+Tk5JhnsM8++zhvv/122LFefvllZ/DgwU5mZqYzZMgQ55VXXgn7zIMPPmiugee56667OpMmTQr5+9KlS833lyxZktT1KIqiKIqiNBRuOSojI8Pp3Lmzc9BBBzlPPPGEU11dHVOGQw5CPjz//POdYDAY8XfWrFnjXHLJJc7w4cPN59u0aePsuOOOzt133x32O16QJZEp3XzyySfO7rvvbuSxbt26OVdddZVTWVlZ+3evfGt58803nQEDBhg5d9999zXXybWvX7++9jNff/21c/DBB5vzzM/PN3LxbbfdFvU+JMpTTz1l5HXkdu7FyJEjnWeeeSbsc1znLrvsYq6zb9++zr///e+E5dfS0lKnsLDQyPjRQBZnPCSjB/Hs//GPf9SeB+No3LhxtXLyLbfcYq4XHaRDhw7mu/Pnz6/9/qOPPmp0k7S0tBAZ3s3EiRPDntX3339v3kPHSQY/HYLXk08+af5eUlLiHHLIIeZ6uK7evXuba7d6nvseX3TRRebauMYjjzwy7DN+18NYSoW+GWtMolvaa5o3b55z7rnnOoMGDTLn2q5dOzOX7N/99Ev364UXXnCSYdOmTWZOcg/Rybbffnvnuuuuc8rLy0PGEeOQOc0cRW+ePn16yHEYH9HGaaR7FkuH8+K9p8uWLXMOO+wwowszFrbbbjvnlFNOcWbOnJngnVAUpb4J8D+N7VhRFEVRmjc0DSS6hWggbyPGlgqp9Bs3bjQRQIqiKIqiKErdIIKcyHcykaNlYij+kEVDJgJlqaJBRgKZCN7sFaV+INucUlzeUsCKoihK3dDyU4qiKEqdISWYZm2kQLcWSKFPtvGhoiiKoiiKEgqlXejlRrkXauQriUEZnVilsxRFURSlpaCZGkqrg/qx1EqdMGGCrFmzprFPR1HqFZLxqqVayMlLCwQkLZAmAUm8wWNrJVibrc69i/++OeJItRPc2thSJF3Sk2qsqbQsiDqlxi81evn/jAyNLVEURWkJVFVVydSpU42OQU81bx+zlkRRUZHp5RaJQw89tEHPR0mc2bNnS2lpqYwYMUJvXwNAv4pp06aZ/jSKUh/Q+4O+Lry6du2qN1lpNahTQ2kVZXFomEYaLorG9OnTTaO2gw8+2DSvUpSWyubKzTJ/y+La/8aknpOeLQML+quBPQY4I2YXL5b1FZu33jlH2me1kUEFvWPeu+pgtczYNFcqg1XGuVFz7wOyQ9sBkpuenYpHq0R4Zo4EcT812fFNw0yaf9LYkyatBx54oNmLaI7ar1+/xj49RVEUJQEWLFgg77//fm2wVHp6ujEoUYYzO7vl7vcVFRWyYcOGqJmsStN3apSUlMjOO+/c2KfSKlCnhlLf0AydvYim7DSuJ4AKHWPfffc1FRUUpaWiTg2lRYKgTU3W119/Xd59911p166dMRqxsGNE6tSpU2OfoqLUO9M2/CSLSpbUGtYth3TdT3LTc/UJRGFF6Vr5YFV4FOLBXfeQHrnR14/VZUUyYfWUsPd3aTdEhhRu36Tve0WwUmZuniclVaXSNrONDGrTT9ID6dLU2Vi5TNaU/2KcGhmBHOmes7PkpBdKUwWHBkqHdbZPnjxZhgwZIsccc4x54Xhvqo4ZRVGU1uw8Z+1Gv+A1c+ZMGT16tNEvMCCxduPYUBRFUZTGgFLQONnRMXhhFzvssMOMfkF5P+xiitKSUKeG0mKg9iqN0VAyaMY1bNiwWgMRqbVqIFJaGzM2zpT5WxaGOTUO63agZKVlNdp5NQfmFS+Tz9dOC3t/n04jpH9Bz6jfXVe+QT5Y9UXY+yPbD5NBbfpKU6UqWCUfrZ4sxVUltWOmS3ZHGdNpjya9fpZWb5ClpaEOqPRApvTN21fSAs2jvBMN53HAs3/hkG/fvr2MHz/e7F9jxozRMlWKoiiNWFbq008/rXVkYCDCMMT6jKGobdu2+mwURVGUJumIp+yZ3b9+/vln2W+//cz+hZ7Rs2d0nVZRmgPq1FCaNfTE+O9//ysvvviifPHFFyZayi7SWspDqSul1cVSVl0s2Wl5kpfRdKO+I7G5qlgmrZksQXo7bH1vu9weMrK91s+NxYaKzfK/5Z+FvT++x77SLqtN1O9yvz9a9aUUVWzc2o0jIFlpmXJY932bdPmphVuWytT1P4a9f0DnvaRjdntpqqyrmCdFFfO39j7ZRq/cPSQnvflFI1GiauLEiUb5wFFPXfYTTjhBfv3rX8vee+9t+nIoiqIo9QfrLhl06Bcvv/yyyb6wjub999+/RZeWUhRFUVpuyUQbBMweh15x8skny3HHHWd6cihKc0SdGkqzgwgpFmIUDVLr9txzT2PsOf7446Vbt27S6ghuEnGKRCRDJK2bSDOJTG7qrCxbIMtKZ9f+d9fsfrJd3qAGPYeNletlWekiCTrV0im7q3TN7plwxPzGyk0ye/M8qQhWSKfsjtI/v6+sKV8jlU6FtMtsL4WZzc9ZE29kSkl1ueSkZ0l6IDkj8JzNS+TLdTNM1gKOiVEdh8ugNr3iLuP044bZxrGRn54rO7UbJG0y86UpM6d4ofyw4eew9/fttLt0y6mboMs4LK0qlTaZBZKfEf99qAyWy/LSuVIeLJHc9DbSI7e/ycJws75ikaytmBX23T55oyUrrWnf83jKVNET6oUXXjAO/Ly8PDnppJOMArLrrrs26QwaRVGU5iY3fPvtt0a/eOmll0wTZXQLdAxqkmtZKUVRFKWlsHLlSqNboGN8/fXXpkQ7+92xxx6rGYhKs0KdGkqzaUhHSY7//Oc/5v932GEHs+ieeOKJ0qdPH2m1VK8QqXaXyMkVydxLJNBwpYUcp1qKq36WCme1iUnPTesjuenbp9zYVtMEuFoCkl7vhjwyNH7eNDns/cFt9pCCjIaJWN9QUSTTNoaW1OmXP0h6521fp/JCU4u+kk1Vm2rfG952J+mZ27JST5eXFslbK76V0uoK49A4oMuOMqxwu6SOVVZdLsVVpVKQkWuarLdklpSskilF34a8lxHIkMO77SfZ6VlJz9ufNv0iC7Ysqn1vx7Y7SN/83jG/W+1Uyc+bvpCKYFltFkZeeqEMbrOnpLkcVdVOpSwu+UKqnIrazxWkd5FuOS2r7GBlZaVpMI7B7bXXXpOuXbvKaaedJmeccYZmJiqKoiTJ/PnzjX7x3HPPyapVq4xBBx2Dht+ZmaFOdKVl4jhBIz8EmkEPMUVRlFSzaNEi+b//+z/j4KBEFSUW0S/4/6wsLVmtNG3UqaE0+WZ8Tz/9tDz//POSn59vFtdTTz1VBg8eLC0Wp0qkZIZI1WoRIpJzhopk+WSgOI5I5UeY9FxvBkTS+ohkDGmw091cOUMqnBUh7+WlD5bc9NhGy2jPnhIsZWVl5lVSsUHWlc+QiupSEScgvTruLFnBTrV/J5o5IyPDKJ/8v325/5tSAfGWbVlfsVLmbwnvp9A7b5h0zu4Vs2RBUVGRadLFdWBU5Xd5UTcfw2Q85zF/yyzj2ADssjxuDLk7t9vTHJMazh07djTNvuKNHpxfPE/mFG/LPjHHloAc2PXgZtEMOh7KqivlyYUfS2WwKqQY0Um9Rku3nOZXiqihWFe+WV5aMlkyAkFpm8VYoy9FuozptLt0yu6Q9HFXl62Rr4q+CXv/wC5jJS8jL8Y5LZeFJdPD3h9YsJsUZnYMea8qWC7rKxdKlVMuOWmF0i6ztwSSzNBpDhBBbB399OLYa6+95De/+Y2JKm7TJnp5NEVRlNbO5s2bTVkpdIwvv/zS9MawBpzc3NzGPj2lHkFnYA9Ff0DX2FwxT7ZULjalQwtyOkuH3B2lskJq/47M7tUpvP/m1RBBFJz36tWrZcuWLea8rI7BNVHJINHylPac0Vcs6EsdOnQwOgbZoYqitC5mzZplnPzoGKw1p5xyipx55pmyyy67tKhgMaXloE4NpUmmwrGQPvXUU6buH7XEWUhpltoqaokXTxGpXBX6XsE+Ih4jnjiVIpUTwr8f6CqSuYs0FOsqOAcinLaREWgnbTN3j9p0kedMRByCOMoAjap43sCGmZOTY15Z2ZmypvIbkUCVBLY+/+LNJdKjzW7SLr+rEb4x6nNM9wvngfu/UUxwOAD/JsOnd29/x0tJ1Sb5ZfOXYe8PyB8pVZtrzg0nm2X27NlGmUApYIyiDHTq1Mn8m9/kfa4TY2O80Q7TNkyVDZXrQt90REZ3OMgcc/369cZxgnKzbNkyc+xx48aZc4vETxtnyLLSpWGNw/ftFNvA3FxYWrJOXlk2JeQ9xK/RnYbIyPb9G+28mjrvr/xBZm1eXjs2uGcZgTT5Xf9D6yTAziteID9vmhn2/p4ddpMuMUparSlfIotLwsth9c/fRdpldUn6nFoarAE4/jHOsRb96le/Mg4O6r63ij1TURQlDpDD6FfEWvnqq6/KoEGDjH6BwaZLF91TWgLI2ziskIv5fyCrEWcAcjP6Ai8cV8jL1WlrpCQ4X9LSAkbHKC0tl/RgG+nWdo8aHSQryxzTrVPwb8aSfY9qAvzb/j7OEHTWRGUnvsc5o0PYYCXOmya/di/nnBirBQUFRhewOgZ/LywsTFpeM9nwjmOOx2+iX/Bavny50W923313GTZsWFLHVhSlecJ68OmnnxqbHGWqtt9+e7NnEmDcKku+K00WLb6vNAkQDok2ffTRR83/U7v2yiuvNMYZBLdWQ7Ay3KGBebFiSbhTw0xfyuGUh76d1rA9EgKSJo7HqUGJqMWLFxvHhR8I62yGI0aMMFFOCPJLliyRkSNHmv92U169UQLlbUPe69y1g7TNzJN2mZGVUBQDBHEiDFA4GGMI7AjoODUQ/COdH39bVbZZ1pUtk+rqoKSnp0lhZmepyp1nIpc4X47L5zguvV369+8v++23n3Tv3l3qAsLDjz/+KBsqi2RteehYyM9oI3Pb1zgwNm2qKSGFU4fIQpQgzgcBhHPjVVxcbF4oWyhw66VI1lavk7YdCiUjs+Y+E43fWGWViqu2yA/rfzTlsHLS6Duxg+n7URey08O3Ncz02WlaQiIaZdUVIc4u/lVpGszX9BNJlrx0f2dZbnrsSNjCjI5h60t6IEMKMjTjRkypjIBJ48LAcemll5oXxg8MdhjpMH789re/lbPPPlu22y658muKoijNHeTLJ554wryQ3yjbR3bGTjvt1NinpiQBMu0PP/xQm12A7OvONEBGJlAKIz8sXbrUNMAdMiQ8i319xQapCIbLnV2yu0XM9sSRsW7dOiNno09wPvw+8vaaNWuM3jp16tSY18FxbLYFsGdz7gsXLjSyPO9TBgZ9hh5a9JD06kiJwNh/7LHHIjo+OBeyv7keGwhGZDY6BsFnXCP6FNdpdYySkhKj05HRgZ7BPecYiqI0f1gTsG3weuCBB0wwADrGNddcY7IbzzvvPDn00EO135TS6GimhtKoYPhGyXj88ceNsGQNMC2mT4ZTLbJljgg9DNILRPIHiaRlRHdqbHzH82ZAJKu3SP7OPp/fIFJFaZeqrR/tKJIxUqQByq4EnXIpD86UyuA6mf7jHKmoqpaq6qB06tRWdhhwmEz9cpZ5jnU1plUGt8jysvD+Fu0zB0lhZt+w93Ey3HvvvUbg5vfHjh1rBHSUAgR6hG2i8+LJmNhcWWT6a+Sk50ubjBqngR+MXaKlUZx32223JK80/JgLS+bIkpKFxqjbMauzDGmzk1RX1jgtuI7vvvtOvvnmG9lxxx1r6z4jgKBcoFSRTcL/8zeufV3ROvngx/dk0crFMvrwvSVN0mTn9rtK5+y6NYFOtr/HxNWfSnlwmzGd89mvyz4JNZL2u2/005i/ZVWtMb5tZq6c3HtfyYo29zzH+HHjYvlx4yJzZsMLe8ku7fq26JTbb4rmyeR12zIquHddc9qasl02ag8lnvGEsSDe8hym8er6H2RF2TYH4sCC/jKkcFBc399UuU4WbpkhlU6ZZKflSb/8nSQ/I9TJ2aqorhRZ9KlI0byaunSdhor03jtszWfNozwVgQIffPCByeJC+UAJqYtRRFEUpTmAwZggqUceeUTef/99OeSQQ+Tcc8+tNdAqzQMyulesWGFke4zsBENR4pXyKMlkQ3jZWPGjlAW9AU5p0iX7QN9j40x5/fXXjZy9xx57mH5WlJZFxub/R40aZeSkeM/LlqmNBo4Egp2IkiaAqb7g/hIwhX6EUwPZAYeFO6udv3F9VsfgxbNBLyE7hvPkuSAnKko8Y475jH7BHCIwEAdkS9a3WgI4Xp988kljv+NZYb/j1atX9BLdilJfqFNDaRRF4+233275xhaihoo+FamoaaBtYp+JMO50gEi0/gWbp4hUxVF+qvZ3KkUcovYzRAKFNYauembdujUyfdbrUh0sNSnbPXp2ks5d2kt6ekeZP7NMKkqzTM8HMiGIPEJAsWnSCL+8bPo3AnG0tH8E/rUV06Skmvto80CypHvuXpIeyDKCNCVXbDo4ChDHtuWf+A2EJHc6d2NTFayU1eUrTIPjtpkdpTAzclRTTfQZSk+4o4prR7GKJPyRqTJ37lxzX2xWBxkyGW0zJBiolsLMtpKTHrlcVX2ytnydfLkutBE67FA4VPrm9TZOKJQrlETOm2eI4MuYQpkiGowx5qc8VjtBmbZhoawp3yQFGTmm7FROevxGjB82LJQJq2fU/vecL6dJ5w1p0q9NV3P/cCjZrBjOEYP/+PHja6MCmyPUkp6werr8vGmp+e+OWW1k/zZDZNHMuWYOcd3MISLziHpE+WBs8gzIUormJDROv/I1UlJdKoUZbaRjEj064lH8WwULJ4msnVXbEN3QfVeRnrsnFDzAK1L5PUVRlObc7JS1jjXPGlsIltL1rvnoiHPmzDGyH88PB9Tw4cNry8x+8cUXRu7gfWQv9AtkFKtbWF0DuZH30QWiBWFUBjdKUYWVRWv21YKMgZKf0c/sl4wn9ArOxWaAt2/f3ugu6Bgcn/JW/EZLklFwpNj77wf32D4nK58hIyKru8vzNkWQX61+wb8ZQ2TIoEuC1S+Q9evbLkFJNMr62L4tQ4cOrR3HPAM73oiUb0kwZnBM4sxAv2AeMWdxYJLtZEuqEZyoTo7mFTyATY/ggRZl01OaPOrUUBoMyv488vDD8uhjjxnh9JxzzmnZZTHK14gUfRL+frtRIrm94mwUniWSM8S/UXgjMWnSJOnUNVd69isKcxJkpw2RrLReRhhBYEQRQCFAaMToaWvZ2rJN/D/Pn8yJaDhOUDZVLZKK4CZJD2RLTrCHLF643AiDGPVJKcfYjZOMKC4yF5pqo8fKYIV8v+FLKQ+W1jq7BhYMl245dZsHKFukhAKK3gEHHGCExMGDBzcZZ4673vHspXPk6zXfyHYDeplxsHz+MineWCw903vImjmrjKGcsYGyiGDE80XR4D2cOSgjCPsY2RlfKFGpypJ5auEnsq6iuPa/qyoqpW1GrhzVZkeZOXOmcaTwu4w9hG0UufqMnmvoMlSVwWrjDJoxY4a5Tp4Pz6Fv3/BsFZ7B/PnzjfLFHB8wYECzdu5A0ClhpkpA8iUQaGJC+Q9Pi1SVhb6X20FkhxPiUj7ee++92jKPBx98sPz+9783qePae0NRlOYKBkDWtgcffFA+/PBDLYvRTJk3b54xcg4cONAYciNBEBOGz19++cXIfmRKI/Ozj1l5l0wDjoEMjIE6GpXBTVJSvUQcp1qy0jpJ6cZscy7oKRhVMbq+8cYbRs4ZPXq09OjRo0U5MOKBMrvIv5Sb4r5Sug05nZK8TQnGBjoQ4wNHJo4Jzhl5FujdiP6w8847m7/ZgCneQxdl/FiHBzomchN67PHHH18vcpJ1XqAbkXmPHM1/80Lu5l5bg39LAj3w+++/N4GPzCUcl169nflHRgDPErgfPNOmpNMq2yDgjWACStzx7MiMxMHBeqko9Y06NZR6hU168uTJ8sA/7pXX/veWHLT7YLnwnLPk0DP+0LI2JVvL1S3kli0TWf9F+GfbjhTJ214aG4Q1ng/RKfGm4vP5jz/+WNp3ypayqhnSoWOh9NxuW+mi7LTBkpXWO+TzGM8oX7PPPvsYgSQepk+fXms0thkYYJUIzhdFwxqS+R2EVgRZ6s9iBMdg1xRZsGWWLC1dGBJpTdmlvTseFLF+b7xwDxCKEcybYgMvBNPPPvvMRCJ16txJvljztcyZO1sCaQFp27Gd9Oq7nYzpOFpWL18tP/30k3FWoJiidDAW/LDRPDiz4m3CHosnFkyU9ZVbQt4rzMiVc7c/UFoKFcEqmbJukWyoKJVuuYWyW/tekhZFSaesAMoFz4GyAjYTyg3PCiMD8w8FhTUGAwVODhTfSGv+yrIimbV5mfn3wIIe0iO3cZRk0ww0+ItUS825kP2WlbaLpAeaUH3oH58XqahpflpLfleRocckdBieJ86Nhx9+2BiFcG7QXDyaIUlRFKUpwR5DCYx//etfJtjh/PPPN4YULX3TuCC3Yyi25YnidQCQWcz3MHSyH++www5Ro30xUH/11VdGH6DsUzy/Y42pNssXo7XtXwH8P04QSj3Z30YHYaxRYhbD/v77798kZez6hmu3/T2aog6PvmkzZ/h/MkmQS3l+lArjnNEtcXCQEWAboxMsZeVZb38WxnIqSikrkcGBwzPhWVG5ASO418Fhyzyj4/MscfJgROd58aybenZQa4L5RpAB+/JHH31k+uNedNFFsvfee7c6Z7DScKhTQ6kXMK6+8MIL8sD9/5CF8+fL2UfsKb87dl8ZgAGc0kujzm+QMkn1DoJPyS8ipXOJtxDJ6iHSZlcRonurS0VW0x/D3USbGuiHiGTWTySzbbhNhAlCAcZgvw0ERQDHwbBhw4yAikABCBG2cSLHQehD0CNCG6EB+OzEiROkY/e10mf7jpKT43aIpEllebrMnxWQubNXmHGAQZPv7r777nELHRi+EV4OOugg43ThHKzjAoWHF9fqvjaMrTZiHoWoqTJz8zRZU74i7P09OxwgWWnJG+W5X1/OmCwDdtle8jLzZLu8PpIZx/EoO7SmfKGUVRdLVlqudMnpK+mB+qs3zbjDKUX0XKVTJb9smikbKzeZhtJDCweF9dPgunCMMk6PPPJIySnMkMpgueRlFEpmWv1ELn1VNFc+X7utvwTs3XGQ7NUxvj4QTZ2qYLX8e95kWVa60fTOoB34zu16yK977RpT4GTeudPD3YYAjAQogEQ48nzJ1GI9sSW6UB6B+UmEH59fXLJG3llB6Yea49Bf5dBuu0k/DPUNTFVwmVQ6P3vezZScNOp213+forhY80tNTw03/Q8Rad8vqcOxnr/22mty//33G0MPDXRxcGgDXUVRmirU7Scr49lnnzUGVgwmxx57bMoCG5QIpWDXrjWGRO4zvSQiBZu8/PLLRr8AnE3WSEwmJwEOOBLoTYFMgF5AtrU1kmM4R74gWwPjtFsm4TjIFfST4zP8jeNxXGSOeMBw+tRTT8lee+1lAmxwXNjscRwXNmrfyivAueHwR7/A4dEaMxtxHmHYb8oOQwLopkyZYvRN5MtYMA6nTZtmxhOBOHuM2k3KqjZKelqmZKcVqgG2gWF+EwhF0I1Xx2fNwZFhMv1nzzYZU9gEmP+sS6wzwHdwjGC3UBofHNU4N8jgYM9grz755JMj7h2Kkizq1FBSCpG8KBrUs0X4ufj4sXLK6H6Sn+s2PgZE9rqgQZpZ1zul80S2/Ohp6t1TpHBrffPylSLrp9T0vZB0kXa7Ry89lSQYEonQ//zzz42xkAgi3kPZwyngBkWAz2FoRJFASOc7COkIg0Q7YUim3A+GLY5D2jUOBoR5BH+MzFnZAamWxZKeWS7lFaVb69g6smTRKlmzZqMcc+TvZED/YXELhXwfYQaFiUgafpvfI3oDwaU5OS6isbR0gcnWcJMVyJY9OuxXJwH6pY+fly7DOxgjNYbhnLRc2a3D3pKZlhlVgJy/5RspriqqfS8nrUAGthkladH6vtQRxhdCKeOUsRfpujFc2BJljNufl02VzcF1ZqzssMsQGdh2V2mbGbkfS7JwX3BsTNu4yCTUDG/byzg0omUyNCe+X79UXlzyfdj7lwwcIz1zk2vCzTNBqcBhheLBc0MJJqLKKpsoI7x4ljhNTzzxRHll6eeyunxjyLE6ZLUxjo3y6kppn5UvmXE2eK8rFWRpOMtC+1WYDLTRkhZoQgI4TcLXzanZQzsPFWmbmj0Fpwb79/PPP2+MAtZQqHVxFUVpbDBA4oB94IEHZOrUqXLqqacaByxlZJT6A3mIvZ2MGAyL1nlA6U0yKbx8+eWXxjGB8RtZncxhgpswUpLJucsuuxgdhHKhyPEEQRBohY4B/DcyIo4EZAl+H+c7MgbHwBmCcYw+jIk4sZBBCMiwparI7kXXwKiN3MLvUY6oNTsuIoHcRm8uv+fdFNcJ9EbGCqWIIwXTMZ4oVWf14G+nfSVFVbOlsqpcum/XWfr1Giw983atV11IiR+ckawDzF1kUv5NRj/rCc+cuWx7MbL+ML9xdipNA/aQ5557zuzf2HnodcX+rc4nJVWoU0NJCRjU7777bqNwHH300XLxxRfLvvvuK4FVP4vMd/eVCIi06yUy7KiWcec3fCpStc7zZrpIp6O3/acTFAmWixBVnoAjB0HeRirYhsRs3O4UWJpsEdGA48Ju6gjtY8eONemy8YCBEWeUbfqLwkK0EimepO7iUECBQDAkssldqgoHR0nZCqnOmCHr1m6SeXOWSXpGujjBoKxZkS0nHHduVIOYMarPn28irzguERdkmFiHhTEuf/WV+Te/j8Jh09lRZuorjZFI9iWly6UiWCmdstpLx+z4SrJsqNgsn6/9UTZWFktBRp7s3WlH6ZzdLiwzgmyNdRU1zeAzApkyvHCktInSLDwWpdUl8vh7D8sOuw8NeX9AwRDplRc5WqW4ar3MKw5v1t07bydpn9Vd6hvGKooS4xYB1GYDAcIqY54xB19M+0SmzJgoo/bfVSorKuXLCd9K0dqNcvX5N0uHdtu+V1/UZLSslyqnWjplt5fsKM6i+qK0ulJmbV5pel70L+gsHbKSS7f+bM18eXvFTx7TvcjZ/faUwW26pLSPEsKrdUgSOcdaxf+zxmBc+LxijjiFmZLmKmWQHkiX8mBNybmctEw5qsce0i2n/ktAVQbnS5UzL+z9nLT9m15vjXqESFWiqlA+WGMvu+wy0/8qnshHRVGUVIJ8wHp03333GZkQZyvrUUvpY9VYELCE3oDxHjmf7AQinG0ELX+njwJyNwZ/W+IVWf2oo46KS/7mGASwYNDi3zgycFTgmCCICeMzGRDoK9TKR9ew8KwxZHJ+/Ca6gC0NhPESeRGDZjQ4ZxscgzGUQBp302HkEwIsuD70D/Y4rp9XUyyv1FjgjCLzn7HQXGC8oUfgzAKrS9hecIwLHBqMecbUnf+6WkYftJN07tZBZnw/R36eNleG9d9Fjj/yrEa8CiUaOLd5dsxVdAurX/Df2DVYa5jzWpqq6cDzohoH2eEEzBI4dcUVV6SsJ6bSelGnhpI0GNqpX3nXXXcZpwZe10svvTREKDXlmZZMFVn2XY1xH4fGwINFMptnlH0YG78QqawxTtdC6Z6ORya10GPooyEZgrwVwgGhjEgm0i179eoVIrThfOD/UfAQ8tnU62Ls5zyIckAJ4Xw4FkoA50LUgzt6CYVh9bp5smLNF1JUtEn2HTuiVunYuLazdOs03Cgj/LcjZKsEJC2QaY6PwIFThCi7aOnMfBfjKIoHzg/Oi9/lHgG1GhG2U0VlsFI+Xv2lbKoq3trGW2TXdsOlf8G2XiF+VFRXymvLJkl5sNJkSgS2GmeP6TlG8jPCa4OWVBdLlVMl+eltJKOOkejrS9fJ/33+vGRmZ0m/IducWe0zu8igNkMlz/P7lk2Va2TBlu/C3u+ZO1Q6ZUe/3lRBtM1bb71lnGlE9DFe3BF9lkWbf5I5y6dL0doNZtx0266LtG3fRoa02duUoqrXcwxWykervpL1lZvMf+PQOKDLntIhq+EaYm+qLJVH538um6rKzPjJCKTJ6X1HSb/8xB06i0vWy4NzPw95j+NdPeRAaVPPazPrGPMYRQNl8n8zJskrr78iPYf0kx6D+0h2Xq5k5ubUFu1jHuWmZ8tZfQ+QtHrO7nOcSikPfi2OkBlWM/szA4MkIy0+B3FLA4PXK6+8YvZ4nM+/+93vTMBCa6wlrihKw0LQA47Vf//736a/wZVXXinHHXecZo4lCXoCcrQtHYmsRXAQuhzBJRj+CEZzBy6xVyOr8zf0Cwz/8fbgiwQGZQKmbN8N5ACMjzg13D2dkPP4LJ8hAIKALhushYyIs8sdCGNBN/j000/N8ckQ2XPPPaOeM8dBx2CPw1mCfsHL9loYP368tGaMPrdxo8nkJ2DO2+ugqYPzgnFM4BRjDWwWsdWVg061TFv5pgmU2rh+sxS2zZfuvbpI+1x6vEV3nLVE3KVlm9t5kwWGY4MqAOiXPGtKIGE8532CN9Vh2TRAH/z73/9uqruQHY5zgww8zZJTkkGdGkrCIDCSQkZmBsLmJZdcIhdccEH0qClTT9VpGSWn3BBtv8nTDDxvmEhefLVdEZpxYiBwAfcQwR5jEsI8keoIkwjnCGB+JZcw8qMgIOTj/EApiVUuxNbGJcuDTd/WpUVx/N///meEP4R5zoPPofDYYyLQktJL9BXv9+7dS9p2WSq5+dW1QpDjZEh65a4SrE6TvPxs2Vg5TSqd9TU/XtleZn2/RZYsWWYEjCOOOCLq+aLYvP/++6YcVUM0avt501z5edPskCh2Sjod0+Ngc3009fYT9paWrJYJq78Je3/vjjvKwDapLznG8ybawfQcyQjIz1t+lIzcdOnZt0ftZzZV0i0hXUZ32k06Z4c3X6Y3xcxNn0lQaqLiLYPa7C256aFly+obxjnjmOfNuKO+LcKnZU35Ylla+kvY94YX7ldvvTUs3xT9LLOLFxlnFfD0CzMK5MgeY6S+sYr941++K7M3rRRJ26qEVVYZZ95tB52R1HG/XLtQ/rd8hrmmrLR0OaX3SBlaWH99LDAScC1EVuGcxYgBZJ28teALmbNuiVSUlYtsqZKiTRskIydbglVVtd8/ovtukpeebYwUZHPx/7GUrpVlq2V9xUbJSc+W3nk9jZMxFo5TJdXOcuOETQu0k/RA4zQtb0qwX0yaNMns+TT9o+/GH//4R1OPXFEUJZUQqHPPPfcYPYOSRDgzxowZ0yyNbI0NOgQGPeR8DPsERSHrkyVhgpJWrzY6HY5qgpa895i1H0cIRn9AJotn3Uc+Rb/gd4DfQDfhOMgA48aNM4ZFzosAFtu/gs9RNhf5ALmfvZ6+Grzchkh0In6Dz7lLUNkgKErkIkcS/UuwTDTQwch6P/DAA9XY6YLgM8oQo/Ohe6KnMX6QvZobjC+cVuizGEy5HnffsJpSvBMlKNtkTiT9tpnbSZfslivn2GwodHnmqp1j3C/+RmBZcyj1jG6BLcQGgFJG22KrWFhnJWseTlr+7V5TbMYHayGvWHaUimC56Y9Z7VRL+8xO0iYzudK9Sk12+EMPPST//Oc/zf6EfkF5SasnKko8qFNDiRs2Bept41UlmgaPqi467GyrRUrns62KZPcQye4bVxN0GhyyeCOsk+pMhJAV7NlwETTIlMBojRDJZo0gb/oJ7LBDrWA56dOJMnyXdiLpZTJ50nQZNmhfyUjPNdkP7ugklBuaodnUciKc+AybBk4VhFecGTZdnA0fYYAsDVuXEqMz0XMomAgGGFv5fEnpZimtXCCOUyIByZbsjD6Sm9PWCAUr1n8nFcEi49NCaMjKzpT+vXeVvt33jFtJ5Xs0mGYMxhI07DG5ThoNJto48tv102XBlqW1BmxL95x2srGyprFy/4LtZVDBwJDzX1a6Rj5aNTXsePt0GiH9C+qnsR7Pgig6oo869e4o0zd8J5VOhblfpdWOlG0Nd89Jy5YjehzoW7bqxw1fS1Za2dZ+EQHpnbdjg5SeShQiqeYUT5WS6m39F3rkDJKuOck1SE6ED1dNkdXl2/qOWE7pdVjKDC3MNYRxFAt3lIpNq54UWCFLyjZIyfpNsnlNkVRVVEpe20K5//Dz6lTOalNlmbTPypWsOmYLMeaIiLMZVIAyzLlT4oH1ymZtEW3pTQenfwZ8v2G+fL12tpSXlklOQc1axZw7f/txJpuEdRJjCesZx2KeY7TwKl44JmdtnlfbY6ZdZqGM6TwqLsdGMnDdGEYwtLhhvcJZbUtbNHfDHOUc7r33XmNwPPjgg+W6664LUSAVRVGSgUjwW2+91ThO0S0wbNhm00piUNaJtZrAB+RD9ie7NyFf2H2ZfZN9mn2UPRXYmwkqAT7D+zgy0B9saRfKEHmzJOjJZMtFoVegX7DvsU9//PHHxmnA/oeugexqdIeSEqNP8B0Mk2+++abJBuB3cDYgF+H48NtXOXf+xgtsM3J+kwCoRIyx6DLcMz99IdKejRxjI/9bItxXHIzoXezxLb23VnHVKllRRo/MmnGUEciRXrl7SkY9B001FNgT6FfDPHOPaeYJzkPmM3YG64xkfjal547NAeeLtZMAzgccVYxV1iT+xloWq08DQXR+gVGsN6xP2ET4N2sKgaaso+7PlleXyvcbvjT6ts3qHtJmZ+mcXX9ZzO5yvm5Yy1nzsMvVNYuusWGdt0HTrMlUf6Hvhpa+VeJBnRpKTDAi4cxgkcG4ff3118vhhx+u6WF1BOHBKhVsrmxMCBtELSFEImQgNPtFTpFeieKRnZ0py9Z+KbuN6ifl5RUy8+dF0rlzF5k9vcKk8GVn5ktQyqSstFpe+e8b5vkRaWUVFo7F77GRYBhk02QjwQjHZ93ODkDI4fOMCTZPWyKL83YbM90UlX9jmooP23H72mvJCLSRjtnb6pvGi1Va4jEMoojhnEHY4doQ2IoqiuSnjT9LWbBc2ma2lZ3aDpec9FDFZ17xIvluw08h7+WkBSSdslouR8fwwh2kT37vkD4cb634XDZVlmwtPxWQnPQsGd9jjGSn16+g8fXXX5tMHvo+fLHma1lVEW6AP6bnuDCD7lfrJktxdbERyGyprcEFw2S7vIYpPZUoXN+GSnpKVEh+RlspyIiv10ldmbLuR5m/ZVnI889Pz5Fjeh5Qp+Myb3AUMucwFFC6z20oYL6xRiDMT147V37ZvEpy27eRNl06SmZWpnTObiO/H7CfNAasGTga7ZwEHKAoE3Z+EjnFtbGWxWvML62ukJeWfC7FVaVmDpFrtG+nYbJzO3/nFb9Bo0/WSlvGrrS6VN5b6e7lVMOItsNk+4L6KSXFmkgE6v777x9yrbbEBkoi67qF+4bBDiMKkbAI7bZfUIOkXlMOklnPuVaViJStFqFvSG53kbTYjh+MUgQ4IBsQgXvDDTeoc0NRlIShsfRNN91k5FoMGBgyUllStDXC/sIajZ5hA6PQNZAlyNxg32H/sUFObvgesjNGMuR/nAw4KYjcR95HT+CYVo+wzxDjIscjE9M6FNjzvv32W/NZZHGOxzmRQYEOQZkoDKc2IpfSWOgY9vvWecE+75Y13HBcssfdmb3JglE0nv2XzxGEYpuOt+Rm9Th7yOL3GystjfLqzVJSXWSagxdkdJV0ykk3Y5C/mcuMV+Yzz9Eavm3JaxwFVn9nLCNHYwNo7AAcAvZYL+x8xMlAKTl3NglrFddVX2OTe8T9wcFq+sRuvSdzNs+QleXLah1gtj/mXh3DgwdTBWW0rIPFfX7oHugYvLhHFtZ8esawXvM+ugV6RnPIfuDZvvPOOybIgf2KbE11biixUKeGEpczg4j3G2+8UQ455JCUb3RE9ztCanO1BKSLBAINY6hsCMqr10lJ1UJxpEqy07tKXnq4k8K9OdFbgM0ZhaH2GOXlZrPie9SIJEsDQ9n6DWtl6drPpTJYI+xXlFdKSXGpZGZnyLDh/SQrq0ZwqYm/xzA/QN58dapJ7baNATku6cRsemx2bJgoBhMnTjTGOZRMoqgiYTICSktr03n9WFP6qXz++dey+6hhkplZE/GRGeggHbLrrynUlqotWw3fBTLzp5lmM+/Su4t8uvpzqTD9PWruCYbpA7uODanVzzVNKfpelpauNP9NdHhWWrgy1TW7i+zWYWTIe6XV5fJt0UxZX7lZ2mTky24dhkhBhH4WqQCDN9HhPDfmKPy44ReZW8yY23bOfpkaVcEqmbT2o7BjdsnuJju2bbkKWjKUVJXJ+6u+kJLqrZGOkib7d9lNuuUk36CcrAYUfoROb+k+5iURcigYzE+cBWXBKnlq4ReysqwmmjI3PVN+03cv6ZbTtkEFTYwXGBpQLDAkpFpAxjn4/qpZ8sumlZKdliZjO/eXnejFFAEMGkSkck6cD6yv2CCfrPky5HM4SAa12V6GFUYvRVFXBZLnxnrIM2NeWgWS82Td5m92D6BExj777GPWWZRLnFys/YwHq8hxn0eODF1n6kRVqciKz0RKV4uQndO2v0jxfBFnqzJECn2Pg0XS43uuGLIoFUPTP64F54a7IaeiKEo0ZwbrIM2/yczw65GgpAYMcxjG3MFSyLvIG8jxGMFwMCAv40Qgmhk9EAcIegg6A8EMfN+vFCzlfXBIsA9zPL7P/oVuwN6ILENkNXsdzgD2RgySZKtHwpaZiubs51yRpxpr30FvI4isJdbpJ2uK59eUIvaV2DBfP/nkEyODEtTnHpvI8TiqbE8aPtNUSkzZEmEY4Vkv3D1EGwscK+gYe++9d20/mRkbv5H1lWvDPju64yH12vuPdY51GL2LdZj10/Yx5X3+bcpkp6XVZrVgU6LvLesozxn9wl4H6ys6KJkeTRGu64MPPjD2RwIAqRCDc8NtI1MUizo1lDAQYmnOhzOD5r0sJpSZqA+vveNslqBQsscaYIkYHy5pgdSk8DlOuVQ7NLvGDEnkQcMJZSXlq2Tl5ilSXl5pHA7lFZWSUd1VBvTaK2pNUtugC6Xg7bffrhX8USxqMx0QLnOWSSBzkynnlJOTVfP/2ZmSnh6+odp3sp0Rsn5tpdkQ2ejY2BB+3BHXCBMIQTg3iIDG0GaOkZZWG01sN1GboWFLY9nz59i8Z5welaulfY9i6dZjm7LaPmukZKVF6cES5d5QxzIjLdO3jAx//3nTT7K0dIn57/RAhuzSdhdZMXelrAquls0dyz1FpUR2az9CeuX1CDvOxsrNUhGskMKMNjJxzScSrG1dXOMQ6ZHbQ3ZuV5PB0ljYrB3qntr0TJpaT9ra6By4T349Nch6+GTNhyHODwy/PXK2kyGFOzTwlTR9uK9LS1ZJlVMt3XOonxpaPilRcCQS6UdUowWhlDmHwIbzMiw1OhiUJaVFxvDfM7e95GUkVlqtriBUsmbUV28bxvNTC6eajBRGZZoETL+PPw7aT9plhToHcRIQAcpaSGkMd3oyz+q9lRNNrVs3e3bYVXrk1l/fkJqLWCGOs1zWrt0oixcHpbKq5rxYc235LV62ZjEZcVZAZ81EgUKps+sxa791WNb93ByRJe+JlFHnfOu8T9+arVFLQATHT6fEnM6cM2WpcG4wptW5oShKJGcGesUXX3whF198sVx++eXN0pnBGk2vuGqnzGQfZ6a1afBa+LaslP23LSUTj4GdclFEwVK2BZndbbRmP2WvZ9+yL44drbyJjfzmPDgeLz7vjri2RjbkG8rcEGlt4TfQjWzzcnN/KyvNvsg12j2Ra7P9OPgc3yMTpLFKr6AzYzQkEKylwXNDViUYTmk+2CBJAlFt4BHvUX6KZ8p8Yd43NXDEkBnW2Fki1sHCvcLpSoaL+5wWbZkji0vnuT4dkLz0fBnZfp8GOTfWWJzUOKTNrwcCtSV97drJi/es/sD7ZIzbbDlgDW0ODkuu5cMPPzR6hTo3lEioU0MJWSRxZvz1r381zgwiqGjSV5+bS7VD/crVnnezJD1Q9wa8QWeTlAe/xRRo/jsguZImA8WRCkkL5ElGoP7SK1mAX3ztn9KzX54pEUXWBE6HjIxMqSoaZCKLas8zGDQOhAGDe8vq8h+lIrhZ0gNZ0kYGSNnGGuEdZQLDlzu6am3Fx3w75HdNVkbAR/kpRcnIkdz0gZKTHlprEiWEzRtnho2EssoDHn1K4nAOnCfnbUuo4NBA+bA1edkUrbODDZP37fHIWCkPkvkQkNz0npKZlnh0eXHVRvllE/05yo3xvW/+YOmRG3oty0qXyYyNjKltZAQyZGzn/eW/n7wq2YPDlc7eeT1kZPvozok5m+fK7OI55t/8NozutJcpYdXQLC5ZK9+umiOfv/2RDO7eV3buNdhEbLij3TDmripbK1VOlXTK6iB5EbJFFpUskLnFs2qviZTrPdrvJXkZdTPYK7H7wzDfKFngnnM0Y8Yg3BTqomJMoIQb52eNCMx/Ij1Rkuqjqea68i1yxyzWtW0wNg/pOkgO6hqaYcGahUGEe2UzI1DSENhxFKypXCdfF30v1abMksiAgn4yvHBw/SpLDs7UmZ43dxIJdBWpWCRSvrX3UuZ2UhHoK5uLt5i1v8EUiupykXn/F/pehk9UWW43ke7JpdGvXD1b7rn3Dnnowf8zmRt33vm3kIaciqK0TnDYX3311SYQ45JLLjHOjKYaIRoL9utNlTOkLLjNKF+QMUjyM6LXck8VlDp0OxvsvwmMYF90151Ht6Pck43OtWAQw6hle2QkK3dwfI7ht4+hJ7BX215hbh0G/YEocZupynFwEHBOVpfg2uzLykq2t2BTKqPC2CYYzHuPmytE8lPWlhJl3GcCp5TmARlSP/zwg3FcuNdXsvqZR+iLjY0tm8RctvoFcx4nK+dJ1hVrQ2OeHxUrcO7yb+wv9M+xto2OnTrIz5u+r83WyErLluGFu5vqEE0B1kdrq4kWRNvcsM4NgiIoq3bNNdeYLM+mtBcojYc6NRSz+L300kty7bXXmsXvzjvvlHHjxjWIp7za+YaiHd5hKWlygARlswSdckkPtJG0QOKpkWXVX4oj7hrmODpEKrcGqGal9ZS89KEpv04UBWrPFnbbKJ17eI1+AemSvc1RZDf2SZ9+Ih37l0n33pTe2hY53yNnlGSnh29Imyrnyap102T2zMWSkUHEUpV079FR+vajmdXWz2zcInNnL5Fg0JH8PJSdcsnLGCBV5XkmqpZ0aQQfGwnsVmg4J9ItGRsoQ3yeuu9W0eD6SCOnLApRPLaJH8oIUWIoVmyo7nRxjkW0T6KNu62R/puiSVJlmnJtY4fC3aRd1rYIv182/SxLShaHNfnep9O+8vkXk6Wkf6gTCPrn95Gd2kVvRsk1Ly1dJqvLVxsnSd/8Pg3u0ODezl63VN5a8Y2UbymV4qKNEqyqltMOOEZ2aZ98s+zVZSulqGKdpKely3a5vSU3veXXzW1MmCuvv/66URbdxl6USOaOtxRVQ4OSQbNOemNgcLcZWThhWCNs3wf+luq1c2XZZrlndmgvDLI19uvcXw7rPjTi9zg31igctDg3UIq4jozsDCmtLpOstCzJzdi2h3BNnD/Oe+81vPLKKyZq2Guc4Ds4DkmHj+jMcT7jCXveLBSp7CFS+n3tOwsXrZKZC4LSe+C+Zq202XA4Y+rVyBesEpn7Quh7KcrUgCpnvZRWs6eLrFu7Ue7920vy2ENvy0knnSS33HJLvWX4KIrSdKFMJr340DMoHYEhork6Myxl1atlY+UPYe93zNpHMtLqT4Zir0D+xnBJOZRYsDcio5NJcOyxxya0Z1PqBCMe30FuIeDCvfdRaorzsJkVtgEx0eAYJXFS4Mxgn3XX6GcvxXhJpgYlde2ebev6E0xBdgcVAjgmx7CBVugVHJvgCncdfa6T/aWxDKHcB66nOTe2R+8z2fAVFbXBbvQRaA29NFoSODyZP8x362hkHk2ZMiUkM7yxIMOaeYyTgLWDc+NlqzygX2CLaiqGas6L86XHKOdEcBd2HtYreshTxSEnPS+kegTXQxk+b4NrsivIVCTzw/sbHJv3U+GEIEsHfZL7a4NobYZcg/Tsq2e4X++//75cddVVZr8gGBs9oyVcm5I86tRo5ZDqRwMeoltpyHPaaac1aF3QoDNPHFngegeht62UV+dKpbO89r289B0lMy2xsiGl1RPCMhlwalS43irIGCmZSZRB8gOBFkEC4R0hvtxZaSK53GSndZVAaV+jDFDvECHSGAg75MispVNkwJDe0qYwX8rLKmTAkD7SPmuABDe3NYs2GRMImdSiXVs+1RjDuKD27dtIZWWVDBnWR7q021FWFU2TnNwcmfnzAhk8tI/k5Wabu5omhfLFhHXSvXsPs3F+9913RmDledu6umza1L5lI0Y4t43wrLKAUoIygTDC9dq68W4DIPWREaS4Byg6NtqbTR4lafz48Qnf2+KqTTJtwxdhEdw9c/tJn/xtEdzzi+fJnOLZYd8/oMtBMnfWXJkp80UKt2163JexnfeS9llNLw3XC+Pl728+LTn9O0tOQZ7kti2QrJxsyUvPknO3P7ixT09J4Dmy3u6+++4hDbVxatAEL1W/QbSmLRlHFCbOS1s3l3XHZoj5gcHBGtkbErIq/jZromyoKDUNwoE7dGH/0dI3v0PCRoZojUNZ61hPvfsd6xQGll//+tchdYZZI/kOzl0+w9qHQSYEZxLuR88v5YtscUSqKflUAyUJFyxaKyXpu5v11UbZ8kxQOuvsLKLx98afRKpLRLI6iLTZWr4KDWz11yIb7Rq51aGRkS5iS3VlFor0OCTunhpuSqunS5VT04vIsnDBCrn9hnflf/97Q/7whz+YSO2WFDmmKErkNZggqX/84x9G7rvtttuMHNsS2FK1UIqrwmXNdpm7SnZ66ktpYWgmw9PWQI9UOgbjPvs/jg+abrNvoZOgZ+DcIPseWZ9ms7zP53FCIMezB1ESzDopMMARuIS8wn5HJgJODvY/DGReIyn7KToD5SAxihPdbJ0MOEXYPzknrsWWXUQPQX9AX+AcMRSSyYOOgSHROvqtsYrj8330FIJCuBYTwJCRYa6ba+L+NAbcu3gcTU0RxgnBNowLnjX3EflJjYTNC+YzRnPmAM/R7ejwlmlNFuYourzNtmKc8GJNYrxYWZk57Dd+OMfPPvvM2B+aetmjSHAN3Adb8skLayRBozZgyQ06BEFj3l55rK2sb9ZmglMk2cwv1nrWRhzCrJcch+fC/tuSgou4v88884wJmmDtomw+5cuU1ok6NVopNDPFw/npp58aI8Oll15au3hS5mdO8U9SVl0qeekFMqjNcMlNr59yNI6Dj3uGqwRVgVQ7PaS0epbnkwEpzKDOYmbKMjUgL32YZKf3rNM1YDxEKcArzobhjkYqqV4kxZXzZcWy1bJsUamsWSqSlZltBHVetsnTT7O+l5+XfCLtO7SRdh0KzbnO/nmhdCkcIL277GAEeIQUDJ8IzcvLPjSL+QdvT5H9DhopuXk1xrdZ326Rdm26SmV1kWzYsEn23GM3ycoJmEyX7LQesmLFKqPsIHzYtGIcLGx+ZF4QiYzQg8MEZcNvA0SY4bdHjBgRJrDwPo4ymosj4KPY2DryXCe/mYxSW1ZdIt+u/zTs/b55g6Vn3rbjVQUrZcq6L2VL9Rbj9CBjY1CbwdIvf3ujYH/7w3fSZsf2sra8yERvD287WLrmbBP8miIYmDF88lwmbZolyzatke4Dt6UPZwbS5cIBhzbqOSrxw5pLWR733EHAZ16nyqGMUkM0o01BZuwj5KLk4NDE+YpBggwtsqeYm8xdDA8I67yIrkXhYM42ZH3bteVb5PnF38nS0g3GYTe+x3DZpX1Pmb5xozy/eLFZS37du7fs3Mj1gHHqYsQh8qk2u8b5hbhkzye3F9myMsSpUUOGTPohX3r27CkVJeuldP3PUl1RKpLRViSvr4inZxD7MwqUjWTj/33HCyWmVrxT8/84hsjEsGMts6NI+71FNi0QKVle47hov4NIRva2xuG53Wv+PwlKq3+UKmdV2PsF6QfIN998ZwIoyACkLu7555/fJMqsKYqSWjCiPPLII6aELSVQ7rrrLpPx25JoqEwNDPbWmYER38/Ixb6NHM+ehDER4w4GTPYWjI58F0cBcj8OAvYrdAnkA/Z4ZAK+z9+R99FLABmef6N7IANgxKNxNJ+3zWp5vhbkBkqD7LHHHkZuJUgD2YM13/b6IAuSfYvAAf6GYc9t2GR/p5cgdd79+qwguzC+uC6Oy7Wxj/DbXBe/3RgR3pz3xx9/HNJMuDnA2KG3CvIo44WSLgcccEBjn5aSJBixsUm45yVOSXRx1oRUgLOTOct4YU3ACI+NgvXH6jb/+9//jIzK/LbzmM8yd5mrzFvmL/uCNn0Oh/tDSTvuM2WKkwG7Dscgu5zAOZsJ5we/Y3ULq1+wjjaF3ibxwH5E8MQdd9xhnBr8f3POmlOSQ50arQw2tj//+c/y+OOPy7nnnmv+7fbml1eXybcbPnM1WKVRa5aMbLevZMRp6Ag6ayS4NcsiLdBD0gKd42robWqNS66UB+dKeXBRSBkmyE/fQzIS6MXg7anhzdKANhm7S0Zau6SFB5xDKBBE7HoXfzYQNm2ionmxseN59/PsIxCvLPtWyoI1TZ8gTTKlZ+7ekpGWLRMmTDCKAAZIolzXlX8n02Z8I126dZD2HbZFvZaWlsui6WkysN+OYWnYbJJkUeCMoLwMURScCwu/uxQAAgcKkM3iSAQcHowxNkSUITZTq2AgzHA/uFZ3L4F4mbN5uqwuX7Y1drumhuXO7faWzLTQclZVwSpZXrbMNPlun9leOmR1NAobURDUhW1ukUcIkAilCCVLnU2yrG255LWtibbhTgxu01PGdUtO6FEaFhqc8Sy9ijfGA+a2OzMgFU4NL8x/Iitt6i6KD0YQ1gbWLxxnCLe8mMcoRxgeKFXX0OVCgo4jaVvX1E/XrJGDJk0K2RHeHzNGDujSRRoT7iNGHhQ5Y8gw+yY9NVbI5s2lkp8/QNLSBotULgspP2XI6i+rN3eW2b/8KN1y5kr/Pp23VYHK6ibSbu/aslC2TxGCO+uA/X/Waht9hVPasGmWyPrvav6dhkPDXV4qIJLdVaRj3XtW+VEZXCVlwdCeRunSWfIydg4xWP3pT38yTjSUkEMPVYesorQU3nvvPdMvgz3kb3/7mxxxxBHNxjDS1HpqEFiE3Iwzw68EEEZE5ENbAoroX29D23hABsD4hVPAOp9weCBHeMvVIMNQ6hFjpbeULE5+gij4LrI+TgzkBhz/bl0Chwr6AUbRREBOYXwRfIWsxJ6H/mqvF9kKZ4m3l0B9wzVTUofrbG4N75ErrH4ERNy7DeJK88H2gcAxSVaVBVkeZ2Cq+qJg++Dl/g1rRGcOYlMgIwsHGWsB85U1AxmZecvewFzGEcJagD2Adae56eYNAesyzmZbKcPC/eMVrTQc+gEOcWxRkfYQ92fRKdz6Bd/jfexEOMSSKR3e0DDubr75Znnsscfkt7/9rSl7a530SstHnRqtBDaWJ554wtSyZSO67LLLjLLhjShZWbZU5hSHlkyCHQpHSoes2M6JoLNSqp3Q76cHhktaYKvBxbX5En2Ogd57DhXBpVJaTcRrKG0yxkhaILEIHJwl1c56sxlUBIulrJpGrTXkpPWT3IwBdRJk2ZCJEmJTtkI7SgjGQIQINoJ33nnHRCzh/GCDYGPwK78RdKplQ+U8Ka/eKBlpOdIus79kpuWZe4XAjDPCRraWlhXLxCkvyYg9e3mOEpCCjL6y4KctJvoBJQXFx25UCBMYU4nQRqBAIcKr7d4saQ6HUFtfdVRRlLgOxiT3CgNvPJtljeNnickkwqFBk3CvQ8MPxhnXhBMHQxqbe6SU0aYEAiLPwBq6iaj6csoUqehTIHOqcRw6MqCgmxzUdSfJSjKyOhFKq8tlXfl6Uze0S06HkPqhSihVwXLZXLVcgk6V5GV0lNz0DrVrBuXbcPYh6LMOMPdsJCXrRa1xuvZYVTJ/yxzZVLlRstNzpH/+wKiN3ImQZK4wx73QiJwsKhwazDvWBxym/D7GC7+IKVs/lnNuLPaaMEG+LiqqLSaI6kOmxrcHN37ZNRsli1EAxQNDDaspGWsZmZm1jcwDVSulqmSRBAKOSEZnSc/dXtIzMqWsaKasXjrNrIdHHbKb6ZFk6DhOJI6mgzxrUtYxTPE7nbKLpFf+OsnKShdJT6txaoSQJtLj+Pq5GVv374rgfHGkSjICnSQnjUyf0PWJffDhhx82KeP77bef3HfffcYgpyhK84Q1CL0CoxoBUxdccEGDly90Y/u6uY3eqYa1t9JZL9VOmWQECiQzLbVl9XAgIL/iqEB/sL+J7M7fMNbwdww5ONbRQdiPkCESuWZ0FWRwnBAWovf5Xb8SjkYW/fJLo5MgH2DIRLbm38irnCuyBE4OjumWHXguODwov1kfcB5EibOfsM/Y3ln1CdkpVqfDAFnfv5cq0FPtuLKyI8+Q56o0cSpXiVQXiQSyRLJ6i2ytYkHvHPR89F3WPmszoEID8xbHW10yZG3pKNYb73GY7/w2aw9rCp/B/kAZJhwtfhkHpl/l0qXGCam9W8T3/nAPKa2HrcxW2GAdtb0PgfUbJ4R73cfOwV5h+xyR/eZ1RMUD+yjjh7WV30HPcfdIaorg3KbcLbYzsjbOOussdZq1AtSp0Qpgk7vwwguN4HnFFVcYYxZGLARWb6TMqrKlMtvHqTG8cKS0j8OpURmcwhIY8l5xcUCK1vQ1CzHeejZWFkM2XdIWyWJAsMIgw2e6desqJcHvpNrZ1kA8J22QZKdvK7mTLNXBYqmWLZImeZKRFm7AS9aTzsZhjc8s+hgsuSYMXTg+uPdcKxs+G0tdDThEXA/dYYCUZk8L+1thxkDj2EBQ4PdRKojUoPwMkRpuBwILP+dq6/PaSFrOF2eHn2G0rhBVZmv9swmjHBGlHq3+fV2xzQYZ9yjfbMgYkOtjU8aQXVw1S6qcDeQ5SUHGQMlMIhuILCCeIan/NusG4QJHFIINUes2kr2+WVu+Xiat+VqqtmZwtcsslP277ClZaU2nfAxjnblonYu2sST/z4u5x7xDeK5PYawqWCZLS6dItWlqz+840jl7mBRmbhdilMAIzZwk4tEqxCgFvM/4RPjkPL/bMFU2VNoMroBpVD+qw2jj4PDC+CAyxy9LAzB+YIAnmhBHBes/94Pf5bxSFcmVarZ/+21ZQA8hF9vl5sqSI4+UpgL3z/Yy4bkZR3pFhVHsUATBXWaDNYm9r13GMpEts8IyE6XjQTWlqBI8h3XL58iSH16RisoqU3qqQ7sCycjMkNKyCvMKBjLEaTNIBEdDWgfjWGEtZp9o6DIA7DX/+c9/TFNDsjcoiZmqbCVFURpm36VvBiWmCJQ655xzjCxnHeX1bfhgHcWwbxtas38CBiDWM+Qo9jX+juzL3pqKuvINBWv6a6+9VqurcY3I6/w3RiuMVciJyBI4xnF0kFlZl/vOvkWZTPpvRAJ9hnuNIRJdjmxT9AW3YwQI4nD3CqOePIFeOF4OP/zwlJcg5B6gZ3IfODa9BDDuYlStz/r97OU2AITfZ8w19bI6OH+QBbEJ2OdAABj/rYRD5oE1Elv9wq1jsLagY9R7RHvZbJHymbX6haTlixSMqXVsoFezJvLiXG1lBMYn6yFrJvpvMjo3wVdkVfjJaRwXh6UtTcc6goOMtYkXwZKRegEp8WdV2cx6dI1DDjnE7HXY1bzPhOeNPpxKmZqxxRqOPQVYN9AheZ9z4P/dcwM4P7tnNWQ2Dvow2aJPP/20sXP961//anFlMJVQ1KnRgkGwuvbaa+XZZ581Tg28lrG84ZTs+W7951LpVG41sgQkJy1Xdm0/OmZkNgvZ8lVvS1HRatm8qaR2YcsvaCc9ux1qNjg2fXdmBp8hIgijLRsiES81nmRHqoJFkpYelH59h0qXTtt6VTQkbBoYAikbFen3OW8TievzdzZyol9skybv91CuiOpJVAjiXnLfqjKXS+/BuZKWxrNxJE2ypUvOXpJO9IbrGnBS+BksMcLx2zY6HOMbCgfZDDwrhPL6vu8IQBjqUZK4Jxh5U1kTl/uOwoXixbVapYP/TnVEEr+1sfIbqXQ55JhD7TNHSUZage/ng7JZHKdC0gJtTCYS76Go8tyIZuMZMQat0sHm3KdP3R18ifDm8olSUl3quiKRgQX9ZJf2qanRmgx2PjE+eaa230wkxRVBkHGGIo4CSsZCfUQGrS2fKRsrl4QYqQOSJv3yDwybSyjgnDfCoFcYQ+lo07lAviqaHPYbAwoGSx9XLxn3PcFJyJoSqfwCn8HQQBQtQjKKj7dhXVPjzK+/lucWL5bqrc88PRCQE7bbTl4YNUqaKqxrKMBeI08YOKyKJrreCIik54l0PEQkkKQCsGWRyLqpZl1Zv7nM3LfcnCzJaZMjGYX5toKfSKCtVKXvJsVbSo1hjHnBnoTy2VD7LescTvrbb7/d3K+///3vctRRRzXIbyuKkjxEiqJXICtS5oGAHRz0qeoN5YV9ElmWdcLWB+e3kOWQsdlP+bd77cIAw96PUYVoUwJ8kKH4LHsh+yTyVGP097HlQchkiFa2iM9FkmuImvaTl62hlWtzR+PHC/eNaOuQnlE+8BucA4FJfs4ir5HcluSlZCLHre/7bjK8V6400c7oF/xmqoO1kCmR2XGcMEaRR5HZcQo1xf4ayCYEADH20b15RtbwyVhrrs3OU43tBcn/ExzKuuEt6+yG507QHAZf5L56KeMVrBDZ/F74+9lDRXLCxzXyPTok652FecBzpsR0MmMdPQrdNJKMyPXjAEKvwDmKrtUcShc1J3iettpJY2a4YIti3LMHsdZ5+3Awd+yaiI7BeROo2VAloTg/HHkETz300ENy+umny1//+tcGL6esNAzq1GiBsIg8+eSTpiknkVIYCcjKiJfS6i0yv3imlFaXSH5GG9k+f4hvVLCFhYoXdOi0RTp2KpaCAgztNQaZgPSSDOqKJ3D+vPg+CxKCNUqMhUXRHts6BuhpkezCjoDNb2DgM5Gu69aZSHiEYBQNFmoWaQRWBBYWSIRV3uO8+N1IETl83kZLeEEZQ8BGoUk2gggFbdIXb8kuew6Udm07Sn56L5k3Z5Ex2ttm4ETBEkHn9ZDzzBDEieJwR2wg7HAP2IRs9BFGVq6RiCeOa5tJcQ22/iIOHLIwbDRGMteE8onBlejmVIEiywbm7h3DuOF8U11Sp9opkaIKrxE6IHnpfSXfU+qMsVYenC7VtY110yQ7bScpL80z6bpuZY9nwVgjss2vdFl9EnSC8vLScCG6W05nGdu5fkoIRILxyJi12V6BylKRFT9LTqBKdtx1Dwn03a22yTFjFoOpXU9sRtIrr7xiFHUM/yi7jI1UCtwry6bJlqrwZsl98/eX9K2RVPFCyamp67+s/e+K8gqpKKuQ4d1GSP82/oq5VcAY295SVu7PYEhmnjWHyLwNFRVy1OTJ8vnatea/9+rYUd7aZx/p0FIUpbKlIpt/qFFYM9qJtNtTJL2OJfKMAygoUrVZpGR+Tb+P7LXhGSEZg0Uy+4esNRjaGBPJGmTYEzB0WcUFgwn7B3u1Lbdm6wGzB7I3sSbT64sSmcgtDzzwQFQDgqIojQNGsosuuqjWGUnt6vp0ZBCdyv+zHmGgZx+vS+CL1SGsvI+RjvXHGyxhgk6CQRN9jfyQTJSplTWRNTiWXf9YCwnkIdiIXnQYkpFZ+DfXyfucG59n7fQz/tvgJotXz+A3kAGiOSWiwfly7zlPm/2JDIZjgr8hR6EncW5eR4HtKcg985ad4XjsM7y4F+hINuIXnYvzRr/gPvB37gvf4bd4n8A3q5clCvobv8PzTAWMEduD0A3yOn9rivXc0Yk4Pztn0fkIviOQpjVHMjOmWdsw3FsbBNhyru55xGfRoW1AEjoEMjdjdMqUKXL00Ueb9xm3OFpTRvVmkWJ3EAwERLL6ieTW3Yliq05Ei+5n3rIuePvtuOE+Gh23vNxcv2ZoKMCYoFcUe1oypbAs6LjsrewBrGPsCxzT7hm8gLXe6jHYDiiRidxCZiklqZpyCS0lcdSp0cLAO37++ecbAYVUq0QjHrdUbZDV5YtMf4d2mV2kQ1ZP30lvsjKWLzdlSzA82CZGjhOUamemOFLTPC8g3SQ9QJkfjzJAY/DqtVujUjvXpk0mA0IDkfi2CRWGEm+kKUZ3FBcEcnNervRRBGiUFttngePxeVuXls+RvYBAw98QsEmjYyHFUL5+U5HMWzlHth/YTwZvN1TyM7c5OKzB0EZ8pAoUHytMpWcEpF+/PjJ7Vk0JGRw8CBEoRNTdxbGBcuEuSYNQguBljf2RnF4oTUR+E9WNsEP0EfeQ94m0QrDjfnPvUEa4/2wcCDTJRmd409XrCufPdXJPbONjzp9XtPT6ZKgKbpH1lV943g1IbnofU4bKTaWpPe/tHZMueen0OEk3mzLKB/eb80eobqxGam8sn2B6algCEpABBb1l1/Y7NMjvM85w9HD9RHkwzqpKNktg6vOSXl22zVDbsa/ITuNNY2TGPGPfzju+T2YOgtDHH39s3mfcMxdQ6hCwUlEndEPFQllXMTvkvYxArvTO2yfhY1c71TJl3WdSHiwXRxz57tPvpV3HdjIod6jkpm1z4uJARKEq7JAvQamW7LR8ee/d98y9Qnn36yGDQsYYQwmLt69NY2J6R2zZYp50v/z8Biu91qCwJ9XXdQVLRMo/8bzJ/rudSNaOIe8yH5gv1tDHvHMbAiPB33C6sl9gPGKNxVDC+GQdY+/kMxiweLHP4ph3RxHyHSKqKI1x6623GuNpfRlMFUWJH9YDnI30wjnxxBNNaYf6inhEvrWlUpDnGyvinbUJORojns148DNwIlvaElBu/YI1k6htW2IX0EVsdgkQNc9ayB7Mv015UccxMjXfQZZH5mE/dztzuEeUkCWzGlKlYyAToUvy2xglyVS3sjPyPufH39C7+BtyKkEibsMlcjdrOWs3ukekNZxMVa6bPcI2IOZ3uGdcM/uENVghy/B3ZBbuj1+N/lhwTsiGqSq1yZ7GfSByHWc+9wm5ikyXcePG1WtZ3bqCMwl9jWfD82vK51rfEAjIuEAPsLo/YyWSXIztg8/ZYEo+i92FuYH+S9kz7CK27w33lmPXOTOJ4JTNH4iYahoucncVyaopcZss2DfoiYQ9xGbCmZ/cWlqL67HrD0ZlbCfIe6xtfmsPgS2sWTh0WScUxcL6yH4JrD/oHPHaN9DbmZfoKNjiyEa0wbcEJ1v9AtgveM/CbxAIQDNx9BR6+9XFuaI0LdSp0UJgQ6WuLVFTNOnDGJBoPc/iqvUyp3hqSCRnt+z+krapjdngbCM4Fg7zt27dzGISyekRUcgObhIpo/dGZY0Rx8moMaxkdBDJ6J6QUYffsX0hbEQMAjlGf4z1LJQImgjFGOHrWuPU1oq0AvqWqmL5fsMUqQpWyopFK2Xjus0yoGCIdMjrZM7Jlq9CIOBcEKQjZZSw8LL5I1zZ6HKrHCHcowRxjTwHHBIHHLC/bKr8RcqCNAIvkxnfLZNu7UaIE0w3CgHXjGCFYE3TWqKyvdkT/J3Fn2fKb7ijdGxaOSUFvIId14Tg5le2BkULZw5GVu55IinibHTcY2/z8rrC+bK5IXxx/4msIsokoiHXwchGDf9MkQSa05t+CZX0ntjkejcg7TL3CGsiWV49U6qcpSHzDYV45eIesnZNsXlWKBoNnZnhx6qytfLZmm+kemur5jYZ+XJgl70kOz2rwZQNd0YRxo7lP0ySriULpDA/RzoV5ktFVbUU5GZL5t5nyvJNFbXN8iw2OoRxxdh0Gwe47ygkKNNuoy3zHIEnkXUDx+7qshlSXL2y5hiBLOmeM1Ky0yMfg99HoWDOepWekqoS+XH9d7KueJ3kZedJ1YKgdM7vGqLQsz5Pmf2RLFw5x/x3j+7dZbd+B0h1acAYZGzzSq/QSGo4SgdGCeY/a2a8c1ZpZqAMl32EaXLbW7wyukggY+ewZt61n9karRyvYwHnN3OJ/Y61FgMPa+8BBxwQV/YeMgYOt48++sgYTZnDjz32mDYvVZRGBAMC/TLYIx955BHZf//9U3Jc5D1kdlsShbWC9Yb9mb3XzyHfkOB0JXKb/dPK48gSnC/7KucOGFeQu+vigOWY7OV+OgLyAQZodD3gt3EoEXVq+0fw/+znfnBPubfI2TYr3Q17P3oK8ib6E8d2ZzLj1OFecH3IzdwPHB+cK1nsjA9v2SIc2db4yX1CB3E/T4xaHJOMDzec2wcffGDqxfvpAWTW89v8PV64ZgzRnBPOolQ2sbeZDjYzBj2G38PA3dRgHHHv2Je5DwTBNVawVFOAeYGRkzFtgyFtuTKeIXOCuW3nPvOD543dwV1eiuOgL9veZOj/7rGLXMT4s/3V7HeQb3AiJPQMqtaJbPmKf9T8N1kaOcOj2k5YZ/k95p/fnLKlR5G9mF9cm7WpcN3MX3R+1id0BeaPLYGEnQPHqtcpZh29tmG1zdpq6gFUSsMSrbyi3x5JBRTsArY3qy0nHmnv834fexBrIIHfBE9dd911pqefjsvmjzo1WgBEieDIQNg88MAD5aabbkrKEDq/+AfZuLVsChN/3i+LpLS4XMYMOtoIrhyfyKI6N9krmywS3FhjOK4OhlbDyOwtkjsiLscGQhmCNoqPN1WSTRjjCJs3576paoEUVy0zfyvI6CWFGf7OmESZvvFbKaoILemRl14gO7fZ0yhCLNYoIQgMCEcIu379NXAEsPkjFCBI2MhYFnocDQhXLMQ2qpWohzJZLFuq5prvl5dXyLw5y6SsOFMGbleTIcH1c2+I/OKYXmMlGwPCHFHaKCU4PjD0oxxZxwS/402f5hz4Hp/1KwHA9bJh8PtcJ4J9rPE4adIk41hBSamPGruMZwRKlDrbEDliem1ws0jVN9zVmv9O6y2SPjRuZ1vQqZDNVb9IZXCDpAVoFD5IstLCoxkrgwulrGqWbNhQLOvWbpL1RZvNmBzS/1fSrVtNU8ikoUm1UyoSyEnIKRONLVUlsrq8SDIC6dI9p7NkbC3zVN/KBmOMaG/GFPOB58b4HJBRJIu+fFvWby4xzowObfJkc2m5lA86SDr12yEsghSlM1FFFgGe32VsMm9R5CNFjKK8M2+Z58yZ3IIMcaRaMtPIKvA3dKAQMC7BKk1kKRHJyTVyD5hHrCNcN+sAjmSbXVF7nLKFsqxsVu3at2bFOlm3YrP0yxtR21DUOjjd6x7Hx2GEootSwnra1EtRKTy4KpHy1TXlpbI6i6THOcerV4tT8Z0Etjong4GAVGdmiAQKJSNAjeTUGjfYK5h3zAmMJ4nCeKdWPyU0L7nkEvnLX/7SqPWDFaW1gTEMneL++++Xk046yTShJkujrmCIYG9lP8UQjH7BC2N9U8nMQiZHz/DrYcb7yPXs9Q1dwsL0btu4sTaDg8AFAowIdvKLtufzyBq2FIyVfZExbM8MngfrNcdFH/D24cBAibETGQwjLDII/w/oGDg0vLL7f//7X3PvkC2QYezngHPm/tGLw3uuZG8gS3mdHRaMzra8DboFxqxozwCDKsYrgqXsOaca7h2ynHUWpNJpUhfYQxkn6I88f/R39EI14NXI38i86IPI2dZmwBxgXCIXswYwTm0pTN5j3PF8vY6IRHUMjoOdgHnFv3EiWgeKF+YnwUfMVXSb9u3aSFZGhQj9M11Z2366uA2Y4t+UxeJ9m1XGvOTvjAv0HMaudTRGCuZiTHEs9gbWb2Q7roHz9vYRsaWqOG/WJn6T9UVR6oqxU86bZ8YYzrJkGqKzZ5533nlmXD766KMhFU2U5oc6NZoxbEBXXXWVPPXUU0bZv/zyy41ARYTLMccck/Dx5hZ/I5uJAMCYV1YhM76dJb36dZdDhp6SWqG95IOaLI2gU+PU8FIwRiR9WxqzHwgZCLUYO2NFOGysnC8bKmuM/5b2mYOlMLPuzZa/KfpctlTXZK5Y0gMZsk+ng4ywguMAYQWlze1UQJDg72zyCBO2CZm3xwOCg20S7U77hqLyqVLprK9pDvz5dBmxy0DJL8iTslWDjDGY30SI5TvufhIWd0ksaqgj9JDWx30lWoN0aq+SgtCOwEcZH7/7TgQ9z4bNBsELAY9jI0DHilriuHyf4yJYEfESYkAm0ji4UMShhFiOSPr2NQJdDFDcSIXHQcM5RR3LOJsqJ/Gt0PfTdxBJT77/hm3+bWsk1zi1gqb8VNv2adKhY6G0a1cg2elDJTOtjn0+qpeLVP24zdGWMUwkvWEbi9cVFGgUBAQVBGyEFsYmCixjGqNHRslakW9eNPdy6ZoNsqWsUvr26ik5Y88VyQgfF4xLFHGMJpF6TUQDAZ75iEDPeXjXHs7z22+/NeMcJcEqPzgJ3GOOdRvlhLXaln7jOER/si4wXlG8OU/ex6lj5yF/5xxQ4t2/vWDLNNlQWZMZ4mbHwv1l5fLVZj7z3Ui9ahDocHowP5iHKHmtOXoPFm5ZJ9M3rpD0QJrs2n476ZbT+BlTBsqtrfm4plcGpGWJdN5fJDP6nmkJBldIsHq6qTzlMC63js30wE6SFkhh7ecUwh5C4AZ7IVkbqYoSVxQlMpQiITsDQzgKPwYrMqj4/2T2UDfsYxjqmmpTYuQP5HM/2bkpYIO6MMQiHyB3uB2+yDvWQYHMxHOjJ5u3iSvvW2OjLcHpBQMozwpDJ7I95Xit08MGYkTK2Od9zhH5wtbg59/oOn497SZMmGAMyn69KDgOGSE4VpDHOB5yFk4QMsqjGbW4PqLrkdPQdTh/nm0qdFuegy212FjOAuRKzgPcQXPcE9urpC59aFoSyMLI2+jejEF0ceYI45txwr/duidjDb0YIz9Oh0ig79vSzImOA54ZQY/oKIxJ71zk7+j1jH3mKzoRY9+r33P+OBhw/HEOHAd9Bb2FrC7+bZs1W0eyuz8Njh7muNepGQnr4GCtQd9Hb/eDIEnsBuji7B+MR0VpCrCX3nPPPSaAij4bd9xxR92Dt5VGQZ0azRRqwtOcjygYlHx3dADCuBXaEmFN+WJZWhpa43/edyvk+AN+Iyml7AuR4AbcrDWODS95e4hkRlaY2HQR2KNFflYF10m1UyxpgRxZVT5Lqp1QI3VGIF965kZuchUvszZNl5XlNRkg20oNtZcR7fYwwom7xq4bDIYohRi6EZRM5PmAAb4NxaxiQLQRQquNYttcOVsqgkXGdt23fw9jGKf01KQ31xpDKRke8QrsZPsg5OBUQCiJ1GMDYYlz92vgipDINfF9orD4bTsuE+mTYZs3EkGOcmOiRRDSyZ5wapxuxiKHYyNztEiEsinmHm3ebAzNfqW3/H+8TKTSp+58Wg+RjORqgmKI474S5e81BDhOtVQ7q8WRSkkLtJX0QB1r2jolIhWfhjcDztxbJK151MvFcYagjILBXLdOLl9WzhSZ9bFIdYU4OYUyN3cHGbhL9HGGUsu8jBQFGA/MRUo08EzdDSeZAzjPMDKYEllbm9HzOZQi2+iS/45XwawZIxvMv9MD7Uy/FS9LS36RNRVLQp57QNJkRNuDwtYAGr9vqFgiFU6J6b3RLnM7M71QOjAMsH9YA0drZcbG5fL84m9N7xjgHp63/d7SO699/BF45UukqHyVydLpnttH2mamqPZ80VciJYtczzpQ49D4f/beA0zSs7ryP5WrOuc0OeegONJIKAvJIBshom3ABhNMMAZjs9h4WbMO7N/22sYG4wUbk0y2CQYJhLKE0ijNSKPJqad7pnMOlev//N6vv+6q6qruqs4zqvM8NdNdXeELb7j3nnPvrc+tFEc80a5Y4sVJz9P7yumYZYbYPAJH+/Of/7z+5E/+RL/1W79lym0WHI8CCpifACnlGL72ta+ZkrYf+MAHxu1O1jaCY9kI8nzAHkmQcCnV8mfv47gQFizl9YXAK0F6u7deOjh27F+CmNg8BLcRK6TbA7Z/wXkjtiA4mZ4pg61CQBKQRUpmNf0ac22CjT/AcRA8JkA8laqWEj7pTbcBAVw704OgND7LDTfcMF4uiOuRKaMmE7hmCGc4Hmye2QCShfG7WGU7uX+I4mzh2Hxkul9MwI6g7Cr+KCQgPjVkVK7jGJ92Kr+aeYTPyRif6b2w1yDmCn58csNy7HSOnbkNmYd/eccdd5i/QRYC1q708lcLidFYvwYjbeb7y9yNpvQuvhE+F+P0Zz/7mcn4K6CApQR8X+Kq+Opf/vKXC+KpCxDzWz+kgDkHGxnZGV//+tcNm/j+979/kqIWhS9KGtKR7SbOGJTTpWbVeFcoGg+pPXRaCcVV6qpWtXMeDCSaktJTw0GWRiJD49LstecxqDEUpiI0grFjCqHoH0MiMXljz2Wrj8TD6gidUzQRVaWnWmWeyQb82pLNGooNaihq9VDwO/3aVGqlX+KsTaXqsJ0FgvZT3RvbMCkvL9WOy8sUSrSZYJvHcYlGUOVPnKniIw268cYdGcmRqYCSByMf5VMmQwgjCxUKQdpszqytBof4QB0G8WH3PMmnySPfTxAbxwYHB6PO5xmVW90p5yqNSvE2qx9LFvD+22+/PQ/VOePdkWFczkx9hZOIA0XT9EzXlQC125Ef+ZiC6IjU/7IUHZV81VIpYzQDUZjoZwRpIZCcAZQv7BIIrF02uTWlw9GwWarfZMrxOFwevI6pvyAWVd+Jl7Wmrl6KxyTnzMpcsMZyTwnuMNfs88XJx/GGjCMwRCqrXUMaxzvf1NZ4YlQjsWeVYKybkelXwLlDTgd1cSfW5jr/GvVG2hSl7NjY+F0emFySgX4fzSP7NBrrG3/dULRLg6f9pvkpgEjiOMPxoEZjQ/I6fQpMsSZfjLj7/CGr38TYXHKgaG0/onetyS0A0jJ6Qi2jE9mBvZEObS27Ym6IjUh/2hyH8E3u4TM1HGYdYD1MzpJkR8kt02OxQKCNElSvfe1rjeMBefhv//ZvpldHAQUUMPeCKYj7dIEL+zL7NEIV9jv2N/wM9sR89338maWkHsdWI3CJzZFrje/FAtfNbhA+VQCX/Xyq8kz289jIufSAwMZ/29vellcmJ2OIQDKkSnIz8fSxkGm8Jft+HCuBW+4N5Aa/2xkK+ZTNxN+ChEBVj7+CAJBga77jF5+I9y0WoYH4h3nIdV1KxOBShalq8MQTZqywZiEyypZZkAmMXUi/bGPY9pXx6WdDLjG3yGaAsGA9QgRlH79ditrOroJcZNwyLxmL6eXcFhrD0U61jD43HmXpCZ/Wcr9VQps1C0FYJlFkAQUsNiAsyU6l1wbz6h3veIfp67eUxQ0FpGJpW20FTErLxdmAhSdwhvHHJhel98KYgYlqBKOPNGM2XtJ1Yfth9Kdz/tkgE73FUketvB631m/brsHy501gMaU+PbLeYLsUGZK8ZZI/z5IVzlIpcL0U65bCbVIEhTFwSIHdknNyY0DOkwAxxudUBgXZGcmEBvA64wqmVbkqcS+fkszoDJ3TmZHjhtDguJp1XBtKtqvBn/o+j9OjSyuu0mB00CKC3GVZ6+dnQj6p7cOxIwolLBKDkFY4cU7F7pVGec13+5218tXMrHwIRhPXGGKDa2yXSOJ3WGt+Z9xlUlDZoFZnMghO22r2mZQqsJuKmaaAh5/R7u0JrVmT/jkTTdcywS5XlDOpwr1zbZBiR5PIDY/kyq8OLwFsjFGuWS7Nq6aDnU6e4nRRhubcz6QYvT8SSgw3q601oKhvSD6fRxUVxfJ6bcN6/gMHg5E+HR86oGB8RH5nkdaX7FBpEhEYjYd1bvSwhmN98jj8agpsUpF7whHD6MU5tUkzxt2Uxm9oxOrL4yuWIDSmw3C/dN9X5Tp1UsNet4pWrpVu+S3JP7HeoEDEGSGAytiBlLSbi6fDTlPHMbabnHK8rJXU6aR8G+sugRLG8kzKEgRjh8YJDeBUUOHEPjMs3Y7Vcjss1ZjX6dfm0r3qDrcqnoiq1F2t0gwBdAgMi9AA1pgainbqXGfcBDTIjuJc+iLtOjV8YDyoX+NdrpVFWxdN+bXQGIlZzVhtcBWGomM9dnKYq+dGT016/vzombkhNdylUqQvNVPDlbvR7XD45dKusWwN1k+XXI5tcjgWtyFvrkDQQOAVx4M9B8eDrI1s9Z8LKKCA6UFQmeyMb3zjG1kFU7zmpz/9qbHBUe6zZ9ulWfATpqsnTwAaYQzvYa8kGMteiUAgBfGgFO20fnbXWyX25hmUcyFYaDcLnk90h3o0EBlQwBVQvX9CFDHXwI7JN2N/OsykNxLHQclArjH33i6ThW2E6InnWL+xmbLZSZDY6fssfjClQxg/M+nDgm+COhdVPCV06BmTDxC04HMvNLA58YeZTzlnoc+BGIn5TnYL98gmMi8kMF4Q70DEcr6sZ9MJD5OB2I7qClOdN5/LmMx0PfELKaWJb8F4xS/mke3+YYtzj/ksyAD8WPrC2GI/5iIZS/gsvGaxyLVkdITwnZUivHnuyINav3avOQe7GkQBBSxFYPN86EMfMplEBfHUhYdC+akLAGxqf/iHf6j/+I//0Ec/+lH98R//sdncHutq0ZdO7tdQNKJVRWX62KYrFDzXYQytdAeflMXkNEbABmM3pAVssgTkeC3MJJs9QW2ay46/j+Bq19PS0ImJDy/bIlVfMvMTjKO4H7HIDOfkjAUMBAw4yqFMp+qKxLs0Ens+5Tk2+3iiRqN8h6RS9wqVuldmNOBCsVEd6H9S4XhofEu2/3fKqb3Vty5aYK8n/KASaUF8l6NMFZ7cyjpNZySjYMHYQIVnOwiksOJ85nvOGC58Jo7vVO8lbdpuVkb90EkEwNkTan7gbnWNDOvSd14qOdPYKTdllTLXumdsM3ambdwWb5XiBCFjkgMHeqOU6JIo7YUa3rl82mbbnAPzFAeJQDfXDQNzNmOFz4GgtJ08HAocO7up47Hn7pVj6LjqqsvUPzii0WBYTfWV8q3YqO6e0woGI7r8sg2So1ryXDG50TlkyOBpKR6Rihok/8xrnKLqf6HvUcXoezIGp1zaXfEq+Vx+MwePDT2p0Rh9AMYIGjm1qXSvfK7i8ZRwSDMMfMYDzoOtUEo97qj07I+l1sPW71XLpaveKPmKjNNsKxMn4YFvSOdPGCKkuatfAZ9HtTv2SNe+MaXEAYF9nB7GPesfz5njdTjM+shaSPDDHt+MM+4Nay4qLs6F99vlGmYEQ2K1KpE4ZG5bLME1tq5c8m30OLbJTWm0HNEbblFb8GDKc+FwRC0vhFVTahE4Gzev14H+h8YJDRtri3ep0ju7OuoXCr52+mkdGewQdDFjl+tf4oaIK9d1tWu1qTQ7Gc3rn+q5d9L1g9AgWyMvQNpRVoy9i2wZzzIpNip13mf9Dyi/V3uD5K2eAVEKeeOZ8wbhC1mq7l3vepchvv/93/+9kC5eQAEzACQh8wibH4IwWzYu9hRijXRFP/YPtlyyWp49nOxd9mNblIFfgV1Dbfjkvmwp6uJYvzT0uNV3D9A3rfjavIjbfIFim317IRTERweP6cjgRBZfva9OV1RdetELBrDn8DntsWHEGF7veJB5JhkKjKGpSmBhp0FWEPQlgE25qnTyw/Z9yA7OV5H70EMPGZt1vhvbM384TggFxirng407WwWxXW6Me8Hctu1W5gICI3wafG6uMbYwGS6sERwL853enRfKuMVW4LzsjGTGIxUF8s0UY+3CL0nuX5MOrg9kXTpBalcugADmOLjmEB3MB8CxIHTEvyMuY/shfCc+JWsnQlX8HDJWl+K1Pz70oGImY3wCpw63qyqx2Yy1W26ZXBK3gEUEAqlRSqg7pKKVkruQlZBs13zhC1/QJz7xCZOd+Dd/8zeFrI0ljgKpscQBC48SkWATdaTZEFB/t8ZD+uRLjyYF3B2q9Pr1uUtvkcfhNBsihh8GkN1IDlUUATqMFZwJ0hdJMU9WsbPp8nq7gZT9GMdom9T2wOQDbbpd8k1uMDdbcA4EiqnFmEu6M+VaBqOPTXq+yHWpPM7pgz5HB/ark+yRsSubTmxcXXWz3PNRkisH9IQfVsIEoSbgdlSq3HP5rD6X64uCBTIMp3S25QBodsZ9m64mP0YlAWy7Di5Gst3UDAzvf0rPf+7/qKm8WGuryqWGCumtN0huAudkVGyXXNlVaDhQGLJTpuNSviq+P/U5R6XkJDOjRsoQ7LPLImHkozADjE0MUIiMfEptZQPG9w9/+EOjyrGbm/O9qHxwzlAMbagZkaP/kDq7+1VaElDAP6Zwa7pdx04dlNsV1pq1GyXnssnnQbmqlnus8lV2Rkr9NVLp1EqfhFCrn7cIINWMlbKRukLndWxo/+R0zpKdqvU1GTLj6CCBilQ0+NerzrdWjz32mLlPtnMLsZDeiHscLz0oHXtyYlY6HDoVL1PHMquHDOOXNYzPYt0YV0F97/+zsjvGcLClQ1s3b5Ljzo9M+gqMb7Is7MaWyfcdQwfHg++wG4MnZ1xx7zjujIZ7PCyNHJRivZIzIAW2Se40Ui4RkaL7JNfQGInBvbf+FEyJkzvkcjTJ65y+ZISNYGxQp4bT74ND60qulddpOWnD0T4dHnxq0msa/Gu0LGA5hBc7yMqA2Dg72qdwPKGoPdTG/v7BdXu1oTQ7CXhk8Hn1hNtTnltTvFUN/jyCZtz0oSfGFMtjcxRSo/gya4wEz1ukh79ecmV3sC92MB/J2sDxeO9736u/+qu/mrbUZgEFFGDZ1/So+eIXv2gaZN58882G0ECJPFX5FIJw2D74E/gY7I34JaiOsVH4G34DQcP02u4E5tgjk/2LlL1y6FEplpaJ5q6VimfX+2CqEkIEhxeiIfhwdFgPdNDzLBWXVe5WU2BuMyqWCmwCgkAsAfOZZHqkg4xabLPpMhQoJWL33WBMkjlu99CwA8xklOMbz2TPwD/nvXNh8ycD/51gN6QM+1tywBvh2VwEhSE0CJ7j89nCKa4Fdi+/Q5oQI2C+8nz6PKZyRCaSaCkCMogMMe6V7RviR2VqVJ9tvcM/5fxZF1m/bMFctlLP/B3/M5Mwy+5/wVxIroLBfeee8BxjCj+I+YNvlOwL2evnUkTr6AsainakZGqUu5erAV+ngKUFKq50PTzxO3GC2psk79zH8i5kMI+xj7B1KP2fbxnpAhYOhfJTSxQw/X/2Z3+mf/iHf9C73/1u/f3f/70JoLHBMcG+tu9RObYsH1eDoijtGOzTD37xc62tqjUGEEoCgqzpag4clmx16tm0p1R/UHIqE6JDc05qYHSg9MonHdzpCCjg2qrR2Mvjz/mcq3MiNICVzZHcbHfiN5/TL9cUTannGwHXKo3EUnsGBFwzV5ZhQGFYYRxhwDK2UCyhIJkuJRcHBaM42alIJIKKJZqVSIxq167d5ndKnWQDDi+lB/ifY8Bw4/txjLn3se9+RVevqJfL6SByJZ3rlb7znPTbf2AtXdOMCcY58wDHIKvDGk/qScLH8ZmOfiVEgN4vJRpM9oZDZePGMY4QjgUG6VRqndkAAxZCAwMXwxYng400pYbyaIc0cEh1NeVJZWh8kqdMGza/yjgnx04GtWFDBmKg96BFbBiMjfCOp6SS1Vmvq1UG6ekxBSWvOaWEthPuzlpyjeym7LC+B4cX8sEmNChlsbzWKWf/QxKZH74mqWjLBDHTcTJljrZ29WlotEt77vpgyqfjuEDWocAzKC6XwqNjWRCSx+2WoySzyg9nDieXddh2+BhHHKN9nPa4Sh9bWZ0NvnfwiYmADVkrkW6p/KbUoHT0ZclBRotjfN2zb4kzkd4JIT+C1e8qVaN/u86bbI2EyZZpCuwYJzTMdcmQLcdrfZAwFyke7zqnJ7vb5HO59NrGNVpdXKb3r7tWfeFR/eXhB8dIvLFMGYIqXSenJDXWFVMqw6HecLu5xssCa1TtadBApE8+p08+Vw7XMtI+UYLFHu+RVim2VnJXSUW5NUWdC5wdaVfLaIfcDpc2lq5UuadkyaWL33rrrUYAwnynhI4dwCiggAImg8atqA/Zr+655x5j95F1SPCPbG0C0NhmySBLAxsIu4egGwIUSgYlB9zsXlLZMK1oJj48uWeQeW7uwfkgEMm14fVsMWJn1yWB/WTEiEsuPnB9ydQhKExvOxoE8zuB9KmCssmlpezfk8G4zKXkEgFnRFao3Rmz+DUQERBwiLkIOM+mPCzngQ+TLbMpX9gZT5wbx8pelk/vknzANeCaQEJic1ORgXmQXmKY+ZqpfDDkEOQSr1/KzckhZIiZ2GsShABrXC79Y+yxR+88mnMnE188T6YO60emewSJlq1MFXY995Z7bZMazBFEU/iW9uchnOP6pn/+UiU0QIN/q1pGggrG+8cEpVWq82fIuH8FoS8c0jeaj6krFNSW0kq9ccVauZZCtkofVU2S1lZEUv37pdobF/Oolhwo9U82K3FYhOVUzPnUpz41o5LSBcwvCqTGEgRBzLe//e0moEbtRDsoZ6d2E+gvKy1VNBSS02dNqpG2Do129uiq33ifVplmwZlBsNhOdQS2CgQQQGWzJ2gL6ZGxCR09NDLBk+X5GYKgOY8pmwRngde5TG5HlWKJYTmpH+7IPQBT4i7TUNTajJW03HscXm0tW9wUcb9zlRxyKxSnUbhTftcKeZ0zU5dhxOLA4hzghBJUtsH9x4jjuUxGGenctuNqX49EIqz2rp+rqspW8wwomnhWbl1lmmFnAsYyJdFwCjDkIOBQXzG+MRTL68qloYl7Ye7GyJBVEipHsOlM5xRwj2O4lg6a5VotdC1Qlu0MV0sJ7VbzmSGjTMTInO9xwOdzjzB6cboyOl6BOqlmj9T9jBX8dxdL9ddJTrchqJg/WR22WCp5Z0D/GEpRubJt1JAJ9tphv/cIZqwpreN3Fpt+Gnbo1+8MqMJrBX75m99ZomBSwMLQIi+1afXqdaakljmsWEyDPae0cnmPHUeWRo9aGQ6lY0FKX1EK3UgpqT27UlVAjG2cWQjRcVzxGtNTwzQIT0ihuBTddXPWTZAgD0458wACg3kB2WGrUBmvrJeUvsFBnBaU1SBDY+KCW9cz3ColZ0AkKHuW+SMmrpzV68XtyE1plowK7zKVeuoVjQcNgeFMI2rpz0FGRuvoBIFa4q5UlffirIP7o9aT+ucTB0y2I9P6Z21n9A+7r9O6kgqVewOKYegngSsfYQxNAbfTrU2lu8frKneHOvR070OKj1FSKwPrtKp4mqyXhFUWchKoNz+HCMfbFI63GNLN62qUx7EsZX07MnhGT/ccNK3EwbGhZv1Kw15VZrMF5gEv93fp1EifqrwBXVnVKFda5pmdPXXffffpk5/8pNlH/vRP/9T0CFjqDX8LKGAhgf1Piam//Mu/1Ac/+EF95jOfGZ8jdv8F9js7oAz4GaKD4PB0AVzbx7A/0/4Zm4T9EruGvRkfJ6MdRc+9WE9qpgbPzSFYl8lY53wWsgloibs4RShljoWSuJ6Lqx8Q6n5IM9T93H/WY0iwu+66y2S4YvMTQMc+TbfPEVshdMH/TCab8IcZQ/k0xMZ2w26DyMCPRpnPMTEG58KOZ1zP5f6Cv4+4ayH6QzGn8fMA35lvtgmqZY5zKQfYGWuQNslEDeNhuioC6WOZNSKZ0LDLb0GaZRtDjGP8mWzguiWTdRAkZH4gNOR5xj6EMYQzZb4uFLgcXq0s2qNIYtTYjG6H/xVdbmogEtY7nn5QHcFRs5X99HyzXuzv1p9vv2Lxrwul31OA2G4y8V6AlXH4vve9z2SnIQa5++67jXgqV3K0gIVBofzUEgKO+T/+4z8ap/z3fu/39OlPfzqrqqltsF9v+9d/UKK0SLF4TP6Kcl234xL98eY9Uy6UdkokKmZex4NNmw2WoAAbNQpxlBlZP6f7eaMQH0flLqli7lILUfIQOMwpUDhH6A6FdHfbWYViYW0v61JMlnLK7fBobfEWVfnqZpWlEU8MK5o4rUQiIpejSi7HigXf0DCwIAxs5RJGEwSanY4NbCOLsYiRBZGWbmCTTYEyJ5kQiye6FIufUDgc1Zq1jVq2zApmuxy75XRM36sBQw4nJmW8//gr0otPjyvrTcTxkmul1/xGzueM85R8fulIxFsUToyNZXM7HPI4ULCnNS9QiZ58IrHgaYfMVxRrOGE4hBnB9UlElXC4DTFJkJ17DWGU1eHqeUnqSW5wSImHgLTq9VNkaqDq6M7wl5sMyRaJh3V25JhGYkMKuIq1smijPEkNPiPxkFpGDmok1i9XwqO2l0e1dvmmSXWsX3ryP7R9fXoGjEOqfp11bN0t0qPfGDv1hB4/dFrb73ynyrdeNZ4mzlqWsczBYLd0ll4cDsVXbNah5vNGhZHNoWOs2yXSuAeo5wj88D2UreBvrFM5zeVorzTw8OTzCmyRAklOVpjyeUNExlNel3BUKa5liiV6TYYGhIZjml4vs8FgpNfcK4/Tp0pP/QXbd2EqMH7uevynGqFPyxggN66rXaY/3mKV9fvyqX060H8+JRD1luU7tbcmt/2JefF0zwShYWN72WWq9E5BSkf7pMEM46XsZsk1RQ1yMrAGT1gEZaBBKspe1iQcP6/R2Espz/mdG+VzrRq/Pt8+e6+iSb1ycFRXFzfq2prdWgj8V8sRfa/l8HgwcHtZrT6x5aoUYsOe9/RyYj2xy3VCln7ta1+bvq9SAQW8AoA6nWwm1MuUUcBGyAZIDICdxzqQa4ke1Mbsjdgg9r5IEJDAILa93WQ8a8ZGbEga/qWUCI0tOAGp5FqrXOMcgPPB7kW0sBhl6pqHz2p//8Sau7popbaXb138ANcswRgh8MO1Bdjz2K4EkJPFUTbRj+iGv9NAPBkQanwG4yXZx+CzyQzCJmYs5pMdwPijLwFq+7kEfipzKqMAcIaN6jP2kZtH4DPce++9hqycrqcM15H4Ae/B7k3P5FpKIKMMAd7evXtTiBfWPu5brg3C00vRQuawvkE4TJd1xv2EWMkWy7D9cXxxHoiobNKY8cDfWKfmqyJAAfOPbzUf198fPZAuIdT3r75Vq4oXmczuekQKTpRbt/pqrJKq5nadvBhgZ7Ayp2ki/rnPfU7/9E//ZMQhH/7wh+cto66A/FAgNZYISDPECcdAwgnPJZ21Izis75x+Wc3nWrVj2UrduXyjyopzVB1Fh6263O5SyeEyxgr1RgkG2j0OpkSoxyo5RYaGN3OK5UyAwQByNTjmAudHR3TX4w+oOxwyARuPU/rs7m26tLJKpe6KGffQSIwpfCnbE4pToz4+vnm4tFxeV/5ZKFN/X0Kh+JDiisnvLDWZhAR5UM4RfMWwQ6VEUBYjzc62YDHmvZSTwiHF0cPZtfs3EPBNL+XBeMGpsB2xeOKcYgmr5NeJE+fU3tajSy7doJKiK+V0TATuMNQw3tIdOJwYDL+UhoGhUel7X5ROjzWEXr9duuvdkjf3QC6bUHLzynRE48cV0+mU59yOhJxpx3fsWIdczj1zUg84KyIdEup4shJoyBzYpGAorJ/85CdmPnBfSFXmgZHLNSSYR7CdYAGw5+8kMgPiIxKU3F7J6bIyO84/Io1Y801On9R0k+TPXqYtoROm5FQqSuRQbrWuByMd6gwdM07FqYMdumXv61VRNqHEY7wx1irdJ1RdMpKd1AB97Qoe26enDxzSlr03qXbLZePlpnA2cnUCuG5k82RLEwdcYzI/qHHM65KzmvKCSe2932r6nNQNSeU3SK4k1Xu8U4o+O6ZQJYOIHxslx1azVhcwd4glEnrtoz+a5HBcVlmnv9qx1/wcjEX0reb9emmgTW6HUzfXrdet9RtyDkL1R3p0oN8KENpgn1lZtF4riyavJxDf9K5xyCdH6Kw0+uL4u1R0ieSbIjuHMib0yomPBQQ5s+orpPLMysShyFOKaSDt2Hwq81xnfo4n4vqP5p9Nel+Tv1Y31+fZ9HwG6AyO6MMv/GLS8+9fd4muq12ZNZiBHcV6+ZWvfEVf/epXjfNBMPdCDxwWUMBMgH2HX4FY6rd/+7dNpkYuymzehxPPfso+ONU+Od3nsNeSHZFTY2XTV6p7TGxRLc1R2VfOAZsYdeViZnANR0c0GB1UwBVQ+Qyz3LmmCZNjnNaTZAGBv4DynfuJH8GaC4mB34A/QWaE7U+S5Yydzzi44447jA+BfY6oDn832WazG4lnGieQGxAUqOTTyWrU8RAhmey/6XyBmQCCBbt1yr59OX4OfeWuu+66Bc98wGbG7yY7HtLILsdmH4fdm9AuxYoIaKbrwELALl2Gf8o8t+cGdgH+J8/jx+UyZyAZIDXsnhaMO9bNfEqW8Rl2c/LpSmQxNwiYFnDx4EsnD+nfTh02vkYy/v2KG7S9fJF7V5CV0fmQFB3zAbzVUg3VHgpllbKBdZC9jT2Oxwc+8AGzD+Fn2JmuBSweCnn5SwAELWlCQ83R73//++ONktMRTwQ1Gjtp/nc7ylTrW6PSJw9qh9OpDYGQXuo+YAxElOmZHBbTBHxoUN6hFxTsO6HDx88pLq9UeYm8RVVWk+Zc017pnzHHPTQIHGIUE3hfSPzT8UPqjYRNYAs3IRyXPvnSMT158x0zJjMGo4cUGuvX4HYE5HOiWpt4TUwtSiTWy5FHKaWpEEtE1TzynEbGytu4El41PxPUpp2rVbW+1KSEVntXmXIzNtFBkBYjD2MKVR2GF+W+ULeQ/s14wZDLtFCn1xJ0qHpsOYlq3bomHTl8VpGIaV+f8jqCTRB4pIInq3xwVCYZ876A9Jsflkas/gKagaoBMmAqo9JqfJ1aEMDkaWCAGNIgpgMHTioQaNTGLfNJaHRJyY20g/1S5Lz8ZTfqjW984/hmaqsgURpxf7hmqKsohZTVSB/slPb/SAoOWn0pNl4vrdgtNd4ghfssRbeP5ujTjUWaiHMvusZ+R+VI74DpMRLt1bHuJ3TicLOcToe2Xr1eA64TKk9cZo4box4n1cz9UEAafDpZOyL5lqdkkBzvGlRntFpXv+MjxkmBoMO5xCnMx8HntTg7qAKzBTl4HidonNCKhCxyKN9AAte+9Bpp+HmrDBX9Zop3pRIagJJy7quk+Pmx9zVJztxLLlwsGI2F1Tzca2rPriqulgcyLg0dwRE92XPOzNcrq5vU4J8igyED+Ozt5dU62N9j+lLZuKRigoj1uzx655rsiubpEE9nTMb2GS9EYhqi8XOKGHLYKuHm8W6T23ublSZO35MM70lB38tjhEbSl/Y8K5Wtn+hJk3Icqdkj9rM2nA6nan2V6gr1jffvAg1TkJ9zia7w5Frz7CpdEN5ZwBrCg7XyrW99qylBRxkq6rl/4QtfWNIBmQIKmGsQlHz/+99vakJ/5zvfyStoRsCabEXscuYRwicCnjt2ZN73sVHwQRCnnDhxwii6bdEM+zv7c06KRuxiz+Q6/rMBx4Vty3kstqqy2F1kHjNFKN6jvvCLihvy26MKz3b5XfPf6DzdX8Nuw99Mvp7Y8thijAGyhvmd1/E7pAK/U5oK8sEWlKRnXUxFOOHb4jsg5kknNbDnvve975lgNtlAtp+CfzNVKaCZwrY9OZeZjinsT8g+BGaLUcqJ68SDOQrxxL7JveV68RzlvhDyTNsLZ5HBsXLcjAvmuF3SNnkdzLWctT1GIeXsbDZ+p0xypv4iUwGCDrIiW0NxwJo63vuvgIsKl1XW6osnJyqbsEqUerxaW7xw5Vuzgt5+9bdJkX7Ln3WX5+/XvsLAnkJmHms+4uD/9//+n/71X//VPPfv//7vhrAvYPFQyNRYRLBx4myjoPr85z9v6rRlQzwR1kDkCSVMk14ruOB2VCrgsEpAEITGELSdj2TjiE0V5QIOR7m7R8Pt+xXwe7V5fZNlPFKLv/Ja6fyjUqjXKkNTf5VUMk1qqalNH5uiBn/uINiNEZJuiCwE3vn0o3qs21K622BZP3T7XTNq5jQUPabRWLr6n+BY6uv8TgKwc2MotgUPqyt0Wi2n29TXM6DRkZB27N6iQCnOgnUOlM9aV7x3vBEw5AaGOI6BrRTBKcDAmk5ZkgmJxKCippTTqLo6wzpyKKYdO64Yb4RmvSahRx55xBiHGHpkFuDsoAbcvTu3ciZ8BqW8oolmk/3iUoM8zk1Zy+PglPPZmYi+WKJ17JiT4ZY74dXBlw9qaGhUO3fuUnHx1XJAAM4Xhp6Rwi2Tnw9sTS1NlC8oq/PLL0smOJgU6LzkLql6Vepc7j5rvb5qmeTNrOK0Apt8Fg4i9aGnd8IgsfYdfkARd7/Wb14h71gfILCh5Ea5nd4U4glScDT8tCJOCBTJFfeo2HOVHE6/cSBRS6EWS87mojawnXWU9yWKxczYx2mbMv2+t1167DvSUK/k8kiX/4q0ttCMeD7QHhzUv596XMOxsPm91lei31mzV8XuifXy1HCf/vTFRxUa628B6fHpbddoY2l+e0h3KKhPv/ykjgzSwF26o3G1PrB+15w18jsycExnR0/IR4LUmForlpD2VF2voqTAVjwxpFD8ibR3O+RzXi2nI0eypv0xaZg+QGlY9aaM+3QodkrB+PGU57yOFQq4J5SII9GgHux4Rj0RS821vmSF9lRtn5TNNh/oCwf1oefvnaRy+9jGK3V5VW6qKAKrqHT/6q/+ypR7+OY3v2lKUhRQwMUO9sXf/M3fNME8spbyFQzZ5X8ITiOCsUsxpn8Of2MPRxxgB3oRAWDfLQVwPAQ7uQ4XerZWLBFSZ+gxk6UxAYdqvXvlduZH6ucLxFCQVfiS+JhTBWIhkVh3CYgTGCdLw+4zkGsps6nG5bPPPms+AwIj2d9ljWfN57t4nnFI8Ing9HyU8YEIgCybqpRbJuBzcU0IdmP7XujjcrHAWMSXtUtW41vONGMCQDAxzsnOsMk1/FPInpk2lee9kFeIz/LtXVLAhY//ajmpvz1yQJFEXDVev/5211XatthZGgXMCdjPyNxAMPIP//APpuLO3/zN3yxKacsCCqTGogEj69d//ddNQBcnmyDdVAjGmjUaoylvKkrde+R2ZmZ82eTZ7CE1UFaZ0j7dj0ujZye/OOSUojQgTdJHr7pD8mdoOk6AofUZ6dxz1uuLa6UNt0nektxKsIx0jNX7rlHC5TdqMAyRbBkq842/PfKSvnTyyLhmlWDW2uJS3f2qW2f0eT3hx02T8mRgrha7k4uPFMvnvGrODNmHD/5AzS0ntXLdMlXVlGcJ7jpU412jev+GSUF/nA7eg/GF4YehTS1QiAcMMRZujD2cQxwF1Co8cGB5DU5EusOI44Gxz2fiBOMM83rIPOqfk3JOpgjGXj7NlqLxZkUSqXNhqnJejH96a2Qq6WYRJNx7m1DwyuO4RPv3H9fy5dUmFVsKjDfJnTcM7bMaRqfDXSWVWaVgZgSyNJ6y+k+MA/Jn1WXS+rHrER6VHvum1N9u/Q6hce1vSOX1s06pZ2xBPhQ1BtUX5fxSg5M2qQGpgEPCGAzGjisUTy1z5XbUqdi9y3wear9kJ9b01Xj88fGatzMBn8FYzVrPOBqR/vsfpRBl+5LO4ZZ3SlnK4GRFnObgA1ZmjPfiahA6V/jC8Ud0Pjgwnh2AOn9XxXLdtXyC+PxfLz2mlwe6xtdtVry1xRX6/3bdMP6acDym5pEh+Z0uLQsUZ11vjUoxGpbX6VLANbcJrAf7D+nU8BlT1s7ltDI3yAa8se460zg2NUvDqkeeDI9jh9zOHNWB/YelbkqX2UB9VSyt+LWMCiyrZOEJheNnrewRR5P8ro2TCGJeF4yHTR8L7wzLMWYDAVP2APYLap6n712/7GrRF048N05s3Fa/Rr+1ekdOeyeEKupNPpPPJ1ODbNj/8T/+h/7kT/6k0ES8gIsS2DzUev7rv/5rQ+ZRdmqusxNYE5i7zDHspKUamEWhjY2aT3PgpYxgrEO9keR+aBbK3VtV5J6fHgcQBAjjEH5g5+ej2qe0EeME+x/CAR/ALtcE0YSICt8PHwLCBOCropK3e2ggeiNjPLlxOKA0EH4JY5vxR9YHY/CFF14wPiWv5zPxP9LfO5ewyYl8SDwyp2g6u9hZQxcy7JJN4/GNKcAYmq63FgQVYzW9Rwo9H/mO2QQqsUMYh4Vg5ysTkXhc/ZGwqry+BREEFZAaH4CgZA+Zrm9QPiAuxl7D/8Qk+Jm+yPga3/rWt4zAvICFRaH81CJMMNKVPvaxj+kP/uAP9KlPfWrahmcol6OJzvGwaiJNHS+lkgGoDOz69GzEKQaoyz+p3I4JB9FcNB3DrZNJDVSxR+6WBpMCsMNd0rGfS9veMH0wr/kX0qiVFZFwuHRwcIXWbb9yUdULH1y/Wfv7evRkT6f5nU3ns7tn3ijJkWFakSJuqdvjcqhUXufOOXEAIQUwqAMNxbr0akoCZKh1koQYtYrTwGKMMYfBZytRcERwOHA0cEYg4XAyyKRhAUeZxxjD4cChxVh7+OGHTVDKvpcY6/bnYXhSugBnG/C5LPx2j4h8EE2cn3xeotlVZlKDzYyUQbtBYTL43ePYrERijcmCcoheFU75vD75fZXm9wWBtykzqeGYJjuk+yWp94h12yvWSzU7U4OXHl9mYrH1kDW3l2+TDj0iDSRlKoWD0r4fSbe8d8anw7WmRjDZExjxo9E+9UVTM1FK3LWG0AA4rzioOJ6R+ORm5NFE9/j9S0/R5zm+A8INpehMjhWlxZS9hPo7peBQ6nMEfttO5kdqBHul0/davQ9A+Vpp+asylga6mGEF00dN9lhyM3kbnaGhlHJHlIZqoyRbymtGUoon8XMXBN0YWkaG9LEDv1Rb0LrWV1c36NNbr5QvQ4kHxlB5prkyB6j11ejk8GlFaGsTsyjtIpdfRaR+pxyDN+PybZ6fCgT7B05Iw+essi2BZdLo2FrCdzRcnzWl3Mwd13rzmAq8LuCan+vDnGcfY/8heJDezPWamuXaXFqtltFBVXn9WlE0vfiBPQolL+sK5VEYb4g8CPB+8IMfNGqqX/ziF8bxWMrNTgsoIF8QOEYwhZgEZ57SMnMJ5hKBZMgChAhLOesJOxN/6GIhNEC27FjHHPUdydSfwG7OnV52Nlfg45KZQDaDrX5/9NFHjd3GOMJPRYCH8Ai/AVuOcWz36yDbgqAwBFqyAAoxFuu73cAaYYq9X0FmYH/OVF2fDzgm9q58SA1s1SVPaERDUudBKTIsFdVI1ZuXTHkayvBy37n/c/mZmdYKSA4yxGf6XYxxfN8CofHKhcfpVI2voN5faLAXsM5iB7GXsWfwmC3YiyBKIeghVJnf2EO0EUA8hR/zd3/3d3rve9+7JMUeFysKpMYCgsDxe97zHmPM/fSnPzUqjelgnPHYAcUSPePP2ZSE6R/reFnxxKicDisoAVMImFAZ63OWbJZGmq1mxNY3SMUbpeH96d9sNRROx9knUgkN+7XDnRKlQqYqRdX1ojRqEQexWFwHj5/V5g0ueec5TcsoykZO68TQCcUSMdX6arWjfIc8Y4pTlLlfvfJVOjTQr2A8qs2lFSqeRRPBItcaDURTlVTF7o3yOSmXEZdjDpr9ck6QCvyPIxBOjOjk0FOKm/JkY+Wm5FZMkAh2pCyhEpT/acjUPI+xgyNz4403mgWZrA27NBh/Q32f3syd47AD2elKLlRMyTVF2QBmWj/WIWeG2N/Um0ZpaalxbrNlA1EGjAa5yU0tF8IZGod3meQ5J0WS5xY1y6ZIl25/Ruo5OHZ7E1InvRqCUkNSUNBfJjVulc5bTdzHswwGe6QXfmKVj+vvSM0+4LOGJhML+YAxw0ZvG/EBd4VWFF2mztBxQ6wVu6pV79+YEtjEqcXYMH1mEpOJQpzibOXpKC9GEDPfRpDca76XbKQpSxNkJIcSVm+N3L9Mar4/lUDuP2ky1lQzw+bjFyCCsVG9PPCMRmNWNludb5nWl2xLyQ6o9BapMzSY1E7doeq0TMD1JZXqDI2O98LgNetKJvolfPrlp9UZnLjWT3a36RvNR/Q7a3LPCpsL1PnZb7bqYP9hxRU32RmXV11q+lUkw6lqOVWluHrGd3mnauRM60s0Cd37pe4DE2sgn7vsJskTkDyleTeXD8ZaTGYopJLP1aCAc+2sjPKzZ88aB8A0tiUzLho189UmIJnzEA6sFyipCGSlZ0xV+wLmkQtYB/gOVFK2etNej3iwb/GaD3/4w+Y4KP/5mte8ZsbnV0ABSwV333233vGOd+jOO+80isG5LrcD+YjABbV+Ptm1iwHWHNYB9vaLCV5npTyOckUSEyS/y1EsP7245hCITAjkskbOpg9RJl8B4CNgH0JosOZTpsoO8rM3pNvfHAPEht3oPRmQLcmvZ5+xS6ctBNhf7NKSuYBsgHzFXAsO/PmjP5JCY02Euw9Lw+3SyuwiiYUEpC1EW65AbDddbwsykjIJLG1/NpMwLpfjhNQY78lXQAEFzBmo/AGZzdwFttAWH8KeqxAP7GXERRHfUtVhOjH5dEJihLyvetWrxp+HrLfX9L/4i7/QLbfcYsQlDz74oL74xS8uWiWaVxoKpMYCgSZTb37zm02ZKYiHqTbWZMQ1YrI0gL2XYjth+gXcY8aUTiuRoExOuVG4ZCqzMw5qeNMYaPiUlIhKvnrJVycNto1lUIzRJZATpatT3spiceSph/Ty8TPas221VjUmG2WO6YMnNCWmEXc4qiOnz2vb+mVyOcPWccxRw+xMOBc8p8ODh8d/bw+1K94f1+WVEzVQSQfcVp6f4W4bselGjs9Vq3LHZQrGrAC1z9lgnrMwe0KD4DzjCSfAKo8k+RzFWl+yV/2RNlNrFxU8wX+ah0cSVmCvzrdeZTk2X7TLSwEyNnJJ2WNRZyOh1BOGIdcFh8jePLhe4XizQnF6YSTkjTXK51yXtRdG1u9xrlQ43pf6nGPq48OxtUtQTdWEEBDwwlhecBVV6RVSqNE0CDcz3L/GKj+VDT0vjzc0N/OPn8naSCY1wNZbpbJ66cgjlqMSTarFfPIZqaRO6mlJIjYcUmDuN+ASd415ZAIEF4ES1HVr1q3RcMwmca1jQkk+7HAYYjjb+3MC5/jcL6UXnjQ5Uwerl2vjr9w5fTkF+jSs2CKdHeu/wpj1FUlr8lDBQiSHrT4hKRgjenPCwEkriM19LG6S6vbMSU+jhcSRwec1GptoAN0RalXAVazlRRMlGO9ctkv/fuoJRSDdDCns1W0NqZlYv7N2p1HvnxmxxgRNwn93nVWeKp5I6NhQf2pWI2UiBibEAfkgGo/rka7z6goFtam0Qrsq8gtIrC5epVVFKw2p7nZmXn9YL73OS0yfH/Z9p4rlcjRN7USTddX9ov3LxHODp6WGq5UvgrFzGo5N9BgajZ00maLF7lRyNZaI62B/q/qjo2rwlWl9SX3W4yQQhZrbXk/tlG3A/mA3WWUOs8+wTuOozCR7k/fxWenZHukg2EtzP4Qlb3nLW0wjZcr1zNTRKaCAxQS2+Sc/+UmjEOQxVX++2eDcuXMmCD1fpXyoXY9YBz/m137t12ZFpDKXyS6+2IC9XOW9TMNRsv+G5HYUqcS9Zk7EUslCD4JEOTd2nwGSBSgEfrOW/kwC2bSIAunVAeyytunIlNE730A4BVmBWncqIBCDcEP8taTRc1wKpWbHqueYVL9b8s+c5JorsAZBFuRKDkGskWE2VewFWyRbxjfzIF9CAxIFwUaB0CiggPkBvXLYA5KJd8rZsk9AdDPfmfd2eXSyrSh/PtP1FwIFUmS6fRHfAj/n7W9/u+k99d3vfjcvEraAmaFAaswzMBD/+Z//2TQEx+n4xCc+kZeRmCDgnwb2Va/T6v1gIxzp05NP7M8t1ZySFGVpKqvlN0ld+6Vgt+Qplmp2Ww3Dx5hJAg0sHlu9A9q7Y/M7PgYAAQAASURBVK2W1VkLyNHmdvUOjEi1mzXy8CNmsntDndJIp/U5FWsnMj48JRoZDetUS6e2b1huGQiUHpmHtOlktAfHegUkoTPUqXgiPkktmwuscmBHFZdFWjgTDXI7Nqc4FV5nlXnMNTCSSHkjOJ9utNMA3OcsVnvohHrC51TqrjGNweOKmTIvzhk6PTiy0wWJbKC05QHI2kgmEMLxFgXjVno4CMVPGzVwwJVfaQCXo96U76K3RkJxuR2Ncjmmdl5RhHEOkEG2YYrTltxwejDSr1PNJ+QOOOckPXFG8C23HtPBEBA2oZH2/DjRMQbG+Ird0pEnJpeZo5zc1uuljlNScCzgzny99A4tBghEuJ2VKtYVCsetHhweZ508zlp5K62MH1s9mO5g4OBQbsIm+jIh/vh9Cv7wGxoJR3R2cFjba6vkWbdW2nHF1AfGd+19g3T4SamnVQqUSluulfx5NOYkM4w5OBaoH/tgyZ1jphrlANt+OfH74BmL3Fh+sy4UsOYORScTUwOR3pTfVxRV6vc33Kjjw51yyaFNZfUKpJE3lIv665036NQw5EVCa4rLTbNwm6Qu83hNDVsbZHLQpI/eDEPRsMrc3pycVAiNj+5/XM/0WiUgmXkfWLdNb1+V37rFd7mn2esIWLmnWctSYMZSukI0YfWsmgFCZs5Nfq5YG1IIjW81P6mzoz3mmpIpc0XlGt1SnznbCBVtss1jN5iFnKdHDkEEgmfJAdpcyjRgWxEcYk2w9xkCoun1sKcCwV9KorzpTW8y+9W3v/3tOa25W0AB8w1EJ29961vNPKIEznxlmFLeh3Ivs1HtZwskonykqTnBREgT297ERmO/Z/3guxHaTNfsl+NEFTmVHXChw+lwq9QzdcnAmYA1lTUZu9gWNc03sNvyIclsdSwlcLOJXBYDZI/gIzMHk5ulJxP6XF/+nqzwXbIg63tSqeqxklRLAGRtQ4Lmk/EynWiBdYdgKMpuSKpkkOVJlhrrazZhHPcX+4X7TmAVO2Y6kquAAgqYOZhz6f4CZDd+AWWlWZOZ93ZMhzXDjlFNB2wq7A67QgSiKeZ+rjFcjuNnP/uZPvOZz5iYHQ3EEVAVylHNHwqkxjyC9N13v/vdpq7tPffck+K45wqXo8SUxUko1ZA4erhZw4MjZnJ0dfXrlpu2muDBjB0OAm51l086/oPP71Pz8Ze0ccMG/cqHPihHx0vS2SfN30+2WgrjPTe+Rlp9ncKRiJ6791ty9R3VljWNxlkZdVWoZMedKi4tM/X+jzz1iEq9bp3r6NOy+mqp6dp5T2XNRFxQ13ymzZ9jiZNJjaXJpjmvaMJlejPMF8LxQR0987zazrdr75U3ZVQhDUd7dXqE5u0WusPNisZDWlk80Vx3JmCM5bsIQ4TQqC/5fZH4ufGfBwaGte/JQ7r5VnfepIZNbLhc+TWyRvmbXp6IGosop/o9HTo7cErtLR3auWebzo2eVlNgCRujXFdKy9Bwevwaw3aWZ59Py7ZKJ56c/BwB+pvfI7Udk2JRqW61VDw3Ssx80rVxbm3Dw+0sN490EHDEgMHxuOKKK1LU3LfcfLOOHT1isj24z3wvKimU4qxl5nO/8zX5RobkpW4zGWM9/dr26M/lmI7UAATMt868Gbkhl6q3S+eesfxE11i2Rw29cHIA6vsUJzMhjZyzAthz3Lx5vsCa65TLEK3Jz2bqq1HuDegy79QBZjcNQkszj9XfX79Lf35onyE44PmK3G5tLq3Sqx/5iUZiUVV6vPqL7XtU4vbo+NCAilxuXVZZo9I0x/dnbWcNoaGkK/+FEwd1W/0K1fkXrxeUAffdXy0Fe1KDD2TxzAiJaZ87NHDOEBrALv21r/eULqlYpWpf7j1tCBpg6BMAsDOlIO1xOOw1oys0qvs6Tms0FtGu8jpVD4SM+pqyMk899ZTZYyCqyf4gCIfTkW/daoIUKH9///d/3yipvvrVr+qOOxaH1C2ggHzwk5/8xPSHecMb3qDPfvazc9abjnlExirBAMhHgtzsofw/Vw45/gFlh1C3Ezggkz25vCRzmgA7AWA7iEjNe8gPhCgEDNjfWQsIYHBclKjEjiCwyPqST0PrVzrsPmiQSnNNXE0FemfMJPCLnZetnwvnAlFmZwAvFNIJN4Ji2KqUQmSMM9a5vgudRTIjFDdOtgcQlgTmr+F6PsBXQOCXD6YrEUawkvUGm4L1Izl7iMoIrIGsP9xPyBTWKIKkZIbxM+9nzTR9BEdHDfEGUbuQ86mApYmBSMQ8GgOBFEFyAbNHJpuE51j7sQWSSQj2m2xZGtgPvI/5iq2BP8LP2CnEV/FV8i27yXcjaMfX+Y3f+A1Tjoos8UUTzl7kKJAa8wQUg3fdddd4ual8GoglA/V/sftSjUT3m5IUlC/yOStUWtyh/t5BM2EuvfRV8vsb5fO1jaflEqilH0K+wEFgAqOA8YZ7FDr6oG5Z16TKorM6cd8X1VO6S+qvkPqbVVFWrY17Xi1VW4ohr8K6allYodplOtbcIbfLqYBvRG3PPaARb71RT22/7f3yBNvUdv6cukqbVFO6YtzYsJv6cc3mEquKVul8MLW59Oqi1TN2zuLqyPLc/JAaoVi/TvY+piOnjuvSPVvVHnpWtdqhEncq29xrGk2nKmv6o+2KJaImU2MmQPU2k8UX9eDk7A7rep841qrBwWGtWt2g3p4BlS+MICwjyGx66eQBne45Lq/Po62XWffw1PBhVXprTVmcJYuVt0mnfzKmqBorLbf6tuyv33StpexuPmARHyt3SxvGNnevX1qZY3A9R1Dixa6VPx1wCFBd4yxMBwIYGBsp9W87Dst54hFtioXVGfbq8Qd75SmpUpHfr+UrV5rmfziYpbVVco5aAfTa0ZCea+vSplBQC0IJBAekVvqejGXZxOJSxQoroy0nZFOHLBEDmXE48IwU7rIy8Ep3WFlHlJtLkInhkcO1RmuKN+nE8MuG4DDcjsOpZYG5r3t+c/1yNfiL9GRPm3xOl7aUVun39//SZGqAvkhYH3zuMY1wH5LwO2s26vfW0+PDuq7ngyPGCbHfB/ipPTSy+KQGaLpBan1ICtEHx2ERZ2Uzq9/scy5TNNY/6blkDEWDY/fOuh6RYEijA8Nqr+pWVW3xpH0VW4KATrYms8mBR7JCISpAR3BE/+PAgzr5y6flLy/V14IhvXHTbl3S2mrsBYhL3ktpB35HGWn37ch3b2cdoe4tKePUwYXg+PSnP31hBJ8KeMWB/fJTn/qU6Zvx//7f/zPO8lyCoJwdAMSpp3wn+y0BPex45jR79UzU/Nj5djCS72A/h0zkMwkm4oPYYI4nq6I5Dh6tra3mM5i3zFEITuY8vgMqa7uZeXIAgjUIu5TvK9S3ngyu/UITGraiPRe7LxmMRezLTOs8n0llAe49ganFBAFtxjCBNOYUQbYLJsBd2igtv1pqQQiVkFw+ae2rl0y5U2If+fRNYc3I5dpbMZVLDaGaXhINXxhxKuVrINV4LRlhrF+sL4xH2ydhDYIYZnxfMPe8gDkH4+DPD72ozx0/aizmFYEiffuqa7URoW8BOYE5BFnIvp2eJcXvkIeZyhBmys5Kt+nJ5ECEy7wlRoBYgvgtwonkGBb7CpkfkJgzmc/XX3+9iQWTHY4g87/+67/y3vcKmB4FUmMe8B//8R/63d/9Xf3BH/yBcTxm6xiTrVHquUYJU2rCquu4ZtUarV41LIdoBFhpnmMjZpOH2JhJLVkmHKwmAcDioiL98LMf0bKaEh1v6ZDL6dSaphqtawjJsXar5L1CqlxjqY9tRKzGrz6vR9vX24EQp1Y1rJAaLzMqh0cee8KQLfVla4xREBgeNgYfx49xgLM216j0VurKqit1auiUoomo6v31htSYOTLdz/kLfvSEjmv/M4d02dWU97AczZ7wsUmkxlyCcUCaNAGmmZYzsB2OkWibhqPNcjhCOnTghMrKirT70o0aHQ2p+bi0ehFJDVDVWKamsskHQSPjJU1qeIulDW+25h1ZBGPl4qbONLjJeiwAli9fboII2ZRyEB44DmzsBDPtmpfTgTWOtWTcIOg/Jx29b/zvtd6IakeP68xTZ3S+s0dHKurkuvzVKqlr1NHiaiVOnjYluvqCId26Zpkcu6/SgqD9JSsTJln91nFQWnGV5KI5elzq2C8NtVklqep2SYGkEnbl66WBNGVa6RopS4+GBQUBsL7HpajVN0nxUan/aakSstfuGeKQ4udV77tSRe4mhWKDiiX8KvdsVsA9P/NsW3mVeYD/aj2VQkzwc9AQTKn4t1NHtb6kXK9ptPbQdSVlKe8DbodDywO5ZyXMKyDFVr/WKkXGWJhBSUUbPieEQlyjsWaFwyGN9BepYyCqoaF942RBZ3hA7X3Ncvs8cnu96jrdqtqVjRo+3619p9vNazDabaCmxbbg/QTNpmpejKjBrmf93+ePKRiPqXh5o5xupyoa6/W4I673X3m1yb5JBt+Z3AdqpiA4DNGN8h076pvf/OZFXcamgAsPONWMU9TBBPPno2E38ym9RA5OP80vCQAgdsnXsWfPJjOWTCwCvXwG5Vz4nbnG57E+5NLXJr35NEQHdgEBCq4P1wQSlTWH64RqGgKUtSefhs6vFHBPEZ8sZPAV25B7M5NSTPiO69evz9pvkD2HccTrZkJyzyWYN9OVTFuyqN0uVW2SokHL35iFbTEfmG4uQ2ySuYWvgE+bqWF9tvdlW1dt+4Y1hdcRUEVkxdpiqlOMjo5njZHdgR9UwCsX321p1j8dnyi7fS44qt946pd6+ubbJ9mxryQwd1mvmSc8bFIQsFezbvIgNkAMlefIdMOOgES09yrmKc8juMCWyFcUzeshPClDO1VZKb4/p/L+UwBx+913320EU2SLzIcg5ZWOJRANuXjApPzYxz5mSI3vfOc7es1rXjOnn5/cs8HhqJRDqWmgEAIPP/ywCUSjhsLhYTMnKM1Ga5diyaQWQfnC5o9SEURGB3XNjtWqqyqVizIpIDHWKIwGYvxStlza8CsTho6P0jdOKzg3jrgUqB5XObBwwIKyOKDMQr0CiWIvZjgk82GEVnurVV2VX3PXbHA5ViuasJuzWnA75q9U0fm28wqFwjr80kmt27hCgSK/4olISo+PjtBJDUa7JqULl7pr887S6O7u0oGX9uuqK/fOqJwB84CxhNPasLJIvZEDikSiOvDcUa1Z3aCVyxrllEeVxet1Mi2DZjHgd2UOsvmd2YNvSwamwc4SCa6mgTUGZcMkhIfVdvaETrR0GvUCxEZyXfxsYF1gTEGCEvwYR+8Z6zrYDs5ISGrrU3Nbp67duEIODJXel6Rrr5fW/7H042+q8/EHFIMgvu5XpOvndp2eslF4JsQiFqnR/IjUd2LsSYc00CxtvNNaV0GgVlp2i9RzYKxR+DKpOkcjq+uE1EKWSFSq3SCtuHRuy/5BYkRT+2JYn5/cBB1tf0yj8WflckRV5ObvlFCkrFbmfgxziRJX6viKZ/GHOapnejrHSY0ba5v0uqbV+tE5jtPqZfU/t1ymSm9qaZP24Ige6GhRNBHX3upGrStZ4PTiOVBQsu8mwjXa96jVUJNa58uWVZpSDPaejJLae7ZCj5w7pGgorDWXbNHrV12pLWVN46oqyphATrAX2IEHuxzcVKQGr7Udi8FI2IyY0hWN6j54VMWN9ebaBmOMnfnLrSIQQZD1ne98pxGKfP/7359UtrCAAhYDjMs3vvGNZjz+53/+54JnHOA/sP+yHuBr4FcgUmKeM6/5O8HDTM4/AWwCCHaQD38AkiRTU96ZgHUDZTXlflmjCGrT1BOBl73moLYkI7RQ+iEVNAbnnpLZAEE030DBzj2gB+NMYKtmsQPtcoMEphh/kCT2HoIvDGGTazC7gAzANuWxBMEYIsiZPp9tcaddpo616rWvfe20n0eQlTUDO2S6Pi+sI3x2prHF+yFsC+OugMe7O40IKjpmByOQOjMyrI5QUA1LIdN7EcD6zxxlz2Ge2SIEe+4Qj+SBfYFQCV/EBn+HwGDO8xo7O5v4AZkW+cYPIUzI8iBmmvw98wW+73//7/9tskDI2iCr8G//9m+zZrIXkB8KpMYcAUOfmrBMQlIT57qEUi7gu1kgUNxAFDBJMOhwMmBCmbQYfMmEBosAmy8LQnKdOY+/RI0NtVbwbLwBsf3XsR8GWqSeE1L1mAoFdfHqm6TTD0wQGzXbpPKJtDAWIhYzFA40+01nRm11d7ZUsqUA+jmQmRFLWI2MXaZRdX79HfLB6uUbVVnv1ZmT5zQ4MKxAUUB+14TB1RY8pq6wFXCzQd36Mk+9mgL51ZPtCZ3RqdAL6oyfUWvUqxWxS+RzZXY6o/GgBqPnFE8QZKpRwGUpohl3e2/epkefvlfnw361n+uU1+PSlu1rVVTs10g0rsaAVQu3qKjfjNu5qgU9E1R6alXrbVRneIJgWVG0XkXupUkWzDdozGmrJghCsjZQDzIFrAeDBLEdUmlF1gA5852MH8hV7nGk5Vkd/uWPzd+u2bBc6l9hVHUQsLk4HDgwpH+nGC0myyHphZAakhrKi7W/uV01pUVazvEO90mlVdJdv6WBXddaDYFzUIXOGSCBO5NJHofkr5A8AVjkJEJDY9kOMan7qNR0RWqdY1PrOA90nZQO/nTi96EOa11fk7mu6MyQSeEyeUxYeSp2eRHbyGcNWSOnY35JxOtqG7WmuFRnhofG1u3Mr+Pp8iQDk7H2ic2X6K5la9QVCmptSZkpa5WM08MD+vALjygUi5mp8B/NR/Tn267SFVXzty/MF+xa5dmCjawHt625TFct36LBSFBV3mIVuScIHhx95j17e76GOu9BnIHhv7msWk/0TPRhoil5U4Dvmv85S7AYMuP//t//a4K2/P++972v0OCvgEUBjjqqPkRTOMRkgS+G+hxfgrmNf0NAAp8Cf4f68oiV2FOT/QiOmzJREJ3Y9Mmq5ZmW5J0OECp2s9/0YCdqS+zNAlIBucS9ghBaCFLD7oE0U3BvGYsErhmHlFsmIMXYS54XEFrYloXg8tKBnaFtN+LG3rjzzjtn5AOyDvFZ9GSxYwbEOg4cOKDLLrts/DtYl/AfpiOBWcMQXuZit/CZrDMEVlGIp4s1+IxCQ+ACqjzeSZ3q8JZKF8COXapg7b7qqswVEuwSbjwyEYv8HTKcNZ85l2vT7qnAXgSZuRCkhg1IVsgdssIRk3/ve98r7FNzgAKpMQdgY0M9xSD9/Oc/v2gBWoIQpGUlG34ARQuBCAxXFgQCljgaBAjtjR5FS0pTPQxDCIqT96ZlXiTDIYUHU5+qWCNt+3Up2GuVxSBolwaOsbu72zhDNmw1p10Pd6nD5agxj4VAhWedIvFR9XYf1so1m+RzlqnWN6Fs7glPNC23EXCVaUVRfj0ShiKdag8dVnGJX4Fivzq725XQs1pf8io50tKOI/ERtYw8qbgJUDrUFzltjqnMs0yDkVZ1hw9p/dYGtbV2afeVW4y62T2e8DNxfyHfKBmwc+dOLRaYExtKd6o2skyh+KiKXCUq8yyNZngLDZRurCOky0M+PvLII5ObagVHpB98SWo9af2+coP0undLvskNeskKQ1UHcRoZaJf33ONa1Vil6goCpgnpzMNylTQYIpbMramcDpwHGolOchTqNlu9KqIQsAnJaf19Q0O1+keCeub0ecVRcnR0qbHEKtWHE0TWx4KWBahaJ4X6pRbWvYTVcHHD7WNZJhN1xFOQ7fl8cC41q8ygdf/ckhouv+RbJoUgesdgpjljApLJmvOJLHG4hJIzzxKKJULmurg0d46h3+XWFy+7Xl8/c1TnR0cMwTEQierLpydSw0GZ26NfXzG5J8XG0gpttHzkSfj30y8bQsM0zk5YxMjnjh/QV6+8VRcaEDrkop4u9xSZRybk26ybQAfzkSCpvf+/un6Nzo4M6L6OMypurFPsyGl9+M63aaHAuPvDP/xDU2oCwQrBsS984Qt5n1sBBcwG7Hvvf//7dc8995jSBWQ3LhaSS/4QUOQBScDc+LVf+zVDIjB/KcdCSQgCDwQdeH62peFyBbYEwgf8HvoD2uCYbr311mkzQl+pwC9cKN+VgDNjJluj71xAJg6BbMaWHbxOh207LHYJqgJSMygI4iFcIDMIQnSm446YBZ+FqBTFM2sNgVDEm8llv+3sy2wNgm2wNuQqxOC1fDdEiCmlHQgY0hb7gLHGg/Odi6BrARcu3rN2vb519rT6IhHjF5Cx8Yebtqr4FboPYeMz52cD5lY+GZ6s/2TykV2VaX4zd7FVeA2x0IXaK7CfEBJ84AMfGM8Kn0k5xgIm8MqcVXOIf/mXfzHqKdKH6KOxlAwnSjlh1GP44VDACkZHujXQ2yaHy6/S6pWmNBUbL5uy3aBzHOUrpa1vkYbbLWLj1ENp5Y0SqTXfbRDoyBLssMGilryw4YBQu5rMkknH8QqH0+FSvX+X6v2DWll0uVwO37TjLJk4yBXDsbFGs0qotr7KNPGuqCpVOD4qX1pvid7wKcVl9z+xvqsrdFil7iYNRq3Aptfr0co1lqo8mRYrck0ozWHaF1I5F43H9FL/YXWEuuVzerW1fKNqfVagvNI7PUnVEx7Q8SErQ2d1UaPq/BcX+cGmbjuaZGywNtipogQLTNmn+74rjZXiMTh7XHroB9Jtv57V8cCJVefLUvkEARcKR/TCkRbFu36hK2++c1onl+AOnzUSbVEwZim4A+4VCvgapd1vlpqfUXCoVy+0dmj48BFdubpe5UV+M6Lrd+xRuK7RrIc4IygFkxuSLgiYs02XSQ27pXhUSlK3y1MiMZaCYz0pDCjxt3L232t6MaU/l3uDxZxRfoU0VCKFOySnTyrZap1j5DlC5WZtcTmWK6IJ9b1dMswZPS65dymmhDpDzysU7xsrAVejWt9OOfMsoZcNJW6P3r8utdTV9bUN+tG5ZpOFsaGkTG9duS7vBuCdoVGL0LBPy6wVQV1oYF4shhOOyAGBxe233z7+HPWG37N2t96yYotGY1H5IzGdPH5CbeFjxkmBbJiqXxmvGY4NKBQbVZG7dMb9kQgiU6f9rrvuMkEMGvwVbJQCFgLYw69//evNz+zDS0lxjtoSdSOlRlFOEqA8dPBFxSPd6ursVnX9alVVVZu5QuAPsnS6ki5zBdYFApk27CyEFOFWASkg4LNQ44v7MxfkUi79CsgIYn8p9EZafCBcoNQM95/1A2EjGQ/cH4KdMy3ZzTjINBYgVhFU3XTTTcbXnE44lQtY5/CT8IcQaJKZCqnCekOVCdtPIWOEcyysOa9sNAWK9OD1t+rfTh9Xfziia2pq9bqmV26fFeYkVVoWGtggVJzIRoBDeDK3qSxBvIPsq4WoGAOh8uUvf9nEkvF//u7v/s5khRcwMxRIjVmoWj784Q8bBxcFFcqgXDEUHdRIlCbEAZV65qjudgKVcp9isYSOHu1Ub9+ASaXC8WdTZ4Pdtdqr4XNndbT3vDavbtKxtkNqORvX8hUrJpeXseErtR42komNuu0ppaVmA5oBQbDgIBVqy2VGV0ePnnvmgHHQWHRhqqkhXOVdPqn8FM/lCytwOFH/PBa1gqGZenIYJXUacUK9fP51ZCg7Yz5THhW5GlTu2ZTyfCQeVNvICUvR56mXL0uPi7nA0z0v6Hyw3fxMjtGjnU/pxrq9qvROPw/bgz36WdtT42f98sBp3Vx3mVYULU6JGTMOFDb9SdIzaWaC9PRsQ0SMASOeMWdw9kRqUJzANMTGdCBzC+e5s19n23sUikR1+ZZV2h8KGCeHLAwca4yJTMBZcAX6NRidKBMWiRD8jivgXyZtvFnPPf64rrjrdXLd+gY99s0vqtHrlXPDZfLd9Bb5nK5xEpcHBMeilD6jYTuPdMJjzW1S84PScIfVH6HpSql0DoIMdZukvuRsLodUt1FzDno+lY41b0+G99qxjBOnXA6nvHGI0gkFrT8ek0MdUvRF9cSdCsX7x/8WjHepN3xM1b78yuhNh87QiI4O9qnU7dWOimrtrpxd1t3WsiqdGBoYJzYolbSp9MIjPHHKTVm2BQZlQlhjMqHM4zMPkn7sTFQyO8iQxW7JFBwz/XeGX1ZH6Oz4c2uLt6nWt8xkPbKnuSHecgTBPupy42xQnuIHP/iBqYlbQAHzBZTHEGmvfvWrjcO7VDKE8CXs7G/sT8RJEAbbtqzTxpphPfn0IV13yXL19J1QW39YzdGoCRgsZolR1haCngXldHbge0GiEXSys+UoRUXQdj4wbk/OMwhMvfDCCwVSYwaIJ5CZxMZ8jNkLNiEu7GwvMm3e9KY3jf8tlxK0uYCxiwgBQgGfwlZeUwKXWvz5xGrSASnCPMHuYE2xjxn7AP+VDHDbP6fMN/bUBdskvoA5Q2MgoD/dkl/ljIsVzMuFzpZk/mNHIQzJ1h+P19jZp4C4KSTsQrQS4LvJxoUYpRwV+9VnP/vZQix0BiiQGjMAacwMPJRHsHr5sHlnhk/q5PBEuYsVgdVaX7pZs0E81qszZ+5RW1unaepdXd0gn3elMSAoMUWJn1L8oXM/UTQWUzAU0dnzPdq+ql6+5aukohwD4NUbpZLGidJSRXPTeBsQ1EQleiGUnloskN6fHoRm8SMjon7TGvVH2uR0OFXjW6VKb/7B0ArPCvWGzyqWiOj0iVZt2blOFZ7lGYM/fleFRmI0JrfhkNdZbILrpZ5lCoZSmwaXe1aryjs5kNo70qEj3ftUHBwxRmFb8LjWl1ypIndmkoHx0TJ6Rj3hbnkcbq0oWqNST27Km1AsNE5oJOPMSGtOpMazvUdS1NhgX++RRSE1IvFB9YafV0yowR0qc29UsXvVrNe1TEpKuy72OPFZVCKNDE405sbZKcpSlycZ5SuUKF2uE88f17WXYOgn1O2ol8tdY5wAAiQEKiFjMymwIT4rVtA8OBUj0WYFXNZ4ZwzhLKmqXtd+4JMmALOcWrdJJAKv4UFpLEqf8b1LAt5iaf0d1nWdy4y/hq1SNGQ1Co+PNQpfN3PHbkZIIkY9zpVyR88rkegx9Of4mcY7FIyRGpx6h4NxMsjmDvt62vXnLz+lyBgxd2lFrf5s29XyzCJDYX1xlWKJU+O3rcbn18c3WeUfLyS0tbVlrXU732DekgKOAnI6oLrkOFkvUImnCyF6I50phAY4OXxQ3eHjSsjqE1blWakG/2aNxPrUFjykSCKkIle5Gv3b5Mm05/n9+spXvmKcDZSf//zP/6zf+q3fmvV5F1BAOhhnH/zgB/WXf/mX+v3f//1FzwDH10EEgJgLMQ3BQvZWGkyjhDd9roaelUKjZm89crxVq1fU6+o1LjmqJsQRiwVsCpSazOH56uVxoSNdPYtNBsEBmcvf5rK5OiVJFyqwhe1ayNLID9j8A9GjGoqeMb+7HSWq9l4itzMw64BmNrtjthkUNliTIBfsShDYCGRlQ6pCMEDIIm5KB0KJ6bIqWANtYQVEBnOCc0ruU8pajf+CL0UGCt9dKHlXQAEWKZgtU2K+gV3CvpMrIEHp/UuMYKGISewostfoMUS7AMpRLWSfj4sBhWJ/eYKSLCj12NBg/fMhNIajgymEBjg7elp94R7NBGyWHM9TT31XgYBLO3asUSgUUSQyoB07AsbpH2+WFaX8h1RTVabr9mzRto3L5fN5pUhaT4zpQNYGZanmkNCwgxQE6XE6sik2C0gF9xXWualpmTqPBbW57DptLL1WVd4VM7pUBHLWFF+tjuMhNVWt1vLSHWrwb8342grPahW7JoL5bofflMgCJe4m1Xi3yeMokdtRpArPWlV6Mm8K9z/131qxtnG85Am5B+dGJ1TcIJ6I69TwET3d85B+2XW/jg8dVk+Y/h/n9WzvkybzKRdko8ti8ajC8czGdjKCcSsYloxQLKy+8LDOj/YoFJv+M+YCiURcPeHnxggN84wGokcUTCGZ8geKBJQJ6WCNYYMfJxquGyPXuGdkiBBwedUd03+Bw6nY6lsUWH2V1HCJtOYWVe68Q719fUbRhDMAwbB//34lFFJCx9TX/4T2H7hXJ0+dHKtTO/kuMmaS10ScWAwRzoXAC+sJv+PMJJc6Y8zZtTbnhUwd7JHOHpQ6zkwQQLlgrgNYfN6KS6Wrf0e65n3Sxpsk1+LqGeiWwYxPPVOnnI7J9U5dGZ6bKSLxuP7P4X2KJmUaPd/Xqf8+N3nc54pjg/361MvPqDcSV184rsFIQh2hiIovsEaAdump+QqgUkLq7MiggrHMZd8InNmp37kAW2HdunX64Q9/OF5KLhyP6fHus7qv/bQ6gpM/JxKfcGp6Is3qDJ3UmZFnFIwPKpYIazDaZX5njc0Ers1HPvIR850f/ehHzWPBy9gVcNGCscT4ohH4j370I/PzYhIaNPh+7LHHTFkGbACCdxAcBAzJVMLHYA6aY4xhhyV05aUbtOeyjaqvK5cjPqKlAIKKZHoSaKRvWLbgagETYC/AJqTGN+KpuVrnCB6T2WNKmc4zOGZKKy9G9qENbMtgLGz8mAsFI7GWcUIDRBPD6gnvn/XnslZgn6d818iIuUeIjOYCCCOSS1uTbURvQECAEAIleSzjF/AgmDidwIk5wZpI2T3Og8+CuOD4UXYjDEv2JfhuPhcyuIACXulg3iCyXgxgo1CGH6I+VzB/iSOwPtgIxQbUHzljesfG56LnZRqIKWNzUbGCWDPxlwJyRyFTIw/gyL797W/Xn/zJn+gTn/hE3s7GcHQ48/OxIVUoQ2+KDCwnSgE2TWMoBYPavm2bzp/fp9On2+XzebR3r1WvUmNqxPGa6pF+u11CEhJSjir3+QQbPoQGDhMPzjFXxeZMwLWLJMJyOzwms+FCB4YawdsnnnjCKJJmyipzH5599nmtWbZrWieAjIyGwC5TyoP0ZI+jKKUEEtkaPKbD5kvW6vDBIxrsH9L6LavHy1El4+TwYZ0PNpu4cGxSbDih1pFmbSpLrZOfCfTQqPFWqTvca3qOWDXXE3qu76x51PsqdWvDZQq4Mqt1Gn3VGogMT0whczxOfb2ZkmySx+HSaxsv14qi+W0gH02MKG6aLyfDoXC8R37XzL+b9QylEgQDKdsEArhGOB0Y8Kw/bPJas0X6jY9KR9hsHdLWy6W6ae41mR33f0/ucycVO98nrfug1LRWzz3zjHFmcBRwEHAajh49pEsuZa0M6+DBl3TJJes1NNSqpsYrFHE1aySWapT4k3q0sG5wjJkyPRjflLeC2GDO4IAQGOUB6UEwlXOGsJ51qYzml6SnfjRRpqtpk7T3jRYRVIDkXi1FOlOvhGuNKl1l6gy/kEJ3VHgmmtPOBDSJPzPSq3A8Kr/Tp5G0oDq9G86MDMz485/obh/nrOJj39cTDuvoYL8umWVZK8AcjCZCpjxgplKAcwUc8+RGwHOJ+9vP6u+Osl7E5Xc5VOsL6IbaFXrj8k1yj80J5izN8ggCZEsRTwfBVrspaCgW1WcOP6azowPjps411S5tKUvK0kox2RwajHak9Z9KKBQfMg+/K7ttRMNhCBiyJ7FXvv3tb8+Z0rSAVybYX9/ylreY/ZexRQBwIWH31iNIxz6Mf8E+yXFwTJR0IcidNfjIfIlSOjBpPrlyb+Y5nzhw4IAp7UBGF//z+2LU9b4QwVigjDFj0u5VMlPf7OzZs4ZUQpE6VT+kuQL2HPsDQW3OYb58ymzoCvXpwY5nNRILmdKbe6q2aUPpzERnC4lgLF1oic88YIJ4s+lvRikzSjZhX9v9W7DDGQsE8si6nAsS124Mjz/A2kVmBv4x44H1jSAlJC3Z3xAc+Lv4ztOV+CNDjfJTyf16kr+Tyhh8NnEMVOGsoQRxKesGGYx4k+8gYLnY2XcFzB2Go0F1hwdU5PKp2ltWuLdZgJhgMcvLExtj/YH0zCVjxC6VReYXGIqeV2foxfG/90VOqSmwRy7H3ArY2KfwKT7zmc+YvfIb3/iGXve6183pd1ysKJAaOYDNiuYtf/Znf6avfe1r44378kUgS6+AbM8DNmWYQjZEu0QDiko2RCbm2ZYWrVu7WqWlyRskPxdNEBqdD0nhromAWjxhKYeL10qBxW/KjVLaTt+E3MCIni/jcyg6oJcHnlMoHjS9H9YUb9aywPw3A5pv2AoTgrYY8KTYs3nwoFwAAZdsTgTjG5UJY43U2nxqN3vG0pGDsRENR/vldnpU5q7OeVMvdpdrw7a1ajl9TqePndWq9StUlNZnpj2YPXOHgFQsUyPkLNhRsVUH+w+rN9ynUMKhUFL2RXuoV984c7+WB+p0Q92OSeTG5VWbNRgdUWvQyogIuAOmNr+NSCKmu9ue1btW3yJPes+EOURmpyIxY2fDrgHL+CDwj9PJPMRw5z7aZafITMP4N+OocZX1yOkL4tKPviR1njM/h3u7pB//qyJ3fdAYDdTTRz2B08n6dqb5Gf3yl8/K43GrqqpUgYBPgYDVw8WXIOia0GiMMeFQkWu5il1rxs4jruq6hPqGn1dJSZW8jpVyJF0T5gIqCNZPAjSoIOxxylqKA8JrWI9wdAyBMxPQIPrpH6f2HTl3RDq9X1prpa7PKygxdeQRqe2IVXZr9eXSyktSM0CG+61j7DknBUqkS2+XGrIH0SCdCLpxfXDsWFNm5ZQ5ayTPFZJRA8YkV4PkXKEi1DSOKzUcazP3t8TdKK9z5sFiiIwvnXxKJ4ctJ93vdKvU7dRgNJ4y/hv9ue83Pz53Vt9pPmXWnjcuXy2/y5UWGLfA87NFMDask8PsV9Y6U+9bq0b/+nlxmnD2M6qosBXCg1ZWFmUn8/zuM8MD+psjzxpCochckoS6w6P6z9ajGoyG9Z61VoYfYJ9i7yLtOxflJkEQ1iWCrw90nFLLqEVO2Xfj8e6Y1pU45XU65HNaBBbAEvK7IKKD5t6FJolopz9Hgr0ojt/85jebNfInP/nJoiqCC7hwwZ57xx13GDEBY2ouS/1MBfZ9bEYa3bIvsr6ztvA8x4BNiD3I3mxn02ZF8TYp0i3Fx8RbOPqli196CnR2dpr1hLUeteiSKTl5gYBgLH1RGCOQzthp2ACMF8YHY4X/s+1LBILxMQhkZ+3fOAWw7QajPaY0brGrQl5X7qIT9hSIDcoQ7d27d8GCapF4VPe17xvPAo8l4nq8+0WVe0pU51/a/bYsXyJdBYnHPDObxs6+ZHwgRCCeYZMaBAx5YH9QOioTYZAPsE+xV1Flk/WNQIJS3IxXsoMYuxAdiLU4jhtuuGH6tW0MHCe9jjKBc7OFmdgvfL8dDOWY7OwQRFWor/G1L1hio7dVOnS/FByUSuuk7a+WAjPfs1hXINS5DwSbuV6mjPAFgJPD53V/+/Pjpak3lizXDbU7L9x7O09gDiZnUCUjkYiY6gwOBeSgN+M8AluGGBlZptPFuhiLELGsTU1NjeoKHZwkMO2PnFaVd+7LUzF+ENBDyL7tbW8z8WcyeAvjamo4EoUmBlOCoNuHPvQh/fd//7d+/OMfm0DYbHBi8IiaR0+N/97oX6ZNpdtTBipsP8phNkECbdwilA1syjjSk1TECfoXPDemEwX8/QrJ4ZOGT0q9+yYfSOXVUtGKuS91MkNgKEBkYBzPV2CA4Pe+nodNlkYytpddrkrv/KrrFxIYCBhsjF3UIqhRCNTSKCnZyEQ1hUMLCGZhSMwEPeF2HR96YTywV+au0qbSy3PKgqEcyImhfQrFh3WuuV2DXUEtC2xVQ13juFLx8a5fKGaakEtJschxbC3bpXp/5ubSNnAs7u94Tm1Bqz5/DT00Eg61pfX+IH4XTfD3Mr1xxbXjQbCJvyeM6opzfa73pF7sPzOpz8ZvrrxOVd75rRvZFz6o0bhN9jhMI78a39VyMefzGCeoFtiwCSSSpbFr1y4zFzH+05UMEB1kbNg1ZXM/2E7pq58Z//Vs76DO9A1p2d6b9aK/zqidrSyhZ03g/Kqr61RWRhA69boORHxKJALyO9ap/dyAcR5wWFk7TYmpU/cr4exXcUmRPB6XXI5i7dh01yQjCaeK99gORyagvs4a+AiOSD/5pnT8oOQvkm6+U9pxRdL5tkn3fin1PcyFdZdKPofU3yb5S6UN10ml81Df++X7pJYXU6/f1lul5WPlHmIx6ef/Ig31jhEvDmsfePV7pIqJknI4XziHdtkOjDvuE+sJWTU7d+7M2RlcLNxz/rDu7zg2fiWYzX6XV8/3Bsfn7YaSCv31zmvlz6Ek1/dbTut/HHg25blPbt6h77YcV08kpFgiYQLml1XW6oPrt+lHradN744baht143TZTGlgrTk0+Ng4oWFjVdEOVXnnVozAfaXmPOUqUxAZkU7dK42O9TUpXS6tvlly5k6g/uz8aX322AvyuShTaBnrNmio/s2rflWutHWWOcq6xPFMZ8QTmCB48WSsT0eK4+YeJOOjG7ZoVVGtQvFetYeOmtBMuSdh/uejeXkozrrOqx3yO0u1tviqnJ0H7LTf+73fM83DsRNxmgooIFc8/fTTZg+kKfg//uM/znv9dfZ95heBfjsTk6A130sz7VmpiCnHECYDLy55aqQMvWkWA6xvDz74oPEtEDZwvgXMHNgE+BZcV8YPIhH6DCWTcazL+LG8Dt9169atM7IXaFZ9YugZ0/vItnZXF+9Wmac272MmaI5fxLjHjp2uh8Js0BHs1T1tT6Q8x56zu2KDdlbMT0bkXCESH1Zn6Mmx0q62X7depZ78muaSoQBJi63NeMC/YCwgRLAzLJNBNgV7/myIJ+7tvffea7JObTIDogQigwdjEHJrKkDCcbwQE6yL+MuMcfZ6xgyfyffgN6X3IcRPwrdmnckmzsTv5m8XZHbnSK/0+Ndp5mqNDfYK/Jm9vyW5ciciuKZ231euKzEI7g0+IHsTAWf2o6UMSk5/vfk+Q1gm46ba3dpQmn9f04vdzmFup8/5aPysIokjY+uMS17nDrkc89v3ij2AdQkfNlMf0WQwz4kHtHWcU9POoBFbJqPY1aA6/855PV6EBL/6q79qsjX+6Z/+6YIh/BYDhUyNaTa2N73pTSY9kuAfiuLZYl3pJlX76jQSo7xBkSo9VeMOBM7G17/+9fE0KTZTHAyCu1M6Oo5KKcEmTYAWo7FmojFrjBryk+pOSe7AkiE02NwIoM23kTkaG55EaGBk9kd6LipSg00jOSCNA0sKWzLuu+8+E7imLvJUDmwwFtRobMRkE/ld/iwOx4EUpfJAtEftoWY1+rMHjZP7eGwq3avR2KA2bCVrqdSUscL5IHCKUdkQWKHWUavkkIvSzWNf5ZRLa0rWZyU0KANzevic+iNDagsS0JpIqSZV1O/0ZpoZ5vfOMHUTh1XpLUlRfT/ccUitwR6VuPyq9ZdkVGiTgjrfKPdslSdWpki8z/QhKHavHic0qA0fiveb6+NzVqSUBbNBYJoSDATgIAQwKAkiQmhA3KJmRKWJc2GTGIwpDE0cD5yTnAMDad+/vKJE5wdHtLyhTsFaKx0UBwAlIAZua+vL8vvD8nqtjZugI1e5uaVVZ5vb5XQ+pW1rbzeOAYYSa+Txky/psr2lKi2tG++VcfxYi8Kx8/K5l6eW8olGpy3RxhzCuM7odHzvS9Lxly1CYGRI+t4XpQBNvsf6zxSVW+ecbOjy81CL1DtsndBIn/TMd6Sr3j4zhVNoUOo/ay3tFaslT9K9OH9o8qjmOZvU6O+QBpObb4+99txRhYsqjWKK/cYuEZBsQDEemJMobuwm6wQLWLezrSOMK67jYhhi50YH0ooLsQ+E9Y+7r9PRoX6Vejy6sqpB3hwzq7586tik57559pS+uedGfenUIbUHR7WptEJXVtXqfc9adZy53T9rO6uPb9qlNyzPPSgQNfM4vSY9JZN65pzUYE6wDzOf7PtIKSjX2Uel0aRSFIOtUtuzUtOe8afYu7OVa9vf164fnHtJDQGHRqJk1aX+3S4DmG6LkCXFmLHVtVPZP3w/ttPGhmV6eaB5/Hk+0ed0a1PpWpM5V6oK+ZzFGoohKumfeB0+ucuhWNwnv6tSDYHNeQV1OTaahjMXbrzxRn31q1/VG97whpzfX8ArFzSC/O3f/m39xV/8xYI0BGfPZ04RZCQgx16JEnCmgpZJwO/wTS0wWQxgS+K/zVXd/lc67LKdtp+M/ZZMaBCshGgmky6fzO9M6AydGSc0AIH2M8MHtL38xoy27VTHzJ5mB7WwdQmszxc8GYh/9jvvFIKAE0MdeqL7uPE1NpTUa2/NBlO2aqHhcRar1neVhqNnlVBUPmeNitwNOb+fPZ0+LNiFlHmjbB1rG8QiCmn2SkgCCDH8C9vO5mfuCzbmTGvv8z0EKhmTrHN8J7YN34mvANGC78DPyYB8o3QUx2QLpvBNTGnLUMiMb8gRezxzjtjA6UFR1hoIvKnAZ0GwXJCkRufJCUIDYL+RITvQIVVOHcjnmnH9ucbsPfgRkE/J+x7Xhgd+CLYdYwjyNNs6YpMji9FQeSA6MonQQKjTQ2ZzASnAxkC4aPsbrAl19QFFEhP9KsjaD8cPyO+8Vo6xeAZzD99xLgV0fB4xMUoqstZMJaQ2ZcqHh1VeWimvu49CfCl/9zrnv/E5MRl8M7J5X/va1+q73/3ueHWbAlJRIDWmSAf/lV/5FTPxqPWYS/21XFHhrVSFJrODTFpKGcxIReQgqJAhsOBh4KcHXp2Se3E204MHD5pgIQsFGxqGhm1EzCehAeihkcnInM865TkBoiXeYd0nyrKYezkB6pjSuC2eCMnjLJPf2TCt8zsYGdDJ4ePq7e/VsGtEXp9nUtCIYMxUn9My0qwjQy+P/76xZLNWFKUSFeF4SPGxLIoJOBSMWeXScgGOSZE7NbBLfVO7qfOaoo1yyaXO0Hk5HS4tC6xWra9xTGmb+fgZX492Pa/mkTbzukiaeteUrVLc/M0KrFnBrck9OyY+779a9qlltNvMpi4N6uxot6p9ReqPjIx/zt7qzUYJPt/gvIvdkKypRGso1qf20HPGEQFeZ7nqfZeNl6biPJiDGBXXX3+9ce4gOOyUcAhUAtfUm7XXweTmXgQFeA/p07nWvVdZlbRig9Ry3BjADup1x+LybLtSW6objFGK48F38TOEw/79TyoeP6FLL12vg4ea1d47opr6Kl2+Z4t17i6nVizbbAxiDN4Vq0rVO7xPzWc6zGdYTcWdOnL0iMqLY8Zwoe8MDngu5DSfS1YKxkyK80OWxrGX0m6GU3rp6QlSwxuQLnuN9MxPJl5Tu1wKTzjmVkOWqNR+zCoPlQ+GO6QjP5Hs5vYtT0tb7pT8tpGTYU4wwFsPWQp7b5HO9QxoJJxknDH4y87JU3ze3Ovp6l1jFOLMoWAhoI3hCXBGbOfCbtqOco2yKna5gYVEla9IzkHHpGyq/2h+WjfUrdc11avyCiSGY5NTxUKxuOr8AX1yixUwAX/wwhOGVE3+1i+ePJQXqcFal+s+NltgcyRnGDDuUVOuGH5JK+pKte/gabmcDnNO/rJ+7bjjcjNGcCa/973vmRIO6QHDztCwPnf86XGnz+PEAXWM17vmql9V3TTeUyMd2AccE/bXVPXQcZIJzNbU1qr5xKCe67Oy78gKec/aLSmlAEs9dYqpU6OxCVLDxvKiLfK7ZuYY2w3EsRd/8zd/0zjkf/RHf1RIFS8gI5gDf/M3f2PIjG9961tGhbcQYG0nMLcQ/QwWA/gSqBrxJbBnsHGwc1FOI5ooYG5AOaj20GkNRrq0/9mXdft1r5tkv2MXzJbQAKHY5F6UcUUVTUTkySMzOd1+mYtjmwoVnhKtKmrQGeODaKxcql9rijPbQWeGu/RdbLkxtIcGNBwL6faG+VUBT0VsVHg35/0+7D2ClwifsBEpO8Z+iI/PXMTW4IFdja1OKRg7a4N7QrCROYxNOdOYAAp/fBp8iVtuucX4OD/72c+Mbcox8b12OV3sCsgM/B5smGQ7A5vVFk2wZiPQQcRj+ReWLQNRbIukWGdyOWbOn8/GF+O9i9lnIG8Yki2Do5xkR3ONKGk4+SVW2fRcfAFsKcp4MVe5powH2z+0A9wIjrknjDl8j/mOIaWjxJ1B6KmEDvSfMuWsb6jdpbJkwdkrGOn9+ri3J08/rbUb8delp544pIrKEkUjMW3dvEwNddba89BDDxnf/c4775zTNZuxSAyDuc8aZcc80sHcZ3xhS4TifWoLEl+xYl4BZ7XKPQtTvp64Bb7QW9/6ViP0uueeewxxW0AqCqRGBqAwgdCgdwbp4PPpAFAS6dDAYXWEOkywYm3xasUiUfldAXmccxC88DdJJRuloaNjTzilqqukBVCTpwN1BPEFlBuoMeya9aRy4ozMN7im9b5lag9Z9fiB1+lVvX8RUxwTw1KYGp12BgmFvq+UnFaAMpGIqTv8tKIJSAIr6l7k7FW5d+uUDemf7nnSkA0nTpzU8rXL9GL/fu2qmAi64fBNxU4PRYdSCA1wdOiwKjxVKk1qLu91+kw6uJWmPH5S8jtn1xMFw5KAKQFrjJhVxdYjV5CVAaFhHU1mpoKMitvrr9QLfSd0fOi8iFdaxXgcqvdXKBKP6dneU0btW+MrNSTGxBlyKxJaXdSoSm/AqL8b/JVaOc9NwqfD+eALisQjcjnJaqF5eL/6I6dUOVbzEaMQUtFOv8aYRjkFUfHwww+b7B3AJs66x0aKg5EMjEw+h81+2rUxHtfQvkc0EvXqfNCj0PCgBuVSw53vlKot5RcGKgrnZCVGTc0dGhkd1t33/Ejb99Rq+abMDUdxkkx6eKJRzkClqmtSybEirdULz+6TM16v0opNJviZjxGGk8I4ZK0yjkcm1RxLSfrz9M6gt8ahX1rkhdtnWkdMRhYWbSqceczqm2EjFpaaH5c2vsb6feVu6dSEc2zQ2SK1njA/jnpKNOSr0Mbqsd5KWJSkjb/qNqk4d/UHDmK6OoprxfiAPLLnLmOEDByyryCX5kwVnANurd+glwfa1RMeSbldg7Gw/vv8y/K7yNTIvdzhHU0r9Lnjh1LKWf1a02SSbChKjdhUjKY1KJ8OkO0NvrVqC50c3694rtY3cbw2QTDXYP0l0ND26CHtP3pWRX6vdm3iPB0aUIVRMKIyRJH49re/3RAb7CnJfWqOD/UomqRi84x36XabzJgrqhr0ztVj2UNZQGCM48BeYD5SAi0dpJBj7AdK43r9shFdVe3VcDShxoBTpe7TiidWpPQbcjuyBBASFjE3G1BGiCDNa17zGpMB9/d///dLvkRbAQsL9s2PfvSjZs6w5+ZdznEWmO9A7mKDYCrZAbaC2s7wIlhJ8GI6BXUBueHs6CF1h1us8mWJfh0f3qeNJVepaEwsx/Nz5Tf76DmZKo41WcizJfeZC5QgoSTKfARD2Qevq92tQwOnx5oI+7W9fI18YyV6ovGYTgy3aSQaVmOgQi/0N0/KHH+h76xeXb89p1K+SwUE6pmDdmauHSzE/rOD2fgbdtm7TGIF5imvz2VtZKxRrggRDUQG6ys/J5OY2JyonJP3Ymx6jpWMCfyhbHZUchZqJnErAhCCopA02Ci57vd8FiQzojHWKoQ/FwTqNkjHH5eixCzG/IeSGqmsPsUPwC6bjW3Ke9PXa8gS1nYIMrtsst0rid6z+KUI8xbK5qLv5tVVW/RED9nxE4grbqpD/OT8k3rz8uvlnscem/OJ+fIvAPdtcNinE6cfVHfXgPZcvUVlZcXmO08dHdCJY780AgzKpt9///3mwZyea/+RmAC2Oj4GJEf62GEd41gpz8uYXlH0KoXjg3IaP2Zhm8KzZvzoRz8yWb02scExFTCBAqmRBhTDkBkf//jH9cd//MfzPmD39x3Q+aAVfJWC2t//omlo6XY4taVs57S9AqYFx19xiVS83ipFRUA6QxkhjfZKI92St1gqaZi+NFUsKHU+KQU7JKdXqr5UKl45ZY3O/SfvVtPqCv3ooXv06uvvGlcnYITwWAhsKNmuEneZBqP98ji9WuZfJaeiiicccs6DAnZaREi9S7bYY1L0oOS9xvw2Ejs3Rmho3NwdibeoOL5K7izEwbnRlnGSIRwKyxfwqSPUrlAsKN/YvSfgOJUhT0P1TBiMDqSQGqiJ1xXv0PHhA+PHV+quVL1/5ZwE1XA6pisVlAmjjM8kYFLYI8zSCEtXVm1Rta9cN9dfqm3lvXq867BGYkE1+qvU4K/S1848Ou5glFGuLQPoubGjfGmw5WeHcTQnAnMl7oQpqxKJT6TC4mhmWtPsxo8Aw5ASUxjZqJTWr1ouDXVK/jIrOD9mCBx6/GFt7z0mDfZamQjXvk4qmjD6T544obZv/6tKmo+oDDX/4LCuvP4Ghd/8u/ImB1e62+TsOGs1rF65CZbDPF0UKNb2rVeopPjc2Byx7waNf1OD6ZTgCjh3azTOOCR47JBfVXLFDmtFE7+3qKIoKiUqpGxBzQzAOeKakXFgDGyf3+qf8dIzE4QAh3WJNV/H0XZKev7eid9bj0s1fHfceh9jkPOszd6cOytCzM20okrJ6c7rr5Hc3rFG4W5poEeKTMznQHRYvtpGnXN71OQclQJl0s6bcyc0IiHpwf+STh6UPD7pqtukbVbWDsoRnDzGTvL6YqeUk5qPUzKTOZ0NI9FenQ++pHB81JQXagrskN9lrVElbp8+tvE6Hehv03+27DdEZPLwf763JS9S40PrNysYj+lbzSfNZX/jitX6yMbJgbK9NfXa39+dkpK+s7xa/3jsgEZiUV1RWaeb66cn0xv86+VzFWso2qNwMKaRtoTOeFrG53BySzSeQ6E4Hrjkb2dfkDpPSPQLWXmplHyulBDo77L+Vlo1ac/n8xp33abG8vsnsn8cTpWtv057i2qMeop1gr0EdSWOeXK5qECG+spka3xkwxXaldS7ZTow//hMxCaQremKL64BBP19D96jq25arqbAxDUhVZz90+uYGNuU0hh1nDK9k8wpcVxmOs5NwJdADPvWbbfdZsb71772tQVXEBawNEFQ5h3veIcpzcIYsYU9BcwediAd8oI10O7JBugNtZBk+sUMSs5CaIChgWGVlhdb2cvhs1rptpo8I2qYq/IYNb5V6o90aDQ2MG6/rywmYDq7wCUCHuzbXIPnMwFkxLbyydmZEBrfb3lS7aGJ7N1Kz+SAucki14UFbDsCzOk96SgFhR0N2MvxN0zpSr9frlGug0MqrjR2CGSHnSGRjRyDFIFMwF9B7ICoAvsT25P3pZc7zRToJmiKYnw2cR58VYiPmfR/4BywDSBWEAjNdz+lOYG/RLryrdLRR6yyU+X10sbrZRSrY+Dec2/wE+ey7CzrOkQG14lAs33f+J/1HrKM+cw4mMvqKlNhZ8Va1fkrtb/vhE6PtI8/z8wdjI6qK9xv4gkXAhiHdtZ9Jh+DOYswLef5EhyWRgelkkrLV0xDSdFqbd68XQmxtluUrsvRoK1bdoxXleB+ckwIhRBYTifInQkgWxkvCJIgTtLtdf5G+SdEnvg5AVfmhucLAcb+5z73ObPO4Xf98Ic/NJnyBVi4AFbQhQN1yt75zneaAcP/8w2yNCYIjQngbLscCR0aOKAyT7npZzBrYDBlMJoMOl6WTlv1vw2q1kvrbs5ObLDItT0kmR4FCasMSvujUtOtkn8i2BhPjCoUhz0fVW9kQG5/XC8+f0xXXbdLgzquQIxzqzalbLKlfs01WIybAlYQmn4EveGnlBjLkih2r1eJO79GaLMHCuL0QuMTquK4ObbJnR/i6dKllL9NqGOTFT7Jz08HX5YAj02KJKPa16gid6mGov1GPVXuqZkzZVHCkdBIdNgQUPlkLlXRCDwJRoyekNaVLJfP6dXq4kbV+CZeQ5bFXcstZQ+b6T8e/3nKFR+MBhVwehSMR1PcjA0QgEsAA5EudYQn6smDoagVRHQ7U9cPu0Zs8sZNsNA2+lHVs2FuX9cktT4rndwnRWJWNsKmW6W6japwJdTy4Hf11OiI6eJz+eo+OXrbpTf+vgmSkg0ycOyQ9oa6pHrLAIhGI6a5tvfUYWnLbuuLD+2THvjuWKCfvPEN0q++m4M0v+K4BhLbJAfNxAbkdPhU4t6UkdBzO6tV4rheCYXkSDjkiDxonq+tLdORoy2qq6uQYmck9wYpFpEG2q2gf1nd5EyLWFSJu7+v4Xt+oq31tXLecJuU2GINJLJMSiusRuH00rjhDmllGjnR/NIY4ZFUd7a7zypRNdQl+UqkTTdIxTkau7y/94jUf4pJLcWS1wSHFEgysvjeNVdaD/DD/zNxHPZnjfSre+utahrLzMkLP/+WdGz/2GcOST//pkX2rN+ZUms7EzCIMYxxeGbaODQZkfiomkeeGSdxQ/EhnRl+RutKrpUbst1k6JGNsUL/fe4lM3+Tke86RamkT2zeYR5T4W0rN6gjOKr/aj1l7tL28kodGezRSwNd5rb99PwZtY4O6x2rszSjT9qv6J/B44WjLxgnPJuTiHNPZhGBU1NmAfXYqafsT5K6TkmXvlGqWiEN9Ul3/5vU1zmW27xJuvVtkjvts8tXS+vvkPrPWOOqcsN4mTOMexSZlI0jRfwtb3nLeLkosr+2l9VpTVGFTtM/Zgyriyu0rSz/JoBcBzI8Xz56SPc9+YD5udpXaZ7/xS9+YRSPN998vYYTVtAkGU6lnpPbUaGAs17hOA6oNY88zmp5HHOXZUewmvIW1L+9/fbbjeORXHO+gFce2FcpoUAgi7FBcKaAuQNrgZ0VyDqZvC5Soq7QT2NukJyVHY3G5DaNUynhOvE8+1A6+TxTkJ24vmSPBqNdiiUiKnZVGKJ/LmCrvRcaL/Y3pxAaoC8ymOJzsDNtLGlYlJ4aswFkFkr9dKB0tv18bBQyZBzhUenJ70inxoLBVcukPW+SPH5DOtk9LSAM0ksWQQxjb9j2EAIkxh02Za52JWsD75stsGk5pxTbd7hXChLQrZZ8mccrAiDsKEotLXpGJ8Ko889a/1NxoOEyKVspZc7p0tdn/ShIJYKvlCdN710yW0xFTjMWGGOQZ9yPhZrbxA+6AtUppIbSRJRLHVwv9k67WkMm4NcjSmOsQiJNSW4cfkLaf5/lpyJw2vsGqSm1Tw7+vc95uaIJhLijcqpELsey8fJuHAtj6cc//rEhNJjvkJAIF/KpupALyDxHPIVQC//Cttch6BFrQRwsFFE2Hbg2n/zkJ83cwsf4yle+Yvo/F2B1lS5AMmWmfud3fmec2FgKIHg6lKSwnRdQluP0o6nP9RyXeqxSJRkRG5FCqFDTTLChs+O/0f9hJPaUoonziqhHMUXNRDRGsNs11nehy7yWTQiDhw1woUBZp97w8+OEBhiOHlcoNhbkWSg4UBQnbwwOyTGxcFoK03TSw6mOYJue771Pz/X+QqeHXzTqKRu1vrrxwLtJH5RDJe5S+Z1WtgGbwnSqkgpPperHGj7am3Kdr15VnswMdcBVolrfMlV66+aM0GgbOq+XhvfriZ5H9UjX/WoesZqF54JyT4muqtoxfuz8f1X1dl1Ts1OXV21OITTSEYpHTaO+VCTUFKjUqqIaU46q0lOsu5ZdoXr/1IGqeCKu/shohs+bW4zEsjQmS3hV7pkg6gioMCaSCQ0IDso2UDrGdkrOHnpGoQPfkQZOST63VIxxG5eO3CuN9kunD2p7fYX2rG60Yts4tH0dVpmjsVTz7U1Z6tMTUDVfPCo9+P3UgHvLMellOxArYzida+lUufcS1fiuV5X3Knmdk/sRJRtJTkdADsfEfHjxpdNavoyAJSRDWBrqkR75V+nJb0mPf1168ttjqdRJ+On35HjwHjUmIuo636LOb31ZLT/6rjHoRiIR6fY3Sx/6tPQ7H5fWZShpYYy9NIMvnpB23CFd/7vSVW+TKvNQdnUekM49IQ23Sebckq4ZY3ClVU4sI0wGRtKxOBzqirqmJpLpHfLTr0pf+KT05b+Ujr5gPU8JpaM2oZF0roefy8sBtJVcqPxng+EoPW7ikwjf0aTGojb21qT2AwJXV89PlhXkx8c379YjN/6aHrz+V7W5tFyRRNxkisTG7t2XTx9SNJ470cz9On36tKkjnAk4GijUuLZDg4NqeQaHwsZYdhCZG+DB70hJmSRqOSo984vMX1xcLzVdKTVekdS3ZQJ8J2nb//mf/2nWFYgOUrnZBT6++Rr9atMmXVm1zPz/PzZdk7WHxnToDvXqVGmLWova9a8//7Ie7XjCCENMmay2NpV4V8ilVELN76yXy1GUoR/RThW7t8nvWqVi91aVuC+Z86xcHGoyf1FxoqgisFrAKxOUN2CcokB+4IEHCoTGPMEOpqWvi5CMrEkFzB4uh0clLmywib4CoNxj2Xvs6QSCZtQbMgvwKfj8Ku+yOSM07Hm5GGV/aDBM9mYyuIrX1WxUmdsvv9OjbWXL9NrGMfHPBQREiumBUYLMBKPtUlPY9Siftf9nVoNpG73npJfuH1dHE2TEtkjfO/FjIBCSBR7ED/INYrM2IACB3Jop8J94f4q6+/DD0oNflJ74lnTfP0vnUssT2WDs2aWzGItkuEDMLDgio9KxH0t9J6WRDqnrkHTyZ5ZfNwNAFkFGzTWhkSsgVLkvC3kt1xQ3yut0p8Qcqr1lU8YalhKYT4xH/LLk7Iz0wD8CIjIWpvTfOs5I+/Enxj4HEeHj35eCk3utOhwueZyr5HVultu5fJIdzjii5xgxBeaHnaGDLzTXoHoMtjoxMnvNIR5iZ38tNbzrXe8yMWv+/6d/+qfFPpwlgVd8pgaT93/9r/+lf/7nf9Z9992nPXv2LNjFdzlcavQ3TMrWoMGlDY9znssWhGiYmbaAsahQjiorsgQlkhajaKLNlH4wT489t2pdk5wU+zegDIh73LBgE5xKyUi67qNdh3RquN0c7ZqiWl1auU4VlMuaAaKJkfHjMzHZsed7Ii+oJL5GJe5pWOi5gnuzFOm3emsYeCX3hEHoc1WrNLFBg9Fj5neHXIolGtQeOjP+mu4wi69Dq4utIGWVt1rby3bp6NAheX1eOYacumT15eMbAf9P16iL12wr26naUL1GYkMqchWrzpdH2uEsceL0Cf30xR9p7faJgPyxocMqhZxxFenlgcOm70eJu0RbyzYr4JpcHmpD6QqtKKozKaAl7oCpf5kLIC1KXD7TpM8eF/zfERo0gUmMlNsadqjam7nPg43WkV595+w+DcfCxoG5pX6L9lTPTyaQb4ywSkeD/xK5ksotoaJmjbPr0NLAD8eA55iHqDUeevBBbSvrlddTnJqt5XVLoag01DEe1O4eGjWB2eRxgaGDIeJevtqUWBoNhdU6OCyvnUbetGqC3KAETjJIYe5LJTdnVpuZ68F5h7VubaN6egbl8bjVfK5Hjv13S8PdKvF5VVdRLGffOenYY9KWmybe/vSjOjMwqEg8ruFIVHUBv6qPvSj/695sauCScTCl8nrNbulEcqDfIa3YInlzKHHDtSULjgw4X5WllupKak5OpobPY/2t4QqppN7KOMmGy35V+uW3xomb470h1V37+uxzme//yVekszR2j0ujw9bvb/ygRKPryYlj05crTANjDscX4xEDeaYKmGwlKDI9f1v9ZhMs2N9/Th6HU6+qXavt5ZOdLho/cm85rtmq5+gd4XWS6RWZ5ChAcITisZyD/ATrKO1ASnZ6L5N0cOw9z7p0uLldm1bUjd1rsirHyFXKvSU7rCb7cuZOAo4GPbNQoENqQJCieCIo8fpls1dUce329T6naCKqipoKU1LxkUcfVcX15dpZu81cj9aWNi1bfpWGovTRCMnjLFORa0XGcc5zPtf8N64niEMN3Pe+973mulAXGFVmAa8csMfefPPNpm/UF7/4xTktx1GAJpW4yZQpSNCQBrUFzA3WFO/WmZEXFS2JqPnYee3dulUV3noT+KGsmt2zbamCgCclkLA7FkMhX+MtMw2Fk+F20Gdqra6pSVUzX0hAcIEPAZFPKSDsO8rFEBRkbgIEVFxzQ3w8+tTkLOKe1AbTKLSxZ5JBgDO5ljy2B8IGFNb5gtJV2PQzXR8gM9jnsSkIhI6cPSztv0dul1N15SWmH5me/4mVIUvppjFwnQjScp34n3Nk7eJ6kemSXr5rXkEGeDS5ZDPZ3J3SSJdUPLWtmclWe+655wyJv5hAuAnRNdflbrOh2O3XnU3X6KmeQxqMjKrWV66rqrdMyrSCvCJDBx8SkmApgTkAGc14TC7fmGnMI7IiY4LzINsoBT2tqZUKbEFcf0fKHMgV+D5kPONTMNeYG6wLrN9zva8zj8k0h7ShggXfdemll5psDQiPpQYyNchWp2dQV1eX/uzP/mxB+3wsNbyiSQ0W3z/6oz/SN7/5TVNLbTGayO2q2CnvgFcdoU5F4mE5HTHTTwOlfa2vQeWeualLmhW+DIsqC9FUCnT6CxQtk0ZouG3DIZVOOOvJ6ln6DngdCcnj1toNqJNpLU3g2AoqYFxiBE/l7P2ifb+ODZ0fNwNfHDirw4Mt+tWmK7SiKP8Uw+T+GammZUJDsZOGcClxT1b2zjkcPslzjZSARKJGP3X3U69DiXuNilzLFEuE5XYEdHhw36SP6YuQ9jihvG4MNJnHdTfeZMa2lkkvvvyicTpyNfxYGOv9C1deCceTmo6oeroGOzUYHEphx1E+9IS7dXr4gEJxCIeEaYreF+7TdbXXZixP5Xf5zCMfcN6/tuxyfb/lqfEMC767LzJqxspwNKxvnHlc7117gwJZ0nN537fOPq0gCgWjHk/o3vaXVecr0xqaqs0xKjz1RslG7WEbDf61KnKnzuNdu3appaXFGM4opwyJ6xigrZliCbd8vgZVlJep7USn2jo6taK+Uo21Y2uQ3ejXUySt3ibtu1dD4YgayyE/nFJZlYKlNXr8sceMuupsb6+qbnuLer/3ZTWUFBkSQbe9WVo2Vke8tNIiMZKJDX6uTDWiMZggYPJSYXE8nsulyDNCHPb8/hMKReu0fuM1cpx+WonSKg2NhnSyrcfKZhp6QTHnMuNgYKSFh0c0Go1pc1XS9UOJ5XCYoCQNCackNWqWSze8TXrxIYkU+8Z1Vt+K6UDGVetD0vDY2koJpeW3mAB0MBTRibMd8kEu8VIfmv9heb0tpsZoVueclP5b3id1nTHkx/r6tTrf2W0cJ1Qopvky8+zpe6XjL1j3pP3c5Ot55Dlp5QZp+x7pRbJpkkprbb9KM20ESNDPThVPL4s2HUrctXI7/IqaJs9WNoLXWaRQLKSzI48ploiqzFOr5YHNpv/PjXXrzSMbqOPMvKipqTGGfXKtdlRnyU0j88GllTW6r/2snS9h9vjVxaUqTi/3lANyqrtM2aq1O1XcekQvn2nXyroKlRb5Jfvc/cXScH/K61U8u9RqHAycR/YbSA0UXQS4qFE728ARZEYoPqEKCxQHtPXKbXpq31Oqu7Ta7B3cN3rrlHmWVlAIu+bLX/6yaQ6Now+xMdep8wUsTeD0Q2i8+c1v1t/93d+9op3NhUAm1T02JErq5MbBBcwOlHZcV3KZ1pVITdtb1XVqUKrpMhn32H52v8SlBjJICHLiZ2BXzjZTdKbYWrZcZ0e6dGTIsrMIfP5KwyVG6X0hgyAtdhI+PUJF9rl0wQr2NdefseJt7tbOGp9c42JHx6SgJ1kZxkYds8Wwy7CBsBXplQMZQFkcfJuZrq+zVWFzPIhhsMObqgPSslpjk7T3Dam1Z8DYfPH9zyhWVm9sSMgLfBoyCtJtSvwKAt8LCiNwcaj5fJeC4YiJ2YB4/KgSRb3Ti7gyBIXt/imQWYu1HmDHc63tcrfcE+yx+dqHK70luh2h2RRgPGMnQ8Ixfu1sN+YJ4pzFLkPGMeXSH4bjRsxEPIH+emRDjl9Xyq1lyvag7PIMgX/I2sLxEbynfB3l6VjD5yMjCNKG0rp2A/GluqcB5ht+180332zG11//9V+/Ym3NC3sHnQXYcD/0oQ/ppz/9qRkMc1X/cybZGtvLt42XqjkfbNFobFTFrmI1+K3acvMKMh1WXSudeWziuYrVUvU016PuWql3vzTabjUer9wpeScIGLejRmFN1LcudkvuuFuJRKVcTp/K3WvkHuvbwL2YKpBFlsbRofOTn08kdH/7Af32mhyChWlwOfwKuFZqNJbah8BGMNa2MKQGcLikaep5E6zhYf08edODJsr40Q6HCVyTEsymPp91vSkFcnKIZtUdZlyvKFqnBv/UmyMbEsag3RCO4CbBqZXrV2jgZK9KyiYycSAxQrGwgvFgynP83hXqVmNgbggYiIgjAy2q8RbL6/SoxlemJ3tOpnznSCys08Nd2lKWOXW9KzSk0TFCwwbZGmdGuueF1DCN0op3qy/SoXA8qCJXqUo9k2uP4hzYDgKIJVoVTZAabcvvz+iyKy7VUyd+oVJvQuUlSQZ3LCGRaVLeZAVBf+19Gv7WvyjY16PekE+d/rWqPPiyMYAxPAgwtIZCcr/7E+od7FXJ1h1WLwobZC3c8lbpF9+aUI2v3iJtnTAKMUIxYFCAsXHn1XDXWS55bzA9atZs2KTikio5MBh9xXIEB02Q1wR6OZeGjUps3GiMS+qKll//agXu/VHq5+21MjkwihmrkAIYzVmVIg1rrUc+6Hl5gtAAZGuce0i98Up1nGvR1nVNE3tC4x6peqNx7nD0WEcp7YSBx2uYU2Rm2RkCLlexKRWE0cxreBDsIUV7xfEnpYNjZb+ypB6PNwK86U1SoFQ68aJ1D/fcKq2auaoMRRCGMdcTBRNKHAx8jg8DDeOf5xi3k1KTHR6tLt6jztAxheNDKnb55HKW6czIRGYLTU2pxY26dDqg+MOgx4BNtgm4lvwN4x1jN599mevfPDKq/rEkCVbqjaUB/dWO/ImgvDKXtr1aPjLufF6d6ujTSONu1TeNEd97f1W67z8m5j3kymW3ajZgbKFUJHBPaQmuI2pK7CtEC7NxCtwOt1GyRpPKLBJI2HvNXnW3dBtibEGVjXmC8fL3f//3RgV3/fXXG2VVstK0gIsP2DW33HKL3vOe9+gv/uIvXrFO5mKDNWmuG4sWMAGyviHvsCVY2xY7KJcOAlPs3cw/Apzs4WTNoc4nEL4Y4Fhua9itS0NrNRILqdZXZpTeFzq4vlMpvO3xgniIwH3j5a+W8+xjE+Uc8G23TDS9xR7EB2B/x0YEkFHYuQizEOWQuZpPH410MDawM2dTp59zJiuF49FQqbGh3S6XllUn+dvbdipRXGnOCZsQPyKbTYTtCynAuWPXzPfekShZpiOn29RQVaqVjfgzDku8unmPEk638efsALydUQKY88wn8xmJhAk8cw3w0xC14JNAKCymrZNc7pZjtEkk9gSuP/4F9x97dap+gHMFrgnXMT0Qb/erIEaDD71YwNfJJ5uU68b1Yx5Cchj/ZOU26dg+q5wccxr/fs0lUvnMe5wwv5kPxLEgBMkKJyZA1hZzaj5iuPg0+Pf0DEyOmyxFcF0eSSI2KEe11PbihYAjka142kUMJu273/1uk06Eao6B+4rHcJeVagjJUbY873IimRCJdygUP2T6VtAAyO/aKZfDClIz6Ugfw8lnMWfjgb2elMY2Rmp8/sTPJj3PEboc0vvXvWZcWZAPGPrB+Hn1R16eVJfd46hQjW+s0e4SQ1+4QyeGn095bllgoxr8Ywr4RcLhgf3qDKeST5tLd5uMo0zAUMVQgnHHCLLLvvAzBmzRRp/OB1tNlgREAiWwVgTWaH//i5M+65KK3WoKzJ6tjyXi+lbzY+oK0bTP6keCeqqfJtdpeH3TpdqchdToCQ/r88etRtU2GKG31m+dtxJU+YLGjuHEQ1avjDGMjob07FO9umb3DfK3PKC+vl4dPdOuhrp6dSTqFatYJcdYcJt1FBUOTuMll1xijFuMXIyLZPKhp6fH3M+shi3lpujFESiRllHeyGnmJk4nBhIKEDZnxgvOEMYo60a+hv6409J5Unr2B2PBezLIAtLet0lFSYQLvQ4evFt69nGrafl1r5aumJxOjSGXfr6zwrmHpUGI1oltub2rX6GGV2ulr13qP20ZiTXbpNrdk9Zp0mVx0rh+PDg2W9kPgci9wqhm/YU44OfG2hqVfvMzE44l1yUcmyA37P4gv/4RqWH+gkOMHxwi7qt9HpAZGP84sBw7RnOmTIVEIqhw4llGsFpG4hqITly/jnNdGhke1Zaya1VbU2vOm3GViRywSyZgIGb6HggksnRycTSHo1H5nE79or1VH39xXwq52eAP6O5X3SbXDPatmabT47xxfuOlHDqapdMvSy63tOFSk2k1W1B2gDUBYpqfsa243qSNE0AyTv8McW60Tc/0vjDeLwrhx3W19NjxmnF8oRjwf/mXf2lU+z//+c/H+xgVcHEB2/bVr361Pvaxj5mGjgUsPNib2UdYG3ggQGDdnFk5ywIuNCCQotQR4hpsSGweyHYIDRS+2KNLrfzLKwHYV9hO2OLYqAd++aBKhtsVikQ0WFRvesDZthV7Oq8jcMleifCFoLRdysr2Q7Avrr322ryOA7GU3YcDe4UsHoKlBHT5/Hz9C+xV/JLy0hLpqe9I3WcnSvCs25NC1uQKMoexe+eb2CBrZmNjsXwd+6ToiOSvlFbeMKmHGusoIijsYK47Ad/kUqj481xHgsz23LJ9uMUGY41jI8ZkhEbNzeOZ+dw37HpIjelKc2daZxCPMVYZO/grpvyy253xntlZONm+Bz+XADoxscUA441zyFeExHhgnjJWzXuJmZx8XhrplyoapFXbZx1XZD4g0EUkxXrOz6znXDPEiMQh5mOe2GHyC0GYcubMGd10001GYPClL33pFWfvvOJIDRaxt7/97SaASg+NXJuExRMJPdR5Ss/1njd1sm+pX6utZROLeSgW1QMdx9QeGlK1t0g3121QkXvppivNB+KJUcUTvaZ0k1PVpgEQMOVd0hYDFqNt27apZbRTP9j3c/X29yrcNaw3XPUa7dk12dG/5/xzk7I1+PRKb7F+c9VkY2Ek2qphk4URV8DVpGJXdiOFGtyD0aMpz1V6dsnvWprMbDQe0eHB50wzXKecqvI1qd63Vt3hXpPGXOerkXsB05jZzLifz/b8ctLfyj2V2lK1yxgSbPIQWAQoMQQITtsKOlQp3B9eB9tMfUAW49Zgi86OnFIwNmJirlXeWrUFe0w5EquUi8OoeK+vfZV8eZaZyoSWkW59r+WJSc875FYwHhsjOhh7DlP/9lW1mybVzLTx49YXtL+/xQQxOdZyj1/vWXud/K6lUVM7kQgrnHhk/Pfh4VE98fjLWrlii4YH6uSg9Fawzzh/vSGn6uobJgV6WU8J4NilHZjrGDY4BXZTQO45mRa5KikgQDBYJqUtB3sV6jmrE2fOK+StkscXMGtIe/C0OkOWiqvGt1wl7mKNxpgLXlV4l5n/IUgwEsfX+0GIlJNWL4rGTVa67AzHPsdrlzHgdxykuqpKq4aoL89yRR3PSL1kziS0/3Cz/F6PyeTadNsfWGTGHMAmPMaDwA9932rObjtg/E86fjQuVdZbpYquvt0qPbWIwKGyia5ktRgIx59XQj3murWOxtUfSWh4cETtrZ2qa6oxGV+7ym9VT3ePGVvcL8YuCrvkMcZ1GRoZVkdbe1bFIesXDiyAzOI9fJ69v7QFR/SpF5/XiVhEgeoq7aqo1JGhPtNDIxn3vup2NQaKJjk8GO5TqcbIysHpsedXVthZDWP7MMBp5/znS8jBtWD9xtHgPiEcQU2Fs8c6QQAhOSCRLwYig+oK98jjcKvRX7+g+1wyovFBheKsOQ75navkytLTKBvI2vj0pz9tMoW5VgVcPEBBSJ1jaht/5CMfWezDuWjA2hKKdymeCMvjLJfHOXXQh8D1pVdcpp8cfljPH31Rw72DanJX6fd//X25lfArYEll29hK/XSw7yKGIojK3klwi/0YewHBFHsPdhl7EVmE7E/srwtRZ7+AVGADYIcjdGIOcr8QkHB/7AzddOBb4jvYf4OIwKewe1MZYuTAgbyybiiPhQ2VSRFP0B5/hfHEsdY3lCsUO2xKvTpULJ9rk5xj4kwbnA8BfY7TqNwpo0tz8NFBqbxOqps6e2UqEIjneGz70s7emK2Ig+vInGIu8B1zqXS3Ky9caOA6I66yy6hNBbsUGvfBJsFYdxgHtnAMEU+mzyFrhIzvbIBwgTgCjHvIefv+J4dssePzIR84Psibqc6Pe4efhYAsX3BsnNs4sTEPYMxCmvEddvY8awjjmdgDsYhXet+y1tZWkyVMqfmvfe1rr6jr8YoiNZisb3vb24yCB0JjumabyfhR62Hd3WYFvu1CLe9dc7k6QkMajIZ0oL91vNwMf6/1FeujG6+74Gtk5opYokvh+AvjCmOHSuRzXjHeDDyT4fDy6aM6oGbVrLDIg1MvHFWRx693XnmX6mvrJmVrPNx5UIcGWkRome084PLodU17VJvW/2Mk1qr+yMGU52j8XerObFgwBUZj3D8CVU4Vu1fI78qvOdZCgWM9OLBP/ZGxHhwmIyahgYhTsTHFPUHd62quki9Lv4e5BMYlilxUNC8Ef2mC/mdPtKr11DmVVpRoRc0q3bj1dvM6gog4HxiKyQYZhgROC0bp3XffbZwNNqc777xTxwdfVmswtURYFb0jokHTT4Nz3VG+Q2We2dWDt3F6uEM/aH160vN7qjbq5HCXWkcJnE5gc2mjXrfssqz36vm+ZrWO9JmeGssCFVoWqFRjYP5KgOUDE4xNPGqaadvr46GXm1VdtUOrV6SWxuFvbJQ2CYUhgWqK+wixkB5gxdjD+ObvGGd2/4RcgAHHI4Vw7jslnSHzZWy78ldquPFGHWh+Qc0DR7Vm00rzHR56aJO+NbZK02/B0dmo+trGGTejzguJuNp++g0N7h8j+Koa1XDXe1Vanxt5bpr1Nd+t6Gi/zpzr1qqmar3QWanLrn/dzFQifa3S+YNW+m/dRqkmLUuI7f9Lf2KRGMnPAY9fes+fa6mBcQsJilPMuOTRtKpFXq913EPRhJ492alwKKJlqyyntdLTqNXFqZlCOB4orPjffOZQp37Rflj9AwPavHO7fmPV5WbOTgXIWdN0eixTh8+547H7dWxoQOHBIcX6+uQt8qukriYlF5A7+csbf1WlScYmx8LaCCmG2gYHBMeQ7CcUcTifzCcyjrJlklgnFpM6npCGxhp/l6yU6vaON5PH+IfcYH7h3My1+ghnzlZWMvdRx6Ki5HvIMoEUwti+EFRPmRCKnddIbKK8GShxXyGPM78slC984Qv6xCc+oXvvvdfqbVTABQ/qL5OhQU3j3/3d313sw7loQFZpT/g5hRMT9le5Z7uKXE1T3ounOw+qeFu9IeqDQyM6f6pV2wIr9eu33rVAR17AbGAHrAnKJNuPP/jBD8YV0QS3+R8yA8U1e1py5iyf8dhjjxmCAz+DQBj7EhkAudSOL2DugC1DUJX9P91+4d5g53Dv7MAqfgbkQ7rABP8Af4R7yz3nZzvjPxfk2oi7ufmkuvofU01dsaqryTzAZvGoyLVXjrHel/g52DUzCQLPBHY5LtuHRvyCLZcvicD1pUQUQXhstqWQTbFUAMnDeMSvJW7BGEv2R7kH2OGsPfwtG+zqBcniXsY22Ua5lETkPYx1fNd0e9n2X/ATuHfTjX3WPMYp/ro9f1hX+RzOF6EYJB+kAWMpV8F3pmMmxopAlRjrfATUESywhnDOZBnxPawTnCP7/myzwi8GdHR0GGKD+/2Nb3zjgiQZZ4JXDKnBxH3Xu95lHGzqo+VDaHCJPvzC3QonNbQ1W5vTadSXbgc/T37fO1Zdpl0VM1sY8gZBq1jQaiy7wESKVcaJMjZjRcPH4HaslseZXQ2zv/e4vv6T72j9FVvl8XnVevi0iitLtWGoRjdddV3G9DvKA7UFew3JUe+vkD9D4L4r9JQiiaQmqIaq8Kren3/651wCYxr2OmsN/hxAM/mnex5Iea47lFBymzOyF1YXL9fuiuxKgNmCjdoOLJLyB15sf1bdiTa5PW4d2X9MDodTe1Zfq97WAeOQkP6fDhh3O4XQ3rQJusH2c61e6nteTn9Ca7euGV+UvU6frq6+cV7Oi34aXzn9oPnfzsqgh8k7Vl2vA/1n9WT3ifHyJzY+sO4WlRIAzjI3fnr+RUNu2Li5brP21ixOD590xBP9iiQgIy1C1qk6uR30DHBOOo8f/vCH5h5iTGMw4BROt1FiZNg9Uy699NKc1RukqGOkEOA1a9tLX5fGmrZbcEh1u/SCd1AvvviSEvGEvH6v6iqKFApFTGCeY66srlC0p0ivuvQ1WhC89EvpyZ+M/8oIavNWKnTdm3JXqMfCOvXSo6qrKlVx7TrJP8P1oueMtP+HY7uVdTTa8mqpYUsaqfFJK6sk+Tlww5ukrUuzBJ9NKNiG/IGD31FFlVN9fUOmFKGbEgBllUrIpzJ3rZoCG0yj8GxoDw7qc8cfNuSjyWKh/rbLo49tvEkBl1fdoVF99cxBtY4MaVVxmd6xapsqvJOdiO5QSFfeP3H/DYZH5O3rlbPEcn4Sfp/evXW3PrJhm1Fk4SABnIBMewPrLM4TzgbnDOk7JSHQ+YzUfzjpCYdUtl6q25NiD7HOEggyx5RIzGlDRxSZGNM4b+x7EBkcNz+bEl8njqp3Vbm6YqOq9hXr9oYtqvRmdw7nHYz5YI8UGZUClZInexZMb/j+lJJ9wCGfKrzX5f211L391Kc+pQceeGB8Hy3gwgTiDuoZ//mf/7np11fA3GE42qyBaPKaBhyq990g51hwMR2saZ954Etm+atfs8z8fvjx/dq1bae2JZYZe+RiAusqwc6Z9gVYSmBPJOiKAp7AKxl+BO94EODjeYJZBAYRTPF6gtrZ5iWvS/Y/iAOwB9rCG1Tq+cQECtC8BCrxE7gv+BWQDsb+nwY2+QHJlWvvHEQdvGc6Wyoab1dL+yM6evisqqrLVFoa0Oho2GRruJ2146ItjnWxgoZku0DSEOTNBwSesdEKyAxbMIVNTLyDNYef7Swwxs5MsmUyVS6ZC5EXxwLBxThk3nCsrI2UneX7TN/Ndesyfjfvx9/GPue9mWI1+QKijMA6ayzXC7J5rnqFMOfItqKBt01aI5yyM/Xwqzif2WSFzzliYSnYbcVn8ennqPLCVOjo6DBlqMiW/7d/+7cLpkTvbPCKSCNg0H/wgx80A5/U05kYLwTT0xEde845XrUjdbEIpQTh5hGhbqn9ESk2agUwqmjIs3CGbTR+bBKhAeKJkSnfNxQb1bort+v0i8e07tItaty0Wmf2H9G2q6/T/Q/cr8Y1K1Ts9WfNOGjRQMbne8LNiiaGUp5zyqNB30Tj8nxAA3dKO4VjYZV5ylQ6RcBjOqdjrmu4MrZj6c8pocHI8Kw/eyg6rONDZxSNR9Xgr9XyokbD8ENCsGHA7KMU2Ldvn9lI/YGAOs4OqHFLja64/ArVqEndZ/rMRstmk2nBRe2QTGgANtQbbrjBGBS+Aenxxx5XPGYZuoByU/MFAplvWLZHd7c9r97wsErdfr26YZcqvMWK0uArpduBheTmtek4MdSZQmiA+zsOa1Npg6p9i1MzMxlOR7m8ukYJMV/cJr2ae8G4wojD+CfYilOJciNfRTFB5yuuuMJ8HmOFewqRhappqgAqfyeIi4GyZnmDurp71TMwrMrSYtVUWtctONClE53ntWbjSvn8XsVJeY5EVJzU3Lz1bIcGulLXgnlFK2vhBCg85ululbc4j4Cty6tVO240xM62FTMnQHXazjhKGrGnnkwlNZh3W66UXno89blLblzShAZILs+0ZdOr9fyB78nlcmj7jjVGRedxXC6nI7c5xjy1yUoz/nEWYxG1jvar0V+hjx94RD3hoCE9Tg736/Bgj/5+143ypTmyRW7XpDXCVVKs123arPJ4QoOxiLZ6i7TH6TcBqPQyWtnAMeXc7HYkqdG8QSK1+fxYrWrsINsWwulAQYkDguprtuQGSinqmWNME2zA9sKhYr0vqyjXf587qI6D3Vp3/RU6HxzQqeFufWTDDYtTshPj7eyjUvcR63ecjdU3SRXZlIuTbUH6hs0Ev/d7v2f2xltvvVUPP/xwIchwgQIRBhkaf/Inf1IgNOYBMeNLpK+sCcUSwaykBli2bqWOPPuSqpfXG7HN2ks2q/tUh6q277jo+ikSRJqz3l6LCMgKzgUi3yZqsB3tPQlbEgL4uuuuM2pqiP5MYE9jD8J2TQ/SEQyzS+QQ+CMwXiA1FhYEJgmich+wEXggkstXIQ7pBVCbE5wFdgA3G+yG1i+88ILxNZg39nuxfxgX2EKUxe7pHtA1r9ph/tbfP6xly2vld62V29lgxidlzfg8HosBhCNTZQtkAzbghVomaiGQ3HcPsRGZDfxOtYnZrBVznaHM50GwQLYwZrmnzCt+J9NkWhHUGCCL57KaAWMymVRgfmL/c/2mK+01HZivrBWIpRA/Q+Tz2fhTxKdY7/mfMZ6z3zSfgMw4+wspPuYnBOqk5bfMuwC9rq7O2DnslQhtPv/5z1+wGfK54qInNTCAaNZHWRs2n5mkVDEILqlo1LO951NU2pkCnMlYWzyLgFSugDhpe2hisnBEPc9J3gop0DD/X58YUExnMv4tve5kOs4MdxhHo6i8REeefFHVy+uUCCfkcrv1QqxZ7UUuc+13lq/VlVWbJ6ffJaIaiLQqlgjL7ypXsdvaaIaj3kmqLnpqlHnWz4jQ+GXXPlPHG/RqSJdV7tSKovyaSQE2HAwn23hio6wscUojx637SEPtwMopmyl5nF5Ve+vVHW43v3NNXDh3aZkaMyVebECK3Nfx2DiZd2qkRbVnTsjb6zTHjXKBTYX6hcm1HmPRmNxDbm1bZSlAlldkdhrZgDAIqXGbbZE1BkSoRr2dfTp56Iw27bLu3+ri+auFy/1+oe+YwvFhFbsZw2FDWoJ1JXXa13sy5TpXeYtV7sluUHaHhzKuEzQSXwqkBqBEnEMTqZoYCtQFxynA+IDcwDm0FeUz+w7HeHozwfpcgqYYPzizh46dUnFE2riqQd19Qzre3KH27n4t396oy7Zfpc64VWbH6XIp4HWp5XSbOdbyylIFR4K68fpfmfJ7MAIhb2x1y6zg9U/0phhDxOFUkSe/z0VFNmtVS9SqyTpJLZKOvb8qeXzSyRclt0fafYO0MUk13t4qPfeYlc2x9VJp7cKk2ecKVDmsR5fteptCkXM6fPC0tm25XE5P7vOLMpGZ9vIftrykrnBUXeEJUhZio3V0SC/2d+ryqtR7FHC59a41G/Rvp46NZXk5TLPw963drDUlM3MYSN9mrcx5bDo9uT3XdlrqaZfKquVcts7MdwJBBJEgMrOptXCWWCOmUlFyzDjbBIwg8iE5UMfu2LFDz589rtKda1WqtePXkxKehwbbdVnlWBPzfACpPHpcig3BIEmB9Sl9RKZF38kJQsN8Xlw6/aC0vUlyZwoSOjNmaswUH//4xw2xgcof0Y0dpCngwgABUdL8P/zhD+uP/uiPFvtwLkq4jC+RvkI75XIEprQ5dpSv1dCmIZ0+cNTYB1WNNVpXVG9sWNawuawjvxRAkN/2L7CfV65YIVf0jBTtkRx+a23Ms//PQgE7jNIh3BtbBEPAOTljH5ELin72Q/YYmu1marjLPvaLX/zCNJTNFqizA5Z8p934uBDgXRhwzRFz2CXEsDmYj/k2aU4GJAUPbMFcfBXGDw/EU+y/BEYJgjLueD+fs27dKm3aBDnC5yVUXl6igYGwWjvxf4dN+TKOeTpCw+4jORcK+ExgvM8maF/A1PeObAfWIYhWSiuzPiHsXErAP7B9hAUptTwdQiOWyA9fuGnd+PxkDSdLiOodmWI/XFuyyO0+rNlAr08EtcSgmH/MW/YFvgMhJcT1XIFy7x2hc+bnOl+T6RWbF849IsWtahgGo51S94tS7fxnZzc1NY0TG/hkf/u3f3tRExsXPanxP//n/9R3vvMd4yzOhrF7+6rdJkDx/Fij8I2l1drf32b+FolLPmdqWpmHQNYC9DRQZECKpwevHNJo+4KQGgmNjCu77R6z1hH4TfmprO9LJDRCuSyU+csb1HOuUy2HT6mholaP9B1UKBEZv5YH+k+qyluqDaXLUwiNlhHKTJGR4JAiCVV61qrKt15FrhWGfBqJnjH/0yg8Wz+N6XBquHmc0LDOV3qu96UZNSllgU5ODe9oPa4jL90jOyOMusFFtTsV9qwaZ9xZkAjuJmND6U55h4+qP9Itt8OjFYEG7e8/Mp4xEE9IRwc75dJJbS9fM6MF7OjQSUNo2CRe8+HTeqmrXzc17TXHlJ62ymby+OOPm+enM9zs0ieo5KY7tvUNG/V7b/uofvHLn6ne16Q6f5OqvFmCbfGQ2XwccqrSWyXXDDI6DvSd1OkRizCys7HuOf+03rbqFq0qrtFrG3frgY6DpjxVU6BCv9Z0qVkXsgHSI1OwlAb3SxUY+jD8tsPP+JtUbzU2Ig0etkreeaulkg05pVMyrnNVE1L/E0WeGSMrKqRTv1B1RYl5uEvr5a7fqmXlq+QL+vTSyWc1OhpSmatC1XVVCpR61dczqEr/MrUc7dVotXVsGKaoVzDmGbN8BxlDGFg4NxwfBuGMlSQ7r5NOUW+fu54wBl3jza/XkfZ2ledR4xOHDyMNw27alFEy9fpftojtQKNUttm6F/TPGLbquVpwSIEqqfWgVLVCCoxljeHYXPUr1iMdraekf/+bCZLm2Uek179T2pnac2VeEY1IA91W0/Xiyf1ocEjt0j1u9wbt3L5uPB0b5U4u4217eaMe7DyqgYiVjQFi8YRag4MajGSWLtiZmun44807tLq4RE90d6rM7dHvrNkwY0IDMF5ROSWrw1Cy2j2KJqFyh9T2cKrsospSGo7jqZ9JzyWVMdx8pXTDG8w1Q9kFUcS8SP5O+mDhmOM48f3TlYZgHyCzD1KDOcW9wDns6u6S6nPLhp0WvKf/MStoZ59vuE0qf1Xu6d2mT1IaUcFeGh6Q3JODFcWu7RqOHUh6xqFi1w4NR08pHO+V0+FVsVFy5q6epAQV1/Smm24yWS1LKm2+gKxgjeae/fZv/7bxNQqYHxS5likU7zSNwi04VOHZJuc0Nt4lFRvkc3r0wLlhBYdHVXEmrsv27jJE1MVYeiVZtMf+cPKl/1YifG7MR5Nc7n0K1N2goZGo8cHwSQjULXaQg+xdAt0cB/4BQan0gCuBb/ZBCMTpbCKEOJxfLsE9Mqwg3KcL8Fo+a58iiVH5naXyu5ZA4PACBfcZoYM97uYy2M+9z6UJPLY/Pmj6GLEzPBgTNTUNcjgr1Tv4vFpbzygR96kksFmbNm0zvgI2EeOCHmjMN84D+wiVOs+j7occQQjCPEPMZzeWnstGynZ/uXxICt6TbuNlBLYQAVj8Ypdfqtkp+fIM6l7AwIYlaG7b2txH7il947jf/G2x18/ZgrGbXhaLXiuc24xKFg32SD/7V2l0aELs9+p3SVUN5joSXCe7lT3Y/nyIZQgjxiWvwR+fyg7mdcnjnR5+xHnZAyH3Z0OQJqMn3KmDA88aEStoC57VtrLLVOXNMTOL+RMZTH9SCqX2Z51PrFq1apzYYG2iROrFioua1PjsZz+rf/mXfzEZGlOlIuYCv8ut31lzmTQW24snEvrO2Rf1SNdp4wqH4qg9rVIG/B5LxPRo10nd1jB9M6pZgR4ak4D1ujBlHChXw2JIc9ZYgtJA1vMlbgLW1vDi72dHz6ll5LzpT7CmeIXq/bWq8JaqLzyooZ5+Na1faVRUg0fadeLwETWum1BsspjQRyOZ1CBDwyI0xs4XdXPkpMo8K+R2+sz385gthqIj5vuTM3Tiims0FlSpc3ZK+7riXtWtg3ia+OyR0XPyrrhW7rHmSizsbC4EmO3F3+VwaW1Jqlq6zl+vZ3sP6/hQq8ImNjOiJ3sOmb/tqEhrDpwDwvGoOWcyL449f0SjQyOqXVanvddeI08GMgdDkg0oF+OUHgsE6hgXnB/nRbCMjRXikQ2PDY5auNTJrfRWm+yMzWWpjX6TMRjp1/7+fYolLIVOwFmk3ZV7TP+NfHAWBj0NXIcTQ+e0rXy1tpcvN49c62KuL6nT7ooVeqHv7PhzN9VtVs0sszT4/ie6j+m5vtMmu2RL6TLdWLdVbufslTcY25ybraaCiEspkQOR0f6LMTI1IY2elSD+qq+e9pjJiECpYYz7/m6VxMKqWL1O3romc//5XhwBHFvGwfg1Lm2StrxJGunU4EhYncFuNchpHBNes7vu+nG1VDwRUzg+IleJR8MDQVNWh8+CrMF5JrDK53NOqKJRA2JgQWzYjZkZlzNqIlndJN35IenlJzU0OKAWd7ncgUbVVObnBDA/qNPLdeJ+cPwZjTSue9t9EwHZUIcUHZaqr5BW75FCw1Lby9bfUK93n7UezOFL75SqpiH6H/4pLGRK5onu+8HCkRrd56Sf/fuEcUzw/ZrXpQSsIS1YL2zygvWE+4rTyX3EoWSMsIZmm7OUnXv/ulfpgY6j6guPqGV0QJ1RKzsDwUJythXfXOz2amtZ5rWO7/iNlWvNYy7AmsoYJhBn7wF2jVrGMv+nkHAlK6Smm6WBE9bvpWuk4qSx03UuldAAh5+W1u+UVlhZAqzDXDtb9cv3cRwQE8AuVTWVkop5niwkYfzy3PLiKj1y6FlVblqlhNNyFdwOlzaUzKB8Q4TxbjsHY3eI33nem6OowxDMGQiVLNmOXle9XI69CsUp6eWU37lCA9EjCsUtkQsIxTpU7bt6SiV5+pj5P//n/5j16bbbbjPExmKVsyggN7CXERC988479ZnPfOaCD2wsZdDjq9JzicLxHsUVlsdRJrdzemEI92Rb+RoNlXdoz6v3GBsEu4b/cyn7dyHD44xow0rG5MTaT+Az6BhQ03Kr/j52N5mzZIXm0r9gPoC9h0/A/k15U2yfTMAuy1V9S6YxQXOCzuyZ7F+IHwjosA8xLiBJALEBvptrkW1MMF7Ojb6kvqil1gUNvs2q9l085csWEgSGybgB2BRzFYC0Vd7cRwgwOxuE+4u9YjeU52+Mh2wtZRkbjEP+t4i2Gu3YvDdljcf2QezFawh840fgB/MzZDf+BllH+BGMXUQL+FJ8J/4GWauzBUFgxjafmW/WBfeAMkX04+D9/J6RaDn/mDRoV+NwWKVMV98heee2lPZSBeOHa5QsIOK+8iCjHh80+bkLEcRuIGns+48vjv3J2GZ85V2Sdt89UjCpBHokJD3xI+m17zO/8lnMBa6r3Uic7+E5e47Z1zVX8D6yNtjfk8WZs8XpYes4kmOAp4eP5k5q4DESh0oRn9OIeWErdaxfv94QG1RH4d6SWXwx4qIlNb797W/rT//0T3XfffeZzWaugTr711fu1GsaN6p1dED/cuLJFLfYKYf6mcjzDSZGyVppiLI4YxuuK2A9twBwOkoVilUomug1v9ui1v7IafldVkDj5PAZHei3AuzgfLBdV1dfppvqduun555Sb3u3Vu9EUeXVkZ42+Yr8Ki4rMksIWQcgvSF4LMG1nVzYh1JU7gylIGbanKnUjdI+9TucciqAYiFfGPXnISnePjb1GDGpn10U8MBajP+OQcQGQ3BzKsfD7/Lr7EjvGKExgWd6T+iX3ccVikW0vKhatzfsVrF7+mOv81Xp8LmjOr7/qOLRqLwBv8p8pTp+5JgJGGJAjcaGdXr4sIKxYXWe79OOZZkb9aUDdh6j0m7ExWaGWoZN6Oc//7k5T1s5gEPCa6dTWx8efHGc0ACj8VGdGj6mTaX5NUunDE0mpBu/uY4lXndH407tLF+uvsio6nylagxkUFfniWd7T+mX3UfHf9/ff8asSTfXz745vCEJxprOYfjbZWPIGjAYPiXF0+oYjzZL0R2SO/tGTf1aMiVYj11P/FyefferbXBYB/uG5br6Fv3/7H0HnFxndf2Z3rf3Lu1Kq957s1xxb7hiOoSawB8SIIFQE0ICIQkEMDV0CDhgjBtucpFldVll1bXa3vvOTm//3/nevJk3szO7M9u0kvb4N15Ne/PK977v3nvuPbfwmlvEeGAglOOLzkkMKPOVWQmj2YdcZ3KtTDaElrPourqaYhrw8vcTlQ2T0BA9LJYuFfcYdZ258E9Io5qSRNvuRmNdnRjXPKaJGLt0mrhfcgCN2WCjNMDtDDrHOWYjF4Ds1RJxsfhGoPZa4OwrQMuJ6GeCAeDEX4BrPjD2TjhHYgkNwj12r6QpA7Pwn/9FrHF85gAaPSr4K5cJJ4PnlX8TyXXx/MmZPpxPeE059mQjOh6UjlqbXSB6CD3RfgE9HpdYf9RqFQoMzNgJwR8iYa/FV5ZtRUaakmKTwahKqTB4zDx2OiQ81oi2srlYeiQCq15SeH0sUk8OFI0FBujjA1Q8/3988U94eONGPPXGGwiUFqKsqAj3lq6cWKNwZUl3Kq8nQk4t0H8BcHRFbYri9YC9B/A6AGsBYI0lsDRqC8xqiQCirr+S0CAoCOnyt8GqkLy0+4Yx5BsURHueIV8keMTPu0zEefjhh3Hrbbfh5V27YqRX5jC7JCluu+02sbbwms0RGtMPnmODJv11lIS3TL7yupGoveOOO3BFwtsD2I9L9pl2dNBRo9HCYoyufwz60sZgcJOkuTJ5arpBm5qJS/xt/ibtLhIck6qUDYM9+UhScI2i/cXrzwAXbcrHHntM2I6cW2kXyGvVWEFhu78nhtAgOj1nYNMVQD9L5bxmM+QeFBwDDMwfOnRIJLBNZh6lr8LxRP+UiR4yUUL/hWOL44D2Ed/jOCfRkKwXBQmQVCq5OJ5of8pJs8kSEWincoyR9OC+0G4j8TGZikyeOx4DfZSJSlbKvUxkooWJVDHBaz8r8pXy4qw+DwBDF4D8NbgSQfKJvhbHBucMJpQlI7/oE8t+MZOAOH9NFUE3k+AxJhrvHM+cQ3mP0m/ivJnSPUpfQnnO+G9WbyjA7U11bJZjl/c213n6zKw4X7NmzaTWNH9otGSzP5SGf8HzVbwFaHsl6qtrzUBu8iTd6cLixYvx9NNPi2pHxkEefPBBXGm4Iluhk8h43/veJ4yXdBvbpotMnRELrXmwavUypSBACYsSozXpZDilyNsoZeZa5wGZi4HSmwHNzDWMC2B0kCgIt5CIIs7Zo30IZJy3NyBHn4EHyq/Bxqxa1NiKBSFRXluF6lW1YuJkkJb/mTR6LM2IDeYZNAwMxwWaoYEuzsB0+Ptwzv4qTtmfx/mR1+EKJG4ungxVlnIUGKIBDV7j5Zm1owISKcF7HAg0ASE3wEbmeiegTWBI+wdHTf50POicMfMoVXgCIQz6PHAFvGI8tjj78ETboZTGpKo7gLY9DdAadMgpzsem7Ztx/7o7RVYNnRBf0Iu6oX0Y9PXCHXSi296O18+/iBd3MWt8NNiTQQ5S83i4WNKwowHA4BmfcxGik0GnhhlZXEzpaBw7dkzoJ44FEiyxCMHhjy/5Gx/s3TLqXECFMvPEs2Y5listuViZVTYlhAZxaji28S+v6Gl7fIPgqTF2yOxTA5NGufRjXNBVaQUSmdlNg4hl4caeVkFoEBkGPWwGLdQHdsHZ3iKMEX6WGRzJqn5I8EUIljFAg5/6mqmA44yZSnLlx7IlS3D26EGEWOkQziJhxv9Y4H1FR5rB871794rjYLOy4cFBwN4LOPpHEwQpgvcHty3fu9wfZjfu2fcm9h05j8MnLuLA0QtoaZcD04qAM8kN5/CoJqvwjEjkxliYTykrxbUWTZTDDlRvF7D7eeCNXcBIevNqSiCh4hiKPWcqNfxdLcKJ5DzCc8x5Zbz+I/zssmXLRPA/0fznDfrwQtcbeL33MPb1H0Oh0YkMrdQwnDBoVCg2qVFuViNLH8S3zu3FN868gS4l4TLD4HHQkR90DAJ64Oixo6lpK2clmcuyU89qopNO6YLx9k95rvnv/f1H4CkP4NDpfdiyYyHWBdS4wZOHSssEs4R1/F78WqwOv54iWN1WcxtQeS1QukH6d28LcOIJ4OyLwOHfAO1KualYhMKyj6NeV3S6anW2YF//Hpy2n8SxoSM4MnBQVNglmoe+/ZMfYFDjxx333B0Jus1h9oDrwFvf+lZhp/ziF7+Y0yWf5WCwXpaj4Tpw5513pmwXXFbwDQEDuyX/gaQGCY6QKs5WCwG60fM8A5skzmlrz4TPyt94/PHHxRrCIDPlppg4w+AsM+kTgXPhU089JQLDiQKRrG6TwUAzgzY8LvoX/MvfoT3GtYu2AJOl+HsMfNF+SJbsQHiC4UrROLAieA4Th9zQm8F9+ogTBW1jBl7ZQ0VORmIgkw/K0fB3+Bn6MLQXSX7Qt0lUaUobiokiqYA+a6rSWfR/OHa5D7JkEcegDPr34917JGbo11B9hHLPzKRntS6/OxkIn2fZMmFPy+A5eH3369j75gXsP1aPw3UNOHBcytwXxMYVCp5jEk+cM3iu6ROmkvHPeYZBdZIbV9K54Hwsy5ulXD2RXRgr/8p/J/M7kmA8XybZ+7zfeY9zXmG8ipJUrJ6aKLJ1eSm9NiZYPV91B1CwHijcLP1be2nI8I0bN+L3v/893vve94pY+ZWGK65SgwvjvffeK2Snbr755hn5Ta1ajb+avwE/vLgfzoAPOhWzOdV4ofsk9vfX46GK9Sg1TaMRzaBTBo326WugPBY0bEAXVzWhApsPSwF7udeDEnzN4XfhyYMvwJsPDLm6xcKamZclq9ELlJvysD1/JcxxDTstmgJk6aow6JOaBPO3io2rYzR2aXA2OY9AEgTjcweaHIdQY9sGrSq1LFuSF1ty16Hb3YsmZzO6PN04OXwa9SMN2Ji7Fhm6FEswXR1AsH2UfwG9FvAHRvcrSAAusmyMJM5LWAZEiUUZ5TjYLzU89QZDkaoZ+adYcdLpGUSrqw/l5rEnZTo4n/vgZ3D0xDEsWroIRrVBXJ/zzefFYtHrbY9hq1VqFdra21BYUS7KimU2n+QEHSU6DiwHHwt0PGXnk8Yds9KZZUNCY7zsAJPGDGcMsaGCZYyqgWTI1ttwW/Em7Oo+IsggEmrXFqxGln52ZcxqEpBqrCCaDshlnTQOrrnmGqgNhYA9WnklBjXLK8e4F2jM06kU6IlmvHWNOJFh1MPrcKPKooN28WLxezK5QcNFbjYsZ9zR+WRwmk5wwp4CYYOHsm1ypUMqYGaOyOhwDODEY9+GxufAxbPPYsBYgmD5amHcbd1xzajsLmZ10VnmX2amk6ijU/WXv/wFd73leuiO/gl4XSJxRC+LtfcC2vSz/GlYs9qFzhnBUt3smmyg53X4fJQC0mDvkQsorVwIdbwsoTUH6GsCVOHGR6KLNT8zTgBjx23AQC9QJ807KK0E7noXUH8GePRfAZ/UQBFPPwZ88stA7uTLfSNgDw2O8/jgr9EixojceC5V8NoyeCH3eVDi9HA9hn3KwEUISzP12N/ngzfu9+l3DvjZv6cX/3J6N/5l+fWwsMH6DILOcGtbK9pUbXAYHNAZdPDb/HD4HLAZbONXErF/yr5no69VLhbN/FIFs1yZlTYW6FzwM/Ja1enuFuunJcMCo8WElnPNaD3fAk+HS1zHRGvauNBYANsGYOQQIJIotIBtnfR6OiCxkROuqug6DfRFnXuB868CedVhqaq4XVCZoFGZEQgp1+0QDGppjfUFfThjPxnznQFfP9pcLSg3j5YveW2wCXd88+/xu/d/Du9897vw61/+asayp+cwNriu0BlkMOuVV16ZWCXfHGYMtCNl+0GuTLhiZac88fNxiMaJNGcxkYr2oXkZoEscXKJdRSKA9jsDH9NZfcRt33PPPWJ9oM0kJ6mMJaNDm5D7x2QOBqR5LzIYzmtLv2i8e5Fz6P333x95ziorJs/we+Nl5RuSSJ3NVWlMDUgM0KahzZ+sKnUsMNkvkWwZbXa5jx/HDMcPxznHDpPzqL0v24OyPBXf44MVXfR1k90HDF6Pl0wTD25PlrYiEUHfmJnk9HO5fyTKlX0GZHBfGVDm92h7bd26VSRO0TdOx8cZCzxOWXaY4O9s2XEd1M1uBN0D4t60O1w4eb4VyyvfgisV9O+4vnOspCtBTLIqXdmk2ZwMwPuBPjZ9WlkiPCWsvwXobQOYcCX7cpvvTntOIDmRjDTkPcKYQMKfX78+QnJzTL/wwgtivZnImjbfukj0a+33SdLk2bp88VraMGRJj1mAW265BY8++qiIldOO5Rx0peCKIjXI1N96662i6eI73vEONDgG8XJ3i8iI25JXmlQDeyowz5KDLy25EXXDHfhz+9GIZJHd78Yvm/bh/y24Xuh2X4mwaueLJn6BkKRBzohZpm5pZAIpMRai2RVrcOcZcvFk2+uoazuHBetiF2V52uEZXJVdM4rQEJ9RqZBrWIhMXYWo8NCpzaOaBo74+yKEhowAfHD5h2BLYtgnAn/LF/KJgIwMd9CN/X2HcX3hNeNXbTgagb59QC4D48qs5ySfD7B0r1TSwE8wWbNMlAZHfLb6ykwGpVQ4M9SEDpaNhqEkiYj/a92P+8o2odycO+Yxi2aKi5ZAr5D+kiWh4kOh+cW56O3sQ19fP4qzzcJgIwnDjBgu9NxX7rcsazQe+Ps0blM1cGtty3Fc9NSQFjmj2oh5lomV5JaYcvH2yhsxm7E2ex7aOwZGvTZdoHHDRly8nvyLrLXA4JtSRYDGDNiqgX42Jw4CxkrAXBOT4c+SUBJbdChMJhvOdPRCr9Ug06iHVa9Dsc0CVW5h5Dsc33zIpa9y03I6Pnxs27Yt0m+Gji3HC8u55c8wY2aipa3OA48hSxdAUQFLbwPoP3kCG5wdGHF5UF+/D85l18CvNYh7gUQDgyY0vOLHKh2Ozld/h3KtIlje3wqcfRVYmv744j1Eg5lzQDTDLBvIWQvd4AlRKVNVvRitniqMEuaavwnoqQc84YoKnmeSkmdeBJbcDDSfBo6/ImmfltUCa24AuF4xWP/W9wO3PgwE/IAlQ/ru/35FCpjIMwGrKp78X+DdU6jTyd9efzNw4Jlw1k8IMFmBBRM3wHidWFoeT2rY/Y5RMoPMtGeywvfq90Xn0VCULGb1GyvhTg/3YF1OtEHrTIAZrsZKI4IjQZggZfzo8nQ4bj+OrYat42+gtEZqEM9sJ95yLWeAuj3AshS+GwaDg8peJvHgvc4MWhnOgGwfAKXVZag/cQHF80qwbdUO9LX1TYzUIAzFgP42SbOW5OpEqiiVcA4lINNCgHs4IalBvf8s3RoM+Y7BH7KLBAubdhH0Yakcd8A9amyx+m/Enzj7d8jrgs5qwt3f+Tx+966/x999+lP4j3//5uSOaQ5Tgs985jMimETN/vg5ZA6zD6zqpKTKVQ3rZkmSmP7ROHMjiWjO2wz2M2liuiueWGmoDBwyyKxsdq4Eg4x8n3ah8Ml8PuGLULqI1cTMXifxkapkHwPEqQZzbNoCZGqLMeTviO6PoRZ69QQkE+eQELTxGVAnUcAAfzqQZZpJeNHuJgnASgPaaxzHHGvKZCSOG1kmiDa17F8QrEpnMh4DpkyekqWyGGBlchZ9IZJx9C9SqRhXQo6J0EdhAh99BvpUHL+87+gjMTGXfo/ck4y/x9evu+66mKAsq1JefvnlKSM1CPpS9GeEjyej7HqoO3YDrh5kZefgzFA+YE6PzLmcIBNbVzNI9HEeVc7FY1WyjYI5A7jzo0BXo5QFVlgJ6NO7p1lRxxjUWJVQyUgK7iuJ+SeffBIPPPCAuI95z6V7vxIalRZLM9cKdRKx7YR9jC8/vPOd7xTxExIctGmpVHEl4IohNbjYkNCgHvHf/d3f4fRwH758ao+4nzjun+tqxCcXrsdmNnKdJhg0Wjj87pjG0vy/K+BDl3tYSNBcidCo9MjTb4Y70IUQ/NCrc6BTR7NFV2YtEc2121yd4tzMt1YiFNKg8Xw9iuYn1x9cl70IhcaxZSS0aiO0SJx5pQ5Xiox6fQJBj35v/6iG4SQ23AEPzOOVkQ0clf56/IBBGw4ohkkNDQ36esUBaQBvWKLKuilW+iUMkgXMJGB2q3JSF70QsqqFVNd3Lvwl8no8AcFjeLm7Du+sumbMzBcuAEp9TaWcSJYuTzQsl0kEW6YNW3dugr4jDw67I9J4j9UWNBJIaCRzVqYCGbosrM/ejkFfv7i+OXru3xUzvY3CogzpXL4pNwrPKMPqrNSaFl4Y6cA5e5vo+7MkswIVKUprac0B9LguoMtths1YDHPpW6XsaHcHYD8U/eAIpVoCgCXqFMv9D2icm4xWLGUG0Pnj4XshBKzbCRRGnVs6IaxKoHHJwIRcRUGnVq74oSPD7Co6GHIWCUuoZcck3cZ5RJbVjANHjmLHcilrfc+h89iUnyP20WrUY7kqiJ62N3G2YqNwiuiU8F5kpiCdbr/PB9/pA8jqOI3uMxexbVERPSjFfcw0//RlwngO6OTwnhxVMm9bKB6hYBBZ+VIZ/ijojEBOGdB5RuyD2+tDW+8QKvwnocuYB7z8m+hnT+2V+lhsvy/6mikumDsQp5lKJ6B3bDkiAa8HePl5oKcTKKsEdlwvZcknw4rtkixS+0Up26d2HQLNE5dZ45hg+T/HlTIbjr2T4js0GdV61Nry8YH5G/Bs5zkMeF3o9bB/AiaNYZ8Xv2yqR4/HjWWZ2bi3tFLILY4Fzr3fO30aT544gWybFTfrzdCogtCoVLCFWzEN+4fFfDDuGnc0rO+q/MmDL6RFavCepvOTjNTgGqIksTN1sRUk1ctrMNw3iIKMfNTkT9Kg5vEycDcVMGeNrg7iiTImlw7Uqs2iMXhIfE/KeJTB/lusoqMNpFyDezztqApUwRRXVVJmzkK9oxfWvGzc870v4gfv/DSq583HRz/60ak5vjlMCN/73vfw05/+VDiA6WbozmHmwTmefuF09FSclTCWAY6zilVMBWizpd4aaWSoMoBL0pz2RrJK2MmCthoDVrK9JoOkRDI/gWsJAy8MUDHgTFKC+8gEF/ZD4zqUSE5oKsD5vNS0HFmBUviDbhg0Npg0c6TmVEOumEgXJEH4kCVy6Gsoq28SjQv6IrRRWDlBf4O+Lq8zg6j8PB/chtwMmmON1eFM5uD8z4DgRBoRczs8Tv4Wt03fRvaz6S+TyJD7D5Js4X6wgoVJXNw33iO8L1lJEn//TBT0n0im0D4eJfWsswAVNwv/gvOIuj9ctT2OrUpZLG5vKpo1zyS475MhNXjd6BMm668y24+dPjbXzUmPLZ1BSpCbBMaqUOY1GqvygvfVfffdJyTEUumPMx6uFDJDCcbKOa8wdk45u2Q9ey8nXBFRP2YLsrSIxus3vylltP2+5QyCDMIiauP9punUtJIaBKsx4rPy5NevZLBKwqxNTFBo1Vqsz1mFdeEgGCeiPW1H4Rx2orgmcbPfVZkLsDxzcoEOVmPoPEb4RFNx6beNahqj6bO1+iQTmi5JY+kYhBlejLiBoAHQa6SsKdMaQMugpxFwnpUcD3V4kvZ1AQF7wmZ/NNho0NOokeWa4vcpV29Dv3ck4VjkKyOBxBqcnOCYXUvjKX6Ck3VJWSpLuPxatDga0dLags1bNmFVzTaYcqUADQ1TuTeCKGPdsmXaJTQMGiMKNTObMX2piQ2Z3EgVJ4ea8UqP1DCaI63e0Ylbi9dhnmV082wlHP5etLoOY8TfjUFfKwZ9LSg1rYFVmw+4FKScDGd9DKlBcEwxC6i6ZgHrsIELdcBQH5BXDFTFfpZjjNlCHHMMQjMDS+7JQRJDZMz7fOJ9Bvpp5HNs0pCmw0GtZNm5ScW4ZDDSE+yDOTeEysJ8XGjvRb/diQKjHjpl35tQEA2n66Cr3ChlBIaCyG8/g+scDTj1P4fRG1Lj3IHdqC3Ow5YFZYDdCQT0gEUO/KoAQ3pSZnRieE8m6w9FA5SkoRxcTpZBPGR3oLuVJbQhGHRaVBZmo6lrAIHuZzAvEIROI9+fIeDiMWDL3YAmyfxWUg60sGF82PjnvV02TtNDvw/4ty8CDeelz7N0uO5N4KOfHjvoUl4Lb+E8cd39rR1JmzumCmbucV6SM494vqptZeh092DQZ4/Iu23KXSVpDWcWiceIz4t/OPGSkBNilQZJQTYLLzJa0e6yo8BgEVKU42HE78P9e19Gq9MppvvftTTg6EAf/nn52pjP0eHluJbnzQ899RR+SL3e3Fw8mOOHMyJTGMKIHyg2USZDlxpp73GN7u/i90iv1Z8FHvsZMNQPVFYDD/8VkJUTGYsMBMgau0mvhc8N//k90PpOAUYbsGA78qx5qLXW4Cwb2Yfnn3xnLl5/abcIFky4UmOqUVAL9F4EeqX9FHu68FoghUbmrNqIh1atw7LMFTg+FE5ukMU6QwGcHa7DquzY+/r6wgVocQ6g3tGHnMoSPPLfX8ZnPvQZQaDS+ZjDzIPNFT/96U/j+eefT2hzzWH2gRnQnFeuGtBXyN4BjLBq0w3ocgHbyrQIDRkMAtGmmGpSgzYb7TIGcfkb8UEpBnUpG6IkyrkOMjjK5qZcqzdt2hSxe5jAwex++v7THZDhvlq1V2Zi4mwB7dzJ2HccN5R4Gi+hiXYMg7eUcaKvyooh+iZMGKQ/Qd+VRAdtL/ra9CPof9BuZFIVbX8mdDDZKN2KJvomtGU5dnkvMFNaCe4Hxzb9GDmJ8KabbhIVxpS5JYFHYoX+0VQRBiRRWAEe0yRcAVZDyVI/yfoT8H3GD+Teg9x/3u9yD0Qmfc1W8DyzEofZ/DyOyTRw5xji9WV8huA55TxKabHZJCNKv5r3iSzHyHFH255z8LQ2O6cf+MqfgPPHJBnmjTcAK7ZE3uZ9wWvA+TZZfyWC1Vg812OB55t9I2g7syovrWqTqwAqlQr/8R//IXrEUYrqueeeu+wlVS97UoOTETVuuSj9+te/jixmQz7PqHDuSGDsRq9TgeWZpXi99wKGfdQwlULKtdZCFIync32Fwhv0YtBrF4H2LJ2UncBr1nWyGVUrYp1DVkJk6axYaCtHrS21rPOxoFHpMM+yCd2e86K/hlFjQ4GhZkKVGlWWSjQ7W0RDWYSv7AJrNXTqFCZJYwHg7pKChU4PwFhUxlKJ0GBmJ4kRMW7jRmy40XoiyJlKyXBHyVo83nYAQz6FDFUohP7WLgR9AVRa83EhdCHmvRG/E7bMTNQsSHyO+Js0aFjuyioRZsPrYMAd9z4QCSofqzsm7kUGvWgYzAUAZhcODyiuefjvkYF60bvmoqMdroAHBYZsFJtinbcez3kxRiQfVPpmr+e8RGok6suQoOEds1ciUgMcXwuSN3+n8SEbW5zT165dKxpw04Gg4cVsPo5BuZEZDWlmXNEgk7/Pz3KM0tmls0SjMhGCIT96PYfgC0nSTNmbF+P8X/aLVjfl+dmAPzZrZ0N1GQIbNmL3nj3YqbNj+I3ncby9B0adDottJmzYuAznewdxsXsAS0vzoXb7AGu4moxVCQu3Ix2Q0GE5fCLwuHmsJIzGKkPn55o8RiwvzVUEElSoXrYOwUEPGg7Wwx8IYl5+lpAFGxePfAj47r8AQ2EJtOIy4I6Hxv7OkQPAxXPSv2Ut1MP7pdeqk2f08LryOtMwnQqjlNuQs3eFTrDdjv6eflS682FyqlBQWoyK7JJRFXhWnR6fW7wdv2g6jg63HcUGK3QaLf6h7mXxfp7ejE/XbkYxJbLGwJ/amtHipNwVx5702h/amvCB6lpUmKXvci6lY8QsI+6j0+PBj9lUMi8P2ToVrsmPNd38IZIlwKrcFLOSK2qBdgUZyfuxdAHQ1Q5856vS9eE9fPo48N9fBf7h35juKJx93ldJrwO/098Jf90z0PY3AflBwNEPDLYDm9+JRRkLUGYugdPvglVrgS5fKxx1jk9K+vA+v+Sa97w/ltwCDLUBXgdgpdzC5AJmhcZi2Bzn4AoTUeIOVAGOwGgJKr1ai/fP34wutx3eYADFy27BDfoSPPTQQ3j11VeFFvwcZjY4zgpwVmkwOWMOs6xnhp/3iQ8ZWhsMYalUWaboqpMI0+cCOTunZFMTmYcZLGZgkzZbsixarh/JgnuyXCHtHQZ2GcAl2c3vyFnE3D7/zUApA8xzgaorB8y+nkiFtQyOhVS+z8CdHMAnucbAOysUaGtybHHs05fgOGXwl4mzHHfKBuIMqPL3OCbp//I7qfw2/RD6QbR3WHkUf59wW9dff72oJKGkLqs1eB8w4E6fh9nn3A++T7ufhMtkwO0qq0WUoN3N88G/yZKqZLCCioRNfFCU/hf3l+eYxzZeIHqmweNjljp9gqmKVyhJAdq2PMckTUjy8FxzTpvOnkXp9KGRezRyf6aiomFcvPB74NTBaIzg+d8BeiOwaI2Y/3m+UiEK6R/FyKQlAI+JJCclKGU1BT6fQxScsxg7v/baa/G+970Pv/zlLy/52LyqSQ32z+CExGwNJcO/IjMfrS57JNzGrMql09hTQ4ZJo8MH5m8XxIbd50aRMRNb8kYvXLMKnFyeeIJd1pm2Cjz8MLVdJr3ZXk8/dvcegj8cnGdvjc25q3Gy7iS2rdqEQZMHB/pPIhAKIkNrwbUFa5Cln1ryR6c2oNQ0+RJNo8aAHfnb0OhoEpm6uYYcFBtTlCDI3Qh0vwb4wgaRuQLQ5wHNTwBsbs0M0HjlDJUhYZWGDJaHM3MkGbL1Vry7aieGfS5RsfSH83twrukCcsoKUZKZj/vKNsKiNYr3utz9ODxwGgO+YcDXhlMdzbi+YD0s4aAeSQoaVTTsWJJIR3HXrl0is4NGCoPKlGSQ+xhMNpM6HoHQCHzBLgRCDqhhgV5TLBqzziF1o80T9MOg1sIXHN1Yyxv046mON9DnHY5IrK3PXoQVWdHFPxjyweP2Qm+MGr4BuVG8qRKwhxuCyTCNHpskImjYcozQeWHWE8kvGu3x8yOdCY47lpTv3LkzIj/AZuUcg3Q0aOBzQZYz9LgtfkcJGkfMhmKpuGFZLhodHUI2bVnmfCyyVUpNyf2NEUKDcJTkYGheKa6vXguV2wMcfUOaI2VJmhXXQEMiITsbe37+Q1j0WmyZVwq1XGWl02D9/BKcauvBj187Cr3RhLvf+15kZWYAJUsAS3qVYoJwHBlJ2BT7mWeeERlR48mh8N40FS+AyjYfuLhXysrPrQRqr4O6swnVTXUIBkNo7B0UIjnV67ZClaxKgygsAT77DaCpXiJkqxaIoPeYsA8neT1x5pcMOn7MGp0OaQlef85ncvCrNhQSxn5Lf7NwUul8Kh3VIpMVn14kBTaf7riA/22JNoDu97rw7QsH8LXl1435m4Nej5CaCsQRfwNeLyrC0xqzBzmPyjqnwz4fgqdPi39bEpwGjrxCQzHKzCmu28u3S/fsyb0SKVlUCVz7IPDaC0AgGHU4WIlDoqO9GaiYH8ngZQb0KJvG6wb++COgrxmNVp2oBJIQAgI+oOscULlWkBl8KMc3nTxZdoFzA51jBq3oDNPRmdbMsUTgsWVN3gZSwqK1wsMMagUJbEwimcXxUWyKrv/UBWaA47bbbhONfGdNVcsVDgaObr/9dnz+85+PaTI8h0sPzhsHB44KWVuC6/rm3LXIUmeI4CR16ucwc9eC51xOJkn3uwwY817jnM/seQZIub7IJKIc1OU6QMJjVvvTc7hkkHutcBzSduCY5HiM77NCG0OWuSIxQAkzgrYG7RASFXJlAQO/fNDPYABYSu5SRb7LZC0me/A3U+3fw9/hZ8eqCKC9z4Qm+i48BmXSEv1u+lOPP/648L/5/o4dOyZwxiRbUw5sx4P3JZuZp0Kc8BwnIyt5X/PBe5j3Mvd9uqTt0gWvJX3QlJtgpwn6Xqyokatq6LvS96VfQRWBRH7dTIDjmH7jjJLC9KFPHx6d9EiSY9Ea4fNwXMiKC2MhXkI4GRgv4HrCtYS+HePFHIv0MXgN+N5YRPvVAIvFIvqPsAryi1/8Ir7yla/gcsVlTWr8/Oc/x3e+8x0xSONL2x6uWIIejwsHBqTGXrW2HHxwfmpNiicLq9aAm4vGZzwZFHQHhqFWaWBUZ146Q+3b3wZ++cuo9vvTTwO/+Q2jkJNjv/uORAgNot3dhcMtx2F3DlArCOqQBneVbINRY0xNxukSg8TGoow0m09T5qnzDcDVK2XDZi0GMmqAtqeBcD8KeJ1ASA+YmeEQANQWwLpBkqhKtllmqAecONhzFo6AG0WGHKzPWSjOI8m0F7pOottjR47eglqXBTdlLMY9N2wW4ZRcvVVUYviCfjzXeQC93gFwOpfH37DPgVfaDqLSniOymBncYyBabt7GhYHPacQcPLxfyGswi2M6FgVvsBOuwInY1/wNsGo3QKO6yrLwJoBmZx/+2HoEjoBHkBrVlix0uPsiTY95zW1aA7o8veLzslzZwYEzomLKGM58NGty0dDdgMysqHNg1oSzl03V0lh2sgokBBgrAOtoIpG/xXma1RPMwqORToOahBgXVblXhkxgUIaAf+XmXsxwkrOAaIDSaVE2/qLTIfdukccyjRYaLLtbjqDnyGkYLSZkFeTgQP8paFUa1FjLcO78GbgCPcLQCgSCKC0vgMdmQGjhNqhUOqB8FXBqD+DzStnsCySZoGVLFgPzE8yRYXttSWm+eDhrNuCXh5uFcbV1QfrSdzTGmNXF78cbwGweyEz3lBugFS2SHkqULQR2PAD1sV2Yb82EM68SJ7MXYMl4RqORPS7SIIyrFyZuBl4xdqk3ryUN0JloXsrfksliZgzR4OV5T7Q2nx/pi3lOSSomUngCftFfKxnW5uQhUM/eJhJ4hq1aHWqsUUKfcysdTTrLrFDJ0OmwPjsbbw4OotvDqroQzBop+C0PufmWNAL/vK7b7gI2s8F2QNK/lV9P9vkw2ci5n0Qhr0kMXnsSaKsHTDr0DjlQXRKXRDKqT4UEZkiyCotVGiTKZV1sXm+SlHR0+JhIk7/ZhGrLIth9++ALk8EMwi6wpp4Z96lPfUqU5pPY4HyQrPJsDlMD2j4kNO666y6hPTyH2YUGR3OE0CDY321/3xFkNZjEPDKHiYNzcKr2PNcpztfMck61ukPW3CeRweAUkwdo50R8kOFhEWBhdjPtRa7BU9kQeQ7TD2XwP9XPTzawTFuJ9jB9AGZy06egDccxSntFDs7Tj2DwlAF72h/yftK2YeIV5w9+L14ijcHveNlnBqbpZ9CG4finja4M2NNPIfHB7SglO8eTeWGijUy2JAuWP/jgg+Lf9KEeffRRvOtd70o7qZDEDP0wZrLHz5u0hUnAyBVUY4GEI8/5eEQNH7RjmCQ5W/pt0C8lAUY/NJ4Am2rwN+TKM1mu61KcB1nJgGN55iSHVAnkEPladK1hJQ/XE/rzyeTQ5ITHVMDPcXzzPDMZiA/GH9hDhmOQ9yrlrmVZw6sVhYWFIkmS6zCrWTiXXI5QhWTRvssMDHi95S1vwRNPPCG0NpNh2OcRGZFZOsOsyu5wBQbR7DiMIPyRAGG5eY0gOGYUDgfATGjlMGBm6tveBnz84xPerCvgxlMdu2Je83v86KprRdW6qoi8Ef9uz9uETN0VGqBuewkQjpfi/GbWACMJmvnmbwUYmBqnwTVv2SN1R3HM2gV/KBDpOV5syMG84Qw81sbeB5RfY3Y5Jdhz8Ik1d8JM/UIF9vedwsnhBtFgNhIcC4XQcOIC9Bot/nr7OxMGThhs6uxtRo/zNBYsLUdmpg3Z+mpoVE4EQ25oVVkwaeYl1BdPB+xzMOyntMvoYJhGlQ2rduzSw6sdDr8H36vfJaozlBVrNTor9h46AK1Gg/nWIujUKjS6OkVQljAYDTBlWHFt1gpRQSWkBNQhHD63Cyu2VEZIjlLTKtFLJ6ksxPCwMCiUhgkdBWphsmxUafjTuaBxpTTIuQ1mKtHIlEtG+Rqr8mgEcr+YZSJncdNo4XuJMpa++dr/wGAzw2A2Yqi7H8FAEPmGLGzNXYHsUh+86rbI9s+dbkZf9zDuu/mT468Zjz8KtDfEBmyXrJPkdtQ+QM1mzGqgchV29ahx/kK9qDyhAcHjT7b9QLAP3uAFhOCFWpUDvWohXn99b8JjY1Bg9+7d4r2x9peavMx+ScWApXPJ+zxRkIIG4oQ1cl99AfjlDyV5I+7Hhz4JrEotAEVDlFlOM5mlTqOXDliiZrM/bTiGV3qaIvcNYVBr8KO1t407bn7WeB7/frYubJvo8d01m7EmOzYbjuOZWW0y2dfucuG+vXuxt68PCyxqfHyhGWpVUMz9G3MWYXX2FJRVs9n7v3yaAyAsjRjulfJ3/xRDePCc8L6LGUs//zrQ0ybWnf0IYNPK6rADE3ZaNr0dsMTKOPF+43HSKacxnciJ4WdYoUXi/JJLU0k7JCUiaA3Je84kgTfoQZ+nByEEkaPPT1qpMdZ9SQ1uOsZ//OMfJyXVMYfkYPDp7rvvFsGwZ599dtoaEM9h4nhzoA5NztaYvnEt55rxlqprUTsvzeSjOcSAZIPcu08JyiHyoSQ8OD8nqrZNBmZrsyKPwU8GTRORJy+99JKwHel/JEsqmMPsBMcDbUcSUonWc15LEgFyj0Zef86v9AvoA6TaYJlrIYPtctNtgs9pK1A6SmmzcZ9Y4RgfuKRNSR+DtrM8Drnf9FP4fRJvrKiQj0OWQo1P6KBNTFuNr7Nygr6PDPo0JFlk0IblmsL+WFOpasD76le/+pUIVnM/ZHmsVMEAO+3dRPI8JHLkyuWxIJObqYAECBMH4tdWWe5KbpI+k2BSHK/7TNpVtKVJLF0KqUQmqHKMjFcVMaV46Q/Am6/FvnbPXwHVy2J8Wo6lZJVP6VT7cCzJVVvJJNRYmcV17arqwZUE7EHCRB72j2PV5OWGy5LUYBk+GeUvf/nL+MhHPoLLDTzlF0Zegz8UW/KXZ6hGvqFmZnempweIa1QlSI077gD+8R8nvNlgKIjH255HMByQDvgDOHOgDqs2roBKFzViRTDeWIT1OVegTjSz1+t/O/p1Yx7g6x/9euE1gHn8bNtz586hz+bFCVfTqG4GJQMWHDeP1um+s2QVlmfGGiRPt+9Fp6cfWsacwq9dPHoW+eVFKC0qwd2lO5NWGLU6d0cIORkWjQpyr2GdKg82rdRod6IgQWL37074nhpm2HSX34Q7kzhv78LvWw/GvNZ9oRnrLGV4aMetgtTg9bkw0oZXe6JNbN0OF3wjbjxccxMMWr0wCmi00witXSxldWjViTMoaBjQGCHkPity4zj+pZyOUkqG26ZzIjeWT2RUsBKPkgMcpzi9HxjuR2dAi4sqmwi00jmmgUODlIY8y7X5l8YijXs6xr9ueg71J8+hcknUYC8x5uHGog3hnhoH4QtJMkgH3jiFm695BCbRL2QcuBzA878GWs9LVQfrbwLW7ASOPgM0s8JIcYcu3Iqh4hVCo51OEg1ZGs9yRhuPQzRgDg3DHTgQ8101cnDskE8YZYnuKcrR0ZHk+UtWzsxzzGCFLGk0GfA68zxzW2kHml1OONrb0DzihMZkEueARAUNWY4FPqeDyXMUHzxhRg3X/pksFaaDyrFPZ1F57ns8Tny+7hW4An4xf5I6fG/VSlxbkFqTQYffJySnCowm6BMcDw13+TcP9XfiqY560WNhbXYx7iqpFpnJF0a6oFVpMc+aJ3oxTAlaG4HHfw0M9AFVNcBb3wlYrKOyeDneGIBggIrjYN6R5xG4eBJ7GzuwqCQPeUvKAbMRoOxn7bVATnnCccT7gcEtZlnGB0E4Z/B3GCggiXbJezTZ+4B9jwEj/RJhs3gHUDuz6xAdNM4D99xzD/7t3/5tRn/7agGbgjNhiiT55V4hdKXizPAFnLZLtgbR2dgh1pAPbn03tJdB5fdsBQPDXO8SzbXpBC0TgfM95/PNmzfPERVXICiTyvWcdneyQCntBxIHtGvoS8hSUBwbTGCifzCW38gEKGZqMxhL0k0mEJS9H5RBaZII/Bzn8kT2M20LxpSUleK0O1ixwW1zH9lkmPYu7RPuJwk32qoyoSeTAvzseKQMA6jc/+nS9melCskC+j08nzxenmNmvtPnSAZ+htnryarcWB3KZDLaesmC/pOdH5T7QiKFpM94REoy8JqSpOHx0yfisXMs0HaVibR40ofEEIms6a4IjwePlXbGZPujpAOObT4men4nDFaG730OOHcM0OmB9dcDtatG+Re8fpxDSDpyPPAe5P1FIob30Hg9XmS8/PLLYtzy3Caak0imcps8F+xlM0egA9/97ndFfJ3+9liVV7MRl53lx8n6jjvuEBrDl5LQcAf8eL23STQkn2fJxuqsopRvhiACowgNwhMYW1t8WsBJlM11W1ujzVv5d+1ywDcIaG3ABKpHWIGxNnsZDg4cF8/rj53DqvVrodYxrzUarOO/PEFP8g0FXMDICcA/BGhsgG05oEneIHt2geNBHVdpoAKoJ04JCj/Jh3CdBStVUujRQbaZRtNQqBtwcfshKSu+ZwCuEScWL9gMOEeTGom4S5vOjC7PgMgWZsC4o74VuaUFyMzLxvqcMZoOB+2jCA3CHwpBE6ZHfKFeBOGCBhPPRFFBH56iRv+WRjU79DhnMxJJ4HhdbixcvRA6RQZMtaUEna4+nB1pEc8tVguun78DhebYklgahVz85RJxGoU0GrnoyYHtaDYE7/IhMfZVYCaVLqEDdOjQIeFw0JDm3E4HIz77gs7Gvjf2YGv/Kai6mzm5oCgYRNG8ZWip3SGcKBJ9suHCsU7jnBURrE6gsZLvtmCwJPZ4am2SzBCrTfING+EO9sLrdaHYZk6N0CBMFuCuD0i9Bzj/y2tAJ4Mtcfdc5zlkLtouGnIxMEljjfJaPF46ddxXnlutsRNOdxsKi7JhtUqZ3EH0Y371YmFkJDLmeOw0imkcM8jMjLR4skEQJvIcrwCJDr5HIijVNYznmU5SfCl+MpCwkjWMabDqdAYUV0p6stwnjitWi+m9HgR1OgQ0WuFY8lrKpA8/SwN8prVPmUHFc0mSIT7o/je5tTjht4N3REVIh9JhP7pCXeL8jJdlZtHqxCMevAfoWDGIz+M+PNCJr57ZF3n/tL0froAHvd4e9HikuT5TZ8JfzduCbPZnmixYmfE3n0v6NjMeeU3oYDA4QQk0Xr+9xmKE2l7FmrJCmPU64GIPcO8HgHnJm5fT8ea8ImdkcTu8znKFIANgvOazIoOKlStv/A5wDUUrNi7sAfqkXifIrQJqdgBhyb7pAu9zWf+WkiyXa5n4bMXPfvYz/OhHPxLjfI7QmL2otlai1dUBu38EjqEROOwOvHX7XXOExiTAoB/tgfigHu08vj7ZDGbKnbDXyVzg6MqETFSMlfnNMZSoypd+LYPKclIUtyNXYsiJUAxu0i5MVSqGklD0DZgAQBKBfkF8c2Ha37S7SVbIQWXaeZSlos1Km5vrLT9H252Ehhxwl4OulKmiP5NKgJjkx3gNjieD1atXi/0mKcPzJ5M1tNXk6mueQx4Hk8Fke5p/E8n6ymDWNmW0eA7psyVqKs1t8jeVFShKGdVUE6C4LyTGeJ5T6Z1AH482szyvkMzhMcp2JY+J2+I15PbkRD3ZJ+Fx81zQ/rwUtiaJMRLJHKvKY+W9wn3mPcC5mcdB25v3Cq/BROZRHr/sa46SkJ0JqDXA1lulRwJwrHC9IXHBB/1L+gT0awmei1QJDUI5VnnMPHYSc/J55rjg2L6ae2rE46Mf/ahYq++8887LTur2siI1OBE98sgjYkH8r//6r0tKaPzL6dfQ6R4Rci7M0LylaAHuLUvuvCuhhgZqaOMCwyro1OlJEUwJeCPzXH7yk5xNpCqNt98ErPQAPc8DGjOQe41EbqSJKksZMnRW9LAaINOP2yuvw77+w+j3DsQQG3n6JOw0+3EMvAYEneFmoyNS9mjOjUCSTPFZAUEgMMipAbKXAAN14TfCCxD7auiswMAJgM259ZlA1gppsh8DnHwZvOSkntfrQFcDS+8lwiIrPxcVpeVYV1yLIxc74Qz4xDnmLzKDd54lNkg74nfBqDZADbWQsOJIHBlyYP2SNViZsxC5huSkgSZBgFpxdIrTEBj9YhqgfJVZswLOwNEYYkgNK4ya1BqyXc0oM+WgwpyDFqdcFaRC9fxqGId8aFO1RRwFGkbb8ldgWeY8uAJeZOmtMGlGSxTRwJJZe9F83OMRjgczKmgs0CigY8CcdX/oMCno8Dd10GINVKysUICVFSz7ppYrwew9VmXQEFVmFAmjxKbF7j+/js3VJdAhfJ801KF8xXaU3XabuDfodPOzzILi97lPtowMPLnnObj0ARRUFcGgM0On1mBpxjxUWIpixppRnQ+12gu/twUOfwO8wS4eNcyaShg042iexhtEGt4jsU3LlcFOOg08dh4vDSoGwOl0cZ9H3G4UGrzo6uxHe1svFtZKGe48plBQl1D7VpxlnU6UTtN54LnlX1kCgOX4iYw2njM6ILyONKj5WX4vFaRqUPN6MBtGzs7kOk4HRHZwxLgyGYFvfAVoa5GIobsfAO5/JEIS0THivlIuYMabRoed3HhNb94DPLbsQSlrj9eUxh+dW+4rX5MzzRKdK94/dHB5Pvi+0iFTll0/3XFRTKNKiuzQQANsonRJAvsoPd52DA+Ur0CjoxmBkB8FhnwUm6aurJzHw3udVRU8zu9973v44Ac/GOlDInqRbN0iNQJkNlbNCqAwuZPvCnTCq+pHR/8pLPEtgEFnFY6dLDNGx43nd7Y0lITLDjgHo891GsBqBHxhvenO04DPDSy7fdp3hePj97//vZBIotMnN9Sdw+RAB44OHas0LonDP4eUoVPrsDN/C9pcHTjaeBTXbbkPJZbiuTM4QTAYyXWLNgTXNv6bgUEGwbj+0T6ZTOCHZD1tGK53c3JuVybkQCvtPdpD6Vbxyj0XZPuINi3tKQboOWZkuapUwM/SZmGQn34J7QmOvUQVG+znxeQoJlYpM5P5m1xbeW/Q7mZiC+8HOcufx8dxzapw2i3jgb8vf2c6CXPuH2XbuL/0AXjfyQ8eI21M2laU2eLxyn0kaKNzDWRgP5HdxXPIB/0Wzg201/iX5I7cvJznQq5e4fmnb0bbmYQBrwk/k2qQlNuVK++TgXMVxweJWFnCiceYqFH5WOCxcJxwvE13X41ESOTXkHQhYcNzx3tLJqF4TRnw57HTv4iXCST4Hiv4OdYI+b7huGdlfLJ+FZcS3F/a/iQU5XtKlkCiWsNEz6s8PuhrkjQlOSf326TvO0dojMa3vvUtIXX79re/HY8//vhlc44uK/mpz3/+8/jd7353yTOoXuy6iN+31I2S/vn6ihuRrU+NmBj2daLNdSzyXKcyocqyKamsS0pZhB4nwN8fJzie+Psh0t2AvwHwnFO8wSqCTCD/pontFyAWJU6sdBLZa2Nf3yEM+6VFp8RYhLXZKyM9NmJgPwPYT4QLHsJNzImMdVIzYgXcARc63G1C9irPkI9M3SUaH95GwHUy3PA7AzCvA+xtgLMNUOskQoPyU2mCBggXXY57LmgiEyFTgzf6T4ms3QJDFq4tWAWb1oQ+zwie7jgmsnjZKPyW4uUoMkoGSrOzBy92HRONowXhEU4ur7WVwXO2D50NbaIRKbOwk0Eslp6jcLK5chjcllUrBzpVUMOALN3WSffVkGWo/MEhQOWDGhnQqGxzmV4pgv009vXVo8djR5bejKIhFfq7egUxTOOaxjqN86mQJCKoZ7t1WyGCkKo+orBAp94c8wqrNJhFxMwfOfNK9Is5ckSM85h9OrUP7pf/D4ebOqHTqGE16LGEjYhvegdQtTTiMDAYxWwqfp+6tZ4qPXoNLjG/tJ5pxMIltbi3fAf0vBfjMrpo5PFc1J17DbUrbMjJjWqcZmhXwaCJEoM0tmR9XeViTyOeBEJZoA849mz41XBIesNbgaLYqgYeK7NIlL0JAqGhsPwUAwEOtDR3Q6fNxsol9wpDjA4jg8t0QBKBRjD1MIVMmFYrPse5g/tFg52BdmZUkcSgs6C817ltvp9qIJlzOw1RBkISBSoYGOE54u/yOBMaRlx7PvVRoKNNqniR8ZFPANuvC5+HIbFvzKRLVXN5NoD3F4kLmbSgQ0zHhNeI75FAGi/79R/rXsfJ4d6Y12qsGujjTmW2ToclGWqxBhIktZdkLEK1NTWSKtm6I0ukMVOKGYZ8TgkI7jud04lki9n9FzHivyDuDZfLg5NHG3DrzvejraVH3IMcl3Q86Iwws3dWwOsCnv7P6HOLAdCLRS/2c9s+DNgdwJ/+RO8aYFZmknt1svjOd76Df/qnfxLzyKUg+64kcI6kxMEXvvAFQWzM4fIBg2zTSexx/nMHO+ELDkOjMsKsIVF9ZfWzkfXkmSnLNZv2QrpBaV/Qj929dbjo6BAJUysy52Ft9gKxRtA2ok3GIOh99903bccxh0uLp59+WtictFFpR/LeYTA9mSxqOqAdSbuV8/R44BimbyFLD8l2rlwlzfkiXn6I2+b7HKv8jfieF7T5GWy98cYbhc9Ae44Be5l8oT2cSFaKtiu3Sx9DzpTnNsbadwZfWbmiTHjhd/lbJCxS6Y2XrJeIEjwe/g7vd7nSnZI9PP5k14y6+7yPGUzng7YHj58JFrIEFddT2rjxZCirRZL1SkgEEjO8Dty/eJAk4e/J+znRfnuil2hDgzgmxqkulwCu3Gyc51n2I+iH8XrIVc5Tcd9NJ+gLkYziPcn9le9T9tmjv5FOX5hkoH1Mf4VxAd4PJDXlvpEcW1MV/7jS0N/fL87VQw89JPyMywGXTaUGy/9YnSFr3LJa4qx9QLxXa8uGMc2GjZPBsM8tpDIo26OE3e9NmdTI0BVBrzbD4e8X8icZusKkGfDjorsB2P9HKUuQGcJrbwfKkssHJQQXTQayesPSChGEJBkqBkrSDFD7gz7UDZ3Arn27sGzDcqjsDMYswM78rXAFGGjUwJggI1zA0QgMRUkfBIKALhxAiDvvTv8IDg3sE9mpKqjQ7LyIpRkrUWCc4awtHyWhJLktgSAzO/cDmdcCWZOrLGAQkg8aNUqDYJ51dCZursGKd1aN1vnu99rxZPvBSGPbQDCIAbsTjs4edPgacF3eKhFwG48wpIFVYFiJYX+zkEzTqg0wqc1wicbGPmhUVti0y8cnNHydgK8X4Lg3VALqxA6UWmWEXmOcFoPAEbgId6BD1H9YNBUwaWdYX3KawGPr9drFPLk+Zx6MonIAQhrHpDWI7BxmQtHIpsEtGxaT+T2WaTNDJ4T2BJ9wRGSEZNB4fPXVV2OycPg+s6boPHCup1EjguUF5TDqtNhaU4ZgMIQ/HT2HyvxsWPKigTwadSTkWDbJMVxQUYSjxnYxJzDAm1mYjcamRhzTFiJjRCf2h8YMt0+DnIYgjWJz0QUc3FuHzGwrFi6qENt1B1oFqSEHd/kdZoAlanLHQP6e1hHkZa1EjW5E6jPDzB9XFzBoBLIkw5sGNM+9rHvLaqT581lRkQGjeg0cntPw+FzIKirHQG8Ix+oOYdni1cLh4XdICvH8uAN2eIMO6NUmGDVSqTIfvJc5V7z++usi24VGO4+RziaPm/saT14yM4rBjVSbc5Ico8PI6xWf2UyDke/LfVPoKNB54r7HnDf7sFShoQSdihNHhdRY03PPQmc0YtFbH2KqGC4n0GCOPy9ydYyyeeRY2JZXGkNqiOGkMcAX8kYqHjnGS00aBEOsG42uj2eGz2G+pWr0tQxS/pDNrs0S2Z4EQhZMrRbrDucLZQYZr+FECA1W8EmEhngGk0mP0oo8vPrGn2FSS1UgfMhZgbMGtO2qNwD1B0YTGUowY/Nd76Heg/S5n/9cqoR929umfJcYfOc8QDlWBiJmY/bd5QDOxffff79o3no59um7msFA4lha8VOBYd8ZOIMtkQQFZ6AdefoNVxSxQduNNoLc22siIKHBHm1cgQIIYl/XKfS3dyPLqRf2Btc9Ze+COVx5kKVJuYbzmnNNYnLCZIOrTHKgTZkqeUm7neOYQW+lDUbfgEF++hfxPf7kzzHASoKDY1Vp4zBAz/0gSPgppVdp19K2ZoCZwUD6FCR16N+Q0KD9JFf7cr8YtKV9Hm+Dy6QLqyUS9XZgIJ+ECm0wkpBjJSDRfmPVAqslaIfLMrUymCBJsoDblBNtaJfyGnIfePyJts/Xn3vuObFeknRg8gttff4O94vPmYiklPmRwfEg9/xIBTxGebvKeYm+BOd++ioMWvN88Rh5HOkkPdO25bHyd1IhimYT5KoLJXgNSVRxrM12cobzA+8z2Sfk9eT9wNdlAm8qQF+d8QmeL96H9Lt5X3LszCE5eC1YpUGSmuTG7bdPfxX6VVGpwQmNwZvvf//7gjFig87PntiDTrdDvF9kNONry7cjzzAz8k1vDnTge/XRBrxc8gxqLb6x8qapJ1d4eVwDQMADmHIArWG0JMLzjwIBSede2iEVcN37gcxxJFMSYeAA4GqKFbtQG4Ciu9Le1NGBN3Gi/rhYaIorpcDlItsSVFpGM+6jjrn9canvhBKcoHUGSX5KE73WJ4eOodvDwHQUOpUe2/KlLN8Zg6sO8DaM1tK3XgdoZr6cMR5vDl7ErqYjaKo7j96GNnidbsxfsxjli6pgNpvwvvmJNQ7TQXzgOincF8IVLeHPqvRAxs6kxMZ0wO47D2eA1yuKDN0ymDSpBRun4lx5w/1k9GrDlFWfMFP7T+1HcNbeKW1bpcGD5RtRZpaMhfi+DAzS0yCciF4yjWsaUdwuDXQak57AMajQExf3M0Kn3hZ5RiNarnZgQIIGZTy4XQbYaYTQ2SjsbwRef0IQrL6QCnWla+DOLRMVADIhQ4eAJcR00F898gZOW7rhcbhg7x9CKBiC2WbFypxqXL9wszCs6STR2eKxcwHndvae+ClG7E709gwKo5AVG1pVJmy6RWJfaAilYjTTYbh46hiC51+E1aCBzx+A1+eHqmwtAlmVEWOLhnQgdBEu7znUX2iD16NBdsY6tPW1wKlugV6nx9CgHUtXLkCwpwhWU66Q+qFhdqH9KAoWRg3XbF0VCoy1ooRX1iPlPjOTlWQRzzmNRf4mnbJEjenokMjBbM7dPCdjlX8TzMri9wgap3SM+PsMkHBscHu8njzXdPa4Xb4nnAivB3j3/TFktTMYREtBCTQtDdBrNKiwsrw6BPz9V4DFs6C/wgyC5+8PbefwRPsF+INBbM4txb2l8/Gzpn1whdd9vVqDa/NzMeQbLYFwa/FN0CiDb/ZGoGMPhu0jsFnNUBVvBTJGZyrRiWRAING9SZKK84hcGp4OAiEvuj2vxL2qgkldhH272sS9y7HD8ZqorP6SgmO06RjQ20zrGRihrSRDBRQsAF48D/zqV9EeZQTn1tdeo1D4lO8S7zsGeljR8u1vf3vKt3814G/+5m8E+ct5kskjc7h85kZWh1LKcbr6NPiDDvR494x6PVO7BOYrJAlmqvCThr+g7WIzWk7WC5tLbzTg2puuw7tW3THrA2xXIpjk4A95oFUZRBLhTICNqhlQlQPEtP34bwbU0wXtVbnHBgmxVJNtCJIPDKwzfnTDDTckJOr4nmwTx0tSUSKV/gltZPo28nuy/Cvte/oxDOaz8oJzEcc41w8SAXI1tCxNSvuXfgyD8TLJwAQXnhd5zZFJIPbaG+84aZ/Rf6Efw9+lnc6AvuyD0+5n5rtSTpY+A211mWBiEJnHR/+H2+O+8Li5nzxfu3fvFuclUbCfRACvB3+bx8P9IOkjV3tzm/TtEiVasLqA547g3/EC8DxXPFZui+eTvgO3IZNKtIFIyDIIzvPAc0zSZKzEIVlej8et7As5h6kBrxmv7Vg2PO8b3huJEirjKzemAownyBVGHDO8z+YwPn7729+KZB8mUCWqRJtNmPWVGlwI3vrWt+I973mPIDSIR+uPodvtjBYquF34Xv1RfGHJ9JT4x2NVVpHoofGsaAYrERofrdkwDYRGEKjfBfRfiGqyL7wFsCkCTIMdsYSG+F4I6GuZGKlhWwK42yOEArM+fbYKuH37hdNuUJfDoEmtAqJloBm9Hb1Ysj5aNdLt6Ryf1GDwKp7QENAAWdtjCA1CDgwrIbJYUw2wTwKBkBuuQANCIS+MIZe4oUb9oiqNccEAHxf3BA1kU8Xh/ia81nsO3mAAC6wFuK14OQwaHc4dO4W6Y4eQmWVDxaJ50Op1KKgqgcFkRK5+4ln6SqR0vtlrw3VKfhL+4wXc9YB5dPMx6f0Q4O8HQqxGypCaxk8SrkDr6Nf8rTNCaviDfpwcfhODPvagALJ0uViasQraMbKmU8WhgcYIoUF4QwH8unkv/nbhzdDEVc/wHmGWxEQIDToMsjMgo8vdiE53J2qsauhVKnHZWLGjUcVWjtHYoZ7rtm3bRKYPHYt444WGsayjSeLloiuIVff/HUxeB3RNdVg91INAoBPH6/zQWDMFMSP3bCA2LF+D5/7w3yioKUVxdXmk6XSBOkuUTzNQT4OeJAgllJh1xOytwvJqVC9ww+XyouFCG5avqoFNuwzGFOc9GULDlkNJWw67wwWDXgs9q800wwit3whV2IgPhDoQQAP0eh0WL6nCubOtqKy2Q1tsxJnTKhSV5mLeAimbzFKhRpFJkt7LKbDC3taK/hN+VMwvgdlixICvEVZtASyW7IhGrJwhSYdAqRecDHSwlJltdOx4vcYy+pXZbnQmaTzSUaFhyt+mw0MCRwadKTolwkDSG4C77kfo8d+hx+eHSaNBdxBYwJ5VGWEyWFQKqoBnn7jqSA2O6fvKasVDiY/X7MQpe6e4x2ptBRj0dseQGqzeyNDZYgkNrx2httdw5KRE5o44PdiwwgvTolxA0UeJhj+dyETNIAmSZhMNUqnZFUdlRiAUteG4DgRCZvTpBlGU50J1UY0IHjBDaFZlz3EMVq2SHkRfI9B0APC7gZwqYP4W4Pe0leLAOYlSVNNwLLxf//CHP4jEH2agvm0aKkKuZPz6178WDzpsc4TG5QVm6FJ/ezrt/CC8CV5lL8VEr1+9oI2w//FdMGRZkFWUh9yyQlhzMlFQXDRHaFwCDPk60eI8gRCCQhC43Lwcmbqp67GVDPF+N4PrE5UFYtU1fYSJ3N9MuGJCxs6dO0UCRqIKD7n3A5OhSGgzqC8Hylm1TMKC5AUryinHxOCrnAdM+572LYOjTDISyUmBgPClXnrpJRFU5/ZoS9F/YXUDbWMSAPRr5Gx0rtkTOT6SDrJ9Jku8KsmXRPEP+jx8Xa6GpT9AW10Z3FUmOtH+ItnPcxLfBJ3ffeqpp8QxkhigDyBXiY8H5bZIUpC4GkuSiudUrnLh/tNnJIFBH4Lnn+eez+Vrxwpfrg3cNyWZxXgizxN9TV4X+frPYWpBv5P3Ln1GEmiJ+pORVOL7ycYLr4/sy08VuD3OCbTzSKgwBhHfK3EOo/Hwww8L2S7G4jlPxsvyzSbMalKDk9eHPvQhMei//vWvR15vGBmOSOgQ/Ddf6/G48IvGc+j1urDYlo23VSyAdhqyQ7hQsCn4dQXzMOxnPwPL1BEaQg+bkUAN0H0qSmgQAS9w/jlg9TujUlC6JIGmZK+PB60VKLgJcDaJ4LNXBzg1HZHYszNAeaoQDCkEfhtONaB2TexCpUklwM9jY08Myl4pqx6sC6XX45Cpy8JgTIaqChnajGknNIIhD4Z8+xASbbZD8KuDyIpQGuH91pWlVn1Aku7PPwMaTku0yKotwI33p90f5dRwO57pPKF43iH6KjxYsR7aQR8233oNVGqFbn0YizJie5RMKwRhlaBALDSanJJeDwGOw4CvLfqaeZUkWTW5HcGlQv3I6QihQfDfF0ZOY1HGikltNxgKoW4wvp+FNEfu77+IeV6LMNZlkEyYiOwUHVga6syQ7PMMY8BnFxr/3e4z4r47bw8iQ0ctWCBPvwQ2XSxhQUOHRjWNGrmvxlgZGTS6afy+/tpr2DRyDqbhTvTbnTjU2ImMrCxodz4otiE3/BOark2t+ODNb8frQ3VwB71oP9OEikAOlt5ai5GOQWH40mlhiTSN5BdeeEHKtmrLQFtrN4LqYdQuqoFVW5s2oRGBj9ULIdgsijkg4BOCWDKCIc5d0VbQwWAAff2dgNGGeTVlOH+6CQuXUBJCI7LcZVByqrq2Am/uP4WXnnoDt7z1GvEZb3AEZmTHkEKTAc8Pjb9kAe54yLJBvF5ylQfHGYOGzGCTqz+UGR+dO27AoC+A4q52OLQ6qDdfA/UPvpGgajGu+fpVDJvOiI0MpIeRpavEiN+BRmezeG7VWrEuO0572t2LY7wPSnKRn5MBl9srnm+q6o0hNVi+niwrko4h77EdC2qAvXuAgkKgOuwcMnD/wjOSpNjiZcCG0Ykm3GaObjX6fUcQCIWre1TF2Nd3AT2+HpztPIeWDDMWrVogDGgGNaaqDH3KkVslPZRgSfuTT0af0walPM4UZpwlukd/9atf4cEHHxTkLh9zSC3wxWb3jz322Jyu8mUGzkPEdPdX1KqsIijM4HAUIehVqfWdulrANf6a1ZsxUhmbmb08c+I9neYwMXgCTjQ7j0dsSo5dPl9otcGgmd7qRwYjldn5tAMnOjdzXZuoH88sfdrA9DHGk1PjHMKqU5IUsgQbf5dNs2l78HjoX9CeZgCd5ARtXCZ2KCWo/vSnP4kqDvo09Cko7cTt0HZmnztuh89pPzFQz32cijgFtxUvE5Vsu/ycLE/E8yJXmiTqWSH373jllVcE6UACWbl9HnuiSt50IFdXJEpsSwT+rhyIJkHE/ebx00dhlRD9Sq4N9Bnl607ShVUzfI+/RTJkuiULr1bw3HPss3KY14qEIH09ZeIkiSgSehx78ZAlnHntplp+luQK4xYk6zhuSKbJPWDmMDa+8Y1viH6ljMn//Oc/n7V9bWc1qfGzn/1M6PZxECqdWspN9XvdEWJDDRVyDSa87+DLGPR5RWBvd08HTg8P4F+Wx5YUpgOn34ejgz2id8aKrDxkUvpIgSy9UTymBAzYDB0Hhs9IhoihEHCEYoJdAswI9LEheDiDNbcMyJ8H9DRGSwRs+UBJHDPKBqytxwF7L2DKACpXS/03EkFjBmzSQuXyvjbqbU+wJSVSoyprHgL+YMxkVmkeP1NYOq4tQM8rQECSGIOxLLJP8ai0VGPEb0efV9K6NGlMWJI5/bqtnmC76CEhI0jNeIMOmcFcqFmNoM0F9Ck2IHr2N0Dj2fCTEHB0D2DLArbcHPnIsG8Ee/qOYdA7DJOGAa1lKDbFTvokMZQjhpU250a60NPXiwXzqrEwz4Z9/XKVRBTaiTSXl8FjJSGhMkhk3Hjg59QmIKgMUIYAbU4suUdizT8CqPyAvzVWx9x5DNAVTkquyqQpgzPQGPda4marwZAfDv85+EIDUEEHi7YGevXEg1QDCWRiBnx98AW90Kp0Kc1Z3qAPTr8bZq0x0vj65e46dHvtCT8/5HOipaVfZPTKxgMz5ieSqUCHg5lPdcMNODTAOUtCtg4oM6kRUKkw4JNGoUXjhy3BVCMfI52eVLTguQZsWliBfd/7OXbUVqCutRs7F1VAr9Xg1GAbnAXVYps8PpZNM+OoJqsG5ZlFcAU8GDYPorO9Q2Rt0WCiw/NsRweODA6i2GDA8rw8lBUXh6WWpqjqj1V1A3ESNZa8GLJSFbcML1pciZ6eQZw73onMQh1MJgPcLg+sNjNMintEp5ayJTi/3vHgdaNe53Fw/WQmWKqNvxOB55QGIDNv+JeVPamAlSI0Wklm8C+vB0vhacwqs/w5Bn1+Pxa9/d3ieWRP960DjuyPbR6+ZvIkzZUKXqflWUuxOKMW/lAABrXkQCuz9rp6h2E06AShQZyub8fyheUAe3T4fMKhp3PJ8n06fbxmyrmI1Vl0/DcPDUL77ndEr81b7wfe+S7gU38D9IV7STzxB+ChdwAPPDJqX7VqC/L12xCEW4z/NwdOIogg5i2bj5P76rB00zJccDXg2s3bxfihE6KscuO6ppuCqrZpwT33AGfOAI8/Lj2n1MN//IdEbkwjWGn2iU98QvTXYABmNmdTzRbnm+fqb//2b3HLLbdc6t2ZQ5pgtuVEA6bpQK3SIVu3CgO+Y6wnE6/ZtDUwaEZLN17N4NrxwLbbcd7VgfqRdmhVaizNrEKFeQKKAXOYFFyBuIRAAfaCGZpWUoMJK0rbjgTHRPqz8L5m4pSyAjhdUGKGc3w6ti+TAdgXgwFymVBhJTqDsLSl6YMwsM/1lbJa8Q2GaXOzipUSTKwSGPF7pMTCUAC1G1ejte5cJNh7qcDzwl4drMDmeWagl/4cq00S7Rft+DvvvHNUxSztC1Y9kPiZiAxp/Hnj/EH7UiaUUh1vJDa4b3IlOo9P2VydoM1KkkMem5Pxh+YwNkjY0V7n+aevx/tfjgGygoPXmX+ZXBnfJ4VgRQAreaaj9xJtBv4u94G+KMkM7i/H3dyYGBuc+5j8w/uIpMa73y3567MNs5bUIHv2sY99TJzEeD3vD1SvwN8f3w1ngBnygFGjQbUlG/v7+mJyaV7r7UCzcwSVlvSlano9Lnz6+Gui+oOwaXX4l+XbUGWZpslw5DwwzCz9MDzdgNaYuIpB+Tqfb3kAuHBAIizMmcCCTbGEBQmTo38Gus5Ln+fzzrPAxoeBcStMEmWzj870j4c74EDI50GFrRKeUFD0DKg0VyLHkJt6xUjRrYDfLsk3kWhJstBRXmN55hq4gy7RT8CkMUOdZlPziSAU8o8inUhsBPW1UKvTHHOs0BBVOgrUn4yQGgzkvNi9H+6A1BzWEXDhlZ5DuLV4GzJ10d9Sygu5R5ywd/fBlpuDU70nsX3bdjgCbhzoPyMCSDLUUKPQMMFsN38X4H1TtAUULWz1KwDtOIYor6N1E2DfK8lJEfoqQB/OFOF56N0NeDqj55fdlg06xRgIAUFnaqQGx3uwDQjR0NcBmipBrFi1NUKixRXoEBJJZk0ljAnIOgYEh33H4BMZ9RKGfEeQpdsAnXpisl0MyHkUpI501Tw4NLALRrUZizLWwDRGH5YGRxve6D0uiF2SuptzV6DCXISjQ00UaRO1Q6Mw5BFZS7KBwWyjiUq7iGZw8MYQGsSAj5VT7E0Sfc0QJxc3qlm7w5Fyg1uTToO1VcU43tKF9fNLsPdCK1ZVFGFJVTmwRCJr5OoA2ahVq1SwaI2wFBahof6iyJqiAfOPdXX46unT0KpU8LvdWNrTgyMf/zimFKWrAWc/0HtOem7MAGrfEvMRjapCSFBJ9xARglZVgSKbDqfPvY6cAhPaW7uRbS7F/CXzhEFGJ4vHmG1iFuRZhc53GcwaifhgyT+D0+NKiwX9wFA9EHABxlzAUjZqriWRwfPW9OZ+dA70oXr1OqiyEwcruH9yGT6NSDnbRu7RQMeCGVO87vwcDcpEWWJ4/18DP/ADRw9JJNBbbgfecgeuFDzf2YK/dDaLU317cRWuLUjdgae0IJMtTAnWb61aC/4nfc6FBscxjPgGEPCG0NPgxi1LVgPuLjHrVJbk4Uy7ByuXFQiZATrsdHLpjNBZJJHFTD1ZkoCGrdXrgek734olm/7wGOBySISG8vXf/Qq47S7AMnou4/2pgSkiF0lwbMxfVo2m041YtLwaFzxvolN9Aceb1Vhathan7SfRQXlMyq/pc7Eic/WlJzdoh/KYdeE5jI7z5z4HvO99wMgIb8RpkZ1KhC984QvYtWuXCNQ/+uijM/Kblys++clPinH9+c9//lLvyhwmAK6BM6WDbtDkoUB9jagsU6v00DAxZw4iM5rrAxMPGSTi+rBEV4ElM1n5PYdR0LBHYQIwYWo6QflWpcyoHExOF5S+HEuOKBVwbmDQnYH7VFvH0ibhOGYTb9qvJCYY9KT01N133x35HI+RElN8XQkGSUmKkAgZ9Drx/YuvwUWFDajQeOA4PnbLg7Miw5n3Ke1x2uC08egHspk7SSQeNys3aIuR/OD+Jjp//ByrUniupgI873KWP/cjWZCZ+8JrIvfMoLyZ7OeQTCL4Hm1Yuc9ffCLVHKYeHE+83+jzyT6nPNblHo8kKklWcPzxmh07dkx8TylhTSj996kGxxl9HfqcHBOcn+SK8DmMDV4XFhuwIpznazbKt81KUoOL0COPPIL3v//9uPnmaKa6jHmWTHxvzfU4QIkOaqfnFOGJ9kbpJoibfGXiI138pOEE+jzhgCtvSr8f/33+KL656pr0jyfoFkF+vdoEA4PzieCSHPUoeBxuwJABeIaiwd3KbYA67rIxsFE7Wi8ygqEOidAQmw0HHIY6ge4LQPGiMfddry6GJxjNNGYgpdsdwjn7LmhUOlSYFyDXEKvT6fTbcXp4H3q9bShBJtSqECotq0QQIi0IGarUSCRee5IZMwmdOheuYGymvxoGaFQTyIShrrxPodHLsaxofN/vHRbZ5kqQ3Gh39cSQGmuzKrDn9Jti9OjNRrTVncfmRSux4y6pkaJNbcZNRWuxq/uoyPRnhv91Batg003g3DEo7z2sIHWCgPcowED/eKQO+2Jk3ggEHQANbSU54e4IExrSUQoEgiyXoLUe/Vw4I31cBM4AYgyHv0uCQ7cVKhIbugXiMeZhwhNDaMjwBDsmTGrMtyzE8aFDglShFJFWYW+RnDs9fBirsxI3vxQVO73MGpT3L4Q3+o7BppPGHW03TTAaIidKjFnI7goid1k5nuuUKnVWZJbC1z/6uFIBDZEh70jC9zy8TuFznasvQYY2cZkvM2roGNB4TaS5mRA5JcjIyoG3o1dM9Wa9DgH+XHH1KOeChgszPpQ66TSWabBnVFcLQkMcCzdkMOCkVouv79qFf7zhBkwZOIctvBGo2iL1PiKpEUe4qlQm6LERgVCzkLJTq3KQacuCx9WNmzY+go6uVng8Xlh1GTh79pww5OmE0IC3+LNRnrkKalgE4cmVIifkgz7s1LLsl44liZ6EFTkkNJr/AngUElg5y4D8ONkiYs+fUXlsN9w+P07uegxVd7wN1o03jvoYM2+YoSc36Yu/trLxy1JzfpbPSYSMKgc3W4BPfI4GAdDSRM0zwO2SXr/M8ef2Bnzj7NHI8wP93fhCaB1uLBxbezoQCuJ7F47j2c5Gcf+vzsrHPyxaD5scUI9z/upHDsMddOLwnhPIzLYhs8gGR/F22Bx9gHcIlgwDzl08Cc/BQ8KxkLP7aezzujHzjU6vTGrw/WBHB/YPDmGN1QKd7Cjyb1tcNZ20E5IUVQJSQ4k8fR56PJIcnyXDwkGCTD3gCAyhqDoXB4/shdfqx4BvOPKdfm8/Tg2fwMqsBGNVBtfUZx8Dzh4HjCbgujuBpWN8Ph1Q73f3H4FTB6X7pmIRcOPbouu2QuZvpsD7iTJUdNRoO991110zvg+XAygT8r//+7/CqZ5IJvEcLj0oJzLZwGc6UKu0UKsm38vtcgfXFWZnM6jBhAT287n++uunXCpkDhOHVZsDiyYHjkDUrjNrsmGlesA0gsFKZYUgs6IZyEy1uleGLEc0EWlc5TZo+9LeT7dJOckKBs6ZyMN+e/Fg0JbBWa4fyoxy3gPsBcZknVd7zsEV8EUEwEpXLcKjzzyGR9/1qVkRYJdJCZII9CfoJzHpiIQAfQUSS7zXeR3GSn6j3U5fi+eB350MOHb426yC4e8mqtogQcFxxv2Ve4PE+w7s4cd1ncSWkEM+fz5hVYDiC0BLI5CRCZSWJ02gnUNi8HrxPqNPp5Qp5rlnPFfub6KURaUENeWiqZygBH1WEmWsHOJjOsD7dHh4WOwD70XeCzPRg/dKwC233IL3vve9onefLM83mzArrfkvfvGLgn392te+lvQzlJu6pTiq1bkhpwA/aYhmDTN7OVuvx7wJVGkQzU77qL4dba7EQbyx0O9pQ4urLvK80FiNImMCdktkUMRJTVHKZ+ndUl8Nv0eSMlE2CU8V3iQ65F5lo87EMGm4ryF4g2TAVehyqzDsH45kV54bOYrFqnXI0kcXlXbXBQQRCIc1peNpdpxGjn7mnfzphE5Nw3EJHAyaIwg1zLDpVoqs/7Sx7Vbgud+FF9PwxLr5psjb8Q2e5TOrrEjxBHzo8doRPN2Byq2rYMnMQNkyFT58W2x2SLm5AO+svBGeoA8GdWpSRwkRTFTizNcHxic1CO57oobfzBhPBCVhaVwGOBsB50VpH5ixbl2cIKjmCRMa4kn4rxcItgBibF8aZOvzsCZrM7o87bD7+uAmuaMQDWMVhzvgRIBBbqhF1YZ8nXo9QwkKywG7z4EyUw7aXAPQqENQhaTX12fPx478RXiqZReeOvuqaBBPvN51HjtDRZBpzWbnILrdI2hyDKDROQidWoPrC6qxNDNW91JucKcPJl4+qi21Yu4liWvT5iQdXzR+GJSIMTR5jbtPA30XJPK2aBmQpTBsKPd347vgv/ivuNDVj/y8XGTf9m4gO3Yf+Ztyk7trrrkmsg+UQaKebA8lYeKgMZnQk4qcRcANdB8C3H0AiaT8tcB4lU76sQPxJDa0qmiAhjwM10Bmk9B4Z/YXM1toFPI5SQE6iy2tLTAt1qHVJUnvAcPo9/Rifc5WkbFPJ4HyU/GGYwTD9WFCgwiPqv46IIv9ixSOaMs54Nhu8U+jToulxbloffEPaFVbUb5khSCoZLBChGOEmV/xervcZ1bl0JiUwevP60MnioZvjEa63w/8+1eBg/uk51Yb8Pl/BhakGMzieDq0S3r4fUDNCuD6+yUS+RLiN83hJAMFftt8flxS4/ct5yOEBnFssBf/ef5NfGFJbKaTXKXBeUXcr4EAapZUQa3WYMjVBluHS8yV+rJK3Hf/0qSGKa8VnUsl1u7cCfcPv4/dQ8OoNhpQycHKSgVek+b62Pmd2Xa54we7qq3z4BA64FI/IItWyvTSqQCTx4E1FVkYCUQJDQkh9HmjfYkS4v9+CtQdjK4dv/4u8N6/BWqmoDng4ZeAU4r7quUs8OofgJvejqkG72m7fxhGjRH5hqIxK1EZCGCVxvve9z4RtJDJxTlIYMYgE6a+//3vJ64Qm8NlAc5NJM/nmrvPLKh3Lq8JDCjSvpipipk5pAb6oFWWtej3NsMTZEKlBbl6ygtNXzCdPkF8Y19m3sdLSHW67Xiq4zQGvS5UWbJxe/FiGOOksEmCsFKUa9lEwUA8bX8GLNP1cWlrM0BL6ctkfenoS/BzvB+UwVdKUtGWHTK7ROKhDPpdAb161gROuf8kbEgiMEGKDxJBcn8BnjdKwzLpbKx95ueZwc3zMFVNl+lDcOyQuOb8zufyPnC/WFHMfUvUh4PVGfHkCq8Jq8r4WbkHRwTH3wT+7SuAnMR87Y3Ah//ftMuFXkmQZcPim8kT9MPHIsW4dsg9apQ9GemnM+mRfRymmgSk/8l7V/ZDuf3Zcl9eDvjXf/1XkQT3pS99CV/96lcxmzDrSA02lfn2t78tgjDpGErLMnPx+cVr8e/njsEV8KPEZBb9NCbawLvCbEOrcySmb0epKb1sA2/AFUNoEF3uepFFwUcMMmoBV2sssZGxBNAagILUGrQmRUaBJN8RjDU4kDW+3IWQ5dHWwoxaBEJ+DNtfjP8Eer0dMaSGT9HwWS7784vm0FcejJpSGNQMGgSgSqUJejKs2gaYbQCzd3mtVm0FSqOkXbY+E3n6LPR5B8XoYH4/qywqzBLJxRLXnza8jpf/+Ax8Tjd09jKssAN3rL9OBEAGvCPo99ph1RpRYMgSE7hRk5rkT1IkKXEGJrldfaJeFZSsWkMWB9BkAe5OwB5tiI6Rk9JfW7xRlahSixVdqY9HVt/oVFnwCfkqGSEY1JMj6Wy6TPFodp5Dm6th1Punhg/CG5bnytDmCEkqjUqb9Lrx9duK1uAPbXsx7HOKmGK1pRjXFkjnpCknhJYDp5FfUwlzdgYCfj9eH6jH9diIJ9pOYldPvSiGUeL8SC8+Wr0JiziHhEH9VSFJY8vFumAtDg1E5Y8WWMtQY52XsoEwKnOm/SjQLJUzuzxe1B96A9blt0CfP08YTszAEXJVO+6HPRjAiu07xtw2M5a5v3KDO64p1LT9y2uvQe9ywauo4gh0dGDHddHeFAnBSrfWFwFPmNDzMcPnOaDiNkl+h6XmtgJAN87aFbQDPga2PQB7s2hrRvWjkTVJadTHkwN0+micn208A3+HCzkFMhEQEpU+vZ4uFJnGnt8Z7G5pvAizewS5WZIObQR+Vyyp0d8Vszbxs+XZNoTyMtE2MCAChTzfdD5kkoLkhNJw5LHQSeHvksxSru9y03A6RvycXBmAJx8HDu2P7ofDITkgP/pVatlUdfuB15+KPj97RArA3/ZOXEpQPioengSvxeNgf1cMoUn75MjA6ExCQh0eT6LXxrrFOPLGCdQur0FBdwtwSiJ61W++CPXN7wdyigDHEHDyFWBkAMgqApbtFCRivDRcp1aHkdtuR+kf/w+9gQCCLjfm8b754EcAsxF48o/SB+lY/sOXmL457nFxH1dmLcPSjEWiGuWPJztEUd7STA3OtQ1ifkUBzvtZxRb7Pd1Yay6rNE7EkXmcFN98Y2pIjaa43lQkTpqjc+FUoX7kLJqd0fUhW9eGFVlrxyQ2Hn74YTz77LN417veJbTAZ0Nm6GwA5xaek9tuuw0PPfTQpd6dOUwCDFRR+z5eAmYO0weeb2bRMjOatthMSoDNZgRCvfAHmWzgh0ZVCK0q9b4A0wWuD3mGqpn7PbVaBCOpiS/LyfAcMJFFJh4HvC7857ndwv6h7dLqGkKraxgfq9kqZGLlOZoVFiQkJovxqvCYLERilPY0/w4MDIjMcmadM0BPv0GZsBMP2rHs36cMulOyifbw8PE6qCrM0Yp6fxAZqmifs2kF/ZT2Y8BgE0B/sWQ1kBGbEEu7nPY4yQ2SEvEy75SW4Xlg/4PxwOQp+mXx4BxBH4DjIt15gtvk+SW5wUoLgvOOTGJwv5k4pSQ1eD1JrNHHUErjkMRgVQCvLStT6L8KeDxS0pRXoYLx8gvA4mXAddGk0jmMDZ5P3rO83iS5lPfdeBLXnB/iK7K4ztDvIFnI+4s9W6ayopb7pBzXnHPmkDp4L//mN78R8/xNN900JXP1FUlqMPuUzUfIAk2E8b25uAJvKSqHOxhIqDWdDt47bxnODA+gN1zlYNZq8dcL0tOGdAcTV3a4AgwuxwVuDXlA4Q2A/TzAXg0MSFmmyCAx2oDVdwFHn5QkUOgML70RiMvAnigYYFfCqs3GiH8QFTVluHimCTVL5sOqHZ0ZPdvgDwZwaOA8OlwDMGsN2JCzENlyQ/YxIBkpU3ArLVwpPUTQy4FeVx0CIa8oG87RV+K6gvU4NngOfZQN0ZqwMqsWJo20YBzob8Cgz4XF121E64lzOPPyAWD7OjyQn4e6oSbs7o2Sa0ts5diRv3xc40pk+GIYoZBXlN2rVXFGCYOx6jwg2BsNeKqzAE3B5EmNzNXAEHt1ECogewNgUtwPrnDmthIkBuJJjZARCHHfgrH9OJC6hA3PU4ZuFUb8Z+AN9gtNZYtmAXTqqemvU2goR5e7BX7Ro0XaP51KD6+CHBz296PFeQFVlkUoMuahxJiLdnefIFvpHBQb88TrB/rPYsTvFNwPcdHRgTPDrViUUQaPBihcNB8jPQOC1FBpNXD63Lg40icIjUSys9zM7t7GCKnBxZ/ztBx0Xp5ZjRJjHgZ8I4IwKzQkr8xICe3yNQcutvWiINsGDF+EJ6dCZHwxA4dOkySPMz7ZS8Odhq8Sf/nLX4Sj8h/z5uFvu7vhCRs0f3vrrchtbsZgTk5MJUEM3P2AZ0DxQkgiMo49LgWDCRLRK+8GMpKQXuwF43kj3JuI/V4GJJJDvzYmUE/DgUZiMm1ZOopbd27Br5//BbLzJaJSRiAUGPVZbovnj9lNnZ2dwkgsK6mGu6EBZxs6oNdpMa8sHypWyOjjyv4zuF6NHiCqzFyU5RSKuULWw5VLjPmabIjS0WD5N51HZo7TmZKJJrGd8L7TiOV7EVKj4cJoZ62/T+pVkEqp+8kD0m5Hbv0QcOG49PcSBh2uzS/FY631kTPKPbmuYHSWUzwsWl18PWdSW0enNiBbV4QBXyfMVhNWbliC9ovtcD67H7biXGSYDKLv1cFffQea9bdgadseGNnbguemr1V6XP8eQZ69/PLL4vqyooPO7/LP/ANw8y3AhQs443LhzJq1WMRr/Z4PAHffJ0lOFZUwnTqt88Lqou72dswrrEGO3iUqNaqrS1BX14ialQtwZCAg02riLFRbU5StiyA1be2UoGegJu5q6MIOHO2svhYpkSSnLPzZ9OH0j8QQGsSArw89nk4UGseuwPjOd74j7rEf/OAH+PCHPzyh37/SwOoMZgBTMmcOlzfk9fFyQqOjH6/11MMd8GFRRiG25c2PBHMvBzC5gokHXMeZgHjrrbfiakcg1AdvMGq3+kN2ahtAr6i8vVrAQLKyspP2n7Ifw5HBNniCfkVNOvsD9qPDPYxSU2ZEyoZJLjMR/Cf5wFgTfRoG0WmbkqiTA/QRO3QMkNBg1QCltgjarzyGcp8RNnMuGp1S8DTTaMYda66L9OiYVjS+AXQejz4faAKW3QvYYmM+DPKPBdp+tN3ZLHyspu0icZVV1XFBac7RJDR4fuQ+gOk2ZOZ1oRQqSSeOLV4zufpUWXHBY+Fn+NnW1taYzxEcT9wn+n6sghfB9t4ewBlHxnCbDYqK4zmkBLl6hpJsvOfXrZN6XLIKg34hKzmU5J8MypYx8ZDXS/a7eQ3l+4nfoeLCVFUEct+4j8oeGhxHJFfmCPrUQaUNqim95z3vEYRWuhKDVwWp8dnPflYENT7ykY9MeBtSb4XJH1a+wYzvrrkObw52IxgKYXlmPrLSlKzQJWlirIsPDstgE+1UG2mni4Jq4PqPAm47YLCyFjLtTTBLPFdfhD6v3O+ACCHfELvYlZpq4AqMoMvZg8zsDBjVFsy3RgNYM4Fz9g6cHm6VslQzKzDPUjDuRPdc5xE0OqWsV067TY5uPFSxAxkJ+k2waiUQ8kGnMkx5Sa+PzV1H9gkJL57fEX8P3AE7Ss0rsC4ncSB32OcW++z3eNF+8gKqN69CRkk+ej3DeF1BaBCn7C2otBSiylI45vlwB+vgD8nXWgWjegV06oK4vh/rAX8TEBoBVGZAyybcU3A+bAsBczngd0hN4zWpLGYJjOCQE/B6AZ02HH8Kkb0CtJ60Zj+1SocMXVQPcirBRtorMregw90IX9CHDF02LjrClScKkChsdjSg0VkPjSqAaksGtKosZOptWGAtF87xyaHmUd87OdwsSI15llx0dnVBo5cOfKC5EztXrEOXJ7msHp0OZk6L7Zw8KYx/ZiIpkWvIFI+JQM7ep0HBzMsMRbZ6aX4WTjV2wuTUYunGwkjGOL9DAycdo5j6mcwGYYm1/P2tGg0eoEzS8DBKTCZUW61i3DMzhNUciZ2qBEFRuwsYifZfgt8L1D0NbHlf4h0KtEYJjciJ6IbPO4yWtj7hRPC36QzQOCcRkSzTOlOfhera+Thz5CyCwRDKqkuRlZOJrLhqJ14zZljKRr4yacCsGkFOVh08Xh/ON/VAVbgRhrauWD3TqsVAzUrgwrHoa2uvB8KEBjO66HAw8M2gIZ1ZXlMapiwjppNIo4fb5HskWWQNUwam6ATK17e3t1c4UMJIys2T5hkl48Z1WKHbnBRsEniiDsjlZ5W9eC591vqHqpfBGwziWTYKB3BnSRXeVTl+EOTB8gXCJpFOB0ln4B2VyftiVZqXw+C2oNvTBI0uhOt6hmCpKsLus80oyrRixOPFvIpK2LI0OPVqA/zBILRqNdbMLwEGOoC+NnHNOG54regMklQUDsDKVcDKVUK+jteYjouoKMrOkR5pgA4NHV46podPH0b2ymx4A9J1MpkMUKtVMPi9WJejR7srF6GQDoXGYuQZxpC2Yp+RZeuAk4ej44d/V4/RfywdcPy310fHFre9/ibA4wT2/BoYCWeCkdDY8jbAFh7LacAddCd+nRJ444Dz3Y9//GPce++9Ivh4tUstMYvzM5/5jOinMRmt9jnMHlxOQYhm5wB+UP+GkKThbFTv6MOQz407SiZZiT+D4BpAYpsNoT/2sY/NVYCJZDjac7EIhFoQCi285NUalwIyicEgN4PMyh4bgWAw3EUw1o5mjIU2BW0LZuNPlYwRwQpv+hjM6KePobwmXAcYgGfzYLnvBqsCSDqkGnynfcT7gbYuj52VCdwuZXdo53Z5huELBlFotEGv1gpbmWvRZKS1xgR9qE6FikEYoc4T6PdqRYa6HODnPtPHSCQbJIPnjEFL2uicbxmETjSuadfzuOgj8nwqK1xk24N2HiWv6OPI0rqpgHbn0aNHxTWhn0F/QpYrYnUNrzHPu3wc/CtXdxD8t7zP9P8YRBdBdwbRGa+Qe81KH5D8jjmkDVbP8MH7jfcyrwP7JdKf5jjja7TxmfgmExgkl3gtOFdwTPJ+5PWUSQ2OOVZqkNjg3/jK8VRAH5P3Ju/Luro6Mb8oCTEqOlCeLL5h+RzGxkc/+lE89thj+NznPodvfetbmA2YNaQGA0n/8z//Iyau2VIqb9bqsDVvfImmZDBpbMjTV6DX2xzJ6LNp85Cpm2Qm+0RB3UpLesGGeNRYl0PnMGDQ1yuyKstNNSIIGy97scC6BoacHHi8HizIrJ1y467R0YTz9nr4QwEUGQuwPHOp2B+ibqgFz3VJwTf+6vmRTtxZsg4LrMnlgob9rgihQdDk8oeCOGNvFRUbSnS5L6LdLS2YWpUe8y1rYNFOTdY+MehtjRAa0f3rQEGwVmTfJkKRMQPHh0JwDAxj4Y51KFpQCZNGL4Lk8WFYGpWUoxqL1PCHOhSEBkGS4wS0KvYn0MY1c4/KZE0pNCbpkQjm+cDw0dGvjUJQOo1epQyVCtBOYcbuFBEbVZaoxFCr6wK8Qc8ow7/eEZU3oVauRW9ErW3ZmHnIsgNxW8lyYcz1dPXCY3fA6giiqM2DHlwEzKNjxzJWZ5UKg4RGwHiLPg0Tud8DDVEaEjROSIjQkOXcTqOGBjAD4DRoWGIsZ/5pOwdRY/OioigbWTYztiyfD2fBGmFU83s0hNNdH5hRwIA+DSJmC1FnXjamaRYrG1xynmJgNlE/CAFDDqCzAT4SQeESAF/UIPaTkIAKGhJFfi+C4TlJNuhplNuMQXFeWtt64HJ5RcCWONfza1QtmIfKopUwa4pFdhQNcOrzxu8Lv89f16n12DH/OpzMpeyiE00nm2EbzIE9OAJdjl4EIOgEcFt0MumgjDp/bAqetRAGvxsLl2SIcnUalTQuSYaIz/M+v/ERoHYtYB+Q5IpKpPuN14YOmtJJ47UnucF95zXjeVeWifOc09iVj4VGJscMHZ78ni6ce/Q7KF5Yi+K77gP2vg70dEcH6If+RsqmGg/HjgHHLgLXLYutzLAWX/JGgGyw/be1q8QjHSzLzMO/r9gu+mrQUd6aVyIeyUDCnfO1Nyhl5AW0Gqg1auxcXIX+EReyAmbkz18IaDWCyPD4/Nh9ugnt/cMoycmQGskrZBwYoOB99PTTT4tAuVxaznHFuYWkYbyMQSqgo0HHc9A1CNViFex+u5DCW2DTittsxYr5OHasHuvWLUMm+1alarre916AfdWUjcKnQnqKKKsB7v4wcGq/5AhXrwDmLwPefBpwKPqQeN3A6z8HMoyAJQ9Y9BbANE4PnjAs7KWUIAjE3hqJ4AsG8FLXcWHzaFQqrFk9Hw888AA+8IEPiAq1qzHIJs8xPAeU5WJT4zlcGbicxvPeXqkXkvJO3tPbgFuLFyfsmTdbwbWcwYyJBJeuTCS2uq9W0M5lkJ92I5OJGHRmBQfJimWZRXi282ykvpGV5jl6M4oMVrzx+p5xZWY4jzOgTd+BNiZtRlZT0OdgBZE8Jvkeg6FycHT16tWigbe8P9eFZWYpm8NtMhjPfeZv035h4D9VUoPHSX+Cv8Xv0E/hccgoMsZuh/YwA7TMQI/p7ZAiIlUGYcgNsHleuN/lJcXCYnC6vWjpGhD+hTCd7exdYMaWLVsi26mvrxfnhPb4WDJBclU1iQP6aTwG2vMMEtPn4zXn8TOgHZF2SgA5qYrnnP4BA9tK/ysRaFNyzmHgWz5f/D59BY4DuYcG90m5HvC8sCKd+8zfpY/JfeT3eL7o31AqSfeu9wM/+6GU7EQ7rrwSeMvtKV+POYwGKy14/nlfkdwTcsXl5YLk4PlnvPeOO+6I+Y5879IXpC9BkoH3LUE/luOW92jyhMPkoP/LihGOH96r8WOOv62sKJsDUp77fvKTn4jrdP/998dUv1zVpAYnJjY1/Kd/+qcYHbzpBrMS9/Z1YsDnxdKMbFRbpy4wLaPEtAg2XZ7ItNepTcjSFV1Whng8SFjMY0PmccBjtJky4XVKWQFTiTZXB04MRfWs21ztIpt8ccZi4fzv648y9PI0daD/wpikRrxcS+T1OK29IV9PhNAg/CEvLjoOY2nGNREN88lCIjRGIyTkiRIbHmuzq9DqGgCqgdbj50RGyP1l62CT5TCU20EoYfWJEgFWXowSOgkiCBc0SEH2Zbph5jyhApz10UbhlgRSJGxYrrZIkj/KY9FOjfTadMDp74FVE0K/YuiJKpxRso8h9Ht7EQwFI/rqSzLK8eYgm6cjRm6MMGl0+Mcb3wZ70AuP240Ca5ZY5BlQ7IcOb/r7EfB40X26HlqdFka1DhtzygFvO/pNphi5oEhPhpYWYTDIxiaNRRqYdChoRPIzJ89IGqbKrBwSHMzulpvSEfPnlaNV40dhUQiwt0iB9OKVMJdvwLrwHEJDON0sW+4b1xUaUjnFhfjJy88iZ91CZGiNuLloGcrNsQFGuVyafZ24/1y4mbnF7Rw4dBiqQA4CPS3INPmweHEN4GePl4vimHYfvwiryYAANOgdeQ6bNm8Wx8n+Eqw+oEPV1e5AyNuGkuJcWMqMYlR29Q7DGnTClqNBv7cOKr1KGIB88BzLlSbcnyOD53ByuEGQXBXmAmzLW4HNedeIOUxTqBHODasdSCTRwaLzwfMcf/1iwP4Zih4adM6YaUVyhwYff1tkslWOnvv5uXgHkA4SySQZ8RlZdGZo9NIZ4nu8rqJU/ek/w8Q+GioV+p97BvWvvITqb/w38MZuqZ/G8pXpNQnvHARergMWlUpESEsvcF/yPiyXAxZlsL9O6skJrJqU5/KmFdUw7a2D0eFGjtUEZOYDG29niR8OXuyAVgWsnV+CbJsZ0JuB7NGECckHEljxQS1mX9FZnwipwfFAsvFYzzF0BjvFGuUIQMhNLcvUwKDVIhhkIH9t6oSGXNVz19Q37o6geJ70UGK4O44dZnVgmFR39AF1TwBr3w4oCM9kMGiMWJSxHGeGT0SIDV6jFtcp2LQ2mLWxazEJjTP2tnBSBrC37xze9fmP4eGtt+KnP/0p3vve9+JqBBOmWEnErLI5zOFSwCd8jNjACe9pruPsH3S5gHbA5VQhM91gD41gqCfmNTUKLms/fzJgsJK2MoPNTIKgzSr3YSkxZeDmUAH+79R+uOBHkcGGTcW1OHzwkLBP4wkNboPzNu1Y+XzSr6DNyDFIQoPBSv5bGVBjMhBtbf6VA5i0gRlQlSsyZHC7rCzgg7/HbHIlKZEKuO+UlWLwlfuaqIG1Eqx24O/weEkK0D8h+cIAPIO5tKm5HWaQKyseSEIwUM/zS1KCx8b4GX0unhc+v9DQCNWgBgaPAwsrpHHI83A6VCh+V2mjM4DM65M0iSuBnbZjxw7hX5BsYNIS7UCSBiSKUk044z7xenAb/G0eDxNjEpE89KtoUyrf4/flqoBkYEIWE644BunPcZ/pxzDgLp8TEhsLb7oN+vk1wJlTQEYmsH0nYJib3yYLjm1lPFce2ySfbr755qTf43hkpQf7ninB8crt0Z+WezCmCm6T9xL3Kdnaxe3znuM9PIfUwfv4K1/5iojhsygh1eqr6YIqNAvoqU996lPCGeZjIsz1RMBGVZ94cw+ODkkSAZyK/37xGtxWfHWX6E8laCBwclIupFOBg31H0Olh49pYtIe7iLoCDMrHDutcvRXvrtqZdJsMDP+2+TUM+ZwxWZH3lm5GsSlqnLS5zgopj3jnZJF1FUzqNgpDMCQIqBYCqolNjg5/H5qdhxSvqKBTUR5n65hSV7yV2+19OHHyJHZu2AIjK3OoYzpwAfv7oxn+8y1FuKlwzZhGtzfYAk8wthcBYdWwUuMyy9AKugDXUSAwAFD6zbQM0E6yWmqaNPk9gSG0u/fDEwjBFwpFiAy9muNCDwf7NyjALN5r8m+KltaGgjjQfw7n7O1CjmpF5jwsz4zNYIkHg8sM+BsrCnD4xHHceM21qM0uGrec8/Dhw8LIYLA+0fb7vSN4puOw6LehVWmwI28JlmZGJY0YyBZGj7hXzyMUasTBg2exYQOzM5jBbpnSc8yeOf+293HYVT6YsmwizMsMyQ/N34FcSvIlAUkCLtY0iGSCAK4LaL/4Gi40dMCo1UDVNgyPw4E1C8pgNhmApbdi14lmYajT8aLTEYNAB+AlMeuFJwTsO9OK+Yuia49enY1C4/rIcxrfdDpODTfiwMDpyOs8hkpzEa4tmF6NXgaylQSUPN+Q0OJfZuGkCzocDIRHxk5jA/CR98d8psnlQt4/fhmW629Mf6epl/u2twGDQ1IGFh0uSiEwsDlOdtiVhHZXPdpcUSJeFQjCOujAwox1UOeVAxqtWKtPvPIcNgSaAZcdyMgHNt0NZCaeJ2mrkehTSksQJOA4HiLSZYEA8PKLQFsrUFYO7Lw+YZUNCcQNGzbgrP0sGhwNoyoTrsnbgWMHjwvHRDaa+TueMFltUMc1ur+UOPxnoP10LLHBaqwsRR+nVQ8A1tTXoLqh/Rj2DYjpUO6XxD5lSzNiZfj++/yzonpViVJTDizHB/DII48IonMsbewrEZyjSLCyseFcD4ArC8zcZPXorLn3x8DRgTb8tuVI5Dmz1KutuXj//M3jflf0rAo0whPsggpqmDVVMEy2d90EIc/Vc4jCH2yBL8TKU6az5EOnXhRb0X6Vg/Yjg/D0NZgAQwJhPDBbm99hQtRESDQSGAxUKhNupnvs0i/igwROqpVMvLdJDpAIYaCd8xkDqwzms7qEc5s8v5HgkJOFSPbQr+DvjSIkfG6gfhcw2CLsuzZNOazVG5NWn5AY4n7EEz4zBRI7JDjiJTJ5/XluSL6k2yia5I9MfownfxwvqZwy6upEXzmwAmX9+kteAT6bQWKTzdpTuS9IQNGXkCs1lNizR6roSge8V5ggOMoPV4AkKCtLlL9JiXl/0AWt2gTNBON5VwMCgYC4JiQ8v/71r1/Sfbnkqy4DG9/97ndx6NChGSM0iCfaGnEsTGgQjB1+/cyb2JlfIppxzmGScDlg8rrhYMPQKUaixnpKbo6ll/GoSVClwSDnwYEL6HIPiSbH1xaswN7e0+jyDAlCYHve0hhCQ5abSlRarAE1LOVU+hHWegLYMKH+EhZtLoqMS9DlPosQAjCorSgzrxq3d4cwfka8WFZeHSE0iDXZNSg15aLPa4dFY0SFOX9cJ1CnKoUPnQhiMPKaQV17+REahNoEWMZ3HFMCNc6dh4EA5w4tYFwMGKZOfssRIFknyY1oVSqqwkRg0ahE9rIS5eaqmGvJio1NuYvEI1XQWGSGT93BE9i2aHFSQoMl3qzqkHWVuYAly8xh5dSf2/djxC9JaDHQtqvnBDJ1FpSZ4/sGdTB8LbZLOSaXawiDYH7xAAEAAElEQVQmE6XF0jNcxkOXxw5/thEmGOHoH4Jao4E504oz9k5sNSSvEKQjEWNE+QcBVx1KinPEQzrgIELucrS290K3cBV02cUoLw+JCouE2rmaYsAkZbT3OHdBNcpgj51j6ATSuD/hbwSssZ9qcUVl86YLdCrpJMg6p8x8YuYNM6gmqk/PMUSnIrLud48mqitYxXH2LFBRJcYpA7IpOYv9vcC3vgRUZQH1HsDlBeZXA1/44lVFaBCFxioMeLvgDEhrcUijRn7FVqgNUhUGnWkGuzffeDsng5S2yZ40dC6uueYaltqS5QDcbpSvXYs9jY0SqUEi6atfBA7sk4gMEhz79sD1jnejs6UZvQzrGaRScBrFRImxRJAaSmTrsmHSmEUlB+3EFetWiOqTXi8b7krHZNJkYL5lLbTqNNcn2g1dXfSeKcScmqzZeFiyE+hrBtyKXkXWuKBMClUaSvhDbmjiplpPgIROLEjSxpMaJJSZ9cbHJz/5Sfzud7/D1YRPfOITuP322+cIjSsQXI8YDFM2hJ2tWJVdimG/Gy91nRNVGwutBXigPDX5QUfgPFwBJlNJGPYfQwZWwaC5utay2QqtuhxapJ/YcbWAwepnnnlG+Ok33HDDmMF9SgTJ35HtzYmAyUTsX6EM5DOwSbKEyTTTAQZN5cApg+VMBBrP1+b7iYLqtIvjewcy+USW+OR3nnrqqcTrms4ILIq+XhwMCrnXZKQGbTAGkVk1MeEA/yRAm55BZSZHyT4lbVKeS5JaE5GkZ+UKg+NjgdslkcNrJY8ZJq6lRJL/6EfAD34QfX777cAXvzhHbCQAybmUfTdAkIIcDxyT8QlzHPtjydwGnQ70naxDh90Ot8UGs8UiYhdj9Y0hqKbA+cfr88CncsId6MeQrz7MU6mQp18Mm27sbVyt0Gg0ohqaEnHveMc7pm1+nfWkBgMaH/7wh/Hxj398ShtDpYJW14jQHPYrguH8d6/HPXtJDZ8X2PcE0HQKUGuAJVuAldfOrkmUDZd2/xk4/rp4ahv0wF5eAltleuViY6HSUoF2d7TfAy+hMthroAR8UCUy3cU+aA1YocgQl74TwlMdh9Hs7Akr46tw0dGFRyp2wKzRJ13UcvWl6PE0wxdyRyQ98nSZ0KvjyRs+Z1BjYgG/bH05snRlIrgtSwuNB2bBcFJOpGtXaGTmd2o63gQJFLNmLfyhboTghUaVKR6jEPABHYcARwegNQJFa4ExenVc1uB4cu4HwoE0wA+4T0ikiW7ixncspHHHKx6vNpWhy0KxqQRtrlZBGuQa8lFmqoQ74MKgt1/In+Ua8qBJM0OMAUUu+g899JAIXDMLW9aY5GLFTBc6GixzZmAxFfJ50OuA3R/bzJaZiU3OngipESUiByL3UkGBJEsEuEQVQzK5tYlAeUf3XGyBs38I5kwbTpV6obb1RBypcQMkAcnpIvz+AOpON8E+4ka3vQdF5ctQliWNBZ4zZpaPB4uGTZibYl/Txu4Ds9PpbOzbexKgbJACM6HHzYx8Zr7xmOSG0ZM1XOjw0dGKlChXVI5q2sdrtmDLVn5YlAZzbDJgPm758RO/BuxDgFEHLC2TqjRKyin2issZrc4RNDtHUGKyoIq9IhT3UbI1S6PSYHHGJgz6uuEP+kSGv1K2iFmUXDPSyXaWq5ac7e0w//VfA83Ncg03dH/1VwBJwKNHJEKDCATgDgSwZ8/LyL9wFMVWMyqzsxF618fQrddHMjdtOhs25EgVG56gB5miV5UGe3r3waw2ocXVBEf/IKwarvOSVjThCtjR6jqFKssqqVEm3xjvviCR8dnPAq++Kj2fPx/49rcZKcWkYMoArn0/cOIloPk4kMXeUIp9yapIuadGZJMaGzyiabg8Z6rEa/FYmz0fb/RFqzKJ1VkSqfrNb35TzCEvvPACbrxxApVPlyGef/558WCwZg5XHrhes1rjciA1iB351eLBOTvV+ZafdQVaRr3uDrTOOKlB+Ru5weuMob8ZuLhPSCQitwqo3pw2KTyHSwsGqG+66SaRHMNqCTlILTfv5YNji9JAUxVU528wxqQE5wn2B5yJygPKSzEJjL/JfaHNTDteNKhOETw/rMTgtmiD07+/5557xHucP0hGpOKP8ffHE2Vh8JgVGwwmM8A706DPyTEgz42cZyZTVUpSQ64kH2uupR3Lh+hz2NoqSC9Wy4wpo3PxYiyhQTz1FEDCbhb0FZhNYN8bJrBFqrfT8A857uNJDY5NjtNEpMaxZ56E9+c/QG7QjwUmI0zbdmLkofego6t7VFV5IvhDHlxw7oUvJCVj8s4yayV3otd7CgYNY32zQH59FmLJkiX42Mc+JmL6r7322iXrjX1JLYOf//znYhL5x3/8xxn/7SpLRoxEEac8PVlbNpNMESN+F7wBPzJ1ZmhIMkw39v0ZaDghBVcDfuDYy4DBDCyOZfOnGpzsh3x9cAcdMKotyNTlJl8kTh6IEBpERsiDkT//GLa//pcpI1/yDLnYmLMOF0YuwhnwoNfjgjvgF7q0gRDdfinYIVNTbJJKGZwHy6OBG0rjMMAaOUY2wg54cdbehjXZiRpOS2Am6CLbZvR4muALeWHRZCFHxwkwUUXK5JTdROVFTCh2fDkgNmWaKk1AEhs61RgBHo7Dpl2AvS3aNLn+GWDBXUBchcsVgZA3JqAtgY2iOwHfMOBlM2MtYF4C6CZGZlm1xRj2NUGn5riNjiCDWgc/ejDk70G23opC42rRo4dkxrHBQ6LbCcGM5jXZm6BPI2OZ1XJySTMzo5TZUSQY5IZs6ZRt6xLMh7zH9Annyeh4HR52wGwunZblqdCYgUJDBro9dlSsWQL3kB2OrgHkBw1iAaYxn9K9o44aR8+/fBT5eRnIyrCgvtOLW1avjswxzDZh476xyp+JLP1CWDTNUINkqho2bSUsmtKExuE8YzEuKqqniBWZU0cYJwN/mxUnU6mXyTEVc76LioG/+X8Iffs/hYwakw7wwMPAKqkcmOeSWVv8TkS6LBm62qVKARn8d8/oSpDJIhjywu47BW+oT6yT/pABJk0psnTzplwS5fct9fiPc8cjc8J7qhaiy+PAvr4Ocb89WLYAb6tYKLKAu90OWLUGZOmlCgES4zn6oqTXgQ+ldnMqoLPR8JWvYGkb53+g0eNBp8OBrJ/8BHj3u4GB/ljTAEFsXVwJo1wNwgbav/ouij/3nzEVIjn6HGzO3Sx6xLzWswcOvxM97T3oaunEvMXzxHnWKQgNCSE4/ANA/fPAUKM0L+cvAco2Jyc3fvxj4LXXos+bmgDaonx9snDagYtvSv8ecEqVGtSNKl8J1OxI2xaqMi/GKbsdXkop0lZV6THPEu1ZI2NDTg0MGh3O2zsE2bkqqwrzrNL8w3mdfevY5JeO+1iNQa8EcM7isf7zP//zpDJ+LzXSCYBfbeB5mU7nmf0uTg83otszCJPGgOWZ82Fh8s4kkf71HO1LxEv0TScYHGZAlWNxRhMQB9uBI3+MHv9IL+AZAZbdMnP7MIdJgckonIvloGJ89QETZRgHolTrVM7TrCSOJ+CYwDUTiusM0N93332iolnuA8jzkGqmugxW0b7yyivC7mXVCbPNlYRDqvMISZZUfBtK7abaIH2qwXMz1T11eb1jqsEVrycig1hZz+pjJkEwCJ/UJk5EjPFazABhdrmBfrV836W77vHayd/jGGZPDr6WqD+kz+OB5+c/wAba2vI9vm83rPMXYsENCaqZFOC2jx07hgFVC2yhqAQbc6U9QcAYHiqewPAcqTEGPv/5zwspvF/84hd4N33Aq4nUYICCvTR+/OMfp+1MTwXuKK7EG72deKNPyvhnAOXzS9bBpBn/lDBL+oXOozjP7PQwITLfkocbCteKBs3TBlZoxC/ITSenldTghHLRcRLdnuhkXWSowDxrEsO2oyEm29bj86OnpQW+82dQVlM7ZQ5IgZG6pUY827knEgBjA01fEHAFSWuEF5mw8d/jHYYj4BEyU9GmffFg5U7iJt3xxEaxSaEvH3IAoXDD6vB2AAb/xmF0eS39LinrSDMxWSeWTdLwYZnmli1bYowmV8CL1jBxU2bOh2mCv5EUfidgVy7iglECBi4ApitMc9fbB7jC2cjx19DbKV1H+d70dQAZ2wF9Xto/o1dbUWRchwHvBehUbmjVFmhVJoxQ6z78c57QCNpdB1Bq2oRTw8cjhAbhCrjQ4DiPWtvogFcisJyZxnYyrUlmZDPTiBlWzI6Qm+6NBzaiX2AtxvkReY5UwaDWYnFGecThiAbVmMHRjoaGFphMRmFsBlEuRLgmFcphY2lmzwwOsjMfNJs24R2VG/FsZx3aXEPILM3DTWtuQ4kpzcxDbR6gLwW8bdi+aSlOnmmCW5uNhx95AAaF9i+Pj1nwNMLGbp6sgklVjlLz2I36mpubUVhdCZfDhGH/CDJ1Bsy3FKPGOn5JLOdIV8ANvVoHnTp90pNZZlMdBOV4itfQxc234UJmNtRdnQhm50BVWobQ+fPCqOXcJmf78HmiPh8RFJcDnW0SmSH636iB7Lxob40pI/uPwh+SiU5Kxrkw5LuAQMiLPMPUlfE3OIZjCA3ip43nYNUwqAf4A378rOk0fCE/jg82w8EKOgA3FlbjraVLxnQmWKXBIDcNezoLMRlN9XXAyf3SOVyyAVgQdSYYMKhrbETdyAgGAwEUabXYRFvO5ZIaY1cvkNZgBgbtDpjzM2AMO5IOrw8Xh+zoc7XDtOtFWErKhOMun1dKW9lDIzg1LGXYZ+RmYulG6X0mLgRDrPyKdZJ0rGQdkufoENBzEtCagOIk/WaOHo21pyiBRY3kqcBgh2K7QWAoLBW1IH9CWcYGjQkrMrfC7hsIV7RkJ6zI4/kgkcFHIjCLig3Dv/GNb1ySZKKZBI+R6xqP+XJEn8eO57uOiAQcs8aAawuWo+pKrYKdBJjQQzuYyQNTHZB7vfc4zo+0hu0QVnO34Z7SHYLgmCnwnjaoi+AJdsRKQmqmn6ijfchAEu1ArtXjJWhMOdpPykW8UXSeARbfACgkducwe8F+dGNVLPOe5YOk2XT0UlImaLHSl9XmMwEmADERKKH8bIqgHBKPgfEyVrDEZ7szCM8qGOVxJgJlmFKpsKINeCmqNKYLnLfiyQv6MiQt5LijHDSnn8ZrxX8zMMvPkERixcco0G8J27YR8N956fv8Vzpk35c2PUnLdIir2tpa0e+T14qEGytoEvmhXKf2fe/b2GTQiuvAa9ricKHR4YR+10twaUwiPiZ/l7JWfMh9WhiXpP+h99C+jo2x0nwXJRsiTnwZyq/PIHidvvWtb+EDH/gA7rzzTlEBddWQGp/73OcEY3/XXXddkt/XqtX4txWbcGywD4M+D2ptWULSIRUcGaiPEBoI3wIXHb14vec4ritMsWGrzw4EPIA+E0g1yCR0qX1xr03vJRz2D8QQGkSnpxl5hhLYdAkWSWM4IMKsxBwrFhZkwt1rR6/BhJdfflksGuNKh6SI08MNIpNK1AiEYxvMcC81FuO8o3PU5KRVZGzm6W2ivwQrPaIZTyHRbyJtqDhu1gIhNvBl1UYGoFo6tvyFdxhofUkaB0T2EiB/bcoZnDKzTD3I+MwXYtA7gifa98IdlBpLG9V63FmyWWT5J0MoFEBQSGaxFaFtfFZdIROT0uuXK1xtQB+rj1QAF01dXIYHCQ1CPl8cTvaDQO4tias9XMcBf6/USJ49OXSx0glGDWWmoiXK3e7j0lfDnJH4yZAHLc43xN+4H4CLZFMKIJnB5l2J5MriQUOXpcGpkhrETYWrkG/IRKd7QARl1mZXC1JRLvOVg5iieXtoEzo7e7Bx81L4QgMIgnNOG7RYDI1qAtISIyNStjizrxn1ZcDyr/8alne/G/eVrcWkwOtsWSeIDZtpBJuKdwC64lH37sWLF0WpOSWWkpEadFaYzZWK4/Naz2kMhjQRabIMnQmnhocQCp3GiqwyXFuwKKEU1YB3CHv7DsETnguWZCxErS29OZgG/1QR0iS0SMQyQy9R1pw/KxuLN28Zcxt0gpkBlJRsufsRoPEC0NslRcGZc9PaDHzzi8BH/x4wTz6RgrJ8UUJDCj6JKgIVMORrQa6+dsoyrC+O2BPm5fpZ/aSg/p5oq0ehIpH4ha56VJqzsD4neRk/M/jY9Ft2+iO9cs6+CTz984g0nCA4bn4EWBINCmzavBnepiZB4Ytj5feYbUlnoWoe8NH/h9Zv/pswBypNRjh9fpwdGMa5/mFkGfXIMRrhUmvg6O6OXEs6kySrAhkh+AcSZ1VSbjJTG3VI+V8JG8PHn6WhpuSkRm6utL/Kip6pCooarem9ngJIYmTpJyc3Qyfu0UcfxXXXXScah6fSsPVyBOfcf/3Xf8WuXbtmtFffVMEb9IueVExMIWinPttxGA+Ub0OuYWJVoFcqOJYZ3Dt16pS41iRmJ9JYOFElPgkNRGYVqZr7nL0FK7OmNqN4PNi0i6Hyq+EJdkuNwrVVMKjHSpSYHDivskKXgWYGg9Jt0psM/pALQfYHUpmhUaVADNGXCF0FPsYVCgYOmYySSoXvVM7TnP/lALUMBkXpv1yKJNrJIJkcF30HBt1lOdhEdjDtdlaKcD4cj/BNVNFwuYLXX7bH48FxQHJ2vKognneS5ZRES0hqfPzjwH/9V+zrTBTx+YBb5irJlKD/ywdjCPSLWZWVChgUZ3Nxju2x1qCD+/Zh0+G9gCaELpcbB3sGYNSohetnsNpEohYTt+iPc5yzIfj27dtHbUfnNYSl5aOg70KY1LkwaeZIq/Fw9913i2KFz372s/j+97+PmcYlEb2i3Amlp8joXMqyajacXp2dh2sLSlMmNIh2t5QtpwSnzgYRSB8HnGR79gMtfwbanwOaHwfcKTZ6XZog+Lh4ihogJ0GiZpTi9WCSwOmKzYDFDKyrAZZVAEvKYNy+BGV5Jlx//fXCUKahMxXwhXwJS7DXZFeLa6scWYtspTAqKhW0ag3uLduAHL103Y1qHW4pWoMi4wT1YlU5gHoroL4OUK8DVKaxx0DbLsCnaCQ6cAoYujDmT1CT/GsnTuD2H/8Yn3rqKVRUV4/OdA7j9d46eIJRAoz/5mvJEAy54AjshTNwAM7AfjgDBxEKxRFo8dBZAUECxd3DtnKpTNwXH3C/TDEYlhHhWPP4AK9fou/9AcDpkf7GGE9MJY5dGKM9OQ4Cfs4TPiDkBFyHJYJjDKhFRi7rFmIRhB85utELvVlrSazN6nNiwGuHPxgQWe7Uto2v7kmGZAbi2PutFkTGbcXrRJYpqzfkbY0KOqiMKK9Yg9f2vI7WdlkWjnI+pxAMhYm/dPB//yfp/AupvnD11Xe/y9QmTAm4bulLANNC6W/cOkbCiIYYDbhETgnfP336tPhLOYfx9D5PDrWgGcOw9w2KklheiWGfC66AB46AF3v7LuLl7tG68ZTweUNBaBCnhs+hI83m4jQESURMBWTHiT05dMzo/6+vAw/fBbzjPuAP/4uy0tKU1oj8N15D3z23AnfcCDzyVqBOIv8EMrOBv/9XoKQq9to01gN//NWUHMeoeS8MOQA2WflBJYpN5nENOO4Nq7aUv8oKxouO0fZKItBpILlBGUOBAy+G31Fs8cALsd/58IdhXrQoascxePHVr0beD73lVlz4+Keh3bYVv21oxbneQRSYDFiUk4F1RblYee9D2H691DiU+rl0wEl60enM1WdDq9LGnGVV2MkIhFQY9LGyLQuFhvlYYNsMqzfB+R6rOpG9PxgI4Hjkg8fwyU9iSlAwDygOVxHJRGNJLZA/8azNqQKz3dg/6TOf+QyuVHz6058Wx8hjvRzR6xmOS7iRKo7ZA24OsSAByyxkSq8yWMJ5hHIXkwVla0dDJQinmYZKpYFNtwR5hp3INbBSpGJafOc+zyCeOvoc/ufZX6Kpt0UEfqaK0LD7L6LHsxt93oPo9rwGVyC28iQhimrj1lGV1FdDe2VL513uoJzlG2+8IYLtK1euTOk7HM9Tcd8S3E58hj2D+uwdyB48VwJoK7GSgwlniQgNNlNmUJ6JC6n0M+D5Z8b7TMhzxYO/KcsUTQV4HCQtElVyM1DO3xr3OAcGoP7P/2SUliWuQHwl0dvfHmPrCtCf+dKXWCY0FYdxxYEJzazQ532YKkhCjrUGMTmO0bZnmprxamsX7D4/cvQ6bMzPxvbyYqz+wEeFjcB7hLYBm5Ynm5PKKB2u8DjU0CDPUIZc/RIh+T0nAzo+eI4Y22eMn0TSFV+pwYnkk5/8pNC6naqM/anE6eE+PNNRD08wgPU5xbihoHLUQGbWcbKGnONi5CJgVwSvaTh3vgpU3guM9/3lOwCDSZKcooQBZadKk8hvTBFMGmtqr3OBcB2jSAqwtRpwKxwCFYP4u4GF9wnHg9nhDFqkqy8Zj2JjHpqdUZ100hi5+kzkGzNxf9kWHOy/gGG/E/n6DGzNGy3tkqO34e2V1whpllSbcU8Jgh6pUiMGKsDVBWRJ15P71Oq6gH5vt8jQLDbMw7176kQmrXrhQoQCAbx57BheuuYa6BJkUA/5HKMcYr6WDO5AHfPQop8PDiLgeQNaGAFNFmCoGT0+eV/MuwloeT3cKNwEGEuA/Y+Fm7WqgaU3AqXhjPzpAquXmLk1XY4Or5cSJDXE78rZYgEp0mbUhxvUqhJPrayqCMQHhqmZxnOXPAMgU1cJu689QZA0hAxdBvp9wyJ4TVg0VsyzxM4JfO/FrjfR6OyCY2gEw/WdeNuG27GVzXwvEaixG5/9X1xqQ17xYrz26nHodFrk50sEYwjMhk+zOVdv7+gsbM5RDMxnpJ/p6gsG0Oayi94FJUbruMYNpaKYAZ9IR5Rl8dTHZYVVsuoHfs8d9Ig1RafSYlf3KdjystHT2A6D1QydQS93sYng2GArbiiMlQVkPwKvgtCQ58lebz+KTVHt0PFA54AkzFSUk/K8MMtMZOt85z+APa9K14mNm3/9M9isNrSXVMD3+GPQvbpLCjjffhfw9ndHpaN2vQjzr3+O+pERFNmsUA8NAZ/7FPDDnwOF4QwsgxHo644rEQ8CF8+lvrMkZvkwWUdV3qlVeujVBfAGJYJIdpIogWjRFIj+KFOFJRnZuK9sHv6vtSFSqbPAakOnWyL85HGQyTIRBVjJmJHGvEjt52w4UP/Ef6HaniCASoknJRg4+OlPgSNHJNkpat2yAiIc2KAe9IZNm5Bn1qKk4RwO9w7C7/Qgx2REyO7FXms+3M8/L7Lh6HyzgovECmHQGLA5dwPeHDwOh98Bs8aMlVnLRF8Jd9ANmzYDeQZF5QIrMrgWRc5ICChcOXr/f/E/wN7XAfZP+/jfAL390ti75hpgDJmMtMB7fsv9QNNxwN4P2HKByuUAM+85THSXNijHPhM855QEuJTrwHSANtJf/vIXEdC5XKGsKpYRCifjzCE52MeK8wermOV5ZKLI1FmEr8fEgWgtdwglpiszW7PT3YMnTz2H4b5hVK6YhyZ0o8LTiyLj5JuRe4L9GPErk7ZCGPTVQa+mjN4YVTUkMNg/o/4NqVF4TiWw+HrMKLiu0wdgn7qZ9BMvY7kpBqcp85ROf0dmcjMQz+qDyYKB0ERyuSS5mcVPSTW5P9vlCqo1sKqVlbXxrzN4y2SqdJqu00+hNBDJkqSyrtME/jbnbpJgqTR0Hg+UnWQFfLKkT9qarBpIKoVEcuIjH4HtzBl0UD64vV1Kgvnd7zhQo5+zJ0i4IzFz/jwwiSbnVzJYLcFG0lNBmDMxkwmSa7Ztw/rFtTjf0YnuITv8UMEGNfZWLUHo3HkhUfmWt7xFjC/OC/RzEiFDl4+Fts2w+3qF/5alK4JOnaatfvw4QDKsu5uZewCTh64gSbdUwPvqIx/5iIj1P//88zNKBs04qfH000+LRe//mEk7CwmNL53cIwxXGrFvDnZj0OvB/eXMFoliXXY1Lox0xPRfYD+HVdkplCSLqow4kVAGnSgZoxsncMeBUbtBekwRQqEgAkJCIwSNKgOqOJ1mSkyVmWpEgF1GhXkhLNq4ScFbD/jCetZKPajI+yNhbXOVcKYZ1JMXTgaDONnQwEin/HGBtUKUiJ8avih+MkefgWvyJUeGMjdO/wgc4jGMrtZu3F6yNZItrsR4hEaXuxdHB+uEc5Oty8S6nBWwaCex8FJ2aJRQLHckSvI0OE7FyH7taX4BXccaEKqsRCC8EOzu7cWLXV24JYG0TbbeBocrmunHQCZfU8IbHMGA9zz8ITdUGIFFG5IqXEIhmN1uqEQZ3hDgp4TLAGDeOPq6skfJPCnTFs4h4PX/iZaG82/d80BGIWCbvGM0CiROGl8DesIZ6rZiYMFbAF1clQxl3oaZOREErCVAgjEwJtgbw9MVd8/GXTs+Z8UGrw3HeYbU3DgWSSb2cSZ8ndqCMvNmtDoPIMgKDwWs2lxszFmFId+ACIBn6XNGkauUyyOh4fN40X62EQvWL8NJTScWIj1SmZkOzLBIqDGaJqiVWVdXJ7RLadgw8E7yjovf5u3L8dyLR2AxG2HQqmFQe2E194ly8pQ1s9nMkoYpR3AggAavF0GTCfl+PwrS7A/R5Xbga2feQI9Hqk5blpGPTy7cAMMY0n8MztKhYEaQDMr7MNDGDCJmWCVb6J1+F17tOYTBsDRdhakE7nDWaF5lMXoa2gSpkVWSj2BIkVWi2F6DoxdNjj7o5dpZBTgncE48OXQCVq0V5ebKcedAZaPHaC8ZF8BG1Gk6+nQqadiKbex7PZZ4Iva+jgUFxTj12O+w1GKWztNvfwWwWuH+h6TPPPlH8foSqwX1DhcWWM2SM3HkIHDLHbEVGy5nlNgQvTWkoPuY4OdPvgKc3ys9N2cCmx8EMmIDWhna5XAE6uEO0B7wiqZyZm0B8g1T30z17xauxPa8Ylx0DKPMZMW23EI81dmIPb0dolfFvaXVaHb246mOs6JCg4RGnsGMa9KpDhjqQI3zBOr6O9Hs84puNxHwOtSMbtAHJickkEDkeIkEFa65Eaa6Y9h2eB/29/Sj0xvA+VtuwuoVK2MCD/EBjSx9Jq4tGF0mnhBsDM6Ek8GL0nVmIoMtTrru+/8NvPhcdDz84kfAZ78EbJqGwD73oWpVlHg//ATQGQ60F1QDa+4EtJdGo5cBJFZqfOITnxCZq9PZbHkmQaKcx/T3f//34/Qxmt3IM2SixJiDDnd/mLxWiZ5oNbRf5jBuQIuBPSUYZOVrqcjgyKAd9ZaiDXix6xDsfhfUUGFj7hKUKkiNVmcvujySvOYCa+llSzqxYvT/Xn4cbq0PFYuqIn7DiaGzU0Jq+IOJqm1D8AVHoNGMIxVWtEh6XAp4eoGe3RKpQbs6ex1gvTIl+6YCzMxlwuJE5l7aC5SFmQrw95k8xIrf8nKpj58yoE1Sg8Qnx70cc6AdwoqGdCWYZDkb9q1gsJTHz8qx6QzkUW6P/kQ8adPZ2Sky1zds2CCOPx25YIIBYiYujdlPYpowVYQGfS76lGONQR4nk2oTNZMXYFXG+fMo1Ghw1usV/eJUbjfw0ktShYaMZD005nprJAXvLyY3x0jdTiKOwJ4yasZdPvlZLPi3L8HQR8l8D94wZ8G7eTt2btkSSTij3z8eaWrS2MRjQuD89aEPSbEH+rVMrqRixC9+IcWGrrKm4TU1NXjmmWdw2223zdjvzuhZ5gLyd3/3d/jyl7+cUtOiicITCODEUD/8oSCWZ+bAok2NjWeFhkxoyCHnJ9rP476yhTELFPsSPFKxA/v7z6HbTe02HZZmVKCWsjtj7lgf4GyhtSz9gsgACf/aeIbdNCAY8sHhPxTuo8C9MMCiXQuN6BERRbm5Brn6QriDThjVFpi1CRY6pYQOew7I2ezhLYMB9fA55OLFxVd5Tsmk0uEg4ZHqJMfvr8lehJVZC0TzdjbBlbGnpw7DfkckjMyg4Atdh/DWstishvEw7LNjT+/BiJHf5x3A7p4DuLFoe2qVOYlAxydvJdB7NBroZuVNzpII0SQTGnTGTh05h6ycTDxy8yp88WSsrFFfnPMmY1veMvy5fS8cAenzdLq250UrJvxBNzpcDJJHrxPjLpm6EHT+gCiuiTHJ/N0AHZPBLsDeJsl65C8FjIog83BXAq3bEDDUOT2kRvubUUKDsHcCF18Gam+Nvua1A+efkkhDmTiquRUwpRDYlJGzAeh9FfCF9fN12YC7L+5DvIdzAGYOs1olUZNwMv7aorD8lAK68TOTSGyUmjeg3XUYgbDmo1mTh2z9fJFRUDBG08hOt1Qd0nKyHvNXLxK72uMZTFhFMBboHFBjOZ3sn6THo9MJQuPQqSMYKHCIJtZqqLEow4RsnRslZQWoXlAmHAabrgYaf6kIhtOA4dpBY2ZM54OapidO4MwvfwlXMIjFeXnQfOMb6DGbhUNDYiHVBn7fvXAIfR5XVApquAd/bDuLhyuSN+fjHEaJL2aM8ZzxeDnvyftNo5tZaWzCHo89vW9iSCFN1+RsE0HqQPh6Fcwvg9flRmd9KwzZmTBnSQTzumzpeA70N+C5zpMiCMN5q8Ksg00XFIEx3o/8O+zrw4hfer/f24dVWWvHHAucm1nSLdDbCBx9EvC5pXlg5a1AYeqZXTx+8Vtc/0gMUYNWBl/X6aDe/QqqTUacdjhRbNAjmxl1rNqQSQ0qwQWCODJsR5bSWIwnmh54N/Cdr0WD2LQF7n7b+DvZUhclNAjXMLDv98CNH44hIXnvWbULxGO6wXO2KbdQPGTcWTJfPGSszMpHuTkD9SMDsGr12J5XCXOK9o8AJcxUwLL5JXjzbAvUTi/KSJwxQL94HbBdQRilAJJXvN8ow7byAx+Hoe0eqPbuxZbb7oBdqxPO5ZQ6/znV0iMR6GTseiG2coe//dLzsaQGq7koU8d7c5LVpBGcehnoPB993n0ROPkSsPLSaS8zi+oHP/gBfvvb34r+GlcCfvOb34gMTR7b5QwS1LeXrMfB/vPo8QzBpjVhfc5CQWzMYXzQn2DGceR8qtVivWV2djrNxJkodX/ZtUKKSqfWxpD/hwfO40D/ObGech2tG2rCPaVbLjtigwkYDMqW1JYjoI9N1lHKVk4G6iT9M1Lqq3GpwGPvfhWQZXiZxNi/H9BlAoaZb4B6OdxzDLRPhkymbThV/dtKS0tFEhHt1ngyk8Fz2uIEf482CNcNStMQjEOkUpUsGhTv2yckZEmWkGzgdpi4u3PnzmkjNijjTQKFFc/0L+hPML7GZCn5uPh8IuA2eOycF0iMyNubbtAna29vT+gTpQNm4ifsgxEHElisqOfnGXyNuVZhG/G40ynkft3BIEz0N+Mlq9iXgRKX+/dLQWsGs2+9FRincfvVDtr99A3YBJw9PSfay4VrOZOjWXHM+ETlt38C8+GDyAkGMe/a6zE4HK5kn6lKAZJeMqFBMNGOFcMXL3JSwdWErKwsfOlLXxIx/5tuumnGquJmlNT44Q9/KAYXO6NPF/o8bnz4yG40OaWgUL7BiEfXbEe5eXzGmT0LOPQF5xAG9efjZT7kIHGx0QqLJgSr1oxqa/HYN47opUFphDjNQH4le1XqzcKnEO7AuQihIXYRXrj8J2HVja4EMWttMI8lAaPSR6mgTLPUe8AfjAbsFWQCJzAuKPGlgcykkh0RTlCpZlWRXIgnGDpJIDGAF74mDOAO+0fSDuRK2vOxMk6OgBNDPjty9JMg5nJXAPoMSbaJgfbsWqlHheLXPG4v6g6extK1tTCajajvjtXy1YogV+LgPCtSHijfgXYXA9ohFJtyYVCMMUegK4bQIPxsP0AyI5HWpC8AnHkS8LvDHatDQN9ZYNE9gNyHRJ8kyyLZ65PFUEsCAiW2qT3aD0QbeRPMeKdEycK7Uv8djQkouAnwO8JZ6Wqg7c9hERjFb1trAfM4BIVpDeA+DQTYKFwPGGolea8UoFdbUGHeCl/QITSWdapwFvs4MFN+Jlw9pQn34DCo9Sl9l/ckA5I0mnnvpEoEpAKVRoVmYyf66ntRWFUs+gGc6B+GsaMVq5dKOeJ0bpyBZhSYaoTjIGfi0PmgfFPyjavQzoya9YuxujgDKJwP5FSA5jKN5hN1J/Cb5/+EoE6NeQVlWL9gWVI5vAbHUEynAv7rHCVlkoDnif0nli5dKrJCaMC5XS4Yw/MZ32fFC50GNq3jPMd94rFSdq7XOxh3KCpUmq1ocAxHfl9tMEJdWobh5g6U5RRibf48bMyZJ2SyXug8JT4n73Oz04/1OYUoNBrhDXgw6OsWsVyZqO319mDIN4gs/dglstzHzsYLKDr7JCDrilNO580/A9veA1jTdPS5E3feC/z+19HnnFduuws4c1Y4ESUGPbo9XmTTYWhoAO68GVixErjuBhgunBOExmKbRVExEJdxX7sM+Id/BY4ekMjkdZuBvCgpkBS9zdK9Hqk6CwGOQcA9ApgmmMUzQ1iVVSwek8Xq2nKcuNgOj16H6vs+w457aW9DJB6sWSOkqE6fOYNVlHfqGwSyc9IVlBslqcfqNI7gTG0WtLQxJgte429/G/jlL6XnDGrw+RSQuOhpiKvMDIVfG2d/Ql6psnMaZE8Y2Pna174mqhre+ta3Tklz5UsJBpj+4R/+QTQITycjf7aCQfQtCWRT55BasCNeWoRyh0ym4oMZyFzPUrGB+BlDHJnk9LsFoUFE19FhnLa3YHnmpe+bkyqYkc2gLgNLh/qPo9HZGpmleGYKDVMjtWVUF0CvyoE31B/xE83qcujUs3gt9Q5ECQ0lPN1zpEYCkBhLJZgcD9rBrASWq6ymsmqQAc9Tp06Jv7J9z+oN/o4M+fcYVOeDiYSUyKbdMl7lwKFDh2JkdDjv8MFqDZIdvLcY3CsoKEi7amIssMKAc5g8z5HA4Dwl7wftLVZskPBgvIVEUzoN0rktxmDonzCZjITNdIPniYTQZCEnYKViA/AY2eOXCWcx9g/HcWUlShsaxHgwMSDLx86dsRtgMP5b36IEjdRHg5K6N900rvrCHKhca40QG5RAnWjQm/cVH7xnGVM0bdyCrsZGqDRa0W9mDpcOH/zgB/Hd735XxP7ZcuKKIjU4yVLH93vf+960Mjb/df4EWl3R3gH9Xg/++fQR/GDt+Bn6FWYb6oZjG6hybmpyDmGeJRp4ZODplZ6D6PFIgS1m6rS6unFT4RhZOiE2F45rrs2NM7M7a+qlKsYDJaeCoU5wb2nESqGbEAIKkiMtGBZIvQG4JU70hVlAsETqFWAujJH8kbMKZDAbgHrtdDK4eHJxIYNOQ4cLHQ2ERKAmorLZkzfkgy9fyi7SqkjRKDNqpX+zokObRoWFaJCc4HVmQU8atirpEb9tlRoZ6jy8fPAlrNy0FNpwIHpl1nxYNGfg4CKrUeM/Vy5GlTn5ws3KlSpL4nPHapBE0KrLodaFAJ9C/5YnoHtYyqiWe0bwEfADXceByvC9lV0K5FcDPfXRgGBOOZA3TQaR0IqPk/GKl/PwDI4OJnnCFRfpgMejlIfL2wT07o1u27YQMKWgocmxZ5p4jxG1SgODJr2eEGuzFuDIuTpkF+ZFsgq35I0/59Ag5vhnps50ZDqQZNRadAh2S2PR5XChp7kLW1ZXQa9XLk2xdyANFa4hYxquoRCa3nwKm2rNgGoY6G8FQsuB3JViDjiVOYI2nRYBfwDnh09j/+NHcO3CtcIJ4bbZ70l2AjJ0egyGm94HfD54BoZwsqUN33U78bZlS+ELsfeACjZdKUyafDF30TliFlVJnhkYPAajfxiNbcPwWZYAhgLhZLBEnKDjwKoOlsaS7OX8pJQ25DUrMWZiW95KvNp9DkeHOuBmb3re42WFcHQMYdMiKVvf5ffGtYqW7hBvUIdVWcvQ7GzEEKuu4uDn+jQO6EA1HX4VeT4PtMrMGt7nA61jkhp0VpjFxvMb4yg++HZ6ssDePYBBD9x2D7BqLXDPfcCPHkWL24PlVot0sMyA4ePwIYAZOO/9AHJ++hP0+b3ILa8AvvDPifullJRLj3Sg57iKn/lZRTLxzFJf0Icz9gaM+J3I1FlRa5s34Wo/fzAoqlG9wQCWZubAmk41xlgoWAS010WeLp9firNDGjR19qGyMo7UoNTX738LOEaA9RuBh98pOX7xOHwY5u9+F16WYTOLbf36MXdBChKy8pP3XAZUiL2mnoAbhwcPwBW2pwxqI9Zmb4BJMw55zuDFdTcB/5+994Bv87yuhw/2BvcekihR1N5btmRb3iO24yQesdM4s232/DLaZvvfNmlGm2YnzbSznGU7cex4SNaWtSdFiaS49wKIPb7feV68xAsQAAESpCSHxz+YIojxjmfce8+95/7t2WjGHX/uuEX697PPRgkNYmgI+OAHgaefnnrpeKJxo0tBIvh7AechidSglWZdA+izr9H80EMP4Stf+Yqo2PjABz6Aqxk8B+pyP/jgg5f7UP7uQfvSEWiININWw6KthkUzvj/hTIA+AjW8uZ8z+EF7hlnlzOJmQJOBkEQJDfQ/uC/LNgbtDb6Xrx2lpGkCn2CUST9XCZhAxoCnXHm7MncJXEEPuim5xK3AUIhVWfJNWdGYr18Dd6gLwbAbOpUNBvU0VG9nEwo54Njnr94+DNMJzqlMAufMkGdwnvZ8NvpoJALXGxIarNKibc0APedzqmoSzgn23jhy5Ij4mQyMYTBWkagvAG1lSuxw7REJHWfPCn+Kti9tYJIcDKgnWg+ZEMXELf6Nx5nIx+FaxM+RewPQZ6F9LStg8D0MGItEqVBIVEDQL+F3ZiIpxfvJxLZsVc9MhJSSUClA4oHxIJ471236X+mACXscH+MSOrgffPvbKHjsMZw9eBDF7P/7sY+RBRn/Ibz/d2eQJDmLmPHFebJ3715sZ0+7KYD3kGNVJBFGSNKkcA4Cvc1SvKisNnsysDfeCPzoR1KsjNUa9JOZXEGy6+8QOp0Ojz32mOiv8eijj2ZFXu6KITVIZrAc8J577ol53hsM4PRIJ7yhAGoshSgxTi1zo8E5LGQ6ZPDfF53xTZkTozJJBman2xlDavR6B8cIjWgTZgc63D2otiTZLNmrghl3MZkfqstSxhoKe+AJHoZKJWehCrdZ1JCo2Rh6MqAGnXU74LskfZKWREbigDo3YvbUkMHNdseOHWJTZuMfZkjIVRzc4GTnIx6cIHKpIuVrfnniDwiMBJFXkg+NOgyGEXyK2L1ZY8y4NLzSVIZzIxdEkJH3WRTW6HORo8u82XAmDuFg/TC2bb4WXp1b9BqoNM1HcUElTt9SgidaDsKoCSIQbsJv2vpwb8VmIYGWCczaIgwK4iKal6VVGWHRLBSBc5hyIo3fWUpnAPyB2OwD0R8l0q9CBv++6nVA+ylgtB8w5QJVK6LNfbON8jVStcbYdA8DlXFGqIFSUcMx5zlWWTIVWOZIhCQlqRhM06cvaTDTyNVbsTpcidylFSKgX20pRplx4nWHxn3SRmpZABtgyw4vjdKelm7MWTwXOk3seDGoxxuoy5cvF1lSXBcYyCIZGlPC6mpHscmJ3oEAigsi92bwJGCbgzPuEbS4JPkwjVYDc44VHbZhzKmbjxyDRWTt0OGnM0Ki4f6iBfh+xxm4+vox3NKBnMoylJbmwuNqw3Mvn4RGo0b1vDIUl3YjV7VESGTRoThffwZG534xh0Sl2MgglpS0QFuxfKwqSzbsGCyho8K1rkyTh1ZDX0QuSiI5l9hrkKO3ocfng1tZ7KdWocEZlf5jLyGLxgCXorkpSY5yzkWuXbrx910NjWi6nA4W1tWh5a97odNqUBVp5C4Q34zaMQiMOtDlDcLhC4h7k7D5INeMO+6RHkqQ1DCaYHzyN4DHJUkCySW9/Hn+LEvKUHLHXWi69noMm0yYV1GZDapZQs064NJxSWJLSEWGgEVbJ238suLzhZ79Qs6QYPepLk8frivaGNMLJR04/D689+hunHdK5GyezoBvrr4GNdbE93DI58aRwVb4wyEsshVjjiXF3M8pA5bfBTTtB/xuIK8adddci30HD8dWVp46AXzu09G9oPEi8Kc/AxarlOXGrBw6pr/7HfDYY+JlepcLp370I3gOHpTK9hMgLNIrKMuotK/qoELUmax3noUnGK2+84W8ODtyGmvyUpMlAv/4Pk64aKPwNzwAbNoi/e348aiEACFr4jJ7cKpNHxdeCxz6bXQP5TWruybxa0MewMF1Q57oQcD5KpBjA9IgtAMhzn0mb0ws7cX18/Of/zze+c534h3veEdGQakrCSSGWXXygx/84DXTH+RqBgmN0WDUxncEKNMkkRszDWrd0/fkg0E/SsOwYox7LoN19EVof8SDARJWW8rjia9lHzA+l6Mzj0s+4D5baJg+vyCbYECW10VZ7crKoGsL149JTqVbzZsuaOeZNVdRTxhdLmAqB9wdUa0GrQUwz/wYvtIxmUA0KwlmoiE1A+Wcy5znJDWVVRqpgnGUYWLDbdl2jZet42sSrRtKcP5wT+W1IUFBf4LPcR1irINrCmMYJFwo3UUihaQDEznpM/D7ebz8LlbBMGDLQDx/kgwak4SNyHqTzIjvIcL1SyaNWKXCB/26dJOLx+RiZwBUAiDZJa/RE4HXiIQRQfs0U/uF1ybpdSguBr7+dVQ5nTjV3Cw+/wquK7tqwTlBkm8ya0h8JSarNTh/UkrgdTcCe34l9WQlmIx3/VsBQxYC7vSRvvOd8Y3C/876aSjBmD8LGr797W/jIx/5CKYb2pli8P/jP/4DP/nJT2IWR1fAh2837saAb3Qs0+XB6rVYYp+8dEKlyYIWl3OM2OBnlhnTG6xlpsQMdqkx9nm/LL0RB1YKJAXPu4Da/Hui2eWUtcmZee29YJhBMEUmcOSWqMJsRjiFcneNFTBNfD7cRJSVGvKmKZduKjcZLnLpLHQXnJdgK8pBX2cvRgaHYcuzQ6+OkhqUXrqpZF3Gp2TWmnBd8RacGq6HO+gWhMbynOnJXCcCITc63K+ia/QEKqxLUajOgU41D4N+B9zBVuzqvQC7ju6T9P2DPqcog9/G/hYZQK+2otS4Bv3eswiEvTCo7Sg0LJMIDfGCCkBHByQkNdtARCImHvGNWOkAksiYCdhKgaWvB3rOSEHHvHlApK/AGCo2Au4+qbcGwUayVWk2np0IbBY/lYbxMwi7wYJ1+emVhjPLgUTjdJduWrUWVJpK4Sn2YLhnEAajASaNEfMs1fCHKC0WhkFdArtuUUJHZcsWKRhJI/jw4cPCwaBhIyq7/E7RKNkb09uHi7cTw36p6adc0RAOhaHV6+FRBZGv0QiydMWKFWPXghlWd43o8GwohNINy2DVqaBVq3BTVT4GWzQoLS9Ab88gThw5D52qHTU1W8Q6VlZgkhpMqtVivVhWV4VT9a1YmtsOTaQPhhJ0SJg1yWBDqM0PQ5VNBBoWWKthj5AgOpUmvjYJqmB4zCBkgPz+qnV4opWZ7NIauzKnEmtyJUfcprNjqX0Fzo6cEnJfWpUWK3JWw8B5kQaMZbWoWbIS508q+gHZCqXGx+JihoHdTwOHXkAwFMLwkAt17/wYMCdD55UVcrfdgcC8+VIvjT/9XvG3yM/z9cCFBsw7dxauL/y7kNKg00qSa1KgkdtwCmASRMVc4Ia3A42HAZ8HKKoGKiaftdrh6RGJD0owMaLPO4BiYwb9fdjruvEMLigSNUb8Pnz2zKv46YYbxr22z+vE/zS8Am9IktZ8qacBD1avwcrcFEH6/LnSQ/6Mvr7x+szPPysZDgz889E7AoQiFXB0Mk+dAv7rv4D/+I+xt6wym9Ht92NRQ4NUBZFwX2dmd7y0Wz3CKBY9vwhnwDEm+ULw33wuLdC2eNu7pUc8uN7FN63nOcrBDKcTeP6vNGiBVaslGbR0UTIf2PwQ0BapgqlYChQmCI5x/rhbgYAPILk7ZmewerI/JanBCuJuzwk4gz1je3yFcQ206tSJKnfddZdw2Fkm/vGPfxxXI775zW+KgMidd955uQ9lFiLJiIHg8c/NFKnBQAn3bgb/mJzAQAf3VyY/UBZGGdRKV1qFryWhwSAaf95csgbPdR8WvRuJZfY5mJ8sqe0KA/dKnkM8aKcY07QFXvPg2lt4DeA4L0lR0da3LZqt1EgSXE5UsZDstawcmGzPh8mCawCJg3TBwL8s7cSKLcpYcQ1hXx6eK/0NEgnpgK+lXyHHDWSSldeCVSRck/g3JmspA/OUtuJ3k5SgH8Lvo33LxFAeG/8mg7YvyZJUfSlIeJBI4WexmiEdH4/fxzVPlv+dToiegcXFgoTiuj1RZjcrb+jzTVY6k7EnflcqgofjhvecpBivL79vsj0gZjEeUsLfiBjTUyE16HNTxooKCcnUXQQO/SnWzh8dBM7sAlbfmp3bQyKD1RqTwblTQP0ZKTls6/UMCuBqh0qlEolTb33rW4UcVSaVYlcsqfE///M/YgG+jc1bFXi5twFDvqgkE4NMT7Ydw+LFpZMOGr+/drmQZRjyR7JNNGp8cvHqtN67yF6AW0vm4dnuqNaxSeicx7LxBYYcIRtBXWcZzKotnqjqwlItZeh6uqWqDf4eX+LKAMqhPwPiGFRAfjlQWgPUrhnfBDXLMGqWQKsev8nJEk9p3RMuFt0Xpeza/DIgpzjly5XyUQQNCDokk5H2IArKCtHR2C5IDeLmknUicMeGfwwQTgYMKG4pXJv26xlglOWzaAQx04Ia+swmpFGRqhFWr/c0fMFR6CnFIvqeDKPDdwQdHiloMcLGFwpIjX7TDOjEwaQpQKU5SbYoIe63BtBqgPw6YKA+8qWRY8ivBShj5OgHvKOArQAwzHCmp6UImJeibFFnARbeCzg7pOO2lgLaq1s7fDLgPEunnwxLeGmwDxdYcHi0G7aOQVxXPFc0Hc42eCzr81chX5+HlxtexsI5NdhWvCUSYF+QsaYmz49GvmjQrXZB5/Ojdm5clYfOjqKQOkaiSa3VQEXtW46VONBYXrlyJfhf74Vd6BBVP9GeNnPnS3O5ak6peGhVFpQZFopKDxInyqxh/ruiNB+XWrtQk7s4pROx0WYTVRsLFsyDSRc1bLYX1eCcoyeG2Hj9hm1jOuFMIKjIy8P7a3eIgDYruPL1sedVbqpAibEU/pAPerUhpvnphFCpMFx7I0x+E2DTAuZcYN766N508aQgNMYuN/fIP/0IeNfnAENm844ZaSLgZLwNePqP0vwVxijl7yIv4u/1Z2Fua8XixUtEA0U6YHTYGIzntUyLnGPG3eP/AzSdiz63+QZguEPayypGgKIawDA5I1Pen8Y9n4bsVzwaHLE9XlhDKPdbicffus8LQoP7hPyOP7afSk1qxIFSCON66chZToSHpdaKfYn3hM425ZziMhlLZOcxaYYjMw/jaTvxJbTmxL9MGpNIMFC+hs9NGW96E/DHP0rVGZy3DICw/xuN8OFh4B/fAXSyB5ZaOv6PfBx4XQayAwVV0iMZOFe6XgFGI32huFbbjNL+K35P7HgHQkMIwYUhX78gNOSp4Qs50eU9hUrTurScjocffhj/9E//lFYm65UEOsT/+Z//KZqEXw55o1kkQqL7kPzeZNrrbiIwy5rBDQYOmfSwenXUB5xKJQ+DWfJnzikuxiNzdmDA54RZoxcVsTMBBiUZzOO6TMiBIGa+89wYgJsoC5sZ3lfbPL8soG1kz0JPpdc4GFCU+2GmAv1gBodZmcB5NFNgXIEVCnKyUibgXJID+vTh2c+Pfj3nXyp5KiWSrTlc8xgPSBYTiJGHWxlNoqCPw3OK/1zavTw+ZqonW0/5nsWLF4teJtw743ubxoPrC9c7Bv/pX9Cenu5qSB5XOg3DuRZOltAg0UTihGMxHfA6kRSjj8z7wjEvX79ZkmNq4H401QbxBGMXKQkN+i3si6gE/UrKUV1uPP8M8LPvS/4Fj+m5p4HPfhkwX53V00rcfvvtQtKbXAD73l3VpAYXQWr2/vrXvx63yA75XeP0vz2hgNCJNigC+Pv62/FEy1m4gn4szynCO+athCWJhvSxoT7k6nTCfF5sz8VHFq5EZRpNwmUssudjZ1+T8NHVKgauwvjmhYP48sqboYtIFzGbeFvRWuztOwpvyC/KkDcVrBzLpk0Ikc0TBvT50iPha8LAzl8CPc3RZsxsTtp8Emg6CdzyVqnZ6RShVRXDBxogDKhIbdBVMEGnKhnnaLS4zqHbK2VN5+tLUWNRZPMnWjB2PSGVdwmogA13AfOSlxFyk1COCzLzNBwyZfOKDQW4OEpRj0jLByE3ZUK5sSjrjm4qB4yGBktGt23bJjZbLtacA8w+oCFEw48l7DSSmC1F/UwaLXJ2sTfE4FR0TvBrzFr2QQiJ79SowqKZ99jfoUJegmBs1lG5BTDmAM4uKVOpaDlgygdOPA80viq9hqTRhnsljcIrCZTmykltuGUFnONtB4Hu09I9LFwIVG/NypzNVuZish4UnId0lGnAvuLtw1ON56Fhtnw4jJ29l/CZpdsnRWywWuI3bRfwck8b9BoNXl8xH9uLos4MA+q1tnkILfAJOYipgPODJdok0J0OB+y+PGA4QsQRJCb1dizU2bAipxonhqX1wud047Z56wUBcGnUCZ1ajTKjadwc31Fch5+3HIzMOqB5NIQlds1YCbqWUlaaYmHwCxJJmwPoCwDfgHhNW+cAnO4wlm6PyN1McL8YnOB6QeOZ6wexyF6Md9dswit9TaIpeIVHg/mjWpxrOIfjx4+LtYQZZTTWa9atxBlfP4waLdbmlcOkkKgjKa/JJBDMsd1/GOGRC2ipb8HyddukysP4sd3VIhll1OBVqeAPhgD2IxnqBUrS72shZ8AJh2VeDfBf/w08/lOgqRHoHd8TBJEkBrkh4549e0TPEhLMJDnoQMv6wwlxdE8soUHsexEoskuGQMNRgMTBHe9I3ATQ1SYlK3BttM6XZCoUKDLkj/WzUd6D/EnI1lWYLDjjGFRUowIlSapRHX5KEcXaWK6gT8zLdGWvKL1w9OhRESRkUEA4cNdeJ1XQEImaThEJdOoFSJCwCXdCWBL3MkF0rC60LsargwcQiFTG8jousmWh4pXk1+OPA7/+NdDRATAAcu+90t9+/Uugq0uyyWRC5r+/Btx6W/LzzBSDZ6OExpiz5QFyrVIVrH58Frg7cB6+sCTzMxoMQ6+ShitBG9YTjK96SYxbbrlFrB10Oj71qU/haqvSYJDmZjbonMUVAbO2Cs7AxXHPxWPYP4yjg8cwGnTBqDZiZe5yFGahOTX3AHndZ+ArVSJVpmBQj0FF7jVGjR7ltIOziIkIHuqP086RiWYGV2lz8HcGHtl4lRne9KN4DWhLUNJ3FrOYLnC8TlQFQV+XVQmJKoSmGwxcc55MNfgs9wCQ+4ekG9yfKmlLe1iZ7Mm5fvLkyRhCgrYuk3p4rul8F6s/SIRSLpd7f6Jrw7WTawjjGL///e8FWcJz5msZq1ESLdkCyRb5eyZCJpU38aBvlan8GYPmXHt5jCR6GKhl8hWPlRUcs0kVmYPXjOOI+xbHoezrTgvor5pzAJcizsa5Yr/M/Z08buAXkeoO2V7p6ZKIjXvux2vhHn/hC1/A/fffj/e+973TmlAx7aTGD3/4Q7FwsGdCPEoMdpxBV4wLq1dpcHq4C6vzKsSFODnci683RIKmAPb3dwhN6U8v3jxuAXm2swX/ce7o2O+HBnrw+/YmvK92eVrHyiDRnj4G8KXgMZ1+Htto0I9+nztGhqrUWIh7K26EL+SHTq0bCxCIYBYkTWM1TCKMgeGjgCti4BtKgbxNiZuQuR2RCo0I+Jmid4Ea6GoEWs4Bc6duEKhUepg06+ELnhcZfmqVDXp1nTiXFtcZoVdt1dqgVWvR5Y3q4vb7OoX+eo01SaPjxqMKQkNcDeDQ00BFXaTxahQWow69p59CkdUPf3sj/HWl0NmrxCbN4CrLMDNBpbkMywKjOOtogNmgg9obwLKyBWKMMDNEDpLxdwY9M9l8SFRw4+fGLjfeIvhvOhF0GOSSUb6W2RJy8Jgbv7KEknOBn8VyUy7gJDfYxJBOCI9JqzIgrAkgFAmccDz5FT5Zvl6Nfi9HlzRr7DoTNhRMLRicFjgGSWTwIaP9XJTQICjLduj3wG3vT90E9bWKjqPSQ8xZlSSLRQJwztbLelh0bpkhFZ/RwiAlHQ1Cdoo9COGpY3vFc3LQdMDnxks9TbirPPNx9pPms3i89fzY72dGBgRZck1hbFYG5w+Pc8qa7l4H1C17YPcMAsY8oHSb6L0gep7opYxGzrObS5ZjVe4c0dizQG/D6foLuHnXCzjrkCoxbiopw3fWbIRRYegvspfg7fM24+hgm5h9dbZyWLXdONFwCK3NXbjztnuQyz4h3r+hotCJ/Qc7UHvzo4CjHg1njqOmZhX0RasBtSGjJoeswqABTgOaz5HY4IPrEIMrfA0bcTMbQnZMDnQ346N/+AlKVtSJY/1LZwM+seha2Cbb6HroJOBoQCgURFNbL/T6PbAW9KCcvaZ8LoyobbDWbsSlQSe0/cPwRJyN6ryI8WK2AS4n8MLTwAAJjgrghjsAfeLjYVZdjCxI3SLgc48BrS3AP79DCixzfNLxyc0DFkSl1ZhJxQw6ZtjRQOaDn0eHb1zvFRmDvZLBq6xAIEjK8Hl+V/sFwO2UzkWJkXPAkEKOy3kBgcIboFX07mHCw+aCVTg4cELosOvVOmwpWC0SJDLFP85fglcHe9Hn84hv1KrV+NSixNWo1ZY8XBztG7OxhBynyY4e7yBcAS/y9ba0MoyZ6czrx2tLpw7rNgL/+F7g978BhkYApzdaSSPuSS51jRiBAw6x4XXkCDi/v/WtxMSQACurKI/ZHfmdr1sCFaL2kkVrxaaCa9DnlcitAn0RjJO4jgnBcfvSS1KlCRuE79wpSWj19Y4/ZspnOEaAgiRB2J42YN9fpXFfVQtsvjl1ta1X6vMTAzIT+hrAskjaS5SHGhoeIzSI+FHNo9WlaedwXSGZ8ba3vQ0f/vCHJ539ONMgUf+Nb3wDP/7xj2cDClcQrJoaelGiUTjJXLO2elw/BV/IhwP9h+CPkJOekAeHBg5jW9E1sMSRwpmCAUAG+Wg/M1mCiURyAC7dqtV0g2n0AeQmwByPrMZOSaAnAIO9cp9BHheTIOR9ijYRExyUgTVl4Ecpt8i/MehKUp/Pk9iX7QdlfzR+x0w1/51FFsFG9CMR4tteecVUnbMHAu1TJTiGm5ubx+YbE6YuB6FBMEmSskzZQqZBOc5lrhnpSnTF40c/+pHYm2Vig7Yt1wx5TeMaxHnOtS6TdY0JU7w2TLBMtG4xFkP/gr4ZJX8Zl5E/n4kuJEXie41MFfRJ29vbkZ+fL4gXxlJ4fnzwGtJvlWWjJitlI6//kyW5SGoxkYLg9WFsSK4Q53FPNzi3uHZfqSSKO+gVvYeZ8F1izBeJRxMlCjAGxn1rbG9johqD/VabFH/KFjbeC7zyBBCISMfllABLsiRJPlnQj4j3PXlvBxP4BFcpbrzxRmGDcC37wAc+cHWSGgzwfu1rX8N//dd/JZx824oWoMnVjyY2FY4kprlCQTzRdhSNrn68oXKlqNJQ6p/z58mRXkE0xGcOP9MZdfDE5wF4qqM5LVKDDcu/eHYPGlkZEYFeFRba6YQtQZYyz8mgiT4fDgfhCp5EINwrflfDCovbArVMaIgv6gaGXgXyJ87WTUh6ZAlqlQVGbTQQQimSE8N7haPBKzcSGICaV55VD4pbN+hnsGEZQLLD0yhJJhgqAWMd4BiQFp+IxqwA/01WVElqhMNYWtCNo0dOwFtgw+qFBTjyt//DxjveB6s1T2QZTwZ5eitMGqCiugSdzZ34W9/zmGurQUATQkFxAeaYq2AKGIQ01EQll8oNlhkR69evT6jvyM1l9+7d4m/ccDnmJ5I7iTfsSGzImpUFhkXo8hwZiwGFoEKnJ7rYGTTADcWLEYZOND2vNhdBP0lZrSljqGv8/Q4GpFK+vKtDW3gcHN1A406pB4e5EJh/nVShkg5IaDAIKhBppD5wccZIjUDIgWB4FBqVGVq1ZJzS2GI5skyaESyZpdFIiQZlpgoNvSeUJFUEfBt1+yeDP3YoSU4JT3U0jSM1mFHJgOmUGpPTSDn3R8DP9SMsCA64eoGlbxrXyJrXokTcV+nePnbuFM6bow7iC92d+K/zZ/DpxbF7xzxLoXhEUYzty5fgmzu/CWuQJLaU7d/TM4DW1gZcqP8LAuolKKi9HfqiyWWD0FDmmsg1go6FDDo1vH9cdxiE5E8a6eI+dp6B3m7BaN8gzIV5gpj6S1cD3lSVhJCeCKOtY87ZrdtW4GJLN3JUvbhw8FURtLKZ9ehvb0LxNbdD19MI/VAPVOwJwCD3xpuk9f8bnwF6u6WN/tRh4MIZ4J8+yQ8d93UMuCR0OKqqJXLjv/9LkgmaOw/4+KfJHse8LF5+gw4bnSA62Ql11IvKxhuV4oQnMKa59g0dR0/fMAaGnNDrtfD7Q/A0/gXzVt0R4yRWmctQYSoRyQNTacLKqoyfb9yBXb0d8IVC2JBfjKok1ag3FNeiwz0sJMuIXL0JNWYTnurYJ37nEVxbtAJ1tqq0sp9F4K23C/j2fwI9nYBRBTz8IFBVB3zpS5I8U00N8LnPSb0o2DCP1Q+NjQDLyx95RJJzSgKpmoVjlMdDp8PGkOi41xnUBlSY0q/8SRuf/ax0rDL27ZMa/y2sA579S/R5BgNzciVCLRH6u4DHvy7th4IQawIGe4C73pr8u0V/pjjpLZKkFl6PEOA5D4QcgNoCGBYgBKkX3dghMQdG8btUsZr+GKM8LAOhP//5z0XT8KsBP/vZz4QTfOutWdJDnkVWwLXNqqVsZZxsnQJDvqExQkMG+zz1efth8QaB3ZSCawds+cDW1wEFcVKSE4CNZuWqBa79ctKELAc7lWxB2iusiKTUBYOlJB3k9ZzPp6s/z71abg5MIj7RnsA1l+fBPZ7JWcw4T0XKMAh43XXXxRwrfRRmEsskBvclBiSnu2/aLLIISp+e/xMQiDSHJpG/8C7AdHnvIZP0OI7kYCTHJoPhtEc55iYbyM82LieBx3jDuESdDMDESTZBVs5rytOSSOL1ZdyBSZKTsSlpZzMmQduYxIh8nXgfmSxF/4PEBYkP5eeTUGEQmutrNsFqYFY/kByghCyJYvo9csN3XkOOLaUvlCnk85oMEkn3cc3lms/EnxMnTghyaTIy6slAf4j3m/eG+wB9eN53WZrsSkKfdxAv9jBZQSL+c3U23FSySSRypQLHFu+p2Nte+TPw8lOS7ZxbADz4XqB46vJUAgWVwK3/DAy0SSoeRXMuv5pGXoFE3ow6owlgTNybG0sUX81QqVT42Mc+Jh7vec97pm1fmNbd5je/+Y0ILtwrl/DHgXJOb5u7Gc931+OFnoYYh+zAQAuuLayBh05hAn0DEh3ZxHPdTWhSEBqEj728w2HcU7EIljSkV7yhxjFCg6DTGfbES2Uw2NaV+ANMNql/Bqs1ZPkp8ZZIoLSwEtOFfl83fGxqG+dgxE91IT1FOarRaEUM3OekY7QXxga4xRvUUrmXEgEHVL4BrFk2F7sOnkNZcR50Wg0cXWdgm7N1wpJC9pZwBSWCx6yxjWnCX3BGNT3L5pbB7/Oj29ENs82OdncH2twd2Jy/Lq3mXtxEaKxxE6WUVDJjgQbB9u3bx7JRJmM4MZDAgK7c56LCtBkXtQ7kaObDFzYhEGZzUSngNsdUiYW2uVcGQ8/xGn+/CUVFE/o6gIN/BhyDQFEVcO29wGSzxacbnhHgzB8jwc0wMNIBnPkTsPIBafNLhf5GSWZHCU5fpdZ8KgR9QOcBYLQL0JqAkrWANX1iyBVshDsYJU+N6rnoa9cLI5XNs+QAMQk9/lvOMlHi4ugAjg2Nz2ZixcYC6+SyT+RqDyXkxprxmKosBBztgF8Z6GNzXZeU2Zaf2jg4OTKMoCk6LnmEe/uja3kq8HquXbsWhw6+hGs2S/esu2cIy5fNxYL5JkBfOy7LOhncQQ88Qa9ooq7sAUTSND5QwsoaZgwxC5X3k+TrunXrRA8FdzCAnDkV6DxyGqb8HITUavQr+ldlDAZXI2DgvqQwB339I6itVBI1g4DZALzlo8BpjuURoHQOMH8ZcHSfFASXwVvd3AA0nQcWxI5FOjIps7/WrAN+/ETSP8uSf/GOBw1mOoF0aOggMMBFHVexlq7YJDUJP3tEoaVtlPYvQVCqgIpawBQXkA8HcPFSFywmAxYtqIDfH0AoFIYhbw5Ot7aOI7C5V2WjEWuOTo+7ypMHDJU21lvnbsCAzwV/OCjkXnb1nogePoBXek+i2lwM0wTHxesUJkn13a8AfZFKCs7Z5/8EPPyPwJNPjn8THbtHH83o3KRA/OQbBqYFnkd8xgZx/Hhsvw++7sgR4H3vA06dBF58IVpx8qX/l5CQEzh5IFpNJBAG6o8CN9wHWJIEU/OWAs4WQPQLiZAbxdTspgzpfiAoZ22pAH8X1OZY6QeNimtr7EdqVeln8vL+fvSjH8W///u/i6zQKz2LmzYaE6ao0XtF2EOzyAjJZGw1Ihvtu8Bwv2RfOoaAP3wLeOAjgCX9rGBmhjO4xGAcCY5XX31VVDFwL2VQaCqkBkkB7se0r7jGK8cfiQ65n1MqkJxgAIzZz6myexkQ5UNZvZEpmPFMuRRZb5yfxySJq57UoK1+chfQdh7QG4Hl24DSyQWOr3i07ZUqNWQEvUDrbonYuAzgeKQUGokLZX8MVm1wbmQzqJsNTNm/mALkxuKTBStlf/CDH2DTpk0iqM24BNexbAa14/um8XrxvnKdYrBeVqaQyQS5uj++Ciwb4JiiX8OkrUTjaCqEhpysOhVSg0lmiYhlWf6Wew7vuVxpMhXIfWi4l/EzSc7LcmpMTpzJ3jTpYHffsZhegcN+B04ON2Bt3sREP++17/gBGF76U/TJ4UGp3+H7v5g98sFoASahPDFtYID/A58EvvpFwB3x07deB2y/Ca8lvP71r8cnPvEJwQ08+OCDVxepwQn/5S9/WZSypzLEKNskZZqPbw7569ZTeHWwRwSYZPBVG/PLYU7QU+PO8rk4MtQX89rXpeH4Ez3eUUGUsKWmEo/MWYntxel9RiAU32wmjJBqPDGgDBDFPq8Ctt0PHH4WaKsHPKORzG8VsPEOoGj6SA0SBYkgddyIotxYA3ibx7/Q2wTMuw1orwc6G6Lns/5uydiMQfQTC3KtcIy6MX9OCc7UN2LjnK0iq5ZMPTe2eJB4OTNyCO6gc4zUWGJfB53aMK4Zq06vQ06+Dn7yQ5HnzjsbUaougCfghVqtESVylB0rMeaJoA7HLbXpuXHRYEjX6OfmNhXHWmnw6NVWzC1dgZAzB6WFhbjZUIgRv1Nk99pS9W2ZacxZCVw6AQx1RqXSFm+TyA5iuBf407eiGdCs5Om8CDzwieTBoMuJoUuShNYYItn+zh4gZwLDoTMaKIyBPY3sAl63lheA0e5IIN4JNP8VmM8srIK0KjSUhAZxvnE/wu4abNwYWyVCozSZATrgdUnVZ+owvIrlYFVOMTbkTy5LYkdxFf7c1RyzqvK5eEymj844JHNahi8BeTUpJG8Au1bHkHyMTE9xBo2tSSb83w+exdJFVuTl2TA66kF5Oe8dv3PidYHrzumRetQ7pCxxqU/TWpQYkwdGaOByfeJ1Y2YcAym8v0a9Hvl6EwZ9bhQuqkHv2YsoWVqLStMUysRzlwA9u8d+zc+1Qj3swtlLXSiwW1Asy0wFfIDFCqyKy97yKJxxJbyecesggy+JSLdxuHgB+MOTkhG4dj1w6x3iHjNrltmzNPjpZMQHjBjY4YPEB50P/l0Ee97wTqD1IjDqAEorgdFB4OBfEXI50GUqQumOh8b1oWhsakVRcTnsJqlnlk4X2dsNRQgEXMLhk+VEOO+mM1BMApHSbvHg9xcYJDmXNpckB6Pss8F/j/hdsaQGKzD9A9Kc0uWLfknMBgw6HUBXe+wX8JzOnwY2bceMQJT0Xoj0MNED1jpAl5N+mfcXPgVcapKO+/Z7gIffFl0bSKa5I5mwBF9TUCDtV//2OeAf3kYvE2DGZSqpvKA/cb9zkaSTolKj+g7A0QzQljGXAZz/gT4FoSEuABAagTbkhkE9D95Q01gVZzCgvLcq5OkXj60vA74GDPuliiubtgyFBmZ2xo7Hhx56CJ/+9Kfx9NNP43Wvex2uZDz11FPC4Z8u52gW04t8fR5ydDmCaCW4LlFGrmTEI/VgkkHfxOcBms4AyzZn9B1c15kwxOQ6JkvRrmcgjpUPY4T2FJAoMMYKWMpCJiM1KJ/CfUeW3Eh3T5hKP4D4ngdyYPSqx4GngYbDkV9UkkzzLW8HitNr/ntVwTMUt6GwaZI0d2YatGc4hyhvFq9gQLLwSiM0rnYwiE9idufOnaL/FW3kiUjTqYJrhiyfzapw/iRhxXsug7Ea9hDlOjhl/y0OJMZYPcJ1iuRGtppxkyDgcU8W9Bd4LbjGy700lGs4rxv9FyZdMImQSbR8TXz1uBIkKPj3+GvIc+dnKJOjZIlmVrCQnOeeJsu7kVC5nGCvvlGRlBMFV6whxjQmAM+Be1Tg0lkYlFLA3P+HByRyI296x/xlRd0S4GvfB9pbpaqN0vKUMYurERqNRnAC5AYeeOCBaUlGmjZSg4svy+3e+tYU5fYRVJtzxzWzZFDp6FCPyD4zqCmPJPmIN5fMw0NzEusy3lJaJQLUv2y9IGQZbiqpxKNzo0x2u9uJx84exnnnEAr0Rnx44SpsyJcyVypMtnFNy41qLbYUVmXUqyLeifWZrdBxMio93FRNLUkAbLo78maPFAi25krM4jQiV1cQE+wQcuUqFeZblmHI3y2eL9DTES4HfAlIDYIL+7UPSI3OScjklwG2BEFZrRVe5GB0qAMDw04sXUiyRg2VUypfJQvNjZILNrOYlJtZk/MM3MFoNrYr6ETj6FnU2Vah2FCMFrfU/Fc+h3iqxhv04pT6Ep7avwvF1aUIhMIiu1Gn0mJHwWpcOHJWlD9mksEUCrvhCV5ECB5oVTkwqNkwKv0NuKuzDSW5lJe4BOgKAY3UX4AsP6FX61FomH6dxoxBffBtjwCtpwGvE8grB4oVWVLnDo2XdKEm/andwMoZCoBlhCQLbDoLLwOgiZBOvxNWF7BCIx7DjWmRGpScIhwjozh1olFk0ufl20WlQCb4ySUpWMnAqEktrQQ883srFyfdfEYDfuzq7RKVAZTBqTTHrlP/vGC5+Lydve2iAfd9lQtwG7P340ADbcqOkL1C0hhWZrPJ13GwEsiP9l2IxwcWLsZnetoFUcDz1qnU+MjC9CQk5CDBtdtuw85X/ootmxZDo1Gjv38E5y+GsHDJxEGLDk/3GKFBsO/C/v7DuL1sR0zFRiLQGGafAxrqrNZg1ci7a9bhGw37EbaYYbBZkNvjxM0rx5PEacNSDRRvAxwkz3iFrMhV9SF3fjka2npRTJkmBmWTESfzF0mBYZm85XjS6YHqGqF9TieNY4xBSqV+eFI0XgQ+9J5oNvyeV4DeXuAtj4rPkbOXWJXBABIbD8ZXf9D5pvNBh4IOinRYPC4bfJ29Ihtr5V3/KOREWPJ+sbVNqlYIh0WwjFm6/Ex7/i1Az05A3pesC8Rj5cqoHAnfz+qQ6SgXZ++wL505jF6fRzQR/9zS9VhsT7x/2XWWcbYWj9LG6jBlBujAy0CAzfS4NZvhs23GS68cwobVq4A/RAhsJeLm/bRi5Bgw2hA9ek8rUHgToEtDx/6TH5AazcsNv596kmnXwN1vlP7+4Q8D/9//F6nQiWRQvfvdka9SSU3O08GC5cDRV+J6UpUDtgnIF0qK5MaNkTiJHuXzRs0CaFVFUm80mGHX6uEKci8Jw6Qphk4tOcmD/kYM+aN220iAuuyUddXDE3RCrzaj2DhPBE+od/vVr371iic1KG37/ve/f8pZm7O4PGDV2sb89WhwXsCI3wGL1oxaay10XZGeAeOQWaY19xKSynIGMtds2hnMnKWN/9JLL4lgkVy9kO2gb6KeFUw+YJbwli1bZrS6iI1tWbGrxFVf3USCuCFSWSkQsVhJcrwWSQ326PIxOBglrcVzMwgm/HEOcewkIjRmMX2gPN0f//hHUXHG4C9tSsbZOK+nW+KL9jMfjMsoqzXkhC72BaVEdzaJDZkw4HhjktJUiAgS27KaBq/VVBsV09+jz8VEH+4x9MFY/aZc7+VKFu4FJCZkUln2IXgduRfxWjLGRXKC5BH3DRInfB8rZJL1l5XvCV9PH4P7HefjlPtSTgGMGZo1RriCUR+ccUV7Gj2yOK5ZAWnOyUucoGicWsXLVQH6UbVXnqRYNvHoo4/iM5/5jFi7lHJ62cK0rYTf+c53xMGns+nNtxbijtLF+HPX2UhASYMKUz76fT2R7MNoYvdtZTXQpyhBYrUGH/HwBoP40LHd6PWyFW4YXR4XPnlyH76/9gbUWO24sXgeTgz14PiwJBfFoNb7alkBkH5w2qiZD2eAGXVRyQGdabnkGLOvBhlH6kCbKpMbaQefAS6y6Sg7EK0D1t86tZIr/yUgwEy+EMBGfbqFMU13+r3duOQ6J3pqsAl4QFE25g+HMeQfwkLbmtjP1FcDgYHxzxE0lEsmKP9VqXC0Mwfa0W5s3bgG0JmBgtUI9TQKVpvBTW7gDEaR2FASDM7A+GwV6Tk28l0keoJ0ReS9mPXljAtwjgT8cGt9CAaCohm3lhr0gtgI4P9e+DU+csvbMyoXDIW9cAYOIAxJJi0YHkQgPAKLZk16TkPIC2/PS7Bo/YCT2WmUPdkiWHcG++IzwYLhIHo8Q4IRLzbmThjwnBFiY26sDMYYvEnkbobiJdmuEOTXAK0HpGxz2UEy5QLWNJxevodSJUoNfkpP5afjXE2tLFqjMsHr9eHEsYvYfM2yMcOKz8eDRHOiSo0LTie+dbEVK3I02FqgkcZuOIxyU6kgnRNhwOfFPxx8GW1uKZhL8sKo0iGsUuHOskp8pG65WKvfV7tSPJQY9nvR7XGjxGhCjs4g9gkGl6cEEhoL7wTOPhlp1i5SA6Q1aag5JalxW/VcLKyuxsvDg4J8eWPlHCxgtkQGWLp8CwYGBtF0qV84GnpjBV492h/TTC8cdiMMafyrUASVStofB33D4zLoSWw4A6PIY5PzNMB1i9fx8OHDQoP3i8t24MRQN07lVsHdP4Q/7HoJNbY8QXpMqmLAUik95Ez0rrOCkFPpNAgbtFAV1iTfq4rLgLe8D/jl96TKCgZ3H3kPgmYrOurr09YgHwMrNBiU5pyT8evHgTe/JaYKTK7KoLNAEkKp/SyDz8WT2CQ5SK5QXoEBKDotcn8MOjLUN2bm3FjPjPI7gKALUOmA4CAw+Fcg5AE0OQj6TOLecC2npCHPNVsBpTaXEx8/sQ/+yHXodI/ig8f24FebbkJugibsC6wVaHR2otUdXYM3FyyFWdlwlKRBINK/SwT/XWg68TQ2bLhLuk63vR7485PRwD8rmq67DTMCVtKNERriACW7avQCkBtnp8RjoF8iNAj5+vP8XnwuSmrccAPwwx9KzcJpt915J7VaMj/OOXXAbQ8DO/8IeFxARQ1wxyOTa3io4djk++JqljVSooNWHe0NRNjV4+0vp388aT7sb8PoWA8o9k3qQa1tk5Ce+rd/+zdhf12Jms0EpXP279+PJxNJns3iqoFOrcMSe1xVHis52UfDOSTNbc5VVubPSaN6LwIGkJ599lkhHSuv91x/GdziGkb7+vrrrxdBuukgNWhj8buUPcvoy5DQ4P4707hS+hpkFUJdIIHtnKg31msBVVulnhqiZ1zE3q2euQa3zNLnHEol28Pq2NdEBdAVCAa+ScbSn2CVBtcYJt6wZyJl9QgGy72hbgTCLmhVZhjUJVklL/n9tH1pw8pVATwu2shM2mH1M4mIyUo7JSMQSGxMFiQUeL2y3dBcmVDGuJVclU0iQ1mVwesT30uF94n7A8+LexU/QwleS36msjF7MjAhjHscH/Rb6JNMx56WLjYXrMTLvYcQjKi/WLUmLM+J7oMxEORFUKjXkMShxBbKS4Eju6XEaIK+zaYbAdPlI2tmkT0wRsFih+9+97tXD6nBSfb73/9eZI2mfJ13BKepdx4OY6GtHOvyb8aw34MCvRnPdzfi8GBEt1lRvWGfpB7/xdFhdHvd4+Tu9/V3ClJDq1bjo3Wb0OAcgMPvwzxLLgoMmTGDGpUNVu0m+EOdFJ6CTlUMrToX4CEb0iibOvwccP5w1FA7tx9gYGL1jZgUAq2A/5Tid2bZhgC9FEBy+Adx3hntjSHCuHG6zMOUoIiHgU4+KwsuSIalgURN+g4HsXbDJuza5YO7aNMYs7xokR719fVi4yS4ODPQqQw46TVG+NgQWHk4aikgo1FpsDpvtQj8Ewz8Hxk6jq5IX5NKUznOUXefAUCrCT6PDwazUYQLfIEgPPDDYMxsfPlDXQhDyqSUt55geED0U9EgjawF11lUlJhx9ORFlJbwPEOA8zCKiraJTU9pHIz63fjdxZfh0UubhUVjxB3lm5CjuwIXewa6c2KDh2MoyFLDp2xDbwaWvR5o3gt4RwA2hJ67NT1SUWcC3F7JoRJGSBgwplntQ0kxc7EkcyUay0aC8TnpaQMzqHX+hAvrNi4aC1Yb1JXQIFcYTTQKmQXCbD0SZZxjNKKUxlJ3xGg8MRxE82gQOToVRgPABxcmD+x/9+JZdDJgFwGrnpzwCemqn126KAjpf1myatz7nu26hK/UHx2Ty2HF3IqgVsg2pItQeARhuEQTYbVKkaFtypcqACJrwBgmuIckUpdrNNhWMflsIF7P8spFIltJb5Xm7Zq1c0UWk9SXx4kQDiMcDiAoqsgaoMUi6DSVMKoN4zLoCWX/BVYhuoIBkY2dzNBl5QED+Nx78xfMw/9ePC4Ifb46ZAzj3UVzxPEkyrJjVhBLs2n8MxiT0pjurwcCDOKrUFmSj9MXO1Dj9cNcsQ7QJ1n3lq4GPv8tSXKKgXCVCvVnzkiGbKYgMRKfzUOSg9lQCcrU5awmBpXofEgvDwrHLJGeuZyJxnERfx3osPB+8nPGSA0Gq7VWIDAMjOwX87+tow+jrnYMD7iBletEII3zcypNCuNxeLBXVKXK4L+cAT9OjQzgmsKyhJlUt5SuQ6u7F66AB4WGHPGIQWAwrldSGIN97ajbFFnPbrtPIqnqTwMmM7D9FqBgpsrekwSsFMkYSSFr1SohyyYqQfsjYoNMCUvXS4+pgvaNZSPgejVStaEBzGsATfqkq6gcjTtN2qhKeEJOjPgpa1GK++67D9///vdFz4orETy2N7zhDRntGbO4SsD9++53AzuflHqykeC49m7Aln71tNxElQFArvFcw7lW0w5iEIl/l2UBUzXdnizkz1eCJCGzmi8HZPmpicgNudfIVVHFwT6X5bVA5wVF/8kwkETJ4aqHwQ4seZPUO46naysHtDMj80TlAI6fcUl2kYok2kQkNFiJxGAsba0rrV/LVTGmJwB7wrE5t0yM0obntaZcNwPnI/5T8ITYu07yQUlq5OhWZO3cmZzDHqL0IejnyAmgwn+KVBTQ92DcJl5CluOCx8mKAq7NmVRL8DwZD+H5ZwKu/1zfp4PQUILnT59arsqQ5cT5M97XJvg75w39jESVFXyPTJIkA68nrwnXdH4WSQ36bSQ2LiepUWoswJ1l29DjGYBGrUGFsQjaRMm3gUYgyASlEEYcGtjkvp1MeHv3p4EDLwKuUWDOAmBlZrKTs7iy8a53vUvEeckVZFsybVpIjZ/85CciUzWefVSi3T2AX7fuG5M4OTLUhLvL12N+JCt6R3EN9va3odvjFBmsrK54U9VSmCZq2JsErLyIB4NIykoMOvx1ieSSMoBGZYFGM8mmSZdOj888aT49BVKjLTHRESE12BxcmR0s+mYi9hD0asloCoPVEGfp+gIqG2BcAhX7a0wScmCIGzQ3R27SDPpQQ5Gas3RWqXnLvyszaeeaF+PMyEHRxJxQQ425ltjNk+SG9JP9V9YKkkNqPgrUOzrE+ZLM8Iy6xU+e7nDvIMrKy8eajqeLsAiysC9MeExvnWQKJak0qjRIjaATWq0agYAiwECtbL1+LDvh4MGDYmP+88ldONt0Hqtu2oSi6lK4gl7s6j2Ou8q3IOsYaQccbFptBAoXTtwoW4lLZ4FnfyoF6JVlVkRhBbCYzU+vUJjygMV3ZP6+qrXA+UgTWdnBmpNmQIvjpnQTcPrJaJaZPwC4hgDTxGQoA7V51oUoslQLKSqNyoyOVgc6fReFsUXjh9kizOyhkcQyXmaM00CUjdIldjssGg1cwSBGAqxoks5hU4qgUYvLGdMIXGl/8dk/dbSMIzVaRh34z3NHxpYYvp8Ex1dqVsPsdqdVJRUINyIYjko1qcNzoFPXRg+iaBnQczz2TYWpKwFI/NDYzpbTITt/DGKzakJUT6xj2XEAbsbexQXg/87CGPZirmUOmkZbMBKQ9juuUXW2+TBRjgbA0x0X8ETLGbEPFhnM+FjdRlSZE8vt0MmhMf0ff/wVvHMoTRO9R0/0NuL7194ssrs4Fij1R4eBDUv5Ph4rS72ZKZSSbGC2INfKcAhGgw55djN6Bx2Y43cnJzXk+6MoI+a6xuuesdOxbiOwe1f0d+7jixZLjalJbOx6CejvAxYsBFZHM2NJYChJDDog7EXC/YhOgexgyH2dGITinsTryeulrHBJWGrv6xavrb/QjrKSPFSWF0JcRZIdugIRWMsmqWFkpVwCmFJU8In7ThI1GdQku0Zi7hEtMLjbpCpT3sN1W6XHTIMSn7pcQOjwK5pwG8cTOONQUiqNPZIbysXqhlvGv5bNwb/8ZTJ9APWM/+Vf2AUSlw3aIsB2KxD2SdcgwyBFrm4OeryxSUaUdI1HMCJ1RaeDxMaXvvQlsTZeSaBdRB+DiVOzeI2CBMad75jSR7DSjgT/M888IxrsMluXDXdpTzOzmGBwjdUarAzPdtAzvjExf5/OnkrpVI4o93T5+Bj4o33CABvlhbj33XrrrVeHrNu2NwL7/gh0NEgB/lU3ANWZJdhdVdDogdyZbYTOcXL06FExh5TPcazISR20eWg/0Y+n/06bjgQZJXlmkT1wjWLAmjakHLhmZQDXsNxCHfz6TvkOif+zasMX6odBk91eBKwM4ZrBtSK+UTXJDSbQ0Q+iXU+fiGsPiTGut7Sx+Tcp0Su9NZdrN33WTMGqIT6mg7hOhPiqDPrelIbiWkoSh3NE3gN4PFyX6X+Q8Obzyt6sqSqeeP+5bpM4SnReM3W+yWDVmmG1plDpoURqsH7s18bGBixaVBdRWFABlM+96T68ZsF7+4NvA3t3AwY98MYHgVvvxN8LFi5cKNaQn/70p/jIRz5yZZManEzf+9738PnPfz7l63b3nROBGy69sum3q+/sGKnBRuD/tmQb9vW3icqJWls+ltgzZ3SYpXpmhJJAISyx5eGcY0gEeVj1YdFqcUPxBA2AZxKJAhQJGqKnj0SyNlFPlgH8RK+QOH6G1lSYZ1koMqKBI4r3MqBwGGFsgWp8G/S0wUASWX+y+tzk6GiwrJGbl5yBx+eee+453HzzzWKRtulysSJ3KwZ8UhVPgb4URo15rNk5nXKtKjaTWSY5iOU5C3BiuAEmqxk9LV0w2cyiGsY95MR9m3dkfg7qQoRwMaYbgxSUJKGUxnjV2AF/HwrzbejrH0FhgR1QW9Dd0zNmtHBD5AZXUFuOeTkqjPQOClKDs2fAF5EJySY6jwOt+6J9YLpPAktfLxnUE4F9YP76s2hDVJGSHgCWbASq64A5S6YmpzZTGOoARroBNtgtWhCVWkmGsqXS/KUkj/x7UZKSy0ToOhErpUM07QIKUhOknCs0nCiFQ6NIB4n883h6kwalSXRQS1rONOc4y9Pr8futW3Hf3r1wBALizn9x2TLclCLjY4HVjoMDvWNBc9ojyvVEk4AgZD+j+DWHv5/sbsfC/IkN70BoEMP+C4IU4BQ3aUgoXkIoXAS1KiKTVbpWIuMoOSX6jVAqaSdQsBLIW5z0mtC4nCq47tBwZdWDLHvBa0yyZnCwFdYcbUwlHOEJNUKvKcH1xVvQ7GqDJ+hFvj4X5Sbp2h8Z7MIvWqIGfb/XjX8/uw/fWH2TqDBMBPbj8ZsM8Llc0JpNY0Yu91KOEzqozO6hHBnPm0kIfJ5ZViST+XxKMCgeDqGtexCjbi/KCnNgt9ukbMIMwHHIMcxrlJERfvOtQF8P8MvHgYAfWLIU+OS/URsK+JePAydPSHOWc+qhR4CHE/f2IqlDMAuR94yEjnwcJC0oucDsp7RJF5UaIw6XWM/zchWkR5IeS7wvnZ4eDPlHYNaYUG3OjFi/prAU5UazqEIlQUi7ZqEtBytyp5CcYV0MRCQcib4BBwrz7dJzyaQzZwq8N/nXAAP7AH9/RK5xmSTrORFoS33uP6VG4Wx4Ttx0O3BPRHpKRnMz8J5IvxaOnwMHgH/+Z+CJJ2i0RF9HcoSBvyTEUtYhsk4ml5lr05WJcT3ibxPrtFlbhFYXnUrlYkSbWCL8GHCgDfa73/1ONA+/kkDJKWZ3scnyLGaRCiTqb7/9dqHXTRuae5uyWoFjnGsw/05N+GyCez73Ue7FymDV5QCJfFboJgLPn/YPkxsefPBBQRbSprwqSA32oNx+/+U+itcsSE4weE07TTlvGFRl8DbeLmLgmoFdzjsGXWnbMZCdrSbPk4VoQBzpaXC1g4FwxkuU2fhMvtm552ks3WBKKJGdbTB2Qx+CZApJjfggOtc8PihHxYA97WyutVxXmFDFsUPiY6I+GaysY+KR3Cg9U9BOIJlAUiWR7PJ0Q/a15eQtVjLR75KrBOXf6atnsj9wbiVLGuecJKE43dUpU0KoN6bPsMfjg9HAhBqO1SsriWZa8L9fB158PpoA+82vR6reb8DfC971rnfhs5/9rGgcnk3bKOve2O7duwVTf++996Z8nSvoGxfccgdjmUlWZdygbDycIfq9Hrz36Csio5goMZhwY0klWl1OFBtNeOe8JSjMUGJqWrHsWinrJOa5ayb/eRorEBocf8cj2X5FhnJ0uptjMnmtmlyY9LkisFJiLIdFa0UYbL4dK0chLT7M5sybuNdAw37A0Q+wvKx2E6CQEONgpsG9cuVKHDlyRGR6ULNc+ff4cmiTxoIKU2yVSK/3EjrckpOuUxkw17IGZu34ANsy+wLRkLDT3YuadRVwdI4gPBpCgaoa9mDmi6lGZYeGmZNQHrPo9DHxm6lL72EkWIOauSXYe7Ae125ZgZB1Nc7VH8CSFQswGugd28yHutRo7+5AfoUULFVF9AqzCkp7tVI+hYiMC88w0H0KKJ9As5wY7pOCjEowSEdpp3mJG15dcbh0BGjYGf09txJY8/qJyZjiOukxGfgYfI+X04nIWcV/b2AUGDiEwZ5WhHpGsWbrG2JIF5YGy4ZcfLYgjUSC84nBWjonLFelwUcCo+Ouu9DodKLMZELRBI273z1/MQ4P9uGsQ+ppE5/9+1D1+EqufDqhCWDw+SdscMZzGfafhU+RoO0MhGHXAloNidfc2GoNZyPgj+wpIT/Q+yrA+WIb33OJDlu2nB5eV94DWfaCIFm7k42WtyQOCgfDbujVViywjj+20yN9QqZLrorhej3g96DbO4oKU/IS7hprDjrDTgycuwj3iBP5q5Yiz2jCSMAn+piQxFBm0rEUlBl5rJjjmsweFHScEhocuXOA0lXwtD6HurmlACsD5t+cXA6BwWHHoBSEiNNGpVMk68KmDR7Tm/8BePARqTJDDsA8/6xEaMjfSTz+M+CmW6VM/STg/WfASwnKKTATipJtJDx4XUSp7Eg/0BSRdZy7FMhRkHGGSuTk5KKxqQ3FMqetLRC9NWTIpenEsaGzuDDaPFad0zzahm1FG9ImNixaHb637jr8sOks2l2jQkrzbfMWib4wk4Yu1hm61NaLlUvmCt3bKwJMYijaEZGYU2dWtTC3Bvj+48DIkNSUj/KenFfPPssuqGywIo0bZb8W/ptEB2XLKKnQ3Q189l+BhnqpCvGRtwJvZr+MK1vewqotFQ8ZOpUVza7jIhGE/dSqzcth1Ehzk3P+ne98J374wx9ecaTGj370I3FsrwU5kVlMP7jPUW6RjSkTVchxn5uUBOIEYFCPgSxWPXKssqL2coIBNga7mEDAQJoM7mtyE3FWQTEreqqNdGfx2gCrMa655poYUoLxHdqJtHNptynB8UNCg2BQlYkhDGpzLlzOIKuoZr/C5LAmC64ltJkpFSvb77w/hfkV6O2+iCIhYx2FVj19c5k+GX3Kl156SfTSiO/BxXWE1XEyXnzxRXH8JEQYK6TNnYrwog2eSL4pE7DSNN4PnmnIcaz4dZW+Nwkf7hFENvYhzjkmql3RpIbCl5BIHvm3qyDhdarg3rvzxfGyty+/8HdFarz+9a/He9/7XrEOZDNBKete6i9/+Uu88Y1vRFgbRL+3BwaNCVbt+EW1ylSAQZ9zLIxHp76SeuhZxNcbTghHX0afz4M+rxffWZv95iRZwcJ1kp6sslE4M9snCxHw1kpBH4JNjLVcNChrpIdJY8XSnI1odTWIRuF2XT6qzLUxlQ0SkgVIJthoGJDd/Tgw2Bl9edcFYPs/jMtu5GKvZLCVYLBRlqRKBIe/Hx3uc2O/+8NeNI0exmL7NqjjzoWfX2OpEA+BSDyKEyuRtno6UMESkedSPmecmNBofArwsupFDY1ejXlV83Gq24G59qPwehvgCOkx7L6EQv0CFBjmY2PBYnRtGICXAdpIBco1hVkmCgIMesctttxxRNA9DVgSZGqzeY31Ct5glfCOxhIaxFAb0HEaqMyCznoyWIuBYWV2PMsQ8sYTGgzk9bwkiI3zFy9h/cr50u9ltwlNf2Yjch4pDclUYNCaTu6pU6eE8Wg1GLAiNze9Q9bq8JMN1wliwxMMoMfjwZPtl4TG/53lVXh07vhKlVW5hdhaUIo9/V3QUuc5HMam/FLcOX+FyFqhYZwMIfjgC48nC72hMEyRaq0xDJPQ4NyKg7M1IamRzQAZP4vSF8xS4085sLJ86a04dvgpLFkznqinZFgyWDS6cfaP/Hwq/OOWHTjz25+g1x+AdV41PH0DGCktwb+d2ouvrrpOECUymEHF5rs8Xo4HOk0sm2aWlOwQkHSiE0LnUKzTZeugXmwGqkoA9mWIIzQ4FplRNNzSiMIDTyOXPTiIFVuB7feMNU1mUIXjj9+bsdQNg/fKjFI2gqaTFLeHoK83ltRobAD+wvXXI0lZXXsDMDIM/PanQOsloLQMebfdh8amJjEvmClGB6SIe+fT35eqz4gjfwPueCdQXD3W/6BlZD6q5uUD2iCgzQMojxi51iQRZed6xO8UhAYhS0D2+gbQ5u5EtTn9CtI8vQEfrcs8iy0p1HrAvABwXZAaT/oC0DH4b0mS6cbx8fQfgOf+LP1+yx3AHXdPf5A/SfVLWmMmV7HX//d/Az/7mdQYnA4He80kmnBy/41/+xTQ3CQ9x3H24x8yJRzYcRNmHOwlEuiX9gX23lCbpEcChMJDCIUZhFJDoyqHVVeApfbrBamhUenGrYHM2v7kJz8pgr7ZbPw5FVBOiIQxy9ZnMYtMgz20jbjfMOjD4D3BfYdVq3K1a7a/k4Eqft/lzhTn+b3yyiuiOp57e6IAGvffu++++7Ic3yyuLHC80u6JDzpzLJPQSCfzne9lpjoD8CTUJsrMny4wYYbkCn18ZRPnqxWsdGDMgmSRfH+WL12LF3ZegtnqgcUi2dFWbR106syqpzMlSvfv3y/WlInkoVgFTr9UJnlJgLASWtnXiP4obQ1+7kS+mVz9wKQj+i+UuUr2WtrcShLosuHQK8Arz0t25oZroataKIhBrsU8Pp6H8tyTIRlJIzcgl/3OKxaaaiDIeEcIFxs7MU8kxs0BVFf/3Eyv4lo98XOvcRgMBtEX71e/+tWVS2pwE/zNb36Db/30mzgwENW7rjDOwXzropgFZ3vRYgz7XWh2SUx/mTEXN5VkN2hY7xhCUBGgZaZrgyNBoGs6wWzrkUYpCGIum1gjf94K6TEVMOgdZBUF+xmopIZqY+A9iDq9Vm0OFtsnal7HlFM2GVdm4FNaI8lmyQX3zH7g9D7A0QeY9YCRzXsZbOwGuhuB8tjSOadzBGFdP4b9x+FXdcDnd0GvkwJ9HPDUfGcpI/Uj4+GkU68oZSMCYTYsHoWJ8k4TgNkAzC6ZrEOjVS2EP8wG73IgTQOtagLGffB8hNAgQoAvhAqNH2fb+9HV04U1K2pgMKrQ7Qmjz3cBx/c2Yu6c+XjjnO1oHu1GOBxClbkYtsg1yhr0NoDNiTluZbBprCVN6Tfq/G+4FTj47JjmPorYR2PD1I+t4zxw8aBEllUuAWrWZT9w5k0g58Xz8EQ15qcFrIJx9gLDLdGm5bU3j3+dbwAIOBUazQy0BYHRJsAQQOuFCygrYFAumHbQj4YUDUmWmdNIliV50gEzwjcVSBn2JDUaRp3iJ3vdJDK72Hfmc8s24bmuFrS4HKg0W3FrSTU0ajW64uW30oQK1qj0lIzhSPPI+PEx2UBohiABS+OdgX1ZxqGwsAxNTasQ8qqgNkRJUJNmoejFlAw3FM/Bc91NQjpKrtS4sXgucpNUvciwmy1YV7cUJ/7yFKzVlfCPusR7L4wOo9XlwNwIAUmnYtfuV6BaZMeenvN4es8+3L5pB9YuXRqzZ9OBYFCRASA5Q4tBor4hJ9DfAHiGAJ0RKKgDdCbhPObY7cg/+GecabiIRcW5yLeYgBN7gIJSYHm0FxAdXwZa+HNKmL9gPKHBYHWlwpm+UA/868ektYljZP9uoKcbOH4A6OuWHI7udpgv1qPi/f8qemvQARPBr999F+jvER/Da6DVaDB331NQ3f2esY/3BbUwFI3vN8HrTKlFOZvNHZR6JinBq+1Wrr0zjFA4gGF/EwJGNSyqSvS1dKKqZjVQtF1qhA5g7969wtZj9rMgof70e+CH345+yPf+V7qGr3t98i/idd+1E6g/x4kB3H4nI2qYcTB7moQGIQcdXS5pzMjVGgwasNS/tpaddIGLUqP5MdBuePXQzJMaIRfg3AOE3YpjUQG6SsCwKmbtC4a7EQifHLOTguEW6LAeapVVyHUmAolN6t7+9re/FRlVVwJ4LJQkjdfxnsUsJoLcKJvBL9o6MqlBe4drO8kyBuemowKICQPMeL+coH/DebNv3z4hjUVyRwZlsihPdeONk+zfOIvXHGjvxWeW0+bJVK+f/gXJPVZLUDaJe8rl6C3DeU9fP1EM4WoE1S1YScM+eATvyXXX3I2Dr+7Fug3LRKKURmWcdqL0Bz/4gTiGVNUQtKHpB5FcYnIQwapnZZNg+hJM+uFaKfeTYMA/VYUbxxXXMa5nqXojca3nmKP/IlcSzTgO7gJ+8d3o761NmHPn/TicVy7IHPre9K2UJCKvCf+mrLpIJS1FQmOqlS0zAvq7ui1AsBk9fZ2oXbQFUEtxB/pIJMo4X1P1Zb5qwXtz253AU7+PJk/x522T6OV6leP+++8XyVNf//rXY+QNrxhS4+WXXxYTMn9VbICm3XMJ+YZC5OujC5hOrcXrKzbAGfCI4JdNa8z6RKwwWdDplgI5BPWmy6hbNlNgZmrLn6WmqsxWJ0o2APnT2MjMPwCM7AUiDR/BoDftkrFruzQpG9rpbkfT6HkEwgHk6QqwyL4MOrUeKhgQBvVmuRnRgWYwbAFUySo4ju0E9keyNglfQFKGIbEhSxwpwM3wUs9uuAIt8IW0sOZ7cKHjeSyqvgNqlVaMC27gNIjItPN3V6AHntAA1JD+nqh/CLMP0wEZcrlx4GSgVtmhxyYEIRF0GlB2ZgJZKJG1HHvcPI/tW5ciLF+nSLPzjo5+lFUuExs8M9kX29MPOmd+Mhqg9hag4S9SNQlRuAjIW5A4SJwI63YAJVVATytgtgG1q8f3huFn9bYBzmEpwKmUcEmEjnrgwJPR3wfaAb8XWJRlR9GUGyVjxo41BFiz22htHCjfU3e7FBgmASqqNBItz3ITsQA0rLyS4W+Exx+C39sHLauxPIcA48a07hcbIXNeMaDMrBcaVqmyXhJhwOfFXXteQK/XK4b0HztacXJ4EF9dNV6nmhUCt5WNz5ihYUqDjcHwRA3D1dBDry4Qje+UsGhTVLMpDW2eT+7UynvPOxzo9HhEY/WJ5LkopUfjjKSsfC3Lysrh6A+gsnohQvAIMkOjStFYm5UMeiMeW74df+lsxLDfi4W2fEF0pIP8shKojXqMXGiCfUFiZ45BjtACC9p9vei61AG3w4WjA+dh0OixPKcmxoFght6463vhr4CW+0LkeuhagKVvlPqaeFyA34nNc0uxt6kDK8qLYDMZgPbGGFKD14fOCTWZJ5IhS4kNm4F77gP+EFkraCh97FNAjoL0YlUB57SSRPv9rwCLogFzKITw8BA6X3kJSx76B/EUnYuSHAOgiZb3n+8akNYwBZI57DxHZZagXWcdk52SwX/l6qYvsy4VwuEgujyH4A9LxK5LBxzr7cNd171jrGKMzimrdXgezNikIyYqXuLx56dSkxrf+ibw5G+kik2S1H/5M/A/35oSsUE7whV0IhgOwKyxQZuiWfoYSFLEg/ePwb3+/mij8A9/WCI3eHxyxYYM/s7qjpmG+wQQjiPGaGf62wB1DqCPzt1AWNbTl487iGC4EWpV6gSaBx54AE888cQVQ2owq+vNb37z5T6MWVyFcLlcgoinbREfgOPew8xz+gIMegbDPriCrQiF/dCr82BUF0/aP+U6mVTGcYbBc0yUYc+KTAYc5eszi1kwKSe+4pvV1EyAYuAxXfA9HP8MYDO4zKA1icSZljijzcLxTZKF/74cxEo2wetHokBZScnAYCighk4lVVNPN/gdN9xwg6jCSNbYmnELVmxzLLFahgQyCdT4SjHeDxIOkyUdeC1kCfNEYJUOx/RlIzV2PTfuqcY//RbLvvy9Mb8g/pqQ1KDNrSQxeJ1SEUjpVHpcEVBb0d1bhOLyawCN5M/yvEjU3XrrraL3ymsWb383pQ+Ava8ABiNw35uA9Zvw94btkSQSSoPu2JF5T+NpJzUoPXXjPdsTauSNBpwxpAYhNX6ePgPqfQuW45+P7IIzovFv0Kjx4YUrY14z6PPgiZazaHM7UGW24aHqJUJvPBWC4SAuOs+gz9ctMpIrTfNQYZo7fhMZOAX4XEBAkTnasR9gA9iJpLZazwHdlwCjBahdC8T3/uCi1nYU6KmXAg4Vq6TGxI4DUUKDIKHimS99J9mNJDIn/d5enHMwk09Cn68Xp4aPYXWelGGvAt8Xe+2S4sQr458b9UqkBo+1IFZXPhAeQXGFDv6QDUcOnUNRcR4azjPjvBl51qgBzgWfDf0WrirCkP98lBQIa0W2YWDsvMPI05VDn0SGIV4DNGPJkwQgiaFFBmQDG+0iossuhxrUKoT146dkjt2GgcZhaBM06mZWb1uk2qnSXAQTqyymCns5sPJhwD0IuP3AY/8F7P24JPPy1rcCb3/7xMHyqoXSIxE4dnf+Djh7QPqdn7X9vtTVHI2shInDhYPZJzWYab7sVuDUs1Fio2wJUDJFrUv2lBFVSzlAbhIpD9H5egLdV30eHF4dDh85g81rGGCWyhZHPG60dw1iUV2lKOdEsA+gVJNKchxY2eMK9gtn3ajJEf0blMFqWRKHxhOdf5YSy7I76eA3rZdEhYay1uLJ9hZ8cOESVFO7Pg0wi5DGLr+TBjID6LLjISoDgl7YNMvgQgO8JDRVOti0tdCrE8hlMRjvjjY7FjBXAcbJNVDm97//2DF8k7r6orpCg19v2oQ74/owKMHzYE8GGvwrVkjBQwaBKQFBIzaTbON8vQlvnpN5FcM1hRX49bWb4BoaRv+JsyheswLFbj/8Xb1oM4wIh4jX+a/uo2INMlrMKJ9fhYtH61GyJT+G1EgIzyAwfEnxBIOqLqlyo2S51D9JrYYqFMKmuWU41NKFTfMqRV8NOhhKx5LXmLbDlEgNzqF3/TNw+12MJgHVc+jNxL7GPRpLaIxl6ceO9fAYqdwR7bdRVAWMjoi1IUjig9OPPUKOPgeQaKqoS+pwxDsjJo0RG/NX4eDAsbHEiyX2WpQYp5lATQJ3sH+M0BDOxeF6lM8pgjc8AJOo1pTks5hpSSJ0LPiV6HxTaRh3dkiEBhGMVEc0XgSe+yvwuslJn3iCHhwc2AdXkFVilGbTYmXeBlGJmhK8rxwfQ0PRMcGxcMMNwPXXj389HcY3PQj86nFprHH80iG9JwWB09sONJ+VCJy6NYklGieDIO9VgutMYiPI7Ebl3I3rcyXemTgIoQTLwz/wgQ+ITO7LJR0ig8fAbEw2L3+t48WeHvzzkSNoc7mwKjcXP9mwAfOtqcnvWaQGkwtI4DN5g8FN7j/KTFDuzaxCs+da4DeeQUj0DVTBFWyBVVMDm25yjWaZPZvtJuTZBtdyBkTZO+qyS7TM4rKDPTNYbaxMwuB8ITFB+4zJT+mC40rZz4L/JtHBQLdIiphBkLijPUe/n/9OJmk9Xci0ymUiUGaI/gSvqRzMZkIak6lYPTETAW5+H0kqrp0kR3m/6UNy7PDBJDlZXob2PfsLknwgMZbNPiccm6zEYYURfVp+PtczZTxS2a91xsHknTjYtGpBWnAfSpSpzusZ3+uPJNHltsWyAVbgcE1RVjDSxqM/eiUkAEwrOCbZE5KPv2NoNBq86U1vEtzBFUdqUFrh93/4PR77yScT/t1Avd8ZBptm/mzjDuzu7RTBNmq5lyoqNdxBP/7l1Cvo87pFUOGicwhnR/rx5RXXwRDp+dDuHsArvWcxGvCgwpSP64qXocV1Dt3edvF3LlNNrnpo1FqUGavG9yeIl8Igug4B825JfNCeUeDgX4CLR6M9K84dAO7651hio/kA0HIw+vsIg7BBQDNe0kJICalIaiRHj7crrnIgjCH/APwhP3TqDHXuEp0zP5YBrnV3A5bYjSwMKbBRUVUMo8mAluYurN+8BMeOnsT110YdCWZ5eDxu7DvyZyxePi96rKogio0V8Ic0CIR8MGtzUKBPb9Fva2vLSG4na7BVA0UrgN5IU1u1Fv6q/LGGzzR+PFS/CGuwoHALTl26MG5DHvQ58FTHvrEeG1qVBreVbkBpNnrTUB/fVgr8yweYyi0FfDwe4DvfobYOcO+9k//s5tNRQmOM5HgSqKpL3ntD1rBXQllNkU2QwMgpBxy9gMEC2IqnJnNFQubYs9Hf568HVtw0qc8MhoDDLTZsu/5mqAPDomlun0eF4eELWLwobsyLRrocQyF0uA/DHYpmV5UYlsOuS5yxYjabheNPg4NGvyzTkAojAb+QlgrFBTNHJjAi+frftjVib1+XaHr8UPUC1OXki7HO76dUD8f533pehTPgFivUqtyFWJW7TboeYQ/8ISe0KjNUSk1K+3xpzAyclsaJbQ5QnDiDJx082d4+RmgQnmAQ9+/fLxqr56TQ6KWhTVklWfqCoBFHp+7gwYMi2DGdBly12YbHlm/FD5pO4WxHL+rcYbx//fWwafXiGjOAwWPUXNKgq60TlhwrjFYzymurce7QKVR2m4UBrSwTj4FczRUDVfR5Gm9bbgd2Pw2NVotcswmHOgZgWl+E6tJSkbU1LaDclFJySonV64Gjr0Z/55q7ZLlEOjod0lqnVkOt16Pu1jtxoV+RlbjldcBwL8L9nTja0o2l7CfC6qr6A9j3p18jb811yNuQXIaIzjSdPDpeLN2vMpehyJAPZ4BSiUZYtJch4z+CUGQf5t5zeP8Z1NRWIr8wB6HIOqJMAGBFFbMvBXbcDPz8/2I/7MYk9g2RKMuT92AgtgIrXfB49/fvhTskVYDyaEeCAZwaPoyN+dennl8kbdlT44MflEgwQYq9KzGhIeMd75LIkCOHyQgD970RqEpiQzSeAp75cdS2evUF4P4PAjlZCKaoLYCQMEtAbAQj/WsiUCEHYQzHvFYtymdTg4EfZlORSCC5cTnBY+Cx8Jheyzg3MoLbX3kF/lBI+C0HBgawY+dOnLnlFpizVKL/9wjuv9x7GXxjNi/JBrlxtgwSts+++Djq1thhYkVhZL44g42waOeIRIrJ4ErPCmfSxa9//WvcdNNl6As0iysKtE8YbJZlmri/0obl7yQ0mHCUyXhmYFspwyr3hGBQk1UbTFacqflBf0YmMjj/SebR35mpAB7jZNmSWZHBtYwkAdcugpUIrDyjf0E/brK9QjMB7zHVNETS6cKF4jvpX7Aqhscl22C0ewlKl7KfhtznhOMh3QS6dEgeVq6wymHKcrbZxPprgfZfRH9XqVB83c0YTVIZR5kuVuPI41NuJs65lGwM8bWcvzx3/numScN0weQo+sDcj5X2OQlHSojN4u8Hb3rTm3DPPffgO9/5TsKCiEyRtdWVzDCd35UbFiOoYvZ99G852jwUGS5Po8Eigwn3VibONj021Iseb9T5I7HR6RnFieFerM8vQ5/Xgd+27hPyEKIdxEg7To30YYU9CFOcbdvr7RxPahjygXDj+C/2J2m63NkI/O2nQKSyZMwBdQ4C5/YDKxWOdsfxBO8/A1TyliqDvcwmnLhigRUniUB5jIxRu0rqp6EMcK7ZIT0SBBi0KjtU0Apyo6AwB6NON9pbe5Fjqx3ndJRVlOBUexjDgw7k5Mnlq2GoEES5KfOm6jSsmGkw4+B1KFkrSZEFPFAZbNCqHAiELyAMP1QqGwyaCsy32oWM1ooV5nEN1Pf0nYYvQmgQgXAQz3Tux32V25Crz0JWHzNWZUJDiV27pkZqDPQkkHgKi2BhUlKjYgnQ36Z4QgXkVwAn/iJlwFavBmxZzHA22qTHVDHYGUtoEBcPAaXzgZLMxx2di9XrNkKtKEftP3MIdTWlCASCGB4ehZNVUZQ/U0vHP+xvjSE0iB7vaVi1RULeLRFoaNDZoJHBTJJUDbyJVTl5omG3POO5NVGeaYE19TX83wun8dNL58fkAV/saceP1l+HxfY8QQScOn0Kx/StCETWW37+0aHzyNVZYAp1onugAUUleVDDgHz9WujkChTOrzzKpkn9C6aKI4OD0KlU8EfWNP7fFQzigtOJtRNkG8lZkMpry+upN2hxpv4kltQtiyVkpgg2aj83MgytWoU6Ww6W2Avw1ZXb0Vm8UDiliYiEFbk1aL7QhIIKKWBIcuN1N22DVaVDd1u3aOS9devW8Y4HCVStSaxhSkIcdoUxveZ6IKcIaD2PRWtN8NetxZGGJnS0t8GuYfWMWSI/Zgq33An09wFP/U7KniKh8cFPAK5R4KffBjpagaIS4OF3A3kFCPb0Kc7XCtzzXgw31aP01D6cOXUQFaEgSnNtKLSZUetsBPKTB4vlBn79/f1jzWqNGgPa3Z04NHgEAX6WsRircin9OD6ARmKB92A6ggAGda6QtTty4AwWL69BTi7lsdTiedkREUQGCXfXaLTC8Q0PSOv3WKPw24H77k/+RUwi4HuFVF1kzHBvW5z5/k04Ao4xQkMJV9ArpKi0EwUi2ePk6ad5gixVk4iKVODacsdd0mMivPS7yDlGzpON6ff/FbjlIWSERNKPpuWAc3fCKgyx/iugUy2DP3wEYUj2rgqF0KiYGDIx7rrrLjzzzDOXndR4+umn8brXvQ6vdTzT2TlGaBAByrO6XDg6NIStaSQYzCI5uPcyyMb+GcweZuBPDgbKf9+wZQX27H9BJFYpQSmqTEkN2uzZcNSnG7RPSGikk8Ayi9cuGAxldrtSjpnZ4vSRud8zKE27JZPAPG1OZu7H244MZjNoKxMmzK6fSTCZkZV/tMVJBCRKfmAPBs6JbDQXlyvQs01q8DPjiQv6HAwYM8CvlL+dTpBI4TVNliTKscX7LV9LylGRMGPVD3umMgjPKo6pgNf2+uuvF34rCTOSJTNFWk2I625jqQjwynOS37FhG3Dnm2DpHxD9YuOrVnhfSQqx0n/58uXinjK5LJWyiLKqg6oHkwHn6nReM5KJrNLatGlT0kqmVPJas3htYfPmzeJ+cy2eShsAGdpsOhxbb9ogDDiacJqwVAXPtXSRbfkVWU7EAELC5yPB1npHu3BF5enFU9DAi0+d9uHfl5tgoDZFKlIgfwnQeyoS8JFBiZkEhiOP5aXHo80qleAXUzKDYB8B13DiLHWSJfq1gE8hzURCwxSng54A5aZKdHraYnL+Sg3l6elSx2PLnVIgvOGolIG5fCuw5oakmel0FHJ0qzHiPyFKvufMrcDpQ26ow4FxDoEaOixdthj7976KDVuXjz0vB14udzlouqDhSAkPOj0MUMnHwOOx2fJEhrpa0TMhkc7tsN85LkeTxNzBwbO4uSQL5e689jRAGHySwfs5Vbkue37i8WtLkVFSsxYI+CTJKWbg5xQDQy3ASIToaD0BbHkEsF9BWZyUnNoZaUIbAxUw0jcpUoNOhVJfk+NHR0klYyXOHP4TqspzpMCpaf1YU2x/eHz/ljDzssNe6JOQGkojiX0OGNQmyZHMuP9Bc2zzXK6s769dDGMKhz4QCuEXLVLTOEJI8IRVeLyxHg/q7SKAqreb8NfDh+Hz+KAz6ETQnUTrhaaDMKsGkJtnQ8O5FkFsqPKPokgfm/mRLVSaTCK4FI/yNOYCDW2Sp0rHY9h/Cf7cepw+cwqWyn6UmlbDqJncGqZEu9uFh/a/gsZRKSNqbV4+frrhGth1OpG9xOwoZu8wW00ZGF9mr0FHXhtCRiM0KjWqzflocJ4S/ZXYQqksv0xId2zbti32+mp0wMI7gIvPAd4RgIH4OdcC1rh5OH+Z9GCAFcDGCifafvgV7O3owsZ5FdBcdzcw4gB87JOzAlgwNecmJXj8b34UeOAtkvyRPlKib88BPv6FcS/nfWPJ91i1is+LXK8bw10dsOq1GHZ5BakhIQz43FJVYjxExUOb6INQkG/D2bNDYj5ri/U4NXJ27GWdni4EB4PYVLAu5u08BpbU09nh/eP8yCa54Rz2oumoH+s2roCWSpHQodCwAtpIlW1FeTmOPPl9lJscQH0zVKEWYN3rAL0JuP/N0iPlFzjZMVfaPz7zeeDznwHckSbX//AosDG7mrJcJzQTrG9jYKAh282nuV644np2cN9zpK9HDncP0LkHCDilRu1lWwFTZG5pbID1esD5IlfT2PdpYx1klcoIHTYhDFa7ccyk38PuzjvvxMc+9jHheM+0DroMJrdQd5fZXK916JLM6WTPzyIzcM3k+kn7O1GAyKwvRH6hHd2d/SgpKxjr5zWZprtjRPAMg1nSDGoxQBVPrDC4SP9C2SicSFqNOYu/GzDLO745L+0NzhNK+3AssVIuk74rJDVo/8aPN4L+BP0VWY4qE0nWqYCyqwymM6BGKTo5u51VyQzoct7wmOj/sM8OQfJjKvYWSY3pkD/inky7Pr5BNP/Nec6M/5lquEy5K66rXPPix4jcu1EJHiOD+evWrRM+prI/yFTA+8ZxStKa3zlT4yoleG9uvlt6KMDjJAETT2qw9xGTxUn0MAmKpB9tsHTl0vl5cg+RdOw2jn8eB+8h5zn9xClJAceBsS3K2jF+ITe3T+QrM5mRZM6kvvtSMzDYD1TPBfJnVlpuFpMDbZPbb79dcAhXHKnx7o9F9cEY7+fDpLbCOEVJBSEvMNCOU8O9MGm0uKmkBiXsNTFFLM0phFGthS8UEFlRzBRmEG6JPUo6KJt4ymgZDWFXXwA3lUQDfGWmBOw0g4rz7gSaI83CCUMuUJagd4DbCXgjDn48SBBQy7uzATj8BykQY9IDhrgAo9YHdB0BCpdJ2a/MKjJUAuqJy/psuhysyl2PS65G+EM+0f9krmWSFQzMnN/+eumRJnTqXOTrrxXVGqzaKC+5IDa3+IWNm2CJaS0qq7rQ2d6LsooiWDXMCvaj17tH6v6hqYJZUzmh004DY6azqBiUYr8CbvrcpBjU4oMld/K50gB8+eWXxXM8RhpSibKr8/V2uNxSPw0lnKIJeRbA6/eWtwDf/770u2zQPfCARLJxzJKgiG8CPhHmrwAuHAOaz0Sf23Sb9FmpjqVui/QQWcHfiG3YymBR4wFgVRrZszOFY89LmRnjhlgYsGQewE6UvSAbgb6QFvaSW5BXOWcceahXcVzFvpeBLa3KKKSpWl0dcA56kKfPRbW5Yty84bhkGS+dZDq+8Tq0Qz4fdvf1jmsGfnI4te4uSYJg3Dl5+/rR7fJj0Z1vEOPeG/Qhv1xytkeHHWhvaBGnUpprxbwVkqGaly8FIM6drYe5dgWslgl09CeBt82bhx83N+PVwUEhs8Xj/sLSpShLw7ljRhLLsjnPmcXE3gUDPimLhhI/Fy80QVUbRpV5W0zlDKW59vW34pJrGHk6I64rnif2v1T4yLFXcYkVBxEcGxzEY2dP4t9XrBH3tXxRHX7XcBqDp49gZU4+FlilcUgHJNdvxMayjaJn1Is9L0qERoQO6wl0omRumcj8Yul4jGFtLgCWPyhJTpEEnyhY6nYBv/wmKg1q5JcXYm9jK64JPAkV+/dwI37lr8AbHgXWSxJj0wau/Wms/xzzsp40hvtx9iufRndPj7iey0oL4Mk1weH2CqdABPhN9sSERpiN7xjkltatxYvKceGiHz2egdiXUrWorxk5vRboFGsrHRPZOeVYohNIh01Jch4bGsJHjx9Hq8uF9fn5+PqqVShMoKssZ4ExWEwJRp4LnZmbrpcy4eV9WLkWaFuOIdR8Au65ZdKy29MIHH4a2PzGia81s8be8x6pdwXBBqQ/fVySnCLZV5BZhnA4HEAgfBYh9EGn1sCiMWCUMpsK1FjitHlZJfHyM0BXG5BfBNxwF2CZxiA9v7uoAujtiJL4fK40TblL2oxtLwCReSiIDf4+725Atqk1RsCyCRjdHyU2tCUxTcKjh0MLN3O7mVm6fDz//PN4/evTt+myCX43nfzLUlU7w3hjZSU+e/o0RgIBsc9wL12Tmyses8gOOI6YgR7fCJkwqkuxvG4rXt79Z0FqkNDI13P/zDygybU1WePa6QIbrbKij9nvzIrlGsj9Wu7rxYAVZV8YrGXQk8FdBtauxMTDWcwsmMCUTK+fgX7q3WcKjj0SB6nAjHoGbqdbjor+NIP8HO8c+wT9cJ6XLLNF8oEVGnKFA4PDtJdoA5Lw4PWZzFyRZbiyDcYwmMm/e/duQdIoK0F4njNJatAe5TkyaZNrkAxeLz7HAHuyxDiSWwzic5xNpAyQDngdGCRlIJ3/vhzkcrog4adMGnnqqafE8XLu0O+g7805kYn9w3vP93IPiCc1uAeQZFLGFPhvzj1eK/5dloiSq8szhUxkHj58WHw2/SPOs1R9bPj93L9YHZVRvxuex3f/B/jrM9LvnAMf/gSwWerlMosrG0yc+tKXvoTHHnvsyiA1aBzRSHrorrdhSNeKAZ/UoNWqzcVC69QNuj92nMeT7ecE6UDs6mvBF5ZuR4kx83JFsoB79uwRpU/5BhP+bckW/O/FI+j2uFBqtOC9C9aMNQpfaCvHwYGLIvAn68V3e8Lo94WhVxXDpuXzGlQY56JAnyRD3GADau8D3P2RRsCFkvROPEjSsIl2ouqRJVuA8hrgb9+O/t0d2RxFUC0IWIxMMZKe6zsNzH+D5PBmgFx9vnhcLnDjU4k8XqQ0ntjkeOPCN2Dv/l2oXLAFrmA7nIFopvhI4KzI0jRrU+sJdg32AGb24QhCy2s/AwYjyyyZRcBgNDduNhKmtAM3HmYEcyHnT2ZEM/uCmxE3Oy7yssapjK2Fy/Db1p0IKuTGtCoV8nVZ1KmnvrhBDex8GTAapIxaXzfwfz+X/s4+L7c/CpSlJ2UhZEZe+SPQGCE0GODZfAdQnub7BdgINd44ZIPiBP1kLieGe6TKKzmJVqsGWH1TsRgoy9zIZBlq/LxgUJJZKDQauLYlCijn6CoxGuwVjcIlqFBiXC6IjRd79qB+pAFlrnI0uVrR4mrDNYUbxxntctZPIjkqyhwlAoMxqUACeV1eIY4O9sPPIGtHJ3T5ebhn/aYxp8ag0WOpfR5OjzTBmmOHwWiC1WRGgTM244nBh+LSAvR1D6C9TZpbkykZpyGWDE+uWIEv19ejy+3GrWVluKe0NOXrlaAxSsNUNOZelT9WOVNQlIu2S12in4E/NAqDJhqg/uml43ilrwUaqEQVy97+NvzL4mvH+j0lwonhwRiiKIgwjgxKx9jmcuLdR16GKxAQw+TZ4TZ8rrIc24sqhCPBa87z0dl0Y4SGTsUgj3T/ndou2CsLxDnEr0VjVRsyeAyd54DhDkBvAapXAbrIftR2EaBsnpZVnTr4eTC8HBxHcqXiU08A666dWi+bLEKeX5oXnoQt5Ie1IAeVOVbsaurAVlsFzvYOSedcPD9K/sagO0JoiE+L/OxAMEgZoNjXt19sg9vpwm2bdiRtpEhHnA4iiQkZzaOjuPall+AOBsUYuDg6ijMjIzi4Ywe0cQ3ZWXVD551OUyINW3kfjkFnA9YtqMArZy7BxqQKnm/3xcTSSPH41Ke4WEV/P34cePxx4H3vw2TgD59EGNJ6plYFsSo3jHqHHUP+UWhVWtRaF6DSXB277/zoq0Bbo3S8vB71J4D3fSa2V1m2cevDwO++AzgjZA7tuI0p+o0o4e6OEhoy+LurG7Ar9kttPmC/EQiOSIksanvW580dd9wh7JTLRWrwu3kMVwPoD5F0nKyeN4ny/Tt24JMnTwrZqXV5efj35ctj5vAspgYGJ+WgZjy41+Xo6zCvyAWTrxw5tqIpyUPOVOIU13X6F1zXuVfRTuPazsAUG7AyuEnfgnsKxyZJHQaR6WPQf6e8ZLalcWZx9YDBaPqoygx7ZWb4ZEmvdN9H35fJe9MlR8VKE56fUEFIsJbyOJkVT+Ij/pg5p/g+vl/uA5FpkJyfQZJzOtYDWdKW++Rtt90WI/WVDemsTMDvju/lwCA57VWuRakkphiXo22aDVJDBscz/ZsrGUxM5LhiPIjrOOM9TB6TZbR4DnICbCZ9CHne8ZUdnOeMLXEcJiPc+TfGqUh4T3aukTThfCHRlm6FL8+NezOPj7KnaWPf7iihQdCP/Np/AMtWArZp6ts4i6zhlltuwcMPPyxskqn2OM6KBfOXv/xFMKL5efngfzVYiWyBEiW/b6+PypMw4S4YxF+6LuKtczP/HrKFIvgXMQILA8DXVyXuul5ksOP6otV4tuswjFTiUauwwKrCDcVa3Fleh1XpZk4xe9UywQbIQNWWe4DdT0alYmx5wI63AHnFUvAgnvBgZmvJPMDELIhQXIDXlTGpcbVAsMsqv+g1oVEZ4Aq0jnuNK9ialNTg+w8OnEK9sxnNFy+i2d6H64rWo5BVNNMEbuqsZuJmzw2DDoRsWJGlZCYLA4oMXMsBZJZkyqChR6dEGdS268y4p2Ir/tJ1AJ6QT4yaHL0VGwvSlG5h9rC7RWo2SjLLkGCMtp4B9O3AzXXSuGreCQxS/iwSmPO4gWd+BPzDvwK6NBp97f4TcHx3NGu1vRm4cCIzUoMOZn4lMNge27elILa09bKCMnGjcdIjgRBgzQc23Jtx0IkOKg0bpeYxDQcaihM5DnTIy41r4A4OIBj2w6CxQ682o9F5CSMBR4wMW59vEO3uLlSayyaUo2L2Dw1Yq1aHe8qr8MeOVrFCy+7CA1UJAt9xeGz5Rnzwb0/jWE8vcufNxTvmL8ZtpbGZYuvzFiFPZ0O3dwAGux5L7HNx6eJZhMK9UIlvVInLqVOXoHzuPLHGkyTk/GIGWLpZX6l0PNkY/MH9+7Gnv39M97xQr8c1GUg2MAuFFRvBAEkNZYaM9FOp193lcQpCQyYmiE6PA/sG2nBd0dyxvbHV7YJNq0WhQVrrS4wmjDodY7sBCZHyiJP6w+azcIjyd/YgksbL184fF6QGDUk+mI02J1eaR3wFCQ0lDtTvxv03vGXikz33EtByNNI7Jwy0nwY2v1kiNs7zeVluT5IJCmo00Ib9sVn1DETHB1nonDzze6C3C6ioAm67R2r2PE2gE8F1mXONmYSePfsRGB7AjQukbL3V5UU43dYLdY4ZyyqLpfmdCGH2j4hcVAVKSnIx0NKDEc0wjGYTett7xHi9cdONgjyiY5/MKWUWFtcFmdj8dVMTRnt6EKZdolYLYoM6/MeHh0XfFzrrrBLk/GWTcmWFR1rQGqDVaLFybik08pyi3TLRWkZ7KyLhMAZWtUxS8zcc5oyIbSpOKdBVuTnQqbcmftOlC0DrxdjvH+gFzhwDVkc19bOO3CLgLZ8A+jsl0q+gJHFSSyIkS7SIyArGPqcHtNOnh09C4aGHHroscp38TvoYj5MEmyZwLR32e2DTGaCfYoILbTk5cMW5xkAxq6AywUKbDU9moRR/FpNHfl4RHMM+5NqnRibNVF8NBr9IqNFGpO8gBzZZ4UQ7iPI+so/BwAEDx3IAgXYdqzAT9s2axd8F9u7dO07+g2TYVPscZILpkKNixQD9AY7xiT6PexvHP4OrtKvibSRWrTPwLDc5l5typ3tucuxpOkD/kCQmM93Xr4/KT3Pt4fdeTsKSx0D/gteW/lkyn4wkWjYljwh+X0Y9Gs6cAA7tlhJftlwPzGfsY3rAvYGyskxOou9FH4N2uizBxTFLcoBxH463TAgNgtecVUeMHXEPYPUFxwmJbZJynGd8PlXlDX0Gkng8JvbTIUmSjKSgz8K5xjnBvWQy4Pzid2RUrdV0Uaq6V/adpa/b2TFLalwF4Hjh3kM7/93vfveUPkubrdJwMi3TAUfAB7UqLDLQCVZL0Fl3UYJpEmj1ueFeXINXHf2o8gTx9J+ewgMPPCA2r0SGp1UbRgEz1RV4U5UetdZpIAxq1wB5JUB3syRjMXdZNFBsSLTQhwFzHhAeHe/06ma26dZMIRR2wxc6ijBGobc0o3dAB5jHb1iptrCLo224MNoKjVaDUCAIb8iPl3oO4b7KHVBnmJFFY4GbDjcjQnb4yZCTaTeHnIBnBBpzHu6/P3nzVG40NLj44AbHTYifwc+jgULDks9xg2EwWQ505RvseFPV9ejzDktqF4ZcaBIFPcZdoADQ9xLgH1SQaMsB22Lp76x6OLMTaDwC6DRAgPIpkWA9m+WMXeCwJJvGJt+FKQxGGnPP/hw4fyz6HKsW+NUkNbbF6kxOiFWvAw7/DhiWqsJEJvi8WA36y4r+9sRVV6zSmERQiAw2DSD+VIIGiVwtoMzajgfHkVkbW8456BsWP9mrIuALiJ/EgG8wKamhlKOiEcbsKjrRX125FpUmM17s7UKeTo8PLVyM1Xmpq754Ph0NF/DfW2+E1W4XZEiigBmfq7VVioeMIV83VD4VDBr+PSz4omC4B4V6yWCmEZhpyTj/Ht9wT8aXzp7FPl7fSPYYa4LeUV+P7jgd24lAp6O1qRk589ijg2S01BTNqimHTh2VaXRwnsUfH1Rw+KUKpQtOBx45sBstlHIC8HD1PPy/5avxhWWr8JYDu6UZHQ7DpNXgE4uWYVdvF55saxur4rBpwyIQPOz3jgUqeT9EAE7NBu8L0Dx6IebcHMNO6PQ6tHsakWtIURrscUiEBiHL7riHgfZTwNx1knRd5HONOi02U9aquQNbrVbouQfTmC2rFk3Ze9rbo44o+2184ZNAe2QO8FxOHgc++fmsNxqnxjLL5WkXcIzT0WY5N3rOAQ0nxs7LbtTD4Q/AOezE8ppqoHa8nAm8/cDISaAg3mZQIze3ApvzapDXno8zPeeg8+iwum4lCl25KKsrE9U9NPKTgRltdNqY3dZQXw8VHbjGRiloTwI0NxfHDh5EMC9P3Et+1qQzIGs3AF3nkW9TyNnVpeG40JnmvBocjDJ4vF+KpoaZIdl8SzEPfUmq+DimphMuhzReaK/O49qfwL7wjADtx6U9N7cCKInsEeZygFWX/jhyfLgesFamT45kAXQ4GOSh/TFZOYLJggQ6gx3Z0NtNhOND3fjWhVfhCQWgU6nx9prV2FyQuso3FZQVAC+++KKw1W688UbhX8xK/Fw94L7DdTWZHE+6FZoM8kxF/oX2HX0M2jNyoId7NgM/K1dGE/voIyQLQHPs0bbhg8fEvY0JFvxJ353+B4NQVDEgIcI9Ynas/v2A44nBTxJbSnC8MHGCYECUY2cmxoXs10xVjopBVu4dnBeZHLfccydZ4oc8l3jN+GDwd6LmyrStOa+S+RjZAo9bCQaveR3iqycuF5LdS45BZugnaiA9FfDecxzFJ4USvHcM8I9VMxzaC/zg69Fq6z0vAR/4NLBYku7LJmgbkKSQm3/fdNNNCccQyWfKciWr2k4FjnmS2nKciuOavg2DyPxuyqEzcSuZTyD7+pyLv/3tb0UyFPchzineR8a4SKRzbeD94+fTTpvqGpGKaEmIomIgmKBPa4aytrO4fLj55psFl3DZSQ1OlhdeeAGf+MQnpN9DXvT7LsIXcsOgtqLQMD9GJzxT/KWzQUg/yZBVKpbYMh+sz3Q242vnGRCXsMJegI/ce69gK7mZs0yKDWyUmZHDgVERTIrvreEIuGDRTgOxwcBwouBwTglQtQxoPRXJfg0BeeVAzbVAz37A0RR5oRoo3QpoXpvZNr7QcYQhBfKcDje0hj6o1aVwhWIzN82a5AH2Xm9UMkZuxu0L+9HrGUSJKX0dP2Y8yfqP8cYeN9HAhVcAZ0QWi3+zUCaiHChfA+iTB5aY1ScHDqiTKWd6cUNmOSIzMcjmc7wSOrUWZRkct8BoU4TQICJj23ESMM+Tsj73/BIY6pT+xmvEh8sLONzjGSP+PpEU3P5ngfPHY5/jJqTVZN6TA5HvY2Nwv1uqhGJn2ysJySSCzJPr90AjgkaPMuMzPvskU0MihwEzdzuMZiM8Ls8YqWHXTVwqyu+is8HxSKed4/+Ti5eJR7rnQ4KGAaCJshiZQds0OiD6H9XaJLme4rI8nL9wEvNqlQGHEILhALQcv4qScRptky0Zl3FqWCKAot/EdcSLATZtziALlxkugwMjWLxkE0b8lxAMezG32ABvXy6g8DnKTTYY1Br4QnKdhlSpuMCaJ+772w/tQzurpCL4eUsTFtnseHTeAvxl2414rqtDJALcVV4pZMAePPBSjCyVg/2xVcDCnNyxcaNshl1rrYVepUGb+7z03aEQGk41YvWW5fAEJyjlZqPsePA75OdZeXjp/BgxQGJja00V9hypx4riPNjKKnFy6RYEjhwRWc/MVhLHeOQQ0BrrsOHcKaD+DLBkObIJ2gBc18c1LdzxeqCrBRiJ7CE6PXIXLICBMpN3vxOI9CgZQygA9O8CQj5gOATYTZFeQCpAvRJQSWNnUcVC2FVWoETK7jYXmoXTQOcjvsl8oqAv79vHVqzAz597Dr7CQoT6+6EJhTBPq8WG6mosT0GMpI2CKuDaR4DGV6UgPSX0qtO87v/6r8BHPyqRLRyH1MultOEkwMozdbgcIXTEPK9RpSDVq2okmSmSG2Ie8PqrgZrEEjTjwAqhXbuY9gmwgjKdAMFQH/DLb0i9p/h9u58G7n0XUFUbSwAe+SUQiMgpkjRzDQI1W6V9rWgt0PGS4uT5nm7A1QFYEhwD59Q0kB3cd7Zv347nnntuxkkNfie/O9Nqh3Qw4HPjfxoOwh9Zi/jzexePoMpkR6V56rIFO3bsEGvYz372MxG4oKzoFdG0dBYTguvwVKVbuG7TPpoMmKlOH4NJIwwsxY//AwcOTOpzRV/CkhLx4J7PIBfJbtpL7CPAqnDKjpCIyUbT3llc+WCgkgHMG264Ial/wcD9dEtQxc8d2suTlaPivJP7ZWQK+iS0eVMFfAlWanCO0JdhRUgq2duZIgm5TjABQc7ql6uwuO9cyUQlEyboU06HXBbXNya4UZ6P38EkVJJ1HGMkdsfI4T/9UvpJO5Xg9Xr6t9NCanC+KdU4koHja+fOnZOufJAJNRIFvMaszuO4JvhvxqpSVWPRV+d1oxyUUsqKlV0ECQ2SG5cV198E7HwBOHNKsut5/x5+2yypcRWB8a2vfOUrU65snTKpwWwPOt8kAxhQuuQ6gECYWXGspuiDOziIavOGSWuSnnHENqAVTc9Uamwvykx3ixmu32g4FhOPPTnSj1eLK3HP2rWCzdy1a5dgHflvGZQ9iSc0SHLYp9j8PGNwcV19J1A4F+A1YTPSOauk4ClJjLzFQMANGPIAXXbL92YaobADoXCP+LdaVQq1aHQsy05IcjkOhwt+fwBGox6qsAZqVQ1cwY5ILw02Ck+eXUVbTZYB1+p1IqBrMBnR6xtOm9Sgw8GxmEiPnERHT+NpLMrtkr6EiyxtCfcg4BkGBpuBZW8EdBNreXNeHWSmbTAomoHRQOGDmwyfl+dexpksIVe0QkOJruM8OWBQETCSG3KThEhUAmOyANYJgvWtDcnrZ9Zen/h5BoMOPwMMdEjjfc0tgDKDkseln+F5mC4KKoD8MmCwKzrYWG1VtXjSCz6zNRjckQ3TqRqo861z0ORqgd/sx1DvIGx5Nti0Vswxp5/RI8tR0fFIt0yd2TF0AOio03GinKAhySbW4OjDdxoPiOA+Mdech/cu2Ix8Wwm02rNwjozCapfWB63KICTp4kGnRFkyTiMx06yXGotFut4RR49X3qrVIjdDqQaet6gKVOmQp5f2mcKlEE4Hj0suEbdo9Xjfgg3434uH4A6yaTMbyC7BYnsRRvx+XBiV1kEZnP0HB/sFqVFns4uHjOe72+GTjXQFjFod/m1JtEydhq4yiFFtnocO9wVh1LY1dmLewmqpobR6AjKf1YMk/APe6Jxn0DA3kpm/5Vag+RzAnkaEVgf969+Na9+Ri6aGBjSrtFi8bJnIWqJjypJnQbZEqlLGQdEYPVtIWq5vywXe9kmghaRMGKhagKUGk2jsF7TmY9woDoxIhAYx6gVcPokgNtUCOVHpMpLUvP7xGb10mplhdbjlAp7tPA+3JowV8xfiweqVyNEZBcHNChJZhu5v27fjfUePolWrRW13N762bBmKMpWZSgWuv5PJYr/2WqmHBgNxlEPbsYMCupM+DK1qEYJhg2gUTjNWq5oHtSqFDAQbgr/tw8AT3wGG+gGLFXjD24CiNIJ2rDB529uoxSH9zjn/ta+x7Cr1+/Y8A3jkMRuWKvee/zXwtk9HX9NxIkJoKPbGtiNA9foIUR+UsnjiQVtPCXcbMHwYCHkBbQ6Qt1mq8pgs5ICWYo/hHkSC4cMf/jBmEszeuvXWW6flsxudg2OEBhTkcYNzICukhizr9+ijj4r9m0HqWVLjygftEpIGDPJPBdwvJys5Q5uKWa+JbHva/1OpIJHBfZV2EX0JkhrMxGVDcZIa/H4SHtyTRJXiLF6zYCCccQ8G50leENkMgGck/5NEjoq2s1J+Nxk435jExCDuVKSMSKQwu38iv4bXiddMlr2V+1tMV7PzicCKfaVMEY+D/s9EwevLBY4NJscxpjKdCRNcx+Rm9Lw+JAl4r7gvjyG+9wbH7ahEAGT7nNNtGs/7x/tGUoaEzGTBsUyfmwFjObmP5097hOs8QV+e67889zmWjhw5IpIx4sczCY4rplcJibDP/QewdxfAHpLzFwLLsk9EzWL6wP4uHHfkFBLFVdOFOhsOh1xa7Qx0RyQ1ohuYJzQMd3ByzWYIm9YwTlSgwGDOeMPt9bpjMlUJZrB2iCw6iEATy3C5ebIkSxx70I8KUxFqrdHMKn7rtYXLYZ6OKo14eEaB3b8C/vBfwLPfBXqapczIpTcANeui2eC8FsYCSZLgqic0BuAPH0QQzeLhDx9AKDysGK7SkD17+hLWro8s8CoHDBoHCg21KDZeC6t2bsrxUR2R1uFwqFw4B5dOXxQjVpeBljKNv2QlcocOHcI1KxdEj4FBCdEMVxXtd9LPIP/EoEPMigwaZ9z0ZXDjITtOx4Yl4xlDx6BunKFJWanGQ0BXpIn3OCS6piqgIkoCJoXJOl52ieOXjVSXxi1gvDFsrPriT4COBsDtkEiWl34KONJrzHzZwbF0/VuABeuAwipgznLg5ncAhsmRMDQgWHUgl4JnAyRrrylYi+X5i1CgysUS+0LcUCwZepmAY5NOL5vgpfy+cFgYVnLWyM8uNaPsqT8i/4+/x8a/PS8kleJf/3/Nr8KvkPG65BrE890XkKevxpIFq9DdKVVokSSoNK9OeezMyqFxT+eIhq1skNEZIWnIn8nw8UWLsEihI8q948fr10/YDD3RfGbmbqINnca18hqSwPjqylvwxaXX4xurbsUtpdI8M2s00McZmDzvAn3iTObcJL1uPlS7EhWcl6lKl63LcOFkI/KKcpBflCtW32rLBJlvrLxacw91zaLPLdgqNdGWSdBHPgrc9Shw65uBRz8JVNdCm1+E2o1bsJayfZEybJbMs5+FQN2SWJkpQWoagPmZZ+KlAgM6LNdPGnzkdy5YDtSuAIySLcKAL9fhcc67Ou6e8O+U8lMQQ8x0oswUxwXJjXgYSvLxDLrhL7VjxOnAmeFu/O+FfRgcHhprwChja2Ehjtx0E3rvvRff3boVzceOifOZbFAhq5g/H3joIeDee6dEaBBMktGq50Ov3gi9ei3UqjQkHVit8fH/BD73HeDTXwfq0nR6vvc9oENB8vMefeYzE79veCAqv6bc15Qg8ZdoDQlGHF72ukq079Lek+EbBAb3SYSG+MwRoP9lqUooU/AY/Q2A5znA81fAe3SsWTnLw5kxyPVzpsDvYrIRv3s6YE1S4Zns+amAshpcUxiQviLm4yySgveHAdVs9Jegb8mKiEzBIFKiwCgT72hzMaEkG2CgjLal7PcqNa75YJBhJuf8LC4PGDgds7WuMNBfoE02EUHICgUGf3kuU+3NwPlPX4U2VjqQZW+Z/c5jYIyA6wgf/Aw+ZmLd55oVb0fSP6OdSqI2lZ8znWByVKKqF/qETOhh0uZ0g/JIJG5JOsm+Yswau2RFVHqK4GuWJZCUzQJhngnBxEoSEgwkfzIFxx39XY4JJhQm2p9IaPJB4kcpX0ZCg3ZLon2IBAnjv/QvlLGpywYmoW27Abj7DbOExlUIcgisbGbi1FQw5UoNDmp2LSdCbDycACFmm00S91TU4Sv1+8bcOgbi7qMufYYoMZpFIEiZsRoIhzHHEnWuWZrFrPdDx46gpUKL1oiW8ercKtxZthnuoBcFBjtyZoI44HHu/AUw3C05ml6X9PvN7wRyJyehcjUgEKbciXLjDyMQboBetU5sQjpVLfzhetQsqMCl5i7ULWLFjgdheBEIM8AZhEaVOpO0zFQIu9aKkYATao0G9oJcuAecqKpI/7oma2glkx2alJWyKiDkl+6rZ0gKbJiY3Zw4KMmNiNnK3JRo2ClBLVyWMDIgxqAaMxC4aXKjYTY6NzQ6KCQ/LBYGCmns6KAyVgHmXsAVKY2nHErXkHRMotdFNCtdHC9/9wYkgobkhwyjGdh658QXbMNNwJPPAA0d0u2dVwx8+l+BhXEGgyAzfgEMdUu/8/u0kQ2VxmX7OWDRVdJAU28E1t2etY9joFfpeMLnAgYapYbvbO5sSr/RvcPfjybXMVFdx/tbaclDnS0SdJ4EmEHFLBgGZ0keyDJGMjh2+TfKVHHMvtLbi388/OrY3886RnD37ldw9OZbxwL21DhnT6V4dHsdGPIP41hbGwK2fDgDViy2rYBJM7FsVnzJOOcN5wozV5SltfHI0elwcMcO/KmjA0N+P7YVFWHJJAOziRwb3ttrrrkGR48eFZu7HKhm09oyU+x5adVqfHrRMnzmzAkhMcVPs2t1+MeaxMH9NXmF2FJQjH39PULKkd+/xJ6HHSUV4t9sBsnrkEjiKE9bjKUla1BZUwIV1Cg2VsDKTPCJkFcBXPduwD0iVVOxQXg8MVC3Kq0MwrGMpvJK4L0fA777dUkGiNn2/D0vQ+m9JOD38PpzfMrSfumC7yHRzDWYDuRYxYXWAphrABfJyIgVozEDlnnCeZCz6pihRoedY5IOlxLHhzulQrmARLx5nE50qoCXXz2Au1l2HQfeSxLrlG4g2cK96krN0hMn9uc/AL/7FdPCgDXrgH/8IGCWqzNp8fUgHHZCpTJChbJJV/2OIVN5AxKNysAGj7mvT+oTlar5ZkkV0NOqqHpQA4VxgcjcSqDrtOIJJqjYAX3EttDbgJLNQDezCWm7snHWesCgmKveeEeVVSEeIDAM6DOcG8FLQECRcBHqBDj9DKvF+KEtwT4D11+fpLoyy6DEAe2YVH1lpoKFtgIssxfh9EivWBvZs2+uJRerpsHGJonOucgAMtcZ+hqzuDLBIA73Ybnf1FTAecM5E28TpQJtkmTNWJn4MBUpkkQgcUF/hhUbSvDcGWykbcQxK+/F3KtIdPAY6WMwq11Jrl8WcK396+PAUC9gzwduehAoz1Cb/e8cl7OZ9ETgHsCkU9pADMDGB1oZ8OX4zIadEwqHcGn0HE4NvwrHSAfKjXNRakxPfkuWvWXWOytGOJflCuiZkHOj/clEmfgeGrx+JA8oac0KtJmSouJ3ysF4pQqKDPq0U8nMzgbG1vmH3iFVfp+K9ARctwW4O3kv1ExBX4t2P+39TNUC6F+w5wh9BP57oh4uJPgorcV1mvee+xkJpInulUxg058n4ZFI9pNVfPx8ki30G0l+KFUkZjGLyYD+6i9+8Qv8K+WKJ4kp7WBctJld+uMf/1j8bmEjWm+spI0aWpg0OTGbBRvRBsNB5OlzoVenzoSpsxXi04uvxd7+VuFwrM+vQJ0t8yCGRavDJxatw5fOHhIVGzo1JQxUeLzlLHQqFW4unSM2RDLJr/Q2wKuzwZQnBa2ODrUiV2fGdcWTb/aWMRz9wJAygB3RgW47e3WRGswW9A5KzcspjZVi0ZOCfe4xCY/Q2EiKBja16mqowkaUFPWjof5FeNxemM3RgFkg3DwhqUFN/ltKN+Hw4Fn0+0ZQvGAZDO2BtKtvuNgn09nkGBKbTU4ZULxI0shOIOUAWwXQ9AIwECEV2AOl9jbAOt7oobMwkcPQ5xyAx+iHXufHNas24/DZ/WBf5TDUUDv8eGX3n1FQMozlK+byy6BXL4cmdy3ArGsGQFpOAu6e6HHmmCP9MxhksQF11wJ/+xWgDkWDK8zyvv9DgDlFMLnxLHB8D8AKA6ebTXek5y92A8/vAZbHGTM7fyU1HZdBAoUPDYkVZBfUhW86AIx0Sec4b5Mkc5UMvI8kVfpaJX32mrWTrryYDDi2xjIHKWN28kkgEMmgU+0HFt8J5Eys1x0I+dA4elRBNofR5bkIZ2A5rNrMm5Eps2DkRmR04OXmg60DA9gzMICCqioUq1Sg6Npz3V1i/SWxTHBNbna5RLXGEru0X7CHhlmjgysYzTqivBwrD/b3H0TXQA9KqkvQ63XAETiM7UXXQsN1Jg0oS8bZkyCdxmRmrRYPVGcme5gIvIcMZicyFlevXi2kqOiYpNKVfEdNLeZarNjV1wObVos3V89DuSnxWGQ1yf+u2YLHWy6i0TmCKrMVd+cXC6knXgcSngzKJ3yvRgMjrJhvnURZOHsCsI/QVDE0AHzzMaCjBSgsBT7z70BOPmC1xWZVTdGWYUCHZEYqciumwbTLCdjzxo6BAV8SUwzExshI5a2TgtC+AUBtAqy18AclcklZTs6AgtwskdlkMqSeXsBQSzvy5lYIIp7Q6/QJM6joYDDDVg5Q0MGd7uaUk8bul4Cf/iD6+6H9gP8rwP8nVUIEw/UIg9VLEsmuQic0WDN1YiPTChPKZskJMbzmDE5MFADaeofUf4XBNlkCi5VJShTVAu4h4BKDiWHAlAMsvTPOVqgBzBWA3ymRZPF2iljzEmSAprkWxiAYmzQhEJISDLhWMJOKPfRmitTgd/E7p8tZJpHxoYWb8LfuRrR7HCg2WHBzSU1GVbvpgsQ+5yEDB4mq9WYx/QiGPXAF6hEIO6FRWWDR1kGjSrz3ZYt84tiVNfrTkaThHsBqHu4liTAdmdbKXgrJQJuEthIVDUjQ0Q+i7UCCg3sWA87MVM+EvMkaXA7gd98B/F7JTmfPqz98D3jk44Bt8jbtlOHoBHpPSX5w7lygYFFKH/i1Cs6BdMd/MnAOMSjOADkDr/Sz+ZOBYs4ZjrtsVS81j55Ft7cVQQTgC3nQ7DoHtUqDEiYDpgmShAwmUyKUvsZMyVHxGjCpJVFjcMYtGBDnMSltzGyD90PuMyL3NUm2h7MiJsa3nWFwHHE9E0mq9KHe90lJ6pY2Zjq+QJrgXkKSYCqShrxnVGtgZcREpAbt/vh+Mkw+pH/Oa82KIiXkBD85gYRVW4n60TDBlvdXbuhOUvtySa3N4rWFHTt24L3vfa/wxydbaTclUkNuPiRnFurVFlSaVqPTcwrBsA86lQnlppXQRBq3+kN+7Os/iOFIBYROpcPmwvXI0aXO/GTmFB9TxfaiCiy05uBzZ/aj3e0UodkhvxffuHAUuXqDcDoOHz4MdW0pTPpYp6bB2TOzpEbCDSBCbEwRJJTODDeg29sr7sFiO2WbspPtGgPfMND6PBCMOHCmEqDiBinYlQBhtEOjYommdPq8A0H+G7HjQ6MqFo9Nm/rx6qtHUFBgx4JaeQNPryrIoNFjS+HKqPz1pT1iUU8W3CP4d2ZeEMkMApaZi8WeJzD/BqBwITDSCQycB3xOibyYuw1w90UJDXHYPuDCc8DKhzNu9Nnq6sS+wWPwl/lw5Pg+eMoCGHE2YE5AuiY68yCuud6OUyf7cfDAWaxZuxDQHodBvRVqobttB6wlQFghO8VqjcICYNPbJCkl4u48YPcfAccQUFQBbLs3NaHRcBL43feka0FHbBO/9yLQ1CM5Hn/7G6DU52bwuj9B+TNJDS17k2iBysXJCYqhVul7civGZ4bHg99/7I/AYGt0XvU2ApvfkrxPx+mdwLndkftDqa6jwI2Tl5TKFAyIUHpA4NK+SM8C+XyCQONOYPVDE36OO8S1b/w8cQYGp0RqyKCBQ8OJzod1wQLc1dgommqDwVWdDruuv170o0hUiM3nZdAIfsucNfh+08Ex6cASoxVL7VZcHA0Jw4rfxVxud9CNId8wCpQZzGkeayrjkBUl7z92DG0uF9bn5+P769ahIsUakQ5YrXL2/AlRvqtVmcYZ+zQqqc0b318hHjeWlInHRKAz6RgcxLVBNa4x5mCgbwABo018fqpgIR0SBlcmqwmeFXjcCD3xA8BmkOY2Jcr++4vAv3wla4QGxxEzVakbm1aDspf/DDz7pCQtZM8F3voBoIJkMcay8ZiNxexVAV5jy3zpEUFne0tCoppjQ9a3lUHbx6ZTQVuSj6G2DqjtOTDk2vFMy1ls82+BTSnzpZBN6HCP4vGWCzh99Aju3LYd93K+XGkBlQN7Y6sCeY+PHhJVEGF3M8IWWY5N+nsYQ6JyQ4UZbGD7jncAhw8DZ89Kv3O9eOyxid9nNAEPfADobpX2J1ZuxEvE8dznbACq1kgBOf8o4HcAPn20WoPQGKRHIpjmAM5zkd4tkeuoL5Z6a2SMRONfHeN0fPe738UXv/hFzBSp8U//9E/T+h2sfLu1LA0JzSmCzj8zJRlUynam/SwmRjgcwIj/EEKQej2Fwm4M+4eRq9sCdYJeXEzQoM1PP5fV0Kl8g4lA8lomSJLtubK2PNd/VkckChQxO5bk+eUAfX0GyhhQo00g203cayifyWvFLHCeq9yXYcbQ0QT4PHEyjz6g7SKweOJmvNMCRwfQ8Ex0TR5pBfxuoOzKrdCaLvJYll2dqiQUweogEhm01RhgpQxztoOqfd6OcRXVvd6OjEgNGczIZ2wpURLTdED4RClkrrh+ZJtU5+fxXjBZi+sD7zPt34kShBigZ08fEv2XK/GG9jrX3ZgAfpIEscmCBAH3j/gq7MmA15WSnFQXyHTccyzyEe9jEByfnP+0UWR5rkQxMc47JQHFxEX6LbNVGrOYKjjuSMrSjmDyxIyTGnIWlRIWbREWWK9HOBwal01X72jASITQIPxhP44MHsf1xdswU6BWbmukj4YMNVTY29+BD0YyY/bUPwenImDIbd6kSUOygIHFIOVhQoCGVQkZyhwowe+zFwEjctY6M9U1QPVSTBWHB06g1R2VLNjddxDXFW9Gvj7LxnLHK0BQYWi6u4H+E0BRYqNOysiM5XN4bzSqxME9va4cGzd50NTUiQsNbYLYUGNyDe3otJCw4CJO450ZSNxsudmRNaSRwMWdgcBEhhmzlCgtwIDS2OLOn7lV0qN6g5Stw3vI55teHt+om80/afQqghncfFieSUOExxUfgN13YD9e7tkvmlvq9Dq4nC40NDehwGwSx+xyulFo0WFgwI3SsnxYLEYc2HcGW69djsGhFlw4PySO12a1oq5oEdS9EmkjyJeld0QJDaKoErj3Pelf1APPj2XYjgWuFrNCJVIREm/kUQeUm3R8Jhr1rdl4e/XNgDVB0J0yTEd+A7gi0kwkNFa/AbCmaCrn7AMGWxRPUEvcA3SdA6oTjE+fWyI0xEsjx+dxABcPA0um1kwyXdCAGdN39bL/RJzhStIsDWgjJHO6z2cKpeH2ngsXMKBoiDYcCOCfjxzBE5s24X8azmMkEBCEBWfCvRWVqI7IzshYmlOCTy++HhedAzBqtFhiL0EHm+ImQLaNqnqHAzft2gV/KCQI8Oe7u3Hjzp04fvPN43papAuS/S7DOdR3vAJDaSfybKUoNqyOCaowm4ZyQamymyYCJSq4lvH9HDc0ZFmNwn9TBoyGfKrPpnPy5IsvomTlSiwrmAbCO1001sPqGYVDB9j0OmltYBbVmWPA5uxkizP7lJIFaREaZ48Df/lN9HfHCPCjrwGf+MqYrBHHPytYmdmaiDAjWcSMqPhMqUTo8zrxZNthGNRsr2CHPc+G3p4hdF/qgCbPhI//+Ql8/c6HYZD7a0WcjpOXmvCh5tPob76EcDCEM/XHRZXOxxZlLzuP44hBNu6JzLTjuM14vIprFrcPcs/pP4vwyDFg/njih1KTycBK4E5PNzxBL3J1OShgdehUwarMH/2IqXaCmBVVOt1d9C6lv6UCx1R5lPBKCtoF7fuBgYizSdt5/k1Sdm+8jTl4CnB3SSRH7lLAWAQU3gg4TgNBl9SHw7ZkchnB2rmAL07/Xxtdz2nvv+Md7xD3XdmMdDrA4AjJxngf42oFgzZ33XXX5T6Mv1v4w4MIQdkbgukQPvhDfTBoEicHMNuZeyWJDcprTDZ4SvudJDarAWmXU26FthyzmelnkCTg2snXkFyPX0flhDvu6ckqOCYDBhPp99BvYaY7v1/53dynWIXBfYzPk8CQ+6JReoS+EQOX9FGYPMEKQWaJy3KelHTma7gnMRhHomhawD5eiaDYF2ccPafG2+jdx4DS1VdstUY2KioSgT4zx1q2SA1lheu0ZImrVPCMemEwGWIqZqfS42KmSA2C85h2bbLG21zTSAwxzjEZ0IZl4o5cDczYCNcPznnagvzsiQgNJkyxcoE9eS8n5OqFRFUJ2QCvB+ND2Vq3Od5Z0c+KbMatkvl/mc41fi4robgX0f+kH0P7i2s69wflXsbnuZ7ztewfwz0r2+B6QZkr2pk8LtqD07Z/zOKKgLIa/LKRGh/72MeSHFyCLBO/Y1x2rjMgBYyTOcOBEMO14ayVgydr7KpTHO/1RXV4qvOE2MLkjWwbZQJSIewD3GzWGGl2ywChaTOgTk/nPQZndgPH/hb5RQWYrYC9EFh5o/RzCvCHAjGEhozm0dbskhoMYLOBZTxC1KKuB1RaQF0JqJQb/fjsApVKD1USckgDSsYEMG+eGqdOXsTBfd2YN2chSkr8MYtwOqDBQSOdoFHOTYELKBd2btipsqto0DMISAP+7rvvTv4lzkvAwMlI480EBg7HoKKvBg0hZmxTZ47OBTcaGnTKDO4AgggyyB5mBbYLZqsZA939gNUIR5t0nzv1GqyaK20GDfVtWLdBMgjr65uwZNEm4VwxY6K1eCHmrF8vBffN+RKZMBUw+KO8p6L8RjGPH3kk+u/BbuDZH0o65bKRyuvB9zDD1V6QvCLiwiuAS9GAlRmvZ54DNqSoWkjWRJUZtYngTZTZwj4jo7gssBQBo32K68t1Ir0N36i2IE9XhkF/51hA0aAyI0+f/eznBodjrMqC4L8bnE6Um0zYfcON+HL9OXR7PNiYn48PLow6KkoUGaziIaPUWIJ6x/mxtZk/rVqLCGJmE39obxfyWDLFxn+fczhwYmgI6yaZVTTgOwtfaATrtyzF8cPnUVg8CO18EwoNsU3yaGDTWFQ6b+mA6xGdGWZKJio/J2iM0iFJlTX0qb178dWmJuoyCXnG/1u/Hm+e6exLARUW5NhwdnAEq4uUAersBAW4znNtT1syo/GcFHSXm9dz7XWyMXMPUBqVflu/fr0gNpJlZKfjcDBw/GLbSfgCfnTWN0JvMcFgMaG4JB+dHsATCGBgZBhvfe5X+Pdtd2KORRr/zAb+/IvPouvUCehKimAokmyG37Y34V3zFyMnrrJjsqCTwQxdOh4MzvGcSeRkhJvvAPa9ElutcdudQOchST2JVXrCEIvebxUrC5MQGvv6D6GPMl8RLLHXodY69ew4Qb7YbcC/fErSXCZKSoDvfA8omlwiRQz6z0cJDXlcNb4ArHyLlOAio3c/MBqp1iNIbpTfLMmb5W2Y+nFoigD9eiDA7wgCDPZqooEPBkG4brz88st43eteh+kEm5IzAzDZOjaLWWSGZNnLqZv3ypUI7ItBKRWOf/7MlMBlBiIftOVfeuklYctzn+YjFaFOP4BBN76Pr83Ut0kF+iw8Hx4L+/VxXisrFuVqWFnyioEm+kP0eViVTmKb/gMDg9zTmJHMgKkc0KREFQOrPAcGwiYbqJgQlQuA/BJgsFdaO+k7UHZq7iJcNrBv4rjnLk+T5kz8YFnaKZvg2GAm/9WCUmM1jrQdQG5BTsxzkyU1xvrCzRC4TjCDnhn9lFSNXzOY4CQrrWRKCvFzSa7SN0m0BvJ8GeyeqB8RyU5WLV4pyEb/pHhw3eS+IUs1ZQskgzhHOae49ia6trSd0pEIo03H+A/BcUrZRaoIcG8g+c7jV/Zg4U+OKZLdXPu5X0xHLx4SQdw7ONYo6cvjZWLBLF7b2LFjB77yla9M+v2THok0ajioM9HWtWjNop8GSQoZJrVRTJKTw8P4bVubKHSnbvkCqwW/aTuJ/f2XxKuX2UvwyJw10Ku1+G3beTzffUn4ureWzsO9FQvSXozMWh1uLK7GCz0tY2JO7EV8a2k0I25d/hwh6XB6uFOUpq/Nm4OKiZrwes8CIUWmdNgPeI4B5gyzuHtbFISG+CDAPQrc9HYgCxJcymtP8HqzD3OvtxsXnRbUWOZlZ2EXAWxjbKWGzQTkhYBQs3RebEqp2wKoJAOYTUDDiC2LUyG5RqbIHFIthCZcizUrrh8LUsmZQhMt6snA4EyqQBMNd9lIoGEvN3y6+eabkxuDTmpr71M84QGsZsA5Gs1Urd4aI83F7+CxcFPhgi4bC0psXr8JbU39UOuj7wv4Azj34gFs2FCH0kop4FJhM0Gn9mHuPCkjjZJedbU1opycGxIdkbGsjWRV9rKzkC5qVwC9UoOw6MHpADYFu/NO4Lbbos+/8lvA65ICW6xGoKHFa6wJAf0twECr1M/ipneO73sx2j+uuTxc0cBWQrCKgxUx/sh3yihIErhlwNBgkY5R/i5ej8LJZbpMFmNlxXM2AaM9EWKDlqQZWLAj/X4S5uWw+vLgDjqgUxthtvqgIdGYRQz7fBhVNtgV1SAqLI1k+M61WPC/a9Zm/LlGjRFbCjbjuTaHIDNytDlYbF8EdZZ19oVUT4Iy7qlI+HiDJN/CImiwcPEc7Hn5GCrKK1EYF2fmfOQ6s+f8afhzbSg0mLDYmj9hNQENQZKeqbLC+DcGGRIZ8SRO/9bYiK+ePMmSA/GcPxzGWw8dwvaiIlRO1tnlXLm4H2g/I60hc9cC1VH5v6SYXwdjSSm8A8PS71wXWBq+bDWmCq59dNBSrffjwEoiuVIr5vnY68L7JF/neIeSAR86ktTxTbRf8PUMYvGnN+jDcEcPSurmQaPVYKS7H86+Qejz7HC3dcFaUYru8434fyd24zub7xDv5z01VZTBrvLGEIrEaCCQFVKDzpqsFU+pFAYtxqrIMsHiZcC/PgY89TupAmfdRuDWO4BTP4MqAGhaehCsLh7jsNSq+VCrEldftLraYwgN4sxIPSpN5TDRFpkq/t+XhBzaGBik+db/Ap/53NQ/290vzQvl2CLx7nMApgiBSnkpJaEhEAYcF2Mbh2eD2OAjhf7+TJAaDPymo/U/i1mkA60qDyroRXWGDBW00KknTgZhtigJagaLmBDAvWOywRxmuqYa1/HSINyjGNyhzy3rnWcLlO5hxjT3ItoD3JsYRJJ7BtK3YQBLbpwugxUnJLMnag5LO4aVKHzNhJJriXoPZlKpcd97gP1/Afq7gNwiYPNtgD57mvgZg1V2DqWcLnsVVl+xVRoECY20eoplCLmnzNWCKlMtWtRtsFkM7EqJMtNcFBgml/RFO47rxkyD1RokKpnRn6gRN5M49+zZIzLx0+35w9cwBjJRspWcOBXfq5BrC9cOEqMzLlGXAiSbuc5mqyeLfK14fVlVMR1VOrwHrN5LRGqwsoEVdSQoEoH3gaQF7wF9RpIkTOxifEmu8KEfyb9xz+EeISf9EtwfMvKbJgHuSRwn9JG415LYmMVrHzfccAMeeeQRYe/IsdVMMOkoFlleDnoaLemizlaLXm8f3JFANzeLVXkr8FJPD27ZtUuECvn4j3Pn8N+ra3CSGpQRnB7pwa/bTkCvsuCJ1ohEDoCfXDotqi/urkhfE/d9C1ah2GDC0aFe2LQ6vKmqDvOtsYRBna1UPNJGaGR8YFVJcqSC0FL2ASYLMBAXBBYfFQKGerJCaujVOhQbCtDrHRDtR/UaqWFpIOzHOQcb6AVQZ8tS75CSTUDHzsgvYSBfzk6Vr5MfCDYBWslQV2FOhHahvAwlacqhwsRZlkqjmoF5PphBxMwgZq1mqzxV3gjoCCgNfJInJFJSli86mhLPvqqtUrDCWgbYyhJKFqRyBrRqDa4pW499/cfGCKsySzHW33s/nOoecW8LDOWwaBYihFaE4YIKZmhVc2As0KCgII3Kn6Fe4PlfAH2d0hhlL42a2MzyhNhyK+BxAcf2SA7LotXAbQ8BOn3i71AG4GgAU/LJEjGw+TdWkDQfBxbHEYXmPMDRG1u1wIarqcDs17X3ASf/LElR6UzAYlZClSR+PbOzt94P7PlVtDqjbgtQuQRZR08L0HBEOp95K4ByiZyL0bdks9hl90nEBq+VtTg2ozeNOVNoiBIyTtV4jc2p4LmuLrx9zx70xOk/s2fGd9ZmTmTEw6azYmXuCiwomj4t9DdWVuLzZ87AHWQ1VFjsM6tycrAiZ/IVIWqVHg7nMLo7ScQB229ci/OnW1GTYIq3ldnx38/8Drm18+AfdWFp0IC31q0ZW3tkJ4T3kkFl2ShNx4CmMZvI6aAh4VuwAGqXa6xCRa5SOTMyMjlSg1KOF/YAja9Gnzv1V8m5r1JkarVeALouSX166tZIjZgNRuCDn4H2y19AQBOAtqQCeMNbANvUqnJo8NPRTFa+nRQbtgP7XgQcEZKFc2/zDVLD8DhwX2ClXSLHgk5fMtkrOlYMLjB7dt2IHo2aqENsK85Hx8U2BPItgtAYvngJuYsX4NzRY3CtvwnmSHXdxoJiPNke3XM4dsuMZpQYs5eBqdx35cy/RH1CJsTSFdJDCUMO4B2BetgF1ZkWhA06qApWQlUYO16VcAXdkabqsUQO7c2skBrt7bGZtiRx5B5HU4XeloAsU7EhVvTXRGSaQCjzDOKhM0DAAbC6LWdx0j5nicBA5lQyqTKp1Pj4xz8+7d8zi78PUOIxR7cOzsAZBMOjUKtMsGqWQB1TLZ4a3GeZxcrqBgasuH5nszE2ExlIhMeTFyRVpiMblmu4MliVCIkqQ7hv8jgnSoCbqC/YmG1Paddze6UKyNIFwPrXZU5I0De5/g24YlC4RJIT7j4urd05c4C51+FKhlyZ8/cOjmtWZizInbp/wWREVlnNJCj/RBuSdj5J0kSNuLmmsAKNcRKSHmweTkKL60y8DBnHBW28iSq8ZdAHoXpFfOIUk0AvW/UlExKbT0v+CHto5UfjezweXodskRo8dyajMv40XTKdqYhC/o1kB5N8E11rVugwYCykx21RNRnee+49HDNcu/kZHEccO9yb+L6ZAo9NOXaYqJzsfGbx2kFpaakg5dhXYzJyrdqpOByZlgIxw3Z70TXo8vSIZtVFhgJYtBZ86NhzImgkT09vOIwDA+0wK46OjuqZkW70ecdnqv6t+1JGpAarL948Z7F4ZAp/yIdLrrMYDQzDoDah2rwIZq0NUDFgH0dsqEyps1FEs+IXgNPU6g8DtgKgbjyjLmCahIxVEmwsWIOjg6fQ6+2KHEp04Wgebc4eqWFlL4nbAVdHREIiXguf18IXu4gJSankgYtMWGxmFFAChIs2HZGpGmtc2ONlQ+RNm1mqKbOoE2aRq4Bial9P7biqzGXI1dnR7xuCQa1DibEwYda6Jg2CaBwCfuCp7wOjkbHNfjTP/Ry4731Sj41U4PW46Y3AjfdJtzrV9SepONIX7b/Bx7iXq6Tjicf8a4ChdsDrjOroLr5p4nOzsBH6I+lXoOSXA7e/D3ANSw4XKzeyjY4LwHM/jlbvnH8VuO4BYN5yIUcUE4Qm0ZKACJsMaMwkyiqPhy8URItrBHq1BlUmW0Kn9rsXL+I9R44g6HIxLTHmb5SZmj+RDv0VAlaS7L7+enz0+HG0ulzYWFCAr61aJfaPySJfX4czLadRuziapWRWVYy79l0eJx5vOYOcmjkicK23WdBYnQdHkR3r8ssSNhhloJ7GQDqg80LDm0YxMy65VsqOS2swmDBMmnGDdM7lS3si2tLMXLQAIy5JUohoPR4lNQ7+DdjzTHRfPLEXeON7pAzMvALM+dCn0Tw8nPb5pUJPT48w1FetmkR/CYsNeP9ngd3PS7JTc+YD665NOqcSNWxkqTelDRkQ47VnVpRyHnEcyGNhsb0Mt5QswYs95+APU4pTgxFbHjyDwzAV5sM2pwLOtk4U1c7HmeMnsS5CGG4vKsf7FizDty+eFoRUlcmKL6/clFR+M1MoJUkIHj+dt7QzPfneF19kaSUZEYCavMpjq7kZuPisqFRQBUNQGecCBamJdLvOlqASldJ0WSJyqucAFxqixAbX35rJVYOOQ9FiSX6KJPXY922RyGsZagNgKAK8StnBMGDOoFqQ1R8dzwN+knKRWmXKkZbdmLYdQrv/4YcfFvJjzPCbDjAIxCDLrNzALLIJjcqCHN3UM0yZXMLsUVY20i5jICgbjW65Xyg/R94/WO0wmazF6QLX/6xlHzcdBU7LyW80fhqAQ38Ctr4J0wJK1Tr6pMQn9q2cLnA/K18HlHFPpl9zZZMF9GEnVW2ZZfR5XRj0eVBmsooeqKlwNVR/0JabSfkp+gLMpJeVF1hJe/LkSUFgxINZ8IxbcL9lbCMRCck1SPYTSOCmK39HQoXyRqxM4/fL78s4kSgbcDmAP38XcI1E7MzngW33A3OWJLRnpwpWozAZNZtSgYmQKKZFX5JJvfQrSETQ34mXhU21Vyn7wtJPYbUGiQTeQ+5B2ZamSwaOSybpyWQ+yTDG82ZJjdc+tm/fLjiGGSU1qNX3oQ99KOP36dQ6VJlj2b5OjycmiMJ/u4NhWLSxtQ8GtTaxkvYMlXNSs/ms4yDcQWZOhuEJuXFm5ACW52yFwbBIkhAIy00sNYBxRaxW/5kXgI4z0u+U3tDlAqdfib7GOQDU7wdK5gHdTVE5gvlrgLzs6d2zWmNjwWrs7z+A/ji5hkRBmEmDWTdcdO01gMYE+JmdHNebQJXl5uQKMMjC0kpu2GThuUDKlRyTXWRlrUGlFiQz6CfMorLPl3pqKGGriQTxJ3U4sR+ls4hH1jHQBTjjs0xUwKVzE5MaYy9nbwxaqx3AyX1S34wFy4F5iiqHrfcCz/0foApLenDifQxwKuTzORdKEhAzRhuw4WGgv1lqpJpXLT2XLjJxNkiYkHxMFzxm9hYhCZJqnSJZdOxloP6AFDRTvvbI3wSpwXJMahTLPVxEFU5BQcLy00xBg5aZ6zRAk6Hd7cAXz+7FgE+qtFuRU4SP1W0UBMfY6YbD+P9OHEeY5xBnJPIq58ZlC00WNNRmwrhamZuL57Oo42nU5KPctg52rTRXLdpS5CwJjsvo7/JI2fk6iwlqvQ7eYQdsITZBHl/GftbhwIsuF2pzclCXTrPrSEYonR8GS0gAKxsK3hcK4aaSEtEYnXJhDIp/sLYWSzMNYPaciRIahIZ7gRkYipyDvNWQMCWhIZ6LPMmKjdMHgJVScz2OcV6jqZIaHJ/M9smkwnQcrHbg1vsmfFmioC8DYNyH6CBcs3UDFsy1oPH8fsxfuA5I0jtqU0ENNubPAymNnb2X0Og8gUD3AFDIBHstjPl5uCenEkWFhThw4IBwGrlXPTynFg9UzYc7GMD/z957gLd1XufjL/bm3puURO0tWdvy3nYSx0mc3aZOkyZt06Zp07RN0l/btGnSpk1n1r9Zbeo0zvTeliVLsvaelLj3JgBiA//n/S4+4gIESIAEKcnhqwcPRRC4uLj3G+ec95z3OJJVx80CJC8SQcc9rUAX14XPfY5N2RT7gL+zF9Vf/EVs3TPnASveDficSgWaumIhBSrMZai1VKHVoyRPMEViQ/5aGLVZ+u5/9hfA73+SGpTRDywHPvHJ7ByblRJLHwKGm4EgKxRLAHtC1aDoL7UL6H8T8PYCWhJ+awFrBtmFni4goN7PIwpJ4u0DLOnNCWYzykz1++67D3MBHptO7IwqfxawgHkAAz8kphkMYxCJSQV8jmR5YlZ0umDAhqS33DukBAOPzazftyS6LsX/Thugpylzudt00H4KOP18zPio2wQsv3VuYwji2NeJ5FTbOeDNp5QKeibH3PwuwKEEOCm/Q5+ZWf70NWhDkOSgAsF8VG/QNvu/jvN4qvvyRJ/T31u8GetTxD4YMJ+VHTcFOKd5DbIBXrusxlWmAc9bXXFAApaB7lQg2fH888+nlLZTy4ymC34e4ySMvfAeMQ5zTSuATu9RfGxC3osDvwRqlk/MfV6nVJKwmcDpdIrxM9eERjIwLkCZJpIWlEgsKrPh7IVTsObqYDdl1mRbVuow0Y2EFmNelLuiDzbXdhk/m74Er6Uk8xeqyH69SI2vf/3rM3rvjEgNZnUeP35cfDDlbCDkgsjwF0GDzDMfthcW4qnubhE8IZhNaNFxIFMiSclY5l/uKVuKTo8PP2yNEgNRqPthzCVYneEJqSWlIgghiOFAL8qYSWjdDQRZ/RBRNIm1qsXx4utAx+mYMdVyFDDlx+so8/s7h4A7fwPovgq4R4C8EqCa2fzZN4oqLBWTSI1yS5YWK98w0PtqrKeGfRFQsB4IHeUflec0lYB2Zs23kiEU6YM/TGkyPzRwwKhdDa3GKgK1lIwiLjVdwv88/zishTYsW7wMjbkN0Gl0QnuSjLSUc+EGry7Lo5PB0klCzehzE6EDfsstt8SVWnJTZ+CQDDk3dm4CmvLdwNBZIOQHxiPA+SNK4C6vElh1n9Lj4VqB0k6jUf3XvKpYw3JmSk9CJMXzU6C3Hfjxv8QC3WcOAne8B1gdbaBVWgvUrwLazsZXO4XZm4XzyQCsuxsoSTHXDWagLM2mgJ5RpReF0U49l7lzaFjq+tpPAJIAFjtwx/uA8iSkDB2Mn/2rQh7pdTFSR4IyXNHsVUoFCM38aNY8x+2FCxcEUZdO8+FUoBFEx3kqUuOfLx0RGVQSp0f78bPOS3i0OmYM+8Nh0UdD09dLUde49/O8fz8LmfZ0ZkgiZlP/dL7AdcFqzEO+MTaOjXkQ91CNMnOsmsVaVgxP34AIAFdwHKnw56dP429V712Xl4ejd9wxZd8PrlOtra0pdb1ZifLMzp34cXs7WsfHBbFz30ycR2eClCLPieM7WoSE6qiT5IpKOalBoswZC77KdXWmzfxI2PE7M0jEbLRsSoakAg1yuYfwvEmsk4Tk57dcPYOOpidQVZmLwHi7sgaYtsXbDImOJTS4tbgOgXAYPxlxwzPmwuKqGrxnxQ5URPSC6CM5RRkoEhvM1uO9dGQrqK8CHSc1scF1iGRNWvfm4EGF0CDkfvDLXwJvfzvTClVfWguYczOTUslbhTp7DXwhn6jcsDCZIlvg2vWjHwMnTyjSaOwHlGn10lTgmC+cZn2kjFbZ7uw2r53q+WkyqeaK1JhJJfgCFnAtQJ9BBgS5JnKdZ0CGxJ8MyHDfJ5HNn/QfSNIzOKReL+l/yEx5Nakh/QcSJer9j2uu7LPHtX6+MmizDiYKTRgFUYgAaJbtcs9YPKFBtBwBiuuA4hlUsd9oGOgEXvtx7PtT6vrF7wNv+z1xD6RMEslk/p/jmuOVfgGrL2fbA4FJD1PZb8dHeicIDYKVqf925Qi+vu6uSRUbPA4D0DU12YsfqH16kpQkLG9EJCNQ6Nel0qiXEkTZ7HFBf4Z27lxJL2UM+taJ8p30q1m5GpVtlnKxmZA3Ely3mXDI99M337lTScaaa7hcrolqBjYNJ/kgK0SeffVHWLm1GPm1YRw6/wtsWHkL8gzpVRZz7yIpJavIub/xnpKwZ1IYY2Fz2eSd6w79mAceeECMWe6ZlHifi/m+gOsPtP0//OEPx/nQc0pqsLSKGSUVlQyAvanS9O1EBEuhQWaZ8N/cuFH01DgxqgQ3bioowFfX3IRBvxMHBluFNNX6vAqsySsXCzZJD0pOkfC4t7we95XNXq5oNpjYQjRGwJBi0vUw8JSw2QSTZAJww2fT30VJGqH6xwHPsCJ7w0zGWaLaUoVgOIir7maEIyGUmcuxkvrKswU31d49QMinWn2jzSwdu4HISKxKI0sB5XBkDP7wydgpgL8fhUm7A5potg8lzzpy+mFZmQPnsBO/2vsUcnU5WJO3XJAPW7duFQs4F9QTJ06IwBe1zznmSFCkMnLoyDz77LMiYEVDUGZmMNBMbUJu7CQ+uCDX1NwDdJ8Drr4QO8AoKxieBjbOUbn1dBgfBk78VGmaTZjswLpHAHMOkF8KVC0BOpk5FS2hps79kgwb9VJehpU7aoPrjadipIasVFLPEfHaCLD7A0DbSWDgihIUrVo183HTcx64+FLsc8pWAI23z+x4Y4NKlhmvSc2KeIk49sBhHxJpSHncwLPfAx79Y6VngBoXjwDu0dh3FtUpqvOpWCyqgThGEx1XZsHwQX1+VhFlEvDlGJUGMMfwVKRIKBxGG51C9fvZm4AkrJCtc+E7zZcxGvCj0WbGZTrnOt1En6TVubn4jw0bsH2WVSUMTHOTy4bUgyQq+UhWAcB9558uXcKTXV3IMRjwuWXLZn3+DEQkyy5jkINGo6zWKDPb8KHaVfhB6xlEgiFoDAZ4glr89tEDWGzLxd+uZgm3No7QIE6MjOALZ87gb6YwzDmWpsskVSQaZ+nkCGI0IVjB/1L2oW4zUB3V8s4rUkhStbQc14ri+IpOZgxynGfqdNCZZhCegf75dFRZAcOAAEumuafwvCVpWFs+ikOHegSpoWMFCys8/ecA89Sl+Zzfd5ctEg+Se5yzhbmFcWOasigMCvPndI3lZwpJzgi0XUTLM7/A9rWrgdEBIHeaOdKVpG8Y0dsbT2rMALw+eewTMRdJck4ncOSIMjbXrM0uoTFfMBdPnpMaHZBhFh8d5//6r//CXIHz9bHHHpuz4y9gAXMBBn+Zlcw9hwEuBpmkDCHXf2ljMYuWQRu+ln9noIg2TWJlH9dZ+h7Usqftw9fKNV0GKPk7s+wZAGBT77la8+cMi28COi/Gr0uN27KfbOSiUkBiwFejSFFdj6QGfYfhq4qsriV/9k3GOy7G5D3F8SOAaxgY7RdVG6wQYm8FteoAg6O0Yxiw5RjLJENb+hbyp+zVkAot7hGRuBFS3SPK3XZ7XFgSrSZR2zmymX02QTuN1bScqzNJnkmEJCrnU9Yr2XnTv5HrTbJq2mxXUZAcmwk5MGcoLAc6SZip5N/t+XF9KDmeOK7SkWFOBIlsxosS+63ONVhlw0bwnGP06Xbt2iXuvzc0hIhpBH5/nvibyWzEaOAKrLoSGLXTB4n5HeiLJhIblFvkT45rxrayIQecDByjrEyUUlNSQustX7l78TzQ0QaUlgMrV8+bCtH1Bt539m8h13D77bfPPalBiQNufkBLtEJDjSZEUBWtsEgPJWYzjtx5Jy6MjYkM06UOh/iZZyzEInu8s8XJ9Y7KJeIxG4z4vaLJeKt7DJUWOz5ctxJFpqkzXWz6HNFHwxf2TiyObHaeb4jXq0uKZI0YGQQ1uJUm4dKIWbU7eRb8YBNw5eVYkLR8HVCzLe4lXLxGA+3whEeg1xiRZ6iDQZta35rXssFeLx6+kBP9vnNod++FQWtDiXklDCmyRqcFqzNCiYSNRpE5EHZItIFphIG1DYBm9mx+KNI/yVmPwIsIXNBAOT57uYwGxpTMhIIc8SAai5ai0FiAYyNtOD3aLu7pxsZajDQrAU9WJpHFTwYGI7kAJ2ZdSchscpIjDFpyA9pg7I5/EY0+EhuhQEbNntMBySoNlcWnWhwvvaI0tJNgI+ym14FVDyiL6r2/ARx5CehrB2y5wOY7AVuG94yyOYkZJL4ECRMarsPdqtdpFALv0P9FbyvJsial4dfiFL1npgJJwUvMEFadR885oICZWhluzn0twCv/rQS4iNOvAnc9BuREA3pdzQmZIREg6Af62yd0PCfg88TGbojv0bJcTfkbM/a3PYRjh49OVBslAwO+DKAy0EgHWZaPy34vDGQny+RTN+PiGE2FVvYRkd8kHIZGNpAjN3X+LH7j4F54QyFFf1WjQVV1Odqjymofrq0TzcFn04tCgsGCbBAaBJ00Gm6pDLPfPXYM37h6deL3p7u7sffWW7FjFsQGs8CS9RzgOdCpZHkvjTnek/vLF2FVThH+/ch+PN85BGtREAZNBJddo3jsyF48XJm899G+QaUJeSIY9KBRSgdmLrNsJlC2Fhi8rEgvCkSAmq3K3qWGyQLc9yHg6e/HXrtmO9C4blKAh4GdTLX8KafADKN50yTnd+hphTYUxNK6GrIbk16iiSj7o8sl112Smeoq0Bg4p9o9Axj2u5BnsKHGWizGB0lyOha8LgQJEwbLuOfQPpMNIOcCJOx5L5aFR4HXnoC2rReIDAFn3gDe+ftAQYJ0kgSrO37yk8nPcw2aIwdpVti/Hzh7FjDogSd+Asi5xQzE//gG0/twQ8GQo0hY9R0AIgGlT0fJDiDDviMcV5/85CdnFACYDjwm18Fvf/vbWT3uAhYwX2CAKZWci2x6ymQnEt4kCOljJNMKJwlCH4MVutPtebT5mDzF5Ba5J9wQKK5REpcuH1J8oIqlQMMcSG0xmWISIimev8ag79D0PDDaFvMNSlYBNTtmWRGTRAZJpxdZ/MwwTyWjzPFEW4N+BF936tQp4ffy/0yI4iOVfyF/Tjcm84xmQWgIEoQVHVF/wRgICd9Znhv/zs+ai0ptEogkcbJBaDBznoHy+agKlkglc8Vrx+oBJgswSJ14L1jJPFswm57BbvpVcb0frwesulmJX/RE/TmjBdj9nkkvY+IT7WaSw5kinQbq2Qb3hGRxgUBkHGWVxejpHIDX40P9EmVvCYTdaZEaBO06zjN1v1gSC4yJ8f7yOnHfmqtqHLUiyrWS85pX/M/3gB//T+z3+x4EPvZ7v7bExpYtWwTXMG+kBuV2gGQl6wzyqYXw0wOrLzLW7J4Cp0YG8ZWLx9HjHcciWy7+fMUG1EQzpH2hIP7szF70eseFTnXr+BjOOwfx9XW3wTZFYyqtRofljpvQ7D4Ld4jNcs2os62AWZeGQ9iwBTirys4nFm8D1pcpOvqUdSlrAOpi2uZxwVg1oUF0nwByKoC8WDZtn+8cxoJRCSFo4Ax2o8ayHXo6rlMgGPahy3MYYSgBpVA4gE7PYdRYd0CrmcEQEXIXCdmA4vlQjNBQvhgQOQaATUJnG/Dk+5Nt6LHj+llqmAR8/s2hq3i57/zEc+2eITxUtxYXTl8Qi3ay7AYu6AyyMqCcClyMadwwkEnDgtlbK5ZYYZ6ULcnm2Nlj+P1hN7o9J+GPkNTRodi0FLmGFD0wxhMqJPh/tyowSpJt672zO6GaRqDjSux33u/qhCDWujuA3hbAKxt+64CCQmAsKukm0XRgZqSGJ0kJKs+D3zVTUuPQ01FCI3peAR9w/EVg93uV342m1DJZiahoAI6/EvudxAYfOi1CN78bx0+dEQb8VJs6xyeD/SQ0OC6ZkS51VKczBpyBAfR6r+Kyqwc5hmKUmRdNVDdJjAZ88PcPwDvumxiuAZcH+YuMeNoYQai0BFopHxit3Tu+43ZBWPORDdBxyrg5JXusBJoABl81ZsCwWEj8sJfEVE4WJX7UhIY4FIDfOXoUp+6+OytOB+8Vgx80FEl20Cimc8kse27oQgs3HMFr7lGEfbFGg0ZtBIN+H/TsP5MENaoMcjqhdAh57VjhMhu974xB6aBV7wJ6zwCuPiXjsI/62RqgbE28sbZoFfDYF4CBHoUwLUweFOf5M+OMVQhTOp4tV4B/+0egqwNhgxnaj38KmA/nkjJSv/omMBglrikX9tBvAwUJ1TlaG/R6HXr7RlBWSrKF638s85DySadHzwh5yFCEfcf8GI/ypysc1ViXtxjP9jShqbUJW2zAXWWUUdROOB90RiShmSpzjGPRGQwItbtUDTn5mrFAO9yhPrGP5BlrYdEViLXm7JkzKD/xJKyaEMJsLh8KQctZcuwV4I7oOpiIJ55g+uLk59mfbQpS9Zrgm98EGFjn9WOmJccbyWb+dLmAv/874FvfwQ0HaxVQ+wgQ9iu2WrpOExNVhg8B/gEsy2Mze72onFq/PsOqzWlAmR6u8wwuLWABb1Vwf2cSChOdUhEWTFRh4sVUmdQMMkmpKq7LzG68995Z2uvzjeJa5TGXsBcoPjf9B4mSRUDZ7BIk5wQjrVFCg4jaeexPVrQMsM6QsGpYC5zZBwR90cQxjZDD7fWEcP7C2Wnl/pjYx8xtBulZbUy7NZ0eBFSDuOS8iO5AF0xDJixxNCKX1ZQJ2GgrwhO9o+j0uaENRxAMhbCrsAqBodG4fnNzCfpM2bKPWd0y33uYWmKKttvJkydFAhMliugH0m6mCgVjEupz4/ox034STBqiL0N7k4lx8yW9FAf6FldeB9xDgCUPWLQrnqxkDOPODyk9Qumrs58M+1wmgHEajmtWQd/IUkcGjRWFxblob+2Bw2EVtpp4XpuZRDXHA8eFlADi/sIYFtcAEu0cX3PV+F328uB9YMIWK6jesk3Cm6/EExrEM08C23YBa7NrX98oYAyEvbszRcYRay6UJDU++9nPsgCWfLTqr3SOHCIz/Fqi0+PGH5zYJwJTDK6dGxvG7x3bhx9tvQM2vQFnxgbiGq6S2Bj0e3F0uBc3F08tnWXSWbAsZwaTmI3BmYXfxZ4BGqB6DVAWzbTdOE2QzDs8ORjLY7AvQJTUCIa9KkKDiCAUCYjnCozJGeRAOIDzY+cw6OsHNAEUGbWw6pVgeyjigyc0DJu+eGba0OyfMUTCQhPTghaMLjNT1cE4BuuYsT87PVi9phzBCCuHYsSFFgXQILaIFxrz4qkEaSt6h3FoSGkuqsbh4RZ8aNs2PPPMMyJIrA4Ok6yg882sqKnAgCI3AmofcsNnMNW8qF4pKxb3NHoSNZuiOrKzRyQSRqfnGIIR70TnFxJeBo0FVn0Sg5hGAHtqTFwZDWBNkdXc1wm89BNgeAAoKgfufhTIS8PI3nw7MDoInD2k/F5aDdz9/vjX2PKA+z8BdFxQCIPyRZM1cGUmtJDCSjMYw9f2XwKGkgTTeA9mkqklKhcSpLLYA0eC1Rjsh8PSbsnxsm9IWRLHjfJe2x4ADj6jnA/nD1+36U6gpBbupo4piTMJZvSx15HMEEwns8EVHMZVN/vcKPD6nILkrLbFk6uuq+2wOmywlhTF+ju3d+PejVvw7avNwHD8cfmaYrMpa4QGQcM5owwg3hPfUSCs9MIRNyHUC1huFkb9hHxOEsj+Tolo86gqmmYAGoj8HszUlNU0XCPUVTU0FpmpzDXnP9rOQWc2ia5Saj1irSaCjfl5uCk/H4eGYxffptPhX6NBRpaAM6Nu8+bNEz1Y5h2mHMCUB3SeiD3XMqD0UqhMMNaYdFDjSEu/nFmDKeeEcwz4qz8DPOPic6oDLlz40udR+N3HgeI0qipngzefVRwnCdoZL/8YeNen4l9nXI3FDb3o6OxGS2sfVq1shMaoVHDxPh8ePoqxwNjEfS81adDpCWNkxI2XW97E9z1HELFbMD4ygtaOs3jzzAl8sGF9XHaT1AhONs7dwQC+cuEQTnB9YjPygnJ8unEjTCKbM4Zh/xUMB2Lk3rinHxWWzaLn2ZabbsIT3/0Kiu0WrKsqwdnuASwrK4Czsx0pa6k6O2PNwdW4Fo6wBM/l0JuscWfJG7B8hfJ/WSkgpSO4JvC0SWzwPW0y6HQDQhA0GRDE3JcGXweCTrG6a+HD5rUNwhfINqnBY3LNuqaNRReQFQz7e9HpuYxgJIhcfRFqbMugm0mi1FsUDKAxs5lJDYmQVeJTzQPaEPQpuNcz8YU2IAOYC0iBxl1AYS3g7FfkdUsXX5+ZsH5X6udnSmqwyv7+jwEnXgHGxwDGOtbeCk9HpwhqT1edwL+TXCAJJ7PDpwuC03Y5MXIMg34lSc4FJ4YHh7C9aAdsenucLCsTcP7pvvfhjcEOoaRhHRnH7kXL59V2zVZDbyY7zlvykAqsDJG+AxOZGBCmf8FgPdcYJlYyQEx/g+SnvI+UimIfEfoemVwrfhaTw6R09zUBq7tO/hTwKraJSF509gKb3h+fRMhEvcLpq3toN0u58BsVJm0+HPoahMPnkZvvQF/PEJZUbZq2SoNjhPuOTILjvsI9hWQZ9ymSGTJJivGFqRrQzxYkTvn5P/jBD/De975XVBPxvLhOcJy/pSo3ulNI8nZ3/lqTGl/96lcz7qOZsXVJBpMMNCUyAC4YJAfkDWEAeR5kLabALzqb8e2r5+GJOs2aKGkx4Pfi/NgwNhWUIJjoUE8TyMoaKlcoj0zBhsaTEIl7XlZZxEMjiI1k4EA5PnwUw4FoQCwCdHnDqLJoYZbSN7NB7jJF6sDbB2gNgGMRoO2cLEEkMPvFSaMxw6S9CYEwAzBeaDV50Gsa4iYDG4duyl+HI8MnlWARBarCwOmxq/CzIXUSTX13JADfqjJ8/qffwuLqOtxesxpVZeWCNaYBwAAlNwIaMGp5E2YvcPFl8FJmhDPgJJqrOoqBTe8B2o8rkkQ0siuypz8ZiHgQjCQGXzVwhwaSkxqNtyk9NYLeWGnm4ptjf+d8YaDO7QR+8V9KVRHv47gL+N+vAx/5M6XPxlRgoP6u9wK736GQEhZbcmeCEnCLVKXnpUuAQVXwiO8pacjMEWk7BLQfjmZDqzRlifxaoCR1cDsl8suAgfbYsXjcApXBZDACb/sd4MRrwNiQIseydrdyHZJhzS5gxVZFWouydFFHlq9mds2BAweEHiohJJ7C4aQZ2DREkskbpcKQj2RefNXQUKATlZEV0Gq0YrwyMLpx+Ur8vrcC37hCsiws3vFg2SIsthdgV9E4Hm8noRiruqu32VGsGhMzbfCsBjUWGcxOO/sp4kIk3K+qGWRZuw9w74dvuBlhtxtaK/uzTF5/LDodzFotvAl7RcUMSJpA2AdXkE6dBmaLUawHXDdoLJLwTNQJ5d/43BsnjuOQr1fIMxnychEYGoaxUAkXV1tsWJ9fhP23346/PHsWe/v7UWOz4WurV6Pt4kUxPmgESo3Va4qeM0meOz2Z1MggEESprpSkxsVzgDsWFDBrNfB5fcCZE8Ctd2FOMdAVv77w/8O9k1+ndSC37H6ca3oOmzatw/mrw1ixUgkQeEIejAZicm+8f6ODoxjtGUGYiQElJdC6Q3APDiMUCGCsrQvnzCZYSgvRfvbixFzjOOM+lQzfunoSp9j/Ioo3h7rxg9Zz+GhDzH7jGOr1NgtJCKNQxFPG0WigTZAaRrMZd6yoh8vjRbHDCrc/gFOd/bD6tDi3b1+0h1SCY0i5pkR9aQYtSuaYbEoFzu+//Dywb2/sucd+G1g3jfwJ197a66yyZC4RcgPB+J5KW9aR1DiAj3/841n9KAZb5ko2bQHzh7HAIJpcxyd+H/B3IBjxY4ljDqSFbmDQj2BAkY1QGchhgJm+A/dvEtKs1mC1OO0FafMx2EM5SSZLMdAs93gGMJP17FqACoU1yuN6RlLiQqP01pgNcgqBm98V9xST7TiOOKZkzy8m15BMS7QdGRTPRP6VtowkNCTod3d5ukTFhvSV+XkyYem2EmVf9RR6RIA1neByNvwL+f2yIanDc+Ecnm/wcxlzYEyC6wNlbRPvF306rim0o4Uc9oYNwmckQZoOGNxmDJAPJhjxONcUo52AV22bRJT+oEOtQOnMKmV4zdTzYTpwvGQqizuXEPa/aTlWVPtgMEQQ9OmA8SJgCp6NVViMH5C44HjgmkAygz43iXLKlnV1dcUR8Ix5kVTPliR0Iri38T7w+NwnJUFHv4brE8m4TOId1y0qUlSgVGbWn/qtBK5LTD4l51CbQX/PjEkNlq6RBY6x5ysQAYMLdFRNGfXSyDae7m7F319UZYQmCGHpo5IqK3KKkKM3whUMCMJDCw1MOh3W5s6f9mHGMh4V64Gu47EApKMCKFwcV26m15gnsvMVRGDVJc/q8Ia9MUJDBVcwArNOC73GJAIXs4K1QnlIhIzxAR9+Fc2ipEHFmUCrscGkm5ocqLKW4/DwBbiDnrj8f5tOC5/sjxDFUkcpvnN1H9whPyxranF5eARDrYex8rQZ9XX1E5kr3OQZiOQGKBltbvR0QtRZJtQgnwjIOkqAFTOXsZkKlAlJhpRSYrZCYPMHgOF2JTifXxPLcGCD6199G+jvAIIhwB+Kz950jgCdV4GGNMm66ciPRNRvVHp8XD2sfF5hHbDilvQrNUigtB+RJ8yLAEQ0QG4FULEWKKyfmfTZtrcDL38/1uA7rxRYf+fkXgEkKt58TjTTFSTQTXcrzycDS2T1k40ijjOSZpT64AZOY1k+R8OD1UJSloljMBMjIyJSjycTjeFwCKFwSBi/slHYFqsVq3JK0O9zI99ogc4XEMHlBxoa0DruwtcunRPE8BK7A9/etA29PT3CsKbxwQApHQ8aTDMxgOjcMDMj3SZskUgAocgFREyKFacNhqANBqGhlg9c0Gu90IY6leC3bWfSsfS1devwiWOsNlPAV3w9w6xkT2gMV1yHEYoopHOnqx/1xropK05YYsvvay0sgPvV/TAvXwpTWQlc5y8JUsOm0+MHN90CQ5T4+utVq8S1ZbbbmWjmNMfDdWPsTaoyFE/O6pAcjykd2WQ9qSTRONdg0KC/U/Wdqe2UwjHSGGHNqYUnkIfyCofoSGiRTAABAABJREFUh0GHNNl3Gh0cQeWSGgwFIhj1hKDT62EvLQJKixDweKE3mxDUasRcpS60LNOmk06HJFEr+8RIv7B9JPi/4yN9sd8jEZwaPYlenzJuOdLKLVpYdLzusT3AbDThbEcvekbd0HGesyFuZTmq160TBimrGeMaRr797cCBA8CePcrvzLb60pdY95/6moZHgNB5gHOI9ox2WRbkKqN4/bV4QoP4zreAb/1/yjnRgYuzWaL3xpEDfO5zmHfwXMYuAa5m5RrkNAL2+SBXJl/vDavr8ex/sEdVdkEf48EHH8z6cRcwvxj0dU1KmBgJ9Im9cKFaIx70CxhspO1GJ54PVivRnuPaTftJSlGxLxYz5Jmtyt5GajAANF9SPQuYQ9C/r9gIdMkqag1Qd7NS+ToHYHD6xRdfFEFw2gt80A7h2OND3VMyk2oGEhjJQN+CYMCUPkGyYDB9ZwbaU/Vt4pxgkFNWRJAY4bmSpMm0WTPfyznH99KnmS2pwWPNVRPlqcDAOgPNfKQiG+gX0E5kEJvrCK8h1wzZHyWV38BgNt/D603/hXHA66KaMql/McXzaYDfj9eFcrfpgFVyjPtcL6SGRGPDGpEQyb5NJM5TkTT0HZlIp/bNSXBwXHC+S4KO35M+vQTHONUh5orU4GezrxRliOjTcnzyPOkncbxSivjOOxPiLjci6uqB9/+G0ldD4sF3AKvTr5x6q8FqtYp9iRJnc0pqUM4iscmoRmTaT6fbPiwMWjZhLjJVwpROH4oM8WRXa9LnSVrU2xxYmatMPIfBiL9auRP/2nQMnR4nSs02fHLRehSmCjbOJ7gQn98LtJxUHOj6DcDS7UD1VsBRCYz3KxUaBax8iG3c1MCvsGxAt+e4yNTnXSk2LoVNXzRpkRgPUVrKmzQopIVeECHFpuXT99NgxuWZ/cBQL5BbCKzZmTqgFB4EIucSviuz9ee/uRKJt0RTq8xswiJ7FU6Pdogm9Rvza2HWGuEKxVhpa36uEM9au2E7jK6AcDKYTUXngtkmMnueG0GyhmsMVNJRmWuwObxDXy56qiigIJwOOfrK1G8yWmNZDSQCmo4A7jGg7bKSfXytwPG5fDfQuAM49yLQfR5443tADkmEtyuNxKdCiPJmspoiejz+yv4qRYtmp897/yeBoW6lqoJVGolVGCSEnviXWJP0nlagtw14+JOpKzZSgOOMhqp0NGjkc7xx4Vcb8MzOJvlBozxV0z818ozlGAn0xDvG7T5cMV4VxyVhol4jKN9n00cNI4NJGHJ0CD65eBk+1tCI8VAIOVEH5HJn9yTpG5J/NLx5bvxONFgYgOVnsGSbDoXaUKaBzePTgOH8oVHGPYjG1FSl76HIaUQ0JJyUcw8b9NCwwXk0S1xLcosDgQHT8Bigm2yM/s6iRaixWvF4W5tocv5YfX3GTcLbx89OEBq8X8FIAF2eC6izT02O0NFbTn6RJbjnL8G2ZgX0Wg0MGuCLKzYi32gS6zcbjaqbuMleE8/09OD82BgW2+14W0XFlFlsbPD+/ZaLaHKNocJixW/WLUVeqp4wM0HJMqB1f8IXnF3ghXNBaohPworVQFUt0NWuZOIz4zAvD5H1m+Ym5YJz2zWm9NFh76GuK8B41PCnnNMt8ZmRajDgT4Od940VUbyHz/7yWfToemEqM6OspgzBQBB6owHOoLKObS2owS9Vto7JYkGuwYRysx0GrU7MSQk6ACRLWOWkhl1vwAi1hdXCoaq+Gu3jbej1xdYFuofdnjDqbVrY9DFn+ZxfCXp4gyFsri0WqS1PNQ8g9803hbYy98U4cE366leBM2coAM1ULHbNTX1tQ8OAX6WBHnICBs7XLdmRDenqSi6HNTYGfPnLAGVWZWbau98FbKZcQwTYsJFC1Jh3jJ4Dhk/Gfu+PVtvYaoDxLiDsA0zMyMvyudFeN5UDvphNsWZFPc6eO5+UNJspGMCijnKij7GABbzVQVuHQSM+aN8wK5YZqQwicq9nUhT3B861ZE4+X3NdBBkXkB1UbAIKlgB+J2DOS6HYkB3QPpTkAe1UOeb4PG10NRi4ZeJEok2RDFadFQ69A66ga4Lg4Of0tw9Ak3NRJP5NFQiWlUrJqrNp55CEUPsB3IsYeKfdQ4KQYKWHTC6k/8HAqOw1xp/0mRjUJUEolRc476aSp50OvI489nyDNnE6ZArvK307XiuCdhrvOX0wNanB53mvua7wO/GaMN5xXSG3EjBYgYCUNqe0pgEomHk1lpTrShf0t0kaXG/gfeM8pW/B/wtp28OHJwjwu+5SKtfpk6dSQOBYUc8xdbyBe1G2JNuSgeOR9iBJVZ4/K3jZoJx7I0k1XveZ9oK57vCe9wObtgDtrUBZObBsBqo+bzHQD2C856GHHppbUiPTwOygrxtX3XTEFCe0x9uCFbnbYNHN/eJIE+++8hp8YvHKicxWotaWg39Yy2bn1xnO7wMu7Iv9fu41xelu3AbkVSuPFDBp7ai17hRSVAxiJzb7DYZDeLX/GDo8io62RatDjiE0ISvBYP/SnM3ISdLEKyn58vR/KRnoPD8ubFdOAw9/QgnkTHp9T5Lm4W7Ry4MVPvOJRkcNjg5fiHtuWU4tlufU466yWI+MA4NXk7U7RzASQlVJiXAwuKBz8+NGT0b8/vvvn9LBny8pmFLTSjEePKER6DQmFBjrBNkxLUhovPw9YKBDyQQVPSf0wLhfNK5WKrKiEJmieUDVLMiBdNFyRCE0JJx9wOlngU2PTP0+g0WpdPKNxZMbJAcHmoCiWWTTMAhYMgWDfOQFwKPSxuUc6WsHetuB8jSya1kF03FJECAWrUYY3Rw/U2lJ8u80PLnpp1O2nWsoQZVlJXq9V8S64dAXw2QyYGljemW7DCrTASDZwOwJSWiQsEjW1JsEBo0Qzht+H1aa0BjknKETQMNKPUf4WjpZnF/8DH6v6XSm2VMmgqHEJxHW6aENhNDXP4LSYnXGSmqj7P7ycvGYKfzhWEl3f/cgSsoL4A3H+jlNhYqiYnxm2Xr8s+Uc+k+dFSTyByvqsdmhNGtraWnBxo0b47JveF1ZXcIm57xCDNXeXVqKVdRMBvDemhpsUMnkUWLvU8ffwImRAXEVmACwt78bP9xymyCwsoLytUqPHDYM5xwgyVE9O3KXY5sZq0lJDWbu/b+/Bx7/AdDaDJRXAktWIWKxZp/UGOwH/utrQG+U+N19D/DuPwRazivEaXWjUr2RAhzHnDecLwTHPufI5srNONN3BhqvFoWOEuRY8lBb1og8gxUFRgfyjfn4n9ZTCETCKDZZ8buLbxKExuRLYUwqK/CB2hX48oVD4n5LvLcmRjSNBcei5H9sbnAsOQx1cKjI8S0f/yz2fPXPsKXAhKb+Eazdsh0P3/sbQoqKhBuDDMzsko0Go1+a1mp61zeoON1xCAyJpqNjQS3KzHnI5Ro/U1BCKpHQ4Pmx0qWkFHjmGaWxOcnM66FJ4djF5M+NXgLYG02AEo3bs1vBIeQVtwPOs8rn6Mxo2Hgb9PrPivWfDmY2wGNxL2homP9klwVkF0xeG/Cr+/wB+YbShSqNNINqnAN8cA1lIJiZw8wAn0r3/ppLTS4gu6Dvwsccg+OGiTQkA7j+TmVf0+8l0ZDucTfmb8aZ0dMYCQzDqDXC7szB8tXL06okllXetHcpq6YmWGhXJBIrPHcG6/k3ypPy8zln6KPzOPyO6mbDtJeZLCWTt2TfgEnJGBliqoqHuUa6a4D0FXltSQDJNYaNn0ny0Neir8l+Gdc19CZg7cPAJfaKYaPwXGDJrYAxs6bYs5E1o+96va69JP4o6cm5LeTPAdFLhfEq+h28x/QTUlVEXUswhkAig9eXld+EbExOsoMECyXUrklz+rnAosXKYwETpAb79mSCGZEav/Vbv5XRe9rHZTBScZDZvrtzvAmLHemVdiXCEwqiyTkssmeX2PPFT+LBilqcZDPiKLjEvK9mCT65OL7p7XWN1pPJnyOpkQa4sOpSVM0cGb6AziihQVAv3hSyoNCkEcbGEvvSeEJjuANwDwKWHKCgLj4zsqtZITQIGRRgFjqDOYuSSUClMpLmfyNY7qD0iwZNTsotAUvs1VjmqMN40I+xoEcEjsw6Axbbi/FSbyywwyCPTWdEObNm5NlrNMJ4IZNM42iq7Iz50NhkOS8NOpZN5hvrkZGAGDN3jz+vEBrq8k2zEfAGlKiWxQBE9IqEUnElcMcjSobyXGMooSmr0Kqf3Nx9EqitSZJNkzjWNCxJmB2pMRXYd+TswdTE0XRgVcevvqX0XGEwwOnH8YAfBdW1kzLyaIDKUmOOPxrvic31aLTyuWRN9wpNVeIhszf0tumD7uPBAPYOdAoJvzVVZXBoDCLIzColGhrM7knV80CdVUGyYiq5BPVrOc94bL6HAf3U7xMlOfFkhWiMyyqLTphNRlxq6sT6tYugM7B/SfZK+nlv1PfHpLVhPDSGgN8Pl3McZZUlMGr1cAcHYdWx8mbqzMp7126E1emBr6IRo109WDIeEdeZhBBLihMdp0NDQ4LQIGSo9vneXrzU1yeuyD9fvoxnd+3CHdHs+DOjQzg2EuutwP4J7R43Xuvrwv0V6Zd8Tgle+6qNyiNL4PemQc6xntQBtzuAxz458Wv44MG5yWL93teBflWl057ngOJyYGv6CRMyY5DnJxvvFRcWo3q4WvD+Q+1DIiGjwRaraNhdXItdRTXwhYOwMCstCl4P9R5EJz9ZM79thRX40qqdeL2/Q9ye20pqsNQRI4jMWvMk6Qjuf0WmxXHOm8aRj02f+TJe/OXPsenO9cCKNeJ+2wxGUX3C0uEXXnhBjFcG57Zt25aZ85eiJ9izPSfQ64uIhIy3V2xEo2OGOvLbdwAPPAg89WT0C2mBP/i0QmgQzCJNU4JgXpAsKy44rjxiLwL6DwDWKkCbxabMGh2QEyOjSKFRxoI+QbZIDR6L1UsLGec3PhyGAiyxb0Cnp0n09ss1FKPKOjON87cCQpEw+r1uUQlebLKlvQ5KCSBKsnHvTwUme2SrYioVGBxmEIn+zjXX0l9A1iD6do2OimqgZP4FEy+khCxfSz9BDf6tr68vaT8Xk86EjQVKIJI423c2o4A/JUho07ACnUlPUm4qlcQUn1cn+kwlXcLvoh7HHNf79+8X34P++kyblPMc6Yun7Ps2C6SyeUneyP6dmZynrCrh+sHrwSAiiR0SqnMlK5R1WPOBde/M6iFZ8cLAOQmv6UB/dCpJ4esBsiclwXnMikCeN5OepORcOuN1LpuDpyJlOCbVslcEbU6uQ5S/euqpp8Sc55jl91rAW4fU+O53v5vRezKygDj4ydDHaSRHEYn4EQabUjIj2gQtliAQ0cAZ7EAg4k98NQJs2DoDdHpc+LPT+zDEoCE3IVsu/mb1Dtj1RtxfXiuagD/efkVkMN5dWi2kNG4oJAu8ZEk/usc7FBemYNBiPBzAO4qTNE9t2ge0qxiykqVKDwhpiHtTNJWi1E4yaCuBUEL2g6ZU6IrPF8IRyr/4YdAYsSKnXjwkDg0144Wes+L6sPfK2yvXY3lOOd5bsxlPdp2CM+hDicmOhys3wJSsEoVZq1u2YN++feKnNLzUG0HSsk1nJzB4AaBOeV4DkD/zIDs3YDo1DATTEKVEy/PPP49777130vlMgt8DvP59QBXgjAOrNEhy8Lu/7WNAxTxv4KLHR0Kgmhka0+HS84Bncu8YAVUwMOtgc/DEDGCC2e8lVcBgL3DoBUW2prIBuOlORZpF4tWfAKGY8VBj0aPz2KtYctuXJhm4fDDjRjoc/EldcgaIaLDLHhvMQprKQGMWB6VqpmvE7Qz68ekTe9BFBz16Rz7duAHo7RWZFSwZpaE8F5krdFA4l6YqDebnaiM1CEMtR6iBVtsAWKuRoz2NJYt0GBzRo6R2m9ifRv2dCEdCQq7Ppp+ZIc/rS8eO35/XmfOw2roKTa5DuHz5KhpX1sGsI3Ewhi7vUZi1eai0bISWwcIUoJN508aN4nrude4V/TKmQmuKZn8i0N3SAo3RiM+eOoWjUR1Sd3Cygcq75g4FMeQfxwVnP2v+sCq3DDa9Ed6QH4FwEDa9WTSSvx5KU6fTvZ1R1lx3F/DCs0DLFaCqBrjjbqA2Yc3zjAPdiXuaBmg6lxGpIZvy0Xhn0IprtdQ9JjifWXJNaQR1liGDY5LQ4Gvo1PK7qrPcOW7orCTDqtwi8UiGWlsdur1dGA+NT1RsLM9ZAV2SsWrPycXN9z8kqjHGnM4JPWrOQ94bVtVQVo7nxazAjLSmtflAKJYMwLWGlM2gPzJRafSrruP41JK7klaqTAverz/8DHDfA0BfH8BrVxWthmVWW08Ptc7mX2rKPwr4RgC9FTAXxewuVl8kVmvobEBQSi9Ewb2a102rqpDJBrxDQP9RIOAGLEVYvWqFmIOPPvpoVkmNBbw1kGcsEY9fd4z4vfi3pgPo9iqBmUZHET7WcBPMKXyJZKA9zwAvgzhsopkIZlkzA3euQP//zaisIAO/tC25XzG5QGbPLuDGBRMPaHck9mlRBzulj8FKBkmwsXqCBAAD6ok2SiK4/2eij64mKmjLyEo+2hpzITnDz2AyBmWZ+F1mSkrQlku3miVd0M9jVTt9H14DaR8SrKrg9ciUhBCSuNEm0LynDBzPNEGBviO/M6vHZ0oGMX7X6xsXPW8pE38twfHM652OvCabpl+LHirpQiZNce7Kfjnqeci1nbGrqcY713n6GNeC6OJ+x6RhnifnqKwoIZFx6623iv2I/iDjHgukxg0M+i2jZ4FxJpzrsHpRgVhTMiGYMyI1yPIxMJs4aCj3EQabWLuijtU4QpEj6POFhdOp11CyJ/5YDv3MmlD/06WjGPHHAlrN7jH8oOUcPrFYCWy8rbJePOY/e47npJ19kL5hI3D6ZUCvjcr9iKjBhDb4bGDWGYGEGJZJa5y4h8FIEyLoh8blg6H9dPwL+y4CZUuVxsoEA7PMlFBXJtDxLkthsGhyAN1mIHSFHjugYcPP+dsE+n09uOQ8jTBC0Gv0WOZgJYMS0OkcH8bzPTGZi2AkjJ93HkOl5TYstpfgDxvvSKsMkX+nNBtlN2j4q7MpmAURNylHe4GOI0BQleU71g4EfUDx9JkBanChZ9CLQSMGQWmcvOc97xE/lyyrwN6DP0XD4npUl62CXptC8q3pTcAzqjTSTgSDRetuVUgAVuHkz7OTOtQOjFDLWy4iUXJjyTQlh6yIcPZMVj0jmMFaNocVXDYG9pJ88OqdSjD0f/8JYECZi3jHFWCgG3joI/GkiCor16TXolanFVlydGzpfDBQyAA6s6gIOT75k8FvjlmOO5IUssKBToVs+icagIVa4A33iMBl2xU/1qzYNe1X+1lHE3qipCZpm4B7HH/78lP40Z3vRklRkTgnZlbMRZYSQaOM5MlUc1Kr4drC17AyTQ+tphZaTa6y49lvhcXoR/fVq8hDBM2u/UJ6i/dr0N+McvMq5Bmn1wtOBJ0KZjhxA5ZBdLPOjhxnA25qrIZf3xptzK7AGx7BSKAVBcbUUisMUvO47LswlbMosToqMyV0TplRw+qw/n5eNGo2IdLVhX4VIbQiJx92nR7joeDEmTFYXmUx4e8uvAp/tKGjo9uEW4tL0eFRZJbsegvuKNmM3DnUeZ4ODJ5zLHCMT9XYkWM/I93XyxeBz/0REIgmYxw7Ajz9S+ALfwOsU1WbkChO7MfA8WjJrOydQQQ2vGN5OIkxOoVqcIwzaESnI9kY4PpP2yxZnxkSmtMS2klg0BqwrXCHIDYC4QDyjQVC8ioVpKPz7LPPCmeQ15zrFPWyee2p9UyCo7m5OTPnz7AaiDiBsBIMjECDn/ZF4uzJQCQkKiwLZzoWec+WLVceEkeOAH/8x9S4UH5nVdif/En6slmzwcgloFdV5ZezCCjbHpWAihJ4rhbldzYKZ83EhPSU/E46QJflHnEBF9D2nJKAwX0t4MTyMi32X8ieljSDCG8ZKYEFLCCK/249jl5vTIr0snMAv+o6j3dXZ0bgkdhgsDVZTwMme8yVBArnJTP5GQh76aWXxF5F8Dmu70yiYqA5sf/CAm4cMJj96quvir2bMR7aDkzSUVdEyPEliQ/aX7R3pdQtx4k6icQfdqHfdxGBsAcRvxFh5MyoJwMzynkeMuBO8luOwWyD3ykb0lGcF9mUoZJBadpRMuNegjbwVBXvqUAfkvYYyQwSlm9/+9vTfq+897JPokjiqq6eHOtIE5ecI/jsqQMY8HuFD/OB2kZ8tH7FNZV14rVmbGU6Ca7rVXpKgjECxoNS+cy8d4wTkLRIJlvNuAGJBI4x9ftJiiWTt802ZEUV/aM33nhDjDOuI0we5DlwveKawB499DW4Ty7gBsTwCcAZ8ycqjYOwWi1ivUs32SmjKDkZE6lTGA8aa3T+1IGDMCw6OvsR5Bo10KnekmcoQYVlZjr8ze5RUDE99ikRNLniSyHnFREvEDoAhPYAoVeBEHXDk2Rop4vFW4CqRkCvU5xWPii9c2nvrE91Q16j0NCW/4jN+cpGGIycRxhtiARd0HWnkPXxsAonCkc+cM+HAMmmM/C95Q4g2ow9KTT5gH4ToN8O6JYqjncmoEa5uwNwNQPB9HTpifGgCxecJwWhQQQjQZwbOwZfSKn26fRMHj8k43q9se/LMT8W8OD8WCeuuvpEKXky0IDhRkjDnwEcLvg0bOKanQ22Awf+G3C3T5aT6Ds17feh0cCsrJdfflloJZ4+fRrbt28Xn8OMgbvvvlsEv6+2noA5vwMV9XoMjl7C83u+g0BIdQ8T760oU9EBJlUFA+/rjkeALfcAm26ff0JjfBQ49jPAr9o4OSdW3wdUTbPIMeCYLJuc8mFr3w1YoiXKI53A1QNA6xHAn/64mhIWO7Dtvuj5Rs+huArYfCdw9s0YoSEQ7UfjVFWUFJTEy71ptKhfvloYoGz0xfvOACcDh6kyJzhmGVSUTgmNAHWFw3joKtwhSkO4EAiPwR26DF84vmk4jSBWOKmrQloH+uAfG0M4GETIH4Cnpx+m6goYc5SsYAZRaVzMBejMS4NlKkNSyPBpaqDXboReuxZaTd6kQDczP/o8TRPrgty/er3nZ9T8jNea2Sz8bF4nCe+4HxWF1XGERvQs4Vf117jq7savug7g551v4PRoszgHOg6y+R8NOmaqpQJfb+zvx6d5Xdi0jk0AmW3O7HM6PFar2PBvLi6eeA8bgn99/Q4UUU4u2kD671dvwesDlxGIEhqEK+jD6wOxqgR30IOX+47MaZO4KXH4deCvP4UNP/smTv3dXwBuVe+aBKSSYEqJb/5rjNCQIHn/rX+Lf45Ztnc/HFtr+OB+yL4aGYDjhXOGjjudi2QGOV/D7D86oYmgA8nxnEho0Inh2JEkZqbQa/Wottagwb5oEqHBcfncc89Neg9lGyiLxs9kQIT7H7V7uVY9/fTTwmnKqIEmbQTjTsB4M2DchiPjS9DsiZ9HlKBy6LOoX+1yAZ/+dIzQIC5cAB57TCE7UoFretN54OIpwDPDfYRyib1vxj83dgVwtceuR+FGoPadQM3DQN4qILcRsKgDPFqgZEd2pacIZ2uM0BCIYEmVA5cvp16TMoX0MRawgLcSmt3DcX4r/3fFFZNKzgR07mkHMXGEwURKcKSUYZwBuKdzL2LwiIFO/qTtyGxZrt8MkDFDlus6SQwm0DCRhVKDXPMXcONi165dYs/es2ePsB3os061HsuqCQna/VKaKhj2oX38EMZDgwhExtHRfwU+W7uoiE417qR8LkkyBsxpZ7ACgPaRuoKchN5cSBTy81mBko2eTpwnshF3tiB9ObXdzes90yAuj8d4AmMHlE9i8tRUNj3JD647TLBhVjwTOGnzMejPTPmZNm1mhQYJDanAwjP4YeslvNIX35NpviHt8Rt9XWPQnz0puE4n63VJkLDmup/s/tPH4Fqv9rk5V5iMNVdVetxfZB8NCRKZJF2ohsK9j2AsjA9Wu9Onn0qi8YZHfy9w8gjQ3oK3HCIRwBVP1nK8LWmozqjqLSOvhwEVypykC43K6SwwMlCsQaV5J0w6y4yZzSKjBd1e94R5yCB9qfkaZoeE2AND3Qy4E4hYAc0MN0Vel2iJchy6zgLLds/8PFlcYc7HQxU70eTqFAG2Wms5Ss350UqbbjGoDH0jqbtc2Arjs+A7ryhBHE0EsBmB7uPA8GVg66OAI8tBzZAf6H4RCIzGnPvSWwBLTGc8FUaDDBarHIpIBAOBIJ7qfgNmrQm5huRBYbtK3qjZ3Y+fdx6eIDPYV+Pd1VuFPv6k99ntYoHlRiKNMxpqNHC48ePS68oElqSVGuGpg28sM6VjwWA2A52yLJIVGgyq0hjjRnPXXXdhXOuk0JvyxtI85OXbcOzUi9iy/p1iQ6KBwmshDCKLQ9zHEbcXwy4PfIEA/EENwjc9Au1wAM5zz8LT1Yrt27bA2rhGITvmAyT0VMFVASGDlYZ0FMmEqk1A+6EYscCfS+9RCA0e9/JrQPcZJRDEe9J+DNj0XsCcBdmO9bcAJdVKrxmrHWA1GeWnGPxKVj2iDrze9ijwy2/E5NwoE7PtPuRY7MLxoDHJTT1TXWPZEI5ZFx6VHJzb5UFOLhvLd8CsUwJkXCd+vPdX0Jj1yDPYsKN4Nex6Kyqgh6uzG6b8PIz3DKBgFRsYm5CTUC6szgoJhMM4NjwsyEI2qjan0MOdCiTseDw6CzTOSCDMJDOQ14COEveytp4TsMdi/AIKycGbk9keRYOQlRp0FHh8Ol6yLFynMYLdjiITBAoRgVGrnH+zuwcv9h6b+EufbwT+cBCNtbWCiGKAevfu3SLAwAAxj8n7SOJUOpGc05z///DII/ik243Xz5zBykWL8MdnzuA1VmtQsik3F/+WIGG1Jq8QT+28B55QCBadTlzjp3qOJ0gVAr5QfFDGGRyHLxxQKgDnE+dPAD/5L/FfzmrHQD9c3/5H2P/gi0lfzu+Ttr3R1ws0pTCghpPI2N3+IFBYAlw6A5gtwPbbgaLp96Rk58j7x7GT6lyZnURDn+s8iUw6oyS6uAfIvhz8yb2GpDbnxnTSXJkgLMjPS4jAi4vn+6HRGCYF06TeNvtocF+hDjYrN7hnMeDBJrcZ236iH46S3bkhPwcXnL3o9Cr3gke6t2xt0n14xmBj8GTZZyQqv/c9diqc/DeSat/8MtDTEauu/e3PAmUZVnwFxpJsDBpFjioVaAuV3QJ4+4CQDzAVAoY5qKBK4uw21pXh8uWmrARVeQxmZGXiYyzgLYzuFuClnyjJHkXlwF2PAnkJm/V1iiH/CE6OnIcn5EOhMQ95RiN6vUFVrbEGuUJSdWag/cbqRNpAnHdcX2lzpNvYdqo5SCkPEugkK2SAkvYWg0WU5vnhD38obFAGMuVnMdAkKw5pk8osde5R3KukXAiPz8xu2jR8cM+Sfcj4O4PXyaS1FjA/oC1Bv5KkFeM9mSZEcDxKuUt3qC9aAa3AO+6FqSICb2gU1qjEK+0X+jH8XCEbq9WK99PGZ4Y87Wkpj6QGxxQDXZn2kJgODI4y+5vnkVHyhQocy5wrPMdsVi5xDsrkHDlveG3od4i4wgzAeUt/iDYbz5eBbZKT0m5kUJlrCz+Hc5frAT9Lzmfu12pCJTGhK11QcooVGmroNRqcGh3E7aXTV6jPJUjqkdydyteWDe2v115g9BOlJHSivJwavLf8rqwW55xkEhW/V6IPRfKAcuckNOaiOo9jm2OdYzsxQZJ/owSj3PO4fvA8+DvXLjXJ+pbCvpeBH307Zoffei/wrg9PjiHeyEjmYzRUTpnMmQh9drKo6EDR+FHr+mrgiVMm0sKuy4eZGsGMD/hHcdXdilAkhHJzCaosFWkZY7+7eB2+cGY/AtHPMem0+GCNSjZg3m9AkiqR8BBA/fZZHzvq0Iuf2Qki5xsd2FyQokwxHIHWHwQMOiDfBgyrsg1rNgH5qs1l/1PA2QNkrJTIktunSBdxUT/xFLDrN5BVjJyOOvxRMNujfz9Q845p36rXxAfAqcftEraWD+MhH4YCY6i22NHucQmSjBlVq3MrUWbOnVhEn+o6Fled0eMdwaGhK9hZNLn/ADc2EhpkxNW6hTT4hdMhqg4igNevXOuJca8BcqfWGyV5wWarapA04XFpAHKO8qdopMt7qUJevgOdrT0iy5/BLy7+fC8Ny2UDbfD4Ajh6pRNr6spQmGOFN7cGhweGsMrhgv+F/4FNE4Fu4CxQ2wg8/HElQD/XSKU5nG4/jMqNgMkODLcBDL6WrwashQqhcernwFh3lFxSxj+CXqDtCNB4a3bOv3KR8lCjYSVw9NXY7yRa8oqAPJWBXlgGvO9PgJ4WhUCqaJi43lwn6VDyPmZKajDbiYYKnRWSmZcuKg3YrTYzBgdGUVFWJ37v9Q5hT/8JQWiU1lUIJ/yCvh+PVO3Gb0Tq0RQcx6HONhhzHbAajPjz5TcJ2SIJaQgRAz4f7tizByejmRWLbDa8esstqM4wo4ekhgx4MbBPB4TkXiagoUSDTL7vSg/gzw3AaFSu7eiwC+7hAPLLkjc9lKBTQQdOyh4xq41Go2xQSGeAD5Kb8pzLzKvQ7WUlVnTv0uYgz6CU+p4ZnZx5wWqNjXVLxDyVmULyvnP+8kESI1n1ZL3NBn1FBUocDryyezfaPR54xsfhbmsTAZZE8P1WlW5sudmB9vFYVSSPblWXW0YDM4bZBpPpAF3cC3RdVOb0ki1A5TSavqePxsk+Lc934NSxw9g07uZAjnup7C+TtqP1zK+UvVeSzspBlP8vThFsXbdFecwCHEvcM6SzmArUTOcaTyOPASOS1+p7z0w5jg8GhqaS5MoU4YgXvvAhpZV8JIL+wSvQ6M0IBJj5FQvOkciTkgYct3wtz4lBCp4z98bZOH56rQ7vr92GJlcvxkN+VJrzUWLO3vcUmCrrMaFZ4QSeewLoVaTZJkiOH38L+NT/y+yzk5IRkalJCu8AMHBIqV5lRU3xHGkeO2qAwRMqx0ODhvpFItjCzPGpnOV0wGAnj5WNhpusLuzytMAVHIVRa0KlpQEmXRareW5k8P6NDijJFfmliozs9YbRIeCJ/1T6ivF8u1qAn/wH8OE/BYxp9FK7hnAGXHit7yBC0crMcY8H6/IseL5HMyGNoNdq8WDFzP1WBpJoa8igLm0V/k7/gITETMH30zZMlPbhms7AMv0Frt/0MRJtDpFVuWSJqCTmus/sXiZ4McBLWV4GRBkwpW/C8yWBwdcymMrA6i233CL2rgVcezCQyLEgE6AyCZJTOYC2ui4HGOwfEY/8whwMDoygtKIQMMcCV1zvOd6SfQbHNv2bZPI2JMBSZZzPFPwc2kyyGoJzjIl/mQZJGejnd+L5ccwzoCz3NM4LKRucKOWTCBINTGCRr+Fxpd/CuAKDypxDM0oUiYLflXOZ15Pfk59Hn4WBbZ4rxwErw9IdAzznmfSWYA+NxDw/uuO517ivBsH1jtcpVW8NBtaZvMq/z0Tqdb4gSbGpeqZwD+EazjnMRDr6m1LeWoLJufSnSHDPlewWP5vzJ9kcp+9NW5PfhXYn5xH3Ju4h9DGY/PeWw9BAPKFBvPossGItsGrqPps3DDiWbHWAO16NYEnj8rmr1OCB2ZRl8rlooY1sRBgs+2Hgik1El8Cs7YYrpJSPGbU5KDIpcjHD/hHs6Vc0g9mAstPTIzJaGh3TEwElZiv0Oh0CIZkNEMJrAx14X018oJ4L8p7+bjS5RlFusYqm4TQksw9ewoRGnLPpq9FzGRjpj0nTkChgb426eJ3tbEK5f6UIa7oVkoKfV2QHcizAuF9pYF21I/5Nl45SeFseQKnW4GsdFsCZotn0bBBIlDfj6uZRyI1pCJ9CYzFsOgfcIVYmUEpl8mtyDVoUGCtg1BpQYckXpIZcsL3hgHgkYsifWvKExj83Ohr0cs5w4aURf1NhrSKr5PIq99cWbYKdXw9Ubk99CQIMvk4eW/wcLvKyOTiZa36+Xp+HYISkWyTW82PjDlj18UF2khpHXzwGXSSCW1Y1QBft5eKwaGALmLHvv/4Zdq0GJQ4LTNzUu5qB468Dm2/HnKO4AWDQyueMLugapQooP80MWN7D4mXKQ43eCwqhIV9DcH0IhQFfliSoUqFqEXDvB4DXfqFUYpTVAPd9cHL1i9kK1CU3QHgvaXRzfElDV0hDtbYK44obP4OkiYFNmRHF1zn9QVTVlIgqDbPZiPwCBzqb/chrDOPNC8cx5h+e2ES5TruCHowG3Cgw5uDvttyJV04eQ25lOcKdfViRE58xpQ4i/+GJEzg7NhbXzPqxw4dxZ1kZXunrQyGbV9MwmaYZr8xm53emsUOHI1Oniw6FmpjfuvoO7D31JEoaDGLPGB0Yx63rHkZbc/eUpdQ07GgAkszg3OMcUhuLdGKYLa/ugWDXl6LWuh3e0Ai0GgNsuiKx9orrlUTOLqwyYOjccX4zGJBIaqYCrwuvD69VjdWKcy0tcd/dGwqhbXwcxSYT8hPWlffXrMe/Nu2Hkz1+6OSabKi1hCYqTTgeNuQ3QpdM3i3sBfwngDCz6Q2AcQWgj+/DNYEzLwHNR2O/H/kloDUA5VNI0CQ4FkZhD7B6S5dUC5YBGl6/tBxht1tZDxIzRoqKgd//DGYEEqg+D2CypuyLRVJDzlmlctIPLZhZFf+d6FxJ7WSSc6zYkuOBc45BIfYkkM4XG8H/oOUy2j1uLLLl4AO1i2GaQRAzHOmNtugGzp9rxciICztvboDByPERCxbz/HmtWVFCApXZljLzketVWlrLAR+w/zmguxVw5ALb7wPyYxnaHHNLHbPU0+b93fcSsP9V5X7vuA3Yfpvyf87Zhx8Gfvaz+Pfwb6n6PXS1xcuO8v9qkiNdkLwo3qg04+Y+zK1JawJM8fJ5cX0uul+KfjYTJfqA7peBqgdmJj+lJvMSYcwBqu4E+kigjIuKEGPZNhFgoW8wW1KDx2BAdraBAa7jF50nMOSXkhEaDPh6sT5/BwzRHnLZwIjfgxMjXSLZZWVuGcqyUd0516Cs3nPfB9ovxSpAH/ptRU72ekLzOSCokgDk+KbMcE8rUHN9V/K0e3oSpKYi8IbH8buLN+KyaxRajRab8ytRYp55NRXnCAM/spcSA8AMKHPdJUkwU0KbyRPJmo3LCkJKz9x///1iriarqOBem0iI0AZiAJb2FO0idbIIf2fQl/YU96yZNJFewNyAdgYrg0lIyWxsJlIwkCht7mQBbNohokpncBSBSBhLlteivaUH629ageYL3VheYBfBbxIfJL34fwbWE8FxQz+WdlGiTj7tWnWvj2yA34vfh+Q6P5fjnT5GJolT/N48L2lnch4y6C2raJl5zsQPHptzKFVVIs+F4LmQIKFPz+CyjEnQjpKySNkILPNas6qW58s9eKbEKM9FHfgf9HngD4dF3I6KLanApuDsoUHJKVZokNAoNJrwjsrZy4BlA/SbeL8SxwJtDa7BDzzwgBgz1ytok0sSTDQLDweEIoFBY5o0friX8MFKPZJbksQmGDfg3J/LalrGL0iqSEItEZRa51jl3sH7IvvLcP/j3jPbasWsEhE/+yHQ0wmUVwMPfwDIn2FlGX2JRJ+UvmRX+1uH1CAKNynx3GijcOQux5KVAez5zneQLjLyeqbKAtFoTNAhXuqg0FSA/MhSIXWkOOjKQLvMJofC1IvhgrMJS+z10w7GX3ZdEcEY9Xv/t+0iHqlaAqMqKPiViyfxs85msZBS8uS57nb807rt2SU2eK7aRiB8Tj6hPLRJMs3YJG4g2tyxuB4wJgmWMZh6+OfxzjFX9/oNc0pqEHrNCoTYQ8IwElsUzGxWrgNK1k5+A0mMiLqSRBM7X+scOHeGHMCjbhZNS9uSVgWLVqPD2rwt6PC0wBN0o3U8XqdRyFH5nRgJjMGuM2FH0eK4cWjWGmDSGoTUihoFUzQmJYtNfUoaMjTYGfCkEcLjHnE7sInExmALMOYBdAUIr3sIh0+exejFV8WGweCQ2rkf8A3h2PnjsFjMonErG7mqGXgaEtwMaHzKvgl2/So4A8cRisqjGTUlsOjqkzsia1YC7hgBwvupsebilqUbET6xCFcGRzDi8cXGRvN5YMVNgC2Ne80A9/nDwFCfIh+wcnMsgM+SUx7PkCLYSAmwLe8FmvYD48MKobFo++zlr0iSMCCbrDdKTuoM/axh2UblMVUQaSq4R7HK2YSWpiZcPL8fSx/6IJqutojAkgwcMvifjNTgGKFhHYnUi54adiv7aGhg0dXAGrSICojyinK0dQ+it6UbpfUxAkkfnW8cA7X2XCwurkDT6HicIcHgu9pJOTw0hKBqQ+b/9wwM4MW+PjHauEb/rLMTx+68E0tTZEXRMeD8YYkpqxNmottKx4jvU2eKa7U6VNhWos5ehc6uTmxbsg1GnU04Erx+fK2cWzT86ZzQMaHzw+tIJ09WviSClRo0DNWg3JSUnFJjsb0CPb54eaPF9lhwgAYes1CYSZVuE77EXhJ00GSTxtf7+/DugwcwGgiI7NG/XrUan26MVZ2Vmh348+W3ocU9LCpwGmwF8IcDuORsEz/LLUWosSoZPLz3rlAzxoMdYj4VRXjMADSKaBXgPw5ozGKdiwPHROuJySfO56YiNW7aDRx6XUU+aFC8ZSf6x5woLo7Pxqb8F9dijhs2kmO/hyn1h9euB55/OnZ+/AwSGv/xX8BMMgObTwP7fqpkRZPUuPW9QHlDUqeDY3PY2QWN70JUNlCDHP0y2PTJg8WcYyRtlFONCAeEgSbpVPpCIXzk8Ou45ByLZsFFsH+wF9/atGtKBzMZpLXW0zOErs4BLF5SidxcjuPJ5cKcE5wbshxfjjl+P/bV4DximbuGernMwGG2MZ0kcT/DwC++A3Q0Ra+/Fmi9CHzwTwBHisD+TLDneeCJ78d+b7uq7FO77lR+/9znWNYGfPe7/NLKubGJ5m/+ZvLjFRQB7TxG9HowWWGmDkzuImD0LBCJzl3+JFFR/QCQWG1AWyZOo5zVhm7ANwBYMtjHXH3AlZcA7yhAu2bRbUBOksQBzvm6B6MfFQGcbairKhW+wWwx2yxzCU/IrSI0iAgCER/6fF2otMz++ESv14l/ubwPPvZ5A/B870U8Vr8FjY7ia1h90aHcP2sBkJOCRD78AtChynobGwJe+hHwjk/iukIqP+06lfeIR3JN+nKLHctS3ZcMwXkie2yRaCARwSQLrqvPP/887rvvvkkNihkg5YM2AQPWiYFkPk9fItEHp90g7S3uLdw/M5GDYBCM2eTJQBKDwSiePwnwmTQYXsDcgHu49GOZKMH9nPYUkxW4r9O+ZWJF4jhiQJ/+Bf95Q9Xo851HXb0RJp0Dt6zfjqtXWsSxGZTkmk+yKxmpIcchx4ha5oi2Du05JktkW+5G9M4cG5sym32qYzDwnSiVpfY3+BlyXpJ8ZKCZMQK+hs/zO9J/4pzh92YcgP4G50Wi1Bafy2YgnT7MVETLdOB3k7Yne2R87dJR7B1QYi1VFjv+38rtgtxIBTYFX2TLFZJTrNAgoZF/nVTl8b7QL07ml8qxyz40/D9t8OsiqK4Cq29IALS0tuCS8xT6fUrCjU2XgxW5G2DUTq5i5XdQSz/Rf+bYZD+LuQLnEIl5xr5IpqaC7DNJn06S69wTuZ6QEJnWz5treMaBr30RGBtR/Iq+bqC1CfizrygyxZmC/kUieNyiee5xO9cQPQM3KY8o6uq6M/Iv0iY1GNQheywyKeg0hf1AxAf4Lin/N5QA1hWAJv6Q2oTfCQZlE80+ylAlwhMK4KXeKxj0uVFhycGtJQ1wJmn4yawYTyg4QWpcdo4KQkM5rvJJh4b7ReXG7aUZahxPBw2Dhlx4fVFCYxmgSQjMjfUBB/8XiGa9CkJj2/sBm2rSdV4E2s8yQh3N0FORBSbbnOumMSNUT/mOxAa8bCBrTBJMKKwAVI1jVQcC1tybvRNzdSiNMpntShIjFC1F5bgqTl3VkAgy094QNeC9KDba0B9tCK3ErSiVpgS33SE/9g1cxL3lMYKOf3+gfB1+0XV0QoKq2JSDmwpigUxm1yqvVQwYGiqyQoNlcdzwWBYndXA7ireiZNlt0Gl1OHnxCrzHTglDioYBjUc6KjQMafBfcbbgp3t+AavDhvKGCrzatx+3lGyDUWsUr+XxuWnx9XRo6IBI5yDXsBVheKEJDUIbbgb8rwHaQkDPuarKcF99N3DoJ1FJl4hCJrCHi8UOrcmEJcX5uDo4ij7XOErtVqD1EvDtvwYe/V2l0iAVeF2e+i5w5UxMMqbplFKp8Pr/AZ1RB7t+NbDzncklrTj+V96pupl+YLRTyURlQ4Rk2eLTge+Ly6yNrkiF9UD1PDLfM5nXrO549luCBK3TRHD4zEVcdfbDdtv745xCGsaJOp8sMaZhony0FmZdEQyi0koHg6YU1hKzCLC6g15c9HTAVeKcOAaD2I6ofKAazCCis0xChUY2x6I6g6vOZkMTM9Gj15jf2Keq5ODzzOb55pUr+FqKHgB0NGiU0RFglgYdh+lkbGhwcq7xmjDTid89MZtYZlAZtBYEPBHkVOVOzHnOR0nWcF4ys4ygMyWNd87nVFmRdNrSNW5X5NTCHwkKySmuMYttFdhWGO9Y8fszmMD5zcDwdMemw6lurii/C4mMRw7shzuqfcw78ednTmNdXh5uK4mVGlt0BizPiRlNBq0O6/MnOzzuEGVelGwZdg3RJVYu8o6HeiaTGqkwXfPxqjrg438KvPIkwD1r6WrU774XR46fiHOO+f0ZkKEBzIy2e+65RxjM1F9NGTzZcTPw/t8A/vcHylpVUQn8xV/PjNAY7gX2/Dj2fVit8dIPgUf+SKyrifeF8oBvnHoNmpYwbtq+UiwNY8HzMGjtMGonG+h8H+cmH7IyQj0WX+/vwQVnfC+GI8MDODY8gM0FUwdfe72jODvaJsbiInsp8o16uEecOH3yKqxWExqXVpMGhQaTSUjOUV77F198UWivJ5Jq3NOOfOMb2PyDH8T6CD34IPD5zwNDvUC7KujKNZrX7SJ7Hd2GrGHPc8mJDklq8OK/853Kg4EDJnekqiDg/fV6Y4QGwf/f+8jMzs3TGyM0lA8Awj7A0wPYE4LyKdeADPbEgAe48KQi9UPQNrr4NLDmUcCUIuNb9J96FXC1obZQL4JMswWPkY1M7VAkSSkuNCmenxme6T4PXziWXMWqup91nsafLsviGE0XvBdXXgX6VA0yK9YDdUnsY8pZqtdXUVGUxI6/1li0GnjjGWXu8xxp47FaKyqPeT2jylKG82NNcf0zcgx25Oizm+wlqyK4/lNikUFP+un33nuvqOBjQgWTIGjvMfGCQR7aLswmZ3Y8g3QM/tCO4h7JAFEy8oF+vyQ1eLzXXnstzq6YLYT0pdU60SsuUUN9AdcOHBsknKgwQCJCVl0SvE9S7jhx/+f4Islm1uWixro17u/qsUMbOZW0CMkR+raEOjmHr5+JxNFUoG0vK4h4/iQMEyuOEkECkNVSDMByDpKcISGQaJfzb/QxEjPIef04Z+VznIP0nfi8lLHl31LJMXI+Z3Ou0Cbm8ZjMRRttul4JPFd1lbz6mj3RcQn7ooQG0eVx4ysXD+Mf1qaWBuJ3Zf+Ma91DIxV4n3nPZbIpx6SUGaZvxvtB+4X9Hq63vkC0x7kfXOg5jT0H3kReYS4qasuEeglJjlW5k6vzCKlEwD2EgeVkVXzZBBOzmJSrXmeSgbJYJFvp70hfnPeD7+F8ocwh97ZMVByyivMngRFl7RKgL8nKjYtngLWpyZqUKK0A7n8EePqJ2HM8zrq5vR/XAzinaIOkkn+bMakhy+EqTJ1A5xuxP1hNShDe5wLCHsAxNYvnC3mQq7eiLxrfl0ZfkakgbsFnk9R/vLgPvaxwQARHhjtx0TmA1bnleKU/ZoSzB0KFxSY0+ST6aQgngEfuS/L8rJj9cAcMEToSkWhL2TAQPg9o8gGNKov/zAvxpdR0Is+9Amx+p/L76VeBc3tjmeMMsBv1sUxU9gSYFyQLJqVwmjfdCTz/3fjnbDnArR8F7FnSdHa2At2vxz9XsAIwU5ezBEgSYE2GQNiPEyMH4Y/wHkRg0UdQEDHCE9bAGfDCFYjAF45lpI4EJmt4NthL8ZG63ej0DMOk06POWiz0vUlmDPrPwR1SmG+rrhxFxhVxsiE0kmi0c1JKTUOWXDe3tIhFlyV2zNJ49dVXRYCYTgclRbgwt7S1YF//IZQ3VCKvSDF0xkMeNLlasCKnUSzifK3sd0ON9cTGrTrKwYROx75MuEuR+DBsjgVFimqB7R8ALuxVsoqrViikG/9+z/uBp7+PIpsZ53qGYNbqkGvQK1UWT/0QeOzPU1/8jqsKoSE+N3qRW84DL/43MKgKhDSfAaw5wE33TX0z3YPAmV8oc4jIKQdWPpR+jw2JwgagYg3QdSq6kOiAhp3Kc9dZlsUktJ5VKr+i2FBXhj3nz+G2B+KNABqoNGYSNTEJ0WQu0g1v+Kwyx0ksoBU23RZoNVbY9Ga8rWInXvcdwYjHDUteDiIRA5rdfWIuyGPwQbKABILcJ1hJocY/rF2LHa+8Alc0iG7UaOBNErimlGAy0MiRY5o/OX8uXrwospjoZE/Xg4NEC69BMiOJzgbnG7M7kh1L7kmcp1P12EiFdEth+Zr1eYvFY6rX0CGk88T5zmPT4ExWtcL7znVHfmc6IEdcLuy5cEE0bR+LZqVJsOT74OBgHKmRLsajEpPi+6Z8VRIDlZ9fvQpojfUZEaiJz3RLirolwEc+PfGrLnqPeM/pKHI8kmhjgEc2faRBJHuS0OBNCp7Te94PvONdSrZNTm7m64F3HGi+APREqw3isuj9wEAHUB2TwyPxJqoZdF5s3bkCvd2DaG3uRl0Ds3o18IeHk5IavH90/BlkIlGTSK451TaHCmOUoJkCXZ4h/KT9wMQdOTPWjlqrFh2vHsO6xaXYtHlpdOzwFZyzOsVuaT6rNPUtLBfjVGYTc31gkIP7GdeGfJKt//Ef8UTRk0+yYQiwPln2pYZZNcgqokkMcUjVd2W67GE6K3Ri1OD16e0G0hjKk5ByvCV53loNaE8Cooo0Ks/IqlbaSMlA4qL9DWCsLSrztkGRSw35E0iUEDDWBRSnIDWcbYLQIGrKC9DarFQMzQZ0mrNBalj1Dhg0RgSEvScRQZ4he01lh/2euFpz/m80EN/odN7ACg01oUF0HQeKlijJG4k2emKFqnW+/IsMwArgRz8FvP5LYHQQKKkCbn7bJOnB6xE5BgduLt6CEyPn4A35UGDMw8b8VXOWvcv9jWQEA89cdxkIpt3D4CQ/k3YNfQT+TnKDtgBfxwDkSy+9JOwaEs6pNNJZ1cFMeQZnKb/Cx1x8F3lMVgPwnK7Xpru/bqB9SRuczYMTJZ+S9SojycHxIgPzsw0iE+rgpLR7sgkmvUhpIY492o+pe8gqYAUFA6iyaXeqRBna4SQM+R0SKxHV8+iaZparzpXnQT+O9rOs6k+c7/y+3K/V1TIXWpsRWFSFY11NODbcG98fAxFccg4L8l/de/FGAtdBrrGUP+L1IAHLyjgm2Mn+J/S5afNKP+R6gSTWCutzYahailNvnhWkBi2XscBwSj+V3497BMfAXFZoEBxznHP02aYj6ziXuF8xWZEJj/R9ZI9RzjFeex4vG5W/WfMviHR7OybDA+8Clq4COluB/CJg9YYbpHJ1dlAkmSPCLiDZOh30mWRRVVaUQe9NaGjq8Sk9ATghAt1K1UYK3Vo27mt2XxAnaKVsVjSGVWjMx+b8+AyREyM96PbGN2W84OzHfWWNeGflEvw0muFdbrHhCyu2xk3IRfacCdkpCf5vWVS+YNjvxf+2X0Svdxx1thw8Wr0UllQNiVPAH25BMHwZRsowxYGyTIPxpIabsiLq7KgI4I6yeAxOktAQz09E1pluBpj1QI4NaHsdGDgDNN4J2Ocwi8VaB4ydnPxcMlQuAXY9Ahx+VsmmKq0Bdr8HsE2ti58RhlSBeAlnB1CUmRTXoL8XflYVRcFNNccYxM7cLfhZxyn4wgpxRnAUlUYbhCciz2gTDzWG/ZcmCA1iPNSNIb8ehaZ47UVORrLQUl+TxpI0omgccH5xs6QBSUkRBln59/NXLmBxRSMMpvigvScU70CTMOG8SuoIiPNTteHi+AsPAHqfIgsjDuBX9OzHOHY1QF8z4BoCVt0GLF4DfOhP4XjiP1Hp9OB4Ww8sBh02V5VBO6yWeUiCcVVzdzX6OgBtQsBPVm0klVboAvweoO0AoA4ejPUAbYeA+hRBylQQjX93A2z0/vV/A17aA+h+ArzrXcDHP359Ns9UB6ZUuv86SryYGZhKaAyfl4fnnntObAo0SrjRM/NCjhVvWJbTyvsQgj/cDLNOMVQdBis2Vq3Gvx/4GfKNYWgwgtNj7bizZDXW5NWKYzJTKXGjSRyD7JVx9u678dPOTrEmP1hejncdOCD6bEhZKv58IElmFJ0aOkjqcnAGp2lg0gBKRkTwuzELkd+br52u0R/nGR0UzstsgefG65zK+WfW8JC/C8GIHzZdPnIyCLhxjWDQgt+T15+kDIPiDFKoq0pk7wXio08+icdp2I6MiHvAO2TQxgxZPkcN25mASQkS3MG80MA0QfQrr4A+RfbV6ruUiqtu2Sh86/SNwlOAmUj79+8XTgavP4mLxOtPY5jzoP+pX6L4lz8BXE5g5Wrgdz/DCRN7IbOxMtH2p6H5xPeA/h5lfhq1QK4FyE8SMEyoLOP94x5QWGIH7EBpeSEOHzgbJTV4HQ1TfudUDQrX5xfF2UG8EqxmXZU7ddLBm4OXRcBWvTpf7Xdh+/IqbFi/SHVNfQhFmqGPLAGe/wHQcm5iXRpdshnH9cXCOSFZzyAECSURjLhwAVr2/TEaYzJYDFYyY/POO4GcAsA5Eh94bZjZmEiJLbuAZxN6Zmy5eWbHIrmfCH4vX4ZBbt8oQAk6kg06q9IzTBIVOhNgTZI5qrcAFXcBQ8eU/hrGAsU+SiXL2foaMKLIv3KmonUPUJqiMnEqiUd+VhQ15YV449XZkxq0gdSVPTOFTqPDytzNuDB2DN6wB1rosMi+AjmG7AWNamz56PKOTcwRJldVWWJ70bHhTvHg3ry9sBZL51KWirKxSZ8fnUxqbL4baLsUTbKK2oQ7347rEqzMeNtjuBFRbCrAnaUp+u/MAWhz8cG1lkFZKekkCX3ui9z3SH7QbqBdeNttt4mkCL6WNsVURAXJRvolUwV5swHaebRRf/GLX4jK9uspMPjrDNrSTL5ItDNonzMznXa21L8necbXZwOJNnli5Xk2QNufc0Y9/pkERfIvVXawrNrm36bLBucYpt1DGy9bPgY/n/Nkpr1zpgKvg+wFyHsuK2m4TkjpLCZlqgPG7e5R/POlI7DqYhK6vEvqEK5Nb7hhCQ2CBA/9Lsrk8SevvfQ/Wckgq1Tof3C9TZk4dQ3AecP4U84yZayWVBShr6sfJRXF0GmUPpvJwHF7++23T5IynAswNvbwww9PWyFEcA+jagHJVhIbPE/OWVnBxf2MBNQ1IzWWrQIsNiU2SiKDaxZ/XzJLP2bJcuUxE4RDgLMToGSqvQwwZC7hfS3A9ZX7Cf2DrJMa1ZUl8cFRRP+bhi68O+gUhAbBCWTXU88tgjV525BjyEsqPZUMnnAQH6lfiUerG4XkVL6RTcnjP7vUbMUXV2zEX507OhE0+51FK4STPx4M4I9Ovi6qOcgek1E+MzqAr6yhznR6m6USELwqskOTI+Ev7AMwTL3xWL8C5BTHpGQSwb/nlQImf2xbGB9SMtQ3fgAwzNECY2tU9JndlBJhb4x6wDFFquGSDcpjpn0BpkNUrzgOM5AQSNaEV3k+hAcq1uP/2g9iPJqpWGrOw47C9DUlPeH+FM/FLzw0mrjoMkDLCcomwtwYuVmQ2ad+oNw4uEDTcKMBadIYYbfY4CNZGAXDTfmG+GAuF/FUjcTjMjwDIWDUrZBmmieB/I2AvQFoOakQGoKUi47TSweARZsAS45wMDWVi4DmFiwuyoPDZMCeli7sWrd66kWEGXaTGu9qeMJAHGmpAYxJMl24IZx5Chi4GntOr1Ux1Oz8PvkeKH+KMGU9qotflHyMfue/gaeej50fNdSZcfORj+C6Rfli4PhLcf1PwgYzQjnFcSsPDU8SZTS+GOSncU3jRmk4x1ckGtgRhOOyW4EXm47A5LDGBTn3DlwQpAYNeq6FanSMu/HEhTPwhcK4u6wcWwuVda7KasWnVA7xUzt34tGDB7GfzQL1evzd6tV4MIkjxGwhZhUmGl38XVaKyHPgnGHWBp0Hzq2U2VwRLxBkY98goCuERlcqMq7oxFDjdiZ6umpwPvOaJ6uQCUeCCIR9aHIfgz88PrGfVpgbUWpOXmaeCrwGcpNnpiWdD14DXjNmsUicGBnB4+y9sHTpRIA7HH2Yo0HvRocD72fgwjWKf7x0DJ0eN2qsdvxR4wZB/KtBSSJ30CP6DJl0Rlh11RgLRgkyjQbD0KFQkwejkKEyAcZGQJsiG5jJBGvuUh6zBK8HxzulmKgPrB4z6v8vDfhw4Ct/i+LCqN1x/Cjwpc8DX/mXme1jLDP+t78F/L5YIN6vAWym5FJaNC5V4LzkuL18oQ3Vy+ww2l0wmYwIBIIwG3Ng0aWWQZD7RDLU2xz4ypqb8BdnjsATCsGuN4jfS6fRdPWE/JMqbgw2KzYunkyGRBgYbzmrEBriiQj6nG40v/BL7Prjr8BjyRFzK85RKSlBqcGAvmAQ5TIgQGefTiEDCI98AnjmhwrxzSzy29+l7CPZxL3vVPaWA68qc3DH7cCdD83sWMXlSv+M0eFYJhZ/LluT/jFGLgPd+2O/O6qVxtx+7m28GwFg7CKQu2ryGDXmAmWK1OWU4LgbUWRZYyD5MgJYCgHPUIxEMTmAvCmqJkz5caRGW+srmC2Ej6GWBySp44k2YDdXAAk2z1Sw6R3YWLAbwXBQkBzZziy/v3w5ujxjaGWfL8bfjRa8t0Yhh/YNNOMnHUpCDj+VzcQ/1rAFK3Iyr4JLC9YUZI0lyfMFpcCjf6TIudG+rlsBlMyuwfsCrj04vkliMFOV/gWDbGzyzExb2bhYJjnwtUz04HO04eh3TDc/5kvGg+dCW4aNd2mzSu30BVxbMMjN6mh1NQEJDY4tkhmsDmK1A8cfx0omVRpTBfoTK0No7yT6HLMF/WZWKCQj8uhL0JaU5Ab9J+5TPIdMek8w6YhVDSQGGYidLVlHG5/Xfa6rmWQgX/Yx4T1nEJnnr7bpvr7vRehKi1SSe4qbHgxrhLlCH+MxSkzf4GAVEscC/UR15YI6oVRIyeblTUiwXQ/gWk+Cqv+8B4bFWpRWleLcsYuC1KizTS0lOB+EBpGJZBf3NiYjchxy3eF9SfT3sr1OZARHLvAHnwd+9G2lnwbbHryfKjZz0G84HQR9wMVfRW18LqQGoPF+IKq6cb2DcY50JW71mQRqKspSNCWZyLYrnqjSCEXc8IeVLG6jlhrtk7OJOPCa3V246GRGVQClpjzsKloLq96MxfZCkQEqQ2kyy7DWqiwSVr1BPFLh7rJqbC4oQfu4SzjxZdEGRW8MdqHXF5MXogt6wTmM82NDWJWbbhUEzyksQoLBiIrCoDQ9WLUSHSicVBxM7Adw6MdKI3DCnAMsv135vz1faZIsJCFUmfTldcCQujERj+UFnD1AQR0ikSDC6AFFY7TIg1aTBckn3kfHCuWR6fvUoP6zj00zfYA+HzDWzCxYZK8Ghs9Nfi5D5BmLoHFrRcN6CcoTOPS50GsNeKz+VvR4RwWpVWHhtUzPUHAGnejzBhBCGHY9Gy0r31GbYlpxvMtMJwbfyPgzmEXHg9kuLBGXBAgDc7IktthUigODRxCIEjpVlnLU2+IZS0rspHRMdBVAuFUZVyPshyPHWQgYOgRQ65cVQ5PIhyjpRlKDuPlBeI4cRGM0Vry1oRoHy1Ziyny0/BLgrvcCL/5YCarw2jJIZbcBrz0eHRdRonRdEj3q7jPxhAYRDPMGxt5rTrJRuFzAZz4DHDmi/E5pk698hdZl/OteeGHyd37xReA9b1dIF1tB/PF7rgCdF5RgbN06hXycb/CzeU6jA8rvuQVY/eEP48CRYxPZ6XQyGOxUZ/KQ5GBmv/IaLbSwIwyuSbHvr9PGB44GBgdgqYy/vmyOSoMhUXf2wtgoPvDmGwiXlYo7+p9XLuE/N27BOyonz1mSHPtuu000lGO2dqqxSyM6lY4iM9yZFUYjU/TFMBiEs85MmZRzgRKJ3n1KkJAINgOG5YChQTgeNNRp/KWTLZIKdJCYdULDV0o18Hr1+6+g3ycbO8tXK//p8l5CobFKrEczAb83H3SaKEWkdnZOsclWQkULr857q2tRaDKiwmzGxxoWCcnHT5/ci/Eg95YILoyNiN+/t/lO5BiUfX3IP4pX+47Ay7Vd9AJpwNqcRupXYTzUIb6PVV8Jg26Ga74E5Yb4HTJ02iSxMeVr3nwDRoNe9HExTvT5uQQMDihNwTPFhVNKRo4avMG+oLLWTlwHEsa0k0yTzpkZShzn7L20elMd6qpNGOkKYVUj5eBmLrlyR2klbikux0jAL5ouptMgvNZWjF5WDajgMFA6NCIKUOKcB9gAZ1fc3tE8MIr11aU4feQQBo1K8IzEpNTejuTno+zTn8bZr3wF5SQeef3XrAHe9jbloHlFwPv+EHMK3vcH36M8ZguuTx/9Y+AH/wr0dAImM/D2DwINaerOBz1A94H455ztgF1NJrJa8YyyVyf21UgH7JPBKo1kYFXHircBHYcVp4eVqlWbAd0UlUr2CqBwFTB4BuXFuejuTZFYkAGEjyGJ7aAT6H8p2ltEAzjPAAW7AHNmEoB6VoHNAdhv6HcX70C3d0wEbCrMDiFHSrzQE6s4lcv8y31Nc0dqsPk0e2hQckqidhtgSxE4c+QDm6L+xwKuW1DalntqMOKCTmOBTVcTJ2ubDDIzlUEA7idPP/003vnOdwpfgsFbSU5QKod9OJhpnBg4TgUGY+caDCJL+036RcmaUS9gfiElTtX3gkk0MgGIezzJqL179yZN5kkFklaZyC5lO1BJOz2V1A3nhazgpuQrvyPnDwmemQZ6SZDQL5gtqcFzkBUx8yFbxflIkodrAP3IxD4f7d3dMDbG/DzeJaqf3F3ZAF8ohJsKyrAm760xh2UvIzV4P9lbQ6oCkEBmfGeuJZvSBddSaWN1tXeidEkOunUDWO7YgALTjdVsmmOQ6xFjXpQA475GWUVea+mbXFNCQ6KyFvjjv8F1ga4jgCdWRSUSWppfAVa/FzcCON84dtNB2ha/KK+qXhzfrJlg7weCjqleacIdDI/AGTw6UWXgDV2FQTu5bNUTiuCSKyZn1e0dwou9h/FgxU5UWnLwm3Ub8N9tJ+APh2DVGfFYwyY4SACkiQKjSTzU4AKbUGsicGCwDU90HBcBu3pbAd5VtUZkXyUDA4I6TR5CkVG4ERZSGzQLtciHTrdWacDsGQUuPhMdSJS62QiYo9niBdWA7AHCnzvfA+z7sdLngFi2DSisTCA1otAyOzqIQOQQIlAyfdmuUIdG6DXTl+bMOVhR4NwDsI8D4W8FgiOAbXIDumlRtE7RemajcMJRn7H0FGHV2bAyZyMuu87AH/bCorNjmWPNRADRpDOg1paZrNdIYBjHhnkPlJE0GoigzAxBbuQa6tNuwMamx5xblOaQoDPCSczgqgxK3112C8YCLhi1Btj1tklBW5IlPJbBokXH+AV4wy7k2HOxoX4XcgzFSv8MzxkgnEguagAvibLKeLkPcSLG+P4o9hx47v2gEuEKBmCpWQLD+UvT9w1YsVmRDxkdUqRFWIYnm9BfPaXMiSUbgZIk45cybYka0PK8CYMFqEnSLOmrXwWOHYv9TnLja18DvvCF+Ncly3JmwPaN70U/RgusuhsoX65Usxx5MiofEwGuHgN2f0iZq/MF9jt57v8D3Kr7ODYCm1nJxKNTwYyHxCbRdHKZYUMjWDqxFt0ajIeOIwIlIKvXlMCoiQXMeF+rbEVoQ7wzm2ewiqB34n3/x0vnRaBYozIovnDmZFJSQ0KfELTmxsXj8jzVTdmSgX+TEm5pgySGIDRUO0DgAqCvE/eVhjudNRqpybK30gGdH15vOkJSd3cs2DNBaEiQlwuoToNSVPoppIamA9cREjOiP4MK1oEBaEpL4/Y8/v+x+nrsUgULXurtgYvjKwre49GAHydH+rGruFJUvJHQ8EUJDeLc2FVEImMwa11iLBSZ6mEVwZcZEhrj48Bf/RXwyitK4JmNmv/wD7Orpa7XC+W7SXQJ5wUbj+/dw7p7YM06YHEachupvmsgqGirsueYfF1BmSLVmAAZbCLhyEDO5s03ixJ2V7kHOTkGUR3T6x1EMBJEkTFfJH6k/XW1WhQx0J4mthY2YizgwQWWKnPb1WtRFPLg/IgGq/KN5K+Ur4Nc6DT1QKGquo/2XiCEN6524qZHNmJVcbkIdlDflsEQzu0nn3wSOx96CH5eb95jOvi3386LgHkBE0hazys/WX3IPWm2KCkHPvO3yjGZbJPJ+Pc7k1ikmpgNlbhXZ0pqcO+88owiRzT5j0DRMoVoq8tQLqd0E5C7COXWDrjcnxeVbolrT7rge2nvTAQLxk6rqnKj12b0KGC+H9cLWCFeqZKckvAnqSZ2BXx4toe2ErA+rxzllixn7LEpePESRYrKkgdYF2R7bmRwnRwOnIRPVH0rHqs31ItC403C/0wHtKFY8cCqcAZ81NUWlC18/vnnJ4Jd6YDBU1ZPyCQTKVOTzf4G3COYkSl766xbt05opS+QGtcevBdHjx4VwVuSZ1zrpZ3HJCASFCSkaPemC9lkORVSNd2eTUBU9m7iuGU2faqm96yGoF8+mwSnqQgJ+msztZPpn/BBgoTVAHPVryeZ5A/vceLe3VBYgiZohM8g5Rjrbbn4YG2WZUOvU9BXZMKgJHs4RufjnqQLOWc49uhjV5sWw7G2CFdOtaBg87UnNejr85GO/UiCnvOWxAb3Mc5jVm5I8D5wnJJgmgu5uhsSnoQWCPy/b0zxDdK0J64l6BcwvpH1Sg3RFKj8HmD0FDB+NZZJycxpIipdMh66mKCmF4YOPSgylmPA3z1hpIUiNmjgnggM8+dQwAlXcBw5Bhs2FlRiXX65kIyy6Y1Z0eNbl0cNOUVyg5/K28nfT4y0RzOGgbNjvei+vA9/uuxW0RQ6GawMCAZPIoRR+KCBUVsLM4kbmbF44WmVExkBuo4BS+4CihZNPlhJHfDgHwBOZsnZlIbJrPBofVPJsJOSAMy8yilHCG1RQiN6bFEZcwk6VEAzRVanLxRAl3cw2ly9EIZMs9iYPbv3V0DTaUUCa+tdwLIEPWZ/m5INHfdcC2ChBMk0DTcTwayksm1AaZTtFsHtCOAdUhpjUgJhqmxCFfKNhbipYDeyhasuRXdcjSG/FvW2dbDq0s9ISDQQJFhyzYVaLsoGrQGFKsmHZJvWipXLcd65D8UaOto5GB4cxYHLL+LmpQ/CoisCjCzveybhnYzuGYCKRmDpDuDiGzFCY+u7lCoiEWscF8ZgcWUVvaWJd1Mu4vXXXxfO0ZQZIxzXfKhRsVh5TAVWiSQSGhwHdVsABvaKFivERiJIYqibMvH/hw9Pft0HPgD8wz/EP7dJJXXCzz7zPJBfBZx5NaH3TRg4v08hJecLI72Aa2Ty8+0XUbTuVhEUZUNeBhLVxjoNAD5YQi6h1dhg021HGOPQQAetJv460nDYXb8Wx0JdODvGLHwlRts27sIXzjwDpyeMFb1N+O2bbhfjtN/nFQSrelUZoiRPFOfGxvCtq1fhDYXwUEUF7kvIdqETISURSNCRtEglPaDWMM0IYo9K0gdJSCUpawmdLFY8yMqPmULqVDOgi4IouS0rD1XJ+wR1TY3amZf58n73jo7iFY1GGH3bCwuFnBerWepyc/HthgZ87OjRCfmp/7dyZRyhoZxD8v1VyjJSckpWaEjwHX2+QVRauDYDPd6LQu6lwKgisviZ4+1AYBQw2AFrbWpD6stfVggNzlc+fvxjRlOAx2anrc77yOolQVTddheCP/x+PKHGc/zZj4GDB4Durhhx+ZnPAbuTVJCpsWI9YLMrjcXlmqNlHwQt4AoBq1cCrA4tLAc23qVUWqUAxxzXcmYe0WCn8+t0OzFcEYDXqBBOvL67izehNFUz6FmC9/ve8vW4rWSVcFK7OtpxseMc2pvcWHnLNuhNTAzRC1JDBNiqlgDrbwWOK+vjqspi5N79KPTlyhjgeJRzlQ4H7UgSNiuoOdzQkLZDzaxlX3gEWpDYr4N+JvPF4wZ+9m+xKjfei/t/C6jKksRJtKIps/ckc+SiRFHcnGQvtmZlHhVtBwxprk3eEeUx6XNtQM0uwDEL/XNzPvJrcsW45Zo8U6kY+hc8xoQNIRKnEtZp0WPk+seqnDIcGe6YOHsWa3V63ejoUvbeZ3ou4vcXb0MjZWmzCVux8ljADY9AZDRKaBDKSApExuAN98GiS79aiUlRsgJcDfoUtBXZu499NtIBe6ipewzQ5mNQlet5NgJ4TABh0FmdBc7jcj9k1jMrUBdw7cB7wXvAdf6FF14QPVnUYMBUVohnC+pG5Px/qh4X6YAVFzIoTz+INgXlslKBPm+cHGKWwDnJa8RzUPe9mwk4VzgH6bNk87onAwnNZFLA/PzP3HY//q7pMJrdSsyr0GTGJxenLyd0o4NjUl3Jdl1UCqQAEwFJFHPscM7y/6w8yoTgzjaoTEJCg3Njur5NPGeOdXVFGMekHJfcR0j0kWxbIDSiYPU1K+rVAnHsqXEDEBpSvYa+cNYrNdgwRjQspK5voH2y0xHtjRFWNWWWiMCHRsdmFPpL4Q25YdbZ0OoeRbc3QVpGxAO0cQ52JtUZ06HK6sBfLN+Cf758HCMBHwqMZug1vglCQ2Io4EGzewjLcpJvelqNCXbDTaJqQvDS6sFBImKSE6kBRtnkOoXTx+9YoArSMXNu7SNA60GFHGElQe0W0bwxkhBYUn1wyls67HfiV10H4Y32ZnDorXhbxTbYMsj4FBJC5w7HsjKf+p7ixC9SGgvHgoZJamFEA/kMSQ0JeW0pX9T1GuBWMkihNQHVdyj6X/5ehQQx1SjVRHMMv6rHhUQ4os2I0JgOLIulPFW6uow+uBHSxDKt8wtz0XalE03t52GPFMNsMsHVDRRb3cjLJcGgUa6hLZr5ufIWoGGjIpPGCg1ZTRQNcHMzSQzyMhuMAat9+/aJTJ60g8D9HcDRFwCPEyitAzbfM0GgxH/AGqCvSWkULqABlt4OVKjGXDIwONLXF9/HJhnp8p73UDQSePZZhaDdtRYokg1aESMv3MPxDcqVPwD+9LOSsoKkRCQ18JTn6QCyUoPZbiQGEkkzGl7qjFpRdcbuxCmyqBik2uS34sBge9ys1msjCBuAl65cQmlVFapGnagfHsWeji4EnEoTWZLQaxsWTfR12P7KKwhEnZRvXr2Kb23ciDW5uXitvx+e1lZ8aPVqVEUD7VMRZHRO2ChPkOyZQpsPhKLrh3IFAEHmGCaVidPpSJXFlQ5Y2TCc68KhC0ewKFyAshxT3B4jhybvQL11PbTTSEukgtB4vXIFH+7pwWWnU+xlX45E8MUVK3D/+LgwVjeZTLi7tBQXnU7U2mxYnCQjhnKNRUazIKIYzNZEZVbKzcprjQkEsllLGUiNyNxn1SV/p2E54u+JkRr8koNvAuOsyIyOIHcbULwruUG1d288GUns2TNrUoP3kSQxpama/UGE128CrlyI3QT++NXPYwNcEpf/8jVg526liiMVHDnA730e+MX/AAO9QGkFsDpKdNQvU/pCZADOXwYN6BhxD9DWW/H8Mz9F3apFyCnIRSgSwhsDx/GOytvnNBuMFYw8h662Ltx384Ni7SBhygrDSdh6L7B0g2jwXUjJQUrcRME1hGXivPbcQ9iwkLInmQQMRgMX4A7R5iQoddaNUvM26DQZEhuHXwDGorqy0qZ4+XHgw3+BawY6F6U3Ab2HaPDGJNcinG/+yVUfgRGg71Wg4v7UDcHVUM+ziWOxz9RyIHf21b10HOlgzobU4HvjmrWy4XlgOMEJm3uZjWyAVd6BSBgnR7oFt2nTmTAS8Ma+SQT4v/Yz+IsVt1zjM13A9YrE3max55P3mpwJGEDKVFJKHVDmvKfePoOdTOCg7UnbjA8GzDLdm3guyewtGUhnlcDGjZlX6i8gu+A6/eCDD4pkNlYyqKWYKAXDijtWEaQTUJwu+MskFPoh/EzaEBxT9F+kFAk/g/bSdJ9F/5UJFZTL4jGmIxOYiJRKliob4DWjrzyb6kYpI8fvRLJmNr7KdKA/yXuQSJxwzvJ6Ftrs+PLq3bjiHhFVxYtseSkTgt+qkLLDvCf0va/XXkAcb7JfDO1wxghIRlLlgUT3fBMBsjKP1V8kN7h3TEVccrwlStzxmjOxj3OK+xrHKsmNBURRsQlgYiqrM6RPUD9Nst51BN7PV1+NJhVPg7RXHU7WiYHEoHHuZmCUmc/RTcm2AjAqBIBek4tARDY4jL5FkxuVp4hlmRi0dpx1NiMcrZogqizFsOnmtjHN5oIy/M+We4We+6nRHnynOaq7n4B0uNaklRGpqgfUz7ecAk6/qgRKS+qBzQ8ApoRu9CY70HjH5M9k81X1eUota89ZoXFdbq6DWRd/rD39p+FVNQB1BT04MHgOd5SmyabT+T93JL7/gChrORRPauiZPRrTFFZeZwR0WSjhZH8NSWiIc/ID7S8AeaaY4+65DOTdAuhmbiikg3xjAdweJXirQIN8tdPdfxm4uk+5v7kVwNI7AGNm14Blr1yo0yU12CUhEdUNFTB6TagurhYGSFnZu3DhyLPIK7ECETasX66k37t7lD4NlEVIIo3AIPKE3nUCOK+5GTIQx2A0nRsattxoZKYNM2ImHJ2RfuDZbytjSvT56Ac6LihNK+vXAsWqQAsNo/XvBAabAb8HyC0D7GkQR7/7u8Dv/368Jj+fm3zywNvfrjyI7gvA6WeSWKEOoLgO6L0SPwdKM2vuPGswYFhWD/S2xDJ5SQZZbcCe/1b6pFSvQk3DBrS1twsjgU3ohZEQDmL1qlVoaW0VGfzTGe0kq2jc+/OU9TiueJFrttcLV+8gfvziM/jLrffhz5fsxlBxEZ7rVRyORnsOPm22CQfk71pahDSVrBQg/vDECYxTDtDvR3hsDP8TDuPNoiIUTJNxREdgqgyrKaGvUSTYQtGmUxoTYNqUVC6Gxh+rRxiITQfsRfEfV87h9OgQykxW3FlmgTPYgeLqEly+2omSNdWiSk5x9iPIN1Yhz1ApCH4dJQtnAN4HZjC8ZjIJQkM0AJfVGCdPYnu0zF72MeFDgq/78oVz+PbVq4LE+EBNHf5x7S787YWjODY8CHcwgisuDza99DIO3n47lrBfSc5inBlrEv2Dcg1KrxB+H2cQiOhZwciVUHUt/UNRQkOcrfLD2608LElk2+ggOynHEwWvleqc00ZHK9B8WWnMtnojtHq9WH8YGKEBvZjZQFcvTWraPam/js+rSFKRuJhOfui3P4NsgNeT5yod+jZXO/JK8tHf3otIKIzc4nz4wn4EIyEYZtFvIx0wQ1ZKvMlxdPbsWREUmCQDkV+qPBLA1zU0NAjim2Qh94Rdu3alfQ6hiB+BSDvyqOokZNoicAeDcAfbkWNIQx5MDVZoqCv/eL+ZXchxEO2JcE1QsFwhK5wqibqQT2kCrtcpfyOia4eoZGDFBoP/08GUq/TAYEaDrPrldy1YnFWnI93y8Gn9C8KxGvAzkWAw5nPkXx/61NOBwZzfrNskgjtcC//q3KuTpP9G2R9vAQtIAYOW+w1t13iC36jNbuPZqZo0pwMGOaUEFY/FtZ7Sp0zGYsAsXfD1U/Up4PpCX+iVV14RfhGD5/xsYYdGIqLKi0Hd2QSIF5A+GPjcuXOnIBpoq7CKh+AaTq172ghT9rZLg9Tg3zguaN/yuPwcypCpA54cE0xW4blM9VncX9hYO13wO8jvNFegP33q1CkxbmfTjJnfm3OBPt1cNHWm78/7nSzRjJVe8rqy8nmpIwtSnjcoeB9Z9cDAOsnXbMryZRtcs+nb07YnARPRsT1oHva+sRc377x53qSzKIkt+3wSJIIYs2DCYibyzxyD7BFFkFDlXLhe+plcFzBYgBWPAKNU0gkq1dmma9S0fI79i7Q9YjrXcbqW5mrAWAwEXYrDoQpaW/XL4QyMIwwl6KvTOGDVT2bGcw123F+2HcdHLsET8qHMXID1eY1pTShm/Pb5xmHXG5A7w0oOLsLV1lwljhFVjZLqUTkGExrYJHhGBzYC5WuB7pPKgQMhpQbdNQa4BhX5mEO/jL2++xLwxk+AWz+UlhYzu3gwOKiWC2HDcn94CKOBIQz6u7A6ZztMUWKDxkGfbyReECwSwZDQcs4EadA8hlLAshLwsME3L6gJsG8BshF8Ge9TriPT3+T5sFLGpwPM0aAgfx+/ADjmtlR5sX0pvCEPBvxKiXiOPhfLcqJZ8SOdwPnnYi8ebgfOPAmsf09GWttksNW9NqaDRZcDmy4P7pCsEtKIviENpctEw0wag86xMTh0AWA0Sg6RvR3zAG5vtPfLzUDl5HJ0suhkvmUjwkTQ6GHAiqWDzByXZePiI8bGRNY7ZUhEZg2GoCfZMWHQMrA0BjQdA64cB25+FKhsVB1cBxRnGIDhdfvud4HnnlOu+T33ANGmdlOitBHoOgMMMujNxSAMWPKB/f+jVE/lFMakS2pWAcvTD85lBSTv7vwwcOwloL8dsOUCFfXAcRURM9Au5Otqlm4XxMXel5/F1ko3LFovNFoD6su24nL/8LSkBh1EGj4VpUUiGC/1Ul0Dwxhu74FLZ0TZhhUoseVMZD5976bt6PJ6RO+iWptdrFHMAOkPBuMIDcIddagjAwNkUNDsduMfL17El6ZxPuhwzFjnlmPBtBqINCpNaDWpSzDp1MixO10WFNfT3z+xH0eHBsQ6q8Mw9gwAH2swIs+ohc5swoGznVi5pBL1jkrYdPnINVTM2nhsamoS867v8mVxrSWhIdDWhiJWV6bAP1++iL+7ECvp/NemS+IYL/eOo8kVmtgvqBH/udOn8cT27ViTuwR5Rgeuus4hjEDc+XuCCqlRyGo5CXX/rcTmyMnwW7+lSFDxuHIz/vCHkREO7AG+9++x9WVRI/DpLwpDfoKgWrEKeOZXsfeQ/KQT4h6Pf46EMomRLIGBfTqgdHhotPMhZcrUoFFOUrioogi+znOoXqqsu5eOnIfFYYXDaod+hpU96YA2A4MFDCSo1wlWaVA7+/Dhw2I/SSoJwr4s/D6qTD0SpDRMKeuWag+ZBCYEXH4DmrEO5FuD8C8qg8ZshB4R2PUkO2aQtVxQCnRcjq/gs+dfW0JDwp/Y8yICBJxA3npgRHHY4pHmOfM7NtwFdB0C3L1KZUj5ZsA0DVGXAThGGPyYKSb5F5TELLoVCI4qezCrwOdwvM8FpGxfvS0f/T53nN54nfXGqDpZwLUBK9DyDWsxEjhFoWalg5FhBQza7O1F3GOyIVmTWJ3NQHOmAWFm19LWnEpeiP7EzTffPCH1og4aMqBFW4i2Iff4uZANWkA8eB9IJpB0YHBSNg0XdktRkUhsm45kYnJSIqHNMcBgOe8l7Y/7779/yrHHgCbJgVQyarORrJpL0OZjFTV7+PH7Z0ICJoI2FYPTJEpm24Q8cY3g3pzM/2EPBCaozAWRciOC9/NaB9LZxJ2kMs+F44n+BZNik41/KdnkdvjRhi7o9Dp488fxypuv4vatc5/FzzWDpOhdd9018RxtQI5f+tysAGQyFP2G6SB9EUrGkVBdkCpMAp0BKEjS/uAGQCb+hT5dB5cHnBQEo6axcfKCptUYkWPYglDEHTXQ2Ng4eeCo0JSDO9hwMANccY3g82cOYtCvZDu9s2oxPlo/M13PYpMNH65bjx+0HhexcjYQLTXb8TuLtsaVzw35BzHsHxK9DcrNleLnlKjdrgRD208Czmgfke7zQM8lwFYR3/yYDvZAm5KJnlitkQRaQWrQqVckQmQYSwYNKU/R62tHjVXZiJpcXZMCivwtR59BBiyd/uWbgPOqag3+XJlEisK8GDDVKVJUlIfIhm6bbwRwdcYIDXmvRY8Nf4zU4DdLaLDJ8XvZNYhB/zjyDHqMh/pE1Qp7VDTaF8XJnaUL6pqvzdsIX0iRFTBpKS0TPafBK7GAnDwnVz/gcwLm9AMJdDgyaXTEz19k34huz2WMh8Zg0JpRYV4CAyWmouhuOoZGhyvBMjQDHp9CGDXtEX1b4CiZZDySUeeG4bDbUTPQChx5VZGKWXUTuhvWTRg3iRsonRVWC0gjad/3/wXbtEGY9AnfS7CKTP94NZ7UmCkoT5SpRBGv9fqHgd5LgGcM6DwHjPZFGysp6xl2vAcoJKmbpjF36hTwox8pTZCZofzII5k1kk0EJd+23Bf7/bXvT37N5UPA0u2wmo3YVe3CvkMnsXlVPWyc8l17EQlwbVgyUZpNw1qdNSUb8skKnUeqNuBHl95Az+UWmB125KxcCk1I+Q63sdJMNQYrLfHrCp2dNQcP4jXVWiX7GqneKG59h2d63XQaaTTc0pE6Y6UFHWY+4kCylY8oeDwa79zjWFnCQLO6MoTXYSpn4YprDIeHomQX12C2Xw8BT3YHcG+ZAcWVxRgbGkXvUBA7S5L30UkHdPR4frw/dCpoBPJctxYU4Guq66kZH4edjdSnIK5+3B6tVomC7368vRVdHn8cAc771BJt/sj7W2stR5enCeOhhKCyRoMa6zrkGEoSJCmTyBH2XwIG24HiVYAxB2g7DnidwJalwJe+BLz4otI4+uGHgejakRa8HuCH34ivuLh6GXjteeDOB2PPUVKq5SrwxOPK77l5wOe+CPzoB8DRaO8diwX4s7+c3VxNAOcUiQGugyQH2DuD/5fro5oEoIPiLB1BXi7la9iDDFi0rhHNJy/jo/d+aM6yqRgworPB4ESy4BTnH0lFzq04sH/O0/8NXDim3PM1W4C7H1UaZ0fHTtol+aEgcPBxsW9qaecMA+ZBJ3zbl0Gj1wnJMz1mEODbdCfQeQUY6IoloNz5PlwXEBXKCXNFa1R60IydU/qIyb+ZSgFDTmYOTbWSDTcX4LrJ9XOmkGtvHGiX3SCSU1PhkaqV6PE60TKuJJvQv/hAbXp9DBbw6wuzrhil2lsQiviET63NclUeSQEGfLMJVmuoG5LPRCKSgXBK2ar3N+5JTDCRvoWa0OA+yqCw7NvEIPtspUMXkD6Y7U1bhcSC1OVncFFWU9NWpTwS75+ErLCRBJUktJk4Rz+TQcl0E5doj7Cvg6waTgSPV1OTmcxiuj0RaLvRHiepMJU9xnnGa8Rxy2sjE/5kYJn+12wCsTwGyR0eZ7akBr8PCSnpb6Tq+8mqZ0p/LeD6Ae8XJQHp73K+UVaKBCErRxJ9YPqPXe4edOp7oYsmyJjzrDjVdRFrhlajuGDu+nORgOa6TdIicd4w3kVbkH43/fJ0SA0J+sKJvtQCbnxI/yLVGq9GWlYSJwcX5UwWS5IYek12skooNfByX5/I/t1ZVIgvnDmI4SihQfy0own1VgfGQ2EcGe6HTW/Ao9WLUWdL7/O3FlZjVW4J+n3jKDBakMsm2Cq0j7figvOcKCWnUFbbeAu2FGyHgQ5nKvDClywHTr+kqirgj5BSrZH0PekFr3WaAuhQiBAGhfJ5JMImvRGMRmVYtRoGQGLGao93eOLc1ViTm6F0zl3sP2BVGoUbTUqjcLX0VNx30WenOkOi51A0UBXN4J34HJIHCa/VxypsOAn+u/UEDg5JPW5giV2LersOA/5BOAMubC5IaHaeAUzJpNJSZRRmmGnoyLWjvb9ZGHw2nSLfNh3YcLjKqsiFJIM27J4cOOFxKXHhj44ZV98kUkNq5TIj//Kvfgxc3D/xt7YnH4d95wjKH3p/0s9UOzkkPnY89G688fXPY2tDJcyG6DURmdnRF/mucUNQEhvly5R+GedenTxHB5qB8jRlT06fBj76UWXskgDav58RdOATn8je+TIAmOo53wj08GHXxkYcOdsMk8GA1Y3V0Hj6hLHPaiBmdNA4TZST4f1mmT8NZRoYD5kbkH/PDlz1u3FoqAv5kQh2FNXgdhWpkQzcN97ORs15efhhV5e4DmtyctDc3o4RBkajZc0Mnq9XSa21j4/jb86fR6fHg035+fjTZctg1umEk5SObAIdWzq6PHcSa3Q8GKiVYDkjswp5LM4xjm0agXQOkjXDY/m1fL6rq0scWxKOvkQZoyg8Y27s7XdhXb4O2mAAZbNoDst9mA4HA8OJ9+qRqip8prER/3DpkhhrtvZ2/OqjH4Vlil4QhiT7jUGrxbq8PBwcGpognUhAbU4oPS8zV+CqO15msMpSj1xW6sUd0AEU3AQMHYqtOawKI4HBCT98BeCS5ByJruUngMpVwFe/OjMyYXSEHmT8c7xH/b3xz/HYH/wI8PZHAJcTKCpRSJQvfgl49ingymVg0WJgUYbyRtOAjj7nEyvaOH744BxkVpJa55njkYGA8cIxcaqUXvKzuE2vR6UjD2XmudF75lxgZRUzYnleycA5wCyre1j9psZLTwAXj0d/iQCn3wQsduC2d2R+IkMdgLMv9pnkuz1+6PpHESpX9nezNn6Pcgd9eKn3Mgb9bpSZc3B7yZLJus4mC/DO31OIjaAfKKsDrNdJGXbBasDTpdqaI0DRBkBvAUrvBEbPKJVPpiLAsRwIe5SeWNdBBQPX+NlUakxHGt/IsOmN+OOlu9DtdQp7tNzimKjiWMACpoJGQwJ3BvKLaWAuSHHuC+nK5SaCQQtKQ9JeSwxecH9M1ciWa4eaLGeQnckCJDekdOIC5hasqKCNTduBdgzvH30HVtCQtOBziX0s+BrGlXj/KNtD0EbfvXt3xp9Pe4rBWzlGGLDl53Pc0GZWN59PBzyv6QJoTAaj+gAJE9pq9C3U5ImUhqWtTj+BAVwp+6tOmpK2Pe0tvp/v4zWjbxJXvZgCJEukP8SqFx5jptJH/B78zLj+VklA35Gvu54lln4dQSKXvW4o78p1mA/OKT5HiSd1LIbE8YsnXoGm1hAXG6xaVo0T507izp2Tpe+zAZlASaKFYzcZ6IdzL5ixzPQC3lKgb8A1mWvipATVBOjTNTbIqE93sLlAr9eLXa++issuJbs8R6/HihzKQ8UWXAZd/rf9Cs47R0VclOXdL/S247ubbkVtmsSGXW8Sj0Sw6uGiU5HokBPfE/KgdbwFi+3TZJOzEoP6ZfFPAvYcYGQgPrhMKZs0M7+52Vh06xCIdCIccaHJ3YnRQOxzmNFp1sayHCxs7p5EOqqQWsuZgBmXtz2sPOYbQiorSgwlNrxUl1AbyoBohQpxdqwvjtAgLrvCKDFrYdNr0OXtgSfkhSWbfVzKlgNdJ6OVONETLmwATAmZJwyEdp8CxgcVfbuKdYrEkdDnd+Jgy8toXF+PvrErsOvzsMyxSZAWs0FEyMQNJTwZQTgQRO/gKApzbDBGe3/QUGLwluw/jTIapdwUzVdOY8zrR45Zue7eYAjVVyiP8f60sl4M5XXY8lt/jNd+8O+4pbYIZs5lA0mwKGFV1oDrA1lw+h5/PEZoSPzwh8DHPx7f72M2qF4JDCt9LBRolOeIaEWZTqfFljWL4HR7cPjMVYybw1hcERHNe1MZrzTG+UjUouWqd09ZZsHeHdu2QbN/Pz5cUQGnx4Pq/Hx0VlfjQxcvYvTkSQrM4h3V1fjdqHPax+z1l17CoJ+9AyJ4trsbh4eG8NTOncIh4NjkuFSTFGrQkaFBJKs0GDxmFhjfx/HIslw64InZfKkCa3SaaIyRUOFeyNfxd/n+JfZcVJit6PFSdlFByOWCztWH3NWNqCoqwI6ixbD5zCJLhU4LzymZbig3b76moKoCR5z96OzoxGKTHbX2vJTZh7yHX127Fn/Q2IgDx49j465dqE9oppaIjy9ajN85Ft9P6hOLluCesnLcvmcPWqPVGRvz8/HlhIBCnbUB4UgInZ52scJVWCrRYEuRhW+vByzlQGAMuPi0ak+UndL90bU9+nvnGaBhC2CfgfxjXoFCuLNqQIIOX3mKvijslaHul/H4fyvVGpybLzwLvPYy8Lf/oFRHZQGUCmNGIwMAsgqCTigr4KRTO3FqDgec3mHozDpxf03R2HWhIy8taYeZ4NixY+JzKRPFcc5qDDrihAxU0OHg+J+0bjSdia+Q4f/53ExIDVGVEI+ING6EjVcKjarSyhcK4l+b3sCwn/Mvgi5PPy67mpFnsGJjfgNWqRtik+iouQ6zeM2FQNV9Sl8NJr/YagBrWYwcLNqm/N/XD/Q+A4Q5xrVA/kbAlsGeyfvSdQzoO6v8v3gZULV5VhW1DNQwIDJT0MeYrnnrjQytqGCcf/9pAQtIBQZ+sy3LwyAn7apk4PO0nxhok3sHg7kMxPJ52kLc/3heDEZLOSPabMzaT1WxThuV+5I6YMcAMgPs6n12AXMLXmdWdTOZiPa3lKRKZaNzDPCeMYCeSTZ2MjDAzj2E5Ijs7cIEOlYZ0A+gfUWd/kzsNNr3zHpPBX5HSZrRLmfAjd+d45TzimOaZIr6+6eqPqGPQpuO457v5zyi3cWEs1QScbTH+H35HeVxed4MFNOH4OfzvJL5dyRjOM8o1UZfRPpGDIAnEi6J4Fzj6zPpi7aA+QHHPSXLWfHGxCTee46nbdu2ied4z2RCHP2HsJ9kWPwewNdPq0QzQ7BKiYQz1wlJoNEfkmOOY5rziK9bWLcXIMF1kOOWa3xWSA2WCdHgmK/mMWr82enTuBothZM67GdGI9heFJt04XBEEBpEJCr9waa0P+m4is8snV2ZdyAcmFThwKoHyg6lJdmUVwGMdsc7+xVLgSU7gXN7lax0BnFX3JzRebESxqipVhaBQIIMBBNfg2NQinGBVbm1OO9sgzvoFedOp39TPrMY52bhmhXYHNM3pMgumApi5IW5AAi4oj01oq/l32yVQNl2IOJVMhZJ5qjGaZ/PlUz8BOOhiCA1xEcygJBNWAuAtY8ArW8CAQ+QVwnUJmgtcjxcfBYYifZukLJVa94lpCKa3CdhL7Cg9XIH6pfWwBUcQaenCTXW2Tn++twaeBGAOahquuP0wO8NoLVnGD5rGfwDfgR7zwmDk0ZPolFakmPHYH/XBKlBUMs/Vb4oF6PEAPSQtRCrP/4FnGxpxpZgG9AVzfouWwRsSMgAvlYwMkN2sdIcfGIERYCqDOSDKKeUWMrMTHKSHNkiNZZsUbKOKTlFIq16FbD2zuh3YNC2GnAqxJ7DZsXWDSuBRW9TMoDnAK1uN44ODyHPaMDNRSWidxHHUmIG1kaWmzc2Yu+qVRi4cgXvXrtWvJb4UVsb+n2+CYKAP5/p6cHPOzvwcFW1cBYYeCXZIMEMPzoAfNBBkHIE6ubLEgzMZlKSLhtRqkGngY4Pj0Mn4Zsbd+ATrz+PKz09IE/XkGuEw2ZBJBzG2rylqLQUAxalXJ5gwJiOn/q86Cjxe+TXVOGTz/1YnKe1qBDW0iJ8urIM04UuTW43FlutqFd991T4QG0dRgMBfPXiBXhDIazLy8cHa2qRYzTi/D334PjwsKjcYPWMvC/xUneN4pEWSBpzTZ9E8qsJahUCJFRmQGowc+yxTwHf/FqsWmn1RuDm6HyYCv19CqFBSBKSPUdefhG4J7Wu85Tg3Oceb4rJMDKbkD0puC5KZ54ONzWR6ZDK8cBx1XOhG6hXGg4TlEos0pTMiY4xK5foXHCcc70muSGrStSNw9evX5/cOWdSBnsjTYD9a2Z4ntwzSfBzXRMWGLVBNYgUVkGvKYdeE595eXasR1RoEGxgbxebUQQjATde7jsNvUaLZTkpiK3rCaY8wMSVMQVIZAy8rvQOU54Ahg9zYwdMhekTGh1KZqwAf+ccrEoiJZom6B9wrZoppI+xgAUsYH7A4BXlPbIJ7gvM4mbQKhFcH2iXyc+U8pmsslDHFmQD8HSlgJjRywCZmhTle2hn8VxIwC/o/s8PeB+TVWXMB1JV5bC/Cu0tjpGpSAo1JJGgln/mmOI+JRtmJ8qs0V6SPoJM/khF6CSDrNxNrPTg+dMnkAQHxzQJCJIeJDEY6JP2GK8/CT05v2hT8jUyaMzvw2PS7uS50b4jqcgs/nTje2+++eaCxM91DN5X9pchicGkRUmSUfaWRC/74Egyu8RYhNGQFz5dYELNpd5WjaA++2oZHM+ch7fffrsgLjiPOHY5vuWc4phlYhXPcQELkODaJH0Mub7NWn5qNk2MZoMzY2Nx2uv8vzesmajQ4J8cRiNGPYl9FEiAzKCRZALYK8GoNcIfjmo7RV3sHEOa12PN/cCJXwFjUfmL6nVAzXrFibx59lrOPJewaCQXj6Aq09GsM+JdVbtwbqwN40EfLDo97AYjer2DKGV24PWC8V6g85WYw26rBipuVoJBZVsA75DSOJPEBgOyNXcCZimLkrwMstRkT9re3KrjEq6BVWdBJ7WONaOosRbBostORq6Qb1ql0nBPBCWeBKFBRM/QOyKIjUjxUnhCLowMjmHxilhTVXcweWN3kjK+kA8mnUn0+pgKdfX1uHDei+W1K4HWvYCT1zQIs8WIogI76rbcBeTXTnmMrrJFaByKVQY05OfglL0Cy1JkzjNThouRZN5pWHFTo+FLAw073gf4og160+gpM69Y/yBw/jVgoAWgLN3SXUD+ZIctJW6+GXj99djvzJLYsB4Y6gHyKXmThfHGtYSkaDJiVASrbgEGzgCeAcBgAYrWzhmh8VxPF37z0EH4o/2CthYU4afbdwnZqGTIMxrx4NKlCC1eLIxlGmGEK9psPpEQ+s3DB9HkcuJPlq0Qf5dkGUvYWQ4us/Y43qbS789GAI3Zyfzs48ePi2oWZgV+Z+PN+J/hi2gdVyT/goEACkb8WGKfLBdEwpDzgFUZMruMxiaDyN9g5VNJAfReD8ylhYKI/ubVk9hSmHrs8XszI40ZOulg2O/Hly9cRI/XJ6phXurrx11792LfrbcK2art0zSSnxKRASBygWnlDDMDmlVKfyVHJeBkPwPVffWp92k2mDYASa5X2li3GfibfwHaripVkYuWpkcgDibpCcD3JXs+HXQ1A7/6LuAaVapH7n4fsHSd+BN1X5mhRNtKONqRCFYttePC2YM4e/RNrFx7B/LyilDpqEJkPAJDkU6s7RXmKrQOtorgUDZtMjrIdHBl9l2yfjXM6OPanVLreue9wJPfj+93tX0KgpoVIJSd8PsBajOr5yT3gZveDZx8CnAPQ2NxAGvug8menIj0qcgym26ytMrZsY7ZkxotzSwp4CbKyY9rAt+gitCQ0AD+vulJjbFO4OrLsd5QavRfmBWpwbHItWymuJY+xgKyC2/Ii0F/v9j/ikwlwn9awPUH2k6T+thkASQQklXRkqynrTNdXyUGttTZ4jLrPVXVBYN2ies9M8n5OQykMZidSrrqLQnuq62tXJSBWVZAvFVAe4vyVPQR0pVH43iV1dgcR7StScJxDNNemipjmISHuvn5TMAxzUoXBoJJTNDHoY3PILCaUEkl+0gChFUcrACm7Sb9JR5TBrVJljAAni6hQRleBhWvOUlIGyh4FghTolQH6BcBuqnjFr9O4BjnfaXsFKs0SGqI3otbtwpigwQH7fgtm7dg7xt7sXhDI7wRH/INeaixVuBQRJX0kiXQ35FjT5IYJAnV+wFfw/NewAJm6mOkTWqk05SViERCCKMNkYgHGo0dWlSlbBKeDlY4HDg6PByn793ocODzK1bj9MgAHAYj7i+rw6dO7sdV15io0iAYBNpaMLtNheBivzZ3A46PHEUwEpjQE6+ypJnla3YAW94HBLyAVj/RNDNbYOamXZ8LV3AsLlCUa4gP2pHYWJ+3CPsHT+KCq3Xi+eWOemzIvw50RxkE7Xot3mF3twMjFwGen8GqZJePR3W2rcUT0jpTYUVOCXYU1uKNwdh3XuYwwKYHHHoHrrrGcXpU0QE3aw14V/U2FJly5qciJcXzHHNj/W7YHFaYrTHjQZuEsOjx9uLI8ElBbGihxfr81aiypA58Uk6qkI6M0RGVQIsG7uTPoavTkhpaq10Jzkd1PPUr1mPd2x/DlebmpA6L7AOgdjhopBHc3NTZL1kBg6YkwIx2IKd2do1+2Uh29V0zf//b3karE/jud5Xg3aIaoEoL/OCrAK/jOz8OlFZjTsFxU5x9Q2Es4Me/NZ3E6dEB5BlM+I3aFfjY0UMIRAkN4tDQAL5x5TL+oHHqzC0G9GmE06Hlz3vKyvDFs2fjqqz4f50G+PuL54RE0upFi0TWETP1WCmhztSiMzIfoGHIwAB/yiyw3ysqwGt9zej3UdvfjvKRoJAfSQbuq3yQlPSF/bDpFWd+0O/BWHsX7NWxuewM+hGKhFPqsZMU2rJlS9oOys86O+Mas3OPfXNoSDx2zIrQcAKR46o7NwxEjgLYBtTdBjS/DLi6FLK6dC2QGwEu7VGC4JxvG96uEIj8PeADDKbM53BhsfLIBJVVCskYiCUwiDWuIc3m1mp43MAT31DOn6Ac1lPfAwr+GChWAjMMtJAQo6HmMHUD/ktYttiO9o5+nD76OFZvfFRUcLBp99b6rROHZpYdx/tsg8B0dOmg07Gls0yHRw2u02rHnK+ZEqtuUnpWnD2s3K+124G6FDJPzOp/7DFA9u2glNZ//ifTLWOvySsHdkf7EU1z/xfbi4RtqE6AUcMfHka/903kGVfAoM2whwaP+c9fA37+U+V3BhQ+/5fALbdiXhF0A6NHRMXKxHmJr8vrM03g2O8CLj+ryFolxeyqsLmGqRvBZopMfIwFXL8YC4zi2Mihiepng8uITflbYNWn1/R3AfMH9jtghnY2weQM7me0yWYK7m2JckRM9OBelQpqaSoGoGlHSlKFgdxfG5w5A3zqU9T6Un5/5BHgs5+dnQ/0FgFtKco1Jdo50xETrAgn6MMymM/nmGGe2NdurkDfmElYqRKx6HtQrieZbBR9gVRzke/hfE133yV5QpsxWRXWvCN4GghLtQkSHOdI4wC66SvUf13A+8okQVY+yDHP8cukNxIbkuyora5FXiAvjuDOlioPYz9UJJDyguoeMVyjOf7Un8XX8XGjgHvN6dGrODvWKv7f6KjChvzGlP7+Aubex0ib1EinnwYJjWCEGt1KE1De5AgGocO6iYHL57q8HRj09UOn1aPGUgeHIfWx/27NGuwZGEBzVILKptPhu5s346aCAuwsii2uX1mzFX96+k1cdI5Ar9HgI3XLcFdpduQG8oz52FV0C9whF/QaA6w6a2aTnq+llE0WEYmMIQIPNLBjmWMdzo0dxXhI6TtSYqpEhSWW4S/R7R1AyzizZGM472xGna0SBZSpmSuE2Rz4orL5IA/QNAKhYeVv+lJAawGCXkBVDaNAA/iiryNICtkz21B5n95bswZbCqsw5Peg3OxAlTVXjMMnu49iLDg8EXrzhgN4qfc0Hq1JbujzPWfG2tHnHYVdb8a6vLqZS3jZoqSMyDCNhW0jjgocepOyVVbULbfGJar3eQdQYx2HRacYL+NBDw4NHZ+QRwsjjGPDp6DDOJzBNoQjQVh1haiwrIY+mi3HwJQorU0SGDUbDejqG0ZhlS91A7DRQYRe+D+gQDVeLp2CZqhX6HgSNHzoSDCbmKXfiRsXNzOZaUINT9mEOSvoOgT0n479TlKj7vZrZ9Tzc3/rt4CPfAQ4vhd4JRoYk4HPn38H+NgXZ6VnLtDdBJxiFu44UFoPrL9n+h49bBovms3aAW1mmTecC188exAXxoYFgTzs9+HPzh4UFRZqcHNnZUUqfP3yZfzVuXNwB4PYMjSEn69eLUSHNhUU4H+3bsVHDh8WkoO8OnbRdkW5j5ddTqzJyxfZVEePHhWZJxIcf/PhcPAa7B0YwBvt7didlzdR2WDU6nGXqufI5TGlMVqyPUOUmDsv4yJ19EXzbgO2FW5Egy0P+7QaaPXK92CfqAqLLSmhwWOQ0CCpkkkGlYfzMqk032zl+PoSz5AMtfLQO4Al9yv9hPhd5DWpXgP4x5UkAPY86G8HXnscGB9TSI0d7wBYXTaXYG+NP/lz4Ct/w4VSee6hh4GtSvVQRuhtB/xJJCrbLk+QGpLYOLB/P3auV/ZuorqqGC6XFx3NB1G15AGRdaVuJE4ygwGp2TSEZKYhy9M5b5I5tdwnDvdfxmAognBfGEsdFdhdvHJC0oPZgkmxZLXymA7//u9AZ2fsd/Zv+eIXgf/7v8mvTWPtLjbZ8Rt1N+HxtuNwBwPRtSL29zqbBv7IKPp9h1Fq3gGdqh/HtNjzWozQIDg2/vovgfUblGzY+cLIkWgfDQlNlOm1A9ZpkmxcPTFCI0kFHMrSuGdTgP7BbEmNbPXscwXHcXb0CjwhH4rN+SJxh8k/C5h7XHCejZNzDUQCuOy6gLV5U0iqLWBewSATH8y4TtWnYqagbT+Vb0y/gHOdeu6pPjtZUIvHpC9BqRwGlVmJK2Vt+VPtPzBxSy3jQ0zX9PktAdren/40oM5mfeIJRvOBB6dQDUgD3lAQT3ScxgVnP6w6Ax4oX4Y1TDq4wZCpX0B7h2OOUNvWJDVS9bebb7B6iYSfWiY0HXANSPc9/L6seGIg/JpD9KpVyWdLhLoXSI0EMNbDtZaElOwXyTlAMpv2PyWh6Ufw/0yIk8QYCQ61zzET0C8nkbFhw4ak8+7ClbPQV3hwbPhFGLVm1FpXiubhJFx4XjfCen12rAVvDlGRQMHxEYV431RwfawNbyWk62Nkl9QAM7/lhqo4TSQ1ImADb6Xk76r7MlrGr0Zfo0Gvtweb87emJDbKzGacvPNOvNDbC184jFuLi1GeROKmzGzF9zbfivFgEEY2asqysabX6pGrTa9scS5B4ywUuYQIYs2v9dpGrMvbAX/YJ5w3Q4pybzp7yeAOjqOAmbFhljFSu7oQ0GSpooTHDKuCzGxSHTygkhw5D9i2K445HU9Vlrfy5Waf4aXovxdiUcJzA96xCc1+OWL7fWMpr/sLvSdxbqxDBBhJJJwf68D7aneJIGbGoAzQ8vuBi88phA77rzTcivYhjzBSVpetxJHhfRNJmUp/1DD6vF2ojTbkHQmMiF4Watj01BFXAqSEOzSIDs9x1NmUnh7cwNjMTDDmRUuAAZnRrkFlST5Gq9eIksXbbrstuQHY3QZNOCz6BMRtOp3NqFq8VpS6ytJcGoQsGWfmFkt/6cTQYVFr4pmMRviajgO+VsBsB5ZtA8wzvOfe4XhCgxhrBcbagNxrXJrKa9XTFj/Gee9cI8C4C7AlrH+jg8CxaM+d2qXAsqhkXTIMdgB7H4+Fp9soNeUEdn8g9Xt854GJcaIBzOsBffqEYa93HOfGYhrq/GT2kTBqNfBTHi4Kjs+GFA2N/7etDX9wgg3mFezt6MDbfvpTfPemmwRBVjMygv+vsgK/ffQQIl4PInoDQkaD+DBz3RDCFUq/FwZ21c30OAankziYLbge/N7x4/h3ZnHx0d2Nz65aNamhtjPgQqupCwePHUVDbQPW5q2EXbWmdXt7JwgNGQQ6MHgUb6u4GQdzTkNS0LkGIz7duHnSeXA+7d+/X2ShZUoM3lVaKnpmBDifo1WQBUYjNs+aYExGlcjno+B6pwaJCz4IEnMv/gAIRgO4rHbY82PgwU8A+XMsp7BtB/BfPwLaW4ECNm6eYRVVsl4SnO8JRCPXWDqWbxz4IbZsboQ+SmItX1aNg0d7MBY4J8q1STJIx4O/07mkXjKdgGTwhUJwBQPIN5omsobU+tB01DlmUmXpPX/uANqsbujDih1wdqxdyMnc1rga+/btS01qpIurVycq/Ygurxd5zc2IyzXkGnb+dcA9AuSWAst3xcZIEixzlOAPG3fj4EAL+nxD8IaZhBJAo0OHGmtUFxtBeEMDsOkzaB57pSmuMnGC2Ohon0xq8B6zKoeElqhozGIj3uBI/LzibaWdVnLH9FWr6r+L4RAlNkwOoGwNUPrWIDWY6PFszxsIhIPCRuv09mHYP4adRetnfewFTA9PKDErPoLxUHK/YwHXBrSPpMxntkF7jIlK/IxkGeIMBLe2tooHJTuTgXtLsiAtA2SszKXvQmKdxIXsC0AbkJ/JwB2JFbVMFV/PPh6UwX1Lg8H3xL5GlBli9cYsSY3vthzB+bF+saaOBrz4TvNh/N7i7VjiuEYyjDMAA7tMtJMVP5SiUle7qvs60VZitRD3JFY0qJuM0/ZPJnk2V+A5TCXbxvOYru9MMqT7HUgS0o+nzXl9BJo1yX2M6+Lcrj9w7DLpjeNY2u1MKl23bp2I9XCcs/qGiVKURGaMiDEavod+uJQqmw4iiT06Nwi+V90vUo1QJICW8TOoDLMSPAJfeByXXUexMmeHeA/X90x6XqZzbqy4iptDnDMH9wBHDyh2+u47gRWKPHC6uOic3M/4orN9gdS43kkNbgTJStsSEUEqeQLl+XAkjNbx5rh3cFlq97RihSG1U+UwGPDOqqoJyZOftF/CSMCHKrMd24oqkEe96iisaU7AOUPID3jI7GsAK6sQsis3FcFQHKFBhCOXoNUUwRTN4E+FXEPyAEYONV381P2X988MGLcCmixUl0T6Jm9A/LyJp4KA9zRg2wmUbAV698dex+qROZTGSrZB+1ltFA5BnxB0Y8NREhqEEgIEhgNuQWyszZtcFZMWciqATb8JBD2AzoSIRoure/YIHUzODNH0Pm7fVsiUiXMVjns87NHm50QgEERXG69/J3z2HBh0JrEocD4LyZPFtytBjZF2QG+GpnozWq72ivLElBktFhvyTEYMuL0otinjg5UYF9u7YbSXiuNzwyShwcwWykxJqSlmVfG5uOt+6EkUtB/HgfNebFlcCW3LaeDej82st4bfmdnz841E4oKgAZD4XUcGge9/RQmO8VKdOgAM96XWp287G599y599LYDXBVCLPhHBPhWhId4AeI8D1kJAa5rx3GHw9NHqCvyorUv0aCA25BfgdxYlb873885OUYEhKB4aRCYT9rE5fX09dOGwMKa31tbiuMWEbxw8AG1BPjQGAz7e0IB9/mH86xPfQY7OgPfWr0BiuH+uje/XBwYUQoPSBszgMhjw9xcv4l3V1dgYJQUoKbVv4CD8ugCcbicG/UPi99tLboYhui8M+kcmGrSpiY1gxIdPLl4PU0UJvOEQaq05MCcESDmHmQ2zadMmkY2TKSjj+OSOHfitI0fQ5fFgqcOBH23ZIvqczA4kHrjPqys+mBCQJlk52KXINarBy9PTPPekBsH7N1tip6wGqG1UKjMENEBuwURPDTVK6Twbt+P1N/bg1ptXT4zdLdtuh9NbKBxsGuTMniKhQH1oSg6wIilZBurjbZfxH1eYMR1BicmCr6zZhpIw8Itf/EIY9RwrzLxjNlQqXB7phL4iJ+7yd3m70OPVo8d1Ga7AMtgNmQc0SHgzAKWpq6NAsyAKRANBjweLSkuxUn4fvwd47XuAz6WsZ/8/e+8B5thdXo0f9a7pvc/Ozmzvva97wcZgTAmmBQIkH6GkEuAjhXx8+f6EhATSICFAKAaDwRjc7bW3997LzOz03tXb/zm/qytdaaQZSaPZHdtznke7I41Gurq6v/K+533PGWwDhjqAHR9M6o8y4HHg61f3whWImh4+VK5DrSXO5D5dqSVKcCXqXiqKS5KdOgD85seSfBnfgtKRD35AkuXKBlj4IbpZFYJ8+nwgFc8C+omYCgDXUDgnoAJMecDid0gdsDMEr0fuKzJFqjHGdLjh6IgQGjJuOrux2r8IlhR9pF7s6cbegX7k6nT4UG0dijI1u38Lwqq1YtQ3Ejn/HGtWdufNY06Ayf1pZQTTAMlxWeea8zZfe3R0NKnvEp/P9WeqCnH5dZRrG49bNhhngRTJeJmk4P5HTqIxFonvXmRyjIUfXHuymSibc6BXBNdGZacLfw5XaGcKp9+Li2Ox3bcc18eHO99QpIZM5skFUPFEATsR5Pu89np6esSN15SyaIrX4Uz9MtIBx1MqhFy2u5GEtE64GJF7zjkDfkZ1NRCMSooLaN7EY3sG4DVB+SkWsyqveeZoWCQlK2vwWpcLUEk4U86ZJHIy8jkeLLri/E5ChMTyVDFp32gXdEbuy6P7NLoDj/kHBPmSqu9NomuWnydesYCfgzEU5//IWN7zHPDk96PX1NnjwP/6PLAs9QKYRKMt7fhiHlmNMVKKZiKSNdNAjdyYynf5URWk4FhK1ManYdl5kJrcxajPg8+e2oMBjxve8Mt84/oZ4anxqYUrb7+OGZOn7S8AcrUSK3Kr7gV00yRz2i9KiUluSOpWA6X1UlDPCn6jPaaqNSRkPCYjBCdtr6d8mxJjPpbaF+DCWDShuTZ3MXJUzXHmkx7AdwnQr0npY08+mADgbwaCZNUob0PN50kHHEUwXMmVswAw5EqkEAN1W03WSSElCgw2DPkmn0/KUFnjSA2H35tw8uKGb0bgNUu/EL5vuDWbFddqNU3MrXAGeHxygAgUGkoiFeBnRi4mOCY5gROCY9yJnDwbcvNtqLdUQqOyQOXuxrWTJ9B+7DSWrFwPVK0DajaLqpXe7l7B5E8paVLdgOLCAtzs7sWV/hFR5W3UatBo0UM7TXX8JBNxVuA2n0JjWSHOtfXC4/PD5B4Xj2FxBlq/vHYSVXAYZ24KnRWs2w1cOg44xqTvnQHHrkekaiolTrwmERrs6JA/yoHngQ13JvHkSTbvJXk8OJrgPFGfnRI4qZEaTJauzCnEudFBQfLJtTN/2LAEn2pYhqNDgzg0OARPICgkpj7FZGrc56R5eMQMfITV2DmiW4DygVBUH/3ditV4qKwce44eRVNxMZ7tbcGAUQtNeTE4c3zX2YNl48NYaJMS0dna2PP9n+5qx4WxEZQZTXhfdR1MYWLh+sSE9P3ROFhRtc7HZVKj3zMIb8iHge4B5JcViLWPvhl8vNwkJecNan2CNZEdL3qxntVYEsvbNDc3i8Q0CciZSEjcU1qK9re9TXTUZG39VHE+2wCEmND3hCUHF6ZeSZWwGp9+G5lJLc0aKKv2/K+BwX5JRjC/EKioBrbsktbyd3wcOPYq0N8F2POBjXdJnh03TwNMEpjsQO0a4SNiytuApUsncP3GTSxsqAL0C6DS1cKul84ZOzJoVMn5mT4crK6iFwaDB+V8fXiwF9+8fj5yf8Djwh+fOYhP+wziWmHVKq8X2YyPATuDeZnEZjUMAxsGBcqVLVcHrM6luXw7KhbZ8KtXv48Hd70XeYZoRSwDGh5LsoCE1X48dpoF1v+v/wWcPCkkqEYDASxixeSnPy0qxwTZ0nUF4Fogg3PBcBcw3AkUJO4SeaHnqpDJ4GiSx9TLvX4ssnHMhsKroxYGTZqJmHvvB55/Djh7Jpo0+tjHAWWioeUK8MvvKY6X65tbMk4vLANKs+CblLsGGNgT3auxS8OeYgDG/cyih4HuU4B7BDDkAGWrs0JoECxkmEmyNNUYY9rXoaxdAvhTjDH++doV/O8L58QaxK/wOy03sHfXXSi53caobxAsti3DiZGjomucMGlMaLTOAc++eQhwDUlWPZsJuFYoE8Hynu2FF14QFcLyXozrDJPKHOey99hUYGKMnYiyTC07MLhuTAVWFCeqKuZjXPvop/CmJjVIJHFd/eY3pZiC6xS9Sd797hm+cOJ92xstdTfdGsPrhElfFpHwmmYnULyMGcEYXS7Um21wn89jmK5oKduEBveV9HJjZ9WtJHBShnYRENADQRYQ06u2XlIYmUdS8Lsk8aAk8rjP540dS7yxO4O/53dPIi0VY2aipaVFxCacp9mhx9eU41L6ajBW4TXK8cfHO/puoqh+8vfFHTolr44dOyaer/Rv4bjk+3D9ShTzco3hcTOX9eCDD8b8ju+/bds28XuuP8Kz6YVfRZ8gF4S+9ExapMZiew32D5yb9Ng8so9UY4yskho0BtdgKQKhS+H6Ww00KlYeSkG3RqVBjjYXY/7RmEROgT61IPOZrhsYVBAaMn7b04o6qx0PlUdlbW4L+g4DAUWFKeWe+o4CFVMYSjKBe/w34TsqieBoWA4MhTtaDFZg1SOATTLYUSFxtVmyx+OxKrcJNeYyTPhdyNFZYNdZAc+NyQlORDW+04KQXzgBBMMm1AhfCtqwfrpoP1BSX2TdFRXsxgLpdgtQYcrHNWpNK2BS0zMlmiTqcY/hJ20n0OeZiOj6G9lpEk6ctDo7sdJfDbM286CX44vBBhnme++9V7RTcxOzrGQNrjkuYsw/Ar3agAbrIli1dniDPrzad0SYBsfvYzwBA1Q6jzjNJrMRHTd7oA364TQdhi5UCNPAFVQUatDc5sBo9wXY/S5cGiwSVcCpaYTS58SNmlxbOIhhnkcFdMmScmlAoTnv9PrCSVVVYi36KV9nAnANA3oLULIK6JWM3wVsFYBtDhibERYb8ME/A84fkWSlqhdK0lLxoNdGfKKbBAcrgBORGjXLgevHokQFz2NJfXIZL9GBlaBdWZX6NcwN9F8u3YhvN5/H2ZEB0S330bqlqLdKfjVfPH8BP2tvF8khVoxTaurwnXfCpOgA+v0FC/DjtjapW4OPd3biA1VVuNnSIl5DaYy3tagEWx98CO3OcXzrV8dhrZaSqfwU/PuDg92C1MikDVtGIOSHN+gWY02j0uEL50/iifbWyGf4ZWcbfr55FwwaDRZRtqenh4K2Ma/BbgcluBFzTThRWK4wYVOEg3WWKrQ62uEIOCPV5Y3WehgVc5ASp4aH8eGf/RSdCGHlokX4N4dDdFzMFFkvCFDZAFUSUpyJR3rfDN8AVFqgZAVQuDhKehRUSMQ+OzPEa3Hs5AI1cUkNEn8vPgXcuCiNrbseAWqnT5hkBU4H8Nd/Bgz0hdc2Hme48uj8KeATfySN1c33Rv+G1+apZ4CuS2EZuhDQeRHY9gFAo0NJ7b1oPngQCy2bExJATBLJPhjUFJeN/pQ4MdwfY5jNlXbA7cSEXpIFIYEhywJyDMvSCiQ1OHYYsDOIVTms+G33ycjr1ltYnCI+hPjXajPjZ7/6AT7xnr8Q99kuTpKN+0Rq8TLo4I3BDdc3VoKxupbrGo9DkAM/+Qlw8CD6W1tRuH07CpqaMHr0qPSGgVhvngiSPS66Kd2RLsrI1xQIQa8qRACUojIjV7cImulMtePBxNo3vgm8+gowOAAsWiz5aShx/UJi+UxK8bVfzw6pocsDiu8D3N1hycByQJPGvkOjByolGcpsg987K6UzSa5wjoyvhs0U5aYiXBqP7kU4p5o1Rti05pQ8hv76okQIyp2G/R4P/uXGVfzN0lhZwXkkBg3BN+dvx6hvWMxhubo8EffN4/aCVbiMK9ghwXHGylUm+JN1VKQK+bVY1ctxL499rilcB/g41wVK/zAZleoY57rBCmHOC9nwR2OXxlsCH/oQXdWBU6ckacSHH2alwoxe0qzVYUVOKc6N9oRLA6TYd0N+Fta0Wwh2PJC0SLY+ybLI3BtxXzQVkZAJgcC1cdzvEufOrp3el1XuPJqO0JCl2LIF7gFZfMLq/kw922Yd3GtpmZyfXZnhNxsoy0cFhERm7yw6pccfvVaoFsI5M1WZWXYvMS7hGOP1yGuWY4jXMMkSuTOPOS7u9yqrKnF5/DBcwgdYmlXoV5yrKxaEGmWw2F3HzioeK/+GXhskE0lOy6SG6PpWqcTfkIAhWZGIhOPYY7Ewu/rYsSFIjUQJcsaUaWCRTTo/F0dbpdjdVonlObc5D/0mBfcOWSM1lMa+00GtKoMKrGBjnR8vuFhGbXnuapwfPY0R37Bg5eos9SgzpqZvPOKjCVkCKT1WoIwOpU1qOP0+HB3qgivgx9KcIlSbZ6jp64nTPObP4rEpcHFf7PP12iihIV7TAZx5Gtj6u2IiV6EQKpQgBMm8ilCjDipV6pvTPL1d3MQ7hoJwhQwIhfwwqkJCGUo6o+nLmUgvOBZLaMgIUl9aB6jsgI9K8XK1oREw3Z6AkUbf7c5B3HBI51Kv0uDh8nWRBJ8n4Md/Nh+Ck5Ji4QTRmB/QqkLQqlUwaVTwBN3YN3AG95aw8pXkEGvHmWRckLIvCY2yyW7LmvhkqjlBtzW3Y3njWjGhc+KWJ/IBoRfugTaSw45ujkzaHNSYazHgacbg8CXYdGrYNNI16VMNQGsyCBGY5Yuq0dY5gIHh06hd9UGYw1Xu04LvZTCJxLsUxMhSFhkER2ODIrF3s38EeRYjDDqtlBQqTkPOa/AqcPP16LiLr/Ie7wTGu6YmNgI+YKxLSjLay7JbEU4DXAZU/G55nqizzo6LqUCy4+Lx6H3OoXlFgDFJYia/DNjxO8D5PYCbRuG1wMq7k1fG0zvD1wYEqb8bvoh0DYA6ve/QotXhc42TqxrOjI7ip+2SRJ4vnBw6OzoqSI4PMaEZxuaCAuzZuRNfu3IF/Wo1dtfV4Su7d4ukbDL4aAJOXw0FZD8IItNqpSFvN1od58TGhIkwo2aBIDSUCa7zYyOic+PdVbXYWliIj9bV4b880U3Q3yxdilWKKvViYyEGbw6grFa69vi6BrUBhYYoWUMZqt3FW9DiaIcn6EG+PhflxsQSS51OJ3b/57fhyctHMC8XhwYHcffe13D2nvuQk+L6PCfQcQgYUHSZte+XKsYLwoQE57m7HgfO7QOGewFrLrB8x2Tj+59+G7h6Vhq3Az3Ad78OfPKLQHmWqjHHx4CbNyVZh3hvjcP7gP5eafjIxkfyHHTqCNB8FVgQR1iO90uEhnhqMPYxGqULKw4jvFMUkdB079ChQ6LqKFGyx6rVTSL2Ai43iopyxNggcc0kE6ux4scK78sa0w3WMjxYthZnR1pFF0++3gOVyoOezn4M9o2grKoICxbWRgIaviYDIgbEDGS4+eSNkkIkUbh2USqNz4lUWVFu6K67UDA0JJJeeUrZjOI6qbsgUnlPqSUjMIU5aa0lF9cmqPstgbulMpMNRfQLSgR6tfC6SyVpxsrXexQEVTyEBGoSQjXZvJ0JKDFquTVVoulAjg8y6biQA5VUY4ypUGoswKb85Tg+fFF0Z+TorNheuCYlo/BRnzcy18vgCBlQzPHzSM1/sMAgFWHNY26ABIPSfJUVtSyk4nogJwvSHX/8G1bAMvEaDxIm/B2JFCbLMjVVzgahwfiJFbpcM98SoF9Klj1TPlS7Br/qvIhLY30wa/V4sGwR6q1zpAM+RXAfkorXHiVOlJ4s2YAn4MPzPcfQ4xkW90sMubivdD2MLDRIAnY2TddVxSRxOsnn6cB9GOcKdjbNDf+MeWQTnJfZBZGI1CBYSNja2iryT5kQZYwfuI5wzk00hpQkWaN1HTpd1+AMjMOgNqHC1AitWo+DR/YKgvGee+6JEHqMeditztiB64kcY3DtYfc5X5frDbv7aIKeCIyLYta5VRskTw3lnm91elKxHCOL7dXiNo/ZBb83chFZ69RIZ8OjEpU5iTsHmNRZm7dR+GswyZPOxLnIlofnu1snvx9Uwkg1HYz5PPjy+b3o9bBCVnqNTy9ch40FM6jqptl5wBOneTwNUeKPY55oFCqXQ0T07sclcsNoE+dLA3ou8Dgpc2WFWpWZ/lwg5EWX6zi8Qb6OGmq+qjYIA/RAqxO4+s9SC2vdSmDZzqRa1jFI1uavZrI4HIxbG4GAtLhDw2PXACPngdHLUrLHUg0UrMuaNEIyaFRqvL18Hfo8Y0Jyqthgh0mxyeh0jcARJjQmJX900rXLJGifIK5YYSrrXfP+CBDaEK7eTQLXKNB+Ap5Lp2GxbQVy10eez6pWTtasLJEnY7LXUtIp3NJNRRbF8NGpdFiWswgmjRHlmiYMBM+ifqlisg0BARIHLilIr64IV4+nq2V9xyPAb38UvR74/9Ykfg/Jqm2PPQ9cOSq+76s9g7hr+QJpcaG8T1mKSRuvI5bQICgDEwOVRGwkIzWo2X7+l4BsEE8psGXvAEyZjakIGOj9/d8DTz4p3WdQ94//GCtZkgzLNwFDfcDRV6T7uQXAox+fWr6npE66pQJeY6ZNgJ9EDk3q7YA2eyaKiZJA6iSPby8qErfjx48L47KpCA0iMDCMjQsX4fz4kCAZ+bo0u95dLG3queilGwi7Aw60OM5FriOOaaf/OipNKnS4otcWj21QUcnx+SVL8Mn8fCE5tchujyE0CJ/Lh61lGzFiG8e43wG71ooVOUsifhoyeL/RNj0h/x8vvghnUTFUNmmjx2r8HrcbBwYG8ECGlVr+YBDnx0ZFQflybkhnIGWVMoauJiYnZVKDIAG+6o7krzExBlw5E73PuUPFToiD2SE1Tp0A/s9fSoQkce8DwKc+Fx2D1PYUMmnye8ddt+OUeIsDJSUnIewfEQaDbiabVq9OnIjnhp7VRpRpoh5uvA/Bw+W1+EVHs/Ack6+Rt5XXIlcrrWsOVtOHr5/p0GAtFTeiz30Fg95WQWqsXM+qXDWsWilxyTWKVVms7qN8FfeKvDFZRkkqSihwrWSAruy+kkEyn8kvvk4kkLIVAJseA07+VvIG4v31b59MbClwT0kj2p2juDQu6X/n6Iz4cI2ktx4Dyv/95ntAV4s0F667A9j6wMyMJldvBQ69JHXwyNCogaLShD4qtw3saBxulT5rfoPUCZwMXBu7Lkh72rwaoKBmzpMaxAJrlZDbDCKYVpdAscGICpMJPS5XxA2IJMeG/HlZi3m8ccGkJ5NDylibySHKOdGwW+6w4mOJyO5kYPcdO7yT6f1P551xq8DKXHYHpmp4O4/J0Ku1eHe48OKNCLkwMFuvlS4ODl5Ar6LAtc8ziv0D53FXyZop32e6a5ZyPJkShvFgQQp9F1gdP09ovDnBOZ7XFcmwZF04LJxi9x0LlVL101AWUqXqjUYCo8ayNOax4eFhkfdirksG5aT6+vpE8S9fmzEQ94ocGyQ1GC/xf+bMGBMlAnMC9ArhNc7uQYH30M/WD5w8JBVQ7b4PuOuhlI59Hm9w+anZQCqVU/G4s7gazROj+FVXc8RAWZIEopF4em1oT3ddQz8lYMKpLCay/qP5FDbkl2U+oZdslDw1hJljuN2/eBqDpfKFQOvZaGJWJEjin6SKqR4XFfKYeaA15LkObzAahJNm6gtYUNVlAE48F33iebKZQWDlNBXmhJCSYlAbRwYodawZaGoV98euAsM8B2FMUD4gBBRtxmyD57LEmFi3XpeEVGFhrvIaMQr/jfiELbUIuYlJUs1CourkE/C4nVCPdUPfcRTwjQNNd0WewgSQMglE6RBuOkrKSmDRmOEMuEQlPC1LaGy+q2iLIDTEMYJyEAF4vUwyRJMFqpgNGSUsSoVJeVpYvQ2w5UpV0tx0rd4OFKWRVD32HHDpSOSaX11WiGNX26SKWbUW3v37U6uq8owmkGlKkGBkgjQZWvYBHoVupM8F3NgjERszwRNPRAkN4vp14C/+Aviv/5r+b3n8u94uSdcwiW6llE+Wk818PV1045BNrMzNhVmjEXIe8rfD/7cVJpcZZGCdStDJ8fB7JiNesObgwtggCvRGfLBmCSpMUnKOrbPxusmhEI/DzT4JqCh3FAdngIRWXIWuiiS6Dp0ub+Q3TBCvzs2PBPRM3q3Lzxe3RGC1S1VxJRZnYOIdD477/KJCqNhVFAfhQZIBSDI9uH8vTtPPROih2/G1FcvwvZuX0e9xY7EtF19YvAYlxtSkDVNHguNN9zMkCyzj5X8ygdsFfPWvWAYXfeyFZ4FlK4Dd4fl50VLpGMRhx41NzmNVCQhGe5E0F8V8hyEgPzoOSQpQEiTexFIJ/p4b+0RBSZ7egP9evxs/bb+OIa8Hy3Ly8VBZDY4fO4Z/uHoe3225Kq7nOosV/7pmC6rZOZYCCg0NovhBrbki1j6TJgdlxqUiQGLSjC3fbFmPRyrVXrIxLMf2Knp9XL8EDPQD5VXA/X8YM6cz0GKyLN4IkNCpNfhE/UYhFUmvpxKjVTwmrgnvNcBPo1kdcOAM0B02meTvjr0M2POAFTOobrXnAp/4EvD6s0BnsyRZ1bgc2Hx3uItjDmCsE7jy22hlWtcJySjcnGD+co8Bx58AfG7p3LefAhbuBCpXAe5RoPM04PcAORVAyZLIfmgm8n/ZhFT8kx65zQ7dn27aincd2i/IYuKjtfX4YE32PAjmMY9bDSZzZA+l+ESPLLlDcB6n5AfXIHaLT0cy8vdMNqViYnw7wXWU54CyJTKYOMumt8g85jYoiaNMlE6FeJP6eGSSH+p2D8VIrvPnHne4sHMG5AljpmwQEIy/Tp48Od+h8RYAi6L279+P7du3J425qRzCmDNViALfLPi6sEtK7haXwViIawzlrZId63RgZwrlqx577LFop7jBCPzuHwIf+ZT8IcTYH+zrkyR45zuV5hTkaywrpAY3L6zEmwsf6uMLVuCxqkbsH+hCm3Mcdq0eD5TVooCSOGlgwOOcZNBKGSp30A/TVEnQ6YyKax8GnN3h7EMKmsdr7pOSHO3U2VYBpYsBV7dUvSk8KIJA/WZhJppteIJM5saeA2/IA9y4PPnJ10+mRmowaWhYD3hPASFeM1pAvxxQJyYOBCbCCQYlHO3Tkxq8wB2XAWe4e8fSAJgbZlZxqUCFKQe15nzcdHJDIoHTIX1H5Qmc77Qhn9Ws4e88BlNsSnpYAelDS9cgGuSOid5LQN1myRsiAagnyNZAi8mCRY4q9FlGMeIbh01rweq8xbAqdKNV7NpYvBtnL7yKRUsoE0IjYBMMpkWA54JEvJHQyEvdFCkGDcukWya4cSbmuiu0mVFoN1M0D6hfhaNqvVjcKFfCBB4/d0LzWX2ChJw86cn+HCQW86eoFnMOTpaMc1KWaYY4Ro+LuM6NM2eAb30eyCkA7nwXUD5NUMVFl7c3GIoMBjy9lcmhQxhll59KhW+uXo2NBYmJWFZzy9Jr04GyCQNXBvD7TStFt93pkR60OYdFp16hwSw6NZQbNX+wD57gufBYVEGvZqdEbNeONonG/odqm3Bo8BIcAb8Y559ftBybCooiQdJ0VYipLsLTgfI+JFEeX7ceX3/pBQx6vYJgIZlRY7YkJIue7e7GH505g163G1sKCvBf69ejNC4R/MdnTuPcaLSj4OrEOB4/egj1Fo04W8eG+/GHp/bjRxvvzG4HB/0z+hQkNpGOuRol1saHgco6oPNmlMjguV42TRFBKujtJWsV+xg7yK5fi5IaCxcBH/l94H++A/gDADvgCCaCPvKHQPg6iYHeDKx7J3DiV1JCmMTi0juBvNg2bRrZMQnDcZFMH5YJqGQdSQUGI/4gbm5+pasdPxqJfodtTgc+dfIQnt56V0qbd7VKg0rzKlSZJrDQul74UvDvWOnLZAErrJTHR88PaveyMoqJs+nIDaGhe+AAVl08BsOx/dFfvP19cGy7S5gNyvMEiUv587PaWKkLLxUpxHnMuM8CPkkOT2BDCdDTDgwqfMNaL8+M1CDyCoFHPojbCjHfkARKMF5b98eSfuyYbD8MND0w+bltJwE/E/uh6Jp6fR+QXwuc+VmUmOu/CrhG4OW+K6x7my6UXR63G8tzcnH27vvR4nAgV69DadYJ3XnM49YiVb8aEsVMHpEEoIwHSeqp1gYS65x7KVnDfdlcBT8DZQ+VIMHBGIOSJew2IcGRDamrecxNUMopVfkxXv+MsxORXowvEpkUTweTxiD8NGIfy35OJxNwv0Z5H1a5Z/LZ5vHGAvdb9OObSpKP8z/31qmC+/dkYyYdMHaP9z+SCx45V8tyVOw0YUzA2JjrG/1fpgLjD/4tc0rr18fGiJTX5evw9ViISNksrg8cF1wbUpGsm8fsg3NvKh5gKZMacyHgkJGnN87YFLzanINjQ9FENKfyXJ0RxplKHmlNgD2NYyNZsflRYGM42OSiQqmpTgbi1OKvAopnZ1Dp1GZ4grFVyjqaCCfayKbDE5DAMO4KS1GFDcKnQiKJgFQq0ycuARMXovfHTksHSnIjC2Dl3kfqNuKV3qvoco/CrjWi2GiCR5jB+5Gnt6LWUooiETD0xpEYDNSnIHICPow63OgdHsOiGkXiKkElduRXgYDYdHCiZbKoIViJ4rLkVVImbT1sxn4Y1PlM50KvLoeKCQTTbTZ4S7Rx4ve9YDWw9n5ozpwVuo+sJGC1Cpl6th7KFWSRpLXBDlDLcKwt9hpjEsZWCRhsQPFKQDfFREiZKValKiXjknTupAWa8zFIUuoNU46E3WEDXcDP/xX44J8Ducm7F24lgiGay/ughlmciXZnK4a8g9Cp9ai11MOqTc+M+q6SEvQ+9BA6XC6UGI2wTtGFwYCDLa+pgsFnj2scX728H+N+qSPMoNbgMU0JVlVF595gyAVPUNEFhxC8wQvQqGxQ08g6DJs2H3ZtIcb8AxGzbk/AIEzJv7FqCcqNRSg1mZCvqLjmOOSmh62viarGCXlzxs1hEAEM+/oRDAWQqyuEQTN9skzWj6TGLTefDM737LoDf372DK5NjAu5qK+tWAVz3Lk9MTyMhw8cEJs1fvIXentx3969OHH33THyXseGBiOG0gR/nvBHZzHev+mcwPWJUSxmJftMwLWAEoPOLkCtB8zFwETYF4ptlzcPAfbK6WXfblwAfvmfgM8rjXPK+1CKymwB7nkXUJsFuQvZ/0ZJSNHboSBurO66B9i6G3CMS2N9bBTILwRMU7RfF9UCd39K6tQzmJP69zChxKrZRKQGrwN2I9E0PBXCgOiwGBHo6YSmpCjy3d5wjAvSMTeFhBevdcpLFReWQquOHjMDAQbDJDfYss41Sk4U8frnLRWzScqD+C+dge7Xv46Zy0/+57/A41ehafM2QW4rk2wMdhikJGs5lw7cH0toyGgqBw5eja4905HHHjfQ00tWlboqk3/PqsoLJyVvF1sOkJMr+UxV1Ga/yy4ZRi4Dg6elz2wqAUq2SftRGT6FNJZACPAqiB0l2LEYz8eSEOk+E96jKH7ZeQq+yrKMJaTmEqlBGDQaISc4j3m80UFZz/jO1akgF2uxYIOJLUp1TNVBS8NV7s/nMqmRTLaHiT2us0xocR3h2sXPzbVrHm8ukNRLtfKa+5VEPjGMuWkizhg0XWzMX4Tfdh+J6dUY82nxk7Yz2F5Yi0pzbMwpy8GxE4q3ZDEGjylTUpGFgiwU4X6N+0iSlG9oOPuBnnAxBmVTS9fOuoT5GxUygZ2s046dfZwTuTeLJwGSdUIwHpkJqUGZKca6lD+TwWI+HitJaUruUqL6zJkzopuD8zT/hrJT04Fjn/N9fPcTx/kvfvELEUNwrZNjFbmDkeeAMUyy8TePW4dUbTC02XQdR8gBhGiEyeDJDKiWAGkYWHMin/C7xQVo0Uiu9rOFh8obcGlsABfGJFNro0aLzzaun/l7MhHy6qtSVTaTI+98J91z0kv0GixSd0bM6wYAMv2UCorTZM8UBfqFcAWGEGB3hkjnqlFkXAosMAGDnbFPblib/hukqmec0wS4e2Ifs6ewcXA2J34sS6SGrCV6f1kKjHWIHQ8kWHguec0vm9ooPK8aWvURFNgtimS6XbolAZlwJnbkSZf3lew14Q96helxIOSHXVcIvaYARs0cY5qXbAZOvhz72PbHgHqpmoqLCxcRbuqYwJY1EEdHR8UiI7dQisoqSxEw3pFAhioIVGyZnlCr3QZMPCUlcQh2aS3YNfPP+MEPSvOAOJaQlPRaWRNNlrJK9sZ5YG0W3itV0B+GcwhJnvBmj3OuM3AZ3mBH+Co0oNNlRY9H0qXnI/2eXmzI3wKL1pp2cmiB4toc9npF98DBgQGhXf7/rVghpJsYSJK0SlW/k3P0E23n4VD4EQ20deClCjXuLIhuwIKhyZ1owlfGsx/qoA7QLwO0peL1GqyrMeDtxLhvDAcHOnFxbAJBjCOELizLGcXj9rUxGyFWojOpykCAm8JkGyuum+cunkW/oRWWfFkaTo3F9vWw66YmCpiMYGKblSPyutRgteIXWxIbocn4RUeHbP8uHW8oJMzb6f3RpDjOSpMZrU5nhNjg3+jVicndGWPgKDDRIv3M9xsZjftqQkDnSaDhjqk7NGRCQxywCnAMAo99EliY2rWTFMePAj9/QurQ2LQF+OBHge//Z9isOghQguaBt03+O26ywpJkIpmdCtj1YZn6u+f3za4EXmeJfCg4/zOAYEVRMlKDfilPdrTgyvgIRu0W+C+PgEJs+hIpiNFABTM9vFLAkSNHRMVSfMJHdCuqVIKUoDwiK2mYKCLJwQ3onXem0N0ZRoNBJ+2DFGTSqsI8HBzoj+iiUzYkVekFBjChoE9ILsZUIJJkIMEsd/Pxd2t2Jj+wo0eAL35B8lHhcz/zWeCxd0d/z+N94j+A00ekjh2bMbru1C4C3vUJ6TufTUy0AQPHo/ddfUD360DlvdFj4Vo51hVL4FsTFEVcuABc7wZsSilUFWDKURi3x8LndmYshSGv87eC1PAG/Zjwe2DTGiVpsnnM400M7lWSmcLGg/sZefxy/81iDM7pEQ3yBJDn/zcaHnzwwUjcxDWFN7mAip+ZxsvZMl+ex9wYB7xl2o3DynB2nk41FqZCuakA76zYhuuOLrQ5RnB4qA/eAOUwVTg02I4/btyGGku0oIfkCeNdegjwmKeqFu/p6RHJYMZR6Xw+7uUYd5HY2L17N97QcI8AN54Nd6LSh3ZIkqeuvTtryh1vNvB6YXyZiNRgcp/kAvMtqSaTeY1S1opdE+n6FzHO4by7c+fOmPVE7v7gHpHHw2JXFvjxmNhdxMLXVAmHRHEUxwtJG7lLg+NbOYa4dpL0TrQWMHcikyQ8vjfiOvhGQqp+fdqsuY6HfECIkityFRcrKalhuRVIIu+hhCvgxS87j6GTRobUfDYX4eGKtSKpPBvQqzX4wuItuDExLGSn6iy5sKVpNp4Q//7vkm4+BzUv+KeeAn74w9SIjWQQAeprYa8OFVC4FshNv1ogHlq1EVXmLXD6+xFCECZNAXRqE7BgjbQ4hI2cUbcCWLI9szdhwB+inA/ltKxAIlNzM3WZdwKjVyTyxloD2G6/wVxaUHHCTOMc5dfAsvxeTLT+j3Tfkg8soVnp5KzimG8cl9svQ61RIUQzXIWWIDcz8kZr0NOHGxNnEYRfSNN0ua8j5OXGfY6RGit2SRXKzaelpOHizRFCYyqjKbLzZNTZNs5kHpl3mFihkkDih+bgo61A7jSVA6wMX/U+YJgSaCEgtzqp/Fda4CaU4/7nPwcG+wF3J1ClSAjykGdjERzqANrOSMmn8kVAadh8efgy0Mf5mYkqLVC+E7BWwBvsjBAahC/oRo9HKTUoVft3utrRaMtsQ0+wa+CBfftwbHhYqhKfmMCO117DmbvvxsLc3EiVd6JNOb9vbpLkKgpuJAZ8LgTD37vP5YZKq8WYNt4XI8FGjH44ImnqAbwnANVWQJMrjI+LDFW4Ot6C82PsWoni/Gi30OmXZW1YGcVqLXYOETQ5TkZqcO201KrR0eFB/9U+VNaVQ6vT4sbEOazO25H8fAWDYpNH0kSpe50KknlsxD/+/1asxJ2v74E7vDHj7+stWqjFahCCGio02nKwwDLDquWgP0poyJg0ZENhuZspMNQXJTRkMMnc1TozUuPMKeCvvhAlHJuvAw8+DHz174HLFwF7DrDrTmZ6cCvBa4wt4srKJSW4ieaYSaRly8c+f+4oXu3rEqQU79sWL8T4pWvQlJaIMfjJBYvEPigVyB2C8Ukw5XilbBav25deekkcO6uq0kJJWWx3TJhQ23bf/QgUlohzwTE3XecHz8np06fFnlUU5Iz1IhQYw9JFVbBT5pCnKm8JsNAI6AxhT6hYCbAI6Dfz+T+P+qtwrPzjPwA051wR1qm/cUkiNAhr3DXSegU4/hqwMeqVNStwcA5XUpkhwDMg7Rllz6y6XcDlZwDPWJTkqIornuF69Xd/J42rRzcAG8L7MBqKL39Ikmvsvaj4A4ns8EI7I6PvlGKMGeLcaAee6zmLQCgInUqDh8tXo9FG6dB5zOPNCc7bqfiVcX1gglPpi8S5nYn/+MKpNwMSfR6eK65hvLHDhXu6hLK3b2HQJ+/fb1zFxbFRVJnN+FRDU0wH81wFr2t2HiXyliEonylL4CaSjG1ubs6oQ0OJAoNd3H7W9izckYLxEBjSv9R7DR+rjxZksTp87dq1kfhnKrBbiqQc4xBer7yfCuhPxmRuOl3ycxbD16KEhkBIKnb0O6dWangLgzED99Mkz+J9LGQwmU9iYTqpZYJd5bxueV2leg3KIHHAPFZ8HMNOJHnccS+/detWkfMiAcG4KJUu8OlA0oQ3dn2wG50FszI4nkh0x5MaJALpOSLLIbGjhPFRU1PTPLkxS2B8kLVODSYZp/fUYOJaGZSEwgQHH5/+An+p9xy6XJJhKdHq7MfrfZdwd+kMKzDjMOaj8WtIeHFw+Cy0JTFyDsMb9OLcyGUM+0Zg1piwLGcR7Dpb8gD4u9+Vfvb7o/rcDBQ/+tHY57JSmwbhrnEgrxQob0yc5GR1dfceIChXsYWkijxDniQxMENoVDrYdHGVPDyOheul20wgKtTPAyFWB4ahbgDUUrfBJGKDt3Rgro+Vn5Ife4NgxFwFrHgH2kqL4Q+pUJ9AeqXb1Y3XW/bB5XCiuLIE+wcOYEvhZujV+siixOTnQKAP14YkXxa1WgWr1YSq8nx0uq5jbWjL3JpoeSxLt0i3DCAvJExedY9rEJowo9LiiPuMqrCReArQmYDimZOEk1BbC/zJn0gSVD/6OjDYI228eJxMpi1MvMHOGAM3gcM/jd7vugQsvxcorgD6oiaJQp6k6zWg/p3wYzQmGZbMBSY4lT/MFBj0eEQCtc/txuGhqFcJ6329wSB+2t6OLy1ZIro0aFQX3+pKdp6bB25ouGni/ww6asw56HaxmyIEZ98gcqsrUGmKTb6rkQsNChEAu/EkfyJ1KARNJGhRAcE+BNUW+ENjolPNHfBGJKiUcCtk4bj5IdHAanRWcCUj4WS4Ak7kF+Ugt8CGjuYumCxGFJcl8FxQgC223Gil6jWixOM1Nfj7q1cRYnWamONV2FVUhPo4PcrVeXk4cfe9+EVHu/i0by+vgApB/PP18+h1O7HUno/PNi6HdsY6u3FBIq9/dgjQi0IJWxkw0gNQUiynRBojSlgTbLyZZE61QyIZnv9NrNwU/3/uN8Dv/QGwIoXEPJ//4x8D//3fUqfH9u3Al76UWKYoDTDJwvk9WbcGwc69119/XWjaKuWZrk6M4pU+ad1VSowtz8nDsvJqbCooxgOlUxtnOvwe9HtGYVDr4PK74Q36oFd0ibJ7Il7ahMdMIi4VSaxJWLIK2HoHcCDc4UY88j6gpFxYPjPYYKUWPyMDMY4NJt6UpA7b6TlfrF69Opq4Ci5HwHEC584ch8cbQl3jNhTWrYJ6wTRFCNzz/su3mGGIfZzj4ezZKKkxMhi5roPxnU38eSCB35bXKUk/UeowiQRZUly+TF0Z6fq66y7pfxLV03XLUo5x+XskYoLFEzQIVxZRjI8DX/tadFw9eRh48Szw8Y8Bj71fKkAw50lG4W1HpOue9xc/COfl5mnnwZnHGJmj3zOG33afjsxEvlAAv+o6gU/U70aOLvPjzgqGm4EeFiL4gbwFQNmqWydZNo83NUhIkNSgNMiOHTsSxgGyjCaTSvGa+lxX6O/EuVRZ7U7ymusNXzsbiaW5BiZ6WTjFfSmr5tktm4qe95sZ3Ed86OgBHBpkEaS0zj3f04UXd9wJqzY76hGzBe6jmHDlGqNcp3jtcy/BoioSA0xMMmGpLCSSK4SzEUOzuMsb1+3IWIOFtTLkccbELbswOIanA8ckk78suCI5ybE51bjkZ+JnvuOOO+ZWbiBTKL3CUnl8HgLsxmOxEMmCRGAXB/fTTNpzrz8dycuxlcr1Gg+5AEkJrjHK7kEZHKOzse7ws3Gd47zPGJ+fedeuXeJ/5XzBGITzCbtElMdGMoedKjxnPFezIefG95el6d5qcMbN3TMiNfgFkjGbTbQ7B2MSSfypzRUOFrMAd8CP/3PpOA4Odgv/jBydBv5QEJUmG/6ocS3qrbkY93nQ4RqBUaNDDYM1hHBg4BhGfZQhoTSWA6/3H8adxdtgVmoVK0mNeJafFx8fV4IL2J4fAIMdUvDCiXfRZmDl3ZNf0zuqIDRkqKTujSyQGrOK0EAsoUEErwOqEqlrY6awLpaSBsIonF4aCwBzAsJkjkJUVZSVobe/XyRH5Y2UP+iGM9CLsfExPH/pGDR6tSA0CGfAhRsTzVgclufiZpseHy1dl1FdEGWTRwZG0NLcJUyaWXetEmmhNw+4mLCaSkjzVKzF0f3fF0Mvz25GZUkeAoGgVKQxPp6S5mIEfJHWY0DbCanboaQRaNotyVJlClYzP/YHwGu/AnrbAfoT7Hg7YMtyFdj1Q+EfFHPQ1QOAbXdcFW9Yzs4zDJUhdiOhY+GtGnAHo4l9/l+onzoJHw+n34/3HTmCX3dJ439DkqSsvOXkZoLfE1u8ZbkEem1w8y3LkTH4YGDCDXtp0I8Wxwi63dIGKkdvxO/WrZ50jRg0K+EPdSIYHIY60AltkGMhchLgD/kx5jsg/ESIZTkW7OkLISj7zQtDPxrGRgkTHgMru7hp4ULLwH8qWDQ2TPhHxUakuqES46MT6LzaB9cqV8KND6V+GGBNRWiQLPpNdzf8oRDuKy0Vcl4yGm027Nu9G184dw7dbje2Fxbi/61YkTB4qbNY8CdNsYTeN1dPLW+VNpgIN5YCbnpohE9srhkYZzV5eANc2Ai0XgL6npPu603A1vcBuYqCCI6bzfcAh14My0JxfFYBy6c2iZsW8eQKIV8AqeC554B//MfofcrOUUrn61+f+u8muoCOA4DPCRjzgKqdk/x8li5din379k3aQMsg0RewmXDqxlVcO3YQ962XCOzR+I4WQfKpsCInH3+9bHopyVZHP57uopxRACW6EDpHmzHS40CduRrLc5ZI3VIDA6IyieD+kNctj5OEAiueUtHiVaKruxvODbvhK6hAbsCL0uUroaqOJhc4/jmGGPwwgGdQw/dllRcDCQZGrOKadK7Uemhsm7Fq22bxHCapWo4dE3/Hys2EMiMkMn7vY2RumL2J/R0T/rmKsVkmETtOrw9Pn7mKmjwb1lWWQC+kvUJATpzededpoGV/lHRouhsoTLGb8oVngf/9l2FCNigRad//PpDTCIxfDw+v8LVrb5ysKc1xk0hyiujvj/WAIhxeoKNf+jtxvCqgah1QsVral7ILRKUS34PcuZYJZjvG6HSNTGoOY5Kuxz16e0mNkVbgxkvR+64hqZCpaoZz2jzmAQipDhIanDNJjiu195kcaW9vF5Xg3FMlqoKUTbaZRCLkjg/O/yQ7uEfJpPBiroOJOc5HJO4ZY/B/7vWYPGaxCfejPCdMPjOOeyuYjJ8aGcIBdp3HeK458NvuTrynqhZzHex8UJJ7/P7YvcHvl5I53BtwLLAgQ5lgZRxCVYRsgERQo60QV8cHIp3mxCJ7NL7ivooEGvcplNxkTJQqOL5ZAMPqcu6PEvnp8Dpm8jUZyfmGRE4tMKAscGUgmw/o3lwdZtkG5y2SBCTPknVXcD/NmJfJfnZHTNX1x3iWhUXpQvbOUIJEO+Mfea3i3p7HwfiCnVP8m3SIA173/JzMKfC6575f+fdCiptyzIGAGDdcDzkP8D3lwikeE+eCRF6HPI88VyQW2TXV0dEh4hJ2iWXLk4PzF+coxl2cG95KGEsxxsgiqcGkFZkzr6ytEjZLTs3AyKTRw8nNfBj8a7MmC3JQYXyn+QIOD3aHk1RSlTDn807XOP73hQP4s6Y1eKL9lGDR+Zxacx7eUdGEEV/0c/NTMQnW5e5BgzWBrA2TcdzgjZKICKfruBmM14q/eV4iNMSLhp93+RCwYC1gjUv+JTwHoaikwJxGksq7UFiKaqbgF0hig7c3KDgxcZKVGUhf0IEe9xHcuH4TepMepXWS5n9sVUds5ag76IY+LjltNetFRX7IG4Q7MAKzdg4Z+THhdvgZoO2SVI29ajfQGG35SwXchCpRaXs/0HEIQ6MOtHYNQpNXC63DD+fwFTi9flF5lYoeHzrOAjcORO93XZSIjqX3pn5wboc0/k2soA1/d/z5/scxq/BRIiUudcM5VcwVCZK0GgOMmgJ4g1200BazrmiisS/ANccQRn3D0Ki0aLA2otCQ3JA+Ef783Dn8RrEZPz40hFydDuMMBFlpIJKswDsrot1ZTFRy0eZ6I1dZc6MlbzyUVSJ2jQZ/vXQXrk8MoVXbjF3L1wjyIR6UldKpqgBVpeQnIro2xG+Eq8A4SKZHSWOt2oGP1JXhuy29gvSm9vqHatfDoNDE53jlRoebIm582MI9VWV6lXkhxvyUOZQS+Hm5edhSdTc6b3bCYNOKzZBRbYkZ51NJTjVPTGDLq6+iNyyJY9NqsWfXLqxVJBf48ws7kstb3XIUbwMGjgCuHonkKFwBNNRKpAa/txvHgT5KwIXhcwNHfgHc+79iX2fX24GKeqD7JmC1S4TGTGUjd+4GDoWTzHIhwuZtEhmZCkhiKDs9OPb37ZPW/mQBALvIWl6Mrv8s4Gh+Dmh6NIZA5TXBDSwTSEwuxWNv/018r1WqQO89ewndRWZ8pHYVGq05MGu0ogIw2pAfwo6mRSI4IRjEc/3hxpubcLlyi/I8z3SfgD8UQJVJjeGOHhSWS0F3i7MNVq0ZdUYD1KE+IDgBqK2CxOC45RxL4oHjYqq2dgbq3PQzOcZqWCYYeJ+6vNqS7YI0PNrdA8uYMyKJwtfmMVPWSl4vSYBSaorBCveolCecKlDnazApxfHK4IvBCuccjkG22EcqoPidhs+JpFkXfk3+TxkupTl5RQ3wtvfC/JsnsLOkAOfHHXj1RgdWlhWibMFCYL3CJ6blMNB8OLyHo7dHCLjyIkAZJEo8TQVnJ/CVr4QJt/C32tMDfO97wGc/C1TeBwxfBIIewFQG5Ka5L+IcxuBL2ZnCa3hBPdB8QJJo1BqBmo1AbkWU6Egj4LhdpEaitUF6PHsxRkboU0p5hdF/AajcOK8FPo8Zg/MwdcpJjCsJDSZvmKjnfJuKkXh8Iksml1mM9WaTppLno3vuuSdyXz53XKfYocu1g+eESUFWvPMcpmPIPiOwmMPjAPTm2fdqUmAigecRV0WHrEoxx8Hvi0lS7lUYD/K7Y7JR9uniz4mSj9wjpOsRMBU+XLsG324+imaHJLW+o7AWdxZHizFZ3MX9nlzMxTGWDrj/4V6G6ymvzXgfEFly6k1lfmwpAWruALqPSTK2vF+5fX4NTQGU2yPJRXI6WfcDrxV2Sp89e3ZKuTK541YZY7ALhAVEJCIYXyTaJzIOSCShK487SmDxGNi9RJDYYMdEMmle/i2LXvk/34+5BcYJHBdcE/k4O1A4Rvi5eNx8f65rzDMopeYYf9CknMWLJDwSERpKcK3gja/JvyFxynUj2WdPBxs2bBBzgxzz8P6bomtjuBO4cRDwuoD8amAhZcF1t5nUEPrl64HQZS5/klmyipX0U1c4+4MBdLsHsNhWggODE0L6Q8b2QqkKMFV0uRx4ubdDJM12FpWjnkmPME4M94mqYK0iNiX42ITfh/9uOQmtOgg9CV4NhNzUE+1HUG/VJGwhTAgmTVmxyQBT7s740IeAu+M6MFxj0Q4NJYa6gPYL0uPlTVKVqs4O2OqBcZpihw+a7LNtGq+AOYEk7bqq29zuP4fAqgwmlB544AFxf8R3XSR0LBYjikrzMT4ehD/ucrPFGTabNOYYyRyjGhgdGERDUw1uNnei230CJYYVsOpmWT/a6wHar0s/V9YDhiQs+v5fAK2UJWOlthPY/5REbtTNQGquaBlgLUe+awj5rHzuPg74TomB7q5YIxY4Lkay0XpS9F6NeyAkPZYKqUHj6ld/CrSck+6X1gL3fhAw3qK29ZIFwGhP9D4nuaJ6wFYLDF8CPJR/4gJIyZ46wJAvKofsus3wBDsQCvmhVedBry7COnJisoF0BtU8L/X2xghWSXOvCg+UlgoZqnKjEf+4ahWWxSU96ZPCDQT/Z7XSzZ4eURlhSRBU0Oi1yVoAjWU4adIq5lwY1gG+K0BgCFCZENI1IRiQu1uiKDKo8TfL7heSUyTW4z//uNWKn3Z3QzMygm3BILbXTT0X69R6rMjZgjHfkOiasmolcsZV1IfT1y6jSlcuHmuyrkVHW2dC4zYl/vjMGQwo9OedgQA+fvw4TsSvM3MJTB6WJJD7kTtgRhRdHASvPeeoNKbipQ3onzGVhwZJBX5nqV6323YCfzAG/OzHgNsFbNgM/P6nkTJY4aokNQhuNKfabI51xK3/9BRxAq4BwBpLkHHeYpWtLIMhY9jrwvdbz0TOmlqjwb6BNqzMLcGa3DKszSvEvoHofPBgWTXuXBzVTCcYfFBugcktBs9sRR/1OOAocAqiXOXzYbBnEEs2SBVTxKD7Oup1flhN3Ti8/zvYtPV9gnjkhl8GX2uqKipWG5HwIJnAwJufSzZqlSETHbJ5H4MNVkJxLmdQw+fyd/yZ1VEkKlJtSWewxefy3DIZxXWYQRLnPN4vGxmWxr2Q0AxJG0fe53c6Ogz84R8A3/kuMxDSC+64D1i2FuWD/ShFAAf3vo5TvgBMD70PuYZw8qDnMnBDMd/4gwrD+4GpSQ16Y7TvAdy+ydc6iQ3CkA+UbkPG4Pf11a8Cn/88T5D0GBN7Cw1Ax6losdK5XwKrHgNsJW8YUqPBWoIKYx663MNir8Qq2QWWIlSxmvO2IkEskSy+mMc80r26QiG89tprk4okmCRisibTSm3Kc3A+5jz+pqn2TgFMssXHELzPhB3JcXYDzGqyePAmcOY3gN8jkcpL7gbKl+BWYEVuniigIYnBlYvfOuOHzQXpdXHfTnC/wWuWEk3cOyilZZJBua/JBmw6A/64aTucfp+Qdo33NWMylElRroncR2U6vvj38VXznA+4p0rXp+8N063B2zzSAq8vFonSNHyq7h2Olen2eEywKzu0GVtwXuTeXCZFuK8nccfxx+uRsU2iLkEWc/FvSbrIJLqck+D9RN43ys/Ebju+J2Mcvgbn7paWqLcj4w3GKIw/ZP8aFkzxPBAk/RmLs/OJcQfjjUSFZVNBJoP4mWUvEI5vfrZM1gmeAx4rzyfnsKk8F98wGOsDjj8Z3veGgIlBwDUKrH777JIarLqbFkxWq1I3HXIFPHi++zDG/JIRUqFBh2JDEUwaI5baK1GskPyQQcLi6FA3Bjwu1FlysCxHMq+8Nj6KT5x8HZ6AFCh+t/Uy/nHlVqzLlxZcmoCrwutXokHrCQagU6sEoSH/3sWCiEAIBk30+WqoUWacgqljVeEzTwPHfwv4eoEcC9B5HKhYF02y5JdPJjQoE3D6WakKg9uFqweBTY8BpQ1A8WbAWCQlJSl7lbNIqnad61AVAKySDrVHH1M3Aaq3tjapDDKtbG1Vytf4Qy4M9I9gQaMkiVFlVuOmI+poUKAvQH1clxD9NRbZl+Py2DnJL0anglOrRVtrF/QG6ToZ8l7PDqnR1wn89n+AwV4grwh44P1AWQ0wNgz8zz8Ao2HJOEorPf456TlK8PqWCQ0ZJPjO75WSgxX0lsmQeWaCgpWkF56Q5JUEQjAOnMCWlQ+jpc8pFtUpF6ZExrkpmuni2ItAy/no/d42YO9TwD0fwC1Bw2aAckw0CieK6oAV90nHX30fMHIF8DkkP56chsh8pFbpYdJM3uTOJFgt1OvBOmf5uuUrFRkM+PW2qZNu3KywsuPpZ5/Ft/1+vMSk3cmTeE9lJf57wwaYFNXzsrmlLIGTCMEQPT1IluuhCQWg8rN7xAOERqEKWMTjUpdK5FNDrTJCo1LDkkDr/tDgIO49eBABJjdDIXzN48FzpaW4e7rNnkqNXH3UaPnq+Ck4A+PC/2awbxgoBm46L6O/xzGpCyke1yYmYnwS+HPzNGaCcx70Q4knBnj+U6lEZCVb82VgYhzY/ypwnR1geuD+dwL3PJwaufHAQ9ItHTDxe+kcsGIJ8Morscf/3vdOTWok+53SA0EBBgCstuVmVq7M6XU7YiQMxMtCJbpPx3yIITSIZ7vb8YcNS1FsjJINrPRRgpt4h9uFX//sG9AYdAjadHCOxSaudCqpMnP5sjocPXYF8J8FQjmiCpIVX5Q8YRDBsZnMLJwkirz+cZPO14/vduL4prfM2OgI8qzSZ9bockSXFAMPBuY8J/wMyirkdEgNZbKDN74nj2l/IIgGjwdlcnefkJKKHBjQ0w38+lfA+xVzO/ea+UVQhULYWLtIHOOJs+fEfCYCmDYSA3EgscHXZtXtVPCPA2YtUGAFhtkJqPjeGxuRNbC76+mngStXAEoGLqwHDn1H8YQQEFIBPZdiSY3RkfRkHjONMTIE5/Pfqd6EE8OtGPY5Uai3YnVeze1PyOYvlAhOJQoWzleYziMrYFKIhLGyS5kEMKvTZ3rts/r2zeinkQm4FrFzl3M+k1iz4r/BrtZTT0veO3Isdf4FwFYk3WYZNAT/4cZt+MSJw+hxu2HVavEPK9dhsX2Gnma3GCySYpU2yb5kXgIyWGA4W9e4OQUfEiZSWeGdKdjpTskcVp6zMIQdsrxWb/u6N485BZIKvEY4LpRdCkqwqCoVw3AlWDSk7GyQuzxOnDghug1IMHI9YhFjPDjudu/eLYqN5NiAx0lSTs6ZJfLikMHXZMEwyXvGTPGxjkzOk5BWgmQ9Yxf52EhqMF5hTJMpSKjIHecyqcpjz7TTguOa54eSv9PmtOY6qIYi4tiopgD6b0gFzwZzDKmRSoyREqnBtiRO7kpDxgioqxt0AGqjdEsDJ4YuY5zViWGoEYA/6MDussTECJM3/+fSYZwY7o0oxL+vahHeW70I/3rjvCA05CCfcec/XDuDH2+8S9z/SO1i/PnZg+K3jAdZ1y7ajaBCldkGk8YFNb0HYqR+qC0dxOq8HPiCHkG2rMxdAptumg1LzwnAMAQwoUz5l64TUqVqWdhYsqwBWLINuLg/SmgUlEiMlcxWEWdfkkgNHlNOepPJnACPW7MECJVHJadUSS5KkjwhH6DSv2UCOrLGTGAeOnRITL6UxdCr7DGTnEWrxvIcOwyaBmhVWuTqchNuSEqN5fAGHbg+dAlt7X3QcwPT1oeV66SEb5Dm0DOFcwL48T9J1cz8vvq7gB//M/Dx/w28+DOJ2JDhGAOe+zHwO5+JexFVrL8DEzq8jfcDe58AKhcD2x/LnNigJnWE0FC8p6MXdXUrhIYwF9NJyTbnEOAcloiAIYUMDlGdhKgd6ABOPA84x4CCCqCnM67aPAh0RiuPZwS2ePfclD5bac1kE2WC5AVJjKV3heVNFJtmzjH50Wrr2cZXli3DPXv3ir4Qee7+arwMXxJw4/LrnBy8whZWdkF4PPhZayuKDQb8bUODqHTgBp0bA242ElV5EL5gPyaYcA1TK7oge8doBB6G/wasugaMC9I1XAECA8ya5N08nz97Fj761AwOisS0uqICf3LmDM4opApSwbifYyWEitoyDPYOobO1G96CIKyG6Q2Wl+fk4PL4eITYoBH40hm2tc4qeJwTzdFuQ3sDnXqBsU5JGq14KdC0Bei+KnUxCrkf7oAfmH4tGBsB/vWrwEAv4PZGk73sGnv6J5L3wcZZkOEigfLVLwKdYcK+oQKwsOpbLSWG3//+qf+eFWU9J6X9gVwBbyoAzFHii+Cea9w/Bn/Ih8XLJHk2rhm85osUGz6/2wONQS/2P8UGCy6MjYnrQkl+8Xc3nRMxpEYiWIwmbFy7Bn0aD/IMGjSsVEWqoviaDYptnk6nhY+yd0PXYDcuF3JWsgY1ZR6SSVDJJMZUsm18zqrli4G+PcBEuPNVl4MVS1dBb7KLxBH/npVYJDWYqEtV35wVkIlM5/iePPaaD3wAZwYH4fnB91GrS7BNTuSVFgYDDeWcxGo1ock9aV0Kw5QnmXhPBbVJGgt//nbgr38OOMKSFOtXAI9nWdaQchxhSQ74klSyykU5Xgdwcw+GLr6M/NzM5yA5xphNaNUabCyYY55rJDDYhSOMwgNAXh1QNTnAn8c80gXnHc6PlMtk1SqrtklaszI23eSUDK4DJDNkQoNJoXlI4JxPQp/EPon6GFkuEhGOHmkvRGmcTGTvmB+QCY3oNyJJd9wCUoNYl1eA43c+gAm/X5Aab9TkOAuhOA5YrU3I+xQWTLAzg3sL/p6PMy6/1eBxML4hUTaTc8xrkElqVqPzc7D6nNfoPOYRD5IPJC4or5fIQyZdD4upQC87uVs7YV45jJh8bCgkSBEWMpHU4BiWffwSgYVElLadCowX4uW0OF64drKQjO/P80GymrENn5+KWfVU4NzC92TXBj8Lx2M6nkw8D08//bQ4Do7vVLrN5jRELBHn+Rp5PPxjuKOHBFNWSA1WsHFjxC8hpv3D0w2MHxNmkgLmpYA5dTZr1DcxyRx8lJXESXBgoFMQGvJziZ+0X8au4ir0elwxVYv8acAT1QZek1eMb67egWe6W4Q2JBvQvaEAasx2vL9mMa6N9+On7ScnvSdzL022xai1xCYbpsTQjQSPXY+SGsTyO4D6NYBrHLAVAAd+PLntnLqZbwaocqVbMjiuA6OnpeSjxgLkbwV0WTZRDiMQCohum1nZjPH7G78AOK5KP5uqgNw1gEqbVEOQN7aP8T43MVAFUVXBTZRUPa5RGVBiWAmdeuqJtGu8Ey+ffhVGsxEN1eXIN2pQy0RbGKZseGpQWsqluCb5Gb1uoPWK1MGh7D7iZrGvK3EyqGk9cPmINI8pK2CJjktAxxWgKkOfFHYyTUIo8jg3iSSN2LYX8dmgtEazQlO/ZKHkTxHwS0bhVQmqjccHgZe+JwUZQipnnFn0ydXmisRjxuBr/+JfgeFwK7E1F3j0DyYb0Mq4hTq7ybC7uBgH77gDP7h5UyRW31dVhe1yoiwFvDI+jiAXekqrhEIIjYzg6cFBfKG8XGzMuTGQDRsTIRQKYMJPGbDoNelTheBVqWGIXKcq6EM+5Oo2wxschAoa6NXFUE8hmcgKtRArt3h9V1aKV+9VatCnCL3aAH/YQ6qgJB/jYxM4d+QSfv+x+6f9W8p2nRgeFh0bBM8vDcM7aFo+ww3XrGD8KjB4Inq/vw8Yc7INUhorvReA5Y8Bd3wU6Lwkee4U18aahCfDb54AhvqjMkFKcCyeOxlLagz0Az/9H+kY6huAdz8u+Qiki6d+DHR3hr9MDWAgETMBPP57wHapkGJKcD5qeEgqgPCyCr8QKF0XQ+YGQ0FcGDuFIa9kzqlRabBw5VJBgnPDzST+uyuX4GcdFwWpoTUZsDq3FBvyKzDik2Q4Y06H2F958e5Dr6Db5cQiWy7+atkaVJgmF2mUFRRhfKILDn9IbChNGlEGgmWWECyKzlWP2yttIrvOAme80OZvxqXWLhFEk2jgmpaoCitlDJ8AfIoKft8Y8rQ3AMu2SCKJ1ZasqGJS/OrVq2JuYNfIVIHCVBVeMlZ+9rM4s2ULuhFC2d/8Jd1jY73SlsZKeRE8Vwwy2IJPmSzKWsma3ShdLJH3SmjV4jPh0tPAsndNNvaOPM8M2BYD3F7/xyeA5h7AXgBs/kBmCbJUwc7HnApgtCu2kqpooTTm6AvjHsbAyAQKizLXlGeMwXP1lgPnqOJl0m0e88hy0ZSyEpQJKxZPZWpqzb9nDoBVorKB6zxiwXPLJBuJDRJHgrSnHO713wLesehc3vAAYEizw0FnTO/xWQJjVluSQqI3EthRw6Qo9ykcI9w7MElIqRiuR7zWZ1KdPVPI+v/ZuCZZJf7yyy+LJO+bQoN/HrMC5kS4RrA7gsSs8lrJZs6MnQtysdNUr0tyj2OARKPcMcVcGYu7KKFIAoKEfTZ9nfiZlcVWJHi45pHI57nh+/F90+0Ojwe7Dhgn8LNQ9itV8P3pJchzmJJP7FwHc2ztzP+GIYr4ywCDJaZLQ56Xp0NK2S9efJwYGThGSA2aEY7TDEahCxy6BnjbJHkhVuirYxdtT8AJX8gjTFG1aj3sOisGvWMRYoOXtl1nnhQkDnidIkCn3AI7K+IlF/rcTiy35+OmYyLyO1YULrHnxbzOwcEevNgrVVbm643426WbsJAyOTSeyatAkcGKp7tOYsgbfZ8mawlqzGlevKyYnlQsnuBUW3KlmzigSmAsnJyRz2d+NCk9mwiFk3w01b3l8PQCowoyKeAEBvcCJQ8mleJICJHUYqeLU+oG0cRe/E6/A+dHT8ERmJCSQ9bFKDNFZZ+UoFHqK73XcGGsB0aNFncUL0STLQWjZJIZEwrjR1erdFXnRTUGlRMnB6nMUvM+tVk5zjiJe0PjgiTQqW1QT3Meunq68ErLi6hurJa0BEOAIQBYw5ecSV2AIkMWzNSTyTAxSMovBkbZJSHPB+rJ0lMyNr1N8ploPg14pcRsFCpgYgYVm5SgylsADN+I+keYCoHcaLUN2/HJuFPaJOQaAS69gPICOyqLw/PFcAuw4p2SGWoy3LwYJTQIfm5VmHGWF2k+tvE+zBh7nwZGBmK7YF7+qURszGGsz88Xt0yQo9Ohq6oKOH1akAeorkZJebnYXJCtZ+JwqoqJIDvDJk3CQECsU8pH9NCoLDCRTE0BO4qKcMPhQIAbLJVK+IRsS2GhjUe1eRGujB+PHMrowDju2/awGL/XxvtwbqxbrEHr8qpRaY4leEuNRnxn7Vrsfv11cZ+vQZLjvn37cPruu4VObzbAuenYcCcuj/WLeXBXUR2KjRlsHkevxL8wYDJIUmkEjf16zgLVm4GyJuDwr4HTr0lzxNp7JUm6ZOhujyaa48HvWrnpGxsF/uzT0v/8m3NngatXgL/+u9RNwWWwQ4OvodeGm894XQWBH34byCsEliWWXYqBwQ7U7E7+Fq6bEUJDJuSbvZexbdsu0QXBxPz9FVybCnH8+mXkVZpwZ8NKoXN9d0klXujpwGv93ZG//1BNI7547ji8wYDYtZ0YGcDvHd+HX2y5C6Y4IrTCVIAbjm6Qv2Bhh89P2akQRoN2hNQ6qEJS5y6/S1VL+Bj9HpT7WlGy6x5BvDBpwMR+DKjxfOaM5N/AdvDpNuUeznvKAUsiPSxxmMCcj51brK5ksoJ7VUo3JAriGSSlYv65csMG0QlS9ndfAz7/p9xdS7/4wAeBnbvEj5yLfvvb3+LRRx8VFV18TyYUSaDTuFzGsK0OF4aOYVtRQOrQ0ZEMC18/rmGpcyl3coVcBDkrAErY2YaASiNgqU28r8wmeF0veQC4tgcYaZck4Wo3A3lVkpShmz5NwOCYC42LMq8W5nen9FO5lXAFfBjyupCrM8KifRMEifOYR9jrgUkYWZ6T+95MvG+YRKIeOPdeSk+neSQG1xsSG0y6cR3U9J6CytGNNYurodGoAb8LaN8HNLwtvVPIRE9hHTDQEvbkDAHWQqC4Yf6ryACMs1mdzuubnZv05pL3BExgZtrNlA2wWCsbhIYSLPaYzuR4HvPgdc89NIlZzmO3sxuL6xePh50TMhnPY2Iumt293MffCpKOhAq7Q3hj3MOiLa6J3OvzODIFcxjMZ7BrZSrJQn4XXHs5L7ATnrEVYw3ZC0TGyy+/LDo/ZtXXKdtg7nvlQ8DVvYDPDeRXSV5RiuuO3zfn5qx5avCiZtDBSqpIK56fwZ0imWDWUzicvxB65fAfBXTbhCEr0eW6il6PZNJC0ada8wqszWtCn2cIE1zkhZGsFlsKo9pg7oAf/3bjCK7SOISTssaUUEO6wmTFjsIyQVh4wtWaJQYTvrA42la0d6ALP2mPmgCPeD340oXD+OGGe6ALD4pKcw4+uWAnzoy0C2KDursrcqX2v7RMc0tXAO2H4x6bRnpl6W6p+nvgZlRjfMF6KXGSZNDymELUhxfn1JD25BMMBdDhvIhhn1RRn6srQ5V56bRJ9KyTGjGtRyQn3JKGdKrdGqJj4DQQjCZwoKkD9IsjFa+nR47BQyIunBy6PH4eRo0ZefrJSddfdZ7HYYUEUUvLEXyifjMWcAM5FVwK7xAZbj42mdQgOFkzkJc3T8rWPoMq9cCjb7gX5QvKYruUvCGM+0KoNdeh3JzccyAt1DRKRMXIYDiJrwasOUD9UqCkCvjB1wFXOFGpNwD3vTc5ObJyF1CzGHjhPycnrXJTIJCSgWOAiUJbBeAaBPRWoJAEa+xUxwVEGFoNMJFSgwPnmlGcZ4NelhqhHNVUpEYiMGC5671Ad6s0butXABVZkLvoj+uC4c+Dimv9TQjKVz126BBUubkIOZ1CDuWvwq2krE6YrkJBpUogz0XyUKwP8jypB3RTJBET4OsrV4oOide7pDlzdW4u/j1uY5EKcnQFWGbfgkFvj5TTdGtRX74Qp0c68GTHKbGuESeG2/C7dZtRZ4kl1p/nJk+lEh0aBP+n5NB1VpFkSYrquZ5reKb7cuRYDg224y8W7Uif2IiX3REm3vLvQlGZG17Xe34IDLE7Jyh1gb32Y+De3wMKk4zFojKgt0sab+z68offi3MT32eXglQ8sBcYHYklIi+eA1puAA1pVuSVVgBXL4X3PIrPxbnt3InUSI1pMME1MA6+kBd++MRmlt1mrPapt+chYJE8IUhoELw2vr5yEw4M9KLX40STLRdnR4aEd5g827JQpNPlxMXREazNj13bltirRdfsmVFpz2bVGrAxrwELbFWi0xEkNS4/A9elThzT90HLuY+v7BkTwQcrp9haHeN3c+IE8JnPsPxKus/f/cd/UCMh9kNeOgm89JTUFViWB2yqB4zy/E1JyyiZyfdgYKGsmmIRDrsf2blBUiWRlm46YHLhlM+PpT95EvrBAb4BTjS3IBA2Fmc1Gau5+H2wspMVn1zLSbAooTcY0IV8tOcVocpzefIbCT+1aWAql263EqwEXpKgg0xBqAyMOLElA3JXBq9dnr9bjVPDXfhe60n4Q0Exz727ajm2F90ms1F5TzWPeWQB7BCjRrpyDk6X0CAoMSUXk8wjNTAWZ65E5Esud8IxApy8dBPrlzF/EhLdbWmDa/uqh4H2M8DEgJQjoDTuHOjMfiMTf3v37hU+E8oih0h35W28fki2ZCthy70QC/nmMY9UwD00u3rYsTHT/fNMwDwNPSOcTmek45tjgmQAOwe5nmVLEivZ2sfi1507d8aMTcYccscICYaZHAOLnxinMA/IDgyCMQXnJbmbheeB3h/cJ3NuYtEWu/XjodfrxT6ax/uGkgakOgpvSUDugTFeKp8p5dWQJ5PsdQRqRdKIAa0IapUIAMFeQFOLUV9/hNAgQgii1XkWy+w78XD5dnS6+kV1fJmxAGa2u4fxy86LuBYmNAin34VqkxltLsmHg0HIHy5cDVcggC9dOAofJX3C9dm+YAAWxWJ/bnQgRmOa5MiQ140+j1OQIpGPolJjTV400SVYubGruD7RIo672FCMtXkroJvKqLt0pSQJMHhNClJKlklauVOBGvnbfgfovQ6ceRbwTwAnnwJyy4ENj0kVcgqEQj64AmcQACsmSQjlwaRZBVUalXvdrmsRQoMY8XVD49Kh0pyFqv5UkUzqZQoJmEkI9scSGkSgBQhWAGo7nAEHPCRKlC8PlaiCjSc1eB0eifdUoE72UNv0pEYiMmgKgohs+EzMNeUqUaM+8YRaYChDRRpycNOCRMUH/gh49ZcAK4DpA7P7EYAa7bzRW+MGTcC5W1wiER6JcGoPcPwlKYinyRw1peVUW9MmyXNmJuDEV5AikWOUjrGxqgjtfcNYUBHezBqnCf6qlwDnXpMSURyAHOc5RUDtMqBeITOXDVBmip0akS4YFWDLvDpgLiMYCgm93ndWVOClHTvw3cJC9J48id+trsbCiYmE1bysWGcVg1K7nxJSJk0TXIFol4AGNhi0lUBoJEpoJCI/poBdp8OenTvxQk4OnqSReW8vtrz6Kv60qQm/Vz/ZbH0qmLU2cSM6dVLF80u9UrJTJu+5nu3pu4q6ulgJH71aHa9AKWDIUFoiHv5gEL/tvhJzLKzwf6WvGe+rTtOUzFoLjF6K3ud4cfukmywZRb+e8WFgUClZx9+pgbYLyUmNt70XaL0meWuw8p3VbeU1QF4BcMeDQHV9rLF3vDycbDSeLh59H3D+dIKuspA0T2YBRs3kahsSCtrw2shgg4Z73KNw088KHnk94SaYG+/tRVEJrwujiRMpGiUxE4aQRSxcgvX5jfCHAjCq9bEbSlU+1EE7dq5YIDb3WiEjqAIs0vw5ybOI5/yLX4w919euAd/9LvDpT0cfu3kV+Nl/RO+3ugHHBPAAZRzDfkx5qyMbXQbq/NyJWsHlCipKp86k6pEVnDy/Zy5cEJVhq0rLRIKPppvKNnQeB+W2khnF8jFWet0cmkC5WQ2N6OwTJxOgaaizHXC2AdZKIGfBrfMWY3VU73mJWLSVAgVhH7fpwD1pfiMwdBUDw6MzasefFF/cAgx5nfjv1hMxMcET7WdRY8lFdVx33KyCBRRXXpD8wBj/LNgFzCHvj7QKuuYxp7432TNgJrjdVetveBjssJjGEIh8F+wgzbDwhEUTNUk8/uaR/ulUq8XazkppFiMkIxE4jm6lFFU2CQ2CCVH6GMxjHqmC+2fG1exUlqvl5Z95fTKpnsgvL5sg2cikf/z+nXvpu+++G7ONCxcuJO16kM3Hz507J/b8mYLnkPJT7OwjScICAn5mFobxc/I8s1CMRAffi/MVi7aSfWcWi0V0kjD+e7NAJjVSQcoZcLatsQUo+pd2wFALeCixkwzSJtgVCJt/xnheBOEOOmDV5qHWktgskoRGTA039wKaAP5p1W4MeFzC4LvEaMHPO24IQkN+LrcOg14PLo0NYx1lcURlrGFSLoOwTdNu3upsw9WJqEdGn6cfJ0fOYmP+2ml0cpdIt3TAv7u2T0ryygHESDdw9QCw5I6Yp7qDV+ALDcMvF7qGhhHEGVi1qVcNj/n7Jj/m42O3kNQw10myTcrEtqkG0KYmByNAyamEj7MDyC7kpib9CiFoEhBAQlFj0nMlkmxaWBngxwXmluTJdbLM3EgxEZWpJiAlrHItuTBaFuKG45oga/jZbFo7Gm3Lsh+IWuzAQx9K8jsbsGIa/fTmc8CxF6L3x8ckj4jtDwN+X9iAtRfIzbBNdrQdGLgsEQA04cybJtFsLQIq16AwdALNXYMSqcEuj4uvAB4nkFMKLLsXMMaRT9Qzv+vDUaPwwkpgw4PJJbpmAp6bnm8C7vB1TgPw3e/Cmw1Ptrfjo8ePY9zvR6XJhJ9v3ozvb9+OI3o9tipkXBIF7yQ74jdYRk01tCo7/KFRumdAry4JS+wllp1LFRxTP2htxU/9/kiv4sdPnIBZo8H7ExispQImXgl3QPpfOfc4w94bMk4ODwtZIEpfEUzK8VPdU1KC2ix5avhClCiKnQk5rzjjji8l5K2UxiONwnnMlP3rPBHrgUFJBVOS6rip5jB2GPzJV4HLZ4BAEGhcCuQkIfxWrQH+57tRYoNzDUnV+gwSiPZc4P98A/jm3wHXw4QNX4+b7213IhuoMtWh39MLVyDqY8Q5XR2u5uamWnSbhduTudGVO5gYgLA9mWvLihUSCXVHcTm+df0ihju6oM61QWcyod5iw1KFTGc8dGotdMm2iQvugPri01DLEoLmfKAm8WZbkBnxnglM8rTG7R8vnJDOo5wA4nXTNwroFjCSAcyVgE5KCLFSi2Nfrm5KBCbjGNDHdIxkAFaG8VxTpoKBDrWH2Z3B9Zst8jwOBoDKiq5E4PeRV12N/ecmsLVoAhq/AyqTDdAFgDGei5D0P6Wdim5BEoLSb+eeBDwT0rjoOQc4+pN/j/Go3Cq04XuGHCIYyxST4otbgHbn6CTfGaLVMXzrSA3Opxd/DXid0e+DBMeKx6S9yW0EZWnHfFfgDHaI+yZ1GXJ0NM+8hZ3c85ixbAaJ1pkkOLjOzOvwzwDlmwDHM6IL3OvzQ28wSfPmPOYEWKAwlT8fIa/xt4rYZWKSnh4zLXqUwaQoCyBTeT1XwI+9/fRT82N1XiHqGPPP4y0J7ml5Y1KZ40OOJUhqMMHOa5QdHcrYm4+xw4F/l411I9sybOmA412OzZOtryx0SlYIkklnH7sw+D/jOZ5HdnHIEvU8/7K3SCIsWbIk0sFPg/PqakmS/o2Onp6elOOLlEkNviBfOAbWVYC+CPAPA6E+QOUPp2F4EjWAWkpM6kQ17OQvWXo8OXJ0RvS4xyN/yVe1aQ2oteSIW/R1EletKrXFHyqrw7PdrRjyeoRiBOU63lvVCLtualKj2907KbHT6+6fnQVOGE0PTpbjGZsc7PmDgxFCQ4YnOARTyAVNWPJrOtAYNx6JEv2zClajFt0DTFyWZKd0BYAlzUr9ZFJN9HZhclNtQpGhFP0e+fqlFr4WZcaKhNeMFir4E1Ib04Cvl79NMj5nMoZG4eb6KcdUcXGx0F1l0iUVE5z4CvWOjg5halRqKYVdl4NR3yj0aj1KjeUJyZzbjs7rYT3YoOKaHwY6rwKtZ6LPW3kXsCjFxIqM4VbgxgtRAnWkFShfB5iLAFMeYEiyoavfClV+LUJje4D65cClPdHjG24Hjv8c2PLByYRFURVw3+9h1pFXDLz/T4HmC9Jx1S4G7HNLCoCkMk2I8/QG0RGXLs6MjOB9R46ITg2iy+XC/fv24cYDDwjCj0naZEy9Uh4wHlp1rqgoZ/t1Tg4rytVpdyycGhmBJxjEmtxcmMMt6r8aGUGQiVrFMX2vtTVjUoObGCamF1QX4eJYT4zP1EJrVI7t28038OlTkgdRgEptWh3qrFbsLCrC3yxdmrU1yaTRocJkR7drPEJu8F/6N6QNjveCtdJNvFAQuCBV/MRgogcorQd6W8LdFGF/mropkrskQp9/Cjh9RCIUPG5g652JiZCaOuDzXwb+9RvsKwYqqoA/+gvAlCERxO60P/oy8OKvgasXAasNuP8dQEniIo3Jx+4Cxm9K8lyWCsAQm0jVqnVYm7dZrFv+oB85ujzYdDm46RjEoHcCeXoLas0FYvNKrfRLrc3osZtEonZdXrEw0GYCXq6uIj4bMuFJnQH9IxOoHnPj8boiTIyOJtWF7XL1Ycg7IrpGas0V0CrnQFMOsOp9wAT3firAWpKc1OVGnPIlw9wrhscq92fVcebSyYKgnCaJUFeA1zoDqKl0otnJkS2vBqGPrtFE3o9BBjs10gENdtlW7tfZ0Ve+Ca+/9hoe2VAMk5em80qS7wxAKdbZDkh6L0qEBt9b/l66TgNl3NunMC649y5ajp7+oRmRGnJ8cSsTR3Z2RycAY4xbBucg4I2SlhGM3LztpMaE/wacwaikqivYBZVfI4iNVEDZV1fAAz2J0ak62+cxK+A4oqdPd3e3SHTw53THFrvR3gxJkdsKemctehRV+jNoGx1Fw6KtgC6Nor15zCpYoECZHe6XkoExNsdCMk8ZdnowuZmJvFuy9zt//rxIUspeAjMBjcLZ1cvjZ7doMoz5vPjEidfR5pyQVE9UKvztsg3YUXSLJS/nMafw+uuvi44BmWjj3pedPyQ3Xn311RhSg4l4Ppdjivs5jgnuk9+IxDjlG9/73iQy6nH5B+U6ydzcVORDIpA44phnAZXcqcX4RenLNx00Go3o8mBMyNegdBY7zNKNU94SpAYn2UmkBr9EQ6V0C3kA/yXJT4NJdc1iQCUx33n6cgx4O+AMjEYSjiWGOhgU2siJ8Ej5Ynz96mBMNdW7KpdOet7OonJ8p/mSSKoxAUNZqgVWO5YqEn+5egP+bc1uPNPdghGfB8vtBdhZNL1mPpPf8ZArJbMOnk/qlbsVWtqisjUnmgxiwtw3hpA5ENUlV8AXHIZGMzWpwQF4cvg0hn1jKDKoJfWc8GuVGGe57V35ZjJ4vDmSpERG0OQD2gbAr0he6OijYI1q4NlXoN1pw6hvBHq1ATWWehgSyHuwIyOqOi5BXLGpEq/GcumWIjhxkZFl0EFGOp12PraFs1pUNkzO1xeK25yGPkE1DKXrlIQGceZloLwRsCf4PCMdQMt+qbrRXg407AR0JqDnVPgJii+rSzaqZXKUsg5JWohzK2CoXArP2AgMyr/nF+8cBiiDZ5+B18dMwYTe8mm6YG4TXuzpwJcvnBAygHatDn+/ciM2FqR3rl7t6wt7BEkgpTTs84muhDtWrBCJ2anaD1ltxU1BPDFIw162bbKC/caNG+J5JElSWSDHfD7cu3cvDg9J0lBVJhNe2bkTC202aKhP29HBFguRTJcDgEzBYKO9vR3vqFghujVuOKSq9hU5FbizWLpmh71efO70qcg5Ijk/EfDhQ7U1+FRDevIQx4aGcHRoSJiOv728PKG5+O/Xb8C/3jiCLve4+Hx3lzRga0FcEjoj0HtCCwSVFTBsPTEAO98LnHwR6LsprYWr70rssTMxBpw6CJw8BLRRajB8Vn7xfUn+acP2xG+9biPw3Z/QKTp9c/BE4GuQyOAtHXjHgbZngQDlmFRA/ymgcrdEbihfXqVFqTEahL7cewFHhlrgD4bgDQLFBhveXr4CS7Zuxsd/+UP48u3Q5+bApNbg/63cJpLoSqwNBPAuxedmIM6ODprLxSevzo1excWx65Huv+aJNtxRvDmW2GDXWE4K3kN87a98Bfjc5yQZMIJ64x/9aOzzVm0Bjr0e7abh/40rJhEaBKuWpquI4u+nqrRKB3K7+Uxfg0aHDDi43lfX1OCp3/4W79xeK6p4TQb6BWnDpDpvs1yYQMmpRJJs7BhIhdQI63XzHDNGyBT8WwaCDIhnYryYDmrNeViTW46TI12CiCehXm/Jx4rczMmZtJEw2c8usttPArgoHxwHd6AvJVJj1DuGA4PH4QpIsq+LbA1YYl84nyC/DeDYYoIlE310JmvnpaeyAK0RhQs3oPnoUTTMExpzCowNGEOzCzNZIpIxAwsIE40P7tvpx9HX1yeKFAlWSE/n+zcVZN1+dohkIyHJ46MkKItcpiI1fnDzCjqcEsnOHQFzb3978QRe2FE2P3e/hfGOd7xDyB6dOnVK5Ktkoo372bvuuivyPBHDUzlArY5cZyTVeR3PtFv6duDDH/7wtNc99/E8N0rJWd5P12eDf8+970xl4qqrq0XhFecyEhzPPfecyIHwdVkcmm7x9FwAuYdU44u0OjW4KUoKdl3oEptjkgRYaN2AYW8XvCE3zJoc5Ohiq5BuTPTg0OBVOPxu4avRZKvE6twafGHxLhwd6hABx+q8MtQkaAsnYfHtdTvx7zcuoMvlQKMtF59csDRiAK583gdq0lsgFlhq0ePujUlzL7TWz94Ev/xe4PhT4SCT+txmoHGbdH/wAOCm5rgKupAePuvkag+asE+Ha+M30OWWCKo+dxAWLdMWwGLbauTqM5T9mQ6hXiBEmQ4vELIBKlYhZrFaRdcIaMokySm+rlp6bU9gHN7gBLRqI6rN039vWpVaSJI5/N5o8hAqFBosaHMO4MxImzAab7KVY7E9TSPpJOAxMdjg+Nq0aVPKfzc0NDSl9MacxNLNwPmDklSMjLIaYDyB9AS7luJJDRrkXfh1tJNi8IYwp8XKxyQph6QIAa2vA3bKlyRO1nAhDokukoS/TOnjvdVwfWIMnz93NJJTHvf78OlTh/Cb7fegiG32aXhVhJI8PlUnhozc/DwcO3cS5lxrxJeJ0myy9if/nvqcfK1UK7e/eP48jrGyPIwutxsfOHoUh++8Ex+rq8M3mJylZEpZmTh2PpYpZO1rk0YvjMFdAa/wTzAofKG63a6IMbgMSlC1OhJU+k6Bb167hk+fPh0RhNxVVIQXduwQPh3EhdEBHBvugUGtwcfrNojuFHp1sOI2K+AcXLsBaD4gPyA9VrVG8pba+NDUf0//jG/9tURs8MJjEtjnl1pXiOMHkpMaMuIJDZ7/U8cliSTKUS2c5Q34IOWyZFmx8FrfcxhY8GjSP+lyjUQIjTE2xdLHwTWOb904AKM6B+qaCnjbOuDs7AEFP/+qZxB/tkCqpuIml8SZsuqPY+I3Pa142tmHb37vX7C7ohZ31TWJNUVlUAtCQzo66byyCKLF0YGFtsy6kcCKo5//HDh1inpOAI3/4iUfSquAD/0R8NozgGMcqF8M3PH2pC851XpO0v/y5cuiSjFV8JxwA81EBU1ElUQq5w92a3CNnsn+j9+B/D2wOrTG7oMmcB1XW3vQ1T+C5QurUM/rj7Je1F6fzUppexnQfWYyuTidp5QCDFoZkGUqoUnwb5lY4mvdKlKD3+FH6tZi8WAxut1jKNBbsLWwWvjq3TJQsi2nChhlwizcnUZj9sLb72GghobOiDGQ5Bun79AgoeEOExrE5fHryNHZUGnOnPiaR+ZgMYecfJGLoKYDu/+U5snzmBnmO17mLlgcyOrmqXwzmANjt4Yy5qYEJpO1/G65X+D/JDpYYDXT2Jx7BBGTZql7kTJ0icyFlehxOSP7PRmOgF/crPT9msdbErwOuUcjwcaOH8lHTyuKcJmHYvdGbW2tWFuU12q704H/192Kk4ePoP7YQXx80TIsKCuXYow3QAfgdMfInB3Ji/g1lVJv7LJIBJKn7ADh+eN8I6+xfB2uz0pyNFMYwuQs45dHH31UFAuxaIgEB+MYeoCk20lyO8G4INWOlbQ6NfjCmYLERgE7OhKg3TmA33SfiNx3eXzY57mIc6Pt+GDNNjxcPj0RUW6y4G+WzayKLhEKDPnYWrARzY6bIpldZixBNXWdp8JEN9B1FPA7AUsJULFFMgBMBUX1wPaPAP0tABNapU0AjaA9A2FCgwjB6PTAbTQiyOA4PO7UMEOvnp6F6w4TGoQ7CLi90iLmD83SBjY0BoSUgfM4EOL3vXVKI+20oaa8UFRiaMTbikHv1ch9m7YCRYYlU05U/N17qtbg+61H4Q8nzkloLLTm46fthyOJwGsT1Dr3YU1ebVYOnRsYTjKcfJJNhvHg8+IZ4mQIgQEmdZuNUCE7uvsZYaQvjnxQAaNDwg94EqwJkhsD1+KqSkOS/Il7BOB30Z3YDFd6Ki926rObky42RlYFU34joJDSyy0DLJmboGYdbgcwOgCYbYAtTRkqcU2Hk8hZwGt9XWJM6MLfH3PM7mBAmBLvKk6d1HisshJfvXQJN51OQWDzNe8oLsZa2exXq8GfH9uHy14nSgwmfK5xORaHvQB63UN4te8EJowOvPziEawobsQiW41YxGUtaeWYT9U8k50Myi5B/kyZLOJrK1bAptXih2NjYmP3R01NeKyqCplC1syUQXIjHtVmC0zUxlVoePpCISylL0QSXBwbwydPnMDl8XEsstnwf5cvx2dPnxa/kz/Z6/39+O+WFnxiwQLs6WvDN66dFJXL/Oi/7rqBv1+5CxVJpFoyRvVaibAfaJaq/StXAvYUK6Rff1ZKeItK/nA3FY2qxZhlNmaYjBbL01J7PV4Pf/cV4OC+6GO/+3Hgne/GrMHFTpz46ngH4GsGtNVAgg7R4eehPFUAAQAASURBVLBMjTMu08hXmQiMIaRRw1oX7aSZcHlFEMtNc1tnJ/74yR/j+OgQ8srK8Mk1GzAWcOO7LRd5sQELKvHLgAdlATcmrl7FgHMIvlI/NDrpOOROpLbOm2homqzTGh94c12aJA313HPAj34kdWrcey+wY0fic1PdAHzwc5gpSGiwA4XHxp+ZlOBYTbb+M6lBMoPkz9q1awX5yeCD0nDc/xYVFYnEIDtbIlIVRw8Az/8a8HmBjduBBx5JmwCvXLYT6LNhnU6LU5da0dw9iKGJU1jnHwXM5UD5rtnxaiLy64HKdUBHuKORhMYiekOlvhdkXDCTLg2C34kcY9xKg0Ne01sKs9F9liF4LS56AOg8Lu1j9Fagav3UXTJBvxRfjLcBaj1QvBLIy36HtUVbhxHf2ZjHrJppPMrE/OSOdGjIYNlUv2dwntS4jeC4oj73VDI7SlA2ZCYx/zwwKdGVbvXuPG4NSKTTH2u6eJtkBfcF7HDlmsXvU95PyP+zQ4OV0dkA9yIkTqbqrkgV7Kac7vqrt9rxen+XoqCTSisGWBTFVfN464J7YN7kosFRyuk1NGBiYgK/OXEc/3bpPEb0OmxsbMKfrViN9x95HcM+LwJ1VRhSqdAz3IN/KygUUsvcV8df15wjeSMJr4Rc1CiPMd7nfp1k5O0iRzgueZwkM1kIxXwDyRrGW/wM8WoQzC+dPn1a/J7kEMcjCSIeP/36+D8LpvgYP99UsrrpwGQyRcb92972NvH6lBNjIXWqucbbjXRijJRnKraxMOCbEtSG5jSY5kV2ebwzInFA8M/pa9DnGceZ0Tasy8u8AjZbxAZvKcE1BLS8EK0kH70pSU00PJz6ebEWSDclgpSpiIKvlDM8Bld+NfxaHTQqM8waVgpM/5WqkxAJmtkKnDEwySgeIslOLedp5JY4mQnjdHVaLfneoCOG0CDG/Z2waIth0UqT8qjPiZPD10VVdJkxDytz6wT51mgrwh837UbLxKColG6yFeOXnVKXkvITHB68ljVSg2B7KCcbmo2mMlFzQbh06dK0SYAQSIaxS0bWxq+jQBtuC9qvxhrB8pjGR4Ela4DOy9HnLd0J5CRgq5OeFxVQthbwOIDBq2Ej4ATPZdJgKlDqbcP7gGv7Ac84kFMudUrNlaqCmxeB156IJnBX7ATW3Tv93/lGgcGDgH8MUOmAPHqNzDyZ82xPW4wKnnBTUrHDIr3Wa3ZkHLnzTvzfy5dx0+HAytxc/GlTk0g6kUz41lgPLra2QF9dKbrxPnZ8L57YdKeQT3q17zi8QRow6lG9pB4j8MNUlIMmS+IkORf4VALMOosFJ4aHI8QGN/glHo9opeX4/KDRiIdXr5aMBuO9AdIEg6DpKiesWi2+v34jPnD0sPD4IN5VWYkP1iaegwY9Huzcs0fIePEzDHm9eGj//oi5ubLboyXc7fGfLefE//JndgcC+Fn7FXyuMeyHkS1wPJUtkW7pgvOFktiUx6YsV9R+E/jG3wCf/XJqxMaBvbGEBvHdbwPbdgLFs9C5OHQNcA1LsnvKeUWnAXyXAH8HYJxM+BeGPYESNS1pqegFVcT/hD83lpRGzC+fcA7j1VwTgtYSDAwO4NO/eAKVJq0whZQRCgTxbEiL/9j9MI6dO4HfvPQq1t63RVzr/e29cAwMY1X+YhwcPCiCmN27dwvpIRpxEwz0Oaa4eefPJBAiG/OXXkLoS19Cj9+PdpIa584hcOECerZvxyOPPJJyYELzYn5CrtP+YAAv9rL4pUt0WG4qqMeWAqkbi8EWj5FJPI4tVlMy8Dh58mTCpJ4cJO1QEC1yu/yBAwciG2oGAQzq2AVS1Hod+NbXoi9yswVwOYHHHkda4GcvWQtV8WqsMT+D9rZW7D12GUsaKnDs6KsoqhvFkq3vTJ7gHrkIeIcBrQXIWyb5lKWDqg1A6QpJcorXWJr7QMYFjA9mipRijDcjmDCqTr1DF+37gNGwqTwLVdpfB0iC22f+HShh0pSKzm9noDN8v0w8Nh30Sfbp9Hqbx+0D9xespiVRm6oUCOfzbBoWv5WRji74PG4tUt1/MIE7HVj4wIRvJmDBFUkTucCJ+xLusbjvmEknpPxa0+H91Y04OTyAUyOS/K1Ro8VXlm14Q1TVz+PWgsl5uZN5UK3CFyaG4C0vgW9iAr86dQIHTp/AiFeRtwyFcNVoRNfyNVi7ejVeeOEFMU64JvF/JttZDMW4gWOAr0+/G+7h+XveGM8wtuDP7BAhqaEE43rGI9z7i+MaHBTyibMhocj34ZrK9+D/7K5gfMEiY96P9xChdNfq1atj4nx2H3DPy5ts6s2CKhZOZYvUiCdvmV/kOWWXCbvYKe398MMPz+kxnk6MkTKpwYuHmoK8mCa1pAbHAf9JIMRKcA2gZWCVbuvd5AmXgbnDH5vMn/MYlU1OZYQA1yDgGQWMGbBifK2+i8Bws2QoaDVIchs8P6EQLJomQJdeu/5iWyMODcUatRrVBuTqUvdzSA8c3Ak1fab+M1Z79e6VulQIaz1QuEEiOKaBL+ic8vFxnwtPtu+DN0gHjRBaHL0Y9I7jrhJJQi1fb0Z+frRajknT+KP2hTLbuCQDJxVOMgw64nU03QEPet3SeSg1FsKgMYjnkxlubW0VC0PyDo0ooSGhBSHkQYXbYDbNiu9Em6vV9wEL1wOOESCnCChIUplS1Ah0nAqThuFOClspYMyR5FzGu6Mmw/H+LeXrk5qFx2z4aIa85hHMObBDQ0loEGdfB0pqgaokQSrPE+efgX2AyhfmFn3A0CEpCabPvAOF56yThnKKU8yfc7Q6rMxN/3ULDQZ8PYGe5I2JUZy7cR26fGme4zfvDQbxcl8n3l5eFjM2o1WhI6hOQmowyZlKxdz/WbYML/f2CjJAImtU+MuamkkbJG7EWOXF8cjKqlQ6p+LBzZq8EZsKb6+owMV778eZ0REUG4xYl5eXdDPySl8fBmT/gjBRQYKDJIZSxordHstycsTvJ2i8rQBTyCPe7FSdZQ1VdcC5OClMkjz8TL6A1C504wpw7ACweWfi12DQ+dLzQE8X0N0VR7SG0dM9O6RGz0k60Eukq/K7KwjPTaFxgIlEdmwoUGK0Y1dRE37bfTlGvY/7pAZrIYa9E+hwTUSkNj/XuEZ6uVAIT3W0SrOlVgtdSTH0JcUwmTTQK66DUDAIx9AIjhw5gi7rKNbdt0Ucn9ftxUBHLxY2LUR5sFjsARlg83qlRAk377wG5QpF7g/laiQZ3p/9DK+NjaHRaMQasxlX3W4M79mDnX/yJ1B959vAjetCxg0f+jCQl5+QzGhzXhLebESOrhiXxtQ4MdwWWdlIcBjUWqzLrxGB0f333x/zGgw6WFVFkiJeGoLnKJkGNv+ut7c3EmRw/FOGqnDv85NtzV5+Nn1SQwnfGKrK8vHeBzdBrVbBoNdibFBKKiec23teA9yydKMKcHYAFQ9ISe50QMkj3jKAXDGXLQmQeUyBYECKMWKgAoavZ05quMeAzhOSR5mtBChfHSG2jJpicUsHJDUabfW4Ot4s1mLCoNZjgXXm18g8ZgbuTzj/UfYilSQGEy3U4efzmaxNhpAQKuvjwsrUCVSYWQL2zQhW+WdjnpxH9sGq6UyJiETINEEox/Lxe5G2tjYhfcW4hcWMmbw+E5rTSdtQZvafVm/D+dFBOPx+0Q3PTo15zGMq/Ka7E95AUKwCapJvVitGaREcl6oLOBy4dPoM8sorRdeAnOAnocG1hgQ6STxCvsb5GGNqxsgcp4w3GF8wV6YcBySN2QlBzw+Sgyz45XqXChGZCeiNFw8WP7ErnLny+PdNFmNwPHNN5ucn+JkYv1C1ZarO8kyh0+mwefNmQQ6xAI2vz/hmrhLunJfZFZPq2pkyqcGLg18K20BiGBN2Z/iOcFkIB+hBwE+pIZqFp5ZsX2KvxMWxjkgOku/DwcG4vSwTIuC2YopK8kzQcQzoikpzweUBiuzscZSqrfXp6w8XGguwPn8Nzo1cEIl5u9aODQVrZpGpY5UjAzFZ0odg4mKajW//IcAzGL0/0RyuRlw+7Tvq1OYpH7841hYhNGRcGe/E5oJFsCSQCmuwlqDHPQIDG0bC50mnUsPp98BMyYYsgSY+nBSVch5jvnHs6T8Mb9hYlxVvdxRtgk1nFRUcnJCSg0muOBJB3GXF/m0gNRatlzw1fJ5oNxOrwGkEm8AMNqEG9fJHgNaDYaPwMqB+uzRx9J6XuisEQhGpelGJai0FbMnb186dOzdri1/WMNIfS2gQJPiY9EpEalCTnV1jJFQJrRqwGcPztAqgDN0MSA1en+zIGKH0ivwYgM0FJYIAyBaYow66PNDmxF4fHCPGBNWfHNPGKZJ6qc5zC6xWXLj3XjzZ0QFPIICdNhvKEhBy1NNk5SOPh8EHryUmQDX5BgyFxqFTa1FvqYRJk3ie8AX9aHN2YEA9hBv9LVhQNHVnYqXZLG7TIZlx+acbGvDN69cFmUG8v7oav1NdLZ6/0JorSCS54l94LdnnkPQaseVuoL0lSmyYLEBvf9QsnCBJQVm7RKB81998ETh7WkreRaTm4sZVeXY8kyaBnYeENwCogtEuDd6kNwdCiYs5thYuRI25EE92nEWHi84ZQIXJjvfXrMaHa7W4MDYoyKkl9nxYwhrI0jQ4+bq1aYwY9bsiv1Gp1Xh0w1ZsqmzAUx17pPMUJhTshblwud1YvnZ5ZGPODTH1o2XzWW4+4xNfHBOsXgoMDMCm0aDX70e3z4cmoxFLGPx89SvA2bMSoUQpzQP7gf/+gQiKlOh234gQGsSorw9nRnyTPtWZkQ5BaijfP0i5S4SghlUcH/VkWZ3EoIHBjyxHF2+qLoPrwt69e0VSgJ9dJi9/ff4Sdvt9sOsVnzlFabvEYLuNHqGAB0fO3EBFSR4W1pTi+eO9WDw6KgKcGHiGFISG+LSShFn3a4ClCshtzJ68p88p7fV11kldi5zz5IBsJuBr8LXmcQvhmQDOPQn4OSeFgJGbgGMAaLx3Rt2py+xNwkNjwDMUJjRqYUyy/s3j1oLzGSthU61eZbKDnW/JDEZDjP1B+TrZ24uqC8ugwiz5M74BQWkS3uKrd+cxdwinbEg8ycg0l8KkbaLEp7y+cr/FKmvGHCQnZCmgVMDxfvjw4Wn1+hm7rcx945kJz+P2IVF8wXDMqNaIAkTGk7yuivIK8P4dd8MWFydwjeF+nHEzr1PZR5Pjkt0NzHNxn87HlL6ABLumWQhMQoHz64WzZ2HPzRVxSfxzZwUsNneOSAWzJrso5mLBF2MMEX+EYwx+hkTzAh+jzwWLB+QYhLLBHOcc73fccUfWD/nmzZui04V5Cx4Xz99cJTVI5vI8pupRlDKpwWCQL8qgI4bUCDJhFmfQyy8u2JoyqVFhKsBDZeuwd+AiRrxO+FlwGQI25S/AQmv6GyOegBuOMQx5PVhgsaPAkFkFWkbIrQP6z4ZzJOGKcXMRYFAk5JgsOPsK0HEFoEzLkm1AdYKAmknf7lNxD6qAQCFQfmdausfxKDWWoLT0Fm06aSKPjUCIMhWs+s0BVPXTB00uBuxxk6WrJyVSQ6+2IF+/EEPea5HHbNpymDXSYu0LBkTCLj4FwccTYUN+A66Mt4sODxn+kB/7Bi7g3lKpInbSdzfcCvjcAK/hNDwZqDXIig3+T5wcviASn9Fj9OHUyEXsKJKSSWSwmUjlxEhGm9f/iUv74cE4GuvLUBh/+Ytqfamil5MtFxMy5plUmKcNay7wzk8Bp1+XOg9Ka4FlqWn8RkAiY8WjiRMvEiuqeDAE5NVLnRyJ4HVi8MzLCLS2IN++EchZJ8lBzEWY7Ymvs0SPyzIVNFGXwepwpxewcDySQZ755/yjxhX48oXjERKD5tK/Vz+9B1I6WGC1o8qei2GvFyG9XnRK8f3uKK4QpuDL7PU4PyZVhXJzZddasNCWvGqVUgrcLCUL0pUoMRrxKQXZlUx3l5JZJ0eGUWA2obGuDtdHWrH/yHFUNdWJS/LyWAvuL90WMTFXjuW9A4cw4XfAXxrCr048g7dtfQBNtvQJth63W3holBuNWGS3426aQ5tM4nEmutmhwc/z5aVL8ZnGRuEPQvkuZbfHnzatx19dOIgujk1BUJXj0crk5om3Bdyovu+TwD3vBDxuwGgC/upzkhSPMrlcHaf/znlhbBQ4c0q6ETJJKD5/KPrzH3waKEw9YEwLvDZHmsPEa/g9xZiMHCigTr53qjTn4bMLd2DU5xbBQq7OFCGw1uRNDlj5uwfLqvFM183Iesd3/XDdYrQ5R/Fyb7sYTw9X1OMdFZIsoU1nFtr4HE8GkxGVC6tRaMiNBNysgqLxJLVgKZnIyp9E8ml8DpMFtt/5HRz/0pew1WKJEp5rVgMnjsXui6jh/toe4G2xZvEjvv7UunsVe4pQyA934CSCkEhdFYwwataKLhNloDCdESd/x8/HaipWZ7FKjFVDukcexanvfAsVJiMa7OFk/2aFTwiJ++YL0h6gogFI8N3EvRFQvAHO5lcQCIbg8wfQ2jOKxjV34MyZMzHSWNKBJ6gs5fXk6gOcvcBEO1B5V0qdrUnB/VDb69HuAGM+UH8PoIvuFRgT0LtkpuA5ZQfMPKYASdjcBcDIDcWDISA/wzm6/0qU0JAx1CztG5LtmVIAx0y1uULc5jG3wMQRJTRTJTU457NwihW08t8wacOYg/MicFNBaCB8LV1ECMViT0ZJEFbbztT89I0MJrhIps9j7o4JfkfJFA/SBQs+MjH4ZhKWMXk8+RUIBXFhbAAuvx8NK5ai6+oN8fqsCE+VjJF9+xK9/jzmMRPcX1qOf7h6CaEgi4ik+LzBasPXlq/GX108jQ6XA41WO/7v8nWTCA0ivgqfXQQ/+clPhCQ7i6XYoZRIMpEkIGMR7o1P79uHpT/+MSouXpSKvj/2MeBDH5pd6fDeK8ClF6MFunWbgNqNMXN9vB9IIjC3zq53Sloxj8d5gNJUnJNeeukl3HnnnVkdsx0dHSK/yK78FStW4Oc//7mQ5Z2kwjQHwPiCRWRTdYoqkdYn4IXX0tISp0ksd2goIL7EaPVuMkz4nbg83ioSOyXGQnyoZhc8QT+GvA5YtQbYdembatFk9u8un8Jvu29GqumpCbi9aGqTkTEf5X0mkKs3osgwg+SuMQ+ovx/oPgb4XYC5GCjfFHuOjj8LNJ+KBhIHfi6ZpVY0Tj6P8mCJPhj2C5h7F9+UUJkB1bL0/obavHFeIkij2itPXwezpgCe4Dh0KhOMmmjyrsZShNOjlGGKQgs1LEm6LpgsCYaCMV8j/7bd2T9588JEwMVfA2NdivKoO4DixWm1iMukxrjfEcOE82c+Fr8gcHLnwO8cvwxziR9jvcMY8WlRaAjLnERaoYDh4Qn09l4SiSgytWRDZ0N3MCFY+b0jgUb4Sy8BTzwhGclSOuR970u+IFGWrvtCuFujFChcAFiKgb4Lsc9jEjmZj4bfA+ehH+Di2cvYtrwOuHkMGO0CVjwys0TQbMHOLpUdwLm90vFxbiiuBhZIkmkCTFCMUK4kBDj6EpgRh43C2eFgnnkr/P1l1Sg2mrB/oEcQGg+V16DCZMGoz4cLo6PI1+vRZLPNqAtMp1bju4+8B3/x/NPoLbeh2GDCnzStQK1FkutZk9eEAkMO2pyd8AQmkKNTodVxGoX6IuTqqyZ5CDGAITmRCqkRj0Qbi6c7O/Gho4cjnQ/3lZZgm+8qSmor0NvWjZKaMniCPlwcu4F1+bHkdaujTRAahNagE1I/Z/svoM5SnZYG+S86OvA7R46Iqhi5G+Mbq1Zh/+7d+MypU7g0Po7FNhv+afVq5Oh04ladoNujxGjBt9bciW6XQ7SiFxmm7wi5LeD1VKgg5T/yaeC//1mSlSLufyewRCFl5pgAvva3UneGsqNDiQ98BKioBGppnpxdffoYVG4JS+WF/QNyq4ACuTeVF/wiIEy+JwPHU64+9f3RFxevglmjxSt9nUIn+aN1jWLsEp9YMLlIYH3eEjzfcygisahRabAhP7p+s5KI5CD3g1w32D7Otm8lGDyzwpcdDkeGhrDDao0SGvw/EUHIxw+8Is3DazYBi6XvUJOAgG2w6nFpPHafuSE/mpTwBq9HCA0iBA88gfMwaTfEvaUqpcQeTfzYscGiHs4hZTSS97hw7aknYVRpUHnnvcD7PiL9gdsJ/PybUncdwXnjjnexrQvQ6oGS+sTkub0OlqZHsMh0Hp09g7CWN6C0slZUZk4+qHxpHhcdnAqCTP6fRSHOHsCSrhSsAn2nY+WO3MPAzdeBhgciD7EAIxudGnJ8MY9pULlN2teMtUt75JKVgC1D8oDXTrzdHRHfETqPNxXkrqhUxi3ned5YEct9k7wHilaJRwu9lITylSvnoNOaRUKKhMhtJzUozbj/GaCvE8gpALY9BNyCinRKonCNlP2t5jH3IF/T2Ur402uARJ7dbs/4WGRQUeJvLx3ElXGp81iv1uBxczkW2u2i4poJ4FQr0pkwJRm5MoHM7zzmkSkarHb8bNN2fPnCGXS5XFiTl4+/W74aJUYTntl2V9qvx+v5Pe95j+jyZhESk/C8duMT27yWly9fLuKP3H/9V1TQ348xsNsNfOtblD6hO/bsfLHu8VhCg2g5DORUAHlRojHV3Ac7PM6fPy86NEhycH1l0Rj9NXge+Dmn891MFexi4b6dazPP6Xvf+945SWhkEl9oM2lbjYEqkTwUk2ZTS9uM+514rns//MJcHGh2dGLMN4FVuU0oN2UuOfVSb0eE0CB8oSD+8sIxPLPt/ogcQzxODnfhu60nBRtO3FvSgEcqMjAwlWEpARqSDCQOuJbTcVGECmg+PZnUYFWWvSKcHFc8P/dNrsvp6QXGzkpXp8hZyJMCBfvT+14MGru4xaNEyJrFRnN+BHFprAMrchNXa5DwcAZiSRbq+bc4ulFvVSQOei/EEhrEjdeA/AVSQiMFcFKTzXHYxu/xeCPEBqufcigDEQcmmgIhHzzjrOLTAb3DItEquIxw05DP60NzczesluXi+QRJGbai3VaQ0PiLv4jev3SJZgUS256I0Dj5U8A1KiXBOoJA1VqgbouUyJeJDRJgC+9Lanzq776Mo6cuYPuKBeGFh/ILHcB4n0SUzAW0XgROvALQ16BmCbDxXqm7ZaATsORIhIacHOMie/LJqAQXN8d2ygAqNslMhljqANuS9A1lk2BtXpG4ydg/0I93HDiAsbA/w+PVNfjOuvVJ5ZBSQVVuHn6/aqFYjOM3NvzuzBp+ewPQawAXbUTcTkz4B1Hg70W1eT1UcSRVJoEL5QPi39vp9+Ojx49GCA3i9b5ebM4NQm/UR6o0OHZd8QQtv7KgN9JhQhRVFOHsgTO4r+rOlEmNAY8H71cQGsQ/X7+Ol3p70e/xYKHNhl9s2YLl8fI1SaBRqVFpTuw9M+QZQZ9nQEhqVZkrYk1hmQAP0b+HkoE6QNUAqG5RN+CajUDjvwG9XUBuPlAQl0D59r8A589KPydK4nGO2LELKLsFlcWURmO1u9xZwgIFce5cUlejKvuGuiSoPr94pbilgly9DQ+X70AHq/4RQoWpGBZtLInCCkG2fHPTyQ13BCSWuikVRZthqZpqh9sNDTfNYUkrsSj1DwAFuXT3U8g2cQ7u4aYMOL4feP8ngXVbUWasx7UJhQwngDuLl6DS7MH50U5BumwuqMPSnOg6HAyNxH0qWYoqCiYxOH+kEngwANi1a5fobGQRACurnBV1WPCN/8CJkydRoTTb45w9qpDO5Od75Umg2C6tWSYb0LgeKGkA7IprldfEeDuKTH4UNVYARQuxZ98REeQxqIupyuR1VHYH0PWK5JMkfejYg47br6SNie64B0JSF4hiTuT3n42CCMYXfC1eL6lWZb0lwbmqfKN0mykYS3SeVDygAgxWYAYx2DzmPpgk2LdvX1rJAiZZlHI30VwAY5HonNDbO4zBQScWLNgOY1glgb5jtxVck576N2CoT0pCDfUCXS3A438KJNnrZAPsiCEhNBumr/PILriGUQImZi+TIVhdnsyjK10803UdV8OEhqwm8cREBxq6coVkDI2KUyUMWWDC6m/+3fw1OY9sYn1+IZ7bfmfWXo97QHYr8PomsZFoT0jykOR8Y3U1bNeiqiwC3Gfv2zd7pIZjKEHRuUrKHylIjXRIR/qE0JeQBQQkbGRzdMYeZ8+eFSRHShhqlY6De7nipkk5MJ5L2UCd7/fUU0+J+ILne64ZhqfTVZo2qUGzMDJJMVAbAPUCIKhoh1YxkTa1LviV8VZBaCgr0C+M3cAyewO0SZKQ8RjzeXF8uFd0Z6zNK0ae3ojrE6OTjFA9wQA62f5ky03YoSETGgzA9Wrg9f7rcAe8eHfVyhkl4pIjPqMii/8nQMPdwPWXgLFOSR+5Yg1QtCjcxcEqK032dJPnAnzDwNC+sAQHRx8vUR1grAQoyWJI30MkEcZ9krSGEjRcHfE5ImTFs90XcX2iH2aNHveULsLavAV4vidWDoweG72e4VhSwz0araSXwZ/pcaBNzceCXRoMOpjEWJO3DHv6DsEdTopSm3h1buKul2CYJCSYHHC7vXj5cheWludAr9egu3sIy5ZtgFodre6X9QtvK558cvJj7NpIRGqwQ4OEhrI6tf0EwIRW5UagbBXgpyxNrtQBlQD8vAePHMPGJbXQKJP+RCBOTi+b4PGe2AOceE2qhmxaDex8BEhEuHZcA577XvT+yF7APQHc8R6gKoHE07XXJI1sGUxwTbiBnHDnGefVmnulbrIMwHn29f5W3JgYglXLMdGA/LhqcV8wiHcfOhhjOP3DtpvYXFCIj9XHyQGlCWprMuhgS2o8Olytkx5z+ElEjmDc3we7LpakYhUVzafkYJv/sw1zqgWdxF9+fuz47XC54JITtWH4Qyp4ApNfx6qZXFmfp8tBM2vIXR70dfTClmdHXn4uzHEJ5KlwdXwcngRa/pfHx8UMNzw0hF2vvYbL992HohlUetx0dOL48JkICXNlvBl3FG+NaqWHSBoMhJ/tBUL01lp367x7rHbplghnT0UT5/yOBbERnjuYbP/Mn94aQkMJZbclOxFU2UuuOPw+0ZExE28bSqU12qZOenGzTvPtCIYHgX/4iiA1OKtuWtAEfOYLAKUNE60xX/oy8M//JJm105uiphAw6qLf1XO/EKSGXVeIRut64avBuTtfX4pcfQmKjMC2wsRSbSr6usV5SqlgiAk29uzZIwoIlixJrViCZCjngHyTEfj3b8J/9CC6AyHc9Z73x84d9HOJ/7xizxYm1Vys8toHtB4BVj4MFC+Q9ghtL0vdFeGjpYTU2lW7cPbCFVGVOUlqgnMKu37k1447AzDO0A9HzENxLKCiW5adFazwipGkzRB8Db4WX5OxxjxuASjlyS7ilv3SdUTPMvpppBiDzeONC0rtyMbEmYDzJ5MuHZ0heDxjyMsLYnBwDIWF+Viy5GEh9yeD45rPvW3VoL03gcGe2HiMEpvN54FllNDKPug/Sukt7ivnkRo6XeN4rrsZzoAfK3OLsKOw6pYl2ZggTSYvm4n8FCVuOUZI/DNxyAKQTLrDO7hXUIArcV9nN3KX3wG72YIbN25MS2pwz8RCDHbO3nXXXSKOmic15vFGAMclb8m6e4VSCUlrEgfKWJyFi6b01X5SRkJFn5BEJITBvSylY9/+9renPI+RxOBeWN5Tcx5h8RU7OVJCy0EpHyars/RcAFa8M+meju9HLw/mN0ggZTJHzSY4J6ezhqa1wyBb8stf/jLBqywEgrlAcFiqMlRXTqvXTsmpRKBPgRbTb6gpj/G503sxTN1i5jO0OnxtxTbR7kT9cCV4KRUZEl/clJySCQ1T+G158Z0YaYdZq8XD5WlKJk0HDrSaFUArE0CK46xLUkFJCa7FD0uSRkyU80INTADjh4EgA3ZW/S0GTG+SINAVluSQoeG35wdyFwPaaYzF04BNZxIVyXJ3DkF98vywVNFP2k7gynif+IaG4MR3W47go3UbBOklF0Py0Ph1TDIrNuVNZnA5oSgmu1QrF7lh4f/3le5AP41BxbVcICqlE0GrMkCvNsMbdCG/KBdD/SPwajQIGJZBp+XklQc1u6jiJtjbzs76EswHccni6HNdk70zeP/Cb6Tq1aVvA3KnTlBSu3Dhqk0wde1RvI5K6qSxzpKWPnH2ILDvmej9c4ek97/r3ZOfe/l43OcMAVdOAjsfTSxdMsHK4Dg/EZUOKF4pzR15CwB95onTH908g/2DbWI+ZWL72HAn/vfiXUKyT0any4VByocpQJKZfhMzBasVGCBMR+bFI5DAdFluZWVCk9f+sWPHxOLJJG2yyjrqSMc/Xm4yQa9Wx3RJ+JxOmPm64XHF7ih4fRjUdyEYaoI63DVCnWrXmAPq0RDGQqOobKiCTq1D2VihqDxPFclMw+UrgevhkNcrOjdoCJ4JGBSdGpEKGmQy2B3w4Op4M1ZwbhaV4jKhIYPXbvetIzWmgtUm+WnIY4lEZkMT8PufBoroeXQL/IRuAdqdE/izs4fR7KDkohqfaliK91an789CeAIetDpvwhv0oUCfhzJj2fTrxPfD3TIyWq4BT/4AeNs7gB/9SOq+47zO19m1C9i2Hdi+Q/JG+ae/Dnd4KOB2iU29TD4wMcCxmwr0mga4AlwzZSkdFQzqqAQkE3rcLGckefStfwCOHIQ2GIQIPX7030BRMbBjt/T7glKg5XzsdKwOk2kyOGfwerz4kkRquAcVhAZBczkH7BiO9axwtAOONmlON4TXKrnTMPJ+KqBsO6BPX/4iBiWrJJmjyH4mBJRviKmi4v4kG5IdMkHG15wnNW4hWCRV2CR9x/NkxlsGTJqwcIpJoUz2/yy8YpUsSd7OribodFZUV6tgNpfEkMdzAkm8EqMdgtmF8PW8cSMrXkNvJULjL869LgqjuM4cGOxAn9uJxxIVcM0SsmUsTAlpEhqMMdixwTWNCUN2efJxrpmpvlep0RLTzU0YoEKe2RIpSKShMAlDpTwN34fdVLKPBo+JNz43WzI285jHnADJ8scfB77/fek+96Nc096dIK+SAK+88or4nzKva9asSW18WAuBipVA55loniavCiiKxls9PT1iL5upFB2RFsngGpUIDUKOdcd6gN7LQFkC3+Yw5rIcHefORx9N4KGbBGlFIvxymPhJWNWtLgK0jYCmNiUDWnpoKCdpTtp2rRWGFCU3/u3GWYz6vDEyIP907TQeKqvFEntezIf7VMMy5OkTX6RyQk4XfrJyc7d/oDUm6Z01bHgbsHC9ZPBrLwQ2vQOonGbhZrAhD5zxQ0BQ9lSgMeRFwEsd/TcDZi+57g16MeEfRyDkh16txV3FK8V1J6PGXIQl9io4/B5cDhMahFxceX60F2vy2EnEjh6VyFFQkmOxPU4OjN4Z1NWLfCQ1sPBuKeGeBpjA4aTI8cZkZ7mpRNySERrirVQq1JjXwqC2IK8gB2VVJVi/ZBdcQzrk5jVAoy1I6FPBZDGrS24b7rsv9j6Pkb4aiUBpKOW4VH4eVhte/G2saXAC8LyWNSwDFt9PMwPpQZJOyx8BdLOofUtSQgmO5/jHIr9LNPeEEgdswjA8L+67VUmP0cieyakpCI1x3yC6XdfQ526BPziZOKApMQkN+QhIADr9PuwfiEr9EYUGgyAx4o+YhtUzBaveEmrLC6JvsmeSMRwzGDWTO/RycnLEa3G8sIKQlSA0ImOwwU0/NTw5HrjekXyQg4R4WLVafGv12piFdLVGix0LypGjZa1iAK6eXmj9XjTfaMH5y+dx5cqViHQDCwUeWvcAfmf1u7GzaCvuKdmNXH16Bq30xvjrpdKGZarK/JmkHemtEFAQR1atCqVGFbzBTvS4WxIW4UuYI62sj38kuo7Ka+njH5b8M97AhMa18SG82NOMY0PdIhnw2dMHcdM5EZHe/Mdr57B/IF5CKDVCY9/AAdyYaEa7sx2nRs7gyniCKkavCzjzCnDwF8ClA0DztdhEEX++cRUoLQV+8APggQcAmst+4hPAV78ana8oU7JkVez8xXVz0XKxqaePG0lNBufUl2UXYjxYDamEWmWBSbMZenUjdOoGmDSboFFHK744rimnwrUvrU5FzgOHD8YlxOgF8nr07prdkkxg5NcqIDdsJh49wPA5dErrQMI1SxX2zAiD0pJ9+yRSY6IVGDwa7dSVO5B4K90K2LIgU2oqABofAYqWAYWLJb+4vIbw8fpw9cqVrBIQcowxj1sMXjvzhMZbDtzzcD+SCZisJcFMTz6T0QSzqRQWc3VCQoP7LXbG3jaU1EhyrbIMKa93dkfXzE7CnGRPvPntPKbGs93NYg/D2EJeWZ/qvAr/LBFPswlWP1sslhgJKjm+4I3SMuyaYMGGUv0kUYzxcHkDqhQSadzjP169NKIiwgprvhb3M0wAcjzzf7kgkjEG35sSPvI+ad7fZR5vOnzqU8Cf/RlAz+e77gL+67+AFDuwaTTOPAANvg8dOiTkn+LBGGFSjmzhTmD5Q8IcHIvvkfxYwwU+HNsk/JljuGW5NW/UazcCzhOJHn8DgOec8UA6MUbanhpknERCsGxq4+3pUGcux7jPgQtj10XSy6a1YFfR2ikrRih/Qj1Bg0Yr5KS4+EV+hxC6wuam/7JmB17t68CQ1yMIjpVTmIHRFJweGq/1M8kUn4wLifcUzQLZBKus190v3dJFkPrT8RcozRL6Af0tls+YDZiqAIdyk60CdAWAZmaJpzZnM1ocUrDMKugl9tVotJWj2JCDPs8oTBo9Kk0FUuVDktfg9bAmtxH5ejt63cMwavRYbKuGQUlWMPHXsQdQjQBWk5RwqNqVsQ8KZTEuXrwo2sNSBTs1Gqxbhb+GGhrhJ+DUt4ikMBNDiUB/jcuXLwt5C1aq33KQUWdS6sc/lloJSXJ85jOJn1tQD1SvB9qOJf49PTfoL8GEfgIwYR2Zv8iq02ScCaV4qSrKUJ1/Bei+IknFLNwE1K5Jbl4+HVwOwOuZrOmfzJR84Wrg+hnF81RA3fJolwYTiid+DfQ1S6/BDjBKg1B6i2DXSeMd0x7WgKcNnS56IUgH1u+5iUbbZugoLSgfehLjUFecVBeT/F9bsQqfO3NKJNH5MessFnyqYeaa6/SYSdYyXW2uF0n3LtdNBBGAUc01BSg2LIZJkyMlBtnZpjYC6tgxIEsjyGCgTjkqtmvrSktx7upVMXZY5cSxyP85TzBQJ95fU4NVubk4PjyEAr0Bm0wmXB2+DJVJJQieigWSZExeUR4WlywWBGU8KDdlFnI5meHLS5Zga0EBTgwPI0+vx1cuXkTX6CgCra3QNDWh2GDAvUwsZwidSguTxgh3wA2LVoVCg1psOFQqSjtKhQ5lBrbA04NB2SmUwKSY1xLnSZlMvBXYuAX4m/8P2P+6lEzedRewsAlvZDzTdQ0/brsYud9gyUcH5xgFGAAfGezDtsL09ms3nW2iCEBZeHLD0YwF1vro9csu2Rf+E5gI6z23npOqQ5TdZdzg54U7ddhO/Zd/mfxN73sUGB2RvDQYfNc3oWX5ZuT19Ig9JxME3IMygGdLt9y1xUpEXn8M1plIUnZyqFVGqFWT114GHEzGcRxzTeTfJlsbJ4Gfj50+fiWxLoRqo/d1euAdnwS6W6XzRFOrE88ofFToXcDnqwBbYbjlM1+av0nMKxcIi+K7Gw77wih/b8gBPOxCCnfAsDBo4Cgw0QIUbwK0iYnglEEZR0V3BlxdwPARIOjFldMvoXFh9qq8mHzhPmQe85jH7IOyNUyApqP7naxrg8nUZBIZsmwnizk4h99ysKjx0T8AXn4CIMlPKeo7HpMMw7MMrkUkNbZv3571134zwxnwTZKEZl6HRtnaLHQCpvK9JSqWyBZkE3KOM3aIcg/C/QcLK5i44//cg1BFgHsTecyYNDr87bIdODXcK85Rk60Ajg6pUKWvr08k/OiVkSrYPZKtz8kCE479dN5/HvOYFcidGSl2ZxCUdOV45P5fFA/rdILg4BjkWsW8AMckSQn+njEHffUincl8z8J66RYH7mO3bNki/o4+Nko/qlmDOU8qTlF2JjIWm031kVkE4z4qZKSzZ0iL1CDzzPZwsswzJTU4ma/MbcRS+wIhOcUOjakIjT19N/E/N88Jf4xKkw1lJrMgMWRig34I1WE2W6dW497S1GU2aArOGHPfQNQXhK/XYC2ETlG9NOobEZX+Jo0ZeTqJgUsZvLA6LwBDnYDBDNStBfQZBJuUk0nn8duJTHw/dLlA/k5g/BwQdAP6QsAeV8GZJoa9gxFCg2Dy88LoKWwq2IlcvUXclLBo9OK7vzExGNlk8f9VuVIys85SJm4JMXgBcND/hGQMP7MK6D8G5FRn9Bm4seGGZ7rqimAoiG53N1wBN+xaO4qNRdAorgnq5k4VdPBzkdjgZM6JhGACOdOWubTBc/OhD0m3VJ5bt1lq/Ws7DnTFSbnJsm0JwM0cP2NMwCGSVAnGz9kXgfbz0dc+95JEFFRlYCTX1wE8/R+AxwVoueiEgEA4KbZ6R+K/qV0C3Ple4PjLUmKsZjGw9WFgYhg4twcYbI0awbJTo/U0sHgnYOdmOATkVwNx13ai66bLJSeRpM/pD3nR72lFuSma9C0ymIV/xrDXFTnTnHv9CRjAXcVFyNHpMBreOPOqV8ozZQKSCqxIYtIr2fVbb20Ut2DID1/QDa3aII0Bbw/gOM6RLz3ZuAhQfDYG9PHyKT6dDr/ffhP7TkmtnFtHh/FXQ0PYvHq1IDvYzSGTGsTSnBxxI4aHh1GqK4FD7RCEotw6XmepS0hoZAt3lpSIG0EC4wM//CHaSkqwUKXCv+/eLciOTMHzu7lgLfYPHIVNGwgTGtH5rN/ThjLjdiB0VWEUvgBQKbpkeI1eeR3oOC3dzykDVjyURJt0FrB0uXTLBlqvS10JNjuwakNsQpvobgbar0qPN66TqkSziEGPK4bQIK4I+bnJoIxmuiChkUw2NHINt10AxuPes64AOOuIrnX8/I8+ntqb8jjf/wngsQ/jZmsruoeG0VRTK0gMGujJVY8cd6yoEscTnmMYjDAAYfCQijwVA5YNG6REPU0zmRxIubKWc8X9DwPPPCXdlz/rvXGGhNw7Vij8Rooqgb4WoO0U4BqS/o7X/ooHpd+T0Ki+B+h4DfCNS+RE+VaJ7JD3U7J/hvJ9SWjXPirJUvVRljT83Tm7JRPxqgeTE+fpgsc1eEDM/sTZi634w/s/mJ3XBoRJ67/8y79k7fXmMY95TA0mWE+fPo21a9dmfKq4F+AczKRPMoNkxjL8HZM9TCTx+ZlKX2UEFje+61Oz/jb0K5iXz0sfK3KKcHAw2s3DnXOtxQ5zIr/BWcCpU6fSKh5MB9wvK2MM/qz0weDP7GRijM+iQo7HzexoDUOv1mBjQbRA6GJ438Mi43Q9cdihzvhlphgbGxPjmPlAJoLnkUVQjvXccSlfsGAxUBrnpTaPGYExPzsyZN8Kkoj33ntvZGzKHVUECxxlhQiSeLwvdz1NRWCyWFIe87x/S6AzAYvuBS69IBU5EVVrgIKpPa7nKmiOzu8hmUJHImgz2QDxje655550/zTxAag103poXBjtx3+2hBMhALpcE8jR+VBoMKKPg56Brk6PzzZONpBNFQ+XL0GuzoCX+64Jk+gmazHeUxV9vRbHDVyfiCbGy4zlWGqf2lg29kO8ArTSvCVcv9xxHtj+YSDOaHdaMKFgpP6yTMBQckALGLJ40TJoHrkEDF2QBoa1GijeEGtsOh0CLmDkIOAfke6bFwLW5akl9qkTbZi+wjxVjPmGJ2lSsprbGXAgJ4HcGb/T91evw6+7zkeMwu8tXYRaSwpVPe5w1WoEIcDvkCrF05SfksFgg8kaSnAkS0wfHTqGQe9Q5HMusNRjkT02ccsgYjrUl+ZLyfGAF82XOzBa1iC6N26750YikBRk29/wTcA1EjVnr9uSUEKKG7mjR45gc10OVFdfkf6+grJM5iQkJJOGcVn7jouZkRov/gjwKuRRyKKa7JJMyZopNoSNa6SbDBqFv/xfUpeGPiyjowTloBq3hD9wNzDRJslOWSoSjj1288RXR/Ez+8Om9DLoP/Px+nX4v5f2hQuxQ/AEgV933cC6vHIstEV9Ez52/BgmFJ0PzQ4HvnjuLL69bj0yAd/ryJEjKesTq1VaGDThTQc/h4MdPdFNRW/bYYwHR6QOsDDiCcM/PXMKBwejCdvDY2P4T4MBZa2tojJJlo9KBLaBkzwsD5TD3+VHuakcBfp8VJhuXScdJam+tmqVSNwePHgQ9dNswlJBnj4H95XuxuWxQ/CGJIP12K4MXo9Rz4JJYDJXJjRkrc/zzwFr34U3FF5/AXjiu9GOhLqFwOe+LFXnE1dPAHt/Hl3rzx0AHvlUmGwMg/MU1wSuPxnMrQOULIoDvV1W5thwZnREEImUJzBrtHhHRfp7g3x9vujWUMKoNsCoUYwTMZ/FtZ3lW4E/+AzQ3iV9/o3bJM+SNDDscKJ/bBybNm0S96eqAlSuafTFSaoNy+/p6mEgvI/0DgIhEiNqtVjfDh8+nJ5cyAc/SnYFOHoIMJqAR94FLJ+mY8GcA9SuAmpWAhMDUiegrSiWUKfc08JHpY4OMZ5U0euln/5L/tjPxN+by6W9hU+SHVM8AfCOAt4xwDBZgi/hORq5Arj6AH7P+UsBXRzh6O2LzKUM1M5dacuqES5fi0kSuap1HvOYx+yCSVTOoywakavDM0EqMQaTQXJhFQtVKL3DQsl0khZzGSyYItE+b8KcPnYVVaPX48TTnddEwVSNxY4/btyIWwF21rBol8nNbODSpUsxYyFeGjMejK+5DyHhRylcVnXHFw7JIPnB5xIcryyiSkd3n0REIpmrdEF/EF7nsrQcO9jnkQU4JoB/+QowQNljdt6qgPf/AbBcKuSZx8zBfNqqVaumJScIeW1icRXHTSp/w8JgZdzCccq/VxZCzhoKFwAbPwK4hqWiVtMteM9ZArmGdOOLjEmNW4mzo31CRkE2AOeCN+xz42+W0jzZIySiVuQWwMoq6hlgR9ECcYuHw++IITSIbncXSoxlKBKSG9OAiUgSGkqdfNc40HYGaJAC97RgWgaobYB/QDJmJ8mhyeKmcKwZ6D8Re5/HXZaG6dnoYcA/Gr3vvCZJSJknn9/ZAiu2XYFhNrAmSNxSUiX59WLU6PDuqtXpv6nOPDnRQ9JpBhXa3Bxxw0Bj00RVGezQIKFByJ+TUiFV5kpYtFJSghskMsdTgh0Ae78Xlr9QoT4UxIRZhysOh2jB46aPk/OcSjZQwmb1e4DeS5KBuL1c6lBIAAZQm4u8MLYfDCckmYG+Aqx9b+LODpGUjNv8ZVLxyrEzMlmjEUVlwNpd6b1W+0WpeiOhSFrY6JzoPS6RkjLsdUD5dvG5Q6EgPMEr8IXo1yL5GXEOVe6fjZrJEmS+YAjjca0ZrKa6MDYQQ2pcGhuLzNUEf97T3yekkdZm0KbM743Jymmv30QIjMcQGsT4hBsNS3IBU/KWxr39A5M+w4HxcWEgRkIjmZEYqzg4TggmgBfaGtCQK72P1EHigkZljOmimg2wm0SWkeOxMtBOhdScDvTzKTFWoz3S3SOhwJBCJdFQrP+KuPiGO6Txka1K8ulw8Tzwi58CTgewdgPwjscY5aUXcPzse9LP8vXBro19LwN3PCA9duiZ8O/D1x27rE7vAXaEzc4mmoGh41LBANftou2AIb1kUqnRGrMnIvjzR2ob0eFy49TIIHJ1ejxesxClxvT3BmXGUoxbF+D6hFQ8YVQbsT5/bcTkXqCkNl5HTyJ2Fq8GViUm4FNNCLAzI1UwYUDijp2G8tibBBIa5yQzQIfbi9zeIWiajwON0v4r7TWN18w73yPdUoWyw4JkxlSILyAZb5Y8NOJhqQdyw+cqGTmWKmnWexgYvRaVYR1vAWofipWvUsxbLW198Hp9SbvnMgFfi63+3Oukagg/j3nMY+YdUnv37hUV15kWMHEOTedvGVOwMp4J0ZaWFpEgZXFJKkmjuQjOWyxokcn4eaQHXjvvrVqMd1U0CUUOyy3q0GCikqRGNk3dudfORGaNf0NlBnadJiM1WJwnEwhMknLspENqZKNggMfG92WhJV+LBSVzhtQ4dQr4yU8YjAG7dgE0GZ6LRZnJsOc3wJCcL6B3WQj42X8BS9dE/BrmMbN5mrd01hnKTrMrSu7ung6cTzZujBKytzxnpjelXzD/ViY1fvnLX+JWwqjRJjQizdEZsMA6+1p+rsDkqkjCyQr8VGTBWVWd0LwlweOpQOgvM6GQXtthymAwGwOak98EjE3Asz8EetsBSn3d9S6gIUHVOpM1vqEEs0nvLSM1/EE3Ol1H4Q+5RcJWp1LBx01CuJOhzFgFczjhn1UULAfG2qTuDJncKNs840WVGwa2xyYiNSg5Fd+JIj8ukxqcVKetzrh+WCFvIb2W9eZhLHrgj+FwOkVrOfV3WUUyk4qurIOJfEpRTQePA0bPtbAsWvhceSaA7gtAdVwVBL8v+mfcOBL7OCtt0wUTgdZcYIIkn5zYUgP2uM4f/v7aKUkPkVJThUn8CGTOjJrulDgTzERYy37BRqlbSEloEGMtQE49YK0MExod4mGOCd6UaV3uoUKhyYuwSfbyUIDXXPzjtRYLrpDYkJ8TYqebE+tefhn/d/lyfD6JBFoyUFMxFc1WkgYjvuvwBsehVZmQq1sALT004qCivr2/DXAOAPo6QDv5PBcZDOh0OSN0CM9GscGI8vIE34kCg4ODMeQBgw2SMgVlBvhMNyMWiPm6xbDppNbXbOPll18WyV25fZ3JAhqfTXfsqaLIUA3aOVJyisFNgaEC5STWk+HoUWDfPqBOA8Sr2fHicDsAU5Z9fMZGgd5uqUsgN3ztXL0MfOFPJLKB73vhHDA4AHwiDUmK0eE4g+iwHNFgOAjh2CWJoQTfj0UMhLsfGDwS29HY9xpQ8VDKxHco7PP1sbqV+E7zmYgE5+6iGmwqKBeB8GNVM1tn+RpNtkbUW+qE5BQJuhhCg8gvBza/Azj6jDQvUVZzx3uk/6cACTYGxKw2jK/Q5SaW12k6soes+KWE1JSSqC2nIj/e7B9BXXEe0HxSkBo8n2kZhacLXi8nXwSuHJWuu9rlwCaSBWkkbUTRQrwZE9tYlkYJQWsdMHw+/BT+w31iEaBL4Vz6XWFCQ/5bXpteYOQaUKhYW43lgNYO+Mdx9lIblixamBWyVAblaUhO8TqYJzXmMY9bA873JBQYI2RUPJIhGJdQAoSJVlay8kaSI5tE6a0CiVh2oczJrvY3EOifcSs8NGQwpqV3ZTaR6TXAscf1byqQ1CDpIcvb8MYOj2TS0vHgWJuJfw6Lurh/W7NmTWSumDPXPAmNT3wiuu86fJhBWfSxNwKGB6L5CRksZKQklenN0dE226BfDUkIqioowRwWC6CSqZ5M1ZVEf4xUwfGgHBNc1xYunLmv6FsNZ8+exaMkJdNA2rsXtuyQtZpO4z/bbYnP99yA0++PBPBbCipQOE3wnCq6XON4ta8FnqAfK3JKsD4/lnE2J+mCsGhTZPoseZLEjSAxQtFER8HsJLVmjET+FxygP/83YGxYOvaJEeDp/wIe/2OgJP5zqMM3ZfInbGCZCcQEzxRpArmdhE8Podd9Aiq4oVMBQZpxGagTboZRUwQrPScMM/OESQqtCah/SEoiU16C3humBFUUHoeUNDDmpMS+c/MgG6PGf9ZRnxOOsGmpXs1NISkOFawK0oabmGl1/ShpEr+YMlkV9IskKW9MLnNTM6dIjRShEpIek6WWMNg8mdQgluwEdAag67IkEbJgA1Ca4cJ053uA3/yXdD4Jaw6wUdJwFGAnx1Pfikq6HHsJuO+Dkq+GEmUNwNlXpeOWPTmYVKpoAurXA5y7xmNlYyLwSvIk7NCQEZDzV+FTIUGFAGKN5Ia8TjzddQEFBg08gQBcAV4qKvz/7H0HeGNnmfVRb7bce/f03kumJJNJJ4UQCIFQAyxl6YT+s4SlZSkLy8LCwpIAWZYSSAKppJfpvRd7xh733m319j/nuypXzZJs2eOZ8cmjjCXLulf3fvU973tOrlaPzfnhWfr/vXotbtnxOiwyEi1wnK+cOIG7ysowP0kzepIEybQ1Vp90Ow7B6ZWqwxwYgs3Th1LDJqhIXDgvBIOCUhO3S9JU9kFAzwqX8PHgW0uX4c27dohRLIBvRmSPkyx9pacHPQ4HVmVlIWNkRARYWXrKjQdlFVjOnmHW443jf8Cc+aHrNOA6A60qCzplej1rOB4wA1Kux8t5mvq36QIXa8X6GvFIiKefBr7xDSmzfXEZcK8sG45judsDdJyR+la68OqLwK9+KhEMPMYHPgbceCvwHCsoZIQm8exTwAc+Eu2JEQ+5BYy8cnUceo3tvNi/ZiDBl1sCDHaHKjWIQr+0kd1fUh7sbMzEcgCuYUCXONuu2z6G/27Yhx6HRVRJvaVsDqpNucLvpsaUlfbNJf0zxvWBqVkBVC2V1jZcj41TccONBucOzmUk56kbzT4SkOqgjAKDaqloRLO9c+GbMMtSXtHCTT1L+v0IXDNm2ZJwCUg7JMTQIECZvTy/0Xc8nHwDOL079LzxmOQfsvGOELHe8BIw2gWo9UDVJiB/fvhnxFyD0gtKtganfxnnKeGpwTHNDJRck1xCRRwPlajXWUFSeB0wchqHzz6LFasmrsM/3h6D2sV33nknLgdwnjg02Ix22yAy1DpszJsj/p3FLGYSKHvD5IfJ+mVOBAzMcm8R2F9cigjoqM/i0gLXHckSAskiXUkS3XYrTgwPCBnRhVoD+rt7xPo+ILXJpBAGbrlPYt9NxoyY7XQy60SukSiJPSNjAH/6k/SvPNbxyCPAhz986VRrFFcAJ2VKKTxvxgsoczqLccG5g+QypWSZIEPfDFbOBdr7wYMHBTkRz/cpFkiyc25MBZH9i6oJJDZoTM5+O2NIwBkMxlLINcSVFI6DlGdgbjhZ8sascXngZCqRozXg20u34cmOcxhxOTA3IwdvKklPxn+bdQTfOvMG3P5BcGdfK97usOEWBg/9YEb//IyFqB8LSW6UGSqQRxPrZMBAx/q3AQcek4LZ3HAu2AoUpV6eOC3Ing9YpEzuILRlwHBE1jq/R8OpaFJDDMKLgTGaLAc6rwIwTSAg7OoGbIf9huM6wLgGUEdfdx+YCcvAnQ5jbhrI06RX+p3Sx+AMHx5Um9K7eIkJGn7mxDkOA12nXgI6TvrT37OA1XdJxNc44CDIag0OinLt71Mj53B+rDX43O4FOPWtzV0epn/ObPeEJCRJtq5AtmZAJqMwykRbmLM21wOvPC5VF5RUAze/Q5p4ZzJ0GYCbJt0RFSujPSF9cjkYoKM/RcCjYjKomAfc+3mg9bwUQK1ZAmhl92P/84DTEVYlgx1/iyY1sgqBrTQPf0bK/qZRsUYN2PolDUWUAdo490Ev6cUqoAyGVEmCRdKPPL5emQmX1y3khugx9KP63cIknKQySbMspQKrsitwd8WiKNm/DXl5OHrjzfjy8WN4tLUtUsAL58fGkiY1mBHEIFciOL0jQUIj8B28cMLq7kamcRmgyQc8QxK5wTR3+b12NUeRGtsKC/Hatu14tE0iiN5eXolVsmoRzhd3vPYanjt9Wiyg6V/wPzfeiA/4MwyZ2UEZHRKB+aV6mLNMGB4aQ3Z2RnBEHHScQZF+fdgCZ9A5hi73MM73t2FOblnKix/q0UeWvbPf0wj5ouDHPw4F/vtHAacbULGqSESXpaZO8jdd6GgDfvmTUBCb/z70c2D+IomIiOI0vRIBsmYjsDGJfs4x9IOfAX7179J3ItRK4PBOYP0WQKcHrrsX+MfDwOigP/C/DFjh980RBEGMDW8SVRoMjv78/F4M+Cs82Rdf7W3AhzJyUMtKsIsFGmLrMxJqjbe2tooMXFZVBKqYdu3aJQJZ3GTU1dWFlWwnAxIjlKpKmHlYswo4STIYsDpdMGg5BofkJRmMYnCDwQGado676WE7+rdvA3t2Ss8XLgK+8V3JYyMWmsMN3cX952skNdj+6p6WPKGE/5YNaKDfU4YkpRhA1gJJforkV4AUy1oMdO8EPBb4NDnoaDqNsgJZ9al7RDIPNyexztRkSjJTPL6ccKNfRySYnJK9EvtOdEwJ8cAS/yeffBKXC57qOIbjw20i0YQ4OdyBD9deDeMk5XJnMYt0ggTzjh07BMk8EbkMZo+nAzx+W1tb8uTyDAEDzJQfDch+XhboaQaOPC/tNfLKgLW3AYZLUx4sHtItDcPEjMlUQgRwsL8Hn3njH7COSlW+8/ML8esb3owMjVaQJlxTMXmKlR0MunL9lAypwbVNIo+P8QKNTE5JVE1y0cDvFZm8yXgFX0vDPYmqBh8eAPKL0ltBse0WoPEs0HBGes49xXs+fumQMhcBbJdMbiLhRxIjIC3FsZivMzAekF2LJx0d73NJkqRS2UFpq0hyu7KyUlSIkDzlHj2dPnRTBp8XGD0DWFvFHq9rLBd5FcvTWpk9HsgxcG+YSpLbhEgNBlm48aRx63SRGkSBzogPMiswzfhH13kRoApUgBBPtJ/BzcVzwgJKVaYa5GrzYfGMQq80IEuTnVrAKbsEuO5jkr8GTYyZAT5TQVPh0muBwdOSMWVGFeCJxcr7JMIm5mcskLILHV2SpwRlp5g5mAo8FsC6X1bdQtPffUDmdYBMVsYHBvXrgs8t7vBFCm8TiQ2lYgaU7rUcDREahH0EOPoksPl9Cf+UGRkcoFkuGyhlqxuN1tnO1OQIc2I5GFBKuBCpXQuM9gEtfs8cYw6w7i1Rb3N2t8O170lomO0qtIXOAI/+HHj/F6UA10wDrzFNicd6pAXISEf474NEwhQvGrLypUcskBySZ3YTVr9kTSRIuN7+aeDEc1KWOxdt9BM5+bzkMVI0DyhaD3Sz78hk0YxSMFGjrITTK2XDUZe/1MBqNSW8fvrB4tbjqU4pU2SuqRw5umL0RxgTc7ycn5ktJADjGVW/u6oaf2wNJ0d5hZMlNIhAmXUiBGSdYr7OAUDL/lAKeJqjF4dxsqpIYsiJDDkebmrCcwcPcrUiFss8+kdPnMBba2uRpdGICg0+2FdVCjOyczPR09kvSI0AXL5hjLlbkKmRSMqTwy14qfs4mjrqUZ9lwRJnD24uXpXUPMNNDrO3GBCINKnkZjudlRpJgxsJ/4ZMoGcYGLIAmQbJ/E5AARSmUZKwqTH2/aw/A1y1BdjxWvTvXnoBeP454L4PSx4bibB0FZBtljR7JUduoKMFeO1Z4Ka7pD5+9/3AcJ8kjZcpa0OmamDkrCQ7FZjXDOVJzY1DLpvoh/LWwGqNs6O9WJWTHmmxceG1S0kGhKYYUCa3hul45BHY//pXXMONxi23AO96V7A6ce3atSJ7in2cRrWsRkxl00GyPqmMwQWUgCS7cgTQD0Ox/PqgnwbBzMPAhoQmguNWfjzyMLB3V+h5fR3wkx8CX/9W7PfHkpkKvGYd9JPRMrC/DzaFkxokvcpulogNVvawSrhvnyT1SY+5ng4cPHoWyhW1KKG0lvRBgKOPo3ji60MCv/x6oP1VwMU+qwQK1wAZsXWyuUHkdXrwwQeRbnB/8bWvfe2yMAsfcloFoUEE5EHH3HYcHWrFJho6zmIWMwRcZ6xatUp4a3Bvn8o4zHEzlezX8cCgCZNCLkVSg/PRZYPhXuD13/uDxD6g85z0/MYPXzba/pSoievDNUEku2dJtJ7/wgtPwm3UwVAurQPaocDvW87ho3OWiL5KEpKJGMwAZ4JIskQKkxIncn4kM5g5HUuGh32fBEu6r2XKuOYaYJdsbcZ2unVr+gmNF58Cnvg/aa9BH7kPfQZYnqaqVX7eP30BaG0E7DagvBowTQ9R6vC40WUfgU6lRpEu85KoKPANDmD/N7+GzRoF1CWlQOH7AT+pwb0B2y3XqkK2N8V2MBHZ5kjpKYL94oYbbhA/c5/Dc0rFB+eiYPgYIPOSPn90N+obmnD1DanJQU0U5Bi4F0i1DU5o5A2QGgH4fB540Qefzw2lIhtKReyBzel1YMg5KLSZc7R5UMWSOZpmWD2uMEKDcPm8cPu80EScX6YmUzwmDAZ8jTM8mz2AjHLpEQAH78r5QKvfk4CbYK0OWBxDticAfYX0mCg8/TGyWqlZ3gioCwFlLnwKVxihISF2xlCBbgZkFwy2hz/ntbT0A25XUhrbZHiZvb53714RiKHsTjSiBwEO5gkzSHhPV74JWLQNcDsBozmmnIi2oxEaLhYCx+a/fZ1AXxdQOEPMwgIYaAJOPiMFfxqaAXOkB4wCyKuePqPieCiqBHpaZGaySqBgnGvJa955Jrp/dLByah6QuwgwlQLOYSkLVx8KrGoV1VAo1XB5O0XVRq62AgW6fDi9duzuO4U2Vn34cd7ShnxXbC+WRFPNLcXF+KeaGvzPhZBHzw+WL8fcJA26UimT1irNUEIrqjPkZ2hQRSwc1LymocomAU3qbfb0wIAIuHll/YneJM0WC5ZnS1nz7G88f60yCyZ1IXzevqhr5vAOIRNVsLgdeKn7BNxuD3yUx9GocXa0A7WmIiwwj39+XKwx450Z6yRSIq8hsx4vCjhGLFtGp3epqoESeY+8AXz0VkDpkSQZl90IZEtkW1qQHSfAffwo8Pn/BwwPAX98hNHwUEZXoOLit/8DvP4K8E8fA5b4M2mYffr0Y8DpE1JV1B1vk3w6KBPHCo0gfEBvV/hcnxNOLgWr+EpuAoZPS8SGNhcwL0iYhcV7/EJXHQxqRfC5wx9rMEZU0k0J3MPAyI7Q3KrQAmZuFsdfDzX/9rfo//73sTrgnVFfzwgY8KEPiacMnKWiVRtrPmR2MT9j3I06r++CTfDM3QBP1j6J5IgTUEuYiXSUlaOycZft6FjIsyMKS7YAr/0x4rWt0r9tB6Lfz8+OlRxA6adA1cXwGSnhxI/+wVEsmlsKuyOi6ilJ4klAlw3U3ClJTpFEGWdOpJkpA5lTkXHGz2SGHAnhS1FbXw67N7oKjRUbsV6fxSwuNpjZyqxU7u9ZEU6/i2TAcTyWPO5EwL4/4wM+cUiNi1YROxVo4xzjX2QE5qXhHumRk8Y120UE5xia1acTJA0mm81scbswaB2DPjeU7EJSvDki0Y1B11S1+k+dOpVypQXbNTPWt27dGrUn4/6C1bednZ0TMkdPK97yFkaNgd/9TqqoJaHxwAPpPca5M8Djvw89dzmB//kx8J2fAebs9O2dqqb3WrbbhvDwhX2wUhYdwKLMItxbuXb6/G2YrOP2J02J+F5iewO31YpdH/8gViroBaWS9mDn64AH/zN4L+bPj5ByTQGssDh06JCIm/HnZEB/m/H84Eh6puIZeFHgY1yyIewljUYFhY9JUphWUiNVTJjU+M1vfiN+JpHh8B6AD/4MBR+DOMuhUhSG/c2oawSHh2jcLG3EjCoT1uRsgHaiPgspwuPziYoMXURQd7G5AEeGusIyH2tM2dDMxIzziwlOZHd+CNjN7PAmIDMb2HyL9O+UHTOieXIupXSJh34VFwBlDqArjIqumtQKDLnCg72ZmmroEgRgpgVag3QtI7OJh1uBvPgDYWTFBhcR7PSMV0cW0VQaozVxw7Ie3f2Ai9UKzGBnpnnEAEtt9HH8akwGI2wuFwycROSYaaw+F+Rnnvdns8qqNrIrgNFu4RWC3GpgocSgX1SsvxHoawc6GqXnlJO57p3j/IEitnGsvDPosqRH5DsUCmhQBgX7DxRQK4ySUaTKhE77YJThvAdWFOhM6HfQONsnxkiOo8uyxt/Y8DN/uWYN3lddjWarFUvN5mDAPxlESq2NB6VCjWxNJUbdjf6qDRVyNMuhUUYQ7LolgJqkhloK2mlYQZY88cpAHnU7CywWeFkh09UleSzk5kKjUIgKFfnmhpt9Xocc1VJ4vRHm7VBAxeAwu7/LIq57Z30TSuZJ35nXecDvgxILDCKwlJUZUrEIDYLnyrL0yOqNacN3vgN86lPUH5Ker7kKuP0zUubUVIwXi2QbVPkY29stGYPz94/8BfjRvwE7X48uVWelxwNfAX78C6CiEnjoZ8Du1/2BZiVw9CDwrz8EDCbAxuqlwDEUEtmRDCgLmLs6pa91eKgdh4bk5JQCOqHipcLVBUl4m0wWY3vCkwV8TmD0KHC8F+hokK7HupuA0lD2OYmX9r/8BZsizMDx6KNBUmOyoBQqqz1YpcSfExl+ckPPvjIeEmZzZWVLY4c8oYCEVzxULgK2vwuoPyi1t9rlQK2/6niohbpi/uHcvy7gvwUJZDJF2w6N/2UluXhxxwlsXMmNsCLUzrJSlNvksUm8JQDXHjQKnYpSdGZ98rOZuHGpkxp52gyYVFoRKAiMFJxDq42paTTPYhbTBfZpVqpx7UBNciZPJarCmGj2dyxQcoKkaUCm8FK6bkKa93JBvPXZTNvnTQKpVoZOB6lBVQWu6bP0Boy0tEGh00FbkAeNTo/KCOkv7ukDJEMy8m88N+5hkq2oIGHBB9dE69ati5lkRpldkh6pGvpOCXh+XFt+8IOhNXu6ceFc9PqPSakdlPq8iDKwkwDX6r9vPgibTAr4zGg3dvY1YFvhNBhce8YAy05pXyGgATI2R8emInD6+WexGi5kBhKCub4eGwUOHwC2pSeuw/mPc+HOnTuFjNV48oKMs7Faj3uReGCyY7qqGqcUvvA4kMmgR9fQ9FUico/xQfbj6SI1GHDija6qVYQIDQEfnN6T0Cu3QSHL9Do1cjxIaBA2jxUNY/VYZB5/g5mOzvqrxtP4Y+s5QWyszs7HN5asRw6rDABsL6wRppsvdktBlwqjGR+fu25Kz+mSBa/Ztmkyb/QOAL4mQK+VtNddfi12edDWS6NOhxj/5DDRYsCjgNPLYKcWGepKZKpT02WbMlSvA7rqpPMOQKMCzjwHrLoHMCWXoUTtQG486s7V442Te5E5Pwd6vQELMmtQa4oO0jI4zD5bXaYFrAdD15EeA6bNgDp506+Ka27E4R3PI4tGJf6qneyqWuTnz7BNCLOp5dc5AJUW2PLR2D4aFwuUcbrjw8BAt2Qmnls8fuUOz7tsKdDmlwoLoDyyEiUabq8dvY5DcPvo7wPolLnI160UxAA9NNwyg2/CqNLic/PW4E+tx9FmG0G+1oi3VyxFttwTJO5pKrA5Px/JK1KGwBLNgCzMeGCFSZ/jNBSKPnFZpDvqgdtHWZcIjVmFCm7VHPhMCyZUWss5j4HT+T4fdigUeL6bnj82KFpa8F+33IJs2WIlUJJN/VpmhC2ffw1GPPXBYKQS6uC4ZNYYMTYgeYLoTIZg8CtLG7354IafZefc0DBAy7EgHrgAY/bUeJkjUwoGJv74R4n84eZxqjMweU9ZrTEQqDbyB38bG4CvfE56afU6YM064I1XQ38TAMcEtv/dbwC3vhnYJZOrEgSIG3j9JeDejwC/+6m0mSFKK4Btt05pFpUKCnj8odHAKX+oeq3wHJtSeAYl6alIWLuBukPSNRsZAJ55CHjLx+HNLRFzTUNDA2piLd4jxpd0ZMhSLoWZgiQ3uPmOB8voKMz0PzlxUPI2uvpmYHHIW0NUX0USXZF49/ukyh+fIvR9Pvjh8f+mfIH0iJW4Qb+wAEHB22vIA/QJKnqN5cAgx36pPRgNemSYc9FoKUXR3ErJ94JVHXIj8TStp5kd+sQTTwjt4qkCP/vxxx/H+96XWJZzJoPJUe+s3IBHWw9gxG0XRPWNxYtRm5FY+3wWs7iY4LqBElAcUynBwWSqeGAQl0FVjg/pkCwhQSI3DOdn8vgzWY6OUkYBLffLAhVLgNM7/PO1f6+UUwKYL4+xi211KtoTSRLOkamaCxPd3d3CA48VUj8ozMWXju+Bw+OBvb0TFTl5eFdFePY+1yoMklK9gV5gib4vA4WJ9lQkPZidTrDfJ5rnGcDlZyb07JxOiI3gFO3tmdQSSyEj8xJRYYkBh9ct5G3l4NXrsMt9KqcQ9hP+dXAAbsB2HMiIloENrM9ZoW3v7sHyWARiTAWTiSOwh6YvBiu7SLzHAuesyVSGzBgoFICxCrCGVDYWzCnBjicvYN3w8LikTTqIZt7blpYW4a83LaQGvxBLxJlJVRnIOAsDJ0ESGKENrZX+CDIwK3XMHV5K1+8YQ7t9UATRak0FQqZqsvhbxwX8viWkC3ZsuB/fPH0AP14pdRYavN5buQxvLVsEp9cjTG8jF2WS1INLvFebhKHnZYuhHuDQS4B1BCisAtZcJ2mGpxveUcBNWQZ/BqMwljXIWFz5e2MHSNi+GHMHnEKCZsZoA1J+bNnNwImnpOf8boKsoVRSc9KkRgAL5s1HdWWVMEzlYmp+cXXM78rAJxcrsAU2CjIW1l4HZCTvj6PILcSaL34XeP3vwOgQUFqDc5XLIM68q0UyvC4sB/RTHGxLBGccT4qA9EM628TYIDA6AGTkAJnJE0Rh4HiXF11lExeLtkv+PD3nJTP3mvVAQZzgtc8K+Ch95sWAcxBuPvfD4R3AsOs8crQLsSp7Pnb3nwiamhLLs+YIAuOjc1KfYOLhlZ4e/Pz8eTi8Xry9ogLvrqyMarfJaKq7vHbUj+5Gppqm5uG31Oppg1kTvcDg4oQkQyqLD5IT3DiQHBSVLgoFntm6FS91d6PbbsdKsxm5VqsoPaUZGMFz5yaXfTOgDan1mGD3DEAJFUzqcqj9JbbWgRFUDOlgWhS6fzWmQizMjJaeIqGRbFlmQE4n3dloKYGZ72UR34NVSZQxYF/hXJJOfPBjwA++LWVpMeDORbBbNk8cOQgsWiyRFs/8PX5fjJl1qZDKzRcsBz7/XaDpnDTOzV+alHzgRGHWGKJkMtnUy43TkBnmDffTEeB1HRiRZfNI5NHJf/wNQ9UrxEb7mmuugaq3F/j3f5edtAK47bYpOc2SkpKgwRzJvshsSZIslY0ngROsOvGjsQ6477PAohVh671xwWqf//wF8OLzkjzZlmuA5RP0eytdDrTQG8NPaBCOMYASACTf40FrBkq2A30HAY8V0OVhydYNOHGmAd6clVMSrKH8REdHh1hHsEJsIllUyWLbtm14+umncTmgxJCFT827TlRr6FUaqC621OUsZpEkGKikHBVJagYaKFsTTxqKJsVMREnGrDgRIpMwaIqccFyeAaTGjJcVSQVcm21/P3D0Rb9ReDmw6qbY2e9indUB+EYAhR5QVkQrLcww0NSdCRHpBj+TFRFMZkol0M+AKMm8gDHu+txC/GHD9Tg+PADjcjUWqvVob7wAd1FR0EeMvhqsPuUxx/MW43qMvgLcm4yXBBWY59nP4wVuI8F+mZSv2eWC1VcBrz0PNDdIfYGk3+btQFly8kQzEVqlWsjtu2SqFowBmNXTRFSJ+LB8fOd4Eh4zJtinmFDDds+qCePKlcDZw8AwE6+YxUz1BS2wPLVK+GTANTXnQs6Dy5Yti+of7AckJZOJK1wSfnE5a0QSKGytYizXZC/EinU1grCdClKD14/VMLwur732mri/EznOhGcdbljJWt1z76Y4Hxu+oTSojBHEhgImmh36cXqkA092HAlKn1QYcvGOig1QT0AGyuX14qfnjuO5rlY4KTEjA6s1Dg32CikquVYcjXH4iITD48QrPYfR5RgQz2tNJdiST3mtK0yeamwI+PsvpMx3LmCo/z/QAdz8gfQz4t7O8OfCjNUNeHjNI0gMBUvBRsM6hi3sLSSkeqFRzgDpqQAMZkAbo+tNUPKMAUsGOWkGTKKRmw4OrPIgcdDILxYxRAP2VFFcAdzzidDzM6eBP/9cMg0nKEfC35dcxIk+XsatOQXiIBmc3Qsc/EdoUl51g6SjPtVge5m/VXqMB98Y4KMHknQ9nJQ7iYCD2dgA5mVWwKDSocnaCRWUmJ9ZibwYElaTwfNdXbhlx46geMrTnZ3odzjwmYjFANsvJ9BAVQIzAakfK5eH6XO0wiMqAGUyQKEvHvP47AecLLu6upKSOKDGMzf1kbI1NFm/Sf73ublh2YUEMx3ZHwN90aAqEI8A2tvbxSaCWV3vu+mt6HGMoMcxjAy1HtXGgiiih9noqWwguOFhFvuMATfHh18EGo6Ebs/CDcD6W9M3j6zfBHz3x8DBvdLzP8r0bwMQlRsPAO94F/D1rwAtTaFFMcmJrdcA2TlA9Ryg5UJoLCGJvtpPKOUWSI9pwFW5VTg02Iou+6jI9ibBcUvxIpimIqkgEsoMv+xjxHh6pCnsaUPPILRzKkX/JAkoSIV3vEMih/70J2nz96Y3AR//+JSdKokNjhWHDx8WkmuBAAE3EZ0dHdhSdzjiLxTAnlfCSA3294T+UzVzgA//8+RPWB+DlHLbgCHKUSYwktYXAqU3St4aSh2KFQoUl09dRRbL6jkeMXBHgndcM/VJIiB/w03iRZPOSyN43Uzqi0gsz2IWkwCTOagpTqkZVooyqTGyKoGExlRmqc50UoP7r8thrApDbimwPUG1HO+L+6Q/acq/qvd0AJqNUlBshoIEAoOknGcCbYuVzemQPWPQ9eTJkyKrO5ngJUk77hMiJWRLDCbxCKAgJ0ecb2APwDUKiYp4a5WARC1JFp5LMqQbpaSS7cdMcrisqpOSAde1n3sA2PUKMNgPlFUB6yaiRTBzwITtO8uW4a9tRwWZwThspkaHawqmQXqKoDQ8173yfbwyvK1yTU45xOuuuw7Hjh0TbU/IzX7lW8BD/wW0NQP5hcD7PwrkFUzZOo7+fUxePH78OK6++urgHp2vJeNVw/7KSqjxKspnBBT0hVwjPfzYtGnqfF54HbnH4PjLdT85holgUqTG1772NagVP4XX1w8vhvy/UQpPjchgzGLzMhwZOgCPnwnUK/WYY5I6jMvrwdOdR8O03NtsAzg81Iz1ualv0n7RcBKPt18ICE8IyE9Hq1SJgFQy2NV/At0OKeBHNFo6YVIbsDYnRa3iSx3nj4QIDYL/tp8HhvuA7BQGEFEWxsHLX32RLHi/tIsB58ngwOdTZMKrrvQvpqSsUbuXFUARi980rIXZbvcPnEObtR96lRbrc+ehKFZAIhmwGsNcCoyQvPG3Um54JzmBcMFCJpkDAglH/hwAA6fCUMzW6zdjkl0U9eQlYQxnDqHh0D7MCRib2a3AE78G/vmbuCig+XrTPimOzzanlPX3vDRq0A91AwefC3/tyItAcS2QV4oZAR8Nn0JMHy+FJ6JPqBShgEu5sVA8pgo/rKsT/8pDpA+ePRtFanDDwQ1BYIHNRQ0XDvKqg07beQy5utCtBDL8PDrNtnNyzKgsiS/Fxc0nNx1cwDObisfhwp8ZgvLP5zGZ4T0ZM8FY2VosraR2bn5xLlZtWAa9KkPMmUX6LPEYb/OTrLEyiUySNzNGv5PGk3sel/ojZQVZPUET+rP7gMrFQEkaA7LzFkgPzlN/e0xIhAXBTOl8/5yVlQN8+/vAr38B1J0BcvOB+/4JKPFXlnzu/wG/+BFQdxowkqh9L7A8JFc0HRh0WvFY+1GMuazI1ugwP6MQK7PLMC9zmmQgVFmAfhFgp3Go/zXtXCCrB7A3Bf0dukfHsGn7reiBLqSvznn7ve+VHtME9m0+du3aJfoviQ5uIpZwA/RsZGUnAzDhiS/8W2Zxjqedmz7EWZwkKp8Xkl+ngGH69PgAdSZQsBXQpDdTmOMjg5gcG7mBJLH76KOPiqBmspmcEwGDNsyGe+ONN3D33XdP2XFmMYtZJAeuTzgGMLmE4ykJ40AQn+Ns4D1TAcrx0BuM/87UaohUM/MvH1j8e3D5fDYqVW6okverm25wnc+98qpVq4KkABOd2M4S+WqRLKDfU7z3sR9wv82sbq4/gklNBoNIdJKD+wCu02N54iWDWOt7rv0Z+CU4jyaqzpAnWbGPJduP6bcxEYmYSx6sBth2My4nrM6pQL4uAw1jvdApNWKPYZyOpCnCsBwYo6eGf5/GeIRxedhbuF9nW2NVEkn2oCl9UQnw1W9PKxnKdTBJFZIsrCjguMGAfDKkBuevmU7QTxeYbMYENI5XjMHcfPPNYuxhxcZ3v/vd6SU1tm7dKrI2env7UVCwxk9quKBEFhQsP4xAliYbG3O3YtDZL2Sl8rQFUCulw4+57fBEbOIUMpPUXocF+/rbJU+MnGJUUMJnHLzc3R6cWsOIDf/z91Ylr6feYeuPMs7tsPUBU7efm5kQG/8YxsQRAYFxQckbF7Ua/RU7iiJAvUIKMsmhLAa8IS03cVxFlrRA0udJfhtQw6Mcgg+n/NrWPnh8eoy5o6UytMrJZZqzbT7XeQQXqCHuvwqttj7cU74FeboJBD7Y9pbcBjTvB8Z6AF0GULUBiKGfPxFwozEwMCACwFx4sa1z0OXr2eYVgHW/pJNOqIulgFUi0MD6zF6ARlI1y4C5q8KYwnKfDf16LXrHbCjIoFSYDxjul6So/P41UwpmUtuGpSxrHvvgn6Vz9bdXq9UBnd4ILLkVyJxgwJ5tvX6vJJtjygYWbAIGe+KTHTOF1ECoOodFGkYlMCr2oFK/odlwlnbqMzI4kR8YHESL1RoVyrN6PPht03mcHB5Cid6AD9XOE2Z28oU+NxCR5EK+MxPN1qPiZ4OKD4Ugbax9RnRfUKJXGV454RNSRN1wOoeFz3Pt/I3Iys4WmcfsJyQDuagnOMlSUoALmHRu1tkv+dllK80YdLWgbqwFGoUOczLWwMCMlXHA7Pdkz4UVLhPdKKUdDiuw94nwYK1aJWX/s1GO9KeX1AiA1+rjnwX+/cGQETOz3N72znCT589+Kfbf05/jK9++aP47TPh46MIeDLtsojrD5fHgxEg7thUmyOJPN3TzpLnCOwYoTZKB303VwM6/A52NGPWpkH39FqCwAlNHh6YGkvqc99jfqAstKkdWrAeO7Ak3wVvpr7wZGwP+8Q+a1sB77bXAVFc4cX6iF4nL5ycmuM5hpRD9MKJl58JgbQWGmeDhh3sM6H0DKEljxZO/0otSUBwHOUZyDfH6669POIsqFfAYPNYsqTGLWcwcMKjDLNNXXnlFZGlzncYkDVa4TRU4dnPtx4DzTCU1SOzMGJnj6USs6n+xt4j1+swCpdLkxASzhJOp1CCxRwnbYPKGDBbXGHrtvRgbHUNpVpkgN1hxPjQ0JP4licHjMqDHY5OUmIj/RjwwQMh9DJOfUpWdZTJHPHm5WJgNzl5eqDTmiMe0Q2kAMq8F3AP+tXBelHwdSeOAwf1M8K2g3wz7MdfI/DeVyouEvn1XCI4ePSrGR87pHLMCnn3cs5FjmFZSgwMfgz3Uvnr7298OFRJLYuhVepQYojdrlNpQK5RwywIe3MDnajPQah3Gd87sEJt7TpRPddbhs/M2YklW/K2zJqLcj1tGs1qDNTkF2JRXjJuLK1PSmnPJAvecqsliXnGoXAgc9RurEtx8Z2QnX6VB3WfrDmkBxKgjzbHRDXgbAVVESRPLztRrAU+d9H5FDqBe7Dd/Momgis83Bp+PWYqEFKBQKezQKmkQHh4yVcqy0FPF0aEWvNB9QgRMA+Cne30+nB5txVbd4ug/YlsdPAWwwkeTAeQuBVQR50C97Nqpk28gY8yADjMdmXG6evVqsZjKzl4MmLYAPru/xC+JzCJW5Dz3sP+JD2g5K1ViLJOdvzkHeSYj6nsHJFKDoJE0MxomC6cN6G+XfCP4mef3SEFSarzO2yRpkB96DLD6q8WyigC3MyxwdbyhHWve9SWpnHoi4Ni0609AT3PgBeD8ISAjV2rPkZJOCYjX6QUXKfTRAAZcUvtl/Ixt2KAuhVldC7Uy/dqycnCy+vChQ/j1BTlZKYFVc6V6Lb5x6pj4mef3ZEcrHpq7RGwcAh4VsZCtLYLDOw9d9nNCdo7EZo1pNcoqokui686ehcp9Gj53H0waNeYtzEFr5y709s7H/AULBNHA7GNuMvgvs5O56UhF+zLRYoVZz/zMrAoN2u2ha+HyOdBoOYrFmVvStjFmFhWzSGYE6DcT6X/E/slry9fN6dvU4eRxYP8eprAB198MXH0tUFoGHDsCGIzA1duAjBTJ6IsUrOi0D2PQZY2Ye7w4O9qDgiQIdbvHg0GnAwU6fZjc5oRAwk1OuumMwHXvFH37+O7dIuMxLtgvfv1r4K9/le77HXcAH/sYI2SYSkQFKe56H0CZURqFkwDf9iZg7VZgaEiqJunsxJDFgkVPPAE88EB6/T/oI9PVCGh0QNVi4OjfgZHuENnmVQM5BcC86wBtgvHY0R2RZEIJEHpx2AC1EWBFtLNXmre0+ZJxeIrgfeWDWtwcCwMydgxmTjSLKhWQTGE1+CxmMYuZBa6Jrr32WuFfxLGBZCeNvJnpXRbpn5VGcO00E/XImaV7ZVZpcBriWjtSGppJIDMkoSYBAZBQajIGSGZE7ktY1dja14oTQ8fghReZ+ZnoMXRh58GdWFSzKBiIJTHHLHPOrew7qVRSM1FpPJ15VpAwe5zyjZGeYsmA/SrZgCuJnSuSxJtFEGzDwy6HiN9mTDbWQxJDUxi33afaR6cD7GMr6euRAqYs2dDtAlpPhmJjBWn2qpwCkKQigUFwn8bx59VXXxXVZRMleie1o7z++uvx8ssvC1JjMtAoVbijdBX+1n44aIZZbczHmpxq/OTcPuGRIQ2zPih8wO9bTuDBZdfF/bx3Vs7Dj+ql0rsAPj1vBW6iD0CKWJOzAG/0HQszzl2ZPU06czMJBRXAde8Cdj8J2C1Afhlw7T1SgCARvDZg5LXwjA6vX4LEOyithyKhzAOU8WVWfIjtAxG+1FVArciAikTIBNBqHcBzXcf9dz568o6sLpJOzAd0vAJYu4LngLEWoPLW8Y0/pwAcJDgwUFOTxAYlHaj7KjKdUpH+Ov6G/wdZ4P7Ya+GkxqabgPpjUPYNhgKAb7p38sHAoS7gtd9LxIZgFDWhIFB/KzDSB7gtUpVGAKM9UcelZqRGOYmSP5IqPX4NeRGAomyJDRgg2UIpNf9r/F31MqA4jRJXUe3rODDcCWiMQOVqqdJnPChqha/GmLsneAfJw0hczPCUExrE3zs64hIa1xcW4tRIj7hHbj8R1Wy1YK/XhQWjo6E2GwdF+loU6KqEt4ZaoY270FZhGHOr2QdDxNYczvuGwqD5ITfnzLwgqTGRTUG8Y3OjQA1OloFzc3PBEj4/EU6v1f8dNJPOjApsmCbyHaYExjj3j9+Hnhrp6i+vvQT8+PuSOTk/+6m/AT/4T2DufOlxiYFVrYH7aXFLSf1sYQ1jg9iaIKHuyY4mfP/sMbh8XpHU8eCyDVibpAcI26HDY4VaqYGWWVRxwPNihg03+OOabj7yCPCrX4U/Z5+bQn+NmGDV4N0fkB5y/O53QHe3aDNKYdrtAx58ELj55uSIF2GczrnIDRRwPIlY1DQeA3Y+Fpq7zu4EApc10KddbmDOdkmeMhHijRFMuPE6gP7XAPeI/71aIO8aQJOd8li2ffv2sNcYtCQxS8JhqsGgKQNALPNnVtwsZjGLmQMGmdauXYt9+/YJQoPjBQO7U42ZRmgEAlWXnZ9GsuD8ol4FuFkx7U8AVS2Q9vAzHEz2O3DggKjknCy4T3EWOlCSG+7ZqK3VID8vNKeTlKD/DBODU5WG5d8FZXciQHUG7jFYoTHRdT+DrWzLyQQUmXA2EzLmZ3FxMOJy4D/q96HBIql+bMwtwz/Vrp588lQEmJzLSsAZ70GRJEh+k0xNK5jE++rvgGFZktTy64H5kx/XphJUkokEOQVyCxPFpEgNGrZ8+tOfRjqwILMEH67dhk77EAwqLaqM+SLINeS0B4kOgj8Nu5hlHh93ldVAr1Lhuc4WETS7o7Qa1xWF6xgmizkZZcJDocnSJc5nXgZ156Y5C5sNdJRSPjbAXCBlq18MVC+RHkTHBeDYLmnzvmgtkDOO6ITQ3aYUkAxuyo6wBU6sikIBU0w5LL1qHly+Dnh9DmiV2TBrFk84m6DZ2hc0ZaX0GYfqwGdRkmyOKUaZqr1PRmhI74RrTCI2sqbOZCceuLhhcJPgopsEB1nQlK4JB8xIwSDxmgwmM2zv/Cy0e14F8rKA6oWSmfhksfdvQKC/kzwIC+r6gK56yXRd/n3iScUEPGE6TwMd/iqfsmVASRLyWy7ZJBQrrkyGYO46oKgGqFg4dZndda8AnZQd8X9+91lg3bsA3TjEHQOjipXwYFdI+s0Pj6jYmXqcGhmBWkZaBPD81q0wa1R4257esNfF2O9yYs2aNSJomsiUVqlQiUfqZfJkHOxhxCo1cFkCyZLSVMAsqVjl3gz8Ut+f/S6QoaHhRjBi/FJACVWC78D+zAVRorLyZE3Qp5XUWLoNOPlaaNGVmQesflN6CcCHfin969f4FvJ3j/4f8PmvYqbD7rHj6NBJDDiHoFNpscy8GCX6XJTps1A3OgRnwM4KwL6BDizLaseanNhZsaeGB/CdM0eCz0fdLnz++B78bdNNyE4gB2hxD+GC5TA8/jk7V1uOckPseZQbW/rQJNwEP/ts+HPef7423aRGPDAY5x+bitVqHLJYsIpzPiWpEmVVsY3994+A44f8WoyVwGe+JkmXBRI49j4p/RwY/4QpYgyyqHUf4BmVKjvL1gFZcebQzHmApRHwBtZV7E8LJVJj6CDgHg29l/dx6ABQcENKl8RHA3Kx5gltE1ilwTF5Kv00AuAxGHTiMd/97ndP+fFmMYtZpA4mSzEQyn+5LmFwNWBknG7MVNkOrsmm6jtfElDmA5pr/X6Zuvik+wzMFOZ96+zsFOv+ycLBvUSC17guJ1mfisxTAFyDxVqH0RukublZSDZOpnqC14JrunjEiRzs88l4CMzi8sSvGg/jgiXgpcw9STsK9Ca8rXxRWsd7JtHQkPtyARMbA1J0qcYY4qLhkCSLLt9jHH8ZqF4BaFNIYJ4BIKnx05/+9OKQGmxoHEj5oHEL4fY60GU/DZtnBBqlHkX6hTDQaDIJ5GhN4kE4PG681tsItdILnVIBh1/ehUHmWtP4GyoO6reWVIlHOlBmKBCPiwIu4vY/CbSckJ5TumDrO4H8i2jAdf448MzvpEAub8uR14G3fwooiFN27N8cR0MBqGLrqHt8o3B5e0WgT6MshjLCp4W+LUrfYnhxOhgYVCpqoVFWQ6eqTvkrDTotGHCOwqwxoMBPWumU6qCfit3D54x9+mBU6bC1YDEqjDEWJcEgQ5KvTyPYL8iMcpKIxZDGBaUyupthdbjQOTwmVU2Vz4fivORX4HY5UdZ6Cp07X8K8/GzAsAEoSENmpTBD7Uvs9E5vHsptBKGIH9xvPw6cfSX02lC7JNFRmsAImtk31DpniV+886lcJJEaUwXKbAlCgwhk99qBzlNAdWKzNrUiG26fJaqSaTowx2SKIjR4l+ZmZCBHqxWZ5GNuV9A8nNJYG3LzRVYeM8BJGEzG8FpIFqhZLSCr6AkgYn7iwp7VGvw3lZLXgFxV5HH37t0r9KDlJaeF+hoMurrgFkSLRG6UGeh/M36WCzMiqW0/niQX0dTUlDD77OxIP97obYVKocQ1BRWYmznFgcrFW6V5a6ADMGQCFYujM9onA7avMVkwNzB/Dvn9g6YKPG7dPqD+gPTznFXAks3RXlHjgJJSu/sPYMxtEXOOy+3C3oGDuKZgE95XvQGfP/68/1Ak2KW/2dvfGpfUODrUL0j4QH/in9g8HpwbG8a63PgJCD6fF02WI0FCgxhwtsGoykKeLjwxhBtz9suksvVi9aNANc0LzwAvPSe9tu0G4E1vnn65L86HL7wgfqzS6cSYtEOtxnKvN7F92pOPAicOh553tAIP/RS4/4EQmS7mDRmcntgSZ2Nt0rqKOPccsPAOICMGOak2AcU3AaN1gNcJ6AoBk3/ucXOMiyD/A1UbScDn88BLrzIENkn5UIK+Qmqx4WAy03SBx+IxZ0mNWVzKcHs96LIPiuruYn0OdJRSvUxA42PuKbheqqysFIauXOukWlHBuY3BZYvFErZn4euU+6Ex+UwF9d5T9S+47CAScqZnP5FOUNOdEovpIDXoHdvrYEW8NP9S4YOvRYLBTMpBB/z7JkPqMTja2NgYlIecDJIlRCi3FvA3mMWVibrRvqiE89PDvUCaSA16NLEKkNWAlxsYOz99+rQYAyjbO2kZN9toKFkwCB9gH7ukSA3GLViVMxkSa1KkBiVt6Eb/0ksv4YMf/KDYEDdbD8Lp5aLEB7fHjmbLftRmbIY2BYkTp9eDn5zbiQ77iJgUdCppn0fN9CK9CR+oGUe7OQ3gIqp+rAe9jjHkaI1YlMmg+kXSDmw8EiI0CJcT2PUocPtng5Ip0443/iZ1mICPAKMse/8B3P5ByUz57AFJQz2nCJi/WtLJczSEfwYzObQM/EQvgkhmWD0BeRYf7N4LyFCvD5OR8vpssHqahHYlr4JGWQG1YmIB5eNDzXi1N2S6uTKrGlcXLMbSrHLsH2jEKIMS4jyAhZkluLN0dfxBiEbmQgJCHsRQAMaLV5rMxQc3CvyXgeGEmw1WJTTuAcb6ASMNsTcCNguaXvwbFhTlQFWzFLj67pAB+L4X0frGc+geGEGpTomMw69Lwcptb57ciVMbkOca8LShqTCrNQLgPTDlAnPXA2de8gcRqdOvlt4n/s6H5p5BFBYWAVmlwCkpcBWG5kOJSQ1WQmx+B7D3MWmiCIvPK6TqqZzJL4rHRWR1jDi0QgqaJYFMzXy4nSNw+6TArxJaZGmWYjpwd0UF/tzair91dARf+7dly1DlXxj/Zt1mfPjQHvQ7HdAoFPjXJSuxLlciDefMmYPz589j8eIY/jVJQkgx6XMBEpaOQF9XALplgDJ8DHJ6vXjI4cBffvlL5MyZg68sWoR3Vib2YSL5QqkUOahvywqNSPksrVKPhZmb0O9sF1I/meo8mDWJS75p2Duepm5A65bkSrwxyu314lun9+LwkBS0ZK95rrMR/7JkE1ZkT7HNc2G19JgK8PvOXwjUM9DrCb22ZBmmFPX7gYP+oDxx9CWJKF2WvJkyyYxReiJEoNPWjfmZc0UyBz3HxtyhaffAQC+67RaxJoqEWaMNEhqRr48Hl9fhJ9rCYXEPoPfCmAje8EFwQx6mn84FNQkkelVkRvh93HMP8O1vR7/2/NPAI/8Teu3/HpaIqNvvwpSBlRVsF/Jrce+9wLFjwOuvi6fmrCxc/ZOfYF99vSASx5UWOXvKP+/INhX1TLYIgP2Qv/OGyBpKTZlKAEun9JxzltG/0JWj/5wgNRjMiMrSJLGRszr6fFQZgItEnmySUiUfgPCCyQpyGRluXutEEgkJht/85jeYTlJD2lv4ZvW7Z3FJwuZx4O/tezHIim1maqq0uKN0I3K1Kfo6zeDM08CcQDC4n3CPMdoKDNRLY1T2HCCrRqyd+FmxpOYY4KK0zkw0JuY59ff3i8SVWVx64JzK+TUdc8xi8xIcHLRizF8pmaHOxCJz9L6FBCBNuQMyVKmcqxysiiKhkQ75rHjHiAXq4C9fvjxtx5zFpQejSgunrAqJrSZzEr4arF5gwh77YkA6mQoNJLQvN7CPcb5g/2eMgITkpMae7CJpfyEHk3AZv0sSM8GrivuLDRs2ICNj4uT4pFvLDTfcECQ17N4ROL3hG3MfvBh19SBPl3wg48RwJ9rtUmZZgPHWKBV4b9VKrM0pT7tmWySe6jyJfQNNQXGQpeZSvKNinED2ZAO3R14CBruAzFxg1fWASdYQBzulYG2wwfqkvyEzZ0pdBosBNLvHBq1SB80EzCMFbOESNmIjbxmVAknPPgR0XvAbwHqB1nphJgrjMsDKzb9XCiJmbogKJgY/3nMmItOQ59wAk3q5TF/8KLx+KR1eGYe3FUqFGVpFahUCwy5rGKFBHB1uQrWpEFWmAtxXvRV7Bxow6rajRJ+NtTnV47cDIRtxHdD5uiQxwWBF0SZAd/FKkykNwsUPSQ0upMbNsOA9PPgXYJRyQNQJbwH6mjC06HbkVa2EiuRAZP87cwgV2Zk40dEHk9afgXbm4ORIDbapHX8MyciIc6PWuRrQaSXihSTC6jskU272md4Lkpl4+TLAZQPOvgzP2CBarGpsvfdjQMdJwGmJcawYWbOxUFAJrLwBOPB3yUODJAvB63H1u6ZeFs6QBbCSzUnj4IAWjRfISS7bR6nQIFe7AS7fsPg7tTILSpm0yFSCMoCPbdqE57q60Ga1YlVODtbLyvXX5uZh/3VvQp/TgWyNVsgHBsAgPrMaEsIzAHiapWuiYmVNaCwI+ktoKgBVMeCjR4sRUEZn133i8GHh/+HLykLbyZO4l31HrcYdCXTdqScdKcvCY8bzA9EodSjWx65UiwdmMSTKyGLW5HjZ839oORskNAi2Yno1/KHlzNSSGhxbDr0ENByT+unyq4EFa9J7DMpM/etXgdYW6flVW4C3vTP5vx8ZAn71n8CZk4zWAHe/B7gmgb7nuUMxXjuYEqkh9+wKe1143yiwNb8Kz3ZeCBIaAfLtFw2H8Y0lW6P+7vqiMvyh5RyaLGPiMyifuNSciW+e2i1+vrqA+rfLhJ+ZHCoS8jK4nC401LXAZzmFa1ffg+ysGkGMR82BI8PA9/4VqOPcDWDzNcAnPhciDu68U6rMeOwxaV1Ao/C3vhX44ieiv/TL/5gaUsNhB/78P8Apf1XFqquAt97HTir5ZvzwhwCrDyk5NW8eFBkZYKjg8OHDgigMEjj8/V8flTw4KNPAdh24HoF/OQb19wJ5BUBPi0Swy+dNzm95c4B1d0oVeKzAPfnHGCct3XBufLjp4DlQ7mtcmJdJJuH0MhPnpAKy/dlufM15AvAOA0w00iwBVJEbn/4YH9ovKnO4Cdu8eTOmC9zUclxNubp0FrOYIdjTdxZDrtC60+5x4ZWeY3hb+fiSmpcSUtobjzQDLa+EExweF1igEc9knGQHqz+4j5lp4NqUCTezpsmXLlilQcnWyVZr6FQ6XJW3CWP+BJVMdWbcdsEKp4aGBjGvs3IjUfth3IOVsZGyU5SASlfb4/yeKKA4EYPzWcjwzDPAT37CDDVg1Srgm98EJiBFdrHx9orFQoKKCVeEWsnEKzu+euJZ5GgNeGv5ctSaEifqkcxmxQID6itWrBBtecZ4QU4xSGjyu+7YsWNyEltVy4G+FqDJnwzO/fXGt0oJZknA4/Hgb3/7mzifyXjyTBYvvvjipPw00kZq/Nd//VfMsji314cxD+UL6pFh68K8jKUwqmMPmCMuG5os/YKwGHHG1nnP1OjGJTTOjgyiy25FtSkT1SYzHB4PflR/FC/3tIug2tvK5+CDNYvGrbposw0JQoMIxA9OjnSgfqwcCzLTnG3Pje6Lv5W00ITRZAfQfQG47eOAzl/ZQpmOSLkbkhyB36eAAWcP6kaOwQspiFttWohivRQQTaTlHoaSGqDtfIho4fUsnwM0n5EIDSLQHhqPS4bSRXMAXY0UQI4InESCfhg+eY6jSIAMtQkf3PAiOqvV7R2AVpkaqTHojP4cHrPfOSpIDaNah+2FKWaIU6qs5q1StQa/68Wq8pFlhZw5c0Zk1jKbvK2tTZIE8/ilnVR5ktkbMdgmGW0HwHZpG0Zf/RHMpRl4LPjN4jdUFaPPYkNBhjE5A/nxYB2R+kMY2M4WA2tvjfbNyKuSHgEYzMD6e3Hk4EGsesdCgAuw+oDheQSKF4W++8nnJdNxYy6w/BbAHNHnuxv9kUZ/P/SfFiYq3cMxgBUYLBFM1E4YgFxxJ3DiKYCkL48/ZwuQlzxhTHkjrWLq9dBjgePureNsGji2F+sNcTe1JOTimhGzLTv3h557uxn2BdTStWFQMpjxoST5FJuAouzVb5uapPGHbYab7PZ2PHzhQkJSY3h4OIpMCPjZpAMkNLjpTySJxfMYr6plT39kv5KIjVG5b8xUYN9zwPEdoeevPSqNE3NXpO8YNGn+yS+Bnm7p/skMGhOCY8pP/g1oPCfNX6Mu4OH/ArKygZVTWwKdoTYhV5ODQddgcO5jfykzSG3u7oqleL23A0Oye8T3NVtiywoZVGr8z9pr8OfWBnTbrXB7XTjABAk//tElrXH+ee7KsL9TQoWRJgWa+87A7XJDrVFjwZJqVOcaYVRZoFPHkZj8+Y+Bc3Wh57vfAEiAv+u+0Gu33y495IiVeRsnG7fZ0oUDg2dh9zhRpM/BlvzlMDCJIFk8+QfgdMhnBEf3Ahlm4LZ3SM85/s6bF/Vn1MDm2CNgswGf+BjQ1iq9/4V/AIvnxD5egNQIjOuRa2T2Y477gfJwcwUw0iZb7/mAXOl8OHd7PW5Y+hvRam9GRe1iQB9nPaoyAgU3Ao5OaY2mK5Je49rLsVcidEWlrVN6rr9aIjiCiDV3a8SGY+vWrWIsni7wWDwmjz1LasziUkS/cySYnEfw51j7jssFXGexcoOGqDHRezzma0pV+FwUCUpcyytCZgJYQUIpntkqjUsbbFuUukmHBJVSoYRZEzuRKRKsQqd8O7PUE/lUUZqN8m6R8yMNyuMlTk2EoON8mygIHauaahZJYN8+4AG/LClx6BDwmc8A//u/Fz1OlCo251cgW6PDwcFOEQJptHShz0F5cp9Qufl1417cP38b8iL8PtleGY9iIJ1xY7YlKv5cqaRwIuWFpMBrt+Y2YP5VgMMieS+nECNmTIHEKqsk6P85WW+eiYBtgQUSn/rUpyb1OZMueWBjpEnW0aNHoVeaoRXZ9woRHBpmvI57J3gx4h7G8eH9cMXwFmi3DeK/G17DU51H8UT7YRwbviD3bRUdRqNQopyZynGY45+cO4aPHH4ND5zej/cdeBl/bWvAT84dx3NdLXB4PbB63HikuQ6Ptko+APFwekRu8hzCEDO/043eVmCoO7SJFwHkMaDNHxxgRUZBtZShTQQa2aqbkmbgAnB6HTg7wuqGUFZ6k+UsXu5+AS90v4AjQ9TRTjJj/cZ3ArmyDXXVQmDjTdK5xwKrSsT5KxMSGk7voMgadvsf/FlSHQrde/psRIPSDNGbcZvHgrOjx0Tba7LUwxvxHemhEQnejSxN6qRR+OnQL0Q7IyYqloMHMjw4UOk0PtgGXwSchwDnYcD2OuD13yNPPE+QcdrGqq2ik+ZlGjHsdEnfma9NBrHIS35uoN0neV05cYrME9F/YwTLMkhArQdsI8DBxwDrsL/yaAA4+FfAGdHvhRayLCtXnAfvdYrMNo9x6nXgie8BT/0IePFXwFgS2v8Z+cDG9wObPwxc/c9AxdRK8c0UMBuJWs1x4W6Mfs11PrpSI1Uw8JhE+StN8yjHGAlukiIlqbw+F2zudljdLXALqcbE4PnX+6VwxgNl5uISP7KAdyTYipdlTbFv1BkZ6RTA2RivTRa8ZyWlqREaBP04zteFB585Dh3eN/7fzY0hATQ3tQoUjssb89ag0liBTHUGCnR52JK3UZAdBH1P5mXkBLOixN+Qy9XFrw7LUGvwwZqF+Oqi1RhxhweDOBLu6GuP0mZmxtCCsqW4dvMiXL11KbZctQjVuQahiuTFOKTXyWPh143j2/Gjib84PTQicW30az32QbzSexijbitcPjfabX14sftAanIkdcfDCRP+fDZGgC1GH2ZAQeC1V4GWZum7soqQn9HUGv1HbDeF/gBJUZVUhRskwSl7ZwQqInxx5lwP5M0HNCbAQFnFm4BM6TMqK8rReuIZLMztRkf9ToycfxoYksmSRh1fCxiqAGONRGgQ3iHAJ6vyE/AAnu7wP0WgEsQ/t/lfe+GFF0QS03SDx+SxZzGLSxFZGlNYJZ7IgVFfOjrXyQZFmDhC0PuC2thx4XWn4LsYfZyZBMqiLl06PRKus5g6cP1FMo4k1XSDQV2SGonAZKXIPQbJmHH7WgpgpQo/P1Ewk1n1keTKLJLEa6+F+8txDXn2LBffl+QlXJJViPdVr8DGvFJYPM6gxwb/7/J5UScUPyRwrX7gwAHRXumTwYoAVuKy8vhKJTQCYLJie3v4fixl8Bqa84GCqgklvTNpiFJ29PjgfZpuHDlyRMQ5yClcVFKDgaLt27eLTCpmAVcZ1yJDXQCPTxMRQvTB5XNixBVdPvpUxzG4ZcFmq8eJpVl5UPs3gTRs/lDNemRpYm/gDw724PH28KDWT88fx8s9bVFhzFd64jecPocFL3adi7lRLtanhwlPuLgjhC/FPuAP3wGe+aWU8VexHFhyDXDte4G5qWeNWt2jQgpMDn5PlX8s6bJ3oW5Elmk5HjKygHvvB973FeC+fwHu+JCk31YQbiQazC7PS47V5/mMuKKDDGwZWkVIYkehUEGnlAf3+CWU0CrDJ1qHx4YjQ3vQ6+jCsGsArbZGnB45EnZ/qWu7Pndu2N/NyyhBrSm5qhy314kBZyv6nc1wJBmgnCrweyUT5KkqGUNLizwQ45IkKQj6TvBeBjdhrEpQwWeOYVYa/HOHFMQJBPgZAevwV+xMFPoMoHR++Hnw86vHz+aKRHCy1GUAscaPihVSsKmfgSrJg0MCGTU7MBSR1T5nney7+jFvfeqVKS0ngTM7Qscb7QN2/TlulnLEl5Kye9NpsjzDQWKOG9pgxnRSm2JPSqTGwYFufOLIq7ihSIvlWSqoA7fYbMb1brdU4RQDzB5kGXisLC+SENwEBM/I50C/Yw9G3Kcw6j6LfuduOETF1PigkWEyurmUiEmU0Xx3ebQ0VY0pE++vmWLviRSy8tMKZncmc5yY5JWMSI2HBRuANQxA5wIZOcCK7cCy1EuJNUoNVmYvxfbCrdiUtx7Z2vAkjndWLoFRrQ6Gmrk++mBNclUugbWUHKxeJTpGe/GfL/wWDx14DK75JmTk5MKo0iNLq0SmRhGsbFUrxskoMkVU4PJ4mUmsmW69E3jH+4CCIqnK5u53AXe8LeptTdausMAgs52ZAT1GiUcZmJgRdw7URQQS+b3iVIYF4XZDceAA9MePw0tig8RXJOFucQCLZfMSf//ej9FkB7BZpfn0lg8BlYsAcx5QOhe49SNApBcKkyBqtgEr3g0suRvIllXgWduwZn4m9hyux7oVtThyqhn27sOAy5+MkBQUSb2uUNAYfC0UKBEPJVbD5crBa6+9hhtvvBHTDR7z1VdfTWvV2yxmMV24Km+h8NEIgFXx2wovLz16BkNZsRBYc7NKI25VhVlWUe2Hx1h2SQa2+B0TJZHM4tIAPSLo2zLd4L6Ec9u5c+fi7m8oUxXLVJx9hkl7TCyeDJiUxX3KypXj769jenvNInnEk+y6xKW8AnuJeK8zse+NN97AvHnzRD+baeT0xQYrtiZNakwSRUVFgtTlWESi9cSJcZKmpgDkEMglTNZDRZ2uTKrHHnsMX/rSl6BW6lBhXAWdsgPDY9ETxKBrBCZ1NvQy2YAhlzWMfOCGVQEf/m3ZLcKk2azWjys71WQZDfpfyBEI2AfAp7pxOtP5sT64uSH2MsAQMo2ip0YV5WjSjfwKaWPLigyxEWfGt0qSnHrxkXCSo+4A8Lb7paDJBBDpdRKA/Jr1OHuwGCHZEhq/O7zN8PiGaNcOvaoaSurQE7wf2RGZvSQ1ttwJ7Pq79H0Y6L32nqSleSgr5aNkTAy4fENQIbR41CnniHOh5BQrNEhoqALnFvg+jg7hISLHoKsPdq8VBplx5lV5C1BpLECfY0RkUNWYCpOatJ1eGxote+HxG6uygqTKuAYmdURbYeClbSdg7QHUeqB4vSQ1kUacGm7EsaHzghws1udia8HKuNIcCkg66yFjNB/OnjkNdaakp18490aYm3ego60FVq8GyoXb4NGOs3A/9FrwR09AkqzxJFwOOzTjZBKPC57XVW8FTrwK9FyQjLqXbQNS0PynFjeD4SHppjuAo38PGWuXLAFK/VlW9D6JheajwKkXAVb0zN8CFM4Brv0AcG4vQJm8whqgdgK+APxOcq8c9hcSGxwLYhj/Xs4Ycjqwf0CSPNuQV4gs6svHABdDO3ftxJpNq6TFvNCr9c8LqkLAHZHtpCwUiyluFFipNJ7Z79mRATxwaq+YezgmlhqUyFQrsX/ADW9mJn7ncuEqpRKDJ04IooK+NDwHVgJxMxIvW4/vYQZjwITL4j4fkfFOIvcUClTx/RfYTznRj6d1y/c82taEnx3YhRzbIO4ur8a7K2NnwFyVX4pvLLkKL3Q1w+l1Y3NeGa4vjg40pB0L1wInd4e/tmAcgt5qAf72Z6C9FSgsBt5yD2BO3vgMZ04BD34L6OuVAuyf+yKw4SqgqRGoP0s3aGDthhCZYTACy1YDJ/yeCwSvX6xqAjn4nkWbpMcUosSQgR8s3479A5zXfFiZXSReSwa3l9bi1Ei4V8I6uwrPvfw8dg2eRNniahQYCtBs68ZwlwW3lSzHmOcoU1HEe7WKYuiV48xZ7/4A8JPvy7LQFMDd9yY+MV67O94qPcaBvEIl/M8Vou1fsLTj/Ng5OLx2KKHEAvM8zM2I8J644U7gj/8tjbti2vMB198R/6AMyn3yk0whgsFmg/V//xemz30OCjlpEiBG7vs4MDQgPUrKgH2vAN/5pPSeyrnAOz4GbE/iesSDewxGgx4L55Riz+FzuGr1POw9cg5bK8ag0CRpOMxqV0UG4GPyhX+9yZpoVfS4qFBkQ4FQX9u79w0RvFu2bIqJzzjjPo9NX5HxdIedXifGXCNQKdUwq7NmAy+zmBHI1BhxT8XVaLL0CNWCCkO+eO1yBjO5GYhlECug1U8zba6BdNpclGfPh6vnDFo6+6AwV8KjykVxaeLKSo71gbUU11UMjl2sACvXfqOjo7PjzGUCekRQxonJS/S7SBasICeJNxlyi7K1bNvsM/ws7lUCSVgMdvK84q3/+V4SEnPp7zVB0KA54GeQ6H2JqsWveHDduGMHy+Ylz4wq2d6K3nJ//au09mS1L683E0WyU9jXzEAwPlqiN6PbPiqqNbhe1ytVaN11BO68NpHwx7XbLBk2s826V61aJWT4WAHGmMN0Ss2xGvttb4tOaLsopMbNN9+Mz372s2HafjnafGgVOjhFwFfaBHq8PhwfOocTQw3YmLcaJQYpQJmnNaHXwUoCCczIK9RnChPL3BjBVKvbhYcunMKx4V6YNVqszi6KJSyD20qq8Ue/3FSA9Hg7vR/igBUhBGWPWBWmUDDzHVidM0WldjQWvv79wO7HgaFeqQJi45uBgRgSWBwA+zsnRGqMurox4DwLnZKG2tJr/F7MY6a8UwAaRXhGqtVzEm5fQJpAAZe7G5nqq6BUxAhUBzb6izcCtcsAy7CUuZqCebJCNEcGRTxJZBMqhCn4eB4aUoA9mu6KJbNVZsgVj1TQba+HxxfKHmQlTIf9FOZlyKSXhDzFC4Cd0kI+gDJQLS8DtbcCxtTkXgacIzgyeA42j0MQFyuz50GtVGFP/ymcHJZKUJld3mUfwOs9h3FzSRxDYUUGykrz0d7Rj/KyfDQ196CsvAqZBXPFwoolaL7lb4OlsAvzFiwQzC0HuhAJEgGZZFWWXodzvYNCfk5ddwYOtU4stiZkKsYM6VUTywwly8wNDwMiQWSXAVs+CIz1S5UORllfKqgF9JmSaWvAr4PjQS8rwHx+earHgY2UXisH1vi14fl6fytgyvH73yQJVo2IdGtVwDSGDdNfJTMBeEYBe4NUsaApBLQVM0L6LBFISH/s8BsYcknEYI5Gh/9esxWVxuhr6VN5MWzuxSvn/4GcgmyYVJlYnr0OWkqtqOcAnGtoFM77peQ1WAqrtTmpxf7rve3icsmNmDM1JMZ9cHmBI0NDuO355/H02rWiDDwgKUXijMbd4y3W+DtmQJAIcQvz3vDxiCRH3L4VqKhLkNnyp9YmfGnPG/DAB+3wEE4MHxVG0h+sifYIINbkFInHtGLjbZJMmzAKVwMrrgbmx5BuItwu4HtfB1qapLmPC72TR4Fv/kgy8E6E4SHgX74sBaYJZth/5xvAu98H/OF3oflqyTLgXx+UDK1ZifO35wGzDjBqpcbAMaJs5pTaZ2v1uLE4NXN5YnN+Gb68EHi6o1EkbmzMyIf65Dmcs3ShZuOC4Fgh9N5do0I6NF+7FR4fCXANlDCMvyHZeq3kPbJ3pzRub78RqE79PONhbkYZTo82iXklEI4vNRTApNJjf/8pdNhbRBILz5GBwzMjdTCqDCg1yKqnVm6QiKsje6T2tHYLUBshASXHI48AxyTjvWyVCvsGB2H4t3/D4i9/CdkP/YrlX0xRBr7xLem781FVC+x4Djj4euhzWhuBxx4C3vuZiV8ALTe9PuTnmqHTagSx4fX64FEak1/E0zdNvwFwnpaMwhX081gCn0IPr88mpPBIurp8Y1Ap9DCrF0OrktZEzz77rFjnX4wNF49500034bnnnotLagw5B3F8+GBwbZejycPy7DVC33wWs7jY0Ku0WGhOPlB6qSFy/cI+G6iYY/Cf0oaUECW4FqprskGdcTXm3jJXzD1cryeTbc5sdQZWAx6ePAaDyakEodMBynwePHhw0lIZs5hZYBvdu3dvSu2JJMRkCAV51Q/32lzrk1hhIhb7EftL2D42hmcmpWkncw4kCJPZo3O/w6zyWcQBq9U+8AGg0a8cw4Sp738fCKxbSAg99JD0GBwE1q2T3n+Jg0nnH629Cs90nkG7fRh5WiPKOh3oGLmAuRuuGjehcBYSmHz5/PPPCz9MkgoXAwrKIG/cKIhaJiFMtmoiWZA72LlzJ379619P+rPScsYc5DigsnzkrW+VMu40Si1WZG9Ag+UsRlxDGHM7MOaSNqQM/u4fOIJbS64XQdnbS1fi/5r3wu732yCRsa0g9maTi5gHzx7A0SFmvTCAa0H9yCA25BZinz/bl3h/1UK8v3ohyo0ZeKW7HWqlAm8pq8Xm/PhGUIvNRSjQmdDvsEracD4Fyg1mzDHlYcrAzPM3fTT8NWscSYEkszIjMehs8mc2+6Dx+kkbnxL9Tk+QROKdmZsRmhS9Pjuc3i4R0ON1ZnBPo/DC6W2HXiWb1Bx24MU/ARdOSoGqNdcC62+cULY5zzFTvRij7vCyJwW00CpT1EcX7agArbaGsE/SK/Uwqsa/jiRDqGHOtub2eQW5FgtOb6Q+NeDyRpRcOxlsjpZcw0hTSqTGiMuCpzp2S/IaJFQcgxhyjSFLnYljQyFNTd4rDbzi9y6vG5pYFQjaxTCZhnHufDssFrvgWaopoeK/B+zP1LcLLJKYMUIDIW48aIgXFWAlUVgvBX+KzSYUwyRl7C5ZBp9SJTLZIw2UpwK8X9xAEZa2emyszQMah4Dy5ZL8FKHWAdkxiDBWB2y8Fzi3E7AMAqZcoEX6TkFwz9Z+SiI1iIYDwKmXQ79czkBekh4XVcuA5sPh7UetBEZ6gNw4ZrzjERrDr/nvPomzNsBjAYx+A/QZjB/WHcWoTFZk2OXEv9cdw09WbYl677nR09Bmq9HX2SdIDYtnDOdHT2Nx1kq/JNdiwMfvTFJq/GAWq9A8PhuUCi2UCk1c/kfcHasVnoEBdOXlwV5SgrL8/DCjPm4++O+Ye0zMdZz78rT5wYAa+0/AXFejzITLEz4eqBRS1Uc8cGMTaNfx8Ejzebi7uqCtDWVR/a7pfFxS46KAY8LGN0mPRKg7LVVUBMAgRk8XcPQgcFUCv56hQeD/HhH3LQgGWKj5/cdHwqWoTp8Enn8WuO1O4KWXJNK5Rz6G97H2n6l0mE5wvdRqHYRWqUK1KU/MR5MFiQ0+CAaGhpbOh8WXCZvCFidIRQm2FEzslq+SHsmC92PHa0BvDzBnLrAmfoAoW5uJW4o34shgvZ/Uz8OanAUYdI6ibqwFOdro/tNt7w0nNYgFy6THeCAR9s1vMnUo+FK5Visep2029NfOQfZTz1HkGsjNiZYePH86/DmTKy7UhcjyiUBfAmQuAEbrkJlhwDUbFwN56wF9iiaDTEjRhYhEn88Di/sYXD5J+zigiOzxWTHoOoQ8xSaolSY8/fTTeEBucDnNuO222/Ctb30LDz74YMy2enI43BNu0NWPVusFVJlmAzAzFbxvdm8X7J4eUeVsVFdAq7y0M1avVARkaeRgZvnZs2cxMDAg9g8BcC3E7E9qqyv8JCmDtgzk0rtoPLNmBpAjg6rcX3B9NB2SJjwO223dqePYUK2Hfvgk4C4CsqoviSSiWSTGxSDumUjI/rNw4cKofsT2PR6oRhDYX0wG4yVWEdPVxy5p/OY3gNzjhPs2rpteeSU0PixcCPzgB7jcYFRrcTflvP3Y370fd95557gKA7MIYdu2bYLAZHD/YpEaAUy3TxSrNFjVSX+VyUKdzk0HNz4BUoPQq4xYYl6No4On0OlqCQv/UibH5rEhU5mBYn0WPjbnWrRY+wXjV2XMjxtIHnQ5cHgoRF7wM1VKBeaYMvG28rnosltRazILTw7ijtIa8UgGOpUan563Fc931Ql/jWJDJm4sWjCu9NWUgNrLxTVA1wUpo5ABneqlQEHFhCYqZt4FtU79l1UJFWpNq9FhkzKOywxlyNOFyBsG/Gz0wQw+93tbKCO060loNJ7wl9M5gb3PSxnry1OX4qCJt80zALdXDZXCI4IqGkUOMjULROAxnqdFn7NBkAk6ZQbydTVQ+g3DzZpsLMxciYax08LPhXI1fB4ve6/N2o/nuo5gzM3PUgtCgwRHrjYDt5euhVGlRbd9QGiMl+jzYVCZYfeOhH0GzyEM8RYJKQap6kdbxbnI+9AFS7esksYP/31i1mpACz0KSiNguBrLVs+HSqXEOQb+leaoMjQ5GJgloXHq1CmRzRLmT3DDO4GuVmDEH6zlcW96lwj2CLeTKew/ZLdp1sd/RfWOVgtfx2kscDYBbZTa8AFtx4EN75IqMcYDiY+lN0s/008jktSQY6hLRmgQNMZ9HsirADKTIODo3yFuT4TpeH9L6qQGKzQChEbwtXrAwAzsmZGpurOvG//XfEGYh91eUoE7SsvxRPsFHB8eENUFAbCFN1tjS+WNuIciupMPI+7h8DcFfF3GgdM7hAHnkaC0ToZ6DrYXVuDJjsawuq4+hxeO4VEpOO7P3NJFtGVuwFtaWmAo0eHkSEhuMUeTi9U5a6FUqMRmQPRFnwUmdR5c3gG4fBJxrYAGWZrEki6JvHLcXh98Hi8Usk0HJYouWcTL2iSJPh46O4DPfVKq1IgFuZk1wXVGV2fs38X7mylGi3UAv2vaJ6TBiDJDNu6rvkqsT9KFpv4O7FZ2Qmc0IFsptROhlQwFzBoj8nRT4CEWSWg88GXg5HHpHng9wF1vB973oai3eoUM5hjMai1uKFoXtvEe89ikRJkYG3I1KxMmgp/9TCK4YsCo0cDIoBuzGgviJCWwGiRQfReAVje5oBf/Nnc1QEktjxXQmAH15DeLdg/H5JCZI9NbWO3JxBeOhA5vH1qbe4RXz8Xw0wiAx37Xu94lAqE0IpbD7XOJ9V0kxtyp+I3MYrph8TRj1F0ffG5zdiFPy8rLWWLjUgPHX2WMtREzdEmgR47NWVlZQu5GDmbHk9RIVf6nsLBQZJXy36n4XpT/4YM/c9+j8Dig794Nvd6/X+4/DRSuAIonIEM7iysabFNnzpwRc9pE5atY4TEhJYSIvsrqcxoWx0OsuXcWEaAvgnzdJ2SlR6UKjgiT98sZAZnAWUIjNZCc5Nx4peHpp58WHEI6kFZS4+67746pCWZUG/xK5SFw8yz31SDLt9AcP0Nj3OCO/6X1uZMvcTKptbirfPp1g8PATf5N9wH1B6VAMWVC5q6a0KaYm1KVwu3fpIZgVOUgX5cvHrFg9wxHSXoxWK5gFn4AvBes0AjcE55fXgbQuh/QOYGaDZLMT5IYcp6EzSsF6YUMjM+LTG0ZVJRJiAH6ZTRZ9sHlkyRdxtADq2cAVcZ16Hf2otl6Tmx4c7WFqDWRnNKEBUqY8cmycBr3jbpseKJ9fzDbzyEzcR90WvB4226Y1Ao4/dVEZrUJNxavhc0zDLtX2jyrFTqUGyLajiYDMJUCFgbO/MIZDDRnp5ZBGPSqkCHynhJSJRSwKLNafK+4UGig0lVIwSBldKZuLDBLgwwuiQ2yqsyaCgZs3vdl4MIpwG4DSCLmFUcZmKdbT5EZLDQ4I9lCaR8BBscG3gB0xpBfhdsONB8CFmxLTR6qYE5Ifkr6IkDZEunnkQgyKQBWWiRDavBaqPza7uK8SQr6AOsA0N8E5FYl39+FBFpkY+A1Z6BHG/KdiINhlwWHBs/D6mYGdA5W5cyJmRnu9HrwVMcFtFnHRAXc7aU1IpM8EV7p6cQHDuwWX5V35MXuTjx8oQ7ttpHgJQi0DRUUgpSOBZ1SL43/suuiU8b234gHr9eOAeeBsPlozN0gKvK+s3QTftt0WlSLzM/Iwb8fOgOlwwFvUZEwPFufm4tVOeHyfyzPdLgcuDASnk016BpAm60VlcZq2G3sX2e44gWvao5WCbdvPry+DGiUWXEJ28jjsEQ8Xjnom7LzcDrQH/20zl3lFzfbY1Ioq5AkoVjFw37MNQW/++IEc/NvHwJGw4lmAf59YREwMih9ptyvqsIvL7ViGVBGuR0fMGgBbC6Am7g0SAukgkdbD4kquwA6bMN4vfccbixOT+UV29H+1rPQLCoWhOKwi+seSfixyliAzfnLxp870oGdr0mERmDMJh5/FLj+ZqAsFNCye8bQZDkEl08is8zqIlQalwfHtGzOr2K+ZhJNaH3I8682TbD979wZl8hSeb2wHD0KjLP5x9ZbgLrjgILVvv5KjevuRFogZKjSF/R1+yiLGYIYh5lB7/FB6Wd4n3nmGSH7dDE3W9nZ2di6das4l49//ONhv1NzLQMVPGGypdxfTD57dRZThzG3rBJPwIcxdxNyteOb1c5i5iHWvpxjSSCzO9nEJgZXKXGTSgCVktdMbEo3qcFgMWV36SGwbt260HfoPgIIQkP2nXuOAflLpUrwWcwiSZDQYOVR0PsxBvg7tsXgfjsCJAHz8vL8fZD77NQT2UhmUE5tPFKDEnLpyKS+rEF5KXlCC3+mX8YVVq3AJJhZAmxioAzjVMTLZirIGVDelr7cM4rU2LRpk5Dh2L9/v9DkkqPWVIVWayeGXKGs2pXZi6GRBZmTRa5Wj2XmPJwaGRBZvVKgzIdraFJ9OYFZmYvCr+NE4PU5ofXPcU6vtAzT0Nsy4SIz9qZeSaPJyPNkhQZRkQcYmDHgAZoOAz2NwFXvTsongHIwAUJDDqunE3pV7IzIMVcPXD6ZxAizvTxD6LG3oN5yNvhat6MNLq8TS7Ik2YVOWx/e6DssMmFpKroxbylcXoWoHop5bqKdueD0hgaZUbcVRwbPY3P+RlgFAeSFQZUFlb9KJAhBgqgBFz/bBxhygMqrAV1qgYkqUzFOjlyQn1RcVBoKsC43uQAYM6NyIgK144ELexIb3EQQzC4RCyHqqM+LvRnlZoOLNw7SDIxMxniI2nssaedAyM+J0rR1O0NkhvxauZIjbsKw6nbgzCtAb5PkDTNvS0h6Sh8nkzlRNUgAJFlE1MiPQHfsPwcMnAcK5gFLbkmO2KCHhrM9+JTf3mY0wOvdIf1aUQGdckHMSXLMbcNjbTsFYcF23mrrRZ9zGDcVrQl7P0m1r57YjZPD/aLqgNr2e/u78G/LNyWUxvllg5SNKb8rR4eHkaFSwOBX3QogR6vD5xeEZ/AFMDdjEfpHeuFxSQFf9t05GakFeq2eE2GEBr8HuSSHZwArshfgxytDht1rrAr81uVCk9WKtTk5+PbSpYLciITT6xA6/pGkvdVNQ14mdfexYYZ+R0k7RSOguEbSuU8CJDMCOtKxsMXlg/eaa/HnLimj8O0V1fj03JkvPxYT3Z3Ad74KOGXVGjT6/ujnJMPw8dDVETsgXVwM3PMugOTnDx+UyAxi/VVSIL2tGfjNT4BCs0QuFpgBXS7wwHdCRuLTAJfXg2FWicnA9trjSF/W+dGjR1G5dD5aPMNBcpzEBo/0gZo1aZG6Soi+vlAVqhz9fWGkRov1aJDQIEbc3eh1NKFQL22uzRoTrspbir39J0Vlkob5Ahoz1uWuQKaf8EgZ3IBGVlr4UaHVYnd9PYq3b48v+VBWDXz4K8DBNyRvmIUrgUUpyHJNIxTQRWfYiXFSeq7EMJ566inceuutuNhg4hTPJZLUELId5qU4NRKqrDSoDKg0zgZgZipEoksM7zwfPcFmcUki1vqSVarJeGVE7hcoWUWfgIDpeLyAbuC4QhKqrk4Q9pTwmahMDj+Hx2WyFD+DcY2wqvRAklQseBxXBKnBmILN0wKvzwG1Mgt6ZdkVE4BLtYJ6PPT19aGgoGBcQoPgPpfV4PEIBRL+nT2HkOupE2s4JfKgVy0TXmjJIhHpyH4lKnmv0PucNN73PmD//qAXGzhuUTLzCrpubCscPzkOzyJ1ML5G+ffVq+P4TV5m2Ldvn2gz9CZNB9K2W2fQhfppjz76aBSpQd+MbYUb0W7rgsPjRJ4uB7ki4yx1cFD92uIN+EXDcRwf7hOSBO+vXoyF5nCTZwarXuxuRbOFWcUm3FRcGTMgFQn+3YnhLhFYqDBmoYba+rLAnpLhqktogNIozeJ8dSrKa4X26Q5f/7h/p1PlAW4pcB2AElpo5PJKvA700BCSU1rAKJ+cmXU+CPQ0AKVTE1zzygKFcvQ5e6MMwgdcPXB73XD5PHit91CQwGAwcnf/cSw1jz8AU85JDm7J6GnBrAiTehxSoOlVYLgldC7WfsBpBVKsNKUx+LaCVdg3cFr0oUJDrmij9NqQL6vytZm4sThcoiMWmPnBsnCajDHLIxXws1mpESAZWLnBbKZ45bMkMgJZngEyJAwOmxRkpGfMOOfNEvDW1laRNRV308IKC0M2YB+WBaV8sX00EoFk3DK/HFUkCqqBkgVAZ51UeUMipXxpiPSIBLORnTapconVDST85OD3ZnQ/8P17zwG984DCJDwRtJWSh4ad1QI+2I16eGVMgcvXKmKHRlV0G68bbRPkXqSs2ajbJmRoAjg82IsTw/1hskYcf48M9mJtggo5iyf88wNw+2XtmNCs8NHTx4xfrb0GGSTIYoBycptKt+OlphdQY5qPAl0xDCqjRKqOngRcQ5IkS+ZSgK/HDKQMBX/usPnQaZfOzKRqx8a8SlFVSHR1dWFVVRVukPlnxINJZ4LP7YOCui2BY8EHvcKAA/v3oqLMHa6nL/7h+EOiLWPSmychMeh04v61a3H/kssgy/U3vwBGZLJi3HDVkuSLb5YYxNz5QMP56GB5dxfwkx8Cn7of+MXDQON5ydh54WLp8595zF8Vwvvk/xsN3bJT93KaDNScT1RaWD3OYJ8hQUafMeLc6BCaLCMo1BuwPCt/QmsRJp8sKqhES9cJmVCbAhXG3OkhNIjaudH3iAkSGg/QVgcUVsGr0cDhlYhBOayecGmx+ZmVwjR82DWGDLUBWRMlMwL48IeB+++PTWywYutNb8Ku/ftF9ULc619SCdz+bsx0GNS1cLlIuoYCzKOu0Hdu7j6LV155Bb/85S9xsXHXXXfhy1/+spCaiVyzFOpLYFRnYMjZD5VCgwJdEdSx/MRmMSMg9iTKPDi8XFOE2psuTgLTLGY2Yq1PmEFOQ+0FCxak/Hncl/DBz6XfAOML4+mMBzz7ODZwT5JKopacgNmzZ4/Y24x7zqYioJ+VtwFw7a4HNKn7SF5q8PpcGHTugxckdhSAtwNu5QgyNYtxuSDgYZcMMTZRnwm26+7ubqEykAhs+/E89Vg9cfzkq1i/mftrqQ960Q+H5yT06vQlUjApkbLTs0gAkhhcKx05AlgsjFBP+x7iYqO5uVnEg2YxMXDuYrViqjKMlyoeffRRwR2ky5Q8rav+e+65Bx/60Ifwwx/+MIr5pRxApTFFrfg4yFBr8YUFa8edML5+cj9e7e0QRAaDcK/3duDBZRvj+wz4SYtfNOzDmZGQxvBbyhZjY14Znu88jC7HkNBp3pi3ACuyL41OS2NaJYzC+DHw1QNmkONBq8xCjmYZhlynRUYVJaDytPSjiGgyNAWnh8aFIwyVR3+QJ1rrOBZIDhhUpbB5JI+PAIyq+G3GqMqNIi+UUMMnhDRif79B50jMigyN0oc8bQYGnCQJov+WG2R6fsgDTVmJFrGUEBlujvymwGADkC1boHNDQKkghXbcoP6cjFLxCKDfMYKnOvfB5r/GRbps3Fa6Ydw2HgAXVMz8SJQlkkzp9+JFC3HhxBG43R5UuwagPb1HIilqlgLXvFUy4Y4FvueNvwIN/qyGwkrghvdI5EYEmD3FYFwkYRoFfveVdwBHngDs/uzmsuXSI53gcdbeCXScBSwDQEaeRHLEuvZd9cDJfwAel0SULL/NX+EUQcqF/akCsA0nfy7GxfDpF8Dpey2mVJnd0wKnR41s7dyozPDIPhR6PYQRV+x+HO91OW4oKsWJCI+DwOwQOFMe/c6ymriERgAmDT2YykJZuPyu/a8BAW8NVz9g7wIKbwaUWgwNDQVLtxlI4filUbjQ6QgRGgSDyAcGDuHqgs04fvy4yKDKT2Ix6vF6cGawEe4iNdxOt5DAUXncGGwYhkmXhaWLy2HOiHEfhRJd8n2voqJCSDIENu/i2D6fIJVOnDqFdVWXxnyUFNpawgPe/JmvJYP3fwiorwMaZHJgonn77/WjfwBuvAUoiqj4GCUJGtFv6O0yGXPnCYBt9K3lq/B/LQdEP+Y9Nqp0WGoux59b6/HwhZAJ9Y1Flfjc/FUpExtcmy3IKMYR3TlYmV0qyBQ1biqaRtnN1WuBt70T+Osfpedc0G5eBux9XHquz4Dilg8ITxpvhKyQJobkHMkMPtKCq68Gfv5zCr1KJvFn/VWfXNN+4QtQL1iABVlZQv5Q3h+TBtvUYAdgGwXMBUBmakkF4Z/lBLhmYttV5cOn5DXgGoqlNzlQIPqzHZ4h4StE2TuDqhhmzVVwejuE5OiAsxcyTgMvPrkLa9aumRFSAjwHZq89/vjj+Kd/+qeo39MzjY9ZXBrI1izDoOs4nILYUMCkqhSPWVyaiJyHaHo6EUIj8jMpzzM6OioCqyQ66NORKCCdKri32LVrl6j8DkrZxkNWDVAwAPT65RNJaFTfICUrXeawe9r9hAYhTRR2bzuMvpq4MtGXGtjGWCnEPUAiMMOYFRepBHFZdcHqpbmTkDWl/wWTErlHuWpLLXyKcBlJD/pTlrBhv4n1Ny6XSxCF3OvPIglwLbtu3RV7qRiIP336NIpZHT+LCYHz5o4dO4QKyqWURJ8qqD5BUuPhhx9GupBWUuO6664TASQuDqh/e7FwZKhPEBryrOKdfV04MNCDDXnxF0QHB9rDCA3iifbT6LG3Y8glZQwyIL6z7zQy1QbUZlwandakrsKw60xY0qFBmVh/1KguEZteChIwuBAT7HA0BafEwhsPSYHbQICUWZ85yTONOZrFUEIDu7dXkBOZmlroWTESBzpVBsoNK9FpOwkPXFBBB7fPjF5nj1+nP/TePK2UuaeNI3lmVOnx9opN2Nt/DgNOZnzqxLdgFnuxPhtVxly82H0gSIjw/WtyEpXXxTMJl73u6AaG9/tJDTVgXgvokyP/aOZ6b+U29NiHRTVUoY76/Mll2jLTkQs3athOCrYxKF74HWoHOuD1+tDcOwiFx4fq/Cyg0b/ov/7e2H975BWgIWSujN424I3HgJveF/a24eFhjI2NJV+OZ8oDNt0nkRokEfxZzmkH72NZguwVEh7Hnw4FVSmPdfTvQNVaoGFP4IOkPhOW8eMDZFViSZ8P/y6CkxOcGa+juxEGVT50qlCVXJWxEEeGaDQeOBMFMtR6ZGvDN3YLzTlBgjgAPl9gTpwRl61Ri4kmQOGwhVImhqA0nkGlwrsr5+Hu8uTkQsImeUdviNAIfmEHYGkEMheKCiGSAYFSWK2yBj5fPSxuoL+rH8ZMIwwmyfNpxD2KCy0XRKZeItO+MbcDv286DBsDMmOdKC0sg9fjwakTF1CuLcRbrrpL2hzTEDzIG8l0VpEvfG2SRUZONppamoXuLQ02Wfvywcf/D6d6u6HJyoTZM4JvGXXYnD/J/jwTUFwKjI2EiA0Gk/laMqAZ349/JhEbP/gu0NMVPt7a40hHzF8CnDsTukc85pyFob8lyXfoFaD5jCRFt2Y7UDY1XhvzMgvxybnb8GxnHV7tbYUPNjxw+nWMyKPNAF7obsHm/FJslPkXJQPKJj3TvB9W9hOZP9XhofO4vmgaK33ecx9w/U2S5FTbUYn8DZaSWqHY+ThKb7wDbbaTwTGS8o4FummQFeKmNLAx7exk2jGZRUnGTKiZFaOhoSF1UoPf78izQHNAKkkBrLoFqJ7AdffaAMcurlKkj3YrpEpAldt/vZrgw1woECIkxtwtGHaFpDnH3M0o0K2HQUWPr5Ew03DiH4/vwDvueS9mCt7xjnfgT3/6U0xSYxaXFkiq5WnXwCfW1MrLevN+JSKd9zMzM1NkijNJ5cSJE2LcjZWQRfJjPNIjHg4dOiSkppJK8uL3KlkLFCyVJKdYGXgFEBqEV5Dl0UlQQjbuMum+JAqoCpAMqcGAPzOM2e7YRuVgLIyVFAzyyvsCX09l3cBqp8B+hMmITKbgvp3tlZ9rdx+NIeSXesUtz2nnzp2CbOHnk6xhMiHl17jnmMUskgHH0FQlB2cRjcWLFwtJxctZxmvnzp0ioWD79u0zk9Tg4PfWt74Vf/7zny8qqdHviB246HPGCWgEfu+wCHkpenTIZYcGXWNh72Pgr9naM3NIDW6UqcNN6Z0YC0mTqhw+nwuj7iaxLNErC5GtTa5cVOgoisqHBGDgeM1dwNEnJXklFTPSb5Ey2AMY7gA6aSzuBfLnAgVzo6o1srXM7Ek+uydTU4gM9bWCeOm2d6J+jAEQ/5LLn2RbrKtAbYY0MORps1BuKEKbrVvcR76JchWVxmK/TFr8ctA7y65Bl53yBkqUGvLjEiRBcKGbtwDop9ZlAD4gzz9IeWzAEAPb/iUJF4bD+wD1DUCSGYc8h3Jj6uWNDLhSYmfS2PUEMCh9jlKpQE1hDuo6+qKN5P3tklkqnPSYeY728+GLY7aLzlCAXfoIH44dO4YtW7akdl689sb0GapOGEPMoo1gGRggzSwAlr9JCuSxaoPVKR0ygqdkCZCXWvY9+4/CZ4ZKMRJGPghJKn8Tc/rGoJMZzZYYcnFd4Qrs7DsFh9eNPCFftjpKhqbMkIEvL1yLH9QdgtPrhU6pwhcWrBavj4ezI0P4Qd0J6NUKeLySXrs0pgD/sngF3llRG2YUngy4ieBEqNVqJdmtSPC7k+zIXCg+NyMjI7jp0CirhY58Z8PLMJj1GBsaQ39nH5QqFXxeL+YX1SYkNIjfXDiI84PdqLQPsFxDEBpn9p3EnBXzUZRTLMv24/XJA0h+iK/Ikn0loEpcdk4MOG34ZeN+tFiHxdy0PacCzgMH8GJXK5pMWpgLpbHS5fPiX08fxDNbboUmSVPOGYv3fwT4zv8DLP5515QJvCeFIKYwFF8CbL9eqswIkklKYOPm2H/zprcAXe3Awd3S89IK4EOfCv3+jSeA0/v8TxTS2HXnPwMlU5PBTlm213rbwkIHZo0CTq9PeGP5zwLttvC1STIeSvrsDBw+sQNlC0PjC4/TYRtI/URZQXPisGTqvmGL5H0SC7wH1BpubweoDb3SH8QvKZUeDa+Hj5OcCwZ7kKMtg1ZpxKi7D0qoxPNYlRpTAkoI/OpXwNAQsGED8LGPhf06Smu94WzoWly1DciNMS931ssIDcIHHH0OKJojVbymAjerkVzhn8W1l/A1C1zL8/ChHAqw0tSNYZd8LcIknTFY3K3I1NTAoDKjQDcPvQ6pyqm3qx8Hd5/AY398O2YK7r77bnzhC18Qa5fZTMDLA4okfaVmMQv6BzBjnIRyQAJXjoD8FAO0Yn2YBBjAZiA75ap1VmjwcQVBq8yBzcM4QggKaKBSTFHi2EUA9wlsQ8mA+xDKorENyYOP3G/wNfrBHD5xBC6VC3qVHgaFPiVDe67XSIywWokybCQ4Nm/eHLZf0qiq4fGEJyNolDUpk4pMdOQ+m32LwUY+p879LNk8i1RAz1MiLWbXbjewbycwOABUzwGWXjnkGvsf++LljD//+c+CM4jaS00C6qnIpKIM1Y9+9KOkFxWxfC0ovaCZYPbD/MxswVPLxSTYtRZmjh/kLDGYwwgN8Xe+2J1SEynDdLHQ2wgcfxZwM2NEDyy/FcgPD7RwYGHVAx9TbvBaR7klpob7AHMjUOgnLobagON/k533OcB1LVC6dNKH5fejt+uIa0gQFQH5KP6fp1FuZGmsKvjeawpWoW60BUOuEZhUBiwy1whCIxFMaj3mZKQooVaxBaAsxkiLRPQUrwYy/dnULgaRInMsSFD1JU1qTBRJTzasvOmtA5wWIKMIyI0I4vW0hMu28HPln82Avf85S824yOO4wIyTIr0pWrecmaYyMFuERmmJjMxmLOIZBzLbO68KKJMFt8tXAJZ+QJcJmIsmJHujUVBm6ygUGBV9gibAo24pSCpOJ0aJ+PzMcvHw0jNonEqfqwvKRFb4gMOOXJ0e2iT6zNnR4eCIqlIqoPQ/+dqilbi3cmLjETcR1O0Um1q1ObwCIphpH/qeLOGkl0sgk6q3G1havBHnNI0wZJrQYfPgwKAbdo8PF+xt+LStFKXjBBjtHhcaLf0Yau/C6hXlaDlxDm6XBzqjHnoTjyu7bzwn1SrAewHwDQMKPcCsaErNJQAXhf/dsB/tNmmDxbnppeEWvH/BKuhy9dB2tYSRVzaPGwNOO4r0l/gGs7wK+LefAScpachKQI6ZEyh9f+d7gLFR4B/PSO3i6muBD3009nspe/bhzwLvuE/y1jBnAR3NIrCO4grg9P5I7TDgzL4pIzV4zyPXImwPtG0JCL7xtxXGDJHEccEyinydHtUkgMYBF8n72s/AqXaFbTq45rJ7Uhxvjh0E/uPBUEXN3x8FHvgBkB+R4chr/81vAk89FXrtvvsAueGzOV+61oG5hOeVKVWB0bNqXN+qdJiW79snVcpt2kRNRUlyiiQGvxsf3FywYuN734v9GQd2AQ//pz9j1we8/Azwpe8ARREVRqN9IQ8m+fVhRV+qpIbXGpYUEMyfjZJMY4shqeEIez/vucurwBiGkKH2ClI8X1eLTHURnF4rnn3mNyIblPPvTAHHcp4TN0Of/vSnL/bpzGIWs/ATvAzwyhNCUvEmSAUM9MYjNJlMwnXhyZMnsWxZcnKKTLRKl0np5Q6tMh8m1XxYPPVBn02zZuVlRUymEohlpQaTpuRtnJJUJCCWL18ufGRb83pETIJ9Ya65Btn0cksCrOjgg3JYJPGps7927dqo81MpsqFXrYPL2wr4PFApC6BWTGzO5mezUmMy0lizuHJBMpBtn211AxOBJktofP8B4OwpqXKe6/A77wHueieuBIzno3k5wOl04i9/+YtYy6cTaY/MX3PNNWKQf+qppwQDk+pNfKazDi/1nBekxvyMfLy/eg0y42ny+yFtyN0w+vXYq0yZ+NLCVfh+3VER9GGG6/0LVmBOhmRWHA+rsktwVV4l9vRL+t38u3urVsLmGcIpBqX9VRrMYl6SNQO0X61DwJG/hzbIrNbg8y33AQbzlHe2sMl1dBB47S/hAepDLwFlc6RH66Fon4uWfVGkhtvngNXdLd5rUBVAoxw/QOf2unBm5BgGhNllbGgiAogM3C4yjx+MEgZ1li4MOS1CiqfWVDwx1plBjrL10iMS8eRnkgh4Thb8flx8JSQ0TjwOWCjx45Xurd4MLL0NyPAHrkhM0Ojbf297RyzIz5AFzldcHbYAZDYUy8MZZC5adS3QLplbSyflBdbdFHYKzEyJLOu9pFBQIxEUIz2hqFNOGZBTEf1eyk2lKjkVaRjtG4PXVyHIi1F3F0Y9baGPV5VBr4z/+clIl5HIKDYkb4hYrA8nUQJdaP5EgtR+cJEfzGDQ8vtwjLCGxh4yOKYQYRKs6gDEgovtaV7JXDR3W9Aw1ooDg/TKUUChVKDDPobv1e3G95ddBx0JuRjg+O+22aHW69DnUiIjOxNavRYOq1QJODcjYm7ghk+V+ibB6nGhNcJXhZfv9Ggvyg2mqEWPVqlEjnaastjTBcsocHgfbxKweAVQ4ieNufHbfO3kPpsVGx/7FPCRT0htI5ngijkbsFmAh74HdPv7DtcNNA1XR/x9EtrdPjE3K1KeO3IiyN0QeR+65zcXV8Hi8uDNu54XlToEJdzun7887vGOHj2Ksk0L0GrplpI+/J/Hf358bhC3l9pQYkhSG/vhn0sbjcA5jY4AT/wR+CdZhQuxd284oUH85jfAjTcCgWzbtTcD3U1CzlBApQE2vwVTjro64CMfAcb8x6XkxEMPAU8+KX2vAGHDf19+mVqIQFZWkKQP4tHf+N/nbxOsFn72ceC+T4Qfj9WDMTyPElYVOjoBC+XRXIC2GMhYygE7LCdC3IVI3U1wjSFlE6sUelF1S480l9eHIRf/xodhdw9G3PsxN2OtkPfSqUyiOuY3Dz2CL37xi5hp+MAHPiB8+z71qU/NZpHOYhYzACQz6KHBSooAuL+YiixvStGS3IwFEik8Jqs55Ou+8ZBu0uVyh1FdBYOqXEhRKaG7LMdgtiPO74mS6djG5FnG7APcm5BQc3qdODR4LJhkyXZ2wdKCQl0eSg2JVT4om0ttfV5fSk6RaGAWfCyyjsSGSiYrPItZXAyw7XPcpxXBpMeFPa9LhAYRWGv/7c/A1u1AQeoSg5caWKGVjK/npYonn3xSxGLIGcxoUoOTAPVuf/WrX6VMauzqb8bz3VIGAHF+rB+/bTqET87bFPdvnu1swn+eOw6H14MygwnfXLIBtRlZuK20WuiLd9gsKDEYkcvM6ARgJ3xX5Qpsza/GsMsusnXzdSZ4feXI1pjQbuuHTqXBquw5yNGOL7syLRhsj94gC3PqzikhNRig8fjq4UO7eK7wFUOloMSLChjoipbZ4Q67r0MiNVhJEgnhvxGCy2tBl30fvH71/SHXORTq1kCvip+lWT96ShAaDPYwKz0ADSVuFECtaYHw0kjte/rwUvdR1I91BCs/5meUCr3xtC7gtPmAJl+qzBBQSJnnuvTImvF7NFkv4MJYI073n4ZhSIdF5iXievB7kGQYFz1nJELD5QnxUcwaP/QosOE9EsGx/k3AS/8bzE4fsDmxYNEyicyhUfiSUAYUjc0ChmrCzI/G4G/+OFB/UDINr14KlIeXlJMgHRwcnJBO7owA2966e4DmQxIJSdKiarWUeZBG8F6Puo+FaaKb1AthUK+B22uBWmmEXpk37RuQDbkFuKmoDM93twc9OW4rqcCanImb44Z9BxIxhduBfgbG+wFK0+StBnQh8oYbW5aDc2PARUJOjjSe9DisaLJG5sNT8smOFusI5mXGJoBYQbghpxKvDp5Em80L5YgTjnNtUHk1WJuzDDWm5H2EEhFIkerFHI/0SjXuKpuL13s7cHY0ZMCuhBev9LTi5uKLbOpLgrvlDOB2AbklUj/XxZh/B/uBB78CDA1KgViOGZ/4MrAkzSXGqfa1l54AeiRPriDxwkA/jWAC8y3nujmsiooNn88GLygnN8otJxS++VAqElf5OTxu0b6qTdm4tqAar/Y2BSUxV2QV4baShWixjqJQb0S5IQO37/pHkNAg/tLWiDU5+bi2MPaxVq1ahcK5tfjzCQseq7NjVbY0Nx4adKPD7sOx4eHkSA1+f943+ZzPjUdfT/R76UcRC3w9QGqwKuMtnwZazopsQ5TOAzKmYZP+ne8AVlY8+DEwAPz4x2ROY6xnQkQWgxfBgBm/d0AqLQC+Nhxu3ilQvghoPyPJUAWwdDtgHCfhxtkLDAf8l3jwBsDrkGTZKGsnP80w4o33dgUUfn1trtNytSsx4DyKYRddeUKweobRbW9EqUHS+t6zZ4+Qz6Dc00wDz4lVGnv37p3NsJ7FtIDrqwZLPdpsLeLnIn0JFmQuDlaAX+mgTxO97+Tgmr21tVVU1qYLPEY8w2Ku8QJBaL1eLxKikiE1JmIsfqWDcwn/u1zBPQL9W5hANR64hyapwQoh7i+4N1myZIn4d8xhjVb+gALDrtGkSA2OMwFihYoFJDlYqXHFg+sy7i+GeyV1kpI5QFbehJQNZpFe0D+Gc4GQF58s6LXHPWEgUSiAgf4rgtRgvOxy7u+/+tWvBFeQbhWWKdFQuu+++/CNb3xDZMXWUj85SZwaZoZ+CJwQ6sf6RNVGpMY7cWyoDz+oo0SFhE6bFV88vhv/t+FG6FTUX9alnLnKyajKFL6ZVioUWJlTKx5pA6sqvMy6y5j4YEwT5JivT022rtd3AT6EAhQ+dMLjU0KtWEQn2xh/4QsFJnJrgFH5/VUAOeGL3UFnPbyy1EMq8A84z6DUEJ/UIqEhqg4i4g98vjhzKUoNqQcYO+wDgtCQzkH6YD5fbK5EmXHiwdgoCCP1LYClHvCMAioTYJwvZXanAe22NtSPShraXnjRYe8Q32d59kph5JRQx46SU7xPkbEdEmenX5TkqIoXALd9FGg+JdqxZ6Ee9blFweCxyC72egWDTxkLbjJofhTUFc0rAa66PfbxrSPIb9yJ8/v3Y97IJmDptYAuRpUAJ72GnQAJUZIIlauBsuUzZ5HDCrI5G6f0EA5vZ5TJq8VzFjmaq2FQpbHNpgje/x+uWI8bu9vRZBlDjSkTNxaVTppcYZsKZlKpjUBR/Kx+Hmvp0miZu2yNUQSMoxs4oE2QvXfvgg3Q2l043dOGjOwKXFu+ENlVaszJSMNizg8Gt28smofnu89JCdhUxlcqcW1hrZjfivU6NPsLVJi0rVYq8LPzx7C9sCIpabApQV878PSvAJsdcPpJU57LDe8AFq4Jf+/f/wyM+AMhIiveAzzyC+B7/42Lis7mCHkgr1RJUrsKaKkDWDm6/kagenFc8t8LrksCwXJWAp2Bz2eAQpEb18/r980H0O0YFWudm4oW4j1Vy7E8uwht1hEU6IxYl1sm1iJz/TKaJ4cHhL+NHGqFAnWjw3FJDY7LuS4t1uSU4W8dTWizhRPb+clKhrL/llcC7a2hLCrOZ9S9jYSfyI5CZLBLZwTmrca0olV2/gQDXE1NwD33AI89Fv3+D94HDA/hTIYZi7/3feDAXuA3vwDcHurrhV+fOTG8wXiNNrwV6G4AbKNAVhGQUwK4nVJ1Sqxx0U5Jz4hxytEGZKwXxxRiaEHFKTV8vo2AgusoU5Qfmk6ZiwLdJnQ73og6jM0TImZ++ctf4j3veY/YoM7ErHCeG89xVjZmFtOBFusFNFsvBJ932tvFfLzIPHn53MsB7JORHn0MxKY7GYlyPKzUoPwoJagCPhh8ra2tLWgmzkeyZszMFuXfM4EqLka6gHOvAY5RqUp9/nWA/hKuIJ/FuKBRNtUEEpEajD1wDxJLotGgik7k4f7bqEpuTmX7PX36tKjwYDLKkSNHgglZVyy4T3jjL0DDUcDmDOkqUwb2jg8Bupm3XpkwRFINF3aXjvR2QUGB6DfEocEB/PRcPUbcLtxQVIyP1M4V+5ekUVUTTWiw+p4efJc5AoTm5VpF2NDQgNdffx2PPPJI2j97SkgNLjZuv/12/PrXv8Z3v/vdpP9uxG0X/djflcU2jhn3UuApGocGe4LZvwESpF9k2Y5iXgL/jIsGBkgaXgN6JTMdGHKBRbdKOvoTkbah4TB1mgPgJjk3fYE1ObyIzsL0gYHURUBeMbB8C3B8Z0jbvnIhUOP3DKhcIwXJaRTOu5tbBcwPd7x3+0IyRqHXos3dXV4XWqxNsHvs4t7TOyD6vOjLMrEMnDEXzyMaO/qO4/bSq4S3RtpAAiNjEaYCXfbQJiMgVcPXllFaq7ExzNgsJkhaxMpW5Wt9TUB3I9CwF1h3N7DmRvGrxf5j0bCPx+DAzKAyF30kUah1S81bPefpE69LgZ3cUmDOyvDJ22kHXvst4BiD0sLM7+NAfxtw3Qel4I8c594A2mXmq/WvSl4eNNu+QuD1MYAaHaD3+OxQKi6uJBEXMjcXp6d6IQCWYjMziuXZE8XW/Pk4O9qNXqctuDbmFaw15cKYwIyY3+ntK7cGn+/fvx81U5BV8ebShSKgfWa0F0aVBtsLa1GslzbfZ0cHxPzoT8T2z50+jLldyNVepMXQ7icBlyNEaBBcmL7wR6CoAsiRmST2dYcHlPkzqzf478X00MkpALrawv0dmAl203uk5yPdwKlngdf2AFojsOgGIE8euOecJcv+lz4EPvRBgehNMqsMf9u0D4M0eRZ91otnu04jT2fCyuxi8YiFghgbOM6HRRGSb3Iwg5XZhZvzFag1KXDB4pNUjHzAXWUlWJPKpvlj9wPfeyBUkTB3PvCWd0S/j/3i3e8Gfv/70Gv0Q0gh4WXKQMLl1KmQlBg3EXPmAMv9pHjk/NfRSWdOeFtboPncp4Ec/1qA783QhtrtsjXAzXHks/jeYr8cXc9ZYM+TkrcW1xVzrgXykrsuPpjhFt5BksuKyqeAUrUKCkU08e/y2tFkOY4xz6AwXOdDnkDC9qlTSVKfrIx89NFHceDAAcxUfPjDHxaazT/+8Y9nAz2zSDv6HL3otncLSc5yQwW6HeEBe6LH0Y1FmCU1CCYrMVFKXlHBuWZcomACIEnCB49FXz7K/3C/QfKV+xkSG6y8SHVdOG6SDSVAjz3uD7D5gMFW6fnad0n7jFlcdmDlBSt9JmN2bFQbsChzHs6MUmJZQr42FxXG5IKyDGgGpKYu5wBnSuhsBBqPAXZXiNAgupqB158AbrwXlzy47zi/A2g7Jq0/C+dJe4zIuMcMBdvpkaFB3PTGq+IWMS77ck832m02fHtp/Or2KKxcB9x8B/CPJ0OExkc/K0kEX+bgmHM59/df//rXuOOOO+J6Y00GUzYjf+QjH8G73/1ufP3rXxeLG2LEZYXD60KOJiPKmPnIUAdaLMP+ThBCyTjZEEYVs9Kig65GNv6Zio5jIUKDsA0C9S8Ay1KT6hJgVvr6e4ALBwDroCRtU73eb1Y5FVCN/9qm24Hy+ZIUFUml2uWhTT4D1vO2AXMYCGRqcfQ90irNcHlYHRC4pwpolZkyvwAH4FPiwOBeWD2WoDRUvCWHcoIVD3m62OXNY24rXuw+gDeXbrkkdERjeSTwNUpnMLMq4XdgdU3ZSkk6KbKbeWRa6mdeAbbeF/wVP5cZwXI9QOosnjlzRmTMK5iV+uwvgTEapTNwdADobQGuujP0+V3nADulW/zgscb6gb4WoCgiG7jrdPS5d565okgNlQhkRcu/qWIYg18OYNtixQ/LXANZdnKTyuDi0BOwVY5GpsaAj83Zhn0DTdjV1wmbx4cTw1b8sL4b36t7Gm8rK8ev164TVRGJEMjYSjfYlzbnV4lHJEjoR3qS82lWAg+qSYN90TYgScaZ8sPnm7FByX8n6m+8QHdrOKlRWQOcPysFlJ0eSeaO+OxHgBVrgeIS4NrrAYMxPee84xVg56vS+W6/EVi/OfZ7r38L0FQPWDn+0OhdDdz+rlCF5dHHpcx6gkTE8aeA9e+W+eHEai/sm7Hb0bDLhn5RFSfB4fFhwOnD987uFzJUH6ldFdO4nuTFR2oX4ZeNZ4ISVUuzcnFrSXy/LwabiqtK0TPSgwcWG/Bitxt9Ti8qjSr885zC1OY1Vmp8/+dAcyOjWkB1bfy1x2c+A9x8syQ5RSKBxMF04/UXgOf/BjgdwIp1wD33AV/7muSpMegnZphZ/LnPSW0yHqFPvl+hwOjgILIN+YBeI70+6pBKpu7/GrBybeJKQWb/1r/EEiupnVFS6vzzgOpNQLasv+sqAHtL+GloS+FWnGCDDEo/Sj7vAbEp+Sn70DB2GDavVIkRIjNCJLhOaUCxTiJTfvvb32L16tUxq9tmChjsYfYqz/Wzn/3sxT6dWVwGcPvcosqOZMapkZNif0G0WVthjiE3nIwH2ZUCVmnIqzL4fDIJJ4nACo3Kysoow2+uAVPVISfJP65MVf8FqTo9CB9gH5aUB7ITS0rO4tIE2y89yFasWDFhGbWF5rnI0+VgyDkCvUqHMkPxhMYNVj0xC/6Kh2U4FH+Qg+uv9sbL4/I0HwRaQwo06DknrasXh/uNzlSQgPufxvMijiuXX/t5wzk8sHgpNMnuk7l+vvcDwLU3SclupeVA9sQ9Ry81XK5G4Xa7HQ8//DB+L09ySyOmLPp//fXXo6CwAP/vv76FN73rzRjzeNDGQIgoy9Pi9tL1KNCFdISPDXVI+7qI+9hqG8HZ0T4sMkcP6DcVV+HR1vMYcTmDnWdbQRlKaV48UzEi+VGEwGBttxT0mcgimVJT87ZgOqBSVMPjOx71WtggVLVQesTDOIRLjnY+nPZRuHxSMFul0CFPuwTDrkGcGTkCl88p5jJXwNzUf8/5fzXUcPu9OLgZ0Sq1KNRNrPQ5X2fGlvzF2NkXCparGTBUAP3OEdi9ThhUM9+Qt9JYiV5HT7BagqgyVotsJlZLJAT/pmaLRG6ceFoK6Cn8ut1Od6g80hEKyMUbnFlGKwgNfmbjUWC0P/Bb6Z+GI8CSrYDZL5XkLzu0OpzQa2TDVGQ5onSisc/9CoJWWQyttwdOX6iayqRaBOU0mM5fLDBT98UXXxSSAz09PeFBuO5jGDjzGrRWG+A9DdTeCMQgKzkXbSuYLx5v270LL/eEqjYeb29DtYk+TUvHJS2sVivUF4FIv7GoCo+1S6W+AVB6SpAd6YLXDngoG0ij7ULAqwZO/xWwj0i/J3G88HYg059xkVcmGUbHgjEiMH/HPUBDPVB/NkRoUMaHkkB88Hs89xTwvf8AjJOc0196DvjdL0PPTx4F/vl+YNPVsSs1/vkB4OwRKbg9dwmQWyiNd5S4c9r8DJL/OnPuZvamn9RQsDLKx+shz+5VQ4HYGXo6GcHv9vrQ4wjNbI1jQ/j2mV344fLrYKSMXQQ+ULMQy7JycWZkCPk6PW4oKh9300BJgz2H9wIVgE6lwG2l0mdKAbwYZFQiUJ5oYZLkMSsDE1UHThUO7AT+/HDo+d7XybRLRt4kMX7wA8ksPDOTq26A8ojr1wOHDoUbwvslpgbcbiww6KPnGS5gqfebTB8cag1JVgXbkg9o2gGslJMaRYB5A2A5DfjcfqNwVgYdjPhABXy+PkARXm3j9jlg845GvTdHU4QMda5I/tApszDkskLnc4nqBz5mOkhm3H///fjEJz6RWEpzFrOIA6/PiwuWU+hzSnuzET9fHRqFWQIZPaZWGePI6l2B6OzsFEQokYzBcrrB9R/XYBMxVmXVx7jrt3hj+RW2x7jSkJeXh4GBASGTQmKDkmdyYoPtnGv/RCjQ5YnHeEjUZ+insXLlyhS/wWWIXP8+I6qKViF5jF0O6A3f04m9V9+lQ9iwwmDUX+UUWUXu8nqTJzUCKCmTHlcQmHzGarHLEY888oiQnydHMBWYskgMg5efv//z+H/f/joWvnkjnLL8MbvHiWc6DuJ91duDwVa1QhkzMY4YYBAhBuiX8cu11+KPLfXC3HVhZg7eWj4HVo8bphgBgBkBIV0UIRMjyspm/gJJqWCW7Up4fVz8U7aiBEpFUdp0AlUKLUr0G+D0joiNBCs3KMVxamRXUEoqsNGIhF5lRoZGD7vHBqPKiDkZ86BRTrwNrMiuwZBrBPWjbZKevez2qC8Rc8B8XQFW56zFsdajKMkrxYLMhYLUOK84n1pG7lgXoKJmuCZ0DzP0wIhVujDZccppKUMz2o/65nYsWLw8tGhzMCgoM90NwMEFon/xV1gLr0KFA+c7sGFemXQcjQHIiyGtVrYMaDkU/lrpzM0ynQrwfmaol8PtG4TX54BaafZXb8xc9DpG8VzncZGlnqfNwJtKliHfL8Pn8bnQbT8Fq4eSPTS4rUWONjxTihtRZuuyTLysrCxo1Gf2DaL/+HPIzjSgsiQPsA8BDf8AFt097kb0td6eoJQhwdb5/PnzuFelFpIG9IeKrAZh1dO+ffuwZcv0EMtyvLdqsVgkPt/dLMbF6wsr8RFWx6ULzOy27vJngxMqoN0RIjQIjst1TwNrPyQ933InMNQD9HSFZyiQ6K7wS+4EQJmkL34LePDrwOmT0nhAUiP42T6gox34xzPAXW+f2HewWYEnHgVefCb6d8/9PTapQXCDtEb2O1alHHkS6JFtOLTqUCVihAyFEovhQwZ8GCTFDgVqoIhTNWVUa7Ehtwp7+5tEtZB8hmOyxrDLgfNjg1ieLatykWFdbqF4JANWzeqVOmSoAYvbFha4KzUk9xmXJA7sDn/OtnVoD7BxO/DAAyEZtLo64K67gDvvBL75TeDBB4G9eyXyhiOCywmr0wm9WgUFK070CsBulf6eY8uS5UBZRfKeaJGLC/7M5IHAOTpbAPcgoNQDOdcASj9JTanOqKUQ11XRa5Po2g3/4ZU65OsqcHa0CYcGXxev7X7iNWi0GtzJ7z/D8Za3vAVf+tKX8Je//AX33nsZyE7M4qKgw94YJDSISHNfCQqsyFotjMIpF1isL0GJ/soKtIwHuTwOAzLTqf3P9R9NwudMoPqPCV6s8h13P5RfC1zYA3hcIWFsYw6Qefmb1V7pYPJfb2+v8NagLGN9fb3wYCERRkKDCVWTAQO/3Lew/7BaKJYHLSvSs7KykjK9v+yRVwqsuwXY8zQQ8IMTCUZKYPNtuCwQS2YqhrLJTAX3GDdmmfEkjb79YKLdprz8ma2iM4PAJOD1TKqaangtgJMekh5AUwSop3YPyHHu3//93/HVr351ytRuprSFcaPx5a9+BUde2o8l14eMcrkssHjssHocQX+CzfnV2D9AHevozymLIb0g15X+1LwV4udnO1tw045nYPN4UGYw4d+WbcDcjFA1yIxA2WpgoFGWcU7viasumawPpSJfPCaEhkPAiZcBt0vK6N1wF2AMz55WKJTQqUKaeSOuoTBvDKo7yNtIIAbZ6xzAgEuNawu2CC3LcRcRY+3otA9Ar9Jiqbk6rkfGiqw5uGDpEJuYQPBnUWYVNJfQBFOgK8C1NdtxePAwqk0TzCwbi/BSYVtV+dsrTfOWxSiLpJzUzj+hu7cfuUoFdMwgXXWz9LeFlRGEBgkLLZAlq8YymnEocxlWLzdAT436zHxgze2ANsa9nbMZUGmB7rqQUXjRAukYfZSxGgL0WUA+TdgvX8kAThKaOEbEMw1WtxO/b94Dm8cl+pbNNoj/bd6Dj9RuE0HeLvtxWD1SNY8PXvQ566BWaJGpKQn7HGoy7t69W2xouZEm+TB25hwW1pTIhlRWEw0DLgsQQ0YigFytFhZbiED3DQ5iWKdG6dxaIel04sSJoMYtQd8YLj62bt16USo1aBr+kTnLxWNK4DjFPG/ZCx5gLNyMXoDSDG6HVDVoygLe+hmgpwVooFeBW8quWrIhdt9jIKSwGDh7KvxQ8gGfpccTAeeZb30VuNAgfU7kHOuKL00Whcb94YSG+Hs3oNcBugygYG7UPKYAqxhllYzjYE1OFXb0tsMaRy4tJYO9BKD8wTUF67Gn7wgGXMPQKjVYlb0YBbpLY+yYENjOIrP72CZ27oq99vr73wGzGfj3fw+91tUF33//HAcPHsKmzZuAj39CIs3++gdgoF/yFLnrncmv5QrmA617pQ2F/G9M/nnQclTacASSYBytQNY2QMFNrx4KFPg9zQJQQxmjGkit1CJXU4oBV0fwNVbm5GvL0e8YwqHB08G10bO/egyf/+znLgk9X57j5z73OfzgBz/AO9/5zktCEnQWMw/DLpknob8q2x2xD83T5SFfVyges4gGE5a49mKfZHZ7dXVy895kwTGLWexLlqQuNcugNEmNjRtDsYmYYKLNqruBhjekhI6MQmAuCeaZP0bOIj2m4R0dHUHSjAQaqzgmSzKw7dJjkjJX/Cx6xLDiiFnMgd/T14p7nEiptSsaS7cAVYuBlnqgp10yB5+/Esi/TAykK9cCQxGKLpSVv0RAGcLNY2P41pJl+LezZ2D3enBNfiF+vfbS+Q4XE5RuJIk55Wtwzyhg2RGq0Hc1AfrlQETyaDrx5JNPiiQErtenClMaieFA/dlPfwYPP/T7MFKD4JZfL2Mka0y5+OTcTfhjy3G02yXtX+LO0kWoNiXO+jg+3I9vnQlla3faLPj00V147KoboZ9JZl7GXGD524Ge01LAJ6dKelzu6DwPHHku9HygHdj1Z+D6D40bBFBHVFswuKNW+MI2HQ7aO4jsbjdabe1YkBmREUzzS8coOux9aLH2oMXG7G/pmHWjrXhb2VYYYxAb2doM3FG6BSeGG+DwuFBqyMcS8/Qs1gNwe2ka24Uuux3rcnKwagIZUBwcyZBOGBpmp0ewSSQPttwHZOZFByvZrnc9CrfdhkGLDQvLCoDzByUiq2o5UFwLrLkJOPyCFGTS6oFt75T+DXxvtxvezHxkXn9r4vPj8Ws2SI8A+LnnXwQGL4SqQgYbgXm3hLc3xwgw4pe6yar2f9dLAy7vKJzeflHJoFcVBaWmnF47GsbOCtk2BlcLdWWoMtZOWAPa4fGITHH2vbkZ2dCkYTPXZO0LC+CS2ODzZmsfFmQWBwkNOcbcPVGkBpGdnS020symYlvPysmVFruRDHmCyq1vLV2G9x/YL7V0khsOJ7Kr8vGVE7vx3blrxHHkupAkNFihcdkG07y26GtIMjOWApyc6GWVZOkc6ZEM3vRmYM8b/kPJDMYJIf80HxPCqRNAQ8CkUSkZqsvv1YYkq2von9FOgicCPN/iJcCcqyRCZxJj/M/O74PF4xYBNafsqnOdVKw3YX5m+ggHzgUZaiNuKN4sCPt0EiYXE9TD98EpVcaQ0Dr2PNDTCGj0wIrFwLED4cTGNTdJfu7xvDNefhn41KdCrxUX48zb34H5H/1nqAMGd+Ys4OP3R//9yCAw3A9k50v+YrHAysNldwOn/w54/NUZ2kzJLNwz5ic0xMmEsqocLYB+jmQkiGXwogk+3xAjb1ApWA0UO0Gj0rgEOocJo+5+QQ4X62uhV2Wg1Rry6ji16yhGuodw330hf6yZDp7rAw88gFdeeQXXXXfdxT6dWVyCYH+Qw6hm0oUCbv+4UKIvRa3pIngAXUIoLy8XBAHlecxmM0ZGRqalWuPChQuoYcXcBECPP1b6JgVTHrD8LRM6ziwubZCg27NnD0pLpaA5KzXSAVZgBAgNgnsYmt0HcPz4cXHsAMkxCxm4Hl6yEbgcbTPza4CVbwHaT0hxi8L5QPFFkm2dAFjFRDnQT9XW4pNz54uQeVolkS9jMO7FarCrr46jIJBOOLk35t2R7X/sp6eU1PjhD3+IT3/601NadTbl0f6PfvSj+NZ3vo3z+09i7vqlQXPnZdk16HNYUKDLCAbb5mXm4+tLtqPHPoYe8TuTeP+OvlaYVBoszyoU2amxcGCgR3ScgHwIb9WA04FGyygWm9O3uBp0WsQjR2sSjwnBkA1UbcIVhc76cMkh3qfhbskM2hDbmJswqTKRpy1Ev7MnGFjP1JhQY1iKXQP7IkJuvP/hEbdOWz9e7aFJpjN4WIZkPf6/tHtcODXSjHW50aZ2bq8Hbq8bi83VyNOap90YkNIyt+zYgZd7pEoJfvufr16Nj06gzJpBEGZ+BAKw8p8TomyNVF0U8NTgRay9GojhcyNgGRK687vrmlFgNsHpdkPLSoz+DonUIBZtAuasliSnWK0TQTwGFnwTxmiHRGiIL+tvc0MtwHAbkO2XB7F0Aw3PSZmyRNchYO7tgD5OEGoGweHpwZDrWHBCsrgbkavbAJ9PhSNDe+DwOKT4nRdosZ6HxWXB0mypoi0V9DqseODkTnQLaTCg3JCJf12yGdkyAmoiiNfyON5Lv4sg0fzZ77HAjQAloK666irphfzFQD8NqAMBch+QvyRh4PmeikocHezBn1qb4XV5kZenRYYGON7YgANeE26QZZocPnxYHO+yJTQIpRnwWGT3QQGUlAPNTeHvyxnHIDpZw+lv/1iSiKLc1HFmqPsJr5tvA67ePrHPtctkKwWp6yc2uKC66XbgzW9L/BmsqNz7J8DiN5JWRMgHLbxu0lWWfU4rBp2UgpKgV9I3CjCqtFiRXYR7K5dAm6asUG44WB4ewLQTGq3NQHs7wABBZfoSBNzeTjjonSNWfmoY97dB0cvKX1Y4WIGxAeC9HwB27ZaMwleuB256Mx04gd/8BhiJ4QMju07E2NiYyDJavHjx+Cdz8DXg5cf9clAK4Ia3A6u2xE9yWfM+wNIrnaspXyII3ZL/XDhoOucMGw9VqE1KuZTvJZFRzPfLwPWP3W8i9OTP/iL8KSIl9mYyeK485+985zuzpMYsJoQyQy2GXb0hIlkBLM1agmxNoVjzT0bG9koBs8mZVU5Sg74W58+fn3JSg5WyNHCmaXiqYxb3P5wL+bezmMV44Bqf+vas0EgXoUGwsokV3pSwZTtmfwnsJ4aGmKhAa69ZQuOKRG6V9LgEwaqmgHSSlHwzi2Rx6NAhrFmzZnoumFeePheA3yt3CvaF9Cai2sUzz8SQgr6USA2W0Xzus5/F0z/7K9719F2weZw4PdKF13sbxKNIZ8a9lRuF5EgAhfoM8Tg02IUf1e8PZszMy8jBvyzaBF2MygujSiOyDqNfT99X3D/QiFdZYeHHtoJF2JA3m8ETBQaOO45KQUWaTJesiK0TSMR73Q8OiosyV6LD3oIx9wh0Sj3KDDVQKVTCO8PqCdcFL9aHFgGjLite7DkAl8xcOpCoqfSHP9h17V5XjKxwG57t3ItRtxTMJalxc/EGIVk1XXj4wgW84ic0CH7LTxw5greVlyN/AovxwIKJpeJyDdyEoLzK8nuA3jqA1yqrAjBHZ8wHoSfZp0BJTibmFuehrqMPRdmZyDFESP8wMB4nOE5d3omUlAfhimPgRgkitksGjtp2hctg8fWO/ZKpdDrAz54iImzYxXFI7v/ghMXdAJsnG06vRGgE5UZppOjshN2zAHpVamTEfzccRS89UPzosI3hoQsncP+CdUn9vcPjxpjbKUyOX+05j0bLAMxqHa4pmIMMtQ4Wt1P0W1IZfF5tyhcBuGxNJYZcgUxlCVma2Fr1bM9k/rk5MFD7nnIBC94C9JyQZJFoYp2XnPbtIrMJczNU8GaYYevshqW5DeoME+YtWhi2aGP5+cWQnJpW6JYAthHA56+cpAF2wVWAfinQtk+SncqpAcqSawvjoqwceP9HpJ+tFqC9Dcg0A8XjjDOJMH8RoNNLQWwO+mJ9oAK+8x9AaXlyn9HXBAx1IopnE/4JN6Zl8adTqkTloXz1olMqcGtJLW4rnQSxG0dygxriFwWP/Qn43UOh5/e+F3jHeyb9sR7fKBzek6EXXA4oelrD38T7pHUB938j/HUGDX73O+DrXwdOnAi9l23l/e8PeysX5Al1brtbgZcfCz3n57zwKFAxB8iP05Y5R2RE6LOrMiWZKZ98beIDNBOU/oyBTtsA9gzUiXZ3dvcxNJ9uxGef/SwuNTDz6z/+4z/w6quv4tprr73YpzOLSwwZ6mwsMV+FXkcbvPAiV1OEbG2chJ1ZxIQ8SSqQQDXVYGY7M1rpdXD27FmRBJVskklLS0uY6fMsZjEeuBdlwHHDBpkawCQxf/58UY3BffjSpUvDEg6PHTsmZG1nMYtLDWzHl3Wy3xSBpCkTzqbNIFydB3jk8rWUlc+eEkKDY9q//Mu/CLlYcgJTiWmJytz/ufvxs5/+DEOHmzG8MBv9Tn+pPYNtjlE8330Cb2E2uAw0iP7p+UNBQoOgBMrTnQ14a3n0Rv+W4gr8oeUcBl2OILlxdX4Jqozp2cD32EfCCA3itd4zqDLlo5h6/THg87ng9kkZd2pFLhRCC/kyx3A7cPrJ0PPRTsBlA2pXA42HJfPYwD2tXhHbIyECDHKWGaKzOq/KW4eDg0cxJHTBtViWtQi52lB2EOWmWG0R/XmhU6BgRaneb04tw47e44LYCGDAOYp9A6dxTcFKTBcaLRaoFQq4ZH2AlUgtVmtcUsPr82L/wHnUj3YI4md1Tg0WmcvDNhkB7duUoDUCZUmWavOeLtsGRddfoVCqsLCsEK02oNeTBVN7uzB1Hg/UFaWU0KRgLIjO9ueNHzgBdO+RDFU9EaV3/Nk1iknDOQT07ARcI4BSB+SvB0xJmsemILMS8So8XrvYlMcD5dlSxQXLUJhxJn9uZCVOEni15wL+r+WEaLNsx0YV5eSkWowzoz34cM16HBi8gD7HmKjYu7F4SVCSMF87H2qFTkhOqRQa5GirYZB57USCXhdHjhwJbTp0ZqBic8rf9+qCMvyBWq0+HwwlRVB4vJiblYsKo5ShReKEkgebN6f+2TMZNJe3uc/BgzEoYYJRPQ9KmhMbtwLeIX+JGxc8aiDLBGQlSQpMBEYTMC8NwXzKkH3lX4GffA8YGpTOmxzjd78I3HAHcMc9iRdwLkfoZ/lQMfcqoCxBxn6SOD0SyhIOHtbnw/rc9BvRsizcGaiCmU7Q10ROaBB/eARYvQ6YP7nSeq/PX0UTQNxbGucXFRVStcYrrwBPPSW9dvvtwPZQhRD7Pa9dwnmzO0ILWcAnyeHFIzVinqoGyNgIjO0DfE6/Oe0SQJO+YOuRIXrE+MTa4Jn/+D988f4vhEnsXSpghuvnP/95sWnasWPHlbWh9g2TquSgCShmmH/gJQST2gyTOj3j+ZUIVj0EJCWYkEQJqukKnnGvwEAQiQ2C3geJ5C1IalA6dBazSAZMYOLcz6SQdFUycj3BfUvkmuLo0aOC5GCy1ixmMYsrA6dOnZq+Kg1CO0eSuXW3iadQZgCG1ZgKvPTSS+L7PRXYX13qpAaZmS984Qti0/GO330zmFlP8Oc2a3SQbNTlhI3a/DIwGNYp89uQI1urw8PrtuF/m+vR57BjYWY27q2cl7YNTi+19+O8HovU8PqssHoOBIOP1Hk2qtZBqZjAhEhD031PAy1nAI0OWHktMG8aG38q6PJnO8rReRyo3Ahsvw+o2w0w87uwCpifwKAtAUxqI64p2BRXSkmtUCXMGlqTPQ+1Jr8+tgx9zuGodtpHs+FpxCKzOYzQILRKJWrGYXJ39dXh8FBj8PkL3cegUijFJoMlrdMVtPDMvwrKYSdgUgA6IypqVoq2y6wqehHMnTs35saDATdq3U5aU5ASbzXbgKbX/dUYCsCUIVVqEJScCrwul9cxTDITlpUsXa8AHn8w1OuQCI7Sm4A0GfEK2RGFCR6f/7v4oVaaka3ME+MkiV05eadV6mFQpT72FOiMGHW5gsQGNf4LdYk/59xoPx5pPh58TnJ61M1qC0negT4CdWN9eHtF7Cx/9lsSGXwkA7YlPsJKxHkvLC1SmSUruHTR5GUkqk1m/GDFZvyi4QT6HXYsycrDp+etENKG3Ljv3bv3ssug8vk8GHUdhBeSh4YHoxhxDSJLQ3ktDaBKfN0uOtjQ9+8BOtslOas16yXCYtFS4L//F/jRvwINZyUZKvp0/OMJICcPuPqG8T83t1yS1pKT43xeHjKMnyxarMNh0pkBuORVZDFg97jRbB1DloYeCcn1bQZ/KKE03fA1N8Hp8cLh9YrvlRcY+1uaJk1qKBCRLKJWwV2aC1XHQIjG4KUNSB/GA0kMGZEhx8GDB0PyduMhnn9GvNfHgyYPyL5Z8rZRav0G4SF4fE6MuXsFyW1S50OjTJwgIofT6xKX5fQbh9F3oROf/cxncKkiUK3xwgsv4KabbsIVAW893alCz301gHLexTyjWVyhYIUsA74EDY/nzZvedkgZqUWLFom9HqWvGHiOlzzF4AqJjyuK/JzFpLFixYrk1wFJIpLQIDHH/QsrwWcxi1kkDybr0uuS+3TOR0K14RIBE3nZ70l0ThsUSsC4CvAymcMDKAxTWqVBDmCqqzSIadPP+OQnP4kf//jHuLDjCAo3LZaHEWGmkWMEMjVaGFTqMGKDgeUSffzKiwKdAZ+bn7pufDIw09QxBrLUsV+3e87AJzM95c92z1kY1RNgwnY+BjSfCulD73xcktQoqQGyCyenZ55uRHhaSK/5M+KzCoH1d6b9kPEWpxXGQphUBljctrBs83xtFq4uWAGT2gCt3OBWBpNKLzb9wWNAId4/nXhPVRWe7ezEX9okJpXZ7r9dtw4542QhnRxuifna7XPXiJLWtWvXTktpOMkJXcUCIGJjwaxKEivnzp0T/8o1QymJtXv37vR5FRQsALKrJDNwVjA0yYzqCZEJw4Cl/z7rc4DSDZOv0giYvsph60obqUFkaZZj0HkoSJpqFDnIUNdCoVBhqXktTo8ehdsftNIqDFiRvXZCnjD/VLMCXz+1E05/UFevUuG+6sQB3br/z95bgLeV3tnDR8yyzOzEdhKHmSfJMHOn7UyZGba0pa/73+4WtrTl6bbd0hZ2y0PtzHSYM2EmJ7ZjZhTj/Z7zXskCS7ZkdqLzPIqtG1m6uvi+v/M759j7BQESe97xN9q3k9SIZMZMJ9auXSsk4sIihkRGxxOyWiaCgu2AJd5TPhnW5BTgRxvjbUw4UOKxye6+S812imrCkOj2jUKCF/5QH7SqKdg/zRZ4PfvO14AXn5PPaR5XN9wCvP+j8iCNY4jz8SpLgZNHJiY1mPe0+TXAkUfkTCHmsmy4HTBNHzmcpzWOsc7kKZKTZFwUwanhAXzy+F4Ms+EBwN1l1fjnunXzplDDCUZzc7MYrBPKnl5oHU7ow92HZ+wubM+1Ql089eNLpSiieR0kOEZJ6sD6zVAZBoHuRmDADox4gUf+CGzaBayZwEIqASdPnhRFsLTO+8XLgLr1wLmj0WNx5WagImxT2tsA1D8r2yNaioGVN8oEfCrwmq0a28TgD7nR7HpVEBviZV4lKo2bYVCl72NfZSxGl2cQf/vu7/DZz3x2Wv3CZxtc909/+tP4whe+gBtuuGHenAczBhEOn5BthCZAKsoqNrKYE/D6yKBTFmT7+vpQWDj7Fl4870moRJqnaEkVWzhub28X84xI6HMWWaQLFhwj1s0zoaIgocFziA1/WWSxUMFr8EydI7GgIrChoUHUmviZPHdIbtPCqbe3FxUVFaikCnueg7UFnvtz1iypnNlcKWZoNDY2iuy72cCsVWfo4/y5z30O//3dn+PN2/8DivABzw7F64rHyn7ZXf7RJZvwn/UHEAh3LC4x5+K20rnJsKgw5GFNTiVODLeO9navtpaj0picUQ8hNmCVkMLLMgQLMhdPjQ102R8OW8kpBG58B2CaJ9LzvFpgMNYLXyHnasxyyDahVWpwW+lOHBmqx5DPDp1Kh+WWKpQZ8ics8O4sWI3Hu/YJOycprPrYmpeeL/90gefGH7Zvx8f6+9Hl8WC9zYaaCfzQY9Ul0WXygIwTDoJs6UyrNsYjTngDop8oO7ooA6+qqhKvp3UE5XfTGt7HwiAfviQKLxY+ClcDplL5+KRKY6oEIW2txkACAsmVXpOFRmlBgW4XAtIIGMWlVlhHCzk2bT525l8rBhaSIgQVLYMmiSWWXHx3/TU4ONglzuVteaXI101M7pnU2qTHYoTQ4P+tsib4yE9n19Pw2bFWYn37AfOiFPsoNWg9Q4UGCY1Z7aSYJSTbT3HLAx5ZgUeVE23dilbMyfU8JY4dlgkNIkKUPfEocO2NsgqA57RGKyseI+D4I91OnqIa4IaPyqQGryXTVDD1hQIY9HmwPa8ce/tb0ewaHh1b3Fe5GuaYnLFYUOX0z8dfFWrWCB7oaMJKay5uKxvfJ3xK1iB+P3DgJWBoAFhcC6yUrRip/KivrxfXbZIZDNTmuUjPctrBievS9u3AYB/w94fE37iDQRxZvga+YTvyz57F8uWTV2uQyDWotsAfaoYED5QKE9SqKijWKoHDLwMv/ir64sYzQMAPbEjPPo5Wczzn0y6C8by48x3A2SPAQC+QXyyTHNwGI93ASY7bwucVnx99ANj2FjnjKQP0eusRjMnb4Oiyy3MK1ab0LVXW22rwt78+BEf30KxNOGYSH/rQh0Tj1F/+8he89rWvxaWNFJlhYo4xT+YCWVxWIPHLIhOJhHPnzs0JqRHbPMX7HFUZK1euFAWvtrY2ES7OjvsssphPeQEk4Kg0zxIaWSx0cA4wnYQGa0MsinNeQfUFCWsu4/WdWTfJ6kU8j3gv2r9/v1BvsJl3umzjpntb7d27V9hZX4qNOMFgUNT9+ZitLMdZbTn94Ac/iPvvvx/GF5qx5XWUiCuwwlqCPG3yL7sxtwTfWXcN6h2DMKk0WJNTCPU0s38v9HZg/0APTGo17iqrRqkhubUPD7ibS9ZimaUE/V4HzGoDhv1aHBocFMXmxPWiL3kQMX7cUEBJ39tMEUn6TVUjHumXlRskNuYD2B3PLIr2I+Eg2cVAzVVztjpGtQ5XFGRuFVKiz8Pd5bvR4uoWKo3FplKxz2cbPO52FqRvibTSWoFjw81jlhG8+JPVLikpEZOOmSQ1eANhoPJ4KC4uRmdnpyA3IoWjGesW5TUmpwYYbowJRdICecsBzTTe7JjporHGKwQIXysQ2gQop68orlSooVWkVn/IA4upXy+L9CbckiGZvD2/HE90N6DHIxO5pAbzNFooFQGY1DrcWbYKNebplVjzmC6InCuBZAQyrYe8gDq9/e0K+PGLM0fw6sED2LJjOzZIISw8x/mJoVbYhIVPrLKQCiaNMk8OWj/1V8BrD9+HzgKOLqDmmhmRqk4KvT0plnfLpAbX8+a7gYf/EL6fhh/X3Jr+Z4ig6em7/h8f6sIvLx4SCiiS16+rWCNs5ex+L2rNeVhmSX1u9HrdIjtsdNUEga/A0z0t2FNYCisJnBSgtd+WLZMIdieBQguvixdGFQgDe27C2UV14lrPwhHvLbzmpAy6e++HgO07gdYWGMrKsWXDZrFd2T1LFdTOnTsxWSh4LVQluUa9/I8ky56YkNTgpIlqQlqqrF+fYY4WiY0VSexB+5sSxnIS4BkBnAOApSiqdA2cA6R+7lVAvQRQ5o/axAkDQIUCvlBi04ys3sgEPq8Pv/rK/fiPr3x1Xk74MgWPuy9/+ctCsXH77bdPb3PEvEOq/bXw92MWCxPMtuA1M9aOai6vK5EAZirtmF3A+8x0Bj1ncXnB7/djZGRk2ouPdFBgo+HixelZ7WaRxXwFiYfpGnexCZfNUiQxSJhTecF7CgmLdEgT/g0ffJ+XX35Z3Avmk60bm8FIutBZYjabJSUwg5B1MDbFsX60RDTGzgR+8YtfiKZQ1v5nC7NKavBg/8Y3viG+4Pk3vy2tAiYLanzMBP6v5Tx+1HBSFBWIh9qb8PMtV6PckJxk4c1sibkYaoUZ1zz/PJrC3tRbcnPxxJ49sMXYAulVy+EKHozJ1NCIZRmDXabMz6g/mPz/qWLpDQe9zAdwW5ZtkB8LHDkaM9bkzA67OF3YXbhSBITXOxgUrsRGW40ICk8EL6JksCnVmymkY3PFwRxJDXYQT6VbNy1U7AYMeYCrFyBBRbJrOgmNyPFvKQOGHbItDi8tapX8M+gClLPbRdno6MTe/lPwhvwo0uXiqqJ1s0LOGVQa/MuKPYLYYDf6IlMOripcDOUMFsJ5PHPgM0ou0Y4jUWapSu94Zzf8+//+R5zvbIdl+RI83tOGIyP9wpbKqJ6fao2gFMSRwSb0eIeRr7VgnW0x9CTuJoBSoYVFvQnO4CmR06KEASb1KigVeqD/ZNi+LaaG2n8eKNs0vm3ObGJxCkuxqphJ4k13A7Y84ORhQKsHrr4JqJrYimwmMOhz4+dNB0cVqMzS+EPrcXxm+ZWoNE58fSBpEbF249DexMuLgoHj/XjfoWfwnfW7UZJkzESZM6/7k7JPe/V5oLlB/HqybxAOfwC5D/8R23/4ayjz5Y7cCSczXMl1G+VHDEhE0gc9GbjOR44cwXYqPSarLklnWQx4L2KhgTYmVBROG6jGSHZLjFVp+I8BUnfM8wMIqjdiMNAIv8TzUAmLegl0Siu8IaoPo0auOgb9ZYDvf//74v779re/HZcK+F1+8IMfiO9GD99LFgobIFGVFdvAslhenkUWc4RIwZe2H7TUYM7FTIGd7RPNYVj84uuoWs4ii6mA46aUDRuTAIt9zOhgkba0dAHYvGaRxSyEXZMYOXr0qLiuk5Rgw1QEkyFMeN7ykeo+wWZIjoPZ7DtbOHHihJjb0HJqNu2sJXAOcThm3sAaNu3Sp1+9SAKYdrA//vGPZ7XBaNbNwe+++25873vfw9e+9jV85StfwVyBRaufNp4aLSoQnmAQ/9dyAZ+iXcA4ePuBA2gNh6IRh4eG8Onjx/HTzZtHl9ECwaTaiYDouGM3bL4cujoZ7LgDYJZI6xnAOQz4Yj37FYBxYRXeLyWEpADcwU6E4INWkQOdaopB01MEiYzdhSvEIxnJEGG4q6urRzuY5pLUoA0WZWmUhk8LwcJu1t4jgKMZoO1S3iogZ0m0g3YSqp2Moc4BNInMN73RZ7drrcszgKd7Dsc9f7xzP15TsWdGyYUIWPy/qzxKVLmDXhwZPI0h/wjMaiM22FbAopm+a1dNTQ0uXrwoJKmwLgM83YCrXf5PHgtFuye0TeLkl92Gey9eQLPfjcLVy6DiJVYNFOvc+HvnYVxbvAJ52kla+MwQPEEfftfyAlzhgPoGZxdOjDTjTVV7YFBNPKBQKS2wKpMUjmk9lUwqGP6ceYGldcCb3wH89pfycx7b7/oAUBljxcRlO66SH8nAjKqTzwIDHQCJhTVXA5bpy8CJRatreJTQiIBbt8kxkBapYVJr8IHalbi/4RQMqvh9M+Tz4r8bT+FfVm4dM4Fm3sykrTcGB4RCo2VoBFqlEtuLwx1P9mEgTGpMFhzwsqOXg/zYwS992Tn4n9KAeM0W4MXH44r/YhlRfw5ovAAUFgEbZdUItxMJDU42pl0OXlwHtBwEAr7w+iiA3ErAGM7BYD5GLKERjosZ8J9EgEF+AiHYA/WwaFbBExqGTxAbvO9rUKJP/97GrBOOvx988MExYaULGfwu3/72t8U8gwTHXFrgzDiUdYBUHJ6YmrKExiWdnzIg57+BVqkTNyrMNXjtZJGotbV1xnzNSWikc2/gPISFK9rcZpHFVI7p6bDVYbGPilm+F5s1LkVL2ywuL/AayxoOj+mpFOk5/ybRx/H3dI5LqYY4fPhwnFKP68zPogX6HXfcgdkC5zRUjMxNrlNnkmW9ovleQWX4NII1fjY13HXX9OcozytSgzcGTjp40L73ve8VvstzAdo+BBKKrux8HIn13U6Bw4ODcX9LUmT/AAed8SCJoVFMA/tHtcam6+XHQBfw958AwUi3oQLYdjtmC73eLjQ4zsIf8sGisWGFZS10aXY/X4qERr9vPwJSNKDUIi0Rgc1Ji+2Oc0BgSC5um1YAaRQbp3V9Q6HRQg1/0pMwsZA03ZOOicAL+yOPPCLCPacF3QeAkfMxz/fKGQrWaswajNWAtwPwMoeCUAC2zdNqPZUOmp2ydVokG4E/B/0O2AMu5Ggy7zjiIOC53mY82yMHlF5ZuAjXFC1OWvzja0+OtKHbMwyzWo+1OZV4rvdVOAIusR78+XTPq7ipZDf003Qe0L9xdCBE8qJoD+AblEPDtbmQlFq0tbaKThA+uI4MqudPFvl4LpBYI+G3LH8jCuv3QauMdh/6JWDY34uH2vtxa+kOFDFYfp7g5f6zo4RGBO6gD0eHLmJHft3k35iqIySoBKn+MMyf7y5wz33Ajt1AdydQWg6UZND5dmYvcOxJudDMfT3QLgdM3/R+wDD9dnipsjJe7GvD7sLk51Mi3rxoGWrNOfjy6X1xBAnHMG2u+Pwgu90uCA3aTo3XaUh7BUq9+fqBgQFBNFD5xHPFKalg7+qFRqnE2vxwNzhtrgonMb5hM8nJfXLFftVWYMka0a1I31wOgikXJ+HO+xW7bAVJOVlcc4dsnXX4Jfn5xl3A1bcDf/o98IufRl+352r4Pv7PIjtnRggNQm8BNt0LNO2T7dxySoHF22Js3MY2AXDPBiBnYUWhQEAawiLjDriDlJKHoFfZBLGRLr74xS/i6quvFo9LDddccw2uuuoq/Ou//it+9KMf4ZKGUGZk1RmXLCQWIU7GNBY0AxKvGfPfWo25FsywmCnQ7pBjt4mu1SyyUX0X2/GbRRaTxXjHHOe89PLna2gvwzEUbZZJ7vE4ZIAxyT52s89mh3YWWcwkOG4uLy8X2RXjgddhzid4X+B8m/ML1p/Y3MrreVdXlzg3prvRhsRhrFqP830SizwXWYOeLWKRDZOsMcwNoUGkajSeuAE5EzQ3N+O73/0uXnrppVnPCpmTqyoP2nvvvRef+MQnRKjfXIBdxDUmK5qddgRHi37AhtyJO+0rDAacs9vFhJOgfdWiaZQljou8EuCujwCNx2XrqaqV8rJZwIh/CKdHjsY8H8Tx4YPYnLuwQ25Y/Gt3DwiVQ4UhH5o0w6JdwfYwoUHIx5A9cAEGVQVUsd1Uot3yJcAX8X5XAJ4OoOD6WS1086Ie22nCwRYv7jPVScXwM6ow2EE/OhC0dwGObkBjEKHyLk/0xjZlcDvbZYuUOIw0zi6pwYJ67i55f4e8gCYXUM9QVsg44PGcyfKJ8HRPE37TfGL0+a+bjyMohXBDSbyXPff1413HcXy4VdjkkMQ4O9IMgzpadOcyb8iHDncPaszTc/yRpOP9hIMUejqzWChpc0XHd1vbadGFzWONgy8Wd3k88vjn+rLgy8FWJET5XF8rdKILPopuL1ARAGwaCYeH6nFTyfzxZ+6LWEQlIJHoyBjWMmDRLqDlFfl+o9YDS2+SiY3ZQMMF4Okn5ADw3VcBq1anfm1ZufzIBK1ngUOPh5VVkRR7SVZDtpwC6iZpezQOFptyscxcgHP23tFlAQk4PjKAw0Pd2JSb3v18R34xlppzcM4+JMgMgudbtTleRcRJNom6CKHB453B0kqSvWEw24gWULwXUIrN1/N8oA85z4nC3degwjEI40tPRQmNd38MMGWotDp3FHjkl3Ihn6t8/jhw85vhsJaIAgAJFXZPcXJEYoMkB9dn0lCpgVvukx8RdLTHExq0uXruabxitmDnez8w/cUGZot1nATcw4AxD1hxg5xLMgZagBlJEokKeX/y6pkMSqigVChhUmfuEcyOtV/96ldCkXKp4pvf/KbIQnnPe96DDRsWvhVqFpchROPcmciT8E9f2FZzhq1ap0mZnY5ae7JgEeyZZ57BlVdeKa7ZqeagLKCxUzeLLKajcYr3z1iLHRIZHCdxLsEmDFpW8ljk3JcZACzU0s+fxyt/XkrKyCyyINgMxTF7qmswz5sDBw6IOQizl2jtynOF6g7OM0iA8zmXz9T5wXUgeP5yPXgucm40VbusdEG1ILfPtFraZoxiALFxBYpwU8z0zuc//vGP47777sPGjfFWw7OBOaOKv/71r6Ourg6PPfYYbr755jlZh6+u2YZPH9uLFrdcmH5NeTXuLJu4+Hn/xo24+cUXhVqDgzaLWo2vz6CNzxjQFmPd7Idv93nHdn67gg64g04Y1QvTAqvXO4I/tu6FJyQrX/I0ZtxbuUMEjE+EkJTcmiUkeeNJDf9gDKFBSEDQCXja5K7+uL/1ISiNQAE1VIqcaSeLYt+Pxd6ZDE5aWlKChm9/G30eD/wVFbBduQa1qna4PD7kWo3odD2PBl8Bti0yAheeAGzVQMHSKX7qPCF7kSkEAAEAAElEQVTXuJ11vIGkD2+wD+6QfI4ZVOXQpsjfCIQC8Ibc0Cp10CiT34xCEm1KPPCHJ5XcKiyjLTIVwTRJZdXT3QkZFQCe6m4aQ2oM+JyC0BDrET43hgNuGNRjC3mRa8l0gITd2972NtGVHumGILFGIoPF0WT2ZuzU4DV8b38bWlzDyHUZcFXhIqHkSwZvUIKkUQgidD4hT2sRWRqJKNZNQxdv8WqgcLlsRcUMmkmSYhnj1Angc58KF3cAPPIg8P99Edi5a/o+o+1c8u+jCBejZwC0fruioBqHBnugUiqE0tMTlJd3eZIF3KfGJ+o24p+PvYShcHB4qd6I99bEEz+0nKK/OR/ltTno855BCAGoJD36Lmjgc0uw2WxCoRALkhkRkk/gTe8BbrgdGBoESsoAtxfo6gSKilMU6ZNg3xPyz9hi174nMbj5NvFZPHdZpOJkg2TG3/72N7zlLW+Z3vtgd0RBF1kVCa8O27GroACa8ch1r4eGv2xBlnM5Dr8CUBWzeBlQm6LISDLu2IPAULt8nJEYZGD4mttjFBph8LlmIxA4CYTY4ayBUrMMxuAQXKHW0QNTASV62yQ8d+Jh3HjjjRk1BLCxgZl2n/zkJ8UE8lIFJ45smvrABz4gQuinwzYkiyxmFxyDJI5DeN2cR9aPMWBnOgu5swUGK1NRyC5hdtqyaEVVH38yr5PnPO8ntKDLnv/pg/dDv+QWx5pGYVzQDYvTjZ07dwq7mmeffVaQ5vydClcei2zeS9xWbNTgcmI2feWzyGI2QWUsu/J5fsQe57w+cw5OooJW54n3h9ly6uE9geQj14U1LzY2cn0579m3b59Y75kESU+uw1yrBRXIhQTWqplhyPltLiAthyR+V0IR0+j2wgsviOZQNnxmgkcffRTPPfecIHHmAnNGatD24z/+4z/w4Q9/WHTkic7aWQYDwX+z7Tr0eT0wqFSwsPswBbzBAPYPtMMe8KHWnIuj11+PRzo7oVEo8LrKSpTPwfrPlKWSK3he2BsooYdRtQRqpdxlzs7ApDmXs1XkmgE83nVUhChHMOh34sW+s7ixZGLvcY3SSu+xuGUKqKBSJBwLUopg0oTlgdAAnMGjoxMZ5rAYVeuhmKHty851DrrGRfd54MwzgM8F5JQAa28FDGnkCYwMYtO3vo6LrR3o8PmwNS8H9h0mnOkehMmgxbmWbuRZTdi9OAjYw8TQQCPgdwGlk/R954DSugQYTriY5sz/4o0r2IFhf8RmQFYB5Wk3QafMG0MsnrMfQyisE6s1rUSZYaxX8MHB8zhrjzLyPEoLdDm4tmjj6MCbKgtnwAejSgt1GkWfCEERi0geUSySFfx9QZ4ZaoSELk4SpTkqRkr1E3ieBwPAxVeBwTZAawAWbwesqckiduzRN5Od55S07tmzZ8JJ2a8uHsMLfc1CcReSJLzS34o3ViZXBJjV8nuVG+Y2OycRO/Pr0OLqjVNmLDYWYZV1mlRYDDPWzjJx/ZtfcjQaLYBzP/7qZ9NLakRC32M/g7/zmls6c9cNkhB2UbOKko5qSKjM0O6qymjBzzZfi1MjVBoqsCYnH3qqE2LA45+k3smzh/D4C49DpZLPdZ/Xj5Wr67Cx7OY41ca4oN2UOQf49/8HHDogL6tbDnz5a0BOGgSaLwkZ6Pdi7dq1gnTZsWOHKER1dHSI7i0GBU57UYVqnsh+5rVyxI61ZiM01eMEx//tL8Cffyf/jS0XyLcCjmEOfmTi4o43AbuuH/t3AxdlQoOI2ISR1BjuAGxJVEW0kNJElQUDvovo9bZCo5SgVahgVBXAqlmKtv4zYiLGbcXrXbrdbT//+c9FR+nnP/95XOrgd/zd736HX/ziF3j3u98916uTRRaZgTlgEucSLDDHYn7leUXA8Vasqi7ODnQGwI5fju/YAcwGFjav0FKEHb/MVqO9CQvPE85xsoib/3d4jsIdlO20tUozyvWboFZmC/IRMJuFRdFITgvHKVlkcTmD19hdu3Zh7969o82DtJEluXzFFXPv5ML7EOcWrAvwfP3rX/8q7h9s8qFjyUyCShTaMc00cZIuFEKtIX/noOTGgO8QApJdPDepqmFRLxH7i/uRhC0t89LNpmNN8SMf+Yio7c9Vnt2cmvpRGs4JB1Ub9PidC7A7skg/PiHhCQbw9bMvoc09IuwdWNx7XcUq/HPdFLzK5xLsQvaNyBYiMd767NCwB44IQoMIwoHhwCBsmh2iUF+sL0er66IoTEaQpy2ETjmNhA4LsY4Oed3y6gD1zJJFAz5HXKmWBVeqN9KBXlkCo2oIrqDcRcmyba5mPZScjMSC9kMsVsSRGApAWxT9XImql+NxnVkMmfeFmqFTzaJ1UiyGOoGjj0SVKMOdwME/A1e8Tc55SQV2OP/220B3LxZrNChRqXAAQey0mZFniymMJvMS7zg0PqkhCo4pbpAkp3Jr5UKTo10uxOauAizzPyDQ7idzHr8tHP5G6HRRUsMbdOOs/WicuqHBeRoWdQ4smnhVxwWeQwlwBn1Qh/fbBUcfftt8EO6gHxqFEvdUrMOG3Ipx13FXQRX+3BaxQ5Cxu3Dsti3QmaFRqOBnjkwYvGauyVmJdk8bhv12mFRGbM5bDeNE5/fpx4C+xvATBTDYCmx+A2AaX2FUWppepkKH2y4IjViChoqNbq8Tb1m0Gr9tPhmJ9MVyixJGtQKLjMXYaAtLSF19wMXnAV4z9DZg8ZWAYWYCpseDSa3Hm6uuxAVHF+wBN6qMBSibg/WYVgwNxXf08/eRsWqUKWHZFuDCYZk843WDe5uZFztfC9hmZrBLMvGXF0lgRsFvucSch7W26D2B6HA78Z36E2h2OYTV1CeWrUG+Ll5xxGaM7fkTW1aV1dhgqGA+RTwRybBpvSoDi6ef/xQ4cij6/Hw98J1vAV/88sR/u3QtcOCZ6DrwWr5EVrkuX75cWDSwwYWdX1RrzMjAuLgE+MBHIN3/PRwYtqNMr0XubXcC23Ykf/3RA8Cffht9PjwEjAwBBRYgFL4XPfJ/wOZdQOJYks0AyZBqeQwcgV70emWC3h8C/AjCGxpGLsndsGc9FQm0kUpH5s3JyWc+8xn8+te/npMmormYaP/gBz8Q6j2GFRYUzC8iOossJgbHwofDtlMEr4dzk0M5EeiXPtsZmSQwWDSjWoPn95Tyl7JAn/f8KKFB+EJOdHtPodww+zYi8xksks5113UWWUwVrDs92NGEJ7paRC2UTjU3lEyuXkK1HEnm+QpaTjG7jwQDCQ6qRjh2JvE9U6D1IdUh84XQSMSA72iMhT5rRE1QKwwwquVaELfTU089heuvT9KwlSIcnPfhuWwiUs/1jeG//uu/hO3Bm970pnkrh3++9yLa3SNx3cp/bjuFKwoqYUoR+jlv4e4HGv8BBNyyjzh9tlUawFCOYE7VKKERRRC+UBcMqmoYVEZsyN2OZud5+EJeWDV5WGySWb1pQd8poGt/1EKo/wyw5I444mW6YdUYMRhDbLCDPC/NjmR+7xzNCphUixCCD2qFCcpkYZ20CMrbAwy+AoTccgdWzmYg4AU8jXKIsdYICWMVHUFpfDsSu9+DB9uPoNU9AL1Si+tLVmJNTnnaF3myyCkl470NcR2t4qdrUH6YxykQjLQAQwOjf6tXKqGyuwGvH9BpxicoSIgkW+7uBrpfAQJOgFZnJVcAsV3+3h45t0QKW8bkLwcsa1ITIOKz3ECgEZB8gNIGqBeP//oZRFDywxWQEAp/dYMKCKnijwdH0J7UrskeGBpDaqiSdF6rw4ofu9+LXzUx4F4mHfxSCH9oPYJivQVlhtTFzVtLl4pQ4ud6SAJIIij8jrKx/pB6lRb3VGzBA+0H4Q0FxNl8VeEK1FkrxCNteJ0xhAYhyd3OnbwuTE+3/kjYticWJK7tAS9uLV2GbXll6PW6UaQzQqOUSXBDJNicqqJzjwBB7icJcPbKz1ffK2dPzDJ0Kg1W5cxMPs6cYP1GoK1V7oQnqCZaM80D0JxC4Kb3AKdfArxuoKQGWL49fTulSWDY74OLJEoM+GnFetOY1711/3MY8vmEvqnRacepkUH8fvu10KfogGUDRlAKosM9KMiTKmP+qJWiUgz3xl4/0lZpRHDsSHSfEPz9BAn5NLDrFoAWWyd5nwewYhNw5Z1xRSp63RIsvFOxQVsTEh7TitvvwgFJgSVKBfJqa4HlK1Nf+8+c5GBVVg0RvD9xMwaZMRPedrwuOe1jSQ1ryViLSl6HLfHkVTK4Av1j/jYg0VLQLTqiWURk51m6Hr0kNGjtddttt+FyAb8r5xf87lSpZJHFgoLCAkgc67DwwGuNac7GqJkqvzm/j/iYzzTYdUt7kawqY2pwh5jnFAtaYybWBLLIIotLAX9svYAfN0YbrE6PDIjR5o2TJDYWAiLEO5WFrH3RKolNVNNtUTgyMiLUXFSwzLVSJZUqL6LQiIU3NAAjonWadC0l6+vrRZbdiy++OKe5QXNKahAMaSGrwwd9Cuej9+WgzyOKWbFWK1K4ILagSA2uf9OTsjc6J+MmnaweYIHFcQFK30UgP9FCIpqhQZjVFqzK2Tgz6pGusJ1F5PNopUKio3TmQt5uLF6HP7e9OtpVblLrsLsgswKKWsmB/AQSZ20eUHRruOiuAvpeBZwXR/9bkbcBMPLYDyUUnPTjsuz/27IPvV6SMhKcQa8gOCxqHRabJu5KZIjZuJka7OpPFvQ3URGM+21FOfDX6N8WQon93/s7Nv/zXXJDNGEskEm26BsDORVjJ21+J9DxDBDp/Cex0f4MsOgOWclDhUYsoUE4zgKaPMCQooge8gCel/jm4YyTDiBkB3RrMR0ISX4EJTsU0EClMI97U+PNxemny30YzDtn4zjUODb0MvwhnyAQi3RRsmo0eF3YoI2Vhq+31eCZnvgg2HU22V6l3T0Up6IIfyQuOgfGJTV4Dby7fLl4TIRFpgJ8aMn1GPG7xTWSREfGSFjHCZcne6kkodvbhD5vizhH8rXlKNWTiJXvM+UGC7RKVVyGBonrWpOscmBXdKQzegxG2oE4qy0J8LqAplcBSxlQUB21N/IMyio0fm5OtZxNsZDA7cN1n67B2UAf8OCfmTAHVFQBd90DJCgQ8PZ3A50dwIF98vMVK4GPfGz8922sBy6cBgwmYPNO+edEyC0Grrgn+pzXExLOzJ6ZgcGoleeDUgVPzDHH8680gdR4sbcL/b4o6cbxBxUbhwZ7cUVBvDLDEfDif5oO4oJTvp4alCTsScBpcV/VdhTrqeYqxZC/SRCokXss7Yw0igybBnLzgNaWKLHBbWRLU+lBa6wb3wBcf6/8fJzxHi2zCHbhTjc42ShZvwF5VSkmb+2tQMtFIC8fMJpT3AcV0Z96I5CTRBlF8n/5dcC5p2Xig/fUFTcC41xnI5AVn0lIKKiFfSsJH97D05l0PPnkk/jzn/8srF4vN3zve98TRNkb3vAGXHfddXO9OllkkRnEeDsDJd0cgVYesfN32j/NFphpQG90ErwzbSdyKUOt0MEnCLQo4vIhsxgDu9+BI0MnMeJ3wKw2Yr1tNWza+WkRl0UWsfhre8OYDfLXtoZLmtSIgHkaDDfnOLq1tXVaVYa0bTpy5Eha9tdzBYVopUvMBFaMOs3wfs5MlHQUznwta/h0X5qt4PV5S2oQX/3qV0WIzI9//GMRYjjfUGW0xhEaPAx0SjXydbNcmBIdguGJ8WRAdQYLxIQ2XLCOOeEUvgCUwSBCHJjGLNcqZsEbTeRaJCkckICZQZQZcvH2xVehxdUnPMmrTcXQU7kyE+A2pZLD2RpHaIj/GjgCg3En3Dg/ukwJE3TK1BfaYb8bPV77mC7zc/buMaRGor8tu1/ZFTtueFnZSqDpYLQTnUd+/iLAOIF3urEYWFoKvP0q4LcvMCwENYU24O0fQG/5WhRb1IDGABjygbZ9QBe7fCXAWg5UXzP2/ajSiCtk8zzwA54ewLwICDriCQ0BBeAfSE1qBFuihMboslZAWgaMQySlA39oSNi4yeFLgEZRCIt6bcpsFFdwAIEEMosY8PfDFf7a/b5O+EJu6BQ56PEOibVWKSQU66zIj7Exi2CZuRz9XjsanV1CobHeVovlYZUEC53JYJjm416jVCFfN4UcBp0FMBcBjt7ofuI1q3BJ2m/R421Gpyd6TpHg4LFRZpBVgRaNDh9eshX3X9gPbygoru2vr1yF5dY0rErGkG/Mh/EAw7SLOCyfJ1vvA3yDwMWn5HZ8PvoOA2W7AetizHs4BoC9fwGGuwF2/K+/AVg8ycybyP777x8Bf38ofvmh/cBX/5OhKNFl9Gb9t68ybU4mVfILxicZ9j4L/PEXcqGcn/PcY8An/g0wpZlTwb/pOwIMhAu/nJiWXwtoM8u5mAjMr/nY0k34Zv2B0XFFjcmGO8rij2uqopIhWY7N/zYfQaMzahvhDgEqET7ux6Odx/CO6j2iOFFh2I5BXyMCkhc6lRW5murMB93veBfwyY9F9wXlZe/5QGbvkUHzynRPCgYHB8X9ry5iH9rbDZw4Bmg1wMZtwN4XgZ/fHyUytu8CrDbZcopXCB6LnAB5wh2sJOPe9tH4YzcWpSuBwlrA65CvaWk2wtg0lRjyt8aRUDnqCuFvrtNJ4jvwvs4w9RtuuEHcz5OBakxONmjzSg/wyw2crPK7cxucOHFiVsOMs8jickVjY+P0K+xSgNc+KrJIgGdJjckjX7sUbvdgTCOjhELdArXZngX4Qn682LdP/OQ2G/QP46W+fbiueDf0bIrJIot5jGRzDCq8LydwHE2l33SCOU/MuhNzFzZ/nT0uu5dULAaqxsnum0UoFEqY1TVwBBpifGqUwnlmNI7AbkdLS4uwBR7P2pGOS21tbXjssccw15gXpAYnGT/72c+E7+0tt9wiui7mE7bnV6LePoCX+1vC4VkqfGDJFvEzAh4AxIywcnzvhr1Aw6vyhDqvClh/O5ApqSKKmYnMXBT8H6vPB6dWC79SBaXCCJN6+WhQ+IyCli4ac5h0iQ6oYJx5QsWqMWD1bNq2+O1J94M2oIdKvx2B0CAUCjU0imIoxlFFeGjVlAAOrCI2Q7HgZD7WRzsSAjQu2E26/Y3AhVfkggyDTZfsmLh7mV7+5buBq5XAFcuZHA2svBmBHg9UDBm3xRSMq3YAldvCZF2SyxFVGe6LrARGLT8iiGSXKJMNHqUUyyP/HUi9fAqnsJxLc2yU0CD8Ui88oVYYwjeLsX+TfBChTDhGBnyDGI7ZZcwYHkqxD5/vPY0jQ03iNsVjQj/SgWWWcqG2qDTaUGcpRL29d/T/aT21ikHw8wUs0J56CnAMhq9bAbmobqoE2ppkAiFJnkciBnztSZdFSA1iTU4Rvrf+JvR6XWKLt7qHsX+gDausReMr8RjCTWs82lAJ+6mEzkT3MHBhL6AeBJTMaogonySg60VZrWGY2IZmzsB7zYv/C7jCORZULxx8BDDmAEWTvEf/4+/xhEakcHz2NHD8CLBxy9i/SWfAGQgAf/l1eL3D59NQP/Dso8BtYVXARBhpjBIahM8uK8IW3zHtio1t+WV4f81GfPf8EbiDQRwcHMJjXS24uzw64N2RXwSjSg0PGw0gCdLdptFig22suq7e0TtqjTm6+kEJklqBAV/UwlCt1KNQP0Uv6BWrgPt/Ajz9pHyM7L5KVtHMAKhEiAQPTgd4fT5+/HjU+/f0CeCr/4+p6fJzEmfMbYkljl59CXjvR4C+HsA+DCypA3ZdA/R1Ay4HUFw2sSKI166wDVi6IHmxyLgDg76LCEg+cRs9NTKEg4PPwtkxgrXFK9C49yhM+Tl4uP0InAo/SvU27Cmsi2vM+OxnP4vq6mq8733vy+jzLyW8//3vxx//+Ed87nOfEzkbWWSRxfSBHZ18xEKtVs96l2pkHp7F5KBXWVFl3AG7v1PMC8zqoszythY6hruAgVb5Xl26fMIGhH7vALyhWLU253sBdHv6sMiUgdVuFpcPIteoedDBf31xpbCgir1qXl98eTW+MPeCWRvThYsXL6KsrEyet3Au+svvASfZ6BjGHfcBV9+K+QCzqkbkJfuC/fBLQI8niC7POeRocqBQKkSNkOpLWkumQlNTk5hjPPTQQ8LSfq4xL0gN4tprr8Ub3/hG0U31xBNPzCvJDguBb1u8DjeW1MIe8KHMYIE5fLPzh4J4vOsETo+0i3XeZFuMq4uWQ5miK3tSaD8JnH85+pxhuUcfBrbdl+EXUQOlW4DO/ax4AFp1vFpDqxaFEysLpSobYNqOWQPXYdF1wMUngEA4RDN3GZA3O50+swoNSaIkg2+1WVgVqVTpkUjHhmSSLRZ813W0cEpSHIoNRKJqg5KxCWHOlwm0TJG7BMhZHLY6Mwh1Ud/pV5L7fwtbmyTnS9ADdD0BcNAoXsNjVcn2ArmL2hAuwqsMgHm5bDkVIQLUVsA4TsC6qgAINMWuBKDQAYqpqa8k+MQjEcm8CyMwqPJGyYX494qHL4Qxr3MF3XAEHLBqonLnPu+IIDTk95Bf2+TqQYOjC0stpeJ69tZFW/FyfxO6PSPI1Rixu7AG2mSk0kxhvMB3nxvY+7+A3x0dAPJYtfcBoTbZoY3Lt90O1CUpgseA2yvZ0kToVGqwdPzd+ldGycIcjR7/vGxXakUeJz7L7wTaXgVcVBMk5N+IDBqqhdwyKSc+OuazB04D5fOY1CCh5EzwU+b519UweVLj1In4nJ7I9uBzR7ztQUZwO2UbxUQMJ3pEjwN77PVArBTgG5bt9KY5I2XE78M3zx0VhIa8JSR8u/4YlphzsCZHJi2K9Ub8dNMufOnMEbS5nKgxWfDFVZtEMHgiqBx1CUVdgjBQZETNgKK0ugZ498wXyZkZwQnCdIDdRpSEM7ttdHz5o+8A/pjr9WA4C4ogCalXy8/3vwh88l/j1RhFpZhpaJR6FOmX4+zIBRwfrhdENnGhvQG+XAU2X70ZP37qQZSW14lrfctwD0421eOftt0JlVKFF154Ab/61a9EIOJ8tHadLfC7s3GKY6DXv/71oqs7iyyymB5wfhGxC0w7s2+a0dXVlVVpTAO0ShPyddNX5FswYJ3l5D+ic8iLB4DtbwI0qcd+qepU86l+lcU8Ad0mBg4DzvA8w1wL5G5IXvuYJby7epWYej3e1SJqf3eW1+B1lZffuT9d52t7e7tQLDBHQ+DovnhCg3j498D6bUBuGm4Qs/C9jaoyqJCPY/0vwS/JirP24VYMuezYuGGjIH1iVRq066J1F/Or2ETAmj0zsa+5JonLyuVMahDf+MY3hPctJx/cUPMJ3PmlBgsSp7FPdZ/CqZE2uTAhSdg/2CgKZLsK0gtvTAs9sWG54QIQuwlYxKFHdSYoWgPobYCjk73+AJPvGZSsDgAGdhKGT+65kJzqc4Flr5U7ZNmdPce+8/X2VhwarIc/5Ee5oRC7CtaKMN4pw1gBmKsBR0wRLW8jJHUAIYkBquwazYESy6EYx8+UeQVjHfEAa5IMAE4sKLOLZVJpPZW4bFrBInk4dJ2hSVZrhj6jzmYgFO6gFYclq3QKwFIBFGyJt2GzrgU0+YC/X1ZokNBQjrOvVEWAZiXgPyNvQZIZuk1THmAwQ0P2GIonjFRI3aWrVtIWZgNa3Udi9qYC7kgFKwy90gh3MEz4jRP0y+MiGey0nxv9TCWupCXKbCPUDoTOycoL5AIqhrknTBr6LgK+hO/JghyLjD4RNgLQlmv/34Ea7vfU27ZAV4VW96m4ZYW65J0ov2s+KiyoImCg+l/aT+G9NeMQJ7STqb1e/n3o54B7JG4fClJQ4wICA0m2xQRKqblGJA8kDiQMp3ANNFviSY3Rz1LLYc2TBS2maBHETvpYIqxiHGJzjCKsO/n/jXcdmSQaHCNJwsIVODbUN0pqEKty8kQw+ES4uWQ5/tJ+Iu5+YFIrhJr05tIp2IXNMSorKwURUViYXLFJYp6dUSxaJyp82TnM8LpIMZ9juLVr10YVizxOenvij0UeM7RpJAlpCt97ebxerAf+72fAW96P2QYnDmdGLowSGq4RJ9QaDdQaNY64mkU2WvORM+FbrhL2viE0LdsmVBvvete78OUvfxm1DEO/zMFuvC996Ut45zvfiaNHj86LrrIssrgUUF5eLjItYn3JS0tLRXbRbFlQsWN0+/ZZbMTL4tIBx/2nnwo/idxoh4GLB4Gl4QJlEhRo82BUGeAOekQxkE0kWqUGxbpZsOzOYmFh8JjIrh2FvV6eW9imJ8dzMmAd4ANL1ojH5QrOC5j/lMqKnRZMZ8+eFVkRsY1BtH7lcta22CTMcTrnKVdccUX0j6nm5t8kNhD3984LUiOCTk87fKwDh3HhZAOWrl0Cv9IvCI2DBw+K7cPtxPkT51w7d+7Ef//3f4t7/F//+lfMF8wrUiMnJwc///nPcc8994g0enbUzXecc3SN6ag+Z++KIzUcATtG/EPQKnXI0xZkruJgESmxEMT3mGznHW1T+IhFoA8IdMmFOE0loMo86GrQN4QGR6MINi7UF6LGVB33XXnSMw6ZQZcpmVEWqkm6zDFaXN14sY9ZDzKaXd3w9xzGTaXbpv7m/O4F22WmPugEtDZIGi1CYCBu5OJHOxE3lNLWlNuK4a/nHd2xJVRhpZWs457n0unTp+NCfFgw4kU5scNqusELYHNzc/zFPhn8gwCL9uocoVqRLaKS2KXZVsh2ZYkwlMuPBEhSABJ4bAfYtwyFInxsaxjkzEkYC9nhjtxp8Ck0qVbAGYwW0pUwQK8av7PdrCnEUtVVsAe6hR2VSZ0PT8iLFhdJNR9yNPko0i3GS317EZTY3S1vk0JdAUyqePIvX2dJqvwo0s2xjDxEpUVsUC3392FAlYalWQSjrwvnC3mcE5AaFeII6vUxL0VCnrYMhbrkNmC0n4rdZrTz6fFkoB5Ycwtw6C/R8HASGrU7ZaXRxYfl9Y39nuaxx+qsgGSK6wwQGAJ47BhXyj8TQZupipVA22n5Oded6pSpZGrceQ/w/NOAiyqcUPT+9rn/BxRNIeCT98J3fgz46TcBV1gxw26YXWkGA5PQoEVYonjNsmjyGVbjwKoZS5Tw2LMmUWGkg12F1TBrdDg53CXIkUqjWSiMqoz5ME+zymQ2Qa90FsdefvllaDQaUTSLeKZ3d3eLAXVvby9uuumm0b8ZHh4WFlMMuNuyZUtcllQceDyXlQOdJFpD0eNo6XKgPdxIEnu+7n0OeON7ZNJjFsHjIhQ+MAP+AC6easDybavl51IIpcsWiasWx1cdpxtQuXYZtAY9Pv6xj4tt95GPfGRW13c+46Mf/SgeeOABfOITn8BPfvKTuV6dLDJASAqi0dmAQd8AtEotakxLYIlRqGYxd+AchY9AICBspyLLSByyKGSxzJyFscfjEfa6+fn52Q75LCYHv0cmNmLBW78ntcKeUCvV2FO4HSeGzmA4YIdZbcLanBXQpcgtzOIyhqt17DJny5ySGllAqHc5XyBJweZfZu1xrsHnhw8fFo2/vKdFCA2Os0+ePCmWk7AfL2sCJeVjCQ3WRAvnkdU3aGUeHK0ZtZxvRX5xHjRaDYJSAFZLjlA283tTeUnVN2t5bBj75Cc/KQiNjBuWLxdSg2DgIVUatKLiRDakUqLROSikUTWmXBE+O5+gUSiR2Betiemc7nS34aw9WsizafKwzrY5M2Jj0UagQ+7EGy3w1mydXtmaukB+TBLD/mHs7X91tCg44B+EK+DGWps8+XYEBtDiOoag5BOkRqVxDaya+Wu90uTsjCunC0mWp08Egg16u9Dt5Q1KgWJ9JYp05ZkPpvn6GD99SWIRJfbix0/mgIoFuuTy7Z35tWhzD6DJ2See00f7nvIoaRELsqu036BHHi/YkS5XLuMFzRlwQ6/SislipiB729PTIy5svPgPDQ2JBy/6/DyGk1555ZWp34Bknf0o4I5RJFk2AvpSYPhEzAsV4eyV9IvzkuRDCFS/yIHzIu5cWg2lInxTEefQ9Mo/9ZTzKUwISINQQA2dskRkpEwEKjZytVGyUacyY03OjrjX7C7ciXr7edEZlKfNxTLLkjHHXo7GiOuL1+LJ7uOj5+P2vKWoMI714p9VBJujmShKRXiz8xinGiem8FqwGKBlTsR+KvIIhCcdgtwl+aoDjBPfTPN1FeIxEUr0ZrS4hkezCVgcLmMGTLqwlQK73gEMtcsdOHkkh7nf9UDN3UDH84C3X1733OUyOTfbIJEw8rJMaPB7UkHi6wFyrwWUScihrXcCtmKgrxXQm4Dlu2SyY7IoKQW+81/AY4/IdlPLVgDXXj89heJFtcC/fAfoagMMRqCoLAOyTBUmbdhVw+MtfFnImZkO9xqTFdcVVeCpnjYxtuEhTSLiuqLJZzutt5WJx6UG3j/44P2K4bMXLsjdbgz342Cbkm8G87FL99SpU2LywUF3WnZLH/kU8KX/D3CGycuKKuDT/w/42XeBc7EEbASz79nOsWKRLh9NA+04f7QeSzeuGP1uS83F6PI2IxgKCULDnGdDaXEJXn30GfzpT38SE5CUpM5lCG6L3/72t1i3bp0gwu6+++65XqUs0sSJ4ePo8bI5RUaftxfb83fBpM4qbuYDWNxhcWjjxo2jyzgfYCFkqqQGCWyOc/k+9PceHBwUcwzOXzifoQIvVadtFllMCM436HDg80Tv8RyUWSZWXBhUemzN35DdyFmMj2T5qONkpmYxO+B9g81PBAl4KsNJaLB2xXsZG6uo7OW8g6QHi/m815FEnxBrNgNb9wD7X5Cfc475+ncAObmYT8jXFqDBcR7tje0IBUMoriyBTqmFic3FYVCRwvkErbW4bVijf+9734vrrw87VcwTKKR5mKzFIimT43dfdw00b74Rg2TRKXE1WPCZuitgGaczd7ZxePAi/tEdP/m9p3wzlllKhG3Ry33PjOmYXmpegQrjorF+e6Dlilb29k8WYEUpZMAPFFYDlevmRdBQBCeGT6LV1Tbmu95UcgNCUgDn7C9CEh3xMsgKLjVfAZ1qahOSQMgHR3BIvJ9FnTfGimeyeLH3OM47xn6fqwpr0OFpHn3O0kKteSVK9DGWNv5uINALKDSAlp2+E3fKhqRGSEiwGRPvvx0KRWpPWp6+XZ4R+EIBFOutcQGhyXxvz5w5M6rWaGhogLHEjH1Dx0S4GbHaugwrrEsyPl+ffvppYXHBix399nJzc4XnXlpkj7cTGHpl7PKCmwFPDzBwQD4/mJ1ReKVQtqSLkFQPCSSgYvejEkpcfcl3ddn9bgz6nbCqDbBp53jiH+wGvAejz7ntVVSb8efVgD8AtJ8GmAtAUoOKgFNPyDkazEtw+aj3lP+WRWeSBle/ESiNBitPFZ1uO759/mU4ArLSolBnwieXXSGyNaYN7AYTYQdz5KNKe7bh8AArFub1gD5Nq6ZLEVSvtD0q21DJ1KecfVR+s2yjNwMIShIe6biI844hFOoMeF1FLUxTsfaaKbReBP7+AOB0AqvWATfcOnmV6BRw0dmCenuDIOELNQVYn7cG6nCTC7t1Dx06JIrVGXu4Mxi8/gxAlczKNQBJ/2MHgR9/M/oanrObdgDv+ifMNnhPfWnvS7jga4ZhcS5UWvkYqTFVoNhhxdGL5/B8+2lYFhejpqwSm4JFuHLLDvz4xz8W+RFZjMUf/vAHfPCDHxRFWFrnZDG/4Q168ELfs3HLOOZfZKzGUsscWOVmkRS0oNqwYYMoBBEsACXN0csQr7zyiphTUAnCBi3OMfiIqEKmBFc34GyVC4zWWjmvL4vLD4PtwOG/AuHxP4pqgXW3z4hSN4vLEPYGYIANljEo2AGYJplPmMWMgaRGYjMQVeEE72cZ1Y5YYm9pAIYGgbLKeafSINgsdqL5BLz5LuQW22BUGbHOthE6SS+IDG4PNpWxcYzf/TOf+YzIvn711VfnXTPBvCQ1CFrlbN66BVd983Mo3rxmtHN2R34F3l0T7QSZDzg53BYOCldig60KS8zFo7ZTBwZeHjMQLzdUYaklpktXGpJtWOAPdyUXA6qlgCpmch7wAp1HAa8dYMd1yVr5ZkvC5+JhgDYp7BSuWD01soNFHdoAsRCvzU/7vY4NHUebu33M8huKr4MrSJXG0TH/V25YhTztxB3UqeAO2nHecQBBSfam1ymNWGreCk2yjuMM0eMZxN8698aRGkvNpVAoesa81qjUY1PeVfITbwPgofVQeLsxE8N8ZTyxwX0cHJEL9bRaUtCPz5lgP0VYoERq+6mJcHK4HU90nYY76EO5wYa7Kzbi7OHjgjAkLjRdwBGco7Qo7u92F2xBiT4zT9C9e/dix454VUHacJ4DHKeS2EztAPz1QMg+WmeEuhIwpE/ohaQTkDDWK1+JK6Eg6ZTF7MD1LOCPkXKT0OCD57+/Bnjl14DXFXYbk4B1twJlK6OB1fX75S4qSx6QVwbklsjKgWmGM+DDBUe/6I6us+TPbnj6bMDXC4y8NHa5aS1guMx994MeYOAY4B8BtDlA7rrkNneXE9pbgC98Us7visiob7gNeGuamWeDPcCRZ2VisqwGWLd7UkWCdncnDg7GjyHK9aXYnLceM4Z9LwCP/hXwuIE1G4FrbgSsuYA5Z9YLhdXV1cgvpJJWEpMLKjXqz9aLLmVmRUTAyQc7p2jT9ctf/nJW13Oh4e1vfztaW1vx5JNPXtYh6gsBzBJ7qe/5MXOpSmMV6ixTyGLKYlrBAgiLPpHcIna3xl6fpkLs8r03b96MacVIE9D1Usx8TQlU3Qzo5lcnbRazBNZT7L1yUxVVGpd441sWswznxXCWKvMWawHT5JXZWWQxHQgGg4KgiFj40upWpVCJ5c8//7zIztDro/XLZ555BnfccYdQx8+0df1kMG8rNitXrsQ3v/4NfO6LX8Stv/lP6HNzhC1IM8Ob5hlW51SIRyL0SgMUUApH5AhYJI+V9Ag7kAihEQjJQbhokx/aJYB+hdy9fIqT6/B37z8PjLQDNdcBL/2PHGjFm2/zEWCoE1hzw+S+iKsFGHg1Wlw2VAJ56fndF+uL40gNTjhsGhs0Sg1UoeSHmSoNS57x0Ow6OUpoEN6QG+3ueiw2TT30qEifi5tLtuHY8AV4g35UGAuxyJiDM/axpEZAqGzCahtP2H9+VMLqk4kOQ9h3j4qIob2Av1d+zpBk23YoNHlQShsRQn3YjscGJerSIjRCUgj19i44Al6UGmwoN+SixTWAB9oZPC2jzT2EX5x+DteZosdpQB2Cy+GGMSaQnfut3zuYMakxJYsLodZJwq2GHDKhIa9YeKVbgVAtoEotZw9JHvhCzZBAq7MAVAopYTvq5vOlb1xEOOgFpTLhORpLaBC0oeL5r1wNNDwTDgYP2/4QJ58ESlfI1x5zLrDxxllZVZNai+UWC3q8Z9HmOged0oIi/Qpo0lBbLQiobbISUISCRTa2EpiMFSAte558EOjtAorLgOvvBAwL2ApEpQcKpyEz6VLCU4/JCqlYX9gn/gbc+1bqtsf/2+F+4C/fBwIBeZzTchYY7AauuTfj1WhzdYxZ1uHpEtfDGbsWbtsjPzovAg/+DPjD9+TlG64Errxz1goeFauX4DuP/ga22lIU5Obh+uL10AwHhPIyNiOL+MY3voGWlhY89NBDs7JuCxk/+MEPRFf517/+dXzuc5+b69XJYhxwLmVWW+AMOEYbjfizUDeFHKYs0kaf1yFsbmn/vMJSAl0KRTgVcxFCYzpB8pZk7rSj92DCfC0E9B8Hysaxy73EMaP31PkOqrJpG5tFFjMBqjKyyows5hFUKpWw32JjMgkMFWRCg+pILo8lNJhf+Ja3vAXf+ta35iWhgfle2aM8/PGnn8LL//IdXP29f4FapUahLkmg6TwFQ6RWWNfgzEjU275AW4RSfSwB4ooqNAShEQPfBUCVBwwNAR56oMdguBVoeEUmNEQxMDwoI7FRuy1z33OG2Q7siy8uu1sBVylgmtiWpERfjNXWVai31wsrowJdPtbb5EBZkyoXRpUNrmD0O+iUZljUaRTOxfeSktq1eBmyHf9ieILjB3tlglJDvnhEMOwfSPo6qybc1SMsnJIU5yWSFGE4z0YJDSLkBvqfAXS1UNjWQ6XYKi+WAvBLDigkH9QKU8pBZlAK4fet+9Diole/jGuLVmLE7xHKpkg+AI+/YYUPPfboPiiw5cPd5YLREj2n+LrJhJwVFhYKb/NJWTnoygFdGeCNKVyZVsnWRMmCwsX2TE5qhCQfXMF9kHhOhf9OJemgU4UlxVBDibULbtAeCAXxct8p1DuoCANWWRdhW97yzLJ55gpUJSWFST6vqT5LFAwybDsUAMaxU5sJBEQ4+36EIJ/L/qAHXpcdi01XTJu13ZyCtl05uwD7AYDXSpI15o2AOkPPa58X+OGXgP4eueBdfwq4cAb46P+Tg79pk9jbI2db2Kap67GvG/jtj4H2ZsCWB7z+ncDSbJfuhBCB7BcBieehHlBUJ7e4TAZKphW0e1OEVaQx+38iUuP0viihEcG5Q8CO2zImv2bses3vxIyO/n6AHcUFCbliVKg89HNZRRbBkeeB4kpgRfL8qqmCHvQM2OV3dgW8eKTrIPzKEHQGPZwBL378zB9xZ+U2bNsU37VMC8gvf/nLeOGFF2Y0mPdSAbcRbaj27Nkj1KvXXHPNXK9SFinAc2GDbTNODh/DkH9QNEstMy9HHtXkWcwoztt78PvWA6Nziec19Xh3NbNMxl7/Z3JczfyMaQfHmXGgil62u77c0Ojow5/bjmLI70aBzox7KzcIhX8WWWSRRRaXBkKhkMil4hwjgpycnNHG5OHhYZErQkIj9jW0frzvvvtEXuH73vc+zFfMa1KDA6T//Z9fY+2mjTj6k//Fno++C6+vHCdpfh6iWF8KqyYHdv+ICGHO0eQmDPy0UZ/4MVAAoeEkA68w2N3M90osCPrcmZMawks8sRNGAfjTV8YsMlWJRyJoy1Vt2oxebxM8QQd0KiMKddUTFwm97YDjiNzpTSsuy1bZrikMrdIAD7v5Y9Z3qhkd48GqtsGqzsVIYHB0mVqhwVLz6qjVlMIASDFhY/xJYioCWnvFInIsjJyVA7DNNfCFhjHgOxwuzLNLrRg2zRqxHRNxYrg1jtAgnu45jU226jH0ikqtot/c6PN8cx5KFPniU6jQIKFhUZuwyFgxYSfPiRMnUFNTM+pfXlBQIJZNitTgNsjZDvi6gKBL7ibnZDXA7zXmWwDK1J63fqldKDRiEaTyRdoQPt7MC9J26tWBMzjrYDaIvEmODzdBo1Rjc+7U/YpnHDwvkkEdPlasJUD3hdg/AIy2WSc0CGewF6HweSdDgl9ywx0chEmdUPBcqFBb5WBwXgsmW4Q4c1xWaETAwnVHi0xs0J7nW/8ODISvS9feBLzlvVPLYWAR/QdfAUYGZRKlpwv4r68B//wVoDTbWZcSgog4xh6b8AKOF2jHRwXmBOR1Zztw6qjMK0dyYDhOqaoGzGkUzSPe1EmXZ3afXmysRKdHthFsOdcMt8ON9dVroChTiK4iEuoMkyW5vnjx4vS3zZe+BDz8sPycWRpf+xpwZUyXLo83tyOBGFQBXRfTIzUkNxCigpVZOoWAMmYskABOJhh03tbWhte97nWi67nV14+G42dgsJrFZKTpwFkU11bCXB2vrKKNEiccVB8kqjeySA1uq+9///ti2zGTpbIyey2Zr9Cr9Nicl1XSzTYe6jg6SmgQw34Pnuutx62lYxXxzNFzOp2jxZDJulvzWk5yl3OMyHw5UoyZViWIoQBw894Ys56GSahWFzgGfS788uI+0STHLdHndeJnja/iU3VXJyWvssgiiywuRfAeQ2sl3svWrl0r8qEYkt3c3CxsEKlQmAk14kxDkiSRqcu5EudJdXV1QoUxMDCAkydPisYe2kUODg6KRp9ES9Z/+Zd/QWdnJx588MF53RQ8r0mNSDfVow89jK3btuEzt96Lkg0ZhkDOAxhURvFIClEIrwUUsUW9CFh00ss+8okd6+y4LVoCtJ6KfTOAIerm1BPnlBDrl9gVLwGxVllTAAvKxfoMvFUDQ4A9JlSJqozhl4G8G2TbGgBVxlW44DiIUDiAXKPQotwwc0Vekgqrczajzd0EV8ABvcqASkMN1NwX8gsA0zbA+WqY2OBKLZLDwiNg2LU/ZjuPDvoVIhRbMlVj0Hd0lNAgPKFuOIM2mNUJ4fJiguGOU2REUGXMx5GhFvhD/B9J7NmllmIYR2LC2hUKLDfXwpBrwYB/GAalDrXmKlEsHw+0t+BFnUHjtMAgw0t5OEOEJg1uO11p/DJ1PqBbBcl/GkGtBpJSCYXCApUiAEWEDEyEUMtEt29IkuAKAMPScagUOti0y2BQZWatNR/k2o3OrrHLHF0zTmq0urqxf+AUPEEv8nU5uCJ/PSwxdmVpQWkGNOWAn8W98HZSGgFtuIBUsxUY6gB6G+XnWgOw4Y7o+THYK3dM5zFraGbVEvMzYWqGMJVjliRDMng9wH//UO7wj+Dpx+VC+NVTsBBrbQKG+hM6KoPAT74OfPxLQM7C8sDmdenFvm70eNxYabVhVcL68/95KKqmfF1hs0KMMlC8K/ddJ9sQxv/TRx+Qz7vYdWAOzj99FrCPyMTGeETVohXAyVeiz0mK2AonlUlRpC/EJts6HO48ClVQgRt2Xwtzv1FIpHnvIZlOL/fz58+nT2r84x9RQoPw+4HPf15eHgkbF4qShHERCTxjGqSO5ASCtPOM2FNepFkpoBxL/Pf09IiwPk4sSGq8/PLL4r7yQudJFNSWwm13ovXkBVRvWA6VRg1NjHWn1+vFa1/7Wtx111145zvfmd53z2IU73rXu4T0nkQSPYTnW/BhFlnMFVjkdiY01fHONCTsQseiqKhIXMuYAUTk5+eL51yeCc6ePSuKLgwiZXGF4+vYrI5pQ8kuoJ32p2EFu5mWy2txuaHB0YdAjKKS+9gT8qPZNYiVbDrKIossLrlru2ohOD3MMpjdxHxYNksdPnx4tA5Ngp3/txDz1yRJEnMK3lM5X+jv78fDDz+M9evXiyYEzjuozigrKxMKjUQ88MADuP/++7F///55rwKf96QGQWbsFz//Od79rndjy7r1WLp06aweDE91t+LIUC/Mag3uKq9F2XT7hquWAAoLoLkACIsjRbTDX1NBSQKw5Fqg8TnZkkWtB5YytLIMWD4MnGWAngRo9cDm1wDqzO2DRCCqbSMwdCi6TFuUlvXUjMDXnVBMYMepFwgMAxpZcm5S27DCugt2f78Y9FrVhVGCYYbAAJ1FxnHIGZUVsFwHhJwAFQGJXvymFYC3CwjFFAUjtmMqHULwISSKTvHwh5Jb+BToLGMIDRbCFpny8M7qXXiht15kbVQZ87C7YCkOdRyMK8LbbDbo/XossqVWWPhDPjgDdmiUWhhVZlRUVAgme0okRpqQtOUIaFpksoJNxnAiIB2CGjugSJLJolLkhYtH8rnrYL6t2DwBBKQA+rzMGbHCpqmGRTM1P2ZP0IOTI0cx7B+CWqHGEnMdyplDMwNQJxl80N94JtHvHcbzvYdGj64+7zCe6tmPO8r2ZDYY4rFm2Chfz4LD8jmhI5EbPlf5PTa9BnD0y6o0c4F8DfN7ZeuX1vPy63ILgde8H7BOgrRNE1RjKL2qUaKUB51aoYVBlZXhx6F2OaDRyjZTEcWHVgcUlsi2U7HgILD+7NRIjVRklssBPPQb4K0fnfRbh6R+BKQL4QyeXKgVy5NeW6YLQUnChw/vxXNhpQuvxJ9fsQ5vXlQr/u8HF07i9y0N4veri8rwrys3wkRLr0khwdJy9BOj5HZKkLhI9DHnvv7EBwCXCzCZgY99BtiQIry1qg7Y8xpg79/lc7mwHLjhzUmtJCeCL+RDo7MegwM9KCjJQbenHfmlq7Czdufoa1588UXhCZs2zp0D1GrZIisCrxdobwfq6uTneiOw8ybglcfk45jf35ILrNs18fuHGsPbOeb+HDobR2qw+5gKR0q7ObEgbrnlFlHUY8DuX868COm8HbmlBajeKHvYchWqTdEi4cc+9jExAaNKI4vJ4Yc//CF27dqFj3/84/jRj340q5ux2+PCn9suYNjvw9qcfNxaunhed8JlcfmA47xcjRFDftfoVYxHZpE+uVo6Ly9PND1FSA0+P3fuXMakhtVqFQ8Wl2YUGhOw6FbAz/maCsi0YecSgTpFoU5zKViuZpFFFqO44BjCl04fQJvbAZtGh0/VbcCO/ISG0tmG3yfXAWa4aTEdkLRgo1Rioy7JedbLFlrTS39/v1BirFmzRtyPqUDhvIOqcH5HKjdYK2O+XGx+RgT19fV4+9vfjl/84hdYvnw55jsWBKlBsItq3759uPvuu0V3Hgc8M40Tw334fydfhYvdiiJOVYEnu1vwzbW70OOV8xzW5BTCwuLOVKEsBgzFgKYHYPYEbYzY3RwpAOQvBfJqgYBXJjUik54l24DSZUDbMXm0yWyMycK8BNDmAb4BOTRVXzapAsRkIEkh9Pkuwu4fEMREiaSGNlk+RUKxSavUI5+ZDPMJ3Gapgqxpj5V3HTB0APB0ssIlVymUWsBSByVYvOI2jy8mUWWQDCstZWiy9uLESNvoMXpb6QYYVCzEanFPRbwVBWXhVFfwQcuo4uJinD59Wlysk2HA14dTw4cRCq9Psa4cdZbV4sLf19cn3o8XTRIdmUCSvPBLp5n0IVQXKsUyqBRj7X0ksDtbJjRiO40lDEGBsa9XK/Ohw3J4Q+cE2ZPo6saLd0AaRofnKEqxFlbN5G7mfJ9jQ4fgDMrWJCRMztpPQS+Ox8krQXq9w7D7XbBpzcjTRo+h9bZavNh3Mu61a3NqMJNoc8cTi+zecgRcGPY7kKe1TkKJUz3+/1sS9icLiW0xCjZ26j/+O+D1H8FMgYHglcat6PacFrZTzP4p1q+CcgaL3NMBSWLxlOow3YwW5EeRVwC86xPA//0EGB6UMy7e9AGgsFQenIYSiuZTvV9XVgMVi4E2mbAcBSfjbY3AS38FHENAQTmw/uq0if2QNAy/dCT6HF3wS15osHHGCot/62gdJTQQPru+euYYbiguw6NdLfhNc5jEA/BsTwc0CgW+skbOWsocVBxwW8SOC/iJaRCDdSuBY5Eg1TBIcrjD7+VyAt/4d+D7PwMKUxStVm0HVm6T/24KE5Zm50W4gx44hx3IK5RVLWdGTqPcUC5UoCycVVVVjfrCCpBwO/ECMNQDmHOBtVcCuphO39JSWe0TC+7zxALcthuAgjKgo0lWbqzeLpMdE0FkPyWOYcIZIwql6JBicwAnE/S0jfjGHz16VBQFeU/efdO1aHL3QkUn0jChoVNqR9WUnGj88Y9/FB1lySYkWaQHbrs///nPwo5q8+bNs6Z46fG48N6Dz8AZDIh9+2R3K5pddlxTVCGKDuUGE1Zas7kRWcwd7qnYiN827xOd+wRzFq4sTN5YqFarBcFqt9vF7xFlBW07WEDJpJGRKo3du3cL249FixbFX9une74WM9a+HLHcUoxcjUFYi3HeRDviYr0F1aaZayDKYuGA81252VKCEvos6b5A4Qj48M/HXhY/iWG/F/96ah9+uukaLDZZ52CFRoDf/RfQyGYfJbD7RuCm107NqniKx3kyy0SqG1gnuzLWmnYB4PTp06JhinZSkfksw747OjpEExWXca6Raq5L2y3W3N/znvcINfhCwPyu1CTga1/7muhqe+Mb34iHHnpoxgY5PKh/33oeP286JcrLo56eIog6gM+eeBEKhVzkzdFo8e+rdqPMME22WOoi+ZFq8KVJkN/6nMDRv0TzNdqPATU7gUVjJURpgaQGH7OMdvdpDAp7Grm50SkpsFyhhYJ5GpHCgKZIVkKkgUCY3FGTLJglUibAPAfJDaWwSCpJfeMnYZS3C3C1AvQJF4TGUkBtFCXkHPVyDAdOjxaUVQo9zOrklhr8DAaDU0VkD3hQayrGSip4UoBeeryo8WJHxnn16tUpCY2gFMTpkSOjhAbR7W2HTZsnJGosJNHugwQHszVSvU+ybeWXmBniCpfKqaI4BgW2QKmY+o1Vq6yERlGOQMgOO/alfN2g7+KkSQ1vyAtHQig9JwK9vt5Jkxp7+0+LrIwItuetwDqbTFystC4SliMMCidxtcJahcWmqSlNUsEf8mPE7xT7P1nw/axJVllEjB1gsBjY1TLjH6tXWbHINPMqpOmCJDEH5ESYCFVAIa2AUpH6GjBtWLIC+JfvyhZFqpihxOvfAvz+V3IRm/uPNkU33Tm1z+L7f+jzwDc+K5MoQhmilu2QqO45f1g+PjobgJ4W4KZ3pTUwDkldY+yFJNA6i/cPHUJSCIcHz6HR2SHO75XWxVhpTT0ITActLgfUCgUCMcc2f2t3u/Bcb2f8+kHCC31jrefShiC4NgHSUbrFyplEihWAIo1rdc3S+NwVriRJeGGLGa6w07Lp3OnUpIZYB8WUO7CoiuPneVweaPXyPZ13Dl6rjh8+LO5rLHyNgqTaE7+UjwWuMte39Sxw+wdlhRFx553AY48BJ07IxwqJl3/6JyA3iZVZ7Wr5MR64PQLdskqTlnvKXECKtUzjSljEOI4WU+xoZtEuMo69ePGi6JrivTXSDbaneAVamweENQjz2nk83FIs58oxEPwjH/kI/va3v8V/9ywmBdqW/elPf8Ltt98u1ODcNzONhzua4AgE4tS2f25rwCMdDaPXmHvKl+DdNRMce1lkMUMgiXFH2VqcGu6AXqXFVYVLoR3HopbHLa9vnGewM5TIhNCIkIwbN24Udhe5ubmioTEjFV4WmW1vlQYfqN2FJ7vPoc/nRIneguuL66CeYTV4FvMfbJiyB47BHx7LqBQ5sKrXQzlRJlsW8w719iGMxGTdSWGr20ODPXNDavz+p8DFevl3jr+ffwzIyQOuuG5ubPgaGsaMpdm0yywK3n8WioLW7/eLhil+l0jOLRumDh48iNLS0rTupWxOeMMb3iAalll7XyhYUKQGOz/+8Ic/CEnQZz/7WXzzm9+ckc/5a3sjftJ4SnTHJR7ELBt5gkEY1PJyu9+P/248hn9ddcWkPssRGMSwvwcKKJGvLcs86Lr1qExoCHum8MSocS9QsX7Wg3a9QQ86Pa2ia92myUeBLj3JcVAKRAmNMAIKoFtfiRIWVoNuOTzaWDehDzzfq9FxDCOBPvHcqi5AjXkdVDPYvcwivSd4CCHQl1UOblcp+qFTrkp9ERT5G1XyIwFGdQXUSjO8oQEooYZBVQplinBrT9CH/2t9CSN+lyi6XXB0whX0YjNVPeKGFcLe/kYRJm5UabHNthj9bW2jKg0SeOyqSv7e7nBhO2a1oYAjMIISfbmQopEcIZubiSSPZAZtpBIRknrGkBqyGoPfPZIxwu2pgwJy4ckbHIErSLsbJczqEmiY1RDOP1ErrdAp88R2FJ8bPj8C4dMklqzJFKkK+5Hl3O7ekF901SrDxwA/v83di5GAEzaNGWX6gtHjo8PdH0doRMLBq4yFyA13kS21lIvHTKLH048Xeg/BL7JJZCsz2uBE6oLF+nxY1dNov2cflEN3mQVUuSy+OG62AYrWeGIjHS/7ywiSxM6643E2fRJOQ5IsIn9mVhC7z4hb7gJKy4HTJygNA1asAY4flnMK1m0C0rVSOn4AaDgLGE3AjmsAqw1476eAn35dlisTLEbnm2RCg+Cx0tUEDHTKqo1kEMdTOyDxusDrUOoglUODZ3F6JKoOOTh4VlgQLrdOvohca7bGERqR86zSaIJRpRqTbKWfavMGjwPF7nDeEEmNNAfmZ06Gw8Fjti2hC09muZzWibShSgdCucD3ilGapgmrxorTbaeRWxRtuPAMe3Cup14MujlQjwPJjO7mmM9mW1ov0HoGqFkX/h464Kc/BZ56ijMXYPVqYP36jNYr+v4S4D4CBGLGMZpKQFgcygHngAF2Vw2OHX8JJSUloxMLdoExpJrf4Yor4seRhTor3r54D06OtAkP5CXmEpQbctHU1IR77rkH3/72t3H11VdPbp2zGINrrrkG//mf/4nXvOY1oqAasdGZKTgCfvlUSFSURtOn8Jf2C9hZUDopxUaIY2tfK/ySB3qlFTmasgUzMc9ifmBffxMe7Topmmk4ujhn78L7avfAPE6ANIkI2lww8JtkIQtDzNfIBHRjoGVGxOs7i5mFRaPHayrC98YssgjDGawfJTSIoDQMR+AMrJoFfqw4R4CjLwEeF1C5FFi2wL9PGtAlISk51tAnzuFmA5w/XDg9Nsjy3PE5IzV4n6Ltq7x6IUHMs3mXDS4LYdwkSZKwrmXYNxXgEaUk5wsM+aYSOd1a3Wc+8xlB8lAxydr7QsHCWdMw2A3+yCOPCOnMqlWrhNfXdOOPMZYnkUJo7AHN5tAI2GHV7pYtaDLFoK8TF10sSIVlQd6LWGbZDkMq66Jk8LvH5nvzid8zq6QGCY1Dg6/AL/nF6rS7m1FjqkOlsTotUiAZAiyomDILbWt1nR0lNAj+3uY6h0UmubtxJhCUesKEBkZ3RFDqRAhVUGFy7LdWaROPiXB06KIgNOT+WfmzX+o7gzU5VdCpNHik4xhOjsiFFu6XensXlvaFoPAEhMS7tbVVEBzJJh1a5diLHz9DF84JYWcpL5CZZ2skIwRip/FR+EIcQGkhSQFolAoYVXlQK+l5r4Ir0Itu79HRvx/2N6HMsBVapWX0nC3QrceQrx7OYBdC8MMfip4qFvXklQ7MFynRlaHL2xFD9yhRrq9Em6sbL/cfRUAKCnXFFQXrUaYvxIt9x9DgjLweWGFZhO358nE56EtOLB0fuohFpmIsMhbO+E2VBFYsoSFDQoE2Rzas0Vqw0bZi+taj7Tzw6C/lTn+CFi93vl/OBiJ23gw0nwNEZ0n4Inf1a6bnsy8ZjCQtysu2bnNIAG3YIj/2vwL8xxeihXF2/3/uy3JBeTw88SDwxAOylRW/36vPAR//d6BsEfBP/w6cojJDAtQB4OwrYwfGMd1IYyDR3kkmKlioCSZcdxTCmkku3Dc44sn2yLKpkBo3lZTjuZ5KPNLZGl4H4N9XbUCBTo+3LFqG/QO9cQaE71gczneYKjIl9vWGBKVUwjbmdcBqAdZMQASQGA8cB0JhxQmJa81mIIWlYjJUGRfhhP84lFaFmGxcPNmMam01lAXKsYQG4aMVW4oge05maUNFco0dxDffPP6H9zQAzYfkPLPiZcCiTWNJmWB/PKFB+FsBzRXwK6rRO9COhnNdMJs6hE98RJ1Bqyl2UNHySKtN3vlo05qwqyB6DLCJgGoCdlG9733vG3/ds8gY73//+4Xf8B133CGsbmcyGHFjbiEeoiIxjMhRlXiH5RwjU1IjJAVx0bkf3pB99P7pCgyg1LB6QUzQs5h7BEJB/KPrlPg9oiayB7x4pa8BN5SsTEloUMEdOW+Yp8FO10xJDV7n2VlK64yFGM463+ELBXDe3iPmKYtNBchJdIHIIguh3KdyOR4BaeyyBQXHMPDrbwBuOpwAOPIicMUtwI4p5P4tACy35mG1NR+nR/rF/ILzn3ydDns4955tcAxC1bQvJkOWNb9Yi9hZX6WoRRMbdqlWYNbafB8vcT1JZgwNDQmVMWt7BMkN5mlQsZGJ0vGXv/ylsLalQjJdB5b5ggVHahDcafS/5cSOrBoPuumEPyQP34SbgAjpjf5fhcEIu989WkbiRaFikt3Dbe5z4d/kd+OndrobUGPOoFswpxTolAedMuhdb5Ifs4g290VBaERL60CTsx7lhkVQTmBXo1JoYFBZ4RZ2PtGOYyvtpjLEiL9/7LLA2GXjocnRjf0DF+ANBVBrLsb2fOY9pP4ODJhNupydqTN8LXQGvKKcHt3q8hZ0B33CsiJCaESW+6UQcjbUYIe1WhASDocDK1euxIULF8SFMTbQT6PUoNa0HA3Os6OTYpPKgrJwGDaZ38kE+SlgEEoL2eYlAiWUivjilDfYjyF/1O8+KKp8GuTQvousuo/rFfu9gxjwnUeJfmP0XRVq5OlWwiYtR4/nDHwh2UbGpqlEvlZWs0wWK6yrYXKZMOAbgFapxWJTjXBnebGPGSTyepEgeKH3MLbmrY4jNIgz9mbUmstRqLPBysDCJDgy1IzDQyQIi3F72eZR1cdMwBlwJxAaMlVjULNTmsStA/WOQdSYN8GgmqLdHi+uT/1vvKd9fydw+Flge7jImF8CvOXTwLlDcpgv7V+KZyaIfVzQyobd/143UFgh+/MnA8OQG0/IBdWyGiB/NgaKyQuhCqFummPQmuin34sPm25qAP7xMHDH61L/HQvPTz4o/x7J5nDagZeeBG55HZBbAOy6QV5ORcbZvdHrAM8PvSn1thfKs6jyQglJbKmASHBShYPC60YHsTz+EzHVc5B///W1m3FfVQ16vG7UWXJQbZLHEDvyi/Gjjbvw57ZGMQ65vrgCt5SOVfNNBEnyh9VwOiiZzzUZ7LwSeOxBYGRYPl8TuTNuBxKSE3XxBBuihIZYOTvgPwZo088J4f7YXLoVpy+chrfbi3u33os82zhFssJKOVeFuRqxEbeHngReflBW+Gy9BVg9gcK2txE4+lD0+XCXnGu2JOHvQlTLjsXRpldw9uJ5WHJMqF1TiXy9URAaVGfw3svvtXVr+tuB92jar1JSTpVGFjOD73znOyKwndv6wQcfnDGr2z2F5XhP9Ur84uIZoYgs1Bkw5HePmURXGDKfY4z4O8OEBkbPgeFAB/JD1dBN9f6dxWUBTyiAYMKFn0emg9fAcebnNTU14lpF0paNgVTTkSjkXCPdAhELS5WVlVlCY4bmjb9sehmDfvm+pVYo8caqbVhkyub3ZBEP2kyFJOf8m19MBYeflwkNNtRKMRmOm64CtAsrCDoTUBH+tbU78dvms2hwDKNYb8RbF62YnlzgTMH7wLW3A4/9Wf498tgzd8QSyXMqE2inHptDMV/h8jjxwuGHMejqQu3SRahbshb52kLREOB2u8U9N1NS5sUXX8SHP/xhIR7gvXyhYUGSGhGZ+Le+9S3cdddd4iCMSIamA9cWVeBPbRcY7SA6JmlXs9RsxVdW70C7245vnTsAV1BWJFi1OrynOjM1QQRBaWwxPCAsGjIAu2XsvXKWBqE1AmvumLWA7wj8SQLKRVqC5Id2go5MnnCLjBvR6joOZ1C2XCrRL50UqcGQcT/9t2OXpbBuSoYWVx8e7Dgw+rx/wA5XwIfrS1Lv4+Q5EAqRrTHTKNHbcHw4arPBY5K+txaNAXa/RzyPPGTHfQlOtxvnOs8J+6gIeP5QZhdLahAVxsUwq60YCQxBo9CiSF8q7FciNwBOWDK98PP1GqxDQLogAr9ZeFMraqFUxIevuoNju6Q9oXZYJaoFlAgmOVeSLRPrqlCixLAKJZg+xQ7fc7GpVjwiaHS0xfljR2yueryDKYkEkhoVhgIsM5eLzIwI/KEoVdXo7MY5eztWWDMLZM8EtMpKhOxbTzWAfD3xSV40Og9jpWWKckwSAO4ECzIWTod645fRX3Pr9WO78LvPymq03AogZwbJg6AfePI3QHe4m5bKgavuAyqj546A1wU88CPZ4oZnGzfNNfcBSyZpZ5M2GDBMizaq0yKSPS6bfFj9tME+HN+FQ/CY6Z4gI0JMNsZU0GViIxF5pcC1b5KDwj1OIKdQ3j+0M0uKhGDo8JGtpTpDuWbM/9VZF+HY0Pn4ZZbMSYZE8NzZmJu8gLA1r0g8Joug1Atf8BhUoSCUtI5T2KDSbMhIGSFgzQG++C3gb38BhgaAjnagvTWepLLIAdfjIpTYVECCJPNOP1o28ZEWmHF2/duA5/4PoJKWVilcb56nYp1CwKt/A/JKgLLa8e09E9F8eCypEZP1NTjowLkLbXCRqC8twsbtciFPdB6feAE6dxNyzcWiqaCuLjMVzqc+9SnRgLDQJOELDbFWt9zmJDlmCm9cVIfXVS6FK+CHVaPFr5vP4PetYZ9pAK+rWIIV1sxz7gJJ5hfyci90yJIaWUwMWtZa1XqR1xe5I3N8y5yN8UAVN+2nWBxyOp1CtZFprgaVHczmyGL68VzvOUGeRkBrw4c6juKjS6/Nbu4s4mBULcFI4GDMEgkm9bKFvZU490x0OOGcgw1plzCpQRhUarxnvmR0XXkLYM0FzhyV52w7rwUqkufHzgaomJ7v4DyCykfaxg8GG1G21Ixqg6yabOw+iqMtp2FWF4l7Lq0fM6nTcG7BYHBasLLGvhCxoGdFH/jAB4TMlR1Ve/fuzVjemgrvrVklbvKPd7WIrso7y6rxzuqVeKWvHd+uZ3aCfCVcbLTi31bthC1il5IhTCobHMGhuCurWZ3h5IUH7LKrgMVb5EKhISds2TG7sGpy0T1qxSNDrzSIQng60Ch1qDFvmVSRPBblhqW44DgcI+CXUGZIn/A6NNAgkwAxOQinRlpwTfHqcXIUaBVVB18oorxRQqdcDaVicsdFJlhprUCnZxAnhuUAZa1SgzvKtoh1HfG7RS5MJOtVGW62rbOVob3prGBxeYGMgP57zc3NY4KSGAzORyIiExb+zBQKhRoaRUJheAxSe913e84L2bQyvK+4n9xBwAUf/KGTKNXXQqeafRkjt38y5KiTb6Pzjm6cd/QIe6mrCtdhqbkCLa5eHBhoiEv8oCJsKNxVNVPQqbSoMhYIYi8CjYL7P/a4l+ALuUXBRJNpkTQWHMCwo55+prFd9rYJivEkMg7+HnCH82v4t3XXAOWTI5YnxJlXge5oZ79QDrzwJ+C+z8VnSRx9ARiJbLfwifb8n4GatWkFVk8WPPaV0lpI4LWXahqqoCoE6TcjoIriod/Jg1AO/q+7E9iyO3VRnBZGHk90H7MzqmwCYo7ZGezCZyB4xJqQ2706xUSqagXwxv9PLlRPuK15fvJcjLWM5AWSxNBYrMtZArVChUZHuyAxV1gXo8Y8s7k2U4Ek+eALHYc6GIAmrDgFBiAFX4BCvyd9YoMKDIdd3odveY+8jITGv/5zmKjijSUEvPXdabxZsjHALHT6lSwG7v2sPFEd6Qceuj/+/3mOdDSMT2rwuNOoAIOWLDYQCDJsauzrVDa4Q9U4evAxmE16bN20DD2aQtgl+Tjz+fw4su80lq+uwaKiGhTqMi8KfP/738fvfvc7MdZdaJLwhQja6Dz66KOC2GDnOUPZZwoapRI54WLK2xavxPb8UmE5VW4wo86SQhk4AYyqsX9Hi0xd2J4ziywmAue/b6jagt8274MzKJNk63IqsCVv8YQqCxLQzNRgdh/nCFRrHD9+XNhGp0PI8jUBKnSzmHYMeJ1j1P3DdKCY4vw7i0sPGqUNOept8IY6xZGiVRaLZQsa5TXAiVejz3nMW2xAWDGdxSyB233jTvmRxbjgtbmxsVE4pPAeyjlAvSNqP93c0AGnw4UN6zZjsWVbxluzr68PN998M972trcJC9aFigVNahBUa7AQS8XGk08+Cb1ePy0TjI8uXSceEXiCAXzvfNRWhmh2jeDFvnbcPt6keBwsMq1Fg+MQPCF54pujLkKJvmZyK601yY85Qqm+As7ACDo8sk84cxdW52ycVBf/VJCjKUSdZSv6vZ3iepmnLYVZnZv2RaPDMzBmfUJjuobHQqOsglpRghC8UEIPRQbqkKmA63dd8VpszVsiLKfytGZolPJpfXy4RaZ2wptUqPtoa28twoChGRs3Rm2aCFpaRPz3xoPf70d7e7u4CFZVTb1rORX0qhJ4Q5GQVRk6ZbE4X3q9DThvD6HPJ8GoUmCFVRU+Mz3w+Dow7O/BSusVgiybTZQaCpCnzcGgb1g85zoVanOFB3+fz4Ez9hZBxKiVCviCCpwZkYnA0yNtGM53YUveEti0ZuwfaIwjdXjd4b6dSfhDfvhCA8jVKOALSWJCm6slaTf2tapM/fkTwYPxujcAj/0qmqnBrumNSUJvGeL7la8Ax44BN64HNkcsqMLbp/45WbE2E2Fnw33yusZeA6gUYfc3g8wjcCTpPKf1DS2rDDN7XSaBQSJjVvDHnwGnjsgFbZcD+PMvAIMRWL1p7GuZWfDBTwLf/3rYBoimrquBG24b/zNITLzr48DP/lMmNogrbwY2JbEKGmgF+prk/Kjy1YB+gkmJuL9sACR24Ie95hW8f5ekvL6uzqkRj4WAEKhyCQpCA3HUvh8INAOaZekFtP/uJzJ5QVLqrR8GVqwFyiuB//ge8NKzgM8PbNoKLElDaaBeAvj74qwloZ6I0J4mcH/To1cfrwIcXY+J/HvzyoFglOQVgWoWrUx2hJtHWHg7duyYGD9s3v0eaFQ+QGmCipkafgfsw06cPn4BG7athFargWYSzQ4PP/wwPv/5z+Opp55Cbe3UbBOzSB/c1tz2119/veh6o+XtbIBExmTJjAiM6lwU65aj23s23BihQrlxPdRJFJlZZJEKZQYbPrbsWvR5HdCrNMhLY56Zl5eHnJwcXH11dDxH5QaJQvp8FxYWTkiK0J4vS2rMDAp1FjS7BkaJDY4TcrXGLKGRRVKolRbxuGSwaivQ2wEcek5+bs4B7n7vrDucZJFFOiCRQRUFm2sicQuCgIYSISmAsycaYTQZsHLdEmhUmY/vPB6PqKGvW7cO3/zmNxf0TlnwpAYtcH7zm98Iqcw73vEO0ck2E6Fi/T6PyCOI+2yFAh0xIeHDfg+e7r4AR8CHxaZc7CpYPK7/tlapx3LLTtH5zMIUn88YnP2AZwQw5slqjmkGiz9LLauwyLQEgVAAepVhwiyNmQJJjHSJjFi4gl6RQxFLrPDCYdUYxs3UiECh0EKVwuN+pmHVGMUjFvRoToZYdUYseN7QEmM80Cf35ZdfFkFEZIsno9JIF3pVMSRpFRyBBpGXoVMWwqpZjpFAL/b1B1HvCInBuEXNAKxYdZJsezbg60SxfnaljLTmur5oG87YmzDid8KqMWOFpRr7Bs7jyJCspiFsCgO8IWbzRPfRq/312JRbA7Naj2uL1+Dp7uOj/7vCUo5l5iSBuDHwBH04MnQRzoAHxXobVlsrM5qkRCzkdCqFeBCBEH8q4wqSpfolUIYtyKaEymXAG/4Z6GySlRtVdWOJCWZufPSj1EXKv4e8crgKi4sR8LpMKb1qBgbdwvZDGlusp71NLEjIXAhbAAooZDKDReFLBeyaPHkofhmPr2P7kpMaxPrNwDd+BDSep7QLWL5qfCVhWyPw6lOyIuT624DaVXL3lDHJdab9JHDyH/JEhNc62gJtfzNgnOD+xowJxQ5A5McoL4mJDO9TzmAb3IFWcT1M/qIwsTQeejqB//lh1GLK4wZ+8V3g898EaJdVWAzcfV9mK6fMATQ7gRCt9aimKQaUs+zdbckDlm4Ezh+O7m+jFVia4riNQKuODwUXBGcQcA8CpgLRiUzbxk2bNsFkii/22TSLcfL8AQz0D2HrrrWyqgoaWNWZEZAMy33Tm96E//mf/xGqgSxmF8wN47ZnvsZzzz0n9vVCQZ5uEXK0ZQiEvNAoOSaffRV3FgsfWqVakBvpgioNWkeRxEi8TyVeJ5NZVw0PD6OsrAwbNmyY9DpnkRpXFi3DRVc/er320f17V3l2W2dxmYDjuKvvBrbfIDee0QJpDhxOsshionobx/9UZSRmY/B3M8rwzCuPorauEnkF8v05Vzt+U3KyeiBr5/ws1tJnon4+m1jwpEakA4TdVJzwfeELX8BXv/rVaf+MfK0eGoUyjthgB39ZuLhl93vxjbPPC0KDODDYhjbXMN64aHxPdR6YOlWyLsJpRONLQOvhyCcCS68GymbGU0+r1InHQoROqRkTus39s8Scpo/3PMNyaynO2uMtwXhJ/FXzcyj3yhez2AsYv+tEgZhURZHQoKR8NmBQl4tHLLxBjSA0CCkcfjUWCgQTQq9nC2qlGmtyogFLPZ5hETwfi6HAWCspqjFIqmkVSqzJqUK5IQ+93hFBcpTpc8clKLxBP37f+jJGhEWVAqdGWtHlHsT1JVG12UQgEcn8GRJCsetUpFsGBXwISn5Y1LQim8bzgd2o43WktrYC587FFLIHgC0JncoaPUIaHSTJQ3ON6e02W7EDaD0H9MoKNFEQ3XXPWPJl7W6goxFoC3uhM3jt+jdfEgXzUUSC3BLJ0okGQQWF8iMZIu/F922/CPz6P8Oh1BLQdEbO0bj27uR/d+bZ8O/hezLDSxtJsNyQ5ve5JIY/As5gC4b98nniCkqI3IEVsT97eoEL/yVfNUtXAkt3j53INdXHZ2YQVNm0NMqkxmTBLj/lLKkzUmHPPUBRFdDTChgtwJpdKRQcMRB2gonmy1yuFlYqvF/u3j02X4j31mOHT6LEugFLt7jhlzzQKc0o1q3JaNLA++1tt92Gf/u3f8M999yT0dfNYvrAbc9iK/cF80wmUrPOJ6gUGqioZMsii1kC/bxHRmiNEQ8qu9lxOl4AKbtSSSRmMXMwqLR4d/UuQWz4Q0FUGfNgYu5UFllcaoj4bycDG89mWEmfRRaTAe+fhw8fFjkfyZqH6ZRSf7IH119xF3yqIaHEzdUugjHDCAPWzPfv3y9sbWlBv9BxyczqGXD82GOPYefOncK784Mf/OC0vr9epcY/Ld0Yl6mxJqcQN5VUi99f6W+GPeCLK4jvHWjBzaV1yNVmfqCEJD+G/Q3whezQKIzI0S6BajIe9oOtMYQGIQHnnwVyK2dEsbEQwG4hT0guKuuVUcmtWqnCjvxleKX/nMgw4J6k3HpT7sK0e6izlOLGkjV4ofecsKbit1QrAIffjWc6z+M6XAVtOAA6Aob50V4qVahfJBw8KAVHA8NnCyQphny9cARcMKpYvJOXjwQkuAISDCpFzNhFQo4muU/+bIG5PHa/G+cSiKVkIJlWoLOIjqkIaDeVruXUGXsbhkczN+Rr0Gl7W9jOKr1BG5VVa3I24sTwIQTChFC5oQpl+jIoQAuLQcDfCzhI0GgBbQWgncGQbiJxMPrqeaCmGFgXLiqptHCt2IRBnywjVkGPfN0GaKZLKq3WAje9C2g/L3f0FFYCOUmOK5Ict7wD6G2XX1dQPnuDZd8g4O6Qi/SmRYBqhhR/JDy3Xgnsez6+yMtlmYJKjAf+R1Z+8H133Qg4maERQ3QQ+54Grr5zLHHCbvmwx3d80F9C+PxlAkcgqgJzh3jvUiEXVLLJIxKF3QCcPRL9g9Yj8jZduif+jVIds7QYW+ggwbhim/xIFwXLgK6jsuVc5Ji3LYLdrxT+tlQrssMp4hHv9XpFVhV/rly5UliwJKLbM4jzjlZxuNaYy1BuSH6f4qTlxhtvxGtf+1p8/OMfn+SXzmK68IlPfAJNTU246aab8NJLL01bht9cwRHoxTAt0iDBoi4Vj6yffhbTgYaGBmHXlggeXxMdYwu9U3S24QsFcHK4Ba6gB8U6G5aY0zuPOd9dYi7CvEIoADjPAoFhQGUCzCuABdokmcUc4+IZ4Jk/Ac4RIL8UuPFNQP7CbFDN4vLDvn37UFpaCrfbHUdq0PqdDTZUb1x55ZVTGrPdf//9+MlPfoJXXnlF1NAvBVwypAZRV1eHv/3tb8L/ljuIk8HpxO7CCtSYbThvH4RVo8U6W9FolzgzN5L088ETZNdzZqSGJIXQ4zkIvyRLQ30YgsfTjxL9DigzzWpw9iX7BMA1OG2khiQ6uzlYnf+HEy12zowchj3AoGFaF9mw0roJ6nDAM4vALCS3ufuFcoMd8yb1zAd+zxTW2xbBHfRiX//5UcKtv7UbARXQZe9HVU684oJy8aGhoZSet7oCLf747O+RX5GP8vIKrLOtR652at7P6cAf8uLMyKvCsom4sUSDl3oD6PWRYAGe7/XjllIrJHhEZ2KVcSVM6rkLM2t3D+DB9gPwhGTVgzrJjWepuRTnHZ1hyaAJt5VO3tbCHfQLIi4284eIfH66sGlzsSP/KriDTmiUWuiFJd6hMKERBJwMCQ7D3wlI6wDdDFp8MbOFFgTM02AHObfjn/cDt74DKMqH2yBhMHR69OVBeNDnPYwSPbunJzE59jiB0y8CrmEgpwhYsUu2m6pMo8ucn1cUyfuYJbjagL6Xw08kYOQ0UHIDoJ4hQuXON8l2UAwK1+mBq28Faldk/j4P/ho4vl8mIpip8szDQOXisSoQZheI/IKEfUnyz1wg2yrG/o1thkm2eYuE8x4K9MKCYt0mOd/p6P+O/ZOus2NJjZXrgMpqoO1iVJVTUwfUzo3KYsjnFKRwSAphqaUUBTrr9H+IoxdofkUmxCzFwOIrgMg9n4TwqnuA9kMASWNTEVC2ERalCrfccovopqIlkdVqFeQGmwFIdKTqeGp39+EfXftGNTT1jlZcXbhBkBuxcDqdQhWwZs0afO9738sWm+cBOHnkvrj33nvFvmG+yURWOvMVjkA3Oj1Ru0RXsF94M9u0M5eRlsXlA1ryscFwMsHTnLtTDcUO1XQCxS93QuNPrS9j0O8QjVEc/6/zDGJP4SosOFBxO/giEGCOmmjFAHxdQN61YcXkJYLOs0DnOXlMW7UeyJvlOcPlgP4u4G+/YHewfCzx+QM/Ad76WUCbJcmykMH705C/E56gA1qlEXnassnVDWYA1157rSA0zpw5IxqoCM4xaMvIe+tUG1D+9Kc/4bOf/azIombt/FLBJTdioGz197//Pe677z4UFBTgqquumtL7Dfu9+FnjMdQ7BmDT6PHWRatxVZLC1TJLAZ7qiVrM8HCzavQo0GU+6fGGhkYJDRks3HrgDvbCpE5RtGHxwdEA+IYA2llZl8mFH32KIsBEgappQJICCOEE7yDhBYVQYjUU4Q7+kBSEMzAsTj6jKkd0grM4cWqkAV0emTRYZa1Fvm72is+NjjOwswskDP7e6DyNZZaoTU+tuUQ8LhXoRPd/tOiVW14EjV6HxrPncbL/kLh4RrI0aOWWKsjPFXDipOME6rbUofV8K47sO4zOig7cuepuGLUz28nb7j4Pb8gz+pyRD1vz1fh7p1zM31O4AhtzawUhONc3JU40Hmg/AG+YUBB1W/ZOx9yE1ucsxpVFq8RrKf82qrRTuklVGPKwP9Y2TVjBadJSevhCLgx4Lwp7KUoXbZoKWDRhwlMiiREOa/YkIUjc9TNLatgHgNdsAa4sBxweoNEJvPV9wLLVkKQgvP5XYRL5HxJ8jNaQaJnFbBw3NIoMr71+L/DUz2VCgzutvR7obQGuesvEFktzhYGD8QVt5qIMnwTyM+hGzwRUpNz4GvkxFZw6PJbA8PBYi1nG83hxnUwqJcP624FDfwHcYZuL4mXA4s24HGFUlcEeaIxfxrGCMlxcT+YXnExpx239oc8Dzz8O9HUDxeXAnhtkNc0so9szjD+2vjyaDUULv7vKt2KRafyQ2YzgGQZOPSATZzz2+JwNH2teE7WOI5FSEw28jUCr1Yox5nXXXScmR8RE1/Ajg/XhIzx6nB8arI8jNaiUfP3rXy+IEXrcTmQJmcXsgfvit7/9rVBrkNx44IEHUqpa5zMGfc1jl/mbsqRGFtMCBoQzU4PkBokJEhTporq6WtjbsnuUgeO0eqOdVRZjcWakFQN+Odcz0rR2bPgiNthqYNEsMCsRfz8QGIhZwImTE/B2AIa5sfvzBvsxHDiFoOSFRmGGTbMWauUUiOzmI8CpJ6PGoB1nga2vAwpmN/vxkkfzufD8QooSZq4RoLcNKF+YzhtZTC84Zm9xHceQv2vUYpYER42JjWAzMN9ng3vADWhMaeW3ULHIppnIvXMyDQKp8Oyzz4ocjT/84Q+XXE7fJUdqEOyi+sEPfoA777wTzz//PNavHz/XIhWYmfHVM3vR4hoRHRAkOL569hV8bc1VqGTIZAxWWItwT/lqPNB+SryWllPvq9kGzSTChxiKnHx58oBncfHu3Qs4L0b9n53NQOn1QH4NkF8L9DdEX68zjPWEnwRCOBclNAR6EcJ5qLBchJ+fsx8QPwm90oRlli04OHgWjU4Ghspr2u7uxU0lO5GrnVwHJk90du8zglOXhu3KiBg0xRbTJIz4h5J/PymEXi89RwPI09pgVC+wQWIYK60VODzYBEfAM3qx3FKzCpvMS3H69Om4cHB2nrLrNBkGfAMIhY/ByqWVgjUe7BnEky88iTuvu3NGv4MnSGul+CKoQQX8y4rroVdpoQ4XnTO9GaVbjEoEtyWtpXK0JkFIxGLA5xglNMRnCKUJEABvSvLpqgyrmmg3FWs5NVlUGguwp2Al9vafgVIhQa1Q4+aSzRO+NwmNJsdehMQ1R8JIoEssK9YnYe6TBs/PYG4JSYanfwV4HHJWudUAbDIDi+QCoDt4BgpFxHKL2UTC80sQGxkr2ojW04Az9lrA62ozMNAOFKTRTUWv/oPPyOHKVUuBjVfNbPgc90cM0RdeCPgcgKNbWHNBb0vtJzuXYAemP8Y+iuet1QZs3wO89Jic41C9Arjz7anfw5QH7HqnXITmdzXMQBf/AoFFXSuKGq6AfG81qStgVsvWmAKVG4DT/4j/o6qNyd+MCpwb7sJc46W+M8LCL/aq83zvKbzVNLVGlTj0X4gSGgIcO/XICtcMrDnSvX/E3hci8MXeKyQJ7373u4XEnGNXvX7hqkQvVXCfPPTQQ9izZw/e85734Je//OWCU9LI9/uEZTF5gVlkMRWwOWrZsmXCq3syXaD8e55fDocDhw4dwtq1a8eEjl/K4LloD9jFvNaiNqe8vriCvqQKbVfQu/BIjVQZiHOUjegPOTDgp3W3vG3ZaNrvO4hC3RWj87eMceGV8C8x+6thX3qkBhXNzz8GNJ6V1dJX3waUVExuPS51cH6RbLqazZbKIgxXcDhMaBDyweIIDGDE34sc7TRnxvafA9pfkck1qs4WXwtY4rNiJ8J0jTGPHDmCu+66Cz/84Q9x66234lLDJUlqEGShuru7cfPNN+Pll19GTU1Nxu/R7rbjIrt2Yw57qtle7e8YQ2oQVxXVYFfBYmEFY1ZPvvNaq7RBCQ1CiJ0AK6FXpgiAYVFeEBqRtQz7rDtbAEsNUFQDDDeNKjrFIOHso8C6+6ZY8OpPuazZeQq+mIIbMyyanafR6JTtdqJrKqHB0YrNeZnJZQOhoAjfPDtyFK6grGrJ0xahzrJeKEJSQaPQwQfZQoevUiu5vfl+XqhjvDuZGfFS3wEMcDuK1yqxLX8jSvTT2CU6S2DR/41Vu3BosFEU40v0NqyzLcb+ffuwZcuWuNfG+oMnQpUwkGPXYkFpAZTOme8kNajMsMd18VCBYoRZo5/0pGFf/2nUO9rE8yXmcmzPXwVVGqTIwYEGPN93RvzOCcVNJeuxwloeF8IX/1nyz9i4gBPDrdhdGD/Zo2pjX38DBn1O5GlN2JpfmxHhQTWXSS3nc5AYPTVyAaWGzeOeD4O+llFCI4IB30UU6mrDA3d+F3qH9wMaFeBNmGBoZlDRNNABuOMVa2Jg33EeqNsGv9Q95k8o2tAqq6BSxO+DtCB885OYCIrlE6CvE/jTD+UCKXdy23lguB+49vWYMfDarbEB/uHoOtMibLAZ6Ap37duqgcXXzL/A8j03A//4i7y5c0yAXgPw3tBtAT76Vbmwns46kzSiDdUkzn+eJwutGJkK/B45mqXikRRlK2W1Udtx+fgsWyU/5jF4r4pvPwCcDIOfTsR288Wi/0xGpEa6KDcUYtjvGP1EHn2RTA0SGp/5zGfwwgsviC7lZHkcWcwPcN8ww++KK64QEv6vf/3rWEiwqIvR74u9twJm9TRP5LO4rEHFGe9LiSoLzjHSBb3Emc3h86UxBrtE4A66sbfvAJxUKVBZr7FhW/5maJJYMJXqmZwVf//inIF2tgsOmjw5Fy6OxOCAfm683r0hWnfHb1uqwP3SCHSKzMJ4R8FmnTHL0jy2//xL4BitKxl8rQTOHgM+8q9AwaXjKjFtWLIO2P8k4HbKhWSO80sWAUVZEigLGaz5JYNfmuZ7jasXaHsp+pxNTE1PASteD8wy8dzQ0CBq4p///Ofx9reP0zC4gHHJkhoEJ4g9PT3CXocTxcrKzLwLUxU8xiuDsGPcMsVgK2YCFOo3od97AgHJCZVCjzztaqiVKSx+gslOTgUQOWlHOsIdwzHdiO5BgJ37UzqpOMhKvADIAy+ZaIgvSbiDI8lXP4MOMXY17u07gi5vHwq0CuiF9YyMAV8PWl0XsMi0LOXfV5vqcGrkoCh+sgBMKBQeXHC+jGrTNujC0tILjqZRQoOgQuHAwFHcVnrdgiyEGdU67C5cMYaUSLS2oJ8tJeOUfSeiUFcIo8oEd9A1KnX29wdQpJv5wMxyw1JBariDjlGioM/nxNGhQ1htpSQ4fsDf5u5Cp7sHaqUaS0xVsGjibZgOD9XjrCMarktvc41Cha35K8ddjw734CihIdYDEh7rPAIXB6YKBSoNeSjSW7EupwrHhlvGvVYkknT/17JXWK5E0OjsxZsW7UxJtFBJ1uTswIjfJSYyJ4cbxbEZOes6PX1odnahOsGvPTF8Pfl7B2VSQ0hL1nILAXrmF7gBH8lKCdCUAUb+3wwhVVF71AqKP0Nx9Um10gyrepL+kMXV0RwB8fkKQK0DctPIaTjxipz5EatmObUP2H1ncg9Xrx1wDQC0bTMWTJ5cLtgJ9DwLBN1hC0KvvB4RDDTIYprcGiC3Mr1OpbOHgX3/AHxeoGYVcOWdcmD6dOLKWwCjCTjwFBBwxahlzgPPPyCH+s0AHAEX9vUfxpB/BBqFGutsK7HIdJlMdEqWy48FgjJ9LgZ9sQSAQhRxMoY4Ly7K1pxqM2CtjV5b8mqA1v3xr1cqZKJQnMvhIsRkSNIk2JxbB3fQM9rcQZJjZ8Fq8fuXv/xl/PrXvxZjVQYEZjG/QW/jf/zjH9i9e7co3H7hC1/AQkGuplpkaAz55TGQWV2CQt2l46ucxdyDivBkod88V8ZThMciFAqhpaUF69ZF7YEvdRwdPAGXUKbLGPQP4czIOay1yfeJWCw2FWFb3jLsG6gXzzkPuFUotBeeJZ4IBM/dDQztA0Iu+Z6bsxlQz431GFUyyZdPoYmPDabM04itjRSlYYfksAPHXo0+Z82EDV4HXgRuft3k1+dShdEM3PtPwN7HgZEBmczYftP8tRHOYtahV1mSNjEaVZkr/iWJNTE2WUpQoBAKRcw1yzm2+VIQtx42is7e3LOlpUXUwt/4xjfi05/+NC5VXNKkBgt8//mf/wmXy4Vrrrkm48limd6MJaZcNDqHRrsh+PP53kYstbDbfeYYcq3SilLDFen5qGlpMaICpNgOGImpzvKvCZ3jMhRTluIpUYsQjkffDxKUkBUxWqUegWAs4UF7GCPytWoM+mQ7L3ktJVQY0+8QOzBwAt1eWQ2iS3J/GvbHd/MnIkebj3W2nWh2HYAUo4RhcbfbU48q4wbx3O53jrncBaQAvCEf9KpJklaSAwhx3VWAskTuSpljsGsqkdhI5eFNgmBb/nY0OhpEvsZg2xCKUYr1WyZn75YJSFqstO7ABccZdLhbEZDkfdPr7cHpkZNYa5P3my/kw97+w+jxDowWwpqcrbiuaCesmuiN5qIzIjuM4qKra0JSo9sTb1XGuhftjp7oORX+POC20vW4tmgNSvS56PIMYdjnxgVamiQEuMeiydmLrhhCg+j0DKHZ2YeaJB3DvC4803MIza7usDKDN1NAGXO94L/OYKI9UTzM6gIM+2XLGhkKEZgVp3QQx+lK+Q3J+RnD3c0z3f2fXw7YioHhHnlD83tpDUDFCgQlF5QwIoSRhOyhEgz4OmBQWWBUZzg4YTD4ztcC+x8G/B7ZzojPadc3ETjBSLlcN9bypuFpeWJCFC4Hqq+aHLGhsQKlt8pFWH7WwMPR/wsEZZJj5BTQdgow5gIbXzc+kd14CnjsN9HnJ/YCXg9wy1uSvpzFCVrYbdu2LTOyl6/dehVw5hVgKIbU4H4msTEDoDrjpd79cJEAEh05ARwcPA6jyoBC/cwTs5ckqEzidWAaiP4ejwMXXQMwqDRYYSnCnsKV6Pc50OmRmwuYD3RdSYYkKo+nnlcAe1N0AkOCo+xaeb2NeYDZCrjsYV9AJaDXyWSmb3/4fs0LazGgXZ88hyQJRvwj8AgLEDMMquj5plaqcHXRRlwR4nVBGi0+fetb3xIh1Awdp21LFgsD3FcMDGeGADNQPvnJT2IhgNfqAt0y5GtlZddCbNTJYn6D8wiqNZKpLzwez4SkBse4vB5STX45ZWoM+YdHm8YiGCAhnwJb85ZitbVKWE7laEyTsryeV2qNwpvlWkaa99qZgl5VDEegIc4xQ6PIgSa2YJkpVt8oe+v3NMhjJgaF126fnMKDiLVwvURB+zmqtfLzMxyjW3KBG94wU6uVxQID81aH/X0ISD6Y1Dbh/lFlXI1W18nR6225YTmM6swU0pJkRwjMtmT9lbWYJiil9VAowsdrqnphGnb504WOjg5BaFClwZr4pTzem/uq6gyDO+9HP/oR3vnOd4pARw6SkoUgJ4NSocDnVmwXQeH7aYXC8pSKVkoB/FfDAXxx1dUo0ZtnfP0nBE+Ooj1Az4th6aYCyN8ERKySStcCvefkm6mABJRvlIPEp7RuRVBKGyFBDtpRoBQKhRz6XWVciXr7gVHvXiVU8IUUMKjsMBgU8AYZ0q3COtsKlBvSl5d2eXpHL0ChhCIuoVFO3FFpUluSuJBK8IfzPwiz2jjGlEKtUE2+AybUAwSORGmSYAOg2TFtHaCTnZCzILlmzZrRZQwJp/0UVQCKJMefVqnFcqus+Nh/YT/Wb515QiMCpUIlrDtIIsSCxAYRCAXwTPcrsMd0OfFYYTHznL0RW/LWxe3LRLDgJP5GkkQuBu2g8nWWOAsokzr+RsRjMNEi5bGu41huKcXqnErUmotx0dEHo1qPNrdcIFuTU4ltefHdOV5R5BoLdxwxGEWbu1cQGpHvGPnsWPB5XjirJhDyCTs4rdIQp2qxakpEhkav94L4CyqVKowbxr/uiP9L8f9eN+AckQeUmike28z9ufZtwNEngYFOwJwLrL8OQb0Kdv+rkMJ5HvKaaNHn08EeiBbEKwzLUajLMGCwYjlQXicTBKkCqpOhehVwOqbjmwXT4gpAn6CuI1kSS2gQvWflgO+cciBvWQoSehzw+KRaSsisY8htV8Kx4xoCml4Flo0NPR7FmUPxahX+PHcEuPGNScOijx49ilWrVmHfvn2iUHHVVRnmHejJkvXHH72J22waVRrOmGsDwnetTk9PltTIFF4HcPZxwN4lH3+LtgPlk78XnBjuxG+bD43elcv1OfhA7U7cW7lTXIt5jSOp4Q/5cWb4OFxBJ4xqM2rNy6AdTxnLBghBaCB6jLm7AWcbYK6Sn1fuBNqej17TePznmqKEBhHqBvxnAe34ll28d5wcOY0WV8vo8bXOthblhni1V+w9hd62X/nKV/D0009j9eqx3bhZzG9w/PTEE0+ISSPzNj70oQ9hoeBSntxmMfeg0oKPiGKD18fW1lYsXZrCJjEGJESoGL8UCQ06DnAuo1NqYWVwbAzYNOcIROcDvIcYJiiAUYnPxyWDOSY0CJVCh3ztNtgDF4S1r0ZphUW9ZGohwhodsPkeeX7B90lXOcC8OeZn9HREldj8uWL25t9zARZjef7z54ULFwSxsWTJkrlerSwWGOg8UW8/CGcwSg5Xm9YgT1sGi7pAZP9qlHpoJuGyEwJrJ5GG8kht8hxU2Bm1gO49CYSbs8RrchYDhtlppOvp6RG17127duH++++/5Md8lzypQXBA9fOf/xxvetObcP3114vk93RDx5iNcU1RFU6NRLMgiKAk4exI34yTGmnDWApUvUa28mDhNba4r88B1r4O6DwhW1XlVAAF09MNqFDkQYGxVkUmdQ5WWq/AsF8uOHMA1+FpHf1/nUqBCkMRlpgzswRjAHIgXLQb8kvI0yjCTdwUiypQZUx9w/OFBjHkP4mg5IZVo4Q9oBD7MfxNoI+RnS0xV4tiF21K5P9VYFPuunHzCVKCnxE4kVB29sjEhjreEmoijPj70ONpFNYBVm0RinU1kx5k0ReandaxaqCmtosYKHRjX9vpsD3LctRGij/zAIm5HvIy1ajllCOhaEmwIEaCIhZrcmrwQt+xhGW1ggB5tPMIzjvk812v1ODuim0ih4RYYi5GlSEfLe5+cbwFkoRnB6SQCPDz+YP4TfMr4nfCqjbgbYupGBnbKV9hSH496vEOYxXGShSd4dD3sVDEfZ8yQwH6ve1ocVNJQqJKgUrjKuRroxkgBboa5GsXCwKS1neTxvGXgJcflo93Eho3vhWomqSlhWMQGOyQ7Zm23h6nCvEFzovMkAi4BxwBL+wJ26TNfRY2TbEYrKQNWi5pNJkRGlQyUPW2bg9w9oD8HhW1soVS4gDCS1ubJHZ7Q82Aqw3oPQrYagAqhnTyMZc2uI0qrwBaXogGQMWBIcjjK9mSclUpxkB9fX1C+ciOS040zp+fhMJix83Agz8JW4mFr5U7Zya8LBmROd7ydMHrpzcUgj6Fwu2SA/fR6b/LYdoEr61NLwE6C1CQhpVCEvvJ37ceiWsz6PAM4/neBtxQUieI5QhpfXjwVXhCbmEfKYL+fD3YkrdnjP1g9M1TXCcD0QYGcb6pDcBIs3wO5S4FpENj/0Z4bI+Pbm/3KKERufccGzqOAm0+dEk6tjgupb8ti+IbN6YIbs9i3oP7jhkbN9xwg1BssIkqiywudyxfvhxnz57FypUrR4kKEn9a7cSNG3RY4Ll0qaHb04/new8JpShRa2KT0+rROdhq60rsGzg4ev/gnHOFNWsNNxeg5XeudgYsdtmwlQlIfrztY8Affgo0XwAMRtl2aun8zkWbKpqbm4UKnJk6x48fT2qLnUUWE6HH2xJHaBAXnadEfUCt1IrH5JHM/j+moZANTEtuA/pOA34HQAvd/OXTom6fCAMDA2JMunbtWvzsZz9Lagd5qeGyIDUiUtjf/OY3eN3rXocbb7xReOGmS2zoUhQr5l0RgydPuDN7DEhsVO/KPOCm64hcALCUA8Ubwtkc6UGnMqBIJXdKt7hignLCGPQnCxofH6tzluLg4ElRZ/MEgT5JwlJzCUxqI4r1FTCoTPAGPWh1XxA/zWorKowstPgx4DvEvn3xPkpFCFY1iRG5/scO9WLdsjirpT2F29Ht6RPdofm6XJjVkw1f42cmykfpnxRTWEkDDn8/mpzRYovbYxcd+BXG8S2TxkNdXZ3I0ODkg8W5FlMvOpq7kF9WELZnOQmjSo/SBDXN0NBQUmn5TGOxqRr9vt6EZbLlGfdTkphngcTcj1pzuShmMiicREaelrJirQgBjxAahDfkxyMdB/Hu6mtl4kyhxGsqtuH0SBtG/G4M+Fw4ORJr38RjSQ2TWocHm/fCPaqOojLJgye6T+G1FZvHrF+OhpZPLPDFW7v3eZPn0OTrxp7n/O43lWwXx7hJbRAdYMwgaXGfHH0NJ0ktrpMwKnOgVY0gJA1DAQ1UikrRmTRpdDYBLz0UL4t+/H+At3weMGRI/LadAfY9EC3+F9cAV9w7eu2JJTTEc0mCJ3bDxcAb7sCYEAPdwMM/BwZ6ZELjqruAtVeM/zdcv7//H3D4xeiyvHzgTZ8G8lKoAbXm1O/FwqzkB4bqgeEGoPpWQJ/hID6/DiABZ+8AXAdkZUhsLLFpgvdbtVVWZoxCASzfnFSlwe1OZZfD4RCFCk4+MkbVMuD1HwXOyJN4LNsAlMvn81ThDXrR4DwvrPLM4a7+ckMJ2t2y9RwJPhKii0yZEeuxeLanE58+fhADPh/K9AZ8f8M2bMi9xK2sSB4646/B4jgZvDgpUsMZoIos/pzmvun3yUGpEQz6+gShoaXb1egL/ai3H8NKem+nsuZMdldItBszl8qPCDw8FxNVchNPfEb89lE7wAj4OxVCiaQG8zM+9rGP4e9//zu2b0/DgiKLeQ3uw0ceeQS33XabULu+9a1vnetVuiwtJkTzxjzo9M4Cogh55syZ0cYpkhnpzBv4+vr6elRXV19SmzEoBfFCDKFBNDhbUajLRa1Zbl4q1BdgT+EV6PLI1rLlhlIY1TOjXs1iASEnF3jvZ6JWvJcBSGoePnxYXDdqamrmHanR7+1An7dD7I5CXSVytelbqWcxe/CKZtf4eQDrJP6QV9jiTw20q5KzXmXwcxLqM2x6LJ7dXKjBwUFR6+Y9lLXvVLbylxouG1KD0Gg0+MMf/oB7771XZGywOy4dK6ol5jzxaHAMiFOCHdoFOiPW2y7hMEf3AFAf7roWHb49AAusi6+Z1NslUzhQdZEpqBqgZLfV3SX2A58X6KLklD/kw7GhvfCLgoSEIX8f7IEhVBvLRgmNCHgjKtMvgUaZB4MqZ8w6suBVZpiGm5SYYOkhwTNajOFWVSgyK/b2+9qSLGsVPoCTVWsUFBSITipONPyKALyaADxOF4KBIFRqlRhUd3h64kgNFjFPnjyJK65IXvRlPskFx2kM+HqFZVSVoQalhulRe+Rp87EpdytaXS2CjCjWF6NUXz5KXCiSlLCqjRVYYh5rQ7TIVIJ8nQ1/bduLeodcpGMod+zfyyoADzwhPwxhWyC+Zk1O1WiXcbd3BL0Mfg43MeuVWrG8L2ybEgF/7/XIr0sEJ3z0ko8lQWh/l0pSXqizYWveCuwfkEPLuZ925q9GiSF+0Mdu5mRwBuuhVLJrX/62QakDWmyHYrJ2aF3N8bZFER/Y/i6gIkY9FQzKXUepBuUMXD/wULyaobsRaDwMLNkinmqU+fCGWkcnvz1eCZ4k4gdCp0xjwEIp+F9+DDiGouv91J8AW6FcdE+Ffc/GExrE4ADw918Db0nhra41AVU7gJa9Y//P4wMM4e3P799/Eijfg4xhKpYf+mLg+INR20GjDaieoHi6aDlw+zuAfU/KgfAMCr/itqQv5b0zXSvHcVGySH5MI9jVf2BwLzxBjzjvBv2DGPQPYHPuduRorOj3DgibhzrLEkEATgaNDjvef2jvqNqvy+PGOw68hGeuugl5ycLhLxWkamyYpDWjWa2DXqkWtp4RcJ8V6eNtR0IICeJXtkWMLh8O9MEbdIsmijHQmIHiK4DuV8LNBbzp0ZoznDeWCpo6wHc4XqakmdgyhfksiX7oRKJ9yE9+8hN86lOfwkMPPYQ9eyZxjmcxL3HllVeKfXrnnXcKO773vve9c71KlwU4Dhj2n4UzKI8LdMp85GnXQjkV5WkW0wKqOElsRNQaZWVluHjxovDJT4UDBw6Iv5tvRcypggprXwyhERm7D/iGURujyGb+X2wGYBZZRA+Yy4PQINavn7/2Wj2eVjS75CxNgnkNtaZ1yNNdwnXBBQrmZyQ2NtESfzJ2U4lQYilCgtSI1FqMUDKDdA7R09MjFBq8x/7+978Xte/LBZcVqUHodDr86U9/wlve8hYxAaGP8UTh4SxifmzpdjzadR4dbjsKtEbcWrYM+iQSQnrgswjKzuuIb3IgFBT2NyyKLhg/s/5zUUIjgsEGoGKnbG+VAYKSH0rFWFugcsNYW510UGEsEY9k6PN2wi95x4SHe0LJu2f1qhzoVekpdqYCSbUECEY75mlzISnLUzm7JH+PJFZHBC04cnWTD61nEN/LL7+MTdvkbtfS2gp0NrYjFAyiqLIUKkt8Eauzs1N44qY6luvtJ9HnCwdxS35ccJ4W9iCF03SzJ7HBRyJytFZszdsgwn9pUaZXaLE5bx1KDKkLry/2ncZIjBUJyQiSCVFbMvn8p/oi1T7p8zrinH6G/C7sH2gSHvAd7qHRIhcnL/m61ETWVYUr8Xj3MUHW8S/UCiW256UupNFCq9pUCrvfJVQZiXkfhCaF+kKhoEoqlr7xIYg2qDHJLnmDKZ7QiF1OOO3AH/8baDwjS6+vvBW46taxA3S3fWzoNl/Tcx6o3STOG42yEAZVHdzB87AHgvCmIDQqDSvTG7QM9QH2wWi4tj9MvJw/Pj6pwe+SCG6DrqjNXlKUhgfqra9Ga6aR7RAMAWqebxKQIk8lbdjKgG1vBYbaZRVfXpXcMTIRlqyVHwsYfb4euMOB4DIkOAIODPuHsMI6PZ68r/b3iutE5KjnYWgPBHBsaABXF13CExuSu6VrZDtLAYVMdHDZJMDr7ZsXbcKvLh4Q1n3EYlMe9hTEX4tsmnzx2mRaPDYx6JCCnLIslu05Kf1mx2s6JJaqBNBuA4Jh1Z66AlDaRsczCqiSNmqUGcrQ7u5Any+qQq2zLIsLC//ud7+LL37xi3j88cdTNgZksXDBhilaUd16663CQodqnCxmFo7AxVFCg/CG+jHoO4V83fwtil0uKC4uFl2jtKfkvIF2lST8qMSgHUYyj/xInsalBj1rAGPuYBIMqcJks8gii3mJLk/jmGWdnqYsqTEPQRXNsL8XI4H+0VpMtYlND1NXLygUaigl1s6c4Su7aWq5O1NER0eHyNBYt26dUINfToTGZUlqENzJv/vd7/Dud79bdMmR2KiqGr+TXKdS4+7y8fMPDgw04ume0+Kw1ihUuKt8I0b8TrzUd1YUNumpf3vZZhQksY6Zd4iEzY5ZnqKCOA7cQTtUiqCwe4oUIGkhoVNN/4nPonQyqBRWqGAQgV+RIaVaYYJOOfOEBhFStoRDfIVGQy5iKpqgQvrBoDnaYgwH5HDoWAz626dEapDo27lzpwj9ra6tQJOrDeVLKsVm6mrqQG1NvD1LbW0tXnzxRRQVFY0hNij/HyU0YtDr7YwjNbxer+jeinjxcxKzYcOGKV+AK4ylwmLGL/mhUWgmJBFp75TYWRupm0VsRK4pWp0yS8UZ9MURIOLvocCQz4VbStbgt7SgCsmd8ia1FjcUp/ZAXZVTCYvGgAZHNzRKFVZbK2FjZ/84MKsN4pHy/1V5yGcQocIJSAo4gxK0yjyYVPH+kuKYpPXRZDnXJevlTI3+TtmXnnYydZuAvPBx+cefAk1hopRKiKcflMPvNiVY4hkscoE01o6Gf+PvA1qeA6quFjtIr6JiqxIOP4nCzjANFEWtcTMsSYivpIh01Xv9gDOGEH31BWDbDYA5xfWavrbJYKYcFdEwv/42WS2RVwZow/vKYJP9xcaDaRoK4yTRiifwYyahc2Yv4HOT0QRq1i2YbjBmC/V6z8AZ6BGdNzZtNXI0VSnvA1R3TReManVSqztjuNmh3eXEb1saMOL3Y3t+Idbl5OLAYC90ShWuLCyFhbkzCxU1e2Sbs+FWgGqy8o2AcfL30jpLET5Tdw1a3IMwqrSoNuUJMjkWtG+qNtahxX12TM4SbSfHBYtGGRaOJGUeggoTlJy0KNQiTPCi4wjcIVltV6SrQYmewaHRc4X3iS15m9Ht6YYnxOaWHORpo8U5BoJ/+9vfxlNPPYXNm1NYZmWx4MFQRu5jyv9JbDA3JYuZgyfUm2TZxBk4WcwOaG/LximO93m95NyBj4aGBgQCAWHXlkiEMFC8snLy1pBNTU3is2gzffDgQbEO5eXRLLm5gFapwQbbchweOjs6v7CoTaizTK9SNYsssphZJJtLMJA6i/kHkgxLzJvgCAwiIPlgVOUkV3ZP+v05BzDPiwyaa6+9Frt37xYZGpeL5RQud1KD4M5mSOOHP/xhcQA888wzolg7WbS5BvBUz+nR534piAfbD0ITMy+np/6D7Qfwjuqrx0zY04E72IshXz1Ckg9apQ15upVT88EfDzmLgb7YTmQFYCxIr8sxRbizWqmAOuZrK2fg8MvV5qPZFd8Lw6wEszoXCsVW2AMXEAg5oVGaYVFTbZD6pA9KI/CH2KkpQaMsgUqRYXBvHNxhIiO2WDhWvTIebJoStCAxcJxIruDIBPSsZBjXzrzNsGhM6PH0Q6fSYk1VDTxDblgKoxdsdletWLFCWFCtWZPYnasY4ykuL1XGhRedOHFChGty4sHJDbuynnvuOVx//fVT/i6ekA+v9h9Dj2dAqKXW565AtSn5ZMamMWLY74pRUwAFWivqLBUiT6PSWIDyBEunyICG4dRUcPDhjbFOYeBtoc6CIr0V76u9Ck3OPvG+NWYqDMYvZFYZC8RjuuAOXYROJftJkhvQqRSwqiugULCLPVYJQM/jKRB8zKG4+0PAyVcAxzBQUAYsp7JCAQQCQEOCqoHLzx4bS2qotcDm24D9D0ePa1oy5ZiA4YuAd0gO2goPJHSUlfqj+07+qYRJk8G5asmV1/WV5+OXezzAC48Ct9wXzQl5+m9ASwNgywPWbwVOH45XllBlcfMbwq/3Ai/+LzAQzlwhobH7jUBu2L9fYwT83A8RopMXy/B5krscyJsFCSsJjYd/KH834vxhYLgP2HgdFgJ6PafhCMokahBB9PvOiQI0lVxKKIVlUazdoU1kLEwPri8uwyKjCW1ulyA2qbBab8vFptx8QWi85pVn4AwfGw93NMOqUYrrDPf2z5oM+O/NV6JQt0CDUHn+lq+TH9MEm9YgHqnQ6x1CtzeAgFBd9onxBMcWdeaNo2OM6YI/5ESv9zCC4dwrq7oa3d4+uENR79webyO0SgPydfGKUxIbpYbSMYq+L3zhC2Ki8eyzz4rgviwubVAByzENu+ZIbHzpS19aOErtBQZFkrkESe4s5g9ycnIwPDwMmy16D160aJFoblq9OhqUTZD8YONUSUlJxo1OvNYeOnRIzCmY9cWmqauvvlrY7Lrd7qTKkNnECmsNcrVW9HoHhZUy5yaaFErwLLLIYn6C+RkMoI5FnnbyzaVZzCx4f7FoLj31XwQXLlwQhAYz3X7wgx9cFqHgyXBZ30m50++//34YjUbRWUXJ+GQ9/Nrdg0llpSywRQqmcmCkRxRRaU2TCXzBYfR5j8R1IfV6DqNYv31mJkrWCmDRVUD7PtkGhUW4RVdOqoNXr7TArM6HIyz94hSEtji2GbgBmNRWLLesxwXHKcHIsoOzzrJeBH/zcLdpUnfKxyIgDcAdpKe2DH+wFQYl32eyHvKmhDAhboXMPFO5n/O05RhIyNbI1U5P9xHVSmfPnBXetyusYYIvH6JjihfMRFBdwQ6oTZs2jR6D/Fmir0SnJ/5mX6KPFn66urrEhIPKkGAwiFAolFZXFgN/W12tCEgBFOgKUKwvSTqheaH3IAZ9sgIjQnBQ3l2SxEd9d8EqdLW9Il5HaBRqXFu8FoW6mG77JATmA+0H4Qr6RCFzra0SR4faRq1Tak2F2Jwn+wUzMHx1Tnr7xxv0YcBnF0RMntY6Lee1M9A8+nvk7dzBNthUG+CXjvJTxTKVogYqRXwYfMZg5/mGq8Yu582VHQPM04iuTVQhkYiqNUBoGGg9Khf5TboYe6Z4a7kCXRWG/T1wBSPKEwWqjGsyl5VedfdYUoPX7ZHw+5IR+tX3gfqT8u/8TqeOAG/7MHD8VaCvEygoAXbdLP8kzrwIDHZE346h3fv+Ctz0Ibm7ve4OoPkFwD0I6K1A1W45c4NKlUnmE2QMKjRIaMR2HR1/Dli7RyaYUoF5G1xPkllzBJ7rjuBY5Zoj0IUyQzk25m7ByeHjIlzaqDJhdc46aKfBQzUCk1qNP++8GvdfOItWlxPLLFZ8sHY51Eolft3cIAiNiIrLqpFVhJFzus/rwc8az+BzKzZO+B3b3M1odl0UXWBFuhIstSwXeU+XEy44OvBMD8c/MmFOtUupToJS6cex4aNYZ9skVBHTAW7zWEKD+P/ZOw/wSK4q+5/qHJRzmCBN0uScxx5nYxvjhMGAyWDSkoyBDcCyhGVhyf8Fk0xaw9oEYxsbjLNnPMmT83hyUM6pc6r/d151Sd2tltSSWnHez19bo1KH6urqV+/dc+89ncFz8CZJwuOcJlHUSITXuE9+8pN44oknsGXLFpExLLk8oHi1detWsdjs6OjAD3/4w8sye260yTRVwh+Ir8zINA8/SU2Sfriu4Hdhw4YNojqcsEKjqqpKCBtMrIqF3hu8P9fk+fkpVt3G9BRnm6vjx4+Ln/To4Dg8UXr0cy2SbD0ikUgmB9Md80VyY2ugTsT+2OKozC6vOZKxh7G0m2++Gffeey++/e1vX9bJM5e1qEH44fMkoGEyPTYef/xx0RM32UL3krcJLf4OESCdlzEdphjDTBr6Js+f75tFr/fn7w66cNZ9AcFICIXWfMx0TOv3ZPSI4E28hXFQ7UZI9cCsDNJ6Ybiwn/8APf1The9ppmMlmv3n4Qt3wWywidYNxlEy8cu3FosbLzj9tQ0ajED4bJ/Pzh853SNqRFQ/QpFLUBGAQcmBSSkbcCAxYBEioEiiG0E7aXOe2r5EOhCItInKllLbHJGB1hlsFCV1RdZK5FpK0yZqsHKCZeL8ty4yJIoNra2tIouKpW70o2G2Vaxh8GznfJgNFrT6m0TwbbqDmUkFcYubxsZGzJo1SwiKzGJ0Ogc+h90hN3a2bu8p+azxVqMqcz4qnJVx9/OG/cJ0LxZWjtR4GpMuItje6Z0zN+O8u0kEy2Y6ipAxgGeMPxzEn2v2IBBtKcWqjIMdl3B72UoRSKfZ9zR77pAvKk2+drzQuAfBqIngNHsRrinic45Ube87/qjg9yITFrBKgiKBSfSFTBuXzmg3ZyawaLUmXlxxE7Dlb9rf+Z54fDZc1/9zFM4Fuk7Fb2OViy0+04LHfE7GGnQFm+EO0Zie79cr2hIxYz9l6P2RnQd0tcd7g5RG2xI21gInj8S3lfK4gEvngTvem/w5O5rin4v/drVrj6UoYssGqt6EccUf6zsR2+4rkFzU8HQDzz8MNEZFy4UbgI1v0t7PODBQVViuJQ9XFl4trt2jNcmjIfiXFvatVujSK1969kkvUdYIQ0Wtjz1Y+xKMhMX9Ob+o99XilKu33VKtr1q83wVZqbctTDtqAAhH270YC4DRqhbVX05V8WrLkbj5lD8SRluQoqYB/ogfB9r34MpCVsCOPGAcVv1xgoZGMi8PZdAxhr3judA4duwYduzYIa6Vksuz9c5NN92Ee+65B7/73e9EBrkkfViNuSi0roUrRF8NFTZDERymyZ01G1YbEVYp1JhgUqbDoPTT7nISJRGyze3+/fuFmMHqDH4PKGboJuKESU6stDh8+LCoHud9huI9xOss277V1NQIMZHrCz7H5Zq5KpFI0g/X5pUZi1Ghaomyl3MgWTJ+0D7hrrvuEi1OP//5z1/25+FlL2rog9G//Mu/iFLXN73pTfjVr34lFh9hNYwmX4swHa71tuFE96WeIMqp7mrcWrqxR9hYkFmKve3n0ejrFPdhsHOOsxTNgTb42E89WqmxJGuGMPTtDrrxcvMOEaTl9hpvPdwhDxZl99f/PPmAydeaLBeAYtvYqtgjCQhH4lrzaKhRQUJVA/CFd/X8DrUOEaULVmP/niuKkgmDupGRTiFxAAx8D75/3lAtukLHeoIqBlxAqX0dyh3Jsz15PokGUMO8wFZWVoog0pNPPim+D+vXr8e0ab2ZqBQyWLXBv7HMjZlUFEJiRQ2+r5mOOeLWH+yZqzOYoEEuuM/3fFd0TnefwkxHRdx77a+t20DnAkXKhVnJq0ROdddhW8sJ8R0ut+djcfYM0ZYq7rmhoCXgwhUFA5hKDwCP98tN+3oEDVLjbcLxrgtYnD1M4+4oNkMJvJH4yh67URPBtOOW5uDKay8Bf39UEy4oQO18EfjA54Hr79A8NCgMWG3ApuuBaTGCVMsFoKNOa9FUtghwFgPlm4C6ndrzUGiaeb0mbMTA4GMg3AJvuB5+tUP4hngjNIWuQ5l9XerCBo/FOz4G/PYHmlhBKquAtdHKk0Ag+WMSgtdxZOQAzfQqiQmG2liJMYEW16WzgDP7499TZr62n8l4+Q9AU8z5dHwnkJWrVXYk0t0C1BzXPr+y+UBOeoNMPH/pn9ERvBC3Pdsc/10ej8XGuvxCPFHXW63GUdmYMGbMifVeiQqmzzQcwEWPJhhonjr0gIqnwVc3fqJGxA34d/a2rguaAet6wDC0qsOhwAqXQEx7P51QzNeKHkrukAtZaajWSDZmiHPNlI3OUG9FGP8rtPYvUjAz//bbbxeBOQa1h5ptLJk6VFRUYNu2bWLOxIAr51exbXgkI0e05E1je8HxJBg5j5DK6mjt2hVWa2E1cD4zSglsYwQFDK4paGBKoZeJTfw9tsUUq9k4ZnLsZEUHvytsc8uKi1RhNZQuIDNxSiKRSEYDKWZIxotHH30UH/jAB/Dggw/iPe95j/wgZKVGPO9973tFsPWtb30rautrsfTta9EZ7EKECbbR1gN6ULU92I3TrmosyNLazFDceOeMjTjQcRFdQS+KrFlYkj0N7jCzCM+JVjVl9lwsztIyf8+5L/QJ0p5yncP8rDlJsw2dplJ0i1Yyvfe3GmimOUY9uUN+oGEv4GkBaHReygzszGEFcAMRturpggE2WI1sH9OMVj8DZSryLNOQaxm46mEsMCl5CKrxnhfGqOdAUK0RFRqxhNQamNVZMAyQtaqIypTU21fRdLsrpHsRaJ97BH64QxeQaY4Xv/xhL067DsAd5nE1YoajCsW2aJY5z9dAKzxht2jHlWvOG/D4cqFxyy23iGwpLjB4X/7ORbguQMydO1dkW7G6aefOnThw4IAw+h4tgpFAn4zsSPQ/Y0y4kD4gMxyluOSp782QZk1MxsAtQpJR42kVQUYdBhtdob6Z7fwWs0JjuLD1ld7+Sof7zRZaI4XniRpU4Y/Qd8AAh2km7Mbhmy8OiM8DPPNH7d96O6OmWmDPK1qlxrprtFsi53YDp1/tFUIuHQTW3wvkVwG5c7SWUxQ1EoSpYKQNXaEDQnz29nRP0s6RoOqGK1SPrIQA94CUVwAPfBPYvRV4+o/Awf3AkY8A97wfWLURyM4Fuju1SgvxUiowf4De+As3Aw1nAU+0cogC+OrbMKGYvRzobAYOb9WOHQWN69+VvM0gP5u6s/EiDak+3VfUYNut7b+PngcKcPY1YN1bgKKRiXSJ5FnmwqCYhVE4ZYMcy0w4TOPf1uH2shk47+7GL86dEmdktjh/w3CFNEG0KjMb91XGi+AvNh3BJV5foxztqsbS7L5tKsf12hikb1isqBsCgkcB64ZRe0nOrbLNTnQF3XFXAKsh/jiYh9GyjfORkOoXnhy6mMGf9NDoCp3vCSqySrLEvhRZ4TZ0B1vF/dn6zkZPnyQwS5jl4Axm/+EPf5CBNYmoBmdWHROm6OP3j3/8Y9yNiyUTD877Q+pZ/bfoT267BIvSf+LUZILfAYp8rNj+9a9/LdpCrV69WlRT2O12sc7g94VwO6s7+DMjY/zNWCUSiUQiGU9+8IMf4Etf+hL+/Oc/i7WGRENWaiTAk4MLj1veeAuuOHk17v3X90JNkuXNQKknob+72WDE2rz4oA1b2VxZ2NfwlS2nkrWGYYAumahBY+si6xp0Bk+L9ghWQy5yLPPGJsARCQNn/w742rV99rYCrnqg6i4t2DiEAII7dBRBtaGn8qA1UI3WQO9xdHs7EEFYBAyGA1/DFwnCZjCP6NhYDfMQCfsQhhZkMiIHNoO2oFDVYD/tKPiZpq8VhwoqaZG+W1V/n/d8snsvvBFNhOHxu+A5DovBhlxLEU53nxB92XXKbNMxL3PhoMIGW0uxFy0N/p5++mn4/X7R57aurk6YYOoZUazS4EKE/hpcnLSF6uAJd8KkWFFsqxCtqNJhAN/ob4z7/mWYMpN+V9bmLUEwEhSigMVgxpq8xcg2D12AO+Oqj2tvw58tgW5UZZTipKteZFuLgKXZjsX0oBkmVoNZVJgwI7kXBY4hfLf6Q1GMyLawPDY1L5kR4eqK92YQO2AAOjlu9EPAqwkaRH+su13z06hcG/WXSJ5p5woxwBrpE2OPvjAi4ns6RDgWPflIbwUGjZ4f+QVQNgP48OeBhx8E6qu11lp3vRuYHt/+LA5bBnDDfUDtSYDVekWVmmgwXvC9HH8ZqD2uHddZa4DZa4FVNwJLrwZCAa1Co99xQdFaUtEAvWeTAiQzuz7+SlT84YejAuEI8OwfgDV30gWUzbTT8pY4huVa2IJvgM9hHOB+3T9vMT48a77w1si3WIWgcbyrHTajEYuz8oT3RiwXoi3wYmkPqMhOGD6n2Yd3bUwLKquYYvdRBaLXndHkhuKV+Fv9btFekDiNBuSYe9uPldnKYTcOLSPXH3ahzrcfIVWrhskzz0KeZbb47LLMc2AyZMAfbhOiWYZpBkwGG/KM5cLXaiDYx52thm688Ub89Kc/FeK/RCLOW6dTeKt8+MMfFt4CFDZiW+9IJNqcP1kz42HMZyYorMBga6hTp06JdrYul0t0SGBVBYWN2EpurkU4hjLJii2rZMWbRCKRSC5H2J6RnYWYDMBY9dq1a8d7lyYUcrWVBJ4kO7bvwA033YCWmmZ89HufBszxwWoupAutwy91LrIVoNrbayLLxXmmKQPmAXwmrMZsFBlXY8xhOwxfW8wGFWDGeudFLZs6RSJwRwWN6HOI3uPxAXrS7L84LFHjorsJLzQdEK0qGCi+vng5ZjiKhh0MthuXRysyaPhu7REBjEqeyJqKuz8sUJDeqhkFJhhgFdUZsZgN8WXYgYgPXrYFSXh0R7AFBsUSJ2iQOl81imylouf8QFCoYEbhyZMnRVsq9obu7OwUradiS8HpucGFCas3/rr1UXiNnZizoBImswltgXoszNoA0wiFjRmOGaK9SLVXO+40/l2R07cyhALP7raDaA20iO+UPxLAoY5juKZoU5wHTir017LqhuJFqMgoQL23A5kmG1bnVcI6gkoNvs6G/CXY1nKoRyrLMFEoGWFWe7gL8B0DIl7AmAPYFgNpEJj6hZ4UbC3l9/cuyimIlgxQLRFIEhDl98wXbQE1ABFoVTPGpDF4FVa+56FSc0ETNhI5exK45mbgga9rhuepmL3STPvcXu295JYCGQN/30adYy8BFw70fjYUOChSVKzQTN55Gwh+LmtuBHY81Vs1w20L1wF7HwfaagCrE1h4LeB3975Otxf43atAMyuPHgOqqoAf/xiYAu1XGn1efPfUUZx3uzAvIwsPVC0WPhs6DpNJ3EiW2YL1+b3BGh2KmRRILQYTgjy3omhVZnYsyZqPS54LCCOMYmsJZjrSW+0yJJRMQIgAetBNAQyjnz2bb8nC26dfjdZAt0geMULF8a4zwjtnuqMIMxxaxWyq8DoRK2iQtuA5WAwZyDSXiGs9q2N5GwovvPAC7r77bnzqU5/Cf/zHf4x7xalk4sEA7UMPPYQvf/nLwiuAmXYM8EokhP5mCjKgIuYaKqrFtErxqcS8efOEoEH/DLah4lqCQgeFjNiWVTT51tciNBWfPXu2SLqSSCQSieRywOv14t3vfreoXKRHH2NuknikqDHAZGvf7n140+1vwtfe9gXc//MvwJLfO6lcmj0bMxx9AxSpMt1eJjw0TnafFQJJljkT6/NWTsxFsBruf3vXRaBpr9aeylkClG7st3pDjfEM6NmW9H6J1QmD0xX04B8N+0Q7IkLfA/7+tulXIcs8vJ6q/CwoZiRCs/AI5iIYOa3dD1ZYjStS8sgY6uvnWFagPbCvx7/DSgNE44w+ZsnJYBWDL5w8i1bbnpfSPlDMoDrMxQRFjdgFB2F2VXV1NYrK8jFzRSG87kycOnoWkTCz6FXYluZiXskAbXpSgPuxMHsR5mbOE9VMVkOvyBRLe7ATDb4m8W8967kr5EKttwEznUNr9UCfjcOdFxFRe/OnZztL4DTbsDxnprili9kZ5aLFSqOvTVSXVDhLYTaMYHimkOHaFq0eivbCp8iRsblPG6e0waD4PR8BHnmwt9Jh6TpgBb1k+sGeBZisWpWAfpT5/c/u9V9g4NIf6RIG0FZDVs/3zAgnwnCL88BhVOGJGabyLPNgNw5RRGDJx9nXk293xgRu2UKo9rwm4BSVJ69soFj78q8AD3vwK8C5fUBbHbBiHMtEa472HXFrjmmiRqos3gQ4soCLJwCTGViwDjjxPNDVqB0nVt7s/hNQNAdwUQhXgb/tB1q7e5/j9GngO98Bvv51TGa6g0Hcs+sVNPl9CKsqjnV1YH9HKx7feJ2oxhgMd8iPx2r344Kb7YwMWJhZBLe3MdrwSPv/6rzZIgGiyDZBTG/Ni6KeGroYQDFsbPw9OB6W2HLR6GvHU/W7ezyImvwBlNqnwTJAQkgiFDNiBQ0NVt+2CVFjOPziF7/Apz/9afzkJz8RCw+JpD94zfrqV7+KOXPm4LbbbhOtBO677z55wCQCi2E5ApEDUWGDc/kZMCrDrwaeyLClFA3E6UFEnw2uJxL9M1i50dTUhAULFog1BX38XnvttZ7qJ1ZwSCQTnrZmYMszWlV75Txg4/UTy2NPIpFMSBoaGoTPFEV+Xvv09oySeKSoMQA8aV556RW8533vxZfv/Cx+/9gjWLZ0uTAWdppSy8oPRAK44D4HX9iLTHOWMDZmEJqLmgVZc1GVOVsEaYfTD3rMcBQCfL+hmAxNBtIpXtS83Hu/7mog9CJQcUvSQJ9RcYq+51pbJQ2nSUFnMD7Qlm1J3XdCp8HX3iNoxGbAcvtwRY2BsBgqYFamC7Ehtooj3bAqo8B6JUKqS/T1Fscw4bXY3qnQUo7mQG10C8O/BhRZp0fbnPXFYRpadi1LwhctWiQWFOfOnRMqMTMOMzMzRdYU21KdOn0S1e46VJ+vg8Npw4zKMjTVNaPL2yz8Y4Zi3M7XaQnUwhVsF1UexbaZop0Wvyf8rz8CCSbeOmxHNVQKrJm4e9oG7Go9BW84gOmOfKzPG54ZeGqvlyNuaSFIT5FQQpuYLiDcCZhGMeNv9kLg/v/SvDQcGf0H/XVY4bLidmD/E0A4KoRMWwaUaq3eAhEXGnx7EVa1v1kN2SixrRJ97zNMi9EV2i++gyaDghyDEw7jIpgMjjiflZR56g/Ac09q+6vvMv9dOg1Yvk77vaEaePiHgCcapJ+zWBNyGOCP5eJhwN3R236JnN8PzNsAOIfwGYveWmp6hKhkzzGc5521RLsRChedevUdet+rMxvInw60XgLq2qhM9d6FbamOHMFExx0KwRttHZVsfN/S3IB6X6/HDoWNc24XXmtrxlWFvYHxUCSCP9e8jj1t9bAZTbitbC7W5ZfhTzX7UO1pF0cspEZwuKsBV+RXIqgGxPi9JHsGSu1jlJ3r6wAubNN+2vOAiis076xEDHbAdiUQiVZuGvKAIYgJ6eClpgMIxSRItAW6sL/9NNbnLxyRGTgxDuO9MIuY5eBsn/LMM89g8+YEfxmJpB8oftF35c4778Tp06fxzW9+U8y1JJc3BsUOq4E+RZz3cL049Zfq9OyjuOF2u4VHHxOpGMCh7wxFjYsXL+LMGZqnQ2SqLl26VKw/KHBIJBOejlbgh/8B+L3avP7IXqCuGnjrB8Z7zyTjAJMl2wJ+ZJjMKSVBTWp4vlfvB+qPaQlb5UuA8mUDxwYkPRw5cgS33nqrWFuwypftGyXJmfozpRHCk+eR3/+fyKq66/rbhekj+yWnQigSxO62HfCFfSJrvNHfgPZAG1bkrO4JkjDQO5Rg77hgtACzbwEubdF8NSxOYNoVgOtSgrcE/TaagZAHMGuG0rEokU5khINwGRSofP8qkGPKQzDSCm9U57AZAasyeOuZRNi6IxlsQzVasEUVRZrRhgEYizJwILTSuRh2Ywa6Qm0wKxaU2ithMzrE8ax0zsV5t1ZVQmY6ZiPbPLzgOc9bihi8UXjo6uoSpeOhUAhGkwFelx9ZOZlYtGw2OhpaMWNGMRx5QVx078ZM55p+q0oSueQ5gSa/fn4BLf5aLM7eBLNh4ME8x5wlKlQoFMaSbx1ecLDEloM7yidjz8L+qp2SGlCkF/pNVM5P/f5504GrPgS4WwGLA3D0npvN/sM9ggbxRzrRHjiDfOt8mAxZyDFvQkjtEDKeScmFQqPlS88CgW5tDJqxGcgoG3wfQiHg+b/2TsB4mGhEPK0S+MQX2ANB+9sffwp4Y8ans8eA7c8BV72xb1stMcYlHG+/JzVRg9UqtbuAtpPac+TMAqZfAYykeqdyFXBqe8K2lRgR/U1KuZ8b3w6cexnIewVwB3qPBY9r4Th6i6Sw2PjW60fw24tnxGnAtlI/XbUR5fZ4cTygG8YnEEzY/psLh/FS08Web94PTu/BA8paXPTEtnTUaAv68JZpqzCmBH3AsSd6kxb8LuD4E8CSt2ntyRJh4N84/CrVkUBx3MXWlzHwuLYHhjZnoHiRa65Ee7DXDJzm3znmAVrlJYEBuHvvvVdUMTJ7ipn3EslQ4EJ1165dYtFKYeN3v/udyD6XXN5oa8TLL3jBc1/vEx4MBkUVuJ5EZbPZRLX4VVddJcS/QCCAlStHOIeRSMaC17Zogkbs/HDvq8DNbwYyJ38rVknqnO7uxOcO70Sj3wujouBjsxfhHTOmcCuhS/uAczFrz9NbtHk3ExglA0LftXvuuQef/exn8cUvfnFidvOZQEhRIwV4ErH/LVtSsV/yN77xDXziE58Y9ORq8DXAG45fgLPXf3eoC1nmbEwqbDnAvNvjt7lqkt832XFhkC6wHyaEkR3RAhG8l1txI8OsICNGewiic8i7N91RiEJrNlr8vY/l79McE6tES2utxeCRJa3ZVzwXKWSUoq9hboVzNgqsRfCE3HCYnMJgO12vSRPx1as1nxcKG7N8M1EbOg6TEoBzZm/gyyeC0dXItw7e+zwY8UcFDaKFAkNqAE3+apTbBw4a2YxWbMxfhV2tBxBUgyLjeWXuYuRaJtn3baSYmCnOVkr6BDq6QD73GhBwAxklwPS1mmA5EaBnUU68+EDRjJUadiM9RtgKjucRDX7pzaBBE1+LEq3sCnqBc/8A9OqkoBs49xxQ9WbAOsg5z3ZZiQKEqgBFJYAtWpVHr432loT7qEBDvL+OgFUKiW30zLbUjcIbDgCtJ3p/7zinCQVFS7TqCn+3JnoEKZIUaYLHYBVpVVdoQepYo/CyIYhPyaAAlTcNaK+NHj9WuRiA8oXadSCzCHj7JuC7T2tm4cRkBD79qbQEuE9116Mr5EWBJROVzqK0TPgerT6P31zUMkLJWXc3Pn5gFx7feG3c/TYWFMJhZIu/sPiWUeLONJuxKjc/TiB5pflSnJTIPdzafEn4aERi/sIz3JKi6JtWOi9pHlk9sI2YG+iqBfImhgF7MBLGy01nUefrRFvAgCxTWFRn6cdtONWY+ZY5sBic8IbbYVQsQtCgGXiqMNh2xx13iGvgzp07kZc3zp45kkkLeyPzHHrzm98sRA6aidNfQCK5nDGbzaLdLW+cD/p8PpFoKKuZJJMOP9f9Sean9CBMz5JckmY6Ai5c9DTBoCiY5aTHWurzw/4IRMK4/9B2tEe9G1nh/T9njqLSmYkN+ROkzWy6qTuSfJsUNfqF17v/+Z//wb/927+J6oy3ve1to/oRTRWkqDEE3v72t4tS8bvuuguHDh3Cgw8+OGAZUGyLhFS2J7+vH96Q5hFgNxbAxPYPA8HnjrAdSBgw5APKKJp45swG2k9E487RgFZGOWBKEmAQ/atDMQao2r9MSTLHDQO0F+oP9iS/vWw9DnScQ2fQLfwJVuTMEtsnCqrahggO9x4HtQoGZWwWrhQy0iVm9AczqfIziuEMOXDesyPhrwqCqlf4I3QGLyAY8cBsyEC2eWaf6o1wku8HA1fhFFtIsQf9rWXXwRf2w2q0iMqN8b44kZEGXEORVoTVNkAxwayUwaAMkMVnzACcGwDvEc1fg+PAuTO9Jt7uFsDdDCy8Y8KWgGpeGWbkWMLiGArzXkWFxdBbuRGHpxFIPEdYseOuH1zUYBb+jEqg5mJvJhVFiQXL4j1DhBF6TC9+ji/Jsqx8XX0rNaYv0sSbVOg4m7BBBdpOAe2ntOeMzfZiUNr/DDD/zoHbSXF/5qzTbulCmIffDRx/CWirBqwZwIKrgMyomFwwH1i6FPiyDTgQzYq/9W5g8cgydCgWPFm7Bxc8zWJsYCXk8pwKXFM08t7au1r5nL01TVx0HO/qgCcU6jH9JiU2B3695kr865F9qPG6McuZiW8tWYXcGKNwkqiViW0ANuTPwvbWs3HXxLUTRESYSPCz/tX53TjtaulJhmgPUKzndV9BpsmOVblDbwvI8STLXCZuQ2Xbtm0iAM0etz/60Y9EmxSJZCRQFHv22Wfx8Y9/XCSK/OUvfxFG4hKJRBuv7fbU2j5LJBOOqsXAtud6f2ebwZx8IHdiJV5KNGq9Lfh7/R6o0dSjfe2ncUfZRuRYRhZTq/G40RoVNHQ4j93X3jJ1RY2ki6Ax6BoxSfH7/fjYxz6Gv//973j++eexYQNbUUpSQYoaQ4Qn1969e0UP3GuuuQaPPfYYSktLk943z9I3c8+kmJBpStKrOglB0U9+NyJ6EDxoRLFtjTDLjage+MNHEYFLM6o2zIeRgcvgLgDRrMuwAphWAIYijAq2PGDmTUDTASDsAxwlQHE/rTMULvpjQ0VEhdlQCETq41pYZZrnDdtIdG0Sz4PdbW3Y396OMrsdbywtFReQsUZVA4jgkCY26dtwEqqaCWWQ1lKTDYvR3sc7he/WojhR79stsu8FYcAbbkGpje3YeoOxFoMdZsWKoNp74WfQMtOcessatnRzpOh7M1owEH/Bcw4XPedEVnmBpRALspb08c/h/VoD59ERqBa/51imI99SGSeCBCKX4I+c1L5DKl0kLsFhXAeDMkDmiCkfyLxa+3fdgV5BQ3tVwNUAeNsAx8RtBURxUlU7e44Ff1oUf4/IEUey1kycOLW1Av7zQEG5Jkz0x30PAA99H7h4VqtkuPlOYM0VvX/n6936TuAvv+z5HJCRBWy+pe9z0RicmeSxw11TolDRD6y+YJWJ/pp8nR6BJElLK76Ir63XD2GsoVCzrB8DdH4mc28FCs8Dq3xaVQlvI+Ssq0EIGvrYQA52XBA+FAUxXhANPjcOtDfBbDBgbV4psgb6/KNkm80iM4tiRs9bVAywJul7uzwnD89ceUO/z8Xn2VRQjm0tNbHfPFxZOB1rckuRbbbjtKsJNoMJ6/NnodQ+DhVl2dM1f6yQPj4ogNkOZA092D8Sdrc1YVtLg+gvfEdZBcrsWgueGm8nTrl6K6S4h7ThKrVNw+LsIsxwFIlr/1ihG4J/97vfxUc+8pExe13J1Ifi2M9+9jOsWLECN954ozQQl0gkkqlA1VLg9ncCf/sDEAoCBSXAez4JTHU/hUnKthbG13qTxwKREF5rex1vKNE6UwyXDHPfpF2xlEz0ZZxKlC0Gzu+M31a6aLz2ZkJTX18vEufpKcVYMz2lJKkjRY1hwJNs69at+NCHPoQ1a9bg8ccfFz8TYYupJdnLcLzrqOjxbzVYsSxnZcqm4G2Bkz2CBmGQuC1wHCXWNfCF90EVbYy43QNf5AAcKIQS3aY/AqEjgPna0cvGdhQBFW8Y/H5stWReBASPxmzLgNFchUK1Ap5wrVDEbYZCWI3pC7J+79QpPHCIYoLGzSUl+OumTTCNpRkjg/MMSBvCff8E+gFMHlGD7bNCqkfU05gUe9LqA3qAlNuXotZ7SHymJNNUDIvBgs5QfO9zf6QDvkiHMIAOq15RfcB+5/MyV+O0az8CrDKAgmn2uci1jJI4N0rU+WpwLsbLpDnQDLXriBgDYqGg0eyPuV/03wXWWT3H3B85Ff2rFhqlrBGIXITNWNX3hT1NQBsrqEJAxnQgZ27fVkg6/W2PJRIG2pq0MSS3SMswGiN4PqjJSraT4SzVhFb6/oixLwIcqwG6Xo/OJnOAOz4C5PSTGcW2QZ/7ulaJwQlmssXG4jVAXiFw7nWAGfn8nYbofYiGsGO/H6lmpnTXRs3Bo49N/DnZoECUl16vge4EX4We7UFvj6hxrLMF/3F8R4/3Ra75BL69bDMKrQO3KnpfxVw8XV8DP8/7aKXGJ+cuGLYY/sFZy4VB+N62BiGM3FE2D2vzNMFgTV6FuI0rFDAW3A5c2Ar4OgGak1deBZjGrp/7X+su4CvH9/cc40cvncXDa6/BTGcm/OFklXuAw2TDwY5q/KPhCDJNNlxbtBjTRlGgZX93ihl//OMfpSG4ZNTgnOqjH/0oFi5cKNrdsir8+9//vmjFI5FIJJJJyqbrgQ3Xau1uWfUtmbC4hMdcL0ye6m/dMRSKrHbcWVaBx+su9Mx3c81W3FY2zuuA0WRmND4qjMIZRF0KTF8xrrs0Edm9e7dImL/++utFcgs9pCRDQ1H13iiSIcND94Mf/ABf+tKX8JOf/ATvete7+r0fW06xSkMPAgcjXuEdYDE6YBJVDH2p9b6KkBo/iBpgQZl9OXzhPX3ub4/YYEA0wzcW8w3AOLfg6SHSDoTbo2ajpZrYMUrUer2Y8fTTfSyTf7tmDd5dUTF2gkZ4hwhCR4x9g8EKFsCgTA4lNhTxocm/D0FVO8dshjwUWpcLESMZwYgP/nA3jAYLbIYsuEK1aAkc73O/XPMsuELsO68Fr7LNc5FlrhTfG1ZrmBRmTk+Q83cIHOzYh9aAlk0ey7WFb4gTg864XhXtuGIxGxyYk3FlT5WPK0xjrXhMSgnsxiV9WzBdfDb6S3RoL1wOOKYBR/8cI2IoWkumpW/Tgs794ekG/voLoKVO+71oOnDbfYBt6D3sh0NYbUZI7RUliQFlMBsW9vMAv+ZHQWHj9XPAhQu9YgKrgYqmAW/5pJYptfVZoIG+AYXA1Tf3emekgzOvAcdeit82b6PWmmkw2s8A1Vv7/3ti+yl+lvQOoufRZBU+hkiNpxV/qonP/GEbqvdXXtPjr/CRfc+LSg19gkMPiysLyvGZqsEzrS64Xfj9pbNwh0K4srAYN5dMG5X3IdHmR1dveRoufiejcLF3U8l0fHXRanjDQfzniRfhCwd7Pkt+1vMy7WLhycWmEq3Oe9fMzcgfYXuAZDQ3N4sAc1dXl/A7mDlzpvzoJKPOxYsXRYuznJwc/OlPf0JhYdQ/SiKRSCQSyajwZN0ONPo6eirBOcesypyOqwqXpqWlKhN5jna2iXa1b58xB3kWGcC+nHn44YdFMsvXv/51fOpTn5KG4MNk4hgOTEIYmLz//vtFpQZPwk9+8pMIBAJJ78fqDD2Q2eQ7g1OurTjveQ2nuregK6h5ZiRiMTDjNDZIpcBi4II9eeBKVZIF5ewTR9AghlzAPAswTR9VQYNccLv7CBomRcE5dxLhZ7SI0ESYwZpoIJIByR4d0QkFk6eHYmvgKIKiSkPDF2lDR7DXUDcRs8GGDHMh7MZsce5bjbl9zl0FBnQHL/QIGvxrd+g02gIHEVZ9sBhsk1LQIMn8XGhcnkiyb7NwCVAD4gaYofB73Of5k7SqaaNoxPMrRqtuOaq1JZp3M8AsdrZqySwBFrxpYEGDvPIY0EqPnijNtcCrT2KsMCqFMCmLoYDjnh0GzIBJGcDc2mgFytcDs28G3KH46ggKOhRn+D385feAZ/4MHNwFvPgU8OOva9lT6WL2Wk3AsGUCtgxN0JiviVSDksl2QPy8+xEoeB3h52bPByxZWhUE3+9lImgQZuRvyO9tNcgg940ly+IMo1v83rjuX+yMW+9LbeyvcGbgCwuW4RtLVk18QSMcADpPA+3HAT+rlCYXrIRxxwga+ra2aN9hu9GMD81aL1p1Ebbqur1svsia0xec/D9b/J1hS71RyJ5atWoVSkpKhJeGFDQkYwXPte3bt6O4uFicgzwXJRKJRCKRjB7XFC6PMwbPt2Rjfd6CtDw329LeUV6JLy5chX+as1gKGpcxjBl/4hOfEDFkxpJZDT5S/9XLGdl+Kg3ccMMN2LdvnzCOvPrqq0VGVX990FyhFjQHenursz1PjfcQ5hmvgskQX7GRZ5mPgK872u6HAT4r8i2LYIAVBmQhgq64j1IxLgDCRwFVD2yYANNyINQFdB8GIm7AlAtkLgUMaVKFGTTsqgX8XVrbiszk/iKjCdvzdIc6hMF0hikbZoPWNmNORoYQMUIxgU3+e1FWap4m6UEL1ohMUlUPwXDDTCiYBWUSBez9kc4ETxTAz6qbBFQ1LEQKBZa4wdlicKLIugzN/iOilZoBJuSaZ6MzdLLnGLGYhR+XP9KElkAbCizrYTKMTVVAuplmn4kmf2PcthmOeK8MkmOejia/dgwIZY9yuxER6Nn6BbAbF8MbPgwVWqDPpJTBnMxkPtK3VYswyxYvNANYfu/Q3kTDxfgWVfx3/QWMJUalRNyGTFYu0FwTU6mhABnZwIXTwOloxZD+t/oa4NgBYHmaTLT5WhQyeBsqbPsz51agbjfg7wSs2VoFijsasLXmANOvmtBeKGPB+vx5mJ9ZLoLbuZYMZMQsQMh0RyYuuLuiNn9apUalcxw8K4ZwHWsNXIQ33CXmAgWWWT3Xsn5hiXzNM0AoKta0KkDJFUDGzHGruqA3Rp3PjZmOTKzMHTyznK0gF2Tl4mQ3r+G9WXEraaIZZYYjB19aeL1oRWUxGNHk78Lu9mhbuRiUNL8XloA/8MAD+NrXviYSWORiQzLWOJ1OPProo6IFFX386OXy4Q9/+PI5F+kx1VmjfbmzZ2j+PxKJRCKRjBJMkLpn2lVoCXSKZES2tWU1sESSLmpqavDWt75VGIMzhlxZWSkP7giRokaa4MnIjKqPf/zjWLlypViEcAGSCAMWiYbZFDb8EReMSi484Tq4Qw0i8zTDPB1ltg0imMxwOH0H9FY/NuNKBCJnEKGJLuywGOfAoDgBZS2gdjDaATCTm33B25+PBjZVIOwBQp1A3nVaO5aRwADE+S1AS0xwoXQFMD1NgcEUiKhhvN69F90hLbhugBFVmauQZc5Dsc2Gh1avxgf27u0JlryvogJ3TxvDzFslD1A1A2h+6orYDwaq5oz8+I8xRsXSpx0ahbbYIJAvcg6+8DnxuwEOZJiXw8jzMorTVASH8VpEEIQBZvF8uqhBX2Wir9UpfLjDl5BtGCAzvx+0rno0kx6/Y5xrycPKnLWo9lwQnjqF1mKU2/sKEXkWClwK2oM14vcymwlmQ3fMPWiSa4PTeAUi8ECBqX+DcHpouKOtogQM5JcPP4vfmQV4XQnCwFiKgiNg/S1AzRnA541++QzA1XcDXfG+Lj14xrCCazAsmUDFdX0z8kWVhuytrpNjcYpbMu6ftwpfPLodnUFNCGSQ/V0z+2lbloRgJILDna3whsNYlJUrysRHC45XNd7D6ArpIqiCrmAjZjs39kl2iKP9KBCKbV2nAk27AOeMMa/c4Xv45uv78Y9GVidqvKV8Nj4+d/By/W8tWYuPH9iOix7tu3lj8TS8a2ZvJY6O1ajNfwqtmSi2ZgtxQ2s/pcCkGFCVmR5zc4/HI0rBn3vuOemfIRl3KGB85jOfwerVq8UieOfOnaLlrcMxORM+UsbTCpz8q3btI6xgXHCHVnEqkUgkEskoYTIYUUKfRokkzbz00kt429vehttuuw0/+tGPpH9GmpCeGqPAQw89JEqJvvzlL+Nzn/tcXEZVe6AWdb4Ys+woc5xXIBBpQUewN2Ob5FuWw2EagUGy5yzgiu9JL8i5CrCMMNOX2VMnn+67fdHdgLMfM940U+M9g1pvfAsks2LBipxreo77WZcLhzs7UW63Y0V2Jqq9NIENINecgxJ78ejuoOh/fxZQ9eocG2BcCSiZmGx4Qk1oDhzsyYdlIKnEtg4Wg/ZeAuEGuMNH4h5jgB1Z5k0DZhW2+Y/DHa6BKYn+YDeUIseS4BsxaLbz6+gOaWbLDmMxCq2L+vX9mIiEVfpnxLdjARwwKhtTO99aDgKtx7SqCgoaZVdobZmGQ+1Z4Mmf94oaNAl/43uAMIUCA1A6F7AmDypPCNxdwBlWqYWAmQuAvGKgqwP4r89pvhqx7+szXwMmeqshyZBgW6OT3W0wGwyYn5kvfqaCJxTCx/a/iiNdmlieYTLjRys2YUn26Cxw/GE3zri39dlebK1CgXUA/6f6LYBbE83jmHXPmItf+9qb8JlD2/ts/9nKqzGfVVODwMSDOq8bNqMRhdbB/W284QBeaTqOBl8HMs02bC5ciKI0BDvPnDkjqm6zsrKEKXhp6dhXn0ok/VFXVyeEje7ubvzlL3/B7Nmzp+7BOvE44KYvWa+bjqjWmHvTOO+YRCKRSCQSydCSv/77v/8bX/3qV/HDH/4QH/zgB+XhSyOTJ9I3ieBJunz5crEwZkbVr3/9a2H0R7LNpSIj2xvu6KnYyDPPhNXoRIt/b5/n6g5dGJmokdAuaPDtQ4CtUZJu7xozUcMb7pt1HVQDohUVDabJ7IwMcQtFQni1ZSdcIZcIyDPDc25wNuZn9c0ITRsM5hvnAGpFNFBtm7S973kelijr4Ak3iePnNNGwuTdTMKi29alCioB9z/1Q+L77IdeyAJZQNlzhkz3eGjoWw9CCiB3Bc+gOaRUPxBNuRKvfiELbYkTUELpDl4QpN71pMk0zxrWSo19UDsvBhF4qKQYoeW4VrgAKlmufw0jfX/ls4G33a8IAn6u4FDj4NBCOii6HTcCMKqB8EVA4BxMOVposuyJ+W1YO8P5PAw//WKvOsFiAe+4bHUHD1wUE3YCNXkITtG0GhZ1xGpPog9AZ1KqSss2ZaS/vdprMWJk7dOH6ofOv41hU0CCeUBD/emQ3nr5idIJpkYRxT0PpZ3sM1twEUUMBTI5xqeahIJGMep8nJVGD5uDTHambfNuNFtxcynEufTz55JN4z3veg/e9731i4WE2y6ooycSirKwML7/8skiYYuXGb37zG2EmPiXxJbY8Vftfd0gkEolEIpFMQNrb2/H+978fBw4cwJYtW8T8TZJepKgxSvBk3b9/P9797ndjxYoV+MMf/oC1a9eKoE2FYw06grUIRfywGTORGRUt6EUgmkRF5/BGRds2IizsRX9MhE20xQHblzgA8+BBhkFhoC7pdk3AGQushmQGyiZxS+Sip1oIGkR3tzjtOosK5wzYjKMccBT7M/m/blZjtriRiOqFP3JOVASYDIVQ+gm8s13SQLCKgwKJxehFWL0g5KaAsHGYDrtxaO1EPOHmpNtYwdHg24OgygCqAndYhTfciiLryonVm1oNwBDwImKhoby+UYFBGWI2pnhPaXpfeSXA2qinxUsPxft2hENA9Qmg7TxQdS1QPnirmQnB3EXAf/wIcFGAzQSMafa24SB+aSfQEK2Sozn7nBuB3PHxOkhKsBPo2KV5LtFjKXs1YBu7rHRf2I9tzbvRGdJEjSxTJq4sXAvbcKuK0shZV6e4YupEosH5QCQsPB3SjdWQIdr7hdVYs3oVGcZBqilzFgHeJsAb9VuhmFGyGeNBJQXEJLDt12Qw6/v85z8vElB+8YtfiEx4iWSiQrHtBz/4ATZu3CjWGFwof+tb34KFAv1Ugj59rsb4So3+1h0SiUQikUgkE4zdu3fjnnvuwcKFC4V/Rn7+5e2JOVpMwDTlqQNP2qeeegof+9jHhL8GFyEsPaKwkWeZjiLbHGSZi0VQldvDMCLMbkXRIEpQZTbiMKs0KIZ4DwKeLYDFCNBwVLEAlkIg90ogHQbVWWVAybL4bfTTcIxdD8Iy2yzYDL3tb1hBMMu5JGmg2h9hxUCy7bGBJEkqhFU33OGdCETOIqBegCe8G0bFDgU8r3oD6lZDBZQUWj9FUA8VF4SvBj87q1GB3RgcsuCgvX7CNsUoqks0QQM9C2RfpBX+CCumJhDBS1BCPhh8ASihsLgZfH4o6gTpne3u7FVddSKq5vDefQhoehZo2w5ExcMJDVsRsWoj3YIGab/QK2gQCkGnnwVCmr9DH2j03H4AaN0FuCgUpqGSbiAiQaBtCxAVFBDxAe3bNaFjjDjYcQxdMedJd8gltg2ViKrisZrT+OyhLcJDY3+77ksxfModFBl6xx7+K9dsGRVBgxgUI2Y6VsEc9cpRYECZbTEcpkECeNyfsuuAaTcDZdcDM+8AbOMzWV6cnY93z6yK2/bRWYsxa4J78Jw/fx5XXHEFtm7dKhYbUtCQTBZ4rvKcZdYfz2Gey1OKiqvijcEtGcCMTeO5RxKJRCKRSCSDwtju97//fRED/qd/+icRE5aCxugx+VPHJzgGg0GUiW/atEmYwrzyyisiGzA3Nz5YQTPwkNo34GVQBu8tnRTfMSBY3StdWcKAtQqwprnV0owNQP4creUUM6jGUNAgJoMZi7M3oD3QJFpO0SDcbsxAMMKs/yAsBqcIGBF6aOgVGj2PV0xwGidIwHgSQTEDoq6IaMc0qJ5Dlnk9fOGzMCjdMMAKo5JagC2s9g1ERtAkLghDETZyzJVo9B9I2DYLETXRoyL6Gv1sHzdEprYCJaJCCcRURIj9TKGayNsBeNoAWybgLEz//mUXAq2aX0kPNEOZkQ9YTECoU8v8D7QAhW8ARrsCaqIi+oDHt2ITQnPbWaAowayaRs8Nz2pCg3jsBU1cyF0xevsX6gAiidcbtvZoAMxaJdZo0xboiBuP+W9uGyq/uXAMj9dqvko84oc6mvG1xRuxPGf4bRvvq5yPbS0NqI22VKIJ9VcWjW6psN2YhbkZm0XLKQNMqY97vF+qQga/n356PIUAcxFgmZnW1mMfqFyIawqnoc7nwnR7JmayCmoCQ08CZrm/853vxHe+8x1p1ieZdMyZMwc7duzAZz/7WaxcuRK/+tWvcOedd2JKwKrvxfcArnptdM8sA4yyJZxEIpFIJJKJS1tbm2hly3ZTL7zwAjZs2DDeuzTlkaLGGMEycZ7Y733ve0U7qkcffRTr16/v+TsD8MmgD8CwCHERkECwPv2iBmHwdCQB1EhYMx422YdVQcJWUwVWrU0Rg+BNvuPojPoqGGBGmX0F7MYclNiKMSdjFs4wEzoqaKzJWwkTW8NIhvaRqb4+21QEoCAEs6EpGsz1III2KCqNukuHUTQ29GCbw1SIEmUVuoM1IkiaYSqB01SCQMTVx3ub2dAWwwTLIma7maB2fmqwdMWstYwbjPrDwPlXe39nv/nKNGc1rrwVeOXXQDAaEGdpTW4GYI0NNLDczA/46gDnLEx6Lh0DzuzTqicql2m3wQLBFlaPJam2aD/VV9RwnY4KGjH37z4JZC8CDKPVTqSfcTYdFXwp4jDa4Av7YhuLwD5EEYzj/dN12veFH48/oooqxy8e2YV3zKjCPTPmxVVcpEquxYpH1l2LLc318IRDWJNbOCYBegoZxlT9c4ZKuAvo3ho9z1RtPhDxAPaE83EInOxuwQV3B7LMVqzOpceSUVRmTPTqDL/fL5JN/vd//xcPPfQQ7r777vHeJYlk2NhsNvzoRz/C1VdfLdYY9Nz49re/Dat1/Fv5jRiTFcihJ51EIpFMMfwe4PBLQGcTkJkPLL0WsE/sZJDLEa419rbXiTmv02jBNUWVyLFcpkl7kkHZtWuXaDe1bNkyHDx4EHl5Y5vwfbkio7ljCEuO/vrXv+J73/serr32WnzpS18SfZyNRiOshiwRZNVCMr3YjMPtH2sY14BVynSdAxp2alnM7Adethlwlg//6UJ1PYIGiSCIet8BVDquEqbQC7KqUOGciUDED6fROSaCRijiRlj1wKg4YIpplaWCWcBst8IWMLw4LoCC0R/4ghEf6n1H4At3wKiYUWSdj0xz1DMhRYxKDiJqbKsanr1ZCKtsfxB/DofV04OKGiZlGoJqvB+GETTyHnpA0m7MF7dYaAxeYFmC1sAx8R1j6LDQugwmeglMJEzFgGU+EHhd+50t42xrB//uskIjVtAg9QeB3BlAzvT07V9GHnDFPcD+PwPhiCZqsI1TUuLPg0nJhSPAjsd6f288D4QCwLy1WhTd3aL97syPNwIvrAIubdcEWx226EomXusVGsm2j5aoQU8lcwEQbI3xWrIC9jSeK4OwNGchtjbvEmbh+giyLGdoAXbueZjypcp2cr11H95IGL+8cByucBAfmrV42Cbjt5TOwJTBd65X0OjZdgawzQeGYdD+j/rTeKz2BAzC0FzFy00X8LmqjULYmMicOHECb3/722EymUTrntmzh+hXJJFMUCjOMWmKi2kmTT3yyCOYP3/+eO+WRCKRSBKhJ+FLvwW6W7T1RFst0HQBuOkjgHkKCNJTiKfqTuKp+lMwCtdP4NWWi/jSwquQaxlmNxXJlCQcDgt/s69//ev42te+hs985jMTy7d1iiM9NcYYntwPPPCA6N/MMvHrr78eNTU1MBmsKLGtEG0nxP1gQJF1CazGYSr21jl9t1lisqZ5AQ13a9mbDCqFPYDnHOC5AKTLY4ItqTrOA66G5D3i/e1A/TZN0NCDeLWvaO1YhvuS4U7hmxF7YrMKJhRTWcBs4Gxz9pgIGp7QebQHd6ArdFD85O9EBStw9lGGiQaZvAAORIWO0c02qPHugzfcLoL7bHlW5zskfh8KVsNsGGMEGAV22I1L2Jgqyb0HaPHESqRILQxqN0yYBwW5UJANozIXxqGaYw+C01SK6fZrUW7bjOn2a2A3FmBCYpkDOG8CHNcBjuuBqDH7oKJGMhh0Tzc504BFNwAZdsDGrHJesPWfenWJEbCOnen0qPH6jiTbdmpixfG/A/sfBQ7/Bdj9W6AzpjqOY0vBLMBkAkxGwGyiuyuQkcT43sY2SbHjowIYnYBxFCfLnGTlXQk45wGWIsA+Eyi4ThM2xog8Sw6uL74Si7KqxI3/5rahYFAUbMwrFV5UyVxIHq89K8Y8SXSs7XOUNFloqHQGfULQIBQ0yAV3O7a1XJqwh5rnwc9//nOsWbMGb3jDG0TLHiloSKYaPKd5bt9www1YvXq1ML6XY6BEIpFMMChgdDX3xkf409MJ1J8e7z2TxOALh/B0/SnxbyZRcc7rDgXxYtMU87CSjIjq6mpcd911+M1vfiNivIz1SkFjbJGVGuMEFxtsR/XJT34SS5cuxS9/+UvRB7fCeQ3CakBk0LOyYNhYKrWPN8j+94rWO1vPxmdww/0aEG6N/m4FRO/waHCj2wbkXwuYeqsK4GsFOk5pAkTGDCBzkAzW9rPAhZd7gyg5lUDFtfEZoV62KUqAz9+wHchbBDiSBAAHgAs3o6ELuSK5WUFYVeEKUdRg8Gu02rj0TzDSBXdY6/Wuw9/NhnyYDOEkAgCPFT8T5+jtk+rRWjHFoaA72AT7EKqCaL5tN66ECo+4xBvgFOerquZCRWyAnRJTTv/+EeHXRJsqwvxeo2EpYBg4GB6JRIQabmaQeIhwH01RM94JDc3VUzBY74EeGsmo3gWYzEDx8LLV+6V4EZA/Fwi6NfNOiobtu7We/QzI56yKHz8mK6Fo+z+zUfMOoSl6OAjUHQZaY9qEcdvxvwHr3987xk2/Egj7AXeD9ntmOVC6tu9rOGYA2d1A51FtDOBxK9w8rOz5IUHhJWspxpMMkxNVWSMTLz85byVed3XikseV1ERcEsVcHJ0PxIpnOVp7uyHSEfAlFZjaAxTnJ2Zv2/vuuw87d+7EE088IZJJJJKpisViwX//93/jxhtvxLvf/W784x//EOLGhG2BwHl/5+taopPJAbBi73L145JMiPaEU6J1m2TiV2oMZbtkXPCGg0mTptys0pdIov58H/zgB0Uc9+mnn0ZGRoY8LuOAFDXGEZ70rNa46aabhFnls88+K1pTORxpMq62TNduOiqDPuzf3g6Y/b0Jmj62P4oZstkPv/swkBs1tfE2A9XP9v69+zwQXgfkJPHnYJCzYa8marA1DYOAhBUbbWeA/JjH9JcV7GsEGpuB7AVA3ooUsk8ZFLfBH6FI0t379FCQYVJhVOYI34104Ap1odXfKILjhdZS2AcwGQ+L451suxsm9Pe40Q1kxtewxLzqMAKoVKCVBAHGoFRCVd1QoQtWThiVRfEPVGlSXKv9jAoaPUSOAUpJv54F7E0YCAR6vj8LFw6/H3zv/rD9TVu0oiQLUBKC8aoXUBkMDANKAZCi+fmY4sgHpq0Calj9E4sKXHgVsOcBWUMTCQfFZNFuAjNQeJ32ikM0d5/QzFwMnNmuVaToAXKWhXc3JRiB06PAq92En0a0F/jsW6KVZ0rUM6if40L/jKz50ZZT1rSaN0917EYT7p0xH998fW+fhcfVheUT9lwMRLoRiLhhVhywGkfoQcFzc1Cfl2mah4aPGWcRgCJ2xpphvVyh1QmzYkAw2jqMMIlgmmNsTOaHwiuvvCKMwFetWoXDhw+joGCCVuhJJGmG4t2hQ4fwgQ98QPR2/t3vfoerrrpqYh1njl2NrwIeXXBVAPclYNoto+gpJZH0xeVyYc+ePcjOzhZCOD1q2KZQIhkVCmcA9GWgR6FYXzDRxAQUMylVMlHINttQYHGgLeDtqUzmzyp6oEgua9xut2gx9cc//hE/+9nP8Na3vnW8d+myRl6tJwD8Eqxbt65n4f3www+LSo60Itov7WHqsRY3Z+Yxe7y79Ytp3J2BUK84gLZoBnEsrQf7ihohP3D2aSDk1QQNcY02ssmclnXsS2hxlDEdsOYBfgaUozAuw8eSzhNAxmzA0k/AJ9IARI7E9O7PgDn60GA0xsP+h7nmaUgH7YEWHOvaFz0SKqo957AsZx0yTMn3z6Akbx9jEJUCzKzPiraf0mHGLFvRjB6sUnAaC+EO9/pXUJrIMqcn4K1VQiyFqvqjn4stPqgYaQUiexMCwrFQaaNQ1Zs93NXVhVOnTsHj8WDu3LlC9Dt+/DgyM9NgpsagnEohQD83uV9LNGFF/N0NqLt6zzH1IoBFgDJ835dRY8Z6LaBOb42eTlD8hwJ016df1EjAH/HhjOsQXKF2ISLOcMxHkTU9371+s5nYBmo0e89WLAZqdmv/1s/jkA9IkqkuxjpTQnYpH2NOsWKFjzcm9yOgUKSiCyoCMCADSj9jy+XKdUXTcMHdiUerT+sOIdiUX4r75w0iio8THYFzaA/2VvFlmSqQb02SJKDDa3SgGgi7tEooJiuIa2ot0LkfiPgAUw6Qux4w9TMu8ly0VwE2vk5kRB5bDpMZH5q1Cj87tw+hqLCxuWAm1uSO7hgz1GzbL3/5y8JAmabJH/nIRyaswCWRjBaFhYV48skn8dOf/hRvfOMb8YlPfAJf+cpXRDXHhCDQHiNo6OsPN+C6CGTNHccdk1wOsPL77NmzQsTgPGvz5s04efIk7Ha7FDQko4vVAVz9LuC1J4CuViAjB1h7OzABk0MuZ1iF/Im56/D/Tu9Ca7Qa+aaSOViXN4rrW8mEZ+/evXjXu94l5lhMHpkxYwp5ME5SFFU2W50whEIhYTDzn//5n/jXf/1XcRtulkhEDaPVfxq+cBuMBgsKLRkwG6r73tHlAzz+3ooKAYMfNBiOtkq59A/AF2/iLAIqc++N39Z2CqjbnuyNafHr6VcABQsSdjQIVD8P+Fu1SBSFltjAQ8m1gD2JiTWz58M0Ru7db/6LIT81qqd4w/ypIM987chaeUXZ2/4qvOF4z4tccwEWZycXoPjV6g6dgJ9VCVFshnJkmBaI4Irmq3Emxih8jvCmGG3EuRE4A0+oHSbFinzrHNiG690yVEIU1mJErD5YAOPVCEciaGlpQV1dHc6fPy+yaylmsO0UvxM0wzT0a1A9BChSqCcTNvIcvEYL+gnRjO2DYr8fJsBwbT/PFwbCbdFM6LxhtXaBvwlwndWeg8bNbE+UKt524PCjfbdXXAkULQI8F7XvGls7ZM7VgqGB6PlpKQf6EegGg+f6ka4d8DLoGnOsqjJXI4eG1OmEAd6jLwKnKTaoQF45sP5uwDYK5Z5tNcBrj8Rv4/g0a51Wfebr7BXo5l0HlKShcijJsQ2pxxAR56HYAZiURTDqwlsaX6c5cBbtgRrxe55lOgossyZMIJiixY/PHECd14UyewY+Nmc5Kp3xre3Y55a3fKsdxgmy38kqNGq9O/tsL7WtgS1ZC0Ce7649QLCu91xjKylrFdD6Usz3jWbvdqDophEJFkP11qjxdGF/ez0OdDb0iBu3lVWJheB4cfToUZEkwmsEs9PTUtEnkUxymAzC7wUDufxeLF6c5raUw8HbCNS/kLCRnk/LtTZUEsko0N3djY6ODhw7dgzBYBBFRUUwGo1CDJ83b54IVEkkEklsO1u2WLUbzSKxRzI2NPu78HzjYbQF3MizOHFj8VIUWEdY3T7CWO03vvENfPOb38QXv/hF/PM//7O4dkjGH1mpMYFgsPYLX/gCbr75ZqH+/e1vfxNVG8xOHyqNvkPwhKMmwWxXG2xHviVJhwpusJgBP51Wo4bOxgwgc1nvfZzlCaKGktzvQjf87oMCZJQC+VV9/2QwA7lVQNNOTdBIfJy5n4FLTWiZpT+doiJCAUMFLAa2npqbFkGDBNiWK0l2ejASwBnXUXQG22BSzKhwVqHAWiKCgZmmBbBGihBWPTAqDlgM+T1BQkV8/eZjrDEoRhQyIDYuLWMH6kFpAozL0e1yYd++faioqEBra6swdp0+PaaNWjphJUafqhFmHvOzZosw+n6EE87NUPJ2Lzw/vDuiz8mntQD2DYBhCIIRW6+1vNK7T94aIBIAMuak9nhbjuZ10Xpa2z++LV782fatfT/gogFddL9dZzQtTX8bvtNA5ibAPPSS2kDEB284prpLoKAj0JR+UePcXuA0vViitNcDux8HNr8LaScjX/OeiMT0uOVnnzddEzaaT2kVatnlQFZ6RQYdihm9gobYASFyGMCxJH0T6+bAOTT7KaZpNPkpuCootM7CeNMZ8OPLx7bBEwqJsm8KHF8+uh3/s+J6ZFt6BzKnySxuExm2nOpve1JRI9QSFTTQO04FG4FI4vRN1dpLBTsBS96YleVvdV/EKy2sYNN4qv6UEJTeVJbkej/KMFj7/e9/H//+7/+O+++/X/ycMBnpEsk4Q3Fv165dolKD1eFf+9rX8OlPfzo9CSLDxZKrtZniPKcHFbAXj98+SaY0bEPIJA768tlsNlx77bXyOiGRSAaEiTr5rK6RjBmekB9/rN4JfyQE9ito8HXiD9U78b6Kq+Fgi+cxhl1D6FNGUXzbtm1YuXLlmO+DpH/GcSYr6Q9+SVjWtGHDBvHvn/zkJ2ICliqhiL9X0IjiSdQb+HQMiphmAVlXA0W3ALlXAHmbgcIbAGPMYEHT7uwYYcVeBJRs7PvCGWV9zW0ZEJx5NTDn5vi/sULDWw14zmuLl6x5fatFCtlKo58LSBLjbyX2/9RDDJkwKbnwhmvgDzdBjen/PRyy2N6jJwKskWnMwYmu/WgLNCOshuCPeHGy+yA6ApoJOwUMq7EADtMM8XOiZD2PlFDEhbbALjT7n0er/1UEIvHnW78oybKf8gDDXMDIigQvjhw+JErAKWo4nc7REzTE/rA1UOJ3i+epFThzFPifR4DvPAX89Hmgni2qlKjvRpLPMXAcUGM8QigS+g4NbX+6T+gP7t3WdWwI70cBZl+rVWYUVAHlK4HFb9b8QoSgoT83Da/pARHs/Z1ijoet5oYnlA1l+4hoPB//O7/XLZe0VlTpxmIHVt4OGGMC5XNZcVah+YmULgamrxo1QYPQoyZx3OHnpWJopsyXPE14tfkIdrYeR0egr99Ph16xE7stzlh6/Dja1QJXKBjXz9YdDuJoV0IFYRSvd2IYVvvCbnQF24TwrUMPjWSYDf1c6yJ+hEJhbNt1HMFgrIEkr2dJ5gVjVKWhs721bwXojiTbRpuLFy+K4BTnSy+88AK+/vWvy0CVRJIART5Wgz///PN48MEHcd1114nvzrhhtAAl1wBGe+/4VbgRsMp+5ZL009nZKUQ8esxwPbZ8+XJ5nZBIJCnDyi7ephKXLl0SAfuJxiVvK3wRGrXrjd9V8Xu1V4uxjRWMwXJtwZjsxo0bReKtFDQmHrJSY4LCfp7MOLz11lvx3ve+F3/961/x0EMPobw8lV7+fQMd9JjoDBUhx8ygqw9QMgEaONti2h3ZSpM/HcWI4vVAIU1FI1p1RTKs2cDMG4Da7ZphuDUHmL4ZsCVkjYZ9QMtLWn9wgRHIv1ITT4S/hgGw5vYvaAiyNd8DlRnMighyqaKlUy+hiAWecG9Gt1nJQbZ51bArN+ZmLMbRrr3wRPebIsc0RyUOdLANFuI8KloDjcixDL4ooxASjHhhMlhhSiLUTERUNYTO4D5EhLE2zwgfOoMHkWteD5NhkBZAhjlAhMbbeuZ5vlYZhLMiAX7//lMoKCjWbFXUCLIyLbhw/iwqKmeP0ruhYNIU76mhLAbamoHHfs7UX21zlwf4w07gw28CHEuTP1WE/ihqQuZ0YvXCIFDsS0SNDWSmgPi+JrSVCCQYsvc8t5rEe2fomA0W5FvK0Bqoi7GkN6BwIE8NvpbK4GcQUDhGFKdmkE0PDVGFErPvwl/ADThGoSS1cBZwzUcBTwdNaYBIJ9B9FnCUa2280phl3tTUhIaGBgQCvUFwxdiK0mluFBbGt1pSRJlNahzvuoRXW46IsUn7/SLuKN+E/Bi/Iv1v8UwMEdbUz3lhjBnLOelkcK6mpkaIocykYZUXr6VDgS3uamtrRfs7ZjYzk3OoiH3xvI5G/8Web8OcjGXIsxQLU/BsUwU6Qxd67p9pmgabITd5BU17EMf3nEAoFBHihtkcnbbZKwFvQ7TCMvpdsBQNu4XccIn9DHQMaaqO1Kn1dqDZ70KexYEZjrw+x/q3v/0tPvWpT+Htb387nn76aWRkjEIrOolkCsGF+cGDB/HAAw9g6dKl+OEPf4j3vOc945N4YysAZtypVWtwfZHm8UMiIaz8ZmtCVikRdkHYunUrrrjiCilsSC4LU+P6+nrhHxNLVlYW5syZIz1kBoHJUkeOHOlJMs7Ozsb8+UPrtsHHulwunDt3Drm5uePuw8B154EDB8SaczhrndHG0M8aNPl6dXTgevCDH/ygaFXIWCyTpyQTEylqTHCYRcVBlAv2RYsW4Qc/+MGgCw+jYoXVkA1/XJBVhZWZ8ErywElKGJgBOkgWKKs1qt6SvD2PTvcxIM6bIgx07AaKbwVMqZrqsn/4UkAtgKq2QlWa6RAj9o4vHVYz4BHeBr0E1Q74IrWwG4eX+W812rAyZyM8YbcYUO1GJ0J6y64Y1BQH3O5gM6q9h6AKc2ygxDYf+ZaZmIiE1QA6AqcQjLhgUDhs+BPOQRWBSOvgogYXq8ZlgLooeqR4/DQfltdeO4F588qRl8eg3Gn4/eeRmdmK/fvPIifnLcjJTfBjSQdi8bwq6vMRjFZhOIELW+Iz/7mr/iDQUALM7q96iOduvKcE+su+7g97ORCMPW8VwJYG812aB/dp8cCvc+z3WQFMw8+OnOVcLL4TzEynyFFmmyV+71/QoK9AVLBRawCFwlUK4tXc9UDtCa1CQz/W/PdLDwGb3w1kpbndlS6kWA1A08u9bfY6rEDJjYA5Y0QTytOnT6O9vV1kDxYXF4tJcuzkMhDw4/UzT6O56QIWLqoQ24zKPCgpiqCcRL/WplUA6dkuYRU42HEW1xX1GmnTQ6PBH+8vw23p5MKFC+jq6hJBtKGwNKcQRVYHWvxeTcCOyi1nXe1YkZEvMoz4vDNnzhQBCr3vKVut6L/3Bx/Hz4CTevZF5efA5AEKGnv27MGVV145pEUPFywRh79H0CDBUBAnO/djTf51MBksyLPOg8NUiC53CzrbfKjvDuNS8IBmCK+q4rzQoZfQnEU3wN9xEHa7Ndr+cZkmYBRcB7iOA2EPYM4DMhemJgymkeuKKvH7S0f6bEsXLzedwktNvefl+rxKvLFscc9i40Mf+pBYlP3f//2fMEKWSCSpQfHvZz/7GW677Tbcd999+NOf/oSf//znKSZPpRmOW7EV4pLLiu5gPbqC9PNSkWkuQ6apPK0CG+dYnCewClx/Xnpq5OfnizYiMlAlmYrwHOd5zzklk33Kysowe/bsnu8A55usXnrxxRfxhje8AZMZVk9s2bJFfMfT2Xa0sbFRiBB8TlZ4Wa3WnjZ2FEo5hgyUJMVkKyar6esLfg707Xn99dfFvwd6fOJzcX3Bz4yCiv4Z8rPljc8fO2ZyG8c97iPXJlwT6dt1YYbPydgi3wur1iYaMx0FyDTZ4Ar5xcqPsbUMk1VsH230hCm26Lz99tvxyCOPICcnPrlQMrGQRuGTCGYgcgFPk+TBFh4MQjf5jsEXbodRYRBlLjJME6BHbbhLMzcNJfFWKH3LsAIyYZVBcW98gjwK0BFMbE1CIWI6Mkzp7fX9etcBUZnR+yoKluZsQMYAGbPBiB+nXVuhihYivVQ61sEh2lxNHGgs3ujbhZBordQbsGeMN/YCmmGaP3TBSGUAfx9OnLiErCwHysu1C1U4HMHWrYexYMEM5OVlwkLfF6zXKozSjQiOs30WL/g5ANvDHN4F/O3hvvd95/3A9H78LdjP3rsdUHXvFSNgXw8k65M/0L50HADc9DZQNUEjb33/1VFDwd8CNG/tFTayGABtA8Id0d3NATI3auLHaBNhkPJS36oy5VpAiGaD0NkEbH0YCEYrS3ge8lY8G9jw1tHZ57q/AcGu2J0FHNOAwoGD5v3BSgKWsC5ZsmTQSa3L1Y1du5/DlVeuhNmUB4OSPSRzu1+c/3uf7dPsBXhjqZaxqE/g2oKXetpQ5VqmIdc8PS2BBU6oOWnm+2xubsb69euH/LwtPg/uP/QSPGH2VuVH70f3uYt4+4yFeMeGq5CZ2XdsqK6uFq9HsYiLCU7quYDzeDw9k3tmqXGRpy9UEkuyuRjh/fj8FDoS95uLDC4afT6fCBLy9nr9ITR4WIWkiRQmkyYezrAthNOU2bOgYBUJDUm5D1ww6WZziT3u2Y4yO9MOkyEIX0BBWDWKxQiFmP5aU/K5CgoKhNAzmvD1tzRfxNaor8bmwpm4qmBmWs6bBl8XfnxmS5/t76tYjy1/elr4Ztxxxx2iqpVZbxKJZHgw+MHF+5NPPplS8pREkk5Bo8kfL4znW6qQk6YkLwY7d+zYESdoMEjJORivjzJQJZlqcM569uxZUZWxatWqQaswdu/eLVo9l5b207FjAsM5KOf6TJriXJ/z6ZG+Dz7n+fPnUVdXJ56rsrKyz7xczH23bBHrB33uzrUOhSTOz/l3jjccY4qKivpcT/kZvfbaa+Kxfr9fxPYSq4x5H65DuB/8DClmEL5Gjz+roojn0F9TXxNwf/Py8sS6i2sNPp731W+x7+Pvf/+7WItyP7iu4Oty3NSFkGTwOWfNmiWEmdGkK+jFK83H0BZgtXYGri5chCzz0Crwh4qeMMVqViZ+sGuOZOIjRY1JxqReeKgBwP2y1iYmkDBQsgVR8S3Detqw+kKfbRHVhA7Rfzw+4JNhWgi7sTztQf+LntPCR4MZ6jMcc5BlHjjA4gq14KJnX5/txVaajGvZ2KNJRA2hO3ReeGMYFTsyzbOE+JUMb7gFLf79fbabmVgn+kRRxjEjz7IBhqG20BICwKvYseMoNm5k9QZ6qjYWL6anRuyFaz6gpNlfg62d1L28bEY3MDi+DAhkAr/8L6CrXRMa+B0rq4R676egGE0Dn+MhtrOKAMZCwDDMC69ehZDu/visMgh5tIxIihec/ESibeBYZTPIWBKM+MT5bjHYh93GTRChd0d9ElHjKkBJMVvzqe9qJt2xZBUC192HUeHSn/q2AuP3vOymlB7OSSgzc4iercOS72TB9P5Kxxnc5qSXE+RkXjOcjPJ+eoCXXg6XPOdx1lWNzmAA7UG9VgNYm1eFFTkpGtD3AyfDzGLigkKvLkh2PWLAvqqqSkyC2R6Kt8HMafl8fJw+qXaHgviPY9t7vh8GiwU5lTNwdVklPjWX1VbJYZYSBQfuK1+TiwJWPwz1usnWYAyExO4395HPxayr2OqaJl81znv6euEsz74KVr13/BBhOyy+B54vXJxwP/jv/t4HFyTPPfccbrjhhknbWuNoZx3+UB1/nexqaMG+bz2M88dPiuQOWZ0hkYxP8pREkg5qPLuinQV6MSk2zHRuTsvzM7jL+VZJieZ9RjGDczG2p5RMHLi2oC+lWbGIilbJEI5dJCKSayhi6AIGWxvp53wqHD9+XMSYKPIxiM+5bbL5NOfQg4kk6YJB9jNnzojkof7munzvvE5NmzZNrBeYQMUA/0Dw71yPxLb6JbEVDPT1ZGXLQPDxPCb66/E5h3N8OF/nmMSEq8RqC36OFFZGM9anvw+uY8xmsxBJ+B7472TwOLGVGYWfTZs2YarA9/Wb3/xGJkxNUqSoMYkXHh/+8IdFKRxVxFE1U074wneF6uAPd8FksCHHPD3ajigFQo2Ab48WSHX7eo3BGbjN3qhtY4ur3PJoq6vUCKs7oKq8EPTuI8NrEbUU3SF6N2ivYzEUIcu0dEKIQN5wF8652X4nnnLbEuRY0tBuaABomN7i34OA2hndoghho8i6Puln6Q01oSVwsM92hzEfRiUCg2KH0zRbPMfwdqgRJ06wD7oN06cXsREXdu58CRs2LEy441JASXO1kXpWu8Vh1CoGPC5g29+BzjaohXmIbCwBLGxRZYMBi6CMpJXbJIPnTI33KDqCFCIAi2JHhXMVrP21lxoIelJEzmleJkIT07+PdkC5IvVqrVd/B9CQWM9U5+OmLwFWjVJGRf1zQICVRbosoADOCqBgfZ+7UlhgSyNm7egZNMweYmspPaNnuHDSziwSZu8sXrxYTKAZ8ObkkmMbKwrYUikUCeGU+xjMWSY4nA4YjEaoNiuaIkZUZUzD5sIlw/Y+4ASYgQJOgrmQ4PVnpO8rEd2IT59U+8NhvP21p+JkMPZbvamkAvfNWoaJRESN4ETXa3CFOcYKgyCU22ZjmmPumO0DBTAu9MYi847X3AMd57C//SxCahiVzmJcU7QElhFWmNV7O/Hg2a09r3HoiRfw4rd/jbe++c34/vdkdYZEMhowsMUqqCeeeALf+9738L73vW9CzJslU5Nq9w4EVN1jUYNJVhXOq9M2l9i+fbuo1GAyAK+NXDv3F7CTjD3dwXacdu1HONrSucw+B+X2kSXdTFX0ygRm78e2IGJyTaqtjAZ6bq5f6B+gVzHrVQt6qyWKgnpFAIUPJlHplQB6JcFI94GvxTUUE3LofcN9STdcnzGIL69tw/uMWN1x8803D5qgNlngd4px1UOHDsmEqUmKFDUm+cLjM5/5DB577DF84xvfwEc/+tGewFIgEkRboFsEFXLNGWkZtDmINfiOoStU2xOosRoyMcOxDoZUMspDzYAvatwtjC8i2k+1AjiwvdfMOKsIWHsPYE7NtCiitiOi7u29uKsqgiK53gaTshgRNSgqCExKbw/C8YbHstZ7BJ2h+p5jaTNkodLJYzm6Fwh/uA0tAVYnxJNrXgyHqa+gwklmg3dbjzE4UWBEiW2TELbSghrArl2vYNmyVbDb83Do0B/hdHoxZw6NphnG5IRmTfoNJCMsedcC9f1VDKiqHxHs0LxfeqAN9kZxjl0ONPvPo8F3KmaLApshA3PZrmoohOuB8KGec15gZHWMA1BWAMoQ/CncHcC23wOeqDiXXQxc8Q7AMkplqWw91fgiEI62vDJnA8XXiaoXLprZpohVCxQdONHnRDzRpJriUHvwEjyhdpgMVhRY5gw7I41CCSf9zGBiiTGzeWKzg+q9NTjWcQiuDhc8Lq9Y9HS1deEt19yLQlvhkF+PiwwKGXoFBbOY+Lpjyf9ePIbHa09rWhgUWAwGfGfZ1Si3j0JbujQIG62BOgQifjhN2cgxD9IDVni1iJ5+actwY8YaPy9WyYxmi43jXdV4uelwz+98B5XOEtxc2n8FTaq81HgSj+19Bc989UG4q5vwq188JKszJJIx4G9/+5tY5HP8YPIUKwslknTTHjiHtsCZuG3ZphkosA3NhHcgGIg9efIkVq9eLf69f/9+YRg+Ec1xLzfCagiHOl4RP2OZm7ESOfQOu8zRfS8oYjBhiTCRiFUEiTGNQKQbbhFXUOEwlsBqHL7IwGQpVlUTzveTzSEpgLAKnQIB1yMMcg93PcNKBX43+Z4onvA1x6oiRDJ0mFhHIYCdA1jZM1k/K557Dz74IL7whS/gzW9+s0jkkO1sJydS1JgC0OCJCw/2z/7FL36BkrnT8UzDbvgjWhC6wlGM64pXJg2WC0U8UIPWQK0IRORbpyPfkrzc3B924YIn2v4jhhLbImSbGXzG4EEb76tAhCbhUatXBo4P1ACult6Ma26fsQxYdENK75/vwRd5EUo0SMr/C61ENAEywWlkW6SJN3HlfncE6+CPdMOs2JBrYdVLmtsNJcEXbkZr4ECf7TnmBXCaklf8cKLU5j+KoOqGSbEjz7IIVnowjPBCwsCbXuLKUkZmgIse8Cr7SL6G6mqahBdizpwrYbUN0XR72JUaJkC5pie4qKqNiCC+3y9RsAgGZfL1Hx0OF9z70U1RMoFFWdenfs7ySxl8sdccXKAAhhLAuHR4wdxwEOho0MSunFL2dRp29QOz9wgFirVr1yZvC8Ux1d8qxpZLjR7U1TcKIZkZf+zlysldfxM7ft9rvPvgCbfGFJcYUOm4ApZhtiQaiBrPBZx2ae2udE7sP4m3XXMv8qypm6xx0UIjZl5fmAk2nlk5PIbPN13EkY5mOExmvKmU1Q8TT9AYEqoPYHu/SLsmapjnAqbZaRM3eD5TjOKiWF8AsyUBBTd+thx3KXxs2LBh2J/t03V7cNHDtnu9UHT6yOybYRjB++C+f/vb38Z//ud/imxxJm+MRsaeRCJJDoNoXOz/6le/whe/+EV89rOflRnukrRf19sDZ9EZvBRtE1yKAmvVyFqcRv2u2HZKv+6xfz2FDMK1Bvul87UZPE3W814yNnhCXTjWxcSx+PlDsa0C0x3p9cAcL1hdrZtNcw6zYMGCAe/PuRlFOJ6nettUihjJvON0/OEONPj3xGxRUWhdAQdbIY8B9OXg2mmoUMxgki4969JR6SEZW+hbSIFDbxXMmA7HU563FMV4LjOuM1YdZYYCE7/uu+8+kbTHxI3rrrtuvHdJMgImp6wmiYNfwiNHjuCrX/2qmLDd8P478IaP3Q2zTQvKXfA04mjnBSzNmdXnyDX7L6LWR9NeDTeznlWKG+VJzceTEdKNhweDwU/7RsB/AmD/VGZlW+cDrgdjBA2iAl3xAZIBn1ZRYFYWIKgej3sarcN7CEG1Dlal73sfb7jfuf0ISKOJ2ZANBSaoCcFli6H/rGuLIRMl9g1p2wdmnHCCx4sfA2o0MmPPema3a7tjwIyZG8SNi+qjx06Iyd3SpUuHZEpF75DO4HH4IzyfjMgwVcBprIhZvFRETcL1VlzMlF6SEFBMvrBitcrlgkl4pcRUV4ijYoTSz7FJDr+RiaZjVB9Dww/gGs1A/sgmSjyv2JqAYyfPRwpte/bs6TFV07NQSDAM7N5/VgR12HZpKIbX3nCHEDRI70MiqPUdQqWzbwurkZJjiS9Db6xpQm5eDjLMqQWFudhn5hV79DLgPdT2UjUeD7518iTqvV6sy8/H/XPnwjRCQYTH+sbiCnGbMvj3aS3ZBBEgeBKgCG9KIVEgBXiusu2ZDsdbVhUx4MMsVZ7DzLTbtWsXVq5cOazMVaPCRmDxDjkUM0YSIuL+cLGhJ25wPyUSydjCANz//M//4N577xXfx0ceeUQkT+nBYYlkpPC6nmedI27p4tVXXxWZ5QyW8vpXWFjY0y+fMLjMACyTqy5evIidO3eK7HBWJUnGlmTVynR/MxtS9Neb4NATgr5uV155pfid3hc0rucag+ck/Yt03zPelxUSXIswyD+U+Vh78HQfn8L2wCk47KMvavB9sFpjKNA/ghVTXF/FzlElkwuOrbzpcG3Bc5hrDX6uTKDi+Mq1iC52jDc8V7/2ta8Jb2L6FH/pS1/q01lBMvmQlRpTDKqO7/3A+1Df0oh3fO1jmL9xmQgszHKW4driFX3uf6xzKwJq/IXIbsjE/KyNScWLc+4t0BwrepluXwOHaQRtSF75BeBlUCemN37pAmDZG+PupqouqOzDL8KrxVCU+OB2MMJWH8e0So2erQosykxYjWPXy3wyEIh0oM1/GGH4YIAZuZbFsI1RNoeemcEFBC+EzCBmxhTL/dj/PfbiGAsXH8wE4QQoVfOz9sBh+CL0Vekl27QQDtM00QqoLXAW3aE6UeWTbc5Htmk2FEN8RYiqhhHBbkp+0S38RtlhwDooY1BZM1IuuhtR422GWTFhQdYMZJqHXvHiD3tw1rUT4Z4WXCrK7QuRZ0lNUOBnx4mMEnyVU9n4PxrnaLd+JsoUGSg8cILERQAn+yOZFPH5mD3CYD33i8F6imX9GXYzy51tpXg/ZqKwfUGq5t6xdAcbUeeL96bhGtugmDEv81qMBk2+Buyv342aC7VASMVbrn47smluHrtf3d1CZOQkj5nx+sKfP3WDuqHS6PNhyXPPoi0QFO0AyVunTcMjQxCBLgso6Hmf7bvdWAZY+16vRxOOwwzucPFNkYP9x1NdUNd6WvFE3a64bStzZ2ND/tAXqjIzXCKZmPA6/J3vfEdUTn3gAx/A17/+9alTOaVGAO9FINQNmLIA+4z0tzuVjBkUNRhEFn5PBw6I85Ri/hVXXNHvYzgP4jVwKMkqkvRwyX0Cjf6LokKDgobV4MCi7I0wpujZqc/lhwrn9Jzz8CfXFZzbD2d+r8PzjQFdVmZwvOTz0ttMdCDoZz3CKnEGfLnGYUtXVkQPhzrvdtFNIRau76c7rsFowX3me6UYw6SYxEoL/p3fK4qLPB66oTaPE4WcJUuWDKttkS8cxtHOTtGCdnF29ogqgiWjDz9vJrLyPOA5we8qk1nHs7sNY1BM0OD6XzI1kKLGFIQXjR//+Mf4ty9+AYuvWoU3/+v7sblqDdbl9y13PNr5CoKqP24be+UvyNqU9LndoRbUeQ8iEg1uFlqrkGcZYdZs6yVg72NAJJrFbc0ANtwL2HsXSyp9M7A/Rq6gp8EqKErvBZRBand4B1QkijSrYBqgCuFyhgF7Ybc7xhMCXuC2bt0qFh2cSHKyw8xcXvCuv/56UY3Bz7M1cAm+cDfMBjsKrDNhVMzYt2+fED4YcB3sNRr8L/TJXLEo+ci3rkKr/zQ6gufj/lZgXYBsc99AvaoGoeIsVLihwAkFs6CI6oXRh++j0d+K7qAbmWYniq35KX9eRzrPY1frcbFQIGbFiDunXYEs89ANvgMRL9oDtaL6JdNciAzT4IZ0nNBzssvyVH7OJoMfPvchLF0yDbm5mYBSgA7XLLx+8pR4T0rYB3d3O4rLK9HR5RaTXQZWOfnl4/l8LGXVTeoohPW7AOB9XCfhajmD5jYX2gN5CCBTPB/LYDmhSfU49ogyI2mlE/HinHurEDL4NHrSoMWQjVkZ6c9C5wKegkxGRgZmzpqJ7Ix4TyFOLJklxcUUv0v8zvHf6RgLPrBnG359oT7hmwecuflmzM4Ygm/KVIUVO0G2vgsCakfCHxXAOA2wjt9E+8SJE2KhzwzCVKnxtOBgxzkEI2HMyijB0uzYirjB4ff5//7v//C5z31OZHexFLynck8ikUwYWMHHoACTU9ge7h3veMfkDgJT0GjbDvh7Pe5gmwbkbkhbG0DJ2MJe7wwY6z4wNDo+evSomOvQJHwgcZ9GsZs2bRpWkFwyPHj9bws0wB3qEBUahdbpMBkGN3LnmvH48eNi/NHn6HrAlP/m89L0msk7+ufJ7VxL8HfeuCbQRQXOe7geJfzJirSBDOX5PBQx2L5GN9BmNjqFjKFUWfB5RupJ0Oo/AVe4OmaLAruhEEW25Ug3XBMxGZHHSE9+Skw247qPnw3NxinWcB3H4z3Sa8V5txvXbdkifpIrCwrw9yuvRMYk9XS43OC5vmXLFixfvnzExvZDgcmMXF/QK4yJGR/72MfkGD/FkKLGFIbq+KcfuB9/e+ppIXB87jOf7Slx7LmP9xQa/fGB3VLbXJTY+m/XFFHDIkBnVCxivs8WNIYUsyn6xd0OtFwADCageE4fs9+wSoPx7oQH5cCorE7YNze84cOIwCXaDdkMVTAbxr7FkwQpTUZ5ji5atEj8zskgM1aYKcUFhT+rAd2hphhTeidmZ6wXmTvsNcrHr1mzpt+LkiYGvAQ1zuAbsBoKkWdZgQuuVxBGfOs0qyEL0xwTp82JOCZtx3DGrfUaJnMzZmB13uJBH8sM+V9f+IcwLNahuDE/czquKFwyKvurV0FwMqsvMmbNmhU3cVEjQby26xVkZ+fC7VERCoexbu1aKB2HgO7XEQiE0NHtR9GCWwF7yaDBFVZR6IuOuMmy+xTguYQMpw2F+VnIzXbCRGNv6/gZD7b6z6PZTwFH9/0xYJZzA6zG1AL91R4PnmtsFNlJbywtRV7CeB7LU089JY47M6ES+/ByAcaWhcysGm6mrS8cxBmX1k5rdkY+7GwFxtL67hbc+9pW7GsP9xE1DtxwA5aPomH1RCKidguBXUEGDEpMdVS4HfDv7CO29mIAbJsAw/hmQDNgyQAPv1tcwPInF+lc+PN7TbGM4vJIshpjK0w//vGPCxHuu9/9Lu65557JHSSVSKY4nJv84Q9/wAMPPCCCVj/60Y8mb8ajrx5oYxVpAvlXj+t8QTIy2FZ048aNPdcSViC+8MILwv+Ma4z+Kn5j2+IwOC2ZQN4r7e24cOGCqComrAzg+BMrPHD9wRZPTHqi4MAkCQoNsQk/9IgbTETgeoY+LPocJ9mchOtPPhfn2rH+LeMBE86a/Qfhi7SJ3y2GLBRZV4pYTSrw+Db5W+AJe5BlykS+tf9kUIqEXH/R52Px4vj1KOeLFBAJrwnpPiZXvPQSXmtrQyiaGWZUFHx8zhz8YHn6xRvJ6MBz7aWXXoqr7GH7J47JXGPw32wfyO/wSM8fCm/f//73RWXp7bffLhIxhtOBQDLxkaLGZQAz4hkw4BebvXFvuKHXgJvZ8HW+0z1G4QWWGSixzR50EAlF/Kj2HoAvrPUCzzFPR4ltwahd0MPqVg5NCVvtMCrJK0r4vkQIVwZGRg1/uA1BtRMGWGA3lqZs6scJJydE+uLh6quvjvs7g60Xa88imNOIOQviq4DK7YuQZ9F6zTNjn94HV111Vb+fc3fwDFzhc3Hb8swrYTUW4IL7lT4+MRNN1Gj2teGFpvi2LuT64g0otMa3EUokGAnhNxf6trapdJZgrX2uEAM4ceCklBMJBjEZ7OZkgxnSXPhxcspFAW883oQLAWZYcDxhFYa+nVgsZpSW56GwoARGw8BZSnxOTliEKOWuBlq2xd+BQum0O4AUsrX6wMlu/WM9zjo9MPsyr29rvbHEF3ahK1gv2k5lm0tT7hv8WmuryE5yR8u3S202/HnDAlR7axCOhLXPNX8+jNGWaBQHWRZOPwz9+8HPnAuRkZp+twU8+PGZ7egI+sTvWSYrPjZnIwqtGXix8QwePHsUf6nVFp2Er15oteDiG2+FbZyzH90hH9qDLmSa7MgeRsXSYPD7E4ycQhi9QiQ9n0yGqEeG/yAQru37QEMxoJgBc+W4Cxqx70Vv68DvOxf5+qKDWYkcy/mTRqv0mBnq9ZZBpn//938XJeCf/OQnRV9biiUSiWRywO8/gwX/7//9P3zoQx/CV77yFRGMmFR4zgMdsQa7UXLXa22oJJMGXq+YHc52mpzvsI1ibOCM219++WUx92VS1EDXGwZm+Xde3yRDmzdQeCAM9jNxhvOHbdu2iePJ+QMrIDhf4PqBnxPnArrRMCup2WK4vr5ezGF5/x4fytxc0copMUEzEa4teS4MZKw9FRFzNtUnWniZFHvKczI+bl/7QdT6ets1V2XMwfys5NWy/DyZs+YnNgABAABJREFUhEjhTw8Q8/Njggr/RiFptK4D9scegy96TuiszcvDa9LkedLB76heUcWxmT/5OyumKGAyTkDBksLZcPz+nnvuOXziE58QwiQTL3g9kExdpKhxmcCLzYMPPigCCDQW/973vtdvj8dUuOjeAw8zTmOyTYus85Bv1Qx100lE9SGs7ocCT0IleAmMyuAZ6+MBRZ/WwAn4I10wK3bkWapgNWaN+uKSPQs5geeNk3YO5OkQdkKRbvgjFxFRgzAb8hGMBOEKn+35u1nJQr5lzaAeE8ye4cWK2eMD7dfzW/4OY0kH6qqbsHT1/J77ltiqUGjtXWBwIsz3zIzzZPAC6QlfgjfcKPbNaZwJm7FA/I1+Gu2B3vdACq2LkGWeOJlZ51012NV2uM/29fnLUOkcfD8fq9mK9gC9aHq/p2Xtdkwz5YuFAyf+zI7muaILTDxmFJb4OTGAyQA4b3oWEk3A6G3CCSsD4/qiMKy64A4dgAot0G1WymE3pih0th8Eul7vm71eehNgGVi8SQpFTSFqJDyftRTI18z6JhtLn30Wx7u6euqO1uWZ8IHK+IUdq3DWZlUJsY8ZhrrBOSeOzDzkZ8XFxkjHhF+e343Xu5oQiR5f2kSzWuMjszdgb1sNfntxP453hbG1OUQrD2Sbgcc3bMI1xWUYT052V+OV5t7vU5ktD7eWprd/dlhtRSDCVonxWA1XwKDYAf9+IMw2JwnYbwImgUdPIhwv2L+cppYcJ1KBQYrf/va3+Od//mchkDIgKo0iJZLJC5MiGDxg655vfetbeM973jMhDEFTItgBND+XsFEBim4CTJdXUHSgcZ4JERy7OffjfJDzxpG2zNGeO4L24EV4w+0wKRbkWiphMQwv4eDZZ58ViRwDVaAyUEZBnr5qNAYf6LpFg1u2YRxOQO1yhHNNJlLyes5zg23AGOSmVxaPI6s7eewpevCcokceqygoVjB4yW1sa8QAJ+ewTJaQbcBGn3pfI3a39Z23Xlt4JTLNGUl9BtniS69i4e+8BtBzkGPDaDLrb3/DBY+nZ3XHSo07y8vxpw0bRvV1JeMDY1tsTx6bkD0YTOr7zGc+I/wzaAj+0Y9+NC3XKsnERn7Clwn8MjMTkm0d/uVf/gULFiwQv//rv/5rH2OnwdACxVp5YyzuUGvaRQ0KGp7wLqgIwWpgQymdLBhQhYkIJ+gNvr0IqjRE1rIm6n17MM2+CaZBstdHAjNaOFknzKjVb7rxr76dk/jYUtzBCEdc6A7RKFvLjAiFW3rKPnWCahc84Vo4TQNntXGCyoArM/w5GeLklkFyihLMqGEgnZNgV7sXc5cVwmA04vypasyq0p7XaYwPcHNxxUAt21HxfSXCYKXTNFPcEsk1zxKt07qDdeJ+2eYZE0rQIFkJk0md7H62J3JD8Sr8o2EPOoNaNUVGs4pptvyensLsc8rPJBYei4HaSPB4X3vttX3HhNBBqOj15wmqtTBGMmE1pmAmbrQnb8cz3O8Lq4ZsZYCvLv557akZm09EzrndcY3U1uYZe/w5dE67arHEPEOU5rPqhqIf/82FJoXEdGXBN/i6egQNwn83+Lpx1sWAfhjlwg+pC4syjfCrKq4vmjXugoYr5I0TNEidrw3bWo7iyjS2Y4uobH3YF/rxsLoQxpIEUUMBDAWTTtDgOcVxnNccBh9S7Y3LknP2teXjfvrTn+LOO++UFZUSySSHQUxmRT7++OO4//77RVYk2zwkzhUmJOYcIHsl0HkgOl9QgJzVUtBIgMFMigXC/6CtrcdgWYf/ZvCf88uhBKIb/cfQHeq9JrpCTZjh2CC89IYKK4+5HuC8lvMfBtO51qB4LtrrNDWJCgDuK1tTUZAfKHuXQVpWGFAokcLGwPB8YPBx7dq1PYFtihiJcFuy7fr6YyheXpL04Al5km8Pe/qIGmxLqlf3M6bAuSDXkvwejUV3jB+vXInbtm8XIzW/05kmE76e0AJLMvlhpYYupF9xxRUpPYZj/n/913+JrjSMd/JawK4TkssDWalxmcKsXQYWmFXFlg9UMQcr59ThReRU94s9ZuE6WaYSlDv6N2AbDr7wSQRVGl9pATT9cukwXjNyH49RwhduFyJGInmW+SJwPhowq4UZRcx24YWAE3kGM3kx4CKEwpWuUvNv7CfLLKvBOHDgAIIKRQUrjEZD3Dngj4tBK3AaK5BlTs3YlQEt7p+etUNVnZMhLj5oOsb3wUqgvedfQnNjM+YvmYMy+0LkWcr7rQDh+6N/w1TjaOdpHOk83fP70ux5WJStmR+mgsvtxtGTx+B1ezG3cnYfESMRd8iFtkALnXJQaCuGJYX2SDRS7wq90me7WSmBw5RCwDgSAhqeA4KdvUad2YuBnBEEmyNBraUE+2UzYJy5AHDOm7TGn2tfeAH7OzoQjgqKn5hjxeIsmu713seoGPC+ipvEd4kLDd5SHdeHwi/O7RLeGbGVGnajBe3RdlRkQWYhKpy5KLdnY2l2ybgHrqs9zfh7A8XZeDKMVtw78/q0vU5YbUIgcqjPdqthIwxKNIMteAEI8jsdAgyFgHWZ1npqEsBgFsdbfp5sU8fFbCqwlcfnP/957NixA//2b/8mkipkkEgimXpwPsrqq2984xsiGPHf//3fPd5pE5qwDwh7AKMTMI7cK2gqwfGbCUgUKzh/Z2Uv5/Acw7nG0Ct5mZXP+Xsqnzef49VtryBvvgc5ebGVFQpyzRUosKa2nkiE+0W/Pu4Hk6XYJodrFt4Y4GK7HK41eD8KFmxhO1BVEe9Hf44rr7xSVg0kwGPKhDomo+mtaQcy1pZMTJp8zdjZtrfP9uuLroLTFOMJFxsD8PvT1g1iqBzu6MBT9fWwGgx42/TpmObou4+SyQfXrBQiKE5w/OYaI5UKC153fvKTn4iqDIrqTKjor4OHZOoiRY3LGF6UnnnmGRFoYIYF1c277747pQtUW+AiGn1sF6PbDyuocK6DLc0tlrzhowipDX2yuJ3GzTAoE2PRwWPHUlpmq4pqgxhRo6vDBaPJCIfThnzrglETNcilS5dE5iwzsvnZ6pMNvS8hJ+acbPLvsYHOSCSI5tZq1Ne1IODXqnr0kl/2x5y3xIrjr++NOy/C4QjySvJRPr2wZ3uOeSnszELmBKmpSewPK4K42KG4wvv1d27pfVUZMON99eqS3NwcVMyeAZPBCsMgnh0nTpwQ74umcVONzkA3ukNuZJqcyLZkpjQxoGDJz5ziFY9JKiXBFDMOd+zraVdlVixYlbcedqNj0OqkrtBLCd9TBRbDdNiNKVZUUdhwn9MCC5Z8wDGxqmbGG7aeuuaVV9Dk16phbi3NwG1l8X1lF2VVYGOBFkzwhAI4524REtGsjAIhOqSLFr8b/3NmG1whzZPGohjhCvdmbOo8MO9KIWxMBNoD3fhjDb2Z4skxOXDPjGvS9jocuwKRw4igqWebSamE2ZC6EDnRYFYrhQwuNChicKGRaiYug0tse/n73/8eH/7wh/HFL34x5TZVEolk8sJ5J4MMP//5z/HOd74TX/3qV6VB5zBg0g/n5fQZiB13Oc9jRSbH5NFurcG5JE2TGWiiMKAnIVGY4D4wWYrXPq6D2No0FgY/Gfjm/F7P6mZCE68nbZ1NaAwehrvb05u1pgI5tnJsWn5Lv8lXrPjmc+htlLlu6O+axNdk8pQuxuhQyGAL1YFaVenwcXz/gwkglwtcr7H1L48FP3Pdm08yOeF392jXCZxzX+zZtiR7IWY5h9+mXCJJFV4bWJVB2HUjVT8Wnrd//vOfRdcZCuxMoLj55pvHPYlOMj5IUUMiAha/+c1vROCBE0QqnKmUenUFG0WZsAIjci3TYTOmv/dsIFIDf+RE/EkLO5zGTRNm0OJAzLJsljQzOG+z2xBxNGLhigr4fX7s2XEUhUX5WDDtShgVq2YkGwyKrJ9kcIFAUSBVhTpRYGFWEttL8fGJx4h/p1+CLhqE1W5RDZOb50BZeQEyHbNgCM0WgSguBHhhycoNwR0+EvMsCqDacK66Go31zWKLzVAEp0lrPcaFDhcr7LfJSa9uLsvn4+tyAcF9YzYfFzoUXSgGccHGxc5I+qcyC5gl5ZMBGhaH1YgwLE73uczjQPFqqCZ5O1u2wBfxxm0rspZgUfbyQR/rD1+CL3Kyp9JCgRkZpvUwKLIXcbpoDwSwo7UVZkXBlYWFqPM24lDHWYTUMGY5S7Eyd64Q/1r8Lvzq/Ha4w5rokGGy4v0Vm5BvTV+vW3cogJPdTULGorjx55qjfe7z7pkrsSZv4MqgdNPsa0d7sBtOox1l9oK479Zfa3eg3k8vqF6uLVyKuZnpbUvGcS6CFqiqF4qSCaMyMYSdofSwpSjN65lu3McxmwGtVGGQ6zvf+Y7w77rllltE0sRUFJwlEsnAMNmGQQcmUT3wwAP47Gc/e9kZ+I4EtvXimNzQ0CDWBPQhoHDAue6TTz4p5thcu3HuzfGaAXwG35MdY/792LFjQlxKtWVgsn76bFGarJ0lE7y4n7EBbiZTMVGKr8drCQNYFBgoWFAgqQ/tQVD1xiXF5GERqs80iwzc2H3n4/m6fM98LCsEuE1fYxAKFXxurkMoCPFxXF9QBOGxG+58m++L4sZUrAgfCqzYYdIUz7+Jsg6XpIeOQCc8Ya9oOZVpSk+7WokkFgrQHLcZ62L8URe658yZM6T4D6vs2HGGYzwTJt773vdK34zLHClqSHpgAOO73/2uEDXYP/TLX/6yaFM0nogSx8gpBNVL4ncFNtiNK2BURv9iy76RnMQyw52BeE5muY0LBQ7AVosF4XAjurqa0N4egM1WCpvdLibXHV0taPGdQvH0DCgRM1z1TnS1+XrawjC4w39zsZeY3cJqg4qKih7VmsFpTtAphHBBk8okUs+i4QWCx5CTeZoGxz6W2fWuMDOXg3GPtRkWwmyIz5L3hS/AK4zBIzAqOcgwLRXLj1DELYQaRWF2cjVUNYRwMANqML/f7CeKGNw3HlcuNFJV5AeDixy2y1q/fv2QH+sN+7C37RBaAx2wGixYlrMQZfbR6cMYVsN4tuEgzrpZgQQUWLJwW9kaOE3pC/5zwsB+p2vWrEFGVoYQTywG86DnzitNz8aZipMsUzZW5aVmwBaM0G+lFQpMsBimTZhqqsuN317YifPu1p7Pku2hKp0FeHdF73fjWGcDnml4HZ5wAHMzCnBH+RLYjcNrG3De3YbvndrWZ/vnqjZjhiM93+9UONJxBvs7TvX8PtNRgqsKV/Sc9xwLt7cexSV3E4wGA1bmzMXczLEVXSYaPCa8zjEQRtGbCw4GjXgN0luKDAWKGexny7kEW5BwPjGcMVkikUwt2G+fQYjjx48LcePjH/94SlnyUxGuA/SAO48BA/8M9vN3ri/YMlZU/QUCQgBg8IfjMe/HMZqJR6yE5pqAwfvz58+LNQLhvJ8tOJjApIsCHMf5fHweBv35WhzzmWXPsZ7wb6m0DuLr8DPk9UIXFBYuXCgEg+EQjHhQ7zsEf6RbJMkVWquQbZ6W4ONYjUCkQyTLBLuzkZ9bkrQygMeVVYV83zyGFH/SYTTNfeD6guL+5S7Ica1FYYw/eW0PqQEYFfOg1fQSieTyg7EzXq8Y++G1i7DNOONSw6kwZCvA//iP/xCVc0yQ4FxitM3pJZMDKWpI+kD19Pvf/77oibtu3ToxeAxL3GA1QNgLGCyAYWSl0aoaoOU2FDCAPnoTJ06EmYnECT+D7Zzwc0BmoIYTdi4+mKXS1toKv+8EjMZWZGY6kJvLzKFCuL1l4v6x2UX9wYkxzRUHChpx0si2SnzO2Ak8y+z4eE7a9ewnHV40WAXBiwUXKHwcLyjMoKI4wgwrTvIjqhfucGIgUoFZKYfNuKDPvmjVHWqf4x9WXXCFXotmWWlBVKthFmzGgbNyI2oIrtBFhFQPzEoGMkwzh/XZ8vPgxY2f1+LFi0W22lDg+3qhcRu6Q664gP41RRuRZ0l/QHZn6+vY206BSIOt26bZ83FH+bq0PD8XdVzMUqR0LsxCtbdGbM8xZ2NN3irYBugVvbdtB7pD3XEZc+X2GZiXuTAt+yYZG75z8nl0h3r9LUi22Y7PzNO8Iyh4/OTsjp5PmefgnIx83Fe5ftiZd0/WHscLTWd6fr+pZB7eWDofY0VX0I3Ha7f02X514QrMdJaO2X5MBvRsXl4TOF6wdQSr+1LxWUpFzGCA6ytf+YowCZaZnBKJJHbsefHFF8W6Qhc3PvGJTwxP3FDZ8pDZ+ZZJ4ZXF9871FZOZCBONOBdnFQbXDBQz9JaxTKDSk5zYro9zec7peOP8Xm/pqlddx6Jv5/pisOPKamlW5VGo4BqHwgbXDFxf8Mb1ReJ1gesSvj7XGLzxsfTc4HZWMQzXmDWiRrRmxgmfZUfgGLyRWv3dwQATCqwbRULVWLBv3z5xXJiEpbe7upxhJwEm3RWWZSOc3yxEDX5uZfYFyLOkt+pVIpFMPvRYGgV3Xj8oYjBGM5IWdazM4LqC8Z5PfepTuP/++1P29JNcHkhRQ9IvDOxT3PjhD3+ItWvXikVIKm2pBL5WoPZlIMT+pQpQuArIG9/AKKsDOBljFirL3Lhw0A2q9Um0bjY9aPWA2gaofU21oKwFlNHPTObigQshLny4SNLLsnW4OGGwSjcL1+F9KWpQrGFVhSv8cp/nthhmwWpIvU2IJ3QcQVVfcOgoyDIxoJUkk0r1IhTpQkfwtBA09HZFVkM+8i0rhxQEo6Cxe/duIboNVczQ6Q668FxjfK99TtDnZVZicXb6g7J/qtmOBl9H3DYaPH9s9s197usOedHkb4VRMaLUVghzP+Kgnm3NxSk/b1b3NKiNONF9Ku495VlysbGgf/HEE3LhQMceqPDDrFCcsmNh1jpYjLKF1GSr1Ljgbo0z8qavxrtmahnzf6k9jNdaL/X8XefLC28UraqGyyVPB5p8LhTZMsa0QoPUeZvxfKPmZaTDkWRlbhUWZ8u2R7wWMJuX13XCLClm6I40i5XXIIoZbDPFygzOE6SYIZFIBoJzlpdeeklUhFPc+MxnPoNPfvKTqYkbDORHXgeiFdxABmBcCSjDF2VHChOP+D4472bgm4IA1xcM6sRmozIIo4sZExXOISmecI2hV/DFVjtyrs395zWFwX4d/o3XF/Y0Txdh1Ycmf18vrAzTbGSaRve6zvdDAYrHggbYlzM8B3gseE6wDeWcebNxyrUVYSEs9jLLuRZO0+RqtymRSEYO40tMlOJ1Qfc3ZeLtSHn11VeFmMFYjxQzJAMxus5ikkkNJ980+aMaSnGDfbEpbnzhC1/A1Vdf3X/wmYa/NS8CYc3QVmR9N+8FrDmAswzjOSnj4oIZqWzNw/e3evXqYfbg8w6wffSDeVxU8MYMruH2KVcUE6yGufBHTsf4INhhUYZqZt7XIFjL9KeYEi9qeMM1cIU0jxSDAnBZF45mmvkjraK83GrMHVDMYWBOz2JjxQq9SVIpme+P/kqmmQ823PZSHYEu8f3IMWf1eX6H0Ro92r3YDH33v8nXhq0tu0XrKJJhcuC6og09lRZcXLDNFH/qPYPZbkrPhGhuOR73fKxCaQu09xHA4vbNlIG5GdPRHNArSXy45N2HWc51MCjycjFZuLV0CX55fgfc0THYYbLgjaVL4gSuZPS3PVUoZIy1mKGTZe7bkpDfsewk2y8X+F1nJi7HCV7neK1g65F0wODVj3/8YzE3oJhBs75rrrlGVmZIJJJB4RzkuuuuEwIoxQ2KoRRGud74p3/6p4EzMClm9AgaxA2EDwKm1Npkjgacd3EuRqGYAgfFAGansmptshFbqcEqvvGEFd19YSVKsu3pqWDUk4N4owDFllOXGxTnmCil+ytyvclkQL31ljvU0UfQ4OfiDrVJUUMiuUxgXI3dRFg9yHgUk2ZHEo+JHYtfeeUV/Od//qcQMz796U/jj3/8o6zMkAyIjFJJhiRu/OAHP8Cb3/xmUc3AXnZ33313X1Eg0A2E41ufiKC5p2FgUYMteRr2ATS3ZZ/zaRsBoyVtnxAXFxyADx48KILjDMQMT9Ag/QXKJlcAzWKogAGZCKvtUBQzzEqZ+DkUTIZ8BMONMVvYFTdTiCaxhFVPj6ChY1QURNTepk+RpAKJFkBjFhz7JnLRyLL6odAZbEG15yRCkQAyTLmocC6EiW3RhMhgR7G1AI3+lujea0LHTGe8r0gqeEJebGvZDXeYFSgMqGZiU/5aWGPO47V5c3HR0yzed1cwgsOdYSzILEGNxwOHyYc6b71YTB2qPY3WtlZ4uqJmvVDQYqvHguzZ4ncuLtgvuL/+vuYkQgkrPgaqhOHx6RU0NPwRF9oCNSiwan2XJROffGsGPjH3apxztYiuHJXOwji/jFW507Cz9WKPuMafCzKL4TSlb7wdCYFIEC3+TpgUIwqs2Sn1as4w2bEubxFeazvWs21exnRMsxfhcoNBCC402BKKBq3se52uNlAUlSlk/OpXvxIiqhQzJBJJOsSNl19+Waw1vvnNb+IDH/iAWHMwqNyHiFZt1guvYl2AGgaU8amAYIINk71qamqE3wDXG+nIUr3cMSkOGGBDBLFrSq2yezRaTLGCkVUZQ1kbhiIhHO06gUZfM0wGE+ZnzkW5fWK3vORamF4tXFsxIJk4P6CwxUSplStXJq0qMiZNclJl8pNEchnAcYPJwRQ7ea0bSevaWDgGc03xne98R3RXoe+WFDMkqSLbT0mGDFv+/PrXvxZZVQyucuHBBQjbOgmCbuDcY4mnGlCwAshfnPxJu2uAc8/G3z+zDJh1U79Bm3/84x8iqMJJaOyEjIMig8Isf4uF+8q+r7qizIDwiBRl9Ryg9vaQhzIPUC6/wK9m5n4W/sh58TtFEqdpOQxKfMsif7gZXaGDfR67/dUj8PkDyMrKRL5tBQzoNbTWqwp4W7Vq1bACc+5QJ4537YqpjVDgNGVjQea6nucLRcI41nUSLf52UQmxKGsecixD7/G8vWUPmv29Bs2aX0YpVucti7tfW8CFv9Wfxsf2n4M7pFViOE1GfHl2BOq5CzCaTTBmZSAjJxOOLGdP9UWRNR/XFKXmvdEZ7MS25l09+8KfS7IXosLZf09gX7gbZ907ErYqyLfMQIlt7PwRJKPPqe5mPNt4Ep6QZhR+a9lCWEbofUTOu5uxveUMfOEg5mYW44qCuaK9Wqq0BbrwTP1r8EU0X6Iiaw7eULIu5X3rDLjQHuyG02RHIasDLyN43dP9dLjQGLSN4hDYs2ePWGg88cQTuOuuu0QvfFY6SiQSSTrZu3ev8Ob5y1/+gjvuuEMkUHGu30P4KKDWJdS7GgDj9Wnz1mDyE+ddrGyjYKHDOSnXEAziJM5HOe7SDJ3VDVxjcG0iGTnBCK/pB0TrWq21bRWcptQqytlm+Pnnn+8xQ08GP0e25B1uVcrutv2o98UmdgEb8lajyDaxPn+euzRRZ5sYJojx/GQrqeEEJPlc1Z7D6Aw19FT6mxU75mZuEKbhEolk6sGxgwlT9FqqqqoakUdGYlzxl7/8pUiY4njMdpTvf//7pQG4ZEhIUUMybCgecNHx7W9/W/Ta/OhHPyoM/0pLS4GGHUAnA/7RSb/JDsy8FTD105v/0lagnfdPML1b/K5+qzWYMbp9+3bRv4+Z+3o2CX9yoGU1BrNtOEByXymEMOtL7N9ICLkATmCZEWbPARQG35yA4sTljCraJIX7rfQIRbrRHqS4EI8/FMbp1+tgCkyH1ZgtJsuccLPke7g+GbGwQqPBd6HPubUkezNsRgfSyd/qXxBZ5rFkmJy4oXhzn/vesGULXm5uFuclGhqguFyYmW3BD29fAKPJCHco1rZcN3OegZW5i1Len65gNy56qkVLrBJbEUpsxYOW+p/sfgURYb7ZyzT7UmSbJ3bmmWT8ueRpxe8u7ow7b5dnT8cby+JFvYH4S80WdATdMcIgsCirEuvyJ18bj7GE18EdO3aILNN0ZQhTJPn73/8urvHMZL3vvvtET9uBAkQSiUSSDtifm9XhDz30kBBQKW6wDa5B8QBhziV1vzgVMFQBhvSNS1w/sJc31zYc72LbYVHkYIsp/d80RdUTqdh+Y/gV4JL+4LpARUjUgSfz6hssEMesX/EcqirWh6xgHPFaMJoQ9beG5+K2cc5Sbi/DqtzU5z2jCYUdGntzrcFzOR3vu8f4PnAJ3nAXzAYrCiwVPRXwEolkasG2fPSlHW6CaTLq6+uFJ99PfvITEfP53Oc+hzvvvFNeQyXDQooakrRMbDj5Zxbnc889h7e97W34xMc/jlVzMgFviyZk5C7UhI3+qN4GtJ1KImq8G4hpm6K/XuyAygkby+BYdcG2WMw86cnAD4XE/dPR40/gawKat2hl7sToBEquB4zjZ1A4UlQ1jDAuQlW7haeGgmIE1ZNQwd+tMBsWwKikp9TbFTwFb+Riz+8WpRBOUxWMilUsVNguhQtEfm6HDx8W2VNsEzYaosbS7M2wpihq6BlO3D8uXvXqEZZnc3Gkn28vNW1HZ7ArTogotOZjU0FvliEXw1wo3/zCC6jz+VjnDRQXAxkZyDUreHidJo7Ra8QX7t3rbFMGri3eAEuStlLpxBVqRbXnQI+wkWuejlLbAtkrXzIof607gKOddT2CBOE345/n35JStYYv5Mfvq1/os73Ulo9bSjWT80kNrxv+DqDzFOBrBhgAyFs6Yq8ptpFgez5mM6ejDJxZUw8//LBYbNDolga+H/7wh8W1VSKRSMYSzvF/9rOf4Yc//KEYg5g89a533oUMB9tQseVUAWAoSctrca5H9Dkd53tcX3AfiouLhQl47HqC4gfFjHQFeSSjB4P6FDgoOjE4x8p9VgKNRIRiwtDT9YmiBiu0y7Ayd2nKz8Pzi0FDtoUSz6EoQjCjj4XDMbTkK3poMVion8tMcuDzSLFNIpEMFY4jr732mqjsGq6HayJMkuL64tFHH8WNN94oEhbojyqvo5KRIEUNSVphWdqPfvQj/Pa3v8WSJUtEP7y3vOUtfVpB9cHdCJz5W3zgOWcWMPOaPndl+wsOssw20QdAttmgAThN3jg5JPwbS8AZdO739Wlq3nwA8DQDZgdQuAKwDpDlWvsUEHbHbFAAZyWQvxaTER7HoHqAFtIx27RPQZ8Q8/8G5MFinAOjMvIM4ECkFaGIC0bFDouht3XYM888I4wVGZTjxJ4VNyz9H+lFzhPqxvGuHXGB1kxTHqoy16T83DyvubDguaRXBHGxSxM99lDmPjNDry3QITw1ItEMQpNiwlWF65Fl7vW9oPnV2rVr8a6DB/EkTfiix9moAMuyjfjq4t6gZLmtHAXWIuGFUWTLEz9ZEVPvu4CuUDvMihll9lmwG9Pr5RJWg/CH3SLrymJIbzWLZOryRO1+HO+iqBHP56tuhtkwcK/zo52ncKzrTD8VSuXYXDgxsh6Hjb8daHwZCGgZvnGU3wiMwPuDAYwDBw6IYNsNN9ww7Oc5efIkHnzwQfzmN78RWVMMHjJJIR0VcxKJRDISKCAwCMJgCDPP3/ve9wpTcc4T0wXNkZ966ikRBGbFMOfBDAZzvcG5H+d7TLrRg85MvGEVhwzGTGz4ue7fvx8LFiwQVY1sIUaBKh0tGve2HUStrz5uG730Cqz5KYst27Ztw7p16+JanTEBislUbGvG5K5U9rW2tlYIN4sXL05baxiJRHL5wlgHRQgmTt16662i9dRwr9/0y2CMkG1y3/Oe94gYIcdkiSQdSFFDMip0dnYKYYODF7PbP/ShD4lMTy4A+qW7Fmg8CIT9mlF4yUogSR91PZjMG//NgHKO0oXOutPw+ENQcmdh7uKVYgLIyR2zXziJ1Sd4/MmJo8Nuh6NtN6wMDpsMMJmMsNocUObcAZiTtJJi8Ln6D323WwuB4uswGYmonQiqe5KUefNvifdWYDOug1FJbkw9UpgRzAsdxbB09oIn3cE21HhPIRgJINOUi+mO+TClWPHA47Fz505s3Lix3/sw6+vs2bPYsGEDXCE36r1NorVzma0EjoQKJU4O2Iuyy2jE1a+8gtMuF09qTFcUfLXKCaPaAavdisrCSqzIWSaMB2M56zqClkBt9DcFBhiwJHtT2ltpXe50Bb041HEJ/kgIlc4CzM4YuHWXBDjZ3YA/1+ztORSUDOdkFOGt0wcWfWs8Ddjeul/8OxRR4dO7igBwGK24rWyT8MiYtIhrx5NAyCO+6/EoQNYsoGhD0ocygHbx4kUxPopWdXFP2+s5xGxMChFDDa7xOdliitfqLVu2iCQELjQovMpAnUQimaiZoxyzGCShQTfHrJtvvjmpqfFwKtXY+oo/OQYyQ5WvyYo4/szKyhJCCtcVFDm4ztDHYUJxmUkwvHF9wt95Y3KVzJYfP3jOMNGNYkY6PwdWaxzvOimMws0GE6oy54p2r6nCqnS2Rs7MTL624jlHrxZe37n/A8H5Au97xRVXxD2eiWIUSfRWzKwyl6KHRCLRq8TYJUNPZo0dO3hd47WLY9RwWtvyGslKy5///OcitsNr9bvf/e60tcmVSHSkqCEZVSg6vPDCC2Lx8eyzz+L2228XmVWbN29OW8DEc34XLu5/Ft1uvxiAI1BQZ6nC+s3XJxVRuE9igtdeD/eJxxEIhhAMhhEKR+Dzh4C8KqjZs8WEj6XunPz1mKDX/R0IdceZTiNjDpC3CpORiNoqKjVi0frOxnUrjnm3VjiMa2BQRifAyM+Fi0leZLlwpIKfzok3s7V4o6iQSpsWHgtmd9FUnpVAA9HY2CgWvYO1y+L5x6xqLix84TCOdnYKc8sN5eXIdjgQMURw4fwFrF21VhyDWCjK7O94KeEZFZTaKjDDUYXLlcSWdCOlM+jBb86/KgQNPmsEKq4rWojVebPS9hpTlYMdl/Bq8ykEImHMzijEzSVLYE1oIdj3MSdwqvtCTzVVRFURUumlMRcLsypg68dXadJAMaP6CU3c6KMWK0DmLKC4V9RgsIwtT5ihySCd3tOdY2G6znMG4n7961+LygwuZj7ykY8Izwy2WJFIJJLJAOddv/jFL0RPblaUfexjH8P73ve+YWeTJptbsBKON11UpsDMcfj666/vMx7z/gwcM4DM8ZsVARzPuY3jLP+tw+Qqzit5S4cYIxn8s2QCEoNs/FyYQJXuwBpN7rlm5NozlWs1k/4oasSKEAO1eWZy1WCCjF5BHgvPNVYe8bE8B3n9l4kLEsnlCcdB+tJyXKHQwDUGhYt0xVv4vFu3bsWPf/xjPPnkk3jDG94gxAxeM6WYKhktpKghGTM4gDKA8qtf/UpkPjGAwvKzwTJPBoRBooO/6vW4EChA/jxg5lUDP9bbCpx/KmGjArDNSeFyEXxmcJ2Lme7ublF+XlGWBTS9AkRoDs6UrByg+FqtN/okRFWDCKjbGXWL2aYFFXUxo28Izg6ncQMUGqWPIhQIeM7EelhQiGCgn+fMQH1mmb3HhaJeyq1nGvDz5ESeQgXN5fs791hmySw9wuwEnq/9Xbi5KOECgucIs/FoojVSuHimGMjMLb1ChG20Xu86gCDi29ewPU+RdToqnGNrpNzib8Hr3a8jEAkg15yLRdmLYBnj74E75MNLTQfQ6GuHxWDCurwFqMqaPuLnfb7hKA50XIxrWUZPiM/MuwmGIZpU9kedtwMNvi7kmO2iEuRyzoo/0XUWhztP9tl+W9l1sBt7Wx95Ql1oCzSIf+dbSmE3jU7VWNphm8OLf9JG03BipQaAsusQthaJigwG6RjgoqDbX+bmcOF4xWoMZkz95S9/wfr168VCg8kGafOdkkgkkjGGgVq2pmUCFTPy77rrLlEhftVVV03YaysD62yZyzUG57n0RJKVHGMD59hMHNDn+TxHeA1k4J8JBExo609ooifeoUOHRLKTDj83ilYUNPg4Xsv7EyoocFGg0Ct5BmoVxfvy/GAyFpO+mDQVa1o/XOi7wYoOBhmlV5ZEMvVhrIJjF9tAMfZBf4x0CwyM27BLCxMNeG17//vfLxINGEeRSEYbKWpIxhxOzB5//HERWGFbnzvvvFMIHCwhH/IAy2ARRY1EciqAWTcO+FA1EsL5l3+OTGsYZpMRBoMCtzcAV956GBwFIijOCg198ks4+RTtsfwtAIP6tkLt5yRGa0F1mB0PGbqFEXMRiJyGilBSUYPYjathUsbWNJaLPi4CeWFmlhGz4HQY/OPnpWcz8370weAiQ1/Q8t9cbHDxoWfTbdq0KelrcRHBckzChQ4fwxvPBT53LBRZ6LXRU80zCHxt7ntbW5t4H7GZezrcZ2YalpWVCWEmrIawr20rgmpAeG9o9+m9f1XGKuRYkgsvo0FXsAs7Wnt9SiisZJmzsCFvw5gFEHgc/1L7KtoDrjjx4eaStZjmGNmxeLJ2P0529/WG+NTcN8A2SNVBKmxrOYPnG0/0/L40uxx3la+YsMGX0SYQCeK5hm3whPndUsTnWZVZieU5vb1Wu4KtONW9N7ZGDlWZa5FpniTm1Z0ngLYD2oAqWlApgDkDzZHpOFnrEaIr22JwHEv3ecCsLH2hwXGHveg/+MEPCmFXIpFIphKcrz/00EPCG4jzKI51TKAarNo23XCs5VyPgXLOPTl/ZPIL55DMjmVgifNK3u/gwYNiPir9i8YPzsUpcjD5iZ+JXpnDz4ifFc8fihCce3J9wHUC4ZqD9+Xj9UQq/p1r2/6EEVZ08DF8rL7G4H1Z4RPbZpLbmFDHCspUq3m4tuF74PqC66Vk8DxjNQm/H5frvFMiuRzYs2ePGFMYJ6FXVLqvMYzN0CuUMT0mFrANNxMKOP7FegRJJKONFDUk4wqNSfXFByf5evXGkFpgvP444GmJD79P2wgULR68PO7Ff6DhxCsIejoQhgk5M1dgwcrNIlDNSa3eU5cXgqmczaL1UeRE2oiQWodA5Li2vR9Rgy2ojEp6fS9Gsu/8nBi442eW2BOScDHAz5SfIW/8THkhHiwrThc/OCHgTy5AuOjgolRr06X2eLTwxgWP/pzMrGIrLb3KhDd93/RsMC4o+jWxj6Ez2Iajnbt73090DcKqgQrHAhTZRl6dMBROdp/Eeff5ODGBbC7YDKcpiR/NKOAKefHIpfhWXBRXFmTNwKaCgb/7g7Gv/TxeaDwW97y5Fgc+WHn1iBeAbQE3fng6sYUY8Lbpq7EgqxSXKxQ2zrguwhcOoMCag+n20rhjfbRzO7xhtv7rJcOUgwVZ6zFp8NQBnmrOvIDM2YClUBjecmxJt1kex7eXXnpJCBlcaDBrlAuNO+64QwbOJBLJlIcZqUyg4hhII2aOfVxjXHvttWPSAoNtgHbv3t3jZaC3EmTWKgPgzGrlT85JucaQLagmJvzs+FlxjcGkvGSw6oKtrDin5zqAj+FaYLD5Iq/TvK++zuA5QEGC/9bXC1wj6GsM3vT1C4Uwnj/6a+hrDD4HzynuC9c9UrSQSC5POK4899xzwm8q3eMAq8oZu2MMjy0Z2faRCQT0m5JIxgMpakgmzOKDffeo9LIP30033STEjVtvvXXwAEzABZz9B+Bt034vXAxM2xCfyq6jRoBwF8AWMobMPvfhftTW1vZMKnUjcgacUgk+TwUCkQsIRk73I2rQmNoJh3EdlDS14RmrCzsz5BIzsAjFNC4yEz9ffvYUJrgw1e/PSQEXC6zK0IUK/o3nDRc7FDv4WoTZV8uXL0/LRKI72InDnTv7bK90LECZYybGmlPdp3DOfa6PqHFVwVVwmEbXsJyLtn3tZ3CiqwZdIY8QeHgm8jBTfFiSXYl1+QtG/BrPNhzBoU6tWifbbMdbpq1DvjW1apyBOONqwsMXX4vbxjPk+uIFuKJgDiYaoUgYe9qP46K7XohoC7MqsShr1pgvlA+2v4ygymqyXqwGO5bmDNJmcCLhOwt4j/b+bpuHrXuaheCQriAby8tZlfG///u/YkzSFxoMmkkkEsnlCMVjPYGKc3oalfI2luMi5xXMnmcwSK805vyRQgez8SWTE36unPt3dnYK8UNPeiJcV7AiIlmVEM8BVnSwmkIXJLje1St4YqvM9fUF1xq8H38uXbpUmu1KJJJ+YfyCsQ/6iKYDjjtPP/20uI7SJ5f+uB/+8IdFC9vLJUYmmbhIUUMy4WB2+8MPPywGTaq/b3/724XAsXr16v4DaZxAhnyAwQT01x4m7AG6twORaNsiUz6QSfFj4Gx9Tjxp7MyFB9sMTXXCagd84T09v+vCBr00jEo2rIYqGJSpcfHi4oDn2Llz58TFmu1fmI2lt61if1y2gBrvPsfcT1ZqdIXao1sUmBUzVuReAfM4+Ll0B7uxvXV7nKhBX411eetGPdi9u+0U9rRpopsOhQ0TxQ3FgLvKr0SOZeTiA3GH/AhEQkLUSJeXRnvAgx+efrFPBdQ7ZqxFVebEM2ne2XIYZ9zxppPr8hZjXuaMMd2PM90H0R5sjJNZCyzlqMxYgkkBq0y6+lbodGMZDh6tFr49vMYMp1ybwZQ//vGPQsxgqTmTAdhiiskB0itDIpFINJjZ/swzz4ixksEZ+lhwrHzLW94yLgFizu0oRDMYLj01ph5cS9C/gusKimmsnmDiHOfJrO6YMWOGFCYkEsmocezYMSFszJo1S6wzhrpG5zWK7fJ4zXzkkUdEIiivme9617vEmkUimShIUUMyYeFAypJxDqQM2NCUjeIGRY5Yg7b+HsvFCzNbqB5zMomuV4EQqzliwonWWYBzaUr7w4UHM/eZpc/n003eOMCPd9A73QQj1QhETkaPlQk2wzIYDSM3p5vI8JyhuR8zqgYyIR8v6KtxyXMG7lCXyFCf4ZgDq9E+bvvTFmgTbah0o/AFWQtgNoy+2fBvzr8AN31tYrAoRszKKMLynDnIt2ZhorOr9Ryeaehtb7UyZwZuK1s6IdsEPHLpWYTU3somUmzNw40lY9v2KRgJ4HT3PrjDnT2tp+ZmrIJpDM65tBCoA9y9YnEPjmWAtUJk8D722GOiTJzi+WDnAq9vL774oqjIYIsV+j3p10cGTiQSiUTSPxQSHn30UZFAxcAPe4CzeoPmyYPN6VnJy+x5jsPsVT7SSjuuVdhOiM+jrzF447/1ymDJ5IZtclkpzvWr/DwlEslYwesUWyEyhnXbbbellOxEMZYiBq+PNTU1eOtb3yrEDHo/yfFLMhGRooZkUsCetAzcUOB4+eWXsWTJEtGy45ZbbhHlupz8czHAwDSzVik06GKG3hpI7dgKRaFBK5DhtCMz0w5HRiHsxTeI7NhUFiV8fmbecAGi39jSiC2HYgd53WeBF45YTwXemPkvRJYJjqrSKDwIBdZJ1WpKMn50B91oC3TBarSIwPdoTHySiRoFlizcM+NKTCYafV1o8neLKpDpds3nZSLyh0vPIaBqLdV0Sm0FuL547ZCfKxQJocZ7Dr6wB3ZTBqbZK2FUUjO/JKLtQsQjKpUo7E3UY5aUUAfQvaXv9owNgLlIBMm4cGBGFSvH6LkTu/Dge+d92K6Cpnx//vOfxTXu3nvvFWLGokWLxvb9SCQSyRSBogbXF7///e/FHH/VqlVC5Fi2bJmo1qZwofsVNDc3i4xXfY7P9kG6xxqvSRREuAbRE5/4M9WKObaj0lsN8cbrAW8k9nrHsZ9rH641uF3/ydfjGkMikUgkEh3GxlgxRnH17NmzokosFl6/GGvbt2+faAe/a9cuXHPNNULIoB/VREz2lEhikaKGZNLBfrR/+tOfRIbVa6+9JgZdZqdyAcLycSrSSXv7dTwHRDxRY2kful0+eENZ8BqrxCKCAaP+4ELGYA6L7lYBL5CQuNxHzNAXMfw3F0jMCGMmLgUWXlh4gRhOmxGJZKJCz4XtrQd7WlKV2wqxuXBV2to26exuPYU97fHtpzYXLsKSbK0MNqxGUOtthD8cQL41B3mWsW8pMZU41HEKhzvPxG27unAVpjuG1iorrIZxqGMX3DFm31mmXCzNXnP5iKaeI4D/XO/v5mmAc2Vy/6eE0m9mTLFikdequ+++G+94xzvS6sUhkUgklztcB7z66qtivKVwzHk6M1S5xmALXIoOFA8GMvXmGoRtTSlKMEjEn9zWH3w+XaRgghQf0x9cVzidTnFf/puPY2IV1xdcW1BgmTt3rvBzk0gkEolkIHit+stf/iJiakwaXrdunbjecZ1RXDzxWiJLJP0hRQ3JpIZZqwz0cDA+cuSIKBunYPCmN71J+CHEEWgAXLEGvUYgezNgzBp0kVPvPog2Tw3CoTCczgxMz1wDmzGn3ywrKuG8McOKCw+2A6HB9KCm5xLJJCQYCeHPNS8ggnhhcE3uIszLnDkqRuGvd9dEjcFnCkFDmLarYbzUuBstAd17BFiTtxhzMsbW/2EqweN9ovuCEK2MigHzsyoww5EwtqZAs78Br3cf7LN9SdYa5Fguo3ZJwWYg3AUYnYCpOKmgwevIjh078MQTT4gKRQas7rrrLrztbW/DtddeK30yJBKJZLSH6miLP64vOA5zHs/kKa4xNm7cOKCwMRS4xmDyE298TiZFJatC1NvqsnJEX2NwvcGqEFb2paMNlkQikUimNmxD9dRTT4k1xgsvvCC6n1DIoIA/WHt3iWSiIkUNyZTh9OnTYoDmjRUcVJu5+OCNmUs9LUCCDaypACzTAOPg5XSdgUtoCbwet82oWDDTcdXkan8ikYwSncFuPF3/atw2Cg7zMmZidd7CMTvuJ7vOY3/HiT77cde062GZLN4LU5QGXzVOu3p9RHQWZK5AgVVmAzGbl4sLXr/++te/imsLxfnbb78dN954o6zsk0gkknGC4sFzzz0n2nJwfCbsTc71BZOpJkNLWYlEIpFcnvQXI6NQP2fOnPHePYlkxEhRQ3JZqNCVlZUiMMTb1VdfLYz3UqXJdxTdofp4g3EAMx2bYTLIFlISSSASxGOiUiP+O7I6dyGqMrW2UGPBvvbjON19sacFls4tpVci25wpP6hxxBNyYX/H9rjPxgADVuduhtV4+Y2jzLp9/fXXRaDs+eefF2XfrC7UM4E3bNiQtkxgiUQikaSH2Eo63rjeYBvcG264Qawx5s+fLxOeJBKJRDJusFPIli1bxBqDt/Pnz4trlN7NRLaWkkw1pKghuSwGdpaQM3DEgf3ixYsiYMTBfebMwVvjuIKNcIebErYqKLQuTLtfgEQyWWnyteGMu7rn9yyTEwuzZo3pd6TR14pz7pq4bQYoWJW7CCaDDBCPN65gJ+r91YioERgUI8psM+E0pS4wTwXoq8SAGK9FbW1t2Lx5s7gWveENbxBm37L6TyKRSCaPOE2T8WeffVasMRhEYpsqihtsUSVbzkokEolkrGCMi9cirjMqKirEtYhrjOuuu060KJRIpipS1JBcdlCt5oDPCo7m5uaUHhNRw1AR7mlnoygm8VMikfTCLHxV1WwCxuv7QV8NmoVzX1gJQDFDflclEwX2PF+zZo1YZGzatEm2lZJIJJIp1KZq+/btQrTeu3ev8MuQSCQSiWQsKCwsFC0RucZglxKJ5HJBihoSiUQikUgkEolEIpFIJBKJRCKRSCYFsneORCKRSCQSiUQikUgkEolEIpFIJJJJgRQ1JBKJRCKRSCQSiUQikUgkEolEIpFMCqSoIZFIJBKJRCKRSCQSiUQikUgkEolkUiBFDYlEIpFIJBKJRCKRSCQSiUQikUgkkwIpakgkEolEIpFIJBKJRCKRSCQSiUQimRRIUUMikUgkEolEIpFIJBKJRCKRSCQSyaRAihoSiUQikUgkEolEIpFIJBKJRCKRSCYFUtSQSCQSiUQikUgkEolEIpFIJBKJRDIpkKKGRCKRSCQSiUQikUgkEolEIpFIJJJJgRQ1JBKJRCKRSCQSiUQikUgkEolEIpFMCqSoIZFIJBKJRCKRSCQSiUQikUgkEolkUiBFDYlEIpFIJBKJRCKRSCQSiUQikUgkkwIpakgkEolEIpFIJBKJRCKRSCQSiUQimRRIUUMikUgkEolEIpFIJBKJRCKRSCQSyaRAihoSiUQikUgkEolEIpFIJBKJRCKRSCYFUtSQSCQSiUQikUgkEolEIpFIJBKJRDIpkKKGRCKRSCQSiUQikUgkEolEIpFIJJJJgRQ1JBKJRCKRSCQSiUQikUgkEolEIpFMCqSoIZFIJBKJRCKRSCQSiUQikUgkEolkUiBFDYlEIpFIJBKJRCKRSCQSiUQikUgkkwIpakgkEolEIpFIJBKJRCKRSCQSiUQimRRIUUMikUgkEolEIpFIJBKJRCKRSCQSyaTANN47IEkfPp8PgUBAHlKJRCKRSCRTBovFApvNNt67IZFclsj1hUQikUgkkqmGXF9MDaSoMYUWHPbSUqCjY7x3RSKRSCQSiSRtlJSU4Pz581LYkEjGGLm+kEgkEolEMhWR64upgRQ1pgiiQoOCxk9+AsVhj+stZlD0n9F/JP1b8u1E6fM3pc99hvR8UAZ8TGrPpwywf6k8X/J9GPrzKSm834H3IbX3O4qfh5LmzyPFx6Tr84h/vsE/D/3fypD2b4DnS3zf/exDys8XffzI9k/5/+ydBZxc1dmH31mJC/EQEjS4JEhxC1DcoUCRIsVaoNCWAqVFSlvaDwq0UCgULVrcPTgUh0BwEgiEQIS4rd/v95zlLHfvjvvs/h9+Q3ZnZ66ce+T1Y56qmGVxvMTPo+14QdD6g/+3JfKv+5u1fy/62WR/ix4/7mdbEn8m1XHde/460/lsouP6gyRri3htk+CzoY8kvId41xe0pPE8UrRN0s9G3k/WFtk853jvpfU8LPPnEa9vpdPGmTyPRM8+m+Mma4t4bZ3weVj6z6MljXvJZHwk/WzkuiPXsaC52UZNnOjkHGVrCFE++kWmckP0b1H5LlM5rsPxIvpF++vL5HjfyWE5ynHp3W/ya0gmi0fl7fbHy1y2z+Z5JD9e5s8jma6V/H4z17Xy/TyizyX7+23/eyxPzyORfpH58dpfQ2bHS/w8EuoXyeSGtOTqJHJSUjkzhVydVI6LnCfZuZLKfC3Zy2ZxZfAE15vs/qL6RbLPZv08EnwnG9k+Z90yjsyc6nkk61vZ6ETpfNay1X3zMD7iPo8E15vsXMnuJZfxkaxvSb/olMip0dno2dNivXq5H73YEIsIl/H/Fv/9XAW7TIT9bI3ouQh2mShL2To1chHsMlG+KuV55EsZztWpkapPJL/fZMeLfw35eh7ZOjWqSuLUyEFYS+uzuTo18vHZDATtTIT9ZN/PRvlKdn95e3Z5cmp0heeRL6dGrspwPsZosvvLZoyG34uF3hNClJV+EU/HiKdHJNIxJMflx4ieL6dGNs8jE10r2bmy0bXSO176ulZXex7lqvum59TIQo7LWOaTHBe3HUrxPPLl1MhZBs+DrlXuum9XeR7SLzoV4XVSCCGEEEIIIYQQQgghhBCibJFTQwghhBBCCCGEEEIIIYQQFYGcGkIIIYQQQgghhBBCCCGEqAjk1BBCCCGEEEIIIYQQQgghREUgp4YQQgghhBBCCCGEEEIIISoCOTWEEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGkIIIYQQQgghhBBCCCGEqAjk1BBCCCGEEEIIIYQQQgghREUgp4YQQgghhBBCCCGEEEIIISoCOTWEEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGkIIIYQQQgghhBBCCCGEqAjk1BBCCCGEEEIIIYQQQgghREUgp4YQQgghhBBCCCGEEEIIISoCOTWEEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBDWlvgCRZ5YutSDW+mPw3Vttv8e++yHu3+K/Dy2hnx3+ONH3w+91+M73Pwbf/ZLoGtK7vliH6/Meuir/e+h4Hf8Wi/t++PbSO953x0nwnfCtJ7qGZOdK7/raX0Pmx4vfFpkdL5bG/bZ/P1/Po/3xUj8P/3Mso+tLcrzofSe4hrSP9933c7u+WI73m/h5tB0v+G40+n9bIv+6v1n796KfTfa36PHjfrYl8WdSHde9568znc8mOq4/SLK2iNc2CT4b+kjCe4h3fUFLGs8jRdsk/Wzk/WRtkc1zjvdeWs/DMn8e8fpWOm2cyfNI9OyzOW6ytojX1gmfh6X/PFrSuJdMxkfSz0auO3IdC5qbQxcuhCgX/SKejhFPj0ikY3TQL5LpGOHfE/wtql+0v7741xD/b7G4+kWmclz0b7GMjpdato/K2+2Pl7lsH8tCrk5+vFgG95vseBG5OKPjJda18v08os8l+/tt/3ssT88jkX6R+fHaX0Nmx0v8PBLqF8nkhrTk6iRyUlI5M4VcnVSOi5wn2bmSynwt2ctmcWXwBNeb7P6i+kWyz2b9PBJ8JxvZPmfdMo7MnOp5JOtb2ehE6XzWstV98zA+4j6PBNeb7FzJ7iWX8ZGsb0m/6JTEgiDc40SlUldXZyuttJJNnz691JcihBBCCJE3hg8fbp9//rn16NFDrSpEEZF+IYQQQojOiPSLzoGcGp1M8WhoaEj49wULFtioUaNs6tSp1q9fv6JeW2dC7ah2LCfUH9WG5YL6otqxUHTr1k0ODSHKVL8Azf/5Qe2oNiwX1BfVjuWC+qLasVBIv+gcqPxUJ4IIxnSiGHFoyKmRO2rH/KB2VDuWC+qLasdyQv1RCFFJ+gVo3soPake1Ybmgvqh2LBfUF9WOQsRDG4ULIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaXYju3bvbOeec4/4VasdSo/6odiwX1BfVjuWE+qMQotLQvKV2LBfUF9WO5YT6o9qwXFBfFJ0VbRQuhBBCCCGEEEIIIYQQQoiKQJkaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqdGJOeGEE2zo0KG20UYbtXt/2223tTXWWMPGjh3rXp5vv/3Wxo0bZ6uuuqrtu+++VldXV4Krrpx2nDx5sntv9OjRdvzxx1sQBO59tWNqVlxxRVtvvfVc/9t1111TtqmIz0MPPWSrr766G7PXXHONmikDampq2ubAo48+2r332muv2dprr+3633nnnaf2jMM+++xjAwYMsP3337/tvUTtpvGcWTtqXhRCVArSMQrXhtIvskfraP6QjpEd0i+yQ/pFfpB+Ibokgei0vPjii8Ebb7wRbLjhhu3e32abbYKJEyd2+PyvfvWr4LLLLnM/n3LKKW0/d3USteO+++4bPPjgg+7nvffeu+1ntWNqVlhhhWDhwoUd3k/UpqIjjY2Nwaqrrhp89dVXwYIFC4LRo0cHs2fPVlOlyaBBgzq8t9FGGwXvvPOOa1t+jjdPdnWefvrp4IEHHgj222+/lO2m8ZxZO2peFEJUCtIxCteG0i+yR+tofpCOkT3SL7JD+kV+kH4huiLK1OjEbLHFFjZo0KC0P//ggw/aYYcd5n7+yU9+4n4X8duRDIKXX37Zdttttw7tpXbMjmRtKjrio+OXW24569u3r8t4efzxx9VUWfL1119bU1OTyyAiyurggw9W/4sD2Xz0t1TtpvGcWTsmQu0ohChHpGMUpg2lX+QfraOZIx0jf0i/SA/pF/lB+oXoisip0UXB8LTBBhvYFVdc0fbe/PnzrX///u7nkSNH2rRp00p4heXN7NmzbeDAgRaLxTq0l9oxNbTb1ltvbRtvvLHdfffdKdtUxBeScWh41F6ZsWDBAttwww1tyy23tOeee07tmed+qPGcOZoXhRCdAekY2SP9Ije0juYH6RjZI/2isH1Q+kXmaF4UnZ2aUl+AyA2McvX19R3ef+KJJ2zEiBFxv3Prrbe6v82ZM8d23nlnF+29zTbbtBmTPdHfOzOZtmO8vR58e3Xldky3TV966SXXrl999ZVtt912NmbMmDaHWpiu2nbpkKwPitRMmTLF9cH33nvPZQfdeOONas889kP1z8zRvCiEKCekYxS/DaVf5NamWkfzg2S47JF+Udg+qL6ZOZoXRWdHTo0K580338z4O16IJip+v/32s9dff905Nfr169eWZYCxedlll7WuQqbtOHjwYOcUYmFlgQ23V1dux0zblKiL7bff3iZMmOD6YqI2FR0heiWcyUJ7bbLJJmqqDOfBddZZx9Zaay3X56Ltqf6XXT+k3ZLNkSJ5n9S8KIQoB6RjFL8NpV/kp021juaGdIzskX6RH6Rf5A/pF6Kzo/JTXQxqn3/77bfu57q6OleDn0wN2H333e2mm25yPxO1vMcee5T0WssZjHSbbrqpPfzwwx3aS+2YnMWLF9vChQvdz/PmzbPnn3/e1lxzzaRtKjpC6S6yDDAo056PPPKI7bTTTmqqNJg7d25blB/G9g8++MA5N6qrq+3dd9918+Rtt92m/pemoByv3TSeM0PzohCi0pGOkTvSL7JH62j+kI6RHdIv8of0i/ygeVF0CUq9U7koHD/96U+D4cOHB7W1tcFyyy0X3HPPPcGiRYuCDTbYIFh33XWDtdZaKzj33HPbPj9z5sxg6623DlZZZZVgr732CpYsWaLHk6Ad4ZNPPnFtufLKKwfHHHNM0NzcrHZMg8mTJwfrrbeee62zzjrBlVde2fa3RG0q4nP//fcHq666qhuzV111lZopTV566SXX9+iDY8aMCe699173/ssvv+zmRfrfOeeco/aMw4477hgMHjw46Nmzp5sPX3vttYTtpvGcfju+8sormheFEBWDdIzCtCFIv8gO6Rf5RTpG5ki/yB7pF/lB+oXoisT4X6kdK0IIIYQQQgghhBBCCCGEEKlQ+SkhhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEJ2Ac88918aOHdv2+xFHHGF77713TsfMxzGKwZQpUywWi9mECRNKfSlCCCGEEEJ0CqRfSL8QQohyRk4NIYQoEDgFMLbzqq2ttZVXXtlOPfVUW7x4ccHb/B//+IfdcMMNOTkFMjlGNsyYMcO1y8033xz378cdd5ytt956BTu/EEIIIYQQlYT0i+RIvxBCiK6DnBpCCFFAdt55Z/vmm2/ss88+sz/96U92xRVXOMdGPBobG/N23v79+9syyyxT8mMkY9iwYbbbbrvZ9ddf3+FvS5cutf/+97/205/+tGDnF0IIIYQQotKQfpEY6RdCCNF1kFNDCCEKSPfu3W348OE2atQoO/jgg+2QQw6x++67r11K93XXXeeyOPhsEAQ2f/58O/bYY23o0KHWr18/22677eydd95pd9y//vWvTmjv27evM/zX1dUlLR3V0tJi//d//2ejR49251l++eXtz3/+s/vbSiut5P5df/31XcbGtttuG/cY9fX19otf/MJdV48ePWzLLbe0119/ve3vzz77rPv+U089ZRtttJH16tXLNt98c/v4448Ttg/X/swzz7hskTB33XWXu6dDDz3UHnvsMXcuHCyDBg2y3Xff3SZPnpzwmGSXRJ0xtDnXFubBBx+0DTfc0N0L7f+HP/zBmpqaEh5XCCGEEEKIUiP9QvqFEEIIOTWEEKKo9OzZs11GxqRJk+yOO+6wu+++u638E9kL06dPt0ceecTefPNN22CDDWz77be3OXPmuL/z+XPOOcc5Jd544w1bdtllXQZIMn772986p8ZZZ51lH3zwgd16663OKQKvvfaa+3f8+PEuq+See+6Je4zTTjvNXed//vMfe+utt5yDZKeddmq7Ls/vfvc7u+iii9y11dTU2FFHHZXwunbddVfn9ImWucLRg0MFJwblun71q185BwoOk6qqKttnn32coyZbHn/8cecwwUlDe1x11VXuGryjRwghhBBCiEpA+kV7pF8IIUQXIRBCCFEQDj/88GCvvfZq+/3VV18NBg0aFBxwwAHu93POOSeora0NZs6c2faZp556KujXr19QV1fX7lirrLJKcNVVV7mfN9tss+D4449v9/dNNtkkGDNmTNxzL1iwIOjevXtw9dVXx73Ozz//PGA5ePvttxNe/6JFi9y13nLLLW1/b2hoCEaMGBFccMEF7vdnnnnGHWf8+PFtn3n44Yfde0uXLk3YTqeffnqwwgorBC0tLe73zz77LIjFYsHjjz8e9/O0F8ecOHFi3Ou//vrrg/79+7f7zr333us+49lqq62C888/v91nbrrppmDZZZdNeJ1CCCGEEEKUEukX0i+EEEK0ovJTQghRQB566CHr06ePK3G02Wab2dZbb22XXXZZ299XWGEFGzJkSNvvZGYsWrTIZSjwPf/6/PPP20ouffjhh+5YYaK/h+HzlI4i2yNbODcZJltssUXbe2zyvfHGG7vjhwlv7k0WCcycOTNpCaovvvjCnn766bYsjZEjR9oOO+zQdm5Kd1EiinJcvlzWl19+mfX90M7nnXdeuzY+5phjXKbKkiVLsj6uEEIIIYQQhUT6hfQLIYQQZjVqBCGEKBzjxo2zf/3rX84BMGLECPdvmN69e7f7nZJKOALYnyJKtpt2k5KeK+z1AdF9KXg/+l74Hv3fkpWKWnXVVW2rrbZyG4bTXpS3OvLII12ZKdhjjz3cniRXX321a0OOtc4661hDQ0Pc4/E9f72JNmHnGOyhse+++3b4Pg4oIYQQQgghyhHpF9IvhBBCaE8NIYQoKDgt2HuCjIyoQyMe7J/BfhrsRcH3wq/Bgwe7z6y55pr2yiuvtPte9Peo0wDHBvtRxKNbt27u3+bm5oTH4Px87sUXX2znKGDfDK4nV8jWYC8P9uz46quvnFMDZs+e7TJBfv/737tME841d+7cpMci82XhwoVuLw6P368k3M5sYB5tY17emSKEEEIIIUS5If0iPaRfCCFE50aZGkIIUUZQcolSUmySzcbeq6++un399ddu03De22ijjezkk0+2ww8/3P285ZZb2i233GLvv/++K88UDzIPTj/9dLfRN44JSkjNmjXLfQdhf+jQoc7p8dhjj7myT3y+f//+HZSnn/3sZ/ab3/zGBg4caMsvv7xdcMEFrlQTx8iVH/3oR27T7uOOO845L1ZccUX3/oABA1wprn//+98ug4WSU2eccUbSY22yySbWq1cvO/PMM+2kk05yG6FHNyI/++yzbffdd3cZIJwbR8a7775rEydOtD/96U85348QQgghhBDlgPQL6RdCCNEZUTiqEEKUEZRrwoHB3htHHXWUrbbaanbQQQfZlClTbNiwYe4zBx54oDPK46jYcMMN3X4UOByScdZZZ9mvf/1r9z2yHTiG3+eCrJBLL73UrrrqKlfeaa+99op7jL/+9a+233772WGHHeYyHSZNmmSPP/64czzkCk4I7pMsDO7bg7Phv//9r9sDg5JTv/zlL+3CCy9MeiycLjfffLNrx3XXXdduu+02O/fcc9t9ZqeddnL1iJ988kn7wQ9+YJtuuqldfPHFLqNGCCGEEEKIzoL0C+kXQgjRGYmxW3ipL0IIIYQQQgghhBBCCCGEECIVytQQQgghhBBCCCGEEEIIIURFIKeGEEIIIYQQQgghhBBCCCEqAjk1hBBCCCGEEEIIIYQQQghREcipIYQQQgghhBBCCCGEEEKIikBODSGEEEIIIYQQQgghhBBCVARyagghhBBCCCGEEEIIIYQQoiKQU0MIIYQQQgghhBBCCCGEEBWBnBpCCCGEEEIIIYQQQgghhKgI5NQQQgghhBBCCCGEEEIIIURFIKeGEEIIIYQQQgghhBBCCCEqAjk1hBBCCCGEEEIIIYQQQghREcipIYQQQgghhBBCCCGEEEKIikBODSGEEEIIIYQQQgghhBBCVARyagghhBBCCCGEEEIIIYQQoiKQU0MIIYQQQgghhBBCCCGEEBWBnBpCCCGEEEIIIYQQQgghhKgI5NQQQgghhBBCCCGEEEIIIURFIKeGEEIIIYQQQgghhBBCCCEqAjk1hBBCCCGEEEIIIYQQQghREcipIYQQQgghhBBCCCGEEEKIikBODSGEEEIIIYQQQgghhBBCVARyagghhBBCCCGEEEIIIYQQoiKQU0MIIYQQQgghhBBCCCGEEBWBnBpCCCGEEEIIIYQQQgghhKgI5NQQQgghhBBCCCGEEEIIIURFIKeGEEIIIYQQQgghhBBCCCEqAjk1hBBF47DDDrPzzz+/IMdeccUV7e9//7tVIhMnTrSRI0fa4sWLU342FovZlClTinJdwmzbbbe1G264QU0hhBBCCFEAnn76aVtjjTWspaUl78c+99xzbezYsVap/OAHP7B77rkn5eeOOOIId6+iOKAboCMIIYQoLXJqCFFiEEIxVPOqra21YcOG2Q9/+EO77rrrOgj3UcN9EAT261//2vr27esUglR89tln9uMf/9hGjBhhPXr0cIb0vfbayz755JO0rpVrvO++++Lew9577530u++++649/PDDdtJJJ1lnAoHWPz//Ouigg9p9Zu7cuc6h079/f/fi53nz5rX9fd1117WNN97YLrnkkrxfS/hF/8mWdJ5xIXjnnXdcnx01apT17NnT1lxzTfvHP/7R7jM4eeLd72OPPdbuc88995xtuOGGru+vvPLKduWVV+Z8fdFz83w33XRTe/DBBzsoP/yd649yxx13dHg+zc3N9pe//MUp+dz3wIED3XGvv/76uHNH+LXzzjvndE9c63rrrefaafjw4XbiiSe2/e3ZZ591c8ayyy5rvXv3doaCW265JWUb/fSnP7WVVlrJ3csqq6xi55xzjjU0NLT73Jdffml77LGHO+7gwYPtF7/4RYfP4ADcZptt3HGWW245O++889w8WOjnLIQQQnRFkC1POeWUDu+jDyBzhGWHZZZZpt1nPvzwQ6dr7LvvvlZfX5/wHKeddpr97ne/s6qqzmOa8HJf9FVXV9fuc1dccYWTj5BZkF1eeOGFdn8/66yz7IwzzsjJ4ZPoWsIv5Lts4Ht8P6zXFIPGxkY7/fTTnQ6F3Ihu+5Of/MS+/vrrvOtp2RI+d7du3Zz8+9vf/rbDWPCfeeWVV9q9z+cGDRrU4fk888wzNm7cOKcb9OrVy1ZddVU7/PDDrampqd0zifeaPn16VvcyY8YMp3fQzpwTXePTTz9t9xmOTduhO/BMNthgA7vrrruSHvdf//qX0zn69evnXptttpk9+uij7T6zaNEip4swl3hdkO+FOe6441z78vchQ4Y4XeWjjz5q95k999zTll9+eTfW0GO41mh/EUJUHp1HchCigkEw+Oabb5zxj4UcQeXkk0+23XffvU1AiYLRE0PhjTfe6Bwa2223XdJzYBzEWbJgwQIX8fPxxx/b7bffbuuss47Nnz/fCs0///lP+9GPfuQcMNnCPRciissLx9lyzDHHuOfnX1dddVW7vx988ME2YcIEZ2Tnxc8IUmGOPPJIJ6Bxj9nCc/XX8Nprr7n3xo8f3/be66+/bpXGm2++6YTTm2++2d5//32n9KIQ0J+ihO+VV3hMfP7557brrrvaVlttZW+//badeeaZzmh+99135+U6/blfffVV56Dab7/97L333mv3GQT8mTNn2ssvv9zufRyYCNlhiLbDgfnHP/7RPvjgA6fA0M9QvOLNHeHXbbfdlvV9XHzxxa6NUaBp76eeesp22mmntr//73//c8oH7Yaj8qijjnJKZNSJEwalgnHLuOCYOO9wNPAMPPT73XbbzWUrvfjii/bf//7XnQOnrYe5izkMhYq+fNlll9nf/vY3d83Fes5CCCGESA3rNGsxMsSdd95p3bt3j/s55AqMo+gIpZLjC3VsjLRRGQ2Dqgc9DIcRchcyC+21yy67uCAPD7IRetrjjz+e9fUfeOCB7a4Bw3FUd9l8882tkliyZIm99dZbzunDv+hABOlhuC6EnpYt/tyTJk2yCy64wC6//PK4GTUEb4UDl+Dee++1Pn36tHsPOZo+QgbP888/74J9kIcJjIzqyOj60f43dOjQjO+B4CEC2wiOvP/++11fXWGFFWyHHXZoV2WANuOcDzzwgLsunJn0PT6fCBwVf/3rX+2NN95wL3Q3HBLcp+eXv/yley7ogjhK+Z0gSa7Fg0OQ9uPvjBWueccdd2ynV2NfIZCMa0QvmDx5su2///4Zt4cQoswIhBAl5fDDDw/22muvDu8/9dRThB8HV199ddt7K6ywQnDJJZcEdXV1wT777BOMHDky+OCDD9I6z9tvv+2ON2XKlKyvle/fe++9ad+Dp7m5OVhmmWWChx56qN37c+bMCQ477DD3t549ewY777xz8Mknn7T9/frrrw/69+8fPPjgg8Gaa64ZVFdXB5999lkwY8aMYPfddw969OgRrLjiisHNN9/c1jaeefPmBcccc0wwZMiQoG/fvsG4ceOCCRMmtP39nHPOCcaMGRNce+21wUorrRTEYrGgpaUl4zbZZpttgpNPPjnh33k+tNsrr7zS9t7LL7/s3vvoo4/a3quvrw+6d+/unnsy+N7nn3+e8rr4DJ/luXvef//9YJdddgl69+4dDB06NDj00EODWbNmtf39zjvvDNZZZx3XrgMHDgy23377YNGiRa6tOFb49cwzzyRsj5NOOin4zW9+EwwYMCAYNmyY+34++fnPf+6eZ7J7jXLaaacFa6yxRrv3jjvuuGDTTTdNei7uh36YiHjnXrBggXvv0ksv7dCXTzzxxODoo49ue3/q1KnuuZ9xxhmuD3vom+eee27Sa0s17jKF8cg4HD9+fEbf23XXXYMjjzwyo+9ccMEFbtx5HnnkkaCqqiqYNm1a23u33Xaba5v58+e736+44grXhsx/nr/85S/BiBEj2sZuts9ZCCGEEOnLuegDYVOCl3MAWbZPnz7BqaeemrJJkRn333//Du+z5q+88spBbW1tsNpqqwU33nhju79z7n/961/BnnvuGfTq1Ss4++yz2+QCZFzOf9RRRwWnn366k6nCXHfddU5WQMZYffXVg8svv7yDXHf77be7e+czfD5Twu2RiI033jg4/vjj273HdSEThjniiCOcvpRKJkxX3o4+U3QQ5HbkKdqS6wrL+eiO6F3oa/x9rbXWCh5++OG2tgq/uI5k7fHYY4+5e0QX2WmnnYKvv/46yBevvfaau4Yvvvgi4b1mq6fFux+OnYx45953332DDTbYoN17nOv3v/990K9fv2DJkiVt7//whz8MzjrrrHZ6F7ouum8y+CzfmTt3bpAPPv74Y3e89957r+29pqYmpyuG7RQ80+g45TPXXHNNRudDfwx/Z+211w7OO++8dp+hDWmzRLzzzjvumidNmpTwM/fff7/T/xsaGjK6PiFEeaFMDSHKFCIVxowZ06GOKimYRO0QwfDSSy/FLWcTD6LdSesmDTSXbIBsIKKbNN6NNtqo3fuksRKVQUQH0evIdURZhyOiiMShDM8111zj7pkIE75HVgsZKtwPqdtEwHs4Dm1EGuwjjzziov1Jgd1+++1tzpw5bZ8jaoaIDaI1iMoB9vwgKibZK5oaTvkdyuWsvfbaduqpp9rChQvb/sZ9kcq8ySabtL1HGSHeIzrNQ1oyzzt67HxBdA5leygXRJsT8UIq8QEHHND2d8o8EXlPlAupy0TY0JbcE58LZwUki+j6z3/+47ISyFogKokSQU8++WTb34kwStXGySBijZTrKERn0T+22GKLDunOPAcidsIQPUhb5DO6j2NdffXV7meipqKQXUVkHv3alwSgXSk7F4bUbfr3rFmzcrqeTNqaZ0SU17Rp09y8QvQUz33q1KlZPY9MvsPzIWuMLIzw8yH1nvHrP0MfDkd78hlSx/0+M8V6zkIIIYToCNHlyOBkH1x44YUpm4ho86h+wDHIWCdbk6xXSsuQ0UzWahhKWRLVTVQ48isyPe/9+c9/dus+JWbQEcIgo3FtfAZ5F7mfaH9k1zCUNiLTk88gR5A9kUqeOv744zvobES0I0+RfR+OWCeDHvkmKrPwe1g/ADKAC6UfAG2LTkmWLDobWTPh8kInnHCCk8d8ZsD//d//ufslu8BnwvqsgGiJ2DDIvmTY3nTTTe5YtCk6RlifStXGyUqeIltSYilaBi0felq+SurSzvH0AzINKEPm2xPZmzaKZoygH9DO/C0XMmlrXy4rnGVUXV3tdFeyqz1bbrml03HQtdEn6E98N929R7BP8B2yP8gmCh8XWwH6CXop8wBZOeFM8jB8n6wN2pM+Gg+ukftDn433PIQQlUNNqS9ACJEY6ukjXIahHA0lnChJk0kKKfXnL730Ule39g9/+INTIEjDPOSQQ1zd+XTB8I0gEwaBBQUmERgc+U74ehGUEVAQ7ryBHOEC4YM6vT4NHUMkCgkGf0CIoUQXdUe9AHrttde2c+4g7CB04+jwBlCEaI6LsfvYY49tUygQrHH4eFBIvKE/WVt6aD+EJoRMFC9KIyG0eiM+jpV4z4n3onVNOW6hNgGntBWOnfBG7ZQ9or1pUxQvSp3hyEABA+rUeqhRynPmPlNBeSKUSqDOK6WiKGNE6SDAQbV06dKs7gPlA6WV/Vk8CN6UIMKZgeOOfkW6MwrqoYce6j5DW0cdB/zOPX/77bdO8c0F+jDn5r4Q5NkfI14/wqlEzVf6IYoKTg2unZTuMLxHSjTtjRLG8VHccVKEeeihhzo4gVDEUdAzbWuugWunj6CUotD9/ve/d8+NeQjlJQr3QYmJaCp/Mkj3JlX+oosuansv3vMZMGCAO6cfJ/wb3RfGf4e/MQ4L/ZyFEEIIER9kSeR3Sj9SxjIdkHvDAQ1eZieA6ec//7n7/Ve/+pWT+3kf3SVcNghnhscH5xx99NHu9z/96U+uPGh4Hwv0KOQP5F1AdkCnQo5hXwIPZaH8ZwA5wgdAJSs3FdbhkPGQpSmfiVyFnIqOgGyMTIIRN57MEk8/wAGAjJbvfUeQyShb+tVXX7U9Bwz/BD9hGEYm5NyUVfV6QVhv9AEq6DVRZ0IUdDrKjyIHA/skEPgUDk4KOxfiEW0vD8+YPkefCD+HfOpp2YAOiyzOvaN38vwoQZXIuYRuhu5C2xPoF9ZRgfFFeSWCfLgnHDAE7VEKNnzfgDMt2o9wPmXa1vRldEPajnFC4Bp6Cu2Dg8WDQwP9i31Aampq3N4bOCj9804EOjtODJ4hOg3fWWuttdr+jv2CMl7cD8elDWlTnB3RtsbOgVODa+YZR3UXdCT0UhxstB16lBCispFTQ4gyhmiE8CZ8PoIHAR0hM7xpeDoQaYPQg9GfKHpq3HIcjMDe4JwK6uFTQzMqICTL/sCoinMhfC9EPiGYhAUqhKDVV1/d/c2DMIKRPPq9cFQXgktYkCbyCcWK40WvA+Hdg4AWFRYRzjOJOkfI8hBpjqLCtVHfFScCRJ9homeL48BH8Ocb2oTnHi8LgjahXyEUo7AQ+cLvGNUxLGdK+HkBhuRwJk3YKZQJZOpg2D/77LPb9Veir6iv6qH92XuCLBHv1IBoe/sNpuM9n0xBkKcf4iBCEUZpS9SPULhRVthHg36K0hLdIwRhHuWL50YUFBFZbKKNko8g70G5j26WFz5vJm2NsozShfLgIwdRdFGa6DvRiCiyebgeoh5xvKQDWRVE/6GUeaODJ51xks4zLORzFkIIIUR8kGMxNCIX4GBIJ5sc2TwcAe5lfR+A5MEhEM0CiGZ48L1otgTGUp/hQfYrEfBkzYbldxwWBHIkOza6x+jRoy1dMJjyCl8/egFBHchZyWSWePoBMhrBRfycT9BXOOdqq60Wd5NqIGPlZz/7mT3xxBNOB8TBEZX10wEjd9jAHdUPCNrLZu9FZFc2/6aNopk5+dTTsgGnCplBOLbIcMHxQPvFA50FxwxBRjjEwv3EQ5AgOgQOOzK6cfaRdcSx2U8xHLxDdk+4PenD2bQ1mQxkkDBu0DG4BvpBNNCKQCj0L+wU6GY+SJHrCAfKRUH3x2FIVQfOg3Pxueeea3Ns0A7cJ/YKdHd0Ihye3GvYJkFbox/iaMEBSnAZwZPh+eU3v/mNu48vvvjCBXliF8GxIR1BiMpFTg0hyhiEc6JLwmB4RrjEuIsjAeE4ExBgiM7ghUCEoZJ/03VqYOCMCvUcE0EkEQg2GOuJUPERE97QmEqIRHgP/56OgRKhFkEHo2uUsPODSJMoOHnC2QzxIFOEzfzigYCM8EcmCj/TXpR5ioJiFY02IhU2VTRLttAmGMUReqPQVgioRLSQao3SQr9CCMf5Fe2DqYim8fKswpvXIQSnSqPH2B+GKDpKsqGcIDSnAkUybPznOUQjrlCkEPCjzq9sIOMFRYkXjiMUlkTZVAjdRBKxUSDCdFjJCEMkEhsB8sJpwwZ5ZHfwXPwzoQ8nU7IzaWuvCIWjo3D6MX7Dm1YCygb9iUgt7iFdhwZOGAwM//73v9v9jedDXwuDYoSi6sdJomcIqT6Tr+cshBBCdCUwwlLWJwpyfzQyHFkSQyYyEOs9RtewTBEPZAzW+yjpGPvjyfHJ8LIoTpdolHo0Cz16bOSgVPeCUZqglmQynS/pxH1zzngySzz9AIdAvh0avk24DoJoom3gA6EIQkFfJEsaHYGywGS7sFlzrvpBWB8kY59SY8kgUwA52oOciPH6888/d/0t2ifzqadlA84yL6cjxxMERIUBDOtRkFMpU8bfyFpAhg+XygpD0BI6AS/0eJxS9D0M9R50hUTZM5m2NeWxcDwwF6DPox8whrzzjwA5ArQIyPKBTr6sMpkpicYFYBvwbcTxyADHgcn5cXqS+eXL2gEONa4Fx0XYqUFb80IXQw8kMI/v4WD1MO540V44XdHfcJiEy10JISoLOTWEKFMQzEjHDEege3BAEFWAURFhFCEimwgDvkN0eb5rhsYruQMYef3PKAZERmHI9OWnZs+e7SLdk0V28Te+R61caswCqbRhpwpCKkoChsxouZpUZFp+Kl42AQK2NxAjJCEAEj3jr5d75r3ovhQIgmRHFALahOgX2iOREZ3+QCQZL7IhiIZBGCTtH4EzX3uxZFp+ijbFoUHkDtFI6UDd4nC0Es/hwQcfbPcZFDOE53zXUiUlnGgwrjVebWGinHAqUkYrmZAfxSvTpFUXoq157n48+ZR1FGlKJPiSZICzEKULB1k0kjIR1MHFwIFSRIRZtHwCz4f2IrrKPzeeDxlefMd/BsUm7BzlM5RL8OO8mM9ZCCGE6OygJxDMEwXDIxHWUVi32Q8QeZZ1n/KjyESJWH/99Z1+EJX1yVINB02gq6TK/ODvGCjD3+N3D0ZqZHgi4cOG8XRA1sik/FQUjPd830esI8cg3xBQtM8++7R9jt8JXIvqBz6rIN/Q/sj3OFMSBWwBxl90JF6UIcIxhFPDy2P50BEyLT/lHRo4KMjGSSd4JRc9LVeQQ5FjaT8M7Tiq4mVzk8FNFYSokykRGO+5n0z0g2xLffmMJtocXZxybuArDUTle+4hHNiWDowVv48Hz4pXNscNHyfR3yHZZ4QQFUCpdyoXoqtz+OGHBzvvvHPwzTffBF999VXw5ptvBn/+85+DPn36BLvvvnvQ1NTU9tkVVlghuOSSS9p+f+aZZ4LevXsHP/vZz4KWlpak53n77beDPffcM7jzzjuD999/P/j000+Da665xn3/vPPOS+tamTLuvffeuPew1157Jf3uBhtsEFx22WXt3uM7a621VvDCCy8EEyZMcO0wevTooKGhwf39+uuvD/r379/hWHxuvfXWC1555ZXgjTfeCLbccsugZ8+ebW1DW/DemDFjgsceeyz4/PPPg5deein43e9+F7z++uvuM+ecc477ey5MmjQp+MMf/uCOyTkefvjhYI011gjWX3/9ds/NX+/LL7/sXuuuu657tmH4fiwWC6ZMmZLyGfDZVPAZPstzh2nTpgVDhgwJ9t9//+DVV18NJk+eHDz++OPBkUce6a6VtqTfcS9ffPFFcMcddwTdunULHnnkEfd9/rb88ssHH330UTBr1qy2ZxRlm222CU4++eQOz5k+kg3vvfeeu+5DDjnEjRH/mjlzZttnbrjhhuCWW24JPvjgA3d9F154YVBbWxtcfPHFbZ/57LPPgl69egW//OUv3eeuvfZa95m77ror6fm5H/phuu3seeCBB4Lu3bu7MR2vLy9ZsiT49ttv236n7zK+Pfvtt5+7fp4LfYKxvummmwarrbZa0NjY2GHuCL94PtnCs1p77bXdeJk4caLrp4xR/7y5Dtrxt7/9bbtzzp49u+0Y9K/VV1+97d7pe4zr7bbbzr0X/p6HPrjOOusE22+/ffDWW28F48ePD0aOHBmceOKJbZ+ZN29eMGzYsODHP/6xu7Z77rkn6NevX/C3v/0t5+cshBBCiPhyDjL2z3/+cyerf/zxx8E///lPJ+MgK3qicg5yw9577+1kuHfffTdh01566aXBhhtu2O49dA3W7n/961/BJ598Elx00UVBdXW1k0GS6ST//e9/3XWx9nOdZ599dtC3b9928v7VV1/t7ufvf/+7+wzXdt1117lzJJPrMuXcc891OgjyNsdC3q6pqXEyUvh6uU+uF5nllFNOcXpZVBdAFk2lqyETotukQ1RWR8ZeccUVg7vvvtvJUa+99lrw17/+1ek1wGe5F/6GnrrxxhsHBxxwgPsbch36C7I4svnChQvjnjOeTsfzy9YchSyMXousSL8My5b19fV519Pi3Q/tmIx4OhHXtuyyyzpdJV5fRodFjvf3MHfuXPd33/evvPLK4Pjjj3c6HPeHnnTaaacFVVVVwbPPPus+w2f5Dv07qiMk0t9SwVjnuPTn++67z+ks++67b9vfOS6y/lZbbeX6ONeGfE7f8P0I0AXC9gD0ieeff949H8bimWee6e7liSeeaNeO6Cacnz5I2/fo0SO44oor3N+5pvPPP9/ZBNBh//e//zl9ZuDAgcGMGTPcZ7gmzstYZHw9/fTTzlawyiqrBHV1dVm1iRCiPJBTQ4gSgxCK4MELYRfhf4cddnACdnNzc7vPRp0a8NxzzzkHyHHHHZfUsYGA9Itf/MIZDvk8Qj5CGwJH9DyFcGoghGGUDTNnzpzgsMMOc0IuCsZOO+3klBdPIqcGQtluu+3mFBcM7TfeeGOHtlmwYEFw0kknBSNGjHAKw6hRo5zQ/uWXX+bNqcGxtt56ayc04QBAMKKNwwZe4HfOTZvz4meE1DAIY9x/KrJ1agBtu88++wTLLLOMa28EexQo+g3KFOen/9GuGM/DQieKyg9/+EPXd8LCdaGdGjwnPz7Cr7ADAEVqzTXXdMZs2hfl+KabbupwLIR9FBmeFcobynIqsnVq0KYY9nE4JuvLiZwa//73v4Nx48a558H10s+POOKIdopueO4IvzhvtsyfPz846qijXB+hX9Nf/JhJds6wYueVKd9Pufd434kqsigijGv6JufGoRFVNFB4UJjoo8OHD3dGg+i8l81zFkIIIUR8MBYiIw4dOtQFE2y00UbBbbfd1u4z8eQcDJ0EaQwePDh455134h4bXYB1n6CUMBgsV155ZSfDI5Mi66ejkxCEw/mQV5FZMPhG5X0CYcaOHevkhAEDBjhZnkCJfDo1kK+R3TgHstyOO+7ojK1RLr/8cif/8TkCwNDrwuA0oA2mTp1aMKcGzwkHEDIT50K+Qv7zzijkMXQcZC/uBd0tHJiDw4XvYMBOJO/n26nhn1O8l9dR8qmn5cup4fso7egdQIn6cjynBoE/hx56aLDSSiu55zFo0CB3jwRTReXweC+cNtnwj3/8wzmQ6B/069///vdtjpewnomjg3kCnQxHUXTc0tfD/RSdw/d/2oTgprBDw+v96EDo9Dgz0HNwQnr5n+CpXXbZxZ2X6+M6Dz744HZzCn0ZvYq+QLvR13EO+QAsIUTlEuN/pc4WEUJ0fqgNSpr6f//7X9WtjEDaK/U/2ZTZlwBKBCWiqBubaVktkR3bbrut2wyblxBCCCGEyC/sM0a5H2roi/awsTFtE92LLApyKroB+7WJwsNG3rzi7d8ohBCieGhPDdEloTYjtTPZBEwUDzZ5Zi+QeBuydWXYb4DNz+iPbLCYCvYIiLcBtcg/7Cfx1ltvJdxoT4hMoCYwNbST7csjhBCickGmY9PlTOvId2XYUJh9O9j7Ld19BLoK7HXB3iSp9AM2MieALB09QuQOugE6gtpblAts3s6+MNpDUHQ1lKkhugQkJLH5LZuv8WIzMTbnYtOzzsLChQtt0qRJSTeCSwYb9LK5djz69OnjMglE6UFRZsNFNmIUhYd5g40HBw8erOYWecnK+vDDD13W2o477mg//OEP3cbyvXv3VusKIUQFwua8zz33nAs4QcdAbmDDaslpophMmTLFbdrNhuai8ODQmD17tpPnhCgHpk6d6jZsxwmKfoGesdpqq7kqD0J0ZuTUEJ2WhoYG57wgguLhhx92UfBbbbVVmyFp3XXXdVGznYWlS5e66LBEjB49Oun358yZ417x6NmzpyKLywTSyk855RRlDhQJUsvHjh3rXkLkg7lz57q1yRvAUEK23HJL22uvvdxLpeWEEKL8Dcj333+/e7344osuSMrrF9ttt51kNFF00HfJKqZsqig8EyZMcC+VpxXlAtmBEydObNMvnn/+eRs2bJjtuuuutvfeeztnB45PITobcmqITsWCBQtc+jKC3SOPPGL9+vVzk/gee+zhHBoY54UQQohyYfLkyW7dwjhGbWYc7qxbvPhZEVZCCFH6jO93333X6Re83nvvPWc8xhG9yy672CqrrKJHJIQQoqwCXl944QV78MEH3bqFnYxy1+gXO++8s7OTCdEZkFNDVDxM0EzUbED91FNPuTQ7bxDaYIMNZBASQghRMVkcOORZ03B0sHfO/vvvbwcddJArISgHhxBCFM+R8fbbbzv94q677nJ7G+DAQL8g8nXAgAF6FEIIISpiPWMfGO+Y/+STT2z77bd3+gVrmhwcopKRU0NUJNQLpKTUbbfd5gxA1K498MADnfEnVZklIYQQotxhw0/Sx++44w6ngFAnG+WDF2ueEEKI/MO+RzgyeH399dfO4HPAAQe40lI9evRQkwshhKho2IcVZ/3tt9/u1jwyONAv+Jd9Z4WoJOTUEBVDY2OjqxF46623ujIdyy23nP34xz92E/Aaa6xR6ssTQgghCubIx4GPkQ2HPhtTsvYdfPDBtvzyy6vVhRAiB7744gsXKMWLCNawgUela4UQQnRWcGrg3GD9w5FPWUVsbOwTVVtbW+rLEyIlcmqIsocatv/5z3/s5ptvdpsbYcRhoh0zZoxKcXQRgqDF5jV+YXUtC6wm1t0G1K5kNVXdrdKor6+3L7/80ubPn+8280pUSoYUUU91dbXb0J4XggWbAA4aNMi6d6+8+xdC5KfkIo59lI/x48e7/aLYqHLfffe13r17q4mFECINFi9ebHfffbfTMag7vsMOOzj9AoOOSnF0HeY1LLDJi6ZYU9BsI3oOs1G9Rlilgd7w7bff2ldffWVNTU1x/w7oHf5nr1v4F/ID+gV9n9+FEF0L5oYJEya4ACqCiBsaGuzQQw+1ww8/3NZbb71SX54QCZFTQ5Qls2bNcpMpisZHH31k++23nzPajBs3ToJWJwIjPy/S+XFYUU+edMiwUZ+fZ9d/akta5nz3TsyqY7U2vMe67t9sGDx4sMv0yZdjgEWfyAYUCpwVnrDTgvvAKbHCCis4x0Q6CgPf4Xj+RbYSbTRnzhzXbmH69u3rSq8pokKIrsP06dPtlltusRtuuMGmTJliP/rRj5zygaNDRgkhhGgPshQODOZMSm+suOKKTr845JBDbPjw4WquTgLy88KFC11gEKVUeO6ffvqpCwoIs6Bxkb01511zWkfMrCVosdF9VrIVei+X1TnRZygVOWTIkLzdy+zZs91av2jRonZ6RVTHwCExatQopweks/9WWL/gxfHRLwi8CuthNTU1TmcaNmxY3u5JCFHeNDc32zPPPONscTj/qYqCfkFwcT7nNyHygZwaomwgsoTyGtddd537d7PNNnOKBvtkYLAVlQUCMsrDvHnzXOkUni814sELyzgySOsnWg4jPZsustE7SoinoWWxfbHkpQ7HH9xtNRvQbcW0F2auhU0ecQrw7yqrrGL9+/d318SL8/O5KCi8GAdxWixdurTd37heHCNs6MvmkQj9CP+58Morr9i0adNce+HE4F9ejAHOxzWjrHC93Afv8x5/457CbRd2jETfF0J0vg1tMdQREMC8wPp55JFHqjyVEKLLQ5bs9ddf7+ZIjN04MTDQrL/++sr6rkCQ29EvkO2RlZGJfYaC1zHIOED+xUiPDIx+Ed3c/bXZb9vUpd+0e686Vm17jdgxrX7BuTg3QU3I5FwP/WuTTTZp0214EfwUdhQA+s/KK6/svkcwX/jv6EcDBw50ZWHQQdCJ+/Tpk1Nfpc3uvfdep6d4HYMXbUO7cD50Gq6DtqXdKG/JtRCQhdMkCnoT15fLdQkhyhvmNYIAcHC8/PLLtuuuu9pPf/pTZ/vI1e4hRD6QU0OURR3ba6+91r0QrDDCoGgg6InKwRvPEWwRcB944AFnZCcrgigpFj0imDIVfOua59vUpa9G3o3ZwNqVbFD3+JvCY9QjsolzrbPOOk4RIK0aYZ1ILQR33kPhQangfV7RhZn7mTx5suuXyy67bLu6yl7ox2GDw+Ott95y0Vl77LGH5QLKD5FSRFpxPfzL+cMRVFwX18s9vfjii7b55pu7v8dzygDfD2eQ8F2lkYpMoc9LcS1/MJ74AIHHHnvMbW577LHHutrwUj6EEF0FDLfsQfTvf//bnnzySdt5553tqKOOcgYZDLWicvDZBKxhlFiiRMq6667r5HieZTwZPh1emvW6Ta+f1eH9fZbb2apiHTOqySYnkAl8IBbnJ5CAgCSfeU6wETI6v3NtvBeVnzAUogOTIYTDIJxdiS6AjoFjhHvlntnjZejQoZYL6EboCl6/4F9+j2aBk1GOboOOg0OD60imX3iHDI6TDTfcUKUwRcZIx6gMPvvsM+fcQMdg7OPc4MU8IUSpkFNDlAQiQx566CGnaDz11FNOwcDostNOOymivEJAGMfgz7NEEMZRgHCLEP6DH/zACedkHfAzwn22tATNNmXJC9YcNLR7f2TPH1jP6gFtRjyinIh2QiBHqOb6cEKgaHAtPgV9pZVWckpGZ+CNN95w90gaKO0fT6GjLb755pt2pbFw6Ky99tpWiQIvz7rY+4n4TB8cSvR3FFMUUAQ7rgnFlmi2zlj6CyUdpdv3rXAtZhRxspNU5qh8wRBCdPI111zjDHwEDRx99NEuA00IITojn3/+uQuUwujCusycx9w3cuTIUl+aSFPmQu5AbvX6BXIX7yPHr7nmmm6/RZwGq666ak5646SFU+yd+R+0/R6zmA3uPtC2HrJJm8yD7OczvVlH+R3QbZCNuDbkUl/atjPA/VLSEv2CF20ddcq48sCzZzsdA+cHIA/yTHCKVBo+a6WYMi1t6LOOcBx5nYJ+T39DX6PsF32rs0F7v/POO25c+77ldQz6G3Iq9y/KE57b448/7mx5BFKxJ9Uxxxxju+++e6fUh0V5I6eGKCoISEx+GFkQAJn8UDSIcBeVAanQCF8I82RiRA3MOBUw9pJ+jcDiHQ0IaH4zOpQS/9nVV1/dttxyy6TnrG9eaF/XTbCmYKnFrMqGdF/T+lYv6yKmEKh9lBYKEA4LhCAyK7wi1BVAGCZ6DIULoRzFinvH6cH4QiAuN+NzOCqH68ZhQcbJ66+/7q43+uz4DH2Hz4WPQaQexopClakjwpN+7PdFwVGHIO7Lj9EHt9tuu7Z+3Vn43//+Z1OnTnX368uwhYVZajxjNPfPIN58IMoDntcTTzzh1l8il+mvBBLsueeeyt4QQlQ8yAdkCF911VWuDjiGFeY4MtVUfrMyIHjkgw8+cLIfAUgY06NyIAZ0MghY05DLeLboHHyOn3mhn6BzIEeiXyTbCwL5ZeL8j+zTRZ+73wd3G2CbDNrA6hfV2ccff+zOg6yHnAfoG2RLENTRleQdgqh8IA/yHvoVP/OM0LfKTf6Nbozuy5PxTHFKRY3lPgvI/+yPQX/iebNXSSH0SfopJcFwAmEjIaOWvsb79Ft0O9qb7JfONtbZp4EAO+wA0eBHdEGc09w/ugdZADyHrqLTV2IAnA+gYqxh22P9VQCVKBZyaoiCg1BANsZll13mSmGwYB933HFO0Sg3I6tI/SxRFjGIpQvPnigU0v4BBcGnMSPEpRJQEC75PlFb8xbMtSqrdt/hfTbG9tErCEgIQDhT2PuC/tWVoF3JVkEopn232WabsjNWEnX00UcftRkYcE54JRQBFoGV54pwhNKKsoQwjzCLc8a//Hd9Vs7EiROdE4dSBIXElxxDqUYAx1nU2QVs+hXOQrKyiJxCAYnXr1AS+QzRepQ247mJ8oT+i/KBg4Pn+7Of/cxFMuda1kIIIYoNhu2rr77arrzySidLYEjBoIKhVVQWr732msvCSDdABXkSuX/s2LHOAIz8SDAV8ghG91QlxpDpMHQjm37x5RfW2NRoNVU17jhcA8ZW5B0+h0Hfb9hNoEdX619+/xL27tpggw2ckb/cINjIl88CdEF0BvQLHFP0B19lANmdZ4gsT9/hc75UmN+jBT0DeYmyxfvuu2/BbRacD92HcxOohczd2eEZ8TwYs2T4xJNDfeYWcz3OM5VPLl94VuPHj3fBBQRQsefGiSeeaNtvv32n15dFaZFTQxQMBIcbb7zRLr/8cicIomjgzChHQagzgfDt66UmggiQ9957r10ZJgzIGIa9EuAj5/k9fCwEvKefftopjwj2RGdH4bsoGghnfB4FgX0f0vXY33bbbS6axke/I2TSb3w6M/dI//LpuX5zQOC6MLx2lhJT6UIbU+6LdirXMcZzeuGFF2yLLbZIqWxyPyiPOGl82ScfKcXz9/ug8EIxUSRm4cFh6CMXwdeNxoHBv2Sx8Nyef/55Nwb5LJ/xEXP8zLNCaZFDu/TwHCkD+c9//tONywMOOMBOOukkN68LIUS5G8CZu+644w7beuutneGEoCnJAoXFZwMnM1Cxxxz6A+s86z/yOEbacPY0xkxftsm/x5r06quvutJSZGqgA8QrY4Lcj6GZfwmCQX8hMycdoxn6KEF26CM+SAP5hRKiXtfh2tEv+Gx0Lwnkl64YfYwuh2Nn4403LtvSMhjH6Z84o1LBc0W/QM/A6YEcSz/CoYCs6isAoHdWYimtSoN5gufnM6IYyzgWvX7hy59hV6Afsu8qc0BY12d+IdisKziDKgEy/Qk2IICKAFTW6J/85CcFq6ogujZyaoi8Q+QMigabCK211lpuEsNY0pXSdEsBwgDlYlj0EcqIOkHIj0ZMY5h86aWX3GJD6RGEOCAa+80333RedaLfX375Zfc3hHuMyhi6SNfl2M8995xtuummTnDwm8TxPpEUn3zyiRN+EfoRLhAyOE66mzJyPs6NssEiyPVyzPAm2F7YYV8Dom/KLSOhFFCyiQi3cq8/yvOln9E/yaxIZoDwTg2eN32tqrfZJ7Pft1iPmI0etaqt2ndt61alzT5LhZ8bcDDywvEEjHUcpCgem2zSWpcamCN4pkRboXhuttlmJbt20bGs4BVXXGE33HCD1m0hRFmCbIoTAx2DOeuII46wE044wdZYY41SX1qnNzjed999bQZ+jMAbbbSRe0XB0EhWPus7zwWZFAcGOgffI8qfElNkdSIrcEzkOx99jX6CQwPdweuNyAtkCnBsnrvfHw8dwesB6d4HGc1k9xI57K8X50gYrovz84q3l0RXA9kOZ1OqUsHlAAF1OLpwbHj9NllJLXRL9JCBy/Szbks+sQUzP7PVRq9gNYPXM+uzUtGuW7QHuwJzg9cx/H4jvoQ1AVLoE+i9wN8Yxzg90DHI3mL8ivJYt++88862dfvwww/Xui3yjpwaIq8lpv72t7/Zs88+awceeKCbsIjoEIUDpQCBnAXDl+PBOYHSsNpqq8X9Dp9l4UeQQ1CdMWOGE9h58Tci3jAkIxiggPDCacCzpIYsEEmB0ICQgeCBkMH3UTr870RPEJGfCq6DRc5vMse5yP4gMoN+dfvttzvFCQVGEXiJQeEIG5DLHZxVKJY8UxSPcJScV2Jx0tEf4aXXX7Rn3n/C1ttsHZv37TxbvHCpNS9qsSN2PqbsHTldFUoBMKdEDQL8zjPD4NHVjQXlBsrjTTfd5JQP5nmCEn7+8593yk0qhRCVAXItTlfmJYzYzEuHHXaYyhwWEORv9sxCF2CdZm1Az8BBsc8++yTMtuRZ8Tlke7/5MZ9FlyDQDT0CfREZH0cHn0P+W3/99V0GAL+jg/B5X0qI9zFuchyOx+8ExSTbKyN8PX4vCEDepKQpsqcvX4WuopKZiSFgzWfaVgI8azKFybbBAM7zjjq+CL6jf2IUR069+YpzbbsNh9uA/r3tg0/ZK86sz6jNbexmO5XsPkTyZ8zm1PEcV8wPzCmsFaL8Miyp4IJtZ9ttt7VTTz1VpalEXpBTQ+QERnEmJpwZREbgyMAAotrcubUpgj7RRGGDH0Il5XfIkPAgjCOsh1P5aHsEuGyMhX7jZr+PBVEs3nGBkZksjngZF3wPozpKCd9HIfnyyy9dVE+8MlA4MLgflCSOTVZGIoWCY+N44XgYwhFS+R7XwwvlShvNm8uewflTaWm3KK233nqr7bHHHm2GU/oEmxrjpPGKyMufvGBvfvK61dRWuz7Bq+8yfW2PsfvZMj3Ti9ITpYf55P3333fP2M9RGC9wZHbFkg7lCuOLMXjRRRfZiy++6GrU//KXv3T7GAkhRDEgaOeSSy5xGWTIkxhA2C9NzvDc9gVD/kb28u3oM63J3vayOPI1pSTJqvUBRQSeEIyQzabQXr8A9At0HAIfcFRwLgKXEu2L5ktfslkyznaulWsZM2ZM3PP40rfoU5QOShZAwbWQ6U67EPDFi/e4JnSLvfbay7o66KS0SyXuZYAeS5/ZaafvnRPsC0IfZg8HVyKtpdn+d/d5Vlvbun8K1NZU28iV17Oha+9RwqsX2awZODIZ735uYX4jQE6BkeUDgbE+UAHbFms7VV3KtbSdKH/k1BBZgVGZjfn+/ve/O6P1r3/9a1cnLxtBt6uDAMXkjpDOzwjqTPBEH/A7xnwESqIOcFbEA8EdR4LfeyJdhY/Fnu/iqGDR5/kh7CHQ+/r4RFzxvndmUFYGQzPGST7r36debrrOBSLwcVRwnv333z/pIkbU1t133+3uHecN6aQoXXyHtuqqyq139uBMpI18qq5Pqa8kUDhQKrkPIviJyKO/8/JMWTzJvlgymTtv990fDNjSetV0/kwNlHqEdb9/DOMOZ2ClCYAYyClL4ZULnjnl5vyG8fzOHEOElVdM/AuHHQaKdMc8Y6Supc6VKKuOJS5zJpIzYcIE59yg7AslDVE+VDpMCFEoWBMuvPBCt9Eomd/oGPEM2CI1yPbIV8gQwBqKPI3uRkAB5V35DA6DRGViWYt5sTZnUg+drA7kdI6PbIdhnAxddApkVkDH8HsWcH4MzazxBD/4clc+MyOdUrPopzjkWf9xwhNslQzOg/5FBgm6DS8cOZyrKxtBeXbonzh7eB7I6VQBSCc7ppxAZkZ25j7oR/Qx+jL7tbQRtJhN+W/HL/de3mxo+ZfcyhXGIkGSfj8L5HD2rKi0Z818QeWHcFk8xjb9OFwCndJU9AH6htcvcIAQWJduqWyob260xqDZeld/vy+QyAyCXClXf/HFFzub0CmnnGLHHHOMMudExsipITICAyqODDb9waD2m9/8xkX2dGXBL1v8BmWA4ECGBZFFXqgAn62BgY9/aWc+iwPBL6DsTwBEObNR9M477xz3eSDgo0ywaPhIFD7HQo/winODFOxJkya5xd5v8Ee6uHdwIDBwPs6BoYtjpbPhL+dBaUGo9EZZFCOM1umUDuJ7nI8+x3G4Vu+Q8QZ9ryD5Dd78fh+dFb+3CQ4eoqc6g0OR/kZfpL9FjfVLmhbZm3P/Zy3W+pzNYtavpr+NXWaTTiNM+ghD5oXwHjL0bcYJJeXo2/R9xinzxbhx46ycrp8xiZGCfykvwbPxcwrjkbHM/jzJamCjXJGRhQHFH5cXBhi+799jLkDxiufYmdcwz96Y+6bVtzRYzGK2Zr81bKXehc0E4ZlhUGFO5OXn184yD+Fc/8c//uHWfwxMODeIYtWm70KIXGGdu//++13mN7LqcccdZ7/4xS9coI7IDNZK1l1kdGQGMiGRGZEvfIYG/xJQhSHPZ1RH5XICTAheYi1G7vCZ1fFgzcZ4zLG9joFzgHWR93/0ox+56/HZGf58yDW+zBRR9TgiCF4iSySdDZo5F9eLE8LLB6y5lErilWp94vvoF1w398Y9Ir94/SKsY3AOv4F0Z5C5k8Ezow9RnqmzZNH6Z8oz7NAvZr1ituiz9u8NG2fWqzJKbqUD+hXVHbyz0EPfJlMKOwTjgHFPibgdd9yxrMr7+v070TEYi+j53Mt7773XLutsq622SngM5iDGO05M5j0/VzG/oaf4vQGZQ3D+xiu92kLJ9ZkT7d35X7rfh3TvZ/uM+IH1re1phYR75d59EJifjzqDDsy4ZP0nmIE9l4499ljn4PB6oBCpkFNDpG3M+Otf/2rXXXedM2jjzNh8883Vejkwfvx4J9D7xYjFlUwH3vN7TOC84OWFLxZdlBLq3BIpy3epT8h+FygxTz75pFsAiEqgVJQvRYQg8Mwzz7hj4RQJKwos8DhDiNLi3BzHb8TF4klZKS8g4Igh6gElBEGHa/YOD4QgHDJcN0JFWwrvd7Ut+R0jZ7aR5XwfIdvX02Ux95FUvHwbcc28MC6ySKLUZFuOqxKgrXE0dYXxuKBxnk1e9LE1tNRZ/9oBtkqfNa22qrIyFaLQRzHi+6wE5gBeYUM4Y4sx7fed4W8I5HyulAZlP+ZRlHjh9GasIWQzNvm7Lyvh9+PJF35TQBQ0xjuOEj+3NLU02dMzn7XGoFU58Wwy8Ac2uHth9obg+ZGFghOYa2NO5Nkyv3qHK/C8mH9xVGeSdVJO8FzJ1KQsDManc845x/bdd185N4QQGcP8SDbueeed5wzjlLkjUhN5UWQHsjovb3hnncE4hzzMuonMgWGeskzhkqV+rzMcCxg42XMCOYPgGXQIPuuDmcKbRrP2k/FAgA3nCWdUILcju5Bp4/fZ8LXu2RQc3QNdwmdtoltgREeO4H3OxTmRNfguMq8PjuK+0FvQe/i+D6TIBnQGrpVrD+sYPkDBl7Lx+4qgi9CGbEhdaWVfMwEdcJ111unU9+gIms3mvGO2ZJoZesUya5v1rnyHKvIazimM9TgZCQRijPlx4u0KZP57WZXxjr0gk6ysfMO1MK8wdzDesD0wzhlzzA3cD/YKfqd/5jtr3VfAQLZnLsQx7HljzmR77tsP234ncGrZHsvYj5dPvY9otjz22GNu/mZO985WX1rLg+7B35lfeYWzUyoJqnng3OCef/rTn9oZZ5zh+qMQyZBTQ6TtzKDm/VlnnZWw5qnITw1zBA2Efx+R7TMS+JcFikUdwR7hm+9gLPRleoi64n0+E46sQSHA+EdNWp4fCovfSI1j4egg6hvBHSGG47CBEwsLqdsIQuHrRCFiIeW4XBvXjDLBIorg66Ng+Kyvy0s9y2KXyuH8GPxRsjpLpFFUWOVZIPCpHExlgfGGsg+AsEwfDSvjjFfGIe+hYDDGy0GpZJ6gv/Ev14WQzfyA8aFUGQkYaYj+ZM6CeQ3z7aXZ/2v3GZSOVfqsbKv3Xa1g10G0GHMiSiOZKPGMK96oQxYeBiSUMco6UW7QZ+QBxhTaF6cBr3KEe0U2OP/88939nn322bbffvvJuSGESNuZ8Yc//MHNiWeeeaYzYFSqIaYS9DkM1BjhkSei+gUvdAfWcgKrMBiyXrGPH+sSf/cloQikCjsuCLpgTeNfShQi6/O5t956yzlFWKM5FjICf8OoypqB0ZCSRmHoCxhYcR74zHL6hA8E8LKG3//Pn5fAhmKDHoTehn7cGUGfRE/cYYcd4u6PKMoTxgVjGB0R3ZySbuGySsjwyKt8jrHEGMWWUOqsW+YMnAhcN/Ixejt6D4Z65o1SBAF52wzBnd5JfM9Xr9nnS2Z2+Owpq+5q1bHCtCHPjABUX5o5nj3FVzvwJZ1xGJP1wM/M7WFbDu2KjoH9J5OSW8UEB/sf//hHe/DBB+2oo45yzg1lbopEyKkhEgq/f/nLX+z666935aVwZmB8EYVf0ImYQHlAYSBKCoWDxQdnAwsPQkh4w70wKAk4LOL9je9g+ENR8LAwch7KGJFizILNYsj3ed5kfiDoEKXAK5GyybG9k4OFluPwM8KJL7FVCoUDuB8UMRxFnS3yj8gylMZtttmmIiO+uzKMbcYN4y4M4wVFEsN4uaXdMp6JlmLewMBQagUIpw8RaMxLjG9vZFnStMSemfVch8+v5UpQrVTQa2IexMCC08pHTXnDkXf0ejDGkGGFosG8z1wVToVnfKNwsjFuOcM9X3vttc65gdGJzA05N4QQ8UCGveuuu1xmBsYrnBkYLOTMKHy7s2ayNrGuYDQkeAK9Ase5L93qA5WisH7hEEkUvc16xvH8dzkWQVToFzxbX9qGtQ79wu+jhlyOnsG/iWQK1kMfRIUhjuPgKOF++JfAkFLV/6c8F+1ZiZtopwInGIblSttboavDOHn66afdviFh4zfjCDsAciYyc7ntyUfmFmMZp2epy17RhjiGmCfRxwjg8jz6zdv24cJp7XZ4rI1V20mjdy6oLs5zQw9Dx/DZasyHrKPMneHSf25Pwbo6F1DK3IxuicMD+Bz2Jt7baaedyjZwyiPnhkgHOTVEO0j1+/Of/9wpnBlB0GxLmz+0xmCGi9PtXrWCda9auWjGX0qRRL3fvja835gKfB3XaH1Wb7D276NIoBTguMi3MZGFj2gcIrNYIBEmiBzmXxZ2FkSUB+8QCS+c/nfuFeMcnyFdlKgeos85TqkFJxQn2rJUjpVCGBHJosHZhUJHFA5Cl6gM/N40GAjCyiLvMc79RpnlBgIwTgTKzzEXldopxGuTTTZx84yfY/z8+s68d+2rpdNchgb0qO5hWw3eoujlypgL/d4ameLn2HLsC4nmJZ+5gYHq3HPPdc6NSrl+IUThYD4jM4N5Afn2t7/9bUU7MwJj/ztKkFD+AwPc2hZz/xYeMnSRy9NZM9AZkCmiIG/4vbtYo5DVWdfzHZHv9wojWA5Yr5FdORe6DEZDMj/pE2G9wn/X7yfoN+/m8zhouC+uuRyClSgDzObEpQ70yBfIVhhP0euQ+7AHiMrBG+PDfZIMqHfffddF75dD1nciJxrzDzaRcCZYsWHeue2225xTliAu70j189G39Qvtli9ftOagxWkYLRbYdkPWtvUHFDZoKgrzIM/ZX1+mJAqQLVfILiJz44EHHrAjjzzSfv/737ssIyFATg3hYFK84IIL7OKLL7Zdd93VKR2JNoOrFJY0vWeNwTft3utRtbp1r24t1VRIEAJ5jR07NuVnUSpeeOEFl+KdiTLBYoaiwLPjGH4T4TC+TJVP+wavNJA+jrcfRQPHCfUKfSYIjgycHKSGs7kf10V0F5HEKB54+3GEbL/99m217UkX53w4wTgfpXXCjhtq7ZfSEEraJm3cWeDZodiSpSEqB8bNNddc45REXzYOfAQlNa5LCWOfyMNEwq5PbyYVu5QbYDNP4YRl7mFe4rp9uTuMZOzj8erHr9rcuvnWo7qbjeg5wjk0mMt40fbxjGlEijEPMifi1PX3yM/pbFqaDlw30XI4Yrhmrof5l2ipSlIwUjk3UD5oZzb+DddfF0J0LQjyOfXUU528iSGikp0ZEBgb7b7ifmqFeRuH+eYWs8Ia41jrKOmK/J1usBoOkHT0keg6hY5BoBIOhGjmKHI+mRl+P7BwyVxfgpF1jrUa2R9nht9rCj0CvYdrIsOBe0JnQodAvkAWQgel1AgyE/oF98BaiY5B5K6POgZvCC2VTMI+h9BZ6r7zzCm5Q0nPUkfMi8xgg2/GDBH4YccAczCZwaV2vJHtzXhOBNfOuEeGL/UYYD5iLvIOV7+XBfPb1/Nm2RtTP7KmoNmW6zXIRvYc6GR65iLmvnj7CDJPoLvjoGVc+XJQzJ8EuOXLkYPxn4Aqb+9h/mX+LtcyU5lC9Q3slI8++qj96le/cvv8lnL/F1EeyKnRxUGQxMDG5EDUMBvzEPnaGZjf+DRid7v3qq2/9anduCDn8xtZhzeVSmSgQuhHuSPqGWMmizjeZtJB+TdqUPS/E+GNs4Hn5p0PODNYwBACeIYeDJMojURosdCyECNMoDigXLBYc53hvSY4D9fFCwMbQgXH5jyUm+F73B+GRBZtlAyOzWdZkDEEvv766+46SXv198BxifxBSSpVtgRRaURdVOqiTqo+CmQ4Ko37CdfIFOUPY4nNMFHA/WaZjA+/70w+jNrMDQjiRO9k2t+5FpQf6lyXazRXKjB2MN5Zy8JZYrSL3+STeZS24R75jC+94Z1L3D/zmI9kRalhzgScG8yr2RpQMDLhkPTPhvkfRYfnRW3uzuDYANqPzcT/7//+z9XkZn+u8BolhOjcMM9SB3v8+PF2+umnu03AK3VdCRPYl2bWuidWezawmBWmlAcBTNRHZ31Ya621khqbKUeCPoIRjfUMXQBDXDiwJyyf+5/DTgr0AIza/MuahZE0HBz1yCOPtGVK+jWSYCZkHIKY0G/4e9h5xVqHToEORAAH98Q1sgZjdOOe0F0wCHLdXAdGQvQLzoMzA92D+0NfCrfNhAkT3PpSiuApZAt0n0rVn3meDz/8cLu2Qz4Nb44sKgOCEdHNydLw4xrdnz6aL0cBujzHY7xm6iTBoYGzlP06KxVfOi+aVe/L5HGPZJ/5sn5ex2D+pGoEujv2Eu+Y5Vh8h+/zGeZqrx9mA8FZ4ex1+gPHp5RtOJiu0iGzh2AJ1h325jr66KNLmuEjSoucGl0UJjnSt1AyAKMDkcOdxZgCCxqftcAa271XHRtofWo2zHtbMqGiCOBgIB063mdwJqAw8GIBQ3hk4cIZgcKAoRMHA0ZqFjoifhDU+Rnj0BZbbNHOUMeCSUQGxlEil3jPOyT4GaEBx4L3XnvDKXUhfTkUzoECgqLg6+jyd97H6MY5SFUNZ5BQ9ojrR2mJtzhSP5cFG08650TA4l+/4ReCVilACMOxUqrzZwvPhWeJwofSxr/h2p6isnjmmWfcpu7hMUU0I/NAvuqacg6OR5/3JePoR34sY1TAiE7UVrzsA589xlzTmZ1mfh8g2oj24V//QllJFEmMsYW5hLZjT5Rs103mRubYqHOE+Zu50m+Q6B0vlbo+o0yhcFDWkght9twodfkyIUThwDlMsJQf82effXanqssfGJH5H8X5y0YWs/xk9HlYx5HRWQsSlWVhHcOJgNxO22NsRI5Hx0CuePzxx91nfBATegUGa/QF5HPWGORKjF84TJABHnvsMbc2YXzEyYAREyOSPz/XNW7cuLZrwDmBIwXHyRtvvOGy81izWONY6zB+Ytjzayaf4Zzh0qkY9e677z4nv3DeaFAGjhPuk2v22R1kanKPXCPHKlU/Q4fjmittQ23WZ66ZZ47TTFmVlQtjnzGNrO9BnsXhhg0hH2CvYKwThMMc4MtqE3CHo5J+dO+99zoZmiyseHIr8wF2jUqvCJIM5kuyJbxOEdYxmJ8TbXjNHMg8ip6BLYV2zgaeA3NSdA7lebCWeN2CF3peqTN4sqUr2DNFesip0QXBME60FBFUKB14Nku950EhqGv+3OpbJrV7r1f1WKut6uh0yCUzAyEiUY15FAdqW/r0RZQMHBDR/TPiwaLPMfksigTOEs7pI2dQPnCkhDcaRtgni4Io43hRxBjiAKEHxQWFgAXURw2E68OiGOC4iOL3y4i3ANKnMLwTDeLLqiDksLhwbtqAcxW7Bi7PAaGO5xQvJbScwUmE0oZgghEVJVDRU5UJTspnn33WRa2HnQW8l8/N3hm/lJcLwzgnA4PIS8YmmwjSp/z4ZO4Ij2kM/kR3EQ3JnCUBMfHG6WEFMtPnhOOE9g3P135fIpQ+Xl4xisJzxLFcKU5O1gCitul7/EvaeKUZgIQQicFwftFFFznDAuWROmt2VmAN35WfagyVoOr3nVOjKq/ODGQF1oioMyOcWY0sj/ELGZc1IdV6zbHRSzCssdZzHtaZcLZotFwNaxBGMp5nPDmaZ8/fR48e7fQXAnAIyPGOC39c1k10BbK242WSsubFy+ZBhrn99tudYwadFaOhX/8wzOPQ4XvoGMWWVwhsI5Oz0vbVQF8kOA4DKPopfYFgOFF5eJsEY4HysOGsAsZLviL0sT0wf0SzPjg34xm7AY5MbB/Id+giyH6Ma8aGd4LwGX7HMarSQR1hTKIbhqteZALzOwFzrB3hYFvmTR8s6/WMcKmqMOgn2JgqocJFuPIMei3l9LPVzURlIqdGFwLD6Jlnnmk33XSTnXDCCS5yqjMvJEzQDS3TrDGY7pSMblWj8ubQABQJFgyMkdF25Nw4j5hkWTBYUKKGq0w3V+T7KBN+cSPrAsEluthxPpwgRNv4PTK8wI9yhOKBcRUFBYUmnMINfI7rzEYAYqH0KZbx4Fow0nuDK06OQhvkEMB4FkSpILD7VPlKgj6AEshzY5GuJKWpq9IcNFgQNFt1rEfbGEW5YAxEN+3EMYryz1jMVRlHOMWxiDEiCvMC2RcoH4w9ogqJxmQuILIl2q9YM1BGiCqKzhOiFTLSKIWBQYD51D8/0r+ZB3FEeucwWXoYZlgPmIdQWkhFxzGCkznT58+8wHyNQYu5HWMP5yt3BxRZQARWYAT6xz/+YbvttlupL0kIkSMPPfSQ/eIXv3Dy8KWXXtrp9/sKjNIhk5F8v3NorJLX/TQoB4ThMF42IGszGZe8j0yBfpCtbIsegwEMg7zXZTA4YriMyipeFuWcrFle5iDzk+9QGsrvi4H+s+OOO7ZzXPN95v94uksqvDEuWfky2oXrQ87BYI9RrtB6Lk4BAo2QkzhXJe4Vg96InoQxUFmUFUBLs1ndQrPaHq2v78YWZf4wgkd1cMYc4zsfmdfoF8i70fkGHQaZFvkWXYM5iawOAqZwYEYzE5gfkImRf9HPO0NZwnyDboGOwZzCfOufH/obTmfGKs+Cf7Hz0N4EjfIM+BfnMvM7/QGbEPNzpvMCzil0RF/Fo9yDoLGTEFSBU+Owww6z888/P6dSXqJykFOjC8Aig/cShwZpwSgdCH4YlomoKSYYdBB+MeIh0LIIE/ET3tehkuB+EBZYbFAAaFcvfBMxhYDLgkQUD3/jWRAJRUpmLiC0E5WVyCHAQuQXMtqZhQyBAyEEBckbLxFi+T2cpYGRExDSowpNPmCBZNHhnFwbi3Uh+6EvD8aizGJMlBjCXblHCBM5h3JIqSIfoYfSiAKpTI348Gx53rQXY9NvbI+ySaRgMZ4555/T8KEtam7dOLIm1tOGdt/AaqtaBXb6Iso2fZ/rxLHAzwijCKCMa8ZkNnVBURIwpifaxBsDut/om7kJpQNHC04OzhuO7hKZPXOeH2sbz419kQAHMnMuShvQB5mDfJYdfyeqlOfB+oHSl209WL8vB30LfMQcc783HrEu8TlfcsunnpfCScpa+Nvf/tauvfZa54D7+9//rnlNiAqENeSUU05xpUlPO+00p2cgbx500EFFd7Ay/7GOEsDiDf7oOqXaQDpXWCuQmQlownnNfbHeYNxi7eDv7EFBeyP38z7lvnJpd9oMgxm6Sio5nmtBlve15DGKe8MZawwyR3gfD9YodBPkfiK0820g8zIQ8g0Ofsrk5GuvskT4vUS4b3Rbv6diuXPPPfc454/fAJ5nyRjOV5mizgb9G52MPovMxHP3ej3GXmwZRZnv5k83e+Mes4bFrb+P3txs1dZnxpj0Tkde9EMM3IyLd999t620arYOUIKcGLvx9ubAMYq9g7GHMR79Ar2C7C3G/e677x633K1Ir+8x5/Iv7Ytsj3OJuRXdg3mH6hqMZfol6wLPgLJ8zEf0C3SCeOXR04G5gXMwnzLXA8ei3/ssDj7D39BneKHLoHvz91IEWtEXf/azn7l/cWxQlaZS5QCRHnJqdHJeeeUVO/HEE90Ed/LJJztPLR5LjOHplEDKFgyvCNneyA9MaggCTMYsqN6p8eCDD7bVQKWe549+9CM3CTIJM3mXMu0NoYBrTDYRomhgoEKgYQEJG4gwZrHw4LSh3f1GtUz8tEH08+nCcVnIKDOVylDrHS8IqtG2JMqXRccvdHyW6DCOy2JVjIWIaDOUMc7FguwF7HziU/RZZBHuWOQow4PjqRxBSEYxQ/hEWeI6vZGePsTzEd/DGKWUD5EpCNy0Fc+aMcccwlxCX0cgpJ/Rt5kDC9G/FzZ+aXMaw7W2Y1Yb62Ujem4RVwlgzguPSwRSopdQALxxPF0weDDnJ1PiMZ7jzMSxyrlpi3hl5kT2Y5c1wUethQ06xYS+zpznN1sF1hrGB4YvFHOfeu4VYGQC5ppiOXwxQBLRd/XVV9ujjz7qNvyjLFUhZRMhRH5g/qC81N/+9jfbdddd7bjjjnMGFNZW5NtCOUuZq9BpmGN9WT6vT2A0Q9720fIY/JkLkeF9KSSyXTG6ICeUOrPNyyrJQIfgOtExonMz2TG+LCl/8zXbuf94n0/3mpAj0i11SiQ2a128MmOUfSVK24NDg34zduzYouzXhREaQ6yvI08AVb7Py70jf9LH6IMY/jgfGYjlmrXB+EG3QEbmeZCZz3pMW+2xxx6lvryybC+cBQSeMD545hjs0eF57jxz5D6ePzoHASwF6d/NTWbPXmXWQHZYqFzQ+nuZDW+vz3ItOBWZL/3vvkQVcwY6RqYwfplXku1bw3xFu6Br0KcIzFN1gfyBPouTiMwM5tF87cWYCX7zcXQMxoIHvYJ+7/fooQ+gl/o+6MsFFmPN5Zw4gtAtLrvsMmfn+uc//1kynUwUHjk1OikY2YmYov4o0ZAYCwplqPCTm98QGpjUME4z2aY7efFdrpuJGsMzAgOeZr+BNSD8s6CyQLJY+oyIXKN9OA7H4/wIwUR24ZVGiOH8COVcj9/42isQqUBxwmjPcREcuU6EXK6f93AoROvrpwuKCwIGsLD5NkDgInKOSAqMpdRkZBKPXi/3gvBBe4YzPnCY8Az8Z4jkxfCJwTjZs+ReWWh5HtksWDwDDM8Y2XLNZAmDI4DrIiLew32h6JbjJsjcPxsl8kzoH8Dz5DkgHGYbadFZYDzyPBk/vp/R9yjdE40CYoygbGBg8ZutUSqA5090eCGMp7Pq37UlzdM7vD+q5ziriqU3T3F92Qp9OEUw5ODETGQsoY8xLxGlF2//DdE1QQHB4cu6nUuWHkoMTneUe+Yw1p5U/ZlNYwnAwDBASaq99tor6/MLIQrL/fff7wKlkB8vv/zygtauZh3HcM7ahuzMXMKajtM/E6MxMh/Zrjh6WfsxRvrMNo7JCzkLgx2GI3QBzpGPcjzIKMgsvJDhaDfkd+QZ8IYW1mZk1nTKvyInIK+jk3BPfiNuXpyPe0AuynYzXgxWHB8nETqAB32PZ4KOh3xFOxGVHYV7walBAJGH+0U+8dfPNdPetEeyADave5ENkW20ty+xRKZ2PmU/shto53D5HPoaOlk5RgZj6MbYxxpPn2cM0OcwxKPrdnUDNGOJvoYu7bO96Jvo2WE5xts+GGs4O2g3xgP9G+dZeL/LvLFottkL17V/L1ZltsL6ZmumtxeKt9Nkq2Nwf4zBeNkaHl9+Cl2NuaMzlzoX6cE4oj+wpqB75mKTZB3HaeJ1jFTzLPPxhRde6IIwyCClPFUlliIXyZFTo5PBYnXzzTe7etUYtahrW6goXF9bFWHIl5AqtACHkEG0FSDc+mvAYcACjSKAEhJPOGZCxVGBAO4N9AggTIz8jCGHSQ6BBqM+PyOYILyzQDMB8zcMNgh+CD0YDbkGrsULiGHY98JnafhyS/nAb+zERE3UBQoRP/uIKa6baG/uwW+6F4644j4eeOABp+zgQEimGOIUQXDjOD7SAwEuutk3AjzKH4sWP6M4ZIOv+58vyFLxJZx8mRieO8+43A1nKIBETqGc+v7YVRUOHJyU5GIchrO9EkG78VkEavovxlKUEn4mQwFByJeooF3zVUeY0lMLmyg9Fd50LWbL99zeYigfOYDBmXEIvgSFj+AMz3nMBbRVOEIyCg5N5gScG4wPITwoHRi0WLcwXDGf43BGeWd9YQ5CmcBR6NcB1iPmK8YRfdPXg/f9MRm+RCLnoyQGARnU4ye6qhCZe0KI7EB+Oumkk5xse8EFF9gRRxxRMJkEuYd1zJcojcqc+YZ5i3MiG3IuXsyDOCL8/m/MR+GSfmGYH5EtkTWZG5E10Df4LLKI30uJLAj0ATIbmEf5Hf3D7wdBRr3f+w6dCt2FUlPRmvM4ofkucg1yQLalC6MwxyNvIR/5kp6U2CW4ht+5B+QN2gbDJWVtoiUv2TsPwxMODZwWiWQ1jkdfQq7xZU143sgm4e/QBugdHJPPsbYkixZPBM+RPpwvgzPrHn3U6yz8jk6GHoRuVe5ZsOjEPFeeHf2x0GOsXKGP8xzph8ghPLdkejH9lvGKvIMczljg88wPyNb0Ue8UQdegv+Wl1Fr9YrOnr4i8GWstPzU6Nzme8U6pKO/wY15nHqM9ogGqlKdjnkoE945DkzmTdin03pmickBPIJjOZxMyj3sdnbWHscjfmIvof6x7rIesr/RN3sMpy1qLfu+zzZPh11/WTIK8cURfcskldsghh5T9HoQifeTU6EQgmBx//PHO8EXk1D777JP3czBxMJF4L2k+jYHZgmDBCyEYQQPjC4sw18bCSootygELNNeKQoJg6+tgpqMIILCwKCOsMrmywPvIDKA9cHL4c/jNlIg+9aU9eB9BPNUEysTN5O438/Z1C8MKwyOPPOKEJCZqFAzum3JFpDtzfT6l3m/IxwSO4yOsfPoam35jbxaQqAEJ42/0fZ/hwftE9mRadzMZLDgoQ5QyyNdCE04/B5xevMfzyEYpKjS0L/0NxYu+SY3irhzlQnvQTxmvKO/0YcY78xD93+9TEIZxj2E27MjDeI9w5CMjvROT4+MMZAz5MUbfRXBC4Mo0m6exZYlNr3vFWsyn5AY2oHY161eb+75BXCcGF1+rlrFOlB3zBXMFfRwnKn2bKFBf6g7nRrSNaE8ciN7p4ze6zJdhRFQ+3gBEP2Iu9/tveOMfhi8+A76klVdMGF/0TxQOX2M80ZzuMyXph75ONQ4N1rS//OUvTq7pqs5cIcoBxuiVV17pMr8JBrnooosKkjWKDIgugxzM+o4xv5TR7sx1yI/IjcxvyB0EVvnsZ+YsAoh8kAF6gXcKoIekszErcykGReZXouh9iWBvYGYN91kdnJM13v+N4CQ+y/yIrOize5M9R+ZX5B1f95zj+UxWwCCPPsOcz/G4Bx8ExVxOO3AdGMQJkKJt+Hw4w5r2YC4nMxbHht8sOLwGsK5gRI2Wy0Q+px3YcyPemsE9PPHEE7bzzjunbNvo91jPeOWrBAnHpH/wvHkGPugGmTXZniSlBMcLzjOeP88TGborr6/0N8YXcjVjj7HIGKB/8jt6QBTaL1xujf6OPM3nfQaSb1fGN0ZYD+/5cUX7Z6zrfvSs2eevt2ZokHXRo4/ZFj8x65Zb9hHyGuMO+wH2Bfo2Y5i5gnHOfXHdVGOgv2Nroa2YJ8KZXB7GAPMh//Id5iyVFhUe1jX0WXQGZAnWMdZ6xgd9kb5HP2O+Yl5lfWUs8T52K/op6y/fob8mW/uY6/xehoxrgnVxarBmXXXVVQltWaKykFOjE4Cgy+D8wx/+4Eqq3HXXXXmJtmCxwkHCBBBWKjBwF6smXipQMHzmQBiMwjgamBzJoChW9Em8zZRoR6KceA/jNJOz9zrz8qmgTNwI21wzwg6fwzFD2/O+90TzvHkG0c3VUVQQHng2XgDhZyZ+niOZO2HBFeXx7rvvdu2DMoQDA0HfGzW9YhkvrZyIFgzC8aIvWHTYbJxnwkKRjtMLYZB24r75TjQiLZ/PhvvCmVFOG/lxbSzuKIZA26EolsMYKxdlI7zhGP2cMc2YQcDxigR4JyJCdHjeos+muwE3fRFhCuMFY9UL8snKI3Ad9FvO2dSy1GVrBNZkPaoGWa+axGOAcYvC76NTwmXSwiWBGPfMB4w7FHocf1G4b/oQfYmxzhzBnMO9c9/sxRLuU8wrKCbMEdwbcyYR8kLkY66lL9In6cMYqDJxmPG9O+64w+2xwXr373//O+syKkKI7GH9OeaYY1ygAOUbDjvssLw0J+sPx/QOd7/pNDJgORi/0H0wWCMfh9d+HxCEjI/sUUyjMPIIbeYzQLzcQ8AHQUw4gfgML589B3zObyaOnOINjOgmPAfkFy8b4JShnFj4nvk+pZt8mS6Oz3yObEb/QBeIGja5Ho6LkR/DMPIF1+dlLIKt0FmjEfHoHUR5J3IKoC9goEIeou1T7btIW/lsGK+/FuJ50SbIU1wbulQp94OM15d9gJ/PEirW3lnlHjCFrME48BuAM+bRW32QBeOFZ+nHGmMKI2zYqcsxCJyif6fS27xMhOyPrM7ncQwk05UZL4xLDLvu6F9/aDZvWqsjg9JTCRwaPrDTG4hxMIbHmw/qQl7j2Bh5uQ/mtahdBTgOwZ30Jxyh2A+8vQUdKXwP3CfjmPtDx8B4zVxa6kBYUfmE5zPmXfoq60Em0GfJ2qDc9znnnOMq3OQlm0qUDDk1KhwyAY4++mi3yKL0syixOGUawRIFYRiD95577lmW6ah+sysWXl7lCs4ForgQYIhSYhJFkOSFYMm/CFFeWWDB9+8BQj9CEhN4sk3TEUJ4ZggZRLxihPUZCVwDpaCighbCFOfi+SLsINzh+PCCCIJ/1HHC8TCQJorMQGEiggUBjHtBaOOeUXySRaz5vUc4N4bncNRYPuDYPAOcGZQoK0XkH/fmNw9mEabNw/0ZQQ/lrytHTIXbimwg+izKOs+OceSdXb5UDdDHUD5QMLxzI55S4SPO6dOZOou8IM88y7m9szGqaHON3lnGdXhlPgpjFcWJccI18x3GOvfH2GVsesWDMeqdMSgfRJAxnlPNy36PGtqG+6UNt9tuuw737mtVIxxyXSi76USXClEMWFMI2GCDv1//+tf2+9//vmw3XxWiM8H69Kc//cllZVBy6qyzzrIbb7zRjjrqqJwNoqx3fm+rcgzeYM0nEIm1txyvzxsskTlYv5GTkCtZ931Uq8+eQ+4hEAK5gaAIZAkvExBBjqzOsRKt+94B4TekRafwWRkEQ+FsjrdZLfKKz9BDx0EWwqnggynYyyuq06CHcL7wPoFh+B46LvoQOgqGLe6X60n0nLyMg/yNDoD+kk8dgHZknwFkN5wspdKZ6bN+r0l0Ne/QAp45BuiunPUdhohtnHHo1/Rdfma80Hb0F7+nGPoaYwx4tn4z+Hh9jQAsPpNpG4dldY6PfhEdizzbe++91/VdP/fyTH32erxsf18KD2cDMtODDz7oyqQx/hhb3knjs7uRtdDv+Vs6eySFHRxcE9cebz9M9HtfBprj82+5zqmi60FGOEEbrEXXXHNN1qXTRemRU6NCQXhF0WdDTdLBTz/99DbhEIM0C3WuSgcLEYt0vtJ08wVCBoJ4uURzpdqMDQGFZ8HC74UI8Onq/B2BGOMmykUYhHucBNwnigRg8McAGT4Wx/ERUDi6yEYg4pvvxXM+RHnmmWdcBAfCG04IIrrjtS1KCgJ7vLIDKAwoDiwIKEgYpVFYuU4UIhwmqUAIR0lBSeAYCD+5GPk5N+2Bg6dUtdkRWBlHPprAp+7zHCXYdQTDOg4C+iDtxBjye8Ik6wsourRxsg02mdOIcqTt6RPZwDhEkGe+ZXPI8DVx3Qj1jEfOxbhiTDNX0Z/5Hsomf/cOzPC8hpLAPeS7/izHRmHxzsLmoNGaWuqspqqHVX+3eTnXjpGB8eKdf0KUC6yRBHAwPm644Yak+8UIIXKDmtdHHnmkW6tQ9DGeeZkUw3M+xh+lh5BTyy1inIhi1u9My6cWE2Rk9i1iTUe2RXZmnQ/Lvsga22+/vTNi8sx23333DjIn8pDfkxBDLwZedBVe4XNRPx+Zh/n31ltvdYFKPvMhVYlOZHo+g0MFWRiDbbh0T7RPxNvjCzmavxEowjmp6Y+8grEWgzDXlo6Mj5xIYAhthZwTDdzKFJwIBE1FM+GLCcZw2tjrZlwHP5dTpkg5gQOD/kQfRqel7yfSaz3I73wPmT8ZOBKQ43EkZFPKlfNwTTxTskfCQZuMceR0xqLfdBm5iHKAviwv3+Vvfn9RP96ZD3wwHU6XfFdDCGequ3O1sPdfzHpUte4lyrinTbxdicxdIcoF5Bo2DycT9ZRTTnGZGwqeqjxUPLuClQ2MzvwcLQ+EUY2FLldnRLkpGoDwjeGd6KlS1tlNB4yyvBBEEDJ86ZwwPCeEUaIi4rU3k2o48gFFA6EA4SCeAI2AjcNj//33z6h9uDaUBIyviTb/QlBB0Eq0DwWR5/RFjNAoQSgc/ppTbeLkweDvS4z40kPRcjyZgADoS/aUAp4V18A9lGPGU7nh93lgHxgUAvoSSkQ6zh8ijXAaJAPFmv7LPJItKPJEO6EcUNIAx53H1wLlPER/YAjyfY/7QklJtEEl47VQGRIc2zs0FjR+ZbPqqe8bWMyqbEj3ta1uXk3bPjyM1ULUShciF5hDGW8XXnihK5F28sknO8WjHOUUISoV1q9zzz3XLr30UpeZ8Zvf/KadcQ6ZlHUaI0CuSj86TDg4pxzAQM76Xc4ODSAr4MADD2wzViI7R42oyCjIIcjA8RwaEN3Imu8wpyZ6tmRc7LLLLhkFXvBZapiTqYGuk6jEB0bZRIZg9CRkN+/cwIHhr9FnYacDcqIvf0Owld/IOBtwAiFzojOVKkAJvYvnX64ZT+UGAX88b8YEgT7ovuls5k6/RD5PNe8RRIjzI1v7hN9HEacfAargHRsck2ft96vhXghGRO/guggk5Pd456Zv+OytQuD1i8aWevtwwZu2uLl1r7W+NQNsjb4buDHKvML4TbXvjxDFhjF99tln27777mtHHHGE3X///Xb99de32bFEZaBMjU6kbPgo4scff9wN0HCEPotepilVLJzPP/+8i8iKmxERtLRuVFXkDcFJCy208Ma9819VEe+vlKCk0kfitSuRTX4jRJwpicp9ESGCMIVBFKGFz5NlgqMF5042igMCO8IbjhEEz3g1PpPh+2+hBLlUETtEwWEA1+bLqaG/oIBjvCTiB+UaQTiTDBsUTJ82ngii6hI5RXHaeccdzotU8wzKdbhvcw844pgvcRKgiKM0cy8owDvuuKOVkvrmhfbV0pcj78Zs2oRq22qLcc4Bi/Gh1NcpRKoxjOKB01iKhxD5gSApxhXrL+MqGjCFQYpAHNY99AkfWIN8hiEu0+ARZKRwKaMw3tlRLEMt5yOIAr0p3+VP45+QrIoqbtC6io4RT+YK10an3yXakwA9Av0PWd5HoGOUpj/yzLLJuODcBJsgv6FfZ7qxN7oJa1E4sKVYIKuRVcu9Z6oXdVWQ7SnLhMwOvvRaunMM/YVKBsmcIMgkZFDEy0Ti+1QzwJGSjjONrA0COcKBhowDdAz6HNlXjCnGjS87Xep9xz5a8JbNbZzlgqY8Per6We3cfm49of251nhzvhDlAOPuggsucKU3FTxVWShTo0JA8CI7g8UrXnaGh8UOoTD6d76fSNDEaM1C7etvYoglqnynnXZyhrkOkVR1M8zmvmrWvNSsurfZwE3Nuhc2Ep6FEMEDo3rB9+pY9JF9tfRL59QY1G2Ird1vPaut6tybByEYYRCmhBR9zDl1gsD1D6Ka8FanSq0Ob9KEMZryWzyvaEmtMPQ7v6k6UezRbAa+yzVQjzPTkjxE43NfxXRoIND6yCkM4+mU3EoGkTgockDKcFgAp0QCP/N8OB+1jRnDPCeMD15BrBSIMqK/+LRo+mKirIZEoCzw3JOVpeOYRFLR36LOJtobRZn242c/9zEPokj7Wrko4IwXfqd/4owh+pDvEeHJHMq94OBDkULpLIdSAPUt3+/jEla0vpo+w82x9N14pR9ECcH4FdSZxXqYxco7O7FYIN/4rA0iE3/xi1+4gA9lbQhRmIApwDnP2uZ1kfAawpoaz6mBLMhajiHLf5Z1hv36CFhg3QkTBM3WYh8h4Xz3xnJWZatZrIABRok2BC8IrMENb5kFS1tV8G7rmlXnt9xkOYKMTzY4MjnGV5/B7Wv+p3IkESUfzmSlvyFvI68kior3JYOIYg+X6PXwrMkKJmgrXLorXQjkKrROGgZ5FH2JF/ImWcy5lGBmbOJQop24f9rHR9HzfBibOE1op/AeDUCQDhk7lZId4vex8w4C9CoC9DK5fq9fJIN+Rl+m5Fs0IAu9gb5GP2Y+9CXeaFP0W3RG+jLPAt2Y/oXz2AcHcq3hvTj5G/oKxylGsGc6LGya186hAW9OeNM2X2l7N/7RoxLZr0TxYU5Z3FxvMYtZ7xrtVefH45lnnulkFGQdn7VBgKoob5SpUeawWLF3xsUXX+xKCu2999622267pa28I6yQTs1CusMOO7hFj8WWBZPJjIURoygLKgu2jwzwgj2KSzvPf9Nis+mP4g75/j1qsg/f1ay6MGUgWNjDGwMXki8Wf2aTFn8SeidmQ7oPtfX6Z18CqdzBEE7kHQ4FoifocwjM2daHRfFlMzAiSZIdg/JSGIb9hmRkVRApFf4OCjRRL3wmU+GdYyNgprOnSDagkCHY+w2ogXbDaJ4PR4rfZwWDOOOWzaMRyskgQDgmld+/z3jFSO/nBYRgxoxPsy93GONhxxgKFIpBuiW7UFpxQviNEmkb2iNRJBTPjgi7eFGBHIv+i8OCaCvmSfo0zxklEPgbNWu9sYb+iSDEcbkX2h1HSLmxuGmmTa+b0OH9wdVjbMm8Ftfe5bxPUZej4SuzpTwvyvdVm/Xa0Ky248bzXRmMVj5r46abbsqpXKEQXQ3kjEMOOcStg/yLjsHamS6sf6y9yCDIj8gmRCN74x86BMY9X94QXYRAKi/nYRQOr5XNAQ6Nr9qdI2YrWVVsFSsErOnImEXZEDxoNKt7Fqm7/fvdtzSrKkJ2SIlAnkPeQr5HNkbnyKV0GSWw0FlT7YuGXoMBFXkNPRhZjhK7HuR2gq+S7fGRzl6Eheo3rGk+m4lzYOxGn+GV6zkZp9w7ehp6C8dmLmBc0i4Y18lI4PzIxOghjGN/XgzUiUoVlxvYNQiMo/9xX8jpZGlkktVAEJl/FuhZ6CrJ+h+f5zzxPsN4YM6k7Zk3GQ98Hl3OV02gf9MveUbsJ+iPg46DLpJpwFexmDDvRVva3KonefpWL2MjbQ3Xn/O9X6DInqXNDXb3V2/Y1KWt+7WO7j3U9lpuA+tWpXj38NxB8NQf//hH+/Wvf+1KVCUqnyhKj5waZQyR14cddphbxFDWUQQw1iGYMah8CmUYFAYMrR4EECKHfQQViykRCigfWRmtF39mNre1zmM7Bm1h1jO/xmPuEyMuApyPkC40b8x5xea7SIPvqbIqGze0c5Zjob+ggPpNBokUIfonVbkkng3f89FWvBBY+Jf+SsmnVAZpHGsIiF6wxHBMOi2RV0QOErmPEJ1LbWMEWbzr+Yq+85t+c9+MLcZVuLZvvuBZUO/XH5eIf9qB86OEp4pqo/1Q6Lj3cjdSoyhgGPFRdChbzFHplhOg75IpwXzo2wWDCo4JBOhEG14ToZYsuon2Y1+iRH9HSWcezkYZLhVB0GJfL33T6lqImuW+AutdPdSG9Uhv35LOypKmJvtgwXzrWV1ta/brb1Xl0BbUJF6EASxMlVnf7c2qil9Or5xhDjj//PPdRn9Emp922mllv+eWEKVW1hkvf/7zn52eQZkFZDJkbf6GsyGaeYFxE/knLAci52F084r+U0895dbNbDeibQ6ex9wcebeXVcfyX+IHuQN9KVXJyrzRPNusIU7WfM0aZrXfG9s7C/QXHBDoBOiuBBqRvZzKGeH1AYy8yLz0M47lg4d4L509IwmU4nNe/qdiAfIg14JuSWBVtqVxgSxcjNH5zIjmmATGoJ9jPEf/TacUaqZwDvo+56F9aQfGMPI3OkOq9ZP24zlk4gAtBVwjeiDVBnywGQ4Nxny6ZYEJEuM4/ju0lw+4S1SyjDmUzzA3xgM9Dv07WRkmIsQJYq2k8sXzGr61jxa+2ZarQQbA2v02tr615RfkVUxm1S+y+Y31NrxHH+tTJhkR90570z5eON1VJQFmmA2WWdF2HK5MmijYppCTGIvYY8t93uuqVM5M2YVg8SQN/He/+53bCO4f//hHO6M+RsBoyjYLKGWpEBaTbWxDRBSCfCKHBsZUhEkGblzDaSxBl8lzWQzaAKcO0RDFLClRE8dDXd0JS37QvtRj9dFz2223nXvu4DcWTLSXAQZzlAMEPC9sIwDznDIxJGFwRrBjMzS/kTaCNf0YJSNZWnm6IFASTZ9Nvd0opPlyrWHjeaHwe5OgdKCQ0ebAuE3n3DwLnh+KRzznZ7nglUK/3w/KAopCJhFUPBP6Slgxpc1oq2Sp4hhCkymKKJP0HdrRO4n8RnuMD4QaotXox0WpwZ0HKOExoueGtqBpmjW2LLFuVX2sb01rfequyscLF9jBr7xgM+tbx9jmg4bYDRtvbj2rSyweNbeWa2lPi1nzPDk1ImCMYdPwXXfd1UWbP/TQQ3bjjTcmNCgI0ZXBoPmTn/zEOTHuvvtuV0YGvKL+8MMPdwiGQO8gkAX9IlmQCLJHMocGazJGbgJo4q878WS+6oLIHsh0RXNoQCJdIpFeVcHQvhiP0THIXCVDAicZjgQizceOHZsw4tXvscH8TR9BlqPP8cpEVsGhgdzGM/a6MY4V+j/BKLlGuyOjc7x8Zh2igycLpskXOHaQvXkGPjgN0g0gJMhq/PjxThYuh9Kq8UCf4PmjX3qHBjI9c0+6jgKC9bCZhJ1oPJtwu8Wzp2CjSZaVQJmvsH6CvYOxge5HX0X/IAMcY2o563BRluk22Nbtv5nNbqB8YMwGd1vWetV03Y3B6YP3f/2+PTPrM/d7TazKjlhxQ1u3f+kzVr5cMrvNoQH8NGXJ90HR4ntwPmKbIlODoF2CQSh7m21FE1EYOp8kVeFg0KOGGx78xx57zNX7jMKChwEagRGBAkWDhTociZBsgcaQjFHaC02+Bj+OEdJPMdBx3riGuh7LmlX3MWte/N0UGDOr7W/WPX9lbrgOFngEzmKnea3QayWb3dB+Ul+xd+cxjPi0W4QnDMdkQxA5RTv7qLxtttnGpcYS5YSxOarcPvnkk24jYfoP/QnBjH9RFBDyM8kCQtng+5yL86IM53PTPZSnfAmEROYzLooVAUy6N9Fpvo3TBUUdQRiBupyFYZReFMLwHg5+L5dMoM/gdPBODaL7iKxKtUE8Sh19NrwXTBiUiqefftqlgDMn0d9RxH2JKdoZxcjvRVQp4NjoX6uNJT0nvPWqzW6ob/v9ldmz7B+ffGRnrFniaCXKOsZ9vzwNCKWEMci6xDxwxRVX2H333efGKmU7jz766C7ttBPCw9p69dVXuzIK6Bl//etf42ZyUlYGYyDyDvIH8jgBLOmUm2EdpoStlwFZOzHgsV4ij2EgRDYhAjkeMVvRAvuw3XtVlntQShj0JdbxfAS7ZESsv1nVILOW1n3SWmNju3eqPTXQLTDOE6SE4RvnhJexfClY5FnK6sTb2BhjMBnc6LP0Pf/CAce/lJNKtxQxxnYi6ZEz/Z4pZCDlax8M+na+dFQM5NyfD/ApNMjNlJdiTKLT4GhEf0sF4wabA8/jhz/8Ydk6NLhO7Bw4r6K2DO/ASQectGEZPxwQSEnvRDC3oGujxyVyFHFsnMUETnEOdHB0Yp4JBlTOVcw9IfNF75p+7iXM3lswvc2hAU1Bi90w5U07b+0drXdNacdOj+paW9L8fVYkq1Gv6vIcz6WGNYQ1C9sXczTZ4A8++KDdcMMNboyL8kDlp8qI//73v3b88ce7vTMuueSSlBETLJgIQQyodISRRCBgIpihdHDMlBvFNtebLXjPrGlhq0Oj39pmVfmZCDHiEqWDoFsqD+i8hrk2bemX1mItNqT7MBveI3WqdLlCe6JgIHwD/yLYo8giNOGA8PWM+RslexCs6FMYdFFQcS6h0Ib7C8dEmEUo89HrHIc+xDFQZjLZy4LjIQRmW7IgEQi1+djcifvCqVHsjaIw0j366KOuLXFQxHOoYKigNB2pzgjBPFsE+XyXxMoXXC99BMJ7WuDQ9WW90o2w9hupI2TwPZyy3DcRp+kYMnGscD2J9h7xKeL0eb/5OuXZROegOQhspYfviWxraLbV4KF266YdAwqKStBitvjF1syM70qFWc0Qs16b4pkq7bWVIYxjIk8xuCDP4Nw89thj3bx53XXX5SQjCVHpsNYdddRRTjFHEccgmQycD8gUrKcYpbM13jIuMZhi5H722WedDJXKUBcEM6zFvnHzXpVRx7/jBuTZgpyB7EpQQ0kIms2aJpu1LDCL9TSrHW0WK09ZLR0wymKERd7yMhL6G+3MHByWmfmdgBACqdBdMQohq6KHhOU1DEj0mbB+wXHRiZHDkcd8uaRMMnoJdMknyIesN/nY+JjrQzcrVqllDw5LXmTrh/W8RHvW8ZzQD9MpIVYqCPpEFwhn1aNL+VLLOA7SqQDBfaND8lzQEdA16H9kUPgN1VNB2yYqVcN4Yex4JyvH33333TO8W1HOPPLNR/bkjE+tJaJlnLLqlrZS7/jjrVh8snC63T3tDaty+kUrP15+U1u+V+H3r61EmA9wjjPvM1f+7W9/c5muV155pR100EGlvjwhp0Z5QCTLSSed5AS8a6+91m3Uly5E2rMoYohGecDglq5nn4UfARMBH6Mt/5KmWaqoRq4BoSNdg2Q50xzMtebga2eIqo4Nt+o8KmXpgoOKyReBO9wnMCgjQBMth9eZz+BIIu2Zz9KnUGSJhkIR5lkkK2kWDwzxCJSk+YahtinKbXSDZgRF0tPzvf8D9Z85bi6KAu1DZE4xszQ8fpNqhF2MDDwjoO38xoGAwE3UVbnXkfebEzLGw0ZGFA2U4HSdGTg/eCYoVjjDaCfKpqFMk1mRLmweiXCSzobkKEVcZ7GVTlFY1n/iIZep4VWO6ljM9h+5gv1tzIalb3oMYA2fmTUvMavua9ZtRbOY0p1TgcGMbDWMX5dddpmLOqccVSpDrhCdkSeeeMIOP/xwl2lx1VVXJTRexou6R1ZErkA/wJjJK52AI5/RyFhkvUZWIdglKhMWEwyzBN+kG+lfrgRBszUFUyywxRaznlYTW9FiiTL7CoQvQ4vcycvL8zxvyv/RZzAG86LdCbxBTsURgfMZozCfwSmC4yOTEp6UZeXc0WwfZGWyvpHVw5vPA0Fcmeox6YDciZMmW5BdWau4Xtqx2DA20S9oU54d1xPWzRjHjH1k83Tk5FLCtaP3eqdXeJ5KtYdeFPQRXr7cHv0X+wSOsUzsE+igUaddomvnOUT3MRKVzf9mf2G3T/1+HyrPOWvtYAO7lX6/y6+WzLEPFn7t9j5Zt/9IG96jvMd4uYDtANsr88pvfvMb23PPPd22AbIPlBZlapQYBK2DDz7YbV7G5jOJ9jFIBAoHC7cXRljQOV40ioAICyIG+KyP2keIJEoGoSbfEfKZgjCKoFtJm+4mojn41hpa3v4+upaa37F1rKZq2aJ5k4mwoz8ka8/nnnvOCWv0A4Q9n2KL0oGygYCF8peNoZx+zXejRmqETaL1OCfKLRGAKLs4Oyg/le/sHL9JHCnomTrKUNoQSGkjnDDltlkb7VbuTgygH6K4oiCw4OPQiLYlSi/OiXTuh8wJUt+JlvLRVjjimEPTNdZEyx3guOL8lZjqLXLjsenT7Pg3WzdvbQkCG9Stuz281XY2omfpFQ6RG6w3GIweeOABp3D87Gc/c7Vwy7VkhhD5lgXZmw9Hxt///neXqZGJHEQEMWstzg3kNfQL1tjtt9++wxpPBCNBC8hcvs488h26COt9KeUnrg/jQyXtf5WIIGix+pY3LLDWPfAgZn2se9XGFivC/n88W54zkfA4yRI9V/oe8y5yGYFFGMTRNekXyFkEsaGLRp0P6eAj5tnnIJqRjO7BeZAlkdvpf2R+8DvXkW/oVwRjZZKZ7sFIjvyJnpYoW7hU+FKwlVAzHl2NbH/6HLpsvKxMnlM6Zcc4Fpvbk8nl9Vf6E040+numeiS2GZ4z/VBlaroeTS3Ndtmk/9mUJXNdRgQZGzsNW812XXaNUl+ayBHmR2wX2FbPP/98ty7deuutec8IFOkjp0YJDZIXXnihnXfeeW6Dy1NPPTUnA6UvIRVNx0X4ZLHH+IeAV45GUCJEcLoUQuAsBXXNr7VTOCBmvaxHdfbRPOn0JzIw6AcI2ER7pBKyfUQOBme8zih7KK0IcvEmZRQQlFyOHc2oiLdZGsI6L5RghEn/dxwuCJ8oOQh7CH3plCTIdTNq+n+6cI2kypP5VK5lnCoBIjWJcKIvItAnUgj4nN8UMhk4mcjEQFEO9+P//e9/OUfLoSRn0kdE5+H9+fPsuVkzrEd1te05YqQN7p66NIHID6wFKAOM/WQba2YD6xLzDwYFyimeccYZbp1B8egMARRCJIJ9pX784x+7n2+77bac+jvrI7oEQQnhNZr3WbuRryhTWo5RxlwjRgeyRPKdCVwKmoM51tDyZof3u1WtZ9WxYQXV03BcIQ/TD5DBUrUnbU+wFHIbIOsTlEKpjnjlf9hLAwMyBv6wPsE87rOTPXyO46L70Pd8FgGfJVsDhweGbJwnXG8+SkQlgsApzpdJOSyukT4pQ3f24IDAuUaQAsFSyXTIZPvneeirPtMnHPhAdjl9LBcdFV0YOSSTTHLReRwbb8ydZgsa62xUr2VszX7l5cDszLAGsa4gv6Bf5Nueg+0S2xhzB5uIE5yOTZfsjUpwCHc25NQoARiGDz30ULfIFcqrh6EawyzCXCk23E4XlCGE07CRstKpa/6fSwtvTzfrWb1N3s/lI+SYtDHIZhOFRk1AyvigMCAYcgyERcr7YGzaY489XLQe/YlodpQbnCdewfCReURcRTf9A45BpBSfJ6qLhcXX1S2Wkw2jdyYbkOMgQgAu+8j9lmaz+nlmVdVm3fqXVa192pAMsHSclcyJ9J9USjIbkKI8xlMq6U/ZChEYPlGO821UFUIkh/XL109nDsiXLMA6RUAHx8OwxZrFOvnII4/Y9ddfn1XkuhDlDjIhe8icfPLJLjPpT3/6U94VeRyRBAHwL3JSuUWZe1jTmQMogdRZsrOag5nW0NKxnEltbE2rqco8WyAVGIPIdiM4iXbMdL7E6MwxkPX9RsjIaegE6BXIiGTdvvjiiy7CnnWAgCIfdOU3daavUVIoWt6DZ0wwFnM84NwgS9yXCioGOGtYxyjfnC6FKoeVbxpb6qwxqLPuVb2tusglzpLB86ZSRbJsoUydGkRdo/9GMz2wUyCjZLvBvIKmhCgNjL3777/f7RXMOkHVjnw4G1iXqBDBsXBWUm0G5wbrwB//+EdXShAHRznvPdQZKa96Kl2Axx9/3Dk0dt11VzfQ8l1/jbr7KBtERmPELWeFHacOwkhncmhAlQ225ohTo9ryX8OXkjmkyeK04pUt++23X1zhjkkbIZBJG4cGiwL9KdEkjWEaQT1acxQFw9cwxpsdrqdbLDCW4zxLV7FFSSnnseNoWGD25RNmjd/1td4jzEaOM6sq/bTuIzvTzXxAYaY+ZbI9NVBqExlniMIjGjObaDwcGjjxOts8JES5wFrF+EY+Yf0IjzUMZURHYtjCkY5jI5cSMeFSd/HKDu68885uM87DDjvMZW+wyZ/q4IrOAGvu8ccfb0899ZTdc889bs+0fIJM6DdqHjt2bFqb7ZYKZE2MGATalGOGerZUGRkJGGVajf2txKwqNqAgGS7IRgSSZNuGiQz9GJnpP+guOKAxJPtsonh7S9D3CE4i8CpcZpTr8nKf35SeNSCbclDZQvCTL9/bafQLZOO6T20We3q19jAb1XOs9a3tWNqpFDAPZbLPIQZHXskcIGT1xwvCohQV/TWTPTnC/dY7VoUQ+QfHNno8TnJ+psKGt/UwXrFdEVzLWoQjlMog2cI87/frY6+c6D5DHHvfffe1E044wclIODZ22mmnnO9RpIdyY4oEi+mZZ57pDMiUnfrPf/5TEEUazyGRC6S1FkJoYsIgpZcJIhcod4RA2xkjo2urRluVDW/7vcqGWG3VGnkvLYDCxrMuRJQciiBRRPyLokw0Var+hLGKCZ5nixErnpBPxhDp1kRcFRP2qqHN0gHjG0a1clbYHdOeM2tc8v3vi782+/ZdKwd8Vk660NY4uhBKkikciYydpHQjZGQKjjj6qRwaQhSG8ePHu6hZavIj8Pt9jjA+hecLnOc4xCn14Pf9yhSMWjg0UGJwqCZaszD2Iisx32y00UZOSRGikqEUD32ZoBH6dr4dGoAMRbQ9Ru5CyUfs34ERMBcwGnOt5VpyNxdise7WrQpHgY+ar7FuVWOsKpa/fRGRpZijedY4mwvRhjvssINzOhNwRNYGBuNksD6QjctcTR/B2eKzODzM9+gg6C3F1i2RIwm8SWeNIvgrk6yOUrCwcVabQwMCa7GpSydYU9Bg5QD2E8Z5upBlgc6cDALfEpFNthtyDM6Xcq6WIUQlw9qBo4L5l/UeRwM6BDIEcy2wTjD+WBtwUGLDzBbmEOYSHKpRh0Z4brrxxhvtggsucDZf9jXLVqcRmVH6kN4uAFEj1Lal9AHCDJEmhRrcGA4K4czAUEA0JfdAJMO2227r3keoIOOCCEwiLNOp28uEgiE+mw3iKoFYrMq6V69rQdD6nPO5eR+T88svv+zKQBW6JrhXWkmnw+OcDvQN+govhLm99967LNL+iQLDiEakV9jYjmIRFjZRlHDK4Cwqa1is6+Z0fH/pLKtUmBPIEErmmKL8FFk/8eaOTIUG+ih9EyFHCFEYcESGM6hYu3Ai+nroKAa852umE/UaNValiy8xkY4MhNHrySefdOV5cIJccskldswxx1REBK0QHhT3f//73/arX/3KTj/9dKdAF8IIjcGWskAETOUb5C50IwzWBDdgSPfgeOTFZ5DXUpUEJQiHwBQM2511LFfHBlqPKsrZIvPU5PU+aTtkIzL9C7m5u5e7MSTzczolQXyJXPoh8jx9AuNSOUAQGLpZ2NmHTMvaFoZ1j/WwkG2bD5a2sC8k/arVMOgdGw3Ni62mpvQ6Hdk6BFcyX6SD36A+GTwX5qF4kdzMs+gY6Ton6NfozhxTtfWFKAzYIKNZowRdkP2HYx5nObZRv05gC0rl3Exnb7F0OOKII1yg1gEHHOCuhe0GiplB2BUp71W1E/Doo4+6Mgd77bWXXXbZZXnbqI5IeKKMiVjhmKTr8vs22+R33wYEXGrjM0Fsv/32zgDoBWgmk1dffdWlejFxIAhjNGYRTxRRzeeJqu4K5R7y6cwABKqXXnrJReOROVEsvJc7HfgcJYToG0R7JXNo0LcwVBcLritc3giFiDq+KG9E4fgIKgxcZQ9jsLqHWXNd+E2z2uL1i3zDnJZsXmDeQVjhGaHQRCP7mJuYf1AiwyXS6L/MjbzCY4nz5Xu+FEKkBiUfAyV1sX1ZB2DNYA1JxxFO9ocvYUn2H2sJUeqpIn7DYPxlUz8UnkMOOcSeeeYZu+qqq3IqfyVEscCwe+yxx9pzzz1nDz30kI0bNy4vx2XNxJFAdiQRkASrYIzNd4aG30SaMkec47jjjmsna/I39BtvGCSoAaOE35chCvoQWSrMLZ3VoeFpvb/8Rn8T0IPRppgyMEF/8cpNJQJHAa9Ue1J4HcSXvi009FsyT8JgPCOIz+/FgDOG9apY15QLNbHu7Rwabe9X5Xd/nmxBByAaO5O+k84x0R8wQKJnh50gzEHsDwP8LTz/0M8IyArvNcncKYeGEIXFZ2NEIcsQuZ75NxzggD0znUohyCbYK9ERmAcIIqYMe6b76nBu1qqTTjqprRzVLrvsktExRPrIqVEgiEA8++yz7dJLL7UrrrjCOTbyCcK+L5HDQooxkMGHEOXLBcXbtDkdMPjh2ST7gs1vEMB++MMfukUcJQrDAe9jFA5v0oVwgWHBb9rmF33aAkcGiggRWER88RkMzNHICT7b2dLF8zVxY7AlgqSYZZGY2LNRDlFSE+2ngLJB7XT+RYgs1WbctCPp7FwLfZg2pu5pxaQJD9/UbNqz30VTYSmsNRs8xsoF2jRVDdswOB1SbSpOX2QMMP9hdAmXGOC7vEg95VjMJR6cHFFFhPmLfY3KJZtIlIbGlha7Ycon9tqcmTagW3f76Uqr2+p9O2cWYbmBnEJQhAfZIlwKjjkZWQT5hnmZNHNvEGLtQGnhMxjGUED4128wmwnbbbedM4ay3xnzBPsRZLM/jxDFAuWc0gbI3fRdsrTzBVHGyEWMAYKVkMkp68B5kJt8dGS2wTVEWOPM5DjoA5TKIigBeZNgF4y/vI8DI7y5L/oGAVY4N7xcgQOGdR39ggxODPJklHOOqDzB3MDnpWN0BJkJvY3nXEx41tFshlTwDHklKgmEIQv9leddSgcCZYfo56xNviRruZed8ixTO8LmNnxldS0L2jI2BnVb0bpV5ScwM1eQ2TPdxyQdmEeRLahMQSCnB+cqTjTsK/yNDBGvY6DDMtdEs0bQU6j1X6wN60V5Mm3pfHt65qe2pKnBRvcdYtsOWcWqY6r+X2iYc6MVTXgvbOPB+UiAIxnjrBs+exwZAlsDaww2VspZYnNANkIuyQTkpOuuu86VpCJr4+STT7Y//OEPkkMKQCxI5OYSWcMgOfjgg52Cfffddxek3BSPDa8h3ki/kLLYhqMGiEDPVLj87LPP3EDDG8nxGYwoDPyNyQClAeWDQZ+J4YAJAuEOoQ4vKcZOlCUEARwk/vhEWkkA6Ahp+Rhmed7FVHLoRwhymdYTRekkqi8KigbKOH2TZ82zT7Y5tEjB0m/NFn3Vujl4v5XNastD4QCiqJk7kmXjEBlIlDXjnz6Wbv8mwwaHVKI5CIcFx0OhYJ5BAfaf5V/mT4we9O1KiJoTheOs996wh7/50sUkVlnMaqtidusm29nKfRSt78HAiFyBQZNXeNzhRPTOb8YVBqqwgZUyhARDYOAhuCEZREJiSA0rHWx6TMo2MgMKCkoH54tXdiIbh0YYjn3eeefZxRdf7DI2kOOEKDduueUWtyE4JacIniqEkR75jDGPvM86ztjCoUAw1ZtvvumcCJko94xfIqs5JkZD5AIcJ+hH6ErMMRgqmTuIxM8k0IBjo19wfcwzrOl+nwbWfmQA5gyMF8jR+XQAdQYw1BBNWsyyqzwznj99KFMdANmSeT5ayoM+St+k/+Acx5mQy6awXZ2WoNnmNU6zxpZ661ndz/rWDC2rDKhUz5e5Bp2T4CfmHmSLdK4fvYHvJSrxzHhhzqUPoyMzr4A3pzFf+jI1BEmUU5uJ4jK9boH949MXrDloact7+sGAUXbAqPRKencFWKMZL4wb9Iuw/I/ugI7hq9x4x7AfU9iQ0DGwTbL2pyr1Fs3wQ2bg+JTVxN6JfIC9El0l3rjNVccg4JeNxFmfKEdVTJteV0CZGnmGAbP//vu7hZZov0KVWWJQRaNbOBeeRpQDnBIIjESeh5V/ohUY9PGM1CzORDlxbIyCLOwInAgEGKhzGcgoMUwkKBo+wpqoTBQNlCSf0sXfRXswvjLJFmovlngLDJM8/SmberU8U7+JX3iBIQIP5ZVj0pdQPFFE5dTIgZ6DW19liDdaxHNq0DdQSJhfSPXGcIJAkgoECsrO8L1k8xGCEfXyifLEiRo2+vjauMx3cmh0beY3NthD33zZ9jtqR1Ngds+0KXbq6sWNWC0XGJtkRITHF/IC0YjIFsztXnnnX5QAHJMexjzvYzhE3kDZ4HuPPPKIK88R/my8tSOaKcf+XeGygCg1iZzsuRoPmCeIoEJ+I2sD5/zf/vY3ZXKJsgBj2q9//Wvn1Lj99ttt1113Ldi5GLfhvav8JsyUQCVLA/kdQwTj2Y9Zxj1GiHgbaKJPIOv7zCyimFmnMUog2+aSgcycgK7D+Ql4QMbEQek3BCWbhTkDOTrb/Xo6M5QvI4s+V4NNutAX2G8Ag282JZkJuItmPPBcyWanH3hDEWsWBud0910Q7amKVdvAbt9nTpYbrNfxZAbAzkCQJoZKgqewP6TTt5FvMDwmK02Lw5VjE4xFRYxwuUovG6FfoPvIodG1eXX2l9YSBO0Kub0+d6rtMWJt61ldIVUh8gxBBsgPHsYI9ibGDjpDeH9M3kNv8LYkgi2QQQhq5HOs88z32AUYc3vssUdGY47v4gTHJunXCeyVich1PCProM8cddRRzjZx1113JS2jKDJDTo08wcAjsg+F449//KP98pe/LPpihjJBWScGqM+MYDFnYWWhxqiM9zGcUuk9laRWhQ2FKBiUnMo3GBlJ3eRcCNFR4wQCCJNWZ91EPBuY7H2dTxwA4fIchcrQQNHIdiNyFEwycIiw3WGHHZzgRz9E0Q1H1fiNYaPOD1FaeF4ICD4609e0zhS+j8KB0SJs5CB6ikUdhRYhAgGC86WCOcxv/JVOXUuOjcASjWKlzyH8ZFobU3Q+6kMlysI0tMR/vyuA4xlDZXhfmkxgXBMVSTQtRiXWckoRhjf/TYTP9gjLTozfYq8PGItxzlDeB6fKnXfeWdT9n4SINy4pXcA6SDR6ITbsTgWBCBgQfXQyhgkMzN5RgJyHASK83mN0YD7AARI2RIfLzuUL5g3kY64PGZRrDc8lyBoYLBUd2R6MKxiBmfMoBVZocGihG2SrI9OPCIBDT+U5+7LLONvCDgxkPP5WKSWfugKs72RkMi/wM/MZ1RmyKUPsy83y/bCMwBjn+Bg+gb2y0slmC++ZkapvYsNAV4oGrnoZBt2nVKWVRfnQGMTXJcjc6KpgY4pXySMdvD6BLIJzkfFHABVzfbbrCTID618m+/HlAk5QdAoywtkH7aKLLnL7ickBmjsqP5UH8Cz+7Gc/s8cee8zuuOOODpuFlRKUDgYQRkQioX3aNQs+hkT+jgKC97BYhgPqtpIu5o3aPrIBIQHjfXjjUPE9CG8Yl4tR/99vSJ5p2aloGTaESwxkKODxJmz6H0JtpjV1ReHmMgwmlCHAUEENbaIcs81oYJ6hD+BwRQElsoJ5Jpo94feLSaZ4kLGEwzPdzB7OjfKM0zaqqKMA88rWcCs6B6w9B7/6tH26aIE1hypxXr7+Frb54K5ZogRlId2oxnziI22ZB6KRl8gLzEHhPXSKNR+ecMIJ9vDDD7vIeBwcQhSbZ5991g488EDbfffd7Z///GfZGMt82RUcLvyM8div4QQzYHgmuySd0nP5BB0C+dPvlebB6I3eo4CGjtBOzP3ZGpsygdI+OMN4HrnA9SJP0r8SZWMkKoUrSgOOA3RCHIysr+gY3vmQDegFBGWiF3tHCWXJwqXJmIvob6n2iyFDNZPy12QcoZeTrRHNQOWasg0MFJ2HDxfMsOumvNb2OyVul+vZ304avWWXNGJj7ymVo5mASmwCPuvbw5yB4xP5vtgBVGRJItvtvPPO9q9//atsZLtKRU6NHEGYZ6NZjL943srNSIZHlOgpDAVEDvDz/HnzbN78+W1eTwR/HA2lVNgRTIh6wLGBQUOZGh0pZn1Yv3lSoaMBWUxwuKnubXmAM4tnwZzAXEGJAKIaeU4I7dkaFX3Jp6ixkowyHBUYLElJTbS5PJBpBqkipnFicCxfd3P8+PHOMcM1ILAQ3cX7OFtQRkTXZmbdUjvzvddtwrzZ1qemxn4xeh3bd2Txo6DLhVIYgSgTg7ERIwBRU/GUPRwbyCl+n55iyVrMG1dffbXLvr3gggvs5z//eZdURkXxoe9dfvnldvrpp9vf//53O+aYY8rqMbCmYwj0RkRkeNZejAcYEnFkoBv5vfKSlXUohmzDXm4KmooPz47nVqwMoGht80KB0ZySVLmUNxP50/OJiCY4jyCGBx54wK33PtiJQLpswXkazczyQVU8f+apVIFT6fRJn3nm97jEsYGRlt+5J2wYzHOUp0JfkpFSvDL7C3t0+odW19xkK/UeaAcvv4H1q+2a8xFjB1tCuGRbMeSo++67zwVEov/Hc4AT5Mjetdg+GLPMGcUIIPZ2WrYtwEZ77733dtgrSqSPyk/lKCRTnoDoKRSPXKLa8wkCAgYAoiBYYDHcvfzyy9a3h9kKPafZzC/eszVGjbJha+xgH30533lOGcClhKgGDCmkjStLIz48JwQlDMFMukSbxaslmg8oOYSwVmhkHCofmDdYxFnUgYUfJQAHIw4uHLjZOjV4zr6vch76MEZMzkEWB4IG50AxSSRI0P/j1eoOgwLDuXBieOMswpOfUxAaGEOchzr/Kn0mhvboaddstHXR6omXM36DvGLD+MRpybhNhC+Bx5jFCYLRNJ19eHKFPnHsscc6OQp5DyMZ0fLlIu+JzgnrE1lCDz30kNsfCoN8uUCGLUEPGAgJREBup9zcoGEN1q3fVFvUOM822+YHVrdguH311deuJG6qtbvQmQHIMV19fk8VyET5X3RH79zIZCP4cgXZEpmvWHsSisTwHMjS9us4JSnpYzicCNrLhbDegIESRyoOTI6NLYT1e+LEiTllB+HQIGsOBy26hn+PPuYzNXAOcl70GsaTnBpi00EruJd0jNaxWUyHBrDukw3GGpcoo48yVj5jjOAp7LtkdBTDGY5DlEwR5D0cu+zHXE7yXiWhQvZZcs0117iSJmeddZaL4iu1gstkidER5wXlY1hgUSKIWsZ4uMVmG9vYYbNs7uzp1tIS2IyZ0+3jV+6wNVcZ4QYPG/WWEqI0tK9C6omPiZcIdDy5GFco1+FLAOQTou2KVeoD5T28MZRITRC02PzGz2xG3Vv2bf371tiyJOdmIz07vF+LjzhiXFJGIl8lwnA2IDTgSA0bO1B2qOmcCIyY0fq1UVBkMHSS+YHTj8irsACFY4V9fVBw+RxzphAgg1er0QEFvRRtjyM9XComEcxHzB3IXBjgigUKD9GelM/Ybrvt2m10KEQ+oW9Ra5lSCayJ5aDgYhjkeli/MUz4iH6iG5FHN9p0ORuxQqN9NnmqG8cT33vN6lsmOcNAuCxVqeD8GBpTyRBdWb9gbuW5YlhBlkKfJAo9nXk5m8zdYq0tOGlEZkxbOs9un/qW/WfKa/bK7Cl56QM8cz8PEDyFnQKjIcFG+epjGC6RC+jLbPaN04Exj57BOejX8cDJkuoa/Ob26BQEfBGIgd4UzkCjxDc6BvoMOosQnq6uY/iMrFKAPSNdWxnOyK222srJOsVaO9BnsCX//ve/d/sZX3vttUU5b2dDTo0MYfE98cQT7YwzznB1lvGslXKi8pvuMvhYlFlQ+RejNIoGkcpEJlQ1zjNrrrOVRg2xzTdc1TZdf1Vbf+0VrKfFX+CLDUICwgYR1LxE/GhWJmaEQgQ0okUQrBDgiA7JFyisROwWo1QAqYgYy312gEiPbxsm2rzGSVbX8q0tbv7apte9Yk0tdTkrtRg14xlYcAZgdMyHUIMSg+LMmCcqnIwg0i9Z1Jm/mLs8KBle0cD5lcp5TD9ijsaRixGG8+Dg4L5wpIQhggohJ98KuxCVCmOnUNl/qcAQgKM+XXC+YKzA0Bp1+DYHhTGYYYR54YUX2gx/yZywQmQDjjP6FqUSnn/++ZKWImBtJAsD4zbZ1D7ogTIyrK9EWuNwYY1tbGmVQbfaZoz9YJM1bONN17ThI6vKwpCDnEG2BvOb9Iv4IO+TKYdBB8MT+gYOKd5Hv8wnzPPp7o2Wq7xJBoDPDhDp8fXS+favyS/aO/Om2UcLZ9j9X0+0x2d8lHPzkU0RdSowxzC/5KvGPrI+dg9KVNKPWbOJugZsIcgLYZnf/8z3UpVew96C3uJL3BKUxThhbiFTLQr6jAKnhGiFDCbk/FKAHMJ6gNySDtgScGwgj0WdIS1Bk9MzCnGN2JfJzqXk6EknneRkFpE+siRmAIZe6p4h0KPMlmJzYwzOLL6+dAoGCDa/8hEDCKXRTXAcseq2QdOje6i8S6w8ugBtSvQlxnoM3Rgs8mFE7ezg4MCxgaHF74GRiyccAzNR+XH7UAFA8ETIpDRAsVMSYWbdXPtgwRfWHDTb8r2G2eg+y5WFEp6MppaltqQ57MQKrMWabFHzNFumKntFESN/NJoSgZ+NrBBEcAqg8OYCx2fe8lBfl7kMyDBjfkNx8MYToqJZ1Hkm0Y344oGygRJBv+I8HJt7YF5kfsSgQVYa/Q1FBCGH+QZly0eNlfvzF6IQEOFYynIjGEa9sSCcMZYMylAxbzGOUUJm139hM+o/tsAC617V20b1XN+6V+c2Z0XhfDfddJNddNFFLhKUiKqDDjoor+cQXZPbbrvN7Zvxhz/8wX71q18VfS1izWSdRB7nZ9ZeZHGyGjFIEKmMIyNeiciYtcoOPXp8/7dYmcTNIWdgnGeOARw1xTCqdwYwzCI34dggwzUXOZ0+RRAe8hjHLTTIm+x7gIxH+cJijyf0irfnfmaz6udb75oetuGA0e7fcud/sz83bP3hcJ8XZk22Hw5b3apj2Y/pMWPGuMyusJOJOQXdArk9H2OSZ+yfM7I/ZfIY+8xfyBfouLzof8xv7LnHvMBnUxlc0Q/YGBw9myAw9AvGBt/nZ3Rnzs19cDzOR7YGOg32DPQS6Reiq4LOXcpS99jKXnzxRSe3pzMOsfmS3UnQMGWrsL3MaZhgjcFCJ930rVnZvfI9psnSxZnCfs077bST3XXXXSXdj6ySKA+LdgXAgrjbbru5AcnmVonqshUC0idZLP1eCjgyEA4ZSCzKCAk4WLbccsvEg6v7QLPug83qZzsjKAPSqrub9U5tKCw0OGmIRsMY7yOztalb+vDMt956ayeoEfGC4JSpww3vNdFTRL4SeVcswQvjMt5wImmI1CnmuJpRN8ce/oYINExgZlOWTLelzfW23jLlrey2WHOcd2MWBPHezwyMg2SgYcTwG2ofeOCB7n2UWuaYXOB4zGf+XGRUUMaPmuEIGsxt119/vTOweqdEJg42lAsUZu/YC88jvqwOWSFEnTJGcAgDc45XSCBsAKlI5k83m/WZWVW12bJrmvXMzWHoU/NLXUIkG96eO9WenfWJ1Tc32Rr9htuuy65t3aok+sRbh3PZqDMfsFE4axjjL521gMhLFA4cGgsbZ9n0+u+jSetbltgXS960VfsgF+XXuMo8ceqppzojHw4N5MPf/e53MliIrGBu/dOf/mQXXnih3XHHHbbrrrsW3diAg5Dr8OUg+ZlxxRrNe6yVyTbO7Fa1gi1tmRN5r/iBX/GghBKGApB+kTkEfGAERkbym75nuokq6wuyF/2oWAFMzNN+jcAYjUOuWNDPHpv+pn25ZFbrtVjMPl883Q4YtbX1rC7OBrTZ0tDS9J1W9D0tBE8hA+agGiKX42BCV915553de+ga2FV4HycATqhcYM7yWd3socGePxzXGzXRCQiWQtZhzkP/TFfvpKQt1+kNjOGsVmwz6LM4OXDc8TM6h/8M14Cc4KnovUNbmsxmfmRWv9Cs9xCzQasw2HI6pA9MqTSWNNXb0zMn2jd1c613TXfbavBaNqpXbn24M8KYLNbG24mgf7EeEJTOGpYKgrzI8iTIivl8dsMEawp81kZgC5smW02sp/WqGZH3a8Vxil3skEMOcTYQ7DKlynKpJGKBam+khCj4ffbZx4444gj7v//7v6IYdohcYMFlIsDLz2MiSppBiTEOg1vGhueWRrO575o1zDWr6WM2YF2zmvxGMWYDQgBtjJBBtAaCgDbXyh6U0Ogm3wiTUYMVfQrjMZEs/J0ommwWncaWevt88URb1DTXamK1tlzP1WxQ9xEZG00xmjO2fBp8oRfA8TPesC+XzGgnutfGauwnK+5k5Qxpj1/XvWRNAeWmvr/6Yd03sh7VmXnzUTJR+BlvGOiYb4iUxrgYVTxRSnkmuZbDICODMY7QT3kNnBlEh3ItRD8x9zHPcS72dUlnvyL6D3MHzgqUIpxzOPfizSNsOM7c6h0a8eDvKDAVycxJZhMedH5r1z2qa802+bFZn+ycNBgxyASkLX2WjV+Hyp0PFnxjt335fYkgmmTtfiPswOWLZ9yoBFgLmH+jDsRWw8Y0itB913qjLGZDC3otOD3ZK4r5KB3HIpGeRFMNW6O7LTIy2NobY1bts5V1qypc1idBJbvvvrttu+22ZbG/mqgsWHOPPvpolxFJ2YFk61I+xzvrPBkM/Oz3q/LyF3N7NvJXU8u31tBCGZbAaqqGWW1sRFk4+pBxWfe5J9Yu5EuRfX/FWBzOuEVmI/o+uhk8uh2GXALy6FPZyo6TFk21t+d+bI1Bkw3vPsg2H7ye9SAoL0O9iFKqPiu30FmJs+sX2B1fvdDh/S0GrWXrLZO8zFGpeWvuVLvzq+9LQVZZzFboNdCOXWXzjNdyIqORHVkXcTD4qhLRACnmIT6LkyEXGOcYLdkDCwcEJaJ8CTWMk8x7GAz5HHMef08H+jhOPRxk6C/YY9BP4s1v9HnOmSi6mnkXR0pF7u/T0mz2/n1mi2aYESxCGZ5ha5utvE1Wh6OPoFuG2xEHB2M018oAhQYn3+1TX7DZDYvanICMlQNHbWmDuxe/8kQ5w5rBuIs6EOubl9pXSwk6W2K9avrZyJ6rWk1VYW0/lIpjDOLYSCWfME/gpFxxpVFWPfjTDn/vWb2sDey2bsGulfFx2mmn2X/+8x+79957c54fOztyaqSAMgPHH3+8XXLJJXbssccW9GG4zfUmTnSLPqWEiIzBy8/Ch1e/MyvLCBwYMCo6eqFMQRBDqAwrFGRlkJ2BQIeyka2jjj770cJXbEnzwnbGpFX7bGT9agdldTyiBtknBCWpkJkbj37zin1dR+bS9xBNdeSKuyRc6GbUzbPpdfOsd3V3W7nPMKvKcwRwujS2LLZZ9e9YY7DIlX0Y2G0N61OzXEbtjDHOOw+IAMDZwBhknkHwpw0Q3sPKH4YXv8F3tsYKssvIzGCsI8wSPYWDA+GBa8L5Fq+EGteMUopCBJwfxwuRXkQ00I9xivA+nyUaK14tZcqdYThKdv3Mw6UoV5AXnvu3WX2oBij3MHS02Zg90vo6ShtG4tavxtwYpGRXGJ9JU4wSErnw3y/fcI6NsJmbJ3rO2rvlVEahs4FRk9rjjEPWApQPFPLApqCiRz69XsEdG4xfImu5hlR1rvksRoyFwTc2bI0eHZwaq/cZV3AlCQVpr732clGg99xzT87RpqJrQPQuAVMYiskAj86z+QbjMmsb8zcyoQ+OwtjHuStyvUsD2pcyM9tvv72ywPMMcgBBaWSL+/5DIAT9jJI7yJZRZ0cmTFs6056eGQ5MiNng7svYTsM2zaq/4mjxJZwJ5CoU0+vm2r3T/tfuPa79BwNXc2Wo4lHX3GRvzp1u9S1NtmbfwbZsz+JlrkfX1GdnfWpPz/zUmoIWW7n3IPvx8htan5ruGckUyOCs4cjSzD0ffPCB2+/C752F7sEz8M+RwCbsH8jnudg7nn32Wafv+v1FCThA1/GBWYnKQNEnCIzCgYEOgkGRyG76jNdNfFlsSuVyvQSAhaG0HTpTsmwkvwdmucvPcZn1kdmkpzu+P+Ygs14D09L/vA5HP6OdeR7R6hzYANZee+2yXpPmNCy0W758vsMY32jAKrbpoPb9oqtDoJLPxEGnxI5A2ez35r9ojUFjm9zeq7qvrdlvs4LbVrAzsUYRyJVqH0HmsqeffsrGbtfdunULfzZmvatH2jLd1rRCc9VVV7mSpPx76KGHFvx8lUrl5XoVCSbbc845xy699FK777773G70hYYFFGMS9RgxGiIIlvOEng3cH1548BteIxww2cmhkT+cc6DxW1vasNSmfvOlbbf19h3+TtRMrk6DxqDeljS3GkC/J2bzGmdm5dSgvyMEs+BR17mQTo1RvYa1c2ogjIzsOSThmHtn3hR7euZ7bb+P7DnI9h25SUmMo7VVvW1Ez82/26zq+xqy6YDAjhEQgZHUSpxe9AfmHRQAlAycA7yHoRPlwgvoRFchtKOg4EzI6tpra924Z7wTMcnxUH44HkoIwo/fqBLlAGcH2ST8i+EFJ4i/XyIwiaLgmDjpOJaHYxHFjbMDgdlHp6JIpGovn2ZOtHgue9SUhIbIRmgUR65rv9EZMA8jrPHyUZe0EX0i1VzMc0H5Yz73yZ4oe/SbcHshOOIsI7qkFE75eM+Zq31u5qe2/oBRNqCb9m0CxiNl4LwcQt8nsnXM2AXG45w0aZrNmbPQqqurbM01e1jvXq1ODeZoAhK22267vGZX8twoFULGFMaEZNHrzBf01wGD17FJi152ipJXkAbUjiy4QwOI2MSQ8pOf/MQZbUgVjxo7hAjD/ElJW8puEoVXjD3kWLdZyzE0M4eXuhxEIcAoynxGYBh6FOsR9amZ31IZL0T6LGxcYjPr59on735km62/Ubu1lghr2jof5Z6+XDLdyeY+Epp/Z9XPtYaWRuueRRkn+jz6BX2ikAzq1teVmaprbgi52QMb1TO+w3thY4Od+8HzNr1usfu9OhazX622iY1dpviGb57luKGr2bZDVnVlpzLVcZDXqTCBowv9AvkSRz86P8feeOON25xfPAf/O/IjfYeMDWSKbO0fjHsctsiyBGqgI6BXcjyitNED0C2R77km5gze933DZwjgzMABw79cP06MMFw/GeI+YBDdym8mngx0EOSrUu9jlr1+4dPAQzQuoXBv26/oBbQFbRTenJm+gLyW6tmijxKA5tdFPu/7R/hY6HzoGb4PFZd4+kVg05bOtk8Xfm2j+8TP5OmKUNbc9wv6BI7w4asOtMYeDdbY0GgfvjPJqquqbJlB/WzUOmtYv26t9iOqOTBGsrU3JIK5AVsCAZGsU4myphj76Bh77bW3LWyebIuaPv/uL6xKVda7Jr39/3LluOOOc0GmP/rRj5xexL5r6lsdUaZGAk8y6eAMJpRTFj6ROz6NiwUdowlRD75WHZMG6aIid1qCFps4/02b1zjbvp7yjfXq3cs2W3lrG9pj2XYKNc8BgS8XGlsa7N35z0Tejdmw7ivYyF6r5xTBiGDMYlMoZZ9+9/rcj+y9+Z87QWS5nkNs3JCx7RSlxpZmp1wQOXXV5Cc71Jndbui6NmaZyillgMHyqaeechGLCPMYthHKmfNImabObXih9GXBvEDiYbxG30sFggwRSgilnIvoKQ/XwLUQsYMyzLPh+fM+wgeRXtkYTX1ZHYQi7gXlI93+RJsgVCOA5xJpWHRe+6/Z/G9anRmOmNkKG5itvk3bfTH/ck+MfxSwbDK1aFva1H8XhQ4nE8/K7wPFsVEwUTxwLDHfF1MQm7xolt0whX1zvoezV8Vi1q2q2o5ZeUsb0r0CSwAUAZxd739ws1VVtdiiRUtt++03sIaGRps4caa1NK/ZVq4QpR/n53777VcQBZ3sLCIscRbEczCiMJPVgfzQ0LLUZtd/bk1Bo/WqWcYG1ha3vzEezjzzTLvmmmtcKSGuWYgo7BtDyTI2BT///PMrz3FepjAXsb5j5GLe2GOPPVzbIu9i1Kw4A2KZ8tWSmfbUzLesxVps8tsfOePQzsM3sdrQXlWsCbyfK6/Ofs8+XTS1g+x90Kgd250vU8g6xnBayM2bKUH1+Iw3bX7jEreP19aD17VV+45oV74GHaN7dY3d9MVEe2L6586JAFxR75puduUG7WXycoe1GCc/L6/LEbjIv8jwyIdhsAng2A07HHEeYEzMpMQp8iiOArIt0CWQbylhBMijZIkg/yPz0p5cE+f2G3tnK5uio+CcwElC4A7nTPc4OFOQswiAqJj96uZPM/vg/vbvxarNNjjM7LsgIeQ1ZCF0Pdob2T+btuW5+XbheBzXO6toc/oUzw7dkgAbguSKERwQ7nN3T3vZZWW1K2PN32Jma/Vd3rYZWrjSRJUMbff8W+Pts9nv25JFS23F1UbZkGUH2bfT51hsZj9be5UxbkzybMliZVxhM8j3XEi/Yq1irvFzVhSCLjk/fW9J8zSrb5ljMauxPjUruADTYsI8xp5rvtytAjXaI6dGBKJ78IQhED/yyCMJO7nIHCIyiLj0dS7D5ZBwIBHVIXJn2tIvbNKiD93PH7z5ka25wepWHau2zQdvZ9WxViWASZzIhnwsEJ8tmmBzG1vTaQHvNemDPatzy7JA6ERAZs+FQoJiEViLayPPoqY6e/DrN1ypKWpkrt1vlE1c8GW77/H++gNWsq2HrFVxBpV4m28j4PtoF8ajz25AgIzuL4FTgsiGZOnTCAt8DmETwRThBCWXKCmiLtLZFA7DOOVnctlADoM6Akk2Uam+5BXrQsVkki2db/bm3WZL5rX+Pmh5s7F7te6t8Z1TgwwWItLyLRDhtGLcAs8trKjh9CDtHCNCMZ1EHy6Ybs/P+tS+XjrfGQyY8Zj2iLNZq99wO2BUaTfHzqQvokATHODnbZ8l42tD59s4GhjryDR3nu/XilWtoX64c/jxfMmeYmxjTGCuSFUuKhsYf2SX4SSIlinw5Z+YZ6IGk1Jx+eWX2xlnnGG33XabM14L4cHZ9eMf/9jtz/fzn/9cDZMnfHAUGcg4N8gq9fMhTg7mTv4mcm/nW6c+6TaTbqxvsG8mT7UV1lrFxvQfbesPaJUTWQswvmCszpU5DQvs0W9eajP2wyq9l7PNB+dWOgqDKbIhaxiG0ULS5IKjqtrpWy9++7ndN+19awxabFj3PlZl3W3igtYNrcNct9FuzulRKSDz+2CWMBjvWaeRO2l3DNK0B2s7WRlRiKBOFejIMXFY0ifJGsaozbnpf+nsm4OOgo6RS1Yl5+YauK9sQJ7DJkKQSMVslD3tLbMvvwsWqqo2W3VHs4Hfy33MtZDr/ovxINgNvSXax3wmPvoN47lYjsD65kZ74dsP7Isls2xJc72h8YRPfdCorW1At8oInGIsou+G5X1+Zo5E18/3Hid1zUtc+amWoPm788WsNlZr6y6ztX315TT3PHmWzBcEl1OSjGDKQhjyKUWFjSDeXEAbkFmC/lEO2a3Mo7vssovLXLrzzjvLfu+ZYlIhM2hxwPNLOjgGAqI4UqUQisygPTGckvoedhaR9omhS+SHJU2L29K1WSh4EVFV31xvvUJCU74W/RV7r2fd6ybbwsY5rtTHsj1WztmhAYzDYpSsIWrbnCjyPTg0ZtTNdz+jTOHQCKfA+/dJMa80EAiIggovzizaCNeAUZqIBMpTWd0Cs0kv4PEw6z3IbPTWZt17O0Hj6aefdopENCXYK4z8HaXWO0RQHjBmZ2JYQEDNtZ+Sssm5M9342xtJEJy5x5JDeveib1qjovou1+ak6EDP/mabH262eE6rwtFrQDspm+eP4Ma95dthiOCbyNFFH0Ew9Zu2Fque8Jr9hrvXeR881C5jnrG8oLG1z1cCtBt9MV5JPsYhf2fc+f2Tko4bnCEfv2I29X0zol1X38RsZLz0bsYM68iMto3CzZa37t1bS0hQXgBhH+MAQjZp0SgH+TYecs8YKb3REueJB8c380SiTTlLwQknnOD690EHHeRKmB511FGlviRRBlx77bV28sknu3JTZDaJ/MF8hxMD5ydRjN6h4fcPU9BUfqhvaXQODahbvNR69OnlltUFTa1lk7xR00fJ58rAbv1sx+Gb2nvzJ7tzL9tjsK3bP3cnhHfIewNsIalBDgvx0cKZdudXE9t+n1W/2Gqr6owe67dfZ7Ud0K1HRTk0gKAGxmDUqYFj0WdbY2vBQNi6d8cXdt77zzsRddzQFW3Lwa3lXLARYC8gWpsMq2gmAwZOYN88nAEcC4dKpvsS+XLYucw7XFs4qyBdkJe4bu6v5A4NZEKyMNiPD12vT5KstuU2MBuyhlnDIrMe/c0ie60gf+J8wLaTr3nAk6gMEc8BRxYyaTFLB3evrrUdho2xt+dOtlfnfNwhowxHxwArfzsBMjxBZ7RbVHdgjBCwRmYRYK9LZUjHDjV50ce2tHmp9avtZ6v0Wd1qI6Vge1T3stX7bmSfL37PGlrqnN1o5T7rueBbniVjH4cGGVg8d+YMnJ1Uecl3aXL0Cs7D3EWFEN8GOE4JkmDuQs8oB6cGcyM26n333dfGjRvnKgopC7UV5Tx/B2lteAAx1pGhIYdG/vF1FaPZLwgDRG0wYYjc6VXTu8PCWkUcUHX3NgNYPp0FbOi0XM9VbY1+m9joPutb75r8RGH7KORig8JGhka4DVnehvdYxmVneFbrs6yt1S//kSiFhDYlGiMqQJNe6evWEwnNeAyonfrmHWYzJ5kt+tZsxidmb91h1tzqaCC6CucHteSjzwqBFqNneMPcbJwTzMfMzbmAYN0uurul2eyj58yevtLs2WvMpn6vXIbhepmrEKxKHgmxZJbZR3eZffms2RdPmX1y73c1bBOAEt13iFnvge3DhkLPGyNxqZReL0AXk+Ui45eflk9jY8NygTmbvoxDMgryCmMFxx1jEoWfaMWEvPes2TtPms352uzbL81eutPsq486fCxm1eSzWMzGWcy2tZit4py7HlL9qVXPuSjzxvlxLmBEzDfMWezNgoOSdvBwPuaZcsuk2n///Z2yweZ+f/rTn0q2nonSw7OnD5x66qlOv5BDozBgxMLYETZm+ahTb5ARudG9qtaVUmqT6YLWtbRfTe925YNYh/LFkO4DbNzQjWzn4ZvZmGVWzdsmshjMCHopNh8tmNVOFiFAihK3K/T+PpCyR3WNnTT6B1ZpMM6izx7dnz7hsyeQV/j9iRmf2Q1T3rHJi+fapEVz7erP3rbnZ7XKhTwXsheQLXBuhPGZGWRmel0G42Om5ZSZJ/h+LrYHro8sZOwYnsaWBTan/jWbVfe8zWuYYM1BfFmMAE9AxygpyCafjDd77z6zT8ebTbjdbNrbyb9Dqak+Qzs4NIBnQ3vk26GRDsikZM3g2EgqA+eZoT36d7C7kJ01oLb8HRrecYFTLrpvDDBGcGQg3yNn45QMy+BR6pvr7M25r9i3DbNscfNC+6bua5sw73VXGj1K39qBtt4yW9tGA3e0tftv3i4glvWFMUK5bEpQMe4JTmCfxvA+LfmCElTop+zp48czwbWcF0dKye0AIQgQRZZkniSjLVc7SWdBTg02AH7nHaRtBYsAAQAASURBVOfQoEzALbfcUpINTbsCLNws/vEmEiYvhBKRO8v2GGXLfLdJd3NTs7U0t9jqfdd13m8ipDFoIiyWMxg9icAtRPpqKlyaeIcNwGI2qHtfO3KlcbbniI3soFFb2K7LblBRtW6BKGoUAa/0k8ZI9LPfXwHBhvmQvzd882lr1E6boBa0ljaa2xrZxr0TMc1CH24HBADGcrjEkN9/IVOYi7P5XhgEtXZO6g+eMZv0itmS+WaLZpu984jZtA8SCuZEbtBuRIFhUKWsQtENlF8+b/ZddKSDCKlv3sjqUDisUDpLWcMXZxHPxJcOKAb7jdyg3cbgq/QeYuOGdkw1JiKpHA3QKBUoa0T3pVLqGONkbrAxcFw+jbNJ6qTsNk5l7FO7nvP58mI4Efk93/hNRskmYxx6+YE+jbO23CATikyWf/3rX3biiSfmPJeJyoNnzrO/8sorVWa1wGB8iZY2RJYhmhsDtsjPHLzN4LEuUKq6tsZlawzq1t/W6d/qHEBWwjBU7rIxayMyXrxyhoUmUfbFL1fdxH635hb269U2sYvG7GCr9a2coAvAOUDJGG+s9xttY4T8wQ9+0Ob0oFwla/iT0zsa4p6c8Vk7+Z8I6egmvsjh0SAGZIBssjW5ViK0s4XIbf+C5palNrfhdWsM5lmL1Vl9yyyb2/CGBXEMurQVBmKCQLgndAyM8ZTSLCpzPjeb9XH79z5/qTVTP0OQnTF4p1P+q5DrgJeBi2VXWq7nYNt04PclyGpiVbbjsA2sV8TpQ78vR1mV/ot+wbzNWEoEf0fmJniAMsbxdKVZ9TPcvnZh28GipoW2sCm7Z0GlBJwKjz/+uPsdIz5BVGFHYr5ANyZTgxLcZP0gP/A7doByg/nx1ltvdXtsYMN+5513rKtTWXmNBYAIADbHPe200+z0008ve0GskkHgoK2jsJiTMhqO6hbZQxTTev03srmN39qoTUfbFx98aUO3WtYJmCwERGiXaz9ngWShRMhk0SxFSRGcGhsPHG2vzvnU/Y6Dg83C119mJetX28u9KhUWaTz6vHBgMO7YXyNcPxMhkBTUoG56/IME3wsSKLDRsk6M57BAi4CLEJdtRBzCVrRcViZgZKXfY0Sn9FLs1addtkkQKj9W2/SMDR6wojuPj/YiYgUhL6o8ESGCYTmXOrwZ04ByEdjU6XOsvqE1qiyY2WA9YiulLjUUwafK81wy/W4+wSGGUIYSR2pxoZ0sy3TrZSeM3tZm1y92Y3xgt14d7t1nDWLsYCygHDEHYSgrhznTlzpI53P0XZ4x99Jhw83QGG4jB4M718T4RmHjeVK6DmNGIcoPAPMTx2Zc4+QgYILnlu+U9HylteM43mGHHezII490JYhKXmpCFAXWWEqP0U/pAyWPyO3kYHyIBwYJlZ/KHyN7DbV9l9vaZtbPtQ/nvmeb9VzTbdqNXOTLH5YrrIcYkSmHxDpSCjYbuLw9P+szq2tuctHdyKIbDxhlg7r3cq9Kxe+TgV0FWYp1jjYORzgTNEJpKoyHjXGii5ta2htJCcIjGzRaCiesd/AZyj1mI0PynVyCDXyJZ/o+99wYm2lLmr90yQ+emtpqW9J3sA3qP9LpIOgy6BfIwPE2Q0d2ymemU0qWsv9ezJbW19vUGfOsqqpV1m3p8baNWGPDjOUq5HruD9mvmBt3x5OBi1nudv0Bq9jqfUfakuY6ZyfoVtWxRDB7VtAmtA/XyNjA9lUOZY38s0unJBt9l/tA3seJHR7j0YyVtvdzCBZD30a/wNmAgxS5n4wK7Fn51h25L+QFZDbmGfpOuQYk4XS55JJL3DWOGzfOHnvssba9UbsiXXqjcCLoyM4gLfykk04q9eV0Wdi0mg2ERWEgGoQoNQQTDCyliExKpWTgxMBYzIuFC+V/5syZLtK1FIs90+L7C6balMWzrHtVjdsQfHD3rrHHDs8AI8CUTz+yzXt8basMx8jPMhEzq+1htunhrf9+F3WCgoiQgTOD6DcESAR12hAnAVEWCPvZ1nwkhZhj57IJMFHdCEVO+Hn0YufU8CCsNC4z0mas2Lq/hE9ZRliIt4EknycigvGEIbcYGQ/Bh3c5Z9+IIf2tf1+UhJjZwFVtyaANXeQbbY3hHWUxnvHdL/Phv5E1h2GY75RyPwIEaMqV0dZkbqDMoQyFHW3FciiQuURUoFfiuDaMH0QWQjkYbIjm85GQ6YBDkfkVBa/NmP76Q2afUV4gJP5tuJvZ6PhGwXSgfzLGGT84Tal/y/NkfGS7iWYqmH+IAmVMslaUMvsonb6FY4N57Oabby7IZoeifGDtO/TQQ93aM378+IxrvYv8wNyNEbSoQQhdCLcvwrPPOjmdtbGUEdrxQPZERvIb4DIPY0TEeMc6n6hGf6GZXb/Enpo5yRY31duKvQfaNkNW/m5/v86NfwbsuTexYb59s+ayFgut2/uPXNP2GPG9w+Kpp55yhkvkMDJBcZygH6LHkhnCsyQQK9vyk8h4yMHIndmWHffltZBhFzd9YYua2mc9NDY2WY/mdWzx/FadCHmWADMMpvH0ceQ1rivehusFYfZnNufVO+3beYtt1VFDvpe9NzzMvp6zyOkK6EPI5YkqmUTldJ+xgT6Y6X6G+Yb+RrtzTcinzFPtjPBF1DHCWUacl35An+b6mKvQKUvp4EC/yGSjde4BxxG6kS8tz34ar895ye3l2krMuld1t00GbWXV7AmZJTgZsBXiNCVYkbGP/ZDAzELYtbg3SvnyXBiPZIuUM5deeqmdddZZ9tBDD7lSvV2RLuvUQMnYZ599nIfr6KOPLvXldDnodhg+UPTwtmIAiaaNi84NAiklplgMMdKxuPu9GF5++WXXJ8rNAdMVwEmBwIdgE8yfbovfftj6xOqtvqaPfbPMOjbnuzKlfqEnqoGfeSGAh5Va/o4Qx7+Zbh6MoOfTsBGwOAZKKMJMpsZLvst844Tr98abTXmz/QfW38Nsucyi9rhfDLluQ/UCgrD78YSXbfWaydat+rvlmg3qR+9hVtu68SKg2KG4A8IlgrzfPBUw/PKi/Uh552fuAcUpus9RKdYDsn4wPHFNfvPOsGODfpaNQ8Hv35GO0kp/Q4FLFFFN+3JNxdrkPB9ODd8GGFdRmFzkXHOT2dtPmE1977uNwjc1W32zuPuvpAvPiM3rEKZ9ViDRTii2tCslYAqhOPrnS7RYucO4I+oU48Add9yh9a2TgiHngAMOcPPYE088oSzkEjkzCJhBrkTWZJ0uxsaxonxg/GFwQ5ZlzcYBjqxDf2CMlipToytDkAVBU5RLoXzZTR+/aa8tmtlaWrLbAFvf+ljjd4Z/Psu6jl6I7IdsRoBEuBwVMhnBEwQ1ZAJyJn0D3cCV221ocOsxgRnZ2CKQy1jXa2pbbHbD/ywwnw1Lpn8vG9SNjPiqjPsv8lqhg46+Yn+Ez1+0kVWhslcrbWm23Ni2X312N04BdHbaCrkuHJzBZ3B60H5ed2P+9fs1lhKcYlwTcwHt6ksLeZgTkKuzcSige7He8PxTrTFkNnCeeM4hb0Qn8KVUmeGMCfSfTHVsglAZn14On9cwxz5e+IHVtyy1PjX9bM2+61rPmtyydpDz/XNC9yZIiwBKnB3YF8geKQTcW7wyeOXI1Vdf7fbxu/fee10QVVejSzo18GIddNBBdtVVV9khhxxS6svpVKTr8SZVk8Vwzvw59va0j+3rr6fZoD4D7KAd9ylu2qXICyxmKJBErPg+wPNFuGEhCPcJhEeEI4RRDF0sFnyfFGIEXdIL8cbzvigePDciIFD+EPgQEHBI4OBA8EcIQyEMj0+ECoyXOKB85DQRS+GoSIyOXrFMB/oCQmc8YztCKQIGwjN9CyGyvnmpzWmY4f4+oNtQ61HdK66DhJeLGGej8I9fNPv6QzNqG6+8sdny2QndXI8X7GkvjJb5yt4gWgtlBucEzphYU53Z4m/MiHTpM8KsujZpJFy8dHz+xvNBUSqUAFgoaGeeIf0onfI93CttyHyD4EsWEUoycxF9kn4cb61J5dgg8w2DCGXKfPYRx0ap4Zy8fIRdpoqJ36wv2fdQ8H3t20zhu4Xc+J72RSljPqAN/X5ltCnKHBFVXT1DAeV6l112cX3kvvvuK1l5BlEYWDv33ntvtxY++uijGW9eK1KAyprG3IccyVox9fOJNnf25zZ7zkJbbY2NbePNtlUTVxjIe8ybrCm+NArrH0EujK+wIZL115dBZH32AQAEULH2oaOwVmcaZCNyB/kNYyTPgefoy30Ccg/yLjI/BnOeIwZismz4l2AcwIFB9nfYwJhpoAcyHLJKW/b2dyAXElyDfMT5OQ/r86RF021u42IbUNvbRvcZHrdsKbIVAV1cFxuFL2z8yJqDpVZb1c/61q5l1bHs9mr1e6jRNvRd73jJFWRY2oGsAdp0DXS2BV+37qPYe5BZ78FJ1zhe8UqGc1z0t0wDb0oNz5tniN6Urg2KeYb+iS6KPuWDsfzxWH+iTg7eRy4eO/Z7h1G0/QjAQ99Gv/Xll8k29FUE6LMEBGdjI2HcJdOfcNAwNvx4ywTmVcZzIZ/966+/7vRh2hwnNfo2awCOUvpjJQQ3FZqbb77Zjj/+eLv99tttt912s65El3Nq3H///XbwwQfbjTfeaPvtt1+pL6fTEAQN1mLvY95hyrWYkUqbuH4wBuyWWGAf955ldTVN1tIS2KS3PrRlawfYiXscWdZlJETHCGsWSgQtFlmfJuzrREY3xcKoheCAwAh8l2wdInypB+g3e/SGclEcENBI5eQZEtWIk8o/03jlixBeELgRrsL7ZfC8cWIglCEYIejx/XRL0ODwQkBMVMcVYZo+4hwVC2bYVy2f2IBhrZFVVVZta/ff2PrU9G8npPEqZD1zokfo9ygeKAjcOy/6eDZzGe2FQ4m2519qZZbDng6lBuGe/oRwj0KKwkDbRw3lzCkoz22ZCXFASUAI5/nQ3iiLzEs4fXBIpDI2812cWMBzp89yPRyLsYPT1kcSpdMHfJkoroPr99kg9F2iFL0iwphD8c82aoh24XiF2n/Cb7rJffOsmFco8cEzwwFK1F6lOdTyDW1C6VP6y8MPPyzHRieBtYlNG+HBBx+siMi+iqFpmlnDB6y2ZlX9zbqtb1bVK2nw2pg1h9ioITNdtPT0GXPtw4+/shXX2M1WGl36yGGRHqylOCQw8GFsZq3HYcHcybrLuoIM4PF16jEo+mhoZCjWYNY8HI2sTcijpcy47IpghMQxhfyBU59n542n0YBIL8MRMEL5qbBxmHLKPHPkY2Q1nxWarqyN8TpR2TEvv3F9BGi8Om+SzR9cZdXVNW6vgNX7jrBdhq/f7lrblbctEASGIJMiD6JrMA6492xLZuEIQj/xpa7KIZuiHECmR7fEsUH7+gz3qCMAPcEHTMWDNqWNw9/jmNg2kOFT6cN8H/nZ6zfo2b5yBe9xfpxcmZQPZh6k3+KE8/0HvYNr9A4q71TJNouN/kngYbbl4FLBs2DrAL9HFsELBE5xP7Q3Y3f99duPz67I3XffbT/5yU/stttusz333NO6Cl3KqYHBFEfGLbfc4iKpRP5oDijnQt3x77tTzNaxqljiOsJvffOh3fbMfbbGZq2L6eS3P7JlVxlpo+cMsJ23/WGnm5RYSMLlYCodhE2EPhbncN1PhC82OkMJ8YIYAlOiWv84NKhVyILNgsiiyvfJ1vCLq7I2SgsC1p133un2zqAf81wR0BBckvVnBGYEM5RMBI10oE9QAitRnwnz3vxXbcLEt6y5qcUGDxtoywzub92be9l6Azd3wg3XiAKcy34c2YLAScaEd/hlYshFKPTZHwhxnWXOyBe+nBjtwovf6Yv0HZwVKH3MS5m0G/MO36XdvcM1V1A+UMBRZOKV+OLZ+iwPfg7XHqYPoCTj5ENx5R45HteWq1OAcekNQYwxri2f2RvM6VtssYU7NgEMRE8RTcXzwbDBuOzqEVUYd8jYYG174IEHEtaqFpUB8wfKI/9iOC1UNlSXpHmOWf3LoTdiZrFeZj22NktQ0oX59NlHLrTNNx5tPXp0s6amZnvrnc+sptswG7nqD4tTq77IxHPwVyq+vCfzJPpBeC0noxhDHOsjch7yXaI1kTWdAIMxY8Y4/QJHI45ksgb5G+OUCPvOpm9WGhgneaZEeRMUBchwyUpB+WxQZKMNN9ww7TkXmYzxn8rp/NWS2Xbb58/b5NfetwHLDrbBKyxrTY1NdtDKW1mf+lYnGUZwnzFUTLysS4AEfTfT6HgM617elI7REeYMDP7MO6zpON/oi+h1vpxtJhkzPC/0ZuR9HCH52jeDPsBx0X/izf1cL32UfoJNxjvBuCf0JMYMTj4/fzLvZpsJ7uFcvhQyIOPSfvmaYzk25+CeuQ90DOZzUFb49/w/e+8BHtddpn3fM+qSZcu9O3Z6cWKnN9KdSkIKBEiWvrChk7CUXV4WWPrCAgvh4wUWXnpPSEjvvfc4zUkcp7h3y+pt5rt+Z/RIR8dn+pmqc+fSFWs0M6f9y30/9aqrrnL6uv3973/X6aefrvGAcePUuPPOO3XOOefoF7/4hVN6KkRwiMeHFNOdu7we0UxFI8lTfJ/a8bJuefZ+9XZ1a+7eu6mvp1evPv2S/umot6hr3Y6sjVLlDoxwbCiF8mAXM0UTccAmyGZldeYBzwtyibjC4UEkDqKDKANEhfe7iLDhfnjTWImU4HshajSKO+WUU8L64yUGWRyWDuuXdpwMCA5IFeMAEcpPupqdEDGiSlgDOB6k2zIh3MTo5heuVHd/l6bNmqqd23aqo71LrS2tOqD1SEewYBgmooXxUyowB3BuJIvo8QNGbq53vBt+MwXjBWLLuoPzLNfotULBxAxj1yISGdP8n7mEIE5G+HPpoZENOA/mCAIOh0kQ9471mzlPNCTfT48knBy33nqr8/0YLTAkmRApOfo2SzufkmI9Ut1UadKhUk3h+zmxJtJjg4jhK664omoMkuMNRGy+7W1vc/YbxniYoRH0DX5BGsTQ6ZGrjSdI0SQZZ/G4Brb9Q3ff94xOPuEgh5s+v+INRWsna1vvQofLFCpbrVR7DJyZqPZKBmsike88L3gi3Mkc8Mwz9hQMigTL4PA3J/1FF13ke0/InCWS1w3LAmC/wzCOkbLS71ulg+cBFzLem0mJUXeGHGPBNAXjI13gB8/cml8D+A+fdwfQPd++Wr+88++avnCu6hrqtPm1dapvatDRk/bSEQv2d8YqDhI4ZynLZrsdFJmCwDEy8sOqGJmBbDHWCozzrEvZjM9CA46NfQm4Tbq8TlApDrxUQTOF1hg2P9Hy6OAgnBsETrGuM4cJXCQwi3Wd8lRoDOYlwetlUf4zPiQNrpBiGxPlo2v2lGqyL++VC/70pz/pQx/6kJO5euKJ1V96c1w4Ne6//34nIu7yyy/Xe9/73lKfTtUhHo8ppjs8r0YU0WxFI8lT2Nb3bNUNGx7SS488q7aZUzV9wSxte32j3nPgOVrx7PMOeS2njSNfsPhSwxLihhHDuTam39AOKT4o1bZJkfI1arBh4ISgpAtiAU+5bfC2SXF9CCuafafqswIZxAiJoyPVpoOIIaqKSC0jnyEKC+450dRehyLP9dxzzw1EvECg0s1tIkYgLowvxhoCFGMpJBySBElbO/iKmmfzPbaNRVTf16I9Ww50omoQLjhCSmlkglxxDdkYLHNxhIxnYJQn4gjRwbiqpijcXBv3ZYRYjI1p5FfqA7M2I5DzPR68ixKCfA+iifGP84noNtZ8nB44u4NALN6ruHoUUZOikSydEQPt0pbb+JbhFyJS7URp2rKkUeBBAiPOySef7Dj3//jHP1YV5xkPgBdhUGWdv+OOO8pDRFcb+l+SBlf6ODVOlqIp6op33a/XVq3Qxs3btXi/3dTS0qhHn+nT0iPOdZ4VvLJawLoBZ8BwhIEVww68u3eoT+0DHWqsadDE2gllnZGA4Yu9HKM2ewYcz52pjZagrBt63viUn76wsrj8P13GL1kfcF4zkIUoPFgrrZeGAU4PH0hWFipT4ADDwOvu65fKsA8/YZzZeIJ7M244n77amO4aelUNzY1jDMWnN+2n/eYleucR5BcEX8oHOO4yud58HSHjFYwHu8elbuRdCBTKqeEtW01WBZocbZ4v94c3o1d4Fhzn7rvvdkpSYaNiLcdJyVwNJMAtHpMG2xP/prR1trpg4Gkptm7sa7WHSDXFKX/461//Wp/85CedakVeB3+1oerVE81jaJTyne98J3RoFAiRSFSR+HzFtXrs65qX8nOzm6bqyCn7K35YXMvvelSrHn1eZxx4oqZPnqrXhuubVxsQvBh2IO/9fT1qir2gyNB29fYNaOaM6Zo4Z5li0YlOFBIbQbkY8jEq4wGH+HHuEE/vZgFJIsKcTA03/DZ/SK01eEpHUClDxTHL5V5UO7jnPFuieNyAIOQLa/6XieEO8kgkN84Norr5nGWIQDAhYbtHFun59kfUPdSp7VvaNdAR096T5qm3JlFKiIwixEYypwYCGmLEOOQ4ViYqSHHLuRNJmA1phLBlXI6GKJCejVKsX2qYLtWNr7InGCUsNZwfnilCk5raQaV4lxJcA0I70IbSG9+QbvmD1L5VapkknfpOad5ezn1EbBD5hDHJPU/Zt6wRIueTrqQbDjnmFg4Zxj77B2s+GVg4O4JyaAzEVqs/tmLk9/rovqqLZrFX9K71GEvjCQEz2CHVJS99ERRYb4juJ4rqAx/4gCNAQuNaZYA94/3vf7/jqCcbPHRoFAi186RBIlGHRl+Lzkzt0ABNh2jhogHF9bL+etW9mtg2T4cdc6GzH8NTSxldXQiw/1l5UAxV67s2amVkjbo6ulRbV6ND9zhYh089yDH4sE/CqQPdV/IATgjOG42BjmCPcK+DGJnR85RAdQeIJCtrS4AVfYvSgQhfAgeqyUhZ7uA5o+u8zyyIGFvmdibVENAHrNfMAbiNdx6QLXTw/vurYfsU3bX5OQ0NDGrbuk1a0jRP8xZPd8YMc425hGbyc2pgxEUXM44Zs/zAsYIuNWl9ErJxrHBuGX+me7u0c6NU1yRNmV+UYI9yAc8Zp6eVqMOxwX7P2PErKRsiEei8pudFbe1f4/ShmVw3Wwua93eqbXD/cL6jDbzNyNmTCExjPqHFU5X3gjdjBwA8F/Zzqkkwj/mOwLIwh3qlHfdKgzsTvxPw1HZc5pncOES8Dg3ne9cWzanxvve9z1nj6Pd22223VVUwx7hyarDwnHHGGfryl7/sdIIPUThEhMe/UXFtdRqFR0WKWXoP6eJJizS1v0mHHrNA++6+l+656x7HcFJNkbZusPiy6DplZXpWqHsbfTYmOjV/aWS4cfnVikx8k9NIjQhAjEsYhUoZac7mg4EZQkaard9mgREWkuTO0EhFOiFT6erW40jhuonANmNaMgzFh/Ra13Pa1k8prKhmNy7UnMY9QqGSA4iKg1xAxN2G4SAMbZANnikClX/7kRYry8M88TNK83fGIpk+GCWaB2eqc3tMBy7cV3P3XqCaaIKk4xS1Emc4N/guvpd5ZcZaiCkGV6LGKHVl5QoYm6lq+WYKhDWEONsoLgy+ZMbg/OMeQQZ9DfSxQWnD7VLf1uEFJirNOF5qDqYnRCWA5+ReS/g398/WGZ45r5WL8SZbsBfipOE6GbtkHjE2GFOMY8RWVuOrp1P6x8+lgb7E7907pWt/KV38WWnSVOe7cFiw99haz5zj2ET2cT9xTPhFyI45TE/PmAaGEGnqZjN32S+CaBYei3eOcWgAfq+JtCkaqZwmzThrERtEmn3qU5/Sj370o3DvKnMw/nlWCGnGdTYlGUNkCRqCNx4rDayU4n1SdLJUl0GQAE6PlhO0qTOi93/4Pdqwabuzp/LsKrkMbCqwfrNnTJ42WcvXrVRkR0QzFszUYN+AHn7hUfW2dWivaXs4a7n1LkCPlMqozx4NR8M4jO7xBtMY4HtEmWYSEEMjWZwfqQAvY1/DqI2Bm3+n2ke39G3R8zufVe9Qr1prW3Vg2xJNqK2e8mXFBLyCYBTGnWkAxl8QY9D6VqJhWJP9eDNjDn5idf693JCgDRxr2JCmNDXpsB1TFW1t0H5vOllTmhJ2DXNgo1PZAzBwc1zrT2FBYIxnHBscC+5j5dSCWH+YM3yf9X7IBpwXvBJuzJxKGtG+8UXp2RsTVSXAlAXS0vOkYZ1V7WBNQsdZthjjiaBOOCw8GGCj4PlXqmMUJxvZTcwLrpNxiibm/9n2gwQbeldpS/8bI79vH1inaE9UC5oTazv3ijWX+WUOPuwBrMWm5bi36XqWuMc8dgW0EN9LpYnjjjsumOfR8VQiwMnAvzuelNoyLZ0bGf7xOGyLPFY+8pGPOGseNnGy6LPN7KoUVK1TA0MsjVEuueQSXXbZZaU+naqHQ0i0G3HJWX9207qN2mevvdTU0OQQBdLTWJCqEgPbpP5NUqRWGtqu5ubRiI3Zs4YNQG0LE38fJh5sqNRqJsq1mJsmwo+G3TgU2DzoS8DG4wdKlmTqiGLThPCx8XiPh+GM6+SaOS6eZUgEm20q4NDY0j/sDXeiBFaqJlKnWY3Zj8fxDp71qaee6pB6oue49wjEfMceRmacCxAlnBoYPImGYkzxjCGGZii1seAHa3yGA80cFHtH/NOoERNLly4dUyeXscc1uaP9eI3xBoGFwDHuceDlc80YTzCuc+xcHEJ8lvuEYOHcIGy7NEJsf0Hq2zY2KmTzA9KCtxadNBULPH+cPPZsIMEIWHfUD/ebdcSeLeuJ1eiuNDBO2QcQwswTenNwbYgBXiMiF0KfcXPzDa9L/b2jvzslEAeltSsdpwbgXuHYcK+7bmMSYxontnte+e0J7vJpPC+ib1n32eP5ofxgPnMsFneJDReG4h2ZOzWa5kudOEbsWiNS3eRERFYRwXy/5ZZbHMMdou8LX/hCUY8fIjt885vfdBow0i8mqKyjECkQbZUaDs76FmFKiEcapGiDEwTB+lOtZVcI7nmje636hvpUF61XTHFNaEusg/QCmL1ortomTNPstkSEMfsI+ynGTct2LCYwDJLBx15CcAlGbpwNfoZo9sFMS/OxH7Lns8e4YQZneIBF+Z5wwgnOa6myITsHO/XE9secqGOwc7BDj217RG+adrxqo1VrRikYyAKFW+BUY+xhJM6m6bIfeLY0DUc7wImwJWAHwjjLeIArmfbEeYUzgvXArywsZangP/Y5uLcfGKtwc4IR3DzGO4a5PgIDuW44EdogiMAODNGcZy76woJX0FKsARiDd3G0DA1Iz9086tAA296Q1j4jzV+qagTjg+eHxgCMDz8tyrOz54dGQytjKA46C6cYwJmA1jRejyY35zLjItvM9x0DG3d5rX1gExatkd8JOsTpaNlZjOF8AyfRQJwv85VyVOiLvIOnsNl5M7kHtmf+edaF6Fwp5gnMjaauZFMIfPrTn3bWQ/r4PfDAA7tkylQDqnI3ZrPAG7Vs2TJ9/etfL/XphEgDFlMjkxCDAw88sGI93inRu1ra+eio17amTop6PLhOT42xERAQcDYaDMAYXYshPHgmN954o7OJEVUDKYOIsdFzLt7SYGzqvC9ZnU4M5AgS3sNn/UqLEXFChBVkE8MORIJNlpRzxkQqkKHh91ro1MgdiAJSOllP6W1BI8Vc5yXPFHHgbRYPLKIKUWMp3fzujvL2GkvJquBcUvWpYPx4G/8lI0+IG0qcQfA5D8gpv+diBIcQImBw4EAGIclvectblAtYF63RG6TZPQ+Yh8sffkhTGzu1cN70kQyX2tp+KdZXlEbHpQARPOYAs7GVan3gWSMkrSRHIRviFQqMY4sidUeEM/4Zq8xR9gdEc1rUJpkz7EcuJMZSbdJ7mszpaHBnRBkQfJw/+wB7CRHu6foqpUJE/gIyqizGfm2rNPVEqWO5NNQt1U+TJi4tiVOQcUrdW4wkzPkPfvCDRT+HEOnxi1/8Qt/97ned8ZvM4BWiPOAucwGvZf2pxD0gHQZjQ7pn84NqH+xwOhrGCHDwAKN8c+3YtRF+xL5h/KdY94b9iua7PA/mELoGgwu8KVlpQxwROD68xjW4gDuw4ZBDDtnls7yHEnEEBcCpOC7OYwy6cNNUmZxb+jaPODSGv019sT7tHNypKfX+PDVEasBnGGsYS3E0whfyKecDt8Mo764kwDNGm5KFwA/GanggXJlgp2Sw94NUhlz0CvzLq4v8PoNmIfuVsc01WwmjXDSVlXpmvuIUPOecc3KubmHOJOYdTkD3fvbqime1bcVr2mvudE1oqtfAYEwNDfVSlyuQqsrAfWAtZF20Z5OuHyJjDg2C3sNBUMrKGrnC1j+u262ZsdUwtrD/sEZn4nyM0gzb+5rHrgVSZcalK0VnetcL9AT2CpzVODlxbqfrq5QSNc1SrHdM307VpCl76UXtAdJQfaJROPehZg+pJj8nbq74xje+4Th+sJFjb6u2kqlV59SAoGBAgvj87Gc/q07jeBUBguGeVJmUL6pIsECTspb4JfE/omOjdWMjRJv9jSkYgPCcQ4ogTKkiZIMAhmNqnvJ/DLts2kQjskH4OVXYgCBXnCeecTZ1Nh02SkTEddddpwsuuCDlMfkMPRQQJhBcPk9aL+QyXX1ESk45UeouRDV+6n4WEjxPRC/p/35iMRNAEv0Mrsx9DNR8N4ZZjHqMISKQWBtYv3n2vM9KkOE0wKiaztGFyMkmIhMyimiGtHFcBBAElzmAww0C5u0XY+DcESm8h88iYCBWCB4ETBBgHjCXmBuIKe7B0oMP0Y43HtUTz76q2toadXT16PijDnKiUqsVrCm53FMcVzhAkkWCVhIsGtHNb4g0xTiTtiH9nN2l6XOlLesS+xJrZ2ubtCgDh8gwMD4grFOlMDOvmTvesgbMSeYG4p65TMQQjbItMi4bRCOTVROZqaE4YiERIMDvvJ4VMEzh2CgDIMD+8Y9/OL3gEJO5OkRDFAY8m0svvVQ33HCD86xClDcw1FlzTLirt5Z/teD17tWOQwNggHfK8MTjghVb6NTkukla1DI/Kf+Bd8FlcjW2ZgrLyGXdx+js5mloeD/AB9nj+L8ZpC17g1JG7DXpIk8JzkI7cX3sX/A9vpOAnVSIJukhEGqM/ME4oDQlJVGYn7mUCUWXwkm8OpFxBs9gbOBAwy6E4wOHgJUOgrPj/IAXwq85H8p7YtxPxUlwkMIjM40udzLK997byVgnopz5hp5nTOLUsT5lfpHlXAeBUswN+D/XwTwl8IuxG0SgI9/BWskxOEfm2cJ5c3TgHvO0cs1GdfcOqLuvX3vPm65Ze+VfOrRcAW9lfcg2oI1xg8ZlXalEp4YXzAWczO7xzTzKxKkxs2GRXu1+auxrjYuyOj5rO3MiWeYL5+VXwYPX0SVkW/F3NEY+tgu1HiRtu8dlp0Mv7RqcmRLsH7VopdKXfIpEIo5t/Pzzz3e0xc0331xwe2IxUVVODRb7iy66yNkA/vznP2ecqhqidMAQaIKjuhGT4t7IVkdmSM1zE3+rnS7VpiYnkBgWaG+zvKCBA4OSVxZV644O9yN67rI4EEw8wWwunCfCAYcMpDEVOI4dC7KYTQmy2Y2LtKbn5bHX0Dha+iREfsAgDMn1RvJkA3emAWML4kiZJ0g83+ku4eEuHcR7LAUckoIxF0MpgoDXbMxAgHCeMEaZG8yVbNNZETjsH4gMzse+kzUKEYSTDfDdkAOugX2H+4KxxH08BDZzJaimv1wrkcFEziACnSiX2IBmDG7WjGkJw/GKV9ZrW81+mhI6833BvWPskfFWyWAs4gDMyTlTUyud92HpkVukbRukSdOkI06T6huzrltt5Tz8wPgnetavVjPO69tvv90xRDC3cXKQWZstOI+G6IEais9QTF2KqsVxalR6MAtRZr/97W918cUXO5kb6YxuIYoDxuk//dM/6fe//72TTROivIFRxvbqakfPUJ+ToeHOKIhGIjpk0mL1xwfUGG3QvOZZqvGJojWwLxKchPE133JAqcDzwJgLh3P30MBolswgCKeD22HkIsuDz1JiGu6FkRejb6o+T7zuzgTIJuBlZsMsvRJdqYHYwMj9nVQ3SRPriluisJoBf2Z9ZV3NljMzJsxZgFaBUxCQxPw3Tk/pS+NL8HYLpmQsYcjnfeZ0YB4QRMVr8CwbUwQ3YSTl/ZYlm+15moYxhxxcjowVeBK9ZayhONeBtsC4TolDtJPb4cH5ExASZOUGrocKDSeeeKIzPx00nqX9a25KuErpUbimR7PmVa8zn3ufzlaRCtWw1zDusOGMjIEs0VY/U7tHDtHWvkSj8Cn1szW5fnbWepfskFx6z6DzcJbjyGQsk6HHHpFTKao6Ap6WSX1rE783zJUqvJdSbW2t/vKXvzhlxtEYV1xxRdZ9P8sVtdXWtA8vKRtjpTYFHU8YT4JDCImalkR5C3caMxGiDZkb3yHljHUMc4Wsh0cUCtEsbuKfSjCwYeDtxYEBYYM8cn4IDgxYEEU852wwhcCcxt1VE6nVtv6NTvTUzMYFaqsvTXpftYKxAOnOxalhESyMAYg9BJ6yEERPJHM+40SBWAFr6Md3IADMsYYDDcMpIgaxgbEf4UsmQy7p2GzsHIOMMc4T4WzlGIxcWfNxDLqQJM7BjLxucD7JyihkCzIMiKK0KJQRkOk151SpZ73j4Jg1oU4bt3UpLIjgD9aoZH2BKgUIasZeXtkmDU3ScefmdR6IaoxQydKXmd/Jot2YZxgveBaI93Sp5qnglMmKVF9PAzIb2efPPfdcx3hRrY39KgVwGyLbvve97+m8884r9emEyPCZjY+gKamtbqKnRJIcB8a85tmqy7DvA3sK94ugDgKaCqnNzDHvBhHyyYxYaAfKiaAb0RV8Fk4ELyWwBUMcnNFdnjEoNNQ06MgpR+vlzpfUO9SjiXWTtOeEvZJmcITIHugAAolwRmRrfLSSxth+4P3wI3gJjjn4hR94D+sDGQ9W0oxju5t4w0/QA5a9QWYeGpxxh4bJZX7g1LCsDcYsDgsyu4EFL3BO11xzjd7+9rc7zhV4gNd5AQ/MteSUF/AvNDrHQvuM4XSz9pVaZ0g7NyhS16Sa1o1V3SQ8iDWPZ1ypRmLGAvMi30b2k+qmOz+5IhPHZqpnRTY/dgLWgAsvvFB5ASeGk2lRPWhubta1117rrDnYzi+//PKqsMVWjVPjBz/4ga688krH2JSsFnuI8lo4MRiOq2i3SUdJ2++T4n2J3+tnSc3ZNywkGgUCgsHXrzdFvs4MjFF+kUwseBA+otchXXjC2bz5gYzaj7fOLUQTAVIoh4adG/0zwh4ahUU+WQcIB8RAJsY5sjosso6IAgQIwhViTyQN4xBSTwq3bcQ40xi/vA8nCFlgOCSyydizbAjAdzG2vcKBCHOMtTY/EE2sZew9NuaZo/wEkS3ItRNxgtjwSxPtHxjS8y9tceYl9yIVMcHhg3DhOzlXfohKS1ferRzAubLm8VzyAWMIocrzIcOsUogcBhsMOoyBghq4B/qlx+6Tdu6Q5i2U9j94TElEMpeYg9w7jADJnBrsD6nS8BEczHOisSjrVqkisJD48Ic/7DhozzrrLGd9KWT0dIjkoHY5z+Bf/uVfdMkll4S3qgLAPsfeXSnre76Y2zRLW/t306qu10ccGkdOOThjh4a3jAqZD+lKfOb6XNgb0AruDG+AoZg9DgMzQVVETGP4tWAOb/lPa3LOe9AXhSwr2VzbrCVt1dkcuVwAP/WOiWwAF8eYmY5LcBzKXR188MEOt4bnm5MCxwW/e7MxLHsDvkMmOfoCZOtEYywTDILhGOectyIB4x6N8c53vtMJrEI3cV04PzgfzgHdz5gPyn7CucDHkvVNXLujR2vWUNquQxuGA81ScXSuD6Az+F50VSWsw4wLgtT8soszBWMDPcgz4rlVSsUYK2nL/3lehVxLB2Lt6o9tUUQ1aqiZrZrIaIkpHIk48ZI5IzPJ6jNgP/jrX//q7A1BOQCrCVOmTHHKqJIlx7p52WWXqdJRGbMtDf7+97/ry1/+su64445dIj9ClCeI2CaKuRI2usBAaalpp0uDO4czNybm3IwUgoVxKVtg+IRIJTNOQ0Lw0Kd6LtSvxaiLp5fUXYxsVuMeYzNGGOoxAgyH+TSYDlE9gKhkSiwwXLPRMh4ZawgMshVOOukkJ4qQMQfp4TsB74NMI4isBwf/h6RZFFQmYF5AgCC2iGu/aBVECIbYu+66a8RRx3ER6oBrRIwz9oMABBmhlazuJQ5Gjk+daYSOX51Rzgfxz31BJJnog8DixEQo4Qgqx1qwbpGEUMi3jj3GFMYOz5dyAgbuBa+nI9PFAM+QMoMWnYeYZQ0teLQ+Do2ffFNa+zqTQYoNSSeeJZ31dufPrPeWfcH9S2ZkN4dZOuB0JGKXTATEYCGzDysV3/rWt5w9lYwNjB3VVP+2kvr0kQX4zW9+s9SnEyJDnkvgz3gq28b+sKRtf+0xYTf1DfWrta5F9dHcDFNw+1zXGYI6MEz5cX74CXt4svKP7M30rIGzwVUw8PJ97hJVaAuCY/guDKbsifkYwkOUD+Cp+ZRVRgdkEhzBe8gGwagHb4bvY8Rl/MEJ0RUWHGvBQpwbzm0LZmKMEhyDATwbAzCaAp2MI8HblBnA+dAYlIGDa/HD9/NjeogAF7hTUFkFfN+xxx6b9D1WcQEkCyqCo3Bd3uBG9k+uFQeNu5RXuQCeijZi3HGOmTjFUoHP4gxGr2KjYZ2ya+b/3INyCOAxBwZgbHG+jO1C9xvsG9qonYPLRzo9dQ+9psn1R6om0uSMLcYRc4sxmaqnDe9Ll02CnmevQavwfipDFLJseyVi4cKFTsYG/a34N702KhkV79Sgxvl73vMe/eEPf3Dq94coHwzEBvVM+wva2LtZ9dE67T9xb81umuksMKAcjEdFR6Q2UaMvT0C6ss3SgJiwwaZzWviBzYbILQC5IQ0cggX5ggiQmovQYEPEaJpNvdoQlYGMmhCnAPMdA7Wllpvx3Y/g8TcIlzkZEK04EhhjOCvI0EB4u+cAEVMIEAMRMghjnAKIbDIdMgWGfsSOn1hmvBPJyLVAlHAIENnibkCKAKIBFwL8lFNOyauHA8dJVsaKaDPunz0XrtkvMoi/c7+8pcNYB+zcuGZ+cMbkE6kUNHgGPD/rsxIEuB/u+trudZUIPMZSIcpYZAoEBmOc8c08YP0tiuHm8Qekta8l/o1DA9x1g3TUSdKU6c7YYP+G/KbK7knl8HCDtYC5gvGR+c14po6vGRTYY/iplGi3QoA18De/+Y3TcwSuS+ZaUH16QqQGY+/d7373yDMI73uZYXCT1PuCFO+XaqZJTYulSJ1jEMxmv68mTKhtcX6KDTNIwSeyDehgj+OzODDIxoBjYQjFgLtkyRKHe9JbjWOwL/NaaJyqTuRj9PaOCbiFn5GW120th8vDi+HHd999t5O9Abfxls70C9LAAI5jA8M9hvxMxz0ZrmhxP6cr12+8D72DY4/v57zc18J8+cUvfuHobXpw5cqRcOgkCzZjPnIOGJjtuSTjoclsCzhtMFKTBcG94hlxzuXi3LDSwUFmJ/CdaCivjuIewXP5O3qmlM4NNLYFSaGb0UOFdmiAjsEXhv+VmE9xDapr8BVNrFvszEHr1ZfOvsWcyKTNAM+V62PMEZCL5uC67d4nWyPGE4444ginT9y73vUuJ3DKbcuoNETi+RQzLjFY1Ln5X/ziF/XJT36y1KcTwoMHtjyqTX1bxlR5PWbyYVrx6PNO2mS5bGqVCLzsVus/HYggtwbP2ZSZYdOD0GCgwmNOtJTVHYXsYHwighoHB2SOTTp8ptULMgLckUq5gKhsSC4iFUcYQMBaXWQDx4FsE1nhHlPXXXedzjzzTCeyhvHo/hvfyetEyXjHIaVbeL9fJgKEx/py4ASB4GMUSeckh3gR1QUZTGbsQoTTeA9RgEEyVeRJMiBqcCgSSeW9LgzwGMAxBKQzuHHvM6mTSoQr5+mOtio1GCsm9ooB1jzGO2MzWV+IYtS1LbqD6darpduvYXCPff1jX5R223PEycb5pRofjz32mOMUzERo40hjDrJ/sOcwTu3es79wLET7eDcoMyYJKCCS6jvf+U6pT2dc4LOf/ayuvvpqp/RHKZ2cIXwwuF3qvs/1QkSqmaKX1kxVc0tLXoEE4x2suRjfMtUYaHGQbSQ23Ib1HmMf+sSMwvA1GjtbmWI0DIEdlVAmM0R+Qar5GNQIIoLLw3MxYMJjMVoSIMJ64OYQlEWGo7jHFGMe4zs80y/bmgAm6/3hBjyFLFbGrBeMYfg/nIZ/w3MInkE/pFuj+F7rD5IMzA8aIXPfcg3sRe9wj7zXxb3gPrIOpHPYMJfRUOmCWfhO5jzOpHLIWAA8FzRWsaqHsN5hk8Ewj4G9FA5a5grjMt/eGdne5y39t+3yel1kitrqD834WTAn0BiZBi4w100/Mw9xrnEc7AHY0nAYlkLnlRt++MMf6hvf+IazDuMEqkRUrFODxR6BR8oMDU5ClBf6YwO6fv3YxSuiiNqf26q3HnNuSE7zRDIDJZskhlab1iz+IJuNC8IFuYNwsLB5SQplUSCC1nMAYjjeDU6VBggV4wQnFc+XZ82YQQQQJe1HJiAGkIh8ohqIuKdMIMTeSgowZonQZi13jyMM2ZAPHBsmPDDgM/4g8PyNCCA3IeQ1v2ghjP98tzsNGDB++TyEnv9jOIToIdLPOOMMBUkgySakfBZkPtv5gmDgGvxEE3sh64G33rQXkDfueybH5t5jaGZs4GBCrBi4d6wLxZ7zrEl+dbiryZniBfWeGf9FwwtPSb/6n7Gv1dVL/+f7UvOo+Md5yNxLJjrMKJUp3KLDwPhjbWBtQuRY+YPxDOY5ax/Nqt///veX+nSqGr/85S8dpwYCr5jCP0SG6H1W6ierbFTCtrd36cW1c3XEUWPr1IfIDskMlHBE9kR3CRr+jTMj09KVfAdZFxg3KbXD3PIa9QgEwahFUBU8MXQoVhbYuwl241ljkHePDfSFX3AP3BuHRL7lRemJx3GtB531uWPcujmylfjkPe7yZrxm4x4+4i6JiZ6Gh3r3Azg4QVgEYtjnDDhHCHziPvB5xjQ/lDMMsloFmeFco/XiyBbmvPDrSYtTH8NvuqCsTAOnuPc4gch0QRMSKOktA1vs3rhWsrDgJV5L6Ezxm3Os38U06G/rf0hDcfqzjKK5ZpFaakcd6Dix0czJzgvnIsg0cMEdOOW+9xyDeYnOYiyG/ZilT3ziE46TlJLA5VSxoaqdGiyI1LjF23n99deP69IEleTUeP3517TfnL11+gGnlOy8qgEYGRn73vIpTGX6DUA+3A2Ds9ksIX/8QLiSAZLGZohx0Wq+h6gMQCAh7RB1DNP2LM35hdC0fijesYVxMdW4sPfhLIF0uLcWxgnrNA4zjHLeCB0+w7l4yyNxXjg83M30IDzWSBwxTGYGjhaM9swNiDmkmHMwUoRxFMJKdlE6EAECMQ96bHMt11xzjSMM3M4F/m9OlXSiI1lkCj2KiKRKJf65b4ivbFLkGS98p9uRhbEDYUcab67GfsYGjjUrf5VpxJb1YiqmQ4Vxg1Ot2IYV7g+k0sq1FQ03Xyndfu2oQ+NdH5P2WzLmLQgh5hrPjkhBjAL2DBH3jDWcd+mAkxIDF2OKdcHPYQXfY07yDNzGh/EKnMLnnHOObrrppl2ajIYIBhjGyAi0WsMhyt+p0d3dp8eefFnHnXapIjVh35lCGNpwZrPms067/5apxjA+B2dLFX1ONC17CMcKs78ra+wQWISGIBjDMvvZ53mOZkhEZ3ifK6+z1qYLmoJ74BRgfNh3wJ1xRNDDAA3h15srWaAF54vTxa2nCSDifHB4WB86jkf2LDyHuYHjj78bZ4ETwxHT6QY4Nfolm6CPTIF2xyGITnNzKa4hHX9Ff8HlsB94AT/j/qXrj5mpUwMwFnhecFzumft7cc5wf9Fyudr3eF4Ys3mumRpneeY8H/RYscB9Z1zBbYupa1iLed7FdqgMxrrUPvCEYup1fq+LTNWkuiWK0GfWBQuc4hm6s6OwfcHPKEudyf1CM+LQYN4mCxBDF8OnL7zwwnG/3wwODuqss85y1mH6W5VLNlVVOzU+85nPOGVIWGRTEaMQpcVDWx/X+t5EI991q9aoprZWFx5ynqY2FNlIU0WAIGKEtsbcBotG4f+5pt1bA750husQlQuieRAOuaS74pCAaLLJmSEageEmjDjVEBeICnczakgDZNFtUOd1orms2SP1bCkz4yVYGHchYN50SMgghn6+F2cFxAVCjbEV0cBr/BvjKu/FceKdN15wTkTmFkJwuAm7lXiiNjTXxjUiHKxRtfUT8ZI8yL+fU4L3c808D+qM+pXgSiY6rDljtsSWjBaEg9cR5QeeEfef80dccQ+IJuNcIa3cA79rtmtDmHCejEHGSrENHZwjxyeSByM7YpbnV6jzYNxiCAiyj0hW2LFV2tkuTZ8pNY11NDBneY5mrKB0HIYBRDRz0GrWZgLGD04Qc4DwrN33lIwpxgqRwIia0MCVwE9/+lP9x3/8h7P+VWqaeLmCMYfz+Otf/7ouueSSUp9OiGQY2iF1UX4qroGBQd3/0As67rgTVNN6DFb28L7lANZfDLqst94GxhiiMb7BWXIFEd8Y0TLN6ghRWYCfwWVzKYMEB4Sfssdj10FHwDHQF8YLGZ+33HKLjjnmmDFjCO4MZ+Jz7jr76BXGHA5qDLhwT28gDt957733OgECftoDXg3nxsjH2OVYfBfnS6AF5wc3YW6wF6dzyhDEAT/yzq8gjZKcH5qL84WPoe25P2gy5jb3049rE6GNsdiPZ8H3eT4AzucX1Y5jx68RNrww2+wR7i/3PpP1hvdaCWG0AtycTBA0IOVS4anJ9AVA/xAAx2fQtsUO1rTG8ehmu/eM70IZlG2MwLtLUfoqHh/SYLxLEUVVExnrILcARwuctd4ZjD/mG2OZAKhM5487GNDM3e7j3XXXXU7JacZtJj06xgO2b9/uZMYTPPXd735XlYSKc2r86le/0r/+67+GKeEVgMHYoJ5tf1Gb+jZr5ZMv6YKTztesxvTNQ0P4g8UckmaGLqYuv0NW+DfEKx+xgDEXMhqWkqpeYCDEwJzOuJ8KGHXJgoCoQjogjJABiAjZEpmWbYKwIjCsN5L1jkCweMcgQoCx7XfeXBN/h8iyEeNYgfAQcQTZJTOFv/HZdOnTCHfOoRiOPUgaoskdPQKh435aszQEibsEBBHr1kA6GSDxkBKiXCCvwKKdvE4NxgIOFnNqZNssL9PILO4pkUjmlGXsQOAzDUrgHHk2CLVSNYDlXvGD0OBcINvJwHrMfUQopTLE84xxmLgjF5lfiO8gSxMEBc6T+cq5Mq8QwBbhyN/cTeszAfeJ0lOWcYDA4L5x7Ywr5jXlKMJsXP80ce4XaeKhkTAYsL6w/+D4/9GPfhTQt4YoGAY3S70r9NJLqzRt5iJNmYNDI8zczwWsxQROuI077NMYqtnrrL9Uro5l9geMhkUtpxii6CATh1KS+QAtwLhDXxD4wP4Pt4B/MRYz4edwKyK64RJoZr4LAydj2Ns7Al4Kn2Ht9/JfDM5oFZx9VAixagZmWCUSHGcGvCiT0lk4WeCwhY6AZj5z/VyTcTK4pTXr5u/cIxwUNqe5D1yrX/8+rxOIvqhehwXPBn7vznRAI6Bn0CI4C7Lp1Ye2Yy1Kl2lhpWk5D54N58S1ooEyAeOK4Bo+y3WVxtAfd2w53Cv+jQ60Cgp+4G/YfNL1GeJ7rIyuHYdj+DmfSg0LdGS+MnaZi6eeeqpz7jxP5iLPOJs9iO/jWhl7jAuyD9C5jA1eS1UBYTzj5eFSt9///vf1vve9T5WCinJqsFmedtppYUp4hYGFBK9wocnsQKxXsXhM9dGmqovoxGAMaSLCg40X8Q3RwygIacv0evsdR9Nr6hjs0bT6idpv4gJFhz+LU4NNhMW+UFEkIUoL0n0hgBAhhAFkjnGUSw1Wr2DlO2g4lylBINOOTdOdGcG4pr4lzgnvOeEwwRHAmPdLKcYhwB5BI12IHOQd0gIRwuiHOEpWvgYyzr1hHmGAf/Ob35zWuM+945xY3ww4IUiZznQ+cg94DsmMttaAExJmjh6Lzk7WkA9hgjGY8+J+WqQT54VzgEgkSDvrCNdgTgmLjOIeZWpEhvjw3mTR4lb2C0cLYiEfh6nbWVMJsDEFcFIgIPhh7jEmGTs8B5w75b7emsMSgYlRgOtinuDUyLfHCeMDwcv4tEg5Ox4On7e+9a2BXUc1ponjqKWZdRiMkB9Y/8477zxn3QzL2lYWjEsUkvcTXRpXryKqVyRSfKNXocF6i5GMPYo1mXsJByOAIl0wiBvb+rdqc99GRSNRzWmcp5bahNEN/gUnA6Fjo7qzwTFqwwvYy8ke5t/57E/wJZvbGCnT9Y8z/kX0O/qYY1tJKrgxHAy+4QacgwABjONwfzitez3BqIq+4FoYv3wP7+N1shbg2nByP16OmQ0uw73gh/eaUyDVfkTwGAZpA+dD9kE6Q7aBY3EP/EpKAfY65iSczrITcChhr8EZkmw95T7BXU1PoHuIduc+WKAT303lB+snYt+N4T7TElXWEBq7R7LsCZwRfK/ZLnKFOa/IXshmvSsVrDoH6yr6we4PHN30HX9D+1ZCljNjHT1q2v722293rinfAENzbhI4xd6GvYH1g3mBVka/hk6N1KVub731Vmc9qARUTEgLCzv1zr797W+HNW4rDGYoKxRwZKzpXq72wY3O7w3RFi1sOdRxblQLWIwhT0SLsOhnWhffjYHYkK5a+4C293c4Tdtjimtdz1adNO0gx1jIpkfkZzEbZYUoLhhDkHnIAiIWIkgGEGnK+URDQwJZo7PxkTOOIRbu40Jm2DwxUkBQIcoGzhthAllGXJBS7v0+IowQPYgOvtc9ls2IjyhBVHAvrF8BmzfH5Xe+h2gqd4STGxBo1jTuIVFOboM0hnc+C5GEHKeLhOF+QfxNdHmN2wgryB6ODIgpESd8L2niGHHc98fAsXHwcP8QM/yb8yQKk2wVxBXrB4TO3WOEc2CdJnsk00wexAnfw731rkk4oPjhda4rV1HL/WZsca2V4tAAXDP3nWfMfbBII0QYay1js9wN0YhWjAPcewQy44VzZ0xhUA9CKDGmcYYSKYkI457wnHEEVVDMTdHB+vaXv/zFEX3f/OY39cUvfrHUp1TR+MY3vuE41RiLYWZQ5QA+wJpRSKPNYGybemJP8y/n94bo3qqPpi+7WEnAGAbXgPOwr+eyN23oXafndi539AVY0/2GDp18pCK9USfwhTW+mE1pQxQfGNzhqPBIuC2c3jhormBMYpxEF2T6PfAveD68Ba5sWLp0qRPYBadm77RxjhbC0UBAFEZ95oM7qwAuj77A2Gr9/LzZ32gqnBrmxID78v04QPgMxwZcB5zWL2vBotWZLziHqJ5gaxv8kT0KHcN9SNcnA6ckmoTr5Ty9vUb4O6Vq0f2WHYsDgveSQZushwbXTmALAWToHSLrWTvc70Xbefvf8d1k4HjLjCYDn8XgjN3D69SwUnncY3hjpo4SP/Dc7PmUOyc3uJvYw9G5BwCnEWOHsZXPnCsWcFoxZxjbrBX88Gwt8DJf8Dz5HsY4TjfuD/zObJKhxkgOMpaxuWN7pwJGNllWpUJFZGow2JctW+Ys8L/73e/K3uMYYhQstkR05lJjM1Ns7F2pTX2vuF6JqLlmkvaYMEpkQkgrdq7WnZuXq3tnp9a99Lo6trarbcYUnTr7EO0xezeH4GDQrZRNPUT2IBqJdGk3WTBimGkzNTcgTwgWiCrjx0h7JmDrIeKHLARvHxj+BnlHlECgMc67e3RwzhiKIbLuUkyWtg6Zg1RjOOb9EG7+hjBC7ECYGOcY5O17IfKch5V149ws6oq/4QxBIHEPcZqk2of4XpwIACKAkLcmifwfwcS6iAOCc+QcEIHcT+tNgKOR93G+EDEcHFwzfyP7AlFmTdH9jNGkgfOcEZZ8FwZpM1IjECz13AuM7pwH9wdDPM8nXeQSzwpRyD3hnnFdXL/1M+Ec3ICMpyqZwz3inLmPkHWEV4jigyg+5piNO8YyY5WxhVgOKliB5w3Pw+hllLTQkdfVAgwgGFuuvPJKnX766aU+nYoETSLf9ra3Oc7yfHoGhCg+2Acp+1Ko6Np4fECdQ/cSEjLm9abooaqN7lpXfjzjvi13qnugW+teXa91r61TlP5rU+fpkDmHO88H53imUeYhKnc+WlARgMPBFXIxsFpACEEzcGcirLMptYiDAh7vty/yN/ZOeCv6x61B0B04NhizGObdwU1WroYsangsn4ejwtH5wXBqpYHQHdwHODffZYZ3eDX8mOtCd/H9cH1+4OgcM1X5Ub4fbgZH5nvRAdwX9AnaxXoPcHw4Gs4EHCE4ItATfJ4ftBLHsaBJ7jd83TK20Flkzfs9F/gGeybvx7HBGsyx4YYcD76IdvA+L+4FWof7wvdwPun6gDKG+Az3h/uNLRBHhJXLRYNybOOL3BP+loo/onG4R3w3zqFkme8hCgfmDfefOcP/sSFYoCFONcZeUCWyyGxmnDAHGLPMsXROwRBy5ui73vUuZy257bbbSlKareqcGvTQoDkU3u18Sx2EKB78ajoWAqs6H1XX0LZdXl888bTQKOLCfW88pRufvleb31iv/p4+1Tc1aOGSffSeQ96sec3h4l5sbO7boI29a5x/z2ycq+kNidr0hYa3hiTk0JuKnQmsaR8OB8hBNs4wyDRlpojSJmLJUkP9AAlm7cdAjih21wbl3Pk85wCRRwBBbomgQmBAhCHVkGaILseASOHwsJR2fiD8NCnnu0ifxujP+/kujKsQdN7DNRrpyvQe8Z0QKT4LeeN4CCqcGd4SWxaJhWENkYHjgygoju8tM2e1bTk/v/JhCDaIHI5KMksQmtkSd46BuEMYZWJgtmaPCA2EDUSVa+LecU32HdwPBFiyrDAcK9x/7lcYNR08mCeMCeZFsrlPdJ81dveCcUV2E+VAC7E2IYwxGhQyGKKa8Nvf/laXXXaZE03FmhoiczDOMJb98Ic/1Lvf/e7w1lUQMAqyR8MNCoXB+Hb1DD3meTWi+ujuaogm+tuFSOAPj/xG61ev1/bNO5zfJ02dqJNOOUmHT9nVMBqisIjF+9UztFKxeJeikRY11eypaCTzfmm5gqApjPeWdZyrvgDsZ1ZuNtsmvmQzw4vhkPD+VJH8cCG4rpeToxXg4DgebI0xjmL9+tAdXKs5AuC4cCf0h/Fdy7pgb8Zxzvvhtzh7CEiA48KH4clw9Gw4L9/NPeaz3CPuF+eNrcwvKwo+j6bgHNAmHJdjok+89jWMwHBFDMBecDzuBdcFH+S7KEeaLeB6fD6TKHCeEeeLgdX6iHIPAc+Nc+Gec0/QlzynZPeSZ8594J6FwTPBgrlBdhJjBA7vF3Bg/RyZV373H6cd8ymf/p9eMCZwvvG9OOLQtcVuCl+J6OrqckqE4xz+7//+b5Uzyr781F//+lf98pe/dFLCQ4dGZZadKrRnr9aHqNVEEo1vQ4xi+R2PKjY5pnn77a62WdNU11Cn2kiNpjZkH6EfIj9s7F2rlzqfGfl9+8AWDcWHNKsxdcRKvrD6mwYMzrlm5uBoYKPLhRRgKMU5QJkZDKusFcmyPDhfHA4Qd4SCEWyIMIQWwQGBgsAjfiBKwEivlUWyXjS8x2ramiMFgoOjg4wCCD6RHGzk1h8DUpZLJCif5XjWTDkdLGILcG5EgnGNEDvvesbv3H+EH04Pb88KXuOect2nnHJKTs4BjsGxMfwhANKB4xORx3PiuRAJhZjE0WHNRhlzXkMUDhyEoB2Te59N1k8IZeVQZI4wp3AeYYTwNrjkdeZBsuwtogzdJR2CAmMWQcT6wBxkXofjID3e8573OOsxkZNEuFVCTehyAMYP7tlFF10UOjQqDJYBSImUQiIqP0Nw3OmtEWIUGDVXPrZK8w+cq933X6SJUyY6e/mU+uQR5yEK1/+lY+BRxdTjjFXF2zUY266JdUcpEilsc2CMzu79J1Wz41SAO8ILczVqYtwnO5lzwZGQyqlBJjCBRrwPHm1cGaM+nBZeQgARDgh0hTVbhq+jEUxH8Hmyo8mgQHeYUwG+i34xbQIP5j7hELF7ZaV3soVlk2QKNIXpQBwJODm4Hj/7GrqFPZIIbe6lu3wVz8b6ZfD5XEvDwvXICOH/6TQK6wnnyXlYGVSeK8eHk/KcrBQyesX9ffBenotpJHRraFMMHlaBgTmFlsW5iP50By3yHjRlsnHLmoGGZL4FCcY7c5A5SxYIc5qAlnAcpAb3h8wsHFToPspRlSvKus4MnroPfvCD+s1vfpNXvbwQpQFGLKIkCo0ZjXsoKhZMjH4Jw9/sxsqpvV4MQECW7LW/3nXieZq52xzHoVEfrdWZsw5TU00ozoqNNT2vZvRa0MAI4BYJbPC5RPbiAOBzuUY5uGtgQ/DTJQyasRwRQQ1OABnH4AlBsV4F/J0fK0/l7vOAIwNxQeQV4P2xeFztA706cOkSZ72ilBXRPfxAqNylrYoF1kz2PpwREH2cN8xfP5ARwTVxnd57CKG3Opj5ZDsgfjgONYIzAc+VjBoEBOODc7CsPcYb3wOZNZLLc2E8MS55xuz1oSG7MECgMrbcdZrdezTPyVK0/QS2Ncvk2RUiwolsHqKpAGMawWzNZUOkxg9+8ANnnl966aXhrcoQn/rUp5z9gnsXorLA+uXtQVWQ46hZtRHjEYk1M6oJqosUJ7O2UoAR6pK3f0SLD1isSVMnJXhAw0wtasktSj9E7hiIb1VM3QmHhoO48zuvFxqW/WzIpRgIn8m3bDVBU3BLkElgJRndBAlhkCV7AK5jJVxZZ9ATOAMw7sNlrcyS21gLV+fzGGXRIM61D/arrblBU6dMGekRYT+FCAxJB3gdGdScH7qJ4CL4XjLg7CFL3BsogSbgczif8i3dBOdHz+GUyAQ4gzC0cp8xjuMw4jwJ0LFMe3fmB38HZOJwLH7C3j6FAc/AGrsz73hO7jmCBiHwxtvfxYBGJEDHG2gVBNAUzFuC5lij0PhkGyXT1yFGgTYnI/yf//mfnTlWrqgt5yiciy++2HFqnHvuuaU+nRA5AIHNpulXGiVINNZM0J4TjtH2gbVOhEpr3QxNqA1r3TKHuP94y4lEx2AEcdu7da56hvrVUtugmgJH7YTwRyw+5NvwvtDjAQJqmQCW9ptLpgbfE1RDec4LgpwJOCYR/RjGIUuUaqKsEuWrICoAMgQp8jbRhsgSmUGEOoTm1a7t+j/P3KKdg33SwJD229ivj53zdpUaCAjmKc4bro9MFv5PVBTiAWGFwwNnDs+O9/vVK+a+EL3E/c03cptzwHnCeVCrNlVkC+IPhwbniXMCksp5WlNFrsNdQgzhiQANqnZqiORAyCLS7f4zZ8w5gRjBsYcY5HmzZ/DcGEdmFGDNwNEW1Nz3E9wIVuY3ogfHKefBPlbutVxLDXgWjcPZ50kTp4lniOS46qqrnExwq6seorLAfgF/KTScTM3o/hqMT9ZQfKcikUbVR+YVPOK9EgAnsOa/8IxZ02ZpZnym+mK9ikSiaogWVvuFSIJkWsJHdwQJAhDcxmT4Yi7NZeG36XoipANcEwNqNkAboSUwuuJwgHcQ5EEWAnrCAj2s8fCb3/zmMZ+HG8HHnWvmGbx4h7Q+EUTVualP+x71tpL3lIHToR3gWDfeeKPzb+Yuz86yG6x/hTU9t4wXN/j91FNPdYzCQazl6Da0HcdP9+wxqlpvQs7dyQgbDs7BcO3WJ+wRODxyLYEWInPwTNAQ5uTC6YSjitfRg5Rrs1JuOJr4O4GD7sBKHGXMwUIBRynz2yo1MJdTVYoIMQps8Tg1sM0T3FaOvLlsMzW+9KUvOYvRt771rVKfSogcwWZTaIeGoaGmWbMa99Lspn3HvUODTcUaYF1zzTUO+SIy2hp21UVrNbGuOXRolBB+/TOmN2RP/rMBAsPdSyMfQ3IQkQ2s75BnSgtm0/wP4zjkl/8jIjC0QoTdkeMYYhFGbnCtGGPJHOgdGtRPVj6kDhwakK+IdPumVXpizdiG1qU0FnCuNAfn3rCOWhM+ylHxOsQeUpgqEg4BRQQUhDJfWDq/9SpJBlK/cVwYSYQ0IihwKvEdZAtBaPnhOZozKkThwBhAfHodXDxPIiIffvhhZ5wh5BGU/JCezd/dmRIIEJ4fY6BQwIBAzWQDRnpER4j04Ln99Kc/dYKBMs2sGo/g3nCPuFdhxGZlItcs01yA0awuOkeNNfuqIbpQkUjZxgMWBcatcDjffvvtjiGKMh4jTqCaptChUULURun75uX2NcOvFw4EtLAHGeCt2XB7A06yfJv4YvCGp8Bt0MSZgvELX2V9wfBJRrc3oxWjOf0b4ONuUG6bTFNHH73xxIhDA7TGurT2vityylwpBDD045RAh5GJAmfnWuGK3HueAYAzpgoo4e/mZMgHdt8xiHMOyZ4Z95ZAKPQEwTc4YkxfoAnRSZyPaQzWprAUeWGBhsPITTChe/5bH0n4O7qbUpHoQhx/VsoIp6EbjL2ROVQAYAfA9mEOT9Ynzr9c5mW541vf+pYz57/85S+rHFGWTg1qsP34xz/WH//4x6IZxUMEj3AjKQ0QGRACPOK2eWBMDFE+WNC8p2Y3LlB0+L/ZDfO1W3NhS+yxibvLNbC5+23k2/q69fDWN/TE9rXqG/KPhOSz2QgFP2DshkzQXC5bwxL7AmSVH8a4F0QQQHKJPncDosS5r+vZqe6hgZHk/GhtjWYdsLfue+4plQOICkMwQdIRHTgpERzU4eU1BAnCCocQQioZEF7MfwhlUGs6hiRIIVkgXlEHiMJBWGD89oJzRoC4f3De4IBNGnX78APSpR+RPvRu6aeXS33ZRd+NdzDHMTZYyQPvmsBYw3GAo8wLCD+fdeOMM84YMRYUKlLaW74izNLIHO985zt1zjnn6L3vfW/O9cyrGdwTepC85S1v0Tve8Y5Sn06IHEE2YDHK24YYC7gIBiwA92LfMIdGiPJANNKg1tpDFVUiKzuqJrXWHqJopLGg66o365tgo12Mk2iODSul156Sdow1aBoIwiOqOx+gf1kfCJIgOzsbECAFJ8VIm8y5Qs8AOLA3G2QkQ2R7whliWDhrivq2rVe8TPZk04JwcDgWGoFgEnSTlXZCb2DATBX8hqEafmY98fIFx0bT4DjFOeEdP5wLwTZkWJJJ4wbjjwxfrsH0BesTPwQy+BrKh/qkNfdIL/5VeuVaqdN/TIZIDpxQBBISCOXtH8k4Yw4xB/2qQvB35pqBZ8tzo4ybNzAxKKBf4Q/udSq0V2YG1rc//elPuvzyyx1bfbmh7JwaCOh3v/vdTod1dyPRECFCpAekhI3A6omSilnqdNeywOAOqfc1qX99glSXGNFIVHtO2F/HTD3V+dmz9QDntUICYzRpl0bCIaze9MGXO7bo2yvu0J9XP6Xfvf64vvviXeoczmYwEFmBUyHfetYcO9s1nnPn+BhTGetEUiUzeBJVzt/ckSBE9mDEffaRxzU0MNYgu23VG9p3vyLtOYzBgU1S32ppqMP3LZAshIVFzdNszZw/iDX+RlZEOoMvhC3o2uMIRsgnYoh+GGRumAGV80ZY8LtXdFD+iPd7xQXPhe+AHI8Ricufkr75Fem1VdKmjdLN10v/891Ar6XaYT1OuPdeMC4OPvjgpOXJ+KxX0OLMZO7y7GkgmWkd5FzB/HX3xgmRHggOIk2/973vhbfLA7QFa82PfvSj8N5UMNgXU2UMhggeBDHAuzA8sv7DIf2c4eMOsQGp8zWp4xVpIP+s2CBQG52kSfXHqq1umSbVv0m10cIGtmGwhJe7g1nIEB4TmEpJpoevkB7+m/T0jdLdv5ZeeXSX7yJgIoia+ozNbHp/wVkxduJQIWMAHZ2snyvciB4cBBa5QZAWwVSvb6Kfx2hwxtBQTNG6OkWLVWaVcdi+SupYLcV2NeajJQgKI6MFLs95o6dME1LSlkyHTEp4wc8ItAoK3FuCt3h+jCeyhd2Gb84ZpxL9NNwgmAoNYVkmBsYAmoP3W7P2ER32+h3SjlXSQJfUs0V67Wapp/C9Z6oJVvLZb6wQtMb4SOY0QCN69SljiRJuzMNC9NQjyJexEiI3YLf57ne/69jqCXIoJ5RdDu3HPvYxJ+rjkksuKfWphMgTeGcxsFeSQWLnQKe29bersaZBMxumVpz3lugFDFh4UCFkGEPHPXpfkbpd5UtqZ0itR0kFdiJkgmKOL2sYTcQKhBFi6M22+NMbT2rQVY93e3+Pblr/ot42f1RgkDFQjAZ3kE+MFm7Cg0Di/BFPRGClu3+8B4Hkjh5BmB+w2+56Ye2zeqMOz37iO2bPnKWmtdsVXxAv7HPh/nY+LA1sHH4hIrUcLDWMzVZJF2WNKGGuM+e9EfhecD2WpcL/iYBChGKYsP8jILKNiEf48YNhG/FhYsSiOCGOVl+V4/L9OGR4r1ss4njlh+fK+CKl3MGdt6GWuRl2U6QH7pV6e6TGwjeJrRYwD5hPzAWeERzLnBU4xuz++4H3IxR5ftZkEhFDNBWChKhdsoFS9VnJBowltzOMdYpx5JeRFcIfZNiQ6XzSSSc5/TUK0XSxEoGx6T//8z8dfpRLWZQQ5QPWA0pqVlKtdPqm7RzYoqH4kCbUtqmhprL2MPaAm266ydEVd9xxh1NKpNI0UuAY6pXW3SINDjsz0BUzT5SaCltONlMU8/lY413jb3C9MRlVa56TNiaaNo/g2dukOftKTa0jBk20q/WvKCQIynAfh3sFjyHrgAyFdBke8FkM5mScG/+hPCx6Y333du0W7Uzw/TjfLfVP39cJ3C04l+laL62+fbSHSsMUabfTpZr6XTRBMqC14A04bdCJqQKjcIRwvwxwShxa8Hk0J1qG+0SgWTbgs+gE9An3FXsSAVDs3X7ZI2QQk+XBvgBvdHNaCwjDSM73OfMCR0b3hl0PjDOoKeSbmYKgKDJ2KBsFsPlRJQAwxnCEc//91iLeyxhjzuAkZ5ygAayhO3OR8lU4sYIC3+kuk4XeGBkTITLChz/8YYcLYLMnc6NcUFtujftuvfVWZ0EKB1flg8XJneJV7ni1a60e3vr0SEmaOY3Tddz0QwseQR8kMGLQRwNieNZZZ4XzKNY71qEBBjdJ/aulhtFNbbwAEm4GQ4iGm2TG4nFtHxjr5Igprs19iZ4HzGWi7IMWGxBfv4hwyBDElFR0N6GG+GTqrLN9xAz6kGuikyBS79n7SL0Y7dYb3Ts0qa5Jpxy0hwY6upwG3RhpC4a+N1wODRCXup6U6mZpMBZ1zhVBgXMAAo5BOhWZRExhrOZZJmuwh+GHZ8d3kTJPZJb7fnNfiGKy+4RwyKb0I4QQYzf/J1qHzyLeELe8xj23UkdEybjLVvF3axDI+GRc4mAJOUDwmVr88JwQEYxxi7zlviPMeW48e/e6cMwxxzjZEohW3u8m/3yOpn44NihhlU1UZDJwbm4nHXOVDDPGUjgmMgf38LLLLtMHPvABJ4qO+TWewT7z/ve/X5/+9KfTOoFDlD9YCyppTA/FB7Vi5yPqHiKCm2CKqPZqPVQT6yrHeMa+wHpPqZrzzz8/dDSD7c9Ig66+YBixNz8oLThf4xlwSjgg/H0EXdsTTh9vI/OedsUbE/3fiPwNmn/DXfya2hLQw5h2G1zh3DQAz9TxYNyajFfA92DDcjjUPkdI6551Mnmi0/bQcVMXOWV1cAYGFQSyC8g+WHvP2Kbwfdulrc9IMw51HBTwPK6d/7sdMn6A18HvcDShSZIFA6Ad4IZwRDSm19nMcyVbmDWb78jGwcE9pfIEeofgHPSLlQzimIw1xg56EfB3skwscIpjo1+5Xj5jWihEcEALWLAjY5xnjB7g3zg46L+EtuQZ4qCwUlRk9cPxycpgHuIUdTcKZ8yhUdhzgihzyPjk2IxnA+OEeU9WSYjMwDz62c9+5jgRr776ap133nkqB5QNI2TR+chHPuKkhGfrzQ1RnoBIFGzjDhj9sQE9snX5iEMDrOvdrFVda7TnhOzq/ZcSRDmzmZx22mmhAQjE/Po+RKSh8dmcmLRciB2kE9JHFL012Y5GIppW36Kt/V0j8yCiiGY1tjqREzQUJhojF8MikdYQScsKgNSwPvAav0N2+EFIQDYYw5AZDKxWkojML6v/mg0guIgMjvnUU0853wt54Vx2adc+ud5xoBQ0mipGmjZkPKbVa7eov3/QuabOyMOK1LY6x+V+c38g8mSmQPySAVLB54mOuueeexxBaOTewPPFsMmzh8B5HUjcC8uwQJyQSYHzwbIsUoH3Mp64v9w3oqV4XpBHDIkIDo5LOruB34nk4jzIGrGsG66Z43KuGB5rTlom3XFLIsyN5w4RPvKYMEsjDzAWGAM473ju9LZxO/9wKhDh5t67LdMJpxkOKt5jn+MZIkIYezhAcul/cf311zvfzWdxvHgNEMzhUIhmj//4j//Q3//+d6fk0r/9279pPIN0edalL37xi6U+lRABoZKcGut6XhlxaICYYnql82ktbTuporg6TnF4YCpOMq7gZGh4OOlQT8JwX0EBcUEBLgjfZ0zDFeDy8HiHc7ZO29WhwT1qmezoC4JexjhBMgQcE4O5NfxFK3BMjKEEZGBIRftQXonfzeB+8sknO+/B8GrcGO4D786U/1vGO+fANWOUJcNjJMBj7xNHL1VyDL8EIZHVUhDEBhPZQ2S9dPVow+b2hPF/S0QbX+pxDMc8I84XvgcP5L4kW4PgYgRLsneixeCFfg4BeDvXDn+zKH03yNaxjB0cDGgyvsevz4IbPAueGeMIbYi+YFxxDeZAQTfAES0Qi2tBezAW0E+8h6AYroGxSWAXY2Qaz7h5ptS9aewcnjScKR4iJ6DdcBJQRQB9yr1mXFjwGxkd6AcDz4ueHIDMYpxorAM2XpjLwALysgXnwpjhGIxhxrsb2JyD6gkznjBr1iz98Ic/dGz33NNy6HEWiZdJy3eaGuLYIMq8kgheiORgw2FDGiklUsbY3r9TN224b8xrGHT3aV2ogydnv4iWCmRosGhT6zMEBK9P2nHTrqKj5VCpYVfiVe3A8A2hxKAMubVm0xglMRC83rVd37n/Gm3btNl5//SGCbpw3hL1d3c77/EaG9k+SAkmNdivaTB/53WyOyA3EFgIKAKG9YE6rvadt9xyi0OyiXhis/R+DwZzSA3/9pKSdIBEca0QK1JZ3YTK75wprWARSQgdi+ogSghxZM3qvM4Drot7C4nDMYHw5zP2PseBM7he8e7nnX/PnztNTU0Njr3++XULdcCBS8d8n5WKwiGQjvwD7jUlVuxZ2PbOZzEaIxozBUKCrJh0EfI4x5KVAEmW0mvZGXw/gtOujdc5T343p9vUl1dIf/qd1NUpHXqE9IF/kRoK1/ByvIJxyvrA80JsJsu6YDxiCMCB4c7mQTASbZVLyUPEME41jp+s8T3vCSPsswcGFIw3GC9yEYTVAPYNovwoO1WM0okhioNKWhNe7nhCO+ij5cEhk5epJlI5zhlK9tDfK9Tpw9j6hLTzxbEao7ZFmn+uxiMIHIIXYOgn0AUjNBrB4dzxuPof/rueuf9ODQ07AeJ7HaOhGbuPaAQv+B6yiOHVfoAjY6BnbzOjGgZMuAQ80qK7MWzC3eEpy5Yt2+V7MHyzT8CDxjglMgCGcng3x0J/o2FSBXfAfeHXaB90CTXquRecAxyKf+NU8dpO4Mvs4zhecCIQgMWxuU4LGINtR16/UUMDPZrQ3Kg5M9oUiUS1vWa+eiYk+hu47x33g2eENsoEOAn4MZjGQONwHamairuBRsERBd9PVQqSZ4lu8cscT1UyCNsT95nz4t5YVgf3jiAdNCv3bp89F0rrHk6UoapplGYdJk2onJLplQLGNjqcZ8DzTsbzrVcf9glv5gSfRydkmxHOvGbcW9ljv1K72EGYv5no7BBjn9c555zj2Dx+/etfq9QoCyZ1ww036B//+IezGYREqXrAhl3oJqJBoaW2yXFixF3ElH+3Qk4znNgD8c2KxftUG2kteFO2ZGADr6TotYIj2iC1HCJ1Uetx+NnWz0v8jEPgLPA6DNjIiajg/5tfeEX/suBwRY+YrLpojfacMFW1UX+SynoNccRwj3jJNEKbdYGICYyjbifJqaee6kRoeM8PsC+waeJYSVUHNhk4HiIHwZDuPDkWot3mNWIFcozowhGDMReChrjgXCFoCC4EE0IEsoYDxv7O/RmT8RDfS+qsTzQKTxxRz77WpD32G81kMCAQIHEY+N2ZDsnA3LeIl3xhNU65dhOofsLE2wzcjWT7Oa+bcOM4fkTSSmVNOfIYRY7KMaoNwWUZHiFSgvmRiTOcKDze63Vw4pxCZDMXkvXnSAXGQqroSJyhYd3b7IERn0iqf/7nf9a9996bsdGhWsB+QQkuav+GDo0QpUJjTYvk2SprI3WKKrP52DXYrc19WxSN1Gh24wzVRbPPiAsC8KBQp7sw+UCpd6PUP1xqOVInTS9QFH4FgNJKbmBMpIIAHJKfZ/um6YiLLlWjBqWJM6QJU5IG6MG90bQEzcApMh13cBRviRn4M1HiyUpboQvoIQF/z3aPxMGALuB84cPpMj3g9JYZTZAZ+7LdJ3QYc4zAEfQQ5wMPJwuF1wjognfj0OW+cJ1c25h7M3eCtPqOkRJUmzuj6pw8R4s8/U25TpwqVl7Ur0SXF+iZILK00CpE43NNXDfR8mg8L3C6JIu9TjUeuBauj7HgDT4DHI9rf2PtRi1YkF2Q3BhwbmEgdlqgIxmn7jmZ7JnybJhTfqXQcD7kErTLPLEScX7AIYquqaQewOWASCSin/70p85cvvHGG3XmmWeO70wNBDDe3S996Uv64Ac/WMpTCREwiJ5g462UrIFVnWv08LblI7/Pbpyu4zPoqeGUjhmkwfLWkdeaavZSY01mkQ9BAmMqJKmSmicWBZSbGmqXIo1S7eSQhHhAhgFOCgh1qrHD+CJKx4lw2WefvEoF+kX433777U5UcaFFM4Yui97JBAgKHLQQMvdnLA0acoxzw02eqQGK+CCSdJfjsO0Obk5kEtW26eVVG8Y0zvYC0cE5ELlVikgSCCmRXCb2EFmIEWDCKltk0vyNe07ZIcYZ0VoZjwsaiv/999KdNyfKHRx6tPTuS6T6zPuEjBewfxHNTx+TTOczkX2sA14DhkVjZWI8xsDB88QZkknENU5DawQJNvSu0Ybe1Y6relbDXM1qnB8a21Lcaww2n/rUp/Txj39c4wmXX365U9YWg1OqZqchKjMqHEOJn9Gq3DAYG9CKjkfUM9QxHMpAT41DNKluV0OeF5v7tuqhrY85JatAU7RRx08/Wo1EFhcZlZQdUzRgPO6Fzw1KjdMSEd8hRgAPpgoHPPHcc89Nuk9jWIdXsM9jP2Cc5cp32fPQK1a6BhC8hdEyk7Kq+cL602XKweA36Au3YwGDPpkjVg4UZ4d9J9dHRjn2FV8HykBnoqxStE490cnasm27b1koOz76j3tTqhIyBLm5NZD14cu0DK4XVvrMzzjuBtH7XH+2fQTVs1Vad4/Uv1OqbZZmHyNNCEvy+YGxjd7H8ZcJeB449LBHeDkb+oKsrHRcjvUDxyhzhowgazCf6piU0KW0ojPHOtul+66Vtm6QpsyQ3nSO1JraITOe8Ytf/EJf/epXnUDEUrYdKLlT4/Of/7xTz/Duu+8O036qEBhLSDutlOjA9oEObetrV2NNg2Y2TnP6DKRD39B6dQ89u8vrk+repGikuCKa6HCMzakW78DB5t6zWaptkOg/kiSyP0T5gkghjNOvv/66Q2qZr5BySDDkAXLJ2IJcJot8yZfEAjZEshvSRXPkC6I9MMxjKEcccD2kQVsDvWxA2UQyOHAmIshIxSTqC6M9huJMotb97oUXGIxxLpSiwR3iEFLK/SJKCycYBu18HCzcN64pE0M6TjSELveY9S1t+vENf5f+8efR33FMH3Oi9J4P53y+1YpcU7qT7e30Q/ELZIBqPn7lXzV0x01OKbGmvfdT/LSz1RdP9FghCisV+PzdD96paQe0qHuoUwOxgWETXwK7t+ynuU27ZXUN4wmU97vwwgsdJ6FfNlw1Amcw6+UVV1zhZAKGqC7AS6jNHlR2YqERiw+pfWCrYvFBTaidrIaazPTBLRvvVM9wnXxAVvn85rk6uC11UEDQgCthGC5qoNrQgLT9VWmoX2qdIzWXvm53iOyAYd5KjVKKitIyBOnA8+F/ODngthg/4SJE7AcRvOPl1XAI+EkuJTKzAdqJ5rkEIcGPrM8H+262thA+C/fmO9m7ceJaGUmaL2d6LZloDL4fzZJJxkYhNBkGaO6R9QjMV+tk2oMBpxvajSAqxqk3WGcXDPVJK//uNIAfqQCBxlj0FqkhOx5d7cC5gH0hVd+WZM8EbeJ1oJNtxbMiGN6L9WtW642Hrpc6Niha36AJexyhjmir49yghw3rTyo8+tBDOjS2VdFXlkudO6ShWCJAzun70yr902elhjAwJtlz5hnjFPr2t7+tUqGkdWrwDBNFhTgO65hVJzCAISzd0RJBgs1vW/9ODcQHNaV+ourzTMmeVNfq/GSDmGhGzWI91j84FO8tqlMDcUfERlEdGjteltY/MPo7UUq7nS5FwxJYlQIi7pmflAGyWqOkBkNAIMIYDjBqU4c2lybA2QBSWQwvP8TZ0p5ZoyA9P//5z50skXTZA96oRe4X4ozvO+uss0buUTYELpP38r25lN4KAjgfIJGIHUQp/853zyaCyrIwMnkvhBTihHODdT9lBNaTj4z9nWyNJx8OnRpJnm22Dg2/vZ3vsabxflh1712ad9UfNKuxXqqJS6tekO6rly79P2kz53jujz31iLY1rFHDYKIvDJ/APGAzYl3Pa6FTIwVOO+00nXHGGfrMZz6j3//+9xoP4FpJhw8dGtUJ9gSiuwuJWLxfQ/EORdSg2mj2ZfXcoHTU5PrM+1oB9jq3Q8N5TXGnHFUxAQ9Eq2O0KBoG+6Tnr5Z6h0s7servuUyakijdE6IyADe2cYPGwDAJfyNIBWM2FTvgE0E6Gxiv3sh7eHYxAiyZszhurGE6GRD33XefozOyqUhC4A/3Bz1mjga3kT5TDg5/yuS9cHH4dRDlpbIFGorAOpwZPCNvP4VcwDVjHE9Xkpu/E9SG84igPZwhOFSS6rKeLVKsf1eN0b0+dGp4QMAfcyHb6gs8E2/MPXYI7FzJSjGvuusvOnZBozRjOHOp/wVp6VulibPTHs/pNfrwbYrWtQ8/T8oWRxJmPZ4tmRuvr5D2Tl7CajwjGo3qJz/5iZOl/573vMfX6VQMlMzyyGD96Ec/6vxkY0QqJ+zo79DLnas1FB/SguZZmtNU+JTGSgObMp7/QoD7fsemx7ShN1H2qSFap1NmHKGpRfaU10QwwnoTniKqiRQ+JR7iRmod/8dQm2mzr8AiqNY/5DmhLdL2F6Wp/k2gQpQXWIdpwkevCoPbyGypyERaQQ6DcmpAsvnxOjQgFsWIEoK48sM5YJhFaFETMl0khxc4Ee1a+Lw7K4PvRJCwyQeR2UIWDeSwFGB9IdIMohnUGoMB3K/UmWVkQIIRpTi5EIdE9sEVLKqPsZs0o8evzFQJos8qAYxhBDciMps0f4Q6WbYIYJ6XlRPzE6M8u7V/+LWOr69NiAVHJwxp5+OPaMvTT2rSvAVj6inzfOEOBgyXU3Zv1WyXQXBEIw1vve5+WCH88f3vf98xitAw+6STTqrq20RpjmuvvdbhR5WIWDym5Tte16a+nWqta9IhbYvUUFOaPgrlDC+PCBL9sc3qGKQkbeIYDZG5aqndr6hl7jjWhNoWdQ52jb5Gb4ssg6/yMUhbqUkM00Xt2bf+Kal3h+sFnOF3SpMXJaJnQ5Q94Hluzkjmt5WOITIfYzJcjgCXIEE0t7fcEscoRukp5oj17oC7om1wrmerAyyYh/uF/iIS2g2cQVxnOicEPT641+nAPEfHlBJBZpGyN3jXKxtr9joBPZahgdaAD3O/LcvDd62PJHGMRcJgTi8Yd/B5HJmMrWz2Dz5rvSrJEGQukCXoZ4dY/vC9WthEgPGo9hsYGtKWZ+5X+9TFji6xZ0nGIXrFQLAg2v2Ehm7b6hMCw/qlmHOlgFyjGnDQQQeN2PXRGKXovVWyGUikGAP9uuuuUyVia1+7btrw4IiQxrlxzNSDtMeE8dmAOBnwqLJ4sCFDJoKsCf9s+6oRhwbojw3o7s1P6IJ5xRXrdZEZqo/OVX9s7chrzTX7KxopbP12jH8IdkqAlKRWtBMp5l3kI9JAomZwiPIH4yeTiBiM6Tg2mL9BAMLojbYg66GoUYDD0QX5RCWRKo9gY/P2rmvcLwgYTqNkUQsQbN6XyfwlAr6oWVguZBLtlC1wXkEmvQSVdc3uF2ID4ebu52H3nYgdBInvfnLaOdLKFxL/dlKI6WESl+69XTou0QQ+RAI4lrifL730kiNqUzXTc4P7zudw3CEIkqWX8/ye/MsfdezObVJjnToGBvTk1h3qGhzSlt5+zXtjtWp2djqOVT6PgwQniLehJ5kYO7rXJT2fGQ1hPeN0YK37z//8T0d0IBJLUWaiGMAJRmNwavxWYuNH9oUb1z+plzrXK6qIozJe3LlOF+/2JtWHWbBjQPAA3IFnzpqRVV30FKBElNuhAfria1UXo2xU+sjPIHHo5CV6YMujGognsuAm1k3Ufq2pS8kE1WuJkpMEZpSkOXifj5agbwUZHHVhGZByB/yOzN50vJ6xFSS/JCACXune3+AVGK6DyADIBul6OqTjWCeccIIv/0YPwLsIOOInWUAW2SHwunSlnJjv2QZ1lTv8snLIrGdNI2CPa8bphDPDXU0E5xNaFxulb4/J5hlS41Spd1siin+QPSKSKJPXMluqK11PgXIDY4oMLMYhZagoJ5VpNQbGLL0p6Y9CcHSyfi9PPvGEZrQ/o7nT25xn+sJrG7S9s1svr9mitvk7NO+IhWOepfX922XNefDPHpPWcAUW9r66Bml+YffcasBXvvIV57n94Q9/0Lve9a6iH78koQ4M7s997nNO1FipjDT54pn2lYor5jg1zLHxxPbKjAgr9IKG0GBRY4O5+eabAyuhQtkpN3gKXUM9GowVt0QLhKy5Zj+11h6pltolmlh3rBpqCiOke4b69OTLz+ju++4ZIYtBODTYCHqHVqlj4H51DDw4xkGTFGzcu0QmUCA9bKZU7oAkYwhg7ELwMiHXQc1bxhqGB68xG4JOrwYIeiWBdc1rWOcaMbKQBZLMaUIqrfUQydSxUuhSG8UAAgtSiSjzE1FwAjgCYJwQpeMnTmY1N2v7n/8oXXWFtPqNsX9ccpj0iS9IM2YnHBpg+1bpVz+R7r2jQFdWuWAdQOwjNhAQ2RgUESk4oZIZvShhN2XdGt2+frMe2bxNz29r147+QU2srdW7TjlRhxx/ghM5hXghsi5ZA/kp9TMUdQpOjUVtpFbzmhZpt+awJEkm+MQnPuGIuR//+MeqVlDWlv2lUpuib+nvcBwawFTG9oFOvdiR3Kk3XoGxgzWIOujUmCeIIAjE4j2+QTuD8eIH7bTVTdKymcfryCmH6pipR+j4aUerLs9Su8myg7Z1b9VdD96p++6/zzFOYNwLxKEx2C5tv1facpO04yFpiPubBk7/DHcGXkSqbUz8hChrwH1p9ptpv5sg27vi1PBmNRMAQ2kjMp4rDX4OH4J6MNKiCfx4NFyKQBWcOZlEyHP/0YWlQNCtfck8ppSUX1YOlQhwbADWNbK9/TJonHvavl5643Fp44tjo/TJEltwmtS2VyJYivPHubFztbTy+oTjNcQuuo7AJ4KgMn3ePB+CdmkYnsyh4diuunbqqeXP6OHnX9ODz72qNVt2qK62Vicu3VPnXvQBJziOUtOsC2Q2MQZ858N+YwOpHJ8GQSTT5kgXfERqSZ/tNN7R2tqqH/zgB/rsZz/rBLSNC6fGd77zHWcxpmlhpaI31r9LsYP+cCFLKz7wjuKtDQIttY1Owzw36iK1qilBWrITaRKlp8eMgpWdeqnjDf3PHb/WLWse0uvzutS4++TAoqf6YivVF3tFMXUrpk71DD2v/lgaAc1iP/f4sWngrQsSG32IsgREF8cBDo0DDjigJE2nGbN+JSMwSnA+Tz31lKqhjigkDMHh7VeAcOAarU9Fpk5Ji6YPWgBkAkoDMW6CABFROCm4Hj9QMiCtYX3TJrV99jJtuvx/pJ/8WPrQ+6XHHx37nsVLpS6fuuN33pTP6Vc1yJ6C/BPRmC8Y34899pgzXvfffZHmNNQr0jegGQ0NOmTyJB3W1qr7lxyjN1avdkQGnBAxyv/99rXGmmYdMOkITahtU32kQZPrZujQySfq6KnLtKiF1PIM9/1XX5Gu+qt04zXSzuH6ueMIiDlEx9e+9jVnnao2cE1cG0FTRS2VEyB6aYrsQSTJ6yFGgyLI9sJwYeWS8oF/pnVc0UhpDOr10XrNapyh6Q1TFS2AxumP9evhTffqVzf+r/rmdqhx/6iaJwaUDYEDY/s90gC16Lul/vXSjnuleJpAmVlLEs3B3Zpjz9PS9mAKUToQ+c4+jkELI2amJZfgyXw2KHiDsAg8Ov30051yNmSCVjoIBME475d5QvY3WgGbS6Yla7k/PINScQLGS1BGUOv/51eeFm6JNiOoLCVefUh67ib1PX+X9NyN0tNXj3Vs1NRLDVMTzowRxKX+DqlzQyDXUW1A91GiiKC2IICDAsfpXvvsq+OW7KG+oSEtmj1VrU2NOmK/3RSbOEf3P/e6o7nJeMLRytzwzb4Bx5wjHXyS1DpZmjxDOulC6aPfli76tDQjwyo8gwPSY/dJd1wnvfScxiMuvPBCR99j6y82is74EcyIjbvuuqs06awBYXbjNG3u2+4SHBHNbPT3JIYYBZsJkT8Yrbz1LrPFgZP21JrujeoabqLHaDp6aqLmumEgNuj03miI1lf0eNva264/3/sP1dbVadaiuU7k3kNbn9WMhsmaXJ9/tpNfZkb/0FrVR9NknLTOl/a4IJGGSfQUjcKLeZ871ksbn0tERkzZXZrm30AqRAIPPfSQ4zjItW4qBsog5hHefCKmqKdr4Hsx8LMuXH/99U4UOEZWjKPU1synwZ9FIBXLyEXNVq7Rr7QSZJoooGxrx3J/WDufe+45p9ZrMRoeGjhfd7+QfEAkbbprx/GRco/49S8Vbd+haXV1Wt/bq9lEVX3vu9If/zr2fWMExzDCuqgpQck0Ag/IrsynlAtrBRk3Tlmq+XO1+Obr1dXZoce27tBgPK7OE0/W3scc68xvsnEYY96a0V5MqJ2kAycdlfM56eH7pe99005QuvLP0n/9UJo6vvqhLVu2TMcee6xTioqshmpLf8e4zTVWKqY3ECBTOyZQClf2vKbhBpghfMGeSFQnhg53OZFcEI3Uq7lmL3UPvTz6/ZGJaozOHbPGDcX7VROpy9ypWqZ49I0H9ciTD2vx4furvrFenYOdeqnjBS2etDT/L8eJMVw6K4G4NNQpDWyT6lOsvdEaad+zpc6NEg69lulSXeF7FY7pG7jyQal9k9Q0UdrrGKkxGB5UjYDbYkw/5phjsi4zTVQ90fX5lGsyYMwnk2GvvfYak8XAORE8Rb13+Dkag4AK9EG+VUOIBseQXgw7A+eLloA7ue8zjhzuPzoHTpVtD0TuB88AuHucFRrcs6A0BvcE7ZCqUTHPnPGB08N3nPa0O06NPeZO03OvbdDiRbOl7W9Im16UZo02ak/exi3s75bKBkh/DetZkg/4HjTitBmzpL2O1nHNT+qxFW+oe3BQ69q7tXniPk4gNc4MuAGBnCmBpj76rMRPLhgYkH72bWn1KhabhNZcdq506nkaT4hEIo6dn759H/rQh/K29WZ17HiRwz6pscUF/+53v1MlgzTdB7Ys16vDNZ6n1k/SSTMOU1NNYfsoVANIAcXo5y65QookEQeZNLNygz4ar3Wt12B8ULMap2lKfeLzDOtHtz2vFzoSqaZT6ibq5JmHqqW28uqwUmbqnmce0rbZg2qZOHbTf9O0JdpjQv61xNsH7oQqjXmtRpM0oe4IlS12rpVWWE+e4WVswTHSrINKeVZlC4zhEAqIay6gLBRkEaEQBIgIgpz7Gbgh1myEEHSEAsZRyCqRRDg6MnVOEHFOWRucJew7kH6+h74BQTTvTgbKYNAHgvOHSCEw+DeCDUJNJCmEOtPaom5wz6zEBk6OYjg3rLleEJk93AdvPxU/cI94VtazZAwu+4T6nn5Kr3T3qC8W08ETWxMk8uY7xjpV//Y76carx372ovdLp56d93VUM7jvRFtS3jBbYewGEZFkhTHf2nq6pX/8TY8897wWn3Cinpo5T4uXLMl6z88LH3gHXTNHf2fMnHSa9JFPabwBUUlpkCeffLIkGXuFQDVd05rurbp23WPqjQ04fTVOmrFYB7WNBgGESI5bb73VMWy6QRQwBs1sMRDbrsF4u6KqV3101ojzom9opzb2PamheJ8TUjWlfm9Nqqu85wOfIHL2xd5nNXef2WP22oZoo46ZdmL+B+leJXX6ZOC2HZfaqVFKYBp56C/S1jdG66o3TJCO/4BUH5a/8htHOBOT9dbKBBawE0TfTTgr3+cXwIW+IJIYWwTZDnAc1gc+g76Br2ez55Bhgsaw8rA4SPI12KYLDkaPkZWG8wGOTDNwrsf0UaY8O5k244fvLpZzgyA3HFv56jJ6J3h78CXjuGga3ruLM2X7GunJK/T6xm1av2WnDtpjtpqbGqVFR0sLXTaRgS5pxZXD5abiw+XxmqR935rI5AiRFIxP5nnSrIkMAdcj+GrfffZRdPML2vDy0+qLRRSbvq/a+xNOzKLhoTulq3676+v//j2pbfwFvb+rBPb+ojo1ELd4bmhOW0zPTSHRNzSgmGJqrPBMgGICrykEyD0G+J20MIyORFjlYuxz44Wdr+mRbc+PyaSZVj9JZ805RpUE6otDUNoWTtctGx/Z5e+nzTxCs5vyJx09Qy+pPza21mhTzf5OA/SyxYvXS+2eMjVkixzyvlKdUVmDpo9ELeQyXzFOUjIKpwJG+SDA1gMB5TvdtU8x2EMy/eqhIh4gQ4iOZOWL3OnZ1nzcTXBZa+655x6HrBPNU4jsjZtuuskh50S7IzaIqsIBYY0LuXayZugdkGt0EmslpDxVRFKQwKGFUyzftZkeC5k6xhh3CNAlS5aM/cNPLlf8qiv1UmenFjU2qh7D+8JF0s//39j3UYLgqj9JD9yViPo85UzpjHPD8hUZANGMc44xnE8DScYp+xjOC+YAc5Ia+EUHY+EdPs6spYdKX/y6xmt/Ddbb666z4IDKxtlnn+2MsR/96EeqBhA81TXYp6aaetWyfoXICH5rDEY6IsktGzQfvRaLD2lN970a0thyYLMaD1VTTeVk02DIpUQg9+qF3uXaOTi2/M/E2kk6dMrR+R9oqFvadttwualh41+0SZq6zKcvX5lgx3rpPh8D1YGnS7sV0VBWIWAfgfNmqw/gGZSRgRNjaA7SGQD3JjiG77T5DqfE1uAXtY3GgZ/iHMjEOfP00087WsSbFYaNC4cDBlt0TNC2IfgU94rvRUPgEGBdmzNntLICDh04F8GjuTqJ4Pw4aILInkkHnj/aLt9G7ugFOECm14xDl/ExpgxwX5f0wC/16rrNmjqxWa3NjYlneNBbpGm7j/2C7i3Smgckerw2Tpbmv0lqGFtyOIQ/mCcg3wAU9nXm4sknn+xkbzB/8x1HOeHmv0t33SB5+/p+/D+k+Z5xMw6wevVq59lSmYneysVAUdnEv//7v+vSSy+tGocGaKgJvllbMRCPxzQUX6e4ehXRBNVEZhbNKYMR0d0MmLRNNiIzzkFwICKWHprsvDAUsoAZIBdWQ3F9z9h6iZRr2ty/wylFVROpDHFI3X0IEaSEDX9B8yy90b3BcdBwPQuaZ2pWYzACqjG6pyKKqj+23vn++uhuqosUptl5oKnhXsR8XgvhiNdsG8rjQGAuQpRzze5IBeY1pN/d0A0nB/M4GYnmdQQ4EWHpGljSAwKDrDdih/UH8oPggQjh5MCxwPpjPv58IsW4b0SOcK44Vrgefvhusq6sbixihHPAaYSY4nqyiYrnulgbWCvzLbWRCTgGzyqXTB2uHZFnmTLZCGVfkfveDyjy/HOqe+LxxBigb8kX/mPX9/Hs3/auxE+IrIAjgzI+zDVKuuTqfGOc4pSiQSBjPCNY48UgDbmMhbnzpfVrR0uQEXW9e36RYpWML3/5y84zsaycSgYl0/j59a9/rWoBvRNa6yovu9jBULs0QG3xiFQ/V4rm5wzPBxgqMbZi6CB7k98B9eaTOWzZk9nvjAuwz1ggxUCsexeHBtfZM7StYpwa3AcMehhvnYjZun311PZHnCA9EFVUe7YGZGCuaZbajpc6nkr01KidJLUeXL4ODauNvgsi0mDY18YPGBIpXZkp4IFwX8YhGQVBZGd4YUZ9HJpoDPg4DoBkZWg4B9YI+DoG11QOFs4fO4Yf98aQB+dlnYHDclxKcsHvWVfyza4mc53vQmOgZ2xd4jhoNo7BtXMdHB+NY1ni2QBbHc4RAs8KbR/i+7n/uZY4xo7Ec+bauQ+ZBF9ZNsou2rihRdrvNC2M36yX12zUxJYmad5SaerY7D8HzdOkvd+S9fmGSMwTHIzLly93em3kCsY5+oRnz7Pk+acFjgf4f5Djeu6CXR0atXXS1GACQSsN8+fPd2z+2P5vu+22ohyzaIyCGoaUAbniiiuKdcgQKRwa/bEnFBM9QZjQccXiW1UX3b8ojg2iOUj5dKePIS6IXOb4GC3Z3DD44bm3zc4LNmq3hxcSwsLGZl9fUzti/DfQQJz/KgEQEYiElejiHpww/WC92rVOOwe6NLGuRYta5gT2vEipb6zZ0/mpGExe6GnIFZHaFpbwhMoXpEhnm4ZJijOp24UQG25AYvnhHBEImZSjQQSRbYEAgaj7zQPWAW+zQDc4DsZagDEMoyvXCjHih3WK45jRg3NkD+Nv5vxAKEGo3M3AMYTMmDHDaUhoqe+8H8MhZXjc0VRmKOY7V61a5bwvm5JSHBfnbjHAPSbThDWZe+5tgO4HRB+OHM4RZwjPJJsIGpxDlt0yBgiWH/5Yuz33nF548UXtefIpasyiZECIzIBgxtjNuM8nu4Kxg/EC4ZlWcGx9WXrtXmmwT2qaIu11mtS0a8PHnPCvX5C++gVpx3A/tAMOlN56kcYrEPSXXXaZvvjFLzocvVKzjVk3uQaupZi1wEMkwcAmqfvh0d/7XpYmHCfVFLHU3DAIIkBLEKFNEBSGSvZ19lw4DmuS336L4ZC9ysqh8D3wcspaRX2N8XFFi9+mMic45XkffdRZ243fTapr0+FTjtWmvgSnntEwS821ATqi6tqkKQGUsioWJs2U6pukfgLwhnUky+P0UGP4GfjhCtnsH/BddH+2wVbZAp5PsBTnZvM/HdhDyILGpkDfP79z5HpTFTrheFb+icAjsl5ZS3jdevzx3ZTftfuGkZ1jwnktc4WAEI5vGeV8lh/eA6cycI3oHQLF3JrNHB4EFfHdplkyRT5ZutkCbYQjCJ3gLR+YDDjGKF8OrJRUptnkfC5p+aNZ+yrSNlfNLz+n1XXNmr93WNa6EOCZkS3IWM+n1C2am7HD/1PaLPq6pXuvlNavTARNLT5OOujEYJwbBxwqHXuqdP+tow6Nf/qI1Fy6gI5S4zOf+Ywzl8nWOPHEE6uj/BSHIOLv9NNP13/8h080ZYiiYii+Uf2x5bu83hA9UtFI4UUH44H0cCuF404Vp3kTBrNsNl339xKdTcR1x1C3rl//gIaGG8Xi3Dhs8r46YFJlpIA98MADTmRHiBRg6Vr9oLTx2URUL/Wmdz9Zqg372uRbegrDI8bodCWeggAGb8iMGb4zBcQesUCEmEUFQIpNIFhT7nSprRje+Q7IlRsYMTDgWwNA7gkRXpYNBjAEQqAxkECkOCeEximnnOJEoHBOJkYg0GRxpCLrfJ5jkhnjPk4qYHDGeZJPU+dClZCyGsk8k1wMpgjLdE4Q1n0EMoYpRGKlGmbLFdxfxjnZTfmAetPs9YjIpI3iOzZIz1/leoEo7xZpyUVSNCCDIU6V11dJ9Q3Swt0TfTWy+Sy9QdomZ/e5MoatSQQcsW5VIogCe/vb3z4SDBKixOi4XYp1uV6g1vh0qSXzSO58gJGEAAL2BHgM+zMBExZcgB7NBXAK+ADftbn3WXUO0VMxsd/UROo1t+lo5//lDqK54RrslyFSoH2j9PjVUvcOqa5ROuhMaXZufQqqGQQpwo+9HDoZmIdo/bSNewPkqzg3MNBnsz+wN5LZAafHIQEXdRv5M81wxJng1SHmVLUmxhZIRQa3cViOf/fddzu6hr/zOvcZwy28mgAqM8rbPeX3VFqAZ8X34nDJhCtzjmibYmSDZ9sXA30Hr8RmlEvwHboPh1G6MYFTnFI6nFO+5XdD7AoLLsi3Xyeam/lExljSsX3b7xIODbfp+6i3SHsfFtyj2bxB6tghzZgjTcjCpkoGeUe71NgkNVRP36avfvWrTp8zAlELrc+LElZyyy23OFG4N9xwQzEOFyIN4k5jO5/X5f960PAOardfDUNirjXi+V4+C4GBQJw9+1it6Hhdg/EhzW2aroUtwZfQSQauiUUacsF58TtkxBoWpwLkJR+P9bgB95HG4POPDr5USZUh040Ekki0EOM041IxeYKoKKutmQ1wFkAy+WF+QYwolQP5huTi4KS0QjogUqzBnxtE/NB01hwffhFL9IjygohQnC1ETbn7deCkwJCASEpmTOD9rGEQaIh0JkYHHBoIFaLeigHWp1QZMAYibxBpuWb6MBYzEaBWFoT7jhOEsoVkuHGvC9EvZbwBp1kQTZdxDKY1ADg9khLZownEpf5OqWe71BJQQ1miLvfN0pgCR/l/v5D++PvEv+fNl7757cT/KxyUpSCaioAjHFeV5hRk7efcuYbQoVEmiHm1RFyKjZacLcaYxnBHcBT7iAVnMLbzcf4T5YzhBCPntKYD1DA4UX2xnY4jgybhxXRosL8x9k0rWAR3JpHV8JOS9DWqNJCtcfIl0tBgQl9U2NpYzPmGMTEdrCQTKLZDjSyNbPcHrstK4cIvCdJhjpE9QSCNO+s6FfzKKvGa6RfL3PDyVY597rnn7vJ98Nxk9g9KyqFbku3jaCO+F32EETndeoGusGdWLHBv0GTpsnhY23Nthm7ZK5lk7uAQ496iMVk7eWbw2SBKiY13cA/pEXPmmWfm/V02n5IfbEhat9KlL4axZkWwTo3psxI/2WDDWunn/y1t3ZTYZ864IPFTBbj00kv1wx/+0HFsnHbaaQU9VsEVPwv5l770JX3+85/PqKxIiMLDycbYJT+HYk271szuHtypbf1rnUyHtrpZaq2bUpC0VQMEnc3Mt+RIBmCDsU1mUv0EHTm1OJEgBsgJpWzw5iOeuBZz2rBBE61NdApii2tng4TcuQ1/4UaZJdgAKqRPSqnAnEhn5MUJB8krdoMtyDcGiFyamLu/g3nEDw4BCDvRmJkQTovCCioFm7nPvPY7Nvc3k2g2MjwQYYgoDMrJrgNHKc+NdYX3Q/K5B/wb4VUIAyWZJElTtrMo/5UKrJk4arJxcHPfuVdcOxF01qeFZ1tNfbyKDcYRexoGgYJHqSXLxggqSyNX3HqL9Iffjf6+bq30hX+TfvP7qjB0ffKTn9T//M//6KabbgpEXBYTnDMRq1xDiDJBzWRpaMtY4wGveUEwSs8r0uBOqaZFatozkLnO2o+RFZ6dyf6eDdhLcGzgKJlYV5xAAjcIAIEvsb+iG6xHlXEE9nwLxmAPtf5ebuSaOTluURMGR6SCad10YJ02g3Cxzw8umk8pJbgP5Wq5Tnrx4dCw8tCZINV8yzb4xpyrXqM/Gs9KXKW7HwRD4axgbSDzIxnQFFw76w5Zb/BAAof4f7Y9OjIB6zX8P5OyZPmURuZaUvVsTaYxzTGHliMgj3HFnhDkHjOewDMkU5h7mmn2UM6ghwb7JE7qkdcipa/uQYbGz74r7RjuBYzN8MYrpZlzpYNzt4uUC7D9f+5zn3OCj0499dSCPuOC79R33HGHk0r2sY99rNCHCpEhopE21Ub21mD8peFXIqqLLFY0MnYT6Rzcrpc7Hx35fUv/ai1sXqLJ9Vl6IH0AAWczgJi7m4ZTlgIxkk8Uh2VG2MQh+4MNDGRaC94LhAORGYgJvptFmP8TveFuWMz/IQDJoqD4DOmkVrKGzZDSQDQ09hqgQ1Qo2JDKSDDaOGW8eV83QzhzkXGXibG6EICkB1UJkTmVjREbQZFN82ovKHmC0HE7gyD97r5BrGmsQxhZMo0UNSMyWY6sh3yn37G5dzg/OP4JJ5zgHIvXiOYKIsLeDdYla+ZuTgM/cK2UuPA750zAep1r5gn7CRFoJriojc79CVPHcwN7NBlPlHThHiYtHRUEpu0trX8q0U/DDKJtC6TGsUa5ouOpJ4fF0LCTjrV0zWpqyklTgg30KAWYp2Q6fOtb36o4p8Y3v/lN59xzbWQfogBoXip1PSjFOhO/10yRmjwOavb79gel/o2j2Vl9a6XJJ+YdpIKItuxPDE/siRiwgJcHZQt4kperkH0JhzADTbaine+Dh/E9ZpiEw2A4mzJlyhinhAWAJDsG0cSUr+U72QMxJFPSxhtUmGtD3hClhY29cnp29I/zcxgwJ/ibGarhjMV2aADmUlDZBpz/4YcfrmI77infbs+cNeGpp54ak/lKIBeOgEwzF6znByWYWCNw0nrHFOX24NNweTj5smXLnPWP1yh1xfMMuuci18X1pQpuZSwRvJZRQ+gUGiXTTBs3uEcEklnvV84FHcbr8ONympeVAoLXGIfsW8cee2zhDsSzOeBN0vK7Rn+He+xXnLKYSbFjm7TN0xuTzMCXn6sKpwb4+Mc/ru985zuBlDIuqVPju9/9ruPQCOvQlRfqorupNj5LcfUqIjz7u5Y7Wt+7a5rWut6XEk6NoV6pd00i0qpxtlSbnfGKzZRoWsg5xisMZWyqGEyou52PU4ONig2XYyAQ2PwsxZDNB1GTab16NizSOfk/54fhkM/zPRhD2XRpuEdkFMYzoidSRYez4UFE+A7bsImypuSPNbgMN8UKRcdGaeVtUm+71DBR2vMUaWLxSp6lKh8DaXATRAgqDg2M007jyxLXh0d0QJyLVULJDe5LPk4NiP1vfvObMVEm7Hc4KzEWIjQQdbnUC2WNIaKK9QZjBoYZ1heOg9PE+g/xDC3l3Ig6jQnzyXpLdj4cBwOMNeczYg+sxJ6Jq1zXMpxw2UTBuUHZLve95hlY6jjjnqi0oMvUsJ6b4czq7/LcynEtX9O9SQ9ve049Q/2aWj9Rx01bogl1zWmfO1GKCOdZRPXdfoP0wnKpZYJ02luk+QE1T6V/xuK3Smsfl/q7pAkzpTmHlN5J3OpjMGfNrKLovEsuuURf//rXs+6/VEo89NBDTtTsddddV+pTCeFGtEmacKIU60gYDaKtu87hgW3DDg2N6ozBdqlvvdQ4T+rflPi9plmqn5PVGsC6yx6EY599iEwza8iL8TVZOclMwR5s6z3aBe7OnsL+l20wAY4Lym+w35EVx1oLN7NME9Zc9AuODbIkeX8qwKE4H4schhvABdwGUO4N96DQTZpDBId4PKZNfS+qfWCN8/vE2jma2Ui/stJyd3QvDgN3D0h4J0GABEQwR3LlckGh1JlJ8HSMtrkGhLCu4Hhw81p+h+szz/l7ssCndOCc0CesM9g4WBdND2KXYO2hpC+8wByj6BvWKvZeehcFCfoh4Ngg2x37jfu5cZ1cL3qDc8pVL+aTvUcAINdv94jzI7AVzcL6zJgvROlmNBcBcRyb42DDSpVhUyrE4oNqH3hefbHNiqhGE2oXqblmQdr5xzhEoyW+pFMaekWK90vRKVLNokSWRRBYcpLUPFFavUKqa5D2O1KaXuJM/ia/sRiXmqqnf0tLS4vjC8AnULFOjeXLlzuNQX73O1fafoiyQSTSoIiSRw0PxnZNJx2KD0qDndKW20fr5u5cLk09XmrIfIFlc4QE4cDAM4uDw4yr+UZSsVmxEWMIZFN0CwyOYRtCOkD62ciJciJywMA5uhsoIwwY65ABNtp0EYPeVFMMbhzHnBog33tQNqCG4ZqXpJ5Oafo8aUrpjfwFQX+39MI1o2mNfTulF66Vll4sNZQmgpRoEouutrGFYdzEeC5RKqVOXy8U8nHqMN+5n27RgWFj6dKlzjpBRFC+oo51y4zlVtLJiDlGDgQNItK9LiE2cMgiEoIE18qx+X8hUq4t4jVIWOo4sAjeoBwbiHrIOAKLZ8BYxvnNcTKp11tMbOvfqTs2Pe6UkwSb+3bolo2P6Ny5x6kmTXQ0Ys1x/v3lV9JdNyde5Dk9+bD079+S5gbkkMQhvPuuvWpKivPfJt14Y6JJOGAO/tO7qUunagFG03/5l39xRAdNwysBnCvnHPbSKEM4pR5SrLEYLPyA7uh8Vup1RVbXzZImHpWVY+Oggw5yjG443+HY1pjYStzm49RgP+Z7+U6MMTb+0AJ8r/XTSweMwewfZMK5jT58n30n3I3joGc4LvM0nYHIvS/jgOG7OI4ZPdm7MRrm25i1LLBzq7Th1YSBav6+Um119iPc0v+Kdgy8MdojYnCNon01mtEYbDZuNoDvEFxDpQEbk/BgywIodbCUG6V0ajD/yHrIFawPV199tZNJz3VgH2CNgWdaiet81jPWC9ZGAsvs+/jh39hLCNyyHj52H7HhcB7udSUIMGYskKtQpZDRUbn24mDd95bzM6Mt54veI7ue7w+q3waOc54Rz9x6euAky6VPTKHRPvCcemOJYIW4hrRz8EUnaLq5Zk5mVRP6d6gu/ggkO/EiZSzjnVLdkmBOkPFL/4wge2gE4dQ45Wzp9usSmspeO+5UVRM+/vGPO2OXIA7sqhXn1Pjv//5vvfe9701ZpiJE+WJi3TT19g2njzuIaELtlIQTY4zDIya1Py7NyK5sAeKCBZlNgoGOIRAjEJHFbAz5lBPwOh6yAR53ohbYvHG8pGvajehwOyRyLZflBoQBI2ZF96HBwH/zrxKCw0FEetP55bWZBIWO9dKQJ9o/NijtXCtNL26PCgD5sVROd6S+Rd6UG7y9dSoNiDpSxFlzWHu4/xB95nZQhJ+1wG894Jnyg4HEXQoPkUOkJwQ+6J4SHIfjsTYGnXLN9+ZTNos9hb0EEYCT2XtuCA9KIiDGGXO5lAsx4CTk3iMIgftZIwitrFu5lPpY3b1pzO84NzoGu7Wjv1NTGzIQR/19ow4NYM73u2+RLv6gqhZEWP78l9JVV0qdHdLBh0inVJfgsIZ+GDoxTJWqFGE268T1119f9CamIQJCLYYhjD6evkvRBqn76bGvDWyQ+tdJDdkFB+DYuP/++50sBQKP2FcY30E4+wmM8guOMud2svnDXoCRiv2H96ZzLLBv2P6ST6Q4e57tT+gVb+R3ReKNFdIdvx/dh9pmSmdfItVXj7PZ0MEc8L42uFEzVBqnBkEbcBt36WRDrtq7kAiqvG0pAI/HkG5ZYBizCbRkbci0nG06oFuS6QSM9IB1y72uEbhFFhxlb4Pkt2hW7IaMMYLvgrSDcO8y6TuSDDwHHC44qfx6aWC3QlNQtQNdm+/5c+/ZJ6zajQUj4kDHTmZOjXLQGJxDb2ysxgC9QxsycmqgJwf7XlddvSeoN7ZOiu8rRUrc+6KQOOed0qy50soViQz4E06X2iq/tK0bZBa95z3vcXwDVLcoBApmRcKb+Ne//tXxyISoTMxu3FN9Q91qH0wsUi01bVpAXdzuu3cpS6Wh4QjGHOrIEkXFRkGKtG2UCBEMskF5ut3gWDhS/AQJRJ+/UWIlnTMjKJBS6S0RUxXNwl981OXQAHHp/qulhYurT3RQ/9D39eIZ6plPkEAiZzCqI+SLNYbzBcZhv+iXYiIfUkjkIz9kJhJVxRqCc7QYjklzqmKwgEhDqA0YLVhLEQj5RHJ5YZF4jDOLgM0VrHUmNNgPiHzNJ8IP4sQPhJ/5gPhzNzTk3PmdH7LxiETjeMwVK9vBe6hvjsMGAxUOCt7DD5/jXvMaWU/JnIR8lu+g5BV7Dedhdd1LhRpnfO8q7qNpxj1zAyfplMam5M6OageOjY9Ud284RPqFF16o73//+/rJT36icsYPfvADvf3tbw/cYRuiSKhpkiYdJe18WCIDXFFp4iHJOVMse43BPmzBUewr7MesxXANopILURaHPcKyKb1gz0Dn4FgvptMQI5w1DzeUUxR9TsBIfc/fRh0aoH2T9PRd0uFnqNoQJfPJs3VT3qXYPB0nMlH7cMpyd3xXEyjZQrYG6xcOUTgo/Dbovnl+MI5LRQ137w1sFDiOKRcVdBkq+DI/aBrGm7taRrZAi1nzcWyT+egVAqHQVXwfGdpoCIK83FobrWWOPfYZsiq4Z+w7OCGw96DTrTwwgWd8rzmp4DT8G+1BYJZf+X4cXTiqmY98PzqTZ1Fqx8ZIf6wxSL/X8JypOlCze7LgKv89tWrAczvi+MRPFePTn/60M07phVcI/lUwi9svfvELp8t5xUeCjGNEIzXafcLBThkqIjprI/WJBbNusjSww7VwRYajrjIHUVMYsthcjFxj3GKjsI3ytttucxwbQTeAxOBF41hvGjcODVDsJmBset4eIiOlPioZHdsSqXRu0UEPlq726nNqTJwnNU2WemxeRKTGiYkmt0WAGR0xNKfKxLA+COUGSFuytN5iAGIJwcwn6glnBg6EG2+80YkChXQSxcTrbgNCLJ6Y11GfPkb5AIKAUwXy705JpqEdZchY6xCh+WSVecH9yjdSDGGGQEaose7lI17cYN9gLkCUOQa/c4/caz7iw1LcWW/JuuA8cFbgHOJz3kw8BAiRvnxXKkFpJUQo1cUx+Fw+dZWDwMKWOVq+4xUNxoecPZ07Mb1hstrqUmcTcd6Iq92oYXzrntIbq0bXdf5/0KHFuYAQBcenPvUpnXTSSfqv//qvQMtKBAkMAr/97W91990E2ISoWDTMlKadLcV6ExkalMBzAqR8DCOpSll5gBHs9ttvd/aSww5LZAYzljHKYhRk3SYCFqezuxdAUGC/8K716BsL1ip2s2T4oTfApZIj1x0M9En9nkbB8WHdUYWYUr9I63vHBolObQiol1UGYDwzX9DHyRxiBJKUsoxsNYN1C3sJnJS5fPbZZzvPA8O3u4fDUDymzsF+TaitV03A/VbgxE8++aTjwHA3LceWg7MWjk2QapABmdhGyBDJVRfAFQgswuiPEyAoR5yVtGVd5xngyCajwtunyAy3rLfWK4lgMCvbxee4Z+4gV/q0skfg3EjGwXCCkLWBbua97DVUGGFvK1UwI/ekpWa+uoZeH/M6r2WyvpDxo9hmaWC9+1ulCE6dsP9TNWDvvffWsmXLHB/Bl7/85cC/vyChGhDKX/7yl07jwRCVj9poveqiDaPGoIkHSnUugRFtlCYfkdV3shizSbuNRSzgbD4A4yaDH4NQIcD3s3kY2HAwZpUi8gQnC8d2AyNfPjU4ywKTpo91aICaWmlCaSPyCwKu64DzpZkHSBPnSDP3kxZfINUUh1xY6aFUpf4Y40SpIOzLDUTSBO28zAZWBzsfEOHPunLKKac4cxpiT3QkjdpBLD6krX1Pan3vnc7Plr4nnKZqQQKDCcQW8myA4FIiAOcGhBqHbjKw3iaLMk0GxALZDrn0AcKhCxln3CIICpHJgBiC6ONg4DxxXvODUHKfszW1JMKKbAz2KESQ1wnEPoWhzCKrkgEnCcfhGFbmijGG8C8VJtQ26azZx2h+8wxNrZ+kfVp307KZh+96HRi8OldKWx+Utj+u1a++mIiI530f/ay0J6ngEamhUXr7+6RDgu3bEqJ0YGzDvf785z+X7WP405/+5MxpmteHqIbeG80Jhwbg3xOI+nWtSU17S/WZ9+xjvSVQwm18Y/1i/zPg8C5UyUsCGjBaucG+y3gttkOjakEPjcaWseOEf7ZVZ7nriXVzNKdxqVpqpjk/sxsP0qS6eUU5NtoBjXHkkUemzPAhcAPdWo4Os9JHsOcPmnOjM3DE8hzglHBU9BN4pn2jPvXUTfr007foE0/eqCe2u43D+QMDPYb8hx56aMzrnAcaAw5PIJedjxdw4WR/S/Xc4NK52EM4Ho4fgorg9mjkoMp1GXgO2I3gTJY5YRqDgCr3daAPuX+8F2cFWR7uLHKAUwSthh5hH0kF66sBuC6cXhacWyq01u6t1tq9VBeZpPrIFE2pO0QNNbtex1C8W52DL6hjcLk6+14bdYRFp0u1i0dj7iOTpLrDsuqnFaK8gW8Ap0a2toZMUBBGR6QqOOOM6ksBDTFc83baMqmfBTuWyNyIZme8xVDEYkx0OVkZGDQRHUQas8ibF5oG2hi8giYkbAAYmKweIobeUvV+sf4ZbrD52eYEUTTvPhs05SGC3pgLgr0Plda8KL3xwqh4Pf7ChBipRtQ1SrsXP3WQjQHC5258xtxyiwtIIWKakjiIbf7uzQ4qJTi/QjSdznY9yBesZWShWbYXcxtjOnO4dWaneolCGUZfbIvaB1Zocj0ELhhYjWOiphAgPG83+aY5F44EIkZpxmcZHaw1EHIMQYwN/pZpZBQOHK4ThxmiJhtAyr1lMQoFRIS78SBjDkMT88a7npKVwPUgVqhjjAOeeeMW66mcONxLIqhwkLgNaexxqeqtFwNt9RN00ow0xuD2p6TOl4Z/iSi2ZZWiscOk6ARp0mTpX7+SaJaNgaMaxUZvr3TddRJCmsw3Isiq8TqTgObbP/vZz/ShD31I5QjOLQyaqmI07ibVTZeGOqRos1SbfcYQDoRHHnnE0Rqst+x/aAqMTlZBAANSobLnvJqFgIFS9chD27AnsVf7BSFSPob9zAzXFVFWiPt70kXSrb+RBoez2qfNkw48QdWK1rqZzk+xQYlRdzS5lTRDezBuzPDMHIN3EljDHAuy5GmIhE4BaBX7N1oDR8Liow7Xj1c+qkGqITj6Ykg/XfWYvnbASZrZGFzAGIZ2+DG2GwIg3E5a9MRxxx3nOD1Ya3AioEkIIGWNYXwwJsjmyaYiBg5oa1ieDShpZD0oCg3L3HADrYX28+vtBwjMQA9gf7IeGfY+r1PcDeYduoR9C93MGg64v6UuW875T6hd5PwkAw6N9oGHFMd+qLhWvfKEZsx3Zb2T2RHFYRtP2I2qEStflJ54lEktHXeyNCW1A6uacOaZZzpaGl8BGWdBIhIvgEv9nHPOcRa7QqSWhKguYPghDZyNBwMcxn3qFGJUA3i6n376aZ144omBOjYgYhjyzBDMObDxlKrcAuVM8LIbUbE0RTY9Jj4GRoiB1WDkflQEIFgbX5d6u6Spc6TW6mp8VGpAfBg7GIbdYxfC4zbgGqwBLOMLoy3jq9QkCFD2h62oVI5FImas8Vu+wJBCRJXbUE4t2oVLBxSpGVtSLqoGzW4qjAjHscE+7JeKzDrCWGC9RYwSacX6x7hAdDCm/BpAegGZRjhAsiH02YpYDD2Qelvviw3GHI5id6k/9z6Dk4cIL/YpDFKZNP3mvdzXZE5Dt1GtLBHrl9ZdNfJrT0+/Xnp1vZYccZrUtlRVD5ybH/wgddHwEjLIpXe/m7pMGk/7CuOe8k7llg1B5hv8h6zeci2PFaJ8QLkWMqHJYiRYin3RvbdRToW1Pejmxhjh+E7jV/ACInBLAfQOJU1wWBgIGON3DG8EYqAxuD/cjwsuuCCwMpAFR/dOadNqqb5BmrUoeX+7EFkDroM2hh/BaUdueXe30//My5d5HQMtewf8EiO3nyOtFChW/4lkMO0VhFbBaWRl9cxOctNzj+v2Jk85NkkfWLhUx04LvgwyjhUcFe41xQ2cqHBrNALGdjQq6wvjBu0JB/brFeEFuozrs/Kx2QJ9guG/VOPQG2AI0A8W9MS5cX+4n8Zn0mkMKzPll2mI/YzA4HIOfO0afFG9sdUjJSYff+QFHXL4vppaf7IikeL1IS0ZHrpP+sG3hh02cam5RfrW/0gzS9tzsZj4yle+4tgZrrnmmkC/N/DRw2J18803l32TwRDlASJgSaUkEoTIDwxB7rJQEGsW56AzNdhEyIaA6HAOkLZSGndxrmCIJtLb5pEZxch4suu/44470qYklhVYtBEaIQIHhmR6JdCzwT12mUeMZwxT1tyYv/N/I1aIDUgi4x9hUqoIQgMOPLIISgXWmKBqATOHccS6DRhLly7VQ89cqf2WjiUtQffVcIP1DZHJOIBAu+8vjlFv5B3i44YbbnBECmTZHKipYE3Cc107rWQX51mKTB3WVXc2i1tQmBhB1HMfOD+uM91exFwq1941GcFTEu3V1Zu0aP4MabgXTNXj+usTDg3WyuEIOP3ud9Lb3kb9BY0HIK4vvvhi/e///m/ZOTV+/vOfO+cWOjRCZAKMP3Bpoplxhnn3KqJd3ZojKBClTIlDi+AtZUkeuJ/1LPTuXwSU8cPr7MMYGtBEFYPmidLC3Bv/hkiOZ555xjEGuzOZ4IXoDpwcRJa79YVlazCWMFrjTCSIA2N+Kcs/kY1VUbo5BQgcYh67OTN2knjfgGJ1g4p6DN3NBSqBbEZz1hUyMwhMcutIxo3XkcC4IRgBmwYaKV22Bk4RdKpfgF6mgN8zBkvl1LDeegbbB5gPOL7RWswbdJBxmnRzhXufrHQimYnoFoLUyhVxT9NvbgnXzOuRwrV6Lh/84v9LXHR8+D50d0lX/En62Kc1XvCBD3zAaRaOrTPIsRp4Xs9f/vIXp9lLEBGvIcYPMLCxEEOIvHXWIFGFEARscggeNggMwZbCVwqwceOpJ2IdEO1iPQbcGxzRDqWMNglRPrBoHbdIZ57gqCBSA9KLYZbIGEg9abBucsXnMEgTBcPfSglIeSkNwQ6hCmiNQXQgNtw1YJnLbU2LtG7NpuHCz4k5PbFuz4LeU547pAGjPJFTqUDkpvU5Yj1GqPAaQgSnk7fcEveLlOl8ncEYVFh/ywIrnlXkzpukZ55UT3e3c32kvpPJQgRUJmAcu+eZdy8rRB3RQBFtGi73khijO3Z2aWJrk9RQugbnRQV7sF/d8OG9ebzgve99r/72t7+VVeNXzoVzes973lPqUwlRQWAdZ01m7SVz0V1qkr3N2zcpCGB0IlgJ5wbGuVI7utENZIsAjM2WiQH3MY3BfTr//POrov9AiPwAv8Np4S3NBg+C31p/MMY13JKMH7SG23mADsFghVYJorxrPlo/Hf+tJNArCOejG285+njVvrTWMZZGFXHY28LmNh04qXDlyjDGkwmB4wAHWCowNlh/0SL8YHdhPUJjEDjk12uD/T4IZxQaJdteHgVBT6ciLz+hyEuPSV3tTtYNtlKcNmj1TDVoqveh+crdKV0X5ZkmrmHH9g5NnNiiGrUoonHQbwod3bFz19e2jS99sWDBAsdX8Ne//jXQ7w3cJUZzwQ9/+MNBf22IKgebnTUgY8F2RxRhYKPXBhGDhWgmzOZKumypo9VxWNx+++3OJueNKsCjD7nEmx8iBHCnqxqIgsFInE0ZJwiplYGDXKVqBFgokFZsvQuqATiLKDnlfg6HLT1ed9x9oxrnz1Y0GlFzzWw11BSuxAOCEgGK8MQwj/EmWbQS5Scw3PPscXIxJsgAAjh7GVc4NwwYhliLiXBlbaLckq3bycYq5T/8xhavseYFlZ6fM674nXTLaCrsrCOP09ZzLx4p5ca1cY44rSxjg/vpvSbun7u/jYHPc68YG2UN9t1px0tb7lNn+0Y1NzVKEw+SmuePqaVdEXXXcwHPx+t4IirRk9FT7aBED8ar2267TWeddZbKAbfeequz7mRSGi9ECDesDAoR5KzDlrlIxDllVDDoe7MXgwB7BUFKtp+WCsat4ATsYyeddNLI39jHKQPJ3lQIB0+IygMGV79sOPZ+xlKmke/MK6L4mXMYqEsxvqyfDs48b2PmSoT1HHVHObe2TNBXz3yHrnruUdXvNlszG1p02sw9VFtAPUcG+h/+8Ae9733vc84lGVhf6F9Hvw2cGMCqUgAM+qw/BOKZ3QdbB7oUhzB/Zzym+n7W9WT9M3Asw+O5V5mUvCoI2rdI1/8sUYob1DVo8ekf0JPLlzv3gnPH+cf5wbsIIsMm5bV5cR+SlZYikI7PluwaM0RDdKZiNXuqe+gVvfziah1++KFqrVvqPHvWF8YIjjvWjqoD83Hh7tLrryacGYAxv1fu2UiVine84x1O5vWnP/3p8nRqkGpLShmRHiFCZAOiPIxsQHrw6hv5gTxBhvDqm5iNx2MaiHcpqjrVRvNrRkaEAZtzqWFNftnY3cTLSsggjEpVkzdEeQEDqV9mEcblbAkNET+QJwgFdZcRtoVwHqaDZWmVQ4+PfMG9xOjrbbA4Z+YiRbpnaHKR6lVDDH/60586RvZk2QOkZjNmINXs30S0uUsyIaCIqnA3ASeCCicJf0PgumuH+4FxiZPnTW96k69jg/HHcUv2/Fe/Osah4eDhe7Vh6hztcUaikZk5KhBQCA6rU86a7I5sTeYU5Nq4zxURBVs7QZp1hp5/7X4dfNI7E/XKqYXb1eUIJ9YHnj/BAFWHY49N9NT4xS8Sv9Mn5tvfpo6AxhMYx4gOsq/LxanBuXBOpXC8h6gOpwZrlztql/2GgCn2Pni2GXKH4r2KxwdUE2lWJJL7nmQR6qUor+gF5V4IUGAfdu9DXDucslr4V4j8gTHZ28OA+UFQRzbZpkTyM/cYVzjTMFAH3b8mE2Akx7FSDU4NM9QT7Mn1WN+82dNn6OCGKTp60SFFOQfWUoKheK7JApp49mR949DAzsMYYgy49Qj/dpfH5buwd/B/DPjWdD4Vd7bMlWSODfg7Wscv4KgoeOQGqa979PfBfm296Q+afuLFzvPjh3vA3EJjsFdxn1iv3eeM9kpm7OeelXW/PheaahYpMjBLU5omaGrTUWN6pqGZsfcRyV+V+NTnpW98Udo8nD120CHSBRdpvOGCCy7QRz/6UcehyXoWBKJBC47TTz+9chqM5YHt/du1omOFXu54Wd2DroUqRE4gCwESRSQF8KbiQYgwfGFI6Y91aHXPvVrb86BW99yjzb3P5Vw6BsOUpUSWAxBUXtJFtAKbWClr8oYoHzAOiGSATHrLTuXSSI15hQGe8gT0U8BoCfksNrgeIiZxBEDOKh0YfEnPdpeh4j6TEVOsuYzTgVItlrHhBc5jRIaJTIzumZRaYr2kTjjvRSymM4QwxiDsRGsZKLWHGLIfjD+ZlngKHFt2LU0wFIurZ+P6XQyorMXcJ9ZquI5XaCV7tjyLsi895QJ7cX1ji+qGHRqW0YMBkGuv6v2IbGN6a/z619KNN0pvepPGI975znfqqquuShklWSxwDldffbVzTtWOwdiQVna+qmfaX9Ab3Wure64VcT/GuMa9ZK/x9tEgmAODCpqgY2CFtvXfq+0DD2lr/70aiLXnfFx4WSEyQHIBexnRyl69w9xCd5j+CjG+wTgg49Rty4GTo7/RGNk4lTFqw3v5OeqooxytT0Q63LiYgKfBwZiPGLerYU0loMYyHww4T4tZ6u7UU091ODE2G78SY5wfz517zzpzyimnODov1f3nWREsRYY044XvTxcMRKULjKNWJheu7dYXBF2jd0qGnVsTfRQM8bjaN2/YZS1GS+Es4jlaz0E3CHLFweOHigiYGgbP/5FHHtWBi5eMvMYY4tq45qrM0jDMmSf94OfSt38k/eBn0he+iqjWeMOUKVN02mmnOb6DoBCoU4PaWERRVTvW96zXQ9se0mtdr+mVrld039b71DHQUerTqmiwsBM9hDHVmtRi/POKDhwfK9bepaH46ObZObRWHYOrcyZvyTz75QJIJJHOlbRhhSgcEN44ICxKGhKHsZjo+iBKqGGohlgw/7x9FAoJ5j0GUwwAGOARUJUO0opx0lhWDddIJA1ZC8UyIDAmWFsREggMImDsBxFANoeJVkQJ4hPyz5gKUngiUljHLFKLaFkTu/ywvrsddUXFrF0bldVEIzrkxJMdQeQHxLE7o4nnTAp+KgcP95jvK5seIklAjx2uxZ3ByFrAHmT1iUuRzVVUEFyweDGRBhqvYD0mU/amm24q9ak458C5YLyoZgzFh3TPlof0TPsKvdL5uh7fvlxP7ni21KdV8WBvoQwjey/8CQeHm9+wN7Pe3fPAdeoZGnWuxzWg9gGMcLlxIQx55V4OhHleLYbeEPkB7g0vPOaYY0Zeg6vCXYgYNx6QKXive54xF+B6pTAyE7zD3CfrmAoNxRzv3vsQBNBpcGd3sBDBQ2THwO+LcX1cF1HWBDmxR7s1Bv/mb2aghv+jMSj5R5YJzpcgz5FxRZlc7jPlpljz3RqjpGUrp8ySIi6TaySqPffb39FDfs4g61NjJYzplcEaTZBcMp3PvWTuWkngcgUOp3vvvdcZB27nBWtCPk3hKwo4MXbfU5o7P1F+apziHcPZ4GXn1CDCkoX17LMTpRqqGS90JIzt8eH/ECErO/0NHyGyA4KVxZtoIkiUN7L1sCOW6vnnXtzl9d6h3CKpMJyWu1MDcI5hP40QjFcIjXssYFyFqCeL3sgFlHuz8kmlaLDG/E9XR7USYNFhbkM3hjlSsREexcxIwXlBY3miu+yHchQmUCHL1u+B9xIhRWT+gw8+mLYJYKbgmDimETSZ1mQuCmbPld7+vrGvnXm+ovscMJLa7wZChJR5DAAW8YtjntcQd8mA4LReFKVsnJkKOF0Ylxi6ku2VXH/VOzVCOGvDeeedp2uvvbbkd+Oaa64ZF02MV3evU/tAwiCBvgCvd6/RzoFdM+1CZAcrc4JeRWsQMe4GvGr+wsl68YWxGYNx9Sum7LlIpTgJyLwjKjjspxHiySefdDK2DWRsENxiJUkZ09lkauDEILrcDdZwNAv8HiNssUFJI4ynGL6LBfghwZpBA93nXmd4NvB3rs+bxVFI4PDCaczYMX3Bv90lSllnMGKzzhJohbMM5wcaIwgtROAU4xTHNdy8rErpHXm21OrqGdnUIh17gXOOfufJ/eDeEWAEmENcWyp9gROHv/MssKOVI9A9ODTQF96SyMyPZP1CQlQnzj77bIeHBVWlIbCeGtdff73TCHQ8GF77Y7tuTH2x8jRQVCLYCKlVzoJHJIfb+FUTqdfig/fW88tX6cCDrXZgxHk9F0DW/IxWxQCEDkMiNRNZyCEnGEDZlLzn5K5nH2L8whtBBZllrDBuLPU2KFLA99Lgj+9lbBbT+UdpIrIbIHQmonB0mOMG8oPxFXGEcZVG0+WKZMQahwJknn2zGGBdwaGQrHal11GMY4sfQMQQmTuIliDGFIIWwQVJz6VkWkGw7M3S4qXShnXStBnSvIR4YOwx3txp4ggzsi2Ya5Axiy7ya6zphs1NngWOIu5FpsKL54MDE+NTITJamGdkgZES7K5vbODajHhma9gIUbl485vfrIsvvtjZW0r1zDn2DTfcoD//+c+qdvTF+hVRZMShYeh3NEboSMwXaFTWL7iEnyFt1qw5emnVk+rrw8AyuuZHlL1O8NaOL/ac4fhwKAxJ7BvwRNZ3i/51g0CLEOMb8G70tnudR4MTiIGRmLFkTaqzcWoky+yE+/G9GLXgUMXS4swN+BvXSVS78RsCecxpji5nfeB3eGq+5XC4NuwNQQafAb7PL6OadYd7TjYExv5Cw+4ba6tf4AGZB+5yZvBedIB9hkAntG2+Y4Dxyw9j9YEHHnDsSWXh3Ghulc77pLTxtUQZqhkLpPpG7TZhsqNlvRkKPDvmBXOE58vzJJgoVXl/5qaBZ5Dt/oOTieeERixEdiEOUnQPz9mv5DvjgJ9S2uVCFBesTdhA4PcfpuxvuTg1rrvuunGRpQHa6tq0Y2DHGNExub7wm8Z4AUTDNmlvNG8kEtX8KUu0SqvV3dWr5pZG1UTqNKlutLFtprAFtJjgeESHsLhznZArNhA2Hq4Z4xmRMizopMJXe1RiiOwAaXGPCUg3Ih0HAGPLosCDBOMTsYNBFeJVyDGJ4ZYUW64Jg6+BecLriCnmDs5AIj04F4geBncM8GUV/T8MiLsfYWf+c53e2sWFAsdGAHEfDRBX7hnjKlVqPMQacYvQC8K5xfEgMpYJVLLmfV7Mmpv4ccEaIdo5UpaJH8YejjbuC+M2m34ZPHvGBWs92TN+QFwQnWVNydkf+Ayv8Qw5Hs6RfMQ26wYZQ1YWDGdGsrFIhpg1b8waZKX88IfSnXfiEZI+8AFCdHI+7xDFAxGVjDkacSYbq4UGzjaCQIrlAC4lpta37eLQqInUqLV2/JZBCxoEQbAn+42nppr5WrL0AC1/+jkdegS9MOJqrtlD0Uj2RhYMa0E1wMwUrOMYw9hjmDNE+7JPYNhjz2BfwZALlwszM0K4Ab/zazQM1+Fv8I9s+T/ONHdfOS8w3lJyFl1MX5dCc2HGP5yN63QbgTEAMy8IIISXEcAD14XvwJHQ5pSvcn+mnDI1cNJ4g124r8UMnOLeucu1sgbhQMVhhIZMFhTFmML5YOcahBOCNZ6xxDoPhykLI3ltnTR37PyyTA24uI0tSiOaPsShRlkmAqKyWa+xBTDWue/WM9ENxiJzGqAt0BjYodDdBC9ZsG2+AbUcn6wRnJdc3/HHH590DWGvQltZX42s0LdZ2vGkNNQj1U+VJh8m1QTrQAxRGOA7wIdQNk4NjBK33367vve976kSYKUTcjXOLWlboke3PaquoURZlhkNM7TnhOCNieMZbELJMKluNx1/yPl6/OkHdeihi9VaN1c1keyj0zHQFCOCwQ2ik9lgMCaa4KEOJXXlrE4iGxckkBQ9FneirEjlDNPyxjcgB95oJ8YJ5DzbOrfZgjHJ+KTEIMS5EFEcRIkQVcT1eEktBA8RDmFG+EDC7HohzJBl7g/ODYRIqaIj/QA5g7Dff//9jlHQ/Qw5b2rAMr+LAa9Dgr2bdYj7SRklN7H26w9CXw7emyoFOhsgum655RbnmZerA9cbnc55ch9xolGei2dKBky2xn7Wcz/izjywyC2cJtbHwsB45wfBgjDJxamBM8nKSuDIQDhlItY5p5xqUX/rW9INN+CdTPz+la8knBunnJL1uYcoLjAGnHHGGY7oKJVTg2NzDmVhmEgD1lDmVa7OxmkNU7V44r56dueKEYfGkVMOVkPN+GskWShgPHGXRXEjGqnXrNbj9Uptp2pjs9VcP0MNNbtmNmQC9tdi9tPAOc2eBJ8g2539AwMVBlsyQwHcCIMXWoQsXBwd8Ds/Y3aI8QXGj1tnugP/4EG5cDS4S7peEnw3XJiMYMZkIRyBrMsEg8Db/IzrzAtz3mCIN9sAr6F5mCecG9fD79neC5waGHYLAUrp4ZCBm7mzbLmvuTphcgH2Cvc6wj1Dl8FVuV84XpI9Wz5LIOfdd9+tk08+OZDzYe3FSM99KedeXDgOMOa7S7yxhhPMwdpt+j6bniy8H6eOlcf1zgO+10rMeoMlTd8l6yeYDuiSO++807EZ8AyweeHcTDdnuH7sXswVdz+/tKBc5+a7CH9M/N67LvH7zNPG9jEJUbZOjS996UtO8BTjJB8EstoxeInkrIQGL9y0//3f/9WHPvShtKUikqGppklvmvYmdQ91O4KjMdpYtgaZakVb8xy11sxXW33u5MccBsUAm/utt97qLPIYBtnYrYzQmWeeOVKzHjHMYg6pshRxXqNG5FFHHZV3CmyIygQEBEOku0SART6Y0bPQTb0hGhBEokYYh0GVo2J8Q7IY75aOnAyWieLXZBthzg/CCGFiDc+zAYbeQoF7d8cdd+icc84Z2S9KXcIHwmnRXaxRPNt0WRNBRnciohlLWe2fmzcmfmbNkaYEdy7pSDpkm+fFOZ944onOeZPlgmDDGZdLRBPzF4cIn+UeMBf4fpxgiNRU9xqHCNGNOIYyjWxD4OCY5Lkj8vhcNs5y5ii9NbLKiCKD5cYbRx0agOeNkyN0alRMCarLL79cX8EZVQJQ3vZTn/qUKgGsE/BKnDC5Yq/WRVrQPNcpa9tc06TaaPEMUyFwbNRp7swDNdjZqgafUk2Zopi6EIMrGRpojKefftpZ39mjMBTxujUOtiAQ9IQB4xX7gvXUCjH+QHYCY8XNSeEXxcw0KlQ5KjgLvApum4orMXf4sZJtbm7E5zDY8zr3heAknIaZgmuBK/qV3QkCpn3QUm7HAmtQspJQhQb3jCA0fgCcNlUZS3hpvkZNLxhL7h4xaTE4IG1em+Co0+dxESo0GBNUQrBAJXNEsYZj+yGYhPmZreZG0zHuzVjMvec1jodTn9dTBQDyPuZOsgAALxhnONFxXqGR0BvZZlzwrNDJWQXx9tB3xB1oFZcG26WBdimsolP2wO7DGMOXgNbIB4EwZSItIfCFWjSf2rFBd29+XbF4XEdPnaejpma+kXjBxP7kJz/pLBqkICJA8BJl682ORqKaUBvWty01vLXOswGEP4jICRZysj5Y/NnE+Z207yVLlowxXhLpzO/uup3AvenjRWcTZjPguzAeQTQxZhPpzSaXqzMuROWCDASMl5AeDJ/8YID3NvPNBd7U13Qg6oK1ExKGSMjHME9aKg4bNrWgDPwII8gfKePcJ0RZMaOVkoHrIwLG3XC5nJqJMg5SPQPOExIcZOQpApLnxZrnLjeWFNdeKf3td4l/s4a+7yPSSacpaDC3EIe2TjOOyB5kXUcUmHOZsUXdXtZkvzrl6cDaTjSvNazkuHwn99mibJM5S9j3WBMY5+wXzCXLAkJI8D28h2wunIBkqPI7kXz5ZP2xj2UF7qEfNwwDQSoGp512mt7//vc7UZfFLlnD+EXQcw6FQPfggK5bv0Lrejo0o7FFZ8/eRxPrci9bgJi3AAM4m5V0yBZkZoTZGaUDezT7Ui7ruoFIWcZvPt9h+w+RvKzzrOGs5eybbucyRgHTF+7gJ/Z1tIiBvcAaCLOXEVxFsAjOdQKusoqQDVE1wPhJyR74Kbra+j8UygifSTkqxrf1dcsFjG8ra5VN0K1VS/BzWsCd0CvcL3QZ781kfltPjUKCNQHHgdupwf5TiqoUfkBfpNIYPKugg6OxneAYIPMhrcOkY7t01f+Vdg4Ht02dLZ334URT74BBICs8312JgD3HnFCAfzO+rNdNtmW5mL/wfXQEe4d9N9+DFsWRjQZLZrtFe6D10ULoC9OI9nnOCScfQYScI+sGn4EDgVx0ButN9gEhyWzPYbB5JYDxxzPHl1A2To1vf/vbKgQe27ZO/98rjzpDk2m+vH2jeocGdeKM7HsoGMzAxQLHgk/9OiJVLrzwwqKmCofIDyzGGITyIV35GDuJzmUTZkJCGCCEJmYRDizyRnasZEkm5+MWFYxNKw3C5kxKOZsSwsVSFUNUP6wmLSQBcswP49+dHmrly7IFax7kKpvPIzR4P9kjjEO/JoCpwBgmKwBjcC4klnNOFUHiThnnOJAwjLzpHO+FrufLOoFBwb0WcE7eqLBSAJGWymFKvdtCRHLyXDCoEF1qxheczZB+1vdly5Yl3rjyxVGHBoD4//r/SvstTmRtBAichwhXbxSTN1OOZwfJZ63PFYxljoXDBGMVjg3GA6IBIZHK4ci4RvRg9KVWre2FODqI3OU7EN44P0vGbRCx554r/f3viWcG+D+vhagIwDcwzFNmllKZxQTHRJhb4EeQGIzF9IOX73ccGjHF9XLnVj2/c7O+uN+JaqzJjRu6y8UxN1lXf/vb3zr7HIaVMKu7MsB+nK8Rkr2NtTkXpwZagsARgj5Y1zGw8j1mOKJspdupwd7kranvB3cQFfzRdAp7BbwSJz3/Z84F3dQ4RHmCMQVfgP/BNfhhDMA9TGNY375irF9BlKOCS2HMhVtlawxmLmHMZd9Lxr0sqyPTsrdcE38vtMZgzmLANq6KXQKdVqrSkZnCesgF7Xwx2wkBzFb+l3GMoR5HMWNrxJZyx1+ljh2jH962UbrvGunUixQ0uNZMMkg4t2uvvVYnnXRSTsdhvlo5KTIorJ8rwPmFPk7Vi5PxyjxifFtvDtaCu+66y9Fm9FrjdfakkjVkb54vdTwvxa2vYUSqa5PqcrOJhCg+Tj31VH3hC1/I+3vydmqQpseml+uES4cbNyQ2VHcs6/XrX87LqWFg0ee8raboVVdd5SwyYW3RygCOgyC8+rkQNUggUVw0tfJGHvB9GOj8mjNlC0QL0X4YkblW2wRvvvlmx9DNZlQOERghCgsilyCquUR8pgPGToRztk4RRA/ng+jAcJNpaiyECLEOmcw11RhnBt/hzXryApJFNkmuKeNBwzKw3MCJibOy1E1wWU+SGUUgwqwzhepVYo1McfhgzGFvpga4Za05Au2NV3f9IMbx1a8H7tSwCNtMrpfMBUoL5gMTWYgu93hmDyGainlizmy344k9EKFywgknjPkc541DiAg9RFHJgzU++1magYw2Cn/f+wihK+05hcgKZEowzovt1OCYhcrSeLlzi9b07Bz5HcfG1v5uPdO+QYdPyX+fMH6KgfiGG25wNMYFF1yQ9/eGKDwwBKYy9mSCfMqCoiEwSML9vMDgGsSazn5PFDPfhe6Fw/FDwAglQNiXOX7oiKtuYPhlrOFIKKcSx7mWoyJyHM6fTxAOc5/vSVdWNIiyt0Eby92OGLgj9w0OX2iHSj7Ajnj00UcX5LtZhwn6oUcF6zEalPWOHzTHiFNj01op7lqv+fcmShsFD+yOmYD5yHjKN+iN8YnGd2s8nOWMDXNcMm7cwbJoDgJr0SFuBzrvM01iFUVKitpWafpJ0o6npaFuqX6aNPngsJ9GBQFbvDka87HRRIMQHBhaC2Xs6IsN7vJafyyzxSBTMCnZMPHmMsHLpRxIiPT1P/Ml2xAX62eRDZh4yUrmkMKdS3SIH6z3BsfBuGU4/fTTHQMYHnSMuyGqG5AKyBjOh6ABafI2E8t2jPJ5HH3pwPmTHo1Qzrd2KvOf78oEljLOPkVUFVFBpQDk1Lu/QCwRb0ReFjo9PRU4L7/1DNKPqEzXayNfQGTgEggbHD0QZwg4WRwOkvXPmBp8ORwcYJlmwmEQwrGR6Vj0AoHF2u7XiJM9hO9mn0KAse8YWA84JrXRvZ/DAYWIZW8rC6c3IvvjH5euvFL6/e8ly74JUVGRVPD9YvJjjsUxOXYh0J/E4By0xmDdp/kpzg0rORGifMHaSrZbEEZAKxeaLeBTfqXecFbDYbIuA+gD9hcc4gcffLATeesOGjnrrLMcBwevF6v3YIjSAO5ByRh0dbnBylERPJXJfgEnwo6TSdZSOl5Htkemxmc4PBoDhyD8EV5XClimjRusFcuXL/ftRVgssAb6Zb3AU1ljWOsK6TxljBMohcYggAxDPMcbc8zWtrFlUfl3a/D8Gf2Zjc2UkjxkROSqDzkeOsrPEY7jGn3BDzyFCiQG5hLzyM9pgR4kizbT+VFw1E+VZpwszT5bmnqUFC1t5YMQ2YGSyWQy5xsgGIhTo1CCAxw2eWwEJsvNYZMza1qTDWxxw2hA5Ero2ChvsDgHEamEaEE8ZCs0ieL1ExxElTCOgvRcsyExJjlHmv3xQ2Q3P5QsMUMpzhQiEXB0EDHC+4j2KhW52gUbV0t3Xy3ddZW0/rVSn03FARJBVHYhyF6+6x3EB9EBcUr2XUSKQJgQKPn2z4jFY1rd9bo2xTdoY+/6jM/fmmRyfAwDOFlwChKpko9jJ997jQEdQcQ8L/Z85Xx4bn6CAuMOrxPlVIrUYiKLyAJycNAh0qHD6do2fk46XVqUXzStNwL2nnvucZ5FNqUJcewhOHD+ZGMA4r2MP8YkWRgI4nQOSBMdrO1+hi2eJ2Ob80c44SxDOIYIkQ8I+sEokqvzLhdwLPgP/WwKgd1bJquppnak8jL/r4tEtU9r8I5S5iLrLGuE2zkZovzAPo02CGLPow8e3DwbwAGScSQc1Tj+gzQA3nTTTU5whVtf8H/mn5U8RfPwGg54MgC5Jn6nRGJZgNIj21+QNj4sbXtOCtgxWe2ALwTV1y5ocF4EZ8BrcOr5wfgXGSf59rABPUM71R5bp60Dr6s/lpk2sLK3GIjRapT2Yc9EX/BTjCwYjINuw7SdF85L5rC7ZHGxgHOIZwfH9YLzQV8UOmgqGcasoyecL0Vrhh0blDBqkI49J7BjoadYN7HR+GXgJQP7EGs++hA+lKne5X045wGfT+cYpCyhBZFhF7MSbF6gm/kubHCcG9kcIULkC3wJtLMoWfkpJiieuo997GMqFM6Zs7fTyO+uza8prriOmjpf71wQfAkWM7ohniBtbER+kzlEYRGP9ymuLc6zjgrPvX8aJ6SB2q9BiF02UxZyDEuZAM+0Gfu8QKxmWoYnU+A8ee973ztGYHF8NwFFZEBYLN2WSBHSuSCAODoQLEQ/lyyNfPVK6e//d7SO3FP3Sud+SFqU2T0vGLq2S52bpYYJ0qTZZd28FiIBYS1HMBYxBOMYsKbmRqpYSxmfjGOimfIFDo0ntj+il157UVNnTtWzO5/W3MZt2ndi5qnmljKO849zJlKxWOCe+JW8IyIN4sm6BskvNHAWYETnPEix90uZ964zxcaYe8R5fOLz0qMPSJs2SHMXSAcfHsic5XlgoOEZYLzNZZ1k7cV5QO8Ra5SX6ngYjhDffI7jUWKQSEnGo18NafYWnoWtAYgOv2gvjF58r+1nCFxEVDmXHghR/sAgQ4QjnD/oZp7JcNtttzkcr1DGoNa6Bn1qz2P0/157XJv6utRW16T3LjxY0xqCL9fG2sA6C3e9++67S1oGcbzC2Xu1TXF1KaJmRTQ16VrPOhtEk2/GbrYlqNCg8KlkxtugAwwo74ZR2+6Fu1GtAQMtzg3GMP9mrySDGKMkew7zFA5REo1BmZi1d0rdBAUMd+DsXC3NP620JUjIXn7hWam/T9p7P2lSefJ3QyG5Xr79OAgUZC5SFo5xZ3yVwBDKoKPLKYcbxPjbObBJr3c/qe2D67SxL6bNfau0x4Sj1FiTWe9A5ieZtZwb5aPR4sWaFzh10P9evsfxuT+sLdi6gtBiqcCaB++Ep7JWkKWY7B6Usrwdaxrn6PSjm71Iuvgz0qvPJ3TFHgdKE4KZszh/Ka9DIFIulW1wLrPGYs8B6QJnzXnFnLHj4YTgdbSv15bA/CSbx5xLOC38HC+Mad5HBRHbh+Azxeq3E6J6sWzZMv30pz/Nayzl5dTAK070OCkjhUJNJKqLdztQFy1Y7NhDowWcNCxsGCSI5i1EU8IQqRGPd2kw/ihxSs7vMdWoVocqEpnoS74w/EAYSCnMp1k40cAs0pmATAw2FaKv/IBBKtumyengJ2C85JMNCmMchIWmsBbNwiZGuiUi5G9/+5tDroKIYskaD9yQqH8/EmEQke69trROjbXPSi/cNtqxZ9Z+0gGnl61jg2dcNlFxKQQ86yjEiegcxh9raZAO4o19G9Q+uEODg0OqrU9sYWt7V2te8wJNoLZmFsABWexyIIgJnCl+ogKhRukHmg/6ZYLlC9Ynjk0kKOseBDYVeeD9pS49McYgxLp3ZPBR26z/GBtxLOQDhCTPkDGVqrwaEVAczys8ca4li6TjObhrSiczlPHMGEMGnIyFqlUcYnzhlFNOcZwaH/3oR4tyPI7FMQuJ3Vra9J8HnKIY5fcKuPdjdCN4BkN5MZ3oIVzPIP6i4hrNkololmqUcCp7gVMBPQiXzrQUYTKwzhPckU5XWpYd5+PunTRy/kNDGfcVyAbe7/S7H5Q55H6gb+ANOMt5H8ZbDKnoIvY9IsLz0WM5oWfTsENDo3y+d7PUtVaaMF8lQVen9PUvJHp+geYW6fP/Ke2xl8oVhTJKYpBFF+TbF4DzQ7/CkeBZBHrB2ciOCNLRt67nhTG/xzSkjb0rtVvL0qy+h+vFTgHfLsS8TabBqOCQDATRMI8L4dSAk7K/EcSGjYJ1IVV/ERy03CP4cCH0TiawUq0jGnXSNGlpsEFlcHfGLDaafMAYwtGAcySdU4O9xNsf2BqGcy5epwa6wT3/0e9+PaXQpuyHNt+4d1bKK0SIfICt0noo4QTNBdF8BQcRpcUgMEyYQgoOgBcbsRGUtz9E9oLDHBrDr2gwviKlQZK+EmzQIyVKcgSLNCl0GLfcvQHYGCDvpAzydzYTvOVewcH7IC5sDPn2CvB+b6peBURCcE6QO8gBZIJNx1s6hciWc845x0ldRDTZdyNCIEAFL7fW3eFyaDhHl3o6VTL0dY51aIANL0ibXla5gudbagNzJiAjCDLEHEH8Bt1vqT/Wl9Xr5QYidC0l2A+IIOZ0PsD4QaSNpb3bDwQUIw2R1hi+k+1z1iCOFPpSpYUbIM+FLMmFCLNMiSCAk50sjFRrKutwtr0uWMM5R6LweI6s8az3qcB6sUvd4IDAMyllD5gQxQcOhjvvvLModZQ5BscqtFPDUGh9gdEGbUG2S7lmXFYzYvH2MQ4NENcGJ3PDD6yZRKNiqKOeeT6wcjT0p/KWGGQNhQ/wN35wgPiVFWS9Ze1PlsGRKzh+Mgc5f6PMIeeMDoE3kNHItbgdNBguuVdLlixxAs1Ms/B/NFHBS2oO9WX3ejFwxR+lta4mw7090v/9vsoZlkUcNAhuSlY2KltgQGUdZU6hLdwG1qAwGO/XwMCgampGTWQD8eSOglTAPlZsnoRWxDiY6u/5ltvFEeHVF6xjGMNZwzCop3JoEKDHeocTppR933C8kOlTSKAHggr+JhgNx3KqEpaMt2znMXYrDMnoIcoOss9w3n7zj/cYCJjlHgYNs31lm+UYonLBWolPAd9Crqgt9yiqYiIoo0aI3BCX3yabmkiwOeP9ptEUHuxcI8IxNPID8aL8C2QJYz9kiX+zwCfboFl0Sb8jBfutb32rggJCgPpyGB+t2SCbDpuaAeMn53bvvfc67zfDN+cNyWCzZqHAAMZ58jeLOsNRY2QQxw3GS3eEb6CYv5fUvnXUsUFKOK+VCj003PZs+pxTV1h7vtwxsXZXJ0lEUbXUZJelUSpgLGEOIv4hut4ILuYkBJM5mQvZx9HJ2oVzxK8vBOtBukwQjOUIk1I7NABReIglv6ihfAFpppxBkOW+eH6IbhzkGHm8wAHNM8/G0YDI4P1EWrHes+5zDAxMGJPcY8hKM/J+1vxCZdJicGaMII5x0lAP1S+6OET1gCxQnA1wB/5dSFCKDWFb6OMUC0SuFoxfhcgAyYx4qTUG+yjrL+ORKOdcgvhYi8l0YD0mCpGsDdZNxgRrNwFarNPJ9gT2dPQ2+iabWuzpgLOGOcZ5oR9Yv9lXDJwn8x2nhu0pVnqOz6G70EXoCoyTaBTeY7rijjvucHQVfyeAKt9I5aRoRLtjgHYbwCJSYwmy0g1r3kAcjv7OvzeuT2igMg2aZGwHkVHh59RgLFVKCcymmknasvk1TZo8ymeaa9ryKm8UZLBjOmAnwCDNumLR+X5/hztmC9Yt+Dj6IRknp09cuooQZC0WqldWNmCtKmSQBvoCDRNk8DdrMPoRB4Pf92KLIrgwU+CkQJcwTxkvPGOcJqzdPEu3I53xzL7FfsCewfuDrk4CsF+hU9lPzBZXsP0jRNllg1966aXFdWowmDHkfv3rX8/1K0KEGIOIWhV3BMZoiaKI0i+WLLCUVSJ6iOhiNigybnIhESzqRNqygPo5MSDt1kgJ4ASBsLPok84ZZEon52BOGjYvSCFNX08++eSR90AKMJ4hmFj4ERYIECJ5uRbKjvA9ZvTi3pjg4L2W4cLfCxotcfxbpJ3bpNdfHE6zWSidHJwDKGs0TvSvy9sUbFZBiOAxuX6K9mjZW2u1zvk9qqgWT1qihppghVghwTqBwLv66qv1tre9bRdjBpGa7K+QuGyi0CCBrFvUvU4GxAZE1c+pQZQVJBwDBOtgOYDzhOAG7dRgLUQYIOyCzmQgQAJDmF9tUJzk1DROVjeU1xAVREBZ807Wfz6HQYy9hnvBvzGEEe3mFoccm2uCo2XT7DwbcO441zE482+eD/tg6NSobjCe6BeD6Ci0swGDKMcKOgo3xPhEci2RXmPg3CfyGWMR+wbrXKY9+NxgLWdvJ8qZ/d9v/Yfnsw8b4PLs2RiycjFCpgLGNjIxyALEsEV2J1G3ll2LUQuDLHsJZaVY41kD2HO4H+gO/obW4FrMQWJzFl0Bl+Bv6cql5IW6CdKc46T19yUahhOgNPMYqaGEfH7WHOnF50YdGzzrqdPL1qFRDGdJpWB+84Fa1bVKU6YlbAgtNVM0q3HPirl25ijan2BHbBZe7sy8ZD7inMi2tB6fgYOm4sysbaxxfoHCVIpgfStWOa5MgFGecwqav2KT4j4VotQXAVPoOL+sPhzx2IFSHZeAV+w/5qRgjLAH4EBHP1rwLPuPt7cUDhPvWh80CKyjHBHnxnH8skZCVKdT46tf/WrO+jVnxUvUCgf0m1AhQuSCmsg+Gox3uCKqGlQTyUw4sHGwkFv9yvvuu88Zm7lEhkDQU6VMQvTN4YHhicgmIp6CrpPMNdG7A8cJUdss8F4QUeM1PnLNnA8bj3fDcf+ecRo7xv4XH5M2r5GaW6XFb5IasmzcWd8onX+J1IUTJS61TCotuW9slfY+QXrp7tHXpu8uzSpOA9RcgJguZdPmcsLClt3V3dajeZPnqammWXXR8iHImQCDAGsHcxdDAkYF79zHYI1jA8MeDkveT9kSK0GG4QHR4B4TODUymddWR9a7PrKvYyAvyTgjgrG3O7FW+BDlIBvRcT9xgLOmBh2VaCAqm/3CLzrbSoz5ZcLgxEE4WgNxN3jN6o3yWRzspPljbPPWIC2UQ8PWIvt+E230cypVXeQQxYM5NT73uc8V9DisfWT/hAgRBCKRCYpqL8Xio6Udo5HdFY1MytjwZY489mzj/LnsSamqArhrmVs5IJwPheinBg/AWUFULk4av16B7FVeYDCFw3gDv9xZGiDjSg49O6TNz0tDg9Lk3aS2XaPL02LCAmmPt0uDPVJtkxQt3P6XEd52sfTcU9KmjYnfMeJ+OLfo02KhEFkaoNLKeddHm7Rb8yGa3zrLyQJviCacdjl9V319ylJQhQBGYIzaZGrwf/SF9/xZY3AwwEPhkASmuEscowHgde7xQEZuJv0TeA8Gd+86Rz8/1odC9uFNCdaX2KBUN3bd4v4QhOSXWZ0rKBWILQrOXgiwHyUr6YZOxBFBcBTBq15wXjgpvGWjeA2nOmMG3cKYYGwQTIuTwz0WvGt90GDfMx3K/y3Yy+96QlQPDjroIGdckQ1Oe4FskfOub1GkoZEtRFCIRBpUq6MUV6I0UERtikSyXzQxtkDUiUDCEMgkCYKomSHSvRGYp7qQUbE4WVI5Wrxg8Q8sihJD4z1XSi8/kYh+wiGx8inp/E8kjI/ZACI0oYwyIRYcIrXNlTo2SQ0TpKkLyzqKCkJYqGg3K1VTSet5fU2DJtaV0XjKguwSEYQRmvuNAxbDgVcokGmG4RoDN+sLRgeirozU4ZQgEgjyhyEEA0Om5feoh43osPJBGKQRX6wzJRkDm9dK//il1LE94dA44XxpybFjyDYOm3Qp7Zmu4xikcN4U0vDPPUYwep0aGA641zgALCrKDZxVyfrQ8Gx4xkRY8R6MUowNXjchUgxw39wp+5YVadcWonpBqbavfOUrY7I+gwbfzbr4ta99rSDfH2J8oiaym6Karri6FVGTIpHMebUb7NcEGhEJTaRzqszIbEA0qrtcjHEC9vdClPiwY2RbE519OIi92EH3Nun5v0ux4f0E58bC46QZORgDcWTUl0kJxElt0jf+R3r6Cam/T9r/QGlaAbNVAkDBe59UEKKRqJpqfDL6cyw/VSzAb9EMOELh8+gCdIKfcxJNwT6LgwP9B5+0rCu0hvXSYT/G2YEdxcrPpQOGdaLt+T9OYM6L34N0HGRlx1h5r7R6uDdS60zpoHMSun+4akW+PUbcwPbEdfuV/goSybQaQVs8U3Qdz8ubFZMq8M10izl7+A6Cv8jOw+ZbTAelO5CNAAKC7krmEAtRFKAp0Ob4GIru1CBiK0SIIIETI6L8626yEJKpwOJOeSZ3yniuRjtEBZ+ndIg7ra/gTbZLiZ1bEw4Ny9gAGB557YBjgiceLz4srXgwIXB2O0BaeqpUU8CIq4kzEz/lgtUvSq+vkOrqpf2OkCZOHZMuSoQc441/M6YR1EGQDIySlAlIJZwHYzH9bc2LenjrOjXU1Oi8uXvp6KnJ01sL2dSca6/UBmJs2kRQGVhLkkX0kxWG4QSxwbrljlLBIWGZFnYvMl3bGEOkh1MXFQM1QoPn5e7XUzQM9kt//5nUMxzNhrH8jiukKTNG+u4gDojcCMKQgkMAI1QhHRp2HO/9ZI49+OCDznODpLM/JUvTTwacIYhUnBrcDwQj10MGB2OroGU+UqDQkVshygNWqxnh7hfZHQSY64ynkhhAQlQ1IpFmRZR/fXv2ZBx8ZMvZPooRKJ/ejMwn1nHWdXdkKpoDLlCVWP/EsEPDpaNWPyxN3z/4IKONG6Qff1969RVp+gzpw5+U9sm+jFjGaGqWjip934AR9PdILz+S4FpT5kqLlozcY3eJGZxoRGzDNTDOFgvtA+1asXOFemO9aqtr034T91N91D9IgnMsZABFUA6eQvds8AIt4e6Lg76Ac/o5NQDBVQT5oAHd+g9DuNtZm23gJs4R7ISsk+hUgnH4fEmCXtYuH3VogM5N0rM3SIe+feQlK3MURFAXGRTF4C5+2p/9A6fWaaed5vRfIhsn12wRvp/AOoJ5+S7GESUHixH4xvjhPtqYLKdyZSEKC4LSWTv+9V//NevP5jQyWaAxtHDgECHKGUQpUOKFhZjNnRrqLPheR4QZihHq1mfC+3c2PEgfEVru1/lOb/mYqkFf966vsZFSJiZovPKE9MRNUne71NspvfiI9MTNGjd4/iHpxl9JLzwsLb9XuvKH0o5NI38m0sXShTFcMp75nQyOfIGBFFGTCr9+7RldtfYlre3t1Kqudn3/pcf02Db/kghE+GSaNZAtEPpcdyXOOT8HKAZ7IvaTgYgbayqaDPwtG6LJ/g1ZpUY3kS9EYJXEoQG2bZa6OxJOTQPX8sZLI78ihvIVmUSeYSxlnS9EjVsvLJvCQKbJ448/7gSDDA0OqKvjMc2Z9YI2rv+HFHtp7PWnAM8ZBxTXAxCrCFkisjBEWIPlQoJ0dKLuDOyNXG/o1BhfkVSFAt/NMcLxFKLcwf7NPkp2NOswZQ1xVnuBgQbeQkkPv4AMi0o1I5KVe0FvozkK2vOulBhw91EcxhCR7QHvYX290hc/Kz37DBZx6bVXpS9+TtqwXuMC/b3Srb+QnrtHWvWk9Og10uM3jHkL4w59QdNgeCdjlozeIAKUiPhPxUu6B7v18NaHtW1gm7qHurW+d70e3fZo0s/4ZbkGBWwEQc23cii9laqvh2k/ygylQjYODZywjJszzjhDxx57rMO3cZqULIt322gfVAeMqfZ1iVJUw8BpjDbIB/BvtFW2mW9BgHlCph86btmyZXry3lsUffwPmr/6Rq25/kdSZ2p97waOedOkPDPuDWME58g999wzxgZWCKB3CUZ295ZiL/Trcxui+nDCCSc48ygXZ3BOoYqkI3Ewi9gKEaISQMQJ6XPUjyWVzgxCGJso5UI0gQkKDDY4Q6xsFRu0/dsdiYVBGeLHZohYYeO3BtyQGfP+8zrzpeJKc7TNlOqbEoTYRAYZG7MXBX+s15Z7XognXjv8zSpbDPQlnDy1eT5XSNbDNw7/e1jwDg5IT90lnfh2h7AQuUC0hBv8jgChHqhfff5MAVlItYFw/Ns3vTFGZkLVb9/0ug6bsqvzgjlRiFI4zEuIo1+/gWxhjspignXGG8lpzgicVn6ZMjx3K+8TBLhmnndg5SPyRUOj/3zw9O0x8ZXtefOcIeKsy4ybbEr55QOeM0Ys7rU9Ywy1TgT6QU1avvweHXroXqqrYxy/hrmYhO+Mvpv0cSKyuB4Mv1YTmf0MQYDzJJfU3WzuqXstwpBXyOOVDczAUgaGilKCCHXKVlx22WUF+X6+m2OECFEpYB1mPcahgV5gHSYjj30LIyn6gUAM6+dEmQ8LzLDSttao1rIy2CuIooZLPfLII45jHy4AVyPrj+hVM/qS/VnocicFwcS50s41rhcoUztzuNxtgFj50mh/CwD3G+iXHntYOvs8lSU4x+5OqXlCItAjH7z6lNRJIALjZXgfe+Vxab9jpZY2x2hI8J87MwNjNHwRHgO3yKeePWM1FWfHiTGkUQ0SV1w7B3eqY7BDE+vGBtzYPAgazCXKwxKQFUSGCudZDo55HFQEbHr7bxpYo4LMXMapwbHKwaHjoBbbDefiDpyqlVzlzVlj4R25rKE4Asm4JrPJOH4xgD2J7AkccOhibExOIFVvh7TyHsX3na8JjXXauG6LtPwq6fB3S3Xp5zD7FkFTVpWEfYlyZjg6MDiTmUgmSqFKIqKZuJe2H6L7yU4fF6Wn2M/LZd6UCFQxYO18/vnnd+kVmQ45rWIYhFmwCl2+IUSIQgDBwA+TBrGBGHAbaEjdJKqBDAyEtRki/cp68HdEB3/DOw8BJKqFRdm9sfEaGwHfXVHNVOsbpNPfK936O6m3KyE0jnqzNKcQUfI+C3m5Lu6kcd/7N2l9IqJOuy+Vjjwn91JZODJwkIx5LS71JBqBsbgnq2UKiUEom8EXhxoChSgmjKk4Pfg9Vfom4x2nXtLTGxYZfq8hyDm2OUUQBoVy3kHcMq3pmgkRZQ8rJvEmwsUvw4T9lPUGo4i3ebe7KXMQx2csQVDLBpOmSvsdJr3w2PB8jyRE/AFH7GLIxzmRbS8vmwvFvmaeIxG8zC3mpzsTpq5uqwYHR+dL4h8bpUhm6yrXz1jBEGbOTGt2yPxgDeBvhcpmYp1xi3XWlpJl+hQDZAldfrl05ZUJQ9NZZ0mf/axUoCbz5Q6MX5dzPwoENMall5Z3U90QIfwA97eAPwKmCPDA4Wt7OOskegH+YU16MRb5lSth32KdhV+x//HdaAnWfy+fY+3HmY1BoJJ6o2n2Eqm3XdqyIvF78xRpz2XBHyeZk6Rc79UTD0m/+2kiw2TCROlDl0l77ZefZoFfeTMf+ro11NjqcEO/MjWMJfQxwawWTIRuxsGBYw59wU+6vhG5cthYPOYED+LEcwcjFaIcG/OMawrCocH94J5hmPVy+mIDjYjRnkh+1iKv5glKA/Fc0Bf8v2wcGmDBodKml6X40LBfIy4tOmqMfYHzRSPYmpwNsBHBiYoN9hnmg2VWjcyxHas1fVKTNu/o0FAsppmTJ0iDfdLO9dJU/zJkyfouWgYhe4/1h+FaiaRHixXCacfxmDfmNCFAADtdWY2poPHSi9I3viatXSPNmCl9/t+lJeMzcaC2ttZZp9ABRXNq0DgmRIhKnzhsBH7AKMsYxwuOQ4NeGl6wuLPoI0ZMYHjr3huI0sLAdcUVV+iCCy6orDS6mbtJF/97okRMY3P+WQnJsPfh0iYill3Yyz+ypOR46Fppw6rR31c9JTW3SktzFGPRGmn6PGnLutFMDTB7d8dAieMgVV1Ma+4FCcDwjzET4WHN3xjnyZwaQ/FB7RzYrJ74VkWH4mqs2TWSPRqJ6Ngpc3Tj8sc1NDCgmoZ6xWMx7V4zxSk1BRkstJObawmKQDF3+a5kdWaL0fgMYwfrAOfBukGED+WRMIS4s24Qc/mk//NZDB6MIQwq9GUputFjoFvaQQp4XJq0QKr3RPicdpE0c7604XWpeaJ06IlS09j3cN8w2OBEziYrgPtZqDIFmYC9ZFcnX+L+r1y5VjNnWpmD7J6JjRuDZQYCxjV7V6GcGtxPnPSWjVXMmtElwf/7f9If/zhqEPrHP3iw0uc+p/EISu2w7nv7iwUBMmnZwzhGiBCVDAKYkgUxYTijVCCG1GTGGjic20kCLGPcCzgDUea33367Tj31VFUMcDbsfqK04OhEOZi65sIEM+21j7TbQmn1GwnHNHslPS+OPFZlh7VvSL/80agW6OqQfvJf0ld/KLWOlrTMCtMXSM/f63oh4gStxSdMdbKA4FbJwPiEU5ANapHscBrGGw4AeGUqR8Dm3nZt7NuhVzrXa2HLTNX4OJhmNs7Uk2ue1Pat2x2aWN9Qr7p4nV7b+Jp2X7S7wzUKbdTEOJytQTsZiCzHNlBMro1WcNsf3H3dcAKRpUxgEBHv7vcRCe/WJtmAz7Ff01uIfxNUU/RSefCy+FYp1ilFmqXo9LFryIRp0mEXSWufloYGpKkLpZm7Bsdxj8ii4//5ZCUVEzyzXeZetEbzZ0zWM6sSpakP2Xu4HJYrMyUTYE8gIMvuBXOfHn7YExjb6Eo/21gQIBPLGs1ju0AvVy3ad0ifuYwJnNib6P30b5+VfvVbaVbwFS8qAdac/oMf/GBxnBpf+9rXcvloiDLD9v5tah/YrrponWY1zlFNJMy+cS+qEByin/wWbsQI0e2pIuAR6HjxESIYfTH+JhMlZQ2M7hMK3DBuwQHSMUPSioeGG4UvlvYvQ8EByNDwRjytfTl3pwZY9k/STb+Stg/30dj7UPXve4RuuuZaxxGWDhANInEwMkGkMzHYD8YGtLLzYfXFEoQhooh2a1mqSXVjs5Ior3biULPqDzpMj+/cotqhIR3WX68LDy9eqm2yZtq5gMjIUmcrcE9J8WU94P8IDozF1kyaTZ3nyPNEMGQLBAZilXUHg4el8hYdPdulFf+QhoYdM9E6aZ9zpBZXGSmE38Hpy80g0LhfrKmZRr9RSoCxU7Lr90OExokJEbhx43ZNmtQiRbJPezdnAtkoCEn3XCSbIpdyXZmAccned8011zhCB+GRzTOpONx++9j1nn/z2jh1aiB2yTpFC2SyN2UDvpPvLlaZuBAFBL0ROl6VYgNS80ypsUxKHpYJcFbcfPPNYxr7Gtgb4AKpMmhZ/wmEwOjEPs/+WDZlJXMqEVNAbYRO+9p3pF/+VFr5sjRzlvSBS6gTqbLDyhd23W/I2Hh9lbQ4ufMhJWbtIS1ZJj19eyK4hPKeb3qHHn3qaWf/TrfeoofhGFbaEt2bifH62fbXdffmZ0d+n904RefOPUI1LgMrkdirX1utxRMWa+u0rerq71L/1n4dMusQzZk+R5UGcxAUO3gIHWH2CNYOAt1YGzgPzod/49AgMAh9aHoKhxbG6myDYOCdBHZSpYJs85JliA2+IMVcGik6R6o9yOPYmCrtc3Lar2Jso5sIQq1YTFmoupY29fWv1rS2ZvX1D6qhbYbUNjfrccwPVU3g9zjoTWMw93n2hQJ7ItkgOBpPPvlkZ6wlC0KueDz7LDUoPet9n/TkE9KZZVx+vYDA/vHlL385689lbcGmcQvpZWGmRuVjdfdrWtm5wjFkUkZmTffrOnTy0aql1mCIkQh4Um8hAWz8tqBjJCNSkfmAscxr2LWUQKKkDzrooJHXSaOt6hS6fLHwoMRPJZTlchocuqOe8ozsaJ0sve1SqWunVFsnNbaI+G6i7p555pmUkVTOGUQijlExm8jZTX2rRhwagHVgdfczmjjx5JFxyjgnKvzA/faXOxEQwlzMsQxhJtsgCGMX5L8U8xDxxn0DRGAS1UQGCteGgYLIFCJSeIZEwtNkj/PkmvlsJtFDRkQpZ0XqZhCp9Hlh9YPDjT+HQTTmG/dJ+52f09exHiOsM62vypwgwrC8MF8LdjtJDz90q3bffZ7Wb5ip2XNmpxyvXIPbgc7eQsaRCQ72Ijfxx2lHQ+9CGbmIZmM+Mi5x/uOIw7DGeOaeM7arZq/zC0SoxOCEAkRSFcKpEeqLKsBQr7T6RmkQfhGRtsWlGUdLEwuTPVaJYH2kqSu13HEU2l4NFyCLg7+jI5w66S5YyUIiZdkPjRewD4RloVNgUpv06X9T2YOMeL9G6WSW5IN9j5H2ONQpOeVkxEZrdPjUeY7xkD083djBqAlHTRXI50bv0IDu2fzcmNfW927Tc+2rdVDbwhFuAyeGq7r5wv/P3n+AyXFm5/3o22FyHoTBIBIEARCRYACYQII5LLk5Shska2VbupJs/eWVZcuybMtBDvpLurZXupIl2Za0SVppE5fLnDNBIhEkAJLIOU3O0933+VXPN1NT06GquzrN1MuniZme7urqqi+c95z3nHOx+mLR+1GgSjf2eD7ANivFPMQXQYNl1gxss+uuu24yw51/udaIqSh/R5Ylz/MzDzJL3AQ1zNqDLwNuSAmiktp58Z7pAQ3rOSoeLJFC3kt9c9/IQrIHiLLBlGUqm56pBIi3fEZ1Z4a1eEGDDnWNasOmTyicwbfHeMCWtwNRFGOKvxHEwPdrF+WaBvSF+N7MQfjvo48+ah0fXxy2oemDSeWKWVPyNt31w880R3HjjTda5fvS9RpNB8+rLqoQlI+FaAIboHhIKrSTUVZTK38wNqBTQ8e1oqG4JVnKHSilWDyJ3gMMBogEEX2MIBzObPQELzBmUFWjkmATsBsJJtIdYBbgmrukV7431QOAObRpR/7HJS3bkRHDOKJRn9nMMUoZX6hjcGLivDRGJcYVY9BtNtBoPFmn1lmOKq6YIopa6eU4RVMpsCHSJj005bFHR625wLlx7rwuVV8at2Df4XwwbvIFjuBSGOKo3ZxqE0PcMKZNuQqcFSjgcUqjgMJBzAafrSQLr6E2MkYgQbBCNXLzhJEeB0FHhdKb8+EMQXOLsnSsh0IKhZZK4XVa0HGjRSiZ0+maX9IfgzHgJNumBCJ/Y+4zJ02g3VynXMsKuAF1bgn4Q3A5d+YmATlKfhGUs6sBKxpf+Yr0L/7F9JrkPDeHwbr07W9/2/fjErD8whe+4PtxAxQZXe9K48a+mJgzF96Qmlb63wS6gsG+T28+9m72fQIa8AqeYw1n7ae8C2s7Nh8OJprSmlIv9lK27AWUlwxQ4diyVXp8iXT+zFQPgDUbpJU+lEaqqkk+JoBtQMaQacqKY5OyZwhpsCMYZ5RgNvBiew2MD83oxRdWSL2UI53gxNgKqLKdNgqfyTllygbBqY4IyDjas1VOyAb61vDd8y1LCy8qRWYwDkCysu0cyfALbEd4IfyDe8w6g1CG9QP/BtcfXpQpGIONimMZWw8+56UMbMGQGEzz/HQHvRfAqRkHbkuRYQdjA2fKqis6ahq19v6fsTjzhm0b9P7hw2n7UbLf4DdwclN+Z44xbgw35XgGXB8CkplKY+cDxiK8n8oKjFfGshlz7JeM41khgKF3BmvO0aNTpRHxldw0d/fyxYsXW/cecQeB04IFNYgCp2poFqCyMBqf2dSLjI2RuF19HsAAp65ZPFn8mWgstJSLwRBEFY1xhkMJZ2Sqnhlskl6b3gQoU9AYvKZeOrY/SdCvuk5aMFG3sgCA0LKJ8y+GJZs9azEGPAasyV7AoYiR4bbOZW24UT2aKHc1gYHuYb1/NqlsZ1NJF7RAqQ3pQEWYymkKGYckmL/hbCcAyHMYJ5Rm4xh2Ry2vQSEDKcCQgsRjuOD0NU29+X75kheOXQpn92RT6CzAWMTBATHiGhNETdW3gKARDg/TPJHrUigDM2fUz5dG+myBjZBUl1/JB+6fl+BdWQY2JGue4sRFUWfS/9lLmB+MAUiEUYylUg8SvLKXLiGAAFE1hJqAJA6wQgXTGaOcF/P67rvvtn5nfkJ6yaR55513ZkdQ4557pN/7Pel735tqFP7gg5rLQMTxm7/5m74GzTjWnj179Lu/+7u+HC9ACTEZ0LCBRq1wj0gF9ZQrAlhD7dyANZx9gX3A7OfPP/+8Zd9RJ9+e/W0Hry/XvS6AB9TUSl/7HenJH0qXLkiLl0t3P1SwpuY4tY0wAbuKMYTdgFIbYYw9qMHfcIK66QvZVFVn9c+I2foE8nPP8XM61JW05+394+zgb9j/iLj41wnTB8I4YrGP4djYSvBy83d7UAQRCKJEAiHYVYis4C/YXgQEsJ+xp7kOcJ9cQXCgFEEN7pmbBurwSJPRjy+Dxs/cZ2eGCfcZ/sH3gV9wbU2wtWwQavT2fAGyu5kf2MBlFdSYuM8mYGH2Bbg085p7aao/MG/SBTyc9rudh7BuMG8KBc7drEOsE/Alssr4zDvuuMMam7MmU+MP/of0F38mHT0iLVkq/dxXpTlegnXz5s3WvCp4UCOdQRWgclATqVU0FNV4YnzyORQVTdFZks5VQOBIwwjAYCKwQR8DFvpHHnnEavabztGGYZCPIzZAmWHJmuSjCMDZj6HOmMPZibFiml3bDUwMUgwsAmxuHJkLa69U3/hlDca6rd/DiqhucLGuXn+1q/PCcIcgODMCOE8ThDCAXEAgjLGIUc3PvAZygSKE1zB/+I58P/7O98NxC9lg/mDgeFGLOUFQyA0h8xuQKbfpslwTk53BzygwWT/sMH9DMV3Wjoxlt0iDlyYyNjDgGqQrsvfPyAQCx6y9bpVUJluoZDV/04D5wPg2jeA5TwIVfD/mFXOYeeS2ETfvt48F5iXjvVBgjDI2CbqZEhKUZuTcyS5jzLt1gJQ97rgj+QhgAWcrhJN1OR8HkB2MHdS7ZReYDeAdNe1S/1HbE6FkMCM8d0squIVx1LJ2s76yP5CVS5mqdNma2Fzltr8FyAP1DdLHf6ool9CUJgKImIwj0/R8c45NxqQb4VR1uEr3dmzRE2d3KT4halk4WqdtKzZpgQt+YoItqQLn8AC7shw7ClEI8wRbBKEX84FzhVfwHbFH2Kt4YJPAOch8ImhCdgY2C2IM/s1nTzMlZIsNLz4GuBtcketEVi3ZCVwPc00RaOLbwKdRNmWVUiHcJEWulmK2/g6Rq6Rw7s3KvfIprrmZP+UGxjNVRuDY8AjmjXnOwMu+4bw2heSezFECTPB/qhbA9/C9Mb/4nXWK6kFkmlU8CIL+0/+n1GdRViDWQMzBC3IKavzKr/yK17cFKDPQpGtjy7Xa1/O2YqinUGXXLrEeAdyBxRznEw8cN2xqGFWldDCiascATFfGJEBlAsU144p7i3LJboSYPhOk62G4Q0LcBjXCoYiuatyqgfFuxTSu2lCTjkVPuD4vPpOMC4xh44w1ahCCMDM+LxyepsziZwxxgjbMHbtBzvewv5aMFJTfONII6qQ6vhvwWRhFxQbkyW0GjQHXBOUyTg3uKX0LzHXkepSbMiglCGJs+IzUfy6ZrdHQIUXyC+6a+q5ugxoY8+Xq8GGtJoBnD1wQyCALBycvc9tLMNy+//C+Qo51iDH3gvluStQZZwDjlaAk5AlywhoGcQ4wO0DAjPUJTuBXUINj4VQJmoTPArReLQ1fkAYm7IlwldS5Y3rz1gBZ9wbjsDGNmjPNDbeZoPkA+4lgfLnupwG8g3tp+rZgL5C5YMDvjD32cOx9AgNextmqxk59aUWrLoz0qD5So+5j5zTfQ3N29hbsCLgt4glsIz4feykV1zY9IuzvJ2uD1zpV5/xuf473PffccxZ3yicDEX8A9k+h+pmlAgFQr6JJzo+gD/9ipxHkQFRgyvOihq+IeR5dKYUXSokBKVQvhfMvu8t8cMujCRTYs5nKCdw/wxcJENrBPWceMa8JBrop7Vxs/xblpl544YVJYRT/8iBjiCwjeDXzje/IXlnWAbgAnoMaX//61wsX1MBhRipIkKkxO9BWPU83z7tDA+P9qgpXqT7SUN6K3wKia3RAz194z/q3o7ZFdyxYp3qaLbkEiywlRFBSpWqchYHkbMJUCKOGTQqjD+cpm6yptz5X7+tsAUYH6iFUM841GYMfRxCbO8BI4WfuOeojlLQQUVPSxolQKKzGqvZJ9X+6NNRU4JgmkEGtVYwLo4pyWxqI8erW+GdcU9MTYpUrUFDxmZn6geQCvjOGoUmVtX9/SnHxuV7nIdeTdHKMTcpR5FM/lGvG55ekuRoN6pr9C5jzPSCrGOnMjUyAoJVVurwDjEXOEXJNgJDvxtqN0Q4gmRBtgniZGsUTVMcZYS+3wLELrSBj7UE15eyXAxmkiTSBSAIezA3WoUw1sgOUOSgj0n9coiZ67TyLCxB0ve+++3w5fJAJPotAWc5Ft0ujNHIdlWpapfDcdDhg/18YPa6u0dMKKawFNcvVVu2tLB9ZcMw19odUKnD+5jbInyvM52MnYoui5saucFPyJkB5A+EMtpRTEIe9zbiCX2LHsn9jO8MteT02CXYHdgY2Sir7kjJUPLBlOL4XOxgbnQfCCc7FcA4vwj23dj52FzweMQmZwIbbeAUl2inF6XdQA/EL94DApl3Uxb3hfCntmwtv4X0ITjh2rv49jgPXhKuUJBASJtjrX7keeLDx52QD5ZsYN+UMxgyCSPYiHmRrmPuGjc78yhbU4P6arPJigfnOubLn2MEYI2uRLA58D3BjeMgtt9xS1PML4C+GYwPqG7+ksMJav3GdxQm8VFnwtPIwIXCypKuBGKDyQDCjtbpNDdHGOev4Hhwf0TeOv6wP+s/p4mif3u09pW+feFXjcXclP+yqVRw89kZKk58xOJiy2bJfwKiEcDA3Ue6jomS+sgnxb4DKBsY2KhxnYAwnKMoE5i7EkgeGOIQTJyOGNX/DaMWowalox3h8UIPjxzU4flIffHjAUv5nahSXDhyX42MIQm5wYBbKwOH7ug2YpAPzw+9aoBACVG2sA/YNmOe4d0YJ5wU47XFg0Iw5k0M7E6jBjaKF8/OaymnHqcEufef4m/q/R1/RixfeV9xWJ7kUQCVOQC9bKTLGYzmXP8IxwPqNM4B/ncSCfePhhx+21vdUgKBwb01jQ3twgXHoLFtWKDUV4/6JJ56wzgVHl7kvkA7uE045ykIEqFAw308+LZ1+QbqwUzrxuDasnG8F2/2C6R8UYJYATkEwo27hnA1ogHMjR3Rq6IAGY70aiHXr6OBedY2eyclZyvqaKlCNs6lQAWOOTa8n7CZ7WVHsEjIKA1Q+UOVjJ9qB7WrsZIII8Esc9TSjx9bH7sdmwe5gn3dyzXgioe+dOqU/OHRIT5w6ZfHRXDKsTTY3/JoH51koRTZzCJ6RrxjE76wpjvfaa69Z/I7ghh25BjQI3mBvwitRw+cSxEFEx33n/fyLqCsXUDHk3d5DeuHCa3r90tvqGetVOVTisGctZbo35Syc4rtwj+GhJgvHzhEIZJlS0qlAEBM7j7XfaZ/lUwraLfAtENhgr0HgRXDUlNXFF8Dag9+Nn51zI0DloHfsog70vayTQ+/p+NB+afEla9zi53ILT0ENHGREc4P0ngCzCQQzhmKjVk8RwL+XRvt1eni6A9itg8o4mezOJJw6qZqd5QMi1zgBqIdpVPgYFjim2WgwMHD84RilbA1OrwCVC1QJZMqRjUO5J+49wY5UJYgwVCCgkBDj5DSlogxG4126NPqK+sYPqG/8XV0YfEM1dd4DmzgqMSRMHXSc8IVqTExQnXnF98uHNGAkYRj5STwgWxyP87MHhrjmuc59k04OgWRNwbjMBD6f16BcefLJJy0SxDrA2oCSKNe9+8xQj/7P0Vf0fv85HR+8rOcuHNSjZ95RKYFhjpMnnbPfgIAG46ZcQbAKAg3hN6ngTphAXqpAHEYf6z332B5MY06aOrR+g3GWqpEigRmcH4xVHrwOEsK5sHYVW+UVwEf0HpEGpzti13TAC2aKOHIFHMNLpmCAAJWACyMzHf8XRo7ndCwUqQQY7L2SWGf9XltxIhO0QMVLlihOJRx88AmcYNg17DvYNtiknJPb3k8Byg/YmoiiaMSLKAkBDKVeU/VwMb27cPqyXhu7A9vfBDYIaHz6lVf0qVde0df27NH9Tz2lPz5HCVJvYExhO+BMxQbCtsYPVagShamEJbmAzFvU736B650qkwbbKleRF/YkQRy4InayGx8BnwfnMxwDG5P7T0YDx8qVY7x1ea8O9n2oS6NdOj18Ts9feFV9Y4VrQu0GBOCwy8uZP7gBPAl/ETwjnWgxU/8CKj/AI5wlthgHflY7SCWit4P1hfFlL8WMgAq/F/uSvWdngMrD8cH9k35YC1UJLb9imcUL3MKTJJcBXOj01gAVhIunpOPvSuGItGqL1FS4TAS/cXm0T6eHLioaimg0nlrJilGWCzDyvvWtb006ODH8MQQgIfluABwPRyULOwYOizjOVBZ/HN3MT9QXOLquvfZay2FmUsPZsPhbLvWvUV+wsRERz0XJHyB/cN0hljitn3/+eT344IOeDUhTDxcDuXfsXUa59fzQ0IiqqsPqH39frdXXTL4eo9xOVE0tW9T/KMghvhgafpCAbMAgw2AheILDNFcwH9nLmI88/CJHlGLgfkAOuD7MTeZrPrV5CVZyrszthx56yCIS9nRojs3cZ0xgBHJvIBYEMFBvUX7Mjwy8t7uPWaaGfUXc1X1c9y/aoCrW/xKBa4wzJ1N6qulFU87gPnMPIazp1lcyHUiH37Fjx+Q9xZBnDGPkc6+NWox5SbDn1ltvLYgIhX0EtaS9USfgHkDmCbiyZ/BA/YW6E0cJc4157LX2c9mD8fXC49JzP5HGx6Rrb5Y+9gUpOou+p+VgYNxNzaXVKxbp/fe/79tHBBwjwCTo9Td8XIoPSJEWqWZpxfTjIIuxd/ysxuOjqos0TyfqE0j1nBvgwGQdxZ6HT5iSkn5lnmI38OBzcIbBJdhnmZtwCvYg9hYc2qj7+Xycf+xDOJqcJVLdAH6EMxnnalDKqnTgnnIvuccIk7xmVWAXGMfnD06f1vcnavhbLKO7W/+jrk6/0Nur9RMlqrCTeY/ddjO9Mkx5WP5l3NltBuwJVNuoy/0Ex2Ss88gn25z3ct7MST/7iJnApd2eJRCRa5ksBG9wKTgEP3MfnH0kDN/jOxHkgt/A+fA1mNLHBrnadsOxEZ0aPjvD/3Js8JQ2tpRW5ECfBrIUGIOZkA/PKzSww7lv8FFnmVgD7hvcET+PKW/IeGMv4G/ca5MNxHdlD4Cv5JIh5AZkXjDujFgSwI144MMimMY5mL2I78Y4nrX+qTOnpf/yn1BISws7pF/7mrRxk2YLEomExhLO4GFCK1YttWyPe+65x9VxPN19oiV+lZ7C6Osa7dF4Ylxt1a2qpolcgMrBiQPSU3898UtC2vuC9PAvSO3+NIwsJI4NnNPT59+aJBV1kRpVhcIaT8StZ0IKqSFSrcV1uaVyYxigXjKbPQY7mwoGU7b679mAIgrljL0UDQ49VFMoWXA0sain2oBxij3++OPTymBBWDgeZAIQMDEGplHXMucxbNgM2eRm7aZRIcDQv//++y0Fndc6now/Nn7GTywxrIsXujU2Oq7W9iaFQgnFElPqP8B9dzouAWMZ8sLYSzem/TbyCLBg4HBM5he/48j1ChyuGGKMc4xVu9FkJw5e1E8YVGYOcs1QxfAzn0UgMFeY78fxcU4zFyGcODGMUhPDkjHhJH6mV4N5jnuVa6rweJr3xRJxVam0adeQK4yedApvE1gqZzize1KBv6OmwnFklJKMMeafmaN8T9ZsxqAxjDyCAAEAAElEQVQ9+OE3mP/MQdYSe2kvxuKzzz5rfR8yyJgTOCAYg7yOoOysC2iA156Tvv+Nqd9fepLFU/rsz2rWoAZ7aPo8IqiBs4NgeS7l9ezACYSd5Jdwqm9syCon2hCt1cKaEvQRCpBfqbOel6Txy1OBtLHzUuN1ZR/YgNseHXxTQ7GprMr6SKv6xqdnWXrtqWEHgWPsFDKxAXMQhxQB5HxEGmSv4tS0ByawO1jvEa7gRCZgjoPBcAb72s8a8Oabb07ut/yLrWnq9HP83bt3T74Xhyzfg/fRQwpHVhDUKC2wGRDOYGd4teGxQ3CSs+cfHRhQaGxMiUuXaLCVDPyHQjo2MDAZ1GAs4fRPZRNgO8NVTeldO7Dd/S57Y8SHJihj7GWv/SHYw3DuY6vjEHbujVxTgh1eAzLG5jJzDaGTETHlmqkBj+CB/WxKD5Nxxfli23F8hDL8zj3NlkXJ9culnwalp1Ihnub5YoL1Cz9Jurlgnueel2sVG8Yk95H7k+kewovxIWG3852NH8v+vRnPrOH4kwpVUp19zPTucYLMGa453BiezX7BekWGGWOWUrizDmSs/NNfkS5eSPIKyoT92j+V/vdfSktmRz+rUCik2nCDhhGx2LBq9ZWFzdT4yEc+onwxHh/Xyxff1OWx5IAloHHrvG1qrfaXeOCIuTw6otbqalWXUE3qCaND0v6npe6zUl2LtOFOqakwpVzywmuPJImHQWxMeusJ6d6vqNzx0sW901RSw7FRbWjp0KnBfvWMD2l+dZMe7LxG1TS3zREYYWxyEAQICJFvNgN7FNwrWMgxGJy19U2dUsiBqYmYagPm9wceeGDacxhtkBD+xobHcYw6hw2Cz8PQwYHKRpdrE7EA/gIHIYYx98WejowzEaLI/YQsOA1MxiJqfxyhPeNdCkfHNDY2qpPHBy3O0X1xXPNswzOdMxhlBI0CMWpSNWsulGqFY0KMMeJRDXEeXmqZMoYZ38aBzM+QBaN0gkhBuI1CJN13QFXItcepwHyEyBljkfMhkIHBBRnwo2ke6wnzG8cCxh5kI1uPDbMGGUKYT+PDq5sXaW/PVOkyAr/L6ttUGym9gxoSms7owSimdEa5r1s4pZiv2RpLMt9ohMf8QqnEXLA35kS1xBwvZOYU8w6SBKlw9iohyMKcIfDGfeE8mausUcytskL3ZekdaoiHpE3XSS15ZFHufHn676ybb708u4IajcukljVSj5lrIbWvu39y/m3dujWvw8MvUIj60RfgQN9p/eTMLiVlKtLmluW6Z+Em3/ek0fiYEom4qsPJvlYVgd6j0qX9Ej3jWq6Q5m0qv0DB6OmJgAaYsEEo11S3SooWptyFX+geOzUtoAFG4t1qr1qinvHz1t65oGaFFlQvz/0zurutvRx7hvUewRHzhlK0BAdyHYvYjqlEGHAD9hmyQ8jeYA9wBjUAji4nOEcTiGc/wAFlbBJsI77HM888Y9lQszLgXaHAocme7RT9YKtgHzPenOVosKvhoIyTeYODSpw5I1EZgLJGdXUKnTypdTZ7BdsAnpnqvpveFowffnaW1vTbgWzKxHL+fBZCITiOlx5PjG84/7p16yb5BTyAuWO4C5kp5nuns/fI+sZeZ44xP7D94XbG1sOHwH6Jfe9HDyoCGnAaOAzZvXAbU+rLC/hOXhq4G9RH6tRS1aTesf5pZcA76wpfAcANyAzAz5KqygVrGzyvnMWe7A+MF+z1bGusEcSyxjN2GcPmPQQ64Bis4YVsBk/ZQ8ZSqibt+KAJhrIOEXxH3Mi58HCuRyUFHODdPdL5s1LHYmldHnbWgfekc7ZMJgK69A6hBNdnPqvZghUNm/VB/07FEsl+RvWRZl1z9fV6/LEnXB/D0yzMVRnrxHt9H6hrIqBhiMGbXbt1b8ft8gtvdV3Qv37ndfWNj6k6HNavr71WDyzK3Yj0BVYQIJR+YEMyXvmW1HchOSH6L0kvnZLu/KpU62+aZd4YcqQ6c76DpW3s5AYoi4fj0xsJsXkmEjH97Modvn0OTt6nn37aWng///nPWwYCxgcbIMZYKmOG1xJkSFceCiKTKjKOAQSxQXWOMYXRRADFTakrNgJq9KYCGyBOW4gOmxmvK+RGFsAbcG5CYkmPxdhFucNYQFmB05t0UWdmHWSE1zM2xuNL1DW6U3ElMxOioUZdPNZiGTIYMRjomZSzGHEYSxj+9qAGBrjf4wQSwzkxRzC4+dmpIMkG5haBILsSELUU8wVw7bgufBfGO0Ygr+W6cn2Zs6aXAGpInNCmfidGrT24wveHwPiV2cg85L7gcHBL5rg/9maH3EtTH7lvbEz/ev9+vXbpkpbX1+s/bNyoNRmUY2ubFukjnZv03PmDGomP64r6efr4ki0qF5i+GU4nO2siJTJYD1HyYASXo/ORecScramt1rnhQxqK9agqXKcFNatUHZ4ZvOI74ERGVWd66lB+jO9fyIAGxBrCAXlwOjvMuGecGYcHzm7WKcB5EYhhjypUPWzXOHlM+r1/Iw1NZKbVN0i//u+lxTmqnlIFVmfbXsm8WXST1LpGGh9MNoCuarQ4ATZCvkENv/jFYGx0WkAD7O05rhX1C7SmKXd1vFNZurtrn04PJ3uMtFe3aWv7dVZwo+QcI5Rh3PUek04+N/X7+csS5VcXesv4LDjiw96eLyOMxYdmlGkDC2uXaUUk96xNO7Ar4BJwAtbVm266ybKJCN6zJ6QqAYUtgOOW16dzPKZSG2M3mT0TGwmnJ5/hViCBDZUu+wJblOwMgpnYeHyPAOUB7hn3HdEbazu2LzYo4wfbG9sXp7szS8D08kNG8l5Dg/7zgQOkcFgNXP9g9WoNHT+u/fG4Nc7gCnCSVFkLHAdbH3uBgJedh+DwT9VLMB9g82M/YTPy2dgoXkVA7IPO82KumYxpbCMThIBf8Dk4j5l3pl8AvAHxIwFCrg3nA5xlfrCv/OofwLnBcTgXjpuLfWZElwYXRi7o6MBRa6/sqO3QFfVXpLW9ef7meTdo5+U9ujzapapwlTa1XK0FNeXhpOaeMg+cvhnWQ64VtjBrIkGhQvWTzPf+cu7wIPWfkS69myzv2LxCaluT0ifJOGCewyuwy7i/CCLZWwrpB2JeMAfuvPPOGXwOwLOpUkEmOkFX9hBAAIT5ZaoklJTn4Q/96z+RXnpm6rkd90k//fO5HS+dcDMyuzhGfaRZ65tu02CsR+FQRA2RFn24qkt/cvRP/Q9qMKDtitZ80DPWO6OaaP/4gG/q3q7REf2Lva9qhCCBFTSJ6z+995aubGjWmqYSqHxi49LBp6VzB5LG7uKN0uodyV4UdpCd0Xt++sQYG5ZOH5SuLLOUqgVLpbNHp7I1uG8d+Y+NQiMSCqs5Wq++8cHJMciIa/c5S4h5wqbAAkyqHs5SNjwWaX5nQTbkAUcrizPGD4YVBhtGEA5IYzDiWMWAMQu4s8eBcXxybMiBH+oNgAHLg8+hNBXn73cd0wC5Aac1BgbO2ptvvtkiuEYhZIwOZ/o0zlMcjYzHaLhB82pu1Vi8x1p3q0KtmrcmPG0ddmO8YAybFGveC0lNpdjLB0ZFhJEP0fGa7YSRZHrO2MHcMd8RJY5xqpEFgRGI6orgBX9j/nE9uYYmYJipVArGbq5p4U6YzBsCSFwHN/sk18mu4jdjgVq1D730kl65dEkxFP9dXXrq3Dntve8+Lc2gsrq+bYX1KEdAohnXzowM1kKUdzhzWB8hHjhlyi2wwVwmYP3dJ/9cnatatGTFIikWUt/4BV3VcIui4ZnjiLlmAmkQDwx6r+XovIC5QgYGn5vpcyB1EHHGqtOZBXnnb7zGb6eEJ3zrz6URm4N0eEj6zl9I/89v53a8W+6W3ndkomy/V7MStQgr2qfZOnCDfOEXv+geHZgW0ABhhXRhpNe3oMahvg8mAxqga7Rbu7v3aVv7zKa6RcHwOeny6xIO9UiD1H6TVJPCudKVoqn75ffKL6gRTZWtE0721ihz1EbYc6ePv5DCqg57VzCnA9yBjAz2NmwUGqVS2s84iHHsmKAz6zUBQ3gFjlGCIXAN7BhTvorX4LRL5czEoWzmpRGW5Ftqzr7vcZ48sCEpr5tOZBWg+MDOZs82Da8ZB4b/YSunEk4xRkwmxe9u2qQvsz8MDOjq5matbGiYzP42NhhOzFTAtjFlOfnXAFscu9bPTA14t91Wh/N6OT62NfOEeeXMoLaXgLXzKRzGBEGYh3wW7zdqf8OfTHmoVCAg4uc1QBjJveC6uxFE2n0PAPW8ybK8OHJRO7t2Tv1trEuj8VGtbUpf+qguUqvbFpTn3Oe+8X2dPkq+vxUomFjLsGEY+/mWGfcbhvvuevlJ7XvicV25dIEa6muk/lPS+LC0cKqPph0E18jc4HvCnfi5kBkp7FF8JhlT6cY9gAOTDebsucG9MY3D88lYzBvvvzc9oAGef0K68XZpVQ5Cx6vXSVeuko4eSWZpsI4gxLrdPyF2uSAarlJzeP4MfuE2PhD24hTCSeK1cVQqNETqrRRcO2rDNb4NwEN93RqOx2YETvb2XFJJ8MEL0tkDySAFQYBTe6Ujr898nb2c0yRCaZ4vMW77jNRsi6IvWildf58qAXcuRFE3lYLXUdOuLa3+1HF2Gh0YOBjpbPhsdhgMpi66eWBIEKzAeOQ1GJIoY1DhmwbgLPREolMFFDCkcPpi4GBUQFT8Xsw5HkYYBCpAeRlbGMDUsXfWyiQAZfpD2O+j3bAOh6KqicxTdbhdoQmFp33sQHJNSbN0wIAzTfVwrmI8+T3+mDccEzLNOeEwtROdVKA2KKSL+cW8wvhxkgCuBYSGhym3ZoBRxX5nmh8z/iFwbgx+4PZ1bsH3xhHM9yFwlOm+mLXEKF2s+sCkxo+MaE93t168eNEKaAD+7Rkb03dOnFClgmuDMWvqLHMvn3vuOYuc8jfuL+OUOVJuZZCYoxDqcG1M12y/Ur3d/err4d7S42ZUPWPTGygamDq+BHMw7gsZ0AA4nLAB2ZtSKajs50V2BvfC1IBmLHI/MEypeYtjoqS4cC5JDgz4medyxZZt0ld+SVqxSlqyQnr489IDn9JcAGskZDRfcAw/+EVTdObYJMjRXJW5XJ8XXBi5OCPb99JIifjFeL908YVkQAPEBqSLz0uxFFkNqbhEOfYbqponNdizGsJS0w1SxL97WCg0RzvUVrV0WkBjad1mRUL+llZinYVjYBOw3mLvwDfY81inn3rqKatUCKIXOAJ2omnGzRqOowo7gjWZYDV7Y6rSUxwPBxLAhiKztRDNYbHtyk1sEECTNidjyM4/jaPXCYQM8FUD+mc82NlpBTSA6QfmtpcBsDtSsfPSVTPIFXBsY6swtgkwZLNR2K+4LnAMHlynVGIrzh3hIq+3O2qNUAn7lKAHnwvHSNXDMBXgMqlKwOUDuBtrB+sC55upFx3f2d4EnQDN4qVLNBYf1/HBmfYAWRvl3tsuExgfjAsDAsfY3qyrxoZHiMoam42bFhumj+ZNq2q1atkC7T1k43oX96V9H3MZrs0egt3uVzA73TniD2ONydaUHT7OudjFk9wP7g/nzDwk66hkuGgTp9txKc3z2UBQ9A/+u3TvfSzI0o03SX/0J9K88ssK8htwAnwd+FDdwHXIjQUOJaEfi+i65qt0buSChiaMbgIc17X5p+xtSlEzjqW0MTr1/OH+bp0fGdSy+iYtqSuw8vwCKYKJmc+tSpZlmETrIqm+RRrqnSAaoWQ2R4f/Dve80dgqffKfSD0XkufYPL/86vKmwfyaFn126Z26NNqjaCii+TWtChf43HEC0XsA5yzziEwNow5hA2RBJiDBYk4wA0MH4oEjiM00XfMjDCMimcZR51eTzVTg/FDZshZQV9dvp22A3MB4ctacBYwbFAtkDEFYjYIKB6jbqDeKagz+VAo+niM7hHFgjoVxl63XgxeYzA87WaAMFN+XcZiuXAmqda5LNuWvmT9kIflZ+gDiRWqy371FWEcIrHBciCMZCNwHro/9czC2TdYO+Pabb+qfHDqkSydOqD5FKivvHfG58WKxwdqJc4bgL+sUwWTnWGTsYPgWoiZzroDwglhsTFXVVVqzcaX2vXVQW7Yls+3iSt8sEScUxp5fZc7SAaKLWubjH/+4q/kNkef6Pvroo9bexRwmmG8vn1iovjuusHSF1NczFdhg7eS5fHDtTcnHHIOpiZ8vWM9pQJwvmqrqdNv8q/XixQOTRYCW1c3T+mb/GiqmKjMVtTmtx+NjGoh1W9ymMdpmpdIXDCMQZcfanRiXRi9KdY7v3HKlNHh25nPliLrVUs3yZLCGLIdSl/ZyCda0xXUb1F69XOOJEdWEm1SVItOuEPsI9hh7HGsuQWWC3tgMiE4o2YF6lbUcjkGWrylhxdqczmGFIxbnJcEMnMmF7E/FOWCLwYsQegVBjvIATsRUgjrDXRkjJijAWPNSniZTSSJTPtW8JpfG3dnA+DcNsc1nYTtjQ6cr5cl5YHdhf2Xr6cfcIbjImPYjaG+uOxynEDBZBqwTBG2w07i39pJ1fHd+NzZ0PBHXrgvv6MjC5N5SG65SKDTdvrP3Ma1EwLEIHMOHGRfwO8MZsY1NoA0ewv02pX7LBfD+ZsVVX1ej2poqDQyOJLM1KEOFvzHFPGTeI2QlaF5ovkRpK+ZTqrK2qcC5sX+ZQD7fj4oVxg+BT6xkpcDSlbHtzMMGxd/2m/9acw1NTU2T/h43jeldBzWYtGbTmqb6iY8kjU0PRnttpFZ3Ldyu00NnrR4HC2vmqanKv4jz1U1tumXeIr166azlrGa+rmho0h0Lkov1/z6yT4+cmYq4fnXlJn2kM5mGWxCkaqZqC7BMe90tPyXtflTqOZ/so7H5Pqkx/8aJBQHBjDZ/FRPFQk2kSovrirfgYaCjYqdpLcYCUWnSxTHQWJj5uyEVpo8FgQN+dhOowLFp1AKFAo4qzhkHFQYfAU6/ylwFyA/2NGC7Y53N3pTmQQnEcwQC3JJFo3xPBY6NKsUe3PKbhEKkMbKdhMrZL8L5Hq6H21ImHJu+Czi7+TnfngQEK1F5ZWo0ni84rintg4rBNMqGYEHSWDMM+Ts/PKyff/ZZjUyUmhicGCvs2PwUmng83OlPaZZSgfWINZNeRhg/6ZzvOGVw8BQ6s8EtWLsp/dHT262OLdVSZGyyqSrZU03R9PsUxp4fjZWzgX3ls5/9bFYCD9jb+D7MvwcffNByehBgsu9N7HWQkEIqvzLii/9Q+r1/K12ecAy0z5d+6qsqGUb7pNMvSUMXpWi91HmT1OitxF6pgKPGr/JTTo4xFh+zMszIrPWylm5rv0qL69p1brhbjdFarW5cpHCmXhMesabpKl20ZWbgsFnXnMyUHI716/3+NzWeSPZuqwnXa3XjtsI5tkNR98+TkRwfSzYKh79RU7tjm8oWXLMiBAQKgdoINktxSrViDxGw+N73vmcpvbGNsMtwzpq6/KYcB/sNTlFsJGwd7MFsjmKEMTg4/VbIO4H4hnUAhyGCHByDfqvRA3gHJZKwq50iIsYZYwzBHrwWRzf2qBeRRSruYuAsdVMIexpb3Znlbr4zf3PyATgR9iP2vRt7CGCXMg+xheBhbt+XKUsC+7+Q85F7aQRU+C7gNVb52njc+tleCvvV4zvVXz0owwSH42OKhPCzTB2vo6aj4oOUBCzIWGY9tQct4NXGT8P6yrwwpdPKAcwxBEY7NnUKlrfhqiV65/1Tum79FVLTsoyCZL9LSacDgZNsTcwN4LsE1ngPQRfGqAloALgGwrWS4YqrpI99Xvrhd6ae++RPS8tKVHI3kZB+8kPpB38rjY1KN9wk/dwvSrXln/lq5xjO8vup4HrGsWlNq488ekHqfk2yDPew1HydVOde6QZJuaLBESTxCQQy/uPGG/V3pw7rcH+PFtXW63PLrlJtJKo93eenBTTAXxzZpy2tHVpcVyDjacU26cCTjufSNFWsb5Vu+enCnEcAX8FC2jc+YKkUmqsas5JmCPtXvvIV62cMQIx2HG8YORgNxsGDMYHRQ9qf24ZdxVIeQ2xwCmKcEHjBuHKbLhugcMDxb78XGFSs2SboxHiihCD3z4szl40kHUHBmCi0EgIigCIjFTCACORBPIwBg8HNPPLac8OUV0PdkW9Qg3PmeBhdhWzabMAaYUgZn4lBZ3cWv3LmjIYcRiu/LW9o0JmhIXXW1urr112nzbMg84rvTRac0zi2kynWVxzvhVD95QKUtPfee6/lhNq99y0t3dKgtvYW9XcNa/2SWyZqtJcWXkg4+xaEj72NHk/MUef9YL0i06hkQY15C6R/8/9Kh+kxEJKuWitVl8h5Su+3Y09IY2Ts0EetTzr+lHTlRyf6V5Q3WGvZa/KFnWNYys/ufTo5lDxue1Wr1a+iJuLexlla1249CgEag9+24GYdHzhplbZaXLtIC2qTe+GxwXc0npgKuI/Eh3Rq6KCuaCiQwr22U4o2SuPJknXWeK5qlWoWzHwt6+C8DclHgLLHWHxUQ7EhSwhYnSW4w77/hS98YfJ3VMXf+ta39PnPf36y/r1xsrE2Yw+aLEE3KEbpGMODCIizP8ORyLzM1wkcID9gxz7//PPWfTH3Av5HkINxh3Odci/whXRio1TgtV4cv347xbED05XSxB5DwIdAhiAGgT1eD+BVXh3WZLPA9eFpqYIoXsDcIEsbblboQAHHt2eYEJhxOrrffneXOtc7OVdYVaGo4kK4vFAbm2eWtqtEUEHD3icFICxCTGaEVAS84JKFzGrzAsYdY/bAe++ptm2l2kInNDYek5qWS0u2qxzgNqDBPsTehVCK+cg+V25ZMRYe+rR07Y3ShbNSR6e0qIQipReekf7qz6Z+f/l5nETSP/nnqgR44RiuV2VSriajwmRndL+STHFOPiH17pSizVJVeWQVRMNhfX7ZTGfricG+yZR0A34+MdirmnBYTVU1qnY28M4XizdI0epkXw0+vHOjNH+qFmGAysNofEwvXtipi6PJOm/N0UbdsXCb1ezKDXCs4ciibANOIHvKHWpxnJM4c92mq5r0LIxOJ4HxEzgBjRFFDVHKvQRBjdKD+07KK5kTEA0a+9kNZ8YbaeGUJshUD9+JTPVvUdPZa6r6jWzlaTBsMO6NKt/qS5Cnk5oxjdGO+izXY0EAeUDQOUc3KZN+wAR4nPWuu2jy6GjYzJ737zds0Bd9aMxbbnBjHBMIsvccKTVM0+y1q9ar91Sv7lp/g1WGo2lFCsdkmQMVH1lPrA/sYTgHyFCEDALIHnsGfy8puPfrUzdILCpGupKBDCf6TlREUANeQCmMfJSJOLc4huEYB/s+mAxogK6xHu3q3qub5qUuw1kKtFS1aFPrzKAcmRpOhjEU61MskexvFVa1v44o+sMtuEfqfSfZX6OqWWre4Cl7PkD54ezwGb3bu3eybMuaxqu1rP4KT6piHLHUQ8c+stsz2IZkiD/00EOe1nXmKfsr/MLOBfwC9pxZQzg+meEExsvSaTXHwP7NWEIRjYAOe9P0iOB31OrY4F6ETnBWY/u4HR9+jjls9HT8le+Do5rX4JzGgepWZJgOBIS4Vux1cLFcgYOa/ZaMAbflevwAdp09sAVYC6oUnRF4rAnX6J6OOzXbwPhzcgzWWa6NEeDhwzH9YMoBJpt72403Jpto3/ZFNcT360z1fHV6EIqUA4wYij6izMf77rvPCrjCubk3zFMCrvxccuEaZajSlaIqJl57afrvVOB445WppuNlDngBMQg3cM1ATD1NC2M9toCGpmdvlElQIx0W1TbMqOxXHZa+eXyXRhMxVYXC+tKKLdrW7vNAXLg6+QgwK7Cn+4AuTQQ0ABkbb1zapx0L02TgpMFdd92V8nnq3WJAuu2RgSGJcYEKBGBgkAaMg9teCzNXsDlg3DnLEWHUvv322xaBqvTU0koHKc4opQyJtRuZJnPBlCny0lwsk6GE056x54eKDyOQ8YsRgpHIsadlBzpeCylwW2LKa8o1gQ3qe+YzpsnM2rdvnzVHCp2GzPXn3jobffL8VTU1umvpUj13/rwVU0etu6axUZ/wmM1SyWBM2Z2uhS7VlysgRWRTse4zn42xTqCqVOsrWYU7d+60gnOpGsmmmkOsDUbNx1wyjgOyu3AW4Kgql4BSyZEuw7NCnNKMT9YZguamHrdX8F5T8gNccDTdxrF7cbSEjR89oDpcr+G4PUgVUn1kTKeHnp/4e6sW1FyrsJ+NoyM1Ulvg+J0tIDvDHtAAh/oPqLmqVS1k4bgEoqhUwijWcuwc1N7pbCwnWM/Zm7ArTRka9iRK5PoBMlyxOckeMSWnjBhnmv8hQEnAvaDcCwIF7hE/GzAmcK4jFEFR69Zhzxhyk4Vj+EW+Yj34BZ/JcbDLMwXmGHOowQ3yDWgYIASDSzOuzX6XC7gOpudhumx2P4FginnpLAfHmnDnNbdrf/z9iRzw5Kq1vrmwfd7KbW5gJ9tRjv4Q05z+yNGj2rhpk1UmliABa3gxytimAzwZXoAfKds8Y8wzj8nIN2sHfgpzveEqzHGCh+WQiV8WiEaTWbp2Pw3XpgzHaCqw97Meu4HrO84BJxdOlEEpj1b+Eb/r2zq0ff6SaRegrTpiBTTAWCKu/3N0l07TrDtAgDQgoDFdi5eYFuTIFyzGbDLOjTKbcwHnEQ82LoxMFPt2h3OmHgmZgDMKY8aZdoqChc+j8WAx0tMDZAZOfgJikAtURk54SQ1nzc9UPgliTOABcO95MF5RYENQ+XwcyW4AESIgYxqFYbCTLWGUYE6QkeJX0z0nUOFA+FHe5AsUVTh1Cz03SJNHfek0pDH+Vq9apR9v365/v3GjPrdsmf752rV65a671FAm9V6LAYICe/futX6G0BLwLUfSATDsH3nkEWtOQKox4mmiV0rCgToT9ZmboCjBGLujjHkKiQcEMiCAXhWasxo1bVIdzg0zHkNSOCq1VEY2L+SRILRb0pEKvJdxbkpoUp7WiSo/gwAFxPL69QpbHYuSaIqEFQ5N1XcejXfr8ui7JTq7AJWA/vG+lI11+8b846UEznFoeeEk2BjY+4il+Jc9wQQk8+UY7HfYXc7gBX2ZjBM4QGnB+owtQAlb7FonuE9+Bp/gAXAZsj3hGgS9eMAxcIK6LZ8GD6GElAnmMX7hs+kCcvAYt+VwchU88X0y9RNxA/gZ54k9VUhwvbGZnXwQXoNvYO3i1dqx4GatqF+i5fVLdMu8G7SiYe6Ipsy1MGWQWcvKtRcQ9jhr6csvv2zNVfgG1UKoDlIKMNe4bqwr9CvJxpXhEPTAtAdD2ZvM+3ge/xfrQ4AJ3PfQzGbw93+0YoIaxB7c8ovcyk9FqdfaKY2QDjJxUSKNUm0ZpNlkAY6MX119g+5YsFznRwatRoQ/piyUDRiThwe6tLiu9LWsA5QnKDPVO9Y/jXbUopbzEQQ1WJhzzbRgrOM4YsOyG2hsIF4bfGOApUvTxfGMA/rJJ5+0nIesE6b8CBsM54AxWa5OxNkGrjMGAoEmjHe7iod7AxmBnGZTPJmeFZl6tZAxQZkcyAVBDF6L+g9w71FkE2TJ9FmQYN5vL5uUKYUd45oxV8jxhIofwpFvszeuAc4DUxquEODYkA3nfcLIQ11lsr1+c906zVVAMBhnlNxg3FDWolyBo4g1k8AA6yiBPtKpydq49dZbi9p8ELsP5RRrAeOXlG/OzUv/Jr4PwVTKTrFPMNcfeOCBsiV9RQfr2PJ7pQtvS4PnpaoGaeF1yX8rBF7Sw7PyC6sR9yqdH7noaMRdGcrPhmir1jVvV9/YRYVCYY3EDms8MV2cMhJLigECBEiF6jQCwXTP54p8bSiU5ginUGubY+GkYo9IJ0hJBfYEbK5053PDDTdY9izOQpxyOLEQbGCjwS34vEI6oQNMB0FsuAH2FLaUcTDCN7gvKKfdZABlC4Dh/MfuMUp4xhTHhmMwBuCY8ORs2ULYT/ALYzsx1jJl6hKc8dLsPBdwrRBnua3GkA5wJTh+tnK9uQIOBMdIVeYK+9T4BebVtFmPuQqaGFNOnAAcY9Krj6WYYM4ilOJcGesmKxy/AOOyWGDMki1C1hdrCJm+iBu9Cp6Yq1So4HsQ0MBHAFcKMIFNW6Tf+LfSj78njQxLW2+RHvqEKgUFKT9FNHYypZCFs+UmafBDabxHitRJ9WukUGUoP1n4r21LRpwP9V3Uj1MEgBoigYEUID02t6zV0yNdVkNLg+va/N3EmHP59qvAIYUjyW5E5qJAx6hko0jX2BVigQEKscBpjpOVz8WZheFmVAEYcKVMc5wrYI1jU8eRSP1W07AMMsL1h4Rma1aHgYEy2/k6DBGMXIgFJIN6x+kIJQQU4kOzx3TgWF6yLkxQo9Bg7kHQGPP5qM/M3Mk3QJIKNGc0WV1OMO/8Kg0xG2DqcpcyuMpchEzj6M90Hswn/g5hfXr3MxprHVN9Z70efe5RffTujxblOzC2mJv2Egz8TKCSfQQi4bZfDNee47EPMB+CgIYD2JuLpq5zpQFugL2SK6bxi4lG3DsW3KJjAyesRtydtR3qqK2c/jLV4VrNq0k69c4Nn5QcQY1wqPyz2gOUDs3RFnXUdOrcyBmFFLKCem1V7Zpfk3u5GicQN+WrFAfOvmrYll7XdxzVmWrQG4EW9hNrBc5ssgV4HsHVU089Zdlp2LfYbUFj8cIDfsA1x5nI/m5sGjgeHNNNiU/+nqpZN2MBHskYpTpApjJNZGAQ7EpX+jCXHpOMn0LbWAhDcCrD0eBY+YxZ+JM9wOAnCJjYfQj264p6GtFagGQGwf33319SfgHPZD6x/maae3BGxhtz9uTRgzrdtVMPbGvXvkNv6XR1VIuXFqYKgnP8IIgkEGH8B3AKguTwBDit23nBus84JKuc7MN77rnHk/BqTmDL9cnHLOcXnoIa09Sz1AFuqPweEVc1ztP65gV6r/fCpPG4or5Vm1qC+p0B0qOtukUPLNquYwNnlFBcS+sWqbXaX0crAQG/nT9sGLmU/cBooncGQRIcvM7gBhsURiNZAfbMADYrNiVSyNnsMeBwhjvLWAUoDFCLYPBTZxK1G0hVRxYSgcOVdR7nO45HDGTn63BMQiJwlqOuyGbAYegR4Jqxf9jgtWEzxopJ880Gt+olXmdqOZu+NLyPsUtpJ849n4AExhrkgLnn15zGeOV+pFKU4XTmvmcKJs01lEOmGPWoyXhgrJmSbQSDnYScc8Up0F3fo+GOZIPhPvWrf9GgXtnzim7dUlgVEgQZgvDxj3982vOsC6i8cBKgSrv33ntdH9MQjwCzD6yP+QY1nPtDS1WzNrcWrwlqodBSdZUujLxleyah1qrK504BCgfW/w3NmzV/ZIH6x/tVF6lTZ+0ShdP138kB2DZ+ZzcYB7LXWuacC33fUO2yT6RSC+Oww9ZxlsCBi2CP8jfsIRrhskf51QMhQHpwjW+//XbLCQmnw0kPGFf2LAx+JvgExyAgxfgmkwPbx56twPjZtWuX9S8Zqm54Aa8jiyddUIPPzFRGNxW8lIt1yzHINIG7UJqXcQp4Hw84Fc7dXGH6mNE4PFUAIlfAhZiLqRzLBC8D0VR5cQw4JmOB7As7R966deuMc7NKUccGtW7RGSkRkWLd2rwqqpfe+qE6Fv2CIgXMCIfbkCnC+mHWDMA5Mt54MG8RUBkxWjaw5zCH8plHASqfX0TdOk8wKDKVBKlUhEMh/eKVN+r5i0d0dqhf82vqdefCKxUNGswEyILGaIM2tPivjDBAjY4BlC47IhdggOVKZNh8CGiwYdrPCQONDShd2iIGrflMMgZ4P849t5tVgPyAsY9D3iiqMHpMDUrSslE2oNhADQUx4V5BPDCQjAHO+wl2MCYhMV6MN4IjL730kvU+O/h8gmxe+nwAiIGbWrqUXjKZDIxXZzM9DCvTA4R/CTZwLSBahhBzjigRyVjJhSzwfq4b14v7wPXMNajBsbgnfH9+5nulS12HGJIOHaC8wBrK+LKPAdRK/G7PPkKhxHwdWD1dvdoyr0UHdh8saFBjz5491rngLEg1zxl7P/jBD1KWIwgwN1GIoMZsQW2kXR21N2pg/LQV0KiPLFJNJMhWDZAZrL2LalM7av0Ajko/MjXswI7Kla+wB2J3YQ86HcUIS7BRU4FyJca5SkDEONlxbvnJnQKkBnYoYgWclCZTBocq2TeMBxz2cEDUtgSuuI8m2xuxFf4lAh7cb15HmSi7o9MNuM+p9hD8VtjMTu7hR1CDucO4NCIxeIJTHQ7/gE8hMIJ3mXK+drvdXAt4SLoxnu08OAbXLN++M9wHeJPh69zPVOWvzT0rdImuAN7AOoggye5boSw0wWJnyV348MkPd2ppq51/J3TN2jbt3/u6Nl9XGI7BmkDvDEpkm157TiCgZYzBQQIEmD9/vuXzcZP95yqoYaLKbksNVBoIYNy9MCjTEaC8kK2fQbGAkUNvD1PPlE0JQ8qoN9g0MdLSpQk6VVs49QAObWfqeoDCgIAFSh6aJTOuWNMxgMkcSKWexri2O8UhmrkGw4zzHQIDqWE8cQ4QGM7La4MyxiDv4VjpgiuMUQiNqWvK68kSgvywQbIxohDjZ/a1dOWvTLYGhAxyxO98H86fc8+UFg/RQO3OZ/Jd+R1DjXMzBmi687dfb37mPRAfSES2TZ17y7qRC0EKUFigmEJJypwz95eSTjyHkW/We8YF47wn1qdwZPr6WVNf48q4ywU4iFjPUTYS4LOrClFNMt5xHkB2s5WvCzB3wHjIx6HCGPOzwWy5oTrcrGqfM3kDBMgHJlPQT2DfeN2XcIBj2yD+YE9hHWHvsfdkMKKQVDACDwPsNTJUEdIEmarFAffnpptusuqec93hi9xHbBiCS84xYcqJmaoBJriWawkm7HxEINjUjAfsGI7F52NTIbqzlzfMBmx2Sitl2pPIVMAeR/BlL+sMpyDAwDUgUMF3Z1yns/V5nuxd+AnHMsEdbDxTNjgdTHY935P38H54CvOB75uu1LMRWxkbFIchHIljuBFvweXsZUkDlAcYd9hijAHTo5LnCGAwvux9PvC9vPH8m1raCueY2geaGus0fDZ9KcB8wLh7/PHHrUbfCCrhFKz9zH9Tbpfn2EcQyRajzHSA8oeJPcAT0tkBnoMaLPLFbFAZIMBcB8Y+jlkMFr+QjsRQfgiljXEUYxBhoBqHrl2RgUKXDcmoozCOMhmj/N2pvMLYotcChpu9QXSAwgED1ig4vDaVy7dMAUYVxj3GM8aUXUGH0x7Fhgl2ZQPnjRqMMmbp3gMptteXZbzxIDhggioYe26JDsYWxqKdQJO9wXdKpQZkzGNE8vpUDcL5fGoGM4fs8xtjjrnFZ3BdTBki5iVkI9sezOcSSNmxY4er7xWguGCdpGwbSlIcLqZ2MwENSijgGDAlF6wAQvclxedNX7M76hb6rrC1z0PWd+YJawXE3WTgsUdw3iZbK0AAA9ZG1rtcAccIMn8CBCgesDV4+O18wLmbSgBJBiD7neklwH4Cx8GmwSY0HAJbEaecAfZPJvvTBOCdQRD2UXpt3H333SUvCzNXYMoPG57p9rrn2wPF2FDY9ny2vTQuv9MEmR6DbnkMYwr7PlNQg3M29rgRbmGXERygbBNj28uehsOZoILhFwRFyASBe6e6jvBzOBB/T8VB+BvHcJab5nl4mKmggHCF78sx3DiROSc4jd+l6wL4A+433NLeqxKejC1vL0/GmIqFyYiy9/sKSeF6KVKY0n1kbcF74LgEO00JZc6TeUog9IknntB9990X9N0LMAnWGtYmAri+BDVYGL2mAwYoX7B4xBVXJJSfIRGg8I4Cv8u+sSCkypDAsYoxagwzDKZ06hIcX3YCgWMYYzJd3VI2VKcDmg2XZk6UXglQfJSC5EF0U5FdxhkkAGe8214rjFdUUAYY6DjVIMyQAohFqu9IKrWXpuR2mPq3BqapGedO6QTzN5OOTuosKdoYas6MDoIhEB6j7uL8IRUor3jeHIvvhPHntkY1ab2kGZecxCfGpJFjUnxEirZKVYu5gKU9pzIB995kbKB2Y0wybiHdBA3ofcN4spo/PvehGhc06XKiS2GFtarxSnWFLxeEULIWc17OOtbG4UBpEMZhUNYsgBPwA5wjuSLgGLMMiYl6+j72gAjg/z7kt53A8RBW8HAKMFCd4yjGbsI+Y59J5UR1Cq84TwIb6UBGKkENOzg+HIUHqv1c+ggGyB2lsD+xiey81H4u2FTYWyjE3QZQ4Cp22x1OzD7F++1ZDnbwN/h6rpzdbufDB7ALKdWMjWgEMOaBSAt7DUd1qqAG14I92WRucCx4BnPOXCf+zjxMdd1SgXlFQKQcsnTHE5cVi1+SQhFVhRYrHHLfl3G2Aw5JAMGenYFvh+oEVGswGUBXXLVJ+0+9rw3LhqT4cJKrtWxVQu/4fk5wW3xL9vlBNhQBMjMnCXQj7PK7l2yAucMxXAU1OFCQBjQ7cG74gnZ27dVofFR1kVptbduieTVBfd9yAyoPFnwUJ34C4yadytdsLPybyciBdNjTiTH+iLxDHlKp0tnM7NFVfsdQxDAzhKUQpVTKEocPSM/+SBoakK7aIN39CaxxzWUwDjC4yNxJRYbTwaRsk+mDYc+/5r2m2XehQXCQ+WTPCrED4w2FTKoyVSbjxN6o3K7KgkQRKHQb0MBhwLxNVQO3qEiMS70vSHH6noSkkYRUs0qqD+qjOhtsQrTJquEeQ5J5jnmAmop1dfut263+KA/ceN/ke88sqbPGRq7BuWm3KpGwlFKMHeagPaBB0ND+O4S20mqU8/16xs5pONav6kid2qo6FQocrQXr/5UrAo4xSxAfk06/IvUdS/7eulpadGMQ3ChD4NwqRFNVwzGcdpwp5QnSlcUBCEGc9hKZquyD1157bdrPtAORFfYg6z9CjzkT1EjEpLGDkuXsrZaia6Sgf4/lJEXsg3DKrSgD2x0nsBEpmX4fgBKdXnsB5gLEWzjz0vELYIIVqXgC+7Lx3XG+OI3N78wNk5nrBsxpsq3KIQt8LH5aw/H9SX6RSGhUx9QQuVHhUIm5TxkBUR3iUTJrTBCKrGu4gxG/4Zeh38tQw7ZpFQP8DEqSjcFnMtac5QDx/diDjASnK65kYM8l6eCuZAmvVZukebO3jGolcIxwKQjHaHxEF0bOq2v0suJG0ROg4OgfH9Brl96yAhpgKDasVy7t1EgsvQomQPHBBkB90Ouuu873Y+P8TRV4yBbwyFbCCtKQTk1FgMbeGwTSYiL1vI9gyJzAySPS//l96egh6exJ6eUnpL//C3+OPTYifbBTevdF6XxlXk+cul6MKRzDGGUokXD+GxLN+CxEeZ5c4aZuNd8bxaF9nqAKI1vLDTBMKRnkh6M7b1gZGqaR+8R3H/lQihemTmulAmMeJw2KKvsaTCAb8m16KjGWIaMGjAnISj6ABFPuCnLDOIPo20stMGb3vP+ujtb267kL7+r0UJcVZMEW9LtkSaFgkfbBfTo2uEfnRg7rxOA7OjKwy/c68gHKK6hhlTEY7tKxgXPqHw/WnKLi7BsTAQ3mWELqPiRd2FPccwiQEewriEHYV/yuwIAwA+dpPiIlgufO88I2Yv9Jtfc4+QXZ7UbpW/KM1WJjdJcUOyol+pKBjdHXpHju67IdF0cu60DvB/qw/5jGCF5WGBhDjBUvwAnMeESIR2CtHAUenFe6hsupSrgY2EtIu7EXySImM6TUc8rqTRg35emMLRfTaPxICc+qPAEfRLSEWNCAihxUzTC8g4CZs3Qo99heCSEXwFHgGPBzuDp9BO1j5+ixY+pviuvli/u1t/uwxuMxKxBD4KxicP6k9Jf/VXr5x9LLj0p//XvSyWT1hQCl4RhFz9ToHu3S2907FUPNiZCnqk3XtW0NSiEVARdGLiluawgExhPjujzarc661KWDAhQXGEtEq1HsFqLECM4rNhl7wyj7Zkc5HIgDm0s6hTgGTiqgEMdBxsMEOCA4GJKU6UGdxeZqTy2EqKASnhN4+6Xkv8apxr/73pQ+/mWpNg+Fyeiw9NSfS/2Xk4pIAsXX3i+t3qZKAuOGcQJMU23GlKmlyHyALENgKXnG+ORvb7/99rTxnKnXRiGAaiudUgpwjnw3ryTIbcYK5IRzKJuyQAnmPsarw3lMKaqwf/2BZgNw3nD/7D1lTH1oMjbYB1BUEXyAoJCJw/gnMM1a7ZaUOsF8Yr13liE02H/yQ70QP6rarkbrTr7VdVgfW3zD5LlQKstPpOzxw/rYc0Ya6pUa50tN3ko6DMZ61DV2xhzM+n/f+EX1jV9Qc9XMzKkAlR/UiFM//cIuHR5I3vewQrpz4bW6oqEz72MHcIH+EzPX/f7j0sLUCvsAxQflbLDvcfL6DWx79jR4TKpsDOw3smj5N52Nxh7HfugUX7H/IcrC1tu9e7flKGPfIKOd78Nx2Q9R1/O7AQpkBB8lz2Atht0VPz/z+fGTUvVMvucFRwZOaHf3OwoppIQS+qD/iO5YcItqIlPBpHIHwS4cvCaL22SFMy4I8BGQ42d4BrY648vsTc6x6AykFRoIAVOV7gVk02Yqz5YOfGdnqdFU4DoR0EBkaa5JacH+4hStJRRPeAtYzRWQjcf9w2Y3nBLbnzUarsx6S2YcAQjse15DPz2Etflk5SDOTVdphHX72VNva3RRtcKjIcsv+cHAaX188c2KnIpY89StoC8vjoHAe/xiUt9fNV8Keewb/cIPpfHRKZ9OPCY9+3fSl/+5fycewP9MDSJ2ftQ4s9R/PbsmAxqge6xLRwcOezrO5dFhHR3o1SgDKIBrRNNM2Gg4aABfLmCjKWQTLjYxDHx75N5u/KMexrDLpMaAkDhTb1GyoCbGwcxmhnqEciYQEH7GCY0jzTQ9tgOHMOnisx6xNFlpaYJErvH+G9JAV/Jnk/m2+0lprLKMPAxmVCM8mAPsOThdMbIYc2YcYYjj0CUYRs1bJzk2wZBigfPJlBlCFkmhxjdqG+Zd2QQ0QATy5VTDRwvWfK7Swb1jTbYrlCDMkBECCKyprNfsDThzAGPey5hijX300UctZxFjlbmTrgcSeO7oLtU2N1gODCOEeP7Cu9a+5LYcmhf88Ic/nJ6xB0l472npjW9L+x6VXv1L6djbno45RhDNw/MBcgcO0nyUfX5xjA/6T04GNABj97kLuzUan2nvpAPZ431j/RoOMpi9I1zl7rkAJQEcHBsfB0EhFNfwAhxS8IFU4HOdwqZU9pRTUc/+RRlP1gn2QFOylHKdOM5wSOP0xWHN8/bvhg25c+dOzyr9ykO6DMT8MhNjiZj2du+fOFJissoDgY1KAspxxgr8guCXKf9KAIPKAcwLnL0E5RAJYSMxnhiP9h4Z2GrFrPkP5+F8sqEQGajMq5deeslydhciCJoLKB8aFtd/+voVCZdH5kw58mruH/fR7hTGN4PYDo4BL+D+mtdg4xNEpgKAWxBMfvbZZy0fD8dNJ34FZwcv69TIJetnwy8ujPToyMBZzxUb3ID96Nvf/vb0J8d7pJ6npP43pP7XpJ5nk31FvKCveyqgAfi5r8efkw6QE8dw5c3GGPCj5j2bI6WnnOgfd0eGWLT/+/v79DcT6T3t1TX6vWtu0dqmoIm5G3TWLlRDpF6DsSHLOGHZaK1q0bzqoKdGOYDxTZ3bj3zkI74cD0MIR5FdXYHBxlwmG8SpPsFpxmaWTr1rgHOZbA/qvqMg4X2QFcgED6eKBaMMpKsLSmMoNlNzbrMWm7ZKb7049TtZFSvXSPV5GshDfTOV8QQ3RgakqspQUmE824mCqWPLvyj+MHQgyxBXXmeyHjCenASjmOnRKAeZL5mCkGzEfiuccB7Q2BlSn8k5XRJUL5Jia6XhgxNPRKVG6qoHzq10oObt/v37pylcWQ9pJA5YU1FbUa4PRRVrJmulW8Ug6lwIOw4lgiSs0ybwh8HPPKKUG89xzIHRYUU1PatmKDY6GfxO1z8pEwim8B3YN8yeACBBfDbHRE1pZV1dOiqd3Dv9AAefkxaslOrd2St1kdQkvC7iXynVAEkwBvNxGvrFMbpG+6zsDHtGciwRV//4oNqrs9/3nrFevXJxp4YneMrK+uW6pnV9yUtuVAzmb5bOvDL9uXmbSnU2ARxg7c+1iXEqUCbRXosdDkPJHj4H28QOnFxkJbrp48E+BEfBtsFZxntxRHNMPsMuykL0YspuplKz4wyhzCOBDb97FJYXaqRQq5TAqWbWv4QUyS9LbSw+PqPCAxiqsKAvzlpsD2d/F2Ob27OtjfAOm8UZoCMzqFg9WrAH4djZMnJTqtDzBHMV4QzZwoUQsuSDushmDcZ2KaFkecloaKGqQ3Okb04OYI0m64Lsb/saaO+nQtCY7AiEcqzhjHECHvZ+qJk4LvMEu549gTllyqfDVQmaYN+ZYx384JBaF83ch4Zjo1qxZIm1f9gzS9yCz0G4S6a7HewhiMfgH/xrzfmBXVLCJs6ND0qD+6XG691/YOcVUvfFKTEpPp3OMigBPYc5hqsRw6D0I9UuEopY2QKUPJpCSHURd6ran5w9PhnQAN2jI/rne17V391yv6JltuiWI8jI2LHgZh3s+8AieS1VTVrbtErhoHFmyWGyHDDOKcGD0ylfQ8I4hUkvtKeZMp9xIPFZvIZoPE5lHMXZAhoABy6bIefJ5mCaUBl4XSs4Rxx4EJVZHdS4ar30uX8kPfk9aXhQWrU+WXoqX7R1SocdKubqOqmucpx3JtPHpIZDMOxjgcAFQTqMfP41xlgp69oyb+hDkC1gATk3DQb9AMEdHOA4vMt2vtRdLdVckSw5RYaG17TeOQjGyJtvvmkFLJxgTYVcEtDAKURAC1LuxuiH8Joml6zZN9xwg7UH8GAMQ3ggMYxTCAHr+/XrNmtv/4lJVwZlJ5bUJh1GkALGH0pG47gyQRfGI2s56ezMTY7F5xOswKnE+ToDgKbpOQ4sSJSFgcupS5iRkeYyqFETqdeyuo06MbR/8jiLa9eqPhoo+vwG45PxlItzhff4xTGaqupnOOAYuw2RWlfn8eqltyYDGuDI4HG1VDdrZUPxyhlWNGgMHq6Weg8nCT6/N2R3igQoLBjbOHawsVh/yZr2I7hheoE5hRXY9DhEeZ65TWYt+5VxKmcDTmVTKgguZG8my2d6zWTHfiw3x6zvYN2tuUEa3TfVKLzq6onM2dxRE662HiMTvTgBosg2F0HicgLCDcMvzO92YIPgFCXTm/GHUI9xZ+81BjKVmvV7zmIbpSoVbYcpm+Xn5yKCQVxjRDXlhnCoQQ2RWxTXoEKKKBwKytpmA+sm4wRbPBVnZZ2ltwbiUyogkNnkJqBhxH30WyEQxnsYO+w1CJYIlrDu8zs/cx6rOlfog4FuS3Bix8KaNjXVNlmZJZS/IrBh7EL4BvMBXxbzE57B9+B4zEmEtpn6/XEOBDb5XtY+FEMMarcVE1LMYwnVHR+XLp2Rzk30tGmbL93zeW/HCOCJY/iWqeFHORwG34bmzdrbQ4Q1OZjqI/Va2XClq/e/03NZkVBIsYl0H6bDxdFhXRwZ1qK6WV4v0ydQA3Nza371NQP4Cwx+1BhsCkxcnPss/jiFnAaVF3As5i0bjd2pimIXQoNjig0Cp5ZTVZUNGHW51nN3go0IA3PW17wFm7clH37iymuliyekYxPK5qoa6dbP0YlYlQLGI2qPdNk8gOAcBjfp336nYFOS5tTQIQ3H+1UbbtCSujWqCmd2hHG+2Yw+jEO+m18qKow6iNdtt91W/uphrl+WaxhgCsZxk84xzN9RTxEA9KI4NeXYTPACAsLPfAZ7jMkMsZdxWxqPqff0sI4MJlWKC2qadP+iayaDE7zf2IQYmqZmL3sOazkOKcYpf+NzeB7SREDS2QSWQMeMcohW4CLFXHYZ0DBor1mipqr5Go0Pqjpcl3VOB8gNJniFstWrus7cez84xtqmZTrcf1rnRibKMUq6ed4GV7XfR+IjVlkVZ0Dk8mhXENTwguYVyUeAsgFlQRAf0azVqM2x/+1KXa9gvrN3HDx4cEZQg89BOAWPwX+AE8mrIxjVsF911cmAdyPYqngQyKi53t9DhkK6ad71Vgbb2ISyeUndIl3ZUFlznOCbKTuVCthIjBHGbCFKLf3VsWP6X4eTpdZ/buVK/cyKFRlteIKCzlLNqcA8y2ce24FzGOEMgZR0PTzKBZShilhlqAK4BX4gnP/pyhWzThNMYP32ul7zevgF45Ggucn6Zmwyzgks2/1M9w5E9NT5XRpPxCw7a/v8DVpY22qt1YazwCXI/mBOst+QbYK9aIIuBPTMZzNXeF0mbsTrJn0EiO2mBTZCUprs7rSoa5B+6v+RLp5Olp6av7ii/C6VBPiBr5kaftX4X1jboZui29U1elnRUEQLajpc93Rora6ZVr4MsCU0Faj/QIAAxQDOURZ+E5HGKQABQVmFsoRFnPnHhuHFkUkwg+Okeg/HszfTKyVef/11yyFW9k7acgXXbdvHpHW3SqNDUvMCqbqynHcYQm5IJ2rybIa+18AGPZ4O9b+uUaueZkJDsT4NjHfp6uZbFcmQYWBXD6YjCDh33aoTswEVPQ5tVCwBZicIkjHGCeClAoEHnFFk9UFM3BAP1FJmbyGInU35B6rCEX1yyTb1jQ9bPQZaqpJ9lt7ctddK7bar6iEhKKZ27do1Yw03cxGVFgE5GhA6QeCefiJkjUyqJ+evlDrXSWfem3rhVbdKDd5LZVaFa6xHgMLBHuDyGtQw6is/OAbZ4B/pvEnHBs9Z5VEW1LRaDzeoCldNNsK1A5VygACVCtZggtB2u4k1l+dQv7Jm4yxAqe7FmQtvwcZBYJEKXssTFgrsp+xXZVems4LQXt2q+xftUO9Yv7VONkWTCulKAufr9pwzvS4X4dSfHzmin9+5c/L3Fy9e1FAspl/MIAzEYZtN1Ahv8qsUFt+LMtCUaiuX/hkB/AX2P7Z4pnFP2SgCG/iHcgkqu133VzR06Csr7lX/+JDqozWqCSed1vAVO8fFPsQPRpANX5F9bpq5iJiGLHT8XensT4KEcKdJP0PDdVLfS5KpHBSqlepzCA7CwRam5msB/AP8wLdMDQZMNgeOFzRGG62HV3x26So9euaYLo2MKBySxhMJ/cMr16shGgQ1AlQmWJTTpY8SxDBAIYvziAWdaLubUg3M2UpIuUa5i5MOQLT43dn8OUAWsNE3+1crudhgDrgxpI3yOx1QIHrNbuobv6zRuN3QS2g0MazesYtqoz9EjsQGNUk657RX4EAgqJGqNFGA2QPWPQgFBny6zDUC4GRLuG1Yybyyl11wC+ZZc9X0tH6cWM69B5LEup0qYGHek8mhAAmBzPDvvffeaz5c2viAtHiDNNwrNc6XWnLPWgxQWBgiOSPjxiW/AH5xDMqprmzozCkgsr55jfb3HrSCG+wD1eEqXdUY1OoOULkgcJEqq5osPXv5TkRQCKnILHWjEAeFaOrqN/iO7H/YUKwx7JnYZX73OZvtIJgxr6Yy+2+myhBNh2x7WC5BjT84dGjGc3946FDGoIYbYDf5NY6xKSltGgQ0ZjcIOiAiStfbiPWRQDXZ1+kC1n4B8VRbdWPG7CTWbtZtAh3p9ppsJeHIKNy9e7fFLyZfF22VWu6RxshGD0lVHVI48CWXK1Jm9Kd6nduDET0rNdqqa/S/t96lH54+qp6xEV3bOl+3LQjqtQaoXKBONU3uMsH0rsBRyiLPxpTNmKGuOdHrck+7NpsrxiJph0TjIR2cOw7qOVGWag6DvcWtQwtVEuVz0qnN3aibnEg46npOPm8VOEyvkPIrYJENbOTUuKWnQoDZDYx2stYIbJARkapnCuskZNZNQAMwt9woXNyenx30uIEkpFqjIRqkobNPZdurcI6RhTSt9Bb/zls+jfBQ6oSgCp+Hoy5wTJUH8sm2yCcg4jfWNF2ppmijLo5eUlWoSlc0LFNtJMjyCVC5YI129r1LZ1vBLVDzEtywi6pSAecnQgscRmXb22vCSUfZErPGYCPSDBdxGIEOnGjlHpgJkB8oS+OmQT1AUEKGdTqVei5jZTQed/WcHW72Q7/GLdeHMj+l7FEYoHjZ4PAHeHS6KgKmD4ZbYIfTr4I+NH47rsneTmfnI+zi75RUzwT8ZfQrxN82jbdTjrZm2aS4ZtfOnZYtC6fhOrE3VIIweC5gbGxsWm/gvIIabtM+ihXY+JkrshtoAQKUO5hTNGa66667XL0e4oADmM2GNDocu5kWXJQpOIoqBRhoKH7ZwAhssFGxuaIExonF9/arDF6A8gFzYNOmTa5ea+rzp1NjeUkxN2iMtltlpihDNfk5iuj8sBQNDau5auZGiuHFBotxSFkd87k8b5THnA9GUa7KJ3McFFQ07Kt442q0Rxo6n2wk27BEcll2cq6BIAQBDTLz6LOUbu/A8YSx7iYgyLqKM8dtICSdc8yMSQPOgcbjjFFIDUEJyBJzgdfjHEDtlS34QCmtxx57zApqc64GONkIjHA8nGemlwxkhr8xJ8qljOJcRj5BDXvpqnJAZ91C6xEgQKWDvkZuHAEGrN84c5mLCKgQRqUD6zDBEjI8/OqvV2jgNIMXYZsR2EBQBtcAiL+CElWzE9jlbqobAMYAtoUzqMHcIOvJbfNkO356+XL9zrvv2qv3a/v8+Xrz8mVd19Zm9Yp1fpYJ9sPhsXewdRCK2PdJ/papT0gmYDfiT8DmIrhH2aGKBuK0/pMSfbFqF0i1lZlVVAwQwMbJz/1PFZBmz6DXK7aZm0AX+wAcIJ9KAox5+mTY+2qaLG98PxwfPoTvy/TSYJ7CC7IFNfiOlFWDj9iDGvAZMlL4O/OBnh8EZjgXAiBPPfWU7rnnnsrn3rMAbttgRP3sOh4gQAD3IMLM5uJWpc4iy+LNIs/iu2/fPssZnGnBrUQFEkp7NlWUI6Y+I5sY6YMYp3x3vxoIBig9cPpDMt1mWDCmnemmOFA5RqZG4+lQFa7WVQ1bdXzwHQ3HBzQcC+uJc4O6NLrTIhtfWXG9trROJzJ8Fqpxyl2ZgAPnxKZrn884FSBCNEn2OhdxBKNO55h2R29Fov+4dPqFqaZs1a3SsvslFw185yIwsg2BdRpyjCOUp+wHJuCVDTj+X3jhBSsokIvjmeDF5cuXZ5AWzg/7kOc5PsemUR+kgGyTj370o66yKSDwDz/88IznKYXCHICQ2LNB+Hnz5s2TDqkApYVRt+VSQor3MKYDjhEggL8gk4IAuVuw5wBsMfYX3p8pI5W5m0s5nlKDYA2BdNYdSj7yHdjjcOThxCOLw8+S2wFKCzOu8wFiDbKXvAQJDf71+vUajsX0p4cPK5ZIWKXT//r4cetx2/z5evS229Ro6wWAkA9uQdlp49Q1Dl57yTfmJueFXwDnrheYrHfEKOnEMxWDeEw68YQ0bBNxLrpFavHOB+cK4AQEdlOVjcXvgv8FG/6+++7Lyl2x37HJ4bu5lA7H9qNBvbOPh+kvSPYUc+HRRx+1gm+U4EXwSvUCt9lFqao7sMYznzhn9jzjU+D7IkhEtEjgMAh2lx6Ga2ZD2M+u4/lib/dJ/fWx1/SNY6/rvd4zBf+8AAFKCRZqnKNuwPzD+DBKWyY3hg4lmmYb2GDYUO3ge2N44cCDaEFAAqQx7i6dkC4elcYLv2b7AZRGXmr+Q0DtAQ2MfbIl8imzVh9t1tXNtygS2qRvnRjQpdGJBmSJhP7y2NvqH58qv2jSXQlU4DAwjSghO04izFg25AHFuRegGKFxn9sslrJWUJ19eSqgYbI2uvaX8qzKHhj4qJLSAcWgW4cSpINgQ65BAAx7MjKcTfrM78xHAi18Buf82muvWa9nDOfjHEJVy/hnfXj++eddf1/m6Kuvvqo33njD+hfVcYDCAfsk1yxK46gpNMcYHB/WKxf36rGzr+r1S+9oJFYZ+2OAALnCS9CBkpp25w37C44kPxzC5QaC5Pa9jJ+xQ9m/cGYRoJ+N39sPxBJDGold1Hg8qZauBJggXa4gG4msnlwCGgBx1H/evFmXP/EJraiv14htbL1y8aL+ncPOYy4iFKFkFtyC/RGhCz/bxy3ng7OWIAgiK69jFqEK3LriA3jdB6cHNMDZ16R4IMZOB4Ry2FzO7GsDghQE8chucANERtjZjCmvYLwTqHCKVRE0GbEjXPrBBx+0xjwBEMY6z+Vb8gqfEg5zOAvnYQeBwlQ+OvZUeA78gocR3AYoPcdwlanBYkqKWiGx8/JR/eTsO5O/Hx64oE8uuVYbW9w1LCskLgxfVtdYr+oitVpS16FwBarfA5QfSCnFmMAgYWNJVxqEvx04cGCGcxMDh4WUB8chSk7Umt9xBlW8oZICOM8w9FAQ4CwO0gJtGB2SXv2W1Ddh3NU2Sjf9lNTYrnKGUUWQepqqvwyBO+aAycJwGg8YIn6VPzg51KOwQorbHPCxRFznhyndUzOZFQL5Z+yxyWZzGjBPIR4YaMxjvodJLU8HCBjBkk9+8pOVP8bjo1I8hcE3VjmkuBQgw4G9gfGebrzwGkgEAfJsgAy4LcHgRCqlFmTH3nzTZFARvLvjjjvkFyAtECaUiwQ2DIkh8O08L67Vrl27rHlpMqiYn6i6IP0IAcj+COAv0pUxcItCc4zR+JgVzBiMDSuhhC6OdOvcyGU91Hmr1SC8pEiMSQmCbnEpNE8KBT3EAvgDnDWsydgsmdY97A32B2czZYQiZC8g4ICvYMNg+7DOsg5jl1W8feIA14CAPI6qii/J4zOGYifVP/7e5O91kSvUGF2tcgfOWTijqXTgBE5SeCX2FAIO+zzAfmB/y6d0p0EcZ2hv77RufbiU37Y5Tg2fMDafGx5PAJI5iY2DYzhT2TgDyu6QkTQr7KGxvomiXnYuFpfGB6XqoE9IOhBIIFsjXb8Z5g3OfsaUm0oDjEOqarjhI3Ywv1JVI8CRbR//nAN+LvYjp/A1V7B/kZnCA35hgjh8FrwdMbEdCCjhE/zNzB3Kt1HOi+8AV5lte2IlcQxXQQ2MF25aIfHqpQ9nPPfapcMlD2rs7/lAe3sOTf7eWbtAty+4IQhsBMgbkAiUUKixMZhSpYnzd1SqbD6pNhWcuTiDMWhwDDNPcVxR8saNYVPJZI0IudMxPqeb/r33rNRvywYYGZB2/1ja/mWVOyDPzAMIs9m4GMs49jHuMWAwJDD4nWWqMHz8alTZVlU3LaBh0FJVN+3zOCead2PAuS3vwHfDCGI+s6emqwNKFhIOXFSDswLhmuQjPpXtYpGPgGxkBQbynj17UqaIA/YFem/wILOjEASVOZcqcEf2EU3N7cY+85RgM8pG/vUTzB8c35AaiA6OONNnysx/MlEoK2IveWXKNUKECCripOP78BqumV/7BQ4+Pp/vnUvd7UpGvk0iC80xTg1d0EBsaPJ3Ahs9Y/06N3xZi+v8Icc5ITEsxV5js554IiyFr5PCQXnNAPmD0h04alCb3nvvvTP6e5k65qynqdYsuAQOKmwvfsb2Mkpc1t/Z2lwYpxR7mD17DIcVe4i9DOJcy9CwBzTAUOyoqsPtqi7z9Yo93thKdgcutjb7NmIHAnamxJNdJIUtnq1mv1sgiF1UW6szwwTXk4iGQlpuG1PsgyjEmXMmKOkGcBK+B0EZbDPEU6mUzfgKcFRjp8yKgIYpZ+vkbYgVotlLn85lYHchnGKdSyV4Yl9gHOGwZ55k47qsjcwXr8Cud3J4jmPP3GAusPeYUrqUnvLb12N6dnA9+Dz4vZ37EPDAJ2F6+xmwr3JtyBDnWgGOARfJtadmKrBXm3K82YSRc5VjlE1QY5wSFTOeS50WVSz0jw9OC2iAM8MXdHzwtK6g0WmAAHmARWn79u3WQomjxg4ME9RVpnZ4usWbYxBNNzDOHJQmBDZm68K3bds267ua68LGw2ZjAh3meTYmNuU5UROx5xwXYup3fjZZGxVyTzG2jcFAmTEakBnVQzry7GdG0g1tS/XG5RP6cOCSQgpZzq8HOtZoXs0U6WC+4kjFYGHMpTMI050r3wnjjAbpjE3ne1G6z5qABmAuLr5NOvWsZPZ0GvG2zaxxGmA6WONYwxkTqQgoc4NxyPqHoU8JqEyAOOB4Z8wxbiH4BNchLKyVqcorMN6daeG8HsJs5ibzFmOb9HDIMqpIHGrp9h+IOkSGecRexfGee+65SaPVOBeYH+azCQZi1N91112T6zuGLp9N0If1ArVVuh4epjY14LsSLKI0FYICVFf57pWmJwTnw3dH7TtrFFsDl6Se01K0Rpp3pRSZfq3gB+Uc1Iil4RJk4ZUUccou2stgxaX4O1I48zwOEMANWA8JRLz99tvTnEam6TFrImtVJkc96699/Uc4ZNZj9gY7/5gt4Fo98MAD064LmSp8Z9O02YCfcTLNxsx4O9KVm+L5cg9qAMYwgTh7thF7NvY4SNeTAmGhX0EN8Kc33KBPvPyylbUBW5tfU6Pf2bBh8u84mck+J8iI/YMQwwsINPJdGavYQma+2n0LrAuZeuVUHFpXSwOnpIGTE0+Epc7bpPDs9H/4CdYuHPF2gZIdzBUe2N4EATOt96yXjF1sOfYWOLupsMC6SWA4lS8rVTY6QWXT64UxyzxgreVcCFASfCDAkQ7wGziG6UlJD1rmll2oxfzgWCaj+5FHHrEag9szTeAVcHXTxxaOlc4fx/c1PQ45X64X64dfmSV8JwQKP/nJT6zPmS3C5UQirpH4eSU0pqpQq6LhmYEgtxzDdVDDmYLjN9Y3derNrqPTYq3rm0urdhscn1J2GeDoGkjxfIAAuYCFlGavNGOyL/A4PY2xlQtYTFGbOGsEzhY4U4ENOXMaalxfNhc2SFLJc6377RonDkrvUsszLl21RVp9rYqGhrZkEGMysBGS6vKrN1lM4GjFwMDAIsBB9gbjOJfm37kiGg7rl666WW93nVLP2LCW1bdqbdN0YwSygaMAEmv2Rq+p6RhNkChU4/bv5yTMswb1ndLKT0jDF6VwdTKoEZqF37MAIKhNkAAjNl1GEuufm3rPBDFYEyHzvIdAgqmpTGCBOUiQAeJiDHfIjJM88Hr2KZwEzAfGLMTfBOg4BionZzkTA7KceA/fh4C+k+zwN4g5ikMC/wBiwOsMmTElUPg7Tjs+m3IKbkAwBbLPOTN/OR+uC987V6LA9ScYSV1hSmChkIYAVfx8Pn9IOvDElBKyYZ50zWek6FQwlmuYjyKt0Byjs3a+VWbKBDcYaVXhKi2o8dZc1XckBmYqTDWc3MPnasZpAF+B8II13i6ewK4i+Jxrhit7BYFn1rbZKJziOzm/lxGYOcE+R/ki7NWCO4rHRqT3X5b6zkv1rdLq7ckys0VAJFSX5vnc+kyUAtgNCBnYq02p5mLjoc5O7b73Xj129qzqo1F9dulSK7BhgNMWxbdpuOy2J44dzEucsKl6FWLP4cieVYBLLLlTGjovxUak2napqjjzotLBHoDdSxmqTEECUy0hE/Cv4MtifzB9JxD58DNzDsc04iw4g+EG6cY39r7JvGaNxR9m/FlwA0qtpYMJUCBiIihx9913W+81tj1cg/WdoIbxMfDcpz71KYsTEdgwgRY4E1kjzzzzjMU13GaH8F74iBEQcO2wc/keufqhOE/8elxTyiNyLAKUlYxEIqbusTc1npgSNTVFN6g2Mt3/75ZjuLJGOFChgxp3d6yzsjX29ZyyCMf17Su0fX5pazU2VTVMqnUN+Lmlyr90ogBzFyyalNxhs7A7PpyOzlxANNfU/Z/LMOpcIvQYs/QlSafIyRtH35We+Mup2p4EOEaGpI1FUt1fvUO6dDzZWwOEI9Km+1VJwFhBXUGgD2Mrm5OSkjfpHKe5IhIKa2t7klCkAo5VnKIYLTh1UXnnOjYJYmAAGpUf6nGjTpl1iNZLjTN7pgTIPk5oGplJTeX1eMbBRe8Lu4HNuGZOYYRD+tmLMOhT1RZnnGJcm7IlJkiH0c0xM/VIgDxhlGNXQhoI8kFiDCACzC3OxZ4pgtOIIAgEAXKOIwlC5nXO2LOrIBoEjHDSQXD4DvwNMpWL+hbiRHCDzBmcXdyzii2JGI9Jh56e7ngfuCyd2iWtSCrS/Cg/VWiO0RCt0z0Lt+qVS3vVPz6k5qoGbZ9/jWojufWY8Q2hZinhbETZGAQ0AvgCxDyoXO2BWtZ41qN8SnayLmP/BM20kwIrsovZN7BbyRosyHrPWvzGd5IZ2azHl09IF45Kt/0Dqcqf8quZEA03qi68QkPxKaFcdWiBqsOVoxbmvlAmhr0ZYVI27mAySv3GhpYW65EOhrczf01wIxcQiDOZKYA9Np/eamUN5lz9HKjIUAAg7mGcUB0knX+E4Lgz6ycdjN1MEBg/l+EYzCcyFxAhMQ/JgCDY4OwXC7D5cdrzGvYvjkNQBEGWEWFlmudwDDLcmeP8/vDDD097jcnWZn80/jb2NHgPf3v22Wet+YPAkrXCWfbaLfhsI1Bm/vGZHB9RQS4ZYGYN4zhwKPwRlZyxMRQ7Pi2gAfrG31NNuEMhW787txzDlceTQcFgLySi4YgeWrxZ/2Ldg/qNdQ/qno71Je9bQWPwbe2bLBelwVWNy7UElWmAAHkCdSgLJkaGXTGCQZNPKjNGGJP/qaeeysvRMJvAwg/xQPlrGkH5jr0vTPxgcwDteU5FzdTY8dVkIGPjvdKOn5Pm5W4QlwoYThhBODozKRpwtmLYFLsuLOeEUcJYyiebCpCZwnFM7U6jIAkQwA5IKIbrkSNHUl4Ye2DMC5zzC0cXxjbEgwfOedbNVOC1GP3GsWWayeLwYl4QGEnn9GI9xjC/8847UzYVND2SPvOZz8z4Ow5wHHOor3gdgZVMBCcVUpWMY9+F3HBeBMJxfPA5XoFzizmN3cz3hyxWLMaGpHgKNevw9AAE/CCf2vrF4BgLa9v1iSV36EsrHtTHFt+u9nLo6ROGTNtFUlVSZCbBDxDAKxDysPYwL+3OE1OmLx+w7jJfcboGSIJgPtcVdXEu6vqs6Dol9VCvfuLYfAbr8LnM6mk/0Vi1Ri1V16khskbN0c1qrrqm4gL2nC9CA1PiKR2wKVCv52vj5wrmFzZJPhmQ5vthkwDsRzI4AgRwggAETvJ02UtUD/HaAJy5ZucYZjwTxCCwAWeAXyxfnlrsRmDDZASypjJvEafyO+stHCMdGOfwFwRPqdYozosSsZQZtAORIp+Bb44HJc0RWPkB/HF8JhyD65np/NPBlBqGa3E/zNyu5F5NmuZlB3HFp5Vldc8xXHlPGFg4Xdx2H59NuLJxqUWGesb6rCBHWRChABUPU9MPpxAOHgwsU/vPaykbu5OXSDYLPpsFKtaCl1uqIJgIN04wrs+kM3ywS3r/qeS/tc3SVXdKTTkoPsZHUzyXrLNeNNQ0SCu2qNKBepuSO2QxkULNOGZDIwOJOcK8IbCQj4opV7AHUl4mVyW3HbwfRQmGCQ+MqAABUoE1CyM4lRo0354GTtgzMzKpgDgHe0DBNO6GCBBcR02UT6mDVGTEniLPHsf5cU3MayFn2UpxEdTIRNBYX0g1J5OSf73so5AViAvXgGySinYiVNcn+2iM24M7Cal+eg11hAL5BJfhGAUTG5QzQgQxbprI1ohJodbkcwEC5AnKc+A8Yh0muIHthMOGYEcudgvOLmwU+AVznXU9yNSYDuxVE2gnu9Lak+jb89Yz0juvJAMRa6+XbnwQ48/bDYilKZUUKy7HoH9GJfTQyASEQ9jvqMAZy3BnnKRkgWIbYEPwN0RHpeiVgioZ0UU6Z68XkKVBmR2yRjkm/ClAACdY1/GPvPzyy5aN7eyjB/wKYGJ7sz6CdD3wAPsLfjL+ZezCLzhPgnNwCzgPIqx8Mo+c34nzoQm5AfY/eyn2PFwHgQCfmS9YW8g4h9N54Qim1DAP1gnTy69SEQnh75wuAggporCmYg3YMIjs3HAMV5kaZnDPSdKBOiFaryV1HUFAI4CvMM4gymdgUGE8sUB5TUnjOChDMdRwfHEsZ4Q8wPRSLqRSYuQdevcdHX/yf2ms64wUG002RH3n+9JI6qZ4GXGFox4lm6XzuQCugWPwscces5yWqBJwlprN315zttiAFBii4BdQKqPy87uUVoDZBcY/2QT0a4B8G+CoKgVZZb+y7zOcm2n4DEEqhsHNPKR8FAFBgqE084OAZMqy4Npl2x/5O0ovApiZAOFC4QYgW+zHiIA4r4ceemgybZ3PZN+puFrV6x6Y3nCzdZm05JppL0vXyN4teC/HmJPgGofbpfCCIKARwDfg6MEZgAMJ2wJHFTzBbd8hO+gTREADfsGDEiWztZ9GvmAtQ92P2pe94/CPv6NLz/5AGuqXhgekPS9Ibzzm/cCtnVIVwXrjhAslS8zOr+ya6qUCvAJbBRECDkJKTyKSwMaiF4Dh5KUAzlo/AhoGCCWZr3CqAAHSgXmAQ9+UeC01OA94vr2ROHPUNAzHV5ZLNrVXsJ7DL+A0rAmInRAs5Qv2Uvzq2Xr7wCvIVIBPIfZkbWDtQnzGORkcPXq0MFmCBURdZKmqQ3buEFZz1WaFbD03TezBTaaQq6AG5I5FthSk443LJ/XfDr6k/3LgRT17/kjF3bAAAdI511Ge20suUG4jUzpsOphIb7HL8FTytcf5xWawoDamao3rpb0fTKwtiaTyqSuHDWvLHdLm26RINEk2Vl0j3frRQnyFOQFTK5INjftlghj8SyCwVChEur0ffXQCzA0Q+GLtwmli7CGCGm4ahfsJnPkEG/lsE7yAbKAO5jnKUBU60ALpsRMClFykm6O0RMmVrlyX23nM8fguPJyfy/FJSydLhPlLzxOCGfT9IKiCKtqePYOqipKQhajRXVC0LZe2fkXa8LB0zaelTR9L7m82wA1SKfvcgveWgl8Mx0b06qU9evTMS3rxwi71jw8W/RwCBCgECF4cOHBg8necMayLXgMRphQhjp253qPPLXC+EBCnjGHz2Q91qrtPRy7aeuccetv7QavrpG2fS5aZBTQIv/7TUoO3kjABpmCaEDMnTIYnNgsBQLI1SwVsEz+zoLDPggBkALdjj3mBCIdShaUEGYaIg8gyNAFG/GQmawORUCE5BgIlZ/AC/wPZG3AvxGX5+qT5PmSC2I/D3IdDwCv4jvj4yP6j7BRrEyIDeCBrlF2ctW/fPqtkXiWB4EVz1Ra1Vm1Vc3SL2qtvVXV4ui8TboC/1I1Q27V1g8O02JkaBDT+/MjU5n94oEuj8ZjuX1Rc50/vWJ8OD5xQLBHT4tqF6qwLmhEFyB8sZmwa+TSuZiHMtwfHXN6829papXnNGhwZ1YXufi1sm6hfaosSuwaE76aHpBs/kgyO5HKMANNAkA+jAlUG+08qA4LnSActZmaS38F1jJhgDgdwCwx6iMczzzxj1ajF6CMjgKZ3/I29pdBBDhPAwOAm6GgH2XAE7AtZegnSxWfzOTgk7OC7o6wilZ61IZ9AJN8F5SbBCAKPBFoJZuCIh/SgtuL78xkooQle0IDdOZ9NM3R6afnR8L3oZQ1rVqb9c77lp0rBL7Dnnzz3uvrGB5RQQt1jfTo/clkPd96mmiI2EOezJZwH9Ckh5X6FQvTWCBAgz/XZa6+hVGBPyaWhaYDkPlTb2qz5Sxfq9SOntXL+RCZurtygZZG04+eTZawqrJdFud4fxjc2AiIMgnfpMh1ybRScC0wfAb+A6h7bI0AAL+WR6A9k+klgZ+M0h18g1inGfCCzaO/evZbtbK9iwH7E3gb/KGSwDuESmVypsqbIfmT9yFeQyLEJXCBSY51BDAXXIBODAArfk96DiLfgPPQigdukKlPf0dFhrR259D8pJazKMpRe9YFfRL3Uiix2w8Nnzs1U2T1z/nBRgxrdo7169vwrE8RDOjpwQte2btSVjf6lBuaKRCKuscRxxRIDCofqVR1aPq1bfIDyBmrPfKPMLF5BhkYeaF4i1bVq2cK49n54UgvbmqWqOqk9j5qJFtkICIdfQQ0MKZRGEI9Ujn/+RnCQtNRKDWp4JTDDsZhqwuGKa9IYwD+gXKFZNqD+M7/zwCCGCBDkg8jm2qPJDXDwp+q5gbFfaEcYAQ3IhDOLwg6yJnBW5JIBaQCJI82bOW+CFpS7IguEPZzvyt+47qij02UsmDnO9fJaR7fcgXIMjpArSsEvzo90qXd8qswkNv5wfEQnh85rVePSopxDklegrOO7s5bz+zkldKNC7ulZ4TB0Qer5MHlaLSul+kDQVSnAuYEKnXUpnwA3jpRiZwHOKmy6VXrmO9aP7BOWzcZz+SCw+3wDHJw5gtM0nc1CUKCYQQ14jr3kTr7ARvJiB8YTCY3F46oJxJJzGti5gLFIuW7sbco94WSmDBKOfWzsQmXwcVzEW6nA5xYa7H2ZfHQEEShbl2+VBURoPAhkkI1x1113WZl++D5MkML0TdyxY0daAeTatWutElQEYxBO5dNrpFL5hesVk8WeZkrFxHhipsokRtOtIuJg34dKKD6tjck7PQdKHtTAOBqKva2YKF9EM7KExnVe9ZGt02qRBShfoC7FKcJmgRo0l42BzabSGwWVFJSK2vQpVR15SWPHuqV5V0orb52oXRug1GDzRolONlO6jRznbbHnAHPV7z4GGEfZymp90N+nL7z2qvb39qohEtF/2XyNvrqydKW4ApQeONkpA4XCB0BeCXSjOkT9g+LKNJZj3D733HNW8IPfqRvN/KpEYOyTQfHggw9mDYrmE9QwQJGFk9Ds0wRReZjeJm4IBHs+6eGsV9wDHPnOLJNKBIGjfIJYpeAX8TRcAnu/eKDclQnmGJZBkO4MhQ5UUgyckY4/OfV790Fp6Z1SU+kFXQGyg3XqnnvuscriIQjJZQ1EJZopaBzABWgMTjD7mR/pfFVUHTfeKW28Jbh0ZQKECJS3ydSjjxI4OBWLZSvhsMXewEnpB1CDYwtSmhORRia/0n8+8J5+98B7GonHdcu8efrmjTerM2guPqdBiVWyJuC7PAgAwlWPHTtmjSsEOvAJxhY2uelzgV3HvKrUsoVw8mxZ1an8EvjlvFZegMNRLpIAqwmmkJEBz+F3+EW269jS0mK9Fi5Ilg3rFmuIn/0/y51fuB5pRKmLTTpunDc9EoeOaWv7kqKew2h81NGXPRlsKXVvj1ji8kRAAyTPJa5ejSfc1SXuGevR7q639ebl13W4/8OSf5+5ChZ8UuiIentNFcchwnuKEbGe1aiul9bep9DV9ytx9QNS7VQd9AClBao2jCkck5lAYMPeONkAhQMqdpTrftZsxxn8wQemD0v+wHgxab7pgHLqoy+9qAMTjZgHYjH98q639ez54iqcA5QXcJAzDh9//PFpwT0MWQxynmPsM16ZRyh9CKJj7EJKqNVaiWDf4/tlylZiXcjWhM8tUKcxT53NNiEbbhVRBJxwMJKtATGEAFI7t5IBGSOwlo+Slff29PQU1YG6oKZNteFqhSayKvl/NBRRZ23uGSfekSoYz5mUgVDlwq4JbmEeSOZ4zgUQpHXvk84+LV14WRq19RMIUDSw/qH4ROnIWu8V2E7U8A6QJ9Zer6U//690YssDySyNINOibICzjNI6mWx5XoNjzQkcjjg+4Rd+1rLHnkCY4ldPA1TO2C67d+/O+LpvHD+mf/vufiugAV6/fNkSUQWY24BLML7t4we7G1uWbHDEPoxV7FmyCeAXZHmw/5B5UOzSon6BvZNslEwgm8opqsSO9dqTx2SLfeQjH5nGJwhkGEGaG9xwww1WOatrr73WCqw88sgjVtC2kkHswS2/CJezkuruhav00c61aoxWqz5SpTsXrNSnl2wo6jksrJlexwsCNK+6reRlP8jLSI3sZKh3rFevX3pV50bO6fLoZb3ff0jv9u73/RwDuAPqDxYgCIQXYIjlU/IhwMz7wOYcoLyAcWCUH+mA4QG5cAIyT7NGlCTMF9RPEJB8wfqPUgXjwS9kM1oOD/Tr6OCgYjbyFQ2F9Pi54u7LAcoLlDtCgQeRQJ2DqgpbDZKOUYtCF+JB8ByD15QSQQnUuXqVdg926du7XtelwdT1pMsV1IxFmZQOEA2uRS7qylTOdY7jx3zHOCeghKrt4YcftggfQQEUb/QAqbTMS8QVjLN8e2pwjGKWoKoOV+mejpvUXt1sBTOaqxp198JtaogWtrn9dDSmSJhnfS+DesjjKRraxzLvw5O4+JrU8440cl4aPCGdfUIao2dIgFKAEh6sXV6aDxOsZI8Ien35Axx8fvQ4CeA/KP2Syb+FvY+jkTlhB4FC3ku2JQ5J+AU8Ht7hh13n/Lx8gNM5VW8AOx47e3aaUxCu8drlS+r3SRgSoDIBdyAoxjxABEUZVmxkOAZ2McENeDZ+LAR/gNfiN1534zo9tfMpHbt0rOLE03yvTGBPxXZ37qtm70wHbHyn2IpqFFRu8bJHp0IoFLLK5SKgotcfmfomA+S73/2u9RmVBtZmt5kaUS9EDMJVTIRDIT28eK31KBVWN61U//iAjg4mI+atVc3aNi91jbdJxMakCwel0SGpqUNq8z9dOxJqttXgNQgpnKHZisFJSIbzuaETWtt0taLhMqjjOwdBdNssjm4jskGDcH9BQKOQ9ecD5A6Idaa5wfOQCuMUQ5nAJk55GDZ5HLmmUR71cf1Sr/gV1IDsZmt03kCpNAdY/ZuiQVPZAMnU4+3bt1vGMgSWDABTk5kspnOjw7o4v01tLS26p2OJzg8P6F+987IGo6Ma+mCP/mrfm/rllZvUHI5aBKWSGs2lAqoyDHq3iinmIJkskDXWC/5lX6Y8BdeRlG4CoqwrhrjlCwihKZ8FAUHVxoPr70fJrGIABSu1hfMpMcD6TvYKx8pGJP1ES1WjHlhUuqbt9M1ICD5BoH10wqZfq5Cy2PHjXdLYeSkUlaqXSuH0JUVyRsNiqfuQjWOEpAYXxDI2LA0etz2RkCj11X9YasvCnQIUDNg/9PJhPXOD2VKPu1zA/hIEiMo365MGvJkcZwinCFjwL0EL7ifOSVPOyVRMwG7wKysc+wC/AHZIvqAhuumRkA6N0ajld6OnhkEkFLL69wUIgI1q+DV7iSnBjE08Nj6m6rawOlcsUlN1q1qibdrbs1enh08rfEVYj+1/TO2xdi2rW2a9h0BJpZalAnxneNYdd9wx429cl1S99ZiD+B/g+gR4WEPox4E4GT8FgSPsfzLQs/kD3GKjLdOSvZ9sGvwWfFalXH94AdzWDVx7sFmw/UqFqySEQ2Fd375Zm1vXWf08akhXz5SlQUBj799JAxengg4rbpKW3eDzedWpLnyNhuKUZUH9EVZteKMioey102KKTTY+tyPVcwGKB9QeOKJQ3LoBxhVKkWI2SJ7tKHUGVoDUwMHHRpyp7i2BdzY/nJgoodORSIyFXJtnElihTBTvB/zM3ug11TTVcbMR3qX19fqpZcv17RPHJ8kGJORnVhTPCRig/IEDHsew3Tn84oWz+ndvvqDBA7sVHxlRW1W1ljfU6GJ8TIlwSIlYXNH2Nr09r1a/uW6blWrO47bbbrPWRIIikGv2J4KHfAbZIZB6yA1/4/NwbjMHyZqC7Kci0QSPyZriwbiHKPm97qKUwvGA+omyRvQX4XyYp5ApJyAYTz/9tG6++eZpmR18L2rS87x9j2bOZ6pNnQs4Nxq/c3yIzac//WmLAOHszycLotCAF6BWzRdzlWMQwEjoNklkQVQppCy1mEdOSANvTfGLofelljuksM99wDqul8YHpf4JEVT9ImlRZqeYhRS9EDM+H6AowHFCE1HWXdbFbGCNx4aabG4dIC/g6C7ndXwuw4imMoE5ANfGJoCHMD9SKc9x2OaTYUHvDmwXjs05MWeN/ZEP3MzhX75qtb51/JjlCSKwwb//bM1aVVWI8zNAcQAH4GHvT/ZOz5s6cvqwdr2ULKfaHJ2v82OXVF1brUQ8oXAkrNqrarV+0XrFB+J65plnLNGRCSQyr7B3sdcRl5rm2QgVmQPMN7gCXOTgwYPWaz/72c/OGNfsWay1zEHeS9CgEOsuWfHwHfgPdisiR+YrgiSEZUZQZkB2C2Aum3Pm9RyHNYPvzAP/H4Is+JXf2Lhxo/UZXD+y2AlywNXSNWOvRI7hOqhB2trx48fnrIFTFa6Sq7jZ2f0TAQ0wseEde03qWJ+s3Z8LRiEW56RIldTUKYWTpCcaXqDG0B1KaFQhEWxxt/EsrOnQ6aFT00pqtVS1WN8xQOmAMwW1LeUoUkV5ncCpwnx0q+RITAS/TA3pAAEqBQQi6I+RDXalVbogAUZPripElFqotE1qKT8fOXLEMmLyDW64qf3/p9ffoA0tLXr90iUtqKnR19autYIdAQJkwr/d/5YS0ahqVybrw46HQhqrC6l+dEyJWEzhaERD5y/qrdde06vdMcvGY2zzL3vMK6+8YhmVd955p0UYTIkFAhjMBcgGAT4CAwQ6MJwhE07DlNRn9jn2K8g7gcVC2JOsF5ACiAXBTpOBR2CUbAxnajjnAElzlqriPDkW5e9MEIOgCGonUrwLcd6otCBxEB6uEft7OTvD4AXZ6g574RhzEUmbzEVQAifawB7zy8Q/I9LQQakhqaL0DI45ckmKDUnVbVLVRLYqfGDZXVNlqCI17noBROqlqpaJclPG6ZeQ6orbDzHATFB+kLU8W/NTAwLVlK7AUZMVloM3IbnkoXMNlNDwq/FzAP+RrcQtwL632/jpbJdcFdDYVggwCJ6YY2AD4BDFFkHclau9xPHsdkwqbGxp0Yt33q3//sEh9Y6N6d6ORfr5lVfm9HkB5g7Oj5xWz3iX2he2WQ8wGk+oaaxVYyNj1tiD3x7ef1ihD0KaVzvPsmlNNjjiXLIYCBbCO1grEVXBOXC+4+jHNqb0FUF55gJ7k30uwFP4u8n6RTyF+LBQ2QhwHtMQnPlqsi8QJKWy15nHZEfYwbmxJ+/atWvyb3AifBSpAiN+4IorrpjsEUSACK5BFme5NhPnmnrhGJ6CGgwS0o7cqDzmLEYHkkYd6dbTnh/MLajRe0Z670fJDBDQ2CFt+JgUSTrlCGSE3JAhGzpqO7Sueb3e73tfscS42qvbtaklR0IUwFdg9LIw06DIDajrj4OFiHc6JDQ4Ud6AfhFhJbRGIQXNxe3AQWdXHgQoL6DKcDsnssFLiTc7UH4QdLQ7RE1vDZPBgYGAE9Vr6R5jEGUzZKLhsKWcChDALaiLfHF0ZMZzkVCVVB1SfMLx2LRksW5ZsEQ3r7l+GlmARGBkE1TEwY466a677rLGrCn5RmCP+cFcgHjbiT/vwzGG7cgcRiUEcYcUFKrcH+cGsXECksR5smdyXsxlbFvUVunWf/bWt956a3L94T1cD8gLhCTfLK1UQNRA4BXyRoP3XNesYgDCka1WtxvM5aCGeyBMcQa/E1I8Rf8LN8ARff5lacA0kQ5JC2+RGm3Zf1GPGSCsHQvvkC6+Io1clMLVybJTdbk3kg/gD1jXsWFYi93YuzhAyfBgjWfdTx/MOErXL6wrKUGfo81SyOfMoQqHCeIHKE/+57ZmeyGBoNEZ+EJoQVAR/xt2Ck5UHJNeHZ7YMW7EGJtbW/VnN0x3vgYIkAkjsSFLmGGv9oK1yliN1CfFhdWq1pota7R93nY1VU0XDyE24rXwZ2xe5qKZB2R7Y6uzfuJ4xwHPnmTsYWxxgn5keGCbm7LQ2P87duwomAg/1f7JZyEY+P73v29xHH6HcyD04vxTJQXAVZjL9gxKSt0+//zz1ndIlVmeLxKJhMXT+GyuGf6Vcs3W4LpwLd1yDNerIgONm0hELQhqZEDjgpkBDRRPdTnWRDz0uBSzkZj+89LJt6QV+aUjLq9fYT3mauZNuYKFmkXZSx1NFnWMHRZ7cy95Pwtp+zwWyV1UAJx4NWPzgBKqo+V9Ab9J5QCHEU43Sq0EKE9g8Pi1VnGMXFQQEPx0ai7mLZkanCO9p9grIbBOtXom8FoaDQZqvgB+gjJlKxuadHyg3yo8aY1XSde1depQ33l1jSXHdGddg766cuOMOUaAjrJIdhDEIIgHsAftdVvtgIxgMKO0evb739fp//pfdX00qigGdAHSq2dg5Jg0/GFy36P3QO1aa580/XVwFGDTUlYqnYIRox+VEBkehmCQBYKSjADNmjVrfFU5oS5jDSHgY9Ro1PsmgFKO4Po5x0cu4Bo/++yzvpzTrAU9NMINUnxg+vPRHAUZ/UdsAQ1AkONVqW7xpHAqJ0TrpUX3JB3eAb8oK+Aw8SKcQs3JOomNgzMJYOcw75MlDmmw/IHtHWTokE3kolTZHAH8LCgTXL4gcOBXfzyQa7Nf9vp0AgbsAbgBAZgXX3zRcniyZ7oVhmDHuMlGCRDAKxqiTTPK11OybGHNUh0fmhKqXN109YyABnDuRfi0GOMIe9h/CMQZm90J9jICdogJX3v9CV23dYWuuGq+qsK5ZzW5Rl+39JNvS6ePS23zpQc+p2jHUn3mM5+x/sxcRqiD/yKTjUyDdURSZGswT/H98fpHH33U8iXgW/Dzu+zatcu6tqwHBEwpBQa3KceeTybm4MyiTwdP0i8iJXxAgAyYv1rqtEXWaLy97sHcCALZGWR+TFssEtJg9jIsbhEENMoPRlHhFjhkMG6IVqPAwjgjjZUIZzKYMTSjoXwiccFSgJoHdfVKjpEh6cge6cO3pcGeonykKa3CphnMhfIF5BlHqh9ATc6Y94p0G/6FkWH93JsvafMT39cdzz2m3itXWA5SyIaXhoGos92U2AoQwCv+y+atarY1ntvU0q5fW3ON/vj6e/Rv1t+kf7fhZv1/t9yptmp3SlIIBEBRhfI33X6F4x9Dfc9LL2nz17+u9U8/rejjj0v/6T9Jv/u7hb2R9B4Y3C3F+5JO4OGD0tC7017CHGW/zdYfA7UVGRmmLi4gkIPSiZq4qWpr5wqOiREP6WCdItheDirSYmRqBPzCBRq3SSHbeK3qkGpTE/6sGO2e6M1hR1waJ6vXBwQBjbIDDlMcJwQq3ABFJ4FbuAX2DIIp1nv+TcJp47AW9urSxbNWQJs1jBIiZYGzR6V3X5VOHJjIMCk8KKuCStePvkMBCgPmA819/QJ7dy7HY26m4qF/ceSIlv7oR2r63vf0ix98oOaODssZ6XW/RKBCNkiAAH5iXnWHOmum+l2StbG2abM2tGzQrfNu1bWt1+q2+bdpZcNK11yb9ZKgAGIe9o9U5ZnxCVCyCpv5nXef1R33zVdL+5DGEyc0FHtd8YRD/OEnxsek//v70oE9Us8l6dj70v/+PakXv9vUfMZ3kakXqD3DgwCNPfD4wAMPWAEH9g8/sWTJEut6sqcTLCAbZrZkgke9OoNQkgbIADakVTukxddIY0NSHTVqc0w5JSBC6repaZv8AKmW9N4AsxUoyDGKKJPhtrQFThmUQBAVVKMsWknH7UxHbG/vgI4fP6K1a+62PgeHTMnn9UC39NRfSMMTRCtaLe34kjS/sGWyiFijfilUGZQA/gAn6ksvvWTV28wXbN651KrEwHISDprp/fzOl3Wwr8cq6TMcG9Sv7nlD/2XBUn1y9WpLoeelHj4lHvie27dv93x+AQKkw9qmVv1o+/16t7dLtZGINjS3WaXMwA3ti3JyAmCEY3DySFXqCaOZlHCc/sveeksLMMztCsbvfU/6h/9QSvFeXzB6NMVzx6T69KUaM4G1h+/K9zEgG4vgxuOPP67777/ffWB8fESKx6SquhmOXwidvcE6QXf2dK4x5K2cgO2AnQE3yBeGXwTZw1kQbZFa75XGe5OZG5Gm3IMHVv8Mp3M3lMy0CDBrwZpFST0y6LxkkiKUwllLg1FKhSQRmWpaP7EmHD58RjU1yaw4kIuIxHe89aS065mp31duku76QkF7gMDHyOZz28MkQOmAqIgAlMlGygfYBflka9jxyOnT+qotKPiD06d1uadH32xrs4IUXqo6MId/9KMf6b777itI2cwAcxOM2auaNqizbrlG4yOqjzSqJpL0ezZXNVuPXOxtggE0zbbKWKUQFRKgg2fQg+O6G1sm5o6xZ+Iaix9TTWS9CoKTR6RL56Z+p0IPwtyDe6Std3g+HD4JeP+ePXusTBV7YAPRMbZ/pjLz0xCPST09UkMjEdYZfyZIYAIF7FHY3ax/qXhcqeGVX3jy7KDWQHkRwAXqWpOPfMAEXX23dOAnUyWtCGgsLc8yBAH8A9kXXkkHjqYZDVBVo4RQeZ6xfv/wwzMTjph7rebyBn4qTXPCnqekkYHpWUo7fyQ98IsF+0jIBopYP4zYuYLesRGdGupVU7RaS+qai5rdgoEDkU6WPPAHGEQEDwlqZVMq4KBFlW7HqaFBvduL2jUJZlE0FNIL587qkx4bBkJOCtnYLMDcRlNVlW6c56/RajeOUxnpH/3oR5O/vPEGk2F6UAOg9i2UIe1iS0NxjCHvdg9wfl+cIBjdrht48v3fe0o69U7y9+YO6dpPSDXpy1eRpQGpeeKJJ/TQQw+pnECtfUiRlzJ76cAxUKRRXiNt/f4ASRDMqPLWtyklmlZJ/celYRs5n3eDNOGQCDA7gYMEQRNz162gh/Utda+w5RP8IqG+vkGdOHFBq1bdpJra5dP4RUmDld3npwc0wJF90vEt0orCOL0o/4vCmEy7IAvcHRgjsUSfEhpXJNSkMH2/igT8W5S88YMP2htys6fxM/MtF/ztyZNWCVFEU4B/nzt9WsNbtmjZggU6ceKEq6BGMth42KqdjxOzELX6A8xtUIaqQe7KBLkBc8bZXNsOU5I1kYhpMOZY362CWBO9iItGMELTMgDh8wRl3Prx2JftZWaZs5SexU/hOlvh0AHp9/691NMtRaLSP/gF6d4H076c/R+/4VNPPWUJswhulBOIObA2Fyyo8c1vfjOX8wqQK9qukLb8lNRzSopUSe1X5FfrNkBFwDTwSeVIdQsWQpTiJ0+d09KlUYVCvWpvX6X29i0KaXqz45Ib3X2Xp6eD83P/lLPYb3BdaT4U9C9wj3d6zuuPP3hDoxMB1pvnLdXPXnGtwkUaO2zqkEQ/ghoQTpyRBBBQLJHiibHPz2zqqVQhpGo6DQvqhjoRj8VUUzM9uJhufqJ8xPBB/Ygam3rXb+AADhBgNgFD/S//cup35g2N9rKkZQPWaco+QXCYH66zrGqWzyzVWT09IGGyFHN1ZPA+T+89tnMqoAH6zkt7H5W2fjbtW8jAxMlfjsFO7Avq/fqh+mT941gcMwhqFAmhiNR5lzR4SooNSTXzko8Ac0I4ha3htreGEzhSmavYL329cXV0UB4kpPXr75c0PcMapy610bOV+SsY+qZKgkwCu7W3cOU+sVVvvvnmsqxTXo5IJOLqH9+t8USyPFJIUTVGr1M0XJzKFHBgvxq5sx+acrk4DXFscmwyzgkMeunBVZ1q3x8YUMeCBZYtZFd1p+K5zLtkKeqkMAw7gucDBJgtCIUiCqtJcVHpY8qPFAm5E37AARDTkB3iOlthyUqpbYHUfSkpOifjj0zqtddMvgRhMnse/N4pOHb3vUK6/vrr3b9hcFD6z/8m+S+gH/Of/U9p2XLp6vRZHrfffru1Tvi1/vkJbIwvfvGLhQlqkEoaZGpUaNZHgIoDSopXX30159RlUsSN0hMjBodBOrCglRStHVLPuanABoSjpXAZFO+++25GBUCA6RiJjev/9+GbkwEN8Oqlk1rbNF+3zs+/nrobEATwiyBSj9aANHHSOpknEHT6AED4MShQWUEcMEggD87P76ip1d0LO/Xs+TNUIk82qerq1lce2GH9nWPgkDWOWEPqmW88KBNnlBIBAsxa4Dj71V+V/sf/oAugREm23/99JkTWtzKHmCs4CijNhpHvqmmcFcCIOxqFJ/dEQNYXgUxKEBYNl6aaJlpgv+s6mbWhcrmWivCqonLLMVA3BygSIOMN2YOLAWYXsEkITNDHK3UGRmbg/DEOIGycVVddnVYcRfAY+61kQQ2LS9hLk0ysvW2FyRIkAwabrmTftwIxHD82GdAAZGv0j+9RS1XlZbrY54YZD/S9hGtQKgc+TnYn84I+NZmEEf/wyiv1f44etbgFHIMr8Sl6fE1wCtMjw1RbYE6T9Q1fwU4igMK+aq4h4qyAbwSYbaiJXKPh2G4lrMAGFROWKRpyV76ceWlEhvSkpXF3VlRVSz/za9KPvymdPS61zpce/ILUktxLTRAffo+Isihz7uQxacDRK4ug6P59GYMaiKjLrbRt0TI1SHXzUus/QIAAuQFFBWQj3zqfGDYnT57MmP6Nc/XAgQNW5kJJDMhr7pEun5J6J5oOVtdL2z5ekI8iao7qN1BQucel0SGNUKfRBlKiTwyaZpGFB+S7FXW3z2BeMNcwQHgwB0zw3jQQZO+DHNCYD3JiwFz5wy036g/f3683Ll3U/Joa/fSqDVo2kakBsbAHUAia8HnFMHD4HAiPl54eAQIUDF/6kvSZz1CPgY6VNI9wNYaxNbdu3WoFH1AU8a8JwjMXTUNtHAcoYydVkOxjNSuTDwcgGcxNDHnmJMfLtTyEJ1j91RzONfpHVZjTxq6iMnXz/QDrLGUhAwQIUHiQ+UbPnnx7eJHBSr8hu23k/LvJwCqEDZcVze3SLR+VXvnR1Nq74VZpiX9rl7Os4XXXXVeQY89WUHbKiYRGrOBGSMVxuOXaB8ONHWPsCzLNcZxis+BIxQ6hDDJBRnxrTlX3tvZ2Pb1jh/7Te+/p8uioHuzs1BcmHJDYLoixTAY5n0PAhFKO6Xg839GvrE84GZUkAi4doNQIh+pUF6HME9wgohAlOrPA+MSYL2RqwP0JOiIm5nnmJOJibFL4BVwEIdQkCGD89C+nPDbiSPZX5ihzvSj7QUOKUpIEOyu0ZyxZ+vguvXAMT0ENDBKcrKic7XW/AgQIUBiQbfHCCy9Yzpx8gg0YUihTKWWRChgmGF0sxChIMLKKWu6itkG67x9JF44lmxyh/q8uTCocNQpxkgVwj5aqGqcrzmqS3V5dnAwDjI9CNVxknNtTuEnBtCsDUFIR5MABSmkqCCtGvMmEovHyv7h68+TrcZBCWggmOudsMdM79+3bZxloOCwCEUIBMDKcVOuUYVmgsgXj38UcoCY1qdumNCJ7E9lUzFUyNUyNdtLGIfFk3TGHaeLtxkHH3CAbCzCnIR5FCWqs3CZd+HCqRxqEY3V+DsVSAnvhwQfT1+v1Cu4xTdcDBAhQeLCe4hRFrEhpmlxBIJljpBNO8RwOI0ryoIalDE/R1eLrb5aWXCV1nZca26T5iwvyMQhJsPPKVflarggrRcAf56Q3N1XOOHPmTMECbox/ezABEaEBdgvcnGAGnAInHvzBnq14+4IF1sOAACHgtfYmunxOtu8AF2Cu5guEIQhMyAwJ+HQBQICN8kFwjACukNx73GXHsRcxf1ineR/7IGBv4sF8NT0xECayx/Ez1RuyZYqb9zLXeJj5WnAsXirdskN65XkpjGgsIc1fKN12pyoRxBrgZ17K0XraLbjxEMG9e/cGQY0AAYoA5hwLKpMbwp8rMLKzlZiCZGzcuNFKTzVGFp9dtOAGTY0WrSroR7C5oGopigNrFqEhWq3PL9uob594Z5J2LKtv0Y4FqZV5foM9B4PfD0UQxgZjPBXZSAUyHXhANFBWMQ9RXqQDzfhM49xSps3zPe+66y6rtrPbRmUBXODcael//b509lSScHzqS9Jt9waXzicwH1EPE8BMt06bYIddwYNS2K1jDkWkKQ2Cs41Ad1HKETYvlG76onRyb5KwLlglLSzsnlfodfk3fuM3fDse/OK//bf/5tvxAgQIkBnUEadBMk4d1/2KUgDnEPZRJmc+oioC1mSfo3xFPFXU/jmUoSpgWVsc1AR6EaEF8IbayBUai59XXEOTzzVE1hfFhjZq6nwzluxCKOaCsVMY6+k4BpyGOUhAkDJuqLszZYxwLGMX4WjFlqHEjVtg9zAH84VRtvMvimovfUICZABCl51PSW8/kxR4diyX7v+y1FCc3jJzAQQ08IdlKnNq+m3eeOON1u/MT8a5m7nGse3z3WSEF1zUGApJv/LPpDVXS0c+kNrnSQ99UqpvqFh+YUqBu4VnC8YENQIECFAcUJuTIEM+Zd/c1vRj8YBo8DDNlFnMMbryITzlAMgG3yEfRdpcxt0dV2pFfYs+HOhSU7RGW9sXq8pSAxQWGM3cN9cNvLIAlRIE3tSg5V83JZpQVxFcRImXLkAIGeFcSz1XOD9zLszpTKXnig5KzHWfk+qapPnLKqv0Do7or//nZHM4MDYqfecvpAWLpKs3lfrsZgUg9qR8uw08M87JpKKOtBvFoCk7ZQKkRZ+rjfOlq+9SpYPSEyjduFd+gWOxPqPmxgYJECBA4UFpDFTX+SiusTHcZCfgVDX9/cgkRWhEpnhRgxsFAN//ySeftBzjZWNrVRDCoWo1V92k0fhZJRRTNNSmaLi5KJ9thD9+3bdz585ZpdhMRqmb8ilGLQ5PxZYhAyNVIOT06dOTx0PFTIaJl6CGX1yA70hGO6Wx4UWu+hAUA+Nj0on3k7b5kiulhuKMId9w8C1p55NTv58/KT3+V9KnUpc5CuAd2JheAs/Yoya7283cYQ7bG3yTzYRIuCiVGsIR6cGPaTbABDW8IKegxje+8Q2vbwsQIECepOPNN9+0aobnAqNI9wIICkYL76PMB8fAIVSpAQ4ULoFiPT9c1TTPehQTBNZyHfepwDj2QgLsQEWVzViifJudwFM2i3lULKJLpgjORjPWIT5l01vj8G5p5yNTJQaWbZBu+mTlBDYunJMuX5hpRL67JwhqeEA6Yo2CFwLhpYYqZIHMK7eZFs7a78bxEMAbIHkIBPwMPlDeFocnxw7UzgECFAeorFGW8yimbW9KfbDuY+eZ/gJ+CViKCZzL2Ie52pYBMAOjqom4a+7rp2iKgJqf5cJMg2CvMILCTICPG9sJ4Ydp/O12zvD+fDLe4RJkonC9zHfMVgWiaBgelP7mf0gXzyR/r66RPvWL0uIrVDE4cXB6GTZKlZ47Lo2NSFXuSisFSM8x8KOZoLpbELSDX7gNSjgzFvE9mb43AbwFNb785S97LGOYQ1Bjz549s4oEUhv+xGCPPui/pGGUmAEClBnyVVdAVFhocwFqEZxMlNThQepeJaKoPUIC+ALSpCEIlaB8Y44yz1566SWrzj/A6YfiyjQed4N8vytBjWuuuWZS6U79T9JmS47hAektW0ADnNiffFQKalNluyVc9YkIkCTENOHjQYkpey8bfiZLI1PZqVRgblEGwe28gWCgDJ5xFxOJnPfIuQh4gFcVlRtwTGoXzyaMxUfVO3ZZQ7H+Up9KgAAZy0eVAqi94Ras42SAVSLKRjgSwBPgswj1KgEEDxB9UC7TAFsfvuG2pBR2Vr5BDc6DUtUGZcPPXn1MunRu6neyNX7yV6ooELhwXs9QWApXnpC0VHYp/IJetGQ12YHAkIoL9j402cC8Ynx7ybJw+prgFjxH9ZMA7sA1y4Vj5JSpASnEcVIpG0EmjMVj+voHb+i9vqT6silarX+6+marXnyAAOVEOHCYokoteoM9GzCG7I6oACVGz1np4hEpUiV1rpNqKrN2Yjqg3vOi2i4mXr14Sn/24R6dP3NGSyN1+pmVG7Rm8TLdd999VmYTBhWKEIIKzBsMGrdqsFyJB4QH1ZndaYsxZnqIlBT9Xcl6sU5jnXJUlYLWdmnbbdIbLyaJB4+6eumWyi8nVEgwnqnbzpg25TkItBHYMHVtUeWQkeiFIFO+hH3RS4NPCLldjcznsa9yfvycqc5uWQEF3+BJaXxIqmlTomZBUZ0Lb7zxRkEahHJM1HSzBV2j5/V+/24llMyUXVizXFfUrysfR1CAABOl33Ai5NpbyC+hI/aSm2as5QZKA7OP5FoiuCyByPPMu9LIgNTSIc137wysFBiHo5/H82Nt7x8f1X9//229ceqY4l29emjRSt2/7Cprf8TRSjYjGVb0qcEXZ8Qd2YDtk09mBVlV9Bkoy/3r0tmkXWTAmtRzKdl0u1JEjZu2S4felmUuWGtqQrr2DpwvpT6zsgYcl0AG+xiBPkBJRTgHnJgMJcaumzlin8vYuaavhhuk8o/hB6A0IesMZakI4lcE+i9LF45aAbVE52qFqovne8SHAkf0Wt7Wc1AD1eyWLVv0+uuvz4qgxk/Ovq8DEwENMDA+qj89vFP/fuPdJT2vAAGcYKFGBU5ZGa+BDRYIv9LfMIqyNVcOUAScPSjt+fFUpuqRN6SbviTVVVgN0QwggE49ynLDwb7L+jdP/L1C0ahq57frYm2tvjV2Ub/fltyAzd6IMgQjCqOG8gRu5iB7LOTYa/o6afR8nrPZISSb61hyWI3ubGnVAALS2O7v50BgHvme9MrzyUbeD31CusmfBpAWvvQL0pLl0pH3pcZm6d6PJYMdAVJi9Nw5vfgnf6Ity5dr3v33T6rQIOTMk+eff94ao9Rotqv/3O5rZHZ4gempYQekheDfqVOntH//fm3YsKG87ybz5uxz0vBZ69eh4VH96LVLeuhzv1C0hp1wgC996Uu+H5d78c1vflOzAePxMX1gC2iA8yPH1Rxt07yazpKeW4AATruD4DDZcuvWrfN0ccjw8CuogZKWPhuVGNSg7yHlPmdNQGPnd6S+80nxCXvOyhulVbdotgBbwO9ya17ES5nw7155UrtPHVO4vlY1nfP1aKhfGxqrdMVEzz6cfdj8CJko2+iWj/O6XDKymN/s+VdcccWMa8bfysIn0LZQOvHBVGADW7Ox1f+ABtkgz/8wGTDpWCrd+Umpzie7a94i6dO/Iu19SRodlpatkdblFmieK9i3+0WFNKiP3H+9otVT5Z/xVSNWouQs+5tXUE6aUrVeMseZ/86sDoIYd911l3UOBF4M3yhrnD8ivf63yWb1kl767jm13v4FbdqaLGtdaLDW0KfH633LaTXnhvCBP/3TP61KB2Wn7KYYS+H5kQErg6MYTXADBHALDCVUpKTWUaPfS2O9fOtoOiPis6n8XMXi3aeSDmJzK8aGpQ9fkzbep9kCr31gioU3Lp9RtKpKdUsWWb/HldDJoT6dGRrQ0vqZZJy55/a7EIDwGtA4evSoNS9TNamEaJRFzVsag197v7TrsannFq+RlntzZGfFd7+ZfBgcfFf62m9J23wi4xCkux9WsfFh/yU9cvpd9Y6PaFXDPH1yyUbVRf2rA+0GOGdDHqqWjh4+rJc/9zltj8dVw3X78z+X/uiPkAVbf6f/DA/mBuTc6x6FE47AhheBTaoUcCMSIPgIASp79B+bDGiAcxd7tHFJTGdPHtaqtYVvWE8JCq57rqruTOCYZOhRhgZnTSVjJD6ouC2gAUIKaSDWq3kKghoBygvwCtY/+P0NN9zgaT32s8myn/0NihnUIHNw1uDM/mRAAxgn8ZHXpSWbpNrKCjilA3sYTno/4UdfmpFYTLvOnlD90kUKTTjkmV2vXjqt7fOXTL5ueHh4UvXtdv4xv70KHAn+IKjESews3cnfcPzCQbyU9SkIbnlAOnZQ6p4QKkei0gNf9Pcz+nukb/yBNDKcnBcXz0rnT0lf/pp/2RTzOqU7P6uiIoHzmjLJZM4zfq+UQsXtbWT8Sl72kg/efVzRxBGtu3q5FN+DikqqvtYKaHEc05sNoaDX8k/MLfbCzs5O13sSPMYpLLKXr8JvR/C77IMaux9NCgQnUJMY09CBV6UiBjW8ZMgYhPMJaswGtFXXKWxtF1Ooj1QpijIhQIAyA8YSjkucCqTFuS0F5aeKAqOmLFNP5xKInhPEsAODYKRPswXl6tRCzX352AmN9M3sU1Flm2MQDlOKgLnnJqjhbDSeDZRpOHDggHp7ey3C4ZyXkOyf/OQnKhus3ird+/PSDR+Vbvsp6dbP+a+i+skPZj73GL08Khenh3r1xx++omODXbo8OqidXSf1Z0deL1pwOaFuJfSipGeU0AtKyF3d89d+8zd1ayiUDGgA6j7/x/8443XMDxSuXvcoMirYA7FHIS3ZQEk4GtOmA8fKRdFVdIyTeTU112uqozrw4RlFVZzgJbYHNfALsT4zDghS8RmVjqrwTOKaUELVKZ4PEKAcgMOTdfXll1927aT3q+SOcSRVYm8jAuM4q2YNKDmVyg/C87MEheiDkm+mBqWlPjh0SKNdPRofGpkWDK9y3A9e67VqA0ENsqHcAM5ChjkBDcpepepF9vjjj1vZXan+VnTUNSaDCw//rPTAT0s/+y+lZVf5+xmH9kjDQ1OBPv4lqHH2mCob78JAcf/DLOlOISWK098okYjr/PB+HR54SocHntS54X2KW0GWzDh5/D2Njx5NBjQM4mel+EQw1gbmideqD7zn5ptv1muvvea6lyyB0kzBPbh6Ofo1pgFeOdQ7rarCwPCILp07o2Kh6EENapWZZqiVjI8sWqPmqhqLHprHl1ZcEzhtA5QtIA/r16+3iAc1yZ3NkNIpWv2qq4+a9eTJk74cK0COIIusYZ6joVhIaknvsKs0HDt2zHcVVb7ZH/SSIlDx+W23qaVjgQZPnlXfsVMavnhZ17d1aGFN/bQApCnLQFmobGUJcKhiOGX7ztSZRBXF3Od82tra0jbTwtGLk6AQ1zFntHVKV26ROq+a2RDPD8TiqUspVDB2dZ2y7MyEzTF6eOCyLo0W3omSEMR6FxR64hkc55TUSR9EwLnDGG3u6lK1PfDC3Dtzxnd1MWnKNM+EgGdCNjUhQZWK6BlVTQ+RqevaubBNHfObNabiBGQIlOZCONyCY//whz9UpaM6XKOldaYn1ETZtUizFtYsLel5BQiQCWSKonCFMxBczBZkwInrVxNU1uBKzATHwVz26lsvaFo4vT8BoFlxvfv+VeWOQpRLysd+wF7HKbr+6qv1U3fco5FLl9V/4rT6Dh9XIhbTg50rp73eBBLpFeAGZEBm4wLMPbKlTMNlsrzJnkzXK4agEEKQsim7RqPtNVuk9duk5gI4j7FhU9GWMq0q4ApWACFVADu7b8kPXB79QH3jpyZt2v7xM7o0ciirAPDY0Q+0dk0KWyqRXeDkFoxtKqTA5wn0Z/IfECSFb1e86DdE2Tbm89T3uG3DSvXEk6XniyFo3b17d04cI6ccOVRUOFJY8B544AFVMlqra/Wv19+hNy+f0kh8XOubF2j5LNq0A8xekOLGYkv9WeYiPTfSGR6oU1F0M2/zBdFrFm0MJAMMHwItAYqILR+Vdn5XGpnolzB/RbLmbQrgBJXI7GBDqvNUQqZUgCQXQjXNHMBo91K+DUMfsgFpNXPoD+/8hL5z/IC6x4a1aDihOxIt05RfGEEmAAGRyKSo4vgYTDQRy4a33nrLcuSiIMlmPHGuzE3SZ+cMtt8hPf3Y9Kbkt92pSgblzUoHguFOoh6feH76mMYJxtxC4btw4UKtYTw//TRMP/kC0vML0IuNmrf01qDBND+nIthkNbFnZpozFUNG6pdKTVdJfWYPDql5+U16a/d+Xbl6fUGcNQSq3n33XWtdRkX11a9+VYUCds2fU6psFmBJ3So1RlvVP95tBTnmVXcqHApK2wYof1x99dXWvGe+s56TnZVujcTWoPSGHxwDJbmdX2BXXXfddWVdlgoFbjn2f8sZC1ZJK66Xjr01FdDY/LBUlSZwEx+XYgNSpFYKl3+2I3axl1r5bsH4py8XPMCLPcHcQSxIXzHe94/WXKeFDU3a2XVOtQrr+pGIQmcuKt7QOrm/81k4d5mj2RogI36ktC1zONvr6NUBx3DTnwvucssts6fPSlas2iC98KOkUAqOQfZMU4u0yJ+epXMRA+MzMysGYue1QDN7OxFkh89aXHvBUoVCKYRMIW/lm91g2bJlVjYSWRvpuDfiRTeOeD8zGwuGGz4uvfItaTQZIAq3LlLVigYr2MBeXAgg6oQ/co/hcLmUs8spqMHNuOOOO6wPrvSgBmiMVuvOhdMj4AECVAowPlBSoaxgsSS44TT+/V5EWeDteP99ajEGKCqIpN/2c1L/JSlSJTW0p1S+JyyH5L6JWpmgXgldq5DDIVmuwNjHgCHbyI9UcZQUGB8QArekhoAgdWPtQcPl9c369aun15TH6Wc/RwJ9kPN0AQ3mJX9nI6d8VDYSgRqQc8hUQseZ7eK1fm7F42f/cbKk1SsvICGVHv6UdHdl2ynXti7Rc+c/nGyzTsnMZfWtaq9OHcT2FxFXz0Os9+7da80pAnpWs9lf/3XkgaQ4JV9E2vVv/3bBzpQ68Dt37rQC/cwn+7zj3G66KXs9WIh/2ZMOzm3+NqlptRQblKpatWFFna7aPFKwZp2QGa4LQQ1sjR07dqhQ4Ni/+qu/aq13FVEOLAtaquZZjwABKg3YGwSMTRkaRI2pSmT62SQbO8jufGXdKeeAhglquC3rUxFgj1l9u7T0mmTJKfhFuoAG/Z26X5USExk9TZukxqtV7kEN9ha4M8EEFMjYyvn2w8BuwBmHCCpbAMGAqidwHJp/G0RCIX166RrrYcDeC1+A8wPmG98jEx/AnnnnnXesOYVNlA0ESSg15aafDp9dEZmtfqJtgfS5/4/0xN9IvV3SwiXSR74oVfkfICsaEFkk6J/hDC4Up/JDyPr86c+FU7injY8LoSBzyxLrjbdLY/unXhS9Uor4W1LOgKA1n4sAEVHk6tWrpwXeCXpkmzf4Bzj/lSvL3Ofc0iHd/Y+l7jNWQDvcvkQPDnsvd+cW3FfEE/hZ8KPAAXLhYDmv3nzgd7/73VzfHiBAAB+BIYbKG2KBUwdjxxg+AKONslGFAsYWqbOFWvACpAHBjKwlp47YAhqAkjXvSNpa1peVnhQEH6g/CZFGPURwIRvpYHOEVGQyLpgbGPr8izIJw7ypqSll9gYOUoJ46bKg7HAGSXAw2uehAQSK70LABiPJbhxlAmUfKbXjBnwGmSJ+KCcrCtyDn/+l5GOWYGl9i/7xqpv0w9P71Tc2oisb5+kzSzcrXBTHOyn8KJ8mMsIsEHyb7sBi38H5hUGKM9oCY++v/xo2kszW2LiR2iYFO1OMYAg5+xHEgxIqzEH64LAnuiHqBD1ZeypiL6thbifnN6EMv8+ZNYSsG9ZHyl3iGKHsHQHmVOuaX0B1CkFEOEXWRoAAAUoLHEg4T3HWEtyAb9jXm3x7CWRzBFFqs+SNiDMAW4uyXbMOdS3JRzrER6TuVyZK2Eygb59U1S7VFLfRsBewlzGWCWwQzMBOoDcEdn62EivsTaZBdypwDBygcAuOTzkVbBMcds45wrhB1ORmP+W9TtFCuixssmaxxTgXxB5ugjVks8Jh3NhJRujAsecclq6Sfu5fanZhI7kGtkbhq6RQYYIDTrRVr9S54b0znnMG25izZDI9+eSTU2M0ukIKz5cS/VKoXgo3FfRcyVjkQbnbixcvWnsT9vG+fftcCX3Yw7Bryz6oAarrpIVTe64bH4hXsDYCONdDDz1k+WH+1b/6V/rc5z6X0/HyCmr8s3/2zwqWwhcgQADvYNExjY3sKkciw4VUEaHKxYDCQVuOyko/G6VXHlL1UqEJVHkDwoyhb6L1BBxw0GPYZ4rgY2CYxnXMB45D2SjTrPvM6ZMaHOxRJJwsocZnYPBjlEByIAkQEoIpOEMxWvwkqxAHHMBkVHkt2cZ3cDu/UH/53QQxQOmwpmmBvrb2jqJ/LqXqEoK4km1Bk1AM2ysVsmVq4NCCcEA07GpDC9iHW4sbQGWNoM8Myh/WA+ZBtvIMBqwVQYA+iRdeeMFy+HB/UWpTW/v555/PWUXlFhybgBSfFQQ1AgQoDzAvsfERSJH5xnpgavrjCygUsPuw0RCYFDKYmg/mLMcY650e0LAQksYulXVQg7EMp0AwZUDgnr0um0+LQL8p54y9AMcwvAT7/tjpE1ZpopGBYav0LKIlMibYTwkM8j4e2PKUXMFR6ycQP3E+ZKZ6GZPs8V6drYH/b5bAKolZmhLijdFFCtVG1Dd2ykrYaK5arIbo9LWDACH7DXNsRoWgMCKr7KXS/ARZT/gZEF5yTgT5yzq7uwxBJQmCGqyDrD133nmn5beknP7Xv/714gY1cGLi6CHitH379lwPEyBAgALANE7FMYvxhNKcxdcqCVIAsJhzbJzCblXnxYQpHzQ3kcpAL+9UfoCTFLWRPdPAjQGNYxWSgGOSB8dABUB5gJHBU6qtOq76+ipVKaKh0Sv04YcRy/CH3KCYgnTwPgIeqAaYQ26RrcElqiwIB07BQhNgiFeqMhEBAnhFyDIV0zuTCJo7SxKWGsxbAvxegZFdEEUwa8PIoBStlqLlv/6ylrFuYucjWDAZYs8995w+/elPF/zzWSO/973v6bd+67cK/lkBAgRwDxy5qFXJ2CB4jEMCIQgOXWymQoDj8jnlGDwgcF7IoE5ZI5JKZJOQwuXfNN3Z3B4e64ZjwBEQStg5BlUSunq7tavrPQ3WjCscDqlNTaq9UGfxCuYHTjtKNSO4IhDI/krfGrdgjGXLuCB7grmZi4iReZWNw9hRjKbBAeYGGqILrEcqMCZZY8spaMBcIQjqFTjt3WZCecbYmDQ8JDU2pSxFXm5gPyfoy9qJAJVrSkyBvd7LuuhLUIPBddddd+npp58OghoBApQZiHzi0Dl+/LiVto1iA0VIIVPEWZDKadOxA5XM3G1kjhLp0kSzX1OV351quZRgU3vxxRctxW4u4wr1lL08wu63X9W61cNqaloxebzh4T5F6q5TVXUyvR5VMvMFNaCXYIYbENAgQwPlca6k3AvhgGgVKogZoPxxYWRI/+foezo7PKDVja36mSvWqS6SX73odAENAoZu60dXAnwnHf3d0nPflLrPJdfgDbdKW+4pa+LBGondQJCHf00GC2W9/uiP/qjgn3/33Xfra1/7muX44bMDBAhQPsAZQcbGY489pgcffNBaM3HYFiqoUc4cA6f1nBVNRZuluiulocNT/CLaKtWVfy83srKxXxjLXkFwgbFuxjuiwd72MTW2z1NrTZJjczXOJ/p054qp5sGMX1PyyisoI53pfWRPwVtyrcpg9ni3oimCJwHmJuCig7HTGoqdtbK5G6LLVRvxv28YPitKnhI8nw3AD0CA03c8/WPp+99Mlvmdt1D6xV+XFpeX0MwJStexprGGmgwxYgrY/rnu83nJHfhgTiBAgADlCQwgszhg6FCXsFDw4nANUDyErLRMGuSyaWAQ36CQZvaOKDdAYEnxJOPIDzQ2jKu5uX7aZllbW6WqCCV1pogKj8meAB6AaimdM5RAyf79+y0BQL4qQzfzDOVDRfQECFAQ9IyN6Jfefk5PnD2u3d0X9XcnP9Rv7H1ZsYS/yjrT4HI2pV4z//levuL5b0k9pgljQtr/kvSBP+taIUHvDLJWTFABcolCtRglYCgXhoOGzwwQIEB5rpUENoziHTVtIXkAxy7HfQZ7cU4HXluuk1q3SQ2rpaZrpPl3TpSzKW+wt6EWduvIzwRs/8H6MUUnAhqAmdAbpYfhFBB/UNEgF8AxUmVq0Oyc0lb0+cglWGJAOS6CFW6QazAowOxA//hRdY/t10j8kobjF3Rp9C0Nx+y9O/3Bm2++qRtvvHHW9Ibke+DI93Wf3Pe29Hd/lQxogK6L0v/8XRYGlTNYy1hDWLfMvm6CGrki76AGtfuJPAUIEKC8QVotD4y4XMFCTIo5TlrK8/Cg1qGpj1fIZuQBckdIdQpplUJarZBaK+ZSkqaNwZ6vAZB8f5q08tD059lkcyEd7IOpGmnRl4NMIeqB5qsAJzCJQZQNqMZyTd8MUPl44cJpXR4dVtyi1bL+3d97WYf6UvXXyQ04Agg4um1cXyng+7C/USbOF+IxOix1nU2Wn5pESDqLsrWykK+Kygv4jEA4FSBAeYOyg0YshZOY3mT5lKXB3oNTGH5B9ocRmZRruRuyycqxl2DRwH5Qt0JqvkZqXCOF/M8ILRSuu+46qzyxH6gJz+QYNZHpzyFqgocjPPID2ChkT27dujVt03C3oIqDmzJqBC9BuZWBC1DcoMbM5475+hnMSwJtsylgjF1L1glVKBCE+YKD70hhm2+BfbL7snSRzPDKAX4SYgolC2pgwOBk4eYECFCp6B4d0N+ffEX/6/Dj+vbxF3R2qEuzCURDIQqAoAMLR1b0X5TOvy/1GXWpLHIBycAYI9UaxQkPeh9Qc5sSJNnqfZYCnG85nlcAd6B+bT6BOMD4r6JpYcgEdCaccqH25MMGDHU+k9JtXsA8cM4tggsYLn6lzkKcnXWAUyEo2TK3MRp3Nu5MYiTN827JMw/GM2XUqN2cTym1cgXfh8AG6dDYtobA5wz6Z4TCM51A1ZVH1PJVUXlFENQIUOmw1syRI3q/7zkd6ntGZ4feU8LnjLlSgmwqStQBnLUIO7IGPeNjUv9Jqe+EFEs6URFLkc1KgAQxi+EXPBByUD63XPcaMvtSCVoClD+wqXPJzE6FLa3Jsr6hyf+kayaecwqnEE4YXu4WzqAe5/38889b9opfmdlu5hh+gHLsnRmgeEho5h6WSMTy3ivht/BmAnVwanpdzjbMmzfP4k5kO7kRKWZFPdU4Ugiw6orbPD1fwLcQSZhSVLkgbwvhnnvu0ZNPPpnvYQIEKAlG4+P6/qnXdHa42/r58miffnD6dfWO+aOiKJcF1JAOVz01jr0pvfHX0js/lt78pvTBi5bxhbGDIUPk3K44h4DQr4LAhlcjrRhATTxb6jHORZBO7TXA4IQ17qtrpKptUmStFF6S/LfqhpkOxwmiTiDs/PmpoJ6rwIljbkF2/Rx7EH4CLplA+jhzNECOePUl6U+/Ln3j/0p+KWmKjOvbFioSSpJqEFZI7VXVeq/3vL5zYr8+6E/uB25gJxmkgrOXUBaOnk2zOVhM6SXKamFo59UEFgXVph3Jn3H2sd7Q2+Rq903Muf4omPxyvuSCrq4uvfXWW0UPahBAoz9QgACViK6xEzo/ckjjiRHFEmPqGjuuc8MHNVtAAMMZxMgY1BgbkA7/UDrxtHTyGenD70sjPZZwBR6BQwNnlv1YOLbIDHRbGqeYIMjf2Ng4q/fC2Q6U4H5kTiypW6j7Om7W6sbl1uO+jlvUWTt/xusY04z19957z1M2qHNekdHEcfyq0e+Wv2MD4lcIkAOG+qVdT0mv/Ug6ss+RwVs5qA3PbOodDtWoe/SAlbHhNsDB+IcnY2fDL8j0oxzRrbfeajm4ZyuYy/AL7Opcy9FN4ta7pYYmIpLJB9h+j9TirioHwi2CowQrS4mnnnoqb36R9y5877336j/8h/+Q72ECBCgJzg13ayA2PPk728t4IqZjA+e1qXV21IvEQUsJHJp6ZVWd9l+SPnx5+nPH39Lx/ohWbNyW8a2U1yk3UBKLbLKAcFQujCGPSilXpd5kMI86v9ErXb1n8eLF1rxho3fTBBnDxFlrPm+Vtw2QLo6XTRGI+oMapAFywHe/Jf3V/6ZAcpJsPP6I9Ad/LM2bSUzLGVc0NOt3Ntyk//fQLqsM1dK6OoVCo3rsbNJ4/vGZD/RPV2/T9W2dKecK6ybzhfHPv2T40eNgrgHnGmXcWANwHuQMghpN7dKZD6WqGmntNqnZ/ZjCocf1p7/ELbfcUpJSJ88884x1Drk2Ic0FjDvWVD77U5/6VNE+N0AAv9A7lizPakfP2Gktqls3Ky+y6auRNrBx7s1kYGPyDcPSmVcVCq3KGAxhHT506JDKCXzPd955R7fffnupTyVAHmBsEWDwo5Tmgpo265ENcBn2NsYPVU/sgbx05W2dPJbn6HHlpwBw06ZNWc8jyErKI6Dxg/8pDZPRH5Lee03acqd07T2qNLRWr5dGpaF4ssRRJFSvYevnkBRLaHD8rBbUbFUohWjw4sWLVjCY9ZOfGcM0jZ6LfSCZb9i3eWWkELz4l7+bbBbe1ytduUba7j44wFoEz2P/RZycF9fJA0888YR++7d/O69j5J2pQVSFlNFyVFAECJAN4TRGdDk2o8sVTU1NVmq3UaRQmiatOmQwtYI33n85a2AAhZUvqXQ+gvPJJ5UtQHmADZ/yZ/kokHIJbOHAc6OkgshjFDh7ZlCOwY8mhIB9NhvpIvCTSjkZwAVQwZOdAQhGkerf1y/98O8r8vLdOG+R/ubmB/XE7Z/QhpYWq0k4vTV4MKK/cfydGWMHUsuDZnaMIYJjftRqrmSQiZh33zjm48rN0i2flLZ+xFNAA2zcuNEiG9u3b9err75a0Ga86UBGNiKmYoPPDLLBA1QuZu7Fs3F/NmsSmbUExdNihPK+9vUrIY12Z70mOF7KTZxESSwc07Pxfs7FTI1i76uIEyhFRenmbEBg4mzMzdjDLvADfHdswGwBi6BfXx5479VkQINxZkoQ7n4u2XetwhAORdVes1mLa+/R/OptiiWMjZycQ2OJHg3Fpvd0uHDhgl566SUr04dABg9sWoKKczGgYUCmX95+gtZ26dNfln72l6Tb753K2HAB9i/OAQE0P5fCj0emJmvZXXfdVdqgBiloNFoibSRAgErDotpWtVc3TlS/TNbCrA1X6cqGzCVeKglOgxvDCDV3StS1pHw6UdPk+XPKAVlLbQWoCJCOikGUK+lwVXYtDQhMZCt/QkMzJ+EwKgw/SAfH4DyykXrq9KL6CpADhgaTgQwnXBDOcg/cd4/RNHw6+samShlhUL/wwgtWRhKBDOw6nFP5NrafDYBsERQtRSDBgHlP5hjOQkp/vf7660U/BwIL9913X9E/l88MghoBKhXt1ctnPNeW4rlKBg4R00+M9TKjcKqaUjl2rhBSItpY0vU1V1A6hAz4AJUPSiuXIhMI0SGq9UxZ3YwzAi9Ojg0nMMGIfEB5zeeee25GpnkqUAKzFJmiswLDlDhz+kkI6vojfCsFGJMJUjZSID7xPGOUYBjl0igrxTiDY/iZZVTJwKZHtFhKbNy4UXv27LHuDQFU1pxighgC5bjyLWsX9ktJRdpIgACVhkgook8suUlrmharvbpJKxoW6tNLb1V9dPZu2hhHzmbD586ds/oHxBvmS8tvmP6Gzg1SS+UpdTOmwAeoOKxbt86qt+kFEG1Kx6A8gADkApy7OH1JT0/VY4PjE9BIFXDgM/3oM4OS65prrsn6OgI/BIACZAEE0ulEaW6ROhZNV7jQWHv9hoq/nFc3zZtGpeixsTxUY/VooaQRYxuy4Vdt5tkGHHXOPbPYIOOQjBGyLllvvK6F+YDSeoyVHTsmeoMUEXwmPZUI2AYIUGlorurU4rrNqou0qDbcrIU1azW/enY1P8X2sTtlsZnsak9sIOavpUbt2CpFbPwqHFV/0ybLuVtp4DsHgf/ZAYJTlMLx2reKvQkOkI+dTyY6QkPES84ABXMG3sGcSgV67Hnp/Zcu4wjHKkrtTEBhn+01ASznQ5I7ONGxYipDA+CfqG9OPioYVeGmlO7kYx9etNZ9OAbrOxw28MkopZ8g38BkvgiHw1YJeTJpCC7AL/LqJegRxBD8yAT3JZfz/vvv1+c+97mMNc/PDp3Xvp4DGomPan5Nu65t3aiaSG5OpgAB/ERdpEb3dGyZUxeVDQZHKU4smjRhyFFmgw1oZKRN67Z8Wnt3vqrG9kWKhzssY2a1i+Ni2GHgsRbwQMFFXexiA+ULtUr9qJEaoDxABB+lMoEKxpUbUBbRTT+MTMAIo/wOwQKa9RoFA8+Tsk4zs0z1cHFEQkxyTa9lHrkhzhggQWZSFpw5Lf3u70iUMmuol/7xL0n3PpD8G7bLb/2O9Du/JV2YIIkf/aR0z8TfKxifWbpOp4f6tK/3gvX74tpGrf6wWy8ceEGf+cxnAuVdFlCSgUaGqJk6OjpUKkAKUXSxJhBkINBbjJIsjz32mBX0ylb3uxBgreezf/KTn+iXfumXUr5mND6u586/o2ODF1QdrtJN89ZobdPiop9rgACp0FLVaT1mK5wCItZLe1ADBTx2GM5XbKamum0a7z2poaFhhRs7dXTP+676UsBXTBlSAgp8Js7eYpcuYf3dvXv3nC6ZMhtBqU3Kb950002u34PdnS/HYBwR2EAh/cYbb1iBA+YU/jREDJl4LKVCcUAS3MgVZKK76ZkGt8fhGSBDMGPfi9LbT+EMkTpXSnd+QaqfCNheeY10+az0zovJ3+uapHu/IoUrOyM6EqpVe/VmdY3utQrcgv5zLXrtpd3WeMF+C5AZ+NjIwC5lP8z6+nprDSSwgd/O61qYz35KUONv/uZv8j6WL2yIxoU4RXfu3JmyWfDl0W69cmnnZBXN00PnNBQb1h0Lbg6idmWO8Xiv4hpRJNSoSCgw4GYLMIAgB2Rt4CjevHmz9TyBDRSpb+/Zo8Ubt1slL0y9O5woOHAzgdqIdpJDwAQlSzqVSaHw3v59uv3a9QoF1admFVasWOFLoMILGMuMYfa4m2++eVqAAQUzpCITWltb83I8Wum9LkozQDhIoQ+QBihJf+s3pLNnkinf9En4/f8qLVgobbku+ZrlV0h/8n+l8+foEp3M3pgFqI1E9etrb9aF0UHFEwmNXuhS96ouizwHpQSyg8wI9jCMfbcOgEJhw4YNVtAe0lGsGvOPPPKIHn74YZUKDz30kH784x+nDWo8dnaXjg6ctzjGYGzU+p0yoisagqy1skZiTEpQ2jEihVqlFE1FA1RmVjQOWQIP2E7wDdSoRuCEMrxrpEHX3XCb9fvK1eusYC1B2kzCDNT0POz2EO9jTSymAhjxV+e8FnXOb5FiY1IkIBqzAYzRUijJCfSRqcHYdjoR4emZzgnOnmsWuoHb7xxkJmXBh3ukN34y9fvZo9LT35A++gvmQktbH5A23y6NDEmNrRUf0DCoiyxUTe0diiWGFQnV6LXTb1pjuVRNpysN+NHxl/3gBz/Qxz/+8ZIGNm6//XbLL1isAMubb75pBYf9CH75wogwQh544AGLdKQKapwcxInAopk0RGhTSaBjMDakhmjmpkQBSgOMxoEYmTVGbRNSY2S9aiKB+m02ACMG1SXKECeJ4HfIhb1hGEp1SttgeKXqHZDq+Mbgyjc11k12CMSJz4FIhQa7tfDkawoNv518weIN0oZ7A8I8C4ADtpgpkSgISAmn9EsqlTJknSCLCf753c/DzCU3qalkXgUGZJYsjdOnpj9HgOrN16eCGua5ztm3zzGOFtYkx/D7w2e0ZMmSvOuXzhUYpwH7YjmgmPeNTLNnn31Wf/iHf6hSgYDKb/3Wb1nn4lyHydI4MjDdxqA32sG+00FQo5wR75XG32SHTP4eapaiWxUoUWZHUIMgMA5ZMmsRo9iBPWUvk4lQhP5jBCgQrCC2ygT7Z1GXnc8oZPkqAtkEYrA9+eSBD1/THSsi0qmQRKnidQ9LTbOnD+NcRrGDGvBWsjGwLXL5bD960cD1OYdMcwghRWAvZsGJA8nAhbknlJo6d0yif12VreReTX3yMctA8/BwqHFyTSfYHMAdyELkgYC41AiHw9YeXSwQOyCG4IdIK+wn6UDNlQqmCbPb5wOUHmOJC7aABkioP/au4glv9SYDlC9warFwpXK44jhwGliQEBZdFOHlYCTSjPDll1/Wrl27rBIVRJW3bd2qreHjWrPQZpyd3i+d2FOQcwgwe/ukECyjhBnZR+nKrvA8jrZMyNQA0C/ywmekK/0YYAI1KRwlXNPauZeBSIA6qI3sHTjP5lrJkaefftrK0HTTRLRQIDMGlfczzzzjnl8E9KK8Mb53KqABEr1SzJttGaB87TICwdhOZKqmsk2cJUR5P04wsiBM03E3IKhBL4RCAK7z6quvWo4mBCMWx7iyVXdeEZ36vuOj0ns/Tl1DP0CADKBxMqInSk+l4zXZRE0oqvMt8UwgMRuvZ15eeeWVeX3OrIeVseW4j9zXWZKN4RZkHJSiVOlsgF/+gkqCn5ngvnlBiLLQOR3ltxPLGxZPm+b8vLBmnuoimdUYAUqH8QRGpXOTTSiWGCzRGQUoVGDAS/kRHGGkyOLshUi4UYlwfF6L0cTn5QsU6dT6e+utt6zMMGoAck6WUTg2LA33TmaFJRGSuksf/S4WEomYxuM9iiX6fVHxlBOK+X0oLUUN/XzVAyOjw0rouBJ6Rwl9oIS8Z5qgXMyUocK5BoQjC1CG7rhzytuJo4WAxn2V3zPDC1CckmEUNOzzDuYYe99sW1fdEI5Sjhc+m3P40Y9+NONvVeGI1jZO5xhkg69vzlwqM0AJYc2fFI7rRF8pziZAmYhNOAbBA/wI7FNu+AL2GeV7ULyn8j/k4lTisyk1yLEpOYqKfpIn9Z1zZH0npPFhaWTujN3x+KBG492KUz4uQE7AnqfXHuLCTMBWO3fuXNq/W70wTx3ROz37tad7r84Op39tJn5BcCWbyKtY5S4rFhtuTvIL+1q4cbsUmVvXDaFpuWQ1VxrKJVujWGDP3rt3rxVDKKugBmoJ6mF973vfm/G3lqpm3bbgRs2vblNjtEFXNCzTTfOuD0h1GSMi1IgziXs4FASiZgswlDDeKS3lddFFUUV64YEDB7ISCVQkEAVS0Pm8XIFRBdGgtwefz3ozI8skWj2zzBT2xSxM9UwFAhl94y9pIPaG+sdf1WBstxKkwM4SoO7zouLLFfTPwIB3k/3Aa9IpqS5euqjGRkjGIQqsSjoq6Q0l7ApVFzBNA9OBoCF7cIAs+Nq/lL78D6Qbtkl33yv99z+WOuZW2QjKpQUBsNxgSjNihM8FsOd+//vf18c+9rFSn4pVZ5h6w5yTE/d0bNaW1pVqq2pUZ22rPr54q5bUtZfkPAO4gOX0cXIJnEFzKwtqtiBVkJd5evDgwZwUu2Rm4eyFo1D+M1vJUTLOsdfgGfnYh5SxJTODcjzwi5Sldqvqp8rLTHu+dk7c556xA7ow+pIujb6h8yMvaCRWmCyZUsKtaAG7P9cMaTIf3JR4ycR5OM839r6h820XdHzwhE4NndZbXW/r6MAx+Qk4vlc/wZzEvMXSx35RunKztGytdPNHpW1zSzTFmk3pQXvvyQDugW8LkaIfAuBKwN///d9be61fpe18DR9+/vOf17e+9S398i//8oy/za9p146FN/v5cQEKiOpwh6riZzSWuDT5XH3kqqBZ+CwBgQHUGblG01FU0SCZR1dXl1ULlxTaVLVweW1HR4f1c669BVBivfHGG1b6d8byH6R5rr1DOvBMMriBcQoJWXGD5gIGx/dMywQYT1zUSPyoaiOzI20YAuFFJZ1NfZQKBN5QUHmZG85zgoSg6K6pTej6653BhuGJAId7JTHEhpTeVIGLy5cvWyUeArgASrOf+tKcvlRzKcugEKAMI2UjqLM+2+fdc889Z5FTGgeWGpwDtsTzzz+vu+++e9rfouGIbl+wXrcHfcErB9FN0vhbuAYnnqiWIqtLfFIBcoUzUwNOQKZrrs4tylchhGK/IssbboFAKlVGCEEIHnwWtpuztJUbcL7Yl9u3b8/8ws6N0vn3kpkZVv38uLTsRik6+4Maw/HzGowdn/w9oZi6xvZoYXiHVU9/NoCxQ0lZN2MoF36BHY/jN11ZNjdgTtCvhjKi1ctqNF4Xt7ITDQ72HdIVDdP72GRDthJXZCwFcIH5S6Q7vzBnL5XbHpAB0gM/F82zaR4+2/Gd73xHX/ziF307nq9FuD/96U/rtddesxymASoboVBYTdFr1RjdrPrIWjVHb1BdpHiNYwIUFoYA+AECG6SMY6ihzMLJ6ifYIF9//XXddttt7uqZL98iXf9pacX10upbpZu/LNV6JzmVWHYqrpnl4WKJHs1VoHbwkh0E4WBuMJ7dllNgfJq5RDADpR8k/IYbbtCWLama//G7NzKEsn737t3W+dkDfZRgI3iCsjFAADcgsByQjvxAU1vm3WzHt7/9bX32s58tC9Ud58C5cE4BZgHC86SqW6XIeimyUaraLgWZ4LMGZGj4sW5gP61evdpyAh86dMiyrdIF5glo5NLziOPixHYlZCF4cc3npBU3S53XSFc/JC2bG6KpsTj253R7lsDGbCpLjXCIYIEbkI109OhRT/YUPJn+VJSVcvsZZh4x7hmr9JJkPqBwrmuZWYUgloh5Fq9QxorAnnkf//Ld4DOT5Z0DBHAh+qGpfID8OBrzu1C9osoFxArw7RE78Au+htZRY99xxx3627/9W/3ar/2an4cOUAKwif3/2TsPMLmq8o2/07a3ZNN775WQSiihiXQREKQoKoICgkixg6jYQMQKCAr8rYCKFEFBaYGQkB4I6b237XV25v6f98zezexsyczu9Hl/PMNm7s7euXPvnXPO971fyXYEIuxFesEBk9EX0WoWy3uFUVWEwkZH+2VZH0bBRJKSzvqMM2bMiMxAKh0aeGQUzuYhPbg8hyMtS8aFW8O5sLDQRFOFpomzBA8jrbkfljlgSTW+jinh06ZN69IxsUwCRRT2eLFrz1qgmOYJETFoNET2veO9f+qpp2LNmjXGcOdzfh5GQkbSE0dkNrzH2fSZZTbaLa3RFepqAzWDs7KQKfC719Wsw1SBJV+YGs6eGsnCZZddZkph/epXvzKR3CLFceQDLjUUFQhrLcfgDQZz7Nq1y8xjoTCoJNJxgWs2OuEiikR3ZwMDp2fcZXM5sjsoS50+a1CWQeH6KJzSUJwjKYQxcIqVCmxob3z44YdmjcC1OoOSaK+YzIoI7086Nuko5rqfDkC+H0URm97ZvXC48agT2QEHemb1jFiE4H75PWCJZ37XqqqqzHHPmTNHgoaIyMaIajY4M+HYu8fB0uKZI6zx+8hsjXQuK/3UU09hwYIFRjSNFlHPF2QJqocffliihhBJDBda8Y7W5UTHUlVcbHEBGI4zlgIIHUh0OovO4XnNdY1DnY8RxJz8LTjgQbYzvTKsOAEy2incSCcaJ4w4svsIsO4yMx7GjRuH9ZU78cf1/zaG8pD8vrhg2qlhL+Bp+NAgpjjCv+eD2RnBOOCGBYokq2gCNV+XcXAg8rI1/B5MnTrVGFL87rZX6k2IzmAkHu9XRrN2W9SoqgAe+SmweX3g+UlnAJdeE2jAngGke+TiK6+8YqKX6dRIFuh4ZCYdj+2cc85J9OEIIZrXJlxXxaqBeDBc+3S0TzpiuS7iHBdu36hVq1a1WbeJ9slzDUKtbzearOoWG6PAPbJZ7Egf27i9e7kjBy4D9CorK1ttZ018Zhc53BbWH16GF1YsgtuRhYkDj8ewYeHdl/zuMDub78EeBQy4Ymm00GbdI/KHo6apFjvrdrb0sJ1eMhVdFXRYEYGfRza36ArswUbhrjv9Zlqo2wZUrgiUqHTmACVzAU9m9EpLd/uCMOv6+uuvRzSJuvXJNBKm5WdCar4QqUwsRA1mYnQkVjCNltFVVKDpZA4HiiB2Pw5xbLKc/ZHvmoVs5wjkOMegwD037TI1WFf5WM3pQxcH9gKBi3VmEo0aNQqbq/fgzUOr4S7NQ9HgXijv6cO7R9aFtU/eyxQxGDHF8ms0pDsqu+ZAMYATmx8L4MBAdAcuGCVoiK6wdOlSY7jynu02j/8K2Lrx6PM3XwFeTZ6o/liT7iW8fve735lat902TKMIj+XKK6/E73//+0QfihCiGYrkoY1NOxI6ugvXfsyubQ9GmHN9yPcN970zIesuWjgcLvTKmo0i93gUuIajh2c6Ct1HMxQyDQYY2dkYNux1wVJOHo8bG6reQ4O7GgOG90afYcU45NmMel9NWPtm9gczk5j5TbGO2RqsrhAKbZspJZPwkb5n4Iy+p2Fe6Vxku7onMknQEF2Bfl/eOxyHu71uZPZRZVDPLX89UPY24I+8j00qQv9COtv5a9asMUF20Sw9RaJurdBYvvTSS/HII49Ee9dCdIjf8mNn7XasrXwfW2s2w2cFl+AR7UWBcEEWbbjoYlRJR2m0nPC4CAu3lBQNk0xQrKOJ21lsGoNnu4bCyZTNNIORSpEayxQzWIuZGR7sl8H7b2NVW2FkY/WusFJnmelBI8MWOBiZ1RlMCXcgm8XAkBF4G4Gm6I8vonswu4lZcsHlC7oEvyPr36dnv/X2D1cjE0j3Zut79+7F888/j2uvvRbJxuc+9zlzbCwhKES8aPJXo6ZpA2qa1sHrL9OJDxE16IQJXaeFbuv2NWjuL9CeTcAx2W7cTKdauO+d7uJ0LISNfPdgFHpGIccVWAOnG8wGDOf+4f1mR6Uzc5tO3f79+5ts8lpfFer9FDCOrhUs+FHu3R/2vc7j4L1O25mPzsQ3t9ONLGdWZtjLLElUVxP4KZIGBrQyCPW0007r/s4a2dcm5F62aFe2zopKV+hTCDfbMBV55JFHjFbAUvjRJOrlp8h1111nUsN/+MMfmkFZiFjCxezqihU42HDAuA4tWNhfvw8ze86By5H4BpfJCBc+sVjMM+2bC73QbA1uZyQXF3yMaA9X1GDUFZsh8+86peoAcHAr4HQB/cYCOYXd+RgiyYnk3qVxTScu78ng6JH21/7hGQTsGUMDhvcxjQ+WuGI954yHYsbbfwO2rQUavYAnBxg8Bph7LpCvEnKJhuOoPfZWeGtwoL4Cee5sDMiJsAYzX5udE+in0bLNCeTlZ4zBEU7N7VTl8ccfN7Vuk9Gooih38sknm2P86le/mujDERnSILmy6b2W5/X+nShwT0G2U1nEhHY+1/jBUFhg8Ec0o74ZNEUBpb2yVuxhwLGBv2NvgHCdJVwfMsCr014HdJ7Wbwd8dYCnGMgakFH13TMNOmYpmh9r/mNwFddTvO/sMlE29EV0B9oYvN9p6/A4WN2A9nDGs+UD4IUngYa6wJqzZ19g1qnApNkZf2oSDe9TZoMTy/LDD4r/TXCiGI5IK0Y46Z5uJ3jImRlZdczOYvnXdKS2thb/93//h5deeinq+3bHqu4t00OfeeYZXH311bF4C5Hh1Pvq0OCvR54rH7W+WiNoEAoapKqpEgfq96N/7oAEH2lyEtxjIJqwoTeb+IVmgTDChE4gpo5zkRauQygs8ePQFmDVc0fnv21LgJmXAfmZUXsxE6HBSsOVpXTCgYZwKGMLB2NHbWDcsBlXODgs5y5fw4wPRk9NnDjRpFIyVTzjWfoysONDoL4xEM3vqwU2rgT27wAu/TLgSYPay34fsGMFUHUIyC0Chs0INA5NkUyNAwcOoL7EhVf2r2yZL4fn98VZ/WbAGYmj5rxLgaceP9pDgwbmGecjE6AwH9wYNJ3g/Pzb3/4WP/nJT5CsfP7zn8edd96JO+64I6nKY4n0wLJ88INBCk44UYA63+Y2Dpbapo3IzpKo0V6mBp29dAqz1Gw04ZhLoYQlfoLXaRyz6FDmNgab2EEs4R47y+Z2KGpQ0Ch/C/AebukjgdwRQCF7pYl0hFnYzOwO10amnRpqq+a6CpDrKkSdr7pl7OBo0sMTXi9ANgjmPnkfUzRkOaqMn+vKDwP//B3Q5A2cUn43D+0F/vVHDjrA1HlIC2oPAoc3BD5fyXCgKDXELPs7YFlNaPAvgwU7q8KJLOd0uBwR+GRyhgA1GwF/3dG5N3sA4Er/gFUK8+EG/qYiTz/9tClFH4t+fTERNbiwoNHx0EMPSdQQUWdbzSZsr90UuNfgxMDcoe2+zstUNdEmst0uPRULFZgD8dChQzuMbmV5OkagRJV1/ws4UG1Y9mbzO8CUc6P7PiJpoNG6cOFC09SOdCXlelh+P5zaZzpWlW9Gk+XD8Pz+mNFjdET3ut3vhVlIykpkwesNQJOv9feRVB4Bdm0Ehk9CSsPPteKfwKGtgUhNfsz9G4E5nwRcyR9BxDJsfQb0xYsHFrcIGmRrzX6sq9yJCcXtlw5sl1POAop7AqveA7KygRNPBwZ3s/l4isDmoDU1NWkZSfXqq68aB+X55yevQMVju+GGG8yxnnnmmYk+HJFG+K1a1ProkAn0iHChB/xW2zreFmRf2HAsDF7/MGiKjZJjAfsV8BEKM2VpY0yaNCmi9aBdQqhDGnY1Cxqkec6s2xIQNtzKPk1HeP8wcIrlarnG70rDe4fDiTEFx2Nn7TrU+MrhceZgcO44ZLvCr15i9z5j1DYznzKe3VsAX1O7AfxY+np6iBrV+4CNQb3pDq8Dhi4Aeo5CKsDvSZO1LUjQIH40+tcgx3lS+N8jZxZQeipQsz7QT8NdAuSNyogMueA+oOnIww8/bDSCWHzGmIga5FOf+hS++c1v4t13342JGiMyB7/lw976LahtqoTPAvY3sNbe0RqVu2q3mggIJrsFU+KJQjPUNCF48GDtz476XsQKptEyqj7S+nlh9dRoCK19agENKgWUzjBiiVGAK1asMP9mtF1opkQ4de9HFgwwj+7A91Fd5mayOkkxjkHT0LhTsTcgaBD7/qo+FBA2BkxAssNSIFt2b4cvq/Vc6YQDR7xdGDOnzwo8MgyKmCxbko7cf//9poRsMjfPpRPy+uuvx09/+lOJGqLb+KwyeP07AcuHJtTAQsPR36EMDkdhiCPNAbdD9kWwTTFq1FGnG0tyxnN8pDBBG4NZs5HSaZYG8dUfzdAIho42SNRIV2hfvP3220Ys44PBeqH267FsDLczC8MLpnT7WHbu3Gmqn2Q8LHnaERQ70oF9y5v/EXRv7XkvZUQNjqdNvqp2OjYzCIA2YATzgjMbKOz+9yfV4PyZrn37Fi1aZLIpY1XFKWZ524ykYJPB++67L1ZvITIAfrE3Vi/HvvotqGw6hDLvobYvctA5OQouh7ulluWEokkoYu1T0aY2H7M0cnIirG/YDRjtwhTarjQEWrduXYeZHy0U9QnpheAAisJL8RWpnSJOY5Z1Znk/06jlYoCwvvPatWu7XYOW4w/T0PnoaJGxcuXKmEUlphzTTj1ajqgFB5CVCwxIg3I93obIticZNIzrymqMiBGMHxZKPJnRD6O7UMCk4RaXOdTvBbxlgVrucWD16tV46623cOONNyLZYabGG2+8YY5ZiK7i8x9BvW8pfNZ++HAIFoLKXTTjggMex9GmyC5HEQrckTvQ05W6urqWTA1meTGTLV5wXUY7oSulrmgL8dFp4JSHdkvo2s+ZEWVQMhneEwzI3bJlC6ZOnWrKP5WVlbWyC9oraxsp3CdtFWbRtge/T3xNtBvqpiTDxgN9B7Xf+nDC8UgLmurbjje+1MkKZHZRfV17F4hBMulbUimasD/UuHHjYv9GlgV4K4GGIyagIx5QE2CWRnvZltEgpqEUt9xyi3H2cPAPjuIQIlxqfZWobjrS8pxtwNujT3ZfDMkbhnpfA7JdWS0ChwiUyWHGQyKg84eLta44fbmI5LH36UPRohMmngUs/xtQ37woLBkIjEyDNFRxTI4//ni89957xvigcWo31qNR3ZWovdB7d8OGDSYDhOKJ/W8bvs+SJUuMo5j1bwWAIROAj3waeP8dYNfmgLO/pC+w4BIgLw1K9VBAZZkpExVmz0UOoEfqRNFluz04uc9kvHbgqDN4YG4pxhcNTuhxpQoffPBBfPrnNO4HKhc3R7exSPdYIH9CzA0OZllTME52uC7gsTKz5Iknnkj04YgUpdG/7ZivcTlzUeCaAr/VYMr2OZGd1uUhIiU44IN9xo65Zo8i9C+wHGmk/Qa4fmMk/rx5x7AVsvoA+eOBmg+bNziBopmAq/sObZHcMNuIa3sKC9OmTTP3Nu0AfvdZ7SDcvi2dZWDY/fnYJ4b3cHAAIvuf8f1U7aTlggCf+BKw+D/A+lVAxRHA7QmUnZr3EaQF7J9RZ5e7Iw6gMHV6w9L2bqzLQm5+LSzUNG91IMsZWVnATIV+DPrMYuX0b4Eixv63gLo9gefufKDfAsATu+xDjnEvvvii+RkrYur5ZSOQSy65xKSI//rXv47lW4k0Lj0VjNsBsLqtvzkjgwbGwJyhyHMHFgJ57vDrVWYKjKAKbuIXT2hwcMHWFfbu3Xtsg4PklQDzPgVUHwacLiC/NCPqLoqAYEdDwK6LHK3mvTTSaUzY9Zlp3IT2pXnnnXcwZcoUU9JHBNF/ZOCRjmTnA9MvBFY9D3jrAacbmHhmc7ZYasB7d0LRYPTNLsb+hgrkubIwJK83nGz0LY7ZIJwBAiylGFP8ja0FDVK3HvD0BLJik4VIB8tTTz2FNWvWIFX4yle+gsmTJ+P73/9+t7PyRKbS2sZoW2jIjWxnYD5zOrrnxExXGJ3LsZH98ji/xKuhMSPY6UTrinPZ7r8R1t9S1MgZGig55coPlEURGQHtV86JDKCi2B8twZ8VDGiz2H35WO4x2Ol78OBBE6R1wgknyBkcjOnfdl7gkY70mwE0VgNlmwPPC/oBQwK9I1MBZhRRjOvTZzb8zHy0fHA6SuB0yDcXDgzS5FgTc8o/OCpokKZa4MBCYODZMXtLagGXXnqp0QZiRczD2W+77TbMnTsXd911V8vgLUS45LmL4HZkoam56Tcn/VwX0Dt7pBE0CtxFKGUkjTgmXWl21l0nCSO2uvKeFDT69YvAeUPnYpHGl0w1Opi+zRTxaLFr1y4z8fLetcsbBGcbMT2UzyVoZCClQ4BTvgB4awFPbkBITRGCexSVZheZhwgPjgN0bpx00kmxP2U+9jcJza5kREdZzESNBx54AOecc05KldLjsZ599tnm2JmxIUSkuJ290egvb3nO0dGFnnA6e8ABJ9yOfnA64leuNRUZNmyYyWCjqMHMVa79hw8fHtP3ZJAJ12ldKTtlO40jGuvY4DmCJs8iPaDoxaAp3m/RFOvo+KUgb5dvY4S2XbaN6zTaGCeeeKIEjUyD9sSwU4HB8wHLD7iyUypIk+XaeF87HC640Lf9UmGiw+oknD+7mwEWFvWhpfwtoLE8cM/FIMCNIi4zqtlnO5bEPJyCjqbTTz8dP/rRj2L9ViINYRmp0QUzkO0MLCbdDg9GFUzH0PxRGJY/Gr2y+2rSD4PgxRij2+3eA7FuHsh+HnQIR9r0aNu2bTE3ikR6wHTtmpqaqDbWYtQvDWYaM6tWrTICh904104PlUifwXA8zS5IKUGD8HsS87TmNIXN7ZiZFZfAgHYjgS3AGRvnKoMIHnroIXz9619HqvGNb3zDHPu+ffsSfSgiBXE7hsLjGNbSl82F3shxTUW2cwSynMMkaIQBnTC2TcGs1njYF8yk5ZqMpSyYJRIJXL8VFqonhggP1renyBBNGLTHeZdOYApswSXaaXNMnz5dvo1MxpUFuHNSStCwBTlmIInIz9vu3btNgEBccNOn6mhrd8QoY/+HP/whzjjjDGNDxZK45Ijec889xujYsyco1UWICLI1JhWfiONKzsDUklNRosyMLmE7Y2hw0PCINXT6jhgxwogTdAhRhQ7H2KHTmMen+osiXBhxRyM3WvDeY51mGhychG0DmMLJ4sWLcdxxx+niiJQj2tGGmQK/9xToWWYlLrDESa7t5Gg2PFzFQM6QmLzdD37wA5x11lmYMWMGUg0e85lnnmk+gxBdmeuzXKOR5zrNPHLc0+BQT76I+4/ZjiwGgjBwKtbQRqCzmZkaFOvXr19vnMPhwECruDRiFWkB532WOotm4BTLWNGRyWxv9uew12V2WSqJbkJkDvRfdLVUe5comQQ4Pc32RbONURqbslcUax5++GGjBcSauFi3bLDEFPF7770XmYJl+VHpXYcD9f81jyrverNNdB2Han53GbvvQOA8OuJa6opRXKxdy+h39tjgoq0zli1b1u0mzyKzoIFAgza470V3oWHBhsDBTuAVK1aYbYpEEama1UTnvIgMivIUOeNK3iSgcDaQOxrInwKUnAQ4ou8sZKmY3/72t/jOd76DVIXH/sgjj5jPkimUN5bjzYNv4uV9L+Otg2+hwluR6ENKabhelY3RdYGBwUjBpTvjCd/PjnRnRH1nwVN0TlN0sTNvhUhUtgZtYjYit6murjaZR7J/hcgc6Js7fPiwKd8YNzwFgf4ZPaYCJROB/mcCBUNj8lb0/bO0bTRLhHdE7MO1g4wONj+5/fbbMXRobE5cMlHdtBl1vh0tz2t920191gJP6tQrFukVyc7FEpXgaDp+O4IGTrDRQIORz7kwPHTokFkchjqMCbfTQGFzcyEigcIZe2vwZyygoEHxhH1ihEhV5xOjA+MBhW2O9SwlGBzhSIcTjXlGJ6YCdILR6Ah2PsQFBgVkDwg8Ysj3vvc9XHDBBS31vVMRZtOdf/75pmE4s8LTnXpfPd4rew9NVsB5W+2rxpIjS3BSr5OQzRrcQsQRigTB80o8bIz2joHrs9LSUhP1yp+hTZ05jq9evTo+fZFEWsH5n9lAsepNyWCT5cuXq4+GSGn43YhXRjj9TKymUFFR0eo7yblo5syZcalIEg2WLFkSn+bg7ZWgKoltdsj27dvx2GOPmWDleBC3K07l+WMf+5gRN373u98h3an37W1n2z6JGiIhsI46Hb7BPTViOeDTeOgomp2Lw5KSEiNgsH6g3RyNhggnJjq8hIgU3lPRLEEVDI0NllNjEy8hUpVQsTla2IY+jQmKGHYZEDqVWBs6dK6hc4AN4/g3dEjn5uYiGWEDz3R2gjFzks37Vq5ciVTn7rvvNvcaA6dGjhyJdOZI45EWQcOGz7m9f27/hB2XyFwY7MHmxwxa5LgS98y2ZuhMY/AUe+zQ4cUSuPbc9/bbb2PevHkqbSu6BG3aWIgaFDQYNMXG4CoPKlIZfj+ifQ/bQVH83lHA2Lx5sxnP+X1kefPQUoJct9O+4O+Z/RDcrybZoH3Bz5CTE5t+eYmGPv+Pf/zjccs+i6uMxYgwRtHefPPNcUlDSSQOlgkILb8Yg9IBQoSL3cCPQgIdT7Ec6JlpQQOns2PhIPfBBx+YLBIq+5ysqK4L0RXY/DHaxgYXU0uXLjVixoABsY2YFiLWMFuPC+howUaXNDAolATKtziMU4uOpM6+i8zSI5yPGKWUjJkbFObZP2f+/PnxdYL5KwGrjPnhgLNvTNeNd955J66++uq0qC/P2vpXXXWV+UzPPPMM0hlnB6VYO9ouRKzhvMK1kp3Jmuj+TWzEzCw72jq0eeg0PuGEE1Q6VHTZFqCNEe17mvcoy1tynSFBQ6QyHPPD6ZsayRp8zZo1qK+vNzYG988eNPQjd1YCmkFS/D4RliSlmD1r1qykKzlI8Z/HGlffguUDGvcAfi/gKQXcxTF7KwZL/eUvfzHjW7yIq6hBQ/fGG2/ErbfeildffTWtoyXyXUNR2bS2zTYhEgUHdEbSsscFf8YqjZZwcRZOCjodEczY4ASV7tGVIrZw4uTCJVowEoROTTr8QssYCJFqcLwvLy/vdsQM90FxhMYLHVh0FHV1HqG4zUbPNDo6EjVYwiqepZ94nthIlp9zzpw58U1h9+0GmlYffe4oBDxzgBg0Ln7jjTfwyiuvmGuZLnz3u981QRJvvvlm2mbXkF5ZvZDnykOdrw4WLDjgQK4rF6VZpYk+NJGhBDtkKWzv2LHDiAmxLnnVWVNyNnim8G6XxlIvNNFVuN6JdllkCm7sMRn3wAkhYgB9OfTpdHf9zUxurr85p9Besat5dAWWM9+zZ4/pV8P5IBQKJvxus99gvGBm1qpVq4yYYWcSxgWrCSh/E/AF9V8rmAHkRD+gjNeRvv6bbroprp8x7mEU3/jGN0y6zQsvvIB0Jtc9CEWeyfA4epoH/53rVumSdMRvNaHJX9+qbniypwUyMjbWzoxwarfT8GGEF1PF25twhAgXZh6xfmM07ltmEL333ntGJJGgIdIBfje6mw1BA4CRUyzzM3fuXCNEd9cYp6OJjqnQ+ZPPmcVBYTGeMG2d9dj5+eKaEs4IqqaQiCarCvBti/pbMeCABsfXvvY1U1YvXWB0Nj8TP1si6vrHC7fTjTk952Bg7kCUeErMTz7ndpFecBxs8NWjyR+9CNhYw8ApO/s6VnD/4X7H6URK1hKHIjWws1GjwZEjR7Bw4UJz/86ePVuChkiLeYpZR9314zC6n/tgQBHt7+4IGjYU1+ljCoVlcp9++mmTERIv+N2nD5z2RVwFDVK3qbWgQapXBGyPKPP888+bQNOvf/3riCfuRNT2v+eee3DbbbfhrLPOSrp0oGiS6+pvHiJ9qfBuRbk3IA64kI0+OdOR5SxCMhKcDs6Jgv+OZbZGZxFUNhRXmLXFdEJFqojuQKP18OHDYb2WNTd57zPyij9ra2vNgmz37t3mObMz4lUDUohYQpHunXfeMYYCx9lwaGhoMGuz4OhbChqLFi0yUYXRzF7guE+jgxG1dho2v48UNFgPt7uRX5EaVDQ07BIq8aWRs3TINgdg1UX9nf7whz+YsfLLX/4y0g1+pocffhh//OMfTTmqdIUNwScXp25zd3Fs6n21WFOxDLW+GvN8QM4QjCoYnxJrZY6jDJyizR8LuE4Lx3/Ac8UmpaeeempMjkNkDuGWh+K9SUGPpXL4N1zPMHiDvfm4HmP/P/Z2UbkpkQ6wBC3LjU+ePDns7wcrIVD8DoYZ0vRNRTvQhvvbunVrm2yQmpoak1UYr4BajgMMmExYZpavNmBTtOqN4Af8DYArellovLb08dPXH6v5vyMSEtZz7bXX4pe//KV5pKNRJTKDOt/BFkGD+NCA/fXLMSj3xEBPlSQjtMYtI9A5EcUqUpM1bJn6FzpxBcPj4cJPzcFFd6Gj1S4XFbpgYCTG2rVrzSKGYhsjsG1jg98LLqTsqCkh0okNGzYYgY4CQUfw3qfjx86WoAHOhWmw6M3vCxtZxqIcEzMj2NS1f//+5jj4nuxjQyElXtFMTAencZO4zAXOkzy3wRHZFuCIblo8o5aZzXD//fenZXNCits//OEPjVF14YUXorCwMNGHJESX+KBiBWqNIyLAnvodyHMXYGBucvUfSgQMQqET7Fj9gCjQKxpeRAMGXvDBdUooLCNF5y7XSbZdS1HPLvnM+3DMmDGdrsOESEWYBcEytMd6Ddf4tv1AG4PfiWBbnd8r+oxigZ3VR58XbSI7eyNefVxZ/oo9p2hDJSwowV0ENIRUlHGwd1907QD69hlw8LnPfQ7xJiGiBm9qfugLLrgAl156qTFehUg16n1H2qiefjTCa9UhK8qOiGgQmpURXI4qFjAahbXSWauQjmSq+ME1bTnJMBqXCz0houHM+uhHP2rqqfNeo4OSQgadeJxzaPzGs26mEMlSkocO++OPP77N/c+MJaYIU+xr7/fxgscwdepUs+hnSSvOE/zuxrr3kw2jpyisJFRcZ5Nnz3TAu+xoxoajF9BOLzY2LOU5Y4p+pHP43XffbebcT3ziE0hXLrvsMpOt8Z3vfAf33Xdfog9HiIjxWU2o9lW12V7eeDgpRQ06qOLZs4LrO/YkYMYZ7QuO3aHjN8uLcI7Tuk9Eg/POO8/YrLyvpkyZYmwLluPk/ce+XywpkwpZVEJEEwaOsKQSvxOhUMigEMiIfWYnJQLaEeybwXUzhUdmSvB7zP5rLIEe62xwBojRtmGft4RmZ+WMABoPAN79zRtcQOGsgO3RTn8U+lQi7YvFahe0MZ577rn49iNsxmElsBHAFVdcYW42dkcXItWo8G5BuXdTm+0Dc06CO8rKZzTgIB7cSJlRJOxDEKtFGKNs2ZeATipGp3DS4/ed78cHf8/a7IxoEeHR6G/E1pr1qGmqRK4rD8PzxyInimmD6QCnNN53XMTw/k5MKRkhkgcKyG+99ZZZxDOChpGDNDaYwUQxIdENVOl4ogA+duxYMx8wy4/HyWaB55xzTkzfm00EGfWbNOXmrAbAX2EiqLxWFiqbtsFnNSLbVYIi91A4HE5TJoul8+iss0t2hQPnYEYtMxtmwoQJSGcoVDEKj9l74ZZFECKZ1jFvHXoFVquSdA70zxmIMYXhlRGMJ4yApTjOHnn28W/atMnMObGC+z906JAR5Hft2mUcKnZ/Js55dKRxXJejOXzWV+3BqnLOOX6MLxyIqSXDdP7aycrgvcdMR9qwKiMlMh2Ov/xesG8fx2DaFizxxEzrZKjEQSc9j4/CAtfBPD6WiGNmdizXh5yLGGhJwTPRdpaBLv+mI4DlheUqRpPjEHz+w3A43PA4h8LpKGzxF9KHEqlQy2Ap2m4scZsIEipqUL1j9OwzzzyDM844I1GHIUSXoKNhb90iU3bKztgocA1CaXZyOgvaEzViaXDYMEWcw0ywGk5xgwtBGRvh47f8WFH+TnN9ZQ7bDngcHszoMR8eZxJMlkKIqMJxc/GRA9hdV4Ph+UU4rkevLu+LTh5GJfEno2rpDI93vdNwIppefPFFk4bOyEeKGnRYxdIRx1q7yVh2rslfj73178APNvELLNPzXf3RK3uymct5Xij+UAQKZx7ndWfqOx8sz5QJ3HnnnUYso1Epx5NINXbWbsWWmvVwGPsCcDqcOK5kHvLc3W+eGm1oTzDbzS6vw7GcgnGkkZ6RwhKi7777rhnXgnts0MYIp6+fOMr6yt34174VrU7J/F7jMLPnKJ0mIdIQn1WPel+gF2WOqzdcjqxuNcFmNgQFA/p2GNCabD4eZjhT/GaFIB4rBY1YlSil/cY+hPRzJ2PZuUbfRnitbUFbnMh1zTLCBm0M9hvh+TrttNPC2t9//vMfXHLJJUbMYoWARJCQ8lM2NFy/973v4YYbbjApfJ3V3hci2eDg3z93Diq9O+C3GpHlKkaBK3VKqdHIj8fCnwM6B8hgZGxETqW3DLW+6qAtFrxWIw43HkC/nMRHQgghorsg/u7a5Xhx346WbVcOGY2bRk/q8ngfawdTd2Ek08c+9jHz73//+9+mhCGzKGLRyI+RZSwjESz0JxPVTbvgb9VfA6jx7UWJf7TJ0mB2C7PQwjUan3jiCezcuRPf+ta3kCnwszIj5cknn8SnP/3pRB+OEBExOG84cly5KGs8BJfDjQG5Q0yGbjLCUjzB8wvHcjq3Yg3HQgr0HA+DRQ3ZGJGzojzYwRVgedlWiRpCpCGN/kocangPlgmcAZxeD3pnz4bb2bU5ho77ZHTeBzNp0iTz4FqYovv27dvN85hkWr71lsmET7bgMWJZ/hBBw2yF178T2a5AYDazWJjRHw6svEJf/ve///2ECRokgcW9AnzhC18wKfQ8EUKkGi5HNnpkjUZp9kQUugclnSrdGayXR0MkHlA8oQNJdJ3WZQiCtycs2S4l8Fu1aPRvMQ+/FSwKCZG8MEMjWNAgf9ixERuqypEJfOQjHzGCA9PGow0jtChqsHREss7ZgQyNtsdGA5SGI2vJ2xFnx4Jp97fffjt+/vOfIz8/+aK8YwVtiwcffNA0DWdWjhCpRu/sfqbc1MiCcUkraBCWqgitoc1t8YBZfezBZDeDFV3PBg9F9sWxnZdV3r043LARld7dxlkoRCpQ3ri2RdAgfnhR4V2PTIANyc8880wjhMfCN8US2MkqaARob5yyWu4HjmvMgmTQQDjce++9JmucPv1EknBRg4ugxx57zDTzYzNLIUT0CW22ajdNYiRsPGBDU9Yg5XuKrlHoLoEnJDXUCSd6eEp1SjvAZ1WizrcIXv9m86jzvQuf/4jOl0h69tTVtrt9Vx3Lz2UGnLOi3WyOAvvy5ctbZ2j4fIFas0lEroulxoKPyQG3I9c8GAlFQYPZN2x8eCxuuukmLFiwABdeeCEyDWb+nHLKKeYcCCFiR7CNwbJ+dBzFS9RgRlZoRriIjHFFrQVyXs2xheH3bMpEu/pA/RocaFiDcu82HGz4AHvrV5jtQiQ7TVZdO9syx74gDPKJdkYhSzEyy6GVoJFkgrvD4YYTxW0Cp1yOgD+JYxgDp2hjsKxYZ7DHH3349OUnOkMy4aIGYbTcV77yFVxzzTVxSVcVItNgDfXgJkWsd8soxngaOzRwJGp0HbfTgynFs1DoLoYTLuS5CjC5eGZmNQqn87GhLmwHZKNvQ0hEgoUGf2ZEoojUZnh++3Veh+XFpv5rMhKLyFs2yWY/CtNjoaYG+MHdwCXnAp84D/jD75NG3Mh1laJn1gQ4mpfpDisXsIbjcGM59u7ba8q3MoWeDbGrqzvOQPvb3/6G//3vf/jlL3+JTOVXv/oVXn31Vfz9739P9KEIkREwcCqeNgajREX3mF4yHPNKxyLXlYVsp8c0CT+pd3L2iIwFdOQ1+LzwWUej1zuj3l+Oat8++6/N/+t8h1HrU1UCkfx4nPkhTm0GzmSOfWFnMffu3Ttq+6upqTEiAPtRGHatA569D3j6e8BLvwbKkydjONs1tVnY4NjngIWBaPS54fUHxC7641l+ijZGR/A1n/nMZ0w29LRp05BoEtpTI5hvfvObxuCg2vO1r30t0YcjRFrBBj6MVrRhShkjPeMJy33MnDkzru+ZbuS5CzCtZC4yknXvAGteA5jeXVgKzP9E4GcnWGgIa5sQ8PuA8nWAtwrIKgZKxgCOxEWdTO/RC1cPHY0nt29s2falUZMwoiBznDd02kcz2pfGBsX9FmfbL+8Hli4OjCleP/C3vwA9egLnXIBkgCUt2afrSGMZFh1eBq+1xmwv33gYF804zxgR+/bt69B5yEgr1rll2SlGjmUq/Ow8B1/84hfNOijZ6z4LkUqwMWhoGT9GbLZXkipWsKdGcOCWiBxew9mlo80j06jy1uK/B5ahzFsFBxyYWjIK04pHdVqe0mc1RrRdZDi+MsC3JyAkuAcCzsSWJirxTMShhqXwN9vELkcOSrLGIpOgUz64F1N3RVEGTZ1wwgmBDWV7gXeePhooVXUYeOMPwDk3Au7Ez1VORzZy3TPh9zfhYMM6VPt20VNnDre2yYuiolEYNGiQeXTET37yExMk/Y1vfAPJQNKIGmwS/vvf/96kyF9wwQUmlVQIET2jPtTxwSjY0LJUsYIlP/g+iU5NEykKox1W//fo8+ojwJt/Bj76RXZA7vDPnI5i+KzgMj4OuJojE4RogU7tXa8A9Yyw43hoATW7gIGnAY7EJbTeMGoSzug7GLvrqjEsvxDD8zNH0CCFhYWm3i2zEjqNrmxoQE5OTpvf8W8pjNC5xgdTzadMmWL/YUDQCM0GWfJO0ogahPPmsrLVaLKaWubtPUf2Yqd/L8YPG41t27aZaDOuoVlXnsIN19H8rF/+8pdNma3LL78cmc4nP/lJ/OUvf8Ett9xiGocLIaLD8OHDsWZNQHC1Ya8fjr38XTxg7yX5DURX4Bri1QNLUeGtaaksv7J8I4rceRhZ0HHPqmxn+1Ht2c7MWqeJMPAdABreO5oZ0bQNyJ4FmDKjicvU6JtzAhr9Zea4spw94ExgIFeywibYXF+H+spoU7DUFDOluS6nQDJx4sSjfq59IaVhaXPUVwPl+4Fe8SnNGA41voOoNmJbgM3rd6DHwCL0KC3CkSOFJnCKthibqh86dMiIQCwpT3uD/bBff/11c36SgaQRNQiNrxtvvBFXX3013nnnHUVdpGCTsbLGQ8b4LvKUJHVTu0yDhgVr3I4cObJl27Bhw8y2ESNGxPS9Odiz1u348eNj+j4ijdm/NeBctpvwcXFQUwbUVQL5HfeFyXaORb2vFn5UmOcO5CPLJcFchFCzu1nQMDdX4EftPqBuP5DXsUM9HowpLDaPTIQR9Rs2bDAOs1GjRiE3N7eVI2L16tVYt26dKScVPLfR0KCgwVrrFDFoZLQR7/mckb3BzWy5LfvoeyTLuqrGd7T28eZVGzFswghUeqvN56KAwXmcWRmc0ynuUNBg2annn3/epI4nazP0eMJz8NBDD2HSpEnm3Hz84x9P9CGJCPFbNfBblXA4PHCiVPd1ksAMCUZrBkMnB7dx/W9K/cWQ8vJy43hqT9gW4lg0+BtR7m1dwpHZGnvqDnUqaniceeiTPQkHGj5oWTf2yhqHbJdEDRFC47rmfwSVN2VT7gSKGsTpcCPHFb3yS6kGM8Hpb6YfrE+fPq3mqrKyMmN7sMpIcGAQS0xxO187duxY4/Bvd45zZbVui9eyPTqZIdGi0V/dEsxXVVmDmupajBo3xJShGjNmjJlf2buPwWUUNbiG5tx+1VVXGZ99MlVgSSpRg9xzzz2mVM63vvUt/OhHP0r04Ygw8VlNWFX+HqqabOehExOLpqM0O3MHy2SCNQOZIh7s+KHRUVvbfjPa7kKHE9PBORCyjwYFjXg1JRdpiIdRAO2sDtydRwfQ+ZHjmgkLjMCyjKjhSGDkvUhSfB2UC/CpVFmiYUQQ5xA7IqpHjx5GrGCUEAULzmGMmCJ8HZvWjR492jyOyUWXAX/4XUDMsDN0zr8IyYTT4US2M8s4XnZu2IEefXsivzAfee6A+EJjivM6RQ0KQHPnzjUR0tdeey1++9vfYsAANXoNjh5/5JFHzLmZPXt2p2n1Irlo8u+D13o/8B212BCyB7Kcx2k+TxLsHj/B5QL579Bgqmhmf9PBsmnTJtNPg/OEEF3B7WjfFZblPLbzsdAzAHnuXsYB6HbkwO1MjohlkWS0V5JMZcqSYk3IuYtZCO+9954R4ceNG2fmLQYN0XdF+4L+LAZV0e6gqMFAqmOWVhwyEfhwIdBQ2xyQ6QD6DANKkqsUrMdJW8JCY6MXH6zahFknTDbbOZ4R+u7Y9HzRokUmE4VVX+644w7j5/vud7+LZMJh8aiSDDvy7rnnnsOpp56a6MMRYbCtZhO2125qtc3lcOOE0tMUTZUk2GU47HTwvXv3mkG5u02S6Eh67bXX2pQIYd8OGjUcDLuFvwHw1wOufKCDxadIc2orgP88AnibncyctkbNBI47K9FHJtKBxgpg2/MhwpkTGH4+4MmsxnnJDp33Bw4cMIaHnYFAA4QPZnZQ6Ag7MpjjyH//Ayx+O5C1wbJTEwIL+mRi6YYVePXD11DcuwT9hvZHnisXJ/eeCw/cRtThOWFmCtfNNMpOP/10E3n22GOPJfrQkxI2NmTZrldeeUUlMVMAy/Kh3v86F4OttnscY+F2DknYcYnWvPXWW5g/f37LuPz+++8bJ0h3M8VoXzD7LBg6nChwMzutO5kgluVHna+2WTzOlb2aoSwtW4c1FVuaiwOxYbIL5w84AUWe1vedEF2iYQXg29vaxnANBrKby6GKpIBiOUsZDhkyxIjl9rY333zT+MsocjCoKiLfxQdvArWVQI/+wIQTAXdyZWrU1dXi9eXPoLr+CCZNH42sLA9Ks8agJGuYsS9oU6xYscLM5bSx/vvf/5o2EUuXLjV2WDKRlKIGefjhh03WBssLlJZ23gxWJJ4PK1fhQAMH7NbMLT0VWc7EN8QRAai0MpKT0AlE0SEajfzefvvto82Rokn9RqBubfMTF1AwE/Akl8ot4kRNObBhcSDqofdQYMT05ghrIaJA5VZg/yLA8gUahPebDxTKYZYqxKs/VLxh9gWddn2G9MWhxiNwOVwYkNsX1RXVJgWeQQmMIKNzj/zwhz/E7373OyxfvrzDBuKZDjN+pk+fjs997nO48847E304IoyyUw3+d0K2OuByDESWU2VNkwWWumP2kx3IxCwKlg3sLhRs2SuIpT6iSb2vDmsrl6LOF+il0MPTG2OLppkxVmTe+mFT9S7sqT+MbKcHE4qGSdAQUbzBvEDDUsB/JPDc2QvInqFAzRQhXe0Lv9+PN954A3PnzkGTuwJN/gbkuIqR4yoxNoRd1YXB0AxUZgAVA8fuuusufP7zn0eykbRhzzxZL730kkkTZ/3bdLyZ0olcRtGHwEwNjyO5FMl0HnCbLD88zo4X46yBF9zMx66DGw1Rg42Doo73YJCgQXxA9XtA8RmAUnwzD/bOmP6RRB+FSFeKhgMFg4GmOsCdB3QylorkI13XiHQQsnQkAxBGFAxtMUQY8HPSSSe1+txMn2cwEI0UCRodw3Pzpz/9CSeffLLJBk+mmsCiLQ6wDIIzJFPDghOKoo4XPstnygo7OxlnKRba0a3RhMItg7CizYaqVSZLw6bMexA7azdhWH50xROR/HAeHV042DyEiP4N5gGy5wBWQ3Ov8GwF5aUQ6WpfOBwOkwW5a9fuVgEILMdF2yO4dCT9jAwEYulW+uaTEXcyn+hHH30U06ZNw4MPPohbbrkl0YckOmFw3jAcaTzYqqfG+MKpaTsQJBNrKnbhP/vWoNHyoWdWPi4aeDx6Z7ctmcLSU8HiAw17GiFUX7sL92vXHIwaTWVH65y34AN8VRkpauyvP4ItNXvNGRlVMAi9sjOzebAQMcPpBrJUbkokD3379jXRUQ0NDSYogeUeGUE1Y8aMVusrvubSSy/F3XffLSd9GFDIYLQZz9myZctMWr1IThwOFzyOiUd7ahiJowdcDvVEiTX1vga8dnAF9tUfMc2TJxePwHElYzq07WJl87EESLSpNvZq62IVlV7aHZmH1+/DivKtKPfWoldWAaaWDINL/eeEiB4cG5v7FAiRDDgcDsyaNQvvvvuuETUoXDBgiuUd2RA8mJ/97Gem5BT7FiarbzepO6aybtkzzzyDb3zjG6ZWp0hemJUxrWQ2JhUdh3GFUzCz53w1CY8Du2qP4IW9K42gQcoaa/GXnYvR5G9rAFC8oPBgU1hYiKqqqqgYGKy1Z6eqRQ0jXLRTHS8DBY3tNfvwwt5F+LByO9ZWbsdze97G3rrDiT4sIYSIK2VlZdi9e7fJVgjG6/WaBXkqw8/1/PPPt/ocLLvChriMgN68ebPpOccMDc7fwfPxFVdcYYKAbr/99gQdferBczV16lRz7kLvJ5FcuJ39kO2cC49jErKc05HlpKiX1CZsWvD6wZXYXx9w9FuwsLpiM9ZX7Wj3tcz6ZvBUd+F3MfT7yBJ8wfZLNHA7QksjO5DlzDyno8/y46ldi7Dw0Dp8ULETrx1ci2d3v5fy86kQQkQCxzxmBTLgNxjOR7EQ1uNJY2MjlixZYsrWBn9eBvWwYToDp+hrZ3/cUEGDPUW++c1vGp98MreESNpMDZs5c+bgvvvuM9FUdJqGNiMWyQMbrZVm90n0YWQUW2oOwgkH/M3Ofxod1U31ONRYjX45nUfy00joyiDNJq0cFJmVwehGDoQVFRWYMGECokrWIKBhK+CrOJqxkTUUcGVeJPV7Zetarq/N8rINOCc30B9FCCHSHTrMOPewCfY777xjhHqKGYSRRRQAzjzzTKQanIfZ72rAgAFGvGBTPjoIWSKSjfrYr4qRVP369TNRVaGw5NSWLVtM+alkjaBKRrgGeuKJJ0yDdZ5DZrmI5MXpyDcPER/8lh9769sGz+yqO4hxRYFSeKGBiBRgOU7ZGdz19fXIycmJuEcfxz8+SkpKjJDNcS3S/RyLEQXjsb5qpclA4cqamQlD8rrfAyTV2Fy9H/vqy1vZGNtqD2J33REMykteB5YQQkSTtWvXGruCAURce3MOoqDB+YdBRwsWLDBzUqqxfft27Nmzx5SU4pz8r3/9C6eccooRORjYw220H+hzp90RDP+OPvj777/flJ5KZpJe1CDXX3+9MfguueQSvPbaa7Gp398J1U2N2FJdiQK3B8Pzi2Q0iqQhy+lu5eg+ur39evChkTc06ukoiqSvBgd8Gi8cCGls8Hlwr46owWZ9hScCDdsAfz3gLgY8A5GJNPgCjrtg6vyNCTkWIUTsWF9Zjn/u2Qav5cepfQZibmlfne5m9u/fbwQNNqPlI9hhtmrVqnYd/skOPwMFCx47AwX27dtnsjA4L9OwYgTV4sWLzXzL2rehvPDCC/jpT39q1sh2g14RPjxnf//73zF37lxTkuqcc86J6+njmqzCW41GfyOKPYXIdoVGjwuRGOjsp6OfkfxHtwEelmlshz59+mDjxo0tosaQIUPM8zFjxkT0vrRH6DzhGMiIWZbFiIVY2yu7H7Kcs1HWeNBk/fTJHoAcV/fL8aYa9b72bYm6DrYLIVITy/LjQMMO1PmqTFZa3xyWmVPvW5uamhpTecTObGCwFB8MnuJ8lIqCxqZNm4xAwzUug59oZ3BepYDD9e+HH36IHj16YP78+W3+lueAvvePfOQjuO6665DspISowcXMQw89ZC7IbbfdZnpsxIu1lYfx7fcXocYXSKmdV9ofXx8/00TH28cmRKKYXDwIi49sRp3P2yJujCnohx6e/A6NBQ7OtjA4bNgwbNu2rVWDoGPBXhwc+AkHwpimKFPYyDnaqChT6Z9bim01+1quMUedgTmKoBIinVhdfhjXLXuzRaZ+dvc23D1hBs4Z0DYqNi5UlAH/+gdQdgQYPgo441xOIok5FsCUS2Q2g40taHA+4ryWagbHBx98YIyNefPmtURHsSH43r17jcjBeZqft6O+VxQ9rrrqKjzyyCNt0sVF+EyePBkPP/wwrrzySlMzOLg5YizxWxYWH1mFHbV7zHO3w4X5vY5H35xSs66SfSESCe+/KcWjsKJ8Q+C5+Q+YWDSs3dfTWRJcIsq+fxnpygCqcOH3j2Pb6NGjjY0Ry+9BkaeHeWQyA3J7tOleSDGrb05qzadCiI7hmmJzzUpUeA+2VL8oa9yPcUVzTAn5hODbA/gPcMQBXEMBZxGSheCMBVYKOu6445BK1NTUtKxnbR8fAw0YREV7gfYFMytZtrajOZY+d+7nN7/5TUqsRx1WChVN5CKHht6Pf/xjfPazn42LwXHF4pdR4W1oNdlPLe6J3fWVZkA4pfcQfGroZLgjWLAJEU0qvXV469AGVDXVYXBuT8wpHdVhgzemoDGrguUtGE1FUYOlo/gzEjgQrl+/3jiS+DjttNMiMlpEZDT4GvHq/mXY13DEPB+c2wcL+kzvMGJOZBDM2KlbC/gqAWc+kDcBcOYm+qhEF7h5xdt49/B+BFcT752dg3+deHb8z2dVJfDtW4HKclpDgW0z5gBfvC3Q8DABsCwJg1uC5xqmRnNtyO2RZBwmutwUsy8479oiDaPA7IbVdOYdC87b/MyMoHrggQficNTpzy233IJXXnnFZL2wDFis2Vy9A0vL2Pz6KBQ28lzZqPbVosCdh1k9pqI0O7OdriJx0EWwsXoXttXuRZbTg0lFI9Aru+OMMI5rjP5kVt348eNNudpx48ZFPDbTRqFAwnFu7NixJkNPxI4PK3fj3/tXmawcj8OFc/sfhxEFyhIVAMq3Aoc+DKwDe44Ceo5J2BpQdJ2apgqsq3q3zfaheZPQKzsBVTCatgC+9c1Pmu8nz1zAmZiM4/LycuzatatVgBDX6izLZPvLUoU9e/aYgGVmPDLTJNhWYrlVBiAci0cffRR33nmnKVEVr0Cf7pIaFmAzPKlsUnLuuecao4/NEmMJxYxyb0Orbfzara08glx34Av4yv5tyHa6ceXQQLpSV1LB6v21cMKJLGduSihhIrkMjjUV27Ghaqd5XuWtxpjCfuid3b5BzrJRbILERj+M/mQEFXtiRAr3wwcNDjpiKHIw9VzEBpakOLv/HNT5GswYkevKvGbpoh0sH1D1FuBjUzM6nssA7yGgeAHgTPEyJt5G4MBeIK8A6JEZWUkV3sZWggapau4ZEXfeeSOQqREc97J0EbBvD9A//gYQo4WYJRgsaLCHBhfqJ554IlKFHTt2mOACRn3Z5aRYq5dp4TNmzOgwKyMYGlqXX345hg4dip/85CdxOOrMgP37aF/w3D733HMtxmCsKGusbK7nf/Q71mT5UNVUa3xG1U21ePPQEpzV72TkurrWT4BlrRp89ch15cGtIAgRIZVNNVhbuQk1vkAGRqE7G6VZnZdhpsOEQoRta3QldnL69OlmnKMThuOjRI3YMr5oIEYV9EOtrwH5rmy4OyhhLDKMss3Atv8dfV69B2A55D5pkBnqrwUsL+AsCFSFSHN8/KwRbI8pnBN8m4I3NB/MVsA5Lf7HA2DdunWmBGmoSD9lyhRje6QCjY2NJiCAQTnMAA8OmGLWY7i20htvvIGbb74ZL774YsoIGiknahA2aWFU2kUXXWTUo1gudArcWfA4nKa2dfDXLngtx+fvHt7dJVGjwVeHDVVLUe8PlPIp9vTCqILpcGbA4Cqiw4bqvVhy5OjEUNNUj2d3L8Fnh59qGreHQicKHUFMq6OxQWXarhcYaa8als1gHXOKi6kSIZtqMFvM2Tzg0IjMc0e3UaJIcZoOA76qoA0WYNUB3v1A9mCkLDu3Ao/eD9Q0f7Y5C4CLrmYTIKQzs0v7YG1lWYuL0wUHZvbsnZiDqasNLHZCHVLcngAOHz6Mvn0DkaPs5cRoes5jzBJMBVhSig469gKxDQvWtV29erUJEIhEmLn99ttNFBbPgebe6MFz+de//tU0S7zjjjtMY8RYEpjPO3f4UuQ42HAYQ/IiFxK31WzBxupAJKQTLkwpmYbe2Qo+EeFBMeL1g0tR2yxokHVV21DiKcTIgvbXF7QnaEvYzb1po7Omd6R9NQjti/79+3fpb8WxscUmW6DyOF0odmZeTxHRCQfWtL8tlUUN3vf1qwHvjsBzRxaQNxtwpXe5tTxXkVkH+OFrtb3AnahM0NbHYUiEwNIMA6Y4f3Fc5NzDeYvZ0KkgaNCH9/7775sgZQYEsCILP8eGDRtM0DGDqMLJziAMsPr4xz+On/3sZ6aZeCqRkp7Iz3/+86YW8XnnnRfTNHGP04kbR03FAxtXmB4afljIcjiQ7bTCasp8LLbUrDZZGjYV3kPYXbcJg/PGdvvYRWawq/Zwy71J+P/qpnpUNdWj2NP+4pRZTox8Yv1xRoxyAORgyOZIkUQmUg2mEiynSvQ50liFl/etwOHGKuS6srCg92SMLAg0XxSihSDB/ZiLxVTB5wN+9wBQy+yTZt59DRg8DJidWgusSPnc8PHYW1eLl/YFMu8mFffEtyfMSMzBTJwC/POvR59TUCooBAYOTlhDbWY57Ny508w5Z511lsk0TObs1iZ/I9ZseBu79+zCoAHDMG/eCS2ZJoxCZm1bGg2RzKGPPfYYnnjiCRPUo8bg0Yfn9Pnnnzdp+xMmTIhpqdvRBUOxvWYPKpuqWzI2eDeH3tJdCXQ60ni4RdAgdGSsLl+BE3ufgiynMj3FsWnwN5psoWB4n+5vONKhqMH+NHSkMGiKPZA43tGZwnJUtigdDhRFKI5Q1BDRhc6udVWbsLF6K/yWH/1yemNGjynwONUwWITgb8/xnML2BaGYYQsaxGoEapcABWekdVkttzMLowqOw5aalWiyvHDAiaF5E5DvTkC5J55nRylgsaR2kE/VmaAgrubMb4oZDPblPEafWKTBvvGmvHoHVqxeBFgOHDflFBQX9mnVB4RBVMyaDJfKykrjW2d/uWuvvRapRkqKGoQRVEwTv+yyy0yaeKwcq2f2G4oheYVYU3EY+W4PPA7g0W2rWr3mvAHhN1kOpqapvE2UVnVTWbeOV2QWdHi3F+eX3cnilLUBe/fubb4zdKzQ6ODgTUOENXDDhX8vQSP6eP0+k23DNHBS52vES/uW4dJB89EnJzG1JkWS4u4ZiDIy0S1H4/vhTtzCsNtUlQNVFa230RG8Y0vaixoMpLhn0kzcMW4amvx+FHuyEue0Hz0euOYG4A+/DZQCYwmwL30VyE5MthijdZkGzUgku0RTsvZxYmTUlq2bsLtqBfoPKcHE2RSk67C/bjXKtmcZZx/nXhpOkcyhTAn/0pe+lHIp4akGmyrapW7575NPPjkm70Mn4hl952F77V5TJqqHpxgrKz4wjuSAwOFAvjsX/bJ7RbzvCi/ti9b44Ud1UzV6ZknUEOHcn+425dFIdielLRkhyh4adAZRyGA5jMGDB2Pt2rXG7gh3zGYWXjIL1qnM1podRtSw2Vd/AMvKVmNOaYICKETy0mMEsDfQy7GFkuFIaXxlLY2yW7AaAKsecKR3P8JCT09MKV4Ar9UAtyOr3YoeccMzXGWsRQAAglFJREFUFfCuAKxmv6drWKBZeIJgfzr2caIIn8xzD+dUZmrv3v8hrKw9GDt5GLKyPGjEGhw4NApbN+9rOf5IAglYpoo+dc7XLMWairhTPU2ckeI08n71q1/F7CYcV9TTPGwKPB68dmCHWeixUfgJvQZ1ab8cUDiwBONxyNgQ4TO1ZBjWVOwwjm/e/czYmNVzFHJcHYsadAoxEpEKLp0rxC5BxR4Z4UZ/0ujg4MqfInocbqxEja8+ZKsD22sPStQQraFzofAEoGZZoAwVSwfkTwdcgVr9KUku69s622ahFGaOoFfgTpLooJNOA+afwjQJIDcv4VFsnKfC6TmRKOjEY3AAAwdGTuyD3pYtPFjYvH4HKis+xAlTPm4yACLlww8/xMc+9jE8+OCDKZcSnoqw1C3PNc/5O++8Yxy1sYB9LoKj3hdkzcX7letR6a1GoacAk4rGdKkXRkfZGFmp3mtJxA2Xw4UpxaOxqmKDETdIltONcYWdN0xduHAhcnJyTOTrgAEDjGOFdgV7bIQrxrJULvsoieizt/5Aq+d07e6rP2gyOJLZmScSQN+pgN8LHFwbuFN6jAIGzkntS9Gun42ZA5kxN/I7nuXISY7rkDUHsJpMgUxj9yXycFjiO4ntC4oOLFdLXx0rrvQcWgwLgeOtr2/AivfWYUDfahx//LkR94Pj2H/TTTeZbPi33347ZQOWU/Oom+Ei6V//+pepf8uF0le+8pW4vO/c0oHm0V2G5I/H5uqVzYoxv9JODMztWtaHyEzy3dm4cuiJWFW+3Qgbg3J7YkzhgE7/hsbGkCFDTHkFlvSwYTMkpt5NnTo1rPfm4E9VW6JGdHG3W2rCgjtJo5JFgmHqcPGpSBuys4FzLgVe+AvA0o4UN4p6ACeemegjy0x4DfJSWCSLA0zZXrNmjemNMX/+fGMclTVuAbwOWJYf76/chB49CzF97BAU5+R3SSw5++yz8YUvfAGf+9znYvIZRFt4rllfmOeepcL69Il9P4psVxZm9Jjc7f30y+mPnbXbUdV0tBn5wJxBKHAXRuU4RWYwqXgUijz52Fd/GFlOD8YUDEGeu/No5p49e5qSF3Ss2JkZAwcONOPYvn37jOgbDnKwx06sCiWhEdsieeF9MWBW4JEuZI0AvLsCmRl2xkb2+IxoFp6UOFLaFR1zKDgwqIllseym5dx2qDHQf6SivBofrN6CWfMmItfTO2JBw65+9Oyzz5p1biqXtU35O2no0KEm6pxRVfz3xRdfjFShZ1Y/ZBXORpn3gKmZ2ytrALJdyasSiuQk352Deb261oeFAocNy2GEI1Cwn41dPoNNAEV0Kc0qxODcXthVd8hEUNEhwcybsQXdF1KFSAlO+SjQfxCw+UMgrwCYdVLgpxBJBHt6sG4tS62woWBwaZUcVwkaaxqxcsk6jBo3BD17FcMJN7Kckd3HtbW1OP/8883+v/vd78bgU4jO+N73vmcizHkN/ve//yV1JF+o43JmzznYXbcT9b56FLoL0S+n84AXIdpjSF5/8wgXihYUL4YNG9amLN+xStwy0IqOFX7PKI6I6DOqYBj21u9vtW1MwQiJSCIzYBZjwclA485APw13KeCOfcCCEJFy8OBBI2gwuzs4w5uCv8fRAxs3f4gjh8sx98TJgW3OyJu+P/300/jOd76D119/3fjRUxmHRbknDaCwcfnll+OVV14xxp8QomMWL16MWbNmtVnErl+/vtOmQoy0Onz4cJfKZ4jwafL7sKRsIw7UV6DAnYNZPUejqIPG70IIIeILSzWuXLkS06dPR1FRUZvfc55cvPK/GDE9H9nZHjjhQd+cachxhW90MHDgkksuMc5Arm1Zr17EHzpazzjjDNMTgAZgVyLhhMgUOG5R7J05c2ab0rd79+5tI3aElq5i9YVULX+RKhxqOILN1dvgs3zon9sXw/IGS9QQQogkgK55Zn/TRzdp0qQ2YzN/v2TpO3Dl78HQkSVmW7azPwrdE+CIIOuOpVXPPPNM/PnPfzYNwlOdtFk18GL88Ic/NNFUrAfGppKpTqO/El5/DdyOXGS7AjetENEyOtqrn0pDgtGnHTXzY9QoDXsRPn7Lj3JvLTwOFwo94TUhcztdmFcamxre3cLyAX429fIDjAhwJEntf5F6bFoLfLAccLmBmScBfRVFLFID1rZdsWIFTjrppDZzJevAM5uRWY8fPfUSWPDBZ3nhdmRHZGxwfr711lvNvmh4SNBIHMxoZWo+A6Z4TX72s5+lvAPQZzWhwnvY3GdFnp7wqNeGiBLM5A7OArfhGMaxszM4bkrQiAy/1Qg/GuECG9yGJ7j2yu5pHslGeWM1ytlPyJ2H0uy2wQJChAX7NHi3Av46wFUMuIckvCecEOHCgCk27GZJ22C4XmNJ1D179hixo6RkHvxogIMNDByR9YRh/78LLrgAP/rRj9JC0EgrUYPceOON2Llzp+lgTyOwf//w02WTjQrvVpR7N7Y8L3ANRs+scSlvSInkiDAtLCxsV7jgdhokHdXUKykpwdq1a1FaWtqh8CGOUt5Yg2d2LUGZN9DwcHzhAHy0/zS4UrF+rdUA1C8CLLt5YzaQMweIsJxK0lO2H6gpB4p7A4XJZ/SlBcvfAf76W8AeQxb9D/ji14GBnTcgFZkFF/AN/gbTDyDHmZM06x826mMjW2Y89ujRw5RhZL3bTZs2mbIpzN5gSSrigBvOLtQMpqHx1FNPmSAdzrcisfAavPzyy5g3b55pfnznnXem7CVp8NXjg8rFaKDDx/Tx8mBi0Szkqd+GiAK0EZht0dUxnxnhbC4ujk2tbyvqfJta5ppC97QulSBJBt6v2IpFh9mQOsCU4hGYXdp5ubKUo6kBqNgb6FVW3B9wppUbLnkEjbqFgL860DOjaQfQdAjIOU7Chgi5V3yAj8JXTlL19qAvjuLF+++/b/rf0t/GSirV1dXG3jjxxBNbXutC5E3f9+zZYzI0PvOZz+CGG25AupA25ads+HGuueYao3K98cYbKdnwxOuvxp76d9ps75M9A7kuGbeie7CUBZ0vdBAxIoqNh2wHTGNjo0kP76yuHgfVZcuWGZVYzpbOeXLbWzjQUGmccjYn9hqHOaWjUu82blgJ+PYEmqoZHICzBMiZl7BD8llH4LMOmigFl2MAnI5uNjRe9jLw4aLmJw5g1jnAmNYlFEQU+N4tQFXF0ed0Vk+YDlx9k06vMDT5m7C8fAUONhwyz3t4SnB8zxnISrKI8m3bthlBg837Ro4cGRXh5fe//z2+/OUvmxq306ZNi8pxiuhA2+Lkk0/Ggw8+iE9/+tMpeVo3VK3E4cb9rebyAlcRJpeodK+IzneEpabsBuGDBg1q+R1tj1GjRnVqw1MUoS0ydepUBU91QqP/EKqaVrTaRmGjh+dEOJLIQRcOFd4aPLXz9Tbbz+k/GwNyW0crx9U53rQdsOoAZxHgGtw9p3jNEWDFM0BjbeB5fikw/WIgK7wMfhEm3h1Aw+q223NPAlzK/hHNNOwFKpbQk8BGK0DhdCA3uXpKsDrKxo0bTQUVzpsUO6IR2HzSSSeZ4CvaGskSLBYNUmvWCwNenN/+9re48MILzeOll15qNw02mfH6azvYXiNRQ3QbprPxQeOBwl9wxgUNiWOVuaDzhgMimxfRQOHziRMn6sqE4LP82N8Q5LhtZnfdkdQ8V/7KICcIsQB/VcIOp8m/F17r/YD4wOfWDmQ7Z8Hp6OKkv3dzkKBBLGDJi8CAUUBBGJFvPh+wcwPQUAf0HwYUKcujQ+pC5jjGVlTz/hIiwLqq9S2CBin3VmBNxfuY0eO46J4i3nuH1wNlmwGW7ug9ASgeEvafd1Yfviu88MILJuv4xRdflKCRhFBkYimqc88915TiPOecc5Bq1Pqq28zldX47A1OI7mELsTt27EBdXSAbKBIbnvYEheKlS5eafjbszZGbK8dvKE3+8ub179HvsoUm+CyWrU6tgE6WnWqPssbqxIgajOBmcKlVdfQcuw4DWdO6Lmx8+G+gsa61yLH5LWD8meH9fXUZULYnIIL0GQakYsZ/PLC8HWxvjPeRiGTFVwtULA6U0jb4gaplgKcEcEd37LSseljYzFUWHCiEAwx+Cs/9zsxvivvRor6+3pScYqABfeXpJGiQtBwRGXXOtH0upq688krTPyCVcDvbbwjs6WC7EF2BTYiYpRHc9JKGRHtNT0PhQMhm4UyLYw3xTOJIYzX+vmsxfrf1NTy/Zxmqm+rbfZ0TDmSFpBY74ECeK7kijcPGwfHH0c62xOC1NjT/y2p++OH1b+5e2anQz8f9VhwK42Aagad/Cfz9YeDFJ4Hf3wtsOZpGL0IYPuZo6SnChdXINCszILrF4cbDrZ4z2+1wQwwE4QNrgB1vAlW7gcodwOaXgYodSAQsm3rZZZfhiSeewCmnnJKQYxDHZsGCBXj88cfNtVq0KFgITw1yXflt5roc2RciitDu3r59O0aPHt1qe7jFIVjqdtasWaauOLPDMwWeH7/FBt7vwmctht9idnT7OEwNdauD7alFR/0Gw+1DGHWYlW4EDRw9x622dYHqw20Dw6rCsC/I7nXAv38DLP4H8NafAg9/avm24oarvYAyl7I0xFGayoMEjSC8re2O7mKZfkdLYGEfPWywsBN+LIdltfPecZiTr7jiCpNFSR+5XaElnUhLUYOw3jEj3dhk8brrrjOpO6lClrMAxZ6RrbYVuAYix6nSUwJRVWx79mw9+ffr1w+7du0Kex/8XjG7I1OoaWrAn3e8jW21h0yfjI3V+/DUzkXwtrO4pPBzap8JLQIH/8tyulKz9BTJotM5eBJ0AVmTE3hAbaNxLHTjXjTZGO0Y3AUlx/7b5a8De7cdfe5rAv71pIyOjvjEtUD/oGj4yTOB086P+JKJ9CXL2TZjsMulpzg+e6sBfztNavevbGdbO6UL4lCyhdH/P/7xj3HxxRfH/f1FZFxyySWm7wkzNVatWpVSp29Y3rhW3yWXw42RBZMSekwi/ZzztMM76kkULrQvUsl+7y4WtsICe2RQyKmChbUdChs5zgFwwg4sCoiU2c7BcDlSL6ulZ1YRppa09nuMzB+Awbl9EnNAHUX1dyfaP6coREx2AHlhRIX7vMCSZ4FgR+iBbcCmpV0/lnTG1QPInhx0rj1AzkwgBcU+ESM6uhccnVcq6QgL7P3HjIzWPgQLBzmLhfgWWJUgvpUJ/H4/Pv/5z5vSjswG72huTnXSrvxUMKz3/+qrr5pSOTfffDN+/vOfp0yqTYlnJHKdveC1quF25CLb2SNljl2kLuyxEW4DcEZPMT18xowZyBS21OxHvf+oQcYJjOLGnrojGJrfu83rJxcPQZE7D1tqDsDjdGFy8WAUe1I048qZD+SeDPiaa3E7ewPOxBlPTpTAj/JWiwWXoxslnwaPBQaPB3Z+eHTbpJMCDcOPxZEDbcoAoLEeqKsB8tO3hisXSqz5yRJ0EVFYDNz0baCijIMOUJC+50iEj9+yzLeIa52xhaPx7mHWuz061o4rGhv56azdAxxYGKiPzTieXrOAwhFBb9pOtKN5bfygoXHGGWeY5tNf/OIX4/reouvwWlVWVuL00083pTyZvZoKZLtyMbV4Piq8h2HBjyJPabsiohBdhRngTU1tx1EGTh0+fNj8PBYMSuQao0+fBDm2E4CFXR1sG9BmO0uYFHtmo8G/E36rEW5HEbKcxz6vycqsnuMwOLc3jjRWodCTZ/6dML9Hu83WXYHeGl1l3OnAyr8fDa5gGamR84/9d3XVgUCpYFh6ymR+pDdVVVXGvoj4PvAMBdwDAyKUg02g0zaGW0QCMwV5L3lKgax+QOO+o7a7m2JY/8h2Z/pxrGG32uYtxbAwlSGszc87yqbyxzXA4Etf+pLp0ffmm2+mdS/ctBY17CZl//3vf42wwZqcjKxKFXEg21WMbKRWXUyROnBgY1Pw/v37dynzggYHv1fhiiDpQEep850l1A/N72UeaRPd4B6MZCDLOQkN/pWwEEgHd6IP3I4gZ2WkcNF70qXArg1ATTnQox/QN8x6+T16t70LPNlAbnpGQ9gwQplOPfbhYZ+ezhqAtoHzcIn6jghmwNXj1f0rsLf+CNwON2aXjsHk4uGY32sedtXtNuNu/9z+6JkVRm+bYJpqgQNvBkU4+oFD7wLcT3bzvkqGA0c2tv7+lnRjHIkQNgE87bTTcMMNNxhRQ6QWX/3qV02pWwobNBgjGgMTiNvpQWl26jpARXJDO7u9jIxwS9yyVAYDp1jiNrMIrzyXjdPhRq5rONKF/rml5pEUJYw8EwHv2uZr4gayZ3Qv2r9kADD7KuDI9ub+XSOAcMpr5RYALGUcnGlKW7QgvdfPLJPz9ttvm74CbJA8cuTIsMaOFti3IMzeBSLN8e0EfOtpFAAO9s2YChTPAeq2Ak2VAEty5o3sgvjFrLrgEnLso8rAyEAfDAdKEQjVCh7XWfEiPoF8lmXhjjvuwHPPPYe33nrL+MTTmYz4trORI4WNk08+2Qgb3/nOdxJ9SEIknBEjRpga3sGixvr16zFmzJiwjZZ0FDS21RzAkiMb0eD3Ykheb5xQOg5uZ6DvyPD8PshyuOC1fGaKYkmpfHc2BuRG6GwT3cbhyEG2c7ZJ+XSwwFcX00Zb79QJDB4X+d/NOAXY/AGwv7kWP++Xj14Z+JnGBgcjMTmvUgxlVmSqOPRE8sBF98v7luJgQ6UZU71WExYeWot8Vw5GFPTHBE83Fv+NZa1LNtg0HDwqagxhpKTV3CjcCfSeBPSJT1m9bdu2GUHjqquuwl133RWX9xTR5+677zYZa6eeeqoRNqLdPF6IVKR37944cOBAS6YFA6bYg499Mo4FhUI6MtMNy2qC178BfrA/VBY8zlGtMowdGAAL21v9jQORRQ+LKOEZFhLtH4X1fG4xMHBKZH/j8gAzz2suQdXsHO01CBh5PNI9aIpzalZWllkr7dmzJzJRQwjiPwj4Pjh6LqwKwMum4CcEhIxuUdbpNocjH05rGvxG6GD/1QI4MSnsRuHd5a677sIf//hHsy4dOnQo0p2MEDUIm5XR6UIHTE5ODr72ta8l+pCESCgUJdhTg6ngdjpacXGxMc6PtXCgIyqSuripAstIPb/3vZbnFRU1qPM14Kx+x7U0rbt08Dy8emANKry16JVViDP7TmnTEFzE7x52IAnqBzMr47IvAdvWAQ21QP/hzdkb6cvu3bvNWLFkyRLjrGBTz2SjyluOOl8t8twFKHDLGEpGWM7vQAOjm47Cb/X22gNG1OgWHfXgCC61w7F72AJgaHNj7jhl8rJ3FQ32Cy+8MKUyiEVbeO3YC4V9yihSsRTVoEGDdKpERkO7e9GiRS2iBp2T4VJRUWGCENMJ2k2N/lXNggapQ6N/uQnOcToCAo4DdLI5YGFPcze+wXBAY0nCcHgCj0QzeCJQ0g84vAvIzgP6jkjroCk7q4u9xgibGk+fPh3JhNffiAovv8sOlHhK4ZYfIHlFjVbZElZzzyKKDN2dYzzH3OZwlMKF+Wb8j+c6/wc/+AEeeughsx7NlIDDjPLEsd4thQ0aHYww/eY3vylDMk74LT8ONmxDna8KWc4c9MkeDndXm36KqDF27Fi8++67mDdvnnk+YMAArFu3rlNRg2nhTAkdN64LEe1JzodVu4xDzW72xP9vrN6LU/1NLcJF35xiXGGie4UIwuUGRk7KqEwvPpKVzdUfYk/90YjHoXmjMCQvMxZ2qYS7g3RvVzSiIrN7AXkDgdrdR42arJ5AfjtOojgaG4w6pKDBPho/+9nPtA5NA2isPvjggyaDjcFTzA5Xxkb88FsV8Fv8nvvhdPQxD5H47wQDpeicLCkpMdvYoJRlpTrrw7V161YcOnQoDfv1NQQJGkfxWXuPihoOChlcp2itIkIoLA08MoQzzzwTyUptUzVWVyyBt7lxfLYzB1OKZyPHlV5CbHrQkS0RDVGQIvSyoOf0Go1u95XxEjQsy8J3v/td00ea69Dx48cjU0i/2jHHYOrUqaZZyq9+9St8/etf77BGvogePMfbalZgX/1GVHj34WDDdmysfhc+K/0i/VMNlo9ijb01a9jo6GjKOIUN1vpmWZlQduzYYcSQdGzep/FAiNSH0VPBggbZXrsJNU2B/isiefA43ZhYNKTlOZf9TocDE4uikCpNI6LPiUDpDKBwJNBjGtD/9OiUkegiGzZswIknnohzzz0Xv/nNb9KyhGOmwmvJyLizzz7b9BvjGkrEHr9VDp+1FBb2wsI++KzV8FltGy6L+EOHCqOtmdVJmMFEG2Lz5s04eJARtG3Zv38/Zs6cmXZjox0s1d5vhBCpw8bqD+AN8mE1+BuwpYYlhkTS4WIQU8hc4hjQvf449m5AsZ5VClhSke8zAw70SagP62tf+xp+/etfG183fd6ZRHqtGMJk0qRJJh3n//7v/3DLLbe067gV0YPZGVVNwY10mIJbh/LGfTrNSQDr7DGaiv00CEtRMW2cdW9pfITChsAsndFdmPFBWHO3rKy9uoTxZ2zhwFaGh8Puo6G0UiFShlpfTUTbRWKZ32sS5paOw+Dc3hhZMAAXDZyH0uwo1VNnJkjRWKDXLKBkQqDcVIJ4//33jbP7iiuuMFH96ea0EwFhgxFyl19+ubnWH3wQVMtZxAS/ta3ZMXx07ea3NutsJ8n3gSIuM8LtaFVWTaDdceRI26yF4J4a3bUv6ODhY9OmTUlh5zuQ0+wEC47YteBy9EvgUQkhIqXWVx0iRlqoaeI2kXQ48gH3HMDZH3CUAq7RgDt6VRUcKIQDY+DAWDiQuP6qfr8fN998c0sPDfq6M42MKj8VDCPN2QmeJQAYQcLoKpcrvesTJgq/1dTudl8H20X8GTJkiKmNb8PvAh80LGgcBH832LyP9S2De3FEAo2MpUuXmhJwttGTl5dnIreOPz6xjc8G5/XCR/sdh3cPb0Bjc6Pwk3pPTOgxCSEiI9eZ1/52V/vbRWJhZsa0kpHmka4sX77clFP40pe+hG9961sqOZXG0HH7wx/+0KxrWIrqlVdeSbp64OmEhfayvgNBMyLx0H5gSdtgW8LtdqNfv37YuXNnm8bhLDu1ePFizJ/ftTKvtE2Yfc7+mbTvWSbTLpnLTPREjgvZzmlo9H8IP8rgMI3CR8PpKE7YMQkhIifXlY+qpoogYcOBPFe+TmWy4iwEnOmbtcC59frrrzflpihoDB8+HJlIRoeJ8aJT2OANcPXVV6dl4+NkINdVCJejrX5W4M6c2pCpAMUGNucLZsyYMSbScO/evaYuLutF83WTJ08222k8RAojtmjM9O/f39TXpZBB47+7kVnRYlRBf1w59GR8ZvjpOL3vVGVpCJFiFHt6on/O0ZJGZHDuSDULFwmBDjX2crvzzjvx7W9/W4JGBkAH5l133YU77rjDBE/xHhCxweno1fb8o6dOdxLB7O9ly4Jrj8NkiFPcYJk29tCoqakxzhkGTTGTg6+PtCQsX//aa6/hhBNOMPuiQNKjRw8jptiBVInE4fAg2zUFua6TkeOaC1c7964QIrkZXTAR7iC/VpYjCyMKMqd3gUgeGhsbjQ/7zTffzGhBgzgsFZHHvn378NGPfhR9+/bFM88802kDM9E1apvKsbVmJZqsBjjhwqC8CeiRNSClT6ffqmUhLTiRbxaqqQ6HAgoOdvkpu6kRt9PYoOhQX19vhA0KGnzOB2uDRxL9xBQ5ZoWwcSBLWTFDg4Mw9xGvRkpCiPSG41ZlUxnqfbXIdRWgyBNoVCpEPPnnP/9pyk3dd999JpJKZB7snXL77bfjT3/6E84///xEH07aYVl++Kz1sLC7WdDoAZdjSkqvyy2rCU1WNRxww+XIT4u1McvWMjODZTGY8R3slGFGhW1jMFiKvYdoK9AuP+OMMyJ6HwZhsS/gnDlzTFndrKwsjBo1yvwUQoho0OhvRLn3EByccTy94Ham7nwjUpOqqipcfPHFpj/VSy+9ZObLTEaiRjOVlZW46KKLTKT6iy++mJZNkJPBycRSVE6HO6UX6PwcTB9usgIGFOBCjnMqXM70yDxZu3atERqGDRuGiRMntqr7zQGUaeE0PJhpwZ4brJdLcUIIIYQQAR555BHceuutePLJJ836UmQuf//730003QMPPIBrr7020YeTtuIG4IejnczwVKLJX4nKpuUtZbU8jt4odFOkSf3iCsyWeOGFF0z2xJQpU8zP0L5DtD9oUzCLg303zjrrrJS2GYUQQohosn//fpxzzjkmQJjry6Kioow/wam/QooSvBn+9a9/mQj1efPmmUWViC5clLqcnpRfnPqsfUGChtmCev8qWFZ61PBlfVuqvitWrMDjjz/e6rvApt4sFTV37lyT1s2SGosWLTKvF0IIITIdBj585zvfwVe/+lUTPSVBQ/Ae4L3AclT33HNPxGV1xLGh0z/VBQ3eF1VNq1r1CfFaB1Hv3450gEIFA6IoXjz77LPG7mb2d3DgFIMKWT5q9uzZRvSgjaHvixBCCAHjl+Mcyf7QnEMlaARQpkYITHdlmvgf/vAHc6PQcStEMA2+9WiydgY1iAqQ65oLpyN9SpcxY4MiBiOl2E+DTfbYS+PVV181g6mdzcQ0b75m/PjxbaKuhBBCiEyBkcg33HCDWT++/PLLJttRCBs6cxl5zrKdv/rVr1oaJwtB/JYXZd7X25yMLGdfk62RLlRXV2Pp0qXm3yw1xcwM2tuMPmU5KrtJOEtRsd8GbRCKHEIIIUSmwl5TZ599Nq666ir8+Mc/blVNJdPRmQg9IU4n7r//fhNNtWDBAlOKSohW94gju42gQRxIr3qtEyZMwPTp002vCxoYrEfL+uA0RoJ7aLDZH1+7detWU5qK/TeEEEKITIJRxh/72MewcOFCvPPOOxI0RBvYT4CR52+99RYuvPBCs54SwoY9NNqa5g44QbsjfWDvypNPPhk9e/bEtGnTjK2xevVqI/oF99tgjz8yYsQIvP322yZCVVkbQgghMg2WbqRvmj5q9umToNEaZWp0wtNPP41rrrkG9957L2666aaUL5skotfAr873HizQGOU9YcHjHIUs5/C0PcXsncHHoEGDMHTo0DbfBTbjYxqc1+s1Rgl7buTm5mLAgAEmAksDr0gJqquAXduAnFxgyAiq3Ik+IiFECsAGuOedd56Z75555hlT51aIjmDkORs8MhL9+eefN2srIUiDbw+qfR+02BcUNIo9s5sDqtIPlp+ioGGLfrQdgtm+fbvJDOd2NgHfsmULPB6PETxoY4S+XoikhCUHD+4G6quBXgOAPNXAF0KEM3RY+PnPf45vfOMb+P3vf49LLrlEp60dJGocA0aen3/++cb4ePDBB009UCHYP6PJ2mPq3rpQnDZNwo/F7t27sW3bNpOdwUbi+fn5RuDYtWuXETJIWVkZ+vbta4yQDz74wBgefK0QSc2WDcAj9wENdYHnYyYB194KuD2JPjIhYm9sV+0AGiuB7BKgYBAL1OusR5AOTkHDLilEh5sQx4JBIF/84hdNRjiFDZW7FS33hr8cXuuIydzIdvaD05FemeAdiRu0GfiTNgMzwmlz06HDYClbvNi3bx9mzZqFyspKLF++HGeeeWaiD12IzvH7gVf/BGxaFXjucgNnXQ0MHa8zJ9Keel8tDjUcMP6iXll9ke3KSfQhpVRJ25tvvhl/+9vfTLUUlWHsGIkaYUAnLo1VNlD+61//qoYsIuOpqKgwAgfLbfh8PpO9QSGDgy/LKjBVnM3D2aNm5syZcvKI5Hfq3nUTUF0Z+DehU/ejFwNnnJ/ooxNx5v2KMjyxbSOqm5pwYu++uHzwiPTN1OT9vvtNoHJrS2QwSkYD/edJ2AiDf/zjH7j66qtx991349Zbb03f+0TEBDpsWfKWjeX/7//+z5SkEiKTod3AzDf20qDAweCpkSNHmp8sc8vAKQqCzABn0BR/J0RSs3Yx8Pozrbe5s4Br7gI86S9YiqP4LD+WHtmM3XVHkO/Oxuyeo1GSlZ+2p6jSW4FV5Uvgh888dzncmFYyGwXuo2UGRce+tk984hPG38bSU/S1iY6RqNGFG4sRVYo8F+KoUU4DhCIGYTNx9t8QImWoqQa++YXW2+icnDYbuPqGRB2VOAZ+y486nxd5rqyoOZPXVpbh8ndfh9+y4G/e9plhY/CVsZPS83pU7wF2/Kft9mFnA3l9EnFEKTPvsabtPffcI2e0iJo49u1vfxu33XabxDEhmmHfGZa/5c/+/fubAEMhUoq3ngU+eBfwBxy7LVx+O9BD66xkpdHvhZP5ck531NaNL+5djo3Ve81zB7uxOl24cujJKPKkZxm95WWLUNVUEbTFgRJPD0wtmZXAo0p+FFAfOaqlFCYst0OV7JZbbjGR56yZzCZnQmQ6dCYOGTLEPIRISXLzgKxsoLHh6DY6yXtkRlm5VGRt5W68vHc1vJYPua4sXDhwBobkdf96/XH7ZuYqtAga5IntG3Hz6Alwp2OPFW8HjYq9NfE+kpShrq4O1113Hf773//i9ddfV9kg0W3YYP61117DBRdcYErtPPzww8jJUYkGIdhUfMKECToRInUp7AFYwatK2hhOIE/R6skqZiw8uBz7Gw6b58PyBmJW6WS4eM26QVVTXYugQSxYaPT7sLZyJ+aUjkE6Uu9rLuncgtXONhHMG2+8YdoeMJj+Zz/7mVofhEkaWuixg7U9f/nLX+L73/8+zj77bPzmN78xqqsQQogUhs7qy64NGBl2xH9pH+DUcxN9ZKIdDtRX4oU9K4ygQep8jfjbriWobQoSpbpIvd/XZl73WRa8oQZpupDTs4PtPeJ9JCkBs3UZ0LJp0yYsXbpUgoaIGscff7y5pzZs2ICTTjrJ3GtCCCFSnIlzgV4Dm5802xgnXghkp2d0fqqz+PBqHGg40vJ8W+1ufFCxsdv79YZm6jTfDe1tTxcK3EVH73mDAwXu4gQeUfJC2/PXv/618THfe++9xuesXs7ho0yNLvD5z3/eRI18/OMfx8qVK/GLX/xC5XaEECKVmT4b6Nsf2PQhkJMHTJ0JZCtSNhnZWXvYZFMEw2inffUVGFHQvVT+k3v3x8v7jjoTXQ4HppeUIpeNHdOR3F5An+OBA0uPbus3O9AwXLRi0aJFuOiii4zBQcMjOztbZ0hEFZbXYfbPF77wBSNy/P3vf8fcuXN1loUQIlVh34yLbgA2rwbqaoB+Q4G+qm6QrOyvp43R2srYU3cQU0rGdmu/7J1R7MlDpbeuZf9+WBien74lyMYUTsSq8vdQ7681z/Nc+RhdOD7Rh5V0NDY24sYbbzTNwP/zn//ghBNOSPQhpRzqqdEN2EeATf1yc3NNV3o2ShZCCCFE7Hi/Yhde3LuyzfYrh56Agbk9uh0p8+jWDXh48zqTtTG7Z2/8ZOos9GR5snSmsSrwyCoMPEQrfve73+Gmm27Cj370I9xwww3qeSBiCschRul99atfNT+vueYanXEhhBAixvxz9/9Q66tvta1fTikW9Jnd7X2XN9bgxb3LcKChEtlON07pMwkTigYhnfFbPtMwnOXKC93FcHazjFe6sX//fhMoX19fb/qrqW9U15Co0U1qa2tx7bXX4s033zTCxqxZanwjhBBCxIpGfxOe2LYQZY3s+xCIdxqe1xuXDJ4VNWcznYosO5WWfTRERNFTt956K/7yl7/gqaeewqmnnqqzJ+LG//73P1xyySW4/PLL8dOf/lRZ4UIIIUQM2Vq9C+8eWW0aeduc2mc2+nRUrrUL+C2/2X+0bBaRmixevNj0z2DJ0UcffdQEyouuIVEjSs4PGhvf/va3cf/995sGkhk3SLHeuJRXIYQQcaDB58XiI5tR4a1D7+xCzOw5ottN/IQIZteuXcah3NDQYIJWhg8frhMk4s7WrVtNFB8bh1NYGzQovaM6Q7F7HGWcXSWEECIh7Kk7gB21++B0ODAyfzBKVZJVRHld89BDD+G2227DPffcY4KntMbpHhI1ogizNS699FJ85CMfMU3E8/LykPZU7ga2vwZ4a4GsImD4qUAa1wYUQgghRPpHyF922WU477zzTPkfRU+JRFJXV2fqLT///PP461//igULFqT9BbEsHxr8a+Gz9pvnbscgZDnHwCHxWgghhBApWuXn+uuvxyuvvGLWc8zSEN1HYY1RhDfl8uXLsXnzZtPYjz/TmoZKYPPLAUGDsB73xn8B3rpEH5kQQgghRMTRU+ybQTHj3nvvxWOPPSZBQyQcimq8F3lPnnvuufjxj3/cksGQrjT6N8Bn7TMlBvlosnbC69+a6MMSQgghhIiYTZs2GR8xM3DpM5agET0kakSZAQMG4LXXXjN1l48//ng899xzSFuq9gCWL2iDBfgbgZpAVJUQQgghRCpQUVGBiy66yGTaMvP2c5/7XKIPSYhW8J5844038Otf/9rcq7xn05Um60A722RfCCGEECK1oE+YvuHTTjvNZIP3798/0YeUVkjUiAEejwcPPPCAqZV25ZVX4vbbb4fX60Xa4XRHtl0IIYQQIslYtmwZjjvuONTX15t/z5gxI9GHJES70CjmPcp7lfcs/52OOOAKa5sQQgghRDJCHzB7Z9An/PDDD5s+zPQVi+giUSOGfOITn8DSpUtNzTSmF23fvh1pRfHQQB8N2M37HEBuKVAg5VEIIYQQyQ1L+LBnBtdo1157LV588UWUlpYm+rCE6BTeo7xXmbnBe5f3cLqVo/I4h7WzbXhCjkUIIYQQIhLo++Ua7dVXXzU+YfqGRWxQo/A4Nfj78pe/jKeeegqPP/44zj//fKQN7J+xd1mgv0ZuD6D/DMCVleijEkIIIYTokPLycnz2s5/F4sWL8Ze//AXz58/X2RIpx8KFC01T+zlz5uDRRx9FSUkJ0oUm/z40+feaoCm3cyDczt6JPiQhhBBCiE755z//iWuuucYIGazgk5OTozMWQyRqxBEazdddd52JrPrBD36ArCw5/4UQIqlprAP2bGZIN9B/BJCTn+gjEh2wt64GVU2NGJJXiByXyiCKjnnvvfeMoTF+/Hg88cQT6NWrl06XSFkOHTqEq6++GuvWrTMBVCxRJYQQIsmp3AfUlgG5xUDxgEQfjeiARn8TDjVUI9flQY8s2YGiYxobG/HVr34Vjz32GB555BFlZ8QJiRpxZuPGjbj00kuNoPGnP/0JI0eOjPchCCGECIfqMuClR4G6qsDz7DzgI58BSvro/CURfsvCzzauwCv7d5jnhW4P7pk4F+OKeib60ESS4ff78bOf/Qzf+ta3cPfdd+MrX/kKnE5VYhXpcW/fd999+M53voPvfve7uOWWW3RvCyFEsrL1XWDHkqPPB0wBRp+SyCMS7bC7rgx/3bkEdb5Af9ypxYNwTv+pcDjs8utCBNi0aRM++clPoqmpyQSYjBo1SqcmTsiSizOjR4/GokWLTJr49OnT8X//939pVwdXCCHSgiX/Auprjj5vrAcWPZfIIxLt8PK+7S2CBqlu8uKetYvhs/w6X6KFffv24aMf/Sh+8YtfmPq2t99+u5y+Im2gOHfHHXeYe5v3+Nlnn23ueSGEEElG9cHWggbZsxoo35WoIxIdBE09vXMp6psFDbKqYhdWlB+1OYSgL/fJJ5/Ecccdh3nz5uGdd96RoBFnJGokANZUe/DBB/HnP//ZRAleeeWVqKioQFJD51D9QaB2H+A/OrALIUTaUnEwMPbZ8N+VhxJ5RKIdNlSVwRUUMcUwgTJvA45QhBICME2Vp0yZYspMrVy5EnPnztV5EWkJ723e4z179jT3/L/+9S8kOz6rHo3+w/BZQUEEQgiRrtSVt7+9toPtIiFUN9Wjxtdg7AobJxzY09H1ExkHfbhXXHEFbrvtNtNqgNng6p8RfyRqJJBzzjkHq1evNrVwp02bZlS9pMTXCOz6d+Cx51Vg+3NAY5KLMCItOXz4sGnqymynI0eOJPpwRLrDMlOOoGmSjvNiNSpNNnpmZZuWJ8FQ5Ch0q29VplNfX48vfelLuPzyy/HTn/4Uf/zjH1FcXJzowxIipvAe571+//33mybi/A7wu5CMNPj2oty7EFVNy1HufQc1TRsTfUgiQyNtaZO/++67WLNmjaooiNiSW9L+9rweOvNJRK4ry4gYwdDcKHBnJ+yYRPJA3y19uPRJcf5ghqxIDBI1Eky/fv3w0ksvGYPj9NNPN7VwWYctqTiyCmgIciD76oH9byfyiESG0dDQgLVr12LLli2YNWsWhg0bhs2bNyf6sES6M/NsILfg6POsPGDO+Yk8ItEOFw4chd7ZucbssDM2Pjd8kpqFZzjvv/8+Zs6caYTwFStWmKxYITIF1vu+6qqrzL3P7wDXTvxOJBN+qx7Vvg+a3UQB6v3b0OhXRqSIn5jB4MKFCxeif//+mDFjBvbu3Qufz6dLIGJHQW9g6OzW2wZOA0oG6qwnER6nC6f3nWD+TXGDFkaRJwezeo5I9KGJBEJfLX22Z5xxBm6++WaTEUufrkgcahSeRNDwYPpSUVGR6bXB/htJwa6XgfpQA8MJjPpkgg5IZApVVVVYvnw5tm7daiaLHj16mGjDQYMGYcSIEWrSJWIPSxjt3RJwuvQbHmgWLpKOKm8jXj2wA1VeLyYX98L0HsqoiRf766uwumKv+ffU4v7ok1OIRDdMfuCBB0wz8C9/+cumIbjH40noMQmRSLxer/kesCxCMjUR9/oPo7JpeZvtea5RyHUNT8gxicyBIh8bu9bW1ppgKQqBFDmmTp2K/Pz8RB+eyASqDgC1R4DcYqCof6KPRnTA1ppD2FF7GLkuDyYXDzIZHCL2cDzeVbcb1U01KHDnY1DuwIT7fjZs2ICrr74alZWVJiOWPZJF4pGokWTU1dXha1/7Gh599FH85Cc/wfXXX5/wLy/2LQSqt7eKpII7Hxj2sUQelUjjCYz1oBsbG02TS9aFZuMlGhj8HR1WLpcr0YcphBAZz9aaI3hkyyLTTJE4HQ58fsRcDM/vmZBzs337dnzqU5/Czp07TXAIG/YJIQK8/fbbxhgfMmQIHn/8cQwdOjShp4Y9NFhyKpQC1yRku+TgE9GnrKwMH3zwAdxuN3bv3o1JkyZhzJgxxtZmdobsCyGESDz0+bx3ZDn2NxwwOTIWLPTN7oOZPY9LiG+Ux/PQQw/h9ttvx7XXXot7770Xubm5cT8O0T6JD9MRreCXg5FUzz77rPmysO8G02ATSuk0wBVcO9AJ9JmTwAMS6QwFjcGDB2P27NkoLS3FiSee2BIxxUlMBocQIhMdMcnIC3s/gM/yww/LPPhvbkuEsfHEE0+Yxsh0UK1atUqChhAhnHDCCWaNxUxwfleefPLJhPYOcDnyketsnZHhcfRElrNvwo5JpHcpW/bL4PegsLAQp556KsaOHdviIJN9IYTIxEzO6upqJBuHGg8bQYNQ0CB8zu3xZs+ePaZfBn2z//znP002uASN5EKiRpLC/hpceDFKnVEkTz/9dOIOxlMADD4X6D0L6DUDGHIOkKcIKhEbxx2zlXr16mWeszwCJ1shMgVGCpaXl5sFFL8PquscPszi+vDDD03jtvXr13frOiTLuEOHI0tT/ve//zXl+JKNCm99cA6n+XelN74NiVkP/eKLL8add95pUsEfeeQRFBQE9cIRQrRAZy6/I/yuMOKQ3x1+hxJFnnsUCt3HIc81GgWuySh0T4fDIfNUxCYLfMKECUbEGDBggMnoEyKToI29f/9+82DZtUSK2qlGRUUFFi1aZB41NTXd6seQLOed9uZbb72F9957D8lGva+h3e0NHWyPFfTBTp482fim6Js97bTT4vr+IjxUfioFeOqpp/CFL3wBZ555Jn7xi1+0OHyFiCeN/nI0+avhcuQhy9kj6ql/O3bswMGDB02pKXvf7J/xxhtvYM6cOSguLo7q+wmRLI549ozhvW9nIrGvUl5enjE4WLOT3wNGn/fp0wepDIUCLqB79+4d9f2uXbvWGBmMumSG165du4wBMnHixGP+Pc8zRRDuh4YGH7wOjMJhUEE8sN+XD0ZM8dgpYtDZOGrUKPN5mL2WbPxh+zKsqdhrsjTsRoqTi/vjyqEz4vL+f/vb38z6aP78+Xj44Yejfm8Jkc5w3rnuuutMWarf/OY3uOiiixJ9SCID8Vle1DRRWLOQ5yqF2xlcHSA66yw67pidxN58wbXRjxw5YmwMIdIR2hDsG2MH6nBdSxuDcI3JdTPXuzNnzkSqw8om7L8Zbf/EgQMHsHHjRnPeKIqSN998EyeddNIxs7u4pqd4ymPjcdkltHldWIkiKys+vTn4voSlvWlf8P059vH9eW/wcyVbMFCVtwqvH1zYZvspveej0BP73n20v2688Ua88sorZn106aWXxvw9RdeRqJEisLcA+2tQHZbhIeJNpXcjanxbW57nOgei2BOIdooGjEjnoit4UbV69WrjzGXpKTuySoh0gvc8F01sek9nbEf3OJ3cdLrPmBEfR3EsOHz4sKljzcU8Sz9Ea9xYt26dqY09btw4E4EcDCOPKHLYBpxtYFDE4GKeDj0u8HNycszf82cwS5YsMQ2mGVVFcYRCU0eRb8wQ4evs92CWWXZ2tvld8HXleMbjpbhDgn9nN+7l56CIy598z2XLlpkoIe4vGBoiHCPt92RWD/fHY+ZniUcpjZqmBvx262LsrqswzwflFuNzw2cj3x1dp1QovHY33XSTMTYY7HH55ZdrjhCiC3Ds+NOf/mS+Twye+uUvf6ngKRE3vP5a7KxbAp/VaJ474cag3JnIdkXPaUXbmXOo7bTjGoDrA86TXCOUlJRE7b2ESAa4HuV9z4ofLDfYmfOcgh/X5fYaNBXnMK6Tua4//vjjzWeOhgjAYCk6/2mfMbgoeL1OMej9999vE2zEdThtE9o8dtlYltRmZljw39Ouo43BtT5tFF6jjj4bg5rY/4fXx87w4NhFG4DHyf3awVjcH4+Zvwt+P/7btg/4GtvG4N/wPgntP8f90X6xqwXwffhv/j3vJT7i4ZfZUbMTqys+MOWn2FdjSslEDMkbHLeAKZ4X9tGgWCaSG4kaKQQHmD//+c/G8DjjjDNkeIi44PVX4lDju2229/TMQLarNCrvwShBThz2BLllyxYzgXIRIUS6QEcs7227ISWz7jpayIaO/Vw88ztB5zqd4skEHQQUZ7gwpyHFhTyd9vbim5+Vi2ce+/Lly83noBOBr2WkGD8PnfdcsHMxToYNG2ZeY5fgovjA9+C5o2OC55KL8vHjx3doiPF96LgIXXjzvZjNwUdnhh6Pn+/LRT8NJu6HxhI/C2tz8zPys1NsoCgSXF+V783XBG/j/mgI8XPz2MM1CHgOKGrZn9M2YGhc8L1tY4XHaR8zxQ4aeKwZHuv7hU3CDzQE6vH2yS4wzcLjYWzQCGeQh4wNIaIXPMXyffxeffzjH9dpFTFnd90K1PoCWRo2Oc5iDM6LTmYiy+xwnWHbE5ybmQEeTpS1EKkC15V0wnPtx3uca8Fp06aFVfefkfssczp16lSz7k6mIEJ+FmZK2Gt3HivXxMEBPXwNbQH+ZHkgru35uflanhf+Hdf6XBtzzU4bgmImnfYUF7gPO3uBmVz8yfU7Ayo7EzyZgcF5M7iUFMcU/g2P4VjrfFt4YBYH90Xbgn/LtT2Pi8IIPxNFkYEDB7baFz8/Xxc8hvGz0i7h+/J34WDblzwvoSIIz6G9f9offPA9eB55fmh/8J6JNQ2+RtT56pDrykW2K7aZLbQtmZ3x6quvGj/rZZddllTfB9ExEjVSEA6gNOjtdHEZHiKW1Pn2o9y7qs32Ivd45Lujo5YvXLjQZGnYkch8znIiInk40ngY22u2oMlqQu/sPhiaN0ITfSdw0bd582azaOXikotwW8To6gKJC9xt27aZxSwXuHT823AhS4OG++binaWq+AiN7o8WXNjSeLCzqRjJREOAn5WLcj7sqKLQz8u/5WvpbOcins+5oObi3c6qYKo3BQCKCHwdHzx/fA0dFDQY4r3QpLhil4XiOabh0FH2RqKheMb7hdk9qRp9F4qMDSFiC8frv/zlL8aoZ/AUs6BU0k3Ekm01C+G1alttc8KDkQULojZv0G5mpgbhumXIkCEqaZtEWJYPDf5N8FnlcCAb2a5RcDmSqxROso3TvK/pCOc6lGs9Qid8aMZyuHBtzcxnOtO5JmfGgx30Y/er4/qX6246/vv27RtTAYRiJG0oOtX5XlzH0ibg5+X722JNqI1hZxjQpqA9QZuE/6ZNxs/DNTvtDwYKUdzgg3/P1/JBW42vCc3cjgc8TtpyPP+0q/g5k3H9znP8+uuvm/uN1yZdeOaZZ/DFL35RAVMpikSNNDA8FixYYAyP/v3VvFtEH6+/CocaF7XZ3jPreGQ7u5/iSbjYoEh38sknmwmck+XcuXNj5pAVkVHWeATLy5e02jY4dyjGFI7XqQyCDn4642kUcPE8cuRIcw9zu13HmbWjm/z1pma0y9H1iBOKG4zusYUDGjJ21gKFFBo8jG5iNI0dyc/XMKOgq9AQYP8Pfh7uj5kXXHiL5ION5nmteL0pAKVDeZxbbrnF1CBmMEc6GVJCJHPw1M9+9jOVdxMxY0/dStT4DgZlajiaMzVmRe096MDkmmz48OFm7USbg33KROIxTmjfcvhwJGirC/muuXA6jp1lkEnnyY7op8hAsXno0KFGaLAj8wMZA3XN36W8LgsOtBtWrlzZkhVMGJDFICM+py1A0cEuo0po9zAzuDtBQyyzxLmH0GbqThCYiC3vvvtuS3nwVM944/eKVXBee+01ZWekMBI1UhxOKl/+8pfxr3/9Cz/60Y9w7bXXJqWqK1Kb6qYtqGra1PI8zzUUxZ6xUX0PLtR47zICnQYHy8bQGcdFTapPmKnO+xWrsL9hb6ttrG25oPeZabPgpADAqCAu2mnsHutzcWHPGqd0HtuLfgoYLHHQUbO1au8+HGh4HxYCDdt6ZY9HsSf2tUFtWL911qxZXYr656KPUVns/xFOOrtIPDR86cwJjq5jXV/7/rSNZIpgNI6TEQozdK6uWrUKP//5z3HxxRenzZgjRDLD8YGRizT2WcaEYiKdwkJEE6+/HrvqlqDJCpSTYbAHe2pkOaMbMLF48eKW+vecVzj3MTCDDmGROPxWDWp877TZnuUciWznCKQDdNgzQ4g/GewUTg8XZhswSIpBRHY2AoM5WIqoPT+PZTXBj5XM227eUgAnpsPhyE5q+4Kfkz00CTOoYtHoW8QG2hbM4LErBPD+5MP22fDeZe9I2tTxaogeqY3029/+FnfeeSfOOecc/PSnP1XAVAojUSNNeOmll4zhz8nukUceMRG7QkQ7Y6PJqoHLkYssZ/eMAKaQchHDBR5VfjqS6VBm6mdwqQPW0KdDlamYLLVDh5yIP6vLV+Bg4/422ylqOB2pL6KybJPdr4KRQnxMmTLFRCvRARy8GKMxTAGErx80aJB5hLMA9/rrsKN2Yau60WRQ7hxku442so4VXFyyUXckzcZtcZGGBucWkbrQKGZUHYU4jr+8Z3kPM8OTUXcs7ZVM2Z4c8xkhfvfdd+PKK6/ED3/4QzVzFSIBsBzHV7/6Vfzxj3/Ed77zHdx8881J19dJpDZ+qwm1vkCkfq6rB1yO8OrBdwTFfPaUYhkZlp2iE5iRxXPmzGk1x/B1nP9Yaobrv2R0vKU7Pqsatb7QagAOZDmGmTJUqQ6d9rz3WM6J9xmbMjP4ye5BFyxw8LVcp3M7g6TY9yHcEqc+60N2qAnaQrukFC7HNMQDZvWxz1kk7Nixw9hbwaWuRGpCe5FZNnamjd33j4EQFPSYZZ1MYhXt/s9//vPGJmLAxllnnZXoQxLdRKJGGkFHxV133YVf//rXuOOOO/C1r30tvcr3mGjohuaJOotdjBJ9RKKLMOqWizou1pjiyrI4NJzZcKq9hQ0nR4obnCwZaSWDOr7sq9+LDypb91XpldUHU0uOQ6pCwYKi2fbt240zNzgClWMp7zdmJHDBQ8OCxgjrzTIKpSvRqtVN+7G/vm1vmnhka9BAosExb968DpvH8TtmN/nmeWHfCIqOxx13XEJqy4r4wWvPcZgZG/w3x+BJkyaF3Wgw2rApO7NOaeAzSIPGkBAisbz11lvGCcB5kdGNkQjkqVLX30KDqenvcCg7OJVhs3uudyjkc25jOdvly5ebnx3VsufrGLyhAI74Yll+1PgWwWopmxQgzzUbLkfsA35igd37jfYFfwbbrVxjcTvvOa6xuc5hEBUzh7idwkBX1l4+i9kurXvTAB64HCcj1vC4+Tk6K2/Lz80SVRQbaWMw0Iq2F7O/RXrDni8UkO3vACty8JEo2//ee+/FT37yE9M/g4EaKqOcHkjUSEPoEKDhQcfcL3/5S5x++ulIeSwvXeGMGWvewBrhUwCHosVSETvd1E4FpwOZ0RpMWews0oN/xxTyri76RNfZWbsdW2s2w2/5UJrdG+MLJ8HtPPb3j45yjkl0jNMZYi/g7YgNLnRZRo8RO4SvY/bO+++/39IPoj14/VmarKNST+0dB9NgaeRyn3wfluNhpkVnJft4fMwisrOFuurgr/eVY3dd674kpF/ONOS7+yDa8Lj5eWk8cCHJ7L3Qc0UBg1FhtpBoN/lm2TcKjV2NqtldV4m/7ngfhxprMTSvBJcPmYQij4SRVHJe8jsY714c/G4yMMNOB2d0eFoFZgiR4nBOZtaUXe6WDoFwSqkkO43+fajzfUB3pGlTneeaDI8z+vOyiA9Lly41wShcxzCCmHYD1560OTorN8V1J4Ot5GiNL36rHvW+9+FDBXM0kOMcB7fzaNZ+ZzDwiBnUzORn1g2dpcEZ/xyzNmzYYK4/Yfkn3hO0Oex+c3YJWRtuYwUBvjYc+PdcT3PNzX/TpmAmBo/pWI276auhw5fH3adPny6vu33WsiAfiU0+XI72hbzuQoHCznJnpi8DFYPheaCdb/fg44POY55Xvr6rwYl+y8Kysk3YWLUHLocT00qGY2zRoCh9KhFrGDz16quv4iMf+Ujcy+W/8sorppwm70PaGQzaE+mDRI00hQ64X/3qV/j2t7+NM844w9SJYwmRlMVaw3jxkI0DAceEBB2Q6C6MeqdT2W74ysUoJzo60hiZ29FkxwUqU3kZEaJmsckHF7Isz8SoIy5mCUUMLny5mOHClgtdClN8zvJGFBZY05/XnNk4FBFYw7uz6AkaJbwPuJBmxBMNilDDJBgKZsyw4II6ERjxpn41anws40WjxUKOswcG5M6AI4ISXn7Lb/qZHMvwef31103EIYWY9nrSsA4qrw/PczRTgo801uHuD15Dg88HPyw44UDfnHx8a8LJ8DgV/ZoKMHrwjTfewPTp043Dks95D8UqdZxjw5NPPmmEDI4Vv/jFL7rV0F4IEfv1G50DnMt//OMf46qrrkrZfn4+qwbVTaE1/R0odJ+gRsUpCucUZmvMnz+/VTYiexTQvuis1CId4LRHOP+pn1/yQRuQggTX/LzOtBlpB9Du4HqFwhTFAdoVdnkjritscYHln2wnfEdrGu6Xf8ugIK6jac/Q5gjF7nVBKJZRBElUJQHLqoAfFDaO2kFOcH0fYXCK5QeOYZMcPHjQjP3sodGeaGMHIPK8R9vmWnRoHZaXb2617cy+0zG6UOWpUwVm67AkFbPpOMbyexTLsZbf5VtvvdX4mBiIccMNN6jiRxoiUSPNoYOQ0Y5s9vf1r38dX/nKV1Iz8tF6s7n0VDC5gCOwYBWphV3ahlEenNRsuGjkpMNFIbd3FIXPCZAR5jSiGVEsug+NOJ5Tnvv2yoDRAU6xlJkK9qKdC38u+mlMUFiwt9GgYNRRR2ONnerPiDhmW3Q19ZP3ASO0aExw4ZxM9To7Ot6qpj1o9FfD48xDkXtg2IKG19+ExYdXY2fdPiMUjC4cimkl4+Hs4DMz8osZMueff36rxtCMJKNBQhEpFum/rx3Yij/vWBPSOQS4Y+wJGF0Y38h/0XU4FvO7xXGB4yzHaxIc1cif/O4xIrar370VK1YYA4OOhwceeAAXXXRR0n+PhRCB7//f/vY34yyg44+Z4XQEpxqN/j3NWRqtyXVNRpazX0KOSXQdrlM5b3ENxKANZgjbcP3DdS4d2uyh0VkUOgNu2IMj+O9F16HYwLU/g9G4/gyGdgMzFpgtTLvBXgPQOU5BgU5QvsYuj8m/Z1BcR2sFiq68B2iLMLCnO42QKZ5QBEsF34ll1cAyAaAWHGDWRwTlu6yDADgOsjJGbnM1jKIOzwvtC14LWzi0vze2bc65IBYCz2+3/BuN/qZW2wbmluLCgUf75Yjkh3bozp07zfea8GeofUHo4zlWtlNnAuh9992HH/zgB7j44otNhqmCYdMXiRoZAuup33jjjWbC+fnPf46PfvSjSCksNhGrDtlYDDhmJeiAkpPgqJFo77fJKoeFJrgdRXA6srtcXoTRHYys4eKVTm1G07QXgc/f01AO/X3owtVO8RXhwYU+rwEXo3RU8vzxPNO5yAg2+/zz2tiLCrtcEyMpeA35t8yy4IKVad40HFSTMrYsOrQS22v3tBILphSPwcTito0UuVBkjVuKU/ZikIYZr3usM5z+u38L/rrz/Taixu1jT8AYiRppBwVNGrGM2ItkDKCT4pvf/CYef/xx4xRlDzCNIUKkHnRGskY1RclrrrkG3/3udxOWEdkVvP6DqPWtbLM933Uc3E4J8bG2L0iTvx5eqxJOZCHLWdzl92G/Pgbg8P6juMEyUqHOaDrXbccsRYuOIoTtXmQnnXSShPYI4HqA55b2AZ2KdEoykI22xZgxY8z5p1jB+8m+p+gE5/zP13BtQLuE/+Y2Cgq8nsqaiSEW/SvvtsryYD8O4ATA0brUM20/Chq8RixrazuiGahGJk+eHNMo+PZEjQE5PfGxQbEpsyUSB30UzOjgPUZ7NhJeeuklfOlLXzI2MAMuggNoRXqihgQZAku9sMboww8/jE9+8pPGeUnFkhNSajCaMZ3NZVts2jrzMhlGFTHFLrTJHRcbdEhzccnBnU7NSAwGNk+sbFqBJsuu1elCkXsaPM7IjVY6xBldw/uOi1gujhiFQ5HDzgzgTy6IaGzQ0c5jZ6pwe/CzsP47U47phBedw3uAzRJPPvnklnuA0RJ79uwxadO8Jjzfp5xyyjGvIyO0FVEdP3bXHWgjFOyu299G1OD1pNi3YMECc33odN62bZsx7ONhnE8t6Yd/7P4QXr+/pfxUaXYuhuWnft110RpGVnEsoAAdbukZjvls/s3eGRRC6IRitpYQIjWh4/H73/8+Pv3pTxsnAjO37r77btPbLxV6n7kdpXChBD6Ut5SGdDl6moc4akewBjkd1KFZvBz76XCmE7orUbV1vgM40sieiYEVTo6zN3pmTY2oLGdwQ9qZM2eaYzCZsVVVJpAnuMcC7Qaug5nFwQCejvr42b3IOEfxteLYMDuGtpidBUNhiBmfvDfseX7SpEmd7sMuW9vV/nWiKxwJETTQnLFR2dzDtHVfAq7daLdTnKL9SDue1zweYva4wkFYXbGt9Tb11EjbgAmOB/QbhQtLK99xxx1YuHChWZdcd911EkQzBGVqZCB0erGm3GOPPYZPfepTxvjo1y8FUqytiqC+Gv07TIvMROgo4uKd0TFsumw35bLhpECnJhf8fM2FF14YVko1HaLZRWVocgaaONs44EEPz1HHeCRwn1zk8m9pNPC47UwAO/2QRjIXSDRKlixZYgSOzpR8ChtcZEnYaB8aFiz1xUyt448/vo1RKpKff+z6L+r9rUvw9c0uxal9Z7d5rW1k8PtDI6OzUmCxYFtNOf60YzUON9RhcF4Rrh42DT2zVMIhndYQLONBZxYjYYMdRh3Be/Ef//iHKYdJBxjr8J977rkSRoVII/g9f+GFF4xTges5NhXnejPZAyAYvNPo3wk/auFEPrKcg7vkVE9XmOnJ8Z49KbjmDr6efM4H1/N0KM2YMcPU0g9nXXrw0AFYxWthmQbtRynxjEe+u3WAVri2EIM66AzjMTL4hs+ZzW1nBnD+4bxFpyz7K7B5eGeiPDNfmT0QaaRwpq0J7GbX7F0nUgxrN4C17fxiJuBoHZBEgYp9Z1hKjH4E+o/CWQNGC5/lx+LD67HBbhTeYzgmFQ1N+jlGhAfnBc4j9FdQnGaQRDjBESy3T3/mE088gc9+9rMmcCqe96VIPBI1MhhOSnQwUHW//fbbTb8NlX9ITbiQf/nll81k0F6dfC7k6fDnQr6ja2xHNXGRYk8gVLqbXDtQ7z2IkpIC9CgtQkN9oFnaxOEXw+XM7jAjgAZtpLVo7Vq4NCBoRHHxFNzgrbO/47HSmOpq7cV0g+IVG27zHNKI48KA6cIiNdlQtQ3LylobHaf0non+uVq0idjDMZZOC44rdpPNcLMz6DjiGoNCCAMqaHAkqpmmECI+4wUDp+hY4Fjxk5/8BHPnqjxIqsJsT/a7Y7AR15Pt1T6n7cFHR85FrkUZYMU1Ol9De+PNhf+DN3sLcnKy0LNXMbKzs1BdXYfhAydjQMlxHR4P5yGuZyPLOrdMZDmzC9kLjsIH99FZbw0brqX5+nDEmkyA55LnhOeRUCCys71FCmJ5m8tPNQRlbFDMmHHMpuFCRAMG3VI05xjCbLpw/RUUP+6//37TO+PMM880/TNY5k5kHhI1hHEG0+HARes999xjauLK4RA+rA/6+uuvm7qfrFVvL+wZDc+6k3Z9UJ5TlkmyncwdDbp2NFFXFoe204nO/dD6o1yQc8JgdAUbBzNymwt8ChBmMHA4jLHB39MgteucTphWglrfFlRV1uLI4Upk52TB7wOq9g02n8nOCqFYwqhwKuN2Qzg7Koq/p8jBFEIufO37i9FddHTxHPF1fDCyiq/jI5JzwP3TedZRKnkmQWODBmN7pQJE6rKtZg921u6F0+HEyILB6JfTflk2IboLx246oGhoMFKVYzGFUc5h4cKxnYETFNxvu+02Ezgh0VmIzIHrEDob6HRgLz86HChyiPBh2ScGGtk2xpAhQ8z6mMFMzz77rHnO8ZmBS1zbM2KVTXo7ys7kursr/Qm4pmcZY5YR4rGErr+ZIcpeXnxfHguPiUFKtujBtSh/Z2dnc/vgIQNhlXxoPsvhgxVobPSioCAXlfvy4Wzq0XKc/Py0Ddi/jfvmnGI3iub7cF+0K9gUmg52G9pDzNQgfC3/lnMYXxNp0BWjh/n+wfvPRHit33jjDSMG0d5ThHyaYNEXsIW1HQAUABgBOCIfJ4QIB85DnB927dplhAn6jzi3hOv3oZ/q97//Pb797W+b7DAGTsj/k9lI1BAGLgj/9re/GQcEF56Mrrr44otVhy5MOLiyrBJLP+3evds8uOBjZBrL/lBQ4EKQC2wuzmmYsK4oDQD+HR09XCxTZKAjiIttvp5qdSS1BAnfiw2BucDnJMFFPN/Pnii4XworFDNY8ulYZWn8lheV3vfgQ409bKDAPRnZzqPNhs1nq9qDw0cO48ihOuTm5JtmYaH3GLMv+Pns5nD8SZGjo54ZkfLOO++oGRRgSnYRXoNIDTchROZhz0/sy8R5iPMFxw7OPyzdEYnAzHmNzkumgV911VUmOyPUCSaEyBzo8KZd8Yc//MH03vja175mnPHi2NhZ1CwpSXuBYzTX0tdee61xCrGcK8dvitBc/9OGY99ErvMZrEbBgM5n2hn8W9sOsRv9RhpAxSx/lhvi3EARhT+DxWq+n52tbWdldEZN026Uez9oee5xFKN39vFwBDlU6xqqcLhsDw4drEBNpdesbUMjefm+PBcU4mn/8P15fBTjoxGoR3Gf1yLT71tmurAnydChQ439JoQQx4JzAn1kXAtwTqBgzTGc9gXH6UjEkKefftoEYXOOY4nLj3/84xJXhUQN0RoOEL/73e9w7733mkGGCugll1ySUeIGB0wu2rlwi/bn5oLYrj3LAZ4RaxQcqFLTWKHIQOcP35cLcgoiNGZoNHChTkOEE0A4TiZeS/4tjRwq4dw3DZiuOLkD9YYPwoIXbmcPuB1HJyDL8qO6aSWarMPmuQNuFLiPg9tZjHjA88mMGApLXGS3V34r02BEHRsgjpsxDgetA/BZPvTJ7oOBuYM08QshDHRuca7jGMr5hBGsHD+7WobSFjMef/xxfOxjH8O3vvUtky0mhBCEa9rvfve7pr+OLW5w3ZZJUJDguHusjOzgEk/hbCe0G+zX0F5gYBR/skwQBRCO77YDicfB7AP+nsFNXDdy7O+sTG2o0MK/ocDBfdPZP2hQ19aYjf5KNPor4HJ4kOPs06qfSb3vIMq8bCQe+Gy5zgEo9kyM21qWQhAFJZ5b9vfL9MwEXnuKZYcOHcCYqaXm2rkc2SjyjITbqebeQojAXGSXqOM8xzmGwbv0cXVlDA0WM+gz+/rXv47PfOYzqkYhWlCmhmgXOtzpmPj+97/fNXHD8gFlHwD1BwFXLtBzMuApTPhCjMIAHTd21A6FBC7Ogl9DWBoq0gyJWGIfNyeC4CbA9vHyGvEzMVKLj9AJg7+jqMEopnnz5kX1s9X5tqDet7nVNgeyUew5MS6LfxpljEKLVrZHKkMhjiXIeL0HjR2IldUrWv1+dMEYjChQZJUQmQjnCzvSlwYCo2g514XThO9YYgYDIZiZcdFFF+Gb3/ymxAwhRMzEjeqmcuyv32YCNko8vdE7e3DCnc1ce9N2YpkkwkAbfk5uD3b08Pccd5MlWIwZDnQS0S5gEBTtBIoVxBZe7NK53M7PEHquKYLwd8zyO+mkk6J2bH6rCQca3oAFX6vtxZ5JyHMNQKzhPMkAIZU1CZyLLVu2mPJmFL6ySveh3n+4xepzwoN+uXONwCGEyDw413H+YMAU5w2Whepus26OO0899ZQRMzjHfOMb3zBrBpXWFqFI1BCdwkWsLW6wjBHFjUsvvbTzxTgd7fveBGp22rcZ4PQAQ84B3F2LAI0GrMHKrAgaGhwkuSjnIp41/BJtDHUXGiT8XBQu+GjPaKHDm70rgiO1ovLe3lXwWgfabC/xLIDD4Y6ZOMWFNaPDKGZkalMou44xy53xvNDwZDo4nZQrypbjQMP+Vq93OVw4rc8ZKX+/CyHCg+MCxwe7LwYjpRhRGw2HWqiYwcwMZgMKIUQ40On/ve99D3//+9/xqU99ykRfHkvcqGmqwIeV78JqaWgL9M8ZgUF5iVsHcm39yiuvmLJItkOH6yxmqkVzvZ0IaC/xM9GJxEzv0LmDcwzFEJ4D2iF0eEcLr78KhxoXhWx1IN81BEWesYiVY472BQMA+NnZHLyr2YupDq8rhQz+5HVnzxI6KRv9Vdhfv6gdu28sCj2ZlXklRCZDIZt9izhucn6gkGEL+91BYoaIFIkaIiJxgw4MOkxvvfVWY4BQ6GiDtwrY/s/QWw3oOSWQsZEgOECuWbPGLM7oBO+ueiwC1DatR4OfAlZwOryrWdRwRO3asU8I70NCIYMCVbjGos9qwt76PfD6G1Hs6YGeWanT6I+fmWUCKEzx3xRy+NPuS8JUTgpVoWUMlh5ZgsONdhRVAAccOKPvRyRqCJHmMGKYDkMaHCwJMnhw9CKZV65caRr/MhWctWyZmSExQwjRnYxbZm5Q3GBW+G233YapU6e2+9ot1atxuHFPm7XNjB5nJnRtQyc4e8bRLqK4kSyZGKkMe/rtb3itzfYi91jku6PnPKeIQZGe62q7DDDtjHCvYZO/HF7riCm/m+XsD6eje9mP8YKfl4IVI6BpY3C9wLJidhkz3st0UobaWg2+chxoCPTuC6bYMwpFnuiJWkKI5ITBUhwzu1PavD04FjFQ6qc//akZk5iZQX+jMjPEsZCoISKCA8xf//pX3HfffWZAu+GGG8yjlUDQUAbsfDH0VgN6TARKp0XtjHPRFWmDOy7W6BxnRNFpp53WvigjIrsOViOqvEvgR13LtnzXZGS5+oX19yyXZKe68/7q6LrNmjWrVTPCcGnyN2Fp2buo8VUbw5fRfaPyx2Jo/nAkI0zdpHFMaKBzImcGBn/azR2P1dydbK/ZjnVVa1tt653dB8f1mBGzYxdCJBY6JSjec+yYNGlS1AwNOj8YifyTn/wEb7/9tkn//vKXv2wyHYUQIhps3LgRDzzwgAmimj9/vhE3zjijdXbpxqrlKPe2zQ6mqOEM6sUQb/uCvP/++2b91rNnT0ybFj17J5OpadqOyqb1Lc89jiKUZs1s1Ui8IxgExLrudnCU7awP7U3Caz1jxowuiWINvj2o9bHROf/WMuV3izyz4UzCMkwMdqANbJ8Hfm7aFMxEoX1BByXtrGPd+37Lh711C+FHQ6vtfXPmIMvZuoG7ECK9+umwQgR7uzJzK1qBBOzN9Ktf/co8GKjJuf8Tn/hEt8vjisxBoobo8sD26quvGnHjrbfeMioqszeMg4P9NLY/BzTVto7eH3gmkNsnKmeci9NHHnmkxXFj13tl6rPda4KLNDbipgOY5Zm4sGV63OzZs7tkrIiOsSwvGv37Td1bt6Mn3M7wxQf2B6GwwYmLDrh+/fqZR7Su0faardhUc9Qgsjmp16nwOLOQDNDAYIr3/v37zWTOhUJ34XdgU/VGbKvZCj/86JXVG1NKpsLDUnBCiLSDUcLM6mKEc7Simjiv2oEMHKtvvPFGfOELX1CmoxAiZtDB8etf/xq//OUvjfPEdnBwXDvUsAtba94PerUDhe4eGFc0K2rvv3TpUmPbMEqdWW52I21iO8KZAcdj4xhJIYNRq3w913AiujT6y0wjcacjC7nOvmEJGrat+N5775mftClYcpjXM1rlpHgvlHv/19LEPIAD2c7ByHPHpjxWV6C4w+b0DBw7/vjjo+Io9PqrcahhFZqsGjjhRo+sCchzhxfMJoRILehfY0ATS1xz7osWHJeYlfHkk0+afkyc6xl0rDLZIlIkaohuw6gPlqKg4+Pss8820ZvzZ02BY/9CoLEMoOO490ygMPqR8TQyGBnFklLHHXdcqzp+tpDBRRwXsKzTq8yM5IbXio4zpoJzAmWZsO42AN9Q9SF21e1oVX+ZzOk5H/nugoijoHl8bMTN2vTt9S8JBxpYFDC4L35OQmOY+4w2RuSDFbUIRiFEckFRlE44ivvR6i9EceTRRx/Fgw8+aObNr3zlK7j66qujlvkhhBDhrLno7KCNwfX+zTffjM9+9rOozy3D3rrNJmCjyF2KEQVTox6kwrUTg0127dplMi8YQGU7WjjmcjvXqhQ8SktLjZghR0zyl1pifynah5zL2C+jOwFULI9V4X29zXaPsy8K3FMi25ffb/oFcu7l/UQBpqvHxs/H+5Ofl5+b2RdcG8Qi6tmy2Dhe9oUQ6QrnOYoPrJjRVb9HMByTFi5caMSMl156yQQs0MbgeCxEV5GoIaIGF1A///nP8dvf/tYICIzo/OTllyEvP7pN8qgU2wszOltoSFC0YE1eGkBcBLIcFqP95YBJHex+J/YingIHIwJ4HbvD3rrdWFu1ptU2l8ONE3stMI2zw4FGxrJly8x9xcg8ChGMzKORzZq748aNMync4UDnIyd0OyNFqZVCiO7A3hk0OihqUNzvDqtWrTLp33/4wx9MRCcNjfPOO0/ZjUKIhEGH7/PPP2/EDa7FrrjiClP6dsqUyVF1qHIcZeaw3RCbQTXsr8CStSy7Ydsd3EbHs7K+UwOu4dnM1m7gzjW4XXKqO9eQ+6n0sgxTfavtua7RyHGFn3G9aNGilvuN8zjvNz4YAEVBjYJEOIIZPycDDXlvMpqagX4S2oQQXYVj0AsvvGDGlAkTJpjxqKvQZ/KnP/3JZGAys/Haa6/Fl770pahmfojMRaKGiDoctP74xz+aQYsRMZ/5zGfwxS9+ESNGjIia0RFsXNApzNRawn9TUOEgzNdR5OCikw5nOpAZUUWVWQ38kg9m3HABztTwaMLrv75qLXbX72wWNFyYUnxcRM3CWYbghBNOaNf4ocCxfPlyY+SGc+w0XubMmSNDQwgRkVOP8xrHG853dF6w15BdCoUi8NixY7uU2ca//cc//mHmbIquV155pXEYdtSoVwghEgWFV45VtDMovN5000248MILoxIgwvGU6zmOs1zvMcLdbqJM6Njp27evKY/FqHq7rBGdMmymTBuDJbLkSE4uWHr43XffNeVNoi1ENfkrUd20HBYC94jH0Rv57ilhi23sT8njYxBXezCAioIMbZBj2a5cE9BZyKwiIYQId96zbQz6zcrLy42NwdKKNqxQwT5RXRk/mfHIcpKPPfYYhgwZYubsT37yk6qeIqKKRA0RM+z0Mhofzz77LM4880yTvcGmf9FYVHL/hw4dMgKH3fSMxgSNDW4/+eSTW2rbMgWXC0M2SeODA3dHcOCmY4hR+cr0iC9cuHMy5cI9mk1uSW1TDWoaa5DryENBbvjZQzRcWSqKEQqdwehB9sKg0Xus/VHAsUuh2U7JYGhQz5s3L+xjFEKkF3Sibd261URrEo6JfHB+okjP0hThNPTsDAr/zKx86KGHjDOOQgaDELhvIYRIZjg2/v73vzeZZVzXX3/99Sbys7vZvTZ06NAZwzUpRQrbocwyHLQPTj/9dLOd9gQbpzLT2LYxOsIOsmKp0e6UFxJdK8nEtTevGa9ftALtiGU1wWdVo7a2AQV5vSO6rrSTKVh0JoTx3qKYF45dsGTJkjaN0IPh/cn+l9H6ngghUnP+5PxGfwPHCtoWnOMY+Mu5iRljtAu6Cv1yr7zyCn7xi1+Ynww8oA9w/vz5Ev1FTJCoIeICI1Eefvhh8+CCnobHpz/96aj3EOCilZEqfA86jbsSLUVnEkURiiOMnuHAzH2xxFC0mq+KY1+DFStWmHPPiZVZEHZWTjjw+gU7AXnd+JNZEvw3IxFmzpzZ4YTNCAVGO9lRCowG7MxIoeHAe5xp3+wr013syCwKO91J9RRCpA4cR+xMRI5djNw8lkgaKRxTX3vtNTzyyCP45z//iQULFhhD46yzzlIGoxAi5aCowLrcDKB6/fXXjfOENgbHtmiKBnwfri25DqWg3JXMEI7xtFG4xuM6046QZUCMnMzxg1UE+KA9wOA3ZokziCocm5GZO7QhbBuD+7DtCpZHZsYO5+2O+lvxmm/cuNGIZeGW2eV7MmN8+vTp3bab+f4UPmhTTZw4UcKaEBkCfVoU5ekr4xhFYTfaJbA5tz3++OMmYIpz3XXXXYfPf/7zLUHGQsQKiRoirjBChGUuONgxOuWCCy4wxsdpp52W1AsrGjLs+UAjScQXLvyZKcFIJbuptr0wt3uqMKog2BihgcuJlb1WaCzQcOBkTmOAhgaNyB07dmDy5MntvqddfoDRTBRV7H3zPWlc8Jh4T3CfhL+n8MJjieQ+5nGwlAw/m/3gsdqGLj+fSsAIkb7QUUYBleMVYWkTOriiXb6EYgkNDTb/5pjDoALOvRzjhBAiHaCzmPYFMzjoqLYDqDiuJitc6/373/82pZG4ZhXxg+t8BiTR+WaXK7axMzrYRy84oI1ZOa+++qpZ69Om4DWz1+5spEvBi0FZHZWZ5Xu++OKLxv5gydpgpyJ/x8oCFLz4sKsQUHCh+BHp/cHPxM9Gu4XHyJ/8jHzweBk4RRtHCJGecDzZvHmz8b8xkJM+EAqv0YTj1H//+18TLPXcc8/hxBNPNHPvxz72MQUDi7ghUUMkjE2bNhkHC40PLtQ4AF5zzTVxi1bioo5OHrs5NUtO0SnNBSZ/Zy9GOVgzGofOZWVqJBe8djQw7BIt9jXjdaIRYJr4VVa21DrmdaYQwYU9rzf7r3QEX0fhgwuCYEOHQgONAP490zQ7gn9DMYYGE++h0DRwu1EhDW/e/9wvj1ONw4XIHBgtyjJTNAKiLWTY6d908tHQYElGRkwxmEBzmRAiXWGWLTPR6GR58803cd5555mxjyWj4hVA9cEHH5isOBIcgENsG8MOYOHvWGtcJA+8Lgw22Lt3r7mf7PmZ9w/X/rQpGPHMa2iv3SlKUEjggxneHfXA4N+wGsCuXbtaBWsFl5c8Vnky2ihcO9AOssWP4P0T2kEUWWhj2HaG+r0IkTk888wzZv7rTimpjuDYaGdlcByiD+9zn/scRo0aFfX3EuJYSNQQCYeLRTpcaHwwdfycc84xkVUs4xMvB6+dVh66wOQikJGssZgMRGywI5Bsw4JRShQ2eI3thT4NBaZeMsrqWBELjG5gHwzeH9wfOZZRQEOGYghTO9lAks9lSAghQqHDbe7cuVGd6+joePLJJ42xwTGLfTI++9nPdtiIVAgh0hVGqdoBVHTy0r64+uqrMXz48LgdAwNpaF8w+t5eC/In14fM8hWpAwUECgqcW+nI41o/OMuD15X3Ge0LBj911tybf8O/p30RnJkRjr1Am4bloyhahPs3QojMgYGVFE+ZkRVNnx2rYdC+YMYZK5gwYIDCiYKlRCKRqCGSCjYt4kBJhwwXi1dccQU+9alPYdq0aQlZsDEKZ/369WbxOGPGDNUcTxN4PZndwSgsXmMaEkwxZ6ScHf1EMYRwkmYKOkWQcBuXUxjjPjXBCyE6g/MLHV5Tpkzp1vzC8YoRWU888QTeeecdfPSjHzVzJw0NZX8JITIdBrs8//zzxsZ4+eWXTdNljpEXX3xxi2M4nnCNyNKDFDsY2SpxI33gnE77gmIF7QHaAuzLSBuC0c12BjftWmZr08ZgBng4WUQUQminhGuPCCEyEwoQtAcoalBg7Socc1hSj/bFn/70JxPwy8AABggweFOIZECihkhKuNh74403zABKRw0HTQ6eFDkSURuXjm8O6IyqikW9c5FY7EbfNDBpKNDAZAq4EELEGmaSffjhh2aeYSPQnj17RtT0m066v//976ZWLp10n/zkJ7vdTFQIIdIVOpzpnOHYyT4cH//4x83YGe3m4uGydu1aI0xT3JazOv2gCMFrzIwO2pF2BrcQQsTav8HgKWaCcW6bPXt2RL34/vjHPxpfHIOOGQBAXxz7PyVzH1yRmUjUEEkPHT1/+9vfzKDK5uKnnnqqMTxYG5cLQw6s27ZtM44gRrvQQcQ+C6GwhBSj7fk6Gg1dESYYUWXv266HywejcOhE4n7tWqtasAohhAg3e+w///mPmaMY5TlhwoSW+tx8zvmE8x0jipctW4a//OUv+Otf/2oiseyMRvZ9EkIIET6rVq0y9sUf/vAHs3Znc1NmcbDRM/sasO8RBWTaF4x2ZYBTKHbEPcdvRtx3Zf3PsZyOb/60sUsacb8MtKF9wQez7xRcJYQQIhw2bNhggjeZZcH5g/YEbQvaGBRdObcxW5A+t3/84x/Gxvjf//6H+fPnGyHjoosuinqDcSGiiUQNkVIwkp6OnD//+c8msvWUU04x4gbr+dEQYKovB2eWqwqF21lyiA8O5B3BAZ+OIzZUo1hBI4c/bQOCzicKJ9wHJwY+aPCwLip/xx4MfK/2GrcxEouOKooy6tMhhBAiFM4lrIXLOYhCOX+uWbMGv/vd70wtW0ZcXXjhhbjssstwxhlnqLyUEEJEoTzVK6+8Ypw5dOpQxDjrrLPwiU98AjNnzjT2BR90BoVmwtk2AO2L4N4IobARNO0LPmgD2PYFt9t2Ap1MtDH4b9vGoO1Au4L2A38GCx823Mb+bxTE2QtQCCGEaK/PFAOCbRuD8wp9a08//bQpV8U55PLLL8ell16KoUOH6gSKlECihkhZmE5nCxyMpDr//PNNatxHPvIRI0x0BRoRFCtoQPAnBQoaCnzYEVOMwGLEFCcDTgT8HY0h+9GemEFBhA8aMozmYgktpe4JIYToaC6icE/nGuc5lkhhfwwKGeyXoRIlQggRGygsUECmwME+HBQJKG4wi2P8+PFdzpKgjcBIWNoYtC/sBwOibDi2007gewTbFrQ1aHN0BMURuz+DImqFEEJ0BPvW/vvf/zYl3p977jkMHjzYCBmc58aOHasTJ1IOiRoiLZw/q1evNsYH64rv2LHDRK8ykpVOIC7whRBCiGSGjq3Fixfj2WefNQ+WO+RcxmgpivaJaGYrhBCZDAUIOn2eeuopUyKQzh/aF3ywRJUClIQQQiQ7Bw8eNCI97QtmJTILg0I9g6XYz0klDUUqI1FDpB3r1q1rcQotXbrU1MaluMEMjsmTJ2vQFkIIkRSUl5ebZt+MCqbjjBG5nK/oMDvzzDNNRqAQQojkiG6lM4j2BZ1DLA1FwZnZcywry34aQgghRLIE/VKM53zF0lLHH398iyg/bty4RB+iEFFDooZIa9jUm46iF154Aa+//rpJyWbkq/1gUyQhhBAiHlC0WLJkiTEy6Bzjv8eMGWNE9wsuuMA05bPrqwshhEhOWApq4cKF+Oc//2nKeLAR66xZs4xtQUGa/6boIYQQQsSDvXv3GtuCNsarr75qhHj2nz3nnHOMAD9gwABdCJGWSNQQGQPr0b777rtmoOdj2bJlpj+GEEIIEa8SUz179mwlrrPxrBBCiNSF5QKDnUlsGi4bQwghRDxtDGZj2OI6SySy35IQ6Y5EDZGxlJWVGaNDCCGEiAesv846tqrDLoQQ6Ynf78f27dvNTyGEECIeMGiqR48eOtki45CoIYQQQgghhBBCCCGEEEKIlMCZ6AMQQgghhBBCCCGEEEIIIYQIB4kaQgghhBBCCCGEEEIIIYRICSRqCCGEEEIIIYQQQgghhBAiJZCoIYQQQgghhBBCCCGEEEKIlECihhBCCCGEEEIIIYQQQgghUgKJGkIIIYQQQgghhBBCCCGESAkkagghhBBCCCGEEEIIIYQQIiWQqCGEEEIIIYQQQgghhBBCiJRAooYQQgghhBBCCCGEEEIIIVICiRpCCCGEEEIIIYQQQgghhEgJJGoIIYQQQgghhBBCCCGEECIlkKghhBBCCCGEEEIIIYQQQoiUQKKGEEIIIYQQQgghhBBCCCFSAokaQgghhBBCCCGEEEIIIYRICSRqCCGEEEIIIYQQQgghhBAiJZCoIYQQQgghhBBCCCGEEEKIlECihhBCCCGEEEIIIYQQQgghUgKJGkIIIYQQQgghhBBCCCGESAkkagghhBBCCCGEEEIIIYQQIiWQqCGEEEIIIYQQQgghhBBCiJRAooYQQgghhBBCCCGEEEIIIVICd6IPQESP+vp6NDY26pQKIYQQIm3IyspCTk5Oog9DiIxE9oUQQggh0g3ZF+mBRI00Mjhy+/cHyssTfShCCCGEEFGjX79+2Lp1q4QNIeKM7AshhBBCpCOyL9IDiRppgsnQoKDxm9/AkZfbqraY02H/bP5Hu79rfztxtPmdo81rItofHJ3+TXj7c3RyfOHsr/1jiHx/jjA+b+fHEN7njeH1cET5eoT5N9G6Hq33d+zrYf/bEdHxdbK/0M/dwTGEvb/mv+/e8Tm6+Xk7vh4t+7OswD/sn/6Qn+Z3aL0t9LWd/S50/+2+1t/xa461X7PNPs5wXtvRfu2ddHYu2js3Hbw26CUdfob2js/yh3E9jnFuOn1tyPbOzkVXrnN728K6Hoj8erR3b4VzjiO5Hh1d+67st7Nz0d657vB6IPzr4Q/js0Ty/ej0tSHHHXIclT4fBq9ZY9Y5ytYQInnsi0jXDaG/C13fRbqOa7O/EPui9fFFsr/mdVg313Hhfd7Oj6GztXjoerv1/iJf23flenS+v8ivR2e2VuefN3JbK9rXI/S6dP3ztn7uiNL16Mi+iHx/rY8hsv11fD06tC86WzeEta7uZJ3U6TrzGOvqTtdxIe/T2Xt1uubzd31t1u4avIPj7ezzhdoXnb22y9ejg7/pytq+27ZlO2vmY12Pzu6trthE4bwWXbV9o/D9aPd6dHC8nb1XZ5+lO9+Pzu4t2RdpiUSNdCM3F468PPNPe9ngCFlctv+79rd3d2EXyWK/q0707izsIjGWuipqdGdhF4nxlSrXI1rGcHdFjWPdE51/3s721/4xROt6dFXUcCZE1OjGYi2s13ZX1IjGayNYaEey2O/s77tifHX2+aJ27aIkamTC9YiWqNFdYzga39HOPl9XvqPB2xxB24QQSWVftGdjtGdHdGRjaB0XHSd6tESNrlyPSGytzt6rK7ZWePsL39bKtOuRrLZveKJGF9ZxEa/5tI5r9zwk4npES9To9ho8CrZWstu+mXI9ZF+kFcHzpBBCCCGEEEIIIYQQQgghRNIiUUMIIYQQQgghhBBCCCGEECmBRA0hhBBCCCGEEEIIIYQQQqQEEjWEEEIIIYQQQgghhBBCCJESSNQQQgghhBBCCCGEEEIIIURKIFFDCCGEEEIIIYQQQgghhBApgUQNIYQQQgghhBBCCCGEEEKkBBI1hBBCCCGEEEIIIYQQQgiREkjUEEIIIYQQQgghhBBCCCFESiBRQwg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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:01:26\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.2987 (1.4784), Variance: 5464.1031, Range: [-127.8174, 127.5887]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1392, sigma²=4268.6474, nugget=1869.5821\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 13216.8 | 114.964 | 68.2557 | -0.0967 | 0.48s |\n", - "| OLS_sphere | 500 | 4378.53 | 66.1705 | 28.7401 | 0.6367 | 0.06s |\n", - "| DeepKriging_wendland | -- | 10042.2 | 100.211 | 61.5544 | 0.1667 | 55.47s |\n", - "| DeepKriging_sphere | 500 | 5882.05 | 76.6945 | 34.585 | 0.5119 | 21.14s |\n", - "| DeepKriging_sphere_Huber | 50 | 3951.25 | 62.859 | 15.0805 | 0.6721 | 37.00s |\n", - "| UniversalKriging | 50 | 4645.5 | 68.1579 | 26.5931 | 0.6145 | 0.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:03:41\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.8080 (1.4509), Variance: 5262.5932, Range: [-127.7582, 127.9540]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1366, sigma²=3915.8153, nugget=1893.8350\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 13022.5 | 114.116 | 67.4577 | -0.4913 | 0.43s |\n", - "| OLS_sphere | 500 | 2409.01 | 49.0816 | 26.4851 | 0.7241 | 0.07s |\n", - "| DeepKriging_wendland | -- | 5796.58 | 76.1352 | 54.3087 | 0.3362 | 54.20s |\n", - "| DeepKriging_sphere | 500 | 1994.42 | 44.6589 | 28.1365 | 0.7716 | 20.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 1264.82 | 35.5644 | 11.3268 | 0.8552 | 40.25s |\n", - "| UniversalKriging | 50 | 1812.06 | 42.5684 | 21.9783 | 0.7925 | 1.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:05:55\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2132 (1.5012), Variance: 5634.1291, Range: [-127.9036, 127.9662]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1326, sigma²=5293.4713, nugget=2044.1529\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11920.8 | 109.182 | 68.6819 | -0.2524 | 0.42s |\n", - "| OLS_sphere | 500 | 6362.62 | 79.766 | 34.8632 | 0.3316 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8437.3 | 91.8548 | 60.4099 | 0.1136 | 59.59s |\n", - "| DeepKriging_sphere | 500 | 4423.65 | 66.5105 | 39.5378 | 0.5353 | 17.69s |\n", - "| DeepKriging_sphere_Huber | 50 | 2160.46 | 46.4807 | 11.1255 | 0.773 | 39.90s |\n", - "| UniversalKriging | 50 | 3306.64 | 57.5034 | 25.1728 | 0.6526 | 0.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:08:17\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9278 (1.5064), Variance: 5673.1096, Range: [-127.8282, 127.9820]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1388, sigma²=3939.6557, nugget=2317.7304\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 32180.7 | 179.39 | 76.956 | -1.2101 | 0.40s |\n", - "| OLS_sphere | 500 | 7285.51 | 85.3552 | 35.2833 | 0.4996 | 0.06s |\n", - "| DeepKriging_wendland | -- | 12287.8 | 110.85 | 63.1582 | 0.1561 | 47.37s |\n", - "| DeepKriging_sphere | 500 | 7122.46 | 84.3947 | 29.2774 | 0.5108 | 31.14s |\n", - "| DeepKriging_sphere_Huber | 50 | 5528.29 | 74.3525 | 16.9262 | 0.6203 | 37.05s |\n", - "| UniversalKriging | 50 | 6561.17 | 81.001 | 30.051 | 0.5494 | 1.06s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:10:34\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.3699 (1.4920), Variance: 5565.1337, Range: [-127.9886, 127.8961]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1108, sigma²=4950.8023, nugget=1481.6022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25471 | 159.596 | 72.1692 | -2.387 | 0.43s |\n", - "| OLS_sphere | 500 | 1766.17 | 42.0258 | 23.6552 | 0.7651 | 0.05s |\n", - "| DeepKriging_wendland | -- | 7101.36 | 84.2696 | 54.4033 | 0.0557 | 62.24s |\n", - "| DeepKriging_sphere | 500 | 1419.94 | 37.6821 | 19.8646 | 0.8112 | 23.60s |\n", - "| DeepKriging_sphere_Huber | 50 | 563.351 | 23.735 | 8.1168 | 0.9251 | 29.76s |\n", - "| UniversalKriging | 50 | 1049.68 | 32.3988 | 17.2305 | 0.8604 | 1.33s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:12:57\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.6431 (1.4625), Variance: 5347.3846, Range: [-127.9185, 127.9299]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1814, sigma²=4260.2356, nugget=1617.2851\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 12643.7 | 112.444 | 67.6142 | -0.1462 | 0.40s |\n", - "| OLS_sphere | 500 | 3131.2 | 55.9571 | 25.6264 | 0.7161 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6520.24 | 80.748 | 55.5173 | 0.4089 | 58.32s |\n", - "| DeepKriging_sphere | 500 | 3185.37 | 56.4391 | 25.8184 | 0.7112 | 24.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 3093.37 | 55.6181 | 12.4566 | 0.7196 | 37.84s |\n", - "| UniversalKriging | 50 | 3063.55 | 55.3493 | 21.4915 | 0.7223 | 1.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:15:23\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.7111 (1.4705), Variance: 5406.1681, Range: [-127.9905, 127.5998]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1324, sigma²=4619.4008, nugget=921.7888\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7523.53 | 86.7383 | 58.646 | 0.1446 | 0.41s |\n", - "| OLS_sphere | 500 | 3240.25 | 56.9232 | 25.7546 | 0.6316 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6738.44 | 82.088 | 53.5636 | 0.2338 | 63.79s |\n", - "| DeepKriging_sphere | 500 | 3135.58 | 55.9963 | 26.3306 | 0.6435 | 26.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 2124.23 | 46.0894 | 11.2645 | 0.7585 | 38.17s |\n", - "| UniversalKriging | 50 | 2922.82 | 54.0631 | 21.2953 | 0.6677 | 1.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:17:58\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.0423 (1.5031), Variance: 5648.2552, Range: [-127.9905, 127.7956]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1435, sigma²=4616.9726, nugget=1608.1191\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 8025.51 | 89.5852 | 61.7333 | 0.1179 | 0.43s |\n", - "| OLS_sphere | 500 | 3332.25 | 57.7257 | 26.6766 | 0.6338 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7094.43 | 84.2285 | 53.2069 | 0.2203 | 59.91s |\n", - "| DeepKriging_sphere | 500 | 3804.12 | 61.6775 | 35.426 | 0.5819 | 20.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 2387.23 | 48.8593 | 10.3566 | 0.7376 | 39.19s |\n", - "| UniversalKriging | 50 | 3044.47 | 55.1767 | 20.7184 | 0.6654 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:20:27\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.1616 (1.4969), Variance: 5601.9292, Range: [-127.9743, 127.9721]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1322, sigma²=4532.9017, nugget=1575.4246\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9307.77 | 96.4768 | 63.0641 | 0.172 | 0.40s |\n", - "| OLS_sphere | 500 | 4373.62 | 66.1333 | 28.722 | 0.6109 | 0.07s |\n", - "| DeepKriging_wendland | -- | 8272.97 | 90.9559 | 56.8046 | 0.2641 | 61.00s |\n", - "| DeepKriging_sphere | 500 | 8643 | 92.9677 | 59.7013 | 0.2312 | 14.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 3738.87 | 61.1463 | 16.2407 | 0.6674 | 39.82s |\n", - "| UniversalKriging | 50 | 4322.76 | 65.7477 | 25.736 | 0.6155 | 0.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:22:52\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.5146 (1.5029), Variance: 5646.6856, Range: [-127.9684, 127.8338]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1421, sigma²=4456.3290, nugget=2010.0012\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 11064.4 | 105.187 | 64.0662 | 0.0137 | 0.39s |\n", - "| OLS_sphere | 500 | 4253.81 | 65.2213 | 27.0979 | 0.6208 | 0.09s |\n", - "| DeepKriging_wendland | -- | 7388.56 | 85.9568 | 53.0519 | 0.3414 | 61.42s |\n", - "| DeepKriging_sphere | 500 | 4424.96 | 66.5204 | 24.7091 | 0.6056 | 28.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 3089.66 | 55.5847 | 11.7037 | 0.7246 | 44.72s |\n", - "| UniversalKriging | 50 | 3868.06 | 62.1937 | 23.9834 | 0.6552 | 0.76s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:25:37\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.8927 (1.4989), Variance: 5617.0348, Range: [-127.9306, 127.9798]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1136, sigma²=5030.5649, nugget=1464.6943\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 21856.4 | 147.839 | 74.1007 | -0.6825 | 0.48s |\n", - "| OLS_sphere | 500 | 7144.69 | 84.5262 | 32.5939 | 0.45 | 0.06s |\n", - "| DeepKriging_wendland | -- | 10356.9 | 101.769 | 57.9571 | 0.2027 | 56.36s |\n", - "| DeepKriging_sphere | 500 | 11125.2 | 105.476 | 35.779 | 0.1436 | 23.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 4595.91 | 67.7931 | 14.6267 | 0.6462 | 42.44s |\n", - "| UniversalKriging | 50 | 6667.25 | 81.6532 | 28.1075 | 0.4867 | 1.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:28:13\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.0234 (1.4537), Variance: 5283.3386, Range: [-127.9616, 127.4728]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1561, sigma²=3867.3643, nugget=1885.7686\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|----------|-----------|--------|\n", - "| OLS_wendland | -- | 984168 | 992.052 | 172.81 | -111.053 | 0.40s |\n", - "| OLS_sphere | 500 | 2610.74 | 51.0954 | 28.5828 | 0.7028 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7666.02 | 87.5558 | 61.2589 | 0.1272 | 60.60s |\n", - "| DeepKriging_sphere | 500 | 4182.7 | 64.6738 | 27.1766 | 0.5238 | 27.69s |\n", - "| DeepKriging_sphere_Huber | 50 | 2005.95 | 44.7878 | 14.5463 | 0.7716 | 43.72s |\n", - "| UniversalKriging | 50 | 2530.61 | 50.3051 | 25.4388 | 0.7119 | 0.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:31:00\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2345 (1.4800), Variance: 5475.7498, Range: [-127.9250, 127.7404]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1626, sigma²=3763.0413, nugget=1950.3592\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|----------|----------|--------|\n", - "| OLS_wendland | -- | 387112 | 622.183 | 106.61 | -38.2149 | 0.42s |\n", - "| OLS_sphere | 500 | 3098.6 | 55.6651 | 28.8588 | 0.6861 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6854.41 | 82.7914 | 55.6376 | 0.3056 | 62.40s |\n", - "| DeepKriging_sphere | 500 | 3082.08 | 55.5165 | 26.2106 | 0.6878 | 27.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 2188.29 | 46.7792 | 12.1949 | 0.7783 | 40.27s |\n", - "| UniversalKriging | 50 | 2499.72 | 49.9972 | 21.0041 | 0.7468 | 0.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:33:42\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.0042 (1.4656), Variance: 5370.2422, Range: [-127.3421, 127.9991]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1670, sigma²=4166.1290, nugget=1319.8501\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 22569.9 | 150.233 | 76.8433 | -0.6537 | 0.43s |\n", - "| OLS_sphere | 500 | 6302.41 | 79.3877 | 30.8527 | 0.5382 | 0.08s |\n", - "| DeepKriging_wendland | -- | 9806.93 | 99.03 | 56.0636 | 0.2815 | 61.04s |\n", - "| DeepKriging_sphere | 500 | 6126.61 | 78.2727 | 26.7847 | 0.5511 | 25.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 5608.84 | 74.8922 | 16.2609 | 0.589 | 32.67s |\n", - "| UniversalKriging | 50 | 6126.25 | 78.2703 | 24.4218 | 0.5511 | 1.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:36:18\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5398 (1.4768), Variance: 5452.6078, Range: [-127.9916, 127.9764]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1443, sigma²=3789.9346, nugget=3138.0103\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9094.84 | 95.3669 | 64.9446 | -0.1035 | 0.45s |\n", - "| OLS_sphere | 500 | 4912.63 | 70.0902 | 31.9033 | 0.404 | 0.07s |\n", - "| DeepKriging_wendland | -- | 6677.38 | 81.7152 | 55.5806 | 0.1898 | 59.41s |\n", - "| DeepKriging_sphere | 500 | 4098.54 | 64.0198 | 34.9087 | 0.5027 | 18.37s |\n", - "| DeepKriging_sphere_Huber | 50 | 1907.13 | 43.6707 | 11.5516 | 0.7686 | 37.12s |\n", - "| UniversalKriging | 50 | 3045.7 | 55.1878 | 27.6072 | 0.6305 | 0.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:38:50\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.8781 (1.4640), Variance: 5358.5662, Range: [-127.9276, 127.9543]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1637, sigma²=4733.1483, nugget=1484.0985\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 54885.3 | 234.276 | 84.2449 | -3.5029 | 0.45s |\n", - "| OLS_sphere | 500 | 4360.28 | 66.0324 | 29.0695 | 0.6423 | 0.08s |\n", - "| DeepKriging_wendland | -- | 9584.68 | 97.9014 | 61.6892 | 0.2137 | 42.59s |\n", - "| DeepKriging_sphere | 500 | 4567.81 | 67.5855 | 31.0797 | 0.6252 | 22.67s |\n", - "| DeepKriging_sphere_Huber | 50 | 3512.67 | 59.2678 | 13.6301 | 0.7118 | 36.07s |\n", - "| UniversalKriging | 50 | 4207.02 | 64.8615 | 23.1604 | 0.6548 | 0.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:41:11\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.3673 (1.4868), Variance: 5526.4354, Range: [-127.8918, 127.7594]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1584, sigma²=4145.1610, nugget=2190.6429\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 87079.8 | 295.093 | 89.9072 | -9.7245 | 0.43s |\n", - "| OLS_sphere | 500 | 2313.49 | 48.0987 | 25.7982 | 0.7151 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6399.41 | 79.9963 | 53.6607 | 0.2119 | 46.51s |\n", - "| DeepKriging_sphere | 500 | 9219.29 | 96.0171 | 29.9916 | -0.1354 | 37.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 1450.55 | 38.086 | 10.5181 | 0.8214 | 37.79s |\n", - "| UniversalKriging | 50 | 1873.81 | 43.2875 | 21.8736 | 0.7692 | 1.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:43:54\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2114 (1.4937), Variance: 5578.0620, Range: [-127.9808, 127.8940]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1550, sigma²=4496.4049, nugget=2129.0055\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9984.54 | 99.9227 | 68.2315 | 0.1321 | 0.39s |\n", - "| OLS_sphere | 500 | 4274.94 | 65.383 | 29.1838 | 0.6284 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8030.91 | 89.6153 | 56.122 | 0.3019 | 60.47s |\n", - "| DeepKriging_sphere | 500 | 4506.92 | 67.1336 | 31.1472 | 0.6082 | 22.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 3047.01 | 55.1997 | 14.0523 | 0.7351 | 35.22s |\n", - "| UniversalKriging | 50 | 4121.45 | 64.1986 | 25.8122 | 0.6418 | 1.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:46:33\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5236 (1.4917), Variance: 5562.9679, Range: [-127.7895, 127.9189]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1552, sigma²=3953.6594, nugget=2014.1817\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 19309 | 138.957 | 68.1113 | -1.4035 | 0.61s |\n", - "| OLS_sphere | 500 | 1583.73 | 39.7961 | 21.933 | 0.8029 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5649.7 | 75.1645 | 51.5885 | 0.2967 | 53.36s |\n", - "| DeepKriging_sphere | 500 | 2090.01 | 45.7167 | 20.3113 | 0.7398 | 29.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 1258.53 | 35.4758 | 8.5155 | 0.8433 | 37.26s |\n", - "| UniversalKriging | 50 | 1485.26 | 38.5391 | 20.6994 | 0.8151 | 0.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:49:11\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.8401 (1.4778), Variance: 5459.7623, Range: [-127.8541, 127.8672]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0901, sigma²=5604.7713, nugget=1024.2746\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 50354.1 | 224.397 | 72.8802 | -4.0787 | 0.45s |\n", - "| OLS_sphere | 500 | 4070.57 | 63.801 | 29.2235 | 0.5894 | 0.08s |\n", - "| DeepKriging_wendland | -- | 7841.83 | 88.5541 | 55.5251 | 0.2091 | 60.54s |\n", - "| DeepKriging_sphere | 500 | 4125.84 | 64.2327 | 27.198 | 0.5839 | 25.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 2666.53 | 51.6385 | 12.3134 | 0.7311 | 36.32s |\n", - "| UniversalKriging | 50 | 3415.33 | 58.4408 | 20.7491 | 0.6555 | 1.44s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:51:57\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.7851 (1.4901), Variance: 5550.6704, Range: [-127.9824, 127.8131]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1053, sigma²=4737.8450, nugget=1882.6440\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 22692 | 150.639 | 76.7535 | -0.9242 | 0.42s |\n", - "| OLS_sphere | 500 | 4318.28 | 65.7136 | 27.4142 | 0.6338 | 0.08s |\n", - "| DeepKriging_wendland | -- | 9963.53 | 99.8175 | 58.1089 | 0.1551 | 59.66s |\n", - "| DeepKriging_sphere | 500 | 8407.75 | 91.6938 | 57.7178 | 0.287 | 15.82s |\n", - "| DeepKriging_sphere_Huber | 50 | 3685.69 | 60.7099 | 13.3581 | 0.6875 | 34.90s |\n", - "| UniversalKriging | 50 | 4672.3 | 68.3542 | 25.7445 | 0.6038 | 1.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:54:31\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.1370 (1.4773), Variance: 5456.0316, Range: [-127.9784, 127.8074]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1396, sigma²=4501.1405, nugget=1441.1649\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9747.53 | 98.7296 | 64.6526 | 0.1398 | 0.48s |\n", - "| OLS_sphere | 500 | 4848.48 | 69.631 | 31.039 | 0.5721 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8580.51 | 92.631 | 57.1223 | 0.2428 | 61.44s |\n", - "| DeepKriging_sphere | 500 | 6762.13 | 82.2322 | 29.576 | 0.4033 | 27.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 3722.43 | 61.0117 | 15.6444 | 0.6715 | 41.89s |\n", - "| UniversalKriging | 50 | 4386.69 | 66.2321 | 25.5555 | 0.6129 | 0.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 22:57:28\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5032 (1.4821), Variance: 5491.6180, Range: [-127.9391, 127.9854]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1420, sigma²=4882.7879, nugget=1331.3453\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|----------|----------|--------|\n", - "| OLS_wendland | -- | 447943 | 669.285 | 125.08 | -33.0105 | 0.37s |\n", - "| OLS_sphere | 500 | 6151.67 | 78.4326 | 34.0527 | 0.5329 | 0.06s |\n", - "| DeepKriging_wendland | -- | 9449.07 | 97.2063 | 58.2125 | 0.2826 | 67.22s |\n", - "| DeepKriging_sphere | 500 | 6347.23 | 79.6695 | 31.2577 | 0.5181 | 27.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 4985.36 | 70.6071 | 16.9589 | 0.6215 | 40.66s |\n", - "| UniversalKriging | 50 | 5786.48 | 76.0689 | 27.0162 | 0.5607 | -0.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:00:28\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3408 (1.4790), Variance: 5468.4577, Range: [-127.9979, 127.9685]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1325, sigma²=4117.9024, nugget=2174.4475\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9321.21 | 96.5464 | 67.5561 | 0.1484 | 0.39s |\n", - "| OLS_sphere | 500 | 3589.27 | 59.9105 | 27.8512 | 0.6721 | 0.05s |\n", - "| DeepKriging_wendland | -- | 8330.73 | 91.2728 | 60.3403 | 0.2389 | 64.05s |\n", - "| DeepKriging_sphere | 500 | 4127.84 | 64.2482 | 30.5517 | 0.6229 | 28.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 2918.25 | 54.0208 | 14.1138 | 0.7334 | 45.99s |\n", - "| UniversalKriging | 50 | 3845.96 | 62.0158 | 27.9965 | 0.6486 | -0.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:03:31\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.7839 (1.4866), Variance: 5524.9187, Range: [-127.9397, 127.8975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1164, sigma²=4809.6699, nugget=1488.0168\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 22903.1 | 151.338 | 69.11 | -2.0823 | 0.43s |\n", - "| OLS_sphere | 500 | 1943.51 | 44.0853 | 25.0749 | 0.7384 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5651.02 | 75.1733 | 58.9181 | 0.2395 | 64.79s |\n", - "| DeepKriging_sphere | 500 | 805.687 | 28.3846 | 19.6776 | 0.8916 | 23.21s |\n", - "| DeepKriging_sphere_Huber | 50 | 193.451 | 13.9087 | 7.7334 | 0.974 | 32.57s |\n", - "| UniversalKriging | 50 | 1116.87 | 33.4196 | 19.4673 | 0.8497 | 1.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:06:20\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9594 (1.4773), Variance: 5456.3230, Range: [-127.9375, 127.9092]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1370, sigma²=4474.9929, nugget=1484.0918\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 10644.8 | 103.173 | 67.3383 | 0.111 | 0.42s |\n", - "| OLS_sphere | 500 | 5022.39 | 70.8688 | 28.8011 | 0.5806 | 0.07s |\n", - "| DeepKriging_wendland | -- | 8976.14 | 94.7425 | 56.3017 | 0.2504 | 55.00s |\n", - "| DeepKriging_sphere | 500 | 5227.78 | 72.3034 | 26.1702 | 0.5634 | 35.77s |\n", - "| DeepKriging_sphere_Huber | 50 | 3671.78 | 60.5952 | 14.9926 | 0.6934 | 34.30s |\n", - "| UniversalKriging | 50 | 4630.8 | 68.05 | 24.755 | 0.6133 | 1.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:09:16\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.1521 (1.4517), Variance: 5268.5454, Range: [-127.8582, 127.9632]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1626, sigma²=4234.2758, nugget=2086.1636\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 13500.4 | 116.191 | 70.7804 | 0.1152 | 0.51s |\n", - "| OLS_sphere | 500 | 6775.73 | 82.3148 | 34.5242 | 0.5559 | 0.07s |\n", - "| DeepKriging_wendland | -- | 10854.4 | 104.184 | 60.9095 | 0.2886 | 52.84s |\n", - "| DeepKriging_sphere | 500 | 7617.53 | 87.2785 | 32.0495 | 0.5007 | 41.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 6014.5 | 77.5532 | 19.7914 | 0.6058 | 41.10s |\n", - "| UniversalKriging | 50 | 6993.24 | 83.6256 | 32.9578 | 0.5417 | 0.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:12:19\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.0463 (1.4862), Variance: 5521.6496, Range: [-127.7802, 127.9364]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1445, sigma²=4353.6836, nugget=2273.2136\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 22500.5 | 150.002 | 79.735 | -0.2671 | 0.39s |\n", - "| OLS_sphere | 500 | 8098.21 | 89.99 | 37.0819 | 0.5439 | 0.08s |\n", - "| DeepKriging_wendland | -- | 19255.7 | 138.765 | 70.7741 | -0.0844 | 65.31s |\n", - "| DeepKriging_sphere | 500 | 9927.71 | 99.6379 | 36.5116 | 0.4409 | 26.76s |\n", - "| DeepKriging_sphere_Huber | 50 | 7559.23 | 86.9439 | 20.0339 | 0.5743 | 38.77s |\n", - "| UniversalKriging | 50 | 7922.93 | 89.0108 | 31.9393 | 0.5538 | 0.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:15:22\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2424 (1.4775), Variance: 5457.2610, Range: [-127.9849, 127.9520]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1307, sigma²=4722.9782, nugget=1595.1365\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 14689.3 | 121.199 | 72.3572 | 0.0674 | 0.38s |\n", - "| OLS_sphere | 500 | 6606.51 | 81.2804 | 28.9486 | 0.5806 | 0.07s |\n", - "| DeepKriging_wendland | -- | 11616.3 | 107.779 | 59.5425 | 0.2625 | 55.64s |\n", - "| DeepKriging_sphere | 500 | 7846.19 | 88.5787 | 37.0525 | 0.5019 | 20.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 5712.93 | 75.5839 | 14.6121 | 0.6373 | 34.07s |\n", - "| UniversalKriging | 50 | 6636.25 | 81.4632 | 24.9311 | 0.5787 | 1.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:18:08\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.8791 (1.4872), Variance: 5529.6059, Range: [-127.9713, 127.9436]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1433, sigma²=4486.9380, nugget=2765.0946\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9666.59 | 98.3188 | 68.7332 | 0.1334 | 0.43s |\n", - "| OLS_sphere | 500 | 3448.08 | 58.7203 | 26.4867 | 0.6909 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7566.21 | 86.984 | 57.0077 | 0.3217 | 53.04s |\n", - "| DeepKriging_sphere | 500 | 3665.37 | 60.5423 | 25.9182 | 0.6714 | 27.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 2586.79 | 50.8605 | 13.2603 | 0.7681 | 38.80s |\n", - "| UniversalKriging | 50 | 3212.47 | 56.6787 | 23.1445 | 0.712 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:21:00\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4004 (1.4868), Variance: 5526.6378, Range: [-127.9424, 127.6391]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1416, sigma²=4834.9836, nugget=1874.8267\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 12629.6 | 112.382 | 68.9073 | 0.1264 | 0.68s |\n", - "| OLS_sphere | 500 | 6367.3 | 79.7954 | 31.9135 | 0.5595 | 0.07s |\n", - "| DeepKriging_wendland | -- | 11401.1 | 106.776 | 60.9559 | 0.2113 | 55.27s |\n", - "| DeepKriging_sphere | 500 | 7905.07 | 88.9105 | 41.3044 | 0.4532 | 21.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 5475.52 | 73.9968 | 18.3792 | 0.6212 | 34.13s |\n", - "| UniversalKriging | 50 | 6340.15 | 79.6251 | 28.2634 | 0.5614 | 1.06s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:23:47\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.5026 (1.4744), Variance: 5434.9398, Range: [-127.9995, 127.8942]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0914, sigma²=5852.1725, nugget=1295.3641\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 10717.9 | 103.527 | 66.3931 | 0.1001 | 0.46s |\n", - "| OLS_sphere | 500 | 4726.32 | 68.7482 | 31.6127 | 0.6032 | 0.07s |\n", - "| DeepKriging_wendland | -- | 9565.68 | 97.8043 | 60.1545 | 0.1968 | 49.67s |\n", - "| DeepKriging_sphere | 500 | 4869.5 | 69.7818 | 31.4384 | 0.5911 | 21.04s |\n", - "| DeepKriging_sphere_Huber | 50 | 3398.15 | 58.2936 | 13.3807 | 0.7147 | 41.38s |\n", - "| UniversalKriging | 50 | 4275.82 | 65.3897 | 25.5862 | 0.641 | 1.61s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:26:32\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2395 (1.4725), Variance: 5420.3070, Range: [-127.9512, 127.5573]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1332, sigma²=3983.6121, nugget=2400.6627\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7159.35 | 84.6129 | 62.8784 | 0.0363 | 0.39s |\n", - "| OLS_sphere | 500 | 1897.99 | 43.5659 | 24.7536 | 0.7445 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5197.96 | 72.0969 | 53.4979 | 0.3003 | 59.96s |\n", - "| DeepKriging_sphere | 500 | 2433.55 | 49.331 | 32.0336 | 0.6724 | 23.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 1069.03 | 32.696 | 10.1181 | 0.8561 | 41.27s |\n", - "| UniversalKriging | 50 | 1583.78 | 39.7967 | 20.5083 | 0.7868 | 1.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:29:33\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9505 (1.4497), Variance: 5253.9886, Range: [-127.9405, 127.9370]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1418, sigma²=4487.7947, nugget=2081.1116\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 16023.5 | 126.584 | 62.0737 | -0.8324 | 0.44s |\n", - "| OLS_sphere | 500 | 2650.42 | 51.4822 | 24.5479 | 0.6969 | 0.05s |\n", - "| DeepKriging_wendland | -- | 5870.08 | 76.6164 | 50.5599 | 0.3287 | 59.30s |\n", - "| DeepKriging_sphere | 500 | 3025.32 | 55.0029 | 21.6284 | 0.654 | 27.02s |\n", - "| DeepKriging_sphere_Huber | 50 | 1820.49 | 42.6673 | 9.6197 | 0.7918 | 38.07s |\n", - "| UniversalKriging | 50 | 2428.74 | 49.2823 | 20.2655 | 0.7223 | -1.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:32:35\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3982 (1.4769), Variance: 5453.3232, Range: [-127.8661, 127.9624]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1227, sigma²=5297.5662, nugget=1949.3186\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 174478 | 417.705 | 91.4048 | -18.5503 | 0.42s |\n", - "| OLS_sphere | 500 | 3203.52 | 56.5996 | 28.7913 | 0.641 | 0.07s |\n", - "| DeepKriging_wendland | -- | 16219.1 | 127.354 | 66.6747 | -0.8174 | 60.60s |\n", - "| DeepKriging_sphere | 500 | 2814.45 | 53.0514 | 31.2379 | 0.6846 | 21.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 1465.81 | 38.2859 | 11.0392 | 0.8358 | 34.44s |\n", - "| UniversalKriging | 50 | 2906.69 | 53.9138 | 25.3088 | 0.6743 | 1.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:35:34\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.0577 (1.4640), Variance: 5358.2494, Range: [-127.9989, 127.9693]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1284, sigma²=4568.5921, nugget=2095.5399\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 10390.8 | 101.935 | 67.8704 | 0.0991 | 0.38s |\n", - "| OLS_sphere | 500 | 4774.23 | 69.0958 | 34.3458 | 0.5861 | 0.06s |\n", - "| DeepKriging_wendland | -- | 9137.95 | 95.5926 | 59.1294 | 0.2077 | 61.59s |\n", - "| DeepKriging_sphere | 500 | 4721.28 | 68.7116 | 29.4472 | 0.5907 | 29.24s |\n", - "| DeepKriging_sphere_Huber | 50 | 3419.88 | 58.4798 | 15.5806 | 0.7035 | 43.55s |\n", - "| UniversalKriging | 50 | 4316.4 | 65.6993 | 29.0274 | 0.6258 | 0.90s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:38:49\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.3609 (1.5106), Variance: 5704.4801, Range: [-127.9918, 127.9510]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1237, sigma²=4716.9373, nugget=1325.7097\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 128265 | 358.141 | 89.8114 | -8.3735 | 0.39s |\n", - "| OLS_sphere | 500 | 6503.36 | 80.6434 | 30.1464 | 0.5247 | 0.06s |\n", - "| DeepKriging_wendland | -- | 11622.1 | 107.806 | 58.6486 | 0.1507 | 63.35s |\n", - "| DeepKriging_sphere | 500 | 7408.43 | 86.0723 | 31.153 | 0.4586 | 25.44s |\n", - "| DeepKriging_sphere_Huber | 50 | 5460.19 | 73.8931 | 15.0995 | 0.601 | 42.51s |\n", - "| UniversalKriging | 50 | 6660.35 | 81.611 | 26.3501 | 0.5133 | 1.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:42:03\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2112 (1.4933), Variance: 5574.8590, Range: [-127.7641, 127.7805]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1253, sigma²=4117.5227, nugget=2433.1978\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6581.67 | 81.1275 | 56.6737 | 0.2346 | 0.42s |\n", - "| OLS_sphere | 500 | 3206.3 | 56.6242 | 26.9541 | 0.6271 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6200.5 | 78.7433 | 52.1698 | 0.2789 | 60.16s |\n", - "| DeepKriging_sphere | 500 | 2897.21 | 53.8258 | 25.5537 | 0.6631 | 23.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 2079.23 | 45.5986 | 11.4114 | 0.7582 | 43.74s |\n", - "| UniversalKriging | 50 | 2817.65 | 53.0815 | 23.4821 | 0.6723 | 1.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:45:16\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3737 (1.4740), Variance: 5431.8190, Range: [-127.8785, 127.9619]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1521, sigma²=4110.9991, nugget=1665.7297\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9689.18 | 98.4336 | 66.5254 | 0.1468 | 0.33s |\n", - "| OLS_sphere | 500 | 4328.12 | 65.7884 | 29.7424 | 0.6189 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8856.75 | 94.1103 | 60.3125 | 0.2201 | 63.33s |\n", - "| DeepKriging_sphere | 500 | 4585.64 | 67.7174 | 31.7035 | 0.5962 | 27.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 3332.81 | 57.7305 | 15.6608 | 0.7065 | 36.29s |\n", - "| UniversalKriging | 50 | 4046.35 | 63.6109 | 25.802 | 0.6437 | 0.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:48:26\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.2425 (1.4719), Variance: 5416.1338, Range: [-127.7052, 127.9518]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1249, sigma²=4900.4795, nugget=2405.6465\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15230.6 | 123.412 | 73.6718 | -0.57 | 0.43s |\n", - "| OLS_sphere | 500 | 2662.32 | 51.5977 | 26.5277 | 0.7256 | 0.07s |\n", - "| DeepKriging_wendland | -- | 7489.52 | 86.542 | 56.3674 | 0.228 | 64.88s |\n", - "| DeepKriging_sphere | 500 | 3832.85 | 61.91 | 33.5251 | 0.6049 | 20.72s |\n", - "| DeepKriging_sphere_Huber | 50 | 1860.53 | 43.1339 | 9.9102 | 0.8082 | 37.62s |\n", - "| UniversalKriging | 50 | 2822.12 | 53.1236 | 24.0415 | 0.7091 | 1.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:51:33\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.1105 (1.4666), Variance: 5377.0048, Range: [-127.6737, 127.8760]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1351, sigma²=4162.8669, nugget=3046.4233\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 14958.5 | 122.305 | 75.2 | -0.6694 | 0.37s |\n", - "| OLS_sphere | 500 | 3894.95 | 62.4096 | 31.4652 | 0.5653 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7565.83 | 86.9818 | 59.2714 | 0.1556 | 60.40s |\n", - "| DeepKriging_sphere | 500 | 2935.23 | 54.1778 | 25.1894 | 0.6724 | 27.80s |\n", - "| DeepKriging_sphere_Huber | 50 | 2031.92 | 45.0768 | 10.4458 | 0.7732 | 40.87s |\n", - "| UniversalKriging | 50 | 2927.4 | 54.1054 | 26.5815 | 0.6733 | 1.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:54:52\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.5270 (1.4662), Variance: 5374.7019, Range: [-127.9080, 127.7636]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1317, sigma²=4097.7403, nugget=1619.1921\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10253.6 | 101.26 | 70.3585 | -0.0864 | 0.49s |\n", - "| OLS_sphere | 500 | 2921.13 | 54.0474 | 26.5033 | 0.6905 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7990.63 | 89.3903 | 63.7652 | 0.1534 | 62.39s |\n", - "| DeepKriging_sphere | 500 | 3536.57 | 59.4691 | 25.488 | 0.6253 | 27.90s |\n", - "| DeepKriging_sphere_Huber | 50 | 1919.8 | 43.8155 | 12.0864 | 0.7966 | 46.05s |\n", - "| UniversalKriging | 50 | 2570.31 | 50.6982 | 23.1432 | 0.7277 | 0.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 23:58:14\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.4846 (1.4531), Variance: 5278.9050, Range: [-127.6724, 127.8338]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1681, sigma²=4025.4101, nugget=1731.4108\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 10521.2 | 102.573 | 66.6421 | 0.1254 | 0.47s |\n", - "| OLS_sphere | 500 | 4967.31 | 70.4791 | 26.0063 | 0.5871 | 0.07s |\n", - "| DeepKriging_wendland | -- | 9492.17 | 97.4278 | 57.5707 | 0.2109 | 61.15s |\n", - "| DeepKriging_sphere | 500 | 4867.62 | 69.7683 | 24.5445 | 0.5954 | 27.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 4638.2 | 68.1043 | 14.2727 | 0.6144 | 37.75s |\n", - "| UniversalKriging | 50 | 4953.23 | 70.3792 | 25.1831 | 0.5882 | 0.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:01:28\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.5428 (1.4921), Variance: 5566.0671, Range: [-127.9427, 127.9957]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1558, sigma²=4560.4682, nugget=2814.5363\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7945.43 | 89.1372 | 61.8021 | 0.1247 | 0.34s |\n", - "| OLS_sphere | 500 | 3256.72 | 57.0677 | 27.8039 | 0.6412 | 0.12s |\n", - "| DeepKriging_wendland | -- | 7329.98 | 85.6153 | 55.9923 | 0.1925 | 62.24s |\n", - "| DeepKriging_sphere | 500 | 3086.86 | 55.5596 | 29.1978 | 0.66 | 22.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 1866.09 | 43.1983 | 9.3514 | 0.7944 | 38.99s |\n", - "| UniversalKriging | 50 | 2549.55 | 50.4931 | 23.5685 | 0.7191 | 0.86s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:04:43\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.1971 (1.4550), Variance: 5292.3073, Range: [-127.9798, 127.9936]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1210, sigma²=5117.2782, nugget=1513.3592\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|--------|--------|\n", - "| OLS_wendland | -- | 11289.8 | 106.253 | 69.5738 | 0.0532 | 0.36s |\n", - "| OLS_sphere | 500 | 4687.02 | 68.4618 | 27.7199 | 0.6069 | 0.07s |\n", - "| DeepKriging_wendland | -- | 9594.8 | 97.953 | 57.8788 | 0.1953 | 61.95s |\n", - "| DeepKriging_sphere | 500 | 5345.41 | 73.1123 | 35.3849 | 0.5517 | 23.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 4463.46 | 66.8092 | 17.7663 | 0.6257 | 37.53s |\n", - "| UniversalKriging | 50 | 4753.28 | 68.944 | 25.6519 | 0.6014 | 1.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:07:56\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.1615 (1.4785), Variance: 5464.6077, Range: [-127.9334, 127.9610]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1244, sigma²=5321.6280, nugget=873.0701\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11441.2 | 106.964 | 69.1728 | 0.0705 | 0.44s |\n", - "| OLS_sphere | 500 | 4867.06 | 69.7643 | 27.4267 | 0.6046 | 0.06s |\n", - "| DeepKriging_wendland | -- | 35783.8 | 189.166 | 68.7534 | -1.9072 | 58.66s |\n", - "| DeepKriging_sphere | 500 | 6676.96 | 81.7127 | 28.5894 | 0.4575 | 36.56s |\n", - "| DeepKriging_sphere_Huber | 50 | 3983.84 | 63.1177 | 15.5093 | 0.6763 | 32.77s |\n", - "| UniversalKriging | 50 | 4894.59 | 69.9614 | 23.5543 | 0.6023 | 1.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:11:18\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.6505 (1.4818), Variance: 5489.3390, Range: [-127.9916, 127.8206]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1535, sigma²=4370.6676, nugget=1359.9993\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 9149.68 | 95.6539 | 60.3025 | 0.1797 | 0.44s |\n", - "| OLS_sphere | 500 | 5451.94 | 73.8373 | 30.7575 | 0.5112 | 0.06s |\n", - "| DeepKriging_wendland | -- | 8584.12 | 92.6505 | 54.6274 | 0.2304 | 62.87s |\n", - "| DeepKriging_sphere | 500 | 4582.86 | 67.6968 | 28.4549 | 0.5891 | 23.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 3617.73 | 60.1475 | 12.6523 | 0.6757 | 41.69s |\n", - "| UniversalKriging | 50 | 4343.53 | 65.9054 | 24.4169 | 0.6106 | 1.11s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:14:39\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.4036 (1.5064), Variance: 5673.3777, Range: [-127.9801, 127.9851]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1288, sigma²=4961.1201, nugget=1599.4474\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7323.29 | 85.5762 | 59.4708 | 0.229 | 0.46s |\n", - "| OLS_sphere | 500 | 3956.34 | 62.8995 | 31.2791 | 0.5835 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6088.7 | 78.0301 | 52.1073 | 0.3589 | 62.36s |\n", - "| DeepKriging_sphere | 500 | 4368.95 | 66.0981 | 32.3231 | 0.54 | 20.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 2287.41 | 47.8269 | 11.5248 | 0.7592 | 38.63s |\n", - "| UniversalKriging | 50 | 3044.82 | 55.1799 | 24.07 | 0.6794 | 1.04s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:17:56\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.0490 (1.5000), Variance: 5625.3446, Range: [-127.9306, 127.9925]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1490, sigma²=4329.9890, nugget=2578.4048\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7917.16 | 88.9785 | 63.8298 | 0.0906 | 0.39s |\n", - "| OLS_sphere | 500 | 3550.46 | 59.5858 | 31.8583 | 0.5922 | 0.07s |\n", - "| DeepKriging_wendland | -- | 9786.56 | 98.927 | 61.0723 | -0.1242 | 61.11s |\n", - "| DeepKriging_sphere | 500 | 3428.34 | 58.552 | 25.7094 | 0.6062 | 34.57s |\n", - "| DeepKriging_sphere_Huber | 50 | 1922.26 | 43.8436 | 12.8223 | 0.7792 | 42.82s |\n", - "| UniversalKriging | 50 | 2807.23 | 52.9833 | 24.4062 | 0.6775 | 1.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-19 00:21:32\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, outlier_ratio=0.025, outlier_multiplier=5, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 500, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 50}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 57422.79±156398.53 | 171.03±167.84 | 73.73±18.47 | -4.73±16.92 |\n", - "| OLS_sphere | 4302.39±1560.36 | 64.48±12.02 | 29.10±3.25 | 0.61±0.09 |\n", - "| DeepKriging_wendland | 9230.03±4559.41 | 94.28±18.47 | 57.95±4.15 | 0.16±0.34 |\n", - "| DeepKriging_sphere | 4953.65±2237.31 | 68.55±15.94 | 30.74±7.40 | 0.56±0.16 |\n", - "| DeepKriging_sphere_Huber | 3143.49±1552.66 | 54.17±14.45 | 13.29±2.85 | 0.73±0.09 |\n", - "| UniversalKriging | 3915.30±1626.39 | 61.16±13.22 | 24.69±3.10 | 0.66±0.08 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 54.17±14.45)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_without_outliers.ipynb b/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_without_outliers.ipynb deleted file mode 100644 index 796bfdc..0000000 --- a/notebook/simulation/ScenarioII/simulation_spherical_GP_eggholder_without_outliers.ipynb +++ /dev/null @@ -1,3354 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-01-18 05:48:07.231473: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768686487.248584 115892 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768686487.253542 115892 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768686487.267355 115892 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768686487.267379 115892 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768686487.267381 115892 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768686487.267382 115892 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3066.3428 3319.6636 \n", - " 100 2103.5437 2244.5879 \n", - " 200 1448.4603 1315.0983 \n", - " 500 320.1582 311.9547 *\n", - " 700 338.2285 160.1068 \n", - " 1000 1275.2592 488.3110 \n", - " 1400 10863.0342 10619.6484 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768686511.712608 115892 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768686514.136289 116141 service.cc:152] XLA service 0x719d1c012c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768686514.136380 116141 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1768686514.506099 116141 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1768686525.435398 116141 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x719d38d8b640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x719d382cb910> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 52.0841 75.4729 *\n", - " 100 54.2191 106.2949 \n", - " 200 77.2372 107.6480 \n", - " 500 134.4717 139.7123 \n", - " 700 280.8956 247.9894 \n", - " 1000 564.0577 1051.3951 \n", - " 1400 1966.8234 2349.4238 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 4.5391 71.5097 *\n", - " 100 5.7207 92.7671 \n", - " 200 7.4098 125.2781 \n", - " 500 11.1829 202.8445 \n", - " 700 13.5803 288.0089 \n", - " 1000 18.0498 1196.8588 \n", - " 1400 38.0237 2864.7605 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0190, sigma²=7099.8026, nugget=0.0031\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4908, sigma²=3217.5769, nugget=0.0032\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2809, sigma²=1588.3766, nugget=0.0055\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0585, sigma²=162.3763, nugget=0.0021\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.5926, sigma²=0.0003, nugget=77.7251\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=230.9752, sigma²=0.0002, nugget=39.0609\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=12.9388, sigma²=0.0001, nugget=20.8192\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 181.5355 171.5833 \n", - " 100 182.0069 178.5939 \n", - " 200 183.4985 175.4278 \n", - " 500 163.8057 178.6346 *\n", - " 700 338.2291 160.1061 \n", - " 1000 1275.2548 488.3081 \n", - " 1400 10863.0979 10620.0557 \n", - " Best order: 500\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0585, sigma²=162.3763, nugget=0.0021\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8228.55 | 90.7113 | 61.0662 | -0.2378 | 0.42s |\n", - "| OLS_sphere | 500 | 311.955 | 17.6622 | 11.2062 | 0.9531 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4360.72 | 66.0357 | 50.4059 | 0.344 | 49.79s |\n", - "| DeepKriging_sphere | 50 | 75.5099 | 8.6896 | 5.2401 | 0.9886 | 35.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 66.5525 | 8.158 | 5.0017 | 0.99 | 39.72s |\n", - "| UniversalKriging | 500 | 178.635 | 13.3654 | 7.3379 | 0.9731 | 7.63s |\n" - ] - }, - { - "data": { - "image/png": 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9S515HVpA2R3WO87pnXfeCVobuKc8A9awVIB58MADD4S8T/mocIZOypl52X333U3pEsbGxx9/HDReMD5S4ob+N34wblkH27VrV1O+jvInzAc7Tyw8PwyalBtLJTh/5p13reXaMUQn85qTsW/Uxz2nNA7jwr2/vPvuu2ZN9xp6vSW8WD8pDRdu/mdkZJj1mHI1du5xPzAMRzOq1zUYqLlfa9eurTn3e+65x9xzb4NwHCCUy4sFHAw4p73goKccF2s1c89d4ov9mfPxyliUSqIEn9+aOmTIECOjUcIqVgdssuS4WOD6vIEpyDTIBay9/AbXRk+bZJW6igWc4H6OlljmjZWV2Yt4nqzL7sbjlI2jBKTXkUxpVT7P/OX+u2Vb5hkBFswJyjtZ2M/9AgSQB5HjuH+UjE12kA7lSP16ySB7MdeRDdg3uIfIyxb0B+sAY//xlvCkDB+fceP9t6IoitKEaehUEUVRFCU1y09VVVU5s2fPDlsix10aIlzJDl6UCPCW0bHlSBYtWmRKEXjT3ufOnRv0eUoc5ebmBn1uxx13DPqMX1kNysB8/fXXQZ+79tprfc8z0fJTfiUxOnbs6EycODHks5RhuuKKK0wJiWj3zlt2wEssJXsoxeX9TIsWLYLKYdhSMt7PUVpm9erVUcsmUNLro48+qvkMJY769esX8rm3337biZdwpVJs+a5EuO2223xLLSxcuDCoRNFll13mW46ooqIi6Hh+58fYdD/jE088MWxJDH7LloMYMmRITPfNb1wy5tzzhnJZp556qm85MC+ffPKJs2TJkrD3jPnrLdXVunXrmnOPVIaCF9+dN29e0GfdJYliGcveMXXllVf6nuuGDRvMPfP7O9fuV17HXVps5cqVvp/7+9//HtO17r///kFlmx588MGQz/CcG6r8FGXNLrnkEvP605/+5DuWKFW3fv1632N6S5+wxlJ6zztevM+L9dtdrhAojfPVV19FLG9EuS7v+f373/9OufJTsMMOO4R8j/W+rq45kX0jmb8fC48++mjIsd56662gz0yePDnkM9dff33QZ7xywpgxY3x/jz3rxRdfdG699VanocpPgXf/4D5MmTIlZJ6Fm6fe9Y/9ya9kj/dzyB1eWYmXe45Susmv/N5dd90VdKwPP/zQt2ygt7RQMuW4WOby0qVLYy5rxr722GOPOQ8//LBTV+WnioqKnC+//NJci9+e8Nlnn0W9Pl6U5PLuqXafvPPOO0M+T6kv9zzmu5R2ijZ3/UpkIae792TkuG233db3PBMpP8Uz8yvLeMQRR/jKc5Tj8o7HcPdOS00piqJs3WimhqIoihJUvilaM2yb/UDkbTSIpiQ6muaTbohYBBqWehuH0hjRG9lGpCmlc9zRnL/88ospgdO3b1/zbyLL/MpX7LXXXkHv0ZzwueeeM1F1tYUmpn7NfynBQISqFyK97733Xqkv/MoL0PSdSExvOZnnn38+KDKQiECuLVrZpaOOOspE91mI8Cc6kEh+N4k0bgzXrJLIvmTeExpGE+FpYQ5QcsrbYNRGYhLNGgmO5z5HMja85VVonkojYTvfiGo9+uijQ7I1Yr1vNBd3zxvmHpGwRLS6S/8QAemeN0BWARBZTJYKn+G/yTqxZaM4PzdkM3BuNOmMBhkd3s/ZNSBWvM+cCGTWDu95EaHJmOTlhqhdd/SnPSZZB9wrC1kxPBfv98m48Gv87IXMGnfZJrLHmF/uCOVwz5SGq3UN2Sy2AasXxiSl0bgGms96IQrYm/1DFDjZQW5Y74kqp8SJO2qY8kE0MbaQFcQL3yDl8cgQIOOIjBk7Zv3WgFRsGh5uXfKWb2noa67v3z/ppJPM+Hdn3NHcmbUuUukpb+kq7q3NfACiyFmbyBJ0Q+bJKaecIg0NGRjuDCiyXZBZvJ+Jlffeey+kZA8ZQN6GzMgdHJfMSO/6ZecpWXLecnBkCFCa0w0ZrmR6RivXmUw5LtF55o3etxD9f+6550qyITuAVzRoXk82SjR4lmTHerH7JGX83JB5wf7u3v+QJW6++Wbz7N3lHslusJkOvO8ns/Id956MHMd+d+ihh0oyYPx6s2N79epl1gK/MofIWNHkLEVRFEUBdWooiqIocYFDg/r2lH2Jxt577x0x9Z6SO14werkNX5GgrIVVhr0GA2tM9jt/yhS88MILUlsw9HiVeYzjGANSgcmTJ4e8F05JxeDhdmrY70dzavj9nTIGXhKpzexnWLWlGML9LRIYhbzXGO6eYODFGOEte8I9iaRsY2Tzlsfq1KlTyOcoOYXhIFn3zW/MUSKOmureviTMFbcRiRJZGLQoDROudJMf1GWP5tTg76wDtYV76jas4oTjfCm7gkGOF6XeeDZ+Y8NvLlByys+5wrqBo8N9Lyg/RWmhSCX3+H1bbs5dFoXnwL2yJLNOeTLB6IgBOtzc8luvWUdjXUtZr71rO8ZeHKB+fT3C4b6XqQRObi9+97Khr7k+f5/rp++S23FBjxqcKM2aNTPOFW/pKeZwv379Qua/27jO+VPGjflPmauBAweaclqU33KX2mkoOCcM1ePGjatZc92lnHC+xtqnKd69nPe9Tg339/1kJRzbfgEtBCxEc2okU46L1amx6667Bl0TfbYw6FMCz44HW9qRvbwh4Jq8YzscXueUG/Yhb9kxAg5iDe4gEMM6AHEgedcpxqJfEA7yD/JyMvpf+I0R+t+F69ukKIqiKLGijcIVRVGUmKGGNZHcsUZC7rDDDjHVoE8UdxS9u/mmu96wH+Her83vW1CmUwW/+0t9az/83icaPhpE/cbSGDORRtHheknMmTNHEoFIX+95oFRTUz5Z94Qode/1+90Pv2P7GV9ivW/hnqvfWHcbKHFoYMwmgyUeh4a7f0ht1oBYocG21/BFRDl9QsgEoUY8jjkitYkC92YjxDMXeF7e7LJwx4g2F+zx3DR0zf9w0IeFscD/1/V6bbPG6O8Sj3E91nFX3zBP/TJwvOOooa+5IX7fa7DlWET3W4M2mSKRPm+dGMxt7zpGdtPjjz9uelhQl5/PkOURLnK/PvFmYrgDIMiIwqnTEHt5smWlZK8LsYDTxmsQJ5OSvh0PPfSQXHTRRSZLAhmCLAW/Bt51BY5yfh8nfJcuXWL6TqR9kueViPxkIUPCXr/fsyebxQ/kEb99sCnKyoqiKErjRTM1FEVRlCDlhiakFlLbiUwm24LoyXBGu3AkEk0fD0TsRyJcKa26NCrGUr6rIUn2+fk5BNzlfGoDij4GYW+zcJpNDh06NO7j+T33ZN8PPweG32/4fa4uiHbNOAMSNUrFMo+StQZgrCRalZIYL774Yogh1EJUKaVWiJD+4YcfahoJ18ezD+ccS9Z8SAYYfMm0Y+3EIURmhrukFAZnSkpRaooMk7parykH5tegPBZS0SmEAdMvA4eI8lS55ob6fZpFY9x1l3wkc4PSVN7SUxiqKV/ohcwNSmXdfvvtJvo9nHOZsolkcTH/yUjwZsPVJzhXKTnpZ9CNp/RUfaxfDSErxSrH+YE8yvNlPLDehytVyfuUacJRS8aCt1xhbca0zVbm3pEdxFhDZiHrId7yivUhK4fbnyKNo61ZVlYURVEaB+rUUBRFUWrAAOgtW1CXSotfJD4GDb8yPH5QasLip7AtXrw4yKhkSVYUp9/5h6tX3xAQZec1/vJvv+g77lWsmRL1BYb/ESNGmOhLN5QdwRgbb1kJxgglGNxRjxhxiV4MN35S7Z6Eg3P1K+HhN9btteIAoM+BX1YEvSCI+KV8Epx88smm/nVDGi4w/BCxzWv+/PkydepUk7XDnMPRxXtuY9Y999xjxkq45xbOMUK9eT9HT7KiVlMBjG6U2aK+OiV83Ab5FStWmHvs7f/jdw8xLvqtsX5YB5Otse41mFE2hh4nlEzDocLYmTlzZqOI6OW8vWDodJeqa+hrbqjfx6lHZoK7Jw09sIgep96/G3rZhDPwIhc8+OCDxkjNOVLOyc7/jz/+OMh5wHrI3L/22mulocCAftZZZ8kdd9wR9D5jwlumLhrxrF/R9q1Y97pYZaVkynHxwNjFQUuGIWMBRyzllfhvxhflAi04QBhr7GPJgNJhkUpGJXOf9JNbCDby9p2JhC1V5ffsw2VtkeGRrLJ3qS4rK4qiKI0XdWooiqIoDQbGMJp2u6EGsrdhpR8osu4IaOppUxrLDQa7Y445Jug9DLk0ykwGZAt4aw5jaMDA4m6eHQ2/SO54SwGFu79ew8eHH37om+XA+37fb2guvvjiEKcGJZOuvPJKY9yKBtHolKEAntVOO+0UUp+aa//zn/8cotB//vnnKXlP/GDMXXjhhUHvYdQhOtULcwUwWBDZ7AYD91133RX0HsYUsh5SCXp1uPt58LwwxLodG+463n7PjehdW9vfzSeffBIy/4gUj9RPo7GC4+qaa66R6667Luj9MWPGmDnm7gfjdw+5J7E4wjGmuw3qfgZZoq6966ZfLfZUg54DZA95oc6/dQrWxTXHu2805D3HAOt2auA4pBSWO3sDYjEUYwBmrrsdL6x1lEpiPqfS2KFJNeup2yBN+a94Ye6RgeLdt7iH8e7ldv13wx7L/PQa11kL61OOSwS+T4aEu4QTPZBwerhhPCTLqVGfcH30B3HvwTihWbf9+nVFuscEPuDgcPfV4FiTJk0ypQfdECiQjH4a1nHlHSOMZ5yO8WSt1pWsrCiKojRetKeGoiiK0mAcfvjhxtDshmaPGGj9wDCA8oUi743u82uUTBSf19HB8ZOVqYFy6NeMHMMMtcL9ejpQ7sfbqNGvVAFRh7XF69CB++67z5TxcEOkrreBNmVAaB7a0Bx22GEmW8MLEbsYysKVIRk/frxpmEqEcLR7cv311wdFqmLcwWDgLRtC5CkRzanIv/71L+PscSv6ZLNgPPQa521Gh1+mC0ZGd3QmRg3q1SfaxyRZYHzlmYeLKKa8htugCe5rtw2F3WDMod662yjCePIzxhFBXh89izAqul9+fRqSDc4wb3Q89461wg1NeGnI7jWgEonu7hfgBuMiGTMYoN1Gdb+xN2XKlJBySWQNpSqMn3/+85++hlLKpbHWu0n2Nce7bzTkPadUj9cp9swzzwT9G6cEzYn9YM/n8+FK5eHU8K513n+Dd3716tVL6hKOj5yDc4sXe8jxxx8f93GOOOKIEFmJ4AxkHDfIRzSCj7R+0XvE7WwDStDdf//9IUEh3kyaupbjYuXMM880ZcjClZ3ykwv8xkNjwSu3cA8pFbt8+fKwaxPOVuQn9k4LGR9+ct1ll10WtH+SReVdv2oD49e7/rC3UerQnVFjsaUmvdSVrKwoiqI0XjRTQ1EURWkwevToYcrcPPLIIzXvoVjhoMCARokG0uUxWKIAYYy3DQ+9/T34DuVN3MZXlFgMhTQQpqY3kW6TJ09O6jXccsstRnl3G0YxhlNigtrKRA9iJKcsAlkD9IfwlsTA0Ozl7bffNsonhlhrMKAkTDzNRVEY+Y5b6cMIgGGe+4WBBaOW1/EDNLr0NmZtKKi7zjl7yyQQ6UdJpL322sv0fUFhp3QOEZnWIeEdJ3/7299MZLnbOIahnPGGE4TIc+6H1/EDN9xwQ4jxJlXgeocMGVJzDWQh+JV3wIhvoQY4Rje34RzHG/eC6G2MfhMmTDBjt6FhDL/00ksmc4dnjZGUKFWMczxLDHzeUhleJ8ZNN91kyoa4eeqpp8w1jhw50pQioxyXt6kszku/iOimQosWLeSCCy4IKZODcZS1yt2XgPWOdcUNWR44nFjveCascRjbKA8WrrSJX7YYjrmvv/7aOE5wgBA9nqxI4drCvMBJCDilyQjCce3t9wOsERhc6adQl9cc777R0PccJ3Sk/ZdsOdZwP1jTMbqzJnGugwYNMvsTJZ4Ya2TVec/bO/8bCno+JENWopQVTdG9xn2cPazZBGtQdsnrYGTvRAayIFOxDnqdSmRmMX6QWRYuXGjkmlgaVCdTjosVxikOHcY4ewHP2pZp4tz9snFTZTwkAnILwSfuQAvkSe4fexf/z1xgncJBxd5vxwFZM17Z7s033wx6DycT94d9n2eOszpcwEii/fpwnnt7+lASj3MfNWqUkdFZW+lPhHP1yCOPjGnNO+2004zzxpbWYswxHhVFUZStBEdRFEXZKrnxxhupBRL0GjlyZMLHe+aZZ0KOx29Eo7i42Bk+fHjId6O9evbsGXKsL774wklPT4/63e7du4e8x/l74Te8n/Pj0Ucfjevc161bF/T9qqoq39/yvlavXh3x+fldw/Tp051WrVrFdX577bWXU1ZWFnIsxof3s/Pnz0/aWIjEjBkznMGDBydlnIwfP97Jzs6O6zgnnHCCEwgEQo4Vy++NGzcu5HOnn356wvfNb6zEcm922203p7KyMuhY9913X9TvtWjRwtl9991D3ue6ErlOL7GM5VNOOSXuZ//RRx+F/Nb5558f1zEyMjKc9957L+FnGu55+RHr/IoVzifWc1y5cqWTl5cX8vl//vOfIZ+9+OKL434W3mspKipyOnXqFPU7o0ePjmmfSua985uHsb7atWvnfP75577HTfY1x7tvJPv346WwsDDiussaH44999wzrueQlZXl/PrrryHHiWW9jhXGVzKO5zdP/fbyzZs3O0OHDo3rPnTo0MF3Hqxatcr8LRFZyW8NSaYcF8tc7tq1a1y/07JlS2f58uVOIvjNMb/nEyuJrlXffPONk5ubG/c99pMhzjzzzJjuWfPmzUPeT3QvRKbcZ599Yj7vI488MuQYY8eOjfq9Qw89NK7noSiKojRutPyUoiiK0qBQ5ojavWeffXbYKE0vRKRRQsHLvvvua6L3IjWQJqWeiMdkQu10Is68kbmxwnXTlDfW648Hyr7QV4GazNEgCpbIT+pox9uEu64hOpfroBRSPNkqfn0A9t57bxOd7NdY2wuRoIyZl19+OalNr5MNUbVErIaDXiLvv/9+SE1qos+9kffeKH7KWDR0lGs89571gdJJfn1tHn74YVOOw1t+xQ8iRynBQnmVpg6NXJn7XsjA8JbLI2KYVzzzkHrtjCUL36W0Tbim0LZkSSx9c1IBxhOZCEQZE3XsR7KvOd59o6HvORk/4eYS48Nb2izR+c91ksUXbzPuVIfG8+PGjYu5LwT3lAwXvxJb7du3N2tbJJmFcexXyqqu5bhYiGc8kNFDBkos/SdSGbJ/yQ4jEyFWuGZ3rxELz9UvE8JC1gPZGu4svdqCTImcQrZlon1UGC+RzltRFEXZ+kjNGgqKoijKVgVGiCeeeMIYj3FKYHCeNWuWKVFAcCVGGJoCY6TAcYGx0qaa+5WwoMwG9XgpSUFpGhQzSqOQds/3KUOTbDDWUPaAckiUgKDUFb9NI2aUagyknAM9ONzGPQv1kekDQWkkSgGQ+h+uTn28kLLP+VBaB6MWx6dkB+V2KFWE0QNDP86eeBTmhhgnGKvpgcF1fPnll6YXCGWHqMuMYRHjLMZ9roc64uGM8ZTkoEwDZWJwSFEWhXtOybLWrVube0bpMsoYJFoio74bPvOMMUhSrosybMwdrp++IpSvwIjkBQPUK6+8YsqTPPnkk/LLL7+YcUe5COYZTiTKPeHUaUhYH5jblNXCcExZI547L66BZ8a1UooD47y7ibjXGEZJJT7D9WKIYxxQtgMHFsY+1g/KeJ1yyikxOT+aCvQSobyNu4wPazBlZbx9FjBM8Txo/so9pKRMYWGhmT/UPac/AvOQfjiUofFzIO65556m/B1lrzB2sSaxNrIG8XwoK8KamiowdphDGHAZb6zprBP0BTj22GPN2hONZF9zvPtGQ99z+k15S9/Y9yPBnkpJIWQD1ijOm/2VecszQR7AgU9PDo6VaIBBqkMpPNZi5qOVlXhelJVkf6SkJHLGCSec4OvUdUO5Qsr6ISth9Kf8GGOb/kM46JAHGFsNIcdFgzFMCSpKRfLflGViPHjlAH6Da/GTuRojNHmnrB8Oh7feess4reiDRQ8NGm4ja7IPEsxxwAEHmPXXz4GAg4GyaJR0ZM3nHpaXlxs5glJOjK+6mEM8GxziyBWMEcYX+y9jhPPkN5E3cKiFc14g+xGcgKzN+OXaq5OwFEVRlK2RNNI1GvokFEVRFEVRlNjACeU1PKo4pyiKoiiKoiiKomwtaPkpRVEURVEURVEURVEURVEURVEaBerUUBRFURRFURRFURRFURRFURSlUaBODUVRFEVRFEVRFEVRFEVRFEVRGgXq1FAURVEURVEURVEURVEURVEUpVGgTg1FURRFURRFURRFURRFURRFURoFaY7jOA19EoqiKIqiKIqiKIqiKIqiKIqiKNHQTA1FURRFURRFURRFURRFURRFURoF6tRQFEVRFEVRFEVRFEVRFEVRFKVRoE4NRVEURVEURVEURVEURVEURVEaBerUUBQlZXn22WclLS0t6HXTTTc19GkpSsqhc0VRlK2FXr16hcgGqYL3vDjXRFmwYEHI8fbZZx9pKtx2221B13buuec2yTGVzDGhpB5nnHFGyDP+8ssvG/q0FBHzHLzPhuelxL7ubU338MUXXwy6ztGjRzf0KSmKEgPq1FCURgACxTnnnCM77LCDtGnTRrKysqR169bSp08f2WWXXeT444+X22+/XT766CNZvXp1Q5+uogSxZMkSefDBB+WEE06QgQMHSvv27c0YbtasmXTq1El22203IyA/+eSTsnDhQr17iqIoSXBsxvI66qij9F4r9c7y5cvljjvuqPl3enq6XHXVVfokFEVRlAbh5JNPlt69e9f8+9NPP5X3339fn4aipDjq1FCUFGbt2rVy6KGHyr777msMvlOnTpV169ZJZWWlrF+/XubPny8//vijvPHGG3L99dfLIYccIn/96199j9XQkRYN/ftK/bNs2TLzjHG+XXzxxfL666/LzJkzZc2aNWYMFxcXy8qVK+W7776T5557zjjuECb33ntvfVyKoiiK0kS59dZbZfPmzTX/Jjhnm222kVQnlbOEUgHNSlGUhoEsPu/8I9tPiZ2MjAz5+9//HvQe9hXHcfQ2KkoKk9nQJ6Aoij8YfXFSYPDdWhk8eLBccsklQe8R1a+kPp9//rn86U9/ijtzCMHx66+/rrPzaqroXFEURVEaS5YGgTpuvLJefXPWWWeZQCJFiZcDDzxQWrVqFfRet27d9EYqTQLGsnd9HjZsmDRVzjzzTLnmmmtk48aN5t8ElL799ttyzDHHNPSpKYoSBnVqKEqKgsLn59DYeeedjQGzRYsWZsOdPXu2/Prrr1JSUiJNDYSmpiw4NVW++eYbOeyww6SsrCzkb9nZ2cYxRfZGfn6+yTwiewOhsaKiokHOtymgc0VRUjuCcvz48ea/b7zxxnrrDdW8eXNjrI3E9ttvXy/noiiWhx9+WMrLy2v+TYbm7rvv3qA36J///GeD/r7SeCGAh5eiNEXIoPv3v/8tWwu5ubly5JFHygsvvFDz3gMPPKBODUVJYdSpoSgpyiuvvBKSEvnBBx/4Nq3CeDxu3Dh5+eWXNUVSaVCIdKRGu9ehQRr0FVdcIddee63pC+OFUlTULaUu/Mcff1yPZ6woitI0Ya3dmowRSuoTCATk6aefDnrvpJNOarDzURRFURQ3J554YpBTY8KECTJjxgzTF1JRlNRDe2ooSopC5Lqbbbfd1tehATk5OXLQQQfJ888/b4zCbuhpgEGZvhxe6GMQqc8FkXSffPKJ3HbbbXL00UfLkCFDpHv37qbBMxH37dq1M43Kzz///JooVC+1+X2/xqfRIlwpXURfEZqqt23b1jSk5v859wsvvFC+/fbbiN+35+t+0Q8E/u///s88gw4dOph7TnQhx6R3RDjmzp0rzzzzjPncnnvuKf379zf3jfMiirZnz54mqwHDU2FhYdjjUBfVe15E/8Lvv/8uZ599tjkfIkz4G9kS3s/zmXDQm8X7+VNPPVXi5a677jI9M7xgxLjnnnt8HRpA1gZCJM3uf/755zq9l/GMq1jrQ5M1RSQP46NHjx7mepgjNELfbrvtTCm56667Tj788MOgOuLesXveeefJTjvtJK1btzbXRUYWWS1kt5ASTYQrgnWi17R06VLj/LzsssvM+EFA79ixozlX5jVp5pRSoN45Dd4jEe7e0O+HaHTmYEFBgXk2w4cPl0ceeUSqqqqkNoSbn5QtQwHZb7/9TCP6vLw8Mz6ojes3HqPVICbz7c477zTrG88i3P2kTCAOaJoLEs3G82Jt4LmPGDFCbrjhBlm4cGHM1zd58mTzbMi84blwLOYM2XnHHXecPPHEE7Jq1aqw36fcG+d9wAEHSNeuXc16wDkNGDDAzP9YSrvR54bnz73p0qWLuZecB//NM8VpefPNN8sXX3wRFG3tBgf8aaedZsZ+y5YtzVimPAf3iLnLOGdNiHZv+DsR1PTZ6dy5szkPjkN2wUUXXWSyFKMxb948M/f4XXtPuSb2MubaCSecYO7ZxIkTjcFV2QJ7G2Un+vbta8YS94+17L333ktojyYr7y9/+YtZIzkeY5ReCl999ZX5O9/1Hs8r00SC8cj6z3rDOGFNY++nIXUs2ax85vHHHzfyDusZ6xfnydjff//9zXHiLakY7fc45o477mh+i7nOOGWeJzoWaztnwq2xrN3sP3vttZfZdxPti4as6JWZuN9+cC/c58Fa7K1tzvV4z9d7/NLSUnMf3J85/PDDY+6V4e4J57dmeb8Xa5+NTZs2yS233GLGKPtkvHvlhg0bjNzBnGQ9Q+7gxX8jC/33v/81v1HXspD7OF64X8nosxFpbaCZL2PIjnfkGPYf1ptYqEu9IZX3xlh10ccee8zID8ihnCfrFOfN+fNvyvKwZhUVFSXUVxGZluu2Ogz3/uCDDzbPNRpke7O/Dxo0yOzryH/IP/Twq0tqK/tF0uni6ZXhPo6fHs49DffduuyJiV6K/M16ZnUMnuvQoUPlqquuMj1BI1Eb2TwZcww9CPnBzauvvhrT/VIUpQFwFEVJSbKystDcal5t2rRxCgsL4z7O6aefHnScaC8+b5k6dWpc3z3ssMOc9evXJ+33n3nmmZC/33jjjb7XuWLFCueAAw6I+TzXrFkT8/16//33nYMOOijs8Tp27OjMmDHD93jHHntszNfeqlUr54033vA9zvz580M+P3LkSOell15ycnJyQv7G53fccceg9woKCpxNmzb5Hv+aa64JOcann37qxEN5ebnTvHnzkOOccMIJTjJI1r2MZ1x5P9ezZ8+Qz/zwww9Ohw4dYj63F154Iej7gUDAOfvss2P+ft++fRO+piuuuCLm38nNzXXGjBkT9nn43ZsJEyaY+RDumEcffbRTVVXlJIrf/Pzggw+c0aNHh/3Ndu3amWfkB3PI+/lvvvnGGTBgQNT7+d1335lnEe0+ZmZmmvlVWVkZ9rqWL18ecY1xvx544AHfY/B+Xl5e1O8zj8KtAx9++KFZJ2IdI19//XXQ90tKSpxDDz005u+PGjXK9zwqKiqcq6++2ty7SN9PS0tzLrjgArP2+PHUU0+F7KWRXosXL3bqAvc4C7fW1Ba/NcBvvYqV8ePHOy1btgx7r84880xzf2NZd+DVV181a0q453jDDTeY73r/xnV54bq8n1u4cKGz3XbbhT3f3Xff3dm8eXPY62UdiWUdZ46Fm4MQ6zNYsmSJM3DgwLC/M2LECGfKlCkh7zOW6nLO+K2x7733nrPHHntElNdi5R//+EfQMZBfwp3LddddF/KbyKVuHn744ZDPvPbaa0Gf+fLLL0M+c++990YdU5Zx48bFvIZ4vxtuTPzyyy9O9+7dE94rmRd+8pafLMTcC3eMWOdvpHHtd5xIr0TWJb+14YknnnDOOOOMsL/TrFmzkD2qvvQGxkwq742xMnTo0JjPuWvXrmbf8MNvDnHfHnroISc7OzvsMe+///6w58Za0Llz57DfPe6444we4/e7tSEZsl84nS5WOZXvhztOtJf9brR1L9qz84M9FtmA8RfpHJDLbrnllrD3OBHZPFlzzLL33nuH7MmKoqQm6tRQlBSlS5cuvgIjmzdCenFxcco5NXghUCTr92NVuFBM/ASzSK/+/fs769ati+l+xWLowGBSW0O8FfQmTpwYchw/wZXzCmew4/MofN73MUL50a9fv5CxFq/x2c9wwGvSpElOMkjWvUymUwNlpU+fPnGdl9ep8fTTT8f1/fpyatjX66+/HtO9wYDAK9rxHnvsMSdR/OZnjx49ov4mTuGlS5fGpDh169bN9xju+4niHkkR93uFc+4tWrTIzLdYj+NnUL3kkkviOpdhw4Y5paWlQcdYu3atMYDFcxyvweif//xnXN/3Uypx8mHQi+c4Rx11lPmem5kzZ8bl0OClTo1qZs+eHZNzq3fv3hHniYVxEs0IF+54sTo1/L7rfV177bW+c/Dll1+OaoTxvq666irfY8ViwGXubbvttlF/w+8zfoavZM2ZcGtsuDUxEeMghiH3MYYPHx72s34GyUcffTToM3/6059CPnPRRRcFfebWW28N+YzX0V2fTo0WLVo4bdu2TXivvPPOO+M+H4zHTc2pEYtszm+VlZXVu97gdWqk0t5YV04NO7bnzJkTchy/OdS+ffuox2PfmDZtWsjxVq9e7XTq1CmhNbQ2To1kyX5N0amBU2G33XaL61wuvvjimK85mmyejDnm5sorrwxxwHONiqKkHtpTQ1FSFMqovPjiiyGlYyj7wYseG5SkohQAqbak6pL66pdCSdolpWTefPPNoL+Rrsvf3fg15iYdmPIIpPVSdoAyCaS0k15KuRR3OQDSPiklQUp0sn4/GqSZetNI09PTzbFJvaWZ+tixY4POc9asWSYF9bXXXot6fFvuxZbrobbmtGnTgj5DevpPP/1kGrn7Qbo9qa+k3/LiXJYvX27KRLnLWdAs+/rrrzelXWI9r8zMTDNeKFPEezbtncaFpP9SDsjy1FNPhTSOnTJlirlHbv785z+bexgP33//fch7lFPYddddJZnUxb1MFJ475W3cMEdsGSRSpRmblAIIV7bkpZdeCnlvjz32MCWHSJumlNb06dPNfKtt+SYL5QoYy5wr95BnzflxPd6yU9dcc40pLxBtPNiyWjxzSl+wHr311lumNJebMWPGyLnnnivJYtGiRSYFnfnOHOA+edPw6fVCWadY5ru9fko2UUaAkjbukmhcD6WgvKWXuG7KgLDeUcrIW0KQ8nWsIZS5sDB2ubes7V4oocE6yvFWrFhh1h2/0lNvvPGG/Oc//wl5n8a77BGcL31q3M+BuUrJhvvuuy9o7XavFUB5IM6Zc6C0BGOdsUzZk1jGMvsU32e+AmOM50NZEG8ZGQvn9Pbbbwe9x9izx+EeUKbO3bfnnXfeMeOKshvu+80a4KZfv35mbrGHcT9Y93hO4UrCNXYY95deemnEz7BH8JzdUHLFr5TIqFGjzD387bffTMmWaCUkgDJK55xzjinX4R0byC2M8++++87MsViOFw6+y5pz6KGHmjXt3XffDZkvlFFBfmJddX+PPdE7HlkbOT/kH/YQyh+6ufvuu2XkyJFmzscL3+UeeqH8DeOTEnCUKvT7TF3OmWhrImXDKCXKs0MGirXMknsseOUEZMtwUCqEZ+Wex+zzyG7uf3thrYz0b0qSRPpdL4xRyrABZUu8JZ3s32LFrsWJ7JVcL73JvFByCl2Ae0zZWGQi7zmiLzDGkg2yir0H3r2Ia/TKnOHKkMaLnd+UouGFLOstFYX89b///S+kyW9d6w2pvDcmAvIQJV5ZW3l+rOesC+wD7nHLf1Oy2Ns3xw8rE7OucN+RdTlf9/XzO6zblFJzc/XVVxu5yAvrJ2XDKFXEPIh1DY2FZMp+yYKyV3buIQt6ZUnK1vIZ73fqgiuvvFImTZoU9B7rN+sS6xPz7bPPPgvSY3iulHb0lgNMRDZPxhxzQ3lSN8wr9HzGmKIoKUZDe1UURfHn119/9S0rFCkS+Z577gkbXR9P+qiFcleffPJJxKyQt956K+S4l156aVJ+P5YoMtJR/SJ7vKWTOE9vJCb/9pYyCJdZ8vjjj9d8hkiNPffcM6YIaqJ6/KKWLEVFRSbLw3teRE27CReNQ7Tfjz/+GBIFalPOL7vsspDvTJ8+PWI5CF6///67Ey/eqBZeQ4YM8f3sgw8+GHE8+0UsJeteJjNTg/Jf3tIkRJD5QbTZv/71L2fs2LERs2S4j35s2LDBefvtt33/Hus1ff/99yFj3lvawC8jhlIZ0e4NLzLMFixYUPMZxqZfVJv3mcSK3/zkGVMexQ0Rrt7PZWRkmKyIaNFgvEiL90Y32pJNt912W8jnKSNC+RsL3/Wbe9wf7rGFLBi/37/55puDPmefDRHl3rJq3hI23G9K6bhZtWpVyDijFBBlryze6yIDxq9cD3vM5MmTTVkF71jyZkaEK1/GHHnxxRdNBLUbfo+9zH0MSiDxe95MAsqKuT9H1KY7++Scc84J+jsRhH5lIFgrv/rqKxPdzX1qSuWnYnn9/PPPQcf57bfffD/nzZigLIjf57zXx1j0m4uff/550HwJl20Ua6YGmSU//fRT0J7pHSN+1+sdJ7wo3eheoxgjxx9/fMjndt1117j3DOaxX4TxhRdeGLTmsHb6Zb5598ZkzplIMtC5554bUsomXBm7cDC/vMdF/oiEt+wVmZHuEl5+58r42rhxY8165S2jRikhL7FELMfzOTfJ3Cv9yiVRftEdQcxz8ZNRGcN1WYozns8lI1OD1/XXX1/zGZ613zxlbalvvcGbqZFKe2M8oAMyzyLNaW8pptatW4fIT+Gynfbff/8gHdNPN/DqEZQB89OP77rrrpBymunp6UnL1Eim7JesTI1EPucmWZkayNbeMc69mTt3btDnvv3225AylOy3sVxLNNm8tnMslkzBcJnriqI0LJqpoSgpCs3eaOhLxHxxcXFMEZlEXBIlQrRGvFH2fhCNYzMpiG6wkZREcBKx8EcJu5DvEclQXxDd5oV7RmSIG5oIHnnkkSYKyMK50/CUZmKRoLEZkaYWIjdpkuaNEPRrvmYzVrhnfJ6GeEQjERlso1W80fec1y+//OLbXN3L/fffH5Id4s7YueCCC0zjVPdzIluDpt0WbwYNmRVk0cSLN8obiIhOFnV9LxOBCC03/D4RkkT5eiFqnle0YxBtR2SqO5rYRlfRpJlXotisGeYvawUZIEQFcw9tJLVfFD5zmui3aBBBSjSfhbHJb3rnChFbNPlLBkRgeqO8iG5lnLujgnk2NJ6kUXEkGCs0eQw3lv3WHBprE21oIYKZZoas4dxfCw1sOScb6cVa7YXG1TQA9UJGFk0p3bAuexvHn3jiiSHR42QNkanCeuBunkt2hr0f3nHI35lfNJ51w95iI2O9cAz2Igtzgchh737E/DjllFNCvk+Wmfv7cPHFF4f8FpF3RP8S8W6x2SxkE/hdD1GWvLzjjnlG1B+v2kDDX++5W+bMmVPz32TN+K2VcNBBB5lXQ0KGgN8e6G0MSnQojaPd1xbr8VjD7HOy84U5ROPfcFlA0SCC2h1ZSUbf6NGjQ6I32adtlD77g1smsBBt7h4njJGHHnrIZH+4o3TJVCUy1pvpEgnkKG+EMRG+7MnuzAfWTuare6/2I5lzJhz9+/c3188aVJv93S/TLFrUPpG27FUWssU4bxryujMwkFlYa7kXrPVE7CO/EjXtHVPhGvLWJ4nslVyHN/OUMUMkO3Kp+7nwvLzZKMxFxi9Ne5sCZJy690r2GfZ+b5Nor2xeH3pDKu+N8WB1QH6beUi2Cv+NXsr5g1dWpXk395yMl1h0GBp8W9C1yC50y/Le58cccGej2Oh9dGA3ZNode+yxSWsankzZr6nx/vvvh2TG0sCb7Gk3NJs/4ogjTPaKBR2NTMi+ffvWSjav7RzzQlZSLHuYoigNjzo1FCWFwViHwEoq7yuvvBKS8u4HqcnPPPNMVMNdrGB0+8c//mHSNWNlzZo1Ul9gVPBC+Qk/eN9rwPD7vhfKOPkpU168pQOsAITyimHFK4TX9h6ixGLAjAQKDoYdDGmW559/Xu644w5joCA1G8O2m9NPP10SgZIOXvxKmCRKXd7LRKE8Bs8BAzBgMMD4T4kWjCwYgyjzNHz4cFOiwa9cB4qm2xHI88F4htKMosYLJyfKkN89jgeex4033iiPP/54XM8m1ntYm7mSKCiufjDuvaVOUJ6i4TXeusHx4053j7TmYDgird5rVGXNsYotZXe8nH/++RIrft9/4YUXzCsWMPzZvYKSad75hlGCcWzHIf9NWatwyidj2W1AYO/C4MZYtnMBQxvzwessCXc9//rXv8wr1uuxhhv+H4OJ2wGEIRTHor0e5iRzOB6jdDgot+EtZ+IH1+h3nda43dBODb85gnHPC4YCnBP33ntvUo6HYQKjop+jr67WHoxl3rKAGEb8HFw4Bv2Mzj/88ENc48fvfvB7bsOehbEQzamRzDkTDgxAXodGIvg587zORy84IG6//fag93gGGCvdTg1KK2HEotQQ8DcMst7SU/aYDU0i4xU5wRu4wXrmdo5YkEMYl+5yNAQvIO/FEqDQGKAUkNdBE4u8UR96QyrvjfGAExFnATJpPOVPkRmjOTWQa3l5g7Jw4rllTu/z81tDmet+8jVraDKcGsmW/ZoafuMT2TJWWwTjM5pTI5Jsnow55sWvTFe4gBRFURoWdWooSopDBIitJ8qmT78K/p+ImXCGQaIdk+HUePDBB00kULzEklmSLPz6FFC70w+/92OJuvBTGN1RcRYbteSOVsJgFi2SNdF7iFHOr4+KF+qju50aXDOKPwYprwEJYfykk06SROjQoUPIexiNiGzzKhsIlrYOrF+/FS91fS8ThShTDC6XX355zXtcL9FsvLzjiAg0ahtT69Vy1VVXmQgwd812IjIx3LgNaBiVqL/N7yWSSUMUFUZDb83bZN1DjJJ+UbexzJXaEG6+Uwc9EedMpJrjRIF5zx1jpF9EVyxrjjuSz4LiFSvh+rTEivv3MY4xJ9010TFiUDvf20MIZwA1ralL7gYlcty4cUH3mf8mytT2+rH37Pjjjzdj2W0QTub1kK3CGuc2SNkazF7jBL2ciPgNZ9hqrLDm+GUQRoLsnNrMsbo+XjL3ab/xxjmE6xWRqAwR7X6Eu+5Y7kcy50w4ktWHAaddvA7ucH01cGq490c+R0S626nh/v9E+2nUBYnulfHIu/Zv3hr7TSnSOFHZvD70Bi+ptDfG49Agsj6R345FZvR7fn7P0FsRINlraCwkW/ZraqTCPlTbOebFL2vUbw9TFKXhqX19GkVR6gWM10SXYXihERxCHaVDbAMsN94mZYmAIIDRKhFiacCVLPx+K97mldHwE1rdRulwIDAlYoSP9R7GGrWPcc8bMUVpHvA6NWyT1UTwa/KOUOgX3URUJWWxeP3tb39r8HvpZ2T3U5z8oKwPze/IDIhU1oEobj7rdRSSCv3jjz+axs3uNHa/SDEMtETKJ3IvyM5IxKER6z0MN25imSu1Idx8T3RtiDSv6mO9SfbxIuHN1mE+UhaA+RnpuZH1QEaXN4qcDCUaiTOn/ZyclpKSEpORxFgOV7IpGdeDs5SgAMoSRbqvZPRQwsxdEmFrxW+M+2URQCxlLpN9vGTu0/Uxn5O9hiWbWDL2apshaPEryxht/hNRa8smWnBmkLnMWuN2avByRw6zZ3oza8iKqes9qa72yvoYr7WRheqbRGXzhpj3qbY3xgI6YKLG6vqWGet6DW2ouZfK8y/V9qFkzzG/v5GxqShK6qGZGorSSCFqG2M1UQNuRc5u2LWF2vPe4yAkjBkzxjhXEEYxQFAGyC8yqr7gnLw15RctWhSiBMPixYt9v19XkK7tlz57xRVXmDRba9ihpBI1WOMlVoGa50RJG7eTCscYES3eCOxES08BadVEIHqFU8YMpVlS5V76Gc785kwspWQspJrzIjptypQpZkzieMCh8/nnnwdFmT766KPmPN0RZAjrRBnxom8Njkm+T6mIsWPHmvfcjiKMyRhra3sPcWJR95asH1sGhOPGUwKpoWG+++GNUIVYHHaR5pVd99zKJ88cpdPv2NHWHP7bO8545pRJigW/9Yt56Lf++eHnFCeCjRfjjDIPlB5kLFI6Yfz48UHKPX0kyO5wO/Mo/0GWH9mFfJcSd3YskzHmjsjj/jDemA/hrofyEYzPRByrtsY6Lxz1XA8ZVLNnzzbZju7yZFwX9ZrpaZIIkTIi2DO5d0D5N+ZcquKntPvNpXDj2+943vKVHC/WPbou8RtvnINfdqH9WyzHiESs60Sk+x7t92szZ/xIlvEOpwZrhbsvSSxGU29fDfZV6urbcjgdO3Y0axnR0QQAIZeyLlOO1XtvU6H0VKL4Petw+18s47UuZKHGQEPpDam0N0YDhyCBc17ILKbvhZ1rQK+vV199VeqLZK+hsf5mMmW/WOcee1F974uJ4Dc+kaX8ysH5QUmoZOxDtZljsWTWdOnSJeo5KIpS/6hTQ1FSFBpwkl6PshavIOGX1u4X+RKpPqqfooQwi7HLTbja4LX9/VhBCaEkl7cZIvculoalsRr/EsF7D6nPSYaEV5iN9R7WBsqRYUyzvR+4997SMRgcvA2G4wFjBc1a77vvvpByaJSBoTlcKtxLv+amfooPfWzihahSon94WZ588smgRvMoRdRhD5cWT1aNO7MGAxAlidyOjUTGjN+cfu6550IUsvoYj8kERcWv3B6OWS+1LTuCM5mofzJrvGsLjUbd8NxwaEVac1DkvAYjlKxYG9v7rV84usm4iAbKcqQoRhxtI0eONC8L/ZVwvFlwYJK14XdfUUAZt+5yWtQjZtxT291vvPldD40mY7ke1rRIEZ6sb9b5aDn11FOD6l7j8OAct+YSAzzLF198Meg9MtEoneeGsePnKPU7nrcEEMdjT3DDmOD9+oRm4jhd3IZ1MgA4X29fDZxifrX0vY16o+E3V77++mtj0PJmsHzyySdRj1eXcybZ8FvcL7eDIpY+R96+GgQJuPvl2MAejKwc32Zn3HXXXb7Hqu01NNR9pJk4v+OWnTHasYd4S/kQseyVa5o1axZUurIuZCGv4TcZcn6yaWi9IdX2Rj9Y77xGdnoTeOeUlWfrE781lL3DzxkdyxraELJfrHOP4/uVQapPPTsWuDb0CTesxd7m7X7UxfqZyBzz4q0ygI7LGqwoSuqh5acUJYWdGhg2MdYR4eknmKDY3XrrrSHvDx06NOQ9PwEKY1Q4/MroEIHuNZLGGtEd7+/H00zdC2mlXoGSiD1vsz+EntoY2qPhvYcYAN19ExDAcQCQMVHXYLj29sqgl4W3cSW1q2sD2SA0+XPDddLQ8e67765xqjTkvfRrXohh3O00wNASSyYEc+Css84yv+vOxohWK9bd6BxjDZFF4aKxuFa3EO79fqzEMqdfe+21EMUk1aHEkK2jbsHh5VVWUJroKVIXa871118f9PwYj9dcc03IsyeKzB0x6XUS22dA808iJb3GA67VXTLONr32KsF33HFH2PFIlgKZPih7bkcX94vSaPx/uDII0cYymVPPPPNM2OhrlErv2HX/G4Ojd/1gHjImwzlgMN7hsPUa9TC481zcJWrccI1+PVYSmVtNCT/HNpl93v2TNcvbNyjW45G5552flNas7yacyABe5wowD9znwly64IILgjIMrCEn3ibzGMa8wSr8FuuFe4yzByEHRiOZc6Y+2HvvvUP2IO99DddXw43bKO3OVnb/t9cAmYx+GnUly8YC57/ffvsFvcczRg53y1bIC/RS85uLbjkgmbJQuPvDnpHMMkrJoCH0hlTaGxOVF5ctWxa0ZyKj0E8u0dKwicIc8PYTJPPG7ei0gS3eErupIvvxLL3llJYvXx403uglSFnaxrA2Ub4Tx4+bm2++Oaifo1f+ohwu8yKWLI36mGNevDIKzy9c6UxFURoWzdRQlBSGKBmUf14IK0Q9kPJLmRg2bZwdfumRpAJ7ITUfYdwt/P70009GARwyZEiNAItQwG/4OUZeeOEFIzgSqUBDRiJjYm3CHO/vxwqlVg488MCgqGwEbfob8D6RTBjxUFa8gj8GRSKP6gruobs5GUIczxDFkog5Sp8ko/9JrKDkkjURDm/mRiIQ9YoSQVq826jKf+PwwAlHBKzNUkCI90bM1fW95JmTzeRWtFEeaIh45JFHmv/GSB6LAwZjDEI0L85j++23N+WwUFb4GwrFt99+G/K9/v371/w3nyFaHEMa45UmzJQfQmljnlNmw2t8dX8/VriH3ntE+SnuIU4vFODGlqUBzGuMDMx5jDSkmbvHiuXoo4+O2LMkVqjXS3SkW3FCqcXBwP0kyp9IZD9jOuWN3IofkaE8F6/xDQMvPVAwZDCWWOeJPsao8MADDwR9lhJQJ554YtB7KMIYnckYYiwxlplrPH+vM9Mdoc53eHENjGXuJ/sNexHrtTeqmqhcxruF8YNhgbUeZwtRwWRIYJDk91mHvc4a91hmDmEQcJfKw6FPqTmuk3vFGsP5cM+5x+Gab/J8cBjy4jtcD8+fvZRrZV56jfJcd1Oqmcwa582w8MMd7csz8+6pzDEMOnZPJTo8lnUbmJc40NzlXhiP7AOsPZRzYNwwvhoCSk8g27jXe8Y59bk5P9Zh1mC3E92SSBkx5j8OEoyNbiiXgUzHnGXssgfF4mBL5pypD8hCc5eI5Bq535HK49i+Gu4MDzduRwb9gAig8CMZ/TRs3XbvGEfmIYPU/o5f1H8yYNx45VkMh6yjzE9kI6LT2Su8445Mu7qShdz3x72fIfthtGQvY6wCx441G7EuaAi9IZX2xlhgXJDJ5i6ryDhBzmGscx1ktPmti3UN8ipBWMjdbq688krjiKKhNNlLzItwARoNLftx/3Dwvv/++0GfQ5Zj7aCsM+W/4nmGzD0/ve6www6rycjmXN2Z48kC2YrjPvLII0HO1YMPPtj8JvIX50CQFmOKe8T6Aslyrtd2jrlh3fLKJF6HsqIoKYSjKEpKsu222yJJx/3aZ599nKqqKt9jjhgxIur3J0+ebD4bCAScIUOGRP386NGjQ97r2bNnrX8fnnnmmZC/33jjjSHHXbFihfnNeO5T//79nXXr1oUc6/TTTw/57Lhx40I+x3vez/FdN2+++WbU88jMzHT222+/kPe5djfz588P+czIkSOdeBk2bJjveTDeksl7773ntGrVKqEx7HdtybyXcO2110Y9Xrt27aKO7dmzZ8d9bbvttlvQMU455ZS4j/HRRx8FHSOWucLcSk9Pj3rsAw88MKZ5F+3exDunYsHvWIMHD456Ta1bt3YWL14ccjzGmfezzLVojB8/3snOzo7rmZ1wwglmXfWyYMECp3PnzjEf54EHHgg5xsUXX5zQPHNf62effRb390888cSg89hzzz3j+n5WVpbz66+/Bh2D/euoo45K6HrcPPHEE3F//+qrr3bqAvc485tLycBvDUjkvtl1raCgIOr3/Oae3/V9/fXXZn1O5Hh+67fffu8H5xLL8V5++WUnLS0trnt21VVX+f5mLOtiSUmJM2jQoKi/0b1795j2/WTNmWSv135UVFQ4HTp0CDr+NddcE/V71113ne/55+XlOeXl5TWfKywsDPss77333rDHj3VMPfXUU1Hv6YUXXhj3mIjn3t95551xP+eHHnrI9zeTJQtZbrjhhqjHu+eee5x4iXUuxyor17fekEp7Y6zcd999UY/bokULZ/fdd496/bHoTPHMxVWrVoWsI7GuoeF+NxaSKft98sknUb+bkZFh5NdY5NSxY8dGPd6hhx4a972O9dkVFxc7w4cPj3ts+q0licjmyZhjlvfffz/k87/99lvE31cUpeHQ8lOKkqKQDeFN5YwGJX4oueHXgAyohRprU28iHf7v//4vYmkFIgrjaQ4Xz+/HA6UcSGN110qPBBE1RNPUdd10IluJHAoH2SmUyfHW7q5L/EoS1LZBeLhUZKL1uAfhxqMffJbIHm/KdbLvJRGL7r4XXohQt419k9lAldIXzKtEj0HEEWW2iJSLFzJbiDIL9zw4D6La/TK9UhmuKVJULNFhROyF62GSCETYUQffnaUQDtZxoitffvll32dNlBo1qWtTGus///mPedlI2FggGtdGFicylkeNGmWa3ruJ5xicKyU/iOBzw/h8/fXXzRoQazk8vsP5JHouQOQn5RKU6sxKokS95TG8UauXXXZZyPt+ezzR8/Tp8JYMcT8/5AO/8h51ITN4Yc0jIj2WBsCUnyBbyq9fQ6xwTUTTR8q4I4M11tIpyZoz9QHr4ZlnnhlSci8a4XphkOHhvmYizN111GM5RjzQh6cue7HFAvsJGdxk0UUDOZeeGGQH1aUsZCEzjAj/VKe+9YZU2hvjeZbeLFA3yA+cTyKZw7WFrBQybSL1nfSTUVJJ9iMr6JJLLok4DtCxyTyJBTIJyIJqKNgbx44dK2effXbMeh/jOFkZEMmYYxavbYPsLrLoFUVJTbT8lKKkKGy2lCOgjAplMigbQpovdTpJ6URgwOCA8YHUbgwy0RQtFBfK9FBPHaGMdMxI5Q0QVGmURSo/zhLSeREEeJ/f++tf/xpXD4Z4fz8eKLFCOSxKYiBAUqqFpmuUGUHxo6QVhhUU0kgKXLLhWmm0S0kXmoxSrotzRbnGIIThIpESFomCgkKJL3c5I8oxnHLKKUn/LVL46QFAGv9bb71lFOOZM2dKYWGhSUGmpAQKIsoBacIoCxh2vY2r6+Je8tsI3xiCUfg5R+4DY5vyApSC4jPR4NyZl4w9FGRKrJCazot5ynzBMYgzg/JHGO285S+eeOIJ02iQsUu6M+WBeD68mOfU3uW8uHaMQX51sGPloosuMg7Te++918wRasxiyGNO8DeeQaQSZakIaxDGP54jji1qtDPvmfOUpaJkQ12UFMKYxvPGSfXee++ZMUmpANY0nhmlABibpORHS6+nBA8KOunzrF8YT0jRp0EkY4hxjkKFEsw48oMxyziixjZjm/R+5hrnQ8klnDocA8cfjkOvUo4BYNq0aSZFn3WaOcE4ZhxSfoRjUGKAMhfMEY7h13uBUj2s75SUYY3nGJSt4DkxtzE4YkiibEY4gwTGABqSYzinxAT7IGXaOA7lAzCmcC6UNeAecy7cQzf0umFN4Hx4NvPmzTPXwvlwDPYF5pLdP90lbJRq4w33nFJBGPwpZ4PMgXMU5zilmSh74oVyD+H2HtZBnAGsl8wVxgP7Mus3axDPLNbjJRuuhzlHKSr60iD7MF4o8YKhnD2KOcJ8TsZ6wvrEHMFBggGFfYRxz1qPk4Wx7y0hFIlkzJn6gr2GMiG2RCU9HNg/cbSGg/lJ8IK3/4bfvGVMUYYw2f00gHOghxbnT/171knkmXB9DeoKZAH2ApwbzCd0BNZ7O2cwhFJOic9Fcn4kSxayMFesnM9+wLP19gRLFepTb0ilvTFWkD8ZE6yNTz75pDlv5iwyLYE19NNAzufeNQTs71w7Oiplp+gRhmGd8mAEarGfxOOQawjZj6AcxhZlm7i/yFqMO+45uhrPcsyYMTGfG7IwvZjYU7g3GzdurNe1CXkVnQZnDroE450yn5Sa4jxYh5G7cCZQgo5xFE7na6g5Rgk3nqsbvwAORVFShzTSNRr6JBRFUZT6A6OUu+Yxim+4Zm6KkkqgiHgbmWNgSkYErqIo8YGDy1t3GgMPe0y8YBDC2IEhwh15Sf+uWDIolMYFQTHuKOqTTjrJGFAVRVEUpaHAKeSuaoCTjGCpeKoOKIpSv+jsVBRF2YogCtvblNgvOlZRFEXZeiFKlEawfrFPOCCI4PY6NIgIJhPMj3PPPTek0byFjCSiod0ODSCLRh0aTRPKHrnL5VHGZs6cOQ16ToqiKMrWC9lOZLK7ITNKHRqKktpo+SlFUZQmDnVxA4GAKWtEWQ03pJGHK2ejKIqibJ1QOpASO5RoIPOC8mWUpaE8C+UdKOng5cYbbwyr/FOSgpctYUbpFxwmlLWk/ItfiRrtcdJ0Qfa49tprjXMDKPNFaTLGiKIoiqLUN5TtomSehZLIlLFVFCW10fJTiqIoTZxIzdOoeZrsJuGKUldo+SlFqR9o9ovDIVZsP5dw+028jdsxdv/rX/+K6zuKoiiKoiiKomw9aPkpRVGUrdhArA4NRVEUJVFyc3PljjvuME1r43Vc+NGqVSt56qmn1KGhKIqiKIqiKEpEtPyUoijKVgQ1rHfYYQc555xzjFNDURRFUbyMHz9ePvroI/n0009l1qxZsmrVKlm3bp3k5+dLu3btzD6yzz77mAyNNm3aRL2B06dPN8ejr9O8efPM8TZu3CgFBQWmb8ZOO+0k+++/v5x88slBvRYURVEURVEURVH80PJTiqIoiqIoiqIoiqIoiqIoiqI0CrT8lKIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqIoiqIoiqIoiqIoiqIoitIoUKeGoiiKoiiKoiiKoiiKoiiKoiiNAnVqKIqiKIqiKIqiKIqiKIqiKIrSKFCnhqIoiqIoiqIoiqIoiqIoiqIojQJ1aiiKoiiKoiiKoiiKoiiKoiiK0ihQp4aiKIqiKIqiKIqiKIqiKIqiKI0CdWooiqKkCDfddJPsuOOO0lj585//LLfffrtsLey6667y1ltvNfRpKIqiKIqibDUUFhZKhw4dZMGCBUk/9pdffilpaWmyfv16aYxceeWVcvHFF0f93LPPPiv77LNPvZyTImasMq4URVGU5KJODUVRlCicccYZRhDllZmZKT169JC//vWvsm7dugYRiO2refPmsu2228qFF14os2fPjvt4vXr1kn//+99JObdff/1VPvjgA7noooukrkDRPPLII6Vz587SrFkz4wB66aWXQj7jvkf2NWPGjKDPvfnmmzJ48GDJyckx///222+H/N7DDz8svXv3ltzcXBk6dKh8/fXXQX+/4YYb5JprrpFAIFBHV6woiqIoitIwMm9WVpZ07NhRDjjgAHn66adD5B2vHOk4jlxxxRVGPv3iiy+i/ta8efPk5JNPli5duhhZq1u3bkbOmzVrVsTv3XHHHXL44Yeb328qeGV8+/r444+DPjd+/Hgjk3K/+vTpI48++mjQ36+66ip55plnZP78+Uk/F/eLQKxE4fvvvPOO1DdPPPGEjBgxQlq3bm1e+++/v3z//fdBn+G6vNfaqVOnoM8wzvkc4zYvL884h3777bdan5/7t9PT083xTznlFFm8eHHQ5/g9PnPnnXeGHOOQQw4JeT6xzLNwz/nVV19N+HpYGwYMGGDuUffu3eWyyy6T0tLSiN/h3t57773Sv39/o6PxPW/AHLrfkCFDJD8/3+iEZ555pnF0WioqKuSWW26Rvn37muvls955BEuXLpVTTz1V2rZta46FXvnjjz8mfL2KojQM6tRQFEWJgYMOOkiWL19uBP0nn3xS3n//fbngggsa5N59/vnn5lymTJliBL3p06cbgW3s2LHSUIwZM0aOP/54o8jWFRMnTpQddtjBOCRwopx11lly2mmnmWfhZebMmeYe2Ve/fv1q/vbtt9/KiSeeaDJLuIf8/wknnCDfffddzWdee+01ufTSS+X666+Xn3/+2ShBBx98sCxatKjmM4ceeqhs2LBBPvnkkzq7ZkVRFEVRlIaSeT/66CPZd9995ZJLLpHDDjtMKisrfb9TVVUlf/nLX+T55583Do399tsv4m+Ul5cbZ8nGjRtN1ityG7LXdtttZ2SrcJSUlMhTTz0lZ599dq2ukd+vK2pzbCvj25f7PuKowGiNTIpset1115msDORiCxksBx54YIizIx4wJLvPAUcVQVTu98gIaWwQ+IRxf9y4cUYXIEiNe4Vx2433WqdOnRr097vvvlvuv/9+o/tMnjzZOD0Yy5s2bar1OdrfXrJkiZkP/DY6it8zwnnlZtmyZWbuYehPZJ5xPPd18zrqqKMSug4cDwR+3XjjjUZPZc7yu9dee23E77HOoGfj2CAgDR1v2LBhNX+fMGGC0f1Ya3Akvf766+YZuNeDf/zjH/LYY4/Jgw8+KL///rucf/75cvTRR5s5YyEwcc899zSOW9Y4PnffffdJq1atErpeRVEaEEdRFEWJyOmnn+4ceeSRQe9dfvnlTps2bYLee/rpp52BAwc6OTk5zoABA5yHHnoo6O9XXXWV069fPycvL8/p3bu3849//MMpLy+v+fuNN97oDBkyJOx5zJ8/32HZ/vnnn4Per6qqcvbZZx+nZ8+eTmVlpXlvzpw5zhFHHOF06NDBadasmbPLLrs4n332Wc13Ro4caY7lfsGaNWuck046yenatas5z+222855+eWXI94ffr9Vq1bO//73v6D3OZ/bbrvNOfPMM52CggKne/fuzmOPPeYkk0MOOcQc3zJu3DhzLevWrQv7nRNOOME56KCDgt4bPXq0uW7LsGHDnPPPPz/oMzzba665Jui9M844w/nzn/+chCtRFEVRFEVJPZkXxo4da+SrJ554IkjOe+CBB5zS0lLn6KOPdrp16+b8/vvvMf0OsizHW7BgQVzn9+abbzrt2rULef/LL790dt11Vyc7O9vp1KmTc/XVVzsVFRVBcu+FF17oXHbZZU7btm2dvffe27z/wQcfGNk8NzfXyNLPPPNMiBz5zTffOCNGjDCf4Rovuugip6ioKOg+/Otf/zL3rkWLFs5pp53mxEs4Gd+rRyCLujnvvPOc3XbbLei9Z5991sjckeA6uSex4KefRNJ5ysrKzL3mOfB37s/tt99u/sZ/u3UP/h3pfvC8eS7oJDvssIMzceJEJ1mgMzVv3tx57rnnIl6rm0AgYK7rzjvvrHmP8d+yZUvn0UcfDfs9ez2R8Pvt//73v+Z7GzZsqHmP5/bXv/7VjOMJEybUvI/Odfjhh5tjcKx45hmfefvtt51kwfPfb7/9QnTnvfbaK+x3WDsyMzOdGTNmhP3MPffc4/Tp0yfkHjEvLZ07d3bGjBkT9BnWtFNOOaXm36wPkc5FUZTGg2ZqKIqixAlpvKSxEt3hTmkmqv+2224zESlkUFCe6Lnnnqv5DFkM1LAlGuQ///mP+c4DDzxQ6/tPijKRLQsXLqxJmy0qKjLRXER8EZkyevRok6pvMw2I1iH9mPRcG40DpAWT1v6///1Ppk2bJueee67JZHBnMXgha4Law7vsskvI34h64X3OgcwWyna5S0ERkVRQUBD2xd8jQZRRmzZtQt7faaedTKTSqFGjTESWG6KziMxyw/0hE8RGNXEfvZ/h3/YzFqKHvGWpFEVRFEVRmhJkDJAV7O0lhrxJ5ipR0998840MGjQopuO1b9/eyK9vvPGGyfKIla+++ipE3iTSHpmXXmdk4D7yyCMmMvzWW28N+hwyOWVkOU8iuSnrc8wxx5jv/vLLLybam+hyN0TKIyPyOeRdos2JFv/b3/4W9Ll77rnHRL8jPyL/JyrjHnHEESbbgihy7k0s8usPP/xgSu64ZVOuDb2gLoim8/z3v/+V9957T/7v//7PZAa8+OKLNaXCiKp3ZwXYf4eD3yErhOdDSSIyLWy2EDpNpPvLiyj9cBQXF5v75tUjKOlLqSZK0J500klG73Nny6xYsSLoOVAmaeTIkSE6Qm3hd5hvGRkZ5uUmOzvblKZyZ2ugY5LFnox55iXee73XXnuZuWDLe3EPP/zwQ7NWhIOsDEqqoYNy7xkzzMm1a9fWfGaPPfYwWSwcC1/MypUrzbW5j1tWVmbKTrmhBBbz1sL4ZB2hygDzDb2Rca0oSiOkob0qiqIoqQ6RVxkZGSbjgSgtG110//3313yGiChvRgNRW7vvvnvY4959993O0KFDa52pAdOnTzd/e+2118J+f/Dgwc6DDz4YEmEXSzbEFVdcEfbvRPZwf4hecsPxTz311Jp/83cyRx555JGa94gcmj17dthXpMii119/3UTkTZs2reY9onsef/xx58cffzTRXEQypaWlOePHj6/5TFZWlvPSSy8FHYt/cyxYunSpuZdE5rkhAqp///5B77377rtOenq6yVZRFEVRFEVpipkacOKJJzqDBg0KkvOQnYgYX7lyZdy/RTR1fn6+iZbfd999nVtuucWZO3duxO9wbmeddVbQe9ddd53JFnDLoWQOkCVs5TOi23fccceg71177bXmetzfI4LbnalBNu65554b9L2vv/7ayH4lJSU19+Goo44KOdd4ZNzVq1cbveK7775zJk+e7Nxwww3mN1544YWaz5BRgizqBlmV8122bFnNe0T18x7ZK3WRqRFN5yGThSh9r14QT1aA1XmefPLJmvd+++038x46D5CJE+n+8oo0Li+44AKnb9++Nc8RPvzwQ+eNN95wfv31V5Phzj3q2LGjyWR33290BTfnnHOOc+CBB0a9nmj3mWeOvklmitU3L7744qDPcU6XXHKJM2XKFDN3yBpCz0HHogKAO1Mj1nnG76Dj8tvul/1cIveaDAp0LrIvOD46WSTIOiKzZ/jw4c5XX31lsu+Zs5yzV/9jbtvjUpnAXfng5JNPNjrvrFmzzPz/9NNPzf20eh7wO7xYA3766SeTZcP1u7N2FEVpHGQ2tFNFURSlMUA9YSK/iOqh1ifN1WxT7NWrV5uIKOp7nnPOOTXfIZKoZcuWNf8mkoSmaXPmzDGRbfy9RYsWSTm/anm0utEbbN68WW6++WYT7UKNVX6LOsTunhB+EMVD4zki0Yh8I9qFF425w8FxiVKyv+2GHhgW22xv1apVNe/17Nkz4bq4NLMkqsYd6UZDOl6W3Xff3TwbarPuvffeQefivX/e92L5DJE/NM7kHvHfiqIoiqIoTRE/OYiIdbKCidZ3Nw2PhQsvvNDUxyejloxg6uNzHKKo6QMQTub0RmGTLYC85z43Mh2QtYnqpncCeDM8+N5uu+0W9D2O44Zoc+R2egS47wOyH1H7NjPFL1s5Hhm3Xbt2ppGyheNR95/+DTQzjiSbet+38ig6S7KJRedBPuf5IY/Tn4VeLN4Mk1hx6xG2VwR6xMCBA03WzTbbbJPQcbmvr7zyitEn3OOJ/nmW7bff3owHGk6ThXL55ZfHpSMkAveM8Y9e8e6775o5QUZMuHtDz0D0S+YQmfXuKgLxzjOqB9A83du7A+K919xXzvvhhx+W4cOHmzlEVQGeoc1k8mL1KfrykJUDZFxRQYCMH+4N1Q7oI/PPf/7TZCmR7fP3v//dZInwWaAaAmOTMcIz4fnRTNyd1cJvMcdsE3IyNcg2Q9fnXimK0nhQp4aiKEoMYNS3whxp1Tg5cBr861//MoIRYGBHcHNj04UnTZpkUpj5DkIYgv+rr75qyjMlAxQzIF0XEPBoYI0xn/NGwTnuuOOiNi/kfBBqUUwR5rluGmZH+h6KGIoTnyEd2o1XuEa4tPcLcEhESo9HIUTIdDN+/HhTSosmfbEIniispL5bcKyQ0u0GBaljx44118Nzi/QZCynR+fn56tBQFEVRFKVJg6xp5UwLZT4xMh555JEmMIbmvPFAaVZKLvGiXBQyMv8fzqmBjIaxP5pB2c/Y7w3QsZ+JBDLreeedZ67Ri3WW+B07URnXK78SSBVNfsXg3LZt25r3bLkeSg8lm1h0np133tk4fGjAjMOLRtcYy73ltGLBrUfYZ2nPgUCtwYMHR/w+DiFv03R0I4zZnJvbaeIHzxV9iJJU9hkAz8HdkNtPR0gE9CirbzJ++F1K977wwgu+n6fc1EMPPWSM/bbUU6LzjGsL57iI917juMDJYht4cw8JuKOsMSXFKInlhfvJWLYODbBOQ34fp8Ydd9xhHJboucDz4xmNGDHCXA/HYNy/8847pqRyYWGhKSVGWTn32sXnvNfDb7355psRr1FRlNRDnRqKoigJcOONN5poHgRNhKWuXbuaeqHUN/WD+r0oLwhylmTVukW4x9GCsEakCdDngUipo48+2vybaLUFCxaECM7e+qp8D8XURoVxbATqSDWSd9xxR/P/CNT2v2OFmqjuOsBevE4RIn+I+LrrrruMYBwL9PNwKx5EXX322WdBEXGffvqpqdNq7wtRQXzG3j/g39wbN/QdQXlTFEVRFEVpqnzxxRemv4RbdrJgGCUzmIAT5MYxY8YkFLXOd4iujtSbADnXHagCGCcxRrqdGxwDQy7yeTj4HsZPNwQhuUHGw/GQSEZAvDJuLPIrfQfcIL8Sce4+FrIp/47Wly4RMNxH03mATPQTTzzRvAiqImMDZwv9Kzi32vR3sKB/0WsjEt6MeHqfYPwm8Msvu8YLmQM48zCaA7oWxn90AqtzEdRFwBW6SbLBOYCRn3nnp2/86U9/Mj1H6HcTzekQzzyr7b0m2M3ruMDpxRwN50zEWUHGz9y5c012BVAZwZ31xHFxfHiPC97jkoHDWGUOsj7gXHP/FtkfbvitRCsIKIrScKhTQ1EUJQH22WcfoywQ6YPydtNNN5koLgQ6nB0IwTTuI5qMdGWUIaJMyM6gkeEHH3wgb7/9dkL3nqgTIoQQ7FBcyKogOodjWsGO36O5HAomwitCsTtDAmjARsNFMkgoH0X0G99D8EPQbd26tcmG4LciOTWIiEHQpgFbvE6NeIRHHBo0giN9+dhjj62JVsMJYZv8cS+4Lp4NSgaKL9fjjrzh+5SiQvnASUF6N9Fa7gZyPDMijFB4UCIff/xx8/y8DQdxAiWaUq8oiqIoipJqIMMiY2F4phHvxx9/bCKkCSoJlyFLI3HkUD6DcZHo8UiODQykBAgha2GMRZbDMPz000/L1VdfHfZ7RJhfe+21Rr5GToULLrjAyH+UhaWBN8ZKjo0s5xcRbkGmI0OZz5GNQakpmi274VzImKCEDyVtiArHyI1RO1pWSjwyLuWNMPZjKOeccV4QsOQ2lHO+6BycL+dC43BK7lBGySubYoSvq7Ko0XQeMr5xxqATcC2UO8IR0KpVK/N95PSxY8cawzL6h32O8RJvSSRKTqEPvfzyy+YcrB5hG10DDgJ0J7JwyL7AAbJx40Y5/fTTzd8Z02Swo/9R+okX/03WNg6GZEPjbHQVyi3hOPTCvaMEUzgHWTzzbP369SGZQDgGGfPx3mubUc94tuWnuPdkilhdlbGMLsxYALJ50CfJPmE+o7cy73Ca2uwNjsvYp0yULT/F8xg2bJhxvAAltiihzPjj/xmvHOuqq66qOT+cRASz8exwdqBHo+vxUhSlkdHQTT0URVEaa9NE21x60aJFNf+moRnvtW7d2tl7772dt956q+bzf//7300zRZqb0WyRJt0tW7aMu1G4fdH0jQaHNLqjQZv3szRWozEaDf1oEmcby1m+/fZbZ4cddjCN0ux2UFhYaK6Vc6Th3D/+8Q/ntNNOC9s00kKDtd122y3oPb9G5N7mdfE+B/f125e70eFdd91lmv7R7I1nsNdeezkffPBByLFoMkdTSRrYDRw40HnzzTdDPkOTSdsEc+eddw5qNg5Lliwx31+8eHFC16MoiqIoipJKuGUtGvG2b9/e2X///Z2nn366pul2JDkPWQkZkqa/4RpF28bYNEDebrvtzOdpYrz99ts79957b8jveEHeRO50Q1PsXXfd1chsnTp1Mg2/aW5s8crAlvfff9/ZZpttjCw8YsQIc53uRuHw/fffOwcccIA5T5onIzu7G3b73Yd4efbZZ41Mbxs6Dx06NKhJuPs6d9ppJ3OdvXr1ch555JGQz/Tv39955ZVXIv5ebRqFR9N5Hn/8cfM37lWLFi2cUaNGmWbMlvfee8/cc8YX9y6SzvPzzz/XvMcz4T0aSCcCv+WnR7j1EvSzzp07G/m+S5cuzjHHHGMalLthXPMdxhnjhmufOnVqxN+OtVG4nx5om5NPmjQp4lj207VinWd+94XXHXfc4SQCc++mm26q0cnQRdFX3fOKc/Q+fxqwc885Vxq0n3HGGUY39TYgpxE4Oi7P6pRTTjE6mXuOMJd4Nujdf/7zn0Mau9u5z33hc+iCjFtFURofafxPQztWFEVRlMYNdUupdUomirfJYlOFeq4bNmzQqB5FURRFUZR6grJORNSTrRwpE2NrhGwZ5NNff/01pEyPGzJSeJEFrdQ9lACmdJWa3hRFUZKLlp9SFEVRag11S59//nlZs2bNVnM3O3ToYJRqRVEURVEUpX445JBDTL83Sst0795db7sLmjE/88wzER0aiqIoitJU0EwNZauD2rDUQaV+4+rVqxv6dBSlTiEiqEqqhJy89LQ0SU9LlzSJv3nj1kqAhnbimHvG/YsVvlPlBKqbVtLELi0joaaZStOCiFL6tNCHhf9Xo4OiKErTgAa3kydPNjoG/QW8fcy2NmgKTZ36cNA4Wmk4Fi9eLEuWLNlqsqsbGvogjhs3zvQGVJS6gB6X9CXh1bFjR73JylaDOjWUraIsDg3TPv30U6NoTJ061TStoukUTcAUpamysWKTzC1a6HonTfIycmRA875qYI8CzojpmxZJYfkm45SgTmPb7OYyqHmPqPeuKlAlUzbMkfJA5R/f5M6nyQ4tt5G8zJzaP1gl7DNzJID7KWXHN800aexJY3oasI4aNcrsRTQ7pCyBoiiK0niYP3++fPLJJzXBUjTAxaCEoZgGyFsz5eXlpvFwpGxXpeFQp0b9OzW++OILOeyww+r5l5WthUWLFpm96Oeff5btt9/eBFChY4wYMcJUVFCUpoo6NZQmCUI09Vbfeecd+eijj6RVq1bGaMTCjhGpXbt2DX2KilLn/LxumszfvNhkDbg5uNO+kp+Zp08gAktK1sgHy78Lef/QTsOlW37k9WNl6Vr5eMWkoPcwsQ9tPVC2bdknpe97WaBCft84VzZXlkirrOYyqEVvk2WS6myoWCIrS6cbp0ZmWq50zdtJcjNaSKqCQwOlwzrbv/nmGxk4cKAcddRR5oXjPVUdM4qiKFuz85y1G/2C14wZM2TPPfc0+gUGJNZuHBuKoiiK0hBQChonOzoGL+xiBx98sNEvKN2HXUxRmhLq1FCaDNRVfffdd42SQdOzwYMH1xiIhgwZogYiZavj1/XTZU7RghCnxmGd95ecjOwGO6/GwKxNS2Tc6ikh7+/bfoj0b94t4ndXl62XD5dPDHl/eJvBMrBFL0lVKgKV8vGKb2RTZXHNmOmU01b26zAspdfPkqp1sqg4uMRFRlqW9Gm2t6SnNY6a0jScxwHP/oVDvnXr1nLkkUea/WvvvffWMlWKoigNWFbqq6++qnFkYCDCMMT6jKGoZcuW+mwURVGUlHTET5kypWb/+v3332WfffYx+xd6RteuXRv6FBWl1qhTQ2nU0BPjjTfekFdffVUmTpxooqXsIq2lPJTaUlK5SUoCmyUnPV+aZaZu1Hc4NlUUydhVEyRAb4c/3uuR10V2bbtjA59Z6rOufJO8vuSrIHcQZv3ju+0trbObR/wu9/uj5d9KYfkG831KT2WnZ8mRXUeY8l+pyryiJfLt2l9D3h/dcXdpl9NaUpU1ZXOksHxeTakvS4/84ZKX0fiikShRRd1llA8c9dRlP/744+Wkk06SPfbYw/TlUBRFUeoO1l0y6NAvXn/9dZN9YR3N++6771ZfWkpRFEVpnCUTbRAwexx6xcknnyzHHnus6cmhKI0RdWoojQ4ipFiIUTRIrRs+fLgx9hx33HHSqVMn2eoIbBQJFIoQkZzeSSQtq6HPqEmwvGSeLCmZVfPvTrm9pXv+gHo9h/Xl62RxyUIJOFXSPqejdM7tGnfE/IbyjTJ9E/0dyqV9TjvZpqCXrCpbLRWBcmmd3VpaZjU+Z02skSmbq8okLyNbMtISMwLP2LhIvlozraZR+Ih228mgFrH14SkPVMhP62ZJYdkGKcjMk51a95cWWc0klZm5aYH8sO73kPf3bb+rdMmrnaC7vnyjFFeVSIusAinIjP0+VATKZGnJHCmtKpb8jALpkreNZKYHr3HryhfKqrIZId/tlb+n5GQUSGOGMlX0hHrllVeMAz8/P19OPPFEo4DsvPPOKZ1BoyiK0tjkhh9//NHoF6+99pqUlJQY3QIdg5rkWlZKURRFaSqsWLHC6BboGN9//70p0c5+d/TRR2sGotKoUKeG0miazVGS4/nnnzf/v+2225pF94QTTpCePXvKVkvVcpHKX1xv5Ilk7yGSVn+lhRynSooqf5dyZ1V1I+r0npKX0SfpxrbqJsBVkiYZdW7II0Nj2sZvQt4f2Hy4NM+qn4j1deVr5af1wSV1+jbrL72aJd6ToTJQKRMLv5MNFRtr3tux1Q7SPb9ppZ4uKV4rby/9QYqryo1DY3Sn7WX7lt0TOlZJVZlsqiiR5ll5KZ1lkQwWbl4pEwp/DHovKy1Tjuiyj+QmWK6MeWvKoG1eUPPeTq22lT4F0dftKqdSpm34RsoDpTVZGPkZLWRQi90k3eWoqnLKZcHmiVLplNd8riCzo3TJbVplBysqKkyDcQxub7/9tnTs2FFOPfVUOe200zQzUVEUJUHmzZtn9IuXXnpJVq5caQw66Bg0/M7K0kChrQHHZDQ7kt4IeogpiqIkm4ULF8r//d//GQcHJaoosYh+wf9nZ2vJaiW1UaeGkvLN+J577jl5+eWXpVmzZmZxPeWUU2TAgPqNmK9XnEqRzdNEKldWOyfyBolk+2SgOI5I+WeY9Fxvpolk9BTJHFRvp7upYpqUO8uD3svPGCB5GbFFtId79pRgKS0tNa/i8vVSWDZNyqtKRJw06d52R8kOtKv5O9HMmZmZRvnk/+3L/e+cnJyYy7asLV8hc4vczqJqeuYPlg65PaKWLFi7dq1p0sV1YFTld3lRNx/DZCznMadopnFsGLDLOmIMuUNbDzfHpIZz27ZtTbOvWKMHZ2+aKzM2bck+gXRJk9GdDpDM9KahyJVWlcujc7+Q8kBlUDGi03ruJZ3zGl8povpiTdkmeX7BBMlMD0jrbMZammkQvl/7XaVDbpuEj7uydLVMWDM55P2DOu0jzTLzo5zTMpm/ObQc1oDmu0qLrLZB71UGymRt+XypdMpMg/DWWT0lLcEMncYAEcTW0U8vjt13313OOOMME1XcvHnk8miKoihbO5s2bTJlpdAxvv32W9Mbwxpw8vLyGvr0lDoEnYE9FP0BXWN92VzZVLHIyO8t8tpLu/ztpbJcav6OzO7VKbz/zas+gig471WrVsnmzZvNeVkdg2uikkG85SntOaOvWNCX2rRpY3QMskMVRdm6mDlzpnHyo2Ow1vzpT3+S008/XXbaaacmFSymNB3UqaGkZCocC+mzzz5r6v5RS5yFlGapW0Ut8U2TRCpWBL/XfC+RrHbB7zkVIuWfh36fElRZO0l9UVg+FlN+0HuZaa2kZdauEZsu8pyJiEMQRxmgURXPG9gwc3NzzSs7J0tWV/wgklYpaX88/6JNxdKl+S7SqllHI3xj1OeY7hfOA/e/UUxQWID/JsOnRw9/B8Xmyo3y+8bQRs/9CoZK1aZ0c1442SyzZs0yygRKAWMUZaBdu3bmv/lN3uc6MTbGGu3w07rJsq6iMPhNR2Rk2/3NMdetW2ccJyg3S5cuNccePXq0ObdwTFk/TRYXLwlpHD6qw0jJj2Jgbiws2rxGXlk8Keg9xK+R7QfJ8LZ9G+y8Up3/LftZft+4rGZscM+y0jPk0n4H1UqAnbVpnkzdEFoaas92u0qn3MglrVaXLpYFxb+FvN+vYGdpld0h4XNqarAG4PjHOMdadMwxxxgHB3Xft4o9U1EUJQaQw+hXxFr51ltvSf/+/Y1+gcGmQwfdU5oCyNs4rJCL+X8gqxFnAHIz+gIvHFfIy+Vpq2VTYJ4J5GC/LCkpk0xpLt1aDqvWQbKzzTHdOgX/zViy71FNgP+2v48zBJ01XtmJ73HO6BA2WInzpsmv3cs5J8ZqQUGB0QWsjsHfW7RokbC8ZrLhHcccj99Ev+C1bNkyo9/suuuuMnjw4ISOrShK44T14KuvvjI2OcpU9enTx+yZBBhvlSXflZQls6FPQFEA4ZBo0yeeeML8P7Vr//73vxvjDILbVkOgPNShgXmxfHGoU8NMX8rhlHk+Xr9RummSLo7HqUGJqEWLFhnHhR8I62yGQ4YMMVFOCPKLFy+WoUOHmn+7KavaIGllLYPea9+xjbTMypdWWeGVUBQDBHEiDFA4GGMI7AjoODUQ/MOdH39bWVIkq0uXmA0dZaFVVnsJ5M8zkUucL8flcxyX3i59+/aVffbZRzp37iy1AeHh119/lfUVa2VV2cqgvxVkNJcFbaodGBs3VpeQwqlDZCFKEOfD+XJuvIqKiswLZQsFbp2zVgorC6VFmxaSmVV9nzPTMiSngcoqbarYLD+u+1U2Vm6UvIw8U5aoXU5wBH685GSElorATJ/r876yhZKqiiBnF/9VHqiq6SeSKOGyMfIzokfCNs9qG7K+ZKRlSrNMzbgRh3uSxmJlDByXXnqpeWH8wGCHkQ7jx1/+8hc566yzpFu3bjrcFUXZKkG+fPrpp80L+Y2yfWRn7LDDDg19akoCINP+8ssvNdkFyL7uTANkZAKlMPLDkiVLTAPcgQMHhhxrdel6aRYIlTs753UKm+2JI6OwsNDI2egTnA+/j7y9evVqo7dOnhyaoep3HJttAezZnPuCBQuMLM/7lIFBn6GHFj0kvTpSPDD2n3zyybCOD6PrtGplrscGghGZjY5B8BnXiD7FdVodo7i42Oh0ZHSgZ3DPOYaiKI0f1gRsG7zGjBljggHQMa699lqT3XjuuefKQQcdpP2mlAZHMzWUBgXDN0rGU089ZYQla4BpMn0ynCqRolki9DDILBAp6C/iaXIb4tRY/6HnzTSRnB4izXyyLwLrRSoQnCv/+GhbkayhIvVQEzbglElZYIZUBApl6q+zpbyySiqrAtKuXUvZdpuDZfK3M81zrK0xrSKwWZaVhva3aJ1F4+VeIe/jZLj//vuNwM3vjxw50gjoKAUI9AjbROfFkjGxqWKtFFduktzMZtIis21YRYCxS7Q0ivMuu+yS4JWGHnPe5tmysHiBMeq2zW4v27bYQQIV1U4LruOnn36SH374Qbbffvuaus8IICgXKFVkk/D//I1rL1xbKB/+8rEsXLlIdj94D0mXdNmlzc7SMUrEfF1Af4/PVn4lZYHyGmM65zOq415xNZL2u2/005hdtLLGGN8qK19O7zVCcjIyYz7GT+sXyc/rFpp/D2nVXXZp3atJp9x+VzhHvly9JaOCe9c5t6X8uddeNVF7KPGMJ4wFsZbn4F5+t/ZnWVqyxYE4sPk2sm3L/jF9f2NFocwvmirlTqnkpOdL34Ih0iwz2Mm5VVFVITJvvMia2cahIR0Gi/TeS8RjfGHNozwVgQKffvqpyeJC+UAJqY1RRFEUpTGAwZggqccff1w++eQTOfDAA+Wcc86pMdAqjQMyupcvX25ke4zsBENR4pXyKIlkQ3gpLPtViquCA5wIpuiaN8r32DhT3nnnHSNnDxs2zPSzorQsMjb/v9tuuxk5KdbzsmVqI4EjgWAnoqQJYKoruL8ETKEf4dRAdsBh4c5q529cn9UxePFs0EvIjuE8eS7IiYoSy5hjPqNfMIcIDMQB2ZT1raYAjtdnnnnG2O94VtjveHXvnlj/SkWpLerUUBpE0fjggw+avrGFSNo140XKqhtoG7JainTYP7LTYdO3IhUro5efqvmdChFnQ3XmRlrLakNXHVNYuFqmznxHqgIlkp6eJl26tpP2HVpLRkZbmTejVMpLsk3PBzIhiDxCQLFp0gi/vGz6NwJxpLR/BP415VOkuIr7aPNAsqVz3u6SkZZtBGlKrth0cBQgjm3LP/EbCEnudO6GpiJQIatKl0ulUyGtsttKy6xWURqk07wwNGKMa0exCif8kakyZ84cc19sVgcZMhktM6UqrUpaZbWU3Izw5arqktVlhTJhTXAjdNih5SDpnd/DOKFQrlASOW+eIYIvYwplimgwxpif8ljlBOTHdQtkVekG0+B7eJs+cTW6/mHtAvlk5bSaf8/9dop03pQufZt3NPcPh5LNiuEcMfgfeeSRNVGBjZGAE5CPV0yVqRsWm3+3z2kuo1sOkoUz5pg5xHUzh4jMI+oR5YOxyTMgSymSk5DPrShdJcVVJdIyq4W0y4lfKY9F8d8qmPulyKrpNQ3RDd12Eek+LK7gAV7hyu8piqI05manrHWsedbYQrCUrneNR0ecPXu2kf14fjigtttuu5oysxMnTjRyB+8je6FfIKNY3cLqGsiNvI8uECkIo7xqg6wss7Jo9b7aMquftMjqbfZLxhN6BediM8Bbt25tdBd0DI5PeSt+oynJKDhS7P33g3tsn5OVz5ARkdXd5XlTEeRXq1/w34whMmTQJcHqF8j6dW2XoCQaZX1s35ZBgwbVjGOegR1vRMo3JRgzOCZxZqBfMI+YszgwyXayJdUITlQnR+MKHsCmR/BAk7LpKSmPOjWUeoOyP48/9pg88eSTRjg9++yzm3ZZDJwZq8eFvt9mD5H87rVvFN5AjB8/Xtp1zJOuvdeGOAly0gdKdnp3I4wgMKIIoBAgNGL0tLVsbdkm/p/nT+ZEJBwnIBsrF0p5YKNkpOVIbqCLLFqwzAiDGPVJKcfYjZOMKC4yF1K10WN5oFx+XPutlAZKajqAD2y+nXTOq908QNkiJRRQ9Pbbbz8jJA4YMCBlnDnuesczl8yWSat+kK7bdDfjYNm8pbJ5Q5H0yOwiq2evNIZyxgbKIoIRzxdFg/dw5qCMIOxjZGd8oUQlK0vmsblfypryopp/V5ZXSKvMPDmu1fYyY8YM40jhdxl7CNsocnUZPVeflFSVS0WgSppn5sq0adPMdfJ8eA69eoVmq/AM5s2bZ5Qv5vg222zTqJ07EHCKcT1KmjSTtLQUE8onPy1SWRr8Xn4bkSEnxaR8fPzxxzVlHg844AC58MILTeq49t5QFKWxggGQte2hhx6Szz77TMtiNFLmzp1rjJz9+vUzhtxwEMSE4XP69OlG9iNTGpmffczKu2QacAxkYAzUkUC3KKpYLAGpktz0dlK2McecC3oKRlWMru+9956Rc/bcc0/p0qVLk3JgxAJldpF/KTfFfaV0G3I6JXlTCcYGOhDjA0cmjgnOGXkW6N2I/rDjjjuav9mAKd5DF2X8WIcHOiZyE3rscccdVydyknVeoBuReY8czb95IXdzr63BvymBHvjzzz+bwEfmEo5Lr97O/CMjgGcJ3A+eaSrptMoWCHgjmIASdzw7MiNxcLBeKkpdo04NpU5hk/7mm29kzL/vl7ffe1/2HzpALjj7TDnojEub1qZka7m6hdySpSKFE0I/22oXkYKGb1iMsMbzITol1lR8Pv/FF19I63Y5Ulo5Tdq0bSFdu20pXZSTPkCy03sEfR7jGeVr9tprLyOQxMLUqVNrjMY2AwOsEsH5omhYQzK/g9CKIEv9WYzgGOxSkblFM2VR8YKgSGvKLo1ov79vNkY8cA8QihHMU7GBF4Lp119/bSKR2rVvJxNWfy+zZs+StPQ0adm2lXTr1U32a7enrFq2Sn777TfjrEAxRelgLPhho3lwZsXahD0aj8wdJ2vLNwe91zIrT/62zShpKpQHKmXCmoWytrxYuua1kOFtephGmeGgrADKBc+BsgI2E8oNzwojA/MPBYU1BgMFTg4U33Br/rKStTJ941Lz3wOad5Fu+Q2jJJtmoIHpUiXV50L2W3b6TpKRlkL1oX96QaSsuvlpDQUdRbY/Nq7D8Dxxbjz22GPGKIRzg+bikQxJiqIoqQR7DCUwHn74YRPscN555xlDipa+aViQ2zEU2/JEsToAyCzmexg62Y+33XbbiNG+GKi/++47ow9Q9imW37HGVJvli9Ha9q8A/h8nCKWe7G+jgzDWKDGLYX/fffdNSRm7ruHabX+PVNTh0Tdt5gz/TyYJcinPj1JhnDO6JQ4OMgJsY3SCpaw86+3PwlhORillJTw4cHgmPCsqN2AE9zo4bJlndHyeJU4ejOg8L551qmcHbU0w3wgyYF/+/PPPTX/cv/3tb7LHHntsdc5gpf5Qp4ZSJ2BcfeWVV2TMf/8jC+bPlbMOGi5/PWIP2aZre5H0DJG9L6yXMkl1DoLP5t9FNs8m3kIkp6tIi6Ei6ZkiVcUiy+mPUW2QryZNpOPo6jJUdYBtuE2ECUIBxmC/DQRFAMfB4MGDjYCKQAEIEbZxIsdB6EPQI0IboQH47LhxY6Vt5zXSs09byc11O0TSpaIsQ+bNTJM5s5abcYBBk+/uuuuuMQsdGL4RXvbff3/jdOEcrOMChYcX1+q+NoytNmIehShV+X3DFFlZtjzk/T3b7SfZ6Ykb5blfE6dNkL479ZX8rHzpnt8zpuNRdmhl6QIpDRRJdnqedMrtJRlpdVdvmnGHU4rouQqnUn7bMEPWV2yUZhn5Mrhl/5B+GlwXjlHG6WGHHSY5zTOlwimTZpktJCu9biKXvlkT3F8C9m7XX0a0j60PRKpDJsa/Z30ji0vWS7qkCe3Ah7buKqf33DmqwMm8c6eHuw0BGAlQAIlw5PmSqcV6Ykt0oTwC85MIPz6/cPNqeXcZpR+qj0OptcM77yJ9MNTXM5WBpVLh/O55N0ty06nbnfwIvYRY+bvIvC+D3xtwkEibPgkdjvX87bfflgcffNAYemigi4NDG+gqipKqULefrIwXX3zRGFgxmBx99NFJC2xQwpSCXbPGGBK5z/SSCBds8vrrrxv9AnA2WSMxmZwEOOBIoDcFMgF6AdnW1kiO4Rz5gmwNjNNumYTjIFfQT47P8DeOx3GROWIBw+mzzz4ru+++uwmwwXFhs8dxXNiofSuvAOeGwx/9AofH1pjZiPMIw34qOwwJoJs0aZLRN5Evo8E4nDJlihlPBOLsOnwX2VixQTLTM6V5Zks1wNYzzG8CoQi68er4rDk4MtAFKRNGxhQ2AeY/6xLrDPAdHCPYLZSGB0c1zg0yONgz2KtPPvnksHuHoiSKOjWUpEIkL4oG9WwRfi46eoT8aXhPaZbnNj6miYz8Gx2NG//dL54jsmmKp6l3N5FWf9Q3L10hUjixuu8FfTRaDxPJT34dcwyJROhPmDDBGAuJIOI9lD2cAm5QBPgchkYUCYR0voOQjjBItBOGZMr9YNjiOKRd42BAmEfwx8icnYMxdJFkZJVJWXnJH3VsHVm8cKWsXr1Bjjrsr7JN38ExC4V8H2EGhYlIGn6b3yN6A8GlMTkuIrGoeL7J1nCTnZ4je7Tdp1YC9KtjX5K227UxDZ5RH3PTc2W3tntKVoTG9AiQs4t+kE2Va2vey00vkEEtdpP0Omw2z/hCKGWcMvbCXTeGC1uijHE7bclk2VhVbRwfvNNAGdBqqLTODt+PpVYZZoVz5CdXo/AR7fpHzGRoTExeu0SeX/hTyPtXDRgp3fMTc7jyTFAqcFihePDcUIKJqLLKJsoIL54lTtMTTjhBXl00QVaW0RNoC22zm8thnXeR0kC5tMkukGycxPVAOVkaztLgfhUmA21PSU9LIQF8zRyRNbOqm4N3HCzSKjl7Ck4N9u+XX37ZGAWsoVDr4iqK0tBggMQBO2bMGJk8ebKccsopxgFLGRml7kAeYm8nIwbDonUeUHqTTAov3377rXFMYPxGVidzmOAmjJRkcu60005GB6FcKHI8QRAEWqFjAP9GRsSRgCzB7+N8R8bgGDhDMI7RhzEeJxYyCAEZtlQV2b3oGhi1kVv4PcoRbc2Oi3Agt9Gby+95p+I6gd7IWKEUcbhgOsYTpeqsHvzdL5NkfsksKa8okw5d20u/nv1l+1a7SEYd6kJK7OCMZB1g7iKT8t9k9LOe8MyZy7YXI+sP8xtnp5IasIe89NJLZv/GzkOvK/ZvdT4pyUKdGkpSwKB+7733GoXjiCOOkIsuukhGjBghacunicz8wj3kRNr0EBlyVNO482vHi1Ss8byZIdLxqOCG4YEyEaLm4xCOEORtpIJtSMzG7U6BpckWEQ04LuymjtA+cuRIky4bCxgYcUbZpr8oLEQrkeJJ6i4OBRQIBEMim9ylqnBwFJcul6rMaVK4ZqPMnb1UMjIzxAkEZPXyHDn+2HMiGsT4TerxE3nFcYm4IMPEOiz4O4ZR4PdROGw6O8pMXaUxVgaqZGHxMikLVEiHnNbSLie2kizryjfJ+NVTZF15kTTPzJe9228vHXJbh2RG/LZhiqwpr24Gn5mWJUNaDZUWEZqFR6Okqlge//hRGbzLoKD3+xcMlJ7Neof9XlHlOpm5KbRZd+9mO0ib7M5S1zBWUZQYtwigNhsIEFYZ84w5mPjLl/LN1C9kt313lvLyCpn0xY+ybvVGufb8W6Rt6y3fqyuqM1rWSZVTJe1zWktORt1ls4SjpKpCftuwQiqcKulf0F7a5iSWbj1u1Vx5e+lvHtO9yAV9d5NBLToktY8Swqt1SBI5x1rF/7PGYFwYVzZbAs2zJN1VyiAzLV2K/yg5l5eeJcd2Gyad8+q+LFJFYJ5UOnND3s9N3zf1emvUIUSqElWF8sEae9lll5n+V7FEPiqKoiQT5APWowceeMDIhDhbWY+aSh+rhoKAJfQGjPfI+WQnEOFsI2j5O30UkLsx+NsSr8jqhx9+eEzyN8cggAWDFv+NIwNHBY4JgpgwPpMBgb5CrXx0DQvPGkMm58dvogvY0kAYL5EXMWhGgnO2wTEYQwmkcTcdRj4hwILrQ/9gj+P6eaVieaWGAmcUmf+MhcYC4w09AmcWWF3C9oJjXODQYMwzpq7/z1Wy0747SNuObWTWlDky69c5suOAneW0I85swKtQIoFzm2fHXEW3sPoF/8auwVrDnNfSVKkDz4tqHGSHEzBL4NSVV16ZtJ6YytaLOjWUhMHQTv3Ke+65xzg18LpeeumlQUKpKc+04DuRRT/whWqHxuCDRLIaZ5R9COsmiPxhnK6B0j0djkhoocfQR0MyBHkrhANCGZFMpFt27949SGjD+cD/o+Ah5LOp18bYz3kQ5YASwvlwLJQAzoWoB3f0EgrDqsK5snz1RFm7dqOMGDmkRunYsKa9dGq3nVFG+LcjFcaplZ6WZY6PwIFThCi7SOnMfBfjKIoHzg/Oi9/lHgG1GhG2k0VFoEI+WfmtbKgo+qONt8iw1ttJv+aRo6HLqirk/xZ/aRwhXC3fJcLn+O4jpSAztDbo5qoiqQxUSkFmc5PqXBvWlhTKK1+/JNk52dJr4BZnVuusDjK4xWDJ9/y+ZUPFaplTFBqx3yN/kLTPSX5GkR9E2/zvf/8zzjQi+hgv7og+y/yNv8nMpb/KusL1UlVZJR27dZCWrZvLti33NKWo6vQcAxXy8YrvpLB8o/l3TnqWjO44XNrm1F9D7A0VJTJmzgRZX1Fqxg+G/3P67CZ9C+J36CzYvE7um/V10HtZaely07b7S4s6XptZx5jHKBook29NGy9vvv2mdB7QW7oM6CnZ+XmSmZcjVbZNEQ6RjBw5v++oWveciYbjVEhZ4HtxhMyw6tmfldZfMtNjcxA3NTB4vfnmm2aPx/n817/+1QQsbI21xBVFqV8IesCx+sgjj5j+Bn//+9/l2GOP1cyxBEFPQI62pSORtQgOQpcjuATDH8Fo7sAl9mpkdf6GfoHhP9YefOHAoEzAlO27gRyA8RGnhrunE/oBn+UzBEAQ0GWDtZARcXa5A2Es6AZfffWVOT4ZIsOHD494zhwHHYM9DmcJ+gUv22vhyCOPlK0Zo89t2GAy+QmY8/Y6SHVwXjCOCZxirIHNIra6MsFKny54V9av2SAb12+SghYF0rFbe+lU0EW2azlUtjbcpWUb23mTBYZjgyoA6Jc8a0ogYTznfYI31WGZGqAP/vvf/zbVXcgOx7lBBp5mySmJoE4NJW4QGEkhIzMDYfPiiy+W888/P3LUFM4NXk0tnbdspch6TzPwgu1EmsVW2xWhGScGAhdwDxHsMSYhzBOpjjCJcI4A5ldyCSM/CgJCPs4PlJJo5UJsbVyyPNj0bV1aFMd3333XCH8I85wHn0PhscdEoCWll+gr3u/Ro7u07LBE8ppV1QhBjpMpGRU7S6AqXfKb5ciGiilS4ayr/vGK1jLz582yePFSI2AceuihEc8XxeaTTz4x5ajqo1Hb1A1zZOqGWUFR7JR0OqHbAeb6aOrtJ+wtKl4pn6z4IeT9vdvtIANabHFEJQueN9EOpudIZppMK/pVMvIypEuvLQ6eDeWOBCRD9mm/i3TIDW2+XBEok2kbvpZAUN8XkcEt9pC8jOCyZXUN45xxzPNm3FHfFuHTsrJ0oSwqnh7yvR1b7VtnvTUskwp/lxmbFhpnFfD0W2QVyDFd95a6xir2j038SKZvWCHOH2MvUFkpXTp1lntHn5bQcb9aPV/eXDJNAuJIdnqGnNlrF9muZd31scBIwLUQWYVzFiOG7e/xzryJMqtwsZSXlElacaWs2bBeMnOzjfPKckzXXYxzAyMF2Vz8fzSla1nJKllbvkFyM3KkV35XyaSfUxQcp1KqnGXGCZue1koy0hqmaXkqwX4xfvx4s+fT9I++G1dccYWpR64oipJMCNS57777jJ5BSSKcGXvvvXejNLI1NOgQGPSQ8zHsExSFrE+WhAlKWrXK6HQ4qgla8t5j1n4cIRj9AZkslnUf+RT9gt8BfgPdhOMgA4wePdoYFjkvAlhs/wo+R9lc5APkfvZ6+mrwchsi0Yn4DT7nLkFlg6AokYscSfQvwTKRQAcj633UqFFq7HRB8BlliNH50D3R0xg/yF6NDcYXTiv0WQymXI+7bxjjZmLhF1JJqega0qRLbnfp17y6N0xTxGZDocszV+0c437xNwLLGkOpZ3QLbCE2AJQy2hZbxcI6K1nzcNLy3+41xWZ8sBbyimZHKa0qMxUdqOzQKbedtM1pPFlMqQa2xEcffVT++9//mv0J/YLyklZPVJRYUKeGEjNsCtTbxqtKNA0eVV10/nBsFM/d0ig8r1dMTdBpcMjijbBOqjMRQlawZ8NF0CBTAqM1QiSbNYI8n6GuqRUsx381TrbbqZVIRql8M36qDO4/QjIz8kz2gzs6CeWGZmg2tZwIJz7DpoFTBeEVZ4ZNF2fDRxggS8PWpcToTPQcCiaCAcZWPl9csklKKuaL4xRLmuRITmZPycttaYSC5et+kvLAWpPygNCQnZMlfXvsLL06D49ZSeV7NJhmDEYTNOwxuU4aDcbbOPK7tVNlbtGSGgO2pWtuK1lXscE4OPo37yODmvcLOv8lxavloxWhpZz2aT9E+jWvG2cMz4IoOqKP2vVoK7+s+1HKnXLjPyyucqTkD5twXkaOHNV1VMj3iyuL5Zf130lWetkf/SLSpHf+9tImp+5LT8VLwKmSGRu/l81VW/ovdMsbIJ3zwpfXShYfLZ8kK8q29B0B7tbpPQ9OmqGFuYYwjmLhjlKxadWfOMtlYcl6KV63UTavXitV5RWS17KFPHH4uQn/ZnFlhWysLJXWWXmSk1G7bCHmKBFxNoMKUIY5d0o8sF7ZrC2iLb3p4GQ6MeN+WDdPJq6aKWUlpZJbUL1W0cz84n4HmewU1kmMJaxnHIt5jtHCq3jhmPx945w/esw40jqrhezXYfeYHBuJwHVjGMHQ4ob1Cme1LW3R2A1zlHO4//77jcHxgAMOkOuvvz5IgVQURUkEIsFvvfVW4zhFt8CwYZtNK/FBWSfWarKYkQ/Zn+zehHxh92X2TfZp9lH2VGBvJqgE+Azv48hAf7ClXShD5M2SoCeTLReFXoF+wb7HPv3FF18YpwH7H7oGsqvRHYqLjT7BdzBMvv/++yYbgN/B2YBchOPDb1/l3PkbL7DNyPlNAqDiMcaiy3DP/PSFcHs2coyN/G+KcF9xMKJ3scc39d5aa8pWyu8bf6nR/XLSc2Xn1rubnodNAewJ9KthnrnHNPME5yHzGTuDdUYyP1PpuWNzwPli7SSA8wFHFWOVNYm/sZZF69NAEJ1fYBTrDesTNhH+mzWFQFPWUfdniytL5NOV30hZoLxGx9ij7U7SPb/udGd3OV83rOWsedjlaptF19CwztugadZkqr/Qd0NL3yqxoE4NJSoYkXBmsMhg3P7HP/4hhxxyiKaH1RKEB6tUsLmyMSFsELWEEImQgdDsFzlFeiWKR05Olixd863sslsvKSurkBm/L5T27TvIrKnlJoUvJ6uZBKRUSkuq5M033jPPj0grq7BwLH6PjQTDIJsmGwlGOD7rdnYAQg6fZ0ywedoSWZy325jpZm3ZD6ap+ODt+9RcS2Zac2mbs6W+aaxYpSUWwyCKGM4ZhB2uDYGtsGytTN3wu5QEyqR1VksZ0mo7ycsIVnxmbVook9f9FvRebnqaZFBWy+Xo2LHVttK72ZYSTURrvLX0a9lYUfxH+ak0ycvIluO6jazz/gvff/+9yeSh78NXq76X5R4DPJzQfXRIw7tv1kyQzZVFNeWyuLpBLQZLj/zULLfD9a0rX2EyTJpltpTmWfVTU3vCml9lTtHSoOffLCNXTui+X62Oy7zBUcicw1BA6T63oYD5xhqBMP/lqjkybeNKyW3dXAo6tJWs7CzpmNNcrhiwjzQErBk4Gu2cBBygKBN2fhI5xbWxlsVqzC+uLJcXFn4tmypLzBwik2Tf9oNllzZ9fD/Pb9Dok7XSlrFD4Xh/+biQzw5tva1sU1A3Y5s1kQjUfffdN+habYkNlETWdQv3DYMdRhQiYRHabb+gekm9ptcTs55zrSwWKV4pQhm8/C4iMTh+MEoR4IBsQATujTfeqM4NRVHihsbSN998s5FrMWBgyEhmSdGtEfYX1mj0DBsYha6BLEHmBvsO+48NcnLD95CdMZIh/+NkwElB5D7yPnoCx7R6hH2GGBc5HpmY1qHAnvfjjz+azyKLczzOiQwKdAjKRGE4tRG5lMZCx7Dft84L9nm3rOGG45I97s7sTRSMorHsv3yOIBTbdLwpN6vH2UMWv99YaWoUVW6S9eWFRldqn9NJMtMbt5EY+Zu5zHhlPvMcreHblrzGUWD1d8YycjQ2gIYOwCFgj/XCzkecDJSSc2eTsFZxXXU1NrlH3B8crKZP7B/3ZPLaqTJ/c3DwY3Z6lhzd9QCpKyijZR0s7vND90DH4MU9srDm0zOG9Zr30S3QMxpD9gPP9sMPPzRBDuxXZGuqc0OJhjo1lJicGUS833TTTXLggQcmfaOrrl9OajNh5R0kTeq+IWx9UVZVKEWVC8SRSsnN6CjNMkKdFO7Nid4CbM4oDDXHKCszmxXfo0YkWRoYytatXyOLVk+QikCR+W55WYWUbi6RrJwsGbxdb8nOrhZcquPvMcxvI++/NdmkdtvGgByXdGI2PTY7NkwUg3HjxhnjHEomUVTh4HdRUGw6rx+rS76SCRO+l113GyxZWdURH1lpbaRNTt01hdpcuVnKA+VSkFkgM36bYTbz9j06yLiVE6QsECCn5o8SQnlyYMeRQbX6A44j3xT+LIuKV5h/Ex2enR6qTHXK7SC7tw2utVpSVSbfFU6XteWbpEVWMxneZqA0z6o7RQCDN9HhPDfmKPy0brrM2sSY23LOfpka9PMYu+qzkGN2zO0kO7aK3Hxxa6O4slT+t3yibK6qjhbMkHTZv+Mu0iUv8QblZDWg8CN0ekv3MS+JkEPBYH7iLCgNVMqjcyfKstLqaMr8jCw5r8/u0iWv9op8PIImxgsMDSgWGBKSLSBTjuqD5TNk2oYVkpOeIft37CM7tQ7f3wWDBhGpnBPnA4Vl6+XzVRODPoeDZFCLPrJ9y9hKAyaqQPLcWA95ZsxLq0Bynqzb/M3uAZTI2Guvvcw6i3KJk4u1n/FgFTnu89ChSazpjANjyVdbnBitthHZOFfE+UMZym4l0mO0SEZszxVDFqViaPrHteDccDfkVBRFieTMYB2k+TeZGX49EpTkgGEOw5g7WAoZHnkDOR4jGA4G5GWcCEQzowfiAEEPQWcgmIHv+5WCpbwPDgn2YY7H99m/0A3YG5FliKxmr8MZwN6IQZJs9XDYMlORnP2cK/JUQ+076G0EkTXFOv1kTfH8UiliX4kO8/XLL780MihBfe6xiRyPo8r2pOEzqVJiypYIwwjPeuHuIdpQ4FhBx9hjjz1q+smMXz1ZVpSuDvns8d0OqtPef6xzrMPoXazDrJ+2jynv89+mTHZ6ek1WCzYl+t6yjvKc0S/sdbC+ooOS6ZGKcF2ffvqpsT8SAEiFGJwbbhuZoljUqaGEgBBLcz6cGTTvZTGhzERdeO0d2YTP+48Y8ep3RLaTNElOI1LHKZMqh2bXdEIg8qD+hLLNZStl+aZJJoMCh0N5eYVkVXWUbbrvHrEmqW3QhVLwwQcf1Aj+KBY1mQ6ZmeLkLBHJ2miyNXJys01Zp/zcLF/B2m6xOc4QWbemwmyIbHRsbAg/7ohrhAmEIJwbREBjaDPHSE+viSa2m6jN0LClsez5c2zeM06PilXSukuRdOqyRVltnT1UstPjj7I3zptAmYme8WYd2L9P3fCbLC5ZXH2f0jJlaKudZMWcFbKiapWsa1NqHBpudmszRHo26xpynPUVm0xqacvM5vLpyi+l2hVSDU+hW14X2aVNdQZLQ2Gzdqh7atMzaWr9+R+NzoH75NdTg6yHz1d+GuT8wPDbNa+bbNtySwSesqU80qKSlVIVqDLODJxWtQFHIpF+RDVaEEqZcwhsOC9DUqMDAVlQvNZkBfXIby35mfGVVqstCJWsGXXV24bx/Ni872XahpVmVFJ2KicjQ64ftK+0zg5uTomTgAhQ1kJKY7jTk5kD7y37wjR/dLNn252lW34dN7l2losTWCpr1myQRYsDUlFZLXyz5tryW7xszWIy4qyAzpqJAoVSZ9dj1n7rsKz9uTkiCz4UKVmzZc9N/yNbo4Y0kdYDRTrsGtehOWfKUuHcYEyrc0NRlHDODPSKiRMnykUXXSSXX355o3RmGBlY1ovjlEh6WnPJSGte77XwbVkp+9+2lEwsBnbKRREFS9kWZHa30Zr9lL2efcu+OHak8iY28pvz4Hi8+Lw74toa2ZBvKHNDpLWF30A3ss3LOR56Bfsi12j3RK7N9uPgc3yPTJCGKr2CzozRkECwpgbPDVmVYDil8WCDJAlEtYFHvEf5KZ4p84V5n2rgiCEzrKGzRKyDhXuF05UMF/c5TdswS37bOCdId26e2UwO7lz3fRaBNRYnNQ5p8/tpaTUlfe3ayYv3rP7A+2SM22w5YA1tDA5LruWzzz4zeoU6N5RwqFNDCVokcWbcfvvtxplBBBVN+upyc3HkVyolet7NljSp/cYQcDZKWeBHTIHm32mSJ+nSTxwpl/S0fMlMq7v0ShbgV97+r3TtnW+cDWRN4HzIyMySwNr+JrKo5jwDAeNA6Dugh6ws/VXKA5skIy1bWqZtI6UbqoV3lAkMX+7oqlVlY6v7eLjIMPap0HJVZSUoGbmSl9FPcjOCa02ihLB548ywkVBWecCjT0kczoHz5LxtCRUcGigftiYvm6J1drBh8r49HhkrZQEyHyjJ1FWy0uOPLt9UsUGmbaQ/R5kRIPo0GyDd8oOvZUnxUpmygTG1BRwbozrsK69/+Zak99ti+LT0yu8iw9tGTh2fsXGOTN802/w3v80d3rv97tI6u/6i5C0LNq+R71fMkm8+HCuDOveSnXsMMBEb7mg3jLnLS9ZIpVMp7XPaSLPMYIOwZf7meTJr08w/rgj7Zobs3nYPaZZZO4O9Er0/DPONkgXuOUczZgzCqVAXFWMCJdw4P2tEYP4T6YmSVBdNNdeUbZYbf2Nd2wKOjUM6D5CDOwc3+2TNwiDCvbKZEShpCOymdF55oUws/EmqTJklkQHNe8uQlgPrVllyFos4nmb2aTuIpHUSKVsoUvpH76XsrlKe3ls2FW02a3+9KRRVZSIzXwl+L8Mnqoy6wN0TS6NfuWqW3Hv/nfLoQ/9nMjfuuuvuoIaciqJsneCwv+aaa0wgxsUXX2ycGakaIRoN9uvSwG9S6biM8un9JTu9fkp3UurQ7Wyw/01gBPuiu+48uh3lnmx0rgWDGEYt2yMjUbmD43MMv30MPYG92vYKc+sw6A9EidtMVY6Dg4BzsroE12ZfVlayvQVTqYwKY5tgMO89bqwQyU9ZW0qUcZ8JnFIaB2RI/fLLL8Zx4V5fyepnHqEvNjS2bBJz2eoXzHmcrJwnWVesDQ15flSswLnLf2N/oX+OtW20addGJqz5qSZbg2oII9sPk5ZZqZFBwPpobTWRgmgbG9a5QVAEZdWuvfZak+WZSnuB0nCoU0Mxi99rr70m1113nVn87rrrLhk9enS9eMod+YGiHd5hKeLsJwHZJAGnzEQ/pafFnxpZWvWtOOKuYY6jQ6TijwDV7PSukp8xKOnXiaJA7dnmnTZIuy5eo1+adMrZ4iiyG/v4r76UNn1LpVN3Sm9tiZzvlreb5GSEbkgbK+bK8jVTZPbMRZKRkS4V5ZXSqWs76dObZlZ/fGbDZpkza7EEAo40y0fZKZP8zG2ksizfRNWSLo3gYyOB3QoN50S6JWMDZYjPU/fdKhpcH2nklEUhisc28UMZIUoMxYoN1Z0uzrGI9om3cbc10n9XOF4qnOqGgJYdWu4irbO3RPj9tuF3WVi8KKTJ98j2I2TCxG9kQ+/gyG3oX9BLdmo9OHpdzeKlsqJ0lWSmZ0rfZj2lVT07NLi309cslneW/iBlm0ukaO0GCVRVyRmjjgrbbyAWVpaukDVla4zzp3t+D8nPbPp1cxsS5so777xjlEW3sRclkrnjLUVV36Bk0KyT3hgY3G1GFk4Y1gjb94G/JXvtXFayUW6b/mWIU2P/jtvIkV0Hhf0e58YahYMW5wZKkVGWsjOlJFAq2enZkp+5ZQ/hmjh/nPfea3jzzTdN1LDXOMF3cBySDh/WmRP4iifsebOFSEVXkeKfat5ZsGilzJjvSI/+I8xaabPhcMbUqZEvUCky48Xg9/wyNVoNEOk4LO7DkxVZWvXDH5kbG+T+u1+TJx79QE488UT517/+VWcZPoqipC6UyaQXH3oGpSMwRDRWZ4alMrBKSgJTQt5vlrGnCZqqK9grkL8xXFIOJRrsjcjoZBIcffTRce3ZlDrBiMd3kFsIuHDvfZSa4jxsZoVtQEw0OEZJnBQ4M9hn3TX62UsxXpKpQUldu2fbuv4EU5DdQYUAjskxbKAVegXHJrjCXUef62R/aShDKPeB62nMje3R+0w2fHl5TbAbfQS2hl4aTQkcnswf5rt1NDKPJk2aFJQZ3lCQYc08xknA2sG58bJVHtAvsEWliqGa8+J86THKORHchZ2H9crJEakKBKRZZr5kunrRcT2U4fM2uCa7gkxFMj+8v8GxeT8ZTgiydNAnub82iNZmyNVLz746hvv1ySefyNVXX232C4Kx0TOawrUpiaNOja0cUv1owEN0Kw15Tj311HqtC+oIkavzPQ6NllJSlScVzrKa9/Iztpes9PB1V/0oqQrNZMCpUe56qyBzqGQlUAbJDwRaBAmEd4T4UmeFbKiYFvSZ3PSOkl7SyygD1DtEiDQbeJtcmbF4kmwzsIc0b9FMysrKZZsBPaVtzjYS2NTSLNpkTCBkUot2TdlkYwyTqoC0bNNCKioqZeCgntKp9Xaycu2vkpuXKzN+ny8DBvWU/LwcE4efLi1k4thC6dy5i9k4f/rpJyOw8rxtXV02bWrfshEjnNtGeFZZQClBmUAY4Xpt3Xi3AZD6yAhS3AMUHRvtzSaPknTkkUfGfW83VWyUn9aH1sjvnt9bejfbEsE9p2iuzNw0K+T7B3bcX+bMnCPTnLkSaL5l0+O+jOqwh7TNSb00XC+Ml3vfe06y+rST3IJ8yWtRIFm5OZKfkS0X9TuwoU9PieM5st7uuuuuQQ21cWrQBC9Zv0G0pi0ZRxQmzktbN5d1x2aI+YHBwRrZ6xOyKm757QtZW15iGoQDd+jy/ntJn4I2cRsZIjUOZa1jPfXud6xTGFhOOumkoDrDrJF8B+cun2HtwyATRACHTLDjVaRAZHNApLKw5h1KEs5ftEaKM4eZ9dVG2fJMUDpr7Syib8a6aSJVxSI5bURa/LFGpueIrJgksm7mHx/8w6HBPbClunDW9jgo5p4abkqrpkqVU92LyLJg/nK57caP5N1335NLLrnERGo3pcgxRVHCr8EESf3nP/8xct9tt91m5NimQHlggZQFqrN33eSl7yyZ6cl32GBoJsPT1kAPVzoG4z77P44Pmm6zb6GToGfg3CD7HlmfZrO8z+dxQiDHswdREsw6KTDAEbiEvMJ+RyYCTg72PwxkXiMp+yk6A+UgMYoT3WydDDhF2D85J67Fll1ED0F/QF/gHDEUksmDjoEh0Tr6rbGK4/N99BSCQrgW7gk6B9fNNXF/GgLuXSyOplSEcUKwDeOCZ819RH5SI2HjgvmM0Zw5wHN0Ozq8ZVoThTmKLm+zrRgnvFiTGC9WVmYO+40fzvHrr7829odUL3sUDq6B+2BLPnlhjSRo1AYsuUGHIGjM2yuPtZX1zdpMcIokmvnFWs/aiEOY9ZLj8FzYf5tScBH394UXXjBBE6xdlM2nfJmydaJOja0Umpni4fzqq6+MkeHSSy+tWTwp8zO76DcpqSqRZhkF0r/5dpJfR+VoHON0mOYqQVUgFYEuUlJljS6WNGmRSZ3FrKRlakB+xmDJyQjupxAvGA9RCvCKs2G4o5E2Vy2Uoop5smzpKlm2sETWLBHJzsoxgjov2+Tptxk/y2+Lv5RWbZpLqzbVBp9Zvy+QDs23kZ4dtzUCPEIKhk+E5mWln5nF/NMPJsk++w+VvPxq49vMHzdLq+YdpaJqraxfv1GGD9tFsnPTTKZLTnoXWb58pVF2ED5sWjEOFjY/Mi+IREbowWGCsuG3ASLM8NtDhgwJEVh4H0cZzcUR8FFsbB15rpPfTESpLa0qlu/WEgUdDCWocGxYKgIVMnHNt1JUtdk4PcjYGNh8gPQt6GMU7B9/+Unytmslq0rXSk5GtilH0zlvi+CXimBgxvDJc/lswwxZun61dOy3JX04Oz1TLut/UIOeoxI7rLmU5XHPHQR85nWyHMooNUQz2hRkxj5CLkoODk2crxgkyNAie4q5ydzF8ICwzovoWhQO5mx91rddXbZZnpn/oywqXi/NMrLl+O7byS5tusmi4o3yDQuoOLJ7227Sq1n9l35zg1MXIw6RTzXZNQFKT1X386khra9I0fIgp0b1+5kyfkqBdO3aVcqL10pJ4e9SVVEsktVSJL+PiKdnEPszCpSNZOP/fccLzeyXfFj9/ziGyMSwY42stnZ7iGyYJ1K0rNpx0XY7EbJYSlaac5JmXaobiCdAadWvUuWsDHk/P2M/+eGHn0wABRmA1MU977zzUqLMmqIoyQUjyuOPP25K2FIC5Z577jEZv02J+srUwGBvnRkY8f2MXOzbyPHsSRgTMe5gwGRvwejId3EUIPfjIGC/QpdAPmCPRybg+/wdeR+9BJDh+W90D2QAjHg0jubztlktz7fmnlRWmtIgw4YNM3IrQRrIHqz5ttcHWZDsWwQO8DcMe27DJroCvQSp8+7XZwXZhfHFdXFcro19hN/muvjthojw5ry/+OKLoGbCjQHGDr1VkEcZL5R02W+//Rr6tJQEwYiNTcI9L3FKoouzJiQDnJ3MWcYLawJGeGwUrD9Wt3n33XeNjMr8tvOYzzJ3mavMW+Yv+4I2fQ6F+0NJO+4zZYoTAbsOxyC7nMA5mwnnB79jdQurX7COpkJvk1hgPyJ44s477zRODf6/MWfNKYmhTo2tDDa2G264QZ566ik555xzzH+7vfllVaUyed3XrgaraaZ0x66tR5iyO7EQcFZL4I8si/S0LpKeFt1o7AiLLb+ZJ2VVc6QssDCoDBM0yxgmmXH0YvD21PBmaUDzzF0lM71VwsIDziEUCCJ2vYs/GwibNlHRvNjY8bz7efYRiJeX/iglVdVNnyBdsqR7/h6SmZ4jY8eONYoABkiiXAvLfpIp036QDp3aSOs/nCBQUlImC6emS7/e24ekYbNJkkWBM4LyMkRRcC4s/O5SAAgcKEA2iyMecHgwxtgQUYbYTK2CgTDD/eBa3b0EYmXmxqmyomzpH84K6hfnyNDWe0hWenA5q8pApSwpWSrlgXJpk91a2ma3NQobURDUhW1skUcIkAilCCWLAhtkfvMyyW9ZHW3DiBvcoqsc1kXr3TYGaHDGs/Qq3hgPmNvuzIBkODW8MP+JrLSpuyg+GEFYG1i/cJwh3PJiHqMcYXigVF19lwsJOI6k/7GmTt+4Rm6bPtE4poG3rxm4u2zXsmEdktxHjDwocsaQwb5pemosl02bSqRZs36SnjFApHyJSPHPwV/O6SurijrIrOm/SqesWdK3Z7stVaByOou02aumLJTtU4Tgzjpg/5+12kZf4ZQ2bJghUvhjcGmpmgOnieR2EulQN5FMlYGVUhYI7mmUIe0lN3PHIIPVVVddZZxoKCEHHaQOWUVpKnz88cemXwZ7yN133y2HHnpoozGM1L6nRj/JTg/u81YbCCxCbsaZ4VcCCCMi8qEtAUX0r7ehbSwgA2D8wilgnU84PJAjvOVqkGEo9Yix0ltKFic/QRR8F1kfJwZyA45/ty6BQwX9AKNoPCCnML4IvkJWYs9Df7XXi2yFs8TbS6Cu4ZopqcN1NraG98gVVj8CIu7dBnGl8WD7QOCYJKvKgiyPMzBZfVGwffBy/4Y1ojMHsSmQkYWDjLWA+cqagYzMvGVvYC7jCGEtwB7AutPYdPP6gHUZZ7OtlGHh/vGKVBoO/QCHOLaocHuI+7PoFG79gu/xPnYiHGKJlA6vbxh3t9xyizz55JPyl7/8xZS9tU56pemjTo2tBDaWp59+2tSyZSO67LLLjLLhjShZXrJEZhUFl0yC7VoMlbY50Q1IAWeFVDnB389I207SaZTqwjS6XrXKGOi951AeWCIlVZ5mq8YBsbekp8UXgeM4ZabON5tBeaBISqvm1fwtN7235GVuUytBlg2ZKCE2ZSu0o4RgDESIYCP48MMPTcQSzg82CDYGv/IbAadK1pXPldKqDZKZnittsvtKVnq+uVcIzDgjbGRrSWmRjJv0mgwZ3t1zlDQpyOwl83/bbKIfUFJQfOxGhTCBMZUIbQQKFCK82u7NkuZwCLV1VUcVRYnrYExyrzDwxrJZVjt+FsvGig2SnZ5jmoTjcIsG44xrwomDIY3NPVzKaCqBgMgzsIZuIqq+nTRJins0k+kVOA4d6d+8kxzSeYjJ1qhrSqrKZHXZOslIy5BOuW3M/yv+VAbKZFPlMqlyKiU/s63kZ7SpWTMo34azD0GfdYC5ZyMpWS9qjNM1x6qUOUWzZUPFeslNz5VtmveP2MidCEnmCnPcC43IyaLCocG8Y33AYcrvY7zwi5iy9WM554bin9O+kjlF62rc3KxWPfNbyh077CMNjY2SxSiA4oGhhvMjYy0zK6umkXlaxXKpLF4oaWmOSGYHycjvIxmZWVJaOF1WLf7FrIeHH0DU6h/zqsPBIpnRmw7yrElZxzDF77TLWivd81dLdnZGdRNwr4GLedv9+Dq6G2TLLZGKwDxxpFIy0tpJTjqZPsHrE/vgY489ZlLG99lnH3nggQeMQU5RlMYJaxB6BUY1AqbOP//8ei9f6Mb2dXMbvZON6Tcl68RxSiU9rUAy0pJbVg8HAvIrjgr0B/ubyO78DWMNf8eQg2MdHYT9CBkinmtGV0EGxwlhIXqf3/Ur4Whk0W+/NToJ8gGGTGRr/ht5lXNFlsDJwTHdsgPPBYcH5TfrAs6DKHH2E/YZ2zurLiE7xep0GCDr+veSBXqqHVdWduQZ8lyV1KYysFqqnA2mekVWWtcaGYveOej56LusfdZmQIUG5i2Ot9pkyNrSUaw33uMw3/lt1h7WFD6D/YEyTDha/DIOWM/ICscJqb1bxPf+cA8prYetzFbYYB21vQ+B9RsnhHvdx87BXmH7HJH95nVExQL7KOOHtZXfQc9x90hKRXBuU+4W2xlZG2eeeaY6zbYC1KmxFcAmd8EFFxjB88orrzTGLIxYCKzeSJkVpUtk5qZQp8b2LYdKm+zoTo2KwCSWwKD3iorSZO3qXmYhxlvPxspiyKZL2iJZDAhWGGT4TKdOHaU48JNUOVsaiOem95ecjC0ldxKlKlAkVbJZ0oWmTtENRrF60tk4rPGZRR+DJdeEoQvHB/eea2XDZ2OprQGHiOtB224jJTmh6e8tMvsZxwaCAr+PUkGkBuVniNRwOxBY+DlXW5/XRtJyvjg7/AyjtYWoMlvrn00Y5Ygo9Uj172uLbTbIuEf5ZkPGgFwXm3LAqZSiyplS6awnz0kKMvtJVgLZQGQB8QxJ/bdZNwgXOKIQbDDw2kj2umZV6ToZu+p7qfwjg6t1Vgs5sNNwyU5PnfIxjHXmonUu2saS/D8v5h7zDuG5LoWxykCpLCqZJFWmqT2/40iHnMHSMqtbkFECIzRzkohHqxCjFPA+4xPhk/P8Yd33sra8OoOLLKWMtEzZs91ekpsRmtXB+CAyxy9LAzB+YIAnmhBHBes/94Pf5bySFcmVbC7++TNZXVYc9F6b7Fx5aOfRkipw/2wvE56bcaSXlxvFDkUQ3GU2WJPY+1qlLxYpmhGSmSjtR1eXoorzHAqXzZbFP70u5RWVImnp0qZ1M/O7JaXlUlJWLoH0LHFaothkiWS0MY4V1mL2ifouA8Be8/zzz5umhmRvUBIzWdlKiqLUz75L3wxKTBEodfbZZxtZzjrK69rwwTqKYd82tGb/BAxArGfIUexr/B3Zl701GXXl6wvW9LfffrtGV+Makdf5N0YrjFXIicgSOMZxdJBZWZv7zr5FmUz6b4QDfYZ7jSESXY5sU/QFt2MECOJw9wqjnjyBXjheDjnkkKSXIOQeoGdyHzg2vQQw7mJUrcv6/ezlNgCE32fMpXpZHZw/yILYBOxzIACMfyuhkHlgjcRWv3DrGKwt6Bh1HdFeFpgn5YG5NfpFmuSbSha2PDd6NWsiL87VVkZgfLIesmai/yaicxN8RVaFn5zGcXFY2tJ0rCM4yFibeBEsGa4XkBJ7VpXNrEfXOPDAA81eh13N+0x43ujDyZSpGVus4dhTgHUDHZL3OQfTPN01N4Dzs3tWfWbjoA+TLfrcc88ZO9fDDz/c5MpgKsGoU6MJg2B13XXXyYsvvmicGngto3nDKdnzw9oJUulUmH4EbJq56XmyS5s9o0Zms5AtW/mBrF27SjZtLK5Z2JoVtJKunQ4yGxybvjszg88QEYTRlg2RiJdqT7IjlYG1kp4RkN69BkmHdlt6VdQnbBoYAikbFe73OW8TievzdzZyol9skybv91CuiOqJVwjiXnLfKrOWSY8BeZKezrNxJF1ypEPu7pKRlh10DTgp/AyWGOH4bRsdjvENhYNsBp4VQnld33cEIAz1KEncE4y8yayJy31H4ULx4lqt0sG/kx2RxG9tqPhBKlwOOeZQ66zdJDO9wPfzAdkkjlMu6WnNTSYS76Go8tyIZuMZMQat0sHm3LNn7R188fDWknGyuarEdUUiA5v3ll3aJKdGayLY+cT45JnafjPhFFcEQcYZijgKKBkLdREZtLpshqyvoK/ClvmeJunSt9mokLmEAs55Iwx6hTHT1K9dgUwsnBDyGwOaD5RezXr73hOchKwp4cov8BkMDUTRIiSj+Hgb1qUaD8/5yfTTsM3D0yVNhrftIhf3S10BlXUNBdhr5AmhvFBkzVjXGzTuzq/O1EhLUAEoWiCy5ntxqspl3eZSqapyJC83W3Jb5Epmq4LqCQzpLaUyY1cp2lxiDGPMC/YklM/62m9Z53DS33HHHeZ+/fvf/5bDDz+8Xn5bUZTEIVIUvQJZkTIPBOzgoE9Wbygv7JPIsqwTtj44v4Ush4zNfsp/u9cuDDDs/RhViDYlwAcZis+yF7JPIk81RH8fWx6ETIZIZYv4XDi5hqhpP3nZGlq5Nnc0fqxw34i2DuoZ5QO/wTkQmOTnLPIayW1JXkomcty6vu+cH2OGaGf0C34z2cFayJTI7DhOGKPIo8jsOIVSsb8GsgkBQIx9dG+ekTV8MtYaa7PzZGN7QfL/BIeybnjLOrvhuRM0h8EXua8uynihKxZVjQ95Pzt9G8lJD9UJkO/RIVnvLMwDnjMlphMZ6+hR6KbhZESuHwcQegXOUXStxlC6qDHB87TVThoywwVbFOOePYi1ztuHw5Rp/GNNRMfgvAnUrK+SUJwfjjyCpx599FH585//LLfffnu9l1NW6gd1ajRBWESeeeYZ05STSCmMBGRlxEpx5WaZu3mGlFQVS0FGc+lbMFByfKKCLSxUvKBNu83Stl2RFBRgaK82yKRJd8lMjz0d1wrjfJ8FCcEaJcbComiPbR0D9LRIdGFHwOY3MPCZSNfCQhMJjxCMosFCzSKNwIrAwgKJsMp7nBe/Gy4ih8/baAkvKGMI2Cg0iUYQoaCNn/g/2Wl4P2nVsq00y+guc2cvNEZ72wycKFgi6Lwecp4ZgjhRHO6IDYQd7gGbkI0+wsjKNRLxxHFtMymuwdZfxIFDFoaNxkjkmlA+MbgS3ZwsUGTZwNy9Yxg3nG+yS+pUOcWytvwbz7tpkp/RS5p5Sp0x1soCU12NddMlJ30HKSvJN+m6bmWPZ8FYI7LNr3RZXRJwAvLSoo9D3u+S215GdaybEgLhYDwyZm22V1p5icjSaZKbViHb7zxc0voME8moHneMWQymdj2xGUlvvvmmUdQx/KPsMjaSKXAvL50iRZWhzZL7NNtXMv6IpIoVSk5NKvy25t/lZRVSXlouO3TeQfq18E8jtgoYY9tbysr9GQzJzLPGEJm3ubJC7pk5SWZuqs5Y6VfQWq4auJsUZDYRRalksciGn0UCZSJZrUVa7yaSWctoYpx+TkCkcpNI0VwRp1IkrzA0IyRroEhW36C1BkMbYyJRgwx7AoYuq7hgMGH/YK+25dZsPWD2QPYm1mR6fVEiE7llzJgxEQ0IiqI0DBjJ/va3v9U4I6ldXZeODKJT+X/WIwz07OO1CXyxOoSV9zHSsf54gyVM0EkgYKKvkR8SiTK1siayBsey6x9rIYE8BBvRiw5DMjIL/8118j7nxudZO/2M/za4yeLVM/gNZIBITolIcL7ce87TZn8ig+GY4G/IUehJnJvXUWB7CnLPvGVnOB77DC/uBTqSjfhF5+K80S+4D/yd+8J3+C3eJ/DN6mXxgv7G7/A8kwFjxPYgdIO8zt9SsZ47OhHnZ+csOh/BdwTSbM2RzIxp1jYM99YGAbacq3se8Vl0aBuQhA6BzM0YnTRpkhxxxBHmfcYtjtZkUeUUSXHVFp2gmjTJSusuufRvqyW26kSk6H7mLeuCt9+OG+6j0XHLysz1a4aGAowJekWxpyVSCsuCjsveyh7AOsa+wDHtnsELWOutHoPtgBKZyC1kllKSKpVLaCnxo06NJgbe8fPOO88IKKRaxRvxWFSxTlaWLTSNwltnd5R22V19J73Jyli2zJQtwfBgmxg5TkCqnBniSHXzvDTpJBlplPnxKAMYb6rWVEelZrYXidPY5wahgUh824QKQ4k30hSjO4oLArk5L1f6KAI0Sovts8Dx+LytS8vnyF5AoOFvCNik0bGQYihfu3GtzF0xW3r36y0Duw2SgqwtDg5rMLQRH8kCxccKUxmZadK7d0+ZNbO6hAwOHoQIFCLq7uLYQLlwl6RBKEHwssb+cE4vlCYiv4nqRtgh+oh7yPtEWiHYcb+5dygj3H82DgSaRKMzvOnqtYXz5zq5J7bxMefPK1J6fSJUBjbLuoqJnnfTJC+jpylD5a09Xx7w9o7JkPwMepxkmE0Z5YP7zfkjVDdUI7U3low1PTUslEHq37yHDGuzbb38PuMMRw/XT5QH46yyeKOkffOCZJBBYuZymkj73iJDjzF9BBjzjH077/g+mTkIQl988YV5n3HPXECpQ8BKRp3QdeULZE35rKD3MtPypFf+XnEfm3V4wuqvpCxQZjLnfv76F2nZpqUMajZY8tO3OHFxIKJQNW/dTAJSJbkZzeTjjz429wrl3a+HDAoZYwwlLNa+Ng2J6cP0Rwmq9jn59VZ6rV5hHNfVdQWKRUrHed5k/+0ukr190LvMB+aLNfQx79yGwHDwN5yu7BcYj1hjMZQwPlnH2Dv5DAYsXuyzOObdUYR8h4gqSmPceuutxnhaVwZTRVFih/UAZyO9cE444QRT2qGuIh6Rb22pFOT5hop4Z21CjsaIZzMe/AycyJa2BJRbv2DNJGrbltgFdBGbXQJEzbMWsgfz36a8qOMYmZrvIMsj87Cfu5053CNKyJJZDcnSMZCJ0CX5bYySZKpb2Rl5n/Pjb+hd/A05lSARt+ESuZu1nLUb3SPcGk6mKtfNHmEbEPM73DOumX3CGqyQZfg7Mgv3x69GfzQ4J2TDZJXaZE/jPhC5jjOf+4RcRabL6NGj67Ssbm3BmYS+xrPh+aXyudY1BAIyLtADrO7PWAknF2P74HM2mJLPYndhbqD/UvYMu4jte8O95di1zUxynCopqvoKKS3o/dz07SQrPf6MLDfYN+iJhD3EZsJV/2Z1aS2ux64/GJWxnSDvsbb5rT0EtrBm4dBlnVAUC+sj+yWw/qBzxGrfQG9nXqKjYIsjG9EG3xKcbPULYL/gPQu/QSAAzcTRU+jtVxvnipJaqFOjicCGSl1boqZo0ocxIN56npsq1smMTd8HRXJ2yd1GMjc1NxucbQTHwgFs6Cwm4ZweYYXsqo0iJUQaVPwRTZopktlNJLONSFaXuIw6/I7tC2EjYhDIMfpjrGehRNBEKMYIX9sap7ZWpBXQN1cWyQ/rJklFoEKWL1whGws3Sf/mA6VtfjtzTrZ8FQIB54IgHS6jhIWXzR/hykaXW+UI4R4liGvkOeCQ2G+/fWVjxXQpDdAIvFSm/bRUOrUaIk4gwygEXDOCFYI1TWuJyvZmT/B3Fn+eKb/hjtKxaeWUFPAKdlwTgptf2RoULZw5GFm55/GkiLPRcY+9zctrC+fL5obwxf0nsoook7CGXCKcneJqZ1sczelNv4QKek9sdL2bJq2yhklWenCGRVnVDKl0lgTNNxTiFYu6yJrVReZZoWjUd2aGH8tL1si41T9IFffF9G1pJgd12l1yMrLrTdlwZxRh7Fj205fSceMcaZGfK+1aNjM9BAryciRrn7/Isk0VNc3yLDY6hHHF2HQbB7jvKCQo026jLfMcgSeedQPH7orSaVJUtaL6GGnZ0jV3qORkhD8Gv49CwZz1Kj1kzv289mcpLFoj+dn5UrXAkY4FHYMUetbniTM/l/nLq50pnbt0luG9R0mgNM0YZGzzSq/QSGo4SgdGCeY/a2asc1ZpZNAPp+QzNuAtb/HK6ihpmTuGNPOu+cwf0cqxOhZwfjOX2O9YazHwsPbut99+MWXvIWPgcPv888+N0ZQ5/OSTT2rzUkVpQDAg0C+DPfLxxx+XfffdNynHRd5DZrclUVgrWG/Yn9l7/Rzy9QlOVyK32T+tPI4swfmyr3LugHEFubs2DliOyV7upyMgH2CARtcDfhuHElGntn8E/89+7gf3lHuLnG2z0t2w96OnIG+iP3FsdyYzTh3uBdeH3Mz9wPHBuZLFzvjwli3CkW2Nn9wndBD388SoxTHJ+HDDuX366aemXryfHkBmPb/N32OFa8YQzTnhLEpmE3ub6WAzY9Bj+D0M3KkG44h7x77MfSAIrqGCpVIB5gVGTsa0DYa05cp4hswJ5rad+8wPnjd2B3d5KY6Dvmx7k6H/u8cuchHjz/ZXs99BvsGJEM8zqHTWSUnVLzWODbI0ctKr16dI6yy/x/zz+5wtPYrsxfzi2qxNhetm/qLzsz6hKzB/bAkk7Bw4Vr1OMevotQ2rbdZWqgdQKfVLpPKKfnskFVCwC9jerLaceLi9z/t97EGsgQR+Ezx1/fXXm55+Oi4bP+rUaAIQJYIjA2Fz1KhRcvPNNydkCJ2z6WdZV7GyZuLPm7FISorKZN8BRxjBleMTWVTrJnvFE0QCG/4ojVEVXA0ju6dIsx1jcmwglCFoo/h4UyXZhDGOsHlz7kWV82Vz1VLzN0o0FWT6O2PiZcr6H2VNORknWy6iWUaBDG0x3ChCLNYoIQgMCEcIu379NXAEsPkjFCBI2MhYFnocDQhXLMQ2qpWoh1JZJJsr55jvl5WVy9zZS6W0KEv6davOkOD6uTdEfnFMr7GSjQFhjihtlBIcHxj6UY6sY4Lf8aZPcw58j8/6lQDgetkw+H2uE8E+2ngcP368caygpNRFjV0znufNM0qdbYgcNr02sFGk4gfuavW/03uIZA6O2dkWcMplU+V0qQisl/Q0GoX3l+z00GjGisACKa2cKevXF0nhmo2ybu0mMyYH9j1GOnWqbgqZMDSpJjI7PS8up0wkiiqLZWXpWslMy5Auee0lK73umi66lQ3GGNHejCnmA8+N8blN2hpZ+M37sm5TsRTkZkub5vmyqaRMygYfLO36bhsSQYrSGa8iiwDP7zI2mbco8uEiRlHembfMc+ZMXkGmyZrITm8m6WH6EaEQMC7BKk1kKRHJyTVyD5hHrCNcN+sAjmSbXWFZUbpAFhfPqFn7Vi8vlHXLN0nfgh1rGopaB6d73eP4OIxQdFFKWE9TvRSVwoOrFClbVb2H5rQXyYhxjletFKfsJ0mTaudkID1Nqlhv01pIZho1kpNr3GCvYN4xJzCexAvjnVr9lNC8+OKL5Z///GeD1g9WlK0NjGHoFA8++KCceOKJpgk1WRq1BUMEeyv7KYZg9AteGOtTJTMLmRw9w6+HGe8j17PX13cJC9O7bcOGmgwOAhcIMCLYyS/a3mQ5rlpVUwrGyr7IGLZnBs+D9Zrjog94+3BgoMTYiQyGERYZhP8HdAwcGl7Z/Y033jD3DtkCGcZ+Djhn7h+9OLznSvYGspTX2WHB6GzL26BbYMyK9AwwqGK8IljKnnOy4d4hy1lnQTKdJrWBPZRxgv7I80d/Ry9UA161/I3Miz6InG1tBswBxiVyMWsA49SWwuQ9xh3P1+uIiFfH4DjYCZhX/DdOROtA8cL8JPiIuYpu06pVc8nMrqTwlKSn+eskVhe3AVP8N2WxeN9mlTEv+TvjAj2HsWsdjeGCuRhTHIu9gfUb2Y5r4Ly9fURsqSrOm7WJ32R9UZRk2HVw9DPGcJYl0hCdPfPcc8814/KJJ54IqmiiND7UqdGIYQO6+uqr5dlnnzXK/uWXX24EKiJcjjrqqLiPN3PjD7KxEgO9SFlpufz200zp1quzHLLtKckV2os+qc7SoORE5ZayEzW02Eckc0sasx8IGQi1GDujRThsqpgnG/8w/ltaZg0wjo3aMqlwgmyuqs5csWSmZcrI9vsbYQXHAcIKSpvbqYAgwd/Z5BEmbBMyb48HBAfbJNqd9g1ryyZLhbOuujnwhKkyZKd+0qwgX0pX9jfGYH4TIZbvuPtJWNwlsaihjtBDWh/3lWgN0qm9SgpCOwIfZXz87jsR9DwbNhsELwQ8jo0AHS1qiePyfY6LYEXES5ABmUjjqvkiziYRhLiMPiKuhujhQHEjFR4HDecUcSzjbCr/km8Fv5+5nUhG4v03bPNvWyO52qkVMOWnWrZOlzZtW0irVgWSkzFIstJr2eejcqlI+ZQtjrasbUWy6kaRqytQoFEQEFQQsBFaGJsosIxpjB6ZRatFvn3R3MslazbI5tIK6dW9i+QecIGIT68FxiWKOEaTcL0mIoEAz3xEoOc8vGsP5/njjz+acY6SYJUfnATuMce6jXLCWm1Lv3Ecoj9ZFxivKN6cJ+/j1LHzkL9zDijx7t+eU/SLrCuvzgxxs2PL/WTl8lVmPvPdcL1qEOhwejA/mIcoeVtz9B7MLSqUX9Yvl4y0NBnW5v/ZewswSdPq7P8ur2p3GXeXnXX3XRYJIQRNICEESPAQSELg+0jyRf9xAwJJcAkWnIVdlnW32dkd9+7paXcrr//1e956u6uqq7qr2me37uuqme7qklceOefc59xntVYElr5iygC5tY57rF4ZwOmVGm+RvNPvmTbi8XbFYwcMSZtgXCbHpsuxR07H/Gk/zyfYQ0jcYC+kamO+ssSLKKKI3ECKhOoMAuE4/ASsqKDi/9nsoalgHyNQt1ybEmN/YJ9ns52XA+ykLgKx2AfYHamEL/aOTVBgM3Hf6MmW2cSV5+1goy3BmQkCoNwrAp3Y9sjx2qSHnYiRq2Kf5zlG7Atbg5+f8XWy9bS75557TEA5Wy8KPoeKEIgV7DE+DzsLEoSK8umCWpwf2fXYafg6HD/3dj58W+6DLbW4VGQBdiXHAVKT5rgmdq+SufSheTEBWxh7G9+bMYgvzhxhfDNO+DnV92Ss4RcT5Id0yAX8fVuaudBxwD0j6REfhTGZORf5O349Y5/5ik/E2M/07zl+CAaIP46Bz8FfwW+hqouf7WbNNpGc2p8Gooc5nklq5oJNcLDW4O/jt2cDSZLEDfDF2T8Yj0UUsRzAXvoP//APJoGKPht/8zd/M/fk7SKWBEVS4wIFmvA05yMLBic/NTsAY9w22gpBV7BFZ8cOpTzj0Oln2/WGW942j0eOhfCwFB+AZpViWUiNsiskb+5jZ9PFYJ8u8zMa7zUNtZwOv3pDRxXLCFK7HaVq9OducpUvDg09r/agVQFi9xqo8lTr4urLjXGSqrGbCgKGOIUEujGUTOb5pk1ZG4rZjgHZRhitdhbbcOSYwvE+E7tet3GFCYwjPXX/D3tMoJQKj3wNdqp9MHIgFTBKcvXYwFji2LM1cMVI5Jx4P1lYfLc9Lgvpk2E3bySDHOfGZItgpEeelBI0ugWcV0DyXj1tPxbGCYHmbNJb2b88KIWz6M47V0geS7e4UBCI47qS5Z8ZCEAfNZboUkIROR2Vcjkq50E3H1ImQ/fef63kvDD0ciHOMJRxMJjrNsmVFecPSwfvkqJhJQKVOlG5R5v3XTnt5+PUMi9zZQHmA+YiEg3c09SGk8wByDOCDEYiK9mMntfhFNmNLvk9XwfTGiMD5meXo8r0W8nE2dHD6gq1pN13h5y6pPq2KWsAjd/7w60KJ8bkc5aq2rPKTC+cDgID7B92gOOliv0D5/W500+Z9RzQv+ODm6/RutLq/OX0gq3qDXfKJZeaA2tV5Z0n7fmex6TRMyn32iF5qqQVd+T19niiU7HE81Oep/eV0zHHCrEFBI72Jz/5SX3sYx/Tb/7mbxq5zaLjUUQRCxMgRY7hS1/6kpG0fc973jNhd7K2ERzLRZAXAvZIgoTLScufvY/jIrFgOa8vBF4J0tu99TLBsWP/EsTE5iG4TbJCpj1g+xecN8kWBCczK2WwVQhIAqpIqaymX2O+TbDxBzgOgscEiKfLqkXCJ7PpNiCAa1d6EJTGZ7nxxhsn5IK4HtkqarKBa0biDMeDzTMXQLIwfpdKtpP7R1KcnTi2EJXuLyZgRyC7ij8KCYhPDRmV7zjGp53Or2Ye4XMyxmd7L+w1iLmCH5/asBw7nWNnbkPm4V++6lWvMn+DLASsXZnyV4uJcHxQ4zFL9aPE1SyPs9z4RvhcjNOf/vSnpuKviCKWE/B9iaviq3/uc58rJk9dgFhY/ZAi5h1sZFRnfPnLXzZs4rvf/e4pGbVk+JJJQzmy3cQZg3Km0qx632pF4iG1B08robgq3DWqdy2AgeTfY/XUcGQhNAjQuHJnxGJQYyhMR2gEY8cVihP0sZBIBqYKRSQeVmfwvKKJqKq9tar0TDXgN5dt00h0WMNRq4eCz+nX9gqr/BJnbbqsDvN6n88E7ae7N7ZhUllZrt2XViiU6DDBNo9jn8ZiVqMl+0zjY0266abdWcmR6UAmD0Y+mU/ZDCGMLLJQCNLmcmbtbHCID7LDID7snieFNHnk+wli49jg4GDU+Tzjck8QGta5SmNSvGPaCgref8cddxSQdc54d0wlBWYp4YSTiANF0/Rs15UAtdsxh+Zu0TGp/6AUHZf8tVI5YzRLI9/YwKKRGqkVQIXClkBg7bLJrWkdjhXbpeZtRo7HwVp1/Pj0XxCLauD4Qa1nfsRjknN2MhessdxTgjvMNft8cfJxvCHjCAxRymprSON4F1raGk+Mayz2tBKyGp055FfAuVtOB7q4k2tzc2C9+sLtiiI7lhy/a0qmSjLQ7+Ps2JMaYzwkXzcc7dHoGb9pfgogkjjOcDyo8eiwPE6/Stxz60V0oeF7bYesfhPJucRY/HH7Eb13U373r3X8pFrHJqsD+yJd2llx2fwQG+GBjDmekJL7Tz5wiHWA9TB1/zV0vJYzCLQhQfXKV77SOB6Qh//93/9tenUUUUQR858wBXGfmeDCWsg+TaIK+x37G34Ge2Kh+z7+zHLKHsdWI3CJzZGvxvdSgetmNwifLoDLfj6dPJP9PDZyPj0gsPHf8pa3FFTJyRgikAypktpMPHMsZBtvqb4fx0rglnsDucHvdoVCIbKZ+FuQEGTV46+QAEiwtdDxi0/E+5aK0CD5h3nIdV1OxOByhVE1ePRRM1ZYs0gyylVZkA2MXUi/XGPY9pXx6edCLjG3qGaAsGA9IgnKPn5bitquroJcZNwyLxmLmXJui41grEe94WeS/gWyxWdV67nEHDdrFglh2ZIiiyhiqQFhSXUqvTaYV7/xG79h+vot5+SGItKxvK22IqaU5eJswMITOMP4M008E3E5k1rYZI1g9FFmzMZLuS5sP4z+TM6/qSgYKJW7q0Eej1tbd+7SWOWzJrCYpk9PWu94hxQelnxVUqBAyQpnuVRyoxTrlULtkskwBg6pdJ/kmtoYkPMkQIzxOZ1BQXVGKqEB3E5kftJfV0pj8hnIjDNjJxRLoFnp0NmxE9pavktN/vT3eZweXVp9pSE2IILK3BVy5dDPz4ZCSttHY0cVSlgkBiGtcOK8St1rzP3nu/3OevnqZicfgtHENYbY4BrbEkn8DmvN74y7bBlUNtDqTAXBaTubfTZSBXZTMdMU8MhTumhnXOvXZ3xOYrLpWjbYckV5kyrcO9cWKXY0hdzwSK7C5JsIYGOMcs3yaV41E+xy8jSnKxqUWn8ixej9kVBi5Kw62gKKekfk83pUVVUqrzdpWDsK15osFMORAR0dPqDx+Jj8zhJtLd+tihQiMBoP69z4EY3GBuR1+LUisFWl7klHDKMX59QmzRh30xq/IUituOQvlfIhX0cGpR//p1ynT2nU41bJ2o3Sq95hvT8JMhBxRgigMnYgJe3m4pmwy9RxjO0mpxwvayU6nci3se4SKGEsz0aWIBg7PEFoAKeCCieoWKLabJ3cDitrzOv0a1flNeoJtZk1q8JTax6ZgMCwCA1gjamRaLfau+MmoEF1FOdCb6VTI89NBPXrvau1tnTHkmV+LTZGo1YzVhtchZFossdOHnO1bez0lOfbx8/OD6nhqZAiqcSGQ3Lnb3Q7HH65tDdZrcH66ZLLsVMOx9I25M0XJDQQeMXxYM/B8aBqI5f+cxFFFDEzCCpTnfGVr3wlZ8IUr/nxj39sbHAy99mzbWkW/ISZ9OQJQJMYw3vYKwnGsleSIDBFYg8fAwRW5N8zaA5AzoVgod0seCExGOkzPoPf6VeNdzIpYr6BHVNoxf5MmE1vJI4DyUCuMffelsnCNiLpiedYv7GZctlJkNiZ+yx+MNIhjJ/Z9GHBNyE7l6x4JHToGVMISGjB515sYHPiDzOf8q5Cn4dkJOY71S3cI5vIvJDAeCF5ByKW82U9mynxMBUk26GuMN1587mMyWzXE78QKU18C8YrfjGPXPcPW5x7zGdBBuDH0hfGTvZjLlKxhM/Ca5aKXEvFYORY8qfJxJsDx+7Thg1XmXOw1SCKKGI5Apvnfe97n6kkKiZPXXgoyk9dAGBT+8hHPqKvfvWr+tCHPqQ//uM/Nptbd6hdp0YOKZqIqMRVrq3le9V+ttMYWpkOPiWLqWWMgA3GbkgL2GQJyPFamEk2e4LaNJedeB/B1c7HpKGUHhXVO6X6OWQHxAhOjltkhnNq8BUDAQMOOZSZsroicYJ2z6Y9x2afUJ1C8TETpi51r1apa01WAy4YG9ezA48pHJ8awHLKqWvrpsq5LBb6wvcqYYJQk3A5KlTlyU/WaSYjmQwWjA2y8GwHgRJWnM9CzxnDhc/E8Z3uvZRN283K0A+dQgDE+tRy+mn19PTq4n3oi8bS/+65RnJmr+xhbDN2ZmzcRgP5GM2aydxvlFxbpXhPUuoKQmP1jJUanAPzFAeJQDfXDQNzLmOFz4GgtJ08HAocO7up4/GnfybH0Ak11JZrcHhM46GwVjRUy7dmq3p7TysYiurSizdJzjrJd/nURueQIUOnpXhEKmmWArPXOCWr/6n+BxWj70kSTrl0afV18rn8Zg4eG3lMYzH6ACQJGjm1rfxq+V2lEyXhkGYY+IwHnAc7Qyn9uKPSo9+VWpNSeXWrpevfJPlKjNNsZyZOwZ2fl84dN82VW/qGFPB6VH/RVdLNb0qTOCCwj9PDuGf94zlzvA6HWR9ZCwl+2OObcca9Yc0li4tz4f22XMOswDqbOKdE4rC5bTFavfAUFGvKbfQ4dsqNNFqe6A+f0/ngwbTnIuGI2vaHVVduEThbtm3S/gHWmvSKH5qO13jnpqN+oeAzJx/XoaEuQRdzK7j+5R6/VgUqdWvjBu2oqJ8+G7D3rinXr8pTq52VlxV2IDQCh/Rnj3SVS75V1s8dd1uBP+BwS403S77aWRClkDeeeW8QvphSdW9/+9sN8f35z3++WC5eRBGzACQh8wibH4IwVzUu9hTJGpkZ/dg/2HKp2fLs4VTvsh/bSRn4Fdg1aMOn9mVLyy4O90ud91h2CXD6pKbbJM/CkZZkbLNvL0YGMQlSLSlVfDWeBu2omCoL9WID9hw+pz02TDKG1zsRZJ5NhQJjaDoJLOw0yAqCvgSwkavKJD9s34fq4EIzcu+77z5jsy50Y3vmD8cJocBY5XywceeaQWzLjXEvmNu23cpcIMEInwafm2uMLUyFC2sEx8J8p3fnhTJusRU4L7simfGIokChlWKsXfglqf1rMsH1gazLJEht5QIIYI6Daw7RwXwAHAuJjvh3xGVsP4TvxKdk7SRRFT+HitXleO3bx++jW2TacycOdyoQ3WrG2q233rosj/sli9iQFGlPytiuzJpU/FIFds2nP/1pffSjHzXViX/3d39XrNpY5iiSGsscsPBkIhJsQkeaDYHs77g3oueHHk95Jdm6Pl1SfZ0JFrIhYvhhANmN5MiKIkCHsYIzQfkiJeapWexsurzebiBlPyYw1i6d+/nUA13zCkv+Zp7BORAoRosxn3Jn5FqGow9Neb7EdbE8zpmP7/DQc+oKdWSX8JF0Te0tcjuXRrO0L3y/EhnGgttRrUrPpXP6XK4vGSyQYTilc5UDoNkZ920mTX6MSgLYtg4uRrLd1AyMDp3Ss098Wyuaa7VhfTLbzEWAH2fXJbl3S67cWWg4UBiy05bjIl8Vzci0clZLLjJe6qQswT5bFgkjnwwz8xan0xigEBmFSG3lAsb39773PZOVYzc353vJ8sE5I2Noc82oHAOH1N03qPLSgAL+ZIbbqpfr+OlDcjtDWr9xi+RaNfU8kKs6+2NLvsquSGm+VqrIXvo/ce6C7MMAgryoS0rZyBCsR4ZpTp6OrWV71OBfofHYsI4MPzLl783+TWr0bdBDDz1k7pPt3EIsZDbinsD+n0uHH5mcow6HTjuq1LXOalbJ+GUN47NYNyayoL7051JwdOJjDp7v1o6tW+V48x9O+QqMb6os7MaWqfcdQwfHg++wG4OnVlxx7zjurIZ7PCyNvCBF+iRXiVS2U0qpVrG+KGL1j3FR/WWRKXbfR4t+tj/XIZdjhbzOmSUjbARjwzo5mnkfHNpcdq28TstJG4kO6PDQYxmvcKjJv0GrSiyH8MWO4UhInzn1uM6ODZgqv6g91JJ//8iWq7WtIjcJeGToWdNPIxUbSneoOVBA0IybPvSIFOmanKPeVVL5pdYYGT9vkR7+Jsmd28F+sYP5SNUGjse73vUu/dVf/dWMUptFFFGEZV/To+azn/2saZB5yy23GEKDTOTp5FMIwmH74E/gY7A34peQdYyNwt/wGwgaZmq7E5hjj0z1L9L2SgjbUG96JVqgWWq4YUFuGRJCBIcXoyH4eGzUJH9kYlv5XtX75reiYrnAJiAIxBIwn02lRyaoqMU2m6lCASkRu+8GY5LKcbuHhh1gpqIc33g2ewb+Oe+dD5s/FfjvBLshZdjfUgPeJJ7NR1AYQoPgOT6fnTjFtcDu5XdIE2IEzFeez5zHKEdkI4mWIyCDqBDjXtm+IX5Utkb1udY7/FPOn3WR9ctOmMsl9czf8T+zJWbZ/S+YC6kqGNx37gnPMabwg5g/+EapvpC9fi5H9IaeUzDelRZDKXGtVLV39j0Mi1ggRHuk0UdTnnBKZddIrgurAmuhwTzGPsLWQfq/UBnpIhYPRfmpZQqY/j/90z/VP//zP+sd73iH/umf/skE0NjgmGBPHnlE225en7JxJDQ8Oqi7D9ylmtI6YwCRSUCQNTObA4cll049m/a02R/hkezPR0bmndTA6CDTq5BycKcjoIBrh8Zjkw3Pfc51eREaYJws2ByEBv0yXGTFLhECrrUai6X3DAi4Zp9ZhgGFYYVxhAHL2CJjiQySmUpycVAwilOdikQiqFiiRYnEuPbuvcj8jtRJLuDwIj3A/xwDhhvfj2PMvY+NPqmrrtiWNN6S9yThl3w4t+6plQcZYJwzD3AMcjqsVGnY4OOcDskxqERiv+QISIkmydEohyomjGMcIRwLDNLpsnXmAgxYCA0MXAxbnAw20jQN5fFOafCQGmorU/rR+CVvpTZvv844J8dPh7R5cxZioO8Fi9gwSF7bjsek8vU5r6slg/REklTiNfTe2UW421RlZD2PaTPAre/B4YV8sAkNpCxW1Tnk7L/XkhfzrZTKdkwSMx0n0+ZoW++gRoJ9uuKNH0j7dBwXyDoy8AzKKi3JqiRD4HG75ajInuWHM4eTyzpsO3yMI47RPk57XGWOrZzOBt878LAUTUoHUbXS3yPV3GoRHDaiByUH1SFUZVjXyL4lzkRmJ4TCCFa/q1wr/LuS1RrUfTi1KrB7gtAASFlNOXQlzPr3YsX93ef1UE+H/C6XfmXFem0oq9CHt1yn/vC4Pn6QcWhVIBmSSdLPu05NS2psKtstjTjUF+40FX4rS9ar1tukwciAfE6f/K48giDhjiShYX8zz52TohskpMVKC5PEmwtaxjrNw+1wa1v5GlV5y5Zdufhtt91mEkCY70jo2AGMIoooYipo3Er2IfvVnXfeaew+qg4J/lGtTQAa2ywVVGlgA2H3EHQjAQXJoNSAm91LKhdmTJqJTFZzWkhIkfx7BhUCzocEkXwbXs8VVIIX8vyFDq4vlToEheltR4NgfieQPl1QNlVayv49FYzLfCSXCDiTZEW2O2MWvwYiAgKOZC4CznORh+U88GFyVTYVCrviiXPjWNnLCuldUgi4BlwTSEhsbhQZmAeZEsPM12zywZBDkEu8fjk3J4eQIWZir0kQAqxx+fSPsccevfNozp1KfPE8lTqsH9nuESRaLpkq7HruLffaJjWYIyRN4Vvan0fiHNc38/OXK6EBqrzb1RsKKpIYNL/7nDWq9GSpuH8JYSAc0tdbT6gnNK5t5dV67aoNci2HapVxJGhT19aYNH5IKrt6CQ9q+QGpf6pZicOSWI5izic+8YlZSUoXsbAokhrLEAQx3/rWt5qAGtqJdlDOLu0m0G/KuIMh+ZLZ2R2tnepu79Wv3fx2Vfhzs6wEi+1SR2BngQACqGz2BG0hPbI2ofPlaEbmzd3cezYgaM5j2ibBOeB1rpTbUaNYYlRO9MMd+Qdg6IkxHLU241R4HF7trLh4Scsm/c61csitUJxG4U75Xavldc4uuwwjFgcW5wAnlKCyDe4/RhzPZTPKKOe2HVf7eiQSYXX2/Ew1NXY2z5Ciiafl1pWmGXY2YCwjiYZTgCEHAUf2FeMbQ7HS00NeX/qbyFBOaZA8E9h0ZnIK2NLjHLPTkvYxZ2TOgbz4s1wtJXSRWs6OmMxEjMyFHgd8PvcIoxenK6vjFWiUGq6UuumxEJM8ZVLTDZLTbQgq5k9Ohy2ahbyDQEDywVTDZAMSXfbaYb+X3iNNpldAwFWaQgo65HcGVO21Ar9+Z6n8zjIF46Mp8lPS6Rc6tH7dRiOpZTscw72ntGZF7+RXjR2VaIBdkay48ZWkNXNv6RnUFXvTHRTGNs4shOgErv5l01PDNAhnZCUcil76spybIEEenHLmAQQG8wKyw85CZbyyXiJ9g4M4I1hXov2pF9y65sFzUmlKVVO8z+rjnAWTV87q9eJ25Jdplopq70pVeBoVjQdNE3BnBlELqbEqsEXnxo+lrIvVqoVcehHiW+dO6h+PHZCL6+qQfnD+jP7zkhu0pbxKVd6AYlRDpIArH0mOoVxwO93aVnHRhK5yT6hLj/ZSlm991rqSjVpfNkPVS8Kqy5mCeI7nZ4lwvEPh+DlDunldzfI4Vqatb4eGzujR3oOmWgccGT6rV6+4RjXzvOdPh+cHunVydFC13oCurmuWK4Mstaunfv7zn+vjH/+42Uf+z//5P6ZHwHJv+FtEEYsJ7H8kpv7yL/9S733ve/XXf/3XE3PE7r/AfmcHlAE/Q3QQHJ4pgGv7GPZn2j9jk7BfYtewN+PjZLWjPCQfdKdXanjmN3OUdZmKdc5nMZuAluSQ9igtoCfShQCy+yHNyO7n/rMeQ4K99rWvNRWu2PwE0LFPM+1zkq1IdMH/TCWb8IcZQ4U0xMZ2w26DyMCPJjOfY2IMzocdz7iez/0Ff5/krsXoD8Wcxs8DfGeh1SZkLXOcyznAzliDtEklahgPM6kIZI5l1ohUQsOW34I0yzWGGMf4M7nAdUsl6yBIqPwg0ZDnGfsQxhDOyHxdKHA5vKr3Xa5YYjxZTe5/SctNDUXC+u2n7lVXMGgs6J92tOrgYJ/+ZOelS39dEqH8/Y6XONh7fud3fsdUp5EM8pOf/MQkT+VLjhaxOCjKTy0j4Jj/67/+q3HK3//+9+vP/uzPcmY1DY8P6r++/0mV1ZQpHoupuq5Su7bs1fYZAu92SSRZzLyOB5s2GyxBATZqMsTJzMj5Od3PSP0puux1F0k16U3c5gIyeQgc5hUonCfQQ6MjSHAnpL5Qh2l2DNwOjzaVbVedr2FOVRrxxKiiiTNKJCJyOWrkcqxe9A0NAwvCwM5cwmiCQLPLsYFtZDEWMbIg0jINbKopyMxJJcTiiR7F4icVDke1fkOzVq60gtkux0VyOmbu1YAhhxOTNt5D+9MrKcwHrpF8+Y81nKfU88tEIn5O0cRh6xeLzZDLQQZ7Snq8QZkeezSx6GWHzFcy1nDCcAhz91+IKOHwGGKSIDv3GsIop8PV+7zUk9p7hmbDJdKG105TqcHrkYXIxM2GZIvEwzozdlxj0RGVuEu1tmSLvM5JgiQSD6l17KBGY4NyJTzqOjSuDau2TtGxfuGRr2jXxkwHyyE1/Ip1bD2t0j1fsDiBREKPHD2rXb/6DlXuvmqiTJy1LKvMwWCPdOaQ+Zz4up063NpusjByOXSMdVsijXtA9hyBH74H2Qr+xjqV11yO9EtUn2SeV+kOqTQlkyn8oJQYITKe9rqEo0ZxrVJM/aZCA0LDMUOvl7lgONKn0eigIT6qvY0zVN1cmGD83PbAjzRKn5YknHLolsaV+n87rf4XnzrxpJ4daE+rkPmNtXt0Q31++1M4HtajPZOEho09lZeo1jcNKQ0BNnBfxpMOqZrKnmmCYFRgDZ6Y7JVTmlvWJBxv13jshbTn/M4t8rnWTlyfL5/9mSIpvXIgNzaUNuvGhsWphPifliP6asuRCSpvb2W9/mzXVWnEhj3v6eXEemLLdUKWfulLX5q5r1IRRbwEQHY61UxkLyOjgI2QC5AYADuPdSBfiR6yjdkbsUHsfZEgIIFBbHu7yXjOig2qMuipEUsGV6hibLpVcs+P1jfng91L0sJSyNR1BM/pOBKUSTT712hj6falD3DNEYwRAj9cW4A9j+1KADk1Ocom+km64e80EE8FhBqfwXhJ9TH4bCqDsIkZi4VUBzD+6EtAtv18Aj+VOZU1AXCWjeqz9pFbQOAz3HXXXYasnKmnDNeR+AHvwe7NrORaTqCijAS8q6++Oo14Ye3jvuXbIDxTihYyh/UNwmGmqjPuJ8RKrliG7Y/ji/MgicomjRkP/I11aqEUAYpYeHyz9YT+9fjzU/Q/vn7lrVpTsvDk5bQYfVyKdmUkEKySSopVzpmwK1iZ0zQR//d//3f927/9m0kO+cAHPrBgFXVFFIYiqbFMQJkhTjgGEk54PuWswdiYTg8d0/n2Nq2qX61VpRtVWpKn4R8dtYIeNN9zuIyxgt4owUC7x8H0X95rSU6Rrembv9JtDAaQr8ExH6D0+/G+R0zwyQ7abCzdpEpvpSrcVbPuoZFIZvgi2xOK0/8kPrF5uLRKXlfhVSjTf19CwfiIEomYkZnh6wnykDlH8BXDjiwlgrIYaXa1BYsx70VOCocURw9n1+7fQMA3U8qD8YJTYTti8cR5xRKW5NfJk+fV2dGnfRdvVlnJ5XI6JgN3GGoYb5kOHE4Mhl9aw0CqMkJPS/FkIJ2qFN/FVmPcPMEmlNq8MhOx+AnFE2cmxfINEWM5Xak4frxLLucV86IHnBPhLmn0qDUvfU1S6TYFQ2H96Ec/MvOB+0KpMg+MXI6RYB7BdoIFwJ6/U8gMiI/IuOT2SU6XVdnRdr80ei550j5p1a3TSsglhOzT6Yxny+RQbtIoFUORLnWFjhun4szBLt129a+oKkX+ifHGWKt2nlBt6WTviymkBujvUPDw43ri+SPafs0tqt916YTcFM5Gvk4A141qnlxl4oBrTOUHGse8LrWqqSAwIfto8JxaJeOUam6W3ClZ77EuKfq0dc5GDo2XrZAcSHAt36y4CxGxRELX3fu9KQ7HFTUN+ueLLCd2PBbRF848p/0DHfI4nbqjcZNe2bw57yDUQLhPzw5YAUIb7DHrSjdpXenU9QTim941DvnkCLZIowcm3qWyiyX/NIGHyKjU8hMpZmdgJaSGy6Wq7IGSkcjjiild2oXvrfBcb36OJ+L6/Jk7p7xvVaBeL2vKva7OFzqDo3rHU3dPef5Dmy/WzY1rcgYzsKNYL7/whS/oi1/8onE+COZe6IHDIoqYDbDv8CtIlnrb295mKjXyyczmfTjx7Kfsg9PtkzN9Dnst1RF5NVbGFg8mewn5G6R56mPHOWATk125lBVcVLSS/OFz+U11+GxgJSBBNmf0JFlE4C+Q+c79xI9gzYXEwG/An6AywvYnqXLGzmccvOpVrzI+BPY5SXX4u6k2m91IPNs4gdyAoCBLPpOsJjseIiSb/TeTLzAbQLBgt07bty/Pz6Gv3PXXX7/olQ/YzPjdVMdDGtlybPZx2L0JbSlWkoBmuw4sBmzpMvxT5rk9N7AL8D95Hj8unzkDyQCpYfe0YNyxbhYiWcZn2M3JZ5LIYm4QMC3ixYPPnT6sL5w5anyNVHz2khu0s9JSKFgyUPVNT404ko9sJdVS6ZUFqWG81MA6yN7GHsfjPe95j9mH8DPsStcilg7FuvxlAIKWNKFBc/Tb3/72RKPkTMQTQY3HTpn/3Y4K+V3r9fj395vNtmRzjXoTzxkDkcz0bA6LaQI+Mizv0LMK9p/QkZPnFXd4pepL5C2tsZo051v2SvBznntoEDjEKCbwvpg4OXJCEQLJKfrxLWNndUPZLbMmM4ajhxWKnze/ux0B+ZxkrU2+JqZzSiQ2yTFPm0csEdXZ0Wc0GrPkbVwJr849HdSW3etUubFcbodXdb61RlrGJjoI0mLkYUyRVYfhhdwX2S2UfzNeGFvZFupMLUGHapPLSVQbN67Q0SOtikTIeU4nvAg2QeBRCp6a5YOjMsWY59r4yKxKNkefRVY6ZMB0RqVpfG1uzKTBQZ2GAwPEkAYxHThwSoFAs7ZsX0hCo0caeMgKYHM84wNSuF3+6pv1ute9znpJODyRBUmmEfeHa0Z2FVJIOY30oS7p6f+VxoesvhTbb5bWXSytvEkKD0ixiOSvziNwQA8fjB9kwQBZjvlVzYxG+3Ws91GdPNIip9Oh7Vdt0oDrpCoTl5jjxqjHSTVzPxiQBh+z7ggxfX7wr06rIDnRO6JuR4OuevsrjJMCQYdziVNYiIPPa3F2yArMFeTgeZygCUIrEpLcnqxN5Kf/MqdUdZ009LSVhe8MSOV70wkN4GqQHFdK8fYksbFCcuYvufBiwViUyp9+oz27obRWHsi4DHQGx/Rgz3mzZl9Tu0IrAoVl8/LZF1XV6sBgX5rTcVnNZPPHgMujd2/MndE8E+JZWjRxvF7n1PUsGj+viCGHLQk3j2+n3L6XW0QY0iVZ3pOG/kNJQiPlS7ufkio3Zx2viYzqEftZG1TnNPqq1RUaMMdso3me9/5c6A6NZ71nXfTGyQHWEB6slW9605uMBB0yVOi5f/rTn17WAZkiiphvEJR897vfbTShv/GNbxQUNCNgTbUidjnziMQnAp67d2ff97FR8EFITjl58qTJ6LaTZtjf2Z/zymikwrNkfrPAOS5sW85jqbMqA64S85gtIvF+jUYPKKGwqdosde+UZ5ZStHPx17Db8DdTrye2PLYYY4CqYX7ndfwOqcDvSFNBPtgJJZlVF9MRTvi2+A4k82SSGthz3/rWt0wwm2og20/Bv5lOCmi2sG1PzmW2Ywr7E7KPBLOlkHLiOvFgjkI8sW9yb7lePIfcF4k8M/bCWWJwrBw344I5bkvapq6D+cpZ22MUUs6uZuN3ZJKz9ReZDhB0kBW5GooD1tSJ3n9FvKhwcVW9/jtxZOJ3Vokyt0frSxdPvjUn6JNYdkOS1MDXJMm5mPgzHdhTqMxjzSc5+DOf+Yz+67/+yzz3+c9/3hD2RSwdipUaSwg2TpxtMqg++clPGp22XIgnwhqKPKqEadJrBRfcjmoFHBeZnwlCYwjazkeqccSmSuYCDkelq1ejHfsV8Hu1beMKy3ikrLvuOqn1finYL7kD0oqrpYoZ9NrRFefhnnuzHILdGCGZhshi4On+J9QXniqrc2vDHbPKgBqJHtd47Ezac24SzjLsVb+TAOz8GIrt40fUHTqjtrMdGugb0vhoSLsu2i5fGc6CdQ7IZ20tv9rIyQDIDQxxHAM7U4TzxcCaKbMkGxKJ4aSU07h6usM6ejim3bsvm2iEZr0moQceeMAYhxh6VBbg7JANeNFFF+X5PQkj5RVNtJjqF5ea5HFulSNHkBmnnM/ORvTFE22K2fJTE3DLmfDq4KGDGhkZ1549e1VaepUcWsCmUINPWk2Amdv2mCPIWrpTKplDOTqyOvd9xmqQnRrovPwNUl1KSTQOX3eLFI9Kdaskb/YsTiuwyWfhIJYi1jXjIUBiPXHkF4q4B7Vx22p5fZPXcWvZTXI7vWnEE6RgMPKEok4re8QV8yjguVIOp984kGRLkS2WWs2FNrBddVTwJYrFzNjHaZu2/L6vXbrnq9Jwn0VqXPlqaUvREVkIdASH9akTj2g0ZhGaDb4yvXfT1Sqj0iiJEyMD+v3nHlQQmRMCDE6X/m7PNdpeUdge0hMK6o8OPKpDwzRwl2kU/uGte+etkd+hwWM6O3ZSfpdjQt4vlpCuqbtBJUi+JRFPjCgUfzTj3Q75nFfJ6ciTrGl/QBqmD1AGNr4xa6+cUOy0gvETac95HasVcE9mIo5Gg7q780n1hq2Kjq1lq3V13W45F8H56Q8H9fYnf6ZoRpbbx7dfoStr88uKIrBKlu5f/dVfGbmHr33ta0aSoogiXuxgX/z1X/91E8yjaqnQhCFb/ofgNEkwthRj5ufwN/ZwkgPsQC9JANh3ywEcD8FOrsOFXq0VT4Q0GKEXQmqQ3qEKD5J88yPRlQskQ0FW4UviY04XiIVEYt0lIE5gnCoNu89AvlJm043Lp59+2nwGBEaqv8saz5rPd/E845DgE8HphZDxgQiALJtOyi0b8Lm4JgS7sX0v9HG5VGAs4svaktX4lrOtmAAQTIxzqjNscg3/FLJntk3leS/kFclnhfYuKeLCx/faTuufjx1QJBFXrdev/2/PldqeolJQxIUL9jMqN0gY+ed//mejuPN3f/d3SyJtWUSR1FgyYGS9+c1vNgFdnGyCdNMhGGvReIymvOkod18htzM748smz2YPqUFmlZH26X5IGmud+uJxhxQNpmvrbXq15M8SICLA0PKEdO4p6+eyBmn7KyRfWX4SLKOdSb3veiVcfpMNhiGSq0JloXF8+KjOjNEE2YZDpa5SXQ3RMwv0hR8xTcpTgbla6k4VHymVz3nlvBmy973wXZ09d0prN640/VWs7LjMVtAONfjWqzmweUrQH6eD92B8YfhhaKMFCvGAIcbCjbGHc4ijQLYKDxxYXoMTkekw4nhg7POZOME4w7weMg/9c0rOqRTB2Cuk2VI03qJIIn0uTCfnxfint0Y2STeCjLHEUSWUlGGSV27HPu1/7oRWrao1pdhSYKJJ7oJh8HEp3DY1S8JdK1VZUjCzAlUaD30h/TnIn/WXSdtusH4Pj0v3fEka6LB+h9C4+Tek6sIykrKV1DO2jHRWc1ADUaTl0kekTWpAKuCQMAZDsRMKJ9JlrtyOBgVce83nke2X6sSavhqPPDKheTsb8BmM1Zx6xtGI9K2/k4Kj1ppn4xXvkpoK7P0DcRQatLJRfUusqbpM8U/HHtD58SHFk+OFmq9LqlfpTWsmic+PPPeQnh/smag1gM7aXFalf7/4xonXhOMxnR4dlt/p1pqS0pzrLfd/MBKW1+lSyTzLkjw3cEgnR87K7UjI7XQonkgoFJdub7xB5Z7SjCqNlF5VSXgcu+V25jkX+w9blRkTQCO3TFr3y1kzsDjvEL2Q4q1W9YhjhfyuLVMIYqOXHgvJ7XTJO09SMDYImLIHsF+geZ5JTN7X1ap/Pv7MRCXNLzVv0Ds37M5r74RQJXuTz+TzqdSgGvaP/uiP9LGPfazYRLyIFyWwedB6/tu//VtD5iE7Nd/VCawJzF3mGHbScg3MkqGNjVpIc+DljHC8S6PR56Y8X+LaIZ9rYWR7IQhIjCPxAzu/kKx9pI0YJ9j/EA74ALZcE0QTSVT4fvgQECYAX5UsebuHBklvVIynNg4HSAPhlzC2GX9UfTAG9+/fb3xKXs9n4n9kvnc+YZMThZB4VE7RdHapq4YuZNiSTRPxjWnAGJqptxYEFWM1s0cKPR/5jrkEKrFDGIfFYOdLE5F43DQNr/b6FiUhqIj0+AAEJXvITH2DCgFxMfYa/icmwc/0RcbX+PrXv24SzItYXBTlp5ZgglGu9OEPf1i///u/r0984hMzNjwjczmW6Dby6plyFmTHS+lkAFkGtj49G3GaAeoiSyBdbseEg6JZ5ByG26aSGlRmHPyBNJjSwHmkWzr8E+miN8wczDv1U2nM0v9PONw6OL5GG3dfsaTZCxvKNmkwMqD+SJ/5nebGe6ryqxrIBkeWaUWJuJVZFZdD5fI698yLAwgpgEEdaCzVxVchCZBF6yQFMfpUZIDFGGMOg8/ORMERweHA0cAZgYTDyaCShgWczDzGGA4HDi3G2v3332+CUva9xFi3Pw/DE+kCnG3A57Lw2z0iCkE00T71vERAPjupwWZGyaDdoDAV/O52bFMigbQS14ZeFU75vD75fdVyaJEatPlWWaRGJmaSJ+t6XuqltDUhVW+WGi9KD156/NmJxdaDUqBGWrtbOnCvNNg5+fdIUHr0f6VXvGfWp8O1RiOY6gmM+LHogAaiNnFkocxdbwgNgPOKg4rjGU1MrZriOfv+ZZbo8xzfAeFGpuhsjpVMi2l7CQ10SuMj6c8R+D1/ojBSY7xPOnmnFEmut9WbpLU3FC5ldYHDCqaPy+lwpzWTt9EVGpkgNAA/nw8OZrxmLE08iZ+7UuSKWsZG9DtPPaTzQetaX1/XpL/fe4V8WSQeGENV3oWRV2j01+vEyBlFaGsTM+J2KnH5VUpFZNoxeLMu3+b56UCwf+CENHLe6jeEbMtYcq6Zqssbc5aUm7nj2mQe04HXlbgXJvOIOc8+xv5D8CCzmeuNDau1s7JWrWPDqvUGtDaPsn32KDJ5WVeQR2G8keRBgPe9732vyaa6++67jeOxnJudFlFEoSBwTMIUySQ480jLzCeYSwSSIQtIRFjOVU/YmfhDLxZCA+Sqjs2nana2/Qns5tyZsrP5Ah+XygSqGezs9wcffNDYbYwj/FQS8Eg8wm/AlmMc2/06qLYgKAyBlpoARTIW67vdwJrEFHu/gszA/pxtdn0h4JjYuwohNbBVlz2hEQ1J3QetXl0ldVLttmUjT4MML/ed+z+fn5ltrYDkoEJ8tt/FGMf3LRIaL13Qj6/WV8zeX2ywF7DOYgexl7Fn8Jgr2IsgSiHoIVSZ39hDtBEgeQo/5h//8R/1rne9a1kme7xYUSQ1FhEEjt/5zncaY+7HP/6xydKYCVaG5AHF1TeRK+5yWPIVlvz+IcUTBIesoARMIWBCZdXnrNgujZ61GvFZ3yCVb5WGn838ZquhcCZOP5xOaNivHaG5bXh6Kaqu56SxbvNjLBbXwZOntW2TU15/MmN8gcA1PDl6RseGTyqWiJkg076q3fIkM05dDpcuqb5cw9Eh0xi1zF0ut3P2U6PEtV5DUes+2Ch1b5HPiVxGXI55aPbLOUEq8D+OQDg+ppOjjydJi0m5qWgCEsGOlCVU5p5aeZOteR5jB0fmpptuMgsyVRu2NBh/I/s+s5k7x2EHsjMzuchiStUUZQOYrX6sQ84ssb/pN43y8nLj3OaqBrJkwHxpTS0XwxmagH+lFE4SG5acvvVPyTTl0uefkHpeSN7ehNT5jOWErEoxvAMV0spdUtsL1u92+c5gn/Tk962G4QTsU6sP+HnI7psxOzBm2OhtI77EXaW1JZeoK3TCjNFSV62a/FvSAps4tRgbFgGY8XlyG6c4lzwd8mIEMQttBMm95nupRppWmiAbOcSF9BQQCOe6nrrbathuo/+E5Sw25Neb5MWAYGxczw8+pbGYVc3W6FupreU706oDarwl6gwOp7RTd6jem05YbS2vVmdwPK2aY2v5ZL+EP3zuCXWmkBwP9nTov04f1Xs35V8VNh9o8tfroqqdOjBwWHHFVe4u1ZW1F5t+FalwqlZO1Zi93k48cKpOzoy+RFPQtV/q3j+5BrK/rL1V8gQkT3n2fXwaBGPnTGUolRs+V5MCzg1zMspbW1uNA8BcM9KB0aiZrzYByZyHcGC9IJOKQFZmxVS9r8Q88gHrAN9BlpSdvWmvRzzYt3jNBz7wAXMcyH++4hWvmPX5FVHEcsFPfvIT/cZv/IZe85rXmIzB+ZbbgXwkwYVs/UKqa5cCrDmsA+ztLyYgO+xyVCqWmCT5nSqd954aJJkQyGWNnEsfomy+AsBHwD6E0GDNR6bKDvKzN2Ta3xwDxIbd6D0VkC2pr2efsaXTFgPsL7a0ZD6gGqDQZK5FB9Kfx74vhSzZSZM8hcrCGpJwlj5IB2kL0ZYvSLabqbcFFUnZEixtfzZbYlw+xwmpMdGTr48b71QAAQAASURBVIgiipg3oPwBmc3cBXaiLT6EPVchHtjLiIuSfIuqw0zJ5DMlEpPIe911k2oukPX2mv4Xf/EXuvXWW01yyb333qvPfvazS6ZE81JDkdRYJNBk6g1veIORmYJ4mG5jTUVcY4rJIgJSpfYx/QLupDGlM0okkMmpNBku2WR2JoCG94qXSyOnLAkof5Pkb5QG2qUxsrWTEVX0tyvTnQEWi6OP36dDJ87oih3rtLYpJcDIwc0UPAmiWZ5QOBLV0TMd2rlxpVzOsFXB4ZpfSYtUnBs/r4NDk42aOoKdero/ritrJzVQuY4VnsLYW9uIzTRyfK56VTouUTBmkT8+Z5N5zsLcCQ2C84wnnABLHonvLNXmsqs1EOlQIhFTuafeBP9Pjz6jcNwK7DX5N6nKm5+MiS0vBajYyKdkj0WdjQSpJwxDrgsOkb15cL0iiRaF4/TCSMgTa5bXuTFnL4yc3+Nco3B8IP05x/THh2NrS1BN14QQEPDCWF70LKqKy6XQOSlExrVT8m+QPNP0B+g5ZC0GZvw5rJ/7DqeTGmDPHVJlk3ToXot4jKRoMR9/QipvlHpaU4gNyJT5b0xd5q4zj2yA4CJQQnbd+k3rNR6zqqZsQs7n3KSIw2GI4Vzvzwuc4xMPSE8+pLjDoYNN67XlVa+dWU6holZat1M6czDZvZwmOSXSpn2FOYnJvgRpGLUq1/LC4Emp61lr7S5dKTVfmbVXwnLGoaFnNUbj6yQ6Q20qcZdqTcmkBOMbVu3Vf5x61MhHgTK3V69akV6J9d6Ne3R2bFinR61rujJQqg9utirskHg6Mkxz60nw84EBe1wVhmg8rnu62tUVHNfOympdXF1YQGJj2VptKF1jSPVchDnrpde5T7FEm9n3CVS5HCumd6Kpuuq2pUiSZwtROXhKWlm4HFswdl6jsckeQ+OxU6ZStNS9ecr1eH7wnAYi42r2V2preWPO4yQQRTa3vZ7aJduA/cFussocZp9hncZRmU31Ju/jszKrPTJBsJfmfiSWvPGNbzSNlJHrma2jU0QRSwls849//OMmQ5DHdP355oLz58+bIPRCSfmgXU+yDn7Mq1/96jkRqcxlqotfbMBeLnfjX5w2MrdOR0B+1/p5SZZKTfQgSJR3Y/dZIDUBhcBvTunPFFBNS1IgvTqALWubiWwVvQsNEqcgK8jWnQ4kiEG4kfy1rNF3wpJJTXvuuFUN7p89yTVfYA2CLMiXHIJYo8JsutgLtkiuim/mQaGEBiQKCRtFQqOIIhYG9MphD0gl3pGzZZ+A6Ga+M+9teXSqrZA/n+36C4ECKTLTvohvgZ/z1re+1fSe+uY3v1kQCVvE7FAkNRYYGIif+tSnTENwnI6PfvSjBRqJllxPKthXvU4y8Sc32HBkQI89+lx+peZIUFXuTH9u3a1WxudYj+QtlRr2WdIVSWaSQAOLxw7nkK7etV4r660F5FhLl/qHx6TG7Rq7/wEz2b3jXdJot/U5NRsnyQ5vmcbGwzrd1q1dm1ZaBgIBuTlUReSD8+Mp0jrJ0E9nqNtUZWRmy+YDgjzRxDHFZZEWzkSTkTBKdSq8zhrzmG9gJFHyRnA+02inAbjPWarO4En1hc+r3F2nLWVXK66YqdpwztLpwZGdKUhkg0xbHoCqjVQCIZI4p1DcKg8H4cQZJeIJo+FeCFyORiPfRW+NhOJyO5rlckzvvJIRxjlABtmGKU5basPp4cigTrWclCvgnJfyxFnLUPGYCYaAiGfJmKISI6XZOGCMr7tYOvhQepWA3SB8z01SxylpPBlwZ1xd8ctaChCIIBOxxHWZIvE2ky3ucTTI7awXcRQMFDt7MNPBwMFBbsIm+rIhfv9PFfzG5zUWial1ZFS7ag/Is2G9tG+GsnK+68Y3SS88bBFAJRXSnhukQAFyVxC3zEGCzpMfbGXU54ORc1Lbg5O/D52WYiFp7W26UMCaS0VcJgbD/Si/TWBtabX+cOtNOjbcbfa5HRWNKsmoAkQu6lP7btSJkUEzTjaWVZqeGADN2kqPVwMRuyIROtmhep/f9GZA27bK483LSUUL911PPazH+ronhBt/f8tOvXPDzEGYqVJ37hkDVu4Z1rI0GNInM0M0YZFes0Ao3pb1uVJNkhqxRFxfOvuozo71meoYKmWurFmvlzdn148lizbV5rEbzELO0yOHIALBs9QAbT4yDdhWBIdYE+x9hoBoph72dCD4iyTK61//erNf/c///M+8au4WUcRCg6STN73pTWYeIYGzUBWmyPsg9zKXrP1cgUQyH2lqTjAR0sS2N7HR2O9ZP/huEm1mavbLcZIVOZ0dcKEDXyPgnl4ycDZgTWVNxi62k5oWGththZBkdnYsEri5klyWAlSP4CMzB1ObpacS+lxf/p6a4btsEQtmkapOSlItA1C1DQlaSMXLTEkLrDsEQ8nshqRKBVWeVKmxvuZKjDOJe5GIue8EVrFjZiK5iiiiiNmDOZfpL0B24xcgK82azLy3YzqsGXaMaiZgU2F32AoRJE0x9/ON4XIcP/3pT/XXf/3XJmZHA3ESqIpyVAuHIqmxgKB89x3veIfRtb3zzjvTHPd84VSZHPIpoXRD4uiRFo0Oj5nJ0dMzqFtv3mGCB7N2OJBiarpsyvEffOYJtRx/QVu2bNbL3/deOdoPSGceMX8/dd6SqLnilldKG29UOBLRMz/9qlx9R7V9XbNxVsbd1Srb91qVlldIDRfp6GMPqNzr1vmuAa1srJVWX7/gpayuLAsQuuazbf4cS5xSfKKxNKHldkUTLnkcCydXFI4P69jZZ9XR3qmrL785axbSSLTfVGbYCIVpqB3SutLZ9wcBjLFCF2GIEBr1pb4vEj8/8fPQ0KieeOywbrnNXTCpYRMbLldjQe8h8zdTngiNRTKn+j1dahk8rc5zXdp9xU6dGzujVSXL2Bg1zGa5VRo+cY15rjL3fFqzSzr6yNTnCNC/4t1S21EpFpWaNkhl85OJWUi5Ns6tbXggr+ByTSWWCDhiwOB4XHbZZWnZ3LfecouOHztqqj24z3wvWVJkirOWAfdXPy/f6Ii8TodGolEdHRjSznt+LMdMpAYgYL5nDk3bIZca9kgtT1h+ootqHAjkPDXPB09nOJkJabRNikUWtNJtPsGa65JLMdNfaPJZT5a+GtXegK6onT7A7KZvT0X2sfrRbXv1x88/aQgOeL5St9tUWVzx8x9qNBZVjderf7roCpW7vTo2PKQSl0tX1NarIsPx/eH5FkNoKOXK/9Oxg/qlFavVRLXOUoL7HqiTxnvTgw9ls20Ym5jxuRcGzxtCA9jSX4/1ndalNWtVX0Dje4IGGPoEAOxKKUh7HA57zegOjenOjjMai0Z1cXWDGodDJvsaWZnHH3/c7DEQ1VR/EITD6ShUt5ogBZm/H/zgB00m1Re/+EW96lWvKugziihiKfCjH/3I9If51V/9Vf3Lv/zLvPWmYx5RsUowAPKRIDd7KP/Pl0OOf4DsENntBA6oZE+Vl2ROE2AnAGwHEdG8h/wgEYWAAfs7awEBDI4LiUrsCAKLrC+FNLR+qcPugwapNN/E1XSgd8ZsAr/Yebn6uXAuEGV2BfBiIZNwIyiGrYoUImOcsc71XewqklmhtHmqPUASYmDhGq4XAnwFEvwKwUwSYQQrWW+wKVg/UquHUEZgDWT94X5CprBGESSlMoyfeT9rJjYIayjEG0TtYs6nIpYnhiIRDUcjavIH0hKSi5g7stkkPMfajy2QSkKw3+Sq0sB+MP0Vq6qMrYE/ws/YKcRX8VUKld3ku0lox9f5tV/7NSNHRZX4kiXOvshRJDUWCGQMvva1r52QmyqkgdiUjBzXxRqPPaeEkOxwyeuoUnlplwb7h82Eufji6+T3N8vn65goyyVQSz+EQoGDwAQmA8Yb7FXo0C9064YmVbvP6OSdn1ZfzcXSUI3Ud1ZVFXXactXLpHrLkPMmwrqyKaRQzUodb+2S2+VSwDemjqfu0Zi/yWRP7XrFe+UZb1dH+3n1VKxQXcWaCWPDburHNZtPrC9Zq/Pj7Wnm2YbSdbN2zuLqyvHcwpAaodigTvU/pKOnT+jiK3aoM/S06rVbZe50trnfNJpOz6wZjHQqloiaSo3ZgKy32Sy+ZA9Ore6wrveJ420aHhrV2nVN6u8bUkVh3MS8gsqm508d0OneE/L4PNp+iXUPT44eUa2vXgGXpcm+LLHh5dLxHyQzqkhDQg5pGm34nTdYmd1nkvr7Gy6Wticlarx+af38NhRF4sXWyp8JOARkXeMszAQCGBgbafq37YflPH6ftkZD6o549cgv+uQpr1WJ369Va9aY5n84mOV11XKi24dOfzCkZ7r6tTUUzNLFYwFAo+uzz1gNkZijsbhUu9qqaMsHuarKlouBzDgcfEIK9UiQFBV7JP9qCbm5OEFwjxzuDdpQtlXHRw4ZgsNwOw6n1pTMv+75y5tXG0mqB3raFXC6tbuyWm9/6mEjTQUGwmH95hMPaYz7kAR/es+mrfqjrfT4sK5r2/iY3A6HoikOMT+1j48vPakB1twstdyTJDYcUv0eqWp2Wbw+50pFY4NTnkvFcDSYvHfW9YgEQwoOj6qzrk91DWVT9lVsCQI6uZrMpgYeqQqFqACdwVG9/9l7dfbhJ+StLNcXgiG9eds+XdbWZuwFiEvei7QDv5MZafftKHRvZx1B95aScXRwITj+7M/+7MIIPhXxkgP75Sc+8QnTN+Mzn/mMcZbnEwTl7AAgTj3yney3BPSw45nT7NWzyebHzreDkXwH+zlkIp9JMBEfxAZzPDUrmuPg0dbWZj6DecscheBkzuM7kGVtNzNPDUCwBmGX8n1Ffeup4NovNqFhZ7TnY/elgrGIfZltneczURbg3hOYWkoQ0GYME0hjThFku2AC3OXN0qqrpHOPWRaPyydtuH3ZyJ0S+yikbwprRj7X3oqpXGwI1UxJNHxhklORr4FU47VUhLF+sb4wHm2fhDUIYpjxfcHc8yLmHYyDvzrygj598pixmFcHSvTly6/RZhJ9i8gLzCHIQvbtzCopfoc8zCZDmK06K9Omp5KDJFzmLTECkiWI35I4kRrDYl+h8gMSczbz+YYbbjCxYKrDScj83//934L3vSJmRpHUWAB89atf1e/+7u/q93//943jMVfH2OUoU5n7GtMrwbRDdTi0fu16rVs7KofR7Kg2z7ERs8lDbMxGS5YJB6tJALC0pETf+/sPaGVtqU6c6zbVDutX1Glj3Zgc63ZI3sul2g3pgbbwiPnP5/Vo18ZkIMTh1NrmVdKqy0yWwwMPP2rIlsaqDcYoCIyOGoOP48c4wFmbb9T6qnV17eU6MXLaNM5u8jdqY+lcsvCz3c+FC370hU7ouacO65KrkAyzHM2+8PEppEb+bepmBuOAMmkCTLOVM7AdjrFoh4ajLXI5Qjr6/EmVV5Ro3yVbND4e0tnj0tolJDVAdVOFmsunOudj0dHlTWrQOHnHm6XIqDUPITVmqjTYe5v1WASsWrXKBBFyZcpBeOA4sLETzLQ1L2cCaxxryYRBMNAmHf7ZxN/rPRHVjx3R2WfOqL2nT0drmuS68uUqa1yhY5WNSpw6YybLQCik29Y0y3Fp4b0HZoXzB6yqitSZ2v6CtO5qy1GEcOrYLw23W43Jmy+WAikSdlWbpYGMzLTKDQsu35cXCID1I29m9U0SfXwGHpdqIHutKgcTcA+3q9l3hcrcK0z1WSTuV5V324LNsz1VNeYB/qfllJGesgFJEYynr5oMv0+eOKqt5RX6lZUW4b6lvDKN0ACQHGtLC5AeW0h4SqWNr7Z6tjAWZiGpaMPnhFCIazzWonA4pLHBEnUNRTUy8uQEWdAbGlJXX4vcPo/cPq96T7epbk2zxtp79eTZTvMajHYbZNNiW/B+gmbTNS8mqcHWs/5O2wmNx2IKrGqWw+VSRXODfuGI6X1XXTUl043vTO0DNVsQHIboJvMdO+prX/vai1rGpogLDzjVjFOygwnmL0TDbuZTpkQOTj/NLwkAkOxSqGPPnk1lLJVYBHr5DORc+J25xuexPuTT1yaz+TREB3YBAQquD9cEEpU1h+tE1jQEKGtPIQ2dXyrgnpJ8spjBV2xD7s1spJjwHTdt2pSz3yB7DuOI182G5J5PMG9mkkxbtqjfJdVslaJBK/lmDrbFQmCmuQyxSeUWvgI+bbaG9bnel2tdte0b1hReR0CVJCvWFqNOMT4+UTVGdQd+UBEvXXy7rUWfOjkpu30+OK7fePIRPXzTy0wV+UsVzF3Wa+YJD5sUBOzVrJs8iA0QQ+U5Kt2wIyAR7b2KecrzJFxgSxSaFM3rITyRoZ1OVorvz0vefxqQ3P6Tn/zEJExRLbIQCSkvdSyDaMiLB0zKD3/4w4bU+MY3vqFXvGKarOlZILVng8NRLYfSy0AhBO6//34TiCYbCoeHzZygNButLcWSLVuEzBc2fzIVQWRsSNfsWqOG6nK5kEkB2A89x6Se49YvlaulHa+aNHRoHpapGU8j09K6iSwHFg5YUBYHMrPIXoFEmZCb6O5eECO0zldrHvMBl2Odoonn055zOxZOqqi9o12hUFhHXjiljVtWK1DiVzwRSevx0Rk8peEIcmDpRl65u77gKo3e3h4deOE5XXn51bOSM2AeMJZwWpvWlKg3fECRSFQHnjmmjeubtHZls5zyqLJ0kyLBdi01Aq7sQTZ/jueXnwzVMgmuZoA1hsyGKQiPqKPlpE629ZjsBYiNVF38XGBdYExBghL8mEDvGWsNYq0BY0GprU8tnd26dtMqOSBzOp6VrrtR+vD/lb79BXU/cI9i9Pu+9Zek22bfkLQgGEIjx/OQGmfus5ozGjikgbPSjtdJ/mSlVEmDtOY2qec5K4CNxFB9no3HOo9bVSLxqNS4RVp36fxWeEBiRPrTnzOfbxMagNz+mELxp+VxRuVx8vcxuXSGMiItNMrd6cGybDl++Mguh/RYb/cEqXF74wq9ftU6fevcmQlC4292X6oab7q0Sfv4mH7W2apoPKHr65sNGbKomIcMSvbdRLhOTz5oNdRE63zlymojxWDvyWRSu1uqdO+5Q4qGI1q7b7veuP4K7axMVlh0dhoZE8gJ9gI78GDLwU1HavBa27Gg7wkjpnRVswYOHlNJc4OiibhCsahKMu7lfIJABEHW3/qt3zKJIt/+9renyBYWUcRSgHH5ute9zozH73znO4tecYD/wP7LeoCvgV9BkhLznHnN3wkeZnP+CWATQLCDfPgDkCTZmvLOBqwbZFYj98saRVCbpp4keNlrDtmWVIQWpR/SQWNw7imVDRBECw0y2LkH9GCcDeysWexAW26QwBTjD5LE3kPwhSFs8g1mF5FD4nKZypsyhghyZs5nO7nTlqljrXrlK1854+cRZGXNwA6Zqc8L6wifnW1s8X4I2+K4K+Kx3h6ThGMnVPF/y9ioukJBI0X1UgTrP3OUPYd5Zich2HOHeCQP7AsSlfBFbPB3CAzmPK+xq7OJH1BpUWj8EMKEKg9ipqnfs1Dg+/7f//t/pgqEqg2qCv/+7/8+ZyV7EYWhSGrMEzD00YRlElKaON8SSvmA72aBIOMGooBJgkGHkwETyqTF4EslNFgE2HxZEFJ15jyBcjU3NliNaEFaRkTy58FWi+CoT5Zokl288Rbp5M8nA4yNu6SqyYA/CxGLGRkONPvNZEbt7O5cpWTLAfRzoDIjlkDuKSGXaVS9cOUG61ZtUXWjV2dPnTeyTYGSgPyuSYOrPXhc3SEr4GbDKZcqPY1aWVKYnmxv6KxOhfarM35W56JerY7tk9+V3emMxoMaip5XPBFVqbtOAZeVEc24u+qWnXro8bt0PuRX5/lueT0ubd+1QSWlfg1H4lpZYmnhlpQMmnE7X1rQs0GNt14NvmZ1hSYJlrUlm1TqXp5kwUKDxpx21gRBSNYG9CDTwHowlAxi09MghxHBfKfiB3KVexxpeUpHHvye+ds1m1ZJ/StNVh0EbD4OBw4M5d9pRgsOV+r6NGrJcTVVlOq5c12qKyvRKtajkX6polZ68zs1dPnNVkPgPLJC5w1Vq6XOQylPOKRAlVVhQwP3CUJDyWqHmNRzRFqVIuNWtsJ6FIKuk9JzP5z8fahTioalzfNZoZIlwyXLmIgRNJctL5I08sUasl5Ox8KSiDc3NmtTWblOjVBRmMhaW8chc1TVnknCgrH2/3ZdrDev2WAckc1lFVoRSD/WUyND+u2n7lcwFjOf8bkzR/QPe6/SVbVLXIY2C9ha5bmCjawHr9xwia5ZtV1D0aBqvaUqdU9eLxx95j17e6GGOu8hOQPDf2dFrR7smWxcTlPyVYGyBSU0bBAshsz4h3/4BxO05f/f+Z3fKTb4K2JJgKNOVh9JUzjEVIEvRfY5vgRzG/+GgAQ+Bf4O+vIkK7GnpvoRHDcyURCd2PSpWcuzleSdCRAqdrPfzGAn2ZbYm0WkA3KJewUhtBikht0Dabbg3jIWCVwzDpFbJiDF2EudFxBa2JbF4PLygV2hbTfixt54zWteMysfkHWIz6Inix0zINZx4MABXXLJJRPfwbqE/zATCcwaRuJlPnYLn8k6Q2CVDPHMZA0+o9gQuIhqjzert1SxCHbscgVr95VXZu9jaUu48chGLPJ3yHDWfOZcvk27pwN7EWTmYpAaNiBZIXeoCieZ/Fvf+lZxn5oHFEmNeQAbG9lTDNJPfvKTSxagJQhBWVaq4QfIaCEQgeHKgkDAEkeDAKG90ZPRktZUD8Nw863S0Z+mV16kwSGFhtOfqqHJ8FukYL8VrMvSVIxj7O3tNc6QDTub09bDXe5wOerMYzFQ5dmoSHxc/b1HtGb9VvmcFar3TWY294Unm5bbCLgqtKY0JZs9DwxHutUROqKSMr8hH7p7OxXX09pSdp0cGWXHkfiYWsYeU9wEKB3qj5xRo2+nKjwrNRRpU0/osDbsaFJHW4/2Xr7dZCq4Jwp+Ju8v5BuSAXv27NFSgTmxrXyPGv0rFYqNq8RdpkrP8miGt9gg0411hHJ5yMcHHnhgalOt8VHpfz4ltZ60fl+3VXrj70q+qeseVWFk1UGcRgY75W19UGuba1RbScA0IZ26T67yZkPEUrk1ndOB80Aj0SmOQtN2qeVpK1DPZyb/vrmxRoPjIT11tt30UXB396q53Ho/ThBVH4sqC1C3yeqr0fK4RcKU1Eg7XmEdLxUU2ZDr+ULQ+tzU51qenV9Sw+WX/CulYFtGKQRZlBDj1pxPcGuyxuJSK88SiiVC5rq4NH+OYcDl1teuuFH/dfqozo2NaVN5hWnel1oaDqrcXr1t/cYp799eUaVcFPGnTx40hIZpnM0QlPT3R5/Td66+XRcaSHTIJ3u6yltiHtlQaLNuAh3MR4Kk9v7/yub1Ojs2ZBqFB5ob5Dh+Vn/42rdoscC4+8hHPmKkJkhYITj26U9/uuBzK6KIuYB9793vfrfuvPNOI11AdeNSIVXyh4AiD0gC5sarX/1qQyIwf5FjQRKCwANBB56fqzRcvsCWIPEBv4f+gDY4pttuu23GitCXKvALF8t3JeDMmMnV6DsfUIlDIJuxZQevM2HbDkstQVVEegUFQTwSF6gMghCd7bgjZsFnkVRKxjNrDYFQkjdTZb/t6stcDYJtsDbkm4jBa/luiBAjpR0IGNIW+4CxxoPznY+gaxEXLt6+fqO+ce6MBiMR4xcgZfuhLdtV8hLdh7DxmfNzAXOrkApP1n8q+aiuyja/mbvYKryGWOhi7RXYTyQSvOc975moCp+NHGMRk3hpzqp5xH/8x3+Y7CnKh+ijsZwMJ6ScMOox/HAoYAWjY70a6u+Qw+VXee0aI03FxsumbDfonED1WumiN0vDHVblxcl7JyswDAjMZVmcCHTkCHbYYFFLXdhwQNCuprJkynG8xOF0uNTo36tG/7DWlFwql8OXPs6yckCFE0OjsWSjWSVU11ijgb4hVdaUKxwfly9D874vfFpxxdK+qzt0ROXuFRqOWIFNr9ejNeutvh+po6bENdkLBKZ9MTPnovGYnhs4oo5gr3wur/ZUblGD3wp013hnJqnQkz82cs6c8cbSZjX6X1zkB5u67WhSscHaYJeKEiwwsk8/+bp0LqUB49lj0l3fln7prTkdD5xYdR6USs9OPB8KR7T/2DnFB+7W5bf+yoxOLsEdPmssek7B2HnzXMC9WgF/s3TZr0lnHldwaED7O1waPX5Ml6+pV2XAZ5oaN150lcINzWY9xBkhUzC1IemigDm7+lJp5T6LrEjJbjcSYvTPGO9PmbtI/M1DxVo2UjptHZ8nVF0hDR+Swl2S0yeV7ZCoeIg8IyWojnDI6SBT93zKcSSlicInJO9exZRQd+hZheL05pD8zjrV+/bIWaCEXi6Uezz60Jb05my3NDTrW+fOqjsY1JbyCr1t/aaCy8Kp4DCEhn1aGO8hq2LoQgLzYimccJIcSLC44447Jp5Db/h9my7SW9du1xiSU5GYTp04qcfDx42TAtkwXb8yXjMSHVIoPq5Sd/ms+7YQREan/bWvfa0JYtDgr2ijFLEYwB7+lV/5FfMz+/Byyjgn25LsRqRGyZwkQHn44POKj3erp7dXtY0bVFNTa+YKgT/I0pkkXeYLptJr52Tij12FkJa4VUQaCPgs1vji/swHuZRPvwIqgthfir2Rlh4kLiA1w/1n/SCxkYoH7g/BztlKdjMOso0FiFUSqm6++Wbja86UOJUPWOfwk/CHSNCkMhVShfUGlQnbT6FihHMsrjkvbVDVfdd1t+oLZ09qMBLW1bUN+qXm5bOPLzaYk6i0LDawQVCcyEWAQ3gyt1GWIN5B9dViKMZAqHzuc58zsWT8n3/8x380VeFFzA5FUmMOWS0f+MAHjINLBhWZQfliODKssdiI0fKv8MyT7naCLOV+0Wf72LFu9Q8MmVIqHH82dTbYvWs9Gm07q2O9Hdq2rlnHuw7pXGtcq1avniovY8NfYT3soNzxeyaDbk17pOr56SVBMyAIFhykorZcdvR09emZpw4YB41FF6YaDeEa36op8lM8VyiswOGk/nk0agVDswUUo2RSZxAnkBxWFUZ2Yo8+GiWuJlV5k3JlSYTjQZ0fO2ll2Xga5VvAXhaP9O5X23hn8iSkX3Q9rtubrlaNd+Z52B7s04/OPzZx1i8MntbLGi/V2tKlkZgx40Bhc10zK2lmg8zybENEJIERz5gzoLt7alCcwPSZ9Gz3rKDRINexZ1Ctnf0KRaK6dNsaPRcrMU4OVRg41hgT2YCz4AoMajg6KRMWMY2p4woEVkrbb9czjzyiy17/K3K97HV66Mv/oWaHV87tl8l3+6/L53JNkLg8IDiWRPqMHh88UmEq414unbpHGum0+iOsvlKqnIcmg03bpL7W1C+TmtLn4LyAfkoVWarDvNdJpgeQSy6HU554uSLxoxPLhDcakyPRKYUPqE8uheKDE28NxnvUHz6uWl9hMnozoTM4piPDA6pwe7WvulaX1syt6m53ZY2ODQ9OEBsuObQTWbYLDDjlRpZtkYFMCGtMNlR6fOZB0Y9diUplBxWy2C3ZgmNG9mbkkDpCk+N+U9lONfpWmqpH9jQPxFueINiHLjfOBvIU3/3ud40mbhFFLBTIPIZIu/32243Du1wqhPAl7Opv7E+SkyAMdm7doC0lfXrsyed1/bYV6hs6pI7+jWqJRk3AYCklRllbCHoWM6dzA98LEo2gk10thxQVQduFwIQ9ucAgMLV///4iqTELJBL4dDE5jI8x94RNiAu72otKm9e//vUTf8tHgjYfMHZJQoBQwKewM6+RwEWLv5BYTSYgRZgn2B2sKfYxYx/gv1IBbvvnyHxjT12wTeKLmDc0BwL6423pyVQvVTAvF7takvmPHUViSK7+eLzGrj4FxE0hYRejlQDfTTUuxChyVOxX//Iv/1KMhc4CRVJjFqCMmYFH5hGsXiFs3unRkzoxMhkAXFuyTlvK5xawicf6dfbMT9TR0W2aetfWNsnntRrfIDGFxE85/lDrDxSNxhUMRdTa3qddaxvlI4BRujq/L6J3RsUKaazPyiwunZ/G24CgJlmiF4L01FKB8v7MIDSLHxURDVvWazDSYYLb9b61qvEWnglQ7VmtvnCrYomIzp5s07Y9G1XlWZU1+BNwVWksRmNyGw55naVyOpxGgioYSm8aXOVZp1rflimf0zfWpcM9T8o/PmaMwvbxE9pSfrlK3dlJBsZH6/hZ9YZ65Ha6tbZkfd7EYDAWmiQ0UnB6pE01NTN/xpN9Rw1pkzpCH+s7siSkRiQ+rP7ws4qJbHCHKtxbVOpeO+d1LVsmpa2LPUF8lpZJo0OTfSxwdkqzZz+koWqNEpWrdXL/CV27b7N5f6+rSa54nXECCJAQqISMzZaBDfFZtZrmwekYi7Yo4LLGO2MIZ0m1zbr2A39iAjCr0LpN+TxewwNpLKTP+N5lAdbUbb9sXdf5rPhbucuS5jrzlNWno3GztO1GLSock/qxbucauSLnlYj3GV5j4kzjXQomWGvS73AwTgXZ/OHR3g597PnHFU4GVS6vbtDf7r1KnjlUKGwtq1YscWrittX7/frEzklS8EJBR0dHTq3bhQbzlhJwMiBnAlmXHCfrBVnimYkQfWGkFFOJPOnEyEH1hk4ooXCyl9IarfBv01hsQOfHDxuiPuCq1MrAzqx7HkHlL3zhC8bZIPPzU5/6lH7zN39zzuddRBGZYJy9973v1V/+5V/qgx/84JJXgOPrkARAMhfJNAQL2VtpME0mvOlz1fuYFMKOc+joqXatW1Wvq5rjcqxc+nUQm4JMTebwQvXyuNCRmT2LTQbBAZnL3+azuTqSpIsV2MJ2LVZpFAZs/rHYcQXjVmW1y1GmcvdFcjkCcw5o5rI75lpBYYM1CXLBVoLARqAqG1IVggFCluSmTJAoMVNVBWugnVgBkcGc4JxS+5SyVuO/4EtRgcJ3FyXviijCIgVzVUosNPD52XfyBSQovX+JESwWMYkdRfUaPYZoF4Ac1WL2+XgxoCj2VyCQZCFTjw0N1r8QQmMkOpxGaICzY2fUH+7TbMBmyfE8/tg3FAi4tHv3eoVCEUUiQ9q9y2+c/olmWRHkP6S6mnJdf/k27dyySj6fV4pk9MSYCb5yS5ZqHgkNO0hBkB6nI1fGZhHp4L7COq9YsVI9J4LaXnm9tlVcq1pfniRVBgjkbCy9St0nQlpRs05ryndrhX9H1tdWe9apzDUZzHc7/Gr277WOy7PC9PzwOMrkcZSo2rNBNd7sm8Ldj/9Qqzc0T0iexBXXufFJHWTzXCKuEyNH9UjPfbq/++c6OnxYPQStgu16su8xDUeG8jq/XHRZLBFVKJbd2E7FeGxqQD0YC6s/PKK28T4F8/iM+UAiEVdf+JkkoWGe0VD0qIJpJFPhICOBzIRMsMawwU8QDbe81gpFc894EHC5+TUzf4HDqdjG2xXYeLW04hJp0+2q3vdq9Q8MmIwmnAEIhueee04JhZTQcQ0MPqrnDtylU6dPJXVqp95F6lVS10ScWAwRzoXAC+sJv+PMpEqdMeZsrc0FIVOHeqUzz0sdpwuTe5rvABaft+4S6cbfkW5+j7TzNqu5+hLCIbcxPtLP1CmnY6reqSvLc7NFJB7Xn7zwpPnfxpP9XfrOuWR/mFngyNCgPnzgKfWF4+oPxzUUTqhjPKKyC6wRoC09tVAB1LFoVGdHhzUeyy77RuDMLv3OB9gKGzdu1Pe+970JKbl4IqbuUJu6Qq1Z6wXD8Umnpi/coq7QKZ0efUrB+LCiibCGoz3md9bYbODa/N7v/Z75zg996EPmsegydkW8aMFYYnzRCPz73/+++XkpCQ0afD/00ENGlgEbgOAdBAcBQyqV8DGYg+YYjR2W0OUXbdIV+zapsb5Sjqjldyw1CCpS6Umgkb5huYKrRUyCvQCbEI1vkqfma50jeExlj5EyXWBwzEgrL0X1oQ1sS3wH/JgLBaH4uQlCA8QSoxqOHpjz57JWYJ9njgfuEUlG8wESI1Klrak2ojcgIEAIgZI6lvELeBBMnCnBiTnBmojsHufBZ0FccPxkdpMYlupL8N18LmRwEUW81MG8Icl6KYCNggw/RH2+YP4SR2B9sEG8qXXsjNrHzyk6Hz0vM0BMGZsLxQpizcRfisgfxUqNAoAj+9a3vlUf+9jH9NGPfrRgZ2M0Oprj+RFVe2vyYjnJFGDT5IGRvmvnTrWff1JnznTK5/Po6qstvUolsxEnNNWRabHaJaQgIXnmJztiLmDDh9DAYeLBOeabsTkbcO0iibDcDo+pLLjQgaFG8PbRRx81GUmzZZW5D08//aw2rNw7oxNARUhzYK+R8kB2yusoSZNAolqDx0zYdtEGHTl0VMODI9q4fd2EHFUqTowcUdt4i9VAOOP91E20jrdoh2fm0k6/06sGX426Q/1WxUUiodFYQk/2t5pHs79adzRdopLUXgcpWOGv0WBkZHIKJQiiOfW5M/eZXz0Ol16z8jKtKVnYBvLRxJjipvlyKhwKx/vkd83+u1nPyFSCYKBkm0CAydgaGzMGPOsPm7w27ZTe/ofSwaesgPnuK6SmGWSSRoelO78m97mTinUOSus/IK3aqGeeeso4MzgKOAg4DceOHda+i1krwzp48AXt27dJIyNtWtF8mSKuFo3F0o0Sf0qPFtYNjjFbpQfjG3kriA3mDA4IgVEekB4EUzlnCOs5S2WcOiA99J1JMmP1dunGN1kkUBGSe53VeyMV7g2qdlSoO7w/je6o8kw2p50NaBJ/erRfoXhUAZdPoxlBdXo3nGZ8zhIP9HSY4hrWhVhCRoSvJxzW4aFBXTZHWavJ/Sokl8NtHgsFHPPURsDziZ92tOivDj9rCEi/06FGf0C3Na7Rr6/ZKndyTjBnaZZHECBXiXgmCLbaTUEhpw8NPa6xmHUv3Q7rfqSGkpxpJptDQ5GujNq7hELxEQXjyIPmto1oOAwBQ/Uk9sr//M//zFumaREvTbC/vvGNbzT7L2OLAOBiwu6tR5COfRj/gn2S4+CYkHQhyJ0z+EjFbDi1J5RjWfgX4MCBA0bagYou/uf3pdD1vhDBWEDGmDFp9yqZrW/W2tpqSCUyUqfrhzRfwJ5jfyCozTkslE+ZC13BAd3V+bRGY0EjvXlt3S5tK59d0tliIhLPTLRMKJYYMnJUDmRGZwmkzJBswr62+7dghzMWCORRdTkfJK7dGB5/gLWLygz8Y8YD6xtBSkhaqr8hOPB38Z1nkvijQg35qdR+PanfiTIGn00cg6xw1lCCuMi6QQaTvMl3ELBc6uq7IuYPfeFxtY4Nqcrj05qSyuK9zQGSCZZSXp7YGOsPpGc+FSO2VBaVX6AzeF6HhibJ3bNjp3RJNVX+85vAxj6FT/HXf/3XZq/8yle+ol/+5V+e1+94saJIauQBNiuat/zpn/6pvvSlL0007isUJTl6BdBbIxfYlGEK2RBtiQYyKtkQmZit585p4/q1Ki9P3SD5uWSS0Oi8Vwp1TwbU4kRgElLZRqlk6RsWkSltl29CbmBEL5TxCcv6/OAzCsWDpoHwprJtWlWy8M2AFhp2hglBWwx4SuzZPHggF0DAJZcTwfgmy4SxRmltIdrNHqcV/A3GxjQSHTREUaWnNu9NvdRTqU07NujcmfM6c7xVazetVqkrvcy9I5i7coeAFNm5+eKiqu16bvCo+kIDCiUcCsUnyb/2YL8+f+YerQk06JbG3VPIjStrt2soOq5z493m9xJ3ibpCk0RlJBHT988/pd/dcJs8mT0T5hHZmyYnZt1M2daAZXwQ+MfpZB5iuHMfbdkpKtMw/s04WrnOeuT1BXHpm5+UOtvMz+G+bulbn1bk137PGA3o6ZM9gdPJ+na25Sk9/PDT8njcqqkpVyDgUyBg9XDxJQi6kvnGmHCoxLVKpa71yfOIq7YhoYHRZ1VWViOvY40cKdeEuUAWBOsnARqyIOxxylqKA8JrWI9wdAyBMxuEg9LD/5tendF6WDr5rLR5EaQ4CNofvlc6f8Tq3bHhcmn9JekVICMD0sPflXrbpEC5dMUrpRW5A9qQTgTduD44dqwpc3LKIN+8l0tRCKq45GqSXKtVQjaN43KNxjrM/S1zN8vrnH1gLByP6t9OPK4TI5aEVcDlVqXbqcFoPG38rwjkv99851yLvnT2tHnfr61ZrxKXOyMwbiEwD0GbYGxUx4afVig+Zn5v9m/QysDmBXGacPazZlFhK4SGTaWV6YtT4HefHh3Snx962rytxJggNIMf19dbjmooEtL7N08GF9mn2Lso+84nc5MgCOsSwdfOMITnJDnF97mS5g7wO6n6sY6dw/C7IKKD5t6FpiTRznyOBHvJOH7DG95g1sgf/ehHS5oRXMSFC/bcV73qVSaZgDE1n1I/04F9H5uRRrfsi6zvrC08zzFgE2IPsjfb1bQ5Ub3X8jPs6gynW6pbHn1nuru7zXrCmk226LKRnLxAQDCWviiMEUhn7DRsAMYL44Oxwv+59iUCwfgYBLJz9m+cBlQ5DEV6DXFd5q4sqO8eewrEBjJEV1999aIF1bA9ftLxhMJxqyoolojr/u4DqvKUqcm/vPttOZALTWRmQfL77JJy7OpLxgeJCMQzbFKDgCEP7A+ko7IRBoUA+xR7laxsqr5JkECKm/FKdRBjF6KDZC2O48Ybb5x5bUuC46TXUTZwbnZiJvYL328HQzkmuzqEpCqyr/G1L1hio/ec9PzPpPFhqbJRuujlUsmkBFehYF2BUOc+EGzmehkZ4QsAT/Wd16dPPqVoskLn2rrV+u31+0yyVBGTYA6mVlClIpGImERNpwJzIk3zAbYMMTKqTGeKdTEWIWJZm5pXNOvI0MG0v4/HxtQydloby+a/+oS1gQR6CNm3vOUtJv5MBe8Fu2YsEhyJYhODaUHQ7X3ve59++MMf6gc/+IEJhM0Fx4eP6szYpMTLCv8q7ajYlTZQYfvJHGYTJNDGLSKzgU0ZR3pKFnGiT0o8YwWHDAKS43LJ4ZNGTkq9T0w9kLqrpZI18y91MktgKEBkYBwvVGAglojpsd77FU4JZIO9VZeqxruw2fWLCQwEDDbGLtkiZKMQqKVRUqqRSdYUDi0gmIUhMRv0hjp1fGT/RGCvwl2j7RWX5lUFE4mHdGz4SQXjo2pv7dRId1CrS3aoqaF5IlPxge67zb0DtuGQit2VF6nJn725tA0ci591PK3zQSu4We+rlCPh1Plgeu8P5lo04VC9r1xvXnPdFMPE0ppFGimhJ/tOaf/AmYnmwDbetvYG1SLTtoAYCB/UeNwmexymWXid7yq5mPMFjBOyFtiwCSRSpbF3714zFzH+MzMZIDqo2LA1ZfNGX5f0mT+b+LV1YFhnh0a18rrb9XxZs8l2tqqEnjaB8yuvalBFBZli6dd1KOJTIhGQ37FRneeHjPOAw8raaSSmTt+jhHNQpWUl8nhoSl2q3VtfO8VIwqniPbbDkQ1kX+cMfIyPSd/5onTkeSlQIr3iddK+lD4Efe3SDz+V/h6cpi2XSdye/naLSNhxs1SxAHqZB34mtTyXfv323CGt2TtJevzg36Xh/iTxgpSYQ3rVu6XqpsnTHB83zqEt24Fxx31iPaGqZs+ePXk7g0uF758/rJ91HE/NHVbA5dUTvcGJebutvEqfvPg6+V0zk4Jfazmj9z/7VNpzf7Fzj7589rh6wiHFEgnj9l9RW6+PbNmpb547Y3p33N64Qrc3FZZAwFrz/OCDCsWRTEuRNCjdozrfCs0nuK9oziNXmf6HMenYT6SxpLRd5RppU2HyZT9sO6O/OfqsfE6reiLV1oFk+OG1r54gG1LnKOsSxzOTEU9gguBFpGJYJSsdUwimZt9GVXnrFYr3qyN4zCQzVHoS5n8+2nA2cdZ1Xu1QwFWujaVX5u08YKe9//3vN83DsRNxmoooIl888cQTZg+kKfi//uu/Lrj+Ovs+84tAv12JSdCa76WZ9pyyiJFjCHZayVT+Bsm1PJqbs77de++9xrcgsYHzLWL2wCbAt+C6Mn5IEqHPUCoZx7qMH8vr8F137NgxK3uBpKXDw09qJDowYe1uKdtn1vRCj5mgOX4R4x47dqYeCnNBR7BP3z//aNpz7DmXVm/WxdXLu3k0clMDEZpgYx9a+2mJa5MCyQSifEGFAiQttjbjAf+CsUAigl1hmQqqKdjz50I8cW/vuusuU3VqkxkQJRAZPBiDkFvTARKO44WYYF3EX2aMs9czZvhMvge/KbMPIX4SvjXrTK7kTPxu/nZBVneO9Ev3/bfVn4+xwV4RqJBuekdBdiHX1O77ynUlBsG9wQdkbyLgzH60nDEajeiDz/5UkQxpud/ZcLGurlv+FVmLbecwtzPnfCjWqmD8aHKdcanEtVse58L2vWIPYF3Ch83WRzQVzHPiAec7zyu2aUxuT7p91uhr1o7KpG+9QCCR4Jd+6ZdMtca//du/XTCE31KgWKkxw8b2+te/3pRHEvwjo3iu2Fy+VfW+BtNfgwqNGu9kVjvOxpe//GXrdZs3m80UB4Pg7rSOjoNg9DVE08yiINVJdoZylIDIFN0piUyXZUJosLkRQFtoI3M8OjqF0MDIpKfJi4nUYNNIDUjjwFLCloqf//znJnCNLvJ0DmwwFtRYbEwlroD8rkBWh+PEyIG0QNJQtE8dwRatCKzLq4/HjoqrTYbtth0YzuVGxgrng8ApRuUK/2q1jluSQ4xuuy7D5XBpQ+nmnIQGsjMnR9o0GBlVe7DPPGz0hIaMHFWWmWHOpSs0pIHIqGpo3pxEKBbVPV2H1TrWp3K3X43+sqwZ2rnkq+YTlZ4d8sQqFIkPmD4Epe51E4RGLBFWKDZogvl+Z1WaLJgNAtNIMBCAgxDAoCSICKEBcUs2I1maOBc2icGYwtDE8cA5yTswkOHIrqosU/tIUKsa6xVcYZWD4gCQCYiB29Z2SH5/WF6vtXHb0j4t59rU2tIpp/Nx7dxwh3EMMJRYI0+cekGXXF2u8vKGiV4ZJ46fUzjWLp97VTppFY3OKNHGHMK4zup0fOmT0tEXLEIA2SJ+LymVtiY1okurrKz2VEOXTLWhs1J41Dqh0X7p4a9IN/y2VDKLrFwy5/vRPHZINeslb8q9aCObJGNcth2aJDUGuqx+H5NXxXp56xGFS2tMxhT7jS0RkGpAMR6Yk2Tc2E3WCRawbudaRxhXXMelMMTaxoYyxIWksVhY/3XpDTo6PKByj1fX1DXJm2dl1adPHp/y3OfPnNIPrr1Z/3b8sNqDY9pRUaVr6hr0a09YOs7c7h+2t+pPdlykX1uzIe9jR3LKrtCYhEPDkd55JzWYE+zDzCf7PiIF5Tp9rzSWMlYGW6W2J6U1k0EB9u5ccm3P9HfqG+de0IqAU6MRCOP0cWlLaWbaIlRJMWbs7Nrp7B++H9upuXalBpRe1eeUS6tKNshpiM0q+ZylGo6e5kQmXsNX+10OxRI+BVzVavZvKyioy7HRNJy5cNNNN+mLX/yifvVXfzXv9xfx0gWNIN/2trfpL/7iLxalITh7PnOKICMBOfZKMgFnm9AyBVRnLIPq70xgS+K/zZdu/0sdtmyn7Sdjv6USGgQrIZqppCuk8jsb2oNnJggNgIThiZHndEn1zVlt2+mOmT3NDmph6xJYXyh4s0iS4C9ke97GyZEuPdp7wlR5bC5r1NV1m41s1WKDhKAqzxUKxloRnpLHUSsf1bR5gj2dPizYhci8IVvH2gaxSIY0eyUkAYQY/oVtZ/Mz9wUbc7ba+3wPgUrGJOsc34ltw3fiK0C04Dvwcyog35CO4pjshCl8E3wB/sb4hhyxxzPniA2cGRRlrYHAmw58FgTLBUlqdB6fJDQA9tvYoDTQIdVOHyPjmnH9ucbsPfgRkE+p+x7Xhgd+CLYdYwjyNNc6YpMjS9FQuTs0OoXQIEHn3Hh+fT5fSsDGIHHR9jdYE+oaAgrGJ/tVEN0Zix1QueNaOZPxDOYevuN8JtDxecTEkFRkrZkukdrIlI+OqrqiWiOehKJK78dV6l74xufEZPDNqOZ95StfqW9+85sT6jZFpKNIakxTDv7yl7/cTDy0HvPRX8sXVd5q88gEkxYpg1llETkIKmRxJrwM/MzAq3PJtG4PHjxogoUsFGxoGBq2EbGQhAZw5zAy3QuoU54XEmEp1mndJxjqpKSTjXgiqrHYOcUTIXmcFfI7m2Z0fociQzoxckL9g/0ac47J4/NMCRoRjJnuc1rGzurw0KGJ37eVb9fa0nVTGq/SUyMdDo3H8m8QiWNS6k4P7KJvajd13lC2xRAYnaF2E5xaE1inBn9zMtM2+/Ezvu7tekZnxjrM6yKZwTTjGsXN36z+GlZwCw32XJ/3jdYn1TLWa2ZTd2jY/FzvLzXkh1MOk/l9Xd02kwm+0OC8S90YkOlGZDA2oI7gM2LrBT5npZr8l0xIU3EezEGMihtuuME4dxAcdkk4BCqBa/Rm7XUwtbkXQQHeQ/l0vrr3qqyV1m6RWo4bA9iBXncsLs/eq7S9rtkYpTgefBc/Qzg899xjisdP6uKLN+ng4RZ19o+prrFGl16x3Tp3l1OrV24zBjEG7+q15eoffVItZ7vMZ1hNxZ06euyoKktjxnCh7wwOeD7kNJ9LVQrGTJrzQ5XGkYyGiRhbzz42SWr4AtJVr5Ye+d7kaxrXSKFUnWKaL0Sl9iPSxgIlOoY7pYPfl5KyBmp5VNr9q1IguadkmxOQKi0HJaoRvCU63z+ssXCKccZ7Ws/LU9lu7vVMetcYhThzZLAQ0MbwBDgjtnNhN20ncw1ZFVtuYDFR6yuZmJup+MKZJ3Rb4ybdWL+ioEBiKDZV6i4Uj6vJH9Bf7rYCJuB3nn7EknVLed2/HT9UEKmRq3+Ga571WwE2R2qFAeOebMrV/c9rdX25njx8Vi6n08wtf8uQdq+8wowRnMlvfetbRsIhM2DYGRzV3x19wshuAI9TisUdE3rXuCjX1q2c6KmRCewDjgn7azo9dJxkArMEaY8Oj2gwahEW3NX1pVuShIaFCk+DYolujcUmSQ0bawLb5XfPzjG2G4hjL/76r/+6ccj/4A/+oFgqXkRWMAf+7u/+zpAZX//6100W3mKAtZ3A3GL0M1gK4EuQ1YgvgT2DjYOdS+Y0SRNFzA+Qgzo1elrdoW4dfuqwXnPja6bY79gFcyU0bAnGzMS8GKGlRFhex+w+H/tlPo5tOlR7yrS+tEmnR/FBADa7X5vLsttBZ0d79M1zk6oKnaEhjcZCuqNpj5YCEBul7m0Fvw97j+AliU/YiMiOsR/i4zMXsTV4YFdjTyAFY1dtcE8INjKHsSlnGxMgwx+fBl/i1ltvNT7OT3/6U2Obckx8ry2ni10BmYHfgw2Tamdgs9pJE6zZJOiQxGP5F5YtA1FsJ0mxzuRzzJw/n40vxnuXss9AwTAkWxZHOYV84xohaTjlJUnZ9Hx8AWwpZLyYq1xTxoPtH9oBbhKOuSeMOXyPhY4hZaLGG5iSGEm19t2dp3RqtN/IUNX7FrePz3JFZr8+7u2J009ozWb8denJxw6rqrpMkUhMu7et1IpGa+257777jO/+mte8Zl7XbMYiMQzmPmuUHfPIBHOf8YUtMRgZ0IHBpyaUQ0iGXl2Spwz3HEHcAl/oTW96k0n0uvPOOw1xW0Q6iqRGFpBhAqFB7wzKwRfSAYjGYzo4dESdoS55HB5tLFun6kjUZMXPS/OZwEqpfKs0THkXcFrSU67FXfwB2REkxJK5QTaGrVlPKSfOyEKDa9rkX5ns0WCZmV6nV82BJSxxjI9KIUqU7QoSl+S7QnJaLCyN2XrDTyiagCSwou4lzn5VendM25D+sd7HFFNMp06e0soNK/XcwHO6uHoy6IbDNx07TSVRKqEBjgwfNg3tK1IIMSotnHIagmASCQVcc9vIMSwJmBKwxohZX2Y98kV7sNcQGtbRZGcqSlw+vbL5Cj3Tf1LHhtsVi1tBSIiOZn+VIrGYnug9LZ/LrQZfuc6mZCzziWQdry9tVq03YCSpVvirtbZ0YcsmZ0Lb+H5F4hG5k/rxofigBiKnVeO1rh1GIaSiXX6NMU3mFETF/fffb6p3AJs46x4bKQ5GKjAy+Rw2+xnXxnhcI4/cq7GYX+1Rv0Kjwxp2uNX0hndKdRZZgIFKhnNqJkZd3as0Nj6qn9z5fe26ol6rtk5WzKQCJ8mUhyea5QxUq7YunRwr0Qbtf+pJOWONKq/eaoKfhRhhOCmMQ9Yq43jkyhbJzKajd0ZoXHrhQaL7ktenJM+UjixyajPi1P2WzIeNaFg6/ZC0IxkYW3eJdCJd9kCdrVLLCfPjuLdcI4EqbamJW9+PRen2SDe8QirLP/sDBzEzO4prxfiAPLLnLmOEChyqryCX5i0rOA+8ommLnh/sVC8ySkmw8g9FQ/pO20EjOXV1Xf6G4WtXrdbfHz2cJmf1q6umkmTDETRi05HZoDwfUmOFf6POB09O7Fc81+ibPF6bIJhvsP4SaOi454CeO35OJT6v9m5hn3RoyFVjMhjJMiQj8a1vfashNthTUvvUHBvuUzQli83rQnuKqiC3qYy5qq5Z79mYJAJzgMAYx4G9wHxEAi0TlJBj7PvL4ipzD8nncpg+GpAokfgpxRMr0/oNeZzeyXK/JKwjtoi5uQAZIYI0r3jFK0wF3D/90z8te4m2IhYX7Jsf+tCHzJxhzy1YznEOWOhA7lKDYCrVAXYGtV3hRbCS4MVMGdRF5IeDg4fUMt5q9p+B6IAe63tc19RdpUqaxSf3pfnym/2mf0ZiSgWex+Gd81xAggRJlIUIhrIP3tKwTy8MnlFPeFClLr/2Vm2QLynRg+9/bKRDY9GQVgSqtX+wZUqAdP9Aq25v3JWXlO9yAYF65qBdmWsHC7H/7GA2/oYte5ctWYF5yuvzWRsZa8gVkUQDkcH6ys+pJCY2J1nOqXsxNj3HSsUE/lAuOyq1CjVbcisJIARFIWmwUfLd7/ksSGaSxlirSPy5ILBiq3QUvyY86T+U10tVTWl+AHbZXGxT3pu5XkOWsLZDkNmyyXavJHrP4peSmLdYNleFx6c3rdmlr7e8kDZ3I/G4jg/36W+PPKK/3H1z3pXgyw0L5V8A7tvgqE/HTt2r3t4hXXbldlVUlJrvbD0+pNMnHjYJGMim33PPPebBnJ5v/5GYALY6PgYkR+bYYR3jWJHnZUxfVXuDhqNDpn9suduSv14ssGZ8//vfN1W9NrHBMRUxiSKpkQEyhiEz/vAP/1B//Md/vOAD9tmBAzoftIKv4wrq2YHnk9rTDu2q3KMm/xw3Oo6/5mKpfJMUG7cqNLLICGmsXxrtlpDbqWieWZoqGpQ6HpHGOy2CpP4SqTx3cCgcH9Wzp36iFWur9L1779TtN752IjsBI4THYmBb+S6zEA1FBw2hsSqwVk5FFU845KQ52mIjQuldajlbTAq/IPmtZnpjsfNJQkMTW+ZY/JxK42vldmYnDs6Nn5sgGcKhsHwBnzpDnUZKyp/UOCbgOJ0hPxwZzvH8UBqpQQXFxrLdOj5yYOL4yt3VavKvmZegGk7HTFJB2TAeSw9Q2bJVdsYUuKp2h+mt8bKmi7Wnsl8P9hzWaNLBaPLX6j9PPzhhpFR6ps4ZR5IA2Vu1PNjyM6OH1EOT6iTK3fQPcCgcn7yXOJrZ1jS78SPAMERiCiObLKVNa1dJw92WbmpSWgtD4PBD92lX1xFLyohKhJteK5VOjo1TJ0+q40v/obKTh1Xhh9AY1eU33qTwb35A3tTgSne7nB1npZJyaf32CeKgJFCqXTsuU1np+eQcse+GU15nejAdCa6A8yKNxxmHBI8d8idq5Ioe1upm3tuqKn9ESlwrFeAM4xxxzag4MAa2z2/1z9j/+KRBz2FdkS7vpvMnpSfvnPy95ZhUhyxVkkgwfSycUtPm2UlPZYoqBVPKnbdeJ7m90vnDlhzIQK8Unvx7IDIiX/0KnXd7tEJjUkmFdMnt+RMa4ZD0029Kxw9YZM31r5T2Wg4kmSM4eYyd1PXFLimnNB+nZDZzOhfGov1qC76gcHzcyAutDOxWwGWNw3KPTx/ffoP2D7Tray3PmSyq1NH/ZF9bQaTGR7ZsVzAW0xfOnDJ34NfXrNMfb5va2PL6+kY9zXVPAoLxkupa/X9HDmg0FtHVtY26o2lmMn1FYJP8rlIj6RcNRjXeLrX0nJuYw6kt0XiODMWJwCV/O/uMJRdAIGXdpVLquSIh0N9tVe9QTZWxLvB5zZe8Us0VP5tsnO1wqmLbjbq6tN5kT7FOsJeQXYljnioXVQJRlgGv06E/3na5Lq6eSk7kAvOPzyTZBLI1M+OLawBB//N779QVN6+SF+LEPkVFzP7pdUyObZ+zTh7HaUWSL+PMsLmczvkJ+BKIYd962cteZsb7l770pUXPICxieYKgzG/8xm8YaRbGiJ3YU8TcYQfSIS9YA+2ebIDeUItJpr+YQZYqhAYYHRpVaYVlN7aMtWp3UoKKpIb5ksdo8q9Tf7hbo8nqOmzujWUETOcWuCSBB/s23+D5bIB0FERGJiLxmL7e8qjag5OyWg2+qeoJpopcFxaw7QgwZ/akQwoKOxqwl+NvGOlKv1+u8UHL/qDZtMNhyA67QiIXOQYpApmAv0KyA0kV2J/YnrwvU+40W6CboCkZ43OJ8+CrQnzMpv8D54BtALFCgtBC91OaF/jLpWvfKh28V+K+QWbsvEUmYzUJ7j33Bj9xPmVnWdchMrhOBJrt+8b/rPeQZcxnxsF8qqtMhzuaNmpTabV+3H5czyLBlQy7UR3eEx7T2dEBbS7P3iB7uYFxaFfdZ/MxmLMkpuU9X4KjVjP5smrJM9UGrihZp+3bdikm/FOLFvI4m7Rrx+4JVQnuJ8dEohAJljMl5M4GkK2MFxKSIE4y7XX+hvwTSZ74OUspV8/Y//d//3ezzuF3fe973zOV8kVYuABW0MUDOmW/9Vu/ZQYM/y+GcWgTGqmIJjCGEnph8IAqPVWm98acQSA6l+RUx0HpxL2Tv9dtlrbenpvYYJFru0cyPQoSlgzK+ful1S+TSiaDFfHEuEJkSsbH1RsZktsX1/P7j+vK6/ZqKHFCJbFKBVy1RsomV+nXfMMEfkqswA79CPrDjyuRrJIodW9SmTt/eZB5QYLS6gyzNTGZVRw3xza18wPBmulKw7MZcqnPzwRfjgaP2Z6v8zUbXcHhyKCpLqr01M1bZlHCkTCVJxBQhVQu1XrTxzpD2ZWgp81q00tjQ1mzITRsNAeq9YbVVvUCm+nfHv1Z2hUfigQVcHkUjEXT3Ixt5fnrzC4kBiI96gy1pD03HEW/1yGPI339sDViUzdugoX2WCGrng1z14ZmqeVJ6ehjUjhqVSPsfJnUtE1VzoTO/exrenx8XE4ldOnQgBy9HdJbPmKCpFSDDB09qKuHOqR6SxYpSqb6keflPX5Q2p1sSPz849KdX52sWFi7VXr9uzlI8yuOayCxU3K0KpIYMjqbZe6tWQk9t7NWZY4blFBIjoRDjtAvzPP19RU6euycGhqqpOhZybNZikakwQ4r6F/VOLXSIhpV4vtf1+iPfqAdjXVy3voqUkusgfTmd0qV1ZYMFb00bv8VaV1GkP70gfS+Gpxf74C0ZZc03GU5BjtvlcryNHbN+w9L/aeS8eXUNcEhlaYYWRzjpiutB/ifP0+vCDEauAPq3ftyrUhW5hSEH3xJOvxMsjfIsPT9L1qSW9suStPazgYMYgxjHJ7ZNg5NBUTGmbGnjJgcCMVHdHb0KW0qu1ZuMvJNY3CPrqpdo2+3vqBgIr1aIrNB9UxAKulPd+4xj+nwjvVb1Bkc19dbT5u7tLeyWgcH+/TsQI+5bd9tO6vWsRG9c8P0lUOmXN+3wjz2H99vnPBcTiLOPZVFBE6NzEL3CykVOw6p66R0xZuk2jVWk/jvfkbq77L+vHab9Kq3WWRYKqrXS9tfI/WftsZz3ZYJmTOMezIykY2jRPyNb3zjhFwU1V97Khu0sbRKp0cHzDXgSm8ordLuysKr2bgOVHgeOnZYdz96jy7ad5HqfDXm+bvvvttkPN5y6w0aiZ+Y+l6lXy+3o0olrkaF450pjlStPI75c1QIViNvgf7tHXfcYRyPVM35Il56YF9FQoFAFmOD4EwR8wfWArsqkHUydV1Eoq7YT2N+kOpHxKIxuT0uYxPbMoOAfSiTfJ4tqE7cUXGFBiM9iiYiKndXGaJ/PmBney829g+cVUcKoQG6Q+n9v9iZtpQ1LUlPjbkAMotM/UyQ6Wz7+dgoVMg4wuPSU9+WHvxp8s0rpUtfK3n8hnSye1pAGGRKFkEMY2/Y9hAJSIw7bMp87UrWBt43V2DTck5ptu/4gBQckUqrJW/28Wq0+qurjdTSkld0kizV9oT1P4oDKy+Tckkpl9dJV74+50dBKhF8RZ40s3fJXDEdOc1YYIxBnnE/Fmtubyqv0c6xej032DFV7H2Z9K6dCVwv9k5brSEb8OtJSmOsQiJNS24cfkTa/3PLTyWp6prXSSu3ZJEev1ThOIm443I5yuRxrJyQd+NYGEs/+MEPDKHBfIeEJHGhENWFfEDlOclTJGrha1SmEPQka0EcLBZRNhO4Nh//+MfN3MLH+MIXvmD6PxdhtIiKAMhM/fZv//YEsbEcgKFIb4QFBY1rT9yX/lzPcalnanBgAtFRKUgWaoYJNjIZVKX/w1jscUUT7Qon+hRLRM1ENEaw25Xsu9BjXssmhMHDBrhYQNapP/zsBKEBRqMnFIp1a1HhIPieujGQwT25cFoZppmkh1OdwQ493Xe3nuq7S6dGnjcNu200+BsmAu+mfFAOlbvLFUhW6LApzJRVUu2hYapVJcT7QaOvSbXe7EHYgKtMDf6VqvY2zBuh0T7crgPD+/VQzwP6RdfPdWaUBq/5ocpbrmvrdk8cO//z+w31e3RF7bY0QiMToXjUNOpLR0KrAtXaUFonn9OtGm+p3rj6MjUHpg9U4egNhMezfN78YjyavbImkfCqyjtJ1BFQYUykEhoQHMg2IB1jOyWth55U6JmvS4OnJZ9bKuP1cengT01AXCcOaFd9pa5Y02jFy3Fo+zotmaNkqfmuphz69ENJZw55pp9+PT3gfvaYdOCRiV8xnM6f61ald5/qfDeoxnulvM6p/YhSjSSnA53Tyfnw/AtntGolAUuqKkLScK90939ID3xZuu/z0gNfkSIZ0jPf/Zocd/1QzbGQetrOqfuLn9G5b3/NGHRjkaj0y78m/dHfSO//v9LWLIRs1r4WCemS10i3f0C6/rdmbKqXhs79UuvD0ki75Exp0gcYg+utyq6sIEsm9XgcDvXE3NMTycEx6X//W/rHP5A+9ScWiQEgpmxCI/VcX5jUgs7HAbQzucjynwtGo/S4SSdrY4poLJYeMAA31E/Nir6ufmH0UCE//mTnPh24/Zf1zK2v1s6KSoUTcVMpEkveu0+dPGzK1PMF9+vMmTNGRzgbcDTIUOPajgwP69zjd6f8NUkrnEnex599VYJgsdFyVHo0GVjIBMTtmquk1VdM9m1JAd9J2fZ3vvMds65AdFDKzS7/Jzuv0WtXbdXVtSvN//zumaXz3hPq0/HSVrUGOvXZOz+v+zoeNYkhRiaro0Pl3tVyKZ1QCzgb5c4gda1+RHtU6t4pv2utSt07VObeN+9VuTjUVP6SxUlGFYHVIl6aQN6AcUoG8i9+8YsiobFAsINpmesiJCNrUhFzB8lFNd6aiZ52tinQ5LcS2tjTCQTNqjdkDuBT4FvU+1bOG6Fhz8ulkP0ZjIxP+CY2uIzX1W1Rhdsvv9OjnRUr9crmi3ShgSTFzMAoQWaC0bbUFHY9mc86eJeV5GNj4Lx02EqwJIBIkBHbInPvxI+BQEhN8CB+UGgQm7WBBBDIrdkC/4n3p2V3n3xYeuyL0v7vSI98Tuo6lvW9jD1bOouxSIULxMyiIzIuHf6u1HdSGu2Uug5Kx348mZRVICCLIKPmm9DIFxCq3JfFvJaXVDebBCp6+AH+X1NSqbVUH10AYD4xHvHLUqszMgP/JBBRsTCt/9Z1VtqP/5H8nFhEevhb0vjUXqsOh0s+11oFXNvkda6aYoczjug5RkyB+WFX6OALzTdQj8FWJ0ZmrznEQ+zqr+WGt7/97SZmzf//9m//ttSHsyzwkq/UYPL+yZ/8iT71qU/p5z//ua64osCmrXMA0j0r/U1qy6jWcKfMaZ9zgWULjGxJxgJGUHostaltBnIGrScPPJqAsbaqCew1au3GFXK67PcSbHdPGBZsgtNlMlKue1/XIZ0Y6TRHu7GsQZfXbFA1clmzQDQxNnF8rN9s3XxuT3i/yt3rVe6egYWeL3i3S6HBZMWGeULyTBqEPletyhObNRw9bn53yKVookkdwckFvTfcZgzk9WVWkBLiYW/lXh0ePmwWaeeIU5euv3RiI+D/mRp1GZa8co8hSKiUKHGVqslfQNnhHHHyzEn94LnvacPuyYD80eEjqnBXKOAu0QuDRzQcHVG5u0y7KmnOPVUeamv5Gq0padRwZEzlnhIF8uwjA2lR7vZpJBqamBn83xkaNuOw0V+pVzbvVp1v+rHXMtavL595QiOxsDFwXtG8Q9fULUwlkDebpJyRr9knV4rcElnUrHG2Di0N/HAMeI55SLbGfffeq52lvfJ6StOD4TSbD0YmnRCH1DsyrmiyMbdBImEMHQwR95q1RmJpPBxW28i4vHYwc3UyuDzUb0ngpILX9KUTi7PSZnZwPTjvsDZuaFZf/7A8Hrda2vvlePqHhtgo83nUUFkqZ1+bdPgBac9tk+9/5F6dHRpRJJ7QaCSihhK/ag/vl/91v2Y0cKk4mDbzmp4ax55KqahwSOt2St48JG5YkKiCowLOX23J+3WlNCd3OiS/R/LXSiuvsILOVJzkwpWvke79sqWBi4M5GFHDza/IPZf5/m//p0Uw4dSMjUjf+U/pLb8nrcmRfVkgkcmYw/HFeMRAnm0GTC4JimzE6qtWsE649fTAeXkcLt3csEF7q6Y6XTR+5N5yXHPNnkNL1+ukh0dkiqNAT55QPJZ3kJ9gHdIOlGRn9jLJBMfe53PpSGuXtq6qT95rNrokudrRku6wcmzn8yeNM4GjQc8sMtAhNSBIyXgiKPHG1XPPqOLaPd77rKKJqCrrquQN+PTAQw+o+oYKXdSwy1yPtnMdWrHqSo1EzyiWCMnrrFCpa3XWcc5zPtfCN64niIMG7rve9S5zXdAFJiuziJcO2GNvueUW0zfqs5/97LzKcRShKRI32SoFCRrSoLaI+cEl1fu0f+CAImURdZxs1217bjWkBoEfZNXsnm3LFQQ8kUDC7liKDHl69CFPkwpsEnzaa6mGvEBBwgU+BEQ+UkDYd8jFEBRkbgISqLjmhvh49KmMvnIJqT+9wTQZ2tgzqSDAmaolj+1BYgMZ1oUC6Sps+tmuD5AZ7PPYFARCx9qOSUd/LrfLqYaqcpX4vdKhn0mVK6WUptFcJ4K0XCf+5xxZu7heVLpkynctKKgAj46nPEEVdpclSV6Wv1SoeWcioWeeecaQ+EsJEjchuuZb7jYXqr0B/Z/t1+mbrQfVEx7XutIqvXH1TpPglArIKyp08CEhCZYTmAOQ0YzHVPnGbGOeJCsqJjgPqo3S0HsuKc2cMrdJiBvskgKFx+zwfah4xqdgrjE3WBdYv+d7X2ceU2kOaYOCBd918cUXm2oNCI/lBio1qFanZ1BPT4/+9E//dFH7fCw3vKRJDRbfP/iDP9DXvvY1o6W2FE3k9lXvkXfQq45Qt6LxsByOmOmnQaZ9g6/JyE8tKHxZgkkEOwLTfK+7RCpdJY2mGB9MosrJRTA1exaJD5+Tzp1ubdhsNRt1isCxFVTAuMQIns7Zu7P9OR0ZPj9hBu4faNHBoVa9duXlWjeL5syp/TNsQsM+8uHoKUO4lHsWJos3DQ6f5LtWivdbR0GD8IzeHmXu9SpxrVQsEZbbEdDhoalZ0f2RDq3XZOb1isAK87j55pvN2NYq6flDzxunI1/Dj4Wxyb94mRY4nmg6ktXTPdyl4fERU9kzcTxyqDfcq9P9BxSMQzgkNBIdVX94QDc1XJtVngoiI18yY+J7HA69btWl+lrL46Zqw/7u/vB48jvD+tzph/W+TTepJFOuJYlQLKovnnlc42QomDGW0I/aD6rRX65NZfNfElvjaVSVp0EDkcmsJ5oMl7rTA+979+7VuXPnjOFM5pQhcR2UvXcolnDL52tSVWWFOo72qKOrR6sbq9VsN9+2N0pvibRxl/TITzQSjqq5AvLDKVXVKljZoEceeshkV7X296vml9+i/q98Vk0lfpX5vFaFw5pkUK+CgL2L9MrJA4TkqE03ojGYIGAKysLieHyXSqGnRHLYs8+dVCjaqE1br5Hj6MNKlFRrJBjWqc5+q5ppbL9ivjXGwcBIC48FNR6LaVtVitFJJpbDYYKSNCScltSoXy3d/lvSs/dIoTFp5Warb8VM4PxbfiGNJNdWJJTW3W7W5GAoopPnuuXzWNt2IpBQwj0mb/85ozGa0zmvWyW98n1S12kjDbapaZPae3qN40QWimm+zD14+E7pyNMWQdJxbirZdPApad1Wad810jMPT5I1GK48N8tGgAT97FLxTFm0mVDurpfH4VeECpzk8ficJQrFQjo7+pCpEKz01Gt1yTY5HS7d1rTZPHIBHWfmRV1dnTHsU7XayTpLbRpZCC6vqddP2lsnZJhwdDaWlqssS9+JmZCX7rLDoZqNe1V67rAOtXRqTX21ykt8kz1ccCxGUqpZmNsp/XBmAxwMnEf2G0gNMroIcKFRO9fAUSQRNWu+jUBpQNsv26nHn3xCTZfUmb2D+waBW+lZXkEh7JrPfe5zpjk0jj7ExnyXzhexPIHTD6Hxhje8Qf/4j//4knY2FwPZsu6xecmkTm0cXMTcgBzs5TWXSjVSm7tNw63D6qnrMRX32H52v8TlBipICHLiZ2BXzrVSdLbYXblaZ0Z7dHjYygZGYurVKy6Wd7rklAsABGmxk/DpSVRkn8tMWMG+5vozVrxne7SnwS/XRLKjQ8pIFqMqw9ioSVsMuwwbCFuRXjmQAcji4NvMdn2daxY2x0MyDHb4ioZSaaTByO129g2rrWfQ6rFw4GnFyuqNDQl5gU9DRUGmTYlfQeB7UWGSyxxq6eg1foaTxCmedh5TonRg5iSuLEFhu38KZNZSrQfY8VxrW+4WOxF7bKH24eZAuT64JSn7mwOMZ+xkSDjGr1XtljDzhOScpZYh45jy6Q/DcZPMRDyB/npUQ05cV39ZBlmpyednCfxD1haOj+A98nXI07GGL0RFEKQN0rp2A/HluqcB5ht+1y233GLG19/+7d++ZG3NC3sHnQPYcN/3vvfpxz/+sRkM86X/OZtqjT1VO7UnqVV6fvycxmJjKnOXqdlvacstKDAgNlwvnXpg8rma9VL9DMz2iuulnv3SWLvVeLxur+SbZGvdjjqFNSlhVeaW3DG3EqqWy+FTpWe93MnGnNyL6QJZ0XhswvhLfz6hn3U+p9/ZcOssrrtfAdcajcdasjZiG491LA6pARwuyTW9njeNkHlYP0/NXIcmyvrRDocJXFMSzKa+kLreSIEcHz6snlCXGdfrSjeqOTD95siGhDFoN4QjuElwavWm1eo/0a9SAuZJQCiE4mGNx4Npz/F7d6hXKwLz098iGIvo4NA51ftK5XV6TAO/h3pPpn3naCys06M92lmZvXS9KzSisSShYYNqjdOjfQtCanCft5TtU3+ky/QZKHGVq8IzNYMB58B2EEA80aZY4vBERUFcZ3XJZRfr8WN3qdwdV2VZisGNVE79Rkv3lnXpde/V6Jc/peBAr/pjJequ3KLqQ4eMAYzhQYChLbRe7g/+ifoH+1W2c7dUmUKW0ofhFW+RfvTlyazxjTulPZNGIUYoBgwZYGzcBTXchSD032R61Kzfuk2lZTVyYDD6y+QYH1J5wGce5lxWblZiyxZjXKIrWnnLHQr8+H/TP+96q5IDo5ixCimA0ZwzU2TFRutRCHoPThIagGqNll+oP1GrrvYD2rGheXJPWHWVVL/FOHc4eqyjSDth4Bmpv1jMVGbZFQIuV7nWrlxrjGZew4NgDyXaqw89LO1/eDJrPxM8ZRvbL3+zVFImHX3OuofX3iFt2K7ZgowgDGOuJxlMZOJg4HN8GGgY/zzHuJ1SmuzwaH3pFeoKHTf9NMpcPrkdFTo99sLEa3rC5xRLRLShbGYyl4w/DHoM2FSbgGvJ3zDeMXYL2Ze5/qdHx9SfXA64ijsqAvqXi2YXZMu7cmnPy+VzOLTTd0KnuwY1tvoSNa5KZjfe8BrpJ1+cbHQPuXLlHZoLkKEiU5HAPdISXEeyKbGvSFqYi1PgcbjldlClGEsLJFx9zdXqbes1xNiiZjYWCMbLP/3TP5ksuBtuuMFkVqVmmhbx4gN2za233qp3vvOd+ou/+IuXrJO51GBNmu/GokVMgqpvyDtsCda2pQ7KZYLAFHs3848AJ3s4VXNk5xMIXwpwLL+0Yp8uD23UWDSkBn+Fytx5VPEuc3B9p8vwtscLyUME7psvvk3O9kcmRR5IRtp6w8RrsQfxAdjfsREBZBR2LolZJOVQuVpIH41MMDawM+ei0885U5XC8ShI4DYht8ullfUp/s7O3UoEqsw5YRPiR+SyibB9IQU4d+yahd47EhWrdPRsh5pqyrWmiZ4VDskTkHZdqYTTbfw5OwBvV5QA5jzzyXxGImECz1wD/DSSWvBJIBSW0tZJlbvlGG0SiT2B649/wf3HXp2uH+B8gWvCdcwMxNv9KojR4EMvFfB1Cqkm5bpx/ZiHkBzGP1mzUzr2hNTXnvQx4tKGfdIs+unZYH4zH4hjQQhSFU5MgKot5tRCxHDxafDv6RmYGjdZjuC6PJBCbCBHtdz24sWAI5FLPO1FDCbtO97xDlNORNYcA/clj5Fuq9QQOaeq1bmbhBeASLxLofhh07fCqTL5XXvkclhBaiYd5WM4+SzmbDyw11PK2JKkxj8cu3PK8xwhCR4f2vzKWTVjYugH4+3qDx+aosvudVap3ne5liP6w106MZLURk9iVWCrmgNTNeMXEwcHn1NXqD3tuZ0VF6nBn32DxlDFUIJxxwiyZV/4GQPWv8mntuA5UyUBkVDqKtXa0g16ZuD5KZ91afVFWhmYO1tPD4wvnnlIXaFh8518N3JUA5GpGV1vWHVJTlKjNzSqvz9mNaq2wQh9ZfPOBZOgKhSJRFzRBP10Jsf++HhITz7er2v23ij/2Z9roL9Px1q61NTQqC41Kla7Xg6na2IdJQsHp3Hfvn3GuMXIxbhIJR/6+vrM/cxp2PZ3W3I4JeWWvJHDaeYmTicGEhkgbM6MF5whjFHWjUIN/QmnpeOk9Ni3ksH7hOQNSDe+zWrol0rg3PUD6bEHiJ5KN79CuvrGKZ+JIZd5vnNCy73S0Nm02rHOnkGFVt+hNa4OaeCUxPWv3yU1XTxlnaZcFieN68eDY7Mz+yEQuVcY1ay/EAf83FxXp/L//tPJD+G6RGKT5AbfweNtfyCtWDiil/GDQ8R9tc8DMgPjHweWY8dozlapkEgEFU48zQhW61hcg5HJ69d1vkdjo+PaWXmt6uvqzXkzrrKRA7ZkAgZitu+BQKJKJx9HcyQald/p1I/b2/SeZ59IIzdXBgJ6+OY7Cm5UDmZbTo/zxvlNSDl0nJVOvmCqd7T9Uqly7mXcyA6wJkBM8zO2FdebsnECSMbpnyXaxtr1eN/+iX5RZe5S3dRwlckaZhxfKAb8X/7lX5qs/Z/97GcTfYyKeHEB2/b222/Xhz/8YdPQsYjFB3sz+whrAw8SEFg3ZyVnWcQFBxKkkDoiuQYbEpsHsh1Cgwxf7NHlJv/yUgD2FbYTtjg26oFH7lPZeKdC4YiGA41SSeWEbcWezusIXLJXkvhCUNqWsrL9EOyLa6+dpq9cFpAsZffhwF6hiodgKQFdPr9Q/wJ7Fb+ksrxMeu670gDVFknZzzWXSBsLOz67chi7d6GJDapmtjSWyNfxmBQZkwI10vqbp/RQYx0lCQo7mOtOwDdVChV/nutIkNmeW7YPt9RgrHFsxJj4mWohuzKf+4ZdD6kxkzR3tnWG5DHGKmMHf8XIL7vdWe+ZXYWT63vwcwmgExNbCjDeOIdCk5AYD8xTxqp5bzQinXpGGh2UqpuktbvnHFdkPpCgS5IU6zk/s55zzUhGJA6xEPPEDpNfCIkpZ8+eNQotJBj853/+50vO3nnJkRosYm9961tNAJUeGvk2CYsnErq785Se7G+Xz+nSHU0btbtycjEPxqL6WcdxdQaHVecr1R1Nm3NK07xYkUiMKy5Ye7ecqjUNgKznE1MWAxajnTt3qmWsW9968qfqH+hXtGdMb7j6Fbpy71RH/wdtT+vwcHrA3OWQadj89vU3TXn9WLRNozEal8cVcK1QqSu3kTIcOaOhaHojrxrvXgVcy5OZjcQjOjz0tMZjgyboXudbqSb/BnWH+k0Zc5O/Tu5FLGNmM+N+PtGTzPROQZWnWjvr9hpDgk0eAosAJYYAwWk7g46sFO4Pr4NtRh+QxfjceKvOjp7WeHzMmIe13ga1B/uMtrol5eKQ2+HWLQ3XyVegzFQ2tIz16qstj0553imXgrGYkZEiKAmJdlXtBt3UsNVc82z4dut+PT3Qal7PsVZ5Anr/5utNM7HlgEQirGhiskJrdHRcjz5ySKtXb9fIUIMcVJqEBrRn9x71h11qaGyaEuhlPSWAY0s7MNcxbHAK7KaA3HMqLfLNpIAAwWCZUrY83q9Q71mdbGlXyFcnjy9g1pD24Bl1Bq0srkb/KhPsHIv1y+2gSfpK8z8ECUbixHo/BJFy0pK/Wrl91mWxjH2O15Yx4HccpIaaaktDlEqGQtDxpNRz0DhCzx1tkd/rkRxubX3lRwruW5ELNuExEQS+63+k58iWS0pJ8T/l+LG4VNMklZRK17/Kkp5aQuBQ2URXarYYCMefVUL0gUpMkBqjw2PqbOtWw4o6lVWUal/Vberr7TNji/vF2CXDLnWMcV1GxkbV1dGZM+OQ9QsHFkBm8R4+z95f2sfH9JHnntGxaES+mhrtq67WoaEB00MjFU/c8nKtDJRMcXgw3KfLGqMqB6fHnl+5L1iyqiFJQgKcds5/oRI5uBas3zga3CcSR8imwtljnSCAkBqQKBRDkWFTled2eLQy0Lio+1wqovFhheKsOQ75nWvlchY2z6na+LM/+zNTKcy1KuLFAzII0TlG2/j3fu/3lvpwXjRgbYklek2SlMtRIadj+j2bwPW+yy7R/x66X88ce15jA8Na467R7//a7+Qn4VfEsqq2sTP1M8G+SzIUQVT2ToJb7MfYCyRMsfdgl7EXUUXI/sT+uhg6+0WkAxsAO5xEJ+Yg94sEEu6PXaGbCXxLfAf7bxAR+BR2bypDjBw4UFDVDfJY2FDZMuIJ2uOvMJ441vrGSg1EDisaH5XbWaoqzzbzfyo4HwL6HKfJcsf2ojl4cFgqb5BqZ2/zEIjneGz70q7emGsSB9eROcVc4DvmM9PdVl640MB1JrnKllGbDrYUGvfBJsFYdxgHduIYSTzZPoeqESq+cwHCBeIIMO4h5+37nxqyxY4vhHzg+CBvpjs/7h1+FglkhYJj49wmiI0FAGMW0ozvsKvnWUMYz8QeiEW81PuWtbW1mSphpOa/9KUvvaSux0uK1GCyvuUtbzEZPBAaMzXbTMW3zx3W989bgW+79ev7Nl6qztCIhsMhPTPQppGk3Ax/b/aX6o+333DBa2Tmi3iiV5HE/okMY4fK5HHQnDr7+WM4HDp9TE/Fz6pmtUUenH3umEo8fr3ryl9VU336vaFB8z2dL+iFIaREEqZXbonLo9etutI0bk7FWKxNgxECg5Moc280zb+zgSnAe8Zi7UbGqdS9WgFX/mNjMWEMuMEnNRBJ9uAwAb2EBiMOU2UAyt2luqXhKvlcC0+qYVySkUsWzVNjD5ss2nOn2tR2+rzKK8u0un6tbt15h3kdQUScDwzFVIMMQwKnBaP0Jz/5iXE22Jxe85rX6OjwIZ0bh5yaRK2nQYPRoOmnQQB7X9VuVXhm12g4E6dGuvSNc1N7llxbu0XHR7rVOm6V2trYUdGs16+6JCcR+lR/i1rH+s3PqwPVWlNarRWBhZMAK3QsRRMPmmbak5kWLaqt2a21q9N1QfkbG6VNQmFIkDXFfYRYyAywYuxhfPN3jDO7f0I+wIDjkUY408ju5M8nKxgCNRpdfav2t+zX2aFjWrdljfkOmjL7YTuTqzT9FpzdzWqsb551M+qCkIir43tf1PDTDyaFQFeo6Y3vVnlTntk/0aB08oeKjg/qbHuf1jbXaH9/nS658ZdnlyXSd046d8CqPFmxXWrIWAPZ/v/5IxYBk/ocoILlg/+flhsYt5CgOMWMSx4r1p6T12sd90g0oadOdisUimjlWstprfE0a31ZeqUQjgcZVvxvKoOGu/Xj9iMaHBrUtj279Lb1l2l1yfSVBZCzpul0slKHz7nhvp/ryPCQIsMjSgwOyB3wK1Bfl1YLyJ089LJXqyLF2ORYWBshxci2wQHBMaT6iYw4nE/mExVHuSpJrBOLSR2PSMPJxt/la6WmayaayWP8Q24wv3Bu5jv7CGfOzqxk7pMdSxYl30OVCaQQxvaFkPWUDaFYu8Zik/JmoMx9mTzI3RWAT3/60/roRz+qu+66y+ptVMQFD/SXqdBA0/h3f/d3l/pwXjSgqjQYe1ZxQ1xb8Dp3yuNcMe29eKjzoHw7Gg1RHxwZU9fpNu0tXaO33P7aRTryIuYCO2BNUCbVfvzud787kRFNcJv/ITPIuGZPS62c5TMeeughQ3DgZxAIY1+iAiAf7fgi5g/YMgRV2f8z7RfuDXYO984OrOJnQD5kJpjgH+CPcG+55/xsV/zng3wbcZ9tOaWzvQ+ppr5E1bVUHhAh8KjRf81EX078HOya2QSBZwNbjsv2oUl+wZYrlETg+iIRRRAem205VFMsF0DyMB7xa4lbMMZS/VHuAXY4aw9/ywVbvSA1uZexTbVRPpKIvIexju+aaS/b/gt+AvduprHPmsc4xV+35w/rKp/D+ZIoBskHacBYyjfhO9sxE2MlQZUY60IE1ElYYA3hnKky4ntYJzhH9v25VoW/GNDV1WWIDe73V77ylQuSZJwNXjKkBhP37W9/u3Gw0UcrhNDgEr3z6R8rZGc+JoMSHofTBCsJsLszSHP+/q4Nl+ri6tktDAWDgHYsaDWWXWQixWSqGhmb9GZbLq2T25mb/X+677i+9MNvav2lO+TxeXX+6BmVVpVpx3i9br3y+qzldwTuz4/3G23tJn+VAlkC9z2hxxVJDKY955RXjf6p8jGLCYxp2OucGvx5IBwP69HedFmj7lBcsZRZTPXChtLVurQmdybAXMFGbQcWKfkD+zueVk+8Q26PW8eeO276F1y17loNnB8yDgnl/5mAcbdLCO1Nm6AbbD/X6sDAs5IvoQ071k8syj6nT9fWTa3Oma9+Gp85da9p8G3JT9HDxKl3rr9Bzw606uGek6ZaIxW/v/lWlXv8OefGd9sO6Mn+SWLmjqbtuqF+aXr4ZCKeGFTMkJE2Idsgl4OeAc4p5/G9733P3EOMaQwGnMKZNkqMDLtnysUXX5x39gYl6hgpBHjN2vbsF6z+EhNwSM379JRvVAeff0GxeEI+v1eN1SUKhyIT1Qg1tVWK9pXo+otfoUXB/gekB74/8SsjqKOkVqFb35x/hjr9Wg48qIbqMpU2bpQC0/fbyYnu09JT357sm8A/e14urdyVH6nxsjdLe5Zvc1UIBduQP3DwG6qqcWpgYMRUUbnLyxQvr1ZC9G+q18rA5qy9iGy0jw/p74/eb+a2qWJxOExF1ce332IqLrtD4/rsyRfUOj6iDaUVeueGXar2TnUiekIhbf3pD9Oec4yOyTfYrwSSacw5v0/v232R/njbLpORhYMEcAKy7Q2sszhPOBucM6TvtIRA55PSwOHUI5AqN0tNV6bZQ6yzBIIA5zyfDR3JyMSYxnlj34PI4Lj5GSfx0Mlj6lpToa7ouKlsffWKHarx5nYOFxyM+fE+KTwmldRI3txVMP3he9Ik+4BDPlV5ry/4a9G9/cQnPqFf/OIXE/toERcmSO5Az/jP//zPTb++IuYPkXirwvEjGc86VOK6QY5kcDETrGl/cvd/KuGUGtattIjrR5/Tvp17tM+x0tgjLyawrhLsnG1fgOUE9kSCrmTAE3ilwo/gHQ8CfDxPMIvAIAlTvJ6gdq55yetS/Q/iAOyBduINWeqFxASK0IIEKvETuC/4FZAOxv6fATb5AcmVb+8ckjp4z0y21HisU8fb7teJY62qrqlQWXlAwfGwKj3bFHDXTyRtcaxLFTSk2gWShiBvISDwjI1WRHbYCVPYxMQ7WHP42a4CY+zMplomm3LJfCR5cSwQXIxD5g3HytqI7CzfxwNyMNt38378bexz3pstVlMoIMoIrLPGcr0gm+erVwhzjmorGnjbpDWJU3alHn4V5zOXqvB5RywshfuM8oJ8NfOmvDAdurq6jAwV1fL//d//fcFI9M4FL4kyAgb9e9/7XjPwKT2djfEStRvZZnnONaHaMblYJJKSVIuCYK/Ufp8UG7cCGHWXSFWLZ9jGEsenEBogobFp3zccDWr95bvU8sJxrd+3XU1b16ll/1HtvPYG/fwX96h53WqV+fw5Kw7aNJT1+b5wi6KJkbTnyK4Y9k02Li8ENHDvCfUbQqHSU6FyzwyyH9M4HXPVcHVkLcnPeE4JDUXTz382GI6M6tjIWUXiUa0I1GtNSbNh+CEh2DBg9skUePLJJ81G6g8E1Ns6pMZtdbrs0svU4FyhvrMDZqNls8m24JLtkEpoADbUG2+80RgU7kHpkYcfUTxmGboAuamFgt/l0ZtWX6Hvn39W/eFRQ1a8smmvqr2lipDtngWRlOa1mTg20pVGaICfdhzWjoom1fuWRjMzFU5HpRy6RgmNIOQlqdTcC8YVRhzGP8FWnEoyNwrNKCbofNlll5nPY6xwTyGyyGqaLoDK3wniYqCsX9Wknr5+9Q2OqrqiRHVV1nULDnbrZF+X1m5ZYwiNOAZoNKKSlObmba1dGu6Z+1zIG2fTZewQHvN0tcpbWkDA1uXV2r03GWJn52wJDXDyMev/1LyF44+kkxrMu91XSfsfTH/uiluXNaEBUuWZtm+9Xc8e+JZcLod27V5vAl1UCs4kUWLj2EjPRK8GM/5NxV9EreODWhmo0vueuV+9oaBiSuj48KBeGOzTf156s3wZjmyJyzVRyWnDWVaqX922XdWJuAaiUe31l+g6T8AEoDJltHKBY8q72e1opkxHYspzGLfYQbYthNNBBiUOCFlfcyU3yJRCzxxjmmADthcOFet9RVWl/vfcC+p8vk/rrr9UbeNDOjnSq49uu0mlSyHZyfw4fb/UnSSCcDY23SbV5Op9NHUfQBJnNnj/+99v9sbbbrtN999/fzHIcIGCJAwqND72sY8VCY0FQDyBL5G5srJih+RQ7kzQFRvX6NgzLyi2slEuj1vrLtqmvjNdqtmz+0XXT5Eg0rz19lpCQFZwLhD5NlGD7WjvSdiSEMDXX3+9yaaG6M8G9jT2IGzXzCAdwTBbIofAH4HxIqmxuCAwSRCV+4CNwIMkuUIzxCG9ANnmBGeBHcDNBbuh9f79+42vwbyx34v9w7jAFuob6Fd/35CuuGa3+dvQ4KiaV9arxrtBJe4mMz6RNePzeCwFSByZrlogF7ABL1SZqMVAat89ko2obOB31CbmslbMd4UynwfBAtnCmOWeMq/4nUqTGZOgkoAsnk81A8ZkKqnA/MT+5/rNJO01E5ivrBUkS5H8DJHPZ+NPEZ9ived/xnjeftNCItQntd8jxZN+gr9BarppwRPQGxoajJ3DXkmizSc/+ckLtkI+X7zoSQ0MIJr1IWvD5jObkioGwaXVzXqyrz0tSzvTxM7E5vK5N96cEfGodP4Xk5OFI+p5SvJWSiVzb5w8ExKJIcWVHrS14dD0G+3p0S7jaJRUlOn448+rZmWDHNGE3C63no62qDHgMtd+X9UGXVW7bWr5XSKqoUiboomw/K5KlbmtjWY06tVQND2ri54aFZ5NsyI0Huh+Qt0wrGTialiX1ezV2pLCmkkBNhwMJ9t4YqOsLnNKI8elBPr76NevnbaZksfpVZ23UT3hTvM718TlSEyp1Khwzy1gPhQZ1Z0dD01IWp0cPaem0ZMK9DvNcZO5wKaCfmGq1mMsGpN71K2d66wMkNVV2Z1GNiAMQjRucy2yGBDV4Tr1dw/o9OGz2rLXun/rSxdOC5f7/XT/cYXioypxcy1pcm9hS3mDHus7lXada71orOYe5z2h0azrBI3ElwOpAZCIc2iyVBNDAV1wnAKMD8gNnEM7o3x23+GYKG8mWJ9P0BTjB2f28PHTKg1LW9Y2qndgRCdau9TZN6xVe1bq8l1XqiN2xrzeCbHmc+ncmQ5zrJXV5QqOBXXTDS+f9nswAiFv7OyWOYEAtd2bIomI06UST2GfSxbZnLNaIlmc/WiW4OtNvyJRdXDsOYlS4ctvsRpH22hvlR6/36rm2H2ZtGV5ZXeRlcN6dMnetygUOa8jB89o5/ZL5fTkP7/ok5VtL/9m6wvqCkbUFZokZbEBqNh4dqBbV9am36MSt1vv2bhZnzx53KwbRprK6dQHtmzTprLZOQyUb7NW5j02nZ78nms7LfW2S1V1cq7ebOY7gSCCSBCZubK1cJZYI6bLouSYcbYJGEHkQ3KQHbt792491XJCgd0btE4bJq7nUDSkFwY7dEXtLBwQSOWx4xJEPvteyWZpmqqcKeg7OUlomM+LSyd+Ll38m5I7W5DQmbVSY7b4wz/8Q0NskOVP0o0dpCniwgABUcr8P/CBD+gP/uAPlvpwXpRwOkrTyXnrWTra5HyP7TuMbBnR2eePGfuguqlO20sbjQ3LGjafOvLLAQT5bf8C+3nN6tVyhc9IkT7J6bfWRleBfb4WCdhhSIdwb+wkGALOqRX7JLmQ0c9+yB5Ds91sDXfZx+6++27TUDZXoM4OWPKdduPjYoB3ccA1J5nDlhDD5mA+FtqkORWQFDywBfPxVRg/PEieYv8lMEoQlHHH+/mcDRvXavOWjUqIz0uoorJMo0NhjQz0yuMeNfJlHPNMhIbdR3I+MuCzgfE+l6B9EdPfO6odWIcgWpFWZn0isXM5Af/A9hEWRWp5JgTHpJYj1r69evPE/GQNp0oI9Y5ssR+uLVXkdh/WXNizZ49JqCUGxfxj3rIv8B0kUkJcz9upxPo1ErX6KJa5m+V3VRf2AZ0PpitNBLulgRekmsKqq2aDFStWTBAb+GR///d//6ImNl70pMb//b//V9/4xjeMszgXxu4d6/cZE/rpgXZ5HC5tL6/VMwMd5m/0U6WqJ7WszOtwqXQRehooMiTFrYZCk3BI452LQ2rIauBs5F7SWGi/XI7cpV+8fhS5LPTOVzWpr71bbUdOa0VVve7pP6hgPDLxWc8OnFKtr1xby1elERqtY48rnBi1zjeSUI1ng2p9m1TiWm1yuMaiZ83/NArP1U9jJpwcaZkgNKzzlZ7qe14r/YU3KWWBTi0N72o7rqMHfmLGjnVN4iqp36uwb90E486CRHA3Fdsq9uj0yDH1R3rlcXq0NtCopweOGkku69pIR4e75Xae0t7K9bNawA4PnzKEhp293Hr0jA51D+r2VVebY8osW2UzeeSRR8zzMxlutvQJWXIzHdvmpi364Fs+pLse/qma/CvMo9ab/fND8ZAGwv1GLqrGWyPXLCo6nuk/pZOjFmFkV2N9v/0JvWP9rVpfWqfXrLhIP+s8aGSqVgWq9KurLjZSNbkA6ZEtWFrrm121z2IAQx+G33b4GX9T9FajY9LQYUvyzlcrlW/Jq5yScZ1vNiH6n2TkmTGyqlI68VPVVpWZh7u8Se6mXVpZuVbeoE/Pn3xawfGQxt1Vqm2oUaDcq8G+YdX4V6rtWL+CtdaxYZiSvYIxz5jlO6gYwsDCueH4MAhnnUly8U3SieetHhbc+URCzbe/Tkc7O1VZgMYnDh9GGobdjCWjwR5p4JBFbLPmV2637kXjJmmkJ+WFDqmsVmp9XiJ4XJJcV3Bsrv8l65GJlpPSJ//KCvSCR++V3vwu6ZJFbG4cjUiDPZKvRCqb2o8Gh9SW7nG7N2vPro0T5dhk7uQz3vZWrdBdHcc0GAlOJC5AFJ8bH9ZgJLvDGM3hSP7Zzj3aUFauB7u7VOnx6N0bt8ya0ACMV7KcUrPDyGS1exRNQe0e6TxykCl0Ks+l4sEfSY/fPfn77iul299krhmZXRBFzIvU76QPFo45jhPfP5M0BPsAlX2QGswp7gXOYW9vj5QlBmAT6AWB9/Q/KEV6J8831C5V35B/efdYj/Xa1O9nLw0OSmVTM/JKXbs0GjuQ8oxDpa7dGo2eVjjO/uNVqWuD3M78syeRoOKa3nzzzaaqZVmVzReRE6zR3LO3ve1txtcoYmHgdqxQTN2Kyd7PIIsJikxv411es9lU397dMarwyLjqWuO6/Jq9hoh6MUqvpCbtsT+cOvADJULnk3kWDrlcTyiw4maNjEWND4ZPQqBuqYMcVO8S6OY48A8ISmUGXAl8sw9CIM5kE5GIw/nlE9yjwgrCfaYAL583FO1XKBZUqbvcPIqYHbjPJDrY424+g/3c+3yawGP744NmjhG7woMxUV/XpISjWucHn1HrubNyyqfaku3atnWn8RWwiRgX9EBjvnEe2EdkqfM82f2QIySCMM9I5rMbS89nI2W7v1whJAXvybTxsgJbqPd5aaxTcvmluj2Sr8Cg7gUMbFiC5ratzX3kntI3jvvN35Z6/ZwrGLuZslj0WuHcZiVZNNgrffeT0tiw9bsvIP3y70p1VjyL4DrVrezB9udDLEMYMS55Df74dHYwr0sd7/TwI87LHgi5PxeCNBVj0W51hp6d0EkZjp5To2+fStx5VmYxf6Yop+CnpPdnXUisXbt2gthgbUIi9cWKFzWp8S//8i/6j//4D1OhMV0pYj7wu9x6z6bJ7FV6aXz57AH9ouuMydkj7mH6ahDHYoImYvpF1ym9asXMzajmBHpoTEEix/PzD4dKrWZGUatawO1Ek5xm1WvkSAb9zaY/dl4tY+fllFObyteoyU8JZ7n6wsMa6RtU08Y1qmmu0/ixTp06clRNG1dPnqIcpo9GKqkxGGlLEhrJ88WYiZxSpWe13E6fytxrzWOuoBk1Gfl2cB/EFddYLKgK59wy7RsCfWrYQKbv5GePjbfJu/o6uZPNlVjY2VwIMNuLv8vh0qby9KZkjYEmPdF7WMdGzpvG4dKYHu6xsk8vqsolo5Eb4XjUnDOVFyefParxkTHVrWzQ1ddeI08WMgdDkg0oH+OUHgsE6hgXnB/nRbCMjRXikQ2PDQ4tXHRya7y12lC6WTsrMgJzKRiKDOrZgScVpeKFklxXiS6tvkJeZ2EZtGfHuqc8x3U4Pnxeu6vWaW/VKvPIVxdza3mDLq1eraf6Wyeee1njtjlXafD9D/Yc15N9Z0x1ya7KlbqtcYfczrln3mBsc252NhVEXJpEDkRG+8+SZGpCGmuxyivrr57xmKmIIFPDGPcDPSqLhFS1fpO8TSvN/ed7cQRwbBkHE9e4YqW0603SWLeGx8LqjvWpSU7jmPCaSxpvmMiWiidiCsfH5CrzaHQoaGR1+CzIGpxnAqt8PudEVjTZgBhYEBt2Y2bG5ayaSNavlN74e9LzD2tkaEjnvNVyV6xUXXVhTgDzA51erhP3g+PPaqRx3c8TnGbOJ6RgpxQZleovlzZdLQVHpLZkU2PGRk+L9WAOX/46qW6GNfLu71uNp1OzZH/8rcUjNbrbpO//56RxvOsq6abXpgWsIS1YL2zygvWE+4rTyX3EoWSMsIbmmrP0z/jQlut0V+cx9YXH1TI2qK6QRbr7nelVmXxzmdur3VXZKzH5jret22Ae8wHWVMYwgTh7D7A1ahnL/J9GwpWvkVbdKg2etH6v2CCVpYydrrZ0QgM8/5i0dZ+0ziLdWYe5dnbWL9/HcUBMAFuqarpMKuZ5aiIJ45fnVpfV6J6jT6li81olnOyuVCC6tK1iFiX94a4koaHJO0RWMpWMvjyJSW95OqEx8Xz2NdrrapTLcbVC8TYzGvzO1RqKHlUobiW5gFCsS7W+q+Ry5JcVzZj5m7/5G7M+vexlLzPExlLJWRSRH9jLCIi+5jWv0V//9V9f8IGN5Qx6fPlcFyme6DdSb05HuVW9MeP7HNpbtV7Bqi5d8bIrjA2CXcP/+cj+XcjwOCPabEyYSaKDwGfQNagVq6wkAOxuKmepCs2nf8FCAHsPn4D9G3lTbJ9swC7LN/uWSmOC5gSd2TPZv0h+IKDDPsS4+P/Z+w7wuM4q7Xd61WjUu2QVW+417nGcQnovkITQYVnKAj8sS1h624Vll6Usu8AuLLDUkEAoCemO48RxHPduy5LVe5em1/95vzt35s5oRhpVy47e55lHmnbnlu9+3znnPec9DQ3S+sjYAH+b5yLVmBD9WBwn0OPtiL5WZVmKEtMC+TwVMDDMihuCNsVMBSDlLG9eRxJgcjUIry/tFbmhPN/jeEjVUpZjg+OQfyWiLQ/bVsbLJNP2YbIXP8PAN/0I+sH8n2Q3/Q1WHdGP4Nhl0gJ9Kf4m/Q1WrU4XDAJzbHObk6264DWgTBH7cfD7fJ6UaOl8BRhtjjxRAc52YNFtgH56UtqXCjh+eI6UCUS8rnywop4+qPK1SxGM3ZCkka8/fXHanxzbHF+TlqTd+2fA7VTY6R5g92PAfR8VT7kt3gs8r3Ijcf4OX5PvMfm8pgt+j1UbXN+VyZnTxaBflq0Px72WNqlBj5FxqLjkcxUwRRn7qaKmpkYQG1RH4bVlZfHliMuW1Pjtb3+Lz33uc3j++efFYjPTYHb2OxetwZ3FtWh1jeC75/dFFNRj7w/7EysoZgGU2MioBkZpoEUWXK0ZsE2PxEkXKlUG3EE7/GGJdfRFYgND/iYUaKSAxnlHM44wkziCdk83duRegRsK1uKPHfsx3N2P8tVSRlX9QBcMJiMsNrM4l4zP829iQ/BgmOd2rLBPMOyDNokUxFSbM7F/hpLQIEjMmJmxMFmQsfWeBoLdkVsvNKak3mzUscN6FDSIuMAwuDme42HSGNHiGoqefxmv9zfgpd56eEN+lJlycHvxWli1E+97gSEbpzvqcOFYHYLMrjcZkWnIQP258yJgSAPKHXSiwXEWnqATfR1DWF2SvFFfIsjO06iUG3FxMWO2DBehZ555RhynnDlAh4SfnSjb+vTIiSihQbiDbjQ4zmOZbXLN0g0pqm9IYiqR7lji5+4pWYP1WWUY9LlQYMwQGv3Txf6BRrzUG+vfcGiwWZBvNxVNvzk891luOkfDX5aNYdWAgOMCEEqQNnI1A/7V0nyUAtSvZaUE52PNnieh2/sMukZdODXqhGbHzSi47lYxHhgI5fiicxIHNu/VW2C0+JHj1qSsvGNDaKNGyr7q7m6Oa8DL309WNkxCQ/SwWLFC3GPUdebCPyWN6twi4Jr70HTypBjXPKapGLt0mrhfcgCN2WBjNMBHzscIDRmj54Gc9RJxwcbgK64HTu0CWtkQPgISFUefAN704fF3wukYK/uhNFZnEww0/+UngFuR5XJyH5r8GgSqVwkng+eVf5PJdfH8yZk+nE94TTn2ZCM6EWatDpuyC8Q88libR5AavO95PxQaKSUXRkAQ9lr829orkambO/3yMZVSEfCYeex0SHisUW1lS7H0SIahvrReH4/UkwNF44EB+sQAFc//75/7I96xeQv+uHcv/MUFKC0sxIPla6fWKDwqu5n4uqLUeyLkLQX66gBHV8ymKN0MjPQAXidAsiUj3oHRqC0wqyWZqGDYE0doEGEE4Q60w6qQvBzxj2DIPwSD2oA8Q56oKFSC44yJOA8++CBuufVWvLhrV5z0ygLmlyTFrbfeKtYWXrMFQmP2IUmtTj7wTsJbJl953UjU3n57kqrEywHeHmDoqNRfkRLECdBotLCYY+sfg760MRjcJGmuTJ6abdC+Z+ISf5u/SbuLBMe0KmUjYE8+khRco0RvPq1WBLhoUz766KPCduTcSrtAXqvGCwoP+HriCA3igvMscvT5MGqmsG69wSH3oOAYYGD+4MGDIoFtOvMofRWOJ/qnTPSQiRL6LxxbHAe0j/gexzmJhlS9KEiApFPJxfFE+1NOmk2ViEA7lWOMpAf3hXYbiY/pVGTy3PEY6KNMVbJS7mUiEy1MpIoLXrMiP0poiF+V4hjD9UDeelyOIPlEX4tjg3MGE8pSkV/0iWW/mElAnL9miqCbS/AYk413jmfOobxH6Tdx3kzrHh3qTah8DkvVGwpwezMdm+XY5b3NdZ4+MyvO169fP601LRj2p/VaSvB85W0BuvfE/HStCbBPn9ScLJYtW4Ynn3xSVDsyDnL//ffjcsNl2QqdRMZ73/teYbxMtrHtZGHXG7HMlosMnSGuiXMwHEaxyZpyMpxR5G+RMnMzqoCs5UDZzYBm7gIuwSSN+kJgQEgKMp8ZiWSMKnB29AJyDTa8rWwntmfXotZWiFDIj9IlFahau0TMA2qVFGIgobEqMz6YZ9DQYE8kGzTQqeMzIx2BfpwdeQknR55F3egrcAeTNxdPhSpLOQoMseoDBo7XZC4dE5BIC55jQKAZCHsANjLXuwBdEkPaPzxm8qfjQeeMmUfpwhsMo9/vhSvoE+Ox2dWPx9oOpjUmtb1BdL/aCK1eh+zCPGy+cise2HSHyKoRTahCPhwdeg2D/j64Qy50jXZgz/nn8fyu55Nujz0Z5CA1j4eLJQ07GgAMnvE5FyE6GXRqmJHFxZSOxrFjx4R+4nhwBRMDrWE4ApHs7klgW25MHkwGW/9WWPKm10/CkiOIjZkgNIiTw8wSjoFX9ORI/GszZeyQ2acGJo3yWMAwiWEzzkLPzG4aRCwLN3a1CEKDsBn1yNDroH7lGbjaW4Qxws8ygyNV1Q8JvijBMg5o8FNfMx1wnDFTSa78WLl8Oc4dPYAwKx0iWSTM+B8PvK/oSDN4vm/fPnEcbFY2MjQEjPQCjv4kuuDpgfcHty3fu9wfZjfu3X8Erx05j0MnGvH6sQa0dsiGo8Kg1GgB91DCb7OqwyGRG+Nh8fL4Hj80Dqsj90hPF/DCU8BLz7GDImYczlHAMRy/3yo1Al3NwonkPMJzzHllov4j/OzKlStF8D/Z/OcL+fFU56vY3XsIr/QdQ6HRhUydSpDqhEGjQolZjQqLGlmGEP7lzD585dRedHnmiOBJAh4HHflB5xDCeuDosaPpaStnp8heSugPMh7opFO6YKL9U55r/r+3/xCcpQHsP70P265agm0hDW4P5KHSMsUsYR2/l7gWqyOvpwlWMC27A6i+DijfCiy9HehpBQ7/ATj1DLDvF0DrsZRfD0dkH8e8jtjrra5W7O3fi1Mjp3B46DAODBwQFXbJ5qHv/uRHGFAHcNvdd0WDbguYP+A6cO+99wo75f/+7/8WdMnnORisl+VouA7ccccdadsFlxT8Q0DvbsA/KCWdeHqAsCrBVgsD+rHzPwObJM5pa8+Fz8rfePzxx8UawiAz5aaYOMPgLDPpk4Fz4RNPPCECw8kCkaxuk8FAM4M2PC76F/zL36E9xrWLtgCTpfh7DHzRfkiV7JDcv5DgDrKB/QKmCrmhN4P79BGnCtrGDLyyh4qcjMRAJh+Uo+Hv8DP0YWgvkvygb5Os0pQ2FBNF0gF91nSls+j/cOxyH2TJIo5BGfTvJ7r3SMzQr6H6COWemUnPal1+dzoQPs/KlcKelsFz8MrLr2DfkXrsP9aAQycb8fpxKXNfEBuXKXiOSTxxzuC5pk+YTsY/5xkG1UluXE7ngvOxLG+WdvVEdlG8/Cv/T+V3pMBEvkyq93m/8x7nvMJ4FSWpWD01VZg0OWm9Ni4spUDpLUDOBiB3M1Byq0RsXARs3rwZv/vd7/Ce97xHxMovN1x2lRpcGO+55x4hO3XTTTfNyW9q1Wp8uGYT/uP8friCfujUlKdQ469dp7C3vwHvrNiIMvMsGtEMOmUuAZLIa88FNCrjmKoJFdh8WArYB5MsgMFQEI6AG4+//hx8OWH0u3qkw8i1x+Udl5vzcG3+GpgTGnZaNfnI0i3CoD/SJBgaFBnXQa3Q2KUETZPzMMKRAB+bPzc6D2KJ9Upo05TnInmxI3cjuj19aHS1oMvTgxMjZ3De0YhtuRuQqUuzBNPVAQQ7xvgX0GsBf8L5CTgBBZEig4ssGyMRsgyIEsszy/Ba/7nIsYfhVdhHtJVYcdLhHkKLqx8VlvENMTo4n//bh3H0xDEsXbEURrVBGD7nW86LxaLH14GAIoitVqvQ3tGOoooyUVYss/kkJ+go0XFgOfh4oOMpO5807piVziwbEhoTZQdQbkrpeJB8skyhWTol0e4u3oJnuw8LMsis0eP6gnWwp5AiuVjQJCHVNLPEUctlnTQOdu7cCbWpEBiJVV6JQU0SdZx7gcY8nUqB7hj50u1wwWbQwRcMYpFJC+2yZeL3ZHKDhovcbFjOuKPzyeA0neCkPQUiBg9l2+RKh3TAzByR0eEYwInffBsan0NoUg9aShCq3CiMu+1X7RyT3cWsLjrL/MvMdBJ1dKqefvpp3HnDtdAd/APwokTiiF4Wm+8DppDlT8Oa1S50zgiW6mZV3yoyQPx+SgFpsO9wPUoW1UKdOL+xl0ZvU0SaMHIvsfptogDG9XcC/b3AUamkF6WVwP1/A9SdBv7tK1K/C27jD78GPvcNIG8Gm+cZzWN7HYjXLWKMyI3n0gWvLYMXcp8HJU4ON2DIr9Q9DWN1ph6v9PngTTCeebgDAQ+Ghr343ImX8d1118KinRu5Rxl0htva29AabseowQmdXoegNQCn34kMQ8bElUQ7bgde/kvsteoVQFn6TXOZ5cqstPFA54KfkdeqDk83Oj09MNssMFrNaKtrQXt9K/zdbnEdk61pE4JzfCarKg4ATKLg+m/bKL0+GZDYyI1kO3acBnoTEjHO7ALya4AkvZAoMaVRmREMKwNcYRjU0hrrD/kFmaHEgH9AEB0VlrHyby8MNOH6f30Yf3r/Z/GOd70Tv/rFL+cse3oB44PrCp1BBrN27949tUq+BcwZRB+EiP0gVyZctrJTrsRgWhgI+ACDDQi5JbLXuiopqUHQriIRQPudgY/ZrD7itu+++26xPtBmkpNUxpPRoU3I/WMyBwPSvBcZDOe1pV800b3IOfTNb35z9DmrrJg8w+9NlJVv0lhS+h0LmD5IDNCmoc2fqip1PDDZL5lsGW12uY8fxwzHD8c5xw6T86i9L9uDsjwV3+ODFV30dVPdBwxeT5RMkwhuT5a2IhFB35iZ5PRzuX8kypV9BmRwXxlQ5vdoe23fvl0kTtE3noyPMx54nLLsMMHf2XbVtVC3eBDyDEoy4043Tp1vw6qKG3G5gv4d13eOlclKEJOsmqxs0nxOBuD9QB+bPq0sEZ4WrrwD6G0FRgdjvtzVsbk33TmB5EQq0pD3CGMCyUCSXCa5Oaafe+45sd5MZU3L0dcKZRh3UErKNWlyxWuTht4uPeYBbr75ZvzgBz8QsXLasZyDLhdcVqQGmfpbbrlFNF18+9vfjnrHEJ7rahFZ6jvzSrDKPnPNqBJRY83Gv6y+HseGOvFY+9GoZNGI34MfN76Gf1x6nZBXuhxh1VbBG+pFMEzDmVAhU7ciOoGUmArQ7GqPq6vIN+bisdaXcbqtDjVXLIuL9cv/8/NXZNeMITTEZ1Qq5BqWIFNXLiSn9GpzHKEhV2nIhIaybMwVHIZNnX7mPX/LH/YLQkOGJ+TBq32HcGPhzomrNhyNQO8+IJfBJsWRpppfA8zoLgVUmqSTNctEaXAkZquvt1eLYP6p4Wa0e1wJhEYMv27Zj7eWb0GFJWfcYxbNFJcuh14h/SVLQiUitygHfV396O8fQHGWWRhsJGGYEcOFnvvK/ZZljdKqbqisTNvAXW5bJXpqyASaQW1EtWVqJbml5hy8p/J6zGdsyq5EW3t8NtGmnMk7A+mCxg0bcfF68i+yNwKDh6SAM+XuMmqAvhel5+YKgOdeYUCwJJTEFh0Kk8WGs90D0GvUyDTqYdXrUGQ1Q5VbGP0Oxzcfcumr3LScjg8fV155ZbTfDB1bjheWc8ufYcbMVEtbXa/8BnZtAIW5uQgFgxg4fQKbRjrgcHvRcPYVuNZdh4DWIO4FEg0MmtDwShyrdDi6dv0GZWpFlshAK3B6N7Bm8k4B7yEazJwDYhlmWUDOFdANHhcVNItqlqPNX4kxwlxLtgPd9VLzY4LnmRU3J58B1twKNJ4CDr0gaZ9WLAM23wRoddLjbR8E7nk72RsgI1P67j//IEZoEI5R4NFfAB/6JGYM/O3ttwKv/EWqEOFvma3A8o1T3iSvE0vLE0mNYb9zjMwgM+0/UrMJ36qTnDxBtofD8Ec+xobig34PTgz3YUtOCqmnWQIzXPXlRgRGQzBByvjR5ehwZOQ4rspLo99JxWJgn1ZqaM9brukMcGQPsO6qtPeBwUFlL5NE8F5nBq0MVyAmWVdcVYILJ+pRWFmM7Wt3oL+jf2qkhtiRYsBwu6RZS+3aqVRRKuEaTEKmhQH3cFJSg3r/dt16DPuPIRAeFckcGdql0EeyuTxBz5ixxXXaMaZ5oIRBnxs6qwk3f+/z+NN7/hGf/NQ/4N//7VvTO6YFzAgefvhhEUyiZn/iHLKA+QdWdVJS5Q0LTju2bVJWKP2jCeZGEtGctxnsZ9LEbFc8sdJQGThkkFnZ7FwJBhn5Pu1C4ZP5/cIXoXQRq4mZvU7iI13JPgaI0w3mUGYqT1+EXl9n9LVKS+2C9NQMgjY+A+okChjgnwxkmWYSXrS7SQKw0oD2Gscxx5oyGYnjRpYJok0t+xcEq9KZjMeAKZOnZKksBliZnEVfiGQc/Yt0KsaVkGMi9FGYwEefgT4Vxy/vO/pITMyl3yP3JOPv8fVrr702LijLqpQXX3xxxkgNgr4U/Rnh48kovQ7qzpcBdy/sWdk4O5wHmCdH5lxKkImtNzJI9HEeVc7F41WyjYE1E3jg74H2C5IdXVwlNQufBFhRxxjUeJVQqUgK7iuJ+b/85S94y1veIu5j3nOTvV8JxhULjeujklMa1eURx33HO94h4ickOGjTUqnicsBlQ2pwsSGhQT3iT37ykyLQ8PDxvZJRpwL+0tmIzy3biB1s5DpLYDNxZ9AT11hatGwO+tHpGRESNJcjNCo9cvVb4Ql2I4wA9Ops6NSxbNENWSuE1EKru0ucm8UZFQiG1Wg+fwEFVamDQZuzl6LQOL6MhE5thA7JM6/kSpFETEU6qt83MKZhOIkNT9AL80RlZANHIl/wA+yZwYk4Mi6hZSBHbkTE5xrA2wQMe4DMrfHSLxGQLGAmAbNblZM6/1+fVY2VmRX4Vt3T0dcTc7F5DM92n8TfVO0cN/OFC4BSX1MpJ5KlyxUNy2USISMzA9uu2QJzZy6co85o4z1WW9BIIKGRylmZCWTq7NiavQOD/gHR8yRbnwttiv4YlwNWZErn8uBgE4LhEFZlluKKrAmaPkdwbrQDZ0baRd+f1ZkVWJSmtJbGFESvqx5dbjNspiKYrW+WsqPdHcDw67EPjlDqKAhkxIxtuf8BjXOTwYYVO6+D+uyRyL0QBra+CSiKBTXphLAqgcYlAxNyFQWdWrnih44Ms6voYMhZJCyhlh2TyTbOI+xWE14/fAxXrZQIir3HGrAlN0vso9WoxypVEL2NB3CuZrtwiuiU8F5kpiCd7oDfD/+pfbC3nkLPuUZcuaSAC0PsPhZp/pMvT+Y5oJPDe3JMyXxmrXiEQyHYI2X4Y6AzArllQDsJljA8Pj/a+0dQHjgBXVY18PTPY589/gpAWaXrHoi9RjJBiYG++CoPOgG97BU0AVgm/+yTQFcXULEIuO5GehGpP7/+aiC7AGirBwxmYMVmBFunLrPGMcHyf44rJTmbqbOg3R0/VxrVeiy35eOjizfjzx3n0O91o9fL/gmYNob9PvyksQHdHjfW2LPwQNkicT+OB869L7bVYV/dCdisNuQZdXAGQtCquf/Um+d2R8RaO+Ead2CXtDIof3Lf05MiNXhP0/lJRWpwDVGS2HY25FagalUNRvqHUJiZD1v+NHuA8Xg1M1TOTSmsMdJQKmAc6UCt2iwag4fF96SMRxlGjVGsSSFFggXX4G5vByoDi2DWxhMlFRY7zjv6YMnNwi3f/wJ+9K6HUV1ZhQ9/eIL+NwuYVfzXf/0XfvrTnwoHcLIZuguYe3COp184Gz0V5yXM5cDomciTiIOhz5YqaCeRocoALklz2hupKmGnC9pqDFjJ9poMkhKp/ASuJQy8MEDFgDNJCe4jE1zYD43rUDI5oZkA5/PajNUo9JfCG/LAos2AVbtAas405IqJyYIkCB+yRA59DWX1TbJxQV+ENgorJ+hv0NcVyZK5ueLzfHAbcjNojjVWhzOZg/M/A4JTaUTM7fA4+VvcNn0b2c+mv0wiQ+4/SLKF+8EKFiZxcd94j/C+ZCVJ4v0zVdB/IplC+3iM1DMbGpffJPwLziPqAUktYiJblbJY3N5MNGueS3Dfp0Nq8LrRJ0zVX2W+Hzt9bK6b0x5bVCNYNL21d7wKZV6j8SoveF/dd999QkIsnf44E+FyITOUYKyc8wpj55SzS9Wz91LCZRH1Y7YgS4tovH7rW1JG2y+azoomnyJIEYlU/G/j6VklNQijmgM/nOL1yxdkM83a5OeWweWtueuwJRIE40T0UtsRuEacKKwpi8b3ldhgX4y19ukFOmy6POg8RvhFU3Hpt43qDJg1k2dr9SnkqnTpBM7lZqYO9tKgpi0zprSAZT2gLQLURsBxVnI8ZGkYXxfA/h/asU4FDTYa9DRqZLmm+H3VIlefgX6fY0yGKMFXHIHkGpyc4JhdS+MpcYKTdUlZKit2MaBFs7MJba2t2LJtC65YfCVMOVKAhoap3BtBlLFu2zbrEhoGjRGFmrnNmL7YxIZMbqSL40PNeK7nhPifI+28owt3FW9EtXV82SBHoA+trkMYCfZg0N+GQX8rSk3rkaHLA1wKUk6Gsz6O1CA4ppgFVM0xy8Z6dceBwT4gvxiojv8sxxizhTjmGIRmBpbck4MkhsiY9/vF+wz008jn2KQhTYeDWsmyc5OOcclgpCfUD3MOUFGYi/rOfgyMupBv0EOnDLqHQ2g8cxK6xduljMBwCHnNp3Btfx1O/2A/+tRa1O1/CbUFOdhWXQwMO4CgEbDKxKsKME5OGodODO/JVP2haICSNJSDy6kyiIdHnehp75WkcXRaVOTb0dwzhOALT6AyFIIuen+GgbrDwNX3Sf04kqG0HGhqkMgMgt8tn6BSiKXLX3wYOH9O+jxLh48cAj71ufGDLouWwVdcLa57oL0zZXPHdMHMPc5LcuYRz9fijFJ0uHsx6B+NyrtdmbtWjK+19kLxGPX78LGjL8Af8okqDbWoSDSg2GRBm2sUhUaLkKKcCKN+P25+eRdaXC5xD/5fcyMODQzgW2s3xH2ODi/HtTxv/vCVp7Hf0YGMglwEDF4EPLEeLyP+MMrMahjUuvRIey8ZnIS1we+VXqs7A/zyx8BgP1C1GHj3h4Cs7OhYZCBA1thNeS38HgTOvgyt6zhgsgFLr0ZeRi6WZ9Tg9Kg0X/DYizy5eOWFl0WwYMqVGjONwqVATwPQXRd5QQUsf5NEqk0AVm0kQqfWYbV9NY6ygW9si6IXx5nRk9iQFX9f31S4BM3OIdQ5+mAvL8Z93/sSHv7gw4JApfOxgLkHmyt+6lOfwrPPPpvU5lrA/AMzoDmvvGFA8iLvGmD4mNQo3JAH2NdNitCQwSAQbYqZJjVos9EuYxCXv5EYlGJQl7IhSqKc6yCDo2xuyrV6y5YtUbuHCRzM7qfvP9sBGSF5qb88ExPnC2jnTse+47ihxNNECU20Yxi8pYwTfVVWDNE3YcIg/Qn6riQ6aHvR16YfQf+DdiOTqmj7M6GDyUaTrWiib0JblmOX9wIzpZXgfnBs04+RkwhvuOEGUWFMmVsSeCRW6B/NFGFAEoUV4HFNwhVgNZQs9ZOqPwHfZ/xA7j3I/ef9LvdAZNLXfAXPMytxmM3P45hOA3eOIV5fxmcInlPOo5QWm08yovSreZ/Icowcd7TtOQfParNzvw94/FfAsdd5ciSJ423XRt/mfcFrwPk2VX8lgtVYPNfjgeebfSNoO7Mqb1LVJm8AqFQq/Pu//7voEUcpqmeeeeaSl1S95EkNTkbUuOWi9Ktf/Sq6mDETMjGcO0p90VnGuqwS7O6tx7BfkhzgPizPKEChcQKd68sU3qAPQ/5REfzP0knZCWIBOd2KitUx55DniUGiLL0VSzPKsdyWXtb5RMxqtXULuj3nRX8NoyYDBcaaKVVqVFkq0ORsFQ1lpf0NozajWgQsJoQhH/AwizkMOL3Sw75SIjSY2UliRIzbhBEbabSeDHKmUircW7oBj7S+jiG/UoYqjMH2boT8QVRm5KFeXR/3niPggjUzEzWLk58j/iYNGpa7skqE2fB6GHDXvW+JBpWPnTwm7kUGvWgYLAQA5hf2DyiueeTv6wP1qDDnos7RAXfAi0JjFkoY3Veg13NejBGV4pu93vMSqZGEOEv2GrNXolIDNO6WppYio/EhG1uc0zds2CAacNOBoOHFbD6OQbmRGQ1pZlzRIJO/z89yjNLZpbNEozIZQuEAejwH4Q+PiOf27StR/+SrCISAslw7xD8KbKoqQXDTZry8dy+uDg9hZM9TON7VB6NOi2UWEzZtWIbzA8O40DuEFcW5ULu8QIY50s9CCyxLXSGVDCR0WA6fDDxuHisJo/HK0Pm5Zr8Fq4pzFIEEFapXXYHQoAeN++sQCIZQmZMJPavFJsL7Pgp884vAUKRpZ0k58Oa3j/+d1/cBdWel/2Ut1P17pddqU2f08LryOtMwnQmjlNuQs3eFTvDoKAZ7B1DjyUOXU4X80iJUZBXDklCBl6HT42srd+B/LhxDu9uBEqMFeo0WHzv6ong/32DGF5ZvQ4lpfNLqd23NaHJS7iqGX7c24SOLa7HIIn2XcykdI2YZiYbgXi+Oq0eRUZgnKjJsuvhgUCAsERvX5KeZGcUMqlaF9i/n+/JaoLMd+OaXgGBAIjhOHAH+5QvA174t5MDo7PO+Snkd+J2+TgSO/wXa/kYguxRw9AODbcCO92BF5hKUm4vhCrqRobVCV6AVjjrHJyV9eJ9fdM173h+rbwUG1wBeJ2DLl6o3poEiYxEatHXRxrLi6qmQVIKKSQkfrtkqqnt9oSBK1tyCWw0leOCBB/DSSy8JLfgFzG1wnBXgrNJgcsYC5g+ETesfhSfoQ7beBmNEKlWWKXrDSYSxF1/+dTOyqanMwwwWM7BJmy1VFi3Xj1TBPVmukPYOA7sM4JLs5nfkLGJun/8zUMoA80Kg6vIBs6+nUmEtg2Mhne8zcCcH8EmuMfDOCgXamhxbHPv0JThOGfxl4izHnbKBOAOq/D2OSfq//E46v00/hH4Q7R1WHiXeJ9zWddddJypJKKnLag3eBwy40+dh9jn3g+/T7ifhMh1wu8pqESVod/N88G+qpCoZrKAiYZMYFKX/xf3lOeaxTRSInmvw+JilTp9gpuIVSlKAti3PMUkTkjw815zTZrNn0WT60Mg9Grk/M1HRMCEe+V/gwMuxpKrf/hgwGIEN28T8z/OVDlFI/yhOJi0JeEwkOSlBKasp8PkCYuCcxdj5Nddcg/e+9734xS9+cdHH5hua1GD/DE5IzNZQMvzrsvLQ4hqJFvwzYL56FntqyDBpdPjo4h14sUciNkpMmbgqb+zCNa/AyaXrNDDcDrB/RclawDT9DJ1ezwB29x5EIBKcZ2+NK3PX4dTJU9ixdgv6jR682n8KgXAINp0FNxZsQFaCRMV0oVMbUGqefommUWPAtflX4oKzWRAbufpslLBhcjrI2wp0vwT4IgFAS7mURdX0uNQUXG8BjMoQlwpQ6QFN6mvA8nBmjqRCtt6Kv626GkN+t1i0Hzm3F2eb6pFVWoASex7eWr4ZVq1RVDN1uQewf/A0BnwjgLcNx9ubcVPhpmhQjyQFjSoadixJpKO4a9cukdlBI4VBZUoyyH0MpptJnYhg2AF/qBuhsJPtv6HXFInGrAtID7z+nlAABrUW/ohcmBK+UAC/b3sVfb6RqMTa1pylQsosdg388Hp8MBhjhq+sMQlTBeCP7+8hXksAiQgathwjdF6Y9UTyi0Z74vxIZ4LjjiXlV199dVR+gM3KOQbpaNDA54IsZ+hxW/yOEjSOmA3V1d0N3fIcXHB2iiz81fYqrLBJvThG/U1RQoNwlOViaEk53rRoPVSUSzq0V6pIkCVp1l0DDYmErCzs/dF/wqLTYFt5EdRylZVWg42LinC6ow8/3nsceqMZd/3Ne2Fn1mPpCsA6uQCpIBwdjqRNsf/617+KjKiJ5FB4b5pKFkNlXgScfwVgpVZeJbD8eqg7GlHdcByhUBhNA8MIhYHqTTugSlWlQRSVAP/8H8CFOp54oHqJ1ANjPFCWTMpRj399NHbuk4GOH7NGZ0Nagtef85kc/KoNh4Wx3zbQIpxUOp9KR7XYZMUXV0g9K/7Yfh4/b441gO7zuvHNs/vx3XXjB5UGfT5oVCoEEiolBnw+LIrw1Mwe5Dwq65y6g35oj0uyYiQ1xhyHCJwXo8ycZmPD9TuB0QHg6F7pehRXAje9FXj+GSAUjDkcHPckOlpbgMrqaAYvM6DH2DReD/Db/wR6m9FkN4pKoFjDWj/QeQ6o2ogMnVU8lOObTp4su8C5gc4xg1Z0hunozGrmWDLw2LJntnKEJI4vFN9fw5RCMotSZLQdZVAXmAGOW2+9VTTynTdVLZc5GDi67bbb8PnPfz6uyfACLj5ou+7pPYpGl9TnQKvS4Lr8DcjTZorgJHXqFzA34BzOcy4nk0z2uwwY817jnM/seQZIub7IJKIc1OU6QMJjXvvTC7hokHutcBzSduCY5HhM7LNCG0OWuSIxQAkzgrYG7RASFXJlAQO/fNDPYABYJHdFxh+/y2QtJnvwN9Pt38Pf4WfHqwigvc+EJspc8RiUSUv0u+lPPf7448L/5vtXXZW+dKgSPA9yYDsRvC/ZzDwd4oTnOBVZyfuaD97DvJe577MlbTdZ8FrSB027CfYkQd+LFTVyVQ19V/q+9CuoIpDMr5sLcBzTb5xTUpj+BP3pxCrxg3sFqUGfh+NCVlwYf1PxEsKpwHgB1xOuJfTtGC/mWKSPwWvA98Yj2t8IsFgsov8IqyC/+MUv4itf+QouVVzSpMbPf/5zfP/73xeDNLG07d2LlqPb48Kr/ZLBuyIzG/9v8dxkuFm1BtxePDHjyaCgOzgCtUoDkzrz4hlqjXuBNvZ9iPx+91lgw4OA0TYth+PlvsNRQoNod3fjQMtxOJyDCOk1UIfVeHPJDpi0xvRknC4ySGwst02y+XTQA3TuBVy9UjZs1nIgczHQ+hep7wDhcwFhHWBiZlQA0JgB22YpqzvVZoNBDAVceLX7HJwBD4pM2diWs0ScRzan/2vnSXR7RpFrsGCF14pb7ctw3w1bRTglz2AVlRgMZj/R8Tp6vYMiUCYPPzbNfb71dVQ7c0QWM4N7DETLzdu4MPA5jZgDh/ZDq5aaMs3GouAPdcETOiHWQLn9gi/QCKt2EzSqN1gW3hTQ5OzHI62HhNwYSY3FGXa0u/ujTY8552Rojej09InPywG3ff1nscxWBlMk89GizUFTTyNs9phzYNFGgvOWxdJYpuQUaWTTIsA2VvaBv8V5mtUTzMKjkU6DmoQYF1W5V4ZMYFCGgH/l5l7McJKzgGiA0mlRNv6i0yH3bpHnUhotNFheajmErsOnYbSYkJmfjX39p8W9ssRairr6s3AHeiMZSSGUlOXDm2lCeMVOqKijuWg9cPxlwOeVstmXSo2qVy5fRq2qsSc9Yq8tL84VD9eybfjF0XasXJmF7Usnn/FNY4xZXTTOEg1gNg9kpnvaDdBKlksPJSqWAte/FeoDz6MqIxOugiqcyl+G5RMZjZwPVsSu2YRYwiqCBGOWBvWi8RuU8VrSAJ2L5qX8LZksZsYQDV6e92Rr89lR9lmKHRElqVrco/AGAzCMQwhtzs6NIzR4hjN0OixRVBJxbqWjSWeZFSpMlig329HmGoY/FEYwTPmrWKM8bq0mYxKBf17Xa+4FdtwpkRj6SHZdqusdeZ3jgXM/iUJekzi88DjQUg+Y9egbcaK6KMERHtOnQgIzJFmFxSoNEuWyLjavN0lKOjp8TKXJ33zCYutSDA++Bn+04aAGSzPSz4z7h3/4B1GaT2KD80GqyrMFzAxo+5DQuPPOO4X28ALmF+pGW6KEBhEIB/Fi72GUt5rFPLKAqYNzcLr2PNcpztfMck63ukPW3CeRweAUkwdo58jrGaVQGGBhdjPtRa7BM9kQeQGzD2XwP93PTzewTFuJ9jB9AGZy06egDccxSntFDs7Tj2DwlAF72h/yftK2YeIV5w9+L1EijcHvRNlnBqbpZ9CG4finja4M2NNPIfHB7SglOyeSeWGijUy2pAqW33///eJ/+lA/+MEP8M53vnPSSYUkZuiHMZM9cd6kLUwCRq6gGg8kHHnOJyJq+KAdwyTJ+dJvg34pCTD6oYkE2EyDvyFXnslyXRfjPMhKBhzLcyY5xHspcU4Qr8XWGlbycD2hP59KDk1OeEwH/BzHN88zk4H4YPyBPWQ4BnmvUu5aljV8o6KgoEAkSXIdZjUL55JLEaqwLNp3iYEBrxtvvBF/+tOfhNZmKgz7vSIAkKUzzKvsDldwCM3OQwgxiM2JTpONcvN6QXDMKSjJ9eqPEl5UAaVrgarUC+pEcAc9+GP7rvif8gbQe7IN5VdUROWN+PeavK3I1F+mAerW5wBXV3wwz74YGG0Y+9mCKwFrqdRvYxzwlj108iheN3ULR07uSVJizEatw4ZftRyEI+gVQTbGmHNzc/DwFXfArI1fIPb2ncaxoUbo1LFWHtx284l66LVa/L+r3pk0cMJgU1dfC7qdZ7BkRRlsmRnI0VdDo3YhFPZAq7LDpKlMqi8+GbDPgSP4ohQMU/RZ5vFqVFmwascvPXyjg0TGt+t2wR8KREcfK9aW6DOw7+B+aLQaLM4ohE6lxgVnlzReaCybDDBlWHFjzmpkai2SlIA6jIPndmHVNqkCw6LJQYl5LTQpxirHER1TGhRKw4SOArUwWTaqNPzpXNC4Uhrk3AYzlWhkyiWjfI1VeTQCuV/MMpGzuGm08L1kGUvf2P2/MGSYYDAbMdwzgFAwhHyDHTvz1sBe7IdX3R7dft2ZFgz0juDNN31i4jXjN98DWhviA7arNwODrYA6CKjJxqmBqvXYNaTD+foGUXlCA4LHn2r7wVA/fKF6hOGDWpUNvWoJXnllX9JjY1Dg5ZdfFu+Nt7/U5GX2SzoGLJ1L3ufJghQ0EKeskfvcU8B/f1+Sn2LJ8Sc+DWxMz5ikIcosp7nMUqfRSwcsWbPZHzQcxfPdzdH7hjCoNfjN5tsmHDc/ajiPr545IWyTbJ0eP920FZuy4ytJOZ6Z1SaTfaz8/PGFA2hyDcKkUaHUpEUwHBRT4/bcpdiYXTP9A2az989+TOp/wqwquVfKF/8FUMdsE54T3ndxY+lHXwO62wQxul+vwpZVlREHhr2i1MCV7wKs8UQH7zceJ51yGtPJnBh+hhVaJM4vujSVtEOA1yU1QhyvmikJfCEv+ry9oml4rj4Pxkk2N+d9SQ1uOsZ/+MMfpiXVsYDUYPDprrvuEsGwp556atYaEC9g6tjbdwLnHdJ8I6P9fAvurb4Wy6ommXy0gDiQbJB79ylBOUQ+lIQH5+dk1bapwGxtVuQx+MmgaTLy5IUXXhC2I/2PVEkFC5if4Hig7UhCKtl6zmtJIkDu0cjrz/mVfgF9gHQbLHMtZLBdbrpN8DltBUpHKW027hMrHBMDl7Qp6WPQdpbHIfebfgq/T+KNFRXycchSqIkJHbSJaavxdVZO0PeRQZ+GJIsM2rBcU9gfayZVDXhf/fKXvxTBau6HLI+VLhhgp72bTJ6HRI5cuTweZHIzHZAAYeJA4toqy13JTdLnEkyK43WfS7uKtjSJpYshlcgEVY6RiaoiZhSP/RzY80z8a+//JLByfZxPy7GUqvJpMtU+HEty1VYqCTVWZnFde0P14EoB9iBhIg/7x7Fq8lLDJUlqsAyfjPKXv/xlfOhDH8KlBhE4c+xBIBxf8pdnqEa+YQYCE5OB1wHs/2n8azQQCpYBS6auzRoMh/BY67PCeRfPA0Gce/0k1mxZDZU2ZqDyv2JTIbbkxCa0ywbMgD3/q7Gvm3JjUlRKFF0DWCbOtq2rq0O31YcjzuYxnQsWDVtw0Di2kde9peuw1h5vkDzetg8dngGJ1BCvhNF4rA65lKgqLMZbyq5JWWHU4nw5SsjJsGpV0Eb8E50qFxlaqdHuVEGCxBl8Oe41ebZSwYwM3aU34c4lzo1045ctUlN3Gb0NLdhqKcNbr74F2oju8bnRdjzfHWti63W64XN48K7aG2DU6oVRQKOdRmjtMimrQ6tOnkFBw4DGCCH3WZEbx/Ev5XSUUjLcNp0TubF8MqOClXiUHBDm7cl9wHA/usI6XNBmimApnWMaODRIacizXJt/aSzSuKdj/LPGp3Hh1HmULY9VBZSacnFz0eZIT40D8Iel++b1V0/j5qsfglmbhnPlcgB/+TnQTBkmHbD9ZmDzdcCBvwCNx+LJzOU7MFy2Tmi000miIUvjWc5o43GIBszhEXiCvG7KbP5sHDvoF0ZZsnuKcnR0JHn+UpUz8xwzWCFLGk0HvM48z9zWpAPNLiec7W1ocbmhMZnFOSBRQUOWY4HP6WDyHCUGT5hRw7V/LkuF6aBy7NNZVJ77Ho8Tf398N1yBgFgySVB8sHotbihIr8mgI+AXklOFRhP0SY6Hhrv8m6/1d+EP7fXwBkPYkVeEu4urhZTcudFu6FQaVFvzxq0OmRSaG4Hf/gwY6JNkxd76HsCaMSaLl+ONAQgGqDgOKvc9ieD5k9jX0omlxbnIXVEOWIxAZi6w4nogpzzpOOL9wOAWsywTgyCcM/g7DBSQRLvoPZpG+oA9vwVG+yU7adU1wIq5lbmhg8Z54O6778a//Mu/zOlvv1HApuBMmCJJfqlXCF2uODpUj6NDddFVsqe5E+FgCH9/9bsvicrv+QoGhrneJZtrJxO0TAbO95zPt27dukBUXIagTCrXc9rdqQKltB9IHNCuoS8hS0FxbDCBif7BeH4jE6CYqc1gLEk3mUBQ9n5QBqVJIvBznMuT2c+0LRhTUlaK0+5gxQa3zX1kk2Hau7RPuJ8k3GiryoSeTArwsxORMgygcv9nS9uflSokC+j38HzyeHmOmflOnyMV+Blmr6eqcmN1KJPJaOulCvpPd35Q7guJFJI+ExEpqcBrSpKGx0+fiMfOsUDbVSbSEkkfEkMksma7IjwRPFbaGdPtjzIZcGzzMdXzO2Uwoe2Zx4GjkUbh190OrNs8xr/g9eMcQtKR44H3IO8vEjG8hybq8SLjxRdfFOOW5zbZnEQyldvkuWAvmwUCHfjP//xPEV+nvz1e5dV8xCVn+XGyvv3224XG8MUkNDzBAF7saRbZk9XWLFyRVZT2zRBCcAyhIW1zbDB61sF+DiY74B6OBdEYOc5g4H0Q0NmAKVSPULd+Y/ZK7B84LoiLhmN1WLtxA1RaVhbEgnXhSDPxlAi6gZFjQGAY0GQAtjWANnWD7HkFMR4YrFLKbqgArRWgBIWfTUIjdRb6TCCNHh1km2k09Yd6AKckfiKy4nsH4XG4sLp2K+AYO46ScZc2nRmdnkEEQmHo1UDXhXbklOQjMzcLW3NSy2L4QqNjCA2CkiraSEmFP9yHENzQYOqZKCroI1NU/G/xSLSq+aHHOZ+RLMjpd3uxeP0S6BQZMEusxehw9+P0SKt4brFacG/NVSi0xJfE0ijk4i+XiNMopNHIRU8ObMeyIXiXc04JQQVmUumSOkAHDx4UDgcNac7tdDASsy/obLy2dy+2dx+DqrNZEBmFoSAKa1ajddW1woki0ScbLqKS6dAhURHB6gQaKwVeK4aK452NZTap6kSt0qLAuBmeYB+8fjdKbOb0CA3CbAXu/7CU1a4srW2vGyu11H4OmSuvFg25GJiksUZ5LR4vnTruK8+t1tgFl6cdBYVZsFqlTO4QBlBVvUwYGcmMOR47jWIaxwwyMyMtkWwQhIncoFsBEh18j0RQumsYzzOdpMRS/FQgYSVrGNNg1RlMKMoriDZ757hiHwG9x4OQXo+gViscS15LmfThZ2mAz7X2KTOoeC5JMiQG3R/OW4rD/hFwBauGHmWjAXSjW5yfibLMrFqdeCSC9wAdKwbxedz7+7vw2ZP7olJXJ0f6Mezzosfbgx4mJVCzWGfC39VsR7ZhBjL/KiqBh7+c8m1mPPKa0MFgcIISaLx++2xlCHe+hPXF+TAbdEB9N/DAh4Ga1GsJHW/OK3JGFrfD6yxXCDIAxms+LzKoeI/v/jXgGorZSWf3AL2npSuTWwksvQZIqIicafA+l/VvKclyqZaJz1f87Gc/w//8z/+Icb5AaMxfLLdVoNHZgSG/A+4RB9yjTjyw864FQmMaYNCP9kBiUI92Hl+fbgYz5U7Y62QhcHR5QiYqxsv85hhKVuVLv5ZBZTkpituRKzHkRCgGN2kXpisVQ0ko+gZMACCJQL8gsbkw7W/a3SQr5KAy7TzKUtFmpc3N9Zafo+1OQkMOuMtBV8pU0Z9JJ0BM8mOiBsfTwbp168R+k5Th+ZPJGtpqcvU1zyGPg8lgsj3Nv8lkfWUwa5syWjyH9NmSNZXmNvmbygoUpYxquglQ3BcSYzzP6fROoI9Hm1meV0jm8Bhlu5LHxG3xGnJ7cqKe7JPwuHkuaH9eDFuTxBiJZI5V5bHyXuE+8x7g3MzjoO3Ne4XXYCrzKI9f9jXHSMjOBbiG3HKf9EgCjhWuNyQu+KB/SZ+Afi3Bc5EuoUEoxyqPmcdOYk4+zxwXHNtv5J4aifjwhz8s1uo77rjjkpO6vaRIDU5EDz30kFgQv/Od71xUQuMLp/agwz0qAjHM0LyjeDHuL0tP81MNDdTQJgSGVdCpJydFMCPgpLjyduDUk4BrQHqeUwD4zwBdZwCNBci/GtBNflBXWUuRqbOixzsAnT2AOyqvxat9B9HvG4wjNvIMKXTmQwGg/0Ug6Io0Gx1lqg+QdyOQIlN8XkAQCAxyaoDsFcDAicgbkQWIfTX0VmDgOMDm3CQ0slfHyXskAydfBi85qef3OtHT1CZeZ4Nfe34OFpWUYVNpLV6v74Ir4BOyKPxF9lKoscYHaR0BN0wag5D/ooSVLwSMDjmwadl6rM9dglxDatJAjeQlrYnLa5h9FqZRNU75KpN6Ndyho3HEkBpWGDXpNWR7I6PcnI1F5hw0u/ojr6hQXVUFy4hfGFCyo0DD6Jr81Vhjr4Q74EO2wSrGRiJoYMmsPQ0rGh50PJhRQWOBRgEdAwjS9hAp6Mg3ddBiPVSq+DmElRUs+6aWK8HsPVZl0BBVZhQJo8SixsuvvIKti4qgQ+Q+qT+OsvVXo/TWW8W9Qaebn2UWFL/Pfcqw2fDHvc/Aqwsiv6IQep0JOrUGqzOrsMhSGDfWjJo8qIM+BHytcAYa4Qt186hh1lTAoJlA8zTRIGKwOpGrVQQ76TTw2Hm8NKgYAKfTxX12eDwoMPjQ3TWAjvY+LKmV5JZ4TOGQLqn2rTjLOp0onabzwHPLv7IEAMvxkxltPGd0QHgdaVDzs/xeOkjXoOb1YDaMnJ3JdZwOiOzgiHFl0ANf+izQIpFWuP+twNvfHSWJ6BhxXykXMOdNoyNObqKmN+8BHlv2kJS1x2tK44/OLfeVr8mZZsnOFe8fOrg8H3xf6ZApy64fb28Y0179tYELsCkqHplc8UjrUbytYg0uOJvFnF5ozENJGkR5uuDx8F5nVQWP87/+67/wt3/7t9E+JKIXCRu7nnxdIgBq1wBFY6szZLiDXfCpBtA5cBrL/Yth0FnFvCTLjNFx4/mdLw0l4R4BnIOx5zoNkGECfE7pecdpwO8B1t0567vC8fG73/1OSCTR6ZMb6i5geqADR4eOVRoXxeFfQNrQq3W4vWi7IDaOth7DbVe+GeUZRQtncIpgMJLrFm0Irm38n4FBBsG4/tE+mU7gh2Q9bRiudwtybpcn5EAr7T3aQ5Ot4pV7Lsj2EW1a2lMM0HPMyHJV6YCfpc3CID/9EtoTHHvJKjbYz4vJUUysUmYm8ze5tvLeoN3NxBbeD3KWP4+P45pV4bRbJgJ/X/7ObBLm3D/KtnF/6QPwvpMfPEbamLStKLPF45X7SNBG5xrIwH4yu4vnkA/6LZwbaK/xL8kduXk5z4VcvcLzT9+MtjMJA14TfibdICm3K1fepwLnKo4PErGyhBOPMVmj8vHAY+E44Xib7b4ayZDMryHpQsKG5473lkxC8Zoy4M9jp3+RKBNI8D1W8HOsEfJ9w3HPyvhU/SouJri/tP1JKMr3lCyBRLWGqZ5XeXzQ1yRpSnJO7rdJ33eB0BiL7373u0Lq9m1vexsef/zxS+YcXVLyU5///OfxyCOPXPQMqqe7GvDL5pNjpH/+Y90NyNanR0wM+7vQ5qY8iQSdyoQqy5aUsi4TgkEErxNgpuYEwfGk4DAIeIHRc4DjjOINFaCzA0U3Tm2/2He8u1tMrHQS2Wvjld4DGCFBwQnHVIhN2WujPTbiMHIGGD4mBbbY9EE2QjI3AWYpy1qGO+hGh7sNoXAIeYZ82PUXaXx4mgAXSYwgoMkELBuB0TbA2Q6odUDWMsCUZha4AjRAuOhy3HNBE41kbRq81Hca7qAXBUY7bihYC5vOhF6vA39qP4Ye7yhy9BbcUbwaRSbJQGl09uKpzqNwBr0iUMYKDRJzyzJKEazrR3dTu2hEyizscZv8eY7CFexNaHYrBzpVUMMAu277tPtqyDJUwfAwwvBDDRs0qoyFTK804Q8F8UpfA7o9I8jSm1E2qsZAd58ghmlc01incT4TkkQE9Wy3X1mAEKSqjxgs0Km3xr3CKg1mETHzR8684tiic8FxHrdPx/fC8+wjONTaDZ1GA6tBh+WFOcBt7wGqV0YdBgajmE3F71O31lmhR4/eLeaX9nNNWLKsFvdXXCUCIokZXTTyeC5O1u1B7eoMZOfENE5t2rUwaGL3LY0tWV9XudjTiCeBUOrrBQ4+EXk1EpK+8n6gOD5QxmNlFomyNwHHuiQ/xUCAE60tPdBps7Bm+T3CEKPDyOAyHZBkoBFMPUwhE6bVis9x7uB+0WBnoJ0ZVSQx6Cwo73Vum++nG0jm3E5DlIGQZIEKBkZ4jvi7PM6khhHXng+8hylskmSfjE9+Grjuhsh5GBb7xky6dDWX5wN4f5G4kEkLOsR0THiN+B4JpImyXz9x9GUcG+6Le22pVQN9wteydTqssqvFGkgwcWBV5lIsyZj6vc11R5ZIY6YUMwz5nBIQ3Hc6p1PJFhsNXIAjUC/uDbfbi1NHG3HL1e9De2uvuAc5Lul40BlhZu+8gM8N/P6bseeU1jJoxzY+vPYjgCoIDNcDrEI1FwKWmKb2TOL73/8+vvrVr4p55GKQfZcTOEdS4uALX/iCIDYWcOmAQbbZJPYkm7cTo4ERGDVGlJjKoJnr/odzpCfPTFmu2bQXJhuU9oUCeK77BM6Ndopq/SuyKrEtZ4lYI2gb0SZjEPS++5Jn6S7g0seTTz4pbE7aqLQjee8wmJ5KFnUyoB1Ju5Xz9ETgGKZvIUsPyXauXCXN+SJRfojb5vscq/yNxJ4XtPkZbL3++uuFz0B7jgF7mXyhPZxMVoq2K7dLH0POlOc2xtt3Bl9ZuaJMeOF3+VskLNLpjZeql4gSPB7+Du93udKdkj08/lTXjLr7vI8ZTOeDtgePnwkWsgQV11PauIlkKKtFUvVKSAYSM7wO3L9EkCTh78n7OdV+ezxmzns8JsapLpUArtxsnOdZ9iPoh/F6yFXOM3HfzSboC5GM4j3J/ZXvU/bZo78xmb4wqUD7mP4K4wK8H0hqyn0jObZmKv5xuWFgYECcqwceeED4GZcCLplKDZb/sTpD1rhltcTpESlrbrktC8aZ0pNOA0M+b7RCQ4kRvzdtUiNTVwiD2gxHYEA027XpCqBJItGSFrouAK88KjndGh2w5U6gQgr0pQ0umjojEEjMNggD/sFIs+bJTfT+kB/Hhk7ghX27sHLTKoRGgNqMxXhTwZVwBd3CKTAmyQgXcDQCg7LOf1hwBCIzUizu8efdGXBg/8C+aNPUJtcFrMpci0LjHGdt+XsAV6w3AYIjgGMfYL8OyJqeRiODkHzQqFEaBNUZYzNx8wxWvK9qbL+Jfu8o/tD2erSxbTAUwuCwC67uXnQFGnFT3loRcJuIMKSBVWhcg2F/C7yhUWhVBpg0ZnhEY2M/NCorMrSrJiY0fF0Ag78MMBsXAerkDpRaZRSP2TAIHMELcAc7BQ1j0ZTDrJ1jfclZgsjS8I7CE/Rja04ljJwXGIRWdcOkNYjsHGZC0cimwS0bFtP5PZZpM0MnjI4kn3BGZYRk0Hh86aWX4rJw+D6zpug8cK6nUSOC5YUVMOq02F5VIiqT/niiHhU5dljyYoE8GnUk5Fg2yTGcV16IJkMHVFCJAK8tPwtNLU04oiuA3akT+0NjhtunQU5DkEaxubAeB/adRGaWFUuWlovteoJtgtSQg7v8DjPAkjW5YyB/b6cLufnrUaMehYa3gcUKOLuAASOQLWWv04DmuZd1b1mNVFXFigobjOr1cHrPwON3w1ZYhsG+MI6dPIiVy9YJh4ffISnE80PZQl/ICb3aBKNGKlXmg/cy54pXXnlFZLvQaOcx0tnkcXNfE8lLZkYxuJFuc06SY3QYeb0SM5tpMPJ9uW8KHQU6T9z3uPPGTCJWaChBp+LIIUFmNz/5BHRGI5a+9e1MFcOlBBrMiedFro5RNo8cD1fnl8SRGhxOVp0BgZBUjUdwjJeZNQiF4+UdTw3XYbG1cuy1DFH+0AVozZLNkAJCFkytFusO5wtlBhmv4VQIDVbwSYSGeAaTSY+S8ly89OqfYVJLVSB8yFmB8wa07Wq3AOdei0jNjSOZ2f4sEIhIjA6eAvKukBIaZhgMvnMeoBwrAxHzMfvuUgDn4je/+c2ieeul2KfvjQwGEsfTip8JnB09jTZ3S9SW6HC3Y2P2lsuK2KDtRhtB7u01FTzffQJnRtrFCsS+ins6T2Oosxd5br2wN7juKXsXLODygyxNyjWc15xrEpMTphtcZZIDbcp0yUva7RzHDHorbTD6Bgzy079I7PEnf44BVhIcHKtKG4cBeu4HQcJPKb1Ku5a2NQPMDAbSpyCpQ/+GhAbtJ7nal/vFoC3t80QbXCZdWC2RrLcDA/kkVGiDkYQcLwGJ9hurFlgtQTtclqmVwQRJkgXcppxoQ7uU15D7wONPtn2+/swzz4j1kqQDk19o6/N3uF98zkQkpcyPDI4HuedHOuAxyttVzkv0JTj301dh0Jrni8fI45hM0jNtWx4rfycdomg+Qa66UILXkEQVx9p8J2dEf+G6uqhPyOvJ+4GvywTeTIC+OuMTPF+8D+l3877k2FlAavBasEqDJDXJjdtuuw3zHZdEpQYnNAZvfvjDHwrGqMfjwseOvoIOj1T2X2w047trr0K+cW7kmw4OdOLb52MNeLnkkVT5/robZ55c4eVxD0hVFOYcQJsw6bpHgT9/DwhKOvfSDqmAmz8A2MfqVk6I/tcAJwNMimGhNgCld096UwcHjuB4w3HRJLywQgpcrsxcjkrLWMZ9zDG3/V4KvCihjhAvlJ/SxK71ieGjIotKCb1aj515U290PiU4TwDeC2O19DPfBGjmvpwxEQcHLuCZpkNoPVmPgaY2+NxeLFq3FCVLFwln48M1t0z7NxID1ynhOg+4TsZEq1R6IOvalMTGbGDEfx7OYGPca5m6lTBrZierNtm58oWkQKFebZix6hM6k79rPYzTI9I9oVdr8M6KLSi3SMZCYl8GBulpEE5FL5nGNY0obpcGOo1Jb/AYVOhNSGA2Qqe+MvqMRrRc7cCABA3KRHC7DLDTCKGzUdBzAXjx94Jg9avUOFmxEZ78clEBIBMydAhYQkwH/aXDr+K4qQcepxuOgWGEQ2GYbVasy67GDbVbhWFNJ4nOFo+dCzi3s+/ET+EYdaGvd0gYhazYYA+XDN1SsS80hNIxmukwXDh9FKFTz8JqUMMfDMHn90NVsQnB7EVRY0s0SwxfgMdXh/r6dni9GmTZrkBbfytc6lbodHqMDI1i+ZrFQG8hrKYcIfVDw6y+4yjyl8QM1yzdIuQba0UJr6xHyn1mJivJIp5zGov8TTplyRrT0SGRg9l0EnhOxiv/JpiVxe8RNE7pGPH3GSDh2OD2eD15runscbt8TzgRDJbfc6tUbShfezpkxaXQNDWIJtrlFrM0rX7tm8CqN1ZQhOfv1y11eLTtPPyhEHbmleCBsmr86MI+uCLrPu/xGwtyMOgfK4FwV8mN8cG34Uag7WWMjDqQYTVDVboDsI/NLqQTyYBAsnuTJBXnEbk0fDIIhn3o8e5OeFUFk7oQr+1qF/cuxw7Ha7Ky+osK2iYXjrArMaAOAcNKMk4FFNYCRYXAYKTPhvK9mgenVkU7AXjfMdDDipbvfe97M779NwI+8pGPCPKX8ySTRxZw6cyNrA6llONs9WlwBpx4tX/PmNeXZaxEqXlqmcGXK75T9xQ6LrSg/cwFOAeGoTPqcc0N1+EDG26f9wG2yxFM9PMGvTBoDHNGwLFRNQOqcoCYth//Z0B9sqC9KvfYoI+abrINQfKBgXXGj970pjclJer4nmwTJ0pSUSKV/gltZPo28nuy/Cvte/oxDOaz8oJzEcc41w8SAXI1tCxNSvuXfgyD8TLJwAQXnhd5zZFJIPbam+g4aZ/Rf6Efw9+lnc6AvuyD0+5n5rtSTpY+A211mWBiEJnHR/+H2+O+8Li5nzxfL7/8sjgvyYL9JAJ4PfjbPB7uB0kfudqb26RvlyzRgtUFPHcE/04UgOe54rFyWzyf9B24DZlUog1EQpZBcJ4HnmOSJuMlDsnyejxuZV/IBcwMeM14bcez4Xnf8N5IllCZWLkxE2A8Qa4w4pjhfbaAifGb3/xGJPswgSpZJdp8wryv1OBCcO+99+Ld7363IDSIfz9/DN0ehj0kdHvc+PfzR/GNVcnlOGYaG7IKRQ+NP3dIiy2JjI8v3jQLhEYIOP880Cf9DjR6YNltgE0RYOrviCc0xPfCQG/L1EgN2wrA3RElFJiV5M+sgMe/XzjmBnUZDJr0KiCaB1vQ19GHZRtjWuSd7q6JSQ0GAxIJDQEtkLMzjtAgaLQlwhfypx9gnwaCYQ/cwUaEwz4Y4RI31JhfVE1iXITZZ4VZoFM3QPcPNOHFnjr4QkHUZuTjzuLVIlP/3LFTOHPsIDLtGShbVgmtXof8imIYTCbk6qeepa9EWuebvTZcp+QnkT8+wF0PWFaOI4/WD4Q8kqyXdvqNi1zBtrGvBdrmhNQIhAI4PnwEg36p30WWLgerM9dCmyCLNBXs72+MEhoEx8H/Nr6Kzy6/GdqE6hneI8ySmAqhQYdBdgZkdLob0enpRG2GBnqVSlw2VuxoVPH9CGjsUM/1yiuvFJk+dCwSjRcaxrKOJomXC25g7Ts+A5PHAd2FY1g31IOgpw3HTwagsWYKYkbu2UBsWrUeTz72H8itLkFhdVm06XSBxi7Kpxmop0FPEoQSSsw6YvZWQVk1qhd74Hb70FjfjlVra5ChXQljmvOeDKFhmxcGlpZi1OWGQaeFXqcFNIMIb74fqogRHwp3IoRG6PU6LF++COfOtWFR9SjURUacO6NCYUkOKhdL2WTWcjWKTFKlR3a+FaPtbRg4EUB5VTHMFiMG/U2wavNhsWRFNWLlDEk6BEq94FSgg6XMbKNjx+s1ntGvzHajM0njkY4KDVP+Nh0eEjgy6EzRKREGEh2mtzyI8G9+id5AECatBj1hYPHoENQkM8RJijRi/+OjbzhSg2P6oYpa8VDi4aXX4MRwl1ijl9sKMODrweBQjNRgVrFdR7k+xb3tHUG4ZTcOn24STx0uLzZ5fDCtygEMsUxKGv50IpM1gyRImk01SMWeTBqVGcFwzIbjOhAMm9GrGURetguVhTUieMAMoXmVPccxWL1eehC9jcCFfYDfKzUKX3wl0BtLeIm3abyAegYauSe5X3//+9+LxB9moL71rW+d8d+4nPGrX/1KPOiwLRAalxaYoUv97dm08+XEEyU4tyZ7/Y0M2giH/rQLersF9sIcZJfkw5KdibzCwgVC4yKg29OFk8PHEEIIaqixMnM1CuZAvSDR72ZwfaqyQKy6po8wlfubCVdMyLj66qtFAkayCg+59wOToUhoM6gvB8pZtUzCguQFK8opx8Tgq5wHTPue9i2Do0wyop1C34O+1AsvvCCC6twebSn6L6xuoG1MAoB+jZyNzjV7KsdH0kG2z2SJVyX5kiz+QZ+Hr8vVsPQHaKsrg7vKRCfaXyT7eU4Sm6Dzu0888YQ4RhID9AHkKvGJoNwWSQoSV+NJUvGcylUu3H/6jCQw6EPw/PPc87l87Vjhy7WB+6YksxhP5Hmir8nrIl//Bcws6Hfy3qXPSAItWX8ykkp8P9V44fWRffmZArfHOYF2HgkVxiASeyUuYCwefPBBIdvFWDznyURZvvmEeU1qcPL6wAc+IAb9N78Z0zKudwyDIgsy+D9fI7nxPxfOodfrwcrMLLxr0WLoZiE7hAsFm4LfUFCJYb8XhUY2Lp6hUyn0sBkJ1ABdp2KEBkF95rN/BTaygWrkuPQpAk2saJgK2BC88CbA2SSCzz4D4NJ0RWPPriCbDoVhSCPw23SqEUvWxy9U2nQC/Dw2XRbgH4rPdsyolV5PAPtnKDNU6XDYtJmzTmiEwl4M+19DWDR8DyOgCcEepTQi+60vS6/6IOwHfEeAUERmRFMG6FZMWvLrxHAH/txxQvG8UwS1316xCcaRADbfshNq9djirJWZqRu6zjh4rGM60vCEpnASaUSOHgR8ChLCuk6SrJrejuBioc5xJkpoEPz/3OgZrMhcPa3thiiPNJTYz0KaI/f2NWBJwCqMdRkkE6YiO0UHloY6MyT7vMMY8I2K/ixdnrPivjszGkSmjqJelENbDpsunrCgoUOjmkaN3FdjvIwMGt00fl/ZswdbBk7DNNSBAYcLB5u7YbPbob3+bWIbcsM/oena3IYP3fw27B46CXfQh85zzagMZmPVrbXY3zUkDF86LSyRppH83HPPSdlW7Ta0t/UgpB5B7dIaWLW1kyY0oqDED8LIMCvmgKBfiFjICIU5d8Vk9UKhIAYGugBjBhbVlOL8mWYsWU5JCI3IcpdByanq2nIc2X8aLzzxKm6+d6f4jC/kgBlZcaTQdMDzQ+MvVYA7EbJsEK+XXOXBccagITPY5OoPZcZH1w23YCgURlFbG5x6PdRXXwv1N78ydh5wSdUgCwBsOiO258bmwCxdBRx+JxpEpSWQobViS06C9rS7D8fOtaC8KBt52Ta4PT7xfEt1bxypwfL1VFmRdAx5j13F6/fyy/T+AVmGYWQEeOLP0t9Vq4DtY/thcJvZunUY8B9GMByp7kER9vSeR5e/F+iqQ5OtDSvWLBEGNIMaM1WGPuPIq5QeShhzgZEGxQsqQGsak4wxk+A9+stf/hL333+/IHf5WEB6gS82u3/00UcXdJUvMXAeIma7v6JVa4UaGoSEBq4EEsmZ7DW4gCi4xl+3bht6y+Ln6g1ZCfPjAmYdroBTKBjIUpQkNk4MH4NVa4NFO7vVjwxGKrPzaQdOdW7mujZVP55Z+rSB6WNMJKfGOYRVpyQpZAk2/i6bZtP24PHQv6A9zQA6yQnauEzsUEpQ/fGPfxRVHPRp6FNQ2onboe3MPnfcDp/TfmKgnvs4E3EKbitRJirVdvk5WZ6I50WuNEnWs0Lu37F7925BOpBAVm6fx56skncykKsrkiW2JQN/Vw5EkyDifvP46aOwSoh+JdcG+ozydSfpwqoZvsffIhky25KFb1Tw3HPss3KY14qEIH09ZeIkiSgSehx7iZAlnHntZlp+luQK4xYk6zhuSKbJPWAWMD7+9V//VfQrZUz+5z//+bztazuvSY2f/exnQrePg1Dp1FJuqt/riepJq6FCvsGEt+zbhUG/TwT2XujpwPHhAXxv7dRYcMIZ8OPQYA8C4TDW2/Ng18dnDGbpTeIxI2DAZvAYMMwm3WHAWAg4SXAk9JCgTrPPBRgicka5ZUBhFdDVGCsRsOUBpQlajMx0bT4KjPYCZjuwaD2gTREooM52prRouH1jy669oda0SI3qrEqEAqG4yazKmmYgOm870LMbCDik56bS6D4lotJSjVH/CPrYn4Ef1Ziwyj772bzeUIfoISEjRM14swGZgWyoWY2gzQGMaZZq+Y7HCA0i2AqoTIAulkkw5HPgpd6jGPCNwKw1YnvOKpSa4yf9k8MdcSOGRu3Z0W709vdhcWU1qvMzsLeP0hjx0E1HFoPHGvYCKkN6FSb8nNoEhJQByjCgDHyT3COxJq5/APDzfCjuY8dRQF84Lbkqs6YUzmBTwmvJm62GwgE4A3Xwhwehgg4WbQ306qmXRQ76xsrEkNjwh3zQqnRpzVmsRnIGPLBojdHG1892nUS3ZzTp54f8brS2DYqMXtl4YMb8VDIV6HAw8+n48AW8NnA2+nq2Digzq8HuNgM+aRRatH7Ykkw18jHS6UlHC55rwJaacrz2vR/jqsVlONneh6uXlEGv1eB0fxtcxTVimzw+lk0z42ilvQbvyCyEK+jFqHUIXR2dImuLBhMdnkZnD7o9w7AUWZDjyUVxYVFEammGqv4yi4F+5RhTAdbcOBkaFbRx9NqyZRXo7R3C+eNdsBXoYDIZ4HF7Yc0ww6SNjTldJOub8+vt91875nUeB9dPZoKl2/g7GXhOaQAy84Z/WdmTDlgpQqOVZAb/8nqwFJ7GrDLLn2PQHwhg6bvfJ55H9/SKzcD+V+NkqbB5bqoxL0XwOq3NWoEVmbVCdsKglhxoZdZe98AojHqdIDSIMxc6sWpxKaA1ivuQDj2dS5bv0+njNVPORazOouO/ta8P2k9/OnZtHnwQeO97gQ/+DUt1JDL+d78F3v1e4O3vHLOvWrUFeforEYJHjP8DAycRRgiLVlbh9P6TWL55JepcF3DD1qvE+KEToqxy47qmm4GqtllB5mLA0w+MRPqGsG9Y8TVjG4rPMFhp9vGPf1z012AAZj5nU80X55vn6u///u9x8803X+zdWcAkwWzLqQZMJwOdWo819nWispbzKlFjXYIcw0JQTAmuHQ/tvA2nnR04O9oBrUqD9VmLUGmZvOzQAqaHYf9wXG8tgs+H/UOzSmowYUVp25HgmEp/Ft7XTJxSVgBPFpSY4Rw/GduXyQDsi8EAuUyosBKdQVja0vRBGNjn+kpZrcQGw7S5WcVKCSZWCYz4PTg81Al/KIiajWvRefp8NNh7scDzwl4drMDmeWagl/4cq02S7Rft+DvuuGNMxSztC1Y9kPiZigxp4nnj/EH7UiaU0h1vJDa4b3IlOo9P2VydoM1KkkMem9PxhxYwPkjY0V7n+aevx/tfjgGygoPXmX+ZXJnYJ4VgRQAreWaj9xJtBv4u94G+KMkM7i/H3cKYGB+c+5j8w/uIpMa73vUuzEfMW1KD7NlHP/pRcRIT9bw/tngNPnJkD5zBQCSIrUGNNRsv9/ZDEf7Arp5ONLkcqLRMXqqmx+PG3x15Cd1eKfBq0+rwnbU7UG2dpclwtA4YVgSbPd2AxpiiikHxOifpnW+VmlcO9QDWLGDZtnjCgoTJwT8Cneek7/N5+xngyrcBE1aYJMtmV57l5HAHnQgHvCi3VcAbCkGvMQjZqVzDWA33lBUjxbcC/lEpAKixpAwKUF5jrX0D3EG3yKYyayxQT7LCYSoIy1JRyqxrNlU1L4VaPckxpyQ0ZAR7oqSGPxTAU1374Q56hXHqCLjxXPcB3FWyA1n62G9pFMftdbgw2jMAa04WTg2cwlVX7oAj6MFr/WdF3wXldwqNU8x283cB7sOiFkC0sDWtAXQTNNvmdbRtBUYYsIw0UzVUAoYI4cV969kDiD4pkfPLbssGnWIMsHm8M80qGEp/tAOhQal3h2aRIFYytDy3KniCneK+sGgqYEpC1jEgOOI/Br/IqJcw7D8Mu24TdOqpyXYxIOdRkDpc8lXw4vWBXTCqzVhmWw+zNnXwuMHRjj29LC0PC1J3R94aLDIX4vBQM7QqIMInxEEzLEkIyQYGs42mKu1CY8QFXxyhQQz4gcwAe5PEXuPxpALPLZ2AdBvcmnRqbCgvxPG2XmxcVIh9F9qxtqwAyxeVAKslskauDpCNWrVKBavWCGtBIRobLoisKRowr/Sdxf6BelHZ5XW74T7fhS+uneEmsWUbAGc/0H0ucgCZwMpb4z6iVpULCSrpHiLC0KjKUZShw5m6V2DPN6GzrQdZ5hLULK8UBhmdLB5jlolZkJFt01jXlsKskYgPlvwzOD2htFgoAAzVAwE3YMoBrGVj5loSGTxvzYdeQ9dgH6rXbYQqJ7m8IfdPLsOnESln28g9GuhYMGOK152fo0GZLEsMH/0k8O1/AQ68RuYGuP0e4I57cLngic4W/LmjRdy/95Qswg2F6TvwrL4LsEdMkoCBTq2FLmLaeYNuNDiOYdQ/IAo9+xvduHXlOsDVJebriuIcnO3yYc26AiEzQIedTi6dETqLJLKYqSdLEtCwtXo8MP3bv8WTTb/5DeB0SISGeD3y3s/+F7j7HsA6dj3k/amBKSYXKcwZNSpXVKPlbBOWrqzGOfcRtKMeR5rVWF22ASeGT6Hd0yG+k6PPwYasdRef3AgEpDVLF5nDeO8UbgVyVgMhH6CzzUovjWT4whe+gF27dolA/Q9+8IM5+c1LFZ/4xCfEuP785z9/sXdlAVMA18C50kHPNeThqtxr4Q66RH8C9kBbgNQ/gesDEw8ZJOL6sMZeIR4LuHhItSay1+RsgvKtSplROZg8WVD6cjw5onTAuYFBdwbu020dS5uE45hNvGm/kphg0JPSU3fddVf0czxGSkzxdSUYJCUpQiKk3+vCP599Cc6AT/gYbQdP4NO3PTAvMpx5n9Iepw1OG49+IJu5k0TicbNyg7YYyQ/ub7Lzx8+xKoXnaibA8y5n+XM/UgWZuS+8JnLPDMqbyX4OySSC79GGlfv8JSZSLWDmwfHE+40+n+xzymNd7vFIopJkBccfr9mxY8fE95QS1oTSf59pcJzR16HPyTHB+UmuCF/A+OB1YbEBK8J5vuajfNu8JDW4CD300EN43/veh5tuumnM+yQWfr7pTdjXT8cc2JpTiEdbm6SbIGHyddLhnAL+s+EE+nyeWGJ4IIB/PXcEP9xw9aS35Qt54Ak6YVCbYNCkCPC5JEc9Bh6HBzDaAM9wLLhbuQNQJ1w2EhPLx7khBzskQkNsNhJsGOoUMg8oGT9LW68ugjcUa4YZDIfR6QljZGSXyCivsCxGniEmZ0M4A6M4Ofwaer3tKEKmkDuqsqxFTrqEhgwG6PXpkUi89mZWmMwhdOocuEPxmf5qsCHbVDJhuAgk6AeqYoZpv28ErmBsPBIkN9rcvXGkxqbsCrx89qi4D7RmIzpPncf2ZWuw806pkaJNbcbNhRvwXPdReEN+GNQ6XF+wFjbdFM5dyAW4DypInRDgPgqoMwHNBKSONhPIujFCTOjiyQl3Z4TQkI5SIBhiuQQZmNjnSHSlg8AZQIzhyHfZS0NPnVYDbLrF4jHuYcIbR2jI8IY6p0xqMNPvyNBBYeyqRdZx7D2SHadHDmFDVvLml8N+qWIndtbD2NN7FLai7ZHAoAraUBgBxVRYarIjty+MrBVl+EuHRJ6uzyqBf2DscaUDGiKsHEoGb5BVN9J+5+pLkKlLntHIjBo6BjRek2luJkVuCWxZ2fB1DYhjNet14M+hdPEY54KGCzM+lDrpNJZFqfWyGkFoyPeR3mTESJYeTx7cjTs2xqoepg0a0stvAqp3SL2POJ8nGNcqlQlabEYo3CKk7NSqbNgy7PC4e3DD5ofQ2d0Gr9cHq86Gc+fqhCFPJ4QGvCWQhbLMtVDBDHeQzhuQbfBBz2oorpXV1cKxJNGTtCKHhEbTU1JmubzG5KwCCjaM/eyLj6Pi0G54/AGcevq3WHT322HdfuOYjzHzhhl6cpO+xGsrG78sNedn+ZxEyJhycDaY+9xXaBAATU2Am/ONW3r9EsdjbY348ukj0ed7+7vxjfBG3Fo0vvZ0IBTCt+uO4U8dTWL8b8zKw1dWboJNDqgnOH91owfhCblweO8JZGbbYMu3wlF6FWys2PQOw5JlRF3zaXgPHBSOhZzdT2Of142Zb3R6ZVKD74c6OrB/dBTrzWbo5PmJY7otoZpO2glgeCQpqaFEvjEHPV6J2LfYLOjzB2DXq+AIDqOgOgcHjuyD1xoQ66CMAd8Ajg+fwIasBIktJXw+4E+/Bk4dAUxm4KZ7gDUbMSOg3u8LjwLHX5Pum6rlwK3vAAyR+UbHcTq3Y5X3E2Wo6KjRdr7zzjvn9PcvFVAm5Le//a1wqqeSSbyAiw/KiUw38DkZaNVaZEzR3rucwHWF2dkMajAhgf18rrvuuhmXClnA1JGtz0GWLlvIMtPHoI1r12WJ12cTDFYqKwSZFc1AZrrVvTJkOaKpSOMqt0Hbl/b+ZJuUk6xg4JyJPOy3lwgGbRmc5fqhzCjnPcBeYEzW+WvnObgCUrIGz3/Bmlp854lH8Kv3PTwvAuwyKUESgf4E/SQmHZEQoK9AYon3Oq/DeMlvtNvpa/E88LvTAccOf5tVMPzdZFUbJCg4zri/cm+QRN+BPfy4rpPY4vd5XMmqAqIYGQYaLgBZdqCCSY8Xn3i6lMDrxfuMPp1SppjnnvFcub+JUhaVEtSUi6ZyghL0WUmUsXKIj9kA79ORkRGxD7wXeS/MRQ/eywE333wz3vOe94jefbI833zCvLTmv/jFLwr29etf/3rKz+QZTLijOKbVuS03H//VQOkmCcx+zNbrUT2BM50KTc4REcCHInDY6k4exBsPfd52NLtORp8XGatRbErCboleEwlSU5TyWXUP0H8eCHgBW3F8k/B0QbmqZPCmeF0Bk4b7GoYvJGXNd7hVGA5IgQV/2Iezo0ehVV2BLH1sUWlz1YuKCcExRY6n0XkGOQnkx6UOnTobFs1yOIPMVmczNjMydGtEY+TJb2wJ4JfHSWRi1VYnrcCQEU543RP0C+kh1ZkOlG1bC4vdhspVanz41vvjJusKSz7eW3l9lNSY8kQeTOh5Iu9VcGBiUoPgvidr+B1MoZuvJCzNqwBnI+CgdnkYsFQBGcuTBNW8EUIjsm8CPiDYAmgvno5itj4XG7O2osvTgVF/PzwhZ5xomDfkhifoQiDMILcaZo01ep16vSwtjwefjwQcKDNlo809CK0aULMRXZikbzWuzV+KP7fvwu/OvCQaxBO7u87jZnUhZKG6JucQOj0ONDkHccE5BL1ag5sKq7EqMz4jX25wZwwlXz4WW2vF3EsC16bNTjm+aPwwKBFnaPIas5dQb72U3Vy8GshWZP2xh9Ct70Wg6Z9R3zOIvNxcZN31HiA7fm7hb8pN7nbu3BndB8ogUU/WPDJWOsxkMcObSCwmAyUAu18X/QmgswIFG4GJKp0M4wc3SWxoVLEADXkYroHMJqHxzuwvZrbQKORzkgJ0FlvbWqFfpkWzS5LeU2FYyPBtyd4ums7TSaD8VKLhGAUrNAShQURGVf8JIKsW0Csc0eZzwKHd4l+jTosVBdloe+p3aNPZULZitSCoZLBChGOEmV+JervcZ1bl0JiUwevP60MnioZvnEY6kxK+/AXg1b3Sczq53/hXYGmaOr4cT/ueBV59FmBlZ+1a4JYHU/eimiP8tLFuzGs/a6qbkNT4ZXNdlNAgDg324etnDuPrq7eM+ayYQ0JO6X4NhlC9jJlJGgz5u2DLYrDAAn04F/e9ZWVKw5TXis6lEhuuuQae734XL4+OotpgQAUdXlZnLF0K1CuOi447s+3SCCgssVbB6Xeh0SX1A7JopUwv9ukxe5y4otyOoWCM0CBoW/R5Y32JkuLXPwKOvBZbO37ybeDDnwFqV2LaeO1Z4LgiS7HxDPDcI8BtM1+W3eftxYh/REhrFhgLx61EZSCAVRrvfe97RdBCJhcXIIEZg0yY+uEPf5i8QmwBlwQ4N5E8X2juPreg3rm8JjCgSPtiripmFpAeuD6sz7oCra4WOINOoV5Qbi6fVQUD+gSJjX2ZeZ8oIdXuHsUjracx4HWjJiMbbyldBnOCFDZJEFaKci2bKhiIp+3PgOVkfVza2gzQUvoyVV86+hL8HO8HZfCVklS0ZQe0VI6IeWoanQ4+Zq/Nk8Ap95+EDUkEJkjxQSJI7i/A80ZpWCadjXf++HlmcPM8zFTTZfoQHDskrjm/87m8D9wvVhRz35L14WB1RiK5wmvCqjJ+Vu7BEcXBA8DDn5ISpohbbwP+8TNjks8WkBqybFhiM3mCfvh4pBjXDrlHjbInI/10Jj2yj8NMk4D0P3nvyn4ot79AaKSPb3zjGyIJ7ktf+hL+6Z/+CfMJ847UYFOZ733veyIIMxlDaa09B/+86gp89fRRuIIBlJnN+M7aLTBNsYH3IosNrS5HtCE5SZIy0+SyDSj9oCQ0iE5PAzK02chIaJyLzGWAqy2e2LCvAHQGoHCaDngmew9ogFBCwC57ApkgEWxTw6ythRm1Qst6OPB84ifQ6+2MIzV8DCRHIJf9BURz6MsPRk0JDGoGDUjiTON20pZL0kjBrkhj0XJAHQvu5egzkW+wo9c7JEYHs29ISFRaJJKLjZD/q+EV7PnDk/C7PMBIKa5wqHDjxmsF8THgc6DPO4IMrQmFRruYwI2aaZYic38n83q60CcLEFOyagOtdUBrB9xdwPDx2NsjkfvMltDMWEiEJdmWaO6eHlh9o1PZ4Q8PKTcMg3p6JJ1Nlykezc46tLkbx7x/fPgAfGGpOidTl40VtvXQqLQwpighN6oNuLtkPX7b+ipG/C4xRhZnFOOGwkhTtawwOvafQU5NBUxZNgQDAewabMCbsBmPtp7C093K5rYSzo724ROLt2BFZiwwSf1VIUmTkYPNwaXYr5CgqrWWojYj/eZ+YzJnWg8DjVIA2+31oeHAXljX3QZ9fpUwnJiBI+SqrnsQo6EgVu+4atxtM2OZ+ys3uOOaQk3b3Xtfhs/qgj4jllXW09yB+64f29g4Dqx0a34W8A5K87RvFGj6K1B1B+ByQGj82AriJQKTITQKeOsk0o1yUfrFY/rRyJqkNOoTyQE6fTTOzzWdhbfThay8rGiglzJ8Pd4eFJvGlzNisLu16QLMbgdy7JIObRSUolKSGn0KKbjIvpXZrQhnZ6B9cFAECnm+6XzIJAXJCaXhyGOhk8LfJZmlXN/lpuF0jPg5uTIAj/0O2PdqbD8cDuALnwUe+X16zuHRvcCuP8aenzogrYP3SP07LhYoyTjmtYSAQDLs6++OIzTpNL8+MDaTUJZlJHgtVl6xFEdePYna1dUozKEUn1QZyga4ai1Lv3WAYwg4tgsYHQCyi4C11wnyJ1Earkuvh+Puu1HyyCPoCwYR8npR+aY3AR/9f4DJCDz6O+mDdCz/6RtM35zwuLiP67NXYbV9mZBGfOx0l5DRW5WpQV3HEKrL83HGz3Uu/nva8dZcVmkcTpBGYFDn9T0zQ2pcOBX/nMQJiY0ZRt3oOTQ6L0Sf57jbRMBqvADVgw8+iKeeegrvfOc7hRb4fMgMnQ/g3MJzcuutt+KBBx642LuzgGmAgSpq3ydKwCxg9sDzzSxaZkbTFptLCbB5DX8P4D0v+Ru6IsCw+KIHr9UqDSosc9eknWsMg5HUxJflZLiuM5FFJh77vW588dQeYevQdml2DaPZOYzPL79SyMTKczQrLEhITBcTVeExWYjEKO1p/h0cHBSZ5cw6Z4CefoMyYScRtGPZv08ZdKdkE+1h35ETUBWZYvZaIIRstSF6nLMK2iL+C0CgR0qY1VdLfoYCtMtpj5PcICmRKPNOaRmeB/Y/mAhMnqJflgjOEfQBOC4mO09wmzy/JDdYaUFw3pFJDO43E6eUpAavJ4k1+hhKaRySGKwK4LVlZQr9VwGvB/jHf2S2VeyHn3wCWLMWuO22Se3vGxk8n7xneb1Jcinvu4kkrjk/JFZkcZ2h30GykPcXe7bMZEUt90k5rjnnLCB98F7+9a9/Leb5G264YUbm6suS1GD2KZuPkAWaCuN7R3E5bi8qgzsYTKo1PRl8uHolTo8MoCfSU8Oi1eIfamNakenAE0pe2eEOjo4lNYy5QPH1wEidFHQxlwDWGTJITBnAxruBg3+SJFDoDK+5EbBPLiibei2Of8OmzYIjMITy6lI0nm1G9fIqZDAQPc8RCAXx2sB5tLsHYNEYsTV3CXKUgb0UkIyZGbiVNIXSQwS9nOhzn0Qg7INFk4UcfQVuLNyEQ4N16PMOwao1YUPWUpjYhJTyJX0XMOhzYcm1m9Fxog7ndx+A6qoNeDAvF8eGmrCrJ0aurbKV47qCVRMGnkWGL0YQDvugVmVArUowSjQ5gCYPCPYqel/YAW1yrf20QZkySooMslcHoQJyNgNmxf3gTKLjycBPIqkBIxDmvoXi+3GE05cFEbJdurVwBM7CFxqAWqWHRbMYOspszQAKjWXo8rSKqgx5/7QqfRw5OOwfQLOrHlWWpSg25aLElIt2d58gW+kclBhzxev7+s/BEXAJ7oeod3Tg1HAeVmSWwasB8pZWwdk3KEgNlVYDp9+D86P9SQkNcexUHeptipIaXPw5T8tB57X2apSacjHgGxVjssiYujIjLbQeiv57oaMf+VkZwGA9vDkVIuOLGTh0miR5nMRrPRY03Gn4KvH0008LR2WDqhinVazIkwyae3behJ6TjShemxlXSRAHVjV4lZnrYSDgAw4+JgWDCa0B2HAvYC9KLdvmeiXSd4D9XgaA0Ahg3Bg3ydJwoJGYSluWjuK2q7fhF8/8HPZciaiUEUwg8/hZbovnj9lNXV1dwkgsLamBp+ECzjV2Qa/ToLI0DyrKwRkSyv4zeb3H6uqq7LkozS0Uc4WshyuXGPM12RClo8HybzqPzBynMyUTTWI7kX2nEcv3oqSGcGYURD+Nz74+ektS1cZEOL5/rLN3TpLnu5hBhxsLSvCLlvroGeWe3Fg4cZJBhlaXWM+ZMnFDpzYgW1+IAV8XzFYTVm9ahs6mLpw8dx6ZG5fAZrOIvlAH9v0eGizGisaXYAx5pXPT2yo9bv4bQZ69+OKL4vqyooPO7yr2Ibj1VmrA4KzXi7MbN2Ipr/UH/w64/0FJcqqkGNAbJi3x0tPRgarCGmTr3aJSo6a6GCdPNmHx6sUYGJBZDUlSozYjTdm62YCB62HC1ZArgAJ+oK9FsuVyymKSVJOEM+CIIzSIfl8/uj1dKDKNX4Hx/e9/X9xjP/rRj/DBD35wSr9/uYHVGcwApmTOAi5tyOvjpQT6FgcHGuANBVBlyceGrPQTQOYDmFzBxAOu40xAvOWWWy72Ll18BHoBFyUII/AOA2EfYJoB4vwSAwPJyspO2n/Kfgz7+tvgCQaUgsWocwygzT2CcnNmVMqGSS5zcV+QfGCsiT4Ng+i0TUnUyQH6qB06DkhosGqAUlsE7Vcew+KQCTprDs47pOCp3WTGnZuui/bomFV4TwMBRaKcuwcwbQM08QmDDPKPB9p+tN3ZLHy8pu0icVUh9S4HpTlHk9Dg+ZH7AE62ITOvC6VQSTpxbPGaydWnyooLHgs/w8+2tbXFfY7geOI+0fdjFbwItnd1S73glKAdW0e59gVSYzKQq2coycZ7/oorpB6XrMKgX8hKDiX5J4OyZUw85PWS/W5eQ/l+4neouDBTFYHcN+6jsocGxxHJlQWCPn1QaYNqSu9+97sFoTVZicE3BKnxmc98RgQ1PvShqTdrlXorTP+w8o1m/GzjdTg42CNkqNbZ85A1SQddlxgEll9P1dzYkAvkJdefnzYKFwM3fwxwjwAGq1QBMkkwSzxXX4g+n9TLREIYBcb4xa7MXAN30IFuV6+QuTBpLKixxgJYc4EzI504Mdwmgr5rs8pRY82fcKL7S+dhNDqlTFhOu43OHrxj0VXITNJvgkHDQMgvGgZOSW5qHPhCblxwvCYkvHh+HYFeeIKjKDWvxtac5IHcYb9H7LPf40PnqXpUbl0La2EeerwjeFFBaBAnRlpQaS1AtbVg3PPhCZFUka+1Ckb1aujUivPIxcm8CfA1SZnnagugr5RIs+nCVguYy6WeG2yYrUlnMUtiBIedgMcHGLSR+BMzWEKA1iOSk9OFWqWDTRfTg5xJGDQmrLVvQ7u7SVQ02XRZqHckZAGzGMU/JAJcFxwNUCOIGosNerUdmfoMLM1gabkKx4di/W9knBhuEaRGtTUHnd3d0Oil+XG4tQvXrb5CSE6lAu8FOehPbX0a/8xEUiLXkCkeU4GcvU+DgpmXNkU1WUluJk43d8Pk1mHF9oJoxji/QwNnMkYx9TOZDcISa/n7NGSuVIdFFRMbidv1FoSLwiIzhNUcSZ2qZA0HR93SQwZJjmN/AXa+P/kO+VtjhIaMYA/8vhG0tvcLJ4K/TWeAxjmJiFSZ1pl6O6pqq3D2yDmEQ2GUVpUgMztzjHYyrxkzLGUjX5k0YMYosu0n4PX5cb6lD6rCLTC0d8frmVavAEjqn4v1gcCWG4AIocGMLjocDHwzaEhnlteUhinLiOkk0ujhNvkeSRZZw5SBKTqB8vXt6+sTDpQwkliSnBjFpzOSTl8NnxdoaxxLYMxR0+bx8P+WrIQ3FMSfOlrErt1XWom/rYq/r5LhbYuW4PXBnugwJKH5vqrUUlyVltUwqq3o9jQhrAeuX1sGi6oEL79yEoUF2XA43KisrEBGvwqnGxoRCIag1aixflER0N8B9LahvLxCjBteKzqDJBWFA0DHfP16IV/Ha0zHRVQUZedIj0mADg0dXjqmh84cgm1VFvwhacybTAbRJ8gY9GFrjh6t7hyEwzoUGYuQbxxHx53jad1m4OjrsfuWc9mmCaqx0gXHfyv78kTGFn9j202Axwm8+HNgNJIJZjADV78dsHEsTy5Q40noozXR60pwvvvxj3+Me+65RwQf3+hSS8zifPjhh0U/jelotS9g/uBSCkJ0ugfxSCuTcSRR3lZ3PxwBD67Onzg5Y76AawCJbTaE/uhHP7pQAUZ44/sqCtAnMq646NUaFwMyicEgN4PMyh4bgXAo2uNDCcZYaFPQtmA2/kzJGBGs8KaPwYx++hhKu57rAAPwbB4s991gVQBJh3SD77RpeT/Q1uWxszKB26XsjtFkQrt7BP5QECUmGwwarbCVuRZNR1prXNDGCYwdk2FfEwZcIZGhLgf4uc/0MZLJBsngOWPQkjY651sGoZP5RrTreVz0EXk+lRUusu1BO4+SV/RxZGnddEC78+jRo+Ka0M+gPyHLFbG6hteY510+Dv6VqzsI/i/vM/0/BtFF0J1VHvStlJn6/H+SPVgWgGj1DB+833gv8zqwXyL9aY4zvkYbn4lvMoFBconXgnMFxyTvR15PmdTgmGOlBokN/k2sHE8H9DF5b/K+PHnypJhflIQYFR0oT5bYsHwB4+PDH/4wHn30UXz2s5/Fd7/7XcwHzBtSg4Gk//3f/xUT13wplbdoddiZN76Ex3gwazOQpy9Hr68lmtFn0+bCrrtIE6ZGB1in1yisNmMV9E4DBn19IquywlyDTF3WmLLX2oz1MOVkw+vzYnFm7YxnXTQ4mnF2pB7BcBBFpnyss68U+0McG2rFnzuOif/5q2dGu/Dm0iuw1Ja6MmXY78IFZ3f0OU0ufziIUyNt2JYTnw3a5bmAdre0YDKjvsa6HhY2vp4hDPnaooRGdP8CnSgI1Yrs22QoNmXi8FAr3EMjqN5xBQoWV8Cs0cMXkhqVKUGjkpn11UhNagTCnQpCgyDJcQJaFfsTKKYNEhiGKswKtCbpkQzWamDoyNjXEkEDmw8PK5TkU0oJqyTB6YsIEhtV1lhwklUZPmZNKxASjX+ZQYJoNQ+rI5bbJiJbpGO9u2QVThw7gYaePnhHnbA5Qyjt9GIgfAEYh+PcmFUiDBIaARMt+jRM5H4PNERpSNA4ISFCQ5ZzO40aGsAMgNOgYYmxnPmnbR9BjcWD8oIs2DPM2LayEq7iK4RRze/REJ7s+sCMAgb0aRAxW4g687IxTfPIzMqKCDhPMTCbrB+EgDEH0Nsk2SmZ/iRJFkGAhARU0HhGBbkRisxJskFPozxDHxLnpa29F263TwRsiXP9v8KiJZWoKFwDi6ZIZEfRAKc+b+K+8Pv8db1aj2uqr8PxnKNwB11oOd0C+3AORsMO6LL1IgBBJ4DbopNJB2XM+WNT8OxaGAJuLFllAzQGYVTSuCQZIj7P+/y2dwIrNgLDA0BeMVAq3W+8NnTQlE4arz3JDe47rxnPu7JMnA4JjV35WGhkcszQ4cnr7ETd7t0oWlqLovsfAPbsBrq7pQAB7+WPf5KpNRNf+KZ6wOkGTLp4YqPs4ktD6NRqfG75OvGYDNbac/GD9Vfhzx1N8IVC2JlXhKvzx8mgU6mhVmvhj8jtBcPkdNS4eucaDAyMwm63Ii+/HBj2CiLD6w/g5boWdAyOophVUuxDopBxYICC99GTTz4pAuVyaTnHFR1FkoaJMgbpgI4GHc8h9xBCtSqMBEYF7VdLEzXMLLAqHDvWgCuuWAm7fg1U6ZquD32AnccjjcJNwM33ArUzRE6XLwHu/4jUV4OBhCVrgcWrgdf/DDgU1Vys9t31EyDTBFjzgJW3AOYJevBEYNFakwaBTClIfgZPnu46jrOjXdCoVNi0tgpvectb8P73v19UqF1KWeEzCc4xPAeU5WJT4wVcHriUxvPRISnQqLyTDw814aq8ZbPa62CmwbWcwYypBJcuTySTLplf/sVcgnYug/y0G5lMxKAzKzhIVqzPKsIf2s9BRbcsIuudZzCjxGjFvlf2Tigzw3mcAW36DrQxaTOymoI+ByuI5DHJ9xgMlYOj69atEw285f259tpro7I53CaD8dxn/jbtFwb+0yU1eJz0J/hb/A79FB6HjLJIBYoM2sMM0DIDPa63Q5qIVhlEIDfA5nnhfpeVFguLweViz72+SL8AIKzxo3fEjW3btkW309DQIM4J7fHxZILkqmoSB/TTeAy05xkkps/Ha87jZ0A7Ku2UBHJSFc85/QMGtmVyIhVoU3LOYeBbPl/8Pn0FjgO5hwb3Sbke8LywIp37zN+lj8l95Pd4vujfUCpJ95GPAt/9jkRu0E+orATuuTft67GAsWClBc8/7yuSe0KuuKxMkBw8/4z33n777XHfke9d+oL0JUgy8L4l6Mdy3PIeTZlwOA7o/7JihOOH92rimONvKyvKFpAeOLf85Cc/EdfpzW9+c1z1yxua1ODExKaGX/3qV+N08GYb/lAIL/d1YdDnxarMbCzJmLnAtIwy81Jk6nOF5JRebUKWrvCSMsQTQcKiWhF8TQUeY4YpEz6XlBUwk2h1deDo0Km458FQCCvtzO4I4+XeGEMvT1N7++rHJTXkbPS4Y+DrCX1Ihv29UUKDoDxUveMQVmXuFOdmJiARGkleF5IyyQ2PzdmL0OIawPEqCPkpg1qLhyo2IlMRsJXBAEmy6hMlgmFm7yemSIcQghsapNEEfLZhYTMzFeCojzyvBKyxRstRsGE5K0go+aNctHTzt2m9M9CLDE0Y/aGELiDs+p3YKNfXi1A4FHWKWZFxaDBermRFppRxb9Lo8JWb3oqRkE/KorfaxSLPgOKmkA6v+wYQ8PrQf7YBOq0WRo0O23LKoAl2YMBkipMLivZkaG0VBoNsbNJYpIFJh4JGJD9z6qykYarMyiHBwexuuSkdUVVZhjZNAAW5QWC4WQqkl66DedEWXBGZQ2gITzbLlvvGdYWGVFZRAX74wl9hW78EmToT7ihegQpLvBSgXC7Nvk7cfy7czNzidl4/eAiqYC6CXW3INPqwbFkNYM8CPPUIBoJ4+WQjrEY9gioN+nzPYMvWreI42V+C1Qd0qLodLoTdHSguyoal1CjusK7+EVjggTVbg37fSaj0KmEA8sFzLFeacH9eHzyHE8ONguRaZC7A1fmrsSPvakHwago1wrlhtQOJJDpYdD54nhOvXxzY8JyPCOicMdOK5A4NPv62yGSrGptVys8lOoB0kEgmyUjMyKIzQ6OXzhDf43UVpeqPPQoTezKoVBj44x/Q8MwzqP7h/wC7d0tl4uvWT6JJeIgTOOD2A/rI3BwIAdWXtizEisxs8UgXrJqUA+NNHmCpmXMBk9Q4j1sA9TKgeBQHmqQ+FhsWFSHLapYqDHLHEiYkH0hgJQa1mH1FZ30qpAbHA8nGIz3H0BHsEvvqCAAHBoJYbdfAqNUiFGIgf0P6hAbB6tq3vBsAH7MAEnsRci+K4Z6Eii5WB0akGRx9wJE/AFveCaTR782oMWJl5iqcHD4RJTYoydXiPo0MXQYs2vi1mITGqZF28clAGHi5rw5v//xH8dD2W/DTn/4U73nPe/BGBBOmWEnErLIFLOBigFnqieQkn3Mdl+VCLwXQDriUKmRmHboSIBBLiBPQFl30xImLBQYraSsz2MwkCNqsch+WcrMNb9EW4JfHX4MTQUFm3Fi6FIcPHBT2aSKhwW1w3qYdK8cR6FfQZuQYJKHBYCX/VwbUmAxEW5t/5QAmbWAGVOWKDBncLisL+ODvMZtcSUqkA+47ZaUYfOW+JmtgrQSrHfg7PF6SAvRPSL4wAM9gLm1qbocZ5MqKB5IQDNTz/JKU4LExfkafi+eFz+sbGqHyemDQOrBkcUm0N9+ZCxnid5U2OgPIvD4pk7iS2GlXXXWV8C9INjBpiXYgSQMSRekmnHGfeD24Df42j4eJMclIHvqKtCmV7/H7clVAKjAhiwlXHIP057jP9GMYcJfPCYmNJXffA/2SWuDEcR4gcMONLA9I6zgWkBoc28p4rjy2ST7ddNNNKb/H8chKD/Y9U4LjldujPy33YEwX3CbvJe5TqrWL2+c9x3t4AemD9/FXvvIVEcNnUUK61VezBVV4HtBT//AP/yCcYT6mwlxPBb5QEO8/uBeHhvrEc07FX16xAXeVvLFL9GcSNBA4OSkX0pnAvr5D6PB0j80Q8UhD2eGnAxE/rHP1Vnyw5uqU22Rg+GdNL2HI74pzPB4o24YSU8w4aXOdQ7eXEj/x21+WsRZhA3QBAAEAAElEQVRmsNG7B1BlAupaQDW1ydER6Eez66DiFRX0KhNqrNvHlboS2d8j/Thx+hSu3bRNBKWJ1wfqsbcv1ldgsbUItxatH5ds8oVa4Q3F9yIgrBpWalxiGVohN+A8DAQGAUq/mVcD062WmiVNfk9wGO3u/fCGwvDzERlmBjUw6tdjNBBfvcFg5fUFN8ZKa8Mh7Ouvw9mRdiFHtc5eiTX2ReNeawaXGfDXleXj0InjuH7nNVieXThhOeehQ4eEkcFgfbLt93sd+GPHQSHvpFNpcG3+Cqy2xySNGMgWRo+opmED6SYcOHAOmzYxO2MtoLLO6Dn2B4P46t4/YAR+GOwZgijSqNT4+JKdyKUkXwqQJOBiTYNIJgjgOo+O+pdQ39gJo0YLVdsQvE4n1teUwGwyAGtux65TrcJQp+NFpyMOgQ7Ae0poL3uhwqvn2lG1LLb2GNRZKGSPjQhofNPpODHShH39p+Ouf6WlENcXzK5GLwPZSgJKnm9IaPEvs3AmCzocDIRHx05DA/C2t8Z9ptnrRe4/fR2Wm2+e/E4PDgDf/QLgiRCadLjY8+DvvwbY0suUvxzQ7m5Am4KI59m2alRYmsFm01mCPORafeLFp7DJ3QS4RgB7PnDlvUBWcqeRthqJPqW0BEECjuMhKl3G5udPP8U3mKIoOYxJbDwSiJs2bcKZkXNocDSOCf5dl38Vjh04LhwT2Wjm73hJViMMgzqh0f3FxGt/AFpPxxMbGjWQpZBM2/hWICP9NejE0GsY8g9BpQqLCgyCfcpWZsbL8P3buadEhakSZaZsZJ0cxEMPPSSIzvG0sS9HcI4iwcrGhgs9AC4vMHOT1aPz5t4fB2dG2vHXriNxa3e5OQf3lU4spcm5zhFohDvYzTpQWHWLYNJcnIp/ea5egALeRkWj8ELAtFpq0LyAqP3IIDx9DSbAkECYCMzW5neYEDUVEo0EBgOVyoSb2R679Iv4IIGTbiUT722SAyRCGGjnfMbAKoP5rC7h3CbPbyQ45GQhkj30K/h7YwgJ9nTxHIv0u9SgvTcD1uwVKatPSAxxPxIJn7kCiR0SHIkSmbz+PDckXybbKJrkj0x+TCR/nCipnDbqzwGtzUBeAbCC9/z8X4cuFkhssll7OvcFCSj6EnKlhhJ790oVXZMB7xUmCI7xwxUgCcrKEuVv+kN+eIJukVykU19ica85BOcqXhMSnt/85jdxMXHRV10GNv7zP/8TBw8enDNCg3i0tRGHI4QGwaToL58+jOsLioXs1AKmibAPJmMYTsfMN/JLVqqt5ObUcm2rArVJqjTYGHxffz06PUPI0BpxQ8Ea7Ok7gy7PkMhqvyZ/ZRyhQWjFxDaWB9SGjgPqSGo9qxyCo4Bm85T6S1i1OSgyLkeX5xzCCApN9DLz2gl7d3AB1zh9WF1WHSU0iE3ZNSgz5aDXO4IMnQmLzHkTOoE6VQn86EIIQ9HXDGrKiF2CE7vaBGRMbhFMiZAHcBwAAv2S02JaDhhnTn7LKTK+pKxqrTo+J9mqVWE0vv8zFlkq464l743tuUvFI13QWGSGz8mDJ7Bz6bKUhAZLvFnVIesqcwFLlZnDyqfH2vZjNCBpvzPQ9kz3cdj1ZpSbE/sGdTB8LbZLOSa3exgm01EAM1vK2OUdhdduhAFGuAaGodZqYLJZcXK4C1fnp64QpCMRZ0T5hwDHCRQXZouHwIYQwr4ytHX0Q1e7FrrsYpSNQFRYJNXO1RZLDwDdrl1Q6RKX4vg5hk4gjfvjbP6niCNznDS7xhK8Mw06lXQSZJ1TZj4x84YZVFPVp+cYolMRXfe7Osd8ptxgQP25s+xAKcYpA7JpOYv9vcBXPw2MDAE2E8DzW1gM3P83byhCgyg0LsKgrxvO4Eh0ZOUbV0GtlqQo6Uwz2L31hjukJolpgD1p6Fzs3LmTpbZkOQCPB2UbNmBvU5NEalCb+NMPA3tfkbbLRpJ7XoL7b/8WXa3N6FNpoTJKpeA0iokSU7EgNWRwZsvSZ8GkMYtKDtqJq69YBWfQiX5vA9wh6ZhMGhtqLBsi6/MkQLuB0mZeL4WY05M1mwirrwN6WwC3wvaxJgRlFLJ06cAf9kIrptrYXO8JktCJB0naRFJDq9aIrDc+PvGJT+CRRx7BGwkf//jHcdttty0QGpchuB4xGKZsCDtfscxWAmfQi9f664RM3CJLHm4qXJvWd0cCdXAEYv3SBnxHka1fB5NmfOmWBcwRDJXSYwFJwWD1X//6V+Gnv+lNbxo3uE+JIPk7sr05FTCZiP0rlIF8BjZJljCZZjbAoKkcOGWwnIlAE/nafD9ZUJ12cWLvQCafyBKf/M4TTzyRfF2jn26KJUUVmUNC7jUVqUEbjEFkVk1MOcA/DdCmZ1CZyVGyT0mblOeSpNZUJOlZucLg+Hjgdknk8FrJY4aJa2mR5L//DfDYr2PPr7oO+MDHFoiNJCA5l7bvxiTk3FwxHjgmExPmOPbHk7kNuZzoP3kCnY5ReCw2mC0WEbsYr28MQTUFzj9evxceuDDg60eTsz6agLAkYwWKTeNv440KjUYjqqEpEff2t7991ubXeU9qMKDxwQ9+EB/72MdmtDFUOmhxO0XGmzKjn/93ez2omq+kht8HvPR74MIJqdnpmh3AFdfPr0mU5zN4BgixjwiQYerE6Eg1MmyTl6RIhUpLGdrcnXE/6VT48ZTWQFgFX+Ta2rQGbMgqH2NAPd5+EI3O3ogyvgr1jm68p3Kn6EWRalHL1Zeg19sCH4PbEXmmPF0m9GrJEIuBwRZKOE0t4JetL0OWrlQELdPV22UWDCflZLp2RaYs8UgXJFDMmg0IhHsQhg8aVaZ4jEHQD7TtB0Y7AJ0JKNkIWOevtNO0wPE0+hoQjFxrZmW5SGaZAP1MjW9p3PGKJwqi2XV2EfRrcbUhEA4i35CPcnMF3EE3Bn0DIqCVa8iDZpIZYgwoctF/4IEHROCaWdiyxiQXK2a60NFgmTMDi+mQz6zOGAkommdH9HN5v8mkRpSIDA9G76X8fEmWCOB3fSnl1qYC5R3d39gK1+CIIDXOlvpgyOyNOlITBkgCsXs9EAji5NkWjDo86HH2o7B8JUqzpLHAc8bM8olg1bAJc3yDd0uE8JDB7HQ6G6/sPQmY48s7ed1nG8zIZ+Ybj0luGD1dw4UOHx2taIlyZdWYpn28Zouv3MEPi9Jgjk0GzCcsP/7tz4DhIWlbg05puyYnUBy/DlxqaHE50Oh0oNRkQbU1I+4+SrVmaVQaLLdtwaC/B8GQH1atXfT7UmZRcs2YTLazXLXk6uiA+e/+DmhpiTZx1/3N3wAkAQ8ckAgNIhCAJxTC3ld3I+/CMRSZjajIykL4A/+AHr0+mrlp02Vga45UseENeWHXcc3RYE/fazCrTWh2N2O4bxAZWhWMmtgxU96z1X0alZa1ACUj+fpE9wWJjM98BnjpJel5VRXwve8xUoppgRraN34AOPIs0HgUyDKTWYi8qQKyy9PuqRHdpCYDXmFzyPaqKu4aytiUXYU9fbG+S8QVWRKp+q1vfUvMIc899xyuv/56vBHw7LPPigeDNQu4/MD1mtUalwKpQVyRVSUenLPTnW9FH4FA65jX+dpckxqUv5EbvM4Z6Ft0HwGCXiCjFCjcIPm/C7hkwAD1DTfcIJJjWC0hB6nl5r18cGxRGmimgur8DcaYlOA8wf6Ac1F5QHkpJoHxN7kvtJlpx4sG1WmC54eVGNwWbXD693fffbd4j/MHyYh0/DH+/kSiLAwes2KDwWQGeOca9Dk5BuS5kfPMdKpKSWrIleTjzbW0Y/kQShdtbYL0YrXMuDI6bS3xhAax5wVgy5XAuvSv7xsBlLpmAlu0ensS/iHHfSKpwbHJcZqM1Dj25J/h++kPkRMKYLHJCNOOa+B463vQ2d0zpqo8GdjH9NDwq8L3UIJxuHOjJ2HTZcKaxO5eAEQM/6Mf/aiI6e/Zs+ei9ca+qKTGz3/+czGJfO5zn5vz3662ZCComOQ55enVahQa0tcDG/W74QsFYNeZoZkLI2vP74H6oxHiIAAcfB4wWoBVM5SFngKc7If8/XAHnTBpLLDrclIvEqG2KKFB2DLUcAy+hoyMu2aMfMk35mJ77kacG2mAK+hDr9cFTzAIjUpqhEozRq0OwxC5vP6wD39sP4h3VMQaDPX5HLjg7I0dI8JwB304PdKOjdmpM++ZCbosYyt6vM3wh3ywaO3I0XqA8HCykzGt4xRlp3Gh2InlgNiUaaY0AUls6FTjBHg4DhueA0ZoJLIZ9xBw7i/A8nuBhAqXywIs6Q3GKlckqABfF+AfBbxdgFoHWJYDIhg3eWToijDsb4aOm1UUHBnUOvjDvRjy9wqppELjOujUJkFmHB48ILqdEGZmNGdvgT5FQ/lU1XJySTMzo5TZUSQY5IZskynb1ifJROY9lux1IDZeR0ZcMJsNs7I8FZlsKDLa0O0ZRem65fAMO+DuHUABjGIBpjGf1r2jia0Rz+4+irycTNhtZjT0+HDzunXROYbZJmzcN175M5GlXwKrpgUati1XqWHTVsCqKUlqHNaYi3EOJIFiWGef/T5U/G1WnMykXibHVNz5ZmDqUw8j/C/fkCo4eB7f8U4g4gTyXDJri9+JSpelQkd7HDki/u/umrF9j2427MOo/zR84X6xTvrDBpg0JcjSxVdQzQR+0dyAfzpzLDonfKCqFt0eB17u64RercHbKxbj3YtqhbRmt9eJDK0eWXrpepEYz9EXprwOfCi1m9MBnY3Gr3wFK9rbxfMmrxddTifsP/kJ8K53Af3xWXKndMD22nIYtRFbyesB/ufbKPr6D+IqRHIM2bgyb6voEbOrZy+cARd6O3rR09qFRcsqxbLDvhLx55fyLINA3dPAICs9VEDBCqBie2py48c/BvbsiT1vbgZoi/L16YISXucPSf8POIEMo0SsLVoL1F4zaVuo0rIMrpFReCmlKOZXPaosY/vbbMupEVWaZ0c6oVWrsSFrEWqs0vzDeZ1969jkl477eI1BLwdwzuKxfu1rX5tWxu/FxmQC4G808LzMpvPMfhcnh5vQ7R2CWWPAWnsVLNrp661P9nomyvHJr84VuB4zoMqxOKcJiM5u4MLTsWP1DAJ+F1CRWkp4AfMLTEbhXCwHFROrD5gowzgQpVpncp5mJXEiAccErrlQXGeA/r777hMVzXIfQJ6HdDPVZbCKdvfu3cLuZdUJs82VhEO68whJlnR8G0rtptsgfabBczPTPXV5veOqwRWvJyODWFnP6mMmQTAIn9ImTuZL8Fp0U3VgAUrQr5bvu8mue7x28vc4htmTg68l6w/p93rh/dmPsMnC9Tlyj+/bA2v1Eiy+Pr4/RyK47WPHjqEj1IKCUGoJtlH/8AKpMQ4+//nPCym8//u//8O76AO+kUgNBijYS+PHP/7xpJ3pmcA9JYvwUm8X9vRJkxMDKP+8ciPMacgvUFrlyY4jOOeQqgV4my6x5uGmog0pgnYzhAsnExpQMnXm+KySGpxQ6h2n0OWNZTcUGctRY01h2Cqyrgmv14/evlb40YDSsqoZc0AKjXnQq434c8croo8ACQ02OfWHpIeYOlUxh6DHOyJKv60Rh8QfStDxEVCJsvCJQGKj2KTQlxdyUw1xWZSAaeIqDV7LgEsKhGumJuvEskkaPizT3LZtW5zRRJKmxSURN+XmPJim+BspQediRJlBFonC99UBZRPrBF9S8PYDzvhs+ug19HYCrEqQ701vB5C1E9AnyixNDL3aimLTFRjw1UMX9ECrtgjyYjQQIwp9IQfa3a+j1LQFJ4ePRQkNglUbDY56LLONDXglA8uZaWyn0ppkRjYzjZhhxewIueneRGAj+qUZxTjLLLtIJZRRrcWqzLKowxENqqkqgHAHGhtbYTIZhLEZQrkQ4ZpWKCfkB5yNUoafIRcaUxHeV7UFf2o/iTb3EDIz8nDbxltRap5k5qEuDzCUAt427NiyAqfOtsATzsaDb7sfBoX2L4+PWfA0wsZvnqyCWVWGUvP4jfpaWlqQV1WBEYcJI34HsvQGVGcUodY6cUks50hX0CPIMR3nm0mCWWYzHQTleErU0MWdd6E+Nxfqjk6EcnOgKitH+Px5YdRybpOzffg8WZ+PKErLgY5WicyQ+2nk5EnPZ2gN4to47D+KQJTQDkOncmPYX49g2Ic8w8yV8Tc4RuIIDeKHF84hQ6MS7RoCwQB+dOGMSLI4MtQCR8AvPnNLYTXeWr5iXGeCVRoMctOwp7MQl9FUdxw49qo0r6/eAiyNVR4xYHCyqQknHQ4MBYMo1Gqxhbac2y1JTdUulRy9cBhnXG6YCzNhjJx7pz+ACyMO9Hu6YXrhOVhKyoTjLp9XSluNhh04MSxl2NtyMrF8s/Q+Exe4SqsTnCSdzwcMNkWeUVbqJKAzAyUbkh/40UiCiAxKYJ08iRnBgMK5ZbP6oYhU1LKCtBqEJ8KgMWGNfTtG/YPiUth0WUkr8ng+SGTwkQzMomLD8H/913+9KMlEcwkeI9c1HvOliEHfKF7sOYxBvwMmjQFX5q5CuXl8gvyNCCb00A5m8sBMB+R29xzHOUdbxA5R4byjHW8pvQpm7dwRgrynTZoiuIPxATOzZuaq38ezDxlIoh3ItXqiBI0Zx4AkCxOHoQagdDugkNhdwPwF+9GNV7HMe5YPkmaz0UtJmaDFSl9Wm88FmADERKCk8rNpgnJIPAbGy1jBkpjtziA8q2CUx5kMlGFKp8KKNuDFqNKYLXDeSiQv6MuQtJDjjnLQnH4arxX/Z2CWnyGJxIqPMShOSDqjHRkKA/bLMJlzmpB9X9r0JC0nQ1zV1taKfp+8ViTcWEGTzA/lOvXaf3wHW/RCpkVc01anG00uN/QvPA+31iziY/J3KWvFh9ynhXFJ+h/O0YEUCQQSJpMs+kaExWLBd7/7Xbz//e/HHXfcISqg3jCkxmc/+1nB2N95550X5feZxfYf67bi0GAfBn0+LLfZUWpOj1x5faAhSmgQvAXOO3ph6DmOGwvTbNjqG40E29hUOk3jjM5wJFgRO5DZNeyGA4NxhAbR6WlBvqEYNl2yRTKyPwweOTxYkp8FT1EO+lx6vPjii2LRmFA6JE2cHL4gMqmEfFQktqFTA0tMDKby+oRTyrTkGWyC4HAGvIpJLIxKyxTKudnQWH0FEGIDX5at2QDNyvHlL7zDQPPzgE/SBEfOCqBwY9oZnDKzTD3IxMwXYtDnwGNt++AJUcIHMKr1uLd0K7L1qRsih8NBhIRklgpqZEzMqodTVaJMr0Jl3sHVBvS+LJFVBi2gS8jwkGWW5PNFA2doP5CfJDuA18N9DAj0AdR/Ny4DdPHSCUaNXRAbMro9x8dsJhj2otX1KgJhqWeFDI5lV8CZ1mGRzGDzrmRyZYmgocvS4HRJDeLWorXIN9hEzxpmOW7OqRb3nFzmKwcxoSIRsBVdXb3YvHUl/BhACG1AuB1aLINGVTw1QqPrOSAwEiNZM1fDmrkcD1WkCHKmC15n20bAW4IMiwNbSq6W5McS7pcLFy6IUnNKLKUiNeisMJsrHcfnhe4z6A1ootJkmT4Tjg0NIaw6g/X2UtxQuDSpFNWAbwgv9x6CJ1JSuypzCZbbJpcRRYN/pghpElokYpmhlyxrLpCdg2Xbxx+TdIKZAZSSbHnwXUBDHdDTJTkbJKtbmoCvfBr4hy8AltTzYLqgLF+M0JCCTzweVlkN+VuRq6+dsQzr847RpKZ2AGFoFNTfY20NKFQkEv+1qwFVVju25qQmvpjBx6bfstMf7ZVz+hDwhx/H7p+6Y8Ad7wJWb45+d8vWrfA1NwsKXxwrv8dsSzoLXOc/9TDavvZVqFVAhV4PFyUShx2oGx6F3aBDtsEIt0YLZ09P9FrSmSRZFbCF4RlI7mA4/GFk6aXzLVc0lrA3RuJZIsmRitTIyRkjd4aZCoqaUpSom6Y+7khi2PXTk5uhE/eDH/wA1157rWgcnk7D1ksRnHO/8Y1vYNeuXXPaq2+mwKSbp7r2wxOU7Dd30Ivnuw/hrpIrka2fmqTp5QqOZQb3Tp8+La41idmpNBZOVolPQgPRWSUsrsfZ0Vasz5r96kglsnTLxBznCfYIIc8MXaUgOmYLnFdZoctAM4NBk23Sm3q7bp5FpnFApUojQJQyq37uqlQWMHUwcMhklHQqfGdynub8LweoZTAoSv/lYiTRTgep5LjoOzDoLsvBJrODabezUoTz4USEb7KKhksVvP6yPZ4IjgOSsxNVBfG8kyynJNoYFJUAD70H+NX/Skkrsg357X8BPuwDrrp2xo7lcgD9Xz4YQ6BfzKqsdMCgOJuLc2yPtwYdeO01bNn7EmDRodvtwYG+QVERHgyFYciwiUQtJm7RH+c4Z0PwHTt2jNmO3mmIyLwm2Rd9rngsYHzcddddoljhM5/5DH74wx9irnFRRK8od0LpKTI6F7OsWq1SYWN2Hm4oLEmb0CDaXQNjXuOUdsGRhrwFJ9mu14CmPwKtTwEXfg+4aaimgTU7x762amab6SYiWTNK8Xoo+etQlTDSAjR0A639QPsAjG2DKM3S47rrrhOGMg2dmXL8krGqG7OrReWNcmStsJXGVSro1BrcX7YZOZEgPxuD31G8HkWmKerFqrMB7ZWA9jpAS3LCNP4YaHlBIrZk9J8CBs+P+xNsbL6/+wy++5ef4dFXn0JVTfXYTOcIXuo9CS8DuxHwf76WCqGwG+7gPniCr8MTpDN9AOFwAoGWCJ47C0v1Eu5hWxkw0gv4ky8OlxwGD0f+YVWGH/AFJEMmEAScHsAfGOt8JVsY+RkXm4xznvADISfgOigRHONALbjnsfNkCAHk6seSmhbt2MAZjbsRP5tfjYpxxCx3atsmVvekQioDcdz9VqmxOacGd5VcgRsKV4nqDXlbY4IOKiPKKjZgz6uvoK1DPh9hBMKnEQor7pN04aiPEBrSdgSGj0uk0kyA65axBLDUAobiMYQGCSMaYjTgkjklfP/MmTPiL+UcJtL7PD7UigvhEYz2D4ksdR7RcMANV8gLR8CLPX0NeLZrrG48r/We3oNxGqEnhuvQ4Z5cc3EagiQiZgKy48SeHDqS9P/8VeDm64E7bwV+/UuUlpSktUbk7XoB/dddDezYBtx6E3D0SOxNZkz903eA8qr4a3PhPPDrn87IcSS7J2MIz2jgpThFUECdsDesyFH+KtfBeke8XFkq0GkguUEZQ4G9lP0gFFuMvhb5zgc/CPPSpTE7jvv5T/8UfT98x52o/8KXoL1mJ37T3o26wRHkm/RYmpmBK/KysebND2HHdVLjUOrn0gEn6UWnM1efBR2biSccIwmSYFiFAR/l7OwoNFShNmMrMnxJyPTxsnnZ+4OBAI5HPngMn/gEZgSFlUBpbWSnI1epbClQMPWszZkCs93YP+nhhx/G5YpPfepT4hh5rJci+n0jgsiIt2/DaHfHJFMXIIEELLOQKb3KYAnnEcpdTBc+hf0cg0pUw801VCoNsvQrUGS6BkWmnbBqy2fFd+7xDOL3h5/GD5/8PzT2torAz0wRGoFwI3zhvfCHD8EXfgXBcBr+sp3BL+U9oJL6asx01fkCZhSUs3z11VdFsH3NmjVpfYfjeSbuW4LbScywZ1CfvQPZg+dyAG0lVnIw4SwZocFmygzKM3EhnX4GPP/MeJ8Lea5E8DdlmaKZAI+DpEWySm4GyvlbEx7n4CDU3/42o7QscQUSK4luuxv48N/HJ8VQFv7735oVqdvLAUxoZoU+78N0QRJyvDWIyXEmhPHX1na81NyBUX8A2XodNufYsaO0COv+9u+EjcB7hLYBm5anmpNqM1bESb5roEGRsVS8vipz/YIMaBrgPMLYPmP8JJIu+0oNTiSf+MQnhNbtTGXszyRODvfj8fYGeENBbMspws2FFWMGcqrSY60qDZZ75AIwoghe03BufxGovg+Y6PvrrwGMJqDhhFS1Qdmp8ojjPEuwaJJnFpoTXxfduo8Cvmagf1QK/MrgpN+8G1jxgHA8mB3OoMVk9SUTUWzKRZMrtnhwMso1ZKLAmIm3lm/Ha/3nMex3ocBow868sXJZuYYMvLdqpwgEpduMe0bACh1WasRBBTi7gGwpK4D71Oqqx4CvR2Rolpgq8WJ3Pfa+8goWrVmKYYMWLznP4k77lqTZ2YN+Z5xDzP+HfKkz+L3BkyKDKvr50CBCnlegCZsArR0wLh47Pnlf1NwENO8BWBlDaS9zKfDybyPNWtXAmpuA8rH6hzMKZjNyjOmMs7T9+KZRYmwL6TDZmAkC6gDAXhCiQa0q+dQa9gLB/oQXqZnWAWhTZwBk6iswEqD0QKIRFkamLgO9vlEE2bSczdG0Gai2xhty1KZ/uusILji74Rp2YPRCF96++TZsZzPfiwRq7CZm/xeVZCC3aBn2vHQcOp0WeXkSwRgG75VJNucS1TMxGbwogh6pQmaSoCxdq2tUkKGlJuuExg2lopgBn0xHlGXx1MdlhVWq6gd+j0EtNnmmpOFTXadhzc1Cf3MHDFYzdAapkbtyy4cGW3FzUfw85wi44E0gcjhP9ngHUGxKX0qCzgFJmJkoJ+V5YZaZyNb55teBF3dJ9y8bN//4v4VkTMeiKvh//SvonntWCjjfcy/wN++PSUc9/RTMP/kfNLhcKDSboR4eBj72EeC3jzItSPqMwQj0dscTjvyd+knIHPi9gM8jZd4nXCu1Sg+9Oh++kJSUIDtJjK1bNPmiJ9FMYXVmNh4qr8KvWi5EK3WWZtjQ6ZaIO1XkYdfHjzVWMtomIZVC7ees0CgaHv0Wqod7k58PJRg4+OlPgcOHJdkpat2yAiIS2KAe9KatW5GrU6G4/jQO9Q4j4PQi22RAeNSHfdlF8Dz7rMiGo/PNCi4SK4RBSO5swsHB43AEnLBozVhnXyn6SniCbth0NuQZFPq3rMhoYkWdfEbCQPG6+P2lRNV//xDYs1siYD70QWB4RBp7O3cC48hkTAq89lc9ADQeA0b6AVsuULk6Vmmru7hl7OwzwXNOSYCLuQ7MBlht9PTTT4uAzqWKZDYdZxeuBwtIDfax4vzBKmZ5Hpkq7HqLqDCVyCX5GoRRaro8szXbXL149OQzGOkbRtmKSpxGN0rdvSg1p9YYTxeh8CCCYUr0QpGwcgpq2KESlbopYCsFyq8Gug5Jdj4JjdJtmFNwXfe7Aa6jCw3K05KbYnCaMk+T6e/ITG4G4ll9MF0wEJpMLpckN7P4Kakm92e7VEG1Bla1srI28XUGb5lMNZmm6/RTKA1EsiSlrOssgb/NuZskWDoNnScCfQhWwKdK+qStyaqBlFJIlE/90IeQcfYsOikf3NEhJcE88kjMvyCcSYLzIt51ASi4dPt4zSZYLcFG0jNBmDMxkwmS66/cgY1LFuN8Vxd6+oYRUKuQYQlh35JlCNedFxKVN954oxhfnBfo5yRDjiEPV2RvQ7+3V9hg+caiyUtOnTkN/PgHUk/BpcuAD34UyJxisvQlipqaGnzoQx8Ssf5nn312TsmgOSc1nnzySbHoPfbYY5hvODHch08d2ysMLgYNDgx0Y8Dnwdsq4heGLTk1qBvthD8cY5UZS9iQnUZJMjNkIzrTUTDo5HcCE5WV83srtkqPGUI4HEJQSGiEoVHZoErQac7Q2VFurkGLqz762iLzEli1CfvqqZcIDcKfyLaHAZ9DOmaVSjjTDOrJCyeDQZxsaGBMpvxxaUY5RgMuIUPFs5mjt+G6fElqwqYzwhV0whlkQ/BRdHl6cXfJdtgi2eJKTERodHn6cHDgpHBusvSZ2JKzGlbtNBZeITeWJOCqjQVbLzhOo1sh+/Vy3XM4fLodxUsXQWvQim92egbR5upDhaiWiEeOPiNOWouBzGx9fGCYvRn6veeFhJEGDmTowqJ6iXItRqcHqjADw8OAvwsIDADWrWPlsXQmoOZG6X/nELDrv2OyVPx79CnAXgjYpu8YjQGJk/O7ge7Tkeh/MbD8FmmflAh4pd4fHH+2EklnfTIw5AKeBGkTStrE7QtJjiDAJrj8HXsyhzrVxD7+hK9XW1Bm3op21+sIscJDAas2F9tz1mHIPygCHln67DGBjwMDDYLQ8Ht96DjXhJqNK3FM1YVaTI5UZqYDMyySaoxOEtTKPHnypNAupWHDwLuWWdkqFbbsWIVnnz8Ms9kIg1YNo9oHq7lflJOnrZltyAYc0jUaHnGhsbUHobAGeRhAfoFhUv0hOt1OfPHUXnR7peq0NZl5+OyyzTCMo43P4CwdCmYEyaC8DwNtzCBihlWqhd4ZcOP57oMYYPN5Md8WwxPJGs0uL0J/Uzu0Bj0yi/LihqG4dyNocPThgrMfhiRTG+cER2AUJ4ZPwKq1osJcMeEcqGz0KG2Ec7ob0BvHl9lLAjqVNGzFNva8FJ/pRLy0G4vrzuP0r3+NFSajdJ5+9lN6qsDb3i595rHfideXm81ocHuw2GySeiK8tg+4+57YtuxZgMsZW29JTGRJQfdxwc8fewE4szfiJduBnW8FMuOdZJt2FZzBBriDnaKPhifEpId85Btmvpnq55etwTV5Rah3jKDcYsE1uYX4Q0cTXurpgFGjxQPl1Why9uMP7edEhQYJjXyDGW8qmITE0FAHaoaP4eRgF1pCfsTl9/E61CYJ+jM5IYkEIsdLNKhw/c0wHTuMK1/bi/0DQ+jyBXD+7tuwbs2auMBDYkDDrs/EmwrGloknRcFKiVjvb5Cuc/4KwJYgXfftbwF/fSI2Hn7wfeCfvg7sSFIFO11wH6ojpArJjP1/ANoj1VRFi4HNd8et+XMJBpBYqfHxj39cZK7OZrPluQSJch7Tpz/96Qn6GM1v5OgzUWjIRreXGs+RnlQaNoefghTjGwwMaDGwpwSDrHwtHRkcGbSjbivahKe6DmI04IYaKmzPXY5Sc4zUaHH1odszKBLdaq0l0F6iQW9WjP561+NwqX0oXboo6jccHDw3M6QGklXbCkuEI3v8L2dVS4+LgeFO4NQTEqnBfpmLrwEKx+999kYGM3OZsDiVuZf2AmVhZgL8fSYPseK3rEzq46cMaJPUIPHJcS/HHGiHsKJhshJMspwN+1YwWMrjZ+XYbAbyKLdHfyKRtOnq6hKZ65s2bRLHPxm5YIIBYiYujdtPYpYwU4QGfS76lOONQR4nk2qTNZMXYFXG+fMo0GhwzucT/eJUHg/wwgvA294W+1xWiiSvhd4aKcH7i8nNcVK304gjsKeMmuTIZ7+IxV/8LAyDg+jx+vCqNRu+HTtx9bZt0YQz+v0TkaZMDOVjSmhrBT7zSalih37tq1SraQe+819Sct4brGl4TU0N/vrXv+LWW8dv1H7JkhpcQD75yU/iy1/+clpNi6YKbzCII0P9CIbDWGvPhiXNvhN/bGdwXBr8vM343+9az+Oh8nhtbPYleHflTrzadw5dnkEha7TaXo5lGWUT7Fg/4G6htSwFMoXWt+SyQDNLWebjIBT2wxk4GOmjwL0wwKLdAI0qXoqrwlyDXH0B3EEXzBoLzEnkbeIkdNhzgDI9UbAXQUY0IM7Fi4uv8pySSaXDQcIj3UmO39+UvQwbspYgEArBoJCa2NNzEsN+GsyxptlPdR7A/eWTC14M+0axu+dAdFz0eQexq/t13Fq8Y+pZc3R88tcCPZRLUcWIDvbViBBNMqFBZ+zM4TrYczKxbccqNLvig+lyz4xE7Mxbid+37YOTmemissaAq/NXKmRpPJEgeayixu+jMx2Gzh+AKiELHP4eIDgKDHYBI21SMIZBJKPiPh7uStJnIwwMds4OqdF6KEZoiN/vBM49D6y8PfaadxQ4+yeJNCRYtl57O6BwTCdEzhagZzfgH5Ke67IAf6JkFPXkswFTHmAqAZJpn5Px1xZG5KcU0E8wb0SIjRLzJnS4DyEY6aNh1uQiS18lMsILNKmzQjo9kmxQ2+kGVK5j816gxzuUtIpgPNA5oMbyZLJ/UoEEJgmNA6cPoyfXCXfQAzXUWJlpQpbeg8LSfFQvLhUOg01XA22gRATDacBw7aAxM67zYa4AvH04e/QluD0+LFu8CJrCq9A7qhIODYmFdBv4favuIHq97pgU1HAvftt6Du9clLo5H+cwSnwxY4znjMfLeU/ebxrdzEpjE/ZEvNh7RDSHldHobBdBaq5nIqOpshQ+lwc9DW3QZ9lgsksE89Yc6Xhe7WvEXzpPiiAM561Kiw42XShSVhsWrw/7++AIqMX7/b5+bLBvGHcscG5mSbdATyNw8E+R7EU9sP52oCiJ7mwK8PjFb9HQ5NrsV6wVfF1vgPqF51FtNOCM24MivQ5ZNFpZtSGTGkJSL4TDDifsSnIpMfPnHe8HvvmlWBBbpwcefOfEO9l0PEZoEK5hYM9vgNs+Ekfs8t6zaheLx2yD52xHXoF4yLivtEo8ZGzIykOFORPnHQPI0OpxTf6itO0fgc4zYn5YuagIR+raoA55UMrO3CSuVm0Grrt7UvtM8or3G2XY1nzsUzDc3QTVvlex7fY7MarTC+dyRp3/nBrpkQx0Mp7+a3xCCX/7qb/GkxqUWRsZAXhvTrOaNIrjzwPtigqhrnrg6LPAFbfhYoFZVD/60Y/wm9/8RvTXuBzw61//WmRo8tguZZCgvqFwI44MnUe/dxhWrQnrspYIYmMBE4P+BDOOo+dTrRbrLbOzJ9NMPMdgw0Pl1wj5VlZMKsn/AwPn8dpAnVhXuY6eGG7GfSXbLjligwkYDMoWLimDXxdvv8s9XaYL+pbJMfd+b9qgfO6JPwHBiH1C2bFzzwGWbCBjjhumXyL3HAPt0yGTaRvOVP+2kpISkUREuzWRzGTwnLY4wd+jDcJ1g9I0BOMQ6VQliwbFr70mJGRJlpBs4HaYuHv11VfPGrFBGW8SKKx4pn9Bf4LxNSZLycfF51MBt8Fj57xAYkTe3myDPllHR0dSn2gyYCZ+0j4YCSCBxYp6fp7B17hrFbERj7tcQu7XEwrBRH8zUbJqw2Zg9Trg+BEpaM3EKvbTWDw35+xSBe1++gZsAs6enlPt5cK1nMnRrDhmfKLif38B84HXkR0Ko/K6N2FoVCLT56xSYO+eGKFB8G9jA9DcBFTNP2Wi2QTjNF/60pdEzP+GG26Ys6q4OSU1/vu//1sMLnZGny30eT145+t7cCFSFlZgMOLnm65CRRqNQT3BgNBrFpyDQrolQjvEwaIxoNiUAYuWzJ4ZNdai8W8cTobdLwOK6o6oZkTOuouiEeoJ1kUJDbGL8MEdOAWrbtOYz1q0PNZx2EuVPlZ9YLcAHr+UuU4ww6XimuhHOYFxQUksDWQmleyIcIJKN6uK5ELipNjh6R/buzngmHQgt130O4mXcXIGXRjyjSLHMA1ijqQGm8Q7OqRrn71M6lGh+DWvx4dTB89g+fpaGM1GDPniddoZnCwwZCXdPPsXvK3iKrS5B8TBl5hy4kgfR6A7jtAgAiw2CAP6ZFrwrL459UcpkMmTyY/0nAVW3guYIvtgSJFlker16WKwJeEFXuQELf7WfYBf0f+FDkrTHmC5Ipt7ImhNQNGNABtwi2CsBmh+PKEhOqtAlgLWCQgK8wbAcwYI9Er3jHEpoElvHJHYKDdvhz/kFBrLOpU5rbHMuUq6pCpodNKUb1Dr0/ou70kGJGk0895JlwhICxoVLui70H+hD/kVRQghhCP9I7B2tWHdSilHnM6NM9iCQlONcBzkTBw6H5RvSgmVCh3uIiBjFdatygXMhYClGMUZjFUW48TJE/jFM39ESKtGdWEpNi1emVIOr8ExBGWnAv53hpIyKcDzxP4TK1asEFkhNOA8bjeMkfmM77PihU4Dm9ZxnuM+8VgpO9frHUo4FBWqLFaRoS//voY9SUpKMdraidKcQmzOW4TtuVVCJuvJzlPic/I+NzoD2JpTgCKjCb6QF4P+bhE0k4naXm8vhvxDyNInn0tkcB+7GutReOpxyXAjAj7gwOPANe8DMtKogIg/MODNbwH+72ex55xbWGlx6hRMajWK9Tr0+P3I4vxe3wDsvApYtx648WYYzp6FXavBMlZpELx+VyWQ1itWA1/7NnDwNcnp2HIlkJ9GWXhPkxTIj1adhQH2pnA7APMUs3jmCFdkF4nHdLFuSSlONHbCq9ei+q2fBQyTz9rj2KUMDKWozpw9i7WUdxoaAbJzJisoFwfaZYM+qQrKrrNDSxtjylAYCd/7HvCLX0jPGdTg8xkgcdFN6ZVwgj2olGNJArHOeqV1YhbkMRnY+frXvy6qGu69994Zaa58McEA0z/+4z+KBuGTycifr9CptSJpZwFTC3YkSotQ7pDJVHwwA5nrWTo2kOgBluCfOQMeQWgQsXV0BKdGWrHGfvH75qQLZmQzqMvAUrj3OM47WqOzlCoi8TsTUCOPteIIgwk2cnJFKdSqucsEnzQcvZLkVRxUwFD7AqmRBCTG0gkmJ4J2MCuB5SqrmawaZMDz9OnT4q9s37N6g78jQ/49BtX5YCIhJbJpt0xUOXDw4ME4GR3OO3ywWoNkB+8tBvfy8/MnXTUxHlhhwDlMnudIYHCekveD9hYrNkh4MDZComkyDdK5LcZg6J8wmez/s/cd4HGWV9Zneh/13iy5dxt3YxsMpneSAOmBtE3ZJH/aZrPZTTaFTW+bZNMhDQJJKKFDaAaMce9Fkm3J6r2Nppf/Oe8338w3o5nRjDSSbaLDM8gaTfnKW+69595za2szqPadIHidSAhNFnICVjo2AM+RPX6ZcBZj/3Ac19Sg4swZMR5MDMjycemlsR9An+KLXwVefh7o7gSqaoCNW8YqWsxgDDh+ZWKDEqgTDXpzXvHBOcuYomnjJnQ1NUGl1Yp+MzM4d/jwhz+Mn/70pyL2z5YTbypSg4ssdXx/9rOfTSlj880Th9FMuYkwer0efOnIXvxh3fgZ+rUWOw4NxTbtJslxZnQIs63RwCMDT//o2i1Kw6WlS4UWZzeuK0uRpUPNewZFleDCx9Le/OQZv1OFoJCc6oSOcaRwQJv/CihIjoxgmgd4qfsflBb6sjwgVCH1CrCWxkj+yFkFMpgNQL12OhncPLm5kEGnocONjgZCIlATUdnsyRvywV3IDF4vNKrYwLy8xwRCwfR6n0Tex+Bf4ucnjZxa6REHZoPlqAvx4p7nsWzdYmjDgehaSy1aXU3whwIiq315bi2slNtIAr1ahzpL4msnCayNhUZVBbWODbEVetS8AJ2DUi8JQcSxb0S4KVbHQaAuvNHnVwElc4CuxmhAsKAaKJ4ihlpoxcfJeMWTg24GiGObbY7tZ5IGeD46RRiuZCPQtSNKbOTMByxpaMFy7JmiFTOZQq3SwKAZR6YuDmvy52Jv4xHklRZGsgq3JOgxEw8axBznzNSZikyHYZ8DGosOgW7pGrpHXehp6cLFK2dBr1duTbEzkIYK95CUhmsohOa9f8f6OSagt4OF8UDhcqBohVgDDlgdaKrSIugP4PjAcbzx0D5cNn+VcEL42ez3JDsBOTo9Bn2SrnbQ54NnYAhHWtvwA7cT7122BD6w94AKNm0FzNoisXbROWIWVXmhCeg/AKNvGE3tI/BZFwFGqTyfJeIEHQdWdbA0lmQv1yfOcRm8Z5WmHGwtWo5/dNdj70AH3AHpqmgrSuDpGMLmRdIcG/F7YwgY6f2UmtNhdf4SNI02Ycgfu8cR/nBPllSgA9W8+2UUej3QKklkzvP+lpSkBp0VZrHx+sY4iu+9k40cJBkqSoK95W3AmrXAHW8HfvwjtHi9WMp7LDYp7qF+YNcbwPAQ8LGPI//n/4c+vx8FlBj49vd4kGO/nI4GH5lAn8iZlapIJtN49tjwGYz4nMjVW7HIXjvhaj9fMIiDg/2i9xf7bdiyZVOVLQRaDkV+XVpbjpMOLZo7+1BTExeAemOnREiNOAD2ZbjrA5LjF4+9e2H+6U/hPXsWuPZaYM2a1Mcg9nRWwrF/hx1Qxa537oAbb/S/AWdAIquNaiPW5a+DeTxJSAYvrrkWeEIhP8WffI54+ukooUEMDgKf+hTw+ONjK4Ayhd4kyTMqkaoHlLcbGNoFhBhU0wD21YCxAtnGO97xDnz3u98VFRuf/OQncSGD50Bd7re//e3n+lD+6cFqY1egEd4g916SAtUwqMf2J5wO0Eeghjf3cwY/aM8wq5xZ3AxoMhCSKKGB/gf3ZdnGoL3B9/K1zvg+a+EkI4c/2pfufAcTyBjwlCtv1xUsEpXdcjP6cmMh1oerxycLVjTqsAJBdIrefSpYBdFxXiNhLypWl85USyUC51QmgXNmyDM4T3s+G300EoHrDQkNVmnRtmaAnvM5VTUJ5wR7b+zbt0/8TAbGMBirSNQXgLYyJXa49oiEjuPHhT9F25c2MEkOBtQTrYdMiGLiFv/G40zk43AtElXs4d4A9FloX8sKGHwPA8YiUSoYFBUQ9Ev4nZlISvF+MrEtW9Uz4yGlJFQKkHhgPIjnznWb/lc6YMIex8eYhA7uB//3fyi4+24c37ULxez/+7nPkQUZ+yG8/5eHJbhnkBE4vjhPduzYgUvY024S4D3kWBVJhGGSNCm8IwD78VJun32ahCR8FrDpEuCBP0l+Kqs0OGdm1QE1F06iQzbBdenuu+8W/TXuvPPOrMjLnTekBskMlgPefPPNY6ojDg21C8moOdYClJkyC9jF48TwoJDpkMF/149IGa7jodqceLFvczliSI1u94AgNAjpm0IY9I2g1dWNWZYkmyUnj0oHhOJlmSbfeDVTBENueIJ7RUBW7KkhCHLDF6JhPsFsPY0NyNkKeJqkahRdCaBPvLFwI2ZPDRncbC+//HKxKbPxDzMk5CoObnCy8xEPThC5VNHpd+NPBx+BdzCAvJJ8aNQhUfDMpq0yLBpjxqXhNeYyHB1qFEFGBoN5ufL1ucjVTW6cjucQDtYPYfOGzfBoXaJReJV5NkqMlbDpivBI+y64gwG83t+IBkcXbq/akLEsgUVbhH4vq2KieVlalREW7TwROIfFDjgPSGRc0AD4woRG9CDDxIbCueNgWnMrcPYQ4OgFLHlAzYoxDXazhuo1UrVGZLqHgFlx/WZM+XHEhipWMmuisM0CTMWAd0iq5NCfv42g8vRWrAlVwraoAv5QELWWYpTzuowDGvdJG6llKROVEIZ3IIDuli5UL5gFvSZ2vBjVY9eRpUuXiiwpvpeBLJKhMdVajjYUGxzo6fejuCA8V3sPAvZZOOwcRpNTqrRQazUw5VjRNDCE6vmzkWuwiKwdOvx0Rkg0vKd0Lv635SjcvX0YaWmHrbIMZaW5cLva8OxLR6DWqFFdW4bi0i7kqRcJiSw6FPUnj8E49Dqj8cK5GR4awKLiZmirlwI6a4xhx2AJHRWudTXqPJzS94bloqSmsUtz6pCnt6Hb7YVL2bJIrcJxzrUwbFojrFpDuJ+OBJIclSZpfObrx953DTSwx/dISoJ58+fj7JOvQKdRoyrcyF26mXH7xlA/4BhGpy+EEZ9f3JuEzQe5Ztz6VumhBEkNkwnGB/4sBc37+qIlvSzxPnpU/F5y4y04c9VVGDKbUUsnMa2zSOdE1wKn90cbY5O4WbJlwgENfzCAZzp3YijcJ6XJyV4tvdhWsi6mF0o6GPZ5cdfuV3B8RCJn8/UG3LNmM+ZYE9/DAa8Lb/S1iB5gi+0lqLOmmPu55cBFNwMNr0nyGwXVmD//Ury+e29sZeWB/cBnPx3dC06dAp5+BrDnSFluzMqhY/rQQ8Ddd4uX6Z1OHPntb+HetQvYnKRPhqiMOQBAUQkVmg+oot09jg0fgysQlYPzBD04MnwEa/PHVpiOwf/7rNTc/OWXpUbhlDPbHG60efCg5KDSISE43np7ychRywKTwqJLgNf+HK64YAVGCFgcl/Ung+c2yHVDnugBYHgXoL0cSGOe+oOc+0zeGF/ai+vnV7/6VXzwgx/EBz7wgYyCUucTSAyz6uTXv/71m6Y/yIUMEhqeYNTGdwUaxE5j1IwvtZltUOuevicfDPpRGoYVY9xzGayjL8KgWDwYIGG1pTye+Fr2AeNzrISOTz7gPltsmDq/IJtgQJbXRVntSnmtq0rWRCRtjWlW82ZCbGhwAfWEsRYBBbVA35loNZ8pByjKvBrhzY6JBKJZSTAdDakZKOdc5jwnqams0kgVjKMMExtuy7ZrvGwdX5No3VCC84d7Kq8NCQr6E3yO6xBjHVxTGMMg4ULpLhIpJB2YyEmfgd/P4+V3sQqGAVsG4vmTZFBEEjYs600yI76HCNcvmTRilQof9OvSTS6OyMVOA6gEQLJLXqPHA68RCSOC9mmm9guvTdLrUFwM/PCHqHI4cKSpSXz++V2jfWGCc4Ik30TWkPhKTFZrcP6klMAbbQdano9W4TNZdda1Uj++yaKiErj7u8Cvfx5uFL4I+Jd//afrp6EEY/4saPi///s/fOYzn8GbgtQgg/+tb30Lv/vd72IWx1G/Fz+sfwW93tFIpsv7Zq3G0tyJSydUmy1ocjoixAY/s8KUHjtUYUpMapTHPe9NktXKTMyk4HkXrQO6X41mlzOzMG/iWdsTRTDEIFggUr0Q+RlSwaSZRLm7xgqYxz8fbiLKSg1505RLN5WbDBe5dBa6ekczLEU5cHX0YHhgCLY8O/RqFbzhTroGtQ7XlI6TIZoAZq0JV5RuxMHBk3D6XSjQ52J53vyMA1Hpwh90od21B52jh7DKuhgGdQ60qEW/dxjOQAue724QEjMy+rwO7Oirx2WKfhnpQK+2osx4EXo9x+EPeWBQ21FsXCIRGoShEtAzkBOUJGaaf5/4g9h4WwnBTI9vjGQF9lJgxduATgY4A0DhbMkJUaJqAzDaLTHzcvbVrCw1heX8nUzD+GmE3WDB2oL0nDFmOZBonOrSTZuWclqlcBe5MdQzAIPRALPGiNnWaviClEEIwaguQY5uQUJHZePGjeLfNIL37t0rHAwaNqKyyzciHAaPN25N9o5g0Cc1/ZQrGkLBELR6PVwIoECjEWTpsmXLIteCGVa3O/X4eyiI0jVLYNOpoFWrcHVNAQZbNCgtL0Bv9wAO7auHTtWG2XUbxTpWlm8Egh4xJ7i+LZlfiSMnW7E4vw2avLEVBXRImDVZ2NuLQKsPugob9Bot5tuqkRMmQXRqTXxtEtT+UMQg5Lr07po1+F0TM9mlc78otwpr8qWAtF1nx7KcZTgydETIfbE5+8rclTBQoiwNGCvmom7RctQfDvcD4sHYiqQKLXExQ8DzjwA7nkEgGMTQiBvzP/oFoC5D55UVcjfdDP/8BcDTTwF/+cvY19CZOXkStUePwPnDHwkpDTqtJLkmBDqoJw8BJAuq6oBr/gWo3yUF94tnAdUTz1plwgMTH5RgYkSPpx8lxsxku37UcAwnFYkaQz4v/u3Qbvxt4+VjXtvtduDbJ7eLpBHimc4G3DlrFVblpwjSF9ZKjzAY/Bqjz/z4Y5LhwMA/77nHD7SwD1SrdF+OHAG+9z3gW9+KvGWF2Ywunw8LGhqkKoiE+zozu+Ol3U4CoRJAJY3REf9IRPKF4L/5XFqgbfHRf5Ue8eB6F9+0nucoBzMcDuDJJ6V+G2x0n4bTHUHZHOCS90iEPw+9eok0puLBa+lslRpcadUKGYOQ1McpBanBCuIu9yE4At2RPb7CeBG06tTO2g033CAcdpaJf/7zn8eFiJ/85CciIHL99eeuR8kMovAGWbU99rnpIjUYKOHezeAfkxMY6OD+yuQHysIog1rpSqvwtSQ0GETjz2tKL8JTnXtFogixLKdGyBBfCOBeyXOIh8jyTtMWeNODa++ia4G2g5IUFftCVl00U6mRJLicqGIh2WtZOTDRng8TBdcAEgfpgoF/WdqJFVuUseIawr48PFf6GyQS0gFfS79Cjn/JJCuvBatIuCbxb0zWUgbmKW3F7yYpQT+E30f7lomhPDb+TQZtX5IlqfpSkPCgX8TPYjVDOj4ev49rniz/O5XgNWCAmyQU1+3xMrtZeUOfb6LSmYw98btSETwcN7znJMV4ffl9E+0BMYOxEAl/w8NiTE+G1KDPTRkrKiQkU3cRaH81tvcr+1cy4bE0eVVWRiCR8d0fT+y9e/cC+/dLPger2i/QJKP4Oc3Eqfe9731CjiqTSrHzltT43//9X7EAX3PNNTHP/6OrHv3eqN49g0z3nd2Pu3NKJ8wMf37BMhzY+RIG6BQyCKNR47+XrEzrvYtzCnFDWR0e6zgdec6sVgv9ZiUK9TlCNkL5PLNqS8arurDWSKygq0vqM2GpGSuX43UDO/4OiGNQAYWVQHkdsGANoGyIOgUwaRZBqx67yckST2ndEwYF2hupJQMUlAN5qRuqKeWjCBoQdEgyhUwoFZQVouN0myA1iGtL14iM8AK9PZIZnikYUNxStCrt1zPAKMtn0QhipgU19JlNSKMiVSOsHs9ReAOj0BukceEODKHXuw+tzpAIWgyxnEYB0eiXzbAnALO2ANXaTclfIO63BtCZgMIFQO+J8JeGj6FwPlC8GGB/AbcDsBcCxmlehG3F0iMZ9BZg8duAkTZpI7OVZ4eRv8DAeZZOPxmW8NJg78u3YIejCzneAWwrngUbGyxnGTyWDQUrUKDPw4uNL2F+TR22lm6EUTjVczLW1OT50cgXDbpVTtHwfu6suCoPQw6KQ5oYiSZWa6iCQeRzrMSBxvLy5cvB/9pOvoxWV1S6TKdWYdZsaS5X1pSKh05lQblxnqj0oIycMmuY/64ozUNzSyfq8haldCI22myiamPOnFqYOP/C2FZSh6PD3THExtvWbYnohDOBoDovD5+fvw09HgdMGh0KDLHnVWGqQKmxFN6gFwa1Iab56bhQqTC06CqYAmbAyoquXGDO+ujedPKgIDQi14jB9L/8Avh/3wQMmencMyNNBJxuuBH429+kdSc+6Mzfjx6FubkZC5cuFQ0U6YDRYWMwntcyLXKOUnq//QHQeCz63OYrgIFWwDUKVA8BpbMzPofxEh68bD6aIU6MxPZ4YQKH3G8lHk921gtCQ/n6B1sPpyY14kAphDG9dJQZimwkHn9P6GxTzikuk7FEdh6TZjgy8zCetiNYTi7ZBWaNWVRqKIkNkyYLPRRuuw149FGpOoPzlgEQ9n+jEU4S5r3vBdrbpb/9/OfAF78I3JpBb6aiaumRDLQnW18GHOG+UFyr7SZAq1H0LRuLQGgQwZATg74+QWjIK7w36ECn5wgqTavTcjre9a534SMf+UhamaznE+gQf/vb3xZNws+FvNEMEiHRfUh+bzLtdTcemGXN4AYDh0x6WLky6gNOppKHwSz5M2cVF+N9sy5Hv9cBs0YvKmKnAwxKMpjHdZmQA0HMfOe5MQA3XhY2M7wvtHl+TsDKfhIZMxg3oCj3w0wF+sEMDrMygfNousC4AisU5GSlTMC5JAf06cOznx/9es6/VPJUSiRbc7jmMR6QLCaglIejHyKDPg7PKf5zaffy+Jipnmw95XsWLlwoeplw74zvbRoPri9c7xj8p39Be3qqqyF5XOk0DOdaOFFCg0QTiROOxXTA60RSjD4y7wvHvHz9ZkiOyYH70WQbxBOMXaQkNBgD8rvinwS86an5TCkeeAD4znekqg76UPffD/z+95L/cYHj2muvFZLe5ALY9+6CJjW4CFKz98EHHxyzyPZ7XWP0v91Bv9CJNioC+Nt72nBv0zGM+n1YmVuEj89dDmsSGYg9Az3I0euE/bzUnocvLVyOqjSahMtYnJOPf3SfBhP8pabhIXz35Bv42UVXiSxZwqw14vLiVXi5Zz88QZ8oQ95UuBw5qYxawQyGJLmpZOQHgzb/+KNEaIgmkeHmpI0HgMaDwA0flIysSUKjKoYvRAOEARWpDboKJmhVJWMcjabRE+hw08kOodBQitnWJcl1wJkt//wfJVJDQAVcfDMwJ7lRyE1COS7IzNNwyJTNKzUWoMEhNY6WWz6YNSZUmIqyXlWRygGjocGS0S1btojNlos15wCzD2gI0fBjCTuNJGZLUT9TZGeHs4s9QS6u0TnBr7Fo2AeBUmEqMR6VMSTeuWlxpmo2A8Y8YISNzXVAyXLAXADse1rKZiY4Zy9+G1BxnpVn83ino3kj53jzTqDjsHQLi+cDdZuzMmezlbmYrAcF5yEdZRqwz7t68beGemiYLR8K4bmuZnxr6SUTIjaCoRD+dLYBz3W1Qq/W4O1Vc3B5SdSZYUB9gb0WqrleIQcxGXB+sESbBLpjZAR2Vy4wIJUmC5SsAfR2LNTZsDK3GvsHpfXC53DhxtlrYNTocGbUAZ1KjQqTacwcv7p0Pn5zZldEFuqUI4ClOVLGFqHVamDWFguDX5BI2hxAXwB4+8VrWjv64XCHsPjSi9O6XwxOcL2g8cz1g1hkL8Yn567HC91nRMVWtUeDBS4tTjScwMGDB8VawowyGus1q5bjsKcPJg0bzpbDrI0GOriGZxQI5tju3oPQUD3OnmzB0jWXAMXrxo7ttibpuWBArLuiqszrAfq6gfL0+1rIGXDCYaEE2i9/Bfzm1xQCBzo7x77BKyUxyA0ZX3vtNdGzhAQzSQ460LL+cELsfiWW0CBeeQ4oskuGwPE9gGMIeMtHEjcBHCX50QGo9YB9DqCNJZJKjPmRfjbKe1CgT9ADZBxUmSw4PDSgqEYFyoyJM9qGfe4xNhYrZDkv090XKb2wf/9+ESRkUEA4cJdvA557NvyKRF2nwrrEiUCChE24E8KS4PN4nNGxutC+EDv7dsIXlvJktdFiexa030l+3Xcf8OCDEnnBAMgtt0h/+9OfgI4OySaTCRk6IKwMSHaemaLvaJTQIPhdIy4gzybJexrGZoG7A/XwhSSZn1F/CHqVNFwJ2rDugCSTOh6uuuoqsXbQ6fgiyZoLrEqDQZorr7zyXB/KDMIwaKrgDpyKuR4G9dgqjVH/EM6MHoIn6IROZcAsy1LYdZlVriUC9wB53WfgK1UiVaZgUI9BRWHTa/SoSEPKMxOMR/BQf5x2jkw0M7hKm4O/M/DIxqvM8KYfxWtAW4KSvjOYwVSB43W8Kgj6uqxKSFQhNNVg4JrzZLLBZ7kHgNw/JN3g/mRJW9rDymRPzvXDhw/HEBK0dZnUw3NN57tY/UEilHK53PsTXRuunVxDGMd4+OGHBVnCc+ZrGatREi3ZAskW+XvGQyaVN/Ggb5Wp/BmD5lx7eYwkehioZfIVj5UVHDNJFZmD14zjiPsWx6Hs604JRG9UC+BzxkqSG86xfLjTCXz/+9K/Zf+itVUiNj74QbwZ7vHXvvY13H777fj4xz8+pQkVU05q/OY3vxELB3smxKPMaMPhoY4YF5aBr8ODHVidXykuxP6BHvzPid2Rv7/S244hvxd3L9k4ZgH5e/tZfPkoZTEkvNbXhftbTovqjXTAAMxLPXQqpeAxnX4em8PvQ4/HiXJT9EaUmQpxe9U2QWqwIbMcIBDBLEiaxmqYRBgD/XuB0bCBbywDCjdIwY94OEeAdoUjwM+kAc7Fve0UcOYYMHspJguVSg+Deg18wXqE4GSLW+jV80U2aZNT0qu2am3QqbRod0d1cXs8HaAq6hxbEqmjhr0KQkNcDWDHo0DVwjEZrhajFj2HH0WRyQvf2Sb45pVCl1MlNmkGV1mGmQmqzWVYnjOKI8MNMBt00Pr8WFEwR9wXZobIQTKOGQY9M9l8SFRw4+fGLjfeIvhvOhF0GOSSUb6W2RJy8Jgbv7KEknOBn8VyUy7gJDfYxJBOCI9JqzIgpPGLHgPiCoZC8IUltIgivRrdHo4u6bkcnQkbWTEx1eBmULpMeshoORYlNORs59f+Atz8GUD/z1cNgZa90kPiCYHOI1Jj8NlJ9OOnCXRumSEVn9HCICUdDUJ2il2hIP7WuCOSAS5e53Hh2a4zeEtl5uPsl6eP456mk5HfDw/twrdU63BpcWxWBucPj3PSmu7uYajPvAK7awAw5QHll1JXTzJawoYL59l1ZUuxKq9GNPYsMthw5EQj1j//DxxmA2oA15WW4Y/r1sOoMPQX55Tio3M2Yk9/qwhOL7KXw6rtxuGG3Whp7sT119yMPPZOcj2HinwHdu5ux9yr7gKGT6Dh6EHUzV4JfdFFQJryDnKTQ1Zh0ACnAc3nSGzwwXWIwRW+ho24mQ0hOyY7OpvwiYfuReHS+WI4PtJej68u3gK7boLSEn2HgKGTYl0609YNve5VWIu6UG7OAbxODGvssC7YgObhUWj7h+AOOxvVOWHClf0eRh3AUw8DvV1AeRVw9c1JG28zqy5GFoSZct/7PtDUBLzrnVIWvdgbNUB+HhDOZiOYScUMOmbY0UDmg59Hh29M7xUZ/T0RMiYGATZ700jfdbYecDoAS5xBNnQc6A/LcREjDfAXb4M23MdErvbbXLgCO/oOCR122gxbCleKBIlM8cm5i7Gzvwc9HjZcpUqRGl9dkjhoVWvJR/1Ib8TGouxapcmOTvcARgMeFOptaZHizHTm9eO1pVOHjRcDn/4McP99zFwBBoakdU9ujsfs4RtuYAQO2L07Wt3H+f2znyUmhgRYWUV5TJm44usWxVQpWLW8lpvR7ekW87DYUAyjJkv7DcfViy9KlSZsEM7eG5TQ6ukZe8yUz6AUVTK5s84WYPsTwOgIMGs+sOXa1NW27njZrTAzYZwNWBdKe4kCgdBQhNAgBJkRk/DAXmnp2TlcV0hm3HXXXfj0pz894ezH6QaJ+h/96Ee49957ZwIK5xGM6lqooA43ClcLQsOgiSXl/EEvGhx7EAjL+fpCHjQ69mGRndWak7MDGABkkI/2M5MlmEgkB+DSrVpNN5hGH0BuAszxyGrslAR6AjDYK/cZ5HExCULep2gTMcFBGVhTBn6Ucov8G4OuJPX5PIl92X5Q9kfjd0xX898ZZBGUwmQfQS70edVSFf15APZAoH2qBMdwU1NTZL4xYepcEBoEkyQpy5QtZBqU41zmmpGuRFc8fvvb34q9WSY2aNtyzZDXNK5BnOdc6zJZ15gwxWvDBMtE6xZjMfQv6JtR8pdxGfnzmehCUiS+18hkQZ+0ra0N+fn5gnhhLIXnxwevIf1WWTZqolI28vo/UZKLpBYTKQheH8aG5ApxHvdUg3OLa/f5SqK4Ah50uftFwneZqSB5IrQiUYAxMO5bkb2NMTvnKJCTm92+rBWXAGefA+TqeSbrFmafnMsIrASPr17nOXdLUrJvBmzbtk3YIFzLPvnJT16YpAYDvD/4wQ/wve99L+Hku6xkLk45+tA4Kjlz9HtdgQDubd6PBkc/3lGzHNt7W2P0z/nzwGCPIBriM4cfZpaoAnzH39qa0iI1PAE/vnjkVTQ62FRYgl4VEtrpRKJgEM9J2aA5FArAGTgMf6hH/K6GFZZRM9QyoUG4O4H+3UDh+Nm6Y+CamMxQIqhVFhg00ZJsX9CLfYM7hCQJr9yQrx8akQMa68v3ebswB0sAkh0unlcAMFQBpvmSDBEnojIzihm+o0OxpEYohMU5ndi/7xA8BTasnJOHfc/8Butu/ASs1nyRZTwR5OutMGkAU3UJOps68Ez3c5htr4VXE0RBcQHqLNUw+w1CGmq8kkvlBsuMiDVr1iTUd+Tm8uqrr4q/ccPlmB9P7iTesCOxIWtWFhgWoNO9LxID4pVsc0Wvp1EDXF2yCEGVDlqVWjR9ZoO/c4L+DonsUOoTktgY6Zekxy5EDHcCDS+K4LhoFDjvcqk5YDpo2ScFoQjxIwT0NkwbqeEPjiAQGoVGZYZWLRmnNLZYjiyTZgRLZmk0UqJBmalCQ+93jVECWQbfRt3+ieAvrbHZmsRfW0+PITWYUcmA6aQak/s9wJGHRJBdXHvew9EeYMXbpV4qCvBalIn7Kt3b/zx6BMeM0dc81dmBrx0/im8sid075lgLxSOKEly6bBF+sv0nsPlNUKmlypDunn60nG1E48kn4VcvRsH866AvKprQadFQ5prINYKOhQw6Nbx/XHcYhORPGum8j/e0HoPWZoGzbwCmgjxBTD3aXo9310yQFA9XwfHzr960DKdaupAT6kbjG7sFZ2QzG9DXcgbFl1wPXVs99H1dUIky2gCw6Rpp/b/7C0B3p7TRH9gNnDgCfOYrCZuoMeCS0OFgdirJjf+5mxcZoCP91a+O0R6Nl9+gw0YniE52Qh31orKxhIY44XGMaa59/QfQ3TuE/qFR6HVa+PwBuJueRO3K62OcxBpLGarMJSIRYjJNWMtMZjyycRte6G4XVa0bC0pQk6Qa9erSuWh1DuHIcJf4PV9vwhybGX9rez0S+L6seBkW2avSyn4WgbeuDuD73wA62wCLCnjbu4E5S4BvfEOqZqirA/77vyVd2B/8QKp+OH0aYHn5u9+dupxaJHIwgMfjYSKCDVCN3XfZA6bKPAX6/F/5inSsMl5/XZKaImlGkkMG7Rzu88n2+p4O4DffkvZDQYidAvq6gLemyLoS/ZnimAm1DrBxzgYB10kgMAKozYBpLoIhqRdd5KV8p2JISRWr6Y8xysMyEPrHP/5RNA2/EPCHP/xBOMFXX331uT6UGYzxjWaJRzKMBoYihIYMpuuM+Pth5Gh2HAJ8g4DGDNiWAax8zABsNCtXLXDtl5MmZDnYyWQL0l5hRSSlLhgsJekgr+d8Pl39ee7VcnNgEvGJ9gSuuTwP7vFMzmLGeSpShkHASy+9NOZY6aMwk1gmMbgvMSA51X3TZpBFuAaB/X8NZxnTyDECy98KWKY+iJoKTNLjOJKDkRybDIbTHuWYm2ggP9s4lwQe4w1jEnUyABMn2QRZOa8pT0siideXcQcmSU7EpqSdzZgEbWMSI/J14n1kshT9DxIXJD6Un09ChUForq/ZBKuBWf1AcoASsiSK6ffIDd95DTm2lL5QppDPayJIJN3HNZdrPhN/Dh06JMilicioJwP9Id5v3hvuA/Thed9labLzCd3uATzduQu+8N6er7Ph2rINMFAxIwU4tnhPxd726F+Av90n2c6FxcBn/xOoTE8mbFyYioDZtwAuJrJpATP79Z1jNQ2unfSXmCQlB/+YOBEmzt4MUKlU+NznPiceH/vYx6ZsX5jS3eYvf/mLCC7cIpfwx4FVGR+ZsxFPdpzA050NMRUbr/U1Y2txHdx0ChPIG2RbUujxjtM4pSA0CG8I0IRCuK1qYVK5KyU8wdMRQoMIYhQhV7xUBpkbZi8lgNkGlM8BOk5F5afEB4WDxkVT12Sv19MFL5vaKkAl7vipLhpJU47KsS/6pPO4pAedUzRW85xZrtY4Z8Q3ApWnDxctrsH23SdQVpQLnVaDkY5jsNVuGrekkL0lRuncM4tJY4towp8ciVaJlM4qg8/rQ7ujCyarHU5nO84627G5cE1azb24idBY4yZKKalkxgINgksuuSSSjTIRw4mBBAZ0CZOmABWmDTilHUGOZjY8ISN8oSMSecSYnrkKC+yzzg+G3myPJTRkKCqa0N0KvPoYMDIAlFQDl70taXb2OQeD4AcfCgc3Q8Bgm/T7mndJElap0HNaCqrHI12pg4AXaH0dcHRIPT/K10r9P9KEM3AaLoXcg1E9C71temGksnmWHCAmocd/y1kmSpBI3jvQMUbRnhUb820Tc5zkao/xnsuGLASGWgHvaJxW5igw2AIUpiZL9g8NImCIZj9yVG9ndnYa4PVctWoVdu96EZvWS4ZyV/cgli6ZhTm1JsA4N22jyel3wx30iCbqyh5AJE3jAyWsrGHGELNQeT9Jvq5evVoEutkk3F5Tge79R2HMy0FQrUavJ15LNAMojl+v16KkIAe9/SOYW64kePoAsx748H8AB3ZIDbcraoH5y4BdrwKdcZlyp04CjSeA+bEkLx2ZlNlf1DF+5NGkf5Yl/+IdDxrMdALp0NBBYICLOq5iLV11sdQk/PCe8PmqAbsxnCEUkiLE1fMBc1xAPujHqeZOWMwGLJhdDp/Pj2AwBEN+DY62tIwhsLlXZaMRa65ej1srx5fUo2Tmv8xei16vU1SiDvqG8Hz3ocjfOeNe6D6MWeZimOOIv3jwOoW4v/+QhFLYhuGcfeJh4AOVUt+TeNCxu/POzE5O7G1TXA7O8xBR/7h99ODB2Iwpvm7fPuCee4BDh4Bnw5JbJGa++92EhJzA/tekfSSypoWAo3uAa24HLEmyuAuWAiPNYc3f8Cpcul76ObID8MuVHCrA1wm1LbZRuUYF+OOWUK0q/YoL3t/Pfvaz+OY3vymyQs/3LG7aaEyYokbveWEPzSAjqMNJU2OfVwGDrwAB7uXso+QEBrYD+duADCQTmRnO4BKDcSQ49uzZI6oYuJcyKDQZUoOkAPdj2ldc45Xjj0SH3M8pFUhOMADG7OdU2b0MiPKhrN7IFMx4plyKrDfOz2OSxAVPanCN3fM80HQcMBiBVZcDlZNIjDmf0fByrB68zyMlYK14yzk5HI5HSqGRuFD2x2DVBudGNoO62cCk/YtJQG4sPlGwUvbXv/411q9fL4LajEtwHctmUDu+bxqvF+8r1ykG62VlCplMkKv746vAsgGOKfo1TNpKNI4mQ2jIyaqTITWYZJaIWJblb7nn8J7LlSaTgdyHhnsZP5PkvCynxuTE6exNkw5e7NkPvyJZYcA3gv2DDVhfMD7Rz3vtfe1lGP76p+iT/b3Ad74KfP8Xye3tTKE1AbYskSTZAAP83/se8KlP8YZLz113HXDzzXgz4dZbb8UXvvAFwQ28/e1vv7BIDU7473znO6KUPZUhRnKCmebxetPEH88ewRv9XSLAJINLyKbCclgU+uAy3lIxC7s4ARSvfWtlesx4l9spjiU+4PaBuhXYVpKeHr8/KDVuiyKEoGosMQBKlCQCF8ht7wR2Pg40n5Cabfu5EaqATTcCJVNHagRjrnIUspKOjEpTHeA+M/aFfG7ONUDLcaC1Pno+F98C6JMv6gW5VoyMujC7uhjHTp7GutpNIquWTD03tniQeDk8tBvOgCNCaizJWQ292hDR2Jah0+tgz7ODvbXlu3pi+BQq1YVw+z1QqzXocg8IOZASQ54I6nDcUpueGxcNhnSNfm5uk3GslQaPXm3FrNJlCDpyUFZYiGtKizDsd8Cg1sOuO4+aBtWtBE4fAPrboxUbSy+VyDlioAd48MfRDOihPqC1EbjzP7O3OWUTfWdEkDKKEMAm7KzeyBtn7rUeSPx8TuX438s15/SzgKMzHIgfARqfBObfIvUtSaNCQ0loEPWndyLkqsO6dbEVYTRKkxmgvR6nmLKsBnIzHhd+flVeMTZOsPLm6tIqPNLWFLOy87l4TKSPzhgkc1oGzgAFs1NI3gC5Oh1Y6Cl/AvuJlNBJThMkE+751VNYPN+CvDwbRkfdKC/jveN3jr8uiHVn6CSOD0tZ4izb3Vy0CqXG5IERGrhcn3jdmBnHQArvr1GvR6HehD6vC/nzZ6P/xCkULpqLapKQE0X+EqBje/TXXCvUg04cP9uFArsZxbnhOe/3AkYbsCaaUSbgTkKoxD3PdZDBl0Sk2xjU1wMP/FkqUV6/AbjxJnGPmTXL7Fka/HQy4gNGDOzwQeKDzgf/LoI97/wo0Nwo9c4oq5b6Wb36BIKjw+i0FaP0uveMSaY43dSCopIK2I0SEarThfd2YzH8fqdw+GQ5Ec67qQwU03bhuI0Hv78o3Cz+rLMjpvKVoN017HfGkhpMUvD2S2s6+8KopWzAAKWm2ltjv4DndPwwsPkyTAtEr7EGwN0lyXja5gP6NEkQysv922eA06ekhItb3gJ88F+iawPJNJcr9twKCqT9ipUo1LflNaBtkmq98iduDC+yr5JBZwZqbwS4BrA03lIuZZX5ehSEhrgAQGAYGr8Lek0tvCHJHmOVqsOvtKPZb2thZH3p8zZgyNci/mrTlqHIsCCSECLjHe94B/7jP/4Djz/+OG688Uacz3jssceEwz9VztEMphZWbR7MGjucAblJJ31BA3Lplobtewk04H2ApwMwj/UJUoHrOhOGmFzHZCna9QzEsfIhQmhPAokCY6yApSxkMlKD8incd2TJjXT3hMn0A4jveSAHRi94vPwQcPSN8C8qyb+49WNA2TT00JtuUFI1xsZlgmR8zGF6QHuGc4jyZvEKBiQLzzdC40IHg/gkZl9++WXR/4o28nik6WTBNUOWz2ZVOH+SsOI9l8FYDXuIch2ctP8WBxJjrB7hOkVyI1vNuEkQ8LgnCvoLvBZc4+VeGso1nNeN/guTLphEyCRavia+elwJEhT8e/w15LnzM5TJUbJEMytYSM5zT5Pl3UionEuwV58jrhE3V6xBxjTGAc+Be5T/2CEYeK/lmBgTi/p6gL5eoDhFE/ALHStWSNXgTHCmH0I1mTdZso5GoxGcALmBO+64Y0qSkaaM1ODiy3K7973vfeO+ttaSN6aZJR3v3f3dwkk3qtlXQLq/15fV4c7axLqM15dXwxsM4nfNDfAGgri2rAofmR1lslucDvznkT04MTKIQoMRX1ywEhsLpUlSZbaJCSmDl5rNyi/JoDqCvSrii0q8Vjt0/XRIFfnPOUl6UhDsRbD5LQAr+phZSykfay5gmtpgdp6uIIZYEnLlbLxrXYJ+b5d4vtBQhmJjOeCJlfmKgEGCy94JdDYBLockQZSTYOPV2eBR52K0v01Idiyey2usgsotOQhkoblRcsFmFpNyM2t0HINTZHBJGA04cMpxHAvtK1BiKEaTU5JJkc8hnqpxBb3Yr2rGQ69vR1FVKfzBkMhuJLF2VeFKnNp3XJQ/ZpLBFAy5RFPEINzQqnJgULNhVPobcGd7K0pyA4CrCdAXARqpvwBZfsKg0aNIc25LjBOC+uDb7gSaDgPuEaCgEihVOJ1Hd46VdGHfmP0vA6unKQCWCeICPOM+r8RokoasbBY+HnwOqUIjHgONaZEalJwiRoZHceTQaZFJn5dvF5UCmeDYUFck49fMNgLh5++oWph086EM4PNdnXAG/Li4sAjVcZnsn563DBqo8I/uNujUaryjeg5uTNAwmgbapB0hEkjMwIgzqjBI6ZcqoDB5VtPnFy7CZ9paIyWRPNb/XJS+/i+DBJsvuRYvv/I0Nq5fCI1Gjb7+YdSfCmHekvHHT5urK0JoECRaX+nZi5srLo+p2EgEGsPsc0BDndUarBr5xNw1+NaJ1xGyADqrBYU9Dly3KlbzOCPYaGBdCgyxGk6ERZGLHuTOLkdDWw+KuUYzKGtOElyet1gKDMuGKscTK7Zq5wjtczppHGMMUir1w5OioR74wF1SzwsG3l96iTXlwIc+LD5Hzl5iVQYDSGw8GF/9QeebzgcdCjoo0mGR1cuFt6tXZGMtv+1fhZwIS95PtbRJ1QqhkAiWMUuXn2nPvxrofAnwhwNxtrmAbQ6WL4/KkfD9rA6ZinLx1/u68cXDe9DtcYsm4t9ZvgZLcxLvFzk6yxhbi0dp47yREXADPS8C/nCwUWOGN+divPjKbqyl4S33+lIiifzVlGBwPzBSHz167vmlVwK6NORpPv4vQFdYAo1VwH99AMjNAW57h/T3T38a+Ld/i2r4cj348IfDX6UC0pWPWLAC2P1S9Hd+XnEFYB+HfGGVXn5cRltcskYEQR8M+jnQhooQDDmh1phFRbEzIJHjJk0xdGrpvvT7TmPAF7Xbhv0kpmjv6eEKMGHCjBJjrQieUO/2+9///nlPalDa9hOf+MSkszZncG6gUqkxz7YG7a5GuAIjYgyWm+ZAI687Y5BZpjX3EpLKcgYy12zaGcycpY3/4osvimCRXL2Q7aBvop4VTD5glvDGjWN7Qk4l2NiWFbtKXPDVTVy/j+6KGx9qye94M5Ia5nzJz1I2uOVz0wgm/HEOcewkIjRmMHWgPN2jjz4qKs4Y/KVNyTgb5/VUS3zRfuaDcRlltYac0MW+oJToziaxIRMGHG9MUpoMEUFiW1bT4LWabKNi+nv0uZjowz2GPhir35TrvVzJwr2AxIRMKss+BK8j9yJeS8a4SE6QPOK+QeKE72OFTLL+svI94evpY3C/43ycdF/KSYAxQ4vGiFH6EGEwrmhnc+5xwHHNCkhzbn7iBMV/hrWG8yfcJ+fNijvvvBNf/vKXxdqllNPLFqZsJfz5z38uDj6dTW+urRA3VyzCo23HxHZNWapKUz56vD0i+5B9LbThteLG8jrx92SgJEMiWQZPIIAP730V3W4XAgih3eXEJw/swP3rL8Mcaw6uKa3F/sFu7B2QAnvsV/DZeauFfEO6MGpmwyEy6qKSAzrzUkCnAUaYSR0EqAOdTAuaRtqrjwIn90q/L1wHXHy9RBZMFL5mwMtgWRDQVgD6eTFB2j5PF86MnhA9NbTQwKsoG/OFQhjwDWJhTlwTUiOzWOOaWhrCpVz87LJxsqnYAL47D9qRbly8biXABa9oFYIDpwWrzeAmN3AGo0hsKAmGET8lwmKzVaTngMX2BaInSDv7loj7YRzDGo/4fHBqvAj4AqLprZYa9EGSZn78+rkH8W/XvD+jcsFgyAOH/w2EIMmkBUKs/BiGRXNRek5D0A1P1/OwaNl0lLni1OreJFh3BvviM8ECoQA63YNiXpQac89dPw0lsTE72pslBqw0SoSB87T5UWEdcIYyH1JfGclpyAPsaTi9Xo8UXJUXKiIQAgrScK4mWRWtUZng8Xhx6MApbNi0JGJY8fl4kGhOVKnR7XZg/1A7dFQ2CavtcPSuySvDLEviQFyfx4NbdryIZmbKh6sbLEKmS4VbKqrwpUVLxVr9uQUrxEOJQa8HHW4nyoxm5OoNYp9gcHlSoMbw4puAQw9IRpHooqyRTobVGilIjZtqZmFBVTWeHeyHXqXGu2pmYV6Ghu/ipRvR3zeAM2d7cfHFm6A3VmDPgb6YZnqhkAshURPCwyuCKtwvoM87NKZakcTGiH8U+fr0tMS5bvE67t27V2jw/mDFNrGfHcivgqdvCA+9/CLm2vME6TGhigFrlfQgAj6g/SjgG4VKr0XIpIWqeHbyvaq0HPjwZ4B7fypVVjC4+6H/h4DFhvaTJ9PWII/ggQckgkQpd/iH3wPv/0BMFZhclUFngSSEUvtZBp+LJ7FJcpBcobwCA1B0WuT+GHRkqG/MzLlIz4zK6wG/U+qB4B8Aep8Sazt14AMei7g3XMspachzzVZA6azTgY/u2wFf+Dq0uUbxwT2v4anNVyIvgczffFsFGh0daHJG1+DNhYthUTYsHzwA+MOZVZxHfifOHHgMa9feKF2nm24HHvlz+F6zkbUJuPJ6TAtYSRchNMQBSusupSfzV6V+LzO9SGgQ8vXn+T31ZJTUuOwy4De/kZqFM1Bw/fVSxlSmqFsI3Hwn8NzfpH2wag5w6/vTI8jjoeXY5LVWJgioAJ1EeGtUOeIhP21XjyVeHL54GVT2SWrFKEnByO/dmG9bL6Sn/uu//kvYX+ejZjNB6ZydO3fib4kkz2ZwwUCj0qLKHDfGdHlS35ggbffwfsgkIUNso/FUYADp6aefFtKx8nrP9ZfBLa5htK+3bt0qgnRTQWrQxuJ3KXuW0ZchocH9d7pxvvQ1yCrEnhdvPDOTbeIyP+c15lwCHPhrVGKVTcLnTl9yGLP0OYdSyfawOvZNUQF0HoKBb5Kx9CdYpcE1hok37JlIWb2oxFY3QuAYMUOFkqySl/x+2r60YeWqAB4XbWQm7bD6mUTERKWdkhEIJDYmChIKvF7ZbmiuTChj3EquyiaRoazK4PWJ76XC+8T9gefFvYqfoQSvJT9T2Zg9GZgQxj2OD/ot9EmmYk9LF1uKluPZrt0IhKXJmTB1UV7s+UXA1/ChluTZKLGF4iLgxWeBUfogKmk9v+YmwDo5ImoG5wcYo2Cxwy9+8YsLh9TgJHv44YdF1mgqdLmHcWiwVQRyFtnLsX7pVRj0uVFoMOPJjtPY1d89pnojJ0HD7nRQ7xgSQTQZXPrZ03d7T6cgNbRqNb60cD1OjPRjxOfFbGsuCpXNrdOARmWDVbsevmCHaHinUxVDq84FeMiGNMrCXn8SOLYraqgdeU3SCV17FSYEXwvgUdwDH7Nsg4BBCiAN+wZwYmR/5M+hcJY2e4nIGPQlyEA31EjyFC7580jUZBaUWrV2PbZv98JVtj7CLC9YoMfJkyfFxklwcWagUxlwMqiNY3p/8DlCq9ZgTf5K+IMBEbNg5c2u/oNop0wFK0BMFTg20ib+bbKZ4HV7YTCLloTw+gNwqXwwKJoFpwNfsBMhSJmUcrgiEOoX/VQ0SCNrwXEcFcVm7D98GqXFPM8gMLQbRUWXik1PaRyM+lz4c+NLcOqkzcKqMeKWivXI1Z87Zj4pnA4gN8nGWnSeNhHndVx5G3DqFcA9BFiLpSbf6RBHlFhzeqJa7TQuTWlW++itgKUEGOmS5ElC4WB8XnpSC1p1DuoPObF63YJIsNqgroQGucJoolHILBBm65Eo4xyjEaU0lkbC/UB0apXoIyTWAqhQa0luCP6g4RhaXdE1lVVPQ0GvIEV+faZREG/fWBpLZhCPtTfj68f3wR+Wy/mPBSuxKqQVsg3pIhgaRghOqGCGWqWQVWLmGrOIuD4pMU7lFInUlRoNLptENhCvZ3nVApGtpLdJ8/aiVbNEFpPUl8eBIPYiFPILviuIBmixADpNJYwawxj5RcKojq5H7Ikw6vcjR5e8yTQrDxjA596bM6cW360/KAh9sR7qQvh/hTXieBJl2TEriKXZNP4ZjElpTPeckIL4KhUqi/Nw9HQH6jw+mKvWAoYk697y1cD3fytJTjEQrlLh5LFjkiGbKZzOsdk8nDs+X0JpOzmriUElOh8EjWg6Zon0zOVMNI6L+OtAh4X3k58TITUYrKY0oG8IGNwhdtPW9j6MOtsw1O8GVqwWgTTOz8k0KYzHzr4eUZ0qg/8a8ftwYLAfW4vLEmZSXV+2Gs3OHpFNVWzIQZEhvufVQNjZkJ8IYaCnFfM3htezm2+TSKpjhwCzBdh2LVAUSxRNGeLn9XjVDEqMJiDZE41x2h9hG2RSWL5eekwWaur/rgccu8LnyT5lqwBN+k6eqByNmy60UZVwBx0Y8lHWohRvectb8Ktf/Ur0rDgfwWN761vfmtGeMYMLBJTnzdsCjOyT1lPRKHyF9DNNyE1UGQDkGs81nGs17SAGkfh3WRYwVdPtiUL+fCVIEjKr+VxAlp8aj9yQe41cEFUcOr3U56qlPmoL8OecN2mmqylH6u832CqdJyVxx+mDlS1QOYDjZ0ySXbgiiTYRCQ1WIjEYS1vrfOvXckGM6XHAnnBszi0To7Thea0p183AeRBHqf0QUQZhApU6NH5gPF0wOYc9ROlD0M+RE0BpJ8sVBfQ9GLeJl5DluOBxsqKAa3Mm1RI8T8ZDeP6ZgOs/1/epIDSU4PnTp5arMmQ5cf6M97UJ/s55Qz8jUWUF3yOTJMnA68lrwjWdn0VSg34biY1zSWqUmwrxlopL0OnuFzG5SlNRYrWBwWPAwEHhawx7DLAZw8lDefnAN34APPs45SeA+YumT9p2BtOCD33oQyLOS64g25JpU0Jq/O53vxMlcfHsoxItzn78vun1yO+7+s/gtqo1mGeTJuPVpXXY3tOKDrdDZLBSMuHdNYthTtBLIx3oE2SmcslXPk+Hf5F9ck6SRmWBRjPBpkmnSUDEeZ6nDk2O1Bjz3NkIqdHn7YrJDpazs1WKHhTUuCVCYDXEcbq+gMoGmBZBZZq4nIkcGOIGzc2RmzSDPtRQpOYsnVVq3vLvykzaOstCHB7aFekBwmaDfE4JLqQIEzQXF64S1Q08T+L4SLt0301GeJwuQWrwXId7BlBWVj5GY3o8hET2pCSRJuutk0yhJJVGlQap4XdAq1XDH9Oc1AWDXh/JTti1a5fYmB87tB3HztRj6bb1KKwuxWjAg+e7D+ItlRuRdbDp8nCHlA1UNA/QZCDxcPoo8Pd7JF1x3gRlkLG4ClgyBcebLbAyY+kEZDdmrQGOPSdNHNnBqluX3ns5bso3AgcejGaZef2Spr95/AWfgdo86zwUWaqFFJVGZUZ7ywg6vKeEsUXjh9kizOyhkcQyXmaM00CUjdIyo01UVTBwruwbMMuS3DFpGnXE9CCKvC28fvy19ewYUuPM6Ai+emxvJJzG95Pg+L+5F8HscqVVJeUPnUYgFJVqUodqoFPPjR5EyVKgM67HSUlqSSMSPzS2s+V0yM4fg9ismhDVE6tZduyHKyA39OX/jsMY8qDOUoNTjrMY8kn7nUT0z4Y5nEH/cGsDftd8TFyvYoMZ/7VoPWqSNBymk0Nj+qsPPwBPVZEkNxS+Tfd0nsb9m68U2V0cC5T6o8PAhqV8H4+Vpd7MFEpJNnidkT46RoMOeXYzegZGUONzJic15PtjiganuK7xumfsdDAz7cUXor9zjVm8hDdSIjf+8RyzK8iUA2uiGsAkMJQkBh0Q9iLhfkSnQHYw5L5ODEJxT+L15PVSVrgkLLX3dorXnmxsQ1lJHirLCyCuIoNz+gIRWMsmqWFOojNsSqE/LLLJLClICAYPvUORX0XQjwPI2QqYK6V7uGGz9JhusIcGM7l9yopNVoukQZSXsUeFWaoUUjqLV1879rVsDv6d75DpA6hn/KUvsQskzhl0RUDutUDIC1DmNMMgRa6uBl3KBBdBko59XSBMDtHpILHxjW98Q6yN5xNoF9HHYOLUDN6k4BqUu2lSH8FKOxL8TzzxhGiwy2xdNtylPc3MYoLBNVZrsDI820HP+MbE/H0qeyqlUzmi3NPl42Pgj/YJA2yUF+Led/XVV18Ysm5XvQt48a9SD0pWJtJXrktDvvJCBUmMwknIiE4AHCf79+8Xc0j5HMeKnNRBm4f2E/14+u+06UiQUZJnBtkD1ygGrGlDyoFrVgZwDSsq0sNslSsy5bWHicFMTM0u+c/KEK4ZXCviG1WT3GACHf0g2vX0ibj2kBjjeksbm3+TEr3SW3O5dtNnzRSsGuJjKojrRIivyqDvTWkorqUkcThH5D2Ax8N1mf4HCW8+r+zNmqriifef6zaJo0TnNV3nmww2nVk8ksJxFuiPJlSfPlWPBfPpT66WbNv8AuCO9+JNC4+H+qnACy9IPivbNNx6K/5ZMG/ePLGG/P73v8dnPvOZ85vU4GT65S9/ia9+9aspX/di9wkRuFGafc93HY+QGmwE/s1lWwSxMezzYoE9H0tzMmd0mKV6aKhfBIOW2vNwdHhQBHmYhWzRanFlSRqNfKcLibJotJMxLEMpnyMhkEz5RiIBVJhtnScyooF94TxQggGPvQhhI1Rj26CnDQaSyPqT1ecmR0eDZY3cvOQMPD737LPP4sorr5SyrXS5uCjvYvR6pOqLQkMpTOEMrmAoKJxyrSo2k1mjyNJekTsH+wcbYLaa0XO2E0arWRjDriEHbttweebnoC5ECKdiWgFL146a1WmMV61NNAItzLejt28YhQV20VOjq7s7YrRwQ+QGlzu3HDU5Kgz3DghSg7OnN40GTBmj/QDQ9Fq0D0zHIWDpW9Mbix438Pd7o41SRUq6H1i2EZi1QHI4zscm4fEYaAOGOgE22C2ZF9VYT4bKpQCllyjJQ1QsBUqTk7pj0H4wVkqHaHwJKE79GZwrNJwohUOjSAeJhHC7e5IGpUl0UEtazjTnODNr9fhQ3Rr86vQeeMLN0m8oX4CF9uSBvPm2HOzo7RFyfkR84nyipsXsZxQfS+PvBztbsaBgbFVHPPzBAQz5GgUpIOLjGjYXbUYwVAS1KiyTVbFWIuMoOcV+IwzUtb0IFK0EChYlvSY0LicLrjs0XFn1IMte8BqTrBkYaIE1RxsmNKJwB0/DrinBlSUbcXq0Fa6ABwX6XFSapfm/u78Tv22KGvS9Hhe+fHQHfrX6StH7IxHYj8djNMDrdEJrNkWM3GG/V4wTOqjM7qEcGc+bSQh8nllWJJP5fEqwiikURGv3AEZdXpQV2mFn5pUxM3KC45BjmNcoIyP8+hukgPPv7pWqM5gl9vW7pX9/6hPA/v3SnOWcuuv9wAc+mPBjSOoQzELkPSOhIx8HSQtKLjD7KW3SRaXG8IhTrOd5udZxK4V4X9pc3Rj0DcOsMWGWJTNindUYlSYzOiiryaAZVFhoz8FFeZNoImlbCLiiPX56+0dQmG8D3GzUe47tJd6bos1A72uAlxKYaiB3KWAJy1+mAiUBvvcj4N8+LTX7Jq67Ebg9LD0lo6kJ+NjHovJmb7wBfPSjwP33S5+hrBZi4G+6pF1E1snEMnPtujIxrik5xeXarC1Ci/NknI2oglUrEX4MONAGe+ihh0Tz8PMJlJxidhebLM9gBqlAov7aa68Vet20obm3KasVOMa5BvPv1ITPJrjncx/lXqwMVp0LkMhnhW4i8Pxp/zC54e1vf7sgC2lTXhCkBtUUrn73uT6KNy1ITjB4TTtNOW8YVGXwNt4uYuCagV3OOwZdadsxkJ2tJs8ThWhAHO5pcKGDgXDGS5TZ+Ey+2fH6E9h48dhAcgiemPhENsDYDX0IkikkNeKD6Fzz+KAcFQP2tLO51nJdYUIVxw6Jj/H6ZLCyjolHcqP0TEE7gWQCSZVEsstTDdnXlpO3WMlEv0uuEpR/p6+eyf7AuZUsaZxzkoTiVFenTAqu9pg+w26PD0aNS+rnp+zv92bF//wP8OST0ZjP3XdLPUOuvhr/LPjQhz6Er3zlK6JxeDZto6x7Y6+++qpg6m+55ZaUrxv1e8cE1J2BWGbSpNHhqtI0GzMmQK/HjTt3b0cTpXBENrIJ15RVonnUgVKjGR+fswjFlME4X7DiUuDlOI3gFZdM/PMYMPfG6dRrQ5FsvyJDOdpdTTGNQ23aXBRpckVgpdRYDqvWihDYfFsZiuTrKVfDhn7jlJiyP8Hx14DhPsCWDyy8GFBIiHEw0+Bevnw59u3bJzI9qFmu/Ht8ObRJY0GVOVaap8fTjFaX5KTrVAbUWS6CWTs2k3l5zhxYtWa0uXowe00FRjuGEXIFUaauRm4w84xEjcouSBRAecz8fxpZ3xzvLt4PDepqirFj90lsLlyGoH0Vju99A4uWzoHD3xPZzLs71GjrbEdehRQs5dfYs70BUIaoifIpRHhcuAaBzsNAZRpawIM94Z4UCgg9ZDMw9wIpCz+zGzj+YvT3/Cpg7W3j97YpWyA9JgIPx0u8nA7lrAJjv9c/CvTuwkD3WQS7nbho09tiSBeWBsuGXHy2II1EgvOJwVo6JyxXpcFHAuPupVeizzMKu84I2zhSf5+au1DI3xwZHowemuLr7qwdm1FWkEDnnzD4/OM2OOO5DPmOR+XxQmxUHoJdS7UuEq+5imqNZVIfIznbJegDunZJBlPO2D2FDlu2nB5eV94DWfaCIFn7Mhstb0wcFA6EXNCrrZhnG9uD5dBgjyCI5KoYrtd9Xjc63aOoMicv4Z5ry0FLwIGhk42CtM1Zvhj5JjOGfF7Rx4QkhjKTjqWgzMhjxRzXZPagoOOU0ODImwWUr4S79RnMrymRJNrmX51cDoHG23A/oDcCcc3k6RTJurBpg8fE/hl33iURGXKT+ScelwgN+TuJ3/4GuO46KVM/CXj/GfBSgnIKzISiZBsJD14XUSo72As0HpJeNGcZkKsgEAxVyMnJxemmVhQXhZ0KXaHorSFDLk0n9g0cR72jKVKdc2a0FVuL16ZNbDD54751l+Knp46jxTmKuVY7PjpnYcLK1LShj+2h09zWg+ULZ0myMOcDtGag9IqwFJU6s6qF2XOABx8BBgcBrjccN5xXTz/NLqhssCKNG2W/Fv6bRAdlyyip0NkJfO5zbOwgERof+ADwfvbLOL/lLWzaUvGQoVNZccZ5UCSCqKFBjXkpjBppDeac/+AHP4jf/OY35x2p8dvf/lYc25tBTmQGUw/uc5RbZGPKRBVy3OcmJIE4DhjUYyCLVY8cq6yoPZdggI3BLiYQMJAmg/ua3EScVVDMip5sI90ZvDnAaoxNmzbFkBKM79BOpJ1Lu00Jjh8SGgSDqkwMYVCbc+FcBllFNft5Joc1UXAtoc1MqVjZfuf9yc8rR1fXGZSUxJ6nKh0Z7AmCPhl9yhdffFH00ojvwcV1hNVxMl544QVx/CREGCukzZ2K8KINnki+KROw0jTeD55uyHGs+HWVvjcJH+4RRDb2Ic45Jqqd16QGew+GIZE88vPniY8xleDe+8wzY5NYn3rqn4rUuPXWW/Hxj39crAPZTFDK+gj685//jLe97W0IagPo9XTDqDHByuB6HGZZCtDndUTCeHTqa6iHnkV888RBtLiiOsrdHje6PR78Yd1WnJdYtE4K+LNRuCrcKHwypbRCukQL+MLBOg11v7mBUNZID7PWiqW569A82iAahefo8lFtmRtT2SAhWYBknI2GAdkXfg/0k5UNo70BuOL9UoNpBbjYKxlsJRhslCWpEmHY14dW14nI776QB6dG92KxfQvUceciSv6sFeIhEC6m4MRKpK2eDlSwhOW5lDCOT2icfATwDInsXo1eg9rK2TjaO4ra3P3wehowHNRj0NmMIsMcFBpmY1PBQnSs7YebAdpwBcrWYknHMmvwKZozRk6QjVaSNP2OhzXBRsrFO9Hz5yM8o7GEBtHfArQeBqozzxRJG6xQG2B2fHRFFFJY8YQG51TH84LYqD/VjDXLZgOdzwMV1wlNf2Yjch4pDclUYNCaTu6RI0eE8Wg0GFBhTu9e2XQ6PHzxVuzq74ErwAb2btx/tklo/N9aUYV/mT02k2R1XhEuKSrDyz0d0FLnORTCpsJS3Dx3uchaoWGcDEF44Q2NJQs9wVCkWiuCgVMxEjoRjLQkJDWyGSDjZ1H6gllq/CkHVpYuvhoH9j6GRReN/X5KhqUKXCeyya3jSDF+YtPlOPLA79Dn9cNcWw1Pbz+GSnX4zMEd+OXqS2MqaZhBxea7PF6OBzpNLJtmlpTsEJB0ohNC51Cs05VroXZagKoSwJg7htDgWGRG0VDTKRRufxi53vC9u2gLcOXbIk2TGVTh+OP3Zix1w+C9TGgQXV1SJVjcHiKqOpSkRsNJ4NGH6YUD6zcCl18BDA0Cf/wN0HwGKKtA3i134PSZM2JeMFOMDkgRm9f+7WdAILynvvEM8JaPAqVhckhjxFnHHFTVFgA6P6DLByyLIgFvkoiyc02pMRIahFyz2u3px1lnB2ZZYkv6U6HAYMR/LUpvvqct8WSdAzgaxb33eALQkYzkc4nA8fG3vwCP/136/YabgFvfOvVB/nH65KQcM8q9/sc/Bv7wB4mg4F7FjKlEE07ulfTpTwOnTknPkQj9+c/JzAHXXINpB6vQfKxYYbNFY/iReC0JhgYRDDEIpYZGVQ6brgBL7VsFqaFR6casgcza/vd//3cR9M1m48/JgHJCJIxZtj6DGWQa7KFtxP2GQR8G7wnuO6xalatds/2dDFTx+851pjjP75VXXhHV8dzbEwXQuP/edNNN5+T4ZnB+geOVdk980JljmYRGOpnvfC8z1RmAJ6E2Xmb+VIEJMyRX6OMrmzhfqGClA2MWJIvk+7No0Sq88upZWK0uWCxSsqMKJAQSy9RmiyjduXOnWFPGk4diFTj9UpnkJQHCSmhlXyP6o7Q1+Lnj+WZy9QOTjui/UOYq2WtpcytJoHOG7c8Dzzwu+SeXbINuwVJBDHIt5vHxPJTnngzJSBq5Abnsd563sM8DhhtFYtKp5m7UVhZJzynIjjctRMV13Djl7xeCgkkWwXHOvngPPPDA+UtqcBP8y1/+gp/+/ifY0bc98nyVqQZzrQtiFpzLSxZiwOvEqVGJ6a8w5eLasiw0Z1Tg2PBgjOY7/31yJEGgayrhdwNDp6VMYSs1nceRJJq7QnpMBvwu/7AUNGJPgxjZIN6DaHa/VZuDxTnjNa/jMdOJVzbjJPufZLPkNT+0Azj4KptVAGYDYAofw0An0NEIVMYy+g7HMEK6Pgz5DsKnaofX54Q+rMnHAU/Nd5YyUj8yHg5/X0wpG+EPeeEOjsKsGX9DZzYAs0sm6tBoVPPgD+3lCJOfgVY1DuPed1IiNAg2ZfUEUaHy4kTbILq6O7F6WR30RhXa3SH0eBpxaMdpzKqZjXdWX4LTo12ip8gsczHsqXQLJwKDDdAYgICiGTuPz5Km9Bt1/jddB7z6RERzH5R4W5qFhqmtJ4CTb0hyVtVLgHlrsx84c7H6KA48j0TPZxPVq4GRbmCgOdq0fFGCIJm3X/RhiWo0M9AWABynAYMfLY2NKMtnUC6QdtCPhhQNSZaZ00iWJXnSATPCNxVKZdBdbjdODDvQ5XELMjGR2cV+Hd9Zth5PdDSLnhzVZiuuL6uGVq1GZ3zmQppgJlJEekrGQIO0DsWPj/GqbbIEErA03hnYl2UcCgvLcObMCgQ9KqgNURLUpJknejElw1Wls/BExxkhwyhXalxTOgt5rHpIAbvZgrULFuHgk4/BXF0Jv8Mp3lvvGETz6AjqrFE95O2vvoLAfDt2d9XjkVdfx7XrL8e6xYtj9mw6EAwqMgAkZ2gxSNQ7NAr01APugXAPngXiJ53HHLsd+a8+gmONp7CgMAf5ZiOwbztQVA5cFDVk6Pgy0MKfkwJLsuMJDTqx1Qpn5sRx4DOfkALYHCOvvAx0dQJ7XgO6O6Xn21thrj+Oii9+XfTWoAMmgl8P/kzq1REKibdqtRrMevkRqG7/ZOTjvUEdDCVj9eB5nSm1KGezuVhqHQdebcqPnSsEQ34M+s7AZ9LCpqpGb3M7quauBEovAXRScsqOHTuErcfsZ0FC/eVB4Cc/in7Ij34gXcO33Z78i3jxnn9eqnZg9cvNN0vastMNZk+T0CDkoCNlpUhwyNUadDY4rubOZd0/EM6qi4B/37lz+kmNwCgw/CoQkqrvBLgf6KsA08qYtS8Q6oI/dDhiJwVCZ6HDGqhV1nCl6ViQ2KTu7V//+leRUXU+gMdCSdJ4He8ZzGA8yI2yGfyirSOTGrR3uLaTLGNwbioqgJgwwIz3cwn6N5w3r7/+upDGIrkjgzJZlKfatm3bOT3GGZw/oL0Xn1lOuy9TvX76FyT3WC1B2STuKeeitwznPX39RDGECxFUt2AlDfvgEbwnGzfciD17dmDdOiY7mqBSGaecKP31r38tjiFVNQRtaPpBJJeYHESw6lnZJJi+BJN+uFbK/SQY8E9V4cZxxXWM61mq3khc6znm6L/IlUTTjpf+Afzs+9HfTzei5u3vw96qOkHm0Pemb6UkEXlN+Ddl1UUqaSkSGpOtbJkW0JeouAYYOo5u5yDmrr5cIjXoug8MCKKM8zVVX+YLFrw3b3kLKwCiyVP8+U/UU0PG7bffLpKnfvjDH8bIG543pMZLL70kJmTO8tgATYurGQX6QhQYoguYXq3F26vXYsTvFsEvu9aY9YlYZbag3eWMaL5Tb7rCmOUgcCqwaeqZxwHfqNRbAHuAivVAweIp/M5+YPA1KYNPDo7aFME91WJAlZgNbXe1otFRD3/Ij3x9ARbbl0Kn1kMFA0Kg3iw3IzrQDIbNgSpZBceeF4FXwlmbctNjQiY2fLFBG26Gzd2vwuk/C29QC2u+G43tz2FB9XVQq7RiXHADp0FEpp2/O/3dcAf7oYb090T9Q7RJzjMeZMjlxoETATMhtFiPICSCTg3KzpjGHxtxRAzP45KNiwFTlKknJ9XR3oeyyiVig2cm+5Kc9IPOGYNB3wXXACeelKpJiOKFQOG8xEHiRFh/JVBWA3Q0SyTHwlVAfFY5P6vzLOAYBArLgbyi8QmNVx6I/t7bCvjcwJJJyLMlgjlXIgOEtIl8rKw0mYRGfTpg2eWSGwDXgJQFbslPWYrp9fqhUToG3lNwe4PweXpFoBWjuwDL+rTuFxshc14xoMysFxpWqbJeEqHX48GWF58XhAbxYMtZ7BsYwC9Xj9WpZoXAjeVjHQsapjTYGAxP1DBcDT306gJ4gyQxo7BoE/TJUCmMheiTQN7kynvrR0bQ4XZjkd2OonEyaiilR+OMpKx8LcvKyjHS50dl9TwE4RZkhkaVujyc5MUPV1yKR9tPYcjnwQJbviA60kFeaSnUBgMcp87AWpf4PQxyeOssaPH0oLu5Ha4RJ/b21cOo0WNFbl2MA8EMvRjw+p58SupdAq7vQUB3Blh+B6AzAi4n4B7Bhqpi7DjbiWWlBbAZDUBLYwypwetD54SazOPJkKXExZuA2+8AHvhzlND48lckWSEZD/9VClYrSbT7/whYFQ2Yg0GEBgfQ8dLzWPSeu8RTdC5KrDogFA0G1XcPSGuYAskcdp6jMkvQrrVGZKdk8F95+qnLrEuFYCiADvdueINSvwmnHjjY34cbL/1AhAykc8pqHZ4HMzbpiOHRh8Z+2CMPpSY12CTvvvui5MHf/05doUkRG7QjnAGHsF8sGhu06ZSyDycgq3n/GNzr64s2Cmd1Bp1NHp9csaEEqzumG85DQCiOGAuGAG8LoMkBDFH5P39I1tOXjzuAQOg01KrUiUR33HEH7r///vOG1GBW1zvf+c5zfRgzuADhdDoFEU/bIj4Ax72Hmef0BRj0DIS8GPK1IBii1nceLJriCfunXCeTyjhOM3iOiTLsWZHJgKN8fWYwAyblxFd8s5qaCVAMPKYLvofjnwFsBpcZtCaRON0SZ7RZOL5JsvDf54JYySZ4/UgUKCspJRldnlfutKw3/I7LLrtMVGEka2zNuAUrtjmWWC1DApkEanylGO8HCYeJkg68FrKEeSKwSodj+pyRGs88Nuap0w/ejyX33B/xC+KvCUkN2txKEoPXKRWBlE6lx3kBvR1dwVkoXpAP5Eh+Jc+LRN3VV18teq+8afHJT9KhjjYKf897gHOc9HAuICeRUBr08ssz72k85aQGpacuv+mShBp5Dr8jhtQgpMbPU2dAfW7+Mrx318sYCTctNmrU+I+FsVUQ/V437m06hhbnCGrMdrxv1iKhN54KgVAADSPH0O3pEs22q821qDLPGruJ9B2WZHtk+Sfi7A7AVAaMJ7XVdAzoaAJMFmDhWqkhmhJc1Jr3Ah0npIBDzUVSQ+OhnVFCg+D3u2cDoumsHUgic9Lr6cHR4cMxvx8c3I/V+evE7yrwfWn2RNj30tjnRt0SqcFjLYoNyvtDwyiu0MEXtGHf7hMoKs5DQ30jygqakKeQvOCCz4Z+81YUYdBXHyUFQlqRbeiPnHcI+bpy6NXjjy1qgGYseZIAJDE0yIBssPB+hHXZ5VADjSz9WCLGbreh/8wQtJqxGZVOvwctzh5xKarNRTCxymKyyKkAVr0HcPYDIRVw4AVgzzekIPuii4HFW8YPltfMlx6JwLH73APAodel3/lZV94OLN2Q/PPqd419jlUb2SY19CZgxXXAgcclMoOoWAKUL5zc5w73AkM9gCUXyE9iVPE6jLcuGPIx4tFh7/7j2LCSc4OljGoMu11o6xrAgnmVOHWmEwj0AEEHoJEch1AoiNFAn3DWjeocGDTWmGC1LIlD44nOP0uJZdmddPCns03odLtiOu/c39KMLy5chFlpBqmZRUhjl99JA5kBdNnxkAKWHlg1S+BCAzwkNFU62LRzoVfHVWkQuXOA0U7p3/JQtTKDeWLkFL//EwcO4CfU1RfVFRo8uH49ro/rw6AEz4M9GWjwL1smBQ8ZBKYEBI3YTLKNCwwm3FWbeUnx1uJy/GHTOriHhjB4+DjyVy5DmcePQFcPWgeHhUPE6/zY6H6xBhksZpTOrsKZAydRuiE/htRICJJwA5KEkoSQRNj21gOsvOReqtZAFQxgfVUJdrd2Y30NKxYtwsFQOpa8xrQdJkVqcA598lPAzbdIFRW1tSybiX3N6OhYPVORpR871nk9NCGI5uGRfhul1YBjSKwNARIfpCW4R+96CiDRVL0wqcMR74yYtUZsLFiB1/sORPpaLcmZi1LjFBOoSeAK9EUIDR7nob0nUVZTJJIHzGrJdmMmHTMtSYRGgl+JzjeVhnFbm0RoKKsjmL33+OPAW986oWN3B9x4rfd1OANuMQSsGi1W5a2FTTeOlB7vK6Wo2GNDHhM8pssuA7YmkCmVHZDf/U4aa2LP1jPlKPl3dLUAjUclAmfxmuxJMQZIyCS4ziQ2WL2qIDViq2wlhJA4CKEEy8M/+clPikzucyUdIoPHwGxMNi9/s2PQ24dGx1F4gh4h3zvPtmysxOIMMgKTC0jgM3mDwU3uP8pMUO7NrEKz51owrDuGQIjJVyoM+c4iT1eHfMPEGs0yezbbTcizDa7lDIiyd9Q5l2iZwTkHe2aw2liZhMH5QmKC9hmTn9IFx5WynwX/TaKDgW6RFDGNIHFHe45+P/+dTNJ6qpBplct4oMwQ/QleUzmYzYQ0JlOxemI6Atz8PpJUXDtJjvJ+04fk2OGDSXKyvAzte/YXJPlAYiybfU44NlmJwwoj+rT8fK5nyniksl/rtMMfSNDnTC1IC+5DiTLVeT3je/2RJDrXtlg2wAocrinKCkbaePRHz4cEgCkF7/WHPyw9/omh0Whw2223SdzB+UZqUFrh4Ucextfu/feEfzdqpl9aYI7Vjoc3bsOLPR0IhkJCy73cFHUMnH4fPnNgO7o9DMaFUD8yiMNDvfjpRVthDPd8aHX244Xu43AE3Kgy5eOKkiU4M3oCne428XcuU6dGT4qswHJT3ELD4E4iHdX214E51yU+aPYA2fFYuK9GeGIfeR146ycAZZXJqR3A6Teivx96AljqBzRjJS1E42ZVak3kLndHTNYofw74+uEL+qDLVOdO1huPBzN3N94KWGM3shCk11dUFcNoMuBsUyfWbFiEA/sPY+vmqCPBLA+324XX9z2JhUtrow69KoBSYwW8QQ38QS/M2hwUUoIhDbS2tmYkt5M15NQAJSuArgPS72odArWFUt+TSBCX8QkN5hVtxJGzjWM25H7vCB5qfR2ecI8NnUqDG8rXoMyUBUON+vj2MuCl+4BOah+GgIAPOPwSYLQCc9JoGp4MjYejhAbBz372AWDWQsCWIEBNxEvKyP0lpgJs9p1bDgx3S+dqL5mczFXDbmD3E9Hf568DLrp6Qp8ZCAJ723KwZeuVUPsGAa0FvR4VhgYasHB+/JiXrk8wFESraw9cgWh2VZlxKey6xAF5s9ksHH8aHDT6ZZmGVBjy+YS0FNfZ2OcZOEsepObr/3T2FF7u6YJVq8Vds+Zifm6+GOv8fkr1cJw/2bEbI36X4CfW5M/Dqrwt0hmG3PAFHdCqzFApmyuT1KBMWe8RqerGXguUjiezlxx/a2uLEBqEOxDA7Tt3ov2GG5CTQqOXhjZllWTpC4JGHJ26Xbt2iWDHVBpwsyx2/GjlJvyk8TBOdPRgkTeEz67bCrtOL64xAxg8Ru0ZDbraOmDOscJoNaNsTjXqdx/Bvh6zMKCVZeIxkKu5YqCKPk+H4tIbgRcehkarRa7JhN1dAzDZylFdWiqytqYEDMwkC86sWQfsVuydDEwvWy6RjiPDUnBbrYZab8D8629E44DCgb/kFmCgB6GeNuxv7cbiyhJJduvYDrz+6J+Rt/Zy5K2/Kulh0Zmmk0fHi6X71ZYyFBvzMeIbhUlrhJVNsM+h9JS89+zdeQx1cyuRX5iDYHgdUSYAsKKK2ZcC11wH/OoXsR927fXJvyiRnADvASsjJgAe7/beHVHZrhAw5Pfj4NBeXFywNfX8IiHBnhqf+hRTqqU1+UMfSkxoyGDVAoMxu3ZJ2VasHEgmadFwCPjbL6MJGK8/C7z380BeFogrtRUIuhMTGwFWgkahQg5CGIp5rRpJ9loFGPhhNhWJBJIb5xI8Bh4Lj+nNDKffgSPDexEKpwgM+4dweGgXVuVtTtDvbgbpgvsv914G35jNS7JBbpwtg4Tt4/+4D3Ur7MIXkefLgO80cvQ1ovfMRHC+Z4Uz6eLBBx/EFVdcca4PZQbnGLRPGGyWZZq4v9KG5e8kNJhwlMl4ZmBbKcMq94RgUJNVG0xWnK75QX9GJjI4/0nm0d+ZrgAe42TZklmRwbWMJAHXLoKVCKw8o39BP26ivUIzAe8x1TRE0um8eeI76V+wKobHJdtgtHsJSpeyn4bc54TjId0EunRIHlausMph0nK22cQllwG/Px39XaVC8VXXYjRJZRxluliNI49PuZk451KyMcTXcv7y3Pnv6SYN0wWTo+gDcz9W2uckHCkhNoN/Htx22224+eab8fOf/zxhQUSmyNrqSmaY8gXL1i5EQMXs++jfcnV5KDacm0aDxUYTbq9KnG26d6AbnZ6o80dio909igODPVhfUIYezwjua3ldBN54OkO+NrQ4+zHf6hM+eDwpMIbUMBYAoWggLAJvrMMZQdsp4InfSiQEIQcIR/olYoO6czLOhoPhSrQeBWrCEiARsAHN+BULMQHBmHdPIOC24CLgICWwFINgzVXAuisSBnK1KjtU0Apyo6AwB6MOF9paepBjmzvG6SirKMGRthCGBkaQkyeXrzJfNoAKUwIZmnFAw4qZBtMOXofyNUDRYtGcW2WwQ60eQUCMFx+gssGgrsBcm104UsuWmcc0UN/ecxTeMKFB+EIBPNL2Bu6o3oI8fWpJm7TA4B77n8QHTdpOTo7U6OuM9tuQwbEy0JOc1KhZDPSeVTyhAgorgb2PARodULcKsKfZ9yMdmOzSY7Loa4slNOQKk7K5QHnmmX90LlauXge1ohy17+huzK8thd8fwNDwKBwON0D5M7U0PwZ9Z2MIDaLTfRRWbbGQd0sEGhp0NmhkMJMkVQNvYnVevmjYLY8Ufmqx0YB5ttTX8Lsnj+BXZ+oj8oDPdLbhLxu2YklOniACjhw9gje0Z+ELHyY/f1d/PfJ0FlhCnejqb0BRSR7UMCBfvwo6BvqkEwAKFkqPLIBSWjqVCr7wmsb/OwMBNDocWDVOtpGcBam8tryeeoMWx04exqL5S5KuvxMBG7UfHRqCVq3CInsOluYU4BerLkVH+XzhlCYiElbk1aHp1Bnkl0sBQ5Ibt1y5BTaVDt1tXaKR98UXXzzW8TCzIbZJkoKL3P0QkKPYC9dtk+Tlmk5igcEE35J12Nd4Bu2trbBXVwJMNAgnEUwLbrwZ6O0B/vagRJaS0Pjil4HREeCXPwZamoHiMuCDHwcKChHoVQTbzTbgjk9h6NQJlB58DccO70QFAijNsaLQZsbcgXogP3nWvtzAr6+vL9Ks1qgxoMXZgR39e0UVaJmxGKvzKP04NoBGYoH3YCqCAEZNrpC12//GMSxcWgd7LuWx1DCGK6HoiAgiIxSC2zkarXB857ulvULZKPwd70r+RQyW0JFjk3bZRuB9mGBzw2H/SMI+JKN+j5Ci0o0XiGSPE1aJsMk811USFanAteWWW6THeHha1s0Nn6fbBbzyOHDj+5AREkk/WpYBw9tjK3Mjxxhr8+lUS+AL7UMIku2pQiE0KiaGjI8bbrgBTzzxxDknNR5//HHceOONeLOj39sTITQkhOAJuuHwDyNHl73M1n9GcO9lkI39M5g9zMCfHAyU/7564zK8+vrzuGh9rD9BKapMSQ3a7Nlw1KcatE9IaKSTwDKDNy8YDGV2u1KOmdni9JG53zMoTbslk8A8bU5m7sfbjgxmM2grEybMrp9OMJmRlX+0xUkEJEp+YA8GzolsNBeXK9CzTWrwM+OJC/ocDBgzwK+Uv51KkEjhNU2WJMqxxfstX0vKUZEwY9UPe6YyCM8qjsmA13br1q3CbyVhRrJkukircXHdLYDXJ8lQ0d69dBtwx3th6e8X/WLjq1Z4X0kKsdJ/6dKl4p4yuSyVsoiyqoOqBxMB5+pUXjOSiazSWr9+fdJKplTyWjN4c2HDhg3ifnMtnkwbABnabDocG7etFQacJizbwCp4rqWLbUvPy3IinzKoqnw+LENwbLhNnINyeg35XWgcZRWIRmQmy1AnCkoVLga6DomgdRSUmClMnHX+9O+BsFRWDPg9rpFoP4rRwdiAcOTARwHjIsCtkGaiBJM5Tgc9ASpNVaKnhhJlxor0dKnjseVmKchxYp+UgblisxTYSjIGKCOTo1uJYd8hBOFBzawKHN3tgjrkH+MQqKHD4iULsXPHHqy9mM2wJBgSSdCcg3LQdEHDkRIedHoYoJKPgcdjs+WJDHW9NjqmEuncDnodY3I0Scy93ncM15atnfxB8ph4/ZWVN6r4xvMTQG5h4vGbkyKjZO4aaeyf3CnNldxioL8ZGGyR/t50ELj0fUBuOHv4fMBwH/Dc7xL8QQUMdU+I1KBTodTX5PjRsTLHXIljux9BVUWOFDi1rIs0CvcFx/ZvYdDEH/JAn4TUUBpJ7HPAoDZJjmTGPasYlN/AEfNv8xcKmaZk8AeD+G2T1DSOCIv54J7Gk3i/yS4CqDq7CY/u2gOfxwudQSeC7iQ/Gs7shk3dj9w8GxpOnBXEhip/P4r0sZkf2UKlyQR/AkOrPA3pOhraJE+VjsegtxmenJOCtDFX9qHMtFIElSeLFqcTN762XZAtxNr8fDy0cbOoJmH2ErOjmL3DbDVlYHx5Th068lvhNxmhUalRY8nH8ZEjIigMK1C1pExId2zZsiX2+pJQXHgDcPJpwDMsKs5Qdwlgi5uH85ZLDwZYyXOMDqP1p9/Cjo4OrKsuh+bKW4GhYcDrARavABZE1/asg8f//g8B73u/VEkpl+hzXn3l22NezvvGku9ItYrHjVz3KIbaW2HVaTHk9AhSQ4BjhIkSugRl/6wYCrWKxs4FeXYcPzEo5rO6WI8DQ8ciL2tzdQpyY1NhrGQJj4El9XR2eP84P7JJbjiGPGja78OqdcvEEs+9tti4DFq1NMYrysux74FfoFw3CNSfhcp9Bth4iyTZ9947pUfKL3BIjcE5Z771LeALX5CachOsjrj4YmQTXEu046xvETDQkO3m0xwLJMpingsCw+nrkcPZBbS9AvgcgM4KVGwOS4ly7tkA++XA0D/CK64C2ti9lA1DdViPEFjtxjGTfg+766+/Hp/73OeE4z3dOugymNxC3V1mc73Zkey+cN+bweTBNZPrJ+3vRAEiq74QeQV2dHf2obhUyurWqPTQTqDpboQInmYwS5pBLQao4okVBhfpXygbhRNJqzFn8E8DZnnHN+elvcF5QmkfjiVWymXSd4WkBu3f+PFG0J+gvyLLUWUiyToZUHaVwXQG1ChFJ2e3syqZAV3OGx4T/R/22SFIfkzG3iKpMRXyR9yTadfHN4jmvznPmfE/XQ2XKXfFdZVrXvwYkXs3KsFjZDB/9erVwsdU9geZDHjfOE5JWvM7p2tcpQTvza23Sw8FeJwkYOJJDfY+YrI4iR4mQZH0ow2Wrlw6P0/uIZKO3cbxz+PgPeQ8p584KSngODC2RVk7xi/k5vbxkHrC+AWZM6HvppoCK66ZqDyzn10QoG1y7bXXShzC+UZqfOhz741pcMyHWW2FcZKSCpwMr/a24cBQNywaHa4pq0OZcfKTbXlOIUwaLTwBv8iLotPAINzSHCXpMDaQ5QkCI/4QcnTRDaTClICdZlBx7o1AQ7hZOGHMAyoT9A5wOqRgSCKQICipkTLkdz4kBZnZn8IQF2DUeoDWPUDJMkmqillFxipAPX4Q2q7LERrUZ0ZPCcmpAkMh6iwT05AVAYJtt0mPNKFT5yJfv1lUa7Bqo7ykUWxu8QsbN8ES0ypUVnWio60HZRVFsGoqEQr50OV+TYQyrNoqmDWV4zrtNDCmO4uKQSn2K+Cmz02KQS0+WHInnysNwJdeekk8x2OkIZUouzpfb8eoS2pOrsSIP8k4yhS8fgs3Ake2y09IP+atk8YrHyQidBmSHPNWALP3AqeORJ/bfAOQk0I2S7Cjm6UHg0WPfTeuYWsAaHgdWHMzzhvseQqg/NKYIRYaI8GWDhJlL8hGoDekhb38GuRV14whD/VqjqvY9zKwRSed0lTNjnYM9btRoM9FjaVizLzhuGQZL51kOr7xOrQDXi9e7Oke0wx8/zi6uyQJAnHn5OvtQ7fLhwU3vU2Me3fAi/xyydl2DjnQ0XBWnEpVgRW1yyRDNS/fjq6OPpw4fhLmuctgtWRJs16Bu2prcW9TE/YMDAgym8f9tcWLUZaGc8eMJJZlc54zi8np70OfV8qiqZ1biVMNZ4C5IdRYtsRUzrBC8JXeFpwZHRLNwq8smQUTSYQU+Mi+PTjDnhFh7B0YwH8dOYQfrVwl7mvZgnn4c/0xDBzei1W5BZgXroyiA5LvM2Jd+Tr4gwE82/WCRGiEZ327vwMVs8pE5hdLx2MMa0shcNG7JHk6kuDjBUspsXjPD1CpCyG/rBA7mtuw6ckHoHJ6pSLD558A3vVhYGMKCaBsgGt/Gus/x7ysJ42BXhz/xufR1dMtguZLSvPhzrNgxOURTgEMZsBsT0xoBNgXiH0QpHVr4fwKNJ72odsVK8fEGXGqpwkFvVbotNH7TcdEdk45lugE0mFTkpzHhwfx7ZOH0elyYWlOHv594TLkJegRJmeBMVhMCUaODTozV26VMuGD8HOFiFkLtKf3IXj6AFw1pZJMJWUJdz4KbLlj/GvNrLGPfUzqXUGwASl7I9ABYXZwhhnCoZAf/tBxBNELvVoDq9YAhz+2WmOuLU6b1+MGnnkE6GgBCouBq24BrFPYlJ3fXVIJdLVGSXxRoZlEqioetBmbnwPC81AQG/x9zq2ALmxTU9bVtgFwvB59na4UMI6tPmU1mCqFFGAyMEuXj+eeew633norzgX43XTyz0lV7TSjyFCKs85T4fVX2h+tGjus2ikcq/9k4DhiBnp8I2TCqi3FioUX44VXnhSkBgmNMuNFE6qm5NqarHHtVIGNVlnRx+x3ZsVyDeR+Lff1YsCKsi8M1jLoyeAuA2vnY+LhDKYXTGBKptfPQD/17jMFxx6Jg1RgRj0Dt1MtR0V/mkF+jneOfYJ+OM9Lltki+cAKDbnCgcFh2ku0AUl48PpMZK7IMlzZBmMYzOR/9dVXBUmjrATheU4nqUF7lOfIpE2uQTJ4vfgcA+zJEuNIbjGIz3E2njJAOuB1YJCUgXT++1yQy+mChJ8yaeSxxx4Tx8u5Q7+DvjfnRCb2D+8938s9IJ7U4B5AkkkZU+C/Ofd4rfh3WSJKri7PFDKRuXfvXvHZ9I84z1L1seH3c/9idVRG/W54Ht/8JvC3v0m/c4x9/etAlvo0zGBqwcSpb3zjG7j77rvPD1KDxhGNpHfecBf6dS3o9XSI523aPCy0Td6g+2trPe5rOR7JVPpHdzO+t+xSlJkyL1ckC/jaa6+J0ic2X/2fpRfjeyf3osPtRIXJgs/MWxVpFL7QVo7X+07F6RBLjzx9CawaL9QqjWgSXmhIovNrsAOLbgOcYb1mc5EkvRMPNhulBEeifhTLNgOVs4EnfxLtI+AKb44iqBYAbEbAEg5edB0G5t0OaDPLLMrT54vHuQI3PpXI40VK40mvtmLdvLdix87tqJyzEaP+Noz4ozJfg77jInBk0abWE+wY6EbIzD4cAWjZwHwaDEaWWTKLgMFobtxsJExpB248zAjmQs6fzIhm9gU3I252XORljVMZlxQvwf3NLyGgGJ9aqu7os+j8LrlUCsy3HJcClku3Ag1HgB1PS38neXbzB6TxmQ5YdvmPv0qfQVTMArbcAFRlQqCxEapv7KbmTdBP5lyiv0u6djxULl3acBC1ahFQmaSJegqwDDV+XjAoySwUGg1c2xIFlHN1VXD4e+AMyDI6KtFTgyvZs12v4uRIPUpHy9E4ehanR1uxtXjdGKNdzvpJJEelTfCd4nTHcUyMGg3W5xdh90Av/F4vvO2d0Obn4i1r10ecGqNGj+U5tTg4dAaWHBv0RiOsJjOKXLHrZElZgQhA9Hb1o61VmlsTKRmnIZYMf1u2DN85eVIEjK8uK8PNpaUpX68EjVEappz3c5ZzjZXqWgqKctHa3CmCyN7gKIyaaID6V6cP4oWeZkEQkeDY3tOCbyzZEun3lAj7BwZiiCL+e1f4GM86HXjXGy9i1O8Xw+TRvhZ8q6Ic20oqhCPBa87z0dp08IUDpHo1YNFI99+h64StskCcQ/xaJKAkXHgMbceAgTbAYAVqL5J6KhHNjQBl83QakfjgU8tVYerovP7r74ENl06ul00WIc8vzVMPwBb0wppnR6Xdgu3NnbjYYsTxXlZPspH4XNEYfewHdIUJDfFL+AczH4ugCVs28l1rP90G14gTpRu3JW2kSEecDiKJCRltzlG8642X4Q4ERdVTi2sUjY5hPLhha8xc5Lmw6obOO52mRBq2mvA+HIO2eqyuK8crJ87CxqQK3uP2hsTSSPH44he5WEV/P3gQuP9+4F//FROBL3QYIUjrmVoVwJq8EI4O29HvHYVOpcUC2xxUW6pj952f/g/QfEoiGHg9jh4APn83YEw/6zRj3HQncP+Po9UZ3Oc2J+mnlqhKQyYqZPB3ZyeQo3D6dQVAzpVAYEhKZOEakuV5c9111wk75VyRGvxuHsOFAPpDJB0nquetVxuxPHc9mkZPwhNww6rNQa2FWbjnd1+GCwkMTspBzXhwrys0zsf8UifyguXIsxVN6tpPV+IU13X6F1zXuVfRTuPazsAUG7AyuEnfgnsKxyZJHQaR6WPQf6e8ZLalcWZw4YDBaPqoygx7ZWb4REmvdN9H35fJe1MlR8VKE54f530i0oTHyax4Eh/xx8w5xffx/XIfiEyD5PwMkpxTsR7IkrbcJ6+55poYqa9sSGdlAn53fC8HBslpr3ItSiUxxbgcbdNskBoyOJ7p35zPYGIixxXjQVzHGe9h8pgso8VzkBNgM+lDyPOOr+zgPGdsieMwGeHOvzFORcJ7onONpAnnC4m2dCt8eW7cm3l8lD1NG88/HyU0CFZEfelLwOrVUgX+DM5rXHXVVXjXu94lbJLJ9jjOigXz1FNPCUY0Py8f/G+OVZKZyAYoBfVAywnxbzrqhCcQwN87TuHDdZl/D9lCEfwLG4HFfuCXq7clfG2x0Y6bylfi7+3UIpYqT4zq8OZnn4MiQ5qThcFg6zgldQxUXfIW4IUHw/GuEGDPB669C8gvkXobxDdGdvmA8jrAxCyIWA1e+EczJjUuFAh2WSXp22pUBoz6wxJECvC5ZKSGCOr0HcGx4SacbTiFBmsvrihZgyLD5OVfkoGbOquZuNlzw6ADIRtWZCmZycKAIgPXcgCZJZkyaOjRKVEGtXN0Zry1ahMe73gDroBXDJtcvRUbC9J0ppk9PNoMBFyAvgAwJRijTUeAA9ujQZKXHwD6hqIVEi4n8PCvgA//N5AgI3gMXngI2MvKj3AgrOUUcPJgZqQGHczCKqCvJbZvS3GaGbDTARIso2zMGgYP0xcArAXAprcmJjZTgA4qDRul5jENBxqK4zkOdMgrTavgDPQjEPKKwLlebUbDSBOGfCMxMmw93n60uDpRbZY+N5UcFbN/aMDadDrcXlmFv7S2hCveJLynZvz78cMV6/Avzz6GPd29yKmtwcfnLMJN5bGb2oaChcjX29Dh7ocxV4+lObU4e+o4gqEeqMSFVYmhpFOXoHxWrVjjSRJyfjEDLN2sr1Q6nmwM/vadO/FauJnxE9Ta1euxKYMSV2ahsGIj4CepocyQkX4q9brbXQ5BaBAySdHmGsErva24omRWRL6r2emETatFcdhwLRO6x77IbkBCpNIsOan/d+oYRvw+6V6HuyV988QBQWrQkOSD2Wg1OVKJOK8aCQ0ldp58FW+/7D3jn+yRfwBn9oZ754SAs4eAS94nERtH90SKvuRjCWjV0CqvP3sPMBAdH2RxjgIP/QXo6gCqqoGb3yY1e54i0Ingusy5xkxC9+s74R/ox7Y6qaJpZVkBjrb3Qp1nwZKyIsCWJCkglJhwLSnORV9LF4ZUQzCaTeht7xGVQFduuFyQR3TskzmlzMLiuiATm0+2NmOkuwfq3Byo2JQyFMLxkSGcHBnC4pw84ayzSpDzl03KlRUeaUGnh1ajxfKaEmjkOUUia7ygBe2tsIRDBKxqmaDmbyhEGj+2qbhBo8LqvBzo1EkkrE7XAwqpO/H9vV3A4b3Amk2YMuQXAx/6L6CnQxrLRWXpr/3JmkInep7VuOqpk48hofCOd7zjnMh18jvpY9x3331T9h1cSwd9bth1BlH5MxnQlpMDV5xrDBSzCioTmDUWLLInlmiYwfSgIK8Io0Ne5NsnRyZNV18NBr9IqNFGpO8gBzZZ4UQ7iPI+so/BwAEDx3IAgXYdqzAT9s2awT8FduzYMUb+g2TYZPscZIKpkKNixQD9AY7x8T6PexvHP4OrtKvibSRWrTPwLDc5l5typ3tucuxpKkD/kCQmM93XrIlKl3Lt4feeS8KSx0D/gteW/lkyn4wkWjYljwh+X0Y9GvbtAV56QUqyuuJqYNHEKhXS3RsoK8vkJPpe9DFop8sSXByzJAcY9+F4y4TQIHjNWXXE2BH3AFZfcJyQ2CYpx3nG51NV3tBnIInHY2I/HZIkyUgK+iyca5wT3EsmAs4vfkdG1Vpsos7xrZxfJDZaWmZIjQsAHC/ce2jnf/jDH57UZ2mzVRpOpmUqMOLzQKUKQR92pJixyoczUe+JNNDkcWJofh12DvVhlieAJx57DHfccYfYvBIZnmpVCFamv8fBpMnMSUkLC1YD+aVAx2nAYAJmL49K+ySU2woB5jwgFJUbiTi9unOjfTzVCIZc8Ab3I4RR6C1N6OnXAabMmgo1OFpx0tECjVYjNhVP0Idnu3bj7VWXJ+6NkgI0FrjpcDMiZIefDDmZdnNgBHANQ2PJx+23J28gy42GBhcf3OC4CfEz+Hk0UGhY8jluMAwmy4GuQoMd76zeim7PoAgOlhhzoUkWDFEi6Ae6nge8zOIO5wnnLgNyFkeD8odeABr2iIxqsVnwMnt8jLIqYrIhwO0EBnuA4hSVMZyvj94LHN8XfU6+1Cf2A9vegoxAmamdfwEGpKowkQk+Jwt9RLKFntbEVVdseD6BrD8y2Byr/KkEDRK5WkCZtR0PjiOLNracs98rkS5avQ5+r1/0rCB6PQNJSQ2lHBWNMGZX0Yn+xarVqDKb8WxXJ/L1enxxwSKsiWteFw+eT2djI3695QpY7XYxHBIFzATJZ68SDxkD3i6EvCoYRRVBCP4g5ay6UaiXDGYagZmWjPPv8Q33ZHzj+HG8zusbzh5jiPoDJ0+iK07HdjzQ6Tjb1AT7LPboIBktNUWzacuhU0dlGofjpHTE8UGFYfaVoQ03MoKbXnsFTeEMpPfPqsWPV16E7y5fgVt3vBrmxUMwazT478VL8XxXB/50tjVCkNi1IRg1Kgx4PZFAJe+HCMBpDJhvm4PTjsaYcxsZckCn16HFfRq5hhSlwa5hidAgZNkd5xDQfBCYs06Snwp/rlGnxYaqErzS0oWLDUbouQfTmK2sEU3Zu9vaoo4or/8XPg2cbQp/dgg4sA/42rfTkpHKBNRYZrk87QKOcTraLOdGyyHgxAEpKM7raNBjxO+HY3AUS2uqgAUJMp88vcDAIaAwviJAjdy8CmzMr0NeawEOdx2Hwa3FygXLUeLKQ9n8MlHdQyM/GZjRRqeN2W3HTzcgFAzA3XhGJEBoi4ugzcvFvjd2YTQ3T9xLftaEMyAXbBASmPk2S5SJW5wGIUBng/NqYEDB4GnosU3sOJL2FkgxD5NV8VGSaioxOiyNF+4Fs5Os/ZwvLful3mt5VUDZIml+WCsAfQ4QXqcjGDgB2KomtI9MFHQ4GOSh/TFROYKJggQ6gx3Z0NtNhAODXfhp4x64g2wqr8YH61ZiQ0HqKt9UUFYAvPDCC8JW27Ztm/AvZiR+Lhxw3+G6mkyOJ90KTQZ5JiP/QvuOPgbtGTnQwz2bgZ/ly6OJffQRkgWgOfZo2/DBY+LexgQL/qTvTv+DQSiqGJAQ4R4xM1b/ecDxxOAniS0lOF6YOEEwIMqxMx3jQvZrJitHxSAr9w7Oi0yOW+65kyzxQ55LvGZ8MPg7XnNl2tacV8l8jGyBx60Eg9e8DvHVE+cKye4lxyAz9BM1kJ4MeO85juKTQgneOwb4I9UML78IfPNrkg9CPPM08I1vASuzLyFI24Akhdz8+4orrkg4hkg+U5YrWdV2KnDMk9SW41Qc1/RtGETmd1MOnYlbyXwC2dfnXPzrX/8qkqG4D3FO8T4yxkUinWsD7x8/n3baZNeIVERLQjC5k0lwSvAY6LPN4ILAlVdeKbiEc05qcLI8//zz+AKbPoqMJw96PafgDbpg0FhRaJgNTbrNGhPg0faGmIbc/Bd/XRLT9yI9PNJ2BncfZ0BcwkU5BfjPW24RbCU3c5ZJsYGNMjNyyDcqgklCQ1qBEZ8T1qmohGBgOFFwOLcUqFkGNB8KZ78GgYIKYN6lQOcOYEgyPMTf2ExS8+bMtvEGDyIEKZDnGHFBa+iFTlOK0Yi0jgSzNnlGRpe7f8wG6w2yJ8cAyth0OU0w40nWf4w39riJ+k++DAyyaXu4zIdZ+jkVQOVqSZIlCZjVJwcOqJMpZ3pxQ2Y5IjMxyOZzvBI6tRYVpgzng+N0mNAgwmN78BBgrQNUBuClPwL97dLfmLHA8TTqBoZcY9vM8PfxpOC2PyGRF0oE+dlMsZ/AWDXZgEvvBLwuqcppss3Ls41kEkGWiVUD0Yig0aPM+IzPPsnUkMjT56DJ2Qaj2QiPyx0hNXLTIET5XXQ2OB7ptHP8f23JUvFI93xI0DAANF4W44DXhVOOAdH/aL6tUEjpFJfl4WTjYdTOVQYcggiE/NCq9DEl4zTaJloyLuPIUGxgkSHtHo8H/WzanEEWLjNcBvuHsWjRegx5m0Wz9roSA7y9uYBi2a802WBUa+AJRsXlWKk435Yv7vttr+9Ai0Kn+DdNZ7A4JwcfmT0Hr122DY93tEOrUuMtrAxTqXD9qztjZKmG/ZK0FDP45XGjbIa9wDYXBrUGrS5p/WLgvPHIaazYuBSuwDil3JyT8eB3yM8XFAOnj0cIDxIbF9dU4LUDJ7GsIBe2ikocXrMV/n37RNYzs5XEMe7eCTSF9zkZRw4Bx44AS7NXHUrQBuC6PqZp4TW3A+3NwGB4v9EbkDt3PgxmK3D7RwBb3ljyuPtlIOgFBoNADntdsacG5bZWSGsts5Mq5yFHbQXKpOxuc6FZ7E10PuKbzCcK+vK+/euKZXj+tefhLymCp6cPaja+VGmwoqoaS1MQI2mjqBq4/E6gYZdEUlcuAGrTvO7/+Z/AZz8rkUEch9TLZXPwCYCVZ+pQOYLg/hSFRpUiC7NmtiQzRRJDzIOw3NncNLNQSR5u3860T6mkPZ0AwUAPcM+3pd5TvOfPPwy8/ePALIX0oHsYeOOPQCAsJ9pxHBjtA+ZukSp8S1YDLc/HHUsn4GgHbAmOgXNqCsgO7juXXHIJnn322WknNfid/O5Mqx3SQb/XhR837IIvvBbx589P7UOVyY7KRL1xMsTll18u1rA//OEPInBBWdHzomnpDMYF1+HJSrdw3aZ9NBEwU50+BpNGGFiKH/9vvPHGhD5X9CUsKREP7vkMcpHspr3EPgKsCqfsCImYbDTtncH5DwYqGcC87LLLkvoXDNxPtQRV/NyhvTxROSrOO7lfRqagT0KbN1XAl2ClBucIfRlWhKSSvZ0ukpDrBBMQ5Kx+uQqL+875TFQyYYI+5VTIZXF9Y4Ib5fn4HUxCJVnHMUZiN0IO//630s9w0pKw2/70uykhNTjflGocycDx9fLLL0+48kEm1EgU8BqzOo/jmuC/GatKVY1FX53XjXJQSikrVnYRJDRIbpxTXH89NUqBAwckQor3jz38ZkiNCwaMb333u9+ddGXrpEkNZnvQ+SYZwIBS0+gb8AmphRBGA71wBgYwy7x2wpqkh4diGyFzUTap1NhWLElkpIthn1dIbSi36f1DfdhR6sRtq1YJNnP79u2CdeS/ZVD2JJ7QIMlhlxs1The4uK69EaD0CK8JHa66i6TgaflmIH8R4HcBRjZuzm753nQjFBpBMCQ1HlarSqFSWRSyE5JczsiIEz6fH0ajHqqQBmpVHZz+dnGdLJoq8Uj6+Qq5HWape5xu6E0GUe2QLqlBh4PvT6RHTqKj+9QRLDC3SfdNq+aJAK4BwD0E9J8BVtwB6MbX8ua82rVrl5jobAZGA4UPbjJ8Xp57GWeyiMCkUsk9jLaDUjPXvrboc3JD7pgKDQUY0As3HE6K5vpYqSgl1ieWf4NrBNj5GNDbCphzgLXXAkVVscfFxrznI4qrJNKRxJCQS1NL/UdYqTHBBZ/ZGgzuyIbpZA3UudYaNDqa4Tf7MNgzAGuuDXatFbMs6Wf0yHJUdDzSLVNndgwdADrqnIeUEzQk2cRODPfiBw074Q1L78225OFz8zci31YCrfY4HMOjsNql9UGrMsRIOMmgU6IsGaeRmGnWS53FIl3v8BjmlbdqtcjNUKqB5y2qAlU65BukfaZ4MYTTweOSS8StWj0+N38dvle/C86AX3zfO6sXY0lOEYZ8Ppx0SOugDM7+HX29gtRYaM8RDxlPdrTBIxvpCrDp+P8sjZap09BVBjFmmWvR7moURm3r6Q7MmlctNZRWj0PmW/IkmSlRVSL3jwgC+eFxdcn1wKljQB/7TPDG6aB/x79isy0PZxob0KTWYeGSJSJriY4pS54F2aJogB6DUclAzyaSluvb84CPfhloOikZzrPmYbHRLBr7Bez5GDOKfcMSoSGO0yP1wuJabZ4H5EXlgkhS8/rHZ/TSaWaG1a7mBjzWXo9RdQgrZs/D+2atQK7eKAhuVpDIMnT3rNmMrx87gE6NBsWtXfhS3SIUZVNTluuvcg1OF5s3A5QPYiCOmt1s4JdhKb0SWtUCBEIG0SicZqxWVQu1KsUexIbgH/134J4fi2bvsNqAd34YKEmjWoQVJnfdJZWyE5zzP/gBy65Sv+/FRyV5RoLrBtewJ/4EfOyr0de0sIrDG7s3Nu8FatcBWoMkEZlonffHEYvOVqB/NxD0ALocoHCj9HOikI9H8d3cg0gwfPrTn8Z0gtlbV1999ZR89mnHQITQgII8rnf0Z4XUkGX97rzzTrF/M0g9Q2qc/6BdQtKAQf7JgPvlRCVnaFMx6zWRbU/7fzIVJDK4r9Iuoi9BUoOZuGwoTlKD30/Cg3uSqFKcwZsWDIQz7sHgPMkLIpsB8Izkf5LIUdF2VsrvJgPnG5OYGMSdjJQRiRRm94/n1/A68ZrJsrdyf4upanY+Hlixr5Qp4nHQ/xkveH2uwLHB5DjGVKYyYYLrmNyMnteHJAHvFfflCOJ9DI7bkVhfK1vnnG7TeN4/3jeSMiRkJgqOZfrcDBjLyX08f9ojXOcJ+vJc/+W5z7G0b98+kYwRP55JcJw3vUpIhP3858A//gH09pLFApL0C5nB+Qn2d+G4I6eQKK6aLtTZcDjk0uoRXxd8QlIjuoG5A0NwBSbWbIagxm381lpoMGe84XZ7XDGZqgQzWNsohcH+BDk5ogyXmydLsqRj96HKVIT5tmhmFb/10uKlsExHvwqXA3j+j8D9dwOP/i/QeQaYtRxYvg2YuzaaDc5rwUx9ShJc4IRGMNQPf2gXgmgSD3/oDQRDQ3Ft2oHjR5uxak14gVeNwKgZQZFxLkqNm2HTzUo5PmotpZGNpWJeNZqPnhJFA7p0ZJvCoPGXrERu9+7d2LQsXLnBw2BGqPh3mETwOYGecAXHOKBDzIoMGmfc9GVw4yE7TseGJeMZQ8+gbpyhyYvQuBNoCzfxjkfCS6oCqtPIhLHYxgZnOH5vugtYvjGBITEAPPUboOUk4ByRSJZn7gGGYytyzltQl/ua9wML1gEkYNn/54aPSsTGBEADglUHcil4NkCydmvRKiwrWIAidR6W5szHVaWbYirj0gHHJp1eNsFL+X2hkDCs5KyRe5uakPvIIzA+9BCWP/ssGuKMR77+/07tgU/RS+j06ACe6GhAvr4ai+asQFeHNB5IElSbV6ac98zKoXFP54iGrWyQ0RkhacifyfD5BQuwQKEjyr3j3jVrxM9MwPnMzN1EGzqNa+U1JIHxi1VX4/vLL8OvV1+DG8olEsSi0UAfZ2DynhUm6WmTl4R4+eLCFagiIZmqdNm6BA2HTyOvKAd5RVLQeJZlnPnOqql1bwX0ij1ywWagNJwswO/80H8Ab/sQcNP7gI99BaidD21hMeauvxirKNsXLsNmyTz7WQgsXhIrMyVITQMwd+KGfiIwoMNy/aTBR4MRmL8cWLgSMElkFwO+XIfHOO/xFZNcY/0BQBO9Nsx0oswUxwXJjXjoSvLx10AXXMV2DDlGcGiwC989uQMDQ4ORBowyLsorwEMXX44dV9yEr63egFMHDojzmWhQIauYPRt4xzuAW26ZFKFBMElGq54NvXod9OpVUKvSkHSYNQf47x8D37sXuPvnwOKV6X3ZL38JtCuqQniPvvzl8d9H8kQZMOc9kBuGyxAyc4lIi/A4YJJKor8rKzNZcdn7mkRoiOMbBrpfkqqEMgWP0VcPuJ4BXE8Dnn2RZuUsD2fGINfP6QK/i8lG/O6pAMnjRLBNQeUnZTW4pjAgfV7MxxkkBe8PA6rZ6C9B35IVEZmCQaREgVEm3tHmYkJJNsBAGW1L2e9ValzzwSDDdM75GZwbMHAasbXOM9BfoE02HkHICgUGf3kuk+3NwPlPX4U2VjqQZW+Z/c5jYIyA6wgf/Aw+pmPd55oVb0fSP6OdSqI2lZ8zlWByVKKqF/qETOhh0uZUg/JIJG5JOsm+Yswae9HqqPQUwdesmXiANRVhngnBxEoSEgwkfzIFxx39XY4JJhQm2p9IaPJB4kcpX0ZCg3ZLon2IBAnjv/QvlLGpcwYmoTH55V3vmiE0LkCQQ2BlMxOnJoNJV2pwULNrORFC4sU/yGyzCeK2qgX42rEdkuxUOIvqHdWZs82lRrMIBHkVC7o/FEItg61hsDSLWe+7D+xDY6kWzXQOAazOq8It5RvgDHpQqLcjVz8NxAGP89l7gYEuyTFm34Jn7wFu+JjUd+NNikCIwX7lxs8eKpQgWy02IZ1qLnyhk6ibU4Hmpk7MX8BGd26E4IE/xABnABpV6kzzclORyEgX0mIaDaz5uXD3j2JWdfrXNVlDK5ns0FhTGS8qIOiTAgis3mBgw5wvZWYmADciZitzU6JhpwS1cFnCyIAYg2rMQOCmyY2G2ejc0OigkPywWBgopLGjg8pcDVi7AYfE0As5lI4BKegmV5bw3/LxcnN3+yW5KFZsRA7AAlx60/gX7OKrgfrDis8EcOM7gEVxbLpjEHj6XqA/fJ48Dm04kMkgzdnjwJIpbOyaTTCou+HGrH0cA71KxxNeJ9DbKK0V+bMAc/rSViO+PpwaPSCq63h/ayx5WGSfPeFjYwYVs2AYnCV5IMsYyeDY5d8oU8Ux+3JPD+7cvTvy96PDw7jqlVdw4uqrIwF7V8CfsLdEh9sh+oHsPdsKvyUfQ14rluYsg0mTXh8hZck45w3nCjNXlKW18cjR6bDr8svx9/Z2DPp82FJUhEUTDMwmcmx4bzdt2oT9+/eLzV0OVLNpbQWl1hSg/NY3lizF5w4dhFYltUq363T4VBLd7rX5hdhSWIxXersFCcOeVMty83B1aYU4FjaD5HVIJHFUoC3GkuKLUFlXAhXUKDVWwKpNIwOcVRlXfEzqF6A3xxIc4sQMY+d+kgzCSEZTZTXw+S8BP/i21EicDhJ/L8hcijIR+D28/hyfsrRfuuB7SDRzDaYDGam40FoB62zAcSoanNaYAUudcB7krDpmqNFh55ikw6XE3oEOqVDOH0AoEITb4UCbCnhhzxu4ZesVY46F95LEOqUbSLZwrzpfs/TEiT38V+C+P0g9U9auBz79OcAiOb8iKIBuhEIOqFRGqFA24arfCDKVOyTRqAxs8JiZDcYAS6rmm+U1QGdLlNjgcRfHEWV51XFJBExQyQEMYdtCbwfKLwbaWerPz1EBpWvDZEcYrnhHlYPFBfiGgFS9bxLB3wz4FA3VAx2Al+ThSjF+aEuwz8DWrVsxHaDEAe2YVH1lJoN5tgIstRfhyHCPIIa5Ns6y5GJF7sRkClOBJDrnIgPIXGfoa8zg/ASDONyH5YruyYDzhnMm3iZKBdokyZqxMvFhMlIkiUDigv4MKzaU4Lkz2EjbiGNW3ou5V5Ho4DHSx2BWu5JcPyegLOQj9wB93UBuAXDTe4HqqOrCDMbHuWwmPR64BzDplEF5BmDjA60M+HJ8ZsPOCYaCODR4HLu6d6PR1oq51lrMsaZOlpQhy94y650VI5zLcgX0dMi50f5kokx8Dw1eP5IHlLRmBdp0SVHxO+VgvFIFRQZ92slkZmcDkXX+Y5+krjmwOyztt2Ur8J67svY99LVo99Pez1QtgP4Fe47QR+C/x+vhQoKP0lpcp3nvuZ+RQBrvXskENv15Eh6JZD9ZxcfPJ9lCv5Hkh1JFYgYzmAjor/7pT3/Cf1KueIKY1A7GRZvZpffee6/43awpGCNpo4YWRk1OzGbBRrSBUAAFhlzo1akdzEX2QnxjyRa83HtWOBwbCirEc5nCqtXhvxevxpeO7BYVGzo1M/NVuOfMcejUKlxXNktsiGSSX+xqwKjaBlOeFLTaM9CCPL0Z20om3uwtYwz3Av1KZ5XSPyqg+ciFRWowEM0eFsxcN+QnllIIg+ELFVwiji3eGlY7CiFapqdVV0MVMqKkqA8NJ1+A2+WB2RwNmPlDTeOSGhqVGteXb8AbfcfR6xlC6dwlsLT7YU6z+oaLfTKdTY4hsdnklQGli4DOYwmlHJBTCTQ8B/Q2RDN6F14P2Mc6BnQWxnMYehz9cBp90Op82LJiA/Yc2wmqCIVY2eLw4ZVXn0RR6TCWLpvFL4NWtQSagjWAfT4QcANnDgCuruhxUvt9xAUEGWSxA0u2AE/cB6iD0eAKM67f/W+SlEcyNB4F9rwC9LRJGcryzeX7O1uBZXGvf+6PwIAkPRYZBAzaZ7kRsEDABzS+Dgx1AkYbMHejFFBKBt7HluNAd7MkezVvzbTKX3FsRTIHXYPA/gcBXziD7vRrwLKbgNzxpaP8QS8aHfvZgSL8TAgd7lNw+JfCqs28GZkyC0ZuREYHXm4+2NLfj9f6+1FQVYViygcCeKqjQwTkSSwTXJPPjI6Kag32hiDYQ8Oi0WGU90kh/Zen0+O1vl3o7O9GcXUJuj0j2NG3F5cXb4aW60waUJaMsydBOo3JzFot7qgmiTo58B4ymJ3IWFy5cqWQoqJjkkpX8uNz5mK2xYrnu7sEoXHXrFpUJjFySWT8bu3FuKepUVzfGosFtxeW4ExDg7gOJDwZlE/4Xo0GZpUVc60TKAtnJZY1C00RB/qA7/w30NIEFJcC3/4BkFcA2OyxWVWTtGUY0CGZkYrcioC9GSh7lZsfOQYGfElMMRAbIyOVvwbQFwDePkBjAmzz4AtKDeKV5eQMKMjNEplNJkMdJkOGz7YjZ1YF1OFxodfpE2ZQ0cFghq0coKCDO9XNKSeMF/4B/OJn0d93vCpVQnz1bvFrIHQSIbB6SZJ+U6EDGlw0eWIj0woTymbJCTG85gxOjBcA2noT0N4EdJyNSmDd/L7Y19CmdA4ApymBEJJkFpffHGcrzAYsFYDPIVXiauPmquhZlyCJIoPK0wgCnUmf41rBTCr20JsuUoPfxe+cKmeZRMan563Hc12n0eYeQbHBgqtK6qBLcx/JBCT2OQ8ZOEhUrTeDqUco5IY/VI8QKFlohk41HypV4r0vW+QTx66s0Z+OJA33AFbzcC9JhKnItFb2UkgG2iS0lahoQIKOfhBtBxIc3LMYcGameibkTdYwOgz84YeA1yORyIO9wJ9+DHz0K0DOOdz7+luBs/uYkQAUzwEql6b0gd+s4BxId/wnA+cQg+IMkDPwSj+bPxko5pzhuMtW9dLBwWM4PXoW3pAfroAbh4aOQ6vWotaSvuQbSUIGkykRSl9juuSoeA2Y1JKoMTjjFgyI85iUNma2wfsh9xmR+5ok28NZERPj204zOI64nokkVT6++j+SDBXvVxK/aCLgXkKSYDKShrxnVGtgZcR4pAbt/vh+Mkw+pH/Oa82KIiXkBD85gYRVW4n60TDBlvdXbuhOUvtcSa3N4M2Fyy+/HB//+MeFPz7RSrtJkRpy8yE5s9CgsaDKvBLtriMIhLzQqUyoMC+HNkxc+II+bO95A4PhCgi9WofNhWuRq0+d+VlnzRWPyWJbSSUW2nLxb4d3osU5IkKz/T4PvnliP/J0RgR9PuzduxehOaUw6WOdmpMj3dNLaiTW+knxfPogoXR4sAEd7h5xD5bkzEWJMcOMvnTgGQSan43qP5tLgeptUgPMhGiDRs0STcnuE0UBwn6PHR8aVbF4rF/fhz179qGgwI45c+UNPL2qIKNGj0uKo01OX2t5TSzqyYJ7BP/OzAsimUHAMnOx2PMEFmwDSuYDw+1A70nA65DIi9mXAo7uKKEhDtsLnHwKWP2+jBt9No924JW+A/CVeLH70OsYLfHD4WhAtV+6JnrzALZcloMjh/ux643juGjVPEB7CGpshEpnB/ggmdJ2NPqhrI4oLgQ2f0gipIjbCoAXHwKG+4GSKuDyt6UmNE4cAO7/WbQnB8H7KYiNEHB0L3Dl22IlN3oSSBiJ6hEOCC1QkyQLh4Hv/hZJtzyvEtCPY4zwePY8BPQx4BQecN2ngM13RrNk43HwBeDI9vD9CQENe4Br/wUwTo/kGwMilB4QOL0j3LNAPp8AUP8CsPY9436OK+BQEBpROPwDkyI1ZNDAoeFE58M6Zw5uOH1aNNUGg6s6HbZv3Sr6USSqZeLzMmgEf7BuFX7S+EaE/CgzWrE814p6R1AYVvwukqHOgAuDviEUkjjN8FhTGYev9PTgEwcOoNXpxJr8fPxq9WpUTNLQZbXK8fpDonxXqzKNMfZpVFKbN76/QjyuKSsTj/FAZ3JkYADbQhpcbskRGtp+s118fqpgIR0SBlcmqgmeFbhcCN7zc8CklYLKI8PAt74M/M+Ps0ZocBwxU5W6sWk1KHv278Df/ywdT04e8JHPA9VRUozZeMzGYvaqAK+xjVVQ0UqojrazCYlqjg1Z31ZGnSUHdp0KupI8DLW1I2TPgSHHjkebjuGSDRuFRGci2YRW5yjuaWrAsb37cMMll+IOzpfzLaDy6va4/SEI7NopqiBCzjMIWeX9QPp7CIOickOFaUzs+MAHgL17gePHpd+5XtwtkS4pQanB930e6GiWqjrKq8dWLPHc69YDNaulik3vKOAdlqpJlBXBTLhIlnRhqQGGj4V7t4Svo6Fkgj011Cmfo9Pxi1/8Al//+tcxXaTGRz7ykSn9Dla+XVM29RnddP6ZKcmgUrYz7WcwPkIhP7yhPXRMwvPEBW9oN/TYAFWCXlxM0KDNTz+X1dCpfIPxQPJaJkiS7bmytjzXf1ZHJAoUMTuW5Pm5AH19BsoYUKNNINtN3Gson8lrxSxwnqvcl2HacPYU4HHFyeh5gaZ6YLkUfJt20B/Z/Rf5gIDeM1J19exzdDxpYKrIY1l2dbKSUASrg0hk0FZjgJUyzNkOqp51to2pqD472poRqSGDGfkktBMlMU0FhE+UQuaK60e2SXV+Hu8Fk7W4PvA+0/4dL0GIAXr6IyT6z1XiDe11rrsxAfwsjFMlSBBw/4ivwp4IeF0pyUl1gUzHPcciH/E+BsHxyflPG0WW50oUE+O8UxJQTFyk3zJTpTGDyYLjjqQs7QgmT0w7qSFnUSlh1RZhnm0rQqHgmGy6o8MNGAoTGoQ36MOu/gO4svQSTBdsOj2aqdGvgAYqbO9txxfCmTHPH3sWDoXsCbd5s2as0TsGDCwGKA/DzPI8IIGhnDYY3GMJ/KAie54LWF18anvmeKPvEJqd0SqQF7t34YqSDaJyJqtofVlqXi7D2QX0HABKVid7QyS+LINBGBUSB/f0unKsW+/GmTMdaGxoFcSGGhNraEenhYQFF3Ea78xA4mbLzY6sIY0ELu4MBCYyzJilRGkBBpQiizt/5ldLj1nrpaoVZk/y+cbnxzbq9rkko9cQrQLh5sPyTBoiPK74AOzrb+zEc507hSybTq+Da8SJhqYzKDKbxDE7HS5YrDr097tRWpYPi8WIna8fw6bNSzEw1IKGkwPieG1WK+aXLoS6Mxy4oRTW8hujhAZBIuOOT6Z/UV99JpJhG4H4d/j6aOPmB0kNfp+ih0LkWIoqgbXXALYEho9nFHjjz8Bov/Q7G7Cvux2wpcgYG+kB+ppjj8vrAtqPA7UJxqfHKREa4rXBaCPz+t3AsksxHaABE9F3dXMdjTNcPek1TJZJ5jHPq7KTKaM03D7W2Ih+RUO0Ib8fH923D/etX48f1Ndj2O8XhAVHxNsqK0UVgRLLc0vw9SWXoX6kTzS2XpZbglY2xU0ArhTZxMmREVyxfTt8waDg4p7r6sK2l1/GwSuvHNPTIl2Q7B/Vn8CJtlegK+lAnq0UJcaVMQ3OmU1DuaBU2U3jgRIVXMv4fo4bGrKsRuG/KQNGQz7VZ9M5eXb7C1i0diVKrZMnuiaMhuOwOh0YUZtgYzM4Br2do8ChfcCWbVn5CmafUrIgLULj8D7gkfuivw8PAT/9JvD1/43IGnH8s4KVma2JCDOSRcyIis+USoQetwP3nd0LgxrQ59thz7Oht2cQnc3t0OSa8MnH7sP/3fRuGOX+WmGn42DTGXyg8Qj6m88iFAhg37EDaBgZxpcXZy87j+OIQTbuicy047jNeLzSOVKSGtIJAD1HERo6AMwb29eEUpPJwErgdleXyK7M0+dkTHImBKsyf/tbptoBlH9keT7lFEhaJanYjDmXyjQcWe57zTuA3nCvLdoJ868C8mvH2pj9hwHabxoDkLcUMBUBpVcAQ0eBgJMDBbAvnlhGsK4W8MTp/+uix097/wMf+IC478pmpFMBBkdINsb7GBcqGLS54YYbzvVh/NMiCPpmyt4QXHO8CKIPmiQkKbOduVeS2KC8xkSDp7TfSWKzGpB2OeVWaMsxm1kkGfj9Yu3ka0iux6+jvnDCHff0ZBUcEwGDifR76Lcw053fr/xu7lOswuA+xudJYMh90Sg9Qt+IgUv6KEyeYIUgs8RlOU9KOvM13JMYjCNRNCVIVjV3LuWUmveF/6HY287sAurWnbfVGtmoqEgE+swca9kiNZQVrlORJU5fwuPywGCKEhETrQ6Ve1xMF6lBcB7Trk3WeJtrGokhxjkmAtqwTNyRq4EZG+H6wTlPW5CfPR6hwYQpVi6wJ++5hFy9kKgqIRvg9WB8KFvrNsc7K/pZkc24VTL/L9O5xs9lJRT3Ivqf9GNof3FN5/6g3Mv4PNdzvpb9Y7hnZRtcLyhzRTuTx0V7cMr2jxmcF1BWg58zUuNzn/tckoNLkGXiGx6TnTvilwLGyZxhf5Dh2lDWysEpeTL2YPl89HivLJmPh9oOhft4SK/fWjzOgscmjc7XgWCYMFEZAPMGIE2d9xgc2g7seTp6cCYrkFsIrL4ayJlcaa8v6I8hNGScGm3JLqnBIIVnIIHWcw/gPykNPU2ldJ2Uf4+7PSroE2ZRERrQ4fejtlaNI4dPYdfrXaitmYeSEl/MIpwOaHDQSCdolHNT4ALKhZ0bdqrsKhr0DALSgL/pphT9JYabgN5DUj+NmPOWT5aaaFFDgIYQM7apM0fnghsNDTplBrcPARHIEe05HE6YbGYMdPVBZTNipE26z116DVbVSZJt9SdbsGbtAvHvEyfOYNECyblixkRL8ULU0OCmpJEleY+PtOGlA5mit8hGRfNP9tB49BeS9Agbq8vXg9e9rBrIKUwu9XTyZUnCI3JR3MChJ4GL35v8u1kOHg9VkucJZRaY8g0kVM4FrEWAg8EnRd8TS3qyfEa1Bfm6MvT7OiLEmkFtRp4++9nPlDuitJQM/rvB4RDVDnuuuAJ3Hz+OTrcbGwoK8DmFo6JEidEqHjLKTCU4NlwfWZv506a1iCBmNvFIW5sgXGSxB/77xAgbNQ9i9QSzivo8x+EJDmP1xsU4tLcehcUD0M02ocgY2ySPBjaNRaXzlg64HtGZYaZkovJzgsYoHZJUWUNP1L+BHeoOHO4ICvmjG8qXYwkl86YbbFZut+D44AhWFij3p+wEBbjOc21PWzKj4Vgs8UqCc2QI6OkCyqMO4po1awSxkSwjOx2Hg4HjZ84eknop1Z+G3myC3mJCUXE+2lyAy+/HwPAQ7nj6z/jBpTeg1iKNf2YD/8fzT6Pn4GGoi4ugK5IcgftaTuMTcxciL0lD+UxBJ4MZunQ8GJzjOZPIyQjX3wS8/FIssXHTrUDrLklViVV6whCL3m8VEgfTuQ++2rMLPWycHcaSnAWYb5t8dhxoT5DA+N73JGkCghJUJDsyPedE6DkRJTQilXfPAmvujG023/U64Ig2coSzE6i6WpL2LMiCLrWmCDCsBXz8Dja0LwO0UfKNQRCuGy+99BJuvDF7/aISgU3JmQGYbB2bwQwyQzJbNHXzXrkSgX0xKKXC8c+fmRK4zEDkg7b8iy++KGx57tN8pCLUuf4z6Mb38bWZ+japQJ+F58NjYb8+zmtlxaJcDStLXjHQRH+IPg+r0kls039gYJB7GjOSGTCVA5qUqGJglefAQNhEAxXjYtZ8oKgM6A33oKTvQGnIOROQzcwWhGxq3NiKT9g6z0A/WJZ2yiY4NpjJf6GgzlqD19p2wp4f9SnqLNUTJjUifeGmCVwnmEHPjH5KqsavGUxwkpVWMiWF+LkkV+mbJFoDeb4Mdo/Xj4hkJ6sWzxdko39SPLhuct+QpZqyBZJBnKOcU1x7E11b2k7pSITRpmP8h+A4pewiVQS4N5B85/Ere7DwJ8cUyW6u/dwvpqIXD4kg7h0ca5T05fEysWAGb25cfvnl+O53vzvh9094JNKo4aDORFvXorWIfhokKWSYNEYxSbrdwzg+0iECVIvt5cjRmfGns4fxSm+zePXynBJ8sO4iGNRa3Hf2JJ7saBYqNjeU1+K2yvQzWs1aHa4prcbTnWfF53I55ztvLA9LRQBYV1ADm86Aw0PUfVdjXX4NKsdrwus5DgQVmdIhL+A6AFgz1M/ralYQGuKDAPcosPlfgCxkzCqvPUGJJyr79Hi60OiwYLalNjsLu9CPMkkNK2XYzQDjK4EzYYKjCdBfDKjkQD7lOOLK4lTJNTJF5pBqHjShubho2dZIkErOFBpvUU8GBmdSBZpouMtGAg17ueHTlVdemdwYHG4GOl5TPOEBbBZgxBGt2KjdHCPNxe/gsXBT4YIuGwtKbFyzHmdO9UGtj77P7/Oj4cU3sGbtfJRWSgGXCrsJeo0PtbVlkcDQ/Ll1opycGxIdkXGzNmRnIV0sWAF0tUV/FzIsuUBJuVQSvlQRhHn+z4DbKQW2WI3A7zHoAE0Q6GkGes8CZ48B138UMMcFtUZ6YzN9eS0d0cBWQtgZvLFKch/ye/mjKLoOxIABQ8pMsWIj8vogUDS9ZfaRsuK6iyUJM0Fs0JK0AAsUJFEKcN7MsiyF1ZsHV2AEOpURVqsXGhFFzB6GvF6MKhvshknlxeEM31qLRcg5ZQruGVuKNuDp1hFYSWbocrA0ZwHUWdbZF1I9Ccq4JyPh4w4OioHGoMHchTXY8dIBlJdXoiguqYnzkevMKyePwp1rRbHBjCW2/HGrCWgIkvRMlRXGvzHIkMiIJ3F6vKMJ/+g7gfLFkiQL0woeaz+IanMB7KyCmgg4V07uAM4elub2nLVA7crx3zdvIYylZfAMhJMFhM6tGVgxfpPx8cC1jw5aqvV+DNhHKFFpvzmWpOB9kq9zvEPJgA8dSer4Jtov+HoGsfjT4/dhqKMbxfNqodZqMNLVh9G+Aejz7HC3dcJUXorOhtP4r/2v4A+brhfv5z01lJfBEqB+fOyxjvr9WSE16KzJWvGUSmHQIlJFlgmWLgO+9T3grw+wkzKwfiNw803A/nuhIr99phOB2pIIqaFWzYZaldgOana2xRAaxJGhE6g2l4s1Y9L46leFHFoEPT3Aj38MZEOKabRPmhdyFWCkH9kIYCmISlQqCQ2BEDDcCBStRdZAYoOPFPr700FqMPCbjtb/DGaQDtTgukGCUBlg1EKN8RMUmC1KgprBIiYEcO+YaDCHma6pxnW8NAj3KAZ36HPLeufZAqV7mDHNvYj2APcmBpHknoH0bRjAkhuny2DFCcns8ZrD0o5hJQpfM67kmtirSGJPwI5jleR7PwO8+Hegpx0oKAG23ggYsrDuTxTFs2OrwennFdWdt1UaBAmNtHqKZQi5p8yFgsX2eWjXtUJt00MNNeba2LNufKnXRKAdx3VjusFqDRKVzOhP1IibSZyvvfaayMRPt+cPX8MYyHjJVnLiVHyvQq4tXDtIjE67RF0KkGzmOputnizyteL1ZVXFVFTp8B6wei8RqcHKBlbUkaBIBN4Hkha8B/QZSZIwsYvxJbnCh34k/8Y9h3uEnPRLcH/IyG+aALgncZzQR+JeS2JjBm9+XHbZZXj3u98t7B05tpoJJhzFIsvLQU+jJV0sss9Ft7tX6J8T3CxW5y1D02gv7j/7RiTc/npfIypM5XilVyIeiMND3fhD8yGRZfy75rBEDoBfnj4qGj+/tTJ9TdzPz1+JUqMZu/u7Ydfp8e6aeZjHYGvMsZaKR9oIxMvBsMFxrMxVUlAbnw9WZPQqgsCRjwoCA11ZITXYQ6PEUIBuTz/UqhAMGqlhqT/kw9Hhk6KSY6E9S71DyjcALS+GfwkBhXK2tXydfBLBoZUN9Zrw31rDP7nBjJ9lqTSqGZjngxlEzAxi1mq2ylPljYCOgNLAJ3lCIiVl+eKwxIRHwT4RjOxuAQIewF4uPRJIFqRyBtgY+dLyNXil90CEsCq3FmPjrbdjSNUtHIVCQznMmnkIqdhQzQmVygwNalBQoEFBQRrZ/f3dwOP3At3tUvBu223AvOgGlxSXXAe4RqVG4XRYlqwGbnw3kCioxu+QAzrCtwlK1RFWk0Ij1w2c2g8sjWPrWVUyIp2rBDYxH4eEpJwcJar2PyqRInozsORKICfJnKe8y9Z3Ai/+SSIZicWbgJrFyDo6m4ETu6XzmbsCqJTGVYy+JSt6Vt4WPu8AYCuRzilNiOx0Q5TEGlWN1dicDJ7p7MT7X3sN3XH6z+yZ8fNVkw9I23VWXJS/DHNKpk4LnXJYXz12DK5AQFSYsOH2ipwcLAs3Mp8INCo9RhxD6OroE79v2bYKDUdbMCdBZfLpYju+9/jfYJ9bC7/DiRUw4MMLV0XWHtkJ4b1kUFk2StMxoGnMJnI6aEiUL5+Hyu5AzG5GYqPX45gYqUEpxxOvAPVvRJ/b/6Tk3M9SyCE110uNlS12YNEqSZ6OQYkvfgPab3xZNLjXllcC73w/YJ9cVSENfjqaycq3k2LTZcD2Z4HhwWgPiC1XSlmhceC+wEq7RI4Fnb5ksld0rBhcYPbsulw96tXRZAlrcT46TrfCl2eBubwUI6ebYV8wB/X7D2B03ZWwhCWwLi4oxn1no3sOx26F0YwyEkJZgnLflTP/EvUJGRfLV0gPJYy5gHsI6oFRqIabEDLqoCpaCVVxnByTAk6/SyTFxCduuPzu7JAara3RZuEESZymeJJhgjDaYgkNAVVsX41IzVgcxrxvHJAcGTgK+EYAfS6QtyhFn7OxYCBzMplUmVRqfP7zn5/y75nBPwdY8a3DKvhDxxHCKFQwQataCFWiqukk4D7LLFZWNzBgxfU7m42xmchAIjyevCCpMhXZsFzDlcGqREhUGcJ9k8c5XgLceH3BBGjn7noO2P281HuodhFw1TsAY4a2BhMOrnsHzhtUrZDkhM/skWx0EhpLrsb5DLky558dHNd1tpqs+BdMRmSV1XSC8k+0IWnnkyRN1Iibawor0BgnIenB5uEktLjOxMuQcVzQxhuvwlsGfRCqV8QnTjEJ9JxVXzIWcWwv4PUAdQuBkugx8Hh4HbJFavDcmYzK+NNUyXSmIgr5N5IdTPJNdK1ZocOAsZAet0XVZHjvufdwzHDt5mdwHHHscG/i+6YLPDbl2GGicrLzmcGbB6WlpYKUY1+Nici1aifjcGRaCkTHclvJJrS7ukWz6mJjgci0/dXpl4UjKruiDCDtHmgfE1Q5PNSFPs/YDfepjqaMSA02BryrdqF4ZApf0Ium0eNw+Idg0Jgwy7wAZq0NUFuAYByxoU5glMmBVzkLeM+zwMGXpfdRYmfRhsRfHJ+dPglsKroIu/uPoNvTGe5hEV04To82ZY/UsFUDddcDjjbpfFXxWvghIKTsXcLjYNAieeAiExabGQWUAOGiTUdkssYaF/Z42RB502aWauos6kTfrQJKqX09ueOqsZQhT29Hr2cQBo0OZcbCJFnrzBLK8MPZ5+IvPwUc4bHtdACP3QO88zNA6TiVHSQC6GRcc4f0e6rrb88DBnukOSHLjYx5vQrwJyjjnb8FGGiVslrF9+qApWk4D9YCYPNd6VegFFQAt3wacAwCBtPUNAhvqQce/3VUXuf4LuDKdwFzlgs5opggNGVwciaWPRQPGjOJssrj4QkE0Owchl6tQY3ZltCp/fmpU/jIvn1S5nUcqbEuPx+zx9OhP08wy2LBq1u34rMHD6LF6cS6ggL8YMUKsX9MFPn6+Th69qio0pBhUVeMufbtLgd+23QUtroajJxqhs5mwfGqPAwU2rG+oCxhg1EG6mkMpAM6LzS8aRQz45Jrpey4DOoSC3LYFNJ4aYFz+cwrQOdh6fdCGzAwCgTChviZ/VFSY8czwEuPRrPV920H3vX/JGIjvxA1X/gKmoaG0j6/VOju7haG+ooVE+gvYbUD//4/wAtPSWti7Vxgw6VJ51Siho0s9aa0IQNivPbMilLOI44DeSwsySnD9WWL8UznCfhCJJo0GLTmwTMwBGNhPizVFXC2dqBgzmwcP3QYq8OE4baScnx+/hL8oP4ofKEQZpmt+NlFGwS5kQ0oJUkIHj+dt7QzPfneF16QiAKuadTkVR7b3KuBk08A3hGoAkGoTLVAUWqpghydbQyhQek0izZLRA57aNTXR4kN7vcTrAYdg5LFQE8D4Aj3UCNqN8XIUUL9/9n7C/hG0+t6HD9isiwz84w9zEy7szvLkA1vqIEmbdM2adOmbdI2+aeQpPk1hbRNmob7DeNm02WG4Z0dHg/PmJltWQz/z3lePdYrWZIlWx7Y+OxH67EseOGBe++591wTYC4GPJTxkOcZBjKRxWD1R8ezgE+pGAPagcluoPqutO0Q2v3ve9/7hPwYM/zmAwwCMciyIDewgGxCq7HBqMm8OjQeTC5h9igrG2mXMRCUjUa33C/UnyP3D1Y7zCZrcb7A9T9r2cdnDgEHnor+3tIMPPNj4KEPY14wMQyM9AM5eUBB9iVXp8D9bPEOYNH22VegXEPQh51VtWWW0eN2YdDnRo3VDkckSSMZbobqD9py11J+ir4AM+ml8gIraU+fPi0IjHgwC55xC+63jG0kIiG5Bkk/gQRuuvJ3JFQob8TKNH6/fF/GiUTZACViv/slYGJUmZcvPgq87SPA0nUJ7dm5gtUoTEbNplRgIiSKadGXZFIv/QoSEfR34mVhU+1V6r6w9FNYrUEigfeQe1C2pemSgeOSSXqSzCcZxnjeAqnxxsett94qOIZrSmpQq+/P/uzPMn6fQWtArS2W7WNT7rgaB5FvF9dGWUhPJQq3ZLtBbDJQs7l5/DW4g8zUDsMTcuPM+GGsceyAybxMkRCYCtLrALMqA4Za/aeeBdrPRDNU2cPi5MvR14wPAc0HgPJFQM+VaICnaSNQkJ3gpazW2FG0DvsHD2MwTq4hPigwJ1A/lOfgWAQwsOBjdnJcbwJtlpuTq8AgC0sruWGThecCKSs5ZrvISq1BtRYkM+hnzKLKWwxMxGV1OhZHg/hzRK7BJh5Zx2CPYgjEQAO0nJ2Z1JCQG29/F3B8nzIXWOmh1rvd/XbgsW8q85u6cmriTwa7OBfKEwQ1LbnAzg8CA1eVgFNhLWDJoJdNJs4GiRqSj+lCVJx4lYzzVAFFkkXMVDt7QDkH9Wtfe0aQGizHpEax7OFCY5NGR6Ly00xBg5aZ6zRAk6HDNYHPnNmPQdErBVifV4LPLNsCk4rM4zGRBBDnEGck8irnxWULzRY01K6FcbUmLw/PZVHH06IrQJV9IxyRuZqjL0P+8uC0jP5uZhVxg7ZaoDUa4BubgK0yhC739Gbw5yYm8KLLhUaHA0vSaXYdyQil88NgCQlgdUPBsnAI9bYitEwOTmW+b86vR7Epw/5Qfc1RQoNgr5w8GzAUV8HoHFMIDXXWeXcbcPIAsEG59hzjvEZzJTU4Ppntk0mF6TTYHcBDEaI2BRIFfRkA4z5EB2Hn9s1YXGvF1QuHsGjJRiBJ76hdxQ3YWVQvkj1e6G/DZedJBPqVfVur18NUmI93FVahuKgIhw8fFk4j96oP1zfhA7WL4QoGREVqNkHyIh503NMKdHFd+Ou/ZlM2ZW/g7+xF9ZnPRNc9Sx6w+l2C1IDWqFTSzYBKSxnqrNVodXWI3zl2NxasgUndk2Iu+NzngI9+lDdW+Z1ZfX/yJ9n5bNq2K98CDF0B/G6l8s4eN0ZFf6lblb4a7j7luhSuBWwZZBdOdgG+kTh50wHl89KU12A2o8xUv++++zAf4GfTiZ1V5c8CFnANwMAPiWkGwxhEYlIBnyNZHp8VnS4YsCHpLfcOKcHAz2bW7xsSl1U2wlQyxFllX8h21cDF14H9v44mF67YAWy+b34locRn3yCSU5dPAS8/ovgbJVXAPe9T+nVGZM7oMzPLn74GbQiSHFQguBbVG7TNvn71LH7Ail0R79Hi8ys3Y2dR4j2AAfM52XEpwDnNa5AN8NolSm6ZL/C41RUHJGAZ6E4Gkh3PPPNMUmk7tcxouuD3MU7C2AvvEeMw17UCaN+TkcRMlczd4z8Elqydmvu8TskkYTPBxMSEGD/zTWgkAuMClGkiaUGJxMoKGy6cP4X8PB0MxsyabMtKHSa6kdBizItyV/TB5tsu43fTl+C1lGT+QhXZbxep8R//8R+zeu+sSA1mdR4/flzJogoz+ELJJDL8RYAm81LcKks+Ljn7pwLqdEZXOQpxcKhfZNrJKo6HKpag0+3Bt2nwqPCAqh/GfGIyMAZ3UB1UCiMYDmDY14dySx1g2w0EehWZAH0JoFUtjs0vAa0no6TMlSOAuSBWR5mLLYmN+38P6LoMOEeA/FKgbuW8GF2U+IonNSrNWVqsPMNA1wtAMBL8yF0MFK8HAq8r/SQIbSWgzZ6uYjDcD1/ovNDM1cAOo3YVtBqrCNRSMoq4ePkifvDMT2ApsGF54zIscTRAp9EJ7Uky0lLOhRu8uiyPTgZLJwk1o89NhA747t27Y0otuakzcEiGnBt7eXkFNFW3AYOnFfmHyRBw+hBwcj9QUAWspf7rPJAS6YLSTsMdyjgrqI42CWem9DRQOivDDbunHfj+vyhjnVPgxH7gvvcCayPSWuX1wOLVwFU6OCoDMBhZpfRGYOO9QFmSKh5mtFYsT+9YXKOAc1DpqZEb1WvPOq6cBp7+sdIw3WoH7v8AUJUgs5dyVj/+N2BiBDDooqROXJNyjiNKBXCMSRkqjtvz588Loi6d5sPJQCOIjnMqUuNL549gKEJoEMdH+/Gzzgt4f230uvtCIaWPRl8fRV1j3s/j/pMsZNrTmSGJmE3902sFrgtWUx4KjNE9iwowvIdqVFCKMAJzaTG8/YPCwa+KI+v+9vRpfFH13rV5eTh6xx0p+35wnWpra0uq680qr3dVb8bZ8W6M+d0oMeVicc4smiGPxUkp8pg4vmW2Qt2aaBbVtIPQAuPR4KtcV2fbzI+EHc+ZQSJmo2VTMiQZaJDLPYTHTWKdJCS/v/XKGXRe/DmqKhzwOzvZgRWw7Yi1Gab1j9LgrtI6+ENB/GRsEpMTTjRV1eB3Vu1ENfSC6CM5RRkoEhvM1mNVUS6D31kGHSc1scF1iGRNWvfm0CGF0CDkXvqb3wBvfjPTCmMr0ShDlSb43evzV2JRTg3cQa+o3LDqZ9kDJhEoL/mrXwHHj7MMhymH9ISz9/k83+IZqmQpo1WRfh+7aQj5UzS1zTyTar5IjdlUgi9gAdcD9BlkQJBrItd5BmRI/MmADPd9Etn8Sf+BJD2DQ+r1kv6HzJRXkxqK/1AuiBL1/sc1V/bZ41p/rTJosw4S7vH9y5ickW27nBXWakKDaN4PVDYCVVlSJ7iR0dcBPPn/ouff3wk8+g3gfZ8S+5mUSSKZzH9zXHO80i9g9eVceyAw6SGV/bZvqHeK0CC8oRA+c+YIfrPjnmkVG/wcBqBrambXvDsV6NOTpCRheTMiEYFCvy6ZRr2UIMpmjwv6M7Rz50t6KWOMDU+X6WQPTyZYRmIZUi42E/JGgus2Ew75fvrmO3cm0BOeBzidzqlqBjYNJ/kgK0T2v/ojlBYWo3FRCJcu/RpLlt0GaNOrLObeRVJKVpFzf+M9JWHPpDDGwuazyTvXHfoxDzzwgBiz3DMp8T4f830BNx5o+3/gAx+I8aHnldRgaRUzSiormIVInWy5WHQC4aWAJrNM+PvKV4ueGn1ehUmtsOThXdUbsKtoHK8OtokMxU35FVifXy4WbPbQeKq3LdLgux4PVcys8ZcNhGd6XmQUJpl0Xeemf0IgQfMobvgmK9CUQHeeTY1dI0owlpmMc0SttQqBcACXnS0IhYOosJRjlSMLjei4qXa/pPSKkGAzS3Mh4LgVCI0BGmbg52fNcA2Fx+ELnYweAvj7UZi0O6CJZOJT8qzN3g/z8lyMj0zg0b2PIU+Xi7V5ywX5sHXrVrGAc0E9ceKECHxR+5xjjgRFMiOHjsxTTz0lAlY0BGVmBgPN1Cbkxk7igwtyTf39QNcZ4JKq3HqEFQy/AbZeJz3YyWHgyM8V/Vep773pYaUCorAMqF0CtF+MVE2wgbcFWJZhX4QDTyuVO2qD66XfREkNYpwEm+rvfC0fd35IkarpuwzodUDt6tmPm65moPnZ6PdUrgSW3zm7zxsbBNrPKwHY+pWxEnHDfUofEhm0Y2bUo98EPvQZwBa3SDe/pjhcREglvyVR1SiqgThG4x1XZsHwQX1+VhFlEvDlGJUGMMdwKlIkEAqh1TUe+34ar5HA8xWnE1+7fAkjfh/qLSa0BYMIqaoGVjkc+Pr69dg+x6oSBqa5yWVD6kESlXwkqgDgvvPvFy/ise5u5BoM+OulS+d8/AxEJMouY5CDRqOs1qiw2PD7DSvxzatnFO1+owHugBbvPXwATXYHvrJ2k9gH1YQGcWJ0FP+/M2fw+RSGOcfSTJmkJDZWOuaoXSqI0bh6S4439rtho/C6SKPw/GLFsaDUnQTXCpXmLcGMQY7zTJ0OOtMMwjPQfy0dVVbAMCDAkmnuKTxuSRrWlo7gtdf6BKmhYwULKzw9zYA1dfM9zu8HKhaLB8k9ztnCvMKYMU1ZFAaF+XOmxvKzhSRnBFrOofWxX2P7mpXAyIByP1Ohuzvx8yRCZ+FQxl+fPKMD81L/OTEBHDmizEdKl2WT0LhWsJCcpD2kcvA1JI8yW9foOH/3u9/FfIHz9SMf+ci8ff4CFjAfYPCXWcnccxjgYpBJyhBy/Zc2FrNoGbTha/l3Bopo08RX9nGdpe9BLXvaPnytXNNlgJK/M8ueAQA29Z6vNX/esHaXUkGgJjY23JZ9UmMsIm+rBr+D/SpvRFKDvkPrmWhyY9WSuV0Tynqp7TEGeceGlPMvrhQVQuytoFYdYHCUdgwDthxjmWRoS99C/pS9GpLhwviokMek3S3hDQXR7poQSa5q0M6RzeyzCdpprKblXJ1N8kw8JFF5LWW9Eh03/Ru53iSqps12FQXJsdmQA/OGshrgSnOs/DsrlFTJmRxPHFfpyDDHg0Q240Xx/VbnG6yyYSN4zjH6dLt27VLuf2gYVssofL488Tez2QiELgOaEkAzc5CY50BfNJ7YoNwif3JcM7aVDTngROAYZWWilJqSElpv+MrdM2e4aLDpCbBu3fxWEN7AEPxCZaXgGvbs2TP/pAYlDrj5Aa0R50htKFwCwlUZ3Qyb3oTfrd+FIZ9TbLmFxhwxiZrsheKhBp9/Z3WjeMwFwz6PCBi1OMdRbbXj9xtWoMScOtMlR58Lk9YCb8gzdc5sdl5gTCOLNVEjRgaNDZNKk3BpZKy9PXEWfP9F4MJzUba5aj3QENtAmovXqL8d7uAo9Boj8o31MGiT61vzWi7OqRcPb3AC/d6zaJvcC6PWhhLzChiTZI3OCDaCD8SXbmoA9wDA9TTcErl8RkC3HtDMXZc5GB6YFkALw4MwOKaUYHOPux+j/nExNu0FueJBNBYvQZGxAEdG2nBipFNkOW9urMNoqxLwZGUSWfxEYDCSC3B81pWEzCYnOcKgJTeg9aK3iNq4DCvEBrMlM2j2nA5IVmlY75RqPp59XpG8kPA6gfMvAeseUubxmz8CHHoG6GlTdGi33wvkZHjPKKcT71BEKhCmQMN1qCu24Tf7Vuz7ceRShYGei4B3ElhCndoMwfedfS52vSLBVFQHlGbo1PS0AE9/N0LUADj2PPDgHwJ5kYBe55U4+SU2OvcpTcAXqWS3CI876tCx3wCJI52UYckBbnkLjr1+bKraKBEY8GUAlYFGOsiyfFz2e2EgO1Emn7oZF8doMrS6otn04VAIGtlADmHsPduMt+19FZ5QUNFf1WhQVFmO/sjrP1hXh29t2DCnXhQSDBZkg9Ag6KTRcEtmmH3s2DH8z9Vos+Unenqw97bbsGMOxAazwBL1HOAx0KlkeS+NOd6Tt1Quxtq8Yvz74QN4rHMY5oIA9Jowzo+P4V2HXsUH65Yk/I59Q0oT8ngw6EGjlA7MfGbZTKFiLTB4SVnXxHIXBuq2A5VxMhpsCPqWDwOPfFvJnCLW7ZpGnDLAw8BOplr+lFNghtE10yTnOXS3QBsMYAnn1JLp90kT5v4YhtMp18AwEIqT5VJX1rgGMexzIt9oQ521WIwPkuR0LHhdCBImDJZxz6F9JhtAzgdI2PNeLPUOA8/8FNrOfsDbDxzbC7z/kwoZngis7vjFL5Loj8+PgzQnHDgANDcrlRk/+xkwOBjZqxzAN75xYx5zKhhzgYpbgN79StUG+3RU7AIMmdl6HFd//Md/PKsAwEzgZ3Id/Na3vpXVz13AAq4VGGBKJucim54y2YmENwlC+hiJtMJJgtDHYIXuTHsebT4mTzG5Re4JNwVYvfz2PwaOv6LYyItWAatnYd/PhJwE+z9tEvo0NxroO7zwQ6DjfNQ3WL4d2PrA7D9T+JYJUjN1epHFzwzzZDLKHE+0NehH8HWnTp0Sfi//zYQoPpL5F/LnTGOyyGRGSCZasaIj4i9Y/SHhO8tj49/5XfNRqU0CkSRONggNZs4zUH4tqoIlkslc8dqxeoDJAgxSx98LVjLPFcymZ7CbflVM78cbATvuVnzy1gvK7xYb8Lbfm/YyJj7RbiY5nCnSaaCebXBPSBwXmERVVTE6OwfhdnvR1BTZW6iskwapQdCu4zxT94slscCYGO8vrxP3rfmqxlErolwvOa9riv/5H+Db7KcawTveAfzVX/3WEhtbtmwRXMM1IzUotwMwszJ+EQ1OdcXIBAwkZ6zZnQLHRobwd83H0ONxoSnHgS+s2oC6SIa0JxjAJ068KhpSsQH51clxnB4bxHc27UEOZW6SHqMOK3I348pkMyYD4zBpzai3LYdZl4ZD2LQNOKHKzieW7gC2lANnDyoB3spFQIOqD4e6QkNNaBCdx4C8SqAgGojs9TRjPCAlPzQYD/SizrodejquKRAIedHpPoIQlICSO+QXv9dad0CnmcUQEd8X3xGFxhMDwC3qEwOCxwDdrVloosb3J9rQo5/rSyK9wOcPDF3F071RWbM21zDeVrsW58+cF4t2ouwGLugMsjKgnAxcjGncMJBJw4LZW8vrTTDHXx7RSD17DL8vNIku9wn4QiR1dCgxLUGeMUkFlZO9YOKyqfmcBEt/d2XesCcGdUuB9kvR33m/a+OIhI13A91XANk3gP0rikqAsZ7Yi3V+/+xIDcpOxZeg8jh4rqUZftb+R5WMXXlcPi/w2lPAXe9XfjcmIRPZWyORU/cayZYIAiHlodMieMd7cPx0szDgU23qHJ8M9pPQ4LhkRrrUUZ3JGBj3D6LPcxUXnb3INRSj3LxoqrpJYsTnhX9gEF53tPoq4HKjeLEJjxjC8JeWTBnUfCdDtaduuwOlZjNK0mkcnAboOGXcnDIcBLwMrI8CWgtgahQSP+wlkcrJ8odCMYSG+CgAf3j0KE7dfXdWnA7eKwY/aCiS7KBRTOeSWfbc0Okw+oJhPDM+irCq0aBRG8ag1yuEGROhRpVBTieUDiGvHStc5qL3nTFYTbjmYaDntNIA2T0B9FwAwlqgck1cY+jVwMe+AAx0K824k+go8/iZccYqhJSO55XLwL/8E9DRjpDZCu0n/gK4Fs4lydtffA0YjFQjWHOUgE38+Wht0Ov06BsYRVkJgyoaQBvNPPQGvTgxdgZD3mEE2AfE5cNkJNlvlaMaG/Ma8Zuey7h89TK2WYH7yymjqJ1yPuiMSEIzWeYYx+J4wCckPu1Jem6I1/g7MBnsF/tInrFW9IXhWtN85gzKD/wK1nAQoWAIoWAQWo7JQ88B9/9O4uvzy18qmUjxYH+2FKTqdQFJCwbWef3iMy1ZtfHFLwLzWK0wb8ipBhY9DIR8SnVxuk4TE1WGDgHeQSy1s5m9XlROrWNGWRZBmR6u8wwuLWABb1Rwf2cSChOdkhEWTFRh4kWqTGoGmaRUFddlZjfee++9uKlAGziRNGs2wV54a28DTrwUfa56GVC7Ajcc2s8phAYhbUb222vcABTOMlt56Qbg6IuKr0I/iOt+dSP6fGGcO39yRrk/JvYxc5tBelYb025NpwcBJbrbJi+iM9CDvnETaqyNyNFPH++7HcX4/tA4WtxOaMJhYbs8UFqN8OhYTL+5+QR9pmzZx6xuudZ7mFpiirbbyZMnRQITJYroB9JupgoFYxLqY+P6Mdt+Ekwaoi9De5OJcddKeikGTMS8/KrS15aV4ItuASyqMUb79r1/AvR2KOOf/UCpOBEHxmk4rlkFfXNLHVlRXOxAS0svcnOtwlYT0GR2fzkeOC6kBBD3F8awuAaQaOf4mq/G77KXB+8DE7ZYQfWGbRJ+8WIsoUEw+YsS0ZtSV++/UbFlyxbRuztTZByx5kJJUuNTn/oUC2DJR6v+SufInoUA9dzQ4ZrER17fKwJTDGGeHh/Bh47sxeM774RNb8DJ0UF0RRqxEiQ22Pz28FAf9pSmls4y6SxYnjuLSVy/TqnAaD+tXB82Cq+IbCpbZtAlpuRUfDCW19o5MEVqBEIeFaEh+334MObvRKEpsbHoD/lxeuwsBrwD0MCPErMWNr0SbQ+GvXAHR5CjL56lNvR6YOBolNyiFrSDwZtolYsCXyQEOrd+EnpNOQJhVg5FMn1FcLUAGtXnFpnyY6gWaSv2uEdwYJDVE7E4NNyKj2zbhieffFIEidXBYZIVdL6ZFZUKDChyI6D2ITd8BlPNTXXA0GWlykkeRP3mrDXFC4dD6HQdhT+saJ6HEUSf9ywMWits+gSBXFs+MOaJLc3kc4nQ2wk88WNgeAAoqQDe9DtAfhqZ69vvAkYHgVMHld/La4AHPxD7Gns+8LZPAG3nlGxnloQff3I6WcW/qRuIzwS+tu+C0kh82t9CscZPuphk5YKaCAopZeISi1YABaWKHIt8XUW98ogH5b1ufTPw6v8pn8MgWnkdsO1e8XPy6r6UxJkEM/rY60hmCKaT2eAMjODKJOepArd3QpCcNbbYahJ3Szss9hxYSoqmztrV2YMHNmzBf16aHqTka4rNpqwRGgQN54wygHjfXZSLUXrhIKhReh7Zdgujfko+JwEYSE6EdndcdVGGoIHI82Cmpqym4RqhrqqhschMZa45/3b1LLRmkzgXtR6xVhPGhvw8bM7Px2sj0XFn0+nwX5EgI0vAmVG3adOmqR4s1xzmXMDsANqjYwxXKAMRBKrjJOyYdGBbkpZ+ObMGk86J8THg058EXJMi47HaOY7zn/00Cn/6K6B0fhpLTmHf48AQ+2pF4HYBT/8IeN9fxL7OvAaLF/Whs6sXre39WLmiCRqzMud4nw8NH8WYf1zpJxYOo8KiRftkECOjk3iu9TC+7T6KoM0Cz+goLrc149CpE/hw47qY7CapEZxonDsDfvx982t4fUSpp2Izzr9dthFmEskqjPiuYMQfXTdd7gFUWDbBosvHls2b8cuvfwHFNgvWVhSjuW8IS0vyMdHZjqS1VF1d0ebgalwPR1iCx8KKjN5egMGTlStZ764QGkQi6Qi+p60NNy24jugyIIi5Lw0wk5oShGFow15sWl0vfIFskxr8TK5Z17Wx6AKyggFvH1qcl4TMbYGxCI32pbNLlHqDggE0ZjYzqSEesko81TygDUGfgns9E19oAzKAuYAkWH8nUL4IGO5VKjRqKJd9A64zwr9I8vxsSQ17HvDwJ4BDTwPOMaC8Fth6N9yd3SKoPVN1Av9OcoEknMwOnykITtvlwsRxjPkjSXLBCTSPDWN13nZYdDkxsqxMwPnxW96Lp/s6MOj1oMDpwr2Ny6+p7Zqtht5MdrxmyUMqsDJE+g5MZGJAmP4Fg/VcY5hYyQAx/Q2Sn/I+UiqKfUToe2RyrfhdTA6T0t3XBawEP/FLwMNK5zDgHgXG+4BN71N6bUpwnnPMzwDazVIu/KaFpgAabQ1CoXMoKLCjt3cYZeWbAE3qqgqOEe47MgmO+wr3FJJl3KdIZsgkKcYXUjWgnytInPL7v//97+Pd7363qCbicXGd4Dh/Q1VudHQkfr69/bea1Pjyl7+ccR/NjK1LMphkoCmRAXDBoIyCDKYzgHwNZC1S4OcdLfjq5bOiyZQENRr7vR6cHhvB1sIS+KcRBAoCSZ7PGqpXKo9MkbCCJRzzPLMhpoP6lIkNXA6U14aPYsQ3MhWk7HCFUGfTwhyRvpnTFpW/XOmA6+oFdEalUbi2U+kZMA1zX5w0GjNM2s3whxiA8UCryYNe0xAzGXINdmwpWIfDwycirefDcAWBk2NX4Q1NP9tgOISJkA8Ty0vxqV98A03Vdbi7bjWqysoFa0wDgAFKbgQ0YNTyJsxe4OLL4KXMCGfASTRXzS0Btr4XaH0dCPiA4nqgOn1jYib4Qi74w/HBVw0mA4OJSY3ldyg9NdgsnDBYgKWqZqScS4M9wOQ48NNvAF4SICHANQF898vAx/4+cQVCPNH1wO8Ad74doNPFDOZECxXlppaoSMOKJmCgVXUaGqBscWYleS2HgNbDkWxoTewYpPRU+SyygOhY9LVHyUYeT1Fl9O8GE/DwnwJHnld0aynHsul25TokwvpbgdXblOZl7M0RcWT5ambXHDx4UOihEkLiKRRKmIFNQySRvFEyDHrjpNCYYeTvQlV4ueirwPHKwOjGZSvxV65K/MclkmUh8Y63lS9Gk70At5dM4v+1RYkN6uIuzslBiWpMzLbBsxrUWGQwO+3sp5AT4SAJ26mjAEJeYHI/vMMtCDmd0NpWAZrp649Fp4NZq4UnLvhaMQuSxh/yYiIwBA00MFmMYj3gukFjkYRnvE4o/8bn9h0/hoOscDBZoMtzIDgyCn2BssbUWm3YWFCEA3v2BeksSQABAABJREFU4O+am7F3YAA1Nhv+bdUqtF+4IMYHjcApjdXria7TCZ47NZ3UyCAQRKmupKTGWepQR6WczBoNvFx3TxwH7p7nDNYByuepxgz/rSY5JHR2OCoewNmWZ7BxwxqcaxnF8hVKgMAddGPUHw1q8P6NDY9hvGcEYXsugsUlwGQIvqERBP1+THR045TZBFNJIbrOXpiaaxxn3KcS4T8vnsQxQbgqODDYg29dbcbHG6P7EMdQn6dFEHwmnTKviTF/uyA1jGYz7lhSD6fbjeIcCyZ9fpzqGYI1qMfZffuUHlLxjiFlHONJAgYtSmbRiD4b4PxmiffLL0ef+9jHgBRSfwJce2+0ypL5BPu/qcYksWVNAw4fOoiPfvSjWf0qBlvmSzZtAdcOI74hnBk7PvV7j6cT/pAPq/Li5Ad/y0E/ggFFNkJlIIcBZvoO3L9JSLNag9XitBekzcdgD+UkmSzFQLPc4xnATNSzawEqlDcojxsZiYgL3uP8Oe6T7Hd1b2wVJZPtOI44pmTPLybXkEyLtx0ZFM9E/tUbckcJDVUS6YC3R1RsSF+Z3ycTlt5cqfzkHGCANZ3gcjb8C3l+2ZDU4bHw+K81+L2MOTAmwfWBsrbx94s+HdcU2tFCDnv9euEzkiBNBwxuMwbIBxOM+DnXFaNdgGc8TurZBQy3AqWzq/DhNVPPh5nA8ZKpLO78J60sR1mlD2FNGB6fDqMTRUh1OqzCYvyAxAXHA9cEkhn0uUmUU7asu7s7hoBnzIukerYkoePBvY33gZ/PfVISdPRruD6RjMsk3nHDorY2s+d/C7B+/XqRfErOoTaD65AxqcHSNbLAUfZ8ORDm5kRH1XRd9b9+3dWGvz8bNaLjoY9kZKx2FMFhMGLC7xcbLOUXzDod1s/VYJgvMJOcAaAOWfkQBhyVQHE0qMP+F3qNGQE2HI3WIiQOYnNjC3kw7FNllkcw7g/DrNNCrzGJwMWcYKtQHhJBU2xAmaeiXQRospPRoNXYYNKlbk5VbS3HwaFzcAbcMfn/dr0WPl8w5rll9lJ87fI+TAZ8MKysxdmRMQy0HMG602Y01NVPZa5wk2cgkhugZLS50dMJUWeZUIN8KiDrKAXW3I/5gDZJJhzl0xIipwjY8UFgiJmnDM7XRTMcWNH08/8G+joAfwDwBWIDQmwUTVmpxjSbgrHkMxMFocWbAI8TuHRY+b6SemDV7elXarCqo/W1yC9hhdQQTcKqgOq1QHHD7DK1bnkH8OS3og2+C8qBzfdN7xWwZgew70lFy5OZ4zvvS1j2KkDpu5zpc4HjjKQZpT64gdNYls/R8GC1kJRl4hjMzMiI74kUeTYURDAUFMavbBS2w2oVPR56PS4UGi3Q+/wiuPzWhgZcnZzEF841iwDoUrsdP9m6HX29vcKwpvHBACkdDxpMszGA6NwwMyPdJmzhsB9BnEfYpoxjrS8Arc+vEBwhJ/RaD7TBLmDSCdh2JRxL/7Z2Lf7o2LGp3/mK/8gwK9kVHMdl55EpcrnbOYB6Y13KihOW2PJ8LUWF8B4/AN2SJTCUlsBz/pIgNXL0ejyy/TYYI8TXP65cKa4ts93ORDKnOR5uGGMvUbLAHBMIOB6TOrJJpJRwLTLn2HywrzOW7GQFWiJoTbDm1sIdyEd5Ra7oh0GHNNE5jQ2OonJxDQZ9YQy7QtDptbCWskKuCAG3BzqzCQGtRsxV6kLLMm066XRI4rWyWaFB20eCR3skUrVB8NqeGDuJHo8ybjnSqixaWPW87lFSgg0Im3v60Tvhgk6rEZ+5qqoc1WvXCoOU1YwxDSPf/Gbg4EHglVeU35lt9YUv8IOSX1NKxwXPApxDtGe0y7KXXfvCC7GEBvHVrwI//rFyTHTgEmVvMujxmc/gmoPHMnYBmGhRroGjCbBfA/3qBNd7/cp6PPWNF7P+VfQxHnxwjnKXC7ju6PN0CyJfSSBSMOjrRyAUgD5Rj8HfYtAvYLCRthudeD5YrUR7jms37ScpRcW+WMyQZ7YqexupwQDQtZLqWcA8oqweWLcHOP6C8jttgh1vBezzEzxkcPq5554TQXDaC3zQDuHY40PdUzKTagb13FcjGOmdxoApfYJEwWD6zgy0J+vbxDnBIKesiCAxwmMlSZNps2a+l3OO76VPM1dSg581X02UU4GBdQaa+UhGNtAvoJ3IIDbXEV5DrhmyP0oyv4HBbL6H15v+C+OAN0Q1ZTJfYg5VNzw/XhfK3aYDVskx7nPDkBoR1NatFgmR7NtE4jwZSUPfkYl0at+cBAfHBee7JOh4nvTpJTjGqQ4xX6QGv5t9pShDRJ+W45PHST+J45VSxHfeeSduenCtYHIQ+2pIvPvdbPKC31ZYrVaxL1HibF5JDcpZTGsyKjJdU2fbT/hHMOzrF9m/xabK9PpQZIhHOlUZ3Soww3CRzY7VecrEyzUY8a9rduGfzx9Fu2sCFRYbPtm0HsXJgo3XEgzcnnkFuHJMMWQaNwLLdwL12wFHFeDsB0w5CqGhyvqmBn6VZQO63MciWfoa0UfBpi+atki4gl64gu6EQSEt9LDpClFsWpY0OD4FZlwe36tkojKYs/6W5AGl0BAQbI59LmwDNPOso5oAiqMVi3KLCY05VTg+qjQK31JQB7POiIlAlJW25OeC3R5WbdgOy6RfOBnMpqJzwWwTmT3PjSBRwzUGKumozDfYHN6uL8dEgL0oCLYK18FhSKFHaLQC5ZHGhjQ4z7+mlDq3XAT61bJmiTCPRCbHJ0mM5bcAp54GupqBV74N5JUDm94GmGaQLQtS3kxNpJHU4Hj0AyVzMDpzC4C3/5minU/DjlUa8VUYLifww3+NNknvbgF6WoH3fCJ5xUYScJzRUJWOBo18jjcu/GoDntnZJD9olCdr+qdGnrEcI/7YTPLRDi+uGK+KzyVhol4jbHojFknixWgShhwdgr9YshSfaGzCZDAIR8QBudTdM036huQfDW8eG8+JBgsDsPwOlmzToVAbyjSw+fk0YDh/aJRxD6Ixlar0PRg+jbCGmcXKsYdMBmjY4DygBGS1JLc4LhgwDY0DuunG6B8uWoQaqxU/bW8XTc4/Ul+fcZPwDlfzFKHB+xUI+9HlPo+GnNTkCB295bw/ZWUYu3gRxpXLEdACJi3wz6s3otBkUmSKDh2KaeIme0082duLc+PjomLmoYqKlFlsnmAQ37h6ARcmxlBtseGji5Yg35hh75JUKFsGXN0X9xzPbvbgXJAa4tOwag2tedFPQ+ynzDjML0B40+b5Wak4tyfGlD46Ox8AOi4rVW0E5ZzuelfStzLgT4Od940VUbyHT/3mKXRr+2AsM6O0pgwBfwB6owHjAWUd21lUg190RqWPjBYL8owmVFhyYNTqxJyUoANAsoRVTmrYDQaM+KN7G69LrqqfWJurHT2e6LpAt7HLE8JiGyUqo87y2aAReoMenkAAmypLhcLb4+0jcBw+LLSVuS/GgGvSl78MnDlDAWhF7qk0RUOj4DDgPaj6fQIwjAP6rdlJoOnsTNwzY2wM+NKXAMqsysw0Nu7j/s37TWfjejiwo83A0Ino755ItU1OLTDZBQS9gLkIMGW5+S3tdUsF4I70ioEGq5fXofnsuYSk2WzBABZ1lKf5GAu4KZEdMZffDtDWYdCID9o3zIplRiqDiNzrmRTF/YFzLZGTz9fcEEHGBWQHJDUWrVVkbR3FgG3+9hvah5I8oJ0qxxyfp42uBgO3TJyItykSway1wqqzwxV0Tq0GAb8fPS2D8NkviMS/VIFgWamUqDqbdg5JCLUfwL2IgXfaPSQICVZ6yORC+h8MjMpeY/xJn4lBXRKEUnmB8y6VPO1M4HXkZ19r0CZOh0zhfaVvx2tF0E7jPacPpiY1+DzvNdcVnhOvCeMdNxTYW9ZgBfzuaD9fnQEomL18lJTrShf0t0ka3GjgfeM8pW/Bf3NOsyJQEuB33XWX+Dd98mQKCBwr6jmmjjdwL8qWZFsicDzSHiSpyuNnBS8blHNvJKnG6z7bXjA3HD7yEUWGlz0HmZSmTgb7LcXq1atFvOdNb3rT/JIamQZmB709uOQ8ORVg6na3YpVjG6z6+V8caeI9VFGDTzatnMpsJeptufj6BpXEzo0CEhp8SJx8UcmSI7HBRTrFQm3S5aDetks0/GYQO77Zrz8UxHN9R9HhVhxhi1aPXENwSlaCwf7ljk1wGBzpseOPfBO4ek7JfufCdvEk8J4/VQI58QgxQBLfHXsS0FCTL4sBtDSwNLcWrw2fi3lueW4dVjrqcV9FVB5s78CVRO3OEQgHhXPBBxd0bn7c6MmI33///Skd/GslBVNuXgmz3w5XYBR6rREFxnpBdswIEhpPfRvob1fGHe+zVQ9M+gC9LrZSg/MpNx+onbnfw5xx5bBCaEiM9QLHHwe2Ppz6fZTSYsNid1wPDJKD/ZeAkjkcO4OAZSkkSA4+rRAbEpwjve1ATzsQKbFOCVbBtF0QQTeLViOMbo6fVFqS/DsNT2766ZRt5xlKUGNZgR7PFbFu5OqLYTEZsKQpPYknBpXpAJBsYPaEJDRIWCRq6k0Cg0YI5w3Ph5UmNAY5Z+gE0LBSzxG+lk4W5xe/g+c1k840e8qEMRz/JEIGPbSBIPoHRlFarA76JTfK7i8vF4/ZwhuKlnQP9AyhuLwA3lC0n1MqVBYX4zMr1uNL5mYMnm6GNhjE71XVY1uu0qyttbUVGzZsiMm+4XVldQmbnGsjwei7S0uxkprJTP6oqcF6lUwepRk//Po+vD48KK4CCd0X+rvx6I49yGEPqGygah0QCgLdp5RrTUKjbm7yMhzbzFhNSGowc+/LXwH+9ztAy9WIgbgOYast+6TGYD/w1S8BPZGeTHc+APzOXwFXzyiBcvbLIeGfBBzHnDecLwTHPufIxopNON1/BhqvFkX2EuRb8lBT3og8gw1FJjsKjfn4busp+EIhlJqt+IslmwWhMf1SGBPKCny4fjk+1/warQTxO+/9h+oipLaQmBqflmUdDAM5+jrY9dFgxpaP/y1e+eKnsMVhwOWhUazZugNvfejDmPQFBOHGIAMzu2SjwchJ01pN7/r6mxM8N4xOfzfGA1pUmB3IIyE/WzQ0TCc0eHzMgKaMy5NPKk4GycwboUnh6PkEz10ARi4AbllpowHKdwK5WazgEPKKO4CxMwqRojOjYdMd0Ov/Wqz/dDCzAX4W94IG3pcF3NQoM1eixxObEFNsKl2o0kgzqMY5wAfXUAacmTnMDPBUuvfXXWpyAdlFbqHymGdw3DCRhmQA199U9jX9XhIN6X7ustyNuOI8jYnAKAwaI/STdjSuXJZWJbGs8qa9S1k1NcFCuyKeWOGxM1jPv1GelN/POUMfnZ/Dc1Q3G6a9zGQpmbwl+wZMS8bIEKkqHuYb6a4B0lfktSUBJNcYNn4myUNfi74m+2Xc0NCbgLVvAy68ALiGFWWTptsB49z6tGYia0bf9UZde0n8UdKTc1vIn4PqqptFvIp+B+8x/YRkFVHXE4whkMjg9WXlNyEbk5PsIMFCCbXr0px+PsAkr4VKyxhSg317MsGsSI0Pf/jDGb2ndVIGkBUHme27O9yXscSeXmlXPFyBAC5MjMCg1WKpPV9k0RJvq6rDsdGofiOXmA/WNeGTS2bRx+J64eqJxM+R1EgDXFh1SapmXhs+j84IoUF4QkGYQ2YUGLUwaY1YlrskltAYagecg0pQmH0f1It251Xg6lnl31JSqrsVuHwGWJLoviZb8K/9RrAyt15864UJxXBZYq/Bitw6TAa8GPN7UGC0wqwzoMlegqd6zyHEJr0R0idHb0QVr4c8eo1GGC9kkmkcpcrOuBYamyznpUHHskkSGQWZKK4wc/e1JxVCQ13WaTECHr+iMGdl02KmjFuB0irg/neLjP15x2BcU1YSBByfM8HnUkg2TdxY41gebpsbqZHyez3AyQPJG5vNBM6lX3wN8LPSBMifDOC4z4uCmrppGXk0QGWpMccfjff45no0WvlcoqZ7haYq8ZDZG5O2mYPuroAfL/R3iYbD6yrLYNcaRJCZVUo0NJjdk6zngTqrgmRFKrkE9Ws5z/jZfA8D+snfx/scR0cKjVFeo06YTUZcvNKFdasXQWewA9q5lZqrwXujvj8mrU1IUPl9PkxOuFBWWQKjVo/JwBCsOlbepM6sfGDtBuQ43fDULMF4Ty+avBDXmYQQS4rjHafXhocFoUHIouxn+vrwfH+/uCJfuXQJT+3ahTsi2fEnRofw2rBiLEqSo801ief6uvGWyizpefLa125SHlkCz5sGOcd6Qgfcngt8/M+mfg0dOjQ/Waxf/2egT2avA3jucaC0Ath1R9ofITMGeXyy8V5JUTFqR6sF7z/cMSwSMhbnRLXS95TW4raSGniCAVhV5BOvh3oPopOfqJnfLcWV+Pe1u/BCX4cgsu4uq8FyVp9FYNGZp5F93P+KzYtjnDdNbj42/s2X8dxvHsHGB9cDK1aL+20zmET1CUuHn332WTFeGZzbtm1bZs5fOPFa+X/dx9HjCYuEjIerN2JZ7ix15G+5BXjLW4Bf/1r5nWPk059WCA2CWaRpShBcEyTKimO/C596zSZ5fgDIqQayKfND+cq8aECVFBplLOgTZIvU4Gexemkh4/zmR56xAKsc69E6eRn+kB+FxmIssqfZD+sNCPboG/ZNCrnjAqMt7XVQSgBRko17fzIw2SNbFVPJwOAwg0j0d667lv4CsgbRt2tsTFQDJfIvmHghJWT5WvoJavBv/f39Cfu5GLUmQWxINHc3ZxTwpwQJbRpWoDPpScpNJZOY4vPqRJ9U0iU8F/U45rg+cOCAOA/667NtUs5jpC+etO/bHJDM5iV5I/t3ZnKcsqqE6wevB4OIJHZIqM6XrFDWYc0H1r09qx/JihcGzkl4zQT6o6kkhW8EyJ6UBOcxKwJ53Ex6kpJz6YzX+WwOnoyU4ZhUy14RtDm5DlH+6vHHHxdznmOW57WANw6p8b3vfS+j92RkAXHwk6GP0UiOIAwG39iUkrILDHI2wh/SYNzfCX/YN+3VbJ46G3S4JvCJE/swyKAhyxNz8vBva3bAbjDizZW18IdC+H4bjegQHqioxkcbbjJ90USBlyzpR3e7h2LCFAzVTwYDeGepUoIWg/OvAC2vqVL/lyk9IKQhrs5AV4NSO4mgrQRCcdkPmrKs9dNIB6FwSDQqNGiNWOloEA+Jg0MteKLnjLg+7L3yzqr1WOEoxwfqNuNXnScx7veizGzHu2rWw5SoEoVZq1u2YN++feKnNLzUG0HCss2xTmCAeuEhoGARUDh7I4gbMJ0aBoJpiFKi5ZlnnsG999477XimwesCnv8uoNJVnzYuSV7ptMDDfwxUXWPZMKNleqCazbhnwrmnAPf03jECbGA/XyC5yuz0eFCerbRGaby+/2nAOQZULwa23w2oM+Of+QnrtKd+rTFr0fXaC2jc86VpBi4fzLiRDofQwz9xQgSIaLDLHhvMQkploDGLg1I1MzXinvD78PtHX0anmw66ckf+ZtkGGPv6RGYFS0ZpKM9H5godFM6lVKXB/F5tuAYhqIkwDbTaBsBWjVztKTQ26DA0ZkBJ7XaxP436uhAKB5GjL4JNPztDnteXjh3Pn9eZ87DGuhKXna/h0qWrWLKiDhYdyYZxdHuOwqzNQ6VlQ/JeN5E+PFs2bBDXc69zr+iXkQptSZr9iUB3ays0RiM+deoUjkZ0SCcDqsqrCDSR54e8LpwZHxCB47V5ZYLQ9QR98IUCyNGbhZTkjVCaOpPu7ayy5ro6gccfA65cBmpqgfvuBxri1jy3C+iMI1Y55i+cyYjUkE35aLwzaMW1WuoeE5zPLLmmNII6y5BkhCQ0+Bo6tTxXdZY7xw2dlURYm1ckHolQb6tDl7sbrqBrqmJjpWM5dAnGak6uA7fc/5CoxhifmJjSo+Y85L1hVQ1l5XhczArMSGtaVwAEotneXGu4qg54w1Mk3C86juHTy+4SZGHG4P36m79Ren309SnatrLCjVltvb3UOrv2UlPeMcA7Augp+1QctbvYP2MsrlpDx4xEV+zeyL4nARdgzB5hK+AeBnqPAH4nYC3GqhXLxRx817uSS6zNhtRYwBsDRaYS8fhtx7jfgx+0HUK/VwnM1NsK8e7qzUl9iUSgPc8AL4M4bKIZD2ZZMwN3vkD//3BEVpCBX9qW3K+YXCCzZxdw84KJB7Q74vu0qIOd0sdgJYMk2Fg9QQKAAfV4GyUe3P8z0UdXExW0ZWQlH22N+ZCc4XcwGYOyTDyX2ZIStOXSrWZJF/TzWNVO34fXQNqHBKsqeD0yJSGEJG6kCTTvKQPHs01QoO/Ic2b1+GzJIMZo2O+USbYmSkhdR3A883qnI6/JpunXo4dKupBJU5y7sl+Oeh5ybWfsKtV45zpPH+N6EF3c75g0zOPkHJUVJSQybrvtNrEf0R9k3GOB1LiJwTjowElgrFUkZK2qLRRrSiYEc0ZeIFk+BmbjB01Y5ISyQbfUTaS8yOvo81BDPAyDBvDHJZjlGmbXhPqL545i2BcNaF12juGbLWfxySYlsPGO6nrxuPbZczwm7dyD9I2bgOPPKlI/+kjQKNcxpQ0+F1h0RqH2pL4VZq0MioQQDF9GGAPAhAeGlmiDXIGec0DFcqAkEjApr1Ey4CMNvwToeCeT1dE6AP1mIEim2AdoigDdtdsE+j3UmD+NEILQa/RYkbsWBSYloNPhGsHjPWemXhsIh/CzzqP4pHUPlthL8DfL7kyrDJF/pzQbZTdo+KuzKZgFETMpR3uBttcAf1T/HGNtAHt4lGZWWcSFnkEvBo0YBKVx8vDDD4ufjUsrsPfQr9CwuB7VZSuh1yaRfLtwEHCNJh5jlDXZdIeiU9m0BihMoYE+X1UaQ13Kgid6YkTkzpbemvp9HJvjPYmLgRgAq5xH3e4cBsESiJetu0Uh/v73n8l0KefERuv93cDbfz/6urGhmKxck06LWoNWZMnRsaXzwUAhA+jMoiLk+ORPBr85ZjnuSFLICgc6FbLpn2gAFmyFJ9QrApftV3xYvXzXjKf24/ZL6GZAN1INEJh04e+eexz/d+87UVJUJI6JmRXzkaVE0CgjeZJqTmo1XFv4Glam6aHV1EKr4RrEwODtsJh86Ll6FXkI44rzgJDe4v0a9LWgwrwS+caZ9YLjQaeCGU7cgGUQ3aLLgWOiAVuaquHXt0X2SgWe0ChG/W0oMCaXWmGQmp/LvgupnEWJVRGZKaFzyvHF6rCBAV40EawNd3djQEUIrXTkw87+JoGAODK+lyRGtcWEzza/BG+EmHPoTbirtAQdEU39HL0F95ZtRp7x+unrMnjOscAxnqqxI8d+RrqvdNb/5I8o6Kr8fvgQ8Mgvgf/vX4BNqqARiWJB9qoaFXI8WjO7JgwisOEdy8NJjNEpVINjnEEjOh2JxgDXf9pmifrMkNCckdBOAIPWgF1FOwSx4Q/7UWAsQIExuc0mHZ2nnnpKOIO85lynqJfNa0+tZxIcLS0tmTl/hlVKz5uQEgwMQ4Mf94YRaS8i4A8HRdCwiL3GZgPeM5K9asL39deBv/xLalwov7Mq7K/+Kn3ZrLmAUlI9qj4ijsVAxY6IBFSE1BSNwjWAgwS0TiU9Jc9JrxAi2YTPCbQ8ESHrw4BvAstKgQNZ1JJmEOENIyWwgAVE8GjXCQx4o4lgrZNDeL7/HO4vz4zAI7HBYGuingZM9pgvCRTOS2byMxD2/PPPi72K4HNc35lExUBzfP+FBdw8YDD7pZdeEns3Yzy0HZiko66IkONLEh+0v2jvSqlbjhN1Eok/5MSo7wICYTeCPiPCmtxZ9WRgRjmPQwbcSX7LMZht8JyyIR3FeZFNGSoZlKYdJTPuJWgDp6p4Twb6kLTHSGaQsHwzkzvShLz3sk8ibU+Oi2mxjjQx6B3DM71HRM9XYm3eImzMj5UjvtbgtWZsZSYJrhtVekqCMQLGg5L5zLx3jBOQtEgkW824AYkEjjH1+0mKJZK3zTZkRRX9o/3794txxnWEyYM8Bq5XXBPYo4e+BvfJBdyE6H0dGIpKDleGB2C1WsR6l26yU0ZRcjImUqcwFjTW6PypAwchWPV09sPIM2qgU70l31CCKsvsMr0vT46BiunRbwnj4kSSTOxrgbAHCBwA/C8D/hcB/+mobM9ssHQbULMUMOiiAVwGdc+p+mzMEpsKlojgpfyP2Fqo6GgHw+cQRjsQcELHBquJ4FaVnFKq4s2/G20MzsD3rnuAvBTlj9p8wLAJMOwA9EsUOYNMEAoAzg7Fofenp0tPTAacODt+UhAaRCAcwOmxY/AGlWqfjgSZ/MwA7fWMx5bn+t04M9aFSxP9opQ8EWjAcCOk4c8ADhd8GjYxzc4G24FX/x/gbJ8uJ9FLcjA1aDQwK+uFF14QWomnT5/G9u3bxfcwY+Duu+8Wwe+rbSdgzu9ERb0eQ2MX8cwr34Y/GD2nGLjGlOlLMs1kiA3+3/ZuYOf9wLa7rj2hMTkKHPoF4GWzbbnEaIB1bwJqkmsLCzDgKLPJ2fdFrlssVd34bsAaKVEe6QQu7Vcqk/g92QADm7c8qPxbHkNpNbDtbuDUwSihQYh+NCeAcVUfiEJWManlsrSoX75SGKBs9MX7zgAnA4fJMic4ZhlUlE4JjQB1hYMreBWTwcsIhp3wh8YxGbwEr+h9EwWNIDnWZVVI2+AA/OMTCDEQzqqJvgEYqsphyFV08xlEpXExH6AzLw2WVIakkOHT1ECv3QC9dg20mtjGubwmzPzoc1+eWhfk/tXrOTer5me81sxm4XfzOkl4XD5UFlbHEBqRo4RP1V/jsrMHv+w4iJ+178fxkRZxDHQcZPM/GnTMVEsGvt44MIA/F9UCFwA2AWS2OTP36fBYrWLDv6W4eOo9BUYTvrNxJ0rMihNi1xvwX+u24oWBS/CpKo3GA1680B+ttJsMeIQDMp9N4lLiwEvAp/4A67//FZz6+08BztjSZDWSSTAlxVf+NUpoSLDS5T/+PfY5kvoPvjO61vBBAuGOBzI6FY4Xzhk67nQuEhnkfA2z/+iExoMOJMdzPKFBJ4ZjR5KYmUKv1aPWVoPFOYumERocl08//fS091C2gbJo/E4GRLj/UbuXa9UTTzwhnKaMGmjSRjDtAky3AqbtOORqwmVX7DzSabSw67OoX+10An/+51FCgzh/XmnkR7IjGTjGWKXTfAJwzXIfoV3Tcyj2ubHLwER79HoUbwQa3gHUvx0oWAXkLwGs6gCPVumpkU3pKWK8JUpoCITRWGHHpUvJ16RMIX2MBSzgjQT6GOr+RPxXO/XfZwE697SDmDjCYCIlOJLKMM4C3NO5FzF4xEAnf9J2ZLYs128GyJghy3WdJAYTaJjIQqlBrvkLuHmxa9cusWe/8sorwnagz5pqPZZVExK0+6U0VTDsRZ/nNXhCQwiEXegZuIJwDgOrifd/4WtE5HNJkjFgTjuDFQC0j9QV5CT05kOikN/PCpRs9HTiPJGNuLMF6cup7W5e79kGcfl5jCcwdkD5JCZPpbLpSX5w3WGCDbPimcBJm49Bf2bKz7ZpMys06E+4I4QGcWL0Cq5OqpI+rwOkPX6zr2sM+rMnBdfpRL0uCRLWXPcT3X/6GFzr1T435wqTsearSo/7i+yjIUEik6QL1VC49xGMhfHBanf69KkkGm96DPQBJ44AHS14wyEcBoZjq9A53hrrqjKqesvI62FAhTIn6UIOf2Z+Fhm5yTEDdCdMOsusmc0SkxVdbueUeUh90nLzdcwOCZwAwupmwF1AyAboZrkp8rqogulTaD8DrJhbY/NScz7eVrULFyc6BRnUYCtDmbkg0li3RwwqXfcwNMk2tZzi2Cz4jkuA2aRQY7kmoPsY8OxFYNf7gNwsBzW52XU9C/jGos59+W2AdWYt7TF/nEPB5mD+AH7dtQ9mnQn5xsRETA4bUEVwxTmAn3YcEVUcRKU5D++v25pQ8oIOABdYbiTSOKOhRgOHGz/OvaxMYElaxRM3KcAyUzoWDGYz0CnLIlmhwaAqjTFuNHfddRdc2gkKvSlvLM1DXr4Nx049hy3r3iY2JBoovBbCILLkApowRl0ejDg98Pr98AW1CO18GNqxACaefhLuzhZs37oV1uVrFRLrWoCEnvqa8LoxKJZO2T7JhJpNQNth5d+81DSEV96nEBsM0Jx/EehkA2M2RQ8DrUeBre9VrsdcsXkPUF6r9Mew2YGl6xWJqQArlRL0p1YHXu95D/Dzr0bl3PKLgVvehFxrjnA8aExyU89U11g2hGPWhTuoClI73ch15MAd7IRZpwTILkx04cev/h80Zr1oULy7ZBXsBiuqtHq4unpgyHfA0z+IvOVNyDeY4JAEZwTqrBDKAR4bGRFkIRtVm5Po4aYCCTt+Hp0FGmckEGaTGchrQEeJe1l77wmqqMSAJAfXC0n8pgsahKzUoKPAz6fjJcvCdRoj2O0oPEWgEGEYtcrxX3b24omeaHVcr3dUyDwtr60VRBQD1LfeeqsIMDBAzM/kfSRxOuVEBoNi/v/L29+OP56cxKtnzmDFokX4yzNn8DKrNSjZ5HDgq3ESVuvyC/Hq7nvhCgZh1enENX6k63icVCErS9S/hzEecMEb8sM8nzJuiXD6GPCD/xH/5NZj72uB86v/hJxPfzHhy3k+adsbvT3AhQTNmInhaL+uKdz3VqC4DDh3EjBbgdvuVn7PEDw+3j+OnWTHyuwkGvpc50lk0hkl0cU9QPbl4E/uNSS1OTdmkubKBCFBfl5EGB5cODcAjcYwLZgm9bbZR4P7CnWwWbnBPYsBDza5zdj2E/1wlOzOTQW5ODPWO5WIwE96qGJ1RjIuM4KNwRNln5Go/N//ZafC6X8jqfaVfwC6IuSD3QF84rNARayUx4zwjSfYGDSKHFUy0Baq2qNUazBRw1QIGFWN2bOFBGZhU20ZLl26nJWgKj+DGVmZ+BgLeAOjtw14+ZfA+AhQVA7c/jCQRC7vRsOAdxRHh8+JrONiUx7seiOGfYGpKcR1ay5ELO03VifSBuK84/pKmyPdxrap5iClPEigk6yQAUraWwwWUZrnBz/4gbBBGciU38VAk6w4pE0qs9S5R3GvknIh/HxmdtOm4YN7luxDxt8ZvE4krbWAawPaEvQrSVox3pNpQgTHo5S7dAf6ERYV0ArcLg/KKsLwhsZgpqxkRLKVfgy/V8jGarXi/bTxmSFPe1rKI6nBMcVAV6Y9JGYCg6PM/uZxZJR8oQLHMucKjzGblUucgzI5R84bXhv6HSKuMAtw3tIfos3G42Vgm+SktBsZVObawu/h3OV6wO+S85n7tZpQiU/oSheUnJIVGlDF9no9w1iUc317JJDUI7mbyteWDe1v1F5g9BOlJHS8vJwavLc8V1aLc04yiYrnFe9DkTyg3DkJjfmozuPY5ljn2I5PkOTfKMEo9zyuHzwO/s61S02yvqGw9wXgh9+IJkLffh/w8AenxxBvakx3MprqK1Imc8ZDn50sKjqcNH7YCDlqtk3GKBNpkaPLhzlSEj/sG8NlZxsCoSAqLCWotVakZYz9RdNa/OWp/fBFmlNTluUj9Uq1wTUHB1c4tmGWQGho9qSGQEReR/wzck2ytFgWGO1T1RnTEAxB6/UDRj1QaAeGVJmKDVuAQtVi+MpvgJP7lAx4HtqERzlWHufrvwFuz6yZ/IwYPh1x+CNgtkfffqD+bTO+Va+J1WYc9IUwIcamF5NBL4Z846i12tHmmhAbKSmetY5KVJgdU4vorzqPTREaRLdnFPsHr+C2kun9B7ixkdAgI67WLaTBL5wO9q/gPHH7ohU5Ahogry7luZC8YLNVNUia8HNpAHKO8qdopOuLJUjy8u3oausVWf4MfnHx53tpWC4daIPb68fRq91YXVOGQrsFnsJaHBkcxkrbOHyP/S9sCEPXeRJoWAK8509ie0DMF5IFq9LV2ySpYbYrTcEZfK1cA9gKFULj6C+Bse4IuRRpeO93KxUby9PXxE8J9svgQ43Fq4FDz0d/J+FC0iJftXkXVwAf/gzQdVW5BpUNU1VRXCfpUPI+ZkpqMNuJhgqdFZKZFy8oQTirzYyhwTFUlCnjr8c9jOf7TghCo7i2QgT4T+kH8N7aW/H79fW44HXhYFcbjLl2WA1GfGHVFqHxLyENIWLQ68Udr7yCk5HMikU2G17avRvVGWb0kNSQAS8G9umAkNzLBDSUaJDJ913uBQwOPwxGZTyNjTjhGvGjsCxx00MJOhV04KTsEbPaaDTKBoV0BvgguSmPucy8Ej0eEmiRvUubizyDUup7crR12nccH23BlkWNYp7KTCF53zl/+SCJkah6st5mg76iAiV2O1689VZ0uN1wu1yYbG9HXgI5Ir7fptKNrbTY0aqqiuSn29TllpHG0Ya5ZoPTAWp+Beg8p8zppduBmhka8x0/HCP7tMyRg1NHjmDjpBOwxUobyP4yaTtav34ksq9HSGflQ5R/L0lS3r9pu/KYAziWuGdIZzEZqJnONZ5GHgNGJK/V956ZchwfDAylkuTKFKGwB94Qe2wpzvTA0BVo9Gb4/cz8igbnSORJSQOOW76Wx8QgBY+Ze+NcHD+DVocPN2zHhYk+TAZ8qLbmo8yc5b4RqbIe45oVTuE3PwF6OqO/T04A//s14G9ieyDNCEMihyycmqRwDwD9h5UqD1M+UBprH2QNuTVA/3HF9hLQoKFhkQi2MHM8lbOcDhjs5Gdlo+EmMz8vTbRgxM8AmglL7Itg0WWxmudmBtcz57BiA9kLr12CSiZg1eqj/6MkT/F4e9qU39/zl4AxjV5q1xFjfiee6T00VeHKgF2F2YJhn2aqBxn79u0pmX2/RwaSaGvIoC5tFf5O/4CExGzB99M2jJf24ZrOwDL9Ba7f9DHibQ6RVdnYKCqJue4zu5cJXgzwUpaXAVEGTOmb8HhJYPC1DKYysLp7926xdy3g+oOBRI4FmQCVSZCcygG01U25wGD/KIYGRpFfmIvBgVGUVRQCpmjgius9x1ui7+DYpn+TSN6GBFiyjPPZgt9Dm0lWQ3COMfEv0yApA/08Jx4fxzwDynJP47yQssHxUj7xINHABBb5Gn6u9FsYV2BQmXNoVokiEfBcOZd5PXme/D76LAxs81g5DlgZlu4Y4DHPprcEe2jEg6PEnOD5aw2ud7xOyXprMLDO5FX+fTZSr9cKkhRL1TOFewjXcM5hJtLR35Ty1hJMzqU/RYJ7vmS3+N2cP4nmOH1v2po8F9qdnEfcm7iH0Mdg8t8bDkMDsYQG8eKTwIq1wKrUfTZvGgg53UXAaGxVRuOS5fNXqcEPZlOWaccCLcKgDjTLfhi4MkODRph1PXBGmjwygFNiVjSxhryjeKH/4FTGZ4e7B56gF0tzZyYCysxW6DU6+BBQGpoGg3iurwsfqo81ELkgP9/fjYsT46i0WPFAeTX088Ki8hKqg8cMkM5hYeu+CIwORHW6SRqwOfOiTZgvkHDShEsR1vQo38VeHiW5gMMKTHqVBtZ1t8S+6eyRaM8P2eOAr+V7xpWs4KzCnyCDMehWHOwZZKwKTcXI0dvhDLAyARFCIxZ5eg0KHJUw6wyotORhTV7V1ILtCfnhDk2XLxmixnQS0PjnRkeDXs4ZLrw04jcX1SqySk6Pcn9zzMo1zG8AapLrSXMRT7Rp8nu4yMvm4GSu+f16fR4CgnQLR3t+bNgBqz42+4akxtHHj0OHEHYvr4eOY4CZZCYNbBoz9n3jy8jhkLBZYKI8VccV4LUXlebW843SxYDFoVQvyeCivTiWYEsFvr50mfJQo+esQmjI1xBcH4Kh7ElQJUPNYuChDwHP/UKpxKioVaTc4oMLzPxelLi/ighC22xifElDV0hDtbUJ44obP4Ok8YFNmRHF1034AqiqKRFVGmazEfkFdnS1+JDXFMKh88cx7hmZKkXlOj0RcGPEN4kiUy6+suNOPHfiGByV5UB3P1Y5YjOm1EHkPztxAs3j4zHNrD9y5AjuLCvDi/39KGTzahomMzTjldnsPGcaO3Q4MnW66FCoifltq+7Aq6ceQ3GDQZzr2KALt699KzpaelKWUtOwowFIMoNzj3NIbSzSiWG2vLoHQo6+FLXW7fAER6HVGGDTFYm1l0gkZ6cuA6Zzx/nNYEA8qZkMvC68PrxWNVYrzra2xpy7JxgU96LExGq12HXld+vW4Z8vHBCyU+L7TTY02LhwBqfGA+UMKf0zDSE34DkBhEYAEsrGFYAhSbbViWeBS6pAxsFfRUi8FM3q48hUo04nKoESEaDUgmWAhtcvLUeY0kNyP1ODhv2n/xazAoOHHhfAitIkNghJDTlnSTaG4IMWzKyKXRPoXEntZJJzrNiS44FzjkEh9iSQzpcz4Me3rl5Cm2sSjTl2fLiedlnmQcxQuG/q3p8724bRUSd23tIAg5HjIxos5vHzWrOihAQqsy1l5iPXq7S0ltkz7dXHga4WwJ4P7H4QKIiStxxzy3PnqKfN+/vSM8ArzyuM3e67gFvvVO495+xb3wo88kjse/i3ZP0eOllRqJrD/Lea5EgX7FFTshHof12xDQidCTDFyudNgQ27O5+LSBkyUaJf+b3uQSDSLy0jqMm8eJgcQN1dQE+EQLEUwVi5QwRY6BvMldTgZzAgO9fAANfNw0PH0e3pmyJfu1y9uKN0F0xZrCrr9bjwdG+XWHtuKynH4pwsk2vzgYAfOPxLoD8iX2DLB3a+B7Cm3nuvOVrPKccqwfHtHAX62oHqG1uerHWyFyHV/sG9cjLowntq1qPDNS6SP1Y7qlBomn2GK+cIAz+ylxIDwAwoc90lSTBbQpvJE4majcsKQkrP3H///WKuJqqo4F4bT4jQBmIAlvYU7SJ1sgh/Z9CX9hT3rNk0kV7A/IB2BiuDSUjJbGwmUjCQKG3uRAFs2iGs0hkYGkNAE0LT8lq0t/Ziw5bluHK+B/V5OSL4TeKDpBf/zcB6PDhu6MfSLorXyaddq+71kQ3wvHg+JNf5vRzv9DEySZziefO4pJ3Jecigt6yiZeY5Ez/42ZxDyaoSeSwEj4UECX16BpdlTIJ2lJRFykZgmdeaVbU8Xu7BsyVGeSzqwH83k6mCQdTZbEKxJRnYFJw9NCg5xcRSrp4WnQnLHTfGekC/ifcrfizQ1uAa/MADD4gxc6OCNrkkwYQfHfaLxA89FQTi7gv3Ej5YqUdyS5LYBOMGnPvzWU3L+AVJFUmoxYNS6xyr3Dt4X2R/Ge5/3HvmWq2YNQwOAD/6DtDdCVTVAO/5XaBwlpWmfd3TfVL6kt3tbxxSg6jYqsTBxtsiUrur0LhKh1e+/W3MC6mRKgtEAy7iKqkDDVBsKkChcYnQElccdGWgXWBPhBhBIKB5/BKW2OtnHIy/6LwCT4jyINEQ9/+2nsd7ahphUm1y/3D2BH7a0SIWUkqe/F93O765YUd2iQ0hi9AEBM9GT5q5ONoE5IzHCfRdjQRZFwGmBMEyzyRw4FcR3eIImD3O5uHzSGoQOs1yhDxjokpjalGwGJUeC+WJJk1cJYn8GQwDtnlw7owOwEV9RdWo0VnS6suh0+iwLm8LOtytcAUm0eKKDTZwrej3TWDENwG73oRbihfHZJ2zmTofJDfUKEjRJJcsNvUpacjQYGfAk0YIr+vrHgc2FtcBAy3AuBvQFyK08c04cuosxq68JDYMBofUzv2AdxhHzx2HxWKCP+QXjVzVDDwNCW4GND5l34Qc/UpM+I8jKHreAEZNCSy6+sSOyOrlgHMk5r5qbHnYvWI9QvvrcGVkDKNuX3RsXG4GVm8D0nHkGeA5fRgY6lWCU6sjCxfhdSuLsyFJsJHP7/od4MJe5fhyi4Glu+aeXeiZUCokEvVGccxPA7oYrNikPFIFkVLBOYqVQ+fRevkSLpzehyVv+QAut7SKwJIMHDL4n4jU4BihYR0O14ueGjlW9tHQwKKrgTVgERUQZeXluNozhP62HpTUVcZkShMcAw12BxaXVODyuCvGkGDwXe2kHBkeRkC1IfPfrwwO4rl+lqcr8oSPdHXh2J13YkmSrCg6Bpw/LDFldcJsdFvpGPF96kxxrVaHStsK1OZUoau7Czsat8GkswlHgtePr5Vzi4Y/nRM6JnR+eB3p5MnKl3iwUoOGoRqUm5KSU2ossVegxxPt7cMr2WSPjkMaeMxCYSZVuk344ntJ0EGTTRpf7u/HWw4cwKjfL7JHv7R6Nf5SpVlcbrHjCytvx5XJEXF/GnMK4Av5cW68TUhOVVqKUGdTghO8985gC1yBTjGfikJ+aMM+xTUJewHvMUBrAnRxUgEcE1eikltT4HOpSI2dtwP7X1SRDxoUb78FAxMTKI5rykj5L67FHDdsJMd+Dyn1hzdsBB77TfT4+B3FJcAPfgLMJjPw8kngxZ8rknMkKe/+HaBycUKng2NzZKIbGu/5iGygBrn6pbDpEweLOcdI2iiHGhYOCANN0qkkafXOg6/g3Ph45FKF8epAH3689ZaUDmYiSGutt3cY3V2DWNxYCYfDlrBcmHOCc0OW48sxx/NjXw3OI5a5a6iXywwcZhvTSRIHGQJ+9t9A+6XI9dcCV88Cv/9ZIDdJYH82eO4JxeGQaPm6sk/dfo/y+1//NcvagO99jyetHBubaH7oQ4k/r7AYaL2s2GsECYnZOjB5i4GRM0Ao0teFdkfHs0D9Q0C8ZI2Tcy5W0g6BScAzlJY05xQm+oALzwK0AU12oHEPkDe9KT045xc/FPmqsHA+6qpKhW8wV8w1y1xiIjA5RWjIsesJedHu6kKjfe5VIMQV5zjee/gVTIrAuwZfvXwW/7NhB7YVZlY5mTXwXgy1Aa5RIKcIKEhw74hzrwL9qqpAvp6V1be8HzcUkvlpN6i8RywSy/cWm3LQZM+OlArnieyxRaKBRASTLLiuPvPMM7jvvvumNShmgJQP2gQMWMcHkvk8fYl4H5x2g7S3uLdw/8xEDoJBMGaTJwJJDAajePwkwGfTYHgB8wPu4dKPZaIE93PaU0xW4L5O+5aJFfHjiAF9pYl9JXzBagz7zqG+3giD1o4da7bj6pVW8dkMSnLNJ9mViNSQ45BjRC1zRDuG9hyTJbItd8PvpP2SKps91Wcw8B0vlaX2N/gdcl6SfGSgmTECvobP8xzpP3HO8LwZB6C/wXkRL7XF57IZSKcPk4pomQk8N2l7kuj/ncOH8bNOJday1G7H07t2oTaFVBGbghcYc4XkFCs0SGiQ2LgRwPtCvziRXyrHLvvQ8N+0wW+IoLoKrL4hAdDW1oou12mMB5SETrM2F1XW9TDQP4sDz0Et/UT/mWOT/SzmC5xDJOYZ+yKZmgyyzyR9Okmuc0/kekJCZEY/b77Bnnrs9Tg2Eklw6gKuXAT+6T8BS+axC+FfxIOfW3Sd7M35ApUfKrYpjwjq6voz8i/SJjUY1CF7LDIp6DRR/44BC9cFIOwDjKVAzgpAE/uR2rjfCX/YP83sC4QVokK9FLgCfjzVexkDXheqLbm4q6wB434GVmPfS4kMdzAwRWpcmBgThAZBQoM4ODQgKjfuKUti7M8WGmY4cUFgIJkkxxJAGxeYG+sH9v4A8Ec0A0lo0InIUTX4bT8HtJ1RshSZKa8mC4zWeddNY0aozqlkqMcsyDxWShrEo6RCyZqa/kHA+vuzd2ATHcDoZcVx11mBYCSLnuOqLHlVQzwCYT88QWrAe0TWcX8kG1/GrdyR6g1nwIeX+y/gTZVRgo7X462Va/GzzqNTGdWl5lzsLIoGMpldq7xWMWBoqMgKDZbFccNjWZzUwe0s3oGSFXeKqoiTF67Cc/y0MKRoGNB4pKNCw5AG/+WJVvzi5V/DarehrKECz/Xtxx2l22HUGsVr+fnctPh6OjR0QKRz4DBsRQgeaIJD0AauAoGXAC01tzlXVZmc6+8F9v00km0aBgxmYOXtgNUOrcmMxkINrg6Po3/SjVKbBbh6HvjPzwAf/KRSaZAMvC6PfBO4eEohIkjYXTihVCo8/2Og/YLyusVrFM3kRJJW5hxgzb2qm+lTmnszM9teEm3EnQn4PjWhIReVYlYlsersGmE285rk56+/Jn7WhUM4cvICro72wnbvB2OcQhrG8TqfLDGmYaJ8tRYWXRGMQn5LB72mFNYSswiwOgMeNLs64SyemPqMelspciPygWowg4jOMgkVGtkci+oMLmbpXGYmeuQa84y9qqxmPu8LhfCNK1fwb0l6ANDRoFFGR4BZGnQcZpKxocHJucZrwkwnnnt8NrHMoDJqLQi4w8itckzNec5HSdZwXjKzjKAzJY13zudkWZF02tI1blc7akUPDUpOMYOmKacCtxTHOlY8fwYTOL8ZGJ7ps+lwqpsrynMZ8/vx0P79cEa0j3kn/urUKazLy8MdqlJjq96AVY6SGEJrY8F0smEy2ApnQMmWYdcQXTi+qk0DBPqmkxrKUaX5nAo1DcCffw54+tdsBgMsX4P6u96E14+fiHGOef4MyNAAZkbbPffcIwxm6q8mDZ7cdjvQ0Q587zvKWlhVDXzpn2dHaJDEfe5H0bXF4wae+B7wvk+LdTX+vlAecP+pl6FpDWHz9hViaRgPnINBmwOjdvoezPdxbvIhKyPUY/GF/h40j0d6MUQO4dDwIF4bHsS2REayCr2eMZwcbRf7XZO9FAUGPVxjEzh98iqsVhOallSTBoUG00lIzlFe++eee05or8eTatzTXv+f/8Gm738/2kfowQeBz34WGOwB2lQBM67RJL6bjwDb7kTW8PyT05977skoqcGL/7a3KQ8GDmhbJqsg4P31eKKEBsF/P/Tu2R2buzdKaChfAIRoa/cAuXFB+aR7Xwb7CiUXz/wGCEbuhdcJnH0cWP8eIJm0F8+57QVgvB21BXoRZJor+BnZyNQOhKeX4rJaI9Hzs8VXLjXDRZ1x8RvJW+Afz57Ak7vuwjUH78WZZ4DO09Hn6jcBS3dPf+0wg0zh2PeOXN9mrAnRsBI49BTg9ShrgJToLLsxMndTodZahlOjl1VCzBrkGXLgSCgtN3vIqgiu/5RYZNCTfvq9994rKviYUMEkCNp7TLxgkIe2C7PJmR3PIB2DP7SjuEcyQJSIfKDfL0kNft7LL78cY1fMFdz7+PmyV1y8hvoCrh84Nkg4UWGARISsuiR4n6Tccfz+z/FFks2oc6DMsjXm7+qxQxs5mbQIyRH6toQ6OYevn43EUSrQtpcVRDx+EobxFUfxIAHIaikGYDkHSc6QEIi3y/k3+hjxGeS8fpyz8jnOQfpOfF7K2PJvyeQYOZ+zOVdoE/PzmMxFG22mXgk8VnWVvPqafen8efw8QmgQl5xOvOvQIRzcsyfp5/Fc2T/jevfQSAbeZ95zmWzKMSllhumb8X7QfmG/hxutLxDtce4HrQOnse/waygodKCqrgye0AS63adQa0tMIEglAu4hDCwnquLLJpiYxaRc9TqTCJTFItlKf2eKSGOCHuMU9fVC5pB7WyYqDlnFqePAiKoHI31JVm40nwI2xq6HaaG0AnjwncBjP48+t3YTsG7+CKYbBZxTtEGSyb/NmtSQ5XAVhg6gfW/0DxZmYGoBt1ORA8pLfcM8QTccBht6PdGu9jT6SkwFMdnx3mAAnz+3Fz38XIRxiIpH4wNYm1eBZ/qiDW5ZqlZltcU0qe1j8CAO/OQ+Op7ZZPZDnTCIKg02lSVCQPAcoC0ANKos/uNPKYFYCZ8bOP08sO2dkb+/AJx6OZo5HggCJkM0E9WSvCIgu0ikO55kYdl2L/Cbb8Y+x+aYd39U0enNBsZbgc6XY58rWilkD2ApARIEWBPBH/Lh6Mgh+ESgIAyrLoxCgxHukAbjfg8m/NEmuMzqG/FP1/BstJfijxfdinbXCMw6PRbZiqHX0lAJYdB7Fs6gwnzbdOUoMi2HVlVBQiOJRjsnpdQ0ZMl1S2urWHRZYscsjZdeekkEiOl0UFKEC3Nreyte6X8N5Q0VcBQpho4r6MbFiVasdDSJRZyvlf1uqLEe37hVx3npp5Z/BKFuhTwzbo4G1Uvqgds+CJx5RSn7p659Tr7y94c+CDzyLRRZzTg7MAKzVgsHFxdWhPzqO8DH/yH5xW+/rBAa4nsjGaVXmoHHvwcMqNjXy6cAmwPY8WDqm+kcBE48Avgi98hRAax9S/o9NiRIXlStBTpPKL+TcGm6Fahac+M3XrpyCnBHtd3XV5fglfPNuP0tscdNA5XGTLwmJiGazIV74As1T/Xw8aMNFh17Y1iRozfjndU78aLvdYx6J2Fy5CIcNuDKZD8W5yifJxs8kiwggSD3CVZSqPEva9Zgx4svTgXRSaJ44pnpiJRgItDIkWOaPzl/Lly4ILKY6GTP1IODRAuvQSIjic4G5xuzOxJ9lnQ4OE9T9dhIhnRLYYU0XMFi8Uj1GjqEdJ443/nZNDgTVa3wvnPdkedMB+R1pxOvnD8vsqjGI/dCQq/R4MDQUAypkS5cQcU2SE1HJNhHeF3qVgNXT8S+sy5xVmcMFi0B/vjTU7/qIveI95yOIscjiTYGeGTTRxpEsicJDd6E4DF94EPAu97DzpaAIy/z9YDvu3wW6I5UG8Rk0fuA/k6gLlrGTuJNVDPoPNi6czn6eobQ1tKDugY6dxr4QiMJSQ2OBzr+DDKRqIkn18ZVgQA1xvzqgPl0dLqG8aN2KQ8KnBzrQINNi56XjmF9Yyk2bloSGdP8K+esTrFbLp9WtPCLK8U4ldnEXB8Y5OB+xrUhn2Trf/93LFH02GNsGAKsSZQhSVIs8bnMGonWGnWFrBozZQ9T75bN69Xg9WF21qwqw5OMt0TjMKcaGDyuVHPItCBjLmBOEuwgcdGyFxhpVfbMqk1Kr6lgPIkSAMY6AXOSjFWWh48rREZNWT7aWpSKobmATnM2SI1cvV1odNPek7OPdl2JKXsBoB63i91lpn6n+ThA8u16YKg9ltAgWo4A5csAR9x6zv5i8fJ6iarGrzdI+r7948C+x4DxIaC4Ctj5YPIeazcQ8ox23Fm2BUeGz4rmtyWmPGwtXBnj32YT3N9IRjDwzHWXgWDaPQxOcp2mXUMfgb+T3KAtwNcxAPn8888Lu4aEczKNdFZ1MFOewVnKr/AxH5nI8jNZDcBjulGb7v62gfYlbXA2D46XfErUq4wkB8eLDMzPNYhMqIOT0u7JJpj0IqWFOPZoPybvIauAFRQMoMqm3ckSZWiHkzDkOcRXIqrn0XXNLFcdK4+DfhztZ1nVHz/feb7cr9XVMqcY06ioQPDiRTzd0xPjCzBx7bXhYSHLN1/r4HyD6yDXWMof8XqQgGVlHBPsZP8T+ty0eaUfcqNAEmvlDXlw1C7BsUPNgtSgZeQKKjLTidZ0nh/3CI6B+azQIDjmOOfos81E1nEucb9isiITHun7yB6jnGO89vy8bFT+zgrJfIlkz6eDB98BLF0JdLQCBUXA6g03SeXq3KBIMoeFXUCydSboM8miqqwog94d19DU4wWskZ4Avm4lwyxJc59OVyuuTJ4XB2jVMRiiPF9sysfWwtgM3aMjPehSBe6IM+MDeKhiiZCa+kn7JbFoVlps+NKqbTETsjEnVwRp1JIn/NeKiHzBsM8jJKvomCzKycX7a5fCmgYDpIYv1IpA8BKM08I44UijcBURMTkc60SIxg4RFo+kDQkN8fxUZF2RcbLoAUcO0PoK0H8aWH43kJM6w3JOsNUDo5Egr0ROkkWhugm4413A/seUDNTyOuCu9wA5WZSIGFAF4iXGOxTN6Uw+xtsHHzMdI+Cm6jAGsTtvC37WcQqeoEKcERxFZZEG4fHIN9rEQ41h38UpQoOYDPZA69OjyBSrvcjJSBZa6mvSWJJGFI0Dzi9uljQgKSnCICv/fu7KeSwqb4SBJJcK7mAsQUfChPMqoSMQZCaeDEJFxl9gEDB4AU2kRJ1VRAceAcYGIwGZK8oYXXsnsHQt8NHPwf7D/0Sl043jnf2w6PXYVFkM7VBU5iEhnKrm7mr0tgPauIBf+/nEpIbIJuxSiIyrBxRSUGKsB2g5BCyOZgSnBZ7j0tuAyhXA5QNKYODSPkUmbvH22VV/XCswwKcKTOgo8WIyTgv80al4+umnxaZAo4QbPTMv5FjxhWQ5rbwPQfhDLTDpFEM112DF5ppV+Mq+R5Cv59o0ihNjHbivbBXW5deKz2SmUvxGEz8G2Suj+e678auuLmHcPlhejnccPCj6bMg1mj8fSJAZRaeGDpK6HJzBaRqYNIASERE8N2Yh8rz52pka/XGe0UHhvMwWeGy8zsmc/2A4gCFvNwJhH3L0+cg1pG8Ac41g0ILnyetPUoZBcQYp1FUlsvcC8XuPPYaf0rAdHZ2qmIk9njCKZqllz6QECY4SjxCjlES/8goYklRIskKMwVXRKFwPLN0xc6PwJGAm0oEDB4STwetP4iL++tMY5jwY+PUjKP7pj4GJcWD1GuBTfxPbIJpOcyaOMytDf/RtRfuUgWOjTukvVZggISHumHj/uAcUluQAOUBpeSGOHGyOkBq8joaU55ysQeGmgsIYO4jfatLqsDZPVSGaAPuHaFvFyoNe6nfi9uVVWL9ukeqaehEMt0AfbgQe+y5w5czUujS2bCuOW8qEc0KynkEIEkoiGHH+PLTs+2M0RmWwaH8xY/POOwD25xkfia2ka5yurTsn7NgN/EaV9UTsnN4vLi0wmzwe3D8SPZ/yc8YA97AyH5iwEXBHiQrKTlkTZDDqLUDNPUD/USDgVKpqizcml2e8/AIwrMi/IuABrrwIVG5IXgqeDKp+YjVlhdj/ylXMFbSB1JU9swWTTXYVbcbBoWOil4EiP7oChYkqjmeJ1Y4CXJgYF1XiBMfx8tzo57/Y14kX+zuF3O2DFXXYkD+PMgGUkEr2fDypsewWoP9qpConYkOsvg7VJekgrxh44HdxM6LMXIAHK9KvJJ8raHPxwbWWQVkp6SQJfe6L3PdIftBuoF14++23i6QIvpY2RSqigmQj/ZJUQd5sgHYebdRHH31UVLbfSIHB32bQlmbyRbydQfucmem0s6X+Pckzvj4biLfJ4yvPswHa/pwz6vHPJCiSf8myg2XVNv82UzY4xzDtHtp42fIx+P2cJ7PtnZMKvA6yFyDvuayk4TohpbOYlKkOGJ8bG8N7Dx3CREPDlN3Pu6SmvPIMhpuW0CBI8NDvokwef/LaS/+TlQyySoX+B9fbpIlT1wGcN4w/VaxU5m9pRRF6uwZQVlkMnUbps5kIHLd79uyZJmU4H2Bs7K1vfeuMFUIE9zCqFpBsJbHB4+SclRVc3M9IQF03UmPFGsBqU2KjJH65ZtlygGVz9GMalymP2SAUVKq9+dPKpPCbQ+qR6yv3E/oHWSc1qitKYoOjhGhuodaFT5yrySbNJDQITqBcgwZ2fRjr8rfBYZgeCHfJUvg4uEMB/OGilfhA7RK4ggEUGM3TFspyixVfWrURnz79+pRD/4nG5dhYUCQ0cP/g6Csiq0phj/twYnQIX123K+1+G5xQntBVkR2aGHF/YR+AoY4osUGHV8p6iEqUOPB8CkoBE69BSLmkJEaYob7lA4o00HzAvkSReXJS7ikM5NQDjhSTcOlG5THbvgAzYZqMSURfOkNQziXh8wjizZXr8KO2Q5iMZCqWm/Owuzh9TUlXcCDJc7ELD40mLroM0HKCsokwN0ZuFmT2qR8oNw4u0DTcaECaNEbkWGzwquQoGG7KZzamClzEkzUSj4E/CIxSPo4l/b8BCjcC9kXA1eMKoSGkFCLj9Ox+oGkLYM0VvTA0bHB9tQWLCxywGw14pa0Xu9auTr2IlFUnaLyrURZ4t5rw0CTOGOSGcPxRoF+VCco+L5Fm5uJ4J/oTfze/0zepfL8hiYRbVzMwEAnIcJi0vKYElRpu4LI+EoqvPR3T/yRktCKYVxqz8tDwJFFG44tBfhrXNG6UhnN8RbyBHUaIUoIqPHPxdRjt1qngDfFi/3lBatCgVzezJtomJ/GDM2dE36MHyyuwM1KaXmW14k9VDvHjO3eKcmRWB+To9finVavwYAJHiNlCzCqMN7r4u6wUkcfAOcOsDToPnFtJs7lCHiDQrqwvbNatLxUZV3RiqHE7Gz1dNTifec0TVciEwgFBsF52HoM35JraTystTSgzZ6b3zmsgN3lmWtL54DXgNWMWi8SJ0VH8lL0XliyZRmgYIj2n2Mvk/XV1uDgxii+cO44OlxN1Vjs+s3w9GuL65lCSyBlww8ReQzojrLpqjAciBJlGgxGdHoXIg5FyLxoTYGwCtEmqDUlkrL9HecwRvB4c75Rioj6wesyo/73E7cLBf/x7FDsi53XkCPDXfwV8/Zuz28dYZvzlzynVbzJz0cs1zjy9uRt/jctu5LzkuL10vh3VS3NgzHHCZDLC7w/AbMyFRZdcBkHuE4nAxsX/tW4LPnnyCFzBIOx6A762fgvKzKmNWXcwmuEuYbBZsaFpOhkSBgPjpxVCQzwRRv+ECy1PP4pdf/uvcFsdYm7FOColJSg1GNAfCKBcBgTo7NMppPzge/8UePS7QG8HYLMD975b2UeyiTe/U7kPrz6v/H7b3cD9b53dZ7E0vKAYGB2K3ls6DiszKNMYvgh07o+u6bk1Sh8x/0DkOT8wdh7IXzV9jPJ1VbfP/B08puF48oHamyOAtRBwDUdJFGb056dwCM3RsVBTXoD29pcwVwgfQy0PyApTN6uyQ4CpHDCk38zaYczFPeW74Q8FoNcoTY6ziT9rWonzE2M4OabIo1SYrfjiKoUc+k3XVXzl0smp4M4rA10i8WpLYeaVfmkhJ0ngVy1vK5FbBOz5PaDjDBAMAOVNQP416CG2gHkFxzdJDGaq0r9gkI1NnplpKxsXyyQHvpaJHnyONhz9jpnmx7WS8eCx0JZh413arFI7fQHXFwxyszpaXU1AQoNji2QGq4NY7cDxx7GSSZVGqkB/fGUI7Z14n2OuoN/MCoVERB59CdqSktyg/8R9iseQSe8JJh2xqoHEIAOxcyXraOPzus93NZMM5Ms+JrznDCLz+NU23ceefhrOwsKpXrfayE8m1RCMwyWTFr6ZwCokjgX6ierKBXVCqZCSzcubkmC7EcC1ngRV+xk3CpdpUVFdilNHLwhSo8SUehxfC0KDyESyi3sbkxE5Drnu8L7E+3vZXicyAiv8P/NF4DtfBXq6gYoq4CMfA+zz0G84HQS9QOvTgDfSt5M9eWvuAqzzmCSfRTDOka7ErT6TQE1FeZJsIzmYDMVKQ1AR+JiEN6QEG03aEkwGpmdsc+BdcXbj3PhR0YS51JyP24rXwKY3Y4m9SEhLyWAav8Go1aHeljel981HMtxfUY2thcVoc00KJ74i0pzllYFu9Hqi8kJ0QZvHh8VjTV665ekiMiFCghTxUG+7GpgBbakqI94LrLkb2P8TJQucsNiB1RF9aHuB0gxZSEKoMukr64FBdWOiMOD3AOM9QGE9wmHq+fYiDB+0yINWkzr7Mi3wPjpWKI9M36cGqyI8V4GwB9DnA8ba2QWL7LXA0Bn1FynPZYgCYxE00IqG9RIGjRG5egf0WgP+aPFt6HGPCVKr0sJrmZ6hMOEfR5/HJ4J8dr0GFr1yjtok04rjXWY6MfhGxp/BLDoezHZhibgkQBiYkyWxxaZS7B08An9EE7rGUoFFttjrQImdpI6JvhIItkUqHpxR7W8SWIOHAWr9klybRj5EGtyT1CD2vAXuwwfRZFCu49b6KhyqXYOU+WiFpcAD7wee+KESVOHGf8+7aeUBz/5QVRERBjYl0EzvPBVLaBCUZ2MjVtELggGYBBsF592Fp4HxiDRObiWw5F5AHxcA7I309Ih/rmSxIvHEgAHnq0T3ZaDtrKKx3rgByJ+nQEUqMBBscQCjEUItrwCrPvJ7OHj02FR2Op0MBjvVmTwkOZjZr7xGCy1yEAJ7y0TvuU4bGzgaGBqEpSK20sEbUio94nVnm8fG8Ob9++AnaQLg3y9exA+2bMHD1dPZdZIc+26/HQFWD7ApfZKxSyM6mY4iM9yZFUYjk84PnSk668yUSToXQgyU7Y0Spv4WwLgcMDYIx4OGOo2/dLJFkoEOErNOaPhKqQZer37vFfEgotNM+UeX+yKKjFViPZoNeN580GmiFJHa2TnFJltxFS28Ou+tqUG+0YhKiwV/tGiRkHz86NG9mBRa8WGcnRjBR4++il9sv2tK3nHQO4bn+1+Hm0YSgFWOBqx3NNF7gSuo6LVb9ZUw6GrmRnRzrRDzWzsrYiPla/a+CqNeL/q4GHmdRJ+f84rmKZuCZ4ozJ5SMnGmEqk+puORaJZ/j7VZJZcpjZoYSxzl7L63aWIe6ahNGu4NY2UQ5uNlLrtxXXom7Sssx7POh0GRKq0F4va0YPWwYrUKugdKhYZhEuy91DY5NqZ5U7R0tw+NYV1mM00cOY8jsEA4ViUmpvR3Oz0fZn/85mv/5n1FO4pFEwOrVwEORBtQkCH73U5hXsJLh7e9VHnMF16c//VvgG/8KdHcAJI3e9aH0M6rY00JNaBCUdoppjB4GRk8re/Vsml2zsuLKS8mvxeq3AG2vKcQG95aaLdP3SjXY8Lh4FTBwGuVFDvT0TU/uyBTCx5DEdmACGHwhsk5rgIkzQMFOwJTZfmtIVW0yB9gNBvxgy624ODGGQDiEJXaH8E+IH7YpNkW0pwJEZfm8kRpsCl6/WUnIkFhyC2BP4rBaHcCSGyeLdAHJk7HaXB2Y8E/AqrOizlYrqpBSQWamMgjA/eSJJ57A2972NuFLMHgryQlK5bAPBzON4wPHycBg7HyDQWRpv0m/KFEz6gVcW0iJU/W9YBKNTADiHk8yau/evQmTeZKBpFUmskvZDlTSTk8mdcN5ISu4KfnKc+T8IcEz20AvCRL6BXMlNXgMsiLmWshWcT6S5OEaQD8yvs9Ha3c3QpHqDoIRArtejz9pbBS9p5iwdlvJG6OpsexlpAbvJ3trSFUAEsiM78y3ZFO64Foqbay+q12oWuqAVTOIKss62A03133hGOR6xJgXJcC4r1FWkdda+ibXldCQqKkD/v5fcENg4ATgVVX0Ul6261Wg8W24GcD5xrGbDtK2+EV5VfWiSFl8pFkzYYx8BB1TY6X4pz80ivHA0aniM3fwKgza6WWr7mAY5yeUht5Et3sIT/W+hrdW7kK1NRcfXbQB3245Dl8oCJveiI8t3oRcEgBpotBkFg81PMFgfK2JwKsD7fhh+3ER2FmcU4D31a5GgTFxRiMDgjpNHoIYw2SYbcLDokGqVpMPnX6t0oDZPQo0Pw64RiJSNxsBc7Hy76KaqMPIAMft7wVe/FG0kfiKnUBRdRypEYGW2dEB+MOvUQ1PuEzU9tWhCXrNzKU58w5WFIy/rGRDE942IDAK2GbB0pesUxhGNgonHA1AaeZNnK16G1bnbcCF8TOiUbhVl4PljtVTAUSzzoD6nMz0lkd8IzgyfHhKpGPUH0aFWYscgwZ5xvq0G7Cx6THnFqU5JOiMcBIzuCqD0veX34ZxvxMGrQF2vW1a0JZkCT/LYNGiffI8vCEncnMcWN+wCw6SjeyfMXkaCI0lyNTsBYqqYuU+xIEYY/uj2B1wv/XDgEUrpI4s9UtgOK9o+6fM9Fq1BVi8EhgbAnILAGska9tkAS6dUObEsk1AWYLMUOdQtNeMGmEGPDl/LEB9gj4+rfuA8agsmPg3n1scl9GayEmkzNXL345cHi2w5j6gagVw5Tiw/9fRQOvF14G7fxcoznIWcSpQYurRbwBO1X0cG4XNrGTi0algxkN8k2g6ucywoREsnViTbjU8weMIQwnI6jQlMGii94D3tSanCFcQ68zmG5TKjfj7/vlzZ0WgmLqpEp88eTIhqSERXx3HjYufy+NUN2VLBP5NSrilDT/JVqk/H4HvHGCoE/eVhjudNRqpibK30gGdH15vOkJSd3fM3ztFaEgYNIBfdRiUotKnkBqaCVxHSMyI/gwqWAcHoSktjdnz+O/fb2jALlWw4KmeHkyoJMxYwTHiZz+iAdxeUimCLCQ0PBFCgzg9xus5DrPOKWSoik31sOpqZp8ZzT3wlV8CLZQy0gLLtgDb7k8upzMbsAw+0o8rBvyOyUngxRfYhARYv0FUt8yIZOfqCyoVcbKfCF9XXKFINU47JOW+k3BkIGfTpltECbuz3I3cXIMgznvdQ4LYplwnEz/SBedYSQZO+M6iJoz53WiOEMK5ei1KQm40j2iwpsBI/ko5HTig09QDRcEYMtwTCGB/aw82v28jVpZUiGAH9W0ZDOHcfuyxx7DzTW+Cj+sQ5z8dfDaQvFZN/VhRc7VZSSSpaVTkruaKsgrgc/+qfCaTbTIZ/77x6RYp308CNtFenSmpwb3z/BOAJ5FMURgoWa7IWy2KVnelhfJNQP5ilOd2wDn5OVHpFr/2pAu+l/bOVLCAJMZUY+/ItRk7BpTchxsFJAiXxRBPClilqAaP3hX04YmeiyKfZEN+OSrUiRLZwNJbgYqliuSUrRCwLzRbvpnBdfL14WPo8w6IfZV+Ro+nF9uLtgg5tXRAG4oVD6wKZ8BHXW1B2cJnnnlmKtiVDhg8ZfWETDKRMjXZ7G/APYIZmbK3ztq1a4VW+gKpcf3Be3H06FERvCV5xrVe2nlMAiJBQUKKdm+6kE2WkyFZ0+25BERl7yaOW2bTJ2t6z2oI+uVzSXBKRUjQX5utnUz/hA8SJKwGmI8eN8kkf3iP4/fuVaWlaItUfcu9cV1eHj4f99o3KugrMmFQkj0co9finqQLOWc49uhjF+gWYdvKQpw/0YpNm64/qUFfn4907EcS9Jy3JDa4j3Ees3JDgveB45QE03zI1d2U8NL2j4sA+CcU3+BGllqPgH4B4xtZr9QQTYEq7wVGTgCTV5UMRD5k+WBEumQyyEwldRAyBB16UWIqR783qu8fDFuhweRUYJg/h3wTmAi4RDPxLYWV2FhQLiSjcvTGrOjxbSyghpyy+MoyOf7++kg7dBqt8CVPjvWh8/w+/OOK22BK0pjOqlsNV+AkgpoxeKGBUVsLM4kbmbF45jHAHQk88veOo8Cye4DiBKW0ZfXAO/5SaYhntikNkxncMR1U5HPERqFRssYdFQiiPUJoKFeNCIYvQocKaFJkdXqCfnS5h4SRXG0tzDyLjQGv538NXDgBGM3ALfcBK+L6W5DEiHfGva2AuQnQZdiQkMZ7xQ6gXKlgUILbYcBNmQe/IoHAJpdpoMBYiG1FtyJbuOxUdMfVGPJpsChnHaz69LOK4g0ECZZcc6GWizLJjFSa0Ny0lq9YhubxfSguZlZ2LkaHxnDg4nPYvfRBWHRsrk5C6PG4dzKTmA1DlwIrdgHNe5WnSR7uelj5Safc5RLGYHFlFb2lqXdTLuLVV18VzlHKjBGLTXnEyyjxkQrMGo0nNDgOFm1Xjq2kCUhEPrLJafwiLqs21KjdAFyIa0bvUlWV8btPPgkU1gDHnos+J35qgFOvAHveh2uGoV5gIlJCqEbbORRtvEMERdmQl4FEtbFOA4APlpBLaDU2WHTbI2uJDlpN7HWk4XB7wxrk+LtxUlxPIBACWlxO/MXJJzHuCmJtz2X88dY9Ypz2eTwiMV29Sg+psvvOjo/jm1evCmL5TRUVuC8u24VOhJREIEFH0iKZ9IBawzQjiD0qQR8kUXOnrCV0sljxICs/ZgupU93Z1QXk855F6fR4tUbqmhq1sy/z5f3uGxvDixqNMPq2FxaK7ChWs9Q5HPhWQwP+4OjRKafj71esiCE0lGNIbNzI7H5KTskKDQn+pdczhGqr8t4ezwURdCk0qYg+fudkO+AfA/R2IKc2uSG171GF0BBVDUGg+YCybqzfg7mA95HVS4Kouuc+BP73e9DLCgp5jD/5EbD3VXasizZg++zngDtn0JxnwzbKJLkno/JD/GxK5DmDwNpVAKtDSWhsYw+R5PsuxxzXcmYe0WCn8zsxOYH+Mj/cRoVw4vXdU7IR5Zb50RvnOHhTxTrcVbpSEJQ9XR0413oWXVcmsWr3NuhNTAzRC1KDCR6oXQJs2gMceUG8f2V5ERz3vw/6CoXM5HiUc5UOB+1IEjbLqTnc0JC2Qz0Z6IQ3NAqthsR+HfSzmS8uJ/CjfwdGIpUFJCDe9gdAbfpSEikRV4WTFowJ1hgx/uNlPcOA86oyj0q4/6VZys4EG48sO4/Yp1wGjDlAw62AQ0lGmhXM+civcYhxyzV5tlIx9C/4GVM2RNA1fZ2mHNVNgB2FZXiur2PKAzLSbIULj3Ype+/jPRfwZ43bsJRSUNlEbqnyWMBNjxH/qCA0iGji1Bj6PP2osKRv9zApSlaAq0GfgrYie/exz0Y6YA81dY8B2nwMqnI9z0YAjwkgDDqrs8D5udwPmfXMCtQFXD/wXvAecJ1/9tlnRU8WNRgwlRXi2YK6ETn/nazHRTpgxYUMytMPok1BuaxkoM8bI4eYJXBO8hrxGNR972YDzhXOQfos2bzuiUBCM5EUML//6w8+iPZDh3B8VEmcqLJY8L1Nm/DbAo5JdSXbDVEpkARMBCRRzLHDOct/s/IoE4I726AyCQkNzo2Z+jbxmDnW1RVhHJNyXHIfIdFHsm2B0IiAcvWTvbH1w+ypcRMQGlK9hr5w1is12DAGOpOi6+ujvlXcxDUoma2h8PQy1RC8WGrfhCJTKdzBSVh0Nlx1jqLLPb3BoFr+hw52JtUZM6HGascXV23FP507hhG/V1RyGDXeiARK9HVDPjcuOYexUva+mHaMJuQYNouqCZHzqR4cJCLoSKrBv490AMVJJizPsbAi9vcNDyuNkUmOsJKgfpvIJg2rml7Hwpf0lg77JvDLzkNCL5twGKx4e9U25GSQ8YnHfwScOhTNyvzlt5TjbFoVFzRMUAsjns+Q1JCQ15YZcO0vAk4lwCrGYt1dHCSAr1chQcw1mZMns4C68bhEKKzNiNCYCSyLpTxVurqMXkwiqIlmWucVOtB+pQuXOs7BHi6G2WSCsxcotk4iL5cEAwNvJqV3CrFmD9C0WZGikrJoqgA3N5P4IC+zwRiw2rdvn8jkSTsI3NcBHHpSIRAqGpRsbBJl8ahZC/RdVBqFC2iAlXcBVTM0XGIFB+ehGokaI9WsU3po9JxTAkh8TdfZ2NeQxJgcAnzxjV/DgDf9rKSsIGFAlMSU8jwdQFZqMNuNxEA8aUbDS51Ry3VLw+7ESbKoGKTa7LXi1UFqm0dntV4bRsgAPHn1IkqqKtEw5sTSsXG82t2NMLPcxdoNrFu0aKqvw/YXX4Q/4qR84+pVfHPDBqx2OPDywADcbW14/6pVqIoE2lMRZHRO2ChPkOyZQlcABNTkFit+OC4M08rE6XQky+JKB6xsGMydwKFzr6MxWIDyXGpXR/8ul1EtdFhkWwdtmhmY8RAar1eu4AO9vbg0MSH2si+Fw/jc8uW43+USxupGkwl3l5biwsQEam02LE6QEbOtsATFJjOGvV5R/cdDzTUYsMimBE9N2liniQVbVj3HAuAKhGHRKcbliK83SmrwJAcPAc6W6L4w2QaU3pLYoGo7N10Cr/XsnEkN3keSxJSmagkGEdq8BThzKvpd/PmLn0UHuHSm/79/Am6n3ZPi3uQ6gE9/HvjZ94D+XqC8Cli3iemLwOIVSv+gDMD5y6ABHSPuAeE6K5544leoXbkI9gIHguEgXh04jndW75nXbDBWMPIYutu78cCtD4q1g4QpKwynYdeDwPJNosF3ISUHWZEXAdcQlonz2nMPYcNCyp5kEjAY9Z2HM6isQRxHrkAPSs3bMic29j+l9L6QYF+BJ34A/NE/4rqB/Z4qtgDdh5VFUxJqISOg9UXmibqybATofQmofCC9Cib1PJOSjZyLZcuB/MzlPONBx5EO5lxIDb43plkrfQn/SKwTZph/mY1s4E8b18IbCuHVgW7BIS21W+EKeqZIDsGfdpzB36/YfZ2PdAE3KnyqHnrpPD8bMICUqaSUOqDMeU+9fQY7mcBB25O2GR8MmGW6N/FYEtlbMpDOKoENGzKv1F9AdsF1+sEHHxTJbKxkUEsxUQqGFXesIkgnoDhT8JdJKPRD+J20ITim6L9IKRJ+B+2lmb6L/isTKiiXxc+YiUxgZWkyWapsgNeMvvJcqhuljBzPiWTNXHyVmUB/kvcgnjjhnOX1LLfbcXjPHrw+MiIkhTfk58M6S/LpZoWUHeY9oe99o/YC4niT/WJohzNGQDKSKg8kuq81ESAr81j9RXKDe0cq4pLjLV7ijteciX2cU9zXOFZJbiwgguJ1gLNbqc6QPkHlLtws4P186aX0+valvepwsk4NJAaN87cAI4ejTod9BWBSCACDxgFfWDY4jHyRhll9lKeI6soaNDk4Pd4isgHlK2usJcjRzW9jmm2FZfi/nfeJxff4aC++evnIrD8rYWVEouoBbt5qjeIrJ4DjzyuB0nIGdd8MmOOC8aYcYNn0LFENTAkNg6uTzYDGhkpLHcxxgf3n+07DHWADUOW143439g6cxb3laTbnIaFw6nBswEmUtRyMJTX0NAQuxR2wEdDNfuOewuCZKKFBe5kGPpvf5JujjvvkRaDgdkCfhe+bofLDGYg2eWf1S75R5XT3XQAuvqr0QcmrBFbcDZgyK2Nl2SsX6nRJDfYNiUd1QwVMHhOqS6qFAVK25WGcP/Ik8ooZyGXD+mWKRMpED2DJVfpHJJBGYBB5Su86/ns1GrEZMhDHYDSdGxq23Ghkpg0zYqYcnZF+4NH/VsYUxxN7QzCYuWglsHgdUFobG8Tf/LDSV8PnBvLKgdw0yiVrtgHn4qpSahPIVPF4SJBIkoSERmfz9NexbwcrqthTQz0HKpSg/TUDA4aVi4Duq9FMXpJBDKA+979KdnvdKtQ0bkJ7R4cwEtiEXhgJoQBWrVyJ1rY2kcE/k9FOsorGvSfPPL22IRxGyOOBu28QP3nuSXxp+/34h1tvRV9RIf4v4nAszc3FZ2w5wgH5p9ZWIU2lblT9ZydOiAbGGp8PofFx/CgUwuGiIhTMkHFERyBVhlVK6GuA4JjSKJxgI2vLpoRyMTT+WD3CQGw6oHbsv148ixOjwyg3W/BAuQVjgU4UVZfgQksXSlbXQMeVQnxXGAXGKuQZKwXBr6Nk4SzA+8AMhpdNJkFocLZJ+a+/P3kS2yNl9rKPCR8SfN0/nD2L/75yRfz7Q3V1+J/1t+Dz547i5OiQiK+Gwn587PjL+Oq63aiy5mCNYzFOjl2GTaeBw6B8P89mPMCdRQObPk4ywDsUITTE0So/3N2AuwewJsgQ5x4ZTx7OJqmhvRW4dF5pzLZ+M7R6vVh/GBihAb14UQNw9gwZodSf4/EAk06FuJipWfSf/C2yAV4/Hqt06NsmOuAoycdARx9CwRAcxfnwhHwIhIMwzKHfRjpghqyUeJPjqLm5WQQFpslAsGdAgr4BfF1DQ4MgvkkWck/YtSt9gzoY9sEf7kCBUdn22XRywh/AZKADDmPqrK5pYIWGuvKPc4VSfqLfUxYlzjJF0QqlRHw8IrVJBDyAyaFIvPoiSTKRtUNIwLJiw5SGRJ7ZofTA4B4vG4HzXAszvHYzOB3plofP6F8Q9pUKqeGPEFA6C5CXgEy7AWHR6/G5FZsRDIXEXP5s80uiYEuCd2CUduECFpAEDoMD7HoWilE8QKyPkQWkatKcDhjklBJU/Cyu9ZQ+ZTIWA2bpgq9P1aeA6wt9oRdffFH4RQye87tpf/DBKi8GdecSIF5A+mDgc+fOnYJooK3CKh6Cazi17mkjpOxtlwapwb9xXNC+5efyeyhDpg54ckwwWYXHkuq7uL+wsXa64DnIc5ov0J8+deqUGLdzacbM8+ZcoE83H02d6fvzfidKNGOll7yuBq0W2+bYK+RmBu8jqx4YWCf5mk1ZvmyDazZ9e9r2JGDYGDivLA979+/FLTtvuWbSWZTEln0+CRJBjFkwYTET+WeOQfaIIkioci7cKP1MbgjoKS/7JmCiU5F1tZYrldo3CTLxL9L2iOlcx+haWmoAY4nS0I8BdH00YGvTL0PQ70IQMmPXDpt+OjOeZ8zBmyu24/WRi3AFvSg3F2JTflNaE4oZv2z4bdcbkGec3eJBrekaNs2LZJBKjXj+dBjMorfG7D7YCFStAzqPK5/NwEmA8jdjwMQgMDEK7H8k+vrOC8DLP1H0+dM49xBMIjiobvrJj/eEhjHqH8aAtxtr87ZPERs8nz7P6FTT9aksYl+EtcsmjKWAZQXgZrY7g64mwL4VyEbwxdWvBCGmZEMooxAAPH7AEgmE8vfJc4Bjfksfm+xL4A66MBApEc81OLDCETGaRjqB009GXzzSDpx4FNj8noy0tslgq3ttzASrLhc2XR4mg7JKSCP6hjSULYVeqxfG4MT4OOzM/pTZqqPtwKgLcNLJZlrhbqV6IQ5k0cl8y0aE8aDRw4AVSweZOS7Lxgkapsx6pwyJyKzxD0BPskOdJc3A0oUjwKXXFTmnatV6wQBMWYbyIHnVwKq3AoMk2NjHphHISSMQXr4U6DgNDLZGdM1DiszZy98H9CYgr1ghZYj61cCq7EmapQWSdw/8LnD4GaCvHcjJA9jr6IiKwOmn1I8PNSt2CuJi7wtPYmvpBCwaDzRaA+qrtuPS4MiMpAYdRBo+5aVFU5J9xOTgCEY7ezGuNaBo3XKU5TimMp9+uX0HOt1uITHVkJMj3scMkIFAIIbQEJ8TcajDg4NkUNAyOYl/vXABX5jB+aDDMWudW9FYfjUQYlaTH9BYk5Zg0qmRY3emLCiSAh9+/QAODQ2IMATP+4V+Zu0akW/UQm82Yf+ZTqxqqsIieyVs+nzkGSrmbDxevnxZzLv+S5fEd6r7maC9HUWsrkyCL1+4gL8/G61K+teLF8VneEM+2A3U8VbAxuHfbmnG363YgnV5jcg32nF5ohkh+GOOn9UaNr0GRaaamSVjAkkqnNbfrkhQCUQCuGsznGOvvAB8/d+j60vTMuCzXxSG/BRBtXoN8GvVHszsJDqEzsnY5/ILFGIkS2Bgnw4oHR4a7XxImTI1aJSTFC4sL4Kn8yyqlijr7uWj52CxW2G35kA/y8qedECbgcECBhLU6wSrNKidfeTIEbGfJJQEEX1ZWPWmjyFIaZhS1i3ZHjINJLfOvwrNaCcKcoLwLamAxmyEHmHRuDwgeuNkiKIyoO1ilNjgdWdVyfUkNCRIUsSAzeYngMINwPCJBH030syo4zk23Qt0sGKqFzDYgOrNCtmRJXCMMPgxW0zzLyiJWXgbEBhTiHpDnlKJexNBF8l4XGTLR79ncsr+Zm1kg+3mqDpZwPWBRWfGxoJ1ODpyQlTmccyszlsJR7qSc2mAe0w2JGviq7MZaM40IMzsWtqaqeSF6E/ccsstU1Iv6qAhA1q0hWgbco+fD9mgBcSC94FkAkkHBidl03DaLdwPmNg2E8nE5KR4QptjgMFy3kvaH/fff3/KsceAJsmBZDJqc5Gsmk/Q5mMVNXv48fwzIQHjQZuKwWkSJXNtQh6/RnBvTuT/sAcCE1Tmg0i5GcH7eb0D6WziTlKZx8LxRP+CSbGJxr+UbPI5fOjR9kCn18EVdOGl117C7Vvieo/OA7hmkBS9665o4jZtQI5f+tysAGQyFP2GmSB9EUrGkVBdkCpMANrUjgz78d0gyMS/0Kfr4PIDpwXBWFGRoKpCqzHCYdiCYFgJEOg0bGyc2AErMjlwT1lmAeiLE6P45MmDGIxkdL67ejE+vnjmrIBEKDXb8HsN60RDchHGCYdRbrHjzxq3wqySehnyDWHYNwyDxoBKS6XocZAS9TsASz7Qdjyi5a9RssC7zwO2qtjmxwy+9LcpUjbsqTEDtILUUBq5qoWeZNCQRnCvpwN1NmUjujDRjUBcXwK+Mo9N39MFnf5Vm4HTqmoN/lwzXa8VlkbAXK9UUlAeIhu6bdSE5nWUhIa81zwGjy9KavDM4gJpHL8TgWH4Qm6Ewnq0TPbCE/KiyFSAZfZFMXJn6YK65uvzN8Ib9IjqF5OW0jKRY+q/HO2tIo9xoh/wTCjVEGmCDkcmjY74/Y32Deh2X8JkYFxo9FdaGmHQRo3/nstH0WSLI7McFsDtUyo2zr8E5FVM02am8UhGnRuGPScHNT1XgQPPKxmua7ehZ9nGKeMmfgOls8JqAWkk7fvOV7AtGICJujVqCK1vDXDs+VhSY7bIKVUemYDXevM7gJ7zijRW+xlgtC9yLyOVOXveCxST1E0gZ5UInVeBIy8qQbrFq4D1lN6ZQzCbmes73xT9/dnvTH/NuYPAip2wmo3YVTaOfUdOYdPKOth4yO2vIBxm9nXjVGk2DWt11pRsyCcrdN5dvR7/78J+9F1qhcmeA9vyJUK5hbi7NFYDuVpVDUDQ2Vl96BDYuUR+g5okibxRrGUkRGYCjTQabulInbHSgg4zHzEQcyI6L/h5NN65x7GyhIFmdWUIr0MqZ+HixDgODEV0+iNrsSsI/LLTj4cqDCipKMb48Bh6h/y4tXT2zfPo6PH4eH/oVNAI5LFuLSjAv6mup8blQg4bqacgrn7Y1hbzezjy3Jq82G5BDMYxgWCqisBWji7XJUwGY4PK3ONrrWvhMKjIQ2NeYjlCVrINtAOlq5Rs9JajgHuC6ULA7e8Grp5UJNWWbQYqMyjj9riBb/5nbDUVKzaefQJ44C3R527bQ0YI+PEPldfm5QNf+CfgO98GDh9SXsMxwOeymLXEOUVigOsgyQH2zuC/5fqoJgHooIyUjMLhMGHUzx5kQP2aJrSeuoSP3vf+ecumYsCIzgaDE4mCU5x/JBU5t6Y14H7ke8CZ15Vrtm478Kb3KX0rImMn7ZJ8Li77fgiMD0BLO2cYMA+Mwbt7JTR6nWhWrg/Pot/NjnuB9ktAf1d0LX3w/bghQCnI+LnCql9bLTDarPQRk38zlwGGDIIglFism79yc66bXD9nC7n2xoBjiGTGTY6Hq1egxzOBq5NKskmZOQcfqE2vj8ECfntRai7B3WV74Al6YNKaRGJSNkFSgAHfbILVGuqG5LORiGQgnFK26v2NexITTKRvoSY0uI8yKCz7NjHIPlfp0AWkD2Z701YhsSB1+RlclNXUtFUpj8T7JyErbCRBJQltJs7Rz2RQMt3EJdoj7Osgk1Ljwc+rqVEl2qSBdHsi0HajPU5SIZU9xnnGa8Rxy2sjE/5kYJn+11wCsfwMkjv8nLmSGjwfElLS30jW95NVz5T+WsCNA94vSgLS3+V8o6wUCUJWjsT7wPQf+1x96Df2Q8dSDbo8eVZc7LqIVcOrUFyQPSn1eJCA5rpN0iJ+3jDeRVuQfjf98nRIDQn6wvG+1AJufkj/Itkar0ZaVhInBxflTBZLBjj0mtk3WVXDEwzg9NiAqM5YYi/AX5w6iGGVRMVPOi5jsS0XzmBIZMra9Xp8oK4RDTnpff/OomqscZSgzzuJIqMVeXHa/m2uNpwdPyskhhjuaXW1YnvhdhjjNMZjoIloFp94JvJEpPGjyEqPBr9ikGa2oE5TAB0KEcSQkPwIh9mkN4zRSIxJhzACoteHgh7PyNSxq7EuP0PW7sH3ARYrcP4krUpg132x0lNqsDIjRUPUjNFFMiWk0oSW30PyIO61xug45SRonTyDQV9UR3/MH8JYAOj3DmHcP4FthbPfmE2JpNKSkRAZahXaHTloH2hBcVExcvSKfNtMYMPhaqsiF5LwEChbER84Eb0ktAqpQYz3TSM1pFYuM/IvPfIT4ES0uXb7Iz9Czu2jKH/7BxJ+p9rJIfGx480PY/+//A221pXDbIiMeXFfZZ+K69wQlPepcrlCMp58PvZvJMD6rgKVaTpMXS3AD/8tOvevnAUmxoDdKlJirpDsghokmwjPKPTwYtf6Rrx+thUmgx6rGquh8fQLY5/VQMzooHEaLyfD+80yfxrKLqcTD5sb4LhvBy55nDgw1C3kYHYXV+OestTNfrlvvJmNmvPy8IPublH9sjo3Fy0dHRhlYDRS1kwiYJ1Kaq3D5cLnz51Dl9uNjfn5+PTSpTDrdMJJSkc2gY4tHV0aRyTW6HgwUCvBckZmFfKz6FRxbNMIpHOQqBkey6/l893d3eKzJeHoldc7Dp6JSbx62omNBTpoA35UzaE5LPdhOhwMDMffq7dXVeEvmprwLxeZhR6GraMD//d7vwdLil4QxgTrEUvI2W/j7PhITGbxUntsZnG5pQKXnbEyg9XWOuSxUi/mA+1A0RZgUCVVyaowNx1cDTB0GeCSND6irAGtIaBmNXDn78yOTBgZni4pxfPsUyTRpsDP/v2PAu96Dy8sdRO4UAFf/lfgN48CF84DTUsYaUE2QUef84kVbRw/fHAOMitJrfPM8chAwGj+uDjUPAP11IGwXo+qnDxUmOdH75lzgZVVzIjlcSUC5wCzrO65557YPzzxU6D5qPJvrnXHDwDWHOCed2R+IEMdwFh/9DtZ9OnyQdc7imCVsr+bdbGVd86AF0/1XMSg1yUSU+4pa4pJTBEwWYD3/TnQcVlUs6GyHoj0jLnuKFoLTPao5LHCQMlGgMknFXcDI6eVBtqmIsCxXEneIBFyA1QwcI2fS6XGTKTxzQyb3oi/XroL3e4JYYNXWOyiV+ACFpBO8pRNpYKQTcwHKc59IV253HgwaEFpSNpr8cEL7o/JGtly7VCT5QyyM1mA5IaUTlzA/IIVFbSxaTvQjuH9o+/AChqSFnwuvo8FX8O4Eu8fZXsI2ui33pp59TvtKQZv5RhhwJbfz3FDm1ndfD4d8LhmCqAxGYzqAyRMaKvRt1CTJ1IalrY6/QQGcKXsrzppStr2tLf4fr6P14y+SUz1YhKQLJH+EKte+BmzlT7iefA7Y/pbJQB9R77uRpZY+m0EiVz2uqG8K9dhPjin+BwlntSxGBLHL518CYZ6Q0xssHpZNU6eO4k7dtwxL8coEyhJtHDsJgL9cO4Fs5aZXsAbCvQNuCZzTZyWoBoHfbrGBhn1mT5sPjDq8+AzZ/aix6MwxxadHqM+f4zSKLN+v9d6GWfGx0RXAS7Gj/V04Ffbbkd9msSG3WASj3iw6uHcuNJ1XU58d9CN1slWNNlnkMShcxqXzSocVbsDGB6MDS5TyiZRo+QE4PlZdGvhD3chFHbigrMLo/5oIIf/smijhrBF9PiYnnlQZMrwfjLj8p6Hlce1ho9BsDDThkUriGjAiz0FVPfNWAbYosbTmH8ghtAgKAczGQwJya5Ody/cQY8o984aKlYAHSciwYmIhnVxg9IjRQ0GQrtOAM5BpWdD9XpF4khIuUzgYMsLaFxXj77xK7Dr87Asd6MgLeaCMKUnEJdRyf4IvgD6hsZRmGuFMXKcNJQYvCX7T6OMRik3RfP5Exj3+pBrUkg9Sg1Vn2Mw6wNpZb0YKuux5Q8/hZe/+1/YXVUAM9NuqRsuCauKG7PB1hQy8QWPvBQlNCQOPwfc+kB2KpiI2lXAUHfsAdZGsmumGohrsWVVAyYmPTjS3AKXFVhcFRbNe5MZrzTG+YjXouXserAiMzmwHdu2QXPgAD5QUYEJtxvV+fnoqq7G+y9cwNjJkxSYxVuqq/GxiHPaz+z155/HkI+9A8J4qqcHR4aH8fjOncIh4NjkuFSTFGrQkaFBJKs0GDxmFhjfx/HIslw64PHZfMkCa3SaaIyRUOFeyNfxd/n+JXYHqixWdLtdU3tTyOmE0dmPnNVNqCwpwO7iRuT4zCJLhU4LjymRbig3b74mv7ICh8YHhLO4xJSDente0uxD3sMvr1mDTzQ14eDx49iwaxfq45qpxePjjY340JHYflJ/2tiId1ZX4C9O7kNfhFxstOfhIw2xTdkbbIuEhGGHSwl8V1qqsDgniUa/vUHR8KSUztn/U5FwslM6m5Wq5kj7KWDxVsA+iyBnQaFCuKsboJKsrUqSqUebRm3X/O/3gO9+WyFCHn8MePYZ4D+/yrI5ZAOUCmNGI++prIKgE8oKOOnUSrASacgzAp1ZJ+6viY3YuW/n5qUl7TAbHDt2THwvZaI4zlmNQUeckIEKOhwc/9PWjQsnY9c5/vvCqdmRGtPspshoiRDvWpRCQ2lLVeLLv1zYhyEv518Yne5+XJhoQYHBis2Fi7AmrybWjqm/AYNdlkKg/n5glH2bgoC9FrCVR8nBku3Kvz0DQPfjQIhjXAsUbATsGfR24n3peB3oPqNc1dJlQN2WOe1HDNQwIDJb0MeYqXnrzQytRoMq6w1Cni1gAZHAb7ZleRjkpF2VCHye9hMDbXLvYDCXgVg+T1uI+x+Pi8FoKWdEm41Z+8kq1mmjcl9SB+wYQGaAXb3PLmB+wevMqm4mE9H+lpJUyWx0jgHeMwbQM8nGTgQG2LmHkByRvV2YQMcqA/oBtK+o05+JnUb7nlnvycBzlKQZ7XIG3HjuHKecVxzTJFPU55+s+oQ+Cm06jnu+n/OIdhcTzpJJxNEe4/nyHOXn8rgZKKYPwe/ncSXy70jGcJ5Rqo2+iPSNGACPJ1ziwbnG12fSF20B1wYc95QsZ8UbE5N47zmetm3bJp7jPZMJcfQfgt4g9HFhYL6eijTzAVYpkXDmOiEJNPpDcswJVZWJCfG6hXV7ARJcBzluucZnhdRgmRANjmvVPEaNH7WfRV9E+oLwBoMia3HAF31NKBQWhIb4N/8XDoumtD9sv4LPLl87p+/3h/zTKhxY9eAJpdHoj5UX+ZXAaHess1+1DFh6K3DqZSUrnc2GV+/O6LhYCWPUVCvNtPxxMhDUNQ1ESz3X5NXhzFgHnAG3OHY6/VsLGmFO1ND8eoPNMT3DSgYiexnIMUeH3+ekeH00qMy/5VQBFTuBsEfJWNTZYjJ8PaHE2u0GtjoJR4mrrMJWCGx8GLh6CPC5gPwqoCFOpovjoflxYJgSMJHjHbgMbHiXkIq46DwJW4EF7Zc7UddUg4nAKDpcl1GnImxmA72jFp6QH2afKgg+5obP60db/wi89nL4hv0IDJwVBieNnnijtMRuw1BflNQgqOWfLF+Ui1F8AHrYVoxVH/8HnGy9ii2uFqWvDFHZCGy5DzcETFagvJGaXSpSMKxkkqcLZgPHlzIzMMdxnK0E22VbgYAXOH9YIcrqVwEb746cgwPIrQHGleCz3WbB1vXLgaa3AoY05bMyBHtjHB4aQr7RiD0lJaJ3EcdSfAbWBpabNzVh78qVGLxyBe9cs0a8lvhxezsGvN4oQQDgyd5e/KqzE2+vrhbOAgOvJBskmOFHB4APOghSjkDdfFmCgdlMStJlI0o16DTQ8eHnDPT340ebd+KDLz2DSz29MGjCaMozItduQTgUwvr8paiyFgNWpVyeYMCYjp/6uOgo8TzyairxkSd+hsHhYVgKC2EpLcLfVJYhdU0MYJqcxGKrFfWqc0+GD9bVYczvxxfPnRPEJKthfreuDrlGI7636U5cdo6KjOJGu2NaZrEiddckHmmBjX65XiSqKkoUTPXNslrLZAY+/pfAV74UrdhYvwm4496Z39vXpxAaBHvpEM1ngKefAt700OyOh3OfezyPK3KezCZkTwqui9KZp8NNTWQ6pHI8cFx1n+tBsC4k9m2CUokl2pJ50TFm5RKdC45zrtckN2RVibpx+Lp16xI756yCcEbtDrGv8bnZoKBSkYYKKOunENrUahAuroFeUw69Jjbz8tRYLwa8SuJLnkEDu7Bswxj2T+Lp3lPQa7RY4Yj0U7mRYcoHSlOUzwe9QP/LSu8wgRAw/BpgdCgVHOmMx/bXgbaIxBpBgoM2U136/bviQf+Aa+psIX2MBSxgAdcGDF5R3iOb4L7ALG4GreLB9YF2mfxOKZ/JKgt1bEE2AE9XCogZvQyQqUlRvod2Fo+FBPyC7v+1Ae9joqqMa4FkVTnsr0J7i2MkFUmhhiQS1PLPHFPcp2TD7HiZNdpL0keQyR/JCJ1EkJW78ZUePH76BJLg4JgmAUHSgyQGA33SHuP1J6En5xdtSr5GBo15PvxM2p08Ntp3JBWZxZ9ufO/w4cMLEj83MHhf2V+GJAaTFiVJRtlbEr3sgyPJ7EJDIbwBL/x6/5SaS7WlGi59kp6HcwDHM+fhnj17BHHBecSxy/Et5xTHLBOreIwLWIAE1ybpY8j1bc7yU3NpYjQXdLgmYhtci/4F3GRC0IvGqIDDaMSIfzrJ4MyCXii1TCkz5WN/iAg48R3p6hmvfwh4/RFgLNK5vW49UL9BcSLvmLuWM48lhGBCMkZdqfHe2l04PdYOl98Dq14vqlJ63EMoJ1lwo2CyF+h4IaIdTSuhBqjarQSDKrcC7mGlYoPi4gzINtwDmKUsSuIySLOqYkVCZJwKbkQDm86KDmoda8ZQay2ClU3eswHKN61NEQib6IsQGuKIlB/uEUFshEuXwh10Ymx4HIuWRZuqTgYSN3YnKeMJemHWmUS5eirU1dfj/DkPltWsAK68AowNAf4AzGYjivJtqNt2N1CYupFrd80SNA1Eq18a8uw4VViLpUky55kpw8VIMu80rLip0fClgYY73w9I4tKcQZ+Xa4EtbwFOvQD0XwWMVmDFrUBhBplfjauAS6eiv3MsVzUAA91AISVvsjDe+Jmrb1Me0/7Gqo3bgYFTgGsQMFiBkjXzRmg81t2Ntx88KEhlYldREZ695RYhG5UIeUYjHlyyBMHFi4WxTCNMrt3CyI5zaN9x6BD+bnwcn1uxQvxdkmUsYWc5uMza43hLpd+fjQAas5P53cePHxfVLMwK/OmWW/CtwfNomVQk/4IBP0rG/Vhqnx5wJGHIecCqDJldRmOTQeT/uHQcweIC6D1umEoLxb73n5dOYEfR9GCBBM+bGWnM0EkHIz4f/uXCBQxHqmFeGRzEXXv3Yt9tt8Gk02GFYw57Q2gACJ1nGgLzLADdKqW/Um5VpMeU6r56VRkKosG0AZiDTBc2bQP+41vA1ctKk+8ly9OT/ksU4OH7BqIySBmh4wrwi28ocnOswnzo/cByUnkQTiEzlGhbCUc7HMbKphycP3MIzUOHsWLtHcjLK0J1bhVC7jB0RTqxtldbqtA20iaCQ9m0yegg08GV2XeJ+tUwo49rd1Kt69seAH75ndh+V7tTENSsAKHshM8HUJtZPSdNNmDHe4DXfwM4R6Cx2IEND8LE6oUE8KrIshxR9BfroJ8a65g7qXH1qjJGFi3i5Md1gXdQRWhIaABP/8ykxlgncPFZwK2QPzHoOzcnUoNjkWvZbHE9fYwFZBeTATe63YNiGaiylN6YyVMLELbTtD42WQAJhERVtCTraevM1FeJgS11trjMek9WdcGgXfx6z0xyfg99PQazk0lXvSHBRKqhPsBiAxwLRLG0tyhPRR8hXXk0jldZjc1xRNuaJBzHMO2lVBnDJDzUzc9nA45pVrowEExigj4ObXwGgdWESjLZRxIgrOJgBTBtN+kv8TNlUJtkCQPg6RIalOFlUPG6k4S0gdyngUCvInVuagSMqeMWv03gGOd9pewUqzRIaojei1u3CmKDBAft+C2btmDv/r1o3NgIX9gn4poV5gq8Flbk4LIJ+jty7EkSgyShej/ga3jcC1jAbH2MtEmNdJqyEuFwEGEwK5iZljnQoCppk/B0UGXJwRXnqErfG6iw5OCPFy/F8dEh5BqMeHNFHT7y+n5cco6rmmWHsbNo7npsXOzX563H0ZGj8IeVYDsnfY0lzSxfOuM73w/43YoUTLaC5hEwc9Oud0QqM6KBojxjrDFDYmNT/iLsHTyJ5omoVvbK3HpsKrgBpBgo1dT5UpTQIHicw+eAwhUAZZOWvAWYZIApDFhLlOaXM8BhKEKxsQoDvmg1y3hAi0A4CIfBjssTbpwYOy6et+gMeG/NNhRnKss1GzCzPsnzHHPjA5Ow2a0wW6PGgzYBYdHt7sOh4ROC2NBCi00Fq1FjTR74pJxUIR0Zox0YjgTxZMCPlUWDV2YkNbQ2Oz0N1oWL3/WrNmLtu/8QV1paEjossg+A2uGgkUZwc1Nnv2QFDJq6hgCTHcirm1ujX87X9WlkeSfDmu3A5Dhw4Bklc5z67VcvAN/4PMDr+N4/Acoza2KXMXhfS9dl/WPH/AyKn8Dx0UHkG034aP1yvPfwYdH7SGL/4CC+cumS6IeRCgzo0winQ8uf95SV4XPNzYlaTOPvzp7FmyoqsHbRIpF1xEw9llOrM7XojFwL0DBkYIA/ZRbYXxUW4Lm+q+j3TqLcbEfteEDIjyQC91U+SEp6Qz7kUD+f8XWvGxOd3bBWRefyeMCHYDiUVI+dpNCWLVvSdlAe6eqKaczOPfPw8LB47JhLsCM8DoSOq+7cCBB8HdBtBxbtAa48r8xRnkf5OqAgBJyNyLQxO38zq4jMyu9+r/JcpnOYe3+m+39NtSIzxSC7BNe4xsyk1gRcTuBH/wXI3l/8+ctvA39QCpQqwXUGWkiI0VCzG3sA7wUsXWxDR+cATh/5CVZtereo4GDT7q0N0YAzs+w43ucaBKajSwedji2dZTo8anCdVjvmfE1KrN2mkNInDyv3a8NOYFES24JZ/R/5CCD7dlBK6+tfZ7pl9DX55cCdH1XGwQz3f4m9SMiQsmIwEXyhEQx4DiPPuBwGbYa93viZX/4y8POfK78zoPD5zwN79uCaIuAEhhI5m2EgVX83wjsBnHsCCCVL9JlbFTbXMHUj2EyRiY+xgBsXQ94xPNt3SNjXhEl7AfeUbUOukD1dwI0E9jtghnY2weQM7me0yWYL7m3xckRM9OBelQxqaSoGoGlHSlKFgdzfGnS2AD/5apS43ngLcO+75+YDvUFAW4pyTfF2zkzEBCvCCfqwDObzOWaYx/e1my/QN2YSVrJELPoelOtJJBtFXyDZXOR7OF/T3XdJntBmTFSFdc3hPgEEeqIEh+c0QMkkw4LUnATvK5MEWfkgxzzHL5PeSGxIsqO2uhZ53rwYgjtbqjyM/VCRQMoLqnvEcI3m+FN/F1/Hx80C7jUd7lZ0udpFgnmZuRL1ttjKwwVkB+n6GGmTGun00yChEcLrjEZP9YsIYwja8Nqpm8xB0OnuwKB3EHqNHrW2OuQakn/2e2tXoHl8CP1s2isaM+vxscXr0WjPx+6S6AL2tfXb8PHjh3B2fFRUcPzR4mV4oLwa2UC+MR+7i3fDGXDCoDXAqrNmNmj5WmZ5ZxHh8DjCoJxUDpbnrsXpsaNwBZ3ib2WmSlRZpgemu9yDuDKp1t4Hzoy3oMFWicL5DOQHu4Egs3YDgCYP0C4BAiPK3wylgNYCBNyKtMK0DMTI6wiSQvbMNlSxqdtWoNBUCV/IDYsuB1Z9rhiHj3QdxZh/dCr05g748VTPaby/LrGhz/ecHOtAr2cMdr0ZG/LrYE6DWEkIe4SUERmm8gg0CDsq8Nrhw9D4rKhdZp1KVOePXs8gaq0uWCOBT2bEHRw6NkX4hRDCa8Mnode4MB5oRygcgE1XiErLKugjQQ8GpkRpbYLAqNloQHf/MArrvMkbgI0MIvjYj4A8lSF07iQ0g71Cx5Og4UNHgtnELP2O37i4mclME2p4yibMWUH7IaD3RPT3/Hpg8V3Xz6jn9+64F9h+D3DkZeCpn8YGPn/638Anvjj3/hpdF4GjzynNzcsbgM33AcYZqjFCk0CIZGuOkkWfATgX/vLkQTRHGkoP+7z4xMlDmIirjmMw/0IKdv0/Ll3CP5w9i8lAAFuGh/HrVatA031jQQF+snWr6PngStAU/PzEBNbl54tsqqNHj4rMEwmOv2vhcPAa7B0cxP6ODtyal4ftEaOQe9QDqp4jl5xKY7REe4bIABu/iHMTiuNELdOdRRvQmJOHvVoNtHrdVLPuKmtOQkKDn0FCg6RKJhlUbs7LBKRRouudEcLxlQ38BjrZk0pvgKUPKjJpPBd5TWrXKGPXkguwuXNfO/DMD4DJSJXD7ncAi2J7u2QduQ7g7/4B+NxnuVAqz73jYWBXepUvMehpV2SnYhAGWi5MkRqS2Dh44AB2ronOkeqqYjgnPehsOYiqxgdF1pW6kTjJDAak5tIQkpmGLE/nvEnk1HKfONh3Ef2+MEI9ISzLrcQdpSumJD2YLZgQS9coj5nwta8BXapeVy4X8LnPRYkDNdJYu0vMOfiDhi34ftsxOAN+IT+lftuiHA184TEMeI+g1LwDOlU/jhnx4ouxx8Wx8ZnPABs2MCUO1wxDRyJ9NOKgtwO2GYKIE71RQkOrUeQP1aiYWyYz/YO5khrZ6tnnCrhwdfIqvCEv8gx5qLfVi+SfBcw/Dg2fjpFz9YX8ODpyDrex4f0CbggwyMQHM66T9amYLWjbp/KN6RdwrlPPPdl3Jwpq8TPpS1Aqh0FlVuJKWVv+VPsPTNxSy/gQMzV9fkOAdtvP/jta9U68/ipQUQesjfRjmi2YXHL8GaDvqpJwsuo2oDJxf7cbGZn6BbR3OOYItW1NUiNZf7trDVYvkfBTy4SmA64B6b6H58uKJwbCb4gEWEloqOHvXiA14sBYD9daElKyXyTnAMls2v+UhKYfwX8zIU4SYyQ41D7HbEC/nETG+vXrE867kxfPoMMxiQMtLcjRW7CraJVoHk7Chcd1M6zXne42XHFGpNMBtLkUP74hWX/JBcy7j5FdUkM0IJbOuXSaWBrHfheK83fZeQlXJ5UbT3mOXk8PthQykyfx5+cbzfi3tbfhxGg/AqEQVjqKxXPxqLBY8avtt4vgGOWppDZ7tqDX6pFnvIYObBLQOAuGLyKMjqnnDNombMzfAV/IK5w3Q5KsPWfAlfT5Ql7TEO8VtasLFdY7G+BnBlXyO+FhwH8A8EaCRh49kLMD0OUoQS7RXFuFLGR4cXG0G2j05sc81+8dj2k4zxHb5xlPet2f6D2J02OdIsBIuu7MeCc+WLtLBDEzBuV/Vr4JOPtktIqnaQ86RjzCSFlZtgKHh/dBF1nXlf4fIfR5u1GvV6oeRnyjU5VJEjmGMEb8yvwinMEhtLuPo8G2RfzODYzNzARjXtIE9MvmnhpUFudhrG6tKFm8/fbbExuAXa3QBEOiT0B00wkB7VdQtWy9KHWVpbk0CFkyzswtlv7SiaHDotbEMxkN8F44BjivKFVNK3YAllk2wHUNxxIa4iK1AKOtCrlxPcFr1d0aO8Z578ZHgEknkBO3/o0MAq+9rDgozHZesTF5cG+gA3jhR6rAKaWmxoE7P5j8PZ5zgE9mvmkAy3rAkD5h2ONx4fR4VEOdo9CgBYxaDXyqgBkzpxuTNDT+SXs7PnEier/2dnbioV/9Ct/bvFkQZDWjo/hWRQXeS4kaBj1JxpEMC4dhGx5GKNLvhYFddTM9jsGZJA7mCq4HHz9+HF9jFhcfPT341MqV+FKczMG434krhi7sff11LKprwPq8lbCr1rRuT98UoUGwGnDf4FG8o+pWHHScRptKputvlk7X2ud8OnDggMhCy5QYvKu0FAatVlTW8I4x073AyIq+uRKM3HsTZcxrYquH1KCjzAfBMf/4txVHWlY5PPdDIO8TQOHMvULmhFtuBX71KMsUgKJCoGaWGaeJeklwvrO3hgpcY+lY7j/0fWzZ2AR9hMRatqQah472Ydx/VpRrk2SQjgd/p3NJvWQ6AYnA3mMTAT8KjKapKiG1PjQddY6ZZFl6TzQfxFXLJPQhxQ44NdYulpK7m1Zj3759yUmNTKScVORZt8eDvJYWtpyJgmvY6ZcBykPmlQKrditVO0mwwlGCv166G/sGWtHrHYInOAmD1odluTrU25TrGkYAnuAgbPoMMvpY9cW9UE32kdigdGI8qSF6qHgAnxew5igVjdmCb3T6vKKdVn4XoJ3BXlP/XaMiNsy5QOXaNwyp4Q66cWDoAALhgLDR+r39mAhMYG3e3HrrLSA9sG+feoTyHkz4E8idLeC6gfaRlPnMNmiPMVGJ35EoQ5yB4La2NvGgZGcicG9JFKRlgIyVufRdSKyTuJB9AWgD8jsZuCOxopap4uvZx4MyuG9oCF8iLoGIdlZX69xJjYOPAH1XlP3NPQHs/zmw+/1Ayewrcq41GNhlop2s+KEUlbraVd3XibYSq4W4J7GiQd1knLZ/Ismz+QKPIZVsG49jpr4ziZDuOZAkpB9Pm/PGCDTzGBKlYy0kLiQCxy6T3jiOpd3OpNK1a5VYD8c5q2+YKEVJZMaIGKPhe+iHS6mymcAxKOcGwfeq+0WqwWSHVwZPIc9aImyEMf8knup9DW+vukW8h+t7Jj0v0zk2VlzFzCHOmSvHgdYzyjq5ZLPS0zUD9HpUiVkR9Hg6F0iNG53U4EaQqLRtOvwpnw+FQ2iZvDr1rGzA3e5qw0pH8ixMygJti+jYj/m9+FHbBYz6vCJzdVdxpXDcJWzZdCJng6APcCnMPqxlaUkkZYIwhmMIDSIUvgitpggmXep7lEfJoQRw6FkR8Yrq/pkB0zalgmKuCPVP34DoUE89RW3EU0DOLqB8G9C9P/p6Y64iPTVPkM1X1fCFgwiEgtDHBd3YcJSEhjilyLkM+yb//+y9B5hjd3k1ftTbSNN72ak7s703b/O6rSvG2KabkECAJAQCIYQEvvSELwn8Qz5KCCEQOgEDNgaMcd319t77Tu9doxn18n/O7+pKVxppRtJotpg5z6PdkUYjXV3dX3nf877ngMQGOzYyQl4lsOX9EqmhNSCkUqN1zx6hg8mxwXeRzczl41VuXrxBBu6xsPK7DMPn86Ovk+e/B74cG3Qag5gUOJ6F5MmSe6WkxlgnoDVCVbsJ7e2Doj0xaUWL2Yo8gw7DLg+Kw9JY7MS43NsPfX6/eH0umCQ0WNlCmSlZaopVVXwsZmO0/xkUtB3FwXMebKqvhPr6KeDRP87MW8PrSC69cSuA0lPx4HVmNE0nNL7691JijDixDxgeAO58OPHrtrH1VhVLlvS3ScGHOcF7+gYUhIb4A8B1AtAUAmpDxmOHydPfWVSBb7b3Co8GYlNhIT6WxJzvZz09YhsqjpobIoMB+2hOX1cHTTAoNtObFy3CIYMBXzxwgH3YgtT4k6YGvOwdxb/+8L9g0+rw3vpliE/3z/fme+/wsERoUNqAFVw6Hf758mU8WV2NdWFSwBPw4NWhg/BqfHA4HRjyjOK1oUPYXbYD+nCScdgjeW/Ia6FMbPhCHnx88VroKkqEiXedxQZjHHnKMcxqmPXr14tqnHSx2GrFc1u34n3HjqHX5UKz1Yrvb9okCJQ5QUXpiNbwNyt/Lp6TFAnqoe6obJMMvkzP9fknNQgGtnP1XKlcBNQvAdrYoUiogPzCiKeGEqUMnnV3YO/+Pdi1Y0Xk2t10x91weApFgM0NOaunSChQH5qSA+xISlSB+q32q/i3q+cE4V1mNOFLa7agLKjCM888Izb1vFZYecdqqGS4MtYNbbkt9vQ7+9Dn1qFv8iocvhZYdelLlJHwZgJKVVtLgWZBFAgDQZcLDaWlWCZ/HhrF/+a/AfekNK8NtgPDXcA9v5fUH4VG4f94YS+cgajp4RNVOjTkaGadu2YEpVASdS/FJ8mO7AWe/pakac7PYDEBj79XkuXKBnQ5bEOL6eyEgXN2CuM1rxowF0myjCInoAJyCoCVT0gFFXMEr0fuKzJF6jHGzOh2dUcIDRl97j40B5ph0qS2nz000o+T40PI0erxcHmtkFZcQGrI0+VgyDMeOf8ca3ncxy/glgCT+7PKCKYBkuOyzjXnbb623W5P6rvE53P9malCXH4d5drG45YNxlkgRTJeJim4/5GTaIxF4rsXmRxj4QfXnmwmym45kESP96Hjz/EFU+mCa3F/nPQXi7M6z95WpIZM5skFUPFEATsR5Pu89vr7+8WN15SyaIrX4Vz9MtIBx1MqhFy2u5H4enIxIvectwz4GXW1gK8t9nH97XMt3kjwmqD8FItZldc8czQskpKVNXitywWoJJwp50wSORn5HA8WXXF+JyFCYnmmmLR9pAdBfWzcS2nlbnpxOZ0p+94kumb5eeIVC/g5GENx/o+M5YuHgKO/Cj9DBXRdAu5+CqhKR254+ni7FWi/NyJSjTFSimYikjWzQIW8BPWZDEClRVVK1MY+g/cD04wPE4NExgeOv4ohtwuekBTa/d/Lp/FYRS0+0bw6qW75DQNNrNufl6SUCK0FqHtACkZnQsd5qbqanglNa4GKRsDrBGh+bsqNqWoNCRmP6QjBCVVsneM0lBkLsDK3AWfs0crgjflLkBfifeV34AF8FwBD4irQWcH2c3YLBNih4wbUwpU7/oCjCIY7SPKaAEO+RArRXNBWO3sF4hxQZLBixDv9fLoCPljjSI0pv9LMFpGAzZng8bSgkCZzh1uzWXGtUtPEPAfOwFTMmCk2lEYqwI+PnU9wTOoIKTTlcCI334rcAivqLVXQqCxQufpw9cQxdB05gaUrNwJ1G4GGraJqhQsSmfwZJU1qm1BSXISOvgFcHrGLKm+jVoPFJi20s1THTzMRnxwDrh7H4rJCnO0ehMfnhYmJ+KvHgRWSYW1aEKbxCSo4zHMwPM4m7rgXOHsEmLRL3zvb7O97QjJGVuLgSxKhoWzDf+0XwLbd059LJJv3kj0enEhwnkJA0JEyqcFk6bq8IuFrRJJPvuo+2bIMf9a8AgdGRnBkdFR011Fi6sNMpsYRzjQPj5iBj49TV0d0C1A+kMcuE3j/b80aPF5RgVeOHMGSkhL8rL8VA0YtUF4i+gK/5OjDiokxtNgkMiFbG3u+/4+7u3DaPoZKoxnvra2DOfwZrk1OSt/P8DBLCyN/w8dlUmPAMywqUkb6h1FQVijGsTvowZBnBJUmSTPaqDFMWxMJg1ov1rM6S2LfhNbWVpGYJgE5FwmJ+8rK0PXww6KjJmvrp8oMaDYBAfqauMOSg4tTl4BLWI1PkvsWSy46JoBnfyIZiVPahx4e7OzYebe0Zr/jj4ADvwEGeiTDzm33S91GbSeA8QHAnAs0rBe+PaaCTVi2fArXWjvQ1FAFGBqg0tfCZpDOGTsyaFTJ+Zk+HKyuohcGgwflfL1vuB+fu3I2cn/Q7cIfnDiAvwxKWrqsWuX1IpvxMWBnMC+T2KyGYWBjNJqEK5qMfL0KGwpJ5HehssWKZ175Fh6+8+3IN0QrYrl+8FiSBSSs9uOx0yyw/o/+CDhxQkhQ2QMBtLBi8iMfEZVjgmzpugi4FFU5nAtGeoCRbqA4cWLqud4rcAUkol8eU7/q82NZLsdsKLw6amHQpEnGPPQQ8ItfUIReIlQ47v/gDwBlYuPaReAH/xV7vFNOyTidMqkVWUimFawH+l+KGoVzb1SwNrW/5fW4/DGg5zjgGgOMeUDVuqwQGgQLGeaSLE01xpgNSumjVB6Pxw87r+I/Ws+JdYhf4c96WvFf63ahMK7DagGJsaVwpfDUcIWlZK1aMzYULF04XbcIuIYkq57NBFwrlIlgec/2wgsviApheS/GdYZJZY5z2XtsJjAxxk5EWaaWHRhcN2YCK4oTVRXzMa599FN4Q5ManKPuejPw8s+k+Z7FALn5wIY75/jCyfZtt1f6brY1htcJk74sIuE1zU6geBkzgjG6XKg33+A+n8cwW9FStgkN7ivp5cbOqhtJ4KQM41KpmMMnG4U3Ato5+AD+FoDfJYkHJZHH5D9v7Fjijd0Z/D2/exJpqRgzE21tbSI24TzNDj2+phyX0leDsQqvUY4/Pt7Z14G80ul5GaqgNLe04OjRo+L5Sv8Wjku+D9evRDEv1xgeN3NZD3HPrgDff9u2beL3XH+EZ9PZvYpnhGPw8/vSIjUqTdW47IjNw1Wk6re8gHmJMbJLaqhyoAotQwgXw1WaGqjBykMp6NaoNMjT5Yd9DKKJnCJ91DxmJvy05zqGFYSGjJ/1tqMhJxePV0VlbW4K+g4CfkWFKeWe+g4DNTMYSjKBe/DZ8B0V0HFO0qSmYTNhyAE2PAHYJKZehcTVZskej8e6/GbUWcrh8LuQp7Mgl4SL69r0BGdI8udIG9zUuo8BgbAJNcF4UqcJV5HwvlL0SQVoFJUkpiLpdgNQbSrAZWpNx3UFWbTRJFGvawL/034MA55JiZ7TqmAMd0PwGm6d6sba/BpYtJkHvRxfDDbIMO/evVu0U3MTs6p0LS5NXcCEbxx6tQGLc1gdaxOJ0hf6DwtmO34f4wkYoYJHnGaT2Yjujn7ogn64zYegCxTCMHgJlQVqtHY6Ye89B5vfiYtjpaIKOCWNUL6h24VFuTlSEBOu0EdHlChLGYpqbKfHF06qhqt00wE7MZio0VuAirVA7/Ho73KrAVvlrdOp8aH/A5zcL84h6pqlau540OQvPs/NAIVERyJSo34VcOlwlKjgeSxvAIxJNsIqXqsJ2pXT6MziBvqfV27Gv189K4zC2S33hw3L0JiTK66L/3P+PH7U1SUIClaMU2rq0N13w6ToAPqDhgZ8v7NT6tbg4z09eKq6Gh1tbeI1lMZ4O0tLsfORR9Ax5cC/Pn0U5hrpO5WuP3ZO9ApSI5M2bBms8PUGXNCrjdCqdfjoqRP4VkcbdOHP8MOuDvxmxy5BxrRQtqe/n4K2Ma/BbgfFWRIbMZfDicLyxHNavaUarZOdmAyQlJYqV1qs9YLsSIQTY2N46n//Fz2hEFa2tODrU1Oi42KuyHpBgMoKaJMkXOmnQe+bkWsSkV+xGihdFiU9SqqBygagJ9zVyceteUB9XMUQx8MvfgRcOQ/kWIEHngAabpDOMStGPvERYLBfWtt4EXKjzevv1HHgTz4ljdUdis01f3f0GaD7QliGLgR0nQfufK94bmndbrQeOIAm65aEBBCTRLIPBjXFZaM/JY6MDokxJ3dKcaUddDnhMJuFLAgJDFkWkGNYllYgqcGxw4CdQezushw8wwR4GItzGG6IDyH+tVjN+OEz38YfvO0vxH22i5Nk4z6RWrwMOnhjcMP1jZVgrK7lusbjEOfqBz8ADhzAUHs7irZvR2FzM+yUmiOE11QC+JNvase97kgXZeRrCoSgVxUhgEloVWbk6VqgUaWZPGdi7atfBV58kWWaAJNr4ergCC6dmS6fyUOhxFPHleyQGvp8oOIhwEVfNBVgrgBS7D4Q0OqBRfOjic3vnZXSmSRXOEfGV8NmimJ9MdqmohWcnFONGqPwwZsNlGz7WpsUoMqyniyk+nH3NXyoYfmcj+23ATQEf1PFDtGtwaugxFAwret5ATcerMJlXMEOCY4zVq4ywZ+soyJVyK/Fql6Oe3nsc03hOsDHuS5Q+ofJqFTHONcNVghzXsiGPxq7NH4rsHW35NnVcRUwWyTZKdMcJZwpTV3RDPRSqlg2eQwBtXOTLLzRYMcDSYtk65Msi8y9EfdFMxEJmRAIknS4FNtqVKZZX0PuPJqN0JCl2LIF7gFZfMLq/kw92+Yd3GsZFku3BaQMyvJRASGR2TuLTunxR68VqoVwzkxVZpbdS4xLOMZ4PfKa5RjiNUyyRO7MY46L+73K6irYe/ZhzDspYl5pn6ZHraVMEGqUwWJ3HTureKz8G3ptkEwkOS2TGqLrW6USf0MChmRFIhKOY4/FwuzqY8eGIDUCvrTii0QoN0r+iN1OSSi6zFSJ6gR+xguYO7h3yBqpoTT2nQ1qVTlCISbgWcHOCy6WUVudtwanx09hzDcKNdSoz2lAhSm1pOOY1zOtu1K8p9B8Hkmb1HD6fTgw0gun34+VeUWoTVIVmzLc8ZrH1FdWGF0nwpk9sc83aKOEBuGZAo79FNj1ATGRq1AEFUoRwoDi89dBpUp941Kgt4mbeMdQEK4Qq4X9MCIU9nBgpXSGvgasAlcSGpGPRjNiHaDKBbzUofNHE6ymFIxF5wHrC2rR4RzB1UnpXOrVGjxRtT6S4HMH/Pjy9QPiOiGYrhj3h1Copn+ACmYN4Am68erQaTxctgoIkRxi14kVUDWm7EtCo2yy27ImPs2ZOEF3tnZh9eJ1YkLnxC1P5IPuUVHxrVXksGWYtLmoNddi2NOK0bGLyNWrkauVrkmfegQas1GIwKxoqUZnzzCGx06hdt17YbamqKPPN6M0lHNSCmLkx7iBThf2YTGYO4btyLcYYdBppaRQWRrjeOgy0PpqdNyxyluckPB9Rzcw0TMzscHFbbxHem9KgilIrTmDiVfKkcit4fyfgcdMINlxhiRFGJxDC0uSByeFFcA9TwEnXwbcU5JR+Pr7k1fG6yoBXxcQCHvoiCr4RkCd3ndo0erwl0umJ65P2+34X+rNCykl6Xs4Y7cLkuN3mNAMY0thIV7duRP/evkyhtRq7Kqrw9/v2iWqZJPB5/FArY8dV3wL2UA702qlYU8vWqfORjZYJk2DIDRiP8M4ftzdiacW1WFrURHeV1eH/xZyMBL+btkyrFZUqZcaijDaMYyyWmkDKW3cDCihZEwYOrUO95RuxfWpTngCXhTq8yJdHPHocTqx/Wtfg7ugAMG8PNENc+drr+Hi/fcjN8X1+ZZA+35g4Fz0ftteqWK8JGxKx3nuwd8DTr4GjPQBnJvW3iUF10p8+8vAhVPSBTCoAr7yWeBjfwNUZWlTOWFnCZIkexbvrbH3VWCgTyIzNOE9jrwxOXIAuHoJWBxHWE4MSoSGeG4w9rFaaQ00GI3wzlBEQtO9gwcPiqqjRMkeq1Ynum6UCLrcKCrLFWODxDWTTKzGih8rvC9rTDdby/FY5TqcGGsXr1dooAGsB/09QxgZHEd5dTEammojAQ1fkwERA2IGMtx88kZJIZIoXLsolcbnRKqsKDd0zz0oHB0VSa98ZWca1wAmQ0mASQcH6E1AWIY0Eepy8nDJMRTZgXG3VGmyoti4JvncTEm3VJJmrAB+4IHkvxeV/EkI1UykFJMehxmwzq9fUCaQ44NMOi7kQCXVGGMmFBoKsdy2HBcdF0V3hkVrwdq8tSkZhdNgPt6jjMvjmOzts4CUQGnFSlNqhWoLuDEgwaA0X2VFLQupuB7IyYJ0xx//hhWwTLzGg4QJf0cihcmyTE2Vs0FoMH5ihS7XzN8KNC6TbtnE5seA0y9Jvhpch5ftBIrm6Kt1g8F9SCpee5Q4UXqyZAPBkA8jnpPwhpgjAnSqXBQZuC4lH3PsbJqtq4pJ4nSSz7OB+zDOFexsujX8MxaQTXBeZhdEIlKDYCFhe3u7yD9lQpQxfuA6IoiLBGNISZI9VL4ZR0cvY8Q7AZvo6GwWxMbe/XsFwXjfffdFCD3GPOxWZ+zA9USOMbj2sPucr8v1ht19NEFPBBIbMetczVKgNRw/yliUXlcpx0iFqVrcFjC/4PdGLiJrnRrpbHhUrL5M0jlg0BiwsXATgqGAEANIZ+JcZivAczTbnfZ+KuTNYCCZCPTm+MTpveh3T4Vth1T4ZMt6bC2aw2JmsAFOd6zm8Wx6svHyRaz6lJQSwmDnw4SUrDRZxWfVgJ4LnJTI+udATXmPDBAIedHrOgZv0B3uqgmhQh2AAXrg6iRw4XOS1ELTGmANJTVSkTlJUmGpLgd04Q2FdjEQINnDbCST6Rpg5CwwdkFK9lgXASUbsyaNkAxMhD5ZtR797gm4gz6UGmwws5oxjG7XOCYTyEsxj5VPfoYSBaSX3GNAiEloPpdf3jggNi+bpIR0MjjHgY6j8Jw9DYt5G5C3MfJ8VrVysmZliTwZk73mohFp6Q5BIjbC0Kt0WJXbArPWiCpNM0ZDZ9CwTFEhGgICei10YbWvmspw9Xi6Wtb3PwH89H+i1wOTT7ti2/1mBKtwD/wSOH9QfN9XBkZxz7K6cHZaC1SmmLShdFjba7HJpKAvPHbkE6MCJrqTkxqeSeDEj6UxRrDbY83jgHmOZskcNy89DZx8XbrP6qnHPyhVnM+GNVuB4X5g/2+k+wXFwDs/PLN8DzszeEsFvMbMmwEfiRw3oMkFtNkzURxWJPplqJM8vr24WNyOHTsmjMtmIjSI0MgYtixuwemJsYjsFc2u7yuVNhVc9NINhGkofH3qbOQ64pie8l9DjVmFTmf02iLZOaT4DJ9auhQfKigQklMtNlsMoUH43T7srNiMYesEHP4p0RW3Jm+ZIDKU4P0W6+zf3X/85jdwUls3vNFjAq7P7ca+4WE8lGGlFuXBztrtgrBdyQ3pHKSs0iIiEz0mkxoEuxw23Jv8NRx24PzJ6H3OHSp2QuzLDqlx9AjwF5+SOqqIRx4FPvnn0TFIQ04hkya/d9x1a5eC1xh4EnSgCf8IZzQf0dgokk1r1iROxHNDz2ojyjRRDzfeh+Dxqjr8oKsVo2E/Hl4jb66qRYFOWtem/H4EU/RfWWwtFzei383go12QGqs2sCpXDatWSlxyjWJVFqv7KF/FvSJvTJZRkooSClyzGKAru69kkMxn8ouvEwmkbEXAjncAh38ueQNZi4A73jKd2FLg4fLF6Jyy4xyJIuEhZsQHKO8VD8r/Pf01oLtVWsM23wvsejR1ebRE2LgD2PNrYErR4Uqyq6QcWJahjOd8wDkKjLRJn7WYcp8zdHnRz6T7LOD3AEW10u0WJzWIanM1qkxVCCIousNTBb0zSgwmDHtcks9TePwst83RX2cBC7iJYNKTySFlrM3kEOWcaNgtd1jxsURkdzKw+44d3sn0/mfzzrhRYGUuuwNTNbxdQAJwP7ZuBlL/FodcGJit10oX475LEUKD8IUmMO69iALDyhnfZ7ZrlnI8mRKG8WBBCn0XWB2/QGi8McE5ntcVybBkXTgsnGL3HQuVUvXTUCokpOqNRgJje3Hs64+NjYm8F3NdMignNTg4KIp/+dqMgbhX5NggqcF4if8zZ8aYKBGYE6BXCK9xdg8KbHpYKpqiOg73iUs2AcsSEyILeIPJT80H1GkEGzJ2l9Xg6uQ4ftTdGjFQZgomR6vDO2vSq157uusKBt1SMkHWYP5/V0/ijsKKzCf0ss2Sp0YwnAinhAgfmwlVzbFsIeUKpr09jZGiE5BUIT93n4BRzzV4g1FPiSBUGAzloLpTCxx6LvrEk69ISdoNs1SYE+pcgPIOoTgyQKOo3OJ3r9Q/HL8MjJyK3p8Iy46U3YH5Bs9lOX1LEkCXpG2ejyqvEUlSJz5hSy1CbmKSBMRMoh/5PjwuJ9T2Xug7DgJeO7Aseo6ZAFImgSgdwk1HSXkpcrRmTPldooqcliU81ntLtwpCQ3wu6BHwB+D1MskQTRaoYjZkKsBULl2n6WDDDsCWJ1VJc9O1cSdQmgYZeOAXwNkDkSTymtICHL3aLSWBNFp49+1LrarKRfIoboOZKME4ky/L1T2xRuJMMF5+GVjzBOaE469FCQ1isBd49hvAuz8++9/y+Om1sfNhwOOWjP6ynWwmsaGfn+qCVXl5MGs0cNEEOPwY/99WlFxWjoF1KkEnx8NHjUb83DqCM/ZRFDJxWb8U1eyAIRHZ3T1NNzkU4nG4oRKdg9PfY9JP75/QtK9guU2HLqc38hsmuDbkF0QCeibv1hcUiFsisNqlprQKSzMw8Y4Hx30Bz1+CRV14kGQAkky79+7FCfqZAFhqs+FzK5fjP65fxIDHjeW5efjH5WtRls1KcyLR4ab7GZIFlhkEnNNA8/dP/4U09mQ89yywejWw+37p/rKV0nuFuDbH/T3Xg9oEJFVuCaDRxbY9c74qjI5DkgKUBIk3sVSCv+fGPlFQQhm4H22+C9/uuIYRrxurcgvweGUtjh09in+6eBZfuX5FXM+NOVZ8a8MdqLWkdm2WGBrhC7qg0VwWa59Jk4sK4zIRIDFpxpZvtqzHI5VqL9kYlmN7Nb0+Lp0HhgaAqkXAmz8WM6cz0GKyLN4IkOAa+JGmTeh3T8IXCqLcmCOt4VwjPFcB/6C0N3nlGNATLo7hd3jgBSC3EFiXgY+TDGqXf/zvgBeflaQYmdhfthrYTh+VW0TCYbwbOP/z6F6z6xiw8nHAkmAvST+Tg9+VfN2EvORxoGUXsGgt4LIDXSek3+VXA+VR7fy5yP9lE1LxT3oxBknrz67Ygk+eOSDGDvFoRR0eKl+QE1jA7Qsmc2QPpfhEjyy5Q3Aep+QH1yB2i89GMvL3TDalYmJ8M8F1lOeAsiUymDjLprfIAm5tUBJHmSidCfEm9fHIJD/kDcYrdoTgmfZY+uQJY6ZsEBCMv06cOLHQofFbABZF7du3D9u3b08ac1M5hDFnqhAFvlnwdWGXlNwtLoOxENcYylslO9bZwM4Uylc9+eST0U5xFnrteBIIhXM94ThkZGRQxDMLxN6tBfkaywqpwc0LK/FuhQ/1x02r8K6aZrw21Iu2KQdydXo8WlmLYkMa2sLMMXpc0wxanQG/MJo0J9KtT9WouPExYJKax2RbKGUzi9fCxoekJAfNwpntqVgKOPuk5KrQ2goCi7dKeshZhofGwHHnwBvyAAqj0QguH02N1GDS0LgJcB8HQrxmeH+lVA2eDI747psQ4OiYndTgBe64CFBDmefK0gjkNM2t4lKBalMe6i0FaJsaVUhaAFZddALnO20pYOIm/J3HYIZNSc850aXT1jeMRrljou8C0LgNMCSWAaKeIFsDc0wWrJiqRq/ZjjGvQ+gYr89fghxdNPmoUumwbMkunD3/ClqWMijn8ZqgNzUD7nNAwAtQ5qYwwyrS5pXSLRNcORFz3RXlmFFkNQNGPdC4Fkf0erG4Ua6ECTx+7oTms4mqTGVSkO00/IFSUkUzVLJMShJYUdDgdRRzBnXUleA47mkD/uXjkmnwg+8AqmepzqekyW1oUFpsMODZrVvxxMGDsLPLT6XCF9eswabCxEQsq7ll6bXZQNkEJjY/1rxadNsdG+1H+9QY8vV6FBvMolNDuVHzBwfhCZ4Nj0UV9Oql0KljW291SczRP1Dfgj1DFzAV8ItL6u+WrcD24pJIkDRbFWKqi/BsoLwPSZT3bNiAf37hBYx4vYJgIZlRa7FgewKy6Fd9ffj46dMYcLtxR2Eh/nvDBpTFJYI/euqUkAqTcdnhwNsOHUSlSS3O1sGRIbz36D78fOs90GeTVKN/Rq+CxCby0kgcupzAxChQUw90t0eJDJ7rNYkrddJCXx8Qv9/hNXXlcpTUWLIM+KM/Af7zy4DXDxjCewYmgv7oT4HwdRIDgxnY8iRw6CdS9TuJxZX3AoWxwTaN7JiE4bhIpg/LBFSyjqQigxEfXxxLMPy6uwtfC5uOE21Tk/jdowfwys57U9q8swil2rwa1aZJNOdsEL4U/DtW+jJZwAor5fHR84PavayMYuJsNnJDaOju34/VJw7AsJ/dd2G89SlM3f2AMBuU5wkSl/LnZ7WxUhdeKlKIWxdcZwBfZ/T+tkqgrxMYko3IVUDr+bmRGnJH3dvej5sKMd9wDUwwXq/viS0C4L6z/QCw7JHpz20/FvaHo79aeA67vBcoqgOO/UDqiOTDA5cB5xi8VknbOhNfDGWXx80GPaF+uPk+9LqmYNXqFwzCF3DbI1W/GhLFTB6RBKCMB0nqmdYGEuuceylZw33ZrQp+BsoeKkGCgzEGJUvYbUKCIxtSVwu4NUEpp1Tlx3j9M85ORHoxvkhkUjwb1DAggFhfl7S9veYJ3K9R3odV7pl8tgXcXuB+i358M0nycf7n3jpVcP+ebMykA8bu8f5HcsEj52pZjoqdJowJGBtzfaP/y0xg/MG/ZU5pw4YNMb+jvC5fh6/HQkTKZnF94Ljg2pCKZN0C5h+ce1PxAEuZ1LgVAg4ZBQYj3jJHU/Baiw0HR6KJaE7l+XojTJS/mQu0JiAvRRmYCFv41rB2dNhslD4anaekSriCaqBsfsyQdGozPPTAUCSYdaok5FA6RAEJDMtdQIifST373ybq2klBAxmOC8CEQpvdflJ6LxIbWQAr9z5UvxkvDFxBt9MOm86AMoMZ7qAbKpUPhfoc1FnKUMqK2VB/HInBQH0GIsfvhX3KjYExB1pqFImrROZF8q8CAbHp4ETLZFFLqAollcmrpIyaeuQYhqBXFUCl0kOnqoDKrAPMWTAtnQsSdcDwum9aB2x+BJrTZ4TuIysJWK1Cpp6th3IFWSRpbbQBeYuAccmkKQImYazV0u/LVkuSUslgzgPcdgWxwe6VOXrriGMzJzCOpceOU5Kh+c4XgA/9lZQIuwUQDNFc3gc1zGI26Jhqx4h3ROhj11kaYNWlZ0Z9T2kpBh55BN0uF0qNRuTM0IXBgIMtr6mCwWev04H/c34fJsLycIYODX5HX4q1THRHPpMLnuAZxfwWgjd4HhqVFWoaWYdh0xYgV1cEu284YtbtDhjQ6ZzAf69fiipTCSpMJhQqKuM5DrnpYetroqpxQt6ccXMYRACj3iHxf56uCMYUDH5l/Uhq3HLzyeB8/1134U9Pn8YVhwOrcnPx/61eDXPcuT0+NoY37d8vNmv85C8MDOD+vXtx/N57Y+S9joyMxGjI82dnIDqL8T6T35cddqxgJfpcwLVg9Czg7JW6FaylwATnzLCOXttBIL8GMM0iz3blHPCDr0g+NTx2yvtQioqdOo+8DajPQis+EzTx5l3UDS+KG6v3PQjsuoctORLpMT4GFJXMLOdXUgc89CdSJTzJ6ySymUwosWo2EanB64DdSDQNT4UwINrMRoT6u6EqLo58t1cmHRj3+QQhOBt4rVNeqrSoDFoFCchAgMEwyQ22rHONkhNFvP55S8VskvIg/rOnoPvVqzH7hRNf/SI80KF523ZBbiuTbAx2GKQkazmXDtwfS2gQfIll1cBrkim0eL/ZimI4lvv7ARKIiTqvSKydPgoM9kldGyTh6YFUTW+QG5QoGL0ADJ0Egn7AXApU7pT2o0q5xhhwPVLIZSnBoppQgjHcfSpMaCh+2XkcvvrKjCWkbiVSQ/ZWY3ywgAXc7qCsZ3zn6kyQi7VYsMHEFqU6ZuqgpeEq9+e3MqmRTLaHiT2us0xocR3h2sXPzbVrAW8skNRLtfKa+5VEPjGMuWkizhg0XeTqmjDsPR4zzi46NDg5fgZr8mpRwlhVAVkOjp1QvCWLMXhMmZKKLBRkoQj3a9xHkqS8reEalvY/LMawlAHFa+Zdwvx2hUxgJ+u0Y2cf50TuzeJJgGSdEIxH5kJqUGaKsS7lz2SwmI/HSlKakruUqD59+rTo5uA8zb+h7NRs4NjnfB/f/cRx/pOf/ETEEFzr5FhF7mDkOWAMk2z8LeDGIVUbDG02XccRmgICNMJk8GQBNEuBNAysOZE7/JQJUSFHK7nazxcer2rCWfuwuBEkMz7VsnHu78lgz94OTPZLAWVRy+zdGvGJXiY7mrZODyh9Tum1ZpLTSQOF+ia4AqMIsDtDxPpqFBuXAc0GYKg79sktiVu/ZkSqEmN5LVJ3ihL5cSariTB1PfFjWSI1CINGizexe2ZWrAVCTJIwkWkBVCtmNgovqoVWfQiFNovCgNsm3ZKATDgTO/Kky/tK9prwB70Y8fYKk0ybrgg6TSEMmluMaV6xDTjy69jH7noH0LRa/MjFhYsIN3VMYMsaiHa7XSwycgulqKyyFAHjTFrFZWC4eNVsm51Qa9wJnPgR4Avr3bMjqvmuuX/GTfcAl08BYm8crnYNKCrK6Sty5TSw+R7cMLA7x+8EdDmRzR7nXGfgErxBabxToqnTmYM+90D4j1QYcA/gjqKtsGjTk1EyaDRoUFybY16v6B44MDyMSpMJ/7JypZBuYiBJ0ipV/U7O0d9uPyeMXWWMdnbjF9Vq3FsY3YAFQ9M70YSvjHsf1EEtYFgBaMvE6y3OWYshTzcc/gnsGerB2fFJBOFACL1YnWfH7+Wuj9kIsRKdSVUGAtwUJttYcd08e+EMBvTtsBRI64AaaizL3QCbbmaigMkIJrZZOSKvS405OaILZib8pLtbtn+XjjcUEh0Z9P5oVhxnldmMNqczQmzwb3QJhstsPicpYfAw4AjLCvL9xigdh1gyt/M40Hz3zB0aMqEhDlgFOIaBd38YaJHmjoxx8CDwve9IHRrbtwMf/BDw1f+QpKQ4l9Q3AG9+LHFRQkE4oLSlSIaycCJnZp1+ft/sSuB1lsiHgvM/AwhWFCUjNeiX8p2OVpyfGMeI1YzARemca0uKI98rZeJSweHDh0XFUnzCR3QrsmOotlbII7KShokikhzcgN599wzfZxwa6SdGAkCRMF+dn4sDw0MRXXTKhqQqvcAAJhT0CcnFmApEks2UOhR/Gy4k2TTDcR46BPz5n7M3Xnruxz8OvP3t0d/zeP/nS8DR/QClHm3G6LrTuBR45x9LpNd8YqIDGDgave8cBLpeAWofjB5LTglg74n1fCO5GA97f7j7STlAVYA5VyJMEsDndmYshSGv8zeC1PAG/Zj0e2DVGpPKiy5gAW8UcK+SzBQ2HtzPyOOX+28WY3BOj2iQJ4A8/99ueOihhyJxE9cU3uQCKn5mGi9ny3x5AbfGOOAt024cVoaz83SmsTATDJoClBg2wxnoR79rHHuGBjHuo/eXCmftXXhXzVaUKwp6SJ4w3qWHAI95pmrx/v5+kQxmHJXO5+NejnEXiY1du3bhtoaH/qS/DhcRslhjDPDYgeq7s6bc8UYDrxfGl4lIDSb3SS4w35JqMpnXKGWt2DWRrn8R4xzOuzt37oxZT+TuD+4ReTwsdmWBH4+J3UUsfE2VcEgUR3G8kLSRuzQ4vpVjiGsnSe9EawFzJzJJwuO7HdfB2wmp+vVps+Y6HvIBAWpW+sLBkBcIHAY0TCzOfiCugBc/7DyKbpckY9BgKcYT1eugnyemldVYf798K646xoTkVENOHmxhM805oe84MMCOgXBwPnIRaH4sNWIjGZwDQM+rYa8OFVCyPrWk/yzQqo2oNt8Bp38IIQRh0hRCpzYBzeulxeH8gahR+Ko7M3sTnoPQKBByASomUxNU4uZUARW7gPFLEnljrQVy56c7Zd6gKgBUachXFNbCsuYBTD7zLek+da1XPpKwqnPC58DFrktQa1QI0QxXoSXIzYy80Rr2DOKq4zRC8AtpGpXqKuBlEvMWIzXW3SUlA6+elMi8FVsjhMZMRlNk58mos22cyTwy7zAzyZVA4ofm4GNtQMEsHV3s1Nj4FDDaLl2rBYuSyn+lheIK4L2fBE7uA8ZHgEuscA2l342ULoa7gDZW6waAqqVAZbhynfNQH83sQxKhUb0LsFbBG+yJEBqEL+hGn1tZ0StV+3c5u9Biy3zOYdfAg6+/jqNjYyKJfn1yEjteew2n770XTXl5kSrvRJtyft/cJMlVFNxIDHpp5iqdT7/LDZVWi3GayyhACbZpoLezmJM8gPsYYNoGaPKgVqlRaqzBpaFWnB5n10oUp8b7hE6/LGvDyihWa7FziKDJcTJSg2uneZEa3m4Phq4MoqquAlqdFlcdZ7GuYEfy8xUMik0eSROl7nUqSOaxEf/451etwo5XX4U7vDHj76vNGqjFahASSe8l1lwspqfLXMBEqExoyJg2ZENRYjEZRvqjhIYMzh+UoJoLqXH8GPDJPw0fRgi4egV47HHg/30JOHcWyMsH7tvNTA9uJHiNsUVcWbmkBDfRHDOJtGz52IdPHsGv+3tExyHvm5qb4Lp8FZrSEjEGP9a0RBCPqUDuEIxPginHK2WzeN2++OKL4thZVZUWyivipAClbslt99+PQGm5OBccc7N1fvCcnDp1SuxZRUGOfQChwASWtVTDZmMHHdffpcASvdQps3EXUJpEb5t+M5/4BLMa0n2Olc99jl+O5LFCXD4vERqENe7Yrl8EDr0MbEtBunMumOwK05IKFyP3sLRnlD2zmu4Gzj0jdSbKJEddHEHadRq48NL01zfmAGveDEwNA33hDheC150pD15o52T0nVKMMUectXfj+f4zCISC0Kk0eFPFGiy2ls3rey5gATcTnLdT8Svj+sAEp9IXiXM7E//xhVNvBCT6PDxXXMN4Y4cL93QJZW9/m8Hik9YjgGNI6mhv2Azob/0Kf17X7DxK5C1DUD5TlsBNJBnb2tqaUYeGEjq1FblqK77Z9mt4IrFgSISFh0ev482VUSloVoevW7cuEv/MBHZLkZRjHMLrlfdTAWV8mcxNp0v+loX9epTQiMhI94SL+LIQz78BwZiB+2mSZ/E+FjKYzCexMJvUMsGucl63vK5SvQZlkDhgHis+jhGKJOFxx7381q1bRc6LBATjolS6wGcDSRPe2PXBbnQWzMrgeCLRHU9qkAik54gsh8SOEsZHzc3NC+TGPIHxQdY6NZhknNVTg4lrEhnRBySCIzQCqGaXR/hV31n00Pg3jNapIbw0cBEPlqdWvZsq7D6vWLToxcGUYott5opJb9CL0+MXMeq1w6wxYWVeC3KTSbGw7Y2EBiHLzrDlf/giULYm9rms1L5+EpiaAAorgOqWxIwyq6t7aNQtV7GFgMGjgCEfMM89INOodLDq4ip5eBxLNkm3uYCbA/9ZIKTwm9A0AZqGxMQGb+nA0hArPyU/dptgPGcRsO5JdJaVwB9SoZ4J9jj0uPrwWuvrcE45UVJVCvvQfuwo3gK9Wh9ZlJj8HPQP4PLoRfGYWq2C1WpCdXkBep1XsT50x6010fJYVm2XbhlAXkiYvOqb0iLkzkGVwRH7GUkYyMmb2cBNedncScJpKCwD7nlCkq75+j9J0iScF4RJtwFYEjcnzBWD7cDe70VzW13ngbUPAmVVQN+h2CRz5ytA85Pwwx6TDEvmAhMUUnLpY8TjEQnUQbcbh0ajXiV8NW8wiP/t6sJnli4VXRo0qotvdSU7z80DNzTcNPF/zt91llz0uNhNEYJrcAS2mkrUmGOT72rkQYMiBMBuPMmfSB0KQRNpQVUBgUEE1Rb4QxOiU80V8EUkqJRwR+ZfqU2WRAOr0VnBlYyEk+EKOJFfnIvcQiu623phMhtRUj6z7BhbbLnRStVrRIl3L1qEz125ghCr08IV+XcWF6M+To9ybX4+zu7ejR93dYnv/S2VlQgiiH+5dBZ9bidW5hbgL1pWQjtn+Zy4q4rjlFX5vrhrKrccGO0T0nzIL5suzZSTIMHA79I6R7m4Z5+RjknpzfHsz4CP/gmwNgXPIT7/heeAnz8N0GB49Xrg9/4IMM8tmGKShfN7sm4Ngp17e/bsEZq2SnmmCxN2PN/fE+nUkbE6Lx/LqxZhe1EJHq2Yeb1lRfuA2w6jWgeX3w1v0Cfk6GSweyJe2oTHTCIuFUmsaVi5Fth1L/Dqi9HH3vYeoKJKWD4z2GClFj8jAzGODSbelKQO2+k5X6xZsyaauAquQGDyGM6ePgaPL4S6xdtQ1LgG6sWzFGpwz/vFL0rSU7EfkgM0SmqMSd2+9HEKcv2NWWtVwKD0PUyTd3I7pMSQLk2yzDMKsJOO34VlkfQ/vcxm65Y1WoG175SICa5BLKRQEuuUO7348vTXqN0gdQ6TQLQUSEbhlIvjdWUuAFY8AufltlnnwTnHGHPAkGcCv+w7FZnVfaEAnuk9jg/W70KuwovsZqDb2YfLk22iu7baVI4Wa6zM2gIWkClISJDUoDTIjh07El5Xsowmk0rxmvpcV+jvxLlUWe1O8prrDV87G4mlWw1M9LJwivtSVs2zWzYVPe83NBi7HPsJMNIV9m1SAQNXgW2/I3kX3sLgPooJV64xynWK1z73EiyqIjHAxCQTlspCIrlCOBtzMt/PK6TGFY9xSVfITsvjjIlbdmFwDM8Gjkkmf1lwRXKSY3OmccnPxM981113vTHWGqXMc8zjc/c0fCOD3XgsFiJZkAjs4uB+mkl77vVnI3k5tlK5XuMhFyApwTVG2T0og2N0PtYdfjauc5z3GePzM995553if+X4ZQzC+YRdIspjI5nDThWeM56r+ZBz4/vL0nS/bXDGzd1zIjX4BZIxm0+0T43EJJJC4ceyBXfAj8+cO4o9Q32CzMjVaUTFVrXZik8vWY8max4mfG50Ou0waagjz2RSCHuGjsDuowwJpbEm8crgGHaXbodZqVUsQxgrxkM1/XESGs9/HRjqiuruL98ObAibkCrhHVcQGorXdA1mhdSYV4SGYgkNInAVUJdKXRtzhZWyUKpYo3DebhOIqorycgwMDYnkqLyR8gfdmAoMiDH3q4tHodarBaFBOAMuXJ1sxTKbxF5zs+0JuHG15xJqCqNs8viwHW2tfUJOholKjUgLvXHAxYTVVEKap2oDjrz2DTFn5FvNqCrNRyAQFMNO53CkpLkYswm6fhhoOyJ1O5S3AMvukXwAMgWrmZ/6GPDCj4G+Dsko/N4nANscfQricWn/9I3chdcB291xVbxhOTv3KFQ0Z1eA14tZw0R8NLHP/4sNyb1bEsHp9+Mdhw/j573S+N+YJCkrb0W5meD3xBZvWS6BXhvcfMtyZAw+GJhww/6eoB/Xp8YFscGPlqc34g8b1ky7RgyaVfCHehAMjkHt74E2EBBnInwS4IcPE779wk+EWJNnwYsDDDzCr8GNmkaPcoUsHI+BlV3ctHChZeA/E8xaKxx+u9iI1DRUwWGfRO+VQbgsroQbH0r9MMCaidAgWfSLvj74QyHcX1Ym5LxkLLZa8fquXfjLsyQn3MJI/J9XrkwYvNRZLPhkXAXaNzakZqiYMtR6wFQOuPqj12C+BbAHo5r+JS3AtQtA/y+iptp3vhsoUCTH8wqAHQ8Ce38lJVhJQpRXA2tnluOaFSQd44Mf0c2TYkB0YA/w/W9E7x87xDYG4E/+cua/c/QAna9LhQ+mAqB2F2CMDRqWLVuG119/fdoGWgaJPn+OCcdbr+DykQN4cKNEYI/Hd7QIkk+FNXmF+L+rZidqWieH8HT3MUE/lumC6B1rw2DvJBosi7Amb6kIOLiJZ2USwbWK1y2Pk4QCK55S0eJVorevD84du+ErX4Q8rxdlq1ZDVRctUuD45xhi8MMAnkEN35dVXgwkGBiximvauVLrobHdgdXb7xDPYZKq7ehR8Xes3EwoM0Ii4/3vZxvW9N/xulOOzapF4j+nx4dnz13HotwcrK8shl50sYSAvDiN9q4TwNW9UdJh2f1ASYqSmRPXgRF23IUxfgGouA/Ibwbs12KrFfOap2tKc9wkkpwi6OUWf81zf8p1UJZq4nmt3QjUrJOqdpnMUqnE9yB3rmWC+Y4xWDAVP5pJ9vW77TeV1Oh1DeDg6ElF0ZUDvqBfFE8tYAFzBaU6SGhwziQ5rtTeZ3Kkq6tLVIJzT5WoClI22WYSiZA7Pjj/k+zgHiWTwotbHUzMcT4icc8Yg/9zr8fkMYtNuB/lOWHymXHcb4XJ+HgfMKLwp+Ja4RwH+q8AVdktPp0PsPNBSe7x+2P3Br9fSuZwb8CxwIIMZYKVcQhVEbIBvu8icyE6nLH5rlpLtMiJ+yoSaNynUHKTMVGq4PhmAQyry7k/SuSnw+uYyddkJOdtCesiyVMsAsp5Fyx0acwCzlskCUieJeuu4H6aMS+T/eyOmKnrj/EsC4vSheydoQSJdsY/8lrFvT2Pg/EFO6f4N+kQB7zu+TmZU+B1z32/8u+FFDflmAMBMW64HnIe4HvKhVM8Js4FibwOeR55rkgssmuqu7tbxCXsEsuWJwfnL85RjLs4N/w2YSLFGCN7pAbld0DmjMF0SFboBlSpGRgxceRkV4L8cqzIpr59lvDFq+fw+lCfeF2TRqoS5nze5XTgE6f34a+XrcO3Ok4IFp3PabDk423VLRj3RT83P5U/5Ee3qx+LrQkMcfRWSWbKT6kCecEKApa4hGDraYnQEC8azp6dex1o3gDY4s6XwpQzCkrI3NqVEQIhZ/LHs0Fq8Au0LZVutyk4MXGSlRlIX3AKva7DuH6tAzqTHqV1kua/DG6EWDWrhCvggt4QG5BYzTpooEPQG4InMA6z9hYy8vN5gL3PAm3nJRmqDfcAS9PrCuImVIkqy1NA536M2qfQ3jcCTUEdtFMBOMcvw+n1i8qrVPT40HkKuBJOOBHd7AQKASsfTP3gXFNS4ovmxfJ3x58f+13MK1hpG5+6YdW7hgtqggStxgijpgjeYC8ttMWsy8NdmduIS44RjPvGoFVpsdjajGJjeqTGn589i18oNuPHRkeRp9PBwUCQlQYiySp1B8hgopKLNtcbucqaGy1546GsEsnVaPAvK+/EFcco2gytuGfVWkFGx0OlUkOnqgZUVZInkejaEL8Ry58DoxFCg9Cqp/Chhgp85Vo//KEgbDojPlC/EUb6IMin2ecTGx1uirjxYQv3TJXpi8xNcPjG4AxICfz8vHxsq7kXPR090OdoxWbIpLHEjPOZJKdaJydxxyuvYCAsiWPVavHqnXdinSK5wJ9f2JFc3uqGo2w7MHQImOqTCMKSVUBznVStrtEDV44A/W3R53tdwL4fAW/6aOzr3PcWgGbwlJxihwYJjbnKRt59D7Dnteh9VsHsvDN1H4SjB2ONxTn2Tx6ViI1kr+EeB649H13/ncPA1V8AS98WQ6DymuAGlgkkJpfi8fJAB/6zVapAHz5zEZ1FZnyofjWW2nJh0WjhDPgVDfkh3NXSIoITgkE81x9uvLkJlyu3WOzx057jopp9kVmF8e4hFFZIifnrUx2wac1oMOqhDg4CwUlAnSNIDI5bzrEkHjguZmprZ6DOTT+TY6yGZYKB96nLqy3dKUjDI/39sEy5IpIofG0eM2Wt5PWSBCilphiscI9KecKZAnW+BpNSHK8MvhiscM7hGGSLfaQC6uWXgStXEr8IZbgofSijug54/CmYf/Id7CzMwzmnC6+09WJVaSHKG5uArfdFn3vtAHA1TD7T10MTAs4/D9jKpE6KmeDsiSU0CP+URGwUrpX8M0bOAQEPYKkACtLcF5G4JQmi9M3g9WnJk0iYkQ5AZwLqNwP5VTE+cLc6qZFobZAez16MkQmuT8aZ2PMSmerAitwFGYMFzB2ch6lTTmJcSWgwecNEPefbVIzE4xNZMrnMYqw3mjSVPB/dd1903pbPHdcpduhy7eA5YVKQFe88h+kYss+5CMM5AZhyAG12/DVTAmOJdB6/xcDvi0lS7lUYD/K7Y7JR9uniz4mSj9wjpOsRMBMerliDn/UcQ09Yan1N3iJsUMgks7iL+z25mItjLB1w/8O9DNdTXpvxPiCy5NQbyvzYXAJU3gkMHpP2P6YSoGLrgp9GCqDcHkkuktPJuh94rbBT+syZMzPKlckdt8oYg10gLCAiEcH4ItE+kXFAIgldedxRAovHwO4lgsQGOyaSSfPyb1n0yv/5fswtME7guOCayMfZgcIxws/F45ZUTtQiz6CUmmP8QZNyFi+S8EhEaCjBtYI3vib/hsQp141knz0dbNy4UcwNcszD+2+Iro3xXqn7m3F/fjXQsFXynry5pIYO0GwEAhdZ5xA2Cl8yq5+GPxhAj2sYy3NLsWdoUkh/yNhVHNaDTxHdzik8398tqlfvKalAkzV6Ag6PDoqqYG345eXxw8cm/D585doJ6DUBGNRSpfKobxzf7jiEJuv0Cox4aZIIGOTV7wauv0A3WukxJm/y4hJUlJySOzSUGO4BOijXFASqlkhVqnobYKsHJqhJHj5oyl/lpqezflOQzCQ+DfP4NzpYlcGE0oMPSknzUe81kdCx5BhRXFqACUcQ/rjLzUqjZwXMWnOMZI5JDUyOjKKueRE6WnvQ5z6OUsNK5OjmubPH6wHar0j580WNgDEJi/7yj4Hrp6UkIP0bXvkxoDcCjYn1TlNC2QrAVokC1wgKWPnMKmMXK41VcJevFwscFyPZaD0p+i7FPRAC+i6nRmrQuPrX3wOunpbuV9YDj/weYLpB13t5EzDWF73PSa6sHsitBUYuAG52vnEBDErzh7FASKXYdFvgCXYjFPJDq86HXl2MTYVNEW3ZTKp5XhwYiBEdkuZeFR4sKxMyVBVGI/5t9Wosj0t60ieFGwj+z2qljv5+URlhSRBU0BdpqbUQ+pyxpEmrmHNh3AB4LwMBSiKaENI3IxhQyHKFUWpQ43OrHhRSVBbN9LZzR04O/revD5rxcWwLBrG9LgHBrYBOrceqvDsw4RsVXVNWrUTOTBYO4vLVS6jSVojHltrWobuzJ6FxmxJ/evo0hhX6885AAB84dgzH770XtyyYPCxLQLJQfoeg7FRMJxE1ccelMRUfuNM/YyYPDVaWc31N9bq9625gwg58+1ssGQK2bgM+/gmkjEQ6n9wLzLTZnOiKW//pKeIEnEOANVYOkvMWq2xlGQwZo14XvtZKDyUJKo0Grwx2Ym1eKTYWlGNLYRFeGmR3jIS3VNXgvqVRzXSCwQflFpjcYvDMVvRx9xQm853QG/RQ+XwY7R9By0apYooYcl1Fo9aPHGMfDr3+X9i87e2CeOSGXwZfa6YqKlYbkfAgmcDAm59LNmqVIRMdsnkfgw1WQnEuZ1DD5/J3/JnVUSQqUm1JZ7DF5/LcMhnFdZhBEuc83i8fH5fG/bTOBRVNdYDf/33gO99hBkJ6/J6HgTUbUTE0gLJQEAde34OTvgBMb30X8gzh5EHvBeDKvuhr+QPR15wcnpnUoDfGkOJvIwgBgXDxCCsTK+dAZHKcrXoYOP2cNIaIsmZgvAcYvBYtVjrxNLDh7RIRc5uQGo05pag05qPXPSb2SpQtpG9fNTukbiISxRKJNN0XsICMrq9QCK+99tq0IgkmiZisybRSm/IcnI85j79hqr1TAJNs8TEE7zNhR3Kc3QDzmizuvAL8+tuSzCWTPrueBFpSkMjMBnLLpM48QWKE5yh+9wU3iMzJArjf4DVLiSbuHZTSMsmg3NdkAxatAe9etBXugA8aFl0pigMIJkOZFOWayH1UpuOLfx9fNc/5gHuqdH36bgvYFkm3BaQFXl8sEqVp+EzdOxwrs+3xmGBXdmgztuC8yL25TIpwX0/ijuOP1yNjm0Rdgizm4t+SdJFJdHlvxPsz7ZP4Gdhtx/dkjMPX4Nzd1hYtnGO8wRiF8YfsX8OCKZ4HgqQ/Y3F2PjHuYLyRqLBsJshkED+z7AXC8c3Plsk6wXPAY+X55Bw2k+fibQPHIHDy6XCsxbh/RJKOX/mm+SU1WHU3K1RmQJv6AusKePBc70HYfZIRUqlBixJjCcwaI1bkVaFMIfkhg1W++4d7MehxoTEnD6vDrf2XJsbxrsN74A4ERer/K9cu4r/Wb8XmQilBRA8NeagmGrTekB9GlUoQGvLvnQFqHYZg0ESfr4YaFTNVLbMrY+mTwPU9wFgH0H2aBw1Ub4wmWYoqpxMa3KAck4NJFXBhP7Dj7UBFE1C2VWKe3aOA1gzkt0g6yrc6VEWAuhoIhrtSCE3zAqkRBplWtrYq5Wv8IRdGhsZRv1iSxFhkVqNtiqlQCcX6QjTmxCZR9WoDltlW4PzEWREk5+nV8Gi16GzvjXRwkCzJCqnR3w389JvAUB/AsfXYe4HKWsA+Cnztn6P64rY84Pf/XHqOEry+ZUIjcp0wUfKKlBysobdMhswz9b2pT37qe5K8kkAIxoGjuGPlY2gbcopFdcaFKW6DKR1fisdz4Hng2pno/d524KUfAY/Mc4eGjJatgMsBtJ2SFofSBmDdQ1L1bf2DwOglwDspJb/ymyLzkVqlh0kzfZM7l2C1SK8Ha78V7hUoNhjw820zSxtxs8LKjmd/9St8ze/Hi6x6P3ECb6uqwjc3boRJ0eYvm1vKEjiJEAzR04NkuR4aduF5uyWTcDBpaYFKrQ93qUQ+NdQqowg2chLoBB8cGcHuAwcQYMI6FMK/ejx4vqwM98622VOpkaePytBcnDiJqYADKrUKo4NjQAnQNnUJI/1T07qQ4nF1cjLGJ4E/t85iJnjLw5I7neinp0ZctUZCeNzAlQvApEPyY6BxM7s3Hn0r8PBbUiM33vwW6ZYOSCydPQWUhYkGZRL8Po67GeYNpddBCo8zAGC1LTezcmVOn2tKJGfjJaa6XQ7YB0J4ZahfUSIC/LynC59sXo4yBdnMSh8luImfcrvwkx9+ARquHVYtnA5nzFygV0mV/CuW1+LI0cuA9wwQyhNVkKz4ouQJgwiOzWRm4SRR5PWPm3S+fny3E8c3vWUm7OPIt/Azh6DR54kuKQYeDMx5TvgZlFXI6ZAaymQHb3xPHtO+YBCNXi/K44Mt+ftlF9pPfgK8973R33GtKyyBKhTCpsal4hiPnzkr5jMRwLQfn34gJDa0GsAwC/HtcyjWtDjosyj/UtIAbH+/FGjozQB9vvb8h+IJYWKj93wsqWEfT0/mMdMYI0NwPn9nzWYcH2vHmM+JIn0O1uQvuukJ2RpzBQY8cveghEXmipt+XAt4Y4BJIRLGyi5lEsCsTp/rNcbq2zein0Ym4FrEzl3O+UxizYv/Boshf/VNqdBDlrF++YdAUTlQFOeLOR+g9+CGJ4ATz0qyoVTSYLGXbWZ/uFsNLJJilTbJvmReAjJYYDhf17gxBUljJlJZ4Z0p2OlOyRxWnrMwhB2yvFYX1pcFKEFSgdcIx4WyS0EJFlWlYhiuBIuGlJ0NcpfH8ePHRbcBCUauRyxijAfH3a5du0SxkRwb8DhJysk5s0ReHDL4miwYJnnPmCk+1pHJeRLSSpCsZ+wiHxtJDcYrjGkyBQkVueNcJlV57Jl2WnBc8/xQ8nfWnNatjn42QoQJDYEQMNwq+Q4yBlGQGqnEGCmRGmxL4uSuNGSMgH4PlNVQm8IyJ6njyMglTAhJEAkqVQD+0CTuLUvc3sTkzWfOHsSh0YGIQvx7a5fgd2pb8Pkr5wWhIQf5qhDwjxdP47ltUvXqBxuW4o9PUDudiS6GZdJnYRKg1mKFWeuCWhXbAsXnXpsMYkNBLnxBjyBb1uQvm1YpPw0dh4CRaMWhqBpngqwiHOBXLQZW3gmcCUteMHFTXAHYB6JsFXH81xKpwWPKy3xA3TTwuLXLgCBJHJckOaVOclEymcVrifrrvyUBHVljJjAPHjwoJl/KYuhVNqgUk5xFq8bKXBtMmkZo1Vrk6/ISbkjKTZVwB5y4Nn4RXd2DQs+7t3MQq9ZLCd9gSCEpkSmck8A3Pge4nNL3NdAr3f/oPwDP/UAiNmRMTgDPfBt4X3zFsyrW30GrlpI6E4PAi98BapcBd78zc2LDOZIg+aMCHP2oq1slNIS5mE5Ltk2NSreSRmC4PfZ3desTv9dgJ3D4V8CUHSiuAnqYMFdWmweBrgSa7JmAgQxJEib5K2olo/FEhAxJjNW7peNQVriT2CiKrdKeT/z98uW4b+9e0Rciz93/tCI13V1uXH6em4uX2cLKLgiPBz9qb0eJwYB/aGwUlQ7coHNjwM1GoioPwhccwqSfJJOUKNcFQrCEPNFEL4k+YxMc6I5cj2oYYNYk7+b51Jkz8KlUCLFaW62GurISnzh9GqcVUgWpgHJUfM/K2nKMDIyip70P/qIgbIbZDZZX5ObiksMRITZoBL5sjm2t8woep+N6uNuQFQZNrCIAxruldbF8ObB0O9B9SZJVkOe3jY/MvhbYx4HP/RXAjgSPT1rc5a6xH38HoJfL1l3Z/0yOCeDTnwC6OqT7JiNLiyS5qTUbgftjq0ymIa8O6DumkKqkmU0hoNBWJrjncvgn4Av50LJckmfjmsFrvsQY3fAFPB5oDHqx/yk1WnB63CGui4CC9ODPbVOOGFIjESxGEzatW4t+tQd5BjUaDapIVRS7rRYrtnk6nRY+ygmOXIXNuELIWcka1JR5SCZBJZMYM8m28TmrV7QA/a8CdkmqAbo8rFy2BnqTTSSO+PesxCKpwURdqvrmrIBMZDonNK8XLcKip57C6eFheL79bdQmej2u0ePjCV+bgYZyTmK1mtDkjjMIjcCcDxhm2axzby3GAr1eFI/rbEBulv0XjDnSjWA7eCLI5CNJ8msvYfTsb1CQl/kcJMcY8wmtWoNNhbN0at5gLDJXCknby45WIftWZSrDqrxYuZAFLCATcN7h/Ei5TFatsmqbpDUrY9NNTsngOkAyQyY0mBRagATO+ST0SeyTqI+R5aL/kL1XmjdzKzIz1h7qjhIa0S8E6G27MaQGkV8J3PUHUreG9vaN1VkIxXHAam1C3qewYIKdGdxb8Pd8nHH5jQaPg/ENibK5EBC8BpmkZjU6Pwerz3mNLmAB8SD5QOKC8nqJPGTS9bCYCfSyk7u1E+aVw4jJx4ZCghRhIRNJDY5h2ccvEVhIRGnbmcB4IV5Oi+OFaycLyfj+PB8kqxnb8PmpmFXPBM4tfE92bfCzcDym48nE8/Dss8+K4+D4TqXb7JZGfIF/gsfljh4STFkhNVjBxo0Rv4SY9g9PLzBO7bRwsJazHLCkLhk15pucZg4ud20kwp7BHkFoyM8l/qf9Iu4trUa/2xlTtcifBmn2GMbGghJ8Y8NO/LSnDQ6vFyFVUPhn1Fty8Xt1S3DZMYRvd5yY9p6+ELDEuhT1OXFmjzNBSWjIGLoaJTWIdfcCi9dLSZzcIuDV70yXOqBx4xsBasqtxBqgxsBxFRijWWIQ0FiA4u2AfobnzwGBUEB028xLpQK/P/s5wEEtbnoq1AD566abdSo0BHlj+xjvcxMDVRA1ldxESdXjWpUBZcZV0Klnnkh7HD146cQrMJgNaF5UjkKTFrWNUakSUzY8NSgtRWIj8nlp9OsGrl8E+rukhHv0A0pdHYmSQcs2AeeoQ88PGDeZt58HOi5K5EYm0CZadEOSFjjlz6urBTPOtr2Iz0bH8ahxK1HeLLV3U1ucRuGLEpCs9mHgl1+XniOkciZo1BNbrU0YsrAJmHIAP/h/wEhYSsaaD7zzI9MNaGWkUt0+z9hVUoIDd92Fb3d0iOT7O6qrsT2sXZsKXnY4EORC398vzmdofBzPjozgLysqxMacGwPZsDERQqEAJv1nFb0igE8NeDUaGCLt5CroAz7kGbfAGxyBChro1SVQU0oxCfppGsbKLV7fVVXi1QcU60yqYHeVL+whVVhagMmJSZw9fBF/9NYHZv1bynYdHxsTHRsEzy8lF7tpWj7HDde8wH4FGD4ave8eBMamAHd47PSeBda+HXjgQ0DHeSnRX1ofaxKeDE9/GxgelF5HJjRkcCzS20JJarAd/9vfZMYHYGLnqffS3S79z/T9bwM94Q5EPX0IAsDlS8D7/gi4e/fsf8/5qPnNQM9RwDsBmIuByo0xZG4wFMQZ+0mMeIfEfY1Kg+ZVywQJzg03k/jvrlmK73ZegN/lgdZowIb8MmwprMSYtzumm0ecDu65PF488PpL6HE5scyWh39ZuQ7V5ulVpRWFRbA7+jApvqIQzFqWgQCrLCHkKDpXPR4vtPxF1yngmAva0m242N4rgmgSDVzTElVhpYyR44BXkTjz2ZGvuwZYtkcSSay2ZEUVk+JXrlwRcwO7RmYKFGaq8JKx6mMfw+mtW9EXCqH8L/6C7rHRNY5+KQkCJp4rBhlswadMFmWtZM1uVCyVyHsl6KvhmQDO/BRY+7aEewUBrUXyDpuQzTBJWtuA8t3Ju36yAXY+UuN2LEr8irFW2iz9f/l5wDWK4fFJFJVmblzIGIPn6rcN3P815tSK2wIWkO2iKWUlKBNWLJ7K1NSaf88cAKtEZQPXBcSC55ZJNhIbJI4Eac84/uSPAVe4E01vAdY8LpHZ6cCQZG+nKG64IeC+il20tznYUcOkKPcpHCPcOzBJSKkYrke81udSnT1XyPr/2bgmWSX+0ksviSTvG0KDfwHzAuZEuEawO4LErPJayWbOjJ0LcrHTTK9Lco9jgESj3DHFXBmLuyihSAKChH02fZ34mZXFViR4uOaRyOe54fvxfdPtDo8Huw4YJ/CzUPYrVfD96SXIc5iST+ytjuImSdEoAhWQS+sFS0yXhjwvz4aUsl+8+DgxMnCMkBpBNzBO08JwkCeSeVcBT6ckL6RdGk5mR+EJOOENemDUWITOeJ7OgmGPPUJs8NKmCWV8kDjocYoAv9c9JTor4iUXBtxOrM4rRBtlOcK/Y5Xiyrz8mNfZO9yHX/Z1imcU6Y34/KotaKFMDhe4gkqUGCx4uodJhMnI+yyxlqLWkubFmyjITCRrw+Qkb0RxDWAPJ2fEa6iBwqgs0XwiFGbEaKp7w+EeAMYUsgzUhx58DahklW4am2+etwDPn1PqBtHEXvxT/imcsZ/ApH9SJIdarEtRYUp8flkx9+u+Kzhj7xctorvLmrDUloJRsuMyMHFe8abU71MBhRsTTpwcpDJLzfvUZuU44yTuDToQQhB6tRXqWc5Db38vXmx9AdWLpaoOexAw+EOw6qTFwqQuRLEhC9V/ia5hgkFSUSkwPhJN+ojrN8k52/5myeTuygkpoaME5xGHouMjXbDauaARGL0WlrMJAeYiQGHExnZ8Mu6UNgk5x4Gzv0RFYS6qisPz1UgrsO5JyQw1GdrPRQkNgmNIFSY15F4APrbtYcwZL/8EGFUkwybtwPPfB97xEdzK2FBQIG6ZIFenQ291NXDqlCAPUFOD0ooKsbkgW8/E4UwVE0F2hslkuwKB+A2USg+NyiKMulPBjuJiXJ+aQoAbLJVKVK5vS2GhjUetpQUXJo5FVjL7sAMPbnuTGL+XJgZxarxX+J1sLqxBTVzwW2Y04r/WrcOuPXvEfb4GSY77X38dp+69F9osBS2cmw6N9ODcxKDwLLm3tA6lcgV3Ohi/GP/CgMUAuLxRg/ue00D9VqCqBdj7U+DIy4DRAmx5SJKkS4aezlgyVQl+18qOJnZ1fPiDLMuTzDZPnwQuXQT+9d+kOSwdsEOD70tCg+/DG+9//UtAYRGwOgUZToMNqL87+Vu4OiKEhkzIX/VcwrZtd4ouCCbmH63k2lSEo9cuocBgwn3Nq8R182B5FZ7r7cKLg1GPnQ/UL8bHTh8TxRw8Y4dHh/GOQ3vx4s57YYojQqvNRbgy2QfyF2yq8fpC4HIyFrQhpNZBFZI6d0kkqVrDc5Pfgwp3K0rvvF8QL0waMLEfA5JKp09LRBLbwWfblAtpHuV+LxR+LLE5Hzu3WF3JZAX3qpRuSBTEM0hKxfxz1caNohOk/AtfAD72MenaIX73d4G77hI/ci765S9/iccff1xUdPE9mVAkgU7jchljeY04bz+GbYXecKUrZad00nLhHJU6lwpmSG7nrwIMRYB3VOrcsNQlJ0GyBV7XKx8GLr4MjNIo3CiZ+FFHnd5VLvo0ASMTLixelrkMCb87pZ/KjYTT78Ow14UCvRE5rD5ewALeAGA8wSSMLM/JfW8m3jdMIlEPnHsvpafTAhKD6w2JDSbduA5q2g9BZe/G2sYqaEhiU1bj0kvA2ifTO4VlNcCiJVLRV1j+FIXlQP2N68B+I4FxNqvTeX2zc5PeXPKegAnMTLuZsgEWa2WD0FCCxR6zmRwvYAG87rmHJjHLeexmSpVx/eLxsHNCJuN5TMxFs7uX+/gbQdKRUGF3CG+Me1i0xTWRe30eR6ZgDoP5DHatzCRZyO+Cay/nBXbCM7ZirCF7gch46aWXROfHvPo6ZRvMsy1/GLj+upQLYBFVy90xXYD8vjk3Z81Tgxc1gw5WUkVa8fxMRiqSCTkGCofzF0DIDviOAPrtwpCV6HJeRr9bMmmh6FNDzipsKGhBn3sUk36pfUar0mJHcdQsmEat/3blCC46pCDWqjVNIzRIXlSZc3B3STl+0deJIP0rWGxtNOEflke/8JcHe/A/rDQPY9TrwZ+ePohntu6GLjwoaix5+GjTTpwY68KIdwpFhhyszasWSYK0THPZkdFxIO6xWUyQV90N2IeAwbD0jdkGNG+UEiVJBi2PKQRP+Jwa0p58gqEAOp0XMObrFffzdeWoMS+bNYmeVbj7Y+WI+D8JM2pIp9qtwe/GexIIKEyStXWAXtKiJCF2YuwI3EFPJDlE/wmjxoQC/XTC6kddZ7F/uEMWBcFXro3gI013YLF1luSlsyvxYwlIDYKTNQN5efOkbO0zaFIPPAbGBlBeH2WWedwDnhDsPnYi1aPCnHr31IyobwEKSoDx4eh1ac0DFi8HKmqAr3422snBpO+jTyV+HS5O6+4G6pcBP/9KnGRTCMifw8aLY6DhbiC3CnAOA3orUEKCNXaq4wIiDK0GrwK+Rdh/rg0leTnQ68LPo1nRTKRGIlBK6653Az1t0vlZvAqozsLGeJCyVoq5lj8P9uCNDMpXPXnwIFR5eQg5nUBxMf4mXBnN6oTZKhRUqkSVZCqoxbUWnidVekCfXoXs51etEh0Se6ipD2BNXh6+GrexSAV5+kJhHj7skXwPvB4t6iubcHy0G9/tPCFIdeLwSCf+sPEONOTEzlO/5iZPpRIdGgT/Pz8xgWusIsmSFNXPe6/gpz2XIsfy+lAn/nb5TpSlS2zEt5fKJID4ndCCBHwu6Xm//h9gOCzT4HEBL3wLePQPgRLJY2gaSiuAvm5pvHH8+RWkKm/3PhR97muvUiQ5Ot/wb86cBq5dBZrTlPCprAIunotdm/mZOLexOyQVUmMWTHINjIMv5IUPPrGZZbcZq32abPlAjuQJwb0KwWvjq+u2YM9QP/rcLiyx5eHk2Ag8waggFTs5ulxOnBkfw6bC2IT0itwajPumcHxMkgyzag3YWtSIxdYa0emI4BhC55+F60I3jur6oWWyhq/stovgg5VTbK2O8bs5fhz46EdZfiXd5+/+8z+pkRD7Ic8eA371I2ktqSgEdjQCRjnAp6RllMzkezCwUFZNsQiH3Y/s3CCpkkhLNx0wuXAyEMCyZ56BfmiIPes43taGQNhYnNVkrObi98HKTlZ8ci0nwaKE3mBAr6YIXYVFqJ5SFD/ISCZPpYS5UrrdSJDIWKkYRzIUa+rwhBN3ZEDuyuC1y/N3o3F4pBdfvnYcvlBQzHPvrVshyNubAe5T6b20gAVkA+wQo0a6cg5Ol9AgKDElF5MsIDUwFmeuRORL1FcxNeLDiavd2NBSEzVDTRecGx58L3B2PzDaD1gLgFXbY2VmF5A28bd3717hM6Escoh0V97E64dkS7YSttwLsZBvAQtIBdxDs6uHHRtz3T/PBczT0DPC6XRGOr45JkgGsHOQ61m2JLGSrX0sft25c2fM2GTMIXeMkGCYyzGw+IlxCvOA7MAgGFNwXpK7WXge6P3BfTLnJhZtsVs/Hnq9Xuyjeby3lW8OZd95SwJyD4zxUvlMKZd58WSSvY5ArWCCGNCKoFaJABAYALS1GPcORggNghXo1ydPY1XenXi8age6nUMi0VxhKoJFG33d/+26gEthQoOY9LlQazajnUmusCnmJ5rXwBUI4GOnD8MnJH34OJVggshRLFKnxkdEVa2cBCI5Mux1C9mqanNOjKHghoKonhyD1vP2K7jiaBUdJWXGYqwvWAX9TEbdJDVoAjV8VdqIlK0ACqcb8caA7Zx3vQfovQocf05K6h/6MVBQCWx9x7R2z1DIB1fgNAJgxSQJoXyYNKuhUqVeudfruhohNIgxXx80Lh2qzTdQ0zeZ1Es6RujBoVhCg/C3AdoqQG2DMzAFN4kS5dtChRHP8DRSg10aMqFByClQPjYrqZGIDJohSCUbPhdzzYjOYVhaKR5FhnJUmbPYPsuq5w/8OfDrp4GBHqC4DNj9hNT+zNuf/ANwOdxG1rRcMgtPhKMvAQd/LSUubbkUhI+SWsu3SZ4zcwEngeIUk5QmadFYXFWMrqFxNFQUxTyeFLXLgRMvS4kokZhVA3klQONKYPEaZBWUpxsdiiaH+fly35gBZjAUwqTfj7dUVuLFHTvwjaIiDJw4gd+rqUHT5GTCal5WrLOKQandTwkpk6YZrsDlyGMalRUGXSWgHo8SGur02uhtOh1e3bkTL+Tm4sc0Mh8YwB2vvII/a27G79fPMsfHwaK1ihvRp5e6k37ZJ3U1RLyhALzQf1kQG0ro1eo4el+CIUNpiXj4g0E803M55lhY4f9C/3X8Tu0sBH08rLXAuCybEyYy2KWh9MCgfNvEqKQbHX2iWOXRejY5qfHEU8D1y5LnAivfaRBeVSd1S9DXoq4x1tg7Xh6O8Ehkd1p4x+8Ap44DE3E+AHztRH43GcCYwKOMhIIuvGYy2KDhHvco3PSzgkdeT7gJ5sZ7V0mU7CZ5kQjc88SDm8cdxUuxpXAxfMEATBp97IZSUwB10Iqdy+vF5l4rZARVQI7UnTfNs4jn5dOfjj3XV68C3/gG8BFFx1nrZeA7X1Lcd0vye2/eGCXDCtZGNroM1Pm5E7WCyxVUlE6dS9UjKzh5fk+fPy8qw1ZXVIgEH003lW3oPA7KbSUziuVjrPTqGJ9EhVYNDY3f5B0Gj2+yE5jsAGzVQH7jjdMrZ3UUJeDob2crl9rBU3lv6sIXtQDDlzA8NjGndvxp8cUNwLDHiS9eOxaRaeM89422M2iw5KM+58YlgCZ8DhweOYkJ/yT0aj3W5i1DpTkF6b0bhLQKuhZwS31vsmfAXHCzq9Zve5jyYDGNIxD5LlSzxxfJwP3d6tSlShYwM5gg5drOSmkWIyQjETiObqQUVTYJDYIJUfoYLGABqYL7Z8bV7FSWq+Xln3l9MqmeyC8vmyDZyKR//P6de+l775X8kucT58+fT9r1IJuPnz17Vuz5MwXPIeWn2NlHkoQFBPzMLAzj5+R5ZqEYiQ6+F+crFm0l+84sFovoJGH890aBTGqkgpQz4GxbYwtQ9C9tgKkOcIUldhIhvAl2BibiqvElYsMdmIJVxwAi8Qb+kmMkVnhAxaRNAF9ffxeGPCQ4rCgzWfC9juuC0JCfy60DCYuz9jFsDkvg5On00yW3RZJq5orf1qlOXHJEE2n97iEcGz2NO4pmuIj5ucuWS7d0wL+7+JqU5JUDiLFe4OJeYGXsAHYHL8MXGoM//Jn4cxCnkaNNvULU7o/TdxaeJoOoxg0kNXLqJQ8K0UUR/jDmRZKGdKoIOpM/rrYJual4kKBK+HicD6f0XJoOp1BFaW0GPHHn1JY8uU6WmRspJqIy1QSkhFVeTh70OYtxdfKKIGv42axaG1psK7IfiObkAk+8L8nvrMC6WUzIrp4GDvwqet/hkMiPXY8x+ySRo2P9QH5ZZsc33gkMXpQIgKLFwGzGoNYSYNE6FIWOobVvRCI1bJXAqRckLdy8cmDNQ4DJNp1oeOj3gUO/kOSgSmqArY8ml+iaC+56DPhuG+AOX+eszLrvbXij4cddXXjfsWNw+P2oMpnw9JYt+Nb27Tis12OrQsYlUfBOsiN+g2XU1ECrssEfstM9A3p1aVhiL0mCPEVwTH27vR3/6/dHehU/cPw4zBoN3pXAYC0VMPFKuIO+aXOPM+y9IYNSU8KOJjy2mZTjp7qvtFSQ/tmAJFEUOxNyXqFUS9ooXC2NR5qFi4C+Gug8GuuBMdQGmJPI1c0U3LHD4K8/D5w7KRGMS1YCeUkIv3Xrga+HE+Oc6Pm6vGYaM0jYsOrtC/8J/MvfApfCVfd8Pe4n7krBUyMF1JjrMOgZEKS8jCW25ZFqbm6qRbdZuD2ZG125g4kBCNuTubasXLlSPHZ/WQU+f/k8xthlZLNCZzKhKccaI9MZD51aK24J0XwP1Gd+BrUn3J1nKZAkxBKBZEa8ZwKTPO3hzlQZZ45I51FOAPG66R8H9A1SR4elWjLHDmu8c+zL1U2JwGQcA/qYjpEMwMownmvKVDDQofYwuzO4frNFnsfBAFBZ0ZUI/D7ya2qw77QDW/Pt0PimoLJYAV0QsHMvTV+uNsA3CZRmmRxPRmic+CHgdkjjgjJwjkGgIUUz0bodImnXP+YUwVimmBZf3AC0T9mn+c4Q1yfHbhip4Q8G8PrQEXiC0hzvDXpxePQkdmnNyNfPb8IgFVlaX/AyApC6QjUoh07dAtWN7ORewJxlM0i0ziXBwXVmQYd/DmjcAUz0Q6/VwOvzQ88CsGZJunABNx8sUJjJn4+Q1/gbRewyMUlPj7kWPcpgUpQFkKm83pTfj5/19GDC58OdJSVYmqXO7wXcfuCeljcmlTk+5FiCpAYT7LxG2dGhjL35GDsc+HfZWDeyLcOWDjje5dg82frKQqdkhSCZdPaxC4P/M57jeWQXhyxRz/Mve4skwtKlSyMd/DQ4r6mpeUMUo/T396ccX6RMavAF+cIxsK4B9MWAj5IOg4DKr6ht1wBqKUmhE10d079k3SxVsvk6I3pd9BZQkhAGNOTkilv0dRJXrcqyUsTjVfV4prcdIx6PUMlix8Z7Fi1G7iykRq9rYFpih8TGvCxwHAgTcRrSfGy8P0EwNBIhNGR4gqMwhVzQhCW/ZgONceOhSaPTIytgNWrZbsBxEQi4AXZOWNNMMtFDI+HjElFgVJtQYijDoEc6j0z8U+oskacG9eh1KrWQI0gblIWgyTmNz/n3lhrA0jDjmCopKRG6q0y6pGKCE1+h3t3dLUyNynLKkKvLxbhvHAa1AeWmioSkzU1H55XpSSv7KNB1Cbh+Kvq8dbulro10MNomGZfKBCrvV20Acoolrw1DkuukaQdURfUIOV+VpLROvxiu5g4BI53AgR8Cu943nbAgkfGmP8S8o7AUeN+ngWtnpPPWsAzIzYLxexZBUnnM60WhwSCkb9LF6fFxvOPwYdGpQfS6XHjg9ddx/cEHBeHHJG0ypp7zcLJNhFadB3XQJtqvc3NZUa5Ou2Ph5Pg4PMEg1ublwRzu/ntmfBxBJmoVx/Q/7e0ZkxrcxDAxvbiyGGft/TGdGi0k3sL46vXr+MMTJ8TPfEa+ToeGnBzsLC7G3y1blrU1yazVodpkQ4/LETkW/rvElkFbPpPwxeulm3ghyj4dnP48Rz9Q2Qj0Xg93P4UJiMYZkrvccD77I+DofolQcLmBXTRPTnAe6huAv/0H4P/7V0mGqroG+MxfM2ONjMCW48/8I/DcT4GLZwFrLvDoE0B58iR7DCi7yQR2KADkVAHGWHJBp9ZhQ/4WsW75Q37k6fJh0+WibWoYw55JFOgtqLcUic0rtdIvtrWi02oS3YZbCkuFgTYT8HJ1FfEZjRnf1RkwODGJ2ikP3pdbgim7HfokurA9rkEMe8Zh0hhRb6mEVjkHmvKA9e8GHAPS+baWJSd1uRGnfIlS/ovrQA3lOBRIFgTlLQFyYoNrXusMoGbSiWYnR7a8GoQ+ukYTeT8GGezUSAc02GVbud+Qi8HaLdjz2mt48/oimFwKI25i4BRQsnr+uzX6zkmEBt9b/l66TwDVawF9CuNCpUaobBX6h0bnRGrI8cWNTBzlJjG7ZYxxo2D3TURkUaNQYcA9dNNJDX+oFQFEO+cC6AWCGug1LSnLabkDnjAxuiCRc6PBcURPn76+PpHo4M/pji12o70RkiI3FeY8YONTqM47jk77BBrX7wAM2TO2XcDcwAIFyuxwv5QMjLE5FpJ5yrDTg8nNTOTdkr3fuXPnRJJS9hKYC2gUzq5eHj+7RZNh1OsVneeXHQ4pi6dS4cdbtuDNC146v9XYs2eP6BiQiTbufdn5Q3LjlVdeiSE1mIjnczmmuJ/jmOA++XYkxinf+Pa3v33G58j5B+U6ydzcTORDIpA44phnAZXcqcX4RenLNxs0Go3o8mBMyNegdBY7zNKNU34rSA1OstNIDX6JxmrpFvIA/gtA0C75aGiXRPw0CvUVGPJ0Y8o/Hkk4lhnrYNTMHDQ9WbUEly+OxFRTvatmevfDvaUV+NK1Cxj3eoVRuAYqNFltWKGQaMnXG/CdjXfhJ92tGPN5hbH4PSWzaxNrEySGE8k1ZAU8nzRQdjliH+OmiBDVrtcAnx2hnEDCBhlfcAwazcykBgfgkdGTGPPaUWrSRPJHRJlxlsr2uUL5ZjK0JiBfkpTICJpCQNsI+BXJC92yCKnByWZF7ip0OG0Y947DoDGgzlKfUN6DFcr+OEJDumJTZF5NldItRXDiIiPLoIOMdDrtfGwLZ7WobJhcaCgSt1saBuN0fpOJZiWhQRx/AahuBnITJFFHu4BreyWj0txKqepJbwJ6pGRvTIKmS07oqYCmu4HiJBW7+VUw1CyHxz4GgzhA2QCc+rejAGXwcm+iyRoTeqvTJHluEH7Z140/PX1MyADm6XT48trN2FaUpOo+CV4ZHAx7BEngCBzz+XBibAx3rVwpErMztR+y2oqbgnhikIa9bNtkBfv169fF80iSpLJAslJp9969ODQqSUNVm0x4eedONFmt0LBSv7tbSqrrdGKOkP0MMgGDja6uLry1ehWcAR+uTkpV7WvzK3F/mXTNkjT645MnY4aP3efDU4sW4SNpykMcHR3FkdFRYTr+aEVFQnPxjy7ehC9cOYRulxTgPFDeiJ3FcUnojEDvCS0QUFbAUILHANz3FHD4V0Bfu7QWbtwNFCQYd44J4Mhe4PDrQFsrEAjP2d/9LymBvnVX4rfefAfwo59JRuHZkOria7z5SemWDrwO4PovJCJfnN0TwKK7AWtswKlVx5Lvz/edx4GRVviDIXiCQKnRhierVqLljs1499PfhTvfBl1eLkwaDb6ydrtIoiuxLhDAWxWfm4E4OzpoLhefvDo1fgVn7dci3X9XHZ3YXbYlltigzGZeCt5DfO2//3vJbJsyYAT1xt8X1/W3fhtw8JVoNw3/X7J6GqFBsGpptooo/n6mSqt0ILebz/U1aHTIgIPrfc2iRfjpL5/DW+5YJKp4TUa95OvEPQhv812YQC+bRJJsfDwVUiOs181zzBghU/BvGQgyIJ6L8WI6aMzJx+aCChwa7RXJGxLqzdYCrMu0SzQDxIylWbqIbzQCoemdMwGwE7klJbLm0MjxiOzr4pwGtFibFhLkNwEcW0ywZKKPzmTtgvRUFqA3oWjZVrQeOYLGBULjlgJjA8bQ7MJMlohkzMACwkTjg/t2+nEMDg6KIkWCFdKz+f7NBFm3nx0i2UhI8vgoCcoil5lIjc9evCh8+QjuCJh7e+/RoxirqFiYu3+L8dhjjwnZo5MnT4p8lUy0cT97zz33RJ4nYngqB6jVkeuMpDqv47l2S98MvPe97531uuc+nudGKTnL++n6bPDvufedq0xcTU2NKLziXEaC4/nnnxc5EL4ui0PTLZ6+FUDuIdX4Iq1ODW6KkoLGrLrEFZWUS2ixbsSItxfeoBsWjQ15+tik1xVHP/YNX8akzyN8NZbYKrG+YBH+cfmd2D/SLQKODQUVqLNMbwsnYfH9TXfiC1fPo8s5haW2PPzJ4mVCdzz+ee+vT09aqclahz73QEwiqdnaMH8TPKVuDv4oWilusABLd0r3h14HXPTAUEEHI3zW6RJNNGGfDZcc19HjkgiqAXcQFo1KxLXLrWuQp5+nxG1wAAhSV50JDSugWQWo0pCYmg36ZkBbAYScgCoHUEuv7Qk44AlOim6hWnM9VJZZJiiVWkiSTfq9ke+c3i3FBgvap4ZxfKxT+L8stVVgORPqWQCvJQYbHF+bN29O+e9GR0dnlN64JbFyG3B6X9TQl6ioE+3Z02Afnk5qOIaA0z+L+ksMXQPcE8D6twNxMj2xCAHXXpEMxPWJrzsuxKFkhOWCgWdCXHFM4I9PssMi/JX5fHj/sQPYc+dulBpNaXlVhJI8PlMnhoy8gnwcOXsC5rwcmMO+TJRmk7U/+ffU5+RrpVq5/elz53CUleVh9LrdeOrIERy6+268v64OX2BylpIp5eXi2PlYppC1r81avfDPcPq9Yt00Mvkvv7/LFfGEksFkXNtUVJ4oFXzx6lV85NSpiCDkncXFeGHHjsh6eXp8GAdH+mFUa/Dhxk2waLUwqDUwKI5lTuBiU78ZuPq6/ID0WO16idjY9tjMf0//jH/+S8BhlySsDFrA448SGwf2JCc1ZMQTGjz/Rw6TKaZmEdAyzxKMAyfDXkJEeK3vPQA0vzXpn3Q7xwWh4QuGYPeHJXScE9YTUO8AAQAASURBVPjclX0wqnMRqqmEr6sbnt5+TEGFT/WP4G+bV4tri5tcEmfKqj+OiZ/2tuMnkwP41//+Eu6tqsP9Dc3SmmJQC0JDOjrpmhv1TeD6VDearZl1I4EVR08/DZw8KXXH0PgvXvKhYhHwwU8Bv/kpMOkAmpYB9z+e9CVn2oeR9L906ZKoUkwVPCfcQDNRQRNRJZHK+YPdGlyj57L/43cgfw+sDl1k9ULjvYIr1wbQOzSOFU3VqG9qlnzVWK0/n5XSuRVA98np5GIamu8MWhmQZSqhSfBvmVjia90oUoPf4R83rcfKoU5B3JYYzLirZFFCgne+YNNaUWIowqBHIrFJINKvr9p8K+zrNBnFF+zQUBIaxJXJ67DprKg03TpeIb9NYDGHnHyRi6BmA7v/lObJC5gbFjpebl2wOJDVzTP5ZjAHxm4NZcxNCUwma/ndcr/A/0l0sMBqrrE59wgiJs1S9yJl6BKZCytBv1plrBUKx3QTfj9yb6IM0AJuLngdco9Ggo0dP5KPnlYU4TIPxe6N2tpasbYor9U+1xS+NtaJEwePoOrUIbyjcRnqysrF2Lgd5sPZjpE5O5IX8Wsqpd7YZZEIJE/ZAcLzx/lGXmP5OlyfleRopjCEyVnGL48//rgoFmLREAkOxjH0AEm3k+RmgnFBqh0raXVq8IUzBRM0xYbEDHHH1DB+1nMsct/p8aJ/0I6TY934/YateKJq9gRDldmCz62aWxVdIhQZCrCjeDOuTbaHzczLUGuepTLR0Qt0H5KMF3NKgZrtgMIAfUaUNQB3/z4w2ApQy7qyBTCYAfdQmNAgQjBOuuA2GRFkcBwed2qYoVfPzsL1CkJDIjJcAd6kRcyXM08b2NAEEFRW4juAwDFAsy27lYhChioqMTTmbcew90rkvk1biRLD0hknKv7uPYvW4j9bj0Q6NkqMFrRYC/GdjkORROBlxwBcAR82FNRm5dC5geEkw8kn2WQYDz4vniFOihADTCY/jdklk9LF2CCg1OXndzE+IvyAp8GaQBt/kN9njNOOJH/iGgcK6oGe48nfm9+n256U1OBiY1y+Gug4DgQUUnoFlcBsJvE3Eq4pYHwQsNgAW5oyVOKaDieRs4AX+3vFmNCFvz/mmNmxccY+hnvTIDWerKrCP128iA6nUxDYfM27SkqwTjb71WrwkUN7cd7jRLnRhE+1rMTyXOl3fe5RvNB/DJP6KTz/wiGsLlmM5bZasYjLWtLKMZ+qeSY7GZRdgvyZMlnEv65cCatWi+9OTIiN3cebm/FkdeZeHbJmpgySG/FYZLEI3w6nQsPTFwph+QzdXRcmJvCh48dxyeFAi9WKz65YgT85Jc3F8ifbMzSEb7a14YMNDfhNfyc+e+m4IEv40Z/uvob/WHcnqs3Z0faNoG6DRNgPXpe6NmrWSP41qeDFnwOTE+FKftmcWwO4wt8rDbE5x9B7JhXwevg/nwH2vBp97A8/DLzzXZg3uOJkJgnfFOC9DugWAQlkIEfZmSY8VmIf56tM+icQ1Khhro120tjdXhHEctPc2dODD//wezhsH0NeeRk+sm4T7AE3vnL9PI3KgPoqfC/gRrnfjckrVzA8NQpfqQ8adg2Ee0m4h+vo6sDiJdN1WuMDb65L06Shnn8e+N73pE6N3buBHUkMT2ubgA/8OeYKEhrsQOGx8WcmJThWk63/TGqQzCD5s27dOkF+MvigNBz3v8XFxSIxyM6WiFTF/r3Asz8BfF5g253AY0/O7AGTAFUr7gT6rViv1eLkpQ609o9idOok1rOz2VIJVN81P15NRFEDsGgj0BHuaNQZgeWPSHvPFMG4YC5dGgS/EznGuJEGh+yu21WSIUmXBfBz31G0DpcmronOacq8LbE1waiZIeAM+oHOA8BYB6DRA5VrgcLsGznr1HXwBs/EPKZVzU7cuwLuGEJDJmuGPaMLpMZNBMcV9blnktlRgrIhc4n5F4Bpia50q3cXcGNAIp3+WLPF2yQruC9ghyvnbn6f8n5C/p8dGqyMzga4FyFxMlN3RapgN+Vs199ymw0/VdznTqbEaIRtgdxcACD2wLzJRYN2ux2NjY2YnJzEL48fw9cunYPdoMPGpmb82co1+LMz+2D3eRFsqgZ7vr/mHMBf+QqF1DL31fHXNedI3kjCKyETbfIY433u10lG3ixyhOOSx0kyk4VQzDeQrGG8xc8QrwbB/NKpU6fE70kOcTySIOLx06+P/7Ngio/x880kq5sOTCZTZNw//PDD4vUpJ8ZC6lRzjTcb6cQYKUcubGNhwDcjqA3NaTDNi+z8RE9E4oDgn9MEdcAzgRNjXdhcmHkFbLaIDd5SgnMEuParaCX5eLskNdH8WOrnxUaz4rgkathIUAZfKXd4HK6iWvh1OmhUZpg1rBSY/StNJp+VqBU+KwgNTTOKB7jos81xlopATmb87DzmNHR5vcGpGEKDmPD3IEdbAotWmpTHvVM4MnYdTr8HlSbKDtRJXUW2EnxmyV24NjksqpOX2krxoy6pS0n5CfYNX80aqUGwPZSTDc1GU5mouSBcvHhx9iRAiGTYBYWkUh2gasRNQcelOE8N+siMAys3AJ08xjBW7QLyE0zqM52XqvUATWsHL4Wfm+A5+lmqSSn1tv09wMU9gGtCIjSW3Dn/2uapovUc8MJ3w6QL9WTuArY8OPvfee3A8D7ANwGo9UDBesAy92TOM70dMadZ0mGlXnl6rdfsyDh899347KVL6Jiawqq8PPxZc7NIOpFM+OfRPpxva4O2pgrdzim88/AePLf1HlSYjPh1/1F4g37oDHpULanHMPzQl+ai2ZJYRoQLfCoBZp3FIky5ZWKDs2apxyNaaQX5aTTiTWvWSEaD8d4AaYJB0GyVEzlaLb63aRPefuiQ8Pgg3lpVhffWJp6D6B+189VXhYwXPwP1ch/Zty9ibi5Dq+j2+BJ9W8IEDkGC6jsdl/GXS8J+GNkCx1PlMumWLjhfKDtW5LEpyxV1tAP/+Bng0/+QGrHx6iuxhAbxH18G7roLKJuHyuLhK4BzFNDE7ZX0WsB7EfB3A6bphH9xWLZC6a+uVPAjESV/b/y5ubQ8Yn75TccofmkzIWjWoX90BB/48Q+wyKyDU55HxPYtiGdUOnzn7jfhyJnjOPbiy1h7/x3iWh/uHsDU8BhshUtFUp9BzK5du4T0EI24CQb6HFPcvPNnEgiRjfmLLyL0mc+g3+9HF0mNs2cROH8e/du3481vfnPKgQmrwAmu0zRZ/kXfBZwa74VWpcb24nrsKJK6sRhs8RiZxOPYYjUlA48TJ04kTOrJQdIOBdEit8vv378/sqFmEMCgjl0gxdcuA//yD9EXab1ONgd46neRFvjZy9dDVbYWa3OeRVdHG/Yev4ylDRU4evRlFNdPYOn2x5MnuEfPA55RQGcBClZIcp7poHYzULlakpwyWtMiNAjGBYwP5oqUYow3ICg1tSw3DWmG1leBUY65kESEXn9ZIjfyskvOaFSl0KtXwx+kUXgIWjV92mYPtJP5Z7ADZQE3D9xfsJqWRG2qUiCcz7NpWPzbjHR0wRdwY5Hq/oMJ3NnAwgcmfDMBC65ImsgFTtyXcI/FfcdcOiHl15oNn2xpwatDQ6LQSY456KlxO1TVL+DGgsl5uZN5VK3CXztH4akshc8xiWdPHsfxMycRgMIvLBjChMmI0ZY1WLNmDV544QUxTrgm8X8m21kMxbiBY4CvT78b7uH5e94YzzC24M/sECGpoQTjesYj3PsTlFuifOJ8SCjyfbim8j34P7srGF+wyJj34z1EKN3Fz62M89l9wD0vb7KpNwuqGGNli9SIJ2+ZX+Q5ZZcJu9gp7f2mN73plh7j6cQYKUcvvHioKciLaVpLatABeI9L0j9sWdYxsEq39W76hEuiw+HPDuN9wzDeGqdPHAKcw4DHDhgzYMX4Wn3ngZHrgMcB2IyAQQoO1KEQLNpmQJdeu/4yWzP2jRyJ5IC42LFCLE83X8aETM4kWlBnqWjkd9/7mtSlQtgagNLNKUkB+YK8FqfDG3SCdfoTPhe+27lPJENJpl2fGsCwx4EHyleL5xUazCg0RBOVHkXyR353/m02wUmFkwyDjngdTRou9roliYIKY5Go5OPzyQy3t7eLhSEhQq5YQkOA5rQFgCpFoi6boLRFokthw4NAyyZgcgzIKwGKk0xgpc1SJ4VIzYY7KXLLJdNayrnYe6LJzXj/lppNgNE2+4aPZsibnsAtB3ZoKAkN4vgrQHkdUJukm41JQM4/Q3s5KiTWgSTh8AFAmwMYMjcc5znrdE7GnGL+nKfVYV1++q9bZDDg8wn0JK847DjDTU6BNM/xm/cEgvh1fw+eqCqfNg4pFzfgHkNdElKDSc5UKub+cflyvDQwIMgA2TTvrxctmrZB4kaMVV4cj6ysSqlzKg7crMkbsZlAw75rDzyAU+PjooJqQ35+0s3Iy4ODGJb9C8JEBQkOkhhKGSu524O/n1R2UYlzHcKYN97I9iZjUQNw4lDsYyR5+Jl8ASnrf/kicGAvsOPuxK/BoPOXz7HcBujplggGWb6K4Gv19s0PqdF7FPAHmJmPnZ+KrNH9FIkNdmwoUG7KxT0lLXim92LMofJ6X2wtEubhHU7pGsrXGfCZpWsi4/QHne3SbKnVQlNSDG1JMXRmDXKUUgPBIKbGx3H48GF0WuxYd/8d4vh8Hq8gNZoWN6ESJWIPyACb1yslSrh55zUoVyhyfyhXI8nw/uhHeG1iAouNRqw1m3HF7cbYq69i5yc+AdV//Adw9SpAuQZ6bMRVaYlTEgqifeoChrySrnWergTn7BocHu2MLCckOAxqLTYXLhKB0QMPPBDzGgw6WFVFkiJeGoLnKJkGNv9uYGAgEmRw/FOGquj5n0/nzX/18/RJDSW8dlSXF+DtD2yCWq2CQa/DxDCTyknm9p5XAJfsfaACJruBRQ9LSe50wA4N3jKAXDGXLQmQBcyAYAAYvR73oEoiSjMlNSjfyQ5XrxOwlgIVayKdQRpVMTSaBL5mM4DkRWNOPa5NtooYTn6sLgtFFAuYG7g/4fxH2YtUkhhMtFCHn89nsjYZQiG6WdJvxQ8VuCdZMMKOB6v8szFPLiD7YNV0pkREImSaIJRj+fi9SGdnp5C+YtzCYsZMXp8JzdmkbejFRt/AgyMjwlNwQ0EBim8jmZoF3Bz8qr9HxOQMSzTWHMCaA6dWhRJjbK7OPzmF8ydPw1hWKboG5AQ/CQ2uNSTQSeIR8jXOxxhTM0bmOGW8wfiCuTLlOCBpzE4Ien6QHGTBL9e7VIjITEBvvHiw+Ild4cyVx79vshiD45lrMj8/wc/E+IWqLTN1lmcKnU6HLVu2CHKIBWh8fcY3tyrhznmZXTGprp0pkxq8OPilsA0khjFhd4bnEKDyS0E6L2v/KckkXJNasn1FbhXO2rtikuxUROIAqTLdGH3d7CHLbFfHYaBT9jJRUZsLKMuVzFALNwD69M9PsbEQmwrW4vT4efhC1Eq0YnPhuvlj6lRMDLGyTJb0IZj0nGXj278fCCfyBSYozZEDFK6c9S116sS6sfrw42ftnRFCQ8YFRze2F7cgJ4FUWLO1FH1umoyHL3Phv6ESXR5mbfYWfZr4cFJUynnYfQ78ZuAQvEEp4WhQ63Ff6WbYdDmigoMTUnKwAjuORRDZLTuVdnHDsXwTcPp1gIlSuZtp6QYgJ1e6zQZLIbD2ceDaPskoPK8SaNwhTRy9ZwG3Q3qenKwLhStRbeWALTnRevbs2Xlb/LIq3aUkNAgSfEPdiUkN7yRw9ZcSoUpoNYDNFE6kUnuub06khlh8dXqMUXpFfgzAjqJSQQBkC8xRhzweaHKjhJS00oRgTJC845g2zSDfkeo815CTg/O7d+PH3d3wBALYabWiPEG1E/U0WfkoSJ7OTnEtMQGqyjdgJOSATq1FU05V0mPyBf1on+rGgGoUVwdb0VRSP+NxVZnN4jYbkhmXf6SxEV+8dk2QGcS7amrwzpoa8fzF1jxcc9jBFAXBV1iem/k1Mi+48wGg43qU2DBbgL4hsjbR57BKZkwyeJ8Gynd98hPACUogaiSCI/575d9noeU/8fuHiSOvP0pqUD6LnRoC3AQl9gfaWdKE+pwifLfjNDrZScYKd3Mufq92LT5Yr8UZ+4ggp1bkFiAn3KUifbLp161Na8S4zxX5jUqtxls3bsXmmkZ0dL0inZdw9aCtMA8utxsrNqyIbMy5IaZ+tGw+y81nfOKLY4LVS4HhYVg1Ggz4/ejz+dBsNGIpKw//5m8AyqGRlOJ38frrwPe/T5OFmNfpdV2PEBrEuG8Qx8e4fsfi+Fi3IDWU7x+k3CVCUCNHHB/1ZFmdxKCBn02Wo4s3VZfBdWHv3r0iKcDPLpOXP790Bbt8ftjCEl3SyYrTBksLKtFFFwp4cPjMdVSW5qNpURl+fWIQS+x2EeDEwD2qIDTEpwX8U0D3q4C1Gshvzp68J6VU+dnY6Rg3r3DOkwOyuYCvwddaQCbIcL1lZ+vZHwN+zjchYLwDmBoGFu+eU3fqUuti5GqtGPGOQq/WC0JjRkmtBdwwcD5jJWyq1atMdrDzLZnBaCjkQxCUj5a6PSkeqg4x4TRP/oy3IShNwlt89e4Cbh3CKRsSTzIyzaUwaZso8Smvr9xvscqaMQfJCVkKKBVwvB86dGhWvX7GbttuQzPhBdw8iHxV3GPOQAh6tUZ0VAfDxVcl+QV464Z7YInroOcaw/0442Zep7KPJscluxuY5+I+nY8pfQEJdk2zEJiEAufX82fOwJaXJ+KS+OfOCyi1GZwC1CZAbRbFXCz4Yowh4o9wjMHPkGhe4GP0uWDxgByDUDaY45zj/S4qBmQZHR0dotOFeQseF8/frUpqkMzleUzVoyhlUoPBIF+UQUcMqREcB1RydWf4C+MXF2hLmdSoNhfi8coNeHnwPMa8TvhCrIIHthc1ooWVQ2mCJ+DK5ARGPG40WXNRbMisAi0j5DcAA6fCOYRwJbmlBDAoAlImJk+8CHReBKidvnInULciwQcJAl1Kj4Dw6/mLgMb70pYJUKLCVCpuNwQ0kddsBoLXJW8HVS6gbpg9aBLeH3FpC2dfSqSGXm1Bob4JI96rkces2gqYNUWRRKJIjMbLmifpvrijqBHnHZ1wUKIhDH/Ij1eGzuPh8rWJv7uRNknSwVoG5KS+SaDWICs2+D9xZPR8zHGR3Dg6egF3l0rJJDLYTKRyYiSjzev/+MV9cMOBxfXlKImPJcVplyp6OdlyMSFjnkmFedqw5gPv+Dhw7GWp84Am4auTaKvPZG667q2JEy9yh0YEIaCwMbnxqceJkRO/QaCjDQXmTUDuBkBzi8ok0EMj0XVmSfLZ2l8DPFLiU4DV4SRFLZwPQ3OaP2R8eskq/NmZo5IHA69Fmks3ZddkebHVhhpbHoa8XoT0etEppVGrsLu0EhatEaty63Ha3io2TSQ0bDoLltiStypSSoGbpWRBuhKlRiM+rCC7kunutjPYGBtDkcmElro6XB5rx55Dx1DdXCcuyQv2NjxSsU0crxK+oA8vDx6Awz8Ff2kIPz32HN607SEssaVPsPW73cJDo8JoRIvNhntpDm0yiceZ6GaHBj/PXy1bho8uXiz8QcqMRqxXdHv89dKN+OSZ/ejm2GQ1SlEF3lmT3DzxpoAb1d/9CPDI2wC3CzCZgY//odSJJIOJ6rq4c8h5wW4Hjh0FjocLBeTqPPH5Q9GfP/4JlujPz/HnVoerrcnWhd8zR3ldcGwm3ztVm/PxqZadGPe5hf9Mvt4UIbA2Fkw/Zv7uscoaPN3dEZEe47t+qGEp2qbG8Xx/lxi/T1Y14O3VDeL3Vq0ZU363GE8GkxGVTTUoNkhJfYJVUDSepBYsJRNZ+ZNIPo3PYbLA+s534thnPoOtFkuU8Fy9Gjh4MJZs6u0FXn4ZePTRmNcZ87EKOB7TiRolmRoK+eEOnEAQEqmrghFGzTrRZaIMFGYz4uTv+PlYTcXqLFaJsWpI95YncPJLX0Cl0YjGHLN03exQBCAk7q+dBTxuoKYJKCxNQYZqM5xXX0QgGILPF0R7nx2L192F06dPx0hjSQce21UV/jCAaxBw9gOOLqDm3pQ6W5OCREbbK8CYJDEGUyHQ9ECMLxVjAnqXzBU8p+yAWcAMYAdF4WJgRLkOhYDiNOSrlBi6HCU0ZFDaivsGY+ad2xwzVeYKcVvArQUmjiihmSqpwTmfhVOsoJX/hkkbxhycF0Ngd5W0X5BAIvkC1KEScR1QEoTVtnM1P72dwQQXyfQF3Lpjgt9RUsWDNMGCj0wMvpmEZUweT375g0G8PjwkuifWL1uG0evXxeuzIjxVMkb27Uv0+gtYwFywu6wC/37tIkJBzv3SPrzeYsU/r1iLL147jX63E7UWGz6xeM00QoOIr8JnF8EPfvADIcnOYil2KCWSTCQJyFiEe+NTr7+OZd//PiovXOCiBbz//cDv/M78Soez0Mp1Mrp/MjQDxuaYuT7eDyQRmFtn1zslrZjH4zxAaSrOSS+++CLuvvvurI7Z7u5ukV9kV/7KlSvx9NNPC1neaSpMtwAYX7CIbKZOUSXS+gS88Nra2uI0iSlpEvdliZaLxJWGSjh8TlyYaBdJ2nJTET5QvwueoB/DnilYtQbk6tM31WKQ/1fnT+BnPVIbu16txudXbcRdJTNvru0+D/pcDpEgKDXOIbnLzpKmh4Gew2Gj8DKgSpJxiODQc8CV49GB8NoPJe3v6pbp51GuZo8+KP2XhYTkDYXKDGgSEDczgRq8lBSKvgigTr3aq0BfB7OmEN6gA1qVCSZNNHlXaynBsbG2mLQIdbkTdWmIQ1GphASG8mvk37ZPDU7fvJCAOPOsJIUko/keoGxpWi3iMqnh8E3FdJTwZyZB4xcETu4c+N0Tl2Aq9cM+OIYxrxYlzEMp5ZhCwNjYJPoHL4pEFJlasqHzoTuYEKz8vjsBKdF2FrhwQEpuNawClsaNGyX8HqCbCSN2a5QDJU1ATgkQOqt4Es15DEBYi34afB4493wTF85dwrZltUDrYWC8B1j3+NwSQfN53tbuAk68Kh0f54ayRUCzglRjgmKsW/quJ1nFG8/aBSIVwdnw1Hi0sgZlRhNeHeoThMbjVbWoNltg9/lwzm5HoV6PZqt1Tl1gOrUaP3j87fj4L59FT5kVpUYTPrNkFepzJLmeTQUtKDHkoW2qB+7gJPJ1arROnkKxvhj5hmqo46qVGcCQnEiF1IhHoo3FT7u7hc+F3PnwUFkpdgSuoqyuEoOdfShZVA530Iez9uvYXBhbDX59sjMylrUGLbxuL04MnkO9pQaGNCRkftLdjXcePgxvuOqc3RhfWL0a+3btwkdPnsRFhwNLrFb8+5o1yNXpxK0mQbdHucmC/9lwD3rcUzCoNSg1zt4RclPA66lYUVny4T8FvvR5ySCcePNbgZWS/JLApAP4m78CThyP+vnE4/c/SGF/lrICNfMoE7FoOxDwAvZwVXp+DSAuxXCVv74F0M58bXI8ca+SKv5++RqYtVr8uq8HJq0Wf9jQjDdVsPqvBh9tml4ksKVwGX7RdyAi7aZVaXCH4tplJRHJQe4HuW6wfZxt30oweGaFLzscDo+OYkdOTpR04P+XLyf6YMBrvwGGO4GNW4EV0tymSeAV1mzV49xE7D7zjsJoUsIbvBYhNIgQPPAEzsGk3Rj3lqqUEns08WPHBot6OIeUv+2d1LLD1R//EEaNDlW7HwB+7wPSH5AU/N6/AaNhMobzxv1vB1g4QL+h8vrE5HluPSzL3oIWy1n0DIwip6YJZVW1ojJzGthlx3lcdHAqCDJ5z0hiY6oPyKlExug7ESU0xOcalUiO5kciD7EAIxudGnJ8sYBZULcT4D6VXRW8hirWSURpJuC1E293R8R3hC7gDQW5KyqVcct5njdWxHLfJO+BolXi0UIvJaF86fJZ6HRmkZAiIXLTSQ33FHDiN8Bon1RctfY+wDr/XaiUROEaKftbLeDWg3xNZyvhT68BEnk2my3jY5FBX7tH9+/F4dGRiETUFypr0GSziYprJoBTrUhnwpRk5KoEMr8LWECmaMix4Xsbt+NvL5DAcGFNXgH+YfkalBhN+E/6fqYJXs9ve9vbRJc3i5CYhOe1G5/Y5rW8YsUKEX/kfeUrqKS/H+M7txv40pcofUJ37Pn5YoOuWEKD8FwGtIUx8VuquQ92eJw7d050aJDk4PrKojH6a/A88HPO5ruZKtjFwn0712ae07e//e23JKGRSXyhzaRtNQaqRBWFTJoVzEpoPNu7D35hLg5cm+qB3TeJdfnNqKJZb4b4VV9XhNAgmOT5s9NHsWfXgxE5hngcHe3FV64fhz8cDD5c3oS3VaeWgE4IEhnNsZWGEXDAXT0RF0WopMfiSQ1WZeVVSYlW5fOzaE59S8I9ANhPSe6n4vKQJwUVUJCesaxRYxO3eJQLf5PYaI7f/1l7N9bmJz6/Vq0RzhiSRersuD7Vh8YcBWlGDxQloUFceQUoapCS7CmAk5psjpOnt8Lt9kaIDeoU51GGKw5MNAVCPrgo0wUdMDgmEq3BkAoqQbwAPq8Pra19sOSsEM8nSMqwFe2mgoQGyT0ZIz2CdMDquxITGoe+DzjHw8bAx4G6DUDjNsAxCPSHiQ12QS19KKIPPe1lei7gyOnz2L5cMpcV18JoF2AfkIiSWwHXzwGHfyNV+9YvA7Y+JHW3DHZLcl3N6wBNeBqnHM2R/5U0sglujvMoA6jYJOtMQE49kLsM0KRPGifCpsJicZPx+tAQHtm/XxAbxHsWLcI3N2xIKoeUCmry8vGx2iaxGMdvbPjdmXkKVMMwagAXpebdTjj8IygODGCReQNUcSRVJoEL5QPi39vp9+OpI0cihAbxyuAAtuYHhXF51KslBFfc3EG4gx4xnuWxXVhZjHP7T+ORRfekTGoMezx4l4LQIP7ftWt4cWAAQx4PmqxW/OSOO7AiXr4mCTRqNWrMiY1BRzzjGPAMQ6fSYpGlMtb8NeQHgheBECUDdYC6CVDfoG7AjXcAX1oO9HUDBYVAcdz7/vu/Aae4AVUQvEowKLz7nvmTnIp5L71U7U4ZKh4LCxTEuXNJpL0qTT+EFGDUaPC3y1aLWyrI11vxeOVOdDoHxbVZYy6BJc6AmhWCbPnmppMb7gjY/dLdKT6bOlxNtcPthoabZhLWBM8/1xwm+V2uKNHEx+khcXgIOLgHeN9HgC07UGFqwGUHJU6iuL9sKRaZPTg13gONWoPtRXVYmRddh4Oh8bhPJUtRRcEkBuePVAIPBgB33nmn6GxkEQArq5x1jWj4z2/i+IkTqFSa7R16ERhTSGfy8z3/A0k6lM8x24AlG4GKxUCu4loliWTvRLHBj+LGCqC0Ca/uOyyCPAZ1MVWZvI6q7gG6XwwTGwlaTxPMOWnB0Rv3AMlydtBG50R+/9koiGB8wdfi9ZJqVdZvJbifWXSHdJsr6MPRw3hEhkoqAqFH2QLesGCS4PXXX08rWcAki1LuJpoLYCwSlcEbGBjDyIgTTQ3bI4l8+o7dVJCke+l/APtQuGNzEBjsBB75I8A4f/4f7IghITQfpq8LyC64hlECJmYvkyFYXZ7MoytdfPHqZRwNExqEOxDAp7o6sCEvX0jG0Kg4VcKQBSas/ubfLVyTC8gm1hcU4bltSTwMMwD3gOxW4PVNYiPRnpDkIcn5xTU1sNKXTwnusylnO1+kRkCStZ3+uD2G1EiHdKRPCH0JWUBAwkY2R2fscebMGUFypITBVmCiHzBYgYol0RxRGDyXsoE63++nP/2piC94vm81w/B0ukrTJjVoFkYmKQYMwjUNQOB6XFW+VGWeDOzQIKGhrEBnJeuq3EZokyQh42H3eXFoZEB0Z2wqLEGB3ohLDvs0I1R3MIAu5xSW2PISdmjIhAYDcL0aeGngKtwBL55atGpOibjkCKX4GOm7+4FLvwbGu6Vgpno9ULok3MXhpfNn9nSTbwV4x4DhPdL54McyMOGjA0zVQF4TYMiODwQN6JXXHkH5mnHfVMQY/NneC7jsGIRFa8BD5S3YWNCA51i5qIBBrUK/eyyW1HCNRyvpZfBndhWkSGqwS4NBB5MYGwqW48WBg5GkKLX51xcsT/h3wTBJSJDAIBny4uVeLC+3Qa/XoK9vFMuXb4RaE61ilvULbyouxhn/EhcOJiY1us9JhIayOrXtqOSvQf+MqrWAn7I0+UmlpPh5Dxw5jk0ti6BRJv2VmvfzAR7v4ZekGxN8S9YB9z4hdWrFo/MK8OzXo/ePD0kVwPe/E6hNQLpeegXwKJJ2THA53EBeuPOMydO6BwBTZmOI8+xLA224MjkKq1YvyN9CQ2yi0xcM4rEDB+AIExrEtzs6sLWInXgze0XMBmprMuhgS2o8upzt0x6b8gM52nE4/IOw6WL1IllFRfMpOdjm/2zDnGlBJ/FXEGdi3OVywSknasPwh1TwBGJfh1epNS4pTBToc8U85HV5MNg9AGu+DfkF+dMSyDPhisMBT4Lug0sOh3jfsdFR3Pnaa7h0//1zMv1rn+rB4dHTERLmkqMV95ZujWqlB06TYgk/2wsET3GrC6hvkCcHq+JsSYoRjis6NPgdK4kNbpY/9ekbQ2gooZybuI5rEhNJmWDK74NRo52Tt41ZSxmzmZNe3KzTfDuC0WHg7z8N9HSJ/dTmxUuAv/w7ibxItMb83d8Bn/+8JDul1wFV+dKaL39Xz/5QkBp5uiIssW7EoKdLXHuF+nIU6EtRagTuLEks1aaCKSyzGH1fFQwxwcarr74qCgiWLk2tiIVkKOeAAibr/u1z8O/fh76QCvc89Z7YucM+Mv3zij1buJbCOQGcfQ24dgDY8BagrEnaI7T9BpjsixwtJjqxbvXdOHP+sqjKnCY1wXmCXT/ya8edAZjmqI0t5qG4Un6FNwI7K1jhFSNJmyH4GnwtviZjjQXcANBrrPEuoG2fdB2ZCyQ/jRRjsAXcvqDUjmxMnAk4fzLp0t0dgsszgfz8IEZGJlBUVIDlS0nwRjsTOK753JtWDTrcDYwrJAw5X3qcQNcloGl+ZKHoP0rpLe4rF5DiOXM58OJAK1wBP5blFmNrYfUNS7IxQZpMXjYT+SlK3HKMkPhn4pAFIJl0h3MfrwRX4pG+Pljuvhc2Sw6uX78+K6nBeJeFGOycveeee0QctUBqLOB2AMclb8m6e4VSCQupSBwoY3EWLpqyU7iZEOoknXeKdY97WUrHPvrooynPYyQxuBeW99ScR1h8xU6OlHB5r6Q6Iucgu88AG982jdhQvh+9PJjfIIGUyRw1n+CcnM4amtYOg2zJz372swSvshhQ5QGiMk4PaKqkIH0GyKbH8ZM1fQq0Ips9M3pcU/jg8T0YoW6xSBjp8JW121FuNAv9cCV4KVGyJNkiKhMapvDb8uI7NNop5BqerEqcPM4YHGj1q4FW+m5Eq3jRoJDKUIISEysfk7SNeZFyYPgnATtleibDnh1LAUuGmrq3GlxxRpEi4RwAKH2hzV5FjU1ngkalRkBBPNB4uIBGmAC+1XEcFyYGpA2E14n/uH4If9SwCUY2j4S/Ng2LbJk3iK+oZmAYLxvGIDHNiiBWLnLDwv8fKd+BARqDqoBSQ6EwH04ErcogzNC9QRcKivMwOjQOj1qDgHE5dFoTli3Ph1pTME3W6aazs4kMVqdJr4VBg3BlUlL4XquA089K3RmrHgXyZ05QUruwac1mmNpfVIxDylXpAds8tsmffB14RTGHntonrjw88K7pzz13OK6qPARcOArc+9bEC5RjeLqfCCvmy9ZIc0dBI2BIvx1axn+3ncZrQx3S6YYKB0d68NkVd8bI4PS4XBjxxkrCkGQ+PjaGuYLVCgwQEiGgIPNkyGfCH5perSy3sjKhyWv/6NGjYvFkkjZZZR11pOMfp2cFJQ6VXRJ+lwuWyvLwZakS5KLK68OwoRfBUDPU4a4R6lR7JpzQjYcwHLSjsrFadD5UTxZDkwZRncw0XP78XA9HvV7RuUFD8EwJrWNjUkGDTAaTZL040Yo1+STZuZ4rKtMFeO0yQXsLGI2T8LCPR8cHN8DNLcCf/hlQSs+j+avWvJHomJrEh08exLVJh7gu/3TxcrynNn1/FsId8KBtqkPs1QoN+ag0ls++Tnz134E+RZfitcvAt78OvPltwPe+R/ZQCjz4OnfeCdAnYudOqV38s38J9MSt/y6n2NTL5AMTAxy7qUCvaYQrwA5EWUpHBYM66vfDhB43yxlJHv3L/wX27YU2GIQIPb7+n0BpCXD3vdLvi8qBq2diiQZuFpSnj3MGr8fTv5ZIDdeIgtAgQoBvCrbgSKxnxWQXMNkhzenG8FoldxpG3k8FVO4A9JnP9wKUNproVEi2hYDqzTFVVNyfZEOyQybI+JoLpMYNRHELUNQs7bcWyIzfGjBpwsIpJoUy2f+z8IpVsiR5p3qaYdDloLZGBbO5FCr6KN5KSCY5mezxOYJJZMZu2fAa+m0BczF/c2GP8I/gOnNotBvDHifeXJliMi8LyJaxMCWkSWgwxmDHBtc0JgzZ5cnHuWamLBllsUjjU9bmp72iWoUSS06kIJGGwiQMlfI0fB92U8k+Gjwm3vjcbMnYLGABtwRIlr/73cC3viXd536UY+atCWTOE+Bl+vgxdWc2Y+3atamNDyrA6OsAL+OH8P6bnr26qMpHf3+/2MtmKkVHpEUyOMclQkOZPxvvBXovANXJiYFbWY6Oc+fjjz+e8vPTikT45TDxk7CqW1MikRva2lkJDYIeGspKeSbJcnU5MFAjOAV84coZjCsSXFN+P/7vpZN4oqoWK3LzYz7cJ5pXoECf+CKVE3K68JOVm7vXBttikt5Zwx2PAi2bJIPf3GJgx5PAolkqBRlsyAubfT8QkD0VQsDUecAdJ3d022L+kuveoBeTfgcCIT/0ai0eKFslrjsZ9ZYSrMytxqTfg/NhQoOQiytP2/uxoaAJOjWTI2pRBZtDosAWp79O7wzKhkU+khpo2S3JRaQBJnA4KXK86dQ6VJlLUWUqTUpoiLdSqVBrXgeD2oL8wlyUV5di49I74RrVIy+/ERrq/SUIYJgsZnXJTUN9/KSqksi/RKDEiHJcKj8PPSVO/3xWTWie1/Km5cDKh6PdM0ar5Kehm0ft2/OxEipiPJ+nv04CJJx7ElTkMknIQMASR1bxZz5WsR6gmf0MhIbDN4Je11UMuNvgD04nDsa9bkFohI9AEICsBn+VLfwKFBkMgsSIO2KR/J8rWPWWUFueQ844XS6MUlSESTO9Qy83N1e8FscLKwhZCUIjMgYb3PRTw5PjgesdyQc5SIhHjlaL/1q3LmYhXa/R4M7GSuTrWCsegGdgAPqAF23X23Du0jlcvnw5It3AQoE3b3gI71n3NtxTuhUPld+FfH16Bq30xvjbZZIs30yV+XNJO7LYQEkc5Wh5zgFPsBt9rraERfgSbpFW1veHvQ4YSPLG88THGhpva0Lj0sQoftHbioMjvYJY+8Dx/WibmoxIb3720hm8OqhMlKdOaLw2tB9XJ1vR4ezC8bHTuOhIUMXocQFHXwBe/V/gzF7g6uXYRJGQ27wIlJUB3/428OCDwJYtwAc/CPzTP0XnK3Y+rFwXO38xMFm+Wmzq6eNGUpPBOfVlKVE07ZhJjCigVllg0myBXr0YOnUjTJrN0Ci6hjiuKafCtS+tTkWuLfv2xn5OHverr0Tvb7pHkglU/j4/Z/rnE+dwSloHEnYIqiRJKhkT14D+PRKp4WgDhg5FO3VFB1J4yFVsB2xZkCk1FwJLnwDKVgIly4DFD0tG1TzeoA9XLl/OKgEhxxgLuMEQMngL3Rm/beCeh/uRTMBkLQlmevJxbjaby2Cx1CQkNLjfYmfsTUNRJWCyRudf/s9Oycr58REk2RNvfruAmfGbgVZBaDC2kFfW53qvhEmO2wusfrZYLDESVHJ8wRulZdg1wYINpfpJohjjo03NWGqNxm6Mr/5+2cqIiggrrPla3M8wAcjxzP/lgkjGGHxvSvjI+6QFf5cFvOHw4Q8Dn/wkQM/ne+4B/vu/gRQ7sGk0zjwADb4PHjwo5J/iwRhhWo7MuBwwbwQMiwHTGsCyOeLHyrFNwp85hhuWW/NIsV8MeDzuBI/fBuA5ZzyQToyRtqcGGSeRECyfm+Z8g6UCE74pnLFfE0kvm86Ce0rWzVgxwmpRXzAAg0aLTuckKF4V+R1C6HZNwaDR4Fsbd+KF/m7RxUGCY11+cqaLpuCUUXlxgHpsse8tFtdQSFTkZxWUmtn8iHRLF9TdjhAaMlSAdxAwzsEQ8laBqQZwXFI8oAL0RYBmDubtQjqlFdenpGCZVdArctdgia0SZcY8DLjtotuixlw4y/UHbMhfjEK9TUhOGTV6LLPViP+jTwoAna8A6nHAxkQuJX92AXmZJRgoi3HhwgXRHpYqDBozFlu3Cn8NNTTCT8ClbxNJYQYfiUB/jUuXLgl5C1aq33As2Swldc7vl5JFJDk23J/4ucUNQN0moI2dDEjsuUFfCSb0E4AJ68j8VdoEUL6Eiap4CSiaDp/6DdB9SeqMaLkDaFyf3Lx8NlA6yhubeBNIVuW6ZD1wOewDQPB9m1ZJVQlyQvHQT4H+69LCRRKIBqI+V1QmZOk9sx7WkKcT3a6LkWqDIU8Hmq1boKO0YBjOBAk3VYLHmeT/t9Wr8ccnT4okeihcafTRLGiu02MmWcv0InO9kDPsdnUICTaTGsgVifclMGlyARI1nDfZMhrnJSJLI8hgoE45KrZra8vKcPbKFTF2WOXEscj/OU8wUCfeU1uLtWEz5CK9HlvNZlwavQSVSQWtWoXKBongzCvOR0vZklgfijAoN2URcjmZ4a+WLsXWwkLREZOv1+PvL1xAr92OQHs7NM3NKDEYsJuJ5QxBDw2zxghXwA2LluSVWmw4VKoAOl2XwVC0Ul8KhKLa2uLbVytk+WTwmuEY193AarFt24H/79+lxDPH0e77gZZo1f7tiJ90X8U32s9H7jfnFKDTGbs3YAB8YHgQu0rS26+1T3WKIgBl4QkJjsac+uj1yy7Zn38FmAjrPV8/JVWHKLvLOLcVhPdfbKf+679O/qaPvhUYH5W8NBh8Ny1F2/odyO/vF3tOJgi4B2UAz5ZuuWuLlYi8FhmsM5Gk7ORQq4xQq6YnlxhwMBnHccw1kX+bbG2cBs61TADHkxo0AJfBn9/xx0BPm3SeVJQd/FmUbGfXBmW2+HfW4rDPRqE0Z1MGSMlcK6UtR86Ef1D83pgLuO2ATDrSk6X/IGC/BpRvBXSJieCUQf+xqmh3Bly9wPBBMadePvkiFnNNyhKYfOE+ZAELWMD8g7I1TICmo/udrGuDydRkEhmybCeLOTiH33Bwr3Hf7wIHngHGB6SCwk1vAnISeYLODVyLSGps374966/9RoYr4JsmCc0cjC8UgHZOJTmpf2+JiiWyBdmEnOOMHaLcg3D/wcIKJu74P/cgVBHg3kQeM1adDi/tvAsv9Pdhwu/DlsIihPokX6vBwUGR8KNXRqpg90i2PicLTDj203n/BSxgXiB3ZqTYnUFQ0pXjkft/UTys0wmCg2OQaxXzAhyTJCX4e8Yc9NWLdCaLfX+ZdIsD97F33HGH+Dv62Cj9qOYNloJwbBKILY5V+vbdRmDcR4WMdPYMaZEaZJ7ZHk6Wea6kBifztfmLsTK3QVSBskNjpoTyb/rb8fW2s/AEA6gxWVFlMqPHPRWRmtJAhbqwuSllFx6pSF1mg6bgGlUILw9ej/FXaLYWQaeoXhr3Up/dAZPGjEK9xMClDB5n51lgtBswWICGDYAhg2BTlcRAMUGi7KYj4vvBytgULzV9HlB8J2A/AwTcgKEYyKN8TubM0qh3JEJoEKw4Pjt+EluLdiJfbxE3JSwaPRbnFOPa5LDYVImPghDW5UvJzIaccnFLiOFzwGR3eLIT7sXAwBEgd1FGn4EbG254ZquuCIaC6HH1wRlwI09nQ6mREjbRa4K6uTMFHfxcJDY4mXMiIZhAzrRlLm3w3KzYId1SeW7TVqBmNdB+BOiiln9cpa0+8djiZo6fMSbg4Osl8rQ48TzQfib62id/LSWrajNI4vR3Av/7ZcDtlCrFRaVrOCm2/s7Ef0Nj8AfeDRx6QUqM1S0Fdj0GOMaAky8BQ60SgSMvXNdPACvuAvIofxYCimqluWYGSNeNnESSPqcv5MWgpx2VpqikXYnBgkK9CaNeV1TWCCH4ExRR7SouRq5OFzEK51WvlGfKBCQVWJHEpFey67cxZ7G4BUN++IJuaNUGaQx4+4CJo5KMHWFeApij44ABfbx8ik+nw+92dGDPMamzZvvYGP7v6Ci2rFkjyA52c8ikBrE8N1fciLGxMZTrS+FQTwmpKdmDosFSl5DQyBbuLi0VN4IExlPf/S46S0vRpFLhq7t2CbIjU/D8bi1ahz1DR2DV+sOERnQ+G3B3otK4AwgaFEbhjYBKEezwGj3/CtAe7kzKrwTWPzbrNZo1rFot3bKB61ckaSVbLrB+i+TLoUT3daDjopRMWbIBsGbXeHfY44ohNIgLjqiZpAyOVUsGOuaeBN1ahC/oi17DbWcAe5zkWEMRcJLygLJfiR541++m9qacg9/3x8C7P4CO9nb0jY2hubZOkBg00JOrHjnuWFEljic8xzAYYQDC4CEVeSoGLBs3bhQ/0zSTyYGUK2s5Vzz2GPDjH0n3ZRLnTY/GPY8eaIrNeGkV0NcKtB6TpKb4d7z214X/joRG/f1AxyuA1yHdr94W9UASa4ZHIbsoVx2rgYYnAUcX0Ldf2nMRU31A54tA/SOR6rE5w+cABl+XJBMBnLnYhj9+8D3ZeW1AmLR++ctfztrrLWABC5gZTLCeOnUK69aty/hUcS/AOZhJn2QGyYxl+Dsme5hI4vMzlb7KCNZCYPf75v1t6FewIJ+XPpbainF4NNrNw51zjdkGUxJfxGzj5MmTaRUPpgPul5UxBn9W+mDwZ3YyMcZnUSHH4xZ2tIZh1GjwaGVU/eGCr0v8zyLjdD1x2KHO+GWumJiYEOOY+UAmgheQRbhdwOEDQn4Vy1YC1QtdX9kEY352ZMi+FSQRd+/eHRmbckcVwQJHWSGCJB7vy11PMxGYLJaUxzzv3xDozcDqR4BTz0WJjfqNQInC9/A2As3R+T0kU+hIBG0mGyC+0X333ZfunyY+ALVmVg+NM+ND+DKrAMPodk0iX+9Dsd6IflYrM9DV6/GpliS+FCngiaplyNcZ8av+K/AE/VhmK8F7FkVf79rkdVxWyC9UmiqwKndmY9nYD/EicP1oOLgMAR1ngLvelz6xwYSCqRFwUb5EDmq1gGluJrwxYNA8ekFK0If8gHURUL5ZMhpOFQEnMLof8IW19C2LAduq1BL7hhKgZPYK81Rh941FEouRw0MAU/4p5CXYgPM7fV/dejzdfRaXHUOwCFPkJWjISUEbnr4XMZB0sSnVkK78lAwGG0zWUIIjWWJ6//BRDHtHI5+zKacey3OjCWBZ33M21JcWANePiy6F1pFO2CsXi+6Nm+65kQiCHLwDGOkAnGNRY6TGbQklpLiRO3L4MLbUWKG68KI09mrWJiZABAl5bjpZ0nEuM1LjuW9JnRUyeD5zWC12D7BxV/K/Y7cGbzJck8Avvwp4XRSNnz6eBtuApWEdX+qzOzoAvRWw0udo+nfIbp746ih+Zh+TZwpo1Wp8tHED/s/5veEcXgiuIPDjnmvYXFiBFlu0K+a9R49iUtH5cH1qCn9+5gy+sWEDMgHf6/DhwynrE6tVWhg04U0HP8fEkUgSjhhoPwgH7IA+Op7jCcOPnDyJfcPRhO0Bux1fNhhQ3t4uKpNk+ahEYBs4ycOKQAVe7fWj3FSBQkMBqk03rpOOklT/unq1SNweOHAA9VmQWKKp+cPlu3Bu4gC8QclgPQYkrzUzdD+0HYsSGrLW54mfA1vegdsKL/4K+NZ/RpPZDYuBT/8j9Tik3188Crz4Qyn5zaF1cg/w9o8BuYr1g/MU1wTKbWYwtw7S5DQOLOZYm2fFifFxUdfIOdui0eKt1QoZpBRRZChAuzNWWs6oNsCkUYwTjzvO84f2KVbJp6SjW/r8W3cCpekVwIw5nRhyOLB5s9QdMFMVoHJNoy9OUm1YHuPFA8A16frzjmsQIjGiVov17dChQ+nJhXzgD4DcPGD/PsmM8G3vANasnflvLHlA41rJP21iUOraoH+TklA3FwFL3ipJToliEFX0eiFhwe5BGdKXDJgrpb2FbyL+Q/ODAh47YMxPcd93CXAOSpKMhcuBsMdYBO7ByFzKQO3spa6sGuHytZgkkataF7CABcwvmETlPMqiEbk6PBOkEmMwGSQXVrFQhdI7LJRMJ2lxK4MFUyTaF0yY08f2ohoMeZz4Vd9VUUxYbbbhw42bcCPAzhoW7TK5mQ1cvHgxZizES2PGg3s17kNI+FEKl1Xd8YVDMkh+8LkExyuLqNLR3ScRkUjmKl3QH4TXuSwtxw72BWQBjgngr/4M6O8NS4qqgI/+ObDxjoXTmyUwn7Z69epZyQlCXptYXMVxk8rfsDBYGbdwnPLvlYWQ84bSJuDODwFTo5KPrzm7BXU3EuQa0o0vMiY1biROjg8KrXC5K4ML3ojXjX9dsRODXreQiFqTVwSrsv0/A9xd2iBu8Zj0T8UQGkSPqxflxnKUykaNM8HlkAgNpU4+H2s/BTRnMFHlrAC0VsA7LCVFzI2AJoubQsoWDBxV3GcHS1AynkwVowcBH43jw2CnhNYCWOZHwzQRWLHtCowhFIqV0ZChn8G/xajR4d2LZklSJIKQegibBskgGTSHCm1ujrhhoLFpoqoMdmiQ0CDkz0mpkFpLFXJ4zsOJYTLHM8IxCrz0dcmXAirUh4KYNOlweWpKtOBx08fJ+ZZKNjD5svGdQO95SXYprxIoTJycYgC1Jd8FY/vpcDKO7nQXgU3vBhRm1xEIAjJu85fJZ+eYH2UyKA7FFcCmu9N7rfZzYXIkkQY8O07Ckj69RyRSUkZePVC9U3zuUCgIT/AyfCH6tUh+RiTGlBtok2a6BJknFITdH4q5ullNddo+FENqXJiYiMzVBH9+ZXBQSCOty6BNmd8bk5WzXr+JEHDEEBqEY9KNxuV50ryZBK8NDU37DPsmJoSBGAmNZEZirOLgOCGYAF5sa0RjvvQ+UgeJCxqVMaaLaj7AbhJZRo7HykA7FVJzNtDPp9xYgw5nrERMsSGFgGaoPfY+z+9IlzQ+slVJPhvOnAa+/z1g0gFs3gK8411S51SqoF/Fd/5L+lm+PlqvAq+8ANz/iPTYnp9Jj8vVOeyyOvoScM/bpPsT14GhI5JckNYMlO0EjOmZqVcYc2L2RAR//lDDYnQ6PTg2Now8nR7vq1uMClP6e4MKYxkW5zTgyqTUvWpUG7G5cF3E5F56Un3cNBSWYFqxHtiwY04JAXZmpAomDEjcsdNQHnvTcGE/cPJF8eOU24u8wTForhwGlkqFAmmvabxm3vlu6ZYqlB0Ws7WDxxeQcE9GD414Pcy8BqBghfxHiV8r1bHVewAY4z43vA6MtwKNb46Vr1IcV1vXELw+X9LuuUzA12KrP/c6qRrCL2ABC5h7h9TevXtFxXWmBUycQ9P5W8YUrIxnQrStrU0kSFlckkrS6FYE5y0WtMhk/ALSA6+dx6uW4NGKZniDAZgTdc/PA5ioJKmRTVN37rUzkVnj31CZgV2nyUgNFufJBAKTpBw76ZAa2SgY4LHxfVloyddiQcktQ2owpmg9KhWAlDUBtXNT+rjh+PnTwGC/Qu0kBHz134H1LCy+hXIvtyk4T/OWzjpD2Wl2Rcnd3bOB88mmTVFC9obnzAzmzJSAfltJjZ/9LBy03yCYNNqERqT5BgMW2+Zfy8/pT1CVKszJU2zhY1V1PDjJJno8FfBvTXXSbT5gjwuembmYaAdMLcAz3wR6O4EcK/DAO4DmBJWRTNb4pkthwD1ww0gNys90u47AH5JIL51KBR83CeFOhkpjNczhhH9WUbwSmOiQujPk9G/FHXNeVLlhYHtsIlKDklPxnSgE9e9lUoOT6qzVGZcOSISGGGzSa+Vc24+Wt3wKU06naC2n/i6rSOZS0ZV1aPVATQpdWp4pGElOChmP8LmigVLPWaAubrHi90X/jMsHYx9vyKBFn0klys9M2hWJLTUQ7/XjGAcunZAqeOuXAiUKs3kZ/J3MKlD7SReWsuLr8ZibN0vSJkpCQ05QMQFmqw4TGt3iYY4JXzAY4xvE8YLQ9EXYEm4DV15lvObMce3hdRYLLpHYUDzW4XRi/Usv4bMrVuBTSSTQkoGaiqlotpI0GPNegyfogE5tQr6uAVp6aMRBDEVvBxAcBAwNgG667wM9KLqdzggdwrNRYjSioiKBR4QCIyMjMeQBgw2SMgXlBniNHRELxEL9Eth0/z97XwHe2HVtvcSSZWYe22MPM2cgzNS0Sdo00LSvKUOatin9ZXx9TV/ppe+lTZs2xSQNU8M0zOjhMTODmP5vnatrXZEl2bLHnnh9n8aWRpaurs495+y99l6rDBOBV199VSR35fZ1JgtofBbr2ONFgWGWGCMdjgYR3JDQKGX3YDT0NwG99ZFNzDjW2H2UkmSZu/5+qdKpoBDI8s9VtbXA3Z+VyAa+LwkOduN84Yvxv25fb7CXAsENa7ffS4QtvyQxlCBpYx3yT8pdQKdiTnHbgNbXgYob4ia+ec55vX529lL85tSBEYnEywtmYWOuJJF426zxtRsLOcJ0yrlVwul1CYIuiNCQzVcvuBnY/IQ0LxnNwKW3AcbRN9Mk2BgQs9owtEKXm1iO00RkD1nxSwmpUSVR/R0aREPPACrzMoGTewSpwfOZkFF4ouB42fkicGSbv7NnKbDpvZElD6PB0RteLEFkLQ6QFhmVQM8hf+EMn8d9Yh6gj+Ncuqx+QgOB96C3Bx/LV0i2sdtMly5kqA4ebcSCeTVJIUtlUJ6G5BTHwQypMYMZTA4435NQYIwwpuKRMYJxCSVAmGhlJStvJDmSSZROFkjEsgtlSna1TyOwK5y3yQJjWnpXJhNjHQO89rj+jQaSGiQ9ZHkb3tjhEU1aOhS81sbjn8OiLu7fVqxYMTJXTJkxT0Jj69/9Wxgf0FUn4n7Mm0b+Nl2dwd3PBGWoKEmVMklSvdMc9KshCUFVBSWYw2IBVDTVk9G6kuiPES94PSivCa5rNUnwFX234eDBg7jxxhsT+puEdy9s2SFrFUvjP5m4JL8cz7SegoXstT91e35uKfKSxEQ1W4fwUkcd7B43VmQV4LycYMbZzGrKCEjVxsn0pWZLEjeCxPBPVgw8cycmqTVuRKzsUwF/+7Vk5MljH+wHHn0AuOtrQFGof4naf1Mmf1RSV8lYICZ4ytn4jTVjPt2HDvseqGGHXgV4VSoUGKkTngKTOg9p9JwwjM8TJiq0JqD6PUB/nWROTJNPU4QqCrtFIhBSMuJi37l5kI1RQz/rgNMKi9/cQK/mppAUhwppCtKGm5iYun6UNAldTJms8rhFkpQ3Jpe5qZlSpEacUNGgWCTsFQ/yftfpcFKDoD8FNfGbjwJM3JMwKJ4ztje/+nbgX/8XMIpNzwQ2XhP4/74u4O//7TcSVwHb/g2856OSr4YSJTVSxTE/g8f/fTKpVDYfmLMWyC0FBhoiH4NTSiqzQ0OG/BIewWPJJ0YFN4KN5LodVjzadAR5Bg3sHg9sHpIIKuQYjLgwhHx5cNUqXPrWW7BEING+fugQ3ldSgjlxmtGTJIhnrLH7pN2+Bw7vgLjv8PbD5ulGiWk9NMYqwH5mJCkoJTBtkmePdTeQshrQBc8HP12yBFe8/XZQ7fN/LpYroiV4/R0onQ4HlmdkIHVwUCRY2XrKwIOyCmxnT0034q0Df0fVnMB56nEehUGdAYMmucl8fjZWQAbp8RqNQv82WeBmrdhUKW4x0XEUOPmatKZw/lLOL5zL3R6guRaYk8TqxpdeBH71c5bgSXPrpz8PXPce4OknApVPMp56AvjM58I9MaIhl0liQzBxwfcp9q/lGrqoFwM97YGuTKLIT0bb2kKS036fBMoEGWMbybXZhvCrkzvQ4bCILqkPls1GVWoOcgwmVJszkh5c6tQ6cYuKmuXA7CVS9xjJjFG6AhhocO3gWkZynrrRvEZkqQ7KKDCplohGNMc7N76JVFl6GNQr1lz5nLHKloSLLO0QE729AGX2aAA42nnf/wZwkD4UfpzaxwUd2PQ+6b59CDj2MjDUBmiNQNUmoCAkqRexAIOdeYo9OCWrSPAL6UB2zaQDpRfHV1BBKbRIEKblCO7UKLgMGDyCvcdfxNJlY9fhHy3GoHbxDTfcgHMBXCde6ajDyeE+ZOqMuLZoNjL1kxM7zWAG8YKyNyx+GK9f5ljAxCxjCzm+mI6QddRnML3AfUe8hEC8SFaRRKvNit19PUJGdJkxBb2dnWJ/L0ttsiiEiVvGSbx24zEj5jgdzz6ReyRKYk/JHAD9ymRCQ8bpHcDcjdOnW4P+GTu3Bu6Lzt4sYAwd1+82cO0guUwpWRbI0DeDnXPyeN+9e7cgJ6L5PkUCSXaujYkg9PqiagKJDRqT87qdMiTgFAZzKeQaokoKR0HCKzADTra8sWpcmTiZSDBg//nSC/F480kMuByYm5aN95Qkx/ik0TqIrx96G25/wvfNribcMcuG64sDladmrRnz0+bh6FBAcqPcVIY8Q5wtf0x0bPgAsPVRiTVmwLngAqBojAnSiUb2PMnsWglDKdC3JfgxXpcnDoWTGrxg0xYBQ5Qpky9eNZA6huobZwdg4ULlAlQGIHUVoIuwcPtYCcvEnUHIhflAk17/4fikZL9O7UGVObmbl4igwWdOlPdhouvAv4FGv4Qb9e7WvV8ivkYBJ0F2a3BSVGp/Hxw4iePDkmkYYfcCFFJal7MERoX+OavdY5KQeeVA63Hlm0oyGSEVpcKcte4Y8OIjwNAAUFoJXH9H0s1wkw5jGmBhZ0NIsn2gw9/tELLQMPG1YJN0Gy8q5gIf/QbQcEKSaKleBBgUkldbXpASpYouGbz2r3BSI6sAuPh2YNvTUvU3K5r1OsDWDVhZzVsKGKJ8D35ddRXUI1s+2nKE0o98f4M6DS6vW8gNOTxu/PjYFvQ4bKIqXK9WwaBWYU1WGW6btQBpTKQpsC4nB7VXXol7DxzAY83NYUJZp4aH4yY1WBHEJFcsOLyDI4SG/Bk8Pics7g6km5cAulzATX35M2T9gr9rR30YqXFxfj52XHIJ/t4o+QrcWl6OFYpuEa4X17/5Jl5k9T/buVUq/P7yy/Ef/gpDVnZQRodEYE6xEWkZZgz2DyMjM3XkrXudR1FoXBO0welxDqPFNYCT3c2ozpGq7hMB9ehD29553dMI+aygbnNg3mNxmNMNaPznn2SA4KujJFTHgqZG4Bc/CxAXXNf/55fAgoUSqRIK/v9/3wes3wBsikMyyWgCPvtl4Ff/KR0/wc+z7Q1gPWWkjMDVdwJPPQAM+v2VapYCK/y+OYLYjxDwxkH4Mzn6i5PbxXUo7sOHVzpP4zNp2ahJPYtzLw2xTakxtcabmppEBS67KuQupi1btohEFoOM48ePB7VsxwMSI5Sqill5SC+Lfa+KX60OF0x6LVATSMgzGcXkBpMDNO0cNehxOIDvfAt46y3p/sJFwH0/p9tglA8f0jXHsVl3RCI1eF0cfkbyhBL+Wzbg+MuAIVWSUpSRNQ8YrJPIL5kUy14ItL4FuK3w6bPQWleLkjwF+UGPDUsjkBFHhRh9l3RmqWNDSbixKCMU9O/IWo4dh9smhHhgi/8zzzyDcwX/d2Yf3upqEiQksaW7GT9dciHSWTAxgxlMEZBgfueddwTJPBa5DFaPJwN8/+bm5vjJ5SkCJpgpPyrLfp4T6GsGTrwp5S3SC4H5l0pehucQki0Nw8KM8XRCyNjc1YE7X30RtiGpGG1+Xh6euva9QmadpAn3VCyeYmcHk67cP8VDanBvE8vjY7REI4tTYnWTnDWwcDF0fy0Mk/2dq8kEu7a7ugAauEeTPR0LrrsROHoIOHIoEHPc87XpQ8qcBXBcsriJhB9JDFlainMxH2diXJZdiyYdHe11SZIk0tlBaatQcru8vFx0iJA8ZYyeTB+6CYPPC/QfAYYbRYzXbs9FTvnSpHZmjwZyDIwNEylyGxOpwSQLA08at04WqUEUGM34dHXs5FaiYAcIE1SyhAPxSNNRXFc0OyihVJVaiVxDLobdQzBpTMjUZSaWcMoqBq76nOSlQf3+qRzQpJYCZZcAPUek6r/0WYA3QtKdwXm0ypS0eZJeuL1NMjI3VwO6BE1yPBZgeEcg5epzAEPbgcxLAbUiIexjUj9AOFncwZsUfk0akbOeAkz3mT0BQoOwDQC7ngAuuivmn7IigxM022XlVrajlAULQYYuC6UpwckIJpRibkRq1gCDXUDdfuk+iZb1N4U9zdneDNcbj0FH1ohj4NQR4OFfAp/8VmL69JMF26BkSkxz1qxCoL8l+P/l6u2J3jRk5Um3SBjqC67sJixRquvZrXHTl4G9zwHNRwCnC3Bagf0vSJ4axXOB4rVAK68dhSxaqpRM1KnL4fRK1XDU5S9JUaPVRn1+KUk75DLiyZbd4vc5qaXINhQKAz8ZEu3iw7yMTGSyaj2KUfWdFRV4tDmYHOUZjpfQIOQ261iQZZ0iPs7vlZ4PvLnrI3zPkf+WJIaSyFDij/X1eHH3bu5WxJjnK3zy0CHcWFWFDJ1OdGjwxmtVo0pHZnYaOtt6BKkhw+kbwJC7Eek6iaQ80N+IF9sPorHlBI6kW7DE2YnripbHtc4wyGH1FhMCoSaVDLaT2akRN3hNCX8eP7R+42N/V5kEFVCUuPZwVJw+Fd5tRhw9Apx/IfCalNgOwgvPA88+A3z6s8Ctt8V+j2WrgIx0wOqX0+I82FQPvPw0cP0HgMxc4I6vAf1dEoGp8JtBWiXQXyvJTsl7DnOZVFUfA31Om+iYUo4GJkprB7uwKjs50mKjgp1NTnZ58XoqACJIu0VC68MPw/6vf+ECBhpXXQXcdttId+KqVatE9RSvcRrVshsxkaCDZH1cFYML2MmhkmSozINQrbgcmB9oJ2floRyQ0ERw1M6P3z0AvKPovDh2FPjJj4Cf/izy8yMFAXKhAM38BBmtALtdes4Ekxrslpl1lSQD6nEAulSgg3JWUsDe19mC3QeOQ71kNoryZXJFJcmdxUNq8D1nXQ40vgo4h6T7BauAtMiJRQaIPE8/+clPkGwwvvjmN795TpiFd9qtgtAg5Bij32XHm12NuL54Ro5gBlMH3GcsX75ceGswtk9kHua8mUj162hg0oRFIdOR1OB6dM5guAfY+3ig0KqnDtj3BLAmsH5Pd1CiJqoP1xgRb8wSaz//ieefhtNkgK5EKrg6AxX+9/QJfGXeInGtkoRkIQYrwFkgEi+RwqLEsRwfyQxWTkeS4eG1T4Il2ecyYRTOATrZle8H44386uR79v3j78D/3S9dG5wnv/sDYEOSPFk4j37jB8Cpk5LsVCVloyfBYJpFsR43WmyDMGq0KDamTYuOAl9XF3becw82WK3QMh6/5x7AT2owNuC45V5VyPYmmJ8ai2xzqPQUwevisssuE78zzuExJeKDc1bQuw8YCORVT+3ZihOn6nH+FeE5wYkAOQbGAomOwTHNvDKpIcPn88CLbvh8bqhVmVCrIk9sDo8Dfa4+qKFGjiEHGtXZT4Ba3K4gQoNwer1w+7zQhRxfui5N3MZV0Wie4tXsMtLKpJsMTt6V84D64wENf07mi0cxzjGVS7exwt0dIeFIzXJWW+cDGraEkZkPNq0NM3f2I98wBaoLekM6YHguh7ql5F9IxXskkOFl9fr27dtFIoZ0XBgiTAKczGNWkHCjuvo6SXZpFGksfeMp6Pi4nIRntXNnK9DVChROMUm17jpg/zNStcbxRmB5sISQSPzkVZ79TXrRLKC9Idhzo2CUoI7nvqU2vCql+bBEauQulBJS9gFAnwqYAok/vaoCKrUWLm+b6NrI0Zch35ALh9eGd7pq0WjldSfhxHAz8pyRk/6UOBsNVxUW4mOVlfh9XcCj52dLlqA6ToOuRNqkDep0aKCHB8pqfBVMmpCNg46JwpBrUJ948Fzb2ysSbl7F9UR/kgaLBUv8Fdu83nj8lJkyc77ydYddmpTJAmbB4nbg3+2H4HZ74KM8jk6LI4OtqE4twIL00Q34uFljxTsr1kmkhJ5DVj2eFYiOvUJgiIlwP2mYnQqQHyMhQPnIFVcBWUmUu4jWJrxnD/Cd7wN9XwIeepDRrDRvCW8dylSpgAd+C7z2CvD5LwBL/QUUrD59/J/Awf1SYHHzrSwllaT62KExAp/k4SGD4yJHIhHDuvhKr5YqYOjVZcgBMufFJFT5HT/fdhwmv/kN7zv8l6UpxNNmQuDqB/rfBnz+atxhVutfAGhH3w81/OlP6Pmv/8IK2TvjxAmp0+EuicRn4iwRrdpI6yGri/kaowbqPL8LN8Iz7zx4uHdduD5qQi1mJdLuXcG+KuzY4fiKhqUXAC//Jfwxol5BPCvn9lCjcJnYyPQnwntrRwgNoqd/GPNnF8PuCOl6UnRrxoQxE6i5UZKc4pgaJRFAM1MmMiei4oyvyQo5EsLTUVtfCauoHA0G1wRrMrvTZjCDJIGVraxKZXzPjnD6XcQDzuOR5HHHAl77Uz7hE4XUOGsdsROBTrlARJbM9gHD3YClB0iL3REwHcA1hmb1yQRJg/FWMw+7XeixWKDLDBS7sJDs1LDfm02RdE1Uq//IkSMJd1pwXLNifdOmTWExGeMLdt+2tbWNyRw9qZi1VOoqOrVdivkLZgPLFTLPycCB/cD//k9I5+43gUefYBY9Oe/BXOGcSVAVUaDR2o/fnNyOYb/k6JIMxu+rJs/fhrKpLnoT+gBdfEVTbosFW268EcssFmg5LltaKFcAPPbYyHcxZ87YFXHYYbFnzx6RN+Pv8YD+NqP5wZH0TMQz8KzARx/lk0EP6bQaqLxdk3YIMqmRKMZMajz00EPidxIZDu8u+OCvUPCpoFcvgUaVH/Q3g64B7OqlcbMUFKdozFibsxZ69eR0LHh8PtGRYQhJ6i7JyMPuvvagysfq1EzoOKnMIABOGB/4FPDms0BLnSQ1dOF1wEQatbPDI+g+NaTVgLtOuqmzAGNBWFdhqhboC4kZ03QVMGimQFswvVX4OUKrifuagLz4JNXYscFNBC96qlTYsoJT2xUp4UnCoKpHVzfgbPVX3JYD2pAqABq+InrFhTnFBJvLDRO/CyWmGqvP5NDBF/ztp4rOmOxyYLBd8rfIrQQWXYGzjvVXA53NQPNp6T6vr6vuGOUPVFEeUzxuyJBuoc9SqaBDCVS8fqCCVpUiHjNpUtFi6w0znPfCggKDWXRrkADmHGnUaLA8M7gjINL7PLBypejYoFH4ovT0kYR/PAiVWhsNapUWWfpyDLnPiO4Mn0+DLN0S6NUh49i0GNA2++cWtd8oPH7ilYk86nbmWSzwUoatvV2qrMnOhk6lEh0qyuCGwT7PQ452EU57j4S8mgpqlURk9jkt4tx2nKhHQY30mXmeeyKZa/vBJAJbWVkhFYnQIHisbEsP7d6YNMy7HDjybKAavXAeMOfSgLF9srFoSUCeRznHdrRLwQh9UZ5+Hvj+d4HXX5OeJ+sUyp0eX/oC8OCfqLUJ/M9/A2++Jr0W58/dO4D7/gdI4cRrCfKhQUGc5Ax9EHIT8yLY1duMHb0BmUG+n0GcQg0uzo/D22S8GNwaIDQInxMY2Avs7wCaTknng3NYaSCoJfHS8thjWB9iBo5HHx0hNcYLSqGy24NdSvw9luEnA3peK6MhZjVXZpY0FpTExmhBSsVC4MoPA7U7JCKiernkRUL0NkjEmpi6/fsC/iyMlXQIJppLCrLwypbDWLeM518VIDSyEizkEHus2Hty7j1oFDoRreis+uRrs3BjupMaxaZUZGgNGHI7Rr4xxiILMqZf0nYG7w7wmmanGvcO1CRn8VSsLoyxVn9HAiUnSJrKMoXT6bwJad5zBdG2Z1MtzhsHEu0MnQxSg6oK3NNnmYwYaGqBx2CAJjcbOoMRVSEFYYzpZZIhHvk3HhtjmHg7KkhY8MY90erVqyMWmVFml6RHooa+EwIe39wNwJz1EzdWa4+E7/9I6NadSR6pMcngXv2BM7tgURRhHBxox2udp3FF4SR0lHqGgMF3pLiCUOmAtI3huakQ1D79NFYMDSFN3rOzwGhgAHj7bSBJ0qhc/7gWbt68WchYjSYvyDwbu/UYi0QDix2T1dU4ofAF54HMJgPaByevE5Exxkc/+tHJIzWYcOIXPatKFSA0BHxweg/DqL4QKkWl16GBgyOEBmHzWHFi6AQWZYRWTif/Yv3NqSP4c/1JuH0+rMnKw0+XrkG2XzblisJKtNsteKFdalmbZU7HF+esntBjmrbgObt8clqP4GUSrAEwGaSJilrsIomuWKS8fYDLAYTMD2YNx5dKVLIyaZiqLUeaNjFdtglDNWWBaqXjlmHQAUeeB1Z9EEiNrwKG2oEMPI6fPIE3j2yHqSYLRqMJC9MrUZ0aXl3F5DCv2YpiHTC8K3AeHSSoNgK6+Bfjsouvwt7XX0CGjv4UlPdRI7NiNnLzJkECJRE4bYBbcZ5lsCPm4s9OjuRUItfWzZ8BumWypSjMyyQIPO7yJUCDXypMwCdVqsSA22tHl2MP3D6Lv8shG7mGZYIYoIeGO8TgO0Wjx/+bvxJ/bjiIBssg8o0puKN8MbIooxcD3ARvyM1F/IqUAbBFU5aFGQ1O8XlqoVZJnRDiG1V54AF16kOuJ5UGbl01fGlzx9RayzWPidM5Ph/eUanwUkcHSwuhamzE/VddhUzFZkVuyaZ+LSvClsy5AAOeEyNJdzW0yPDPSxm6FFj6JE8Qg1k6ryQ5MvXhwQcDfradM6BhgpZzQTRwA8bqqdEqRyYUhjRg+S0AyRkWCkT4PEmF6AbJ5uDxP8CNmVpq5f7cp6WH1q4D1qwDXn0l8DcyOCdw/L/1BpBzI/CGQq6KQQyNzV/9N/DRzwP/+7OAHwjN/a6YOGPjRtuAkIpjQlR5yJ+ZvVp4jk0o3H2S9FQoLB3AkR3+qp4e4Mn/Az7wBXhzi8Vac/r0aVRG2ryHzC/JqJClXAorBUluMPiOBsvQENK3vwns2yFJg116LbAkMMeI7itlsBoJd30M2Lc38CV4vJLZ/GgonyfdQsGODBFIykQcP1AOYIpB/nKN7zkwEnikmIxITc/GGWsJCrLKJY8Wyk4pjcSTtJ9mdeiTTz4ptIsnCnztJ554AnfeeSemM/RqDb4+fx1+dnwnepw2cQ1/aNYiLMkILvqawQymGrhvoAQU51RKcLCYKhqYxGVSlfNDMiRLSJAoDcP5mnz/qSxHRykjWcv9nEDBXKBuZ7AnQXo+YJ6eidtQcKxOxHgiScI1MlFzYaKjo0N44LFD6nc5Wfjo7q2wezxwt7ZjdnY2PlkRnGDmXoVJUqo30Ass1udlojBWTEXSg9XpBK/7WOs8E7h8zZienZOJiYzrGV9E2iNGkSqeDrB73ehlvkQBnsFGq9KncgJhOSj55spgrth6AEgP9zmU9+fs0LZ3dmJJpCKkJMcYcgxNXwx2dpF4jwSuWePpDJlS109qBTAckHKbW1WEd15oxOqBgVFJm2QQzfxuGxsbhb/epJAa/EBsEWclVXlVpCQaBxQJjEBAa3FLCTQZrAamP4US3Y5hNNv6RBJtdmoeNEnQwXu06Qz+UMdEkoQ9/d342sGd+N0qyfyXBq8fqVyMW8vnw+H1CNPb0E2ZJPXg8kuJTI5JypREbwew42VgeECSy1l7hZQYSDa8g4Brp7+KVkpEiiDdG8noNfLkxYSgTgwfp5CgmTLagJQfW3EdsO8pvyeJRrpxz9hTHzepIWNuzRxUlM8ShqncTM0rroj4WZn45GYFVvlaUJiB2o4CuvjTzqqcAqz81s+AVx4HBvuB0iqcrF4OUXvY2gA47EBRGWA8yx4m9iGFnJNKkbT0L57JHBNDvcBAj6Sfn574ZlY6HjWQCDG0+DLJn6ftuGTeWrNOareNBJ8V8NJLxIteVx/cvO+Hw9uLAdcpZOnnYXXWXLzdfTBIWmpZVrUgML5Qk3grYDS83tmJ3546BYfXi/eXleH28vKwcRuPpjoJjWNDW5GudUMfUvxv9TQjXRe+weDmhCRDIpsPkhMMHEgOik4XlQrPb9qEVzs60GG3Y1l6OrKtVtF6SjMwgsfOIJfXpqwNafCYYfP0Qg0NUrWl0PpbbG19g6gcMKBjfoB8qDbnY2EE6SkSGvG2ZcpyOsmuRkt4XBtDKtm7moG+DulaKUwy4fzZu4HvflvqwOD1zk2wS7FO7NwBLFoM3HgT8Pi/Ir8Gxx29asI+C8vdnMCiZcD3fgGcPi4Z+S1YGtk7IUnI1BmFUXjooZRPhpyl1zKScx8Bj6V7IDC/ip8qHH7hCfRXrxCB9gUXXAANjRR//nPFQauAa6+dkMMsKioaMZgj2RdaLUmSpfzYfmD324EHT9YCn/kasGh50H5vVLDb548PAc8/L8mTXXQxsGLF2A66dBlQR28MP6FB2Idjy1HqM4CSS4HOnZKMmTEHCy9Yj0PHTsObs3xCkjWUn2htbRX7CHaIjaWKKl5ceOGFeO6553AuoNKcif9ZfhmG3E6YNbrJk3OYwQzGCSYqKUdFkpqJBsrWRJOGokkxC1HiMSuOhdAiDJoix5yXpwCpMeVlRRJBSiaw6mbgxDtSUUpGETD3woiyhPxu3L42eH1DUKsM0KrKRBfpVAZN3VkQkWzwNdkRwWKmRBL9TIiSzJONcTflFeC1Cy7H7r4epK7WYqnehLa6OngLCkZ8xOirwe5Tvudo3mLcj9FXgLHJaEVQ8jrP6zxa4jbSdx+Xr9m5gosuAZ54XPJSY0Kde8BrrwOq4lPamIowqLWiAMOpyKcxB5CpnySiijFGcIAheeqGgNcUC2o47tk1kUL505dfBnp6JCKDeyvGugkYe8cL7qm5FnIdXLx4cdj1weuApGQ8eYVp4ReXu0bKvVpoFK6FLncBlq6tEYTtRJAaPH/shuF5efPNN8X3O5b3GXO/KANWslYfuHV9lJcNDihTNCmwKAYpLxizNlDVcHigBY837xvxt5iVko07Zq0bkwyUy+vFz44fxNOtjXCy6lkBVjru7O0SUlTK4MKg0YpbKOweJ17u2IM2uySfUW0uxoX5lNea2gt20kEj40d/LXUYcHPZXg90twLv+XjyWXFvW/B9vrzaDXh5zkNIDDU3kYNBF4aVUukjf0pCqgs69RSQnpJBrwp2Z4Qiko52HGDCkklOmgGTaGTQwYlVmSQeMfJTsuEyaMCeKIpnAXd+MXD/aC3w8K8k03CCciR3fgEoOYsdMhEJL5W0OU8mDm8Btj4XWJTXXhXQTJ9IcG6cf4F0Gw2+YcCzfUSyxCl8BIKvWYeHXQ3A3PQymLR61A23Q61SY156OfIiSFiNBy+1t+Oqd94ZyZM+19aGHocDXwjZDHD8cgGVuxJYCUj9WKU8TLejCR7RAaiQAQp88Ijvz+uAi2V7e3tcEgfUeGZQHypbw6rbK5R/n50dVF1IsNKR16N8LZo0eeImo6WlRQQRrOr6jytuRIdjEO32AaRpjagy54URPaxGTySAYMDDKvYpA+sQsPNF4MSewNez8Dxgw3uSt45s2AT85rfA1i3S/T/9Mfw5J08CP/wx8OGPAF+6R2odJ/nBPQGT4QxceJ5n1wB1pwOVWdw4r/HveXLzpdskYGNuBXb0NKHVPiRkybhPek/xfKTG4cM0blC2kV15zpD5dI9fKs+P09390GdXiuuTJKAgFW65hZoHwD//KZ27q68GPvOZCTtUEhucK/bu3Ssk1+QEAYOIttZWbDwY4mHBMffWy0GkBq/3mP5T1TXA3V8Y/wGnREgc0ES+rxHIi6FPnVIAlF8lVbWpDShUqVBYNnEdWWyr53zExB0J3lHN1McJWf6GQeJZk85LIlg8laE7i8TyDGYwDrCYg5rilJphpyiLGkO7EkhoTGSV6lQnNRh/nQtzVRDSCyViYxRIRZ+18Pj8ksY+H1xog0mzZkoTGyQQmCTlOiOPLXY2J0P2jEnXw4cPi6rueJKXJO0YJ4RKyJalmMVNRn5WljheOQbgHoVERbS9iixRS5KFxxIP6UYpqXivYxY5nFPdSfGAeZRf3w889yzQ2SHtBS+VTKCn8/7k1rIl+HPDPpGbZdF5us6AKwomySNFkw54bcFxPB9TgHtyyiFecsklOHDggBh7Qm72gQeAH9BY/RRbi4Cvfx2YIOlC7n/p38fixYMHD+L8888fidH5WDxeNbxe2Qk1Wkf5lIBaA+StkW5+rF8/ces7zyNjDM6/3PeTYxgLxkVqfPOb34RW9Rt4fT3wgoanhFp4aoQmYxZlLMHuvp3wCHNDkghGzEmVTpDT68aTLfuDDLsbrb3Y1VuP9bmJs5+/PHEY/2yitnogxaU8GoNaIxJS8eDtroNot0sJP+KUpRWpfSaszZ5cE5+zjmN7AoQGwZ+NJ4D+LiArgcQO5Yp8Nr+megKyGfy+DAsAx+GRic9HokJXDh9YgS5Vjdq8Pgwqpb9H/hkf3F4PtvWcRIO1W3QSrc+dg0Iaa44FqblARgkwwE2gf5Qy4M0f34TBDQuZZE4IJBz5uwwmToWhGCVDZDMmGbrxV1aZDu3A6V3bMTvLTx7ZrMA//hf48k9xVkDz9VPb/EbAssyU/zPnJ7Giorcd2Pps8GM7XgSKZwN5iRtQTwi8TLQHEpLcYofSPRpVIOFSnlIgbhOF+44flw5L8dhPjh0LIzUYcDAgkDfY3NRw46DsOmiznUKvqx2davrmSI/RbDsrKx3lRdGlDRl8MujgBp7VVHwfbvxZIah8fb4nK7zHYyYYqVqLrZXUzs0pyMayNYth0qaKNbPQmCFuowU/8Rork8gkeTNl9DsbjgBvPSqtAQa9JOXE25FtQOVioCSJ1+W8+dKN69Sj/2RZXuD/GGTKrfpZ2cAvfwP8+pdA7WEgNw/41GfJRkn//83vA//9n8CRwwB1iO/8KLBiFSYTvQ4r/tG0D0MuK7J0BsxPz8fKrFLMS58ks05NBmCkV0Wtfz7loK4BstoAW730fUKFjiEL1l96LTrVpoC+OufdD31Iuk0SeG3ztmXLFnH9kuhgELGQAVAo0S0694ILX/i3rOIcTTs3eYiyOYklgcXj7j8M9B3yGyumAQUXAvrkVgpzfmQSk3MjA0gSu48++qhIasZbyTkWMGnDari3334bN988elJtBjOYwcSD+xPOASwu4XxKwlhO4nOelZ8zEaAcD73B+HOqdkMkWpl/rsAHi5/QkO5J/w6Lzg2daorEQBHAfT5j5eXLl4+QAix04jiL5atFsoB+T9Gex+uA8Tarurn/GClqMplEoZMSjAO4T4/kiRcPIu3vufdn4pfgOhqrO0NZZMVrLN7rmH4bY5GImfZgjMhO73MIa3PKkGc04/hgN4waLdZkl8I8GUVTRMoSYGgz4PXHacxHmJcFPYXxOscau5JIso+Y0peVAb/73aSSodwHk1QhycKOAs4bTMjHQ2pw/ZrqBP1kgcVmLEDjfMUczJVXXinmHnZs/PjHP55cUmPTpk2iaqOrqwd5eSv9pIYLamRApQpf2DP1mdiYez56nT1QQ41cQx60/sr0YbcDbhEYB0C2sMcpdXZ02C3Y0t0iuizWZBdilnn0yuEXO5qV4jpBxAbvf6wqfj31ZltPmHFus7Uba99F3XYCIvAP1aAQGcT4X4OSN67dgF/LH+pCQLs0vJ1VXQR46hQPUCg/A9CWA5ocgFXlKi28Go65IyPH5fEZMUgphhDo1OOrNPf4vHi2dS9OkRDwv1u9tRt3zNqIPMMYNtgce0uuB+q3A4MdgDENqDyPYvpIBhho9Pb2igQwN14c65x0+Xhm+nJgeAfg9hv36ooA0+jmqgLNp4CDWyXppuqlwLyVQZXVpS4regw6dFlsyKMnAK/n/h7ASc+TSahQZBKI+o/0oeB7b/ubJOHhH69WmwMGUwqwjOb2Y0zY87PXbgX6O4HUTGDhRonUiAQ+PlVIDdl8i6fJB6SogSEx3UrXjQoaZOgn3gyMC/muvj40Wq1hqTyrx4MHTp/C/v5+lJhM+Ex1jTCzU270GUCEkgu5zjTUWSVfEZOGfjoqMSxt3SnoqFOjSx3cOeETUkTtcDoHYLOpUDVnHTIyM0XlMa8TkoHc1BNcZCkpwA1MMoN1Xpd87aKlaehxNqB2qAE6lQFz0lYhRTt6IpXV7/EeCztcxhooJR12K/D2Y/4EuB+U3eP3wUE50JVcUkMGz9W9XwW+/z3/cPcB1Dq+XZFkZ5Lkm9+O/PfZOcAPf3bW/HdcXg/uP70V/U6bKPpwejzY39+CSyergkoGSQxdAeClN0qqVEV1bSXwxuNibRhSaZF59YVA4SxMFbcAkvpc93i9URdadI6sXA/spCGhYgZa5ScJh4eBf/+bpjXwXnQRMNEdTlxPKFnooi+VD9Cw0EMtyU5lxVg72BLedzBw3zUMtL8BlF2f1HHKTi9KQXEe5BzJPcRbb7015iqqRMD34HvNkBozmMHUAZM6rDJ9/fXXRZU292ks0mCH20SBczf3fkw4T1VSg8TOlJE5nkSwLyMcrPae+qbplEpTEhOsEo6nU4PEHiVsR4o3FPB5LfD5ujA4OIyc7BJBbrDjvL+/X/wkicH3ZUKP701SYiz+G9HABCHjGBY/JSo7y2KOaPJykTCTnD23UGXOFrdJhyYFyLgYcPX4i3RyJLPwENJYNrifCr4V9Jvhdcw9Mn8m0nkR07fvXYL9+/eL+ZFrOucs2bOPMRs5hkklNTjxMdlD7av3v//90CD2hWDUGFFsCtcIp9SGTqWBy9/FQZBIyDWkosEygG8efsev9abC4y3H8fV567A0M3rorA9p92P4mqHVYW1OPs7PLcR1xeVxf05KUrkUiXtuWYyad6GvRuUCYJfCNJXBd3oWkB1nCsNjBYbelqSOGLzrmdCiKXIqoK0Jl5TSrQbcx6SErDoL0C6UgnVVqkiq+CipIwgNQkpQaNR2GNQ0CA9OmVLjc6zY09eA59sOiYSpDL469c0PDzTjovwIhIDXDfQcAShZpksFcheHG3YycVEdboKULJAxZkKHlY6sOF2xYoXYTGVmLgDSNgE+Gr+qAL+m/6hgR85TvwskBetqAbsFWK5IbGRmIyc1BSe6+iRSgzAYk+O54rAB3c0A5eFointsi/T+uWXAgk2SBvn2RwCLv6MqqzDMIPxgfRtW3vkNICd8/okLXITe+DvQLpNtPuD4LiAtRxrPNItVgqTHVIEqC/DRRwPo9Ug0hlYljWGTthjp2ipo1RPrf8LF6uN79uDBOiVZKYFdc/kGPe49uF/8zuvrseZG/Gv+IhE4yB4VkZCpL0CRtwZt9pOweUhAGjHbvALGsvCW6OPHjkHjOgKfuxtmrRY1c7LQ1L4ZXV1zMWfuXEE0sPqYQQZ/sjqZQUci2pexNiuseuZrppXp0GQNmHC5fA6cGt6HxRmbkhYYs4qKVSRTAoPdkavkeW75eEYSuw727wO2bJaqqa6+BrjkMqC0DNizm4LHwCWXAmkJJkbOUrKixTaAXmeAqPf5SfbawQ4UkAyPAZpM9jgdyDcYoRuvhiuJDGVLOD2TrrpDXNsHt24VFY9RweviwQeBf/1L+t6vvx741KeYIcNEIixJcetdkg6yMArXAZe/BzjvQqC/X+omaWtDv8WC+U8+CXznO8n1/6CPTMspqSuzahGw50mgv03aS5Hs02ml/dR8eiXFmI9t7SFFJj7ATZlBG6BNAbiXdnRJPw25Qp4qUfB75Y1a3JwLZRk7JjPHWkWVCEimsBt8BjOYwdQC90QXXXSR8C/i3ECyk0berPQuKRnjHjsOcO80FfXIWaX7buzSINRgojFUGtrHUilMdZAAiCk1GQEkM0LjEnY1Dg40Ab5DgtKprEhDSkonDuzfjNKy+SOJWBJzrDLn2sprJ5FOahYqjaYzzw4SVo9TvjHUUywe8LqKN+FKYufdSOLNIACO4W6nHTqVGpnjLV5VaQF9QdRxn+g1OhngNbZsWXBHyVkrNmQR76kDUm4svxwonjgp2mSBJBUJDIJxGuefN954Q3SXjZXoHVdEeemll+K1114TpMZ4QN+M95Uux2NNe0YkqCrNuVidVYH/Or4DTq/XL1XiEwVtf6w7hF8tvyTq6905qwY/Pia13sn46vyluLYofjJDxpqseXi9a/+IcS7/XZE18VXNUw4F5cDVdwJvPgnYhoH8UuCK26REcyww0O59PVAxTtNWJoFTaP4dkPYKgjoH0I9m9hPuAyFyZEFrLJO3qdCqxtYB0WDpwTOtNE2WXiv4lZlciuDZwINoeg2wyL4gKmCoEai6TjJznkRwkuDEQE1NEhuUdKDuq6h0SkT6a+8b0k9ldevu14JJjQuuBmr3Qd3dH0gA3nDn+JOB7Hh45U+AwyqddHqR8DV5LF2NwGAX4LYAVln+DkB/u0Q0KEB/CB0nj7Gipxlo9yehhfkwZUtsgKNZqjjnwHNTWs0HVC+T5KcmAmJ8HQD6W6XEV8VKqdNnNKhnA55hDHs7R1JgPFzevL6BCSc0iKdbW6MSGpfm52Nzd4fYILv9Y6zOYsHbbieWDA0FxmwUFBqrkG+YJbw1tCp91I22BgOonsVrMGDGPptLQkqelFz3dzmx8oKkxliCgmjvzUCBGpxsA2dwc3pY6i5RwuG1+j+DbtyVUXLANJbPMCGI1l3Jz7NoffI2YC+/BPzo+1LimvjXo8D//R6YO0+6TTNw3pK/z2E34GTDCMmxoT5cFKOe4LHmenzvyH64fF5k6HT49bJ1WJcTH3nEcejwWKFV66BXR18reFyssGGAP6rp5sMPB7eI8z6vuQn014gIBl53fFK6KfHnPwMdHWI8qoVptw/4yU+AK6+Mj3jh83u4FrmB/AJJj1aJk/uA1x8JFAYceVNqmRN/608iuNzA3MuA1Di+I3WUvYRaB3gcQNfrgHsw8NzciwB9ZsJz2cUXXxz0GJOWJGZJOEw0mDRlAoht/qyKm8EMZjB1wCTTqlWrsGPHDkFocL5gYneiMdUIDTlRdc75acQJlUoPo3op7F7mXKSYWK+eA4166stZsNhv165dopNzvGCcUj2b3SnBa9WypQZAHeh+IClB/xkWBicqDcu/G5HdCQHVGRhjsENjrPt+Jls5luNJKLLgbCpUzM/g7KDXaccX9m3DoUEph3dFQSl+sGjV+IunQsDiXHYCTnkPijhB8ptkalLhcgLP/C/QoyiSWncNsGRs3Q6TBSrJhIKcArmFsWJcpAYNW+6++24kAwvSi/C5movQYusXngUkNWhewwtH6bXB3/pdrDKPjlvKqmDSaPF0a4NImt1UWokrC8cmBVOTVgKjRo8zljZxPPPSkm+cGxMMglnpysRuZgGgP0sVIbMXSzei+TSw5y3J3HrxWiBnlA2drS7coJrEBmN57VjZXXOYHBbziSnqGri8rfD6HNCpM5GhWzDmaoI6S8+IKSulzzhVy6/Fx2pSIxhO27oVhAbhA1xDwGADcBbIMG5umNwkuOkmwUEWNKFzwgkzVDDI/5ojSM2A7aNfgX7za0BWBjB7AZBAR1RUvPMY4PS3tZOoCErq+oCW44BBGx95InvCNB8Gmg/5DSSWAiVx+CVQRkvxtmHgQr5wLVBUBVT4u4omArWv+I+dXUs0lTgKrP/Q6NJlQtJkOTweGif7pd/88IiOnYnHkcFBaBWkhYyXNm2CSaPBFe8EB8PS3O/EypUrRdI0limtWqURt1HhDchwBYHdYwpQA5ctkGwpTQSskorU7s3EL/X9ed3JFRpa0T0WMn9BDU2Mz8DrmRuiWG3l8ZqgTyqpsewSYP9rAVIyPRdYd11yCcD/+ZX006/xDW4e//wn4Dvfw1SHzWPHnr5D6Hb0w6gxYHnmfJSYclBmykDtYD8csp0VgM09rViW2YK1UTrPDvb34luH946MrkGXC5/auxWvXXAlsmNUVFnc/Tht2QuPf83O0ZeizBR5HWVgSx+amEHwCy8E3+f3z8cmm9SIBibj/HNToVaLPRYLlnPNpyRVrKoqrg3//RNg707pfnkF8I0fSNJlBCsf335c+uZ8irUIEcii+q2Aa1Dq7CxfC2RFWUMz5gBDpwCvvA77gIz5EqnRtwtwDwWey+f07QAKrkjolPggdSirFGECuzQ4J0+kn4YMvgeTTnzP22+/fcLfbwYzmEHiYLEUE6H8yX0Jk6uykXGyMVVlO7gnm6jPPB2gUecgRXUBfLBBBQNUoxTmTLVKYX5vbW1tYt8/fkRKVgbHWNyXk6xPROZJBvdgkfZh9AZpaGgQko3j6Z7gueCeLhpxogSv+Xg8BGZwbuJbh/egdihQTPpyR7Mwtf9M9dj9JyPN9yyioSH3uQIWNspSdInmGKKidjvQ45dCl4ukdrwAzGHR68QXrSYTJDV+85vfnB1SgwONEylvNG4hXF4H2uy1sHkGoVMbUWScBxONJuNAtt4sboTd48arHWegU3lhVKtg90sKMclcnTp6QMVJ/YaSWeKWDJSl5InbWQE3cVueAOr8+sk6I3DJbUB+cj7bmHBsH/DEg1KpN7+Wna8Bd94LFEQhjnxRfDd8KkATOaHl8Q3B7esSiT6tqhDqEJ8W+raofAvgo3mpP1OgQhX0mgrkaSoS/ki9zmH0OIaRrjOhwG/US+kx2U/FThUHNau9fTBrDUJ2apY5wqZkJMkQZ0J1EsHrgswoF4lIDGlUUCqjrR5WpwttgxbJK3bWPKhOSX4FbpcTJacPou2tl1CTkw4sWw8UlCSPzItVnU5iTdk1E21TR+KhcT9w5JXAY33N0jVWFt1UWiCnWJIMc/P7jXI8lYskUmOiQJktmYyRE2QkfPjY7NiVRlpVJtyyn42ik2kyMNtsDiM0+C1Vp6YiU68XleRDLteIeTilsTbm5omqPFaAkzAYj+G1kCzQsdtD0dGjlLsL2dizW4M/E2l5leWqQt93+/btQg9a2XJaZKpEr7MNbp9TdAFynilPmQdVqL9QCFgRSW370SS5iPr6+pjVZ0cGevB6ZxM0KjUuKSjD3LQJTlQuvVBqi+1pBVLSgIpF4RXt4wHH15AimUvw2u7z+wdNFETl/Vagdoe0oZy7Clh6frhX1Cjw+rx4u2snhtwWMRZcbhfe6d6NS/M34ONV6/Cpff/2vxUJdulvNnc3RiU1dvf1hIoTCd+a40MDOC8neouHz+fFGcu+EUKD6HE2w6zJQI4heH1nYM7rMq5qvUjXER/juXvuKeDF56TfL78auOHGyZf74nr48svi11kGA7L0eryj1WKJ14uYV8WjfwP27Qrcb2oA7v858K0fBwgMsW4o4IywJ+JnHmyS9lWOQeDoc8Ci9wLpEZItWjNQchUwcBzwOgBTAZDqX3ucnONCyH8SJXHCJ6ptKe0pEc0+cJ+zSJAbDDhYzDRZ4HvxPWdIjRlMZ7i9HrTYekUxVJEx65ySMKbxMWMK7pfKy8uFoSv3Ool2VHBtY3LZYrEExSx8nHI/NCafqqDee6L+BecaVCoNVJiceCKZoKY7JRaTQ2owd9ClWH+5jwkvimAyk3LQsn/feEg9JkfPnDkzIg85HsRLiFBuTfY3mMG7E3v6ukWxrwyRCuztQrLKlOjRxC5AdgOea2DuvLa2VswBlO0dt4ybZUCW3sAI+N3YhqYVqcG8BbtyxkNijYvUoKQN3ehfffVVfPSjHxWBeYN1Nxxebkp8cHvsqLPsRHXqBugTkDihf8ZPj21Gi21QJHyMGsqUABYPk0FmfHr2KNrNSQA3UceGOtFuH0KO3oxFGUyqnyXtwJN7AoSGHCC/8Q/g5nuTmxBKBK/+S5rC5AuIWZa3nwNu/qRkKH54BzDYA+QUAvNXAfp8wBZs2CsqCo0bJdPRELi9XbCJVlbCBwfqYNasgVohI+X12WBx18MHr9g26NRlMKgrx/Rx9vXV4+WOwyP3V2VV4uL8BViWWYptPWcwKDqDfLB7gYXpRbi5dGX0SciUI322IHJDBZiTsWEaG7j5YKDAn0wMxww2OMZObAaGugFzFrBwvZAcq3/xSczNz4Smeglw2S2BjqF3XkTT68+io38IxToVUne8IY3Ny28a34GzM4nnTpjU+/eHSlkpfgepOcD8dcDBl/xJRGqQ6aQJQ/ydDw2d/chna3hWMXDwxfD3qd8Vm9QwmoGLbwfefkSSX1PmjHgcehOQM8HfcYhPyMh7u+PrtkjTzYHbOQi3T0r8qkEyYREmAzeXleGRpiY81do68th/Ll6MWf6N8b/O24APbt+GbqcDOpUK9y1djvNyJNJw9uzZOHXqFBYsiMPQPgqEFJMxGyBhaZeJIRVgXAJoguW7KHf4B4cDjz3wALJmz8bX58/HB8tjdx2RfKFUihLUt2WHRqh8ll5txKKMDehyNAupn3RdDjJ0sSu3aNg7mqaurHVLciXaHOX2evH1w9uws1dKWnIVeablDH60ZD1WZk2wzTNJv4ki/vh5FywEjtYGOjX42NLENE/HVCWz9dnA/Z3/lsiUFcHSPaOBZMYgPRFC0GJrx/z0GtFx6vJ6hQSVvOxu6+lGm80i9kShIEkYqaY1I4bHkdPrEERb+PH1oqtuWCRveCMYkAfpp3MTTQJJq5PM15X4wAeAH/4w/LFnnwJ+d3/gsT/8n+SxcuMHMGEQXXecsxXn4tZbgQMHgLfeEnfTMzJw/q9+hR0nTggicVRpkSMHA0bycpBXK88xMvz/J1+STpe0J5C7OknMp2hD9TOBruOC1GAyI6xKU/h1rQw/Hm0a4KIkgC+YBIkb3KspO+e6SWHB51sgCIaHHnoIk0lqMLbgnnxGv3sG0xFWtwOPNW8ThVOESaPHzaXnIccQ2xNpulSeymsCweR+zBij4zTQyBjPJ3VKF88Teye+ViSpOSa4KK0zFY2JeUw9PT2icGUG0w9cV7i+JmWNUc0HfIxb5b1cKqAKL0AiAUhTblmGKpFjVYJdUSQ0kiGfFe09IoE6+EuWLEnae85g+iFdq0OXM1BMyhk/cxwequxeYMEer0VZOpkKDSS0zzXwGuN6weufOQISkuOae3KLpZhTCXr3JVCoOBW8qhhfrF27FqmpYyfHxz1aLrvsshFSw+4dhMMbHJgz6Tzo6kSuIf4qi319bWi2SZVlcqW8Tq3C3ZXLsC6nFNoJPvGPNx/Clp76kUrHZRnF+FDFKIns8YCmLjtfkqpX03OAtVcFmwzzcVkjTcAnJXutQ2MyI2YCzeaxwaA2QBdNlzkWrMESNiJYHx6UEklPPgC0nJaS2kxO1B8TZqJIXQoMM9D3AppUIOO8iIQGYfceDak0dMPhPQ2TRlpEufmwuPfD65fS4TMd3iaoVenQqxLTXu53WoIIDWJ3Xx2qUvNRac7DJ6o2YUv3aQy67SgxZWJNdsXo40BjAMovA5rfBNxWKcFevAFgQvUsgdIg3PyQ1OBGatQKC35n2/4BDDKpQZ3wBqCrDv1L3ouc6lXQCL3wkOvv8E6UZaTiUEcPzHpOKT7x2LhIDY6pVx4OEBqE2yN1KrEiikRTdgmw7r2StA3JFwZKTKhRUsplAw6/DM9QHxrtemy6+TNA0yHAEZ44HEmAxgK7o1ZdBWx9HMLcRzYH5/V56YckYmMikZIJGFIBh0QaC3BeyI6va0ut0iFbvxYu34D4O606A2qac00CmJR9fP16vNjejmarFcuzsrBG0a6/LicXJ6+6Bp1s49frYVRUdjOJz6qGmPD0AK4G6ZxoiwFdYC4Y8ZfQlwHaQsBrA0i0RzDQ/ezevcL/w5eRgebDh3Errx2tFtfH0HWnnnSoLAvfM5ofiE5tQLEpMeklVjHEqshi1eRo1fN/rD86QmgQHP1Onw9/rj86saQG55btLwHH9wGsVF15IbBwTXLf49vfBe79EtBQL90//wLg9g/F//c0jP71z4DDBwBTCnDHfwCXXjn63xzdEfmxBEgN2bMr7HGVShRUXJRXgadbzgQV4ri8Hvzy5B78dEl4VctVhaV4qP4UTg8PSXY/Ph+WZ6ThG4e2iOqqi/NL8ZnqxdCHFEbQQ0MJl9OFM8cb4bMcwcUrPoDMjEpBjIetgQP9wLe+AdQe8bs8Xwx85esB4uCGG6TOjMcflzbfNAq/8Ubg03eFf+gXn58YUoOJtz/fDxzwd1Ws2Qjc9nGAgRgDp/vuA9h9SMmpmhqoUlPBVMHevXsFUThC4PD///Y3arwBvM64fsjnQ/7Jz9jVCeTlAx0N0nMEYaEKrG95c4ANN0kdeFzX9v0l6qEz8GHQwWOg3NeoyFgCODslzydxTBogy3+dcd5zHAS8A4AqBTAsAjSh+8ieCC/aIzpzGIRt2DCa11lywaCW82rC3aUzmMEUwTvdR9HnDMRMdo8LL7Xvx62zprbedSJIKDZuOwHseTIgv9lxShTsWOymqCbjJDvY/cE4ZqqBe1MW3MyQrtMX7NKgZOu4uzWErCx3DcP+8U1SQxW1w+n06dNiXWfnRqzxw7wHO2NDZacoAZWsscf1PVZCcSwG5zNQ4I1XgId+DwwPAQsXA/d8NSBVOo1w95xF+Obh3SK2J/QqFTw+G96/7VnkGUz49OxlWJgRm7Ajmc2OBSbUly5dKsbylPGCnGCQ0ORnfeedd8YnsVWzHGirA47vlu5TVeSy26SfccDj8eCpp54SxzMeT57x4pVXXhmXn0bSSI37778/Ylucy+vDkAvosZ9Aqq4dc9MWwayNPGEOumw4PdwtCAtWxgerjUvI0BlGJTSODPSh1W7FbHMaqlLTYfd48KOj+/Fie7PQc7+1fDY+W71g1K6LRmu/IDSgeP/9A61YM1SG+elJNgJjwva53wN9kkEluluBtjPATfcEWoYo0xF6JphEHUNLUY+jE7WDB+D1m3nNNs9DobFMrLmxtNyDUFoFNJwIEC18gVnVQN0RidCQE1jEyf3A8vOBotmAqVKSCQpJnISCfhjyaJJ8LPiYPUjr2TtSCRGA29sLvToxUqNHEWzI4OjodgwJUoNSU5cXJlghnpIP1NwsdWvws56tLh9FVcjRo0dFZS2ryZubmyVJMA8rMH2AJgdQ+Se/HppvdwT+mOPS2o/uY3tQvfGqyG/gN4tfW1qAbqsdeWZTfAbysdrpupuDH+N5LF8IrH9PoDJWRl6FdBtBBrDhQ9i3ezeWb5wHGPTAMb/heShK/N9vTxOw73nAOgCkZgMrrgMyQ3wJWk9K7ysGpn8u4mGkj5G08rgkaRISIrHGCROQK28E9j0F2JiUUgNzzgfy4u9QoryRXjXxeuiRwHn3mlGCBs7txSZT1KCWhFxUM2J3N2DfHrjvaQdYca6XxgSTkiMVH2qjdIsAyl79qZ4dYNyl6QEG2S0t+GNdXUxSY2BgIIxMkP1skgESGgz6Y0li8ThG62p5h+tMCDhbDwid/wnE5ueAPW8G7r/8D4mEnJvEzsuCQuChh4H2NskUOhHNUs4p//k94OQxKSntGgDu/wWQmQWsWouJRJrWjFx9FnqcfWLsqfzXS5lJGnO3z1qE1zta0Kv4jrhG1lkiywqlaLX457oL8Of6U2iz2+D2urCjN/C9P9taJ37eMye4i0UNDYbrVajrPgq3yw2tTou5CytQmZ0Cs8YCgzaKrOB9P2X5XuD+W28AhUXAXR8PPHbdddJNiZFijRiPic/aju09R2HzOFFkzMYFeUuQkogn12N/Ag7uCdzftQVIywBuvEO6z/m3Jtz3ihrYnHsEbDbgIx8BGhqk5z/3HLAwileWTGrIRIZgpBR7OV7H+hTpRmSWA/1NwaJhJD78XTFejxuW7jNostajrGoBkBLFM0ebAhRcCdhbpXNpKJA6Nbj3sm0DfDa/fKFLup9ygUTwBl4gwovqRMCxiR5IUeboiQDfi+/J954hNWYwHdHtGBwpziP4e4+/a+NcBPdZ7NygIWpEnJb3aYq58NR2qMtHl7WjxLWyI2QqgB0klOKZ6dKY3uDYotRNUiSoRFwYuZApFOxCp3w7q9Rj+VRRmo3ybqHrIw3KoxVOjYWg43obKwkdqZtqBnFg/17gF/8VuH/oIPCDbwH/ff9ZzxMlimuKypGjN+L1zhZhC3BksBUttmEhscif363dht8svxiFVLlQgOOV+Sgm0pk35lii4s+7lRSOpbwQ95xz/o0AC9yoJJJdIKmLxAnmFEisskuC/p/j9eYZCzgW2CDx+c9/flyvM+6WBw5GmmTt378fRnU6DKL6noydD/1OVggyYeLFgGsA+/p2whXBc6DJ2odfnXwDT7Tsx6NNe7G3/0zQgfHU6lVqlKVkRGWOf3rsAG7f+Qa+cnAHbtr2Kv7eeAr/eewAnmltgMPrgcXjxu/rjuMvDSEySCE4NOA3WwlBr2xWnEywgq+XyTdF1TU7MBr8Vcl2K1BQKVVoQ5FEXX113AycUlaidpDdDYGq9NOWY3i542W80PYy9vRSRzvOivXrPgTkKgLqqoXApmulY48E63Dg+GMQGk5vHxzMJ/lv/J0F8RpV4Lunz0Y4KM0QHoxb3RbUDhwQY+/08Imwz5ihCw/Q+W1k6MapQyeYIv2UWKjYDi5XeHCiMuh8sPW8DNh3A/Y9gPVNwDsUSLJHgkxSRcKai0QVak56CgbcbqkidfVF4zvoiOSlKjDu4zyvXDhF5YnTGtmbIy0fqD5PIjK2/ROw9EvX4VAPsPUf0t8pIdorFVW54jhUiZM4PJYDbwD//BHw2E+B5+4HhuKoQkvLBTZ9FLjoU8ClnwcqIsiPnINgNRK1mqPCFWFed50I79RIFEw8xtH+StM8yjGGgkFSqCSV1+eCzd0Cq7sRbiHVGBs8/hN+KZzRQJm5qMSPHykRxipH8fLMCfaNOrgt/LFDER4bL/idlZQmRmgQQ4PA8drgNl4SiTu2jv5381aHPzY/sQ4Uzssbc1ehMqUM6dpU5BtycVHeeUjTSRtT+p7MSc8WwcPI37DSZ5TusFStDp+pno8fLlohikaU4Ez4emdzmDYzK4bmlizCxRvm44JNi7DpvPmoyjYJNT9vRBNMP/btC14jOL/tUxAI0XD5VXE91m7vwysdezDotsLlc6PJ1oUX23cmJkdyZF8wYcK/Pbwv5p/xGmZCQeCVV4C6OmmMsPuCr3GmIfyPOG6K/IF/UaXUhTtCgrMoxQxUhJhszr0CyJ8L0FcuJQeYdzWQLr1GeVkpmvY/i3nprWg99jYGjz8L9MgSnRHALtyUCsBcFZCe8nJts4Y4rXgAt6KIQUDuBFF0lqAKL7/8sihimmzwPfneM5jBdESmzhzUiaeKEndMZzApwsIRgt4X1MaOCncEf8FocUeE95lKoCzqokWTI+E6g4kD918k40hSTTaY1CWpEQssVgqNMUjGjHqtJQB2qvD1YyUzWVUfSq7MIE5s3xLsL8c98+mTQG+k7tipj3U5+fjG/OW4sqgYg26nIDQIn7+TfC8Ltv3gXn3Xrl1ivNIngx0B7MRl5/G7ldCQwWLFlpaW8b0Iz2FWPlBclRChIYNFQ5Syo8cHv6fJxr59+0Seg5zCWSU1mCi6+OKLRSWVWqXGrJRVSNPmwePVhfQX+ODyOdHvCk/cPdm8X1wAMqxuJ5ZlZkPrDwKNGi0+Xb0GmbKGfwi293bin03+DgH/BXXf8YP4d3tzmK70S+0h1d8KdDkseKHtZMTcZ7EpOUx4EJTSOkGPe4DD24A/fAd4/H6gswOYtVQyWr3iP4B5iX/pFvcQvCFng5MMkxVEq70dtTSdjAdpmcBHvwF88rvAZ34IvP9TUrK3oDxycJ8XH6vP4xl0KfxD/HD5AJ2qLMiQLNg/Q9S1Qq8Ofn+7x4Y9fdvQ4WgX467RegaHB/YFJUJyDWlYnxNcaTk3rQg1qfF15bi9TvQ6m9DtaIDDE1+CcqLAzxVPkmdW4RAaG5sUf+gC7P7znlUCiOpXeZFhV4IGvsxRKlhYPUyvCznJz9/ljp2xwpQGlM0LlvUQlbQrEnqZkcXSmAaIQDJk8axYLiWZuupHPDgC5qp2oDdkoZmzVhrTykV43jpJTicR0Cfn0JsBooUeNG/+PbYpuvShpOres+Wpc5aIOQa0IxXToWDnUdhjnoRIje09HfjIrtdxXo4ONanqkbmR/gCXut1Sh1MEsHqQbeCRqrxIQjAIkOHxOdDj2IZB9xEMuY+hx7kVDtExNTpoZBiPbi4lYmJVNN9WHv7/1eZ0fGJ2DF+ZceMs6WGzujOe6yoSeSUMm2Jc2/QcWneNlLhOywZWXQYsT5zU1al1WJm9GFcUno/z89YgSx9cxPHhioVCBk1ONXN/9OnqpXG+dvhWj92rRMtQF37x0p/wwM7HYa0xw5yZDbPGiEy9Guk6Sf5Kev4oFUVpIR24fL94qgffezPw4bukDhtKG97+YeDmD4Y9rU54T6iCqp27nYMYcgcnIVi0EHUNDO1u5eeixNhocLuh2rULxoMH4SWxMTgYTrhbHcASBbnM///k52lmBdgs0t7ohk8BlQsBtuOX1QDv+wxgCjlnLIKovgRY9WFg2S1AtmKPM9yEldWp2LbvJFYvqcK+2nrY2/cAzvgNwMPWviiPq4QxOM0Zi/y3FXA5s/Dmm2/i8ssvx2SD7/nGG28ktettBjOYLGzKmy98NGRoVRpcWhDfvD1dwGQoOxbkPTe7NKJ2VRSF7j9U8OROz8QWP2OsIpIZTA/QI4K+LZMNxiVc206ePBk1vqFMVSRTcV4zLNpjYfF4wKIsxinLlo3uPxfR22sG8SNaLDHN5ZbkXK0SPsXjLOx7++23UVNTI66zqUZOn22wY2vcpMY4UVBQIEhdzkUkWg8dCvUFnFiQQyCXMF4PFW2yKqkef/xxfPWrXxU64eUpy2FQt6I/QoK6zzmIVG0mTPQe8KPXZQ1Kd5DtU6l8+PXyqzDociBDZxxVdurM8FCYXBV/H0lK+cG7hlEupuND3UJ7muAUI8/byzJLUGmeAE+EgllSYEtfDVlOh4aRTOg+/8fA85hsPbITuP2rY5a5cYZ4nchQxv+dDuqsB2RLfD4vHN4GeHz9UMEAo6YCauowE/w+skP01wvKgItuBN58QnphVgRfcVvcx0xZKR8iVPEIYqMfGgQ2jwb1bHEslJxihwYJDY18bH502FvhDkl29jq7YfNYkaIwztyUNxcV5lx0OYaQrjNhtjk/rkXb6aVk2vYRY9V2qFFhXolUbcjndVmBxrcBSwegNQIl64CM+HwQ4sXB/jPY23cSbp8HxaYcXJS/LOgaU0Lls/g9TWVjNB+OHT0KbYZE5OTPvwrpp99Aa1MTrNBBvfBieFg9Gg07Xhv51SNXOZ88CJfDDp0hSgt6LPC4LrwF2PsK0HYaMKYCKy4FsuKXgKMWN5PhAemm9wK7Hw8YbpcuBkr9ZmfRCIIze4EDL0ua5wsuAAqrgSs+BhzbCjjtQGEVUBOhUjsW2s8Ee+Xw50CX5JfBz/ouQp/Tga09ksfDhpx8ZFI2KAK4Gdq8ZTNWrF8ubea1aYJIF6BPhlNZ7URyLV9sphgosFNpNLPfIwO9uPfANpEs5ZSYZ1AjVaPG4UEPvGlp+LPLhfPUavQdOiSICvrS8BjYCcRgJFq1Hp/DCkbZhMviPhVS8U4i9wjyNBdEPTZep1zoR9O65XP+0lCPX27bgvTBPtxWXoG7KiMnCs7PK8Z/LV6P59vqRRfjhXnFuKoofs+rMWPhWmD/OyGPjULQWyzAI38FmhokKaMP3AFkJuAjxU3Zt/4fJwIpwf6tbwMbNgKnT7HPXnqt89YHyIwUM7B6LbBL4ZHB83dZFNk95XOWbJJuE4gSUyruX3EJtna3ik7YVdkFKA5NjEfB+0pm4+BAcDXYBqcGL772Et7qOYziBRXIMeWh3tqBgXYL3lu8BMOe/SxFEc/VqwphVIcH1SO46xPAT34YqELjObnjztgHxufddIt0GwXqKPU3JFw49s9YWnB08AQcXrt47oKMOZibFuI9ce3NwB9/HSAluEe5+sbob8qk3Oc+J7pQTDYbrH/5C8xf+AJUyk2TIEZMwGe+CPR2A309QEkZsOUV4Bt+6a3KOcCdnwOuTMDbJRTuYaSYjJhXVYxt+07hvOU12L7/FDZVDEOlj7PgRp0BqFIBn+zJxLmBnWjh86IKvM4C19r27W+L5N3ixRNNfEae9/ne9BUZTXfY4XGizzUArUqLHH3mTOJlBlMC6boU3FlxAU4Pd8Dr82KWOU88di6DldxMxDKJJWv100ybeyCDoRSlZUvhqtuHxs4+qAqq4TGUozAOw2TO9fJeivsqJsfOVoKVe7+hoaGZeeYcAT0iKOPE4iX6XcQLdpCTxBsPuUXZWo5tXjN8LcYqchEWk508rmj7fz6XhER1dfWY358GzbKfQaznxeoWf9fDYQc2bwZIUC1dxskwuAv5xWelvSdzJTzfmy4E0pMgQXQWMTctGxUp6Wi0snjaJzrKUzVa9O/Yjy25LaLgj3u3GTJsapt1L1++XMjwsQOMOYfJlJpjN/ZNN43DhzeZpMaVV16Je+65J0jbL1ufC73aAKeXCV8pCHR7fSLxur//NDbmrkCxSUqK5+rN6HQMjZASbNUtMKZDp9YgxxC+UFjdLtx/6jD29HchQ6fHmuzCiDWgN5ZWCLNM6TWlo7iD3g9RYCChAL9kli/wN2uyJ6jVjp0n13wMePMRoK9TMv6mLlqPX+pACXayUAt9DKTGoKsDPY5jMKppwi095vN/RnZBKCtFlbB6DsPtk9vHVHC5O5CmPQ9qVYREtRzoL9kA1CwDhvulYzXE32atEsORSRFP7GpClUqYgo/moeHxecVYUurZEt4IMltlKTnilgjabSfgZpeDHz540WI7grlpm4LPy+l/AzZ2KPGkO4EzrwBzrgfM+Qlr8+7qPQmrx4FiUzZWZ9VAq9Zgc/cRHOiXWlC1KqDV1oNXO/biuuIohsJqM0qKc9HS2oPSklzUN3SipLQcaQXVUoLozBn4Vn4QlpJ21MydK5hbTnQBEiQE9ITwI8NowMnufuFNoD12FA6dQWy2xmQqRr39NVcn/ncin3lIBDxMiIwguxS46JPAcLfkYaEkKklWsBvLPqQgGHVAxxn/LEB5qseA828HcsqAdTdIf0fZqq5GIDULSElP7NoX5db+rg/5gkxQVm4E7kHAelrqutFTT7F8SkifxQIJ6Tt3voVel0QMZusN+Mua81FhDpdy8mq86E3rwisn/42svExBaizPXA09pVZ0swGfA3DxOiChmg8Yl8BqbYhrs/9qZ7PfuyfwmEnLpKlPyODt6+/HtS+9hOdWrRJt4LKkFIkzGnePtlnj/7ECgkSIm0a9ofMRHNGvLbmjLkZly58b6vD5zW+LpImmvw/7+vvg9HrwmerIhuFrcwrEbVJx/nuka5pG4SQSVl4EzGdFeASwKvvbXwHqz0ibf2709u8B/vu3savrib4+4ItfkDwQiKEh4OtfA+66C/jjg4H1ikHHfb+Q/FPow1F7UOo0E+SsitoAwKxJIHziRJbeiGvYXpwgLsgvwXdVa/Bk8xm4fV5sTMuFqfYUjlvaMWvN3JG5gmtlr3MIfS4g37AJHt8wVCod1DCNHpBccpnkPfLOW1LV2ZVXA7PHHmSHYk5aCQ4N1ol1RU7Hl5ryREfJ1u4jaLY1iCIWHiM7Ug8PHINZY0JpiqJ7atV6aezs2iyNp3UXAnNG8ct6+GHggCTxlKnRYEdfH0z/9V9Y8KUvIfP++wFKOnK/+9OfSgSZINyqgdefA7a9HngdSp7+4wHgY/eO/QToqbftQ252Ggx6rejY4FzlUZvj38TTN820DnAckYzC6aOhXwCfygivj74rFkG6unzD0KiMSNcugF4jrZEvvPCC2OefjYCL73nFFVfgxRdfjEpqdDl68XbX7pEilgJDLjblrUzMK24GM5ggGDV6LMwYhRSe5gjdv/CalTvmmPyntCElRAnuhY7b7NAuqEL19dVi7eF+PZ5qc1arM7Eqe3jyPZhMTiQJnQxQ5nP37t3jlsqYwdQCx+j27dsTGk8kIcZDKCi7fhhrc69PYoWFWLyOeL0ExbERPDMpTTueYyBBGE+MzniHVeUziAJ2q33qE0DdmUD39w9/DGz054TKZgE//SXwyN+AwQFgyTLg5lun/elkJ/gPF23EnxuO4MxwPwqMZizvdaJzqB7V684btaBwBhJYfPnSSy8JP0ySCmcDKpVKKEKQqGURwni7JuIFuYPNmzfjwQcfHPdrJeWIOclxQmX7yI03SpVvOrUeK7LW4tTQMfS7+jHkdmDIJQWkTDZv6d6H95ZcKpKyN5Yux0P122Dz62rm6FNwWcG8iO/FTcy3Du/E7r4uwQi22iw4NtgnKny3+Kt9iU9UzcMnquajPCVNyFCxDeqWsipcmB9dRmdxRgEKDGZ0OaxStwhUKE9JR01qYsnuhEBDl/d9PrIHRSjirMoMRa+zXgzWNK0POq+ftPGq0e2Ukvty4n9OWmBRpDG309suPC1E04XKB73aC6e3BUaNYlFz2IDn/yoq84UEz7pLgQ1XAabENd2kY1yAIXdw25MKeujVsat4QpFjyEMDE70jr6OCQW1EShSzehkcn9Qw51hjAojkWiQ4vUp9agkub0jLNeUhbBH0EvvrEiI1+p0WPN68VZLX8GuM9zuHkalPw54+yfSVIQVpHAq/tdt74fS6ofcTdUEwLIQ5dQAnT7fAYrGL3EwFJZT83wGvZ+rbyZskVozQQIiBBw3xwhKs5dXAMUmbvDAtRdxE0mjRYvjUGlHJHmqgPBHg98UAirA0HcO6WVnAiR5g1jJJfkp8GIMksRUKdmKcfydw9C1guAfgNV8folnOoK3xsERqECd2APtlrW8VsOIqoDpOj4uqZcDp3cHjR6sGBjqBnNLECY1emqAz0PMBjmaAUmipCRrcnwX88Oh+DChIsQGXEz86egC/X7Ux7LnHB2uhz9Siq61bkBoW9zCOD9ViccYy6bsxLBRJOnEOIrTDKsEuNI/PBrVKD7VKF1WYRZAcVis8vb1oz8mBvagIJYqKQhr1Mfjgz2H3MPqd/YJkyTXkjnSR8PqRzXV16jS4PMESjBqV1PURDQxs5HEdDb87cxre9g6oKwMJ+AfOnIpKapwVcN7YdJ10i4XaQ8AZhU8KkxgkHXbvADbFkHbq7QX+8Aep00MGFzFqfj/0h+D2xIMHgGefBm68Gdj+jvQcegLJc35bC9DYAFQkTiSMB0MuBxqsfTCoNahKzRHr0XhxQV6JuBFMDPUurEGvNw12VbiONPcDlHkcVXIqFCtXSbd4wXP92quSvGbNHGDdeaOQOWm4vug87Oo7PmIUviZ7Hnqdgzg61IAsffj1027vDCY1iIXLpNtoYIfG97/P0qGRh0r1enGrtdnQM3cuMt98E6AOdnZ2uGzZ8RDvH47dk0cDZPlYwM+RMQ8YOIa0VBMuWLsAyF8HmBKs8FMbAVNgjfL5PLC4D8Dl65Lu+//1+Kzoc+1Bjmo9tGoznnvuOXznO9/B2cK1116LH/zgB/jJT34Scd3f2r03qCu3w9GN40N1WJCePGJtBskFvzc7YwxPp/DJS9GWQadOoBNvBlMGsiyNEqwsP3bsGHp7e0X8IIN7IVZ/Ultd5SdJmbRlIpfeRaOZNTOBHJpUZXzB/dFkSJrwfThujx/ej7U5XhgbtkvFUoWBwoAZTG+cDeKehYS8fubNmxd2HXF8jwaqEcjxxXgwWmEVMVnX2LTGXx4GGhQeJ4zbfvh94MWXA/PD7BrgG9/FuYY0nR6frV4eJJm8/oYbRlUYmEEAF154oSAwmdw/W6SGjMn2iWKXBrs66a8yXmiTGXQw8JFJDcKkScHizBXY3XsELbbGoPQvZXKsHhvS1akoMmXg7pqL0GDtFeRDpTk3aiK51+nATnY1+OHzSxDUpKbh1vIatNmtmJ2ajmWZEhFxU2mluMUDend8Zd4mPN92HJ12C4pNabimaO6o0lcTgooFktlL6xkpucIujeolQOGsMS1ULr/0FBcsk/+0qqFBddoKNFuliuOylBLkGgLkDRN+Fncg5UoihDe9OkS7noTGiQP+djoH8M7zQEoasCJxKQ52UNg8vXB7tdCoPCKpolNlIU03VyQeo3ladDlPCzKBJvV5hkqo/YbhGbpMLExfhhNDtcLPhZXdvD8iWROCRmsPnmndhyE3X0srCA0SHDn6VNxYsgpmrR5t9h4hb1FiyoVJmw5biKa1UR06gUcZOwkmqY4NNYljUV5Dp4bb4fYFG9trmJCASlStaqJtUFihmXIBFq+YC41GjZN1/YAmPawNTQkmZkloHDlyRFSzBPkTXHsH0NoADMrJWhVw3YeF/JhwO5nA64fsNs36+FN07+j18DUewlzbaaCe34UPaNgHbPqw1IkxGij7tPwa6XenLZzUUJ79vjYFoeH/v70vAPnlQHocBsUkcMXXE+IZ0tmQOKlhPRUgNEYeOw6Y6Uly9loZlXijswN/rDsDl9eLG0vLcFNpGf7ZVIe9fT1CSkcGf6+z+E3rQzDo7g9q2GLiddA1EPwk2bh9FDi9/ehx7IPXL62Tpp2NKwrL8a/mMyPdefzZ5/TCNTgktRH7K7cMIWOZAXhjYyMMRQYcHAjILWbrsrEqe5WoFGYwIHwJfBaYtTlweXvh8kmfkRRkhi62pEssrxy31wufxwOVIuhQntdph2hVm9F0umXQ9+TjHwP6+yL/v9IEnOD5avVrmSrbdJSY5PNYb+nFg3U74PBKa22ZKROfqDoPhggG72PFme5WvI5WGM0m5Mj+1dRKhgoZuhTkGSbAQyyU0PjSF4D9+6TvgMHfB28DPvnpsKey+8jhHRZduVcXrgkKvOmp4YsSkGv8+4CE8T//A7z6asT/StHpkMKkG6sa86MUJbAbRO6+k0EZxvEkvfi3eauA9NmA2wrQb0U3/mDR7qkbITTE28Anuj251+NM6PB2o6mhU3j1nA0/DRl879tuu00kQmlErITT64JddIQHY8AVeR2ZwdSA1dOAYfeJkft2Zzuy9Oy8nCE2phs4/6oj7I1YoUsCPXRuzsjIEHI3SrA6nqRGovI/+fn5oqqUPyfic1H+hzf+zrhH5bLDWPsSjIVp0rzcsBeYfR4wd2LlJ2dw7oFj6ujRo2JNG6t8FTs8xqSEEHKtsvuchsXREGntnUEIGEso9338nd0bvIWYvJ/LkGUCZwiNxEBykmvjuw3PPfec4BCSgaSSGjfffHNETTCz1hQmAcTgWan5b9YasCB9FDNiP0JfJ/CCKqzPHX+LU6pWjw+UTb5ucBAY5L/n40DtDoA62NmFwLxVYwqKGZRqVG5/kBqAWZuFPEOuuEWC3TMQdqYlSS5z8ITNDg15Eufx5aYB9dsBgwuoOU+S+YkT/c7DsHk7Avklnxdp+hJoVJFfw+Nzo86yA06fJOkyhE5YPL2oTFmNbmcX6obpMeFCriEfs81zoVOYOTNRQhkno1ovuoUGXTY82rRTkG3SeQuQN71OCx5p2gqzVgqgiQydGVcXrYLVPQC7VwqetSoDSlNCxo4+FUgrAYZaA+lSJpqzg83JY4EES/hj4c/jeWOh8aL0itGlF1Q6aIxlUjJIHV6pGwms0iCDS2KDrCqrpkaklD75HeDkIcDOBPBsIK8ozMA82XqKrGChwRnJFkr7CJAAbH0VSE0J+FXQ8PvMTmDhpfG/OMctJak6KOnkP9H8WeZnsPtkWbYQ9HfER2rwXLAzg1+WfHF5vIClG+iqA3Ir4r/eRYVq6GDwwcdEj1oPVQxig11AO3pPwuKmrFkWVmdXR6wMp5wRE/8NlmHMMqfiptIq6OMwK3+pvQ03b9vi7yQCnm9vw29PH0eTdQA6NVeCgKE7ibjq1MgJVXZZhY4jgzqy/0Y0eLx2dDt2Ba0jQ+7ToiPvF8s24IHTtcLjY0F6Fu7ffwxqhwPeggJxXGuys7E8izIwAbA90+Fy4ORAcDVVr6sXTdYmVJgrYBcSSEepjisoziy9Gm7fHHh9qdCpM6IStqHvwxbxaO2gN2Tnola+Hv2kzAfZ6jxdwWOntwrbyHgdc0/Bz744RpX9A/8rmTlHWlPZAj3QJ72mMrk+y1/0UOmvAJUT0nxP+iNM8nn8W+Me0WUno9k2gNc6T+LqIkm+Y7zgONraeAy6eQXCQ6zXySorlRB+rDLn4YK8xRMv2/P6axKhQchdSP/4G3DNtUBZQO7T7hnGmeE9cPkkMitDV4BZKUtG5rRsfZoY6+woNWoC5B+Pf3bqGL836iGHkl9+aLxeWPbvB0YJ/nHxtcDR/dIY8u9jcOUo3h2JwJAl3ZIEty+Y/BNzq88Hm8cnedL5gOeff17IPp3NYCszMxObNm0Sx/KZz3wm6P8om0rzZXnvRnBVSdGMv3p1BhMHi9sv0TECH6zueuj1Meb4GUw5RCq64FwiV3bHW9jE5ColbhJJoFLymoVNySY1mCym7C49BFavXh34DCe3sCVdmhzlz316G1C5KqF4dwYzIKHBzqMR78cI4P9xLI7E2yEgCZiTk+O/BhkfJV7IRjKDcmqjkRqUkEtGJfU5Dc5ZyoIW/p6RybY1vJvAIpgZAmxsoAzjROTLpirIGVDelr7cU4rUWL9+vZDhYMsRNbmUqEmdhUZrG3qdgaralVkLwjwc4kGO3ohlmbk42N/jl4iSNlSX5k+upuaEg1WZizeM+2U8Pif0/jXOSSkpBoEqdhTEkGcR6cdwqGk0GXScOqlDg5iVC6RwcfYAZ3YB7aeACz4cl08A5WBkQkMJq6cNRk3kJPGQqxNOHyWgArB5+tFub8SxoWMjj7XZm4W3y5LMFeJ+q60br3XuFQQFuy425C6Cy6eGK4LXhjg2Mc6ccHoDk8ygy4o9vadxft46WAUB5IVJkxFeHSpIEB11qaRNsDkLqLgIMCZWjVZlLsQBSlYFDirKsQIVKXlYlxOf9BAro7JCErWjgRt7EhsMIghWl4iNELXy50vnNxQMNrh54yTNxMh4jIeovceWdk6EfJ0wTVu3PwkaelLYeZEoVt8AHHwF6KyTgpX55wO5fukpdiNFQqxuEBl1uyXtfhki9lMDnceBrhNSO/uy6+IjNvT5kuSUHxymtlQzvL53xKWoU5XBoJ4bcZEcctnw98bNIonKcd5g7UKnYxDXFa0Mej47AT6/byv295MkVYkugHe62vGb5RtidrL98uRx6bgUj+3pG0CauFQoaxd4nxy9Ad+cHzmpMSdtProHu+B2SQlfXrt8LBHYPIeCCA1q9JMIdHh6sTJrLn63KmDYvdGhxp9cLtRbrViVlYUfLloUsfvJ4XUIHX8lmFSzUAJMGBB28wwG/o+SdioaxV8g6dzHAZIZso50JFzm9cF70cV4uKVZfL7bZ1Xga/OmvvxYRFDy6etfCu7KoJHeF78mGYaPhpZWidgMBavr7/wwkGoGvv9dicwgNmwCrr4GaKwHfvHjQEDCnyQ7vv69cHmhCYTL60E/SVgF+H220+snSdi/fz/KF9fgjL/Lib5avU4pIP5k1crJ6Uzt6gx0oQY93hVEatRb9o8QGsSAqwOdjnoUGKXgOl1nxobcxdjSfUjMSTrWC+jTsS5nGdLH2snAADS008KPMr0eW0+cQOHFF0eXfCivAj7/HWDbG5Lf1MLlwOIEZLkmESoYwivsmHD2fy0qDODZZ5/FNdf4OxjPIlg4xWMJJTXYBbc6ewm29+wbmdlTtSmYz66WGUxJiEKXCN55XoWE2AymFyLtL9mlGo9XRmi8QMkq+gTIpuPRErry+wpJqOPHBWFPCZ+xyuTwdfi+LJbiazCvEdSVDn8sEWl94Lr9LiA1vD4nHJ5G4QVHeUq9uuRdk4BLtIN6NHR3dyMvL29UQoNgnMtu8GiEAgn/ts49yPYwzqJJcw6MmsXCCy1exCIdeV2JTt536fccN267A9i9Gzjk79rnd/v9H76rpOk4Vjh/ch6eQeJgfo3y7ytWRM6nnWvYsWOHGDP0Jk0GkhatM+lyww034NFHHw0jNVgJf2nBOjRZqZ/qRK4hCzmGsbUYc1L98aK1+MXJA9jX1y0kCT4xeyEWZgQbaDNZ9XxbE85YhjArJRXXFZdHl+MJ+bt9/e3od9oxy5yB6tTA61L+R8101TSaoPTqdHG8Bg3ETYbDF8HnQQGjOgcDUGiaiySiHjqlvBLPAz00hOSUATArNp5c7C29QPtJoHQhJgJeRaJQiW4H5RRkIRkJPc5OuL1uQVy80kFDSSmgYjLyne6DWJoxenJUVC0qwJCszzUkqiLY9RIVp14D+hsCx0K/BqcFMMdRza8AjcEvL1iOzd21sFNTPCUb/U4b+l2WIH6DsiFXF62OOUZZ+cG2cJqMscojEfC12akhkwzs3GA1U7T2WRIZcpWnTIYEH4xVSm7RM2aU42YLeFNTk6iaihq00BsjJROwDSiCDh+QNQbSk2TciijJnIIqoGQe0HJM6rwhkTJrSYD0CAU/n5OyIfQb0UiEnxL83GQb5c/ffhzomCuRG7FgnCV5aFgZBPpgTzXByy4QP1y+JlF4nKIJ32TUDjaPEBoyTg+3Y9BtEzI0Mnb2dmJff7e/uld67t7+buFttC6G4fSwm68fDN4X3j4+Kamq8vkwLy0df1t7IdJCg0g/KCd3fsnFeLn+Zcw2z0G+oRAp2hSAHSn9BwFnvyTJkrEE4OMREyn9I7+32HxotUtHlqppwYbccqRopaC0vb0dy2fNwmUK/4xoMOvN8Ll9UFG3ZeTz+WCCCbt2bkdZiTtYT1/84PxDoi113MGTkBh0OvHNVavwTRrPTXf89lfAgPQ9CTDgqpkLLAmWxIsIbqRPHA+vtG9rBX7yI+Br3wD++k+ARBuNrRctll7/8X9K5uTKv+vtATKD9xUTDcpvmjV6WD3OkWuGe45cgzSejw/149TwAIqMKViemTumvQiLTxbmzsKZtoMjQm2Sf1j25Elt0kMjlNAgeaT1AA3HhNSmV6+Hw6vwRfHDShk6Beanl6PUlIsBlwWpWhMy2R05Hnz848CXvhQ5ccWOrauvxpadO0X3QtTzXzILuOnDmOowaavgcnFeD3wXA5yQ/ajvOIbXX38dDzzwAM423ve+9+FrX/uakJoJ3bOUpxQhXWtGp6MXOrUWpaZC8XMGUxNCKlSdA6eXcUhgvBmiFDDNYGoj0v6EFeQ01J47N449bAgYl/DG16XfAPMLo+mMy559nBsYkyRSqKUkYLZt2yZim1GPmZ58lJwaAf3cUuIvaJrG8PpcGHLtgBcsNGCpXyvcvkGYtdO0gGYUD7t4iLGx+kxwXHd0dAiVgVjg2I/mqcfuiYOH38CaDYyvpWvQix44PIdh1MaxX44TLEqk7PQMYoDk62/uBw7slzz9FiwE4oghzyU0NDSIfNAMxgauXexWTFSGcbri0UcfFdxBskzJk7rr/8AHPoC77roL9913XxjzSzmACvMoLfsJGtJ8e8HqUReMLx3YiZc7WqBVqUQS7tWOFvx6+XmStnkUkLT4xYkdOESzXvkzlS3EptwSPNu2F232PuhUGmzMnYcVWdPjoqUxrUaVIowfgzF6hYFek4Ec/WL0OmtFRRUloHIN9KMIGTI0BWfV+qk9zE6HvxAr5+MAyQGTphg2j+TxISNFE33MmDXZYeSFGlp4Rdl75M/X4xgMkimQoVP7kKtPRY+TJEH432rVOlFFJv+PpD0eI3lCCZF+hWmU/y/RcwrIUrRWy34kav2oHgg1acXiJqPbMYh/NW8XxqlEoTETN5auG3WMy+CGipUfsapE4mn9XjB/HuoO7oXb7UWFtQv6g+wQoA/MYuCyWyQZmUjwuIFXHwGO+wOEwgrg2o8AKeHnldVTTMaFEqZh4GdffSOw8zHA5pehmbVcMgtPJvg+628CmmqB4V4gLQconR+ZlGk9Bux7XvLRIFGy8j1SJ5ZCYibCGwDW/viPJXUhfCnz4PS+CU+ELiubpxEOjxZZ+mDjVJJ8wVeQ//GQY6OBdyT0R3lciWuLirGfproKyGGAfKR8//eXVUUlNGSYdakoNpWgwuyvWuK13PEaIHtrOLsBWxtQfI24nvr7+0dat5lI4fylV7nQ4ggQGoTF48S2nj24OH8DDh48KCqocuPYjHq8HtT2nYE9Xwu3wy08i7ReNwZODyDDmIFFC0qRnhri+yF/YFX8115ZWZmQZJCDd/HePh929zKoOYy1k2xkPaGgMbeSXODvSvO90fDJTwG1tRKxIUNq55R+f/jPwLXXSZ0bSgz2hxMh1MEdj7nzGMAxekv5cvypfpfYj7i9PiHNuSSjFH9pOIHfnj4y8txrCsvx/+avSJjY4N5sQXohdvWdEBKMhBZaXF+8BJOGNWuB2z8E/PVh6T43tBcuBzY/Jt03pUL1nk9ArdbAG1TRrYIuguRcmi5F3JKC888HfvtbCr0Cp08Dx/xdn9zT3nsvtHPnYm5GhpA/VF6PcYNjqqcFsA4CmflA+jiCXp8TcHPP5AE0efCpScryPjtEs6BCeMGCw9MPh7cfGpUOJk0h0nXnweltFZKjPc4uiKYdP159ZgtWrlo5JaQEeAysXnviiSfwsY99LOz/M/Xp4jaD6QF6SQ24DvqJDcqFlYvbDKYnQtchmp6OhdAIfU3K8wwNDYnEKokO+nTESkgnCsYWW7ZsEZ3fI1K20VA0DxjqAk5vl+6zUGnVjVKx0jkOp7fFT2gQvpHHTD56WZ4bXSocY+wUYgwQC6wwZsdFIklcdl2we6m6OjgWSwT0v2BRImOU8zZWwacKlpH0oCdhCRteN5H+xuVyCaKQsf4M4gD3siunZmfuZICJ+NraWhQWFp7tQ5m24Lr5zjvvCBWU6VREnyioPkFS449//COShaSSGpdccolIIHFzQP3bs4Vdfd2C0FBWFb/R1Y5tPZ3YMIrvxrae5iBCg3ik6Qg67M3oc1lGkoBvdB1Bus6E6tTpcdGmamdhwEVN9wBM6tj6oynaIhH0UpBAHU0mhRccTcEXrgRe+z9JbkFOkTJBnxOlcj0CsnQLoIYOdm+XICfSdFUwaqJ3ERg0qSg3LUOL7TA8cEEDA1zedHQ6OyWdfsVz8wwF0Kq1MCh8NYI+q8aA22etx+buk+h1DsNMvxcVEwBulJgyUWnOxottO0ckqsxaI9Zkx2qvizIZKScpRwfQu01KTpAwylwLmOIj/3IN6fhIxUXocAwITelCI/X546u0ZaUjN27UsB0XrMNQPfcgqrpb4PX60NDdDxXJjex04MR+6TnX3Bn5b3e+Ahz366oTHY3Aq/8Err8r6GkDAwMYHh6Ovx0vLRe46OMSqaE1SFVUEwF+j+UxKm3YmbPn6UBSlSTfrieA2WuBE1vkF5KuGaUclZAqy0n8eNQkyIIfFtLuPqDffQYmTS6MmkCXXKU5H7t6A10jJOvStEZkhVQ7sxOOnW7sZJNzXry/MD12RVyWXieaUOQCYM4ksiSeXkVvJQ3uqpyD2yjbEtfHVFw/9q4AoSGDBOHQaSBjvugQIhkgt8Lq1ZXwaU7A4gZ623tgSkuBySx5Pg26h3CmsU5U6sUy7RtyOfDHur2wenqgGWpDUV4JvB4PjhyuQ7k+Hzetf58UHNMQ3BOSWBfHnyt8beKFOSsDdQ0NQveWBpukV2979G842NUObUYaUp2DuM9kwIX547yepwKKS4DBgQDJwGRycZwVKwy8HvwDcLQW+N53qK8XPN8Kf5MIWLAYOHo48B3xPecqSEp2FWx/BThzBDCYgLWXAeWJ+SLFi3lp+fjynAvxVMtxvNLRBC9suPfgWxgQEoYBPN/eiAvzi7ExN7HvnLJJz9TtgNUXkAXxwIOdvSdxVVHyqvti4mOfkKS/KDnVsA9oPR6QDrRboXr9MZRc/z40WQ+PzJGUd8z3S09NKFavlm4ExxAN6MvKAH+gxoDt9OnTiZMaHF87ngHOyOueClhzLVC9MvFj9NoA+2amlaSXdh2XEmxat/981cOHaqgQICSGXI3odwWkOXWqBuQb18CkoVTTIJwK03Di30+8g1s+8CFMFdxyyy345z//GZHUmMH0Ar2ksvQr4RN7avU5Hby/G5HM7zMtLU1UirNI5dChQ2LejVSQRfJjNNIjGvbs2SOkpuIq8uLnmns+ULkacNkAU8a7gtAgfD7G9+FlUCz4U6jITmuQKKAqQDykBhP+rDDmuOMYVYK5MHZSMMmrvBb4eCL7BnY7yfEIixFZTMG4neOVr2t3748g5Jd4xy2PafPmzYJs4euTrGExIeXXGHPMYAbxgHNoopKDMwjHggULhKTiuSzjtXnzZlFQcPHFF09NUoOT34033ohHHnnkrJIaXY4IHQMAOqM8PvJ3duuIXrwMKor0uoaDnsfE3xlL59QhNXi81POk9E6EjaRZUyo2I0NuVrt6YVTnI1MfX7uo0FEcqaseBUwcr3s/sPMJSWZHw4r066QEs4z+FqD5kFRdnV8DFMwJ69bI1LOyJ/7qnjRdPuZqLxLES7u9DUeHmAAJbLl4NoqNZahOkyaGXH0GylMK0GjtEN8jn5mpS0OluUjIpF1WED1JfXPZhcKPg8RBWUoe9LE8YbjRzZsHdAWSCOLI8vxtnB4r0MOkhH9LQj3hvq2A7gpAG19VBEma8pTEKz2ZcKXEzrjx5mNAT5v4Va1WoTIvE8dbuwPj8tShoGpnVqlw0WPlOZokuaQRMKHVHCzLxMqRAwcOYOPGjYkdF889/UvONnpbwiVMmCClmfiKa6VEHrs2TGlAo6KlvXQJkJ+YJjivH5UvHRrVYNAcxnNs8w8xl3c4iNQoMWXjysJleKPzCBxeF3INabi2aGWYUXh5Sip+sHA1vle7Gw6vFwa1Bt9duBJlEbpqlDgy2I/v1x6ESauCnr4P/qHAV//x4uW4s6IqyCg8HjCI4EKo1+sBd7g8jTjf9k5BavB1U1NTR4IOnbpC6Mi3n3kNxjQjLAPD6GnrFi3k9PVZUFAVk9AgHjizGyd6O1Dp6BXVBiQ0ju04jKqlc5CXVaio9uP5yQFYjSo+ogrwqgFNfJJ8PQ4bfn1yB+qtA0KG6Mrscjh37cILbY04maKDeYE0V7p9Xnz90C68fdG10E2WhNBE4ZOfA77xZWDY7yPBYPHjwTr6o0IYii8BrrhS6sxQEhWswo+E974faG0Gtr4t3S+dBXz+3sD/v/oYcHCb/44KaDwB3HI3UDIxHZuUZXulozmIn8zQqcW1R28s/1GgyRq8N4nHQ8mQmYpdB95G8bzAsZPUa7b1Jn6g9CLZu5vGMcDGCyTvk0jgd7BzJzUEAWpDL/N3zpWUSrfjbwZ7IfH3vg5k60ugV6dgyMV1VyPuR+rUmBDs2wf87ncAu8zWrgU+9amg/w7TWj9xFNi3SzoXmy4BciMkRZqPKQgNwgfseg4orgFSEqyEdHH9dAW/FqUtSeSPrKun4EMpVOxg9bnRT+JD+RK+YQy7m5Cuq4RJk458Qw06HSfF/3W192DP1kN44h/vx1TBzTffjHvvvVfsXWYqAc8NqOL0lZrBDOgfwIpxEsqyBK4SsvwUE7RifxgHmMBmIjvhrnX6Z7wLPDSU0KqzAG9w16wKOqEGca6AcQLHUDxgHEJZNI4hZfKR8QYfox/M7oP7YFO7kKIxIk1jTMjQnvs1EiPsVqIMGwmODRs2BMVLOk0FPJ7gYgSdujJhUpGFjoyzeW0x2cj71LmfIZtnkAjoeUokxeya3oeb3+TEDsyuAZa9O3wmCF5/vBbPZTzyyCOCMwiLpcYB7URUUlGG6r//+7/j3lSEgtXAlF7QjbH6YX5apkiaKRMCvLQWpo/u41Gakh6SDJQIjEigDNWUQMcpYM+zgNsB6EzAyuuB/OBECycWdj3wNuEGr0e54fGXhmedAgr9G8/eJmCvwt2+44RExDB5O07w8zFZOuDqF9+XXEsuWZ8C5eZKIX8mP/eS/BU4OtiAXueQ0OBemFEpCI1YYHdGTVqCGncV5wOUxehrkKSHilcC6X4JKSeTSKE1FnQt7o6b1Bgr4l5sKJnUfhRwWID0QiA3JInXXh+cjBIZa6UBtmaE0GDyl5s8zgusOCkwmsN1y1kFrQCrRWiUFsvIbMqCRGMkMBjKqwDKFgceo0TWULdEcGQUjUn2Rqfi9bQfKgyJa4K+FYMu6SehFfIkwZifXipuXnoGjdLpc0lBCTbmFqLHaUeO3ghDHFqyRwYGAp0dahXU/u/6x4uX4cMVYzNyZRBB3U4R1Ooygjsg5N81gSCLLZz0cpErqbo6gMUF63BMdwamNDOarB5s7XXB5vbhmKUJX7UWozSaEbyQ8nLh5HAPBlrbkbu0FM2HT8Lt8sCQYoTRbApeM3hMmuWAtw7wDQAqI8CqaFXstZGbwl+d3IEmytSI9cyHF3ob8PE5K6DNMEDX2jiyXvFfq8eNbocdRaZpHmDSoPv+B4F9e6SFe8Xq6Mny0fCRjzK6BJ5+ShoXl14GfO7uyM/V6oC7vwrc+XHJWyMjE2ipA3o6geJy4KBfamLkbKuBg1snjNRotA6K7zt0PLDjSRZ84//OSkkT3/nJ4UHkG0yYnRp93BLcJG9tOQqXRpkMlxBKZMbE7p3Aj7/DDZt0NI/8DbjvN0BeSNDOc//97wPPPht47CMfAZSGz5m5bJ0KrCW8btIkUjpVmyVuE4bubrrVSWvV+vVStw8DM5IY7BbijcEFOzZ++tPIr0Ey7Lc/91fs+oAXnga+fx9QFNJ1OdAV8GBSnp+h3sRJDSEpGhgjI/WzYZJpHDFaeERnji9on+3yqjCs6kea1itI8TxDFdK1BXB6rfj38w+JalCuv1MFnMt5TAyG7r47yrU8gxnMYFLBpAQTvMqCkES8CRIBE73RCE0Wk3BfePjwYSxerNhbjwIWWiXLpPRch06dC5NmDmweycRdBT1StcvOKWIykUQsOzVYNKUc45SkIgGxZMkSnBpuw05zu9jLsfBpaVYV5mTG5yfLjg7eKIdFEp86+6tWrQo7Po0qE0bNari8TaJgVKPOg1Y1tjWbr81OjfFIY83g3QuSgRz7HKtrWQg0XkLj218DjhyUckrch3/gduDWqdM5PJEYzUfzXIDT6cRjjz0m9vLJRNJJjQsuuEBM8s8++6xgYBL9Ep9sOYYX20+JytP5abn45OxVSNeNXkHB4MzmccPMxASAqtQ0fG/hCny3dp9I+rDC9VsLlmJO2uiJkVVZRTg/txxvdzeK+/y7j1Quh83ThwMD0mOib0GlxtLM6KZlkwZLH7DzSanzgWAr7M7HgUs+PqGmZfLFFrS4DvYCL/8zOEG9/SWgrBooqwHqd4Yr95/ZHkZquL0OWD0dgphI0eRBpx49Qef2unB48AB6qKUfBbqQBCITtyQyYn3GU8Pt6HVZkK0zi66cMbHOTHKUrZVuYf8XhZ2kt8YEg5+Pm6+YhMbeRyX9WA+TOz6p1XrZ9UC6P3FFc2+7beS77Rq0IFdpGL/yoqANIKuh2B7OJHPBmsukimd5XDDRsz7YmJuVKaFtvdMK+VVARiEw0BHIOuWUAjkRtKNTc6TbGCEMo33D8PrKoFWZMOim4Xdz4OU1JTCqoxsfxyNdRiKj2BRDc1iBIlMwiSJfQ/NizMWjgZv8kQoGQzagNgM0FJbnHuYL0wIE7khXB6ecM2fEeJpTVI3TbRactDRhSw/FnFRQqVVotg3ju7VbcP/yS2FgB00UM2e3zQ6t0YBOlxrmjDTojXo4rFIn4Jy0kO+WAZ8m8SDB6nGhwRosrcVv6PBAp+iQCd306NVq5IzTI2fSwW6MHVv4JUlG4CV+uUKaeF906fhemx0bX/wy8IUvSmMjnuQK35fdD/f/AGhrkh7jWNV5AK3y7ynzFlu7mx2E0q4hwaq5CBWgvHaURRfXFc3CsNuNi976N5x+qa7by6vwzflLo65V+/fvR/G6ubC7gyv7CK/PDqvbjhRtFCI2FPf/Qgo05GOiZNjfHwbu/nLw87ZvDyY0iIceAi6/HJCrbc+7BmirA6z+7hzu5S66GROO48eBT3xC8k8hKDnxhz8Azzzj97rykw/8+dpr1EIEMjJGSPoR/OX3/uf5xwS7gp96FPjUPcHvl5oVTGiMPB4j0eFoBYaPUmMK0BcCaYvDSG/xLfCxoMe5x5C+T63KKLpu6ZHm8vrQ75I6dAbcnRh07URN2ioh72XQmEV3zEN/eBhf+cpXMNXwH//xH8K37/Of//xMFekMZjAFQDKDHhrspJDB+GIiqrwpRUtyMxJIpPA92c2h3PeNhmSTLuc6jJpZMKhL4YNLdD2fi5X8HEdc32MV03GMKauMeQ0wNiGhZvc68XLH/pHiFLVGg0ODDShLycXsOFQ+KJtLbX2eX0pOkWhgFXwkso7EhkbRgT+DGZwNcOxz3qcVwbjnhbdelwgNQt5rP/JX4JLLgIJzQGY5BtihFY+v53TFM888I3Ix5AymNKnBRYB6t7/73e8SJjXe7GrAs21SBQBxfKgHD5zejXvnbYj6N0+11OOnxw7A7vWgzGTGz5euQ01aBt5XWiH0xZutFhSbUpBriB2o8yL8j8pluDi/An0uO8pS0pFnMMPrK0WmPhVN1m4YNXqszpqN7BDN+bOCnuYAoSGDBr+UvClJPqlBeRYv+P1IfiXwFUKNeVKVRjerLENIC05qXS0SqRHJMNxvcC3D6bWgzbYDXkjJ9j6cRKFxJYya6FWaR4eOCEJDqjr0+++KihKaAgPVqXOFl0Zin9OHF9v34+hQy0jnx/y0ElxVuCy5Gzh9HqDPlcyN5aQXOzSMyZmw+TnOWOpwavgMartroeszYHHGQnE++DlIMoyKtlqJ0HB7AnyUbQDY+U9gw4cl4mzDdcDzD40cf6/dhbnzF0tkTs0SYGlANorGZrKhmjDzK6wGbvkCcGSnZBpOY/HyYOkxEqR9fX1j0smdEmBifMOtwJldEgmZmgtUrQruZknSdz3kPgCXQhPdrJ2HFO1KuLwWaFUpMGlyJj0A2ZiTh+uKSvFsW/OItN/7SsqwNnvsi3XQZyARU3Qp0LUNcPQA9MLJWwkYAuQQA1u2gzMw4CYhK0uaTzocFpwa9oSZpLMTpc46iHlpkQkgdhBuzCnHK/2H0Wj1QjfkhP1kM/Q+HdZlL8bs1AS7uaJAr9ZEUC+mB4kW7y+rxqsdLailwbUfanjx7/YmXF98lk19SXDXHQFoIp9bAhTNCuvAEujpBv7fPUBfr7+jRQN85TvA0iS3GCd6rb3wGNDuX+MIyxDA7hedosKea90cv4RSBPhAopcbcibpmUieAxVieyXZPW7xvVelZuLyggq83FEviisYGK/MKsB7i+eh3jqEQqMJ5aY0XPDWiyOEBvHXxjNYm52Hywsjv9fy5cuRW1OFns49wtRetunQq1XQqH2iezEuUoOfn9+bcs3ncbANKhT0o4gEPi6TGunZwC1fBuprJWKgbM5Ip8aE4kc/AqzsePCjtxf4xS/InIbvZwi/CS2TFyMJM35uWSpNBh/rjyDnRQ+mxiOSDJWM5ZcB5lESEs5OoF+WPuObn5Z8g8zpgIcmywoEEW/c9yyFyq+vzX1armEZuh37MeAikRuAxTOAdvsZlJgkSdBt27YJ+QzKPU018JjYpbF9+/aZCusZTAq4vzo5fAKN1gbxe6GxCAsyFo50gL/bQZ8met8pwT17U1OT6KxNFvge0QyLuceTk9BGo1EURMVDaozFWPzdDq4lcclST1MwRqB/CwuoRgNjaJIa7BBifMHYZOHCheJnn80S1m3LvVy3YzAuUoPzjEysULGAJAc7Nd714L6M8UVfp6SEwPwSO33PQXJtuoH+MVwLhLz4eNHVKcWEofMzpajeBaQG82Xn8vX+u9/9TnAFyVZhSTqpQXzkIx/Bd7/7XVEVW0X95DhxkNXMCnBBqB3qhtvrhTbCB9/T143v1QZ06FtsVnx67xY8u/EKGDXUXzaIWyLgYlSZmgVlHb9apcKqrCpxSxrYVcFEriF17JNxtA6WGJ0tY4UPdcxEKB5po4U4NJgPRDIM5uLDykQitwoYVHo4qICc4G6JXucJeBVyTKxy7XYcRWnK+qjHREKDi7+sMy79nZSsWZq5CCWmxBOM1BYnoSG9lrQp4f3FGWWiyiJpEEbq5wPDxwH3EKAxA6nzpMruJKDJ2ozaQUlDm9JCzbZW8XlWZC0TRk4xdewcrF6lpE+EDo6D/5bkqIrnATd9Djh9SIxjjycFJ3ILR5LHQhrM6xUMPmUsGGTQ/GhEVzS3GLjghsjvbxlA7vE3cWrnLtSwPXzZZQAlq0LBJNjxt4C24xKZQgO/8mVTZ5ND2bE50YnZZMDhbQsiNAiL5xiydOcLMuNsgd//b1eswdVtJThjGUK1OQ3XFJWMm1wRPhZyJZU2BSi6ZNRjWLRoUdjjWTozNCoSiuHJS3qGjIY7562F3ubC4c5mpGaU4fKyeciu1GJ2WhI2cwry5JqiGjzXdlLyHaEyvlqNywqqxPpWYjSizt+gQgJXq1bhvuP7cWVhmUiMnxV0NgGP/xaw2gCnnzTlsVx7B7DQb7ws49G/AgN+UoYfgpvXB34F/PbPOKtoaQiupmeCmp0kNSuBuqMA9xQbrgJmh4+pQHcGfRPkZDnXtKPwgdJkkYPkLscw/lC3C232IdEJel3RfNxVuQTLMwvQZKO0VArW5ZQIYnCuX0bzQH+v8NhQQqtSoXaoPyqpwXk5160XJH2LvU3wNEoYNXFqm/L6nVUBNDYEG7pT9zYUfiI7DKHJLmMKMG+SN/FNTYHjJzgG6+uBD3wAeFwhlynjQx8SxMfRzEwsuO8+YNsW4Le/AlzuYEKB56cmgsEfz9Em+recAmyDQGYhkFMiyYfShyzSvGhrCDdndTQDqWup6TdSyCH+0kdvoPMAFcecOSzxZFBno8C4Hh2Ot8Pfxh3wZ3nggQdwxx13iAB1KlaF89h4jDOyMTOYDNRb6lBnOTNyv9XeIuLChRnxSRyd6+A1GerRx0RssouRKMfDTg3Kj1KCSvbB4GPNzc0jZuK8xWvGzGpR/j0LqKKivxU48ipgHwTSC4BFV0yoGsIMzi5olE01gVikBnMPjEEiSTSmacPXTua00igTHgc4fmtra0UnEYtR9u3bN1KQ9a4F44RX/gEc3wPYnJKCBFFSBbz/U9Ie8lyBKKqhlOj0kd7Oy8sT1w3R5ejHof4zcPpcKDXlY2F6RWJxf9XscEKD3ffFsYvDpjtkQvNc7SI8ffo03nrrLTz88MNJf+0JITW42bjuuuvw4IMP4sc//nHcf9fvtIvrWJli0qso9xT5Qtje0xFk7M0Fo9tpR71lCPNi+GecNTBZcuINoKNWup+SAyy6DjCmjU3ahjJArKaXTxqD5NyJkcbyoTPCo0ykzgdyi4AVFwB73wpo21cukKrviYrVUpK85ZA0UedWAAuC5UXc3oCM0chjvnBzd5fXhXpLPexeu99/JdKx+uCOQx4kEobcPI5wvN55CDeWnofUeOU54oFKC6TFZxqcKFrtgSBDlqpptbVjeaZPEI5KY7OI4OY9UrUqH+uqA9pOA8e3AhtuAdZdJf5rgf+9aNjH9+DEzIWMmz6SKNS6peatkev03tcB+gWwmnvuyuDF22kDXn4QsA9DPczK7wNAdzNw5SckaRIljr4BNCrMV2tfBZicK42cdDwX4RX66uF1/R6fHWrV2ZUkYvB/fXFyuhdksBWblVFszx4rLsqfgyODHWh3WEfmEJ7BmtRspLLjI8Znum3FppH7O3fuROUEVFXcVDof+UYzjgx0IUWjw+WFVSii5wplqAZ7oJNd1/2XJee9QRcN38/SZujNJwGnI0BoEJyHn30YKCwHchRJjs724IQyf+/ukX6eTQ+dnHygrTFwbDzHmTnA9R+R7g+0A/ufBV7aAujNwJIrgTxlwQPXLEX1v/QiTPNQPC3s7biGPXBmB3oc0t/QT+yp1iPINZixKrsQqxC5qq/AGL4OcS9UNEpgxwpWVhfqRdJbniuk/VWpKQ85+gRk4b70deDbXwX6+qT7c+cDt9wR/jxeF7ffDvz1r4HH6IeQQMHLhIGEy5EjgeCJQcTs2cCSJeF+T4Q/cedtaICOniBm/3VGVpGyixr/uKUPzHuiGGxznSvxJ9zajgJv3y9dI6w6nHspkBef15BPnQ6vkOeUOl5VHhXUmhVQqcKJf6fXjjrLQQy7+0QZCm/KAhKOAYPfh4idkY8++ih27dqFqYqPf/zjQrP5F7/4xUyiZwZJR4+jC12OTuEzw8Kodntb2HPa7R0zpIYfLFZioZSyo4JrzahEwRhAkoQ3vhd9+Sj/w3iD5CvjGRIb7LxIdF84arLN2g/sfMS/RviAngbp/sYPS3HGDM45sPOCnT7jMTsmebEuew62954YicxKTNmYmxZfUpYJTVlq6lxOcCaEllPAib2AXUFoEK11wCuPAdfdiWkP5giPvA7U75N+L5oHLLtKKo6cBuA47XYM4LnWrSIe5bhvsXXD6rFjTfb8+F9o9Trg+vcBzzyhkBP+miQRfI6Dc865fL0/+OCDuP7666N6Y005UoP4xCc+gdtvvx3f/va3xeaGGHBZ4fC4hHRTqDHzrt5W1Fv6RY2jMozMHyXZn6rVRTRTMXPwT1U07wsQGoS1Fzj2b2DZzWOUtrkNOLUDsPRKevzVa/1mlRMBzeiPseJ+1lygu03q3KhZFkhO8ef8S4C5F/r1zcO/I4M6HS6PJSjZYlBL37/UjeEQUgrbe7fD4rGMSENF23Kox9jxkGeI0t7ssuL51l14f9nGaaEjqpazncrHVGohncHKqpifIXc2UL5Ckk4KhVuhpX7wVeCSu0b+i68rKoIVeoDUWTx69KiomFdRlubJ3wCDlM5g4sgLdDQAF9wUeP2WE4BNIenB9xrsBjobgOIQf4KWw+HHx8feRaSGRiSyQudCEsJTr9I2GeDYYscP21zlKjulSaUAE4WRZO/8SNeZ8IWaC7G1ux5vdrfB6vbi8KANPz/Rgf86/iw+UFqKP69ZE5chulyxlWzwWrogb5a4hUKQ/SGe5LyblWB3YsLgtch1i/KBqfnB681QnzQ3hP2NF2hvDCY1KmcDx/wJZYcbcPmTrB+9Q9rQFhUDV1zFMtDkHPNrLwOvvyIlrq+4Bth4fuTnXnUTcPqoJDslZLG0wI0fDpCtux4DXP4EjtMC7HlSSnCM+OFEGi+8NiOPo36XDV0OrnsS7B4feh0+fPfIDsxOzcTnq1dENK4vNKbg7poF+NXJ2pHijmWZ2XhvSQS/HkWyqWBWMZr6O0EBCX8tmFgp8vQZia1r5RXAbx8C6k5J3StV1dF9S77wBeDKKyXJKRIJJA4mG6+8ADzzL4l0W7UW+NAngG9+U/LUkIkZVhZ/8YvSmBzFqC9VpcJQXx8ydZmAXiv5TQ3ZJHLjWz+QXj/WuRxoA46+JBEh/DuPAzj6AqC+DshRSMgZywG75Okmw2cohlfFAhHXSDelT8sf4e4tkj/YXtg8UidGgMwIkOAGtQlFRolk+tOf/oQVK1ZE7G6bKmCyh9WrPNZ77gnxLZnBDMYAt88tJHZJZhwdOjxyJbXYmmCM4O3HjroZSGCXhrIrg/fHU3ASC+zQKC8vDzP85h4wUR1yQfKPJlPVeVpSVQja//RLPnnZyS3WmcHUAccvPciWLl06Zhm11dk1KDJmo9MxgFStAbNTi8Y0b7DriVXw73oM+Tu7lYSGfE02SR0C0x4ntwNndgfutx6TYqwV12I6gARc7WD9SGwh4/BAHVZlzY3Lu1OA++ePflKK1Xq7gdJyIPvsqU5MNs5Vo3C73Y4//vGP+KuyyC2JmLDs/6WXXoq8/Dx87X9+gKtuux7Dbg8abZLGcIpGj/eWrEW+MVAVuKevNWJhXKNtELWD3ViYET6hX1dcjr80nES/yymqHYnLCkpQmoCR7aSjX6HVLcBkLf0ovGNrM6PU1PwoyZkkQ40KeIVOeAAqKIJvfoHszuAt6otETxBmG+bAaR+C0ysls7UqA3INC9Hv7MPhgX1w+Zyiotrl/65laSj+q1Np4fJJG08GIwa1HoXGgjGTGhflLcQbXUdGHtP6/Te7nYOweZxI0U59Q94Kczk6HJ0j3RJEpblCVDOxWyImhITG+ZJ02P5npIQeH6PMhtPNsmDp7AuZqtEnZ7bRCkKDf39yLzBAQkOx7B3fDSy7EMjwByT+LhurwwWjTjFNReq+iZg8mvqkUzKhVxdC7+2E0xfopjJr5kOtmh7VHWMBK3VfeeUVITnQ2dkZnIRr3YfeI29AL2SQDgJzrgIU642MFK0elxbOEbfrN2/GG509I10bjzU3ozI1FT9etGhU0sJqtUJ7Foj0a4pm4R8hG/krCsqidjaOCV474KYspA/Q5gNeLbD/n5IMA0HiePENQLpf4zS/VDKMjoTQxPzNtwEnjkqV8jKhQf+ehibpxs/x9JPA/Q+wdG58n+OFZ4D//U3g/r49wL3fAC64OHKnxpd+BBzeKyUz5i4Gcv1da/T8YkeF0pCZazerN/2kBs0zfaK7QinHwfERLlFAGBSeT26vDx32wMp2cqgf3zy8GfevuBTm0A41AJ+ePQ/LM7NxaKAP+QYjri6i9Jh6VEmDrXu3g/YeSk6MayYdsxIGCaeFS+J7LjsDY3UHThS2vgX8+YHA/bffkCSjPv1FicT42c8ks/C0NO66AcojrlkD7NkT3gJPqUy3G3MpfRJ6rZHcyC+MT/qwtzHQ2TEylnzA6beCSQ1DAZCxFhiuBbjHMRQCqSQgFIGv+HOSVN1QIbiSzeVzwOYJ8fzgs3QFSNNlC18AvSoDfS4rjD6X6H7gbaqDZMaXvvQlfPazn40tpTmDGUQBpVnrLEfQ7ZRiM6tbuhblWZi/aSPEZhXmKLJ670K0tbUJIpSIx2A52eD+j3uwsRirsutj9P1blLn83RVivOuQk5OD3t5eIZNCYoOSZ0pig+Oce/9YKE3JEbfREOuaoZ/GsmXRPdzeNaBcNRGWLGSn7DkiB6fwFZbgA9pPYrqAHQYWq0WxfkrgfeZpWb+TEErLpNu7CCw+Y7fYuYiHH35YyM+TI5gITFgmhsnLL3/py/jGD7+NmuvXwqmoGmdS+OnWXbir8pKRZGskzwwZPc7IC0e23oi/r70YD9WfQLfDjkUZWfhg2WxYPe6ICYApAUoMhMrEiBbWqb9DUqnyofYtg1cYhfugRhFUqgSJA6GRQhH48O9bo9Kj2LgWDi8TZj7o1elCiuPgwBZ4/IbooROlDJMmDXlaE2weG8zaFMxJq4FOPfYxsCKrEr3OQRwdbBapAmUFa2iX0VRFvjEPa7JXYV/TfhTnFmNB+jxUmStwquNUYhW5Q+2AxgOY9AqvFCMw4E/uRatWYjXzQDdONLZi7sIlgU2b3RJZ2oOPy6RG4Wx41VrsqmvA2ip/Bk5vAvIjVCGXLQPqdgY/Vr4U7ybw+0zVLoHb1wevzwGtOt3fvTF10WkfwlOtB9HtGEaeIRXvKV4y0pnn8bnQbjsMi4dJOhrczkaWPrhSioEoq3XZJl5SUjJi1Jfu6UXPvueQmZaC8sJswNYPHH8BWHLLqMnGN7q64FaMSaZ4/33yJD6k1QpJA/pDhXaDsOtpx44d2LhxIyYbH69aCJfXi2fbqLnvw1WFs3B3TZwJ5njARKhli78anNAAjfYAoUFwXj78NLD+k9L9i24CejuA9lYpwSujagFQEaJxTfPt7/0M+OZXgIMHJLknZZcHvwtW9j/zFPDB28b2GRh0/vNvwHNPhf/fU/+KTGoQqenAugsD90lu7HwyOLhghb68Q6cfQhBI7FN6gx0A/L9KWrxHfCuzVo+NORV4p7sOVk/wCkdJzT6XA8eHerEiK/Jae15OvrjFA3bNmtQGpGmBYbdNURjgQ4kpvteYltj2dvC+i0TUjs3ARVcC3/lOQGrs+HHgfe8DbrgB+P73gZ/8BNi+nU64LOkVhIfV5RKeNirKZ6ldgM0q/T3nliXLgfI4KzplOQHlnMTf6bcmjtEH2BsAdx+gNgLZFwJqeQ22xd0NFKljk9CpDcgzlImquh29b4nHtj/1JnR6HW7g55/ieO9734uvfvWreOyxx3Drrbee7cOZwTRFq/3MCKFBhJr7Emq1BsszV6LJ2ig8k2gUPha/vnMVSnkcJmQmU/uf+z+ahM8eQ/cfC7zY5TtqPFRQA5x8R/IS5JzM55qzgYxz36z23Q4W/3V1dQlvDcoynjhxQniwkAgjocGCqvGAxVKMW3j9sFsokgctO9IzMjLiMr0/55FXAmy4FnjraclTgxDVOWrgoqm/Z4kLkWSmpmo+M0qMUWrKQqvF3/3sL5oqNGZPm9zZ2QaLgNewqGqi4R4G7PVSsZS+SCqgmkBwnvv5z3+Ob3zjGxOmdjOh5aUMNL72ja/jwGs7Mf+SdSOPc8s47LbD6nHA7PcnuDCvAtuomR9hQ1k6iiFXvtGEr86TEphPtTTgvNefg9XjQZnJjN+sWIe5aQloRE8GylYB3WekQSRL71RumDqmxjGgUuUyxTi2Pz6xC9j3CkD5obxSYOPNgDn4+6GGrVET8EMZcPWPEBqEOkqLVoejDz3OQVxesAkpEQy6lM8/PtyCVlsvjBo9lmZUjIzBUKzMmo3Tw61C1kNO/ixKnwW9orJ2qqPAmIfLqi7G3v69mJ06xsqyIVZqKyB3axDpecCyK8P/hnJSr/0VHd3dyFapYHB2AGuvkf62sCLYiJfXAeVLlEk7cwb25CzDisUmGOm1wk6tdTdIxEYo5m6SFn22aTKwon8LdSj5Hl2UseqTqvTzacJ+7koGcJHQqUY3tpsqsLgd+H3dVtg9LpFEsFr78GDdVnyh5iLRPdFqOwirp3vEeLnTcUyQnum64ECSmoxbt24VAS0DaZIPw4ePY14FCVf5WUwO9ktSQYboGs9ZOh2G3QqZgf5+9Ok1KJpdhUy9AYcOHRrRuCXoG8PNx6ZNm85KpwYLAe6es1TcJgR2yiQqzgdla4ZD5gLC65aMjtm9lpoB3HYv0F4PnDhE3TnJb2np+sjXHq/XgiJAdTDS0i/9TY80DhIGk9Bf/SJw6qS0cISusTT/TqQlPLRaipX+Rj1gSJMSH8rDFm/IantFxf0oWJM9C693NcNKSa8ISKbUCdu/L8lfg3e696HHOQC9WofVWQtQYJwec8eYwGAq1HKI53Tzlsh7r6efBtLTgZ//PPBYezt8v/kNdu/Zg/Xr10u+IFYL8Lc/S2N0zjzgg3fEv5crnAfUbZP2gsq/oaQbMbRPCjjkghdKUGVdDIhiDe5Z8vyeZjK0UEXoBtKq9cjRF6PH2Rr46FAhz1CKbkc/dvTWjuyNXvr947j3ni9OCz1fHuMXv/hF/OxnP8MHP/jBaSEJOoOphwFX8PrCkR/am5Wtz0G+MV/cZhAOFixx78VrktXtFRXxrXvjBecsVrEvXJi4LyGT0iQ11q0L5CYigh5m626VvPtsA0BGITCf8/DUnyNnkBzT8NbW1hHSjAQauzjGSzJw7NJjkjJXfC16xLDjiFXM8v/T14oxTqjU2rsayy8EqhYD9ceA9ibAYAIWrALyzxEDaUrI91BuVLFhrTkP0wWUIaQk9Jqcedjbd1IUJhcbc3Bh/vKzfWjTApRuJIk54Xtw9yDQ+4a/fJMVmmeAtOWAaeI6UJ955hlRhMD9+kRhQjMxnKjvufsL+MMf/xJEahBqIREUYB+rU7Nx79z1+HP9ATTbA3I2N5fOR1Vq7KqPfX09+MahPSMxa4vNio/u2oxXLrgCpgj+DWcNrPBYeQvQRi1xlyQzkD05G8CzCnok7HwucL+7BXjz78DVnxg10axV6cIMerU0AZcLLoUGud8Y3OdGvbUFC9Krw5UeHENotnWj3tKJBlu3GH/826ODTbilbFNEYiNLn4qbSjdiX/9p4QVTmpKLJRkVk94a32bvgt3jQLY+A1mJGLn6wcmRDOmYQTPc0IwQiZ2L7wLSc8O/P47r1/8Gt92GPosd84pzgeM7JCJr9jKgpBpYdw2w4wWp8slgBC7/EKA3BnlweNPzkHZZHDqSfP/q9dJNBl/3+EtA7xnp/0lw8Pd5fmJFhmMQ6G+QnpNVCeiSoN8/SXB5h+Dw9ohOBpOmYERqyuGx4+TwMSHbRpKwwFiMKvPs+LUsQ+DweHB8qE9ce3PSMqFPQjB32tIdlMDl9WvxOMXjC9MLRwgNJYbcHWGkBpGZmSkCaVZTcaxnZGUD9qZwgjyGqeNPlyzBrTt2SHcoQeNwwFxejLv3b8Wv5q0U76PUhSShwQ6NczaZJsznQ85htN5hJdFLgrG0RrrFg/fdBLzFzZVTkrVTQsg/jbEa7uB+4MRx/x2/obryu9qo6MQYDfRlaaR/QQiE8+NiYM5GSQZyjHCzeuXEdljdbknmUHHWuU4Vm1IxPz15hAPXgjRdCq4u2uBvBz83xi/18H1wQgU9VPzOdr0AtJ2W1pVVi4E9O4I7BC+7GvDqIntnCA+W14DPfz7wWGEhjt52G+Z87nPQygZ3GRnAl78e/vcDvUBfN5CdJ/mLRYLOJO0FDzwOuO1+qak0ySxcrqCSDkb6Qb8xEhsps8Wco/Ythg/ULu4XkmcqdgOpIhdozEpZCIPajCF3D7QqSnNWwahJRb014NVRu2U/Bjv68ZGPfATTBTzW73znO3j99ddxySWXnO3DmcE0BK8HJQwa7nlU8Pivu0JDMSpSwiuoZxBAaWmpIAgoz5Oeno7BwcFJ6daoq6tDJT2axgB6/LHTNy6k5QFr3j+m95nB9AYJum3btqG4WCoYYKdGMsAODJnQIBjD0OxexsGDB8V7yyTHDBTIyAGWbgDORUGGgtnAug8ADfulDuCS+UDpKJLuUwzsYqIc6OKMMixKr5I8+86RGGOiwbwXu8HOP38SLAUsjI1DXKyHD00oqXHffffh7rvvntCuswnP9n/yk5/ED370Q5zZdRhVqxeJIJ2h59LMSnQ5hoXciFyFOC89Fz9Zcgk67MNot1uQbzCL4O31ziZhCr48Mx+6KDJVW3s6xYXDqnqC79HjdODU8CAWZyQvIdDrtIhbtt4sbmOCKROo2oB3FZqPB5LLBH/2tQO2YSAleidOqjYNufp8dDs7R4zB07RmVJsX4c3u7SEpN37/wTVWLbZuvNy+bySByuerFC3mNo8LhwYasC4n3NTO7fUInw4SGbmG9DEnhcdDaLzVtQudDvpPSFiZtRDVqYkblvE6YuWHnIBV/h4T5ewuOgW47P6Mmw+oof9FlM3WMKvibdh6qgl5qSlwuj3QU/OaRBZJDWLJJmDeaklyit06IcSjvOEbMwZbJRJDfFj/mOtrAPqbgCx/1ctwO3D8OUlCh2jZBcy/ATAGktdTFXZPJ/pcB0YWpGH3GeQa1sLn02B33zY4PA4pf+f1ocFyChaXBUuzEtdk7bRb8eWDm9FmlyQAy1PScN+SDchSEFBjQbidrfy4XJccWlYdXUaFgQAloM47z1/NUrAQ6KyVyDUBH1CwWOokGAUfLC/Hrt4O/K2xAV6XB1mZeqRogP1nTmGH2owrVgXaQffu3Sve75wlNAh1OuC1KL4HFVA6CzhzOvh5ObPHV7VIw+lf/y/w3NNAcxOwd58glASuvwG49LKxva5NIdEjS2FxCHFDdf37gPfHUS1CD5+3/woM9foHpyq4TXzR5ePusqRJeK/T7ncYUiFF44PTK8lSrcgswIcrFyWFSJQDDraHy5j0YKO+HmhqBEpKgQhSC2OF29sGh5cdB5zrtUjZXA8Vjem5VtGEfahHMgR/8x1pbNGI/robge4e4KGHgEGFpJoMxXkiWH3GKqMFC2IEmNtfA158JCBVcu2twOoLoxe5nPdRYKjTL+uYJ62FLsl/LhgqwOsI6mpVIb5zyOcWmapQFPJ8dsrZ3FIf6gv3P4bPffazYRJ7Uxk8Vnpq/OhHP5ohNWYwJpSYqjDg6goQySof5qcvQKY+X+w5xiNj+24Bq8lZVU5Sg74Wp06dmnBSg52yNHCmaXiicxbjH66F/NsZzGA0cI9PfXt2aCSL0CDY2cQOb0rYchzzepHjif5+yRB7htB4lyK/UrpNQ7CrSZZO4ng+hyPkpGPPnj1YuXLl5LyZl7FESEEXu8bluCXJoDcR1S6ef/55TCQmnNRgG80X77kHz97/L9z+zPtg9bpwsL8dr3SeFrciYzo+UrEOZkXCqcCYKm47etrxw6M74fYnJeelZeEnizfAGKHzwqzVRnSLN8eozk0E23rO4OUOqVWfuDR/PjbkJq7jec6jrwloosmqSzKZLl0eXRMwxvfDSXFhxjK02Box7B6EQW1EWUqlMLc0a1Jg8QTrghcp2sMHXVY837ZL6M6PvJ6C2JB/2r1y8jOAIZcNz7Rux6BbSubm6tNxXfFaIVk1WThjaQ4iNIi9fbUoMxXBMIbjkDdMbBVXauDGBCV7Vt4KdBwDSA6RFMiIbHorYKTEjwpFGWZUF2TjeFsPCjJTkZUSIv3DxHiU5Dh1ecfSUj4CSg1FgssijUtWlje8EyyDxc/WtA2ouQpJAV97goiwARfnIaX/gxND7tOwuzPh9EqEhnJT0eFog90zD0ZNYmTEL07uR6c9kBxutg7j/lOH8M0Fq+P6e4fHjUG3E6kaHV7uOIVTwz1I1xlxaX4V0rQGWNxOQTAyPZeqNaA6NU8k4LJ05ehz0SsigEx9ZLMwjmcy/wwOTNS+N6QDi24G2g9IFdBpxUB+fJUu89NTUWnWwJOSDkdbB2wNzdCmmVGtqKDipo3t52dDcmpSYVwEWAcBr79zklXghecBKYuA+m0A500SGuVrx/9eZeXApz4n/W6xAI0NQEYm4K+OGxPmL5QS00xiCy8nv7H5b34Xv/FcRx3Q65ftUVb58/dlVyVl80ejcJd3pAlYzJ1GDXBDyWy8t2QcxG4UyQ1qiJ8V/PUvwP/9NnD/o3cBH/nouF/W42PH2uHAAy47VMJnRgF+TzoX8K2fBD/OKsg//xn49reBQ4eCv+cPfzjoqdyQx9S5bWsEXvhn4D5f59m/A7PmAPlRxjLXiHR/54cMTRrALlWfcm/iA/RjlP6MgBZbL97uOi4uixPbDqDx6Bnc8+I9mG5g5dcvf/lLvPHGG7jooovO9uHMYJohVZuJhennocvRDC+8yNYVIFNPabcZxAtlkZRcQDXRYGU7K1rpdXDs2DFRBBVvkUljY2OQ6fMMZjAaGIsy4bh2bRL2un7MmTNHdGMwDl+0aFFQweGBAweErO0MZjDdIHIO53Kx3wSBpCkLzibNIFyfB7g6FQ9QVj5rQggNzmnf+ta3hFwsOYGJxKRkZb70xS/hf37zPxja34jOOZnodgaSZB32ITzbdhi3lAWzU9Rh+6/ju8VPGSeG+vBEyyncWh5IMMl4T3E5/lh3Er1Oh5BUIC7JL0alOTkBfId9MIjQIF7tPIoqcy6KTJG/JJ/PBbdPqrjTqrKhCpFSOmcJjYNPBlfM0/iyZhVwYrdUMStveGcvl/QQY4AdEmUp4bJP5+etwfaefeh1DcCg1mNZ5gLkGgLVQZSborRHKJQ14Eyolphywp7zZtdBDLkD47THOYQt3bW4pCDxavexwuK2jnSnBMv02KKSGuzu2NpzCkcHW6FVqbE6uwqLMkqDggxZ+zYh6FOAshXxPZff6YpLoHrxUajUGswrzkOTQ4UuVTbMLS3C1Hk0UFeUUkLjAiteQ6v9ebdzP9CyWUpuirGhDL5Y1TuEccPRB7S9AzgHALUBKFgHpJUnVWaFJEbIo/B47SIojwaP8PFJDKeG+0dkGOTr5SQ7ceLAy+11+GP9IdE9p1OpkKL1Qaem24AKRwY78NnqtYIoFh17hlRcU7QIRj/JmWeYC63agGF3F9TQIltfCZMmeuUfvS727dsXCDqM6UBF4kHBJfkleKiOiT4fDEUFUHu9mJORjQqzVKFF4oSSBxs2nFuddjSXt7hPwoNhaGBGirYGGpoTm88HPDR88wE8/yotkGkGlsVJCowF3NTNT0K7dU4O8MOfAj/5PjMgQHaa1Knx/+4GrnkvcPPtsTdw9AqRwbVLfvq8jUDZOEhXBQ4NdoVdtezUWJ+dfI1gtoU7E/ESSRZOngwmNIg/PAisXQcsGN959PoChoQSonyn0b7rsjKpW+P114Fnn5Ueu+464OKAiTyve567mOtmG2XvQuGTtJ+jkRqRwOrwjPXAAD03+H2pgNRFgD55UhS7ek9JOwp6afzqb/jKl+4NktibLmCF65e//GURNL3zzjvvroDaN0Cqkj0rgGqK+QdOI5i16TBrp4/Ex1QDux5kSQkWJFGCarKSZ4wVmAgisUHQ+yCWvAVJDUqHzmAG8YAFTFz7WRSSrE5GIdGzeHHYnmL//v2C5GCx1gxmMIN3B44cOTJ5XRpESg3gHgIcjYFCqoyJMSd/9dVXxed7Vo6vpjupQWbm3nvvFUHHe/74vRHpH4K/N1lDg1JW2TthpZ52EFRooVxRBGTpDfjXeRfh92dOoNNhw6KMLHy4oiZpAU4HtfejPB6J1PD6rLB6dgmNZ+nIKWWyGmrVGBZEGmtvfgqoPwro9MCqSyXpnqmI1gja4y37gcr1wFUfA45sBihlU1gJzFd4IIwBZm0KLinYEFVKiUl9ZXFtJKzJqkG1uTCCJMhAGJnAxyYT6brUoGMgpIr26GPora5j2CnLLgF4vm2/kHdjkMGW1slKWngWbIR6iEa6PsBoRln1ctGVwaoqehFUV1dHDDyYcKPW7bg1BU1ZQPXFwOk3/N0YKiDFLHVqEJScEqSGkvhQAeZxVsKyer35VcDjCLT4tb0N6K4GkmTEy04GjcoMjy+4G0WnTkeKNkcQYSS3AhVz9C8ywqRJfO4pMKRg0CWZecvjr9AY+3WODfbg93UHR+67fL7/z957gLd1nUnDg95JsHdSJFWo3rtky3KvsWM7duJ14vTq3bQvbbN/srtJNr05ySab7sTZOHbiunbibsuSrN67KFaxdxC9/c+cCxCFAAmQAIuE8QNTAEHg4uLec895550ZDLk5TkuFYaqnTg314P6q2BdRsVhVV4tbIuCxxFuERJzfhaVJUuDoCwHt+N9tjTELP1u9BT84eww9TgeWZefi83UroJDJxML9rbfeuuQ6qPx+Lwbd++EDSVw/vLDA4+5Htmoj5CTilanrDk8beJzv3AFcvAhUVgEbNkpF7KXLgD89DnztX4GzJ6Vz3usFnn4cyMsHrh5HlZVXLllr0YZKvE8gdLpyWco2vcE6KI6voHVm8G1c4SqyGLB7PWiwDsOsUqNUl9i5zeIPLZSmGv6GC3D5fHD6fGIsyKMdIdHQMGlSQ4aoZhGVAp6KfChamDcUFr5VM05DAkmMMCIjHPv37w/Z242FePkZ8R4fC+o8IJ+5H3aAvv9RNjgen0uQvn74YFTmQyUfv0EkHC6fW+yW0zsOorexHZ/65CcxWxFUa7zwwgu4/vrrcVnAf45+bmH3qwHZ6Dy5DDJIN6iQZcGXYODxvHkJ5mmlCLSRWrhwoZjv0vqKhed4zVMsrpD4uKzIzwwmjeXLlyc+D0gQ0YQGiTmuX6gEzyCDDBIHm3WZdcl1Oq9HwrVhloCNvDzvSXROGWRyIHsN4Fsq1cO4fkijSoMcQLpVGsSU+Wc8+OCD+MEPfoDmnYeRu2FheBlR2JFEI0tFEkApFu7hS/0yXXzlRaFWh39dlJ7koGxl7BPEzLDHGHB4T8GPkHUA/+3wnoZemWC3ezhe+QvQcEwq3DCD4NXHAJcTKK0Bcop4ZcSMgS9GN7gozvgBcxGw+c6Uv2W8yekcQxFMSp2wkgovDxVqsnF14XIYVTqowwNuw8DgcKcr9P2xUGyMcwykC1X6MrTbu9Bi7xjZhnW5y6Eew+P38EAo+DOIIwNNuHPuGiFpXbNmzZRIw0lOaKrqgKiFBbsqSaycO3dO/Az3DKUl1q5du1KXVVBYB+TMkcLAOWifDwuqJ9gJw87zYGA1iZDyjZNXaXgDoa/hsLWljNQgclTL0Oc6MKLYUMtyYFLWQCZTYLl5DY4PHoY7ULRSy7RYlUtCNfnOnwfnLcdnj7wJZ6Coq1Mo8JHapeP+3cmh3pH8pCD4L68/lDXtDhaKU4Rly5YJibiwiOF32vw84A4jo4s2Adnj2wWuMOfj9+sibUw4UeKxye6+S812yu3vg090+4bggxNuXw80itHB7DMOHM++/h/Ayy9J5zSJi1tvAz71WWmS5vEAp8PsiQRkwOED45Ma+mxg0zuAPU9ImUIMBF93B2BMnV94vlo/oi4N2zqYY8yLgjgy0IePHNiFATY8ALi3ohr/36IVM6ZQwwVGU1OTmKwT8t4+qG12aAPdh6fsdmwwmaAsmYS9WAAKWSGvzvBjeISk9qzbAIW+B2g/D/QMAQNO4PE/AeuvBFYlN8YfP35cFMESOu9r6oBFq4GTB0LH4rL1QFWgyNd5Djj1MuCySZZTy24C9GM0GnDMVoyWo7t8djRa34JXqDj4qeWo1K+BnvLxBMH5UZu9H3//0SP44ue/kFK/8KkGt/1zn/scvvzlL+O6666bMedBehUaYYSGQAPgL8goNjKYFnB8ZNApC7I9PT0oKJh6Cy+e9yRUgs1TtKQKLxxfvHhRrDOCoc8ZZJAoWHAMWjenQ0VBQoPnEBv+MshgtoJjcLrOkXBQEVhfXy9qTXxPnjskt2nh1N3djfLyclRQhT3DwdoCz/1pa5aUpzdXihkaFy5cENl3U4Epq87Qx/mLX/wifvmjX+POdd+QFnxiQSrDTcWjO/XYXf75ujX42sm9cAc6FuebcvD2sukZ8Cv1uViRXYHDgy0jvd3Ls8tQpY/NqPsQHrBKsJ9uAh2SVKtcoPohqhD95lPSz5xC4NYPAsYZYhtQMA/oC19syYC8mrRlC4wFFv/fXrYJe/vOot9lgVahweKsKpTr88Yt8G7NX4Jn2/eIjnfueZVcgY15CzGVYIjrxryVmO8agMPrhFmdNaZKQyAGYSG2X6USCw6CbGm6VRtjESe8ANFPlB1dlIFXVlaK59M6gvK7lIb3sTDImyuGwouFDwZIM3OBvjSGgskFHovXjDWk+iOL6ymASm5CgWYL3P4hyKCASpY1UsjJUedha8HVYmLB/5RxiLtEsMCUg1+u2Y63ekmsybA5vwQFCVjGmVTqUSojIlhq4u+WmUcrpFLW9dR/CnBHWYl1vQWY5iT9HdN6hgoNEhpT2kkxZYh9ro58fyzmtx+RcmqMhUDRomkZz+PiwH6J0CCCdoPPPA3ccJOkAuBxQYVjgAAQILOWoLoBxbXAbZ8BaJupTl03C/Nm+lx2bM0vw46eFlywDgp3LH6C98xZIs6hWKDK6eMHdws1axB/bmnA0uwcvL18tE1jOCZlDeJ2A7teB/r7gJq5wDKpQYPKj7Nnz4pxm2QGA7V5LtKznHZwYlzasAHoaAP++rj4G7tXg0MrVsFlsyHv9GnUhWXWJAsSuTrFWrh9TfDDAbnMAKWiErLVcmDPG8DLvww9+ewJwOMG1iWmBKTVHM/5hItgnNe+40PAiQNAbyeQXwwsXi0dM4MdwGHKrwPn1VAHsP9xYMsDUsZTEuhynIU3LG+Ds8t2xwnUGhO3VFmTU4P/+9tTsHYNTNmCI534+Mc/Lhqn/vrXv+Kuu+7CpY14awmS0xkbqgymHiR+WWQikXDmzJlpITXCm6d4naMqY9GiRaLg1draKsLF2XGfQQYzKS+ABByV5hlCI4PZDq4BUklosDbEojjXFVRfkLDmYxzfmXUTq17E84jXor179wr1Bpt5U2Ubl+p9tXv3bmFnfSk24ni9XlH3522qshyntOX0Yx/7GH76058id2czVt55vVjnLc4qQT6DiGNgXW4x/mfN1Tg11AeDUoVV5kIoU8z+vdLVhl09neL131FRjTJd7JAWHnC3lS7Dwqxi9DiHka3SCYVJh2MQRdqsUUVyOQzwIsyPW/TSTeCk4k6KigaIwECPpNwgsTETUFQnZWg0Mz/DA+RVA/NjWzpMBfRKLbYVJm8VUqrLxTvKt6LR1ikUErXGkilXagSPu/CckPGwJLsCBwciO/iWZktsNQd/strFxcVi0ZFOUoMXEAYqj4WioiK0t7cLciNYOEpbt6jaCOTMBfrpI07IAIUGKFgEqFJ4seN3pc4CXFEkhqsV8LkAeeqC5uUyJTSy+OoPTiz432RRrDWI0OJksCW/DM+21aPDYRXDF3M58tRqKGRuEQh+V/kSzDWmVmLNYzo/P2CV5IlBYpEcpx2YPLHv2+px4+cnDuGtffuwZuMGrPD7kLr+/JkDpcwsLHzClYUgUSbPlTIljj0GOCyBTJpTgKUdmHtNWqSqE0JnR5zHOyVSg9t5+93AY48Erqe8yYEbb0v8Pfg3mtSNEwf72/Hf9Qfg8nlFY8c/VS2FHAoMup2Yb8rFwqz450aXw45eKjWDmybCxmX4e0cLri4qRXYcMoSgtd/atROwrmQOx9e/BNSfG1Eg9F1zC07PXyzGehaOeG3hmBM36O6Tnwa2XgE0NUJXXoG169aL/cruWaqgNm2auB2lTKaEWhFjjHr1/2I89vy4pAYXTVQT0lJlxYokc7S4f5bG2MfdFyLnciT+7YPAcC+QVRR4zAu4TgO+XikoXDUfUOSN2MQJA0CZDC5fdNMMya5QBlgicDld+MM3fopvfv0bM3LBlyx43H3ta18Tio1bb701tc0RMw7xvq/Z/z1mMDvBbAuOmeF2VNM5rgQDmKm0Y3YBrzOpDHrO4PKC2+3G0NBQyouPdFBgo+GcOWM3pGSQwUwHiYdUzbvYhMtmKZIYJMypvOA1hYRFIqQJ/4Y3vs7OnTvFtWAm2bqxGYykC50lprRZ0tMLOM9IWX2KfEC7UMqYTQN+85vfiKZQ1v6nClNKavBg//a3vy0+4Ll/ek9CBUwW1HhLB37feBbfP3tcFBWIx1sv4M8btqNCb4zfYW4qQqHGhD+17MYgi/cASrTZuLdiw0jILaFV1MHm3R+WqaESjyUNdhUzP+PU3tBj4V3wLNR1jrYcmjZwXzJQOtFQ6RkMs9qIFSyGzyJsL1okiL/ooPBocBAlg02pXrqQiM0VJ3MkNdhBPJlu3YRQdSXAUHhbF0CCqmh5agmN4PHP/d1/Ruoa532VApD5Aa8tpaRGIjg33I4d3Sfg8LlRrM3BdUXLp4Sc0ylU+MbSK/Bcez16XQ5UG7JxTdGckbE2HeDxzImPgIZkTyhbZkRmqUjsePf4fHjgmb/gTNtF6BfUor2zBXsHe/Dwuu2CAJ+J8Pi82Nd/AV2OQeRrTFiVw3D18Y83uUyNLOVqDHtPiJwWBXQwKBdDIdMC3UcBR4CgC57O3WeB8rWAboaoA2vjqDerw/JYbrsbyMkDDu8HNFrguluA6uSIulSB6oyfnt8PT0CByiyNhxuP4t8Xb0OVYfwuazPJQcgEUahgfrRSGmaODfXgvj0v45err0BJjOYMypw57k/IPm3HK8AFiRA+3jeAYY8HOX/7Mzb8+n8hL5AsBMddzHAj16yVbmEgEUkf9FjgNh86dAgbqPSYqLpk1GNjB6XzWsRCA21MqChMGajGiHVJDFdpOA8DvgBJx+c698CrWYN+zwWhyiOpYVLOhVaRBaePxG3IyFUjT26u8uMf/1hcfx944AFcKuBneeihh8Rno4fvJQuZGfBXAWgKe7AqYz2VwbQiWPCl7QctNZhzkS6ws328NQyLX3weVcsZZDAZcN4Ut2FjAmCxjxkdLNKWlMwCm9cMMpiCsGsSI4cPHxbjOkkJNkwFMRHChOctb/GuE2yG5DyYzb5ThWPHjom1DS2nptTO2jsA2HaH1g0+C+C3A/rUZzSTAKYd7M9//vMpbTCacnPwO+64Az/60Y/wzW9+E1//+tcxXWDR6sfnToh/BwM67V4vft94Dl9etHLMv2X48hAtOQLocAzh1a5TuLEkpAigBYJBsQkef6+4r5TlQcbOu4lg6x0As0QaTwLDA4DLEUZsMAB59nohz3b4/B7YvO3w+V1Qy7OhJfM5jaBt21WFi8QtFskQZLirq6tHOpimk9SgDRZlaZSGp4RgYTdr5wFgqFEqFuUtBXICfubszi5KXcBvXKiyATWZ73D2m97oU9u11mbvw987Dkbcf6ptL95ZcYWwNks3WPy/uyJEVNm9TuzrO4l+1xBMKj3W5CxElip1pGFNTQ0aGxuFJBXmBYCtA7C2hmzBSq8c1zaJi192G+5sOI96pw25i+eJwrFBQfLagScvHsQNxQuRp5mghU+aYPe68NuG12ENBNSfHe7A4YEmvHfOldArx59QKOUmmOUxCsdUasSSCnpi5MZMF+oWAh/4EPCr/5Hu89j+xD8Dc8JIDT52xdXSLRZ4TT30MtB7ETCYgZVXA2OoJSaDRuvACKERBPfu+eHehEgNnlefWrAY3z1zHHpF5Hcz4HLiofPH8Y2l60ctoJk3M2Hrjb5eoUBoHrJALZdjQ35AITY0AARIjYmCE1529HKSHz75pS87J/+TmhCvXA+8TLVG2HxpZeA4P3UKOHeWkkEgoBrhfiKhwcVGyuXgpQuBhr1S3o+4NtKaswIwBPYlO6eChEYAfFaf+zg8COYP+WDxnEWWajHs3kG4BLHB674KpbrEr+XMOuH8+8knnxwVVjqbwc/y/e9/X6wzSHBMpwVO2iGbD/gLA5ZTeonoyOCSg907AJuXGWVKZKlKoJBNbWPMRMCxk0WilpaWtPmak9BI5NrAdQgLV7S5zSCDyRzTqbDVYbGPilm+Fps1Lk1L2wwuJ3CMZQ2Hx/RkivRcf5Po4/w7lfNSqiEOHjwYodTjNvO9aIF+221JqPYnCa5pqBiZllwnd6AeEg5PR8pdRAjW+NnUcPvtt2MqoZyOCwMXHTxoP/ShDwnf5ekAg289UUVXhnUOjtPFR3Q4hyL84vnvNsfAqOeRxFDJUsD+8eRef4N062kDnvip5AvNRTHX3Vun7qDpcnTg7PBpuH0uZKnMWJy1DNoEu58vRUKj27kXHn8ooDRLORcmVU3sYrvlNOAekIrbpoUJd42nbHt9vpFCDX/SkzC6kJTqRcd44MD+zDPPiHDPlKB9DzBwNuz+TkntlB3jO0kXDDWA/SLgDBaoZEDu2ilXaTRYJeu04FjFn32uYQy5bTCrk+844iTghc5GcSOuKazCDcXVMYt/HEuPDraizT4Ik1KLVTkVeKnzLVg8NrEd/Pl351u4rXSryLpJlX/jyESI5EXpNsDZJ12wNbnwy9VobWkRnSC88fMwqJ4/WeTjuUBijYTf3NyVyD/lglq4FXEfMpwX6Hd14y8tvbijfKNQvswUvNF9eoTQCMLmdeFAfwO2FkxCAZVVOprQ4PelS13ofUpw3/3AldsAWt6VV3BgSfxvj70JHPiH9Dl5LPe0SgHTtz2YloYBUxyS6ZWuVmwvjH3URFKcAAEAAElEQVQ+ReP91fMx35iFLx3fE0GQUL3RZIu0XrNYLILQoO3UWJ2GtFeg1JvP7+vrE0QDlU88V6wyJSxdvVDJZViWEyBeaHNVPIGJ+bljwKFdUmF/+QagboXoVqRvLifBlIuTcOf1il22gqScKG68U7LO2vO6dH/DNuCGO4A//gH46U9Cz7vmWrj+9csiOycthAahNQEb7gPO75Ls3MylwNyNYTZuo5sA+M16IGVhhSCDxz+AasNG2L39Ik9DpzALYiNRfPWrX8VVV10lbpcatm/fjm3btuErX/kKfvazn+GShiAyMmTGpYohdxs6ncdH1hf97kZU6jZAmeZwz1TlWjDDIl2g3SHnbuON1SyyUX0X3vGbQQYTxVjHHNe89PLnc2gvwzkUbZZJ7vE4ZIAxyT52s09ph3YGGaQRnDeXlZWJ7IqxwHGY6wleF7je5vqC9Sc2t3I87+joEOdGqhttSByGq/W43iexyHORNeipIhbZMMkaw7QQGkRUM10I4zcgJ4Ompib88Ic/xJtvvjnlWSHTMqryoL3nnnvw6U9/WoT6TQfY7TjPmIULwxZRCCD4/zU543fas0jH4mCo908mMjamBPmlwD2fAc4dkuxtapYAeVMjXRx0D+DY0OGw+/04PLAf63Nnd8gNu5xbbH1C5VCpzxOh4InA5r0YIDQI6WgY8pyHXlke2U3F4k3PDsDZGXhAJhW9i66b0kI3B/XwThNOtji4p6uTiuFnVGGwg35kIshw1KF2yfapYC5sjtCFbdLgfh6sH/34QP3UkhosqOdvBZxdUoaDKgdQTX1nP4/nZB4fD893NOCXDUdH7v9Pw1FBDN9aGmnjw+/66bYjODTQQgd4QWKcGGqCQRkquvMxp8+FVnsX5hpTc/yRpOP1hJMUejqzWOjX5IqO79YzJ0UXNo81Tr5Y3OXxyOOf28uCLydbwRDl450tIqcgHO0OP6r0fuSo/djbdxa3lc4cf2ZaTsVCNNGRNLLLgOorgMY3pQkRrcsW3gQop2jcOnsWeP45MlbANdcAy8ZQGpDM4C0ZNJ4A3noWUHNSKwuNI04H0HgMWDTxnId4qDXmYKEpHyeHukce8/iBw4N92NvXgfUJXs+3FhSjzpSNU5aBEbUpbak4rwkHF9kk6oKEBo93FsLlYT6qzDaiBRSvBZRi8/k8H+hDznOiYPu1KB/qhf6Vf4QIjQf/H2BIUml18gDw2P9IhXxu8ulDwO3vxXBumSgAkFBh9xQXRyQ2SHJweyYMhRJ4+/3SLYjWVuBnP414mvOFf2BXbi42fezjqS82MFus5ShgGwSMucDSG6XcjVFQA8yx8fWPzCckOnU0mL/CHDeDMnk1ETvWfve73wlFyqWK73znOyIL5YMf/CBWrhxbeZ1BBjMRotnCeSp4T/zf63ehz92AQk2arVpTpMxORK09UbAI9sorr+DKK68UY3a8NSgLaOzUzSCDVDRO8foZbrFDIoPzJK4l2IRBy0oei1z7MgOAhVr6+fN45c9LSRmZQQYEm6E4Z483BvO82bdvn1iDMHuJ1q48V6ju4DqDBDjv8/F0nR/cBoLnL7eD5yLXRpO1y0oUVAuKCINUWtomC1Up4A63LGW2bC6QYvXnpz71Kdx7771YtWrqYwimjSr+1re+hQULFuD555/HjTfeOC3b8IMVG/CJg7vQGOhsvLeiBndXjF/8vL5oCR5t3QtfgPVSyxW4qiB93qGjkJULrI5jo5FGdDtHd35bvcOwea0wKGdX9kQQnY4hPNK8G3av5L2dpzbi/qqNMCRg2eL1O2Jas/j8zkhSw90XRmgQzFewAvZWqas/4m9d8PqHIIMSCll2ysmi8NdjsTedwUnzqitRv/tZ9BxWwK00wmwwotbXBJvThRyTHu1HXkW9twjrK7XAmb8DuTVAQRoG/Ong20gcaJNTaTm8PbB7eZzIYFCWCTuzWPD4PHD47NDINVDFIcU4NlncjpFip9gkWjQZCmFUTkwh9HzHhZiPRZMavS6rIDTEdgTOjQG3DQalIq2LXhJ273nPe0RXerAbgsQaiQwWR2PZm7FTg9uwo6cVDdZB5A3rcG1RFZwilHc0HD7pbLd7xlf0TSXyNCZ0OEcTGyXaFHTxliwFChdKllNq2pxMXoKfEI4cBh78RMhq8a+PA9/4JnDllal7j6ZTsT+PSLeP7pBPDWj9tq2wBnv7ukTGDJsqHF7p8XYHA6ATx78uXIWPHXwT/W6JvCrV6fHg3EgbIlpO0d+ct7LabFEo88EDhV+LvvMquOx+mM1moVAIB8mMIMkn8L6PATffAfT3AaXl9LSSlDH0ok3UlmHH89LP8PP+zefRv/VO8V48d1mk4mKDZMazzz6L+++/P7XXQW5z2Pvz/H/LasOWnByoxiLXqTwcHpKyWZjVsXeHdH/uQmB+nC5gNp7s/yvQ3yodZ5wzdjcAK98WptAID6NfA7iOAd4eERQuV9VB7xuAzdcS1kIjR3erH68dexrXX399Ug0BbGxgpt1nPvMZsYC8VMGFI5umPvrRj4oQ+lTYhmSQwVTCL64M0fMQPzy+GWT9GAZ2prOQO1VgsDIVhewSZqcti1ZU9fEn8zp5zvN6Qgu6zPmfOHg9dPrsovFBK5eafzKQsGnTJmFX8+qrrwrSnP+mwpXHIpv3ovcVGzX4ODGVvvIZZDCVoDKWXfk8P8KPc47PXIOTqKDVefT1YaqcenhNIPnIbWHNi42N3F6ue/bs2SO2O50g6cltmHa1oDIf0K0GHKfZFiCCwv3apZxVQOaXQxbW6PbGG2+I5lA2fCaD5557Dq+99pogcaYD00Zq0Pbjv/7rv/CJT3xCdOSJztopBgPBn9h8LbqddugU9CuNz1Y5vR7s7L2IIbcT8025eP+cK3BuuFMUIhaaSmCaKqXGlGREnBP2BnJooVfMFX7rhJy5ADHAjsHZimfaDsMRIDSIPpcVr3adxi2l43uPq2Qs+EQWZWVQQCHTje7UjAVfZIipx9cHq5dKGO9IDotesQKyNO1fdq5z0jUW/P4u+MDBiUXcLMixBLLozxcLTitWK0+jUT+Etp4BrFtYBctAO0519MGg0+BMcxdys/TYWuMFBgPEUG894LICZRPsrOSE0jwP6OeAHQbzNDLjCcLmaUO/O2gzIKmA8tWroSGLHkUsnhw8Ap8wJgHmGxehTD/aK3hP3zmctET6JxZpsnFD8aqRibfX78OwxwWDQi3C5ccDLaWiEU6ajHwWesZHweWVQSFTCLKFZChLc1SMlOrG8TxnYfncTqCvWSqoz9sMZMcni9ixR99Mdp5T0nrFFVeMuyj7xYUjeLmrSRSY+Rnf6G7BA3OWxHyuScktB8r105udE40rCurQaO2OUGbUGgqxLDtFPtLseFdMMXH9y/+RisJB2TC/x5//LLWkBq/5PISDx7FQENCGSg6Up2/c6Hc5MSguC8GOfDZHUAmUnKJrjiELf95wDY4O9orzaaU5D1p+V2Hg8U9S7/jpA3j+jb9DrpDOdZfTjUVLFmBV6Y0Rqo0xUVQCZJmBz3+OmnPpsUWLgR/+EDAnQKC5YiiHXE4sW7ZMkC4bN24Uhai2tjbRvcWgwJQXVSrKQ98zgP1WK5bptFDFC5wn/voo8MjvJFIiJxfIzwYsA5z8SMfn3Q8A22M05/RckAiNcOl3dz3QfxHILR/9fFpIaULdTX3ORnQ6W8SxweYZgyIfWap5aO09JRZi3Fcc7xLtbvv1r38tOkq/9KUv4VIHP+MjjzyC3/zmN/jABz4w3ZuTQQZJQS5TQinTwcMgzzBoFTMrzysIzrfCVXURdqBpADt+Ob9jBzAbWNi8QksRdvwyW432Jiw8j7fGySAEr9+D00OHMOSRbMP0ChMWZq2BehbYnU0VmM3Comgwp4XzlAwyuJzBMXbLli3YvXv3SPMgbWRJLm/ePP1OLrwOcW3BugDP17/97W/i+sEmHzqWpBNUotCOKd3ESVJqDZVkf+Xx2dHrOgiP3yLuG5XVwkKf3xe/RxK2tMxLNJuONcUHH3xQ1PanK89uWk39KA3ngoOqDXr8TgdIShRpx570OLwefOXEDjTbhoSdCsty/1S1GDeXjLEInsnweQHaldBCRG2M6NCweA4JQoPwYhiDnn6YVRtFob5YW4ZmWyO8Yd1DeeoCaOUpJHSGWgDLRUChBvLqJJuiNKI3zEaMYMG1yzmU0N/qFMVw+QZg9baMEBq56hViMRIBdY5UrPCHkxjsyiyM2Pc2L619QvuWIfMuXxM0irDA2ymE3z8IH0J2Q8AQfDgEuX/D2EQLSZzTT4iQ4TmleSjOy8K+k03YVFeF3KyApztdYgLFtQhiqHX/2KSGKDjK4pNEufOkrmEW9Pk95C8BsqYntycZDLnPj9oXFs+FCFLD4bXjxODhiDyfs8MnYVJlI4vh5GE4a2kb9R5WrwvKgLXaGUs3ftOwHzavGyqZHO+qXIE1sYpsYbiqsBKPNAftECRsLxxdNC/QGEUBzu0LGvtJXv8rzIvRamtFv9sCk1KP9blLYKCd0Vg4/AzQRUuxQLBubxOw6d2AaWxSoaQkMQufVptFEBpiGwNFzgbbIDqcVnyweil+1XAs+M5Ykq2AQSnDHEMR1ucGCt7WbuD8q9J4qjMDtdsBQ/rUT/FA9c37q7fhrKUdQ2475hgKUKafYbkXyaKvP0RoEPx+BkZnV00KizYAp/YBbg/T0qXHVBrgynuAnBTkYcUAycT/rj8W8RiPMTZLrMyJDN1utVnxjdPH0GC1oM5kxpcWLkWBJlJxxGaMLfnjH+8lNWZoyplPEUlEMmxaq0jC4uknDwF794bunzkNfP3r9PwZ/2/rVgC7XgxtA8fyOqmBoK6uTlg0sMGFnV9Ua6RlYlxSCnz6M/B/77vYNzyMUpUaOXe8HdiyJfbz970F/PE3ofsD/cBgP1CQBfgC16K/Pgxs2gZoo8YzV2RBMvQ4w53HxrC7G51OqduJeT4unxcO7yBy1NJ7cHFGRQJtpBKReXNx8vnPfx4PP/zwtDQRTcdC+6GHHhLqPYYV5ufPLCI6gwzGQ6l2BS46DgjbKcKgKECOas6M3HH0S5/qjEwSGCyaUa3B83tS+UsZoMl6doTQIGzeYdQPHxPERgaRRdJp77rOIINJgnWnPzZdwBMXm0Qt9L6qGtxRNrExnGo5kswzFbScYnYfCQYSHFSNcO5M4jtdoPUh1SEzhtCIQp/rcJiFPjDsaRCNFAalVAvifnrppZdw7bXXItFwcF6Hp7OJSDndF4b//u//FrYH991334yVw7/U2YgW21CEncojTSdwZUEljFPlLZ4q2HqAM88BbhugVgKUg5FAMJTDm1M1QmiE4IXL1wGdohp6pR5rcjfgwvA5OH1OmNW5qDFIrF5K0HUMuMjuz8DrdZ8EFtwBTCDUOFFkq/Sj8lFoQZUI+LnN6oUw+qrEokMlN0AeK6yTFkEFVwK9uwCvTSq256wVRX/Y6wFNDvwaA/yIVG4QXv/YdiRDbgcebTmIJlsfdAo1bi5ZhBXmsYvT4YM8WeR4knE/uqPstfjTFriNsY8GmwB3aLu1GhUUClliBAUJkViP2zqAtjel11WZgLKtQHiXv6MT6N4B+AOqmPyFQPay+ASIeC8b4Log5V4ocwEuFqepo8Djd8Ph9cMb+OhaOetlkcfDsMcSQWgEMeQeGEVqUBURDWWAiOIx8z8X9grSgXD7fXi46SCKtSaU6+MXN99eNl8U/l/oaBTbcU3RHNxVvmDU83gcvrNiHf7csg9On0ccQdcWLcKirHJxSxjOYaArSPYQfqnb+eJxoG4bUoHBgG1POEhc8/E7yuZjc34pupw2FGsMUCmk3+mD1nQsTB5/EhBKLz8w3A2ceBJYeR+gmpjF12SgUaiw1JwiZcZMAGWvzU0hYoNk5aoU+5+aC4E7Pg4cfh1w2oCyucCSzYnbKU0AAy4XrFHWVny3Yq1h1PPevvs19Lmc4rw7N2zBkcE+PLflGmjjdMCyAcPj86LV3i/IkzmGvBErRbmY7o0eP8IlxwnhwIFIsoletYcOJfa3298GOGzAYQaFA1i6Drj2rogiFb1uCRbeqdigrQkJj5TirruxT6nCXJ8PuZx38j3jjf3Hj3CyKn1OgtcnbruXGTOBfcf9YRkaTWoIVVmURSXH4azxu8Os3t5Rf+vxO+Dy2UVHNIuI7DxL1KOXhAatvW655RZcLuBn5fqCn50qlQwymE3QKEyYo98Cl88q1HQq2cy1A4pWfnN9H/QxTzfYdUt7kYwqY3KweJjnFA42Oqa4kSSDDDKYEfhVwzl883SowerQQJ+Y3r69fOY3gk4UQeKdykLWvmiVxCaqVFsUDg0NCTUXFSwz8Zrt83vgDig0wuH09cGAUJ0mUUvJs2fPiiy7HTt2TGtu0LSSGgRDWsjq8EafwpnofdnrsgsWM9xqhf9i4WtWkRrc/rN/B9x2iAqdUSupB1hgGToLufMCUBTdZRzK0CCMShOWmdMQ/sIC68Vg92fg/ejh3nUUKN+IdOHW0uV4pOktuAMe+kalBlcVJldAUcr1UGIcRYk6Dyi+JVB0VwCduwBLw8ivZQWrACOPfV+Ukih+cZRWOb9t3IMuh0WQbcMeJx5tOYQspRY1xvG7EhliNnamBrcnVubBOOcoyZqoQbwgJwt7z13EmrnlkMsCr2koAOw9Yc+SAdkVo4tLJDKaXwKCOQfuYaD5RaD2Dim4mAqNcEKDGDol7XN9nCI6fYmt/JtAQdrTBnjZbZ8a1t7nd8Prt0AGFRQy45gXNWH55vGFKC0/4BYe+0oc6t8Jt8+FbFUuCrVlI38zErwubGtGS8NX59Tghc7IINhVOZK3a4ttUHT9hoPfSL21b0xSg2PgPRV14jYeePz9vwXXYdBth0GpFkRH0ggvnI4gYPeSILifOhwN6HY2i0+ZpylDqZZErHQMV+hN0MgVcIbtD55L8xnoK7IqdOIWE4MtQITVFgOmbcD5nVLAdmFNKFDb3iep0Pi+/B5U6SNq0wJeI6jySdXkrKcbePTPQF8vUFkF3PNOIDrz5KMfAy62Art2SfeXLAU+9/mxX/fcaeDMcUBvADZcIf0cD7nFwPZ7Qvc5nnAMU2jTQnJmq3g+KGGPIjbKdJGTx1e629HNwPIAOP9osA5jT183riyIVJFY3E78vH4/zg4HbSP4PhLB+MCcjSjRZcOkKkG/uwHe4JgnnpcPtSzJYzE3D2hsjLQFy0kwu4XWWLfeD9x8n3R/jPkeLbMIduGmGlxsFK9ahdzKOCRgSzPQcAHIyweMpsgMkCCCxwZ/6gxS1kY0TAXAkuuAEy9KhCzPoaU3AWOMs0FIis/R76uQKYV9KwkfXsMTWXS8+OKLePzxx4XV6+WGH/3oR4Ioe+c734lrrrlmujcngwySAseBpJR00wRaeYSv32n/NFVgpgG90UnwpttO5FKGimsJb2ShS5XiENlLDmw89PK6ykY+HaBYDAhr6gwymNn4feP5mI9dyqRGEMzTYLg559EtLS0pVRnStunQoUMJ2V9PF2SijhedCcy2buXI9ZyZKIkonPlc1vDpvjRVweszltQgvvGNb4gQmZ///OcixHCmYY4he1TgLn2rCzRT7NUpOgQDC+OJgOoMV0BqpFGN6oiXOT2Qe73wcWIa9rhaNgXeaCJfIkaxksRGGlGmy8FHarehwdojPPXnGougVcRQW6QC3KdUcliaIwgN8avug9AZt8KOcyOPyWGARh5/oGXBuMMRaZXFTvITQx2jSI1of1t2v7IrdqzwMhlK4AetecIL4Cz2jmNdYSiSilUKBqJL32lNWT5Qsw3duWUoMiqkfAR9HtC8G2hj8d0PZJcD82IUHajSiAhu9kvHi60TyJoDeIYjCY3A1sPVG5/UcDeFCI2Rx1oATR0gn1yXvds3IGzcGL5EqGQFMCmXxbXssnn74Ilx7Pe7e2ELfOweV7sI79PKs9DuGBBbrZD5UaLJRn6YjVkQdaYydDstqB/uEF77a3JrR1QSemXs41uf4uNeJVcgXzOJHAatSepotnSFiooc/4oTV/R1OZvQ5gidUyQ4eGyU6aTXyFJp8NkF6/C9M3vh8HnF2H5/1WIszk7AqiT6+6SF0aAd6N9HzxrAkANsuR9w9gEX/iFdOGi51n1QUhqZJZJpRsPSC7zxF6C/Q7JlWnMjUDvBzBuC3+OPfwj87fHIx/e8Bfz4pwxFCT1GkuO736eGVyK98wvGJhneeBF4+BfS2MP3eeEZ4MvfkorSiW5b50GgJ2C5p8kGKq8BNKldoDK/5gt1q/GfJ/fCEziua41m3FUeaWfp8cUilGM//tvGQzg/3Ddyn+MGxXFymRtPth3GR2uvhFKmRqVuA/pcF+DxO6FRZCFXVZ38pPujHwU+8uHQd8HP8M//ktxrJNG8kupFQX9/v7j+LVgQUJl1dgCHDlImAqzfCOx4DXjoB6Ex54ptgDlHsp3itrD7uLoasAe6WmkH9pHPRB674ShbAhTOk5RnHNMSbIQxqyrQ72qJIKHMqnIo5RpoNH7xGXhdZ5j6ddddJ67nsUA1JhcbtHmlB/jlBi5W+dm5D44dOzalYcYZZHC54sKFC6lX2MUBxz4qskiAZ0iNiaNSPw8nBvtGHCl43ZljmJrvcFZCNIWyGTN4jeb9fYBiCyDL5JBkMLPhCea8RTwWe91xqYLzaCr9UgnmPDHrTqxd2Py1fy/Q0wMwt2/BzBhPZTI5TMoaWDz1wUcE0WFUVoXiCCwWNDc3C1vgsawd6bjU2tqK559/HtONGUFqcJHxq1/9Svje3nTTTaLrYiZha34FTg/14rXu5kBntAKfmr9W/AzvmufSOy2sHAeZMzulmyjuVAHrbgc0SXZYChuKADMXY+Dib7IcTlg1GrgVCshlehiUdSNB4WmFQgOoTQHSJczuiAXyNCNLpcNyM0MepgjuoRgMKaB2a6DQbYDH1w+ZTAmVrGhMaxBajUSDqhoWk6PBxXy4j3YwBGgsMBBc7l8HHzjoOSGDGTLUjH+Mk6youhJo3gHI2eGtAqqvhqfTAQWDcHPDCsZzNgNVGwNkXYzhyGMFrI2SsojHrCfsIhx8fkwSwj82OTGKBBnn8QQh5dIcGSE0CLe/Gw5fC3SK2ASVX4Rnj4YUSR36Ta+rH32u0H1aVfW7R4dyEy92nsS+/gbxGjwmNANtguig2qJKn4NFpkKcsnSN/J7WU8vN6ckQmBBYoD38dymMlyoPklocvxh83XxBMpkvHr+zotd5MeZjQVKDWGEuxC/X3CBsprg3mmyD2N3TimXmQqEyiQtaPdEaT/jj+wFLFAFrGwDO7AAUDBQmy6MMjLt+oH2HlGekH01IzRjwWvPyHwDroHSfVl27nwQMZqB4gjk/zzwVSWgEr0PHjwEH9wPrNoz+mzHVZAF43MCfAvYyQQVBXw/wj6eBOwOqgPEwcD5EaBDMVqJCbO4dKVdsbMkvxSfnrcI3Th+CzevFnr4BPN3WjHsqQkTX1vxCGISiwyuKDCTdc1RqrKVSIgonh7rDChGBzfdJo0evM2QFqJRrUaidpBf00qXAww8Dzz0vHSNXXyPZN6UBVCIEgwdTAY7PR48eDXn/Hj0C/Ovn2FYs3WeGx+BA5PzojdeAf/kM0N1B43hgwSJg+7VAVwdgHQJKKsZXBJEQ5C3Jrtlqw0b0uRrh8bkEUXV0YAC7e1+FvW0IK4sX4sJbh6HPy8ZfWw5iWOZGqc6MqwvrIhozvvCFL6C6uhof/vCHk3r/Swkf+chH8Je//AVf/OIXRc5GBhlkkDqwo5O3cCiVyinvUuX4nsHEYVRmY5l5M7qdzOTzI1ddJB67bNDdCnQ0AGotULNs/Gu2n40N0WswWij3ALKQsj6DDELHTFie3DTjbaWV+HXDuYiVw+1ll1fjC3MvmLWRKjQ2NqK0tFRat7AB6mtfAd4KuA0Q7/8wcFeYM8A0wqSsERkaDl+vcAdpc3jRYj+DHFU2ZHKZqBFSfUlryXhoaGgQa4ynnnpKWNpPN2YEqUFcffXVeNe73iW6qV544YUZJdlhIfBDNStwS+lcDLmdKNdlwaSSil30pX+m7SiODV4Uz1ubOwfXFi0U3dEpQ/NR4NQbofu9zcDeJ4Gt9yX5QZRAxQagZTfg8o5Wa2iUNEZCFhf4ykLAlD7bp1HgNtRcB9Q/LylKCAaFMxvhUoOanb8xJt8qk7AqUigSI5EO9DePokb471UxMjVYHAoPRKJqg5Kx8SCTGaDAMiQNBnabqwGPHVDqhbqo58Su2P7fPFdinS9eB3Dx74DPFejsDRyrbh+gzgb0gXBcWlBlLZQsp4J7RJkFGMfohFcWSHkaoY0AaPUln5z6yg+XuEXDE8O7MAidIlcobKKLktFHiNNL4jTSDs7qtcPiHka2OKYkdDmGBKEhvYb03PPWLpy1dKAuq0SMUx+sWYfXuy+gzWFBnlqHqwproY5FKqULYwW+M1z39YclsiA4ARSe9d2Aj5ZPAVJ26x1S0PNYiPEeElkUCSrvmEHwrdM7R2yBzCot/m3RVuTHU+SRZFl6J9C4E7D1Ab6o75jbSHJGbwfUitHb03tiZpMalj5gOMpjmedp27mJkxqHD0v7IHpiz/uW+OfIuLBaAU8MQnIgFHo5LgYj1XOSndgg4OX1MLUZKYNuF/795GFBaEh7wo+vnTqMBaZsrDBLpEWJTo9H1m/Fl44dRLPNirkmE76zbI0IBo+GVq6EVWS7hCCaLCBDbjoyqdhx9OCDSDeYGcEFQirAbiNKwpndNjK//O43+YvQk6gKkgd+x7wMrUo6Rne+Bvx/XwfCVW7F3K7UbFs8qORaFGnrcGLwPA4NnBVDH1F/sR7OHBnWb1uNn7z4JIqKF4ixvmmgC8cvnMVnNt4OpVyBN954A7/73e9EIOJMtHadKvCzs3GKc6B3vOMdoqs7gwwySA24vgjaBSaa2ZdqdHR0ZFQaKYBOYRCKjcsO5w4Cb/41ND89vgO45aNAPPtZgXh1qplTv8pghoCNee17gcGA5ZN5PlC8NnbtY4rw2QVLxPrjb61SUPj9VbV4f/Xld+6nqt588eJFoVhgjoYAVd/hhAbx619I6u/CohnxufXKUsi9eXit7024fG6xjmgeaIFt2IJ3rlwlSJ9wlQbtumjdxfwqNhGwZs9M7O3bt2MmYMaQGsS3v/1t4X3LxQd31EwCv/wynUncwvF8x3EcHWwVAwPVGrt7L0ArV2FbYWLhjQmhI8r3jhfcnibJ55we1cmgZDmgywGG2MHsBJh872fRxgPotSOlEOhGBwCnHbpcYNG9gGtI6s6eZt/500Mt2Nt3VpzoFfoCbCtYJsJ4Jw1DBWCqASxhRfWCNfArPfD5KWVl12g25KiDbAw/0wFmo0SB355ZPXoSxoUFZXbhTCqtp6IfSylYJKf6JhCalJWVpI3LMH3bAx204pojkzxV9JVA0fpIGzbzcilDg5ZTVGiQ0KBCJB5I2mmXAI6Tku0ZyQzd5CcYzNCQ2vIjCSMF4nf8KOVqVOhWotlOy6pQZL3dE0lr6BR62Bg0HwV51DbTlmz0dgFDJJhG3lOOq4tS152QMLytgPeM1M0kywGUDHOPKhh3NQBh3eUCLMixyOjimCcjSwTsfAqYt3LMbqpCTSWabCciHivQxu5E+W3DYTjDgi1JYP9v83E8OG9d/M+jMQELbpD+3ftzwEZVQ1jB3pQPqG2ANzqAkYdIbJXNjEFMmzIShpPwWM4yRZIaI++lBBZPotvflAVk87o2EEmEVdUm9vfM7bF3xP5dGsi+s5bBUWHhCsiwv79nhNQglptz8X9bx88BeFtZHf7UfCyC5DapIPJibi9bjtmKiooKQUQUUEERAyTm2RnFonW0wpedwwyvCxbzOYdbtmxZSLHI46SrM/JY5DHD6wrHGkNgXOHxev408JufAx9KP5ETDS4cTgydHyE0bENWKNUqKFRK7Lc2i+1sPkxCn5uuwPnefjQs2CBUG+9///vxta99DbW1CZ4HlzDYjfef//mfeN/73ofDhw/PiK6yDDK4FFBWViYyLcJ9yUtKSkR20VRZULFjdMOGcZpcMsggFjgX2/2U9O/gfIBNPSQ2Vl8Xf59xDSPsmKnS5t9xBsZGiATsazO4vNB5ABjg2jeA/lMA60mFacioTRAquRxfWrhM3C5XcF3A/Kd4Vuy0YDp9+rTIighvDKL1Kx9nbYtNwpync52yefPm0B+3t7GDWFJshKOjY0aQGkG02i/CGVaPuHC8HrXL5sIldwtCY//+/WL/cD9x/cQ116ZNm/DLX/5SXOP/9re/YaZgRpEa2dnZ+PWvf40777xTpNGzo26m49RQx6iO6lOW9ghSw+K2YNA9AI1CjTx1wagi5LhggT+6EMTXmGjnHW1TeAuHuwdwU3IqBzSVgCJ5H/E+1wDOWS7A7XehUFOAucbqiM/Kk94HD+QYQ5bMgoKWE4XpRaO1E692h6xIGqydcPkO4tbS9ZN/cX724k1A9lzJXkmTA79aDR/2hBXDaSdiF/ZP8fZViS4Lpy2h44/PylbpYnbc81w6efJkRIgPC0YclKM7rFINDoBNTU2Rg30suPoAFu1V2YCS4ayemDZdyF0k2ZVFg/kZMTI0/H4P/GDB0sO+ZciCIW7qakDFRRgvOMqUyEHpU2hQLITVGyqky6GDVjG2pZ5RVYD5im0Y8nQKOyqjMg92rxPNNpJqLphVeSjSzsGrXbvg8bO7W9onRZp8GJWRxZkCjWmUooP/KmJGwHTC1xMI1AuTbnsOAMpNie/78OexAGkfHpPUyNdIx0O3s0Xsjzx1KQo1sW2rupzWCLUM/93hiCJXxsLq24DdfwY8gcmBMQ9YsBXwu4D6JyWLtfDtN8bJe0k3mEdjPQm4ae2lB4yLpZ/RoM1U1RKgKfCdcdu5ryeTqfGOe4EXX2BrZ8gmSqUC/vMbQHFAeTUR8Fr48c8BP/o6YA3kRq3dBFwVIJzGg71T4iKj5p7InpMWUoNh4dHg8WaO8XgiuKqwGllKDQ4PdAQs5owo0OoxR58Hkyq1KpOpBL3SWRzbuXMnVCqVKJoFPdM7OzvFhLq7uxs33BD6ngcHB4XFFAPu1q5dG5ElFQEez2XlUhh98FjkcbRwEdAU8JkNP19ffwl4/8ekRcoUguMWVWSEx+1B08l6LFgnEYBuvw9F86rEqMX5VfvJepQvnQ+VTotPfepTYt89OAWKmtmCf/7nf8YTTzyBT3/60/jFL34x3ZuTQRLw+b24YK1Hv6sParkaNYa5MKkygbwzAVyj8ObxeITtVPAxEocsCplM6bMwdjgcwl43Ly9vRjk8ZDCL4LRLxEYEZCHr1XiQKQHFOsB3WmoQhR5Q1AGZcPUMojHUGPuxaSQ1MoBQ73K9QJKCzb/M2uNag/cPHjwoGn95TQsSGpxnHz9+XDxOwn6srAlUVo0mNPg6ZTPLms4j8kSlmlHr+RbkFudCpVbB4/PAbMoWymZ+biovqfpmLY8NY5/5zGcEoZF0w/LlQmoQDDykSoNWVFzIehVynB/uF37S84w5MfMCphPKGASFKiwH4aK9BSeHQoW8HFUuVuWsTY7YqF0DtJ4I1HcDRbf5G1IrW1PlS7cJYsA9iDd73hoppNL/3+a1Y4VZWnwPu/vQZDsCj98FhUyJSv1SZKlmrvVK/XB7RDldnOz2HqHa6HN2oM3RIgaBEl0FijRlyU+m+Xx9iKn1+y9EdffznWnHwqJqbPn2Fflz0Wztx3lrt7ivU6jwrso1MZ9LdpX2G/TI44Ad7HLlYxzQaGOklasF8ZYsyN52dXWJgY2D/8DAgLhx0Of7MZz0yiuvjP8CPKYHDwK2+rCq32pAVwL0h3ncC6WGFlCbE942v98FH6h+kfIORC+NfwnkskB+hDiHUiv/1CpKoZAZ4PH3QwYlNPJikZEyHqjYyFWHsl00CiPM6kgLuKsKN+HU0HnYvQ7kacxYmBVmpRKAWa3HzSXL8Fz70ZEi/db8eagyJJBNkE54qC4LFg9p98UvhMc41ThhhdeCOQAtn2hDxWMjePMEJgdiDJQBag1gGJ+oIbERJDfGQqnWhAbrwMg+oyVYeZQyb0zklALbPwz0tUgdOMw+Eko6LTDvTqD1VcBBOyQZkLtQIuemGiyO9u8APAPS2eCWAa4uIO8aQB6DHNr8diCnCOhuAbQGYMkVCe3zuCgtA371O+DJv0l2U5wQ3nBTagrFNfOAb/4MaGsBdAagpCwJskwRIG0UoWOU983pUTPNM2bhpuJyPNfRCqVMBmZ/zzEYcVPxxLOdVueWitulBl4/eOP1iuGz589LylWG+3GyTck3g/nYpXvixAmx+OCkOyG7pc//K/CFzzBNW7pfNQf46jeAH38LOBF+7QlgGjzbOVcs0uThQt9FnD9yFnNXLhz5bAuMhWhzNMHr86Hj1AUYc80oLirGnudfwWOPPSYWIHFJncsQ3Bd//OMfsXz5ckGE3XHHHdO9SRkkiGODR9HlDKnpepzd2JC3BYaopo4Mpgcs7rA4tGpVqEjH9QALIZMlNUhgc57L16G/d39/v1hjcP3C9QwVePE6bTPIYFxwvsj5rSOQjxecK+cm0GhDpbkiZO2cQQYxEas5KoG6QAbpBa8bbH4iSMBTGU5Cg7UrXsvYWEVlL9cdJD1YzOe1jiT6uNi0FbjuBuCFv4fWlP/8aSBvZim5CrX5OG05h7YLF+Hz+lBUXgyNXA2TKlR7pCKF6wlaa3HfsEb/oQ99CNdeey1mEmT+GZisxSIpk+O3XLMdnndej16XVJCs0Jnw1cWbkZVk4GI6sbevEf/Xfkz8O1gEv7diDRZmlcDtc+P17pcjOqaJOtMiVOirRvvt+W2AjNLFGJ2V/e1A/V6AwcDFtcCclTMiaCiIIwPH0WRrHfVZby6+ToQmnx7aAV9YGywJgQWmzdAoJrcgYYDmsGdAmvAqcyEfI1g7GbzadRRnLKM/zzWFNWi1N43cZ212nnERSnVhyhdXB+Dplr5LDTt9x++U9fkvwI/wjIfA62MDZLL4nrS0PGt3DMLl86BEmx0REBrL9/bUqVMjao36+npoi4zY2X8E7kBA9vLs+VhCBUmS5+vLL78sLC442NFvLycnR3juJUT2ONqAvjdHP150i9RB3bM3EBStBwq3AZrESQ2f/yz8aIlSe8ghx1WXfFfXkNuOPpcVWSpdenz1k4GnA3DuD93nvmdhjieQartkK9VyXFI5FNZIigAGhQ91A7ZhwOYMdTyw6ExrsRveA5Slruh80T6E/zq1E5aA0qJQY8C/LtwCM0MDUwUGK/OzT5ePKu3Z+l8b/bhpJaAfI4PmUgfVK03PSjZUQRsB5tRU3pQWpQbh9fvx19YGnLIMoEijwz9VzYUxpuXXNKOxAXjiL1Lhf/lK4JbbJ64SnQTqh5txykIbJi+KVflYnbdM5EYEu3UPHDggitVJe7gzGPzkCYAqmWW0MVQDB/YA3/t66Dk8ZzdsBR78LKYavKbu2P0mzjiboKnKgUItHSNzDeUos2XhYMMZvNJ6AvqqItSWVmIjCrFt7Ub8/Oc/F/kRGYzGo48+io997GOiCEvrnAxmNpxeB97oeTXiMa4hqvTVmGeaBqvcDGKCFlQrV64UhSCCBaCYOXpJYteuXWJNQSUIG7S4xuAtqAqZFDy90vyUzU3qSkCeIckuS3Q2AS/+HnAHLI8rFwJXvTPS5jiDDCaK/rNAe1S+QtkVQPZlvO6aoSCpEd0MRFU4wetZUrUjltjPnAZ6uoHqGkkdPsNw4cIFHG48BkuODeYiMwwKPdblrqLHiCAyuD/YVMbGMX72z3/+8yL7+q233ppxzQQzktQgaJWzZu1arP/WF5G/eulI5+zWgnJ8Yu7MkmsdHWjFUQaFQ4Y1uVWYbyoasZ16K6pYy4k4CY0FpjDLHx9tWA5y+RroBCwClPMBRdji3OMEWg8CTgvAjuvSFdLFloRP/T7AMSx1ClfRo34SxVrPMOAaCHTE5yX8Wgf7jwpftuiD6abia2Dz9KHRdnjU35TrFiMvgQ7qeLB7LThr2QePXwpI1cj1WGBaB1WsjuMk0enoxxMXd0eQGnUmdm10jXquXqHF+rxtgY06D9ipzAnsN8pQs7cB8rCcC9F1PiQV6mm1JKMfnzXKfoowQY749lPj4cjARTzbfgI2jwuV+hzcU7ESZw4dE4Qhcb7hPPb6zsCvinz9qwrWolQX28M8Hnbv3o2NGycYLG85BViOx7CZ2gK4zwK+oTC70gpAtyLh49LnPwY/Okc9LseVkJF0ymBqMPwy4AoLglYGMjJU5YCnBnjttwCtnoJqtDVvAyoCsk5LP3BqtyQRz8oH8sqAvBKpuyrVm+lx4aylV1j4LDTlQ5NsZtFMB1UZVGpEw7Qc0E9DxspMAjNneg9LmU5Ug+WviG1zdzmhuQn49MekEPagRRNJjQ99PLG/7+0E9r4kEZMVc4E12yZUJGixteGtvsg5RIWuBBvyJmGFNh52vAo8+ShgtwOr1gI33CzltpgSJ9VTVSisrq5GXgG7u/zw+HxCIXz2zFnRpcysiCC4+GDnFG26fvvb307pds42PPDAA2hpacGLL754WYeozwbYvTa82fN6jLVUJRaYpkHxmEFMsADCok8wt4jdreHj02SIXb72mjWxlegTf+GLgJ1r7+B6Qg4YtkzIfjmDSwBcY/S1A2xkokrjEm98y2CKMXgBGKDSWAbkzAOyxralziCDdMPr9QqCImjhS6tbNovx8ddff11kZ2i1ocbOV155BbfddptQx6fbun4imLEVm0WLFuE73/42vvAfX8VVv/0eNDnZwhakYTyPw2nAMnO5uEVDp9CKnnBfWKGaRXJDOFlBiWOQ0GAHMjuW0SrdNHMB7SLA6waOPA7YaRlCg/izwEArMP864OXfADY+zsyNA0BfG7Dqxol9EIZO9uwKFZcZyJyfmN99ibYILXaGj4cWHDlqM1RyFeRxJHa0oZoMGq3HRwgNwumzo9V2FtVGiQSbDIq0ObitdD0O9p+Hw+dGpb4Ac/TZODE0mtRgxoEAf9qDWQpBCasLcNQD+kAILhURfTul4iJBFYd5M2SaXMj9q+DD2YAdjxlyLEiI0OAgxGyXYY8TZTozKvQ5aLT24s8tPK4kNNv68fMTr+EmhpQH4FL4YLXZoVfpI763Hmd/0qTGpCwulDwfYnCrtCYioSFtmAR3C6CpHXPR4fM74PY1wQ8X5Mxwkfmj9qNmJg99YyLIQc8qlQm74MMJDcITUFsolwInXwwEg9NmKvD7w88D5YsCYds5wLqbpmRTjUo1FmeZ0Ok4jRbbWWgVJhRpF0KVgNpqVkCZI9lMiVCw4M5mh+IEQsuGLcCzjwGd7ZLV0y13A/pZ3OWo1AFFEyRmL1U897SkkAoSGsSzTwHveT+gGeecGOgB/vg9BkFI85yGk0BvB3DjfUlvRrONeV+RaLV3iPEwbWPh1quk28UG4C8/Ax7+jvT42u3ANXdOWcGjdMlcfPvZPyCrphh5Obm4uWQlNEMeobwMz8givv3tb6O5uRlPPRUIPc0gLh566CHRVf6tb30LX/ziFzN7agZDK9fBqDTB6hkeaTTizwLNzAnbvJTR6xxGg7UHarkCC0zF0MRRhFMxFyQ0UgmSt+wUTTkcUes1ugk4zwL6FJMnswhpvabOdGh0QEmmcz6DNIGqjIwyI4MZBIVCIey32JhMAkMpkwgNqiP5eDihwfzC+++/H9/97ndnJKGBmV7Zozz87y+/hINf/QE2fP/foFAoUUSv9VkCpVyFxdnLcHzwyMhEvEBTiDJdOAFiCyk0BKERBud5QJEH9PUD9v7I3w20AGd2AtaAN3pwUnbhALBgoxTymgxY5OrZHVlctjUD1hLAOP5FvkRXhGW+xThtOSvCZfI1eVhlXi5+Z1TmQK8ww+YdCFukGJGlSqBwLoq4/ph2LQ5vdIivHw5fVPF0EijV5YlbEAMMso6BbFUg2FxYOMUozvsCclbCcjJEaBBeO9DzIqCdB1nuSihk66Q/8Xvg9g9D5ndBKTPEnWSya/PhprfQYKVXv4QbihdhyO0UyqFgPgB/9sOFLkvoOyjIyYO93Qa9KXRO8TidSK5GQUGB8DafkJWDthzQlgGOECkG05LAdx4jKJxE0RgZGg7vHvh5TjFglSVbvwZqRfBvlJBj2aybtDP35PXuEzgzfFHskSXZVdicV5dcNs90wRsgpqLhN0jfsd0y2queFlAM7ptiOx6Pz4km2154A+ey2+OAw2pBjXFzyqztphUkknKuAAb3SmoxKsiyVgHKJD2vnU7gW18Gujukgvepo8Dp48AX/0sK/na7gc4OwGAAcnJTs+1dHcCvHgJaGoCcfODdHwLqAmRxBvEhQqYbAT/PQy0gqwZkCSpQBjjv8Es2cSLbRrwg4HSNT2oc2RUiNII4sRe48m2APjmLKJLtaQE/U1szMDQIlFUCWVHzJo5Bj/13wGs7gH2vACWVwBLpWp1q0IOeAbu8Rlk9TvytbR9cch/UOq24/5OXH8WdVRuwcXVk4Y0WkF/72tfwxhtvpDWY91IB9xFtqK644gqhXt2+fft0b1IGccBzYaV5jVhLDbj7RbPUfGMdcqkmzyCtOD/chcda9o2sJXb0nMUDc5hlMvoaks55NfMzUo5Yawl/2HrtMkKbvRevdh+B1WNHtsqA7YUrUaCZRH5aBhlkkEEGMwo+n0/kUnGNEUR2dvZIY/Lg4KDIFSGhEf4cWj/ee++9Iq/wwx/+MGYqZjSpwQnSn37/MJauXoUTv/wTNn7i/bi/aoyk+RmIYm0JspTZsHgGoZZrYFblRE38AgVkJoWOggwgEeCNM8lyDkvdgtEFQQbsJktqeEgQRHfCyAB34sqYakOluEVDJpOj1rgGXY4GOHzDUMv1KNJWj18kZJF76KA08aS6JXuDZNcUgFquE68Xvr2aNPqhZqnMgsAYdIcIJpVMhToW4MXbqwG5HvDZIyLGoQwr6oX9rfQ3gWNh8JRkeWKqgcs3iD7XwUBhngRQEcyqpWI/RuPwQEsEoUH8o+Mk1uVWj3quXKmALOw4yzfmolSeJ3QhUtHID5PSgBpD+bidPMeOHUNNTc2If3l+fr54bEKkBvdBzibA2Q54bYDKDKjzJa9bZ/R5oQDk8VUabj8t0CIXKj5+Qv/KwPFmnJW2U2/2nMIpS0tIyDDQALVcifW5k/crTjvkcUgyVeBYySkB2qlQCjse9OYpJzSIYU83vGHqL0Fs+O2wefthVM6scK8JQ5klBYPzujHRIsTxg0BnWPc8iY3WJuDMccme59//FejtkX53462SXdFkLF5Ionz7K8BAn/RefO/v/Qfw1e8AZVH5VBmEIIiII+yxCTzA+QLt+DZK16uxcLEFOLw/MgOHU4SaGlaEx9/LzP+KwUmLx5NEjbESFx2SjWDLmSbYrXasql4GWblMdBWRUGeYLMn1OXPmJL5vHvkf4K1Axgz92d/3L8CyMLJgsE+yzgoH7bOo3kiE1PDbAS+Vt15AXgDI4xdhuZhg0Hlrayvuvvtu0fXc5OxF/ZFT0JoMYjHStP80CmsrYKgqjPhb2ihxwUH1QbR6I4P44L768Y9/LPYdM1kqKkJK1gxmFrQKLdbkStapGUwdnm07PEJoEINuhyA2bigerYhnjp7Vah0phkzU3ZpjOcldrjGC6+VgMSalShBFDuANEPcjj11+RJnFbcNzHWzmkWoAg24rnm1/C/dWXAXdBJrcMsgggwxmI3iNobUSr2XLli0T+VAMyW5qahI2iFQopEONmG74/X6Rqcu1EtdJCxYsECqMvr4+HD9+XDT20C6yv79fNPpEW7L+27/9G9rb2/Hkk0/O6KbgGU1qBLupnn/qaaxbvx5X33oPStYmGQI5A6BX6sUtJkQhvBbw0WcvGv5AJy0nWVHVAcp/SxYAjcfDX0wK2DVNYFKm4PZFVyBYkE/N/mZBuViXhLcqi/+Db4XuU5UxsAPIu2EkuHWOYTHOWvaPBJCrZGqU69NX5GVX/HLzGjTbGmDzDEOr0KFSXyMUOQI80Y3rActuwC+F20M9RwoLD0Khi9zPI5N+mQjF9hur0e86PEJoEA5fJ6xeM4zK0cW7frctQpEhXlIQTHnY198Mt4+/8Yt3rDMVwWANEVccmJaYaqHJM6HXOQidUoP5xkqoxgnGpb0FB3UGjdMCgwwv5eEMEZowuO+0pZGPKfMA7WL4XSfh0/I84SBrglzmgSxIBkZDdNiH9i+D1C0eoNd/FAqZBrnq+dApkrPWmgly7Xprx6ja4PnhjrSTGk3WTuzuPQ6714l8jRnbClbAFGZXlhDkRkBdDrhY3AvsJ5J/mgABOm8D0HcR6AiMgWo9sP7O0PnR1yl5+ueXUCuJdGJGBkylC5M5ZkkyxHzcAfz4K0B/mKrt+WekgLTrb574+zXVA30BkiR4XNAW6YdfA/71W4A5RWqQKQLHpVe7OtDhcGBpthnLzDmjfs9jUTHpccUaRmgQfFV+d+0AxiGDnnhMOu/Ct0EhA77wFUnZYMoam6iqXQwcDstvISmfWzhaDZEAirUFWJ+7AvsuHoLCK8f1W6+BuU8vJNK89pBMp5f7uXPnEic19u8KERoEP+tvHwK+8d+ALjDG6Yyj50Us/BgS8Fz3WwE31a8Be0pfI6BYCihGE/9dXV0irI8LC5IaO3fuFNeVly8eR15NCRwWKy6eOI+qFXVQqJSC0A7C6XTirrvuwu233473ve99iX32DEbw/ve/X0jvSSTRQ3imBR9mkMF0gUVuqzeShOaVacAVplwLQ2FhoRjLmAFE5OXlift8PBmcPn1aFF0YRMriCufX4VkdKYNuFWDbAwQV/spiQDMPlxsu2ntHCA0ErnYun0dkS84xZCzeMsjgUoPIZstkiY0Cs5uYD8tmqYMHD47UoUmw83ezMX/N7/eLNQWvqVwv9Pb24umnn8aKFStEEwLXHVRnlJaWCoVGNJ544gn89Kc/xd69e2e8CnzGkxoEmbHf/PrX+MD7P4B1y1dg3rx5U3owPNfRjP393TApVXhHeS3Kk7ROGBfKeVL3ue8c4O4LLaIVuVKQrloOLLgWOPcK4KMlixZYeCOQXQYs3Q4ce1V6vloHbLobUE6gs4KBqLmrgb5AZyahKUzIeiotEBZN4cUEv2Tj5BmUAswBGJRmLM7egiF3r1AaZKsKQgRDmqCQKVBtGIOcUWYD5usAH0OPVVJmRjiMiwFHe6QlVdB2TKGBDy5JWRAFdzBbIgpFmqwIQkPaRpkgNT5WuxUvd53FsMeBOfo8bC+chwOd+yOK8GazGVq3FtW58RUWLp8LVo8FKrla5MGUl5cLJntSJEaC8KvL4VO3SGSF2GQGqh+A3L8RshiZLApZLtz+Rulv/X4Mergw4z0PPH4PuhyHqLlBjroaWarJTdYdXjuODEh2CEqZEgtMdSjXp6fTUxFDpaNKsx1Sj3MQL3ftHzm6up0DeL5jD+4svzLm9sQFjzXDaomkovJMpgW0tdL5Eex83nA3YOmRbKeyCqQxzOUE/vJzoPG09DwWRN/1z4A5fZ10VGPIoRghSnnQKWVq6BRTGw4847FgMaBSB+yFAooPFgMLSyTbqXBwEnjqxORIjXiB7VYL8JffAB/67IRf2ufvhcd/PpDBkwOlrC7m2JIqeP1+vHffLrwY2E8c1v5zyXK8v3qu+N23Tx/Dw0314t/XFpXiW8tWwzhh1VKUpeXIOwaP7zFA4iLax5zf9YffC1htAFV6X/w3YG2c7unqhcC17wBef0o6l4sqgFsfiGklOR54DaofPovB/m4UlGSj03ERBcWLsal208hzduzYITxhE8ZFkgwKiRwLV5H0dgHlAWKE5MYVtwBvPCONUyz8ZOUCq68c//W99YH9HHZ99p6KIDXYfUyFI6XdXFgQN910kyjqMWD30RM74D1vgbk4H1UrF418BTXGUJHwk5/8pFiAUaWRwcTwk5/8BFu2bMGnPvUp/OxnP5vS3djhsOFPTecx4HZiZU4+bi+dM6M74TK4fMB5nlmlx6DbNjKK8cgs1MYmdXNzc0XTU5DU4P0zZ84kTWpkZWWJG4tLaQWbBg1XAn6SNHLp/mUIRZxCnXI22NtmkEEGCeP00AD+39G9aLQNI1etwX8sXoUrC0qmdw+ySY5K6TQ3LSYCkhZslIpu1CU5z3rZbGt66e3tFUqMpUuXiusxFShcd1AVzs9I5QZrZcyXC8/PCOLs2bN44IEH8Jvf/AZ1dXWY6ZgVpAbBLqo9e/bgjjvuEN15nPCkG4cHevDZI7thpa+ymPLI8Fx7M362cis6RLAtsMJcgCwWdyYLeRGgLwLcXVLhj8VwEhrBSUXBfCB/LuDhyU9f7MCiZ8EmoGwh0HxodH5DsjDNkwgDVy+g0AK6sgkVICYCv9+HbmcjLJ4+KGUqlPgUUMfqm44q5KrlWuRrJmB5lE5wnynisJlKA1BwPdC3B7C3SRV3f8CiJ7sOcrB4xX0eWUyiyiAWlmaX4vxwNw4xYyVwjN5ZvhJ6pVrc3lUZaUVBWTjVFbzRMqqoqAgnT54Ug3Us9Dp7cHTw4EjYfYm2DAtNS8TA39PTI16PgyaJjmTg9zvh9p+EH4NCdaGQzYdCNtrexw9aawUJjfBOY2aDjH6+Qp4HNerg8p0RZI9EaIS/nh8e/yAu2g8DWIYs1cQu5rwIHOg/IIIrCRImJ4aOC4uEfM3ElSBdjkEMeWzIURmRpwkdQ6tzavFad7gqC1iZk17CsdnWGUEsct9ZPDYMuCzIS9ZrVyhxqsf+PcmMcLz+DNB0JnS/vwd4+nfAuz+DdIGB4FWGdehwnITLZ4dGbkSJbjEUaSxypwJSegzVYRrSMOl/w7wC4MEvAr95SLKEYsbFB/8FKC4dXSgmsidJClXVSLemC5GPK+RAcz3wymOApR8oKAfWXiMRLgnA5x+E2x+4dopRtwNuvxMqrEpbYfFvrc0jhAYCZ9e/HT+Cm0vK8NTFZvyq4dzI717oaINaLscPVkw0v4ENGNwX4d22fMcElC2LlgD790Q+RvtCuz1AKFmBr/4b8Ns/AIVxCOLlm4FlmyRyZBILlgvDjbB7HbAODiOnQFK1nBg6iXJ9mWg0YOGssrJyxBdWgITboVclpReJiFXbpSDQIHIKAG8UacPvPDtq32y5ESgqA1rqpSwQfqagkmNcv/boOYxHIkZkctEhxeYALiboaRv0jT98+LAoCvKavPWGq3HO1i0EMr4AoaGVa0aUGlxo/OUvfxEdZbEWJBkkBu67xx9/XNhRrVmzZsoULyQ0/mnPKxj2uMV3+3xHCxqGLbihpBwtNgvKdUYszr787HAymDm4o2wV/rd5Dxw+ST1eqjVjS37sxkKlUikIVovFIv4dVFbQtoMFlGQaGanS2Lp1q7D9qKqqihzbUwmO+bL02RbPBlTpC2FS6kQDXFDZn6M2oUQ3uxSwGaQJ/uC6mz/D6k8ZzCoMuV344IE3xU+i3+XEJw+/hcc3Xo1aY/prqqMwOAB8/7+AE0elpqFb7wDue+/krIonAdZ1YlkmUt3AOtmVVybQzDSDcPLkSdEwRTup4HqWYd9tbW2iiYqPca0Rb61L2y3W3D/4wQ8KNfhswMyu1EThm9/8puhqe9e73oWnnnoqbZMcHtS/bzqLn50/IfIxRzw94Yfd68WDh9+AXCYths0qDb6zbGvq1BuqQukWr1iuiuokIbmy/1HAaZMuNC2HgHlbgJoJes9qcqXbFKPVdhJ97lBQ9LAfWCRTQyb87QODjLpQUkIkAA+Dz0VYu3rKSBkP8xz8dshlJihkxfGLYiSM8q8ArM0AfcK5jVnzAaVeTCazlXUY9JwcKSgrZFoYlbEtNfgeNxYvgkmlgsXtwDxjIZZSwRMH9NLjoMbBjozzkiVL4hIaXr8Xx4YOjRAaRLvjInJUuUKixkIS7T5IcDBbI97rxNpXbj8zQ2xBmgEe/xHIsBZy2eQvrCp5BZSyMnh8FvQjqigXhl5n44RJDafPgWFPZCg91UJdzq4Jkxpvdp/EocGGkfub8xZiVYC4WJpdJQpZpy0XBXG1OLsSNWmShbt8buGpy3DyWIZMSak0JgMWEcMnGCwGtjWl/W21iizMMaRfhZQq+IW90LEAESqDHwshQ5SVWzrAkO5v/0Ky7WGXTRD3vw/43S+lIja/P9oU3RawE5so+Pqf/Qrw1U9L1laCJGNnj1xS95zaJx0freeAjkbg9o8kNDH2+TtG2Qv5QY9tXj80wpJhb98ZnBuWzrul2dVYlh1/EpgIGm1WKGUyeMKObf6r1WbDi+E5JYE5x8udtIqaIAQZtxrwk8QlGaEAZAsBWQJj9bwFo3NXhHKDjwUWulwcnTgen9QQ2yCbdAcWx1vOyxw2B9S0IgxcOdw+tyjo87rGwldoO73AM7+UjgV/YBsaTwJ3/UuI8Nq0Ddj/JkASKZhNdvt90vE6al8sk25jQeyPDkmlScs9uRnwh1mmcafJTGIeR4spdjSzaBecxzY2NoquKV5bg91gVxUvRGNjn7CRlFGsCj+uL5Fy5RgI/uCDD+LZZ5+N/OwZTAi0LXvsscdw6623CjU4v5t042+tDYLQoCoriP9tOY9n28+PjDF3l8/FR2pH5xdkkMFUoFRnxm2ly3Da0g6tXIVN+fMi7O+iweOW4xvXGewMJZIhNIIk46pVq4TdRU5OjmhoTEqFl0FSUMtVuL10E/b1nxVz/1y1CWtzFoiGgQwuc9AlwX0oNJeRZQOqNeNnsmUw43ByaAADYZl2nHVw7rG7t2t6SI0ffRs4dTw0Z3/qcSAvH7jpbVO/LbT5rq8fNZdm0y6zKHj9mS0KWrfbLRqm+FmCObdsmNq/fz9KSkoSupayOeGd73ynaFhm7X22YFaRGuz8ePTRR4Uk6Atf+AK+853vpOV9/txyAT8+d0J0x0UfxFxUOrxe6JXS42Q8f3L+ML65bMuE3svi7segu0uEQOepS6FVJNkx0nQQEP6m7PgPLIzO7QQqV0150K7T68BFe4voWs9R56GA9lUJwOv3RBAahEcmQ4e+CiW02/LapfBoQ924HQJ8rfrhwxh0SxdgWlLVGpentdOaRXqH9wB8Qj0gFUcUsl5o5IvjD4Iif6NKukVBryyHUm6E09cHOZTQKUogjxNubfe68LumN4U8nEX1M8PtwgN3Q16t+D2Lcm/21OOCtRdGhRqbc6rR29o6otJgoYhdVfHslUhsRGw2ZLB4hlAiKxNSNJIjZHOTkeSRzPALr/dI+Pxdo0gNGfLhF+qVYMYI9yffS+rWdXqHYPPSqkwOo7IYKmY1BMLplfIsaOS5Yj+K9w2cH54AR+OPUsMkg3gh9woWDcVn8cHpc0MjV0MeOAb4/s32Lgy5JSVGmS5/5PhotfVGEBrEzt5TmGMoEAsMYoGpTNzSiQ5HL17pPAC3yCaRrMw46RF1QaHUyUO2KoX2e0P9QNsFQK0FKhdEFscZOE0CZcTrlzZWM9vPcarhF+qMo5E2faACygQZpmhfhX9nxB13A+UVwNHDgMEILFsBHNoHGE3A6nWscCT2ugffAs6clF7jyusktce/fAn48X9KdkYEu3vyjaFjhOc4j6eeNqCwfIzw7FbAz3GBzQDxk1T29J3GsbDz8q2+U1DKFVicNfEi8nyjKYLQCJ5nVQYD9ArlqGQr7WSbN0QhfWsgb4ikRoIT8+OB7ilBboblP2lUIdUGrRNpQ5UI/M6ASiH5Tr8sVRYG+04ipzDUcOEYdOBs91kx6eZEPQLtjUB72HjKbe/vAhpPAPNWSo+R3PiX/w849JZktTVnLlC7IKntinh960HAzdygANSVgLooEMxO6GCx1+LInjdRXFw8srBgFxhDqvkZNm/eHPGytHn5UM0VODLQKq7lC0zFKNfnoKGhAXfeeSe+//3v46qrrprYNmcwCtu3b8f3vvc9vP3tbxcF1aCNTrpg8bhDItQwBK+3xGOt57E1v3RCig3Oh7scLYIUNChNyFeXzZqFeQYzA4cHGvFq13FB6nMm2GjrxH2VW6FXxp/zk4igzQUDv0kWsjDEfI1kQDcGWmYEvb4zSC/0Si2uLBiHuM/g8oPnTGRzhn8IcB8H1KswqzHYD7z+AmAdBhYsAVZNsBF4FiHWWoJzDd102D5RzX/sSNj6PoBD+6eN1OB1iravBC2aSMyzeZcNLrNh3uT3+4V1LcO+qQAPKiW5XmDIN5XIidbqPv/5zwuSh4pJ1t5nC2bPlgbAbvBnnnlGSGcWL14svL5SjT82hewfgoXQ8ANaKY8kOVrskgVNsuhztaPByoKU9NpdjkbUZW2ALp51USy4AlYQEfADHseUkhoOrwN7+3bB7ZcWaS32Jsw1LECVoTohUiAWPOy0NSZ34Wy2nhohNIhBdzdarKcxx7gE6YLX3xUgNAjpePH62+FDJRSYGPutlpvFbTwc6G8c8buVSs/Aa92nsNJcCY1Chb+1HsHhwdYRQuKkpQPLuv2QOz1C4t3S0iIIjliLDrV89ODH99BQaRLoLOUAmXy2hnycZXwILt8ghj00IvNALZdBr8iFQnjeK2DzdKPTeXjk7wfdDSjVrYNabho5Zws1K9DvOgurtwNevxtuX6hgaJpEpoZarkaptgxtjiAZx70rF5katG16o/sw3H4vVDKlCNcu0xXg1e4jouM7iCVZc7A5X+q67XPHJpYO9TeKoL4aQ0HaL6penxevdoUIjSAK1Gb4ZX7kq7OwNmdh6raj+Szw5C8lmxiCReh3fEIiOIhttwENp6Vu8GCl9/p7UvPelwyYtROrKD/IIxzThrUbpNvON4B//X+hwji7/7/+HUAzjlXOs48Dz/wlpPbY8SLw5W9LWQdf+CZwZL80GVZ4gONvRip6CPcYNoy+s0Agd4eFGi/Ho7BDWiasmaQutLOWsCJ1AHxsMqTGraXleLGzHX+9GLQMBL67bBUKNFp8sGY+dvV2icekMR346EQL7dFIltinxVL4tTl6HwtZoQlYGWlxOAokxl1HAG97qNNPy06/xO2S5uircNR1FLIsmVhsNB9vQo2mGvJ8+WhCY6zv32kHhgcBrV6aH3GyvnachpSOc0D9PinPrGQBULtuNCnj6YkkNAhXM6DZAre8Gt29bag/2wGjoU34xAfVGbSaYgcVLY/U6tidjzlqA7YVho4BNhFQTcAuqg9/+MNjb3sGSeMjH/mI8Bu+7bbbhNVtOoMR1+YW4PHWkKVe8LyPvsJyjZEsqeHze3FyaA9sXouYnfidfgy5+1BjWDorFugZTD+o1n2964T4dzC7z+px4kD/BWwtWBiX0KCCO3jeME+Dna7Jkhoc59lZSuuM2RjOOtPBIPALw13w+L2o1OcjK9oFIoMMCD8toMPBpiCpUXDWgpa5//k5KZOP18JX/w687V7g5kkqymc4lmbnYpU5D4cHekVLJ5up8tVaXEN71akGx3TOeZ2OyMcSsXZNE8ItmtiwS7UCs9Zm+nyJ20kyY2BgQKiMWdsjSG4wT4OKjWSUjr/97W+FtS0Vkok6sMwUzDpSg+CXRv9bLuzIqvGgSyXcfmn6xjW8xx9JYlTp9Bhy20fKSCyKVOkntuhptQX94qVX47u22etRa1yR+IvklAIXaT0SBANbDdJtCtFia4QnYBUV3Df11rOo0FdBPo5djUKmgk6RBbvXEtFxnBXPhmsMDHmiL8DAYIzHxsL54U7s6j0vJn3zjEXYks+8h/ifgQGzMR9nZ2qax8Jhj1NasEbYpwA2r0scx0FCQ3rcD4/PB/2KWlxprhaExPDwMBYtWoTz58+LgTE80E8lV2GesQ7nhhnULFWVjUoTynVSGDaZ34kE+cmggww5AZuXIOSQyyKLU05vLwYoew3A7uU+VSE7UPTudXG7wj+3F32ucyjWhogwuUyJPM0i5Pjr0Ok4BZevTeyvHHUF8tWSmmWiWJy9BAalAX2uXhGiXmOoFRker3Yxg0TaLhIEr3QdxMa8JRGEBnF8qBHzjGUo1JqRzayVGNjf34S9/U3iOHx72ZoR1Uc6MOy1i2M+HNxXeiVgVlNZY8Xp4X7MNa6GTjFJtQYH1/97WLIuCqL7IrD3JWDLLdJ9hpd96MvA8b0Ac43mLwNKpsFmhQX5i/WSxV9hpeTPHwtUD5w7IhVOK+YCBVMxUYwnAZ9alV5MkIz60XdChAZRfw54+gng7nfG/zuHA3j2MenfwWyOYQvwyvPAHe8CcguAq26UHqci4/jO0DggbKkM8fc9C+wBQoOQUwdG60Bo4JcpAkHhC0YmsTz+oxHrsWTAc/ihlWvx7jk16HQ4sCgrG7VUsQDYWlCEh9dtxSPNF4Tl0E0l5bi9rDLp9/D73QE1nAZy2QSLFVduB558TPK9FeqWqN9zH0Vbj8WC+1yI0BAbNwQ4jwDaxDvj+H2sL12HE+dPwtXlxL3r70GeeYwiWWGFpMRwh9lX8nvb/Q/glb9Ki6cttwErxrEY6qwH9v41dL+/Tco1q7si8nk+qmVH43DDbpxuPAdTtgG1SyuQp9ULQoPqDF57+bnWrUs8L4XXaNqvUlJOlUYG6cEPfvADEdjOff3kk0+mzep2e2EZPj53MX5ef1IoIgs1Oli89lGL6Apd8tfbHle7IDSI4Pywx9WGEm019MqM4jGD8eH0eUbmsuGwsmlujPV5TU2NGKtI2rIxkGo6EoVcayRaIGJhqaKiIkNopAEkpv7UvBMDbttIIPhd5etRoc/k92QQDTY3RjkrzHbrqVeekwgNYacawNOPAtfcPH7D1SwGSYz/XrUZv7hwGmcsgyjV6fHR2jpkpyIXOFnwOnDXO4FHfhvINeJNDtz2dkwXSJ5TmUA79fAcipkKu8OKNw89g0FbB+bOr8KiectgVhWIhgC73S6uucmSMjt27MAnPvEJIR7gtXy2YVaSGkGZ+He/+13cfvvt4iAMSoZSgRuKyvHH5vMielUesKupy8rCD1ZsQrPVgq+f3gtrQDaerdbg43OXT+h9PCJIMhLuZIO+SxcDQ12hoHCNHlh1x5QFfAfh8rtGTX2lUGY31HFCroMQYTWGVWiyHYXV0ycK0SXaecieAKlBgkQKzA2BweOJotHag8da943c73ZaYPO4cGNJfFlu7BwImcjWSDcY2ncIoZwBHpNahVp03Qy5HeJ+8CZdvv2wOexCUkf7qCB4/vCxcFKDqNTPgUmZhUH3gFAnFGlLRnxWeQHggiXZgZ/PV2E5PP7z8AuFiwZKWS3kskiG3u6NJAEIh+8isvxUC8jhJWkUhViPiW2VyUXgM2+pAl+zxliLGoTIkfPDraMWgV7GDzvCCZwQhj12FMIsOqXqTGUiMyMIly9kkHVuuBMnhy5iSXZygezJQBsjf0byracaQBpPXD4nzg8fxJKsScox2UkdrXBj4bSPVmJhYOGSQb3hYIZC20nA7QByK4CcNJIHVJH8368lSyOCdjzX3w/MWRT5PIcN+POPJIsb4Z0P4MZ/AhakW6LNjKF8lqvCsiH42MTD6lOGwUF660Q+xmOmPTI3YhTs1tGqAH42EhvRyC8FbnqPFBTO48lcCFz/TyG1zyhEBZgHjmw11Rny0b71i7OrcKD/XORjk1BpBCGK2bn83kZjU36huE0UXn83XN4jUPg8kPv88MvMUKhXJqWMEKDd13d/Avz1z0BfL9DaCrQ0RS4EAwHXY8IXo9MvYAmYDGjZxFtCYKPJze8H/vEH6bhQaaRQcEdgcc7P8MaTQF4xUDHGxL3hwOjH6vePJjUUof3QPzCMM+cugoaQ8vJirNogFfJE5/GxN6CxNyDHWCSaChYsSE6F89nPflY0IMw2SfhsQ7jVLfc5SY504YE5C3Bf5TyxpmBx4beNp/BIc7DhCbinYt6ErKeCuXLRcMdYd2SQQSzoFWoYlVpBYoTazfwo1o7dvUkVN+2nWByyWq1CtZFsrgaVHczmyCD12NlzBoPukMsDrQ2faz+MD9dendndGURCOR9wh2dT+gFFitTD0wWxlohav3LNYbdd0qQGoVcq8an56XMuSQq33y1laOzfA9AW6YZbgdrpK6RTMT3TwXUElY+0jR/yNaBivhFzdVI9oqnrMA41nYBeUSiuubR+TKZOw7UFg8Fpwcoa+2zErF4VffSjHxUyV3ZU7d69O2l5azw8OG+J8Lx+uq1JMJt3V9Tgo7WL8Eb3RfzX6f0joX41hix8a+lm5E5wEDQozRj2sKgbKuCYVEmGdPOAXbhdCgZnkU9vlgpvUwyzKgcdjshilVaugypBRl8l12Cuce2EiuThKNfNw7nhg2EXLL94LFHs7asfIQGkvwaODDbjuuIlcdUaChmtohbA5QsuROXQyJdAnmwRaQJYml2ONns/Dg02i/sauQp3la8V2zrgsolcmGDWqzzQbLvQXIKOxjOCxeUAGQT995qamkYFJeWoc8UtGsEFC38mC5lMCZUsRKrERnyv+w7HObh93pHcGx43Vsok/E44fcdRpq2FRqGblsC9WDCrY++jM5YOnLZ0odpQgGsKl2OBqRyN1m7s7q2PSPygIqw/0FWVLmgUaswx5AtiLwi1DMjVhB/3frh8dkHIqsYhK8cEi4w6gzSJDO+yzxmnGM8xbvcfAWt/qIi/+FqgMgl1WzI4thNoC/Pmp+rgpT8B7/0qoAi7fO5/FRhgYDcRONFeeBSYtyKhwOqJQlJpkXDl2EuSiMd8ubBCSwuoovjLb4FjB6VJ6M13ARu3xS+K09OTfxMkKTjeMG9jLGTnACz4UyIeHJ+43+fGGS+qFwPvXyw9d9x9zfOT56I1ModEFnvusNo8D0qZQqisqONYkj0H89KcazMZ+P0uuHxHofS6ofIG6dU++O1vQKa7Ekj0nKUCwzIkfYcf/Lj0GAmNzz4oEVU8V7m/P/ixBF5MMz1KotIa4IH/D3A5gIEe4NEfRv6ex0rr+bFJDR53aiVg1JLFBjxeYDiY7xQGpRl21ODw3udgNGqxbvU8dGmLYPFLxK3L5cahPSdRt6QGVYU1KNDMT/rj/PjHP8Yjjzwi5rqzTRI+G0Ebneeee04QG+w8Zyh7uqCSy2FWS+fJ+6oXYVNeCVrsFqHQqIunDBwHRuXoY0QOOfTJ2NtmcFmDc+vbStfiiYt7RH4fsdBUhmXmOeOqLEhAM1OD2X1cI1CtcfToUWEbnQghy+d4wpW8GaQMfS7rKHX/kMc+6fV3Bpcg5DmAajPgY8OdH5AXS4/NZnAt8eYrofus7eTkAVmZedWUgmPNFdulWwZjgmPzhQsXhEMKr6FcAzTaQu42jfUXYR22Y8XyNSg3Jq7+DqKnpwc33ngj3vOe9wgL1tmKWU1qEFRrsBBLxcaLL74IrVabkgXG5+qWi1sQdq8H3z5zYITQIBqsQ3it+yLeXj4xG5tqwzKcsxyAwyctfKlMKNHWTGyjp8FyKhyl2nJY3EO46JB8wjVyLZabV02oi38yMKsLUWdaj16XRLAwfN2kykl40GixRXaQhhQOY0Mlr4RSVgwfnJBDC1kS6pDJgPvrhpJl2Jg/V1hO5amNUMul0/rgQLNEzgR2qVD3UR6eXYRBXTNWrYrsIqelRdB/byy43W5cvHhRDIKVlclboyQKraIYTl8wZFWCRl4kzpcux4UR72kZfHD72YHIZzhgc15Ev6sTS7O3xMwFSScY/p2nzkafi5kGEgo0OaK7m6qfE4PNojamlMvg8spwbFA6To8NtmCwoA4b8uYiV23Erl4qA0JjDcuT+XGIkVTB7XPD5etFrpqKDJJ1QK5aJn5GQ5GsP380eDDe9G4pU4PWUkR+CbDu2tHP5YX71cel4N9cE6ALpxyZi/0yULYYUKThnCNRwW0NVw7QVonbZAobV4ZidJ5TUULLqglYhyQDicBIn4InAg//LJRnQfn2H34ueaCuiDGJYmfmZ78EfPM/AjZADJJZBtx6+9jvwWLzJ74APPRfQH+gy//aW4ENUd3xRE8T0NUgffeVSwHdOBlG/C4VqwAvlY2Bbi15LRBlfRd6ugwrzLXiNhvgY/A5s3yoSogg592AuwlQJ1BMP/AW8KuHJK9bfrcf+TSwdCVQUQX8+H+AV19klR5YtxGoi1IsxYJ6HuDojiSR1LH92FMOft8anZShEQ2e01S2joWC8khPaYUcMBsksiPQPMLC25EjR8T8Yc2VH4JK4QbkBig8LYB7GJZBK04ePY+V6xdBrVZBNYFmh6effhpf+tKX8NJLL6G2dnYci5cCuK+576+99lrR9UbL26lAXVaOuE0GWapcVOnr0GQ7HSA0FJhnWimsMjPIIFEUabPx/uqr0ecahkauhFk9/jozNzcX2dnZuOqqq0Yeo3KDRCF9vgsKCsYlRWjPlyE10oN8jQmt9r4RYoPzBLNKnyE0MogN5lTKx2tCnEVgI9bFZuDFZ6X75hzgwS+ktQEtgwwmChIZVFGwuSYYtyAIaMjh83tw6tgF6A1aLF4+FypF8vM7h8MhaujLly/Hd77znVn9Rc16UoMWOH/4wx+EVOa9732v6GRLR6hYj5N+875R3titYRYqAy4H/tF5HkNuF2qNOdhWMGdMD3y1XItFWZtE5zPtdHg/bRjuAexDgCFXUnOkGCz+1GUtRo1xLjw+D7QK3bhZGukCSYxEiYxwWL1OEZoW/o0JMxelbsxMjSBkMjUUcT3u04tslV7cwuEbZeESeDzqOA6C5w0tMcYCfXJ37twpgojIFk9EpZEotIoi+P2LMeypF3kZGnkBslR1Ivydoozgp+DHjDSVkWzPep1tKNGNH1SfStCa68bi9Tgx1IBBtxXZKiOWZFVjZ8857OuT1DRErkoHu5fZPKHvaEf3GazLrRFy/xuKl+LvHUdHfrskqwx1ptiF1yDYSbevrwHDHgdKdGasyK5MapHiCthVaBUyaANiL7dPNhJfKuki/CjVzoU8YEE2KVQtAN77JeDiBckuqKputD8/j9Wnfwn0dkiFdLcSIHEd/rn4uMsO6NJAagjbj6jziAHD0TlKJGTOhPJfxDJRb4hdTJ2tYNfk4b2Rj/F72L8rNqlBrFkP/PdvgbOnAQaHLl4mhX/HQ9N5YMc/AIcduOltQN0ywJAFGGKMM81HgcPPBawW/cCFfcAVDwCGca5vzJhQbgL8JNMYEj77FzJCqeZphc3bgqy4s7oEbGfaLwL//b2QQsZuB37yLeC/fiKpZ4qKgXvvT27j5AwG3wJ4aCVCtrQIUMS23kobeB4vXAuc2hf6vo1ZwKK1Y/8dgwzDxxrxby9g7wMMBaITmbaNq1evhsEQWewzy+bg+Ll96OsdwLoty8RYLIcKWcrkCEiG5d533334/e9/L1QDGUwtmBvGfc98jddee01817MFxdo5yFeXwe13Qi3nPHbqVdwZzH6o5ApBbiQKqjRoHUUSI/o6FT1OxrKuGhwcRGlpKVauXDnhbc4gPpgT2WLrRY9LsvRUyZW4uSSzrzO4TMB53N3vAW58u+QWwLltmnKzMshgomC9jfN/qjKiszH4b52/FK/tfA61CyqQVyCte7NUyTUasx7I2jnfi7X0dNTPpxKzntQIdoCwm4oLvi9/+cv4xje+kfL3yNfooJbLI4gNFo3LA124Q24n/uPk67CwOxfAW32taLYN4oE5Y9ui8MDUKNJc+Dr7BtC4P/iOwKKrgfL4GRGTATvjp7o7PlWgdVN06DaHkPmmBH28ZxgWZ5fi+FCkJRiHq180vIZqhzSYhQ9gPBbHC8SkKoqEBiXlUwGdskzconNTxlfPyOCN4Z8/FeACYYU5ZGnS4RjErt5IX/6+GFZSVGMwHFijkGOFuRIVulx0OYdgUmpRpssZk6BweN34XeObwieXzzoy2IKLtn7cUpq4LZNOoYVKpoLb747YpiLNPMhkbnj9bpiUtCJL4flAa42x7DUGe6Qw6CCcbsAUTv7KAJUWPrUWfr+Dxm+p7TZbugVoPAl0BggpFkS33xtpPUWs2ga01gNNp0PF0Fvee0kUzEcQDHOLIEupdBjnMxYUSrdYCL4WX7flAvDzb0okFR8/dwIYHgJuekfsvzv+UuDfgdGAAc7ndgMrbkzw81wS0x+BYU8zBtyS/aHV4x8xfJKF/2zvBE6/LO27iiXAwqtGW1WePx2ZmcFrIVU2F85JC7/JdPpNlTojHq55B1BcCXQ0S0TZyivGJx2F+iuYVRMGuUpYqfB6uXXr6HwhXluPHDyO4qyVmLfWDrffAY3ciCLN0qQWDbze3nLLLfj3f/933HnnnUl93AxSB+57Flv5XTDPZDw160yCUq6Ccirs3jLIIAD6eQ8NMYstElR2s+N0rABSdqWSSMwgfWDu4v1VW9Bi7xU2vmW6XBiUs3PdnkEGYyLovx0LRpN0yyCDGQZePw8ePChyPmI1D9Mp5dTxTmzf/DZ4FIOi0TRLVQmdIjm7UtbM9+7dK2xtaUE/23HJrOoZcPz8889j06ZNwrvzYx9LxOs5cegUSnxuweqITI0V5gLcWip1gu/oaRKERnhI8I6eZtxWugC56uQPFJ/fjUF3PVw+C1QyPbLVc6GYiId9b3MYoUH4JbuW3CpAn3jnzaUEdgs5fFJRWSsPSW7ZjXRFwQK83n1aZBjwm9QpVFifOzvtHhZlleDWkmV4ueu0sKZiKUUlByxuO/7Rdg7XYhvUUd77DPOjvVS8UL9gOLjXzzyLqe1s8Po96Hd1w+m1JeD96odZNcXdwFFg+B5JhlODowPPo0EyrUBjgiasWJ6nMYpbIjg22IpBt00cs8ER6NhQKzblz0NuAnYBBC+KtIw7PHAAHtHFDlToKlGmLYUMLJj2Ae4uYIgEjRrQlAOadOcLRH3HQ1RkqCWPe0Kpgm35OvS7XhN3FdAiT0OLjRRNVFVq4G0fBVrOSGHgxVWAOYZ1AhUmd3wQ6GwBnHagsDzttlMjcPYB9naA56OxCkhXlgwJz83bgZ2vBMiIQLF38wTCJWlt9OdfAUf2SgTRVTdJFl583XDSZMcLwA13jSZOaP8TaCAYAf/OGQiCvsxg8YRUYDaqq2RK5AjVoUTRy4a0wLH9kQHYPF4WhexBBPRxjlmqjmY7SDAu3STdEkXhAqDtYOBYCxyXOXNgccuFvy3ViuxwCnrEO51OkVXFn4sWLRIWLNHocPTj9FCLeLV5xlKU62Nfp7houf7663HXXXfhU5/61AQ/dAapwqc//Wk0NDTghhtuwJtvvpmyDL/pgt3bjWE3j0M/DMoS6BUlGfuZDFKC+vp6YdcWDc7Zx2s6me2dolMNp9eDQwNNGPY4UaozY6EpsfNYKVeg2hCn2WS64PMA1tOAZxBQGADjQmCWNklmMM04cRj4318CQwNASTnwwIPSzwwymAXYs2cPSkpKYLfbI0gNWr+zwYbqjSuvvHJSc7af/vSn+MUvfoFdu3aJGvqlgEuG1CAWLFiAZ599Vvjf8gviYjCVuKqwHHON2ThjGUC2So1VOYUiSJyweT3R5TcBu5ddz8kVmfx+H7oc++H2S9JQFwbgcPSiWLsR8mSzGmg7NfodAGtvykgN4dctyrIz/3By+1w4PngQQyKgHchSmrEkezVUgYBnZhowl6LJ1gstO+5zqoQd0GzFmtwq2H1O7Oo5N0K49bV2wKMA2iy9mJMdqbigXHxgYCCu560qT41HXvkz8srzUFZWjtU5K5CrSX9omMvnxPHBt+D02UceU8Iv7N2EwsTvh1KuhdPngFKmwhz9ophBmVMFZrM82rJPnP+yAJkUjTpjCU4Pt4t/56kNeHvZmgm/H62nolVGhEOEOyZekGQg/Nb8q2DzWoX3to6WeP4DEqHh8gDDztCTmVvjXwFo02jxZc4HSBy3N4Y6bnqswKZ3iKBxux7o958ceboXDvQ4D6JYy+7pCSyOaSd47HVgeADIKQaWXiHZTc1JIDuA70fSYyphbQG63gzc8QMDJ4DSGwBVmorQ97wXMGUBRw8AWh1w/e3AgsXJv86jvwYOviWpLGhr9fe/AZXVozviSV54mV8Q9V2SCDEVSNe3cBIkpxSXJyL3m80vgwtZKNGulvKd9v5+9J+0nRxNaixbBcypBZouhFQ58xdN7DtOUaDpqaE2+Pw+LDCVoFA7TmbKRGDpAhp2As5hIKsYqNkq1F8CJISXvQNo3Qe4rICxGChfDZNcgZtuukl0U9GSKCsrS5AbbAYg0RGv46nV1oNn2/eMkLWnLS24tmgl5hojj1ur1SpUAUuXLsWPfvSjTLF5BoDzDH4X99xzj/humG8ynpXOTIXN04le15GR+05XL3wqD0xJWhdkkEEs0JKPDYYTCZ7m2p1qKHaoJhIofjnD5fMIhXava1jM/7nGu5hTjWuLp+d6PSlwLti/A/D0B+YzMsDVAeReLZSRlwwajkk3qmTr1gHFU2uRfFmgvRX4xXdDqm/ef+jrwFd+AGhmbz0ng9RCytFtw5DHAoPSgCp92bTZ5kfj6quvFoTGqVOnRAMVwTUGbRl5bZ2sI8Vjjz2GL3zhCyKLmrXzSwWX3IyBstU///nPuPfee5Gfn49t27ZN6vUGXE78tP4wTg31IUetxQerl+CaoopRz1toysffO86P3OdpkaXSonAC4d1O38AIoSGBnfEO0VllUMYp2nDgHjoPOPsBpQEwz5cmAvGCU8cLVE0AfrCb+xjlIIH7LIQvgQxSB7/P78WwZ1BMtgzKbDFYsDhxZKAebfYeaBQqLDfPRYFm6orP54ZPYYhdIAHw3+eHT2JhVigUfp6pWNwuFZCcCS92m0sLodJocOH0OZzsPSAGz2CWBq3c4gX5WT1WHLYcw/y1C9B6vgWH9hxEe3kb7lx6Bwzq9FqotdjOCcIiHF6/TIRu8/gq1y9AkXaOIAQnVMxO8ULjz8374PBJNk40c/H4AWXYNWh1TjWuKVqMG30eIf/WK9STukhV6vOwM8ziShawU8tTj69YcPls6HE2CnWYnvZSqnJkqQKEp58kRiAE2xGypRqB7Ux6SY2hXmnH6QPdWoZ84Mq3A0WV8Pu9cLrfgiGgGGC4OcPifWA2jh0qWZJjr8sBPPc/gHVQmgy3ngE6G4Hr3jtzA+R69kUWtJmLMnAMKEiT9z7JhFvvkW6TwZF9IduoIJzOSIKC+7x2oRQ4Hgtr7wDeehSwBcbz0jpg7npcjjAoSjHkuRD5mKoUkAeK69E2U0QspR339ef/A3jhWaCrXepsu/bmafEbpm3fH5p2jihjd/Wexzsq1qHaMHbIbFKwDwJH/iqRZzyPeN/WD6y4K2QdRyJl7mg1klqtFnPMa665RiyOiPHG8H39ZwNna+g439t3NoLUoFLyHe94hyBG6HE7niVkBlMHfhd//OMfhVqD5MYTTzwRV9U6k2HxNMV4rCFDamSQEjAgnJkaJDdITJCgSBTV1dXC3pbdowwcp9Ub7awyGI0jAy2C0JAU2tI1ZW9/A9bl1SBbNcusRNy9gCew1hBgWKIVcLYBuumx+/P4euHwnYQfTshhhFaxFIpk1xXhOL0H2PWUtELjVKHhqLS+KJubys3O4OSREKFB0FKVio2WBmDuNNugZjAjwDn7vr7DaLG3jzSEkuDYkr82PcQGG9zddqlRKtZ6LIZikU0zwWvnRBoE4uHVV18VORqPPvroJZfTN0MrNZMDu6geeughvO1tb8Phw4cn/DpcTP/biV14q7cD/W4nGqyD+LcTu9FkHe0VuiS7EO+sWDKi3KDl1KfmbRCWRsmCocixH4+TJMCBu2Mn0PkWMHAW6DkItPxDknIW1AKFURdM+kgLr+jJ4swIoSGhm7SB+JfTa8fRwTdxyrIXJy17cHxwF9w+J97sOYaDA2fR4exDs60Tz7btRp9r9P5MFDzR7V4bnN7Ignc8DLr7orpa/Rh0S6qNaJCA6XB0o8XWDpsnpBCYbViSXQ6TUicGbv7HwXJ9zWKsXbRcWCiEh4Oz85Rdp7HQ6+yDL3AMls+twPxVC8T9F994Me2fweG1jepG5kVoWdY2rDBfIwgNIllCg8dPsCCVDCxuBy7a+2GLtsBhndk5PEJoBEFSw8k8a5/0UxHw81fLlcLLdrIXqzmGfFxTuAgahkfJAaNSiTvL10bYWcUjNOqHd6Pf3YpBTwfaHSfR6Twb+8mxdlPApiotcDuB538F9LaFJqgM6NVJiwq79xRkMlsg6oHZRDKoArsxaUUb0cQMh/5QsZ3vR1KjhwHHCaC9GXjiV8CffgzsfiFQKE0juH1RRJ/4kjieWjoAW8DOaSYiuhBIAiM7B7jxbqmTivfnLQbe+ZH4r2HMBbZ/CNj2fuCajwJrbk9osngpIktVC5OyGnJhKKhGlrJG3B9BTYyiUqzHCO7/W+8C3v8gcNMdkgXbNODVrlPCws8f9t9LnSdS+ybd50KEhoAfsHQCw5zLJI5EbFUIp1Duxn+M16IPfOADQmL+5JNPQqvNdBXONPA7eeqpp9DS0oIPfvCDE5o/TDfYEDD6sfGTyjLIIBGwOWr+/PlirTGRLlD+/RVXXCEsrOgrzkarywm87rXbB9HlGBpzfLF6nGJNF+vxWYd4a4l0rjHGgNc/DLvvEPxwBKovFti9B+CfzPYceiXwj4DNKr9aKsMT2iAvsOvvwJ8fAp7+LdAdmZeZQRjiNRpQdZ9BBkIFPiAIDelslMbYbmcv2h1dqd8/XaeAg78Fjv4JOPR7YLAl6ZdIFaFx6NAh3H777fjJT36Cm2++GZcaLjmlRhBkoTo7O3HjjTdi586dqKmpSfo1Wm0WXGDXbgD+QDj4mz0XUcWgyShcU1SDbQVzhOWMUTnxzmu13Aw5GIYcvgCWQyuPEwDjGgAsDWFbyZVyP2BpArJrgeK5QH9DWGCSBzjxLLD6XfEDlBJCb9zHGqwnIjrr7T4rLlhP4FzAbie0pX6csbRgY15yclmPzwuXz4GTQ4dh9Uqqljx1IRZlrRiTZWWIOa2MiGDGhFZOWyonVGHencyMeL17L3pd0mRaDjk25a1CiW72+c7pFGq8d84W7Om7AIvHgRKtGatz5mDvnj1Yu3ZtxHPD/cGjoZArR3Ut5pXkQ21PfyFRpzBiKKKLR8pDUSk0E1407Ow5gdOWVnFa1JnKsKWApOT4pMhbvfV4mRcpcVzIcFvpCizODmVLUHURjrBy2ci/Dw+04Cr6tUcpPHb21AvLFdpRbcqvFaRH4p/JCZNKUq6QGD0+eA7lurG7DvpczfAJEjW0cOp1NaJAUxsgXvhZ6B3eC6gUgDeq8KEuSXj7kgbJBNtQVOeWF2g9CyzaCLe/c9SfKGQ8xyuhkE2gECsIqhihwDGIq1Hougj84XtSgZQHVNMZYKAHuPFdSBs4dqvNgGswtM1uD9DTJIWWEzk1wLxrZ15g+fZbgGcflXa32QhoVYClDWjPAr74vRCxMR5IYmQl37lPwlpQvKkMlZ9G8HOY1fPELSbKl0jHQNPhUFB4xTLMZPBaFdl+wED0FBdrRMEoRtGo+yRgirRmTAUq9AUYGJQ6awkefRWBTA0Wrz7/+c/jjTfeEF3KsfI4MpgZ4HfDDL/NmzcLCf+3vvUtzCbolUUYdFui5lipP94zuHxBxRmvS9EqC64xEgW9xElsuFwJzMEuEQy57fhj01vopd0hL926HLyzch20MRoRy/U58PWGrl+S1a1SrB9mHVS5IgssksTgHHd61txef2+MRjonvLBAiQlaLo9aS7AJKcE5zfOPACcDuWict54/DjzweSB39tUk0o6VG4Dn/wZYLZJKg3PfOXOByuTrgBlcmnAE6oDRcHpTvMYY7gIaw4hLWoKf+wew/D5gitV09fX1oib+pS99CQ888AAuRVyypAbBBWJXV5ew1+FCsaJitG3UWIhX8IjVGRGEUi6HaZLBVgqZCgXa1eh1HoPHb4VCpkWuegmU8jgWPzFVCrLQ4wMXA/YRYeVVdvF6HJM8qTjJir5ISxMvq5fFyMiShC1APoza/BhdY/Hg8rmxo/sQ2hw9KNDIoGMVM4BeVxcaredRY5wf9+9rDQtwdHC/KH6aAke/TObAueGdqDWsh4bhZADOWhpGCA2CioQ9fYfxttJrZ2UhTK/U4KrChaNIiWhrC/rZUjJO2Xc0ijQFMCgMsDGoO/Ddevo8KNKkPzCzUj9PkBp27/BILarbZcWB/gNYlr1sJBMliBZbB9rsXVDKlJhnqkSWKjIAd1/fWZwYCoXrnrS0QClXYnP+2NkJrbb+EUKDYB/xUxcPiWIbVSJV+lwUabOw2lyJAwPNY4wUo0m63zXuFpYrQZwf7sZ7qzfFJVpIsNYP0w/SBrVMiaMDFyQb/MDvL9p70GDtQG2UX3t0+Hrs12YQvDJAei4H/GcAXS/gt0tB2HwXdSlgDNm2pRwxg+j9YcVu/gyRLDwmlHIjspQT9IcsrgnlCIj3lwEqDZCXQE7DoTelyXN4V93hncDVbwfUMbqtHRbA2iNJUY0FEyeXCzYDHa8AXnvAgtAhbUcQvecZNALk1QJ5DBFPoFPp+D5gx3OSHdf8ZcA1d6a+W//a2wCDEdj9d8BNFVYATWeBlx4Dbn0P0oFhjw1vdh9Ev3sIKpkSq3IWocZ4mYQHli2SbrMELOb0Baw1gvOuMt0EignivGgAnAMArwPmuSGSL5/5IW9FPp+ehmwUEedyoLFkIiRpDKzPXQCbx4HzVqm5o1xXgK35S8S/v/a1r+Hhhx8Wc1UGBGYws0Fv43/84x/YunWrKNx++ctfxmwBVVw+vwfDnmZxfukVxTCrLx1f5QymH1RpxAr95rkyliI8HD6fD83NzVi+PI3zzBmGp9uOoM8VmhNREf5y5yncXDq6CWGusQhX5M/HGz2SuppNUHeXrxHWzrMOrJvkbAUG9gA+m3TNzV4DKKfLeiz2uks2GYOTigVSnkb4OqGybvy/s1lChAbBvyc5eHQ3sO1tE9+eSxXM/ft/XwOe/QvQ1wNUVAO33D1zbYQzmHKYVVkxc0hz1BNoJnJZgEHa//oBUyWgDaufDXeMbpaki46tB8hOriY9GTQ3N4ta+Lve9S587nOfw6WKS5rUYPH5e9/7Hmw2G7Zv3570YrFcZ8QCYw7ODQ+MhCzz50td9ajLMmMVg2TTBLU8CyW6zYn5qKlzpAJgBDngB3SBDlalJkYDsiwFFlS1AI6GXk+8gcSEa+RaeERIcej92FlfoFGgzzkE74hOw48qfeL7cXfvMbQ7JDUIFRax7aXiw6zOw6qcTWi07gsEnIeKux2Os6gyrBT3hzzDo3aZ2++B0+eCdoLqAPgsgJ/bpwDkRcBELHJSDHZNRRMb8Ty8WfTfUrAB5yz1Il9joHUAFbISrFi7Iu3bqZSrsCx7I85aTqHV3iLsnPjddDm7cHzwOFbmSN+b0+vCmz0H0eWUjgNetOqtLbi+eBOyVaHJcX2YYiiIC9aOcUmNdkekVRnPT+Y4PNch2aLwmHl72UrcWLIUZfoctNkH0O+y45w1UtK4OifSI7be2o32MEKDuOgYQIO1B3ONoztx+L7/6DiARlvnyIWZ7y0PirEC22IdxzbNqMzHgPti2CMyqOV6KMOLeILcCCipuAuNge7mdHf/F5QDuSVAf0dAZSaXCIKqRfD6bZBDDx8ilRxeXzF6XW3QK0zQK5PMDcopArbdA+x8Qiro67OBK+8BNAnkxXjccR73SGKXcHSdBc68GLK5KloIzL96YsQGJ2Dlt0pFWNrYdD8Z9t5eKdh96DjQfBww5ALr7gXUYxDZ544BT/4mdP/gDsBhB+54X8ynszhx8uRJrF+/Pjmyl8/dfDVwbAfQH0ZqcJ800tYw9aA649WuvSPnBMfzPX1HYVDqUKRNPzF7SYLKJJ6XKSD6Ox3DqB/ug16hxOLsImwvXCSs/C46pOaCfI0RN5UkqS7huNH2JjDEBUfgik6CozKgXuI5wQUwO/r4XC56tWopKNzJ4kpAjcrrtWZlHKJ1NAbdQ7B7nchSGqFXhs43pVyBa4tX4UqfR4zhweLTd7/7XRFCzdBx2rZkMDvA74qB4cwQYAbKZz7zGcweZdd8ZKskZddsbNTJYGaD6wiqNWKpLxwOx7ikBsdHjodUk19OmRpcM4QX2vivVnt8+62tBfOxKqdKWE7lqA0TsryeUWqNghulWkaC19p0QSkrggv1EXUCObIhF4ugCWLT7ZQwAS2npDlT3Xpg2ZXj/x3XEdGQjbHuuIRw4MABodaiVXZSyM0H3v2xdG1WBrMMdOc4Y+mC1eNCpT4HRVoT1uQuw/6+oyPj7XLzIuTQ/SAZsB7Z8Hyo/tp9RFpfBJtJWX+NpQZXTp21bFtbmyA0qNJgTfxSnu9d0qQGwS/vZz/7Gd73vveJQEdOkmKFIMeCXCbDvy/ZiJ+eP4xdPW3iGsRCutPnwQ/P7cU3l16FUl16J1sJHXw8OUq3AW2vB6SbMqBwLRC0SipbDnSekopeAn6gcg2QhLVNzG1DIfxYxUjPwBW2BDJIA8Icw2KcGtobsLZhGV8h8gQMymEYlHLYPcyyUGBNbh0q9InLJ9vs3SMDkNfP140kfdTy8TsqjUoTZCM0VRAMGg4VgE0MW4+CUqaAOkoRkDC8nYDnUNjgdh5QbwRkk1P1THZBzoLk0qVLRx6jdy3tp6gCkMU4/rh/F2dLio+99XuxYl36CY0g5DIFBtzDgkQIB4kNwu3z4IWOXRgW+RsSeKywmHlq6AI25C2PKC7F+n7F3/j9InyP53mBxhRhAWWKuhCxLB1tkfJU2xEsyirBcnMF5hmLcMHaA6NKi2abVCBbYa7AZnbOh8Hhja2YiJXZQTTbugWhEfyMwfcOB+/nBboOPD6XsGtTy3WCIAoiW1UscjW6nOfFX2jkBlTqV4497ojfxfm9wwYwcygrd/Ld/cwDueH9wP6/Az0XAVMusPYGeLVKWNxvwU8bvZEtUaPbqcGQJxSWXqGrQ6E2yYDBykVAxUJprFQmsf3zlkpdU0GwYFpSOZL/MQIGhYUTGgTHZr8LMJcDBXVAlH3ZuODxqc0PFJjDyG1b1LHDAOTzO4FF18R/rWN7ItUq/MkOsdveEzMsmplVixcvxp49e0ShYtu2bcltu84I9PeEHb28yCZAIk1QpcFbOEgIUtGVITWSBJVGx58Dhtql469mE1DJucDEcHigHb++sH/kqlyhy8Yn52/G/VWbxFjM6xFJDbfPjeODR2H1DMOoNGKucQE0YzUZcMEhCA2EjjFbB2BpAbICY8OcLUAjva6DbLAMyDOGCA3C1wm4TgOasW0yee04MnASDbbmkeNrdc4yVOgj1V7h1xR6237961/Hyy+/jCVLJNVGBrMHnD+98MILYtHIvI2Pf/zjmC24lBe3GUw/qLTgLajY4PjILJp58+LYJIaBhAgV45cioeHwugVhr1OokKeJVJEblRq4XJ4IhWLWOI4KzOXj7ZLBNBMahFymhl6xHk7fefj8dihkWdDI5yad2xgBNmVdez/A9R7H3kQJKFM2UFAK9HSE1g5UZM+9tOcLLMby/OfP8+fPC2Jj7txMqHoGycHt8+JXDbvRzDVwYEx9R8UKrDCXo1hbAKvHBp1CN7GG5c6DgTV3WAWmYw8w9w7p37m1QPtRKQ9UrDF8QE41ELCdTTe6urpE7XvLli346U9/esnP+S55UoPghOrXv/417rvvPlx77bUi+T0nJzEbA5NSjeuLKnFssH1UiPjJoZ60kxoJw1AK1N4NeKyAQhdZGNNlA6vuBdqOAPSkNlcAUX7+E4UMlFmNtioyKrOxLHsz+t1S4Oage1h02I9sklKGKkMhFlCqlQToF+oJeLIOuP3IU8ukBksGdYKvGf+C5/L1Y8B9HF6/HWa1HINumfgeg59Eqwh1Ds03VotiV79b6p7na6/LHTuvIy74Hp5jUWVnB+CtB5TJ2YEMuXvQ6bgglCXZ6kIUa2omPMmiLzQ7rcPVQBdaG9GVb8eO5pMBe5Y6zE/yO0onaCcVDUVgAkzLqXBCIwgW/ZlXEY6V5lq83HV41GMkQJ5qO4TTFul856LjnvL1KNFJZN18UxHm6PPQaOsVeRqeGCF+Hr8PVq8LLrcHv7ywS/ybyFbp8KHqzTDH6JRn50As0I5qGUbb41hjWs5FYoV5Lsr0+ehxXkSz7URAzSFDlX4x8jShDJACTQ3y1XMEAUnruwnjwOvAK3+TjnfaNt3+PqA60vIsYVj6pIBwqiTY3RQmG3Z5zonMkGiv/SHa6YWhxX4aZnUR1PIkOiKo0CAZkwyh4XRI4+2a7cDx3ZJHbuU84LYHRnew2wcjCY0g+hoBayvQdQjIqQUKFgPaJK12uECasxVoeC0YABX5e34v1rGVbHHJqhjo6ekRykd2XHKhce5ciFBKGFfeAvz5p6H9xG1Ok5w+OE7EIzMnCo6fDp8PujgKt0sOPI6OPi3ZpxEcW8+/AWizgMK5E+qgerjxYESbQat9EC93nsfNpXXI10hzLI/Pg339u+HwOiCX0c5yCH3ubmzMvSKCqI1APKVaOLnF841qioEGqZiSNx/wHxj9N77A5x0D7Y7OEUKD4Jh7oP8oCjV5MckXzkvpb8ui+KpVEyeFMphe8LtjxsZ1110nFBtsosogg8sddXV1OH36NBYtWjRCVJD4U6vHn1/RYYHn0qWGJmsvHm3ZJ5qmiJXmCtxcsmxkDXZD8RL8b/PewLP9UMnl2F6YgEVRBimHXKaDThFq+ksZ2LCVDLjGv+sjwDO/By42AFqdNE+ec2kfF01NTUIFzkydo0ePxrTFziCD8bC7txEtAUIjOC//ayubT4tFo24i16O4EGsJf/z1BRuYFt0OdB4DnMOAPhcoXJQSdft46OvrE3PSZcuW4Ve/+lVMO8hLDZcFqRGUwv7hD3/A3Xffjeuvv1544SZKbGjjXIDiPT5t4MkTzw+OxEbtFckH3LQdlAoDWeVA6arEOwtoQaXQo1ghdUM2Wd8c9fs+V6yg8bGxLHse9vQdF6U3uxfodvqxwFQMo1KPYm059EoDnF4HmmznRfHDpMpCpZ5d8W70uQ6wb1+8jlzmQ7YK6HdJwxE71Eu08yOslq4q3IAOR4/oDs1T58Ckmmj4Gt8zWibqB8KUIYnA4u5FvTVUbLE7LKIDv0I/cZ/0BQsWiAwNLj5YnLug60ZbYwdyS/MD9izHYVBqURYVkD4wMBBTWp5uVBuq0e2SiLIgaoyS5Rm/p3iI7sSebyoTxczTlhbRBZynyYZGoRYh4EFCI9hR9deL+/Hx2qvFooOk1r2V63FssBUDLjv63TYcGWiNuKRp5ErRbfXrhoOwjaij+P058H/tx3FfVWQ4O2FW6aMSIqTycqcj3F4phHz1aOk+n39r6UaR/2JU6pCtMogMkibb8YiLeaPtOPQKfl4SWiTtVJDLKqCYjGqo9QLw8l9D991O4IlfAx/5CqBPkvhtOgG8+Xio+F8yF7jqXSNjTzihIe77WeD0xyEy7YmRGuyAeuznQG8noFQB194NrN469t+wU+rJPwB7Xgs9lpsHfODfgLw46rNAgXYU+Fnp5c9juO8M0H8emHcboEtyEs/cHOYODF0E7HskkiZ4dHISRbudsbB8Y6R3L/9m6fqYKg3udyq7hoeHRaGCi4+kwUXZuz8jKUSIRauBitR0YjHw7dzwOVi9VtHVP984HxW6YrTYO0aIao4BNcaJ+5q+0NGOjx3ch16XC+U6HX6zdgPW8hi4lEG10XD36NGnt2FCpMawxwUnVUYRryZDd5ivONHr6oHdy/M5SL1JmRenho5gqXlN7BcXxOAo701AF9UlZSqVbkHYVZJ6KmKjxid8Gb4c7dPLf1MhFE1qMD/jk5/8JP7v//4PGzZsGPe1M5jZ4Hf4zDPP4JZbbhFq13e/+93TvUmXHfx+UqN+oerNYPrBIuSpU6dGGqdYPEpk3cDnnz17FtXV1biUwOy8v7Tsj2iyOjTQggp9rlB2EzXGAnygZivOWjpFs97irFKY1elRr2Ywi2AyA+/6l4AV76XdbR0ESc2DBw+KcaOmpmbGkRqdjjZxEz4lugrka4qme5MyiIFel3XUvJzNpxa3E3maSdZxafPPvL5wt4Gg9X8QtJll/XQK0d/fL2rdvIay9h3PVv5SwwyryqcXKpUKjz76KO655x6RscHuuESsqOabcjHfmItzw33isGWHdoFGj9VpzNSYdjBI/NRTgcKiHxjuBOj5P3cM65IxEEvhEKvrfjxQNaBVqNFk7YBCJhMh0AWaEDnl9rlwcGA3XD4WJPzod/dgyD2AuYbSEUIjtE1AmW4u1PJc6BTZo7aRnb1luhRcpMQCSws/HCN90DyOZPLkir09rtYYj7WgXFc3YbVGfn6+6KTiQsMl88Ch8sBus8Hr8UKhVIgLQautK4LUYBHz+PHj2Lx5c8zXpIrkrOUkepzdYh9WGWpQpkuN2iNPk4d1OevQbGsWxfsibRHKtJLqoFCbF6t8hRpDOeYbR9sQ1RiLUag149GWXThDAk985/KI1+BPi8cBu9cNfaB7n89ZYZY+j8fnQ7t9EJ1Oi/R8Rtko1KL7uNtpibiIcrkdfF40uOCjKiScBOGWkByJBW73xryF2N0rhZbze7qiYAlKoorgNk9kTkcQVs9ZKOVBOSQtutqgxAbIJhqIy+6hcNsigtZZ3e1AVdhxTpUVuwXiTcr5N7uo9gg7V9vPA+f2AwukorlKngenr2Vk8dvp9AuCMxY08gQWg5SC/+nHgCWwr+hT+/yfgNwCoHqMTqidL0USGkR/H/DX3wAf+kKcDTIA1ZuBhp2jf2d3AfrA983P330UqEzSzokwFUs3ZhUd+GvIdpBqo7mxz9kR1C4C7vow8OZzkgKFQeFXxVZO8NqZqJXjmCidI91SCHb17+6Tuvp5Dva7+tHn6sOGvA0wW7LQ4+wTUuPFWXNFpsZEcM5iwT/t2TWi1mqz23HXrh04eO2NyNNcQlYQ0YhlW8nTeYIZXSalBjqFUljwhcZdP4q1kbYcVNEpZNJbhUuoBzw9cHjt0FKhGg0Gg5duBdrfDMuwoTXnOMetagHgOhipXFKNn3WhV+hGBQ8SOkUksfqLX/wCn/3sZ/HUU0/hiiuSbDbJYMbiyiuvFN/p2972NmHH96EPfWi6N+mygLA1sp1Gt0tSSWUp81BtWB5fwZXBlIEqThIbQbVGaWkpGhsbhU9+POzbt0/83UwrYk4Wg247HFHNV5znM0cjSGoQRdosccsgg1G4TAgNYsWKqbO3ThZt9hacG5ayNIk+dw8WmpajUJt4bm8GU4NCjXGU4bxKpkAWs/Mmi6I1EqlhD+SmsuG0dJx19hRYTl133XXiGvvnP/9Z1L4vF1xWpAah0Wjw2GOP4f777xcLEPoYjxceziLm5+s24um2c2i1DwlC4/bSBaIIGcsDn0XQHLV+xDeZ3RmUmuoV6tnjZ9ZNn/eoxIC+84B7ixSimQS8fjcUstG2QBW60bY6iaBSXyxusdDlbIfL54x4bMDdB7svdvcsyQytIkmblwnAr5wLeI5HyEn98rIkzF6khVssDLi6kKOZOMHGIL6dO3di9Xqp27WkphwdFy7C5/WioKIEyqxIhre9vV144sY7lk8NHUe3U+qG9vjdguBQyVQpu9iT2OAtGjnqLGzKWynCfz1+LzQyNTbmL0fJGAWsV7tOiIVGEN5A4YxB5EEoZfK4qiwWsLqc9HwPPdbvsmFXzwXkqY2wh4X+SWRoZKEuHNcWLcLTbUfE88T7yuXYUhDfe3iFuQa1hhJYPDahyqCiJhpKeeziqlxOlVQ4feOCD61QQFK9JA29MZLQCH+cGLYAD/8EOHsCUCqBa98GXHf76Ak68zii80X4nI4zwPy14rxRyQugUyyA3XsOQx4vHDHcnIhK/SKo4nz+CPR1A0MBaarbC7g9EvFy+sjYpMa50IQ2Mpi4aez3q1gl7frGXYHPF7ZQ8foAJc83WtbFzlNJGDllwJb3Av2tUiE6f05ihee6FdJtFoM5O+zqj+yWH8aAewBLslOjBNnR0yUIjeBRz8NwyOPB/v4+XF98CS9sSO4yp+vikcADZBoUQFmSId4BsBv1fdVr8D/1e+EOEA+1xjxcXRiZO5SrzhNzsUg9mwSPUFXEIaeyq6XQPpcFoNoyERJLWQLINgDeNunzKcoBhXlkPiODImajBrMzWuxt6HaGVKiLsuZHhIX/8Ic/xFe/+lX8/e9/j9sYkMHsBRumaEV18803CwsdqnEySC86nQ0jhAYx5OkTCtVa48rMrp9mFBUVia5R2lNy3UC7ShJ+VGLQDiOWR34wT+NSA3MvohuvOIMwjZOZkUEGGcwstNgujH7M3pAhNWYgNuTNESHh5wIKczZE31u5CqoknGfigtbTc24EXAFraY1ZsoubJrS1tYkMjeXLlws1+OVEaFyWpAbBL/mRRx7BBz7wAdElR2KjsnLsTnIWNd/BANkxsLv3Av7RQe96iQV8R8VqDLiG8Xr3acES0lP/zrK1KJgNHRjBsNlEHx8Ddq8FCpkX2UqGrAftnrhPU3/is5szFhjy5YMOXn/IjkUpM0AjTz+hIbZL3gKolKHCLwc9WQMUSDzoK0ddhEGPFA4djj73xUmRGiT6Nm3aJEJ/a2vKUW9tRencCrGbOhvaMI//DkNtbS127NiBwsLCUcQG93+Q0AhHp6M94mLvdDpF91bQi5+LmJUrV056AK40lKBcXyysqBjqPh6J2O0cGtVZSwWPTDwkyRWvK1oSN0vF6nGG5bJgRDUx4Lbj9rJl+FXDLqHyIAxKNW4uif99s1OLgYDnLJ0iyJyB4rnqsS3PuBgaa0FkUuQiT22AHFaRqDHsYRh4LowKyiUjtxp+dzKRCpGoWwkceA3obpOObdrJLF4H5Ae+89//BDh/Ujr+aT/w3ONAdg6wIUqJQKsqTjTC7Wj4N64eoOEVoPpqQQBoFZXQyCsw5CZR2A4Zv7Cwr6HWsAbZ6gRtgNQB4sPhBixhlnBvvARsuUEK6YsFfRwVSJZU/BTg5+hplQiKvHJAE/iumKHCAy0a4YdSuB3ORKE1ASXj5JoM9QHHdwJOO1A2F5i3ctZ0g/n8HnQ5T2HY0wU5FMhRV8Osqox7HYj3+ERgUCpj9OQzT0qaVrXYrPh1Qz2G3G5szi/AanMOdvd1QatQ4OrCUmQxu2W2Yv42QG8G+lsAqskqV49vbTYGFmUV4iuLr0ajtR96pQpzjUECIwTaN9UY5qPRdmaU4lOnGMcaktZPuuTUM355LrwyA+QypbjRyu7C8CHYfZLarkhTgxItg0ND5wqvE5vy1ohsDbvXAbMqG/ma0H5hIPj3v/99vPTSS1izJo5lVgazHgxl5HdM+T+JDeamZJA+DAay+0LwY9A9fgZOBlMD2tuycYrzfY6XXDvwVl9fD4/HI+zaookQBopXVEzcGrKhoUG8F22m9+/fL7ahrCyUJTcd0CpUuKZoEV7sPCmal1gXyFUbsTZntJI8gwwymLmgU8SoxyZQH8sg/eBa4oE569Fo7RUZp+U6s2g8Txm4BiCZMQMyaK6++mps3bpVZGhcLpZTuNxJDYJfNkMaP/GJT4gD4JVXXhHF2omi2daHv3eEOnfdfnpn7oMqbF0+5HbgL6178ZHa7aMW7InA7u1Gn+ssfH4XNHIz8jSLJueDPxZyqoGuk2EP0I89H1AlPxDIA4eZSi6L2B+KCdhPjYccdR5k1kjvPJVMDZMyBzLZOlg85+HxWaGSG2FSUm0Q/6T3+ofg9rUHwtqKoZBNZtCySwNfRLFwtHplLJhVJC6iA8elTp/Jgp6VDONiQSZLZUCHo1fYfK2qqoFzwA4UhBQG7K5auHChsKBaujQyRE2Ka4/c/+LxsOOd4UXHjh0T4ZpceHBxw66s1157Dddee+2kP4vD68KOniPocPSJfIs1uQsx1xh7MWNWGTDgto1sL7+dAk0WFpoqhEx8jj4f5Qx2ilEctXicYpHC9wiG/onfwS/kjsXaLHxy3lWoH+4W+2SeiQqDsQuZ1YZ8cUsV7L5G6BQ8zmSCG9BqZMhSlkMmY/E+XAlAz+NJEHws0L7rk8DhNwHLAFBYBixZJx3vHg9wNkylRPDxE4dGkxoMGN94O7Dzb6HjXK8GcgzAwAXAsWokZ4ILVq3CyMF25CXFT8hhUiVxrmbnStv66kuRjzscwItPA2+/X7rvdgHPPwE0nAdycoG1m4Cj+yOVJVRZvO3doVyRlx+WSA2CAfFXvxvIKwWySqWxVKiEhBmddPApA+dJ3kIgfzHSDhIaj/9ICjcnTu8HBrqBdddjNqDTcRLDXolE9cKLHtcZcV2hkksOecTCg8Vvszp1E8+bS8pQYziJJtr1+f2iSLEmNxfrc/MEoXHtGy9jmMc+gMdaG2FWycU4w2/7J9pT+N/121DIwMfZCJ5sFSulW4qQo9aJWzx0OgbQ7vDCI1SXPVDKZeK7XmhalfK5hNtnRZfzIDx+ieTMUlaj09kDu284tD3OC9DIdcjTRCpOSWyU6UpGqSy//OUvi4XGq6++KoL7Mri0QQUs5zTsmiOx8Z//+Z+zR6k9y6CIsZSl9WkGMwfZ2dkYHByE2Ry6BldVVYnmpiVLlkScGyQ/2DhVXFycdKMTx9oDB/5/9s4DPK6zSv/vnT6aUe+2JVty7457SeL0kBBIAiF0EnpZQgltl7pLW5aywB8IELL0EiCUUBLSm+24995kW7J6HU1v9/+8352rKRpJI2nU7O+XZyLravrcufc75z3nPXtETMFZXyyauvbaa4XNrs/nS9sZMp6sL64V1lL13k7h3rA0fwask20+p0QiGZQSSzka/fHuQFI6iuJSydjCjvBaZ/ZyK5ON06dPC0GDM92+973vXRZDwdNxWZ9J+aH/4Ac/QE5OjqisYsv4SD386r1dafz8WRutJTLiFhh+dAe9KB7EhiYdgUgPWgP7+n73RdvR6t+LCtv6sQmU8quA2uuA+u1AOADkVgKzrxtRBa/dmAunqRjusG7JoMCsWGNJ+uziNOVhUd4KnOw9gpAaRI7RgYV5K8Tgb+7uBebMEoVhtRO+CD21NUKRetgNvJ+ResizkjSeECEKhjdTg59zsWU6OlJmaxRZslN9xG6l48eOY8miRViSHxP4SiAqpnjATIXdFayAWrVqVd8+yJ/T7FW46Es+2U9LsBprbm4WAQc7QyKRCKLRaEZVWZ6wB+c89QirYZRZS1Bpr0gb0DzduhudQa0DwxfVBI4coxXTUofDAri2bBF+V89uCi3BbzaYcFP5cpTb8gf5rnfi4frd8EaCIpF5RUEV9nTxeWkJ1LnOUqwr1gYccibG8oIZGYsxfN7sMCm25GXle+0Jx62Q9LvzRRpQaLwCYXU/jyxim0GphUEZYLh1prDjYe31/bfz5MqKAc7TiD+beIdEKjXLgGg3cH4vwG4upzXBninZkqnUWo2eUCs8Eb3zRMHMnKXDHxR64139RQ2qQD1d8X//8BvA0YPav/maDuwG3vdxYO9WrUOltAK49jagLNZhcfB5oONi/P5CfmDLH4HbP6xVty+5EzjzPODrBLi/1WwGOEeAz32E8wmGzaGtmqCR2MGw91ngims1oWog2NXBzzQ2Z2Yi4HfdHenfudYbbsZ0+3SsKVqDgz0HhQ2Vw+jA8oLlsGZiR5Yh7Mh48urr8M0Tx3De68HCvHzcP2+BsI37ydnTQtDQu7jyzZq1nf6dbgv48f3Tx/DFJSuHfI0XvOdR56kTVWAVtkrMz1tw2SXsTvY24l/NXP9ogrnVYMQMuwqjIYS93fuxqnAV8s0DH7OHA9/zREGD9ITOpp3b4wp39BM1UuE57kMf+hD++te/4oUXXhAVw5LLA4pXL774ogg2u7u78d3vfveyrJ4ba8pttehxJ3dmVNpGXqQmyT6cqcHvwoYNG0R3OGGHxvz584WwwcKqRDh7g9dnTF5cnGHXbYKnOG2ujh49Kn5yRgePw5PFoz/bxUsSiWR8me1cIIobWwLxQeEzc+Q5RzL+MJd2yy234M1vfjO+8Y1vXNbFM5e1qEH44XMn4MBkztj4y1/+Ijxx0wW657wtaPV3I8dkw4LcqiQ/NqfJ0q9eXpcyUtGrMlwhN072nkNIDaHcykXOjAF3Rq9I3iSPMA6qvQirXpiVIawXRkrJPO0ySviaahwr0Raogy/iglmxocxWC+MYDfErsZaLC084A9kGDUUwcqbfZxeInuoTNaJqAOHoBX4KMCgFMCnTBj2QGLAYUVAk0YfEOaAgsxNgMNqNQLRTdLxMs80RNivdoRbx2sqtNSiyVGZN1GDnBNvE+W9dZEgVGzo6OkQVFVvdOI+G1VaJA4PnOBfAbLCgPdCqDQrPqUWRpSQpuGlpaUFtba0QFFnF6HAMvg+7wx681LZVJAbJBW89FkUWYLZTEw90vJEAOuhtmAC7JM57W9KKGoUWJ+6duRlnPa0iWTYrp3RQO6dAJITfXtiFQGzYH7sy9nRfwF0zWC1sEJ0YVTmFoipgOLT4O/F40y6EVK2yuzqnDDeWrxrx/hun//FHBZOruTDhypioYYKSzUrnupNA3SnAmQusWKeJF9ffBjz5aIL1mgJcPUg3QPk8oOd48jZ2uaQMQqd4Mde5RthPeMIcTE8h2SdsiWgZkzGc/VFYDHR3JSf4Z8RsAZoagCP6DAF+8FHA4wbOnQZe/67099ndkjxnhP/u7dQsqXju4ODuJXdgQqE4kYqwCAumFzU8vcA/fwY0ndN+X7YJ2HynJvJMAGm7wqA9lyJLEa4pvUacu8dqkceB4P+9rH+ipIcWawnwGSU+B4odDT5P2vsMRSOxph0jGn0Xcbz3WN/f6n0XRPfJkvzkDrlxhfMrIjG7F2MJMFbdovrDqSqebT2UtJ4KRNmVw2SmAYFoALs6d+HasmuzIvZEeG5PEDQ0+pescNtQHSL0jmegceTIEWzbtk2cKyWXp/XOK17xCrz+9a/Hr3/9a1FBLskeueZCzM9dh7ZAPVQ1igJLOYosU7tqNhRtQSjaLtZmtNo0KFm0y5igIkLa3O7du1eIGezO4PeAYoY+RJywyImdFgcPHhTd47zOcGYP8TxL27eGhgYhJjK+4H1crpWrEokk+zA2n5+3BPNUrVD2ck4kSyYOjk94zWteIyxOP/nJT172++FlL2roB6N///d/F62ur3rVq/DTn/5UBB8RNYJmf7tIMrA6+7DrfJ8P5jHXBdw5fVOfsLEobxq2d9Shyd8jEi28znxnJVoCrPjXEhxMvqzIr4bTZENvyI0nW7aKxDu3X/A2wR32YlnB/AF23YEOmFPjQCoS8ONcOTWahHA0yZpHQ40JEqoahD+yve93qI2IKi5YjQN71zOJbFA3MtMZS3HRDmvo5+cNX0RP+EhfUsWAc6i0r8OMnPTVntyfhAHUCE+wNTU1Ion06KOPiu/D+vXrMWNGvBKVQga7Nvg3trmxkopCSKKowfe9xjFHXAaCnrk6Qwka5Iy7TggaiQnM466TqHXMSnqtA9m6DWb3lmOyYgk7k9JwzNWI51uPwh8JoSqnGCsKZgpbqkR4TGgLunFN6cgEQL7fT7Xs6RM0yAVvKw711GF5wei+MzZDBXzR5M4eu1ETwbT3LcvJlZeeAv7yK024oDjw4hPAfZ8Dbn2dZvNEyyla7lxzCzAz4bW11gGdDYA1B6haCjgrgOqrgfot2v1w0G7tTZqwkUBE9SEYaYc/0oSg2g2oCnxRdsQ1Ypp9XebCBt+Ld34U+OHXNLGCzF0EbLpB+7duz5R6m3TbdZyF8fdBuwFgc2iCxmRh+mzgxO7473y++cWAfYDv5BO/BpoTurAObtWuvzLFRoy42oDzh7XXP2MRUJTd4dncfzk/oysUE1hi5JuTv8sTEWxwhsYfGuJdUjwqG1OOGfNSZrXwGPO3xr2o82iCwfL8ahRZ+otOTb7GiRM1oh7AzwH3+vnRDNg2AIbhdR0OBx73E+39dMIJ2iMLQzgIPhvdGumOGdyH8k356AnHO8L4GZZZBxYpWJl/++23i8Qck9rDrTaWXDrMmjULW7ZsEWsmJly5vkq04ZGMHqepQFwuBfyROgSip2OzzljYdBFO0zoYx6qAbZyggMGYggNMKfSysIm/J1pMsZuNx0weO9nRwe8KbW7ZcZEp7IbSBWQWTkkkEslYIMUMyUTx8MMP453vfCceeOAB3HPPPfKDkJ0aydx7770i2Xr33XfjYtNFLHz9WnSHXIiqKlyxmJppVdIR7MWJ3nosyZ8lfqe48faajdjdeR49IR8qbPnCdoaDhHd1nYU3HMAMexGWF2gDyU+6z/cJGjrHes9gcf6ctNWGTlMlXMJKJn59Dvw1KePkyR32A/U7AW8bYM0HqtYC1rwRJXCD0QaEVRcMsMFqrEZ3qA3tASZeVRRbZqDIMnjXw3hgUooQUpNnXhhjMwdCaoPo0EgkrDbArNLCZ+CqVUXhwj1z+ypWnPWE9Spd7XOPIgBP+BzyzMnilz/iw4nefXCH+b4aMcsxH5V2bV8jncEOYd+UY8xBEeeODPL+MtC49dZbRbUUAwxel78zCNcFiLlz54pqK3Y3vfzyy9i3b58Y9D1WhKLBfhXZrFhmwsuU8H3hHJCanErUeTkLRZOC2DUxz5mZBVQiF7wdIsmoc9bTBhe/BynwW5wzCrsgfzTYZ3+lw+dNK6rRkmueDzWkIhDl3AEDckwzYTeOfPjioPi8wKO/0f6tJ/LZ4bD1GeC6VwJX3qBdUjm1HTj2fFwAqNsLXP02oGQBUDQXiAQAk037ewKhaCdc4X1CfPb1JTi1fSSkeuAONyEvJcE9KFU1wOe/A2x9DvjTb4Cdu4C99wL3vA9YfxVQUAS4urUuDfFQKrB4kH1+2TVA4ynAE+scYqXgxgnuzEhl3kpthsbe57T3jgLFLfemtxnkZ9NwOrn7hJw/3l/UoO3Wc7+M7wcntgNXvQGoyK6wXWyZC6NiRm/foPCZcJgm3tbhdTOqcdrdi++fPiH2yHyzDQoi6A1rguiivAJ8cE6yCP6v5oM454lbqBzouYCVBf1tKif03BikwJ4o6oaB4GFN2Bgj2LHC2Uc9IU/SGcBmTH4fzOL8Ovz1SFgNiI4LXczgT87QcIXr+opGRJekfRnyI53oDXWI69P6Tsz0SQOrhNkOzmT273//e5lYk4hucFbVsWCKc/z+9a9/TfjgYsnkg+v+QPSM/lvsZxTB6AXYBymcmkrwO0CRjx3bP/vZz4Qt1OrVq0U3hd1uF3EGvy+E29ndwZ9O5/BsmyUSiUQiudT4zne+g8997nN45JFHRKwh0ZCdGilw52Dgcesrb8WG49fgDf9+r6iyTIVVehQsErEYTNhYkpy0yTXbcF1ZvLVWJyQqD/tbw4SjkbSeuxxsXWFdg+7QKWGPYDUUotAyb3wSHLRLOf53wNupPWcmXnovAkvuBgax6kmXQPCEDyOkNvd1HnQE69GWUO3s8XUzVS0SBiOBj8FKepvBPKr3xmqYh2jEz9Gz4ncjCmAzaAGFqoYGsKPgZ5o9Kw4VNPGO9t+qBvq95qOu3fBFNBGG799Zz1FYjTYUWcpwwnUUF3zxquEZ9iosyF08pLBBayl60XLA3z/+8Q8EAgHhc9vY2CiGYBLuq+zSYCDC+RoMTjrDjXCHu2E2WFFhqxFWVKOFg3+b/HH/fHaj5JpzRcIrlU0lSxFqDaEjyO4ZMzYUL0GBZfiVxCdcTX2dWYSiSlvAhYW5lTjWq/2Nfykw27Esf/iiiY7VYBadJLq1lv4KHexOGCWKYkS+he2x4zB0urcnnvDXYSK/W5+lk4agTxM0iP763Z1A3T5g7nqtq8GQvtLOHT6qSUr9D6Pi/YuK7+kw4bHo978EgjGRiYOef/oDoGoW8NHPAQ9+G7h4QbPWetO7gFmDJOntucArPwDUHwOYzK6sBfImMOHOgeYHnwbqj2qfy9x1wLz1wLpXACuv0wab2znTY5CuQFpSJXan8Lq2NJ/PoWe080biMfLgE4DzdYCjuJ9ANVJ4DCu01IjLZILP69MLl+AjcxeI2RolVqsQNA71dMFuNGJZfhHMKXYYdW7NAi+RjqCKwpTDZ3XOBFoYqexiSnyOqta9Mca8snIl/nJxp7AXJLkmA4oscfux6bbpyDENryI3EHGjwbcHYVUTqosttSi2zIl1AM0Ra65ApBMGxYxcUzVMBhuKjdPFXKvBoI87rYZuuukm/OhHPxLiv0RCmKzlbJX3vve9YrYAhY1E6x2JRFvz9zcz1uKOSwN2YNAa6uTJk8LO1u12C4cEdlVQ2Ejs5GYswmMoi6xoWSU73iQSiURyOUJ7RjoLsRiAueq1a9dO9FOaVMhoKw3cSbZt3YYbX3EjOi624T3fjA12TYCJzjLbyFudK2wlOO+ND5FlcJ5vdoohwQNhNeaj3Lga446nFfAmJibpue4Dus4BZZlXDkXhiQkasfugRQMTaSm0Bc6PSNSo87Ti8aa9wqqCieJbK6/ALEfZiJPBduOKWEcGffqtfSKAUSlCWE0ehK3AAgXZ7ZpRYIIBVtGdkYjZkNwhE4z64YskJ5a4P3UF22FULEmCBmnw1aPcVik6NgaDQgUrCk+cOCFsqegN3dPTI6ynElvBOXODgQm7N/76wsPwG7sxe2ENTGYTOgNNWJy/EaZRChuzcqrhDrlxzqu97w6TA2sK+1fJU+DZ1rEf7cF28R4EIkHs7TqCm8o3pRVABoNCQ7p8+a0VizHbWYKLvm7kmWxiKLhtFJ0atOu6qmQpnm870CeV5ZoolNRiVERcgPcIEPUCxkIgZwmQBYFpQAqKNWspP5OEsXeOw8GnDZKEDSR3Qwn4PfP3DvlwUWjWPCkF2zFUWI0jOD6frwMCaSylTh4Fbn4V8Plvaq8pk2GvFGxO7AR8bqB4GpA7wfYzB54Czu6NfzYUHjjoe/YqTawYbDC4/rlsuAV44a9xUYLblm4Atj8CdNQDView7AZAzIuIPY5BAUpzAbMKHPwDwAGZi149LEF8stLk8+FLxw7hjLtXDAr/7MKlQsDQyTGZxIXkmy24siSerNGhmEmBlEURQe5bMbhb85yyvGAhziUMCq9xjPK4MBqUXECIAPqRUQEMY189W2LNw72zrkF7oFd0xfLIfMR1GlGEUZVThpocrWM2U3ieSBQ0SEfwLCwGJ/LMleJcz+5YXobD008/jbvuugsf/vCH8Z//+Z8T3nEqmXwwQfvQQw/hC1/4gpgVwEo7JnglEsIZGgY4RbyUKG7oneKXEvPmzROCBudn0IaKsQSFDgoZiZZVHPKtxyIcKj579mxRdCWRSCQSyeWAz+fD2972NtG5yBl9zLlJkpGixiCLrT079+C221+F/37jZ/HBH38a5qLCviXmyoLZqHH0T1BkysycaWKGxlHXaVFpmG/OxZUlqyZnECwqbtOgRoCec0DjLiDiBxyVQNWVmlVMuqsnzAzo25bu4ZIq1jOjJ+TF3xt391W7c5Dz3xp3455Z1yDfPDJPVX4WFDNS4bDwKOYiFD2lXQ9WWI1XZDQjY7iPX2S5Ah3BPX3zO6yGMjiM1f2GJad7X2lj5g170x8cRVdHcUbPgWIG1WEGExQ1EgMOwuqq+vp6lE0rxswrSuDzOHHy8BlEI6yiV2FbVoj5lcuH+er7P4+lBYuxIG+esBuyGuIiUyKdwR40+lrFv/Wq556QG/W+ZtQ4hmf1sLRgBvZ2nxP2c7wnPtpcZwWcZhtWFc4Ul2wxN3cGCixONPk6RXKz1jlN/BwxUR/geokp03g1tdsF5F6dtSr5flgswD33AT/9jjZomqzaCKy9auDb5ORpgnFYEw8F/A4XxANWDv0ORF3Cxd5qyOv7nhnhQAQesR/kGFV4Ew5TRZZ5sBuTh4oPCVs+ThxNv52dGTp8bfWc/2EDKmak72wI+oHHHwTcXdqeczIKtDcA616FCePC4f5HXG6jqJEpK64GHPlA3RGAQt7SjcDhJ4CeZu194uDxrQ8D5XMAd4e2rdABmBKOUZ4OoG4LMO9GTGVcoRBu3fIsmvx+Mfj7QE83dnZ24JnNN4hujKFwhwP43YW9OONuh0kxYGl+GdzellhfqPb/9cVzRAFEhW2SDL1l11fg5ZiwITYAlvGZ72E2mFBpL0Szrwt/a9zZN4Oo2RfEdNsM0ZWXKRQzEgUNDQXeSKcQNUbCT37yE3zkIx/BD3/4QxF4SCQDwXPWF7/4RcyZMwevfvWrhZXAu9/9bvmGSQQO0wp4wvtiwgZdAKphMYy8G3gyQ0spDhDnDCLO2WA8kTo/g50bra2tWLhwoYgpOMdvx44dfd1P7OCQSCY97a3Ak38Del3AnAXAta/QuqYlEolkEJqbm8WcKYr8PPfp9oySZKSoMQjcaV549nnc8/Z78ZXXfAK//vPvsGzpCjFY2JmhNUwwGsRZ91n4oj7kmfIwyzFLJJsZ1CzJn4tFebNFktY8SIfGhOMsAygMsDtDT4oxkc5K23PPxK/nugDUPQXMuS1too9D7jguVbNVit21SUF3KDnRVmDJfO6ETqOvM8W+R6uAbfJ1jVjUGAyLYRbMSpUQGxK7OLINuzLKrFchrLpF54aJ72HKY9Heqcw6Ha0BvfOH6V8OZq9COM2AVeI0Dc+OiS3hixcvFgHF2bNnhUrMisPc3FxRNUVbqhOnTqDe3YiL55pgy7GiumYa2hrb0eNvF0LVcAa383HaghfhDnWJLo8K20xYDDbxPeF/AxFMGeI91PbBKLXm4U3VG7C1/SQ8kSBm5ZTgypKRDQPP7PEKxCUrBBtjdmg6KhDp0S6mMaz4m78E+Ny3gaZ6wJELVA6Q9NdhInLta4Cdf4oJG1R8rwBmaHZZwagbzX6KldrfrIZ8VNhWCd97p2kJXOG94jtoMigoMDiQY1wMkyEHxqSxzBnyyG+Af/wpeRuf+/RqYE1sZkDjeeAn3wQ8sU6SBcuAt90HmFL2ybP7gV4KGjy2xY5vp3YDi6/UBohnivDWUrMjRKX7HEYSUM1drl1IbwfQrc2v0YjJf3yNpTOB1nOAxZTy2CrQq3fsTV484bC4lFrTH9+fbmlCgy8+yJvCxil3L7a2t+KG8nhiPBSN4ncXjuPljkbYjCbcNWMuNpVMx6/P78F5T6d4x0JqFHu7m3FNaQ3CakjMAVpRMBPT7eNUnevrBk6/oP3MKQLmbAZsaWZm0QrOdjUQiVlRGmklNr5rlydb9iGcUCBBm8FdXadwZcmiUQ0DJ+xsHC6sImY7OO1THn/8cVx99dXDvg/J5QnFL85dufPOO3Hq1Cl87WtfE2styeWNQbHDadogOsUZM7F741KHM/sobng8HjGjj4VUTOBw7gxFjfPnz+P0aQ5Ph6hUXbZsmYg/KHBIJJOeznbgK58C/D5tXb93O9BwHrjn/RP9zCQTAIslWwN+0cGdSRHUlIb7e8cRoFsrBEbRAqBwweC5AUkfhw4dwm233SZiC3b50r5Rkp5Lf6U0Srjz/O43vxVVVa+94XYx9JF+yZkQioawrWMb/BG/qBpvRjM6g51YXbi6L0nCRO9wkr0TgtECLHgVcPZZba4GLUZmbQZ6z6fMllABbysQ8gIWbaB0Ikq0B85wEG6jASpfP4t4TUUIRTv6qqztRsCm0Ld7eNBuKv32sdvFaVHFgGOsYQLGogye7J7jXIIckxM9wU4hcky318BuzGE5O+Y45+G0+2TfdWscs5FvHlnynPstRQxeKDy4XC7ROh4Oh2EwGeDrDSA334lFy2eju6UDVTPLkFMUwjnPTsxyrEnbVZKO895jaAnQakr7nrQHLmJJ/iZYDIMfzAsteWJweJhdRAmUWkeWHJxmL8TrqtZh6qEOc3sWYVfD3GH4hJfMBG78gJYgt+YAjvhn1RY42CdokEC0B13B0yi2LoDJkIcC8yaE1W4h45mUQiieNqDuMSDgAswOoOZaIC+DDh3OvPjnn/tv58yMT30RsMT2u19+H/AmWL2dOAQ8/xhww+39bbXEMS7l/fZ7MxM12B1X/zLQfky7j6LZ2jF3NMezOWuAY+zeSWA4XRrpGGhRajQBm98CnHwGaD/R/+8pdo6TLdj4zyMH8eDZU+LbsjA3D79atwlVOcnntEDq/JgYwZTtD549iCeaz/V98752fBc+u1BBHTtWEuA72RH04y0zx9liksUK+x8BQjFbKVq/7f8TsPrNmj1ZKhQxTCPvUh0NLFToDceFJML3tSs4vDUDh8sXWWrQGYwPA+e2QnPVsO6HCbg3v/nNoouR1VOsvJdIhgMD1e3bt4uglcLGr3/9a1F9Lrm8GahT/FKH+77uEx4KhUQXuF5EZbPZRLf45s2bhfgXDAaxcuXKiX7KEsnQbHlGEzQS14fbngPueCOQn6ViNsmU4HBPN960Yysu+nwwKQo+v2gp/m3O2BVMTjgdh4HWPfHfm7UuOxRlbl9/ucK5a69//evx8Y9/HJ/97Gcnp5vPJEKKGhnAnYj+t7Skol/yV7/6Vdx3331D7lzN/mb4IskBOL3+XWEX8s35mFKwYnTxa5O3uevTXzfd+8IuisBemBBBfiTSZ+fjUTzINXPoc/yqIfQM++nNdJSi3JqP1kD8tmXWAlQ7ht/1MZaoYgggk0ecwZG9rx/3RQoZvKRCEaPUWgZP2IMcYw5yzXlZe0wOEV+9WkvCUdiY7Z+J+tARmAxBOGbGLVN80R50ButRYh3a+zwUDcQEDaKlAkNqEK2BesywD540shmtuKp0Fba27xPdGexYWVO0BEWWKfZ9Gy3mCsB3LGHQPL9tVuDENiDgAfIqgFkb0ictJwKzDShKFh8omrFTg0InO7r4Cvw8jHBWSAwO8bUopfHk7KnHgEisK4fi6qnHgSV3A9Yh9nkOBk8VIFgxWzGNw2Vi1wkAnW3J1+FtLibPrBGUVscHn+tYbEB+hi2jTXuBtiPx3zvPABRuK5bHZ45c2AYEPUBuBVBztdZNNxiLrtY+7/oj2gB2DgqfMcohtRSgiquAzobY+6doz69qsfYzvwzoOkmlIPl2szaO7nFjNoXHepvQE/Kh1JqLOY6yrCz4fnnuLH589lR8nIq7F+/Y9TKe2nxD0vU2l9IK0ARfJCy+ZZS4881mrC8uSereeKrlfJKUyGf4TEu9mKPB2Vzx7dpcjXGn60KsC1NHBSgSdDcAJRM4vyOBUDSCJ1tOo8Hbg/YAB3lHRXdWfB7Z8LsxSyxzxQwNX6QrJmhow8Azhcm2O+64Q5wDX375ZRQVDdPuTiKJQW9k7kOvfe1rhcjBYeKcLyCRXM6YzWZhd8sL14N+v18UGspuJsmUg4JGuvVpINUGUzJZaA+4caK3BUZFwaK8achjnDpKApEIXvfyFnTEPvewquLzRw5ifm5uUof3JUXXifTbpKgxIDzffe9738OnP/1p0Z3xhje8YUw/oksFKWoMgze+8Y2iVfw1r3kNDhw4gAceeGDQNqBEi4Sk7QNYAqW/jwB8YW1GgN1YApNhCNsrNQREWzTrGaUYMAzPZmhYFM4F2o/G8s6xhFbujPTJNeFfHUoYgKr9y9SXdI1jGMReaLChzndVbcDuzjPoDnlQYHZgTdFssX2yoIKWHQf7bIFUzIeC8QlcaTc1XMup4cJKqmJnORzhHJzxbkv5q4KQ6hPzEbqD5xBSvSKhlG+e2a97I933homrSIYWUvSgv3P69fBHArAaLcLubaJPTmS0CddItAMRtRNQTDAr06Aog1TxGZ1A7kbAe1Cbr6E4geMnYkOwVcDdpl2Wv3bStoBqszLMKLBExHvI3x2KCosh3rmRhLsZiCT+TdXm/vQ2Di1qsAqfXRkX6uKVVPy5NGEYPQdpW+3a3IhE4SM/TeeFz6UNyE5M5tcsA8wZVl52nNIPkgntu8eBjmPafSbeb9c5rTNlyV2D21Txc56/QbtkC97nxru1oePtFwCbE1hyHZAXE5rKFgHtpwB3S7xJaMZqIH/GqLsp/tiwC2c9beLYwE7I1YWzcGP56L21t7S3gu9iNEGYONjTLayoHLGh32SaPQePbLgKH9q/Gxe8Hsxx5uL7V6xBkd7VM0hvFLddXTobz7edjp8TFWBT8fAGXmeFAZu3xqGrK8PP+oEzO3GiNy4odgTZgWgQwWae2Y61RfNGJsqbp4nLcNmyZYtIQNPj9vvf/76wSZFIRgNFsSeeeAIf/OAHRaHIn//8ZzFIXCKRaMdru15gIpFMNRatAJ55LDl2KCoBiidX4aVEo87Tjl+d39E34/X51pN4V+2VKKFTySio87iF7VQi7NbY0t526Yoa6WKJ1CJCSR+BQAAf+MAH8Nhjj+Gpp57Chg1ZjNkvcaSoMUy4c+3evVt44F577bX405/+hMrK9AeiIkv/yj2TYkJehpXyIeEnvxPRWBJcCRlRblsjhuVGVS+C0cNQwVkLVpgNC2CEEwi+zIxa7B4UwHQFYBwjqwh7ETD7VqB5DxD2A85KoHJN+usKr+pEqyqiwmwsA6JNSRZWueaRteGxynVjyfx+29sC3egI9CDHZMMMe5nwKx9v6I0LHIgNbtY5wVcLBZdW66nFaO83O4XvgFVx4KJvp6i+1/GG2zHNTju2eDLWarDDrFgRUgMJt1aRZx56sLkOLd1yMpx7M1aI+SOeM6jznBULo1JrKZbkL+s3P4fXaw/WoSuodT4VWqpQYqlJEkFC0QsIRlntoFkahXABduM6GJRBKkdMxUDetdq/6/fGBQ3tUQFXE+DtAByTd+BUvtkBVe3pey/406IE+kSOJNJVuTP539EB+M8C5RRcB0k+fvjfge99HWCVPj1OX/06YOPm+N/5eK+9B/jdj/s+BzjzgevTDP8+vQswGgBDwvGuOV79PyjsvgjHLK50CysKJAMuAmn71wH4uoCczL8jWYPVSytfmf5v/EwW3QF0ngVoG+Qs1y6j5GRvsxA09GMD2d11Dsvzq1GWMAui0efBnq5WmA0GbCiuQH4GolK+xaLtWwnvt8VggC2N7+3qomJsu+7mAe+LSffNJTPwfFt94jcP15ZVYUNxJQosdpxwtcJqNOGqklpMz5mAc0FhNWCyAWH9+KAAFjtQML6Dabe1t+K51ibYTSbcPWMWZsTsvi54u3E8QdAgHMNVaZuOFQXlmOkoG9cOF30g+Le+9S28733vG7fHlVz6UBz78Y9/jCuuuAI33XSTHCAukUgklwJLVgBveAfwp1/RVw0onwa8/+NarCGZdPyj8ZCI2/V1uz8axlMtx/DG6gFyXBmSlyYG5WPkmSfxXN3RUjAPaNuXvK3wErbbGgVNTU2icJ4zpZhr5kwpSeZIUWMEcCd78cUX8Z73vAdr1qzBX/7yF/EzFVpMLc9fjsOuw2IYuNVgxcqClRkPBe8MnugTNAiTxJ3Bo6iwrkEgSn86Te1VQYFjH2zRUiixbfotED4EGMrGrhrbUa4JG0PBQXeWJUDwUMI2J4zm+ShVZ8EbuSismWyGUlg5fDRLHO45i11dx/t+n24rxQ3lHDQ8jh0cTM4zIW1MnvOg0c0jPqYKqhpFWPWKfhqTYk/bfcAZIFX2Zaj3HYjZbQF5pnIx6yMYSvY+90e74Y90w2rMR0T1waBYhRXIgtzVOOHeiyC7DNjPYp+LQksZphINvgacdscT2W2BNhzqOYiVhclzDChotAbi19P/XWqt7XvPg1F9Joq+xAohFD0Pq7G/iAdPi9ZBxY6wvGqAVcwpM0b6GGAuQPJ1IkBHi3YMKSof2XDpEcL9QU1qWRiEXFpFFQO+2BDjcBQ4UA/0HI39vQB4w31A4QCVUaya+sLXtXZwDv5OF2ysWA8UlwOnj2h2UvzdkaZyR0+IJ34/Mq1M6W2IDQeP3Tb154AV9JOz40ZYXZXMzepdulLmKiRuL4MmahzsbsenDm3tm3FRbLHhe1dsRrltcKui98+eh7801MMfjYh3lO3hn5y/SAgUI+Hf5qyA3WjC9s4m2AxGvK5qHjaWaN0BG4pnicuEQgGDHVunn4sPCp977bjOPXmk/hw+dWiPqFjj3v2Lc6fx143XocaZK4LJVPhJOE127Omqx9+bDiHXZMPNFUtQPYaiHv3dKWb84Q9/kAPBJWMG11Tvf//7sWjRImF3y67wb3/728KKRyKRSCRTlGtfAWy+SbO7tY3eykgydrhCvpQSXBXdtDQeJdPsdtw7swY/P1/Xt94tsVjx1pn9rcMvGUqWaT/1QeEcEl40SuvjS5CdO3eKgvkbbrhBFLdwhpRkeEhRY4RwZ/vFL34hKqnYsfHDH/4Qb33rW/tdb5p9GiptlcJSh10aehKYydpwNACLIQcmQ/rqYS15nLIt6kcUvX2CRhwVquoSRhzJ0LJHc/yecEzVgJILRGmhYwGMlULsEN0rhuyrtp6wP0nQIBf9bTjracQc54zxEzQi22KfQ7pk8NQJVLnvtQb2IKRqVeQ2QxFKrSuEiJFKrrkMc41XwR/pFfu3zZCH3vDFtPfrj3aiI7gfakzAyzfPRZ65BivyrxbdGibFnPGA8clEq582cMmLIgobqV0G3aH+7wu36aKGZleW+r3mvaWxYfI0A2ce77sOeuu1CvmiGuD8zoTEugKwot0xRBLQ0wv88QGgNfYcK6uBu94P2MdnkKlRmYawmlylbUBleisvJs/nvwpo3K11LRw5A7gShnq7XcA/fwW85X6tUuqZfwKN9UBJGXDjqwB7LNltHWIhUVWjXQZj1nLgwFPJ22pWDPFq469w8D+n2Frxs+TsIM49ukyosPWfkUMbKs7W0Pnmyb0IJYh2XcEA/q/uCD69cPBKK9pIPb35evz03Bm4w2FcV1aBV08b+fnCajTi/XOW4/1YjkmLo0gTNiYAHg+/coy2jJqARHyRCH5w5ji+uXwNqu0FwobOFwklTSA57b4Id5jrIRX+SAi/vbAD76q5etT2AOloa2sTCWaXyyWqp2bOnJn1x5BIEuEwZO5rtDi78cYb8cc//hGlpdKqRCKRSKYsLAqTycpJT4U9Hw3err65d4wvptuyU4D6jeUrsaygELs6O1BiteIDs+ehdKi4cyrDeL10uXaRpOVXv/qVKGb58pe/jA9/+MNyIPgImTwDB6YgTKx99KMfFZ0a3Ak/9KEPIUgFPs312J2hJ+Ja/KdxovdFnPHswPHeF+AKaTMzUrEYWHGamLzjIFGnOLimf0LpDop2YDIlhI2FgHk2YKrSujfGEHe4vyjE9643C2p7xkQvxAQN+uBHtaSyuPCPTAzHh2lPdjqCh8UsjEQxojukecKnw2ywIddcCrsxX+z7Nn72KfuuAgPcoXN9ggb/2hs+hc7gfkRUPywG25QUNEi6WR4cDpwZFCmD4kLhS+H3uN/9pxl+3jdcOiHp3XZQEy8W36YJGbRqyasElt2hCQGD8eTvgTbaw8VobgCe/TPGC6NSCpOyBAqt9WCHAdUwKQsGvgGry6s3AfNvo6qZ3B1Bb1SKM/wefv9rwF9/B+zaCjz2F+Abn9eqp7LF/PXA0usAe542Z2LRlcDiBCurwcivis0lGmhfUTRrq5wSbVYIuyAW3DZpZ6OMBazIv6pkXtL36pWVy5MGRrf6kyutGJzQjioTap25+PKSFfjOitWjEjTGBc6R6T4JdB4F/OxSmlpwZklvONRvW4ewywNyTGbcN2c9CszaMdBmMOF1MxaKrhw94OT/aRVAW7KxqJ5atWoVKioqxCwNKWhIxgvua1u3bkV5ebnYB7kvSiQSiUQiGTvunL4CuQmDwSttebixIjvdBbRAv2dWLb6/cg3+c/EylEmR67KFOeP77rtP5JCZS2Y3+Gjnr17OyE6NLMAqqj179ojBkddcc42oqBrIB6031I7WwJm+32nPc8F7AAtyN/fr2CiyLEDQ39vXsWFUrCi2LBYzNBTkQYUr6aNUjIsA9RCgdsW2mQHzCiDUA7gOABEPYC4E8lYAxiypwkwa9jQAfheQw/se/tDN0T+FKFzhbkTUMJymfFgMmm1GntnRN0S277pQUWAZ24HZycSHo6usrtanpCus9KwVsyemCoFoT7+OgUBE39fiqGpEiBQKYt70MSwGBypsy9HiPySs1AwwocgyG64wZ0VobwtztdylAtFWtAc7UWJZD5NhcLuYycpMx0y0BJKTbLMcybMySKG5Ci0B7T3QleYquxFRvBjbUgKrcQkCkYNQEejrYDApaYbMR9N0ddA+ihTNBNbeM7wX0ViniQE6/PfFsxhPjEqFuAybvEKguT7+/Pm+cwbGmRPA8ZgNni56XLwAHNgNrNmYnSfNx6KQwctw4XyDea8GLm4H/D0Aq4M478DdqP3dVgjMunZSz0IZD64smYfFedNFm3iRxZkUgJCZjlycdbv6Et8UPmbz85+k8DzWFjgPb8QFs2JBma0W5ti5bEDYhXX+sdgMlthxZdrVQO7MCeu62NbRgnqvBzWOXKwrHtoy0GQwYGl+IY64eA7Xq+KANbSDizHLUYivLr0R/kgYVoMRLQEXtnUkd2GK2ynZfS1sAf/Yxz6GL33pS6KARQYbkvHG4XDg4YcfFhZU7ArnLJf3vve9l8++GPQCPSwOUoCCmdoMJ4lEIpFIxogiiwP3zbkWTf4eETtU2vNhHE/bcsklT0NDA+6++24xGJw55JqaS9iCbJyQokaW4M7IiqoPfvCDWLlypQhCGICk4ou4+g3MprDhj7rhUArhjTTCE24WyXinuQrTbBtEMpnJeKshv8/qx2pYiVD0NKLoEVXcZsMcKIoDMK8DVM5pCAOs5BZ++E8AaizZSWEj3AOU3AiM9gDNBMSZ54DWY/Ft01cCMzdgvOCskqM9u+EKa8l1A4xYlL8K+eYi2I1WbCpZiq3tTAZrzHHMwKycceyOUIoAVRsALUJQ0aVhBYxzplyjlFGxIKwme9lTaEtMAvmjZ+GPaElvA3LgNK+AkftlDIepDDWO6xBFCAaYxf3pogZddYgeq1P48EQuIN8wSGX+APC58B4SB5CPN4WWIqwpWofznnNCcCuzlaPKXt3vesWWmeL73hVqiM19McFsoMWcTjsz2bAbrxTzc3jYHnBAOGdouBPtrBQgd8bIs31MAnvdyTMinFNkBszVrwLOnwT8Pu3Lx7bvm18PdCe+twnwdU4W2IFRe1P/inzRpTF1LOvGmkKLQ1zS8R8LVuNjB7agO6QJgTWOPLyzZnHG903rqr1dHfBGwlheUIQiy9jNmODx6rz3IHpCumWdgu5QC+bnbhzQnlLQeRjo60iMfUebtwFOWj2Ob9KTr+ELR/bg0UYmIDXeUj0bn1gwdMv5965Yh7fv2oKzHu07eFtlFd5d29+S0mbU1j9l1lxU2vLR7HeJtRGPn2bFgIWcq5MFvF6vaAV/8skn5fwMyYRDAeP+++/H6tWrRRD88ssvC8vbnJypWfCRMZ524MhfY+c+1mnZgSWvAdLYD0okEolEki3MBiOqOWNOIskyzz77LN7whjfg1a9+Nb7//e/L+RlZQlG17J8kizz00EOilegLX/gCPvGJTyRVVHUFL6LBd7jfbeY5r0Qw2o7uULximxRbViDHNIoByZ7TgGtf/+3F1wKWUVb6dtcDR//Wf/vyuzWP93Hggvc06r3JFkiscl1TdG3f++4KedAV7EWOyYZ8kwMXfA0IRIIotBRgmr18bJ8gv17RM4Cqd+fYAONKbbbIFMMbbkVbcH9fRTATSRW2dbAYtNcSjDTDE0kYBC+EDTvyzJsGrSrsDByFJ9IAUxr9wW6oRIFl6bCqnTuCx2PzO1TkGMtRal2cdu7HZCWivtDX4RMnB0ZlY2b7W8s+oI0dW1FN0Ki6euRDf+tPA3/4QXyGA4dn3/kOIEKhwABMnw/Yxme+xojgHI0T+4BIBJi9CCiuAHq6gM9+CAgF42INBY/PfQOYlqb7RTJlcYdDOObqhNlgwOK8YvEzEzzhMN628yXs79bsnHJNZvx87ZVYUTA2AU4g4sHx3i39tk+zzUepbZBB4hefB9xxEaGPuW8EDOMrfu3oaMV79vR/Db9ddy0W5w8974VdGuzwsBuNKLf1t9tLxRcJ4qmWo2j0dSPPbMP1ZYtQTnu9UXL69GnRdZuXlyeGgldWVo76PiWSbNHY2CiEjd7eXvz5z3/G7NmzL90399AjgJsztRJmgRXOBBbcOsFPTCKRSCQSiSRzmHL/+te/ji9+8Yv47ne/i3e9613y7csiUyfTN4XgTrpixQoRGLOi6mc/+xkKCrTq5nxzJTqDDfBGuvs6NlipbTU60B7Y3e++esPnRidq9BswPNT2YUBrlLTbXeMmanjD/aurQ2pQVMZzwLRuQ8VLKBrGC20vozfs7rOlmp87G4vysj+kvA8m89mVoc6KJaptU9b7nvthhbIO3kireP8cpmkwJ1hDhdTOfl1IUdDXPgCFr3sACi0LYQnnwx050TdbQ8diGF4SsTt0Fr1hreOBeCMt6AgYUWpbgqgahit0QcwF4WyaPFP1hHZyDIjKw3IoZaRChglK7lsVK4HyK7TPYbSvr2oOcM8ngRP7tfuqmAbsfhTQPfCNjwM1C4DqZUD5XEw6nHnAqpRZFkywfuCTwIPf1rozWIF/7wfGRtDw9QBBj2bNF5sJMOmgsDNBxyTOQegOaZ0zBeZcGLL8fXSazFhTNHzh+genj+FgTNAgnnAIH963Ay9cewvGAp6v+qMgknI87Ie1MEXUUABTzrgLGqRhgHklF32ejEQNo6JgliPzId92owWvnrYC2eTRRx/FPffcg7e//e0i8DCbZVeUZHIxbdo0PPfcc6Jgip0bP//5z8Uw8UsSEWMkxioq4GPsJJFIJBKJRDI16Orqwjve8Q7s27cPL7zwgli/SbKLFDXGCO6se/fuxdve9jZcccUV+P3vf4+1a9eKpE2NYw26QxcRigZgM+YiLyZacBYBUxj66AWTom0bFRZWGbJ6PhoLDphkzwHMWag4tQ9wH/bxs6exGdMNUDaJSyrnvfVC0CD6nI0TvWdQ66iGLVszRgZCPJ+p/3WzGvPFhURVHwLRs6IjwGQopQFI2tsoQ7xudnFQILEYfYio54TcFBRjEKpgNw7PTsQbaUu7jR0cTf5dCEZ7+4QXb7gDFbaVk8ubOhqAwe9F1JYYyyswKMOsxhSvKUuvq6RSu5DHfwREEhKt/Pe5o0D7WWDxjUB1dpOMY8bCpcC3HgJ6XYAzV+tAybZQcHYLcHF/rBPEBCx8BVA8iTwzQ91Ax3bNjtBgAwrXAPbxm4nkjwTwXOvOPlEj35yL68rWwmYcO5unTDnRy1kccfjvBp8XgUgE1mzvK+I85oRJ2PslDqtXkWsqHvyGRUsAXwvgjc3uoZgxPcOB9FlmDgXENHC2xlQY1vfJT35SFKD85Cc/EZXwEslkhWLbd77zHWzcuFHEGAyU/+d//gcWyyBWdVMRxhi9zcmdGtIORCKRSCQSyRRh586deP3rX49FixaJ+RnFxUPEdpIRMQnLlC8duNP+/e9/xwc+8AExX4NBCFuPKGwUWapQbpuDfHO5SKpyexhGhOlWxFwhA22V1Ygj7NKgGOLdD3ifB6wmgIkizj+wlAHF1wBKFhIz+dOA1EpJztPIGb8v6wx7LezGuP0NOwjmOpemTVQzica/pxKIJiaSJJkQUT3wRF5GMHoGQfUcvJGdMCr22ODzeELdapgFJQPrpyiaoOKcmKvBz85qVGA3hoYtOKQbvK4oRtFdogka6AuQ/dEOBKKTrOovdAFK2A+DLwglFBEXg9cPRZ0k3tme7rhlkw5VWE5479kHND4OtL4ExBLVkxpaEeUXZF/QIB1n44KGPsD96OPawO90cNBzx16g7WWgl3Z1Y+wKGQ0BbS8AYVfsdz/QsUUTOsaJ3Z1H0BOKd9q5Qm6xbbhEVRW/u3ASH9j7PD6+fwt2dupzKUZOdY5DdA7o8F/FFuuYCBrEoBhR61gFM8UlTcZElX0JHKYhOhwMRmDGjcDMW4Gqm4Ba+s1PzAD55QXFeE/tgqT37P55SzA3d3L739fV1eHKK6/Eiy++KIINKWhIpgrcV7nPsuqP+zD35UuK2dckdzhancCsKyfyGUkkEolEIpEMCXO73/72t0UO+N/+7d9ETlgKGmPH1C8dn+QYDAbRJr5p0yYxFOb5558X1YCFhcnJCn+0ByE1OeEl+iqUEVqW+I6IBKmAeRijClgXALYsWy3N2gSUzNPaxO2FgGN81UeTwYzlBRvQGWhFWA2LAeE5JidCUVb9h2AxOETCiBRZCvo6NPpur5jgYOeKZFhQzNCkN6K9pyH1LPLM6xGInIFB6YVBscKoZLY/RNT+icgoWsUJYTjCRoG5Bi2B5BkyBeZasS+kf9z02ycMUamtQIlExSW+PWZdNhTeLsDTCdBbPncMLOAKyoE2Dr5P+B5xGMrMUk08ZVI81AME2oBptwJj3QE1WRE+4MlWbEJobjsFVC5Jvi4HPV/8F6CLq+46INgDFK8cu+fHz4lCRir+ZsA8Pp12HcHupOMx/81tw+XBs4fxe85+ib3je7rb8M1lm7CqaOS2jR+csxDPtzXjgtcT65o04OvLxrZV2G7Kw8Lcq4VNHmcAZXzc4/UyFTLYleM7rR1PzOUA53VksVPt3+Yswk3l04UV1SxH7qTv0uBMAla5v+Utb8E3v/lNOaxPMuWYM2cOtm3bho9//ONYuXIlfvrTn+LOO+/EJQFjihVvBFxN2sE9bzpglJZwEolEIpFIJi+dnZ3CypZ2U08//TQ2bNgw0U/pkkeKGuME28S5Y997773Cjurhhx/G+vXr+/4eHSC5ygTHiAg39t8Wasy+qEGcpdplpLCKOeIHTPYRdZDQaqrUptmmMAne7D+KnlB9rBXJjBk5K2E3FqDSVo55zlqcdJ/tEzTWF6+EidYwkuF9ZGr/hKiKIBSEYTG2xpK5XqjoRFTloO7KETSNDT/ZlmMqRYWyCr2hBpEkdZoq4DBVIBjtP3uF1dBWw+gHy2YVI5OT2v6pwdYVC5Awu2RAGg4Ap56P/161EphzVXaf37pXA08+BARjnz9ba4pzAVtiooHtZgHAexHIvQSGmNYdAo7vBKJRYO5K7TJUItjC7rE03Rbtx/uLGq6TMUEj4fqu40DBEsA4RnYiaY+z9D0cm06EdOQYbfBF/InGImLbcODx/s8NZ/sahoJRFREVuP/Ay3j7rPl466z5SR0XmVJsteLvV16Pp1ua4A2HsaG4FDW0KRtjKGQYY7Ogsg67crp5fOA7zjerCYh6AcfiEd/lUVc7zri7UGC2YX0xZywZRWfGZO/OCAQCotjkl7/8JR566CHcddddE/2UJJIRY7PZ8P3vfx/XXHONiDE4c+Mb3/gGrNaJt/IbNSYrUMSZdBKJRHKJ4fcAe54CulqA/BJg1Y1AziSLSyUi1niq5SJ2d7Uh32zB3VW1KLVO0jmJkgln+/btwm5q+fLl2L9/P4qKsmD5LxkSmc0dR9hy9Le//Q3/+7//i+uuuw6f+9znhI+z0WgUyVUmWdUkJ2/aTw09YDPjJPE4Jqwypucs0LhVq2KmH/iMawDn9BHfnSvc2CdokChCuOjbh9mOzWIo9OL8+ah1zkQgEoDT5BgXQSMc9SCiemFUcmAyxK2yoHKw6mGOg49V4S8EMuxsGA2hqB+NvkPwRbpFAq3ctgB55oph3YdRKUBUTRwUz703D1HQ/iB5H1ZxCsDgooZJmYGQmjwPwwgO8h5+QtJuLBaXRDgYvNS6FO2BI+I7RpuqMutymGJ2L5MGVk9HFwCB49rvFDRy1g793fV2JwsapH4vUDQTKKrO3vPLLQaufROw42GAnSQUNXhJS/J+MCU5sx947vfx35vOAuEgsGiDZhHlbtcspZwlgDlhX6pYCJx9URNCdGjRlU68Hsj+jhZRYyVqsBvDUgIEO+KzlgxWwJ7FfWUIVhYuwjOt28WwcEJbRm4bDnzmUX6jOT82Eu/78EYi+MGZo+gNh3Hf3BQRaRhDxu+YPn7vx5jjOxMXNPq2nQJyeN4ZvhPp3xpP4XcXjsIARXwGT7bU4XOLNsFCS6xJzLFjx/DGN74RJpNJWPfMnn0JCK8SCSDEORZNMZhm0dTvfvc7LFgQt4STSCQSySQhHAIefwjoadPiifYGLca44z7AMsli08ucB88ew4N1x2GiXTyAv1ysw2/XXY8ymxQ2JHEikYiYb/blL38ZX/rSl3D//fdPrrmtlzhypsY4w537Yx/7mPBvZpv4DTfcgIaGBpgMVkyzXQFDTGdiirjCuhRW4wirQ61z02xLCN55Ao30AhGXGPQsPN09ZwBPnVZlnQ38LqDzLNDblN4j3t8FXHxREzT0JF79s0DIO+KH9EV6xNwMY8IxJKIGEUroLLAbbSiw5I+LoOEN16ErtA2u8H7xk78LRAfOHsowsSSTD8C+mNAxttUG9d498Ea6RHI/rAZw0XcA3nDXsO7HapgNI+LKswI77MalNKZKc+1BLJ74PkQaYIj2woR5UFAIBfkwKnNhHO5w7CFwmioxM+c6VNmvxsyca5Fjmhjv+Yy+u7m3AM4bAOeNgDEDOyBv5yA2SFmmqApYzueXA9gsgGoERHW5ktBdYhrXodNjxqEt6bdFI8DBvwM7fg3s+SOw9adAd0J3HI8t5XMAk1Gz5zIbAbMJyE0j2NopKCYeHxXA5NA618YKJrFLNgPO+YC1DMiZBZRxXxu/yt5iawFuqbgKy/Lniwv/zW3DwaAouKpkmujOSDeFhLZUPOZJCM85qe8F3zjdRjBzuoN+IWgQChqEHRvPt8YsLych3A8efPBBrFmzBjfffLOw7JGChuRSg/s09+0bb7wRq1evFoPv5TFQIpFIJhnNdUB3azw/wlwM5xY2nJzoZyZJgN3aP6nTCg3DKrvBVbjCITxcz0IhiUSjvr4e119/PX7+85+LHC9zvVLQGF9kp8YEwWCDdlQf+tCHsGzZMvzf//2f8MGtdVwrkvCsoGdnwYix1miJxWCDZpVimQXo1fisFnbvAMIdsd+tgM8dT2702ICyG7TEmo6vHeg6oV0ndyaQN3Pwx+84DZx5Jp5EKawF5tyQXBHqTTPQlfff9BJQvARwDK9jg4GbSXGh2KoNqhYnnpBmR2Jkxfs4E4q64IloXu86/N1sKIZZYYIpVTzie9UOwDGGz8mLQD8rJgW94VbkDDWUNvEWihF240qowmIqCgMcYn+NqoVQkeiLz8+iYOD5EaHtwqaqb/SLcTlgHLyrIxqNCjXcbB6+TQufo0mZAhUw/O5mMGC9D/sAdi/nXwbMFqCSglMWmbYEKJsLBL3a8M6oD+jYoc2CMOUAxWuTjx9TuZKKWEyaOMGWgAjF1wNAW8KClt0bFDmuenf8GFdzDRD2a6IuyZ8BVKfx1HRUAwXLgO5D2jGA71v55hFVzw8LCi8FyzGR5JodWJQ/OvHyk/NX4khPN+q8/S3meA6QJHSBBRpSxLNCrUNymHTq9nMpAlNHkOL85PS2ffe7342XX34Zf/3rX0UxiURyqWKxWPD1r38dN910E972trfhX//6lxA3Jq0FAtf9HUcBfwdgdmrrf9MUWKdJLkloT3hJWLdJJjeR8PC2SyYEdziUtmiqO5SlAmDJlIfz+d71rneJPO4//vEPOJ3OiX5KlyVS1JhAuNOzW+MVr3iFGFb5xBNPCGuqnJwsDa62VGkXHZXCBf3buwGzX5vzLJoE2K2RcMhmp0bPAaB4o/a7txU493iyZVTFeqAoTVt70A007tREDebkdPeVrrNA+0mgNOE2A1UFe5sBfytQuBgoHWJYLiv9VS+g2BCItkJRaOWkwYfPM7FrY46Yu5EN3CEX2gItIjlebq2EnQncAYjw/U673QPzgAPgxzaROZBQxs6g4d8Xe2KSE9cKaqCC3Sacq0EcMCDFs13lkOKL2k/RoZJA5DBgqBhwZgG9CYPBYN/3Z9Gi4dnVpIXVMSoFvhCg5ANKSjJe9QFRJgMjgFIKGMbeImzYOIqBmWuA87vi28RbqAJnXgByioD8kdu6Deh1zQvh8M6KG8U/hzvcfVIzexlw7CXAbokfI2kz5WpOGQTOGQVegElda2z/4Xuz4NVAiN8HBTDnDDyLo3AJkL9QE/poiXapvH/jgN1kwttrFuALR3b3CzxuKJ8xaffFQKQXoagHZkMOrMZReihz3xzqdVqrNPHRy4qzqCZo5K0b0cNV2BywGAwIJtirUUCaOQm9oJ9//nkxCHzVqlU4ePAgSkomaYeeRJJlKN4dOHAA73znO4W3869//Wts3rx5cr3PPHbVPwe4dcFVAVzngNpXj539okSSBrfbjV27diE/P18I4ZxRQ5tCiWRMKJ8JWOxAyB9fwzGWqqyVb/gkosRqwzRbDpr9vr7OZK53VxeOYpas5JLA4/EIi6k//OEP+PGPf4y77757op/SZY08W08C+CVYt25dX+D9q1/9SnRyZBXaL6k7NQsK5q9ph2IwAJ5AGmsoFQjHxQG0H+xvW9G2r7+owarkk48CIZ/msy+s2hXNe5/JdF+KxVFuFWAr1qqzEtGTM11HAFbxWgaoQo82A2E+t1hiRcmFJfaw4dhj04aq0DwD2aAz0I6DPbSM4mOoOO85i5WF65BrTp/IMQwgXBhEp0Be7EL7KR1WzJZjLGGXgtNUCnc4bktEaaLAMi1rookRy6CqgdjnYktOKkY7gOjueEI4UfgSUGljlUq8etjlcuHkyZPwer2YO3euEP2OHj2K3Nzc7AgaEX6mun0TZwss04QV8XcPEHk5/iTV8wCWAIYsCwTZoHaj1jVx+nntOyQalvhTAXoasy9qpBCI+HHKfQC94S4hIs7MWYByW3a+ewNWM0XDybMsss3s5cC5Hdq/9f2Yx7k0lergLIHU5yK65DKs2BCzCNIfMygUqeJYEYQCJ5QBRdHLk1dUVOGM24Vfnj+pTwjBNaXT8JmFV2Ay0hU8i85gvIsv3zwLJdZ5A9+A52j/Bc0y0ugEbNWxc2oD0LUHiPoBcyFQtAEwD3Bc5L6YMx+w83F4Th757Isckxn3zVmN/3dqN0KxmSjXlc3EhuLpk6ra9gtf+IIYoMyhye973/smrcAlkYwVpaWlePTRR/GjH/0Ir3zlK3Hffffhv/7rv0Q3x6TA35kgaBAVCLkBVx1QOH8Cn5jkcoCd32fOnBEiBtdZV199NU6cOAG73S4FDcnYYnMAr3gH8OIjgKsdcBYAV75W+ymZNLAL+bsrNuJD+7ehye8V8cXbZs7DLRUJRcOSy47du3fjrW99q1hjsXikuvoSmsE4RVFUabY6aQiHw2LAzFe+8hX8x3/8h7iMtEokqkbQHjgFX6QTRoMFZRYnLEoav2u3X7vQVqUPJj84YDhWxVn3GODTK+/1qxiBhW9N3tZxHLjwUpoXFtGUhllXA2UplfWco3H+ibiwQTEkMfEw/QbAkcaOiNXzoReTxBYhZhiMUGPChkc8rIIi83Wjs/KKsaPjJXgjyTMviiwlWF6QXoDiV6s3fAwBdiXEsBmmw2laqCVXxNBgWtgwWclk6FxgHJKV2r5xWszVMCpWlFrnwDbS2S3DJcxOgoT5D31eovoGC2C+FpFoFO3t7WhsbERdXZ2orqWYQdspfic4DNNAUW60RM8B0RMpGw2A8TptH48cAlTaByV+P2hFdP3ANgqRDu11mYpisyaGCb9rvac0wYXWRM4hrN4S8XYBe3/Tf/vszUDFEsBzDgh0AEYbkDtPEzv9sYQCBQjTyCqtua8f7NkGb8Sd9F4tzF2NAg6kziZ8X/Y9BRyndZkKlMwArno9YB+Dfbj9AvDSr5O38bs7b6PWfebrjgt0i24CpqV0JWUBvrcRlQPum/UnAKOyGAalIuuP0xo4g05aFnLmhaUKpdbaSZMIPuPuwbdO7kODz40Zdifun7cCc1KCP7aJ81JqtcM4SZ53ug6NBh+F0mSm2dfAbkxjAchjiWsHEGiM72uWCsA+H2hLsHjk34x2oOLWUQkWw52tccHrws7ORuzq0vbP68tm4a4Z80UgOFEcPnxYFInwHMHq9Kx09EkkUxwWg/B7wUQuvxdLliyZ6KcEeJq0GCAJBShbBZRMgucnuSTp7e1Fd3c3jhw5glAohLKyMhiNRiGGz5s3TySqJBKJRIfdGa0BH5wmM3JNI4jtJSOi3uvCz+r2o8nvRqXNiXfUrMCMCewKZ672q1/9Kr72ta/hs5/9LD71qU+Jc4dk4pGdGpMIJms/85nP4JZbbhHq3z//+U/RtcHq9OHS5D8Ab6S9r/i9J9SJEnM6hwoFsFqAQFQTGMQTcQL5CV7rzhkpooaSXmjg8NyByJsOlKSpuqKfNzs+mreleXLKwF0aKjtJ+rscGjjhgQKGymHWKgzK3KwIGiSYZoA6q9ND0SBO9B5Gd6gTZsWMWud8lForRDIw17QQ1mgZIqoXRiUHFkNxPEkoEt5pLLzGGINiRJltoirgUgaJC3FHTRALrkCv2409e/Zg1qxZ6OjoEINdq6rGqCKC1mVJNkKElcf8rHMAdpwwiZ60b4bT271w//BuBaIx4UuxAjkbgOEIRr5moPnZ+HPy1gPRIJCX4THAXgCUzgPaTmqV3HyetjygdD7QsQfo5QC62PN2nQLoHqW/DO9JoOBKYAQiRDDqh5dV5AmwA6gz2Jp9UePkLuB4QlK4oxHY+ghww9uRdXJLAKMp2eOW72lJNTBvPdB8AggHgMIZQP7gs2BGCsWMuKChbaHIoYDHkuwtrFsDZ4WoodMSYCeBgjLbxLfCdwUD+MTBLfCEw6L9mwLHJw5uxU9X34BCS9zGkMEGL5MZWk4NtD2tqBFqjwkaiB+ngs2Amrp847wXLxDqBizjY5FXYLHhmdbzeKqVHWwaf7p4QghKr50x/ucYJmu//e1v4/Of/zw++tGPip+TpiJdIplgKO5t375ddGqwO/xLX/oSPvKRj2SnQGSk2IoAg0Vb5/ShAo7sivYSiQ5tCFnEwbl8NpsN1113nTxPSCSSQeG6ttKWJXt4SUa4QgH897Et8EW02K/O042vHtuCry27Hnnm8Z97RNcQzimjKL5lyxasXDmERb5kXJnAlaxkIPglYVvThg0bxL9/+MMfigVYpoSjgbigEYNdC0nw7qImwDwbyL8GqLgNKL4aKNkMlN+sVXLrsFqqIMEaI6ccmHZl/wfOnZEy3JZWPiag5npg/itj9ioxKKB4LgDus0BOBZA/P8UGSwEqNmo+9Gnpn6hITE0z32w25MKkFMIXaUAg0go1ZpMxUvLMrApOTmTnmQpwuGcvOoJtiKhh+KM+HHXtR1dQ6zyhgGE1liDHVC1+Tpaq59ESjrrRGdyOtsBT6Ai8hGA0eX8bEM6k6LetGDDNBUwzAcWLQwcPiBZwihoOh2PsBA3x2Ny/Ur9b3IetQKQVCHXwxQKhSKybiZ9fXnr/ev8RIKoNPRdwPoJ///CeT/dR/cYJ2w4N4/Wwi+AGrTOjbAFQtRpY8TptXogQNPT7ZgLUFxuEHfudYo57GI+VIpSlwns0jkXFePOZlAeKAkyqDiaqjhRrDrD2tZrPrc4ivrc12syMGcuAWWvGTNAgKi3QUo47sWFIw7qf3lAbGn1H0ew/jkBKxxnpCl7svy3Uf9tEcLCnHb3hUJ+fLX+yI+NAd9xGLxGfb3IMrPZFPKKggMK3DmdopMMsjkVpiPoRDkewZccxhEKJAyR5PkuzLhinLg2dF9r6d4C+2FaP8eb8+fMiOcX10tNPP40vf/nLMlElkaRAkY/d4E899RQeeOABXH/99eK7M2Fwtl71jYA+n47z76ZfBdjl7BtJ9unp6REiHmfMMB5bsWKFPE9IJJKMYWcXL5cSFy5cEAn7ycYxVzs8keTYj79z+3jCHCxjC+ZkN27cKApvpaAx+ZCdGpMU+nmy4vC2227Dvffei7/97W946KGHMH360J7VdF9PJaQq6ImUosDEpKsfUPIAw2LAlGB3ZB8gMUehYtpGoGKtJjwkJvgSseUDs18BXHgRCHoAWwEw6zrAXpR8vYgfaHkaCLvjSZjSzUDxEs1fl4lZVm8NMoRbG+hcAajawF4e6FRFEZe+1xy1wBvZkZA0KkC+edWIOzcW5C7B/u7dMYsd+qAXoNpRg11dL/WrUG8PtKAwg2pZCiGhqA8mxQoTq9WmAKoaRk9oD6JMlIuTjB89of0oNK+HyTDE/ADDHG1YrfjcSDEgbnNG7Fp795xCSXG5cCFjsjov14xzdWcwq2b22LwYhR6IbSkzNZZq9mZBztpI+C6J2TA5gCmhiymRKG3EEr97FA6SuxeGRO+WStqWmMjMAO7flUuTtwUSLL8SSRVL6c0/AswGC0os09AebEwYPW9A2WAzNWh9FWVCNAwotOoqz2xANudWJHX4xF6zzwM4xqAltWI2cMuHAE8Xh9IAkR6g5xTgrEoWf7NQZd7a2orm5mYEg/EkuMHYgWkzPCgtTfXZzfyxO4P1aPJTMFP6fq91rE+yndMs8dDvWDYZGMhKyphQYcxFJ5NzDQ0NQgxlJQ27vHguHQ60uLt48aKwv2NlMys5hwufS53nOJr85/u+DfNzl6PYWi6GgheYZ6E7dK7v+nmmGbCl69Lg0akrjKM7jwthgxezObZsy6kFfE2xY0bsg7OWAaYBuhvHCGOa82m2rafqvd1oDbhRbMnBLEdRv/f6F7/4BT784Q/jjW98I/7xj3/A6cxwjo1EcpnCwHz//v342Mc+hmXLluG73/0u7rnnnokpvMkpBea+TuvWYPd2lrqrJZJE2PlNa0J2KRG6ILz44ou48sorpbAhuSyGGjc1NYn5MYnk5eVhzpw5cobMELBY6tChQ31Fxvn5+ViwYHhuG7yt2+3G2bNnUVhYOOFzGBh37tu3T8ScI4l1xpqBYonxtLdlPPiud71LWBUyF8viKcnkRM7UmALQ95MBO4f9fec73xky8OBBs963E4GUJOuMgTy7x4J09jw6nbsBN6utEzJoxhxg+quH/xjRRqhqO6KGdu7N8c2qEz1CNEnO0jlNC2A3jrzyP6pGxVwNJvtyjA6E1RC2ddAuKA7/Ns1ejTnOhYPelyvUhnrvAUTFcGyg0rYAJdZhzE8YRyJqEF3BkwhF3TCwkg6d/U4qDuM85LDbIhPUcOyzCQHqFrFp+/ZjmDdvOoqKmJiuRiBQh5aWDuzdewbXXHs3CgoHfz9HDCv9VS7ygjGxzAGEzwEhvWsiAfMqwDTAMHfvbiDcnLzPGfIA5+bMn0v3YaDrYIrVWzVQtgmjgsJI/V9TLB6YBLUAJkOCFd10IH/tyB5CjaLRVycq0ylyzLDXIseUO7CgEdkWGwovNgCG2ZroNRT07n/iIW1+SaKwYbEDN74DyB8jL2Ra8DU+oz0uoaAx4+aBBzNnuKA8deoUurq6RPVgeXm5uCQuLoPBAE6e/gfCkS4sXjxLbDMo82AUgtzQ8HxwvPdZRPvea418UyVm5Czr+709cA5N/uT5Mtk+Jp07dw4ul0sk0YaDNxzCe/Y8h7aATxOwVVWcA99YNRdvnjZHVBjxfmfOnIkZM2b0+Z7SaoUJi8Hg7fgZcFFPX1R+DiweYMCxa9cuXHXVVcMKehiwROwBnPEf7ttOMYLJ/3Wl14vvhrhupAsuTztcHX743Vr1lxgIr6piv9DhLKFCmweBzr2oncVjjwLkrgDsNVphgOuw1nVlKQJyF2vdkePIE81n8dNzyR1e75i1FDdXZMe27KmWk/gXbd5iXFVSgzumL+kLNt7znveIoOwnP/mJGIQskUiGB61u3/3ud4tZZQ8++GBGxVMSSTbpDTXBFeI8LxW55mnINU3PqsDGNdbx48exfv36vvtl4QLPIRQ7ZKJKcqnmj7g+5pqSxT7Tpk1DUVFR33eA6012L+3YsQM333wzpjJcQ7/wwgvC6SGbtqMtLS1ChOB9cg6V1Wrts7HjubK4uHjQIikWW7FYTY8v+DnU1NSI4xFFjcFun3pfjC/4mVFQ0T9Dfra88P4Tj5ncxuMej2+MTRgT6dt1YYb3uXjxYvFaNm3aNOncRHyRED596Dkxv4+xnwGKsL396tJrYR+owDpL6AVTtOi8/fbbReFHQUFqcaFkMiFFjSkEKxAZwGcSeDAJ3eI/IhInRsWCEutcOAdKxo4nEVesSyMluUqqXp9ZpXbqXapbk61YKGqgBN2hVGsSRQgaTlN2vb6P9OxDe7Al4VEUrCzcAOcgQ5dD0QBO9L7ICSBJ22sd6+AwTa6DJgeLN/m3IyzmT2gnQn5KNkOsuns0ghHFBHU3jh27gLy8HEyfrlkORCJRvPjiQSxcWI2iolxYLKze2wAoYzAMWtiSsZWRJ/wCrRsj3ACEEsWFGJb1gDGl80iH1lOeLdocDoERcHCmRuHwnkvHXm1QON/rnOlA6UatenG0+NuAlhe12R+kYLH2/oe7tN9NhUDBJs3feqyJnABUVrGntAYYr9fsJ4aiuwV4+mdAMNZZwv2Ql8q5wOY3js1zPv93INSTIjhVAZVXj+ju2EnAFtalS5cOuah1u3uxY+eTuPKqlTCbimCg+DaMhdnR3if7bXcYizHLsTrpeh3BC302VEWWGSiyVGVlkcsFNRfNfJ1tbW1JiYVMafN78e49z4q5GuxGDPsD8NRdwL2zFuFtmzYjN7f/saG+vl48HoUiBhNc1DOA83q9fYt7VqnNnj27L1BJbclmMMLr8f7ZuZH6vBlkMGj0+/2iQ4CXY40H0OStF8+T76vRpFlC1doXwWHK7Qso2EXCgaR8DgyY9GFzqR73tKPMz7XDpAThDxkQUY0iGKEQM5A1Je+rpKRECD1jCR//6dZzeDY2V+O6slm4oWxmVvabRp8L3zr5Qr/t769dj5ce+YeYm3HHHXeIrlaKUBKJZGQw+cHgPdPiKYkkm4JGayBZGC+2zEeBZWbWkp3btm0TyU59n2aSkmswnh9lokpyqcE165kzZ0RXxqpVq4bswti5c6eweq6sHDsr3bFCFPLW14uiKa71uZ4e7esQ3dZ1dWhsbBT3RREidV3O61BEYfygr90Z61BI4vpcL77iMaasrKzf+ZSfEcUk3jYQCIjcXmqXMa/DOITPg58hxQzCx9Dvjz95H/pj6jEBny8FLMZdjDV4e15XvyS+jscee0zEonwejCv4uDxu6kJIOniftbW1QqQZSzoCXvz2wmE0+tyYZnfiTdVLUWwdXgf+cNELptjN+uMf/1i45kgmP1LUmGJM6cCDMwbcz7IHEgim2OxwOPm0kR00IurT/bZFVRO6hf94aqfGItiN07Oe9K/znBJzNFiFO8sxB/nmwRMsvaF2nPPS3iiZStt8lFi1auyxJKqG0RuuE7MxjIodueZaIX6lwxdpR2tgb7/tFoVF/kpMxjGjyLIBhgHuY0DEIO4XsW3bYWzcuLhv844dx7FkCWdqJLRDKgsBJcvzNdgxou5mvbb+IIBCe6lCIECBwh/fh5RCqNa1UAbzrOc+HqLApQKmUsBgH4XQombfH59dBmEPYLABRovW6RCzU4PROaSoGIr6xf5uMdhHbOMmiBwG1MY0osY12oD1THjka0BIF5Bi5JcBt74fY8KZh2MdRglYCoHqzKrDuQhlZQ7Rq3XY8p0umT5Q6ziT21z0coGcbtYMF6O8np7g5SyHc56zaA9ehIIQnKa4EFlmnYtS6+iq6bkYZhUTAwq9uyDd+YgJ+/nz54tFMO2heBlqOC3vj7fTF9XucBCfPEABW2vHM1jMyJ1VjRtn1ODfF8TFmVRYpUTBgc+Vj8mggN0Pwz1v0hqMiZDE583nyPuaN29eUndNs78eZ9xH+t3H6sLNsBpHdkxgVSlfA/cXBid8Hvz3QK+DAcmTTz6JG2+8ccpaaxzobsQvzyefJ3tb2nHkG7/CuaMnRHGH7M6QSCameEoiyQYN3u0xZ4E4JsWGmY6RFYykwuQu11sVFdrweYoZXIvRnlIyeYioEXjDPliNFlimiB3zZIFrURbXUMTQBQx2Aej7fCYcPXpU5Jgo8jGJz7VtuvU019BDiSTZgkn206dPi+Khgda6fO08T7FLm/ECC6iY4B8M/p3xSKLVL0nsYOBcT3a2DAZvz/dEfzze50jeH67XeUxiwVVqtwU/RworY5nr018H4xiz2SxEEr4G/jsdfJ9oZUbhhx0elwp8XT//+c9lwdQURc7UmGIwWcV2KAYe733ve/GHP/xBqIhjOkw55QvvCjciEHHBZLChwFwVsyPKAFaEqyHAZqYfR2zwsjZTQy3m4pLDtXnQLhAe5JmTA1XliSD+HFWEkGuqRG+CHZDFUAabYfAT1EiHJM92Ds9XcaD5GQMJC9mEA9M7AnsQVPWqcwX+aDvKrOvTfpYDDVg3G4phUqIwKHY4TLOHL2iIh2ZCdykKCy+gvr4VVVVllHYQjR5NFjS0R0T2YXVxYjClAuohQLkOsG4EQqcB1QuVn5epF1CegypmGSyGQuGj3+uxAJYsfBfHylOaIok5oYOIX5qBLKJS9oEL3sPoDjWJ3ylq1DpWwWocQYVGtBuIBDWBhaJY30KNyd5h7EOFlUDb+bgFFe+nKPvf7z4sBUCgI0GIoV1X+q4dCgu0NGLVjl5Bw+ohBtF6Rc9wYVC+efNmsWhnFclLL70kWqG5gGbCm4tLLnrZUcDgJhwN47jnMEy5ZtgddtEtUJBrQ7nTgkLLDJRYZo1qAcxEARfBDCQ4MC3T18Xr6xZRg6EP4tMX1YFIBAWhtiQZzAgFjiFakDNt7R4KCkm8ZHRd63S0+hvQG+YxVgwIQpV99ogFDcKui+Fw4MAB8bmMh6DBc+7urrPY0XkaYTWC2Y5y3FS+DNZRtoeXWh1Jj3Hk0afx0rd+hrtf+1r84w9/kt0ZEkmWYUUivaPZBUVbiv/93//F29/+9qlTPCWZcqSLMVK72EcDk4Jbt24V528WA5w4cUIMCJdMHjoCndjRuRch5ggAzM+dgwW5cyf6aU3qzgRW7ydaELG4ZrgzHhJhJzLvm/ELZ0foXcx61wLX+1xPs7hH7wgQ9qiFhX2dAHonwWhfHx+LMRTXr5x9w+eSCYwX2JmSCYzPmMQfzbmNzy8b3S183uyUmChG8jpo+3rLLbfgUoHfKeZVGTv95je/kQVTUxDZqTGFoaJ+//33409/+hO++tWv4v3vf39fYikYDaE90AurgRX0zqwEJDzRNPuPwBW+2JeosRpyUZ2zTiT2hyTcBni363cGhOmJD6icPWFjdbvevcEk60pR/Z8JUbULUXV3/OSuqghxprNig0lZgqgaEgl3kxL3IJxo+F42+A7FksTae2k35gn7KcMYD0kMRDrRHmR3QjKF5iXIMfVPCkfUEBp9W/oGgxMFRkyzbRLCVlZQA9i+/XksX74adnsRDuz/PRwOH+bMYfKTacw8QFmb/WR/lC3vWqI+CWVzX8eACnYDcP5DYuUHn8dGKMMY1jyVafXXocl/MmGLApvBifl5G4d3R+FGILS/b58XcKYHLb+MKwFlGAN+3V3As78EPN1xkePatwJj1ZYa7AEuPq3NL9BFjuk3iNkaTMDTpohdCxQduNDnQjx1SDWD967gBXgiXTApVpRa5wwocA4FhRIu+lnBxBZjBu6J1UGNvgYc7NoPd7cbXrdPBD2uLhfefP1bUGwZ/twRBhkUMvQOClYx8XHHk5+cPYyH60+JvYfeqmaDAT9adS2qcsbAlm6UcMZMW6ARwWgATlM+Ci1DiBJiVovw9MtahRsr1vh5sUtmLC02DvVcwJMtcbs+9u7NcZbj1dMG7qDJlCeaT+D3O5/Hs19+AN6GVvzsJw/JYEMiGadZGwzyefxg8RQ7CyWSbNMVPIvO4OmkbfmmapTYRp6gTYXdGRQzVq9eLf69d+9eMTB8Mg7HvdxgAc6TLc8hlNIJva5oFSpsmRWSXMrocy8oYnAGHGEhK7sIUnMa/kgvekJNwvo0z1yOHOPI130slmJXNeF6P90akgIIu9ApEDAeGWmSm/EMOxX43eRronjCxxyvjhDJ8KEtFoUAisXs7JmqnxX3vQceeACf+cxn8NrXvlYUckg726mJFDUuAZ555hkReLCSk8Myy+ZW4W+NO+GPaknoWkc5XlGxUgwqTXeybAvUoz2gCRVltiqUcFhwGgIRN855Y/YfCVTYFiPfPHTlrUjaeOjp74klNFmlbYVKD0GF2xKZAQWZLWj5GvzRZ6DEkqRqTDPhzyhMcBhpizT5Fq583t2hRvgivbAYbMK/PiNxaJT4I23oCO7rt73AvBAOU/oug2C0F+2BwwirHpgUO4oti2EdxWJJP5Ew8aa3uLKVkRXgwgOenQEXtqP+wlkUFJZizpyrYLX1b4UdNeoZ7ZIEPXqu7UsuqqDgluz3q8FujannPzoS6tx74aIomcLS/Bsy32f5pfQ/lTAcnCiAsRIwLx9ZMjccArqaNLGLXRpDWBoNeDfhsLB2IhQo1q5dm94WioPW/R3ipVxo86GxqUUIyayyoZcrF3cDLez4fb/g3QNvRLs9oYXXbMeVsIyign8gLnjP4UTvsaRtx/eewJuuezOKrZmLGgxaWJHD8wsrwYayjRpLhPdr8zns7WqD02TGa2fMQfUkFDSGRdQP+PYAkU5N1LDOAyxzsiZucH+mGMWgWA+AaUlAwY2fLY+7FD42bNgw4s/2zxd3os7TmrSNwsZH5t4KwyheB5/7N77xDXzlK18R1eIs3si0Yk8ikYweJtEY7P/0pz/FZz/7WXz84x8f0JJCIhnpeb0reAY9oQvid6epEiXW+aOzOI3Nu2KHq37eo389hQzCWIN+6XxsJk/Ted5LxoeekAvPtyXnFrTCiBosysvuDMyJgt3V+rBprmEWLlw46PW5NqMIx/1Ut02liJFudpyON9yNc96dCd3MKqrtK5FrHn4R00jgXA7GTsOFYgaLdNkpko1OD8n4wrmFFDh0q2DmdPSuOIpi3JeZ1xkvR5nhwMKvd7/73aJoj4Ub119//UQ/JckomJqymiQJfgnZKvjFL35RLNiue/sduO79d8Fs05JyZz0tONB9DisL+/untwTOo96r+byTOk+3UPhLrTPSDh9PR5iJvkxg8jNnIxA4rg0MNzgAG+cksAo+ld6MP2UuRM3KQoTUo33JQqI1L4cRUhthVUbnHT8W8HkXWqanMzEaU8yGfCgwcdRu4rOBxTBw1bXFkItp9g1Zew6sOOECjyc/JtTYLkrPela3a0/HgOqZG8WFQfXhI8fE4m7ZsmXDGkrF2SE9oaMIRJlwM8JpmgWHcVZC8DIrNiRct+JipfTSlITiQIHV2AtQkwWtmyChu0K8KzT/GU7QyW9k6tAxqo/hkSdwTWagtBqjgfsVrQl47OT+SKFt165dfUPV9CoUEooo2LnvrEjq0EJpOAOvfZFuIWiQ+E2iaPAdQK1zPbJNoTn5+9za0IrC4kLkmjMLGhjss/KKHr1MeA/XNqsz6MPjzafRE/Kj1lGIG8s5t2d0SQq+16+srBGXSwbfbiDSHf+O8Pyo2AFLBoUCGcB9NdGSgMdbdhUx4cMqVe7DrLTbvn27sKsaSeWqSWHfTPKEHIoZo0kR8fkw2NALN/g8JRLJ+MIE3Pe+9z28+c1vFt/H3/3ud6J4Sk8OSySjhef1IusccckWtOdkZTmTpTz/lZaW9vnlEyaXmYBlcdX58+fx8ssvi+pwdiVJxpd08zOYh7BeInM1OBOCc92uuuoq8TvtYTm4njEG90nOL9JtQnlddkgwFmGSfzjrsdbAKfG+JdIcODEuogZfB7s1hgPnR7BjivHVaGyzJBMLj6286DC24D7MWIOfKwuoeHxlLKKLHRMN99UvfelLYjYx5xR/7nOf6+esIJl6yE6NSwyqjve+8+1obG/B6774AczbsFwkFuY6p+Hmiiv6Xf9A9wsIRpNPRHZjLpbkb0orXpz1vNDP67TKvgY5ppHbkKhghUbqybACCpakXI8DjfVq0HK6qSf9PRSl1ccRrVOjb6sCizITVqP05kwkGO1GZ+AgIvDDADMKLUtgM45PNYdemcEAgidCVhCzYortfvR0TDw5JsLgg5UgXABlOvysK3gQ/ijnqsTJNy1CjmmGsALqDJ5Bb7hRdPnkm4uRb5oNxZDcEaIK26mdrJ2JbeE3iie/dcKGa7JT52nBBW8bLIoJi/OrkWcefsdLIOLFKffLYpCfhooZ9kUotmZWecHPjgsZJfAioKZ0ZZnmAua5Ay6UKTJQeOACiUEAF/ujWRTx/lg9wmQ9nxeT9RTLBhrYzSp32krxeqxEoX1BpsO9E+kNtaDBtz/l9TH5a8aCvOswFrT4m7C7aScazl0EQipef82bUMDh5onPq7dXiIxc5LEyXg/8+VMfUDdcKGR8/vDz8HB2Sux4vKZwGt5bu0pWQyZCQa/38f5voGk6kLMS4wmPw0zuMPimyHH11VdnHFDXe9vxh4aYtWSMtUVzcFXJ8ANVWRkukUxOeB7+5je/KTqn3vnOd+LLX/7yJdM5xfVgWG2KzedzwKSM7WBWydhCUYNJZK5j2GnK/ZRi/pVXXjngbbgO4jlwOMUqkuxwqOcoznrOiw4NJuYdxhxsLt0IsyGzrjB9LT9cuKbnmoc/GVdwbT+S9b0O9zcmdNmZweMl75dDrIUDwQDxCLvEmfBljENLV3ZEj4TT7i0ICCeMOEbGF7ljE18QPme+VooxLIpJ7bTg3/m9orjI90MfqM33iUIO50iMxLYoFI2gwecSBTXT7Xmj6giWjD38vFnIyv2A+wS/q5nOPhkrdxvmoFigwfhfcmkgRY1LEJ40fvCDH+DTn/0MFm5ehds/9Q5cu2ANNpX0b3fc30UfS84MiGMzOrE0P/3CzxNuR6NvP6Kx+QKl1vkoGsXAWaKCthtM9uliCSsW1ibNK1DRBWBvglzBpOYqKMhPCko8kW1QUwQSu2EVTIN0IVzOqDEf9/FewPME9+KLL4qggwtJLnZYmcsT3g033CC6Mfh5tgcuwBfthUWxo9Q6E0aDGXv27BHCBxOuQz1Gc+DplPphwKIUo9i6Ch2BU+gO1SX9rcS6EPnm/ol6Dp4HaFPFBSPFtFrKZRgP+Dqa/B1whTzIMztQaSvO+PM60F2HLe1HRaBAzAYj7q66Evnm4Q/4pvjZGbiIKMLINZUi1zz0AGYu6LnYZXsqP2eTIQB/734sWzIdhYW5gKEU3d5aHD9xSrwmJeKHp7cL5dNr0O3yiMUuE6tc/PL2vD+2supD6iiEDRgA8Dqu43C3nUJbpwdd4SIElTxxf2yD5YIm0/exT5QZjZVO1IfT7heFkMG70YsGrcZ8zB6DTg0G8BRknE4nZtbORL4zeaYQF5askmIwxe8Sv3P8dzaOBd86sQ1HXG397uu/l1yPMtsIhstfarBjJ3hGszOL8NyWiALwGGRfPkFPDjh27JgI9FlBmCkXvO1iWDiDTc7TWFlQM6x9id/n3/72t/jEJz4hqrvYCt7XuSeRSCYN7OBjUoDFKbSHe9Ob3jSlk8CieCKyHxHRtav1nBmVMlgNy6b067qcodc7E8b6HBgOOj58+LBY6yxfvnxQcZ+DYjdt2jSiJLlk5N/Bi/4mdAV7YDNYMMtRnZGgwZjx6NGj4nuqr9H1hCn/zfs9cuSIKN7RP09uZyzB33lhTKCLClz3MB4l/MmOtMHs9ng/FDFoX8PH4OOxGp1CxnC6LHg/o51J0Og7iq5QfcIWRcRq1TmZr+MyhTERixH5HunFW6o6FgABAABJREFUT6nFZoz7+Nlw2DjFGsZxfL9He0xtC3jwjePb0Bb0it/nOYvw0XkbYDNK85mpAPf1F154AStWrBBx+HjBYkbGF5wVxsKMD3zgA/IYf4khRY1LGKrjH/nYR/GPv/8Dn/nsZ/CJ+z/e1+KoU+89gWZ/cmJ3un0uptlnD3i/UTUiEnRGxSLW/wb+p4zuZKKCJyeKGzzZlfUbEq5iRxpLqgIoSB5EGlU98EUOIiq6OoywGebDbEg/I0QysXAxyn108eLF4ncuBlmxwkopBhT+3Ga4wq0JQ+kdmJu7HkbFJLxGefs1a9YMeFLi/bUEno11WsSxGkpRZLkC59zPI4Jk6zSrIQ8zciaPzQlfw/aOIzjp1ryGyfzcaqwvTu5iSkdUVfHg2X8hosY7qyhuLMqrwjVlS8fk+epdEFzM6kFGbW1t0sJFjYawY/tzyM8vgserIhyJYN3atVC69gM9tBkLo7s3gLLFrwJyKodMrrCLQg86khbLrpOA9xycOTaUFuehMN8B07QbAFs5Jop2fx1aAyfjooZiQK2Di/HMBqS7wz40eNuEjVNVThlsxoGFtb///e/ifWclVKoPLwMwWhaysmqklba+SAgne2mnpWKuswQ5tAJjQtzVhm+c0CwFU4OXLyzajOqcy8MzN6r2QhWdcPT0TuiOoojh4/sTU7WiiaIr3y8FcFwFGCe2ApoJSyZ4+N1iAMufDNIZ+PN7TbGM4vJoqhoTO0w/+MEPChHuW9/6Fl7/+tfLZKJEMonh2uT3v/89Pvaxj4mk1fe///0pW/EYjrYjEO0/Z85mXAWjIguipiq0Fd24cWPfuYQdiE8//bSYf8YYY6CO30RbHCanJZNo9kpXF86dOye6igk7A3j8SRQeGH/Q4olFTxQcWCRBoSGx4Icz4oYSERjPcA6LvsZJl4xn/Mn74lo7cX7LRBBRw6j37ocnZnNrN+SjOmdlzD54aPj+1vva0Bv2ociSi0rbwMc+ioSMvzjnY8mS5HiU60UKiITnhGy/J1899hLOuLsQja2hee83lNfiTdVjE9dKsg/3tWeffTaps4f2TzwmM8bgv2kfyO/waPcfCm/f/va3RWfp7bffLgoxRuJAIJn8SFHjMoAV8UwY8ItNb9wbb7yx72+shm/wnUJ7oEEbFG6twjT7nCEPIqFoAPW+ffBFtFkEheYqVNoWjtkJXcWLPDSlbLVDwab01xeJXEUmRsaQQKQTIbUHBlhgN7JVPzNLIC44uSDSg4drrrkm6e9Mtp5rOINgQQvmLEzuApphX4zi2LwXVuxz9sHmzZsH/Jx7Q6fhjpxN2lZkXgmrsQTnPM/3mxMz2USNFn8n/tWcbOtCbqnYgDLb4NNYgtEwfnL2iX7bZzsqsNExV4gBXDhwUcqFBJOYTHZzscEKaQZ+XJwyKOCF7zdhIMAKCx5P2IWhbycWixmV04tQWlIBo2HwKiXeJxcsQpSiaNP6UvIVKJTOfA2QYft5ElQM6v+Y0P0VI6cKKEl/zBgv/BE3eoJNwnaqwFIJsyGzpHCrvwv/bNqBcMwCLMdoxcK8Ghxx1SMcjWC2swKbShbAGBvcTnGQbeGch6F/P/iZMxAZ7dDvjoAX3zq5FV0hrSsu32TF/fM2oczmxONNp/HHhiN9VXL6T6fJgm8tv0l0C00k7rAfXUE3ck12FFiy3zXC1xpWTyGKuBBpUvi5xGZk+PcD4YbEG2hhmaFM29ctsydc0Eh8LbqtA7/vDPL1oINViTyW8ycHrXLGzHDP/0wyff7znxct4B/60IeEry3FEolEMjXg95/Jgv/3//4f3vOe9+C//uu/RDJiKhGKXkQwerTfdqthKUyGzKxOJZMDnq9YHU47Ta53aKOYmDjj9ueee06sfVkUNdj5holZ/p3nN8nw1g0UHgiT/Syc4fphy5Yt4v3k+oEdEFwvMH7g58S1gD5omJ3UtBhuamoSa1hev28OZWGhsHJKLdBMhbEl94XBBmtfqu99SPWLrIlZsWe8JuPtnmndhzOepr5tqwrmYnVR+k54fp4sQqTwpyeI+fmxQIV/o5A0VueB9+z+O0IJxXqk1lGAzy3aPCaPJxk7+B3VY0Qem/WYkR1TFDCZJ6BgSeFsJPP+nnzySdx3331CmGThBc8HkksXKWpcJvBk88ADD4gEAgeL/+///u+AHo+ZUOfZBa+wzYhXmZZb56HEmv0BrlHVDxV7oYBet4PP3ZgshKMBtAeOIRCl56MdJdb5sI5xoorBJT0LuYDnhYt2HsizITSFo70IRM8jqoZgNhQjFA3BHaEdk4ZZyUOxZQ2UWEJ1IFg9w5MVq8cHe15PvfAYlPJuNNW3YunqBX3XrbTNR5ktHmBwIczXzIrzdPAE6Y1cgC/SIp6bwzgTNmOJ+BvnaXTRAiaBUuti5JknT2XWGXcDtrQf7Lf9ypLlmO0c+nk+fOFFdAbdScPjqrrtmGkuFoEDF/6sjua+ogtMfM8oLPFzYgKTCXBe9CokDgHjbBMuWJkY14PCiOqGJ7xPVKcTszIddmOGQmfHPtGlkWoVhum3ANYRVEpywStEjZT7s00DyqbmouaR+hfRHWL1v0ZEFWMyklicV4VN+fOF2McKQ33AOReOrDzkZ8VgY7THhAdO78ARV2tfpRTHRM91FuMj8zbi5Y4GPHh2T7zvILZg/eCcNVhZOA0TyVFXPZ5pjX+fZtiLcMe07PpnR9UOhNT+Vb8WZRMUDgD37wXCjf1v6LgFGOL4ORnhZ0v/cg615HEiE5ik+MUvfoFPfepTQiBlQlQOipRIpi4simDygNY9//M//4N77rlnUgwEzbSrzhfpXzxiN26EQZF2ifpxngURPHZz7cf1INeNo7XM0e47Cnf4PALRLuEAkGuqgckwsvf9iSeeEIUcg3WgMlFGQZ5z1TgYfLDzFgfc0oZxJAm1yxGuNVlIyfM59w3agDHJzVlZfB/Z3cn3nqIH9ynOyGMXBcUKJi+5jbZGTHByDctiCWkDNvac87TgiZbd/bbfPWMzCi3OtHMGafGld7Hwd54DOHOQx4ax5BMHnkJH0JtgRq5gZWEl/m3OmjF9XMnEwNwW7ckTC7KHgkV9999/v5ifwYHg73//+7NyrpJMbuQnfJnALzMrIWnr8O///u9YuHCh+P0//uM/+g12GgotUUyrqGTc4Y6sixoUNLwi2AjDZqShlA4XrPMxGeECvdG3GyGVllqs2vXjom8XqnM2wTRE9fpoYEULF+uEFbX6RR/8q2/nIj6xFXcoIlE3esMclK1VRoQj7Qgn3CcJqS54IxfhMA0+54ILVCZcWeHPxRAXt0ySU5RgRQ0T6VwEu7t8mL2sVCxm607Wo3a+dr9OU3J3AoMrJmppR8XXlQqTlQ7TTHFJpdDMuRgG9IYaxfXyzdWTStAg+eb0VWQFA2xP5ZbKVfhH4y50h7Ruivx2FTPtxX2ewvQ55WeSCN+LwWwk+H5fd911/Y8J4f1QEZ/PE1IvwhjNhdWYwTBxEwevp2ToiZHbRwC7huzTAd/F5Ptlp8YUpTccX8TrokYqx3svYqWlWrTms+uGoh//zUCTQmK2quAb/b19ggbhv5v8vTjV24FgNILqnDxc8LqE3Rlt0K4vq5lwQYPWXYmCBmnwdeKFtsNZtWPTrA/7o8IDBXbAWJEiaigAhdYpJmhwn+JxnOccJh8y9cZlyzl9bXm7H/3oR7jzzjtlR6VEMsVhEpNVkX/5y1/w0Y9+VFRF0uYhda0wGTEoubAYFiAYPR7bosBiWCQFjRSYzKRYwPUeBQF9wLIO/83kP9eXw0lEdwWPwBeNV4j7Iq0os26AyTD89R87jxkPcF3L9Q+T6Yw1KJ7zebe2tooOAD5XWlNRkB+sepdJWnYYUCiRwsbgcH9g8nHt2rV9iW2KGKlwW7rtevwxnFlekuzAmY0DxR2pogZtSfXufuYUuBZkLMnv0XjYb7115jL8v9M7RJMzC/ZsRiNeM33BmD+uZHxhp4YupF95Zfo5v6nwmP/f//3fwpWG+U6eC+g6Ibk8kJ0alyms2mVigVVVtHygijlUO6cOF4bHe5/pGxauk2eqQFVOdoeb+iMnEFI5+EqvCNbIMV4LZZRzPMYKX6QLjb5d/baXWBYg31I9No/p84mKIla78ETAhTyTmTwZMAihcKWr1Pwb/WRZZTUU+/btQ0ihqGCF0WhIHqyYlFRV4DDOQp45s8GuTGjx+elVO1TVuRhi8MGhY3wdnkgXdp99Fq0tbViwdA5m2BehyDp9wA4Qvj7Ob7jUONB9Cvu7T/X9fkXBPCwr0IYfZoLb48Gh40fg9/owt2Z2PxEjFU/YjfZAGwyKAeW2ClgysEdS1RBc4ef7bTcrFcgxZZAwjoaBxieAYHffDBUULAWKRuHRHQ0BnTsBX6OWMM5bBOTO16Z0T0H+enEL2gOuvq6bYLSfuZaYtfH+2leI7xIDDV4yPa4Ph++d3o7jrrakTo0cowWdobiotTi3FLXOQlTl5GFlAe3pJvZ9v+Btw6ONFGeTyTVacW/NDVl7nIjairDav7vKrGyIJ8mC54DgSSHWw1gG2JYDyghs1iYAJrN4vOXnSZs6BrOZQCuPT37yk9i2bRs+/elPi6IKmSSSSC49uB5l99VXv/pVkYz4+te/3jc7bTKjqkFE4YOB1racGShJOn6zAIliBdfv7OzlGp7HcMYYeicvq/K5fs/k8+Z9vLT1eUxf5EVhUV7KgOPM44lU+Lw4r4/Pg8VStMlhzMILE1y0y2GswetRsKCF7WBdRbwe53NcddVVsmsg3YDvixdFMZpuTTvYYG3J5KTe24bHmvuvj99YdS3yzAkz4RJzAIFA1twghku9twf7u5thUoxYVzwdRZYRFsBJJhWMWSlEUJzg8ZsxRiYdFjzv/PCHPxRdGRTVWVAxkIOH5NJFihqXMTwpPf744yLRwAoLqpt33XVXRieojuB5NPvjVU38r8axDvYsWyz5IocRVpv7VXE7jFfDoIx+QGk24HvHVlpWq3IBnShquLrdMJqMyHHYUGpdOGaiBrlw4YKonGVFNj9bfbGh+xJyYc7FJv+emOiMRkNo66hHU2M7ggGtq0dv+aU/5rylVhw9vjtpv4hEoiiqKMb0qtK+7QXmZbCzCpn+/62t4vmwI4jBDsUVXm+gfUv3VWXCjNfVu0sKCwswa3Y1TAarSLIPxrFjx8Tr4tC4S42uYC96Qx7kmh0otORmtDCgYMnPnOIV35NMWoI7Au3Y1727L3FuVixYV7wBdmP/RW1qd5Ir/GzK95TVjlWwGzPsqKKw0XsGiPgBWwmQM7m6ZibDPvCPxu3wR7U5MFaDFT3huIhAluXPwuZSLZngDQdxyq11bmmDvLOXpGn1u/HNk1vQG449F8WI3kiyyE0+s+AqIWxMBjqDvfjNBc5mSqbQnIO3zLw2yzM1DiGK1r5tRtBOY+oel1jVSiGDgQZFDAYamVbiMrlE28vf/OY3eO9734vPfvazGdtUSSSSqQvXnUwyPPjgg3jLW96CL37xi3JA5whg0Q/X5ZwzkHjc5TqPHZk8Jo+1tQbXkhyazEQThQG9CInCBJ8Di6V47mMcRGvTRJj8ZOKb63u9qpsFTTyfdPW0oid6EL2uuL0wl/+51ulYt/zWAYuv2PHN+9BtlBk3DHRO4mOyeEoXY3QoZNBCdTCrKh3ejq9/KAHkcoHxGq1/+V7wM9dn80mmJvzubus4isMubRYK2VS8GEvy5TwZydjDcwO7MghdNzKdx8L99pFHHhGuMxTYWUBxyy23THgRnWRikKKGRCQsfv7zn4vEAxeIVDgzafVyhVrQG26FAiOKLFWwGbM/kCsYbUAgeix5p4UdDuOmSXPQ4oGYbdlsaWZy3ma3IWJvwaIrZiHgD2DXtsMoLSvGoqqrYFSs2iCvUEhU/aSDAQJFgUwV6lSBhVVJtJfi7VPfI/6d8xJ00SCi9opumMKiHEybXoLcnFoYwrNFIoqBAE8seYVheCKHEu5FAVQbztbXo6WpTWyxGcrgMGnWYwx0GKzQb5OLXn24LO+Pj8sAgs+N1XwMdCi6UAxiwMZgZzT+qawCZkv5VIADiyNqFHmmzIe5Ded9oHg13CF5W9qfhy+iDX/WUFBurcCyghVD3jYQuQB/9ERfp4UCM5ym9TAo0os4WwQiIbQEOmGAARW2Ipz1tGBv9xmEohHMdVZibdFcIf61Bdx44PRWuCOa6JBrsuIDszehxJo9r1t3OIijLi1x7woF8bv6w/2u8+6alVhfPHhnULZp8XcJAcNpsmGGPS66kj83bMNFP2dBxbmpbBnm52XXlozHuSgoKPmhwAmDMjmEneF42FKU5vlMH9zHYzYTWpnCJNc3v/lNMb/r1ltvFUUTl6LgLJFIBofFNkw6sIjqYx/7GD7+8Y9fdgN8RwNtvXhMbm5uFjEB5xBQOOBa99FHHxVrbMZuXHvzeM0EPpPv6d5j/v3IkSNCXMrUMjCdnz4tStPZWbLAi88zMcHNYioWSvHxeC5hAosCAwULCiTd6i6EVV9SUYwjuhjnTrWJCtzE587b83H5mnlbdghwmx5jEAoVvG/GIRSEeDvGFxRB+N6NdL3N10Vx41LsCB8O7Nhh0RT3v8kSh0uyQ1ugR1i1FpqdKEixnZJIsgEFaB63meti/lEXuufMmTOs/A+77Og4w2M8CybuvfdeOTfjMkeKGpI+mMD41re+JUQN+od+4QtfEDZFE4locYyeREi9IH5XYIPdeAWMytifbOkbyUUsK9yZiOdiltsYKPAAbLVYEAm3wOVqRVdXEDZ7JWx2u1hcd7va0e4/idIZTihRM7zNDrg6/X22MEzu8N8M9lKrW9htMGvWrD7VmslpLtAphDCgyWQRqVfR8ATB95CLeQ4NTrytGM4XYeVyKOm2NsMimA3JVfL+yDn4xGDwKIxKAZymZSL8CEc9QqhRFBXBaD1UNYxIyAk1VDxg9RNFDD43vq8MNDJV5IeCQQ7tstavXz/s23rDfuzoOID2YDesBgtWFi7CjJyx8WEMRyP4Z9M+nHSzAwkos+bhtTPWiiRstuCCgX6na9asgTPPKcQTi8E85L7zdMu/koaKk3xTAdYWb8jocUNRzlvpgAITLIYZk6ab6nLjx2dfxll3R5I91BxnCd5dG/9uHOxuxt8aj8ETDmJBXinurloKu3FktgFn3J346vEt/bZ/buHVmOXIzvc7E/Z3ncauLgprGjWOSlxfdkXffs9j4Qvth3HO0wqTYsDqwrlYkDe+ostkg+8Jz3NMhFH0ZsDBpBHPQbqlyHCgmEE/W64laEHC9cRIjskSieTSgn77TEIcPXpUiBsf/OAHM6qSvxRhHKAn3PkeMPHPZD9/Z3xBy1j+m0l9CgBM/vB4zOvxGM3CI3ZCMyZg8r6urk7ECITrflpwsIBJFwV4HBddhOGwSPrzsXjMZ5U9j/WEf8vEOoiPw8+Q5wtdUFi0aJEQDEZCOOpFZ/AAQmqvKJLLN8+HwxQ/L/N5+yL1CEa7RbFMsDcfxYUVaTsD+L6yq5Cvm+8hxZ9sDJrmc2B8QXH/chfkGGtRGONPntsD0SDMBhOMU2wumEQiGXuYO+P5irkfnrsIbcaZlxpJhyGtAP/zP/9TdM6xQIJribEeTi+ZGkhRQ9IPqqff/va3hSfuunXrxMFjROIGuwHCPsBoAQymUXvdqohAARPoY9fiyoUwK5G44GeynQt+HpCZqOGCncEHq1Q6OzoQ8B2D0dCO3NwcFBY6EQiWwuPXrp9YXTQQXBhzuOJgSSMuGmmrxPtMXMCzzY6356Jdr37S4UmDXRA8WTBA4e14QmEFFcURVlhxkR9VffBEUhORCszKdNiMC/s9F627Q+33/kdUN9zhHbEqKy2JajXUwmYcvCo3qobhDp9HWPXCrDjhNM0c0WfLz4MnN35eS5YsEdVqw4GDjP/VvAWukDspoX9T+UYUW7OfkH2p7Ti2d57u+53WbdU5xbi7KjuJPwZ1DGZF19CCXJzzNYjtheZ8bCxZDZtx4PdnR8c2uMKupIq5KvtMLOA8CsmU4UtHn4Ir7E/aVmC24zMLtdkRp90d+M7JrX2fMkWPebkl+OCc9SOuvHuk4Sgeb47v16+qnIc7xnF4Hwcd/r6+/1yXG8pWosZZOW7PYyqgV/PynMDjBa0j2N2XyZylTMQMJrj+67/+SwwJlpWcEokk8djzzDPPiLhCFzfuu+++kYkbkRCgci6RbUrMyuJrZ3zFYibCQiOuxdmFwZiBYoZuGcsCKr3IiXZ9XMtzTccL1/e6pavedZ2Ivp3xxVDvK7ul2ZVHoYIxDoUNxgyML3hhfJF6XmBcwsdnjMELb8uZG9zOLoaRDmZloZVYEad8lj3BI/BHL+qvThTNFFs3ioKq8WDPnj3ifWERlm53dTlDJwEW3eWW5+N8Tgv80YBYQ15RsBi1zrGzWJZIJFMDPZdGwZ3nD4oYzNGMxqKOnRmMK5jv+fCHP4yPfvSjGc/0k1weSFFDMiBM7FPc+O53v4u1a9eKICQTWyqBrx04/wwQpn+pAlSsAUomdlAguwO4GGMVKtvcGDjoA6r1RbQ+bHrI7oFoBxDd3X+7cR2gjH1lMoMHBkIMfBgk6W3ZOgxOmKzSh4Xr8LoUNSjWsKvCHXmu331bDLWwDsP/3Rs+ipCqBxw6CvJMTGilqaRSfQhHXegOnRKChm5XZDUUo9iyclhJMAoaO3fuFKLbcMUMHYoZ/2xK9tpn2LQgrwYrCrKflP3N+a1oTLHA4YDn++fd2u+6bANu8XeICqjp9lJRDTVYtTWDU37e7O65GG3BYdeJpNdUYi3C1aXrBh0SvqdrJ1QEYFYoTtmxJH89rEwaSKYMD559GWdSOjXm5pbgXTWacPbwhYPY2n6+7+86X1t6M3LNI08UnPN0o9nvRoXNOa4dGqTB24bHUwYd8kiypmgBlhdI2yOeC1jNy/M6YZUUK3RHW8XKcxDFDNpMsTOD6wQpZkgkksHgmuXZZ58VHeEUN+6//3586EMfykzcYCK/ZSfQFZvrZy0Eqq4HzBNXrcnCI74OrruZ+KYgwPiCSZ3EalQmYXQxY7LCNSTFE8YYegdfYrcj19p8/jynMNmvw7/x/EJP82wRUf1oD/SfheUwzYbTNLbndb4eClB8LzgA+3KG+wDfC+4TtKGsnTsbjzU/j5Ca3Ol/bel6EWdIJJLLC+aXWCjF84I+35SFt6PlpZdeEmIGcz1SzJAMxthOFpNMabj45pA/qqEUN+iLTXHjM5/5DK655pqBk88c+HvuKSCiD7FVASabWPWeO31CF2UMLliRSmsevr7Vq1eP0IMvce5AAvSFHQdRg0EFL6zgGqlPuaKYYDXMRSB6KmEOgh0WZbiVNvGgJg6TpRRTkkUNX6QB7rA2I8WgcIAugxYtsRqIdoj2cquxcFAxh4k5vYqNHSucTZJJy/xADDSAnHMLRkJEjaAr6BIiQqElr9/9c2Cz9m7HsRv6P/8Wfyeead0prKOI05SDWyo29HVaMLigzRR/6p7BtJvSKyFa244k3R+7UNoDmr3BQN9dh8mJ+bnVaA3QaowEcN67B3Oc62BQ5OliqnDn9KV44Mw2uGODxB0mC+6YtrTv7wMdukdbVU8hY7zFDJ38NAktfscKzJevLzC/66zE5XGC5zmeK2g9kg2YvPrBD34g1gYUMzis79prr5WdGRKJZEh4rrn++uuFAEpxg2IohVHGG//2b/82eAUmxQxd0CCBbqDhOaDmtgl757nu4lqMQjEFDooBrE5l19pUI7FTg118EwmLr/qjDLA9Ox2MenEQLxSgaDl1uUFxjoVS+nxFxpssBtSttzoCXf0EDcY8rYFOKWpIJJcJzKvRTYTdg8xHsWh2NPmYxGPx888/j6985StCzPjIRz6CP/zhD7IzQzIoMkslGZa48Z3vfAevfe1rRTcDvezuuuuu/qJA0AVEkq1PRNLc0zS4qEFLnou7ACbi8quA6isBU/baixlc8AC8f/9+kRxnImZkggZfzgCJsnGY9ZFNLIZZMCAXEbULimKGWZkmfg4Hk6EYoUhLwha64uYK0SSRiOrtEzR0jIoi7J/0BH80rUCiJdBYBUffRAaNbKsfDj2hdlzwnkAoGkSuqRCzHJwbYhF/cxjtqLSVoMnPwb6avEMhosYxfAHOG/bhubYdcIsOJSZUc3FN6TpYacEWY2PxPJzztAkxx6RQ5ACW5peLAd2esA8Nviao0Sj2XzyF9o4OeF2xYb1Q0JXThCX5s8XvDC7oFzyQv685jVBiUoyDJh3D0WCCoKERiLrREWxAqVXzXZZMfkqtTnxi3jU47W4XO/RcZ2nSvIx1RVXY0na+T1zjzyV55XCa4vvpRBKMhsTAQnYolVnzBxQeE8k152BT8WJs7YiLeQtyq1GdU4bLDSYhGGjQEooDWul7nS0bKIrKFDJ++tOfChFVihkSiSQb4sZzzz0nYo2vfe1reOc73yliDiaV++HR5pHFUQE/u6cjgGFiOiBYYMNir4aGBjFvgPFGNqpUL3eMSg4MsCGKxJhShcUw/CHnmVhMsYORXRnDiQ3D0TCOuo6hNdAmuqnnOeei0j65LS8ZC3NWC2MrJiRT1wcUtlgotXLlyrRdReniC0ZyllFaTUskkskPjxssDqbYyXPdaKxrE+ExmDHFN7/5TeGuwrlbUsyQZIq0n5IMG1r+/OxnPxNVVUyuMvBgAEJbJ0HQDZz8Y+quBpSvAkrj1cJJ9NQDpx5Lvn7edGDeKwdM2vzrX/8SSRUuQhMXZDwossKG7W+J8LnS91VXlJkQHpWiHD0DROMe8jDMAwxpArBLHG2Y+xkEonXid4okDtMKGJRky6JApA2u8P5+t9360iH4A0Hk5eWi2HYFDIgPtNa7CnhZtWrViBJznnAPDru2J/RGKHCa8rEod13f/XF498GeE2jzd4lOiGUF80SXxXB5oXUnWgIdfbM5tHkZlVhfnNy63hFw40DPGfSGm/qelxEGuL1enD10RgRUhjwnnIW5sOc6+rovKmzFuLF8YPuoRLqDPXiu9eW+58KfKwoWY7ZzYE9gf6QXp9zbUrYqKLFUo9I+fvMRJGPPcVcb/tF0XAwKn59bijunL4LVOPqA9Iy7DS+0nYI/EsL83HJcUzZP2KtlSkfAhb837YAvos0lKrcW4LZp6zIOlruDbnQGe+E02VFmm5iukYmC5z19ng4DjSFtFIfBrl27RKDx17/+Fa95zWuEFz47HSUSiSSb7N69W8zm+fOf/4w77rhDFFBxrd9H41ag50xyvyuHFM9/c9Zma7D4iesudrZRsEgaWu3ziSRO6nqUx10OQ2d3A2MMxiaS0ROOutEd2iesa7kezTXNR44ps45y2gw/9dRTfcPQ08HPkZa8I+1K2d25Fy2BxMIuYG3RapRaJ9fnz32XQ9RpE8MCMe6ftJIaSUKS97Wjcz/qfU0iziE5RhtuKL8SljSCh0Qimfrw2MGCKc5amj9//qhmZKTmFf/v//5PFEzxeEw7yne84x1yALhkWEhRQzJiKB4w6PjGN74hvDbf//73i4F/lZWVwMWtQNfJWA0wS8TtwJzbAdMA3vx1zwMdvH7K0LsV9w7YrcGK0a1btwr/Plbu69Uk/MkDLbsxmBzmAZLPlUIIq77E8xsNITfga9aCqJxCQAkCikO7XMZoQ/4iA3Z6hKO96ApRXEgmEI7g1PFGmIJVsBrzxWKZC262fI90TkYi7NBo8p/rt28tz78aNmMOsslfLj4lqswTyTU5cGvl5n7X3da+E+3BToQjYTTXNaG3uxcmixkzF9bAaDLCE04cW64JJPNyq7G2KPPZND2hXtS5LwhLrEp7OabZy4cc3n7M9TyiiCRtr7IvQ4FlcleeSSaec54O/LRuW9J+u6qwGndMX57xffz+wgvoCnkShEFgWX4NNpZMPRuP8YTnwW3btokq02xVCFMkeeyxx8Q5npWs7373u4Wn7WAJIolEIskG9Odmd/hDDz0kBFSKG7TBNYRcQN0/AVVfp6hA2WqgOHtz+xg/0MubsQ2Pd4l2WBQ5aDGl/5tDUfVCKtpvjLgDXDIgjAtUhEUfeLpZfUMl4lj1K+5DVUV8yA7GUceCMbvZfzU/2W/7dPs0rCjIfN0zllDY4WBvxsDcl7PxuvtmjrjPoyvUA7vRhnnOmqSudIlEculAWz7OpR1pgWk6mpqaxEy+H/7whyLn84lPfAJ33nmnPIdKRoQUNSRZWdhw8c8qzieffBJveMMbcN8HP4hVtU7A26YJGhwSzp8Dce4FoP1Ef1HjincACbYp+uMlHlC5YGMbHLsuaIvFypO+CvxwWFw/Gx5/Al8L0PxcPJgyOYBpNw/+2iY5qhpBBOehqr1ipoaCcoTUE1DB360wGxbCqGSn1dsdOglf9Hzf7xalFA7TfBgVqwhUaJfCAJGf28GDB0X1FG3CxkLUWJF/NawZihp6hROfH4NXvXuE7dkMjvT97YnmLehmwJ0gRJRZi3FN2dq+bQyGGSg/f3ELApEADEYDyqsrkVuYK+Zf6s+S9lT+SPx3zga4qXwDrCnfh2zjDnfgvGdfn7BRZK7CNPtC6ZUvGZJHGvbhYPfFPkGC8Jvx+UWvhCmDih5/OICfnX+63/bp9mK8epo25HxKQ3sU+r/TD97XCnA+TslywDm6WVO0kaA9H6uZs9EGzqqpX/3qVyLY4KBbDvB973vfK86tEolEMp5wjf/jH/8Y3/3ud8UxiMVTb7371XCGLmoz/Hj8zBu4A3U4cK1H9DUd13uML/gcysvLxRDwxHiC4gfFjGwleSRjB5P6FDgoOjE5x859dgKNRoRKJ2pw3U9RY3nBsozvh/sXk4a0hRL3oShCMOMci5yc4RVfcYYWk4X6vswiB96PFNskEsmIurJ27BCdXSOd4ZoKi6QYXzz88MO46aabRMEC56PK86hkNEhRQ5JV2Jb2/e9/H7/4xS+wdOlS4Yf3ute9rp8VVD/czcDxvyUnnovmALXX97sq7S94kGW1iX4ApM0GB4BzyBsXh4R/Yws4k84DPj4Doua9gKcF4KDZylWAdZAq1wuPAmF3wgYFyK0FSqdmwo3vY0jdBxWdCdv0TyExMVkEi2EODMroK4CD0Q7RTm5U7LAY4tZhjz/+uBisyKQcF/bsuGHr/2hPct5wLw67WD0efz25piIszF2T8X1zv2ZgwX1J7whisMshevRQ5nNmhV5HsBvPt+3oG+5tUky4vmwDCizxuRccfrV27Voc9h9Hs7816Xklihqkyj4dZbYyMVugwlYkfrIj5qLvHFyhLpgMZsyw1yLHlN1ZLhE1BH/EA5NiyVj4kUj+UL8Xh3soaiTz+UW3wjyE1/nB7pM45DqN3lD/DqX5udNxbdnkqHocMf5O4OIzQFCr8E1i5iuAnMG7qAaDCYx9+/aJZNuNN9444vs5ceIEHnjgAfz85z8XVVNMHrJIIRsdcxKJRDIaKCAwCcJkCCvP7733XjFUnOvEbMHhyH//+99FEpgdw1wjMxnMeINrP673WHSjJ51ZeMMuDpmMmdzwc927dy8WLlwouhppIUaBKhsWjXu79qPJTyvZOOuL1qLYWpyx2LJlyxasW7cuyeqMBVAspqKtGYu7MnmuFy9eFMLNkiVLsmYNI5FILl+Y66AIwcKp2267TVhPjfT8zXkZzBHSJveee+4ROUIekyWSbCBFDcmY0NPTI4QNHrxY3f6e97xHVHoyABgQVwPQuBcI+7VB4dPXAGl81PVkMi/8NxPKBWoPehpPwhuIQCmajblLV4oFIBd3rH7hIlZf4PEnF445djty2rbDGuqE2WSAyWiC1WaHsuA1gCVNkpgZ57rf9e8msZUB00aeSJpIomoPQuquNG3e6VBgNayDQUk/mHq0sCKYJzqKYdn0gieuUCfqfSfFoPA8UyGqcxYIQSAT+H68/PLL2Lhx44DXYdXXmTNnsGHDBvSGPGj0019XQZW9AjkpXTxcHNCL0mg3YWv7DngiXhHUKH4VhrAR7b4uWO1WzC6rwZrC5TClfAdO9R5Ca+Bi7DcFBhiwonAT7FJ8yCqukA97ui4gEA2j1lGCebkjTzpfLhxzNeO3F+LHE0qGfN/eMjPeqZSOem8zXmzfK/4djqrwJrif5RiteO2MTWJGxpRFnDv+DIS8PIGl/FEB8ucA09IfX5hAO3/+vDg+8jiRfLfxmUOsxqQQMdzkGu+TFlM8V7/wwguiCIGBBoVXmaiTSCSTtXKUxywmSTigm8esW265Je1Q45F0qtH6ij95DGSFKh+THXH8mZeXJ4QUxhUUORhn6MdhQnGZRTC8MD7h77ywuEpWy08c3GdY6EYxI5ufA7s1jrtO9A0Kn+uci3LGhRnCrnRaI+fmpo+tuM9xVgvP73z+g8H1Aq975ZVXJt2ehWIUSXQrZnaZS9FDIpHoXWJ0ydC7uxKPHTyv8dzFY9RIrG15jmSn5YMPPihyOzxXv+1tb8uaTa5EoiNFDcmYQtHh6aefFsHHE088gdtvv11UVl199dVZS5h4z7yM87seR69XOyBHoaAxZyHWX31DWhGFz0ks8Dqb4Dn0JwRDYYTCEYQjEfgDYaBkAdTCuWLBx1Z3Lv76hqDX/x0I9SYNnUbeXKAkYYjhFCKqdohOjcFEjcTuAdpR2YxrYFDGJsHIz4XBJE+yDByp4Gdz4c1qLV4oKmRi08L3gtVdHCrPTqDBaGlpEUHvUHZZ3P9YVc3AgsGQK+SGwaCgsqASOTY7ooYo6s6ew7rVa8V7kAhFmZ2dz6bcI1vdZ2GWYz4uV1It6UZLd9CLB+tegj9CD2cgChU3ly/C+uLarD3GpQqFoOdaTyAQiWBebhleNW0pbENYpu3tOobjvef6upZovRaOcpbGXCzJnwXbVPdpDnuBs3/SDqZRNY2oMRuYtqlvC5NltDxhhSaTdLqnO4+F2drPmYj72c9+JjozGMy8733vEzMzaLEikUgkUwGuu37yk58IT252lH3gAx/A29/+9hFXk6ZbW7ATjhddVKbAzOPwDTfc0O94zOszccwEMo/f7Ajg8ZzbeJzlv3VYXMV1JS/ZEGMkQ3+WLEBiko2fCwuosp1Y45B7xoyMPTM5V7Poj6JGoggxmM0zi6uGEmT0DvJEuK+x84i35T7I878sXJBILk94HORcWh5XKDQwxqBwka18C+/3xRdfxA9+8AM8+uijuPnmm4WYwXOmFFMlY4UUNSTjBg+gTKD89Kc/FZVPTKCw/WyoypNBYZJo108SBgYSBSidD9ReO/htve3AqUdTNipA+QqgYqVIPjO5zmCmt7dXtJ/PqswDmp4BokHt6pYCoPIGzRt9CqKqIQTVrcy6pYgYalo7JMK5G3bjBigclD6GUCDgPpM4w4JCBBP93GcG85ll9R4DRb2VW6804OfJhTyFCg6XH2jfY5slq/QIqxO4vw504mZQwgCC+wir8ThEa7QweKYYyMotvUPEE+7FkZ59CKnJ9jW056mwVaHWOb6DlFsD7TjScxyBaBDFlkIsL1gMi2F8k8/usB9PNO9Dk78LVoMJm0oWYlFe1ajv97Gmw9jTdV6IGTpGxYBPL3gFDMMcUjkQDd5uNPpdKDTbMcdZcllXxR9xncH+bs5USuY106+HPeHY6gm70BlsFv8utlQixzQ2XWNZhzaHpx/WxPBIaqcGgOqbELGViY4MJumY4KKgO1Dl5kjh8YrdGKyY+vOf/4z169eLQIPFBlmbOyWRSCTjDBO1tKZlARUr8l/zmteIDvHNmzdP2nMrE+u0zGWMwXUuZyLJTo7xgWtsFg7o63zuIzwHMvHPAgIWtA0kNHEm3oEDB0Sxkw4/N4pWFDR4O57LBxIqKHBRoNA7eQaziuJ1uX+wGItFXyyaShxaP1I4d4MdHUwyyllZEsmlD3MVPHbRBoq5D87HyLbAwLwNXVpYaMBz2zve8Q5RaMA8ikQy1khRQzLucGH2l7/8RSRWaOtz5513CoGDLeTDPsAyWURRI5XCGmDeKwa9qRoJo+6ZHyLXHBX2U6yW9/iCcJdtgsFRIpLi7NDQF7+Ei09E/IC/HWBS316m/ZzCaBZUB+l4yNQtjJiLYPSUEDrS21ABNuNqGJXxHRrLoI9BIE/MrDJiFZwOk3/8vPRqZl6PczAYZOgBLf/NYIPBh15Nt2lTvDo6EQYRbMckDHR4G164L/C+E6HIwlkbfd08Q8DH5nPv7OwUryOxck+Hz5mVhtOmTRPCTEQNY0fHiwipQeh7W2KcvihvFQot6YWXsaAn5MILbfE5JRRWCsx5uKpkw7glEPg+Plz/EjqC7qS5JLdPW4vqnNG9F4807MVRV2O//f9T828esusgE55rPY3Hmo/1/X5FwXS8seqKSZt8GWuC0RAea9oCb4TfLUV8ngtza7CyMO612hPqwIne3Yk9cliYuxa55ikyvLrzCECLLb4AYUGlAGYn2gwzcaLRJ0RX2mLwOJbt/YBVWXqgweMOvejf9a53CWFXIpFILiW4Xn/ooYfEbCCuo3isYwHVUN222YbHWq71mCjn2pPrRxa/cA3J6lgmlriu5PX2798v1qNyftHEwbU4RQ4WP/Ez0Ttz+Bnxs+L+QxGCa0/GB4wTCGMOXpe31wup+HfGtgMJI+zo4G14Wz3G4HXZ4ZNoM8ltLKhjB2Wm3TyMbfgaGF8wXkoH9zN2k/D7cbmuOyWSy4Fdu3aJYwrzJJwVle1zDHMznBXKnB4LC2jDzYICHv8SZwRJJGONFDUkEwoHk+rBBxf5evfGsCwwDv8J8LQlz7qYeSVQsXTo9rin/4XmI88h5O1GBEYUzFqJhas3i0Q1F7W6py5PBJdyNYvmo8iFtBFhtRHB6NFBr08LKqOS3bkXo3nu/JyYuONnluoJSRgM8DPlZ8gLP1OeiIeqitPFDy4I+JMBCIMOBqXCpktV+2a08MKAR79PVlbRSkvvMuFFf256NRgDigGH2CfQHezEgZ6d8dcT+8mugVrnQtGpMZ4cdZ3AaXddkphAri+7Gk6TY1yeQ2/Ih5+fT7bioriyJL8a15QuGdV97+ysw+PNR5Lut8iSg3+bfc2oA8D2gAf/cyLVQgy4Z+ZqLMmvxOVKIBrCqd7z8EeCKLEWYGZOZdJ7fahnK7wRWv/FcZoKsDhvPaYMnouAp14YmiF/LmAtFQNveWzJ9rA8Ht+effZZIWQw0GDVKAONO+64QybOJBLJJQ8rUllAxWMgBzHz2McY47rrrhsXCwzaAO3cubNvloFuJciqVSbAWdXKn1yTMsaQFlSTE352/KwYY7AoLx3suqCVFdf0jAN4G8YCQ60XeZ7mdfU4g/sABQn+W48XGCPoMQYvevxCIYz7j/4YeozB++A+xefCuEeKFhLJ5QmPK08++aSYN5Xt4wC7ypm7Yw6Ploy0fWQBAedNSSQTgRQ1JJMm+KDvHpVe+vC94hWvEOLGbbfdNnQCJtALnHgc8HVov1PMqN6UXMquo0aBsAughYwxt991+DwuXrzYt6jUB5Ez4ZRJ8vlSIBg9h5Do1EgH07sO2I3roGTJhme8TuyskEutwCIU0xhkpn6+/OwpTDAw1a/PRQGDBXZl6EIF/8b9hsEOxQ4+FmH11YoVK7KykHCFerCv++V+2+c4FmJ6zkyMN0ddJ3HafbafqHFD2WY4TAPbgmUDBm07O0/jiKsePSEvDIpWtS8+DyhYUVCDK0sWjvox/tl0CHu6tW6dArMdb65ehxJrZt04g3GitxUP1e1I2sbnf2vFQlxTNgeTjXA0gu0dR3DW0yQsuJbk12JZfu24B8p7u55DSGU3WRyrwY4VBZsxZfCfAbyH4r/b5uPF3W1CcMhWko3t5ezK+OUvfymOSXqgwaSZRCKRXI5QPNYLqLim56BSXsbzuMh1BavnmQzSO425fqTQwWp8ydSEnyvX/j09PUL80IueCOMKdkSk6xLiPsCODnZT6IIE4129gyexy1yPLxhr8Hr8uWzZMjlsVyKRDAjzF8x9cI5oNuBx5x//+Ic4j3JOLufjvve97xUWtpdLjkwyeZGihmTSwer2X/3qV+KgSfX3jW98oxA4Vq9ePXAijQvIsA8wmIGB7GEiXqB7CxDRPFRhLgHyNwKGwav1ufDkYGcGHrQZutSJqN3wR3b1285ZGgYlH1bDfCjKpXHyYnDAfezs2bPiZE37F1Zj6bZV9MelBdRE+xzzebJToyfUFduiwGIwY3XhlTCP8xwL4gr14oW2rX0zJ/itLLIUYlPxujFPdr/ccRLbO04mbaOwYVQUkXR/Q9VVKLSMXnwgnnAAgWhYiBrZmqXRGfTia8ef6Wdt9fZZa7Eob/INaX6p7SBOueuTnu+m4iVYkDe+Ytqp3v3oDLUkdeSVWKZjtnPwjrxJA7tMep7pt7kXK7D/SL2Y28NzzEjatZlM+cMf/iDEDLaasxiAFlMsDpCzMiQSiUSDle2PP/64OFYyOcM5FjxWvu51r5uQBDHXdhSimQyXMzUuPRhLcH4F4wqKaeyeYOEc18ns7qiurpbChEQiGTOOHDkihI3a2loRZww3Ruc5inZ5PGf+7ne/E4WgPGe+9a1vFTGLRDJZkKKGZNLCAylbxnkgZcKGQ9koblDkSBzQNtBtGbywsoXqMReT6HoRCLGbI8GV3V4L5C7P6Pkw8GDlPqv0eX/6kDce4Cc66Z1tQtF6BKMnYu+VCTbDchgNox9ON5nhPsPhfqyoGmwI+UTBuRrnPKfhDrtgM9oxM2eO+DlRdAQ6hQ2VPih8Sf5CmCkqjjEPnnkKnkhyxb7FYMRsZzlWF85BiTUPk50t7WfxaGPc3mptYTXumrFsUtoE/OLcvxBW451NpMJWhFdWbhjX5xGKBnGidw88kZ4+66n5zlUwjcM+lxWCFwF3f7EYOcsBW42o4P3Tn/4k2sQpng+1L/D89swzz4iODFqscN6Tfn5k4kQikUgkA0Mh4eGHHxYFVEz80AOc3RscnjzUmp6dvKye53GYXuWj7bRjrEI7Id6PHmPwwn/rncGSqQ1tctkpzvhVfp4SiWS84HmKVojMYb361a/OqNiJYixFDJ4fGxoacPfddwsxg7Of5PFLMhmRooZkSkBPWiZuKHA899xzWLp0qbDsuPXWW0W7Lhf/DAaYmGbVKoUGXczQrYHUjhegKBzQCjgdNuQ67cjJLYV92s2iOjaToIT3z8obBiD6hZZGtBxKPMjrcxZ44kicqcALK/+FyDLJUVUOCg9BgXVKWU1JJg5XyIOOoAs2g0Ukvsdi4ZNO1Ci15uEtM6/GVKLZ70KTrxcFFjtm5WhzXiYjvzr/BIJRzVJNZ5qtBLdUrhv2fYWjYVzwnoUv4kWOyYnqnBoYlcyGXxJhuxD1CkGa1lOT9T1LS7gbcD3ff7tzA2ApF0kyBg6sqGLnGGfuJAYefO28Du0qOJTvkUceEee4N7/5zULMWLx48fi+HolEIrlEoKjB+OI3v/mNWOOvWrVKiBzLly8X3doULvR5BW1tbaLiVV/j0z5In7HGcxIFEcYgeuETf2baMUc7Kt1qiBeeD3ghiec7HvsZ+zDW4Hb9Jx+PMYZEIpFIJDrMjbFjjOLqmTNnRJdYIjx/Mde2Z88eYQe/fft2XHvttULI4DyqyVjsKZEkIkUNyZSDfrR//OMfRYXVjh07xEGX1akMQNg+TkU6rbdf+7+AqFcbLO3xo9fthy+SB595oQgimDAaCAYyijkCowkI+oCUwuV+YoYexPDfDJBYEcZKXAosPLHwBDESmxGJZLJS52nCC237++ZszLCX4vqyVVmzbdJ5uf0Etncmz3y5rmwJlhdobbARNYp6b4sYOF1qLUCxdfwtJS4l9nadxL7u5Pf7hrLVmOkYnlVWRI1gT+d2eBKGfeebC7GiYE3W95FJi+cQEDgT/90yA3CsSj//KaX1mxVT7Fjkuequu+7Cm970pqzO4pBIJJLLHcYBL730kjjeUjjmOp0VqowxaIFL0YHiwWBDvRmD0NaUogSTRPzJbQPB+9NFChZI8TYDwbjC4XCI6/LfvB0LqxhfMLagwDJ37lwxz00ikUgkksHguerPf/6zyKmxaHjdunXifMc4o7x88lkiSyQDIUUNyZSGVatM9PBgfOjQIdE2TsHgVa96lZiHkESgGehJGLbMCuHCawBT3pBBzkXPfnR6GxAJR+BwOFGduwZ2Y8GAVVZUwnlhhRUDD9qBcMD0kEPPJZIpSCgaxm8vPI0okoXBDUWLsz57IXFQuAGKEDNWFMzShrZHI3iyZSdaA/rsEWBD8RLMy02uSJEM7/0+4qrrGxS+OK8Gsxwpx9YMaPU344hrf7/tFDUKLZeRXVKoDYi4AIMDMJenFTR4Htm2bRv++te/ig5FJqxe85rX4A1veAOuu+46OSdDIpFIxvpQHbP4Y3zB4zDX8SyeYoyxcePGQYWN4cAYg8VPvPA+WRSVrgtRt9Vl54geYzDeYFcIO/uyYYMlkUgkkksb2lD9/e9/FzHG008/LdxPKGRQwB/K3l0imaxIUUNyyXDq1ClxgOaFHRxUmxl88MLKJUGoGwg2sacCsFUBxqHb6bqDF9AWPJ60zahYUJOzeWrZn0gkY0R3sBd/aXwpaRsFh/m5M7G+eNG4ve9HXXXY1XksaZsCBW+ougEW4xSZvXCJ0uirx4ne+BwRnSX5V6DUKquBWM3L4ILnr7/97W/i3EJx/vbbb8dNN90kO/skEolkgqB48OSTTwpbDh6fCb3JGV+wmGoqWMpKJBKJ5PJkoBwZhfo5c+ZM9NOTSEaNFDUkl4UKXVNTIxJDvFxzzTVi8F6mtPgPwxVuShgwrlGTczVMBmkhJZEEoyH8TnRqJH9H1hUtwqI8zRZqPNjZcRQnes/3ex63T7sKBZZc+UFNIN6wGzs7t/bZkxEDDFhffDWsxsvvOMqq2+PHj4tE2VNPPSXavtldqFcCb9iwIWuVwBKJRCLJDomddLww3qAN7o033ihijAULFsiCJ4lEIpFMGHQKeeGFF0SMwUtdXZ04R+luJtJaSnKpIUUNyWVxYGcLORNHPLCfP39eJIx4cJ85c2hrHHeoBe5Ia8pWBWXWRZePF7xEMgTN/k6c7K3v+z3f7MDS/Npx/Y40+Tpw2t2QtM2gKFhXtBgmg0wQTzSuUI/o2KBNmRFGzMiZCYcpc4H5UoBzlZgQ47mos7MTV199tTgX3XzzzWLYt+z+k0gkkilkz3jkCJ544gkRYzCJRJsqihu0qJKWsxKJRCIZL5jj4rmIccasWbPEuYgxxvXXXy8sCiWSSxUpakguO6hW84DPDo62traMbhNVI1AR6bOzURST+CmRSOKwCl9VtTEBE/X94EDqsMrZHioUGGBWjDJRLJk00PN8zZo1IsjYtGmTtJWSSCSSS8imauvWrUK03r17t5iXIZFIJBLJeFBaWiosERlj0KVEIrlckKKGRCKRSCQSiUQikUgkEolEIpFIJJIpgfTOkUgkEolEIpFIJBKJRCKRSCQSiUQyJZCihkQikUgkEolEIpFIJBKJRCKRSCSSKYEUNSQSiUQikUgkEolEIpFIJBKJRCKRTAmkqCGRSCQSiUQikUgkEolEIpFIJBKJZEogRQ2JRCKRSCQSiUQikUgkEolEIpFIJFMCKWpIJBKJRCKRSCQSiUQikUgkEolEIpkSSFFDIpFIJBKJRCKRSCQSiUQikUgkEsmUQIoaEolEIpFIJBKJRCKRSCQSiUQikUimBFLUkEgkEolEIpFIJJL/z95ZwNdZnX/8uUna1EtdKBQrxSnuWoYzdIMhgzF0jLENHRuyjbENGRv8cR06GF7cvQynWNGWIhXqljRN8v4/35Oe8ObNdb/J78vn0uTm3lfOe+TxY0IIIYQQQohKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGkIIIYQQQgghhBBCCCGEqAjk1BBCCCGEEEIIIYQQQgghREUgp4YQQgghhBBCCCGEEEIIISoCOTWEEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGkIIIYQQQgghhBBCCCGEqAjk1BBCCCGEEEIIIYQQQgghREVQU+oLEPmjvr7eGhoa1KRCCCGE6DB07drVunXrVurLEKJTIv1CCCGEEB0N6RcdAzk1OpDC0X3YMLO5c0t9KUIIIYQQeWPo0KE2adIkOTaEKDLSL4QQQgjREZF+0TGQU6OD4DI0cGhceaXFenRvU1usKub/XfZD3L/Ffx9i7f4Wa/eZjI5nsaTfSe94sSTXl87x4l9D5seLpXG/ya8hvfst4POI5fl5pPmdfD2PtsdL/Tz8z7GMri/J8aL3neAa0j7esu/ndn2xHO838fNoPV4QtPzg/22O/Ov+Zm3fi3422d+ix4/72ebEn0l1XPeev850PpvouP4gydoiXtsk+GzoIwnvId71Bc1pPI8UbZP0s5H3k7VFNs853ntpPQ/L/HnE61vptHEmzyPRs8/muMnaIl5bJ3welv7zaE7jXjIZH0k/G7nuyHXMb2qyFd57z8k5ytYQonz0i0zlhujfovJdpnJcu+NF9Iu215fJ8ZbJYTnKcendb/JrSCaLR+XttsfLXLbP5nkkP17mzyOZrpX8fjPXtfL9PKLPJfv7bft7LE/PI5F+kfnx2l5DZsdL/DwS6hfJ5Ia05OokclJSOTOFXJ1UjoucJ9m5ksp8zdnLZnFl8ATXm+z+ovpFss9m/TwSfCcb2T5n3TKOzJzqeSTrW9noROl81rLVffMwPuI+jwTXm+xcye4ll/GRrG9Jv+iQyKnR0eje3WI9ergfvdgQiwiX8f8W//1cBbtMhP1sjei5CHaZKEvZOjVyEewyUb4q5XnkSxnO1amRqk8kv99kx4t/Dfl6Htk6NapK4tTIQVhL67O5OjXy8dkMBO1MhP1k389G+Up2f3l7dnlyanSG55Evp0auynA+xmiy+8tmjIbfi4XeE0KUlX4RT8eIp0ck0jEkx+XHiJ4vp0Y2zyMTXSvZubLRtdI7Xvq6Vmd7HuWq+6bn1MhCjstY5pMcF7cdSvE88uXUyFkGz4OuVe66b2d5HtIvOhThdVIIIYQQQgghhBBCCCGEEKJskVNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQENaW+AJFn6uosiLX8GCx7q/X32LIf4v4t/vvQHPrZ4Y8TfT/8XrvvfP9jsOyXRNeQ3vXF2l2f99BV+d9Dx2v/t1jc98O3l97xlh0nwXfCt57oGpKdK73ra3sNmR8vfltkdrxYGvfb9v18PY+2x0v9PPzPsYyuL8nxoved4BrSPt6y7+d2fbEc7zfx82g9XrBsNPp/myP/ur9Z2/ein032t+jx4362OfFnUh3XveevM53PJjquP0iytojXNgk+G/pIwnuId31BcxrPI0XbJP1s5P1kbZHNc473XlrPwzJ/HvH6VjptnMnzSPTsszlusraI19YJn4el/zya07iXTMZH0s9GrjtyHfObmkIXLoQoF/0ino4RT49IpGO00y+S6Rjh3xP8LapftL2++NcQ/2+xuPpFpnJc9G+xjI6XWraPytttj5e5bB/LQq5OfrxYBveb7HgRuTij4yXWtfL9PKLPJfv7bft7LE/PI5F+kfnx2l5DZsdL/DwS6hfJ5Ia05OokclJSOTOFXJ1UjoucJ9m5ksp8zdnLZnFl8ATXm+z+ovpFss9m/TwSfCcb2T5n3TKOzJzqeSTrW9noROl81rLVffMwPuI+jwTXm+xcye4ll/GRrG9Jv+iQxIIg3ONEpVJfX28rr7yyTZs2rdSXIoQQQgiRN4YOHWqTJk2ybt26qVWFKCLSL4QQQgjREZF+0TGQU6ODKR4NDQ2lvoyKYP78+bbCCivYV199ZX369Cn15XRI1MZq40pHfVht3BHoCP24a9eucmgIUSKkX3S+ObfcURurfSsd9WG1b6XTEfqw9IuOgcpPdSCIYFQUY2YwAVfqJFwpqI3VxpWO+rDauCOgfiyEyAbpF5pzyxWta2rfSkd9WO1b6agPi1KjjcKFEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4N0Smpra21c845x/0r1MaVivqx2rfSUR9WGwshREdC65rauNJRH1YbVzrqw2pj0XnQRuFCCCGEEEIIIYQQQgghhKgIlKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGqJTcMIJJ9jgwYNt4403bvP+559/7t5bbbXV7LjjjrMgCNz7M2fOtB122MFGjRpl++23n9XX15foyiuTlVZaydZbbz0bM2aM7b777inbW2TOQw89ZKNHj3Z99LrrrlMT5omamhrXb3kdddRR7r3XXnvN1l57bddv//SnP6mtM2Tfffe1fv362QEHHND6XqI21RyRn/bVHCyEEMVBOkbx0NpWHKRj5B/pF/lH+kXhkY4hKoJAiE7ASy+9FLzxxhvBRhtt1Ob9/fbbLxg3bpz7eZ999mn9+be//W1w2WWXuZ9//etft/4s0mPkyJHBggUL2r2fqL1FZixdujQYNWpU8PXXXwfz588PVltttWDWrFlqxjwwYMCAdu9tvPHGwbvvvuvanZ/fe+89tXUGPPPMM8GDDz4Y7L///inbVHNEftpXc7AQQhQH6RjFQ2tb4ZGOURikX+Qf6ReFRzqGqASUqSE6BVtttZUNGDCgzXtkCYwfP9722GMP9/tPf/pTGzdunPuZfw877LB274vsSdbeIjN8lPvyyy9vvXv3dtkwjz/+uJqxAHz77bfW2NjoMo+Isjr44IPVbzOErDf6aao21RyRn/ZNhNpXCCHyj3SM0qK1Lb9IxygO0i9yR/pF4ZGOISoBOTVEp2XWrFnWv39/i8Vi7vcRI0bYN998436eN2+e9e3bt937Ij1o02233dY23XRTu+eee1K2t8hcEMah4VFb5o/58+fbRhttZFtvvbU9//zzausi9l/NEflDc7AQQpQO6RiFQWtb4ZGOURikXxQe6RfFQfOwKDdqSn0BQuQLDJFLlixp9/4TTzxhw4cPb/d+vP0cvMHd/xt9X6TX3i+//LJr86+//tp23HFHW3/99VudRGrX3EnWd0VuTJ482fXd999/32UV3XzzzWrrIvVf9ev8oTlYCCHyh3SM4iH9orRIFisM0i8Kj/SL4iAdQ5QbcmqIDsObb76Z0ecHDhxos2fPdgsgBjUM8MOGDXN/69OnT2u2Rvh9kVl7E4E9duxYe+edd2z//fdP2N4iM4hyD2e50JabbbaZmjEPeAfoOuusY2uttZbrq9G2Vr/Nf/+lTZPNySK7fqw5WAghckc6RvGQflFapGMUBukXhUf6RXGQjiHKDZWfEp0WjGabb765Pfzww+53IrL32msv9/Oee+5pt9xyS7v3RWoWLVpkCxYscD/PnTvXXnjhBVtzzTWTtrfIDMp6kUmAYZi2fuSRR2yXXXZRM+bInDlzWrOPMKh/+OGHzrlRXV1tEyZMcPtA3HHHHeq3eRCG47Wp5oj8oDlYCCFKi3SM/KO1rThIx8g/0i+Kg/SLwqN5WJQlpd6pXIhi8POf/zwYOnRo0KVLl2D55ZcP7r33Xvf+J598Emy44YbBKqusEhx99NFBU1OTe3/GjBnBtttuG6y66qrB3nvvHSxevFgPKk0+//zzYL311nOvddZZJ7jqqqta/5aovUXmPPDAA8GoUaNcH7366qvVhHng5Zdfdn2Wvrv++usH9913n3t//PjxwVprreX67TnnnKO2zpCdd945GDhwYNC9e3c3/7722msJ21RzRO7t++qrr2oOFkKIIiEdozhIvyge0jHyi/SLwiD9ovBIxxCVQIz/ldqxIoQQQgghhBBCCCGEEEIIkQqVnxJCCCGEEEIIIYQQQgghREUgp4YQQgghhBBCCCGEEEIIISoCOTWEEEIIIYQQQgghhBBCCFERyKkhhBBCCCGEEEIIIYQQQoiKQE4NIYQQQgghhBBCCCGEEEJUBHJqCCGEEEIIIYQQQgghhBCiIpBTQwghhBBCCCGEEEIIIYQQFYGcGkII0QE499xzbcyYMa2/H3HEEbbPPvvkdMx8HKMYTJ482WKxmL3zzjulvhQhhBBCCCE6BNIvpF8IIUQ5I6eGEEIUCJwCGNt5denSxVZZZRU75ZRTbNGiRQVv83/9619200035eQUyOQY2TB9+nTXLrfeemvcvx977LG23nrrFez8QgghhBBCVBLSL5Ij/UIIIToPcmoIIUQB2XXXXW3q1Kn2xRdf2HnnnWdXXHGFc2zEY+nSpXk7b9++fW255ZYr+TGSMWTIENtjjz3sxhtvbPe3uro6+89//mM///nPC3Z+IYQQQgghKg3pF4mRfiGEEJ0HOTWEEKKA1NbW2tChQ22FFVawgw8+2A455BC7//7726R033DDDS6Lg88GQWDz5s2zY445xgYPHmx9+vSxHXfc0d599902x/3b3/7mhPbevXs7w399fX3S0lHNzc3297//3VZbbTV3nhVXXNH+8pe/uL+tvPLK7t8NNtjAZWxsv/32cY+xZMkS+9WvfuWuq1u3brb11lvb66+/3vr35557zn3/6aefto033th69OhhW265pX388ccJ24drf/bZZ122SJi7777b3dOhhx5qjz32mDsXDpYBAwbYnnvuaZ9//nnCY5JdEnXG0OZcW5hx48bZRhtt5O6F9v/jH/9ojY2NCY8rhBBCCCFEqZF+If1CCCGEnBpCCFFUunfv3iYj47PPPrO77rrL7rnnntbyT2QvTJs2zR555BF78803bcMNN7SxY8fa7Nmz3d/5/DnnnOOcEm+88YYNGzbMZYAk43e/+51zapx11ln24Ycf2u233+6cIvDaa6+5f5966imXVXLvvffGPcZpp53mrvPf//63vfXWW85Bsssuu7Rel+f3v/+9XXzxxe7aampq7Mgjj0x4Xbvvvrtz+kTLXOHowaGCE4NyXb/97W+dAwWHSVVVle27777OUZMtjz/+uHOY4KShPa6++mp3Dd7RI4QQQgghRCUg/aIt0i+EEKKTEAghhCgIhx9+eLD33nu3/v6///0vGDBgQPDjH//Y/X7OOecEXbp0CWbMmNH6maeffjro06dPUF9f3+ZYq666anD11Ve7n7fYYovguOOOa/P3zTbbLFh//fXjnnv+/PlBbW1tcO2118a9zkmTJgUsB2+//XbC61+4cKG71ttuu6317w0NDcHw4cODCy64wP3+7LPPuuM89dRTrZ95+OGH3Xt1dXUJ2+n0008PRo4cGTQ3N7vfv/jiiyAWiwWPP/543M/TXhzzvffei3v9N954Y9C3b98237nvvvvcZzzbbLNNcP7557f5zC233BIMGzYs4XUKIYQQQghRSqRfSL8QQgjRgspPCSFEAXnooYesV69ersTRFltsYdtuu61ddtllrX8fOXKkDRo0qPV3MjMWLlzoMhT4nn9NmjSpteTSRx995I4VJvp7GD5P6SiyPbKFc5NhstVWW7W+xybfm266qTt+mPDm3mSRwIwZM5KWoPryyy/tmWeeac3SGDFihO20006t56Z0FyWiKMfly2VNmTIl6/uhnf/0pz+1aeOjjz7aZaosXrw46+MKIYQQQghRSKRfSL8QQghhVqNGEEKIwrHDDjvYlVde6RwAw4cPd/+G6dmzZ5vfKamEI4D9KaJku2k3Kem5wl4fEN2Xgvej74Xv0f8tWamoUaNG2TbbbOM2DKe9KG/1s5/9zJWZgr322svtSXLttde6NuRY66yzjjU0NMQ9Ht/z15toE3aOwR4a++23X7vv44ASQgghhBCiHJF+If1CCCGE9tQQQoiCgtOCvSfIyIg6NOLB/hnsp8FeFHwv/Bo4cKD7zJprrmmvvvpqm+9Ff486DXBssB9FPLp27er+bWpqSngMzs/nXnrppTaOAvbN4HpyhWwN9vJgz46vv/7aOTVg1qxZLhPkD3/4g8s04Vxz5sxJeiwyXxYsWOD24vD4/UrC7cwG5tE25uWdKUIIIYQQQpQb0i/SQ/qFEEJ0bJSpIYQQZQQllyglxSbZbOw9evRo+/bbb92m4by38cYb20knnWSHH364+3nrrbe22267zT744ANXnikeZB6cfvrpbqNvHBOUkPruu+/cdxD2Bw8e7Jwejz32mCv7xOf79u3bTnk6/vjj7dRTT7X+/fvbiiuuaBdccIEr1cQxcuVHP/qR27T72GOPdc6LlVZayb3fr18/V4rrmmuucRkslJw644wzkh5rs802sx49etiZZ55pJ554otsIPboR+dlnn2177rmnywDh3DgyJkyYYO+9956dd955Od+PEEIIIYQQ5YD0C+kXQgjREVE4qhBClBGUa8KBwd4bRx55pK2++up20EEH2eTJk23IkCHuMwceeKAzyuOo2Gijjdx+FDgcknHWWWfZySef7L5HtgPH8PtckBVy6aWX2tVXX+3KO+29995xj/G3v/3N9t9/fzvssMNcpsNnn31mjz/+uHM85ApOCO6TLAzu24Oz4T//+Y/bA4OSU7/5zW/swgsvTHosnC633nqra8d1113X7rjjDjv33HPbfGaXXXZx9YiffPJJ22STTWzzzTe3f/zjHy6jRgghhBBCiI6C9AvpF0II0RGJsVt4qS9CCCGEEEIIIYQQQgghhBAiFcrUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQQgghhBBCCCGEEEIIURHIqSGEEEIIIYQQQgghhBBCiIpATg0hhBBCCCGEEEIIIYQQQlQEcmoIIYQQQgghhBBCCCGEEKIikFNDCCGEEEIIIYQQQgghhBAVgZwaQgghhBBCCCGEEEIIIYSoCOTUEEIIIYQQQgghhBBCCCFERSCnhhBCCCGEEEIIIYQQQgghKgI5NYQQJeWwww6z888/vyDHXmmlleyf//ynVSLvvfeejRgxwhYtWpTys7FYzCZPnlyU6xJm22+/vd10001qCiGEEEKIDHjmmWdsjTXWsObm5ry327nnnmtjxozpsM9jyZIltuKKK9qbb76Z8rOSVYsLfe+II44o8lmFEELIqSFEGYJQhKGaV5cuXWzIkCH2gx/8wG644YZ2SkDUcB8EgZ188snWu3dvpzik4osvvrCf/OQnNnz4cOvWrZszpO+99972ySefpHWtXOP9998f9x722WefpN+dMGGCPfzww3biiSdaRwJFwj8//zrooIPafGbOnDnOodO3b1/34ue5c+e2/n3ddde1TTfd1C655JK8X0v4Rf/JlnSecSF49913XZ9dYYUVrHv37rbmmmvav/71rzafwckT734fe+yxNp97/vnnbaONNnJ9f5VVVrGrrroq5+uLnpvnu/nmm9u4cePafA7HCH/n+qPcdddd7Z5PU1OT/fWvf3XGAO67f//+7rg33nhj3Lkj/Np1112zvp/XX3/dxo4da8stt5z169fPdt55Z3vnnXeSfueaa65xfa9Pnz7u/OG+7eHeotd5xhlnZHxuHIDbbbeda5Pll1/e/vSnP7l5UAghhBDpw7r961//ut37yPms0WH5hXU5zEcffeR0iP32288Z3xNx2mmn2e9//3urquo4Zggvz0Vf9fX1bT53xRVX2Morr+xkTmTPF198sc3fkV0wjqOTIdPwPD744IPWv9fW1topp5xip59+ek7XyzmS6Qa5BEvF6xvFYPbs2U6fHD16tPXo0cM5f371q1/ZvHnzMpY9p0yZYnvttZf17NnTBg4c6I7T0NCQ8zWGz83zRZ6/8MIL28isXoeoqamxb775ps33p06d6t6PPp977rnHNttsM6dvoP+vvfbazhaQqn/SD7MlHTkf0PO5Nu6XtmR+SEY8PQZdJ8yxxx5rq666qjvmoEGDnN1i4sSJcY/HXISjk+NE9Yds9BshRPnRcaQJIToYGCERXhBaHn30Udthhx3spJNOsj333NMaGxvjfgej589//nO7+eabnUNjxx13THoOBDScJfPnz7d7773XPv74Y7vzzjttnXXWaScEFoL/+7//sx/96EdOAMsW7rkQ0V6wdOnSrL979NFHu+fnX1dffXWbvx988MFOcMLIzoufcWyE+dnPfmZXXnmlu8ds4bn6a3jttdfce0899VTrewh0lQYRagixt956q1P2UI5/97vfuf4UJXyvvMJjYtKkSbb77rvbNttsY2+//badeeaZTnFBOcgH/tz/+9//nINq//33t/fff7/NZ1CYZsyYYePHj2/zPg5MFLKoEooD889//rN9+OGH9uyzz7p+hoMs3twRft1xxx1Z3cOCBQtsl112cdfCfbz00ktOgeG9ZONj8eLF7jpo02TggAhf5x/+8IeMzs3cxRyGAYC+fNlll9lFF11k//jHP7K6XyGEEEJkBusvshTr83//+19nfI/HK6+8Yp9++qmT/Uslnxfq2MgnUdkrbDRGv8JhhMyKzEl77bbbbs6A7rnggguc/II8S5sOHTrUyTjIQ55DDjnEOUNwImULjpHwdeKMispjBA5VEt9++617IQMS7IIhH/0KvTgT2ROda4899nCZ8sid//nPf5xeEHYS5II/N8+P54CcjIMgCnIt+nyYf//73y54J6prEDh3wAEHOD0PHekvf/lLOydMvP755ZdfZn0f6cj5tBu6LfosAWkvv/yy039TEdVjHnnkkTZ/xyFIQBdt+PjjjzunEA6JePoyTlTaMl/6jRCiDAmEEGXH4YcfHuy9997t3n/66acJ5Qiuvfba1vdGjhwZXHLJJUF9fX2w7777BiNGjAg+/PDDtM7z9ttvu+NNnjw562vl+/fdd1/a9+BpamoKlltuueChhx5q8/7s2bODww47zP2te/fuwa677hp88sknrX+/8cYbg759+wbjxo0L1lxzzaC6ujr44osvgunTpwd77rln0K1bt2CllVYKbr311ta28cydOzc4+uijg0GDBgW9e/cOdthhh+Cdd95p/fs555wTrL/++sH1118frLzyykEsFguam5szbpPtttsuOOmkkxL+nedDu7366qut740fP969N3HixNb3lixZEtTW1rrnngy+N2nSpJTXxWf4LM/d88EHHwS77bZb0LNnz2Dw4MHBoYceGnz33Xetf//vf/8brLPOOq5d+/fvH4wdOzZYuHChayuOFX49++yzCdvjxBNPDE499dSgX79+wZAhQ9z388kvfvEL9zyT3WuU0047LVhjjTXavHfssccGm2++edJzcT/0w0TEO/f8+fPde5deemm7vvzLX/4yOOqoo1rf/+qrr9xzP+OMM1wf9tA3zz333KTXlmrcZcrrr7/urnvKlCmt702YMMG999lnn6X8Pn2Cz86ZM6fd36LjM5tzX3HFFa4Nmf88f/3rX4Phw4dnNXaFEEKIzkoi+RU5P2w28PILIKP26tUrOOWUU1IeH1nwgAMOaPc+a/kqq6wSdOnSJVh99dWDm2++uc3fOfeVV14Z/PCHPwx69OgRnH322a3rPbIr5z/yyCOD008/3clKYW644QYn6yFXjR49Orj88svbyWt33nmnu3c+w+czJdweidh0002D4447rs17XBeyHiCzDB06NPjb3/7W+ndkG4571VVXtfne9ttvH5x11lk5yaphMtWX+JlroN35+4YbbuhkNi/zhV+J5H2vc/GsOX+fPn2CAw880MnL+eKuu+4KunbtGixdujThvUZ55JFHgqqqquCbb75pfe+OO+5wfWPevHkJv8f9IIMnI965abv99tuvXZ/8wx/+EIwaNarNZ+m/PPew3sd45Vnk2j+zJZGcT5svv/zywXXXXZfR8bLRY9599924egnPkjGGrhvVy3LVb4QQ5YMyNYSoIIgyX3/99V30fZiFCxe6qBKi1omCiFfOJh5Eu5P+fffdd+eUDZANlJ4iVXXjjTdul3b6xhtv2IMPPuii19FliKYPR00QHUIZnuuuu87d8+DBg933yGohQ4X7IcWbCHgPx6GNpk2b5iI+iGTZcMMNXdopKcuezz77zJX+IbrEp6Cy50evXr2SvqIp5LfddptLsyUFmEiccJQV90WKMOm4HlJreY8oNk/Xrl3d844eO18Q/ULZHtJyaXMimqZPn24//vGPW/9OmacjjzzSRcM899xzLm2YtuSe+Fw4mmbLLbdMeC6ii8hKIBqGSDQilZ588snWvxOtlqqNk0FmEeWYovzwhz90/WOrrbZy/SIMz4HInjBE6NAW+YzS4VjXXnut+5lyclGIIiOCj34NRJfRrpSdC0PEHv37u+++y+l6Mmlr0vjpx9dff72L+qqrq3M/069HjhxpufL3v//dBgwY4PpgNLIsnXPzDOnD4ahQniHRetpnRgghhCgc9913n5OtyT6gjE4qXnjhhXZyP8cgE51IeLJZKS1DZDfZqGHOOeccV2aGKHzkUmR13kN2QG4bNmyYk/3DIHtxbXwGORZ5/qyzznIyaRjKOZGpy2eQIcieSCUnHXfcce10MWQTsh7Iqicbw4MMg94RlTn53cv9ZA+jo4Q/g2yDjBPWDYDs30LpBunoS2SLcJ9kk/B3yjch36IHkFEczgpAX0jE559/7kqbPfTQQ+5FSdi//e1vrX/PRv+K6gZcCyWb0pU9kSupWBCO7qdPUMYonb1MMmln9Cr6XDzdAP2FTGwyCIB/aX/KYkV1A3ThaCZ4puTa1lHeeustVz4LO8MGG2zgxif6R7icWiJoF3S31Vdf3WWkh/X5KGTUkLVBWbdwdhH6LN+95ZZbXDmyKIXWb4QQxaPtDC+EKHuov4lDIAzlaCjhREkahIB0IYX10ksvdamZf/zjH52iQZkrhFX2F0gXDN/V1dVt3kP4QyhOBAZHvhO+XlLScWbgmPEGcpwDCCkIvT5dHSMxigsGf2D/D0p0vfrqq62OAgSTsHMH5QhFCMHIG0BJUea4GLuPOeYY9x6CDQIQDh8Pios39CdrSw/th3CFoImQSWkk0m69ER9FId5z4j3+Fj1uoYyzlLZCUQlv1E7ZI9qbNkVBo9QZjgwv4LHXh4dapjxn7jMV6623nlM+YdSoUS61/umnn3Zp9YCDCoEyG1BAUG6p2+pB+CaFH2cGAjX96sADD3SK7KGHHuo+Q1tHHQf8zj3PnDnTCeC5QB/m3NwXJdKopRuvH6FUURuWfkiaNk4Nrp39bsLwHunltDdCN8dHwUdJCINiGHUCobCjyGfa1swrKBech3kGUDJI944qiZmCEYP+Rx1bUuYZJyj1XF+65+YZRveF8c+UvzEOhRBCCJFfkBGRyyk/E92TIBHIs9FSMMjiBCb94he/cL//9re/dfI876OTeChbgzPD44NujjrqKPf7eeed50rxhPexQHa4+OKLW+v4IxOgK1ES9vDDD2/9HGWhwrX+kQNT1dbHWB7WzZDdkJEpi8k+b8ifyP7IvMiUBI/Fkzm93O//jfeZaJmgQuoG6ehLOH1OPfVUd9/APXoI0GL/gnR0A2Rj2s2XIUYGRjfA0ZCN/hVm1qxZ7vnjJMtE9oynG/BZAs2iOlo2II9T7gp9E32WEmU41KLg6EBfQS/beuut3b/8HnWAsI8Izgb6HroaQXI4xtBFwwE/OHiiugF6xBNPPJFzW8fD6zCUzkV/QVZnLOKkQ8eMF4gG6DTMK9wLzwXdhaBOHErh+8EOgP0Cpwb9EB2bZ+QdRswp3BO2jXhjpZD6jRCiuGjEClFhsFCHN+sDhBcEeYzT4U3D0+GEE06wn/70p06IJYqeWrgcByOwNzings2sd9ppp3ZCW7LsD4yqCCfheyFaBUEinMFAJA3RFOHasQgtGMmj3wtHfyHghDeqQxhCAeN40esgUsiDEBV2aACCVyLhKx5EhniI9kHY59qIWkGQhugzTPRscRz4CP58Q5vw3ONlQdAm9CsisxCUiVLid4zqCPeZEn5egMMgHHmTiaAchogfBNKzzz67TX8l+uY3v/lN6++0PxFPZIl4pwZE29tv1hfv+WQK2Rf0Q4R3FGY2IU/Uj1DMiTSitiv9lOyk6B4ha621lnOS8dyI2CLikYgtBHevjAFGABxWYcLnzaStGR9cG8o5+3IwplFuuT4i9Oif2RJ+PvQP+hX9y0fQpXvuQj5DIYQQQrSHNRhjK9kQOBjSyRJnXY9uTowM7wOLPKz7OAbCRDM8+F40W2KLLbZozfAgq/Wrr75y2bBhuRyHBYb3ZMdGp1httdXSfuwYksObGXP9yPvs80XwmCeevBJ9L53PFFo3SKUv4XjCmUQQGPofRmiCczIFQ3d4X8WobpCp/uXBsURgHXKzD6hKV/bMREfLBpxByO30T7KIMNgnynSn79Kn0cvRzwniiu6rSRY8QV08G/o+DkGynhg/fN5nKdDO6KFhwjJ8tm2dCL/fJffInoKAnkOGD/cSdTZ5CEAL69CMTXRz7jHseMRpg95HNhC6AQ4ZgiKZXxh39AEcVqXQb4QQxUVODSEqDIT4aPQxhmeiPDDusiizmGcCgg5prryIdMKAzb/pOjWIxokK/xyT8lKJwOiMQE6kSjiyIh5RQRJBI/x7OkZMhCuEZaIyooSdHwiHURAmw9kM8SBThE3/4oFiQ2QNmSj8THuRFhsFATcaHUSqcTaKQjrQJhjFEeSj0FZk0hD5Qto7kTz0K4RTnF+ZRsBHI4t4VuEN3onMSZXWjJIVhmg7lAGU1fAmf4lA4Qwb/3kO0agrlCmU2agylw1kvODQ4oXjCKE+UTYVwjkRR0Q04WRMFCVE5scmm2ziXihmbJZOZBvPxT8T+nAyZTyTtr799ttdhBOKEef276EEPvDAA25zwnzhDQKUgKP90zl3omcI0bEkhBBCiOTZB0R0R0GeD2cmADIi0fvINgRTUB4TI3IykP0JMImSjiE/nnyeDC9j4nQJB0v5a092bDIRUt0LATIEqyST1ZD7/X1zznjyipdVfGYDnwlnCoc/E9YNogFY+SIdfQlZlcwZDM3oPzgO2FB73333zatukI3+RblfSrgid1PaLF5pp2SyJ88BPScMfZasinzIlfQFZHRelDrmX64hGhzojfoER3mnIb8nyiBCV+SFswmdgKwDgqso5eb7ZDLdIFddN4rvw+FxRDAjlSAYX+nCcXBq+LHkwTHJCx2L9kM34HnTVsxFOHfCmR2AgwR9i6z9Yuo3QojCIqeGEBUEizQpweEoEw8OCMrOYKRGICTKO5uIEr6DABWt35pvKLkDGHn9zwg+RKAgTPqoFdKHiXRPFgHG3/geNXWpMwsff/xxG6cKzgQUBYzF0XI1qcg1JZdsAoRhL+ARdYPSSNqzv17umfei0TpE5hNBVAhoEwRq2iOREZ3+QBQLL7IhECwRGonSwhmVr71YMi0/RZvi0KB8gE9TTwX1jcOKIs9h3LhxbT6D8wahN5USlCmkW6OMcK3R6EMgOgqnImW0EinJ8fDKAunXhWhrHI8I++G5xP8eVjzzga8/7Z9ROufmGVL6Iuwc5RlS3iLTcS6EEEJ0ZpD/MVxGIXKZrOkoGA3Z5w85FccGpYOQdRJBbX3k/qgMT/YpAR0edJBUmR/8HcNl+Hv87sEAjWxOGRwMmZmADJFJ+akoOGX4vi/Zinyy0UYbuUChsOGf3wlIA1+2lvdoJ0C2YZ+JaPARuoH/TL5JV1/CaM4LnRRDMlH43Fs+dYNM9S+i8wnMo19ScSCaFZSO7IlciaxOBoB/D7mSY/IM8wkGdMpHse8I1xFPbyebgNJs0QzsZPDcyNDIRDfId/kp2oo2Qx8nowvQhXEkZLJnBXYAMq5SlQRmzFESGciOIjjTwz579AucPN7BWUz9RghRWOTUEKJMYWFGqEQwJKqfTZzZHJvN58ICfBiMvETN8BkW98svvzypYwOBm+gaIr0xjiKIIjxTt5PyUYWECCMEZxQZ79Qg2gLhnsh76t2S7UGdXoQoL/THA0WLqBy+d8011zhBnHI/4dRRImAQVPfZZx+nHPAdhBw2weO9aOp5mExSckn/ZR8Q0leJxkF5Iw0Y5QPHgFfE/PVyn0DqPc8trDQi+LHJWrzonXxA6TFfNoB0aK6XSCWirXgfJxEKKmWnyC7A8UI2iVc0EZqpPYrASnQTETPZOgMyEZRxaKA8c104V3zkG1FwPnKNKByuhXZHSMV5gZAbVgwR4HH+cQyeBdE67MVCGnIhoB+Qok9GRrz7pa4wNWITZYlgNKAP4fhC8aXWLKnVKJW+rnF47gjDmOD5ZtrWOEvpG/QVFC8EfTZx5Hi+1jV9lGyxm2++udVJx/l50Z8AZyzjmfJajCXaGuMDx6DfYDBBMcaxw2fSPTeRguwHRCo/zg0iuYg0wwGn8lNCCCFE+mA8RS5i3UUuRY7GyI5sRKmheKA7ECCDQRQ9BLkxvP9aGAyL0U26Wef5rt+MGnkNRwlldZPB3ggEtiC/YzRF9kY+DO8JSEYBmew4IMhSRT5CtiXyHtkvEZmWn0IOIVocPQbDOvImOhZ6mIfzoW9xvegj6CtErPsSWsgs6C7IMD7Ll58xTiPrhCHb1u8DkG9S6Uvs6cYzQybFEfP11187Gc6XGEI3INuXfsDeh1x/vI2a0yET/YsMDfQCjNVkMfMceAG6ATpCOrInx0An5lldeOGFLisGpwN6QjJHVrYw1mhnxlC8IDbOi+4QrioQhj7OPaN34iwgoI/+hwMhXHEBu0C8PUHQ79CTMi0/lUrOp63o29gZyFzn2mhP8HtkAvoL9g0cYvQb7oe+hBMDPRjZHv3FOwNxUuKc4DnxXNFBaD/mKtoA/LP0+DLLZLJQ/ipdHUMIUSEEQoiy4/DDD6eeknvV1NQEgwYNCnbaaafghhtuCJqamtp8duTIkcEll1zS5r3nn38+6NWrV3DssccGzc3NCc/z3XffBb/61a+CddZZx32+d+/ewbrrrhtcdNFF7c6TCK7xvvvui3sPe++9d9LvXnXVVcHmm2/e5r3Zs2cHhx12WNC3b9+ge/fuwS677BJ88sknrX+/8cYb3d+iTJ06Ndhjjz2C2traYMUVVwxuvvnmdm0zf/784MQTTwyGDx8edOnSJVhhhRWCQw45JJgyZYr7+znnnBOsv/76QS5wrG233Tbo379/0LVr12DVVVd1bTxr1qw2n+N3zk2b8+LnOXPmtPnM+eef7+4/nWcwadKklJ/jM3z27bffbn2Ptt13332D5ZZbzrX3GmusEfz61792/ebDDz9056f/0a6rr756cNlll7V+d8aMGcEPfvAD13c47rPPPhv3vNttt11w0kkntXmPvkEfyQaekx8f4RfP23PTTTcFa665ZtCjRw/XvhtttFFwyy23tDvWc889F2ywwQbuWa200krBlVdemfL83A/9MJN2Btp09OjRwfHHH5+0L3vou+F7uuaaa4IddtjBPQ+ul35+xBFHBJMnT447d4RfnDdbnnjiiWCrrbZy19qvX79gxx13DMaPH9/ufsPPP9Ez8u325ptvBptttpk7Zrdu3dz18Z1FixZldG6YMGFCsM0227g+OnTo0ODcc89NOu8JIYQQIj5vvPGGk/0GDx4c9OnTJ9h4442DO+64o81n4skvDQ0Nwf777x8MHDgwePfdd+MeGxkfWXPixIlt3r/iiiuCVVZZxcnmyJrI8OnoGn/5y1/c+ZBDkX9OO+20dnL8bbfdFowZM8bJTcgRyOj33ntvUnktU5Cbkck4BzLazjvvHLzyyivtPnf55Zc7uY7Pbbjhhk5fC4PsgiyELINMw7W+9957bT7DcZHZFy9enJOsGiYTfWnJkiXBQQcd5N7jPvjML3/5y6Curq71+8cdd1wwYMAA17bcTzzi6VxRuTcTkEHjyZ1hHSld2fPLL790OiV9FX2O+6uvr096fo6TSq+Jp7PD0UcfHay99tpO907VJ3k/fE/PPPOMG3f+eQwZMiTYddddgxdffLH1O/SDRG2D/pwNqeR8PyecfPLJbi5BF8OW8f7777c5Tvg79GnGDmOIfseYok29ng7ffPNNsNtuu7lj8pkRI0YEBx98cLs5JUyiNk1HxxBClD8x/ldqx4oQonNSX1/vIoDIDCAqSHwP0WREaZE14DM8EkF0F1H7KrdTHLbffnuXGcBLCCGEEEKkB9mqlFv1mcoiM4hyJwuZCPZkSFYtLmQYkFlA1rUQQojiofJTolNCSib7GVBKR5QW0mrZCyTextmdGdJp99hjD9dH2YgxFdR7jbcBtcg/M2fOtLfeeithKrgQiSDFnzrDmZQAE0IIUTkgv7355puqy54AShKxbwfldqIbdovU+ivlviijk0o3kKxaXCZOnOg2dU9HZxOiEFCOizLA+d4XUohyR5kaolNAQhJ1/6kLy+vZZ5919T2p8diRob6or3UZj1SbzLFJWrz6m74+JZkEovSgPLMxIxuyicLj9xDxe1QIkUkG1kcffeQy1KgHTE1fNpHv2bOnGlEIISoQNuNlPzqCS9AxkBHYe0wymSglklWLC/uOsLG7suZFqWBDdfZXYU8Q9Av0DPZd1B6DoqMjp4bosCBY4LwgYoLNs4l432abbVoNSWxiR9RsR6aurs5FjCUi1SZ4bI7GKx5syKVo4/JJeWZzQWUOFAdSy9nc3m9wL0QmsEEpa5M3gKGEsMnp3nvv7V5SiIUQoryhzMwDDzzgXi+99JILkvL6BZtlSx4TpUayanF57rnn3CbdbKYuRClgs3M2bPf6xQsvvGBDhgxxG6jTL3F2kOklREdDTg3RoZg/f75LacaR8cgjj1ifPn3cJL7XXns5hwaGeCGEEKJc+Pzzz926hXEMpRiHO+sWL35WhJUQQpQ+43vChAlOv+D1/vvvuz0LcETvttturhyQEEIIUU7BrS+++KKNGzfOrVvYyShtjX6x6667OjuZEB0BOTVExcMEzUTNZtNPP/20S7PzBqENN9xQBiEhhBAVk8WBQ541DUcH++QccMABdtBBB7lygXJwCCFE8RwZb7/9ttMv7r77blcvHwcG+gWRr/369dOjEEIIURHrGftBesf8J598YmPHjnX6BWuaHByikpFTQ1Qk1AukpNQdd9zhDEDUrj3wwAOd8SdVSSUhhBCi3Kmvr3fp43fddZdTQIYPH+6UD16seUIIIfIP+x7hyOBFnXwMPj/+8Y9daalu3bqpyYUQQlQ07LmKs/7OO+90ax4ZHOgX/Mu+s0JUEnJqiIph6dKlrkbg7bff7sp0sJ/DT37yEzcBr7HGGqW+PCGEEKJgjnwc+BjZcOiz0Thr38EHH2wrrriiWl0IIXLgyy+/dIFSvIhgDRt4VLpWCCFERwWnBs4N1j8c+ZRVxMbGPlFdunQp9eUJkRI5NUTZQw3bf//733brrbe6zY0w4jDRrr/++irF0UkIgmabu/RLq2+ebzWxWuvXZWWrqaq1SmPJkiU2ZcoUmzdvntvMK1EpGVJEPdXV1W5De14IFmw+OWDAAKutrbz7F0Lkp+Qijn2Uj6eeesrtF3XEEUfYfvvtZz179lQTCyFEGixatMjuuecep2NQd3ynnXZy+gUGHZXi6DwsaZpv85Z+ZYE1WY/qQda7yzCrNNAbZs6caV9//bU1NjbG/Tugd/ifvW7hX8gP6Bf0fX4XQnQumBveeecdF0BFEHFDQ4Mdeuihdvjhh9t6661X6ssTIiFyaoiy5LvvvnOTKYrGxIkTbf/993dGmx122EGCVgcCIz8v0vlxWFFPnnTIsFGfn2ct+dQWN89e9k7MqmNdbGi3dd2/2TBw4ECX6ZMvxwCLPpENKBQ4KzxhpwX3gVNi5MiRzjGRjsLAdzief5GtRBvNnj3btVuY3r17u9JriqgQovMwbdo0u+222+ymm26yyZMn249+9COnfODokFFCCCHagiyFA4M5k9IbK620ktMvDjnkEBs6dKiaq4OA/LxgwQIXGEQpFZ77p59+6oICwjQ0LbIZS97nG05mb24OrG/XFa1Pl+FZnRN9hlKRgwYNytu9zJo1y631CxcubKNXRHUMHBIrrLCC0wPS2X8rrF/w4vjoFwRehfWwmpoapzMNGTIkb/ckhChvmpqa7Nlnn3W2OJz/VEVBvyC4OJ/zmxD5QE4NUTYQWUJ5jRtuuMH9u8UWWzhFg30yMNiKygIBGeVh7ty5rnQKz5ca8eCFZRwZpPUTLYeRnk0X2egdJcTT0LzIvlz8crvjD+y6uvXrulLaCzPXwiaPOAX4d9VVV7W+ffu6a+LF+flcFBRejIM4Lerq6tr8jevFMcKGvmweidCP8J8Lr776qn3zzTeuvXBi8C8vxgDn45pRVrhe7oP3eY+/cU/htgs7RqLvCyE63oa2GOoICGBeYP382c9+pvJUQohOD1myN954o5sjMXbjxMBAs8EGGyjruwJBbke/QLZHVkYm9hkKXscg4wD5FyM9MjD6RXRz9+n1E2xh47Q278WsylbuOTatfsG5ODdBTcjkXA/9a7PNNmvVbXgR/BR2FAD6zyqrrOK+RzBf+O/oR/3793dlYdBB0Il79eqVU1+lze677z6np3gdgxdtQ7twPnQaroO2pd0ob8m1EJCF0yQKehPXl8t1CSHKG+Y1ggBwcIwfP9523313+/nPf+5sH7naPYTIB3JqiLKoY3v99de7F4IVRhgUDQQ9UTl44zmCLQLugw8+6IzsZEUQJcWiRwRTpoJvfdM8+6ruf5F3Y9a/y8o2oDb+pvAY9Yhs4lzrrLOOUwRIq0ZYJ1ILwZ33UHhQKnifV3Rh5n4+//xz1y+HDRvWpq6yF/px2ODweOutt1x01l577WW5gPJDpBSRVlwP/3L+cAQV18X1ck8vvfSSbbnllu7v8ZwywPfDGSR8V2mkIlPo81Jcyx+MJz5A4LHHHnOb2x5zzDGuNryUDyFEZwHDLXsQXXPNNfbkk0/arrvuakceeaQzyGCoFZWDzyZgDaPEEiVS1l13XSfH8yzjyfDpMLXuLVvcNLPd+6v03MlisfYZ1WSTE8gEPhCL8xNIQECSzzwn2AgZnd+5Nt6Lyk8YCtGByRDCYRDOrkQXQMfAMcK9cs/s8TJ48GDLBXQjdAWvX/Avv0ezwMkoR7dBx8GhwXUk0y+8QwbHyUYbbaRSmCJjpGNUBl988YVzbqBjMPZxbvBinhCiVMipIUoCkSEPPfSQUzSefvppp2BgdNlll10UUV4hIIxj8OdZIgjjKEC4RQjfZJNNnHBO1gE/I9xnS3PQZJMXv2hNQUOb90d038S6V/drNeIR5US0EwI5QjXXhxMCRYNr8SnoK6+8slMyOgJvvPGGu0fSQGn/eAodbTF16tQ2pbFw6Ky99tpWiQIvz7rY+4n4TB8cSvR3FFMUUAQ7rgnFlmi2jlj6CyUdpdv3rXAtZhRxspNU5qh8wRBCdPJ1113nDHwEDRx11FEuA00IIToikyZNcoFSGF1Yl5nzmPtGjBhR6ksTacpcyB3IrV6/QO7ifeT4Nddc0+23iNNg1KhROemNcxu+tFkNH4feiTndYnj3jVtlHmQ/n+nNOsrvgG6DbMS1IZf60rYdAe6XkpboF7xo66hTxpUHnjXL6Rg4PwB5kGeCU6TS8FkrxZRpaUOfdYTjyOsU9Hv6G/oaZb/oWx0N2vvdd99149r3La9j0N+QU7l/UZ7w3B5//HFnyyOQij2pjj76aNtzzz07pD4syhs5NURRQUBi8sPIggDI5IeiQYS7qAxIhUb4QpgnEyNqYMapgLGX9GsEFu9oQEDzm9GhlPjPjh492rbeeuuk51zStMC+rX/HGoM6lxY+qHZN6109zEVMIVD7KC0UIBwWCEFkVnhFqDOAMEz0GAoXQjmKFfeO04PxhUBcbsbncFQO143DgoyT119/3V1v9NnxGfoOnwsfg0g9jBWFKlNHhCf92O+LgqMOQdyXH6MP7rjjjq39uqPwyiuv2FdffeXu15dhCwuz1HjGaO6fQbz5QJQHPK8nnnjCrb9ELtNfCST44Q9/qOwNIUTFg3xAhvDVV1/t6oBjWGGOI1NN5TcrA4JHPvzwQyf7EYCEMT0qB2JAJ4OANQ25jGeLzsHn+JkX+gk6B3Ik+kWyvSCcYb7hE5u39Ev3e7eq5WxIt/Vt0YJ6+/jjj915kPWQ8wB9g2wJgjo6k7xDEJUP5EHeQ7/iZ54R+la5yb/RjdF9eTKeKU6pqLHcZwH5n/0x6E88b/YqKYQ+ST+lJBhOIGwkZNTS13iffotuR3uT/dLRxjr7NBBghx0gGvyILohzmvtH9yALgOfQWXT6SgyA8wFUjDVse6y/CqASxUJODVFwEArIxrjssstcKQwW7GOPPdYpGuVmZBWpnyXKIgaxdOHZE4VC2j+gIPg0ZoS4VAIKwiXfJ2pr7vw5VmXVyzbza3YbY/voFQQkBCCcKex9Qf/qTNCuZKsgFNO+2223XdkZK4k6mjhxYquBAeeEV0IRYBFYea4IRyitKEsI8wizOGf8y3/XZ+W89957zolDKYJC4kuOoVQjgOMs6ugCNv0KZyFZWUROoYDE61coiXyGaD1Km/HcRHlC/0X5wMHB8z3++ONdJHOuZS2EEKLYYNi+9tpr7aqrrnKyBIYUDCoYWkVl8dprr7ksjHQDVJAnkfvHjBnjDMDIjwRTIY9gdE9VYgyZDkM3sumUKV9aY9NSq4rVuONwDRhbkXf4HAZ9v2E3gR6drX/5/UvYu2vDDTd0Rv5yg2AjXz4L0AXRGdAvcEzRH3yVAWR3niGyPH2Hz/lSYX6PFvQM5CXKFu+3334Ft1lwPnQfzk2gFjJ3R4dnxPNgzJLhE08O9ZlbzPU4z1Q+uXzhWT311FMuuIAAKvbc+OUvf2ljx6a3R5EQ2SKnhigYCA4333yzXX755U4QRNHAmVGOglBHAuHb10tNBBEg77//fpsyTBiQMQx7JcBHzvN7+FgIeM8884xTHhHsic6OwndRNBDO+DwKAvs+pOuxv+OOO1w0jY9+R8ik3/h0Zu6R/uXTc/3mgMB1YXjtKCWm0oU2ptwX7VSuY4zn9OKLL9pWW22VUtnkflAecdL4sk8+Uorn7/dB4YViokjMwoPD0Ecugq8bjQODf8li4bm98MILbgzyWT7jI+b4mWeF0iKHdunhOVIG8v/+7//cuPzxj39sJ554opvXhRCi3A3gzF133XWXbbvtts5wQtCUZIHC4rOBkxmo2GMO/YF1nvUfeRwjbTh7GmOmL9vk32NN+t///udKS5GpgQ4Qr4wJcj+GZv4lCAb9hcycdIxm6KME2aGP+CAN5BdKiHpdh2tHv+Cz0b0kkF86Y/QxuhyOnU033bRsS8tgHKd/4oxKBc8V/QI9A6cHciz9CIcCsqqvAIDeWYmltCoN5gmen8+IYizjWPT6hS9/hl2Bfsi+q8wBYV2f+YVgs87gDKoEyPQn2IAAKgJQWaN/+tOfFqyqgujcyKkh8g6RMygabCK01lpruUkMY0lnStMtBQgDlIth0UcoI+oEIT8aMY1h8uWXX3aLDaVHEOKAaOw333zTedWJfh8/frz7G8I9RmUMXaTrcuznn3/eNt98cyc4+E3ieJ9Iik8++cQJvwj9CBcIGRwn3U0ZOR/nRtlgEeR6OWZ4E2wv7LCvAdE35ZaRUAoo2USEW7nXH+X50s/on2RWJDNAeKcGz9sJqT3MPpj5gcW6xWy1FVaztfuubbVVmldKhZ8bcDDywvEEjHUcpCgem222WevnmSN4pkRboXhuscUWJbt20b6s4BVXXGE33XST1m0hRFmCbIoTAx2DOeuII46wE044wdZYY41SX1qHNzjef//9rQZ+jMAbb7yxe0XB0EhWPus7zwWZFAcGOgffI8qfElNkdSIrcEzkOx99jX6CQwPdweuNyAtkCnBsnrvfHw8dwesB6d4HGc1k9xI57K8X50gYrovz84q3l0RnA9kOZ1OqUsHlAAF1OLpwbHj9NllJLXRLV7KsX1+bvGSKTZn5pa202kq2Rr/Rtnz3jrE/SiWCXYG5wesYfr8RX8KaACn0CfRe4G+MY5we6BhkbzF+RXms2//9739b1+3DDz9c67bIO3JqiLyWmLrooovsueeeswMPPNBNWER0iMKBUoBAzoLhy/HgnEBpWH311eN+h8+y8CPIIahOnz7dCey8+BsRbxiSEQxQQHjhNOBZUkMWiKRAaEDIQPBAyOD7KB3+d6IniMhPBdfBIuc3meNcZH8QmUG/uvPOO53ihAKjCLzEoHCEDcjlDs4qFEueKYpHOErOK7E46eiP8NJrL9nj7z9u62y+js2dOdfqFtZZ88JmO2b3Y8rekdNZoRQAc0rUIMDvPDMMHp3dWFBuoDzecsstTvlgnico4Re/+EWH3KRSCFEZINfidGVewojNvHTYYYepzGEBQf5mzyx0AdZp1gb0DBwU++67b8JsS54Vn0O295sf81l0CQLd0CPQF5HxcXTwOeS/DTbYwGUA8Ds6CJ/3pYR4H+Mmx+F4/E5QTLK9MsLX4/eCAORNSpoie/ryVegqKpmZGALWfKZtJcCzJlOYbBsM4DzvqOOL4Dv6J0Zx5NTzrz7fRm62kvXq28umfDLF6C4bDdvAxm7W4vwS5feM2Zw6nuOK+YE5hbVClF+GJRVcsO1sv/32dsopp6g0lcgLcmqInMAozsSEM4PICBwZGEBUmzu3NkXQJ5oobPBDqKT8DhkSHoRxhPVwKh9tjwCXjbHQb9zs97EgisU7LjAyk8URL+OC72FURynh+ygkU6ZMcVE98cpA4cDgflCSODZZGYkUCo6N44XjYQhHSOV7XA8vlCttNG8uewbnT6Wl3aK03n777bbXXnu1Gk7pE2xqjJPGKyIvTnzRXvvkNavuUt2inAbmlI/9N9rf+ndXNE6lwHzywQcfuGfs5yiMFzgyO2NJh3KFMcYYvPjii+2ll15yNep/85vfuH2MhBCiGBC0c8kll7gMMuRJDCDslyZneG77giF/I3v5dvSZ1mRve1kc+ZpSkmTV+oAiAk8IRshmU2ivXwD6BToOgQ84KjgXgUuJ9kXzpS/ZLBlnO9fKtay//vpxz+NL36JPUTooWQAF10KmO+1CwBcv3uOa0C323ntv6+ygk9IulbiXAXosfWaXXXZpfY99QejD7OFA/2tqbrJ/jLvEqrvU0IHcZ6pramztldeynVaXU6PS1gwcmYx3P7cwvxEgp8DI8oHAWB+ogG2LtZ2qLuVa2k6UP3JqiKzAqMzGfP/85z+d0frkk092dfKyEXQ7OwjgTO4I6fyMoM4ET/QBv2PMR6Ak6gBnRTwQ3HEk+L0n0lX4WOz5Lo4KFn2eH8IeAr2vj0/EFe97ZwZlZTA0Y5zks/596uWm61wgAh9HBec54IADki5iRG3dc8897t5x3pBOitLFd2irzqrcemcPzkTayKfq+pT6SgKFA6WS+yCCn4g8+jsvz6cLP7XPF35uAd6MENsM3MZ61XT8TA2UeoR1v38M4w5nYKUJgBjIKUvhlQueOeXm/Ibx/M4cQ4SVV0z8C4cdBop0x3xzEFh9U711repqNVWJy5yJ5LzzzjvOuUHZF0oaonyodJgQolCwJlx44YVuo1Eyv9Ex4hmwRWqQ7ZGvkCGANRR5Gt2NgALKu/IZHAaJysSyFvNibc6kHjpZHcjpHB/ZDsM4GbroFMisgI7h9yzg/BiaWeMJfvDlrnxmRjqlZtFPccgjI+OEJ9gqGZwH/YsMEnQbXjhyOFdnNoLy7NA/cfbwPJDTqQKQTnZMOYHMjOzMfdCP6GP0ZfZr8TQHzfbotMfbfXdYt2G2Yb8x1tFhLBIk6fezQA5nz4pKe9bMF1R+CJfFY2zTj8Ml0ClNRR+gb3j9AgcIgXXplsqGpc1LrTlosq5V3+8LJDKDIFfK1f/jH/9wNqFf//rXdvTRRytzTmSMnBoiIzCg4shg0x8MaqeeeqqL7OnMgl+2+A3KAMGBDAsii7xQAT5bAwMf/9LOfBYHgl9A2Z8AiHJmo+hdd9017vNAwEeZYNHwadh8joUe4RXnBinYn332mVvs/QZ/pIt7BwcCA+fjHBi6OFY6G/5yHpQWhEpvlEUxwmidTukgvsf56HMch2v1Dhlv0PcKkt/gze/30VHxe5vg4CF6qiM4FOlv9EX6W9RYv7Bxob0882VrtpbnHLOY9e3S1zbvv3mHESZ9hCHzQngPGfo244SScvRt+j7jlPlihx12sHK6fsYkRgr+pbwEz8bPKYxHxjL78ySrgY1yRUYWBhR/XF4YYPi+f4+5AMUrnmNn1pK59tLMN62+eYnrK2OWW9NW713YTBCeGQYV5kRefn7tKPMQzvV//etfbv3HwIRzgyhWbfouhMgV1rkHHnjAZX4jqx577LH2q1/9ygXqiMxgrWTdRUZHZiATEpkR+cJnaPAvAVUY8nxGdVQuJ8CE4CXWYuQOn1kdD9ZsjMcc2+sYOAdYF3n/Rz/6kbsen53hz4dc48tMEVWPI4LgJbJE0tmgmXNxvTghvHzAmkupJF6p1ie+j37BdXNv3CPyi9cvwjoG5/AbSHcEmTsZPDP6EOWZOkoWrX+mPMNov3h37gT7uu6bNu9t2n9jG1SbfF+OSgL9iuoO3lnooW+TKYUdgnHAuKdE3M4771xW5X39/p3oGIxF9Hzu5f3332+TdbbNNtskPAZzEOMdJybznp+rmN/QU/zegMwhOH/jlV7lOx/O/8C+rvvK/d67prdt2G8j61bd3QoJ98q9+yAwPx91BB2Yccn6TzADey4dc8wxzsHh9UAhUiGnhkjbmPG3v/3NbrjhBmfQxpmx5ZZbqvVy4KmnnnICvV+MWFzJdOA9v8cEzgteXvhi0UUpoc4tkbJ8l/qE7HeBEvPkk0+6BYCoBEpF+VJECALPPvusOxZOkbCiwAKPM4QoLc7NcfxGXCyelJXyAgKOGKIeUEIQdLhm7/BACMIhw3UjVHhBwde25HeMnNlGlvN9hGxfT5fF3EdS8fJtxDXzwrjIIolSk205rkqAtsbR1BnG45yGOfbxgo9tSfMSW67LcrZWn7WsS1VlZSpEoY9ixPdZCcwBvMKGcMYWY9rvO8PfEMj5XCkNyn7MoyjxwunNWEPIZmzyd19Wwu/Hky/8poAoaIx3HCV+bmlsbrRxU5+1huYW5cSz/aBNbUi3wuwNwfMjCwUnMNfGnMizZX71DlfgeTH/4qjOJOuknOC5kqlJWRiMT+ecc47tt99+cm4IITKG+ZFs3D/96U/OME6ZOyI1kRdFdiCr8/KGd9YZjHPIw6ybyBwY5inLFC5Z6vc6w7GAgZM9J5AzCJ5Bh+CzPpgpvGk0az8ZDwTYcJ5wRgVyO7ILmTZ+nw1f655NwdE90CV81ia6BUZ05Aje51ycE1mD7yLz+uAo7gu9Bb2H7/tAimxAZ+BaufawjuEDFHwpG7+vCLoIbciG1JVW9jUT0AHXWWedDn2P0BQ02cfzP7EZS2ZYTVWNrdpzVRvWPb9ya6nkNZxTGOtxMhIIxBjz48TbFcj897Iq4x17QSZZWfmGa2FeYe5gvGF7YJwz5pgbuB/sFfxO/8x31rqvgIFsz1yIY9gzedEk+3jBxNbffZDdZgO2sELx2GOPufmbOd07W31pLQ+6B39nfuUVzk6pJKjmgXODe/75z39uZ5xxhuuPQiRDTg2RtjODmvdnnXVWwpqnIj81zBE0EP59RLbPSOBfFigWdQR7hG++g7HQl+kh6or3+Uw4sgaFAOMfNWl5figsfiM1joWjg6hvBHeEGI7DBk4sLKRuIwiFrxOFiIWU43JtXDPKBIsogq+PguGzvi4v9SyLXSqH82PwR8nqKJFGUWGVZ4HAp3IwlQXGG8o+AMIyfTSsjDNeGYe8h4LBGC8HpZJ5gv7Gv1wXQjbzA8aHUmUkYKQh+pM5C2Y3zLUnp7/S5jMoHWv2WdXW7bt6wa6DaDHmRJRGMlHiGVe8UYcsPAxIKGOUdaLcoM/IA4wptC9OA17lCPeKbHD++ee7+z377LNt//33l3NDCJG2M+OPf/yjmxPPPPNMZ8CoVENMJehzGKgxwiNPRPULXugOrOUEVmEwZL1iHz/WJf7uS0IRSBV2XBB0wZrGv5QoRNbnc2+99ZZzirBGcyxkBP6GUZU1A6MhJY3C0BcwsOI88Jnl9AkfCOBlDb//nz8vgQ3FBj0IvQ39uCOCPomeuNNOO8XdH1GUJ4wLxjA6Iro5Jd3CZZWQ4ZFX+RxjiTGKLaHUWbfMGTgRuG7kY/R29B4M9cwbpQgC8rYZgju9k/jN2W/YzIbv2n32B0N2sapYYdqQZ0YAqi/NHM+e4qsd+JLOOIzJeuBn5vawLYd2RcfA/pNJya1igoP9z3/+s40bN86OPPJI59xQ5qZIhJwaIqHw+9e//tVuvPFGV14KZwbGF1H4BZ2ICZQHFAaipFA4WHxwNrDwIISEN9wLg5KAwyLe3/gOhj8UBQ8LI+ehjBEpxizYLIZ8n+dN5geCDlEKvBIpmxzbOzlYaDkOPyOc+BJbpVA4gPtBEcNR1NEi/4gsQ2ncbrvtKjLiuzPD2GbcMO7CMF5QJDGMl1vaLeOZaCnmDQwMpVaAcPoQgca8xPj2RpaFjYvt4anPtfs8JahG9165oNfEPIiBBaeVj5ryhiPv6PVgjCHDCkWDeZ+5KpwKz/hG4WRj3HKGe77++uudcwOjE5kbcm4IIeKBDHv33Xe7zAyMVzgzMFjImVH4dmfNZG1iXcFoSPAEegWOc1+61QcqRWH9wiGSKHqb9Yzj+e9yLIKo0C94tr60DWsd+oXfRw25HD2DfxPJFKyHPogKQxzHwVHC/fAvgSGlqv9PeS7asxI30U4FTjAMy5W2t0Jnh3HyzDPPuH1DwsZvxhF2AORMZOZy25OPzC3GMk7PUpe9og1xDDFPoo8RwOV5b+679m39t20+Xx2rtrGDf1BQXZznhh6GjuGz1ZgPWUeZO8Ol//iXOZOAUuZmdEscHsDnsDfx3i677FK2gVMeOTdEOsipIdpAqt9f/vKXDuHMCIImq2/+yBqD6S5Ot2tspHWtWqVoxl9KkUS93742vN+YCnwd12h9Vm+w9u+jSKAU4LjItzGRhY9oHCKzWCARJogc5l8WdhZElAfvEAkvnP537hXjHJ8hXZSoHqLPOU6pBScUJ9qyVI6VQhgRyaLB2YVCRxQOQpeoDPzeNBgIwsoi7zHO/UaZ5QYCME4Eys8xF5XaKcRrs802c/OMn2P8/PrarAk2afHXLkMDelR3s52Hbm1di1yujLnQ762RKX6OLce+kGhe8pkbGKjOPfdc59yolOsXQhQO5jMyM5gXkG9/97vfVbQzIwhmWbN9xMxnZr2syta2WKw4hjgydJHL01kz0BmQKaIgb/i9u1ijkNVZ1/Mdke/3CiNYDlivkV05F7oMRkMyP+kTYb3Cf9fvJ+g37+bzOGi4L665HIKVKAPM5sSlDvTIF8hWGE/R65D7sAeIysEb48N9kgyoCRMmuOj9csj6TuREY/7BJhLOBCs2zDt33HGHc8oSxOUdqX4+Wrh0gY2fNd6CZXs8BhbYGr3XspE9Rxb1OpkHec7++jIlUYBsuUJ2EZkbDz74oP3sZz+zP/zhDy7LSAiQU0M4mBQvuOAC+8c//mG77767UzoSbQZXKdQ1vW+NwdQ279VWjbauVS2lmgoJQiCvMWPGpPwsSsWLL77oUrwzUSZYzFAUeHYcw28iHMaXqfJp3+CVBtLH8fajaOA4oV6hzwTBkYGTg9RwNvfjuojuIpIYxQNvP46QsWPHtta2J12c8+EE43yU1gk7bqi1X0pDKGmbtHFHgWeHYkuWhqgcGDfXXXedUxJ92TjwEZTUuC4ljH0iDxMJuz69mVTsUm6AzTyFE5a5h3mJ6/bl7jCSsY/HyxP/Z7Pr5lq3mlpbsftw61rdxc1lvGj7eMY0IsWYB5kTcer6e+TndDYtTQeum2g5HDFcM9fD/Eu0VCUpGKmcGygftDMb/4brrwshOhcE+ZxyyilO3sQQUcnODAiCRdZsrzpzVgvM212syra0WKywxjjWOkq6In+nG6yGAyQdfSS6TqFjEKiEAyGaOYqcT2aG3w8sXDLXl2BknWOtRvbHmeH3mkKPQO/hmshw4J7QmdAhkC+QhdBBKTWCzIR+wT2wVqJjELnro47BG0JLJZOwzyF0lLrvPHNK7lDSs9QR8yIz2OCbMUMEftgxwBxMZnCpHW9kezOeE8G1M+6R4Us9BpiPmIu8w9XvZcH8NnP+d/bxtI+tOWi2fl37Wb+u/Z1Mz1zE3BdvH0HmCXR3HLSMK18OivmTALd8OXIw/hNQ5e09zL/M3+VaZipTqL6BnfLRRx+13/72t26f31Lu/yLKAzk1OjkIkhjYmByIGmZjHiJfOwILGp9B7G7zXpX1tZ41mxbkfH4j6/CmUokMVAj9KHdEPWPMZBHH20w6KP9GDYr+dyK8cTbw3LzzAWcGCxhCAM/Qg2ESpZEILRZaFmKECRQHlAsWa64zvNcE5+G6eGFgQ6jg2JyHcjN8j/vDkMiijZLBsfksCzKGwNdff91dJ2mv/h44LpE/KEmlypYgKo2oi0pd1EnVR4EMR6VxP+EamaL8YSyxGSYKuN8sk/Hh953Jh1GbuQFBnOidTPs714LyQ53rco3mSgXGDsY7a1k4S4x28Zt8Mo/SNtwjn/GlN7xziftnHvORrCg1zJmAc4N5NVsDCkYmHJL+2TD/o+jwvKjN3REcG0D7sZn43//+d1eTm/25wmuUEKJjwzxLHeynnnrKTj/9dLcJeKWuK2GagykWWMueWGGqbEOLxQpTyoMAJuqjsz6stdZaSY3NlCNBH8GIxnqGLoAhLhzYE5bP/c9hJwV6AEZt/mXNwkgaDo565JFHWjMl/RpJMBMyDkFM6Df8Pey8Yq1Dp0AHIoCDe+IaWYMxunFP6C4YBLlurgMjIfoF58GZge7B/aEvhdvmnXfecetLKYKnkC3QfSpVf+Z5Pvzww23aDvk0vDmyqAwIRkQ3J0vDj2t0f/povhwF6PIcj/GaqZMEhwbOUvbrrFR86bxoVr0vk8c9kn3my/p5HYP5k6oR6O7YS7xjlmPxHb7PZ5irvX6YDQRnhbPX6Q8cn1K24WC6SofMHoIlWHfYm+uoo44qaYaPKC1yanRSmORI30LJAIwORA53FGMKLGikpvrSNu9VW3/rUbNR3tuSCRVFAAcD6dDxPoMzAYWBFwsYwiMLF84IFAYMnTgYMFKz0BHxg6DOzxiHttpqqzaGOhZMIjIwjhK5xHveIcHPCA04Frz32htOqQvpy6FwDhQQFAVfR5e/8z5GN85Bqmo4g4SyR1w/Sku8xZH6uSzYeNI5JwIW//oNvxC0SgFCGI6VUp0/W3guPEsUPpQ2/g3X9hSVxbPPPus2dQ+PKaIZmQfyVdeUc3A8+rwvGUc/8mMZowJGdKK24mUf+Owx5pqO7DTz+wDRRrQP//oXykqiSGKMLcwltB17omS7bjI3MsdGnSPM38yVfoNE73ip1PUZZQqFg7KWRGiz50apy5cJIQoHzmGCpfyYP/vssztUXf7m4GsLbGK796sMQ2J+Mvo8rOPI6KwFicqysI7hREBup+0xNiLHo2MgVzz++OPuMz6ICb0CgzX6AvI5awxyJcYvHCbIAI899phbmzA+4mTAiIkRqXXvqoULbYcddmi9BpwTOFJwnLzxxhsuO481izWOtQ7jJ4Y9v2byGc4ZLp2KUe/+++938gvnjQZl4DjhPrlmn91Bpib3yDVyrFL1M3Q4rrnSNtRmfeaaeeY4zZRVWbkw9hnTyPoe5FkcbtgQ8gH2CsY6QTjMAb6sNgF3OCrpR/fdd5+TocnCiie3Mh9g16j0iiDJYL4kW8LrFGEdg/k50YbXzIHMo+gZ2FJo52zgOTAnRedQngdridcteKHnlTqDJ1s6gz1TpIecGp0QDONESxFBhdKBZ7PUex4UgiXNk6yh+bM273WvGmM1Ve2dDrlkZiBEJKoxj+JAbUufvoiSgQMiun9GPFj0OSafRZHAWcI5feQMygeOlPBGwwj7ZFEQZRwvihhDHCD0oLigELCA+qiBcH1YFAMcF1H8fhnxFkD6FIZ3okF8WRWEHBYXzk0bcK5i18DlOSDU8ZzipYSWMziJUNoQTDCiogQqeqoywUn53HPPuaj1sLOA9/K52Tvjl/JyYRjnZGAQecnYZBNB+pQfn8wd4TGNwZ/oLqIhmbMkICbeOD2sQGb6nHCc0L7h+drvS4TSx8srRlF4jjiWK8XJyRpA1DZ9j39JG680A5AQIjEYzi+++GJnWKA8UkfNzgqChmXlp5aGSlD1WebUqMqrMwNZgTUi6swIZ1Yjy2P8QsZlTUi1XnNs9BIMa6z1nId1JpwtGi1XwxqEkYznGU+O5tnz99VWW83pLwTgEJDjHRf+uKyb6ApkbcfLJGXNi5fNgwxz5513OscMOitGQ7/+YZjHocP30DGKLa8Q2EYmZ6Xtq4G+SHAcBlD0U/oCwXCi8vA2CcYC5WHDWQWMl3xF6GN7YP6IZn1wbsYzdgMcmdg+kO/QRZD9GNeMDe8E4TP8jmNUpYPaw5hENwxXvcgE5ncC5lg7wsG2zJs+WNbrGeFSVWHQT7AxVUKFi3DlGfRayulnq5uJykROjU4EhtEzzzzTbrnlFjvhhBNc5FRHXkhcJE/wjTU2T3PxU12rVsibQwNQJFgwMEZG25Fz4zxikmXBYEGJGq4y3VyR76NM+MWNrAsEl+hix/lwghBt4/fI8AI/yhGKB8ZVFBQUmnAKN/A5rjMbAYiF0qdYxoNrwUjvDa44OQptkEMA41kQpYLA7lPlKwn6AEogz41FupKUps5KU9BgQdBk1bFurWMU5YIxEN20E8coyj9jMVdlHOEUxyLGiCjMC2RfoHww9ogqJBqTuYDIlmi/Ys1AGSGqKDpPiBbISKMUBgYB5lP//Ej/Zh7EEemdw2TpYZhhPWAeQmkhFR3HCE7mTJ8/8wLzNQYt5naMPZyv3B1QZAERWIER6F//+pftsccepb4kIUSOPPTQQ/arX/3KycOXXnpph9/vKwjqLLDPLbA6i1kfixklZ/NX+oJyQBgO42UDsjaTccn7yBToB9nKtugxGMAwyHtdBoMjhsuorOJlUc7JmuVlDjI/+Q6lofy+GOg/O++8cxvHNd9n/o+nu6TCG+OSlS+jXbg+5BwM9hjlCq3n4hQg0Ag5iXNV4l4x6I3oSRgDlUVZ/gQBG1TXu318YrHvywxR5g8jeFQHZ8wxvvOReY1+gbwbnW/QYZBpkW/RNZiTyOogYAoHZjQzgfkBmRj5F/28I5QlzDfoFugYzCnMt/75ob/hdGas8iz4FzsP7U3QKM+Af3EuM7/TH7AJMT9nOi/gnEJH9FU8yj0IGjsJQRU4NQ477DA7//zzcyrlJSoHOTU6ASwyeC9xaJAWjNKB4IdhmYiaYoJBB+EXIx4CLYswET/hfR0qCe4HYYHFBgWAdvXCNxFTCLgsSETx8DeeBZFQpGTmAkI7UVmJHAIsRH4ho51ZyBA4EEJQkLzxEiGW38NZGhg5ASE9qtDkAxZIFh3OybWxWBeyH/ryYCzKLMZEiSHclXuEMJFzKIeUKvIReiiNKJDK1IgPz5bnTXsxNv3G9iibRAoW45lz/rlLP7LFTS0bR1bHutvArhtaTVWLwE5fRNmm73OdOBb4GWEUAZRxzZjMpi4oSgLG9ESbeGNA9xt9MzehdOBowcnBecPRXSKzZ87zY23jubEvEuBAZs5FaQP6IHOQz7Lj70SV8jxYP1D6sq0H6/floG+Bj5hj7vfGI9YlPudLbvnU81I4SVkLf/e739n111/vHHD//Oc/Na8JUYGwhvz61792pUlPO+00p2cgbx500EFFd7Ay/7GOEsDiDf7oOqXaQDpXWCuQmQlownnNfbHeYNxi7eDv7EFBeyP38z7lvnJpd9oMgxm6Sio5nmtBlve15DGKe8MZawwyR3gfD9YodBPkfiK0820g8zIQ8g0Ofsrk5GuvskT4vUS4b3Rbv6diuXPvvfc654/fAJ5nyRjOV5mijgb9G52MPovMxHP3ej3GXmwZxZjvgmC+NQbvMMLc71WxVaw61lLCjTHpnY686IcYuBkXEyZMaC2tmq0DlCAnxm68vTlwjGLvYOxhjEe/QK8ge4txv+eee8YtdyvS63vMufxL+yLb41xibkX3YN6hugZjmX7JusAzoCwf8xH9Ap0gXnn0dGBu4BzMp8z1wLHo9z6Lg8/wN/QZXugy6N78vRSBVvTF448/3v2LY4OqNJUqB4j0kFOjg/Pqq6/aL3/5SzfBnXTSSc5Ti8cSY3g6JZCyBcMrQrY38gOTGoIAkzELqndqjBs3rrUGKvU8f/SjH7lJkEmYybuUaW8IBVxjsokQRQMDFQINC0jYQIQxi4UHpw3t7jeqZeKnDaKfTxeOy0JGmalUhlrveEFQjbYlUb4sOn6h47NEh3FcFqtiLEREm6GMcS4WZC9g5xOfos8ii3DHIkcZHhxP5QhCMooZwifKEtfpjfT0IZ6P+B7GKKV8iExB4KateNaMOeYQ5hL6OgIh/Yy+zRxYiP69sHGKzVsarrUds5pYDxvSbau4SgBzXnhcIpASvYQC4I3j6YLBgzk/mRKP8RxnJo5Vzk1bxCszJ7Ifu6wJPmotbNApJvR15jy/2Sqw1jA+MHyhmPvUc68AIxMw1xTL4YsBkoi+a6+91h599FG34R9lqQopmwgh8gPzB+WlLrroItt9993t2GOPdQYU1lbk20I5S5mr0GmYY31ZPq9PYDRD3vbR8hj8mQuR4X0pJLJdMbogJ5Q6s83LKslAh+A60TGiczPZMb4sKX/zNdu5/3ifT/eakCPSLXVKJDZrXbwyY5R9JUrbg0ODfjNmzJii7NeFERpDrK8jTwBVvs/LvSN/0sfogxj+OB8ZiOWatcH4QbdARuZ5kJnPekxb7bXXXqW+vLJsL5wFBJ4wPnjmGOzR4XnuPHPkPp4/OgcBLIXo32RoNAYvtTo0PNWx9awq1rYENteCU5H50v/uS1QxZ6BjZArjl3kl2b41zFe0C7oGfYrAPFUXyB/osziJyMxgHs3XXoyZ4DcfR8dgLHjQK+j3fo8e+gB6qe+DvlxgMdZczokjCN3isssuc3au//u//yuZTiYKj5waHRSM7ERMUX+UaEiMBYUyVPjJzW8IDUxqGKeZbNOdvPgu181EjeEZgQFPs9/AGhD+WVBZIFksfUZErtE+HIfjcX6EYCK78EojxHB+hHKux2987RWIVKA4YbTnuAiOXCdCLtfPezgUovX10wXFBQEDWNh8GyBwETlHJAXGUmoyMolHr5d7QfigPcMZHzhMeAb+M0TyYvjEYJzsWXKvLLQ8j2wWLJ4BhmeMbLlmsoTBEcB1ERHv4b5QdMtxE2Tun40SeSb0D+B58hwQDrONtOgoMB55nowf38/oe5TuiUYBMUZQNjCw+M3WKBXA8yc6vBDG09kNE6yuiZJ3bRnWbQerWpYmngquL1uhD6cIhhycmImMJfQx5iWi9OLtvyE6JyggOHxZt3PJ0kOJwemOcs8cxtqTqj+zaSwBGBgGKEm19957Z31+IURheeCBB1ygFPLj5ZdfXtDa1azjGM5Z25CdmUtY03H6Z2I0RuYj2xVHL2s/xkif2cYxeSFnYbDDcIQuwDnyUY4HGQWZhRcyHO2G/I48A97QwtqMzJpO+VfkBOR1dBLuyW/EzYvzcQ/IRdluxovBiuPjJEIH8KDv8UzQ8ZCvaCeisqNwLzg1CCDycL/IJ/76uWbam/ZIFsDmdS+yIbKN9vYllsjUzqfsR3YD7Rwun0NfQycrx8hgDN0Y+1jj6fOMAfochnh03c5ugGYs0dfQpX22F30TPTssx3jbB2MNZwftxnigf+M8C+93mS+CYJE1BuMj78asykZYdVV6exd5O022Ogb3xxiMl63h8eWn0NWYOzpyqXORHowj+gNrCrpnLjZJ1nGcJl7HSDXPMh9feOGFLgiDDFLKU1ViKXKRHDk1OhgsVrfeequrV41Ri7q2hYrC9bVVEYZ8CalCC3AIGURbAcKtvwYcBizQKAIoIfGEYyZUHBUI4N5AjwDCxMjPGHKY5BBoMOrzM4IJwjsLNBMwf8Ngg+CH0IPRkGvgWryAGIZ9L3yWhi+3lA/8xk5M1ERdoBDxs4+Y4rqJ9uYe/KZ74Ygr7uPBBx90yg4OhGSKIU4RBDeO4yM9EOCim30jwKP8sWjxM4pDNvi6//mCLBVfwsmXieG584zL3XCGAkjkFMqp74+dVeHAwUlJLsZhONsrEbQbn0Wgpv9iLEUp4WcyFBCEfIkK2jVfdYTnNnxki1zpqfCmazEb3m1szhuIYnBmHIIvQeEjOMNzHnMBbRWOkIyCQ5M5AecG40MID0oHBi3WLQxXzOc4nFHeWV+Yg1AmcBT6dYD1iPmKcUTf9PXgfX9Mhi+RyPkoiUFABvX4ia4qROaeECI7kJ9OPPFEJ9tecMEFdsQRRxRMJkHuYR3zJUqjMme+Yd7inMiGnIsX8yCOCL//G/NRuKRfGOZHZEtkTeZGZA30DT6LLOL3UiILAn2AzAbmUX5H//D7QZBR7/e+Q6dCd6HUVLTmPE5ovotcgxyQbenCKMzxyFvIR76kJyV2Ca7hd+4BeYO2wXBJWZtoyUv2zsPwhEMDp0UiWY3j0ZeQa3xZE543skn4O7QBegfH5HOsLcmixRPBc6QP58vgzLpHH/U6C7+jk6EHoVuVexYsOjHPlWdHfyz0GCtX6OM8R/ohcgjPLZleTL9lvCLvIIczFvg88wOyNX3UO0XQNehv+Si1FgQN1hi80O79cAmqbGG8UyrKO/yY15nHaI9ogCrl6ZinEsG949BkzqRdCr13pqgc0BMIpvPZhMzjXkdn7WEs8jfmIvof6x7rIesrfZP3cMqy1qLf+2zzZPj1lzWTIG8c0ZdccokdcsghZb8HoUgfOTU6EAgmxx13nDN8ETm177775v0cTBxMJN5Lmk9jYLYgWPBCCEbQwPjCIsy1sbCSYotywALNtaKQINj6OpjpKAIILCzKCKtMrizwPjIDaA+cHP4cfjMlok99aQ/eRxBPNYEycTO5+828fd3CsMLwyCOPOCGJiRoFg/umXBHpzlyfT6n3G/IxgeP4CCufvsam39ibBSRqQML4G33fZ3jwPpE9mdbdTAYLDsoQpQzytdCE088Bpxfv8TyyUYoKDe1Lf0Pxom9So7gzR7nQHvRTxivKO32Y8c48RP/3+xSEYdxjmA078jDeIxz5yEjvxOT4OAMZQ36M0XcRnBC4Ms3maWxebDOWvGqB+ZTcwPrUrG69u+S+bxDXicHF16plrBNlx3zBXEEfx4lK3yYK1Je6w7kRbSPaEweid/r4jS7zZRgRlY83ANGPmMv9/hve+Ifhi8+AL2nlFRPGF/0ThcPXGE80p/tMSfqhr1ONQ4M17a9//auTazqrM1eIcoAxetVVV7nMb4JBLr744oJkjSIDossgB7O+Y8wvZbQ7cx3yI3Ij8xtyB4FVPvuZOYsAIh9kgF7gnQLoIelszMpcikGR+ZUoel8i2BuYWcN9VgfnZI33fyM4ic8yPyIr+uzeZM+R+RV5x9c953g+kxUwyKPPMOdzPO7BB0Exl9MOXAcGcQKkaBs+H86wpj2Yy8mMxbHhNwsOrwGsKxhRo+Uykc9pB/bciLdmcA9PPPGE7brrrinbNvo91jNe+SpBwjHpHzxvnoEPukFmTbYnSSnB8YLzjOfP80SG7szrK/2N8YVczdhjLDIG6J/8jh4QhfYLl1ujvyNP83mfgeTblfGNEdbDe35c0f6Z6rpNzZ9as7FPW2xZ8FSt1cQ2s1gst1LdyGuMO+wH2Bfo24xh5grGOffFdVONgf6OrYW2Yp4IZ3J5GAPMh/zLd5izVFpUeFjX0GfRGZAlWMdY6xkf9EX6Hv2M+Yp5lfWVscT72K3op6y/fIf+mmztY67zexkyrgnWxanBmnX11VcntGWJykJOjQ4Agi6D849//KMrqXL33XfnJdqCxQoHCRNAWKnAwF2smnipQMHwmQNhMArjaGByJIOiWNEn8TZToh2JcuI9jNNMzt7rzMungjJxI2xzzQg7fA7HDG3P+94TzfPmGUQ3V0dRQXjg2XgBhJ+Z+HmOZO6EBVeUx3vuuce1D8oQDgwEfW/U9IplvLRyIlowCMeLvmDRYbNxngkLRTpOL4RB2on75jvRiLR8PhvuC2dGOW3kx7WxuKMYAm2HolgOY6xclI3whmP0c8Y0YwYBxysS4J2ICNHheYs+m+4G3PRFhCmMF4xVL8gnK4/AddBvOWdjc53L1giCRqutHmDdqxOPAcYtCr+PTgmXSQuXBGLcMx8w7lDocfxF4b7pQ/QlxjpzBHMO9859sxdLuE8xr6CYMEdwb8yZRMgLkY+5lr5In6QPY6DKxGHG9+666y63xwbr3TXXXJN1GRUhRPaw/hx99NEuUIDyDYcddlhempP1h2N6h7vfdBoZsByMX+g+GKyRj8Nrvw8IQsZH9iimURh5hDbzGSBe7iHggyAmnEB8hpfPngM+5zcTR07xBkZ0E54D8ouXDXDKUE4sfM98n9JNvkwXx2c+Rzajf6ALRA2bXA/HxciPYRj5guvzMhbBVuis0Yh49A6ivBM5BdAXMFAhD9H2qfZdpK18NozXXwvxvGgT5CmuDV2qlPtBxuvLPsDPZwkVa++scg+YQtZgHPgNwBnz6K0+yILxwrP0Y40xhRE27NTlGARO0b9T6W1eJkL2R1bn8zgGkunKjBfGpQ+yCoz9O+ZRI8GqYiskdGj4wE5vIMbBGB5vPqgLeY1jY+TlPpjXonYV4DgEd9KfcIRiP/D2FnSk8D1wn4xj7g8dA+M1c2mpA2FF5ROez5h36ausB5lAnyVrg3Lf55xzjqtwk49sKlE65NSocMgEOOqoo9wii9LPosTilGkESxSEYQzeP/zhD8syHdVvdsXCy6tcwblAFBcCDFFKTKIIkrwQLPkXIcorCyz4/j1A6EdIYgJPtmk6QgjPDCGDiFeMsD4jgWugFFRU0EKY4lw8X4QdhDscH14QQfCPOk44HgbSRJEZKExEsCCAcS8Ibdwzik+yiDW/9wjnxvAcjhrLBxybZ4AzgxJlpYj849785sEswrR5uD8j6KH8deaIqXBbkQ1En0VZ59kxjryzy5eqAfoYygcKhnduxFMqfMQ5fTpTZ5EX5JlnObd3NkYVba7RO8u4Dq/MR2GsojgxTrhmvsNY5/4Yu4xNr3gwRr0zBuWDCDLGc6p52e9RQ9twv7Thjjvu2O7efa1qhEOuC2U3nehSIYoBawoBG2zwd/LJJ9sf/vCHst18VYiOBOvTeeed57IyKDl11lln2c0332xHHnlkzgZR1ju/t1U5Bm+w5hOIxNpbjtfnDZbIHKzfyEnIlaz7PqrVZ88h9xAIgdxAUASyhJcJiCBHVudYidZ974DwG9KiU/isDIKhcDbH26wWecVn6KHjIAvhVPDBFOzlFdVp0EM4X3ifwDB8Dx0XfQgdBcMW98v1JHpOXsZB/kYHQH/Jpw5AO7LPALIbTpZS6cz0Wb/XJLqad2gBzxwDdGfO+g5DxDbOOPRr+i4/M15oO/qL31MMfY0xBjxbvxl8vL5GABafybSNw7I6x0e/iI5Fnu19993n+q6fe3mmPns9Xra/L4WHswGZady4ca5MGuOPseWdND67G1kL/Z6/pbNHUtjBwTVx7fH2w0S/92WgOT7/luucKjofZIQTtMFadN1112VdOl2UHjk1KhSEVxR9NtQkHfz0009vFQ4xSLNQ56p0sBCxSOcrTTdfIGQgiJdLNFeqzdgQUHgWLPxeiACfrs7fEYgxbqJchEG4x0nAfaJIAAZ/DJDhY3EcHwGFo4tsBCK++V4850OUZ5991kVwILzhhCCiO17boqQgsMcrO4DCgOLAgoCChFEahZXrRCHCYZIKhHCUFJQEjoHwk4uRn3PTHjh4SlWbHYGVceSjCXzqPs9Rgl17MKzjIKAP0k6MIb8nTLK+gKJLGyfbYJM5jShH2p4+kQ2MQwR55ls2hwxfE9eNUM945FyMK8Y0cxX9me+hbPJ378AMz2soCdxDvuvPcmwUFu8sbAqWWmNzvdVUdbPqZZuXc+0YGRgv3vknRLnAGkkAB+PjpptuSrpfjBAiN6h5/bOf/cytVSj6GM+8TIrhOR/jj9JDyKnlFjFORDHrd6blU4sJMjL7FrGmI9siO7POh2VfZI2xY8c6IybPbM8992wncyIP+T0JMfRi4EVX4RU+F/XzkXmYf2+//XYXqOQzH1KV6ESm5zM4VJCFMdiGS/dE+0S8Pb6Qo/kbgSKck5r+yCsYazEIc23pyPjIiQSG0FbIOdHArUzBiUDQVDQTvphgDKeNvW7GdfBzOWWKlBM4MOhP9GF0Wvp+Ir3Wg/zO95D5k4EjATkeR0I2pVw5D9fEMyV7JBy0yRhHTmcs+k2XkYsoB+jL8vJd/ub3F/XjnfnAB9PhdMl3NYRwpjrnWhosdsWxamIte4ky7mkTb1cic1eIcgG5hs3DyUT99a9/7TI3FDxVeah4dgUrGxid+TlaHgijGgtdrs6IclM0AOEbwzvRU6Wss5sOGGV5IYggZPjSOWF4TgijREXEa28m1XDkA4oGQgHCQTwBGgEbh8cBBxyQUftwbSgJGF8Tbf6FoIKglWgfCiLP6YsYoVGCUDj8NafaxMmDwd+XGPGlh6LleDIBAdCX7CkFPCuugXsox4yncsPv88A+MCgE9CWUiHScP0Qa4TRIBoo1/Zd5JFtQ5Il2QjmgpAGOO4+vBcp5iP7AEOT7HveFkpJog0rGa6EyJDi2d2jMW/q1zVhCfd/AYlZlQ2rXtvq5Na378DBWC1ErXYhcYA5lvF144YWuRNpJJ53kFI9ylFOEqFRYv84991y79NJLXWbGqaee2sY4h0zKOo0RIFelHx0mHJxTDmAgZ/0uZ4cGkBVw4IEHthorkZ2jRlRkFOQQZOB4Dg2IbmTNd5hTEz1bMi522223jAIv+Cw1zMnUQNdJVOIDo2wiQzB6ErKbd27gwPDX6LOw0wE50Ze/IdjKb2ScDTiBkDnRmUoVoITexfMv14yncoOAP543Y4JAH3TfdDZzp18in6ea9wgixPmRrX3C76OI048AVfCODY7Js/b71XAvBCOid3BdBBLye7xz0zd89lYh+D5gaolNq3/LGpoXuN9rq5azod02cGOUeYXxm2rfHyGKDWP67LPPtv3228+OOOIIe+CBB+zGG29stWOJykCZGh1I2fBRxI8//rgboOEIfRa9TFOqWDhfeOEFF5EVNyMiaDaLVRV9Q3DSQgstvHHv/FdVxPsrJSip9JF47Upkk98IEWdKonJfRIggTGEQRWjh82SZ4GjBuZON4oDAjvCGYwTBM16Nz2T4/lsoQS5VxA5RcBjAtflyaugvKOAYL4n4QblGEM4kwwYF06eNJ4KoukROUZx23nGH8yLVPINyHe7b3AOOOOZLnAQo4ijN3AsK8M4772ylZEnTAptSNz7ybsymvlNt22y1g3PAYnwo9XUKkWoMo3jgNJbiIUR+IEiKccX6y7iKBkxhkCIQh3UPfcIH1iCfYYjLNHgEGSlcyqgN3tlRJEMtazdBFOhN+S5/Go/moMkFFXQWQzQ6RjyZK1wbnX6XaE8C9Aj0P2R5H4GOUZr+yDPLJuOCcxNsgvyGfp3pxt7oJqxF4cCWYoGsRlYt956pXtRZQbanLBMyO/jSa+mOQfoLlQySOUGQScigiJeJxPepZoAjJR1nGlkbBHKEAw0ZB+gY9DmyrxhTjBtfdrrU+45Nr3/bFjfNXLaBeQvNC3tb3Xe93XpC+3Otced8IcoAxt0FF1zgSm8qeKqyUKZGhYDgRXYGi1e87AwPix1CYfTvfD+RoInRmoXa19/EEEtU+S677OIMc+0iqeqnm835n1lTnVl1T7P+m5vVFjYSnoUQwQOjeiHhXj9fNNG+rZvinBr9uw6yNXuvZzVVHXvzIAQjDMKUkKKPOadOELj+QVQT3upUqdXhTZowRlN+i+cVLakVhn7nN1Unij2azcB3uQbqcWZakodofO6rmA4NBFofOYVhPJ2SW8kgEgdFDkgZDgvglEjgZ54P56O2MWOY54TxwSuIlQJRRvQXnxZNX0yU1ZAIlAWee7KydByTSCr6W9TZRHujKNN+/OznPuZBFGlfKxcFnPHC7/RPnDFEH/I9IjyZQ7kXHHwoUiid5VAKoL75+31cworW19OmuzmWvhuv9IMoIUGTWVBvFutmFivv7MRigXzjszaITPzVr37lAj6UtSFEYQKmAOc8a5vXRcJrCGtqPKcGsiBrOYYs/1nWGfbrI2CBdacNzU1mM/9ntnCyc7hb71XNBm5c0ACqRBuCF4JFjfPs84Xv2JLmOquO1dhKPdex/l3b77vV0UDGJxscmRzjq8/g9jX/UzmSiJIPZ7LS35C3kVcSRcX7kkFEsYdL9Hp41mQFE7QVLt2VLgRyFVonDYM8ir7EC3mTLOZcSjAzNnEo0U7cP+3jo+h5PoxNnCa0U3iPBiBIh4ydSnHK+X3svIMAvYoAvUyu3+sXyaCf0Zcp+RYNyEJvoK/Rj5kPfYk32hT9Fp2RvsyzQDemf+E89sGBXGt4L07+hr7CcYoR7JkO9U1z2zg04O1337S1Ruzoxj96VCL7lSgB6LiNdS3BAzXFD/4sRxiPZ555ppNRkHV81gYBqqK8UaZGmcNixd4Z//jHP1xJoX322cf22GOPtJV3hBXSqVlId9ppJ7fosdiyYCIgsTBiFGVBZcH2kQFesEdxaeP5b1xkNu1R3CHfv0dN9qG7m1UXpgwEC3t4Y+BCMmXxFzZp0Sehd2I2sOtgW7tv9iWQyh0M4UTe4VAgeoI+h8CcbX1YFF82AyOSJNkxKC+FYdhvSEZWBZFS4e+gQBP1wmcyFd45NgJmOnuKZAMKGYK934AaaDeM5vlwpPh9VjCIM27ZPBqhnAwChGNS+f37jFeM9H5eQAhmzPg0+3KHMR52jKFAoRikW7ILpRUnhN8okbahPRJFQvHsiLCLFxXIsei/OCyItmKepE/znFECgb9Rs9Yba+ifCEIcl3uh3XGElBsLG2fY1Pp32r0/uHp9Wzy32bV3Oe9T1Olo+NqsjudF+b5qsx4bmXXp+AawTMBo5bM2brnllpzKFQrR2UDOOOSQQ9w6yL/oGKyd6cL6x9qLDIL8iGxCNLI3/qFDYNzz5Q3RRQik8nIeRuE2a+V3r5ktiJSRXG4ds/7Ja9lnC2s6MmYxNgRvbF5q7817wRqD7zdwhrX7bGk9agqfHVIqkOeQt5DvkY3ROXIpXUYJLHTWVPuioddgQEVeQw9GlqPErge5neCrZHt8pLMXYaH6DWuaz2biHBi70Wd45XpOxin3jp6G3sKxmQsYl7QLxnUyEjg/MjF6COPYnxcDdaJSxeUGdg0C4+h/3BdyOlkamWQ1EETmnwV6FrpKsv7H5zlPvM8wHpgzaXvmTcYDn0eX81UT6N/0S54R+wn646DjoItkGvBVLL5e/IotDVr0JE9tbDnr0dCyn0a+9wsUOdC0xOyrZ8wWT2/5vdcIsxHbmXXwIN5M5w6Cp/785z/bySef7EpUJSqfKEqPnBplDJHXhx12mFvEUNZRBDDWIZgxqHwKZRgUBgytHgQQIod9BBWLKREKKB9ZGa0XfWE2p6XOYxsGbGXWPb/GY+4TIy4CnI+QLjRvz3nV5jcSafA9VVZl2wzqmOVY6C8ooH6TQSJFiP5JVS6JZ8P3fLQVLwQW/qW/UvIplUEaxxoCohcsMRyTTkvkFZGDRO4jROdS2xhBFu96vqLv/Kbf3Ddji3EVru2bL3gW1Pv1xyXin3bg/CjhqaLaaD8UOu693I3UKAoYRnwUHcoWc1S65QTou2RKMB/6dsGggmMCATrRhtdEqCWLbqL92Jco0d9R0pmHs1GGS0UQNNs3dW9aXTNRs9xXYD2rB9uwbuntW9JRqWtqtC8WzrPa6mpbpWdfqyqHtmiab7bwucibVWa9x5pVKaIqOgecf/75bqM/Is1PO+20st9zS4hSK+uMl7/85S9Oz6DMAjIZsjZ/w9kQzbzAuIn8E5YDkfMwunlF/+mnn3brZtYb0X55j1lTfdv3uvQxW2EvyzfIHehLqUpW5ov5S2fZxwva608rdB9tQ7uX9x4e2UB/wQGBToDuSqAR2cupnBFeH8DIi8xLP+NYPniI99LZM5JAKT7n5X8qFiAPci3olgRWZVsaF8jCxRidz4xojklgDPo5xnP033RKoWYK56Dvcx7al3ZgDCN/ozOkWj9pP55DJg7QUsA1ogdSbcAHm+HQYMynWxaYIDGO479De/mAu0Qly5hD+QxzYzzQ49C/k5VhIkKcINZKKl9c1zTL7anhiVnMhnbb2LpVl1+QVzGZtGihTa+vs1G9+tiAPNsLsubr58zmfxnKrImZ9RttNiy3/Xg7ItimkJMYi9hjy33e66xUzkzZiWDxJA3897//vdsI7l//+lcboz5GwGjKNgsoZakQFpNtbENEFIJ8IocGxlSESQZuXMNpLEGXyXNZDNoApw7REMUsKVET5/6qOmDJD9qXeqw+em7HHXd0zx38xoKJ9jLAYI5ygIDnhW0EYJ5TJoYkDM4IdmyG5jfSRrCmH6NkJEsrTxcESqLps6m3G4U0X641bDwvFH5vEpQOFDLaHBi36ZybZ8HzQ/GI5/wsF7xS6Pf7QVlAUcgkgopnQl8JK6a0GW2VLFUcQ2gyRRFlkr5DO3onkd9oj/GBUEO0Gv24GDW480EsVmXLd9/I5jV+Y0ubF1vXql7Wp6alPnVnZfKi+XbahJdtVkPLGBuz3ED7yzpbWLfqEotHTS3lWtrSbEZ6v5wabcAYw6bhu+++u4s2f+ihh+zmm29OaFAQojODQfOnP/2pc2Lcc889rowMeEX94YcfbhcMgd5BIAv6RbIgEWSPZA4N1mSM3ATQxF134ukYifSOHGUPZLpiOTSAclPx6Ig6Bu2L8Rgdg8xVMiRwkuFIINJ8zJgxCSNe/R4bzN/0EWQ5+hyvTGQVHBrIbTxjrxvjWKH/E4ySa7Q7MjrHy2fWITp4smCafIFjB9mbZ+CD0yDdAEKCrJ566iknC5dDadV4oE/w/NEvvUMDmZ65J11HAcF62EzCTjSeTbjd4tlTsNEky0qgzFdYP8HewdhA96Ovon+QAY4xtZx1uCjdqwfY8G6b2aKm6c6h0bNmqNMzOiv0wT9/+J5dPallnqitqrIrN9zMdhma2rFbcBZNi5QKC8wWTS3hBZUvOB+xTZGpQdAuwSCUvc22ookoDHJqlBkY9Kjhhgf/sccec/U+o7DgYYBGYESgQNFgoQ5HIiRboDEkY5T2QpOvwY9jhPRTDHScN66hrtsws+peZk2Llk2GMbMufc1q81fmhutggUfgLHaa1wo9VrbZ877PdIGRPTqOYcSn3SI8YTgmG4LIKdrZR+Vtt912LjWWKCeMzVHl9sknn3QbCdN/6E8IZvyLooCQn0kWEMoG3+dcnBdlOJ+b7qE85UsgJDKfcVGsCGDSvYlO822cLijqCMII1OUsDKP0ohCG93Dwe7lkAn0Gp4N3ahDdR2RVqg3iUeros+G9YMKgVDzzzDMuBZw5if6OIu5LTNHOKEZ+L6JKAcfGcl20saTnzx++bnMalrT+PmHuTLv1y4/tqFVKu+GiK+sY9/3yNCCUEsYg6xLzwBVXXGH333+/G6uU7TzqqKM6tdNOCA9r67XXXuvKKKBn/O1vf4ubyUlZGYyByDvIH8jjBLCkU26GdZgStl4GZO3EgMd6iTyGgRDZhAjkuCy3dsueGtH38gj6Eut4PoJdMqFHdR/rXTPAFjS27JOG/tQ1Vmv9u3ackizoFhjnCVLC8I1zwstYvhQs8ixldeJtbIwxmAxu9Fn6nn/hgONfykmlW4oYYzuR9MiZfs8UMpDytQ8GfTtfOioGcu7PB/gUGuRmyksxJtFpcDSiv6WCcYPNgefxgx/8oGwdGlwndg6cV1FbhnfgpANO2rCMHw4IpKR3Iphb0LXR4xI5ijg2zmICpzgHOjg6Mc8EAyrnKuaekPmitrqPewmzJ6ZPbXVowJLmZjv+rf/ZGzvtbv27ljhjo7pr+6zImuIFEVcSrCGsWdi+mKPJBh83bpzddNNNboyL8kDlp8qI//znP3bccce5vTMuueSSlBETLJgIQQyodISRRCBgIpihdHDMlBvFUodv/vtmjQtaHBp91jaryo9ggxGXKB0E3VJ5QOctneM2Cm8Omm1Q7RAb3K0MPOo5tCcKBsI38C+CPYosQhMOCF/PmL9RsgfBij6FQRcFFecSCm24v3BMhFmEMh+9znHoQxwDZSaTvSw4HkJg1iULEoBQm4/NnbgvnBrF3igKI92jjz7q2hIHRTyHCoYKStOR6owQzLNFkM93Sax8wfXSRyC8pwUOXV/WK90Ia7+ROkIG38Mpy30TcZqOIRPHCteTaO8RnyJOn/ebr1OeTXQMmoLAdnnh/si2hmYbLTfILli/xPWig2azRS+1ZGYsKxVmNYPMemzesqmfaNtcQeAiTzG4IM/g3DzmmGPcvHnDDTfkJCMJUemw1h155JFOMUcRxyCZDJwPyBSspxilszXeMi4xmGLkfu6555wMldJQt/BLs4WTWua9PquZ9YifNZwNyBnIrgQ1lILmoMmm1n1hi5sWWNeqbja8+6rWpao8ZbV0wCiLERZ5y8tI6G+0M3NwWGbmdwJCCKRCd8UohKyKHhKW1zAg0WfC+gXHRSdGDkce8+WSMsnoJdAlnyAfst7kY+Njrg/drFillj04LHmRrR/W8xLtWcdzQj9Mp4RYqSDoE10gnFWPLuVLLeM4SKcCBPeNDslzQUdA16D/kUHhN1RPBW2bqFQN44Wx452sHH/PPffM8G5FOXPRxx/aZZ9NtMZIsN4DW21vG/cr/D6xSVkwpWVPDadfLGPkLmY9tW9fovkA5zjzPnPlRRdd5DJdr7rqKjvooIOK99xEQuTUKAOIZDnxxBOdgHf99de7jfrShUh7FkUM0SgPGNzS9eyz8CNgIuBjtOVf0jRLFdXINSB0pGuQLGeWNM+2uqZv0eisW/Uw61bdtjZxMcBBxeSLwB3uExiUEaCJlsPrzGdwJJH2zGfpUyiyREOhCPMskpU0iweGeARK0nzDUNsU5Ta6QTOCIunp+d7/gfrPHDcXRYH2ITKnmFkaHr9JNcIuRgaeEdB2fuNAQOAm6qrc68j7zQkZ42EjI4oGSnC6zgycHzwTFCucYbQTZdNQpsmsSBc2j0Q4SWdDcpQirrPYSqcoLPu/8ojNW7qk1bFRZTHbeeiKduroMshyCprMGr4wa1psVt3brOtKZjGlO6cCgxnZahi/LrvsMhd1TjmqVIZcIToiTzzxhB1++OEu0+Lqq69OaLyMF3WPrIhcgX6AMZNXOgFHPqORsch6jaxCsEtUJiwmGGYJvkk30r9cCYImawgmW3OwyKqsu3WtWsliiTL7CoQvQ4vcycvL8zxvyv/RZzAG86LdCbxBTsURgfMZozCfwSmC4yOTEp6UZeXc0WwfZGWyvpHV22w+b+aCuDLVY9IBuRMnTbYgu7JWcb20Y7FhbKJf0KY8O64nrJsxjhn7yObpyMmlhGtH7/VOr/A8lWoPvSjoI7x8uT36L/YJHGOZ2CfQQaNOu0TXznOI7mMkKpvbvpxkp7/3VrvAqdfG7mbLdy+D/S7ZJHz+5Jaf+65m1r2y18Zige0A2yvzyqmnnmo//OEP3bYBsg+UFjk1SgyC1sEHH+w2L2PzmUT7GCQChYOF2wsjLOgcLxpFQIQFEQN81kftI0QSJYNQk+8I+UxBGEXQraRNdxNR3zTT5ix96/voWrLnu6xr3auHFc2bTIQd/SFZez7//PNOWKMfIOz5FFuUDpQNBCyUv2wM5fRrvhs1UiNsEq3HOVFuiQBE2cXZQfmpfGfn+E3iSEHP1FGG0oZAShvhhCm3zdpot3J3YgD9EMUVBYEFH4dGtC1RenFOpHM/ZE6Q+k60lI+2whHHHJqusSZa7gDHFeevxFRvkRsvzfzW/vjBa+7nwALr26XWrtxwexvcrQwUDpETrDcYjB588EGncBx//PGuFm65lswQIt+yIHvz4cj45z//6TI1MpGDiCBmrcW5gbyGfsEaO3bs2HZrPBGMBC0gc/k688h36CKs96WUn7g+jA+VtP9VIoKg2RY3vWHN1rIHHlRZL+tRvanFirA3B8+W50wkPE6yRM+Vvse8i1xGYBEGcXRN+gVyFkFs6KJR50M6+Ih59jmIZiSje3AeZEnkdvofmR/8znXkG/oVwViZZKZ7MJIjf6KnJcoWLhW+FGwl1IxHVyPbnz6HLhsvK5PnlE7ZMY7F5vZkcnn9lf6EE43+nqkeiW2G50w/VJmazseSpib70fgX7K25s62KQMQgsF+PWsNOHV3i8rYiZ5gfsV1gWz3//PPdunT77bfnPSNQpI+cGiU0SF544YX2pz/9yW1wecopp+RkoPQlpKLpuAifLPYY/xDwytEISoQITpdCCJylYOaS/9nS4HuFA6pjPWxw7dYF7U9kYNAPELCJ9kglZPuIHAzOeJ1R9lBaEeTiTcooICi5HDuaURFvszSEdV4owQiT/u84XBA+UXIQ9hD60ipJkONm1PT/dOEaSZUn86lcyzhVAkRqEuFEX0SgT6QQ8Dm/KWQycDKRiYGiHO7Hr7zySs7RcijJmfQR0XH4bOFce332DKutqrYdBo+wfqWudduJYC1AGWDsJ9tYMxtYl5h/MChQTvGMM85w6wyKR0cIoBAiEewr9ZOf/MT9fMcdd+TU31kf0SUISgiv0bzP2o18RZnScowy5hoxOpAlku9M4FLQSBZ485vt3u9WtZ51qRpSUD0NxxXyMP0AGSxVe9L2BEshtwGyPkEplOqIV/6HvTQwIGPgD+sTzOM+O9nD5zguug99z2cR8FmyNXB4YMjGecL15qNEVCIInOJ8mZTD4hrpkzJ0Zw8OCJxrBCkQLJVMh0y2f56HvuozfcKBD2SX08dy0VHRhZFDMskkFx3HsXH/t1/ZtPp6W7/vcrb9YJV3KhasQawryC/oF/m252C7xDbG3MEm4gSnY9Mle6MSHMIdDTk1SgCG4UMPPdQtcoXy6mGoxjCLMFeKDbfTBWUI4TRspKx0vlvysjUGbKT+PVXW1YZ02z7v5/IRckzaGGSziUKjJiBlfFAYEAw5BsIi5X0wNu21114uWo/+RDQ7yg3OE69g+Mg8Iq6im/4BxyBSis8T1cXC4uvqFsvJhtE7kw3IcRAhAJd95H5zk1ndbLOqGrNuy5VVrX3akAywdJyVzIn0n1RKMhuQojzGUyrpT9kKERg+UY7zbVQVQiSH9cvXT2cOyJcswDpFQAfHw7DFmsU6+cgjj9iNN96YVeS6EOUOMiF7yJx00kkuM+m8887LuyKPI5IgAP5FTiq3KHMPazpzACWQOkp21tLmGVbf3OIkCFNbtaZ1rco8WyAVGIPIdiM4iXbMdL7E6MwxkPX9RsjIaegE6BXIiGTdvvTSSy7CnnWAgCIfdOU3daavUVIoWt6DZ0wwFnM84NwgS9yXCioGOGtYxyjfnC6FKoeVdxoWmTUuNqvtY1ZdPsEePG8qVSTLFsrUqUHUNfpvNNMDOwUySrYbzCtoSojSwNh74IEH3F7BrBNU7ciHs4F1iQoRHAtnJdVmcG6wDvz5z392pQRxcJTz3kMdkfKqp9IJePzxx51DY/fdd3cDLd/116i7j7JBZDRG3HJW2HHqIIx0JIcG1FYNtMamtk6N2qr81ymkZA5psjiteGXL/vvvH1e4Y9JGCGTSxqHBokB/SjRJY5hGUI/WHEXB8DWM8WaH6+kWC4zlOM/SVWxRUsp57Djq55p9NM6sYWHL731XMFt91xYHR4nxkZ3pZj6gMFOfMtmeGii1iYwzROERjZlNNB4ODZx4HW0eEqJcYK1ifCOfsH6ExxqGMqIjMWzhSMexkUuJmHCpu3hlB3fddVe3Gedhhx3msjfY5E91cEVHgDX3uOOOs6efftruvfdet2daPkEm9Bs1jxkzJq3NdksFsiZGDAJtyjFDPVuqY2QkYJRpMfa3ELOaWL+CZLggGxFIkm0bJjL0Y2Sm/6C74IDGkOyzieLtLUHfIziJwKtwmVGuy8t9flN61oBsykFlC8FPvnxvh9EvYNqbZtPfafmZ0mYr7WjWpzzkZOahTPY5xODIK5kDhKz+eEFYlKKiv2ayJ0e433rHqhAi/+DYRo/HSc7PVNjwth7GK7YrgmtZi3CEUhkkW5jn/X597JUT3WeIY++33352wgknOBkJx8Yuu+yS8z2K9FBuTJFgMT3zzDOdAZmyU//+978LokjjOSRygbTWQghNTBik9DJB5ALljhBoO2JkdO+aUdat6vv7qq0aZH26ZO90SFRaAIWNZ12IKDkUQaKI+BdFmWiqVP0JYxUTPM8WI1Y8IZ+MIdKtibgqJuxVQ5ulA8Y3jGrlrLA7Pn2qJYrKM+9rs2/alyUoBT4rJ11oaxxdCCXJFI5Exk5SuhEyMgVHHP1UDg0hCsNTTz3lomapyY/A7/c5wvgUni9wnuMQp9SD3/crUzBq4dBAicGhmmjNwtiLrMR8s/HGGzslRYhKhlI89GWCRujb+XZoADIU0fYYuQslH7F/B0bAXMBozLWWa8ndXKiK1Vr3qg0sZj77vsa6V61vVbH87YuILMUczbPG2VyINtxpp52c05mAI7I2MBgng/WBbFzmavoIzhafxeFhvkcHQW8ptm6JHEngTTprFMFfmWR1lIT5X33v0ICgyWzyM2aNqe+xGGA/YZynC1kW6MzJIPAtEdlkuyHH4Hwp52oZQlQyrB04Kph/We9xNKBDIEMw1wLrBOOPtQEHJTbMbGEOYS7BoRp1aITnpptvvtkuuOACZ/NlX7NsdRqRGaUP6e0EEDVCbVtKHyDMEGlSqMGN4aAQzgwMBURTcg9EMmy/fUspJYQKMi6IwCTCMp26vUwoGOKz2SCuEojFqqxf13UtCNZyG89WxfI3zJicx48f78pAFbomuFdaSafD45wO9A36Ci+EuX322acs0v6JAsOIRqRX2NiOYhEWNlGUcMrgLCprWKwXfxd902zhdKtUmBPIEErmmKL8FFk/8eaOTIUG+ih9EyFHCFEYcESGM6hYu3Ai+nroKAa852umE/UaNValiy8xkY4MhNHrySefdOV5cIJccskldvTRR1dGBK0Qy0Bxv+aaa+y3v/2tnX766U6BLoQRGoMtZYEImMo3yF3oRhisCW7AkO7B8ciLzyCvpSoJShAOgSkYtjvqWK6p6m89Y9sh9Tg1Pp/3SdshG5HpX8jN3b3cjSGZn9MpCeJL5NIPkefpExiXygGCwNDNws4+ZFrWtjCse6yHhWzbvLB4pssAcnpF2LGxZK5ZTeH2bkkXsnUIrmS+SAe/QX0yeC7MQ/EiuZln0THSdU7Qr9GdOaZq6wtRGLBBRrNGCbog+w/HPM5ybKN+ncAWlMq5mc7eYulwxBFHuECtH//4x+5a2G6gmBmEnZEyX1Urn0cffdSVOdh7773tsssuy9tGdUTCE2VMxArHJF2X37fbDkHX8irgUhufCWLs2LHOAOgFaCaT//3vfy7Vi4kDQRijMYt4oohqPk9UdWco9xCLVTuRMF8gUL388ssuGo/MiWLhvdzpwOcoIUTfINormUODvoWhulhwXeHyRihE1PFFeSMKx0dQYeAqexiDNd3NGsORSjGzrsXrF/mGOS3ZvMC8g7DCM0KhiUb2MTcx/6BEhkuk0X+ZG3mFxxLny/d8KYRIDUo+BkrqYvuyDsCawRqSjiOc7A9fwpLsP9YSotRTRfyGwfjLpn4oPIcccog9++yzdvXVV+dU/kqIYoFh95hjjrHnn3/eHnroIdthhx3yclzWTBwJZEcSAUmwCsbYfGdo+E2kKXPEOY499tg2siZ/Q7/xhkGCGjBK+H0ZoqAPkaXC3NJRHRqelvvLb/Q3AT0YbYopAxP0F6/cVCJwFPBKtSeF10F86dtCQ78l8yQMxjOC+PxeDDhjWK+KdU050aV7W4eGB72jDEAHIBo7k76TzjHRHzBAomeHnSDMQewPA/wtPP/QzwjICu81ydwph4YQhcVnY0QhyxC5nvk3HOCAPTOdSiHIJtgr0RGYBwgipgx7pvvqcG7WqhNPPLG1HNVuu+2W0TFE+sipUSCIQDz77LPt0ksvtSuuuMI5NvIJwr4vkcNCijGQwYcQ5csFxdu0OR0w+OHZJPuCzW8QwH7wgx+4RRwlCsMB72MUDm/ShXCBYcFv2uYXfdoCRwaKCBFYRHzxGQzM0cgJPtvR0sXzNXFjsCWCpJhlkZjYs1EOUVIT7aeAskHtdP5FiCzVZty0I+nsXAt9mDam7mnFpAmvvI3Zp09+/3t1V7Pli7MxYjrQpqlq2IbB6ZBqU3H6ImOA+Q+jS7jEAN/lReopx2Iu8eDkiCoizF/sa1Qu2USiNCxtbrarv/jYxs/8zvp37Wq/WG0NW7NPx8wiLDeQUwiK8CBbhEvBMScjiyDfMC+TZu4NQqwdKC18BsMYCgj/+g1mM2HHHXd0xlD2O2OeYD+CbPbnEaJYoJxT2gC5m75Llna+IMoYuYgxQLASMjllHTgPcpOPjsw2uIYIa5yZHAd9gFJZBCUgbxLsgvGX93FghDf3Rd8gwArnhpcrcMCwrqNfkMGJQZ6Mcs4RlSeYG/i8dIz2IDOht/GciwnPOprNkAqeIa9EJYEwZKG/8rxL6UCg7BD9nLXJl2Qt+7JTnn6rmc362KwulLExaJ2WDcPLAGT2TPcxSQfmUWQLKlMQyOnBuYoTDfsKfyNDxOsY6LDMNdGsEfQUav0Xa8N6UZ58OH+uXfP5xzZ3aYNtMWCQ/Xzl1a0mD5tVi+Qw50YrmvBe2MaD85EARzLGWTd89jgyBLYG1hhsrJSzxOaAbIRckgnISTfccIMrSUXWxkknnWR//OMfJYcUgFiQyM0lsoZBcvDBBzsF+5577ilIuSkeG15DvJF+IWWxDUcNEIGeqXD5xRdfuIGGN5LjMxhRGPgbkwFKA8oHgz4TwwETBMIdQh1eUoydKEsIAjhI/PGJtJIA0B7S8jHM8ryLqeTQjxDkMq0nitJJVF8UFA2Ucfomz5pnn2xzaJGChTPM5n7Zsjn4wNXLKlODKGrmjmTZOEQGEmXN+KePpdu/ybDBIZVoDsJhwfFQKJhnUID9Z/mX+ROjB327IqLmRME45d3X7f5vpriYxCoqlVfF7MGtx9pqvcpDeS8HMDAiV2DQ5BUedzgRvfObcYWBKmxgpQwhwRAYeAhuSAaRkBhSw0oHmx6Tso3MgIKC0sH54pWdyMahEYZj/+lPf7J//OMfLmMDOU6IcuO2225zG4JTcorgqUIY6ZHPGPPI+6zjjC0cCgRTvfnmm86JkIlyz/glsppjYjRELsBxgn6ErsQcg6GSuYNI/EwCDTg2+gXXxzzDmu73aWDtRwZgzsB4gRydTwdQRwBDDdGkxSy7yjPj+dOHMtUBkC2Z56OlPOij9E36D85xnAm5bArb6WluNJv9qdnSxWY9Bpj1GdmSJV4mpHq+zDXonAQ/MfcgW6QjH6A38L1EJZ4ZL8y59GF0ZOYV8OY05ktfpoYgiY6eNSYS88mCefaj8c9aI47YZe7B/ZYfaeet+32Jxc4OazTjhXGDfhGW/9Ed0DF8lRvvGPZjChsSOga2Sdb+VKXeohl+yAwcn7Ka2DuRD7BXoqvEG7e56hgE/LKROOsT5aiKadPrDChTI88wYA444AC30BLtV6gySwyqaHQL58LTiHKAUwKBkcjzsPJPtAKDPp6RmsWZKCeOjVGQhR2BE4EAA3UuAxklhokERcNHWBOViaKBkuRTuvi7aAvGVybZQu3FEm+BYZKnP2VTr5Zn6jfxCy8wROChvHJM+hKKJ4qonBo50Gtwy6sM8UaLeE4N+gYKCfMLqd4YThBIUoFAQdkZvpdsPkIwol4+UZ44UcNGH18bl/lODo3OzdyGBrvvmymtvzdbYI2B2Z1TJtnv1/o+g6AzwdgkIyI8vpAXiEZEtmBu98o7/6IE4Jj0MOZ5H8Mh8gbKBt975JFHXHmO8GfjrR3RTDn27wqXBUSpSeRkz9V4wDxBBBXyG1kbOOcvuugiZXKJsgBj2sknn+ycGnfeeaftvvvuBTsX4za8d5XfhJkSqGRpIL9jiGA8+zHLuMcIEW8DTfQJZH2fmUUUM+s0Rglk21wykJkT0HU4PwEPyJg4KP2GoGSzMGcgR2e7X09HhvJlZNHnarBJF/oC+w1g8M2mJDMBd9GMB54r2ez0A28oYs3C4JzuvgsigguWSi2XlwrW63gyA2BnIEgTQyXBU9gf0unbyDcYHpOVpsXhyrEJxqIiRrhcpZeN0C/QfeTQ6Nzc/fVkayJDcNnv9I57vvnSTl9jPetdKVUh8gxBBsgPHsYI9ibGDjpDeH9M3kNv8LYkgi2QQQhq5HOs88z32AUYc3vttVdGY47v4gTHJunXCeyVich1PCProM8ceeSRzjZx9913Jy2jKDJDTo08wcAjsg+F489//rP95je/KfpihjJBWScGqM+MYDFnYWWhxqiM9zGcUuk9laRWhQ2FKBiUnMo3GBlJ3eRcCNFR4wQCCJNWR91EPBuY7H2dTxwA4fIchcrQQNHIdiNyFEwycIiw3WmnnZzgRz9E0Q1H1fiNYaPOD1FaeF4ICD4609e0zhS+j8KB0SJs5CB6ikUdhRYhAgGC86WCOcxv/JVOXUuOjcASjWKlzyH8ZFobU3Q8ljQ3JXi/8xq+cDxjqAzvS5MJjGuiIommxajEWk4pwvDmv4nw2R5h2YnxW+z1AWMxzhnK++BU+e9//1vU/Z+EiDcuKV3AOkg0eiE27E4FgQgYEH10MoYJDMzeUYCchwEivN5jdGA+wAESNkSHy87lC+YN5GOuDxmUaw3PJcgaGCwVHdkWjCsYgZnzKAVWaHBooRtkqyPTjwiAQ0/lOfuyyzjbwg4MZDz+VjElnzoBrO9kZDIv8DPzGdUZsilD7MvN8v2wjMAY5/gYPoG9stLJZgvvmZGqb2LDQFeKBq56GQbdp1SllUX5UB8qgxymwekendOpgY0pXiWPdPD6BLIIzkXGHwFUzPXZrifIDKx/mezHlws4QdEpyAhnH7SLL77Y7ScmB2juqPxUHsCzePzxx9tjjz1md911V7vNwkoJSgcDCCMikdA+7ZoFH0Mif0cBwXtYLMMBdVtJF/NGbR/ZgJCA8T68caj4HoQ3jMvFqP/vNyTPtOxUtAwbwiUGMhTweBM2/Q+hNtOauqJwcxkGE8oQYKighjZRjtlmNDDP0AdwuKKAElnBPBPNnvD7xSRTPMhYwuGZbmYP50Z5xmkbVdRRgHlla7gVHQPWnr1ffsYmLpjnoqk8N26ylW07aKh1RlAW0o1qzCc+0pZ5IBp5ibzAHBTeQ6dY8+EJJ5xgDz/8sIuMx8EhRLF57rnn7MADD7Q999zT/u///q9sjGW+7AoOF37GeOzXcIIZMDyTXZJO6bl8gg6B/On3SvNg9EbvUUBDe2gn5v5sjU2ZQGkfnGE8j1zgepEn6V+JsjESlcIVpQHHATohDkbWV3QM73zIBvQCgjLRi72jhLJk4dJkzEX0t1T7xZChmkn5azKO0MvJ1ohmoHJN2QYGio7DczOm2vFvjW/9vToWs7X6LGd3br59pzRiY+8plaOZgEpsAj7r28OcgeMT+b7YAVRkSSLb7brrrnbllVeWjWxXqcipkSMI82w0i/EXz1u5GcnwiBI9haGAyAF+njd3rs2dN6/V64ngj6OhlAo7gglRDzg2MGgoU6M9xawP6zdPKnQ0IIsJDjfVvS0PcGbxLJgTmCsoEUBUI88JoT1bo6Iv+RQ1VpJRhqMCgyUpqYk2lwcyzSBVxDRODI7l624+9dRTzjHDNSCwEN3F+zhbUEZE52Z6fZ395p3X7I05s6x3TRc7bY117MAVih8FXS6UwghEmRiMjRgBiJqKp+zh2EBO8fv0FEvWYt649tprXfbtBRdcYL/4xS86pTIqig997/LLL7fTTz/d/vnPf9rRRx9dVo+BNR1DoDciIsN/NWWKMx6gY+DIQDfye+UlK+tQDNmGvdwUNBUfnh3PrVgZQNHa5oUCozklqXIpbybyp+cTEU1wHkEMDz74oFvvfbATgXTZgvM0mpnlg6p4/sxTqQKn0umTPvPM73GJYwMjLb9zT9gwmOcoT4W+JCOluOurSfaPT963hY2NtnG/gXbh+pvYoNrOOR8xdrAlhEu2FUOOuv/++11AJPp/PAc4QY7sXYvtgzHLnFGMAGJvp2XbAmy09913X7u9okT6qPxUjkIy5QmInkLxyCWqPZ8gIGAAIAqCBRbD3fjx4613N7OR3b+xGV++b2ussIINWWMnmzhlnvOcMoBLCVENGFJIG1eWRnx4TghKGIKZdIk2i1dLNB9QcghhrdDIOFQ+MG+wiLOoAws/SgAORhxcOHCzdWrwnH1f5Tz0YYyYnIMsDgQNzoFikkiQoP/Hq9UdBgWGc+HE8MZZhCc/pyA0MIY4D3X+VfpMDOnW3W7ffLui1RMvZ/wGecWG8YnTknGbCF8CjzGLE4Toy3T24ckV+sQxxxzj5CjkPYxkRMuXi7wnOiasT2QJPfTQQ25/KAzy5QIZtgQ9YCAkEAG5/euvvrJhXWdbv9kTrXnuAttxzOY2t/sI++qbaa4kbqq1u9CZAcgxnX1+TxXIRPlfdEfv3MhkI/hyBdkSma9YexKKxPAcyNL26zglKeljOJwI2suFsN6AgRJHKg5Mjo0thPX7vffeyyk7CIcGWXM4aNE1/Hv0MZ+pgXOQ86LXMJ7k1BA/XmFl95KO0TI2i+nQANZ9ssFY4xJl9FHGymeMETyFfZeMjmI4w3GIkimCvIdjl/2Yy0neqyRUyD5LrrvuOlfS5KyzznJRfKVWcJksMTrivKB8DAssSgRRyxgPt9piUxsz5DubM2uaNTcHNn3GNPv41btszVWHu8HDRr2lhCgN7auQeuJj4iUCHU8uxhXKdfgSAPmESPdilfpAeQ9vDCVSEwTNtmDpFzZzyVs2p+EDa2xenHOzkZ4d3q/FRxwxLikjka8SYTgbEBpwpIaNHSg71HROBEbMaP3aKCgyGDrJ/MDpR+RVWIDCscK+Pii4fI45UwiQwavF6ICCXoq2x5EeLhWTCOYj5g5kLgxwxQKFh2hPymfsuOOObTY6FCKf0LeotUypBNbEclBwMQxyPazfGCZ8RD/RjcijW4zuayv1mGmffTnVyScfvP2K2Yy3nGEgXJaqVHB+DI2pZIjOrF8wt/JcMawgS6FPEoWezrycTeZusdYWnDQiM5qC+VbX9J4tbnzbGpq/yksf4Jn7eYDgKewUGA0JNspXH8NwiVxAX2azb5wOjHn0DM5Bv44HTpZU1+A3t0enIOCLQAz0pnAGGiW+0THQZ9BZhPB0dh3DZ2SVAuwZ6drKcEZus802TtYp1tqBPoMt+Q9/+IPbz/j6668vynk7GnJqZAiL7y9/+Us744wzXJ1lPGulnKj8prsMPhZlFlT+xSiNokGkMpEJVUvnmjXV28orDLItNxplm28wyjZYe6R1t/gLfLFBSEDYIIKal4gfzcrEjFCIgEa0CIIVAhzRIfkChZWI3WKUCiAVEWO5zw4Q6TGn4T2b3/iZLWmeaYubvrUZS161xub6nJVajJrxDCw4AzA65kOoQYlBcWbMExVORhDplyzqzF/MXR6UDK9o4PxK5TymHzFH48jFCMN5cHBwXzhSwhBBhZCTb4VdiEqFsVOo7L9UYAjAUZ8uOF8wVmBoDYNBtSkojMEMI8yLL77YavhL5oQVIhtwnNG3KJXwwgsvlLQUAWsjWRgYt8mm9kEPlJFhfSXSGoeL23Nr/pfub9tvuoZttt4qtvn6q9oKverLwpCDnEG2BvOb9Iv4IO+TKYdBB8MT+gYOKd5Hv8wnzPPp7o2Wq7xJBoDPDhDpOzQWN71mjcE0a7KZtqR5ojU0f5Zz85FNEXUqMMcwv+Srxj6yPnYPSlTSj1mziboGbCHIC2GZ3//M91KVXsPegt7iS9wSlMU4YW756quv2n0efUaBU0K0QAYTcn4pQA5hPUBuSQdsCTg2kMeizpClzY3WHDQX5BqxL5OdS8nRE0880cksIn1kScwADL3UPUOgR5ktxebGGJxZfH3pFAwQbH7lIwYQSqOb4Dhi1a2DplttqLxLrDy6AG1K9CXGegzdGCzyYUTt6ODgwLGBocXvgZGLJxwDM1H5cftQAUDwRMikNECxUxJhWv0c+2Del9YYNNlKPYfY6r2WLwslPBmNzXVW1xx2YgUWWKMtbvrG+lRlryhi5I9GUyLws5EVgghOARTeXOD4zFse6usylwEZZsxvKA7eeEJUNIs6zyS6EV88UDZQIuhXnIdjcw/Mi8yPGDTISqO/oYgg5DDfoGz5qLFyf/5CFAIiHEtZbgTDqDcWhDPGkkEZKuYtxjFKyHdLvrSpdR+7GbG2qqet3HMDq63Obc6KwvluueUWu/jii10kKBFVBx10UF7PITond9xxh9s3449//KP99re/LfpaxJrJOok8zs+svcjiZDVikCBSGUdG3BKRVS26RLfakFO0qrTZGWE5A+O8c76YOUdNMYzqHQEMs8hNODbIcM1FTqdPEYSHPMZxCw3yJvseIONRvrDY46mxuclenf25TaufZ71rutmWA0ZZ7y7lX0ufzIx27wVfWtdgVYvFstcv119/fZfZFXYyMaegWyC352NM8oz9c0b2p0weY5/5C/kCHZcX/Y/5jT33mBf4bCqDK/oBG4OjZxMEhn7B2OD7/IzuzLm5D47H+cjWQKfBnoFeIv1CdFbQuUtZ6h5b2UsvveTk9nTGITZfsjsJGqZs1eLGOnttzls2b+l8i1nMRvdezVbvtWre1xWydHGmsF/zLrvsYnfffXdJ9yOrJMrDol0BsCDusccebkCyuVWiumyFgPRJFku/lwKODIRDBhKLMkICDpatt9468eCq7W9WO9BsySxnBDWLmVXXmvVMbSgsNDhpiEbDGO8js7WpW/rwzLfddlsnqBHxguCUqcMN7zXRU0S+EnlXLMEL4zLecCJpiNQp5riaVj/b7v+GCDRMYGZfLJpmixuX2Ab9ylvZDawpzruxBO9nBsZBMtAwYvgNtQ888ED3Pkotc0wucDzmM38uMioo40fNcAQN5rYbb7zRGVi9UyITBxvKBQqzd+yF5xFfVoesEKJOGSM4hIE5xyskEDaAVCRzpppN+7TFqLTCOmY9cqtn7lPzS11CJBtenTXFHp36iS1pbrL1+g6xA1ZYx7ouM8CJtutwLht15gM2CmcNY/ylsxYQeYnCgUNj/tLv7Nu6ia1/W9K82L5Y9Kat0Ru5KL9JycwTp5xyijPy4dBAPvz9738vg4XICubW8847zy688EK76667bPfddy+6sQEHIdfhy0HyM+OKNZr3WCuTbpw5YC2zRd9G3lvXygFKKGEoAOkXmUPAB0ZgZCS/6Xumm6iyviB70Y+KFcDEPO3XCIzROOSKBf3s3m/esC8WfbdM447ZJwun2c9X2s561BRnA9rsQUaPZjDze25ZzcjlOJjQVXfddVf3HroGdhXexwmAEyoXmLN8Vjd7aLDnD8f1Rk10AoKlkHWY89A/09U7KWnLdXoDYzirFdsM+ixODhx3/IzO4T/DNSAneCp679CmRrOv3jOrm2/Wd7DZsDUYbDkd0gemVBoLli6x/379rk1eNMd6d6m1vYevbav3rvy9iPINY7JYG28ngv7FekBQOmtYKgjyIsuTICvm89dmv2nzG1uyNrAaTVzwqfWo7m4r9Fg+79eK4xS72CGHHOJsINhlSpXlUknEAtXeSAlR8Pvuu68dccQR9ve//70ohh0iF1hwmQjw8vOYiJJmUGKMw+CWseG5eanZnAlmDXPManqZ9VvXrCa/UYzZgBBAGyNkEK2BIKDNtbIHJTS6yTfCZNRgRZ/CeEwkC38niiabRaeheYl9tnCCzV86x2piXWxkz9E2qHZ4xkZTjOaMLZ8GX+gF8LGpb9jkxdPbiOldYjV21Cq7WDlDeZXpS162poByU99f/cCuG1ttdWbefJRMFH7GGwY65hsipTEuRhVPlFKeSa7lMMjIYIwj9FNeA2cG0aFcC9FPzH3Mc5yLfV3S2a+I/sPcgbMCpQjnHM69ePMIG44zt3qHRjz4OwpMRfLtJ2b/u2eZkhGYVXcx2+4Isz7ZKYsYMcgEpC19lo1fh8qdd+dOtWu+eL31d1pkg+WG289XKa3xvtxgLWD+bedApDQDUZvNU5cFQow0qypshC1OT/aKYj5Kx7FIpCfRVINWr7X5Nr2d4WWN3ttYbXXhsj4JKtlzzz1t++23L4v91URlwZp71FFHuYxIyg4kW5fyOd5Z58lg4Ge/X5WXv5jbs5K/Fn5jNvsjM0oz9FnJbLlRORu78gEyLus+98TahXwpsu+vGIvDGbfIbETfRzeDR7fDkEtAHn0qW9nxi4Vf2YR5H1tjc6MN7jbANu2/nnUjKC9DvYhSqj4rt9BZiTPq59v1k19o9/5Og9e2TfonL3NUapY2f2v1zR+E3olZtfW1HjWpDYHRtZzIaGRH1kUcDL6qRDRAinmIz+JkyAXGOUZL9sDCAUGJKF9CDeMk8x4GQz7HnMff04E+jlMPBxn6C/YY9JN4dhj6POdMFF3NvIsjpSL398GhMf4Os7nfmhEswly/4hiz9bLTm+kj6JbhdsTBwRjNtTJAoWkOArvkkxdsWv0Ca7bA6Rc4L3+z+rY2vHvxK0+UM6wZjLuoA3Fpc53NXPKpLQ0WW21VHxtYO8qqY4UtgUupOMYgjo1UdlTmCZyUI1YaYe/VfNzmb3xz+e7DbaN+6xfsWhkfp512mv373/+2++67L+f5saMjp0YKKDNw3HHH2SWXXGLHHHNMQR8GizoDn0WfUkJExuDlZ+HDq9+RlWUEDgwYFR29UKYgiCFUhhUKsjLIzkCgQ9nI1lHn+uy88baoaUEbY9JafTaxvl0GZHU8ogbZJwQlqZCZG+O+fdW+riNz6XuqLGbHrLJbwoVuat1c+7Z+rvWsrrXVew+xqjxHAKdLY/Mim9XwrjUGC1vUjS5rWM+a5TNqZ4xx3nlABADOBsYg8wyCP22A8B5W/jC8+A2+s83mIbuMzAzGOsIs0VM4OBAeuCacb/FKqHHNKKUoRMD5cbwQ6UVEA/0Ypwjv81miseLVUqbcGYajZNfPPFyKcgV54bHLzOoYj8vgHoaNNttsv7S+jtKGkbjlqzE3BinZFcZn0hSjhEQuXPfFG/bO3G/bmLl5ov/aYE+rLtHYLUcwalJ7nHHIWoDy4RTypi/Mmj5p++GaMWZVbftDvmH8ElnLNaSqc81nMWLMa55qg0Z3a+fUWLvPDlZTVVgHOQrS3nvv7aJA77333pyjTUXngOhdAqYwFJMBHp1n8w3GZdY25m9kQh8chbGPc1fkepcGtC9lZsaOHass8DyDHEBQGtnivv8QCEE/o+QOsmXU2ZEJ39bNsBdnfr93EUbDAV2Xsx0Hb55Vf8XR4ks4E8hVKL6um2O3fNmyl0P42rcduLptOTC+ntvQ3GifLZhuDUGjrdhjgPXvWrzM9eia2hBMsobmSTxhq7Z+1q16PauKdc1IpkAGZw1Hlmbu+fDDD91+F37vLHQPnoF/jgQ2Yf9APs/F3vHcc885fdfvL0rAAbqOD8xKVAaKPkFgFA4MdBAMikR202e8buLLYlMql+slACwMpe3QmZJlI/k9MMtdfo7L1++bvfNw+/e3+7lZ74Fp6X9eh6Of0c48j2h1DmwAa6+9dlmvSTgzLvz4uXY2hLFDVrNdh5auzFI5QqCSz8RBp8SOEMQa7cvFr1hTsLRVbq+t6m0rdN8s79nVUbAzsUYRyJVqH0HmsqefedqaN6ixLl27tJnPV+qxgq233NpWaK6++mpXkpR/Dz300IKfr1KpvFyvIsFke84559ill15q999/v9uNvtCwgGJMoh4jRkMEwXKe0LOB+8MLD37Da4QDJjs5NPLbf2c1zLS6hsU2ZeoUG7vt2HZ/J2omV6cBWRqLmloMoOGJfnbD9KycGvR3hGAWPOo6F9KpMbLHkDZODa57hR6DEo65N2ZPtsenv9/6O0rHwStuVhLjaE1VTxvSbUuXteGuPIN5AoEdIyACI6mVOL3oD8w7KAAoGTgHeA9DJ8qFF9CJrkJoR0HBmZANCBCMe8Y7EZMcD+WH46GEIPz4jSpRDnB2kE3CvxhecIL4+yUCkygKjomTjmN5OBZR3Dg7EJh9dCqKRKr28mnmRIvnskdNSViyuH20fX3IybEM5mGENV4+6pI2ok+kmot5Lih/zOc+2RNlj34Tbi8ER5xlRJeUwilfFecxc7WPTf3ENh+wog2o1b5NwHikDJyXQ+j7RLaOWWu28Tg/++xbmz1ngVVXV9maa3a3Hn1bjK/M0QQk7LjjjnnNrmR8UiqEjCmMCcmi15kv6K/LDVzHPlkwftkm4S19sn/XEQV3aAARmxhSfvrTnzqjDaniUWOHEGGYPylpS9lNovCKsYcc6zZrOYZm5vBSl4MoBBhFmc8IDEOPYj2iPjXzWyrjhUif+qbFNn/pXPtwwke2wQYbt5GpiLCmrfNR7unrumlONm8pEttS9mNmwxxraF5qtdWZ91/6PPoFfaKQDK7tbT2ru9ripoaQmz2wlXrGzxCpa2qwWya/bHOWLmo1ju43YmNbtVfxDd88y9rYKtY1RkBBkLGBEXmdChM4utAvkC9x9KPzc+xNN9201fnFc/C/Iz/Sd8jYQKbI1v7BuMdhiyxLoAY6AnolxyNKGz0A3RL5nmtizuB93zd8hgDODBww/Mv148QIw/WTIe4DBtGt/GbiyUAHQb4q9T5mWVG/qCVQKrTZumPJojZODfQC2oI2Cm/OTF9AXkv1bNFHCUDz6yKf9/0jfCx0PvQM34eKSbw7YH76ZMFMG1z7jcsK72g2vGyhrLnvF/QJHOErju5vTb0arKFhqX3w9qdWVV1l/Qb0tYFrrm49alrsR1RzYIxka29IBHMDtgQCIlmnEmVNMfbRMfbZex+buPBT+3ThF+591iSCWlfuVZysz2OPPdYFmf7oRz9yehH7rqlvtUeZGgk8yaSDM5hQTln4RO74NC4WdIwmRD34WnVMGqSLitxpDprt7blv2OyGWTb1y6nWvUcP22a17Wxot2FtFGqeAwJfLixtbrA35jwTeTdmw7qNtJV6rpFTBCOCMYtNoZR9V3Jl9kSbMHeSSx1dofsg22nIGOsWUpSWNjdZdSxm9U2N9s9Pn2xVrDy7Dl3XNupXOaUMMFg+/fTTLmIRYR7DNkI5cx4p09S5DS+UviyYF0g8jNfoe6lAkCFCCaGUcxE95eEauBYidlCGeTY8f95H+CDSKxujqS+rg1DEvaB8pNufaBOEagTwXCINi87zN5vN+SakdMTMVtvUbN2xrffF/Ms9Mf5RwLLJ1KJtaVP/XRQ6nEw8K78PFMdGwUTxwLHEfF9MQWzi/O/sss/GO+UjPHIxGHStqrZTR29jQ7tXYAmAIoCz68N3/21VVc22cGGdjd1xA6d8vPfBTGuKrd1arhClH+fn/vvvXxAFnewsIixxFsRzMKIwk9WB/NDQVGczlkyyxmCp9apZzgZ0LW5/YzyceeaZdt1117lSQlyzEFHYN4aSZWwKfv7551ee47xMYS5ifcfIxbyx1157ubZF3sWoWXEGxDJldsN39uH8ty2wZvvwzYm26Wab2Xp9N7Hq2PdxkqwJGI1y5c0579vnC79qJ3vvt/zO1iWHvbHIOsZwWsjNmylBxb4ac5ZSWqXG6Qtr9Vm+TfkadIza6hp7evoH9uacyW3us3t1FztxtZ0rynjFWoyTn5fX5Qhc5F9keOTDMNgEcOyGHY44DzAmZlLiFHkURwHZFugSyLeUMALkUbJEkP+ReWlProlz+429s5VN0VFwTuAkIXCHc6Z7HJwpyFkEQFTMfnUzp5i9ekfb99i7b+zxZrUtDgfkNWQhdD3aG9k/m7blufl24Xgc1zuraHP6FM8O3ZIAG4LkihEcEB6/l3/2sn25eO73Tlf0LvaODALbcsBIO3CFwmWDVTK00/h3nrJv535kCxcutlVHr2hDhg2wGdNm2+Jvetsaq6znxiTPlixWxhU2g3zPhfQr1irmGj9nRSHokvPT96Ys/tq+WzLLrT2r9FzJencpbjYd8xh7rvlytwrUaIucGhGI7sEThkD8yCOPJOzkInOIyCDi0te5DJdDwoFEVIfInSmLJ9vHCz5yP3/01kRbY4PRVh2rtu0Hj21VOpjEiWzIxwLxyYJ3bFbDtNbfY1Zl6/Xd0nqwb0sOIHQiILPnQqEFE5Qz2qj13EvrnTJCqSkMoOv1XcHemjulzfd4f9P+K9vYIWtVnEEl3ubbCPg+2oXx6LMbECCj+0vglCCyIVn6NMICn0PYRDBFOEHJJUqKqIt0NoXDME75mVw2kMOgjkCSTVSqL3nFulAxmWSL5pq9fIfZojktvw9e2WyzA8xqurQ6NchgISIt3wIRTivGLfDcwooaTg/SzjEiFNNJNGHuNHt82qc2ZfFc57wMj98xyw2rmP016Iso0AQH+HnbZ8n42tB5N442fuD21OA8rWtF9Rq2pHGYc/jxfMmeYmxjTGCuSFUuKhsYf2SX4SSIlinw5Z+YZ6IGk1Jx+eWX2xlnnGF33HGHM14L4cHZ9ZOf/MTtz/eLX/xCDZMnfHAUGcg4N8gq9fMhTg7mTv4mcm/n8bOfdhlxS+obbMqnX9modVe1FbuvaiN7tshIrAUYXzBW58qchvn25PSX2xj7V+qxvG02IDdjIQZTZEPWMAyjhQTHRU2sqo2+9dqsyfbotA+sMWi2QbW9bEDXbu3K4cJvV9/NumA0rhCQ+X0wSxiM96zTyJ20OwZp2oO1nayMKERQpwp05Jg4LOmTZA1j1Obc9L909s1BR0HHyCWrknNzDdxXNiDPYRMhSKRiNsr+7FWzic+3/IxjccMfmg39Xj9iroVc91+MB8Fu6C3RPuYz8dFvGM/FcgTWNS21B7/5wD5aMMPmLa2373caauF3a+xgQ7tVRuAUYxF9Nyzv8zNzJLp+vvc4aWhebFMWv2LNQdOy88Xcfhoje2xtX0/5xj1PniXzBcHllCQjmLIQhnxKUWEjiDcX0AZklqB/lEN2K/Pobrvt5jKX/vvf/5b93jPFpEJm0OKA55d0cAwERHGkSiEUmUF7Yjgl9T3sLCLtE0OXyA+LGxe1pmvzL4tFszVbfdMS6xkSmvK16K/Waz3rVtfD5i2dbV1iXW1Ej1VzdmgA47AYJWuqXDu0VRpwaEytn+d+xhD6zrwp7nM4QDy8P6i2MoSVMAgEREGFF2cWbYRrwChNRALlqaxuvtknL5i98mpLavHq27loHASNZ555xikS0ZRgrzDyd5Ra7xBBecCYnYlhAQE1135KyibnznTjb28kQXDmHktOw2Kz+d+0bM633IotG4DHo+dyZjsdY7ZgVksEVa/+bTZr5fkjuHFv+XYYIvgmcnTRRxBM/aatxaonvN5yQ93rV2+Pa5Ouwfidu7Slz1cCtBt9MV5JPsYhf2fc+f2Tko4bytZNeMns83fNqmvM1t3GbJU4DoFqFPXAYn6j8Co2Ch9ptbUtJSQoL4Cwj3EAIZu0aJSDfBsPuWeMlN5oifPEg+ObeSLRppyl4IQTTnD9+6CDDnIlTI888shSX5IoA66//no76aSTXLkpMptE/mC+w4mB85MoRu/Q8PuHKWgqP5AF11Liz6xuUZ317N0SGV3XvLiNUdNHyedKv6593P4ZH83/3JWcGtJtoK3VJ3cnhHfIewNsIYk6JT5b+J2Nm/pe6+8zlyxygVQ1sZYob+D/vWq6VZRDAwhqYAxGnRo4Fn22NbYWl8WIPvXxa2bjrmy579Gbmo1q2QcPGwH2AqK1ybCKZjJg4AT2zcMZwDjHoZLpvkS+HHYu8w7XFs4qSBfkJa6b+yu5Q4Nn0TTLrLnOrLqPWXWSwKPVNjcbsY5Z/UKznv3MurTV05E/cT5g28nXPOBJVIaI54AjC5m0mKWDyaY6cMUx9tT0T+3hqRPb7em2YOmSinBqIMMTdEa7RXUHxggBa2QWAfa6lIb0xXPMJr1sVj/frPcQs5W3MuvSNiCpa1UPW777hja9/kNrDOqtqyurva4LvuVZMvZxaJCBxXNnzsDZSZWXfJcmR6/gPMxdVAjxbYDjlCAJ5i70jHJwajA3YqPeb7/9bIcddnAVhZSF2oJynpdBWhseQIx1ZGjIoZF/fF3FaPYLwgBRG0wYInd61PRsl6pdZVXWrbq21QCWT91IBS8AAQAASURBVGcBdQVX7LG6rdt3c1ujz4bWqyY/Udg+CrnYLGlqdBka4TZkeVu++3IuutuzVp9htm7f/EeiFBLalGiMqABNeqWvW08kNOMxaKgze+0/ZjM+NVs402zaxy2/N7Y4GoiuwvlBLfnos0KgxegZ3jA3G+cE8zFzcy4gWLeJ7m5uMnv/WbPHLjd78mqzL9+N+z2ul7kKwarkkRALp5u9c5vZp0+affK42bt3tjg5EoEi3HewWe8BbRwa4eeNkbhUSq8XoIvJyB792oxffloFhaxCYM6mL+OQjIK8wljBcceYROEnWjEhbzxl9urDZt99bTZtstmTt5hN+qD958heq1nHrOsPzLruZFYzqk1/ItWfWvWcizJvnB/nAkbEfMOcxd4sOChpBw/nY54pt0yqAw44wCkbbO533nnnlWw9E6WHZ08fOOWUU5x+IYdGYcCIhbEjbMzyUafeICNyoybWxWqWZXzTrn5e617Vo035INahfDGwtp9tM2hjGztkC1un7yinc+QDDGYEvRSbzxbMaCOLoGvUNzfawNrvAykpb/LD4S0G/kqCcRZ99uj+9AmfPYG84van+PAVs1ceMPvuK7MZU8xevNvskzfdZ3guZC8gW+DcCOMzM8jM9LoMxsdMyykzT/D9XGwPXB9ZyNgxWln0ndnEB8wm3Gb22RNmS+PL6gR4AjpGSWEM171ttni8Wf07ZoteMFvy/f6EcenWy2y5oe0cGsCzoT3y7dBIB2RSsmZwbCSVgQugX4QzwaFLrKoiHBrecYFTLrpvDDBGcGQg3yNn45QMy+DtYH+Vd/5rNmuy2aJZZtM+MptwX4vuHaF7dX9bqefWtlqvnWzFHltYbdX3zgrWF8YI5bIpQcW4JziBfRrD+7TkC0pQoZ+yp48fzwTXcl4cKSW3A4QgQBRZknmSjLZc7SQdBTk1zFwZBRwalAm47bbbSrKhaWeAhZvFP95EwuSFUCJyZ0T3Fa1/1xahkom5uanZ1u67nvN+EyGNQRNhsZzB6EkEbiHSV1NRU4ULKGoIjtmgrr3tF6vtYAeM2NiOWGkr22f4hhVV6xaIokYR8Eo/aYxEP/v9FRBsmA/5e8O3n5otWfj93gxO8J1nNqdFmOHeiZhmoQ+3AwIAYzlcYsjvv5ApzMXZfC8MglobJ/V7T5t98orZ4rlmC2aavfmQ2VcfJBTMidyg3YgCw6BKWYWiGyg/e8asKRRRtmSB2ZRXszoUDiuUzlLW8MVZxDPxpQOKwRErbWgDQxuDr9lnkO0xvH3JACKSytEAjVKBskZ0XyqljjFO5gYbA8fl/Zfbv/fBK1ldF2Of2vWcz5cXw4nI7/nGbzJKNhnj0MsP9GmcteUGmVBkslx55ZX2y1/+Mue5TFQePHOe/VVXXaUyqwUG40u0tCGyDNHcGLBFfubg0b3Xd2Vmu3StsbpF9S6QaUSPlrKDyEoYhspdNmZtRMaLV86w0HStromYP1vYb8Qm9pMVNrf9l9/EjlllBxvRo3yyD9MB5wAlY7yx3m+0jRFyk002aXV6UK7SlSmNJ3Pg6AjJ/0RIRzfxRQ6PBjEgA2STrcm1EqGdLURu+1erbP7xgy2BSA0LzeZONvt4XFyDLm2FgZggEO4JHQNjPKU0i0rjNLPGiNN3yYdmoeyrdEF2xuCdTvmvQq4DXgYull1pVO+Bttew77NIusSq7YiVNrbeEacP/b4cZVX6L/oF8zZjKRH8HZmb4AHKGMfVlWZ9btaIQ8n/LTBbNNNs4Yysro1KCTgVHn/8cfc7RnyCqNo4EvMEujGZGpTgJusH+YHfsQOUG8yPt99+u9tjAxv2u+/GD9DsTHT68lNEALA57mmnnWann3562QtilQwCB20dhcWclNFwVLfIHqKYNlxuE5vVMNNW2Xw1+/Kjr2zo8GFOwGQhIEK7XPs5CyQLJUImi2YpSopUx6ps8wGr2SuzPnW/U8KLzcI37r+y9e3Sw70qFRZpPPq8cGAw7thfI1w/EyGQFNSg7vt9UtqVrlkGCmy0rBPjOSzQIuAixGUbEYewFS2XlQkYWen3GNEpvRQb/5RzEHhhrKoqZl1ee9oGDljZncdHexGxgpAXVZ6IEMGwnEsd3owhhdcC+2r6HFuyLKosmNVk3WpHpS41FMGnyvNcMv1uPsEhhlCGEkdqcaGdLANqe9gf1trBptcvdPWtB9W2dcaFswYxdtA/UI6YgzCUlcOc6UsdpPM5+i7PmHtpt+FmPON6DgZ3ronxjcLG86R0HcaMQpQfAOYnjs24xslBwATPLd8p6flKa8dxvNNOO9nPfvYzV4Ko5KUmRFFgjaX0GP2UPlDyiNwODsaHeGCQUPmp/NG/6yDbuN/WNr/XXKv+9j0bGbBvX42Ti3z5w3KF9RAjMuWQWEdKwcb9VrRXZ01yWeFkaSCJjlluhPXr2sO9KhW/TwZ2FWQp1jnaOBzhTNAIpakwHjY3Lm0fWRsx/hOERzZotBROWO/gM5R7zEaG5Du5BBtwv7zo+9xzl7mfm02bakFIT+pS85116/6B9R66qtNB0GXQL5CB422GjuyUz0ynlDQTbBqzurp6++qbmctKMps1106w4Suuk7FchVzP/SH7FXPj7ngycDHL3e40ZJRt1n9Ft7cGAVTd4pQIZs8K2oT24RoZG9i+yqGskX926ZRko+9yH8j7OLHbZDG48UQfijg8QmMiU9C30S9wNuAgRe4nowJ7Vr51R+4LeQGZjXmGvlOuAUk4XS655BJ3jTvssIM99thjrXujdkY69UbhRNCRnUFa+Iknnljqy+m0sGk1GwiLwkA0CFFqCCYYWEoRmZRKycCJgbGYFwsXyv+MGTNcpGspFntXh3neV/bFou+stqrGNum/sg0KpYZ3ZHgGGAEmfzrRtuzyta06BCM/y0SspSbm1keYdWmpjcuzQkFEyMCZQfQbAiSCOm2Ik4AoC4T9bGs+kkLMsXPZBJioboQiJ/w8cIFZ0/fp5ggrS/utYNNHtWxW6FOWERbibSDJ54mIYDxhyC1GxkPw9u3O2Td8YF/r24u2j5kNGm2Lh2/uIt9oawzvKIvxjO9+mQ//jaw5DMN8p5T7ESBAU66MtiZzA2UOZSjsaCuWQ4HMJaICvRLHtWH8ILIQysFgQzSfj4RMBxyKzK8oeK3G9OfvMZv4elulY5t9zdbaPOvron8yxhk/OE2pf8vzZHxku4lmKph/iAJlTLJWlDL7KJ2+hWODeezWW28tyGaHonxg7Tv00EPd2vPUU09lXOtd5AfmboygRQ1C6ESwPlOCFDmdtbGUEdrxQPZERvIb4DIPY0TEeMc6n6hGf6GZ07DYXpr5mS1qbLAVe/SzzQes0mpM7sj4Z8Cee31nfG6796mz6vDeBxvtbLb+9q2/Pv30085wiRxGJiiOE/RD9FgyQ3iWBGJlW34SGQ85GLkz27LjvryW0xemTTD7+tU2stXSxiarW/EHNmdpS4AW8iwBZhhM4+njyGtcV7wN1wvC0qk2+5tnbebs+TZq1eHfy949d7Rvp811ugL6EHJ5okomUTndZ2ygD2a6n2G+ob/R7lwT8inzVNgIX0wdI5xlxHnpB/Rpro+5Cp2ylA4O9ItMNlrnHnAcoRu1lpavm2v2xu3LnBjLbAe1vcw2PiTxXpBpgJMBWyFOU4IVGfvYDwnMLIRdi3ujlC/PhfFItkg5c+mll9pZZ51lDz30kCvV2xnptE4NlIx9993XebiOOuqoUl9Op4Nuh+EDRQ9vKwaQaNq46NggkFJiisUQIx2Lu9+LYfz48a5PlJsDpjOAkwKBD8EmmDfNFr0xznrZElvSpZdN7b+ezV5W0t8v9EQ18DMvBPCwUsvfEeL4N9PNgxH0fBo2AhbHQAlFmMnUeMl3mW+ccP3u42afR1JJN9nHbIW1Mzom94sh122oXkAQdj9++xUb3TjRulYtixbBwbbOfmahqD4UOxR3QLhEkPebpwKGX160Hynv/Mw9oDhF9zkqxXpA1g+GJ67Jb94ZdmzQz7JxKPj9O9JRWulvKHCJIqppX66pWJuc58Op4dsA4yoKk4uca1xqNv6h7zcKX28bs/W2jbv/SrrwjNi8DmHaZwUS7YRiS7tSAqYQiqN/vkSLlTuMO6JOMQ7cddddWt86KBhyfvzjH7t57IknnlAWcomcGQTMIFcia7JOF2PjWFE+MP4wuCHLsmbjAEfWoT8wRkuVqdGZIciCoCnKpXStqbZFLz5o3ae8Z7GqKps9bE2bOmiULWlo2bePz7Kuoxci+yGbESARLkeFTEbwBEENmYCcSd9AN3Dldhsa3HpMYEY2tgjkMmf0twazD+5alnGyzKDbra/ZWge07HWXYf9FXit00NHX7I9Q/4GNGBLaW6R2bbPa77PrfXY3TgF0dtoKuS4cnMFncHrQfl53Y/71+zWWEpxiXBNzAe3qSwt5mBOQq7NxKKB7sd7w/FOtMWQ2cJ54ziFvRCfwpVSZ4YwJ9J9MdWyCUBmfrXL4vG/MPn2upRxbz0Fmo3cy656bjQ853z8ndG+CtAigxNmBfYHskULAvcUrg1eOXHvttW4fv/vuu88FUXU2OqVTAy/WQQcdZFdffbUdcsghpb6cDkW6Hm9SNVkMZ8+bbW9+/bF98+03Nqh3P/vJzvsWN+1S5AUWMxRIIlZ8H+D5ItywEIT7BMIjwhHCKIYuFgu+Twoxgi7phXjjeV8UD54bERAofwh8CAg4JHBwIPgjhKEQhscnQgXGSxxQPnKaiKVwVCRGR69YpgN9AaEznrEdoRQBA+GZvoUQ2dhcZ3VNLbU6u1cPtpqq7nEdJLxcxDjKxocvmH39QUvUyKjNzFYak1WbcT1esKe9MFrmK3uDaC2UGZwTOGNiS+vM5n+7bBPwEQkjXnwkXLx0fP7G80FRKpQAWChoZ54h/Sid8j3cK23IfIPgSxYRSjJzEX2SfhxvrUnl2CDzDYMIZcp89hHHRqnhnLx8hF2mionfrC/Z91Dwfe3bTOG7hdz4nvZFKWM+oA39fmW0KcocEVWdPUMB5Xq33XZzfeT+++8vWXkGURhYO/fZZx+3Fj766KMZb14r8qNjIEeyVrz12Xv26bTJtnDuAtt63U1txy0zM36K0oO8x7zJmuJLo9AHCHJhfIUNkfQPXwaR9dkHABBAxdqHjsJanWmQjcgd5DeMkTwHnqMv9wnIPci7yPwYzHmOGIjJsuFfgnEABwbZ32EDY6aBHshwyCqt2dvLQC4kuAb5iPNznp6sz03TWko0VfU0qx7aLviD+0C2IqDLXRcbhX/1csuehBh0V9zarGt2MpffQ422oe96x0uuIMPSDmQN0KZroLM1zTYL6syq+phV90m6xvGKVzKc46K/ZRp4U2p43jxD9KZ0bVDMM/RPdFH0KR+M5Y/H+hN1cvA+cvGYMfF1TtqPADz0bfRbX36ZbENfRYA+S0BwNjYSxl0y/QkHDWPDj7dMYF5lPBfy2b/++utOH6bNcVKjb7MG4CilP1ZCcFOhufXWW+24446zO++80/bYYw/rTHQ6p8YDDzxgBx98sN188822//77l/pyOgxB0GBNwQcWGBuTVltVbBWrjiWuH4wBuzkW2Pvdv7O6mkYLmgP77O2PbPmafnbS3j8r6zISon2ENQslghaLrE8T9nUio5tiYdRCcEBgBL5Ltg4RvtQD9Js9ekO5KA4IaKRy8gyJasRJ5Z9pvPJFCC8I3AhX4f0yeN44MRDKEIwQ9Ph+uiVocHghICaq44owTR/h/HMXTrf6ms9s0NCWCJCYVdvg2o2ta3XfNkIar0LWMyd6hH6P4oGCwL3zoo9nM5fRXjiUaHv+pVZmOezpUGoQ7ulPCPcopCgMtH3UUM6cgvLcmpkQB5QEhHCeD+2Nssi8hNMHh0QqYzPfxYkFPHf6LNfDsRg7OG19JFE6fcCXieI6uH6fDULfJUrRKyKMORT/bKOGaBeOV6j9J/ymm9w3z4p5hRIfPDMcoETtVZpDLd/QJpQ+pb88/PDDcmx0EFib2LQRxo0bVxGRfZXC0uaptqT5YwuMOvx9rXv1ulYV6540eK3PqoPt027fuX3R5n0326Z//o0duOFutsHo0kcOi/RgLcUhgYEPYzNrPQ4L5k7WXdYVZACPr1OPQdFHQyNDsQaz5uFoZG1CHi1lxmVnBCMkjinkD5z6PDtvPI06K70MR8AI5afCxmHKKfPMkY+R1XxWaLqyNsbrRGXHvPzG9X3w/vvWJfjMVhtZZdVktJJ5UT3crNsGbRwbbcrbFggCQ5BJkQfRNRgH3Hu2JbNwBKGf+FJX5ZBNUQ4g06Nb4tigfX2Ge9QRgJ7gA6biQZvSxuHvcUxsG8jwqfRhvo/87PUb9GxfuYL3OD9OrkzKBzMPMsbc3i/L+g96B9foHVTeqZJtFhv9k8DDbMvBpYJnwdYBfo8sghcInOJ+aG/G7gYbbNDp9eV77rnHfvrTn9odd9xhP/zhD62z0KmcGhhMcWTcdtttLpJK5I/G5jctMOqOf9+dqmPrWFUscR3h17/9yG575n4bvUXLYjrp7Yk2dNURtubcfrbr9j/ocJMSC0m4HEylg7BJVC6Lc7juJ8IXG52hhHhBDIEpUa1/HBrUKmTBZkFkUeX7ZGv4xVVZG6UFAeu///2v2zuDfsxzRUBDcEnWnxGYEcxQMhE00t7PZMKEhH0mzPT612zCe29bU2OzDR7a3wYM6mvB0u42vOcWTrjhGlGAc9mPI1sQOMmY8A6/TAy5CIU++wMhrqPMGfnClxOjXXjxO32RvoOzAqWPeSmTdmPe4bu0u3e45grKBwo4iky8El88W5/lwc/h2sP0AZRknHwortwjx+PacnUKMC69IYgxxrXlM3uDOX2rrbZyxyaAgegpoql4Phg2GJedPaIK4w4ZG6xtDz74YMJa1aIyYP5AeeRfDKeFyobqjDQGc6yuKVwyEjdFd+tZvYXFYvHneObT3995kY3cYJR1qe1qTY1NNuWDz21Ez8G23/o/KE6t+iITz8FfqfjynsyT6AfhtZyMYgxxrI/Iech3idZE1nQCDNZff32nX+BoxJFM1iB/Y5wSYd/R9M1KA+Mkz5Qob4KiABkuWSkonw2KbLTRRhulPecikzH+Uzqdm2bZ0vkv2HMvfmArjhhoq606zBoaGq227zY2Z0GLkwwjuM8YKiZe1iVAgr6baXQ8hnUvb0rHaA9zBgZ/5h3WdJxv9EX0Ol/ONpOMGZ4XejPyPo6QfO2bQR/guOg/8eZ+rpc+Sj/BJuOdYNwTehJjBiefnz+Zd/+fvfMAk6wus/6p6tyTc2ZmyHnIIEHSEAURBNOqmHVNoKuu66di1pVVV3F3DZgDgqhITgNDzgw5DDCByTn1dO6q7/nd6nf69u1bVbeqbsW+h6efoaur6qZ/OOeN++aZCW7gWFYKGcBxuX9hrbF8N8fgmrkONAbrOYiywgdACSr6uv3973/XGWecoeGAYePUuPvuu3XuuefqyiuvdEpPRQgPyWSfepN3D3k9pimqj6dP8X1iyyu69dkH1LmzXdP3nq3ujk4te3qx3vuGN6t9zdacjVKVDoxwbCjF8mCXMkUTccAmyGZldeYBzwtyibjC4UEkDqKDKANEhfe7iLDhfnjTWImU4HshajSKO/XUU6P642UGWRyWDuuXdpwOCA5IFeMAEcpPtpqdEDGiSlgDOB6k2zIh3MRo0ZJ/qKtnp6ZMm6Atm7Zr+7Y2jRgxWpMajnQEC4ZhIloYP+UCcwDnRrqIHj9g5OZ6h7vhNygYLxBb1h2cZ/lGrxULJmYYuxaRyJjmX+YSgjgd4c+nh0Yu4DyYIwg4HCZh3DvWb+Y80ZB8Pz2ScHLccccdzvdjtMCQZEKk7NixWlr5kNTTLo2YkioX0VD8slCsifTYIGL42muvrRmD5HADEZsXXnihs98wxqMMjXDR2bdYPcnXBwVNgRF1xyoe8zdkss7+8pUb9PIjz2rfYw92uOnqV17X5OZxOqh+N4fLFCtbrVx7DJyZqPZqBmsike88L3gi3Mkc8Mwz9hQMigTL4PA3J/073/lO33tC5iyRvG5YFgD7HYZxjJTVft+qHTwPuJDx3iAlRt0ZcowF0xSMj2yBHzxza34N4D983h1Al+xeoace+7v22WuGmpoatPiV1Roxgq4Ze2nc5P2dsYqDBM5ZzrLZbgdFUBA4RkZ+VBUjGMgWY63AOM+6lMv4LDbg2NiXgNuky+sEleLAyxQ0U2yNYfMTLY8ODsO5QeAU6zpzmMBFArNY1ylPhcZgXhK8XhHlP/t6pcX3SBtek+obpblHSdNK08/pqquu0oc//GEnc/Wkk05SrWNYODUeeOABJyLuiiuu0MUXX1zu06k5JJMJ9Sbv8rxKJNU01cfTT9zVHZv0z9UP65VHn9PYKRM0cbep2rJ8nT4471y99NwLDnmtpI2jULD4UsMS4oYRw7k2pl/PZinRKzWOk+LheO+LATYMnBCUdEEs4Cm3Dd42Ka4PYUWz70w1kCGDGCFxdGTadBAxRFURqWXkM0JxwT0nmtrrUOS5nnfeeaGIFwhUtrlNxAjEhfHFWEOAYiyFhEOSIGntsSWaNAtDoG1jMfW2t2p8w4FOVA3CBUdIOY1MkCuuIReDZT6OkOEMjPJEHCE6GFe1FIWbb+O+QEgk2Jh2/Up9YNZmBHKhx4N3UUKQ70E0Mf5xPhHdxpqP0wNndyhIdEiJdineKvn01MmIjs3SS3+Xkon+F2JSyzhp3wukNFHgYQIjzimnnOI49//85z/XFOcZDoAXYVBlnb/rrrsqQ0TXGLr6XlN3cqmPU+MExWOpkhx+uHH1g3rhtZe1beMWzdh7tppam9W4pEPveON5zrOCV9YKWDfgDBiOMLBi2HF4d6JT6tsucZ/qRg3pB1BJwPDFXo5Rmz0DjufO1EZLUNYNPW98yk9fWFlc/s2W8UvWB5zXDGQRig/WSuulYYDTwwfSlYUKChxgGHjdff0yGfbhJ4wzG09wb8YN59PalNDMCUs1YkTzIEPxhrb9NHFKqnceQX5h8KVCgOMuyPUW6ggZrmA82D0udyPvYqBoTg3HvJzcxaPJqkCTo80L5f7wZvQKz4Lnc8899zglqbBRsZbjpGSuhhGklRTawMqoj1RMOe4Tz90irX15MH85+Fxpcmn6zvz2t7/Vpz/9aadakdfBX2uoefVE8xgapXz/+9+PHBpFAunf8eQsJZSKmjHEYzMzfm56ywQdN2F/JY9I6tmFj2npYy/o7HknadK4CVrWX9+81oDgxbADee/ualdLx7OK9WxSZ1ePpkyepNFzz1KifqwThcSmWSmGfIzKeMAhfpw7xNO7WUCSiDAnU8MNv80fUmsNnrIRVMpQccxKuRe1Du45z5YoHjcgCIXCmv8FMdxBHonkxrlBVDefswwRCKZDwuJztb7zcfUk2rRpwzbt3N6rSc3z1DkmVUqIjCLERjqnBgIaYsQ45DhWJipMccu5E0mYC2mEsAUuR0Pj87bVUqJHap0sNdZO9GkQYJSw1HB+eKYITWpqh5XiXU5wDQjtUBtKr14m/ePX0pYN0qix0psvlubu69xHxAaRTxiT3POUfcsaIXI+2Uq64ZBjbuGQYeyzf7Dmk4GFsyM0h0b3Mqnz2YHfmw+SGnNwBm5d2i++DMmUo6Nzq9QSrFZxIWC9IbqfKKoPfOADjgCJjGvVAfaM97///Y6jnmzwyKFRHDTEp6u7bzl3fNdrdZqU0aEBTp58qLoTvVqcfEWP3XSf5kyYofeceqGzH8NTyxldXQyw/1l5UAxVfR1rNKF5ubbvaFNDQ71mzjlM8VGHaUdbm7NPwqlD3VcKAE4IzhuNgY5gj3CvgxiZ0fOUQHUHiKQra0uAFX2LsoEIXwIHaslIWengOaPrvM8sjBhb5naQagjoA9Zr5gDcxjsPnD4Z9BXoHi91P6+enl4tf3292ntnarc9JjljhrmG8RTN5OfUwIiLLmYcM2b5gWOFXWrS+iTk4ljh3AJ/pq9N6tsqxRql+kkV7RgNGzxnnJ5Wog7HBvs9Y8evpGyEfk268Drp2YdSwUL7HC7Nv8iptsH9w/mONvA2I2dPIjCN+YQWz1TeC96MHQDwXNjPqSbBPOY7wsrCTIrm7E9S7K7/lRFK6jDFFFSfJ4Y6NMCaF0vm1Hjf+97nrHH0e7vzzjtrKphjWDk1WHjOPPNMXXbZZU4n+AjFQzyGx79ZyeSm/kbhcxSPZfeQHjx2rib1tOgNx++mfXffS/cuvNcxnNRSpK0bLL4suk5ZmW3PqX19t2IarebmBq1dv03rHrtWsSmnOI3UiADEuIRRqJyR5mw+GJghZKTZ+m0WGGEhSe4MjUykEzKVrW49jhSumwhsM6alQ1+yTy9vf14butYqHotrVusczW7dIxIqeYCoOMgFRNxtGA7D0AbZ4JkiUPl/P9JiZXmYJ35Gaf7OWCTTB6NEb+8kbd7Wp9lz9tHk3WYpHk+RdJyiVuIM5wbfxfcyr8xYCzHF4ErUGKWurFwBYzNTLd+gQFhDiHON4sLgS2YMzj/uEWTQ10CPI+O1W6WOVMNqxeqkOadKozI7lGsJPCf3WsL/c/9sneGZ81qlGG9yBXshThquk7FL5hFjgzHFOEZs5TS+du6Q/vQTqbsz9XvbNukv/yN97KvSuEnOd+GwYO+xtZ45x7GJ7ON+4pjwi5B1AxLtbmAIkaZuNnOX/SKUZuFEILsdGoDf68ZLdUEjtMov0HHWIjaINLvkkkv0k5/8JNq7KhyMf54VQppxnUtJxgi5gYbgrXVHqTuxVMlkl+piY9UY3z3r50bUt+j8GSfo7uVJXfJv79a2DVucPZVnV81lYDOB9Zs9Y/KkcUpuflFbt8U0d/YUdXX1aMniR6XWdo0Yt6ezllvvAvRIuYz67NFwNIzD6B5vMI0BvkeUaZCAGBrJ4vzIBHgZ+xpGbQzc/H+mfbStd6NWd7ygnmSnmuOjNLPlIDXVDa8AkrAAryAYhXFnGoDxF8YYtL6VaBjWZD/ezJiDn1idfy83JGgDxxo2JPTQ5o0TNG5Mk+bsd4rqG1O8whzY6FT2AAzcHNf6U1gQGOMZxwbHgvtYObUw1h/mDN9nvR9yAecFr4QbM6fSRrR3r5I6nhwwytZNkkYcXZIs1koAaxI6zrLFGE8EdcJh4cEAGwXPv1odozjZyG5iXnCdjFM0Mf/m2g/SwcO3S0/dN/D7S49LOKLnv835lXvFmsv8Mgcf9gDWYtNy3NtsPUvcYx67AlqI76XSxAknnBDS88Ah0e76vb3/tVQv4KyI9f8M8mnESu4Y/Nd//VdnzcMmThZ9rpld1YKadWpgiKUxykc/+lF95jOfKffp1DxYPOo0W4rNzvmz61ev0z577aWWphaHKJCexoJUk+jaJHWulWL1UvcmtbYMeHunTenPWpg+R4rX7yIebKjUaibKtZSbJsKPht04FNg86EvAxuMHSpYEdUSxaUL42Hi8x8NwxnVyzRwXzzIkgs02E3BorOta7fx/IpnQ0p2vqj7WoJmtuY/H4Q6e9WmnneaQeqLnuPcIxELHHkZmnAsQJZwaGDyJhmJM8YwhhmYotbHgB2t8hgPNHBQxx6k6FIiJQw45ZFCdXMYe1+SO9uM1xhsEFgLHuMeBV8g1YzzBuM6x83EI8VnuE4KFc4OwDWmEuOE5qcOVxp/sk15fKO3/LzUbTcXzx8ljzwYSjIB1R/1wv1lH7NmynliN7moD45R9ACHMPKE3B9eGGOA1InIh9IGbm69aInV1DPxOdCQ1X5ctdpwagHuFY8O97rqNSYxpnNjueeW3J7jLp/G8iL5l3WeP54fygwWtKzg1fF/fFtypMW53ae2i/vJTKI+Y1DpJag7B6ZLjfL/99tsdwx2i70tf+lJJjx8hN3znO99xGjDSLya0rKMIaVEXG6mWuvQ9+jKhta5JLXVNqp8wwVl/arbsCvt/5+upklOxJsViCY0bmzK8t7Q0ac/dp6fWtdZUhDH7CPspxk3LdiwlMAySwcdeQnAJRm6cDX6GaPbBoKX52A/Z89lj3DCDMzzAonxPPPFE57VM2ZBdfW16vf1JJfstU52J7VrW/rj2HHm86tByEXICWaBwC5xqjD2MxLk0XfYDz5am4WgHOBG2BOxAGGcZD3Al0544r3BGEBTlVxaWslTwH/sc3NsPjFW4OcEIbh7jHcNcH4GBXDecCG0QRmAHhmjOMx99YcEraCnWAIzBQxwtyV6pY9Fgi2zfBql7udRUm/3+GB88PzQGYHz4aVGenT0/NBpaGUNx2Fk4pQDOBLSm8Xo0uTmXGRc5Z76/8szg39EYrz67y6lhQYc4HS07izFcaOAkGojzZb5Sjgp9UXjw1DaPRyLZ/1pA4PybdoC0+rnB3zHd33lfTHz2s5911kP6+D344INDMmVqATW5G7NZ4I2aP3++vvWtb5X7dCJkgVO7sp9MQgwOOuigqvV4Z8TO5dKmhwbcto7jwuPCjTWkoq1dgICz0WAAxuhaCuHBM7nlllucTYyoGkgZRIyNnnPxlgZjU+d96ep0YiBHkPAePutXWoyIEyKsIJsYdiASbLKknDMmMoEMDS/Wd62NnBoFAFFASifrKb0taKSY77zkmSIOvM3igUVUIWospZvf3VHeXmMpWRWcS6Y+FYwfb+O/dOQJcUOJMwg+5wE55fd8jOAQQgQMDhzIICT5zW9+s/IB66I1eoM0u+cB8/CZhx/ShPrtmjN94q4Ml3p1S31dUn3m0hzVCiJ4zAFmYyvT+sCzRkhaSY5iNsQrFhjHFkXqjghn/DNWmaPsD4jmrKBRnR88c8kZS2mMSNzTdE5HgzsjyoDg4/zZB9hLiHDP1lcpI+JpxngufTWax0p7nyutekTq2ZlqFD7z2LI4BRmn1L3FSMKc/9CHPlTyc4iQHVdeeaUuv/xyZ/ymM3hFqAy4y1zAa1l/qnEPyAoMkFvukXq3DeiKeq+RCN0x2HAPP2LfMP5TqnvDfkXzXZ4Hcwhdg8EF3pSutCGOCBwfXuMaXMAd2HDYYYcN+SzvoUQcQQFwKo6L8xiDLtw0Uybnjt6Nuxwaht5klzr7tmtEffFLFNYi4DOMNYylOBrhC4WU84HbYZR3VxLgGaNNyULgB2M1PBCuTLBTOtj7QSZDLnoF/uXVRX6fQbOQ/crY5pqthFE+mspKPTNfcQqee+65eVe3MGcS8w4noHs/W/raS9q8arH22n26Ro5sVk9Pn5qaGqXEDtUquA+shayL9myy9UNkzKFB0Hs4CMpZWSNf2PrHdbs1M7Yaxhb2H9boQM7HBp85Uz/0HmbKjMtWis70rhfoCewVOKtxcuLcztZXKTPQGF0+r+WAfU+RGluk9a9Kdf2NwieWxyn47W9/23H8YCPH3lZrJVNrzqkBQcGABPH5+c9/XpvG8RoCBMM9qYKUL6pKsEBvfsx+Gag7WEezcDMOxaRxR/oaUzAA4TmHFEGYMkXIhgEMx9Q85V8Mu2zaRCOyQfg5VdiAIFecJ55xNnU2HTZKRMSNN96oCy64IOMx+Qw9FBAmEFw+T1ov5DJbfURKTpGhMei1XJs5RfAFzxPRS/q/n1gMAkiin8GVuY+Bmu/GMItRjzFEBBJrA+s3z573WQkynAYYVbM5uhA5uURkQkYRzZA2josAguAyB3C4QcC8/WIMnDsihffwWQQMxArBg4AJA8wD5hJzAzHFPTjk0MO19bWH9eSLy1VfF9eO9k698ciDpLrqixYKCtaUfO4pjiscIOkiQasJFo3o5jdEmmKcydqQfvZe0tRZ0rpVqewEIonGjJf2CphO3Z8+jrDOlMLMvGbueMsaMCeZG4h75jIRQzTKtsi4nFA3QaqfLvWudhnypqdezwUjJqccGxUABNg///lPpxccYjJfh2iE4oBnc+mll+rmm292nlWEygaGOmuOCXf11vKvGXQs63douDRGX0Kqc/HgurFS0+y0/AfeBZfJ19gaFJaRy7qP0dnN09DwfoAPssfxrxmkLXuDUkbsNdkiTwnOQjtxfexf8D2+k4CdbPrCDzk3jI0w9B7GYk5pSkqiMD/zKROKLoWTeHUi4wyewdjAgYZdCMcHDgErHQRnx/kBL4Rfcz6U98S4n4mT4CCFRwaNLmecMs7JWCeinPmGnmdM4tSxPmV+keVcB4FSzA34P9fBPCXwi7EbRqAj38FayTE4R+bZnNkzddD+c/XqklVq7+hSe3u39t5zuqbOLn2UeakAb2V9yDWgjXGDxmVdqUanhhfMBZzM7vHNPArk1DjyVOmGXw8O2j1qfk7HZ21nTqTLfOG8/Cp48Dq6hGwr/o7GKMR2IbE3UVXE7EvcjxwzPSmJvefxqZ8yIxaLObbx888/39EWt912W9HtiaVELBlGZ6YKAYv9hRde6ExE0virtYb2cAJEBsFR886nRK+08tqhrzdPk1pnphwbTTT5zZwqh3EVg6a3WV4xQMkr4I4ASZeJwTkhECCBbOh4gtlcOE9eZ2OCiKWLvi8Uy3a+pqU7UyTVcPCYwzWhqbCU5gga9Ox5jvlEphKVB8EwRwRjC+JImSdzgvmV8MCxxXsgUvyd78CYi6EUAzXnYkIGAoTzhAwG5gbfm2v0Et+NwRyjK8eD6PMaaxQiCKIHGMesWVwD+w5igLHuJoCcN4b0MBvcExlM5AwRbk6US1+PtORmqYNeRtJLy9Zp8sHnaPyc/Mp0VAMKybZAsCHWyHir9rmIAzBv50xnh3TvjdKG1dL4ydIb3ySNCNqDIgXmA3MlnajHcIBxAAefF8zjBQsWOPMaRwxiiczavACFxamR2CnFR6ScGjXAJyht9N73vtfJ3MhmdItQGrAvEOH2xz/+UW95y1ui217hQAsSzYzhsObR9pzUDgf2SPpRGHN6pHiT1DhjSCa4Gxh2CU6C4xdaDigIMOq6e2iwD2CE8ovYp+cQ5go4KNqez1JiGu6FtsDoe/LJJxdFS/YmuvTqzgfVl+zZdX9b4mM0d8RRig2T3gLFBjya9ZUsxVzL0KA30RQ4C+DccAoCkpj/8GTGTTq+hDbhM+bcsF5sfIbX+JyNKfQ34xMeaVmyuY43+Ctahc9b3w10CpqG3jLWUDzVL7DXMa5T4hDt5HZ4cA4EhFAWOixwXlRoOOmkkwaCTbtX7ipB5fQofGKt3nDKh2u2pwbXSFZCvhoDXlzt/ZoYd1xHQfvmkuf7G4Unpf0Ol/bJzanA3OA5ZLqXzBdK2vqBz+LIZCyToUdJ7XxLUSWdPhope5g0WTFVv225vb3duSesPddee23OfT8rFfW11rQPsc/GGDk0Kh8QDjMO1jwoNVU/UurdOVh0NE2QRgaPOobsM9YhcMWsh8eCh1HK7cDI1BgWUYG3FwcGpA3yyPkhOMi+gCjiOWeDKQZmt+6u+li9NnStc6KqZrTsFjk0QgZjARKRj1PDIlgYA4hSCDxlIYieSFfiBuMzYgVYQz++A7JpEVREaFF2ABGD2CDiC6cGTrZ80rHZ2DkGGWOcJ8LZyK2RK2s+DumCJHEO1pzQDc4nXRmFXIEDhyhKi0IZOOEGaY9zpLZVUl+3pk5p1LqtHYoKIviDNSpdX6BqAWKWsVdQtklzi3T6RQWdB6IXI1S69GXmd7poN+YZxgueBeK9oNga9qSG2qsNS2Yj+/x5553nGC9qtbFftQBuQ2TbD37wg8ihUUXPzLI0ah71rMPedbROapohxYMFQLGncL+IHMfAW0xthtHM3XMJ4ARPZ8RCO1BOBN2IruCzcCJ4KYEtGOLgjO7yjGGhPt6k3UccrXWdrziNwlvqRmty056RQyPMe1xf7wQS4YzI1fhoJY2x/cD74UfwEhxz8As/8B7WBzIerKQZx3Y38YafoAcse4PMPDQ44w4Nk8/8wGFiWRuMWRwWFvhhwQuc0/XXX6+3ve1tToY6PMCbjQEPzLfklBfwLzQ6x0L7DOJ0jTOlujFS31bFYo2qG7m0Zh0aIIw1j2dcrUZixgLzomDHzO4HpH7yRBDHZqZnRcAVdgLWgIsuKkzrpJwYg/eqakdra6tuuOEGZ83Bdn7FFVfUhC22ZpwaP/rRj/S3v/3NMTYVKxo8QrgLJwZDDBvDBhOPl9YvTDXxsyyN0bl7wolGgYBg8PXrTVGoMwNjFPBmZLDgQfiI5oB0kTbO5s0PZNR+vHVuIZoIkGI5NOzcaAoeNQYvLgpp5IVwQAwEMc7RP4JnioC4+uqrHQGCcIXYE8XEOITUkwVhGzHONMYv78MJQjkoHBJBm0sCHDZkQwC+i7HtFQ5EmGOstfmBaGItY++xMc8c5SeXY6cD107ECWLDL020u7dPLyzZ7MxL7kUmYoLDB+HCd3Ku/JC1kK28WyWAc2XN47kUAsYQQpXnQ6p5tRA5DDYYdBgDRTVwd3dJ994lbdkszd1DOvzoQZkPZEMxB7l3GAHSOTXYHzKl4SM4mOdEVFHWrVpFYDHxsY99zHHQnn322c76Uoro6QhDQbQ/z+AjH/mIPvrRj0a3qArAPsfeXS3re8HAedGyh9TxWv8LddKYYwI7NLxlVMh8yFbiM9/nwt6AVmAPcQNDMXscBmaCqsgcwfBrwRze8p/W5Jz3oC+KWVayMd6qWa1De8JFCA/wU++YyAVwcYyZ2bgEx6FKxKGHHupwa3i+OSlwXPC7OxsDwJMZu/Cd3Xff3dEXIFcnGmOZYBAMxzjnTjjhhEF/Z9yjMd7xjnfsqnDAdeH84Hw4B3Q/Yz4s+4lVWUjXN3HV2u1auXJ1v+MxFWiWiaNzfQCdwfeiq6phHWZcEKTmLZmaCxgb6EGeEc8tDA1YypK2/MvzKmqJ3uWvSS88hRddOuJ4acyAExNHIk68dM5IA2MsW5kv7AfXXHONszeE5QCsJYwfP94po0pJTtbNz3zmM6p2VMdsC5Cmf9lll+muu+4aEvkRoTJBxDZRzNWw0YWGxrHS9HOknq1SrF5qGJN3mQwIFsalXIHhEyKVzjgNCcFDn+m5UL8Woy6eXlJ3MbJZjXuMzRhhrEwPhsNCGkxHqB1AVIISCwzXbLSMR8YaAoNsBcoLEEXImIP0WIk03geZRhBZDw7+haT5lb9JB+YFBAhii7j2i1ZBhGCIXbhw4S5HHcdFqAOuETHO2A8DEGSEVrq6lzgYOT51phE6fnVGOR/EP/cFkWSiDwKLExOhhCOoEmvBukUSQqHQOvYYUxg7PF/KCRi4F7yejUyXAjxD6sBadB5iljW06NH63d3S1/9DWvYak4ETkc69QHrn+5w/s95b9gX3L52R3Rxm2YDTkYhdMhEQg8XMPqxWfPe733X2VDI2MHbUUv3baurTRxbgd77znXKfToSAPJfAn2FVtg2OPWpeyrGR7JLqRknx/AxTcPt81xmCOjBM+XF++Al7eLryj+zN9KyBs8FVMPDyfe4SVWgLgmP4Lgym7ImFGMIjVA7gqYWUVUYHBAmO4D1kg2DUgzfD9zHiMv7ghOgKC461YCHODee2BTMxRgmOwQCeiwEYTYFOxpHgbcoM4HxoDMrAwbX44fv5MT1EgAvcKaysAr7vuOOOS/seq7gA0gUVwVG4Lm9wI/unlaB2l/KqFMBT0UaMO84xiFMsE/gszmD0KjYa1im7Zv7lHlRCAI85MABji/NlbBe93+CiR6Tf/iS1X3H8O2+QPvctacJkZ2wxjphbjMlMPW14X7ZsEvQ8ew1ahfdTGaLYZdurDXPmzHEyNuhvxf/Ta6OaUfVODRoJU3f4T3/6k1PTPELloCfRq6e2vqB1XRvVEGvQQWP21vSWKc4CAyrBeFSWMlRNhadHQ7pyzdKAmLDBZnNa+IHNhsgtALkhDRyCBfmCCJCai9BgQ8RomkuD5gjVgUBNiDOA+Y6B2lLLzfjuR/Cspq05GRCtOBIYYzgryNBAeLvnABFTCBADETIIY5wCiGwyHYICQz9ix08sM96JZORaIEo4BIhscTcgRQDRgAsBTs3bQno4cJx0ZayINuP+2XPhmv0ig/g798tbOszJcOo/N66ZH5wxhUQqhQ2eAc8Po3dY4H741e1mXSUCj7FUjDIWQYHAYIwzvpkHrL8lMdzcd5e09NXU/5tz7Ia/S/PPkiZNccYG+zfkN1N2TyaHhxusBcwVjI/Mb8YzdXzNoMAew0+1RLsVA6yBv/vd75yeI3BdMtcKyZiLEByMvfe85z27nkF03ysLqzo2aNGWl9SZ6Na05gk6cvwBaow3OAbBXPb7mgJlblX6zEszSMEncg3oYI/jszgwyMaAY2EIxYA7b948h3vSo4xjsC/zWmScqk0UYvT2jgm4hZ+RltdtLYfLw4vhx/fcc4+TvQG38ZbO9AvSwACOYwPDPYb8oOOeDFe0uJ/Tles33ofewbHH93Ne7mthvlx55ZWO3j7xxBPz5kg4dNIFmzEfOQcMzPZc0vHQdLYFnDYYqcmC4F7xjDjnSnFuWOngMLMT+E40lFdHcY/gufwdPVNO5wYa24Kk0M3ooaI7NMBff5NyZth86miXbvm79O6POXOQZwHvymbfYk4EaTPAc+X6GHME5KI5uG679+nWiOGEo446yukT9+53v9sJnHLbMqoNVd0onEWdm//lL39Zn/70p8t9OhE8uG/jY1rXuWFQldcTxh+plx97wUmbrJRNrdab5RJBjkPD3VQ56KYHocFAhcecaCmrOwrZwfhEBDUODsgcm3T0TGsXZAS4I5XyAVHZkFxEKo4wgIC1usgGjgPZJrLCPaZuvPFGnXXWWU5kDePR/Te+k9eJkvGOQ0q38H6/TAQIj/XlwAkCwccoks1JDvEiqgsymM7YhQin8R6iAINkpsiTdEDU4FAkksp7XRjgMYBjCMhmcAvawI4IV87Tr3F7ucBYMbFXCrDmMd4Zm+n6QpSirm3JHUx/u0q67poBh4bha9+X9t53l5ON88s0Ph5//HHHKRhEaONIYw6yf7DnME7t3rO/cCxE+3A3KDMmCSggkur73/9+uU9nWODzn/+8rrvuOqf0RzmdnBGGYkPXFt269qFdv7MzTm6aoNnbxjqGt0ICCYY7cm2WixYHuUZiw21Y7zH2oU/MKGwNYK1MMRqGwI5qKJMZobAg1UIMagQRweXhuRgw4bEYLQkQYT1wcwjKIsNR3GOKMY/xHZ7pl21NAJP1/nADnkIWq1/TYsYw/B9Ow//DcwieQT9kW6P4XusPkg7MDxohc9/yDexF73CPvNfFveA+sg5kc9gwl9FQ2YJZ+E7mPM6kSshYADwXNFapqoew3mGTwTCPgb0cDlrmCuOypE3NEwnp0ncPfX2fA6VPfCnws2BOoDGCBi4w100/Mw9xrnEc7AHY0nAYlkPnVRp+/OMf69vf/razDuMEqkZUrVODxR6BR8oMDU4iVBa6Ez365+o7Br0WU0xtL2zShce+JSKnBSKdgZJNEkOrTWsWf5DLxgXhgtxBOFjYvCSFsigQQes5ADEc7ganagOEinGCk4rny7NmzCACiJL2IxMQA0hEIVENRNxTJhBibyUFGLNEaLOWu8cRhmzIB44NEx4Y8Bl/EHj+RgSQmxDyml+0EMZ/vtudBgwYv3weQs+/GA4heoj0M888U2ESSLIJKZ8Fmc91viAYuAY/0cReyHrgrTftBeSN+x7k2Nx7DM2MDRxMiBUD9451odRznjXJrw53LTlTvKDeM+O/ZFj0mHT5Nwe/xnz/6W+kkQMOQZyHzL10osOMUkHhFh0Gxh9rA2sTIsfKHwxnMM9Z+2hW/f73v7/cp1PT+NWvfuU4NRB4JRX+EQLhsc0v6OUdy5V0hU21b9+p/XZM1RuPHVynPkJuSGeghCOyJ7pL0PD/ODOClq7kO8i6wLhJqR3mlteoRyAIRi2CquCJkUOxusDeTbAbzxqDvHtsoC/8gnvg3jgkCi0vSk88jms96KzPHePWzZGtxCfvcZc34zUb9/ARd0lM9DQ81LsfwMEJwiIQwz5nwDlC4BP3gc8zpvmhnGGY1SrIDOcarRdHrjDnhV9PWpz6GH6zBWUFDZzi3uMEItMFTUigpLcMbKl741rJwqKXeC2jM8VvzrF+l9Sg/5//Ia1ZkXJwAK77tPOkc9626y04sdHM6c4L5yIIGrjgDpxy33uOwbxEZzEWo37M0qc+9SnHSUpJ4Eqq2FDTTg0WRGrc4u286aabhnVpgmpyarz+4jLtP31vnXXA/LKdVy0AIyNj31s+halMvwHIh7thcC6bJeSPHwhXOkDS2AwxLlrN9wjVAQgkpB2ijmHanqU5vxCa1g/FO7YwLmYaF/Y+nCWQDvfWwjhhncZhhlHOG6HDZzgXb3kkzguHh7uZHoTHGokjhsnMwNGC0Z65ATGHFHMORoowjkJYyS7KBiJAIOZhj22u5frrr3eEgdu5wL/mVMkmOtJFptCjiEiqTOKf+4b4yiVFnvHCd7odWRg7EHak8eZr7Gds4Fiz8ldBI7asF1MpHSqMG5xqpTascH8glVaurWS45o+pbA3Ac7/ki9Khg+c9Qoi5xrMjUhCjgD1DxD1jDeddNuCkxMDFmGJd8HNYwfeYkzwDt/FhuAKn8Lnnnqtbb711SJPRCOEAwxgZgVZrOELlOzW6Ojq19JlX9aXzP6URDVHfmWIY2nBms+azTrv/FlRjGJ+Ds2WKPiealj2EY0XZ39U1dggsQkMQjGGZ/ezzPEczJKIzvM+V11lrswVNwT1wCjA+7Dvgzjgi6GGAhvDrzZUu0ILzxeni1tMEEHE+ODysDx3HI3sWnsPcwPHH342zwInhiNl0A5wa/ZJL0EdQoN1xCKLT3FyKa8jGX9FfcDnsB17Az7h/2fpjBnVqAMYCzwuOyz1zfy/OGe4vWi5f+x7PC2M2zzWocZZnzvNBj5UK3HfGFdy2lLqGtZjnXXKHyvo10v98R9qyKfX7fgdLH/qs1DB43lvgFM/QnR2F7Qt+RlnqIPcLzYhDg3mbLkAMXQyfvuiii4b9ftPb26uzzz7bWYfpb1Up2VQ17dT43Oc+55QhYZHNRIwilBcPbHxCqzvXOf+/egkLU53efvj5mthUYiNNDQGCiBHaGnMbLBqFf/NNu7cGfNkM1xGqF0TzIBzySXfFIQHRZJMzQzQCw00YcaohLhAV7mbUkAbIotugzutEc1mzR+rZUmbGS7Aw7kLAvOmQkEEM/XwvzgqIC4QaYyuigdf4f4yrvBfHiXfeeME5EZlbDMHhJuxW4ona0Fwb14hwsEbV1k/ES/Ig/35OCd7PNfM8qDPqV4Irneiw5oy5ElsyWhAOXkeUH3hG3H/OH3HFPSCajHOFtHIP/K7Zrg1hwnkyBhkrpTZ0cI4cn0gejOyIWZ5fsc6DcYshIMw+Ijlh0wYsS9K06dKIwSU/mLM8RzNWUDoOwwAimjloNWuDgPGDE8QcIDxr9z0lY4qxQiQwoiYycKXws5/9TF/5ylec9a9a08QrFYw5nMff+ta39NGPfrTcpxMhDTZ1bdUtax9ynBq9Pb169XHK2p6o06cdE60TeYL1F4Mu6623gTGGaIxvcJZ8QcQ3RrSgWR0RqgvwM7hsPmWQ4IDwU/Z47DroCDgG+sJ4IePz9ttv17HHHjtoDMGd4Ux8zl1nH73CmMNBjQEX7ukNxOE777vvPidAwE97wKvh3Bj5GLsci+/ifAm04PzgJswN9uJsThmCOOBH3vkVplGS80Nzcb7wMbQ99wdNxtzmfvpxbSK0MRb78Sz4Ps8HwPn8otpx7Pg1woYX5po9wv3l3gdZb3ivlRBGK8DNyQRBA1IuFZ6aTl8A9A8BcHwGbVvqYE1rHI9utnvP+C6WQdnGCLy7LL2JerqltatSjowp01PZGp4ARwuctd4ZjD/mG2OZAKig88cdDGjmbvf4XrhwoVNymnEbpEfHcMCWLVuczHiCpy6//HJVE6rOqfGb3/xG//Zv/xalhFcBehO9embbS1rbuVGvPbVYF570Fk1r8W9IFSE7WMwhaWboYuryO2SF/4d4FSIWMOZCRqNSUrULDIQYmLMZ9zMBoy5ZEBBVSAeEETIAESFbImjZJggrAsN6I1nvCASLdwwiBBjbfufNNfF3iCwbMY4VCA8RR5BdMlP4G5/Nlj6NcOccSuHYg6QhmtzRIxA67qc1S0OQuEtAELFuDaTTARIPKSHKBfIKLNrJ69RgLOBgMadGrs3ygkZmcU+JRDKnLGMHAh80KIFz5Nkg1MrVAJZ7xQ9Cg3OBbKcD6zH3EaGUyRDPM8Zh4o5cZH4hvsMsTRAWOE/mK+fKvEIAW4Qjf3M3rQ8C7hOlpyzjAIHBfePaGVfMa8pRRNm4/mni3C/SxCMjYThgfWH/wfH/k5/8JKRvjVAsrOnYqEVbX9bSV1/TnlN31yl7HKOGeJS5nw9YiwmccBt32KcxVLPXWX+pfB3L7A8YDUtaTjFCyUEmDqUkCwFagHGHviDwgf0fbgH/YiwG4edwKyK64RJoZr4LAydj2Ns7Al4Kn2Ht9/JfDM5oFZx9VAixagZmWCUSHGcGvChI6SycLHDYYkdAM5+5fq7JOBnc0pp183fuEQ4Km9PcB67Vr3+f1wlEX1Svw4JnA793ZzqgEdAzaBGcBbn06kPbsRZly7Sw0rScB8+Gc+Ja0UBBwLgiuIbPcl3lMPRzX7HlcK/4f3SgVVDwA3/D5pOtzxDfY2V07Tgcw8/5VG5YoCPzlbHLXDzttNOcc+d5Mhd5xrnsQXwf18rYY1yQfYDOZWzwWqYKCMMZr/SXuv3hD3+o973vfaoWVJVTg83y9NNPj1LCqwwsJHiFi01mu/o6lVBCzfGWmovUwmAMaSLCg40X8Q3RwygIaQt6vd2JXj21dZl29HRoYtNoHTRmN8X7P4tTg02Exb5YUSQRygvSfSGAECGEAWSOcZRPDVavYOU7aDgXlCCQacem6c6MYFxT3xLnhPeccJjgCGDM+6UU4xBgj6CRLkQO8g5pgQhh9EMcpStfAxnn3jCPMMC/6U1vymrc595xTqxvBpwQpEwHnY/cA55DOqOtNeCEhJmjx6Kz0zXkQ5hgDOa8uJ8W6cR54RwgEgnSzjrCNZhTwiKjuEdBjcgQH96bLlrcyn7haEEsFOIwdTtrqgE2pgBOCgQEP8w9xiRjh+eAc6fS11tzWCIwMQpwXcwTnBqF9jhhfCB4GZ8WKWfHw+Hz1re+NbTrqMU0cRy1NLOOghEKA+vfW97yFmfdjMraVheMSxST9yeSfepNdqou1qi6WBmiW4sM1luMZOxRrMncSzgYARTZgkHcWLZzo17asVb1sbjmjZ2lSU2pQCv4F5wMRI6N2s4Gx6gNL2AvJ3uY/y9kf4Iv2dzGSJmtf5zxL6Lf0ccc20pSwY3hYPANN+AcBAhgHIf7w2nd6wlGVfQF18L45Xt4H6+TtQDXhpP78XLMbHAZ7gU/vNecApn2I4LHMEgbOB+yD7IZsg0ci3vgV1IKsNcxJ+F0lp2AQwl7Dc6QdOsp9wnuanoC3UO0O/fBAp34bio/WD8R+24M90FLVFlDaOwe6bIncEbwvWa7yBfmvCJ7IZf1rlyw6hysq+gHuz9wdNN3/A3tWw1Zzox19Khp+wULFjjXVGiAoTk3CZxib8PewPrBvEAro18jp0bmUrd33HGHsx5UA6ompIWFnXpn3/ve96Iat1UGM5QVC4lkQi/veEYbu1Obf2vdSB04+nA11VW2oSgXsBhDnogWYdEPWhffjZ5En65+/QFt6t7hNG1PKKmV7Rt1xuR5jrGQTY/Iz1I2yopQWjCGIPOQBUQsRJAMINKUC4mGhgSyRufiI2ccQyzcx4XMsHlipICgQpQNnDfCBLKMuCCl3Pt9RBghehAdfK97LJsRH1GCqOBeWL8CNm+Oy+98D9FU7ggnNyDQrGncQ6Kc3AZpDO98FiIJOc4WCcP9gvib6PIatxFWkD0cGRBTIk74XtLEMeK474+BY+Pg4f4hZvh/zpMoTLJVEFesHxA6d48RzoF1muyRoJk8iBO+h3vrXZNwQPHD61xXvqKW+83Y4lqrxaEBuGbuO8+Y+2CRRogw1lrGZqUbohGtGAe49whkxgvnzpjCoB6GUGJM4wwlUhIRxj3hOeMIqqKYm5KD9e3qq692RN93vvMdffnLXy73KVU1vv3tbztONcZilBlUPYAPsGYU02jT0bdJ6zqfptCV8/v4xn00piF72cVqAsYwuAach309n73puW2r9I9VixRX6lk8tnmZ3jfnOI3sjTuBL6zxJW1KG6HkwOAOR4VHwm3h9MZB8wVjEuMkuiDo98C/4PnwFriy4ZBDDnECu+DU7J02ztFCOBoIiMKoz3xwZxXA5dEXGFutn583+xtNhVPDnBhwX74fBwif4diA64DT+mUtWLQ68wXnENUTbG2DP7JHoWO4D9n6ZOCURJNwvZynt9cIf6dULbrfsmNxQPBeMmjT9dDg2glsIYAMvUNkPWuH+71oO2//O76bDBxvmdF04LMYnLF7eJ0aViqPewxvDOoo8QPPzZ5PpXNyg7uJPRydewBwGjF2GFuFzLlSAacVc4axzVrBD8/WAi8LBc+T72GM43Tj/sDvzCYZaYz0IGMZmzu2dypg5JJlVS5URaYGg33+/PnOAv+HP/yh4j2OEQbAYktEZz41NoNi+c5X9HrHa65XYhpdP0bzxhavLn414vltK3T7uqfVsb1NaxYvV9vmbRo9ebzeNONw7TlttkNwMOhWy6YeIXcQjUS6tJssGDEM2kzNDcgTggWiyvgx0h4EbD1E/JCF4O0Dw98g74gSCDTGeXePDs4ZQzFE1l2KydLWIXOQagzHvB/Czd8QRogdCBPjHIO8fS9EnvOwsm6cm0Vd8TecIQgk7iFOk0z7EN+LEwFABBDy1iSRfxFMrIs4IDhHzgERyP203gQ4Gnkf5wsRw8HBNfM3si8QZdYU3c8YTRo4zxlhyXdhkDYjNQLBUs+9wOjOeXB/MMTzfLJFLvGsEIXcE+4Z18X1Wz8TzsENyHimkjncI86Z+whZR3hFKD2I4mOO2bhjLDNWGVuI5bCCFXje8DyMXkZJix15XSvAAIKx5W9/+5vOOOOMcp9OVYImkRdeeKHjLC+kZ0CE0oN9kLIvxYqu7Uv2aEX7vUqqb9DrU5uPUEvd0Lrywxn/vfgObetq1+bX12jzirUOh9hryixdsNfRzvPBOR40yjxC9c5HCyoCcDi4Qj4GVgsIIWgG7kyEdS6lFnFQwOP99kX+xt4Jb0X/uDUIugPHBmMWw7w7uMnK1ZBFDY/l83BUODo/GE6tNBC6g/sA5+a7zPAOr4Yfc13oLr4frs8PHJ1jZio/yvfDzeDIfC86gPuCPkG7WO8Bjg9Hw5mAIwRHBHqCz/ODVuI4FjTJ/YavW8YWOouseb/nAt9gz+T9ODZYgzk23JDjwRfRDt7nxb1A63Bf+B7OJ1sfUMYQn+H+cL+xBeKIsHK5aFCObXyRe8LfMvFHNA73iO/GOZQu8z1C8cC84f4zZ/gXG4IFGuJUY+yFVSKLzGbGCXOAMcscy+YUjCBnjr773e921pI777yzPD1Yas2pQQ8NmkPh3S601EGE0sGvpmMx8My2R7WtZ/OQ14+fcEZkFHFh4bKndNPT92rD62vU09GlhpYmzZ63jz54xDnarTVa3EuNDV1rta5zpfP/U5pnaFJTqjZ9seGtIQk59KZiB4E17cPhADnIxRkGmabMFFHaRCxZaqgfIMGs/RjIEcXu2qCcO5/nHCDyCCDILRFUCAyIMKQa0gzR5RgQKRweltLOD4SfJuV8F+nTGP15P9+FcRWCznu4RiNdQe8R3wmR4rOQN46HoMKZ4S2xZZFYGNYQGTg+iILi+N4yc1bblvPzKx+GYIPI4agkswShmStx5xiIO4RREAOzNXtEaCBsIKpcE/eOa7Lv4H4gwNJlheFY4f5zv6Ko6fDBPGFMMC/SzX2i+6yxuxeMK7KbKAdajLUJYYzRoJjBELWE3//+9/rMZz7jRFOxpkYIDsYZxrIf//jHes973hPduioCRkH2aLhBsdDZt0VrOh/zvBrTuIY9NLYx1d8uQgqfv/1X2rhyrdo2bXV+bx03WieeerI+MPf46BaVGN2Jbr3atlg7e9s0on6k9hy5lxrjhZWZDQKCpjDeW9ZxvvoCsJ9Zudlcm/iSzQwvhkPC+zNF8sOF4LpeTo5WgIPjeLA1xjiK9etDd3Ct5giA48Kd0B/Gdy3rgr0Zxznvh9/i7CEgAY4LH4Ynw9Fz4bx8N/eYz3KPuF+cN7Yyv6wo+DyagnNAm3Bcjok+8drXMALDFTEAe8HxuBdcF3yQ76Icaa6A6/H5IFHgPCPOFwOr9RHlHgKeG+fCPeeeoC95TunuJc+c+8A9i4JnwgVzg+wkxggc3i/gwPo5Mq/87j9OO+ZTIf0/vWBM4Hzje3HEoWtL3RS+GrFz506nRDjO4f/6r/9SJaPiy09dc801+tWvfuWkhEcOjeosO1Vsz15DjNr3LIrJQa9FG9VgPL/wMfWNTmjGfrtr7NSJqm9sUEOsTpOaco/Qj1AY1nWu0uK2Z3f9vqVno/qSfZranDlipVBY/U0DBud8M3NwNLDR5UMKMJTiHKDMDIZV1op0WR6cLw4HiDtCwQg2RBhCi+CAQEHgET8QJWCk18oiWS8a3mM1bc2RAsHB0UFGAQSfSA42cuuPASnLJxKUz3I8a6acDRaxBTg3IsG4Roiddz3jd+4/wg+nh7dnBa9xT7nuU089NS/nAMfg2Bj+EADZwPGJyOM58VyIhEJM4uiwZqOMOa8hCgcOQtCOyb3PJesngnJyKDJHmFM4jzBCeBtc8jrzIF32FlGG7pIOYYExiyBifWAOMq+jcZAd733ve531mMhJItyqoSZ0JQDjB/fsne98Z+TQqDJYBiAlUoqJuKMvvEimeX34AqPm9meWadTe0zVlnzlqHTva6dc3Z0QUMFVqoCUe2/ywOvralVRS23q2akv3Jh0z4TjVxYpr9sHo7N5/MjU7zgS4I7wwX6Mmxn2ykzkXHAmZnBpkAhNoxPvg0caVMerDaeElBBDhgEBXWLNl+DoawXQEnyc7mgwKdIc5FeC76BfTJvBg7hMOEbtXVnonV1g2SVCgKUwH4kjAycH1+NnX0C3skURocy/d5at4NtYvg8/nWxoWrkdGCP9m0yhoA86T87AyqDxXjg8n5TlZKWT0ivv74L08F9NI6NbIphg+rAIDcwoti3MR/ekOWuQ9aMp045Y1Aw3JfAsTjHfmIHOWLBDmNAEt0TjIDO4PmVk4qNB9lKOqVFR0nRk8dR/60If0u9/9rqB6eRHKA4xYREkUG7u17qG4M5TpFJEy/O0+onpqr5cCEJBD9tpfF59yvibvNt1xaDTG6/XmGUeqpS4SZ6XGyo6lgV4LGxgB3CKBDT6fyF4cAHwu3ygHdw1sCH62hEEzliMiqMEJIOMYPCEo1quAv/Nj5ancfR5wZCAuiLwCvD+RTGpHT6cOPmSes15RyoroHn4gVO7SVqUCayZ7H84IiD7OG+avH8iI4Jq4Tu89hNBbHcxCsh0QPxyHGsFBwHMlowYBwfjgHCxrj/HG90BmjeTyXBhPjEueMXt9ZMguDhCojC13nWb3Hs1zshRtP4FtzTJ5dsWIcCKbh2gqwJhGMFtz2QiZ8aMf/ciZ55deeml0qwLikksucfYL7l2E6gLrl7cHVTHQEGvVyDoz5qXWzIbYSI2sr/z60qUERqgvX/xxHXzggRoxbozzfPYdNU1vnBhp91JjU9dGtfftdBwagH/b+9qd14sNy3425FMMhM8UWraaoCm4JQgSWElGN0FCGGTJHoDrWAlX1hn0BM4AjPtwWSuz5DbWwtX5PEZZNIhTVjPRq+ZRrQ7Psh4R9lOMwJBsgNeRQc35oZsILoLvpQPOHrLEvYESaAI+h/Op0NJNcH70HE6JIMAZhKGV+4xxHIcR50mAjmXauzM/+DsgE4dj8RP19ikOeAbW2J15x3NyzxE0CIE33v4uBjQiATreQKswgKZg3hI0xxqFxifbKJ2+jjAAtDkZ4R/84AedOVapqK/kKJx3vetdjlPjvPPOK/fpRMgDCGw2Tb/SKGFiRP0oHTbuOKeUD03DxzdO0tjG9PUohwuYQ9x/vOVEomMwgrjtN3qG2nu7NLK+WfXxcOoVRsgNieTQTZSxW+zxAAG1TABL+80nU4PvCauhPOcFQQ4CjklEP4ZxyBKlmiirRPkqiAqADEGKvE20IbJEZhChDqF5vX2LvvfSHdrR26VkT5/mru7WR95U/ggEBATzFOcN10cmC/8SFYV4QFjh8MCZw7Pj/X71irkvRC9xfwuN3OYccJ5wHtSqzRTZgvjDocF54pyApHKe1lSR63CXEEN4IkDDqp0aIT0Qsoh0u//MGXNOIEZw7CEGed7sGTw3xpEZBVgzcLSFNff9BDeClfmN6MFxynmwj1V6LddyA55F43D2edLEaeIZIT3+8Y9/OJngVlc9QnWB/QL+UmywVk5s2l/NvWPVndihuliTRjfMUrzIEe/VADiBNf+FZ8yaNE0fnDhV23s7VReLa2R98csdRRiKhPy1RF+RNQYBCG5jMnwxn+ay8NtsPRGyAa6JATUXoI3QEhhdcTjAOwjyIAsBPWGBHtZ4+E1vetOgz8ON4ONcMwFTj25+Tq/uTDkMti/fqDP3PrHsPWXgdGgHONYtt9zi/D9zl2dn2Q3Wv8KanlvGixv8ftpppzlG4TDWcnQb2o7jZ3v2GFWtNyHnznstOAfDtVufsEfg8Mi3BFqE4OCZoCHMyYXTCUcVr6MHKddmpdxwNPF3AgfdgZU4ypiDxQKOUua3VWpgLmeqFBFhANjicWpgmye4rRJ5c8Vmanz1q191FqPvfve75T6VCHmCzabYDg1DS12r5ozYW7uP3HfYOzTYVKwB1vXXX++QLyKjrWEXGRpjG0dEDo0ywq9/xqSm4kb+ITDcvTQKMSSHEdnA+g55prRgLs3/MI5DfvkXEYGhFSLsjhzHEIswcoNrxRjrlGvq69Vvlz2itt5UZFAyJt274TU9s2pwQ+tyGgs4V5qDc29YR60JH+WoeB1iDynMFAmHgCICCkJZKCyd33qVpAOp3zgujCRCGhEUOJX4DrKFILT88BzNGRWheGAMID69Di6eJxGRjzzyiDPOEPIISn5Iz+bv7kwJBAjPjzFQLGBAoGayASM9oiNCdvDcfvaznznBQEEzq4YjuDfcI+5VFLFZncg3yzQfYDQb1TBDE5rQF3OHvUPDuBUO5wULFjiGKMp42L0a09ASOTTKiHEN44eUmaqL1WlcY3ErJxDQwh5kgLfmwu0NOMkKbeKLwRueArdBEwcF4xe+yvqC4ZOMbm9GK0Zz+jfAx92g3DaZpk4j8B1Ldjk0QHdjQrc8vzCvzJViAEM/Tgl0GJkocHauFa7IvecZADhjpoAS/m5OhkJg9x2DOOeQ7plxbwmEQk8QfIMjxvQFmhCdxPmYxmBtikqRFxdoOIzcBBO657/1kYS/o7spFYkuxPFnpYxwGrrB2LM5VAxgB8D2YQ5P1ifOv1LmZaXju9/9rjPnL7vsMlUiKtKpQQ22n/70p/rzn/9cMqN4hPARbSTlASIDQoBH3DYPjIkRKge7te6pac27OWXT+G9a0yzNbi1umj6buLtcA5u730a+oatd925Yroc3rVRnn38kJJ/NRSj4AWM3ZILmcrkaltgXIKv8MMa9IIIAkkv0uRsQJc59bed2dfT17OrCE6+v08yD9tGDLzylSgBRYQgmSDqiAyclgoM6vLyGIEFY4RBCSKUDwov5D6EMa03HkAQpJAvEK+oAUTgIC4zfXnDOCBD3D84bHLBpo27vv0/68Puld75N+u8fMpBDuZbhAuY4xgYreeBdExhrOA5wlHkB4eezbpx55pm7jAXFipT2lq+IsjSC4x3veIfOPfdcXXzxxXnXM69lcE/oQfLmN79Zb3/728t9OhHyBNmApShvG2Ew4CIYsADci33DHBoRKgNNdU06fNyRaq1r3RX0d9jYI9Vc11zUddWb9U2wkdc4SQbDU1vX6u71y7R0Z6qhvBcE4RHVXQjQv6wPBEmQnZ0LCJCCk2KkTedcoWcAHNibDWIZIms7B3PjibMma33bxorZk00LwsHhWGgEgknQTVbaCb2BATNT8BuGaviZ9cQrFBwbTYPjFOeEd/xwLgTbkGFJJo0bjD8yfLkG0xesT/wQyOBrKN+xQ/rmN6QL3iJ98P10pw/lOoYTcEIRSEgglLd/JOOMOcQc9KsKwd+ZawaeLc+NMm7ewMSwgH6FP7jXqcheGQysb1dddZWuuOIKx1Zfaag4pwYC+j3veY/TYd3dSDRChAjZASlhI7B6oqRiljvdtSLQs0VqXyJ1rcZKVu6zUTwW154j99exE05zfvYcdYDzWjGBMZq0SyPhEFZv+uCL2zfoP55ZoF8tfUr/99oT+spzd2t7z+A6p0RW4FQotJ41x851jefcOT7GVMY6kVTpDJ5ElfM3dyQIkT0YcZ995An19Qw2yK5/7XXtu1+J9hzGYN8GqXellPDPooBkISwsap5ma+b8QazxN7Iishl8IWxh1x5HMEI+EUP0wyBzw8Qa542w4Hev6KD8Ee/3igueC98BOR4kEhc9KX3lSxKCae0a6YZ/St/7dqjXUuuwHifcey8YF4ceemja8mR81itocWYyd3n2NJAMWgc5XzB/3b1xImQHgoNI0x/84AfR7fIAbcFa85Of/CS6N1UM9sVMGYMRwgdBDPAuDI+s/3BIP2f4sEOyV+peKXUvlxKVkXk6pmGsjpt4ouZPPlPHTzxRYxvHFfV4GCzh5e5gFjKE3YGpODR+9Moj+sHiR/TrZU/rq8/fo9vWDjWGEzARRk19xmYuvb/grBg7caiQMYCOTtfPFW5EDw4Ci9wgSItgqs0rN/Z34On/7r4+1dfVl67Mak+btG2JtGMFBx/yZ7QEQWFktMDlOW/0lGlCStqS6RCkhBf8jECrsMC9JXiL58d4IlvYbfjmnHEq0U/DDYKp0BCWZWJgDKA5eL81a9+lw774Bem2W0kxR6BIl36appGhXctwgJV89hsrBK0xPtI5DdCIXn3KWKKEG/OwGD31CPJlrETID9htLr/8csdWT5BDJaHiioJ+4hOfcKI+PvrRj5b7VCIUCLyzGNirySCxpbtNG7q2qbWuSTNaJlSd95boBQxYeFAhZBhDhz3aX5V2PD1wGxqnSGOPlYrsRAiCUo4vaxhNxAqEEWLozbb45ZJF6nXV3d3Y1aF/rHpJF8+Zt+s1MgZK0eAO8onRwk14EEicP+KJCKxs94/3IJDc0SMI8/1m767X1jyvtWNo/Zn6jmlTpqpu5RYlZyaL+1y4v12PSwlXCZ/GeVL9zEFvyxbRhShhrjPnvRH4XnA9lqXCv0RAIUIxTNi/CIhcI+IRfvxg2EZ8mBixKE6Io9VX5bh8Pw4Z3usWizhe+eG5Mr5IKXdw+62oZW6G3RTpnoVY1rHI53SuwxnMA+YTc4FnBMcycY1jzO6/H3g/QpHnZ00mETFEUyFIiNolGyhTn5VcwFhyO8NYpxhHfhlZEfxBhg2ZzieffLLTX6MYTRerERibvv71rzv8KJ+yKBEqB6wHlNSsplrpvYk+LW/fqO5Er2a0jNfohuraw9gDbr31VkdX3HXXXU4pkWrTSKEj0SXtvN/lzIhLI46W6gtrnhwWSvl8rPGu8Te4njuj6sFNK/XU1nWDPvOn15/TUeOna1xjyy6DJtrV+lcUEwRluI/DvYLHkHVAhkK2DA/4LAZzMs6N/1AeFr2xfcVObZ5I4FR/EF0sprlN05zA3aJzmbbV0ooFkvVubB4vzT5TqmscognSAa0Fb8Bpg07MFBiFI4T7ZYBT4tCCz6M50TLcJwLNcgGfRSegT7iv2JMIgGLv9nMOkUFMlgf7ArzRzWktIAwjOd/nzAscGe6MIAt4vPN2rPE5netwBkFRZOxQNgpg86NKAGCM4Qjn/vutRbyXMcacwUnOOEEDWEN35iLlq3BihQW+010mC72xa0xECISPfexjDhfAZk/mRqWgvtIa991xxx3OghQNruoHi5M7xavS8fKOlbp7/dO7StLMbp2sM6ceXvQI+jCBEYM+GhDDs88+O5pHfZ2DHRqge53U+brUUpp6zJUESLgZDCEabpJJFNWm7sFOjoSSWteZEmvMZaLswxYbEF+/iHDIEMSUVHQ3oYb4BHXW2T5iBn3INdFJEKm37XmUltZ3aGXHVo1paNbx++2h7h07nQbdGGmLhj6yMzw9CbqfkeqmqLcvJTQQFDgHIOAYpDORScQUxmqeZboGexh+eHZ8FynzRGa57zf3hSgmu08Ih1xKP0IIMXbzL9E6fBbxhrjlNe65lToiSsZdtoq/W4NAxifjEgdLxAHCz9Tih+eEiGCMW+Qt9x1hznPj2bvXhWOPPdbJlkC08n43+edzNPXDsUEJq1yiItOBc3M76ZirZJgxlqIxERzcw8985jP6wAc+4ETRMb+GM9hn3v/+9+uzn/1sVidwhMoHa0E1jWkcGVe9/qDWd213fq+PxXXBjKM0e0RhvQNKCfYF1ntK1Zx//vmRoxl0vSwl2l13KSG1PymNPkPDGXBKOCD83YCWqFNMfbtUdsrkT/DU2IZmJ3iCyN+w+Tfcxa+pLQE9jGm3wRXOTQPwoI4H49ZkvAK+BxsW33v01L31Stvr6kv2aWbLFM2YPdkpq4MzMKwgkCHAML/q3gGHBujcIm18VppyuOOggOdx7fzrdsj4AV4Hv8PRhCZJFwyAdoAbwhHRmF5nM8+VbGHWbL4jFwcH95TKE+gdgnPQL1YyiGMy1hg76EXA38kyscApjo1+5Xr5jGmhCOEBLWDBjoxxnjF6gP/HwUH/JbQlzxAHhZWiIqsfjk9WBvMQp6i7UThjDo3CnhNGmUPGJ8dmPBsYJ8x7skoiBAPz6Oc//7njRLzuuuv0lre8RZWAimGELDr/+q//6qSE5+rNjVCZgEgUbeMOGV19PVq4/hkX1ZKWt6/XSztWav/RudX7LyeIcmYzOf300yMDEBgkNgwxqa8yUsRLDdJyIXaQTkgfUfTWZDtOmZqmEdrQtXOg14RimtEyyomcoKEw0Rj5GBaJtIZIWlYApIb1gdf4HbLDD0ICssEYhsxgYLWSRGR+Wf3XXADBRWRwzKeeesr5XsgL5+Jt1z5iXKPjQClqNJVTbgoyntCKlRvU3d3rXFNb78OKxUc5x+V+c38g8mSmQPzSAVLB54mOuvfeex1BaOTewPPFsMmzh8B5HUjcC8uwQJyQSYHzwbIsMoH3Mp64v9w3oqV4XpBHDIkIDo5LOruB34nk4jzIGrGsG66Z43KuGB7rTj9TuvUWJ8rNEWsQ4eNPiLI0CgBjgTGA847nTm8bt/MPpwIRbu692zKdcJrhoOI99jmeISKEsYcDJJ/+FzfddJPz3XwWx4vXAMEcjoRo7vjKV76iv//9707JpS9+8YsaziBdnnXpy1/+crlPJUJIqCanxsObXtGGroESLX3JhG5c86Q+vsdpVcXVcYrDAzNxkuGnMTycNNmVysitooC4sAAXhO8zpuEKcHl4PDwBLeF2aJjGmNw8wtEXBL24nSBBAcfEYG4Nf9EKHBNjKAEZGFLRPpRX4nczuJ9yyinOezC8GjeG+8C7g/J/y3jnHLhmjLJkeFiAx5HjB/e8w/BLEBJZLUVBoldOMB9ZLzs7tHbjNicbPbkxrnWvdDiGY54R5wvfgwdyX9KtQXAxgiXZO9Fi8EI/hwC8nWuHv1mUvhtk61jGDg4GNBnf49dnwQ2eBc+McYQ2RF8wrrgGc6CgG+CIFojFtaA9GAvoJ95DUAzXwNgksIsxMnHqVOmQQ6Vnnk5lgds9mH967vc9wi6g3XASUEUAfcq9ZlxY8BsZHegHA8+LnhyAzGKcaKwDNl6Yy8AC8nIF58KY4RiMYca7G9icw+oJM5wwdepU/fjHP3Zs99zTSuhxFktWSMt3mhri2CDKvJoIXoT0YMNhQ9pVSqSCsbFru/668r4hZOugMXN07MTq6e1ChgaLNrU+I/Snhm+4aajoGH2k1FI9zqqwgOEbQolBGXJrzaYxSmIgeK1tiy6755/asj6VSTCleaTeN+cQ9bS3O+/xGhvZPkgJJjXYr2kwf+d1sjsgNxBYCCgChvWBOq72nbfffrtDsol4YrP0fg8Gc0gN/+8lJdkAieJaIVaksroJld85U1rBIpIQOhbVQZQQ4sia1XmdB1wX9xYSh2MC4c9n7H2OAye5Tsnulxwb/ayZE9XS0uT8/wtLZ+iAAw8Z9H1WKgqHQDbyD7jXlFixZ2HbO5/FaIxoDAqEBFkx2SLkcY6lKwGSLqXXsjP4fgSnXRuvc578bk63CS8+L/32N1LbDunoY6R//SQpKoGvI0IwME5ZH3heiM10WReMRwwBODDc2TwIRqKt8il5iBjGqcbx0zW+5z1RhH3uwICC8QbjRT6CsBbAvkGUH2WnSlE6MUJpUE1rwj9WPaZX2waX3gGX7HWmGuPV45yhZA/9vSKd3o+O56XuJYM1RqxVGj1fwxEEDsELMPQT6IIRGo0A5yYb/H9eekS3PPqAkv1OgPOm7aWDR0/epRG84HvIIoZX+wGOjIGevc2Mahgw4RLwSIvuxrAJd4enzJ8/9Nlg+GafgAe5nRJBgKEc3s2x0N9omEzBHXBf+DXaB11CjXruBecAh+L/cap4bSfwZfZxHC84EQjA4thcpwWMwbZjy25WX0+HRrY2a/rksYrF4tpSt5s6RqX6G7jvHfeDZ4Q2CgKcBPwYTGOgcbiOoD1D0Cg4ouD7mUpB8izRLX6Z45lKBmF74j5zXtwby+rg3hGkg2bl3u3D/fjhD6SnFkljx1EDXzqiiJn6wxSMbXQ4z4DnnY7nW68+7BPezAk+j07INSOcec24t7LHfqV2sYMwf4Po7AiDn9e5557r2Dx++9vfqtyoCCZ1880365///KezGUREqXbAhl3sJqJhYVR9i+PEoNyOgf8f0zAi8MTuSmxQItmp+vhoNcbHqhxgA6+m6LWiI94kjT5C2v74gOhomiU1D40kGQ7AWeB1GLCRE1HBv1teek2f2+Mo9R07To2xOu07eoIa4v4klfUa4ojhHvESNEKbdYGICYyjbifJaaed5kRoeM8PsC+waeJYyVQHNh04HiIHwZDtPDkWot3mNWIFcozowhGDMReChrjgXCFoCC4EE0IEsoYDxv7O/RmU8ZDcS+pqHlSC6rnFjdpjn4FMBgMCARKHgd+d6ZAOzH2LeCkUVuOUazeB6idMvM3A3Ui3n/O6CTeO40ckrVTW+ONOUOz43JxYu4DgsgyPCBnB/AjiDCcKj/d6HZw4pxDZzIV0/TkygbGQKToSZ2hU9zZ3YMQnkuqDH/yg7rvvvtI1Kq0QsF9Qgovav5FDI0K5ML5xpGJat0thsDM21zWqIRZwPibJCKApJ3vZJCmWe0ZcGIAHRTrdhea9pd6NUsIaGddLrYWXSalWUFrJDYyJVBCAQ/Jz8PounXT2O9VRJ+3WOlpTm0emDdCDe6NpCZqBUwQdd3AUb4kZ+DNR4ulKW6EL6CEBf891j8TBgC7gfOHD2TI94PSWGU2QGfuy3Sd0GHOMwBH0EOcDDycLhdcI6IJ349DlvnCdXNugezN95KCeGht2xtU2brrmevqbcp04Vay8qF+JLi/QM2FkaaFViMbnmrhuouXReF7gdEkXe51pPHAtXB9jwRt8Bjge1/765s3a7bKv5X8hZHhE+iIr0JGMU/ecTPdMeTbMKb9SaDgf8gnaZZ5YiTg/4BBF11RTD+BKQCwW089+9jNnLt9yyy0666yzhnemBgIY7+5Xv/pVfehDHyrnqUQIGURPsPFWS9bAS9tXaOGGgRJUs1om6axpR6guSwoxU2hrzyJ1JwbqxI+s30sj6ueq1MCYCkmqpuaJJUFvm9S7TYo3Sw3jB9JMI+zKMMBJAaHONHYYX0TpOBEu++xTUKlAvwj/BQsWOFHFxRbNGLoseicIEBQ4aCFk7s9YGjTkGOeGmzxTAxTxQSTpkOOw7bJeUKYgPlavvLZmUONsLxAdnAORW+WIJIGQEsllYg+RhRgBJqxyRZDmb9xzyg4xzojWCjwuEn3Sn34j3XZjSnS84QTpI5+SmqIMD7/9i2h++pgEnc9E9rEOeA0YFo0VxHiMgYPniTMkSMQ1TkNrBAk2da3Sxu4Vzv9PaJyhCY0zI2NbhnuNweaSSy7RJz/5SQ0nXHHFFU5ZWwxOmZqdRqjOqHAMJX5Gq0oscXvVigd3laCip8b5M47UnBEBGkonN0tJmrAm+l9olmJHS7Hgfa+GY3ZMyUCpqb5NUrJXqhufCqaKsAvwYKpwwBPPO++8tPs0hnV4Bfs89gPGWb58lz0PvWKlawDBWxgtg5RVLRTWny4oB4PfoC/cjgUM+mSOWDlQnB32nVwfGeXYV3wdKD1tUvt6Kd6gjvg4bdy8xbcslB0f/ce9KVcJGYLc3BrI+vAFLYPrhZU+8zOOu0H0Ptefax9BrVgq/eYKaf0aacw46d0fk/Y7OOfzHA5gbKP3cfwFAc8Dhx72CC9nQ1+QlZWNy7F+4BhlzpARZA3mMx2TErqUVmSO9SU7taNnsXqTbaqPjdCohn1UF4v0YzpceeWV+sY3vuEEIpaz7UDZnRr//u//7tQzvOeee6K0nxoExhLSTqslOnBz9w5t6NqmlromzWyZ6PQZyIaOvtXa3vPckNcnNp2gulhpRTTR4RibMy3eoaN9o7RznVTfLI2ZI6WJ7I9QuSBSCOP08uXLHVLrbOp9fQ4JhjxALhlbkMt0kS+FkljAhkh2Q7ZojkJBtAeGeQzliAOuhzRoa6CXCyibSAYHzkQEGamYRH1htMdQHCRq3e9eeIHBGOdCORrcIQ4hpdwvorRwgmHQLsTBwn3jmoIY0nGiIXS5x6xvWdOP/3G19JffD/zOeZ44X/rYJXmfb60i35TudHs7/VD8Ahmgmk9cc7X6brnBKSXWsv8BSp5zvsjlZN4RhZUJfP6eh+7W+P1HqKOvTUn1qDE+EK03s2VfTWqandM1DCdQ3u+iiy5ynIR+2XC1CJzBrJfXXnutkwkYobYAL6E2e1jZicVGb6JPy9o3OE3DZ7SM15iGgDwqcS9X63qBNW+6FPcv4VEswJUwDJc0UK2vR9qyVOrrlkZNl1rLX7c7Qm7AMG+lRilFRWkZgnTg+fA/9nC4LcZPuAgR+2EE73h5NRwCfpJPicxcgHaieS5BSPAj6/PBvpurLYTPwr35TvZunLhWRpLmy0GvJYjG4PvRLEEyNoqhyTBAc4+sR2ChWidoDwacbmg3gqgYp95gnSFob5O+9hmpo72/d06MtBfpP74vTfF2ahzewLmAfSFT35Z0zwRt4nWgk23FsyIY3os1ry/X63//g7RyqeLNLRr5xjO1Y+xkx7lBDxvWn0x45NFHNG3/Mdrau86pujKqgQBlp6Cb4mrUhKZjFS9ThmQ1POc3vvGNjlPoe9/7XtnOo6x1avAME0WFOI7qmNUmMIAhLN3REmGCzW9z93b1JHo1vomyT4UtOOMbRzk/uaAv2dEvMgb7B/H0ltKpgbgjYqOkDo1NL0mvu3qRtE6S9jpHqqIawcMdRNwzPykDZLVGSQ2GgECEMRxg1KYObT5NgHMBpLIUXn6Is6U9s0ZBen7xi184WSLZsge8UYvcL8QZ33f22Wfvuke5ELgg7+V78ym9FQZwPkAiETuIUv6/0D2bCCrLwgjyXggpxAnnBut+xgisRx4c/DvZGo8+GDk10jzbXB0afns732NN4/2wZOFdmvnHKzW1sSGVqfTC01JDnfTlb2bNnOO5P/7Uo9rYuFINvQN9YTr7KOGSqqu8vmt55NTIgNNPP11nnnmmPve5z+mPf/yjhgO4VtLhI4dGbYI9gejuYqKzr0vbenaoua5JYxoK49b18TrtOTJHh6ITd9jpfRHLmkoJeCBaHaNFydDbJb1wndS5pf+FmLTnfGl8qnRPhOoA3NjGDRoDwyT8jSAVjNlU7IBPhOlsYLx6I+/hCaUIsISf4rixhulkQNx///2OzsilIgmBP9wf9Jg5GtxG+qAcHP4U5L1wcfh1GOWlcgUaisA6nBk8I28/hXzANWMcz1aSm78T1IbziKA9nCE4VNLqsmWvphwb7jWaPoaLn4ucGh4Q8MdcyLX6As/EG3OPHQI7V7pSzEt++1Mdpx3Uk2fzkB6+UXr/5+lcnvV4rEVLNr6gePeAU2pzd8qyN6KeHMkudSc2qblueAQE5TPX/vd//9fJ0n/ve9/r63QqBcpmeWSwfvzjH3d+cjEiVRI2de3QC9tfV18yod1HTtVuGHQjDNmU8fwXA32JPi1Y/7jWdG5yfm+KN+i0KUdpQlPuBppCUB8jEtub8BRTHc3iigyIG6l1/IuhNmizr1BA5NSKBwa/1r5B2vCCNCVKw6wGsA7ThI9eFQa3kdlSkYm0ghyG5dSAZPPjdWhALEoRJQRx5YdzwDCL0KImZLZIDi9wItq18Hl3VgbfiSBhkw8js4UsGshhOcD6QqQZRDOsNQYDuF+pM8vIgAQjSnFyIQ6J7IMrWFQfYzdtRo9fGnljVBIi3RhGcCMic0nzR6iTZYsA5nlZOTE/McqzW/WrX+qN9XX9RjrKPfdp+6MPaeOiJzVmt9mD6inzfOEOBgyX43YfpWkNgxvd802sIpgpypx0XBX44Q9/6BhFaJh98sknq5ZBaY4bbrjB4UfViEQyoWe3va71Xducnm+Hjp2rprooSnDIffLwiDCxumOdHtq0SIn+sk9zW2fp8HEHlrbMHcdKEuix0/OHUSUzSFupSQzTJe3Zt+YpqXOr64WktORuadxcKUtZ4AiVAXiemzOS+W2lY4jMx5jM3k2AS5ggmttbboljlKL0FHPEenfAXdE2ONdz1QEWzMP9Qn8RCe0GziCuM5sTgh4f3OtsYJ6jY8qJMLNI2Ru865WNNXudgB7L0EBrwIe535bl4bvWN6TRqOleH8Zg3MHncWQytnLZP/is9aokQ5C5QJagnx3imbvu0Jxta6TRFhSZVE8iqY1336JtR813dIk9SzIO0SsGggXR7nseRT+NwTpiZ29SI5xsDf4SaYxMoMSt2fXRGOXovVU2pwaRYgz0G2+8UdWI9Z3b9PdVDyrRL6Sf3/66Tpl8sPYbPTwbEKcDHlUWDzZkyESYNeGf3b5kl0MDdCd6tHDDk3rrzNKK9ab4ZDXHZ6gzsWrXa6Mb9lddkevdYvxDsFMCpCy1onv6Uy/dQGh0p2oGR6h8MH6CRMRgTMexwfwNAxBGb7QFWQ8ljQLsjy4oJCqJVHkEG5u3d13jfkHAcBqli1qAYPO+IPOXCPiSZmG5ECTaKVfgvIJMegkq65rdL8QGws3dz8PuOxE7CBLf/eTNb5Veej71/30JLISpfxfcJp16RqjXUe3AscT9XLx4sSNqMzXTc4P7zudw3CEI0qWX8/wW/eEPOm7DWqm1STt6erVo63bt7OvTxu5uzVyxQnVtOx3HKp/HQYITxNvQc1XHMm3ZuTrt+YxvjBr8ZQNr3de//nVHdCASy1FmohTACUZjcGr8VmPjR/aFW9c+pVfa1iiumCOmF+9YrXfsdrwaoyzYQSB4AO7AM2fNyKkuegb0JHr08Oandjk0wNL2FZrUPF6zW0scyRw7SEo+wVn1vzBKiu1Rkl5LlJwkMKMszcH7+48MQqI3lcHREPXHqXTA78jszcbrGVth8ksCIuCV7v0NXoHhOowMgFyQradDNo514okn+vJv9AC8i4AjftIFZJEdAq/LVsqJ+Z5rUFelwy8rh8x61jQC9px+qFu3Os4MdzURnE9oXWyUvj0md99b2m13acWyVP8+zICU3X7qUWnfg6SxUYk8A2OKDCzGIWWoKCcVtBoDY5belPRHITg6Xb+XRU8+qcm3/k0zRo9wnumLG7ZoS0eXXtm8XWO7WzRz/2MGPUvr++edU09sXquE/KshxFSvxnj0XLPha1/7mvPc/vSnP+nd7363So2yhDowuL/whS84UWPlMtIUise3vOJEUiE2zHv3EKV4IgxZ0BAaLGpsMLfddltoJVQ2d20f9DtPoa23w6lbW0pAyHBijG88RmMaDtGExuPVUlcc0dPe26UnX3lWC++/ZxdZDMOhwUbQnVii9t4H1N77kHpcDpq0aBw5tMwUTo6W4vZDiFA4IMkYAhi7ELwg5DqsectYw/DgNWZD0OnVAEGvJrCueQ3rznzq7nayQNI5TUiltR4iQR0rxS61UQogsCCViDI/EQUngCMAxglROn7iZGpLi7b88Q/S1X+Rli8b/MfDj5b+4+vStBkphwbYtFH6nx9JC24v0pVVL1gHEPuIDQRELgZFRApOqHRGL0rYjV+2TAvWbdKjW7bphe1t2trbq9H1dXr3SSfqsBNPciKnEC9E1qVrID+hcbLiTk7GADD4NsQaNLlprqY1RyVJguBTn/qUI+Z++tOfqlZBWVv2l2ptir6xe4fj0AAplSFt6dnpODYiDAbGDtYg6qBTY54ggjCws7dDfcnBnCemmLaVI2gnNlqKHS/FDpViR/Q3CQ8/awdNu7Zto+544G7d/8D9jnEC414YDo3eRJu29TyuLd33aXvP007N8qxw+me4o2Njqd59/ESoaMB9afYbtN9NmJmWODW8Wc0EwFDaiIznaoOfw4egHoy0aAI/Hg2XIlAFZ06QCHnuP7qwHAg7y5bMY0pJ+WXlUIkAxwZgXSPb2y+Dxrmnzz8nXX2VdNedqfJShrp66VP/Tzr2lP4eorGUc+O5RdIPvy510ykuglfXEfhEEFTQ583zIWiXhuHpHBp8V+fWLXrqmef0yJJVemjFOq3c3q6GurhOmjNd533oX53gOEpNsy6Q2cQY8JsPE5uG6vCR9XWqj43SuMYjih6oXCvP+Uc/+pE+//nPOwFtw8Kp8f3vf99ZjGlaWK3o7OsekohE87cImcUH3lG8tWFgRH2zIzLcaIjVq64Macksvg3x0Wqum6z6eHHKTr24/XX914Lf6ObXH9Kr03eqce7Y0KKnehKvqifxmpJqV1Jt6k68oJ5EFgGNQ2POKVLMZWiiUfiE0jcyjhAMEF0cBzg0DjjggLI0nWbM+pWMwCjB+Tz11FOqhTqikDAEh7dfAcKBa7Q+FUGdkhZNX44yO5QGYtyEASKicFJwPX6gZEBWw/q6tRr78X/V+ssvp6aO9K530bl68HsOOUJq85bskHTrDYWcfk2D7CnIPxGNhYLx/fjjjzvjdf+5czSdPj1tnZrcUK/DRo/UES2teuDYk/X6ihWOyIATIkb5129fa65r1YFjjtKo+rFqjDdpfONkHT7+RM0be6pmtOytWNB9/5XF0p//KP39WmpDaLgBMYfo+OY3v+msU7UGrolrI2iqpKVyQkQXzZE9YEZ0+rweYSAogmwvDBdWLqkQtNQNNWDgXmopl0EdJ0ZskhQbX5TSSx19Xfrb8oX6r3/+Qq9N2KqNc5NqGhVONgQOjO29j6k3uVUJdaonuUHbep5Q0uM0GoKp81LNwd2aY8/Ts/ZgilA+EPnOPo5BCyNm0JJL8GQ+Gxa8QVgEHp1xxhlOORsyQasdBIJgnPfLPCH7G62AzSVoyVruD8+gXJyA8RKWEdT6//mVp4Vbos0IKsuI3/5a+tY31PW/P5W+fpn0hc8Ndmy0tEpz9mCgDThe0bUb10mvRgHOfkD3UaKIoLYwgIMCx+le++2nE6ZNUteWNs1tatKohjodNWWiEhNn6YHVGx3NTcYTjlbmhm/2DdqzdV9NbZ6rxniLmuMjNGfEgZo78jRNaHqDY98LBAIPb7hB+vWvpYcf1nDERRdd5Oh7bP2lRsmtvwhmxAY/ZUlnDQkzWwfqPwOM69Obo9SkbGAzIfInl2jQdDh47J5q7Rcesf6fYyemaq4baCAOWa/2etsbO7fpqnv/6YiqKXNnOP/ev/E5bQ4paqwnOTQzozdItsaY2dL+b5d2P13a+83S3PmlrXW7dZX0wq3SczdK6yIikQ0PP/ywU47j2GOPzashd1jzCG++N2KKeYuBH6P2TTfd5BAfSC6RRoVmiUBqShmFRM1WMk68zhtKKxFBRBRQrrVjuT+snc8//3zJG4Zzvu5+IYWASNps147jI+Me8bOfKb51qybW12tNV1dKbHz7W0Pf51dv3VsyL8IgUDINpyfRTYWAtYKMm3nz5in2lrfowIkTtX9zi15fv0Uvr9qopfsfor2PP8HhBDgzKdOAESRTuaBRDWN08NhjdOT4k7Xf6MPUGM/RwHjvPdJHPij98ufST/5bev97pfXrNdwwf/58HXfccU4pqlpMf8e4zTVWKyY2jRpSZoqdd0ZLpDGyGU2I6gwjGruprkkHj0kFfZiiGNcwRruPGCgxTPnhHT2dJc8OLwZue/VBPfbQo9rz8P3U0NSoLd1tenBjfwnHAtGd2KAkjVt3hQImlVC7epPbMn+QKOh9z5H2O0/a+yxp3jul0SUsJ4cT8bmF0v1/kZ68ReqISutmgmUfoy/22muvnG41UfXw4zCAMZ9MBm/WN4Z7gqcwhJLVhYET54BlBhcC+FKp7AxoGe4V99udJY4u4P4TUEKGc649EMm8xbFUascG2iYsjcG1ox24lnSwbI20/ZjWrJZ+8yvt0dKsV9r6A3yeeEy6a8Hg96V73pHGSAv4Pj+M00JBnw7sBROnTdfoN52vE6ZM1Mo1m9W+fptWr9qkDYce7wRSsx7ADQjkhPemQzwW16zWfTRv7Ik6aOwJmtQ0UJIsENBMH/mIBK/++c8lMoV/8QsNN8RiMcfG/4Mf/CAUW28uKHkY05e+9CVdcMEFQ+olVxsOH7entnW3a3FbyvA7qWm05k85pNynVRXAg47Rz200wXBJxEGQZlbuSKo3Tz9By9rXOM6LaS0TNb4x9XnIxUObXtDz21NlSSY0jtbpU4/QyPrqq8NKmamFzz6kCTMnq3X04E1/U9c2jW8Mo4Sb3+YckKA1jkj9lBpbVkjPXNf/S1LatETq6ZRmRvPQDxjDM9WlzAbIAwb5XMWKHyCcEGfSwb0GbkoxITQ4HtFGrBfUw4SsEklENHnQCFwizilrg7OEjRbSz/fQNyCM5t3pAIGirBfnz3UiMPh/DLcYcIkkJVosV8cS58znab4IcHL4lWYKG5SBCqssGPff3YzeD+wDNDfEAWI9SwZh1Wp19fRoQ2+vuhIJTaN2MsZpRIb7vSefJl3318Hi46TTQrmOWgXjCeJPtCXlDXMVxgbmLWVh7r33Xme+jb3y1xrxpz+p5cUXdODJp+ip3ffQ9ObmQOXvQsN/fX+wo2vb1lRE3he+qOGGyy+/3CkNQu+JcmTsFQMI5V/96ldatGiRqhnNdY168/QjdOPqJ9SZ6HHKrJ00+UBNi0p7Bt734SpuECCBQTMX7DNqd41vHKvN3VvVFG/UrNZpquvPTF7dsU2/X/6otvV0Os/n7Gn767iJ4fQcKyUwkBJAsnjrq9r76IEm6ARObejaUu7TSwVJjUpvoCwa4Az3Xy1twEHWzyvWLJZO+4jUWH06shTjiGzedL21soHPkNmMobnQvptwGEouoXm8ja/hM/BmIonRH9gieI31AbsB+ga+nsuegyMAjWHlYQnaosl0MW0C2EvISmOdgyPDl7ke00fojXyaohPFjmah3yIZ2vyUAtw/1u1CdRmB0zzzbLqI54zji3s3xJnSX57qdXr6JXrV3turVno1rVs7+H37z5OaW1LlpuCVjNtRY6TdS9u3pdqAHYLxydhNlzURNCMHroeW3vf8f1F84hTNevgBdTU0qOu4U9TUPMKZ20GzlQoGPaKft16O/YEOODXOOw9PmoYTjjrqKJ1//vmOzf8Pf/hDyY4bS5YwhJ3ov5NPPtlZLDFY1QJIB6fuKhkD1Zx5UkqQAgYBco8BfidqAqMjUbr5RJG78dy2pY5Tw51JM6lpjM6bkd5LW4mgvjgEZfScSbp5jae8iqSzpx2tGS2Fk46uvsXqTQ6ObmuM76+GeIkbIuaCZ/8pbfZE5FEa4LiPlOuMKho0fSRqIZ/5CmlAbOBUyGaQDgq2HpwVfKebfGPIhmT6EXLEA2QI0ZGufJEBx4g1H3cTXNYaDK2QdUhRMUqU3HrrrQ45p5cQYoOoKoSUNS7k2smaoXdAvtFJrJUIsnRNyMMGERc4xApdm+mxENQxxriD+BLtPwg//IGSV1+txe3tmtvYpMbGBpiy9OerBr8PYnnV76S770TpSme9WXrLhVH5igDAiUU0I2O4kAaSjFP2MRxVzAHmJIS35CCb59QTh75+1NHS5T/UcO2vwXp7I2KsBnDOOec4Y+wnP/mJagH0ONjZ26WWukbVO7W7IwSB3xqDoY7IZssGLUSv9ST69P2XFzjPxi2gPzj3GO05MndDYrmAIZcSgdyrBVsXaUPX1l3Xw92Z1DRW504vXDP1JTu1tedB/m/Xt8fVrLENb1DMXb62krB5tXTXr4e+fthZ0u6Hl+OMKhrsI3DeXPUBPIMyMnBiAl7CdAbAvQkg4jttvsMpsTUQte0FGgd+inMgiHPm6aefdrSIu8k0wMaFwwGDLTombNsQfIp7xfeiIchMY11zZ7ni0IFzESSWr5MIzo+DppBm50HB80fbFdrIHb0ABwh6zTh0GR+DygBTmuptF2jpjjZNqK/TKEqn8gy/85/ScZ6m9yuWSlddKW1YL02fJf3Lh6XJZXDCViGYJ6DQoBr2debiKaec4mRvMH8LHUd54f/+T/rd7waXKQO//a3kca4OB6xYscJ5tgsXLixZIkNJMzX+4z/+Q5deemnNODRAcx1RjOE3bCs2ksmEElqjZLJDsdhIxTWlZE4ZjIjuqF8yNtiIzDgHwYGIsJkS0Z3uvDAUsoAZIBdWQ3F1x+B6iUQdre/a6jigLNKq0kHdfQgRpIQNf86IqVq2c63joOF65rRO1fTm4BElmdAY31OxRFy9SZpT0h9ktupjJUzzzge9Pk2To742acVrrg3lcSAwFyHKmVJ58wXzGtLvbuiGk4N5nI5E8zoCnPTxbA0siRrDIOuN2GH9gfwgeCBCODlwLLD+mI+/kEgx7huZDZwrjhWuhx++mwgrqxuLGOEccBohprieXKLiuS7WBtZKr6gqBjhGvpk6XDsizzJlchHKviL3Ix9V7Nln1fDEE6nEDPqWfNOn/BTP/t0fSP1EyAk4Mijjw1yjpEu+zjfGKU4pGgQyxgPBmYfJcEsZ4rycPRumPZCtwTzfe/hG1V122WXOM7GsnGoGvdr4+S0CskZAOYRRDdUZFd6b2KGuBKXd4mqum6q6WPmuA0MlxlYMHUQD8zsggjOdw5Y9mf3OuABcwwIpNna1qa13cHk+sjVea9tYNU4N7gMGPYy3XOPR8f1189qHHUeajb2jx4cTMFEXa9aY+iO0s+8l9SU7nMarI+oxNNdVl77A1dMb9bXxA4ZESlcGBTwQ7ss4JLug0OwMP5hRH4cmGgM+jgPAz6EBOAfWCPg6BtdMDhbOHzuGH/fGkAfnZZ2Bw3JcSnLB71lXCs2uplwW34XGQM/YusRx0Gwcg2vnOjg+Gof35upwwlaHc4TAs2Lbh/h+7j/3LZ9jYUfiOXPt3IcgwVe8n+C2IdqYTJ0v/j/N+c439UrbTo1Gl134NulYHwfvrLnSF76d8/lGSM0THIzPPPOM02sjXzDO0Sc8e54lzz8rnH0uFm6AG44Ur0ODDJ9SZYpUGFg/sPlj+7/zzjtry6lx9913O2VArr322lIdMkIGh0ZPcpGSIrU45hgQ4tqkeu1fEscG0RzUMzSQPoa4IHKZ42O0ZHPD4Ifn3jY7L9io3R5eSAgLG5t9U7xhl/HfQANx/qsGQEQgEhAywD04dfJheq1tlbb17NSYhhHaY+SM0J4XTVYb6/ZUo/JPBSw5SPXfjhPGEJMmzC3jCVUuSJHOtfSCpfEWQ2y4AYnlh3NEIAQpQYcIItsCAQJR95sHrAOZek9wHIy1AGMYRleuFWLED+sUxzGjB+fIHsbfzPmBUIJQuZuBYwihpA4NCS31nfdjOKQMjzuaygzFfCclpXhfLiWlOG5YdYizgXtMpglrMvfc2wDdD4g+HDmcI84QnkkuETQ4hyy7ZRC4b7+8UrOffUYvvvSy9jztNDWXKE1+OAHBjLGbcV9IdgVjB+MFwjOr4NjymrTiAamvS6JP2dxTpeaQIgW/9i3ps5dIWzanfj/kUOm979NwBYL+M5/5jL785S87HL1as41ZN7kGrqVU5TIipEd3YpO29VgJsKTa+5ZqXMORqo+HUSo1NxBEgJYgQpsgKAyV7OvsuXAc1iS//RbDIXuVlUPhe+DllM5IBbP5NBH3eb1S5wt8h7Xd+N3k5nE6f8YJWrozVWJl7oipGtMQTh8tUB8frTHxMmTo5Ytx06TGVqmb/apfR7I8To40hp+BH66Qy/4B30X35xpslSvg+QRLcW42/7OBPYQsaGwKs2fP9j1HrjdToROOxzoDCDwi65W1hNetvx/fTfldu29W9gnOa5krBIRwfMsot/6AvAdOZeAa0TsEirk1mzk8CCriu02zBEUhWbq5Am2EIwidwDobBDjGKF8OrJRU0GxyPpe2/NFppys27xC1PvqwVowbr1neDI0IoYBnRrYgYz3fUrcAzc3Y4d+MNoveTmnJXdL2lamgqWmHStMOC8e5cfLJ0jvfKV3VXzEA7fqd77AIabjic5/7nDOXydY46aSTaqP8FIcg4u+MM87QV77ylWIfLkIW9CXXqTf57JDXG2JHKR4r/uRjPJAebqVw3Kni1DjEYJbLpuv+XqKzibje3teu61Y9oL7+qCMEx9Hj99PBY6uj5u2DDz7oRHZEyACWriX3S6ueTnndcWjse7pUn2oeHyH/0lMYHjFGZyvxFAYweENmzPAdFBB7xAIRYhYVACk2gWCN9LKltmJ45zsgV25gxMCAj9EDAcE9IcLLssEAhkAItNUB5pwQGqeeeqoTgcI5mRiBQJPFkYms83mOSWaM+ziZgMEZ5wkOgFIhaAkporws9T8fgynCMpsThHUfgYxhCpFYrYbZSgX3l3FOdlMhoN40ez0iMm2j+J3rpMXXu16ISQ2t0v5vkzyNk/MGhoDXXpWamikgncrWCAocMjt30u0wt89VMGxNIuCIdasaQRTY2972tl3BIBHKi83dcO8BgxtoiE3U2MZDS3J8jCQEELAnwGPYnwmYsOAC9Gg+gFPAB/iua1c+pSe2rHAyNMDI+iZ9eq83akQV8E+iueEa7JcRMmDLWunhv0k7t6T6aBz+JmlGbfQfChMEKcKPvRw6HZiHaP10GRPF4Ks4NzDQ57I/sDeS2WG9HuCibiN/0AxHnAleHWJOVYzyrFMWSEUGt3FYjn/PPfc4uoa/8zr3GcMtvNrdk8DuKb9n0gI8K74Xh0sQrsw5om1KkQ3udtAwlrIFd6Hv4JXYjPIJvkP34TDKNiZwilNKh3MqtPxuhKGw4IJC+3WiuZlPZIylHduLb045NNyFI2efIE0KsYzz8uV4KGl6KQXU8Q7IIN+8WWKMFdnZW0p84xvf0B133OEEohZbn5ckU+P22293onBvvvnmUhwuQlYMTps2JNO8Hja8g9rtV8OQmG+NeL6Xz0JgIBDnzzheL2xf7jQRn9U6WbuPLF2dQ66JRRpywXnxO2TEGhZnAuSlEI/1sAH3cY8TpN2PTzk1oprTGW5VsI0Ekki0EOM0cKmYAkFUlNXWzAU4CyCZ/DC/IEaUyoF8Q3KtaWE2IFKswZ8bRPzQSNccH34RS/SI8oKIUJwtRE25+3XgpMCQgEhKZ0zg/axhEGiIdBCjAw4NhEqpmqGxPmXKgDEQeYNIyzfTh7EYRIBaWRDuO04QyhaS4ca9Lka/lOEGnGZhNJLGMZjVAOCIDdYq4wRJqWen1LlVag0pAp/I64NyTHWHo1Av9ze/Sf0/c+2//7sm0sopS0E0FQFHOK6qzSnI2s+5cw2RQ6MykEgO1RIJDZScLcWYxnBHcBT7iAVnMLYLcf4T5YzhBCPnBTPmaUbLWK1s3+I4NI6fuEdJHRrsb4x90woWwR0kshp+Upa+RtWGcVOlsz4h9fWm9EWVrY2lnG8YE7PBSjKBUjvUyNLIdX/guqwULvySIB3mGNkTBNK4s64zwa+sEq+ZfrHMDS9f5djn0WTYA3huOvsHJeXQLen2cbQR34s+woicbb1AV9gzKxW4N2iybFk8rO2WDZMPyF4JkrmDQ4x7i8Zk7eSZwWfDKCU23ME9pEfMWWedVfB32XxKC+xE21cMfX3r8nCdGqxtua5vS5dKl15KxEEqYOrDH0791AAuvfRS/fjHP3YcG6effnpRj1V0xc9C/tWvflX//u//HqisSITiIya/jZ3GbUNTw3f2btPGrlVOpsP4xqka3RBODwdv2qoBgs5m5ltyJADYYGyTGds4UsdOLE0kiAFyQikbvPmIJ67FnDZs0ERrE52C2OLa2SAhd27DX7RR5gjIWyXX5q0AMCeyGXlxwkHySt1gC/KNASKfJubu72Ae8YNDAMJONGYQwmlRWGGlYDP3mdd+x+b+BolmI8MDEYaIwqCc7jpwlPLcWFd4PySfe8D/I7yKYaAkkyRtynYO5b8ygTUTR00uDm7uO/eKayeCzvq08GxrqY9XqcE4Yk/DIFD0KLV02RhhZWnkCwJyfu1qGovwQID87W81Yej69Kc/rf/+7//WrbfeGoq4LCU4ZyJWuYYIlYH62Bj1JCkJMhCw1BAb61sKtze5Qolkm2KxFjXEdlMsVvhcZ+3HyArPDrK/5wL2EhwbOEreMGGOxE8JQQAIfIn9Fd1gPaqMI7DnWzAGe6j193Ij38zJYYu6KDgiE0zrZgPrtBmES31+cNFCSinBfShXy3XSiw+HhpWHDoJM8y3X4BtzrnqN/mg8K3GV7X4QDIWzgrWBzI90QFNw7aw7ZL3BAwkc4t9ce3QEAes1/D9IWbJCSiNzLZl6tqbTmOaYQ8sRkMe4Yk8Ic48ZTuAZkinMPQ2aPZQ/+m1FSbcujUnlLhuJTr7kEjbsgYyNn/9coqrD/PmqdowePVpf+MIXnOCj0047rajPuOg79V133eWkkn3iE58o9qEiBEQ8NkZ12kt9SfO+x1QfO0Cx2OANf0fPFr2849FdsmRD1wrtMfIQx7lRKCDgbAYQc3fTcMpSIEYKieKwzAibOGR/sIGBoLXgvUA4EJmBmOC7WYT5l+gNd8Ni/oUApIuC4jOkk1rJGjZDSgPR0NhrgI5QpcCJVUGC0cYp4837uhnCmYuMuyDG6mIAkh5WJUTmVC5GbARFLs2rvaDkCULH7QyC9Lv7BrGmsQ5hZAkaKWpGZLIcWQ/5Tr9jc+9wfnD8E0880TkWrxHNFUaEvRusS9bM3ZwGfuBaKXHhd85BwHqdb+YJ+wkRaCa4qI3O/YlSx/MDezQZT5R04R6mLR0VBsbvJa17WurDQNK/HoyeJTWVuaTQ44+nms6bk45/X389lSqeR6nMSgPzlEyH7373u1Xn1PjOd77jnHu+jewjhI9RDQdoW8+T6kvudH6vj43ViPrBpSXY7zv7nlJCm3b19uvTejXXHVlwA2lEtGV/YnhiT8SABbw8KFfAk7xchexLOIQZaHIV7XwfPIzvMcMkHAbD2fjx4wc5JSwAJN0xiCamfC3fyR6IIZmSNt6gwnwb8kYoM2zsVdCzo3+cn8OAOcHfzFANZyy1QwMwl8LKNuD8jzzySJXacU/5dpuvrAlPPfXUoMxXArlwBATNXLCeH5RgYo3ASetdDyi3B5+Gy8PJ58+f76x/vEapK55n2D0XuS6uL1NwK2OJ4LVADaEzaJSgmTZucI8IJLPer5wLOozX4cfRmpo7CF5jHLJvHXecTzP2sMD4nnqItOYJeyH1z+SDVFbQF7PfRrkLODofe6wmnBrgk5/8pL7//e+HUsq4rE6Nyy+/3HFoRHXoKgv1sdmq01Ql1amY8OwP9VSu6nhlUKNtsLJ9ccqp0dcpdaxMpXO1TJPqczNesZkSTQs5x3iFoYxNFYMJdbcLcWqwUbHhcgwEApufpRiy+SBqgtarZ8MinZN/OT8Mh3ye78EYyqZLwz0iozCeET2RKTqcDQ8iwnfYhk2UNSV/rMFltClWKbatlV68TerYKjWPkfY7XRqbO2kqRvkYSIObIEJQcWhgnEaEF7sZeBDRAXEuVQklN7gvhTg1IPa/+93vBkWZsN/hrMRYiNBA1OVTL5Q1hogq1huMGRhmWF84Dk4T6z/EM7SUcyPqNCYsJOst3flwHAww1pzPiD2wEnsmrvJdy3DC5RIF5wZlu9z3mmdgqeOMe6LSwi5Tw3puhjOrv8tzq8S1fGX7ej20+Xl19HVrYuNonTBxnkbRtyLLcydKEeE8FWfWjddJzyxKNWx/y0XS3JB673Ae+5wvrX1S6mmXRkyWphxSfgOOX5Yxa2YN1Vf+6Ec/qm9961s5918qJx5++GEnavbGG28s96lEcKEu1qxxDcf0OzViqouNGLIWJrSt36EBUvtHQjvUl9yg+thUqX2N1LVFahghjZiVauoZEByLPQjHPvsQmWbWkBfja7pykkHBHmzrPdoF7s6ewv6XazABjgvKb7DfkRXHWgs3s0wT1lz0C44NsiR5fybAoTgfixyGG8AF3AZQ7g33oNhNmiOECLT2zmelzmWp35t2k0bOy2leFAPoXhwG7h6Q8E6CAAmIYI7ky+XCQrkzk+DpGG3zDQhhXcHx4Oa1/A7XZ57z93SBT9nAOaFPWGewcbAumh7ELsHaQ0lfeIE5RtE3rFXsvfQuChP0Q8CxQbY79hv3c+M6uV70BueUr14sJHuPAECu3+4R50dgK5qF9ZkxX4zSzWguAuI4NsfBhpUpw6Zc6E706o61z+qVtrVqiNfp6PF76vBx2R39jEM0moPVK6Wb/i7t2C7ts7905nmpoKIwMP1wqbE1VXKKDI3JB0ojw884ygnoKO6PO1iC/6+h6kYjRoxwfAH4BKrWqfHMM884jUH+8Ic/FPMwEfJELNakmNJHDfcmh6aT9iZ7pN42af2dUqK/bu62p6VJJ0pNwRdYNkdIEA4MPLM4OMy4WmgkFZsVGzGGQDZFt8DgGLYhZAOkn42cKCciBwyco7uBMsKAsQ4ZYKPNFjHoTTXF4MZxzKkBCr0HFYNEn7T0Ram9TZq6mzSp/Eb+oqBrp/T0P6S+fuN453bp6euko98jNY8qzyl1de2KrraxhWHcxHg+USrlTl8vFgpx6jDfuZ9u0YFh45BDDnHWCSKCChV1rFtmLLeSTkbMMXIgaBCR7nUJsYFDFpEQJrhWjs2/xUi5tojXMGGp48AieMNybCDqIeMILJ4BYxnnN8cJUq+3lNjcvV13rn9iV7DC+q6tum3dozp/xgmqyxIdjVhznH+/+pl0S38zb57TQw9Il18hzQ6pDEvTKGn2iaoovOMd0vXXp5qMA+bgBz5AXTrVCjCafuQjH3FEB03DqwGcK+cc9dKoPMRicdXH0nOfJFrC73X1SBuflLa+MNBfp3WGNO3EnAy4Bx98sGN0w/kOx7bGxFbithCnBvsx38t3Yoyx8YcW4Hutn142YAxm/yATzm304fvsO+FuHAc9w3GZp9kMRO59GQcM38VxzOjJ3o3RsNDGrBWBTeukpYulpmZp33lSQ+kzAUqC9helziUDv3ctS5VSGZljf6gQAd8huIZKAzYm4cGWBVDuYCk3yunUYP6R9ZAvWB+uu+46J5Oe68A+wBoDz7QS14WsZ6wXrI0Eltn38cP/Yy8hcMt6+Nh9xIbDebjXlTDAmLFArmKVQkZH5duLg3XfW87PjLacL3qP7Hq+P6x+GzjOeUY8c+vpgZMsnz4xxcata5/W4h1rHIXR09enuze8oJa6Rh0wZmawqgmrVqjhW1+UerpTZZieWyStXiF9+JJwTpDxS/+MMHtoFApshu95j/T73w84b5hTF12kWsInP/lJZ+wSxIFdteqcGv/1X/+liy++OGOZigiVi9ENE9XR1+Z6JabR9eNTToyE2wCZkLY8Lk09O6fvR1ywILNJMNAxBGIEIrKYjaGQcgJex0MuwONO1AKbN46XbE27ER1uh0S+5bLcgDBgxKzqPjSU0PrHz6WVr/a/EJNOe7t0YHVEgeaEbav7S6YYklKiR9q6SpoabgmgIID8WCqnO1LfIm8qDd7eOtUGRB0p4qw5rD3cf4g+czssws9a4Lce8Ez5wUDiLoWHyCHSEwIfdk8JjsPxWBvDTrnmewspm8Wewl6CCMDJ7D03hAclERDjjLl8yoUYcBJy7xGEwP2sEYRW1q1SSn283r5+0O84N3b0tmtLd5smBinx1NU14NAAiI5Yn3TrjdJHP6maBaVr/vxn6eqrSQWSKD9x5pmqNdDQD0MnhqlylSLMZZ246aabSt7ENEI4iMfYyzB6Dg7gqeut63dogH5e3L5K2rlSGplbZC6OjQceeMDJUiDwiH2F8R2Gs5/AKL/gKHNup5s/7AUYqdh/eG82xwL7hu0vhUSKs+fZ/oRe8UZ+VyVeelr64/+kgqfAlBnSR78oNddgffuuVUNf66ZkSXmcGgRtwG3cpZMN+WrvYiKs8rblADweQ7plgWHMJtCStSFoOdtsQLek0wkY6QHrlntdI3CLLDjK3obJb9Gs2A0ZYwTfhWkH4d4F6TuSDjwHHC44qfx6aWC3QlNQtQNdW+j5c+/ZJ6zajQUj4kDHTmZOjUrQGJzD4h1rPfVdpJd2rA7k1EBP9i68XQ3m0DA8dK/09vdJoyvLgRMqPvUphDWlNRhkqUCqCszEKQRkFr33ve91fANUtygGimZFwpt4zTXXOB6ZCNWJmS17qauvQ1t7UrXhR9aP1ZwRB0jtCwc1AHTQ1x/BmEcdWaKo2ChIkbaNEiGCQTYsT7cbHAtHip8ggejzN0qsZHNmhAVSKr0lYmqiWfhzD7kcGiApLbhG2utgqanG0t7jdbm9XgQwnyCBRM5gVEfIl2oMFwqMw37RL6VEIaSQyEd+yEwkqoo1BOdoKRyT5lTFYAGRhlAbMFqwliIQConk8sIi8RhnFgGbL1jrTGiwHxD5WkiEH8SJHwg/8wHx525oyLnzOz9k4xGJxvGYK1a2g/dQ3xyHDQYqHBS8hx8+x73mNbKe0jkJ+SzfQckr9hrOw+q6lwt1zvgeKu7jWcY9cwMn6Xi/MYSxoGugL1bNgmdHc/AaBiL9oosu0g9/+EP97//+ryoZP/rRj/S2t70tdIdthNIgHmtSU/wQdSWeIQLGcXA0xfdXvCuN8bHHHWAVDOzDFhzFvsJ+zFoM1yAquRhlcdgjLJvSC/YMdA6O9VI6DTHCWfNwQyVF0ecF9p2/Xjng0ADr10gLb5bOvFA1B99MyrqS83ScyETtwykr3fFdS6BkC9karF84ROGg8Nuw++b5wTguFTXcvTewUeA4plxU2GWo4Mv8oGkYb+5qGbkCLWbNx7FNFqJXCIRCV/F9ZGijIQjycmtttJY59thnyKrgnrHv4ITA3oNOt/LABJ7xveakgtPw/2gPArP8yvfj6MJRzXzk+9GZPItyOzbQEn0eB2JdgAxLnjNVB+p6yeD0uYbu/sowtQqe2znnpH5qGJ/97GedcUovvGLwr6I5Na688kqny3nVR4IMY8Rjddpr1KHqTXQ7EZ31scbUgtkwTurZ6jKO8FpuBkmipjBksbkYuca4xUZhG+Wdd97pODbCbgCJwYvGsd40bhwaoNRNwNj0vD1EdpX6qGZs3Zgy6rtFB973HVtrz6kxbjepdbzUTp+B/kbh9NWYEFJJliwwoyOG5kyZGNYHodIAaUuX1lsKQCwhmIVEPeHMwIFwyy23OFGgkE6imHjdbUBI9JfdiPv0MSoEEAScKpB/d0oyDe0oQ8ZahwgtJKvMC+5XoZFiCDMEMkKNda8Q8eIG+wZzAaLMMfide+Re8xEfluLOekvWBeeBswLnEJ/zZuIhQIj05bsyCUorIUKpLo7B5wqpqxwG5o6Yrqe3vqbeZJ+zp3MnJjeN07iGzNlEnDfiajY1jPfaR3rtlYFIKv49sgaz74YpLrnkEp188sn6z//8z1DLSoQJDAK///3vdc8995T7VCIUgPr4BNXFTlRS3YqpIdUgvBHnhY/ztSlYHzyAEWzBggXOXnLEEUc4rzGWMcpiFGTdJgIWp7O7F0BYYL/wrvXoGwvWKnWzZPihN8ClmiPXHXR1SB3eYLqktHlwNmLNoGVvqe3xwa+1ls6+wnhmvqCP0znECCQpZxnZWgbrFvYSOClz+ZxzznGeB4Zvdw+HZDKhhHoUd9bTcB2XcOJFixY5Dgx303JsOThr4dgEqYYZkIlthAyRfHUBXIHAIoz+OAHCcsRZSVvWdZ4BjmwyKrx9isxwy3prvZIIBrOyXXyOe+YOcqVPK3sEzo10HAwnCFkb6Gbey15DhRH2tnIFM3JPDhs7R49tcZXJoxTy2DmB1hcyfjRhjHTPHQN/YK2ZOl0aP6EYpxyhxMBGNX/+fMdHcNlll4X+/UUJ1YBQ/upXv3IaD0aoftTHG9UQbxowBo09eLATI94sjcvNqMFizCbtNhaxgLP5OIcYO9YZ/BiEigG+n83DwIaDMasckSc4WTi2Gxj5CqnBWREYP2WwQwPU1Uujg4vTqgHXddiF0oyDpLHUfz5AOuyiVCOqEsBKD2Uq9ccYJ0oFYV9pIJImbOdlLrA62IWACH/WlVNPPdWZ0xB7oiNp1A6SyT5t73lKW3oWOj/be55UMkmEanjAYAKxhTwbILiUCMC5AaHGoZsOrLfpokzTAbFAtkM+fYBw6ELGGbcIgmJkMiCGIPo4GDhPnNf8IJTc52xNLYmwIhuDPQoR5HUCsU9hKLPIqnTAScJxOIaVuWKMIfzLhZH1LTpn2rGa1TpZExrHaN9Rs3XalCOHXgcGr62LpdX3Suse0YolL6Ui4nnfFy+T9juw33HbIn3gY9IbTijXJUUIGYxtuNdf/vKXir23V111lTOnaV4fobqB4S0ea045NEDDSGkypaFca9K4A6TW4HsD6y2BEm7jG+sX+58Bh3exSl4S0IDRyg32XcZrqR0aNQsCo0aMSu1DbtRq377mWdKoo6SGKamfUUdKzaULmkJjHH300RkzfAjcQLdWosOs3BHsYYDm3OgMHLE8BzglHBX9BDr6NmhFx0Kt7LhHKzruVntvuA4+DPQY8h9++OFBr3MeaAw4PIFcdj5ewIXT/S3Tc4NL52MP4Xg4fggqgtujkcMq12XgOWA3gjNZ5oRpDAKq3NeBPuT+8V6cFWR5uLPIAU4RtBp6hH0kE6yvBuC6cHpZcG65cOKk/Zyf6c1jNbt1oi6ceZRmj/AJpOvdIW15Qtr0kHq3vqR6c4QdeIh08cekltbU2j53T+kzXy5p1YsIxQW+AZwaudoagqAojI5IVXBmDdYcjsAq3iRNni91b5KSCalxvBTPzXiLoYjFmOhysjIwaCI6iDRmkTcvNA20MXiFTUjYADAwWT1EDL3l6v1i/TPcYPOzzQmiaN59NmjKQ4S9MRcF9M6gSfiS51K/Q4bP/BepsXaaqw5CQ4u098klPywbA4TP3fiMueUWF5BCxDQlcRDb/N2bHVROcH7FaDqd63pQKFjLyEKzbC/mNsZ05vC4aW3qSQ4Q857kJrX1vqRRDQcqLFiNY6KmECA8bzf5pjkXjgQiRmnGZxkdrDUQcgxBjA3+FjQyCgcO14nDDFGTCyDl3rIYxQIiwt14kDGHoYl5411PyUrgehAr1DHGAc+8cYv1TE4c7iURVDhI3IY09rhM9dZLgbGNI3Xq5CzG4A2PS1tSdZQxLiZWL1W89wipcVQqYupbl6eaZbOm14CxYAg6O6Ubb5QQ0mS+EUFWi9eZBjTf/vnPf64Pf/jDqkRwblHQVA1j9B5Sy1SpZ5tUP0JqzL2WNg6ERx991NEarLfsf2gKjE5WQQADUrGy57yahYCBcvXIQ9uwJ7FX+wUhUj6G/cwM11VRVoj7+86PSb/7caqpLJi5u3TiWapZNM1I/ZQYlBh1R5NbSTO0B+PGDM/MMXgngTXMsTBLnkZI6RSAVrH/R2vgSDjuhKO0vuvpXT2KkurThu6nNT1+rBriQ8sX5QsM7fBjbDcEQLidtOiJE044wXF6sNbgRECTEEDKGsP4YEyQzZNLRQwc0NawPBdQ0sh6UBQblrnhBloL7efX2w8QmIEewP5kPTLsfV6nuBvMO3QJ+xa6mTUccH/LXbac8z9q/B7OT1r0tknrbpeSGLWTWrb0Uc2Z7mocfeJp0hvnp7LAq70Mezo895z0wANEVEpnn80mreGCs846y9HS+ArIOAsTsWQRXOrnnnuus9gVI7UkQm0Bww9p4Gw8GOAw7lOnEKMawNP99NNP66STTgrVsQERw5BnhmDOgY2nXOUWKGeCl92IiqUpsukx8TEwQgysBiP3oyqA02vVUqmjTZo8UxoTpRCGCYgPYwfDsHvsQnjcBlyDNYBlfGG0ZXyVmwQByv6wFZXLsUjEjDV+KxQYUoiochvKqUW7z2G9itcPLikXU5PGN75RxQCODfZhv1Rk1hHGAustYpRIK9Y/xgWigzHl1wDSC8g0wgGSDaHPVcRi6IHU23pfajDmcBS7S/259xmcPER4sU9hkArS9Jv3cl/TOQ3dRrWKRF+39OpAlH5HZ7cWL1uneW84Q5qcKuVS08C5+aEPURctJagQjO95D3WZNJz2FcY95Z0qLRuCzDf4D1m9lVoeK0LlgHItZEKTxUiwFPuie2+jnApre9jNjTHC8Z3Gr+AFROCWA+gdSprgsDAQMMbvGN4IxEBjcH+4HxdccEFoZSCLju1bpBVLpaZmae7eqazpCKEAroM2hh/Bad28jf5nXr7M6xho2Tvglxi5/Rxp5UCp+k+kg2mvMLQKTiMrq2d2kleWPaWp+w7N/J7QeIBG1ofvCMOxgqPCvaa4gRMVbo1GwNiORmV9YdygPeHAfr0ivECXcX1WPjZXoE8w/JdrHHoDDAH6wYKeODfuD/fT+Ew2jWFlpvwyDbGfERhc0YGvWxdJba/sKjH5yKJXdNQheyo24605B0hXJe68U/rSl1KOecYFz53G2TOzN1OvFXzta19z7AzXX399qN8b+u7PYnXbbbdVfJPBCJUBImBJpSQShMgPDEHuslAQaxbnsDM12ETIhoDocA6QtnIad3GuYIgm0tvmkRnFyHiy67/rrruypiRWFKjpOTNcsRghBQzJ9EqgZ4N77DKPGM8Ypqy5MX/nXyNWiA1IIuMfYVKuCEIDDjyyCMoF1piwagEzh3HEug0YhxxyiJ547m868JDBUUPx4rW1ctY3RCbjAALtvr84Rr2Rd4iPm2++2REpkGVzoGaCNQnPd+20kl2cZzkydVhX3dksbkFhYgRRz33g/LjObHsRc6lSe9cEQmKwMF66coPmzpwkJaq8x1NQ3HRTyqHBWtkfAac//EG6kPKCpY+SLQcQ1+9617v0y1/+suKcGr/4xS+cc4scGhGCAOMPXJpoZpxh3r2KaFe35ggLRClT4tAieMtZkgfuZz0LvfsXAWX88Dr7MIYGNFHVgHK2B1TR+VYRnn32WccY7M5kgheiO3ByEFnu1heWrcFYwmiNM5EgDoz55Sz/RDZWVenmDCBwiHns5szYSTpf7FZPT0INDYM1Bb01igEzmrOukJlBYJJbRzJuvI4Exg3BCNg00EjZsjVwiqBT/QL0ggJ+zxgsl1PDeusZbB9gPuD4Rmsxb9BBxmmyzRXufbrSiWQmolsIUqtYeMouc0uca3ZeHwZOjf/8z4GehICSxFdeiaVfwwUf+MAHnGbh2DrDHKuh99S4+uqrnWYvYUS8Rhg+wMDGQgwh8tZZg0QVQxCwySF42CAwBFsKXznAxo2nnoh1QLSL9Rhwb3BEO5Qz2iRC5cCiddwinXmCo4JIDUgvhlkiYyD1pMG6yRWfwyBNFAx/Kycg5eU0BDPHwlpjEB2IDXcNWObyqKa5WrWCGrfM59Scbq3fs6j3lOcOacAoT+RUJhC5aX2OWI8RKryGEMHp5C23xP0iZbpQZzAGFdbfisALzyh2x83S00+oo73duT5S38lkIQIqCBjH7nnm3cuKUUc0VNS3SA2jd43RrdvbNXpkszSiRmuVe8Ee7Fc3vH9vHi64+OKL9de//rWiGr9yLpzTe9/73nKfSoQqAus4azJrL5mL7lKT7G3evklhAKMTwUo4NzDOldvRjW4gWwRgbLZMDLiPaQzu0/nnn18T/QciFAb4HU4Lb2k2eBD81vqDMa7hlmT8oDXczgN0CAYrtEoY5V0L0frZ+G81gV5BOB/dOOKQ4/XMI8sH6ZjG2Gi11IW/thkwxpMJgeMAB1gmMDZYf9Ei/GB3YT1CYxA45Ndrg/0+DGcUGiXXXh5FwfZtii28Q7EFt0mbNjpZN9hKcdqg1YNq0EzvQ/NVvFO6iTUldQ1btrZpzKhWqX50qj9vrQMd7e1riiasofUpCChVja/gmmuuUZgIPUyU5oIf+9jHwv7aCDUONjtrQMaC7Y4owsBGrw0iBovRTJjNlXTZcker47BYsGCBs8l5owrw6EMu8eZHiADc6aoGomAwEudSxglCamXgIFeZGgEWC6QVW++CWgDOIkpOuZ/DYfPeqIX33qI5s6Y697gpPlUN8eKVeEBQIkARnhjmMd6ki1ai/ASGe84LJxdjggwggLOXcYVzw4BhiLWYCFfWJsot2bqdbqxS/sNvbPEaa15Y6fl548+/kW6+btevU487SZsues+uUm5cG+eI08oyNrif3mvi/rn72xj4PPeKsVHRYN+deaq06m61bVmr1pYmaeKh0qjZg2ppV0Xd9XzA8/E6nohK9GT01Doo0YPx6s4779TZ1PytANxxxx3OuhOkNF6ECG5YGRQiyFmHLXORiHPKqGDQ92YvhgH2CoKUbD8tF4xbwQnYx04+eaD/G/s4ZSDZm4rh4IlQfcDg6pcNx97PWAoa+c68IoqfOYeBuhzjy/rp4MzzNmauRljPUXeU84gRI3XCYW/Va688qT33m676eKtG189WjGoJRQIZ6H/605/0vve9zzmXdGB9oX8d/TZwYgCrSgEw6LP+EIhndh9sHehSHML8nfGY6ftZ19P1z8CxDI/nXgUpeVUUrFklfeVzjmPDQXOLDvzqd7TomWece8G54/zj/OBdBJFhk/LavLgP6UpLEUjHZ8t2jUHROkvqPUja/pxeem21jjriMGniCY72YH1hjOC4Y+2oOaAXyTx65ZUBncGYL1MJ5nLi7W9/u5N5/dnPfrYynRqk2pJSRqRHhAi5gCgPIxuQHrz6Rn4gT5AhvPomZpPJhHqSO53UyvoCvbtEGLA5lxvW5JeN3U28rIQMwqhcNXkjVBYwkPplFmFczpXQEPEDeYJMUncZYVsM52E2WJZWJfT4KBTcS4y+3gaLUyfPVW/b5JLVq4YY/uxnP3OM7OmyB0jNZsxAqtm/iWhzl2RCQBFV4W4CTgQVThL+hsB11w73A+MSJ8/xxx/v69hg/HHcsj3/5UsGOTQcPLBQayfP1B5vOtf51RwVCCgEh9UpZ012R7amcwpybdznqoiCpSH43DfrhbUP6NCzL5YaUyKKaDeEE+sDz59ggJrDccelemqQDg7oE/O971FHQMMJjGNEB9nXleLU4Fw4p3I43iPUhlODtcsdtct+Q8AUex882wy5fYlO9alH9bFWxWP570kWoV6O8opeUO6FAAX2Yfc+xLXDKWuFf0UoHBiTvT0MmB8EdeSSbUokP3OPcYUzDQN12P1rggAjOY6VWnBqmKGeYE+ux/rmTZo4Wa++MkYTm0pType1lGAonmu6gCaePVnfODSw8zCGGANuPcL/u8vj8l3YO/gXA741nc/EnS1zJZ1jA/6O1vELOCoJfn+l1OZq/t3dpU0/+S9N+txXnOfHD/eAuYXGYK/iPrFeu88Z7ZXO2M89q+h+fW6M3l9djXPVMG2W6ma8YVDPNDQz9j4i+WsS3/629MlPkiaa+v2YY6QPflDDDRdccIE+/vGPOw5N1rMwEA9bcJxxxhnV02CsAGzt3qJXdryk19peUUdfe7lPp+pBFgIkikgK4E3FgxBh+MKQ0p3YoVUd92lN50Na1XmvNnY9n3fpGAxTlhJZCUBQeUkX0QpsYuWsyRuhcsA4IJIBMuktO5VPIzXmFQZ4yhPQTwGjJeSz1OB6iJjEEQA5q3Zg8CU9212GivtMRkyp5jJOB0q1WMaGFziPERkmMjG6Bym1xHpJnXDei1jMZghhjEHYidYyUGoPMWQ/GH+ClngKHetT+44bfYmkOtauHmJAZS3mPrFWw3W8Qivds+VZVHzpKRfYixtbRqih36FhGT0YALn2mt6PyDamt8Zvfyvdcot0/PEajnjHO96hf/zjHxmjJEsFzuG6665zzqnW0Zfs0/KdS/Xyjhe1pmNVbc+1Eu7HGNe4l+w13j4aBHNgUEETbOl6Sas779W6zoe0puNedfX1R9fmAXhZMTJA8gF7GdHKXr3D3EJ3mP6KMLzBOCDj1G3LgZOjv9EYuTiVMWrDe/k55phjHK1PRDrcuJSAp8HBmI8Yt2thTSWgxjIfDDhPS1nq7rTTTnM4MTYbvxJjnB/PnXvPOnPqqac6Oi/T/edZESxFhjTjhe/PFgxEpQuMo1YmF67t1hcEXaN3yoa1qwf3UUgktG3VqiFrMVoKZxHP0XoOukGQKw4eP1RFwJTbjvHoYzrgoIGAYsYQ18Y112SWhoF+uddeK/3xj6l/f/KTVDb4MMP48eN1+umnO76DsBCqU4PaWERR1TrWdq7RY1se1vL2ZVqy8zU9tOl+tfW6PLARcgYLO9FDGFOtSS3GP6/owPHx8uqF6tPA5rmzb5XaelfkTd7SefYrBZBIIp2racOKUDwgvHFAWJQ0JA5jMdH1YZRQw1ANsWD+efsoFBPMewymGAAwwCOgqh2kFeOksawarpFIGrIWSmVAYEywtiIkEBhEwNgPIoBsDhOtiBLEJ+SfMRWm8ESksI5ZpBbRsiZ2+WF9dzvqSooZQ3uA1cVjOuzkUxxB5AfEsTujiedMCn4mBw/3mO+rmB4iaUCPHa7FncHIWsAeZPWJy5HNVVIQXEBKuE8JjuEC1mMyZW+99dZyn4pzDpwLxotad2g8tvlhLW57SSval+u57c/oxe3Plfu0qh7sLZRhZO+FP+HgcPMb9mbWu4UP3Kgdvct3vZ5QjzZ2LXKyw/MBhrxKLwfCPK8VQ2+EwgD3hhcee+yxu16Dq8JdiBg3HhAUvNc9z5gLcL1yGJkJ3mHuk3VMhYZSjnfvfQgD6DS4sztYiOAhsmPg96W4Pq6LKGuCnNij3RqD/+dvZqCG/6MxKPlHlgnOlzDPkXFFmVzuM+WmWPPdGqOsZStnzx3cqy0e154HHujoIT9nkPWpsRLG9MpgjSZILp3O514yd60kcKUCh9N9993njAO384I1oZCm8FUFnBj0x6UqwjC27b29Pxu84pwaRFiysJ5zzjmqdRA9BZJOo5ukI0KWtPkbPiLkBgQrizfRRJAob2TrEUcdoheef3nI612J/CKpMJxWulMDcI5RP40IjFcIjXssYFyFqKeL3sgHlHuz8knlaLDG/M9WR7UaYNFhbkM3hjlSsREepcxIwXlBY3miu+yHchQmUCHL1u+B9xIhRWT+Qw89lLUJYFBwTBzTCJqgNZlLgukzpXd70n/ffKHi+x+0K7XfDYQIKfMYACziF8c8ryHu0gHBab0oytk4MxNwujAuMXSl2yu5/pp3akRw1oa3vOUtuuGGG8p+N66//vph0cR4bedq7ejd7tIY0qrOlWrrHZppFyE3WJkT9Cpag4hxN+BV0+eM1eIXB2cMJtStvmTuXKRanARk3hEVHPXTiLBo0SInY9tAxgbBLVaSlDGdS6YGTgyiy91gDUezwO8xwpYalDTCeIrhu1SAHxKsGTbQfe51hmcDf+f6vFkcxQQOL5zGjB3TF/y/u0Qp6wxGbNZZAq1wluH8QGOEoYUInGKc4riGm1dUKb33fUSaQoPsfowZK33sEucc/c6T+8G9I8AIMIe4tkz6AicOf+dZYEerRKB7cGigL7wlkZkf6fqFRKhNnHPOOQ4PC6tKQ2g9NW666SanEehwMLz2JIZuTF2JyjRQVCPYCKlVzoJHJIfb+FUXa9RBh+6tF55ZooMOtdqBMef1fABZ8zNalQIQOgyJ1ExkIYecYABlU/Kek7uefYThC28EFWSWscK4sdTbsEgB30uDP76XsVlK5x+lichugNCZiMLRYY4byA/GV8QRxlUaTVcq0hFrHAqQefbNUoB1BYdCutqVXkcxji1+ABFDZO4gWsIYUwhaBBckPZ+SaUXBmW+WDj4s1dBv8hRpVkrEM/YYb+40cYQZ2RbMNciYRRf5NdZ0w+YmzwJHEfciqPDi+eDAxPhUjIwW5hlZYKQEu+sbG7g2I565GjYiVC/e9KY36V3vepezt5TrmXPsm2++WX/5y19U6+h29AWOm8EG8W5HY0SOxEKBRmX9gkv4GdKmTZ2hl19bpK4uDCwDa348lrtO8NaOL/Wc4fhwKAxJ7BvwRNZ3i/51g0CLCMMb8G70tnudR4MTiIGRmLFkTapzcWqky+yE+/G9GLXgUKXS4swN+BvXSVS78RsCecxpji5nfeB3eGqh5XC4NuwNYQafAb7PL6OadYd7TjYExv5iw+4ba6tf4AGZB+5yZvBedIB9hkAntG2hY4Dxyw9j9cEHH3TsSRXh3Bg7Xrr8f6SXnk+Vodp7f2qFaXZ/mSxvhgLPjnnBHOH58jwJJspU3p+5aeAZ5Lr/4GTiOaERi5FdiIMU3cNz9iv5zjjgp5x2uQilBWsTNhD4/cco+1spTo0bb7xxWGRpgDENY7WtZ+uuKCowtqH4m8ZwAUTDNmlvNG8sFtfMcfO0RCvUvrNTrSOanWbhoxoGGtsGhS2gpQTHIzqExZ3rhFyxgbDxcM0Yz4iUYUEnFb7WoxIj5AZIi3tMQLoR6TgAGFsWBR4mGJ+IHQyqEK9ijkkMt6TYck0YfA3ME15HTDF3cAYS6cG5QPQwuGOAr6jo/35A3P0IO/Of6/TWLi4WODYCiPtogLhyzxhXmVLjIdaIW4ReGM4tjgeRsUygsjXv88vY4McFa4Ro50hZJn4YezjauC+M21z6ZfDsGRes9WTP+AFxQXSWNSVnf+AzvMYz5Hg4RwoR26wbZAxZWTCcGenGIhli1rwxZ5CV8uMfS3ffjUdI+sAHCNHJ+7wjlA5EVDLmaMSZbqwWGzjbCAIplQO43PrC69CIq04j64dvGbSwQRAEe7LfeBpZP0sHH3KAnn36BR16JE78pMY07JmXUwPDWlgNMIOCdRxjGHsMc4ZoX/YJDHvsGewrGHLhclFmRgQ34Hd+jYbhOvwN/pEr/8eZ5u4r5wXGW0rOoovp61JsLsz4h7NxnW4jMAZg5gUBhPAyAnjguvAdOBLanPJV7s9UUqYGThpvsAv3tZSBU9w7d7lW1iAcqDiM0JDpgqIYUzgf7FzDcEKwxjOWWOfhMBVhJKc/HYFTLlimBlzcxhalEU0f4lCjLBMBUbms19gCGOvcd+uZ6AZjkTkN0BZoDOxQ6G6ClyzYttCAWo5P1gjOS67vjW98Y9o1hL0KbWV9NXLCokXSD35AyYFU2dgvfYnIvILOPUJpgO8AH0LFODUwSixYsEA/YEBVAax0Qr7GuQPHzNOiLY9pZ1+qLMvExsnafWT4xsThDDahdBjdMFsnHHa+nnjqIR1+xIEaWT9DdbHco9Mx0JQigsENopPZYDAmmuChDiV15axOIhsXJJAUPRZ3oqxI5YzS8oY3IAfeaCfGCeQ81zq3uYIxyfikxCDEuRhRHESJEFXE9XhJLQQPEQ5hRvhAwux6IcyQZe4Pzg2ESLmiI/0AOYOwP/DAA45R0P0MOW9qwDK/SwGvQ4K9m3WI+0kZJTex9usPQl8O3pspBToXILpuv/1255lXqgPXG53OeXIfcaJRnotnSgZMrsZ+1nM/4s48sMgtnCbWx8LAeOcHwYIwycepgTPJykrgyEA4BRHrnFNetai/+13p5psHGiV+7Wsp58app+Z87hFKC4wBZ555piM6yuXU4NicQ0UYJrKANZR5la+zcXzjBO01cl+90vaS83tdrE4HjzlUjfH8spEj+BtP3GVR3CDre7cxJ2pZY5uak9M1snmyWuqGZjYEAftrKftp4JxmT4JPkO3O/oGBCoMtmaEAboTBCy1CFi6ODvidnzE7wvAC48etM92Bf/CgfDga3CVbLwm+Gy5MRjBjshiOQNZlgkHgbX7GdeaFOW8wxJttgNfQPMwTzo3r4fdc7wVODQy7xQCl9HDIwM3cWbbc13ydMPkAe4V7HeGeocvgqtwvHC/pni2fJZDznnvu0SmnnBLK+bD2YqTnvlRyLy4cBxjz3SXeWMMJ5mDtNn2fS08W3o9Tx8rjeucB32slZr3Bkqbv0vUTzAZ0yd133+3YDHgG2LxwbmabM1w/di/mirufX1bQo/DjH2fApTTG/fdLn/hEqhF3Ccd/hPydGl/96led4CnGSSEI5WkzeInkrIYGL9y0X/7yl/rwhz+ctVREOrTUteiYCcero69d8VidmuPNFWuQqVWMaZmukXWzNKYhf/JjDoNSgM39jjvucBZ5DINs7FZG6KyzztpVsx4xzGIOqbIUcV6jRuQxxxxTcApshOoEBARDpLtEgEU+mNGz2E29IRoQRKJGGIdhlaNifEOyGO+WjpwOloni12QbYc4PwghhYg3PcwGG3mKBe3fXXXfp3HPP3bVflLuED4TTortYo3i22bImwozuREQzlnLaP9euoWsejVekifkZm/Ih6ZBtnhfnfNJJJznnTZYLgg1nXD4RTcxfHCJ8lnvAXOD7cYIhUjPdaxwiRDfiGAoa2YbAwTHJc0fk8blcnOXMUXpr5JQRhdC45ZYBhwbgeePkiJwaVVOC6oorrtDXcEaVAZS3veSSS1QNYJ2AV+KEyRdzRszV9JYZTskp9EZdLBLmpURdrEGzpxwstY9Sy4j895hS6kIMrmRooDGefvppZ31nj8JQxOvWONiCQNATBoxX7AvWUyvC8APZCYwVNyeFX5Qy06hY5ajgLPAquG0mrsTc4cdKtrm5EZ/DYM/r3BeCk3AaBgXXAlf0K7sTBkz7oKXcjgXWoHQloYoN7hlBaPwAOG2mMpbw0kKNml4wltw9YrKiu0sim537tfc+JTGKMyaohGCBSuaIYg3H9kMwCfMzV82NpmPcm7GYe89rHA+nPq9nCgDkfcyddAEAXjDOcKLjvEIjoTdyzbjgWaGTcwriXbAgpS9MY6A5cMjQr6dSqgBESAvsPowxfAlojUIQymwl0hICX6xF84kta7Vg3XIllNQJE2fquInBNxIvmNif/vSnnUWDFEQECF6iXL3Z8VhcI+qj+rblhrfWeS6A8IcROcFCTtYHiz+bOL+T9j1v3rxBxksinfndXbcTuDd9vOhswmwGfBfGI4gmxmwivdnk8nXGRahekIGA8RLSg+GTHwzw3ma++cCb+poNRF2wdkLCEAmFGOZJS8Vhw6YWloEfYQT5I2Wc+4QoK2W0UjpwfUTAuBsuV1IzUcZBpmfAeUKCw4w8RUDyvFjz3OXG0uKaq6TfXpn6/1hc+tSl0pmFkSA/MLcQh7ZOM47IHmRdRxSYc5mxRd1e1mS/OuXZwNpONK81rOS4fCf32aJs0zlL2PdYExjn7BfMJcsCQkjwPbyHbC6cgGSo8juRfIVk/bGP5QTuoR83jAJBqgann3663v/+9ztRl6UuWcP4RdBzDsXAzt4eXbvyJa1o366pzSN14cx9NLYx/7rniHkLMICzWUmHXEFmRpSdUT6wR7Mv5bOuG4iUZfwW8h22/xDJyzrPGs5azr7pdi5jFDB94Q5+Yl9HixjYC6yBMHsZwVUEi+BcJ+AqpwjZCDUDjJ+U7IGfoqut/0OxjPBBylExvq2vWz5gfFtZq1yCbq1agp/TAu6EXuF+oct4b5D5bT01ignWBBwHbqcG+085qlL4AX2RSWPwrMIOjsZ2gmOAzIesDhOC5T53qbQmVZZJu+8hXf7DVFPvkEEgKzzfXYmAPcecUID/Z3xZr5tcy3Ixf+H76Aj2DvtuvgctiiMbDZbOdov2QOujhdAXphHt85wTTj6CCDlH1g0+AwcC+egM1pucA0IYU346OtIYVQHGH88cX0LFODW+973vqRh4ZPNq/XDxY7va5i3auk4dfb2aPyX3HgoGM3CxwLHgU7+OSJWLLrqopKnCEQoDizEGoUJIVyHGTqJz2YSZkBAGCKGJWYQDi7yRHStZEuR83KKCsWmlQdicSSlnU0K4WKpihNqH1aSFJECO+WH8u9NDrXxZrmDNg1zl8nmEBu8ne4Rx6NcEMBMYw2QFYAzOh8RyzpkiSNwp4xwHEoaRN5vjvdj1fFknMCi41wLOyRsVVg4g0jI5TKl3W4xITp4LBhWiS834grMZ0s/6Pn/+/NQbX3phwKEBkgnpih9JB82TZuQf6OAHnIcIV28UkzdTjmcHyWetzxeMZY6FwwRjFY4NxgOiASGRyeHIuEb0YPSlVq3thTg6iNzlOxDeOD/Lxm0QHOedJ/397wPCg395LUJVAL6BYZ4ys5TKLCU4JsLcAj/CRG8ioW++8IDj0CBo6sXtm/TstvX63sEnqaUuvyhhd7k45ibr6u9//3tnn8OwEmV1VwfYjws1QrK3sTbn49RASxA4QtAH6zoGVr7HDEeUrXQ7NdibvDX1/eAOooI/mk5hr4BX4qTnX+Zc2E2NI1QmGFPwBfgfXIMfxgDcwzSG9e0rxfoVRjkquBTGXLhVrsZg5hLGXPa9dNzLsjqClr3lmvh7sTUGcxYDtnFV7BLotHKVjgwK6yEXtvPFbCcEMFv5X8YxhnocxYytXbaUH3w/5dgwLFsq/d//SF/8fwobXGuQDBLO7YYbbtDJJ5+c13GYr1ZOigwK6+cKcH6hjzP14mS8Mo8Y39abg7Vg4cKFjjaj1xqvsyeVrSH7aadJv/oV3v9UtgbXh12hxP2sIuSP0047TV+iD0qBKNipQZoem16+Ey4brl+V2lDdPrh/rn6lIKeGgUWf87aaov/4xz+cRSaqLVodwHEQhlc/H6IGCSSKi6ZW3sgDvg8DnV9zplyBaCHaDyMy12qb4G233eYYutmMKiECI0JxQeQSRDWfiM9swNiJcM7VKYLo4XwQHRhugqbGQogQ65DJfFONcWbwHd6sJy8gWWST5JsyHjYsA8sNnJg4K8vdBJf1JJ1RBCLMOlOsXiXWyBSHD8Yc9mZqgFvWmiPQXntt6AcxjiM8QnZqWIRtkOslc4HSgoXARBaiyz2e2UOIpmKemDPb7XhiD0SonHjiiYM+x3njECJCD1FU9mCNz3+eZiADjcLf9z5C6Mp7ThFyApkSjPNSOzU4ZrGyNF7YvlHL27ft+h3Hxvqudj25ZV1BGeEG46cYiG+++WZHY1xwwQUFf2+E4gNDYCZjTxAUUhYUDYFBEu7nBQbXMNZ09nuimPkudC8cjh8CRigBwr7M8SNHXG0Dwy9jDUdCJZU4zrccFZHjcP5CgnCY+3xPtrKiYZS9DdtY7nbEwB25b3D4YjtUCgF2xDe84Q1F+W7WYYJ+6FHBeowGZb3jB82xy6nxymIp0TfwQdbul1O9rcIGdscgYD4yngoNemN8ovHdGg9nOWPDHJeMG3ewLJqDwFp0iNuBzvtMk1hFkbJi1izpF7+QfvxjvGOIMunf/o0HX97zihAY2OLN0ViIjSYehuDA0FosY0dXotfntWCLQVAwKdkw8eYywSulHEiE7PU/CyXbEBfrZ5ELmHjpSuaQwp1PdIgfrPcGx8G4ZTjjjDMcAxgedIy7EWobkArIGM6HsAFp8jYTy3WM8nkcfdnA+ZMejVAutHYq85/vCgJLGWefIqqKqKByAHLq3V8glog3Ii+LnZ6eCZyX33oG6UdUZuu1USggMnAJhA2OHogzBJwsDgfpol0LLO3hBxxgQTPhMAjh2Ag6Fr1AYLG2+zXiZA/hu9mnEGDsOwbWA45JbXTv53BAIWLZ2yrC6Y3I/uQnpb/9LdW8z7JvIlRVJBV8v5T8mGNxTI5dDHSn0RLpXi9k3af5Kc4NKzkRoXLB2kq2WxhGQCsXmivgU36l3nBWw2FyLgPoA/YXHOKHHnqoE3nrDho5++yzHQcHr5eq92CE8gDuQckYdHWlwcpRETwVZL+AE2HHCZK1lI3Xke0R1PgMh0dj4BCEP8LrygHLtHGDteKZZ57x7UVYKrAG+mW9wFNZY1jriuk8ZYwTKIXGIIAMQzzHG3RMjPNuDcT/T50a+rmgP3OxmVKSh4yIfPUhx0NH+TnCcVyjL/iBp1CBxMBcYh75OS3Qg2TRBp0fRQcOTBwb110nfeMbRHaV+4wi5ABKJpPJXGiAYChOjWIJDnD0+MHRvyw/R48P1rQmF9jihtGAyJXIsVHZYHEOI1IJ0YJ4yFVoEsXrJziIKmEchem5ZkNiTHKONPvjh8hufihZYoZSnClEIuDoIGKE9xHtVS5yNQSrl0u3XiPdcrW0IjtBjTCURBCVXQyyV+h6B/FBdECc0n0XkSIQJgRKof0zEsmEVuxcrvXJtVrXuSbw+VuTTI6PYQAnC05BIlUKcewUeq8xoCOImOelnq+cD8/NT1Bg3OF1opzKkVpMZBFZQA6OOEp6Q382i42fs86R9gqvBi8RsPfee6/zLHIpTYhjD8GB8ycXAxDvZfwxJsnCQBBnc0Ca6GBt9zNs8TwZ25w/wglnGcIxQoRCQNAPRpF8nXf5gGPBf+hnUwzsPWq8WuvqdwkhVsCGWFwHjA6/bwhzkXWWNcLtnIxQeWCfRhuEsefRBw9ungvgAOk4Eo5qHP9hGgBvvfVWJ7jCrS/4l/lnJU/RPLyGA54MQK6J3ymRWBHo65UeXyjddrX0yAKpt3wBItUI+EJYfe3CBudFcAa8BqeeH4x/kXFSaA8bsK1nm1Z2rdLSjmVq7w2mDazsLQZitBqlfdgz0Rf8lCILBuOg2zBt54XzkjnsLllcKuAc4tnBcb3gfNAXxQ6aSodB6yg9+uD91geO5/XRj4d2LPQU6yY2Gr8MvHRgH2LNRx/Ch4LqXd6Hcx7w+WyOQcoSWhAZdjErweYFupnvwgbHuZHNESFCocCXQDuLspWfYoLiqfvEJz6hYuGCmXtrZ1+P7ly3TEkldfzEWXrP7PBLsJjRDfEEaWMj8pvMEYqLZLJLSVGfPKmY8Nz7p3FCGqj9GobYZTNlIcewFAR4ps3Y5wViNWgZnqDAeXLxxRcPElgc301AERkQFku3JVKEdC4III4OBAvRz2VLI1/6svT7Hw38/shd0r98UtrrIJUVOzdLOzZITSOlsdMrurEURALCWolgLGIIxjFgTc2NVLGWMj4Zx0QzFQocGk9ueVSLl72sCVMm6LntT2tG82btOzp4qrmljOP845yJVCwVuCd+Je+ISIN4sq5B8osNnAUY0TkPUuz9Uua960ypMegecR7/72vS/femGvnNniMd/YZQ5izPAwMNzwDjbT7rJGsvzgN6j1ijvEzHw3CE+OZzHI8Sg0RKMh79akizt/AsbA1AdPhFe2H04nttP0PgIqIqufRAhMoHBhkiHOH8YTfzTIc777zT4XjFMgaNbmjSl/Y7Vj999Qmt7dyp8Y0t+tc9DtXk5vDLtbE2sM7CXe+5556ylkEcrmDd7UtuVlI7FVOL6mLpo4NZZ8No8s3YzbUEFRoUPpXOeBt2gAHl3TBq271wN6o1YKDFucEY5v/ZK8kgxijJnsM8hUOURWNwf6/9ubTspYGmsa88I73z0+UtQYKjZf2y1L8TZ0lFWFfCRDG5XqH9OAgUZC5SFo5xZ3yVwBDKoKPLKYcbxvhb17lOj295Ums616h3R59ea1uiYye8QaMagvUOZH6SWcu5UT4aLV6qeYFTB/3v5Xscn/vD2oKtKwwtlgmsefBOeCprBVmK6e5BOcvbsaZxjk4/ugMPkq78rfTgA1JdXDr+jdKkcAJUcf5SXodApHwq2+BcZo3FngOyBc6a84o5Y8fDCcHraF+vLYH5STaPOZdwWvg5XhjTvI8KIrYPwWdK1W8nQu1i/vz5+tnPflbQWCrIqYFXnOhxUkaKhbpYXO+bc5Aunn2g01cjXsRJw8KGQYJo3mI0JYyQGcnkTiX0GHFKqd9Vp3jycMVio33JF4YfCAMphYU0CycamEU6CMjEYFMh+soPGKRybZqcDX4Cxks+2aAwxkFYaApr0SxsYqRbIkL++te/OuQqjCiWnLHgupTQsAgD5vHtfy+vU2Pls9LzpLr1n9O0/aWDzqxYxwbPuGKi4jIIeNZRiBPROYw/1tIwHcTrutZqW+9W9fb2qb4xtYWt6lyhma27aWR9+kbXfsABWepyIIgJnCl+ogKhRukHmg/6ZYIVCtYnjk0kKOseBDYTeeD95S49McggxLr3xswOg3zA+o+xEcdCIUBI8gwZU5nKqxEBxfG8whPnWrpIOp6Du6Z0OkMZz4wxZMDJWKxaxRGGF0499VTHqfHxj4cXvZgJHItjFhN7jBynHx0yXwnK7xVx78foRvAMhvJSOtEjDKA78ZJ6kwNZMnWxaWqKp5zKXuBUQA/CpYOWIkwH1nmCO7LpSsuy43zcvZPcYyhoX4Fc4P1Ov/tBmUPuB/oG3oCznPdhvMWQii5i3yMivBA9lhdWvJpyaADbF1ctkZa8UD6N0d0h3f17aVt/qdOGJunEd0ue6hOVhGIZJTHIogsK7QvA+aFf4UjwLAK94GxkR4Tp6Ht+2wvOvwTSgr5knxbvWKzDx/vr/nTgerFTwLeLMW/TaTAqOKQDQTTM42I4NeCk7G8EsWGjYF3I1F8EBy33CD5cDL0TBFaqdZdGnT5DuvBtoR4D7s6YxUZTCBhDOBpwjmRzarCXePsDW8NwzsXr1EA3uOc/+t2vpxTalP3Q5hv3zkp5RYhQCLBVWg8lnKD5IF6o4CCitBQEhglTTMEB8GIjNsLy9kfIDQm9vMuhkUKfEnopo0GSvhJs0LtKlOQJFmlS6DBuuXsDsDFA3kkZ5O9sJnjLvYKD90Fc2BgK7RXg/d5MvQqIhOCcIHeQA8gEm463dAqRLeeee66Tuohosu9GhECAil5ubeeOAYdG6uBS+w6VDV1tgx0aYM0L0rrSldbIFTzfchuYg4CMIMgQcwTxG3a/pe5EV06vVxqI0LWUYD8ggpjThQDjB5E2lvZuPxBQjDREWmP4TrfPWYM4UujLlRZugDwXsyQXIswyJcIATnayMDKtqazDufa6YA3nHInC4zmyxrPeZwLrxZC6wSGBZ1LOHjARSg8cDHfffXdJ6ihzDI5VbKeGodj6AqMN2oJsl0rNuKxl9CW3DXJopF5bo0TSvzQfaybRqBjqqGdeCKwcDf2pvCUGWUPhA/yNHxwgfmUFWW9Z+9NlcOQLjp/OQc7fKHPIOaND4A1kNHItbgcNhkvu1bx585xAM9Ms/IsmKnpJzQ7/kkTqaFPZ8Nw90vYNA7/3dksP/0OVDMsiDhsEN6UrG5UrMKCyjjKn0BZuA2tY6Ep0q7enV3X1dbucG52J9I6CTMA+VmqehFbEOJjp74WW28UR4dUXrGMYw1nDMKhncmgQoMd6hxOmnH3fcLyQ6VNMoAfCCv4mGA3HcqYSloy3XOcxdisMyeghyg6yz3DefvOP9xgImOUehg2zfeWa5RihesFaiU8B30K+qK/0KKpSIiyjRoR84bfJZiYSbM54v2k0hQc734hwDI38QLwo/wJZwtgPWeL/WeDTbdAsuqTfkYL91re+VWEBIUB9OYyP1myQTYdNzYDxk3O77777nPeb4ZvzhmSwWbNQYADjPPmbRZ3hqDEyiOMG46U7wjdUzN1H2rLBlakRT71WLrRjDPRs+pxTW1R7vtIxun6okySmuEbU5ZalUS5gLGEOIv4hut4ILuYkBJM5mQ/Zx9HJ2oVzxK8vBOtBtkwQjOUIk3I7NABReIglv6ihQgFpppxBmOW+eH6IbhzkGHm8wAHNM8/F0YDI4P1EWrHes+5zDAxMGJPcY8hKM/J+1vxiZdJicGaMII5x0lAP1S+6OELtgCxQnA1wB/6/mKAUG8K22McpFYhcLRq/ipAVyaS/ES+hTmUyibKPsv4yHolyzieIj7WYTAfWY6IQydpg3WRMsHYToMU6nW5PYE9Hb6NvcqnFng04a5hjnBf6gfWbfcXAeTLfcWrYnmKl5/gcugtdhK7AOIlG4T2mK+666y5HV/F3AqgKjVROi2m7SfE6KeFytnIvpxfWLLogbHPpHcD/t21J/VuhQZOM7TAyKvycGoylaimBOaZhjJZtXKZRYwf4zLjGcQWVNwoz2DEbsBNgkGZdseh8v7/DHXMF6xZ8HP2QjpPTJy5bRQiyFovVKysXsFYVM0gDfYGGCTP4mzUY/YiDwe97sUURXBgUOCnQJcxTxgvPGKcJazfP0u1IZzyzb7EfsGfw/rCrkwDsV+hU9hOzxRVt/4hQcdngl156aWmdGgxmDLnf+ta38v2KCBE8GNXvxDAiCPHLvliywFJWieghoovZoMi4yYdEsKgTacsC6ufEgLRbIyWAEwTCzqJPOmeYKZ2cgzlp2LwghTR9PeWUU3a9B1KA8QzBxMKPsECAEMnLtVB2hO8xoxf3xgQH77UMF/5e1GiJMy6Stm6SXkul9Wq3PaQ3vUtlQ4tP9kAyIbVGEZSVjnGN47XHiL21Squd3+OK68Ax89RUF64QKyZYJxB41113nS688MIhxgwiNdlfIXG5RKFBAlm3qHudDogNiKqfU4MoK0g4BgjWwUoA5wnBDdupwVqIMEDYhZ3JQIAEhjC/2qA4yalpnK5uKK8hKoiAsuadrP98DoMYew33gv/HEEa0m1sccmyuCY6WS7PzXMC541zH4Mz/83zYByOnRm2D8US/GERHsZ0NGEQ5VthRuBGGJ+Ixfy0Rj2Xvc4Bzn8hnjEXsG6xzQXvwDTpWPO7s7UQ5s//7rf/wfPZhA1yePRtDVj5GyEzA2EYmBlmAGLbI7iTq1rJrMWphkGUvoawUazxrAHsO9wPdwd/QGlyLOUhszqIr4BL8LVu5lIIwZoJ03vulG38v9XRLdfXS2f8iTSxjX8xR46WNy12OjZjUOrpiHRqlcJZUCw4ZO09LlyxRy4RUL6cJjRO098jBpXwq+dqZo2h/gh2xWXi5M/OS+YhzItfSenwGDpqJM7O2scb5BQpTKYL1rVTluIIAozznFDZ/xSbFfSpGqS8CptBxfll9OOKxA2U6LgGv2H/MScEYYQ/AgY5+tOBZ9h9vbykcJt61PmwQWEc5Is6N4/hljUSoTafGN77xjbz1a96Kl6gVDug3oSJEyAdx7aOEIPMWUdWkuIIJBzYOFnKrX3n//fc7YzOfyBAIeqaUSYi+OTwwPBHZRMRT2HWSuSZ6d+A4IWqbBd4LImq8xkeumfNh4/FuOO7fA6exY+x/5mFp7evSyNHS4SdJzTk6jJqapfdcIu3YlnJajRpbXnLfPEra5yTp5YUDr03eQ5pWxuyRLEBMl7NpcyVhzojd1T62QzPHzVRLXasa4pVDkIMAgwBrB3MXQwJGBe/cx2CNYwPDHg5L3k/ZEitBhuEB0eAeEzg1gsxrqyPrXR/Z1zGQl2WcIf4pI9HU4tvcM8xGdNxPHOCsqWFHJRqIyma/8IvOthJjfpkwOHEQjtZA3A1es3qjfBYHO2n+GNu8NUiL5dCwtci+30Qb/ZzKVRc5QulgTo0vfOELRT0Oax/ZPxEihOXUaIzvre7EQInRhvgeqouNDWz4Mkcee7Zx/nz2pExVAdy1zK0cEM6HYvRTgwfgrCAqFyeNX69A9iovMJjCYbyBX+4sDRC4kgNZDS8/lirRNGs/aVYePHzvedKnviu1bZNGjpEaStzXw4sDT5LWLZV2bkn9zn05+jxVMoqRpQGqrZx3a32Ljhx3hKZOmqp4jCzwlNMuX6dGplJQxQBGYIzaZGrwL/rCe/6sMTgY4KFwSAJT3CWO0QDwOvd4ICM3SP8E3oPB3bvO0c+P9aGYfXgzorcn9eOxX3B/CELyy6zOF5QKxBYFZy8G2I/SlXRDJ+KIIDiK4FUvOC+cFN6yUbyGU50xg25hTDA2CKbFyeEeC961Pmyw75kO5V8L9vK7ngi1g4MPPtgZV2SD014gV+Stei2KNDKyRQgLsViT4sljYLj92RpjFYvlvmhibIGoE4GEIZBJEgZRM0OkeyMwT3Uxo2JxsmRytHjB4h9aFCWGxluvkp5/NNWkl99ffEJ6z+dSxsdcABEaXUGZEHMOl8bNlHask5pGSRPnVHQUFYSwWNFuVqqmmtbzxromjW4It19HKQDZJSIIIzT3GwcshgOvUCDTDMM1Bm7WF4wORF0ZqcMpQSQQ5A9DCAaGoOX3qIeN6LDyQRikEV+sM2UZA+tWStf8n7RtcyrK8vQLpcNPHES2cdhkS2kPuo5jkMJ5U0zDP/cYweh1amA44F7jALCoKDdwVqXrQ8Oz4RkTYcV7MEoxNnjdhEgpwH1zp+xbVqRdW4TaBaXavva1rw3K+gwbfDfr4je/+c2ifH+E4YmG+GzVxSYqkWxXPNYaKEvDD+zXBBoRCU2kc6bMyFxANKq7XIxxAvb3YpT4sGPkWhOdfTiMvdjBlnXSjf/HpE/9jnPjDW+W9h0axJUVODLGhXRehaJ5hHT6h6W1r2JFlCbPlkZUkP7xQdF7n1QRcGaMbhgo9ZwvrPxUqQC/RTPgCIXPowvQCX7OSTQF+ywODvQffNKyrtAa1kuH/RhnB3YUKz+XDRjWibbnX5zAnBe/h+k4CAwCMxf+U3rinpRtaepu0ls+lHJ+9letKLTHiBvYnrhuv9JfYSKdViNoi2eKruN5ebNiMgW+mW4xZw/fQfAX2XnYfEvpoHQHshFAQNBd2RxiEUoCNAXaHB9DyZ0aRGxFiBAmUk6MwutushCSqcDiTnkmd8p4vkY7RAWfp3SIO62v6E22y4mtG1IODWANmygj9fxj0mHh1aF3wH18/kHp2fulRK+0+zzpqDNTRs5iYcyU1E+l4PWXpGUvpsTZ/kdLYyYOShclQo7xxv8zphHUYZAMjJKUCcgknHsTCf1h+Uu6b+NqNdfV6+2z9tKJk9KntxazqTnXXq0NxNi0iaAysJaki+gnKwzDCWKDdcsdpYJDwjIt7F4EXdsYQ6SHUxcVAzVCg+fl7tdTMlAu4s9XSO39JTf6eqVb/iJNmCrNSQkoxAGRG2EYUnAIYIQqpkPDjuO9n8yxhx56yHlukHT2p3Rp+umAMwSRilOD+4Fg5HrI4GBsFbXMRwYUO3IrQmXAajUj3P0iu8MAc53xVBYDSISaBo6MfJ0ZbrAn4+AjW872UYxAhfRmZD6xjrOuuyNT0RxwgZrEM/ek9ny3jnr8Nmmfo8IPMtq2SbrlKmn9Smn0eOn0i4rbd6OhSZpVnEjtvNDVIb30cIprTZwp7XnornvsLjGDE42IbbgGxtlSYdnOrfrL689rc3eHdh85Tu/e7SCNTJNxwzkWM4AiLAdPsXs2eIGWcPfFQV/AOf2cGoDgKoJ80IBu/Ych3O2szTVwE+cIdkLWSXQqwTh8vixBL089ID2xcHAQ1Q2/kd45ULffyhyFEdRFBkUpuIuf9mf/wKl1+umnO/2XyMbJN1uE7yewjmBevotxRMnBUgS+MX64jzYmK6lcWYTigqB01o5/+7d/y/mzeY1MFmgMLRw4QoRKBlEKlHhhIWZzp4Y6C77XEWGGYoS69Znw/p0ND9JHhJb7db7TWz6mZtDZPvS1eCxVJiZsvPy49PCN0s6tUkdbysHx8E0aNnjuIenGX0nPPyw9da90zY+kLet3/ZlIF0sXxnDJeOZ3MjgKBQZSRE0m/N9rz+qqFYu1oqNNr7Rt1bdefEwPbVrj+14ifIJmDeQKhD7XXY1zzs8BisGeiP10IOLGmoqmA3/LhWiyf0NWqdFN5AsRWGVxaIBN66Sd2wcbNbiWpS/t+hUxVKjIJPIMYynrfDFq3Hph2RQGMk2eeOIJJxikr7dHO7c9pukTn9e6lf+Qel8efP0ZwHPGAcX1AMQqQpaILAwR1mC5mCAdnag7A3sj1xs5NYZXJFWxwHdzjGg8Rah0sH+zj5IdzTpMWUOc1V5goIG3UNLDLyDDolLNiGTlXtDbaI6i9rwrJ7p2Dt37CHQIew/jO6+6QlrxakrXbFgl/eWn0taNGhbo7pBu+pn09N3SK09ID/5Devj6QW9h3KEvaBoM72TMktEbRoASEf+ZeMn6zp369ov36+UdG7Wua6ce2bRKl7/8oBJpPuOX5RoWsBGENd8qofRWpr4epv0oM5QJuTg0cMIybs4880wdd9xxDt/GaVK2LN5lA1piV+bGqqWpDKp+4DRGGxQC+DfaKtfMtzDA3CLTDx03f/58LXriHsV779esKS9r5ZK/S4mhdq10wDFvmpRnxr1hjOAcuffeewfZwIoB9C7ByO7eUuyFfn1uI9QeTjzxRGce5eMMzitUkXQkDmYRWxEiVAOIOCF9jvqxpNKZQQhjE6VciCYwQYHBBmeIla1ig7b/d0diYVCG+LEZIlbY+K0BN2TGvP+8znyputIcREtTZqq7c0BkIMhmhdu018GrizwvJFOvHVfBdWh7ulKRTvUFPlfu7UM3DRAuAOF68i7p1Hc4hIXIBaIl3OB3BAj1QP3q8wcFZCHTBsLxb1673CkKZ4Cq37J2ud4wYWiGAXOiGKVwmJcQR79+A7nCHJWlBOuMN5LTnBE4rfwyZXjuVt4nDHDNPO/QykcUCr8ydsyH5hZf8ZXrefOcIeKsy4ybXEr5FQKeM0Ys7rU9Ywy1TgT6gc165ul7dPhhe6mhPiYllkpkKdYFW1dJHycii+vB8Gs1kdnPEAQ4T/JJ3c3lnrrXIgx5xTxexcD2wAowVJQTRKhTtuIzn/lMUb6f7+YYESJUC1iHWY9xaKAXWIfJyGPfwkiKfiAQw/o5UebDAjOstK01qrWsDPYKoqjhUo8++qjj2IcLwNXI+iN61QzFZH8Wu9xJUTB1D2nVqwO/x+LSpFmpwIYwQU/A7ZsHfue+wbFfe35QqcuKAvwUZwQciftSCNBSOzBGMl7697HFj0sHvVEaOc4xGhL8587MwBgNX4THwC0KqWfPWM3E2R/dvMrJBjdGnlBSy9q3aUX7ds0eMbgcp82DsMFcojwsAVlhZKhwnpXgmMdBRcCmt/+mgTUqzMxlnBocqxIcOg5s/pi2BvUNg3r3scbCO/JZQ3EEknFNZpNx/FIAexLZEzjg0MXYmJxAqmSH1Peiksm9NHJks9atWyP1PiY1nCDFstsq2LcImrKqJOxLlDPD0YHBmcxEMlGKVRIRzcS9tP0Q3U92+rAoPcW+VCnzpkygigFr5wsvvDCkV2Q25LWKYRBmwSp2+YYIEYoBBAM/TBrEBmLAbaAhdZOoBjIwENZmiPQr68HfER38De88BJCoFhZl98bGa2wEfHdVNVNtbJbe+lHpuiul9raU0DjpfGm3zFEdecFvIS+UyBczjXvhX6RVqdqTThr3cefnXyoL8YKDxA0IWH9GDIt7ulqmkBiEshl8caghUIhiwpiK04PfM6VvMt5x6qVDSgINjZhKJFOCnGObUwRhUCznHcQtaE3XIESUPayUxJsIF78ME/ZT1huMIt7m3e6mzGEcn7EEQa0YjJsoHXS09OwjqTWAn9ZR0rxjhxjycU7k2svL5kKpr5nnSAQvc4v56c6EaajbpN7egfnioG9tYKcG189YwRBmzkxrdsj8YA3gb8XKZmKdcYt11payZfqUAhi/rrhC+tvfUmv12WdLn/+8VKQm85UOjF9XcD+KBDTGpZcOlIaIEKFaAPe3gD8CpgjwwOFrezjrJHoB/mFNejEW+ZUrYd9inYVfsf/x3WgJ1n8vn2Ptx5mNQaCaeqPpwOOk7RtT2QNg3BTppLeHfxxfnpcM33kSFpY9Jz1AFmd3qj/Hie+Qpqbn6FnR1Z66B97Mh8529bWMdrihX5kaxhL6mGBWCyZCN+PgwDGHvuAnW9+IbBw2XQ5HIplwggdx4rmDkYpRjo15xjWF4dDgfnDPMMx6OX2pgUbEaE8kP2uRV/OEpYF4LugL/q0YhwY48hRp8VNSn0tbH3vWoDWB80Uj2JqcC7ARwYlKDfYZ5oNlVu2aY4lNmjRxlDZs2Ka+voSmTGE890jJrVJsck59Fy2DkL3H+sNwrUTSo8WK4bTjeMwbc5oQIICdrqLGVNh48UXpK18hBU2iysXXvkY9Sg1H1NfXO+sUOqBkTg0ax0SIUO0Th43ADxhlGeN4wXFo0EvDCxZ3Fn3EiAkMb917A1FaGLiuvfZaXXDBBdWVRkfN2Y99Q2rbJrWMTPV7KAb2O0Zas8TzWoWuMw9cJ612RZe9+pTUOkY64vT8vg9iMHlWKiXeHU0yY3fHQInjIFNdTGvuBQnA8I8xE+Fhzd8Y5+mcGn3JXm3r2aCdyU2K9yXVXDc0kj0ei+mkiTN0w9OPK9Hdo3hTk5KJPu3XNMEpNQUZLLaTm2sJi0Axd/mudHVmS9H4DGMH6wDnwbpBhA/lkTCEuLNuEHOFpP/zWQwejCEMKvRlKbnRo7td2rQkpVzHz5aaUxE4u3Due6Vpu0mrlkkjR0vHnCa1Do4C4r5hsMGJnEtWAPezWGUKgoC9ZKiTL3X/X311taZMGZeXA9fGjcEyAwHjmr2rWE4N7idOesvGKmXN6LLg17+W/vznAYPQP//Jg5W+8AUNR1Bqh3Xf218sDJBJyx7GMSJEqGYQwJQuiAnDGaUCMaSmM9bA4dxOEmAZ417AGYgyX7BggU477TRVDeJ10vEXSEeeJfX1SC2jihOpOm22NHFaqtwlHJv9trFJ2vNgVRy2rJXuvWZgv6Fc1oI/SBd8JqXB8sHkOVLSXTIw5gStJUdPcLKA4FbpwPiEU5ANapHscBrGGw4AeGUmR0BH33Z19G3V9p51GlVPv5ihXOfwcdN09YtPaOeWLc5lNzQ2any8UVsal2vP3Xd3uEaxjZoYh3M1aKcDkeXYBkrJtdEKbvuDu68bTiCylAkMIuLd/T4i4d3aJBfwOfZregvx/wTVlLxUHgNm8+tS2yapdaw0ce7gNWTSdOnd/yYtui9Vhm73/aV9hxqMuUdk0fFvIVlJpQTPbOjci2vWrEl69rllzm+HHWp2rtyeL/YEArLsXjD36eGHPYGxja70s42FATKxrNE8tgv0cs2CUr4f/zhGgVTQ1Jo10qc+Jf31r6Rgajji6P7m9B/60IdK49T45je/mc9HI1QYNndv0tbuLWqMN2pay3TVxaLsG/eiCsEh+slv4UaMEN2eKQIegY4XHyGC0RfjbzpRUvHCg8Z6xcTuB0mJt0vPP5BqHLjHPOngCi1BsfoVT8RTUlr5cv5ODXDGe1I9NbasS/2+7xHq3u8Y3Xr9DY4jLBsgGkTiYGSCSAcx2PcmevTyjkfUmUgRhphi2n3EIRrbODiag/JqZ6tVzfOO0EPbNqq+t08n9DXpXUeVLtU2XTPtfEBkZLmzFbinpPiyHvAvggNjsTWTZlPnOfI8EQy5AoGBWGXdweBhqbwlR/tm6am/Sb39dcbrGqSDL5BGucYYwu+oU7J+FQKN+8WaGjT6jVICjJ2yXb8f6uZo6pRUb6d167ZozJgRUjz3KExzJpCNgpB0z0WyKfIp1xUEjEv2vuuvv94ROgiPXJ5J1WHBgsHrPf/Pa8PUqYHYJesULRBkb8oFfCffXaoycRGKB/jFxu7V6k30akzDBI1qKF3T4WoAzorbbrttUGNfA3sDXCBTBi3rP4EQGJ3Y59kfK6asZF5lKItoSCSL+h2flO76u7R2hTRmgnTK+dKowaWNKgLrlg/VF2RsbFotzcyzzOyMPaXDz5CevD313dzvU/5Fjz31jLN/Z1tv0cNwDCttie4NYrze3L1CazpfGPieunGa3XqE4i7HBpHY25at1MfnHKJbp6zTpvY2TWjv0/v3PVJzp1afUc8cBKUOHkJHmD2CtYNAN9YGzoPz4f9xaBAYhD40PYVDC2N1rkEw8E4CO6lSQbZ52TLEXrpbWvHUwO/T9pMOPHOwYwOH5mlvy/pVjG10E0GoVYv4JDU0jFRXV48mThjt/NvUNE6Kjc95HPNDVRP4PQ560xjMfZ59scCeSDYIjsZTTjnFGWvpgpCrHk8/Lbn6hzjrM4GMjz0mnVfB5deLCOwfl112Wc6fy9mCTeMW0suiTI3qx/KdS7W47SXHkJlUUq+3L9dR49+g+njk2HBHwJN6Cwlg47cFHSMZkYrMB4xlXsOupQQSJX3wwQORQKTR1nQKXaHY85DUT6WjoTnVZ2QXYlJTgTVeR42T3vHZVEYMPTpaRoj4bqLunn322YyRVM4ZxGKOUTGXyNm1nUt2OTQA68Cy9mc1r+GUXeOUcU5U+EH77S8SAS/pfy+EuZRjGcJMtkEYxi7IfznmIeKN+waIwCSqiQwUrg0DBZEpRKTwDImEp8ke58k189kg0UNGRClnRepmGKn0BeG1+6VeV6YJDstXF0qHZhcYfmA9RlgHra/KnCDCsKIQ3027zT1Vjzx8m3afO0trNkzVtBnTM45XrsHtQGdvIePIBAd7kZv447SjoXexjFxEszEfGZc4/3HEYVhjPHPPGds1s9f5BSJUY3BCESKpiuHUiPRF9aMn0a1ntz2krkSHw49WdLyiPUYcqMnN4ZeNqVawPtLUlVruOAptr4YLkMXB39ERTp10F6xkIZGy7IfGC9gHorLQGUBZy3MuVsWjoSl9OeBCcODx0j5HSp07JfpUxOt05KRZjvGQPTzb2MGoCUfNFMjnRl+yR2s6Xxz0WnvfFm3pWaEJjbN3cRs4MVyV8X6s9tuVYVvqfhREpRsfLwRws3LMQ2wRNFhmzYCbHXbYYbsy3PmXe00wFeXvyLLkdf6fHzJLgjg1bO3BloE2pARRWXnetrWDHRpgzYvS9P2lCbn3x+C5kYXkdhBlg5VlqpieqbEGqeEYtbRu0PQZE7X41a064GAciennE+MBLu8GQVGMKf6GEwPbrzso1xrQF+O6mYPo35tvvtn5fmxxcEPrg0nlipopeZtOSwxjjXH00Uc75fvS9RpNh5xXXaJCiHwsRhPYCKVDT6JHi9te3mXIBDv72rSiY7nmjihOyYpqBZFSLJ547wGEASGBRx8ShMGZjR7nBWSGqGqiJNgE3CTBPN0RagCHzZfuu7Y/EoSfpHTISYV/L9FLODdcYBzRqM82c0gp44voGIyYGC+NVEKuGINBs4G6Eqk6td5yVAn1qU71Tno5RlG/CGyEtKWH+gGyw1zg3Dh33ufXlyYo2Hc4H8hNocAQXA4iTrSbN9rEhBtk2spVYKwgAh6jNBFQGIjZ4LOVZOE91EaGBOIEK1Yjt5zQsdVTMTkpdW7P++tMoAVFRRrWYzHF6mdK9Qdo0vSjHUHJnE7X/JL+GIwBr9i2Eoj8jbnPnDRHu92nfMsKBAF1bnH4I3A5d+YmDjkMEjjl3NGAVY33vlf64hcH1yTntWEM1qW//OUvoX8vDst3vOMdoX9vhNJidcdSdSUs8CM1Z5bsfEGTmqiNXaF9DMoA9n1687F3s+/j0EBX8Nr/Z+89wNw6rzP/F2V6n2HvIsUmUhRJiRIpUY3qxVZc5dhO2bWTTXPi9KzjJJu+cbz5J5t1ihMnuynutlxk9S5KlESxixJJkWLvHE7vA+D//C7mm7mDQbkALtrMffVAnMEAFxf3fuW857znHNZw1n7Ku7C2Y/PhYKIprSn1Yi9ly15AeUkPJY4FV0kNL0V7jZjpM/sKado8dwImtqAJtgEZQ6YpK45Nyp4hpMGOYJxRgtkgHdtryJr/sd0yfBqyAp1RToytgCo71kbhMzmnZNkgONURARlHe6rKCalA3xq+e7ZlaeFFhcgMxgFIVradIxl+ge0IL4R/cI9ZZxDKsH7g3+D6w4uSBWOwUXEsY+vB59IpA5sz9CXgEn0dGR8STs04cFqKDDsYGzhZVl3e4avU8lUPWpx51erNevfwewn7UbLf4DeI5ab8zhxj3BhuyvEMuD4EJJOVxs4GjEV4P5UVGK+MZTPm2C8Zx5NCAEPvDK79e+9Fy08xZ5nDmzdrqmLOnDnWvUfcQeA0Z0ENosDxGpp5KC0MhgcnGBtkbAyE7OpzDwY4dc3iyeLPRGOhpVwMhiCqaIwzHEo4I+P1zGCTTLfpjYcixdL10eZ9R/dFS+csu06akbu6/RBaNnH+xbBks2ctxoDHgDXZCzgUMTKc1rmsCtSqfejCuOd62vv17vmosp1NJVHQAqU2pAMVYTynKWQckmD+hrOdACDPYZxQmo1j2B21vAaFDKQAQwoSj+GC09c09eb7ZUteOHYhnN2jTaFTAGMRBwfEiGtMEDVe3wKCRjg8TPNErkuuDMyMUTt9JIhhvrtPqhkjyZnev3SCd0UZ2KBl0MqVlhMXRZ1J/2cvYX4wBiARRjEWTz1I8MpeuoQAAkTVEGoCkjjAchVMZ4xyXszrO+64w/qd+QnpJZPmrbfemhxBjTvvlL74RemRR8Yahd93n6YyEHF87nOfczVoxrH27NmjP//zP3fleB4Kh8E4Ds2IwhqODKvMVySK1iIBa6idG7CGsy+wD5j9/MUXX7TsO+rk27O/7eD1xbrXeUgD9C28/2ekt7ZK3W3R5ulX3ZSzpuY4tY0wAbuKMYTdgFIbYYw9qMHfcII66QtZ5q+ST35r3htEImGdePe8OiuiJZHs/ePs4G/Y/4i4+DcWpg+EccRiH8OxsZXg5ebv9qAIIhBEiQRCsKsQWcFfsL0ICGA/Y09zHeA+mYLgQCGCGtwzJw3U4ZEmox9fBo2fuc+xGSbcZ/gH3wd+wbU1wdaiQW0CLlGbuc2bbnY38wMbuKiCGiP32QQszL4Al2Zecy9N9QfmTaKAR6z9buchrBvMm1yBczfrEOsEfImsMj7ztttus8bmpACZLl/+svT3f4/jhKaF0s/9HHVeNZWxZs0aa17lPKiRyKDyUDqoDFQq6AtaBMOAjI26siKsL1pkwJGGEYDBRGCDPgYs9I8++qjV7DeRow3DIBtHrIciw/zl0UcegLMfQ50xh7MTY8U0u7YbmBikGFgE2Jw4MmdVLlbn0GX1hNpH2osFVNs7RytWrXB0XhjuEITYjADO0wQhDCAXEAhjLGJU8zOvgVygCOE1zB++I9+Pv/P9cNxCNpg/GDjpqMViQVDICSFzG5App+myXBOTncHPKDBZP+wwf0MxXdSOjCW3SD2XRjI2WEBrpaWp+2ckA4Fj1l6nSiqTLVSwmr8JwHxgfJtG8JwngQq+H/OKOcw8ctqIm/fbxwLzkvGeKzBGGZsE3UwJCUozcu5klzHmnTpAih633RZ9eLCAsxXCybqcjQPIDsYO6t2iC8x6SBs1wXpdGjw77rkyX4WClMbwkBTGUcvazfrK/kBWLmWqEmVrYnMV2/7mIQuUV0nr89Pw3ZQmAoiYjCPT9HyLHZuMSSfCKXp0zqu6Wif79o4GOMOdlVo8e5UjfmKCLfEC5/AAu7IcOwpRCPMEWwShF/OBc4VX8B2xR9ireGCTwDnIfCJoQnYGNgtiDP7NZk8zJWTzjXR8DHA3uCLXiaxashO4HuaaItDEt4FPo2jKKiUKaiy/TTr4wthzSzZJjZn3YkmXT3HNzfwpNjCeqTICx4ZHMG/Mcwbp7Bux1yaX3JM5SoAJ/k/VAvgevjfmF7+zTlE9iEyzkge+gd/+7UKfRVGBWAMxh3SQUVDjM3Rl91DSCPgCuqZxvXa371AoEnWYzKmcaz08OAOLOc4nHjhu2NQwqgrpYETVjgGYqIyJh9IEimvGFfcW5ZLdCDF9JkjXw3CHhDgNalBjc3ndBnUPt1tlp6r8dTpedtLxefGZZFxgDBtnrFGDEISZ8Hl+/zhlFj9jiBO0Ye7YDXK+h/21ZKSg/MaRRlAn3vGdgM/CKMo3IE9OM2gMuCYol3FqcE/pW2CuI9ej2JRBcVFRI63/cakTB1dEqp8lBbIjSaa+q9OgBsZ8sTp8WKsJ4NkDFwQyyMLBycvcTicYbt9/eF8uxzrEmHvBfDcl6owzgPFKUBLyBDlhDYM4e5gcIGDG+gQncCuowbFwqnhNwksfsyoXqnOI+vnRTFCCGcvrxvrSeXC2NxiHjWnUnGxuOM0EzQbYTwTji3U/9ZA+uJembwv2ApkLBvzO2GMPx94nMJDOOKsvm6WlgQb1h7oU9JXrxJlzapnnPFOXvQU7Am6LeALbiM/HXoq3lpgeEfb3k7XBa2NV5/xuf473vfDCCxZ3yiYDEX8A9k+u+pnFAwHQdEWTnB9BH/7FTiPIgajAlOdFDV8S83zhemnaFVJvm1TVkDh7Iw0wH5zyaAIF9mymYgL3z/BFAoR2cM+ZR8xrgoFOSjvne/+m3NRLL700KoziXx5kDJFlBK9mvvEd2SuLOgDnIe2gxpe+9KXcBTVwmJEK4mVqTA40l7fo5mm3q3u4S+X+clUHaqYs4bg80KMnzr9t/Tu7qkH3zrpKNUHnTXpYZCkhgpIqXuMsDKTYJky5MGrYpDD6cJ6yyZp661P1vk4WYHSgHkI1E7smY/DjCGJzBxgp/Mw9R32EkhYiakraxIL61nVlzaPq/0RpqPHAMU0gg1qrGBdGFeW0NBDj1anxz7impifEKlOgoOIzk/UDyQR8ZwxDkypr//6U4uJz052HXE/SyTE2KUeRTf1QrhmfX5DmaoGg1DSxhECm4HtAVjHSmRvJAEErqnT5GDAWOUfINQFCvhtrN0Y7gGRCtAniJWsUT1AdZ4S93ALHzrWCjLUH1VRsvxzIIE2kCUQS8GBusA4lq5HtocgRCUsdx6WhXqmqxeICBF3vvvtuVw7vZYJPHvh9fiuI0RfqtjLCqwN1Cvrz3zy3GID9//zFo3rj8ikFfX7dOv0KbWhOT0BGFhxzjf0hngqcvzkN8mcK8/nYidiiqLmxK5yUvPFQ3EA4gy0VK4jD3mZcwS+xY9m/sZ3hlrwemwS7AzsDGyWefVnur7Ie2DIcPx07GBudB8IJzsVwjnSEe07tfOwueDxiEjKBDbdJF5RopxSn20ENxC/cAwKbdlEX94bzpbRvJryF9yE44diZ+vc4DlwTrlKQQEhNU/ThEuDBxp+TCpRvYtwUMxgzCCLZi3iQrWHuGzY68ytVUIP7a7LK8wXmO+fKnmMHY4ysRbI48D3AjeEhN954Y17Pz4PLGOiQes5IvoDWrFphcYJ0qiyktfIwIXCyJKqB6KH0UOYvU1N5s2qCtVPW8d0zPKAvv7dVBzvP6/xAl/a0n9ZXjr6q4bCzkh921SoOHnsjJbvREa/ZslvAqIRwMDdR7qOiZL6yCfGvh9IGxjYqnNjAGE5QlAnMXYglDwxxCCdORgxr/obRilGDU9GO4XCveodPqHf4lA4fOWAp/5M1iksEjsvxMQQhNzgwc2Xg8H2dBkwSgfnhdi1QCAGqNtYB+wbMc9w7o4RLBzjtcWDQjDmZQzsZqMGNooXzSzeV045z/W169Oyb+s6pV7X98rsK4+AsIFCJE9BLVYqM8VjM5Y9wDLB+4wzg31hiwb7x4IMPWut7PEBQuLemsaE9uMA4jC1blis1FeP+qaeess4FR5e5L5AO7hNOOcpCeChRMN+PPiWdeF46+7r03mNatbDFCra7BdM/yMPkALZHdbBO9WVNUzagAZ44/66+deotHe9t15Gey/qXYzu0/XJUhJKus5T1NV6gGmdTrgLGHJteT9hN9rKi2CVkFHoofaDKx060A9vV2MkEEeCXOOppRo+tj92PzYLdwT4fyzWxTQbDF9QfOq6+wfMWH80kw9pkc8OveXCeuVJkM4fgGdmKQdzOmuJ4r732msXvCG7YkWlAg+AN9ia8EjV8JkEcRHTcd97Pv4i6MgEVQ3a1HdLjZ7fp+Qs7dHkwQQPwPFfisGctJbs3xSyc4rtwj+GhJgvHzhEIZJlS0vFAEBM7j7U/1j7LphS0U+BbILDBXoPAi+CoKauLL4C1B78bP8fODQ8lhO4z0nvfl869Lp19VcuDB61xi5/LKdIKauAgI5rrpfd4mEx4p/OcekKDCo/U/KS3yMWBbp0glTEDB5VxMtmdSTh14jU7ywZErnECUA/TqPAxLHBMs9FgYOD4wzFK2RqcXh5KF6gSyJQjG4dyT9x7gh3xShBhqEBAISHGyWlKRRkMhtvUOviquoYPqGv4bV3sfUMVVekHNnFUYkiYOug44XPVmJigOvOK75cNacBIwjByk3hAtjge52cPDHHNM537Jp0cAsmagnGZDHw+r0G58vTTT1skiHWAtQElUaZ798WBDn3n1DYd6zmvM/1teu3yIb1w8S0VEhjmOHkSOfsNCGgwbooVBKsg0BB+kwoeCxPIixeIw+hjvece24NpzElTh9ZtMM7iNVIkMIPzg7HKg9dBQjgX1q58q7w8uIj2I1HSYcOyaREdOjRRxJEp4BjpZAp68FAKeP7CRMfYixePZnQsFKkEGOy9klhn3V5bcSITtEDFS5YoTiUcfPAJnGDYNew72DbYpJyT095PHooP2JqIomjEiygJAQylXuP1cDG9u3D6sl4buwPb3wQ2GJM9w3usR1/okC50bFNVfXL7NR4YU9gOOFOxgbCt8UPlqkRhPGFJJiDzFvW7W+B6x8ukwbbKVOSFPUkQB66InezER8DnwfkMx8DG5P6T0cCxMuUYL1/co70dh3VhoE0ne8/rsbOvqmMod02onYAAHHZ5MfMHJ4An4S+CZyQSLSbrX0DlB3hEbIktxoGb1Q7iiejtYH1hfNlLMSOgwu/FvmTv2emhBHFma1Q8NYJy/5CuWDjX4gVOkZZ0hgGc6/RWDyWE8yelw/uipUVWXis1FGdNwXi4PNilM32XFPQFNGRrlm6HCXKkC4y8r33ta6MOTgx/DAFISLYbAMfDUcnCjoHDIo4zlcUfRzfzE/UFjq5169ZZDjOTGs6Gxd8yqX+N+oKNjYh4Jkp+D9mD6w6xxGn94osv6r777kvbgDT1cDGQO4fetkY56OsbUFm5X93D76qx/JrR12OU24mqqWWL+h8FOcQXQ8MNEpAKGGQYLARPcJhmCuYjexnzkYdb5IhSDNwPyAHXh7nJfM2mNi/BSs6Vuf3AAw9YRMKeDs2xmfuMCYxA7g3EggAG6i3Kj7mRgfdWxwlrNbSviPs7T+rmaatU5i+cQolrjDMnWXqq6UVTzOA+cw8hrInWVzIdSIe/9dZbR+8phjxjGCOfe23UYsxLgj033XRTTkQo7COoJe2NOgH3ADJPwJU9gwfqL9SdOEqYa8zjdGs/Fz0YXz98RPret6XhIenm26Wf/rRUNonq+w7iYGBjHi/yAAEAAElEQVTcjc2lpQtm6t13f+DaR3gcw8MoEAW984bUeVmaNkdato7FvCQuUCgS1nvdZ9UbGtD0igaF4+w/vCYT4MBkHcWeh0+YkpJuZZ5iN/Dgc3CGwSXYZ5mbcAr2IPYWHNqo+/l8nH/sQziaYkukOgH8CGcyzlWvlFXhwD3lXnKPESalm1WBXWAcn0ORi9bD4Py5y7pyWbVCkW4FfLXWc9jJvMduu5leGaY8LP8y7uw2A/YEqm3U5W6CYzLWeWSTbc57OW/mpJt9xEzg0m7PEojItEwWgje4FByCn7kPsX0kDN/jOxHkgt/A+fA1mNLHBpnadn3DAzree270d74da+bhrlO6tjn9DBQ3QZ8GshQYg8mQDc/LNbDDuW/w0dgysQbcN7gjfh5T3pDxxl7A37jXJhuI78oeAF/JJEPICci8YNwZsSSAG/HAh0UwjXMwexHfjXE8af1Tly5I//mP0qnjUnOL9PCnpMWTqGpSJCIN98Y+qaULZ1u2x5133unoMGndfaIlbpWeonRF22CH1Zy2qbzRKoPkoYTw3n7p+18Z+337s9LHfkWanry+eTHgeM95PXthh5WRAaoCFarw+TUYiYYxfPKpNliu+VWZpXJjGKBeMps9BjubCgZTqvrvqYAiCuWMvRQNDj1UUyhZcDSxqMfbgHGKPfnkk+PKYEFYOB5kAhAwMQamUdcy5zFs2AzZ5CbtplEiwNC/5557LAVdunU8GX9s/IyfUKRfly62a2hwWI3NdfL5IgpFxm8q3PdYxyVgLENeGHuJxrTbRh4BFgwcjsn84nccuekChyuGGOMcY9VuNNmJQzrqJwwqMwe5Zqhi+JnPIhCYKcz34/g4p5mLEE6cGEapiWHJmIglfqZXg3mOe5VpqvBwAgdMtARVYdOuIVcYPYkU3iawVMyIze6JB/6OmgrHkVFKMsaYf2aO8j1ZsxmD9uCH22D+MwdZS+ylvRiLzz//vPV9yCBjTuCAYAzyOoKyky6gAZ5+XPrKP4z9/qPvR52yP//LmjSoxG4YP4+WLpxpOTsIlmdSXs8OnEDYSW4Jp3qH+9Qx1KXKQKWaygvQR8hD5ggNS4/8g3T2KAY1m5d04pB058NFH9igjMoPz7yu8wNtoyHAqxsa9frl1nGzZ0Nz5n0oCBxjp5CJDZiDOKQIIGcj0iB7FaemPTCB3cF6j3AFJzIBcxwMhjPY137WgO3bt4/ut/yLrWnq9HP83bt3j74Xhyzfg/fRQwpHlhfUKCywGRDOYGeka8Njh+AkZ88PR/o0MDCo0ycvav5CRE/RY4Uj/aNBDcYSTv94NgG2M1zVlN61A9vd7bI3RnxogjLGXk63PwR7GM59bHUcwrF7I9eUYEe6ARljc5m5htDJiJgyzdSAR/DAfjalh8m44nyx7Tg+Qhl+556myqLk+mXST4M1M/7zhS1xC1i/8JMkmgvmee55sVaxYUxyH7k/ye4hvBgfEnY739n4sezfm/HMGo4/KVcl1dnHTO+eWJA5wzWHG8Oz2S9Yr8gwY8xSCnfSYXBA+t9/LLVfjtpCZ/qkv/1T6XNfkKbnXlCaFzDGKhqjPTVGLSWfli25IreZGvfff7+yxXB4WK+2blfbUHTAlvvKdOO069VQ5i7xGA6HdWmwX83lFSovoJo0LQz0SvuekdrOSjWN0uo7pPrclHLJCs9/d1yakIYHpVd+JP3Yz6jYsfXS3tGABugPDWpDy0y929Wt9qE+zays0wfmrlUFGSgZAiOMTQ6CAAEh8s1mYI+CpwsWcgyG2Nr6pk4p5MDURIy3AfP7vffeO+45jDZICH9jw+M4Rp3DBsHnYejgQGWjy7SJmAd3gYMQw5j7Yk9HxpkIUeR+QhZiDUzGImp/HKEdw23yB4c0NDSoUyd6rUB5+6VhtdiGZyJnMMoIGgVi1MRr1pwr1QrHhBhjxKMa4jzSqWXKGGZ8GwcyP0MWjNIJIgXhNgqRRN8BVSHXHqcC8xEiZ4xFzodABgYXZMCNpnmsJ8xvHAsYe5CNVD02zBpkCGE2jQ+X1M7Sga6x0mUEfmdXNqkiUHgHNSQ0kdGDUUzpjGJft3BKMV9TNZZkvtEIj/mFUom5YG/MiWqJOZ7LzCnmHSQJUhHbq4QgC3OGwBv3hfNkrrJGMbeKCpdbpV3bo8b0+g1SYxYE7flnxv/Ouvnic5MrqFG/QEI1efnAyBM+Na++Xy0tf2jNvw0bNmR1ePgFClE3+gKc6D2j11r3jNp5S2rm69qm1a7vSR1DgxbPgGMUq0pzAk7slw68JoWHpQWrpJU3FV+g4Mi+aEADGOflO9ultTdL0zOzn/OFg12nrIAGMNZTf7hdN09boB1t5xTw+XT7jMW6fXrmCm72NfZy7BnWewRHzBtK0RIcyHQsYjvGE2HADdhnyA4he4M9IDaoAXB0xYJzNIF49gMcUMYmwTbiezz33HOWDTUpA94lChya7Nmxoh9sFexjxltsORrsajgo46SsckCXe05r8ZK52rfniGprq3TwnRPasGYsYIZtAM+Md99NbwvGDz/HltZ024FsysRy/nwWQiE4Tjo9nhjfcP6VK1eO8gt4AHPHcBcyU8z3TmTvkfWNvc4cY35g+8PtjK2HD4H9EvvejR5UBDTgNHAYsnvhNqbUVzrgO6XTwN2gJlilprI6tQ91j+7Z/LugujgctmQG4GeJV+WCtQ2eV8xiT/YHxgv2eqo11ghiWeMZu4xh8x4CHXAM1vBcNoOn7CFjKV6TdnzQBENZhwi+I27kXHjErkcFBRxg/27p/Flp1lzpqjWZ21nHj0iXL9mOHY5mhO/fJd023qdX0ph7i3T8SSk0UkqzskVL12zU408+7fgQac3CTJWxsTjYdVjtIwENMBgZ0pttu3XHjFvkFl5rvaBf2/O6OoaGVOH363+sWq/3z0m/QZWrsIIAvsQDm8bUW78qdV6ITojuVqn1lHTnz0pV7qZZZo3ervG/W+dLhK24QeS/Pzy+kRCbp09h/dLS21z7HJy8zz77rLXwPvzww5aBgPHBBogxFs+Y4bUEGRKVh4LIxIuMYwBBbFCdY0xhNBFAcVLqio2AGr3xwAaI0xaiw2bG63K5kXlIDzg3IbGkx2LsotxhLKCswOlNumhsZh1khNczNobDc9U2+KbCim4gQV+tLh1vsAwZjBgM9GTKWYw4jCUMf3tQAwPc7XECieGcmCMY3PwcqyBJBeYWgSC7EhC1FPMFcO24LnwXxjtGIK/lunJ9mbOmlwBqSJzQpn4nRq09uML3h8C4ldnIPOS+4HBwSua4P/Zmh9xLUx+5a2hIv7d/v15rbdWC6mr9yerVWpZEOba4ZqZun75ar7Ue0mBkWPOqWnTXzLEyZYWG6ZsR62RnTaREBushSh6M4GJ0PjKPmLMVleVqHTikgVCngn4U5ktU5p8YvOI74ERGVWd66lB+jO+fy4AGxBrCAXmIdXaYcc84Mw4PnN2sU4DzIhDDHpWretiOcfyo9Ae/JfWONEasqZX++IvSvAxtxHgCiMm2VzJv5t0otSyXhnqlyiapvNbiBNgI2QY13OIXA6FBvW4LaIAjPSc1s3Ka5lfPlhsYDIf0u/t26LFz0UDvtY0t+t/rNqqxPDPFrGsgAJBs3J18R3rl22O/t52Thgaka+5QUaGnKzreYkUVPZ1FH9ToGu6T32IU48/9zpmL9OML1rryGdgVcAk4Aevqxo0bLZuI4D17QrwSUNgCOG55fSLHYzy1MXaT2TOxkXB68hlOBRLYUImyL7BFyc4gmImNx/fwUBzgnnHfEb2xtmP7YoMyfrC9sX1xusdmCZhefqBvuFH94WNaf11U8DPYNUeHDhyzxhTjDK4AJ4mXtcBxsPWxFwh42XkIDv94vQSzATY/9hM2I5+NjZKuCIh9MPa8mGsmYxrbyAQh4Bd8Ds5j5p3pFwBvQPxIgJBrw/mA2DI/2Fdu9Q/g3OA4nAvHzcQ+M6JLg2fOn9PfUOY3NKz3z5mrz1y5VP4Etjfj7M6ZG/TypT260H/ZEktd17RSs6qKw0nNPWUexPpmWA+5VtjCrIkEhXLVTzLb+8u5w4OGwpc1EKKccFjl/hkq98+Ny4kYB8xzeAV2GfcXQSR7Sy79QMwL5sDtt98+gc8BeDZVKshEJ+jKHgIIgDC/TJWEgvI87Jb/9/fSS8+OPXf7PdJP/Gxmx0skyvdNMo5R2Sxd+UGp75LkD0pV07X4yid07B//yf2gBgParmjNBh1DnRO6FXQP97im7m0d6Ncv7dqm/pFa8APhsH5335taWluvlfW5aWqTFKEh6e1npHMo3HzS3KulFbdNHKjt56SO8+MnxlC/dOaAtCQ7wug6Zi2UTh0Zy9bgvs3NnpDmGgGfX/XBanUN99oSnKRml8sTME/YFFiASdXDWcqGxyLN7yzIhjzgaGVxxvjBsMJgwwjCAWkMRhyrGDBmAY/tcWAcnxwbcuCGegNgwPLgcyhNxfm7XcfUQ2bAaY2BgbN206ZNFsE1CiFjdMSmT+M8xdHIeAz6a9RScZOGwh3Wulvma1TLMv+4ddiJ8YIxbFKseS8kNZ5iLxsYFRFGPkQn3WwnjCTTc8YO5o75jihxjFONLAiMQFRXBC/4G/OP68k1NAHDZKVSMHYzTQuPhcm8IYDEdXCyT3Kd7Cp+MxaoVfvA1q16tbVVIRT/bW165vx57b37bs1LorJa3bDQehQjINGM69iMDNZClHc4c1gfIR44ZYotsMFcJmD9yNNf0fyljZq3cJYU9qk3dEnzqjYp6J84jphrJpAG8cCgT7ccXTpgrpCBwecm+xxIHUScsRrrzIK88zde47ZTIi185e+k/r6x3/t6pX/5B+n3/yyz4933oLRv9/jnHni/JiVwNNicDdg6cINs4Ra/6BrumeBQJrOMUlTz5U5Q40uH39HjIwENsLvjsn7vrZ362/WbVBCcPyq9/j2pvzua4b3xA1JLHEfy4TcnPvfu9uILasycPzGgAV+it0aRY1p5/YTxF+Ud7gVy4Q5kZLC3YaPQKJXSfsZBjGPHBJ1ZrwkYwitwjBIMgWtgx5jyVbwGp108ZyYOZTMvjbAk21Jz9n2P8+SBDUl53UQiKw/5B3Y2e7ZpeM04MPwPWzmecIoxYjIpqoJLVR6ZbZWc8vtqFGip0ozm6NwwNhhOzHjAtjFlOfnXAFscu9bNTA14t91Wh/Omc3xsa+YJ8yo2g9peAtbOp3AYEwRhHvJZvN+o/Q1/MuWh4oGAiJvXAGEk94Lr7kQQafc9ANTzJsvyuQvn9f5XtkZfJ1lco3VgQH+0OjEvrA5W6p5ZxTn3uW9831gfJd+fdQtw77BhGPvZlhl3G4b7bt/xot7Y/W0tvGKWamqqNBxqVViDqgrE990RXCNzg+8Jd+LnXGaksEfxmWRMJRr3AA5MNlhszw3ujWkcnk3GYtY49Pb4gAZ4/klp063SlclLuMXFwiXSnPnSudNjwpWKKmltkfmF3UCgQqqdO4FfOI0P+NNxCuEkSbdxVDxUB6stkmFHhd+99O13ujrUFwqNMyv5eUebLX0nnzj4knT2QNRIJwhwao905LWJr4tbI9KX4PkC496PS022bIN5V0o3PaBSwO0z1qvc1sNlZkWz1ja6U8c51ujAwMFIZ8Nns8NgMHXRzQNDgmAFxiOvwZBEGYMK3zQAZ6EnEh0voIAhhdMXAwejAqLi9mLO8TDCIFAeisvYwgCmjn1srUwCUKY/hP0+2g1rvy+oikCLyv3N8o1E/e1jB5JrSpolAgacaaqHcxXjye3xx7zhmJBpzgmHqZ3oxAO1QSFdzC/mFcZPLAngWkBoeJhyawYYVex3pvkx4x8C58TgB05f5xR8bxzBfB8CR8nui1lLjNLFqg9MavzAgPa0t+vlS5esgAbgXzIav3HypEoVXBuMWVNnmXv5wgsvWOSUv3F/GafMkWIrg8QctVL9K0O69pYl6mjvVlcH95YeN4PqHh5roGiHqeNLMAfjPpcBDYDDCRuQvSmegsp+XmRncC9MDWjGIvcDw5SatzgmCgpSwu12FT+fP5P58W66RfqNz0nLVkhXLJF+8lPSj/+kpgJYIyGj2YJjuMIvAhMzm8jaiPd8ptjWemEcv2ANff3yWFPcvKK7TXr5a1L/yH7Q0yG9+NVogCMW8bhEMfKLOVdImwkK+sYyoe79pFTrjjM9l1hcM1sr66LrHvDLrztmrHO9VCPrLBwDm4D1FnsHvsGexzr9zDPPWKVCEL3AEbATTTNu1nAcVdgRrMkEq9kb45We4ng4kAA2FJmtuWgOi21XbGIDDxq1ORlDdv5pHL2xQMgAXzWgf0aZf5oCvuj6a/qBOe1lAOyOVOy8RNUMMgUc29gqjG0CDKlsFPYrrgscgwfXKZ7YinNHuMjr7Y5aI1TCPiXowefCMeL1MIwHuEy8EnDZAO7G2sG6wPkm60XHd7Y3QSdAM23OHCsL/B+PHBntJ2Twf44cLvredsnA+GBcGBA4xvZmXTU2PEJU1thU3DTfMH00r9kwQ4sWz9b+fUdt5dePJXwfcxmuzR6C3e5WMDvROeIPY41J1ZQdPs652MWT3A/uD+fMPCTrqGC4eD5xs+9MwPr3y5+XrrspWspq5TXSr/+h1JB9qdZiB5wAXwc+VCdwHHJjgUNJ6MYiuqLuSl3ov6i+cL/1OwGOdY3uKXvrgxONR5bS+rIxh9a7Xe0629+jRTX1WlCdY+X5BZQIkYnPLb1p/HNNs6TqRqmvY0Sl5Iuqk2a773DPGnVN0k/8ltR2IRo1bJpRfHV5E2BaRYM+Mu92tQ52KOgLaFpFY8K0SLeAE4jeAzhnmUdkahh1CBsgCzIBCRZzghkYOhAPHEFspomaH2EYEck0jjq3mmzGA+eHypa1gLq6bjttPWQGxlNszVnAuEGxQMYQhNUoqHCAOo16o6jG4I+n4OM5skMYB+ZYGHepej2kA5P5YScLlIHi+zIOE5UrQbXOdUml/DXzhywkN0sfQLxITXa7twjrCIEVjgtxJAOB+8D1sX8OxrbJ2gFf375dv3zokFpPnlR1nB4kvJeMxlIGayfOGYK/rFMEk2PHImMHwzcXNZkzBYQXhMI0gy/TitVXaM+bB7X+hmi2HWniiYATCmPPrTJniQDRRS3z0EMPOZrfEHmu72OPPWbtXcxhgvn28om56rvjCAuvkDraxxy6rJ0Ls8w0vfm26GOKwdTEzxas5zQgzhYoPtc0LNfejoOjz82oaNaiGvfKFjWWlVuKMPvMrLPVqu4eHtT+jssK+vxa3dCiijT6PqWNC8ejpWtHEZGGB6Kla+fGOJ8XXS1dsDsxfNIVRdpvaP2t0or10bK2DS1RZWIJgDXtlulrtKphkXqHB9RcXqeaYOIgsJv7CPYYexxrLkFlgt7YDIhOKNmBepW1HI5Blq8pYcXanMhhhSMW5yXBDJzJuexPxTlgi8GLEHp5QY7iAE7EeII6w10ZIyYowFhLpzxNontsRBv212TSuDsVGP+mIbb5LGxnbOhEpTw5D+wu7K9UPf2YOwQXGdNuBO3NdYfj5AImy4B1gqANdhr31l6yju/O78aGHgyH9fldu/T4iNN/ekW5Fei339uhcDR/rTS8RBMBxyJwDB9mXMDvDGfENjaBNngI99uU+i0WwPv9VRFVV1eqsrJcPT19VrYGVkwiW5x5j5CVoHmu+RKlrZhP8craxgPnxv5lAvl8PypWGD8EPrGClQKbm2Cek22RKWrrpZ/8BU011NXVjfp7nDSmdxzUYNKaTWsUZB2EByR/ueRzbrRXBip124zNOtt/zupxML28RXVl7kWcVzc06dbps/TSxXOWs5r4wJLaOt01M7pYf+ndvfr26bGI62euXKMPzoum4eYE8RQ6gfL4r7v5E9KOR6NlqOijsfY+qTaLBpa5BJv5NHfS+fMNVFNzqvK34GGgo2KnaS3GAlFp0sUx0FiY+bshFaaPBYEDfnYSqMCxadQCuQKOKs4ZBxUGHwFOt8pcecgO9jRgu2Odzd6U5kEJxHMEApySRaN8jweOjSrFHtxym4RCpDGyYwlVbL+I2PdwPZyWMuHY9F3A2c3P2fYkIFiJyitZo/FswXFNaR9UDKZRNgQLksaaYcjfhf5+ffr55zUwUmqid2SssGPzk2/k8eDs0lzLDViPWDPpZYTxk8j5jlMGB0+uMxucgrWb0h8dne1asL5cCgyNNlUle6o6kHifwthzo7FyKrCvfOQjH0lJ4AF7G9+H+XffffdZTg8CTPa9ib0OEpJL5VdS/MwvSf/jt6WLI8qpadOlTxWQMAx2SSdelHpbpbIqae6NUn38OvTFBhw1bpWfmsAx6H+GAW9xDOdr6cr6JZpW0aTLgx2qClRqXtVM+V2sP/zzS1ZamRnmjHDg/OrSqMr9RG+XfnPPVrXTqwK1Y1WtvnjNZjWV58ixHUfElZBjLF4nDQ1KB7aNNQpfd7eKFgjOci06yxFayuvVkqe4OfYQAYtHHnnEUnpjG2GX4Zw1dflNOQ72G5yi2EjYOtiDqRzFCGNwcLqtkI8F4hvWARyGCHJwDLqtRveQPiiRhF0dKyJinDHGEOzBa3F0Y4+mI7KIx10MYkvd5MKexlaPzXI335m/xfIBOBH2I/a9E3sIYJcyD7GF4GFO35csSwL7P5fzkXtpBFT4LuA1VvnacNj62V4K+7e3bdPjnZ1EQK3fWweifUvLR75mwOfTQ3Pm5lw8mmsQsCBjmfXUHrSAVxs/Desr88KUTisGMMcQGN2weYkaZ0grrlqod/Yf0zXrlqrMNz3pvHK7lHQiEDhJ1cTcAL5LYI33EHRhjJqABoBrIFwrGK64UvrAj0uPfG3suQ9/UlpQoJK7kYjU9o50+S0pEpJq50szb5BsFWtKgWPElt+PB8czjk1rXH3kgQtS6ytR0oFmqek6qWYsFS0VKP+zsDqLqFUSsHD+zdqN+uqJI3q3u1NzKqv1k4uuVFUgqB2XL4wLaID/c3ivrm+eqXnVOTKeFt8g7X9q/HNXJKiFRj3cWz6Zm/Pw4CpYSNuHuq3SBo1ltSlJM4T9J38yWpICAxCjHccbRg5Gg3HwYExg9JD257RhV76UxxAbnIJsggReMK6cpst6yB1w/NvvBQYVa7YJOjGeKCHI/UvHmctGkoigYEzkWgkBEUCREQ8YQATyIB7GgMHgZh6l23PDlFdD3ZFtUINz5ngYXbls2mzAGmFIGZ+JQWd3Fr969qz6YoxWfltQU6OzfX2aXVmpL61frzWTIPOK700WXKxxbDfaWV9xvOdC9ZcJUNLeddddlhNq754dumJtjZpa6tV5uV/L525SRaDwTr10SDj7FoSPvY0eT8zR2PvBekWmUcGCGtNnSv/r76WD70Qnw/KrpIrcq6njApX9kcelQTJ2ItEAx9GnpGU/JlUVqaDFBtZa9ppsMY5jQLza35T6RoIlZS1S8+ZovV2HmF7RbD1ygXVNLfr6xtv1nVPHNBwJ6+6Zc3XjtOha/78O7lQngYMRnOnr0T+9t1+/tSJHyk0yuRE+9bRFyStrXcNMaXqcoD5/W7Ex+vBQ9AhFhhQK9yrgr1TAl3zss+9/7GMfG/0dVfHXvvY1Pfzww6P1742TjbUZe9BkCTpBPkrHGB5EQJz9GY5E5mW2TmAP2QE79sUXX7Tui7kX8D+CHIw7nOuUe4EvJBIbxQOvTcfx63ZQAzswUSlN7DEEfAhkCGIQ2OP1AF6VrsOabBa4PjwtXhAlHTA3yNKGm+U6m4nj2zNMCMzEOrp/tGMHRt3Y+bHGUJWiPGiVgn//7Dn62/XFlbmQKaigYe+TAhAWISYzQioCXnDJXGa1pQPGHWOWYExFRbOqGto1NBRSmW+GaoLOMiNyDacBDfYh9i6EUsxH9rliy4qx8L4PS+tvkC6ck2bNlmYXUKTU+Z500dZPrfNoNClhzi0qBaTDMRyvyqRcjUaFQwPSpZeliKkbF5ba3pDKGqTy4iBhZX6/fmrRRIX70d7OCbX++PlYT4cqAn41lFWoPFGn+Uwxd3VUNXX2nZGG2qul6cXfVNtDYgyGh/TE2e06PxCt89ZUVqv7Zt/gOM0cxxqOLMo24ASyp9yhFsc5iTPXabqqSc/C6IwlMG4CJ6AxoqghSrkXL6hReHDfSXklcwKiQWM/u+HMeCMtnNIEyerhxyJZ/VvUdPaaqm4jVXkaDBuMe6PKh1xl66RmTGO0oz7L9FgQQB4QdM7RScqkGzABnth61200eYxp2Mye98erVukTLjTmLTY4MY4JBNl7jhQapmn20sVXqfNUpzav+LhVhqN6ga1vVYkAFR9ZT6wP7GE4B8hQhAwCyB57Bn8vKCqrpGuKIFun/3I0kBGLzhMlEdSAF1AKIxtlIs4tjjHKMbreGQtoWC+4LLW/IbXcrGLByvpGff6qicqx471d4xpF8/N7PR3qHBqw+hrBMVx1RFFW947/Iu1/Seq+LNVPl666OZpJ7aFk0Tt8Vq2Db42y1cay5aorW5iWqhhHLPXQsY/s9gy2IRniDzzwQFrrOvOU/RV+YecCbgF7zqwhHJ/McALjRem0mmJg/2YsoYhGQIe9aXpE8DtqdWzwdIROcFZj+zgdH26OOWz0RPyV74OjmtfgnMaB6lRkmAgEhLhW7HVwsUyBg5r9Fie103I9bgC7zh7YAqwFdcFgNIvD9toZFRU6lsb6Uipg/MVyDNZZro0R4OHDMf1gigEmm5tAQDRQfLtmNcxQ16Vpqp1dHNkkTmHEUPQRZT7efffdVsAVzs29YZ4ScOXnggvX5s6PPgqNrti+KRGp6/iYCKbIAS8gBuEEjkezqadpYajdFtCwYeBi0QQ1EmFuZU1sdwtV+qX/d3yXBsNELv361BXrtGmay1G1WcuiDw+TAq+3vqMLIwEN0D7Uo5cu7tV9s69P6zhbtmyJ+zz1bjEgnfbIwJDEuEAFAjA6SAPGwW2vhZkp2Bww7mLLEWHU7ty50yJQXv3bwoIUZ5RShsTaDU+TuWDKFKXTXCyZoYTTnrHnhooPI5DxixGCkcixx2UHxrwWUuC0xFS6KdcENqjvmc2YJjNr37591hzJdRoy1597G9vok+evrKjQlnnz9MKFC9FyKT6fltXW6sfSzGYpZTCm7E7XXJfqyxSQIrKpWPeZz8ZYJ1BVqPWVrMI333zTCs7FayQbbw6xNhg1H3PJOA7I7sJZgKOqWAJKBUei0q0ulkvKJRifrDMEzU097nTBe03JDwsDsY0WyWDJsMlinjG7slrHejpHHTwB+RT0DelX9zxp/b6stkW/dOUG1QRdzKytqJbW3+ve8TwUFMPhvnEBDdA+dFDl/kZVBJxntyGKiieMYi3HzkHtncjGigXrOXsTdqUpQ8OeRIlcN0CGKzYn2SOm5JQR44zzP3goCLgXlHtBoMA94mcDxgTOdYQiKGqdOuwZQ06ycAy/yFasB7/gMzkOdnmywBxjDiewQbYBDQOEYHBpxvXofpcBuA6m52GibHY3gWCKeRlbDo414ffuuEMf3rfPKmlryjH+sQNbcTLNDexkO4rRH2Ka0+P0Zz2nTCxBAtbwfJSxTQR4MrwAP1KqecaYZx6TkW/WDvwU5nrDVZjjBA+LIRO/KODzlyy/AOz9rMdO4PhbccDRhTNRHa4SqM+1sWWW7pgxZsQFfNLMyoAV0ABDkbC+/N5OnertLOBZeih2nO9vj8n2iejCQLtrx2cxZpOJ3ShTORdwHvFg48LIRLFvdzgn65GQDDijMGZi005RsPB5NB7MR3q6h+TAyU9ADHKByigW6aSGs+YnK58EMSbwALj3PBivKLAhqHw+jmQngAgRkDGNwjDYyZYwSrBYkJHiVtO9WKDCgfCjvMkWKKpw6uZ6bpAmj/oy1pDG+Fu6ZIl+tHmzRTI+On++fmv5cr26ZYtqiqTeaz5AUGDv3r3WzxBaAr7FSDoAhv2jjz5qzQlINUY8TfQKSThQZ6I+cxIUJRhjd5QxTyHxgEAGBDBdheakRmWTVM06a8ajT/IHpcbSyOaFPBKEdko64oH3Ms5HS2jSQyMWvuLnF+BXlq5TRWBsbZ1WGVA/vQdHcLj7sv79eHQt8uAhHobCZG5NtBkGwx2uXTAC5zi00uEk2BjY+4il+Jc9wQQks+UY7HfYXbHBC/oyGSewh8KC9RlbgBK22LWx4D65GXyCB8BlyPaEaxD04gHHwAnqtHwaPIQSUiaYx/iFzyYKyMFjnJbDyVTwxPdJ1k/ECeBnnCf2VC7B9cZmjuWD8Bp8Az+2fLm23n67fnrRIn1ywQKLb/zEJMwCTwauhSmDzFpWrL2AsMdZS1955RVrrsI3qBZCdZBCgLnGdWNdoV9JKq4Mh6AHpj0Yyt5k3sfz+L9YHzyMoDFOubvGFSWRpQGIPTjlF5mVnyprkirnSP3UuBq5KMFaqSo3TiY3gSPjd1dep7tnLtC5/l4Nhwf1/bMHxr2GdHGIx7zq+A41Dx6qgxVqH+oaRzuq06j37AQENViYM820YKzjOGLDshtobCDpNvjGAEuUpovjGQf0008/bTkPWSdM+RE2GM4BY7JYnYiTDVxnDAQCTRjvdhUP9wYyAjlNpXgyPSuS9WohY4IyOZALghi8FvUf4N6jyCbIkuyzIMG83142KVkKO8Y1Yy6X4wkVP4Qj22ZvXAOcB6Y0XC7AsSEbsfcJIw91lcn2+tzKlZqqgGAwzii5wbihrEWxAkcRayaBAdZRAn0oq8jauOmmm/LafBC7D+UUawHjl5Rvzi2d/k18H4KplJ1in2Cu33vvvUVL+vIO1rHFd0vndkg9F6TyGmnWdVJ56VyfdNLDU/ILUHeVNGAnMRGpvjjqU6fCivom/dN1d2hn2wWrQepzF97V+YGecfziQNelgp6jh+KGP0H/jFR9NdJFtjYUSnOEU6i1zbFwUrFHJBKkxAN7AjZXovO57rrrLHsWZyFOOZxYCDaw0eAWfF4undAexoMgNtwAewpbyjgY4RvcF5TTTjKAUgXAcP5j9xglPGOKY8MxGANwTHhyqmwh7Cf4hbGdGGvJMnUJzqTT7DwTcK0QZzmtxpAIcCU4fqpyvZkCDgTHiFfmCvvU+AU2trRYj6kKmhhTTpwAHGMyXR9LPsGcRSjFuTLWTVY4fgHGZb7AmCVbhKwv1hAyfRE3pit4Yq5SoYLvQUADHwFcycMIauZI8+6QLr8thYekugVSU3H0UilY+SmisaMphSycLTdJ3e9GS1EFqqW65VF1WQnAcmq0RCPOBzov6ftxrlWtm6nhHiYdrm9eoR+e2aYQzXZGsKnF3U2MOZdtvwocUjiS7EZkJgp0jEo2ikSNXSEWGKAQC5zmOFn5XJxZGG5GFYABV8g0x6kC1jg2dRyJ1G81DcsgI1x/SGiqZnUYGCizY1+HIYKRC7GAZFDvOBGhhIBCfGj2mAgcK52sCxPUyDWYexA0xnw26jMzd7INkMQDzRlNVlcsmHdulYaYDDB1uQsZXGUuQqZx9Cc7D+YTf4ewPrv7OQ00DKl6drV+9MJjev8d78vLd2BsMTftJRj4mUAl+whEwmm/GK49x2MfYD54AY0Y0HNt7iaVKuAG2CuZYhy/AOUt0vQ7pZ6j0Z59lXOlytyX2HAL0yuqdM+saBD7zbYTujDQM04A4/ELD8lQ7q9XdWCWekME9qJdICv8TaoKuNdjCXFTtkpxENtXDdsy3fUdR3WyGvRGoIX9xFqBM5tsAZ5HcPXMM89Ydhr2LXab11g894AfcM1xJrK/G5sGjgfHdFLik7/Ha9bNWIBHMkapDpCsTBMZGAS7EpU+zKTHJOMn1zYWwhCcynA0OFY2Yxb+ZA8wuAkCJnYfgv26op5GtOYhmkFwzz33FJRfwDOZT6y/yeYenJHxZmVfHzuo/W17NP366Xrr0F4Fy4NaMC/3AnXGD4JIAhHGfwCnIEgOT4DTOp0XrPuMQ7LKyT6888470xJeTQnUzI0+Jjm/SCuoMU49Sz0uAhkljmV1Lbq6YYbe6rggv3yWiuqKmiatbfTqd3pIjGkVDfrQvJt1uPuMtTgvqpmllgp3Ha0EBNx2/rBhZFL2A6OJ3hkESXDwxgY3uAYYjWQF2DMD2KzYlEghZ7PHgMMZHlvGykNugFoEg586k6jdQLw6spAIHK6s8zjfcTxiIMe+DsckJAJnOeqKVAYchh4Brgn7hw3pNmzGWDFpvqngVL3E60wtZ9OXhvcxdintxLlnE5DAWIMcMPfcmtMYr9yPeIoynM7c92TBpKmGYsgUox41GQ+MNVOyjWBwLCHnXHEKtFV3qHdGtHRNl7rVNbNXr+x5VZvX5laFBEGGIDz00EPjnmddQOWFkwBV2l133eX4mIZ4eJh8YH3MNqgxYX8gI7yx9AUQH5i7Uv/rEOU5x0qVfmju1M2a85AarP/N5VerMjRdQ+FuBf1VqgnMkc/FOtjYNm5nNxgHcrq1zDkX+r6h2mWfiKcWxmGHrRNbAgcugj3K37CHaITLHuVWDwQPicE1vuWWWywnJJwOJz1gXNmzMPiZ4BMcg4AU45tMDmwfe7YC42fXrl3Wv2SoOuEFvI4snkRBDT4zWRndeEinXKxTjkGmCdyF0ryMU8D7eMCpcO5mCtPHjMbh8QIQmQIuxFyM51gmeOmJpoqLY8AxGQtkX9g58oYNGyacG/6l/lC/ume1qzJSoa5Qp2qWVOrpHY/rp2Z9KqcZ4XAbMkVYP8yaAThHxhsP5i0CKiNGSwX2HOZQNvPIQ+nzi6BT5wkGRbKSIKUKv8+nzy69Qc9eOKrTfV2aWVGju2YtVtBrMOMhBerLarS+aWnujl9fbxlAibIjMgEGWKZEhs2HgAYbpv2cMNDYgBKlLWLQms8kY4D349xzull5yA4Y+xgoRlGF0WNqUJKWjbIBxQZqKIgJ9wrigYFkDHDeT7CDMQmJScd4IziydetW63128PkE2dLp8wEgBk5q6VJ6yWQyMF5jm+lhWJkeIPxLsIFrAdEyhJhzRIlIxkomZIH3c924XtwHrmemQQ2OxT3h+/Mz3ytR6jrEkHRoD8UF1lDGl30MoFbid3v2EQol5mvXlePVqw0tDTqw52BOgxp79uyxzgVnQbx5ztj7/ve/H7ccgYepiZwENSYJlte16HdX3KxXW09Zoqnrm+Zoad3ULdPhwRlYe2uCuctOwlHpRqaGHdhRmfIV9kDsLuzBWEcxwhJs1HigXIlxrhIQMU52nFtucicP8YEdilgBJ6XJlMGhSvYN4wGHPRwQtS2BK+6jyfZGbIV/iYAH95vXUSbK7uh0Au5zvD0EvxU2cyz3cCOowdxhXBqRGDwhVh0O/4BPITCCd5lyvna73VwLeEiiMZ7qPDgG1yzbvjPcB3iT4evcz3jlr809y3WJLg/pgXUQQZLdt0JZaILFsSV34cO7j+zUcOP4vpczlk/Xm3u3a+P63GQOsybQO4MS2abXXiwQ0DLG4CAePEybNs3y+TjJ/nMU1DBRZaelBkoNBDDumeWV6fBQXEjVzyBfwMiht4epZ8qmhCFl1BtsmhhpidIEY1VbOPUADu3Y1HUPuQEBC5Q8NEtmXLGmYwCTORBPPY1xbXeKQzQzDYYZ5zsEBlLDeOIcIDCcV7oNyhiDvIdjJQquMEYhNKauKa8nSwjywwbJxohCjJ/Z1xKVvzLZGhAyyBG/8304f849WVo8RAO1O5/Jd+V3DDXOzRigic7ffr35mfdAfCARqTZ17i3rRiYEyUNugWIKJSlzztxfSjrxHEa+We8ZF9Y4D3XJHxi/flZUVTgy7jIBDiLWc5SNBPjsqkJUk4x3nAeQ3VTl6zxMHTAesnGoMMbcbDBbbFhY02g9PHgoFphMQTeBfZPuvoQDHNsG8Qd7CusIe4+9J4MRhcSDEXgYYK+RoYqQxstUzQ+4Pxs3brTqnnPd4YvcR2wYgkuxY8KUEzNVA0xwLdMSTNj5iECwqRkP2DEci8/HpkJ0N668YQpgs1NaKdmeRKYC9jiCL3tZZzgFAQauAYEKvjvjOpGtz/Nk78JPOJYJ7mDjmbLBiWCy6/mevIf3w1OYD3zfRKWejdjK2KA4DOFIHMOJeAsuZy9L6qE4wLjDFmMMmB6VPEcAg/Fl7/OB72XPi7tU34jAamwfqK6tVt+5xKUAswHj7sknn7QafSOohFOw9jP/TbldnmMfQSSbjzLTHoofJvYAT0hkB6Qd1GCRz2eDSg8epjow9nHMYrC4hUQkhvJDKG2MoxiDCAPVOHTtigwUumxIRh2FcZTMGOXvscorjC16LWC42RtEe8gdMGCNgiPdpnLZlinAqMK4x3jGmLIr6HDao9gwwa5U4LxRg1HGLNF7IMX2+rKMNx4EB0xQBWPPKdHB2MJYtBNosjf4TvHUgIx5jEheH69BOJ9PzWDmkH1+Y8wxt/gMrospQ8S8hGyk2oP5XAIpt956q6Pv5SG/YJ2kbBtKUhwupnYzAQ1KKOAYMCUXCCBcaG9VuGX8mj2reobrClv7PGR9Z56wVkDcTQYeewTnbbK1PHgwYG1kvcsUcAwv88eDh/wBW4OH284HnLvxBJBkALLfmV4C7CdwHGwabELDIbAVccoZYP8ksz9NAD42CMI+Sq+NO+64o+BlYaYKTPlhwzOdXvdse6AYGwrbns+2l8bld5og02PQKY9hTGHfJwtqcM7GHjfCLewyggOUbWJsp7On4XAmqGD4BUERMkHg3vGuI/wcDsTf43EQ/sYxYstN8zw8zFRQQLjC9+UYTpzInBOcxu3SdR7cAfcbbmnvVQlPxpa3lydjTNX560eK7hv4VOmvVCDgvmAKkLUF74HjEuw0JZQ5T+YpgdCnnnpKd999t9d3z8MoWGtYmwjguhLUYGFMNx3QQ/GCxSOssAK+7AwJD7l3FLhd9o0FIV6GBI5VjFFjmGEwJVKX4PiyEwgcwxiTieqWsqHGOqDZcGnmROkVD/lHIUgeRDce2WWcQQJwxjvttcJ4RQVlgIGOUw3CDCmAWMT7jqRSp9OU3A5T/9bANDXj3CmdYP5m0tFJnSVFG0MtNqODYAiEx6i7OH9IBcornjfH4jth/DmtUU1aL2nGBSfxkWFp8LgU7peCTRJlNAp9TkUC7r3J2EDtxphk3EK6CRrQ+4bxZDV/fOGIqqfX6XKkTX75tbRusdr8l3NCKFmLOa/YOtbG4UBpEMahV9bMQyzgBzhHMoXHMSYXIpFoPX03e0B4cH8fcttO4HgIK3jECjBQneMoxm7CPmOfiedEjRVecZ4ENhKBjFSCGnZwfDgKD1T7mfQR9JA5CmF/YhPZean9XLCpsLdQiDsNoMBV7LY7nJh9ivfbsxzs4G/w9Uw5u93Ohw9gF1KqGRvRCGDMA5EW9hqO6nhBDa4Fe7LJ3OBY8AzmnLlO/J15GO+6xQPzioBIMWTpDoYvayB0ST5fQFWBuQr4nPdlnOyAQxJAsGdn4NuhOgHVGkwG0FVXrtJ7p4/INz+swfCA6oL1WtWwRnu1z/VzgtviW7LPD7KhCJCZOUmgG2GX271kPUwdjuEoqMGBvDSgyYEzfRf1auseDYQHVR2o1OZp6zS9ovSbMU42oPJgwUdx4iYwbhKpfM3Gwr/JjBxIhz2dGOOPyDvkIZ4qnc3MHl3ldwxFDDNDWHJRSqUoceJd6ZXHpb4e6YqV0s0PSMGprXhhHGBwkbkTjwwngknZJtMHw55/zXtNs+9cg+Ag88meFWIHxhsKmXhlqkzGib1RuV2VBYkiUOg0oIHDgHkbrwZu3gMa3S9JYfqe+KTBiFS+RKryejDENtiEaJNVwz2GJPMc8wA1Fevq5ps2W/1R7r/h7tH3np1bZY2NTINz425VJGIppRg7zEF7QIOgof13CG2p1Sjn+3UMnddAuFvl/io1ls32HK057P+VKTyOMTkQjgzrXP9b6ho+b639DcG5mlm50ptzRQicW7loqmo4RqwdZ0p5gkRlcQBCkFh7iUxV9sF169Yl/Ew7EFlhD7L+I/SYMkGNoUHpmUekowek6jppy0PSAq+0Nk5SxD4Ip5yKMrDdcQIbkZLp9wEo0ZluL8BMgHgLZ14ifgFMsCIeT2BfNr47zhensfmduWEyc52AOU22VTFkgfeFTqtzaH+UXyii3uHjai7fqKC/wNyniICoDvEomTUmCEXWNdzBiN/wy9DvZVXNqnEVA9wMSpKNwWcy1mLLAeL7sQcZCU6XWsnAvlCvzvWftX6eXjFTtUEvIFNIjuEvBOEYCA/oQv8FXR68rPCIosdD7tE51KMXL75pBTRAb6hfz1/Yrv5QYhWMh/yDDYD6oOvXr3f92Dh/4wUeUgU8UpWwgjQkUlMRoLH3BoG0mEg97yMYMiVw9rj09f8jnTwsXTwjbX9Oeuw/3Dn20ID07nZp/0vShWMqReDUTceYwjGMUYYSCee/IdGMz1yU58kUTupWW6nAdXXj5gmqMLK1nADDlJJBbji6s8bgiZGABhj57oNHpHBu6rSWKjDmcdKgqLKvwQSyId+mpxJjGTJqwJiArGQDSDDlriA3jDOIvr3UAmN27+G3daKqRy9ffFtn+9qsIAu2oNslS3IFvsOpvn062bdHFwbe06m+t3S8d5frdeQ9FFdQIxrIatelgQvqD3lrTj5xvv/ASEDDuhPqGD6lVtZ+D0UD9hXEIOwrbldgQJiB8zQbkRLB89jzwjZi/4m398TyC7LbjdK34Bmr+ca3/kl6/Tnp/Gnp2EHpX78onYvfjDdtXDguvfWidPB1abA0bIDYMcRYSQc4gRmPCPEIrBWjwIPzStRwOV4JFwN7CWkn9iJZxGSGFHpOsb93DZnydFFbLqKQekJHC3pexQj4IKIlxIIGVOSgaobhHQTMYkuHco/tlRAyARwFjgE/h6vTR9A+do4dP64j5cP6t+N79fjZwxoMh6xADIGzUkHXUKe2tW7V4e5D1uO11lfUNni50Kc1pTlG3jM12gbb9GbbmxpGzYlio6xJG5o3eKWQ8oDzA63jqueBociwLg20a151/NJBHvILjCWi1Sh2c1FiBOcVm4y9YZR9s6McDsSBzSWRQhwDJx5QiOMg42ECHBAcDEnK9KDOYnO1pxZCVFAJTwnsHSm1ZZxq/PvOTumej9H9N/PjQjCe+YrU1SpR7oFA8bp7pWXXq5TAuGGcANNUmzFlaikyHyDLEFhKnjE++dvOnTvHjedkvTZyAVRbiZRSgHPku6VLgpxmrEBOOIeiKQsUGRhVUE183r3+QJMBOG+4f/aeMqY+NBkb7AMoqgg+QFDIxGH8E5hmrXZKSmPBfGK9jy1DaPD2qSN6JXxclW3RtXpn+1E9OPva0XOhVJabiNvjh/Xx/Ampq01qniW1pNdIui/UofahqILKjMWu4UvqGr6o+rKJmVMeSj+owTh6u5Mg1jnrd598WlV/jaZXTt4m5MWEntDEZvFdwxc0rWJpQc7Hw0RQzgb7Hiev28C2Z0+Dx8TLxsB+I4uWfxPZaOxx7Iex4iv2P0RZ2Hq7d++2HGXMdzLa+T4cl/0QdT2/G6BARvBR8AzWXKO7Uzq4Z/z+6YtIu7dJ92ZpDx/eIb3xw2gJUY57gGP+rFRROteUYBcOXpPFbbLCGRcE+AjI8TM8A1ud8WX2ptixGBtIyzUQAsYr3QvIpk1Wni0R+M6xpUbjgetEQAORpbkmhUXECmLEPhe2+IWHWJCNx/3DZjecEtufNRquzHpLZhwBCOx7XkM/PYS12WTlIM5NVGmEdfsrB7frWGOZAmRBRSJ67fJpfX7lZgVOB6x56lTQlxXHGOyTWk9I/oDUskAKpjenD3UfUCgyNhYp7H+g621tatns1ml7yEWmBhE7N2qcMah2te8aDWiAtqE2vdf9XlrHuTzYr+M9nVZkz4NzBBP00Aj6vQbwxQI2mlw24WITw8C3R+7txj/qYQy7ZGoMCEls6i1KFtTEOJjZzFCPUM4EAsLPOKFxpJmmx3bgECZdfNIj0XqVIEjkGO++IXWPqANM5tvup6Kp6CUEDGZUIzyYA+w5OF0xshhzZhxhiOPQJRhGzdtYcmyCIfkC55MsM4QsklyNb9Q2zLuiCWiAAM6MWDV8UPJ7abnxwL1jTbYrlCDMkBECCKyprNfsDThzAGM+nTHFGvvYY49ZziLGKnMnUQ8k8OKx3aqsr7HoI/+Bly6+Y+1LTsuhpYMf/OAH4zP2cJy89Ij0yJekZ74qffOvpD0vp3XMoQQkdyjskV+3gYM0G2WfWxzjXP/p0YAGYOy+3blXw+GJ9k4iDIXDOt7TpdaB0lMjFxq+ODo5vzPtnIc8AA6OjY+DIBeKa3gBDin4QDzwubHCpnj2VKyinv2LMp6sE+yBpmQp5TpxnOGQxumLw5rn7d8NG/LNN99MW6VfcsgVvwgNS28+Nl6Q1dshvfOqSgkoxxkr8AuCX6b8KwEMKgcwL3D2EpRDJISNxHhiPNp7ZGCr5bPmP5yH80mFXGSgMq+2bt1qObtzEQTNBPRpCvq4/uPXrzK/1/c3Ea/m/nEf7U5hfDOI7eAY8ALur3kNNj5BZCoAOAXB5Oeff97y8XDcROJXcLizVfu6L1k/hyJRhvFeT7vebDubdsUGJ2A/+vrXvz7+yc4L0vP/JO34nrT9O9JL/yr1mwoDztAfmmgjDsR5zkP+OIYjaxNjwI2a90S0KD0Vi65hZ2SIRftLh/fpO6ej6czN5RX686tv1LI6bzFzgnlVM1UXrFb3cJ9F9lg2mssbNMPrqVEUsEp+7N2r+++/35XjYQjhKLKrKzDYmMtkg8SqT3CasZklUu8a4Fwm24O67yhIeB9kBTLBI1bFglEGEtUFpTEUm6k5t0mLleulvdvGfierYsGVUlWW6pe+ronKeIIbAz1SWf7URNkaz3aiYOrY8i+KPwwdyDLEldeZrAeMp1iCkc/0aJSDzJdkQUg2YrcVTjgPaOwMqU/mnC4IymZJFcukAZOBFZRqrpd8nnMrEah5u3///nEKV9ZDGokD1lTUVpTrQ1HFmsla6VQxiDoXwo5DiSAJ67QJ/GHwM48o5cZzHLN3sF9lMVk1/SNlKwl+J+qflAwEU/gO7BtmTwCQID6bY6KmtLKuTh6S3h7JbDN49YfSopVSg7MmnJX++CS8KuBeKVUPUTAGs3EausUxeoa7rewME4gDYYXVH+5TbYLxYMfh7g795p5XdWmkvMoH5lyhzy67Rv6pVsYmQ0yrWKxz/dQ6H0NLRXJ70kP+wNqfaRPjeKBMor0WOxyGkj18DraJHTi5yEp00seDfQiOgm2Ds4z34ojmmHyGXZSF6MWU3YynZscZQplHAhtu9ygsKuAHmb9EOnV0TNwUjkirrs3uuKiZ4wVMLN5ROsBZi+0R29/F2Ob2bGsjvMNmiQ3QkRmUrx4t2INw7FQZuXFV6FmCuYpwhmzhXAhZskFD2TVqG9qpcCRaXrLCP1M1gSnSNycDsEaTdUH2t30NtPdTIWhMdgRCOdZwxjgBD3s/1GQcl3mCXc+ewJwy5dPhqgRNsO/Msd5+95CqZ4zfhxi9XUODWj13rrV/2DNLnILPQbhLprsd7CGIx+Af/GvN+T2PSUO2AEQfgdoXpHUPOv68hrJG9YZ6R30v2J4856FwHMPRiGFQupFqF/AFFPQFx2VqMAiqA85UtU+ePzEa0ADtgwP63L5t+sbGexQoskW3GFHmD+qemTdqX+dhdQ31qLG8Tqvrr5Qf56qHgsJkOWCcU4IHp1O2hoRxCpNeaE8zZT7jQOKzeA3ReJzKOIpTBTQADlw2Q86TzcE0oTJId63gHHHgQVQmdVBj0QrpfT8tvfyo1N8b/f2eh7M/btNsKbJj/HPlVVJV6TjvTKaPSQ2HYNjHAoELgnQY+fxrjLFC1rVl3tCHIFXAAnJuGgy6AYI7OMBxeBftfKlcIZUvipac8td4AQ0HYIxs377dCljEgjUVcklAA6cQAS1IuROjH8JrmlyyZl933XXWHsCDMQzhgcQwTiEErO/rrlqj/d0nR13D2GlzKqPBFkgB4w8lo3FcmaAL45G1nHR25ibH4vMJVuBU4nxjA4Cm6TkOLEiUhbZoo+EJGT/tFx0HNSoC1ZpXtVqn+vaPHmd25XJVB4ujFvZkAuOT8ZSJc4X3uMUxKgNV4wIaBhX+1OskJRB+Z+82KxPc4JEzR3VlXYPeP8dzzDtBQ9k8+VWmzqEzlqKW32uC7paR8JA+mGM4drCxWH/JmnYjuGF6gcUKK7DpcYjyPHObzFr2K+NUTgWcyqZUEFzI3kyWz0w3kx37sdgcs66Ddffjvyj94N+lowel6lrprg9KC7Ms/VZZI1XWSv09Y/sxWQHNs1VKQLhh+IX53Q5sEJyiZHoz/hDqMe7svcZAslKzbs9ZbKN4paLtMGWz3PxcRDCIa4yoptgQ9NdoWvlNCkV65fMFFPB5ZW1TgXWTcYItHo+zss7SWwPxKRUQyGxyEtAw4j76rRAI4z2MHfYaBEsES1j3+Z2fOY+r5y3U90+2asjWU5mV5craJtXV1lmZJZS/IrBh7EL4BvMBXxbzE57B9+B4zEmEtsn6/XEOBDb5XtY+ZFW3sAtBI1JX/AzDRFhet0Ldw13qGo5mwODLXlk/VvrQg/scw7VMDTfK4TD41jSssUpQGeLBIFhcu9jR+/d3XLbqr5GuBJgOrYP9ujjYr1mVpVPbsZCoCJTruqbkm6SH/AKDHzUGmwITF+c+iz9OoViDKh1wLOYtG43dqYpiF0KDY4oNAqdWrKoqFTDqMq3nHgs2IgzMSV/zFlx1bfThJhavky6dkI7tjf5eViFt/iidiFUqYDyi9kiUzQMIzmFwk/7tdgp2KDKgtsFDGgp3K+ivVVPZUgX9yevNcr6pjD6MQ76bWyoqjDqI180331zwhn0pYV2/1DV7PURhHDeJHMP8HfUUAcB0FKemHJsJXkBA+JnPYI8xmSH2Mm7zwiF1h/t1rDdq5E+vqNOdM68ZDU7wfmMTYmiamr3sOazlOKQYp/yNz+F5SBMBydgmsAQ6JpRDtAIXceayw4CGQVP5XNUFp2kg3Ktyf5XKUsxpD5nBBK9QtqarrjP33g2OMadqvi70n1XH8Fi5jmV1V6nMnzpgcnlwQOcHxjcWh2+81XHZC2qkgbqymdbDQ/GAsiCIj2jWatTm2P92pW66YL6zdxw8eHBCUIPPQTgFj8F/gBMpXUcwqmG36qqTAe9EsFXyIJDxsZ9395iIHm/5mPTCf4w1CF+wSlpaWj37CL6ZslPxgI3EGGHM5qLU0s62k3rj8gnLd7qheYGubZqX1IYnKBhbqjkemGfZzGM7cA4jnCGQkqiHR7FgrAyVB6fAD4TzP1G5YtZpggms3+mu17wefsF4JGhusr4Zm4xzAst2P9NnKgL60pE3NRAOyS+ffmrRGi2ubbLWasNZ4BJkfzAn2W/INsFeNEEXAnrms5krvC4ZN+J1oz6C6kaJEljmd+ZibXr7DXblDc03WoENfNq1wTpPJJ4jwA9czdRwq8b/zMqZ2jxtsy4PEqAIaGbFTMc9HZrKK0bHnwFbQn0wN/0HPHjIB3COsvCbiDROAQgIyiqUJSzizD82jHQcmQQzOE6893A8ezO9QuL111+3HGJF76QtVnDdrn9IWrk5mipeP10qLy3nHYaQE9KJmjyVoZ9uYCMcGda5/jc0HIGwRTQY6tZAuE1zKm+UP0nJJLt6MO5xw2HLuetUnZgKqOhxaKNi8TA5QZCMMU4ALx4IPOCMIqsPYuKEeKCWMnsLQexUyj9Q5g/o/XM2qHu4X2FFVB+MBkG279prpXbbVfWQEBRTu3btmrCGm7mISouAHA0IY0Hgnn4iZI2MqicXrpSWrZcO7Rx74fX3So1jZaucIuivsB4ecgd7gCvdoIZRX7nBMcg6Xtt0vS4NXNBQeFB1ZQ2qL3OWmVMXLLPINeN9FJEo7/DgoVTBGkwQ2m43sebyHOpX1mycBSjV03HmwluwcRBYxEO65QlzBfZT9quiK9NZSpg2T3ros9FMSbhF/bQo7yghMM6dcsxkr8tEOEUw4zunxnqmHeu9rKFISJtaEpdMwmGbStQIb3KrFBbfizLQlGorlv4ZHtwF9j+2eLJxT9koAhv4hzIJKjtd99c1zdKX1t2r1sE+NZZVqjoYdVrDV+wcF/sQPxhBNnxF9rlp5iJiGrLQ8Xclsj8JEsKdRv0M19wvvfZ1aXikJUJFrbTytsyyFctKpypGqQJ+4FqmBgMmlQMnHdQGa61HuvjA3CV6/OxxS1Hl90nDkYj+6xVXWZPBg4dSBItyovRRghgGKGRxHrGAEm13UqqBOVsKKdcod3HSAYgWv8c2f/aQAmz0EI0SBXPAiSFtlN+JgAIx3eym/tBlDY/UZo0iolCkX32hS6oJzsqY2KAmSeScThc4EAhqxCtN5GHygHUPQoEBnyhzjQA42RJOG1Yyr+xlF9Iz1sen9ePEit17IEms2/ECFuY9yRwKkBDIDP/edddd5sOlLQ9Ly6+VutqlllnSDG9PKFYYIjkh48YhvwBucQwCGzMq089wrQgE9LOLr9I/vLdfQUoaRKSG8nJ9ZF7i7EEPHoodBC7iZVWTpWcv34kICiEVmaVOFOIgF01d3Qbfkf0PG4o1hj0Tu8ztPmeTHmWV0vTS3IPjZYgmQqo9LJOgxtaL78V9LllQwwmwm9wax9iUlDb1AhqTGwQdEBEl6m3E+kigmuzrRAFrt1ARCGpOVV3S7CTWbtZtAh2J9ppUJeHIKNy9e7fFL0Zf1zhLuu3T0qXjpHBI0xdHq1x4KErEzeiP9zqnByN6VmigmPqn67bo0bPH1DE0oGsap2nzNGc13zx4KEagTjVN7pLB9K7AUcoiz8aUypihrjnR62JPuzabK8YiaYdE4yEdnDsO6ilRlmoKg73FqUMLVRLlcxKpzZ2om2IRsQoZxv9LMoWUWwGLVGAjp8YtPRU8TG5gtJO1RmCDjIh4PVNYJyGzTgIagLnlROHi9PzsoMcNJCHeGg3RIA2dfSrVXoVzjCykcaW3+Hfe0nGEh1InBFX4PBx1nmOqOJBNtkU2ARG38YmFy7Sopk672i+pNlim989ZpOYSy3r04CF2jY7te5fItoJboOYluGEXVcUDzk+EFjiMira314iTjrIlZo3BRqQZLuIwAh040Yo9MOMhO1CWxkmDeoCghAzrRCr1TMbKsK13gEEoznPj3uNgP3Rr3HJ9KPNTyB6FHvKXDQ5/gEcnqiJg+mA4BXY4/SroQ+O245rs7UR2PsIu/k5J9WTAX0a/Qvxt43g7vYLmrRoV1+x6803LloXTcJ3YG0pBGDwVMDQ0NK43cFZBDadpH/lAY3mFPrkwtYHmwUOxgzlFY6YtW7Y4ej3EAQcwmw1pdDh2ky24KFNwFJUKMNBQ/LKBEdhgo2JzRQmME4vv7VYZPA/FA+bA1Vdf7ei1pj5/IjVWOinmBpWBZvkVVFhjxpRPAfUM+xTwDagyMJGwY3ixwWIcUlbHfC7PG+Ux54NRlKnyyRwHBRUN+0reuBrokHrPS4FyqXae5LDs5FQDQQgCGmTm0Wcp0d6B4wlj3UlAkHUVZ47TQEgi55gZkwacA43HGaOQGoISkCXmAq/HOYDaK1XwgVJaTzzxhBXU5lwNcLIRGOF4OM9MLxnIDH9jThRLGcWpjGyCGvbSVcWAm6bNth4ePJQ66GvkxBFgwPqNM5e5iIAKYVQisA4TLCHDw63+erkGTjN4EbYZgQ0EZXANgPjLK1E1OYFd7qS6AWAMYFvEBjWYG2Q9OW2ebMe6xrl65sKh8Z9T2ax9HZd1VX2T1bsp9rNMsB8Oj72DrYNQxL5P8rdkfUKSAbsRfwI2F8E9yg6VNAgSDZ+XIoNSoEkKeCWBkgWwcfJz/+MFpNkz6PWKbeYk0MU+AAfIppIAY54+Gfa+mibLG98Px4cP4fsyvTSYp/CCVEENviNl1eAj9qAGfIaMFP7OfKDnB4EZzoUAyDPPPKM777yz9Ln3JIDTNhhBN7uOe/DgwTmIMLO5OFWps8iyeLPIs/ju27fPcgYnW3BLUYGE0p5NFeWIqc/IJkb6IMYp392tBoIeCg+c/pBMpxkWjOnYdFMcqBwjWaPxRAj4yjWz8jpdGtiv4UiPhiN+He0eUH94l3zyaV3jNZpdNd5o4rNQjVPuygQcOCc2Xft8xqkAEaJJcrpzEUcw6nSOaXf0liQ6j0unXhjLfqlokhbdFw1weJgAjGxDYGMNOcYRylP2AxPwSgUc/y+99JIVFMjE8Uzw4vLlyxNIC+eHfcjzHJ9j06gPUkC2yfve9z5H2RQQ+AcffHDC85RCYQ5ASOzZIPy8Zs2aUYeUh8LCqNsyKSHFexjTHsfw4MFdkElBgNwp2HMAthj7C+9PlpHK3M2kHE+hQbCGQDrrDiUf+Q7scTjycOKRxeFmyW0PhYUZ19kAsQbZS+kECQ3umLnMytZ4/fJxhSIRHe0e1g/PHFNEx3RtU4v+8dqbVGPrBYCQD25B2Wnj1DUOXnvJN+Ym54VfAOduOjBZ74hREolnSgaRkNS7TQq1jT1XuVYqL81yafkAnIDAbryysfhd8L9gw999990puSv2OzY5fDeT0uHYfjSoj+3jYfoLkj3FXHjssces4BsleBG8Ur3AaXZRvOoOrPHMJ86ZPc/4FPi+CBIRLRI49ILdhYfhmqngd7PreLbY13FKXzvxmr5+8nUd7Dqb88/z4KGQYKHGOeoEzD+MD6O0ZXJj6FCiabKBDYYN1Q6+N4YXDjyIFgTEQxyEQ1LrSeniMWk492u2G0BplE7NfwioPaCBsU+2RDZl1sr99ZpTtUkV/qv1TicBjZHPUkS72vdoIDRWftGkuxKowGFgGlFCdmKJMGPZkAcU5+kAxQiN+5xmsRS1gurM1vHlvAbapda3CnlWRQ8MfFRJiYBi0KlDCdJBsCHTIACGPRkZsU36zO/MRwItfAbn/Nprr1mvZwxn4xxCVcv4Z3148cUXHX9f5ui2bdv0xhtvWP+iOvaQO2CfZJpFaRw1ueYYNL1/6twefePEK3r2/D71h0pjf/TgIVOkE3SgpKbdecP+giPJDYdwsYEguX0v42fsUPYvnFkE6Cfj93YFPe3SucNSZ3r2bCFhgnSZgmwksnoyCWgAv8+n+2av1P9Yda/2tpdpd8fwqDW8q61Vf3f4nQlzEaEIJbPgFuyPCF342T5uOR+ctQRBEFmlO2YRqsCtSz6AN3hsfEAD9O+RIoUvaVmsQCiHzRWbfW1AkIIgHtkNToDICDubMZUuGO8EKmLFqgiajNgRLn3fffdZY54ACGOd57IteYVPCYc5nIXzsINAYTwfHXsqPAd+wcMIbj0UnmM4ytRgMSVFLZfY0XZMT50fc3Ic7bmoh+as01X1zhqW5RIX+i/r8mCnqoOVmlc109qgPHjIFqSUYkxgkLCxJCoNwt8OHDgwwbmJgcNCyoPjECUnas3vOINK3lCJA5xnGHooCHAWe2mBNgz2Sq98Teq8MFYv8qaPS7XFndViVBGknsbrL0PgjjlgsjBijQcMEbfKH3QMdVrZGQQzDPi5J9SripEyVGSFQP4Ze2yyqZwGzFOIBwYa85jvYVLLEwECRrDkAx/4QOmPcZyH4TiZnoNdhTibkgEZDuwNjPdE44XXQCIIkKcCZMBpCYZYxFNqQXbszTdNBhXBu9tuu01uAdICYUK5SGDDkBgC37HnxbXatWuXNS9NBhXzE1UXpB8hANkfHtxFojIGTpFrjjEQGrKCGV3D/dZ6fra/Xaf7WvXxBTcr6C+snTQYHtLpvvMKRUKaVTlNtUGvgbEHd4CzhjUZmyXZuoe9wf4Q20wZoQjZCwg44CvYMNg+rLOsw9hlJW+fxIBrQEAeR1XJl+RxG0d3SbseHxOoLLtRWn27ih04Z+GMptJBLHCSwiuxpxBw2OcB9gP7WzalOw3C2CLdneO69RGGeLtzzHFq+ISx+ZzweAKQzElsHBzDycrGGVB2h4ykSWEPhXutosHj+yBGpHC/FMj+vk1WEEggWyNRvxnmDc5+xpSTSgOMQ6pqOOEjdjC/4lUjwJFtH/+cA34u9qNY4WumYP8iM4UH/MIEcfgseDtiYjsQUMIn+JuZO5Rvo5wX3wGuMtn2xFLiGI6CGhgv3LRc4vXLR+I8917Bgxr7Og5rd/tYLcQ5ldN1+4zrvMCGh6wBiUAJhRobgylemjh/R6XK5hNvU8GZizMYgwbHMPMUxxUlb5wYNqVM1oiQxzrGp3TTv/3PS122HioDPdLOR6VbfkrFDsgz8wDCbDYuxjKOfYx7DBgMCQz+2DJVGD5uNaqsClSNC2gYVPorxn0e50Tzbgw4p+Ud+G4YQcxn9tREdUDJQsKBi2pwUoBgEA9btouFivEOFA8TgYG8Z8+euCnigH2B3hs8yOzIBUFlzsUL3JF9RFNzu7HPPCXYjLKRf90E8wfHN6QGooMjzvSZMvOfTBTKithLXplyjRAhgoo46fg+vIZr5tZ+gYOPz+d7Z1J3u5SRbZPIXHOMoz0X1DncN/o7a3zrYLdO9bVqUU3h7KTe4X49c+FV9YWiAR2//Lpl+gbNrCxuIYKH0gClO3DUoDa96667JvT3MnXMWU/jrVlwCRxU2F78jO1llLisv5O1uTBOKfYwe/YYDiv2EHsZxCmXoWEPaIBDr0ozFkkzMs+SzgfY442tZHfgYmuzbyN2IGBnSjzZRVLY4qlq9jsFgtjpFZW6OEBwPQr6acy2OXTZB1GIM+dMUNIJ4CR8D4Iy2GaIp+Ipm/EV4KjGTpkUAQ0QqJOGYm3UgOTPLLNmqgC7C+EU61w8wRP7AuMIhz3zJBXXZW1kvqQL7PpYDs9x7JkbzAX2HlNKl9JTbvt6TM8OrgefB7+3cx8CHvgkTG8/A/ZVrg0Z4lwrwDHgIpn21IwH9mpTjjeVMHKqcoyiCWoMx1m4hymlUkB0D/eOC2iAM/0XdaznjBbXFj6DxENpg0Vp8+bN1kKJo8YODBPUVaZ2eKLFm2MQTTcwzhyUJgQ2JuvCd/3111vf1VwXNh42GxPoMM+zMbEpT4maiB00SbMZdvzcebGk7inGtjEYKDNGAzKjekhEnt3MSJpbNUenek/p8tBYKvPS2itVHRwjssxXHKkYLIy5RAZhonPlO2Gc0SCdsRn7XpTukyagAZiL826TTjwTrX0LqmdILasKfWZFD9Y41nDGRDwCytxgHLL+YehTAioZIA443hlzjFsIPsF1CAtrZbzyCoz32LRwXg9hNnOTeYuxTXo4ZBlVJA61RPsPRB0iwzxir+J4L7zwwqjRapwLzA/z2QQDMeq3bNkyur5j6PLZBH1YL1BbJerhYWpTA74rwSJKUyEoQHWV7V5pekJwPnx31L6TRrHVdUlqPy0FK6QZS6TAeGcJ/KCYgxohSuCl8Xy+8FbnIfXbgr1hhfXG5b1635ziVz97KH6wHhKI2Llz5zinkWl6zJrIWpXMUc/6a1//EQ6Z9Zi9wc4/Jgu4Vvfee++460KmCt/ZNG024GecTJMxM37CHjBB8OOLcowiD2oAxjCBOHu2EXs29jhI1JMCYaFbQQ3wh6vW6TO7XrOyNriaTWXl+syVY/X+cTKTfU6QEfsHIUY6INDId2WsYguZ+Wr3LbAuJOuVU3IoWyANX4g2Crfgk6rWSb7J6f9wE6xdOOLtAiU7mCs8sL0JAiZb71kvGbvYcuwtcHZTYYF1k8BwPF9WvGx0gsqm1wtjlnnAWsu5EKAk+ECAIxHgN3AM05OSHrTMLbtQi/nBsUxG96OPPmo1BrdnmsAr4Oqmjy0cK5E/ju9rehxyvlwv1g+3Mkv4TggUHn/8cetzJo1wOTQsnX9XGuqXmuZK9RO/l1OO4TioEZuC4zZW1s+2SlDZt8yV9YVVu/XYlF0GlCbpCU183oOHTMBCSrNXmjHZF3icnsbYygQspqhNYmsEThbEpgIbchZrqHF92VzYIEklz7Tut2OcOCC9tQ2vnLR0rbQ8vsI6J6hpjJaeGg1sYNhlV28yn8DRioGBgUWAg+wNxnEmzb8zhd/n1w0t1+tM31kNhPvVUNagaRXjncmQDRwFkFizN6abmo7RBIlCNW7/frGEedKgZrZ05YekvovR5uDVMyXfJPyeOQBBbYIEGLGJMpJY/5zUeyaIwZoImec9BBJMTWUCC8xBggwQF2O4Q2ZiyQOvZ5/CScB8YMxC/E2AjmOgcootZ2JAlhPv4fsQ0I8lO/wNYo7ikMA/gBjwOkNmTAkU/o7Tjs+mnIITEEyB7HPOzF/Oh+vC986UKHD9CUZSV5gSWCikIUAlP5/PHpD22RS6tdOk6z8mBceCsVzDbBRpueYY86unKegLWCWe+BaMtHJ/meZUpVcmwW10D/VMyAwka8Per8aDh2yA8II13i6ewK4i+Jxphit7BYFn1rbJKJziO8V+LyMwiwX7HOWLsFdz7ige7Je2Py21npXqm6UNd0s1ebLxq+OJiiIlxTGwGxAysFebUs35xm0zZuu7N96hrZfOqzIQ0L2z5qqpfGwe4rRF8W0aLjvtiWMH8xInbLxehdhzOLInFeASVRuk0GUpMigFGiT/FM2oShPsAdi9lKFKFiQw1RKSAf8Kviz2B9N3ApEPPzPncEwjzoIzGG6QaHxj75vMa9ZY/GHGnwU3oNRaIpgABSImghJ33HGH9V5j22Nbsb4T1DA+Bp774Ac/aHEiAhsm0AJnImvkueees7iGU7uM98JHjICAa4edy/fI1A/FeeLX45pSHpFjEaAsaQwPSq9RNt0WkLzmfmnueNGjU47hyBrhQLkOatw+faVC4bDe6jxt/b6+aaFubFmqQqIuWBO3vnpjmXvpRB6mLlg0KbnDZmF3fMQ6OjMB0VxT938qw6hzidBjzNKXJJEiJ2sc3S89/n/HansS4ICEXB1fAeE6rrpdunRCGhwJulIrfO19KiVgrKCuINCHsZXKSUnJm0SO02wCG/OqE2fi4VjFKYrRglMXlXemY5MgBgagUfmhHjfqlEmHsmqprMQNsAKAcULTyGRqqnSPZxxc9L6wG9iMa+YURjikn70Igz5ebXHGKca1KVtignQY3RwzWY8EyBNGOXYlpIEgHyTGACLA3OJc7JkiOI0IgkAQIOc4kiBk6c4Ze3YVRIOAEU46CA7fgb9BpjJR30KcCG6QOYOzi3tWsg5qsqX3PzVeodvdKh3fIS3Z5Fr5qVxzjPqyKn1w3g168txudQ71qbm8RvfOWqcqAqwFRGN5vS4Nto1eXUZJPbyjVMeLh6ICYh5UrvZALWs84yubkp2sy9g/XjPtqMCK7GL2DexWsgZzMn9p6PuDL0uXTkeFS2f80slD0kd/VaqYWI/eddRPl5ZulN59bYzjzF4mzclcfJdvcF8oE8PejDApFXcwGaVuY2ldvfVIBMPbmb8muJEJCMSZzBTAHptNb7WiBnMu6JVtzASIexgnVAdJ5B8hOB6b9ZMIxm4mCIyfy3AM5hOZC4iQmIdkQBBsiO0XC7D5cdrzGvYvjkNQBEGWEWElm+dwDDLcmeP8/uCDD457jcnWZn80/jb2NHgPf3v++eet+YPAkrUituy1U/DZRqDM/OMzOT6igkwywMwaxnHgUPgjSjpj4/jOsT6wFiLSviekWcvGZYQ75RiOPJ4MCgZ7LkGzvntnr9FvLL/PemyZcVXB+1bQGHxTy9XW9m2wrHaB5lWV8ADyUDRAHcqCiZFhV4xg0GSTyowRxuR/5plnsnI0TCaw8EM8UP6aRlCuY/eLIz/YHEA7n1feUNMkbfkZ6Zp7pavvlrZ8WmrJ3CAuFDCcMIJwdCZTNOBsxbDJd11YzgmjhLGUTTYVIDOF45janUZB4sGDHZBQDNejR4/GvTD2wFg6iJ1fOLowtiEePHDOs27GA6/F6DeOLdNMFocX84LASCKnF+sxhvntt98et6mg6ZH04Q9/eMLfcYDjmEN9xesIrCQjOPEQr2Qc+y7khvMiEI7jg89JFzi3mNPYzXx/yGLJggB5OEbNikHc1zHuKfhBNrX188Ex5lY1679esUWfXfaAfnLRbZpRWfheAKsblqmxbMxGI3vkhpa1BT0nD5MDCHlYe5iXdueJKdOXDVh3ma84XT1EQTCf64q6OBN1fUqcPyZdPDWWiU3pvO72qJgqX7j6Dmnzx6P/3vAhaeOHo87kEgJjF6GBKfGUCNgUqNeztfEzBfMLmySbDEjz/bBJAPYjGRwePMSCAARO8kTZS1QPSbcBOHPNzjHMeCaIQWADzgC/WLBgQdz3E9gwGYGsqcxbxKn8znoLx0gExjn8BcFTvCAz50WJWMoM2oFIkc/AN8eDkuYIrNwA/jg+E47B9Ux2/olgSg3DtbgfZm6XLHrbJ+4hiKkGejPiGI68JwwsnC5Ou49PJiypnacZFc3qGOqyghzN5YUnQh5KH6amH04hHDwYWKb2X7qlbOxOXiLZLPhsFqhYc15uqYRgItw4wbg+o87wnsvSgaej/1Y1SMu3SPUZROWHxpoKjkutyycqaqRFpZ9ajHqbkjtkMZFCzThmQyMDiTnCvCGwkI2KKVOwB1JeJlMltx28H0UJhgkPjCgPHuKBNQsjOJ4aNNueBrGwZ2YkUwFxDvaAgmncDREguI6aKJtSB/HIiD1Fnj2O8+OamNdCzlKV4iKokYygsb6Qak4mJf+ms49CViAuXAOySUraiVBeHe2jMWwL7uBUowSVDQgFsgkuwzFyJjYoYhDEuGPmjWodaLP6e7RUNFrPefCQLSjPgfOIdZjgBrYTDhuCHZnYLTi7sFHgF8x11nUvU2M8sFdNoJ3sSmtPYr08+rp0ak/059lXSVfeFM2kTgcjPZsm3pgEz+cK9M8ogR4ayYBwCPsdFThjGe6Mk5QsUGwDbAj+huioEL1SUCUjukjk7E0HZGlQZoesUY4Jf/LgIRas6/hHXnnlFcvGju2jB9zKQMP2Zn0EiXrgAfYX/GT8y9iFX3CeBOfgFnAeRFjZZB7FfifOhybkBtj/7KXY83AdBAJ8ZrZgbSHjHE6XDkcwpYZ5sE6YXn4li7rp0QC9HZS2xZc1AmwYRHZOOIajTA0zuKci6QB1ZdWaVz3TC2h4cBXGGUT5DAwqjCcWqHRT0jgOylAMNRxfHCs2Qu5hfCkXUikx8g7tf0snnviyhi6fkUKDUvclafd3pf4MmpYujkmhZLOMfc6DY+AYfOKJJyynJaoEnKVm87fXnM03IAWGKLgFlMqo/NwupeVhcoHxTzYB/Rog3wY4qgpBVtmv7PsM52YaPkOQ8mFwMw8pH0VAkGAozfwgIMmyLLh2qfZH/o7SiwBmMkC4ULgByBb7MSIgzuuBBx4YTVvnM9l3SgqUwbjmQclv0z+1LJAWjA9UJWpk7xS8l2NMRQR8fs2obNHsquleQMODa8DRgzMABxK2BY4qeILTvkN20CeIgAb8ggclSiZrP41swVqGuh+1L3vHe1t/oNbdz0iDvdJQn3Rih3TklfQPPHOBVFFtU7X6ooGRefnrOTeZAK/AVkGEgIOQ0pOIJLCx6AVgOHkhgLPWjYCGAUJJ5iucyoOHRGAe4NA3JV4LDc4Dnm9vJM4cNQ3D8ZVlkk2dLljP4RdwGtYExE4IlrIFeyl+9VS9feAVZCrApxB7sjawdiE+45wMjh07lpsswVxiwVppuq35PFxj3fulwJhtYWIPTjKFHAU1IHcssoUgHa+1ntKfv/Oy/uTtl/Ts+fdK74Z58JDAuY7y3F5ygXIbydJhE8FEevNdhqeUrz3OLzaD6RXDKteQtu47MrK2RKTQkNSWwYa1/nbpmluiizFkg0bhmx/KxVeYEjC1ItnQuF8miMG/BAILhVzUS3ajj46HqQECX6xdOE2MPURQw0mjcDeBM59gI59tgheQDdTBPEcZqlwHWiA9dkKAkot0c5SWKLkSletyOo85Ht+FR+zncnzS0skSYf7S84RgBn0/CKqgirZnz6CqoiRkLmp05xQtC6Wb/6u07sekDQ9L6z84QWUMN4in7HP8ES0tBeEXnUMD+peju/SH+1/QPxx5U5diUt49eChVELw4cODA6O84Y1gX0w1EmFKEOHameo8+p8D5QkCcMob1PWd0+lKHjp61iULPvpP+QSurpfd9WmoY4Xk0CL//v0iN09078SkG04SYOWEyPLFZCACSrVkoYJu4mQWFfeYFID04HXvMC0Q4lCosJMgwRBxElqEJMOInM1kbiIRyyTEQKMUGL/A/kL0B90Jclq1Pmu9DJoj9OMx9OAS8gu+Ij4/sP8pOsTYhMoAHskbZxVn79u2zSuaVFOAS131I2vQJ6doPSrf9zPggxwi/wF/qRKjt2LrBYZrvTI1traf05fd2jP5+pKdNg+GQ7pud3wbilJ460n1SoUhIc6tmaE5VtOmSBw/ZLmZsGtk0rmYhzLYHx1TevK1r31yv3v5BXWzv1oymkfqlvgzIG4TvpvdJN9KQKpLZMTyMA0E+jApUGew/8QwIniMdNJ+ZSW4H1zFivDnswSkw6CEezz33nFWjFqOPjACa3vE39pZcBzlMAAODm6CjHWTDEbDPZeklSBefzefgkLCD746yilR61oZsApF8F5SbBCMIPBJoJZiBIx7Sg9qK789noIQmeEED9tj5bJqh00vLjYbveUVFrTQ9cVnMbMtPFYJfDIVD+sLBV3S+v0dhRXSqr0uHulr1R6tvVy3p73ncS471nlDHYIcqA5VaXLtI5f5J2MjVQ17B+pxur6F4YE/JpKGph+g+VNnUoGnBOXr9neO6YvZI4DdTbjB9nvTjvxkt1+HxC1fuD+MbGwERBsG7RJkOmTYKzgSmj4BbQHWP7eHBQzrlkegPZPpJYGfjNIdfINbJx3wgs2jv3r2W7WyvYsB+xN4G/8hlsA7hEplc8bKmyH5k/chWkMixCVwgUmOdQQwF1yATgwAK35Peg4i34Dz0IoHbxCtTP3PmTGvtyKT/SUEBP2ua6wq/CKZTKzLfDQ+fOf/ehOeeOv9eXoMabYOdeub8q4qMNP99r+ekrm1arStr3UsNzBSRSFhDkRMKRXrk91Wr3LdAPp/n3C4VoPbMNsrM4uVlaGSBxrlSVaPmzwhr73unNaO5XiqrklqyqBlrOdBKq3leMQc1MKRQGkE84jn++RvBQdJSSzWokS6B6Q+FVOH35yRrxENpAOUKzbIB9Z/5nQcGMUSAIB9ENtMeTU6Agz9ezw2M/Vw7wghoQCZisyjsIGsCZ0UmGZAGkDjSvJnzJmhBuSuyQNjD+a78jeuOOjpRxoKZ41yvdOvoFjtQjsERMkUh+MW73Zd1tr979HcCGx3DA9rdfk6bp+XHvmfc7GzbozP9Z+WzbIaITved0S3Tb1JZEfTWGA53aChy1jqvMt8sBf2ZC3A85Bc4N1Chsy5lE+DGkZLvLMBJhflrpbefHJ3vls02P8u+d15AwzXAwZkjOE0T2SwEBfIZ1IDn2EvuZAtspHTswHAkoqFwWBWeWHJKAzsXMBYp1429TbknnMyUQcKxj42dqww+jot4Kx743FyDvS+Zj44gAmXrsq2ygAiNB4EMsjG2bNliZfrh+zBBCtM38dZbb00ogFy+fLlVgopgDMKpbHqNlCq/cLxistjTTCmfGI5tHkI6UJzncol3Oo8oYtGdMextP1DwoAbGUV9op0KifFG0GdmwLqg6sEE+z+ApCaAuxSnCZoEaNJONgc2m5BsFFRKUilr/YZUdfllDJzqkliXSlZulMo/EFQPYvFGik1GTaCPHeZvvOcBcdbuPAcZRqrJa73Z16UOvvqp9nZ2qCQT0V2vX6mcLWIrLQ+GBk50yUCh8AOSVQDeqQ9Q/KK5MYznG7QsvvGAFP/idutHMr1IExj4ZFPfdd1/KoGg2QQ0DFFk4Cc0+TRCVh+lt4oRAsOeTHs56xT3AkR+bZVKKIHCUTRCrIPwiQXmPUB5L3HYP91gBDWCEU72hPp3qO6MrahaqkBgOt6ontHNUoDGoU6rWNSrzTwxieig+sE7deeedVlk8BCGZrIGoRJMFjT04wJyrrCk0o/sZXRgu08zVm6R54zMbPRQOCBEob5OsRx8lcHAq5stWwmGLvYGT0g2gBscWpDQnIo1kfqX/deiAvnjwHQ2Ew9rY3KJ/vX6jZlV6zcWnMiixStYEfJcHAUC46vHjx61xhUAHPsHYwiY3fS6w65hXpVq2EE6eKqs6nl8Cv1y6lRfgcJSLJMBqgilkZMBz+B1+keo6NjQ0WK+FC5Jlw7rFGuJm/89i5xeORxpR6nyTjk0t4yNxmNYbm3MfnbNjMDw4LqABhiOhgvf2CEUujwQ0QPRcwurUcMRZXeL2wQ69eXmnXr30ut7tMv0EPOQbLPik0BH1TjdVHIcI78lHxHpSo7xGuupe+Vbfp8jq+6WqhkKfkYcRoGrDmMIxmQwENuyNkw1QOKBiR7nuZs12nMGHDx92bd3EeDFpvomAcuqel1/W2yONmHtCIf23HTv0bJ4Vzh6KCzjIGYdPPvnkuOAehiwGOc8x9hmvzCOUPgTRMXYhJdRqLUWw7/H9kmUrsS6kasLnFKjTmKexzTYhG04VUQSccDCSrQExhABSO7eUARkjsJaNkpX3dnR05NWBemVts+qDFfKPOO35f4U/oNX1+atRPxSZGIwnY2MoXHihSn/oyMhPI73GrOcOO3ov4rO3Ot7Vcxde06uXdql9sDOHZ+oh2fqH4hOlI2t9usB2ooa3hywx+yrNe+DndXL6hmjmhpdhWzTAWUZpnWS2PK/BsRYLHI44PuEXbtayx55AmOJWTwNUztguu3fvTvq6b5w8oT99Z78V0ADb2y7rp94oTfvQg3uASzC+7eMHuxtblmxwxD6MVexZsgngF2R5sP+QeZDv0qJugb2TbJRkIJsqVlSJHZtuTx6TLXb//feP4xMEMowgzQmuu+46q5zVunXrrMDKo48+agVtSxnEHpzyC38xK6nunrlEPzZnheqC5aoOlOmOGYv10fmr8noOMyunTSAc0yuaCl72g7yM+EhNhjqGOrX10jad7T+v1sHLOtB1SPs69rt+jh6cAfUHCxAEIh1giGVT8sHDxPvA5uyhuIBxYJQfiYDhAbmIBWSeZo0oSZgvqJ8gINmC9R+lCsaDW0hltBzp7tbRnp5xSuKgz6fH87wveyguUO4IBR5EAnUOqipsNUg6Ri0KXYgHwXMMXlNKBCXQzKVL9GZPm/5952u61Ftaax81Y1EmJQJEg2uRiboynnOd47gx3zHOCSihanvwwQctwkdQAMUbPUBKLfMScQXjLNueGhwjnyWoqoNl+s3lN2phTYMVzJhTWadfX7ZJLRX5axBbF6xVmW98wjwZGy3lha+HHInDJeI9Fw9vtO7R/s53dXHgsk71ndUzF7apa6i01pfJBEp4sHal03yYYCV7hNfryx3g4HOjx4kH90Hpl2T+Lex9HI3MCTsIFPJesi1xSMIv4PHwDjfsutjPywY4neP1BrDjqfNnxzkF4RpvXG5Vt0vCEA+lCbgDQTHmASIoyrBiI8MxsIsJbsCz8WMh+AO8Fr9xxarl+vvnn9KrJ4+VnHia75UM7KnY7rH7qtk7EwEbP1ZsRTUKKreks0fHg8/ns8rlIqCi1x+Z+iYD5Nvf/rb1GaUG1manmRrBdIgYhCuf8Pt8emjucutRKCyvu0Jdwz062hONmDeW1WtjS/wab6MIDUkXD0lDvVLdLKkxcVpjpgj46ke0ZfZFwie/b6yZTiIc75k4qI/3ntRV9SsU9Oeu6Y6HxCC6bRZHpxFZr0G4uyCgkcv68x4yB8Q62dzgeUiFcYqhTGATpzwMmzyOXNMoj/q4bqlX3ApqQHZTNTqvjVNfl9W/LoeN0jyUDnDwb9682TKWIbBkAJiazGQxnR3s17nmRjU3NOreWXOtBsm/umeren2D6j90Qv+6Z7t+fckaNQaCFkEpqUZzcYCqDIPeqWKKOUgmC2SN9YJ/2ZcpT8F1JKWbgCjriiFu2QJCaMpnQUBQtfHg+rtRMisfQMFKbeFsSgywvpO9wrFSEUk3MaeqTp9feYsKBfpmXN9ynbZf3mllhSOaWt1wlVoqUsy97vNS5ymJhuItV0b7gLmMgK9Z4ch4p1rQl3pN6A8N6ETf2XF7VDgS1ns9p3RN4wrXz9ODM2D/0MuH9cwJJks97mIB+4sXICrerE8a8CZznCGcImDBvwQtuJ84J005J1MxAbvBraxw7AP8Atgh2YKG6KZHQjKOYQl2bc7ngM9n9e/z4AEb1fBr9hJTghmbeGBoSEeCEQVnzdRVDU1a3zhNf/rOm3r6wikNV4b0/ed+qDvrp+uh2Yus9xAoKdWyVIDvDM+67bbbJvyN6xKvtx5zEP8DXJ8AD2sI/TgQJzPvCBxh/5OBnsof4BSrbZmW7P1k0+C34LNK5frDC+C2TuDYG8KC7VYqXCnB7/Pr+uY1Wtu40jLMK/zlybM0CGjs+67Ue2ks6LDgBmnedS6fV5Wq/NeoL0xZFtQfflX6VyvgS107LRzh9RMjpuM7h3jIN1B74IhCcesEGFcoRfLZIHmyo9AZWB7iAwcfG3GyurcE3tn8cGKihE5EIjEWMm2eSWCFMlG8H/Aze2O6qabxjpuK8M6rrtYnFyzQf544MUo2CGh8ypv/HmzAAY9j2O4cfv7CWf3Oay+pf/8uhfsH9KflFVpYU6FLoUFFAj5FQmENNTfpteYK/dGqG6xUcx4333yztSYSFIFcsz8RPOQzyA6B1ENu+Bufh3ObOUjWFGQ/HokmeEzWFA/GPUTJ7XUXpRSOB9RPlDWivwjnwzyFTMUCgvHss89q06ZN4zI7+F7UpOd5+x7NnE9WmzoTcG40fuf4EJsPfehDFgHC2Z9NFkSuAS9ArZotpirHaC5v0t0zt6g/PKByf5kCvhS1mC+9K7337Bi/OLtLWvWhaBlNF1EVWKZIaEDDkYujQY6qQGqHeKK+h/nuh+hhPHCc0ESUdZd1MRVY47GhRptbe8gKOLqLeR2fyjCiqWRgDsC1sQngIcyPeMpzHLbZZFjQuwPbhWNzTsxZY39kAydz+L8tXqpvnjwx2iycb/fLS5eprEScnx7yAzgAD3tp5F/auVWvHz2soe2viJl08/RZerPjovzlZYqEI/L5/dpaF9CnV69Uy3BEzz33nCU6MoFE5hX2LvY64lLTPBuhInOA+QZXgIscPHjQeu1HPvKRCeOaPYu1ljnIewka5GLdJSsevgP/wW5F5Mh8RZCEsMwIygzIbgHMZXPOvJ7jsGbwnXng/0OQBb9yG6tXr7Y+g+tHFjtBDrhaombspcgxHAc1SFs7ceLElDVwIBuOcP7tkYAGGNnwTrwuzbhKKs/Q8TXYK3WdlwJlUv1syR8lPUH/dNX6blNEKLwItjjbeGZWztTJvjGFMeqwxrIG59/RQ06AMwW1LeUo4kV5Y4FThfnoVMkRsYJZ/ik5fz2UNghE0B8jFexKq0RBAoyeTFWIKLVQaZvUUn4+evSoZcRkG9xwUvv/Xzds0NUNDdrW2qrpFRX6nRUrrGCHBw/J8N/37VQ4GFTZomh92AGfT31VPlUNDikSCskXDGjgQqt2bHtN2zrD1h7B2OZf9phXX33VMipvv/12izCYEgsEMJgLkA0CfAQGCHRgOEMmYg1TUp/Z59ivIO8EFnOxH7FeQAogFgQ7TQYegVGyMWJTwzkHSFpsqSrOk2NR/s4EMQiKoHYixTsX541KCxIH4eEasb8XszMMXpCq7nA6HGMqgvFXFXAQZMeJduwl80v0n6E+6cxOadHNmX04x+y5EM0sr54mVUTngM8XUE1wrcKRaK8qn8oczdXqQKUayurUOdQ92vycf+dWeQ3GCw3KD7KWp2p+akCgmtIVOGpSIergxT/gOUATldBwq/GzB/eRqsQtwL632/iJ1sNMFdDYVggwCJ6YY2AD4BDFFkHclam9xPHsdkw8rGpo0NO3btHfH35XXcND2jJjln56kSea9JAcj509oZ3tl1TW1Gg9wD6fVDu9VuHBIfkCfoWHQ+o5fFRPDb6gtU1RoY7JBkecSxYDwUJ4B2sloio4B853HP3YxpS+IijPXGBvss8F9h/+brJ+EU8hPsxVNgKcxzQEZ76a7AsESfHsdeYx2RF2cG7sybt27Rr9G5wIH0W8wIgbWLRo0WiPIAJEcA2yOIu1mTjXNB2OkVZQg0FC2pETlceUxWCPhFEXq0qCMGQS1Og8K+3/QTQDBNTNlFY/JAWiTjkMSJ/SUxzPrpqpq8NX6UDnuxqODGtaebPWNUXTyjwUFhi9LMw0KHIC6vrjYCHinQiRSK/Coo8ANY398kWWye/zmovbgYPOrjzwUFxAleF0TqRCOiXe7ED5QdDR7hA1vTVMBgcGAk7UdEv3GIMolSET9Pv1Ww7LR3jwYOoiXxyIZhbZnwv6yuQr9yk04nisnTtbN86Yq00rrxtHFiARGNkEFXGwo07asmWLNWZNyTcCe8wP5gLE2078eR+OMWxH5jAqIYg7pCBX5f44N4hNLCBJnCd7JufFXMa2RW2VaP1nb92xY8fo+sN7uB6QFwhJtlla8YCogcAr5I0G75muWfkAhCNVrW4nmMpBDccID0sTmohHpKGezI6HI5qsj7aRpuDM+yu2SM1jAUm/Lz0BAGvHLdM26LXLu3VpoE3l/qCuaVw5oT+hh/yDe4MNw1rsxN7FAUqGB2s86348YLcMRo5pMExPs7D8alBVYI38vvQzYSczTBDfQ3HyP6c123MJBI2xgS+EFgQV8b9hp+BExTGZrsMTO8aJGOPqhkb93bXuq8Q9TF6c6++1KgfY+z3iAfUFAgpURcWFVMqsW7FU92+4XQtrxotwERsxruHP2LzMRTMPyPbGVmf9xPGOA549ydjD2OIE/cjwwDY3ZaGx/2+99daciXjj7Z98FoKB733vexbH4Xc4B0Ivzj9eUgBchblsz6Ck1O2LL75ofYd4meXZIhKJWDyNz+aa4V8p1mwNrgvX0inHcLwqMtC4iUTUvKBGEtROnxjQIAOiMsOaiAeelEI2BW/XBenkDmlRdumIi2oWWo+pmnlTrGChZlFOp44mizrGDou9uZe8n4W0ublJYe2iAqCtyNgBRSJV8vlSZ4NMBeAwwulGqRUPxQkMHrfWKo6RiQoCgp9IzcW8JVODc6T3FHslBDZWrZ4MvJZGg56az4ObgGwsqa3Tse7u0QAGdOD6ljl6u/O8Lg9Gx/Tsqhr9wpKrJ8wxAnSURbKDIAZBPIA9aK/bagdkBIMZpdXz3/ueznzhC7o2GFQQAzoH6dUTMHRCGjwapVjBOVL5MmufNP11cBRg01JWKpGCEaMflRAZHoZgkAWCkowAzbJly1xVOaEuYw0h4GPUaNT7JoBSjOD6xY6PTMA1fv755105p0kLsrUr6qWBrvElZKunZ3a81kNjAQ2AU+LoC1L9fCmYeXm16mCltszY6PGLIgQOk3SEU6g5WSexcXAmAewc5j3r03DknAbDh0dfH1an+kJ7VBNMXr9/KgF+5pUJLl4QOHCrPx7ItNkve30iAQP2ANyAAMzLL79sOTzZM50KQ7BjnGSjePCQLpbWNYwLaICA/PqxOYv0vTPY31H80pKrJwQ0QOxehE+LMY6wh/2HQJyx2WPBXkbADjHh9i9/Wdd3dWk5mRIf+UhUpJFLRPqlwf1SpFPyVUllVykYrNeHP/xh68/MZYQ6+C+S2cg0WEckRbYG8xTfH69/7LHHLF8CvgU3/bS7du2yri3rAQFTSoHBbYqx55OJOcRm0SdCWtIvIiV8gIckoGnfLFtkjcbbK+4dzaxIC2RnDKKuty8WEak3dRkWp/ACGsUHo6hwChwyGDdEq1FgYZyRxkqEMxrM6JvQUD4cuWgpQM2DunoFx0CvdGS39O4OqacjLx9pSquwaXpzoXgBecaR6gZQkzPm00WiDf9cf7/ufekl1X73u7riscd0duFCy0EK2UinYSDqbCcltjx4SBd/s/Z6NZSPlZdc29is31lxjf7f9Xfpz6/epL9Yc6P++botaq5wpiSFQAAUVSh/E+1XOP4x1Pds3ao1X/qSrnr2WQWffFL6sz+T/vzPc3sjh05JA3ulSJcU6ZGG3pUGD4x7CXOU/TZVfwzUVmRkmLq4gEAOSidq4sarrZ0pOCZGPKSDdYpgezGoSPORqeHxCwdYercUtM3ThgXS7AxVdn1t0cxyOyhTOkjQJHt4NlXxAYcpjhMCFU6AopPALdwCewbBFOs9/4LhSKyNE7ECG5cunbMC2qxhlBApCpw7Jr3zmnTy4LhmyLkEZVVQ6brRd8hDbsB8oLmvW2DvzuR4zM14a+YPzxzT+7c+rjte/KH+6vx7apw103JGprtfIlAhG8SDBzdx+/Q5+vC8xaO/B30+/eHq6/Sry9bqn6+9XX+86gb924Y79ZH5Vzrm2qyXBAUQ87B/xCvPjE+AklXYzAf++q9173e+o5bnnpO++U3pE5+Qjh1TzoCdNPC6FL4gRfqkcJs08Fo00GGbz/gukvUCtWd4EKCxBx7vvfdeK+DA/uEm5s6da11P9nSCBWTDTJZM8GC6ziCUpB6SgA1p8S3S7DXRWrdVTVJZhimnBEQgL8P20hG+zLM+PJQEUJBjFFEmw2lpC5wyKIEgKqhGWbSijtuJjtjOzh6dPHFUK5bfYX0ODpmCz+vudunxf5L6RhbvYLl0109J05NvBm5ErFG/5KoMigd3gBN169atVr3NbMHmnUmtSgysWMJBM70HXn5Zezo6LKUKc/bh117TVxcs0MeWLrUUeunUw6fEA99z8+bNaZ+fBw+JsLK+Uc/deq/2dbSpMhDQmoYmq5QZ2NgSDVCk6wTACMfg5BGv1BNGMynhOP3n79ih6RjmdgXjI49IP/MzUpz3upalEe+5itT14eOBtYfvyvcxIBuL4MaTTz6pe+65x7kTd2hACoek8qoJajIInb3BOkF39nSuMeStmIDtgJ0BN8gWhl942cMpQN+Laz4u9bZGxVJwjExVfGR9TGjg7ZPKirO+sgd3wJpFST0y6NLJJEUohbOWBqOUCgE+i2OMNK0fWROOvndWzdXLR9W1mYhIXMfOZ6Tdz439vuhq6faHJwb1XAR8jGw+pz1MPBQOiIoIQJlspGyAXZBNtoYdWy+d1Z8doNpCFC9fPKf2ik79wfkmK0iRTlUH5vAPf/hD3X333Tkpm+lhaoIx+9sr1upD867QpYF+La6p14zKaJnmpXWN1iMTe5tgAE2zsYnjiQoJ0MEzzp85o03PPhudO6GQSTGU/uM/pM9/XjlBuD0qlhoF+9+wFDovBdPvMYdPAt6/Z88eK1PFHthAdIztn6zM/PhzC0sDPVF+EZjo6yBIYAIF7FHY3ax/8XhcoZEuv0jLs4NaA+WFBweoaow+sgETdNmd0juPjRGPqgZpfnGWIfDgHsi+SJd04Gia2AC1Qoqg8jxr/X7kyFnL4Lp69V3y2Wolu6k0zQg7npL6e8ZnKW37vvT+X8rZR0I2UMS6YcROFXQMDehEb6fqg+VaUF2fVyUmBg5EGuWDW8AgIhBBUCuVUgEHLap0O4739mpne/vo75ERlcqTZ8/qY2k2DISc5LKxmYepjbqyMt04zV2j1W4cxzPS3/e+90V/eeMNJsP4oAZA7ZszQzqS8jkUxxjyTveA2O+LEwSj23EDT77/3iek43uivzfOlm74sFSZOKhOlgak5qmnntIDDzygYgK19iFF6ZTZSwSOgSKN8hqJ6vd7sJWhqks/GDkB05ZHy091RUvJWVhwk1Q23o70MLmAgwRBE3PXqaCH9S1er7Ay/wINheAXEXV19er0yYtaduVGNVQvGMcvChqsbL8wPqABju2TTlwjLbwqJx9J+V8UxmTaeRlLzoBI6FRfh/pCQ5pf1aBqxG15Av4tSt64wQftDbnZ0/iZ+ZYJnrtwmk6YVuFowL9vXjirwSVXa+606Tp58qSjoAbzDzuC2vk4MXNRq9/D1MaVtQ3Wwy0wZ2Kba9sxWpK1v58Fd6KtPZJNmF+McQz4PEEZp3489mV7mVnmLKVn8VM4zlZoPSVt/WY0qAHnWnevtOTahC9n/8dv+Mwzz1jCLIIbxQRiDqzNOQtqfPWrX83kvDxkiuZF0vqPSx2noqosfs+klJWHkoJp4BPPkeoULIQoxU+dOq+584KSr1MtzUvU0rxWPur/jQCDu+BGd1freMUgQZZuymflBlxXmg95/QucY1fbeX3x0HYNojCWdMu0+frFK9fJn6exw6YOSXQjqAHhxBlJAAHFEimeGPv8zKYeTxVCqmasYVEeJwARCYVU4UAFxfxE+Yjhg/oRNTb1rt/AAezBw2QChvq//dvY78wbGu2lSMsGrNOUfYLgMD8cZ1mVLZAG2iY+ZwtImCzFTB0ZvC+t9x55fSygATrOSTt+IN308YRvIQMTJ38xBjuxL6j364bqk/WPY3FML6iRJ/gD0rIHpPbj0czymunRh4cpIZzC1nDaWyMWOFKZq9gvHZ1hNU0fViTi0zWr71GZb9641+LUpTZ6qjJ/OUNXPC7hk7pyV+4TW3XTpk1FWae8GDEUDunL723X250XrN+rAmX6xSs36oqaaPPcXAMO7FYjd/ZDUy4XpyGOTY5NxjmBwXR6cJX5KEk1Xo8x3NWtmdOnKxgIjlN1x+O5zLtoKeqoMAw7guc9eJg0YN6uXIlBOpapgQ/JYR86OABiGrJDHGcr+BslX3W09NTo5AxI/jFBDsJk9jz4fazg2OmadO21iQMScTPAX/pa9F8T2NnxmFQ/XZqeOChyyy23WOuEW+ufm8DG+ASlxHIR1CCV1MvUKNGsDw8lB5QU27Ztyzh1mRRxo/TEiMFhkAgsaAVF00yp7dxYnVusuIbcpcK9/fbbSRUAHsZjIDSsv7IFNMBLl05qdcM03T4j+3rqTkAQwC2CSD1aA9LESetknkDQ6QMA4cegQGWFExWDBPIQ+/lzKiv1/jlz9OiZM7QitgoxBDs69PO33279nWPgkDWOWEPqmW88KBNnlBIePExa4Dj77Gelv/3bKOmgJNtf/RUTIuVbmUPMFRwFlGbDyHfUNC7IfheyNQqfazUKNyDri0AmJQjzhosxNX7Z71pPRP9NEhwu1lIR6aqonHIM1M0e8gTK7zRd4V3uKQZsEgIT9PGKl4GRCjh/jAMIG2fFshUJxVEEj7HfChbUaCBQF+MZ5md4Rw5ABgw2XcG+bwni2QtH9M5IQAP0h4b0z+9t159YVQUKLLrLYm6Y8UDfS7jGuXPnLD5Odifzgj41yYQR75+7SD86d8JqgAvH4ErcPn2uKkfKypgeGabaAnOarG/4CnYSART2VXMNEWd5fMPDpMMXviD96q9SryhqSz/8sDTSsDsVmJdGZEhPWhp3p4QvIJXfIA29JYVHGoWXr5L8US5vgvjwe0SUeZlzHRelIXu7ghFf2sXjSYMaiKiLrbRt3jI1SHVLp9a/Bw8eMgOKCshGtnU+MWxOnTqVNP0b5+qBAweszIWCGJDX3iNdOh1dlEFFtXTTB3LyUUTNUf16CirnuDDQq35bQAMEfD4dy1NDdwD5bkTd7TKYF8w1DBAezAETvDcNBNn7IAc05oOcGDBXvr5xo37/rbf0wsWLmllZqc9cfbUWjxgwEAt7AIWgCZ+XDwOHz4HwpNPTw4OHnOGTn4ySDHpr4ERzEKBkDGNrbtiwwQo+oCjiXxOEZy6ahto4DlDGjqog2cfKFkUfMYBkMDcx5JmTHC/T8hBpwSrrE+NcC1Zk3hOhCFRUpm6+G2CdpSykBw8ecg8y3+jZk20PLzJY6Tdkt41i/24ysHJhw6VEfbO08UHptUfH1t6rbpTmZF82Lx4oa7h+/fqcHHuy4lTv+FIx3KX2oX71hYZVHcyPwy3TPhhO7BhjX5BpjuMUmwVHKnYIZZAJMuJbi1V1r6pv1t+u3az/d/ygOocGtalllm4ejtpO2C6IsUwGOZ9DwIRSjol4PN/RraxPOBmVJDwu7aHgmD1b+trXGJTRzA0H2VDGJ8Z8IVMD7k/QETExzzMnERdjk8Iv4CIIoUZBAKNiQ9xjI45kf2WOMtfzsh+Ux8m0INiZaV/nAoMsfXyX6XCMtIIaGCQ4WVE52+t+efDgITcg2+Kll16ynDnZBBswpFCmUsoiHjBMMLpYiFGQYGTltdxFZY304M9J549HG6ii/qfJUQ5AjUKcZB6co6m8coLOjfq30yryk2GA8ZGrhouMc3sKNymYdmUASiqCHDhAKU0FYcWIN5lQVYGA/vKaa0Zfj4MU0kIwMXbO5jO9c9++fZaBhsPCEyHkANRxhagWYVmgogXj38EcoCY1qdumNCJ7E9lUzFUyNUyNdtLGIfFk3TGHaeLtxEHH3CAbCzCnIR55CWos2ySdOzSSkRiJ/rvyVpUqsBfuu+8+147HPabpugcPHnIP1lOcoogVKU2TKQgkc4xEwimew2FESR7UsJThybta/KpN0tyl0f4atY1Sy5ycfAxCEuy8YlW+Fiuay6vkk08RG8uo8AdGMxJyjbNnz+Ys4Mb4twcTEBEaYLfAzQlmwClw4sEf7NmK65qmWQ8DAoSA19qb6PI5qb4DXIC5mi0QhiAwITPE49M5AH6QoWFH2cweRsDe47AnBHsR84d1mnnDPgjYm3gwX01PDISJ7HH8TPWGVJni5r3MNR5mvuYcdS3S/FXSyf3RDFzW0uoGaWFp9s8h1gA/S6ccbVq7BTceIrh3714vqOHBQx7AnGNBZXJD+DMFRnaqElOQjNWrV1vpqcbI4rPzFtyg+WWOlFMGbC6oWvLiwJpEqA2W66cXrda/HntrNLhxRU2D7p7pXtPuZGDPweB3QxGEscEYj0c24oFMBx4QDZRVzEOUF4lAMz7TOLeQafN8zy1btli1nZ02KvPgAGdOSf/rT6VTJ6JBjZ/8Gemu+71L5xKYj6iHCWAmWqdNsMOu4EEp7NQxhyLSlAbB2UagOy/lCBtmSrf+F+nYLik0LM1eKs1yL9Mh32Bd/u3f/m3Xjge/+Mu//EvXjufBg4fkoI44DZJx6jjuVxQHOIewj5I58xFVEbAm+xzlK+KpvPbPaZgWfeQIOKgJ9CJC85Ae7p51pfa0n9WlwV6LY4CPL1ibl559Rk2dbcaSXQjFXDB2CmM9EceA0zAHCQhSxg11d7KMEY5l7CIcrdgylLhxCuwe5mC2MMp2/kVRnU6fEA9JgNDlkW9I3/9W1Ea8crn0K78tNRVXA+dSBgEN/GHJypyafps33HCD9Tvzk3HuZK5xbPt8NxnhORc1+nzSDT8mTZsntZ2VquqkZRvjZ3CUCL8wpcCdIm0LxgQ1PHjwkB9Qm5MgQzZl35zW9GPxgGjwMM2UWcwxurIhPMUAyAbfIRtF2lTG/bOXaHFNow51t6mhrFybWuaqnEajOQZGM/fNcQOvFEClBIE3NWj510mJJtRVBBdR4iUKEEJGONdCzxXOz5wLczpZ6bm8o6tVaj8nVdZJ0+aXVumd4WHpz35fah0pk8c4+OcvSTNnS2sc1GH1kBIQe1K+nQaeGedkUlFH2oli0JSdMgHSvM9VmvatuVulDkpPoHTjXrkFjsX6jJobG8SDBw+5B6UxUF1no7jGxnCSnYBT1fT3I5MUoRGZ4nkNbuQAfP+nn37acowXja1VQqgNVuh3Vt6qnW1n1B8a1rK6aZqPyjgPMMIft+7b+fPnrVJsJqPUSfkUoxaHp2LLkIERLxBy5syZ0eOhYibDJJ2ghltcgO9IRjulseFFjvoQ5AMEAuhdFhqSWuZLlc6vTVHg5eek735t7Pf33pX+5i+k//GFQp7VpAI2ZjqBZ+xRk93tZO4wh+0NvslmQiScl0oNfr+0dHL0izVBjXSQUVDjP//zP9N9mwcPHrIkHdu3b7dqhmcCo0hPBxAUjBbeR5kPjoFDqFQDHChcPMV6dlhR32I98gkCa5mO+3hgHKdDAuxARZXKWKJ8m53AUzaLeZQvokumCM5GM9YhPkXTW+PobmnHj8YKmc2/Srr+A6UT2Dh/Vrp4fvxzOMf37PCCGmkgEbFGwQuBSKeGKmSBzCunmRaxtd+N48FDeoDkIRBwM/hAeVscnhzbUzt78JAfoLJGWc4jn7a9KfXBuo+dZ/oLuCVgySdwLmMfZmpbeqCUa5lumha/L0suRVME1NwsF2YaBKcLIyhMBvi4sZ0QfpjG307nDO/PJuMdLkEmCtfLfMdUVSDyhsE+6aX/kEzD+WC5tPnjUvNYua+ix96dUT5kbFL8NocPSv19UmWeS/ZNQo6BH80E1Z2CoB38wmlQIjZjEd+T6XvjIb2gxk/8xE+k8Q7Jn0lQY8+ePZOKBFIbnoa3Bzpb1Ud014OHIkO26gqICgttJkAtgpOJkjo8SN0rReS1R4gHV0CaNAShFJRvzFHm2datW606/wCnH4or03jcCbL9rgQ1rrnmmlGlO/U/SZstOPp7pJ22gAY4+Xa0/mepIF62G7ZQZWYZdFMNEGKa8PGgxJS9lw0/k6WRrOxUPDC3KIPgdN5AMFAGx5u/me6RUxHwgHRVVE7AMaldPJnQOTSg/R2XdLqvq9Cn4sFD0vJRhQBqb7gF6zgZYKWIohGOeEgL8FmEeqUAggeIPiiXaYCtD99wWlIKOyvboAbnQalqg6LhZ++8LHWNZFGD4SHpje+ppEDgIvZ6UhEh6PXocWqXwi/oRUtWkx0IDKm4YO9DkwrMK8Z3OlkWsb4muAXPUf3EgzNwzTLhGBllakAKcZyUykaQDIPhkL548HXt64guhPXBcn1u5Y1aWJOftEcPHpwSDhymqFLz3mDPBowhuyPKQ4HRcU66dDTaj2T2SqlictU1Rb2Xjmo7n/j2qVP65R07dOncOa2pqtIXr7lGa+bP1913321lNmFQoQghqMC8waBxqgbLlHhAeFCd2Z22GGOmh0hB0dM2pj4yoJlZ1yWVDJqnSbdskV56Lko8eFTXSHeUfjmhXILxTN12xrQpz0GgjcCGqWuLKoeMxHQIMuVL2BfTafAJIberkfk89lXOj5+T1dktKkTCUs8pabhXqmhWpHJ6Xp0Lb7zxRk4ahHJM1HSTBTsun9MXD72hwZFM2ftmXaFPXZFenWAPHnINSr/hRMi0t5BbQkfsJSfNWIsNlAZmH8m0RHBRApHn6f1Sf7fUOFuaUfo+n1gYh6Obx3Njbe8aGtRfHNypbaeOSW2d+sDcxXpwwZXW/oijlWxGMqzoU4Mvzog7UgHbJ5vMCrKq6DNQlPsXXGLcOhSRetujtpLVOLkEcM/7pFdeJCIT/S483vdBblyhz6yoAcclkME+RqAPUFIRzgEnJkOJsetkjtjnMnau6avhBPH8Y/gBKE3IOkNZKoL4JYHBTqnnjOQLKFK7QL5g/prW40OBI6Zb3jbtWYJqdu3atXr99dcnRVDj+6ff1VsjAQ3QPTyov353u/6/tXcW9Lw8eIgFCzUqcMrKpBvYYIFwK/0NoyhVc2UPecC5g9KeH2m0c/fRN6SNn5Sq6ifN5SeATj3KYsO21lZ95FvfihqaTU3aU1mpXzh7VntHNmCzN6IMwYjCqKE8gZM5yB4LOU43fZ00ej4vttkhJJvrWHBY9ZHNYB0BZKO22d3PwXn4ja9Lzz0dbeT9kYel27a4d/yf/6y04Arp3QNSfYP0/g9Hgx0e4mLw/Hm9/I//qLULFqjlnntGVWgQcubJiy++aI1RajTb1X9O9zUyO9KB6alhB6SF4N/p06e1f/9+rVq1qrjvJvPmzPNS31nr177+Qf3wjct64OGfz1vDTjjAJz/5SdePy7346le/qsmAnuEhffHQ9tGABnj83FGtrJ+mm6aVUEkMD5Me2B0Eh8mWW7lyZVrvJcPDraAGSlr6bJRiUIO+h5T7nDQBjW1flTrPRx3C7DlXbpKWlUjQ36Et4Ha5tXTES8nwO1uf0s5Tx+WrqlT5rGn6TrhLq6qDWjTSsw9nHzY/QibKNjrl47wuk4ws5jd7/qJFiyZcM/5WFD4BuAT9NEbXIl+0WbLbAY3jx6Uv/R/pzGlpxQrpM78ixcn+zQjzF0p/9JfSEz+U+nqlq9dJt93lzrEnKfZ9/evynTyp+6+9VkFbaVl81YiVKDnL/pYuKCdNqdp0MseZ/7FZHQQxtmzZYp0DgRfDN4oaPWekk89G131JW3d9TY0r7tXV6/PTr4O1hj496d63jFZzbggf+PGPf1yljmM97XYXi7h95/p7rAyOfDTB9eDBKTCUUJGSWkeN/nQa62VbRzM2Ij6Zys+VLN5+JuogNrdiqF868pq0evKoxtPtA5MvfP/0aQWCQYVmzbJ+H45E9E5Xlw53d2tFnCAMc8/pdyEAkW5A49ixY9a8jNekEqJRFDVvIRdr75Z2Pzn23Oyl0oL0HNkp8X//Jfow2LdP+pM/l2651Z3jYxegnMozDna16psn96tjaEDL61r08QVXqybfKelpKt4G33tPr3z0o9ocDqsCwvuVr0h/93fIgq2/03+GB3MDcp7uHoUTjsBGOgKbeCngRiRA8BECVPToOjYa0ADnL3Vo9eyQzp18T0tWuNe4O1kJCq57pqruZOCYZOhRhgZnTSnj/AiXsCPg8+loT7sX1PBQdIBXsP7B76+77rq01mM3myy72d8gn0ENMgcnDU69FQ1ogBHHlg5vk+avjdpykwDsYTjp3YQbfWkGQiHtOHtSlXNnyTcSKGB2vXzxjG6fMdYPoL+/f1T17XT+Mb/TFTgS/EFQiZM4tnQnf8PxCwdJp6xPTrDyFunCUal7pIQd69d173f3My5dkn7201J3j8TeTjnuQ4ekf/m/7mVTzF8k/cxnlE9gp/zHsf3a0X5e1YEyfXT+cm1onp3XczB+pXT2ksO/93sKfve7WkmG3De+Id15p/Rnf2Y1zOY4pjcbQsF0yz8xt9gLZ8+e7XhPgsfECovs5avw2xH8LvqgxplXx9Z9RA+BkPrO7sVCz8vHc93TyZAx8GcT1JgMaK6okt/aLsZQEyhTWamkqnmYUsBYwnGJU4G0OKeloNxUUWDUFGXq6VQCxhRBDDswCAYmT83uYnVqoebuPX1aod7eCX+rsDkBIBymFAFzz0lQI7bReCpQpuHAgQPq7Oy0CEfsvIRkP/744yoaXLlBuuPT0rUPSjd9TLrxo+6rqL79zYnPPfIdlTJO9nbqLw68oiPdbbo40KtXL53SXx96LX/B5eFWqf0pqf2HUvuT0pCtbnESvPa5z+kmny8a0ADUff7TP53wOuYHCtd09ygyKtgDsUchLalASTga0yYCx8pE0ZV3DJN5NTbXK8rLdOC9Mwr68hO8xPagBn4u1mfGAUEqPqPU0VReGbeHX3O51/DTQ3EChyfr6iuvvOLYSe9WyR3jSCrF3kYExnFWTRoMdMe3zXh+kiAXfVCyzdSgtNThQ4c03NahUN9YnwyffCqLsY94bbpVGwhqkA3lBHAWMswJaFD2Kl4vsieffNLK7or3t7yjolra8inp+g9K175PuuvnpOkuN55/4XmIV5SDA3gdPRPffluljC8d3qnHzr1niboRXfzPA2Nl+XONSCSsnqG31T70rNqHnlH30FuKRFL7tk69/LKGH3lEK5kDhgs984z08ssTXss8SbfqA+/ZtGmTXnvtNce9ZAmUJgvuwdWL0a8xDlzL4fF9OHv6BtR6MX9B+7wHNahVZpqhljI+MHeZGsoqLHrIxeDfTy++xnPaeihaQB6uuuoqi3hQkzy2GVIiRatbdfVRs546dcqVY3nIQi1e0xLTUMwnNSR22JUajh8/7rqKKtvsD3pJEaj47M03q3r6dPkh/WfOyNfWpgdmzdIiWz1lApCmLANloVKVJcChiuGU6jtTZxJVFHOf82lqakrYTAtHL06CXFzHjNE0S7pirTT7yokN8dxAKM79LUEniR2vtZ6Kltcd+T2siA51X7YCHDlHuF/q2iZFRoIGkX6p+zUplPizce4wRuvb2lRuD7ww986OZRi4pS4mTZnmmRDwZEilJiSoUhI9o8rpITJ2XWfPaNTMaY0aiuQnIEOgNBPC4RQc+wc/+IEmQ1DjxxdES/kY8dTimkbdMcNlR4sHDy6CTFEUrnAGgoupggw4cd1qgsoaXIqZ4DiYi159mw7qZ41T6lqgd19NkyYLclEuKRv7AXsdp+hVK1bok7ffqcHLbeo9dUa9R08oEgrpoTnjbRcTSKRXgBOQAZmKCzD3yJYyDZfJ8iZ7MlGvGIJCCEGKpuxasFyat1JauGak5K3LSHR/S8FuTJKlsfXS6VGLMjJirzx/IT9Zy32hIxqMnB61aYciZ9UbejelAPD43r1aHitCYk64yDEY21RIgc8T6E/mPyBICt8uedGvzyeVm3LRUdx87XJ19OenegaC1t27d2fEMTLKlUJFhSOFBe/ee+9VKQPF1F+suV3bWk+pPxTS1Y3TdUWN86aTHjwUCqS4sdhSf5a5SM+NRIYH6lQU3czbbEH0mkUbA8kAw4dAi4c8Yu37pDe/PaacmrZQuiL+JhAliWR2sCFx/4o/Ew2SnAvVNHMAoz2d8m1cP8gGpJX3M4ve+NCH9Af79+tcf7/WSvqZlpZxyi+MIBOAgEgkU1RxfAwmmoilwo4dOyxHLgqSVMYT58rcJH12yuCuu6Uffn98w0CeK2FExhXJtD+fpywNxRK2cPT5wPj9BicYcwuF74wZM7SM8fzss2OEj0ymHPRio+YtvTVoMM3P8Qg2WU3smcnmTMmQkZr5Uv1SqdMQP5/qF27Ujj37tXjZVTlx1hCoevvtt611GRXVpz71KeUK2DVfoVTZJMCH5y3XstpmHe5uU2NZhTZPn+eVtvVQElixYoU175nvrOdkZyVaI7E1KL3hBsdASW7nF9hV69evL+qyVChwi7H/W8aYeaW0+HrpvZGMOX9QWveQVBY/cBOJDI9wjHL5fM5r0BcK2MXp1Mp3CsY/fbngAenYE8wdxIL0FeN9n1l5rWbW1uu11nOq8vl141BQwfOXFK5rHN3f+Sycu8zRVA2QET9S2pY5nOp19OqAYzjpzwV3ufHGGzVlcONN0t//HQQ1KtLBpqUEWJp9iIoJ8WPIsI78BJeHwhcSPLdiwvME2eGzFtdetSpans3+Bfg5Bxxj/vz5VjYSWRuJuDfiRSeOeDczG3OGubdIJ56SQtFsMX91i8paqq1gA3txLoCoE/7IPYbDZVLOLqOgBjfjtttusz641IMaoK6sXHfPKnAtQA8eMgTGB0oqlBUslgQ3Yo1/txdRFng73iX90kN+Udsi3fxfpe7WEQVVc1zlO2mcocg+RXRp5JlqBbVOPl9plMDA2MeAIdvIjVRxlBQYHxACp6SGgCB1Y+1Bw1UNDfp2jDGP089+jgT6IOeJAhrMS/7ORk75qFQkAjUg55CshE5stku69XNLHjTtw9B97hmprFx6+GPS+x5SKeOG5rl64twR+SJjKirEF9NJt881fAnMRN/4eusQ671791pzioCe1Wz2N38TeSApTtEXkXb9+7+fs1OlDvybb75pBfqZT/Z5x7lt3Lgx5TEg/kVPOji3GTdIDUul4V4rc2PV4mpdec1Azpp1Qma4LgQ1sDVuvdWlHjVxwLE/+9nPWutdSZQDS4E1jdOthwcPpQbsDQLGpgwNosZ4JTLdbJKNHWR3vrLuFHNAwwQ1nJb1KQmwx6y4TVqwLiqcgm8kDGhcUlj7RsUPvsiV8vuKKDs4QVCDvQXuTDABBTK2crb9MLAbcMYhgkoVQDCg6gkch+bf9t5LP75gmfUwYO+FL8D5AfON75GMD2DPvPXWW9acwiZKBYIklJpy0k+Hzy6JzFY3ge/jb/639IW/oAEQDhjpd39PKuEsLcomb2yeo9cvn7Flg0u3TBvv58kVfHCJmPiJTxPHn/FxIRRkbs1mPP/Wb0lf+MJYYOOnfkrasCEn50nQms9FgIgocunSpaN/Y/4S9Eg1b/APcP5XXHGFihqVzdKSD0h9l6zKIP6qGbpvdn/a5e6cgvuKeAI/C34UOEAmHCzj1ZsP/Pa3v53p2z148OAiMMRQeUMscOpg7BjDB2C0UTYqV8DYInU2VwuehwQgmJGi5FQ4ctQW0AC9Go68pTJfbjZ+t0BPCoIP1J+ESKMeIriQinSwOUIqkhkXzA0Mff5FmYRhXldXFzd7AwcpQbxEWVB2xAZJcDDa56EBBIrvQsAGI8luHCUDZR8pteMEfAaZIm4oJ0sKOEF/7Teij0mChTWN+vVlm/QNW6Pwn1y0Rv58ON6D06RAvRSiXw/EwSf5a6SyGeNexr6D8wuDFGe0Bcbef/wHbCSarbF6NbVNcnaqGMEQcvYjiAclVJiD9MFhT3RC1Al6svaUxF5W0Rx9jJRPdfucWUPIumF9pNwljhHK3hFgjreuuQVUpxBEhFNkbXjw4KGwQB2L8xRnLcEN+IZ9vcm2l0AqRxClNgveiDgJsLUo2zXpQAmfJGV8IpFBhUUD2bGyJBEdViRSL5+veGvHs5cxlglsEMzATqA3BHZ+qhIr7E2mQXc8cAwcoHALjk85FWwTHHaxc4Rxg6jJyX7Ke2NFC4mysMmaxRbjXBB7OAnWkM0Kh3FiJxmhA8eecrhmrfSfX9NkwmeWrlfdsXLtbDs30ih8hdY1Oa9mkA0qA1eoZ3hvzHOLJwTbmLNkMj399NNjY/QjH5E2bYo2bGcuOAwkZgoyFnlQ7vbSpUvW3oR9vG/fPkdCH/Yw7NqiD2qAQIVUOxaod+IDSResjQDO9cADD1h+mN/93d/VRz/60YyOl1VQ49d//ddzlsLnwYOH9MGiYxob2VWORIZzqSJClYsBhYO2GJWVbjZKLzVEFK+XSqeKHRBmDH0TrSfggIMewz5ZBB8DwzSuYz5wHMpGmWbdZ8+cUm9vhwL+aAk1PgODH6MEkgNJgJAQTMEZitHiJlmFOOAAJqMq3ZJtfAen8wv1l9tNED0UDqsapuuPGm7L/wejoqrbLPUdjAY2ArVS5fJxGRw4tCAcEA272tAC9mGOlFOJwBpBnxmUP6wHzINU5RkMWCu8AH0UL730kuXw4f6i1Ka29osvvpixisopODYBKT7LC2p48FAcYF5i4yOQIvON9cDU9McXkCtg92GjITDJZTA1G0xdjkFD2dgggE8Rdcin5qIey3AKBFMGBO7Z61L5tAj0m3LO2AtwDMNLsO9Pnjlpiav6e/qt0rOIlsiYYD8lMGiVsG1qsmx5Sq7gqHUTiJ84HzJT0xmT7PHpOls9/9/kQGUgqJ9bkjqTJxco98+UL7hWAyH6w0ZUHpijcv940RQBQvYb5tiECkEIdnMo2o0Hsp7wMyC85JwI8hd1dncRgkoSBDVYB1l7br/9dstvSTn9L33pS/kNauDExNFDxGnz5s2ZHsaDBw85gGmcimMW4wmlOYuvVRIkB2Ax59g4hZ2qzvMJUz5oaiKegV7cqfwAJylqI3umgRMDGscqJAHHJA+OgQqA8gADvadVWXZC1dVlKlNAfYOLdORIwDL8ITcopiAdvI+AB6oB5pBTpGpwiSoLwoFTMNcEGOIVr0yEBw9pw1cmVScm3gTNY0sSFhrMWwL86QIjOyeKYNYGGrvTyDJY/Ouv5ZTp77fsfAQLJkPshRde0Ic+9KGcfz5r5COPPKLPf/7zOf8sDx48OAeOXNSqZGwQPMYhgRAEhy42Uy7AcfmcYgweEDjPZVCnuBHPJscOLn6xa2xze3isE44BR0AoYecYVElo72zXO50HNFQ5ZNX6r1Otqi5WWbyC+YHTjlLNCK4IBLK/0rfGKRhjqTIuyJ5gbmYiYmRepeIwduSjabCHqYEy/3TrEQ+MSdbYYgoaMFcIgqYLnPZOM6HSRmhYGhqQKEtcRNcqEdjPCfqydiJA5ZoSU2CvT2dddCWoweDasmWLnn32WS+o4cFDkYHIJw6dEydOWGnbKDZQhOQyRZwFqZg2HTtQyUzVRuYB3xINR0yzX+5PRAGfM9VyIcGm9vLLL1uK3UzGFeope3mE3Tu3aeXSftXVLRw9Xn9/lwJV61VWHk2vR5XMfEENmE4wwwkIaJChgfI4U1KeDuGAaOUqiOmh+HGuv1d/d+Qdne7r0cq6Jv3CkpWqzrJedKKABgFDp/WjSwGuk47udun5/5TazkfX4FU3SevvKmriwRqJ3UCQh39NBgtlvf7u7/4u559/xx136Dd+4zcsxw+f7cGDh+IBzggyNp544gndd9991pqJwzZXQY1i5hg4raeqaMrnq5EiONBPj/ILqU4+Oev7VkiQlY39wlhOFwQXGOtmvCMaHJwxpOaZLSqrGOPYnaFubZwXzWgCjF9T8ipdUEY62fvInoK3ZFqVwezxTkVTBE88TE3ARR8/d1wvXDitcn9APzZ3sa5rdn884LOi5CnB88kA/AAEOF3H/lekHU9JkbBU2yRt+YSUp/JhmYLSdaxprKEmQ4yYArZ/pvt8VnIHPpgT8ODBQ3ECA8gsDhg61CXMFdJxuHrIL+kI+jbKryvk1wIFfNfJ7yvuzc4QWFI8yThyA7U1w6qvrx63WVZWlqksQPr8GFHhMdoTIA2gWkrkDCVQsn//fksAkK3K0Mk8Q/lQEj0BPOQE7YMD+vjrL+gHZ07ojcuX9O/HD+u/7diqYZeVdabB5WRKvWb+871cxfNfldovjPwSkfZvlQ67s67lEvTOIGvFBBUglyhU81EChnJhOGj4TA8ePBTnWklgwyjeUdPmkgdw7GLcZ7AXp3Lg1a8V8mmVfJovn5bKr+uizX+LHOxtqIWdOvKTAdt/qHp4XEAD9Jb3jvsd8QcVDTIBHCNepgbNziltRZ+PTIIlBpTjIljhBJkGgzxMDnzj5Lv6y4O7tL3tgl5tPavf3PuK3mhFtOMutm/frhtuuGHS9Ibke+DId3WfPHVQevOJaEAD9LRLz/xbNHOjiMFaxhrCumX2dRPUyBRZBzWo3U/kyYMHD8UN0mp5YMRlChZiUsxx0lKehwe1Dk19vFw2I/eQOXy+KgX8SxTwL5Xf11gyl5I0bQz2bA2A6PsTpJX7xj/PJpsJ6WAfjNdIi74cZApRDzRbBTiBSQyiVEA1lmn6pofSx1PnT+vSQL9CI/MmrIh2d1zW253x+utkBhwBBBydNq4vFfB92N8oE+cK8Rjsl9rORctPjcInnc3MsVFIZKuiSgd8hiec8uChuEHZQSOWwklMb7JsytJg78EpDL8g+8OITIq13A3ZZMXYSzBfYK32+2bL71smv29hSQQ0DNavX2+VJ3YD5f6JHKMi5jlETfBwhEduABuF7MkNGzYkbBruFFRxcFJGjeAlKLYycB7yh6+ffHf0ZyxbLMJvnTrs6mcwLwm0TaaAMWslWSdUoUAQ5grOvif5bHMRrtHbKXW3qZSAn4SYQsGCGhgwOFm4OR48lCouD/boP46/qr869IS+cvRFneorrYXASTQUogAIOrBwpETXRensQaljTLUBuYBkYIyRao3ihAe9D6i5TQmSVPU+CwHOtxjPy4MzUL82m0AcYPyXVcyQRgM6I045X3P0YQOGOp9J6bZ0wDyInVsEFzBc3EqdhTjH1gGOB69ky9TGQDhsRnjM81Eymil55sF4powatZuzKaVWrOD7ENggHRrb1hD4jEH/DDvhAAQFykuPqGWrokoXXlDDQ6mDNfP5C4f1h/uf0u+/9aQeObXP9Yy5QoJsKkrUAZy1CDtSBj2HB6Vzh6Wzh6JBXyhHV5eVzUqABDGL4Rc8EHJQPrdY9xoy++IJWjwUP7CpM8nMjoflddEMRt/of2PPxQqnEE4YXu4UsUE9zvvFF1+07BW3MrOdzDH8AMXYO9ND/jAYYxcT2BjIMjOAvRJ+C28mUAenptflZENLS4vFnch2ciJSTAmLS8QRYJUYx4BvIZIwpagyQdYWwp133qmnn34628N48FAQDIaH9dUT26xAxkB4WBcHuvT1E6+pfdAdFUWxLKCGdDjqqXHkdenlf5V2fV965d+kd16wjC+MHQwZIud2xTkEhH4VBDbSNdLyAdTEk6Ue41QE6dTpBhhiYY378gqp7HopsFzyz43+W3bdRIfjCFEnEHbhgikZ4zBwEjO3ILtujj0IPwGXZCB9nDnqIUNs2yr94/+R/vP/Sq2XSvIy3tgyQ35flFSDgHyaVl6u3e3n9ZWj+3WgM7ofOIGdZJAKzl5CWTh6Nk3mYDGllyirhaGdVRNYf0C65rbozzj7WG8CQWnljY4PwfVHweSW8yUTtLW1aceOHXkPahBAoz+QBw+liFdbj+vRs++oc3hAPaFBvdJ6TD88s1+TBQQwYoMYSYMafZ3SM/8kvfoNadu3pKf/QepqtYQr8AgcGjiz7MfCsUVmoNPSOPkEQf7a2tpJvRdOdqAEdyNzYmblDN3YslELqudrQfUC3diySdMqpk14HWOasf7OO++klQ0aO6/IaOI4btXod8rfsQHxK3jIAB3t0ne+Kv2/L0uvvxKTwVs6uGna7AkO5JmVVfrHI/v0vdNHNOhQQMX4hydjZ8MvyPSjHNFNN91kObgnK5jL8Avs6kzL0Y1i2XUjzcH9Y336lm2Qqpz1t0K4RXCUYGUh8cwzz2TNL7Lehe+66y79yZ/8SbaH8eChIDjT166u4ahSCLC9DEVCOtJzQdeWT456kThoKYFDU6+UqtOuS9LBF8c/d/QNnegLauGajUnfSnmdYgMlscgm8whH6cIY8qiUMlXqjQbzSIsPLnb0njlz5ljzho3eSRNkDJPYWvNZq7xtgHRxvFSKQNQf1CD1kAG+9TXp3/+FAslRsvHEj6S//nupZSIxLWYsqa3X/167SX/w9k6rDNWC6iqFI4P67qkjls37rZPv6vevukE3Tpsdd66wbjJfGP/8S4YfPQ6mGnCuUcaNNQDnQca4+laptlk6e1gqq5BW3CDVOx9TOPS4/vSXuPHGGwtS6uS5556zziHTJqSZgHHHmspnf/CDH8zb53rw4BZ2tI1XYsIxdrSf0gfmXT0pL7Lpq5EwsLHnaanP1rMIAdmux+SbvTFpMIR1+NChQyom8D3feust3XLLLYU+FQ9ZgLFFgMGNUppN5U3WIxXgMuxtjB+qntgDeYnK28byWJ6jx5WbAsCrr746ozK7HhwGNH73s1JnR9T5/PSPpA88LH3o4yV3+X5t2TqFI9JLl04r4PNpfnWNtraeUdDns8revnjxlL6w5maVxeHsly5dsoLBrJ/8zBimafRU7APJfMO+zSojheDFg78gvf2q1N8tzVgQDXQ4BGsRPI/9F3FyVlwnCzz11FP6/d///ayOkXWmBlEVUkaLUUHhwUMqoGaN+3zc4h2libq6Oiu12yhSKE2TUB3SHb/GX7j7csrAAAorV1LpXATnk00qm4fiABs+5c+yUSBlEtjCgedESQWRxyiI7ZlBOQY3mhAC9tlUpIvATzzlpAcHQAVPdgYgGEWqP+vmD75bkpfv5umz9Nyt92v3XR/QmoYGhSJhq7eG6bPxj+/tmzB2ILU8aGbHGCI45kat5lIGmYhZ941jPi5eI930Qen6B9IKaIDVq1dbZGPz5s3atm1bTpvxJgIZ2YiY8g0+08sG9zCZOIY/e+pddDBrEpm1BMUTovPCeHUyP3deTGmz4HgpNnESJbFwTHv21uTI1Mj3voo4gVJUlG5OBQQmsY25GXvYBW6A744NmCpg4fXrywJP/Sga0IBbGMHb976JYk2lhqpgUL+3aoOeuuUh/e91t+hUX7T08jBlaumH0dWmVy5F+60aXLx4UVu3brUyfQhk8MCmJag4FQMaBmT6Ze0nqKmXNtwr3fxhafn1cStQJAL7F+eAAJqfC+HHI1OTtWzLli1ZHSdry4oUNBotkTbiwUOpYU5lo6aV141Uv4zWwqwKlGlZXfISL6WEWIMbwwg1d1xUx28iHalIncZWjIZ9ylJbHkoCpKNiEGVKOhyVXUsAAhOpyp/Q0CyWcBgVhhukg2NwHqlIPXV6UX15yAAQi3i1ziEhJe5Uaxvsl/2bMYs6hsZKKmFQv/TSS1ZGEoEM7DqcU9k2tp8MgGwRFC1EIMGAeU/mGM5CSn+9/vrreT8HAgt333133j+Xz/SCGh5KFTe1TLQLbpo2ObLADXCImH5irJdJhVNkrNm5gs+nSHVjQdfXTEHpEDLgPZQ+KK1ciEwgRIeo1pNldTPOCLzEcmw4gQlGZAPKa77wwgsTMs3jgRKYhcgUnRTo7hy/9o02dXbQ57RIwZi0c4nR5yW1D0XLpTJGCYZRLo2yUowzOIabWUalDGx6RIuFxOrVq7Vnzx7r3hBAZc3JJ4ghUI4r27J2freUVKSNePBQagj6A/rEgo1aVT9X08vrdGXtDP3kwptUE5y8mzbGUWyz4fPnz1v9A8J106XFMaVr5q+RmvJXcsItJE2B91ByWLlypVVvMx1AtCkdg/IAApAJcO7i9CU9PV6PDY5PQCNewIHPdKPPDEqua665JuXrCPwQAPKQAtyTWCdKQ4M0cxaS0LHnqAu7cnXJX86rG6eNM/bIRFwSqLB6tFDSiLEN2XCrNvNkA4662D0z3yDjkIwRsi5Zb9JdC7MBpfUYK7feeqvyDT6TnkoEbD14KDWsa5qrTy5Yr4XVTZpX1aD3zb5Kd81M7TwsJWD72J2y2Ex2tSc2EPPXUqOuuVsqt6lyg+XqXnqr5dwtNfCdvcD/5ADBKUrhpNu3ir0JDpCNnU8mOkJDxEuxAQrmDLyDORUP9NhLp/dfoowjHKsotZMBhX2q13gYCVTEa5q9bOVYhgaAazS1RB8ljMU19SqLyQyAXVVeuGyt+3AM1nc4rOeTUVw/QbaByWzh9/utEvJk0hBcgF9k1UswTRBDcCMT3JVcznvuuUcf/ehHk9Y8P9N3QbvaD2ggNKgZFc3a0LxaFYHMnEwePLiJ6mCF3jdn7ZS6qGwwOEpxYtGkCUOOMhtsQAOhaVp5/cPau/011bbMUrh8lmXMLHVwXAw7DDzWAh4ouKiLnW+gfKFWqRs1Uj0UB4jgo1QmUMG4cgLKIjrph5EMGGGU3yFYQLNeo2DgeVLWaWaWrB4ujkiISabptcwjJ8QZA8TLTEqB06elP/g8OfQSafa/8qvS/Q9E/4bt8nt/LP3h70oXR0ji+z8o3XWvSh0/vWilTvR2aUdb9HstqK7VtSc79NKRl/ThD3/YU96lACUZaGSImmnmzJkqFCCFKLpYEwgyEOjNR0mWJ554wgp6par7nQuw1vPZjz/+uH7xF38x7muGwsPa1b5f5/ovqswf1Kr6ZVpQPSfv5+rBQ6LABo/JilgBEeulPaiBAh47DOcrNlPdqvdr+Fw0yOGfsVDHDh511JcCvmLKkBJQ4DNx9ua7dAnr7+7du6d0yZTJCEptUn5z48bk/SNj7e5sOQbjiMAGCuk33njDChwwp/CnIWJIxmMpFYoDkuBGpiAT3UnPNLg9Dk8PSYIZ3/qG9O//VxockK5ZK/3335OaRgJBN94qnTwuPTpS0rahSfqN34v28CthtFRU6XMrN+h/HnhTA+GQJZr6cNU0nd65V7Ouvday3zwkBz42MrAL2Q+zurraWgMJbOC3S3ctzGY/JajxzW9+M+tjucKGaFyIU/TNN9+M2yy4daBdL11804rcgVN959V7sV93zdzkRe2KHEPhToUiAwr6ahX0ewbcZAEGEOSArA0cxWvWrLGeJ7CBInXnnj2ac83NVskLU+8OJwoO3GSgNqKd5BAwQcmSSGWSK7zz1j7dsvYq+bzqU5MKCxcudCVQkQ4Yy4xh9rhNmzaNCzCgYIZUJENjY2NWjkfmkZPSDBAOUug9JABKul//VenMmSj5oE/Cn/2JhJP62pGmagsWSV/+N+nCebpES/UNk+JyVgaC+tPVm3S+v1chRTR8qU0d5W0WefZKCaQGmRHsYRj7Th0AucKqVausoD2kI1815h999FE9+OCDKhQeeOAB/ehHP0oY1Hj98m6d679gcYyB8KD1e7m/TLMqvay1okZkUBpuj9Z/DlCWaPL1mpgKiJcVjUOWwAO2E3wDNaoROKEMbytv0fqN663fr1gRsoK1BGmTCTNQ0/Ow20O8jzUxnwpgxF+zm+o0u6VBCg1JAY9oTAYwRguhJCfQR6YGYzvWiQhPT3ZOcPZMs9ANnH5nLzMpBZ5/Vvrnfxj7fe8e6Y/+QPr//tZcaOljPyU9+EHKCEjTppPmpsmAjS2z9bWN9+lCf6+ayyu1b/ubati4sWBNp0sN+NHxl33/+9/XQw89VNDAxi233GL5BfMVYNm+fbsVHHYj+OXKbMIIuffeey3SES+ocaL37EiFtaghQhuZ1sF29YT6VBtM3pTIQ2GA0dgx9I76wkZt41ND2SpVBzz122QARgyqS5QhsSSC3yEX9oZhKNUpbYPhFa93QLzjG4Mr29RYJ9khECc+ByLl62nTjBOvyNfzRvQFc1dLa+71CPMkAA7YfKZEoiAgJZzSL/FUypB1giwm+Od2Pw8zl5ykppJ55RmQSUAw49TJ8c8RoNr26lhQwzw3e/Ltc4yjWVXRMfxu/1nNnTs36/qlUwXGacC+WAzI530j0+z555/XX//1X6tQIKDy+c9/3jqX2HV4ODyss/3jbQx6o53oPeMFNYoZoQ6pZ1s0sAECDVLNjfKUKJMjqEEQGIcsmbWIUezAnrKXyUQoQv8xAhQIVhBbJYP9s6jLzmfksnwVgWwCMdiefHLP2y/rtmnDUSdlWaV0/YdLskSvh4nId1AD3ko2BrZFJp/tRi8auD7nkGwOIaTw7MUUeH1bNOPb8DX+3b9P6uuVqmy+ztq66GOSoSoQ1EIaVo+s6QSbPTgDWYg8EBAXGn6/39qj8wViB8QQ3BBp+d0kHai54sE0YXb6vIfCYyB80RbQAAQ59ltZGx4mB3BqsXDFc7jiOIg1sCAhLLoowovBSKQZ4SuvvKJdu3ZZJSqIKl+/YYM26KiWTbcZDKffko7vysk5eJi8fVIIllHCjOyjRGVXeB5HWzIkawDoFnnhMxKVfvQwgkSOkilYQoIAtVcbOX3gPJtqJUeeffZZK0PTSRPRXIHMGFTezz33XJy/evyiJNG7cyygYYIc/QcKeUYeXLTLCARjO5GpGs82iS0hyvtxgpEFYZqOOwFBDXoh5AJwnW3btlmOJgQjFseYW6vbp0dLX1mgGe7278Svoe/BQxLQOBnRE6WnEvGaVKImFNXZlngmkJiK1zMvFy9enNXnTHpYDdRj7iNrX3BqZXKRcVCIUqWTAW75C0oJbmaCu+YFIcpC53SU37FYVDNn3DTn55kVLaoOJFdjeCgchiJdcchiRKFIb4HOyEOuAgPplB/BEUaKLM5eiIQTlQjH57UYTXxetkCRTq2/HTt2WJlh1ADknCyjcKhf6usczQqLwie1n9FUQSQS0lC4Q8PhbldUPMWEfH4fSktRQz9b9cDAQL8GwyfUF3pLA6F3FbY7chwC5WKyDBXO1SMcKTBjhnTHnVF1pSEbBDoeKFxZnUIAxSkZRl7DvvTBHGPvm2zrqhPCUcjxwmdzDj/84Q8n/C3oD2h+1exxz5ENfkVN/nt5eXAI5k8YjhGDELabh6kqNuEYBA/wI7BPOeEL2GeU70HxHs//kIlTic+m1CDHpuQoKvpRngSXGFcmLSIN9o3wjqmBSKRP4UiHIpGhQp9KyQJ7np4yiAuTAVvt/PnzCf9ONYTT545alTXah95Sf+hCRvyC4EoqkVe+yl2WLB76QJRX2IO4H/ww5S80lYDQtFiymksNxZKtkS+wZ+/du9eKIRRVUAO1BPWwHnnkkQl/ayyv15aZN2h6RZPqgjVaUjNfN0+/1iPVRYyADzXiROLu93mBqMkCDCWMd0pLpbvooqgivfDAgQMpiQQqEogCKeh8XqbAqIJo0NuDz2e9mZBlEiyfWGYKrlU+NVQDw+EuXRrYqsuDr6t18FW1D+1SJJK6dFGpAHVfOiq+TEH/DAx4J9kPvCaRkopgXln1eQ2ED2o4claDkWPqDb2eNhk0TQMTgc9hD/aQAr/3B9Knf1aibvG990n//K/SrPTWv1IH5dK8AFhmMKUZMcKnAthzv/e97+n9739/oU/FqjNMvWHOKRYbmtdoae0VFr9oKW/UzdM2aFrFSHNOD8UHHOAWxxj3pOSfGnbaZEO8IC/z9ODBgxkpdsnMwtkLR6H8Z6qSo2ScY6/BM7KxDyljS2YG5XjgF3FL7VbURINysSivnhL3eSh8SIORVzQU2a7ByFaFI62abHAqWsDuzzRDmswHJyVeknEeznPvW9vVtOCyekOn1Bc6o7ah3eoZPiE3AcdP108wJbFkqfQ3X5Ju3SJdv1H6hV+WPv1zmkpgzab0oL33pAfnwLeFSNENAXAp4Lvf/a6117pV2s7VsOvDDz+sr33ta/qlX/qlCX+bXtGsO2ducvPjPOQQVf5Z6vef1UB4zGCpC16p4AQi4qEUQWAAdUam0XQUVTRI5tHW1mbVwiWFNl4tXF47k2a8I06hTIAS64033rDSv5OW//AHpJVbpLefiQY3ME4hG4s3aCqgfWivwhorETcYvqSe0FHVBpdoMgACkY5KOpX6KB4IvKGgSmduxJ4TJARFd1lFWGvWj9+sI+rXUOScyn3zHR8fYkNKb7zAxeXLl60SDx4cAKXZT/30lL5UUynLIBegDCNlI6izPtnn3QsvvGCRUxoHFhqcA7bEiy++qDvuuGPc3wK+gNY2sl576sCSQfVaqed1XIPR330VUuWKQp+VhwwRm6kBJyDTNVPnFuWrEEKxX5HlDbdAIBUvI4QgBA8+C9sttrSVE3C+2JebN29O/sKF66WTe6XezmhwDtHQ8pul8skv+AvrgsKyO8xDGorsVbluls83OVT8jB1KyjoZQ5nwC+x4HL+JyrI5AXOCfjWUEZ2/tFJV1qmO2XVdw4dVE1yQ1jFTlbgiY8mDAyxdJv33z0/ZS+W0B6SHxMDPRfNsmodPdnzjG9/QJz7xCdeO5+ou9KEPfUi/8iu/YjlMqcHroXTh8/nVVLZe/eELCkcGVOavU7m/qdCn5cElQADcKidBYAMDDZUT0WWcPm7Wa2eDfP311y2nhiOCtGi9VNssXToulVVI866OqqumQNmpUGRif4eh8NRJi48F45EghdOmVxAO5kY6TbIYn2ZcEszYt2+fFXi77rrr5At0qze0PeYdPkWUHhlCWU+zXkqtkW5uAn3vvPOORcBQOnjw4AQElrNRGHqQ1dT21VdfTe0AK3F8/etf10c+8pGiUN1xDpwL5xQb1PBQgghOl2pvk4YvSr6AVDZL8pUX+qw8uAQyNNxYN+ApS5cutRoZHzp0yDpmoh4EmQY0OC7vi21oHhcEL27+aenEnmjZqeZ50swrNRUQicAluO52YURIEfXKp2iD4FIHwiGCBU7GEdlIx44dszJ6nNpTBDTS6U/FZ5h5ZIIZlFrjGDxaB97UYGS8qjui4bRLwsErCOxR/o338X6CGZTCGS3v7MFDCuD/gXPTp8VD5hwNn9pkr8Bw8uRJy7dHtkZRBjVQY99222361re+pV/7tV9z89AeCgA2sapAVGHvYXKBBRODxa3gA2PFEAJSzhMdl7I+OGHTSUmnPuO1116bHkGatij6mFLwy6egZdCOwaf/n72zAHOsOt/4m2TcZ93dhXVfnMXdCsUKRdriXqqU/ktpKaVQaIFCC7Slxb3Q4rDsLuvGsu6+O+6S5P+8J3NnMxnZZCae98cTZnI3k9xcOed83/uJgxGQcYa/C/bs7GwTTeXrxGUJHkZa831Y5oAl1fg6poSPHz++XfvEMgkUUSg8WLVn3W4aRsxO8hYx3HDYArvveO0fd9xxRjCh4c7n/D6MhAykJ45IbHiNM+CEAnSLpTXaQ0W5pxFiAl2HvPfam3UYK7DkC40N9tSIFi666CJTCuvxxx83kdwixnFkeR5C+LGWY1kqBnPs3LmzxcBJBpUEOi5wzVZQUBBYJHpyGjB4WuKdM9oSLWR62hA/cz/LoHB95E9gE+dIZnTTiUuhzYL2BgOOuEbgWp1BSbRXKJYEen3SsUlHMdf9dADy87xFkVRHZ9TWF3r9hQ0ptvyARQi+L+8DlnjmvUYRkfs9ffp0CRoiIBsjqNngLDfK8msM5ksgYY33I7M14lnUeOmll3Dsscca0TRYBD1fkCWonnzySYkaQkQxXGiFO0WQEx1LVXGxxQWgP85YCiB0INHpLNqGxzU7eSRK61Y1RlPZkYzMpEFxdeg4ATLaiQ30/IHGCaOprD4CrLvMjIwRI0ZgVcl2PLn6fVRWVWFQdjdcOOF4vxfwVmYSxREa2nwwO8MbpuRnOCagyrkcbrAutA2p9hFIsgVetob3wbhx44whxXu3pVJvQrQFI/F4vTKTqMOiRlERcN/PgK9XeYyN08/01BCOgqj+cBDvkYsffPCBiValUyNaoOORmXTct9NOOy3SuyOEaFibcF0Vqgbi3nDt09p70hHLdRHnOH/7Rq1YsaLZuk20jAO94cJuuFHeaGM4bHTYp8aVbdzStdyaA5cBeqWlTbPhWROf2UV1dhf+s20R/v3JXKQ7UnDSkKkYPcC/UsC8d5YvX24+gz0KGHDFzFDfZt2ZjgGod1WiyuXpbZlsy0Feyli0V9A58sgjzfeRzS3aA3uwUbgLSjb4W28BDzxA9ZCGP/Dgg2w6kRAnJt7tC8Ks6+99L7g9Z4Jef4AlqFhLnA8hRPQSClGDmRitiRVMo2V0FRVoOpn9gSKI1Y9DHJ50R090SplqhIyspOHonDoDDlt8Ob9ZV/lwzel9FwfWAoGLdWYSMTV2beluvL93JdA5Axl9OmNfrhOfHPzGr/fktUwRgxFTLL9GQ5q9LVrCYctFpuMo88hyHIcUex90BC4YJWiI9rB48WJjuPKa7TC//RXwzRrP74zMevtN4NWXEubExHvd4L/+9a+m1m00lSnjvlx66aX429/+FuldEUI0QJHct7Fpa0JHR+Haj9m1LcEIc64P+bn+fnYiZN0FC5vNgWTbFCTZRsCBAUiyjUeSLb6CpgKBAUZWNoYFy0OxlJMjOQmv7foKe1xlyOrbBUm9cvBJ1XoU1frXyJ7ZH8xMYuY3xTpma7C6gi+0bfJSRqN76nHonnoMOqdM7XB2vgQN0R7o9+W1w3G4w+vGlSuB++7zCBrk4EHgpps8WRsJAP0L8Wznr1q1ygTZUTMIJkG3VmgsX3jhhXjqqaeC/dZCtIrL7cLmim1YUbwaG8o2od7lXYJHtBQFwgVZsOGii1ElraXRcsLjIszfUlI0TBJBsQ4myfY80xg8M6k/7HEUQWXBSKVAjWWKGWw2yQwP1ozl9fd16c4mr2HC7JrSnX6lzjLTg0aGJXAwMqsteA3zXLBXUUJQWwPUBX98ER2D2U3MkgukpnOL8B5Zvgxw+dyHS3z7x8Qn8d5snXW03377bVxzzTWINq6++mqzbywhKES4KKsrw9rStVhTugaFtS0HMCSyqEEnjO86zXdbR7H6C7RkE3BMtho306nm72fHuzgdCmHDYeuDJPtgOGzxWRqF2YD+XD+83qyodGZu06nbs2dPk01+sKYMhbXlcDf0H3E3+Ck2lu/1+1q3el/SduajLfHNbkuC3ZaSGPay2wVUVXh+iqiBAa0MQg1Kz7NFi5pmfXOcLilhGhQSAfoU/M02jEWeeuopoxWwFH4wCXr5KXLdddeZ1PAHHnjADMpChBIuZhcVLcPe6v2wmSa8buyq3osju0yHgw0IRTO48AnFYp5p31zo+WZrcDsjubjgY0S7v6IGo66WLFli/q5NSvcB+zcDdgfQcySQnt2RryGinECuXRrXdOLymvSOHuFY0Qw/DQL2jKEBw+uYxgdLXLGec8JDMePdvwNrlwM1tUBKOjBkDHDKBUB2cBcvInA4jlpjb2ldBQpqS01ZhO6pnQIzhvnatHRPPw0L3ltZ2QljcPhTcztWefbZZ02t22g0qijKHX300WYff/jDH0Z6d0QCUFxbjAWFCxqfb6vchvF549Ez7TDr0gSBdj7X+N5QWGDwRzCjvhk0RQGlpbJW7GHAsYH/xt4A/jpLuD5kgFebvQ4o3u9YBVSWArndgZ7DEqq+e6JBxyxF88PNfwyushrXW2WiLFq7Olq0O1qxMXi909bhfrC6Ae3hhGfDKuDVp4HyCoBmYJcewFEnA5NnJ/yhiTS8TpkNTpxuF3ZWFqDWVY+e6fnISgow6yAryyNktLQ9AWB2Fsu/xiOVlZX4+9//jvfeey/o750Uqrq3TA995ZVXcPnll4fiI0SCU+WsQrWzGplJmaiorzSCBrGiIkrqSrGnah/6ZPSK8J5GJ949BoIJG3qziZ9vFggjTOgEYuo4F2n+OoT8Ej/2bwKWvu75nad/0wJgxqVAVnCaoIvogwYrDVeW0vEHGsK+jM3ti00V+5puy+nrl3OXr2HGB6OnRo8ebVIpmSqe8Hz8OrB+BVBZ44nmr68AVi4EdmwGbvg5kBoH6bR0cGxeDJQdANJzgcFTgeTUmMnU2L9/P+ryHfj84IrG+bJfejcc220S7IE4ar5zFfD4ox4xg3/HLKQLL0YiQGHeuzFoPMH5+S9/+QseZP3iKOXaa6/F3XffjbvuuiuqymOJ+MDldqKivhx2mx0ZjixsKN/QOFZarCtbJ1GjlUwNOnvpFGap2WDCMZdCCUv8eK/TOGbRocxtDDaxglj83XeWzW1V1OB8/+ULQOFOzxzH6PCBk4AjTgzW1xJRBrOwmdntr41MO9XXVu2Smo2uqTkmY4NjB69WBlkOzfZPCGWDYL4nr2OKhixHlfBzXdFB4MUngOoaoLahGsfencBLT3PQAaYdjbigZB+wa5VnrOk+DOjSwf53YcK6ByhkvLZzAfbVlJjnSTYHzuo1BX0y/LPXDaecAvzjH8D+/R5bko9jj6VygniHwry/gb+xyMsvv2xK0YeiX19IRA0uLGh0PPHEExI1RNChgbGxfKP53Q47+me0PODXulT+pKXIdqv0VChUYA7E/fv3bzW6leXpGIESVL7+0DPhWdTXAhu+ACacFdzPEVEDjda5c+eapnakPSnXQ7N74IyeE/FV4UbUuZwYnt0TM7sMC+hat/q9MAtJWYkANn8N1Dmb3o90BhUdADZ9A4yagJiG32vhq56sMOua27MOOPIKICn663KzDFu3Xt3xzsGFTZx026v2Y2P5TgzL7uv/m511LtC5CzBvLvPOPY3CBw9FIsDmoBUVFXEZSfXhhx8aB+WZZ56JaIX7dv3115t9PfFEORdF8KhyVuLrkkWocVWZ5zlJnVDTgi1R5/KUOhIwY6H3+odBU2yUHArYr4APX5gpSxtjzJgxAa0HrRJCrbLrG4+gQaxyN1uWAAMmAjnxWX4p0TF9KvLyTLlarvHb0/Cegug5vafhswNfY291MbKT0nBkl1HITfa/eonV+4xR28x8Sni2bwSc9R4bw5e5/40PUaNoJ7Do5YYITQA7VwFjTwV6BVcgDhW8TxYXbsL+BkGDON1OvLd3Ka4eeIL/9xEz/J5/3vOgsEGB/KKLEiJDzrsPaDzy5JNPGo0gFN8xJKIGueKKK/CTn/wECxYsCIkaIxIHDog7KzehvL4UTrcNuxqyMogLLmyp2GzEDf7uTaeUIDRDjRO8Bw/W/myt70WoYBoto+oDrZ/nV0+N2gqfDW6gWqWA4hlGLDEKcNmyZeZ3Rtv5Zkr4U/d+RE4v8+gI/BzVZW6AJYl8IloboTES6xTt9ggaxLq+yg56hI2+YxDtsBTIll3b4MpoOlfaYUNxXTvGzNlHeR4JBkVMli2JRx566CFTQjaam+fSCfm9730Pv//97yVqiA5T7SxEWf0OuN0u7KoqRY3rUNPr0vpCJNuym5WQ6ZSiTGBvm2LIkCGNz1mSM5zjI4UJ2hjMmg2UNrM0CG0J2iC+68kazpcSNeIV2hdffvmlEcv4YLCer/16OBuDpT1P7tHxQJ4dO3aY6icJj7EvzJFvfijq48C+QEOlCXNdeX1HBmnGiKjB8bSourTJGeLvlc5a1LmdSLEFMC9Q1Lv5ZiQanD/jtW/f/PnzTTZlqKo4hSxvm5EUbDL4u9/9LlQfIRIA3thrSpdgZ9VmFNcdxMHaA81fZANGZg9BUsNgSYNjfN4Y5KU0j+ZJRHxr8zFLIy0tfGVgGO3CFNr2NARau3Ztq5kfjeR091HvbUCeah0nQoo4jVnWmeX1TKOWiwHC+s5r1qzpcA1ajj9MQ+ejtUXG8uXLQxaVGHPMPq1pc7fG/gsZwMA4KM9Vd8jZ1YT6GsQCNIwriyqa1XV2wY2cJPU/8wcKmDTcwjKHMhq8rghweqLGQ83KlSvxxRdf4IYbbkC0w0yNzz77zOyzEO2lylmAfTWLUenchyrXAVSae63pXJ9mt6NbarfG5znJORiTG/0idrioqqpqzNRglhcz2cIF12W0E9pT6oq2EB9tBk7l9WguaLB3X7YEjXiG1wQDcjdv3oxx48aZ8k9FRUVN7IKWytoGCt+TtgqzaFuC9xNfE+yGujHJ4FFAz35AUguleSbMQFxQ23z+iRX7wsouSqunhdF0TE2zJyNZPW79gv2hRowYgZDjdgPOcqC+GHC3kP0UAqgJMEujpWzLYBDSUIpbbrnFOHs4+HtHcQjhL8zOKK0rbHzONuAt0SO9OwZmDUC1swap9hQk2eMzirI9sEwOMx4iAZ0/XKy1x+nLRST3vVu3Q8Zki4w7DVj4ElDVkO7YqQ8wVE3DEoHJkydj0aJFxvigcWo11qNR3Z6oPd9rd/369SYDhOKJ9bsFP2fhwoXGUcz6t4J1wcYBF98IfPURsGkdDyLQtSdw7pVAVvAahkaM3B6AI7kh68Sai2yeMSdGSE1KxszOY/BlwarGbT3TOgdWeiqB+frrr8PTP6dmL1DCqL2GuTtzBJDVsTHNH4ODWdYUjKMdrgu4r8wsee655yK9OyJGKa3b1uS5w8bscO8tNqQ50jE8ZzxqnDUmIzzNnhbX5SECxTvgg33GDrtmDyL0L7AcaaD9Brh+YyT+zJkz235h1wHAiCOBtV8cEjQmnQmkxV/pQdEUZhtxbU9hYfz48ebaph3Ae5/VDvzt29JWBobVn499YngNewcgsv8ZP0/VTqwTkgx853bg8/eAlYuBg/s926YfAxwfveUyA4L9M8r2Nw0K6xTeyhodgbb3yNR87LdVoKDWk/3tsNlxUo/xmjP9gH4M+sxC5fRvhHZF+UKgrqGvqD0dyJ4FOEI3r3GMe/fdd83PUBFSzy8bgVxwwQUmRfxPf/pTKD9KxCkuNHXGJ9sAVrLlVirBrAveP6M/spI8N2Kmok2bwQgq7yZ+4YQGBxds7WHPnj2HNzhIRh5w5FVA+UGPwZHVJSHqLgqPYEdDwKqLHKzmvTTSaUxY9Zlp3Pj2pZk3bx6OOOIIU9JHeDFguOcRj6RlAlPPAxa/4cnaoHg+/hQg19NbJRbgtUsBo0tqLg7WlJgSCb3Tu5oa0OLwDcIZIMBSiiGFNfyLFzSsdBqoWAskdwJSQ5OFSAfLSy+9hFWrDold0c7tt9+OsWPH4le/+lWHs/JEYuJG07IleSk2FNQcctIzA7xfpicoJ9XRMSdmvMLoXI6N7JfH+SVcDY0ZwU4nWnucy1b/Db/+dvhsoN8RQFUZkJUPpCirMVGg/co5kQFUFPuDJfizggFtFqsvH8s9egulBw4cMEFas2bNkjPYm5Q04IRzPI94ZMgMoKoU2LvW8zyvNzA6dvqGMaOIYtxFQ2djS8V+1Lnr0SutE/JSwpe9F8swSJNjTcipWn9I0CAsuVm+CMg9NmQfSS3gwgsvNNpAqAh5OPsdd9yBGTNm4Oc//3nj4C2Ev2Q6cpBkS0G929Ooj5N+hoOZGYNN5hTTwL3TwkXrtKfZWUedJIzYas9nUtDo0aOH/3/gSPJEUYuENDqYvs0U8WCxc+dOM/Hy2rXKG3hnGzE9lM8laCQgjNw86SagthJISfcIqTGCd4+iTik55iH8g+MAnRtHHRWGHiL1ZU0FDQMjOopCJmo8/PDDOO2002KqlB739dRTTzX7zowNIQIlw9ENNa7ixufpDhv6Z3SGG/mmV1+X1J5IdYSvXGssMmDAAJPBRlGDmatc+w8cODCkn8kgE67T2lN2ynIaBzTWped4HiKhoOjFoCleb8EU6+j4pSBvlW9jhLZVto3rNNoYRx55pASNRIOBUqw+MeoEwO0CktNiKkiT5dp4XSfZHRiarTLggVYn4fzZ0Qwwv6g/VAHHA0tRlXhKUoXgeqOIy4xq9tkOJSEPp6Cj6YQTTsBvfvObUH+UiENYRmpM7mSkOzyRMUm2ZIzMmYAhWUMxNHsouqd116TvB96LMUa3W70HQt08kP086BAOtOnR1q1bQ24UifiA6doVFRVBbazFqF8azDRmVqxYYQQOq3GulR4qkT6B4XjK8hMxJGgQ3ichT2uOU9jcjplZYQkMaNGJ6gbsoXGuMojgiSeewI9+9CPEGj/+8Y/Nvu/duzfSuyJikOyk/shJGuARDem7tndFr/Tx6JsxGL0zBkrQ8AM6YSybglmt4bAvmEnLNRlLWTBLJBC4fsvObtr8XYjWYH17igzBhEF7nHfpBKbA5l2inTbHhAkT5NtIZJJTPUFTMSRoWIIcM5BE4Mdt165dJkAgLNjZD8jn2rKlhOx6e+CBBzBnzhxjQ4WSsDQeuO+++0wZGWZt9OrVKxwfKeKIzKQcTMw/Ci63SyUyOoDljKHBQcMj1NDpO2jQINObgA4hOp/pHD7cZ9NpzNeoZrHwF0bc+fa86Ai89linmQYHJ2FLFKRw8tVXX2HatGk6OSLmCHa0YaLA+54CPcushAVHJpAxFKhk7VnO224gKRdI7x+Sj/v1r3+Nk08+GZMmTUKswX0+8cQTzXd45JFHIr07IsbgXJ+fMgx5yYzaZzazxsdA4RrfcmQxEISBU6GGNoLlCGZmCB3DzBTxpzwQA63oNBbCHzjvMxMpmNUOeJ0ycIrZ3t49+ayyVBLdhEgc6L9ob6n2dpE+HKjbA7jrDtkYGaERHCjWPPnkk5g/fz5CTVhWb2ywxBTx+++/H4mC2+1CvWsd6lyfmIfTtd5sE+1HNb87cOwa+g6QcIgF3os/RnGxdi2j39ljg4u2tliyZEmHmzyLxIIGAo1a774XHYWGBUUSbyfwsmXLzDZFoohYhMIynfMiMCjKU+QMK1ljgdzpQOYwIHsc0OkYwBZ8ZyEdgn/5y1/wi1/8ArEK9/2pp54y3yVhcJcAri8B1ween3wu2g3XqxI02i8wMBjJu3RnOOHnWQIHI+rbyhRhHw6KLlbmrRCRytagTewtaJSXl5vMI9m/QiQO9M0VFBQYUT5sODKB3OOA9FFA2jAg5yggNTR96ej7Z2nbYJYIj2imhmV0sPnJnXfeif79QxNtFk243JvhxiEDy4XtgNsOh+1QiqEQ4Yxk52KJSnAwHb+tQQPH22igwcjnXBgePHjQLA59HcaE22mgsLm5EIFA4Yy9NfgzFFDQoHjCPjFCxKrziWnO4YDCNsd6lhL0Lg1HhxON+X79+iEWoBOMRoe38yEsMCggrTcAPkLH//3f/+Gss85qrO8dizCb7swzzzQNw1mKKu5x1wDuxV59VyoA9xIAswCbmlmL8EKRwHteCYeN0dI+cH3WuXNnE/XKn75ZGxzHV65cGZ6+SCKu4Py/bt26kPWmZLDJ0qVL1UdDxDS8N8KVEU4/E6splJSUNLknORdNmTIlLBVJgsHChQvD0xy8pRJU6aEN1tq2bRueeeYZE6wcDsJ2xqk8n3POOUbc+Otf/4p4x4Xm9X1d2AcHJGqI8MM66nT4evfUCOWAT+OhtWh2Lg7z8vKMgMH6gVZzNBoinJjo8BIiUHhN8RoKBTQ2WE6NTbyEiFV8xeZgYRn6NCYoYjBritCpxDIfvnMNnQNsGMe/oUM6PZ31XaMPNvCMZycYMyfZvG/58uWIde69915zrTFwavDgwYhv2EPAV5xkdHoRq7VHaJ9EIsNgDzY/ZtAix5WwZ7Y1QGcag6fYY4cOL5bAtea+L7/80pTCVmlb0R5o04ZC1KCgwaApNgZXeVARy/D+CPY1bAVF8b6jgLFp0yYznvN+ZO9Vjve+63baF/x3Zj9496uJNmhf8DukpYWmX16koc//vPPOC1v2WVhlLEaEMYr25ptvDksaSmRp6aZWrVYROawGfhQS6HgK5UDPTAsaOG3tCwc51illFgmVfU5WVNeFaA9s/hhsY4OLqcWLFxsxQ/2gRKzDbD0uoIMFG13SwKBQ4infYjNOLTqS2roXrd43nI8YpRSNmRsU5tk/Z/bs2eF1gjlLAGehp2lfUo+QlJyyuPvuu3H55Zc3MwpjkZEjR+Kyyy4z3+mVV15BfNOaLSEbQ0QGzitcK1mZrJHu38RGzMyyo61Dm4dO41mzZql0qGi3LUAbI9jXNK9RlrfkOkOChohlOOa3VfqvPWvwVatWobq62tgYfH/2oKEfua0S0AyS4v1EWJKUYvbUqVOjruQgxX/ua1h9C24Gw9A3x3L0+YAtO2QfxWCpf//732Z8CxdhFTVo6N5www247bbb8OGHH8Z1tITD1h9O9zfNtgkRKTigM5KWPS74M1RptISLM39S0OmIYMYGJ6j4j64UoYQTJxcuwYKRIHRq0uHnT/NJIaIZjvfFxcUdjpjhe1AcofFCBxYdRe2dRyhus9EzjY7WRA2WsApn6SceJzaS5fecPn16eFPY63YCVcsOPbfnAJksKRT8ffjss8/wwQcfmHMZL/zyl780QRKff/553GbXeOhM050xiQ3PbQ3Pw1iTWQgvvB2yFLa3b99uxIRQl7xqqyk5GzxTeLdKY6kXmmgvXO8EuywyBTf2mAx74IQQIYC+HPp0Orr+ZiY319+cU2ivWNU82gPLme/evdv0q+F84AsFE97b7DcYLpiZtWLFCiNmWJmEYcFdD7gXcQ+8No4BbMEXVXge6eu/8cYbw/odwx5G8eMf/9ik27zzzjuIZ+y23nDYxsCGfPPg7/YQXDgi8rjc9XC6q5vUDY/2tEBGxobameFP7XYaPozwYqp4SxOOEP7CzCPWbwzGdcsMokWLFhmRRIKGiAd4b3Q0G4IGACOnWOZnxowZRojuqDFORxMdU77zJ58zi4PCYjhh2jrrsfP7hTUlnBFUVSuabnOVArWbg/5RDDigwXHPPfeYsnrxAqOz+Z343SJR1z9sUOSyUcBnOcRcAL0A25SQiF8isnAcdLprjJ0RKzBwysq+DhV8f3/vcTqRorXEoYgNrGzUYFBYWIi5c+ea63fatGkSNERczFPMOuqoH4fR/XwPBhTR/u6IoGFBcZ0+Jl9YJvfll182GSHhgvc+feC0L8IqaBi2+wgaPHFrGrI3gsvbb79tAk1/9KMfIZzYI1Hb/7777sMdd9wR1gspEthtPZBkn2Qe/F3EH2V1W7Cn+mPsrf7cPGrphIhSvNPBOVHw91AKMW1FUFlQXGFUJYUNRaqIjkCjldkV/sCam5WVleZ33gMVFRXYuXOncaBSzGC5KUag0zgXIpahSPfFF1+YSCVGLflDTU1NM4cRBY358+cHPXuB4z6NDkbUWvDeZCYBRYWORn4FalDR0LBKqIQVN8cuXyedDXB5xqlg8o9//AMFBQW49dZbEW/wO9FY/ec//4m4hg3B7aMB+zTPTzUIjzuc7ioU1c1HYe3nKKj9BGV1a6M+eMqC42hbJWg7Co+DP+VEOL+wSenYsWNDti8iMfC3PJSVFWutobiesUptcg3F7Az2dgm/U1OI4MMStAwG8neM5f1BG8MXZkjTNxXsQBu+n7fAbmVjM6iWWYXhCqjlOMCASd77/vjHgo6bmb2+wizHqOD64umHoY+fvn76/MNJRMJ6rrnmGjz22GPmEY9GlUgMqp0HUFp/KNvBhRoU1CxFj7QjYQthHez24lvjlhHoNDpCFanJGrZ0orXlGOb+sEaimoOLjkJHq1Uuylcgo4C+Zs0aI15wMUFnKa89LjJ4X3AhZUVNCRFPrF+/3qRws2Fea/Dap+PHcpgxe4ILU+8Shbxf2MgyFOWYmBnBpq49e/Y0+8HPpLBIISVchj/TwWncRCxzwTileWy9I7LdgD24NW8ZtcxshoceeigumxNS3H7ggQeMUXX22WcjOzt0NYOFCCUldSvgdB8SNatdO5DkykS6wz9xOp5hVDAdU4frB0TnmaLhRTBg4AUfXKf4QqGCzl2ukyy7ltUQrJLPvA6HDRvW5jpMiFiEWRAMAjzca7jGt+wH2hi8J7xtdd5X/gZeBYolMNLnRZvIyt4IVx9XBpWx5xRtqIgF8NqyqOj4bOT5aL0/SXugb58BB1dffTXCTUREDV7U/NJnnXUWLrzwQmO8ChFr1LgKG1TPQ4OEC7Wod1chmYNHlOHbQ8O7HFUoyMvLM7XSWauQjmSq+N41bTnJMHKFCz0hguHMOuWUU0zmD681OigpZNCJxzmHxm8462YKES0leeiwnzx5crPrnxlLTBGm2NfSv4cL7sO4cePMop8lrThP8N4Nde8nC0ZPUViJqLhuswMZk4HKhYcyNhzdgJTmjd3ZsJTHjFkzgc7h9957r5lzv/WtbyFeueiii/Dkk0/iF7/4BX73u99FeneECBi32wmnu6x5FKSrMCpFDTqowtmzgus79iRgxhntC47dvuM3M7Y4x2ndJ4LBGWecYWxWXldHHHGEsS1YjpPXH/t+saSMKg6IRIOBIyypxHvCFwoZFAIZsc8MhUhAO4KZUlw3U3hkDxvex+y/xmyNUGeDM0CMtg37vIXS53Z4uG4oYKfChud2wDbOY3u00B+FPpVA+2Lt2rXL2BhvvfVWePsRNmBzRzCX9ZJLLjEXG7ujCxFrlNVtRmn9xmbbe6QdBYct+iIgOYh7N1JmFAn7EIRqEcYoW5byoZOK0Smc9Hi/8/P44L+zNjsjWoR/1LpqsbF8HcrqS5HhyMCQrOFIdwS3eV2swymN1x0XMby+I1JKRogoggIyS1BxEc8IGkYO0thgBhPFhEg3UKXjiQL48OHDzXzALD/uJ0s4nHbaaSH9bDYRZNRvRxuoBw1XNeAsAWzJqLOlorR+K5zuWqQ68pCT1B82m92UyWLTUjrr2GzQXzgHM2qZ2TCjRo1CPEOhilF4zN5T6RkRi+uYg7Uf+5SksyHN3gvZydF37zICluI4S8la+79x40Yz54QKvv/BgweNIM/yoXSoWP2ZOOfRkcZxXY5m/9lXvQc7qrbD7XahR1ov9Envp+Pne4z27TPXHjMdacNG1lEpROTh+Mv7gn37OAbTtmDDb2ZaR0MlDjrpuX8UFrgO5v6xJBUzs0O5PuRcxEBLCp6RtrMMxuXPUlx1cLuzsb92P0rqDsJhS0LPtIHISMpu9BfShxKoUMtgKdpuLHEbCSIqalC9Y/TsK6+8gjlz5kRqN4RoF3Q07K+eb8pOWRkbGY4+yE+JPoOjNVEjlAaHBVPEOcx4q+EUN7gQlLHhPy63CwsL56HSWQE33LDBhmRbMqZ1no0UexRMlkKI4C+ID+7DjsoKDMnKwfTOXdv9XnTyMCqJPxlVS2d4uOud+hPR9O6775o0dEY+UtSgwyqUjrgtW7ZEZdm5elc19lTPgwts4udZpmc6eqJL6lgzl/O4UPyhCOTPPM7zztR3PlieKRG4++67jVhGo1KOJxFrVNZvRYWTJW49TgUb7MhLnoYke8ebpwYb2hPMdrPK63Asp2AcaKRnoLCEKOu5c1zz7rFBGyMidctjmL3Ve/B16Yom2wZnDsOATPV+ECIe2VddhXkF+2CHDUd17YH8lNQONcFmNgQFA/p2GNAabT4eZjhT/GaFIO4rBY1QlSil/cYeOvRzR2PZue2V67Cnekvjc64vxuTMMMIGbQz2G+HxOv744/16v//973+44IILjJjFCgGRICLlpyxouP7f//0frr/+epPCp6asIpZw2FLQLW06yuu3w+WuRYo9FxmO2CmlRiM/HAt/DugcIL2RsRE4xXVFqHCWNz6nsFHrrsXBmv3olR75SAghRHAXxHeuXIxXd21v3HbtwGG4Z+TYdo/3oXYwdRRGMp1zzjnm9//+97+mhCGzKELRyI+RZSwj4S30RxPl9TvhatJfA6hw7kGea6jJ0mB2C7PQ/DUan3vuOezYsQM//elPkSjwuzIj5fnnn8d3vvOdSO+OEAGRkTQADls6al0Fpk8fy045bNGZmctSPN7zC8dyOrdCDcdCCvQcD71FDdkYgbOzcluzbTuqtknUECIOWVNahKsWfYEKp2ed2Sk5BX+fdgz6ZbSvDC0d99HovPdmzJgx5sG1MEX3bdu2meehsN+YHc9M+GgLHrOCZPdUb4U39CntrdmGQUme48EsFmb0+wMrr9CX/6tf/SpiggaJeM7c97//fZNCzwMhRKzhsKUiN3ko8lNGIzOpT9Sp0m3Benk0RMIBxRM6kET7YTp4i9u9erqI5tS7Kj2l4uo2oc51SBQSIpr54uD+JoIGeWrLeqwpLUYicNJJJxnBgWnjwYYRWhQ1WDoiWudsT4ZG831zw2kMR9aStyLODgfT7u+88048+uijyMyMvijvUEHb4pFHHjFNw5mVI0SskerobspNZSUNj1pBg7BUhW8NbW4LB8zqYw8mqxmsaB+uJqXO2rY7hHV83Khy7kF53QZU1e/S8RIxwy/XLEeVk+tMDyV1dXhw3UokAmxIfuKJJxohPBS+KZbAjlZBg7jNWO/rO3LD5XY2jmvMgmTQgD/cf//9JmucPv1IEnFRg4ugZ555xjTzYzNLIUTw8W22ajVNYiRsOGBDU9Yg5WeK9pGbnNeszJQddnRK6axD2gq1rlLsr5lnet+U1W/C/pr5qHEW6niJqIclp1piW0XL2+MRzlnBbjZHgX3p0qVNMzRo2EWuEmuLpDu6+BgdNiTZ0s2DkVAUNJh9w8aHh+PGG2/Esccei7PPPhuJBjN/jjnmGHMMhBChw9vGYFk/Oo7CJWowI8s3I1wEBnto+NI9racOYxt2dUndKpTWrUKFcytK679Gcd0ys12IaGdHVQVcXmtMJ9zYWpFYgX8M8gl2RiFLMTLLoYmg4SUeRQMOWxKyHLnNAqdyk7s0jm0MnKKNwbJibcEef/Th05cf6QzJiIsahNFyt99+O6688sqwpKsKkWiwhrp3kyLWu2UUYziNHRo4EjXaT5I9GRPzpiInKRcOOJDpyMLE/CmJ1SicabLVlX47IEvq1jdEJFi4UVy3NmS7J0SwGNJCnVcuP4dkhab+azQSishbNslmPwrTY4EG3H0/Bc46GTj7VOC5Z6JG3Eh3dEanlFGmzq3BnQ6XewAO1BRjz949pnwrU+jZELu8vHVD9NVXX8XHH3+Mxx57DInK448/jg8//BCvvfZapHdFiISAgVPhtDEYJSo6Rt/0/hiUORTJthQk2ZJNk/ChWSMS57By7q+p9NgZflDnLkaNa6/1x+b/LBVX61JVAhH9DMnMhsNLiObvw7KjM7MgVDCLuWvX9vcq9KWiosKIAOxHYVi8ALjhCuCq84F7bgR2Ni/xFymGZk9AVlLD+XbbkJ3UF1X1SSivrzKb6I9n+SnaGK3B11x11VUmG3r8+PGINBHtqeHNT37yE2NwUO255557Ir07QsQVbODDaEULppQx0jOcsNzHlClTwvqZ8UZmUhamdJqBhGTFZ8CS/9HTCeR2AU68HMhtezHicle3sK0mhDspYhaXEyheC9SWASk5QP5wwBa5qJNpnbrie4OG4YnN681zmh73jBiLodmJ47yh0z6Y0b40NijuNzrbHn4QWLjAM6a4aoEXXwDyOwFnevp6RJrspD7IcvRGQW0RPt2/GHXuVWZ76aYCfGvymcaI2Lt3b6vOQ0Zasc4ty04xcixR4XfnMfjBD35g1kHRXvdZiFiCjUF9y/gxYrOlklShgj01vAO3RODwHA7MHGweCUdpIfC/54HCvTwQwMTjPY82ylOyl2ZLOFvZLhKc6oNARYNTO2sgkBrZdci9oyfiqsVf4ECNx07ulZaBu4cfgUSCTnnvXkwdgdkNDJqaNWuWZ8O2zcDjD3rsC7J3N/DbXwAP/glITUOkSbGnYXTOdDhd9fjy4NdYXcoeG1uNPptX6cKQnCHo06ePebTGgw8+aIKkf/zjHyMaiBpRg03C//a3v5kU+bPOOsukkgohgmfU+zo+GAXrW5YqVLDkBz8n0qlpIkbZ+jWw6P2mBsj7zwIX3M4OyK3+WbI9D/XOSq8tNqTYEysSRfgB60bv/ACoYt19joduoGIX0IdGbeQSWu8eMRZn9OqL7ZUVJkNjSFbiCBokOzvb1LtlVkJrcA5jJmJaWnMjgX9LYYTONT6Yan7EEUccisq0BA1v5n8ZNaIG4bw5v2Al6t31jfP2noI92Fq/B2MHDMXWrVtNtBnX0KwrT+GG62h+11tvvdWU2br44ouR6Hz729/Gv//9b9xyyy2mcbgQIjgMHDgQq1Z5BFcL9vrh2Mt/CwfsvSS/gWgXXAv89zmg+MCh50s+BHI6A0MntPpnSbaWs2aT7Ym1ThN+ULkb2PfpoXI/peuBHscB6ZELNhmQmY23Zs3B0qIC2G3ApPwuSHdEjVs4amATbK6vfX1ltClYaoqZ0lyXUyAZPXr0IT/XqmVN34i2RkkRsH0rMDR6MuC2Vu7Hhoqdjc93b9qB8rwcZOXnILsw2wRO0RZjU/WDBw8aEYgl5WlvsB/2p59+ao5PNBBVVy+NrxtuuAGXX3455s2bp6iLGMPldqGw9iCc7nrkJOclVlmcKIeGBWvcDh58KAJnwIABZtugQYNC+tkc7FnrduTIkSH9HBHH7NroES8sBySd0GWFQEUJkJ3f6p/lJQ9HvasCde4S8zzJlom8lNHh2msRK1DAMIIGaSg/VLkHqNwHZEa2pvSonDzzSEQYUb9+/XrjMBsyZAjS09ObiBkrV67E2rVrTTkp77mNhgYFDdZap4hBI6OZeM/nKam0TJpu8/qMaFlXldcfEmY3r9yAviMHobiuzHwvChicx5mVwTmd4g4FDZadevvtt03qeLQ2Qw8nPAZPPPEExowZY47NeeedF+ldEgFS66pAjbMUDluyKc+m6zo6YIYEozW9oZOD27j+N6X+QkhxcbFxPLUkbAtxWKorgKJ9TbcxmGXXhrZFDXsGcpLHoLTu68Z1Y3bSCIkaojmFyxt+8SpvWrQcSD8pokcrKykZR3XtgUSFmeD0N9MP1q1btyZzVVFRkbE9WGXEOzCIJaa4na8dPny4cfi3OMcxG6Olcra0O6KIotpS2GEz/VWqyipRXVGJXkP6oqy+EsOGDTPzK3v3MbiMogbX0JzbL7vsMuOzj6YKLFElapD77rvPlMr56U9/it/85jeR3h3hJxQylhUtQlm9x3nIOtBjcyegc2rwatWJ9sOagUwR93b80OiorPSOYg8edDgxHZwDIftoUNAIV1NyEYekcHHQwvbkthcHdlsyuqZORb27vFHUsEUw8l5EKc5WSpI5m5cvE+GFEUGcQ6yIqPz8fCNWMEqIggXnMEZMEb6OTeuGDh1qHoflwouBZ5/2iBl8cIw553xEE3abHWn2FFS7arFz/Xbkd+uEzOxMZCZ5xBcaU5zXKWpQAJoxY4aJkL7mmmvwl7/8Bb16NW/+mqgwevypp54yx2batGltptWL6KK8bi/21TAbwLMQSLPno1f6JM3nUYLV48e7XCB/9w2mCmb2Nx0sGzduNP00OE8I0S6MHdGQoetNyuEDHNIdvZBq7wKnuwp2WxoctuhyWIoowVXjv90hwrom5NzFLIRFixYZEX7EiBFm3mLQEH1XtC/oz2JQFe0OihoMpDpsacVps4G3XwHKSj0BmbQxRo4F+jb024gSspMzjKBRX1uHbV9vxLCpY8z2TIcnSIC+OzY9nz9/vslEYdWXu+66y/j5fvnLXyKasLm5V1GGFXn31ltv4bjjjov07gg/2FKxEVsrNjbZ5rAl4cguxyuaKkqwynBY6eB79uwxg3JHmyTRkfTJJ580KxHCvh00ajgYdngx4KoGHJmALep0WBEOyouB1x8FarkIdHuiH0bNAGaeqeMvOk5NCbD1LR+j1g4MPAtISZzG3LEAnff79+83hocVqU0DhA9mdlDo8DsymOPIB+8D8+Z6oqfOPBsYE301hRetX4r3Vn+CnG556N6vJzId6ZjTYyZSkGREHR4TZqZw3Uyj7IQTTjCRZ88880ykdz0qYWNDlu364IMPVBIzBnC567G14lO40bRUXOeU4chLiS4HQSLzxRdfYPbs2Y3j8urVq40TpKMZNbQvmH3mDR1OFLiZndahTBBm/boqPJH5tow2eyiIOGbh+8DyTxvOvw1ISgbOu8lTgkqIjrJ/XkM/DcvGsAFZg4Cu03RsowiK5Sxl2K9fPyOWW9s+//xz4y+jyMGgKr8pOAi8+SJQWAAMGASceUHUZWpUVFXi6c9fwr7yg+g/ZgiSUpIxtdNIjM0dZOwL2hTLli0zczltrI8++si0iVi8eLGxw6KJqBQ1yJNPPmmyNlheoHNnTSrRzpqSFdhXs6fZ9lldjkOKXc3bogUqrYzkJHQCUXQIRiO/L7/88lBzpGBStQGoYmovcQBZU4GUxG14mtCUFQGr53pSxXsOAoZPkQEqgkfpFmDvPMDt9IinPWcD2f10hGOEcPWHCjfMvqDTrmu/7thfUwiHzYG+Gd1RXlJuUuAZlMAIMjr3yAMPPIC//vWvWLp0aasNxBMdZvxMmDABV199Ne6+++5I747wo+zUjsovfbbakJPcB11TVdY0WmCpO2Y/WYFMzKJg2cCOQsGWvYJY6iOouCqBSvZVqvA8T+oGpE8GbOr9l3DQFbZ+iafUbWo6MGYWkBuA81KItnDWAvs/B6obytym9QS6zwbswWlSLUJLvNoXLpcLn332GabPmI69zmJU1lejS2oeuqflGxvCqurCYGgGKjOAioFjP//5z3Httdci2ojasGcerPfee8+kibP+bTxeTPFEelIm4JNJx0yNZJsG7HANuPVuF5LtrS/GWQPPu5mPVQc3GKIGGwcFnboDXoIGcQLlC4G8EwF7dCndIgywd8aMM3SoRWjIGQhk9QXqq4CkDKCNsVREH/G6RqSDkKUjGYAwNLt/oyHCgJ+jjjqqyfdm+jyDgWikSNBoHR6bF154AUcffbTJBo+mmsCiOUm2VFPStmmmhhvJjKwXYaHe5TSl8OxtjLMUC63o1mBC4ZZBWEGnaqlH2LCo3w/UrAfSJJQlHLyuh0/2PIQINo4UoMfxgLPKk6XB0j5xumaNR+LVvrDZbCYLctfOXU0CEFiOi7aHd+lI+hkZCMTSrfTNRyNJ0Xygn376aYwfPx6PPPIIbrnllkjvkmiDfhkDUFBzoElPjdE54+J2IIgmlhXtwFt7VqHW5USXlEx8u98UdE9rXjKFpae8xQca9jRCqL52FL6vVXMwaNQXtVDn1Ak4SwF74vVq2VNViA3le8wRGZHDCMUOlvUSQjTFnqRyUyKq6N69u4mOqqmpMUEJLPfICKpJkyY1WV/xNRdeeCHuvfdeOen9gEIGo814zJYsWWLS6kV0YrcloVvqmGY9NXKTD/VvEKGhor4Gr+9aim2VBaaZ6PTOg3FM1+Gt2nahsvlYAiT4b1rcvI+CsxCJSJ3LiRXFW1FSV4FOKdk4Iq8/HOo/J0Tw4NjIgCkhogSbzYapU6diwYIFRtSgcMGAKZZ3ZENwb/7whz+YklPsWxitvt2o7pjKumWvvPIKfvzjH5tanSJ6YVbGxPxpGJs7EaNyjsC0zrPVJDwMbKsoxCu7lhtBgxTUVuLZrQvMAtUXihcUHiyys7NRVlYWFAODtfasVLWgYbIxWqiOZ/c0L0okNpfvxWu75mN1yTasKtmGV3Z8id1VBZHeLSGECCtFRUXYtWuXyVbwpq6uzizIYxl+r7fffrvJ92DZFTbEZQT0pk2bTM85Zmhw/vaejy+55BITBHTnnXdGaO9jDx6rcePGmWPnez2J6CIruQf6ZsxEt9Sx6Jk2Eb3SJ6tJeBh4Y9cybK/0OPrZTHRewUYsLd7e4muZ9c3gqY7Ce9H3fmQJPm/7JSjYfEsj24AEbPTsdLvw6s4FmFewFmtKd+Lzg2vw9u7FMT+fCiFEIHDMY1YgA3694XwUEmE9jNTW1mLhwoWmbK3392VQDxumM3CKvnb2x/UVNNhT5Cc/+YnxyUdzS4iozdSwmD59On73u9+ZaCo6TX2bEYvoganJXVK7RXo3EooN5ftN9BSNDeKGG6X11dhfU4be6Xlt/i2NhPYM0mzSykGRWRmMbuRAWFJSglGjRiGopPQBqjcDzpJDGRspAwBH4jXunV+wtvH8en4CCwrW49w+nv4oQggR79BhxrmHTbDnzZtnhHqKGYSRRRQATjzxRMQanIfZ76pXr15GvGBTPjoIWSKSjfrYr4qRVD169DBRVb6w5NTmzZtN+alojaCKRrgGeu6550yDdR5DZrmI6CXFnmkeInzO7q2VB5tt31S+D5Py+7cYiEgBluOUlcFdXV2NtLS0gHv0cfzjIy8vzwjZHNcCfZ/DkjYGqFrSYF8QB5Aa5L4dMcDm8n3YV1PcxMbYVnkAu6sL0Ts9eh1YQggRTNasWWPsCgYQce3NOYiCBucfBh0de+yxZk6KNbZt24bdu3ebklKck//zn//gmGOOMSIHA3u4jfYDfe60O7zh39EH/9BDD5nSU9FM1Isa5Hvf+54x+C644AJ88sknoanf3wZldbXYUF6K7KRkDMnKkdEoooZUe1LjItR3e0v4Rt7QqKejKJC+GhzwabxwIKSxwefevTqCBpv15RwF1GwFXFWAIw9I6Y1EpMbpcdx5U83GY0KIuOLrkmK8vHMLal0unNyjN47q6nEQCWDfvn1G0GAzWj68HWYrVqxo0eEf7fA7ULDgvjNQYO/evSYLg/MyDStGUH311VdmvmXtW1/eeecd/P73vzdrZKtBr/AfHrPXXnsNM2bMMCWpTjvttLAePq7JNlWUoqSuFkOycpGb7Bs9LkRkYMBUks1u+vVZ0P2f0op90a1bN2zYsKFR1OjXr595PmzYsIA+l/YInSccAxkxy7IYIRFrk3sBtjSgfp/H3kjuA9gTrzxMjau5fUGqWrA7hBCxS53Lhcc2bsSK4mL0y8jA7cOHIzfMPtVopqKiwlQesTIbGCzFB4OnOB/FoqCxceNGI9BwjcvgJ9oZnFcp4HD9+8033yA/Px+zZ89u9rc8BvS9n3TSSbjuuusQ7cSEqMHFzBNPPGFOyB133GF6bISLlSUFuH3FfJTXeyb3o7v0xP+NmQpHwwJLUXEikkzI74svDm4yi08rW2NUTg90Tsls1Vjg4GwJgwMGDMDWrVubNAg6HOzFwYGfcCAMaYoyDY20Q42KEpXeGZ2xqXxvo4DF0adPhiKohIgnlhYV4NsLPmtsh/vijq148IjJOLdP86jYsFBUCLz6IlBYAAwdDpxxDieRyOwLA0zKykw2g4UlaHA+4rwWawbH119/bYyNmTNnNkZHsSH4nj17jMjBeZrft7W+VxQ9LrvsMjz11FPN0sWF/4wdOxZPPvkkLr30UlMz2Ls5Yihxut24d/UivL9vp3me7nDg9+NmYnKnrmZdJftCRBJef7O6DMVnB9Z5npv/gKmdBrX4ejpLvEtEWdcvI10ZQOUvvP84tg0dOtTYGCG9D5I6eR4JTI+0/GbdC9lPo7v69gkRN3BNcf68eXh7zx7jw+T9/srOnVh0wgnIjNS6/tOPgflfAmnpwJlnA4P990WFGu+MBVYKmjhxImKJioqKxvWs5eNjoAGDqGgv0L5gZiXL1rY2x9Lnzvf585//HBPrUZs7hoomcpFDQ++3v/0tvvvd74b881xuN06f+x5K6moanQw8pZPyOmNHVYkZEE7s3g/XDhqL5AAWbEIEk+LaKny8fx1K6qoxILMTjuo6pNUGb0xBY1YFy1swmoqiBktH8WcgcCBct26dcSTxcfzxxwdktIjAYFbGf/YswZ5qT23jARndcGKPCUhuJWJOJBDuWqBmLeAqA1iaI2U4YE+P9F6JdnDVorn44sC+xvUG6Z6ahnnHhzd63FBaAtx0HVBcTGvI4/KYcSRw9088DQ8jAMuSMLjFe65hajTXhtweSMZhpMtNMfuC864l0jAKzGpYTWfe4eC8ze/MCKqHH344DHsd/9xyyy344IMPTNYLy4CFmtd2bsGv1y5rfM67Kt2RhH7padhdXYGe6Zm4Z8QkjM5VAIOIDHQRrCjegbVle5DqSMb0ToPQs43SthzXGP3JrLqRI0eacrUjRowIeGymjUKBhOPc8OHDTYaeCB1rS3fhw/0rTcmxZJsDp/ScgIGZ3XXIBVCyFSiksOkC8gYDeUMjtgYU7WdxYSGmfPRRs+1/mzIF3wnQBxQUXn4RePoJlgzxPOfPPzzmCaCKAMXFxdi5c2eTACGu1VmWyfKXxQq7d+82AcvMeGSmibetxHKrDEA4HE8//TTuvvtuU6IqXIE+HSU2LMAGeFDZpOT00083Rh+bJYaS4roaFNXVtJi9kZHkGdDf3bMFaQ4HvjuwfVFybrcL1a5K2GBHqj09JpQwEV0Gx/LibfimzBPpV1ZfbjI1uqe1bJCzbBSbILHRD6M/GUHFnhiBwvfhgwYHHTEUOZh6LkJDmiMF5/SejkpnjYmVy0hKvGaGogXcTqBqPuBiUzM34CoGnAeBjKMBW4ynFNfWAHt3A5lZQOeuSASKa2ubCBqktCFLNOx88qEnU8M77mXe58CunUCfvmHfHUYLMUvQW9BgDw0u1I888kjECtu3bzfBBYz6sspJsVYv08InTZrUalaGNzS0Lr74YvTv3x8PPvhgGPY6MWD/PtoXPLZvvfVWozEYKtaXFSPJZkN9wz3G/1c667Gzqtw4jfZUVeDOlV/iualz0DW1fUJ1rasW1c5qZDgykKQgCBEgxXUVWFO2EWX1VUA9sKUyDT3Sctu0VekwoRBh2RrtiZ2cMGGCGefohOH4KFEjtIzI6Y0hWT1Q4axBpiMVSfbQjn0iRijZAuz49NDzir2Aqx7oHOQempGg4ABQWQ507wWkxL9NXdTQf84brqaLaiNQyppzwj+f8/zu8rJ6XnkJuOenkRF21641JUh9RfojjjjC2B6xQG1trQkIYFAOM8C9A6aY9eivrfTZZ5/h5ptvxrvvvhszgkbMiRqETVoYlXbuueca9SiUC53spBQk2+yo86onyqWZ91qOzz8/sLtdokaNswpryxaj2uUp5ZOb3AXDsibAzpI7QvjBN2V78GXBxsbn5fXVeHHHQtww5DjTuN0XOlHoCGJaHY0NKtNWvcBAe9WwbAbrmFNcjJUI2ViD2WJ2r1J3mUlBbpQoYhtnoSdDoxE34K721IhmfehYZetG4JH7gbJSz/Oj5wCXXHMooidOmd21O1aWFDWWgWCK+MzOERKLKysBziEUzrypqozI7hQUFKB7d0/kKHs5MZqe8xizBGMBlpSig469QCzDgnVtV65caQIEAhFm7rzzThOFxWOgjQq4tAAAh2xJREFUuTd48Fi++OKLplniXXfdZRojhpJuaelmjvfF2BmeuFhUOZ1YUXwQJ3QPXEjcXLEZ68o8pYMcNgfG541Ht1QFnwj/oBjx/t5Fxq6wWFWyBZ2SszEip+XrkfYEbQmruTdtdNb0DrSvBqF90bNnz3b9rTg8lthkCVQUMnITsKeIaIODq1veFsuiBp3o/3wK+KIhayE7B7jpR0D/2HHetocJeXnITkpCRX19Y/AUR4CjunaNzDnwDajltgoG6EUGBkxx/jLZiStWmHmL2dCxIGjQh7d69WoTpMyAAFZk4fdYv369CTpmEJU/2RmEAVbnnXce/vCHP5hm4rFETHoIrr32WlxyySU444wzUFra4PQIASwpddfw8Y0N0zwN0phR0fR1qe10tGyqWGmyNCxK6g5iZ9UhB7UQh2NbRYG5NuE1QZXVV6O07pAR4guznGi8s/44RQ0aHWwUxKioQKAaTCVYTpXgc7CmDE9v+Qy/WfcuHtnwP6wr2xOCTxGxjyvA7TEAx6E/PkCP76Ftn30AzP0Y8c4NQ0bizF79Gp9PyOuEB46YFJmdGTcBcHnNCVzn5OYBfQ/tX7gbatPIYCo4Hfonn3yyWXBHc3ZrvasWS9d+grc/+jv2lWw086WVws751moQHojT7plnnsFzzz2Ht99+W43BQwBL5/DYPvvss+ZYh5IL+w5G/8xs87u1jqN94XtN0+4IlILagkZBgzjdTiwrWoYaV+CZuSIxqXbVorS+srGXG6ElvKe6oM3+NBSgeR9ZjiI6U1iOKhAoilAcoaghggudXXMPrMPv17+PB9f9B6/tXIQaNQUXLV4sLfgFvNeFscjcjw4JGqS8DHj8N7H/vQ5Dl9RUvD17Njo19IpIs9vx7JQpmJSfH/6dYRbsEeObB6pNapopEU4Y8Esx4/PPPze9J8466yz07t0b0cyB0u1474t/48P5r6H/4F4mIIeChtUHhH6+WbNm+S1o0KdO3zr7y11zzTWINWI2vJoRVEwTv+iii0yaeKgcq6f36o+BmdlYVnwQWUnJSLYBj21a0eQ15/c5fP3jliivL/ZpzUWHdFGH9lckFhlJKT5XkIc0R+tZF6wN2LVrV3PP0LFCo2P06NFG0WUNXH/h30vQCD51Lif+veMrlNd7nA+Vzlq8vmsJvjPgSJP2L0QjDi5Gea97pxU7AEeX2D1IJUVACedGL1gKYcsG4KgTEM/Qefn78VPwi9HjTYZofnJK5Jz2o8YAN94OPPlH5jQDXboCP7rX09AvAtDxzzRoRiJZJZqitY8TI6M2b9mInaXL0KNfHkZN68GYe+ytWonibSmm4TnnXjoAA5lDmRJ+0003xVxKeKzBpopWqVv+fvTRR4fkc2hTPDf1WPx37w6U1NViZHYeHtu4EruqKuCC2wgdvdIzMKVT4LXti2uLW5C6XSivK280eoVoi2RbkhExvEUNqxxqa/DaYg8NChIUMlgOo2/fvlizZo2xO/wds5mFF82CdSzDksVfFmxofL6xfD/e3bMc5/aJnENRRCk5A4FqH79U7kDENLQlaFNYIgYzloob7I78+O5fdXTXrth75pnYV11tRI72BEwEjXt+Avzy58DqVR5x4+xzgbPOjdjusD8d+zhRAIjmuYdzKgO7tuxZg0rHLgwZNQApKck4iJWoPjAMOzbvbdx/K7vdH1imij51ztcsxRqLJMV6mjgj32jkPf744yG7CEfndjIPi5zkFPx33zYzDs7p3g/HdGtfmY8kWwrq3E2jppJtMjaE/0zKH4BlRduN49tTrsCNmZ2HtClq0CnESESWwaBzhVglqNgjw4qw8sfo4ODKnyJ47K8pNdk2TbFhc/l+iRrC57JIAdJnANXLADfrsKcDaeOAWC4hwB4aXOB611mlU4VZAglCdoClAEPGnJOB4+YAVVWsXRjx5pCcp/zpOREp6MRjcAADBwaN7obOLkt4cGPTuu0oLfkGR447D6NGBV66gdmU55xzDh555JGYSwmPRVjqlseax3zevHnGURsK2Bj87N6HnER/zD4Kz2xZg22VZeibkY3vDhxlXhMoqfaWbYkUu9Zrwj9Yjmhy/jAsKlpnBDZKG6n2JIw9jFNz7ty5SEtLM5GvvXr1Mo4V2hXsseGvGMtSueyjJILP+vKmWTMUrTaV7zcZHNHszBMRoOtYwFUHFK71OP/ZKLxHjItfObQlfMJBaXPQ9kgAWNa2l5+R+yElLx946FFPqVvaPBG2ezj2RbN9QdGB5Wrpq2PFFVvPHLjgOY811TVYuWQd+vWowKzJZwTcD45j/4033mh6/n355ZcxG7Acm3vdABdJ//nPf0y6DRdKt99+e1g+98iuvc2jo/TPHImN5csbqucy/dyOPulDgrCHIlHISkrF1YOOxOLCbahy1qJfRieMyunV5t/Q2GBq3bRp00xJDws2Q2Lq3bhx4/z6bA7+VLUlagSX5BZ76rjVuE+0jCMHyAxNJHFESE0DzrsUePl5T4oyxY28TsDxp0V6zxITnoMYqCkbSZiyvWrVKtMbY/bs2cY4KqrdDNTa4Ha78PXyjcjrlI0Jw/shJ93THDxQseTUU0/F97//fVx99dUh+Q6iOTzWrC/MY89SYd26hb4fRW5KKm4bPqHD79MzvSe2VW5DaX1pY7R9n/Q+yE72lLsSwh8m5g9BXnImdlUXGEFjVE5/ZCW17RDr1KmTaRROx4qVmcEyHhzH9u7da0Rff5CDPTSwV6jNx63raKEHoxCmr1qPyZ5HvHDCacCCz4HiQo+YwZK3512WEM3Co5IoFhKiAQoODGpiuXiraTm3HSj3VGgoLS7H2tWbMXH6aGSndg1Y0LCqH73xxhtmnetvYHM0YnNbnaJimMWLF5uoqr/97W84//zzEUuU1RWhqG6/aQ7eJaUX0hy6uUXo+eqrr4yo4Q3LYfhTgurrr79uLJ/BeuCBNhgXbcMhmeWntlUebGgYakO6IxlXDzwamUladIkEYfVyYN3XHof6rOOALDnjRHThcrlM3VrOgSwl5V1apcpZiK0l87F84VoMGdEPnbrkwo4k9Ms8Eg6b/3NmZWWlWd8ycOcf//hH1JbciudzzB5+jDL/+OOPozqSzxf20dhRuQPVzmojZvRK6yVHsQg5FC8Y8GT1D/K2HWhftDWGMdCKjhXeZxRHWP5NBJcdlQX41/b55nfLATS7yzDM6qKG7CJBqCgDvvzE05h62GhgtH/BnEKEkwMHDhhBg9ndDJryZnvlQqzf+A2KCkowdtIws7brkjIEnVMDK0378ssv46qrrsKnn36KSZMi1MMxSMSFqEFYTufiiy/GBx98YLrVCyHaFjUoSPhGQq1bt85EWLVlrLAJYHvKZ4jA+mqw5u2eqmLkJKdjdpehyE2OHWeKEELEMyzVuHz5ckyYMAE5OTnN/p3z5IJlH2HghEykpibDjmT0SB+PdNMHxz8YOHDBBReYHh1c26oXQmSgo3XOnDmmJwANwPZEwgmRKHDcotg7ZcqUZqVv9+zZ00zs8C1dxeoLsVr+IpaEjSWFW1DndmJodg+My+0nwVMIIaIAuuaZ/U0f3ZgxY5qNzfz3rxbNQ3XaLvQc6MmsyEnqhR5po2ELIOuOpVVPPPFE/Otf/zINwmOduFk18GQ88MADOPPMM009MDaVjHVqXaWoc1UgyZaOVEfi1BMX4TE6WqqfSkOCkYmtRVIxapSGvfAfl9uFotpKJNsdRqDwB772mK6hqeHdIdxOwFkIuF1AUicggIhjIZqw/mtg5SIgKRmYfgzQo+MlHYUIV23bZcuW4aijjmo2V7IOPCOSWZbx1OMvgBtOON11SLKlBmRscH6+7bbbzHvR8JCgETlYspOp+QyY4jn5wx/+EPMOQJe7HhX1BSZWO93RCUnqtSGCBDO5ec/4wjGMY2dbcNyUoBEYlfW1KK+vQX5KhrEd/KFvRmfziDpqS4G6EiApC0j1PwBAiCZUVwGfvg8UFQB9+gOzjveUmhIiBmDAFBt2+2Zn0C5gSdTdu3cbsSMvbybq3TWw2+xwsMdmALAyy1lnnYXf/OY3cSFoxJWoQW644Qbs2LHDdLCnEdizZ0/EKqV1W1BSt6HxeaajL/JTRsS8ISWiI8I0Ozu7ReGC22mQtFZTLy8vD2vWrEHnzp1VBsMPimor8ML2hSis9TQ8HJPTC2f2Hh+b9Wtd1UDFl4CroXmjLRXInAU44qzefuFeoKwIyO8G5ESh0RcPLJoL/P1PgDHA3cDn/wNuvRfo23YDUpFYcAFf66ox/QBS7WlRs/5hoz42smXGY35+PgYNGmTq3W7cuNGUTWH2hlWW0YYk2G2BL7VpaLz00ksmSIfzrYgsPAfvv/8+Zs6caZof33333TF7Supc1dhe+RXq3Z6easwi6pcxBakOlfgTHYc2ArMt2jvmMyOczcXF4fl0/wZ8uH+tKSOVZk/Gpf2nYGBmjM4XJeuAgsWHnueOBDpPRFxRVw0c2OnpVdalL+CIKzdcdFBTDTz0M2DvbjbmAVxOYMM3wJU3ep4L0YDLzaCjGiMItGedHiroi6N4sXr1alMqnv46VlIpLy839saRRx7Z+NpkW/MAgsNBUYQZGiw7df311yNeiJvyUxb8OldeeaVRuT777LOYbHhS5yrH3up5zbZ3TZ2ENEeMLlZE1MBSFnS+0EHEiCg2HrIcMLW1tSY9vH///q3+PQfVJUuWGJVYzpa2eXrzF9hbXWqcchbHdhuB2V1isE5w5VKgbpdXFV4bwFIqWbMjtktuFAHYb9wyQC/YEHgT3iYseBdYPdfzOxe/M88CRjbtPSOCwI+/D5SVHHpOkW/sRODq23R4haHeVY8VxctwsPageZ6XnIcJ+ZOQEmUR5Vu3bjWCBpv3se9FMIQX9oe79dZbTY3b8ePHB2U/RXCgbXH00UfjkUcewXe+852YPKy7q5ajrH5/k7k8zZ6D/pntc0QL4XuPsNSU1SC8T58+jf9G26OtPhm04SmK0BYZN26cgqfaYF3ZPjy/bWHjc848KfYk3D18DlJjzVleVwrseLv59p7HA+n+NZYPOq56oHIj4KwEkvOA9IEdc4qXHAA+eg6oLvc8z+0GHH8FkNZBu0U0Zd4nwAtPNT8q9zwA9G7dtyESi8r6/dhfs8pkU3P07JIyGtnJvRBNsDrKhg0bTAUVzpsUO4IR2HzUUUeZ4CvaGtESLBYMYmzWOzw8OX/5y19w9tlnm8d7773XYhpsNFPvqmxxO0tRSdQQHYXpbHzQeKDw552xQUPicGUu6LzhgMjmRTRQ+Hz06NE6MT443S7sqfZy3Dawo7IwNo+Vk9/FWwN3A87SiO2OG3vZTbrBlCM74MYU2NDOSX/nhkOChvkANzDvTaDPUCC70+H/3ukEtq3zpD33HgDkSoBulSqfOY7lzMoidy2J6GND2bpGQYOU1JVgTclqjM8PcuQm7/OD64CiTR5xrdtoILef33/eVn349vDOO++YrON3331XgkYUQpGJpahOP/10U4rztNNOQ6xRY7Itm87ltVYGphAdxBJit2/fbhqGB2rD056gULx48WLTz4a9OdLT/SvdmkhsryyCHTa4Gu5l/r/GVY8DteXokx5jJatZdqrF7SWRETXc9UDBx0B9SYON4QZqDgB5U9svbCx4A6jxGmdLDwDLPwCmn+3f3xcdAHZtBTKygAHDVU6pNSrLPWs52hXesCm4EMbPWo19NSu81kFuHKxdbbJVU+xBzlitKQO2LwRqSoHMLkDfaUCSf8FZzPymuB8sqqurTckpBhrQVx5PgkZcihqEUedM2z/++ONx6aWX4sUXX4ypxn5J9pYbAie3sl2I9sAmRMzS8L43aEh06nR4By4HQqtZ+MKFhyKFEoGCmnJ8tP9rFNZWontaDuZ0H42spObCKY2NVHuSMTIsbMwlcERXpLHf2DMBV3lTZ0hEx6T1DT8PLUqAzQDauQAo2usxVryTF/l78YHDixp1tcC/HwN2bvI8Z5TcudcAQ8a0b1/incHDgQ1rAFeD0cHjPswznghBCmpZ7/8QzHYrrA2BILxvJbBzwaHnJduBIScDeeGP6GPZ1IsuugjPPfccjjnmmLB/vvCPY489Fs8++6w5V//73/9Mr41YIsWe2SBiHJrrUmRfiCD37du2bVuTMhnE3+IQLHU7depUbNmyxWSHJ4qo4XK7Ma9gE1YW74TdZsO0TgMxIb9lkT0zKaVJFnjj9li0MZJbyVZIjlB526odDYIGaTjG1duB+hFAcjsrgBTvb25fFO3z72/XLgNeedpTSokMHAF8+waVr2qJQcObChq0L1JSgV7+B6uI+KbGRRG1+dhZ7SwOrqjBcnMrXwHqKO67gdI9QNleYOx5HuEtzHPyJZdcYrIoGTRlVWiJJ2KwsLt/sN4xTxqbLF533XUmdSdWSLZnISd5cJNtmY7eSLUr8lcgqIqtr4DRo0cP7Ny50+/34H3F7I5EoaK+Bn/f/iU2VxxEUV0F1pXtxT+2zUedtdD0EX5O7D6qUeDgfyl2B2bFYukpkjbapzG4A0gPXgRB4NS1sK0D1yKFi5YM7iw/mhUu/BjYRUGlAWc98ObfDhkgoimX/aBpGviE6cBJ5+ooiUZS7M0zBpPbW3rK7QTqyz3lJHzZu7xloSMCJVsY/f/b3/4W559/ftg/XwTGBRdcYPqeMFNjxQpG/MUO3VKHI8mrqaQdSejO+V2IIEHxgnZ4az2J/IX2RSzZ7x3l8wMb8MG+b7Cvpgx7qkvxxu4VWF68o8XXTsrrh84pmSaPgDYGmdFpgGkYHnOk5AN5PoEtmf2B9AiVg3GxfJqtle3tJDOv6XvS2Z7th33BoKnXfeyJreuARZ+2f1/imUHDgIu+29Czj7ZrBnDdHUCWekYJD44mvozDbz8sziqgvrK5D6FwE1BX2TT4snw/UOanmBkkXC4Xrr32WlPakdngrc3NsU5cZmpYsN7/hx9+aErl3HzzzXj00UdjJtUmN3kw0uxdUO8uh8OWjlR7fszsu4hd2GOjpQbiLcHoKaaHT5o0CYnCxvJ9qHIeMsgYJUVxY2dVIQZmdm32+vH5/ZCbnIGN5fuRbHdgfF5f5MWiwUEcmUDWsUA9J2M3kNQNsEcyeo4GQrFPtIUfBkJr9B8JDBgNbP360Lbxx3oahh+Own2HUtQtaqs9adBZsdfXKZCFEmt+sgRdQOTkAXf8H1BcCCQlA9k5odpFEWNRqryLuNYZmj0UiwoXNhlrh2cPD/xNq/cAxQs85SQYx5M7CcjwKhnVkvDYkvgRQmhozJkzxzSf/sEPfhDWzxbth+eqtLQUJ5xwginlaWWvRjvJ9nQMyJyFyvoCc19lODohqQURUYj2wgzw+vrm4ygDpwoKCszPw8GgRK4xunXzYw0WJyws3Nps26LCrcZ28IV9M74/+EgsLNyGsvpq9E7Pw7jc3ohZOk3wiBi1xZ4MDf4eKb9HSpfmkdw2R/uzNMi0M4CP/w5YNmRqBjB+zuH/rrzEI2w02Rc7UBBex2gkKCsrM/ZFwP6v2ScAU2Z7ytrmdaJzI1S7KGJMbOe1lGrPQ7qjC6qcBxtt91R7DjIcAc41tBUOzgOqdjWMG52BbkcDjob1VGuBjWG0MdxuN2666SbTo+/zzz+P6164cX+Xs0nZRx99ZIQNpq8ysipWxIFURy5SEb8OMRFZOLCxKXjPnj3blXlBg4P3lb8iSDxg1a71pa2E+oFZXcwjLqDjIyVaUnhZ2olR1mUNzykqDWr/29FIOP7bwPa1QHkx0KkH0NPP9zPCh89VwHRn1r6NYxihTKce+/CwT09bDUCbwXk4P34XV8J/Kuur8cmBZdhXXYgkWxIm5Q/D6NyBmN55JnZX7TKO1x5pPZHPaM5Ao6eK5pmR24MLKFnkafrJB8kfCBRsaHr/5ndgHAkQNgFkqdTrr7/eiBoitvjhD39o+gZQ2KDBGNAYGEEYkZidHKEGvCLuoZ3dUkYGS9zm5OT4VSqDgVPTpk1DItFSOSlXGwZGmiMZR3WNjTHHL9K7ex6RhqJGzgSglDaG25Olnj/DYwO1l679gNN+AOzZ5OmH0WeER9g4HAyMYvBPvdf9xPJKneJb7GOZnC+//NL0FWCD5MGDB/s1djSSmuZ5iIRnf/UObK9aB6e7HlmOPAzJGofuqeNRWr8Tda5yJNnSkZPcD7ZAS0IVrzwkaBCWyC1YCHRrKLtIMbpJfxcbkJwGZHULm6Bx11134a233sIXX3xhfOLxTNyLGlYjRwobRx99tBE2fvGLX0R6l4SIOIMGDTI1vL1FjXXr1mHYsGF+Gy3xKGhsqdiPeQXrUeOsw4DMrjiyy0iTZUEGZ3YzJaRYbop2BktKZSWlondaBzIERLuwIRVuTGUottXBpONHkouP/u2Itp16HLBxFbBnm+c5r5czrjiU/hynBgcjMTmvUgxlVmSsOPRE9MBF9wf7FqOgttSMqXXueiwoXIPMpDQMyOyJnOQOZPHUFXkJGl6wAbklavSb3VDb2moUPhbofgTCwdatW42gcdlll+HnP/95WD5TBJ97773XZKwdd9xxRtgIdvN4IWKRrl27Yv/+/Y2ZFgyYqqioQN++zbMOfKFQSEdmvFHjrMeH+9dgW8VBZCal4piuI9A/81BwBzMy2FPDm/F5fSKwpwKZQ4D0/oCrGnBkeDI1OgrL2Q6dHNjfJKcAZ10BvP7XQ33o+g4GphwT90FTnFNTUlLMWmn37t2BiRpCUHeoPYAtlYcqMJQ7S7CubAnG5s5CbnIHgzSrfbOl3E23pecDI08HNn4M1FYAGbz/TwSSwpMZ+/Of/xz//Oc/zbq0f//w9wkMNwkhapChQ4capwsdMGlpabjnnnsivUtCRBSKEuypwVRwKx0tNzfXGOeHWzjQERVIXdxYgWWkXtt1qORJcXEFKutrcHovT4mtnOR0XNJvJt7fuwrFdZXolpqNU3ocYdLARfihqAREQQNJZmVcdhuweQ1QXQX0Hhj3UVS7du0yY8XChQuNs4JNPaONkrpiVNZXIjMpEzkdKRsgQkaNqw4Ha62GnIfu6+2V+42o0SFai6r03u5IBgYdB7iPbfjw8GTysncVDfazzz47pjKIRXN47tgLhX3KKFKxFFWfPnJEisSGdvf8+fMbRQ06J/2lpKQk7pqD0256dedibK08aAR82hAvbF+AqwYeie5pHpvrhO4jTIPwFcU7YLfZTaPwqZ0kkkYMe7LnEWlGTwa69wF2bQHSs4DBo1jjDfEMs7rYa4ywqfGECRMQTdS6anGwptCsV7ukdkJyNFwnohnFdQfMOTqUBedGlascta5qpDo6OMewzJSvK8wqPWXBbI3JV3iCp8K4zv/1r3+NJ554wqxHEyXgMKE8cax3S2GDRgcjTH/yk5/IkAwTLrcL+6q3ospZhhR7GnqkDURSe5t+iqAxfPhwLFiwADNnzjTPe/XqhbVr17YpajAtnCmhI0aMiLsz8XXJjiaTH/+/rnwPTnTVI8XuGS57pOXiOwNmR3hPRdRBYWtoeCK8oyXTi49oZV3pN9hedag+9eDMoRiUlRgLu1jC0Uq6d1IwoiKTOwGpPYGaPYd63iTnA2ktNB8No7HBqEMKGuyj8Yc//EHr0DgRNh555BGTwcbgKWaHK2MjfJTVlWBv9Xa44ULnlO7onKrSWtFwTzBQis7JvDxPZhwblLKsVFt9uLZs2YKDBw/GXb8+9r3YUska7h482d7A6pKd6J42qnE+nNN9pHkI0YQuPTyPBOHEE09EtFJWV465BV8ZYYOk29Mwu8t0ZCTFlxAbD9htjhZLhHN7h8k7Atj7UdNG4PnjW35tmGwMt9uNX/7yl6aPNNehI0cmzlwSf7VjDsO4ceNMs5THH38cP/rRj8zJF6GFx3hT+TLsrt6Aorq92FezDd+ULYDTHX+R/rEGy0exxt6qVauapIxT2GCtb5aV8WX79u1GDInH5n0aDYSIfYpqC5sIGmRTxQaU1ZVGbJ9EyyTbkzAi+1AKOJf9jFQdkROEVGnTt2WmpzZ2xkAgeyzQ+ZjglJFoJ+vXr8eRRx6J008/HX/+85/jsoRjosJzyci4U0891fQb4xpKhJ7SuiKsLFmA/TW7caBmD9aWLceequ069FEAHSqMtmZWJ2EGE22ITZs24cCBAy3+zb59+zBlypS4Gxtbsy9kdwgRW6woWY061yEfVrWrBqtK10R0n0TLdEvtA7uPu7tzSi8kByOwOrUL0PNEIHsYkD0U6H48kHH48oqh9Lfec889+NOf/mR83fR5JxLxtWLwkzFjxph0nL///e+45ZZbWnTciuDB7IzS+oNNlnC1rioU1u7VYY4CWGeP0VTsp0FYiopp46x7S+PDFzYEZumMjsKMD8Kau0VFrH0eeUbl9G7SqI8OtsGZ3RuzNIQQ0U9FfXmL2yudFWHfF3F4ZnQeg6mdRqB3elcMzOyFM3rORKeUINVTZyYIa2PnTgKyRgC2yI3lq1evNs7uSy65xET1x5vTTniEDUbIXXzxxeZcf/31oVrOIjTsrNzc4Bo+tHbbXilBKVruB4q4zAi3sjdYNYF2R2FhYZs9NTpqX9DBw8fGjRujws7PSUpD3/T8hrKpHnjFjsppIXNQCBG1lNWXN/EV8Hdmb4joI82RidE509E5pSdykzqjT/pQDM4cE7wPSMkHOk0EOk0C0iIX7OtyuXDzzTc39tCgrzvRSFhPHSPN2QmeJQAYQcLoKkec1yeMFE53fUDbRfjp16+fqY1vwXuBDxoWNA687w0272N9S+9eHIFAI2Px4sWmBJxl9GRkZJjIrcmTA2ygFmT6ZXTBGT0n4suGRuEDM7vi2G6JNzEIEctkJGW2vN3R8nYRWZiZMTZ3sHnEK0uXLjXlFG666Sb89Kc/VcmpOIaO2wceeMCsa1iK6oMPPoi6euDxRH0LWd8utydoRkQe2g8saettSyQlJaFHjx7YsWNHs8bhLDv11VdfYfbs9pV5pW3C7HP2z6R9zzKZVslcZqJHcly4oO8UvL93NbZXFCAjKQXHdRuJXume0lxCiNggy5GJIldJo7BBoTKrFbtDRJ6MpGwMyYrfrAXOrd/73vdMuSkKGgMHDkQiktBhYjzpFDZ4AVx++eVx2fg4Gkh3ZMPRQnRkTlLgDnEROig2sDmfN8OGDTORhnv27DF1cVkvmq8bO3as2U7jIVAYsUVjpmfPnqa+LoUMGv8djcwKFsOye+HKAcfge4Pn4KQe45WlIUSMkZ/cCX3SD5U0IgMzByM7ufVeQUKECjrU2Mvt7rvvxs9+9jMJGgkAHZg///nPcdddd5ngKV4DIjR0SvF1VNuQl9JFhzuKYPb3kiVLmmxjhjjFDZZpYw+NiooK45xh0BQzOfj6QEtE8/WffPIJZs2aZd6LAkl+fr4RU6xAqkiS7kjBOb0n4uZhc3DNoKMxOCv+yvgKEe+MyxuDJC+/Voo9BWNzPX1xhAgntbW1xof9+eefJ7SgQWxuNZXA3r17ccopp6B79+545ZVX2mxgJtpHRX0xNpUvR527BnY40D9zFDqlxHbKrctdyUJasCMTNlsyYh0OBRQcrPJTNMqt7TQ2KDpUV1cbYYOCBp/zwdrggUQ/MUWOWSFsHMhSVszQ4CDM97A+UwghOjqeFdcVocpZicykLOQmKxpShJ8333zTlJv63e9+ZyKpROLB3il33nknXnjhBZx55pmR3p24w+12YVP5N9hXs8M8z03uhOHZ44NTMztCuNz1qHeXw4YkJNky42JtzLK1zMxgWQxmfHs7ZZhRYdkYDJZi7yHaCrTL58yZE9DnMAiLfQGnT59uyuqmpKRgyJAh5qcQQgSDGmcNDtQUmLG5W2oXJNtj3w8kYouysjKcf/75pj/Ve++9Z+bLREaiRgOlpaU499xzTaT6u+++G5dNkKPBycSSU8zaiOUFOr9HnesbOLGrYYsDKfZxcNjiI/NkzZo1RmgYMGAARo8e3aTuNwdQpoXT8GCmBXtusF4uxQkhhBBCeHjqqadw22234fnnnzfrS5G4vPbaayaa7uGHH8Y111wT6d2JS1xuF9xwtZgZHkvUuUpRWLsELniqB6TauyI/eRxs7A8U4zBb4p133jHZE0cccYT56dt3iPYHbQpmcbDvxsknnxzTNqMQQggRTPbt24fTTjvNBAhzfZmTo0oEsb9CChK8GP7zn/+YCPWZM2eaRZUILlyUJtmTY35x6nTv9RI0zBbUulbAHSc1fFnflqrvsmXL8Oyzzza5F9jUm6WiZsyYYdK6WVJj/vz55vVCCCFEosPAh1/84hf44Q9/aKKnJGgIXgO8FliO6r777gu4rI44PHabPeYFDV4XRbXLGwUNUuM6gArnNsQDFCoYEEXx4o033jB2N7O/vQOnGFTI8lHTpk0zogdtDN0vQgghBIxfjnMk+0NzDpWg4UGZGj4w3ZVp4v/4xz/MhULHrRDe1LrWwelmmntTozTVPgN2W/yULmPGBkUMRkqxnwab7LGXxocffmgGUyubiWnefM3IkSObRV0JIYQQiQIjka+//nqzfnz//fdNtqMQFnTmMvKcZTsff/zxxsbJQhCXuw77aj5pdjDS7N2RnxI/jU7Ly8uxePFi8ztLTTEzg/Y2o09ZjspqEs5SVOy3QRuEIocQQgiRqLDX1KmnnorLLrsMv/3tb5tUU0l0dCR8D4jdjoceeshEUx177LGmFJUQ3tiQ2kzQ8GyPr3qto0aNwoQJE0yvCxoYrEfL+uA0Rrx7aLDZH1+7ZcsWU5qK/TeEEEKIRIJRxueccw7mzp2LefPmSdAQzWA/AUaef/HFFzj77LPNekoIC/bQsDUzzW2w22h3xA/sXXn00UejU6dOGD9+vLE1Vq5caUQ/734b7PFHBg0ahC+//NJEqCprQwghRKLB0o30TdNHzT59EjSaokyNNnj55Zdx5ZVX4v7778eNN94Y82WTRHBwu+tR41oEN2iM8ppwI8k2BMn2gXF7iNk7g48+ffqgf//+ze4FNuNjGlxdXZ0xSthzIz09Hb169TIRWBp4RUxQVgps3wKkpwMDhlDljvQeCSFiADbAPeOMM8x898orr5g6t0K0BiPP2eCRkehvv/22WVsJQSqdu1FSt7rRvrAjFV1Sp8MRZ8KGBctPUdCwRD/aDt5s27bNZIZzO5uAb968GcnJyUbwoI3h+3ohohKWHNyzHagoA3r0BbJzI71HQogYgEL+o48+ih//+Mf429/+hgsuuCDSuxSVSNQ4DIw8P/PMM43x8cgjj5h6oEKwf4bTvRtu1MFuy42bJuGHY9euXdi6davJzmAj8czMTCNw7Ny50wgZpKioCN27dzdGyNdff20MD75WiKhm41rg0fuB6irP81FHANf/EEhOjvSeCRF6Y7tsO1BXBqTkAll92ARLRz2AdHAKGlZJITrchDgcDAL5wQ9+YDLCKWyo3K2wqHUVo8ZVCDscSHf0hN0WX5ngrYkbtBn4kzYDM8Jpc9Ohw2ApS7zYu3cvpk6ditLSUixduhQnnnhipHddiLZxuYBX/gKs9pRcQ1IycNH3gWFjdeRE/FNTChRt9Qj1+QOB1Pgp1R6OkrY333wzXn31VVMtRWUYW0eihh/QiUtjlQ2UX3zxRTVkEQlPSUmJEThYbsPpdJrsDQoZHHxZVoGp4mwezh41U6ZMkZNHRL9T985rPJkaVgNXOnXPugg49dxI750IM8uLC/HEpg0oq6/D8d164MoBg+M3U5PX++7PgdIGg4OlFXOHAD1nStjwg9dffx2XX3457r33Xtx2223xe52IkECHLUvesrH83//+d1OSSohEhnYDM9/YS4MCB4OnBg8ebH6yzC0DpygIMgOcQVP8NyGimiVfAG8+33RbSipw10OenyJhqHW58OiGdVhYeBDdUtNw+/CRGJgZx07+8v3AN28CrnrPc0cKMOpsICMxgoE76mv71re+ZfxtLD1FX5toHYka7biwGFGlyHMhDhnlNEAoYhA2E2f/DSFiBqaD33pV0210Tk6eCVxzS6T2ShwGp9uFKmcdMhwpsAfJmbyyuAinfvExXG43nA3bbhgyDD9l5k48Ur4b2PFB8+39TwEyukVij2Jm3mNN2/vuu0/OaBE0cexnP/sZ7rjjDoljQjTAvjMsf8ufPXv2NAGGQsQU7/4LWPQZ4LJWlQ3c9EugS49I7ZU4DNVOVuOwIcWeFLR141WLF+Dt3btMZ1aHzYYsRxI+P3YO+mRkxOf5WP0qUHHAqxetDcjpBYw8M8I7Ft0ooD5wVEvJT1huhyrZLbfcYiLPWTOZTc6ESHQYmdqvXz/zECImSc8AUtOAGk8JNQOd5J26RHKvRBusKtmFN3etQJ3baUSNb/WdhAGZHT9fz2zZCBcFE69tf960AfeMGIOkeOyxUl8R2HaBqqoqXHfddfjoo4/w6aefqmyQ6DBsMP/JJ5/grLPOMqV2nnzySaSlpenIioSHTcVHjRqV8MdBxDB5nQE3V5ZecD2ZlROpPRJtUOWsxWs7l2BrZYF5PianN07vNQ4OW8dsgJ1VlXhr967G5063G+XOevxrx1bcOTxOx7iaMi9Bg7gbtonW+Oyzz0zbAwbT/+EPf1DrAz+JQws9dLC252OPPYZf/epXOPXUU/HnP//ZqK5CCCFiGLsDuOL7HiPDivjv2h04+axI75logb3VJXh151IjaFgGyD+3L0RFfU2Hj1el09lsXqfhwZTxuCQ1P7DtCQ6zdRnQsnHjRixevFiChggakydPNtfU+vXrcdRRR5lrTQghRIwz5WigZ0Pgn2VjnHYxkBan0fkxzrt7VmJbZWHj89Wlu/DFgfVBsS984dVQUd9Qmikeyeza8C0tbEAQAtDiEdqef/rTn4yP+f777zc+Z/Vy9h9larSDa6+91kSNnHfeeVi+fDn++Mc/qtyOEELEMiw11aM3sO5rT+bGxGlAmqcxpYgutlYU+sb9oNblxO6qEgzN7ljJpBO798Rbu3c2Pmd6+NROnZGRFKfLpfQuQLdJwP4lh7Z1nwqk5kVyr6KS+fPn49xzzzUGBw2P1FTVwhbBheV1mP3z/e9/34gcr732GmbMmKHDLIQQsQr7Zlx9N/D1EqCyHOg7GOgzMNJ7JVpha8VBuJtYGcCmigM4BiM6dMwGZWZhQEYmdlRVmmApwp9zuveM33Mx8Gjgm7eAmhLP8/Q8YMCRkd6rqKO2thY33HCDaQb+v//9D7NmzYr0LsUc6qnRAdhHgE390tPTTVd6NkoWQgghROhYXrwDr+9a3mz71QNno29GfocjZf64cR1+v/4bVDudOLJLN/x50jR0iXcHdm2Z55GSA6TEcdPCdvLXv/4VN954I37zm9/g+uuvV88DEVI4DjFK74c//KH5eeWVV+qICyGEECHm0Q0foqz+UDli5hkMyOiCb/ef3uH33lJRjqsWLcCqkmLkJCXj/rHjcVG/OG8AzSbhbBjOLKXMbp7qCKKRffv2mUD56upq019NfaPah0SNDlJZWYlrrrkGn3/+uRE2pk6d2tG3FEIIIUQr1Lrq8eSmL1BQy74PnniqwZldcWn/aUFrGE6nIiOo4rKPhggoeuq2227Dv//9b7z00ks47rjjdPRE2Pj4449xwQUX4OKLL8bvf/97ZYULIYQQIWRl8U68vWc5bA1lk/j/S/pPR7+MzkH7jHqXy2SCsy+pSFy++uor0z+DJUeffvppEygv2odEjSA5P2hs/OxnP8NDDz1kGkgm3CDFBlgdbKAkhBBC+EO1sw5fHtyE4rpKdEvNwYzOgyRAiKCyc+dO41CuqakxQSsDB6pchAg/W7ZsMVF8bBxOYa1Pnz6JdRqsHkeJZlcJIYSICBvL9+Ob0j1GeBif1w+9WDZJiCD6jp944gnccccduO+++0zwVML5joOMRI0gwmyNCy+8ECeddJJpIp6RkQANoEp2Ahs/Ahgxm5oDDJ0DZKsMlxBCCCFiN0L+oosuwhlnnGHK/yh6SkSSqqoqU2/57bffxosvvohjjz02/k+Isw5Y/T9gz1pPrGzfI4CRxwHKnhNCCCFEjFb5+d73vocPPvjArOeYpSE6jkLrgwgvyqVLl2LTpk2msR9/xjXVJcA373oEDVJTBnzzNlBXFek9E0IIIYQIOHqKfTMoZtx///145plnJGiIiENRjdcir8nTTz8dv/3tb821Gtd88wmw+xtPJrjbCWxfBmyaF+m9EkIIIYQImI0bNxofMTNw6TOWoBE8JGoEmV69euGTTz4xdZcnT56Mt956C3FLyS6PodGIG3DWAmV7I7hTQgghhBCBUVJSgnPPPddk2jLz9uqrr9YhFFEFr8nPPvsMf/rTn8y1yms2btm3wWNXeLOX24QQQgghYgf6hOkbPv744002eM+ePSO9S3GFRI0QkJycjIcfftjUSrv00ktx5513oq6uDnGHI7nl7fakcO+JEEIIIUS7WLJkCSZOnIjq6mrz+6RJk3QkRVRCo5jXKK9VXrP8PS5pycZoze4QQgghhIgy6ANm7wz6hJ988knTh5m+YhFcJGqEkG9961tYvHixqZnG9KJt27Yhrsgf4OmjwVq3BhuQ2QXI6RXhHRNCCCGEaBuW8GHPDK7RrrnmGrz77rvo3LmzDpuIaniN8lpl5gavXV7DcVeOavD05tsGTY3EngghhBBCBAR9v1yjffjhh8YnTN+wCA1qFB6mBn+33norXnrpJTz77LM488wzETewf8aORUB1MZDRGegzGUhKjfReCSGEEEK0SnFxMb773e/iq6++wr///W/Mnj1bR0vEHHPnzjVN7adPn46nn34aeXl5iBvYJHz3GsBmB/ocAXQbFOk9EkIIIYRokzfffBNXXnmlETJYwSctLU1HLIRI1AgjNJqvu+46E1n161//GikpKeH8eCGEEIFSUwXs2siQbqD3YCAtU8cwStlZWYGSuloMyspGukNlEEXrLFq0yBgaI0eOxHPPPYcuXbrocImY5eDBg7j88suxdu1aE0DFElVCCCGinP07gOL9QE5noMeASO+NaIUqZz22V5QhOzkFvdJlB4rWqa2txQ9/+EM888wzeOqpp5SdESYkaoSZDRs24MILLzSCxgsvvIDBgweHexeEEEL4Q1kR8PYTQGWp53lqBnD6tUB+dx2/KMLlduO+NUvx5m5Picfc5BT8ccJMjM3tFOldE1GGy+XCH/7wB/z0pz/Fvffei9tvvx12uyqxivi4tn/3u9/hF7/4BX75y1/illtu0bUthBDRyuL/AUs/PvR89Axg1lmR3CPRAt+UFuKHK+ehtN7TH/fkHv1w5/CJsNus8utCeNi4cSO+/e1vo76+3gSYDBkyRIcmTMiSCzNDhw7F/PnzTZr4hAkT8Pe//z3+6uAKIUQ8MP9toKr80PPaamDu65HcI9ECr+/a2ihokLK6Wty6fD7qXS4dL9HI3r17ccopp+CPf/yjqW975513yukr4gaKc3fddZe5tnmNn3rqqeaaF0IIEWUc3N1U0CBfzwd2b4rUHokWcLrd+MnqBShvEDTI+3u34509W3W8RCP05T7//POYOHEiZs6ciXnz5knQCDMSNSIAa6o98sgj+Ne//mWiBC+99FKUlJQgqnG7gKoDQMVewHVoYBdCiLiFKeEc+yz4e8mBSO6RaIHVJYVweEVM8YwV1NbgIEUoIQDTVPmII44wZaaWL1+OGTNm6LiIuITXNq/xTp06mWv+P//5D6Ke+kqgcg9QG+W2kBBCBIPSgy1vL2llu4gIhTXVKKytMXaFBe2NtaVFOiPCQB/uJZdcgjvuuMO0GmA2uPpnhB+JGhHktNNOw8qVK00t3PHjxxtVLypx1gJb3wO2/gfY/l9g0+tAjQwPEX4KCgpMU1dmOxUWFuoUiNCS183ToNSCv3ObiCq6pKaZlifwMTpYhkokNtXV1bjppptw8cUX4/e//z3++c9/Ijc3N9K7JURI4TXOa/2hhx4yTcR5D/BeiErKtgBbXwd2fwRsfxs4uDTSeyQSNNKWNvmCBQuwatUqVVEQoSW3lT5eeV115KOI7OTkJkFThPZG5xQ1fRYwvlv6cOmT4vzBDFkRGSRqRJgePXrgvffeMwbHCSecYGrhsg5bVHFgGVBdcOh5fTWw+/NI7pFIMGpqarBmzRps3rwZU6dOxYABA7Bpk1J0RYiZcQaQkX3oeWo6MOscHfYo45J+Q9AjLR00Oyzj47ZhY9UsPMFZvXo1pkyZYoTwZcuWmaxYIRIFm82Gyy67zFz7vAe4duI9EXUZGvsY0OWlShevASp2R3KvRIKJGQwunDt3Lnr27IlJkyZhz549cDqdkd41Ec907gVMOqHptjGzgJ6DIrVHogXSHEn4/uCx5nfaF7QwuqWl4/y+6ombyNBXS5/tnDlzcPPNN5uMWPp0ReRQo/AogoYH05dycnJMrw3234gKmKHB0lPeMGJ5xGWR2iORIJSVlWHp0qXYsmWLmSzy8/NNtGGfPn0waNAgY7QLEVJYwmjXRo/TpedgIC1DBzwKKa2rxdu7t6OkrhaT8rtgWmdl1ISLvVVlWFrscUJOzO+FHmleQmCEGiY//PDDphn4rbfeahqCJycnR3SfhIgkdXV15j5gWYSoaiLOklPM0GiCDeg8HsgfHaGdEokCRT42dq2srDTBUrQpKHKMGzcOmZmZkd49kQgc2AkUHwByOgPd+0V6b0QrLCnajxXFB5GTlIKTevRDtjLBw4LL7cbSop04UFOOrqlZmJjfJ+IN2tevX4/LL78cpaWlJiOWPZJF5JGoEWVUVVXhnnvuwdNPP40HH3wQ3/ve9yLvuN31OVC6tWkkVXIWMOS8SO6ViFNoULAedG1trWlyybrQbLxEA4P/RoeVw+GI9G4KIUTCs6m8AI9umG8MD0Jj46ahMzA4q3NEjs22bdtwxRVXYMeOHSY4hA37hBAevvzyS2OM9+vXD88++yz69+8f2UNTWwpsf6v59u6zgOyBkdgjEecUFRXh66+/RlJSEnbt2oUxY8Zg2LBhxtZmdobsCyGEiDy0K57fugjflO2DHTa44MaonO64rP+UiAgb9EE98cQTuPPOO3HNNdfg/vvvR3p6etj3Q7RMFITpCG94czCS6o033jA3C/tuMA02onSdCDhSm2Zp9FSTTREaKGj07dsX06ZNQ+fOnXHkkUc2RkzR6JDBIYRIREdMNPLazjVwul3G2OCDv3NbJIyN5557zjRGpoNqxYoVEjSE8GHWrFlmjcVMcN4rzz//fGR7B6TkAPljGp40OCnSewJZERZbRNyWsmW/DN4H2dnZOO644zB8+PDG4EHZF0KIRMzkLC8vR7SxqfygETQI7QuypnSf2R5udu/ebfpl0Df75ptvmmxwCRrRhUSNKIX9NbjwYpQ6o0hefvnlyO1MShYw6EygxzSg+xRg4JlAZq/I7Y+Ia8cds5W6dPE0UGN5BE62QiQKjBQsLi42CyjeD6rr7D/M4vrmm29M47Z169Z16DxEy7hDhyNLU3700UemHF+0UVJX5Z3DaX4vqQtvQ2LWQz///PNx9913m1Twp556CllZWWHdByFiBTpzeY/wXmHEIe8d3kMRg6Wmeh3v+dl9NtDrWE/wlBAhyAIfNWqUETF69eplMvqESCRoY+/bt888WHYtoqJ2jFFSUoL58+ebR0VFRYf6MUTLcae9+cUXX2DRokWINsrYw7fF7TVh3Q/6YMeOHWt8U/TNHn/88WH9fOEfKj8VA7z00kv4/ve/jxNPPBF//OMfGx2+QoSTWlcx6l3lcNgykGLPD3pZtO3bt+PAgQOm1JT13uyf8dlnn2H69OnIzc0N6ucJES2OePaM4bVvZSKxr1JGRoYxOFizk/cBo8+7dYvtPhEUCriA7tq1a9Dfd82aNcbIYNQlM7x27txpDJDRow9fl53HmSII34eGBh88D4zCYVBBOLA+lw9GTHHfKWLQ2ThkyBDzfZi9Fm38dctiLCva0xhFxRTxCfk9cdXAyWH5/FdffdWsj2bPno0nn3wy6NeWEPEM553rrrvOlKX685//jHPPPTfSuyQSEKe7DtXOg3DDjXRHZzhsXtUBgrTOouOO2UnszeddG72wsNDYGELEI7Qh2DfGCtThupY2BuEak+tmrnenTJmCWIeVTdh/M9j+if3792PDhg3muFEUJZ9//jmOOuqow2Z3cU1P8ZT7xv2ySmjzvLASRUpKCsIBP5ewtDftC34+xz5+Pq8Nfq9oCwbaV12Gh9d/2iRwimf21mHHoHsYevfR/rrhhhvwwQcfmPXRhRdeGPLPFO1HokaMwN4C7K9BdViGhwg3pXUbUOHc0vg83d4bucmeaKdgwIh0Lrq8F1UrV640zlyWnrIiq4SIJ3jNc9HEpvd0xrZ2jdPJTaf7pEmTEKsUFBSYOtZczLP0Q7DGjbVr15ra2CNGjDARyN4w8ogih2XAWQYGRQwu5unQ4wI/LS3N/D1/erNw4ULTYJpRVRRHKDS1FvnGDBG+zvoMZpmlpqaaf/M+rxzPuL8Ud4j3v1mNe/k9KOLyJz9zyZIlJkqI7+cNDRGOkdZnMquH78d95ncJRymN8voaPLZhAXZUlZjn/TJycf2Q6chKCq5TyheeuxtvvNEYGwz2uPjiizVHCNEOOHa88MIL5n5i8NRjjz2m4CkRNupcldhbvRAu1JrndiShe9oUpNiD57Si7cw51HLacQ3A9QHnSa4R8vLygvZZQkQDXI/yumfFD5YbbMt5TsGP63JrDRqLcxjXyVzXT5482XznYIgADJai85/2GYOLvNfrFINWr17dLNiI63DaJrR5rLKxLKnNzDDvv6ddRxuDa33aKDxHrX03BjWx/w/Pj5XhwbGLNgD3k+9rBWPx/bjP/Dfvz+Pvln3A11g2Bv+G14lv/zm+H+0Xq1oAP4e/8+95LfERDr/MwsLteH3nShM4xaCpc/scgSmd+oUtYIrHhX00KJaJ6EaiRgzBAeZf//qXMTzmzJkjw0OEhTpXKQ7WLmi2vVPyJKQ6gtMMllGCnDisCXLz5s1mAuUiQoh4gY5YXttWQ0pm3bW2kPUd+7l45j1B5zqd4tEEHQQUZ7gwpyHFhTyd9tbim9+Vi2fu+9KlS833oBOBr2WkGL8PnfdcsHMxTgYMGGBeY5XgovjAz+Cxo2OCx5KL8pEjR7ZqiPFz6LjwXXjzs5jNwUdbhh73n5/LRT8NJr4PjSV+F9bm5nfkd6fYQFHEu74qP5uv8d7G96MhxO/NfffXIOAxoKhlfU/LgKFxwc+2jBXup7XPFDto4LFmeKivFzbz21vtKY3VIy075A38LGODRjiDPGRsCBG84CmW7+N9dd555+mwipCzv3oZqlwsf3YoHjfFnoueacHJTGSZHa4zLHuCczMzwP2JshYiVuC6kk54rv14jXMtOH78eL/q/jNyn2VOx40bZ9bd0RREyO/CTAlr7c595ZrYO6CHr6EtwJ8sD8S1Pb83X8vjwr/jWp9rY67ZaUNQzKTTnuIC38PKXmAmF39y/c6AyrYET2ZgcN70LiXFMYV/w3043DrfEh6YxcH3om3Bv+XanvtFYYTfiaJI7969m7wXvz9f5z2G8bvSLuHn8t/8wbIveVx8RRAeQ+v9aX/wwc/gceTxof3BaybUVNTXoKi2Cvkp6cgMQ8AUszM+/PBD42e96KKLoup+EK0jUSMG4QBKg95KF5fhIUJJlXMfiutWNNuekzQSmUl9g/IZc+fONVkaViQyn7OciIgeDtYUYFP5JtS7neie1g2DMwdpom8DLvo2bdpkFq1cXHIRbokY7V0gcYG7detWs5jlApeOfwsuZGnQ8L25eGepKj58o/uDBRe2NB6sbCpGMtEQ4HflopwPK6rI9/vyb/laOtu5iOdzLqi5eLeyKpjqTQGAIgJfxwePH19DBwUNhnAvNCmuWGWheIxpOLSWvRFpKJ7xemF2T6xG3/kiY0OI0MLx+t///rcx6hk8xSwolXQToWRX1VzUuyubbLMjGX0zjg3avEG7mZkahOuWfv36qaRtFOF012NbxQaU1Bch1Z6G/hlDkZkU+vIysTxO87qmI5zrUK71CJ3wvhnL/sK1NTOf6UznmpwZD1bQj9Wvjutfrrvp+O/evXtIBRCKkbSh6FTnZ3EdS5uA35efb4k1vjaGlWFAm4L2BG0S/k6bjN+Ha3baHwwUorjBB/+er+WDthpf45u5HQ64n7TlePxpV/F7RuP6ncf4008/Ndcbz0288Morr+AHP/iBAqZiFIkacWB4HHvsscbw6NmzZ6R3S8Qhda4yHKyd32x7p5TJSLV3PMWTcLFBke7oo482EzgnyxkzZoTMISsCo6CmEAsKv2qybUBGf4zO9dQWFR7o4KcznkYBF8+DBw821zC3W3WcWTu6zlWFJFsakuztr6VKcYPRPZZwQEPGylqgkEKDh9FNjKaxIvn5GmYUtBcaAuz/we/D92PmBRfeIvpgo3meK55vCkDxUB7nlltuMTWIGcwRT4aUENEcPPWHP/xB5d1EyNhfvRxVrgNemRo2pNpz0SNtatA+gw5MrskGDhxo1k60OdinTEQeEyleuhjFdR7HPLHDgUn5s5DmiM6gkUgdJyuinyIDxeb+/fsbocGKzDd92eARCG3IaLfgQLth+fLljVnBhAFZDDLic9oCFB2sMqqEdg8zgzsSNMQyS5x7CG2mjgSBidCyYMGCxvLgsZ7xxvuKVXA++eQTZWfEMBI1YhxOKrfeeiv+85//4De/+Q2uueaaqFR1RWxTXr8ZZfUbG59nOPojN3l4UD+DCzVeu4xAp8HBsjF0xnFRE+sTZqyzrGg5dlfvabLNBhtO6XFS3Cw4KQAwKoiLdhq7h/teXNizximdx9ainwIGSxy01myttG4vdletghuehm090kYhPyU42U7+wPqtU6dObVfUPxd9jMpi/w9/0tlF5KHhS2eOd3Qd6/pa16dlJFMEo3EcjVCYoXN1xYoVePTRR3H++efHzZgjRDTD8YGRizT2WcaEYiKdwkIEk3pXNfbWLITT7SknY0cKeqRNQbI9uAETX331VWP9e84rnPsYmEGHsIgclc4KLCn6otn2/hlD0C8jPkoQ02HPDCH+ZLCTPz1cmG3AICkGEVnZCAzmYCmilvw8bnc9al3L4EJDvzZkIdU+ETZbalTbF/ye7KFJmEEVikbfIjTQtmAGj1UhgNcnH5bPhtcue0fSpg5XQ/RAbaS//OUvuPvuu3Haaafh97//vQKmYhiJGnHCe++9Zwx/TnZPPfWUidgVItgZG/XuCjhs6abebUdgCikXMVzgUeWnI5kOZaZ+epc6YA19OlSZislSO3TIifCzpHAp9tbsa7adoobdFvsiKss2Wf0qGCnExxFHHGGilegA9l6M0RimAMLX9+nTxzz8WYDXuqqwqZyG26Haq2Rg5gykOQ41sg4VXFyyUXcgzcYtcZGGBucWEbvQKGZUHYU4jr+8ZnkNM8OTUXcs7RVN2Z4c8xkhfu+99+LSSy/FAw88oGauQkQAluP44Q9/iH/+85/4xS9+gZtvvjnq+jqJ2Mblrke1s9D8nurIh8PmXz341qCYz55SLCPDslN0AjOyePr06U3mGL6O8x9LzXD9F42Ot3inor4MS4u/9NlqQ9/0QRiQefiec9EOnfa89ljOidcZmzIz+MnqQectcPC1XKdzO4Ok2PfB3xKntc41cGKX1xYb7OiMVMcEhANm9bHPWSBs377d2Fvepa5EbEJ7kVk2VqaN1fePgRAU9JhlHU1iFe3+a6+91thEDNg4+eSTI71LooNI1Igj6Kj4+c9/jj/96U+46667cM8998RV+R5PWmVNQ4x4SlQNjiIwGHXLRR0Xa0xxZVkcGs5sONXSwobnnuIGJ0tGWsmgDi+7q3ZjWXHTvirdU7thcif/HeTRBgULimbbtm0zzlzvCFSOpbzemJHABQ8NCxojrDfLKJT2RKuW1u3DrqrlzbaHI1uDBhINjpkzZ7baPI73mNXkm8eFfSMoOk6cODEitWVF+OC55zjMjA3+zjF4zJgxfjcaDDZsys6sUxr4DNKgMSSEiCxffPGFcQJwXmR0YyACeSzgdjuNjWFDKmw2ZQfHMmx2z/UOhXzObSxnu3TpUvOztVr2fB2DNxTAEV5cbpcRNaqclU2CfsbnzUB2Umxm0Vi932hf8Ke33co1FrfzmuMam+scBlExc4jbKQy0Z+1V7ZwHNyp8tiYj3XEMQg33m9+jrfK2/N4sUUWxkTYGA61oezH7W8Q37PlCAdm6B1iRg49I2f73338/HnzwQdM/g4EaKqMcH0jUiEPoEKDhQcfcY489hhNOOAGxjttdhyrnCjhRZJ470BnpjiNgsylaLBax0k2tVHA6kBmtwZTFtiI9+HdMIW/vok+0n60V27ChfCOcbie6pXbF2NwxSLYf/hzQUc4xiY5xOkOsBbwlSnKhyzJ6jNghfB2zd1avXt3YD6IleP5Zmqy1Uk8t7QfTYGnk8j35OSzHw0yLtkr2cf+YRWRlC7XXwV9ZX4RtlQubbe+TPh7ZycHvD8D95vel8cCFJLP3fI8VBQxGhVlCotXkm2XfKDS2VzjeWVmKf+1YjQM1FRiQkYdL+o9FbrKEkVhyXvIeDHcvDt6bDMyw0sEZHR5PgRlCxDqck5k1ZZW7pUPAn1Iq0U69ay/q3F/THekpgGQfC4etW6R3S7STxYsXm2AUrmMYQUy7gWtP2hxtlZviupPBVnK0hpcaZxXWla1CWX0xku0pGJw5Cp1T/bv/GHjEDGpm8jPrhs5S74x/jlnr168355+w/BOvCdocVr85q4SsBbexggBf6w/8e66nuebm77QpmInBfTpc4276aujw5X5369at/X0wnIvhavCRNH4PZCLNMROhgAKFleXOTF8GKnrD40A73+rBxwedxzyufH17gxNdbjfe3r0e8wt2ItnuwMk9BmNWF2WSxwoMnvrwww9x0kknhb1c/gcffGDKafI6pJ3BoD0RP0jUiFPogHv88cfxs5/9DHPmzDF14lhCJFapcq5CvduT0maRZOuNdIcaFccqjHqnU9lq+MrFKCc6OtIYmdvaZMcFKlN5GRGiZrHRBxeyLM/EqCMuZglFDC58uZjhwpYLXQpTfM7yRhQWWNOf55zZOBQRWMO7regJGiW8DriQZsQTDQpfw8QbCmbMsOCCOhKYHhxVK1BWzzJeNFrcyHDko1/GZNgCKOHFqDbmqh3O8Pn0009NxCGFmJZ60rAOKs8Pj3Mws94Ka6vwk9Ufo8bphAtu2GFD97RM/GL0McYAEdEPowc/++wzTJgwwTgs+ZzXUKiyIzk2PP/880bI4Fjxxz/+sUMN7YUQoV+/0TnAufy3v/0tLrvsspjt5+dyV6DGNc9nK5tVz4Ldpv5RsQjnFGZrzJ49u0k2InsU0L5oq9QiHeC0Rzj/qZ9f9EEbkIIE1/w8z7QZaQfQ7uB6hcIUxQHaFVZ5I64rLHGB5Z8sJ3xraxq+L/+WQUFcR9Oeoc3hi9XrglAsowgSqUoCLncJalyLm2S7pNgnwGELLDjF6XbBcRib5MCBA2bsZw+NlkQbKwCRxz3YNtdLO77Gu3sO9fgk3x88CdM79wnq54jQwWwdlqRiNh3HWN5HoRxreS/fdtttxsfEQIzrr79eFT/iEIkacQ4dhIx2ZLO/H/3oR7j99ttjMvKxvP7zhtJTh7AhHVlJngWriC2s0jaM8uCkZsFFIycdLgq5vbUofE6AjDCnEc2IYtFxaMTxmPLYt1QGjA5wiqXMVLAW7Vz4c9FPY4LCgrWNBgWjjloba6xUf0bEMduivamfvA4YoUVjggvnaC9Jx/0tqduNGlc5UuzpyEtmPw7/HEF1rnp8cmAFtlbsNaLG6JwBmN55JOytfGdGfjFD5swzz2zSGJqRZDRIKCKFIv33o32b8c/tbIbelHtGzMaw7PBG/ov2w7GY9xbHBY6zHK+Jd1Qjf/LeY0Rse++9ZcuWGQODjoeHH34Y5557btTfx0IIz/3/6quvGmcBHX/MDKcjONaod+1uyNJoSrJtLJLsPSKyT6L9cJ3KeYtrIAZtMEPYgusfrnPp0GYPjbai0Blwwx4c3n8v2g/FBq79GYzG9ac3tBuYscBsYdoN1hqAznEKCnSC8jVWeUz+PYPiWlsrUHTlNUBbhIE9HWmETPGEIlgs+E4o0DpNAKjbZJrZbf7369tdtR8LC1eixlWLTEcGZnaZgE4pua0eF9oXPBeWcGjdN5ZtzrkgFALP95f8B5XOuibbRmZ3wQ9HBtbPQ0QW2qE7duww9zXhT1/7gtDHc7hsp7YE0N/97nf49a9/jfPPP99kmCoYNn6RqJEgsJ76DTfcYCacRx99FKeccgpiiYr6+XChvMk2O3KRmTQ1YvsUjXhHjQT7fevcxXC765Fsz4Hdltru8iKM7mBkDRevdGozmqalCHz+Ow1l33/3XbhaKb7CP7jQ5zngYpSOSh4/Hmc6FxnBZh1/nhtrUWGVa2IkBc8h/5ZZFlywMs2bhoNqUoaWj/cvx8byXU3Egin5wzExv2nKN+FCkTVuKU5Zi0EaZjzvoc5w+nDfZrzQgqjxwxGzMDy7S8g+V0QGCpo0YhmxF8gYQCfFT37yEzz77LPGKcoeYBpDhIg96IxkjWqKkldeeSV++ctfRiwjsj043QdQ62re7yrFPjHgKOd4JlT2Bal3VaPOXcrCX0ix57b7c9ivjwE4vP4obrCMlK8zms51yzFL0aK1CGGrF9lRRx0loT0AuB7gsaV9QKcinZIMZKNtMWzYMHP8KVaYPpkN1xSd4Jz/+RquDWiX8Hduo6DA86msmdBRWleO9/d+AXYuJbz7WF74tJ7HIMWnzDBtPwoaPEcsa2s5ohmoRsaOHRvSKPjvLXkXVc76JtuGZ3fGj0YqyDXeoI+CGR28xmjPBsJ7772Hm266ydjADLjwDqAV8YkaEiQILPXCGqNPPvkkvv3tbxvnJRVLTkixQKp9KKpcyxqmWmtbc2deIsOoIqbY+Ta542KDDmkuLjm406kZiMHA5okldctQ57ZqdTqQmzweKfbAjVY6xBldw+uOi1gujhiFQ5HDygzgTy6IaGzQ0c59Z6pwS/C7sP47U47phBdtw2uAzRKPPvroxmuA0RK7d+82adM8JzzexxxzzGHPIyO0FVEdPrZW7GsmFGyr3NdM1OD5pNh37LHHmvNDp/PWrVuNYR8O43x8Xg+8snMN6lyuxvJTXVLTMTAzP6SfK8IPI6s4FlCA9rf0DMd8Nv9m7wwKIXRCMVtLCBGb0PH4q1/9Ct/5zneME4GZW/fee6/p7RcLvc/s6Awb8uBGcWNpSDs6mYc4ZEewBjkd1L5ZvBz76XCmE7o9UbVVzv0orF3RWDYnzd4VnVLGBVSW07sh7ZQpU8w+cJ/pTGcgj3ePBdoNXAczi4MBPK318bN6kXGO4mvF4WF2DG0xKwuGwhAzPnltWPP8mDFj2nwPq2xte/vXicDZW32wUdAg/K3WVYfC2mL0SDt071h9Cbh2o91OcYr2I+14nvNwiNmzu/QzwVPe9tBs9dSI24AJjgf0G/kLSyvfddddmDt3rlmXXHfddRJEEwRlaiQgdHqxptwzzzyDK664whgfPXpEf4q1012COpenr0ayvSccAaRVxjt0FHHxzugYNl22mnJZcFKgU5MLfr7m7LPP9iulmg7RlOwi1Nk9TZwtbEhG55RDjvFA4Htykcu/pdHA/bYyAaz0QxrJXCDRKFm4cKERONpS8ilscJElYaNlaFiw1BcztSZPntzMKBXRz9+3fYhKZ9MSfL3SOuOMXs3vDcvI4P1DI6OtUmChYEtFEf6xbSUO1lShf0YurhgwHp1TVcIhntYQLONBZxYjYb0dRq3Ba/H111835TDpAGMd/tNPP13CqBBxBO/zd955xzgVuJ5jU3GuN6M9AILBO/XuHXCjEnZkwmHr2y6nerzCTE+O9+xJwTW39/nkcz64nqdDadKkSaaWvj/r0gMH98OduwZu06D9EHnJI5GZ1LddthCDOugM4z4y+IbPmc1tZQZw/uG8Racs+yuweXhbojwzX5k9EGikcKKtCaxm1+xdJ2KLzeU7sKhoVbPtx3ebgS6pTR3KFKjYd4alxOhHoP/InzVgsKh3ufDqrm8w/+ChRuHHdRsQ9XOM8A/OC5xH6K+gOM0gCX+CI1hun/7M5557Dt/97ndN4FQ4r0sReSRqJDCclOhgoOp+5513mn4bKv8Qm3Ah//7775vJoKU6+VzI0+HPhXxr59iKauIixZpAqHTXObajqvYA8vKzkN8pBzXVnmZpYwedD4c9tdWMABq0gdaitWrh0oCgEcXFk3eDt7b+jvtKY6q9tRfjDYpXbLjNY0gjjgsDpguL2GR1yVZ8WeCp++2JJQVO7TEVfTO0aBOhh2MsnRYcV6wmm/5mZ9BxxDUGhRAGVNDgiFQzTSFEeMYLBk7RscCx4sEHH8SMGTN06GMUZnuy3x2DjbiebKn2OW0PPlpzLnItygArrtH5Gtobn8/9GHWpm5GWloJOXXKRmpqC8vIqDOw9Fr3yJra6P5yHuJ4NLOvcbSLLmV3IXnAUPvgebfXWsOBamq/3R6xJBHgseUx4HAkFIivbW8QezMr47965qHJWN2ZsdEnJx7HdpsEucVeEAQbdUjTnGMJsOn/9FRQ/HnroIdM748QTTzT9M1jmTiQeEjWEcQbT4cBF63333Wdq4srh4D+sD/rpp5+aup+sVW8t7BkNz7qTVn1QHlOWSbKczK0NulY0UXsWh5bTic593/qjXJBzwmB0BRsHM3KbC3wKEGYwsNmMscF/p0Fq1TkdOT4PFfWbUVZaicKCUmN8uJxA+b6+5jtZWSEUSxgVTmXcaghnRUXx3ylyMIWQC1/r+mJ0Fx1dPEZ8HR+MrOLr+AjkGPD96TxrLZU8kaCxQYOxpVIBInZhT41N5XuMkTEqpx96p6tHhQgNHLvpgKKhwUhVjsUURjmH+X29btxoAicouN9xxx0mcEKisxCJA9chdDbQ6cBefnQ4UOQQ/sOyTww0smyMfv36mfUxg5neeOMN85zjMwOXuLZnxCqb9LaWncl1d3v6E3BNzzLGLCPEffFdfzNDlL28+LncF+4Tg5Qs0YNrUf6blZ3N7X379YY77xvzXQoOlKC2tg5ZWeko3ZsJe31+437y+9M2YP82vjfnFKtRND+H70W7gk2h6WC3oD3ETA3C1/JvOYfxNYEGXTF6mJ/v/f6JCM/1Z599ZsQg2nuKkI8Pqp01+Lp0Ayrqq5CXnI1ROUOQZFfwiQgNnIc4P+zcudMIE/QfcW7x1+9DP9Xf/vY3/OxnPzPZYQyckP8nsZGoIQxcEL766qvGAcGFJ6Orzj//fNWh8xMOriyrxNJPu3btMg8u+BiZxrI/FBS4EOQCm4tzGiasK0oDgH9HRw8XyxQZ6AjiYpuvp1odSC1Bws9iQ2Au8DlJcBHPz7MmCr4vhRWKGSz5dLiyNC53HYrrFsHprrCGDeQkjUWq41CzYfPdynajoLAAhQerkJ6WaZqF+V5jzL7g97Oaw/EnRY7WemYEyrx589QMCjAluwjPQaCGmxAi8bDmJ/Zl4jzE+YJjB+cflu4IRGDmvEbnJdPAL7vsMpOd4esEE0IkDnR40674xz/+YXpv3HPPPcYZLw6PlUXNkpK0FzhGcy19zTXXGKcQy7ly/KYIzfU/bTj2TeQ6n8FqFAzofKadwb+17BCr0W+gAVTM8me5Ic4NFFH401us5udZ2dpWVkZbVNTvQnGdJwuVJNty0TV1Mmy2Q8JLdU05Cop34eD+EpSX1pm1rW8kLz+Xx4JCPO0ffj73j2J8MAL1KO7zXCT6dctMF/Yk6d+/v7HfhBDicHBOoI+MawHOCRSsOYbTvuA4HYgY8vLLL5sgbM5xLHF53nnnSVwVEjVEUzhA/PWvf8X9999vBhkqoBdccEFCiRscMLlo58It2N+bC2Kr9iwHeEasUXCgSk1jhSIDnT/8XC7IKYjQmKHRwIU6DRFOAP44mXgu+bc0cqiE871pwLTHyc16wzWuA3CjDsm2fCTZD01AbrcLZfXLUecuMM9tSEJ20kQk23MRDng8mRFDYYmL7JbKbyUajKhjA8ThE0dgr2s/nG4neqR3Q9/0Ppr4hRAGOrc413EM5XzCCFaOn+0tQ2mJGc8++yzOOecc/PSnPzXZYkIIQbim/eUvf2n661jiBtdtiQQFCY67h8vI9i7x5M92QrvBeg3tBQZG8SfLBFEA4fhuOZC4H8w+4L8zuInrRo79bZWp9RVa+DcUOPjedPb36dO+NWatqxS1rhI4bMlIs3dr0s+k1nUQZfVsJO75bim2nshKGh22tSyFIApKPLbs75fomQk89xTL9h/Yj5Sh+SioLUGGIw3j8oYgM0lBVEIIz1xklajjPMc5hsG79HG1Zwz1FjPoM/vRj36Eq666StUoRCPK1BAtQoc7HRO/+tWv2iduuJ3AwVVA1QGAi5wuRwApORFfiFEYoOPGitqhkMDFmfdrCEtDBZohEUqs/eZE4N0E2NpfniN+J0Zq8eE7YfDfKGowimnmzJlB/W6Vzs2ocm5qss2OVOQlHxmWxT+NMkahBSvbI5ahEMcSZDzfvYb1xpKyZQ3VUT2MyB6GodmKrBIiEeF8YUX60kBgFC3nOn+a8B1OzGAgBDMzzj33XPzkJz+RmCGECJ244SoGXFs9toa9K2DvS09+RI841960nVgmiTDQht+T270dPfx3jrvREizGDAc6iWgXMAiKdgLFCmIJL1bpXG7nd/Bd21ME4b8xy++oo44K2r4xoKqw7jO6tJpsz3KMRqqjF0IN50kGCKmsiedYbN682ZQ3o/C1xrELu6s9PTVssCHVnowzex+JdEfb2f9CiPiEcx3nDwZMcd5gWaiONuvmuPPSSy8ZMYNzzI9//GOzZlBpbeGLRA3RJlzEWuIGyxhR3LjwwgvbXozT0b7rU6Bsu3WZAfZkYNCZQHL7IkCDAWuwMiuChgYHSS7KuYhnDb9Yj7yhQcLvReGCj5aMFjq82bvCO1IrGJTVrUCte3+z7fnJx8JuSwqZOMWFNaPDKGYkalMoq44xy53xuNDwZDo4nZSLCpdib/W+Jq932Bw4pcecmL/ehRD+wXGB44PVF4ORUoyoDYZDzVfMYGYGswGFEMIf6PT/v//7P7z22mu44oorTPTlYcUNVwlQv4Cj26Ft9kFAUuTWgVxbf/DBB6YskuXQ4TqLmWrBXG9HAtpL/E50IjHT23fu4BxDMYTHgHYIHd7Bot5VjpL6+T5bbUiz90Vm0nCEyjFH+4IBAPzubA7e3uzFWIfnlUIGf/K8s2cJnZRFtaV4a/fcZq+f0mkkRuUMjMi+CiHCD4Vs9i3iuMn5gUKGJex3BIkZIlAkaoiAxA06MOgwve2224wBQqGj+YvLgE2v+V5qQNfxnoyNCMEBctWqVWZxRid4R9Vj4aGifh2qXTuaGphwoFPysUFznvPcsU8Ir0NCIYMClb/GotNdjwM1u1HvqkV2cj5yk2On0R+/M8sEUJji7xRy+NPqS8JUTgpVvmUMFhQsxIEaT0kwC0ZTndbzJIkaQsQ5jBimw5AGB0uC9O3bN2j3/fLly03jX6aCs5YtMzMkZgghOpJxy8wNihvMCr/jjjswbty4ll9cvxJw7fbZaAOST4xotgad4OwZR7uI4ka0ZGLEMuzpV1T3abPtGY5hSHcEr2wZRQyK9FxXW2WAaWf4ew6LaotwsKbANFbund4LKfYUxAL8vhSsGAFNG4PrBZYVs8qY8Vqmk9LX1jpQXYT/7J3fzL4YnzcUR+QNCet3EEKEHwZLcczsSGnzluBYxECp3//+92ZMYmYG/Y3KzBCHQ6KGCAgOMC+++CJ+97vfmQHt+uuvN48mAkF1EbDlLd9LDeg8Bug2MWhHnIuuQBvccbFG5zgjio4//viWRRkR2Hlw16KkbiFcqGrcluVgI/Eefv09yyVZqe68vlo7b1OnTm3SjNBfKGisKlmAKme55zqEG/0yhqN3enRGEzF1k8YxoROSEzkzMPjTau54uObu5n0qtmF1yZrG5/zm3VK7YWrnSSHdfyFE5KBTguI9x44xY8YEzdCg84ORyA8++CC+/PJLk/596623mkxHIYQIBhs2bMDDDz9sgqhmz55txI05c3yyS+uWAi1kB3tEjcBsgmDaF2T16tVm/dapUyeMHz8+KPuS6FQ5t6PSua7xucOWjdykKU0aibcGg4BY190KjrKc9b69SXiuJ02a1C7hf2flTqwoWWWc+m64kWZPw+wuM5EahWWYGOxAG9g6DvzetCmYiUL7gg5K2lmHu/brXU68tutTVDtrzXe2OL3nLHRODU8/RSFEZPrpsEIEe7sycytYwVLszfT444+bBwM1Ofd/61vf6nB5XJE4SNQQ7R7YPvzwQyNufPHFF0ZFZfaGcXCwxu2mN4C6iqbR+/1PBjK6B+WIc3H61FNPNTpurHqvTH22ek1wkcZG3HQAszwTF7ZMj5s2bVq7jBXRdjRVrWsf3HAi2dYJSXb/xQf2B6GwwYmLDrgePXqYR7DO0a6qLdheecggspicfxySoySaigYGU7z37dtnJnMuFDoK74G1ZRuwuXwLXHChW2pXTMgfhxSWghNCxB2MEmZWFyOcgxXVxHnVCmTgWH3DDTfg+9//vjIdhRAhgw6OP/3pT3jssceM88RycJhxzbkTcK72erUNsOUDyVOD9vmLFy82tg2j1JnlZjXSJpYjnBlw3DeOkRQyGLXK13MNJ4JLnasI9e4S2JGCFHt3vwQNy1ZctGiR+UmbgiWHeT6DVU6K18L7e/9n1tgWFDcGZPbHqJzoKcVIcYfN6Rk4Nnny5KA4Cotqy/DZgWUoqSs3dsWMzmMwILNnUPZXCBFd0L/GgCaWuObcFyw4LjEr4/nnnzf9mDjXM+hYZbJFoEjUEB2GUR8sRUHHx6mnnmqiN2dPPQK2XZ8DNYUAHcc9pgG5wauzakEjg5FRLCk1ceLEJnX8LCGDizguYFmnV5kZ0Q3PFR1nTAXnBMoyYR1tAL6l4hvsq97eJJqIjMudjYykrICjoLl/bMTN2vQt9S/xBxpYFDD4XvyehMYw3zPYGJEPbtiDFMEohIguKIrSCUdxP1j9hSiOPP3003jkkUfMvHn77bfj8ssvD1rmhxBC+LPmorODNgbX+zfffDOu/u53kZ9bBDgZge8CbJ2BpHGALSXoaycGm+zcudNkXjCAynK0cMzldq5VKXh07tzZiBlyxER/qSX2l6J9yLmM/TI6EkBV56rD//Z92Gx7z7QemJg/IaD34jXFfoGce3k9UYBp777x+/H65Pfl92b2BdcGoYh6drldsi+EiGM4z1F8YMWM9vo9vOGYNHfuXCNmvPfeeyZggTYGx2Mh2otEDRE0uIB69NFH8Ze//MUICIzo/PbFFyEjM7hN8qgUWwszOltoSFC0YE1eGkBcBLIcFqP95YCJHax+J9YingIHIwJ4HjvCgZpd2Fi+qsk2hy0Jk/KPNY2z/YFGxpIlS8x1xcg8ChGMzKORzZq7I0aMMCnc/kDnIyd0KyNFqZVCiI7A3hk0OihqUNzvCCtWrDDp3//4xz9MRCcNjTPOOEPZjUKIiEGH79tvv23EDa7FLrnkEtxw/fU44oixQSs5RTiOMnPYaojNoBr2V2DJWpbdsOwObqPjWVnfsQHX8GxmazVw5xrcKjnVkXPI9/l4/6eodlU32T4iezgGZ/kfyDd//vzG643zOK83PhgARUGNgoQ/ghm/JwMNeW0ympqBfhLahBDthWPQO++8Y8aUUaNGmfGovdBn8sILL5gMTGY2XnPNNbjpppuCmvkhEheJGiLocND65z//aQYtRsRcddVV+MEPfoBBgwYFzejwNi7oFGZqLeHvFFQ4CPN1FDm46KTDmQ5kRlRRZVYDv+iDGTdcgDM1POjRdhVrsL+GzcwBOxwYkTMxoGbhLEMwa9asFo0fChxLly41Rq4/+07jZfr06TI0hBABOfU4r3G84XxH5wV7DVmlUCgCDx8+vF2Zbfzb119/3czZFF0vvfRS0yur1Ua9QggRISi8cqyinUHh9cYbb8TZZ58dlAARjqdcz3Gc5XqPEe5WE2VCx0737t1NeSxG1VtljeiUYTNl2hgskSVHcnTB0sMLFiww5U2CLUSV1JVgYeFi1LpqzfPuqd0xMX+839kL7E/J/WMQV0swgIqCDG2Qw9muXBPQWcisIiGE8Hfes2wM+s2Ki4uNjcHSihasUME+Ue0ZP5nxyHKSzzzzDPr162fm7G9/+9uqniKCikQNETKs9DIaH2+88QZOPPFEk73Bpn/BWFTy/Q8ePGgEDqvpGY0JGhvcfvTRRzfWtmUKLheGbJLGBwfu1uDATccQo/KV6RFeuHDnZMqFezCb3JIqZwWqaiqQastAZrr/2UM0XFkqihEKbcHoQfbCoNF7uPejgGOVQrOckt7QoJ45c6bf+yiEiC/oRNuyZYuJ1iQcE/ng/ESRnqUp/Gno2RYU/plZ+cQTTxhnHIUMBiHwvYUQIprh2Pi3v/3NZJZxXf+9733PRH52NLvXgg4dOmO4JqVIYTmUWYaD9sEJJ5xgttOeYONUZhpbNkZrWEFWLDXakfJCon0lmbj25jnj+QtWoB2pd9WjrL4MNZU16JbTLaDzSjuZgkVbQhivLYp5/tgFCxcubNYI3Rten+x/Gaz7RAgRm/Mn5zf6GzhW0LbgHMfAX85NzBijXdBe6Jf74IMP8Mc//tH8ZOABfYCzZ8+W6C9CgkQNERYYifLkk0+aBxf0NDy+853vBL2HABetjFThZ9Bp3J5oKTqTKIpQHGH0DAdmvhdLDAWr+ao4/DlYtmyZOfacWJkFYWXl+APPn7cTkOeNP5klwd8ZiTBlypRWJ2xGKDDayYpSYDRgW0YKDQde40z7Zl+ZjmJFZlHY6UiqpxAiduA4YmUicuxi5ObhRNJA4Zj6ySef4KmnnsKbb76JY4891hgaJ598sjIYhRAxB0UF1uVmANWnn35qnCe0MTi2BVM04Odwbcl1KAXl9mSGcIynjcI1HteZVoQsA2LkZA4frCLAB+0BBr8xS5xBVP7YjMzcoQ1h2Rh8D8uuYHlkZuxw3m6tvxXP+YYNG4xY5m+ZXX4mM8YnTJjQYbuZn0/hgzbV6NGjJawJkSDQp0VRnr4yjlEUdoNdAptz27PPPmsCpjjXXXfddbj22msbg4yFCBUSNURYYYQIy1xwsGN0yllnnWWMj+OPPz6qF1Y0ZNjzgUaSCC9c+DNTgpFKVlNta2Fu9VRhVIG3MUIDlxMre63QWKDhwMmcxgANDRqR27dvx9ixY1v8TKv8AKOZKKpY783PpHHBfeI1wfck/HcKL9yXQK5j7gdLyfC7WQ/uq2Xo8vupBIwQ8QsdZRRQOV4RljahgyvY5UsoltDQYPNvjjkMKuDcyzFOCCHiATqLaV8wg4OOaiuAiuNqtMK13n//+19TGolrVhE+uM5nQBKdb1a5Ygsro4N99LwD2piV8+GHH5q1Pm0KnjNr7c5GuhS8GJTVWplZfua7775r7A+WrPV2KvLfWFmAghcfVhUCCi4UPwK9Pvid+N1ot3Af+ZPfkQ/uLwOnaOMIIeITjiebNm0y/jcGctIHQuE1mHCc+uijj0yw1FtvvYUjjzzSzL3nnHOOgoFF2JCoISLGxo0bjYOFxgcXahwAr7zyyrBFK3FRRyeP1ZyaJafolOYCk/9mLUY5WDMah85lZWpEFzx3NDCsEi3WOeN5ohHA80gHnlXrmOeZQgQX9jzf7L/SGnwdhQ8uCLwNHQoNNAL490zTbA3+DcUYGky8hnzTwK1GhTS8ef3zfbmfahwuROLAaFGWmaIREGwhw0r/ppOPhgZLMjJiisEEmsuEEPEKs2yZiUYny+eff44zzjjDjH0sGRWuAKqvv/7aZMUR7wAcYtkYVgAL/421xkX0wPPCYIM9e/aY68man3n9cO1Pm4IRzzyH1tqdogSFBD6Y4d1aDwz+DasB7Ny5s0mwlnd5ycOVJ6ONwrUD7SBL/PB+f0I7iCILbQzLzlC/FyESh1deecXMfx0pJdUaHButrAyOQ/ThXX311RgyZEjQP0uIwyFRQ0QcLhbpcKHxwdTx0047zURWsYxPuBy8Vlq57wKTi0BGsoZiMhChwYpAsgwLRilR2OA5thb6NBSYeskoq8NFLDC6gX0weH3w/cjhjAIaMhRDmNrJBpJ8LkNCCOELHW4zZswI6lxHR8fzzz9vjA2OWeyT8d3vfrfVRqRCCBGvMErVCqCik5f2xeWXX46BAweGbR8YSEP7gtH31lqQP7k+ZJaviB0oIFBQ4NxKRx7X+t5ZHjyvvM5oXzD4qa3m3vwb/j3tC+/MDH/sBdo0LB9F0cLfvxFCJA4MrKR4yoysYPrsWA2D9gUzzljBhAEDFE4ULCUiiUQNEVWwaREHSjpkuFi85JJLcMUVV2D8+PERWbAxCmfdunVm8Thp0iTVHI8TeD6Z3cEoLJ5jGhJMMWeknBX9RDGEcJJmCjpFEH8bl1MY43tqghdCtAXnFzq8jjjiiA7NLxyvGJH13HPPYd68eTjllFPM3ElDQ9lfQohEh8Eub7/9trEx3n//fdN0mWPk+eef3+gYDidcI7L0IMUORrZK3IgfOKfTvqBYQXuAtgD7MtKGYHSzlcFNu5bZ2rQxmAHuTxYRhRDaKf7aI0KIxIQCBO0BihoUWNsLxxyW1KN98cILL5iAXwYGMECAwZtCRAMSNURUwsXeZ599ZgZQOmo4aHLwpMgRidq4dHxzQGdUVSjqnYvIYjX6poFJQ4EGJlPAhRAi1DCT7JtvvjHzDBuBdurUKaCm33TSvfbaa6ZWLp103/72tzvcTFQIIeIVOpzpnOHYyT4c5513nhk7g91c3F/WrFljhGmK23JWxx8UIXiOmdFBO9LK4BZCiFD7Nxg8xUwwzm3Tpk0LqBffP//5T+OLY9AxAwDoi2P/p2jugysSE4kaIuqho+fVV181gyqbix933HHG8GBtXC4MObBu3brVOIIY7UIHEfss+MISUoy25+toNLRHmGBElfXeVj1cPhiFQycS39eqtaoFqxBCCH+zx/73v/+ZOYpRnqNGjWqsz83nnE843zGieMmSJfj3v/+NF1980URiWRmN7PskhBDCf1asWGHsi3/84x9m7c7mpsziYKNn9jVg3yMKyLQvGO3KACdfrIh7jt+MuG/P+p9jOR3f/GlhlTTi+zLQhvYFH8y+U3CVEEIIf1i/fr0J3mSWBecP2hO0LWhjUHTl3MZsQfrcXn/9dWNjfPzxx5g9e7YRMs4999ygNxgXIphI1BAxBSPp6cj517/+ZSJbjznmGCNusJ4fDQGm+nJwZrkqX7idJYf44EDeGhzw6ThiQzWKFTRy+NMyIOh8onDC9+DEwAcNHtZF5b+xBwM/q6XGbYzEoqOKooz6dAghhPCFcwlr4XIOolDOn6tWrcJf//pXU8uWEVdnn302LrroIsyZM0flpYQQIgjlqT744APjzKFThyLGySefjG9961uYMmWKsS/4oDPINxPOsgFoX3j3RvCFjaBpX/BBG8CyL7jdshPoZKKNwd8tG4O2A+0K2g/86S18WHAb+79REGcvQCGEEKKlPlMMCLZsDM4r9K29/PLLplwV55CLL74YF154Ifr3768DKGICiRoiZmE6nSVwMJLqzDPPNKlxJ510khEm2gONCIoVNCD4kwIFDQU+rIgpRmAxYoqTAScC/huNIevRkphBQYQPGjKM5mIJLaXuCSGEaG0uonBP5xrnOZZIYX8MChnsl6ESJUIIERooLFBApsDBPhwUCShuMItj5MiR7c6SoI3ASFjaGLQvrAcDoiw4ttNO4Gd42xa0NWhztAbFEas/gyJqhRBCtAb71v73v/81Jd7feust9O3b1wgZnOeGDx+uAydiDokaIi6cPytXrjTGB+uKb9++3USvMpKVTiAu8IUQQohoho6tr776Cm+88YZ5sNwh5zJGS1G0j0QzWyGESGQoQNDp89JLL5kSgXT+0L7ggyWqFKAkhBAi2jlw4IAR6WlfMCuRWRgU6hksxX5OKmkoYhmJGiLuWLt2baNTaPHixaY2LsUNZnCMHTtWg7YQQoiooLi42DT7ZlQwHWeMyOV8RYfZiSeeaDIChRBCREd0K51BtC/oHGJpKArOzJ5jWVn20xBCCCGiJeiXYjznK5aWmjx5cqMoP2LEiEjvohBBQ6KGiGvY1JuOonfeeQeffvqpSclm5Kv1YFMkIYQQIhxQtFi4cKExMugc4+/Dhg0zovtZZ51lmvJZ9dWFEEJ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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:00:21\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0621, sigma²=177.3389, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7810.95 | 88.3796 | 58.7113 | -0.2346 | 0.40s |\n", - "| OLS_sphere | 500 | 381.706 | 19.5373 | 12.4554 | 0.9397 | 0.07s |\n", - "| DeepKriging_wendland | -- | 5227.85 | 72.3038 | 55.5825 | 0.1737 | 51.06s |\n", - "| DeepKriging_sphere | 50 | 103.483 | 10.1726 | 5.07 | 0.9836 | 38.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 167.014 | 12.9234 | 5.6758 | 0.9736 | 40.92s |\n", - "| UniversalKriging | 500 | 199.177 | 14.113 | 7.9103 | 0.9685 | 4.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:02:58\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0709, sigma²=223.6737, nugget=0.0028\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8446.07 | 91.9025 | 60.2664 | -0.1797 | 0.40s |\n", - "| OLS_sphere | 500 | 341.513 | 18.4801 | 11.6416 | 0.9523 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5589.49 | 74.7629 | 50.1417 | 0.2193 | 48.69s |\n", - "| DeepKriging_sphere | 50 | 62.59 | 7.9114 | 4.8097 | 0.9913 | 37.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 72.8595 | 8.5358 | 4.3625 | 0.9898 | 38.84s |\n", - "| UniversalKriging | 500 | 150.282 | 12.259 | 6.8854 | 0.979 | 4.43s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:05:30\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0602, sigma²=182.9708, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 6465.35 | 80.4074 | 59.4881 | 0.0131 | 0.49s |\n", - "| OLS_sphere | 500 | 515.48 | 22.7042 | 13.5413 | 0.9213 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7414.01 | 86.1047 | 52.3249 | -0.1317 | 53.84s |\n", - "| DeepKriging_sphere | 50 | 89.4065 | 9.4555 | 5.5993 | 0.9864 | 42.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 95.6897 | 9.7821 | 4.6663 | 0.9854 | 42.28s |\n", - "| UniversalKriging | 500 | 281.13 | 16.7669 | 8.5084 | 0.9571 | 3.71s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:08:16\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6175 (1.6158), Variance: 6527.2144, Range: [-209.7956, 205.1870]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0595, sigma²=180.7371, nugget=0.0013\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25850.6 | 160.781 | 65.953 | -2.962 | 0.45s |\n", - "| OLS_sphere | 500 | 385.647 | 19.6379 | 11.9214 | 0.9409 | 0.07s |\n", - "| DeepKriging_wendland | -- | 6827.6 | 82.6293 | 47.1242 | -0.0464 | 49.58s |\n", - "| DeepKriging_sphere | 50 | 89.9831 | 9.4859 | 5.9727 | 0.9862 | 38.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 93.9197 | 9.6912 | 5.6297 | 0.9856 | 37.93s |\n", - "| UniversalKriging | 500 | 250.464 | 15.8261 | 8.1963 | 0.9616 | 5.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:10:49\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.9645 (1.6405), Variance: 6728.1055, Range: [-206.4400, 204.6959]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0592, sigma²=175.1345, nugget=0.1692\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25662.7 | 160.196 | 69.8785 | -2.7324 | 0.45s |\n", - "| OLS_sphere | 500 | 278.315 | 16.6828 | 10.6318 | 0.9595 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4620.04 | 67.9709 | 48.0224 | 0.3281 | 53.06s |\n", - "| DeepKriging_sphere | 50 | 26.8175 | 5.1786 | 3.2262 | 0.9961 | 39.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 46.1532 | 6.7936 | 4.0742 | 0.9933 | 38.82s |\n", - "| UniversalKriging | 500 | 96.9091 | 9.8442 | 6.1416 | 0.9859 | 6.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:13:30\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.0328 (1.6395), Variance: 6719.8687, Range: [-213.9537, 205.0478]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0744, sigma²=253.7382, nugget=0.1181\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9380.13 | 96.8511 | 61.069 | -0.408 | 0.39s |\n", - "| OLS_sphere | 500 | 273.664 | 16.5428 | 10.413 | 0.9589 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4085.11 | 63.9148 | 46.0216 | 0.3868 | 54.45s |\n", - "| DeepKriging_sphere | 50 | 40.7923 | 6.3869 | 4.0504 | 0.9939 | 43.31s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.0752 | 7.3536 | 4.3336 | 0.9919 | 37.72s |\n", - "| UniversalKriging | 500 | 102.296 | 10.1141 | 5.8995 | 0.9846 | 3.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:16:08\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.2577 (1.5934), Variance: 6347.5322, Range: [-193.8297, 206.9203]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0549, sigma²=158.4430, nugget=0.0016\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4527.15 | 67.2841 | 52.8862 | 0.2363 | 0.42s |\n", - "| OLS_sphere | 500 | 389.598 | 19.7382 | 12.3285 | 0.9343 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3683.2 | 60.6894 | 44.6958 | 0.3787 | 52.68s |\n", - "| DeepKriging_sphere | 50 | 85.9304 | 9.2699 | 5.5519 | 0.9855 | 37.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 89.9541 | 9.4844 | 5.2738 | 0.9848 | 41.83s |\n", - "| UniversalKriging | 500 | 258.471 | 16.077 | 9.1177 | 0.9564 | 8.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:18:55\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.8672 (1.6373), Variance: 6702.1040, Range: [-213.2814, 206.2566]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0615, sigma²=182.5137, nugget=0.1034\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4703.88 | 68.5848 | 54.8266 | 0.1706 | 0.35s |\n", - "| OLS_sphere | 500 | 275.1 | 16.5861 | 10.5799 | 0.9515 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3475.23 | 58.9511 | 44.0211 | 0.3872 | 52.12s |\n", - "| DeepKriging_sphere | 50 | 69.5888 | 8.342 | 4.3132 | 0.9877 | 36.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 72.8806 | 8.537 | 4.3948 | 0.9871 | 42.85s |\n", - "| UniversalKriging | 500 | 143.687 | 11.987 | 6.9301 | 0.9747 | 3.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:21:37\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2464 (1.5728), Variance: 6184.4497, Range: [-213.3035, 205.0131]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0677, sigma²=200.0723, nugget=0.1463\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4529.19 | 67.2993 | 52.9151 | 0.2262 | 0.42s |\n", - "| OLS_sphere | 500 | 418.391 | 20.4546 | 12.1791 | 0.9285 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3763.78 | 61.3497 | 44.2777 | 0.3569 | 50.83s |\n", - "| DeepKriging_sphere | 50 | 56.2152 | 7.4977 | 4.259 | 0.9904 | 40.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 58.3302 | 7.6374 | 4.3634 | 0.99 | 38.68s |\n", - "| UniversalKriging | 500 | 194.875 | 13.9598 | 7.5994 | 0.9667 | 3.78s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:24:23\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.7404 (1.6331), Variance: 6667.8887, Range: [-209.9314, 203.9581]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0571, sigma²=206.5474, nugget=0.0166\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6210.4 | 78.8061 | 54.9743 | 0.069 | 0.47s |\n", - "| OLS_sphere | 500 | 244.226 | 15.6277 | 10.6805 | 0.9634 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3581.36 | 59.8445 | 41.3392 | 0.4631 | 52.88s |\n", - "| DeepKriging_sphere | 50 | 39.4689 | 6.2824 | 4.1927 | 0.9941 | 36.61s |\n", - "| DeepKriging_sphere_Huber | 50 | 69.6024 | 8.3428 | 4.9367 | 0.9896 | 35.23s |\n", - "| UniversalKriging | 500 | 101.387 | 10.0691 | 6.2277 | 0.9848 | 4.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:27:05\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.8317 (1.6021), Variance: 6416.8169, Range: [-213.4137, 202.3162]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0687, sigma²=193.6669, nugget=0.0007\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10873.2 | 104.275 | 60.2179 | -0.706 | 0.43s |\n", - "| OLS_sphere | 500 | 355.423 | 18.8527 | 11.1298 | 0.9442 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3259.41 | 57.0913 | 40.5705 | 0.4886 | 55.68s |\n", - "| DeepKriging_sphere | 50 | 35.3613 | 5.9465 | 3.5203 | 0.9945 | 37.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.2722 | 8.017 | 4.3365 | 0.9899 | 36.75s |\n", - "| UniversalKriging | 500 | 174.329 | 13.2034 | 7.2182 | 0.9726 | 7.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:29:54\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.7707 (1.5807), Variance: 6246.6694, Range: [-211.4668, 202.6113]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0548, sigma²=171.9504, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|----------|-----------|--------|\n", - "| OLS_wendland | -- | 642505 | 801.564 | 145.702 | -103.767 | 0.42s |\n", - "| OLS_sphere | 500 | 358.81 | 18.9423 | 12.2176 | 0.9415 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4486.17 | 66.9789 | 51.2613 | 0.2685 | 48.60s |\n", - "| DeepKriging_sphere | 50 | 96.7037 | 9.8338 | 5.3134 | 0.9842 | 40.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 110.9 | 10.5309 | 5.9518 | 0.9819 | 36.72s |\n", - "| UniversalKriging | 500 | 207.798 | 14.4152 | 8.0433 | 0.9661 | 5.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:32:35\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.7825 (1.6295), Variance: 6637.9307, Range: [-205.2383, 204.2435]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0520, sigma²=183.0774, nugget=0.0196\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 335469 | 579.197 | 95.8696 | -48.5007 | 0.47s |\n", - "| OLS_sphere | 500 | 234.92 | 15.3271 | 9.4777 | 0.9653 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3757.86 | 61.3014 | 45.605 | 0.4455 | 52.22s |\n", - "| DeepKriging_sphere | 50 | 43.4272 | 6.5899 | 3.9887 | 0.9936 | 33.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 43.7624 | 6.6153 | 3.751 | 0.9935 | 44.03s |\n", - "| UniversalKriging | 500 | 106.416 | 10.3158 | 5.8567 | 0.9843 | 2.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:35:25\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7531 (1.5943), Variance: 6354.0967, Range: [-212.4267, 204.4545]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0541, sigma²=197.3472, nugget=0.0038\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15060.7 | 122.722 | 65.5616 | -1.6208 | 0.50s |\n", - "| OLS_sphere | 500 | 304.557 | 17.4516 | 11.2778 | 0.947 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3262.82 | 57.1211 | 43.9858 | 0.4322 | 52.41s |\n", - "| DeepKriging_sphere | 50 | 49.5101 | 7.0363 | 4.2414 | 0.9914 | 38.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 70.8583 | 8.4177 | 4.2899 | 0.9877 | 35.33s |\n", - "| UniversalKriging | 500 | 174.021 | 13.1917 | 7.4643 | 0.9697 | 7.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:38:13\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.3852 (1.6051), Variance: 6440.5127, Range: [-213.6311, 206.5967]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0569, sigma²=182.2583, nugget=0.0015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5800.7 | 76.1623 | 57.4934 | -0.0138 | 0.42s |\n", - "| OLS_sphere | 500 | 477.091 | 21.8424 | 13.407 | 0.9166 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4327.35 | 65.7826 | 46.5443 | 0.2437 | 49.95s |\n", - "| DeepKriging_sphere | 50 | 81.0736 | 9.0041 | 4.6097 | 0.9858 | 40.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 94.5344 | 9.7229 | 5.0888 | 0.9835 | 43.06s |\n", - "| UniversalKriging | 500 | 291.085 | 17.0612 | 9.3857 | 0.9491 | 8.07s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:41:08\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.5094 (1.6194), Variance: 6556.0034, Range: [-213.5274, 204.3522]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0787, sigma²=241.1883, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 38449.8 | 196.086 | 75.0308 | -4.1849 | 0.49s |\n", - "| OLS_sphere | 500 | 415.436 | 20.3823 | 12.6596 | 0.944 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4189.06 | 64.7229 | 49.2123 | 0.4351 | 53.41s |\n", - "| DeepKriging_sphere | 50 | 145.252 | 12.0521 | 5.917 | 0.9804 | 35.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 153.965 | 12.4083 | 5.9499 | 0.9792 | 34.90s |\n", - "| UniversalKriging | 500 | 186.264 | 13.6478 | 7.559 | 0.9749 | 4.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:43:51\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.2342 (1.6400), Variance: 6724.3286, Range: [-208.6201, 203.6964]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0695, sigma²=209.7583, nugget=0.0018\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 14162.1 | 119.005 | 68.6103 | -1.3172 | 0.39s |\n", - "| OLS_sphere | 500 | 358.927 | 18.9454 | 11.2801 | 0.9413 | 0.08s |\n", - "| DeepKriging_wendland | -- | 9023.64 | 94.9928 | 50.8138 | -0.4765 | 49.65s |\n", - "| DeepKriging_sphere | 50 | 80.6278 | 8.9793 | 5.0127 | 0.9868 | 34.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 74.8534 | 8.6518 | 4.5537 | 0.9878 | 36.60s |\n", - "| UniversalKriging | 500 | 166.951 | 12.9209 | 6.6967 | 0.9727 | 6.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:46:41\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.2790 (1.6210), Variance: 6569.0591, Range: [-215.0015, 203.4196]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0704, sigma²=208.8158, nugget=0.0015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5499.97 | 74.1618 | 55.8482 | 0.2002 | 0.41s |\n", - "| OLS_sphere | 500 | 281.356 | 16.7737 | 10.7755 | 0.9591 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4092.82 | 63.9752 | 45.5781 | 0.4048 | 52.31s |\n", - "| DeepKriging_sphere | 50 | 38.4969 | 6.2046 | 3.9331 | 0.9944 | 39.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 43.6876 | 6.6097 | 3.7995 | 0.9936 | 42.12s |\n", - "| UniversalKriging | 500 | 137.956 | 11.7455 | 6.5671 | 0.9799 | 5.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:49:38\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.1620 (1.6099), Variance: 6479.7378, Range: [-213.2246, 205.0493]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0532, sigma²=196.7699, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10516.3 | 102.549 | 59.6707 | -0.6717 | 0.42s |\n", - "| OLS_sphere | 500 | 227.562 | 15.0852 | 9.8484 | 0.9638 | 0.07s |\n", - "| DeepKriging_wendland | -- | 5106.85 | 71.4622 | 42.528 | 0.1882 | 52.77s |\n", - "| DeepKriging_sphere | 50 | 40.9857 | 6.402 | 4.1658 | 0.9935 | 39.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 42.3656 | 6.5089 | 4.1173 | 0.9933 | 37.23s |\n", - "| UniversalKriging | 500 | 91.3544 | 9.5579 | 5.6303 | 0.9855 | 7.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:52:30\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.6292 (1.6380), Variance: 6707.4985, Range: [-214.6765, 205.8479]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0640, sigma²=183.5553, nugget=0.0017\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 40706.6 | 201.759 | 63.9411 | -5.482 | 0.44s |\n", - "| OLS_sphere | 500 | 320.253 | 17.8956 | 10.7238 | 0.949 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3983.6 | 63.1158 | 47.7183 | 0.3657 | 55.13s |\n", - "| DeepKriging_sphere | 50 | 121.708 | 11.0322 | 5.8413 | 0.9806 | 36.45s |\n", - "| DeepKriging_sphere_Huber | 50 | 114.63 | 10.7066 | 4.4817 | 0.9817 | 42.07s |\n", - "| UniversalKriging | 500 | 175.703 | 13.2553 | 6.9666 | 0.972 | 5.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:55:29\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.1540 (1.6253), Variance: 6603.7393, Range: [-212.9704, 206.5339]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0576, sigma²=172.1535, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 17840 | 133.567 | 67.9295 | -1.6905 | 0.43s |\n", - "| OLS_sphere | 500 | 253.464 | 15.9206 | 10.1372 | 0.9618 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5062.87 | 71.1539 | 48.1032 | 0.2364 | 53.15s |\n", - "| DeepKriging_sphere | 50 | 62.8848 | 7.93 | 4.3578 | 0.9905 | 40.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.3912 | 7.375 | 4.4062 | 0.9918 | 51.59s |\n", - "| UniversalKriging | 500 | 106.527 | 10.3212 | 6.3052 | 0.9839 | 6.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 06:58:45\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.1046 (1.6040), Variance: 6432.4102, Range: [-204.2676, 206.8854]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=115.4380, nugget=54.9373\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5040.36 | 70.9955 | 56.4294 | 0.2036 | 0.41s |\n", - "| OLS_sphere | 500 | 287.368 | 16.9519 | 11.278 | 0.9546 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3863.62 | 62.1581 | 47.3541 | 0.3895 | 52.40s |\n", - "| DeepKriging_sphere | 50 | 80.817 | 8.9898 | 5.4477 | 0.9872 | 45.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 230.446 | 15.1804 | 6.172 | 0.9636 | 38.02s |\n", - "| UniversalKriging | 500 | 287.673 | 16.9609 | 11.2807 | 0.9545 | 1.16s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:01:48\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.3727 (1.6088), Variance: 6470.5322, Range: [-205.0407, 202.1517]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0557, sigma²=172.4160, nugget=0.0058\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 73575 | 271.247 | 78.7006 | -10.7735 | 0.37s |\n", - "| OLS_sphere | 500 | 316.191 | 17.7818 | 11.2297 | 0.9494 | 0.09s |\n", - "| DeepKriging_wendland | -- | 8235.43 | 90.7493 | 48.4986 | -0.3178 | 54.90s |\n", - "| DeepKriging_sphere | 50 | 33.5809 | 5.7949 | 3.6783 | 0.9946 | 39.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 37.7867 | 6.1471 | 3.7492 | 0.994 | 39.95s |\n", - "| UniversalKriging | 500 | 186.518 | 13.6572 | 6.9254 | 0.9702 | 6.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:04:53\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.7215 (1.6056), Variance: 6444.8770, Range: [-213.1257, 203.9100]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0625, sigma²=209.4600, nugget=0.0139\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5247.3 | 72.4383 | 57.2562 | 0.237 | 0.41s |\n", - "| OLS_sphere | 500 | 357.886 | 18.9179 | 11.3023 | 0.948 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4635.25 | 68.0827 | 51.8408 | 0.326 | 46.49s |\n", - "| DeepKriging_sphere | 50 | 168.62 | 12.9854 | 6.0103 | 0.9755 | 39.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 151.341 | 12.3021 | 5.6872 | 0.978 | 37.10s |\n", - "| UniversalKriging | 500 | 187.692 | 13.7001 | 7.1836 | 0.9727 | 4.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:07:45\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6376 (1.6446), Variance: 6761.5942, Range: [-211.8297, 206.1259]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0016, sigma²=53.4845, nugget=114.0700\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9894.59 | 99.4716 | 61.9282 | -0.3583 | 0.45s |\n", - "| OLS_sphere | 500 | 343.254 | 18.5271 | 12.1757 | 0.9529 | 0.07s |\n", - "| DeepKriging_wendland | -- | 5117.02 | 71.5334 | 51.7279 | 0.2976 | 51.05s |\n", - "| DeepKriging_sphere | 50 | 51.6763 | 7.1886 | 4.0977 | 0.9929 | 44.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 60.1158 | 7.7534 | 4.2995 | 0.9917 | 45.84s |\n", - "| UniversalKriging | 500 | 343.275 | 18.5277 | 12.1757 | 0.9529 | 1.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:10:54\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.4394 (1.6160), Variance: 6528.4375, Range: [-208.1850, 205.0659]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0558, sigma²=176.6405, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5433.93 | 73.7152 | 57.5509 | 0.2049 | 0.45s |\n", - "| OLS_sphere | 500 | 338.267 | 18.392 | 11.2133 | 0.9505 | 0.10s |\n", - "| DeepKriging_wendland | -- | 5036.15 | 70.9658 | 47.7453 | 0.2631 | 53.20s |\n", - "| DeepKriging_sphere | 50 | 140.649 | 11.8595 | 6.3263 | 0.9794 | 33.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.5221 | 7.7151 | 3.9863 | 0.9913 | 36.66s |\n", - "| UniversalKriging | 500 | 213.244 | 14.6029 | 7.4892 | 0.9688 | 4.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:13:52\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.0742 (1.5787), Variance: 6230.7856, Range: [-209.3389, 203.6537]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0589, sigma²=184.9165, nugget=0.0019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5830.11 | 76.3552 | 56.0138 | 0.1016 | 0.41s |\n", - "| OLS_sphere | 500 | 445.546 | 21.108 | 12.9898 | 0.9313 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3705.54 | 60.8732 | 43.4885 | 0.429 | 52.77s |\n", - "| DeepKriging_sphere | 50 | 87.1087 | 9.3332 | 4.582 | 0.9866 | 37.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 100.792 | 10.0395 | 4.44 | 0.9845 | 41.03s |\n", - "| UniversalKriging | 500 | 265.498 | 16.2941 | 8.5768 | 0.9591 | 7.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:17:03\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.3946 (1.6330), Variance: 6666.4321, Range: [-212.8773, 204.5095]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0531, sigma²=188.4361, nugget=0.0364\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10891.5 | 104.362 | 63.8236 | -0.6878 | 0.39s |\n", - "| OLS_sphere | 500 | 348.189 | 18.6598 | 12.4186 | 0.946 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4562.22 | 67.5442 | 49.681 | 0.293 | 48.02s |\n", - "| DeepKriging_sphere | 50 | 62.45 | 7.9025 | 5.2561 | 0.9903 | 34.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 61.1949 | 7.8227 | 4.5665 | 0.9905 | 37.65s |\n", - "| UniversalKriging | 500 | 184.153 | 13.5703 | 8.083 | 0.9715 | 5.71s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:20:00\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.9176 (1.6363), Variance: 6693.3193, Range: [-213.0331, 205.5877]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0610, sigma²=188.4106, nugget=0.0016\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7575.43 | 87.0369 | 61.655 | 0.016 | 0.45s |\n", - "| OLS_sphere | 500 | 280.101 | 16.7362 | 10.8703 | 0.9636 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4094.42 | 63.9877 | 46.7198 | 0.4682 | 51.95s |\n", - "| DeepKriging_sphere | 50 | 54.9597 | 7.4135 | 4.0491 | 0.9929 | 38.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 46.7705 | 6.8389 | 3.6315 | 0.9939 | 43.68s |\n", - "| UniversalKriging | 500 | 129.697 | 11.3885 | 6.4879 | 0.9832 | 9.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:23:17\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5136 (1.6337), Variance: 6672.2979, Range: [-207.8768, 205.5624]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0597, sigma²=182.4537, nugget=0.0028\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6089.39 | 78.0345 | 60.8512 | 0.1852 | 0.38s |\n", - "| OLS_sphere | 500 | 282.177 | 16.7981 | 10.8261 | 0.9622 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5305.38 | 72.8381 | 56.3005 | 0.2901 | 52.96s |\n", - "| DeepKriging_sphere | 50 | 73.0085 | 8.5445 | 4.1177 | 0.9902 | 38.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.3525 | 8.0841 | 4.0385 | 0.9913 | 44.83s |\n", - "| UniversalKriging | 500 | 156.943 | 12.5277 | 6.9289 | 0.979 | 8.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:26:35\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.3084 (1.6258), Variance: 6607.7876, Range: [-213.9850, 205.5705]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0541, sigma²=161.3896, nugget=0.0044\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4774.38 | 69.0969 | 53.9581 | 0.2698 | 0.42s |\n", - "| OLS_sphere | 500 | 399.946 | 19.9987 | 11.9153 | 0.9388 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4005.03 | 63.2853 | 46.0403 | 0.3874 | 53.80s |\n", - "| DeepKriging_sphere | 50 | 96.53 | 9.825 | 4.9482 | 0.9852 | 40.22s |\n", - "| DeepKriging_sphere_Huber | 50 | 131.782 | 11.4796 | 5.7772 | 0.9798 | 43.21s |\n", - "| UniversalKriging | 500 | 271.765 | 16.4853 | 8.0478 | 0.9584 | 7.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:29:54\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5793 (1.5943), Variance: 6354.5825, Range: [-195.4052, 205.5495]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0670, sigma²=214.2001, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5554.7 | 74.5299 | 56.3301 | 0.1914 | 0.40s |\n", - "| OLS_sphere | 500 | 311.024 | 17.6359 | 11.6577 | 0.9547 | 0.06s |\n", - "| DeepKriging_wendland | -- | 6007.66 | 77.5091 | 47.1804 | 0.1254 | 50.97s |\n", - "| DeepKriging_sphere | 50 | 41.2696 | 6.4241 | 3.7096 | 0.994 | 42.90s |\n", - "| DeepKriging_sphere_Huber | 50 | 40.4612 | 6.3609 | 3.3964 | 0.9941 | 45.01s |\n", - "| UniversalKriging | 500 | 125.314 | 11.1944 | 6.7201 | 0.9818 | 5.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:33:14\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4423 (1.6179), Variance: 6543.6118, Range: [-203.1745, 204.3926]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0779, sigma²=225.0233, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5323.46 | 72.9621 | 56.877 | 0.1144 | 0.41s |\n", - "| OLS_sphere | 500 | 315.28 | 17.7561 | 11.7617 | 0.9475 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3580.7 | 59.8389 | 47.16 | 0.4043 | 60.07s |\n", - "| DeepKriging_sphere | 50 | 45.9115 | 6.7758 | 4.307 | 0.9924 | 45.63s |\n", - "| DeepKriging_sphere_Huber | 50 | 73.2479 | 8.5585 | 5.1037 | 0.9878 | 47.72s |\n", - "| UniversalKriging | 500 | 127.218 | 11.2791 | 6.67 | 0.9788 | 5.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:36:51\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4683 (1.6400), Variance: 6723.7222, Range: [-212.8139, 205.7783]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0654, sigma²=207.5731, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4217.08 | 64.939 | 50.1491 | 0.3165 | 0.48s |\n", - "| OLS_sphere | 500 | 258.241 | 16.0699 | 10.5226 | 0.9581 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3158.7 | 56.2023 | 39.2977 | 0.4881 | 66.50s |\n", - "| DeepKriging_sphere | 50 | 25.9921 | 5.0982 | 3.2843 | 0.9958 | 42.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 28.7301 | 5.36 | 3.3287 | 0.9953 | 46.81s |\n", - "| UniversalKriging | 500 | 91.3469 | 9.5576 | 5.6831 | 0.9852 | 4.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:40:29\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.6203 (1.6101), Variance: 6480.7422, Range: [-214.8754, 205.1001]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0635, sigma²=147.2485, nugget=0.0835\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 148641 | 385.54 | 84.2597 | -20.8214 | 0.37s |\n", - "| OLS_sphere | 500 | 305.867 | 17.4891 | 11.5304 | 0.9551 | 0.07s |\n", - "| DeepKriging_wendland | -- | 9875.21 | 99.3741 | 52.3362 | -0.4497 | 62.87s |\n", - "| DeepKriging_sphere | 50 | 47.2391 | 6.8731 | 4.5542 | 0.9931 | 37.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 147.612 | 12.1496 | 5.7133 | 0.9783 | 47.62s |\n", - "| UniversalKriging | 500 | 169.246 | 13.0095 | 7.4967 | 0.9752 | 6.43s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:44:03\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.7464 (1.6258), Variance: 6608.4390, Range: [-200.4412, 205.8013]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0449, sigma²=184.1349, nugget=0.0022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5572.09 | 74.6465 | 58.792 | 0.1691 | 0.37s |\n", - "| OLS_sphere | 500 | 381.631 | 19.5354 | 12.2888 | 0.9431 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4138.62 | 64.3321 | 46.2737 | 0.3829 | 58.89s |\n", - "| DeepKriging_sphere | 50 | 63.662 | 7.9788 | 4.8222 | 0.9905 | 48.37s |\n", - "| DeepKriging_sphere_Huber | 50 | 129.429 | 11.3767 | 5.5493 | 0.9807 | 41.86s |\n", - "| UniversalKriging | 500 | 231.541 | 15.2165 | 8.6502 | 0.9655 | 6.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:47:39\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6833 (1.5863), Variance: 6290.8442, Range: [-212.4869, 204.6357]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0711, sigma²=212.0466, nugget=0.0015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 119067 | 345.061 | 77.0487 | -19.3069 | 0.39s |\n", - "| OLS_sphere | 500 | 498.65 | 22.3305 | 12.9536 | 0.915 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4375.01 | 66.1438 | 45.538 | 0.2538 | 66.26s |\n", - "| DeepKriging_sphere | 50 | 67.5028 | 8.216 | 4.1707 | 0.9885 | 47.44s |\n", - "| DeepKriging_sphere_Huber | 50 | 55.9614 | 7.4807 | 4.0418 | 0.9905 | 50.31s |\n", - "| UniversalKriging | 500 | 263.62 | 16.2364 | 8.0687 | 0.955 | 4.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:51:30\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.7999 (1.6001), Variance: 6400.8418, Range: [-213.0022, 202.7321]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0707, sigma²=215.1423, nugget=0.0030\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4336.34 | 65.8509 | 50.904 | 0.2426 | 0.42s |\n", - "| OLS_sphere | 500 | 337.373 | 18.3677 | 11.78 | 0.9411 | 0.06s |\n", - "| DeepKriging_wendland | -- | 2942.59 | 54.2457 | 39.2823 | 0.486 | 63.86s |\n", - "| DeepKriging_sphere | 50 | 70.3905 | 8.3899 | 4.3045 | 0.9877 | 43.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 67.446 | 8.2126 | 4.2168 | 0.9882 | 50.71s |\n", - "| UniversalKriging | 500 | 164.843 | 12.8391 | 6.984 | 0.9712 | 6.24s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:55:16\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.4685 (1.6370), Variance: 6699.0903, Range: [-209.5693, 207.2927]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0016, sigma²=45.8569, nugget=143.0210\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4980.39 | 70.5719 | 56.1854 | 0.2548 | 0.39s |\n", - "| OLS_sphere | 500 | 309.23 | 17.5849 | 11.3114 | 0.9537 | 0.05s |\n", - "| DeepKriging_wendland | -- | 3847.04 | 62.0245 | 46.7089 | 0.4244 | 62.66s |\n", - "| DeepKriging_sphere | 50 | 115.68 | 10.7555 | 6.5897 | 0.9827 | 38.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 78.6632 | 8.8692 | 5.0899 | 0.9882 | 35.92s |\n", - "| UniversalKriging | 500 | 309.17 | 17.5832 | 11.31 | 0.9537 | 1.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 07:58:38\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.9952 (1.6115), Variance: 6492.1343, Range: [-214.9039, 204.2655]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0546, sigma²=190.3350, nugget=0.0218\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11102.6 | 105.369 | 62.8812 | -0.5466 | 0.38s |\n", - "| OLS_sphere | 500 | 325.713 | 18.0475 | 11.1196 | 0.9546 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5077.15 | 71.2541 | 45.7845 | 0.2927 | 64.23s |\n", - "| DeepKriging_sphere | 50 | 24.1123 | 4.9104 | 3.368 | 0.9966 | 45.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 27.6376 | 5.2572 | 3.6334 | 0.9962 | 42.48s |\n", - "| UniversalKriging | 500 | 130.45 | 11.4215 | 6.7329 | 0.9818 | 5.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:02:21\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5562 (1.5995), Variance: 6395.9707, Range: [-207.5052, 203.6785]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0495, sigma²=182.8715, nugget=0.0071\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7964.61 | 89.2447 | 64.9762 | -0.2982 | 0.40s |\n", - "| OLS_sphere | 500 | 304.474 | 17.4492 | 10.9343 | 0.9504 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4628.58 | 68.0337 | 50.0735 | 0.2456 | 63.60s |\n", - "| DeepKriging_sphere | 50 | 60.9606 | 7.8077 | 5.3255 | 0.9901 | 41.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 40.5847 | 6.3706 | 3.6791 | 0.9934 | 42.11s |\n", - "| UniversalKriging | 500 | 171.195 | 13.0842 | 7.4761 | 0.9721 | 7.61s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:06:03\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.1298 (1.6094), Variance: 6475.3687, Range: [-209.5753, 204.1214]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0552, sigma²=165.9968, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7310.17 | 85.4995 | 62.9731 | -0.0558 | 0.43s |\n", - "| OLS_sphere | 500 | 435.116 | 20.8594 | 11.6889 | 0.9372 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5492.69 | 74.1127 | 52.2529 | 0.2067 | 60.23s |\n", - "| DeepKriging_sphere | 50 | 99.6734 | 9.9837 | 5.0551 | 0.9856 | 39.28s |\n", - "| DeepKriging_sphere_Huber | 50 | 87.4786 | 9.353 | 4.5955 | 0.9874 | 43.75s |\n", - "| UniversalKriging | 500 | 284.218 | 16.8588 | 7.7429 | 0.959 | 6.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:09:41\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.6192 (1.6029), Variance: 6423.1338, Range: [-206.6646, 207.4582]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0542, sigma²=178.7079, nugget=0.0103\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5553.25 | 74.5201 | 58.7551 | 0.1797 | 0.42s |\n", - "| OLS_sphere | 500 | 399.88 | 19.997 | 11.4798 | 0.9409 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4508.45 | 67.145 | 49.0947 | 0.334 | 61.03s |\n", - "| DeepKriging_sphere | 50 | 86.3698 | 9.2935 | 4.7221 | 0.9872 | 44.47s |\n", - "| DeepKriging_sphere_Huber | 50 | 79.4414 | 8.913 | 4.3393 | 0.9883 | 49.09s |\n", - "| UniversalKriging | 500 | 255.72 | 15.9912 | 7.7817 | 0.9622 | 8.39s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:13:32\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4212 (1.6116), Variance: 6493.1841, Range: [-213.9658, 204.9198]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0464, sigma²=159.9170, nugget=0.0038\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5398.73 | 73.4761 | 55.973 | 0.1896 | 0.28s |\n", - "| OLS_sphere | 500 | 320.776 | 17.9102 | 10.9102 | 0.9519 | 0.05s |\n", - "| DeepKriging_wendland | -- | 4624.78 | 68.0057 | 48.3854 | 0.3058 | 66.00s |\n", - "| DeepKriging_sphere | 50 | 41.5366 | 6.4449 | 3.8255 | 0.9938 | 43.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 42.8169 | 6.5435 | 3.5457 | 0.9936 | 55.31s |\n", - "| UniversalKriging | 500 | 181.281 | 13.4641 | 7.4587 | 0.9728 | 6.64s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:17:33\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4900 (1.6288), Variance: 6632.0986, Range: [-211.5701, 205.0404]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0744, sigma²=194.9052, nugget=0.0105\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5466.25 | 73.9341 | 58.7023 | 0.0968 | 0.38s |\n", - "| OLS_sphere | 500 | 247.3 | 15.7258 | 9.9925 | 0.9591 | 0.05s |\n", - "| DeepKriging_wendland | -- | 4593.56 | 67.7758 | 47.9253 | 0.241 | 61.35s |\n", - "| DeepKriging_sphere | 50 | 41.5047 | 6.4424 | 4.1068 | 0.9931 | 40.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 107.212 | 10.3543 | 5.1037 | 0.9823 | 50.16s |\n", - "| UniversalKriging | 500 | 98.0367 | 9.9013 | 5.9129 | 0.9838 | 4.07s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:21:20\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.8911 (1.6016), Variance: 6412.9741, Range: [-209.6448, 202.2589]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0585, sigma²=195.7650, nugget=0.0035\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5735.95 | 75.7361 | 58.8929 | 0.1227 | 0.42s |\n", - "| OLS_sphere | 500 | 241.359 | 15.5357 | 10.6156 | 0.9631 | 0.05s |\n", - "| DeepKriging_wendland | -- | 48724.2 | 220.736 | 63.9132 | -6.4525 | 50.13s |\n", - "| DeepKriging_sphere | 50 | 61.8678 | 7.8656 | 4.3828 | 0.9905 | 37.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 67.2791 | 8.2024 | 4.2436 | 0.9897 | 43.74s |\n", - "| UniversalKriging | 500 | 116.189 | 10.7791 | 6.112 | 0.9822 | 6.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:24:50\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.5321 (1.5923), Variance: 6338.7407, Range: [-199.3963, 204.6754]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0014, sigma²=116.6440, nugget=55.4155\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4642.64 | 68.1369 | 50.7486 | 0.2294 | 0.37s |\n", - "| OLS_sphere | 500 | 307.908 | 17.5473 | 11.6719 | 0.9489 | 0.05s |\n", - "| DeepKriging_wendland | -- | 3262.32 | 57.1167 | 39.7982 | 0.4585 | 61.08s |\n", - "| DeepKriging_sphere | 50 | 57.3195 | 7.571 | 4.9962 | 0.9905 | 31.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 30.9023 | 5.559 | 3.5225 | 0.9949 | 41.76s |\n", - "| UniversalKriging | 500 | 307.564 | 17.5375 | 11.6548 | 0.9489 | 1.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:28:18\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9732 (1.6360), Variance: 6691.4902, Range: [-212.6931, 203.7595]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=142.7960, nugget=40.4090\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4701.99 | 68.5711 | 54.0042 | 0.2663 | 0.37s |\n", - "| OLS_sphere | 500 | 415.04 | 20.3725 | 12.6151 | 0.9352 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3430.96 | 58.5744 | 43.7891 | 0.4646 | 62.94s |\n", - "| DeepKriging_sphere | 50 | 36.4176 | 6.0347 | 3.7619 | 0.9943 | 41.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 35.1715 | 5.9306 | 3.7385 | 0.9945 | 49.81s |\n", - "| UniversalKriging | 500 | 414.738 | 20.3651 | 12.6097 | 0.9353 | 1.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:32:07\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9954 (1.5917), Variance: 6333.9272, Range: [-211.4829, 206.6510]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0667, sigma²=197.6412, nugget=0.0022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5068.73 | 71.195 | 55.7855 | 0.1457 | 0.40s |\n", - "| OLS_sphere | 500 | 379.304 | 19.4757 | 11.4376 | 0.9361 | 0.05s |\n", - "| DeepKriging_wendland | -- | 3651.38 | 60.4266 | 43.3067 | 0.3846 | 63.25s |\n", - "| DeepKriging_sphere | 50 | 50.2602 | 7.0894 | 3.6059 | 0.9915 | 43.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 29.6131 | 5.4418 | 3.2733 | 0.995 | 48.43s |\n", - "| UniversalKriging | 500 | 184.093 | 13.5681 | 7.1061 | 0.969 | 5.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-18 08:36:04\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 500}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 34873.76±101310.80 | 130.10±133.97 | 63.01±14.60 | -4.46±16.20 |\n", - "| OLS_sphere | 337.93±67.92 | 18.29±1.82 | 11.46±0.89 | 0.95±0.01 |\n", - "| DeepKriging_wendland | 5553.21±6329.11 | 70.74±23.42 | 47.47±4.46 | 0.15±0.97 |\n", - "| DeepKriging_sphere | 68.46±31.60 | 8.07±1.83 | 4.61±0.81 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 77.40±41.00 | 8.53±2.16 | 4.52±0.77 | 0.99±0.01 |\n", - "| UniversalKriging | 192.56±73.36 | 13.63±2.59 | 7.68±1.63 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 8.07±1.83)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/simulation/ScenarioIII/simulation_eggholder_euclidean.ipynb b/notebook/simulation/ScenarioIII/simulation_eggholder_euclidean.ipynb deleted file mode 100644 index ddae8ca..0000000 --- a/notebook/simulation/ScenarioIII/simulation_eggholder_euclidean.ipynb +++ /dev/null @@ -1,4111 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.014919, - "end_time": "2026-01-19T16:27:12.555889", - "exception": false, - "start_time": "2026-01-19T16:27:12.540970", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:12.581255Z", - "iopub.status.busy": "2026-01-19T16:27:12.580346Z", - "iopub.status.idle": "2026-01-19T16:27:12.605514Z", - "shell.execute_reply": "2026-01-19T16:27:12.601944Z" - }, - "papermill": { - "duration": 0.040627, - "end_time": "2026-01-19T16:27:12.608923", - "exception": false, - "start_time": "2026-01-19T16:27:12.568296", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:12.629851Z", - "iopub.status.busy": "2026-01-19T16:27:12.629117Z", - "iopub.status.idle": "2026-01-19T16:27:15.427099Z", - "shell.execute_reply": "2026-01-19T16:27:15.424259Z" - }, - "papermill": { - "duration": 2.811925, - "end_time": "2026-01-19T16:27:15.429842", - "exception": false, - "start_time": "2026-01-19T16:27:12.617917", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768840033.530680 3622349 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768840033.535597 3622349 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768840033.549349 3622349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768840033.549388 3622349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768840033.549390 3622349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768840033.549391 3622349 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.002597, - "end_time": "2026-01-19T16:27:15.439048", - "exception": false, - "start_time": "2026-01-19T16:27:15.436451", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:15.448304Z", - "iopub.status.busy": "2026-01-19T16:27:15.447104Z", - "iopub.status.idle": "2026-01-19T16:27:16.920782Z", - "shell.execute_reply": "2026-01-19T16:27:16.917463Z" - }, - "papermill": { - "duration": 1.482455, - "end_time": "2026-01-19T16:27:16.923943", - "exception": false, - "start_time": "2026-01-19T16:27:15.441488", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011454, - "end_time": "2026-01-19T16:27:16.948919", - "exception": false, - "start_time": "2026-01-19T16:27:16.937465", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:16.967120Z", - "iopub.status.busy": "2026-01-19T16:27:16.966242Z", - "iopub.status.idle": "2026-01-19T16:27:16.978238Z", - "shell.execute_reply": "2026-01-19T16:27:16.975284Z" - }, - "papermill": { - "duration": 0.02386, - "end_time": "2026-01-19T16:27:16.980625", - "exception": false, - "start_time": "2026-01-19T16:27:16.956765", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006201, - "end_time": "2026-01-19T16:27:16.997512", - "exception": false, - "start_time": "2026-01-19T16:27:16.991311", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:17.010676Z", - "iopub.status.busy": "2026-01-19T16:27:17.009825Z", - "iopub.status.idle": "2026-01-19T16:27:29.608998Z", - "shell.execute_reply": "2026-01-19T16:27:29.605548Z" - }, - "papermill": { - "duration": 12.609524, - "end_time": "2026-01-19T16:27:29.612125", - "exception": false, - "start_time": "2026-01-19T16:27:17.002601", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.002584, - "end_time": "2026-01-19T16:27:29.693212", - "exception": false, - "start_time": "2026-01-19T16:27:29.690628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:29.703905Z", - "iopub.status.busy": "2026-01-19T16:27:29.702552Z", - "iopub.status.idle": "2026-01-19T16:27:29.716063Z", - "shell.execute_reply": "2026-01-19T16:27:29.712953Z" - }, - "papermill": { - "duration": 0.022429, - "end_time": "2026-01-19T16:27:29.718793", - "exception": false, - "start_time": "2026-01-19T16:27:29.696364", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_euclidean(num_sample, seed):\n", - " \"\"\"\n", - " Experiment II : Deterministic Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate uniform points in [0,1] × [0,1] Euclidean space\n", - " x1 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " x2 = rng.uniform(0, 1, num_sample).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term\n", - " # f(x₁, x₂) = -(x₂ + 47) sin(√|x₂ + x₁/2 + 47|) - x₁ sin(√|x₁ - (x₂ + 47)|)\n", - " term1 = -(x2 + 47.0) * np.sin(np.sqrt(np.abs(x2 + x1/2.0 + 47.0)))\n", - " term2 = -x1 * np.sin(np.sqrt(np.abs(x1 - (x2 + 47.0))))\n", - " z = (term1 + term2).astype(np.float32)\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(z):.4f} (±{np.std(z) / np.sqrt(num_sample):.4f}), Variance: {np.var(z, ddof=0):.4f}, Range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " \n", - " return pd.DataFrame({\n", - " \"x1\": x1,\n", - " \"x2\": x2,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.002644, - "end_time": "2026-01-19T16:27:29.727878", - "exception": false, - "start_time": "2026-01-19T16:27:29.725234", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:29.737145Z", - "iopub.status.busy": "2026-01-19T16:27:29.736328Z", - "iopub.status.idle": "2026-01-19T16:27:29.802734Z", - "shell.execute_reply": "2026-01-19T16:27:29.799432Z" - }, - "papermill": { - "duration": 0.0749, - "end_time": "2026-01-19T16:27:29.805598", - "exception": false, - "start_time": "2026-01-19T16:27:29.730698", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"x1\", \"x2\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"x1\"].to_numpy(), data[\"x2\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3a5c9ea6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:29.829480Z", - "iopub.status.busy": "2026-01-19T16:27:29.828556Z", - "iopub.status.idle": "2026-01-19T16:27:29.874390Z", - "shell.execute_reply": "2026-01-19T16:27:29.871069Z" - }, - "papermill": { - "duration": 0.060009, - "end_time": "2026-01-19T16:27:29.877439", - "exception": false, - "start_time": "2026-01-19T16:27:29.817430", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_euclidean_2d(ax, x1, x2, value, vmin, vmax, title, cmap='viridis'):\n", - " sc = ax.scatter(x1, x2, c=value, cmap=cmap, s=15, vmin=vmin, vmax=vmax, alpha=0.7, edgecolors='none')\n", - " \n", - " ax.set_xlabel('x₁', fontsize=10)\n", - " ax.set_ylabel('x₂', fontsize=10)\n", - " ax.set_title(title, fontsize=10, pad=5)\n", - " ax.set_xlim(-0.05, 1.05)\n", - " ax.set_ylim(-0.05, 1.05)\n", - " ax.set_aspect('equal')\n", - " ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\n", - " \n", - " return sc\n", - "\n", - "\n", - "def create_subplot_euclidean(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " ax = fig.add_subplot(*position)\n", - " \n", - " cmap = 'RdBu_r' if plot_type == 'residual' else 'viridis'\n", - " \n", - " sc = plot_euclidean_2d(ax, locations['x1'], locations['x2'], values, vmin, vmax, title, cmap=cmap)\n", - " \n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.08, shrink=0.9)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " cbar.set_label(cbar_label, fontsize=9)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['x1', 'x2']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05, hspace=0.25)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.96])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_euclidean(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=16, fontweight='bold', y=0.98\n", - " )\n", - " plt.subplots_adjust(wspace=0.05)\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.009534, - "end_time": "2026-01-19T16:27:29.900298", - "exception": false, - "start_time": "2026-01-19T16:27:29.890764", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:29.916522Z", - "iopub.status.busy": "2026-01-19T16:27:29.915539Z", - "iopub.status.idle": "2026-01-19T16:27:29.929349Z", - "shell.execute_reply": "2026-01-19T16:27:29.926226Z" - }, - "papermill": { - "duration": 0.025088, - "end_time": "2026-01-19T16:27:29.932241", - "exception": false, - "start_time": "2026-01-19T16:27:29.907153", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T16:27:29.950144Z", - "iopub.status.busy": "2026-01-19T16:27:29.949342Z", - "iopub.status.idle": "2026-01-19T20:43:38.849797Z", - "shell.execute_reply": "2026-01-19T20:43:38.846318Z" - }, - "papermill": { - "duration": 15368.912326, - "end_time": "2026-01-19T20:43:38.852448", - "exception": false, - "start_time": "2026-01-19T16:27:29.940122", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1164 (±0.0227), Variance: 1.2827, Range: [-30.6737, -25.5390]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0000 0.0000 \n", - " 100 0.0000 0.0000 \n", - " 200 0.0000 0.0000 \n", - " 500 0.0000 0.0000 \n", - " 700 0.0000 0.0000 *\n", - " 1000 1.3315 1.2537 \n", - " 1400 1.3315 1.2537 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768840068.378642 3622349 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768840070.824111 3622586 service.cc:152] XLA service 0x78ce1c004de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768840070.824187 3622586 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768840071.215596 3622586 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768840079.439351 3622586 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78ce29ef2560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x78ce2989af80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1310 0.2068 *\n", - " 100 0.2680 0.1818 \n", - " 200 0.3049 0.3066 \n", - " 500 0.6045 0.4026 \n", - " 700 0.6799 0.4229 \n", - " 1000 1.4657 1.3463 \n", - " 1400 1.4818 1.3559 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0097 0.0093 \n", - " 100 0.0120 0.0195 \n", - " 200 0.0075 0.0177 *\n", - " 500 0.0106 0.0229 \n", - " 700 0.0092 0.0184 \n", - " 1000 0.6743 1.2756 \n", - " 1400 0.6693 1.2249 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0487, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0240, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0112, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0008, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1513, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[GPBoost] [Warning] The linear regression covariate data matrix (fixed effect) is rank deficient. This is not necessarily a problem when using gradient descent. If this is not desired, consider dropping some columns / covariates \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5380, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0000 0.0000 \n", - " 100 0.0000 0.0000 \n", - " 200 0.0000 0.0000 \n", - " 500 0.0000 0.0000 *\n", - " 700 0.0000 0.0000 \n", - " 1000 0.0000 0.0000 \n", - " 1400 0.0000 0.0000 \n", - " Best order: 500\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0008, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 0.5799 | 0.7615 | 0.5887 | 0.5359 | 0.42s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 1.8089 | 1.3449 | 0.7372 | -0.4475 | 56.68s |\n", - "| DeepKriging_sphere | 50 | 0.047 | 0.2169 | 0.1213 | 0.9624 | 69.39s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0171 | 0.1308 | 0.0841 | 0.9863 | 88.59s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.73s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 00:49:07\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1401 (±0.0228), Variance: 1.2977, Range: [-30.7513, -25.4807]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0007, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5947 | 0.7712 | 0.5895 | 0.5054 | 0.40s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6529 | 0.808 | 0.5976 | 0.4571 | 61.13s |\n", - "| DeepKriging_sphere | 50 | 0.1124 | 0.3353 | 0.2096 | 0.9065 | 73.62s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.024 | 0.1549 | 0.0855 | 0.98 | 53.83s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 1.55s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 00:52:46\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1128 (±0.0229), Variance: 1.3145, Range: [-30.7376, -25.5249]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0016, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5618 | 0.7495 | 0.5725 | 0.6006 | 0.45s |\n", - "| OLS_sphere | 700 | 0 | 0.0002 | 0.0001 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.9206 | 0.9595 | 0.6014 | 0.3455 | 59.22s |\n", - "| DeepKriging_sphere | 50 | 0.2038 | 0.4515 | 0.2471 | 0.8551 | 75.41s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0273 | 0.1654 | 0.1048 | 0.9806 | 53.84s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.32s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 00:56:32\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1049 (±0.0226), Variance: 1.2765, Range: [-30.6871, -25.5980]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.7284 | 0.8535 | 0.6156 | 0.4762 | 0.50s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.08s |\n", - "| DeepKriging_wendland | -- | 0.6961 | 0.8343 | 0.5682 | 0.4994 | 67.46s |\n", - "| DeepKriging_sphere | 50 | 0.1567 | 0.3959 | 0.2356 | 0.8873 | 100.58s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.013 | 0.114 | 0.0715 | 0.9907 | 78.23s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.18s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:01:11\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1269 (±0.0223), Variance: 1.2447, Range: [-30.7043, -25.5504]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.8807 | 0.9385 | 0.6478 | 0.3931 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5763 | 0.7591 | 0.5392 | 0.6029 | 60.85s |\n", - "| DeepKriging_sphere | 50 | 0.0415 | 0.2038 | 0.1321 | 0.9714 | 114.74s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0208 | 0.1441 | 0.0911 | 0.9857 | 91.93s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 1.67s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:06:13\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1598 (±0.0223), Variance: 1.2467, Range: [-30.6810, -25.5124]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0007, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.1153 | 1.0561 | 0.6575 | 0.0947 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5486 | 0.7407 | 0.558 | 0.5546 | 69.59s |\n", - "| DeepKriging_sphere | 50 | 0.1796 | 0.4238 | 0.2002 | 0.8542 | 113.37s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0206 | 0.1435 | 0.0923 | 0.9833 | 89.99s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.24s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:11:26\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1054 (±0.0229), Variance: 1.3066, Range: [-30.7309, -25.5143]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0012, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7128 | 0.8442 | 0.6541 | 0.4616 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5516 | 0.7427 | 0.5315 | 0.5834 | 73.69s |\n", - "| DeepKriging_sphere | 50 | 0.1156 | 0.34 | 0.1906 | 0.9127 | 82.09s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.02 | 0.1414 | 0.0937 | 0.9849 | 97.27s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.15s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:16:18\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1233 (±0.0228), Variance: 1.2941, Range: [-30.6448, -25.4937]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5736 | 0.7574 | 0.5929 | 0.5223 | -2.03s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5256 | 0.725 | 0.5276 | 0.5623 | 71.66s |\n", - "| DeepKriging_sphere | 50 | 0.173 | 0.416 | 0.2481 | 0.8559 | 83.90s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0192 | 0.1387 | 0.0958 | 0.984 | 42.61s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.54s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:20:15\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1587 (±0.0220), Variance: 1.2129, Range: [-30.7115, -25.6215]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0009, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6547 | 0.8092 | 0.6124 | 0.5134 | 0.37s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | -2.32s |\n", - "| DeepKriging_wendland | -- | 0.8596 | 0.9271 | 0.601 | 0.3612 | 68.14s |\n", - "| DeepKriging_sphere | 50 | 0.1317 | 0.3629 | 0.2 | 0.9021 | 77.26s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0178 | 0.1336 | 0.0846 | 0.9867 | 73.54s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.39s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:24:32\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1229 (±0.0224), Variance: 1.2516, Range: [-30.7023, -25.4944]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0027, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8311 | 1.3532 | 0.6664 | -0.4957 | 0.33s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5748 | 0.7582 | 0.5576 | 0.5305 | 64.65s |\n", - "| DeepKriging_sphere | 50 | 0.2568 | 0.5068 | 0.2505 | 0.7902 | 69.24s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0313 | 0.177 | 0.103 | 0.9744 | 75.14s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.21s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:28:44\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1439 (±0.0224), Variance: 1.2560, Range: [-30.7147, -25.4973]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.8532 | 0.9237 | 0.5605 | 0.2032 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.455 | 0.6745 | 0.4778 | 0.5751 | 72.31s |\n", - "| DeepKriging_sphere | 50 | 0.0952 | 0.3085 | 0.1642 | 0.9111 | 81.65s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0313 | 0.177 | 0.1053 | 0.9707 | 69.85s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.82s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:33:09\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1530 (±0.0223), Variance: 1.2440, Range: [-30.6311, -25.6170]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0007, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.8655 | 1.3658 | 0.6807 | -0.4022 | 0.42s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.15s |\n", - "| DeepKriging_wendland | -- | 0.5853 | 0.765 | 0.5722 | 0.56 | 69.11s |\n", - "| DeepKriging_sphere | 50 | 0.0749 | 0.2737 | 0.1589 | 0.9437 | 77.13s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0212 | 0.1455 | 0.0955 | 0.9841 | 65.46s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.43s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:37:23\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0963 (±0.0226), Variance: 1.2804, Range: [-30.6621, -25.4797]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7323 | 0.8557 | 0.6371 | 0.4025 | 0.56s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.6107 | 0.7814 | 0.5888 | 0.5017 | 66.77s |\n", - "| DeepKriging_sphere | 50 | 0.1334 | 0.3652 | 0.191 | 0.8912 | 85.58s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0227 | 0.1506 | 0.0883 | 0.9815 | 89.25s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.86s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:42:06\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1551 (±0.0228), Variance: 1.2987, Range: [-30.6965, -25.4733]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.513 | 0.7163 | 0.5421 | 0.595 | 0.38s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5874 | 0.7664 | 0.5738 | 0.5363 | 73.82s |\n", - "| DeepKriging_sphere | 50 | 0.0859 | 0.293 | 0.1683 | 0.9322 | 75.17s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0215 | 0.1466 | 0.0922 | 0.983 | 76.40s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.20s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:46:40\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1196 (±0.0224), Variance: 1.2523, Range: [-30.6444, -25.5354]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5656 | 0.7521 | 0.5738 | 0.5205 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.4421 | 0.6649 | 0.4787 | 0.6251 | 68.39s |\n", - "| DeepKriging_sphere | 50 | 0.085 | 0.2916 | 0.1775 | 0.9279 | 85.77s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0186 | 0.1363 | 0.1026 | 0.9842 | 42.48s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 5.34s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:50:47\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1076 (±0.0225), Variance: 1.2682, Range: [-30.7001, -25.5140]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0218, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6903 | 0.8308 | 0.598 | 0.4301 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0002 | 0.0001 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 1.1707 | 1.082 | 0.6468 | 0.0334 | 66.15s |\n", - "| DeepKriging_sphere | 50 | 0.1021 | 0.3195 | 0.1844 | 0.9157 | 89.03s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.019 | 0.1379 | 0.0942 | 0.9843 | 74.97s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:55:19\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1345 (±0.0227), Variance: 1.2845, Range: [-30.7042, -25.5094]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5696 | 0.7548 | 0.5876 | 0.6084 | 0.38s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5995 | 0.7743 | 0.5509 | 0.5879 | 62.40s |\n", - "| DeepKriging_sphere | 50 | 0.0571 | 0.239 | 0.129 | 0.9607 | 72.72s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0202 | 0.1422 | 0.0879 | 0.9861 | 66.33s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.48s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 01:59:34\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1402 (±0.0220), Variance: 1.2151, Range: [-30.6807, -25.5615]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5374 | 0.7331 | 0.5538 | 0.5531 | 0.38s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.5519 | 0.7429 | 0.5468 | 0.541 | 72.24s |\n", - "| DeepKriging_sphere | 50 | 0.3842 | 0.6198 | 0.2944 | 0.6805 | 85.28s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0161 | 0.127 | 0.0804 | 0.9866 | 88.88s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.81s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:04:25\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1416 (±0.0226), Variance: 1.2800, Range: [-30.6792, -25.5373]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0038, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 1.9387 | 1.3924 | 0.6694 | -0.533 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.12s |\n", - "| DeepKriging_wendland | -- | 0.8462 | 0.9199 | 0.6248 | 0.3309 | 72.15s |\n", - "| DeepKriging_sphere | 50 | 0.1828 | 0.4276 | 0.2334 | 0.8554 | 72.97s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.03 | 0.1732 | 0.0988 | 0.9763 | 83.45s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.80s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:09:04\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1023 (±0.0226), Variance: 1.2769, Range: [-30.6563, -25.4917]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6447 | 0.8029 | 0.6272 | 0.4684 | 0.42s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5704 | 0.7553 | 0.5218 | 0.5296 | 70.10s |\n", - "| DeepKriging_sphere | 50 | 0.1041 | 0.3226 | 0.1688 | 0.9142 | 84.37s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0192 | 0.1386 | 0.0915 | 0.9842 | 87.66s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.14s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:14:02\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1759 (±0.0226), Variance: 1.2773, Range: [-30.7204, -25.4908]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0082, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5658 | 0.7522 | 0.5592 | 0.536 | 0.40s |\n", - "| OLS_sphere | 700 | 0 | 0.0003 | 0.0001 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6324 | 0.7952 | 0.5738 | 0.4814 | 68.57s |\n", - "| DeepKriging_sphere | 50 | 0.1526 | 0.3907 | 0.2177 | 0.8749 | 85.11s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0209 | 0.1444 | 0.0956 | 0.9829 | 45.52s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 3.80s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:18:17\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0950 (±0.0224), Variance: 1.2523, Range: [-30.7024, -25.5172]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0003, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6981 | 0.8355 | 0.6308 | 0.4487 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5717 | 0.7561 | 0.5407 | 0.5485 | 71.50s |\n", - "| DeepKriging_sphere | 50 | 0.1717 | 0.4144 | 0.1984 | 0.8644 | 90.10s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0276 | 0.166 | 0.0961 | 0.9782 | 94.94s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.71s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:23:27\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1017 (±0.0223), Variance: 1.2410, Range: [-30.6955, -25.5072]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0023, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.615 | 0.7842 | 0.5961 | 0.4879 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0.0001 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6796 | 0.8244 | 0.5756 | 0.4341 | 66.80s |\n", - "| DeepKriging_sphere | 50 | 0.1553 | 0.3941 | 0.2151 | 0.8707 | 80.36s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0345 | 0.1857 | 0.109 | 0.9713 | 67.57s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 1.70s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:28:01\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0922 (±0.0220), Variance: 1.2095, Range: [-30.6982, -25.5104]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0009, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5899 | 0.7681 | 0.5875 | 0.5385 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.7095 | 0.8423 | 0.5992 | 0.4449 | 70.29s |\n", - "| DeepKriging_sphere | 50 | 0.0694 | 0.2634 | 0.1525 | 0.9457 | 88.48s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0423 | 0.2058 | 0.1597 | 0.9669 | 40.12s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.87s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:32:20\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1301 (±0.0227), Variance: 1.2888, Range: [-30.7200, -25.4777]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6928 | 0.8323 | 0.5993 | 0.4838 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.7779 | 0.882 | 0.599 | 0.4203 | 72.79s |\n", - "| DeepKriging_sphere | 50 | 0.0842 | 0.2902 | 0.1715 | 0.9373 | 78.87s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0269 | 0.1639 | 0.0911 | 0.98 | 60.97s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:36:51\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1400 (±0.0225), Variance: 1.2616, Range: [-30.7434, -25.5192]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0008, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6512 | 0.807 | 0.6168 | 0.4492 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6427 | 0.8017 | 0.5645 | 0.4564 | 69.45s |\n", - "| DeepKriging_sphere | 50 | 0.2169 | 0.4657 | 0.2157 | 0.8166 | 86.04s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.039 | 0.1975 | 0.116 | 0.967 | 46.40s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.31s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:41:16\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1481 (±0.0229), Variance: 1.3084, Range: [-30.7376, -25.5201]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0014, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.7333 | 0.8563 | 0.6335 | 0.4416 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0.0001 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.587 | 0.7662 | 0.5689 | 0.553 | 68.55s |\n", - "| DeepKriging_sphere | 50 | 0.0718 | 0.2679 | 0.1703 | 0.9453 | 89.29s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.021 | 0.1448 | 0.1089 | 0.984 | 34.11s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.09s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:45:32\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1256 (±0.0227), Variance: 1.2834, Range: [-30.7273, -25.5378]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0010, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.496 | 0.7043 | 0.5395 | 0.5364 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.4934 | 0.7024 | 0.5346 | 0.5389 | 69.74s |\n", - "| DeepKriging_sphere | 50 | 0.0672 | 0.2591 | 0.1526 | 0.9372 | 103.01s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0344 | 0.1856 | 0.1087 | 0.9678 | 44.64s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.67s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:50:10\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1671 (±0.0219), Variance: 1.2027, Range: [-30.6815, -25.5243]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6334 | 0.7958 | 0.6044 | 0.4419 | 0.38s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.6073 | 0.7793 | 0.5737 | 0.4648 | 69.58s |\n", - "| DeepKriging_sphere | 50 | 0.1308 | 0.3616 | 0.1879 | 0.8848 | 82.16s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0326 | 0.1807 | 0.0997 | 0.9712 | 72.82s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 02:55:04\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1214 (±0.0223), Variance: 1.2466, Range: [-30.7192, -25.6736]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.6733 | 0.8205 | 0.5629 | 0.4598 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.8749 | 0.9353 | 0.5722 | 0.2981 | 64.04s |\n", - "| DeepKriging_sphere | 50 | 0.1569 | 0.3961 | 0.2162 | 0.8741 | 74.61s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0297 | 0.1724 | 0.105 | 0.9761 | 107.63s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 1.62s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:00:13\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1114 (±0.0221), Variance: 1.2212, Range: [-30.7160, -25.5394]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5432 | 0.737 | 0.5653 | 0.5145 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.4528 | 0.6729 | 0.5094 | 0.5953 | 69.55s |\n", - "| DeepKriging_sphere | 50 | 0.1612 | 0.4015 | 0.2388 | 0.856 | 75.97s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0283 | 0.1683 | 0.1044 | 0.9747 | 61.67s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:04:46\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1001 (±0.0223), Variance: 1.2480, Range: [-30.7190, -25.5135]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0046, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5394 | 0.7344 | 0.573 | 0.5297 | 0.35s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.5104 | 0.7144 | 0.536 | 0.555 | 68.94s |\n", - "| DeepKriging_sphere | 50 | 0.154 | 0.3924 | 0.2082 | 0.8657 | 88.21s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0132 | 0.1148 | 0.0728 | 0.9885 | 87.40s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:09:53\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1126 (±0.0225), Variance: 1.2625, Range: [-30.7105, -25.5093]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 1.0576 | 1.0284 | 0.6299 | 0.1491 | 0.42s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6589 | 0.8117 | 0.5684 | 0.4699 | 71.99s |\n", - "| DeepKriging_sphere | 50 | 0.112 | 0.3347 | 0.1782 | 0.9099 | 113.33s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0163 | 0.1277 | 0.09 | 0.9869 | 41.17s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.41s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:14:51\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0933 (±0.0224), Variance: 1.2530, Range: [-30.7107, -25.4969]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6294 | 0.7933 | 0.5985 | 0.4989 | 0.49s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6517 | 0.8073 | 0.5661 | 0.4811 | 66.65s |\n", - "| DeepKriging_sphere | 50 | 0.1119 | 0.3345 | 0.1798 | 0.9109 | 72.21s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0297 | 0.1722 | 0.1143 | 0.9764 | 41.17s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.87s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:19:04\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0888 (±0.0231), Variance: 1.3382, Range: [-30.6630, -25.5644]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.6142 | 0.7837 | 0.5729 | 0.5492 | 0.44s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.4888 | 0.6991 | 0.488 | 0.6413 | 72.75s |\n", - "| DeepKriging_sphere | 50 | 0.0603 | 0.2455 | 0.1285 | 0.9558 | 122.79s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0162 | 0.1274 | 0.0739 | 0.9881 | 67.02s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.02s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:24:35\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.0965 (±0.0227), Variance: 1.2868, Range: [-30.6708, -25.5365]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.7258 | 0.8519 | 0.638 | 0.3989 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5523 | 0.7432 | 0.5614 | 0.5426 | 67.10s |\n", - "| DeepKriging_sphere | 50 | 0.1099 | 0.3315 | 0.166 | 0.909 | 112.88s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0293 | 0.1712 | 0.1043 | 0.9757 | 59.39s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 1.34s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:29:48\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1409 (±0.0224), Variance: 1.2594, Range: [-30.6448, -25.5493]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.5718 | 0.7562 | 0.5822 | 0.4968 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.5298 | 0.7279 | 0.547 | 0.5338 | 64.21s |\n", - "| DeepKriging_sphere | 50 | 0.0879 | 0.2965 | 0.1552 | 0.9226 | 110.98s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0198 | 0.1406 | 0.0917 | 0.9826 | 91.56s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:35:28\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1261 (±0.0222), Variance: 1.2327, Range: [-30.6400, -25.5062]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0014, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.5807 | 0.762 | 0.5968 | 0.5166 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.5059 | 0.7113 | 0.5098 | 0.5788 | 71.29s |\n", - "| DeepKriging_sphere | 50 | 0.0957 | 0.3093 | 0.1748 | 0.9203 | 83.88s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0177 | 0.1331 | 0.0862 | 0.9853 | 81.99s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 3.92s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:40:41\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1581 (±0.0226), Variance: 1.2763, Range: [-30.7333, -25.4990]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6258 | 0.7911 | 0.5976 | 0.5073 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6291 | 0.7932 | 0.5881 | 0.5047 | 68.04s |\n", - "| DeepKriging_sphere | 50 | 0.1029 | 0.3208 | 0.1804 | 0.919 | 83.61s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0302 | 0.1736 | 0.1096 | 0.9763 | 68.53s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.16s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:45:34\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1259 (±0.0225), Variance: 1.2688, Range: [-30.7180, -25.5529]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.5831 | 0.7636 | 0.5832 | 0.533 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.4772 | 0.6908 | 0.5223 | 0.6178 | 68.19s |\n", - "| DeepKriging_sphere | 50 | 0.0977 | 0.3125 | 0.1779 | 0.9218 | 75.81s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0269 | 0.164 | 0.1016 | 0.9785 | 105.03s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 1.29s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:50:56\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1504 (±0.0223), Variance: 1.2435, Range: [-30.6768, -25.5451]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6766 | 0.8226 | 0.5969 | 0.4399 | 0.35s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.5035 | 0.7096 | 0.5326 | 0.5832 | 72.99s |\n", - "| DeepKriging_sphere | 50 | 0.1976 | 0.4446 | 0.1699 | 0.8364 | 84.56s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0289 | 0.1699 | 0.0996 | 0.9761 | 84.60s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.50s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 03:56:15\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1303 (±0.0229), Variance: 1.3085, Range: [-30.7273, -25.5186]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0009, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.8834 | 0.9399 | 0.64 | 0.341 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0.0001 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.4866 | 0.6975 | 0.5128 | 0.637 | 74.08s |\n", - "| DeepKriging_sphere | 50 | 0.0827 | 0.2875 | 0.1547 | 0.9383 | 114.29s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0281 | 0.1676 | 0.1067 | 0.9791 | 93.69s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.27s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:02:15\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1456 (±0.0224), Variance: 1.2511, Range: [-30.7023, -25.5115]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0005, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6008 | 0.7751 | 0.5909 | 0.5251 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 0.6104 | 0.7813 | 0.5889 | 0.5175 | 69.01s |\n", - "| DeepKriging_sphere | 50 | 0.0828 | 0.2877 | 0.1519 | 0.9346 | 74.52s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0259 | 0.161 | 0.0977 | 0.9795 | 92.30s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 5.09s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:07:28\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1144 (±0.0228), Variance: 1.2968, Range: [-30.7089, -25.4890]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0006, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.734 | 0.8568 | 0.651 | 0.5149 | 0.42s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5963 | 0.7722 | 0.576 | 0.6059 | 69.98s |\n", - "| DeepKriging_sphere | 50 | 0.1184 | 0.3442 | 0.1951 | 0.9217 | 70.34s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0381 | 0.1953 | 0.1431 | 0.9748 | 39.94s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.27s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:11:51\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1688 (±0.0223), Variance: 1.2470, Range: [-30.6982, -25.5522]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0017, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6077 | 0.7795 | 0.5878 | 0.4616 | 0.41s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 0.6976 | 0.8352 | 0.6312 | 0.3819 | 66.43s |\n", - "| DeepKriging_sphere | 50 | 0.0649 | 0.2547 | 0.1679 | 0.9425 | 74.85s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0225 | 0.1501 | 0.0983 | 0.98 | 73.60s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.30s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:16:50\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1138 (±0.0227), Variance: 1.2839, Range: [-30.7106, -25.5556]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0023, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6037 | 0.777 | 0.5961 | 0.5673 | 0.38s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0.0001 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.588 | 0.7668 | 0.5058 | 0.5786 | 71.53s |\n", - "| DeepKriging_sphere | 50 | 0.1079 | 0.3285 | 0.1786 | 0.9226 | 63.74s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0196 | 0.1399 | 0.0948 | 0.986 | 91.31s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.49s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:22:00\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1354 (±0.0223), Variance: 1.2385, Range: [-30.7165, -25.4821]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0014, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.9057 | 0.9517 | 0.643 | 0.1824 | 0.39s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.5697 | 0.7548 | 0.5811 | 0.4857 | 66.70s |\n", - "| DeepKriging_sphere | 50 | 0.0622 | 0.2494 | 0.1347 | 0.9439 | 108.46s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0246 | 0.1569 | 0.0989 | 0.9778 | 90.73s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.39s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:27:52\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1453 (±0.0224), Variance: 1.2519, Range: [-30.6551, -25.5042]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0018, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6749 | 0.8215 | 0.638 | 0.5069 | 0.47s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0 | 1 | 0.09s |\n", - "| DeepKriging_wendland | -- | 0.6454 | 0.8034 | 0.5635 | 0.5285 | 67.66s |\n", - "| DeepKriging_sphere | 50 | 0.1662 | 0.4077 | 0.2204 | 0.8786 | 73.42s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0207 | 0.144 | 0.0884 | 0.9848 | 63.43s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:32:44\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1735 (±0.0221), Variance: 1.2236, Range: [-30.7264, -25.5198]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0012, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|---------|\n", - "| OLS_wendland | -- | 0.5445 | 0.7379 | 0.5547 | 0.5583 | 0.45s |\n", - "| OLS_sphere | 700 | 0 | 0.0001 | 0.0001 | 1 | 0.11s |\n", - "| DeepKriging_wendland | -- | 1.1461 | 1.0706 | 0.6395 | 0.0702 | 66.84s |\n", - "| DeepKriging_sphere | 50 | 0.1813 | 0.4258 | 0.221 | 0.8529 | 114.91s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0475 | 0.2178 | 0.1004 | 0.9615 | 71.00s |\n", - "| UniversalKriging | 500 | 0 | 0.0001 | 0 | 1 | 4.37s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:38:26\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -28.1379 (±0.0222), Variance: 1.2358, Range: [-30.6870, -25.5004]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0004, sigma²=0.0000, nugget=0.0000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 0.6078 | 0.7796 | 0.5818 | 0.5253 | 0.43s |\n", - "| OLS_sphere | 700 | 0 | 0 | 0 | 1 | 0.10s |\n", - "| DeepKriging_wendland | -- | 1.2133 | 1.1015 | 0.5734 | 0.0524 | 73.18s |\n", - "| DeepKriging_sphere | 50 | 0.1097 | 0.3312 | 0.1788 | 0.9143 | 74.00s |\n", - "| DeepKriging_sphere_Huber | 200 | 0.0232 | 0.1523 | 0.099 | 0.9819 | 81.33s |\n", - "| UniversalKriging | 500 | 0 | 0 | 0 | 1 | 4.09s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:43:38\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_euclidean(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018018, - "end_time": "2026-01-19T20:43:38.924390", - "exception": false, - "start_time": "2026-01-19T20:43:38.906372", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:38.963214Z", - "iopub.status.busy": "2026-01-19T20:43:38.962124Z", - "iopub.status.idle": "2026-01-19T20:43:38.995415Z", - "shell.execute_reply": "2026-01-19T20:43:38.991804Z" - }, - "papermill": { - "duration": 0.056575, - "end_time": "2026-01-19T20:43:38.998453", - "exception": false, - "start_time": "2026-01-19T20:43:38.941878", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 200, 'UniversalKriging': 500}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------|-----------|-----------|-----------|\n", - "| OLS_wendland | 0.74±0.32 | 0.84±0.15 | 0.60±0.03 | 0.41±0.25 |\n", - "| OLS_sphere | 0.00±0.00 | 0.00±0.00 | 0.00±0.00 | 1.00±0.00 |\n", - "| DeepKriging_wendland | 0.66±0.24 | 0.81±0.13 | 0.56±0.05 | 0.47±0.19 |\n", - "| DeepKriging_sphere | 0.13±0.06 | 0.35±0.08 | 0.19±0.04 | 0.90±0.05 |\n", - "| DeepKriging_sphere_Huber | 0.02±0.01 | 0.16±0.02 | 0.10±0.01 | 0.98±0.01 |\n", - "| UniversalKriging | 0.00±0.00 | 0.00±0.00 | 0.00±0.00 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model: OLS_sphere (RMSE: 0.00±0.00)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 15391.035827, - "end_time": "2026-01-19T20:43:42.751216", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario2/simulation_eggholder_euclidean.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario2/simulation_eggholder_euclidean.ipynb", - "parameters": {}, - "start_time": "2026-01-19T16:27:11.715389", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/simulation/ScenarioIII/simulation_eggholder_spherical.ipynb b/notebook/simulation/ScenarioIII/simulation_eggholder_spherical.ipynb deleted file mode 100644 index bd3a0af..0000000 --- a/notebook/simulation/ScenarioIII/simulation_eggholder_spherical.ipynb +++ /dev/null @@ -1,4141 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.015208, - "end_time": "2026-01-19T20:43:43.578958", - "exception": false, - "start_time": "2026-01-19T20:43:43.563750", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:43.610497Z", - "iopub.status.busy": "2026-01-19T20:43:43.609080Z", - "iopub.status.idle": "2026-01-19T20:43:43.636013Z", - "shell.execute_reply": "2026-01-19T20:43:43.631996Z" - }, - "papermill": { - "duration": 0.045423, - "end_time": "2026-01-19T20:43:43.639636", - "exception": false, - "start_time": "2026-01-19T20:43:43.594213", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:43.660470Z", - "iopub.status.busy": "2026-01-19T20:43:43.660025Z", - "iopub.status.idle": "2026-01-19T20:43:46.941431Z", - "shell.execute_reply": "2026-01-19T20:43:46.937837Z" - }, - "papermill": { - "duration": 3.292909, - "end_time": "2026-01-19T20:43:46.943937", - "exception": false, - "start_time": "2026-01-19T20:43:43.651028", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1768855425.084838 1747037 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1768855425.089161 1747037 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1768855425.101307 1747037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768855425.101331 1747037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768855425.101333 1747037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1768855425.101334 1747037 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.011419, - "end_time": "2026-01-19T20:43:46.967943", - "exception": false, - "start_time": "2026-01-19T20:43:46.956524", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:46.985701Z", - "iopub.status.busy": "2026-01-19T20:43:46.984485Z", - "iopub.status.idle": "2026-01-19T20:43:48.535161Z", - "shell.execute_reply": "2026-01-19T20:43:48.529823Z" - }, - "papermill": { - "duration": 1.563912, - "end_time": "2026-01-19T20:43:48.539571", - "exception": false, - "start_time": "2026-01-19T20:43:46.975659", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.008267, - "end_time": "2026-01-19T20:43:48.554102", - "exception": false, - "start_time": "2026-01-19T20:43:48.545835", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:48.569140Z", - "iopub.status.busy": "2026-01-19T20:43:48.567872Z", - "iopub.status.idle": "2026-01-19T20:43:48.584324Z", - "shell.execute_reply": "2026-01-19T20:43:48.581638Z" - }, - "papermill": { - "duration": 0.026294, - "end_time": "2026-01-19T20:43:48.587197", - "exception": false, - "start_time": "2026-01-19T20:43:48.560903", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.00878, - "end_time": "2026-01-19T20:43:48.602014", - "exception": false, - "start_time": "2026-01-19T20:43:48.593234", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:43:48.615499Z", - "iopub.status.busy": "2026-01-19T20:43:48.614659Z", - "iopub.status.idle": "2026-01-19T20:44:03.305213Z", - "shell.execute_reply": "2026-01-19T20:44:03.301965Z" - }, - "papermill": { - "duration": 14.700528, - "end_time": "2026-01-19T20:44:03.308435", - "exception": false, - "start_time": "2026-01-19T20:43:48.607907", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.006235, - "end_time": "2026-01-19T20:44:03.325164", - "exception": false, - "start_time": "2026-01-19T20:44:03.318929", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:44:03.339117Z", - "iopub.status.busy": "2026-01-19T20:44:03.337743Z", - "iopub.status.idle": "2026-01-19T20:44:03.362196Z", - "shell.execute_reply": "2026-01-19T20:44:03.358515Z" - }, - "papermill": { - "duration": 0.035125, - "end_time": "2026-01-19T20:44:03.365282", - "exception": false, - "start_time": "2026-01-19T20:44:03.330157", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment II : Deterministic Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " z = (term1 + term2).astype(np.float32)\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.006418, - "end_time": "2026-01-19T20:44:03.381898", - "exception": false, - "start_time": "2026-01-19T20:44:03.375480", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:44:03.396260Z", - "iopub.status.busy": "2026-01-19T20:44:03.395489Z", - "iopub.status.idle": "2026-01-19T20:44:03.465285Z", - "shell.execute_reply": "2026-01-19T20:44:03.460955Z" - }, - "papermill": { - "duration": 0.081671, - "end_time": "2026-01-19T20:44:03.468742", - "exception": false, - "start_time": "2026-01-19T20:44:03.387071", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:44:03.499248Z", - "iopub.status.busy": "2026-01-19T20:44:03.498345Z", - "iopub.status.idle": "2026-01-19T20:44:03.555787Z", - "shell.execute_reply": "2026-01-19T20:44:03.551561Z" - }, - "papermill": { - "duration": 0.076607, - "end_time": "2026-01-19T20:44:03.559324", - "exception": false, - "start_time": "2026-01-19T20:44:03.482717", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.011341, - "end_time": "2026-01-19T20:44:03.584591", - "exception": false, - "start_time": "2026-01-19T20:44:03.573250", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:44:03.603637Z", - "iopub.status.busy": "2026-01-19T20:44:03.602729Z", - "iopub.status.idle": "2026-01-19T20:44:03.617996Z", - "shell.execute_reply": "2026-01-19T20:44:03.613444Z" - }, - "papermill": { - "duration": 0.028759, - "end_time": "2026-01-19T20:44:03.621286", - "exception": false, - "start_time": "2026-01-19T20:44:03.592527", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T20:44:03.645199Z", - "iopub.status.busy": "2026-01-19T20:44:03.644348Z", - "iopub.status.idle": "2026-01-19T23:45:03.740961Z", - "shell.execute_reply": "2026-01-19T23:45:03.738523Z" - }, - "papermill": { - "duration": 10860.111579, - "end_time": "2026-01-19T23:45:03.743788", - "exception": false, - "start_time": "2026-01-19T20:44:03.632209", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4567 (1.4711), Variance: 5410.2669, Range: [-127.9956, 127.9977]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3099.7151 3349.2456 \n", - " 100 2114.4741 2306.5110 \n", - " 200 1414.9084 1193.5135 \n", - " 500 364.7443 283.3091 *\n", - " 700 404.8941 223.0184 \n", - " 1000 484.8952 548.5554 \n", - " 1400 1487.0630 2476.5039 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768855448.361654 1747037 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1768855450.642654 1747211 service.cc:152] XLA service 0x773c580145b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1768855450.642691 1747211 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768855451.016584 1747211 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1768855462.340481 1747211 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x773cb8d3ab90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x773cb881a290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 43.6482 86.1506 *\n", - " 100 61.3468 91.6226 \n", - " 200 88.6246 123.0868 \n", - " 500 134.6397 149.9974 \n", - " 700 313.8306 218.3608 \n", - " 1000 705.8013 792.4564 \n", - " 1400 1970.7262 1836.9512 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.0296 67.4320 *\n", - " 100 5.6644 76.2799 \n", - " 200 6.5463 175.7545 \n", - " 500 11.1412 257.3225 \n", - " 700 13.5379 295.1727 \n", - " 1000 21.6239 1030.6235 \n", - " 1400 37.7276 2513.3413 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0074, sigma²=7011.7521, nugget=0.0018\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4868, sigma²=3187.6085, nugget=0.0016\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2592, sigma²=1459.2079, nugget=0.0026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0606, sigma²=181.8152, nugget=0.1486\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=61.6112, sigma²=0.0003, nugget=70.9271\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.4885, sigma²=0.0001, nugget=29.1751\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9845, sigma²=0.0001, nugget=12.5475\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 181.1100 174.0702 \n", - " 100 180.7712 179.9249 *\n", - " 200 185.7964 170.3364 \n", - " 500 199.8748 147.7476 \n", - " 700 404.8931 223.0185 \n", - " 1000 484.8966 548.5519 \n", - " 1400 1487.0669 2476.4689 \n", - " Best order: 100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4868, sigma²=3187.6085, nugget=0.0016\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8186.79 | 90.4809 | 61.0406 | -0.2354 | 0.51s |\n", - "| OLS_sphere | 500 | 283.309 | 16.8318 | 10.5525 | 0.9572 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4198.56 | 64.7963 | 48.252 | 0.3664 | 62.21s |\n", - "| DeepKriging_sphere | 50 | 55.806 | 7.4703 | 5.0553 | 0.9916 | 38.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.3999 | 8.087 | 4.68 | 0.9901 | 42.42s |\n", - "| UniversalKriging | 100 | 179.925 | 13.4136 | 6.6367 | 0.9728 | 0.43s |\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 04:57:53\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.2987 (1.4784), Variance: 5464.1031, Range: [-127.8174, 127.5887]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5005, sigma²=3017.4818, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8451.95 | 91.9345 | 59.2141 | -0.3296 | 0.40s |\n", - "| OLS_sphere | 500 | 385.581 | 19.6362 | 11.8469 | 0.9393 | 0.06s |\n", - "| DeepKriging_wendland | -- | 11693.8 | 108.138 | 52.4788 | -0.8396 | 63.21s |\n", - "| DeepKriging_sphere | 50 | 116.213 | 10.7802 | 5.262 | 0.9817 | 49.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 155.258 | 12.4603 | 5.9513 | 0.9756 | 40.81s |\n", - "| UniversalKriging | 100 | 189.97 | 13.783 | 7.2821 | 0.9701 | 0.80s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:00:48\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.8080 (1.4509), Variance: 5262.5932, Range: [-127.7582, 127.9540]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5394, sigma²=3528.1861, nugget=0.0042\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8445.49 | 91.8993 | 60.2763 | -0.1797 | 0.45s |\n", - "| OLS_sphere | 500 | 332.361 | 18.2308 | 11.8788 | 0.9536 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4808.09 | 69.3404 | 51.3586 | 0.3284 | 64.64s |\n", - "| DeepKriging_sphere | 50 | 44.5484 | 6.6745 | 3.9967 | 0.9938 | 39.68s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.81 | 7.4034 | 4.4202 | 0.9923 | 44.20s |\n", - "| UniversalKriging | 100 | 176.437 | 13.283 | 6.8416 | 0.9754 | 3.43s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:03:40\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2132 (1.5012), Variance: 5634.1291, Range: [-127.9036, 127.9662]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4890, sigma²=3233.4410, nugget=0.0024\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 6446.6 | 80.2907 | 59.4977 | 0.0184 | 0.43s |\n", - "| OLS_sphere | 500 | 595.938 | 24.4118 | 14.3494 | 0.9093 | 0.07s |\n", - "| DeepKriging_wendland | -- | 8109.15 | 90.0508 | 54.7662 | -0.2348 | 67.08s |\n", - "| DeepKriging_sphere | 50 | 79.6228 | 8.9232 | 4.8227 | 0.9879 | 38.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 80.4893 | 8.9716 | 4.7773 | 0.9877 | 54.69s |\n", - "| UniversalKriging | 100 | 258.931 | 16.0913 | 8.264 | 0.9606 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:06:47\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9278 (1.5064), Variance: 5673.1096, Range: [-127.8282, 127.9820]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4668, sigma²=3117.5568, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 26108.5 | 161.581 | 65.9709 | -2.9993 | 0.43s |\n", - "| OLS_sphere | 500 | 476.316 | 21.8247 | 13.2562 | 0.927 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5106.18 | 71.4575 | 47.2071 | 0.2178 | 62.06s |\n", - "| DeepKriging_sphere | 50 | 69.6117 | 8.3434 | 4.6956 | 0.9893 | 47.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 92.0165 | 9.5925 | 5.4698 | 0.9859 | 42.10s |\n", - "| UniversalKriging | 100 | 318.154 | 17.8369 | 8.8382 | 0.9513 | 3.78s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:09:48\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.3699 (1.4920), Variance: 5565.1337, Range: [-127.9886, 127.8961]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5313, sigma²=3393.6766, nugget=0.0056\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25659.6 | 160.186 | 69.9714 | -2.7177 | 0.42s |\n", - "| OLS_sphere | 500 | 306.73 | 17.5137 | 10.8904 | 0.9556 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4056.02 | 63.6869 | 45.6844 | 0.4123 | 65.31s |\n", - "| DeepKriging_sphere | 50 | 28.1314 | 5.3039 | 3.4214 | 0.9959 | 44.34s |\n", - "| DeepKriging_sphere_Huber | 50 | 26.9824 | 5.1945 | 3.2486 | 0.9961 | 76.07s |\n", - "| UniversalKriging | 100 | 106.456 | 10.3178 | 5.8316 | 0.9846 | 3.73s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:13:24\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.6431 (1.4625), Variance: 5347.3846, Range: [-127.9185, 127.9299]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4758, sigma²=3191.4956, nugget=0.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9369.74 | 96.7974 | 60.9859 | -0.4122 | 0.43s |\n", - "| OLS_sphere | 500 | 305.578 | 17.4808 | 11.245 | 0.9539 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3685.1 | 60.705 | 44.0267 | 0.4446 | 67.93s |\n", - "| DeepKriging_sphere | 50 | 5548.11 | 74.4857 | 61.0408 | 0.1638 | 13.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.6636 | 7.3935 | 4.4714 | 0.9918 | 42.50s |\n", - "| UniversalKriging | 100 | 165.08 | 12.8483 | 6.6083 | 0.9751 | 3.44s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:15:56\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.7111 (1.4705), Variance: 5406.1681, Range: [-127.9905, 127.5998]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4939, sigma²=2928.4635, nugget=0.0013\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4537.68 | 67.3623 | 52.9792 | 0.2336 | 0.40s |\n", - "| OLS_sphere | 500 | 349.547 | 18.6962 | 12.1051 | 0.941 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3782.93 | 61.5055 | 45.6782 | 0.3611 | 60.04s |\n", - "| DeepKriging_sphere | 50 | 84.549 | 9.1951 | 5.3217 | 0.9857 | 45.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 107.098 | 10.3488 | 5.6564 | 0.9819 | 37.65s |\n", - "| UniversalKriging | 100 | 245.89 | 15.6809 | 7.928 | 0.9585 | 3.57s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:18:45\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.0423 (1.5031), Variance: 5648.2552, Range: [-127.9905, 127.7956]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4605, sigma²=3127.1113, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4698.27 | 68.5439 | 54.823 | 0.1716 | 0.39s |\n", - "| OLS_sphere | 500 | 368.502 | 19.1964 | 12.1843 | 0.935 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3656.21 | 60.4666 | 45.8475 | 0.3553 | 66.43s |\n", - "| DeepKriging_sphere | 50 | 72.1481 | 8.494 | 4.8852 | 0.9873 | 45.68s |\n", - "| DeepKriging_sphere_Huber | 50 | 76.7835 | 8.7626 | 5.1995 | 0.9865 | 39.35s |\n", - "| UniversalKriging | 100 | 158.926 | 12.6066 | 6.4478 | 0.972 | 3.79s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:21:45\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.1616 (1.4969), Variance: 5601.9292, Range: [-127.9743, 127.9721]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4954, sigma²=3077.7671, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4538.34 | 67.3672 | 52.9839 | 0.2263 | 0.41s |\n", - "| OLS_sphere | 500 | 297.155 | 17.2382 | 11.0586 | 0.9493 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3954.81 | 62.8873 | 44.4173 | 0.3258 | 65.68s |\n", - "| DeepKriging_sphere | 50 | 54.2107 | 7.3628 | 4.5155 | 0.9908 | 42.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 72.5139 | 8.5155 | 4.9724 | 0.9876 | 40.90s |\n", - "| UniversalKriging | 100 | 172.698 | 13.1415 | 6.9094 | 0.9706 | 3.35s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:24:44\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.5146 (1.5029), Variance: 5646.6856, Range: [-127.9684, 127.8338]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4881, sigma²=3344.4912, nugget=0.0054\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6206.6 | 78.782 | 54.9765 | 0.0673 | 0.44s |\n", - "| OLS_sphere | 500 | 270.872 | 16.4582 | 11.1891 | 0.9593 | 0.15s |\n", - "| DeepKriging_wendland | -- | 3599 | 59.9916 | 41.1287 | 0.4591 | 68.81s |\n", - "| DeepKriging_sphere | 50 | 60.0715 | 7.7506 | 5.551 | 0.991 | 35.85s |\n", - "| DeepKriging_sphere_Huber | 50 | 93.8109 | 9.6856 | 5.7016 | 0.9859 | 40.50s |\n", - "| UniversalKriging | 100 | 108.167 | 10.4003 | 5.5487 | 0.9837 | 3.55s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:27:40\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.8927 (1.4989), Variance: 5617.0348, Range: [-127.9306, 127.9798]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4534, sigma²=3069.8162, nugget=0.0011\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10719.6 | 103.535 | 60.0592 | -0.6877 | 0.39s |\n", - "| OLS_sphere | 500 | 424.408 | 20.6012 | 12.1568 | 0.9332 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3960.92 | 62.9358 | 41.2616 | 0.3764 | 60.77s |\n", - "| DeepKriging_sphere | 50 | 47.5106 | 6.8928 | 4.4416 | 0.9925 | 41.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 39.9038 | 6.3169 | 3.8252 | 0.9937 | 52.89s |\n", - "| UniversalKriging | 100 | 212.71 | 14.5846 | 7.3444 | 0.9665 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:30:45\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.0234 (1.4537), Variance: 5283.3386, Range: [-127.9616, 127.4728]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5408, sigma²=3388.4839, nugget=0.0042\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|----------|-----------|--------|\n", - "| OLS_wendland | -- | 647329 | 804.568 | 145.893 | -104.485 | 0.37s |\n", - "| OLS_sphere | 500 | 378.879 | 19.4648 | 12.3129 | 0.9383 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4609.07 | 67.8901 | 52.8053 | 0.2489 | 65.21s |\n", - "| DeepKriging_sphere | 50 | 127.373 | 11.286 | 6.8004 | 0.9792 | 42.05s |\n", - "| DeepKriging_sphere_Huber | 50 | 93.8736 | 9.6888 | 5.3406 | 0.9847 | 51.93s |\n", - "| UniversalKriging | 100 | 271.636 | 16.4814 | 8.2635 | 0.9557 | 3.44s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:34:00\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2345 (1.4800), Variance: 5475.7498, Range: [-127.9250, 127.7404]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4945, sigma²=3340.6942, nugget=0.0033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 333530 | 577.521 | 95.7746 | -48.2028 | 0.36s |\n", - "| OLS_sphere | 500 | 181.214 | 13.4616 | 8.7399 | 0.9733 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4916.07 | 70.1147 | 45.4684 | 0.2748 | 63.24s |\n", - "| DeepKriging_sphere | 50 | 50.4758 | 7.1046 | 4.089 | 0.9926 | 42.39s |\n", - "| DeepKriging_sphere_Huber | 50 | 56.4726 | 7.5148 | 4.0337 | 0.9917 | 52.30s |\n", - "| UniversalKriging | 100 | 88.0886 | 9.3856 | 5.0034 | 0.987 | 3.50s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:37:12\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.0042 (1.4656), Variance: 5370.2422, Range: [-127.3421, 127.9991]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5052, sigma²=3311.7519, nugget=0.0066\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15024.3 | 122.574 | 65.5635 | -1.6026 | 0.38s |\n", - "| OLS_sphere | 500 | 259.987 | 16.1241 | 11.0104 | 0.955 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3333.28 | 57.7346 | 44.2017 | 0.4226 | 64.14s |\n", - "| DeepKriging_sphere | 50 | 51.4203 | 7.1708 | 4.4022 | 0.9911 | 41.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.2976 | 7.7005 | 3.7738 | 0.9897 | 39.40s |\n", - "| UniversalKriging | 100 | 188.871 | 13.7431 | 6.9783 | 0.9673 | 3.78s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:40:20\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5398 (1.4768), Variance: 5452.6078, Range: [-127.9916, 127.9764]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4670, sigma²=3087.9057, nugget=0.0033\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5796.77 | 76.1365 | 57.4236 | -0.0159 | 0.44s |\n", - "| OLS_sphere | 500 | 541.841 | 23.2775 | 13.9704 | 0.905 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3674.87 | 60.6207 | 45.7802 | 0.356 | 67.68s |\n", - "| DeepKriging_sphere | 50 | 102.649 | 10.1316 | 5.7114 | 0.982 | 40.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 137.259 | 11.7158 | 5.4482 | 0.9759 | 40.29s |\n", - "| UniversalKriging | 100 | 248.148 | 15.7527 | 8.7127 | 0.9565 | 0.28s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:43:25\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.8781 (1.4640), Variance: 5358.5662, Range: [-127.9276, 127.9543]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4909, sigma²=3137.2837, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 38101.1 | 195.195 | 75.0087 | -4.1312 | 0.43s |\n", - "| OLS_sphere | 500 | 359.551 | 18.9618 | 11.8443 | 0.9516 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4062.41 | 63.737 | 47.5059 | 0.4529 | 64.15s |\n", - "| DeepKriging_sphere | 50 | 198.664 | 14.0948 | 7.7248 | 0.9732 | 39.24s |\n", - "| DeepKriging_sphere_Huber | 50 | 155.261 | 12.4604 | 5.8936 | 0.9791 | 41.60s |\n", - "| UniversalKriging | 100 | 275.791 | 16.607 | 8.3706 | 0.9629 | 3.31s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:46:31\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.3673 (1.4868), Variance: 5526.4354, Range: [-127.8918, 127.7594]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5178, sigma²=3436.9504, nugget=0.0024\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 13596.9 | 116.606 | 68.3535 | -1.2311 | 0.39s |\n", - "| OLS_sphere | 500 | 327.162 | 18.0876 | 11.4803 | 0.9463 | 0.08s |\n", - "| DeepKriging_wendland | -- | 12424.7 | 111.466 | 52.7919 | -1.0387 | 63.05s |\n", - "| DeepKriging_sphere | 50 | 77.7037 | 8.815 | 4.4121 | 0.9872 | 43.23s |\n", - "| DeepKriging_sphere_Huber | 50 | 70.5261 | 8.398 | 4.5748 | 0.9884 | 41.97s |\n", - "| UniversalKriging | 100 | 157.591 | 12.5535 | 6.7095 | 0.9741 | 3.61s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:49:42\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2114 (1.4937), Variance: 5578.0620, Range: [-127.9808, 127.8940]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4725, sigma²=3067.1362, nugget=0.0049\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5473.13 | 73.9806 | 55.7127 | 0.2004 | 0.35s |\n", - "| OLS_sphere | 500 | 381.653 | 19.5359 | 11.4841 | 0.9442 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3896.54 | 62.4223 | 45.2167 | 0.4307 | 67.57s |\n", - "| DeepKriging_sphere | 50 | 41.6081 | 6.4504 | 4.2482 | 0.9939 | 37.91s |\n", - "| DeepKriging_sphere_Huber | 50 | 46.1231 | 6.7914 | 4.2199 | 0.9933 | 45.60s |\n", - "| UniversalKriging | 100 | 173.285 | 13.1638 | 6.7236 | 0.9747 | 3.74s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:52:57\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5236 (1.4917), Variance: 5562.9679, Range: [-127.7895, 127.9189]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4233, sigma²=2958.5361, nugget=0.0097\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10468 | 102.313 | 59.602 | -0.6663 | 0.34s |\n", - "| OLS_sphere | 500 | 262.947 | 16.2156 | 10.5404 | 0.9581 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4354.65 | 65.9898 | 43.3767 | 0.3068 | 72.52s |\n", - "| DeepKriging_sphere | 50 | 41.7078 | 6.4582 | 4.3467 | 0.9934 | 38.30s |\n", - "| DeepKriging_sphere_Huber | 50 | 48.1322 | 6.9377 | 4.2652 | 0.9923 | 38.01s |\n", - "| UniversalKriging | 100 | 114.906 | 10.7194 | 6.1052 | 0.9817 | 4.15s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:56:06\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.8401 (1.4778), Variance: 5459.7623, Range: [-127.8541, 127.8672]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4544, sigma²=2959.3140, nugget=0.0018\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 39936.1 | 199.84 | 63.777 | -5.3723 | 0.43s |\n", - "| OLS_sphere | 500 | 452.031 | 21.261 | 12.7133 | 0.9279 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3753.17 | 61.2631 | 45.0817 | 0.4011 | 65.19s |\n", - "| DeepKriging_sphere | 50 | 124.592 | 11.1621 | 5.4924 | 0.9801 | 47.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 123.777 | 11.1255 | 4.6221 | 0.9802 | 50.70s |\n", - "| UniversalKriging | 100 | 262.146 | 16.1909 | 6.9164 | 0.9582 | 3.51s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 05:59:36\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.7851 (1.4901), Variance: 5550.6704, Range: [-127.9824, 127.8131]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4401, sigma²=2965.1149, nugget=0.0025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 17821.4 | 133.497 | 68.0846 | -1.6781 | 0.37s |\n", - "| OLS_sphere | 500 | 249.304 | 15.7894 | 9.8149 | 0.9625 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4483.93 | 66.9622 | 49.6751 | 0.3262 | 63.56s |\n", - "| DeepKriging_sphere | 50 | 65.3661 | 8.0849 | 4.6674 | 0.9902 | 43.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 94.0872 | 9.6999 | 5.0967 | 0.9859 | 42.05s |\n", - "| UniversalKriging | 100 | 188.885 | 13.7436 | 7.4403 | 0.9716 | 3.63s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:02:54\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.1370 (1.4773), Variance: 5456.0316, Range: [-127.9784, 127.8074]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5119, sigma²=3443.5371, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5053.77 | 71.0899 | 56.4628 | 0.2006 | 0.43s |\n", - "| OLS_sphere | 500 | 350.726 | 18.7277 | 12.3678 | 0.9445 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3918.8 | 62.6003 | 47.9738 | 0.3802 | 67.55s |\n", - "| DeepKriging_sphere | 50 | 65.2221 | 8.076 | 4.7322 | 0.9897 | 38.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 230.361 | 15.1777 | 5.9783 | 0.9636 | 44.08s |\n", - "| UniversalKriging | 100 | 181.743 | 13.4812 | 7.6351 | 0.9713 | 3.48s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:06:15\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.5032 (1.4821), Variance: 5491.6180, Range: [-127.9391, 127.9854]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5048, sigma²=3340.4579, nugget=0.0012\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 73384.6 | 270.896 | 78.5751 | -10.7342 | 0.52s |\n", - "| OLS_sphere | 500 | 277.247 | 16.6507 | 10.3606 | 0.9557 | 0.06s |\n", - "| DeepKriging_wendland | -- | 7084.22 | 84.1678 | 45.6094 | -0.1328 | 58.71s |\n", - "| DeepKriging_sphere | 50 | 44.498 | 6.6707 | 4.2451 | 0.9929 | 39.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 40.6099 | 6.3726 | 3.6359 | 0.9935 | 42.89s |\n", - "| UniversalKriging | 100 | 131.995 | 11.4889 | 5.2458 | 0.9789 | 3.68s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:09:21\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3408 (1.4790), Variance: 5468.4577, Range: [-127.9979, 127.9685]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4160, sigma²=2745.2170, nugget=0.0089\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5261.76 | 72.538 | 57.2842 | 0.2377 | 0.40s |\n", - "| OLS_sphere | 500 | 431.443 | 20.7712 | 12.1554 | 0.9375 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4582.83 | 67.6966 | 51.1157 | 0.3361 | 65.44s |\n", - "| DeepKriging_sphere | 50 | 190.696 | 13.8093 | 6.5042 | 0.9724 | 36.65s |\n", - "| DeepKriging_sphere_Huber | 50 | 188.752 | 13.7387 | 5.5389 | 0.9727 | 64.91s |\n", - "| UniversalKriging | 100 | 326.72 | 18.0754 | 8.9236 | 0.9527 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:12:58\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.7839 (1.4866), Variance: 5524.9187, Range: [-127.9397, 127.8975]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5076, sigma²=3284.9419, nugget=0.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9049.83 | 95.1306 | 61.3922 | -0.2426 | 0.42s |\n", - "| OLS_sphere | 500 | 292.442 | 17.1009 | 11.0026 | 0.9598 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4672.3 | 68.3542 | 49.4936 | 0.3584 | 59.76s |\n", - "| DeepKriging_sphere | 50 | 56.5997 | 7.5233 | 4.3696 | 0.9922 | 50.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.4164 | 8.026 | 4.5576 | 0.9912 | 49.27s |\n", - "| UniversalKriging | 100 | 207.205 | 14.3946 | 7.9291 | 0.9715 | 3.59s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:16:30\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9594 (1.4773), Variance: 5456.3230, Range: [-127.9375, 127.9092]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4897, sigma²=3049.2549, nugget=0.0012\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5416.31 | 73.5956 | 57.5477 | 0.2073 | 0.40s |\n", - "| OLS_sphere | 500 | 269.715 | 16.423 | 9.98 | 0.9605 | 0.06s |\n", - "| DeepKriging_wendland | -- | 5568.94 | 74.6254 | 47.2234 | 0.1849 | 63.82s |\n", - "| DeepKriging_sphere | 50 | 106.248 | 10.3077 | 4.8893 | 0.9844 | 40.98s |\n", - "| DeepKriging_sphere_Huber | 50 | 127.866 | 11.3078 | 5.6678 | 0.9813 | 39.60s |\n", - "| UniversalKriging | 100 | 182.841 | 13.5219 | 6.6958 | 0.9732 | 3.55s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:19:44\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.1521 (1.4517), Variance: 5268.5454, Range: [-127.8582, 127.9632]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4746, sigma²=3037.2870, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5821.96 | 76.3018 | 56.0093 | 0.1036 | 0.38s |\n", - "| OLS_sphere | 500 | 399.519 | 19.988 | 12.5434 | 0.9385 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3498.39 | 59.1472 | 42.4938 | 0.4613 | 63.64s |\n", - "| DeepKriging_sphere | 50 | 99.802 | 9.9901 | 5.1301 | 0.9846 | 37.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 86.7278 | 9.3128 | 4.3035 | 0.9866 | 44.98s |\n", - "| UniversalKriging | 100 | 276.932 | 16.6413 | 7.8919 | 0.9574 | 3.39s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:23:02\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.0463 (1.4862), Variance: 5521.6496, Range: [-127.7802, 127.9364]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4585, sigma²=3133.6062, nugget=0.0073\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10873.3 | 104.275 | 63.885 | -0.687 | 0.38s |\n", - "| OLS_sphere | 500 | 330.16 | 18.1703 | 11.5768 | 0.9488 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4361.23 | 66.0396 | 49.224 | 0.3234 | 62.47s |\n", - "| DeepKriging_sphere | 50 | 60.2246 | 7.7605 | 4.8943 | 0.9907 | 40.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 60.378 | 7.7703 | 4.7056 | 0.9906 | 45.64s |\n", - "| UniversalKriging | 100 | 157.942 | 12.5675 | 7.0318 | 0.9755 | 4.03s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:26:28\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2424 (1.4775), Variance: 5457.2610, Range: [-127.9849, 127.9520]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4943, sigma²=3186.0692, nugget=0.0031\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7584.37 | 87.0883 | 61.6268 | 0.0134 | 0.38s |\n", - "| OLS_sphere | 500 | 269.37 | 16.4125 | 10.7466 | 0.965 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3718.94 | 60.9831 | 44.9108 | 0.5162 | 64.02s |\n", - "| DeepKriging_sphere | 50 | 42.2532 | 6.5002 | 3.6275 | 0.9945 | 41.58s |\n", - "| DeepKriging_sphere_Huber | 50 | 57.1001 | 7.5565 | 4.259 | 0.9926 | 41.89s |\n", - "| UniversalKriging | 100 | 164.785 | 12.8368 | 6.3992 | 0.9786 | 3.37s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:29:50\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.8791 (1.4872), Variance: 5529.6059, Range: [-127.9713, 127.9436]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5060, sigma²=3343.8639, nugget=0.0014\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6097.66 | 78.0875 | 60.8726 | 0.1843 | 0.43s |\n", - "| OLS_sphere | 500 | 298.628 | 17.2808 | 11.4916 | 0.9601 | 0.07s |\n", - "| DeepKriging_wendland | -- | 5353.59 | 73.1682 | 52.3382 | 0.2839 | 63.00s |\n", - "| DeepKriging_sphere | 50 | 62.4875 | 7.9049 | 4.0182 | 0.9916 | 43.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.5363 | 7.3849 | 4.1379 | 0.9927 | 44.65s |\n", - "| UniversalKriging | 100 | 171.215 | 13.0849 | 6.2053 | 0.9771 | 3.64s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:33:21\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4004 (1.4868), Variance: 5526.6378, Range: [-127.9424, 127.6391]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5344, sigma²=3553.5295, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4765.57 | 69.0331 | 53.8748 | 0.2711 | 0.37s |\n", - "| OLS_sphere | 500 | 431.046 | 20.7616 | 11.7945 | 0.9341 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4048.87 | 63.6308 | 46.7863 | 0.3807 | 67.93s |\n", - "| DeepKriging_sphere | 50 | 117.859 | 10.8563 | 4.9963 | 0.982 | 44.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 116.113 | 10.7756 | 5.2042 | 0.9822 | 56.32s |\n", - "| UniversalKriging | 100 | 237.582 | 15.4137 | 7.3401 | 0.9637 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:37:05\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.5026 (1.4744), Variance: 5434.9398, Range: [-127.9995, 127.8942]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4459, sigma²=2869.5888, nugget=0.0017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5543.35 | 74.4537 | 56.2217 | 0.191 | 0.42s |\n", - "| OLS_sphere | 500 | 255.436 | 15.9824 | 10.3249 | 0.9627 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4837.7 | 69.5536 | 47.151 | 0.294 | 64.54s |\n", - "| DeepKriging_sphere | 50 | 45.4651 | 6.7428 | 3.737 | 0.9934 | 39.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 31.7796 | 5.6373 | 3.3225 | 0.9954 | 44.88s |\n", - "| UniversalKriging | 100 | 123.565 | 11.116 | 6.3457 | 0.982 | 3.28s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:40:33\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.2395 (1.4725), Variance: 5420.3070, Range: [-127.9512, 127.5573]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4945, sigma²=3344.7937, nugget=0.0026\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5306.66 | 72.8468 | 56.7467 | 0.1147 | 0.44s |\n", - "| OLS_sphere | 500 | 373.712 | 19.3316 | 12.434 | 0.9377 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3640.73 | 60.3384 | 47.6375 | 0.3926 | 60.32s |\n", - "| DeepKriging_sphere | 50 | 44.0801 | 6.6393 | 4.2026 | 0.9926 | 72.30s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.0973 | 7.6875 | 4.507 | 0.9901 | 44.09s |\n", - "| UniversalKriging | 100 | 126.126 | 11.2306 | 6.1478 | 0.979 | 3.53s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:44:33\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.9505 (1.4497), Variance: 5253.9886, Range: [-127.9405, 127.9370]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4865, sigma²=3214.6573, nugget=0.0038\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4156.93 | 64.4742 | 49.967 | 0.3277 | 0.47s |\n", - "| OLS_sphere | 500 | 294.973 | 17.1748 | 10.3983 | 0.9523 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3139.55 | 56.0317 | 38.0635 | 0.4922 | 66.80s |\n", - "| DeepKriging_sphere | 50 | 39.4925 | 6.2843 | 4.511 | 0.9936 | 38.67s |\n", - "| DeepKriging_sphere_Huber | 50 | 35.1483 | 5.9286 | 3.556 | 0.9943 | 43.70s |\n", - "| UniversalKriging | 100 | 111.636 | 10.5658 | 5.9413 | 0.9819 | 3.93s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:48:01\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3982 (1.4769), Variance: 5453.3232, Range: [-127.8661, 127.9624]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5655, sigma²=3553.1704, nugget=0.0030\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 151419 | 389.125 | 84.4477 | -21.2967 | 0.36s |\n", - "| OLS_sphere | 500 | 345.244 | 18.5807 | 11.4279 | 0.9492 | 0.07s |\n", - "| DeepKriging_wendland | -- | 6010.52 | 77.5276 | 51.4341 | 0.1149 | 68.79s |\n", - "| DeepKriging_sphere | 50 | 53.2369 | 7.2964 | 4.9642 | 0.9922 | 41.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 71.7123 | 8.4683 | 5.3627 | 0.9894 | 39.34s |\n", - "| UniversalKriging | 100 | 227.249 | 15.0748 | 7.8564 | 0.9665 | 4.58s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:51:32\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.0577 (1.4640), Variance: 5358.2494, Range: [-127.9989, 127.9693]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4188, sigma²=2903.0402, nugget=0.0072\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5552.87 | 74.5176 | 58.6322 | 0.1684 | 0.45s |\n", - "| OLS_sphere | 500 | 377.745 | 19.4357 | 12.3009 | 0.9434 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4091.12 | 63.9619 | 46.1176 | 0.3873 | 64.81s |\n", - "| DeepKriging_sphere | 50 | 74.205 | 8.6142 | 5.0364 | 0.9889 | 68.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 66.1739 | 8.1347 | 4.6931 | 0.9901 | 55.42s |\n", - "| UniversalKriging | 100 | 205.549 | 14.337 | 7.9719 | 0.9692 | 3.69s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:55:48\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.3609 (1.5106), Variance: 5704.4801, Range: [-127.9918, 127.9510]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4955, sigma²=3298.5170, nugget=0.0021\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 120738 | 347.473 | 77.3623 | -19.5688 | 0.42s |\n", - "| OLS_sphere | 500 | 489.481 | 22.1242 | 12.6407 | 0.9166 | 0.11s |\n", - "| DeepKriging_wendland | -- | 4411.33 | 66.4178 | 44.9118 | 0.2485 | 64.70s |\n", - "| DeepKriging_sphere | 50 | 48.9924 | 6.9995 | 3.9061 | 0.9917 | 48.96s |\n", - "| DeepKriging_sphere_Huber | 50 | 51.7253 | 7.192 | 4.087 | 0.9912 | 47.59s |\n", - "| UniversalKriging | 100 | 214.731 | 14.6537 | 7.4429 | 0.9634 | 0.80s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 06:59:30\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.2112 (1.4933), Variance: 5574.8590, Range: [-127.7641, 127.7805]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5343, sigma²=3221.8628, nugget=0.0028\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4355.47 | 65.996 | 50.9772 | 0.2402 | 0.51s |\n", - "| OLS_sphere | 500 | 284.592 | 16.8699 | 10.9595 | 0.9504 | 0.07s |\n", - "| DeepKriging_wendland | -- | 3277.75 | 57.2516 | 42.2185 | 0.4282 | 67.40s |\n", - "| DeepKriging_sphere | 50 | 57.6846 | 7.595 | 4.0769 | 0.9899 | 41.38s |\n", - "| DeepKriging_sphere_Huber | 50 | 104.507 | 10.2229 | 4.7113 | 0.9818 | 55.98s |\n", - "| UniversalKriging | 100 | 209.92 | 14.4886 | 7.0342 | 0.9634 | 3.58s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:03:22\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3737 (1.4740), Variance: 5431.8190, Range: [-127.8785, 127.9619]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4504, sigma²=3071.1470, nugget=0.0081\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4986.39 | 70.6143 | 56.2552 | 0.2535 | 0.34s |\n", - "| OLS_sphere | 500 | 360.523 | 18.9874 | 12.1111 | 0.946 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4166.28 | 64.5468 | 47.4014 | 0.3763 | 68.33s |\n", - "| DeepKriging_sphere | 50 | 76.4502 | 8.7436 | 4.6431 | 0.9886 | 42.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 65.1519 | 8.0717 | 4.1387 | 0.9902 | 60.38s |\n", - "| UniversalKriging | 100 | 200.695 | 14.1667 | 6.9687 | 0.97 | 1.17s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:07:21\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.2425 (1.4719), Variance: 5416.1338, Range: [-127.7052, 127.9518]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4236, sigma²=2883.2874, nugget=0.0084\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11092.7 | 105.322 | 62.7469 | -0.548 | -2.54s |\n", - "| OLS_sphere | 500 | 313.586 | 17.7084 | 11.5015 | 0.9562 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4831.07 | 69.5059 | 45.9651 | 0.3258 | 70.97s |\n", - "| DeepKriging_sphere | 50 | 26.6238 | 5.1598 | 3.4467 | 0.9963 | 44.92s |\n", - "| DeepKriging_sphere_Huber | 50 | 32.034 | 5.6599 | 3.7678 | 0.9955 | 36.16s |\n", - "| UniversalKriging | 100 | 144.795 | 12.0331 | 5.9389 | 0.9798 | 3.67s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:11:03\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.1105 (1.4666), Variance: 5377.0048, Range: [-127.6737, 127.8760]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4727, sigma²=3182.4654, nugget=0.0057\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7956.22 | 89.1977 | 64.8589 | -0.3011 | 0.42s |\n", - "| OLS_sphere | 500 | 273.142 | 16.527 | 10.5472 | 0.9553 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4498.95 | 67.0742 | 48.076 | 0.2643 | 65.49s |\n", - "| DeepKriging_sphere | 50 | 54.9404 | 7.4122 | 4.6484 | 0.991 | 45.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 42.4006 | 6.5116 | 3.9157 | 0.9931 | 43.03s |\n", - "| UniversalKriging | 100 | 168.231 | 12.9704 | 6.6069 | 0.9725 | 3.65s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:14:48\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.5270 (1.4662), Variance: 5374.7019, Range: [-127.9080, 127.7636]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4344, sigma²=2962.0544, nugget=0.0083\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7316.69 | 85.5376 | 63.0202 | -0.0522 | 0.39s |\n", - "| OLS_sphere | 500 | 410.173 | 20.2527 | 12.0263 | 0.941 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4992.71 | 70.6591 | 52.6406 | 0.282 | 67.37s |\n", - "| DeepKriging_sphere | 50 | 97.3894 | 9.8686 | 4.5693 | 0.986 | 43.99s |\n", - "| DeepKriging_sphere_Huber | 50 | 75.0967 | 8.6658 | 4.1696 | 0.9892 | 43.71s |\n", - "| UniversalKriging | 100 | 266 | 16.3095 | 7.5499 | 0.9617 | 3.57s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:18:32\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.4846 (1.4531), Variance: 5278.9050, Range: [-127.6724, 127.8338]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5069, sigma²=3337.2369, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5525.93 | 74.3366 | 58.5539 | 0.1815 | 0.41s |\n", - "| OLS_sphere | 500 | 458.72 | 21.4177 | 12.3177 | 0.9321 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4375.9 | 66.1506 | 48.145 | 0.3518 | 61.38s |\n", - "| DeepKriging_sphere | 50 | 84.9707 | 9.218 | 5.3952 | 0.9874 | 41.00s |\n", - "| DeepKriging_sphere_Huber | 50 | 80.4466 | 8.9692 | 4.535 | 0.9881 | 48.25s |\n", - "| UniversalKriging | 100 | 292.384 | 17.0992 | 8.1432 | 0.9567 | 3.58s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:22:13\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.5428 (1.4921), Variance: 5566.0671, Range: [-127.9427, 127.9957]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4730, sigma²=3210.9188, nugget=0.0055\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5412 | 73.5663 | 55.9979 | 0.1905 | 0.41s |\n", - "| OLS_sphere | 500 | 309.785 | 17.6007 | 11.4422 | 0.9537 | 0.06s |\n", - "| DeepKriging_wendland | -- | 4573.57 | 67.6282 | 47.8006 | 0.3159 | 63.74s |\n", - "| DeepKriging_sphere | 50 | 59.8992 | 7.7395 | 5.0657 | 0.991 | 41.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 60.0658 | 7.7502 | 5.284 | 0.991 | 38.23s |\n", - "| UniversalKriging | 100 | 167.919 | 12.9584 | 6.9209 | 0.9749 | 2.74s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:25:50\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.1971 (1.4550), Variance: 5292.3073, Range: [-127.9798, 127.9936]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4961, sigma²=3220.9010, nugget=0.0013\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5501 | 74.1687 | 58.874 | 0.0934 | 0.39s |\n", - "| OLS_sphere | 500 | 295.382 | 17.1867 | 11.2978 | 0.9513 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3808.7 | 61.7147 | 44.5768 | 0.3723 | 64.95s |\n", - "| DeepKriging_sphere | 50 | 68.4102 | 8.271 | 5.4842 | 0.9887 | 40.44s |\n", - "| DeepKriging_sphere_Huber | 50 | 102.287 | 10.1137 | 4.8921 | 0.9831 | 44.33s |\n", - "| UniversalKriging | 100 | 140.98 | 11.8735 | 6.3696 | 0.9768 | 3.29s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:29:33\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.1615 (1.4785), Variance: 5464.6077, Range: [-127.9334, 127.9610]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4660, sigma²=3216.7699, nugget=0.0061\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5713.42 | 75.5872 | 58.7774 | 0.1281 | 0.46s |\n", - "| OLS_sphere | 500 | 298.42 | 17.2748 | 10.8428 | 0.9545 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4753.06 | 68.9424 | 48.9515 | 0.2747 | 65.34s |\n", - "| DeepKriging_sphere | 50 | 44.5866 | 6.6773 | 4.0385 | 0.9932 | 57.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 62.3404 | 7.8956 | 4.3133 | 0.9905 | 46.31s |\n", - "| UniversalKriging | 100 | 134.612 | 11.6022 | 5.7308 | 0.9795 | 3.33s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:33:39\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.6505 (1.4818), Variance: 5489.3390, Range: [-127.9916, 127.8206]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5182, sigma²=3282.8688, nugget=0.0023\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4641.99 | 68.1322 | 50.7717 | 0.2289 | 0.44s |\n", - "| OLS_sphere | 500 | 250.117 | 15.8151 | 11.06 | 0.9585 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3359.85 | 57.9642 | 40.1352 | 0.4419 | 65.03s |\n", - "| DeepKriging_sphere | 50 | 25.4199 | 5.0418 | 3.1962 | 0.9958 | 39.76s |\n", - "| DeepKriging_sphere_Huber | 50 | 27.6226 | 5.2557 | 3.198 | 0.9954 | 42.39s |\n", - "| UniversalKriging | 100 | 136.259 | 11.673 | 6.4663 | 0.9774 | 3.61s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:37:23\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 1.4036 (1.5064), Variance: 5673.3777, Range: [-127.9801, 127.9851]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5249, sigma²=3442.3865, nugget=0.0032\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4690.9 | 68.4902 | 53.9267 | 0.2673 | 0.40s |\n", - "| OLS_sphere | 500 | 367.141 | 19.1609 | 12.2395 | 0.9427 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3405.97 | 58.3607 | 44.0378 | 0.468 | 66.39s |\n", - "| DeepKriging_sphere | 50 | 67.68 | 8.2268 | 5.73 | 0.9894 | 39.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 68.7749 | 8.2931 | 4.7087 | 0.9893 | 45.67s |\n", - "| UniversalKriging | 100 | 229.399 | 15.1459 | 7.8073 | 0.9642 | 3.66s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:41:14\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.0490 (1.5000), Variance: 5625.3446, Range: [-127.9306, 127.9925]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4752, sigma²=3114.9994, nugget=0.0035\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5069.83 | 71.2028 | 55.6906 | 0.1468 | 0.40s |\n", - "| OLS_sphere | 500 | 286.68 | 16.9316 | 11.0031 | 0.9518 | 0.05s |\n", - "| DeepKriging_wendland | -- | 3651.37 | 60.4265 | 45.4267 | 0.3855 | 69.32s |\n", - "| DeepKriging_sphere | 50 | 56.8816 | 7.542 | 4.465 | 0.9904 | 38.45s |\n", - "| DeepKriging_sphere_Huber | 50 | 31.4667 | 5.6095 | 3.6047 | 0.9947 | 43.02s |\n", - "| UniversalKriging | 100 | 176.995 | 13.304 | 6.5767 | 0.9702 | 2.99s |\n" - ] - }, - { - "data": { - "application/javascript": [ - "IPython.notebook.save_checkpoint()" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-01-20 07:45:03\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.019138, - "end_time": "2026-01-19T23:45:03.784939", - "exception": false, - "start_time": "2026-01-19T23:45:03.765801", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-19T23:45:03.826354Z", - "iopub.status.busy": "2026-01-19T23:45:03.825468Z", - "iopub.status.idle": "2026-01-19T23:45:03.855777Z", - "shell.execute_reply": "2026-01-19T23:45:03.852432Z" - }, - "papermill": { - "duration": 0.054969, - "end_time": "2026-01-19T23:45:03.858543", - "exception": false, - "start_time": "2026-01-19T23:45:03.803574", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 34974.68±101869.24 | 130.08±134.36 | 62.99±14.63 | -4.47±16.29 |\n", - "| OLS_sphere | 343.72±80.61 | 18.42±2.11 | 11.55±1.00 | 0.95±0.01 |\n", - "| DeepKriging_wendland | 4655.07±1763.14 | 67.37±10.77 | 46.84±3.52 | 0.28±0.28 |\n", - "| DeepKriging_sphere | 180.29±767.64 | 9.52±9.47 | 5.87±7.93 | 0.97±0.12 |\n", - "| DeepKriging_sphere_Huber | 77.98±41.29 | 8.57±2.15 | 4.61±0.73 | 0.99±0.01 |\n", - "| UniversalKriging | 191.57±56.84 | 13.69±2.05 | 7.02±0.92 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 8.57±2.15)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 10884.492107, - "end_time": "2026-01-19T23:45:07.336373", - "environment_variables": {}, - "exception": null, - "input_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario2/simulation_eggholder_spherical.ipynb", - "output_path": "/home/user/MLspatial/notebook/Final/Simulation/Scenario2/simulation_eggholder_spherical.ipynb", - "parameters": {}, - "start_time": "2026-01-19T20:43:42.844266", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebook/test/experiment_spherical_GP_without_outliers_SNR.ipynb b/notebook/test/experiment_spherical_GP_without_outliers_SNR.ipynb deleted file mode 100644 index a4677f7..0000000 --- a/notebook/test/experiment_spherical_GP_without_outliers_SNR.ipynb +++ /dev/null @@ -1,1463 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-13 17:14:45.639712: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770974085.669844 861795 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770974085.674575 861795 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770974085.686386 861795 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770974085.686408 861795 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770974085.686409 861795 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770974085.686411 861795 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ GPU found and configured: ['/physical_device:GPU:0']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770974089.258040 861795 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - } - ], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " policy = mixed_precision.Policy(dtype)\n", - " mixed_precision.set_global_policy(policy)\n", - " \n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " if gpus:\n", - " for gpu in gpus:\n", - " tf.config.experimental.set_memory_growth(gpu, True)\n", - " print(f\"✅ GPU found and configured: {[gpu.name for gpu in gpus]}\")\n", - " else:\n", - " print(\"⚠️ No GPU found\")\n", - " \n", - " return tf.distribute.OneDeviceStrategy(\"/GPU:0\" if gpus else \"/CPU:0\")\n", - "\n", - "strategy = init_hardware()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed, signal_scale):\n", - " \"\"\"\n", - " Experiment: Stationary Gaussian processes + Linear Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Linear Mean Function: 2*x + 4*y - z\n", - " mean_term = signal_scale * (2.0 * x_cart + 4.0 * y_cart - z_cart).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " spatial_noise = (L @ z_std).astype(np.float32)\n", - " y = mean_term + spatial_noise\n", - " \n", - " # Calculate Signal-to-Noise Ratio (SNR) statistics\n", - " signal_var = np.var(mean_term, ddof=1)\n", - " noise_var = np.var(spatial_noise, ddof=1)\n", - " snr = signal_var / noise_var\n", - " snr_db = 10 * np.log10(snr)\n", - " \n", - " total_var = np.var(y, ddof=1)\n", - " \n", - " # Calculate Standard Errors (SE) pre-calculation for cleaner code\n", - " se_signal = np.std(mean_term, ddof=1) / np.sqrt(num_sample)\n", - " se_noise = np.std(spatial_noise, ddof=1) / np.sqrt(num_sample)\n", - " se_combined = np.std(y, ddof=1) / np.sqrt(num_sample)\n", - "\n", - " print(f\"\\n📊 Simulate Data Statistics (signal_scale={signal_scale:.2f})\")\n", - " print(\"=\" * 80)\n", - "\n", - " print(f\"\\n{'Component':<15} {'Mean':>10} {'SE':>10} {'Variance':>10} {'Range':>30}\")\n", - " print(\"-\" * 80)\n", - " print(f\"{'Signal':<15} {np.mean(mean_term):>10.4f} {se_signal:>10.4f} {signal_var:>10.4f} [{np.min(mean_term):>7.4f}, {np.max(mean_term):>7.4f}]\")\n", - " print(f\"{'Noise':<15} {np.mean(spatial_noise):>10.4f} {se_noise:>10.4f} {noise_var:>10.4f} [{np.min(spatial_noise):>7.4f}, {np.max(spatial_noise):>7.4f}]\")\n", - " print(f\"{'Combined':<15} {np.mean(y):>10.4f} {se_combined:>10.4f} {total_var:>10.4f} [{np.min(y):>7.4f}, {np.max(y):>7.4f}]\")\n", - "\n", - " print(f\"\\n{'Metric':<20} {'Value':>15} {'Unit/Percent':>15}\")\n", - " print(\"-\" * 80)\n", - " print(f\"{'SNR (Linear)':<20} {snr:>15.4f} {'-':>15}\")\n", - " print(f\"{'SNR (dB)':<20} {snr_db:>15.2f} {'dB':>15}\")\n", - " print(f\"{'Signal Partition':<20} {signal_var/total_var*100:>15.1f} {'%':>15}\")\n", - " print(f\"{'Noise Partition':<20} {noise_var/total_var*100:>15.1f} {'%':>15}\")\n", - " print(\"=\" * 80)\n", - " \n", - " z = y\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " # Return both data and SNR info\n", - " snr_info = {\n", - " 'signal_var': float(signal_var),\n", - " 'noise_var': float(noise_var),\n", - " 'total_var': float(total_var),\n", - " 'snr': float(snr),\n", - " 'snr_db': float(snr_db),\n", - " 'signal_scale': float(signal_scale)\n", - " }\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " }), snr_info" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat, test_x1)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test, test_x1):\n", - " \n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " \n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " # Load best weights based on validation loss\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " # Find the epoch with best validation loss\n", - " best_epoch = np.argmin(history.history[\"val_loss\"]) + 1\n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " \n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Best_Epoch\": int(best_epoch),\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model, history, y_pred, y_test\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " tf.keras.backend.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🔧 Experiment Configuration\n", - "================================================================================\n", - "Fixed Parameters:\n", - " - Seed: 1\n", - " - Epochs: 500\n", - " - Batch size: 256\n", - " - Num samples: 2500\n", - " - Huber delta: 1.345\n", - " - Dropout rate: 0.01\n", - "\n", - "Experimental Factors:\n", - " - Models: DeepKriging_mrts, DeepKriging_sphere_Huber\n", - " - Basis K: [50, 200, 400]\n", - " - Signal scales: [1.0, 1.732051]\n", - "================================================================================\n", - "\n" - ] - } - ], - "source": [ - "# Fixed parameters\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "dropout_rate = 0.01\n", - "\n", - "# Basis parameters\n", - "knot_num = 1400\n", - "order_max = 500\n", - "\n", - "# Experimental factors\n", - "basis_K = [50, 200, 400]\n", - "signal_scales = [1.0, 1.732051]\n", - "\n", - "print(f\"\\n{'='*80}\")\n", - "print(f\"🔧 Experiment Configuration\")\n", - "print(f\"{'='*80}\")\n", - "print(f\"Fixed Parameters:\")\n", - "print(f\" - Seed: {seed}\")\n", - "print(f\" - Epochs: {epochs}\")\n", - "print(f\" - Batch size: {batch_size}\")\n", - "print(f\" - Num samples: {num_sample}\")\n", - "print(f\" - Huber delta: {huber_delta}\")\n", - "print(f\" - Dropout rate: {dropout_rate}\")\n", - "print(f\"\\nExperimental Factors:\")\n", - "print(f\" - Models: DeepKriging_mrts, DeepKriging_sphere_Huber\")\n", - "print(f\" - Basis K: {basis_K}\")\n", - "print(f\" - Signal scales: {signal_scales}\")\n", - "print(f\"{'='*80}\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outcome\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🎲 Signal Scale: 1.0x\n", - "================================================================================\n", - "\n", - "📊 Simulate Data Statistics (signal_scale=1.00)\n", - "================================================================================\n", - "\n", - "Component Mean SE Variance Range\n", - "--------------------------------------------------------------------------------\n", - "Signal -0.0392 0.0525 6.8979 [-4.5810, 4.5814]\n", - "Noise -0.2303 0.0214 1.1486 [-3.4100, 3.1657]\n", - "Combined -0.2695 0.0511 6.5192 [-5.6175, 5.3562]\n", - "\n", - "Metric Value Unit/Percent\n", - "--------------------------------------------------------------------------------\n", - "SNR (Linear) 6.0058 -\n", - "SNR (dB) 7.79 dB\n", - "Signal Partition 105.8 %\n", - "Noise Partition 17.6 %\n", - "================================================================================\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 50\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=50 | signal_scale=1.0\n", - " ✅ Best Epoch: 487, Val Loss: 0.0737, Test MSPE: 0.1275\n", - "\n", - "Training DeepKriging_sphere_Huber with K=50 | signal_scale=1.0\n", - " ✅ Best Epoch: 411, Val Loss: 0.0535, Test MSPE: 0.0834\n", - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 200\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=200 | signal_scale=1.0\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72b55ee42dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " ✅ Best Epoch: 304, Val Loss: 0.0651, Test MSPE: 0.1397\n", - "\n", - "Training DeepKriging_sphere_Huber with K=200 | signal_scale=1.0\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72b5d09db2e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " ✅ Best Epoch: 307, Val Loss: 0.0570, Test MSPE: 0.0918\n", - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 400\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=400 | signal_scale=1.0\n", - " ✅ Best Epoch: 209, Val Loss: 0.0642, Test MSPE: 0.1241\n", - "\n", - "Training DeepKriging_sphere_Huber with K=400 | signal_scale=1.0\n", - " ✅ Best Epoch: 466, Val Loss: 0.0614, Test MSPE: 0.1002\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-13 17:50:29\n", - "\n", - "✅ Completed signal scale 1.0x\n", - "\n", - "================================================================================\n", - "🎲 Signal Scale: 1.732051x\n", - "================================================================================\n", - "\n", - "📊 Simulate Data Statistics (signal_scale=1.73)\n", - "================================================================================\n", - "\n", - "Component Mean SE Variance Range\n", - "--------------------------------------------------------------------------------\n", - "Signal -0.0680 0.0910 20.6938 [-7.9346, 7.9353]\n", - "Noise -0.2303 0.0214 1.1486 [-3.4100, 3.1657]\n", - "Combined -0.2982 0.0876 19.1970 [-8.7325, 8.6027]\n", - "\n", - "Metric Value Unit/Percent\n", - "--------------------------------------------------------------------------------\n", - "SNR (Linear) 18.0173 -\n", - "SNR (dB) 12.56 dB\n", - "Signal Partition 107.8 %\n", - "Noise Partition 6.0 %\n", - "================================================================================\n", - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 50\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=50 | signal_scale=1.732051\n", - " ✅ Best Epoch: 452, Val Loss: 0.0799, Test MSPE: 0.1396\n", - "\n", - "Training DeepKriging_sphere_Huber with K=50 | signal_scale=1.732051\n", - " ✅ Best Epoch: 189, Val Loss: 0.0490, Test MSPE: 0.0980\n", - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 200\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=200 | signal_scale=1.732051\n", - " ✅ Best Epoch: 420, Val Loss: 0.0758, Test MSPE: 0.1494\n", - "\n", - "Training DeepKriging_sphere_Huber with K=200 | signal_scale=1.732051\n", - " ✅ Best Epoch: 421, Val Loss: 0.0520, Test MSPE: 0.0961\n", - "\n", - "--------------------------------------------------------------------------------\n", - "📐 Basis K = 400\n", - "--------------------------------------------------------------------------------\n", - "\n", - "Training DeepKriging_mrts with K=400 | signal_scale=1.732051\n", - " ✅ Best Epoch: 414, Val Loss: 0.0673, Test MSPE: 0.1169\n", - "\n", - "Training DeepKriging_sphere_Huber with K=400 | signal_scale=1.732051\n", - " ✅ Best Epoch: 436, Val Loss: 0.0646, Test MSPE: 0.1176\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-13 18:31:13\n", - "\n", - "✅ Completed signal scale 1.732051x\n", - "\n", - "================================================================================\n", - "🎉 All experiments completed!\n", - "================================================================================\n" - ] - } - ], - "source": [ - "# Store all results\n", - "all_results = []\n", - "snr_records = []\n", - "training_histories = {}\n", - "predictions_data = {}\n", - "\n", - "for signal_scale in signal_scales:\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🎲 Signal Scale: {signal_scale}x\")\n", - " print(f\"{'='*80}\")\n", - " \n", - " # Generate data with specific noise scale\n", - " dataframe, snr_info = simulate_data(num_sample=num_sample, seed=seed, signal_scale=signal_scale)\n", - " snr_records.append(snr_info)\n", - " \n", - " # Preprocess data\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Precompute basis functions\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " for num_basis in basis_K:\n", - " print(f\"\\n{'-'*80}\")\n", - " print(f\"📐 Basis K = {num_basis}\")\n", - " print(f\"{'-'*80}\")\n", - " \n", - " exp_key = f\"signal_{signal_scale}_K_{num_basis}\"\n", - " \n", - " # ============================================================\n", - " # Model 1: DeepKriging_mrts with Huber Loss\n", - " # ============================================================\n", - " print(f\"\\nTraining DeepKriging_mrts with K={num_basis} | signal_scale={signal_scale}\")\n", - " \n", - " Phi_mrts = max_Phi_mrts[:, :num_basis].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " \n", - " with strategy.scope():\n", - " metrics, model, history, y_pred, y_test = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, Huber(delta=huber_delta), dropout_rate, *parts\n", - " )\n", - " \n", - " # Store results\n", - " result_record = {\n", - " \"Signal_Scale\": signal_scale,\n", - " \"SNR\": snr_info['snr'],\n", - " \"SNR_dB\": snr_info['snr_db'],\n", - " \"Model\": \"DeepKriging_mrts_Huber\",\n", - " \"K\": num_basis,\n", - " \"Best_Epoch\": metrics[\"Best_Epoch\"],\n", - " \"Val_Loss\": metrics[\"Val_loss\"],\n", - " \"MSPE\": metrics[\"MSPE\"],\n", - " \"RMSE\": metrics[\"RMSE\"],\n", - " \"MAE\": metrics[\"MAE\"],\n", - " \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"]\n", - " }\n", - " all_results.append(result_record)\n", - " \n", - " # Store history for plotting\n", - " training_histories[f\"{exp_key}_mrts\"] = {\n", - " 'history': history.history,\n", - " 'model': 'DeepKriging_mrts_Huber',\n", - " 'K': num_basis,\n", - " 'noise_scale': signal_scale\n", - " }\n", - " \n", - " # Store predictions for plotting (only for the first noise scale and K=200)\n", - " if signal_scale == signal_scales[0] and num_basis == 200:\n", - " predictions_data['mrts'] = {\n", - " 'y_true': y_test,\n", - " 'y_pred': y_pred,\n", - " 'test_idx': parts[-1],\n", - " 'dataframe': dataframe,\n", - " 'model_name': 'DeepKriging_mrts_Huber'\n", - " }\n", - " \n", - " print(f\" ✅ Best Epoch: {metrics['Best_Epoch']}, Val Loss: {metrics['Val_loss']:.4f}, Test MSPE: {metrics['MSPE']:.4f}\")\n", - " \n", - " del Phi_mrts, parts, model\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - " \n", - " # ============================================================\n", - " # Model 2: DeepKriging_sphere_Huber\n", - " # ============================================================\n", - " print(f\"\\nTraining DeepKriging_sphere_Huber with K={num_basis} | signal_scale={signal_scale}\")\n", - " \n", - " Phi_sphere = max_Phi_sphere[:, :num_basis].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " \n", - " with strategy.scope():\n", - " metrics, model, history, y_pred, y_test = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), dropout_rate, *parts\n", - " )\n", - " \n", - " # Store results\n", - " result_record = {\n", - " \"Signal_Scale\": signal_scale,\n", - " \"SNR\": snr_info['snr'],\n", - " \"SNR_dB\": snr_info['snr_db'],\n", - " \"Model\": \"DeepKriging_sphere_Huber\",\n", - " \"K\": num_basis,\n", - " \"Best_Epoch\": metrics[\"Best_Epoch\"],\n", - " \"Val_Loss\": metrics[\"Val_loss\"],\n", - " \"MSPE\": metrics[\"MSPE\"],\n", - " \"RMSE\": metrics[\"RMSE\"],\n", - " \"MAE\": metrics[\"MAE\"],\n", - " \"R2\": metrics[\"R2\"],\n", - " \"Time\": metrics[\"Time\"]\n", - " }\n", - " all_results.append(result_record)\n", - " \n", - " # Store history for plotting\n", - " training_histories[f\"{exp_key}_sphere\"] = {\n", - " 'history': history.history,\n", - " 'model': 'DeepKriging_sphere_Huber',\n", - " 'K': num_basis,\n", - " 'signal_scale': signal_scale\n", - " }\n", - " \n", - " # Store predictions for plotting (only for the first noise scale and K=200)\n", - " if signal_scale == signal_scales[0] and num_basis == 200:\n", - " predictions_data['sphere'] = {\n", - " 'y_true': y_test,\n", - " 'y_pred': y_pred,\n", - " 'test_idx': parts[-1],\n", - " 'dataframe': dataframe,\n", - " 'model_name': 'DeepKriging_sphere_Huber'\n", - " }\n", - " \n", - " print(f\" ✅ Best Epoch: {metrics['Best_Epoch']}, Val Loss: {metrics['Val_loss']:.4f}, Test MSPE: {metrics['MSPE']:.4f}\")\n", - " \n", - " del Phi_sphere, parts, model\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - " \n", - " # Cleanup after each signal scale\n", - " del max_Phi_sphere, max_Phi_mrts, dataframe, location_data, location_data_norm, y_combined\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - " \n", - " save_notebook()\n", - " print(f\"\\n✅ Completed signal scale {signal_scale}x\")\n", - "\n", - "print(f\"\\n{'='*80}\")\n", - "print(f\"🎉 All experiments completed!\")\n", - "print(f\"{'='*80}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Results Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Signal to Noise Ratio" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📈 Signal-to-Noise Ratio (SNR) Summary\n", - "================================================================================\n", - "| signal_var | noise_var | total_var | snr | snr_db | signal_scale |\n", - "|--------------|-------------|-------------|----------|----------|----------------|\n", - "| 6.89793 | 1.14855 | 6.51918 | 6.00577 | 7.78569 | 1 |\n", - "| 20.6938 | 1.14855 | 19.197 | 18.0173 | 12.5569 | 1.73205 |\n" - ] - } - ], - "source": [ - "# SNR Summary\n", - "snr_df = pd.DataFrame(snr_records)\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📈 Signal-to-Noise Ratio (SNR) Summary\")\n", - "print(\"=\"*80)\n", - "print(snr_df.to_markdown(index=False, tablefmt=\"github\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Model training results" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Results Table\n", - "================================================================================\n", - "| Signal_Scale | SNR | SNR_dB | Model | K | Best_Epoch | Val_Loss | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------------|----------|----------|--------------------------|-----|--------------|------------|-----------|----------|----------|----------|---------|\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_mrts_Huber | 50 | 487 | 0.0737282 | 0.127502 | 0.357075 | 0.284655 | 0.980299 | 384.048 |\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_sphere_Huber | 50 | 411 | 0.0534953 | 0.0833742 | 0.288746 | 0.234481 | 0.987117 | 389.247 |\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_mrts_Huber | 200 | 304 | 0.0650741 | 0.139708 | 0.373775 | 0.298824 | 0.978413 | 327.555 |\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_sphere_Huber | 200 | 307 | 0.0570114 | 0.0917875 | 0.302964 | 0.243048 | 0.985817 | 343.831 |\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_mrts_Huber | 400 | 209 | 0.0641762 | 0.124072 | 0.352238 | 0.274472 | 0.980829 | 336.572 |\n", - "| 1 | 6.00577 | 7.78569 | DeepKriging_sphere_Huber | 400 | 466 | 0.0614194 | 0.100228 | 0.316588 | 0.250543 | 0.984513 | 326.854 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_mrts_Huber | 50 | 452 | 0.0798902 | 0.139575 | 0.373597 | 0.301611 | 0.992777 | 350.215 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_sphere_Huber | 50 | 189 | 0.049027 | 0.098019 | 0.31308 | 0.255479 | 0.994928 | 389.439 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_mrts_Huber | 200 | 420 | 0.0757561 | 0.149383 | 0.386501 | 0.302118 | 0.992269 | 413.592 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_sphere_Huber | 200 | 421 | 0.0519818 | 0.096065 | 0.309944 | 0.242006 | 0.995029 | 417.475 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_mrts_Huber | 400 | 414 | 0.0673239 | 0.116854 | 0.341838 | 0.265473 | 0.993953 | 428.538 |\n", - "| 1.73205 | 18.0173 | 12.5569 | DeepKriging_sphere_Huber | 400 | 436 | 0.0646174 | 0.117623 | 0.342962 | 0.264065 | 0.993913 | 432.691 |\n" - ] - } - ], - "source": [ - "# Convert results to DataFrame\n", - "results_df = pd.DataFrame(all_results)\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Results Table\")\n", - "print(\"=\"*80)\n", - "print(results_df.to_markdown(index=False, tablefmt=\"github\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualization 1: Training / Validataion Loss Curves" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# DeepKriging_mrts_Huber\n", - "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", - "fig.suptitle('DeepKriging_mrts_Huber - Training & Validation Loss', fontsize=16, fontweight='bold')\n", - "\n", - "for idx, (noise_scale, num_basis) in enumerate([(1.0, 50), (1.0, 200), (1.0, 400), \n", - " (3.0, 50), (3.0, 200), (3.0, 400)]):\n", - " row = idx // 3\n", - " col = idx % 3\n", - " ax = axes[row, col]\n", - " \n", - " exp_key = f\"signal_{signal_scale}_K_{num_basis}_mrts\"\n", - " \n", - " if exp_key in training_histories:\n", - " hist = training_histories[exp_key]['history']\n", - " epochs_range = range(1, len(hist['loss']) + 1)\n", - " \n", - " # Training loss (solid line)\n", - " ax.plot(epochs_range, hist['loss'], 'b-', linewidth=1.5, \n", - " label='Training Loss', alpha=0.7)\n", - " \n", - " # Validation loss (dashed line)\n", - " ax.plot(epochs_range, hist['val_loss'], 'b--', linewidth=1.5, \n", - " label='Validation Loss', alpha=0.7)\n", - " \n", - " # Mark best epoch on validation loss\n", - " best_epoch = np.argmin(hist['val_loss']) + 1\n", - " ax.scatter([best_epoch], [hist['val_loss'][best_epoch-1]], \n", - " color='blue', s=100, marker='*', zorder=5, \n", - " label=f'Best (epoch {best_epoch})')\n", - " \n", - " ax.set_xlabel('Epoch', fontsize=10)\n", - " ax.set_ylabel('Loss', fontsize=10)\n", - " ax.set_title(f'Noise={noise_scale}x, K={num_basis}', fontsize=11, fontweight='bold')\n", - " ax.legend(loc='upper right', fontsize=8)\n", - " ax.grid(True, alpha=0.3)\n", - " ax.set_xlim(0, epochs)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "\n", - "# DeepKriging_sphere_Huber\n", - "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", - "fig.suptitle('DeepKriging_sphere_Huber - Training & Validation Loss', fontsize=16, fontweight='bold')\n", - "\n", - "for idx, (noise_scale, num_basis) in enumerate([(1.0, 50), (1.0, 200), (1.0, 400), \n", - " (3.0, 50), (3.0, 200), (3.0, 400)]):\n", - " row = idx // 3\n", - " col = idx % 3\n", - " ax = axes[row, col]\n", - " \n", - " exp_key = f\"signal_{signal_scale}_K_{num_basis}_sphere\"\n", - " \n", - " if exp_key in training_histories:\n", - " hist = training_histories[exp_key]['history']\n", - " epochs_range = range(1, len(hist['loss']) + 1)\n", - " \n", - " # Training loss (solid line)\n", - " ax.plot(epochs_range, hist['loss'], 'r-', linewidth=1.5, \n", - " label='Training Loss', alpha=0.7)\n", - " \n", - " # Validation loss (dashed line)\n", - " ax.plot(epochs_range, hist['val_loss'], 'r--', linewidth=1.5, \n", - " label='Validation Loss', alpha=0.7)\n", - " \n", - " # Mark best epoch on validation loss\n", - " best_epoch = np.argmin(hist['val_loss']) + 1\n", - " ax.scatter([best_epoch], [hist['val_loss'][best_epoch-1]], \n", - " color='red', s=100, marker='*', zorder=5, \n", - " label=f'Best (epoch {best_epoch})')\n", - " \n", - " ax.set_xlabel('Epoch', fontsize=10)\n", - " ax.set_ylabel('Loss', fontsize=10)\n", - " ax.set_title(f'Noise={noise_scale}x, K={num_basis}', fontsize=11, fontweight='bold')\n", - " ax.legend(loc='upper right', fontsize=8)\n", - " ax.grid(True, alpha=0.3)\n", - " ax.set_xlim(0, epochs)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualization 2: Prediction Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test_idx = predictions_data['mrts']['test_idx']\n", - "df = predictions_data['mrts']['dataframe']\n", - "test_locations = df.iloc[test_idx]\n", - "y_test = predictions_data['mrts']['y_true']\n", - "\n", - "# Calculate global min/max for consistent color scale\n", - "all_values = [y_test]\n", - "for model_key in ['mrts', 'sphere']:\n", - " all_values.append(predictions_data[model_key]['y_pred'])\n", - "\n", - "all_values_concat = np.concatenate(all_values)\n", - "vmin = np.percentile(all_values_concat, 2)\n", - "vmax = np.percentile(all_values_concat, 98)\n", - "\n", - "\n", - "# Create figure with 3 subplots\n", - "fig = plt.figure(figsize=(18, 6))\n", - "\n", - "# Subplot 1: Test Data\n", - "create_subplot_robinson(\n", - " fig, (1, 3, 1), test_locations, y_test, vmin, vmax,\n", - " f'Test Data (n={len(test_idx)})',\n", - " plot_type='prediction',\n", - " cbar_label='Value'\n", - ")\n", - "\n", - "# Subplot 2: DeepKriging_mrts_Huber predictions\n", - "y_pred_mrts = predictions_data['mrts']['y_pred']\n", - "rmse_mrts = np.sqrt(mean_squared_error(y_test, y_pred_mrts))\n", - "r2_mrts = r2_score(y_test, y_pred_mrts)\n", - "\n", - "create_subplot_robinson(\n", - " fig, (1, 3, 2), test_locations, y_pred_mrts, vmin, vmax,\n", - " f'DeepKriging_mrts_Huber\\nRMSE={rmse_mrts:.4f}, R²={r2_mrts:.4f}',\n", - " plot_type='prediction',\n", - " cbar_label='Predicted Value'\n", - ")\n", - "\n", - "# Subplot 3: DeepKriging_sphere_Huber predictions\n", - "y_pred_sphere = predictions_data['sphere']['y_pred']\n", - "rmse_sphere = np.sqrt(mean_squared_error(y_test, y_pred_sphere))\n", - "r2_sphere = r2_score(y_test, y_pred_sphere)\n", - "\n", - "create_subplot_robinson(\n", - " fig, (1, 3, 3), test_locations, y_pred_sphere, vmin, vmax,\n", - " f'DeepKriging_sphere_Huber\\nRMSE={rmse_sphere:.4f}, R²={r2_sphere:.4f}',\n", - " plot_type='prediction',\n", - " cbar_label='Predicted Value'\n", - ")\n", - "\n", - "plt.suptitle(\n", - " 'Prediction Comparison: Test Data vs Model Predictions\\n'\n", - " 'Stationary Gaussian processes + Linear Mean Function',\n", - " fontsize=16, fontweight='bold', y=0.98\n", - ")\n", - "\n", - "plt.tight_layout(rect=[0, 0.02, 1, 0.95])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualization 3: Residual Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get test data\n", - "test_idx = predictions_data['mrts']['test_idx']\n", - "df = predictions_data['mrts']['dataframe']\n", - "test_locations = df.iloc[test_idx]\n", - "y_test = predictions_data['mrts']['y_true']\n", - "\n", - "# Calculate residuals\n", - "residuals_mrts = y_test - predictions_data['mrts']['y_pred']\n", - "residuals_sphere = y_test - predictions_data['sphere']['y_pred']\n", - "\n", - "# Calculate global max absolute value for symmetric color scale\n", - "vmax_abs = max(np.max(np.abs(residuals_mrts)), np.max(np.abs(residuals_sphere)))\n", - "\n", - "# Create figure with 2 subplots\n", - "fig = plt.figure(figsize=(16, 6))\n", - "\n", - "# Subplot 1: DeepKriging_mrts_Huber residuals\n", - "rmse_mrts = np.sqrt(mean_squared_error(y_test, predictions_data['mrts']['y_pred']))\n", - "mae_mrts = mean_absolute_error(y_test, predictions_data['mrts']['y_pred'])\n", - "\n", - "create_subplot_robinson(\n", - " fig, (1, 2, 1), test_locations, residuals_mrts, -vmax_abs, vmax_abs,\n", - " f'DeepKriging_mrts_Huber Residuals\\nRMSE={rmse_mrts:.4f}, MAE={mae_mrts:.4f}',\n", - " plot_type='residual',\n", - " cbar_label='Residual'\n", - ")\n", - "\n", - "# Subplot 2: DeepKriging_sphere_Huber residuals\n", - "rmse_sphere = np.sqrt(mean_squared_error(y_test, predictions_data['sphere']['y_pred']))\n", - "mae_sphere = mean_absolute_error(y_test, predictions_data['sphere']['y_pred'])\n", - "\n", - "create_subplot_robinson(\n", - " fig, (1, 2, 2), test_locations, residuals_sphere, -vmax_abs, vmax_abs,\n", - " f'DeepKriging_sphere_Huber Residuals\\nRMSE={rmse_sphere:.4f}, MAE={mae_sphere:.4f}',\n", - " plot_type='residual',\n", - " cbar_label='Residual'\n", - ")\n", - "\n", - "plt.suptitle(\n", - " 'Residuals Comparison: DeepKriging Models\\n'\n", - " 'Stationary Gaussian processes + Linear Mean Function (Signal=1.0x, K=200)',\n", - " fontsize=16, fontweight='bold', y=0.98\n", - ")\n", - "\n", - "plt.tight_layout(rect=[0, 0.02, 1, 0.95])\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers.ipynb deleted file mode 100644 index 5fb7c8f..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers.ipynb +++ /dev/null @@ -1,1684 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-06 15:34:25.351183: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770363265.365844 1544652 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770363265.369784 1544652 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770363265.381252 1544652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770363265.381273 1544652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770363265.381275 1544652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770363265.381276 1544652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/1, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.8990 (1.5865), Variance: 6292.0918, Range: [-217.1357, 204.6192]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3176.7896 3394.3245 \n", - " 100 2144.7463 2336.1426 \n", - " 200 1498.9122 1313.7180 \n", - " 500 371.2504 280.5685 \n", - " 700 301.0612 268.9745 *\n", - " 1000 1327.3986 570.5023 \n", - " 1400 5146.0850 3237.1375 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770363294.846291 1544652 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770363297.356215 1544914 service.cc:152] XLA service 0x7b42c0008480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770363297.356307 1544914 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770363297.725546 1544914 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770363300.612627 1544914 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7b43185b55a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7b42e8432440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2778.6794 3212.2937 \n", - " 100 1846.5616 1928.8068 \n", - " 200 2813.7649 2894.0691 \n", - " 500 172.2411 180.6353 \n", - " 700 158.8983 208.3302 *\n", - " 1000 257.5057 507.5571 \n", - " 1400 1019.6581 2037.0559 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 42.0895 71.5083 *\n", - " 100 67.6089 94.6592 \n", - " 200 85.7729 145.4088 \n", - " 500 136.6161 169.4930 \n", - " 700 168.2471 286.5142 \n", - " 1000 475.3343 747.7632 \n", - " 1400 1865.0953 2835.3049 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.6670 99.7482 \n", - " 100 5.4108 89.9401 *\n", - " 200 6.7413 143.3818 \n", - " 500 10.1949 219.7720 \n", - " 700 13.1247 336.7665 \n", - " 1000 17.5672 907.9854 \n", - " 1400 35.2322 3378.4390 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0308, sigma²=7188.3099, nugget=0.0036\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4948, sigma²=3240.4010, nugget=0.0016\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2730, sigma²=1552.7162, nugget=0.0070\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0541, sigma²=165.8877, nugget=0.0023\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=233.4325, sigma²=0.0003, nugget=69.7662\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2636, sigma²=0.0002, nugget=35.5322\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7808, sigma²=0.0001, nugget=17.4992\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 181.7744 173.1051 \n", - " 100 181.6322 177.9040 *\n", - " 200 206.7067 173.9930 \n", - " 500 217.1667 155.8603 \n", - " 700 301.0606 268.9739 \n", - " 1000 1327.4003 570.5067 \n", - " 1400 5146.0943 3237.1459 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4948, sigma²=3240.4010, nugget=0.0016\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8229.3 | 90.7155 | 61.0671 | -0.2378 | 0.40s |\n", - "| OLS_sphere | 700 | 268.974 | 16.4004 | 8.3569 | 0.9595 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3928.12 | 62.6747 | 48.0728 | 0.4092 | 62.54s |\n", - "| DeepKriging_wendland* | -- | 3921.94 | 62.6254 | 45.2357 | 0.4101 | 25.65s |\n", - "| DeepKriging_mrts | 700 | 230.569 | 15.1845 | 8.5436 | 0.9653 | 61.34s |\n", - "| DeepKriging_sphere | 50 | 58.5759 | 7.6535 | 4.5001 | 0.9912 | 56.97s |\n", - "| DeepKriging_sphere_Huber | 100 | 100.724 | 10.0361 | 5.4851 | 0.9849 | 44.15s |\n", - "| UniversalKriging | 100 | 177.904 | 13.3381 | 6.6308 | 0.9732 | 3.16s |\n" - ] - }, - { - "data": { - "image/png": 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cC4fWrFmzfEudeR1aQNkd1jvO6ZVXXglbG7inPAPWsHSAeXDHHXdEvE/5qGiGTsqZedltt91M6RLGxttvvx02XjA+UuKG/jd+MG5ZBzt16lRfvo7yJ8wHO08sPD8MmpQbSyc4f+add63l2jFEp/KaU7FvNMU9pzQO48K9v/zvf/8za7rX0Ost4cX6SWm4aPM/KyvLrMeUq7Fzj/uBYTieUb2xwUDN/VqzZk39ud9yyy3mnnsbhOMAoVxeEHAw4Jz2goOeclys1cw9d4kv9mfOxytjUSqJEnx+a+qwYcOMjEYJq6AO2FTJcUHg+ryBKcg0yAWsvfwG10ZPm1SVugoCTnA/R0uQeWNlZfYinifrsrvxOGXjKAHpdSRTWpXPM3+5/27ZlnlGgAVzgvJOFvZzvwAB5EHkOO4fJWNTHaRDOVK/XjLIXsx1ZAP2De4h8rIF/cE6wNh/vCU8KcPHZ9x4/64oiqK0Ypo7VURRFEVJz/JTtbW1zqxZs6KWyHGXhohWsoMXJQK8ZXRsOZKFCxeaUgTetPc5c+aEfZ4SR/n5+WGf23HHHcM+41dWgzIwH3/8cdjnrrzySt/zTLb8lF9JjK5duzpTpkyJ+CxlmC655BJTQiLevfOWHfASpGQPpbi8n2nbtm1YOQxbSsb7OUrLrFy5Mm7ZBEp6vfXWW/WfocTRgAEDIj738ssvO4kSrVSKLd+VDNdff71vqYUFCxaElSi6+OKLfcsRVVdXhx3P7/wYm+5nfPzxx0cticFv2XIQw4YNC3Tf/MYlY849byiX9Zvf/Ma3HJiXd955x1m8eHHUe8b89ZbqKikpqT/3WGUoePHduXPnhn3WXZIoyFj2jqlLL73U91zXrVtn7pnfv3PtfuV13KXFli9f7vu5P/7xj4Gudf/99w8r23TnnXdGfIbn3FzlpyhrduGFF5rXr3/9a9+xRKm6tWvX+h7TW/qENZbSe97x4n1erN/ucoVAaZyPPvooZnkjynV5z++f//xn2pWfgh122CHie6z3jXXNyewbqfz9INx3330Rx3rppZfCPvP5559HfObqq68O+4xXTrjrrrt8f48968knn3Suu+46p7nKT4F3/+A+fPvttxHzLNo89a5/7E9+JXu8n0Pu8MpKvNxzlNJNfuX3br755rBjvfnmm75lA72lhVIpxwWZy0uWLAlc1ox97f7773fuuecep7HKT23cuNH58MMPzbX47Qnvvfde3OvjRUku755q98mbbrop4vOU+nLPY75Laad4c9evRBZyuntPRo7bdtttfc8zmfJTPDO/soyHH364rzxHOS7veIx277TUlKIoypaNZmooiqIoYeWb4jXDttkPRN7Gg2hKoqNpPumGiEWgYam3cSiNEb2RbUSaUjrHHc35zTffmBI4/fv3N38nssyvfMWee+4Z9h7NCR9//HETVddQaGLq1/yXEgxEqHoh0vvWW2+VpsKvvABN34nE9JaT+c9//hMWGUhEINcWr+zSEUccYaL7LET4Ex1IJL+bZBo3RmtWSWRfKu8JDaOJ8LQwByg55W0waiMxiWaNBcdznyMZG97yKjRPpZGwnW9EtR555JER2RpB7xvNxd3zhrlHJCwRre7SP0RAuucNkFUARBaTpcJn+DNZJ7ZsFOfnhmwGzo0mnfEgo8P7ObsGBMX7zIlAZu3wnhcRmoxJXm6I2nVHf9pjknXAvbKQFcNz8X6fjAu/xs9eyKxxl20ie4z55Y5QjvZMabja2JDNYhuwemFMUhqNa6D5rBeigL3ZP0SBkx3khvWeqHJKnLijhikfRBNjC1lBvPANUh6PDAEyjsiYsWPWbw1Ix6bh0dYlb/mW5r7mpv79E044wYx/d8YdzZ1Z62KVnvKWruLe2swHIIqctYksQTdknpx00knS3JCB4c6AItsFmcX7maC8+uqrESV7yADyNmRG7uC4ZEZ61y87T8mS85aDI0OA0pxuyHAl0zNeuc5UynHJzjNv9L6F6P+zzjpLUg3ZAbziQfN6slHiwbMkO9aL3Scp4+eGzAv2d/f+hyxx7bXXmmfvLvdIdoPNdOB9P5mV77j3ZOQ49rtDDjlEUgHj15sd27dvX7MW+JU5RMaKJ2cpiqIoCqhTQ1EURUkIHBrUt6fsSzz22muvmKn3lNzxgtHLbfiKBWUtrDLsNRhYY7Lf+VOm4IknnpCGgqHHq8xjHMcYkA58/vnnEe9FU1IxeLidGvb78Zwafv9OGQMvydRm9jOs2lIM0f4tFhiFvNcY7Z5g4MUY4S17wj2JpWxjZPOWx+rWrVvE5yg5heEgVffNb8xRIo6a6t6+JMwVtxGJElkYtCgNE610kx/UZY/n1ODfWQcaCvfUbVjFCcf5UnYFgxwvSr3xbPzGht9coOSUn3OFdQNHh/teUH6K0kKxSu7x+7bcnLssCs+Be2VJZZ3yVILREQN0tLnlt16zjgZdS1mvvWs7xl4coH59PaLhvpfpBE5uL373srmvuSl/n+un75LbcUGPGpwoRUVFxrniLT3FHB4wYEDE/Hcb1zl/yrgx/ylzNXjwYFNOi/Jb7lI7zQXnhKF6woQJ9Wuuu5QTztegfZoS3ct53+vUcH/fT1bCse0X0ELAQjynRirluKBOjREjRoRdE322MOhTAs+OB1vakb28OeCavGM7Gl7nlBv2IW/ZMQIOggZ3EIhhHYA4kLzrFGPRLwgH+Qd5ORX9L/zGCP3vovVtUhRFUZSgaKNwRVEUJTDUsCaSO2gk5A477BCoBn2yuKPo3c033fWG/Yj2fkN+34IynS743V/qW/vh9z7R8PEg6jdIY8xkGkVH6yUxe/ZsSQYifb3ngVJNTflU3ROi1L3X73c//I7tZ3wJet+iPVe/se42UOLQwJhNBksiDg13/5CGrAFBocG21/BFRDl9QsgEoUY8jjkitYkC92YjJDIXeF7e7LJox4g3F+zx3DR3zf9o0IeFscB/G3u9tllj9HdJxLgedNw1NcxTvwwc7zhq7mtujt/3Gmw5FtH91qBNpkisz1snBnPbu46R3fTAAw+YHhbU5eczZHlEi9xvSryZGO4ACDKicOo0x16ealkp1etCEHDaeA3iZFLSt+Puu++W888/32RJIEOQpeDXwLuxwFHO7+OE79GjR6DvxNoneV7JyE8WMiTs9fs9e7JZ/EAe8dsHW6OsrCiKorRcNFNDURRFCVNuaEJqIbWdyGSyLYiejGa0i0Yy0fSJQMR+LKKV0mpMo2KQ8l3NSarPz88h4C7n0xBQ9DEIe5uF02xy+PDhCR/P77mn+n74OTD8fsPvc41BvGvGGZCsUSrIPErVGoCxkmhVSmI8+eSTEYZQC1GllFohQvqLL76obyTcFM8+mnMsVfMhFWDwJdOOtROHEJkZ7pJSGJwpKUWpKTJMGmu9phyYX4PyIKSjUwgDpl8GDhHl6XLNzfX7NIvGuOsu+UjmBqWpvKWnMFRTvtALmRuUyrrhhhtM9Hs05zJlE8niYv6TkeDNhmtKcK5SctLPoJtI6ammWL+aQ1YKKsf5gTzK82U8sN5HK1XJ+5RpwlFLxoK3XGFDxrTNVubekR3EWENmIesh0fKKTSErR9ufYo2jLVlWVhRFUVoG6tRQFEVR6sEA6C1b0JhKi18kPgYNvzI8flBqwuKnsC1atCjMqGRJVRSn3/lHq1ffHBBl5zX+8ne/6DvuVdBMiaYCw//o0aNN9KUbyo5gjE20rARjhBIM7qhHjLhEL0YbP+l2T6LBufqV8PAb6/ZacQDQ58AvK4JeEET8Uj4JTjzxRFP/ujkNFxh+iNjmNW/ePJk2bZrJ2mHO4ejiPbcx65ZbbjFjJdpzi+YYod68n6MnVVGr6QBGN8psUV+dEj5ug/yyZcvMPfb2//G7hxgX/dZYP6yDydZY9xrMKBtDjxNKpuFQYezMmDGjRUT0ct5eMHS6S9U19zU31+/j1CMzwd2Thh5YRI9T798NvWyiGXiRC+68805jpOYcKedk5//bb78d5jxgPWTuX3nlldJcYEA//fTT5cYbbwx7nzHhLVMXj0TWr3j7VtC9LqislEo5LhEYuzhoyTBkLOCIpbwSf2Z8US7QggOEscY+lgooHRarZFQq90k/uYVgI2/fmVjYUlV+zz5a1hYZHqkqe5fusrKiKIrSclGnhqIoitJsYAyjabcbaiB7G1b6gSLrjoCmnjalsdxgsDvqqKPC3sOQS6PMVEC2gLfmMIYGDCzu5tnx8IvkTrQUULT76zV8vPnmm75ZDrzv9/3m5oILLohwalAy6dJLLzXGrXgQjU4ZCuBZ7bTTThH1qbn2//u//4tQ6N9///20vCd+MObOO++8sPcw6hCd6oW5AhgsiGx2g4H75ptvDnsPYwpZD+kEvTrc/Tx4Xhhi3Y4Ndx1vv+dG9K6t7e/mnXfeiZh/RIrH6qfRUsFxdcUVV8hVV10V9v5dd91l5pi7H4zfPeSeBHGEY0x3G9T9DLJEXXvXTb9a7OkGPQfIHvJCnX/rFGyMa05032jOe44B1u3UwHFIKSx39gYEMRRjAGauux0vrHWUSmI+p9PYoUk166nbIE35r0Rh7pGB4t23uIeJ7uV2/XfDHsv89BrXWQubUo5LBr5PhoS7hBM9kHB6uGE8pMqp0ZRwffQHce/BOKFZt/36dcW6xwQ+4OBw99XgWFOnTjWlB90QKJCKfhrWceUdI4xnnI6JZK02lqysKIqitFy0p4aiKIrSbBx22GHG0OyGZo8YaP3AMIDyhSLvje7za5RMFJ/X0cHxU5WpgXLo14wcwwy1wv16OlDux9uo0a9UAVGHDcXr0IHbbrvNlPFwQ6Sut4E2ZUBoHtrcHHrooSZbwwsRuxjKopUhmThxommYSoRwvHty9dVXh0WqYtzBYOAtG0LkKRHN6cjf//534+xxK/pks2A89BrnbUaHX6YLRkZ3dCZGDerVJ9vHJFVgfOWZR4sopryG26AJ7mu3DYXdYMyh3rrbKMJ48jPGEUHeFD2LMCq6X359GlINzjBvdDz3jrXCDU14acjuNaASie7uF+AG4yIZMxig3UZ1v7H37bffRpRLImsoXWH8/OUvf/E1lFIujbXeTaqvOdF9oznvOaV6vE6xRx99NOzvOCVoTuwHez6fj1YqD6eGd63z/h2886tv377SmHB85BycW7zYQ4499tiEj3P44YdHyEoEZyDjuEE+ohF8rPWL3iNuZxtQgu7222+PCArxZtI0thwXlNNOO82UIYtWdspPLvAbDy0Fr9zCPaRU7NKlS6OuTThbkZ/YOy1kfPjJdRdffHHY/kkWlXf9agiMX+/6w95GqUN3Ro3Flpr00liysqIoitJy0UwNRVEUpdno3bu3KXNz77331r+HYoWDAgMaJRpIl8dgiQKEMd42PPT29+A7lDdxG19RYjEU0kCYmt5Eun3++ecpvYa//e1vRnl3G0YxhlNigtrKRA9iJKcsAlkD9IfwlsTA0Ozl5ZdfNsonhlhrMKAkTCLNRVEY+Y5b6cMIgGGe+4WBBaOW1/EDNLr0NmZtLqi7zjl7yyQQ6UdJpD333NP0fUFhp3QOEZnWIeEdJ7///e9NZLnbOIahnPGGE4TIc+6H1/EDf/7znyOMN+kC1zts2LD6ayALwa+8A0Z8CzXAMbq5Dec43rgXRG9j9Js0aZIZu80NY/ipp54ymTs8a4ykRKlinONZYuDzlsrwOjH++te/mrIhbh5++GFzjWPGjDGlyCjH5W0qi/PSLyK6tdC2bVs599xzI8rkYBxlrXL3JWC9Y11xQ5YHDifWO54JaxzGNsqDRStt4pcthmPu448/No4THCBEj6cqUrihMC9wEgJOaTKCcFx7+/0AawQGV/opNOY1J7pvNPc9xwkda/8lW4413A/WdIzurEmc65AhQ8z+RIknxhpZdd7z9s7/5oKeD6mQlShlRVN0r3EfZw9rNsEalF3yOhjZO5GBLMhUrINepxKZWYwfZJYFCxYYuSZIg+pUynFBYZzi0GGMsxfwrG2ZJs7dLxs3XcZDMiC3EHziDrRAnuT+sXfxX+YC6xQOKvZ+Ow7ImvHKdi+++GLYeziZuD/s+zxznNXRAkaS7deH89zb04eSeJz7fvvtZ2R01lb6E+FcHTduXKA17+STTzbOG1taizHHeFQURVG2EBxFURRli+Saa66hFkjYa8yYMUkf79FHH404Hr8Rj7KyMmfkyJER34336tOnT8SxPvjgAyczMzPud3v16hXxHufvhd/wfs6P++67L6FzLy0tDft+bW2t7295XytXroz5/Pyu4aeffnLat2+f0PntueeeTmVlZcSxGB/ez86bNy9lYyEW06dPd4YOHZqScTJx4kQnNzc3oeMcd9xxTigUijhWkN+bMGFCxOdOOeWUpO+b31gJcm9GjRrl1NTUhB3rtttui/u9tm3bOrvttlvE+1xXMtfpJchYPumkkxJ+9m+99VbEb51zzjkJHSMrK8t59dVXk36m0Z6XH0HnV1A4n6DnuHz5cqegoCDi83/5y18iPnvBBRck/Cy817Jx40anW7ducb8zduzYQPtUKu+d3zwM+urUqZPz/vvv+x431dec6L6R6t9PlNWrV8dcd1njo7HHHnsk9BxycnKc7777LuI4QdbroDC+UnE8v3nqt5dv2rTJGT58eEL3oUuXLr7zYMWKFebfkpGV/NaQVMpxQeZyz549E/qddu3aOUuXLnWSwW+O+T2foCS7Vk2ePNnJz89P+B77yRCnnXZaoHtWXFwc8X6yeyEy5d577x34vMeNGxdxjPHjx8f93iGHHJLQ81AURVFaNlp+SlEURWlWKHNE7d4zzjgjapSmFyLSKKHgZZ999jHRe7EaSJNST8RjKqF2OhFn3sjcoHDdNOUNev2JQNkX+ipQkzkeRMES+Ukd7USbcDc2ROdyHZRCSiRbxa8PwF577WWik/0aa3shEpQx8/TTT6e06XWqIaqWiNVo0Evktddei6hJTfS5N/LeG8VPGYvmjnJN5N6zPlA6ya+vzT333GPKcXjLr/hB5CglWCiv0tqhkStz3wsZGN5yeUQM80pkHlKvnbFk4buUtonWFNqWLAnSNycdYDyRiUCUMVHHfqT6mhPdN5r7npPxE20uMT68pc2Snf9cJ1l8iTbjTndoPD9hwoTAfSG4p2S4+JXY6ty5s1nbYsksjGO/UlaNLccFIZHxQEYPGShB+k+kM2T/kh1GJkJQuGZ3rxELz9UvE8JC1gPZGu4svYaCTImcQrZlsn1UGC+xzltRFEXZ8kjPGgqKoijKFgVGiAcffNAYj3FKYHCeOXOmKVFAcCVGGJoCY6TAcYGx0qaa+5WwoMwG9XgpSUFpGhQzSqOQds/3KUOTajDWUPaAckiUgKDUFb9NI2aUagyknAM9ONzGPQv1kekDQWkkSgGQ+h+tTn2ikLLP+VBaB6MWx6dkB+V2KFWE0QNDP86eRBTm5hgnGKvpgcF1fPjhh6YXCGWHqMuMYRHjLMZ9roc64tGM8ZTkoEwDZWJwSFEWhXtOybKSkhJzzyhdRhmDZEtkNHXDZ54xBknKdVGGjbnD9dNXhPIVGJG8YIB65plnTHmShx56SL755hsz7igXwTzDiUS5J5w6zQnrA3ObsloYjilrxHPnxTXwzLhWSnFgnHc3EfcawyipxGe4XgxxjAPKduDAwtjH+kEZr5NOOimQ86O1QC8Rytu4y/iwBlNWxttnAcMUz4Pmr9xDSsqsXr3azB/qntMfgXlIPxzK0Pg5EPfYYw9T/o6yVxi7WJNYG1mDeD6UFWFNTRcYO8whDLiMN9Z01gn6Ahx99NFm7YlHqq850X2jue85/aa8pW/s+7FgT6WkELIBaxTnzf7KvOWZIA/gwKcnB8dKNsAg3aEUHmsx89HKSjwvykqyP1JSEjnjuOOO83XquqFcIWX9kJUw+lN+jLFN/yEcdMgDjK3mkOPiwRimBBWlIvkzZZkYD145gN/gWvxkrpYITd4p64fD4aWXXjJOK/pg0UODhtvImuyDBHMccMABZv31cyDgYKAsGiUdWfO5h1VVVUaOoJQT46sx5hDPBoc4cgVjhPHF/ssY4Tz5TeQNHGrRnBfIfgQnIGszfrn2uiQsRVEUZUskg3SN5j4JRVEURVEUJRg4obyGRxXnFEVRFEVRFEVRlC0FLT+lKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSosgw3Ecp7lPQlEURVEURVEURVEURVEURVEUJR6aqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqKkLY899phkZGSEvf76178292kpStqhc0VRlC2Fvn37RsgG6YL3vDjXZJk/f37E8fbee29pLVx//fVh13bWWWe1yjGVyjGhpB+nnnpqxDP+8MMPm/u0FBHzHLzPhuelBF/3tqR7+OSTT4Zd59ixY5v7lBRFCYA6NRSlBYBAceaZZ8oOO+wgHTp0kJycHCkpKZGtt95adtllFzn22GPlhhtukLfeektWrlzZ3KerKGEsXrxY7rzzTjnuuONk8ODB0rlzZzOGi4qKpFu3bjJq1CgjID/00EOyYMECvXuKoigpcGwGeR1xxBF6r5UmZ+nSpXLjjTfW/z0zM1Muu+wyfRKKoihKs3DiiSdKv3796v/+7rvvymuvvaZPQ1HSHHVqKEoas2bNGjnkkENkn332MQbfadOmSWlpqdTU1MjatWtl3rx58uWXX8oLL7wgV199tRx88MHyu9/9zvdYzR1p0dy/rzQ9P//8s3nGON8uuOACef7552XGjBmyatUqM4bLyspk+fLl8umnn8rjjz9uHHcIk3vttZc+LkVRFEVppVx33XWyadOm+r8TnLPNNttIupPOWULpgGalKErzQBafd/6R7acEJysrS/74xz+GvYd9xXEcvY2KksZkN/cJKIriD0ZfnBQYfLdUhg4dKhdeeGHYe0T1K+nP+++/L7/+9a8TzhxCcPz4448b7bxaKzpXFEVRlJaSpUGgjhuvrNfUnH766SaQSFES5cADD5T27duHvbfVVlvpjVRaBYxl7/q86667SmvltNNOkyuuuELWr19v/k5A6csvvyxHHXVUc5+aoihRUKeGoqQpKHx+Do2dd97ZGDDbtm1rNtxZs2bJd999J+Xl5dLaQGhqzYJTa2Xy5Mly6KGHSmVlZcS/5ebmGscU2RuFhYUm84jsDYTG6urqZjnf1oDOFUVJ7wjKiRMnmj9fc801TdYbqri42BhrY7H99ts3ybkoiuWee+6Rqqqq+r+Tobnbbrs16w36y1/+0qy/r7RcCODhpSitETLo/vnPf8qWQn5+vowbN06eeOKJ+vfuuOMOdWooShqjTg1FSVOeeeaZiJTIN954w7dpFcbjCRMmyNNPP60pkkqzQqQjNdq9Dg3SoC+55BK58sorTV8YL5Siom4pdeHffvvtJjxjRVGU1glr7ZZkjFDSn1AoJI888kjYeyeccEKznY+iKIqiuDn++OPDnBqTJk2S6dOnm76QiqKkH9pTQ1HSFCLX3Wy77ba+Dg3Iy8uTgw46SP7zn/8Yo7AbehpgUKYvhxf6GMTqc0Ek3TvvvCPXX3+9HHnkkTJs2DDp1auXafBMxH2nTp1Mo/JzzjmnPgrVS0N+36/xabwIV0oX0VeEpuodO3Y0Dan5L+d+3nnnySeffBLz+/Z83S/6gcB///tf8wy6dOli7jnRhRyT3hHRmDNnjjz66KPmc3vssYcMHDjQ3DfOiyjaPn36mKwGDE+rV6+OehzqonrPi+hf+PHHH+WMM84w50OECf9GtoT383wmGvRm8X7+N7/5jSTKzTffbHpmeMGIccstt/g6NICsDYRImt1//fXXjXovExlXQetDkzVFJA/jo3fv3uZ6mCM0Qt9uu+1MKbmrrrpK3nzzzbA64t6xe/bZZ8tOO+0kJSUl5rrIyCKrhewWUqKJcEWwTvaalixZYpyfF198sRk/COhdu3Y158q8Js2cUgrUO6fBeyyi3Rv6/RCNzhxs06aNeTYjR46Ue++9V2pra6UhRJuflC1DAdl3331NI/qCggIzPqiN6zce49UgJvPtpptuMusbzyLa/aRMIA5omgsSzcbzYm3guY8ePVr+/Oc/y4IFCwJf3+eff26eDZk3PBeOxZwhO++YY46RBx98UFasWBH1+5R747wPOOAA6dmzp1kPOKdBgwaZ+R+ktBt9bnj+3JsePXqYe8l58GeeKU7La6+9Vj744IOwaGs3OOBPPvlkM/bbtWtnxjLlObhHzF3GOWtCvHvDvxNBTZ+d7t27m/PgOGQXnH/++SZLMR5z5841c4/ftfeUa2IvY64dd9xx5p5NmTLFGFyVzbC3UXaif//+Zixx/1jLXn311aT2aLLyfvvb35o1kuMxRuml8NFHH5l/57ve43llmlgwHln/WW8YJ6xp7P00pA6SzcpnHnjgASPvsJ6xfnGejP3999/fHCfRkorxfo9j7rjjjua3mOuMU+Z5smOxoXMm2hrL2s3+s+eee5p9N9m+aMiKXpmJ++0H98J9HqzF3trmXI/3fL3Hr6ioMPfB/ZnDDjsscK8Md084vzXL+72gfTY2bNggf/vb38wYZZ9MdK9ct26dkTuYk6xnyB28+DOy0L///W/zG40tC7mP44X7lYo+G7HWBpr5MobseEeOYf9hvQlCY+oN6bw3BtVF77//fiM/IIdynqxTnDfnz98py8OatXHjxqT6KiLTct1Wh+He/+pXvzLPNR5ke7O/DxkyxOzryH/IP/Twa0waKvvF0ukS6ZXhPo6fHs49jfbdxuyJiV6K/M16ZnUMnuvw4cPlsssuMz1BY9EQ2TwVcww9CPnBzbPPPhvofimK0gw4iqKkJTk5OWhu9a8OHTo4q1evTvg4p5xySthx4r34vGXatGkJfffQQw911q5dm7Lff/TRRyP+/ZprrvG9zmXLljkHHHBA4PNctWpV4Pv12muvOQcddFDU43Xt2tWZPn267/GOPvrowNfevn1754UXXvA9zrx58yI+P2bMGOepp55y8vLyIv6Nz++4445h77Vp08bZsGGD7/GvuOKKiGO8++67TiJUVVU5xcXFEcc57rjjnFSQqnuZyLjyfq5Pnz4Rn/niiy+cLl26BD63J554Iuz7oVDIOeOMMwJ/v3///klf0yWXXBL4d/Lz85277ror6vPwuzeTJk0y8yHaMY888kintrbWSRa/+fnGG284Y8eOjfqbnTp1Ms/ID+aQ9/OTJ092Bg0aFPd+fvrpp+ZZxLuP2dnZZn7V1NREva6lS5fGXGPcrzvuuMP3GLxfUFAQ9/vMo2jrwJtvvmnWiaBj5OOPPw77fnl5uXPIIYcE/v5+++3nex7V1dXO5Zdfbu5drO9nZGQ45557rll7/Hj44Ycj9tJYr0WLFjmNgXucRVtrGorfGuC3XgVl4sSJTrt27aLeq9NOO83c3yDrDjz77LNmTYn2HP/85z+b73r/jevywnV5P7dgwQJnu+22i3q+u+22m7Np06ao18s6EmQdZ45Fm4MQ9BksXrzYGTx4cNTfGT16tPPtt99GvM9Yasw547fGvvrqq87uu+8eU14Lyp/+9KewYyC/RDuXq666KuI3kUvd3HPPPRGfee6558I+8+GHH0Z85tZbb407piwTJkwIvIZ4vxttTHzzzTdOr169kt4rmRd+8pafLMTci3aMoPM31rj2O06sVzLrkt/a8OCDDzqnnnpq1N8pKiqK2KOaSm9gzKTz3hiU4cOHBz7nnj17mn3DD785xH27++67ndzc3KjHvP3226OeG2tB9+7do373mGOOMXqM3+82hFTIftF0uqByKt+Pdpx4L/vdeOtevGfnB3sssgHjL9Y5IJf97W9/i3qPk5HNUzXHLHvttVfEnqwoSnqiTg1FSVN69OjhKzCyeSOkl5WVpZ1TgxcCRap+P6jChWLiJ5jFeg0cONApLS0NdL+CGDowmDTUEG8FvSlTpkQcx09w5byiGez4PAqf932MUH4MGDAgYqwlanz2Mxzwmjp1qpMKUnUvU+nUQFnZeuutEzovr1PjkUceSej7TeXUsK/nn38+0L3BgMAr3vHuv/9+J1n85mfv3r3j/iZO4SVLlgRSnLbaaivfY7jvJ4p7LEXc7xXNubdw4UIz34Iex8+geuGFFyZ0LrvuuqtTUVERdow1a9YYA1gix/EajP7yl78k9H0/pRInHwa9RI5zxBFHmO+5mTFjRkIODV7q1Khj1qxZgZxb/fr1izlPLIyTeEa4aMcL6tTw+673deWVV/rOwaeffjquEcb7uuyyy3yPFcSAy9zbdttt4/6G32f8DF+pmjPR1thoa2IyxkEMQ+5jjBw5Mupn/QyS9913X9hnfv3rX0d85vzzzw/7zHXXXRfxGa+juymdGm3btnU6duyY9F550003JXw+GI9bm1MjiGzOb1VWVja53uB1aqTT3thYTg07tmfPnh1xHL851Llz57jHY9/4/vvvI463cuVKp1u3bkmtoQ1xaqRK9muNTg2cCqNGjUroXC644ILA1xxPNk/FHHNz6aWXRjjguUZFUdIP7amhKGkKZVSefPLJiNIxlP3gRY8NSlJRCoBUW1J1SX31S6Ek7ZJSMi+++GLYv5Guy7+78WvMTTow5RFI66XsAGUSSGknvZRyKe5yAKR9UkqClOhU/X48SDP1ppFmZmaaY5N6SzP18ePHh53nzJkzTQrqc889F/f4ttyLLddDbc3vv/8+7DOkp3/11VemkbsfpNuT+kr6LS/OZenSpaZMlLucBc2yr776alPaJeh5ZWdnm/FCmSLes2nvNC4k/ZdyQJaHH344onHst99+a+6Rm//7v/8z9zARPvvss4j3KKcwYsQISSWNcS+ThedOeRs3zBFbBolUacYmpQCilS156qmnIt7bfffdTckh0qYppfXTTz+Z+dbQ8k0WyhUwljlX7iHPmvPjerxlp6644gpTXiDeeLBltXjmlL5gPXrppZdMaS43d911l5x11lmSKhYuXGhS0JnvzAHukzcNn14vlHUKMt/t9VOyiTIClLRxl0TjeigF5S29xHVTBoT1jlJG3hKClK9jDaHMhYWxy71lbfdCCQ3WUY63bNkys+74lZ564YUX5F//+lfE+zTeZY/gfOlT434OzFVKNtx2221ha7d7rQDKA3HOnAOlJRjrjGXKngQZy+xTfJ/5Cowxng9lQbxlZCyc08svvxz2HmPPHod7QJk6d9+eV155xYwrym647zdrgJsBAwaYucUexv1g3eM5RSsJ19Jh3F900UUxP8MewXN2Q8kVv1Ii++23n7mHP/zwgynZEq+EBFBG6cwzzzTlOrxjA7mFcf7pp5+aORbkeNHgu6w5hxxyiFnT/ve//0XMF8qoID+xrrq/x57oHY+sjZwf8g97COUP3fzjH/+QMWPGmDmfKHyXe+iF8jeMT0rAUarQ7zONOWfirYmUDaOUKM8OGShomSX3WPDKCciW0aBUCM/KPY/Z55Hd3H/3wloZ6++UJIn1u14Yo5RhA8qWeEs62X8Lil2Lk9kruV56k3mh5BS6APeYsrHIRN5zRF9gjKUaZBV7D7x7EdfolTmjlSFNFDu/KUXDC1nWWyoK+ev111+PaPLb2HpDOu+NyYA8RIlX1laeH+s56wL7gHvc8mdKFnv75vhhZWLWFe47si7n675+fod1m1Jqbi6//HIjF3lh/aRsGKWKmAdB19AgpFL2SxWUvbJzD1nQK0tStpbPeL/TGFx66aUyderUsPdYv1mXWJ+Yb++9916YHsNzpbSjtxxgMrJ5KuaYG8qTumFeoeczxhRFSTOa26uiKIo/3333nW9ZoViRyLfcckvU6PpE0kctlLt65513YmaFvPTSSxHHveiii1Ly+0GiyEhH9Yvs8ZZO4jy9kZj83VvKIFpmyQMPPFD/GSI19thjj0AR1ET1+EUtWTZu3GiyPLznRdS0m2jROET7ffnllxFRoDbl/OKLL474zk8//RSzHASvH3/80UkUb1QLr2HDhvl+9s4774w5nv0illJ1L1OZqUH5L29pEiLI/CDa7O9//7szfvz4mFky3Ec/1q1b57z88su+/x70mj777LOIMe8tbeCXEUOpjHj3hhcZZvPnz6//DGPTL6rN+0yC4jc/ecaUR3FDhKv3c1lZWSYrIl40GC/S4r3RjbZk0/XXXx/xecqIUP7Gwnf95h73h3tsIQvG7/evvfbasM/ZZ0NEubesmreEDfebUjpuVqxYETHOKAVE2SuL97rIgPEr18Me8/nnn5uyCt6x5M2MiFa+jDny5JNPmghqN/wee5n7GJRA4ve8mQSUFXN/jqhNd/bJmWeeGfbvRBD6lYFgrfzoo49MdDf3qTWVnwry+vrrr8OO88MPP/h+zpsxQVkQv895r4+x6DcX33///bD5Ei3bKGimBpklX331Vdie6R0jftfrHSe8KN3oXqMYI8cee2zE50aMGJHwnsE89oswPu+888LWHNZOv8w3796YyjkTSwY666yzIkrZRCtjFw3ml/e4yB+x8Ja9IjPSXcLL71wZX+vXr69fr7xl1Cgl5CVIxHIin3OTyr3Sr1wS5RfdEcQ8Fz8ZlTHcmKU4E/lcKjI1eF199dX1n+FZ+81T1pam1hu8mRrptDcmAjog8yzWnPaWYiopKYmQn6JlO+2///5hOqafbuDVIygD5qcf33zzzRHlNDMzM1OWqZFK2S9VmRrJfM5NqjI1kK29Y5x7M2fOnLDPffLJJxFlKNlvg1xLPNm8oXMsSKZgtMx1RVGaF83UUJQ0hWZvNPQlYr6srCxQRCYRl0SJEK2RaJS9H0Tj2EwKohtsJCURnEQs/FLCLuJ7RDI0FUS3eeGeERnihiaC48aNM1FAFs6dhqc0E4sFjc2INLUQuUmTNG+EoF/zNZuxwj3j8zTEIxqJyGAbreKNvue8vvnmG9/m6l5uv/32iOwQd8bOueeeaxqnup8T2Ro07bZ4M2jIrCCLJlG8Ud5ARHSqaOx7mQxEaLnh94mQJMrXC1HzvOIdg2g7IlPd0cQ2uoomzbySxWbNMH9ZK8gAISqYe2gjqf2i8JnTRL/FgwhSovksjE1+0ztXiNiiyV8qIALTG+VFdCvj3B0VzLOh8SSNimPBWKHJY7Sx7Lfm0FibaEMLEcw0M2QN5/5aaGDLOdlIL9ZqLzSupgGoFzKyaErphnXZ2zj++OOPj4geJ2uITBXWA3fzXLIz7P3wjkP+nflF41k37C02MtYLx2AvsjAXiBz27kfMj5NOOini+2SZub8PF1xwQcRvEXlH9C8R7xabzUI2gd/1EGXJyzvumGdE/fFqCDT89Z67Zfbs2fV/JmvGb62Egw46yLyaEzIE/PZAb2NQokNpHO2+tqDHYw2zz8nOF+YQjX+jZQHFgwhqd2QlGX1jx46NiN5kn7ZR+uwPbpnAQrS5e5wwRu6++26T/eGO0iVTlchYb6ZLLJCjvBHGRPiyJ7szH1g7ma/uvdqPVM6ZaAwcONBcP2tQQ/Z3v0yzeFH7RNqyV1nIFuO8acjrzsBAZmGt5V6w1hOxj/xK1LR3TEVryNuUJLNXch3ezFPGDJHsyKXu58Lz8majMBcZvzTtbQ2QcereK9ln2Pu9TaK9snlT6A3pvDcmgtUB+W3mIdkq/Bm9lPMHr6xK827uORkvQXQYGnxb0LXILnTL8t7nxxxwZ6PY6H10YDdk2h199NEpaxqeStmvtfHaa69FZMbSwJvsaTc0mz/88MNN9ooFHY1MyP79+zdINm/oHPNCVlKQPUxRlOZHnRqKksZgrENgJZX3mWeeiUh594PU5EcffTSu4S4oGN3+9Kc/mXTNoKxatUqaCowKXig/4Qfvew0Yft/3QhknP2XKi7d0gBWAUF4xrHiF8IbeQ5RYDJixQMHBsIMhzfKf//xHbrzxRmOgIDUbw7abU045RZKBkg5e/EqYJEtj3stkoTwGzwEDMGAwwPhPiRaMLBiDKPM0cuRIU6LBr1wHiqbbEcjzwXiG0oyixgsnJ8qQ3z1OBJ7HNddcIw888EBCzyboPWzIXEkWFFc/GPfeUicoT/HwGm/d4Phxp7vHWnMwHJFW7zWqsuZYxZayO17OOeccCYrf95944gnzCgKGP7tXUDLNO98wSjCO7Tjkz5S1iqZ8MpbdBgT2LgxujGU7FzC0MR+8zpJo1/P3v//dvIJejzXc8F8MJm4HEIZQHIv2epiTzOFEjNLRoNyGt5yJH1yj33Va43ZzOzX85gjGPS8YCnBO3HrrrSk5HoYJjIp+jr7GWnswlnnLAmIY8XNw4Rj0Mzp/8cUXCY0fv/vB77kNexbGQjynRirnTDQwAHkdGsng58zzOh+94IC44YYbwt7jGWCsdDs1KK2EEYtSQ8C/YZD1lp6yx2xukhmvyAnewA3WM7dzxIIcwrh0l6MheAF5L0iAQkuAUkBeB00QeaMp9IZ03hsTAScizgJk0kTKnyIzxnNqINfy8gZl4cRzy5ze5+e3hjLX/eRr1tBUODVSLfu1NvzGJ7JlUFsE4zOeUyOWbJ6KOebFr0xXtIAURVGaF3VqKEqaQwSIrSfKpk+/Cv5LxEw0wyDRjqlwatx5550mEihRgmSWpAq/PgXU7vTD7/0gURd+CqM7Ks5io5bc0UoYzOJFsiZ7DzHK+fVR8UJ9dLdTg2tG8ccg5TUgIYyfcMIJkgxdunSJeA+jEZFtXmUDwdLWgfXrt+Klse9lshBlisHlD3/4Q/17XC/RbLy844gINGobU+vVctlll5kIMHfNdiIyMdy4DWgYlai/ze8lk0lDFBVGQ2/N21TdQ4ySflG3QeZKQ4g236mDnoxzJlbNcaLAvOeOMdIvoivImuOO5LOgeAUlWp+WoLh/H+MYc9JdEx0jBrXzvT2EcAZQ05q65G5QIidMmBB2n/kzUaa214+9Z8cee6wZy26DcCqvh2wV1ji3QcrWYPYaJ+jlRMRvNMNWS4U1xy+DMBZk5zRkjjX28VK5T/uNN84hWq+IZGWIePcj2nUHuR+pnDPRSFUfBpx2iTq4o/XVwKnh3h/5HBHpbqeG+7/J9tNoDJLdKxORd+2/eWvst6ZI42Rl86bQG7yk096YiEODyPpkfjuIzOj3/PyeobciQKrX0CCkWvZrbaTDPtTQOebFL2vUbw9TFKX5aXh9GkVRmgSM10SXYXihERxCHaVDbAMsN94mZcmAIIDRKhmCNOBKFX6/lWjzynj4Ca1uo3Q0EJiSMcIHvYdBo/Yx7nkjpijNA16nhm2ymgx+Td4RCv2im4iqpCwWr9///vfNfi/9jOx+ipMflPWh+R2ZAbHKOhDFzWe9jkJSob/88kvTuNmdxu4XKYaBlkj5ZO4F2RnJODSC3sNo4ybIXGkI0eZ7smtDrHnVFOtNqo8XC2+2DvORsgDMz1jPjawHMrq8UeRkKNFInDnt5+S0lJeXm4wkxnK0kk2puB6cpQQFUJYo1n0lo4cSZu6SCFsqfmPcL4sAgpS5TPXxUrlPN8V8TvUalmqCZOw1NEPQ4leWMd78J6LWlk204Mwgc5m1xu3U4OWOHGbP9GbWkBXT2HtSY+2VTTFeGyILNTXJyubNMe/TbW8MAjpgssbqppYZG3sNba65l87zL932oVTPMb9/I2NTUZT0QzM1FKWFQtQ2xmqiBtyKnN2wGwq1573HQUi46667jHMFYRQDBGWA/CKjmgrOyVtTfuHChRFKMCxatMj3+40F6dp+6bOXXHKJSbO1hh1KKlGDNVGCCtQ8J0rauJ1UOMaIaPFGYCdbegpIqyYC0SucMmYozZIu99LPcOY3Z4KUkrGQas6L6LRvv/3WjEkcDzh03n///bAo0/vuu8+cpzuCDGGdKCNe9K3BMcn3KRUxfvx4857bUYQxGWNtQ+8hTizq3pL1Y8uAcNxESiA1N8x3P7wRqhDEYRdrXtl1z6188sxROv2OHW/N4c/eccYzp0xSEPzWL+ah3/rnh59TnAg2XowzyjxQepCxSOmEiRMnhin39JEgu8PtzKP8B1l+ZBfyXUrc2bFMxpg7Io/7w3hjPkS7HspHMD6TcazaGuu8cNRzPWRQzZo1y2Q7usuTcV3Ua6anSTLEyohgz+TeAeXfmHPpip/S7jeXoo1vv+N5y1dyvKB7dGPiN944B7/sQvtvQY4Ri6DrRKz7Hu/3GzJn/EiV8Q6nBmuFuy9JEKOpt68G+yp19W05nK5du5q1jOhoAoCQS1mXKcfqvbfpUHoqWfyedbT9L8h4bQxZqCXQXHpDOu2N8cAhSOCcFzKL6Xth5xrQ6+vZZ5+VpiLVa2jQ30yl7Bd07rEXNfW+mAx+4xNZyq8cnB+UhErFPtSQORYks6ZHjx5xz0FRlKZHnRqKkqbQgJP0epS1RAUJv7R2v8iXWPVR/RQlhFmMXW6i1QZv6O8HBSWEklzeZojcuyANS4Ma/5LBew+pz0mGhFeYDXoPGwLlyDCm2d4P3Htv6RgMDt4Gw4mAsYJmrbfddltEOTTKwNAcLh3upV9zUz/Fhz42iUJUKdE/vCwPPfRQWKN5lCLqsEdLiyerxp1ZgwGIkkRux0YyY8ZvTj/++OMRCllTjMdUgqLiV24Px6yXhpYdwZlM1D+ZNd61hUajbnhuOLRirTkocl6DEUpW0Mb2fusXjm4yLuKBshwrihFH25gxY8zLQn8lHG8WHJhkbfjdVxRQxq27nBb1iBn31Hb3G29+10OjySDXw5oWK8KT9c06Hy2/+c1vwupe4/DgHLfkEgM8yyeffDLsPTLRKJ3nhrHj5yj1O563BBDHY09ww5jg/aaEZuI4XdyGdTIAOF9vXw2cYn619L2NeuPhN1c+/vhjY9DyZrC88847cY/XmHMm1fBb3C+3gyJInyNvXw2CBNz9cmxgD0ZWjm+zM26++WbfYzX0GprrPtJMnN9xy84Y7dhDvKV8iFj2yjVFRUVhpSsbQxbyGn5TIeenmubWG9Jtb/SD9c5rZKc3gXdOWXm2KfFbQ9k7/JzRQdbQ5pD9gs49ju9XBqkp9ewgcG3oE25Yi73N2/1ojPUzmTnmxVtlAB2XNVhRlPRDy08pSho7NTBsYqwjwtNPMEGxu+666yLeHz58eMR7fgIUxqho+JXRIQLdayQNGtGd6O8n0kzdC2mlXoGSiD1vsz+EnoYY2uPhvYcYAN19ExDAcQCQMdHYYLj29sqgl4W3cSW1qxsC2SA0+XPDddLQ8R//+Ee9U6U576Vf80IM426nAYaWIJkQzIHTTz/d/K47GyNerVh3o3OMNUQWRYvG4lrdQrj3+0EJMqefe+65CMUk3aHEkK2jbsHh5VVWUJroKdIYa87VV18d9vwYj1dccUXEsyeKzB0x6XUS22dA808iJb3GA67VXTLONr32KsE33nhj1PFIlgKZPih7bkcX94vSaPw3WhmEeGOZzKlHH300avQ1SqV37Lr/jsHRu34wDxmT0RwwGO9w2HqNehjceS7uEjVuuEa/HivJzK3WhJ9jm8w+7/7JmuXtGxT0eGTueecnpTWbugknMoDXuQLMA/e5MJfOPffcsAwDa8hJtMk8hjFvsAq/xXrhHuPsQciB8UjlnGkK9tprr4g9yHtfo/XVcOM2Sruzld1/9hogU9FPo7Fk2SBw/vvuu2/Yezxj5HC3bIW8QC81v7nolgNSKQtFuz/sGakso5QKmkNvSKe9MVl58eeffw7bM5FR6CeXbGnYZGEOePsJknnjdnTawBZvid10kf14lt5ySkuXLg0bb/QSpCxtS1ibKN+J48fNtddeG9bP0St/UQ6XeREkS6Mp5pgXr4zC84tWOlNRlOZFMzUUJY0hSgblnxfCClEPpPxSJoZNG2eHX3okqcBeSM1HGHcLv1999ZVRAIcNG1YvwCIU8Bt+jpEnnnjCCI5EKtCQkciYoE2YE/39oFBq5cADDwyLykbQpr8B7xPJhBEPZcUr+GNQJPKoseAeupuTIcTxDFEsiZij9Ekq+p8EBSWXrIloeDM3koGoV5QI0uLdRlX+jMMDJxwRsDZLASHeGzHX2PeSZ042k1vRRnmgIeK4cePMnzGSB3HAYIxBiObFeWy//famHBbKCv+GQvHJJ59EfG/gwIH1f+YzRItjSGO80oSZ8kMobcxzymx4ja/u7weFe+i9R5Sf4h7i9EIBbmlZGsC8xsjAnMdIQ5q5e6xYjjzyyJg9S4JCvV6iI92KE0otDgbuJ1H+RCL7GdMpb+RW/IgM5bl4jW8YeOmBgiGDscQ6T/QxRoU77rgj7LOUgDr++OPD3kMRxuhMxhBjibHMXOP5e52Z7gh1vsOLa2Ascz/Zb9iLWK+9UdVE5TLeLYwfDAus9ThbiAomQwKDJL/POux11rjHMnMIg4C7VB4OfUrNcZ3cK9YYzod7zj2O1nyT54PDkBff4Xp4/uylXCvz0muU57pbU81k1jhvhoUf7mhfnpl3T2WOYdCxeyrR4UHWbWBe4kBzl3thPLIPsPZQzoFxw/hqDig9gWzjXu8Z59Tn5vxYh1mD3U50SzJlxJj/OEgwNrqhXAYyHXOWscseFMTBlso50xSQheYuEck1cr9jlcexfTXcGR5u3I4M+gERQOFHKvpp2Lrt3jGOzEMGqf0dv6j/VMC48cqzGA5ZR5mfyEZEp7NXeMcdmXaNJQu57497P0P2w2jJXsZYBY4dNBuxMWgOvSGd9sYgMC7IZHOXVWScIOcw1rkOMtr81sXGBnmVICzkbjeXXnqpcUTRUJrsJeZFtACN5pb9uH84eF977bWwzyHLsXZQ1pnyX4k8Q+aen1536KGH1mdkc67uzPFUgWzFce+9994w5+qvfvUr85vIX5wDQVqMKe4R6wukyrne0DnmhnXLK5N4HcqKoqQRjqIoacm2226LJJ3wa++993Zqa2t9jzl69Oi43//888/NZ0OhkDNs2LC4nx87dmzEe3369Gnw78Ojjz4a8e/XXHNNxHGXLVtmfjOR+zRw4ECntLQ04linnHJKxGcnTJgQ8Tne836O77p58cUX455Hdna2s++++0a8z7W7mTdvXsRnxowZ4yTKrrvu6nsejLdU8uqrrzrt27dPagz7XVsq7yVceeWVcY/XqVOnuGN71qxZCV/bqFGjwo5x0kknJXyMt956K+wYQeYKcyszMzPusQ888MBA8y7evUl0TgXB71hDhw6Ne00lJSXOokWLIo7HOPN+lrkWj4kTJzq5ubkJPbPjjjvOrKte5s+f73Tv3j3wce64446IY1xwwQVJzTP3tb733nsJf//4448PO4899tgjoe/n5OQ43333Xdgx2L+OOOKIpK7HzYMPPpjw9y+//HKnMXCPM7+5lAr81oBk7ptd19q0aRP3e35zz+/6Pv74Y7M+J3M8v/Xbb7/3g3MJcrynn37aycjISOieXXbZZb6/GWRdLC8vd4YMGRL3N3r16hVo30/VnEn1eu1HdXW106VLl7DjX3HFFXG/d9VVV/mef0FBgVNVVVX/udWrV0d9lrfeemvU4wcdUw8//HDce3reeeclPCYSufc33XRTws/57rvv9v3NVMlClj//+c9xj3fLLbc4iRJ0LgeVlZtab0invTEot912W9zjtm3b1tltt93iXn8QnSmRubhixYqIdSToGhrtd4OQStnvnXfeifvdrKwsI78GkVPHjx8f93iHHHJIwvc66LMrKytzRo4cmfDY9FtLkpHNUzHHLK+99lrE53/44YeYv68oSvOh5acUJU0hG8KbyhkPSvxQcsOvARlQCzVoU28iHf773//GLK1ARGEizeES+f1EoJQDaazuWumxIKKGaJrGrptOZCuRQ9EgO4UyOd7a3Y2JX0mChjYIj5aKTLQe9yDaePSDzxLZ4025TvW9JGLR3ffCCxHqtrFvKhuoUvqCeZXsMYg4oswWkXKJQmYLUWbRngfnQVS7X6ZXOsM1xYqKJTqMiL1oPUySgQg76uC7sxSiwTpOdOXTTz/t+6yJUqMmdUNKY/3rX/8yLxsJGwSicW1kcTJjeb/99jNN790kcgzOlZIfRPC5YXw+//zzZg0IWg6P73A+yZ4LEPlJuQSlLrOSKFFveQxv1OrFF18c8b7fHk/0PH06vCVD3M8P+cCvvEdjyAxeWPOISA/SAJjyE2RL+fVrCArXRDR9rIw7MliDlk5J1ZxpClgPTzvttIiSe/GI1guDDA/3NRNh7q6jHuQYiUAfnsbsxRYE9hMyuMmiiwdyLj0xyA5qTFnIQmYYEf7pTlPrDem0NybyLL1ZoG6QHzifZDKHGwpZKWTaxOo76SejpJPsR1bQhRdeGHMcoGOTeRIEMgnIgmou2BvHjx8vZ5xxRmC9j3GcqgyIVMwxi9e2QXYXWfSKoqQnWn5KUdIUNlvKEVBGhTIZlA0hzZc6naR0IjBgcMD4QGo3Bpl4ihaKC2V6qKeOUEY6ZqzyBgiqNMoilR9nCem8CAK8z+/97ne/S6gHQ6K/nwiUWKEcFiUxECAp1ULTNcqMoPhR0grDCgppLAUu1XCtNNqlpAtNRinXxbmiXGMQwnCRTAmLZEFBocSXu5wR5RhOOumklP8WKfz0ACCN/6WXXjKK8YwZM2T16tUmBZmSEiiIKAekCaMsYNj1Nq5ujHvJbyN8YwhG4eccuQ+MbcoLUAqKz8SDc2deMvZQkCmxQmo6L+Yp8wXHIM4Myh9htPOWv3jwwQdNo0HGLunOlAfi+fBinlN7l/Pi2jEG+dXBDsr5559vHKa33nqrmSPUmMWQx5zg33gGsUqUpSOsQRj/eI44tqjRzrxnzlOWipINjVFSCGMazxsn1auvvmrGJKUCWNN4ZpQCYGySkh8vvZ4SPCjopM+zfmE8IUWfBpGMIcY5ChVKMOPID8Ys44ga24xt0vuZa5wPJZdw6nAMHH84Dr1KOQaA77//3qTos04zJxjHjEPKj3AMSgxQ5oI5wjH8ei9Qqof1nZIyrPEcg7IVPCfmNgZHDEmUzYhmkMAYQENyDOeUmGAfpEwbx6F8AMYUzoWyBtxjzoV76IZeN6wJnA/PZu7cueZaOB+Owb7AXLL7p7uEjVJnvOGeUyoIgz/lbJA5cI7iHKc0E2VPvFDuIdrewzqIM4D1krnCeGBfZv1mDeKZBT1equF6mHOUoqIvDbIP44USLxjK2aOYI8znVKwnrE/MERwkGFDYRxj3rPU4WRj73hJCsUjFnGkq2GsoE2JLVNLDgf0TR2s0mJ8EL3j7b/jNW8YUZQhT3U8DOAd6aHH+1L9nnUSeidbXoLFAFmAvwLnBfEJHYL23cwZDKOWU+Fws50eqZCELc8XK+ewHPFtvT7B0oSn1hnTaG4OC/MmYYG186KGHzHkzZ5FpCayhnwZyPveuOWB/59rRUSk7RY8wDOuUByNQi/0kEYdcc8h+BOUwtijbxP1F1mLccc/R1XiWd911V+BzQxamFxN7Cvdm/fr1Tbo2Ia+i0+DMQZdgvFPmk1JTnAfrMHIXzgRK0DGOoul8zTXHKOHGc3XjF8ChKEr6kEG6RnOfhKIoitJ0YJRy1zxG8Y3WzE1R0gkUEW8jcwxMqYjAVRQlMXBweetOY+Bhj0kUDEIYOzBEuCMv6d8VJINCaVkQFOOOoj7hhBOMAVVRFEVRmgucQu6qBjjJCJZKpOqAoihNi85ORVGULQiisL1Nif2iYxVFUZQtF6JEaQTrF/uEA4IIbq9Dg4hgMsH8OOussyIazVvISCIa2u3QALJo1KHROqHskbtcHmVsZs+e3aznpCiKomy5kO1EJrsbMqPUoaEo6Y2Wn1IURWnlUBc3FAqZskaU1XBDGnm0cjaKoijKlgmlAymxQ4kGMi8oX0ZZGsqzUN6Bkg5errnmmqjKPyUpeNkSZpR+wWFCWUvKv/iVqNEeJ60XZI8rr7zSODeAMl+UJmOMKIqiKEpTQ9kuSuZZKIlMGVtFUdIbLT+lKIrSyonVPI2ap6luEq4ojYWWn1KUpoFmvzgcgmL7uUTbbxJt3I6x++9//3tC31EURVEURVEUZctBy08piqJswQZidWgoiqIoyZKfny833nijaVqbqOPCj/bt28vDDz+sDg1FURRFURRFUWKi5acURVG2IKhhvcMOO8iZZ55pnBqKoiiK4mXixIny1ltvybvvviszZ86UFStWSGlpqRQWFkqnTp3MPrL33nubDI0OHTrEvYE//fSTOR59nebOnWuOt379emnTpo3pm7HTTjvJ/vvvLyeeeGJYrwVFURRFURRFURQ/tPyUoiiKoiiKoiiKoiiKoiiKoigtAi0/pSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoiiKoiiKoiiKoiiKoiiKorQI1KmhKIqiKIqiKIqiKIqiKIqiKEqLQJ0aiqIoiqIoiqIoiqIoiqIoiqK0CNSpoSiKoiiKoiiKoiiKoiiKoihKi0CdGoqiKIqiKIqiKIqiKIqiKIqitAjUqaEoiqIoiqIoiqIoiqIoiqIoSotAnRqKoihpwl//+lfZcccdpaXyf//3f3LDDTfIlsKIESPkpZdeau7TUBRFURRF2WJYvXq1dOnSRebPn5/yY3/44YeSkZEha9eulZbIpZdeKhdccEHczz322GOy9957N8k5KWLGKuNKURRFSS3q1FAURYnDqaeeagRRXtnZ2dK7d2/53e9+J6Wlpc0iENtXcXGxbLvttnLeeefJrFmzEj5e37595Z///GdKzu27776TN954Q84//3xpLFA0x40bJ927d5eioiLjAHrqqaciPuO+R/Y1ffr0sM+9+OKLMnToUMnLyzP/ffnllyN+75577pF+/fpJfn6+DB8+XD7++OOwf//zn/8sV1xxhYRCoUa6YkVRFEVRlOaReXNycqRr165ywAEHyCOPPBIh73jlSMdx5JJLLjHy6QcffBD3t+bOnSsnnnii9OjRw8haW221lZHzZs6cGfN7N954oxx22GHm91sLXhnfvt5+++2wz02cONHIpNyvrbfeWu67776wf7/sssvk0UcflXnz5qX8XNwvArGShe+/8sor0tQ8+OCDMnr0aCkpKTGv/fffXz777LOwz3Bd3mvt1q1b2GcY53yOcVtQUGCcQz/88EODz8/925mZmeb4J510kixatCjsc/wen7npppsijnHwwQdHPJ8g8yzac3722WeTuhZ7jt7XIYccEvN73Ntbb71VBg4caHS0Xr16hQXMBdHzCDjbZZddpH379vX64hNPPJHwc1YUpWWgTg1FUZQAHHTQQbJ06VIj6D/00EPy2muvybnnntss9+7999835/Ltt98aQe+nn36SYcOGyfjx46W5uOuuu+TYY481imxjMWXKFNlhhx2MQwInyumnny4nn3yyeRZeZsyYYe6RfQ0YMKD+3z755BM5/vjjTWYJ95D/HnfccfLpp5/Wf+a5556Tiy66SK6++mr5+uuvjRL0q1/9ShYuXFj/GQTzdevWyTvvvNNo16woiqIoitJcMu9bb70l++yzj1x44YVy6KGHSk1Nje93amtr5be//a385z//MQ6NfffdN+ZvVFVVGWfJ+vXrjRESuQ3Za7vttjOyVTTKy8vl4YcfljPOOKNB18jvNxYNObaV8e3LfR9xVGC0RiZFNr3qqqtMVgZysYUMlgMPPDDC2ZEIGJLd54CjiiAq93tkhLQ0MIhj3J8wYYLRBQhS414tWbIk7HPea502bVrYv//jH/+Q22+/3eg+n3/+uTGGM5Y3bNjQ4HO0v7148WIzH/htdBS/Z4Tzys3PP/9s5h7BX8nMM47nvm5eRxxxRFLXwW+5j/P9999LVlaW0RVjwTqDno1jA0cFOt6uu+6akJ7XoUMHo7/xjNEXTzvtNPPy6mvxnrOiKC0ER1EURYnJKaec4owbNy7svT/84Q9Ohw4dwt575JFHnMGDBzt5eXnOoEGDnLvvvjvs3y+77DJnwIABTkFBgdOvXz/nT3/6k1NVVVX/79dcc40zbNiwqOcxb948h2X766+/Dnu/trbW2XvvvZ0+ffo4NTU15r3Zs2c7hx9+uNOlSxenqKjI2WWXXZz33nuv/jtjxowxx3K/YNWqVc4JJ5zg9OzZ05zndttt5zz99NMx7w+/3759e+f1118Pe5/zuf76653TTjvNadOmjdOrVy/n/vvvd1LJwQcfbI5vmTBhgrmW0tLSqN857rjjnIMOOijsvbFjx5rrtuy6667OOeecE/YZnu0VV1wR9t6pp57q/N///V8KrkRRFEVRFCX9ZF4YP368ka8efPDBMDnvjjvucCoqKpwjjzzS2WqrrZwff/wx0O8gy3K8+fPnJ3R+L774otOpU6eI9z/88ENnxIgRTm5urtOtWzfn8ssvd6qrq8Pk3vPOO8+5+OKLnY4dOzp77bWXef+NN94wsnl+fr6RpR999NEIOXLy5MnO6NGjzWe4xvPPP9/ZuHFj2H34+9//bu5d27ZtnZNPPtlJlGgyvlePQBZ1c/bZZzujRo0Ke++xxx4zMncsuE7uSRD89JNYOk9lZaW51zwH/p37c8MNN5h/489u3YO/x7ofPG+eCzrJDjvs4EyZMsVJFehMxcXFzuOPPx7zWt2EQiFzXTfddFP9e4z/du3aOffdd1/U79nriYXfb//73/8231u3bl39ezy33/3ud2YcT5o0qf59dK7DDjvMHINjJTLP+MzLL7/sNBasE9xr97zxwtqRnZ3tTJ8+Pepnguh5fuy0005G7w76nBVFaTlopoaiKEqCkMZLOjhp+e6UZqJCrr/+epM5QQYF5Ykef/zx+s+QxUAN2x9//FH+9a9/me/ccccdDb7/pCgT2bJgwQL58ssvzXsbN2400VxEfBHNNXbsWJOqbzMNiKAh/fhvf/tbfYQKVFRUmLT2119/3UTVnHXWWSaTwZ3F4IUoGGoPk+rr5bbbbjPvcw5ktlC2y50iTJRMmzZtor7491gQZUREjpeddtrJRCrtt99+JiLLDZE7RGa54f6QCWKjmriP3s/wd/sZC9FD3rJUiqIoiqIorQkyBsgK9vYSQ94kc5XyO5MnT5YhQ4YEOl7nzp2N/PrCCy+YLI+gfPTRRxHyJpH2yLz0OiMD99577zXZHNddd13Y55DJKSPLed5///2mrM9RRx1lvvvNN9+Y7A/KirohehsZkc8h7xLlPmnSJPn9738f9rlbbrnFRL8jPyL/JyvjHn744SbbYo899jD3Joj8+sUXX0h1dXWYbMq1oRc0BvF0nn//+9/y6quvyn//+18TUf/kk0/Wlwojs8GdFWD/Hg1+h6wQng8lici0sNlC6DSx7i+vc845J+qxy8rKzH3z6hGU9KVUEyVoTzjhBKP3ubNlli1bFvYcKJM0ZsyYCB2hofA7zDcyHHi5yc3NNaWp3Nka6Jhksadinnlp6L1mPnIvKQcVDbIyKKmGDsq9Z8wwJ9esWZOQnucGfw2VDBiHe+21V+DnrChKyyG7uU9AURSlJYCAhcCGQIjhH0g9tvz97383BnyUHkBAwnmB0nTKKaeY9/70pz/Vfx5BjXRulCPq3zaUwYMHm/9SKgBlBsWTlwXFjr4RKBkoYgjwCMg4Wtw1RHv27BmWUk6PDBw4zz//vIwcOdL3t/lNjoUS5gVF0Zbpuvzyy40Th/Rve75vvvlmmCLmxe048oKAjjLEPbYg4D7wwAPGMVNZWWlqqCLw8ptWmEVJoEa0G/7O+7Bq1SrznGN9xn2/EPSpM43SoCiKoiiK0hpBdsOw7wb5F1kSmddPDowG8hPGb2Tga6+91jgqKHOFoRbDZjSQOTFEenugUY6HckDUxuc8KcWD3PmXv/ylXj7bZpttTOkgC+Wb+C1kU743aNAg48S4+eabw5wVv/71r01JUqDMDeeNERvnCT0KrNPHW5IpERkXHQO9AmcG54u8TqlUHAW/+c1vYsqvGPmRXW3ZIe6tvVd9+vSRVBNP50Eu5j7tueee5r66zwEjO9DvIEgPA+6p7cPAOMERNHv2bPOMGQc4O2LRtm3bqP+GA4t7RW8NC7oOJdRwoCxfvtzoT7vvvrtx2nXs2LFeD/B7DqlwIjH+GAvoFZRaA0qM+TkDKPfGPSZQDmcagV7cK3c/jUTmGQ4jr/OE+c7nGnKv6VtCoByOjVjgVOAeonPyDNDFLr74YjnmmGPqe/QE0fOAe8G18xmuiTWCMlxBn7OiKC0HdWooiqIEAAEQ5YWoHmp90lzNNsVeuXKliYhCuDzzzDPrv4OS0a5duzAjPA0VEcaJbOPfYwnbiVCXOVzX6A02bdpkhFecMSh2/BbCsbsnhB8IkDSew9lC5BvCIK9YkTUclygl+9tu6IFhsU3YVqxYUf9essoWwivNLIkWc0e6oZDysuy2227m2VCb1S3ses+V++d9L8hnaBCI4sE94s+KoiiKoiitET85iIh1soKJ1nc3DQ/CeeedZ3qjEWlNRjDGTI6DQd9tgPTKnNaRYCFbAHnPfW44B5C16U1A7wTwZnjwvVGjRoV9j+O4wViM3P7UU0+F3QdkP6L2bWaKX7ZyIjJup06djAHXwvFKS0uNE8Y6NaLJpt73rTyKzpJqgug8yOc8P+Rx+rPQi8WbYRIUtx5hnTboETg1yLrBUZUM3NdnnnnG6BPu8UT/PMv2229vxkP//v2Nc+kPf/hDQjpCMnDPGP/oFf/73//MnCAjJtq9wXmEfskcIrPeLxgs6DzDued28ADOQmjIvcaZQRaTX28MN1afss4G+10cGGRaWB0viJ6HoxUnDGsAmRo8O5wzNDBP5DkripL+qFNDURQlABj1rTBHxAtODpwGRCshhAEGdm82g414mTp1qklt5TukiiP4P/vssybSKRWgmNloKfjjH/9oGqIh5HHeKDhEusRrXsj5INSimCLkcd1Ep8X6HooYihOfIR3ajVe4RuC39wtwSMSKbEIhJGrGzcSJE00pLSLaENLjgcJK6rsFx4o34wIFyUZdcT08t1ifsZASXVhYqA4NRVEURVFaNciaVs60ECVNJPm4ceNMYMydd96Z0DExPlJyiRfR0sjI/DeaUwMZDWN/PIOyn7HfG6BjPxMLZNazzz7bXKMX6yzxO3ayMq5XfiWQKp78isHZHV1uy/XYrIhUEkTn2XnnnY3DhybzOLxodI2x3FtOKwhuPcI+S3sOBGoNHTo05vdxCHmbpqMbYdTn3NxOEz94ruhDlCoCm13Cc3A35PbTEZIBPcrqm4wffpfSvWQk+EG5qbvvvttkypAR0ZB5xrVFc1wke6/RD9F3KXccD+4nY9k6NMA6Dfl9tzMjlp4HZDvZa9lxxx3N2nXjjTfWOzXiPWdFUVoO6tRQFEVJgmuuucZEeSBokpJLiitps6Tz+kH9XpQXasNaUlXrFuEeRwuKJjVGgT4PREodeeSR5u9EqpCG7hWcvfVV+R6KqY0K49gIeLFqJCMsAgK1/XNQEi0/RUQVEV+UBqDfRxDo5+FWPIjGee+998Ii4t59912TdmzvC1FBfMbeP+Dv3Bs3pFOjvCmKoiiKorRWKP9CaRy37GTBMEpmMAEnyI22DFSi2NJRsXoTIOd6DZgYW1988cUw5wbHwJBrSzH5wfdeeeWVsPcIQnKDjIfjIZko9YaUWI0mv9J3wA3yK1kd7mMhm/L3eH3pkgHDfTydB8hEp3wWL4KqyNjA2UL5W86tIf0dLMmURKKcGMZ8Ar/8smu8kDmAQXz06NHm7+haGP/RCazORVAXAVfusmWpgl4lGPmZd376BqXRKNFFyeF4TodE5pmXZMtP0VeFe+jONooG2VVk/MyZM8dkTQCVEeJlPXnniR+sDZxH0OesKErLQZ0aiqIoSUCkB8oCkT4ob9QvJYoLgQ5nB8IRjfuIJiONFWWIKBOiVWhk+MYbb5geF8mwevVqEyFE9AuKC1kVROdwTBslxe/RXA4FE+EVodidIWH7etBwkQwSykcR/cb3UAwRdEtKSkw2BL8Vy6lBJBiCNo0TE3VqJJKaj0ODWrE0RT/66KPro9VwQtgmf9wLrotng5KB4sv18LLwfVKUUT5wUpDeTbQW52/hmZHGjcKDEkn9Vp6ftwkeTqBkU+oVRVEURVHSDWRYZCwMz9Sbp7caUc4ElUTLkKWnBHIon8GASPR4LMcGBlIChJC1MMYiy2EYfuSRR0wvjGgQYX7llVca+Ro5FejdhvxHWVj6xlGqhmMjy8Xqd4ZMR4YynyMbg1JTNFt2w7kQCU4JH8otEdGN8ROjdryslERkXMreYOzHUM4547wgYMltKOd80Tk4X86FxuGU56GMklc2xTjbWGVR4+k8ZHxjZEYn4Food4QjgD4agJxOSSCM2Ogf9jkmSqIlkSg5hT709NNPm3OweoRtdA04CNCdyMIh+wIHyPr16+v7IzKmyWBH/6P0Ey/+TNY2DoZUQ8kkdBV6w+A49MK9o+F6NAdZIvNs7dq1EZlAOAYZ88mWn2J8HnHEEb59KhjL6MKMBSCbB32S7BPmM3or8w6nqc3eCKLnsVahv+EY4TM4FylpRRlpS7znrChKC8JRFEVRYnLKKac448aNi3j/qaeecnJzc52FCxfW/33HHXc075WUlDh77bWX89JLL9V//o9//KPTsWNHp02bNs7xxx/v3HHHHU67du3q//2aa65xhg0bFvU85s2bR558/auwsNAZMmSIc+655zqzZs2K+Ow+++zjFBQUOL169XLuuusuZ8yYMc6FF15Y/5lPPvnE2WGHHZy8vDxzPFi9erW5Vs6xS5cuzp/+9Cfn5JNP9r1+N/fdd58zatSosPf69OljrtEN18d1Jvsc3NdvX1yX5eabb3b69+/v5Ofnm2ew5557Om+88UbEsZ5//nln0KBBTk5OjjN48GDnxRdfjPjM3Xffba6B57nzzjs7EydODPv3xYsXm+8vWrQoqetRFEVRFEVJJ9yyVnZ2ttO5c2dn//33dx555BGntrY2rpyHrIQMefbZZzuhUCjq76xcudK54IILnO222858vri42Nl+++2dW2+9NeJ3vCBvIne6+fDDD50RI0YYma1bt27O5Zdf7lRXV9f/u1cGtrz22mvONttsY2Th0aNHm+vk2ktLS+s/89lnnzkHHHCAOc+ioiIjO19//fUx70OiPPbYY0amR7bnXgwfPtx54oknIj7Hde60007mOvv27evce++9EZ8ZOHCg88wzz8T8vUcffTRMfo6Fn34SS+d54IEHzL9xr9q2bevst99+zldffVX/3VdffdXcc8YX9y6WzvP111/Xv8cz4b0JEyY4ycBv+ekRbr0E/ax79+5Gvu/Ro4dz1FFHOT/88EPYcRjXfIdxxrjh2qdNmxbzt+31xCKaHjh58mTz3alTp8Ycy366VtB55ndfeN14441OssyYMcMc49133416vd7nv2TJEnPPOdeuXbs6p556qtFNE9Hzrr76ajO+7Gd2220359lnnw37TJDnrChKyyCD/2tux4qiKIrSsqmoqDC1TslE8TZZbK3Qt2TdunUmi0NRFEVRFEVpfIi8JtKabOVYmRhbImTLIJ9+9913Jro+GmSk8CILWml8KAFM6So1vSmKoqQWLT+lKIqiNJj8/HyT2rtq1aot5m526dLFKNWKoiiKoihK03DwwQebfm9LliyRXr166W13sWnTJnn00UdjOjQURVEUpbWgmRrKFge1YamDSv3GlStXNvfpKEqjQkRQrdQKOXmZGRmSmZEpGZJ488YtlZDjCP/jnnH/gsJ3ap1QXdNKEcnKyEqqaabSuiCilDq/9GHhv2p0UBRFaR3Q4Pbzzz83Ogb9Bbx9zLY0aApNv7do0DhaaT4WLVokixcv3mKyq5sb+iBOmDDB9AZUlMaAHpf0JeHVtWtXvcnKFoM6NZQtoiwODdPeffddo2hMmzbNNGGj6RTNoRSltbK+eoPM3rjA9U6GFGTlyeDi/mpgjwPOiJ82LJTVVRuMU4I6jR1zi2VIce+49642VCtfr5st1aGaX77Jnc+QHdptI4XZeQ1/sErUZ+ZICPdT2o5vmmnS2JPG9DRg3W+//cxeRONTyhIoiqIoLYd58+bJO++8Ux8slZWVZQxKGIppgLwlQ4NeGg/HynZVmg91ajS9U+ODDz6QQw89tIl/WdlSWLhwodmLvv76a9l+++1NABU6xujRo01FBUVprahTQ2mVIERTb/WVV16Rt956S9q3b2+MRizsGJE6derU3KeoKI3OV6Xfy9xNi0zWgJtDuu0jhdkF+gRisLh8lbyx9NOI9w/pNlK2Koy9fiyrWCNvLZsa9h4m9l1KBst27bZO6/teWVstP6yfIxtryqUkt1iGtu1nskzSnXXVi2V5xU/GqZGdkS89C3aS/Ky2kq7g0EDpsM72yZMny+DBg+WII44wLxzv6eqYURRF2ZKd56zd6Be8pk+fLnvssYfRLzAgsXbj2FAURVGU5oBS0DjZ0TF4YRf71a9+ZfQLSvdhF1OU1oQ6NZRWA3VV//e//xklg6ZnQ4cOrTcQDRs2TA1EyhbHt2t/klkb50c4NQ7vvr/kZeU223m1BGZuWCwTVn4b8f4+nYfJwOKtYn53ZeVaeX3plIj3R3UYKkPa9pV0hcySN5dOlg01ZfVjplt+R9m/y65pvX6W15bKwrLwEhdZGTmyddFekpnRMmpK03AeBzz7Fw75kpISGTdunNm/9tprLy1TpSiK0oxlpT766KN6RwYGIgxDrM8Yitq1a6fPRlEURUlLR/y3335bv3/9+OOPsvfee5v9Cz2jZ8+ezX2KitJg1KmhtGjoifHCCy/Is88+K1OmTDHRUnaR1lIeSkMpr9kg5aFNkpdZKEXZ6Rv1HY0N1RvlvRWTJERvh1/e613QQ0Z23LGZzyz9Ka3aIM8v/ijMHYRZ/9it9jIZDLHgfr+x9BNZXbXOfJ/SU7mZOXJkz9Gm/Fe6MmfjYpmy+ruI9w/qtpt0ziuRdGVV5WxZXTW3vtSXpXfhSCnIannRSJSoou4yygeOeuqyH3vssXLCCSfI7rvvbvpyKIqiKI0H6y4ZdOgXzz//vMm+sI7mffbZZ4svLaUoiqK0zJKJNgiYPQ694sQTT5Sjjz7a9ORQlJaIOjWUFgcRUizEKBqk1o0cOdIYe4455hjp1q2bbHGE1ouEVotItkhWN5GMnOY+o1bB0vK5srh8Zv3fu+X3k16Fg5r0HNZWlcqi8gUScmqlc15X6Z7fM+GI+bVV6+WnDbOlMlQlXfI6yYA2fWVF5UqpDlVJSW6JtMtpec6aoJEpm2orpSArV7IykjMCT1+/UD5a9X19o/DRnbaTIW2D9eGpClXLl6UzZXXlOinKLpDhJQOlbU6RpDPT18+Xz0t/jHh/vy4jpEdBwwRdxmFZbbm0zWkjbbKD34fqUKUsLp8tFbVlUpjVRnoWbCPZmeFrXGnVAllROT3iu30L95C8rDbSkqFMFT2hnnnmGePALywslOOPP94oIDvvvHNaZ9AoiqK0NLnhyy+/NPrFc889J+Xl5Ua3QMegJrmWlVIURVFaC8uWLTO6BTrGZ599Zkq0s98deeSRmoGotCjUqaG0mGZzlOT4z3/+Y/677bbbmkX3uOOOkz59+sgWS81SkeqvN/89o0Akbw+RjKYrLeQ4tbKp5kepdlaYmPT8zD6Sn7V1yo1tdU2AayVDshrdkEeGxvfrJ0e8P7h4pBTnNE3EemnVGvlqbXhJnf5FA6VvUfI9GWpCNTJl9aeyrnp9/Xs7tt9BehW2rtTTxWVr5OUlX0hZbZVxaIzttr1s365XUscqr62UDdXlUpxTkNZZFqlg/qbl8vGqL8Pey8nIliN67i35SZYrY95+t/Ynmb1pfv17O7XfVrZuE3/drnFqZNq6yVIVqqjPwijMaivbth0lmS5HVa1TJfM3TZEap6r+c22yu0qP/NZVdrC6uto0GMfg9vLLL0vXrl3lN7/5jZx88smamagoipIkc+fONfrFU089JcuXLzcGHXQMGn7n5Gig0JaAYzKaHclsAT3EFEVRUs2CBQvkv//9r3FwUKKKEovoF/w3N1dLVivpjTo1lLRvxvf444/L008/LUVFRWZxPemkk2TQoKaNmG9SnBqRjdNEqpfXOSeKhojkdvf5nCNS8S4mPdebGSLZfUVyhjTZ6W6s/l6qnaVh7xVkDZL8rGAR7dGePSVYKioqzKusaq2srvxeqmrLRZwM6dVxR8kNdar/d6KZs7OzjfLJf+3L/fe8vLzAZVvWVC2TORu/iXi/T+FQ6ZLfO27JgjVr1pgmXVwHRlV+lxd18zFMBjmP2RtnGMeGAbusI8aQO7xkpDkmNZw7duxomn0FjR6ctWGOTN+wOfsEMiVDxnY7QLIzW4ciV1FbJffN+UCqQjVhxYhO7rOndC9oeaWImopVlRvkP/MnSXZGSNrlMtYyTIPw/buMkC75HZI+7vKKlTJp1ecR7x/UbW8pyi6Mc04/y5xNkeWwBhePkHY5HcPeqwlVypqqeVLjVJoG4SU5fSQjyQydlgARxNbRTy+O3XbbTU499VQTVVxcHLs8mqIoypbOhg0bTFkpdIxPPvnE9MawBpyCgoLmPj2lEUFnYA9Ff0DXWFM5R9ZVLRQnFJK2BZ2la9H2UlMl9f+OzO7VKbx/5tUUQRSc94oVK2TTpk3mvKyOwTVRySDR8pT2nNFXLOhLHTp0MDoG2aGKomxZzJgxwzj50TFYa37961/LKaecIjvttFOrChZTWg/q1FDSMhWOhfSxxx4zdf+oJc5CSrPULaKW+LpPRKqXhb/XbrRITqfw95xqkYr3Ir+f2U0kb2dpKkqrxmPKD3svO6O9FOeMiNl0kedMRByCOMoAjap43sCGmZ+fb165eTmysvoLkYwayfjl+W/cUCY9ineR9kVdjfCNUZ9jul84D9x/RzHB4QD8mQyf3r39HRSbatbLj+sjGz0PaDNcajdkmvPCyWaZOXOmUSZQChijKAOdOnUyf+Y3eZ/rxNgYNNrhq9LPpbSasmIuHJExHfc3xywtLTWOE5SbJUuWmGOPHTvWnFs0vl37vSwqWxzROHy/LmOkMI6BuaWwcNMqeWbR1LD3EL/GdB4iIzv2b7bzSnde//lr+XH9z/Vjg3uWk5klFw04qEEC7MwNc2XausjSUHt0GiHd8mOXtFpRsUjmlf0Q8f7ANjtLSW6XpM+ptcEagOMf4xxr0VFHHWUcHNR93yL2TEVRlAAgh9GviLXypZdekoEDBxr9AoNNly66p7QGkLdxWCEX818gqxFnAHIz+gIvHFfIyxUZK2V97VzJzMwwgRAV5ZWSI8XSu/2udTpIbq45plun4M+MJfse1QT4s/19nCHorInKTnyPc0aHsMFKnDdNfu1ezjkxVtu0aWN0Aatj8O9t27ZNWl4z2fCOY47Hb6Jf8Pr555+NfjNixAgZOnRoUsdWFKVlwnrw0UcfGZscZaq23nprs2cSYLxFlnxX0pbs5j4BRQGEQ6JNH3zwQfNfatf+8Y9/NMYZBLcthlBVpEMD82LFokinhpm+lMOpDH87s2l7JGRIpjgep4ZIlixcuNA4LvxAWGczHDZsmIlyQpBftGiRDB8+3PzdTWXtOsmobBf2XueuHaRdTqG0z4muhKIYIIgTYYDCwRhDYEdAx6mB4B/t/Pi35eUbZWXFYrOhoyy0z+ksocK5JnKJ8+W4fI7j0tulf//+svfee0v37j5ZNQmA8PDdd9/J2uo1sqJyedi/tckqlvkd6hwY69fXlZDCqUNkIUoQ58P5cm68Nm7caF4oWyhwpc4aWV2zWtp2aCvZOXX3OTsjS/KaqawSjcy/KP1O1lVvkIKsfNm5ZDvpnBcegZ8oeVmRpSIw0+f7vK9spry2OszZxZ+qQrX1/USSJVo2RmFW/EjYtjkdI9aXrIxsaZOtGTficE8yWKyMgeOiiy4yL4wfGOww0mH8+O1vfyunn366bLXVVjrcFUXZIkG+fOSRR8wL+Y2yfWRn7LDDDs19akoSINN+88039dkFyL7uTANkZAKlMPLD4sWLTQPcwYMHRxxrWflaKQhFyp3dC7tFzfbEkbF69WojZ6NPcD78PvL2ypUrjd76+eeRGap+x7HZFsCezbnPnz/fyPK8TxkY9Bl6aNFD0qsjJQJj/6GHHorq+DC6Tvv25npsIBiR2egYBJ9xjehTXKfVMcrKyoxOR0YHegb3nGMoitLyYU3AtsHrrrvuMsEA6BhXXnmlyW4866yz5KCDDtJ+U0qzo5kaSrOC4Rsl4+GHHzbCkjXAtJo+GU6tCOV+qteJZLcRKR4k4mlyG+HUWPOG580Mkbw+IsU7+Xx+rUglfRdq6v6e2Ukkd7hIE9SEDTmVUhmaLtWh1TLtu1lSXVMrtbUh6dSpnQzd5lfy2SczzHNsqDGtOrRJfq6I7G9RkkPj5b4R7+NkuP32243Aze+PGTPGCOgoBQj0CNtE5wXJmNhQvUbKajZIfnaRtM3uGFURYOwSLY3ivMsuuyR5pZHHnLtpliwom2+Muh1zO8u2bXeQUHWd04Lr+Oqrr+SLL76Q7bffvr7uMwIIygVKFdkk/Jd/49pXr1ktb37ztixYvlB2+9XukimZskuHnaVrnIj5xoD+Hu8sm2gamFtjOudzQLfRCTWS9rtv9NOYtXF5vTG+fU6hnNJ3tORlZQc+xlelC+XL0gXm7zuW9JIRJX1bdcrtp6tny4crN2dUcO+657eT/+u7Z33UHko84wljQdDyHNzLT9d8LUvKNzsQBxdvI9u2Gxjo++uqV8vcjdOkyqmQ/MxC6d9mmLTJDndyblHUVovMniCyalbd3tBtW5GtR4t4jC+seZSnIlDg3XffNVlcKB8oIQ0xiiiKorQEMBgTJPXAAw/IO++8IwceeKCceeaZ9QZapWVARvfSpUuNbI+RnWAoSrxSHiWZbAgvKyq+k0214QFOBFP0KdzP99g4U1555RUjZ++6666mnxWlZZGx+e+oUaOMnBT0vGyZ2ljgSCDYiShpApgaC+4vAVPoRzg1kB1wWLiz2vk3rs/qGLx4NuglZMdwnjwX5ERFCTLmmM/oF8whAgNxQLZmfas1gOP10UcfNfY7nhX2O169eiXXv1JRGoo6NZRmUTTeeOON1m9sIZJ2xUQRE23/y+ac016k2/6xnQ5By0/V/061SGidSEa2SEY7E7nb2KxevVKmzXhFakPlJmW7R89O0qVLiWRldZQ50yukqjzX9HwgE4LIIwQUmyaN8MvLpn8jEMdK+0fgX1X1rZTV0ogcaBWeK90LdpOsjFwjSFNyxaaDowBxbFv+id9ASHKnczc31aFqWVGxVGqcammf21HaMS5iNkineWFkxBjXjmIVTfgjU2X27NnmvtisDjJkstplS21GrbTPaSf5WdHLVTUmKypWy8erPo14f1i7obJ1UW/jhEK5QknkvHmGCL6MKZQposEYY37KY60Tki9L58uKinWmwffIDlsn1Oj6izXz5a1l39f/fc7Ub6XH+kzZpm1Xc/9wKNmsGM4Rg/+4cePqowJbIiEnJG8vmybT1i0yf++cVyxj2w2RBdNnmznEdTOHiMwj6hHlg7HJMyBLKZaTkM8tq1ghZbXl0i6nrXTKS1wpD6L4bxHM+kBk+U/1DdENvUaI9BmZUPAAr2jl9xRFUVpys1PWOtY8a2whWErXu5ajI86aNcvIfjw/HFDbbbddfZnZKVOmGLmD95G90C+QUaxuYXUN5EbeRxeIFYRBNvjPFQSHSf2+WpIzQNrn9jP7JeMJvYJzsRngJSUlRndBx+D4lLfiN1qTjIIjxd5/P7jH9jlZ+QwZEVndXZ43HUF+tfoFf2YMkSGDLglWv0DWb2y7BCXRKOtj+7YMGTKkfhzzDOx4I1K+NcGYwTGJMwP9gnnEnMWBSbaTLalGcKI6OVpW8AA2PYIHWpVNT0l71KmhNBmU/Xng/vvlwYceMsLpGWec0brLYlSsEFnxQeT7nXYXKezd8EbhzcTEiROlU9cC6dlvTYSTIC9zsORm9jLCCAIjigAKAUIjRk9by9aWbeK/PH8yJ2LhOCFZX7NAqkLrJSsjT/JDPWTh/J+NMIhRn5RyjN04yYjiInMhXRs9VoWq5Ms1n0hFqLy+A/jg4u2ke0HD5gHKFimhgKK37777GiFx0KBBaePMcdc7nr54pnyy/AvpuU0vMw6WzlsiG9dulD7ZPWTV7BXGUM7YQFlEMOL5omjwHs4clBGEfYzsjC+UqFRlydw7+0NZVbWx/u81VdXSPrtAjivZXqZPn24cKfwuYw9hG0WuMaPnmpLy2iqpDtVKcXa+fP/99+Y6eT48h759I7NVeAZz5841yhdzfJtttmnRzh1wnDJxpFoypEgycBanE58+LFLN2uGisKPIzicGUj7efvvt+jKPBxxwgJx33nkmdVx7byiK0lLBAMjadvfdd8t7772nZTFaKHPmzDFGzgEDBhhDbjQIYsLw+dNPPxnZj0xpZH72MSvvkmnAMZCBMVDHorJ2vayvWSSOUysFWZ2kan2eORf0FIyqGF1fffVVI+fsscce0qNHj1blwAgCZXaRfyk3xX2ldBtyOiV50wnGBjoQ4wNHJo4Jzhl5FujdiP6w4447mn+zAVO8hy7K+LEOD3RM5Cb02GOOOaZR5CTrvEA3IvMeOZq/80Lu5l5bg39rAj3w66+/NoGPzCUcl169nflHRgDPErgfPNN00mmVzRDwRjABJe54dmRG4uBgvVSUxkadGkqjwiY9efJkueuft8vLr74m++88SM4941Q56LSLW9emZGu5uoXcssUiqyZFfrbDLiJttpHmBmGN50N0StBUfD7/wQcfSEmnPKmo+V46dGwrPbfaXLooL3OQ5Gb2Dvs8xjPK1+y5555GIAnCtGnT6o3GNgMDrBLB+aJoWEMyv4PQiiBL/VmM4Bjs0pE5G2fIwrL5YZHWlF0a3Xl/32yMROAeIBQjmKdjAy8E048//thEInXq3Ek+WvGpzJo908ybdh3by1Z9e8l+nfeQlT+vkB9++ME4K1BMUToYC37YaB6cWUGbsMfjntkTZHXVprD32ucUyPkD9pPWQlWoRj5euUDWVJVJz4K2Mqpjb8mMoaRTVgDlgudAWQGbCeWGZ4WRgfmHgsIag4ECJweKb7Q1/+fyNfLj+sXmz4OLe8pWGOmbAcZSjfOThOTnX97JlpyMHSUzI43qQ3/+uEhlXfPTeoq7iQw7JqHD8Dxxbtx///3GKIRzg+bisQxJiqIo6QR7DCUw7rnnHhPscPbZZxtDipa+aV6Q2zEU2/JEQR0AZBbzPQyd7MfbbrttzGhfDNSffvqp0Qco+xTkd6wx1Wb5YrS2/SuA/+IEodST/W10EMYaJWYx7O+zzz5pKWM3Nly77e+Rjjo8+qbNnOG/ZJIgl/L8KBXGOaNb4uAgI8A2RidYysqz3v4sjOVUlFJWooMDh2fCs6JyA0Zwr4PDlnlGx+dZ4uTBiM7z4lmne3bQlgTzjSAD9uX333/f9Mf9/e9/L7vvvvsW5wxWmg51aiiNAsbVZ555Ru76979k/rw5cvqBu8rvDttNtunZWSQzS2Sf85ukTFKjg+Cz8QeRTTPryk3lbyXSbheRzGyRmjKRpW/U9dWoJ0Ok+0EiOY1TE9423CbCBKEAY7DfBoIigONg6NChRkBFoACECNs4keMg9CHoEaGN0AB8dsKE8dKx+yrps3VHyc/PCatDW12ZJXNmZMjsmUvNOMCgyXdHjBgRWOjA8I3wsv/++xunC+dgHRcoPLy4Vve1YWy1EfMoROnKj+u+leWVSyPe36PTvpKbmbxRnvs1edok6b9TfynMKZTeRX0CHY+yQ8sr5ktFaKPkZhZIt/y+kpXRePWmGXc4pYieq3ZqZNra6bK2ep1pKL1du0ER/TTMdU2ebMbpoYceKnnF2VLtVEpRdlvJyWycyKVJq2bLhBWb+0vAmM4DZa/OwfpApDtkYtw+c7IsKlsrmZIhtAPfpaSnnNp357gCJ/POnR7uNgRgJEABJMKR50umFuuJLdGF8gjMTyL8+Pz8TSvklSWUfqg7DqXWDu+xi/Rv0/QGg1pniXFqhJMjuRmjozYMbXKW/VDXU8NQl+klQw4W6bh1UodjPX/55ZflzjvvNIYeGuji4NAGuoqipCvU7Scr48knnzQGVgwmRx55ZMoCG5QopWBXrTKGRO4zvSSiBZs8//zzRr8AnE3WSEwmJwEOOBLoTYFMgF5AtrU1kmM4R74gWwPjtFsm4TjIFfST4zP8G8fjuMgcQcBw+thjj8luu+1mAmxwXNjscRwXNmrfyivAueHwR7/A4bElZjbiPMKwn84OQwLopk6davRN5Mt4MA6//fZbM54IxNll5C6yrmqdZGdmS7ucdmqAbWKY3wRCEXTj1fFZc3BkoAtSJoyMKWwCzH/WJdYZ4Ds4RrBbKM0PjmqcG2RwsGewV5944olR9w5FSRZ1aigphUheFA3q2SL8nH/EnvLrEb2lqMBtfMwQ2fcCOhq3/Lu/aZbI+m9cb2TUOTZKRtX9tXypyKopdX0v6KPRYaRIUerrmGNIJEJ/0qRJxlhIBBHvoezhFHCDIsDnMDSiSCCk8x2EdIRBop0wJFPuB8MWxyHtGgcDwjyCP0bm3DyMoQslK6dSKqvKxQmFxAk5snDBclm5cp2MO/R3sk3/oYGFQhQIhBkUJiJp+G1+j+gNBJeW5LiIxcKyeSZbw01uZp7s3nHvBgnQz4x/Sjps28E0eEZ9LMjKl9067iE5MRrTI0DO2viFbKhZU/9efmYbGdJ2lGQ2YrN5xhdCKeOUsRftujFc2BJljNvvF38u62vrjONDdxosg9oPl5Lc6P1YkoX7gmOjvlF4+17GoRErk6El8dmaxfL4/K8i3r9i8BjpVZicw5VnglKBwwrFg+eGEkxElVU2UUZ48Sxxmh533HHy9IKPZXnlurBjdcwtlsN7jJCKUJV0yG0juTiJm4DqkM3ScMLnZwbRRWkkgK+cJbKSDKdMkW5DRUr6pOSwODXYv59++mljFLCGQq2LqyhKc4MBEgfsXXfdJZ9//rmcdNJJxgFLGRml8UAeYm8nIwbDonUeUHqTTAovn3zyiXFMYPxGVidzmOAmjJRkcu60005GB6FcKHI8QRAEWqFjAH9HRsSRgCzB7+N8R8bgGDhDMI7RhzERJxYyCAEZtlQV2b3oGhi1kVv4PcoRbcmOi2ggt9Gby+95p+M6gd7IWKEUcbRgOsYTpeqsHjz1m6kyY+MMqayplM49OsvAvgNlRIcRktWIupASHJyRrAPMXWRS/kxGP+sJz5y5bHsxsv4wv3F2KukBe8hTTz1l9m/sPPS6Yv9W55OSKtSpoaQEDOq33nqrUTgOP/xwOf/882X06NGSsWSayPTxrhGXIdKhj8hOR7aOO796gkjVKs+bWSLdj9r8VzI4aitFaFScgHCEIG8jFWxDYjZudwosTbaIaMBxYTd1hPYxY8aYdNkgYGDEGWWb/qKwEK1EiiepuzgUUCAQDIlscpeqwsFRXrFUQtnfy6pV62XOrCWSnZ1lznfF0jw59ugzYxrE+E3q8RN5xXGJuCDDxDos+HcMo8Dvo3DYdHaUmcZKY6wJ1cqCsp+lMlQtXfJKpFNesJIspVUbZMLKb6W0aqO0zS6UvTpvL13zSyIyI35Y962sqqKBvEh2Ro4Maz9c2sZoFh6P8poyue/t+2ToLkPC3h9UPFj6FvWL+r2NNaUyY4NtkLiZfkU7SIcm6OPCWEVRYtwigNpsIEBYZcwz5mDKNx/K5GkfyKh9dpaqqmqZ+sGXUrpyvVx5zt+kY8nm7zUWdRktpVLr1ErnvBLJy2q8bJZolNdWyw/rlkm1UysD23SWjnnJpVt/sGKOvLT4B4/pXuS8bUbJ0LZdUtpHCeHVOiSJnGOt4r+sMRgXxlfMlFBxjmS6ShlkZ2RJ2S8l5wqycuSYrXaVHgWNXxapxpkrtc7ciPdzM/ZOv94ajQiRqkRVoXywxl588cWm/1WQyEdFUZRUgnzAenTHHXcYmRBnK+tRa+lj1VwQsITegPEeOZ/sBCKcbQQt/04fBeRuDP62xCuy+mGHHRZI/uYYBLBg0OLPODJwVOCYIIgJ4zMZEOgr1MpH17DwrDFkcn78JrqALQ2E8RJ5EYNmLDhnGxyDMZRAGnfTYeQTAiy4PvQP9jiun1c6lldqLnBGkfnPWGgpMN7QI3BmgdUlbC84xgUODcY8Y+qyf14mO4zZQUq6lsjs72ab186DdpbTjzi9Ga9CiQXObZ4dcxXdwuoX/B27BmsNc15LU6UPPC+qcZAdTsAsgVOXXnppynpiKlsu6tRQkgbDNfUrb7nlFuPUwOt60UUXhQmlpjzT3KkiCz7nCyId+4hs9yuRnJYZZR/Bmo9FKpeFv0fpnm5HJLXQY+ijIRmCvBXCAaGMSCbSLXv16hUmtOF84L8oeAj5bOoNMfZzHkQ5oIRwPhwLJYBzIerBHb2EwrBy9RxZtnKKrF6zXvYaM6xe6Vi7qrN07bSdUUb4O413TYGqjBxzfAQOnCJE2cVKZ+a7GEdRPHB+cF78LvcIqNWIsJ0qqkPV8s7yT2Rd9UZb3EV2LdlOBhTHzrCprK2W5xZ9KBWhalNCh+8S4XN8rzHSJjuyNuim2o1SE6qRNtnFJtW5IawpXy1Pf/SU5OTnSt9Bm51ZHXK7yLZth0qh5/ct66pXyuyNkRH7vQuHSOe81GcU+UG0zeuvv26caUT0MV7cEX2Weet/kBlLvpPS1WultqZWum7VRdqVFMu27fYwpaga9RxD1fL2sk9lddV68/e8zBwZ23WkdMxruobY66rL5a7Zk2RtdYUZP9kZmXLm1qOkf5vEHTrzNpXKrTM+DnsvJyNTrt1uf2nXyGsz6xjzGEUDZfKlaR/KC6+8JN0H9ZXuA/tIbmGBZBfkS61tU4RDJCtPzt1mvwb3nImH41RLlYOTDyW4bvZnZQyQ7IzUZEK0NDB4vfjii2aPx/n8u9/9zgQsbIm1xBVFaVoIesCxeu+995r+Bn/84x/l6KOP1syxJEFPQI62pSORtQgOQpcjuATDH8Fo7sAl9mpkdf4N/QLDf9AefNHAoEzAlO27gRyA8RGnhrunE/oBn+UzBEAQ0GWDtZARcXa5A2Es6AYfffSROT4ZIiNHjox5zhwHHYM9DmcJ+gUv22th3LhxsiXDWMHhRSY/AXPeXgfpDs4LxjGBU4w1sFnEVlcmWOmVOa/IutXrZEPpBmnTro107tlZehb3lOElw2VLw11atiVhsuwnTTKODaoAoF/yrCmBhPGc9wneVIdleoA++M9//tNUdyE7HOcGGXiaJackgzo1lIRBYCSFjMwMhM0LLrhAzjnnnNhRUzg3eLW2dF4cGjg23BRvL9JmcKCvIzTjxEDgAu4hgj3GJIR5ItURJhHOEcD8Si5h5EdBQMjH+YFSEq9ciK2NS5YHm76tS4vi+L///c8IfwjznAefQ+Gxx0SgJaWX6Cve7927l7TvslgKi2rrhSDHyZbM6p2ltjZTCovyZF31t1LtlNb9eHWJzPh6kyxatMQIGIccckjM80Wxeeedd0w5qqZo1DZt3WyZtm5mWBQ7JZ2O2+oAc3009fYT9haULZe3l30R8f6YTjvI4LabHVGpgudNtINxYmVlyLRN30lWfpb06LvZwbOu2pGQZMk+nXeRrvmRzZerQ5Xy/bqPJSTuvi8iQ9vuLgVZ4WXLGhvGOeOY5824o74twqdlecUCWVjm7XdAaah9Gq23hmXq6h9l+oYFxlkFPP22OW3kqJ57SWNjFfv7p7wlP61bJs4vYy9UUyM9unWXW8eenNRxP1o5T55f9L2ExJHczCz5bb9dZLt2XaWxwEjAtRBZhXMWI4bt7/HynMkyY/UiqSqvlIyyGlm9bq1k5edJqGbzuDx6q12McwMjBdlc/Dee0vVz+QpZXbVOCjLzpG9RT8mmn1McHKdGQkIDySrJzCiRzAyNBma/mDhxotnzafpH341LLrnE1CNXFEVJJQTq3HbbbUbPoCQRzoy99tqrRRrZmht0CAx6yPkY9gmKQtYnS4I9GWcBOh2OaoKWvPeYtR9HCEZ/QCYLsu4jn6Jf8DvAb6CbcBxkgLFjxxrDIudFAIvtX8HnKJuLfIDcz15PXw1ebkMkOhG/wefcJahsEBQlcpEjif4lWCYW6GBkve+3335q7HRB8BlliNH50D3R0xg/yF4tDcYXTiv0WQymXI+7bxjjZvyK8VJNqWiX3te7sLcMbVvXG6Y1YrOh0OWZq3aOcb/4NwLLWkKpZ3QLbCE2AJQy2hZbxcI6K1nzcNLyZ/eaYjM+WAt5xbOjVNRWysKyn6XGqZVu+Z2kQ27LyWJKN7Al3nffffLvf//b7E/oF5SXtHqiogRBnRpKYNgUqLeNV5VoGjyquugghS8X2TS7riF4wVYiBf0CNUGnwSGLN8I6qc5ECFnBng0XQYNMCYzWCJFs1gjyfIa6plawnPjRBNlup/YiWRUyeeI0GTpwtGRnFZjsB3d0EsoNzdBsajkRTnyGTQOnCsIrzgybLs6GjzBAloatS4nRmeg5FEwEA4ytfL68fIOUV88TxymTDMmT3Ow+UpDfzggFS0u/kqrQGpPygNCQm5cj/XvvLH27jwyspPI9GkwzBuMJGvaYXCeNBhNtHPnpmmkyZ+PiegO2pWd+eymtXmcE3YHFW8uQ4gFh57+obKW8uSyylNM+nYfJwOLGccbwLIiiI/qoY6+O8vXaL6UqVGX8h2W1jpT/0uewICtPjuq5X8T3y2rK5Ju1n0pOZuUv/SIypF/h9tIhr/FLTyVKyKmV6es/k021m/svbFUwSLoz3xqZt5ZOlWWVm/uOAHfrlD6/SpmhhbmGMI5i4Y5SsWnV7zhLZUH5WikrXS+bVq6R2qpqKWjXVh487Kykf7OsplrWV1dISW6B5GU1LFuIOUpEnM2gApRhzp0SD6xXNmuLaEtvOnhFLcqkI5+tmSdTVsyQKgwWRXVrFc3MLxp4kMlOYZ3EWMJ6xrGY5xgtvIrXd2tnyg/rZ//SY8aRkpy2sn/X3QI5NpKB68YwgqHFDesVzmpb2qKlG+Yo53D77bcbg+MBBxwgV199dZgCqSiKkgxEgl933XXGcYpugWHDNptWEoOyTqzVZDEjH7I/2b0J+cLuy+yb7NPso+ypwN5MUAnwGd7HkYH+YEu7UIbImyVBTyZbLgq9Av2CfY99+oMPPjBOA/Y/dA1kV3QH9Az0Cb6DYfK1114z2QD8Ds4G5CIcH377KufOv/EC24yc3yQAKhFjLLoM98xPX4i2ZyPH2Mj/1gj3FQcjehd7fGvvrbWsYpl8s/abet0vPzNfdu+4u+RltQ7DKvYE+tUwz9xjmnmC85D5jJ3BOiOZn+n03LE54HyxdhLA+YCjirHKmsS/sZbF69NAEJ1fYBTrDesTNhH+zJpCoCnrqPuzZTXl8v6KyVIZqqrXMUZ12El6FTae7uwu5+uGtZw1D7tcQ7PomhvWeRs0zZpM9Rf6bmjpWyUI6tRQ4oIRCWcGiwzG7T/96U9y8MEHa3pYA0F4sEoFmysbE8IGUUsIkQgZCM1+kVOkV6J45OXlyJJVn8guo/pJZWWVTP9xgXTu3EVmTqsyKXx5OUUSkgqpKK+VF1941Tw/Iq2swsKx+D02EgyDbJpsJBjh+Kzb2QEIOXyeMcHmaUtkcd5uY6abNZVfmKbiQ7ffuv5asjOKpWPe5vqmQbFKSxDDIIoYzhmEHa4NgW1V5Rr5bt2PUl5bKSU57WSnku1MQ203MzcskM9Lfwh7Lz8zQ7Ioq+VydOzYflvp52r6Th+OF5d8LOuqy34pP5UhBVm5ctxWYxq9/8Jnn31mMnno+/Dhis9kqccADyf0GhvR8G7yqkmyqWZjfbksrm5I26HSuzA9y+1wfaVVy0yGSVF2OynOaZoo+kmrvpPZG5eEPf+irHw5rte+DTou8wZHIXMOQwGl+9yGAuYbawTC/IcrZsv365dLfkmxtOnSUXJyc6RrXrFcMmhvaQ5YM3A02jkJOEBRJuz8JHKKa2MtC2rML6upksfnfywbasrNHCKTZN8uQ2VEh619P89v0OiTtdKWsUPh+N/PEyI+u0vJtjKguHHGNmsiEaj77LNP2LXaEhsoiazrFu4bBjuMKETCIrTbfkFNknpNrydmPedaXSZSvlyEviFFPUQCOH4wShHggGxABO4111yjzg1FURKGxtLXXnutkWsxYGDISGVJ0S0R9hfWaPQMGxiFroEsQeYG+w77jw1ycsP3kJ0xkiH/42TASUHkPvI+egLHtHqEfYYYFzkemZjWocCe9+WXX5rPIotzPM6JDAp0CMpEYTi1EbmUxkLHsN+3zgv2ebes4Ybjkj3uzuxNFoyiQfZfPkcQim063pqb1ePsIYvfb6y0NjZUb5BVVaskOyNbuuV3k5zMlm0kRv5mLjNemc88R2v4tiWvcRRY/Z2xjByNDaC5A3AI2GO9sPMRJwOl5NzZJKxVXFdjjU3uEfcHB6vpE/vLPfmidJrM3xQe/JibmSPjehwgjQVltKyDxX1+6B7oGLy4RxbWfHrGsF7zProFekZLyH7g2b755psmyIH9imxNdW4o8VCnhhLImUHE+1//+lc58MADU77ROVKGDxoTn4h0kQxp/IawTUVl7Wopq5kvjtRIXlZXKcyKdFK4Nyd6C7A5ozDUH6Oy0mxWfI8akWRpYCgrXbtKFq+aJNWhOmG/qrJayjaWS05etgzdrp/k5tYJLnXx9xjmt5HXXvrcpHbbxoAcl3RiNj02OzZMFIMJEyYY4xxKJlFU0eB3UVBsOq8fK8s/kkmTPpMRo4ZKTk5dxEdORgfpkNd4TaE21WwyERTF2W1k+g/TzWbeuVcXGb98klQ5mEnr7gm9Lg7qNiasVn/IcWTy6q9lYVldrxSiw3MzI5WpbvldZLeO4bVWcZZMXf2TrK7aIO1yimRUh8FSnNN4igAGb6LDeW7MUfiy9CeZsYExt/mc/TI16OcxfsV7Ecfsmt9Ndmwfu/nilkZZTYW8vnSKbKqtixbMkkzZv+su0qMg+QblZDWg8CN0ekv3MS+JkEPBYH7iLKgI1ch9c6bIzxV10ZSFWTly9ta7SY+ChivyiQiaGC8wNKBYYEhItYBMOarXl06XaeuWSV5mlhzQdWvZuSR6fxcMGkSkck6cD6yuXCvvLp8S9jkcJEPbbi07tB8kjalA8txYD3lmzEurQHKerNv8m90DKJGx5557mnUW5RInF2s/48Eqctzn4cNTWNMZB8biiSKblovQy6dkgMg6Mg1/UYby2ov0OUgkYHQihixKxdD0j2vBueFuyKkoihLLmcE6SPNvMjP8eiQoqQHDHIYxd7AUMjzyBnI8RjAcDMjLOBGIZkYPxAGCHoLOQDAD3/crBUt5HxwS7MMcj++zf6EbsDciyxBZzV6HM4C9EYMk2erRsGWmYjn7OVfkqebad9DbCCJrjXX6yZri+aVTxL4SH+brhx9+aGRQgvrcYxM5HkeV7UnDZ9KlxJQtEYYRnvXC3UO0ucCxgo6x++671/eT+Xjl57KscmXEZ4/ueVCj9v5jnWMdRu9iHWb9tH1MeZ8/mzLZmZn1WS3YlOh7yzrKc0a/sNfB+ooOSqZHOsJ1vfvuu8b+SAAgFWJwbrhtZIpiUaeGEgFCLM35cGbQvJfFhDITjeG1d2SDiHz+S4x43Tsi20mGpKYRqeNUSq1Ds2s6IRB50HRCWVnlclm2YapUVlYbh0NlVbVk13aVbXrtFrMmqW3QhVLwxhtv1Av+KBb1mQ4Il/lLJCNnvSnnlJ+fW/ffvBzJyorcUO07ec4wKV1VbTZENjo2NoQfd8Q1wgRCEM4NIqAxtJljZGbWRxPbTdRmaNjSWPb8OTbvGadH9Qop6bFRuvXYrKyW5A6X3MzEo+yN8yZUKdmZORFZB/bfv1v3gywsW1R3nzKyZZeSnWT57GWytHaFlHao8BSVEhnVYZj0KQpvVG4anVdvMI6RdtnF8u7yDyUkm1NeeQpbFfSQXTrUZbA0FzZrh7qnNj2Tptbv/tLoHLhPfj01yHp4f/m7Yc4PDL89C7aSbdttjsBTNjeCX1i+XGpDtcaZ0TYnvHxSouBIJNKPqEYLQilzDoEN52VEanQoJPPL1pisoN6FJVKYnVhptYaCUMma0Vi9bRjP9839TKatW25GJWWn8rKy5E9D9pEOueHNKXESEAHKWkhpDHd6MnPglSUfmOaPbkZ32lm2KmzkJtehn8Wp/VlWrVorCxc7Ul1bJ3yz5tryW7xszWIy4qyAzpqJAoVSZ9dj1n7rsGwwHHPuGyLlqzbvuZm/ZGvUkyFSMlik264JHZpzpiwVzg3GtDo3FEWJ5sxAr5gyZYqcf/758oc//KFFOjOMDOyslZCUS1ZGsXk15W8je9uyUvbPtpRMEAM75aKIgqVsCzK722jNfspez75lXxw7VnkTG/nNeXA8XnzeHXFtjWzIN5S5IdLawm+gG9nm5RyvrrxtublGuydybbYfB5/je2SCNFfpFXRmjIYEgrU2eG7IqgTDKS0HGyRJIKoNPOI9yk/xTJkvzPt0A0cMmWHNnSViHSzcK5yuZLi4z+mHdTPlxw2zw3Tn4uwiGdut8fssAmssTmoc0ub3MzLqS/ratZMX71n9gffJGLfZcsAa2hIcllzLe++9Z/QKdW4o0VCnhhK2SOLMuOGGG4wzgwgqmvQ15ubiyHdUSvS8mysZ0vCNIeSsl8rQl5gCzd8zpECyZICEpEqyMgolK6Px0itZgJ99+d/Ss1+hKRFF1gROh+zsHKlZM9BEFtWfZyhkHAj9B/WW5RXfSVVog2Rl5Eq7jG2kYl2d8I4yYRpCu6KrVlV9wLfDftdkZWT4KD/lKBn5UpA1QPKzwmtNooSweePMsJFQVnnAo09JHM6B8+S8bQkVHBooH7YmL5uidXawYfK+PR4ZK5UhMh8oydRTcjITjy7fUL1Ovl9Pf45KI0BsXTRItioMv5ZFZUvkm7WMqc3g2Dig6z7y3wkvSdbAzYZPS9/CHjKyY+zU8enrZ8tPG2aZP/Pb3OK9Ou8mJblNFyVvmb9plXy6bKZMeXO8DO7eV4b3HmQiNtzRbhhzl5avkmqnRrrkdZCi7HCDsGXeprkyc8OMX64I+2aW7NZxdynKbpjBXonfH4b5RskC95yjGTMG4XSoi4oxgRJunJ81IjD/ifRESWqMpporKzfJX34YH/Yejo1Dug+Sg7uHN/tkzcIgwr2ymREoaQjspnRe1WqZtOorqTVllkQGF/eTHdsPblxlKbRIJPRj+HuZw0Qyu4lUzhcpn1O3Zuf2lKqsrWXDxk1m7W8yhaK2UuSnp8Pf83GCmxJUvZNLo1++YqbcevtNct/d/zWZGzff/I+whpyKomyZ4LC/4oorTCDGBRdcYJwZ6RohGg8j59b+INXOZqN8fuZAyctqmtKdlDp0OxvsnwmMYF90151Ht6Pck43OtWAQw6hle2QkK3dwfI7ht4+hJ7BX215hbh0G/YEocZupynFwEHBOVpfg2uzLykq2t2A6lVFhbBMM5r3HLRUi+SlrS4ky7jOBU0rLgAypb775xjgu3OsrWf3MI/TF5saWTWIuW/2COY+TlfMk64q1oTnPj4oVOHf5M/YX+udY20aHTh1kyqqv6rM18jPzZEznXaVtTnpkELA+WltNrCDaloZ1bhAUQVm1K6+80mR5ptNeoDQf6tRQzOL33HPPyVVXXWUWv5tvvlnGjh3bJJ5yR76gaId3WIo4+0pINkjIqTTRT5kZiadGVtR+Io5srmFOYCpxu7W/BKjmZPSUgqwhKb9OFAVqz7bttk469/Aa/TKkS95mR5Hd2Cd+9KF06F8h3XpRemtz5PxWBaMkLytyQ1pfPUeWr/5WZk5fKNnZRCzVSPceHaVvP5pZ/fKZdZtk9sxFEgo5UlSIslMphdnbSE1loYmqJV0awcdGArsVGs6JdEvGBsoQn6fuu1U0uD7SyCmLQhSPbeKHMkKUGIoVG6o7XZxjEe2TaONua6T/dPVEqXbqGgJadmi3i5Tkbo7w+37djzJ/08KIJt/7dBktk6ZMlnX9wiO3YWCbvrJTydD4dTXLlsiyihWSnZkt/Yv6SPsmdmhwb39atUheWvKFVG0ql41r1kmotlZO3e+IqP0GgrC8YpmsqqyrIdursLcUZrf+urnNCXPllVdeMcqi29iLEsnc8ZaiampQMmjWSW8MDO42IwsnDGuE7fvAv6V67fy5fL38/acPI5waB3TdRo7oOSTq9zg31igctDg3UIqMspSbLWWhCsnLzJXC7M17CNfE+eO8917Diy++aKKGvcYJvoPjkHT4qM6cmo/It/C82U6ktqfIJhzsdcxfuEKmzw9J74F7mbXSZsPhjGlUI1+oRuTHJ8Lf883UGCTSbWTChycrsqL2i18yN9bJ7f94Th687w05/vjj5e9//3ujZfgoipK+UCaTXnzoGZSOwBDRUp0ZlurQCimr/Tbi/eLsPSQzo/FkKPYK5G8Ml5RDiQd7IzI6mQRHHnlkQns2pU4w4vEd5BYCLtx7H6WmOA+bWWEbEBMNjlESJwXODPZZd41+9lKMl2RqUFLX7tm2rj/BFGR3UCGAY3IMG2iFXsGxCa5w19HnOtlfmssQyn3gelpyY3v0PpMNX1VVH+xGH4EtoZdGawKHJ/OH+W4djcyjqVOnhmWGNxdkWDOPcRKwdnBuvGyVB/QLbFHpYqjmvDhfeoxyTgR3YedhvXLykHtDUpRVKNmuXnRcD2X4vA2uya4gU5HMD+9vcGzeT4UTgiwd9Enurw2itRlyTdKzr5Hhfr3zzjty+eWXm/2CYGz0jNZwbUryqFNjC4dUPxrwEN1KQ57f/OY3TVoX1BEiV+d5HBrtpLy2QKqdn+vfK8zaXnIyo9dd9aO8dnxEJkPIEalx2buLsoZLdhJlkPxAoEWQQHhHiK90lsn66u/DPpOX2VUyyvsaZYB6hwiRZgPvkC/TF02VbQb3luK2Rabp9zaD+kjHvG0ktKGdWbTJmEDIpBbtqsrPjTGMCyopKZbq6hoZPLSPdGm/vSxf863kF+TL9B/nyaAhfaSwIM/E4WdKW5kyfrV0797DbJxfffWVEVh53rauLps2tW/ZiBHObSM8qyyglKBMIIxwvbZuvNsASH1kBCnuAYqOjfZmk0dJGjduXML3dkP1evlqbWSN/F6F/aRf0eYI7lkb5sj0DTMjvj+22/4yZ8Zs+d6ZI6HizZse92W/LrtLR+rIpzmMl1tefVyyt+4k+W0KpaBtG8nJz5PCrFy5cMCBzX16SgLPkfV2xIgRYQ21cWrQBC9Vv0G0pi0ZRxQmzktbN5d1x2aI+YHBwRrZmxKUg7/+8IGsqSo3DcKBO3TJwD2lf5sOCRsZYjUOZa1jPfXud6xTGFhOOOGEsDrDrJF8B+cun2HtwyATRg3NycMdryJtRMpCIjWr69+hJOG8haukLGekWV9tlC3PBKWzwc6imjKRNdPq/kvZuXakn2eIZOaJLJ0qsmb6Lx/8xaHBPbClunDW9v1V4J4abipqp0mtU9eLyDJ/3lK5/pq35H//e1UuvPBCE6ndmiLHFEWJvgYTJPWvf/3LyH3XX3+9kWNbA5W186UiVJe966Ywa2fJyUy9wwZDMxmetgZ6tNIxGPfZ/3F80HSbfQudBD0D5wbZ98j6NJvlfT6PEwI5nj2IkmDWSYEBjsAl5BX2OzIRcHKw/2Eg8xpJ2U/RGSgHiVGc6GbrZMApwv7JOXEttuwiegj6A/oC54ihkEwedAwMidbRb41VHJ/vo6cQFMK1cE/QObhuron70xxw74I4mtIRxgnBNowLnjX3EflJjYQtC+YzRnPmAM/R7ejwlmlNFuYourzNtmKc8GJNYrxYWZk57Dd+OMePP/7Y2B/SvexRNLgG7oMt+eSFNZKgURuw5AYdgqAxb6881lbWN2szwSmSbOYXaz1rIw5h1kuOw3Nh/21NwUXc3yeeeMIETbB2UTaf8mXKlok6NbZQaGaKh/Ojjz4yRoaLLrqofvGkzM+MDT9IeW25FGW3kcHF20lhI5WjcYzT4XtXCao2Uh3qIeW1MzyfzJC22dRZzEk6U4NAn5ArUwMKsoZKbmZ4P4VEwXiIUoBXnA3DHY1UVrtANlbPlaVLVsiSBeWycrFIbk6eEdR52SZPP0z/Wn5Y9KG071As7TvUGXxm/jhfuhRvI326bmsEeIQUDJ8IzT9XvGcW83ffmCp77z9cCgrrjG8zvtwk7Yu7SnXtGlm7dr2M3HUXyc3PMJkueZk9ZOnS5UbZQfiwacU4WNj8yLwgEhmhB4cJyobfBogww28PGzYsQmDhfRxlNBdHwEexsXXkuU5+MxmltqK2TD5dQxR0OJSgwrFhqQ5Vy6RVn8jGmk3G6UHGxpC2g2SbNlsbBfvLb76Sgu3ay4qKNZKXlSvD2g2W7gWbBb90BAMzhk+ey7vrpsvitSul64DN6cO5mdlyycCDmvUcleCw5lKWxz13EPCZ16lyKKPUEM1oU5AZ+wi5KDk4NHG+YpAgQ4vsKeYmcxfDA8I6L6JrUTiYs01Z35YSVA/P+1IWlq2VoqxcOa7XdjKiw1aysGy9TFq12Owau3fcSvoWNX3pNzc4dTHiEPlUn11T+6OIU9fPp57M/iIbl4Y5NQwZ2TLxu2Lp2bOnVG1aI+WrfpDamnKRnHYiRVuLeHoGsT+jQNlINv7rO15oZr/wjbr/4hjKwpnxy1jL6yTSaQ+RtXNFNv4skpUr0ml7EbJYyn5pHE7pKf6bBBW130mtszzi/cKsfeWLL74yARRkAFIX9+yzz06LMmuKoqQWjCgPPPCAKWFLCZRbbrnFZPy2JqJlarTJ3sOUt03Z71RX1zszMOL7GbnYt5Hj2ZMwJmLcwYDJ3oLRke/iKEDux0HAfoUugXzAHo9MwPf5d+R99BJAhufP6B7IABjxaBzN522zWp6vBbmB0iC77rqrkVsJ0kD2YM23vT7IgmTfInCAf8Ow5zZsoivQS5A67359VpBdGF9cF8fl2thH+G2ui99ujghvzvuDDz4IaybcEmDs0FsFeZTxQkmXfffdt7lPS0kSjNjYJNzzEqckujhrQirA2cmcZbywJmCEx0bB+mN1m//9739GRmV+23nMZ5m7zFXmLfOXfUGbPkfC/aGkHfeZMsXJgF2HY5BdTuCczYTzg9+xuoXVL1hH06G3SRDYjwieuOmmm4xTg/+25Kw5JTnUqbGFwcb25z//WR5++GE588wzzZ/d3vzK2gr5dM3H9Q1WMQznZObKyA6jTdmdIDjOSglJXZZFpvSQjIz4RmNHWGz5zQKprJ0tlaEFYWWYoChrV8lOoBeDt6cGTo1qT5fooqwRkp3ZPmnhAecQCgQRu97Fnw2ETZuoaF5s7Hje/Tz7CMRLK76U8tq6pk+QKTnSq3B3yc7Mk/HjxxtFAAMkUa6rK7+Sb7//Qrp06yAlvzhBoLy8UhZMy5QB/baPSMNmkySLAmcE5WWIouBcWPjdpQAQOFCAbBZHIuDwYIyxIaIMsZlaBQNhhvvBtbp7CQRlxvppsqxyyS/OCrJe8mR4ye5mfLqpCdXI4vIlpsl3h9wS6ZTb0ShsREFQF7alRR4hQCKUIpQsCK2TecWVUtCuLtqGEbdt255yWA+td9sSoMEZz9KreGM8YG67MwNS4dTwwvwnstKm7qL4YARhbWD9wnGGcMuLeYxyhOGBUnVNXS4k5DiS+cua+uP6VXLdT1PMGg68fdXg3WS7ds3rkOQ+YuRBkTOGDPbN0E8izlLZsKFMitoMkMzswSJVi0U2fRX+5fxtZMXGLjLzx++kW/YM6d+n0+YqUPndRTqOri8LZfsUIbizDtj/slbb6Cuc0oa100VWfbG5tFRYeakMkfxuIl33bpT7URNaLpWh8J5GWdJZ8rN3DDNYXXbZZcaJhhJy0EHqkFWU1sLbb79t+mWwh/zjH/+QQw45pMUYRhreU2OA5Hl61jUEAouQm3Fm+JUAwoiIfGhLQBH9621oGwRkAIxfOAWs8wmHB3KEt1wNMgylHjFWekvJ4uQniILvIuvjxEBuwPHv1iVwqKAfYBRNBOQUxhfBV8hK7Hnor/Z6ka1wlnh7CTQ2XDMldbjOltbwHrnC6kdAxL3bIK60HGwfCByTZFVZkOVxBqaqLwq2D17u37BGdOYgNgUysnCQsRYwX1kzkJGZt+wNzGUcIawF2ANYd1qabt4UsC7jbLaVMizcP16xSsOhH+AQxxYVbQ9xfxadwq1f8D3ex06EQyyZ0uFNDePub3/7mzz00EPy29/+1pS9tU56pfWjTo0tBDaWRx55xNSyZSO6+OKLjbLhjSj5uXyxzNgQXjIJdmg3XDrmxTcghZxl4pjMi81kyHaSmfGLwcW1+RJ9joHeew5VocVSXvtTxLGLs/eSzIzEInAcp9LU+WYzqAptlKrQ3Pp/y8vsJ/lZ2zRIkGVDJkqITdkK7SghGAMRItgI3nzzTROxhPODDYKNwa/8RsipldKqOVJRu06yM/OlQ25/ycksNPcKgRlnhI1sLa/YKBOmPifDRvbyHCVD2mT3lXk/bDLRDygpKD52o0KYwJhKhDYCBQoRXm33ZklzOITaxqqjiqLEdTAmuVcYeINslnWOn0Wyvnqd5GbmmSbhuR6Hhh+MM64JJw6GNDb3aCmj6QQCIs/AGrqJqPpk6lTZ1LtIfqyuKyswsLibHNJ9mMnWaGzKaytlZWWpZGVkSbf8Dua/ij81oUrZUPOz1Do1UpjdUQqzOtSvGZRvw9mHoM86wNyzkZSsF/XG6fpj1cisjbNkbfVayc/Ml4HFA2M2cidCkrHBHPdCI3KyqHBoMO9YH3CY8vsYL/wipmz9WM65ufjT9x/J7I2l9W5uVqu+he3kph0axzifCDZKFqMAigeGGs6PjLXsnJz6RuYZVUulpmx+nX8hp4tkFW4tWdk5UrH6J1mx4GuzHh52AFGrv8yrbgeLZMdvOsizJmUdwxS/0yl7jfQqWCG5uVl1TcBxarhh3vY+rhEjmBdLdWiuOFIjWRmdJC+TTJ/w9Yl98P777zcp43vvvbfccccdxiCnKErLhDUIvQKjGgFT55xzTpOXL3Rj+7q5jd6pxvSbckolJBWSldFGsjJSW1YPBwLyK44K9Af7m8ju/BvGGv4dQw6OdXQQ9iNkiESuGV0FGRwnhIXofX7Xr4SjkUU/+cToJMgHGDKRrfkz8irniiyBk4NjumUHngsOD8pvNgacB1Hi7CfsM7Z3VmNCdorV6TBANvbvpQr0VDuurOzIM+S5KulNee1KqahdK1kZudImu6dk/iJj0TsHPR99l7XP2gyo0MC8xfHWkAxZWzqK9cZ7HOY7v83aw5rCZ7A/UIYJR4tfxgHrGVnhOCG1d4v43h/uIaX1sJXZChuso7b3IbB+44Rwr/vYOdgrbJ8jst+8jqggsI8yflhb+R30HHePpHQE5zblbrGdkbVx2mmnqdNsC0CdGlsAbHLnnnuuETwvvfRSY8zCiIXA6o2UWVq+WKY3wKlR60xlCQx7b+PGDFm1oq9ZiPHWs7GyGLLpkrZIFgOCFQYZPtOtW1cpC30ltc7mBuL5mQMlL2tzyZ1kqXU2SsjZZBr50YA8VZ50Ng5rfGbRx2DJNWHowvHBveda2fDZWBpqwCHiesi220h5XmT6e9vsAcaxgaDA76NUEKlB+RkiNdwOBBZ+ztXW57WRtJwvzg4/w2hDIarM1vpnE0Y5Iko9Vv37hmKbDTLuUb7ZkDEgN8amHHJqZGPNDKlx1kqGIHAOkJwksoHIAuIZkvpvs24QLnBEIdhg4LWR7I3NiopSGb/iM6n5JYOrJKetHNhtpORmpk/5GMY6c9E6F21jSf7Li7nHvEN4bkxhrCZUIQvLp0qtaWrP7zjSJW+otMvZKswogRGaOUnEo1WIUQp4n/GJ8Ml5flb6maypqsvgIkuJhu57dtpT8rMiszoYH0Tm+GVpAMYPDPBEE+KoYP3nfvC7nFeqIrlSze+/fk9WVpaFvdchN1/u3XmspAvcP9vLhOdmHOlVVUaxQxEEd5kN1iT2vvYZi0Q24MT3pBF2HSuS0z7hc1i9ZJYs+vK/UlVdY5waHdq3keycLCmvqDKvUEaOODQCl2yRrI7GscJazD7R1GUA2Gv+85//mKaGZG9QEjNV2UqKojTNvkvfDEpMESh1xhlnGFnOOsob2/DBOoph3za0Zv8EDECsZ8hR7Gv8O7Ive2sq6so3FazpL7/8cr2uxjUir/N3jFYYq5ATkSVwjOPoILOyIfedfYsymfTfiAb6DPcaQyS6HNmm6AtuxwgQxOHuFUY9eQK9cLwcfPDBKS9ByD1Az+Q+cGx6CWDcxajamPX72cttAAi/z5hL97I6OH+QBbEJ2OdAABh/VyIh88Aaia1+4dYxWFvQMRo7on1t9VxZVz27Xr/IziiU7vkjJfOX8tzo1ayJvDhXWxmB8cl6yJqJ/puMzk3wFVkVfnIax8VhaUvTsY7gIGNt4kWwZLReQErwrCqbWY+uceCBB5q9Drua95nwvNGHUylTM7ZYw7GnAOsGOiTvcw6mebprbgDnZ/espszGQR8mW/Txxx83dq577rmn1ZXBVMJRp0YrBsHqqquukieffNI4NfBaxvOGV4Wq5LPVk6TGqTb9CDCi5WcVyIgOe8SNzGYhW7biDVmzZoWsX19Wv7C1adNeunc9yGxwbPruzAw+Q0QQRls2RCJe6jzJjtSE1khmVkj69R0iXTpt7lXRlLBpYAikbFS03+e8TSSuz7+zkRP9Yps0eb+HckVUT6JCEPeS+1aT87P0HlQgmZk8G0cyJU+65O9mojfc14CTws9giRGO37bR4RjfUDjIZuBZIZQ39n1HAMJQj5LEPcHIm8qauNx3FC4UL67VKh38PdURSfzWuuovpNrlkEPwLMkZJdmZbXw/H5IN4jhVkplRbDKReA9FledGNBvPiDFolQ425z59Gu7gS4SXFk+QTbXlrisSGVzcT3bpkJoarclg5xPjk2dq+81EU1wRBBlnKOIooGQsNEZk0MrK6bK2mr4Km+d7hmRK/6L9IuYSCjjnjTDoFcZMU79ObWTS6kkRvzG4eLD0K+rne09wErKmRCu/wGcwNBBFi5CM4uNtWJdu3D37K9NPwzYPz5QMGdWxh1w4IH0FVNY1FGCvkSeCytUiK993vUHj7iKRbr8SyUhSAdgwX2Tlp+KEqqR0U4XU1jpSkJ8r+cX5kl3iWocy20tNzgjZuKncGMaYF+xJKJ9Ntd+yzuGkv/HGG839+uc//ymHHXZYk/y2oijJQ6QoegWyImUeCNjBQZ+q3lBe2CeRZVknbH1wfgtZDhmb/ZQ/u9cuDDDs/RhViDYlwAcZis+yF7JPIk81R38fWx6ETIZYZYv4XDS5hqhpP3nZGlq5Nnc0flC4b0Rbh/WM8oHf4BwITPJzFnmN5LYkLyUTOW5j33ejly5bZqKd0S/4zVQHayFTIrPjOGGMIo8is+MUSsf+GsgmBAAx9tG9eUbW8MlYa6nNzlON7QXJfwkOZd3wlnV2w3MnaA6DL3JfY5TxIlhqcfmHEe+3zxkg7XIidQLke3RI1rv6Y9TWmudMielkxjp6FLppNBmR68cBhF6BcxRdqyWULmpJ8DxttZPmzHDBFsW4Zw9irfP24WDu2DURHYPzJlCzqUpCcX448gieuu++++T//u//5IYbbmjycspK06BOjVYIi8ijjz5qmnISKYWRgKyMoJTVbJLZG6dLWW2ZtMkulgFtBkueT1SwhYWKF3TsvEk6ddoobdpgaLcGmV6SlRE8HdcK43yfBQnBGiXGwqJoj20dA/S0SHZhR8DmNzDwmUjX1atNJDxCMIoGCzWLNAIrAgsLJMIq73Fe/G60iBw+b6MlvKCMIWCj0CQbQYSCNnHK67LTyAHSvl1HKcrqJXNmLTBGe9sMnChYIui8HnKeGYI4URzuiA2EHe4Bm5CNPsLIyjUS8cRxbTMprsHWX8SBQxaGjcZI5ppQPjG4Et2cKlBk2cDcvWMYN5xvqkvq1DplsqZqsufdDCnM6itF2eGlGBhrlaFprsa6mZKXuYNUlheadF23ssezYKwR2eZXuqwxCTkheWrh2xHv98jvLPt1bZwSAtFgPDJmbbZXRlWZyJLvJV9qZPvhu0rG1iNFsurGHWMWg6ldT2xG0osvvmgUdQz/KLuMjVQK3EsrvpWNNZHNkrcu2keyfomkCgolpz5Z/Un936srq6Wqokp26L6DDGzrn0ZsFTDGtreUlfszGJKZZy0hMm9TTbX8Y8ZUmb6hLmNlYJsSuXzwKGmT3UoUpbJFImu/EglViuSWiHTYTSS7gdHEOP2ckEjNepGNc+r6feStiswIyR0skrtN2FqDoY0xkaxBhj0BQ5dVXDCYsH+wV9tya7YeMHsgexNrMr2+KJGJ3HLXXXfFNCAoitI8YCT7/e9/X++MpHZ1YzoyiE7lv6xHGOjZxxsS+GJ1CCvvY6Rj/fEGS5igk1DIRF8jPyQTZWplTWQNjmXXP9ZCAnkINqIXHYZkZBb+zHXyPufG51k7/Yz/NrjJ4tUz+A1kgFhOiVhwvtx7ztNmfyKD4Zjg35Cj0JM4N6+jwPYU5J55y85wPPYZXtwLdCQb8YvOxXmjX3Af+HfuC9/ht3ifwDerlyUK+hu/w/NMBYwR24PQDfI6/5aO9dzRiTg/O2fR+Qi+I5BmS45kZkyztmG4tzYIsOVc3fOIz6JD24AkdAhkbsbo1KlT5fDDDzfvM25xtKaK6tBG+bliiufdDCnO7iUdkOUaiK06ESu6n3nLuuDtt+OG+2h03MpKc/2aoaEAY4JeUexpyZTCsqDjsreyB7COsS9wTLtn8ALWeqvHYDugRCZyC5mllKRK5xJaSuKoU6OVgXf87LPPNgIKqVaJRjxurC6V5ZULTKPwktyu0im3p++kZ2FCUKdsCYYH28TIcUISkumY9n75ZDfJFMr8eJQBjDe1GFgyRLI7iyRo7HOD0EAkvm1ChaHEG2lqmiwvWGAEcnCnjyJAo7TYPgscj8/burR8juwFBBr+DQGbNDoWUgzla9avkTnLZkm/Af1k8FZDpE3OZgeHNRjaiI9UgeJjhams7Azp16+PzJxRV0IGBw9CBAoRdXdxbKBcuEvSIJQgeFljfzSnF0oTkd9EdSPsEH3EPeR9Iq0Q7Ljf3DuUEe4/GwcCTbLRGd509YbC+XOd3BPb+Jjz5xUrvT4ZakKbpLQ6UuAsyOpjylB5a89X0VQ4jCwpzKLHSZbZlFE+uN+cP0J1czVSe2HxeNNTw0IG18Di3rJrh22b5PcZZzh6uH6iPBhnNWXrJWPSfySLDBIzlzNEOvcT2eVo0xiZMc/Yt/OO75OZgyD0wQcfmPcZ98wFlDoErFTUCS2tmi+rqmaGvZedUSB9C/dM+Niswx+t/EgqQ5Umc+6bj7+Rdh3aydCioVKYudmJiwMRhaq4pEhCUiv5WUXy9ltvm3uF8u7XQwaFjDGGEha0r01zYvowVZYZk3yXvMImK73WpDCOG+u6QmUiZR943mT/7SWSv0PYu8wH5os19DHv3IbAaPBvOF3ZLzAescZiKGF8so6xd/IZDFi82GdxzLujCPkOEVWUxrjuuuuM8bSxDKaKogSH9QBnI71wjjvuOFPaobEiHpFvbakU5PnminhnbUKOxohnMx78DJzIlrYElFu/YM0katuW2AV0EZtdAkTNsxayB/NnU17UcYxMzXeQ5ZF52M/dzhzuESVkyayGVOkYyETokvw2Rkky1a3sjLzP+fFv6F38G3IqQSJuwyVyN2s5aze6R7Q1nExVrps9wjYg5ne4Z1wz+4Q1WCHL8O/ILNwfvxr98eCckA1TVWqTPY37QOQ6znzuE3IVmS5jx45t1LK6DQVnEvoaz4bnl87n2tgQCMi4QA+wuj9jJZpcjO2Dz9lgSj6L3YW5gf5L2TPsIrbvDfeWYzc0M8lxamVx+UQJSV1JU0un3O2lKDvxjCw32DfoiYQ9xGbC1f1mXWktrseuPxiVsZ0g77G2+a09BLawZuHQZZ1QFAvrI/slsP6gcwS1b6C3My/RUbDFkY1og28JTrb6BbBf8J6F3yAQgGbi6Cn09muIc0VJL9Sp0UpgQ6WuLVFTNOnDGJBoPc8N1aUyfcNnYZGcPfK3kewNxWaDs43gWDiADZ3FJJrTI6qQXbtepJzo4+o6I04oWyS7p0h2R5HcHgkZdfgd2xfCRsQgkGP0x1jPQomgiVCMEb6hNU5trUgroG+q2ShflE6V6lC1LF2wTNav3iADiwdLx8JO5pxs+SoEAs4FQTpaRgkLL5s/wpWNLrfKEcI9ShDXyHPAIbHvvvvI+uqfpCJEI/AK+f6rJdKt/TBxQllGIeCaEawQrGlaS1S2N3uCf2fx55nyG+4oHZtWTkkBr2DHNSG4+ZWtQdHCmYORlXueSIo4Gx332Nu8vKFwvmxuCF/cfyKriDKJasglwtkpq3O2JdCc3vRLqKb3xHrXuxnSPmdXyckMz7CorJ0uNc7isPmGQrxsYQ9ZtXKjeVYoGk2dmeHH0vJVMmHlF1LLfTF9W4rkoG67SV5WbpMpG+6MIowdP3/1oXRdN0vaFuVLp7ZFpodAm4I8ydnnDPl5Q3V9szyLjQ5hXDE23cYB7jsKCcq022jLPEfgSWTdwLG7rOJ72Vi7rO4YGbnSM3+45GVFPwa/j0LBnPUqPZtqNsnXa76WVRtXSWFuoTjzHenapmuYQs/6PGXG+zJvaZ0zpXuP7jKy334SqsgwBhnbvNIrNJIajtKBUYL5z5oZdM4qLQwyNTa9ywa8+S1euV0lI2eniGbe9Z/5JVo5qGMB5zdzif2OtRYDD2vvvvvuGyh7DxkDh9v7779vjKbM4YceekiblypKM4IBgX4Z7JEPPPCA7LPPPik5LvIeMrsticJawXrD/sze6+eQb0pwuhK5zf5p5XFkCc6XfZVzB4wryN0NccByTPZyPx0B+QADNLoe8Ns4lIg6tf0j+C/7uR/cU+4tcrbNSnfD3o+egryJ/sSx3ZnMOHW4F1wfcjP3A8cH50oWO+PDW7YIR7Y1fnKf0EHczxOjFsck48MN5/buu++aevF+egCZ9fw2/x4UrhlDNOeEsyiVTextpoPNjEGP4fcwcKcbjCPuHfsy94EguOYKlkoHmBcYORnTNhjSlivjGTInmNt27jM/eN7YHdzlpTgO+rLtTYb+7x67yEWMP9tfzX4H+QYnQiLPoKK2VFZUfk2RbvP34qzeUpJbtz7FWmf5Peaf3+ds6VFkL+YX12ZtKlw38xedn/UJXYH5Y0sgYefAsep1illHr21YbbO20j2ASmlaYpVX9NsjqYCCXcD2ZrXlxKPtfd7vYw9iDSTwm+Cpq6++2vT003HZ8lGnRiuAKBEcGQib++23n1x77bVJGUJnb/haSquX10/8udMXSvnGStln0OFGcOX4RBY1uMle2SSR0Lo6w3F1LfVtNv9bXh+RNjsFcmwglCFoo/h4UyXZhDGOsHlz7htr5smm2iXm3yjR1Cbb3xmTKN+u/VJWVYWX9CjKaiPD2440ihCLNUoIAgPCEcKuX38NHAFs/ggFCBI2MpaFHkcDwhULsY1qJeqhQhbKpprZvxg1q2TOrCVSsTFHBmxVlyHB9XNviPzimF5jJRsDwhxR2iglOD4w9KMcWccEv+NNn+Yc+B6f9SsBwPWyYfD7XCeCfbzxOHHiRONYQUlpjBq7ZjzPnWuUOtsQOWp6bWi9SOXn3NW6v9OgPmdoYGdbyKmSDTU/SXVorWRm0Ch8oORmRkYzVofmS0XNDFm7dqOsXrVeStdsMGNycP+jpFu3uqaQSUOTaiKzMwsScsrEYmNNmSyvWCPZGVnSo6Cz5GQ2XtNFt7LBGCPamzHFfOC5MT63kZWyYPJrUrqhTNrk50qH4kLZUF4pldseLJ36bxsRQYrSmagiiwDP7zI2mbco8tEiRlHembfMc+ZMQZtskzWRm1kkmVH6EaEQMC7BKk1kKRHJyTVyD5hHrCNcN+sAjmSbXWFZVjFfFpVNr1/7Vi5dLaVLN0j/NjvWNxS1Dk73usfxcRih6KKUsJ6meykqhQdXI1KxvG4Pze8ikhVwjtcsF6fiS8mQOuekwz6Tj+O2rWQKNZJTa9xgr2DeMScwniQK451a/ZTQvOCCC+Qvf/lLs9YPVpQtDYxh6BR33nmnHH/88aYJNVkaDQVDBHsr+ymGYPQLXhjr0yUzC5kcPcOvhxnvI9ez1zd1CQvTu23duvoMDgIXCDAi2Mkv2t5kOa5YUV8Kxsq+yBi2ZwbPg/Wa46IPePtwYKDE2IkMhhEWGYT/AjoGDg2v7P7CCy+Ye4dsgQxjPwecM/ePXhzecyV7A1nK6+ywYHS25W3QLTBmxXoGGFQxXhEsZc851XDvkOWssyCVTpOGwB7KOEF/5Pmjv6MXqgGvTv5G5kUfRM62NgPmAOMSuZg1gHFqS2HyHuOO5+t1RCSqY3Ac7ATMK/6ME9E6ULwwPwk+Yq6i27RrXywZOTWSJTmSjZ7ng9XFbcAUf6YsFu/brDLmJf/OuEDPYexaR2O0YC7GFMdib2D9RrbjGjhvbx8RW6qK82Zt4jdZXxQlFXYdHP2MMZxlyTREZ88866yzzLh88MEHwyqaKC0PdWq0YNiALr/8cnnssceMsv+HP/zBCFREuBxxxBEJH2/G+i9kfQ0GepHKiir54asZslXf7nLwtielVmjf+E5dlkbtL04NL+33FsmOXYcUIQOhFmNnvAiHDdVzZf0vxn9Lu5xBxrHRUKauniSbausyVyzZGdkypvP+RljBcYCwgtLmdiogSPDvbPIIE7YJmbfHA4KDbRLtTvuGNZWfS7VTWtcceNI0GbbTAClqUygVywcaYzC/iRDLd9z9JCzukljUUEfoIa2P+0q0BunUXiUFoR2BjzI+fvedCHqeDZsNghcCHsdGgI4XtcRx+T7HRbAi4iXMgEykcc08EbIgMgpEsvuLuBqiRwPFjVR4HDScU8yxjLOpYgLfCn8/Z/u6Mi1JYpt/2xrJdU6tkCk/1a4kUzp0bCvt27eRvKwhkpPZwD4fNUtEqr7d7GjL2VYkp3EUucYCBRoFAUEFARuhhbGJAsuYxuiRvXGlyJQnzL1cvGqdbKqolr69ekj+geeJ+PRaYFyiiGM0idZrIhYI8MxHBHrOw7v2cJ5ffvmlGecoCVb5wUngHnOs2ygnrNW29BvHIfqTdYHxiuLNefI+Th07D/l3zgEl3v3bszd+I6VVdZkhbnZst68sX7rCzGe+G61XDQIdTg/mB/MQJW9Ljt6D2RtXyzelS02Zq1Ede0mPgubPmDJQbu3n90Vq6kopSmauSPf9RXI3l/6IhVO7VEKh7+qctJkZ9c7aTNlBMjJSV/s5lbCHELjBXkjWRqqixBVFiQ6lSMjOwBCOwo/Bigwq/pvMHuqGfQxDXbo2JUb+QD73k53TARvUhSEW+QC5w+3wRd6xDgpkJp4bPdm8TVx53xobbQlOLxhAeVYYOpHtKcdrnR42ECNaxj7vc47IF7YGP39G1/HraTd+/HhjUPbrRcFxyAjBsYI8xvGQs3CCkFEey6jF9RFdj5yGrvP/7L0HfKR3de7/TJ9R72W1vfe1131t4w7G1NBDSQhJSAIEQkgh5N60m4T80xNCCfdSQwkQgiGADRgbG/e63vX2Kq1Wvdfp8/98f69eaWY0I82orz2PP+OVRlPe8ivnnOc553D83NuF8G25D3apxeUiC7ArOQ6QLJrjmti9SubTh+bFBGxh7G18b8YgvjhzhPHNOOHnZN+TsYZfTJAf0iEb8Pft0sz5jgPuGaJHfBTGZPpc5O/49Yx95is+EWM/3b/n+CEYIP44Bj4HfwW/hawufrabNdtEcnJ/Goge5ng6qZkNNsHBWoO/j9+eCYgkiRvgi7N/MB4LKGAlgL30H/7hH4yAij4bf/M3fzN/8XYBy4ICqXGJgprwNOdDBYOTn6wOwBi3jbZ80BVsUfPY0aRnHDr3XLvectu7F/DIsRAekeIDWBY0IZj+99JrJV/2Y2fTxWCfSfkZjfcqrhE55Fdv6IRiaUFqt6NY9f7sTa5yxdGhw2oPWhkgdq+BCk+l9ldebYyT5Bq7ySBgiFNIoBtDySjPN2/O2FDMdgxQG2G02iq24chJheN9Jna9ftMqExin9NSD/9NjAqVkeORqsJPtg5EDqYBRkq3HBsYSx56pgStGIufE+1Fh8d32uMynT4bdvBEFOc6NUYtgpIefkuI9U7XgITZ818/Yj4VxQqA5U+mtzF8elIIZ6s67miRvat35XEEgjuuKyj89EEB91FiiSwlF5HSUy+WYZ01bsjOCP5veDNh/g+S8NOrlQpxhKONgMNdtkisj2o5KL/xYioaVCJTrdMU+bbn82hk/H6eWeZlNBZgLmIuUaOCeJjecZA5AnhFkMCWyJprR8zqcIrvRJb/n6mBaY2TA/OxyVJh+K+loHj2mrlBLyn13yKkrKu+YtgbQ+L0/fEHhxJh8zmJVelab6YXTQWCA/cMOcLxU8Vx/mz537mmzngOIjd/Zer02FFfmvIa1By+oN9wpp1xqCqxThXeBas93PyaNnE+61w6L0Gh6ZY7H1qm4Dk973qEdcjrmmSG2iMDR/uQnP6mPfexj+uVf/mVTbrPgeBRQwOIESCnH8OUvf9mUtH3f+943aXeythEcy0aQ5wP2SIKEK6mWP3sfx4WwYCWvLwReCdLbvfXSwbFj/xLExOYhuI1YId0esP0LzhuxBcHJ9EwZbBUCkoAsUjKr6deYaxNs/AGOg+AxAeKZVLWU8Elvug0I4NqZHgSl8VluvvnmyXJBXI9MGTWZwDVDOMPxYPPMB5AsjN/lKtvJ/UMUZwvHFiPT/cUE7AjKruKPQgLiU0NG5TqO8Wln8quZR/icjPG53gt7DWKu4McnNyzHTufYmduQefiXr371q83fIAsBa1d6+aulxFh0UIORDvP9FZ5G+V2lxjfC52Kc3nvvvSbjr4ACVhLwfYmr4qt//vOfL4inLkEsbv2QAhYcbGRkZ/zHf/yHYRN/67d+a5qiFoUvShrSke0mzhiUs6Vm1frWKBIPqT14TgnFVeauUq1rEQwkmpLSU8OZIUvDNC7NrojFoMZQmInQCMVOKZwg6GMhMRGYyheReFidwTZFE1FVeqtV7pluwG8p2a6R6LCGo1YPBZ/Trx1lVvolztpMqg7zep/PBO1nuje2YVJeXqo9V5YplOgwwTaP43KNxaxGS/aZxscadMstezKSIzMBJQ9GPsqnTIYQRhYqFIK02ZxZWw0O8YE6DOLD7nmST5NHvp8gNo4NDg5Gnc8zLvckoWGdq+l5Qd+CGTIoeP+dd96Zh+qc8e6YTgrkkBGSCTiJOFA0Tc90XQlQux3zaO4WGZP6X5Ci45K/RipjjGZo5BsbWDJSIzkDKF/YJRBYu2xya0aHY9VOqXGHKcfjYK06dWrmL4hFNXDqiDYwP2iCPMcyF6yx3FOCO8w1+3xx8nG8IeMIDJHKateQxvHON7U1nhjXWOwZJWQ1OoOkDTj3yOmgLu7U2twY2KC+cLuilB2bGL9ri6aXZKDfR/PYUxpjPEy8bjjao9HzftP8FEAkcZzheFDj0WF5nH4VuefXi+hSw3cuHrX6TUzMJcbiD9qO6wNbcrt/LeNn1DI2lR3YF+nSnrKrFobYCA+kzfGEFEnu4TMbWAdYD5MFBYaO10oGgTZKUL3qVa8yjgfk4ec+9znTq6OAAgpYeMEUxH26wIW1kH0aoQr7HfsbfgZ7Yr77Pv7MSlKPY6sRuMTmyLXG93KB62Y3CJ8pgMt+PlN5Jvt5bORcekBg47/zne/MK5OTMUQgGVIluZl4+ljINN6SfT+OlcAt9wZyg9/tDIV8ymbib0FCoKrHX0EASLA13/GLT8T7lovQQPzDPOS6riRicKXCVDV47DEzVlizEBllyyzIBMYupF+2MWz7yvj08yGXmFtkM0BYsB4hgrKP3y5FbWdXQS4ybpmXjMX0cm5LjeFIt86PPTvhX0g9ofNaX2SV0GbNQhCWSRRZQAHLDQhLslPptcG8+qVf+iXT128lixsKSMXKttoKmJaWi7MBC0/gDOPPNPFMxOWcqIWNagSjjzRjNl7SdWH7YfRnc/5NRsFAsdxddfJ43Nq2a7fGyp8zgcWU+vTIesc6pMiw5C2XiqaUyjnBWSoV3SzFeiVnmxREYQwcUsl+yTW9MSDnSYAY43MmgyKWGEkhNIDHGVcoLSGk2L16VjLj/NhpxRJRE+5pHjutbaW71eBPfZ/H6dGVldcaYgMiqMRdJleW+vmZkE9q+2jshEIJi8QgpBVOtKnYvdbcf77b76yVr2Zu5UMwmoy6uL3dXGO7RBK/w1rzO+Muk4LKBrU6k0Fw2lazz6VUgd1UzDQFPP60LtsZ04b16Z8z1XQtE+xyRTmTKtw791YpeiKJ3PBK7vycFgLYGKNcs1yaV80GO508xemCyGj5gRSj90dCieFmdbQWKeofkc/rUUVFsbzeCcPakX+tyXwxHBnQieFDGo+Pye8s0rbSPSpLIgKj8bBax49rNDYgr8OvVYFtKnZPOWIYvTinNmnGuJvR+A2NsWhJ/mIpF/J1ZED63mflOndWox63itZvkl7zXikwtd6gQMQZIYDK2IGUtJuLp8NOU8cxtpuccrysldTppHwb6y6BEsbyXMoSBGPHkggNMsxCiuppMyxdiXVyOSzVmNfp1+7y69UTumjWrDJPtXmkAwLDIjSANaZGot1q746bgAbZUZwLvZXOjjw/GdSv9a7RuuKdy6b8WmqMRq1mrDa4CsPRiR47s8CUQxs7N+35i+PNC0NqeMrSiA2EALkb3Q6HX87EvolsDdZPl5zaJYdjeRvy5goEDQRecTzYc3A8yNrIVv+5gAIKmB0ElcnO+MpXvpJVMMVrfvCDHxgbHOU+e7ZdmgU/YbZ68gSgEcbwHvZKgrHslQgEUoBtM9o24ROsklyLb79QzoVgod0seDExGOkzPoPf6VeVd0oUsdDAjsk3Y382zKU3EsdByUCuMffeLpOFbYToiedYv7GZstlJkNjp+yx+MKVDGD9z6cOCb4I6F1U8JXToGZMPELTgcy81sDnxh5lPOWehL4AYiflOdgv3yCYyLyUwXhDvQMRyvqxnswkPk4HYjuoKM503n8uYzHQ98QsppYlvwXjFL+aR7f5hi3OP+SzIAPxY+sLYYj/mIhlL+Cy8ZrnItWS0B09O/DQhCKL01LEHtGPjAXMOdjWIAgpYicDm+cAHPmAyiQriqUsPhfJTlwDY1H7v935PX/3qV/XhD39Yf/RHf2Q2t+5Qu86OHFU0EVGRq1TbSvepvbnTGFrpDj4pi8lpjIANxm5IC9hkCcjxWphJNnuC2jSXnXwfwdXOx6XBJEV05S6p7oq5n2CM4OS4RWY4pzsvGAgYcJRDmU3VFY33aDz+XMpzFvFTo2B8zAQGi91rVOxam9GAC8bG9dzA4wrHpwewnHLqhprp5VyWCn3hB5RIC+K7HGWq8ORW1mk2IxkFC8YGKjzbQSCFFecz33PGcOEzcXxnei9p03azMuqHTiMAYn1qOfeMenp6tX8fAfK07B4fZZUyZ/Ywthk7szZui7VKsbNWzw5nveTeZpW5orQXmRtkgszSbJtzYJ7iIBHo5rphYM5nrPA5EJS2k4dDgWNnN3U89cyP5Bg8pbrqUg0Oj2k8GNGqugr51m9Xb+85BUNRXbl/s+SskXxXT290DhkyeE6KR6TiBikw99rRqPqf7v+5YlzDCVB258rKG+Vz+c0cPDnyuMZi9AGYIGjk1PbSA/K7iidTwiHNMPAZDzgPtkIp9bgj0iPfkVqOWL/XrpFu+kVDbuA028rEafjB56QLp0xz5Za+IQW8XtVSqur2t6eUOCCwj9PDuGf94zlzvA6HWR9ZCwl+2OObcca9Yc1FxcW58H67XMOcwDqbaFUicczctljCCj+TdZY8ptyOXXLlkenTH25VW3Diuk0gEo7o4sGwakotAmfr9s06OMBak5rxQ9PxKu/86qhfKvjMmSd0ZLBL0MWMXa5/mSeg1UXluqN+o3aWZZ8rpjxL74+nXb8KT7X2lF+V34HQCBzSPzZqZTD6Vlv7ZdtPrH+Bwy013ir5auZAlELeeBa8QfhSlqp7z3veY4jvL3zhC4V08QIKmAMgCZlH2PwQhNmycbGnEGukK/qxf7DlktXy7OFk77If26IM/ArsGmrDJ/dlS1EXB/ulCz+S4hPEMoTG2jsl7+L1NEKxzb69FApiBFLJWXxVnjrtLJteFurFBuw5fE57bBgxhtc7GWSeS4YCY2imEljYaZAVBH0JYFOuKp38sH0fsoPzVeT+7Gc/MzbrYje2Z/5wnBAKjFXOBxt3vgpiu9wY94K5bdutzAUERvg0+NxcY2xhMlxYIzgW5ju9Oy+VcYutwHnZGcmMRyoK5JspxtqFX5LcvyYdXB/IunSC1K5cAAHMcXDNITqYD4BjQeiIf0dcxvZD+E58StZOhKr4OWSsrsRrf3ToAcVMxvgUmk90qk7bzVi7/fbbV+Rxv2QRHZSCbVZ8AP8iD4HUix3YNZ/+9Kf10Y9+1GQn/t3f/V0ha2OFo0BqrHDAwqNEJNhEHWk2BNTfcW9Eh4eeSHolal2frqi80QQL2RAx/DCA7EZyqKII0GGs4EyQvkiKebKKnU2X19sNpOzHJEbbpdafTD/Qda+S/AtUMzwJnAOBYmox5pLuTLmW0djD054POPfL7Zz9+I4NPa+uUEfmEj6Srq++TW7n8tQs7Qs/qIQJQk3B7ahUuefKeX0u1xcFC2QYTul8ywHQ7Iz7NltNfoxKAth2HVyMZLupGRgdOqvnnvwvrWqs1sYNE4FbU3InYtTF8uyV3NkDujhQGLIzpuPG2qVomtLKVSW5ULzUSBmCfXZZJIx8FGaAsYkBCpGRT6mtrIcVi+nuu+82qhy7uTnfi8oH5wzF0JbKUTn6j6i7b0ilxX4F/BMKtzV36dT5o3I7Q9qwaavkWj39PFBCnvu+FB2bykhZdaNUnjn1f+rkIUDbJ8glro+lWIJgPT5Mc/JUbCvZqzr/Ko3HhnV8+NFpf2/0b1a9b6Mefvhhc59s5xZiIb0R9ySe+4l05JGpOepw6JyzUl0brzXXifHLGsZnsW5MqqC+8OdScHTyY4609Wjn9m1yvOMPp30FxjdZFnZjy+T7jqGD48F32I3BkzOuuHccd0bDnUDNyAtSpE9yFUklu6SkbBXriyJS+EnJPWLO0Lr31m0Km3tlfy4r/Sp5nLnVAwbB2LDOjKbfB4e2lNwgr9Ny0kaiAzo29HjaKxxq8G/U6iLLIXyxYzgSMsTG+bEBheMJRe2hNvH33992QDtmIDaODj1n+mkkY3PxTjUG8giacdMHH5HCXVNzFKej7CqLiBxvs0iPQIPkzu5gv9jBfCRrA8fjve99r/76r/961lKbBRRQgGVf06Pms5/9rGmQedtttxlCAyXyTOVTCMJh++BP4GOwN+KXoDrGRuFv+A0EDdNruxOYY49M9i9S9srme6RgT2omWvEqafVti3LLKCFEcHgpGoKPx0aN+CMd20v3qXaGPoKXMmwCgkAsAfO5ZHqkg4xabLPZMhQoJWL33WBMkjlu99CwA8xklOMbz2XPwD/nvQth8ycD/51gN6QM+1tywBvh2UIEhSE0CJ7j89nCKa4Fdi+/Q5oQI2C+8nz6PKZyRCaSaCUCMogMMe6V7RviR2VqVJ9tvcM/5fxZF1m/bMFctlLP/B3/M5Mwy+5/wVxIroLBfeee8BxjCj+I+YNvlOwL2evnSkTz6EENRbtSYij07VtdNPcehgUsEvAt+pPjZU6p6iYpQ6n1lzKYx9hH2DqU/s+3jHQBS4dC+akVCpj+P/uzP9M///M/69d+7df0T//0TyaAxgbHBHvq+KPafuuGpI0joeHRQf3k0I9VVVxjDCCUBARZ09UcOCzZ6tSzac+o/qDkVLbnF5jUwOhA6ZVPOrjTEZDfuVPB+FTDc69jfU6EBhg36tfMhAb9MlyoYpcJAdc6jcVSewYEXHNXlmFAYVhhHGHAMrZQLKEgmS0lFwcFozjZqUgkgoqrRYnEuPbtu8z8TqmTbMDhpfQA/3IMGG58P44x9z42+pSuu2b7hPFm35OA5L/ZWrpmGROMc+YBjkFWhzU21eTdfJ6LBuSDUgKig2NHmd4gOcomjWMcIRwLDNKZ1DrzAQYshAYGLoYtTgYbaUoN5bFOaeCI6qptBSPH75d85dqy40bjnJw6F9KWLRmIgd6JPhwGE9e2/TGpbEP265rg9U9MkEq85pyU2CM5GkxWRsbzmFEBbn0PDi/kg01oUMpidY1Dzv4HpERU8jVJJTuniJm2Mylz9GLvoEZCfbrmFz+U8uk4LpB1KPAMSiqsklUTqlGP2yVHaWbjDWcOJ5d12Hb4GEcco32c9rhKH1tZnQ2+d+ARKTpROoislf4eqep2i+CwET4iOcgOmSJG7FuS3u3FYXrA5A4a9q3y757I1iDvw6nVgT2ThAaglNW0Q1fCrH8vVjzY3aafd3fI73LpDU0btLGkTL+37Ub1h8f1Ry/cb2VxTVx77sF9nWdnJDW2luzRqRHHRKNwp1YXbVC1t0GDkQH5nD75XTkEQcIdE4SG/c1EBFul6CaJ0mIl67VUaB7tVMtYp9wOt3aUrVWFt2TFpYvfcccdRgDCfKeEjh3AKKCAAqaDxq2oD9mv7rnnHmP3kXVI8I9sbQLQ2GbJIEsDGwi7h6AbAhRKBiUH3OxeUtkwq2jG9AdK6xkUzqdnUO7gfBCI5Nrwer4gEzyf5y91cH3J1CEoTG87GgTzO4H0mYKyyaWl7N+TwbjMpeQSAWdEVqjdGbP4NRAREHCIuQg4z6c8LOeBD5Mtsylf2BlPnBvHyl6WT++SfMA14JpAQmJzU5GBeZBeYpj5mql8MOQQ5BKvX8nNySFkiJnYaxKEAGtcLv1jJjNvH3nENOdOJr54nkwd1o9M9wgSLVuZKux67i332iY1mCOIpvAt7c9DOMf1Tf/8lUpogKbATkXGghqPDZrfi11VagxkyLh/CWEgHNLXL5xWT2hc20sr9YbVG+VaCdkqQwgRk9fWuDRyWKpMKwf5Egel/slmJQ6LsJyKOX/yJ38yp5LSBSwuCqTGCgRBzHe9610moEbtRDsoZ6d2E+g3adzBkHwT6uyOC53qbu/V2299j8r82es9Eiy2Ux2BrQIBBFDZ7AnaQnpkbEJHD41MyPb8HEHQnMeMTYKzwONskstRpXhi1ATVXY7cAzD0xBgmHS/9Mx1e7Srbv6xpk37nOjnkVihOo3Cn/K418jrnpi7DiMWBxTnACSWobIP7jxHHc5mMMtK5bcfVvh6JRFjdvT9SVZWt5hlSXM/ImUA9n9kAw1imJBpOAYYcBBzqK8Y3hmK5B7VeehmwyES2Rm5g05nZKXCYLT3Oa5yU9rFa6FogK6GZqyUlLldzy4hRJmJkLvY44PO5Rxi9OF0ZHS962dRfJ3U9aQVdPSXSqpskp9sQVMyfrA5bZHQ6eQeBgALclW2jPpvUw8R+73FD+tArIOAqTiIFHfI7A6r0WiVx/M5i+Z0lCsanvtfQIi90aMP6Taaklu1wDPee1dpVvVNfNXZCIp25bCLjxl+UEt5v6R3UNXtTVUCMbZxZCNFJ3Pg601NDcStAHUo4Fb36zqybIEEenHLmAQQG8wKyw1ahMl5ZLyl9g4M4K1hXov3JF9y65sFWqTgpqylOr6GZPsg+d49cjtyUZsmo9DapzFOvaDxomoA704haSI3Vga1qHT+ZtC5Wqhpy6UWIb104o78/eUgurqtD+m7beX3uypu0tbRCFd6AYmRDJMEUbUraNzPB7ST4f9lkXeWeUJce6/2Z4hPNudcXbdKGklmyXuJWWchpiAVNVbyFQiTeoUi81RBXHmejPI6mlPXt6OB5PdJ7xGTrgGPDzXp90/WqWsRyMOk4NNCtM6ODqvEGdKCmUa40stTOnrrvvvv0x3/8x2Yf+V//63+ZHgErveFvAQUsJbD/KTH1V3/1V3r/+9+vj3/845NzxO6/wH5nB5QBP0N0EByeLYBr+xj2Z9o/Y5OwX2LXsDfj42S0o7wV0niy0peyGAtbu591mYx1zmcpm4AWZegXCIpfZCU/UPdDmqHu5/6zHkOCveENbzAZrtj8BNCxT9Ptc8RWCF3wP5PJJvxhxlA+DbGx3bDbIDLwo1Hmc0yMwYWw4xnXC7m/4O8j7lqK/lDMafw8wHfmm22CapnjXMkBdsYapE0yUcN4mK2KQPpYZo1IJjTs8luQZtnGEOMYfyYbuG7JZB0ECZkfCA15nrEPYQzhTJmvSwVup1ebiq9RxIjgHPI4/C/pclNDkbB+9ekH1BUMGgv63o4LOjLYpz/ddeXyX5dpPgZiuyx+x0sc7D2/8Ru/YbLTEIP88Ic/NOKpXMnRApYGhfJTKwg45v/6r/9qnPLf/u3f1p//+Z9nVTUNjw/q/333kyqpKlE8FlNlTbl2b92nHbME3u2USFTMvI4HmzYbLEEBNmoU4igzsn5O1zNSf1Jd9prLperUJm7zAUoeAoc5BQoXCPTQ6Ah2mH/7Qh2m2TFwOzzaXLJDNb66eWVpQLDEEueVUEROR5VcWrPkGxoGFoSBrVzCaIJAs9OxgW1kMRYxsiDS0g1ssilQ5iQTYvFEj+KJMwqHo9q4sVFNTVYw26nL5HDMXusdQw4nJmW8hw6mZlIAMlN8uY81nKfk80tHItaqqI5Zv5jbAW0Un/gx+f6U6LHHteRph8xXFGs4YTiEGcE9i0eUcHoMMUmQnXsNYZTV4eo5LHU/m/QEzYaLpM1vnCFTg9f3ZvjDbSaLIhIP6/zYKY1FR1TkLta6oq3yOqcIkkg8pAtjRzQaG5Qr4VHX0XFtXL1tWh3rFx79inZvSnewHFLdL1jH1n1B+snnLU4gkdCjJ5u1+02/rvK9BybTxFnLMpY5GOyRzpEJ4VB8/S4da+0wKoxsDh1j3S6Rxj1APUfgh++hbAV/Y53KaS5H+iWyT9LPq3inVJykZAr+XEoMS95kZ9GhBEStViuuAZOh4XKslmOWXi/zwXCkT6PRQUN8VHrrZ8m6uTTB+Lntwe9rNDZF9Dvl0O31Tfo/u63+F588/ZSe7W83/TVs/PK6fbq5Lrf9KRwP67GeKULDxt7yK1Ttq81/vFTdMXPdWzKwBk9bpc6KGq3SLdm+It6uYPyFlOd8Tubtusnr86XzP1IkqVcO5MamkkbdUrc0mRBfbzmur7Qcn6TyLiuv1V/svi6F2LDnPb2cWE/scp2QpV/+8pdn76tUQAEvAaBOJ5sJ9TJlFLARsgESA2DnsQ7kWqIHtTF7IzaIvS8SBCQwiG1vNxnPmrFBVkbLjyQ7e8FdbPXU8OTXdyEbOB/sXkQLy1GmriPYqlOUoJxAo3+tNhXvWP4A1zzBGCHww7UF2PPYrgSQk8VRNtGP6Ia/00A8GRBqfAbjJdnH4LPJDMImZizmkx3A+KMvAWr7hQR+KnMqowBwjo3qM/aRW0TgM/z4xz82ZOVsPWW4jsQPeA92b3om10oCGWUI8A4cOJBCvLD2cd9ybRCeXooWMof1DcJhtqwz7ifESrZYhu2P44vzQERlk8aMB/7GOrVYFQEKWHx888Jp/eupw9Pqf3z92tu1tmjxycsZ0U95285UAYF/rVQ+v5LmL0bYGazMaZqI/9u//Zs+8YlPGHHIBz/4wUXLqCsgPxRIjRUC0gxxwjGQcMJzSWcNxsZ0buik2tovanXtGq0u3qTiohwNf5Ta1G73lEoOlzFWqDdKMNDucTDzl/daJafI0PAtXOo2BgPI1eBYCJD6/UTfoyb4NBm0Kd6scm+5ytwVc+6hkZhQ+CYUVCRB2Z745ObhVFNetfBz+76EgvERJRIxU2aGryfIg3KO4CuGHSolgrIYaXa2BYsx76WcFA4pjh7Ort2/gYBveikPxgtOhe2IxRNtSsgq+XXmTJs6Ovq0f/8WFQeulsMxFbjDUMN4S3fgcGIw/FIaBjI+Q89YynVz0Wol336rMW6OYBNKbl6Zjlj8tOKJ81PF8k36Gk5X6utOneqS03XtgtQDzopQpzR2UoqFJeorl2xXMBTW97//fTMfuC+kKvPAyOUaEswj2E6wANjzdxqZAfERGZfcPsnpsjI7Wn8mjbRaf3f5pDV3SIEZyrQlzkxkaySjRHLkRvQMRbrUFTplnIrzR7p0x4FfUEXZ1NrBeGOsVTpPq7p4qvfFNFID9LUrePQJPXnkuHZcf5tqd181WW4KZyNXJ4DrRjZPtjRxwDUm84Max7wuOaspLzAh++wGz4mkGqa3Wk2gbcS6pPDTE0TTRA8N5yrJSQmulauKuxQRSyR0/f13T3M4rq2q079cbjmx47GIvnDuoJ4b6JDH6dQrGzbr1Y1bcw5CDYT79NyAFSC0wR6zvniz1hdPX08SiYgSCskhnxzjLdKI3avGIZVdYTkdM+3rzT+QYnaGW0Kqu1qqzJyxNRp9QnGllnbhe0vcVvp5PBHX587dM+19qwO1emVj9nV1odAZHNV7np7ew+t3t+zXbfVrswYzsKNYL7/4xS/qS1/6knE+COZe6oHDAgqYC7Dv8CsQS7373e82mRq5KLN5H048+yn74Ez75Gyfw15LdkROjZWxgcYJtjisjNQF6mPHOWATo65czgwuMloRf/hcfpMdPhdYAiTI5rSeJEsI/AWU79xP/AjWXEgM/Ab8CTIjbH+SLGfsfMbBq1/9auNDYJ8jqsPfTbbZ7EbimcYJ5AYEBSr5dLIadTxESCb7bzZfYC6AYMFunbFvX46fQ1+5l73sZUue+YDNjN9NdjykkV2OzT4OuzehXYoVEdBc14GlgF26DP+UeW7PDewC/E+ex4/LZc5AMkBq2D0tGHesm/mULOMz7Obks5XIYm4QMC3gxYPPnzumL54/YXyNZHz2ipu0q9yqULBsQDhAT43YhA/grpIqr5eSxIgFpIJ1kL2NPY7H+973PrMP4WfYma4FLB8KefkrAAQtaUJDzdH/+q//mmyUnI54gjqFZ82/bkeZ/K4NeuK7B81mW7SlSr2J542BiDI9k8NimoCPDMs7+KyCfad1/CzqU49UfYW8xdVWk+Zc017pn7HAPTQIHGIUE3hfSpwZOa0IZXcmQBmOlrFm3VRy25zJjOHoMYXibeZ3j6NIfheqtanXxEVzxS1yLFCPjlgiqubRZzUas8rbuBJetT4T1NY961W+qVRuh1c1vnWmtIxNdBCkxcjDmEJVh+FFuS/ULaR/M14YW5kW6vRagg5VK2GWk6g2bVql48cvKBJxSoFUwotgEwQeqeDJKh8clWnGPGWmfCirJpqjz0GVDhkwk1FJ8NC6MVMGh/lpQk0WjcZ06NBZBQKN2rF1EQmNcLc0MNGwi+MZG5DCbfJX3aY3velN1kvC4UkVJEoj7g/XDHUVpZCyGulDXdJT35bGh6y+FDtvlTZcIa2+VQoNWCWnICZdswUO6OGD8UNZMIDKMTeF2mi0Xyd7H9OZ4y1yOh3acd1mDbjOqDxxhTlujHqcVDP3gwFp8HHrPlARiB/8a1IySE73jarbVa/r3vMq46RA0OFc4hTm4+DzWpwdVIHZghw8jxM0SWiFQxIqwXwzF3h9xY3S0DNWGSpnQCrdl0poAFed5LtOirZbg9HdJDkXtrzfpYCxKJk//ab27Mbiankg49LQGRzTQ90XzWW6oaZRqwL5lfHgsy+rqNahwb4Up+OqqqnmjwGXR+/bbGVtzAXxDC2a2GO8zunrWTTepkji6GQJN49/l9z+uywijNIlGd6Tgr4jE4RG0pd2Py1VbM0yXjOV0Zp6L9k59b5KdYUGzDHbWDUT+bmA6A6NZ7xnXfTGyQLWEB6slW9729tMCTrKUFHP/dOf/vSKDsgUUMBCg6Dkb/3Wb5ma0N/4xjfyCpoRsCZbEbuceYTwiYDnnj2Zs2WxUfBBEKecOXPGKLpt0Qz7O/tzTopGSmCW5F9acSZwXNi2nMdyqyoDriLzmCviiX5FEocnbGOP3NolVw4Z0Qvtr2G34W8mX09seWwxxgBZw/zO6/gdUoHfKU0F+WALStKzLmYinPBt8R0Q86STGthz3/rWt0wwm2wg20/Bv5mpFNBcYduenMtcxxT2J2QfArPlKOXEdeLBHIV4Yt/k3nK9eI5yXwh5Zu2Fs8zgWDluxgVz3C5pm7wO5lrO2h6jkHJ2Nhu/UyY5U3+RmQBBB1mRraE4YE2d7P1XwIsK+ytq9bkEZZotsEqUuD3aULx05Vuzgt5+1bdJUWID9OQsm7VX6Usd7Clk5rHmIw7+93//d/2///f/zHNf+MIXDGFfwPKhkKmxjGDjxNlGQfXJT37S1GnLhngirKHIY6Z8kR10cDsqFXBcZn4mCI0haDsfycYRmyrKBRyOcmePRtsPKuD3avumVZbxSCmL2hullp9JwX7JHZBWH5DKZmlCTW16Hu75s7oEuzFC0g2RpcAz/U+qLzy9rM7tdXfOSQE1Ej2l8dj5lOc8DilgVNdT8Dqo6bowhmL7+HF1h87rYnOHBvqGND4a0u7LdshXgrNgfS/ls7aVHjDlZADkBoY4joGtFOF8MbBmU5ZkQiIxrLgp5TSu7u6wjh+Nac+eqyYboVmvSeihhx4yxiGGHpkFODuoAS+77LIcvyehaOK8ookWE5RzqUEe5zY5sgSZccr57ExEXzxxUbHERPmpSbjljHt09OgRjYyMa+/efSouPiA5FlG9MPik1VuBuW2POYKsJbtTSxPlC8rq3P8Zq0F2cqDzmrdKtetTX9fdYv1bs0byZVFxmsAvn4WDWJxT5gAk1pPH71fEPahN29fI65u6jttKbjE1WJOJJ0jBYORJRZ3D5ndXzKOA51o5nH7jQKKWQi2WnM1FbWA76yjvSxSLmbGP0zZj+n1vu/ST/5CG+iS3R7r+ddK2QpruYqAjOKxPnX5Uoyh2afjpK9H7Nx9QCZlGEzg9MqDfOfiQgpQ5Mb2UXPqHfTdoR1l+e0hPKKjfP/SYjg7RwF2mUfjvbdu3YI38jg6eVPPYGfldjsnyfrGEdH3NTSqi5NsE4okRheKPpb3bIZ/zOjkdOWZgtj0kDafuPQab35axV04ofk7h+OmU5zyONfK7ppSIo9GgftTxlHonmvVuL12j62v2yLkEzk9/OKh3P/UjRdNUbv9rxzW6rjo3VRSBVVS6f/3Xf23KPXzta18zJSkKKODFDvbFd7zjHSaYR9ZSvoIhu/wPwWlEMHYpxvTP4W/s4YgD7EAvIgDsu5UAjodgJ9fhUs/WSiRCCicenbDBbFC7nn1iccvUIIaCrMKXxMecKRALicS6S0CcwDhZGnafgVxLmc00Lp955hnzGRAYyf4uazxrPt/F84xDgk8EpxejjA9EAGTZTKXcMgGfi2tCsBvb91Ifl8sFxiK+rF2yGt9yrhkTAIKJcU52hk2u4Z9C9sy1qTzvhbxCfJZv75ICLn3cffGc/vnkIUUScVV7/fr/9l6rHUlVCgq4dMF+RuYGgpF//ud/NhV3/u7v/m5ZSlsWUCA1lg0YWb/4i79oAro42QTpZkIw1qLx2Ilpz5e6r5HbmZnxZZNns4fUQFllSvt0PSyNEgxOw7hDigZTa+tteZ0UyBAgIsDQ/IR04emJwGudtOtVkq8ktxIso52WOjxQq4Tbb9RgGCLZMlQWG6eGT+j8WHJZHYeKXcU6UDPVXCwf9IUfVSyRWj4Hc7UUZmPiN4eK5HFcu2CG7M9e+I6aW89q3aYm01/FUselt4J2qM63QY2BLdOC/jgdvAfjC8MPQ5taoBAPGGIs3Bh7OIc4CqhVeODA8hqciHSHEccDY5/PxAnGGeb1kHnUPyflnEwRjL18mi1F4y2KJFLngkur5XVlVuEw/umtkamkG0HGWOKEEpoowySv3I7L9fzB01q9ulo1NRAyRYuvXhh4Qgq1Tv8eT7VUedPcP5csjYe+kPoc5M/Gq6UdE5+LGvq+L0r9HdbvEBq3v1uqzE+RlCmlnrFlSmc1BjUQpbRc6oi0SQ1IBRwSxmAodlrhxLmU17kddQq49pnPQ+2X7MSavhqPPjpZ83Yu4DMYq1nrGUcj0tf/VgqOTBA7E3jtb0oNefb+idMcfNAKMvuWuabqCsU/nXxIbeNDk30s6HNxReVqvW3tFPH5ked/rkMDPZO5BtBZW0or9Kn9UzW6w/GYzo0Oy+90a21Rcdb1lvs/GAnL63SpaIHLkjw/cFRnRprldiTkdjoUTyQUiksvr79JpUl14q0sjaReVRPwOPbI7cxxLvYdtTIzJuGQPCXShtdnXMM473D8jMKJC2ZuehyrTE+NdILY1EuPheR2uuRdoFIwNgiYsgewX1DzPJ2Y/FnXBf3jqWcnM2le07hRv7FxT057J4Qq6k0+k88nU4Ns2D/8wz/Uxz72sUIT8QJelMDmodbz3/7t3xoyj7JTC52dwJrA3GWOUbJmpQZmUWhjo+bTHHglI5boUjRxaNrzbscOuRyLU7YXggBhHMIP7Px8VPuUNmKcYP9DOOAD2OWaIJoQUeH74UNAmAB8VVTydg8NRG9kjCc3DgeUBsIvYWwz/sj6YAwePHjQ+JS8ns/E/0h/70LCJifyIfHInKLp7HJnDV3KsEs2TcY3ZgBjaLbeWhBUjNX0Hin0fOQ75hOoxA5hHBaCnS9NROJx0zS80utbEkFQAanxAQhK9pDZ+gblA+Ji7DX8S0yCn+mLjK/x9a9/3QjMC1haFMpPLcMEI13pIx/5iH73d39Xf/InfzJrwzOUy7FEt5yO6eUsEkLNnEoGoDKw69OzEacYoC425dRyOyYcFM1QzmG4dTqpQWbGC9+TBlqTZFDd0pEfSPvfOnsw78w90phV/z/hcOtIcJ027blmWdULG0s2azAyoP5In/md5sZ7K3LLGsgEWk1Pf457TPiNJtSl8jj2LogDCCmAQR2oL9b+6ygJkKHWSRJi9KlIA4sxxhwGn61EwRHB4cDRwBmBhMPJIJOGBRxlHmMMhwOHFmPtwQcfNEEp+15irNufh+FJ6QKcbcDnsvDbPSLyQTTRPv28REA+M6nBZkbKoN2gMBn87nZsVyJBaSWuDb0qnPL6fPL5K6VcFdLzhX+1RWqkgxJcM6HrsNRDpklCqtoi1V+eGrz0+DMTiy0vSEWV0rq90vP3SwPUr55AOCg9/G3pNe+f8+lwrakRTPYERvxYdEAD0dTzK3HXGkID4LzioOJ4RhPTs6Z4zr5/6Sn6PMd3QLihFJ3LsaK0mLGXUH+nNG5ljkx9sVNqPZUfqTHeJ53+oRSZWG8rN0vrb86/lNUlDq55KD4up8Od0kzeRldoJKUxNz+3QQQloTM4nlI8iZ+7glPlilrGRvTrTz+stqB1rW+qadA/7LtGvgwlHhhDFd7FKa9Q76/V6ZHzitDWJpawKG2XX8VkRKYcgzfj8m2enwkE+/tPSyMXrX5DxWuk0QsTi19AarolKynLeftcm+XTzM4+rytyL47yiDnPPsb+Q/AgvZnrzXVrtKu8Wi1jw6r2BrQ+h7R99iiUvKwrlEdhvCHyIMD7/ve/36ipfvKTnxjHYyU3Oy2ggHxB4BjBFGISnHlKyywkmEsEkiELECKs5Kwn7Ez8oRcLoZHNv1gsd97uT2A3504vO5sr8HHJTCCbwVa///znPzd2G+MIPxUBHsIj/AZsOcax3a+DbAuCwhBoyQIoxFis73YDa4Qp9n4FmYH9OVd1fT7gmNi78iE1sFVXPKFBKcvuo1avrqIaqWrbiilPQxle7jv3fyE/M9NaAclBhvhcv4sxju9bIDReuqAfX7WvoN5farAXsM5iB7GXsWfwmC/YiyBKIeghVJnf2EO0EUA8hR/zj//4j3rve9+7IsUeL1YUSI0lBIHjX//1XzfG3A9+8AOj0pgNlkLykOLqm+xl7HJY5Sv43eU4qoTG5ZgISsAUAiZUxvqc5TulkWZpoim2iaKUbZeGn03/ZquhcDrOPpJKaNivHemSouGZS1F1Pi+NdZsfY7G4jpw5p+1bnPL656FEzwFcwzOj53Vy+IxiiZgJMl1esUeeCcWpy+HSFZVXazg6ZBqjlrhL5XbOfWoUuTZoKGrdBxvF7q3yOiiXEZdjAZr9ck6QCvyLIxCOj+nM6BMTpMVUualoAhLBjpQlVEIjqDRkap7H2MGRueWWW8yCTNaGXRqMv6G+T2/mznHYgex0JRcqpuSaomwAc60f65AzQ+xv5k2jtLTUOLfZsoGsMmC+lKaWS+EMTcLfJAVXS+GLyUclFc3gjLc9KXUfnsoc6HhWioSkNUmNuwNl0urdUusL1u92+s5gn/TEd0mnsQiN5OwDfh6y5ulcwZhho7eN+CJ3hdYVXaGu0GkzRotd1Wrwb00JbOLUYmxYBGDa58ltnOJs5ekoL0YQM99GkNxrvpdspBlLE3gyBbwTUj6BcK7rmR9bDdttEIwurpXqMtcofzEiGBvX4cGnNRazstnqfU3aVrorJTugylukzuBwUjt1h2q9qYTV9tJK01MjOZtjW+mUGvP3nn9SnUk9GR7q6dD/PXdCH9ice1bYQqDBX6vLKnbp0MAxxRVXqbtY11bvN/0qkuFUtZyqMnu9LTxwqkZOzaIw7XxO6jo4tQayv2y4Q/IEJE9p5n18BgRjrSYzlB4aPleDAs6N8zLKL1y4YBwA5popHRiNmvlqE5DMeQgH1guUVASy0jOman1F5pELWAf4DlRStnrTXo94sG/xmg9+8IPmOCj/edddd835/AooYKXghz/8oX7pl35Jr3/9641icKHL7UA+InBBrZ9Pdu1ygDWHdYC9/cUEhyrkULkSmiL5HSo2e8VCApEJgVzWyPn0IcrkKwB8BOxDCA3WfMpU2UF+9oZ0+5tjgNiwG70nA7Il+fXsM3bptKUA+4tdWjIXkA2Qr5hryUHpz1Pfk0ITYp6+E1aVhTUvWxHEBqQtRFuuQGw3W28LMpIyCSxtfzaTMC6X44TUmOzJV0ABBSwYqPwBmc3cBbbQFh/CnqsQD+xlxEUR31LVYTYx+WxCYoS8N944Vc0Fst5e0//yL/9St99+uxGXPPDAA/rsZz+7bJVoXmookBpLBJpMveUtbzFlpiAeZtpYkxHXmGKyAozJpfYx/YrdljElnVcCgzZRbhQumcrsTIIa3k13SSNnJ0pANUj+emmgzTJYJhqUyuWTylNLYrFYnHj8AR09fV7X7FyvdQ1JAUaOY7bgSYgm1gmFI1GdON+hXZua5HKErQyOWZsUzx2t4206MjTVqKkj2Kln+uO6tnqqBirXscyTH3trG7HpRo7PVatyxxUKxqwAtc/ZYJ6zMH9Cg+A84wkngMXb+s5ibSk5oIFIhxKJmEo9tSb4f270WYXjVmCvwXJM1JUAAQAASURBVL9ZFd7cypjY5aUAGRu5pOyxqLORUOoJw5DrgkNkbx5cr0iiReE45c8S8sQa5XVuytoLI+v3ONcqHB9Ifc4x8/Hh2NolqGZqQggIeGEsL7mKquIaq69GCMW1SwpslDwz9AfofsFaDJIXhr5jqaQG2PdKqbxBOvKARTxGkmoxn3xCKquXui9YGRyTZMrCN6YucdeYRyZAcBEoQV23YfMGjcesrCmbkPM5NyvicBhiONv7cwLX6PEHpScfVtzh0JHGjdr62jfMXk6hvEbasFs698JE93KH5C+WtlhlFHJ2Eif6EqTArLs5khoDZ6TOZ621u6RJWnVdxl4JKxlHh57TGI2vJ9AZuqgid7HWFk3tN29ZvU+fOfuYKR8FStxevXpVaibWBzbv1fmxIZ0bta5pU6BYH95qZdhR4un4MM2tp8DPzw/Y4yo/RONx/aSzTV2hoHaXV+iKyvwCSJtK1mlj8VpDqmcjzFkvvc7LFUtcNPu+U8VyOVbN7EQzZ7uet3+Z+CcmDZyVVudfji0Ya9NobKrH0HjsrMkULXZvmXY9Dg22aiAyrkZ/ubaX1mc9TgJRqLnt9dRO2QbsD3aTVeYw+wzrNI7KXLI3eR+flZ7tkQ6CvTT3Q1jy1re+1TRSplzPXB2dAgpYTmCb//Ef/7FRCPKYqT/ffNDW1maC0ItVyofa9Yh18GNe+9rXzotIZS6TXfxiA/ayR/sVS+D3jZhSti7H+gURSyULPQgS5dzYfQ5IFqAQ+M1a+jMJZNMiCqRXB7DL2qYjU0bvYgPhFGQFat2ZgEAMwg3x14oGgpvQ0PTn6vZJ/rmTXAsF1iDIglzJIYg1Msxmir1gi2TL+GYe5EtoQKIg2CgQGgUUsDigVw57QDLxTjlb9gmIbuY7894uj062FeXP57r+QqBAisy2L+Jb4Oe8613vMr2nvvnNb+ZFwhYwNxRIjUUGBuKnPvUp0xAcp+OjH/1onkaiVa4nGeyrPpfksutRGadmQI89+nxuqeaUpKjYlfocys6O56TxHglFLGVsUHpOMJMEGlg8djqGdGD3BjXVWgvIyQtd6h8ek+p3auzBh8xk9453SaPdkqdIqt48RXZ4SzU2Hta5i93avbnJMhAgT+aRFZEL2saTSutMhH46Q90mKyNdLZsLCPJEEycVl0VaOBMNpoRRslPhdVaZx0IDI4mUN4Lz6UY7DcB9zmJ1Bs+oL9ymUneNtpYcUFwxk7XhnKPTgyM7W5DIBkpbHoCsjWQCIZJoVShupYeDME5ZPCG/K7/SAC5HvbzOvaa3RkJxuR2Ncjlmdl5RhHEOkEG2YYrTltxwejgyqLMtZ+QKOBckPXHOZah4zAZDqCURGsnPJxMdgDG+4QrpyMOpWQKAoPG+W6X2M9LYhAPDuLru9VoOEIhwOypV5LpKkfhFoxb3OOrkdtaKOAoGiq0eTHcwcHAoN2ETfZkQf+AeBb/+eY1FY7owMqrdVc/Ls2mDtH+WtHK+67ZflA49bBFARWXSZTdLgTzKXUHcMgcJOk99sEU05wLKAbY+NPX74DmrPMD6l+tSAWsuGXHpGAz3m9Y1NtYVV+oPtt2ik8PdpmH3zrJ6FaVlAVIu6jP7bzENw5kNm0rKTU8MQM3aCo9X/RE7IxE6mZ5CftObgdq2/D0XJ5VauL/69MN6rLd7snDj723drd/YNHsQZnqpO/esASv3LGtZCgzpk64QTVgE2hwQil/M+FyxpkiNWCKuL55/TM1jfSY7hkyZ66o36K7GzPVjUdEm2zx2g1nIeXrkEEQgeJYcoM2lTAO2FcEh1gR7nyEgml4PeyYQ/KUkypvf/GazX/3nf/7ngtbcLaCAxQaik7e97W1mHlECZ7EyTCnvQ7mX+aj2swUSUT7S1JxgIqSJbW9io7Hfs37w3QhtZmv2y3GiipzJDrjUga/hdiy88ps1lTUZu9gWNS02sNvyIclsdSwlcLOJXJYDZI/gIzMHk5ulJxP6XF/+nqzwXbHAtpxWqtp+fvlB1jYkaD4ZL7OJFlh3CIai7IakSgZZnmSpsb5mE8YZ4V4kYu47gVXsmNlIrgIKKGDuYM6l+wuQ3fgFlJVmTWbe2zEd1gw7RjUbsKmwO+wKEYimmPu5xnA5jnvvvVcf//jHTcyOBuIIqArlqBYPBVJjEUH67q/92q+Zurb33HNPiuOeK5wqkUM+JZRqSJw83qLR4TEzOXp6BnXrrTtN8GDODgelmFZdPe34jzz7pFpOvaCtW7bolR94vxxth6Rzj5i/n23rMf9ec+urpS03KxyJ6Nl7vypX73HtWN9oAljjrkqV7H+jikvLpLrLdOLxB1Xqdauta0BNDdVLksrqyrAAOSb+mwtiibOKTzaWJpumXdGESx7H4pUrCseHdbL5OXW0d+rA1bdmVCGNRPtNZoaNUJiG2iGtL557fxDAGMt3EYYIoVFf8vsi8bbJn4eGRvXk48d02x3uvEkNm9hwuerzeg/K3/TyRNRYRDnV7+lSy+A5dbZ2ac81u9Q6dl6ri1awMcp19ZZaSqrke+Mrzz6f1u6Wjj86/TkC9K9+n9R6QopFpYaNUunCEHL5pGvj3NqGh8tRLpdrOrFEwBEDBsfjqquuSlFz337bbTp18oTJ9uA+872opFCKs5YB91e+KN/oiLzUbY7GdGJgSLt+8j9yzEZqAALml82jVB7kEiq3licsP9HltLI96nOseQ6JkeJkUvbvohSLLGqm20KCNdcll2JKJXY8GfpqVHoDuqZ65gCzm749ZZnH6h9t36c/PPyUITjg+Yrdbu0qr9QVP/meRmNRVXl9+tfLrlGZx6sTw0Mqdrl0TXWtytIc3++2tRhCQ0lX/h9OvqDXrlqjxsDClnfJG9z3QI003psafCida8PYxKzPHR5sM4QGsEt/PdZ7TldVrVNtHo3vCRpg6BMAsDOlIO1xOOw1ozs0ph+2n9dYLKorKutUPxwy6mvKyjzxxBNmj4GoJvuDIBxOR751qwlSoPz90Ic+ZJRUX/rSl/TqV786r88ooIDlwPe//33TH+aNb3yj/uVf/mXBetMxj8hYJRgA+UiQmz2UfxfKIYfMoOwQ6nYCB2SyJ5eXZE4TYCcAbAcRqXkP+YEQhYAB+ztrAQEMjosSldgRBBZZX/JpaP1Sh90HDVJpoYmrmUDvjLkEfrHzsvVz4VwgyuwM4KVCOuFGUAxblVKIjHHGOtd3qbNI5oTihun2AHYavQZXAPAVEPjlg9lKhBGsZL3BpmD9SM4eojICayDrD/cTMoU1iiApmWH8zPtZM7FBWEMh3iBql3I+FbAyMRSJaDgaUYM/YIRaBSwcMtkkPMfajy2QTEKw32TL0sB+MP0VKyqMrYE/ws/YKcRX8VXyLbvJdyNox9d5+9vfbspRkSW+bMLZFzkKpMYiAcXgG97whslyU/k0EEtX5ARc+zUee14JUbLDJZ+zQqXFXRrsHzYTZv/+G+X3N8rn65hMyyVQSz+EfIGDwARGAeMN9ip05Ke6fWODKt3ndOaHn1Zf9X5puFrqb1ZFaY22XvcKqc4KSnsTYV1bH1SoskmnLnTL7XIq4BtTx9M/1Zi/waindt/1AXnG29XR3qaeslWqKV87aWzYTf24ZguJDUXr1DbenmKebSwmXXtuG0tcXVmeWxxSIxQb1Nn+h3Xi3Gntv2anOkPPqFZ7VOJOZZv7TT+GVGXNYKRTsUTUZGrMBaje5rL4oh6cnt1hXe/Tpy5qeGhU69Y3qL9vyFQ/Wi6Q2XT47CGd6z0tj8+jHVdY9/DM6HFV+2oVcC1Ro/C5YNNd0snvSjGrlqQ8xdLmV2V//e6brR4a5w5at2LjfmnHRKk6b0DaOD/yK1OJF7tW/mzAIUB1jbMwGwhgYGyk1L9tOyrn8Qe0LRpSd8SnR3/aJ09ZtYr8fq1eu9Y0/8PBLK0pl9NvGTi1wZCe7enXtlAwQxePRcD4oHT+GashEnM0Gpeq1kreHMdYtvVqpRjIjMOBJ6Vwt0Rz63LKFKyRKDcXJwjukcO9URtLtunUyFFDcBhux+HU2qKFr3v+ysY1WhUo1kM97Qo43dpdXql3P/WwKU0FBsIhvevJn2vc3A8L/PT+zdv00W30+LCu68XxMbkdDkWTHGJ+6giOLz+pAdbdJjX/1MqyZGLX7ZUqZ1YzZ4PP2aRobHDac8kYjgQn7p11PSLBkILDo+qs7VNNXcm0fRVbgoBOtiazyYFHskIhKkBncFTvf/YBNT/ypHzlpfpiKKRf3H65rr540dgLEJe8l9IO/I4y0u7bke/ezjpC3VtSxqmDC8Hx53/+55dG8KmAlxzYL//kT/7E9M3493//d+MsLyQIytkBQJx6yney3xLQw45nTrNXz0XNj51vByP5DvZzyEQ+k2AiPogN5niyKprj4HHx4kXzGcxb5igEJ3Me3wGVtd3MPDkAwRqEXcr3FepbTwfXfqkJDVvRnovdlwzGIvZlpnWez6SyAPeewNRygoA2Y5hAGnOKINslE+AuaZRWXSu1PWFZPFRVWH/7iil3Suwjn74prBm5XHsrprLfEKrpJdHwhRGnUr4GUo3XkhHG+sX6wni0fRLWIIhhxvclc88LWHAwDv7m+Av697MnjcW8OlCkL151vbYg9C0gJzCHIAvZt9OzpPgd8jBTGcJM2VnpNj2ZHIhwmbfECBBLEL9FOJEcw2JfIfMDEnMu8/mmm24ysWCywxFk/vd//3fe+14Bs6NAaiwCvvrVr+o3f/M39bu/+7vG8ZivY+xylKjEfb3plWDaoTocWrdug9ato9EqQZVKE2RgI2aTh9iYSy1ZJhysJgHA4qIi3f33H1RTdZFOt3bL5XJqQ2ONNlWPyrFup+S7WqreaKmPbYRHzD8+r0e7N1mBCf6+btVqafVVRuXw0COPGbKlvmKjMQoCo6PG4OOcMA5w1hYa1b5KHai+WqdHzpnG2Q3+em0qno8KP9P9XLzgR1/otJ5/+piuuI6SYZaj2Rc+NY3UyL1N3exgHJAmTYBpruUMbIdjLNqh0WiLnI6Qjh8+o9KyIl1+xVaNj4fUfEpat4ykBqhsKFNj6XTnfCw6urJJDcrE7Xq7FB7FErfKvc2aaXCH9VgCrF692gQRsinlIDxwHNjYCWbaNS9nA2sca8mkQdB/UXrh3sm/13rDqh07quZnm9Xe068TVfVyXXeXSupX6WRFoxJnm02JroFwWHesbpDjqhl6EC0kyHIjqyJ5prYfljYesBxFSgm1H5SG26wSgav2S0VJWQiVW62axsko37To5ftygunn8nMpQq8bSqCNS/2PS9WQvXbTeYcUblej7xqVuFeZ7LNI3K8K7/ZFm2f7KqrMA3yt5awpPWUjmiADLnXVZPT92+kT2l5apl9osgj3rSVlKYQG8DgcWluUR+mxxQSk2JbXWiWnGAtzKKlow+dk345rPNaicDikscEidQ1FNTLy1CRZ0BcaUndfi1xej9w+r3rPX1TNmkaNtffqqeZO8xqMdhuoabEteD9Bs5maFyNqsOtZ/1fraY3HYgqsbpTD5VJpY51+6ojpt6+7bprSje9M7gM1VxAchuhG+Y4d9bWvfe1FXcamgEsPONWMU9TBBPMXo2E38ym9RA5OP80vCQAgdsnXsWfPJjOWTCwCvXwG5Vz4nbnG57E+5NLXJr35NEQHdgEBCq4P1wQSlTWH64RqGgKUtSefhs4vFXBPEZ8sZfAV25B7M5dSTPiOmzdvztpvkD2HccTr5kJyLySYN7OVTFuxqN0lVW21BCuIpuZhWywGZpvLEJtkbuEr4NNmalif7X3Z1lXbvmFN4XUEVBFZsbawBvG8nTVGdgd+UAEvXfz3xRZ95uxU2e324Lje8/SjevDmV5gs8pcqmLus18wTHjYpCNirWTd5EBsghspzZLphR0Ai2nsV85TnEVxgS+Qriub1EJ6UoZ2prBTfn1N5/xmAuP2HP/yhEUyRLbIYgpSXOlZANOTFAyblRz7yEUNqfOMb39Bdd921oJ+f3LPBIVJAU9NAIQQefPBBE4hGDYXDw2ZOUJqN1i7FkkktgvKFzR+lIoiMDen6XWtUV1lqCA0D7Ifuk1L3KeuXijXSrtdMGTo0D0uvGU8j06KaSZUDCwcsKIsDyizUK5Aok+UmursXxQit8VWbx0KApnzRxOGU59yOxStV1N7RrlAorOMvnNWmrWsUKPIrniA4OtXjozN4VsMRlLqpRl6puzbvLI3e3h4deuF5XXv1gTmVM2AeMJZwWhvWFqk/ckiRSFSHnj2pjesbtK6pUbQ6LC/erEiwXcuNgCtzkM2f5fkVBdNgZ4UEV9PAGoOyYRpCI+poOaMzF3uMegFiI7kufjawLjCmIEEJfkyi55y1BtmNzkeDUnu/Wjp7dMOmJjkgc9qflW68Wfr9/y198wvq/tl9ilFW7Y7XSq94nZYE2focmPJRXuncA1KvTVo4pIHz0u43TTVlLKqz+md0HbQ+ixJDdTk2Ku88ZWWJxCkvtlVaf9XCZnjEx6VIf+pz5vNtQgOg7Y8pFH9GHmdUHtMTakwunSe5X4uNUrcnp0JL7LKUm7JJjTsbmvTW1ev1jdbzE2u9Q//f3qtUnVbapH18TPd2XFA0EddNtY3aWrrE6rwFUFCy7ybCNXrq51ZDTWqdNzVVmlIM9p6MktrVXKH7W48qGo5o/eU79NYN12h3+USGRWenKWMCOcFeYAce7HJwM5EavNZ2LOh7wogpXt2ogaMnVdRYZ65tMBZVcdq9XEgQiCDI+iu/8itGKPJf//Vf08oWFlDAcoBx+aY3vcmMx29/+9tLnnGA/8D+y3qAr4FfgUiJec685u8EDzM5/wSwCSDYQT78AUiSTE155wLWDZTVlPtljSKoTVNPBF72moPakozQQumHVNAYnHtKZgME0WIDBTv3gB6Mc4GtmsUOtMsNEphi/EGS2HsIvjCETa7B7AKylLhcoeVNGUMEOdPnsy3utMvUsVa96lUzZLFPgCArawZ2yGx9XlhH+OxMY4v3Q9gWxl0Bj/f2GBGOLaji35axUXWHgqr3L0y5yEsNrP/MUfYc5pktQrDnDvFIHtgXCJXwRWzwdwgM5jyvsbOziR+QaZFv/BDChCwPYqbJ37NY4Pv+4i/+wmSBkLVBVuHf//3fZ81kLyA/FEiNBQKGPjVhmYSkJi50CaVcwHezQKC4gShgkmDQ4WTAhDJpMfiSCQ0WATZfFoTkOnOeQKka6+ummoKZBsT2Xyd+GLhgERx1EymaHr+0+Tbp9H1TAcb6PVLlVMCfhYjFDIUDzX7TmVFb3Z0tlWwlgH4OhL5iCco9JeQyjaoXL91g/eqtqqz3qvlsmynbFCgKyO+aMrjag6fUHbICbjaccqncU6+movzqyfaGmnU2dFCd8Wa1Rr1aE7tcfldmpzMaD2oo2qZ4giBTjQIuSxHNuLvutl16+Ikfqy3kV1d7t7wel3bs3qiiYr9GonE1BqxauEVFg2bcLlQt6LmgylurOl+jukJTBMu6os0qdq9MsmCxQWNOWzVBEJK1gXqQKWA9GOyzAtdllVkD5Mx3Mn4gV7nHkeandPznd5u/Xb9ptdTXZFR1ELC5OBw4MKR/pxgtOFzJiq0xa81qKCvW861dqikp0mrWo5F+qaxaesd7NXTtbVZD4BxUoQsGSOCOo0lPOKSiCivDhgbuk4QGSFiZG93HpTVJ/T5KVlmPfNB1Rjr4vanfhzqlaFjaspAZKhkULhnGBERSQnZ5kQkjX6whG+R0LC6JeHt9o7aUlOnMyPDkd0fSD3niL5WeKcKCsfaXe67Q29dtUldwXFtLy7UqrezUmZEhveepnykYi5nT/ty54/rHfdfpQM3SNFtdSNi1yrMFG1kPXr3pCt2wZocGI0HV+IpV7J66Xjj6zHv29nwNdd6DOAPDf1d5tR7qmWpcTlPy1UUli0po2CBYDJnxD//wDyZoy7+/8Ru/UWjwV8CyAEcdVR+iKRxissCXQ32OL8Hcxr8hIIFPgb9DfXnESuypyX4Ex02ZKIhObPpk1fJcS/LOBggVu9lverATtSX2ZgGpgFziXkEILQWpYfdAmiu4t4xFAteMQ8otE5Bi7CXPCwgtbMtCcHnlwM7QthtxY2+8/vWvn5MPyDrEZ9GTxY4ZEOs4dOiQrrjiisnvYF3Cf5iNBGYNQ3iZi93CZ7LOEFhFIZ4u1uAzCg2BC6jMMJacGURWLyWwdl97beY+lnYJNx6ZiEX+DhnOms+cy7Vp90xgL4LMXApSwwYkK+QOWeGIyb/1rW8V9qkFQIHUWACwsaGeYpB+8pOfXLYALUEI0rKSDT+AooVABIYrCwIBSxwNAoT2Ro+iJaWpHobh1juk4/ekZl6kwCGFCBAloWqTdFmjNN5nlcUITF+UOMbe3l7jDNmw1Zx2PdyVDpejxjyWAhWeTYrEx9Xfe1xrN2yTz1mmWt+UsrkvPNW03EbAVaa1xUlq9hwwHOlWR+i4ikr8hnzo7u1UXM9oa8mNcqSlHUfiY2oZe1xxE6B0qD9yXvW+XSrzNGkoclE9oWPauLNBHRd7tPeqHXI7aQZsvdeuxQ4g3ygZsHfvXi0XmBPbS/eq3t+kUGxcRe4SlXtWRjO8pQZKN9YR0uUhHx966KHpTbXGR6Wv/pvUMhGE37Bd+sX3SRlUJ2SFoaqDOI0MdMp74SGta6hSdTkB04R05gG5yhoNEUvm1kxOB84DjUSnOQqrdk70qghZ5MbE37fUVWpwPKSnmztMHwV3d68aS6334wSR9bGkZQFqt0jBQamZRuEJq7TUrldZx0sGRTo4jUzP54sLB6c/1/zcwpIaLr/kXy0F7bWIc+JfVJSQTNacT3BOGWNxyZlnCcUSIXNdXFo4xzDgcusb196sz549oQvjo4bgGI5E9ckzU6nhoMLj1a9s2DTt/TvLKswjEz51+oghNEzjbIagpL878by+cwmSGggdclFPV3iLzCMT8m3WTaCD+UiQ1N7/X924Qc2jQ/phx3kFGurkONWsj77xnVoqMO5+7/d+z5SaQLBCcOzTn/503udWQAHzAfveb/3Wb+mee+4xpQvIblwuJJf8IaDIA5KAufHa177WkAjMX8qxUBKCwANBB56fb2m4XIEtgfABv4f+gDY4pjvuuGPWjNCXKvALl8p3JeDMmMnW6DsXkIlDIJuxZQev02HbDstdgqqA1AwKgngIF8gMghCd67gjZsFnISpF8cxaQyAU8WZy2W87+zJbg2AbrA25CjF4Ld8NEWJKaQcChrTFPmCs8eB8FyLoWsCli19Zv0nfaj2vwUjE+AWUsv3Qlh0qeonuQ9j4zPn5gLmVT4Yn6z+ZfGRXZZrfzF1sFV5DLHSp9grsJ4QE73vf+yazwudSjrGAKbw0Z9UC4jOf+YxRT5E+RB+NlWQ4UcoJox7DD4cCVjA62quh/g45XH6V1qw1panYeNmU7Qadk6hcJ13+dmm4w2owfPr+qQwMAwJzGRYnAh1Zgh02WNSSFzYcEGpXk1ky7The4nA6XKr371O9f1hri66Uy+FLHWcZOaD8iaHRWO+kVrmmvkoDfUMqrypVOD4uX1rN+77wOcUVS/mu7tBxlbpXaThiqWu9Xo/WbrD6ftCL19YlFLmmeoHAtC+lci4aj+ngwHF1BHvlc3m1t3yr6v1WoLvKOztJ1Rsa0onhVnPGm0oa1eB/cZEfbOq2o0nGBmuDnSpKsMCUffqfr0oXzk696fwJ6d5vSq//5ayOB06sOl6QiqySPiAUjujgqVbFB3+iq+/4hVmdXII7fNZYtFXBWJt5LuBeo4C/Ubr27dLZJxQc7tfBri6Nnj6pq9fUqTzAXJHqL7tO4bpGsx7ijKAUTG5IuiTgQNZeJa3eb5EVSep20x8lUCWN90/NXYK7FQuQsZapmWLKOr5AqLxGGi6RQl2S0yeV7pLIeIg8KyXot+SQ04FSty3pOCZKE4VPS959iimh7tBzCsXpzSH5nTWq9e2VM88SetlQ6vHoI9t2T8vg+NaFZnWGgtpWWqZf2bBZDXmmhXeFxi1Cwz4t1opwUJcamBfL4YQjckBgceedd04+R73h395ymd61bofGY1EVRWM6e/qMngifMk4KZMNM/cp4zUh0SKH4uIrdpXPu20IQmTrtb3jDG0wQgwZ/BRulgKUA9vAv/MIvmJ/Zh1eS4hy1JepGSo2inCRAeezIYcXHu9TT06vqho2qqqoxc4XAH2TpbCVdFgom02vXlPDHzkJIEW4VkAICPks1vrg/C0Eu5dKvgIwg9pdCb6TlB8IFSs1w/1k/EDaS8cD9Idg515LdjINMYwFiFUHVrbfeanzN2YRTuYB1Dj8JfwiBJpmpkCqsN1SZsP0UMkY4x8Ka89JGY6BI99x4u758/owGI2FdV12nVzWunH18qcGcpErLUgMbhIoT2QhwCE/mNpUliHeQfbUUFWMgVD7/+c+bWDL+zz/+4z+arPAC5oYCqTEPVcsHP/hB4+CioEIZlCuGI8Mai42YWv5lntT06DkjEZYS/YrFEjp5qkf9A0MmlQrHn02dDXbfWrdGW8/rZE+Htq9bpVNdR9Uaj2v1mjXTy8vY8JdZDzsod/K+qaBb416pamF6SdAMCIIFB6lQWy4zerr69OzTh4yDxqILU00N4Srf6mnlp3guX1iBw6n659GoRVpkCihGUVKnESeQHFYWRmZizyGPilwNKvdMlCubQDgeVNvYGUtl46mXbxF7WTzSe1Ct450TJyH9tOsJ3dlwQFXe2edh+3ifvtf2+ORZHx48pzsbrtT64uXpdG7GgcKmP0l6Js1ckJ6ebYiICWDEM+YMzp9MDYoTmIbYmA0E7rmOPYO60NWvUDiqK3es1fOxIuPkkIWBY40xkQk4C67AoIajU2XCIqYxdVyBoiZp9yv07KOP6qq3vlGuV75ZD3/5M2p0euTccbV8d75TPpdrksTlAcGxLKXP6PHBIxmsrdvuskr3jXRa/RHWXieVr5n/9zVul/pakr/Mem6hQT+lsgwZV94bJdMDyCWXwylPvFSR+InJZcIbicqR6JDCDvXJpVB8cPKtwXiP+sOnVO3Lr4zebOgMjunY0IDKPF5dXlmtK6vml3W3t7xKJ4cHJ4kNaujupCzbJQacclOWbYlBmRDWmEyo8PpUISsYaWeiktlBhix2S6bgmCl7M3JUHaELk89tLtmlel+TyXpkT/NAvOUIgn3U5cbZoDzFd77zHVMTt4ACFgsojyHSXv7ylxuHd6VkCOFL2Nnf2J+IkyAMdm3boK3ebj3+1CG9bEuj+gaPqKNvs1qiURMwWM4So6wtBD0LyunswPeCRCPoZGfLUYqKoO1iYNKeXGQQmDp48GCB1JgD4gl8utiEjzF/wSbEhZ3tRabNm9/85sm/5VKCNhcwdhEhQCjgU9jKa0rgUos/n1hNOiBFmCfYHawp9jFjH+C/kgFu++eU+caeumSbxBewYEAk9QfbU8VUL1UwL5c6W5L5jx2FMCRbfzxeY2efAuKmkLBL0UqA7yYbF2KUclTsV//yL/9SiIXOAQVSYw4gjZmBh/IIVi8fNu/c6BmdHpkqd7GuaL22ls4vYBOP9av57A/U0dFtgoPV1Q3yedcaA4ISU5T4KcUfav6uotG4gqGILnT0avfaevnWrZVKcgyc0TujbNVUaanihSu/xHGjEr0USk8tF0jvTw9Cs/iREVG3dYMGIx0muF3rW6cqb/5KgErPGvWFLyiWiKj5zEVt37tJFZ7VGYM/AVeFxmI0JrfhkNdZLKfDaUpQBUOpTYMrPOtV7ds67XP6xrp0rOcp+cfHjFHYPn5aW0uvVrE7M8nA+Lgw3qzeUI/cTrfWFW3ImRgMxkJThEYSzo5cVFXV7J/xRN8JQ9okj9DHe48vC6kRiQ+rP/ycYkIN7lCZe6uK3evmva5lUlLadbEnic/iUml0aKqPBc5OcQ7qp8q1SpSv0ZnnT+uGy7aY9/e6G+RK1BgngAAJgUrI2EwKbIjPijU0D07FWLRFAZc13hlDOEuqbtQNH/pTE4BZTa3bpM/jNTwojUXpM753RQDSZ+frU0poLQiadkvRkHT+aatPR/1WafvNWlI4purHup1r5QpfVCLeZ3iNyTONdSpoylWl3uFgnAyyhcOjPR36w8NPKDwRVLm6qs70v/DMI0NhW2mVoolzMr3PUYf6AvqzXVfqUkNHR0fWWreLDeYtKeAoIGcDqkuOk/UClXi6EKIv3J1CaIDTI0fUGzqthMLm9yrvWq3yb9dYbEBt48cMUR9wlaspsCvjnkdQ+Ytf/KJxNlB+fupTn9Iv/3Lm7LQCCpgPGGfvf//79Vd/9Vf60Ic+tOwZ4Pg6iAAQcyGmIVjI3kqDaZTwps9V56NSFDvOoRNn27V+dZ2ua4zKsW5KHLFcwKZAqckcXqxeHpc60tWz2GQQHJC5/G0hm6tTknSpAlvYroUsjfyAzT8UPamRaLP53e0oUbX3crmdgXkHNLPZHfPNoLDBmgS5YFeCwEYgKxtSFYIBQhZxUzoQSsyWVcEaaAsrIDKYE5xTcp9S1mr8F3wpMlD47kLJuwIKsEjBbJkSiw18fvadXAEJSu9fYgRLRUxiR5G9Ro8h2gVQjmop+3y8GFAo9pcnKMmCUo8NDdY/H0JjJDqcQmiA5rHz6g/3aS5gs+R4nnj0PxUIuLRnzwZT0iUSGdKeXX7j9E82y4pYvS9qqkr1squ3a9eW1fL5vFJkKL8v9ZdaZakWkNCwgxQE6XE6sik2C0gF9xXWedWqJvWcDmpH+cu0vewGVfvmpu4mkLOp+Dp1nw5pVdV6rS3do1X+nRlfW+lZrxLXVDDf7fCr0b/POi7PKtPzw+MokcdRpErPRlV5M28KP3nif7RmY+NkyZO44modT1X9xxNxnRo5oYd7fqafdd+nE8PH1EPQKtiup/oe13COYzgbXRZNRBWKZTa2kzEemx5Q57n+8IgujvcpmMNnLAQSibj6ws9OEBrmGQ1FTyiYQjLlDxQJKBPSwRrDBj9JNLz8jVYomnvGgyyR260SGTPC4VRsyysU2Hy91HSFtPXlqtz/OvUPDBhFE84ABMPzzz8vkQmUOKnBgcd06Pkf6dzZsxN1aqffRfJVktdEnFgMEc6FwAvrCb/jzCSXOmPM2bU2F4VMHeqVzh2WOs7lV+5poQNYfN76K6Wbf1O69f3Srjus5urLCIfcxvhIPVOnnI7p9U5dGZ6bKyLxuP7XC0+Zf2081delb7WemfNnHhsa1O88/7R6w3H1hhMaCEtt4xGVXGKNAO3SU4sVQB2LRtU8OmxKSWUCgTM79TsXYCts2rRJd99992QpOZSl3aGL6gpdyJgvGI5POTV94RZ1hc7q3OjTCsaHFU2ENRztMb+zxmYC1+Z3fud3zHd++MMfNo8lL2NXwIsWjCXGF43Av/vd75qfl5PQoMH3ww8/bMoyYAMQvIPgIGBIphI+BnPQHKOxwxK6et8mXXPZZtXXlMkRpezg8oOgIpmeBBrpG5YtuFrAFNgLsAmp8Y14aqHWOYLHZPaYUqaLDI6Z0srLkX1oA9sSPwE/5lLBWKx1ktAA0cSo+sLPz/tzWSuwz1O+a2zM3CNERgsBhBHJpa3JNqI3ICBACIGSPJbxC3gQTJxN4MScYE2k7B7nwWdBXHD8KLsRhiX7Enw3nwsZXEABL3UwbxBZLwewUSjDD1GfK5i/xBFYH2yMRYfUFTyv3tBFxRILb/sTU8bmomIFsWbiLwXkjkKmRh7AkX3Xu96lj33sY/roRz+at7MxGh3N8vyIKr1VObGcKAXYNHlgpO/etUvtrU/q/PlO+XweHbhux0TwMWkTpdF3eNBul5CEhJRD2Z3FBhs+hAYOEw/OMVfF5lzAtYskwnI7PCaz4FIHhhrB28cee8wokubKKnMfnnnmOW1s2jerE0BGSGNgnynlQdkpr6MopQQS2Ro8ZsP2yzbq+NETGh4c0aYd6yfLUSXj1MhxtY63WOM+7f08c2G8RTs9s6d2+p1e1fmq1B3qtzIuEgmNxhJ6qv+CeTT6K3VXwxUqSu51kISmQJUGIyNTx5AgiObU/zv3M/Orx+HSLzRdpXULTPilI5oYU9w0X06GQ+F4n/yuuX836xlKJQgGUrYJBHCNcDow4Fl/2OS1Zbf03o9Kh5+21pR910oNsxBpZHb84GtyXzitWPegtP5D0prNevbpp40zg6OAg4DTcPLkMe2/nGBIWEeOvKDLL9+skZFWNa66WhFXi8ZiqUaJP6lHC+sGx5gp04PxTXkriA3mDA4IgVEekB4EUzlnCOt5l8o4+7z00H9NkRlrd0i3vN0igQqQPBus3hvJ8GxUpaNc3WEam0/trRWeqea0cwFN4s+N9isUj8rv9Gk0LahO74azIxbpPxc82N1hkmsSE72DKNjQEw7r6NCgrp5nWaup/Sokl8NtHosFHPPkRsALiXvaW/RXx54zBKTf5VC9P6CX16/VO9duk3tiTjBnaZZHECBbing6CLbaTUFxMI4OPaGxmHUv3Q7rfiSHkuxMGgsODUW60nLvEgrFRxSMUx40u3KUhsMQMGRPYq/853/+54IpTQt4aYL99a1vfavZfxlbBACXEnZvPYJ07MP4F+yTHAfHREkXgtxZg4/4EiEEWvZ8ckielTEnDh06ZEo7kNHFv/y+HHW9L0UwFihjzJi0e5XM1Te7cOGCIZVQpM7UD2mhgD3H/kBQm3NYLJ8yGzqDA7qn/RmNxoKm9OZNtbu1o2wBSoouMoKxdKElNsiQ4onovPqbUcqMkk3Y13b/FuxwxgKBPLIuF4LEtRvD4w+wdpGZgX/MeGB9I0gJSUv2NwQH/i6+82wl/shQo/xUcr+e5O+kMgafTRwDVThrKEFcyrpBBiPe5DsIWC539l0BC4f+8Lhax4dU7vFrTaCscG+zADHBcpaXJzbG+gPpmUvGiF0qi8wv0BduV/PYocm/d4bOamvJtXI7F1bAxj6FT/Hxj3/c7JVf+cpX9LrXvW5Bv+PFigKpkQPYrGje8md/9mf68pe/PNm4L18UZekVQG+NbGBThilkQ7RLNKCoZENkYl5obdWmdetUWpq8QTokR/EUodF+vxTqngqoxYnAJKTSTRK16JcZKKXt9E3IDYzoxTI+UfUfHnxWoXhQDjm0uWS7VhctfjOgxYatMCFoiwFPij2bBw/KBRBwyeZEML5RmTDWSK3Np3azZyIdORgb00h00BBF5Z7qnDf1Yk+5Nu/cqNbzbTp/6oLWbV6jYlcq0dYezJ65Q0AKdW6uuLxihw4OnFB/eEDBhEOh+BT51x7s1+fO/1RrA3W6o37PNHLjuuodGoqM68J4t/m9yF2krtAUURlJxPTdtqf1W5vukCe9Z8ICIrNTkZizs2HXgGV8EPjH6WQeYrhzH+2yU2SmYfybcdS0wXrk9AVx6euflDpbzc/h3h7pG59W5J0fNkYD9fRRT+B0sr61ND+tRx55Vh4PfTZKFQj4FAhYPVx8IuiK8o0x4VCRa7WKXdZxoK6urktocPQ5lZRUyeNYK0fSNWEuoIJg/SRAgwrCHqespTggvIb1CEfHEDhzAQ2if/7t1OyMlmPS6WelrUtQkoig/ZH7pYvHrN4dm6+RNl6ZmgEyMmAdY0+rFCiVrnuN1JSdDIV0IujG9cGxY02Zl1MG+ea7RopAUMUkV4PkXqsi1DSOqzUa6zD3t8TdKK9z7oGxcDyqfz31hE6NWCWsAi63yt1ODUbjKeN/dVHu+81/tbboi+fPmfe9c90GFbncaYFxC4EFCNoEY6M6OfyMQvEx83ujf6OaAlsWxWnC2c+oosJWCA1bGVmUnczzu8+ODOkvjj5j3lZkLgnN4Mf1tZYTGoyE9KEtU8FF9in2LtK+c1FuEgRhXSL42hmG8Jwip/g+14S5A/xOq9cJwBLyuyCig+behaaJaGc/R4K9KI7f8pa3mDXy+9///rIqggu4dMGe++pXv9qICRhTC1nqZyaw72Mz0uiWfZH1nbWF5zkGbELsQfZmO5s2K6ovl4Ldk1nhwtGvP6CVgO7ubrOesGajFl0xJScvERCMpS8KYwTSGTsNG4DxwvhgrPBvtn2JQDA+BoHsrP0bZwBZDkORXpNVXeIulz+PvnvsKRAblCE6cODAkgXVsD2+3/akQnErKyiWiOv+rkOq9JSoIbCy+21ZvkS6ChKPeW42jZ19yfhAiEA8wyY1CBjywP6gdFQmwiAfYJ9ir6LKJusbgQSluBmvZAcZH6OlxYi1OI6bb7559rVtAhwnvY4ygXOzhZnYL3y/HQzlmOzsEERVqK/xtS9ZYqO3VTp0rzQ+LJXXS5ffJRVNleDKF6wrEOrcB4LNXC9TRvgSwLP9bfrs2WcUm8jQOVC9Rr+8/jIjlipgCszB5AyqFNBzMRGUHEVWb8ZFBLYMMTKyTGeLdTEWIWJZm1atatSFsSMpf8cv6wqd06rAwmefsDYgoIeQfec732niz2TwXrJrxhLBkSg0MZgRBN0+8IEP6H/+53/0ve99zwTC5oNTwyd0fmyqxMsq/2rtLNudMlBh+1EOswkSaOMWoWxgU8aRnqYiTvRJsWdM8R4LAcl1jeTwScNnpJ4MDbhqr5eK1y58qZM5AkMBIgPjeLECA7FETI/3PqhwUiAb7Ku4UlXexVXXLyUwEDDYGLuoRVCjEKilUVKykYlqCocWEMzCkJgLekOdOjVycDKwV+au0o6yK3PKgonEQzo5/JSC8VG1X+jUSHdQa4p2qqGucVKp+LPun5h7Z447Q5mgPeWXqcGfubm0jXA8YhRTbUEruFnnI2jgVFswtfcHcy2acKjWV6p3rL1xmmFiZXdYAfYne8/quYHzk82BbfzK+ptU41vcupED4SMaj9tkj8M08qvxXScXcz6PcYJqgQ2bQCJZGvv27TNzEeM/XckA0UHGhl1TNmf0dUmf+tPJXy8Mjqh5cFRNN71ch0tXGbWzlSX0jAmcX3dtncrKuE+p13Uw4ldCAfkdm9TZNmScBxxW1k7G9+lzP5WcgyouKTKEiNNRrN3b3iBHmpGEU8V7bIcjE1BfZw18jI9J3/qCdOyQFCiSXv0Waf91SefbLn3331Lfg9O07WrJ65D626RAmbTrFsshWGgcvFdqPph6/S67S1q3b4r0+M6/SsN9E8QLpcQc0ms/IFU1TJ3m+LhxDu2yHRh33CfWE7Jq9u7dm7MzuFy4++Ix3dtxKlk7rIDLq8d7g5PzdkdphT5zxY3yu2YnBb/afF7ve+7plOf+evdefen8KfWEQ8ax4YpcW12rP9i2S9+4cF7heEyvaGgyj3zAWnN48OcKxSmZllTSoHivanyrtJDgvlJznnKVqX8Yk078QBqbKG1Xvlba8vK8ypd97+J5ffz4c/K5rOyJZFsHkuEHN7x2kmxInqOsSxzPbEY8gQmCF5GyYRU1OaYRTI2+Tarw1ioU71dH8KQJzZR7EuZfPtpwNvGExswW41DAVapNxdfm7Dxgp/32b/+2aR6OnYjTVEABueLJJ580eyBNwf/1X/910euvs+8zvwj025mYBK35Xpppz0tFHI9K4x2WmMpfL7lXRnNz1rcHHnjA+BYIGzjfAuYObAJ8C64r4weRCH2Gksk41mX8WF6H77pz58452QvY/seGntJwdGDS2t1WerkqvbV5HzNBc/wixj127Gw9FOaD9vE+/ffFx1KeY8+5umqLrqxa2c2jI/FRdYcenyjtavt1m1Xqya9pLhkKkLTY2owH/AvGAkIEO8MyGWRTsOfPh3ji3v74xz82Wac2mQFRApHBgzEIuTUTIOE4XogJ1kX8ZcY4ez1jhs/ke/Cb0vsQ4ifhW7POZBNn4nfzt0syu3OkT3rgc1Z/PsYGewX+zK2/npddyDW1+75yXYlBcG/wAdmbCDizH61kjEUj+r3nf6RIWmm5X92wX9dWr+xjXw47h7k9TVwbaZZCL0ysMy7Jv19yL26fUvYA1iV82Ex9RJPBPCce0N7ZpuKd4/J4Uu2zSk+j1hfvXdTjRUjwmte8xmRrfOITn7hkCL/lQCFTY5aN7c1vfrNJjyT4h6J4vthSuk21vjrTX4MMjSrvlKodZ+M//uM/rNdt2WI2UxwMgrszOjqOKsl1g0VusCg4aiRboRxF4Tmt7pTkLloxhAabGwG0xTYyx6Oj0wgNjEx6mryYSA02jeSANA4sKWzJuO+++0zgmrrIMzmwwVhQY7ExBVwB80gHWRKnRw6lBJKGon3qCLZoVSB70Di5j8fOsgNGYbt9J0reUlPGCueDwClGZZN/jVrGrZJDySPZ5XBpY/GWrIQGBMiZkYsaiIyqbbxP7cGplOru0JApR5VhZphz6QoNqT8yqmqaN08gFIvqvq5jujDWpxK3Xw3+kowK7eIs5asWEuWenfLEyhSJD5g+BMXu9ZOERiwRVig2aIL5fmdFSlkwGwSmKcFAAA5CAIOSICKEBsQtakZUmjgXNonBmMLQxPHAOck5MJD2/avLitU+Mq7VdbUKrrbSQXEAUAJi4F68eFR+f1her7Vx26V9Wi5e1IWWTjmdT2jXxjuNY4ChxBp5+uwLuupAqUpL6yZ7ZZw+1apwrF0+9+pU0ioanbVEG3MI4zqj0/HFT0jHD1uEwOiw9XtRibR9okZ0cYV1zsmGLkq1wWYpPGqd0Fi/9PBXpFt+dW4Kp+Cw1N9sTYiqjZI36V602gaiUp+zSY3+Tmkouf+KfYGPKVxSZRRT7Dd2iYBkA4rxwJxEcWM3WSdYwLqdbR1hXHEdl8MQuzg+lFZciHrRYX3xqpt0bHhAZR6vbqhpkDfHzKpPnjk17bnPnzurH9x4q/7l1DG1jY9pV1mFbqip01sff9D6zoT03bYL+otdl+kd63IvKUPJKTtDYwoODUd6F5zUYE6wDzOf7PtIKSjX2fulsaRG7YMXpNanpHVTQQH27mzl2p7p79B/XjisVQGHRqMQxql/t0tpptsiZEkxZmx17Uz2D9+P7dRY3aQBpWb1OeXS6qKNchpis0I+Z7GGo+c4kcnX8NWUxIolfAq4KtXo355XUJdjo2k4c+GWW27Rl770Jb3xjfQdKqCAmUEjyHe/+936y7/8yyVpCM6ez5wiyEhAjr0SJeBcBS3T4HRLxSsvmIMtif+2UHX7X+qwy3bafjL2WzKhQbASoplMunwyvzOhffz8JKEBCLSfGnleV1XemtG2nemY2dPsoBa2LoH1xYI3Q0kS/AXfDKVKTo906ZGe08bX2FZarxtqt5iyVUsNj7NYtb5rNRq9oISi8jlrVOSeErzMBvZ0+rBgF1LmjbJ1rG0Qiyik2SshCSDE8C9sO5ufuS/YmHOtvc/3EKhkTLLO8Z3YNnwnvgJEC74DPycD8o3SURyTLZjCN8EX4G+Mb8gRezxzjtjA6UFR1hoIvJnAZ0GwXJKkRufpKUIDGH9mUBrokKpnjpFxzbj+XGP2HvwIyKfkfY9rwwM/BNuOMQR5mm0dscmR5Wio3B0anUZoINDB7yggFdgYCBdtf4M1oaHWL4UOJ70qJgWfkYpulZzW/Wbu4TsupICOzyMmRklF1pqZhNRGyDo6qoqySsPZxZTajyvgmooRLRaIyeCbkc37qle9St/85jcnq9sUkIoCqTFDOvgrX/lKM/Go9ZhL/bVcUeGtNI90MGkpZTAnFZEjIDkyKEHN96QHXp2SZ3GV5Nlw5MgREyxkoWBDw9CwjYjFJDSAO4uR6V7EOuU5IRGWYp3WfXLWShMlnWxQx5TGbfFESB5nmfzOhlmd36HIkE6PnFb/YL/GnGPy+DzTgkYEY2b6nOaxZh0dOjr5+47SHVpfvH5a41V6aqTCofFY7g0icUyK3anlFqhvajd13lSy1RAYnaF2E5xaG1iven/jhNI28/Ezvn7a9azOj3aY10XSMjxM2SrFzd+s/hpWcIsa7FNnkfp5/3nhKTWP9ZrZ1BUaVstYr2p9xYY0ccphlN8vq9lulOCLDc672I0BmWpEBmMD6gg+axwR4HOWq8F/xWRpKs6DOYhRcdNNNxnnDoLDTgmHQCVwTb1Zex1Mbu5FUID3kD6da917VVRL67dJzadMoN9Bve5YXJ7LDmhHbaMxSnE8+C5+hnB4/vnHFY8f1/79m3Xk2AV1DIyppr5KV16zwzp3l1NrmrYbgxiDd826Ug2MPqWW5i7zGVZTcfpznFBZccwYLvSdwQHPhZzmc8lKwZhJcX7I0jiW1jARY+vZx6ZIDV9AOvA66ZG7p9behrVSKCkriAFHxkTbCas8VD4Y7pQO3y1NlDWQ+zFp75ukook9JdOcoAZPyxEr6OQLqK1/WGPhJOOM97S2yVPZbu71bPWuMQpx5lCwENDG8AQ4I7ZzYTdtR7lGWRW73MBSotpbNDk3k/H580/q5fWbdUvtqrwCicHY9FJ3oXhcDf6APr7HCpiAX3vqEausW9Lr/vnU0bxIjWz9M1wLXL8VYHMkZxgw7lFTruk7rDW1pXrqWLNcTqchiv0tw9qz+hozRnAmv/Wtb5kSDukBw87gqP72+JOm7AbwOKVY3DFZ7xoX5caapsmeGunAPuCYsL9mqoeOk0xgliDtieERDUYtwoK7uqF46wShYaHMU6dYoltjsSlSw8bawA753XNzjO0G4tiL73jHO4xD/vu///uFVPECMoI58Hd/93eGzPj6179uVHhLAdZ2AnNL0c9gOYAvgaoRXwJ7BhsHOxflNKKJAhYGlIM6P3pOPaFuHX36mF538+un2e/YBfMlNMB4jBKvqfIj+ifRF9HrmNvnY78sxLHNhCpviTYWN+is8UE0UU7Try2lme2g86M9+nrLk5O/d4aGNBoN6a5Vi6sCnonYqPBuz/t92HsELxE+YSNSdoz9EB+fuYitwQO7GludUjB21gb3hGAjcxibcq4xART++DT4Erfffrvxce69915jm3JMfK9dThe7AjIDvwcbJtnOwGa1RROs2Qh0EPFY/oVly0AU2yIp1plcjpnz57PxxXjvcvYZyBuGZJsu5kv2O7hGlDSc/hKrbHouvgC2FGW8mKtcU8aD7R/aAW4Ex9wTxhy+x2LHkNJR6Q1ME0aSrf3TzrM6P9pvylDV+Ja2j89KRXq/Pu5ty5kT2rY+ZMbFY08eV2VFiSKRqLbuXq/aRosY/NnPfmZ899e//vULumbzncQwmPusUXbMIx3MfcYXtsRobEBnRp6ZjHmVumtU65tdvLsQIG6BL/S2t73NCL3uueceQ9wWkIoCqZEBKEwgNOidQTr4YjoA0XhMLwweV0eoSx6HR5tL1qsqEpXfFZBnIYIX9Mwo2y4NHZ94winVHZBcS58WjjoCQSzKDdQYds16UjlxRhYbXNMGf5M6TI8GawP2Or1qDCyjsiw+KgUfTWrs7pJ810oui4VNJGLqDT+paAKSwIq6Fzn7Ve7dOWND+sd7H1dMMZ09c1ZNG5v0/MDz2l85FXTD4ZuJnR6ODqcQGuDY8DFVeatUltT8kUwLp5yGIJhCQgHX/DZyDEsCpgSsMWI2lliPXNEe7DWEhnU0iczf4fLpNauu0dP9Z3RyuF2xuBWEhOho9FcoTHmm3nPyudyq95XqfJJimU+MJhLaWNKoam9AY9GQVgUqtb44v5T4hcbF8YOKxCNyT9SPD8UHNRA5pyqvde0wCiEV7fRrjGmUUxAVDz74oMneAWzirHtspDgYycDI5HPY7GddG+NxjTxyv8biPrVHfQqNDWvY4VbDW98r1VpkAQYqCudkJUZNzas1PjaqH9z7Xe2+plZXbM+shsBJshqaN8odqFRNTSo55tdGHXz6KTlj9Sqt3GaCn/kYYTgpjEPWKuN4ZFOLpHYgtnpnhMalQw9Z5IXHrzSBh4UM5dRmxZkHrTIfNqJh6dzD0q6JwNiGK6VTrClJ6LwgXThtfhz3lmqkqEpbq6LW9+OMuL3SzXdJJbnXesZBTFdHca0YH5BH9txljJCBQ/YV5NKCqYJzwKsat+rwYKd6KKM0Ae7UUDSk/7p4xJScur4md8PwTavX6G9PHEspZ8Vz6RiORlJWRDA6UVM5H1JjlX+T2oJnJvcrnqv3TR2vTRAsNFh/CTR03HdIz5++qCK/V/u2sE86NOSqNgpGVIYoEt/1rncZYoM9JblPzYnhPkWTVGzeyTniNpkxB2oa9b5NE0RgFhAY4ziwF5iPlEBLBynkGPv+krhK3EPyuRyGw4NEicTPKp5oSuk35HF6jSAsGdaRWcTcfEAZIYI0d911l8mA+6d/+qcVX6KtgKUF++aHP/xhM2fYc/Mu5zgPLHYgd7lBMJXsAFtBbWd4EawkeDGbgrqA3HBs6Khaxy9YzZFjA3qq/wldW32dyjyW/cXzC+U3Wz0nE9My8DwO77znAiVIKImyGMFQ9sGXN1yuQwPn1R0aNITG5ZUb5Z8o0YPvf2K4w5SzbQpU6tn+lmkB0mcHLujOxt05lfJdKSBQzxy0M3PtYCH2nx3Mxt+wy95lEiswT3l9LmsjY41yRYhoIDJYX/k5mcTE5kTlnLwXY9NzrGRM4A9ls6OSs1AziVsRgBAUhaTBRsl1v+ezIJkRjbFWIfy5JNC4TTr+cykamvIfSmulisYUPwC7bD62Ke9NX68hS1jbIcjsssl2ryR6z+KXIsxbKpurzOPTm1fv0jdbj6TMXeze0yN9+seTj+rPdt2acyb4SsNi+ReA+zY+5FHzqZ+op3dI1129XWVlReY7jzcP6+TZR4wAg7LpP/3pT82DOb3Q/iMxAWx1fAxIjvSxwzrGsVKelzG9q+xlGosNyeXwqMi1tE3hWTO++93vmqxem9jgmAqYQoHUSAOKYciMP/iDP9Af/dEfLfqAfXbgkC5Sg5Zgk4J6duCwaWjpcTq0u3yvGvzz3Og4/ur9UtlmKTouecukDGWENNYnjfRIsMplq2YvTRUNSu2PSGOdkssn1V0pla2bsUbn82d/qFXrK/Tdn92jl9/0hkl1AkYIj6XA9tLdKnWXaSg6aAiN1YF1ciqqeMIhp2MZ6tSFj3F1kp6ISeHDUsBKix6LtU0QGprcMsfirSqOr5PbmZk4aB1vnSQZwqGwfAGfOkOdppSUf4LMIuA4kyE/bDd7zJABkkxqkEGxqWSPTo0cmjy+UnelGvxrFySohtMxW6mgTBgzfS+mgEnBCLMVU+C6mp2q9ZXrlQ37ta+8Xz/vOaaRqOVgNPqr9dlzP580Uso90+eMY4IAuaxiZbDl50ePqocm1RModeMQOhSOT91LHM1Ma5rd+BFgGFJiCiMbldLmtU3SUJcUKJc8vklD4NjDD2h3xzFpsE9qWCPd9iapeGpsnD1zRh1f+rRKTh9Rmd+v0MiYrr75FoXf8zvyJgdXutvkbG+WikqljTsniYNAUbF277xKJcVtE3PEvhtOeZ2pwXSHwyu/8zIF44xDgscOeRNVckWPaU0j772gCj/NyG6Q8nCGcY64ZmQcGAPb57f6Zzz3+JRBz2Fdc1PqG9vOSE/8cOr35hNSbaXkiE8QGfSxcEqNc6itHCS1Oa2oUjBJeb7jZRZJQaNwAgsDfVJkKh06EBmRr36V2lwerWJtKSqXrnpF7oRGOCT98D+lk4ckr1+6+VXSZRZJhnIEJ4+xk7y+2CnlpObjlMxlTmfDWLRfF4MvKBwfN+WFmgJ7FHBZ47DU49P/2nmTnutv11dbnjcqquTh/2TfxbxIjT/YtkPjsZi+cN7qi/XOtev1xzumN7a8qbZBT/dPkaAQjFdWVutvjh/SaDSiA9X1emXj7GT6qsBm+V3FpqRfNBjVeLvU0tM6OYeTW6LxHArFycAlfzv3jNRxSqJfyMarpNokVRElBPq7rb+VV0/b8/m8xitepcaye6fy1hxOle24WQeKa416inWCvQR1JY55crmoogz1lb1O6WM7rtIVlbmXsmD+8ZmITSBb0xVfXAMI+vseuEfX3LpaXsfUNYkrYvZPr2MqVZtSGh7HOUUmXsaZ+ZxMx4UJ+BKIYd96xSteYcb7l7/85SVXEBawMkFQ5pd+6ZdMaRbGiC3sKWD+sAPpkBesgXZPNkBvqKUk01/MoMcFhAYYHRpVcZllN14Yu6BdEyWoEDUsVHmMxsB69YW7NTqRXYfNvaWUgOn8ApcIeLBvcw2ezwWUjoLISEckHtNXmh9TW3CqrFadL1MpomySrJULbDsCzOk96SgFhR0N2MvxN0zpSr9frnGug0MqxkZ2GLLDzpDIRo5BikAm4K8gdkBUgf2J7cn70sudZgp0EzRFMT6fOA++KsTHXPo/cA7YBhArCIQWu5/SgiBQKt34LukIpUkHpYoGafdtMorVCXDvuTf4iQtZdpZ1HSKD60Sg2b5v/Mt6D1nGfGYcLGR1lZlwR8MmbSyp1L0dp/U8Jbgmwm5kh/eGx9UyNqDNJVkaZK8wMA7trPtMPgZzFmFazvMlOCqNDUmlVZOxg2QESjdo+449Unxi/ishh3uVduzab7LCIR25nxwTQiEElrMJcucCyFbGC4IkiJN0e52/Uf4JkSd+Tplz+crVM/b/7d/+zaxz+F133323yZQvwMIlsIIuHahT9iu/8itmwPDvUhiHNqGRDOpOu5XQC4OHVO6pmFCqzBMEopOC0Slof0E69cDU77VbpO2vyE5ssMhd+Klkmi4nrDIoF38mue+UiqaUlPHEuEIoJePj6o8Mye2P6/Bzp3Tty/ZpWKcViJUr4Ko2pWyypX4tNEzgp8gKYtGPoD/8hBITWRLF7s0qcefXCG3eSJBanV5ofEpVHDfHNr3zA8GamVLDMxlyyc/PBpv8yOX5Gl+jit2lGo4Mmuyick/NgimLEo6EyTyBgMonc6ka8i4JDGVXQtpaukZ+l1ebShoNoWGDLIu3rjkwuYn/zYkfpVzxoUjQBOnGY9EUN2NHae7BucXEQKRHnaGWlOeGowmjjvY4UtcPu0Zs8sZNsNAeK6jq2TB3b2yUmp+Ujj0qRSAKXNKeV0iNO1ThjKv1nq/qibFxk6Rw5VC/HD0d0i//gQmSkg0ydOKIDgy0STWVkwSBjh+S9+QL0t4rrS8+9Lj0g/+YylhYv1166/utYPxEabJAYpfkuKBIYkhOh08l7m0ZCT2Xs1pFjpuUUEiOhEOO0P3m+draMp042aq6ugop2ix5tkjRiDTYYZVjqqif1vdD0agS//01jX7/u9pZVyPny1+DtMQaSO/4Dam80moUXlQsvfIN0oa0+r9nnk/tq8H59fRbJaogiPyl0p47pFyNXfP+Y1L/mYn4cvKagDOYlCHEMW69znqAr/9FakYIP48OqveyV2rVRGZOXrj7i9LRZyc+Z1j6zhetkls7Lk+ptZ0JGMQYxjg8c20cmgyIjPNjT080tKQU1IiaR5/W5pIb5EaRb5SeHh2oWatvtb6gYHKGywTZkA8olfR/du81j5nw3o1b1REc19dazpq7dFl5lY4O9evgoEV0/PfFZl0YH9F7N86cOWTS9X2rzOPgqYPGCc/mJOLck1lE4NSUWeg8LJ20M3YcUucZ6cAvSpA4w/3Stz8t9XVZf16/Q3rNr0ieNMKvaoO08xekfkgcp1S7TQpY8xnjHkUmZeNIEX/rW986WS6K7K99FXXaVFyhc6MD5hpwpTeWVGhvef4lnrgOZHgePXlMP37sp7r8ctL6q8zzP/nJT4zi8bbbb9JI/PT09yr1erkdFSpy1Ssc75ycRx5ntTz0IlsgEKymvAX1b++8807jeCTXnC/gpQf2VUooEMhibBCcKWDhwFpgZwWyTiavi5SoK/TTWBgk+xGxaExuj8sq55r0PPtQOvk8V5CduLv8GmPjxhIRlbjxiRempIut9l5qPDfQrPYkQsPu85fsc7AzbSttWJaeGvMBZBZK/XSgdLb9fGwUMmQc4XHp8W9I5zon7I0m6Zo3m8xmSCe7pwWEQXrJIohh7A3bHsK/YNxhU+ZqV7I2GL9knsCm5ZxSbF8aao8PS6XVkj9zpjkCIOwoSi0te0YnYqkLT1j9+kpqpTXXWOKoTCitka59S9aPglQi+Ep50vTeJfPFTOQ0Y4ExBnnG/Viqub2ppEo7ymp0eLBjGgl5qcxfrhd7p12tIRPw6xGlMVYhkWYkN448Ij39I8tPdXukl71VWpNKdIrSsIHrrGbhxL2cpZJ7rfFhnQ6HORbG0ve+9z1DaDDfISERLuRTdSEXkHmOeAqhFr5GeRJBj1gL4mCpiLLZwHX/4z/+YzO38DG++MUvmv7PBRgvtQBAmalf/dVfnSQ2VgIwFFHGLypoXHvqZ6nPdZ+yHtkQpWQSjWbTTLDh5snf6P8wFntC0US7IupTTFEzEaMYwW4Clg4FY1azWjYhDB42wKUCZZ36w89NEhpgNEqDtm4tKZwE35M3BhTcUwunpTBNJz2c6gx26Jm+n+jpvh/r7Mhh07DbRp2/bjLwbtIH5VCpu3Sy0TebwmyqkkpPpVZNZAnxftDga1C1N3MQlmZJdf4mVXrrFozQaB9u16Hhg3q45yHd33WfqeGbKyq9pbqxZs/ksfPvjbV7dHPdXl1bvT2F0EhHKB5VOC34yT1YHajUxuIa+ZxuVXmL9bY1V6mR7IUZQD35gfB4hs9bWIxHM2fWJBJeVXiniDoCKoyJZEIDgoOyDZSOsZ2SC0efUuiZr0mD56SARyrFWI9Jh++RxgakU4e0u6Zc16ytt5QcOLR9nVKHpeBDXbG7PotBOTThzAXHpR9+NTXgfv6EdPCRyV8xnNpau1XuvVw1vptU5b1WXmf2jAJIEKeDOqdT8+HwC+e1uomAJVkVIWm4V/rJp6UHvyw98Hnpof+QImmlZ779VTl+9D01RkPqudiq7i9+Rq3f+qox6MYgeH7hndLH/lb6nT+VtmUwArP1tbjyF6Q7PyTd/J5Zm+qloPOgdOFhaaRdcsWkJDW6AhXSxhuyv5cMjOTjcTjUE3fNTCQHx6T/+r/S331E+sT/lo4+M0n2TBIayed6eKoWdC4OoK3kQuU/H4xG6XGTStbSyG0slhowADfXTldFvyw5c2EBAfnxF7sv15FXvF6HXv467a4oVzgRN5kiPMAnTx9ThAbyOYL7df78eVNHOBNwNFCocW1HhofV+viPk/46QSuQuQF++BWpP2m/bT4uPXpP5i+GuF17QFp77SShkQy+k7Ttb3/722ZdgegglZtd/s9336A3rt6mAzVN5t8/33WDPHN03ntCfTpRfEEtgU595p4v6P6Ox4wwxJTJ6uhQqXeNXEol1ALOernTSF2rH9FeFbt3ye9ap2L3TpW4L1/wrFwcajJ/UXGiqCKwWsBLE5Q3YJyiQL7//vsLhMYiwQ6mpa+LkIysSQXMH4iLKj1Vkz3tbFOgzm8J2tjTCQTNqTdkFuBTVHnrVOtrWjBCw56Xy1H2B5/A9k1scBlvqt2qMrdffqdHe8qb9NpVl+lSAyLF9MAoQWaC0XapKex6lM96/l5L5GOjv0164afmRwKIBBmxLdL3TvwYCIRkgQfxg3yD2KwNCEAgt+YK/Cfen6LuPny/dO+npAf/Q/rBv0oXUks522Ds2aWzGItkuEDMLDkiY9IL35Z6TksjHVLHC9Kx702JsvIEZBFk1EITGrkCQpX7spTXcn/FKiOgoocf4N81gTKtJRP+EgDzifGIX5acnZEe+EdARMbCjP5bx3npaTK8Jz4HEeGD/ymNZ+i1CrFBjMK3W/Ksm+Y7M47oOUZMgflhZ+jgCy00qB6DrU6MzF5ziIfY2V8rDe95z3tMzJp/P/GJTyz34awIvOQzNZi8f/qnf6pPfepTuu+++3TNNXk2bZ0HKN3TFGiYlq3hTprTPucily0YJ6U3bQEjKD2W1NR2GpyzPh9NwFhb2QT26azbtEpOl/0ayoC4Jw0LNsGZlIyk697fdVSnRyxFx6aSOl1TtVGV3swKiNkQTYxNHl8i6Qr0RQ6qJL5BJe5ZWOiFAr0xKB1jMjbME5J3SgXsc1WrNLFFw1GLZHLIpWiiQR3BqQW9N3zRGMgbSqwgJcTDvvJ9pgcGi7RzxKkrN1w5uRHw72yNunjN3vK9hiAhU6LYVawGfx5ph/PEmfNn9L3n79bGPVMB+RPDx1XmLpPfVaTDg8c1HB1RqbtEe8q3q8g9vTzU9rK1Wldcr+HImEo9RQpQJi0HQFqUun2mFJU9Lvi3MziscDymel+5XrNqj2p8M4+9lrF+fen8kxqJho2B86pVO3VDzeJkAnkzlZQzGSiXy5VUbgkVNWucXYeWBn44BjzHPESt8bMHHtCu4h55PcWpBobfI41HpKEJVZVD6h0ZVzSeXHczYQwdDBH3urWmxNJ4OKyLI+Py2nN/zURweajfKoGTDAKe/alB2znVZnZwPTjvsDZtbFRf/7A8Hrda2vvlePp7htgo8XlUV14sZ+9F6dhD0t47pt7/8P1qHh41QWfKBdX5/ao+8pz8b36HqYFLxsGMyuutV0gnnkrKqHBIG3ZZ5ZpmAwZlsE+KhaVAlVXer/NQ0jVyWPfCVyOtvkYqa7AyTrLhutdL9/+H1XsDB3MoorpbX5V9LvP93/x36fxJy6kZG5G+9Vnpl35XWptFfZknkcmYw/HFeMRAnqsCJlsJikzE6mtWbVfA5dYzA23yOFy6tW6jLkuqA2yDxo/cW45rvuo5n8slVp2hSGSao0BPnlA8lnOQn2AdpR1IyU7vZZIOjr3P69bx1i5ta6qduNcTzelBR3Oqw8qxteVOGqcDR4OeWSjQITUgSFE8EZR429rUBuJzAdfusd7nFE1EVV5TYUoq/vzhh1R1U5kur9ttrsfF1g6tWn2tRqLnFUuE5HWWqdi1JuM45zmfa/Eb1xPEoQbue9/7XnNdqAuMKrOAlw7YY2+77TbTN+qzn/3sgpbjKEDTStxkyhQkaEiD2gIWBpdVXq7DA4cUKYmo/Uy7du69XfX+ehP4oaya3bNtpYKAJyWQsDuWQyFf5y815WmSgU1ybfVGvaw2Lev3EgKCC3wIiHxKAWHfUS6GoCBzEyCg4pob4uPnT0zPIu5LbTCNQht7JhkEOJNryWN7IGxAYZ0vKF2FTT/X9QEyg30em4JA6FjzMenp78vtchr/osjnlZ68W6pdm5KxwXUiSMt14l/OkbWL60WmS3r5rkVF71kpMp70REIa6ZJGuqXS6X3MZrPVnn32WUPiLycQbkJ0LXS522yo8Pr1h9tv0Ldbj6o3PKZ1RRV60+pd0zI1IK/I0MGHhCRYSWAOQEYzHpPLN2Ya84isyJjgPMg2SkFP60Rp5qS5je8x0CkF8o/Z4fuQ8YxPwVxjbrAusH4v9L7OPCbTHNKGChZ81/79+022BoTHSgOZGmSr0zOop6dHf/Znf7akfT5WGl7SpAaL7+///u/ra1/7mqmlthxN5K6o3Cuf06uOYLci8bBcDoIcDqO0r/M1mPJTiwp/hkWVYAfq32zwFEklq6URGm7bhakdUvnUIpisniWNzNS59ri1caLZqFNulUwEFTAuMYJncvZ+2P68jg23TZqBzw206IXBC3rT6qvn1Jw5uX9GqmmZ0EjsrCFcStyLo+JNgcMn+W+U4pBIcQkVelpvjxL3BhW5mhRLhOV2BHRsaLoquj/SoQ2aUl6vCqwyj1tvvdWMba2WDh89bJyOXA0/U1Pdv3RKCxxPajqi6uke7tLw+IhJb588HjnUG+7VmdFDCsYgHBIaiY6qPzKg2+pQAE8fPxAZuZIZk9/jcOjNq6/UV1ueMFkb9nf3hccnvjOs/3f2EX1wyy0qypKeG4pF9YVzT2g8ZhFnODD/03ZEDb5Sbaap2gKjylOvCk+dBiJThABNhovdqYH3ffv2qbW11RjOKKcMiesg7b1DsYRbPl+DKsrL1HG8Rx2dPVpTX6lGu/m27fx5iyUa/D78A42Eo2osK7KC2hXVCpbX6dGHHzbqqgv9/ar6hV9S/5c/o4Yiv0ow7l//TthN63Mo4QRhkayAgOSoTi3phcEEAZOXCovj8dGs+2khDnvu+TMKReu1edv1chx/WImiSo0Ewzrb2W9lM40/p5hvrXEwMNLCY0GNR2PaXpEUbKefhsNhgpI0JJyR1KhbK935HunZ+6TQmNS01epbMRs4/+afSsMTjp3LK214hZSIKRiK6Exrt3xea9tO+BNKeMfkHWg1NUazOuc1a6RXf0DqPG9Kg21u3KT2nj7jOKFCMc2XuQcP/8DKyIAg6Uh1LM29f+EpacM26fIbpOcenuoNwr/7r9dcGwES9LNTxdPLos2GUnetPA6/ImTgTJBHPmeRQrGQmkcfVowguKdWa4q2y+lw6eUNW8wjG6jjzLyoqakxhn1yrXZUZ8lNI/PBNVW1+mH7hckyTGRybCouVQlp2Xkip7rLDoeqtuxV8YVjOnqhS2trKlRa5LOaPAIci5GkbBb276R+OHMBDgbOI/sNpAaKLgJc1Kidb+AokogqGJ/KpvIXB7T9ql164qkn1XhFjdk7uG8QuOWelRUUwq75/Oc/b5pD4+hDbCx06nwBKxM4/RAab3nLW/SP//iPL2lncymQSXWPzYuSOrlxcAHzA+Vgr6i6UqqSLrouarhlWD01PSbjHtvP7pe40kAGCUFO/Azsyvlmis4V+8rX6Pxoj44MWWpgAp+vb9ov70zilEsABGmxk/DpESqyz6ULVrCvuf6MFW9Lr/bW+OSaFDsi2CmZlpVhbNQJWwy7DBsIW5FeOZABlMXBt5nr+jpfFTbHgxgGO3xVpV9aZdkknQOjutg3bGy++HNPKlZh2ZCQF/g0ZBSk25T4FQS+lxRGXOZQS0evguGInAineNp7UomSgdlFXBmCwnb/FMis5VoPsOO51na5W+4J9thi7cMN/lK9f/PMwmjGM3YyJBzj18p2S5h5gjhnucuQcUy59IfhuBEzEU+gvx7ZkJPXNVCcSmjYyFKGLRfgH7K2cHwE7ylfR3k61vDFyAiCtKG0rt1AfKXuaYD5ht912223mfH1t3/7ty9ZW/PS3kHnATbcD3zgA/rBD35gBsNC1f+cS7bGvopd2jdRq7RtvFVjsTGVuEvU6G9a/IGJ2nzTy6QzD009V71RqpuF2W66Sep+ThrtkNx+qfYyyT/F1rodNQprqr51sVtyx91KJCrlcvpU7t4g90RjTu7FTIGsaDymo8PTSzegdL2n43n91qbb53Dd/Qq41mo8ltqHwEYw1rE0pIadfueauZ630+E1D+vn6cp1aKKMH+1wmMA1KcFs6otZ15tSIKeGj6kn1GXG9friTWoMzLw5siFhDNoN4QhuEpxas3mN+k/3q7hsKtUcQiEYD2s8Fkx5jt+7Qr0m62khEIxFdGSoVXW+YnmdHtPA7+c9Z1K+czQW1tnRHu0uz5y63hUa0dgEoWGDbI2zo32LQmpwn7eWXK7+SJfpM1DkKlWZZ7qCAefAdhBAPHFRsQTN6i0ld1zNuuKq/XrixI9U6omrvCTJ4I7FpbrNUmWTFQR9629r9MufVLC/V/2JYnVXblfl0aPGAMbwIMBwMbRR7g//hfoH+1Sye49FZNigD8Orf0n63pemVOObd0v7poIPGKEYMCjA2LjzarjrrJD8t5hanRu2bVdxSZUcGIz+EjnGh1Qa8JmHOZemrUps3WqMS+qKlt/xSgX+59upn3ezlcmBUcxYhRTAaM6qFGnabD3yQc8LU4QGYAyd/6n6Va2u9kPaubFxak9YfUCq22qcOxw91lFKO2Hg8RrmFJlZdoaAy1WqdavXGaOZ1/Ag2EOK9pojP5eefdj63Cypx5Ok1qt/USoukY4/T86ydONd0qa5CwJQBGEYcz1RMKHEwcDn+DDQMP55jnGbvh+6HB5tKL5GXaFTCsdHVOLyyeUo07mxF6YuabjV1OLeWDI7mYviD4MeAzbZJuBa8jeMd4zdfPZlrv+50TH1TywHXMVdZQF94vK5Bdlyzly67FXyOZza5T2lc12DGlt3perXTqgbb/kF6ftfnGp0D7ly4JWaDyhDhVKRwD2lJbiOqCmxrxAtzMcp8DjccjvIUoylBBIOXH9AvRd7DTG2pMrGPMF4+ad/+iejgrvpppuMsipZaVrAiw/YNbfffrt+/dd/XX/5l3/5knUylxusSQvdWLSAKZD1DXmHLcHattxBuXQQmGLvZv4R4GQPJ2sOdT6B8OUAx/K6VZfrmqpNGouFjI9R6skhi3eFg+s7k8LbHi+IhwjcN175cjkpq+pIEiPtmGp6iz2ID8D+jo0IIKOwcxFmIcohczWfPhrpYGxgZ86nTj/nTFYKx6PBUmNDu10uNVUnCUV2X6ZESaU5J2xC/IhsNhG2L6QA545ds9h7R6J8tU40d6qhqkRrG+hZ4ZA8Aemya5Vwuo0/Zwfg7YwSwJxnPpnPSCRM4JlrgJ+GqAWfBEJhOW2d5HK3HKNNIrEncP3xL7j/2Ksz9QNcKHBNjGA0LRBv96sgRoMPvVzA18knm5TrxvVjHkJyGP9k/W7p2BNSb5tVWYAyu5v3SxX599OzwfxmPhDHghAkK5yYAFlbzKnFiOHi0+Df0zMwOW6yEsF1eSiJ2KAc1Urbi5cCjkS24mkvYjBpf+3Xfs2kE6GaY+C+5EGaIQ/KOVWuyd4kPA9E4l0KxY+ZvhVOlcjv2iuXwwpSM+lIH8PJZzFn44G9npbGNkFq/N3J6fW+OUKXQ/rI1leZbJB8wdAPxts1GDk6rS67x1GhGt/VWonoD3fp9MizKc+tDmxTY2B6zfilxJHB59UVak95blfZZarzZ96gMVQxlGDcMYLssi/8jAHr3+zTxWCryZKASKAE1tqijXpm4PC0z7qq8jKtLpo/W08PjC+ce1hdoWHznXw35agGItMVXW9dc0VWUqM3NKq/PWE1qrbBCH31ql2LVoIqXyQScUUT9NOZGvvj4yE99US/rt93k/zn79NAf59OtnSpoa5eXapXrGajHE7X5DqKCgen8fLLLzfGLUYuxkUy+dDX12fuZ1bDlmbFHS1SUam0botxbJibOJ0YSChA2JwZLzhDGKOsG/ka+pNOS8dp6bFvWcp+tj9vkXTLu6XipLUHI+ze70qPPUT0VLr9Lun6W6Z9JoZc+vnOC80PSIOUlpvaljt7BxVa+0qtdbVbDZu5/nV7pIb909Zp0mVx0rh+PDg2W9kPgci9wqhm/YU44OfGmmqVfvZPpz6Ea0LfkMk+5A7r8Z4/lJoWj+hl/OAQcV/t84DMwPjHgeXYMZozZSokEkFFE/SLGFfLWFwDkanr19XWo7HRce0qv0G1NbXmvBlXmcgBu2QCBmKm74FAIksnF0dzJBqV3+nU99sv6reencquc8mhVYGAHrvtzrwblYO5ptPjvHF+k6Uc2s9LZ14w2TvaeZVUPv80bsoOsCZATPMzthXXm7RxAkjG6Z8jWsfa9Xjfwcl+USXuYt1Wd51RDTOOLxUD/q/+6q+Mav9HP/rRZB+jAl5cwLZ9+ctfro985COmoWMBSw/2ZvYR1gYeCBBYN+dUzrKASw4IpCh1hLgGGxKbB7IdQgOFL/boSiv/8lIA9hW2E7Y4NuqhRx5QyWinQpGIhovqpWLLBgTs6byOwCV7JcIXgtJ2KSvbD8G+uOGGGfrKZQBiKbsPB/YKWTwESwno8vn5+hfYq/gl5aUl0s+/JnU3T5Xg2XZA2nOr8gWZw9i9i01skDWztS4g38XHrD6rRVXSljum9VBjHUUEhR3MdSfgm1wKFX+e60iQ2Z5btg+33GCscWzEmPiZbCE7M5/7hl0PqTFbae5M6wziMcYqYwd/xZRfdrsz3jM7Cyfb9+DnEkAnJrYcYLxxDvmKkBgPzFPGqnkvfTROPSONDkhVjdKGvfOOKzIfEOgikmI952fWc64ZYkTiEIsxT+ww+aUgTGlubjYVWhAY/N//+39fcvbOS47UYBF717veZQKo9NDItUlYPJHQjzvO6om+dvmcLt3VuEl7k1jHYCyqeztOqSM4rFpfse5s2KLiLKVpXqxIJMYVF6y9W05VyzGRUWDKu6QtBixGu3btUstYt7711L3qH+hXtGdMbzlwl67dN93Rv/viMzo2nBow59Np2PxrG6cHG8eiFzVqsjDiCrhWqdiV3UihBvdw9GTKc5WeffK7ViYzG4lHdGzoGY3HBk3QvcbXpAb/RnWH+k0ac4O/Ru4lTGNmM+N+Ptkz1eDZRoWnUrtq9hlDgk0eAosAJYYAwWlbQYcqhfvD62CbqQ/IYtw6fkHNo+c0Hh8zpEC1t04Xx/tMbXWrlItDbodbd9TfKF+eZaYyoXm0V19peWza8065FIzFTBkpQmeQaAdqNuqW2u2mlEwmfOvCQT3df8FkaHCsFZ6APrT1ZaaZ2EpAIhFWNDGVoTU6Oq7HHj2qNWt2aGSoTg6yBIIDxvnrD7tUV98wLdDLekoAxy7twFzHsMEpsJsCcs/JtMhVSQEBgsEyLW15vE+hnhadudCukK9GHl/ArCHtwfPqDFoqrnr/ahPsHIv1y+2gSXqT+ReCBCNxcr0f6rbIDQK6TTvmnBbL2Od47TIG/I6DVFdVYZV0IhslH7Q9KfUcMaTG8ycuyO9jrLi07TW/n3ffimywCY/JIPC9X5MOPjrlgPEvx05mDqXAikqkm15jlZ5aRuBQ2URXsloMROPPKaE+c91sUmN0eEydF7tVt6pGJWXFurziDvX19pmxxf1i7KKwSx5jXJeRsVF1dXRmVRyyfuHAAsgs3sPn2ftL2/iYPnLwWR2PRuSrqtIVlZU6OjRgMguT8dRtr9TqtGaqODwY7jOpxsjKwemx51f2CzaR1TBBQgKcds5/sYQcXAvWbxwN7hPCEdRUOHusEwQQkgMS+WIwMqzuUK88Do+aAvVLus8lIxofVijOmuOQ37lOLmd+85ysjT//8z83mcJcqwJePEBBSJ1jahv/zu/8znIfzosGZt8SDX3DcqhMDsfMezaB6/1XXaG7jz2oZ08e1lj/sFZ7qvThX/yN3Er4FbCism1spX462HcRQxFEZe8kuMV+jL2AYIq9B7uMvYgsQvYn9telqLNfQCqwAbDDEToxB7lfCEi4P3aGbjrwLfEd7L9BROBT2L2pDDFy6FBeWTeUx8KGyqSIJ2iPv8J44lhr68s1FD2mSHxUbmexyt34fKm2F+dDQJ/jNCp3bK8LR6SxIamiQWqcu4qcQDzHY9uXdvbGfEUcXEfmFHOB71hIpbtdeeFSA9cZcZVdRm0m2KXQuA82Cca6wziwhWOIeDJ9DlkjZHxnA4QLxBFg3EPO2/c/OWSLHZ8P+cDxQd7MdH7cO/wsBGT5gmPj3CaJjUUAYxbSjO+ws+dZQxjPxB6IRbzU+5ZdvHjRZAlTav7LX/7yS+p6vKRIDSbrO9/5TqPggdCYrdlmMr554Zi+c9EKfNutXz+05Up1hkY0HA7pmf6LGpkoN8PfGwPF+uMdN13yNTJzRTzRq0ji4KTC2KESeRw0p858/hgOR8+d1NPxZlWtsciD5udPqsjj13uvfaMaauumNQq/r/MFHR5sVWwisFzk8ugta65VvT+1pNJY7KIGIwQGp0Dj71J35iAVU2A8dlHjMQJVThW718jvmnua3GLCGHCDT2kg0j95rcPxhAYjDpNlAEonFKw+6vEvMjAuUeSionl67BGjom09e1EXz7WptLxEa2rX6fZdd5rXEUTE+cBQTDbIMCRwWjBKf/jDHxpng83p9a9/vU4MH1XreGqJsCpPnQYjQdNPgwD25ZV7VO6ZW6PhdJwZ6dJ/Xpjes+TGmq06NdytlnECp1PYVdaot6y5MisR+lRfi2kYznVZG6jU2uJKrQosXgmwfMdSNPFzEySYUlq0qLpqj9atuTbltfyNjdImoTAkUE1xHyEW0gOsGHsY3/wd48zun5ALMOB4pBDOfWel0z+ZymAIVGl03R062HJQzUMntX7rWvMdXqpLkb41sUrTb8HZ3aj62sY5N6POC4m4Or7zRQ0/+/OJoq5Nanjbb6m0IUf1TzQonfqeouODam7v07rGKh0cqNUVt7xubiqRvgvShUNWea/GHVJ9muPC9v8PvzvVQNp+DtDU/Hf/XisNjFtIUJxixiWP1eta5fVaxz0cTeipM90KhSJqWmc5rVWeRm0oSc0UwvFAYcW/fOaJ4W59v/24BocGtX3vbr1nw1VaUzRzZgHkrGk6PZGpw+fc8MB9OjY8pOjIiOIDA/IE/CqqrUnJBeROHr/ztSpLMjY5FtZGSDHUNjggOIZkP6GIw/lkPpFxlC2TxDqxmNT+qDRk9QRR6Tpp1Q2TzeQx/iE3mF84NwutPsKZs5WVzH3Usago+R6yTCCFMLYvBdVTJoRi7RqLTZU3AyXuq+Sh3F0e+PSnP62PfvSj+vGPf2z1Nirgkgf1l8nQoKbxb/7mby734bxoQFZpLEGW1pT95XLslNOxasZ78WjnEQV21RuiPjgyps5zF7W7aK3e+fI3LNGRFzAf2AFrgjLJ9uN3vvOdSUU0wW3+hcxAcc2elpw5y2c8/PDDhuDAzyAQxr5EBkAuteMLWDhgyxBUZf9Pt1+4N9g53Ds7sIqfAfmQLjDBP8Af4d5yz/nZzvjPBbk24m5uOauWvodVVVukKlNKikLPHtX6rp/sy4mfg10zlyDwXGCX47J9aMQv2HL5kghcX0pEEYTHZlsJ2RQrBZA8jEf8WuIWjLFkf5R7gB3O2sPfssGuXpAs7mVsk22US0lE3sNYx3dNt5dt/wU/gXs329hnzWOc4q/b84d1lc/hfBGKQfJBGjCWchV8ZzpmYqwIVImxLkZAHcECawjnTJYR38M6wTmy7883K/zFgK6uLkNscL+/8pWvXJIk41zwkiE1mLjvec97jINNfbR8CA0u0a889QOFbOXjRFDC53KawCVbizuNNOfvv7HpSl1RObeFIW8QtCIgRiB7iYkUo1Q1ZWxSm225tF5uZ3b2/5m+U/ry/3xTG67cKY/Pq7YT51VcUaKd47W6/dqXZUy/I3DfNt5vSI7GQIUCGQL3PaEnFEkMpjznlFf1/qlancsBjGnY66w1+HNAOB7WY72pZY26Qzh8U7+TvbCxeI2urMquBJgv2KjtwCIpf+BgxzPqiXfI7XHr5POnTP+C69bfoIG2IeOQkP6fDhh3O4XQ3rQJusH2c60ODTwn+RLauHPD5KLsc/p0Q8307JyF6qfx6TMPmAbfVvkpsjKc+o2NN+nZ/gt6uOe0ydZIxke23qGyLPVwmRvfuXhIT/VPETN3NuzQTbXL08MnHfHEoAkWSDYhWyeXg54Bzmnncffdd5t7iDGNwYBTONtGiZFh90zZv39/zuoNUtQxUgjwmrXtmS9I8eQeJQ5p1eV62j+mI4dfUCyekM/vVX1lkcKhyGQ2QlV1haJ9RXrZ/ru0JHjuIemhuyd/ZQR1FFUrdMfbc1eox8I69/xDqqssVXH9Jqlo5n47WdF9TnrqW1N9E/jf3ruk1btzIzVe+XZp3wGtVEAo2Ib8oSPfUFWVUwMDI2YdcZeWKF5aqbh8plF4U2BLxl5ENtrHh/S3Jx40c9tksTgcJqPqf++4TUVur7pD4/rMmRfUMjaijcVl+s1Nu1Xpne5E9IRC2nTP/6Q85xgdU2CwXyouMutJzOfTb++9TB/bvtsosnCQAE5Apr2BdRbnCWeDc4b0nZEQ6HhS6j+WfARSxRap8boUe4h1lkAQ4JwXsqEjikyMaZw39j2IDI6bn3ESj545qc41ZeqMjpvM1tc17VQVZeCWC4z5sV4pMiYVVUve7Fkw/eGfppTsAw75VOF9Wd5fS93bP/mTP9H9998/uY8WcGkCcQf1jP/P//k/pl9fAQuHWOKC4okTac+SpXtTVuEUa9pf3Pd/TROjuvVNFnH92PO6fNde7VWTsUdeTGBdJdg5174AKwnsiQRdUcATeCXDj+AdDwJ8PE8wi8AggileT1A727zkdcn+B3EA9kBbeINKPZ+YQAFalEAlfgL3Bb8C0sHY/7PAJj8guXLtnYOog/fMZkuNxzp1pu1BnTpxwZAaxSUBBYNhlXu2y++qnRRtcazLFTQk2wWShiBvPiDwjI1WQGbYgilsYuIdrDn8bGeBMXbmki2TqXLJQoi8OBYILsYh84ZjZW2k7CzfxwNyMNN38378bexz3pspVpMvIMoIrLPGcr0gmxeqVwhzjmwrGnjbpDXCKTtTD7+K85lPVviCIxqSRrokp0cqrVuwygszoaury5ShIlv+c5/73CVTonc+eEmkETDo3//+95uBT+rpXIyXqN3IdgKsC/ZzrsmqHVOLBWGhUHKgaDER7JUuPiDFxq0ARu0VUuXSqAZALHFqGqEBEhqb8X3D0aA2XL1bLS+c0obLd6hh23q1HDyhXTfcpPvu/6ka169Rsc8vf5aMg4sayvh8X7hF0cRIynOoK4Z9U43L8wEN3CntBKFQ4aGh2yxlP2ZwOuZbw9WRYWwnExrmOSU0FE09/7lgODKqkyPNisSjWhWo1dqiRsPwQ0KwYcDsoxR46qmnzEbqDwTUe2FI9dtrdNWVV6nOuUp9zQNmo2WzybTgonZIJjQAG+rNN99sDAr3oPToI48qHrMMXUC5qcWC3+XR29dco7vbnlNfeNQ073t14z5VeosVSWpSmwwItmw4OdKVQmiAezuOaWdZg2p9y1MzMxlOR7kcul4JjRAikFRs7gXjCiMO459gK04lyo18FcUEna+66irzeYwV7ilEFqqmmQKo/J0gLgbKhtUN6unrV9/QqCpLi1RTYV234GC3zpzv1rqtaw2hEccAjUZUlNTc/OKFLg33zH8u5IyW1MALhcc8Xa3yFucRsHV5te6yWw2xs2uuhAY4PVFGLVm3cOqRVFKDeQdx8exDqc9de8eKJjRAcnmmndteroOHviWXy6HdezbI4fDIbTIFc5tjJ0Z6Jns1mPFvMv4iujA+qKZAhX7zmQfVGwqaLMGTw4N6YahPn7/yVvnSHNkiFx0zkjuiSK6SYr1x+w5VJeIaiEW1z1+kl3kCJgCVXkYrGzimnJvdjqSX6UhMew7jFjvItoVwOlBQ4oCg+povuYFSinrmGNMEG7C9cKhY70sryvXt1hfUeahP6152pS6OD+n0SK8+tuOW5SnZyfw4+zOpa4IIwtmgnnR1toan8ekfMZHtli9++7d/2+yNd9xxhx588MFCkOESBSIMMjQ+9rGPFQiNxUACXyJ9ZeXnIHlSWd/WtGmtTjzzgqqb6uXyuLXhsu3qPd+lqj17XnT9FAkiLVhvr2UEZAXnApFvEzXYjvaehC0JAfyyl73MqKkh+jOBPY09CNs1PUhHMMwukUPgj8B4gdRYWhCYJIjKfcBG4IFILl+FOKQXQG1OcBbYAdxssBtaHzx40PgazBv7vdg/jAtsob6BfvX1DenaG/aYvw0NjmrV6lpVejcq4Gow45OyZnwej+UAwpGZsgWyARvwUi0TtRRI7ruH2IjMBn6n2sR81oqFzlDm8yBYIFsYs9xT5hW/k2kyqwhqApDFC1nNgDGZTCowP7H/uX6zlfaaDcxX1grEUoifIfL5bPwp4lOs9/zLGM/Zb1pMQGa88F2L2ABlq6Rdr5EWuRR5XV2dsXPYKxHafPKTn7xkM+RzxYue1MAAolkfZW3YfOaSUsUguKqyUU/2taeotNNN7HRsKZ1/481ZEY9KF39q1L0WElL305K3Qiqef+Pk2ZBIDCmu1KCtDYdm3mjPjXYZR6OorESnnjisqqY6OaIJuVxuPRVtUX3AZa79/oqNOlC9fXr6XSKqochFRRNh+V3lKnFbG81o1Kuh6PGU19JTo8yTvzqezJCHup9UV8hKe+/WsK6u2qf1xfk1kwJsOBhOtvHERllZ7JRGTlr3MdAoFa2bsZmSx+lVjbdePeFO8zvXxOVITMvUKHPPL2A+FBnVPR0PT5a0OjPaqobRMwr0O81xo1xgU6F+YXKtx1g0JveoW7vWWwqQNRWZnUY2IAxCatxmW2QxICrDNervHtC5Y83aus+6fxuKF68WLgTWU/2nFIyPqsjNtaTJvYVtpfV6rPdsElHmMD1dKmdQGPeERjOuEzQSXwmkBkDp6NBUqiaGAnXBcQowPiA3cA5tRfncvsMxmd5MsD6XoCnGD87ssVPnVByRtq6tV+/giE5f6FZn35BW71utq3dfq47Y+YnWAS75fS61nu8wx1peWargWFC33PTKGb8HIxDyxla3zAv00LB7U0wg4nSqyJPf56Iim7eqxTagUp7LEHy99Q0SWQcnDkoEla+9XdqZVFKt7YL0+M+sbI69V0nbFi8DbC5AlcN6dPm+dyoUadPRI+e1a8eVcnhyn1/0ycq0l3+j5QV1BiPqCk2RstgAZGw8O9Ct6+g5koQit1vv37xF/3b6lFk3TGkqp1O/s227NpfMzWEgfZu1MuexiaGcnNQEUAelo/Ws1NMuVdbIuXarme8EgggiQWRmU2vhLLFGzKSi5JhxtgkYQeRDcqCO3bNnj55uOS3/7o1ap42T13MoGtLhwQ5dWz0HBwSyefSkFB2W3KVS8VZphqycaeg9PUVomM+LS6fuk8qbJHcmwsmZMVNjrviDP/gDQ2yg8kd0YwdpCrg0QECUNP8PfvCD+v3f//3lPpwXJxzFqeS89STUdva3OBzaV7FRw9tGdP7QSWMfVDbWaFtRvbFhWcMWso78SgBBftu/wH5eu2a1XKNnpFCv5PJL5Tsk9zJmxM1ih1E6hHtji2AIOCdn7CNyQdHPfsgeQ7PdTA132cd+8pOfmIay2QJ1dsCS77QbHxcCvEsDrjliDruEGDYH8zHfJs3JgKTggS2Yi6/C+OGBeIr9l8AoQVDGHe/nczZuWqetWzcpIT4vobLyEo0OhTXU3yuPe9SUL+OYZyM07D6SC6GAzwTG+3yC9gXMfO/IdmAdgmiltDLrE8LOlQT8A9tHWJJSy7MhOCqdP27t22u3Ts5P1nCyhKjekSn2w7Uli9zuw5oN9PpEUEsMivnHvGVf4DsQUkJcL9y5dEujVpxBxeslf54E5vEfpfreQ+3Shael9VPZ84uFVatWTRIb+GR///d//6ImNl70pMb//t//W9/4xjeMszgfxu43Nl0ul8Ohp/rb5XG6tKusWs8OdJi/EVB2OlLTyrxOl4qXoKeBwkNSLD145ZDGO5aG1JDVwNlq4pfMQvvlcmRP/eL1ozFLYVO1ukF97d26ePycVlXU6qf9RxSKRyY/69mBs6r2lWp76eoUQuPC2BMKJ0at840kVOXZqGrfZhW51hjV7Vi02fxLo/Bs/TRmw5mRlklCwzpf6am+w6ZJqSfPMl8s0Mmp4V2tp3Ti+R/K6bCcNVMftG6vwv4Nk4w7CxLB3WRsL9urcyMn1R/plcfp0bpAvZ4ZOKHoRCYB4/HYcLdczrO6rHzDnBawY8NnDaFhq5cvnDivo92DevnqA+aY0tNW2UweffRR8/xshptd+gSV3GzHtqVhqz70zg/rx4/cqwb/KvOo9mb+/FA8pIFwvykXVeWtkmsOGR3P9J/VmVGLMAJkY93d9qR+fePt2lBcozc0XaZ7Oo6YMlUouN+0er8pVZMN1d7ijMHSat/csn2WAhj6MPy2w8/4m1ZvNTomDR6zssN8NVIZgcTZUxsZ17mqCan/iSLPjJHV5dLJe1RdXmIe7rIGuRt3q6l8nbxBnw6feUbB8ZDG3RWqrqtSoNSrwb5hVfmbdPFkv4LV1rFhmKJewZhnzPIdZAxhYOHccHwYhHNWklxxi3T6EIPcWi0SCTW+4k060dmp8jxqfOLwYaRh2M2aMhrskfqPSPGwVLRKqthh3Yu6TdJwd9ILHVJJtdRySKpZJxVNrCs4Nje91nqko/mM9Im/tAK94NH7pXf8pnTlEjY3jkakgR7JH5BKpl9DHFK7dI/bvUV7d2+aTMdGuZPLeLusYpV+1HHS9OqxhQv0Sb8wPqzBSGaHMZLFkfzLXXu1qbhUD/V0qdzj0Qc2bZ0zoQEYr6icktVhKFntHkXTULNPan0gVXZRm9ZE86H/kR778dTve6+T7vxFc81QdkEUMS+Sv5M+WDjmOE58/2ylIdgHyOyD1GBOcS9wDln7lcEnsAn0vMB7eh+UIr1T5xtsl6pvzj29m7JTvDb5+9lLg0NSyfSgabFrt0Zjh5KecajYtUej0XMKx9l/vCp2bZTbmXvwkBJUXNNbb73VZLWsqLT5ArKCNZp79u53v9v4GgUsDpxapYS6JxqFA8Q8u7KWnrJxZeUWk317X/uoQqPjqm6J66rr9xki6sVYeiVZtMf+cPbp/1ZitHUi+5Yk0CcUWP8KjYzHjL+BT0KgbrmDHGTvEujmOPAPCEqlB1wJfLMPQiDOZhMhxOH8cgnukWEF4T5bgJfPo5dhKB5UibvUPAqYGwzhuG/f5LhbyGA/9z6XJvDY/vig6WPEzvBgTNTWNCjhqFT74LNqvdgsR8Kn6qId2r5tl/EVsIkYF/RAY75xHthHqNR5HnU/5AhCEOYZYj67sfRCNlK2+8vlQ1LwnnQbLyOoRND1vDTaYYk86i+X/JV6qQAblqC5bWtzH7mn9I3jfvO35V4/5wvGbnpZLHqtcG5zKlk02Ct981+lseEpsd8b3yfVNpnrSHCd7Fb2YPvzIZYhjBiXvAZ/fCY7mNclj3d6+BHnZQ+E3J8PQZqC8Tap68GpOikjp6W6m6RAjgJ55k8wtRy+8VNGe7RUWLdu3SSxwdpEidQXK17UpMa//Mu/6DOf+YzJ0JgpFTEX+F1ufWDLlHqVXhpfbj6kn3adN5q9SIKyOEmlp+Ix/bTrrF6zavZmVPNCRuIElmVp0pAdKraaGUWtbAG3k5rkNKteK8dE0N9s+mNtahlrk1NObS5dqwZ/raq8peoLD2ukb1ANm9aqqrFGYyc7debYCdVvWpOiiKePRjKpMRi5OEFoTJwvxkzkrMo9a+R2+lTiXmce88VwFKU95UimQtNxxTUeC8rjnJ/Svi7Qp7oNsP1Tnz02flHeNS+Te6K5Egs7mwsBZnvxdzlc2lyaWl6sPtCgJ3qP6cRIm2kcLo3p4R5LfXp5haWIzQfheNScM5kXZ547ofGRMdU01enADddnJHMwJNmAcjFO6bFAoI5xwflxXgTL2FghHtnw2OCohUud3CpvtTYWb9GustRGv8kYigzquYGnFE1YCp2Aq0hXVl4jb57zoHksOQhsgetwarhNeyvWa1/FGvMwvXRyMGS2ldbpyso1err/wuRzr6jfPu8sDa7dQz2n9GTfeZNdsqe8SS+v3ym3c/7KG4xtjBtbTQURl1IiBzKy7d4JMpXNucVSAtZdP+sxkxGBUsMY9/3dKomEVLFxi7wNTeb+8704Aji2jINJI6usSdr7i9Jot4bHwupO9KlBTuOY8Jor6m+aVEvFEzGF42NylXg0OhQ0ZXX4LMganGcCq3w+54QqGjUgBhbEht2YmXE5pyaStU3S235HOvSoRoaG1OqrlLtstWoq83MCmB/U6eU6cT84/oxGGoRrK8Fp5nxCGu+UIiNS3TXSluul0KjUeth6Ler1rmbrwRy+9i1S7Sxr5I/vtoyyZJXs97+xdKRG10XpO/8+ZRzvOSDd9saUgDWkBeuFTV6wnnBfcTq5jziUjBHW0GzOB/0zfnfrjfpx50n1hcfVPDqoLnpUmSyO1KxMvrnU49W+isyZmHzHezZsNI+FAGsqY5hAnL0H2DVqGcv8m0LCla6V1t4hDZyxjrp8o1SSNJa7WlMJDXDoMWn7fmmDRbqzDnPtbNUv38dxQEwAu1TVTEoq5nmykITxa54rrdJPTzyt0i3rlHCyu5KB6NKOsjmk9IcY73agc+IO8TvP+3MkJr0lqYRG8vOZXu6ql8txQKH4RTMa/M41GoqeUCjeMXVYsS5V+66TyzFVCm8mMGb+5m/+xqxPr3jFKwyxsVzlLArIDexlBERf//rX6+Mf//glH9hYyaDHl0uXKaF+rFNykeVwzE4ack/2lG/QWEWXrnnFNcYGwa7h31zK/l3K8CisLfUInabWwVgsoaCzT6u2XmV+x+4mc5as0Fz6FywGsPfwCdi/KW+K7ZMJ2GW5qm/JNCZoTtCZPZP9C/EDAR32IcYFJAkgNsB3cy2yjQnGy9Hhw+oItk0+t7Vku9YUFcjnuYDAMBk3AJtioQKQtsqb+wgBZmeDcH+xV+yG8vyN8ZCtpSxjg3HIvxbRVqtrd6WWScb2QezFawh840fgB/MzZDf+BllH+BGMXUQL+FJ8J/4GWavzBUFgxjafmW/WBfeAMkX04+D9/J6RaLnwoDQ4oVLHWhtulba8XvLNr5T2pQLGD9coWUDEfeVBRj0+6P/P3ndAt3Vd2W70QoAEeyclkuq9WM2W5d5bXJI4dnqb9EkmmbRx6mTSJvmT6iSTSXeKE8cp7lW2LMnqjWoURVLsvaH3v/Z9eMQDCJAgCVKUxL0WlgQQePW+e885+5x9oPjsQgRjNyRp5PtPX5z2J8c2x9eEJWnZV9Itx+e4ZHuBF/8s+caRGAOfBV5XuZE498PP5GdMvq6pgr9j1QbXd2Vy5pQxICcwhWM/S5XUoK9KMjDiT0Y+BIwz+/zU1NQIYoPqKLy3rCy+GHHRkhp//OMf8R//8R944YUXxGKTbjCg+Y55q3BXySK0uIbx/87sFrIjI4EPlQqD/gTyH+kGJTYyq4FhGmiRBZflxVlTI3FShUplhTtogz88EAmGS58P+ptQqJECGmcc53Bo8MTIb9o8Xdiatx43FK7G39r3YKirDxUrF8Cg1uFMXyf0JiMyrGZxLSP5zqMaggfDvLajhX2CYR+0CaQgJtucKVPLTPvYfZCYMbOUe6Jg9qf3BBDsijx6dDhit2026aSoWQQ0iLjAMLg5luNh0hjR7Bocuf4y9vSdxfbuelH5UmHOxR0lq2FJKKkRi0JDDk6016HhSB2CzK43GZFlsKL+9BkRMKQB5Q46cdZxCp6gE73tg1hZmrhRXzzIztOolBtxcTFjtgwXoWeffVacp5w5QIeE3x0v2/rE8LERQoNwB9046ziDJZkTk8rRJ6m+IYmhRCqEBsExd3fpKqzNLseAz4VCo1VUeEwVr/c3YntP3cj7/QPnBPl2c/HUpYF4zHLTORr+smwMqwYE7A1RQkOG8xzgXyXNR0lA/VpWSnA+1mx/ArrXnkGnw4Xjw05ott2CwutuE+OBgVCOL5HRrQSb9+ozYLT4kevRJK28Y0Noo0bKvurqOhfTgJf7T1Q2TEJD9LBYtkw8Y9R15sI/KY3qvBLgmnvRVFsrxjXPaTLGLp0mHpccQGM22CgN8KG6KKEhY/gMkLdOIi5W3gwsux6ofQE4x4bwEZCoOPgP4MaPjH0QTvto2Q+XwlidTjDQ/PefA25FT5Rju9AU0CBQs1I4Gbyu/DeRXBevn5zpw/mE95RjTzai45Gh1WFjTqGYRx5t8aDb6xkhL4uNQI83jEAYyNRp8f9WX4Es3czpl4+qlIqA58xzp0PCcx3RVs4okV6JMJAkQ4jVMAqMRerJgaKxwAB9fICK1/+x5/+Gt23ahL/t3Al/SSHKiopwf/nqyTUKD/uTfD6BHhcFS4DeOsDeGbUpyjcBw92A1wFkFgLWWIJBo86AWS3JRAXDnhhCQ+weQbgDbbAoJC+H/cMY9A/CoDYg35AvKgrj510m4tx///245dZb8fJLL8VIr8xhdklS3HrrrWJt4T2bIzSmH6LZKSYeeCfhLZOvvG8kam+//XZclGBSQ+9+KYBCCeI4aDRqZNDHiIBBX9oYDG6SNFcmT003aN8zcYn75j5pd5HgmFKlbATsyUeSgmuU6M2n1YoAF23KP//5z8J25NxKu0Beq8YKCvf6umMIDaLOcQp5hgKRQDWHiUHuQcExwMD8/v37RQLbVOZR+iocT/RPmeghEyX0Xzi2OA5oH/FvHOckGpL1oiABkkolF8cT7U85aTZZIgLtVI4xkh48FtptJD6mUpHJa8dzoI8yWclKuZeJTLQwkSomeO13KQgNsVfJd+ivA4oVErUXEUg+0dfi2OCcwYSyZOQXfWLZL2YSEOevtFUIzCB4jonGO8cz51A+o/SbOG+m9IwO9MRVPoeAoVj/gttLd2yWY5fPNtd5+sysOF+7du3U1jQqIIz6bAKxXV6vBdcCp56O+NJhgEod5TP//CxZsgRPPvmkqHZkHORNb3oTLjZclK3QSWS8+93vFsbLRBvbThQ2vRFLMvNg1RlimjgHw2GUmixJJ8O0onCzlJmbWQXkLAMqbgE0MxdwCWJ0kCgEBoSkIPNJQbjE4pS9AXmGTDxYvg2X5yzCwswiBMN+lC6qRNXqhWIeoKSXKkJorMyKDeYZNGTN48kGDXTq2MxIR6APp4ZfQe3wc6izvwZ3MHFz8WSotlSgiPI6EdCtWmNbPCogkRI8R4DAOSDsAdjI3OAGdAkM6cDQqMmfjgedM2YepQpvMIw+nxeuoE+MxyZnHx5t2Z/SmNT2BNG1qxFavQ45RfnYeMVmvHnDHSKrRjShCvlwePB1DPh74Q650Glvx6tnXsALL72QcHvsySAHqXk+XCxp2NEAYPCM77kI0cmgU8OMLC6mdDSOHDki9BPHgisYH2gNw0GN9QniirzFo5qxU3auMiN/av0kMnIFsZEOQoOoHWKWcBS8o8eGYz9Ll7FDZp8amDTKBUJJAonJPo9kdtMgYlm4saNZEBpEpkEPq14H9Y5n4GptFsYIv8sMjmRVPyT4RgiWMUCDn/qaqYDjjJlKcuXH8qVLcfrQPoQ9UkCdY5EZ/2OBzxUdaQbPd+/eLc6DzcqGBweB4R7A3pdAFzw18PngtuVnl8fD7Madew7h9UP1OFDbiL1HzqKlI5K1rjQoNVrANRS3b8r0OCQHZSwsXBbb44fGYU3EEO3qAF54Cnj5OXZQRNpBQsUxGHvcKjUCHeeEE8l5hNeY88p4/Uf43eXLl4vgf6L5zxfy4+mOXdjecwCv9R5BickFm04FUfQmKjVVKDerMT+D0nYhfP3kbnz5+E50UDf2PIHnQUd+wDmIsB44fORwatrKOUm0gHNT1wimk07pgvGOT3mt+f+dfQfgLAtg74nduPzKhbg8pMEdgXxUWSaZJazj7+LXYjWgmwCJyOq2pXcCNdcBlZuBpXcAPc3AgceA2meBXb8BmhWEYBzCEdnHUZ+LpAUJLa4W7OzbiePDx3Fw8CD29e8TFXaJ5qH/+flP0a8O4LY33DUSdJvD7AHXgXvuuUfYKb/5zW/mdMlnORisl+VouA7ccccdKdsFFxS8A1J/Rf5LWVBKEAtrVmnRhgHT6LWSgU0S57S1Z8Jn5T4ef/xxsYYwyEy5KSbOMDjLTPpE4Fz4xBNPiMBwokAkq9tkMNDMoA3Pi/4F/+V+aI9x7aItwGQp7o+BL9oPyZIdCGcg8TrvDrKB/RwmC7mhN4P79BEnC9rGDLyyh4qcjMRAJl+Uo+F++B36MLQXSX7Qt0lUaUobiokiqYA+a6rSWfR/OHZ5DLJkEcegDPr34z17JGbo11B9hHLPzKRntS5/OxUIn2f5cmFPy+A1eG3HDuw+Uo89Rxtw4EQT9h6TKkNEguZFCl5jEk+cM3it6ROmkvHPeYZBdZIbF9O14Hwsy5ulXD2RWxwr/8r/J/M7kmA8XybZ3/m88xnnvMJ4FSWpWD01aZgK49ZQVcI1dEzkzgdWvwmYfwVQcw2w5n4pSfM8YOPGjXj00Ufxrne9S8TKLzZcdJUaXBjvvvtuITt10003zcg+tWo1PlKzAd8/swfOoB86Spdr1Xiy4zhe6z2Ld867DOXmaTSiGXSykak/Pw0mNSrjqKoJFdh8WArYBxMsgMFQEI6AG4/vfR7e3DCCrm5xGll5NhHDkrdUac7HtQWrYNbGkjQWTQGydfMw4I80CYYGxcY1UCs0dilB0+Q8iHCkqac35ESjcz8WWq6AVp1aKR3Ji635l6Hb04tGVzM6Pd04NnwSZxyN2JK3Dlm6FEvInO1AqH2UfwEDm7rGXR8a0AoiRQYXWTZGImQZECWWZpVjd99p6dyDYXiVRLm4pmG0uwdxztWHeRljG2J0cB56/6dx+NgRLF62GEa1QRg+Z5rPiMWi29eOgCJLVq1Woa29DcWV5aKsWGbzSU7QUaLjwHLwsUDHU3Y+adwxK51ZNiQ0xssOYLaUktgg+ZQxiWbplES7u3QTnuk8KMggs0aPG4vWIDuJFMn5giYBqaaZJo5aLuukcbBt2zaouaAPHVd+QyJRx3gWaMzTqRToihp9XU43Mg06+IIhzDNpoF2yROxPJjdouMjNhuWMOzqfDE7TCU7YUyBi8FC2Ta50SAXMzBEZHY5+HHvku9D4HGg48ncMWMoQqrpMGHeXX7ltVHYXs7roLPNfZqaTqKNT9cwzz+DOG66Bbu9fgBeelr6cVwFsfiMwiSx/GtasdqFzRrBUN7vqVqDzVfj9lALSYPehepRWLoI6XpbQmgv0NEakCSN6SpwDxwtg3HAX0NsNHJJKelE+H7j/vcDp48C3viz1u+A2HnsE+OK3gPw0Ns9jD434XgeEKUOMEbnxXKrgvWXwQu7zoETt0FkMUrZrBGGssunxao8P3jjjmafbH/BgcMiLzx/bgR+suQYZbLA+g6Az3NrWipZwG+wGJ3R6HYKWAJx+J6yGcbS+84ul/imv/CP6Wc1yoGJ8HWoZzHJlVtpYoHPB78hrVbunCx2ebpgzWW1lRmtdM9rqW+Dvcov7mGhNGxec47M3A4N7ACZRcP23bZQ+nwhIbORH7Kf2E0B3XCLGyZeAwgVShlUcKDGlUZkRDCsDXGEY1NIa6w/5BZmhRL+/XxAdlRmj5d9e7G/Cdd/6NP7x/s/jbe94Ox757e9mLHt6DmOD6wqdQQaztm/fPrlKvjnMGGhHyvaDXJlw0cpOOZrjEr3CjEpJMnoi+K4GctcmDcjQriIRQPudgY/prD7itt/whjeI9YE2k5ykMpaMDm1CHh+TORiQ5rPIYDjvLf2i8Z5FzqH33XffyHtWWTF5hr8bLyvfrE0cgJqr0kgPSAzQpqHNn6wqdSww2S+RbBltdrmPH8cMxw/HOccOk/OovS/bg7I8Ff/GFyu66Osmew4YvB4vmSYe3J4sbUUigr4xM8np5/L4SJQr+wzI4LEyoMzf0fa6/PLLReIUfeOJ+DjjVsJFZIcJ7mfLtmuhPutCyCX1qLE7PTh+tg0ram7BxQr6d1zfOVYmKkFMsmqiskmzORmAzwN9bPq0skR4Sth2J9DdAgxHCGhTBnDdmyY8J5CcSEYa8hlhTCARSJLLJDfH9PPPPy/Wm0mtadlrJclt9tYgTMXSZxNFRq70mgW4+eab8fDDD4tYOe1YzkEXCy4qUoNM/S233CKaLr71rW9FvWMQz3Y2iyz1q/JLsdKWvmZU8aix5OBbK6/H4cEO/Ln18Ihk0bDfg581vI7PL7lWNKy7GGHRVsEb6kEw7I58okKWbtnIBMKm2udcbTHmdoExD39p2YETrXWoXh9bgiZ+FiE21mfXjCI0pO+okGdYiCxdhZCc0qvNMYSGXKUhExoyWA3iCg4hU5165j3lR/xhvyA0ZHhCHuzqPYAbi7aNX7VhbwR6dgH5dKwUk2qy+ZWa4OEySQM/wWTNMlEaHPHZ6uts1SKYXzt0Dm0sG41ASRIRj5zbgwcrN6FyjAmW11c0U1y8FHpFgFSWhIpHXnEuejv70NfXj5JsszDYSMIwI4YLPY+Vxy3LGqVU3TB/fsoG7tLMFaKnhkygGdRGVGdMjuQrM+fiPVXXYzZjQ858tLTFZhNtZDbANIHGDRtx8X7yX+ReBvQfkALOdPiyaoC+l6X35kqA115hQLAklMQWHQpTRiZOdfdDr1Ejy6CHRa9DscUMVX7RyG84vvmSS1/lpuV0fPi64oorRvrN0LHleGE5t/wdZsxMtrTV9ervYdMGUJSXh1AwiP4TR7FhsA0OjwdnT+yAa+11COgM4lkg0cCgCQ2v+LFKh6Pzhd+jXKWoDutrAY6/DKyeOOHOZ4gGM+eAaIZZNpB3GXT9R0SlzLzqpWgNVmGUMNeiK4DOesAbyYLkdSYpWfsMsOo2oKEW2P8C4PMAlUuBzTcDWp30etuHgHveDgQDgDVL+u0vPhclNAiHHfjTb4APfwppA4mCrbcBr/5DImK4L7MFWJqarnYi8D6xtDye1BjyO0fJDDLT/qM1G/DfdZKTJwqHw2HRP4tgQ/EBvwdHh3qxOTdFfdU0gRmu+gojAvYQTJCqE3W5OhwaPoor81Pod1K5kPpcUkN7PnKNJ4CDrwJrr0z5GBgcVPYyiQefdWbQynApNGVLqkrRcKweRfNLcPnqrehr75scqSEOpAQovFMqCWcPpclUUSrhHEhAprFfzVBCUoN6/zbdWgz5jyAQtotkDqt2MfQaaX31BD2jxhbXaUdASaJFMeB3Q2sx4abvPYR/vPuz+OS/fwrf/e/vTO2c5pAWfPrTnxbBJGr2x88hc5h9YFUnJVUuaRReJUkSU45ynLmRRDTnbQb7mTQx3RVPrDRUBg4ZZFY2O1eCQUb+nXYh7T0G2OiLULqI1cTMXifxkapkHwPEqQZz8vUFKDQUo8vbMfLZAsuiOVIjjaCNz4A6iQIG+CcCWaaZhBftbpIArDSgvcZxzLGmTEbiuJFlgmhTy/4Fwap0JuMxYMrkKVkqiwFWJmfRFyIZR/8ilYpxJeSYCH0UJvDRZ6BPxfHL544+EhNz6ffIPcm4P35+zTXXxARlWZXy8ssvp43UIOhL0Z8RPt7Ih9dD3fwK4OqGLTsHp1yFgGVq8nCzGTKxdSmDRB/nUeVcPFYl2yhYbMADnwLazkp2dGm11Cx8AmBFHWNQY1VCJSMpeKwk5v/5z3/ijW98o3iO+cxN9HkVUOukxuBCcpsD5OJIZHnb294m4ickOGjTUqniYsBFQ2pwsSGhQT3iT37ykzg21ItPHd0pRSJUwD87GvHQkstwJRu5ThPYTNwZ8MQ0lhYtm4N+tLuHUWWZHSxduqFR6ZGn3wxPsAthBKBX50CnjmaLrsteJqQWWtyd4tossFYiGFbj3JkGFFYlDwZtylmMYtPYshQ6tRE6JM68kitF4jEZ6ag+X/+ohuEkNjxBL8zacSbr/oORH/gBoy4SUIyQGloa9FIzVgGtBvA2AoNuwLYlVvolApIFzCRgdqtyUhcNk7OrsSKrEt8+Lcn7EPG52DyHZztr8b7qbWNmvnABUOprKuVEsnV5omG5TCJYs6zYcvUmmDvy4LQ7RxrvsdqCRgIJjWTOSjqQpbNhc85WDPj7Rc+THH0etEn6Y1wMWJ5VIobP3gG5UXgZLssep+lzBKft7Tg53CbIOsq6zUtRWktjCqLHVY9OtxmZ5mKYrW8EQgGAWsNDe6NfHKZkUBCwRo1tuf8BjXOTMQvLtl0H9cmDkWchDGy5HiiOhuLphLAqgcYlAxNyFQWdWrnih44Ms6voYMhZJCyhlh2TiTbOI2wWE/YeOoIrl0kExc4jZ7Ep1yYMM4tBjxWqAHoa9uD0wq3CKaJTwmeRmYJ0ugN+P/zHdsHWXIvu0424YmEhF4boc8xz7U1dQk4GrwGdHD6To0rmbYvEKxwKwRYpwx8FnRHILwfaWIYbhsfnR1vfMCoCx6CzVQNP/Sr63aEdAGWVrr8/+llGXKCgrze2yoNOQE9sX4GEYJn8M08AnR1A5Xzg+pvoRST//rqrgZwioOUMYDQDyzch2DJ5mTWOCZb/c1wpydksXQba3LFzpVGtx7KsAvzrgo34W/tp9Hnd6PGxfwKmjCG/D//bUI8ujwerbNl4S8W8cfv0cO59ubUOu+qOIdOSiXyjDs5ACFo1hFSWRsXtDov5YNw1bi/LjsOxxPrOpyZEavCZpvOTjNTgGqIksW362AqSqhU1GO4bRFFWATILptgDjOermZjTlBQZOQkah7PkPHlVkFZtFo3Bw+J3UsajDKPGKNakkCLBgmtwl7cd8wPzRmUBV5ptqLP3wpyXjZt+8AX89J2fRvX8KnzoQx9Kz/nNYVL48Y9/jF/+8pfCAZxohu4cZh6c4+kXTkdPxVkJ6zxg8HhkEYs4GIZcQB9JREgRDOCSNKe9kawSdqqgrcaAlWyvySApkcxP4FrCwAsDVAw4k5TgMTLBhf3QuA4lkhNKBzifL8tciRJ/GbxBDyxaK6ypVunPIWXIFRMTBUkQvmSJHPoayuqbROOCvghtFFZO0N+gryuSJfPyxPf54jbkZtAca6wOZzIH538GBCfTiJjb4XlyX9w2fRvZz6a/TCJD7j9IsoXHwQoWJnHx2PiM8LlkJUn88zNZ0H8imUL7eJTUMyu9am4V/gXnEbVTUosYz1alLBa3l5ZmzTMIHvtUSA3eN/qEyfqrzPZzp4/NdXPKY0tvAOZPjXAbq0KZ92isygs+V/fee6+QEEulP864uEjIDCUYK+e8wtg55eyS9ey9kHBRRP2YLcjSIhqv3/mOlNH2m6ZTosnniH0H4P8aT0wrqUFI1RijJT1MF2mVhgxWSZi1ia8tg8ub89ZgUyQIxonoldZDcA07UVSTODtzffYCrMmeWqAjU5cPnccIv2gqLu3bqLbCrJk4W6tPIlelSyVwLjcasrvZcRow8DcawLIO0FJ70AQ4TmKkkQjh6wTY/0M72qmgwUaDnkaNLNcUe6xa5Bus6PU6RmWIEvzEHkiswckJjtm1NJ7iJzhZl5SlsuIQA1qcczahtaUFm7ZswvoFV8CUKwVoaJjKvRFEGeuWLdMuoWHQGFGkmdmM6fOJZVkl4jURHB08h+e7j4n/c6SdcXTirpLLUG0ZWzbIEehFi+sAhoPdGPC3YsDfgjLTWlh1+YBLQcrJcNbHkBoExxSzgKo5ZtlY7/QRqWlxQQlQE2t0cIwxW4hjjkFoZmDJPTlIYoiMeb9f/J2Bfhr5HJs0pOlwUCtZdm5SMS4ZjPSE+mDOBSoL81Df0Yd+hwsFBh10yqB7OITGE7XQLdoqZQSGQ8hvPIZrek7hxI93o1etQ93rr2BRYQ62kLAdcgBBI2CJEK98xk0Tk8ahE8NnMll/KBqgJA3l4HKyDOIhuxPdbT3i/wadBpUFNpzrHkTwhScwPxSCTn4+OU+fPgBcc5/UjyMRyiqAprMSmUHwtxXjZHqwdPk/PgXUnZLkflj5cWg/8JkvjB10mb8EvtJqcd8DbR1JmzumCmbucV6SM494vRZYy9Du7sGA3z4i73ZF3moxvlZnF4nXsN+Hjx5+Ef6QT1RpqEVFogGlxgy0uuwoMmYIKcrxYPf7cf2rL6LZ5RLP4K/ONWD/QB/+Z3Vs4zg6vBzX8rz58GvP4HV7O6yFefAbvPB7oj1ehv1hVJjVMKh1qZH2XjI4cWuD3xu59yeAX/8M6O8DqhcA7/0wkJ07MhYZCJA1dpPeC58HgZOvQus8AhgzgaVXId+aj6XWGpywS/MFz73Yk4fXXtwhggWTrtRIN4oXA931QFdd5AMVsOw6wDD+uGPVRjx0ah1W2lbi8GC0L4coRg0HcdJei3XZsc/1zcUL0eQaFMSGraIE93zvS/j0Bz8tCFQ6H3OYebC54r//+7/jueeeS2hzzWH2gRnQnFcuGZC8KL0e6D0IBNyAqQDIWzchQkMGg0C0KdJNatBmo13GIC73ER+UYlCXsiFKopzrIIOjbG7KtXrTpk0jdg8TOJjdT99/ugMyPNYc/cWZmDhbQDt3KvYdxw0lnsZLaKIdw+AtZZzoq7JiiL4JEwbpT9B3JdFB24u+Nv0I+h+0G5lURdufCR1MNppoRRN9E9qyHLt8FpgprQSPg2ObfoycRHjDDTeICmPK3JLAI7FC/yhdhAFJFFaAxzQJV4DVULLUT7L+BPw74wdy70EeP593uQcik75mK3idWYnDbH6ex1QauHMM8f4yPkPwmnIepbTYbJIRpV/N50SWY+S4o23POXham537fcBjvwUO75VkmG+8E7ji2pE/87ngPeB8m6y/EsFqLF7rscDrzb4RtJ1ZlTehapNLACqVCt/97ndFjzhKUT377LMXvKTqBU9qcDKixi0XpUceeWRkMRv0+0aFc+2BsRu9pgNrs0vxcnc9hvyS5ACPYVlmIYqM4+hcX6TwBn0iUMRAe7ZOyk4QC8iJFlSujHUOGSRi74LF1gosy0wt63wsaFQ6VFs2octzRvTXMGqsKDTWTKpSoyqjEk3OFtFQluCdXWStFgGLcWEsjDTtCwNOD8DWD7blEqHBzE4GDsW4jRuxzIJPAjlTKRnuLVuHPzTvxWCMDFUYg21dCPqDqLLmo14TDUYLvUy/C1ZbFmoWJL5G3CcNGpa7skqE2fB6GHDXPW8cCSofqT0inkUGvWgYzAUAZhf29CvueeTfvf31qDTnoc7RDnfAiyJjNkoZ3Vegx3NGjBGV4pc93jMSqZGAOEv0GbNXRqQGaNwtWZP0OGl8yMYW5/R169aJBtx0IGh4MZuPY1BuZEZDmhlXNMjk3/O7HKN0duks0ahMhFA4gG7PfvjDkkyU7YrlqH9yFwIhoDw3GwjE6nZuqCpFcMNG7Ni5E1cF+zH8ylM42tkHo06LJRkmbFi7CGcGhtHQM4hlJXlQu7yA1RzpZ6EFll2NiYCEDsvhE4HnzXMlYTRWGTq/d85vwYqSXEUgQYXqlZch1OdG4+t1CASDmJ+bBT2rxcbD+/8V+PpDwGBEM7WsEnjT28b+zZ5dwOmT0v9JaBC7X5M+W5z82HlfeZ9pmKbDKOU25OxdMe/Z7Rjo6UeNJx+dThUKyopRmV2CjLgKvEydHl9bvhU/aziCNrcDZcYM6DRafOTwy+LvBQYzvrRsC0rHIa3+2HIO51gtovjskeYmfGzBYsyPVMRwLqVjxCwj0RCc+ssqO6xF+aIiI1MfGwyiJBaJjasLUsxKZgZVs0L7l/N95SKgvQ34ry9I94cO9dFDwNceAr7xPSFFRmefz1XS+yAqkToQOPwPaPuagOxSwN4H7GwFtr0by7IWosJcAlfQDavWAl2hVjjqHJ+U9OFzft417/l8UJJtoBXwOgBrATDFKttiYzHOautGGsuKu6dCQgkq2kofqdmMDvcwfOEgSlffgtuMpXjzm9+MV155RWjBz2Fmg+OsAGeVBpMz5jB7wLlxyG+HL+QT/e0MEalUWabokpMIM+YDZTemZ1OTmIcZLGZgkzZbsixarh/JgnuyXCHtHQZ2GcAl2c3fyFnE3D7/z0ApA8xzgaqLB8y+nkyFtQyOhVR+z8CdHMAnucbAOysUaGtybHHs05fgOGXwl4mzHHfKBuIMqHJ/HJP0f/mbVPZNP4R+EO0dVh7FPyfc1rXXXisqSSipy2oNPgcMuNPnYfY5j4N/p91PwmUq4HaV1SJK0O7m9eC/yZKqZLCCioRNfFCU/hePl9eY5zZeIHqmwfNjljp9gnTFK5SkAG1bXmOSJiR5eK05p01nz6KJ9KGRezSKarR0VDSMhz/8H7Dn1WhS1e//V+qfuH6LmP95vVIhCukfxcikJQDPiSQnJShlNQW+n0MUnLMYO7/66qvx7ne/G7/97W/P+9i8pEkN9s/ghMRsDSXDvzY7H82u4ZHAAQPmq6axp4ayIuNjC7bi5R6J2Cg1ZWFb/uiFa1aBk0vnCWCwVZIpKV0NmKaeodPt6cf2HgYLpQBWmakQW/PW4HjtcWxdvQl9Rg929R1HIBwS8h83Fq5DdpxExVShUxtQZp56iaZRY8A1BVegwXlOEBt5+hyUJmm4Nwr5m4HO7YAvEgBkc1BDAdDw10hT8AzAqAwCqwCVPmGVhgyWhzNzJBly9BZ8oPoqDPrdomLpj6d24vS5ethKC1Fqy8eDlRth0RrF3zrc/djTfwJ9mmHA04rDredwc/EGWCJBPZIUNKpo2LEkkY7iSy+9JDI7aKQwqExJBrmPwVQzqeMRDDvgD3UhFHay/Tf0mmLRmHUOqRttnlAABrUW/ohcmBK+UACPte5Cr294RGJtc+5irFVUSrEXjdfjg8Goj/lMwFQJ+GP7e4jP4kAigoYtxwidF2Y9kfyi0R4/P9KZ4LhjSflVV101Ij/AZuUcg3Q0aOBzQZYz9Lgt/kYJGkfMhurs6oJmaS4anB0iC3+VrQrLM6VeHHZ/0wihQTgq8jG4sBLXzVsDFQ2+/TuligRZkmbdNdCQSMjOxs6Hf4AMrQZbKoqglqustFpcVlmMEx29+PmuY9CbzLjrPe+BzZYJlC8HLDkTvn8MFiRqiv3UU0+JjKjx5FD4bJrKFkDFfid1OwH2N8ivApZdD3V7A6rrjyAUCqOpf0ismdUbt0KVrEqDKC4Fvvkj4Gyd1Juhhj0axiEchgblHPXYz+2KniMJQMePWaPTIS3B+8/5TA5+LQqHhbHf2t8snFQ6n0pHlYTFl5dJPSsebzuDXzVFG0D3et34xqk9+MGaaNZRIgz4vEJqinOvEv0+7wipwexBzqOyzqk76If2iCQrRlJj1HkwM85YgnJzio0N110lNfE7xGZ6YaC0Crj5QeD5p4FQMOpwcNy3twLN54CqmpEMXmZAj7JpvB7g9z8EupvQlG1CZb68foWl/isdp4CqDbDqLOKlHN908mTZBc4NdI4ZtKIzTEdnWjPHEoHnlpPeyhGSOL5QbH8NUxLJLI6PUnN0/acuMAMct956q2jkO2uqWi5yMHB022234aGHHoppMjyH8w/OG3v7D6PVLfU50Ko02Jy7DtmaLBGcpE79HGbuXvCay8kkE/0tA8Z81jjnM3ueAVKuLzKJKAd1uQ6Q8JjV/vQczhvkXisch7QdOCY5HuP7rNDGkGWuSAxQwoygrUE7hESFXFnAwC9f9DMYABbJXZHxx98yWYvJHtxnqv17uB9+d6yKANr7TGiizBXPQZm0RL+b/tTjjz8u/G/+/corU5cOVYLXQQ5sx4PPJZuZp0Kc8BonIyv5XPPFZ5jPMo99uqTtJgreS/qgKTfBniDoe7GiRq6qoe9K35d+BVUEEvl1MwGOY/qNM0oK05/Y99roKvG9OwSpQZ+H40JWXBh7U7ESwsnAeAHXE64l9O0YL+ZYpI/Be8C/jUW0XwrIyMgQ/UdYBfnFL34RX/nKV3Ch4oImNX7961/jhz/8oRik8aVt75q3FF0eF3b2SQbvsqwcfHzBzGS4WXUG3FEyPuPJoKA7OAy1SgOTOuv8GWoNO4FW9n2I7L/zFLD+fkk2YpJgwObV3oMjhAbR6u7C3uajcDoHENRroA6rcV/pVpi0xtRknM4zSGwszZxg82kGD9tfA5w9UuPvnCWAbSFw7h9S3wHC6wLCOsBsBHi9NGYga5OU1Z0EzJwYDLiwq+u06OPC3iNbcheK68jm9E921KLTY0eeIQMrvBbcnr0Eb1y0WYRT8g0WUYnBYPY/2veixzsguo/Iw49Nc59v3osFrlyRxczgHgPRcvM2Lgx8TyNm34E90KqlpkzTsSj4Q53whI6JNVBuv+ALNMKi3QCN6hLLwpsEmpx9+EPLATgCXhjVWiy02tDm7htpesw5x6o1osPTK74vB9x2953CksxymCKZjxnaXDR1NyLTFnUOMrSR4HzGAmksU3KKIXHTPCBztOwD98V5mtUTzMKjkU6DmoQYF1W5V4ZMYFCGgP/Kzb2Y4SRnAdEApdOibPxFp0Pu3SLPpTRaaLBsbz6AjoMnYMwwIasgB7v6TohnZZGlDHX1p+AO9EQykkIoLS+A12ZCePlVUKl0wPx1wJEdgM8rZbMvkaomli9dAlQkKKmOGGxLi/PEy7Xscvz2aBuWL8/G5UsmLpFAY4xZXTTO4g1gNg9kpnvKDdBKl0kvJSqXADc+CPXe51BlzYKrqBrHi5Zi6XhGI+eD5dF7Ni5ENUacMUuDev7YmTO8lzRAZ6J5Kfclk8XMGKLBy+ueaG0+Ocw+S9EzoiRVs8sObzAAwxiE0KbcPATqoteBV9hKh9ganc84t9LRpLPMChUmS7DXQotrCP5QGMEw5a+ijfK4tQXWCQT+eV+vvRfYdpdEYlD/Vv482fcjZCPnfhKFvCcxeP6vwLl6wKxD77AL1cVxjjClFxOAGZKswmKVBolyWReb95skJR0dvibV5G8WYYFlMYYGXoc/QgazJ9Via+qZcZ/61KdEaT6JDc4HySrP5pAe0PYhoXHnnXcK7eE5zC40OJtHCA0iEA7i9b6DyGkyi3lkDpMH5+BU7XmuU5yvmeWcanWHrLlPIoPBKSYP0M6R1zNKoTDAwuxm2otcg9PZEHkO0w9l8D/V7081sExbifYwfQBmctOnoA3HMUp7RQ7O049g8JQBe9of8nHStmHiFecP/i5eIo3B73jZZwam6WfQhuH4p42uDNjTTyHxwe0oJTvHk3lhoo1MtiQLlr/pTW8S/6cP9fDDD+Ptb3/7hJMKSczQD2Mme/y8SVuYBIxcQTUWSDjymo9H1PBFO4ZJkrOl3wb9UhJg9EPjCbB0g/uQK89kua7zcR1kJQOO5RmTHOKzFD8nCNn16FrDSh6uJ/Tnk8mhyQmPqYDf4/jmdWYyEF+MP7CHDMcgn1XKXcuyhpcqCgsLRZIk12FWs3AuuRChCsuifRcYGPC68cYb8fe//11obSbDkN8rAgDZOsOsyu5wBQdxznkAIUhB/wxNDirMawXBMaOgJNfOn8R+xutUugaoTr6gjgd30IO/tr0UuytvAL21rShfXzkib8R/r8nfDJv+Ig1QNz8HOCPSUzJIakR0xWNQtBWwlgGqsQkePrIHag9jr6lLOHJyz/FSYw4WOTLx2+b9cAS9IsimCgG5ebn47Po7YNbGLhCv9Z7A4cFG6BStPLjtc8fqoddq8Yltb08YOGGwqbO3GV3Ok1i4rByZWVbk6quhUbsQCnugVdlg0sxPqC8+EbDPgSP4spQhr+izzPPVqLJh0Y5denipg0TGd+pegj8UGBl9rFhbrLdi9/490Gg1WGAtgk6lRoOzU4wXQm8ywGS14MbclcjSZkhSAuow9p9+CSu2SBUYGZpclJpXQ5NkrHIc0TGlQaE0TOgoUAuTZaNKw5/OBY0rpUHObTBTiUamXDLKz1iVRyOQx8UsEzmLm0YL/5YoY+m/tv8CBqsJBrMRQ939CAVDKDDYcHX+KthK/PCq20a2X3eyGf09w7jvpk+Mv2Y88j9Ac31sY+FVm4D+VkAdANRk49RAzTq8NKTHmfqzovKEBgTPP9n2g+E++EOcI3xsew+taiFee213wnNjUGDHjh3ib2MdLzV5mf2SigFL55LPeaIgBQ3ESWvkPvcU8PD3GTWhvgXwyc8BGzan9FMaosxymsksdRq9dMASNZv9cf1hPN91buS5IQxqDf606bZxx81PztbhSyeOCdskR6/Hby7bgo25sZWkHM/MapPJvkGfBz9v3IdG5wBMGhXKzFoEw0ExNV6RtxgbcmqmfsLdncCnPyrp3jKris5GZRXw1W9LfVAi4DXhcxczlh7+CtDZKojRPQYVNq2oijgwEaflyneOknHi88bzpFNOYzqRE8PvsEKLxPl5l6aSDkhKRKAe8FjVTAngC3nR6+0RTcPz9PkwTrC5OZ9LanDTMf7rX/86JamOOSQHg0933XWXCIY9/fTT09aAeA6Tx8GBY2hySvONjJa6Ztw8/xosmj/B5KM5xIBkg9y7TwnKIfKlJDw4Pyeqtk0GZmuzIo/BTwZNE5EnL774orAd6X8kSyqYw+wExwNtRxJSidZz3ksSAXKPRt5/zq/0C+gDpNpgmWshg+1y022C72krUDpKabPxmFjhGB+4pE1JH4O2szwOedz0U/h7Em+sqJDPQ5ZCjU/ooE1MW42fs3KCvo8M+jQkWWTQhuWawv5Y6VQ14HP1u9/9TgSreRyyPFaqYICd9m4ieR4SOXLl8liQyc1UQAKEiQPxa6ssdyU3SZ9JMCmO930m7Sra0iSWzodUIhNUOUbGq4pIKx79FbD9mdjPPvApYMW6GJ+WYylZ5dNEqn04luSqrWQSaqzM4rp2SfXgSgL2IGEiD/vHsWryQsMFSWqwDJ+M8pe//GV88IMfxIUGEThzvIpAOLbkL99QjQJDGgITEwE1o1//RexnNBAKlwKLxpbRGAvBcAiPtjwnnHfxPhDE6b21WLVpJVTaqIEqgvGmImzOXYuLDsyAPf270Z+b8qJSVEqUXANYxs+2raurQ5fFh0POc6M6F1QOZWCfcXQjr/vK1mC1LdYgeax1N9o9/RKpIT4Jo/FIHfLKClFWVII3VVydtMKo2bljhJCTYdGqoI34JzpVHqxaqdHuZEGCxBncEfOZPFupYIZVd+FNuDOJU8Nd+G2z1NRdRs/ZZmzJKMcDV90CbUT3+LS9DS90RZvYep1u+BwevGPRDTBq9cIooNFOI3TREimrQ6tOnEFBw4DGCCH3WZEbx/FfyukopWS4bToncmP5REYFK/EoOSDM22O7gKE+dIZ1aNDZRLCUzjENHBqkNORZrs1/aSzSuKdj/IvGZ9Bw/AzKl0abWZeZ8nBr8cZIT4198Iel52bvrhO4+aoHYNam4Fy5HMDffwk0nZbkl7beAmy6HtjzD6DxcGyZ7fIrMVSxVmi000miIUvjWc5o43nQiA+Fh+EN8b4ps/lzcGifXxhliZ4pytHRkeT1S1bOzGvMYIUsaTQV8D7zOnNbEw40u5xwUm7C7YbGZBbXgEQFDVmOBb6ng8lrFB88YUYN1/6ZLBWmg8qxT2dRee27PE584sh2uAIBsWSSoPhQ9WrcUJRak0FHwI8+rw/FJP4SnA8Nd3mfu/s68VhrPXzBELbmF+Pu0mohJXfa3iVkV2os+WNWh0wITQ3AI78E+nslWbG3vgewWEdl8XK8MQDBABXHwfyd/0Swrha7WzqxuDgXecsrgAwTkJUPLL8eyKtIOI74PDC4xSzL+CAI5wzuh4ECkmjnvUfTcC/wyh+kPiG86SuvBpZNTvZhsqCDxnngDW94A775zW/O6L4vFbApOBOmSJJf6BVCFytODtfjxHDdyPvOpg6EQyH8yxXvhPYCqPyerWBgmOtdorl2IkHLROB8z/l88+bNc0TFRQjKpHI9p92dLFBK+4HEAe0a+hKyFBTHBhOY6B+M5TcyAYqZ2gzGknSTCQRl7wdlUJokAr/HuTyR/UzbgjElZaU47Q5WbHDbPEY2Gaa9S/uEx0nCjbaqTOjJpAC/Ox4pwwAqj3+6tP1ZqUKygH4PryfPl9eYme/0OZKB32H2erIqN1aHMpmMtl6yoP9U5wflsZBIIekzHpGSDLynJGl4/vSJeO4cC7RdZSItnvQhMUQia7orwuPBc6WdMdX+KBMBxzZfk72+kwYT2p7+K3Boj1Qhfv3twNpNo/wL3j/OISQdOR74DPL5IhHDZ2i8Hi8yXn75ZTFueW0TzUkkU7lNXgv2spkj0IEf/ehHIr5Of3usyqvZiAvO8uNkffvttwuN4fNJaHiCAbzYfU70zaixZOOy7OKUH4YQgqMIDWmbo4PR0w59BmCyAe6haBCNgbhMBt4HAF2mJJs0QVC3fkPOcrzef1QQFw1H6rD6snVQaVlZEA3W8X+e4BgN3INuYOgwEBgCtFYgcxWgnd7ywLRBjAcGq5QtYVXS8VOCws8moZE6C30WYB6fLSfbTKOpL9QNOCXxE5EV3zMAj8OFFYs2A47R4yhev53I1JnR4RlAIBwG+852NrQht7QAWXnZ2JyXXBbDF7KPIjQIbkcbKanwh3sRghsaTD4TRQV9ZIqK3RfPRKuaHXqcsxmJgpx+txcL1i6ETpEBs9BSgnZ3H04Mt4j3GZYM3FNzJYoyYktiaRRy8ZdLxGkU0mjkoicHtqPZELxLQ5Gxz0wqXUIHaP/+/cLhoCHNuZ0ORnz2BZ2N13fuxOXth6DqOCeIjKJQEEULVqJl1XXCiSLRJxsuopLpwAFREcHqBBorRV4LBktinY2lmVLViVqlRaFxIzzBXnj9bpRmmlMjNAizBbj/I1JWu7K0tu30aN3Q1tPIWnG1aMjFwCSNNcpr8Xzp1PFYhR6wqRNuTxsKi7JhsUiZ3CH0o7p6iTAyEhlzPHcaxTSOGWRmRlo82cD7RYMxHiQ6+DcSQamuYbzOdJLiS/GTgYSVrGFMg1VnNKG4oHCk2TvHFfsI6D0ehPR6BLVa4VjyXsqkD79LA3ymtU+ZQcVrSZIhPuj+2YLFOOAbBlewapUeFY6AcH55fcbLMrNodeIVDz4DdKwYxOd5v97Xic8d2z0idXVsuE9UoHZ7u9HFpARqFutM+EjN5cg1pCHzb14V8PmvJv0zMx55T+hgMDhBCTTev922CoQ7X8XaknyY9TrgTBfwlg8DC5L3tKLjzXlFzsjidnif5QpBBsB4z2dFBhWf8e2PAM7BqJ104hWgs1Z6XzAfWHItEFcRmW7wOZf1bynJcqGWic9W/OpXv8L//u//inE+R2jMXtRYKtHqasdwwAHXkAMuuxP3bL1rjtCYAhj0oz0QH9SjncfPp5rBTLkT9jqZCxxdnJCJirEyvzmGElX50q9lUFlOiuJ25EoMORGKwU3ahalKxVASir4BEwBIItAviG8uTPubdjfJCjmoTDuPslS0WWlzc73l92i7k9CQA+5y0JUyVfRnUgkQk/wYr8HxVLBmzRpx3CRleP1ksoa2mlx9zWvI82AymGxP899Esr4ymLVNGS1eQ/psiZpKc5vcp7ICRSmjmmoCFI+FxBivcyq9E+jj0WaW5xWSOTxH2a7kOXFbvIfcnpyoJ/skPG9eC9qf58PWJDFGIpljVXmufFZ4zHwGODfzPGh781nhPZjMPMrzl33NURKyMwGuIbfdJ70SgGOF6w2JC77oX9InoF9L8FqkSmgQyrHKc+a5k5iTrzPHBcf2pdxTIx4f+tCHxFp9xx13XHBStxcUqcGJ6IEHHhAL4v/8z/+cV0Lj87Wvos1tF4EYZmjeVbIA91ekpvmphgZqaOMCwyro1BOTIkgLOCmuuAOofQJw9Uvv8woB3wmg4wSgyQAKrwZ0Ex/U1ZYy2HQWdHn7obcFcOf8a7Czdz96fQMxxEaBIYnOfCgA9L4EBF2RZqN2wNcHFNwIqGdIA3AyEMFMBjk1QO4yoO9Y5A+RBSh3KcCG6L1HAP8woLcBuStj5D0SgZMvg5ec1At6nOhuahWfs8GvrSAX80rLsbFsEfbUd8IV8EnyU0ISRSsyeZWw+90wawxC/isQCoqgnGPQgY1L12Jd3kLkGZKTBmokLmmNX17D7LMwhapxyleZ1CvhDh2OIYbUsMCoSa0h26WMSnMO5ptz0eTqi3yiQk1VFSzDfmFAyY4CDaOrC1ZilW0+3AEfcgwWmDSjny8aWDJrT8OKhgcdD2ZU0FigUUDHAAgihAMcZZFf6qAOr4VKFTuHsLKCZd/UciWYvceqDBqiyowiYZSY1djx2k5snlcEnegAw7SgoyhfdzXKbr1VPBt0uvldZkHx9zwma2YmHt/5LLzaIArmFcGgM0Gn1mBlVhXmZxTFjDWjJh/qoA8BXwtcwUb4Ql1QQQOTphJ69Tiap/EGEYPV3rjvKIKddBp47jxfGlQMgNPp4jE7vR7oDD50dvajva0XCxdJcks8p1BIl1D7VlxlnU6UTtN54LXlv7IEAMvxExltvGZ0QHgfaVDzu/xdKkjVoOb9YDaMnJ3JdZwOiOzgiHFFsuChzwLnmqRref+DwDveNUIS0THisVIuYMabRkec3HhNbz4DPLecQSlrj/eUxh+dWx4rP5MzzRJdKz4/dHB5Pfh3pUOmLLv+a+vZUe3Vd/c1IJMldhEwueKPLYfxtspVaHCeE7KERcZ8UQWZLvB8+KyzqoLn+eMf/xjvf//7R/qQiF4kbOx6dK9EACxZDRSPrs6Q4Q52wqfqR0f/CSz1L4BBZxHzkiwzRseN13e2NJSEexhwDETfazWAxQj4nNL71uOAzwOse8O0HwrHx6OPPiokkuj0yQ115zA10IGjQ8cqjfPi8M8hZejUOlxdcDla3e04fO4IrtlyH0otxXNXcJJgMJLrFm0Irm38PwODDIJx/aN9MpXAD8l62jBc7+bk3C5OyIFW2nu0hyZaxSv3XJDtI9q0tKcYoOeYkeWqUgG/S5uFQX76JbQnOPYSVWywnxeTo5hYpcxM5j65tvLZoN3NxBY+D3KWP8+P45pV4bRbxgP3L/9mOglzHh9l23i89AH43MkvniNtTNpWlNni+cp9JGijcw1kYD+R3cVryBf9Fs4NtNf4L8kduXk5r4VcvcLrT9+MtjMJA94TfifVICm3K1feJwPnKo4PErGyhBPPMVGj8rHAc+E44Xib7r4aiZDIryHpQsKG147PlkxC8Z4y4M9zp38RLxNI8G+s4OdYI+TnhuOelfHJ+lWcT/B4afuTUJSfKVkCiWoNk72u8vigr0nSlOSc3G+Tvu8coTEa3/ve94TU7YMPPojHH3/8grlGF5T81EMPPYQ//elP5z2D6qmOs/jNudpR0j8Pr70BOfrUiIkhfyda3UdG3utUJlRlbEoq6zIuGETwOgFmao4THE8IDoOAF7CfAuwnFX9QAZR5KblpcsdFmY6uLjGx0klkr40dPfswRIJCSMAUYWPO6pEeGzEYPgkMMqCtYulHNAvathEwS1nWMtxBN9rdrQiFQ8g3FMCmP0/jw9MEuEhiBAFNFpBxGTDcCjhaAY0OyFkKmFLMAleABggXXY57LmiikWymBq/0noA76EWh0YYbClcjU2dCj9eBx9uOoNtrR64+A3eWrESJSTJQGp09eLrjMJxBrwiUUQmMxNySzDKE6/rQ1dQmGpEyC3vMJn+ew3AFe+Ka3cqBThXUMMCmu3zKfTVkGapgeAhh+KFGJjQq61ymV4rwh4LY0XsWXZ5hZOvNqLCr0d/VK4hhGtc01mmcp0OSiKCe7RVbCxGGVPURRQY0qtjeCazSYBYRM3/kzCuOLToXHOcxx3TkNXie+SMOtHZDp1HDotdhaVEucMe7gZoVIw4Dg1HMpuLvqVvrqNCjS+8W80vb6SYsXLII98+7Enq1blRGF408Xovjda9i8SorcnKjGqeUUtOro88tjS1ZX1e52NOIJ4FQ5u0G9v4z8mkkJH3lm4HSWDKO58osEmVvglB4KCI/xUCAEy3NPOdsrFh6tzDE6DAyuEwHJBFoBFMPU8iEabXie5w7eFw02BloZ0YVSQw6C8pnndvm31MNJHNupyHKQEiiQAUDI7xG3C/PM6FhxLXnPe9gmr4k2Sfj058Drr8xch2GxLExky5VzeXZAD5fJC5k0oIOMR0T3iP+jQTSeNmvHz+0A4eHemM+W2zVQB/3sxydDqtsarEGEkwcWJG1GAutk3+2ue7IEmnMlGKGId9TAoLHTud0Mtli9kADHAH2jFHB7fbi+OFG3HLVe9DW0iOeQY5LOh50RpjZOyvgcwN/Ucg9mfWAXju68eH1H5NI+KF6IOSVKjAzpoeI++EPf4ivfvWrYh45H2TfxQTOkZQ4+MIXviCIjTlcOGCQbTqJPc5/Xd4O2APDMKiNKDWVQzPT/Q9nSE+embJcs2kvTDQozf5tL3YdQ52jQ1Trr7XNx6bchWKNoG1Em4xB0HvvvXfazmMO5xdPPvmksDlpo9KO5LPDYHoyWdSJgHYk7VbO0+OBY5i+hSw9JNu5cpU054t4+SFum3/nWOU+4nte0OZnsPX6668XPgPtOQbsZfKF9nAiWSnartwufQw5U57bGOvYGXxl5Yoy4YW/5b5IWKTSGy9ZLxEleD7cD593udKdkj08/2T3jLr7fI4ZTOeLtgfPnwkWsgQV11PauPFkKKtFkvVKSAQSM7wPPL54kCTh/uTjnGy/PZ4z5z2eE+NUF0oAV242zuss+xH0w3g/5CrndDx30wn6QiSj+EzyeOXnlH326G9MpC9MMtA+pr/CuACfB5Kact9Ijq10xT8uNvT394tr9eY3v1n4GRcCLphKDZb/sTpD1rhltcTxYSlrbllmNozp0pNOAYN+70iFhhKUhEiV1MjSFcGgNsMR6BfNdjN1hdAkkGhJCR1ngVcelZxuZghveQMwL7nkQ0Jw0dQZAb8iE1EgLH0mmjVPbKL3h/w4MngML+5+Ccs3rEBoGFhkXYDrC6+AK+gWToExQUa4gL0R6DsUPQbGuhjFEYt77HV3BhzY0797pGlqk6sBK7JWo8g4w1lb/m7AFe1NgCAzO3cD2dcCOVPTaGQQki8aNUqDoNo6OhM332DB+6pG95vo89rx19a9I41tg6EQnHYXnJ096Ao24ub81SLgNh5hSAOryLgKQ/5meEN2aFUGmDRmeEL1gnjQqCywaleMT2h4OwBfD8AAs2k+oE7sQKlVRvGaDoPAEWyAO9ghaJgMTQXM2hnWl5wmiCwNrx2eoB9bcufDSEKNQWhVF0xag8jOYSYUjWwa3LJhMZX9sUybGTphtCf4hnNERkgGjcdXXnklJguHf2fWFJ0HzvU0akSwvKgSRp0Wl88vEZVJf6s9i8rcbGQURO8XjToSciyb5BjOqyhCg6EdKqhEgDezIBtNzU04qC9EtlMnjofGDLdPg5yGII1iS3E99u6uhS3bgoWLK8R2PcFWQWrIwV3+hhlgiZrcMZC/s9ONvKL1qFENQ6OinF8m4OoE+k1AjpS9TgOa117WvWUgtKqKFRWZ0KvXwuU9CbffDWthOfp7wzhSux/Ll6wRDg9/Q1KI14eyhb6QE3q1CUaNVKrMF59lzhWvvfaayHah0c5zpLPJ8+axxpOXzIxicCPV5pwkx+gw8n7FZzbTYOTf5b4pdBToPPHYY64bM4lYoaEEifmDB0TVxrmn/gmd0YjFb3krU8VwIYEGc/x1katjlM0jx8JVBaUxpIYgkLUG+MNSNR7BMV5p1iAUjpV3PD5UhwWW+aPvZcgP+Njs2iyR7UkgZMHUarHucL5QZpDxHk6G0GAFn0RoiHcwmfQorcjDK7v+AZNaqgLhS84KnDWgbbdoE3D69WgD9EQIeoDW54CAW3rffxzIXy8lNKQZDL5zHqAcKwMRszH77kIA5+L77rtPNG+9EPv0XcpgIHEsrfh04LT9BNo8LSO2RKenDeuyN11UxAZtN9oIcm+vyYCExil7m1iB2Ffxtc4TGOjoQZ5bL+wNrnvK3gVzuPggS5NyDec955rE5ISpBleZ5ECbMlXyknY7xzGD3kobjL4Bg/z0L+J7/MnfY4CVBAfHqtLGYYCex0GQ8FNKr9KupW3NADODgfQpSOrQvyGhQftJrvblcTFoS/s83gaXSRdWSyTq7cBAPgkV2mAkIcdKQKL9xqoFVkvQDpdlamUwQZJkAbcpJ9rQLuU95DHw/BNtn58/++yzYr0k6cDkF9r63A+Pi++ZiKSU+ZHB8SD3/EgFPEd5u8p5ib4E5376Kgxa83rxHHkeE0l6pm3Lc+V+UiGKZhPkqgsleA9JVHGszXZyRvQXrqsb8Ql5P/k88HOZwEsH6KszPsHrxeeQfjefS46dOSQH7wWrNEhSk9y47bbbMNtxQVRqcEJj8OYnP/mJYIy6PC587PBraHNLZf8lRjN+sOZKFBhnRr5pX38H/rsu2oCXSx5JlZ+svTH95ApvD2WhAh4gIw/Qxk26Ljvw+P+wE3c02M9F+LYPAtmjdSvHRe/rgJMBJsWwoNRT+d0T3tT+/kM4evaoaBJeVCkFLpdnLcX8jNGM+6hzbn4MCMX12lCz94QRKLgJ0ETv9bGhw+j0dMR8Va/WY1v+5BudTwrOY4C3YRTpgqzrAM357wOyv78BzzYdQEttPfqaWkV/hco1i1GyaJ5wNj6y4JYp7yM+cJ0UrjOAgxUtke+yQin7mpj7Ot0Y9p+BM9gY81mWbjnMmtSCjem4Vj5m8orxakhb9QmdyUdbDuL4sPRMGNQavL1yEyoyJGMhvi8Dg/Q0CCejl0zjmkYUt0sDncakL3gEalVPXAKzERrVFSPvaETL1Q4MSNCgjAe3ywA7jRA6G4VdZ4EX/yIIVr9KjdqqjfAUVIgKAJmQoUPAEmI66NsP7MIRUzc8Tjcc/UMIh8IwZ1qwNqcaNy7eLAxrOkl0tnjuXMC5nT3HfgmHw4We7kFhFObmZYoeLhnaxeJYaAilYjTTYWg4cRih2mdhMWjgD4bg8/uhmrcBwZx5I8aWaJYYboDHV4f6+jZ4vRpkZ65Ha18LXOoW6HR6DA/asXTVAqCnCBZTrpD6oWFW334YBQujhmu2bh4KjItECa+sR8pjZiYrySJecxqL3CedskSN6eiQyMFsOgm8JmOVfxPMyuLvCBqndIy4fwZIODa4Pd5PXms6e9wu/yacCAbLb79JqjaU7z0dstJyaJrOQq/RoMJskqbVr38bWLkalxJ4/R5prsOjLWcQCIVwZUEp7i+vxk8adsMV9I884zcW5mLQP1oC4a7SG2ODb4ONQMurGLY7YLWYoSq/EsgenV1IJ5IBgUTPJkkqziNyafhEEAz70O3dHvepCiZ1EV5/qU08uxw7HK+JyurPK2ibNBwCuppoPQODXO9lqIDixUBpMdB/Is4OUAEL3jK5KtpxwOeOgR5WtHz/+99P+/YvBXzkIx8R5C/nSSaPzOHCmRtZHUopx+nq0+AKOLG7f8eozxdbl4mKjTlE8YMzT6O9oRltJxuEzaUz6HH1Ddfifetun/UBtosRTPSjj0H/YqYIODaqZkBVDhDT9uP/GVCfKGivyj026KOmmmxDkHxgYJ3xo+uuuy4hUce/yTZxvCQVJVLpn9BGpm8j/02Wf6V9Tz+GwXxWXnAu4hjn+kEiQK6GlqVJaf/Sj2EwXiYZmODC6yKvOTIJxF57450n7TP6L/RjuF/a6Qzoyz447X5mvivlZOkz0FaXCSa5/xv9H26Px8Lz5nHyeu3YsUNcl0TBfhIBvB/cN8+Hx0HSR6725jbp2yVKtGB1Aa8dwX/HC8DzWvFcuS1eT/oO3IZMKtEGIiHLIDivA68xSZOxEodkeT2et7Iv5BzSA94z3tuxbHg+N3w2EiVUxldupAOMJ8gVRhwzfM7mMD7+8Ic/iGQfJlAlqkSbTZj1lRpcCO655x68853vFIQG8d26I+j0MOwhocvrxnfqDuObKxPLcaQb67OLRA+Nv7VLiy2JjE8u3DANhEYIOPUc0CPtBxo9sPx2IEsxUfe1AZHARvR3YaC7eXKkRtYywN0mZXJGJCz8tnnw+PcIx9ygLodBk1oFxLmBZvS292LJZdEMxQ535/ikBoMB8YQGodICuVeNCnx7g94Ezaz9qQfYp4Bg2AN3sBHhsA9GuMQDpUp03KkiTHKKUluTN0D39DXhxe46+EJBLLIW4A2lK0Wm/ukjx3HyyH5k2qwoXzIfWr0O+ZUlMJhNyDdMPktfiZSuN3ttOCLNVeXAD++3ux6wJGnSxTHt7wNCHkCbJTWNnyJcwdbRnwVaZ4TUCIQCODp0CAM8JxGMzsXKrNXQxskiTQZ7+hoFoSGH1DyhIH7euAsPLb0Z2rjqGT4jzJKYDKFBh0F2BmR0uBvR4enAkkwN9CqVuG2s2FEjNkuZxg71XK+44gqR6UPHIt54oWEs62iSeGlwq7D6HZ+HyeuE7uxhrBnoRtDdgqO1fmgsNkHMyD0biI0r1+KJv/wAeVWlKKouH2k6XaS1ifJpBupp0JMEoYQSs46YvVVQXo3qhR643T401Ldh5eoaWDTLU573ZAgN2/wQsLgMdpcbBp0Wep0W0PQjvPFNUEWM+FC4AyE0Qk9JraXzcPp0K+ZV26EuNuL0SRWKSnMxf4GUTWapUKPYJFV65BRYYG9rRf+xACqqSmDOMGLA3wSLtgAZGdkjGrFyhiQdAqVecDLQwVJmttGx4/0ay+hXZrvRmaTxSEeFhin3TYeHBI4MOlN0SoSBRIfpzQ8g/Mhv0BMMwaRRoxsqLHAMQp0RKf+XG7H/9c+XHKnBMf1g5SLxUuKzi6/G0aFOsUYvyyxEv68bg4NRUoNZxTYd5foUz7Z3GOFzL+PgSakyxuHyYoPHB9PKXMAYzaSk4U8nMlEzSIKk2WSDVOzJpFGZEQxHbTiuA8GwGT2aAeTnuDC/qEYED5ghNKuy5zgGq9dKL6KnATizS0o6ya8CFl4JdNNWSmTTeAF1Ghq5J3heH3vsMZH4wwzUt7zlLWnfx8WMRx55RLzosM0RGhcWmKFL/e3ptPO9kcQTJTi3+hL5KJcwaCMc+PtL0GdlwFaYi5zSAlhyslBQXDRHaJwH9Hg7cWr4KEIIgRb44syVyDekr8dWMsT73QyuT1YWiFXX9BEm83wz4YoJGVdddZVIwEhU4SH3fmAyFAltBvXlQDmrlklYkLxgRTnlmBh8lfOAad/TvmVwlElGtFPoe9CXevHFF0VQndujLUX/hdUNtI1JANCvkbPRuWZP5vxIOsj2mSzxqiRfEsU/6PPwc7kalv4AbXVlcFeZ6ET7i2Q/r0l8E3T+9oknnhDnSGKAPoBcJT4elNsiSUHiaixJKl5TucqFx0+fkQQGfQhef157vpfvHSt8uTbw2JRkFuOJvE70NXlf5Ps/h/SCfiefXfqMJNAS9ScjqcS/JxsvvD+yL58ucHucE2jnkVBhDCK+V+IcRuP+++8Xsl2MxXOejJflm02Y1aQGJ69/+Zd/EYP+W9/61sjn9Y6hGOkn/p+fdXnc+FnDaXR7PViRmY13zl8A3TRkh3ChYFPwG4vmC8mpYiMbF6fpUgo9bEYCNUBHbZTQIII+4MSTwKZ3R6WgWLmQCMk+Hw9sCF58s1StEQ7Ap1fBpe0ciT27gmw6FIYhhcBv0/FGLFwbu1BpUwnw89zYE8M3GJvtaF0sfR4H9s8YUGSo0uHI1GZNO6ERCnsx5H8dYdHwPYyAJgTbCKUROW59eVJZpRiE/YD3EBCK9KnQVAD6ZROW/Do21I6/tR9TvO8QfRXeNm8DjMMBbLhlG1Tq0cVZK7KSN3RNO3iuozrS8IJ6k3w/DAzvA7wKEsK6FjDNm+qB4HyhznFyhNAg+P/T9pNYlrVyStsNUR5poGXUmVGU5rXes1gUsAhjXQbJhMnITtGBpaHODMle7xD6fHYY1ECn55R47o4PB2HTUdQLyDMsRaYulrCgoUOjmkaN3FdjrIwMGt00fl979VVs6q2FaaAd/Q4X9jd3I9Nmg/bGt4ptyA3/hKbruVZ8+KYH8dJgLdxBHzpOn0NVKAcrbl2EPZ2DwvCl08ISaRrJzz//vJRt1ZaJttZuhDXDWLy4BmbNogkTGiOgJCDCsJoVc0DQL0QsZITCnLuisnqhUBD9/Z2A0Yp5NWU4c/IcFi6lJIRGZLmPbDrkRPWiChzacwIvPrELN9+zTXzHF3LAjOwYUmgq4PWh8ZcswB0PWTaI90uu8uA4Y9CQGWxy9Ycy46Pz5lsxGAaKW5vh1Ouhvvo6qL/+pQRVi8pA+KWNTJ0RV+RF58BsXSUcfifOOs+J91atBZty47Sn3b04croZFUW5yM+xwu3x4UhdMzbV9MSQGixfT5YVSceQz9iVvH87dtD7B2QZhuFh4B9/k/5dsRLYeuWo33ObObo16PcfRDAcqe5BMV7tOYNOfw/QWYemzFYsW7VQGNAMaqSrDD3tIJHBlxLsmTV8VvGBCtCaprUKkc/o7373O7zpTW8S5C5fc0gt8MVm93/+85/ndJUvMHAeIqa7v6JFaxFBYQaHZZBIzmKvwTmMgGv81Wu2oL88dq5ekx3NFJ/DzMAddOHk8JERKUqOXb7PyLbCrJ3e6kcGI5XZ+bQDJzs3c12brB/PLH3awPQxxpNT4xzCqlOSFLIEG/fLptm0PXg+9C9oTzOATnKCNi4TO5QSVH/7299EFQd9GvoUlHbidmg7s88dt8P3tJ8YqOcxpiNOwW3Fy0Ql2y6/J8sT8brIlSaJelbI/Tu2b98uSAcSyMrt89wTVfJOBHJ1RaLEtkTgfuVANAkiHjfPnz4Kq4ToV3JtoM8o33eSLqya4d+4L5Ih0y1ZeKmC155jn5XDvFckBOnrKRMnSUSR0OPYi4cs4cx7l275WZIrjFuQrOO4IZkm94CZw9j49re/LfqVMib/61//etb2tZ3VpMavfvUrodvHQah0aik31ev1jOhJq6FCvsGEe3e9hAG/TwT2Xuxqx9GhfvxgzeRYcMIZ8GM/s4HDYayx5SNbH5sxyP4ZqfbQGBcM2PQfBobYpDsMGIsAeyQ7VakQ5vdIWtiGiJxRfgVQXAV0NEb7TWQVAOVxWozMdG06BAz3AGYbULVO6r+RCFozkCUtGm7fq6P+7A21pERqVGfPRygQipnMqiwpBqILrgA6XwYCDum9uRywJWZU52dUw+4fRi/7MzCmoDFhhW36dVu9oXbRQ0JGiJrxZgOyAjlQsxpBy+zXFEu1fEeBkKIZbLAZCBgBXXSyHfQ58ErPYfT7hmHWGnF57gqUmfNHkRrKriM0ak/au9DT14sF86tRVWDFa72UxoiFdiqyGDzXsBdQGVKrMOH3mLEacsccKXS5seSeowkQDeUDgK8ltiGr/RDArKNUCKMkMGvK4AzGavmbNYmbrYbDATiDdfCHBqBW6WDW1ECnnnxZ5IBvtExMv78P/pAPWpUupTmL1UjOgAcZWuNI4+tnO2vR6eU1G41BvxstrQMio1c6p7DImJ9MpgIdDmY+HRlqwO6+UyOf5+qBCrMa7G7T55PurVnrR2aCqUY+Rzo9qWjBcw3YVF2O1//5v7hyQRlq23tx1YIy6LUanKBUU8kCsU2eH8ummXG0PLsG77AVwRX0wm4dRGd7h8jaosFEh6fB0Y0u7xAyijOQ68lDSVFxRGopTVV/rKrrU0qcqQBrfowMjQraGBJqyZJK9PQM4szRTmQW6mAyGeBxe2GxmmHSRsecLpL1zfn19jddM+pzngfXT2aCpdr4OxF4TWkAMvOG/7KyJxWwUoRGK8kM/sv7wVJ4GrPKLH+OQX8ggMXvfo94P3KkGzYBu3fGyFJh0/Q1g73Qwfu0OnsZlmUtErITBrXkQCuz9rr6hmE06AShQZxs7MCKmjJAaxTPIR16Opcs36fTx3umnItYnUXHf3NvL7Sf+Uz03tx/P/DudwPvfRfQ0y16oeCPvwfe/V7g7e8cdaxadQby9VcgBI8Y//v6axFGCPOWV+HEnlos3bgcda4G3LD5SjF+6IQoq9y4runSUNU2LchaAHj6gCG5wtYAlF49uqF4msFKs49//OOivwYDMLM5m2q2ON+8Vv/2b/+Gm2+++XwfzhwmCGZbTjZgOhHo1HqszFqDY8OHxbxKVGcsQI5+tHTjpQyuHW+58jacdLajzt4ufIrVtnmYlzFx2aE5TA3D/sGY3loE39sDg9NKajBhRWnbkeCYTH8WPtdMnFJWAE8UlJjhHD8R25fJAOyLwQC5TKiwEp1BWNrS9EEY2Of6Slmt+AbDtLlZxUoJJlYJDPs9ODDYIVQTqi9bjc4TZ0aCvecLvC7s1cEKbF5nBnrpz7HaJNFx0Y6/4447RlXM0r5g1QOJn8nIkMZfN84ftC9lQinV8UZig8cmV6Lz/JTN1QnarCQ55LE5FX9oDmODhB3tdV5/+np8/uUYICs4eJ/5L5Mr4/ukEKwIYCXPdPReos3A/fIY6IuSzODxctzNjYmxwbmPyT98jkhqvOMd78BsxKwlNcieffSjHxUXMV7P+2MLV+HDB1+FU/SRYBBbg4WWHOzo6VPk0gAvdnegyenAfMvEpWq6PW588OArQtqKyNTq8P01W1FtmabJcLgOGFIEmz1dUuPu+JRrZu7zcxmcpK95EDi5GxjsBizZwLLLYwkLkiJ7/wq0n5Z+z/dtJ4Ar3waMW2GSKJtdeZUTwx10IhzwoiKzEt5QCHqNQchO5RlyU68YKbsN8NulQDkNsSQLHeU1VtvWwR10I4QgzJoMqCdY4TAZMNCtzLCWiY2QeTHU6gmOuWDP6Gsd7B4hNfyhAJ7u3AN30CuMU0fAjee79uGu0q3I1kf3pVGct9fhgqOnHxm52Tg+cBxXXrEVjqBHBKHZd0H5m2LjJLPd/J2A+6CoBRAtbE2rAN04zbZ5H22bgUEGLCVdTRjnA8YI4cVj63oFcHdEr69GDRh0ijHA5vHOFKtg+N1WIDQAqPSAdr4gVqxalp6q4Al2iOciQ1MJUwKyjgFBe+AI/CKjnpn1wHDgILK0G6BVT062iwE5jyB1JEh3zYvX+1+CUW3Gssy1MGuTB4/rHW14pfuIIHZJ6m7LX4V5GUU4MHAONB8SFW1qhiQJIdnAYLbRZKVdaIy44IshNIg+Hyun2Jsk+hnPJxl4bekEpNrg1qRTY11FIY629eCyyiLsbmjH6opCLK0sBVZJZI1cHSAbtWqVChatEZbCIjSebRBZUzRgdvScwuv99aKyy+t2w13XiS+vTnOT2Ip1gLMP6IpcJ1MWsPzWmK+oVRVCgip618LQqCpQbNXhZN1rsBWY0NHajWxzKWqWzhcGGZ0snmO2iVmQp0e2laUtg1kjER8s+WdwelxpsVAAGKgHAi7AlAdYy0fNtSQyeN3O7X8dnQM9qF67AarcxPKGPD65DJ9GpJxtI/dooGPBjCned36PBmWiLDH86yf5I2Dv62RugDvvBu66BxcLnmhvxt/bm8X4u6dsHm4sSt2Bp5McYI+YBAEDnVoLXcS08wbdOOs4Aru/XxR69jW5ceuytYCL402NyuJcnOryYdW6QiEzQIedTi6dETqLJLKYqSdLEtCwtXg8MP33f8eSTX/4A+AYlggNfi7/7Rc/B+6+F0jQFJLPpwamqFykMGfUmL+sGs2nmrB4eTVOuw+hDfU4dE6NleXrcGzoONo87eI3ufpcrMtec/7JjYBfWmN0kTmMz07RZiB3pSSrqMucll4aifCFL3wBL730kgjUP/zwwzOyzwsVn/jEJ8S4fuihh873ocxhEuAaOFM66LmGfFyRe7XIgDdoDKJHwRyk/glcH5h4yCCRCPraKsVrDucPydZELf2faQTlW5Uyo3IweaKg9OVYckSpgHMDg+4M3KfaOpY2Cccxm3jTfiUxwaAnpafuuuuuke/xHCkxxc+VYJCUpAiJkD6vC189+QocAZ+w8dr2H8Pnbn/zrMhw5nNKe5w2OG08+oFs5k4SiefNyg3aYiQ/eLyJrh+/x6oUXqt0gNddzvLncSQLMvNYeE/knhmUN5P9HJJJBP9GG1bu8xefSDWH9IPjic8bfT7Z55THutzjkUQlyQqOP96zI0eOiN8pJawJpf+ebnCc0dehz8kxwflJrgifw9jgfWGxASvCeb1mo3zbrCQ1uAg98MADeM973oObbrpp1N9rLFn47cbrsKu3U7zfkleER1uapIcgbvKViY+J4of1x9DriwRcqT0dCOCbpw7hZ+uvmvC2fCEPPEEnDGoTDJokAT5XW9wHPA8PYMwCPIPR4G71NkAdd9tITCzfmvwABtolQkNsNhJsGOgAOuqAsrGztPXqYnhDkpwFwaqVDk8Yw8MviYzyyowFo3Q6nQE7aodeR4+3DcXIglodRlXGauSmSmjEyFClRiLx3ptZYTKD0Klz4Q7FZvqrwYZsk8mESRSKjhqmfb5huILR8UiQ3Gh198SQGhtyKvHqycPiOdCZjeioPYMtS1Zh211SI8VMtRm3FK3Dc12H4Q35YVDrcGPhamTqJnHtQi7AvV9BxoQA92FAnQVoxiF12Bcj9yaJmKAkmVKaw90eITSksxQIMlBGckOx0KWqT+4/CYiKjMhvA22AkTqtBmTqFojXmKcJ7wihoYQ31DFpUqPGshCHBvcLY5dSRFqFvUWy4/jwAazPTtz8khU727sPK656GNt7DuMO3eWRwKAK2pAkiiaj3GRDfm8YtmXl+Ef7cfHZOlsZ/P2jzysV0BAZ8EaqqOLgCVKaQTruPH0psnSJy3yZUUPHgMZrIs3NhMgvQ2Z2Dnxd/eJczXotghwXZQtHORc0XJjxodRJp7EsSq2X1AhCQ36O9CYjhrP1eGLfdty5IVr1MGXQkF52E1CzVep9ZGSAM9a4VqlM0GIjQuFmIWWnVuUg02qDx92NGzY+gI6uVni9Plh0mTh9uk4Y8nRCaMBnBLJRnrUaKpjhDtJ5A3IMPuhZDcWM0upq4ViS6ElYkUNCo+EpKbNcXmPyVgBFEkEUgxf/isr9L8PjD+D4U3/EvHveCssVo9dnZt4wQ09u0hd/b2Xjl6Xm/C7fkwgZVQ6eYQG++J80CICmRkl6ik7KbGsePQn8uaURXzpxaOT9zr4ufDN0GW4rGVt7mo3C2VPsb+1NYvxvyM7HV5dvQKYcUI9z/urs++EJuXBw5zFk5WQis8ACR8WVyGTFJiuUcgyoe+UkvPv2C8dCzu6nsc/7xsw3Or0yqcG/h9rbscdux1qzGTp5fuKYbmkdnXggpAOHEpIaShQYc9HtlSoVMzIz0OsPwKZXwREcQmF1LvYd2g2vJSDWQRn9vn4cHTqGddlxEltK+HzAX38H1B4ETGbg1nuB1VOXZBOg3u/zjwJH6NSHgaplwB1vBwyR+UbHcTqzY5XPE2Wo6KjRdr7zzjtndP8XCigT8sc//lE41ZPJJJ7D+QflRKYa+JwItGotrJO09y4mcF1hdjaDGkxIYD+fa6+9Nu1SIXOYPNijz6bLwaC/P+Jh0CbPnvbqIgYrlRWCzIpmIDPV6l4ZshzRZKRxldug7Ut7f6JNyklWMHDORB7224sHg7YMznL9UGaU8xlgLzAm6/yz47RQ+6CdxutfsGoRvvvPP+GP7/30rAiwy6QESQT6E/STmHREQoC+AoklPuu8D2Mlv9Fup6/F68DfTgUcO9w3q2C430RVGyQoOM54vHJvkHjfgT38uK6T2OLveV6JqgJGQBu1oQGwZQNMsJoFxNOFBN4vPmf06ZQyxbz2jOfK/U2UsqiUoKZcNJUTlKDPSqKMlUN8TQf4nA4PD4tj4LPIZ2EmevBeDLj55pvxrne9S/Tuk+X5ZhNmpTX/xS9+UbCvX//615N+h3JTd5ZGtTq35BbgR/WUbpLA7OUcvR7Vk6jSIJpcwzF9Oxg4bHEnDuKNhV5vG8655KbIQLGxGiWmBOyWyKyIzfoXFQpr7gF66oCAV5IyUTYJTxXeJDrkXue4PzVpeKxh+EJS1ny7W4WhgBRY8Id9OGU/DK1qPbL10UWl1VUvKiYExxQ5n0bnSeTOQJOymQTlhzI0S+EMMgubzdjMsOpWicbIE9/YQsAvj5PIxKqrTliBISMc97kn6EeXxw7NyXaUXr4aGVmZmL9CjQ/f9qaYyZrl4O+dfz08IT+M6tSkjhIiGNfzRD6qYP/4pAbBY0/U8DsQrWCI3bRiXxkrpb4vdikwDct8IHNZgqCaN0JoRI5NwAcEmmOkvWYaOfo8XJa9GZ2edgz7++AOOWOkuEhsMCswGGaQWw2zxjJyn9jDIsFVx5DfgQpzDlpcA4IkUbMRnZgbq3FtwWL8o+0l/PHEdtEgnni54wxuURdDFqprdA6g0+1Ao2sQZx0DMKg1uKmoBittsRn5coM7Yyjx8rHAsgi5er0gcDO1OUnHF40fBiViDE3e447jQPcZKbu5dBWQWxnbK+j29yJw7muo7x5Afn4esu9+D5AbO7dwn3KTu23bto0cA2WQqCdrGhotHWbKMMObsMYlDmwK3LFH9CeAzgIUXwYYx5EiM4wd3CSxoVFFAzTkYbgGMpuExjuzv5jZQqOQ70kK0FlsaW2BfokW51yS9J4KQ0KGb1PO5aLpPJ0Eyk/FG44jGKyPEBpEZFT1HgNy2L9I4Yg2nQL2vyz+a9RpsawwG61P/gmtukyUL18lCCoZrBDhGGHmV7zeLo+ZVTk0JmXw/vP+0Imi4Rujkc5KjS98Htj5mvSeTu63vwssSVEyjeNp57PAruekjPrFq4HbHph8z6k04RdNdaM++1VT3bikxm/P1Y0QGsT+gV587eRBfHPlplHf9Ybc8ISc0vMaDKF6CTOTNBj0dyIzh8GCDOjDebj3jSuSGqa8V3QulVh39dXwfO972GG3o9pgQCUdXlZmLF4C1EVtMEF0ZNmAgsQVPUostFTB6Xeh0dUi3mdopUwvvRrIcDtwWbkNAxG7QwZti15vtC9RQvzmYeDg7uja8dPvAB/7D2BxGnpO7H4WOLIr+r7xBPDsH4E7RsttTRW93h4M+4eFtGahsWjMSlQGAlil8e53v1sELWRycQ4SmDHIhKmf/OQniSvE5nBBgHMTyfO55u4zC+qdy2sCA4q0L2aqYmYOqYE+6Iqs9Wh3N8MVdMCkyUCpif0dpi+YTp8gvrEvM+/jJaTa3Xb8pfUE+n1uVFtycG/ZEpg0sfYHSRBWinItmywYiKftz4DlRH1c2toM0FL6MllfOvoS/B6fB2XwlZJUtGX7tVSOiHpqGp0OPp161gTMefwkbEgiMEGKLxJBcn8BXjdKwzLpbKzrx+8zg5vXIV1Nl+lDcOyQuOb8zvfyMfC4WFHMY0vUh4PVGfHkCu8Jq8r4XbkHxwj27wM+82nAE4k73HIr8JnPjUo+m0NyyLJh8c3kCfrhY5FiXDvkHjXKnoz005n0yD4O6SYB6X/y2ZX9UG5/jtBIHd/4xjdEEtyXvvQlfO1rX8NswqwjNdhU5vvf/74IwkzEUFqTnYuvr1iPr5w4DFcwgHKzGd9fswmmSTbwnmfORLPLMUJskCQpN00s24DSD0pCg+jwnIVVmwNrXONcZC0BnC2xxEb2ckBnAEqm6IAzKMkAYSguYJdTlpJxZNYughmLhJb1UOCF+G+gx9sRQ2r4GEiOQC77C4jm0BcfjJpSGNQMGpDEmcLjpGN2gAGgFBLFiLQVgCYa3MvVZ6HAYEOPl1qpUjN0VlnMz5Ck2dgI+Uf1r+HVx5+E3+VBaKgMG+wq3LjhGkF89Pkc6PMOw6o1ochoExO4STPFUuRkpcxTLXE2JAoQq4CsdZKhobUB7k5g8Ej0z0OR5yxreezPko27CYxHVt9oVTYEwoPKDUCvnhpJl6nLEq8mZx1a3Mq+CxKODe2DLyxV52TpcoQklUalhTHJfTNqDLi7dC1+37wLwwGXGCMLrSW4sUgyNOtsIbTvPYnc6kqYsjMRDATw0kA9rsdGPNpyHE93RggiBU7ae/HJhZuxjH165OM6dkySpMnMxabgYrzeH5WgWmwtw2Jr6s39RmXONB8AGnaK/7q9PpzdvwuWNbdBX1glDCdm4Ai5quvfAnsoiJUJGhErt82MZR6v3OCOawo1bbe/tgM+iwt6azSrrKe5HcuuH6PiTa50a3oW8AxI87TPLlU6VN8BkPQO+ICsoliJwEQI2QFvnUS6US5Kv2BUPxpZk5RGfTw5QKePxvnpplPwdriQnZ89EuilDF+3txslprHljBjsbmk8C7PLgVybpEMbQywqSY0ehRRc5NjKbRaEc61oGxgQgUJebzofMklBckJpOPJc6KRwvySzlOu73DScjhG/J1cG4NE/Aruk8SDgcACf/yzw2N9Scw4P7QRefDz6vnafFIC/9704n/Cy8iwOnvj1OQF293XFEJp0mvf2j84klGUZCd6L5esX49CuWixaWY2i3DYgJEk4qaGBWsvSbx3gGAQOvQAM9wO5JcDa6wT5Ey8N16nXw/GGN6D0T39CbzCIkNeL+dddB/zrvwJGvXTPCGsm8I1vMX1z3PPiMa7NWYGVtiVCGvEvJzqhVQErM9WoaxtEdUUBTgTY4yv2d9qx1lxWaRzYFbcjNfD6K+khNepjbTtBnDSM7lc1VdTZT6PR2TDyPtfdirXZ68ckNu6//348/fTTePvb3y60wGdDZuhsAOcWXpNbb70Vb37zm8/34cxhCmCgitr38RIwc5g+8Hozi5aZ0bTFZlICbFbD0wE4TgGUJDaWApbF0lpzHsH1ocw8b+b2p1aLYCQ18WU5Ga7rTGSRicc+rxv/efJVeGk3MFHUNSRen1l8hZCJledoVliQkJgqxqvCY7IQiVHa0/x3YGBAZJYz65wBevoNyoSdeNCOZf8+ZdCdkk20h/0Hj0FVbIraa4EQcjSGkfOcVtAW8Z0F/F2SEoKhRurxqQDtctrjJDdISsTLvFNahteB/Q/GA5On6JfFg3MEfQCOi4nOE9wmry/JDVZaEJx3ZBKDx83EKSWpwftJYo0+hlIahyQGqwJ4b1mZQv9VwOuR/An+K+OpJ4FVq4Fbb5vQ8V7K4PXkM8v7TZJL+dyNJ3HN+SG+IovrDP0OkoV8vtizJZ0VtTwm5bjmnDOH1MFn+fe//72Y52+44Ya0zNUXJanB7FM2HyELNBnG987SCtxRUg53MJhQa3oi+FDNchwf7kd3pKdGhlaLf18c1YpMBZ5Q4soOd9A+mtQw5gElN0i9NWgYmcuk7PN0wJQJbLwH2Pu4JIFCY2v1TUB27CI2HpKvxbF/yNRmwxEYREV1GRpPnUP10ipYGYie5QiEgni9/wza3P3I0BixOW8hcpWBvSSQjJk0PEraIuklsmyd6HXXIhD2IUOTjVx9JW4s2oADA3Xo9Q7CojVhXfZimNiEFMBrvQ3o97mw4OqNaD9Wh/pX9kG1dR3uz8/D4cEmvNgdDcCszKzAdYUrxg08iwxfDCMc9kGtskKtijNKNLmAJj/SD0TufWEDtONn5o4JypTlrAX62auDUAF5m4AMxfPQExesIhyNo0kNaraHeWyh2H4cE5AI43WyalfDGTglZKjU0MOsXQAdZbbSgGJjOTo8LQiIHi3S8elU+hhycMjfj3OuelRlLEaJKQ9lpjy0unsF2UrnoNSUJ167+k7DEXRBHTnVM452HB/Kx7Kscng1QP6iKjh7BwSpodJq4PB7cMbel5DQEOcO4KXuxhFSg4s/52k56Lwmuxpl5jz0++yCMCs2Jq/MSAkkNSJoaO9Dgc0CDJyBN69SZHwxA4dOkySPEy1zTQYa7jR8lXjmmWeEo7JeXYLjKlbkSQbN3dtuQk9tIwZXZ8VUEsTA3Qd4lJnrYYnI2PcXwB4xkrQG4LL7AFtxctk2FysPuN8wEOoHQsOA8bKYSZaGA43EZNqydBS3XLUFv33217DlSUSlDFb4xH+X2+L1Y3ZTZ2enMBLLSmvgOduA042d0Os0mF+WDxWrBg1xZf82ktajdXVVtjyU5RWLuULWw5VLjPmZbIjS0WD5N51HZo7TmZKJJrGdyLHTiOXfRkiNM3XSNZEz7Wl89vbQW5KqNsbDkddj33M7pw5J/57HjLmbikrxm3P1I1eUR3JT0fhJBhatLr6eE+YkiRs6tQE5+iL0+zphtpiwcsMSdDR1ovb0GWRdthCZmRmiL9S+3Y9BgwVYVv8SjOwBweehpxnobgZue78gz15++WVxf1nRQed3BfsQ3HorNWBwyuvFqcsuw2Le6w9/FLj/Aamcv6SU3sOEJV6629tRVVSDXL1bVGrUVJegtrYJC1YtRH+/TPxIkhqLrCnK1k0HjKbYsUnIFUCsCuo5JyWS5FVEJakmCGfAEUNoEH2+PnR5OlFsGrsC44c//KF4xn7605/iAx/4wKT2f7GB1RnMAKZkzhwubMjr44WEDnc/Dg42wBcKoNJcgDW2+RdUhiiTK5h4wHWcCYi33HLL+T6k8w9vF9C/I/rePwCEqKwwsXjBxQAGkpWVnbT/lP0Y9va3whMMKAWLccbRjzb3MMrNWSNSNkxymYnnguQDY030aRhEp21Kok4O0I/YoWOAhAarBii1RdB+5TksCpugteSiziH5BTaTGXdvuHakR8e0wlML+BSJcoFuIOMKQBvbO5NB/rFA24+2O5uFj9W0XSSusqo6LijNOZqEBq+P3Adwog2ZeV8ohUrSiWOL90yuPlVWXPBc+B1+t7W1NeZ7BMcTj4m+H6vgRbC9q4sNH2J3SDuWfsccJgS5eoaSbHzm16+XJIxZhUG/kJUcSvJPBmXLmHjI+yX73byH8vPE31BxIV0VgTw2HqOyhwbHEcmVOYI+dVBpg2pK73znOwWhNVGJwUuC1Pjc5z4nghof/ODkm7VKvRWmflqFRjN+s+Fa7OvvFtUaa7Pzka2fmIOuiw8Cy58na25MYoOv6UDxAuC2jwMuBs8sUgXIBMEs8Tx9EXp9Ui8TCWEUMjNFgXJzDdxBB7pcPcjMzhSlrzWWaABrJnByuAPHhlpF0Hd1dgVqLAXjTnT/7DiIRqeUCctpt9HZjbfNuxJZCfpNMGgYCPlFw8B0l/T6Qm40OF4XEl68vo5ADzxBO8rMK7E5N3Egd8jvEccc8PjQebwe8zavhqUoH93eYbykIDSIo8PNqLIUotpSOOb18IRIqsj3WgWjeiV0asV15OJk3gD4mqTMc3UGoGcT7jRcj6zFQEYlEHBKEj+auGcmVYOXsk5eH6DXRngXBqFpSnuVLUvGhVqlg1WXhuzeBDBoTFhj24I2d5OoaKL27RmH1PdCiWH/oAhwNTjOQqMKYqElEzqVDTa9FYszK0QG0JHBaP8bGUeHmgWpUW3JQ0dXFzQR+amhlk5cu3I92j3JZfX4LAQiTgm19Wn8MxNJiXxDlnhNBnL2Pg0KZl5mhqPZ6qX5WTjR1AWTR49lVxSOZIzzNzRwJmIUUz+T2SAssZZ/T0NmqzqMfp8DGVojsvUZCBeHRWYIqzkSO1UJGg7a3YBdIfFHkuPQ34Gr/yXxAflbooSGjGA3/L5htLT1CSeC+6YzQOOcRESyTOssvQ1Vi6pw6tBphENhlFWVIisna5R2Mu8ZMyxlI1+ZNGCGAzlZR+H1+XGmuReqks0wtHXF6pnWLAMWrwVOHVTUFd8IRAgNZnTR4WDgm0FDOrO8pzRMWUZMJ5FGD7fJv5FkkTVMGZiiEyjf397eXuFACSMpn/NN3H2gM5JKXw2vF2hpGE1gzFDT5rHwrwuXwxsK4m9tzeLQ7iubj3+pin2uEuFt8xZi70B3lONBGO+piq3iUWJ+xkoY1RZ0eZoQ1gPXry5HhqoUO16rRVFhDhwON+bPr4S1N4wTDU0IBEPQatRYW1kE9LUBPS2oqJgnxg3vFZ1BkorCAaBjvnatkK/jPabjIiqKGAhIIRigBB0aOrx0TA+cPIDMFdnwhzjmwzCZDKJPkCngxZYcA1rcuQhBh2JjMQqMY+i4czyt3QQc2hMlHkjYbExe3TUhbL4BaD4TWe/C0j4uvwXwOIEXfwUMSz1CYDAD17wdyMqfMJHmieujNd7nSnC++/nPf467775bBB8vdaklZnF++tOfFv00pqLVPofZgwspCNHpGcBjbSTZJVHeVncfnAEPtuanR7JlJsA1gMQ2G0J/9KMfnasAI5xnE3+WuXrWSA3NJGQSg0FuBpmVPTYC4dBIjw8lGGOhTUHbgtn46ZIxIljhTR+DGf30MZR2PdcBBuDZPFjuu8GqAJIOqQbfadPyeaCty3NnZQK3S9kdo8kkCBtfKIgyUyYMGq2wlbkWTUVaa0zQxqFPHv+xtxH9QyGRoS4H+HnM9DESyQbJ4DVj0JI2OudbBqET+Ua063le9BF5PZUVLrLtQTuPklf0cWRp3VRAu/Pw4cPintDPoD8hyxWxuob3mNddPg/+K1d3EPy/fMz0/xhEF0H37BxJ/UGZqc//C79jDhMFq2f44vPGZ5n3gf0S6U9znPEz2vhMfJMJDJJLvBecKzgm+TzyfsqkBsccKzVIbPDf+MrxVEAfk88mn8va2loxvygJMSo6UJ4svmH5HMbGhz70Ifz5z3/G5z//eXzve9/DbMCsITUYSPrFL34hJq7ZUiqfodXhqoKxJTzGgllrRb6+Aj2+5pFM9kxtHmy68zRhUrfSOrVGYYusK6B3GjDg6xVZlZXmGhGEVUKt0mCRdS1MuTnw+rxYkLUo7VkXZx3ncGq4HsFwEMUmZjwtF8dDHBlswT/aJWki7vWkvRP3la3H4szkckFDfhcanF0j72ly+cNBHB9uxZbc2GzQTk8D2tzSgqlV6VFjWYsMNr5OEwZ9rSOExsjxBTpQGFoksm8TocSYhQNogWtwGNVb16NgQSXMGj18IalRmRI0Kvt8dlQjOakRCHcoCA2CJMcxaFXsT6CYNhjQMVRhWqA1Sa9EsFQDA4oAK2FN0KuGwWMa2V5lRooK0M2uckOjxoRqSzQ4yaoMH7O9FAiJxr+nR96zmseiN2Fp1thki+xA3FO6AseOHkVDVw+8dicyXSFUdPrQ394AjMFxbsgpEQYJjYDxFn0aJnK/BxqiNCRonJAQoSHLuZ1GDQ1gBsBp0LDEWM7807baUWN2o6IwGzaLGVuWz4erdL0wqvk7GsITXR+YUcCAPg0iZgtRZ142pmkemVlZEQHnKQZmE/WDEDDmAvpMSXZKpj/9USImQEICKmg8dkFuhCJzkmzQ0yi36kPiurS29cDt9omALXG67xHMWzgflUWrkKEpFtlRNMCpzxt/LPw9965X63F19bU4mntY9GBpPtEM21Au7GEHdDl6EYCgE8Bt0cmkgzLq+hWtA3IWwRBwY+GqTEBjEEYljUuSIeL7fM7veAewfAMw1AfklwDl0vPGe0MHTemk8d6T3OCx857xuivLxOmQ0NiVz4VGJscMHZ789nbUvfQSihcvRvGb3wJsfxno6oxmxX/y35laM/6NP3cGcLoBky6W2KhYcN6DDXq1Gg8tXSNeE8FqWx5+uvZK0VfDHwrhqvziMW0USlCo1Vr4IWXRBcPkdNS4atsq9PfbYbNZkF9QAQy4sbayGF6/HzvqWtA+YEdJthUISr+TK24YoOBz9OSTT4pAuVxaznFFR5GkYbyMQSqgo0HHc9A9iNAiFYYDdkH7LbJqxGO2cmUVjhxpwPr1y2DTrwRUKTLSb/8gYMmMbRS+JE0JFpWLgLd8TOqrQUd40Wpg4Srg9b9Hq7YIVvu+8L+AzQxY8oEVtwIZsTZTMmRoLQmDQKZ4kj8CfyiIpzuP4tRwJzQqFTasqsIb3/hGvO997xMVahdSVng6wTmG14CyXGxqPIeLAxfSeD46JCWcKJ/kI0NNuDxv8ZhScrMNXMsZzJhMcOmiRKTSN+7DqH14iYF2LoP8tBuZTMSgMys4SFasthXj7+2noWIOQETWO89gRonRgt2v7RxXZobzOAPa9B1oY9JmZDUFfQ5WEMljkn9jMFQOjq5Zs0Y08JaP55prrhmRzeE2GYznMXPftF8Y+E+V1OB50p/gvvgb+ik8DxlyBYoM2sMM0DIDPaa3Q4oYqTKIQG6AzevC4y4vKxEWg8vlRUtbz0i/gLDGjx6HB1u2bBnZztmzZ8U1oT0+lkyQXFVN4oB+Gs+B9jyDxPT5eM95/gxoj0g7JYCcVMVrTv+AgW2ZnEgG2pSccxj4lq8Xf09fgeNA7qHBY1KuB7wurEjnMXO/9DF5jPwdrxf9G0ol6T78EeD735PIDfoJ8+cDb7g75fsxh9FgpQWvP58rkntCrri8XJAcvP6M995+++0xv5GfXfqC9CVIMvC5JejHctzyGU2ecJgc9H9ZMcLxw2c1fsxx38qKsjmkBs4t//d//yfu03333RdT/XJJkxqcmNjU8Ktf/WqMDt50g0GBV3s60e/zYpUtBwut6QtMyyg3L0aWPk9ITunVJmTrii4oQzweJCyUwdcxJXtMWfC5pKyAdKLF1Y7Dg8dj3gdDISy3LRUT046eKEMvT1M7e+vHJDVkCZqYc+DncTrnQ/6eEUKDoDxUveMAVmRtE9cmHZAIjQSfC0mZxIbHptx5OOfqx9EqCPkpg1qLt1ZehqwEFTkMkNgSVJ8oEQwzez9e6CSEENzQIIUm4NMNS6TJt+OsZIhYqgBrtNHyCDSZgNoiVWwoz0U38cDbTMEZ6IFVE0afYkiKKpxQeHSjXF8PQuHQiFO8PKsc+wdi5UqWZ0kZ9yatDv950wMYDvrg8XpQZLGJRZ4BxY0hLfZ4+xHw+tB36ix0Oq1oIn95Xjm0oQ70m0wxckEjPRlaWoTBIBubNBZpYNKhoBHJ7xw/JWmYKrNySHAwu1tuSkdUzS9Hq8aPwpwgwAAAn6fy1TDP34z1kTmEhvBEs2x5bFxXaEhlFxfixy8+hay1C5GlM+GOkmWYlxErBSiXS7OvE4+fCzczt7idvfsPQBXIQ7CrFVkGP5YsqQGCNsBTj2AgiB21jbCYDAiqtOgNPItNmzeL82R/CVYf0KHqcrgQdrejpDgHGWVGMSo7+4aRAQ8sORr0+Wqh0quEAcgXr7FcacLj2dN/GkeHGgXJNS+jENcUrMTW/KsEwasp0gjnhtUOJJLoYNH54HWOv38xoMyeQmqPzhkzrUju0ODjvkUmW/XoSjF+L94BpINEMklGfEYWnRkavXSG+DfeV1Gq/uijMP3xj8LB6H/sMZx95hlU/+8vgO0vSf001q6bWJNw9q5w+wF9ZG5mlVbN9FRczRSWZeWIV6pg1aQcGG/yAIvNDIoDOTmcxzMA9RKg1I59TVIfi3XzipBtMUsVBnmjM/hIPpDAig9qMfuKzvpkSA2OB5KNh7qPoD3YKY7VEQD2DoSwKksNo1aLUIhr2brUCQ2C1bX3vxsAX9OAsmrppcRgV6wkFZ9wf0SawdELHHoM2PwOIIV+b0aNEcuzVqB26NgIsUFJrmb3CVh1VmRoY9diEhq1Q22RpAzg1d46PPgfH8WDV9yCX/7yl3jXu96FSxFMmGIlEbPK5jCH8wH6GPHkJN9zHZflQi8E0A64kCpkph2mcsAr9aiSoJL6alxARFU6wWAlbWUGm5kEQZtV7sNSbs7E3ZpC/P7Y63CJhEQLri1ZjIP79gv7NJ7Q4DY4b9OOleMI9CtoM3IMktBgsJL/VwbUmAxEW5v/ygFM2sAMqMoVGTK4XVYW8MX9MZtcSUqkAh47ZaUYfOWxJmpgrQSrHbgfni9JAfonJF8YgGcwlzY1t8MMcmXFA0kIBup5fUlK8NwYP6PPxevC9/VnG6Fye2HQ2LGwpnSkN9/Jc5liv0obnQFk3p+kSVwJ7LQrr7xS+BckG5i0RDuQpAGJolQTznhMvB/cBvfN82FiTCKSh74ibUrl3/h7uSogGZiQxYQrjkH6czxm+jEMuMvXhMTGwrvuhn7hIuDYUcCWDVx/A8sDUjqPOSQHx7YyniuPbZJPN910U9LfcTyy0oN9z5TgeOX26E/LPRhTBbfJZ4nHlGzt4vb5zPEZnkPq4HP8la98RcTwWZSQavXVdEEVngX01Kc+9SnhDPM1GeZ6MmA54Lv37cT+AUkigFPxfy5fhzeUXdol+ukEDQROTsqFNB3Y3XsA7Z5oVYWMVrc0lJ0BqZxViTy9BR+ouSrpNhkY/lXTKxj0u2IcjzeXb0GpKWqctLpOo8vLjKvY7S+xrIIZrQAbO6uyAC0bxU1ucnQE+nDOtV/xiQp6lQk1lsvHlLoS2d/DfTh24jiu2bAFRlbmANjTX4/XeqN9BRZYinF78doxySZfqAXeUGwvAsKiYaXGBZahFXQBzkNAYABgpYt5JaCfYt+PadLk9wSH0ObeA28oDD9fkWFmUAN2vx72QGz1BoOV1xfeGC2tDYewq68OJ4fbhBzVWtt8rLbNG/NeM7jMgL+2rAAHao/ihm1XY2lO0bjlnAcOHBBGBoP1ibbf53Xgb+37hbyTTqXBNQXLsNIWlTRiIFsYPbyWoTqEQ43Yt68OGzasBtRrAJUlrdfYHwziyzv/imH4YbBZBVGkUanxiYXbkG9IrgdJkoCLNQ0imSCA6wza619BfWMHjBotVC0D8DqT78e/AAEAAElEQVQdWFtdCrPJAKy+Ay+daBWGOh0vOh0xCLQD3uNA2AcvVNh1ug1VS6Jrj0GdjSL22IiAxjedjqNDTdjVdyLm/ldlFOGGounV6GUgW0lAyfMNCS3+yyyciYIOBwPhI2Onvh6Ia957zutF3je/iYzJaHf39wL/7wuAxyWNMTpc7Hnwqf8CslLLlL8Y0OY+i1YFEc+rbdGosNjKZtPZIvjCtfrYS09jg+ss4BwGsguAK98IZCeeJ2mrkehTSksQJOA4Hkaky4JB4Omn2AEeqJwH0JlJYOORQNywYQNODp/GWUfjqODftQVX4si+o8IxkY1m7sfL/jQIw6COa3R/PrHrMaCZz7biHDRqIEcxx2x4C5CZ+hp0bPB1DPoHoVKFRQUGwT5ly7NiZfi+deppUWGqRIU5Bzm1A3jggQcE0TmWNvbFCM5RJFjZ2HCuB8DFBWZusnp01jz7Y6DO3oZnuw7HrN1lplzcVTq+5AXnOneoCb5QF1RQw6SZB71SCnYGIc/Vc1DAeQawnwQ49xpLgKy1AHuTzWHEfmQQnr4GE2BIIIwHZmvzN0yImgyJRgKDgUplws10j136RXyRwEm1konPNskBEiEMtHM+Y2CVwXxWl3Buk+c3EhxyshDJHvoV3N8oQoK90dz0eXtEclhbbxYsucuSVp+QGOJxxBM+MwUSOyQ44iUyef95bUi+TLRRNMkfmfwYT/44XlI5ZZw5BbScA/ILgeWrznsF+GwGiU02a0/luSABRV9CrtRQYudOqaJrIuCzwgTBUX64AiRBWVmi3Kc/5Ic35IZBbYROfYHFvWYQnKt4T0h4futb38IlXanBwMaPfvQj7N+/f8YIDeJPzY04ECE0CCZFf/H4QdxQVCJkp+YwRYR9MBnDcDrS38gvUam2kptTIzyq1mFRgioNNgbf1VePDs8grFojbihchVd7T6LTMwiTRoerC5bHEBqEVkxso3lAbfAooI6k1oedQMAOaDdNKlvHos1FsXEpOj2nxZlQE73cvHrc3h1cwDVOH1aWV48QGsTGnBpUmHLR4x2GVWfCPHP+uE6gTlUKPzoRwuDIZwY1ZcQuwIldYwYyJ7YIJkXIAwzvBQJ9AGW4zMsAU/rkt5wBknVSVrVWrYqZoC1aFeyx/Z8xLyO20SSfjSvyFotXqqCxyAyf2gPHcNXiJUkJDZZ4s6pD1lXmApYsM4dZiX9p3QN7QNJ+Z6Dt2a6jsOnNqDDH9Q0KtwPhJrFdt8cLt3sIJtMhQLMV6USn1w6vzQgDjHD1D0Gt1cCUaUHtUCeuLkheIUhHIsaI8g8CjmMoKcoRL4F1QYS95Wht74Nu8RrockpQbleJCouE2rnaEukFoMv1ElS6+KU4do6hE0jj/qi/kU0wFN8Ko8k1muBNN+hU0kmQdU6Z+cTMG2ZQTVafnmOITsXIut+plLuTUGEwoJ6N3hcsEOOUAdmUnEU2E//yp4HhASDTBPD6FpUCb3nfJUVoEEXGeRjwdcEZHB4ZWQXGFVCrJSlKOtMMdm++8Q6pSWIKYE8aOhfbtm1jqS1ZDsDjQfm6ddjZ1CSRGpRk+tSngNd2SNtlI8lXtsP9wX9BZ3MzetVaqIxSKTiNYqLUVCJIDRmc2bL12TBpzKKSg3biivUr4Ao60ec9C3dIOieTJhM1Gesi6/MEQLuBzSLZf4V6zOmwQVddB3SfA9wK28caF5RJoUpDCX/YC62YaqNzvYdkfRxI0saTGlqVRmS98fWJT3wCf/rTn3Ap4eMf/zhuu+22OULjIgTXIwbDlA1hZysWWkvhDHixd+CM8D0qzPm4vjC1hC9X8Aw8oWi/NHvgCKza1dCrx5ZumcMMIWOB9JpDQjBY/dRTTwk//brrrhszuE+JIPk3sr05GTCZiP0rlIF8BjZJljCZZjrAoKkcOGWwnIlA4/na/HuioDrt4vjegUw+kSU++Zsnnngi8bpGOygjSpYWW0JC7jUZqUEbjEFkVk1MOsA/BdCmZ1CZyVGyT0mblNeSpNZkJOlZucLg+Fjgdknk8F7JY4aJaymR5H/5PfDnR6Lvt10LfODjc8RGApCcS9l3YxJyXp4YDxyT8QlzHPtjydyGKMN27Ag67E54MjNhzsgQsYux+sYQVFPg/OP1e+GGC4O+Ppxz1guTmwkICyzLUGwaexuXKjQajaiGpkTcW9/61mmbX2c9qcGAxgc+8AF87GMfS2tjqFTQ7HKKjDe5CS7hD4fRxYa1lllKavi9wMuPAWcZQNcAq68ENtwwuyZR0Yj5JBAxwK3GDtiHqmHNSp/cz/yMcrS6O2J26VD48UbGJYIqcT+JTK0B67IrRhlQj7XtR6OzJ6J8qsIZRxfeM3+b6EWRbFHL05eix9sMH4PbEXmmfF0W9Opo8F9qGjoMhO1S1cYkkKMvR7auTAQtU9XbZRYMJ+VEunbFpmzxShUkUMyadQiEuxGGDxpVlniNQtAPtOwB7G1S/4uyDYB18oborAbH09BuICgZ3aAcmPMIoDYBhnSNb2nc8Y7HC6LZdDYR9Gt2tSIQDqLAUIAKcyXcQTf6ff0ioJWnzx/pLZMqGFDkov/mN79ZBK6ZhS1rTHKxYqYLHQ2WOTOwmAr5zOqM4YA75jPq5/J5k0mNESIy3D/yLBXkS7JEAH/rSyq3NlX0NbbAPTAsSI1TZT4Ys3pGHKlxAySBIcUzF0TtqWbYHR50O/tRVLEcZdnSWOA1Y2b5eLBo2IQ5tsF7RoTwkMHsdDobr+6sBcyx5Z2879MNZuQz843nJDeMnqrhQoePjtZIiTKz9+Ka9nFULNi6VZAaLA3m2GTAfNzy4z/8EhgakLY14JS2a3ICJbHrwIWGc04HmlwOlJnMqGavCMVzlGzN0qg0WJq5CQP+bgRDfli0NtHvS5lFyTVjItnOctWSq70d5g9/WKrEIAwG6N77XoAk4N69EqFBBALwhELYuXs78huOoNhsQmW2DeEP/ju69fqRzM1MnRWbc6WKDW/IC5uOa44Gr/a+DrPahCb3OQz0DiBTqxISWvIxU96zxX0C8zNWA5SM5OfjPRckMj73OeCVV6T3VVXA97/PSCmmhIws4JYPAgeeARoOA9kZgFaeM1VAbgVgTl0+jDBrrPAKm0O2V1Ux91DGxtwqvNIT7btEXJYjkarf+c53xBzy/PPP4/rrr8elgOeee068GKyZw8UHrtes1rgQSA1iTXaVeHHOTnW+5Xc9oZZRn3uCrTNOalD+Rm7wOmOwtwPdh4CgD7CWAYWsgpi5BMg5TB0MUN9www0iOYbVEnKQWm7eyxfHFqWB0hVU5z4YY1KC8wT7A85E5QHlpZgExn3yWGgz044XDapTBK8PKzG4Ldrg9O/f8IY3iL9x/iAZkYo/xv2PJ8rC4DErNhhMZoB3pkGfk2NAnhs5z0ylqpSkhlxJPtZcSzuWL6F00doqSC9Wy4wpo9PaHEtoEK+8CGzeCqyJVtjPgblOHpHANlK9PQH/kOM+ntTg2OQ4TURqHPnH3+H7yfeRG/BhgdEA09XXwfGuD6Cju3tUVXkisI/pvqFdwveQoREBwjDqHLXI1GWNknydgwTG8D/60Y+KmP6rr7563npjn1dS49e//rWYRP7jP/5jxvddbbHGSBSpIs07i4yp64HZ/W74QgHRn0AzE0bW9seAM4ciWuEBYN/zgDEDWDW9zVk42Q/6++AOOmHSZMCmy02+SND4VmQUZVrVcAzshjXzDWkjXwqMebg87zKcHj4LV9CHbo8L7mAQGpXUCJVmDGUaZGrKG/Lh8db9ePu8aIOhXp8DDc6e6DkiDHfQh+PDbdiQkzzznpmgS6yb0e09B3/IhwytDbkaDxBSkhrRrU4Fouw0xUZzshwQmzKlSxOQxIZONUaAh+Ow/jlgqDVyroPAqX8Ay+6dcNDmgkDYBwTj77MK8HUA/mHAy2bGOsC6DBDBuInDqivGkP8cdNxspJkeYVDr4A/3YNDfgzyDBUXGNdCpTYLMONC/T3Q7IcwaMzbmboI+SUP5ZNVyckkzM6OU2VEkGOSGbBMp29YnIFb4jCX6XGrXLWF42AWz2TAty1OJKRMlxkx0euwoW7MUniEHPD39KIJRLMA05lN6djTRNeK57YeRn5sFW6YZZ7t9uHnNmpE5htkmbNw3Vvkzka1fCIumGRpeB5UamdpKWDSlCY3DBaYSnMJAzOdrbdPfh4r7ZsVJOvUyOaZirjcdmM9+FuH/+i+pgoPX8Z3vBC6TnAReS2Zt8Tcj0mXJ0N4aQ46I/3dFifB0IRT2we4/AV+4T6yT/rABJk0psnWxFVTpwG+a6vGfJ4+MzAkfqF6ELo8Dr/Z0QK/W4G2VC/Gu+YuEtGanxwmrTo8cvXS/SIzn6ouS3ge+lNrNqYDORuNXvoJlbW3ifZPXi06nE7b/+z/gHe9gDXnM94/rVLh8YQWMcoDf6wF+9l0Uf/OnMRUiuYYcXJG/WfSIeal7J5wBF3rae9Dd0ol5S+aLZYd9JWKvL3twDACnngYGWOmhAoqWA/MuT05u/PznwKuvRt+fOwfQFuXnU4VzCKg7IP2/3wFYTZIE1fzVwOJrJmwLzc9YAtewXZTCE3q1HlUZo/vbXJ5bI3ohnbR3QKtSY332PNRYpPmH8zr71rHJLx33sRqDXgzgnMVz/c///M8pZfyeb0wkAH6pgddlOp1n9rs4MdyEblG9bcBKWxUytFPXW5/4/UzkS8yccjTXYwZUORZnNAHR2Q00Phs9V88A4HcBFdtm7hjmMCUwGYVzsRxUjK8+YKIM40CUak3nPM1K4ngCjglcM6G4zgD9vffeKyqa5T6AvA6pZqrLYBXt9u3bhd3LqhNmmysJh1TnEZIsqfg2lNpNtUF6usFrk+6eurzfMdXgis8TkUGsrGf1MZMgGIRPahMn8iV4LzrT72Nc6KBfLT93E133eO/k33EMsycHP0vUH9Lv9cL70x9gg1EHhCP+xKsvw7JwCRbceueY++G2jxw5gtZQMwpDsRJs9CLlkWIPDM2RGmPgoYceElJ4v/nNb/AO+oCXEqnBAAV7afz85z+fsDOdDtxbNg+v9HRie48kecEAyjdXXgZzCvILlFZ5sv0QTjukCYyPaY0lH7cUr0sStEsTzh6La0DJz45OK6nBCaXecRyd3mh2Q7GxAjWWJIZtaCCmwbTX50dPbwv8qnqUlVenzQEpMuZDrzbib22viT4CJDTY5NQfkl5i6lRFg6ld3mE4Al5YdZJD4g/F6fgIqOCPawyejNgoMSlKjdlUO1Qfk0UJmABVZgpVLS5Je1UzOVknlk3S8GGZ5pYtW2KMJpI05yLETWVGPkyT3EdS+J3AkDKDLBKF7z0NVGzGRQVvL+CIZCPH30NPh3QfR77bBuRcDRjiZJZSgF5tQYlpPfp99dAFPdCqMwR5YQ9E9+0LOdDm3osy0yYcGzwyQmgQrNqot9djadbogFcisJyZxnYyrUlmZDPTiBlWzI6Qm+6NhyydGYutJTjFLLtIJZRRrcWKrPIRh2MkqKauBIJtaGxshclkEMZmWMWsjnCKlF4ShPwApWyYdWHIg8ZUjPdWbcLf2mrR4h6EzZqP2y+7FWXmCWYe6vIBQxngbcXWTctw/FQzPOEc3P/gm2BQaP/y/JgFTyNs7ObJKphV5Sgzj92or7m5GQXVlRh2mDDsd8CmN4j+OIuYvTgOOEe6gh5BjukmofXMLLN0B0E5nuI1dPGGN6A+Px/q9naE8vKgqqhA+MwZYdRybpOzffg+UZ+PEZRXAm0k2EPR/jd5edL7NK1BXBuH/IcRCMuVO2HoVG4M+esRDPuQb0hfGX+9YziG0CAePnsaVo1KxMoDwQB+0nAC/nAABwaa4Qj4xXduLa7GAxXLxnQmWKXBIDcNezoLMRlNp48Ah3dJ13DVZmBJVG+WAYPapibUOhwYDAZRpNViE205t1uSmlrCvlIq8duTbBpaaIMxcu2d/gAahh3oa+uC6cXnkVFaLhx3+bpS2soeduDYkJRhn5mbhaUbpb+HIokL8U6SzucDBpoi78JA5zFAx+rBJJmRhw/H2lOUwKqtRVrQr2gcy2b1g07p/0sLJyw9RRg0JqyyXQ67f0CMgUxdNjSUP4wDr8f6nHnilQjMomLD8G9/+9vnJZloJsFz5LrGc74Q0e+z4/nOg+j3O2DWGLAtfwXmZUyxF9hFCCb00A5m8kC6A3Kv9hzFGUdrxA5R4ayzDXeXXgmzduYIQbH2qYrgC8cGzAzq6SfqaB8ykEQ7kGv1eAkaaceAJAsTg8GzQNmWuZ4VFwjYj26simU+s3yRNJuOXkrKBC1W+rLafCbABCAmAiWUn00RlEPiOTBexgqW+Gx3BuFZBaM8z0SgDFMqFVa0Ac9HlcZ0gfNWPHlBX4akhRx3lO1I+mm8V/w/A7P8DkkkVnyMQnGczyX6QoYB20WYzDlFyL4vbXqSlhMhrhYtWiT6ffJekXBjBU0iP5Tr1Ovf/Q428VaHw+Ketrg9aHJ7oX/hebjNmSI+Jv+WslZ8yX1aGJek/2G394/q56d8p1Nd3IlAUwXv0/e+9z28733vwx133CEqoC4ZUuPzn/+8YOzvvHNsBm26oFWr8aO1m0Wj8AGfD8sybSgzp0au7O0/O0JoyIO+3tGDl7qP4qZUG7b67ECQwbas1I0zOsORYEX0RKZXKmsoMBBDaBAdnmYUGEqQqUuwSMo9Fxg8cniwMD8bnqJc9Lr1ePnll8WiMa50SIo4NtggMqlEdVgktqFTAwuZzWzn/QmPuucy8g2Zoo8GiY7oJBbG/IxJlHOzobF2vSS7BTYKzwS0K8aWv/AOAY3PAz5JExx5y4Hiy1LO4JSZZepBxme+EAM+B/7culsQGwQJjXvLNiNHn7whcjgcRAgO4bypYR2fVU+W8TIDmTAzCmcr0M2MXhVg0AK6uAwPJaEhY/B1oPC20Z+zgZvrSKSBmx4wLQX0sdIJRo1NEBsyujxHR20mGPaixbULATamV4Bj2RmMBNDGAckMNu9KJFcWDxq6LA1OldQgbi1ejQJDpuhZw6DMxtxqWLTGkTJfOYgJlRHQbEFn1z+wafMyBDCAsKpN9NrQYAnUqpLJERodzwGB4SjJalsFS9ZSPFi5DlMCn4vMywBvKawZDmwqvQrQF496dhsaGkSpOSWWkpEadFaYzZWK4/Ni10n0BDQj0mRZPhOODA4ijJNYl12GG4sWJ5Si6vcNYkfPAXgiJbUrshZiaebEMqJo8KeLkCahRSKWGXqJsuYCublYMs6YpBPMDKCkZMv97wTqTwNdnYz4S581NgJf/BTwmS8DGcnnwVRBWb4ooSEFn3g+rLIa9LcgT78obRnWZ+zDCfNyAwhDo6D+/txSj0Jj9P2THWdRlWHDlrzkxBcz+Nj0W3b6R3rlHN8P/OV/o88PCY673gmsiq43mzZvhu/cOVL40rnyd8y2pLNQXSMqb1q//BUhPVdp0MMVDOB0vwN1Q3bY9HrkmAxwa7RwdneP3Es6kySrAplhePoTryV2fxg5eul6yxWNpaInS9z3SXIkIzVyc0fJnSFdQVGTdWKfpwCSGDb91ORm6MQ9/PDDuOaaa0Tj8FQatl6I4Jz7jW98Ay+99NKM9upLF5h088/2PSP2myvoxTOdB3Bf2RXINUyuh9HFCo5lBvdOnDgh7jWJ2ck0Fo6HI+AWhAZGZpUwPEEf6uwtWJ09/dWRSli0S+AMquELdQvr3CgahadP0jcenFdZoctAM4NBE23SmwyhsBsheKCGGepUAkSXio9xkYKBQyajpFLhm855mvO/HKCWwaAo/ZfzkUQ7FSST46LvwKC7LAebyA6m3c5KEc6H4xG+iSoaLlTw/sv2eDw4DkjOjlcVxOtOspySaKNQUgo8+G7gd/8nJa2Q0CC++w3gw58Atl2TtnO5GED/ly/GEOgXsyorFTAozubiHNtjrUH7Xn8dm55/jlIX6PJ4sW9gCEaNRqjxGDKzRKIWE7foj3OcsyH4Vsoax0HvNERkXqOQZ5BsXR5y9BNPVL3UcNddd4lihc997nP4yU9+MuP7Py+iV5Q7ofQUGZ3zWVatVqmwIScfNxaVpkxoEG0uasDHglPaWcfoRqejv8gMwt1A4+NA81PA2b8AqTZ6XXPV6M+mWXoqUTNK8Xko8edQlTLSApztAlr6gLZ+GFsHUJZtwLXXXisMZRo66QClv+JZVWJjTrWovFGOrGWZZTGVCjq1Bm8u34jcSJCfjcHvLFmLEtMk9WLZdFV/BaC/DtBtAFSmscdA0wsSsSWjtxYYODPmLthccE/XSfzPP3+FP+96GlU11aMznSN4ubsWHva7iID/52djORvu4G54gnvhCe6BJ7gP4XAcgRYPXrsMlurFPcPMxh/uAfyxi8MFi/6IjAjHmtcP+AKSIcNgqdMD+IOjnayITEgM+B3nXsDfAfDahpyAcx/gH6eZmeCeR8+TIQSQbxhNalq0owO2NO6G/S6R/clxxCx3atvGV/ckQzIDcczjVqmxMbcGd5Wuxw1FK0T1hrytUUEHlRHl89Zhx+6daOuQr0cYwfAJhNmbZqKwnwEC8u8ixz14RNJlTge4bhlLgYxFgKFkFKFBwoiGGA24RE4J/37y5EnxL+UcxtP7PDrYgobwMOx9gwhGzmgowGfWK4jZV3rO4tnO0brxvNev9uyP0Qg9NlSHdvfEmovTECQRkQ7IjhN7cuj8fuCrXwauvwa49Ubgd79BWWlpSmtE/gsvoO/KK4FNG4EbbwAOHYr+MTsH+K/vAxXzY+9NfR3w2/9Ly3kkeiaj4B1KX+Cl1GQe14BTRapIlXvlOnjGEStXlgx0GkhuUMZQYMfTkb8otvja07G/+cAHYF68OGrHMXjxta+N/D18512o/8pXoL12G/7Q1oW6/mEUGPVYnGXF+vxsrLr3AWy9VmocSv1cOuAkveh05umzoVNpY64y/69WAYGwCr2+MHRqG4oMVVhk3QyrN74LES/AGHMbe38wEMDxyBfP4ROfQFpQXAWUR557mQysWAIUTT5rM11gthv7J33605/GxYp///d/F+fIc70Q0eMdFkRGrH3L7MOoZOocJJCAZRYypVcZLOE8QrmLqcLHxIhRUAm/Y6ahUmlg0S5Fjv4qZOuvhElTMS2+c7dnAH8+8AwefuI3aOhpEYGfdBEa3lADnMHX4A7uhzO4A/5QClItNpKuymdABVhLJ13ZPoeZAeUsd+3aJYLtq1atSuk3HM/peG4Jbic+w55BffYOZA+eiwG0lVjJwYSzRIQGmykzKM/EhVT6GfD6M+N9JuS54sF9yjJF6QDPg6RFokpuBsq5r3HPc2AA6v/3/xilZYkrEF9JdPvdwEc+FSU0CMrC/+A7UjLVHEaBCc2s0OdzmCpIQo61BjE5zhQK4amOTrzS0gV7IIgcnRYbbZnYWlSINR/+mLAR+IzQNmDT8mRz0mLrshjJdw00KDGWYaFlGVZkrZ2TAU0BnEcY22eMn0TSRV+pwYnkE5/4hNC6TVfGfjpRO9SHv7adhTcUxJbcYtxSVDlqICcrPdaqUmC5h88CQ4rgNQ3ntpeBmvuA8X6/7hqAPT/qjwIaHbDycqAyfRIXiZChSZzRao7/XHTrPgR4zwH9dinwK4OZkE3bgeVvFo4Hs8MZtJiovmQ8Ss15aHJFFw9ORnmGLBSasvBg5eXY1XsGQ34XCo2ZuLpgtFxWnsGK91ZtE9IsqTbjTgtYocNKjRioAFb/5EhZATymFlc9+n3dIkOz1DQfL3XVi0za+asWY8CgxXbnKdxl25QwO3vQ74xxiPn/QV/yDH5vsJZ5aNHvhwYQcr0GTdgIsCLHtHD0+ORzsfBmoOkVYLgDoLSXuQzY8YdIs1Y1sOpmoHK0/mFawUA1x1hEWiz9248GhAU4toV0mBxECzJ6CJjYEyEyV7C3RjzCXiDQF/chNdPaAF3yDIAsfSWGA5QziTfCwrDprOj22hFk03JBaFhRY1kwKrD9dOchNDi74BpywN7Qibdvug2Xs5nveQI1duOz/0tKrSgoXoJXXzkKnU6L/HyJYAxhCBpMMMM5mIBUEhvzTMohpixds8sueheUmSzjGjeUimIGfCIdUZbFUx+XFVbJqh/4OxIWbPJMScOnO0/AkpeNvnPtMFjM0BmkRu7KLe/vb8EtxbHznCPgEj2FlOA82e3tR4kpdSkJOgckYdJRTsrrwiwzka3z9a8BL70gPb+ix8JPYLVmor2qGv7f/ha6Z5+VAs733Qe8//3RAPHTT8H8s5/iLJ0XkwnqwUHgQx8E/vIYu0FK3yFxRudC6bxwP2cm0DSYxKzPK2XYx90rtUoPvbpAZM8SspPkC3HdLBA9idKFlbYcPFhRhd81N4xU6iy2ZqLDLVX6qSIvG5tNKMBKxixd6iXT1H7ODtlx9tFvo3pIOq8Y8FoowcDBL38JHDwoyU5R65YVEJHABvWgN2zejDwNUHL6OA70DCHg9IgKjfCwF7vzSuF57jmRDUfnmxVcJFYIg8aAK/I2YP/AUTgCTmRozVhjWy76SlBmL1OXiQKDQv+WFRmNco+MSHVJyZq44/cBD/8YeGW7RMB84F+AYbvUNHzbNmAMmYwJgff+qvuBs4cBex+QmQdUrYpW2k7gnkwH2GeC15ySAOdzHZgO0EZ65plnREDnQgX7ocQjnKqPcQmDfaw4f7CKWZ5HJossXYaoMOU6HK3lDqPUdHFma7a4evDosWdh7xtC2bL5OI4ulLl7UG6O1RifDALhAfhCZxWfsPn5cWhU2VCzUjcZKK1Zvg3oOhhpFF4KlG7BjILrut8N0O+fa1CektwUg9OUeZpIf0dmcjMQz+qDqYKB0ERyuSS5mcVPSTW5P9uFCqo1sKqVlbXxnzN4y2SqiTRdp59CaSCSJUllXacJ3DfnbpJgqTR0Hg+UnWQFfLKkT9qarBpIKoVE+dQPfhDWU6fQQfng9nYpCeZPf+JAjX4vUXCesY+mBqDwwu3jNZ1gtQQbSaeDMGdiJhMk1155JS6rrsKZzk509wwioNXAaghi98qVCJ+pFxKVN954oxhfnBfo5yRCniEfG3K2oM/bI2KCBcZiGCbQn1Sg9hjwo+9TXxlYuhz4xL8BtotH0i0V8Ln64Ac/KGL9zz333IySQTNOajz55JNi0fvLX/6C2YZjQ7345JGdwuBi0GBvfxf6fR68NY442JRbgzp7B/zhKKvMfg7rc1IoSXZ3x/ScEGDQif0J9OOUlXNgLN8ivdKEcDiEoJDQCEOjyoQqTqfZqrOhwlyDZhd7RkiYZ14IizbuWN31EqFBMHM9di9SVUJE25zONIN68sLJYBAnGxoYEyl/XGKtgN3vwrGhBnE1c/WZuL5QkpbJ1BnhCjrhDLIhuB2dzT24u+zykWxxJcYjNDo9vdjfXyucm2x9FjblroRFO4WFV8iNxY0BQhOdPBscJ9ClkP3aUfc8Dp5oQ8niedAYtOKX7Z4BtLh6MU9US8QiR2+NkdZiIDPHEBsYZm+GPu8ZIWGkgQNWXVhULzHzwGh3QxWOVOP4OwH/AJC5ebQ8FjXLF9wk/d85CLz4UzIi0nv+e/gpILsIyJy6Y5TQeDjzMtB5QnqfVQIsu1U6JiUCXqn3B48nswzQT/DeGfMBd5y0iTI7g2CHelZu6CLPT1YiiaNkE/vYE75enYFy82a0ufYihNjsQYs2D1vz1mDAPyAC4Dn6HPGvEvsGzgpCw+/1oaOuCdXrl+MQOrEIEyOVmenADIuEGqMTBLUya2trhXYpDRsG3rXMylapsGnrCjz7wkFkmI3Qa9Uwqn3IMPeJcvKUNbP1DL5L92ho2IXGlm6EoEG+qh8FhYYJ9YfocDvxUO1OdHml52F1Vj4+v3QjjGNo4zM4S4eCGUEyKO/DQBsziJhhlWyhdwbceKFrP/r9UqXJPHMJPJGs0ZyKYvQ1tUFr0COrOF8MOxni2Y2g3tGLBkcfDAmmU84Jdr8dRwaPIVNnQaW5ctw5UNnoUdoI53QXwEbUEwze06mkYSu28crLsfI/xPaXseB0HU488giWGY3SdWLzae7/7W+XvvOnP4nPl5pMOOv1YgEJDGZ57d4N3HNPbMWGyxklNkhM5EhB9zHB7x95ATixM+Il24CrHgCyYp3kTO0KOINn4Q52iD4anhCTHgpQYEh/M9UvLF2NawqKccYxjEqzBVfnF+GxtkZs7+mASaPB/eU1aHT14bHW06JCg4RGgcGM6wsnIDE00Iaa/oOo7etEcziAmPw+3gdFT40RMDkhgQQix8tIUOHGW2A6dABX7H4NewYG0ekN4sy9d2DNqlUxgYf4gIZNn4XrCkeXiScEG4Mz8NR3VhqThcuArNLY7/z3t4En/hkdDz/4PvCNb0mERrrBsbYgElglmbH7MaAtQqgVLwA23Q3ozk/GMQNIrNT4+Mc/LjJXp7PZ8kyCRDnP6TOf+cw4fYxmN5icU2zMQaeHGs+S/cZK42rLJKQYLzEwoMXAnhIMsvKzVGRwZNCOurl4A57r3A97wC0k9DblLhXJVDJaXb3o9g4I8qPGUgrtBRr0ZsXoH158HE6ND6WL5o34Dfv6T6eF1AiFIzK7MQgjFHaMTWoQ2dXS63xgqAM4/oREarBf5oKrgaKxe59dymBmLhMWJzP30l6gLEw6wP0zeYgVv+XlUh8/ZUCbpAaJT457OeZAO4QVDROVYJLlbNi3gsFSnj8rx6YzkEe5PfoT8aRNZ2enyFzfsGGDOP+JyAUTDBAzcWnMfhLThHQRGvS56FOONQZ5nkyqTdRMXoBVGWfOoFCjwWmfT/SLU3k8wIsvAg8+GOtfJEKyz+cgni8mN8dI3U4hjsCeMmqSI1/7Lyz45L/BMDiIbp8fu3Ly4bvmWly1ZctIwhn9/vFIUyaG8jUpNJ8D/vUjEilGv7Z3O9DaAvzs/yRJ3kusaXhNTQ2eeuop3HrrrTO23xm9ylxAPvnJT+LLX/5ySk2LJgtvMIiDg30IhMJYk50DS4p9Jx5vY3A80qMhEhL7U8sZPFgRq43NvgTvnL8NO3tPo9MzIJyNlbYKLLXGLp6j4OkDnOekrG7uQW4uxP9rpinLfAyEwn44A/sjfRR4FAZkaNdBo4qV4qo01yBPXwh30AWzJgPmBPI28CvK4tlzgDI9I2AvAutIQJyLFxdf5TUlk0qHg4RHqpMcf78xdwnW5yxEIBSCgdUrEWzvqsWQ3zESLqY28VMd+3B/xcSCF0M+O7Z37xsx8nu9A3ipay9uLdk6KnicMuj4FK6Rso/kIyTRkb9shGiSCQ06YycP1sGWm4UtW1egyRUbTPfEZWHLuLpgOf7cshvOoFR9QafrmoLliux9TyRIHq2o8ftIDIWFJIwqLgsc/i4gaAcGOoDBVil4xCCSUq5riFnR8RIgYek300FqNO+PEhpi/x3AqReAFbdHP/PagRN/k0hDgln6i+8AMiaQbZe3Ceh8GfAPSu/12YAzXjKKz3AuQAfQVAok0j4n468rluSnlNCPXxpMYqPUvAHt7gMIRvpomDV5yNZXiYzwIk3yrJB2tyQb1HriLOatZvNeoNs7mLCKYCzQOaDG8kSyf5KBBCYJjX0nDqI7zwl3kDrLaqzIMiFH70FxWQGqF5QJh8Gqq4EmUCqC4TRguHbQmBnT+ciYB3j7cOrwdrg9fixZUAlN8Tb02FXCoSGxkGoDv/8+vR893mjlx5GhHvyx+TTeMT95cz7OYZT4YsYYrxnPl/OefNw0upmVxibs8Xi55xAG/NEMoEZnmwhSUx9UZDTNL4PP5UH32VZobZkw2SSCeUuudD47exvxj/ZaEYThvFWVoUOWPhQpq+VzrcKQvxf2gBqt7jB6vX1Yn71uzLHAuZkl3QLdjcDexwEfsxf1wPo7gJJFSBU8f7Evrn/MlKMElQx+btBD/fzzqDYYcNLjQbFOh2wahM89FyU1hJxFCAedLti0inEQbzi+81+A//pCdF5iEPmBd45/kE1Ho4QG4RoCXvk9cPtHYkgcPnsW7QLxmm7wmm3NLxIvGfeVV4uXjHU5+ZhnzkKdvR9WnR7XFMxDxkT6brWfEPdg+bwiHDrTCnXQizIyZzznlZuA6+6e0DGTvOLzRhm2Vf/2GRjONUG1eye23H4n7HqDcC7T6vznLZBeiUAn46knYyt3uO+nnoglNSizNjwsVfxMsZp0BCTI2hTyBZ31wOFngcsUa9UMg1lUP/3pT/GHP/xB9Ne4GPD73/9eZGjy3C5kkKC+tfgy7B84gx7vEKxaEy7LWRgjoTqH5KA/wYzjkeupVov1ltnZE2kmnqPPxJvKr4Y35BcVk0ry/+DAGewbqBPrKdfZ48PncGfJlguO2GACBoOyhYvK4dPG2u/sIZIO0LdM+PlsbrzKKs1jfwdkGV/Kjp1+HsjIAawz3DD9AnnmGGifCplM2zBd/dtKS0tFEhHt1ngyk8Fz2uIE90cbhOsGpWkIxiFSqUoWDYpff11IyJIsIdnA7TBx96qrrpo2YoMy3iRQWPFM/4L+BONrTJaSz4vvJwNug+fOeYHEiLy96QZ9svb29oQ+0UTATPyEfTDiQAKLFfX8PoOvMfcqYiMedbmE3K8nFBKJQ6NkptdvBFatBY4clCrKmVi17VpgwcxcswsVtPvpG7AJOHt6TraXC9dyJkez4pjxico/Pwbz668jRwXMv/4GDNqlxMAZqxTY/nKU0CD4b/0ZoKkRqJnZ6qfzDcZpvvSlL4mY/w033DBjVXEzSmr87Gc/E4OLndGnC71eDx7Y86rIzicKDUY8snEbKlNoDOoOBkTYR3AOEQTCwRGSQ4kMjQGlJjJ6ZPbMWGApHvvBEb00Xh0d9OVP8taeF41QT7BuhNAQhwgf3IHjsLAnRBwytFbxSgq1PkoFZWUAHn+0QSszXOZFGydxAuOCEl8ayEwq2RHhBJVqVhXJhfhJsZ0EkqKBOC//oN8x4UBum6isiZVxcgZdGPTZkWuYAjFXuFpqEu9okyo0cpdIPSoUe/N6fDi+/ySWrl0Eo9mIQV+sTjuDlkWGxGVtrEh5a+WVaHMz0y+MMlNuDOnjCHTFEBpEgMUGYUCfSGqS1TfHHpcylngx+Z3uk8DKewFT5BiSVUAYpp59kRADzXEf8CbHfda8C/Ar+r/QQaFc1jJFNvd40JqA0puAAJ8VGttqqSeOqOdS7DtrCWAZh9jMWAe4T0gkIJ8Z4xJAm9o4IrFRYb4c/pBTaCzrVOaUxnIGCShRJKWCJlJJYlDrU/otn0kGJGk089lJlQhICRoVGvSd6GvoRUFlMUII4WDfMDI7W7FmuUT00LlxBZtRYKoRjoOciUPng/JNSaFSod1TDFhXYs3KPMBcBGSUoMTKWGUJjtUew2+f/RtCWjWqi8qwYcHypHJ4Z52DUHYq4P9ODMfLiEXB68T+E8uWLRNZITTgPG43jJH5jH9nxQudBjat4zzHY+K5UnauxzsYdyoqVGVYUO8YHtm/2mhEqKQM9pYOlOUWYVPBPFyRVyVksp5oPy6+Jx9zgzOAy02FKDaa4At50e/rknqkRP7e7e3BoH8Q2STrxgCPsbPxDIqO/lXSjiUCPmDPX4Hr3gdYU6iAiD0x4I1vBn71i+h7HtLd9wJHj8OkVqNEp0N3ICCRGmfPAmwgTlmRm2+G4eRJ2DQaLJHXCd6/q+JI6+WrgG98D9i3WyKTt2wFClNwuruapED+SNUZ5RUHALcDMM/uRr3rc4rFa6pYs6AMxxo74DXoUP3AQ4Bx4ll7HGeUgaEU1clTp7Ca8k5Dw0Bu3kQF5WIQDAcx4JOqoGw6G7S0MSYNhZHw/e8Dv/2t9J5BDb5PA4mLTkqvhOPsQaUcSwKIddYLqChrmP5KCgZ2vv71r4uqhnvuuSctzZXPJxhg+uxnPysahE8kI3+2QqfWYjPtwjlMKtgRLy1CuUMmU/HFDGSuZ6nYQKIHWJx/5gp4BKFBjCQ8+YZxyt6C5Vnnv29OqmBGNoO6DCwFu4/ijKNlZJbilUmX1JZWVQANchBE/4ifqFOVQaOayiowzXD0JOjBpgIG2+ZIjQQgMZZKMDketINZCSxXWaWzapABzxMnToh/Zfue1Rvcjwx5fwyq88VEQkpk024Zr3Jg//79MTI6nHf4YrUGyQ4+WwzuFRQUTLhqYiywwoBzmDzPkcDgPCUfB+0tVmyQ8GBshETTRBqkc1uMwdA/YTIZCZvpBq8TCaGpQk7ASsUG4Dmyxy8TzmLsH47jykqUNjaK8WBiQJavq+L62jLu9PmvAK+8CHR1AOWVwOXbRitazGEUOH5lYoMSqJMNevO54ovPbOXmzTBt24aupiaotFrRb2YO5w/vf//78aMf/UjE/tly4qIiNTjJUsf3xz/+8bQyNv918ijOuaKB+l6fF585dgB/2DR+hv58cyYOD8bqSHNqanAOocYSDTwy8PRC1z50efsj7rAKLa5u3Fo8RpYONe8DcT0NOPGZCoCc5Bm/04WQkJzqhC4SR/r/7L0FfJv3tT7+iGWRmSl2mJOG0yZtmjZlXFcYd3S3O7zbOrr77e6O7/huu/9R13XrqN1KK6zcJmmY0bEdO46ZUZbF+n+e76vXeiVLsmTLTtL56Ud1LAte+MI55znnOV5hyQbgU5AcSYE9F1zU/fdLC30BF5NiQJcLWAoAheyTnFUgg9kA1Gunk8HNk5sLGXQaOtzoaCBEAzURlc2eXAEPnDlSdpFGFR6Zl/cYNlJNRpdYCv5Ff37SYBM80QgvHMwGS1fn4PWDr2LZusXQBgPRs8wVaBxpEEQbs9pXZlTAoo0diCCJUWmJfu0kgbWx0KhKoSar4VRklfICtPdLvSREkZEq2JXWC7QeBWZvkV6XXQrkzwE6zoYCgjllQN4UlY6LHhoRMl4KCS8BJwPE4c024YzsZ5IAeD46RTCz8HKgjZncweuYsQAwJ6AFy7FnWoqJQq3SwKBJLqi6LmsuDtWeREZ+jiDCGOy+Mnd8eRwaxBznzNSZikyHQY8dGrMOvk7pGjqHR9DV1IHKlbOg1yu3pvAZSEOFe0hcwzUQwPlDT2P97DSgi+vScSBnOZC3UqwBhy12NJRq4ff6cKqvCnufOIyt81cJJ4SfzX5PshNg0+rR75F0tf0eD1x9AzjZ0oIfuRx437Il8IBVaipYtcUwaXPF2kXniFlURTlpQPdRGD0DaGgdgse6GCDBUFgoSsQJOg6s6mBpLMlerk+c4zKYCVqSlo4tucvxcmcNDvW2YYS96TkFivPhbh/A5sXSHBv0uMMIGOn9XBN1WJ21BOeGG9DnGdsrwZNA81M6UOf3v4EctwtaJYnMed7dGJfUoLPCLDZe3zBH8f4PsJGD1OOAkmBvvwdYuw54xzuAn/wETR4Plsr3mBkwfOzbJ2XSf/KTyPq//0OP14tsNkL84Y+A9CgEYdks6ZEMDNHGlQrQGyfVePb04DkhmZiht2CRrWLC1X4evx9H+3tF76/l6VmwpsqmKloENB4b/XVpRRGqh3U439GD8vIIUmPvHqmfxpBdIpuoORztOA4dgun//g/uxkbgxhuBNWviH4PY0zmnGFCyAarw9c7pc2Jf7z44fBJZbVQbsS5rHUzjSUIyeHHTzcAz/whl3PGnXBr9wgshQoNgn5ZPfxp49tnJl45z3ES2tKJ0Wyy4O4H+fUDALe0ZtjWAMUJKKwV4xzvegR/84AeiYuNTn/oULmXwHKjLfd99913oQ/mXB6uNA6hDAG3BHaxMelyAYA99BGp4cz9n8IP2DLPKmcXNgCaTq6IlNND/4L4s2xi0N/hevpZN3CNB22rYG+pLd7GDCWQMeMqVtxtyFsHhc6I52Iy+KC0HG3JS45uyojFNsxLeQDv8YFWuBVpV6oK8U4KovTMDUnXqDMaAcyqZwDkz5Bmcpz2fij4a0cD1hoQGq7RoWzNAz/kcr5qEc4K9Nw4fPix+xgJjGIxVROsLQFuZEjtce0RCR1WV8Kdo+9IGJsnBgHq09ZAJUUzc4t94nNF8HK5F/By5NwB9FtrXsgIG38OAsUiU8vtFBQT9En5nMpJSvJ9MbEtV9cx4iCsJFQckHhgP4rlz3ab/lQiYsMfxMSahg/vBL36B7G9/G1X79yOP/X8feIAsyNgP4f3fel1SxzuD0PjiPNm9ezeunKQELO8hx6pIIgySpDHhGgQGWwFKSKeXSz2KU4EtVwN/eDhUrcF48OxKYNbUk4IXI3Q6Hb797W+L/hr3339/SuTlLhpSg2QGywFvv/32sOedPi+OD7TC6fNhriUbhWmTy4KsGuwXMh0y+O+aocSCmNSpjoZmhz2M1Oh09glCg5C+KYB+zxCaRzoxyxxjs2SvCkoMBbXRg08ChiSzW1MAf8AJl/+QCMiKPZUqIOxXHKBhPsGgDXtsZF4NOM8BDMjpC6RHFHAjZk8NGdxst27dKjZlNv5hhoRcxcENTnY+IsEJIpcqMnPqkaNPwdPvQ0ZeFjTqgCh4ZtNWGWaNMenS8HJTIU4NnA1W7EiSTFn6DGQoA9xT4BD21wxg04ZNcGlHRKPwUtNs5BtLYNPl4omW/XD6fdjVcxbVQx24r2zDmEyy8WDW5qLXzaqYUF6WVmWEWTtPBM5htUmN30nGBQyAJ0hohA5S+p2Z2jI4mNa+TQqKsTkqdehnrRzTYDdlKFsN9AbHUZCUQ8WG8NekZQEjSmJDBRhTwN5bZ0mEJLPqGUybTNXOFCNTb8E6lMCyqBhe+FFhzkMxr8s4oHEfs5FaijJRCWF4+3zobOpA2YJZ0GvCx4tBPXYdWbp0qciS4nsZyCIZGlatZW9Gnt6Orl4v8rKDc7X7GJBegeOOQTQ4pEoLtVaDtHQLGvoGUDZ/NjIMZpG1Q4efzgiJhvcVzMX/Np2Cs7sH9qZWWEoKUViQAedIC1564yTUGjXKKgqRV9CBTPUiIZFFh6Km+jSMfbs5SYRzMzjQh0V5DdCWLx2typINOwZL6KhwrStXZ6JO3x2UiyLRSFmuSmTqregYccOhaFkUUKtQNRSSQmMvIYvWgGHRT0cCw0ulJml8sudKJBhYZ9PlRDBv/gI0PrsDOq0apTmKMR8Z7B/oBeyDaPf4MeTxiXsTtfkg14y77pYeSlAOx2SC8S9/kZrx9fSESnpZ4n3ypPg9/8634dz112PAZEJFGQNnKcK8dUD9kVBjbBI3SzZPOKDh9fvwYvteDAT7pDQ42KulG9fkrwvrhZIISFy998BOnB6U7JpsvQG/X7MJc7lmR0GvewT7eprgDviw1JaPSkucuZ9ZDKx+G1CzU6rKy56F+Qu3YM+BQ+GVlUcOSwF/eS+oOwu89KJEKjHLjVk5dEyfeAL49rfFy/QOB04+9BCc+/cDm2L0yeB1DhwBoKyEomReSKLv9OBp0ShchsvvwsnBk1ibNbbCdAw+94DU3Jxl4gwWvOe9wOagM3XsmOSg0iEhhCZuNxk5allgUlhyJbDzr8GKC1ZgBKTnooHnJtaN4ESnPTWwD9BeI9lZ48Dr59xn8sb40l5cP7/+9a/jQx/6ED74wQ8mFZS6mEBimFUnDz744FumP8iljADOIoBQ1WwAtcGw/ziVrFMAat3T9+SDQT9Kw7BijHsug3X0RRgUiwQDJKy2lMcTX8s+YHzOpjONST7gPptjuLir+JQBWV4XZbUr5bWuL1gzKmlrTLCaNxliQ6e6hHrCWHKB7Aqg51yomi8tHchNvhrhrY6JBKJZSTAdDakZKOdc5jwnqams0ogXjKMMExtuy7ZrpGwdXxNt3VCC84d7Kq8NCQr6E3yO6xBjHVxTGMMg4ULpLhIpJB2YyEmfgd/P4+V3sQqGAVsG4vmTZNCoJGxQ1ptkRmQPEa5fMmnEKhU+6Nclmlw8Khc7DaASAMkueY0eD7xGJIwI2qfJ2i+8NjGvQ16eSKwqtdtxsqFBfP5FXFd2yYJzgiTfRNaQyEpMVmtw/sSVwBtsBmpfkOxqcQDpwILbxvZinQhKy4D//Tnw82Cj8CVLgE9/9l+un4YSjPmzoOEXv/gFPvvZz2KqoZ0uBv9//ud/8Pvf/z5scRz2uvGjmp3odkspbDR575+1GssyJi6dUGa24JzDPkps8DNLEmSHimOQGsVp4c+7GeiNkYkZEzzv3PVAx85QdjmDoZmhPgfTBX+AQTDfaPXC6M8AmxFOotxdYwHM42ehcxNRVmrIm6ZcuqncZLjIJbLQVQ+dhzk3HT1tXRjsG4A10wa9WgV3sKGzUa3DjYXjZIhGgUmbhmsLNuJYfzUc3hFk6zOwPHN+0oGoROH1j6B15CDah49jlWUxDOp0aFGBXvcgHL4mvNxRKyRmZPS47djVXYOt+cmNI73agkLjZeh2VcEbcMGgtiHPuEQiNAgDG2ozkOOXiIuG30f/oMhGrHQASWRMB2yFwMq7gbZT0gaVM1tyQpQo2wAMd0q9NeTsq4oUNYXl/J1Mw/hphM1gxtqcxJwxZjmQaJzq0k2rlnJaBXDmOjHQ1QeD0QCTxojZljJ4/E1ijTSo82HTLYjqqGzcuFH8m0bwoUOHhINBw0ZUdrmHREWdyx2xJruH0O+Rmn7KFQ0BfwAavR4j8CFboxFk6bJly0avBTOs7hvR4yn4UbB2Caw6FbRqFW4oz0Z/kwYFRdno7uzD8cM10KlaMLtyo1jHCrOMgN8l5gTXtyXzS3CyuhmLs5uhyRqr602HhFmTOd3d8DV7oCu2Qq/RYr61DOk6aQ/SqzWRtUlQ+QKjBiHXpffOWoPfnWMmu3TuqzJLsTZLCkin62xYnr4MJwZOCrkvNmdflbkSxsgKpxgwlsxF5eLlqDlxVNrHeDDWXKAg6Jxy333lSeDNF+Dz+zEw5MT8T3wZqEzSeWWF3B13wLtwIfD888Bjj419zenT1M9AxcmTcPz0p0JKg04rSa4JgQ7qGRKyA0BpJXDDR4CaA1LvkLxZQPnE92omPDDxQQkmRnS5epFvTC6x4ce1p1HNSpUg+j1uPHD8AJ66fOuY13Y67fhu9Q7RZ4x4sb0W75+1Cquz4gTpcyukRxAMfo3RZ/7HPyTDgYF/uTdYc4v0oJNJ0umHPwT+539G37LCZEKHx4MFtbVSFUTUfZ2Z3RHSbgE2184HgvrrQ96hUcmX0cb33vBrGxO0LT7xSekRCa53kU3reY5yMIPkGscirz0b3SfgdI+CjcG3vFfq1cJjJ7HJMRUJXktHk9TgSqtWyBgEAHd3XFKD612H8zjsvs7RPb7YeBm06viJKrfccotw2Fkm/vnPfx6XIn7+85+LgMjNN998oQ9lBmK0tsV4rnTaAiXcuxn8Y3ICAx3cX5n8QFkYZVArUWkVvpaEBoNo/Hlt/mV4qYM9zqQ1Y7GtHJWxktouMnCv5DlEQmR5J2gLvOXBtXfRjUDLMUmKin0hSy+bqdSIEVyOVrEQ67WsHJhoz4eJgmsAiYNEwcC/LO3Eii3KWHENYV8eniv9DRIJiYCvpV8hx79kkpXXglUkXJP4NyZrKQPzlLbid5OUoB/C76N9y8RQHhv/JoO2L8mSeH0pSHiQSOFnsZohER+P38c1T5b/nUrwGjDATRKK6/Z4md2svKHPN1HpTMae+F3xCB6OG95zkmK8vvy+ifaAmMFYiIS/wUExpidDatDnpowVFRJiqbsInHs9RGjIVRtth4CyK1JzexYvAX7x64m999Ah4MgRyedgVfslmmQUOaeZOPW+971PyFElUyl20ZIaP/vZz8QCfMMNN4Q9/1JHDXrdIb17Bpn+1HgES9MLJswMf2nBUhzp60EfnUIGYTQafGPxZQm9d0l6Dm4rrMTTbfWjz5k1avjkjLkgcvTpIruVus4ymFWbbxgn+9laDuitgKNdqtqwsOwpIvOTDWl3/QNopd6yCsgpAYrnAIvWSKVSU4g0zSJo1WM3OVniKaF7wqBASy21ZICcYiAzfkM1pXwUQQOCDkmykAmlrMIctNW3CFKDuKlgjcgIZwaVnBmeLBhQ3Jy7KuHXM8Aoy2fRCGKmBTX0mU1IoyJeI6wu1ym4fcPQG6Rx4fQNoNt9GM2OgAhaDLCcRgEGdLrdCQZ0ImDSZqNMG2chF/dbIzHYuQuBLikjYlS6I3c+kL8YGOyWtObTcwHjNC/C1jzpEQvMiF9yNzDYImUBW4uCslX/WuA8S6SfDEt4abB3Z5mwa6gd6a4+XJs/SzQdTjV4LBuyVyBbn4nXz76B+eWV2FKwMRhgn5O0pibPj0a+aNCtckDn9mJueUSVhz4deQFNmEQTqzXUfj+y9GPHLo3l5cuXg/81VW9H80io6o/ExqzZ0lwuKS8QD53KjCLjPFHpQeJEmTXMfxcXZOI8JbbiSA7SidhotYqqjTlzKpCmyCC5Jr8SJwc7w4iNu9dtHtUJZwJBWWYmvrjgGnS57EjT6JBtCD+vElMxCtMK4Pa7YVAbwpqfjguVCgNLbkCa38zNUarGmrchtDedOSoIDRk6ErB//QXw2e8BxuQyYZiRJgJOt90G/P3vweB5RNCZv588CdP581i4dKlooEgHjA4bg/G8lgmRc5TS+80PgbOnQ89t3gb0NAEjDqB8ACicE0OWanzESnhwJyD7FbUaVTF+mcBRG+y3Eonn2msEoaEc7482n4hPakSAUghjeukoMxQj9Rl5T+hsU84pIpMxX3YeY2U4BmgPRtJ2BMvJJbvApDGJSg0lsZGmSUGW1d13A08/LVVncN4yAML+bzTCScKwSX1rq/S3X/4S+PKXgTuTaJyeWyY9YoH2ZNMbgL0ptP/a0gBt0IGmzRgFvkA//AEH+j09gtCQV3i3345210mUpK1OyOl417vehY9+9KMJZbJeTKBD/L3vfU80Cb8Q8kYziIZo9yH2vUm21914YJY1gxsMHDLpYeXKUKLNZCp5GMySP7MsLw/vKtuKPjf3Wb2QFJwOMCjJYB7XZUIOBDHznefGANx4WdjM8L7U5vkFASv7SWTMYNyAotwPMx7oBzM4zMoEzqPpAuMKrFCQk5WSAeeSHNCnD89+fvTrOf/iyVMpEWvN4ZrHeECsmIBSHo5+iAz6ODynyM+l3cvjY6Z6rPWU71m4cKHoZcK9M7K3aSS4vnC9Y/Cf/gXt6amuhuRxJdIwnGvhRAkNEk0kTjgWEwGvE0kx+si8Lxzz8vWbITkmB+5Hk20QTzB2EZfQoD+q7LE6Kkke3X+aVjz6KPD970vy/fShqFLwhz9I/scljhtvvFFIepMLYN+7S5rU4CJIzd7HHntszCJLWYRI/W+n3yt0oo2KAP72rhY8fO407F4PVmbm4pNzl8MSQwbiQG83MvQ64Q8utWXiq4tWiOqNRLEkIwsvd9YLpXwu21p1AN+v3odfXnYddEHpIpPWiK15q7C96whcfo8oQ74iZznS4xm1IpsnAJD4iEV+MGjz0iMASRXRJDLYnLT2qPS4/cOSkTVJaFR58ARogDCgIrVBVyENWlX+GEejYfgM2pxS1nSOoQCzLUti64BzwXj590CLbNyogE13AnNjEwLcJJTjgsw8DYdk2bwCYzZq7FK5u9zywaxJQ4kpN+VVFfEcMBoaLBndvHmz2Gy5WHMOMPuAhhANP5aw00hithT1M0V2djC72OXn4hqaE/was4Z9PSgVRomoQLD/SfDvUCFrOpypWZsAYwYw1CYFMAtWAKZs4OA/gep90mv4/Ka7gZL5uKhAvcTMaWjeyDnesAdoPSHdwvwFwOxNKZmzqcpcjNWDgvOQjjIN2FdGuvD3mhpomC0fCODljgZ8b9lVEyI2/IEA/ni+Fi91NMOgVuO+sjm4Jj+kn8uA+gJbBVRz3UIOYjLg/GCJNgl0+9AQbI50oC9IxBH5awCDDYv0VlyWUYbDwabyHvsIbpu9BkaNDvV2O/QkH9LSxszxGwrm48Fz+0dloersPixLlzK2CK1WA5M2Txj8gkTSZQCGHMDVI17T3N4LuzOAxVsuT+h+MTjB9YLGM9cPYnF6Hj49dz1e6zwHt9+HcpcGC0e0OFN7BseOHRNrCTPKaKyXr1qO464epGm0WJdVBJM2FOjgGp5UIJhju/MAAv01aKxuwtI1VwL568eO7ZYG6Tm/T6y7Hl4bSjj1dgJF8Z0nJeQMOOGwUALtwQelB4PllAOKIMMRfL3ckHHXrl2iZwkJZpIcdKBl/eGo2LcjnNAgdrwE5Ng4SIHTBwB7P3D3x6I3ARwm+RFMVrDNBbThRFK+MUuMG2UgnvcgWz+2Ymc8lJnMOD7Qp6hGBQqN0TPaBjzOMTYWK2Q5LxPdFym9cOTIEREkZFBAOHDXbgNefDH+G6Po1AuQIGET7mhQWcbeWzHfQmN1oW0h9vbshScgEUWsNlpsS4H2O8mvP/9ZqgoiecEAyB13SH/705+Atjbp2GRChg4IKwNinWey6D4VIjQIftfQCJBpBbRWwDDW6XP6auAJSBKMw94A9CppuBIsnnH6JJnU8XDdddeJtYNOx5dJ1lxiVRoM0mzbtu1CH8oMglChBAHUj3kuEsPeAZy1H4PT74BeZUClZSnSdZNvTs09QF73GfiKl0iVLBjUY1CRew1lXwsTkPJMBuMRPNQfp50jE80MrtLm4O8MPLLxKjO86UfxGtCWoKTvDGYwVeB4Ha8Kgr4uqxKiVQhNNRi45jyZbPBZ7gEg9w9JNLg/WdKW9rAy2ZNz/cSJE2GEBG1dJvXwXBP5LlZ/kAilXC73/mjXhmsn1xDGMZ588klBlvCc+VrGapRES6pAskX+nvGQTOVNJOhbJSt/xqA5114eI4keBmqZfMVjZQXHTFJF8uA14zjivsVxKPu6UwL6poyXCXUghSR52gVuKO5wAD/6kfRv2b9obpaIDfYqfAvc42984xu455578PGPf3xKEyqmnNT47W9/KxYO9kyIRJHRihMDbWEuN+U1jve3YU1WibgQR/q68O2qA6N/39nVKjSlv7N045gF5KmWRnzlFPWYJbzZ04k/N9XjiwsSY+cp6/NGZ5MIPjB4TKefx0YypcvlQFFa6EYUpuXgntJrBKmhV+tGAwQimAVJ01iNNBHGQO8hwB4M9BsLgdyNgDqKI+wYClZoBMHPpAHOxb35LFB/CpiTfKZBJFQqPQzqNfD4axCAgy1uoVfPF9mkDQ5Jr9qitUKn0qLVGep90eVqgxoazLHGkOGoOaggNMTVAN58EihbNCbD1WzQouvYU8g1ueFpaIBnbj50GWVik2ZwlWWYyaDcXIgV3mGcHKiF2aiDzuPFypw54r4wM0QOknHMMOiZzOZDooIbPzd2ufEWwX/TiaDDIJeM8rXMlpCDx9z4lSWUnAv8LJabcgEnucEmhnRCJNLCgIDGK3oMiCsYCMATlNAi8gxqtDs5uqTn0nVpuDx7GkgEZnMXLpceMhpPhwgNOdt559+At31uUg11L1k0HgQaDwX7jaiAthPSdZuz+YIeFp1bZkhFZrQwSElHg5Cd4pGAH3+v3TWaAU50u0bwYsc53DUBsupXdafxUEOo6fzxE/sFWbIlLzxLi/OHxzlpTXfnINT1O2Fz9AKmLKB4C3X1pJ4nwb4nnGc3Fy7F6sxy2L1O5BqsOHnmLFa98jKOD0iVGDcXFuLR9exVEzL0l6QX4ONzNmJ/b7NY2xbZimDRduJE7QE0nW/HzTfcjkxWNjleQnGmHXsPtmHude8H+qtQe+oYKmdfBn3+ZWOb2Y/T5JBVGDTAaUDzORIbfHAdYnCFr2EjbmZDyI7JrvYGfPzxh5GzdL5YKZ5sqcE3l2yGTTdBaYmeY0B/tViXzrV0Qq97E5bcThSxB5Z7GIOadFgWbcT5gWFo+wbh9EoB57L0IOFqYY+eIeCfTwJdHUBRKXDDHVJz8ChgVl2YLAgd4R//GGhoANgImM4MxyfPl8FoylQFwUwqZtAxw44GMh/8PDp8Y3qvyOjtGiVjwuALNnvjd52vkfZocwQ50n8a6KXdEdxTBmvhzb8W2rSMsGq/TTkrsLvnuNBhp82wOWelSJBIFv8xdzF293Shy+UU36hVq/GtJdGDVpXmLNQMdY/aWEKOM82Gdmcfhn0u5OitoufOeGCmM68fry2dOlx+OfD5B4A//hEYHJKahROiOZ5akpa65RZG4IADB0JEBef3//f/RSeGBFhZxUa17cHfmaGwGFCF7CWLltdyEzpdnYIkyjMwuJii/Ybj6vXXJfKMDcK3b5cktKiPG3nMlM+gFFUsubO2RuCN54DhQaBiAXDlTfG1dZ2h3jij4N6fNgewLJQahivgCwyMEhqEIDPCEh7YKy0xO4frCsmM97///fjMZz4z4ezH6QaJ+v/93//Fww8/PBNQuIigAtdujaJReCnUqnBpJo/fjaqhg/AFyUl3wIXqocNYln45jJrJ2QEMADLIR/uZyRJMJJIDcIlWrSYaTKMPIDcB5nhkNXZcAj0KGOyV+wzyuJgEIe9TtImY4KAMrCkDP0q5Rf6NQVeS+nyexL5sPyj7o/E7pqv57wxSCI8z1EcwswzQp6BCMQVgDwTap0pwDDc0NIzONyZMXQhCg2CSJGWZUoVkg3Kcy1wzEpXoisRDDz0k9maZ2KBtyzVDXtO4BnGec61LZl1jwhSvDRMso61bjMXQv6BvRslfxmXkz2eiC0mRyF4jkwV90paWFmRlZQnihbEUnh8fvIb0W2XZqIlK2cjr/0RJLpJaTKQgeH0YG5IrxHncUw3OLa7dFyuJwp6Obc5ekfBdkpY9bi9bJgowBsZ9a3RvczmlYD/79KVyn6q8Bqh9HvAF+8EyObcocSWWKQErwSOr13nOnZKU7FsB11xzjbBBuJZ96lOfujRJDQZ4f/zjH+OHP/xh1Mm3NX8uztp7cHZY0lCm3zvi8+F354+g1t6Ld5Yvx/au5jD9c/480t+FIa8HtojM4SeYJaoA3/G35oaESA02LP/yiTdRy2zMIPTqgJAYIaIFg3hOygbNgYAPDt8JeANdwQCCBebhNKhlQkN8UTvQsx/InYB+GwMqKYJaZYZBszLMwTjcv1tIkvDKDXh6oRE5oOG+fI+7A3OwBBg5D4yclTJ4jSWAaQEw0CNNRGVmFP8+3B9OagQCWGxrw5Ejx+HKsmLlnEwcfuG3WHf7p2CxZIks44kgW2+BSQuYyvLR3tCGF7texmxbBdwaP7LzslFpLoPJaxDSUOOVXCo3WGZErFmzJqq+IzeXN998U/yNGy7H/HhyJ5GGHYkNWbMy27AA7c7DozEgXsmWkdD1NGqYMb4Ifuigox6wOU80+Lsg6G2VgvZBTWEB6vizSXj29JUWpxQD7UDNa1I5IhsFLrhGag6YCBoPS0FQgWAj9e7aaSM1vP4h+ALD0KhM0Kol45TGFsuRZdKMYMksjUZKNCgzVWjoPVQbIpBl8G0DnsS0YyPxWLOCpA3i7831Y0gNZlQyYDqpxuReF3D8cUBIGgZLSu2dwGXvkHqpKMBrUSjuq3Rvv3jyJE4HJd+I59va8LVTp/DdiHL1OZYc8QghH1ctW4Sf7/g5rF5Wd0jZ/p1dfWhqrMXZM8/Dq1mM7IU3Q5+bO6HToqHMNZFrBB0LGXRqeP+47jAIyZ800sV9bDoNndUMR08f0rIz0eMawVMtNXjPrOTI4lEMSVUt/Pzrr1iGuqZOpAc6cJZawKTF0/ToaalH3pZboWuugb63HSqZJNh8o7T+f+MLQGcw2/3IfuDMCeCBr0vERAQYcInqcDA7leTGt74lBZrpSH/zm2O0RyPlN+iw0Qmikx1VRz2vcCyhIU54HGOaa1/vUXR2D6B3YBh6nRYerw/OhudRcdnNYU4iifdSU75IhJhME9bCNBOevfwavNLZKqpar8jOR3mMatQbCuaiyTGAk4Md4vcsfRrmWU14vGWP+J1HcHXeMiyylSaU/SwCb+2twPe/AbS1ACYVcPd7gflLpXvCaobKSuC//1vSheW9YvVDfT3A8vJ3vzt+ObW4JhyjlATgmmMFVGP3XYPGgFLTFOjzf+1r0rHK2LNHkpqiBARJDhm0c7jPx9rru9qA33xXIvo53hvrgJ4O4O4Px/5u0Z8pgplg5Y+V18MPOKoB3yCgNgNpc+GH1Itu9KV8p2JISRWriY8xysMyEPrHP/5RNA2/FPDII48IJ/j666+/0IcyAwW4tqlAGzu2nc0qDZnQkMF0nQFPD4wczYNHAU8fQILDtgLQJRc8Y6NZuWqBa7+cNCHLwU4mW5D2CisiKXXBYClJB3k95/OJ6s9zr5abA5OIj7YncM3leXCPZ3IWM87jkTIMAl511VVhx0ofhZnEMonBfYkByanumzaDFMLRDxz+W0g+hTK6K98OmKc+iBoPTNLjOJKDkRybDIbTHuWYm2ggP9W4kAQe4w1jEnWSABMn2QRZOa8pT0siideXcQcmSU7EpqSdzZgEbWMSI/J14n1kshT9DxIXJD6Un09ChUForq+pBKuBWf1AcoASsiSK6ffIDd95DTm2lL5QspDPayKIJt3HNZdrPhN/jh8/LsilicioxwL9Id5v3hvuA/Thed9labKLCW0jfXiqdf+otC4Tp+4q2QADFTPigGOL91TsbY//FXj0j5J/lZcPfOm/gdLEK/3jwpIPLLkXsHdI6iKWwguvpsG1k/4Sk6Tk4B8TJxTJepc6VCoVHnjgAfH42Mc+NmX7wpTuNn/7299EcOEOuYQ/AqzK+NicjXiu7Qz+2V4bVrHxZs95XJ1XKciGsfrKlG5ILUP5bFs9zioIDcLt5/cEcE/pwphyV0q4/PWjhAZBpzPgiGyYR+ZmbBM9AZMVKJ4LtJ4NyU+JDwoGSvOnrslet6sDbja1VYBK3JFTXTSSdjYCQ4dCTw5XSYtPRu5YzXN1UHddCc8QVK4eXLaoHDsOVqMwNwM6nQZDradgrdw0bkkhe0sM+ySCx6yxjmrCVw+FyKOCWYXwuD1otXcgzWKDw9GKRkcrNuWsSai5FzcRGmvcRCklFctYoEFw5ZVXjmajTMRwYiCBAV0iTZON4rQNqNMOIV0zG66AEZ7ASXE3iFmmUiy0zbo4GHqTLZzQkKGoaEJnM7DjH8BgH1BQClxzD6C/SBsRjgwCRx8PBjcDQH8LcORxYN27JQmreOiqk7KolOD0VVTZjBuQb9otyXuxh0LxOsCWuMakw1ePEV+IQDCqZ6G7RS+MVDbPkgPEJPT4bznLRIkaey8O9o3NZmLFxgLrxBwnudpDCW+MazJZWQj0NwXLSkc/Ufqdz+fEJ0uO9PfBq8h+5KjezqB5AuD1XLVqFQ7sew1XrJcM5Y7OPixdMgtzKowiABmZZR0LDq8TTr9LNFFX9gAiaRoZKGFlDTOGmIXK+0nydfXq1SLQzSbh1vJidB05BWNmOvxqtai4mTAUx6/Xa5GfbUN37yDmFikJnm7ApAc++p/Akd2AfQAoqQDmLwf2vQm0t4R/5tlqoLYKWBBe+UdHJm721/r1wDPPxPyzLPkX6XjQYKYTSIeGDgIDXNRxFWvp6iuAM8eB40FSj3uKzRjMEApWXpXPl/ZoJfwe1J1vh9lkwILZRfB4vPD7AzBkleFUU9MYApt7VSoasWbo9birZHxJPUpm/vvsteh2O4Rc2YBnAK92slm1BM641zpPYJYpD6YI4i8SvE4B7u8//CbQEbRhOGefeRwoKgEef3zsm+jY3X9/cicn9raJNwxMCDwPEfWP2EePHQvPmOLrDh8Gfvc74Phx4KWXpOdJzPzgB1EJOYHDb0r7yOiaFgBOHgBuvFeqWoqGnGUSeehV9BUpXC/9HNgNeOVKDhXgboM6PZSUQmhUCJOmJLSqxCsueH8/97nP4bvf/a7ICr3Ys7hpozFhihq9F4U9NIOkoIrRy4lJbOjZDvhY/UW5NwfQ8zqQex2QhGQiM8MZXGIwjgTHwYMHRRUD91IGhSZDapAU4H5M+4prvHL8keiQ+znFA8kJBsCY/Rwvu5cBUT6U1RvJghnPlEuR9cb5eUySuORJDa6x+14G6k8DBiOw7lqgNDlJmUsGtW8AXoUNx0QjJmCtvOuCHA7HI6XQSFwo+2OwaoNzI5VB3VRg0v7FJCA3Fp8oWCn74IMPYv369SKozbgE17FUBrUj+6bxevG+cp1isF5WppDJBLm6P7IKLBXgmKJfw6StaONoMoSGnKw6GVKDSWbRiGVZ/pZ7Du+5XGkyGch9aLiX8TNJzstyakxOnM7eNInghfYj8Ch6Bfa4h7C3txZX5o5P9PNeu3e+DsNf/xB6srsL+Nb/A/7vd7Ht7WTBGMt0SJInCgb4f/hD4NOf5g2XnrvpJuD22/FWwp133okvfvGLghu4j4oLlxKpwQn//e9/X5SyxzPEKA/ETPNIvWnikfMnsbe3QwSYZHAJ2ZRTBLNCH1wGnfx9vd1hr72bQZUE0O50iGOJDMJ9uHIFrslPbPB7/VLjthAC8KvHEgOIlVnPBXLbu4DdzwDnq4CRYcDDjVAFXHkbkJ9YQ6OJwB92lUOQOm6EUJJWCYycG/vCkXpg7o3A+dNAc3XofDa9LW5z1ex0C4aGRzC7NA+nq+uxrnKTyKolU8+NLRIkXk4MHIBDODwSqbEkfTX0asOoxrYMnV4HW6YN7K0t39Uzg3UoUefA6XVBrdagw9knZMcKjJkiqMNxS216blw0GBI1+kVm2iQca6XBo1dbMKtgGfz2dBTm5OCGglwMeu0wqPWw6S6ipkGzVwJ1R4GellDFxrItEtlB9HUCf/lJKAN6oBtoOgt88L9StzmlEj3ngLDGvQHANQQMtgOZ4xCKTSHZuzBkjNWRHgOuOWdfBOztwUD8EFD7HLDwDsCUk1CFhpLQIGrq9yIwUol168L7N9AojWWAdrscYgwbNAG4FPb36sx8bJxg5c0NBWV4suVc2Mp+Q+HYazmRPjoJo68eyJ4dR/IGyNDpwFz2gII0zzcm7pSRTPjdb57H4vkWZGZaMDzsQlEhqwf4neOvC2LdGahG1aCUJc6y3U25q1BgjH3/aeByfeJ1Y2YcAym8v0a9Hjn6NPS4R5A5fzZ6z9QhZ9FclEXKJiWD7KVA6/bRX7MyLFD3DqOqsQPZNjPyMoL3zusGLFZg3Zbw9ztjVN85nWPWQQZfopFuY1BdLWmODg8DGzdKBqBKJbJmmT1Lg59ORmTAiIEdPkh80Png30Ww590fAxpqATbdLioDhnqBHc/CPzyEdls+Cm5535g+FPUNzcjNL4bNKBGhOl1wb0/Lg9c7Ihw+WU6E824qA8W0XaIle/D7c4PN4pscbWGVrwTtrkGvI5zUYNNqd6+0puuzhc3CbEDf0BDQQvk1BXhOp48DV46VGJ0SiF4TtVLVK2U8bQsAfYIkyOAA8Ln/oEaZVIVzx13AR/49tDaQTBsZCT+37Gxpv2IlCvVteQ1om8Rbr4Lya2MgknRiQGcCKm8FBuoFWQZzEWDKBdxdCkJDXABRsaHxjECvrYA7INljaRpKpSrtaBUy9QtH15cedy0GPE3ir1ZtIXINC0YTQmS84x3vwH/+53/i2Wefxa233oqLGc8884xw+KfKOZrB1MKqzYRZY8Mwq48E6AsakQkdEExakkAD3gM4WwFzuMTNeOC6zoQhJtcxWYp2PQNxrHwYJbQngWiBMVbAUhYyFqlB+RTuO7LkRqJ7wmT6AUT2PJADo5c8Xv07cEKqOhR2VlMtcM8ngaKJZcRf1HD0RfSbCkjPXQDQnuEcorxZpIIBycKLjdC41MEgPonZ7du3i/5XtJHHI00nC64Zsnw2q8L5k4QV77kMxmrYQ5TrYKr9NxJjrB7hOkVyI1XNuEkQ8LgnCvoLvBZc4+VeGso1nNeN/guTLphEyCRaviayelwJEhT8e+Q15LnzM5TJUbJEMytYSM5zT5Pl3UioXEiwV9+gkngN+tS9jGmMA54D9yjviWMw8F7LMTEmFpHY4CN/YkTUJYEVK6RqcPom9EOoJvMWS9bRaDSCEyA3cO+9905JMtKUkRpcfFlu9773vW/c11aYM8c0s6Tjvb+3Q2SfGdWUR5Lu782FlfhAZXRdxluLyuD2+/FwQy1cfj9uLizFx2aHmOwmhx1fPnEQVUP9yDUY8ZWFK3F5jpS5UpZmFRNSBi81m5VfmVuaVK+KyKIStyUdOld3uKxAehwJEPYiuDKYecHM2sFewJIBmKY2mJ2pyw4jlkTVgUqNeZYl6HV3iOdzDIXIMxZFJzXkqoxr3w20nQNG7EBOMZAeZePVWeFSZ2K4txm9A3Ysnlsibq7KJWnukoXmRskFm1lMys3srP00HL5QNvawz446exUW2lYg35CHBockkyKdg5RxrcSI340jqvN4Ys8O5JYWiH4VHFsk1q7PXYn6w1Wi/DGZDCZ/YAROXx38cEKrSodBzYZRiW/A7a3NyE/3AY4GQJ8rmswyyEuWnzBo9MjVXNgS46hg9cK2+4FzJ4CRISCnBChUOJ0n9o6VdKGE2uHtwJqrcdEh1gIbI6MwDPYYzgWbhY8Htx2wR1RvcfD2nE2I1KDkFDE0OIyTx+tFJn1mlk1UCiSDUwOSRA0Do2kaaSXgFbmvbGHMzYf9hl7paMeIz4uN2bljZHA+O3+Z+LyXO5qFXNo7y+bitqKxx0UDbdKOUHqxlIHhiahI6K0DusuA3Nj34suLFuOTzc2i2TfBY/3a4hi9g6KAQYJNm2/C9jdfwMZ1bLqnRk/vIGrqA5i3ZPzx0zLSMUpoEOy7sLPrEG4v3hpWsRENNIbZ54CGOqs1WDXy6Xlr8J2qPQiYAb3VjJwuO25enVxAKAzWcqk/SX9tcB+zIgMdyJhdhNqWbuTxulE+xxQjuDx/sRQYFvJswcoHVmxVzBHa53TSOMYYpFTqh8dETQ1A24IGLx/sg8AG4h/5iPgcOXuJVRkMILHxYGT1B51vOh90KOigjGYPp2XA3dEtsrGW3/cpISfCkve65hapWiEQEMEyZunyM22lNwDtrwPeYIaNda54LF8ekiPh+1kdMhXl4ru7O4Vd0+FyojTNjB+uWIOl6dH3i3SdeYytxaO0ahWJBz4n0MXzCQYbNSa40y/H6zsPYC0N70jJQX5CDPmrKUHfEWBI7tOjArjnF16XmDzNRz8MdATJYzpNf38UyMwA7n2n9PfPfAb4whdCGr7MoPq3fwt+lQpIVD5iwUpg3+uh3/l5+SWAdRzyhT1WsiMy2iKSNZTPGzRzoA3kwh9wQK0xiYpih086vzRNHnRq6b70eurR5wnJsw56SUzR3tNjxMeECRPyjRUieEK92x/96EcXPalBadtPfvKTk87anMGFAf2LBba1aHHUiiQloyYNxWlzofVIfa3GIrlMa+4lJJXlDGSu2bQzmDlLG//1118XwSK5eiHVQd9oPSuYfMAs4Y0bx/aEnEqwsS0rdpW45KubSNLQxwgbH2qJ5HgrkhrsEcckKzlOwfvH56YRTPjjHOLYiUZozGDqQHm6p59+WlScMfhLm5JxNs7rqZb4ov3MB+MyymoNOaGLfUEp0Z1KYkMmDDjemKQ0GSKCxLaspsFrNdlGxfT36HMx0Yd7DH0wVr8p13u5koV7AYkJmVSWfQheR+5FvJaMcZGcIHnEfYPECd/HCplY/WXle8LX08fgfsf5OOm+lJMAk74sWiOGvU5Fao0KGbrxj4njmhWQpsysCPI2CNOFO69pA+dPsE/OWxX3338//uu//kusXUo5vVRhylbCX/7yl+LgE9n05llzcEfxIjzVclpMBMpSlaZlocvVKbIPtWo2w5Ree3txpfh7LLBaI5osg8vnwwcPvokO5wh8CKBlxIGPH9mNx9ZfjbnWdNxYWIHD/Z041CcF9rQqNT4/f7WQb0gURs1s2L3sDxKSHNCZlgIM+LCvBgMBpjLAXBo7i2/Hk8CZoLTT4vXAFbdMTu/N3QC466Xv1hUDhvlhQVrKTtUPn4HX74YaahFMk3I3WRXhR6+nDwvTI5qQpjGLtSfi5IN6d/zsonGCZ2wA350J7WAnLl+3EtCbgbzV8PfXC1abwU1u4AxGkdhQEgxDXkqEhWerSM8Bi20LRE+QVmZwivthhD2CNR7yeODQuOHz+ETTW51GI8gslsv95uXH8KUbPpBUuaA/4ILduw8BSDJpvkAfvIFBmDWXJeY0+Jxwtb0Cs9Yj9XOgUZ61SbDuDPZFZoJ5/T60O/tFYKrAmHHh+mkoiY050ZvUwhmu+T2KXrkJ7EWGnNlAPWU+pL4yImBmygRsCTi9LAVncFWpwU+ZpezJlDgm5sRrVGlwudw4frQOG65YMmpY8flIkGiOVqnR6bTjyEAr9GpJdk/SxAbWZRViVqR8XBA9Lhdu2/U6zjuk+0zywqTRIgAV3lZciq8uXibW6s8vWCEeSvS72UjMgUKjCRl6g9gnGFyeFEhoLLkNOPposFk7F3KN5Pz11sclNW6fNQsLysrwQm8v9Go13jNrFuYnafguXrYRvb19ONfYJRwNfVoxDh7tDWumFwDXI7n5Vy5UkPbHHvfAmGpFrsVD3mFk6RPTEue6xet46NAhocH7k5XX4HBfB45klcLd048ntr+OubZMQXpMqGLAUio95N45LSeFvJdKr0XAqIMqf27svaqgGPjoA8BDPwM4XmwZwEc+A5/Fitbq6oQ1yEfBCg3ON2VJ/8MPS5n0ChJcrsqgs0ASQqn9LIPPRZLYJDlIrlBegQEoOi1yfww6MtQ3ZubcaM+M0lsk2SD2QPD0Ap3PA/4RQJcBn8si7g3Xckoa8lxTFVBqdNjx0cO74QnKPraMDOMDB3bhxc3bkBlF5m++tRhn7W1ocIQa0G3KWQyzsmF5/1HAG8ys4jzyOnDu6DNYu/ZW6TrdeQ/w+F+C9zpADw+4/hZMC1hJN0poiAOUbBtWbmStjv/enu4goaEgsHl+zz8XIjWuvhr47W8lkoyBgptvljKmksXshcCd7wde/Lu0D5bNBe76wMQaHmo5NnmtlQkCKkCbLf6lUaWLh/y0TT02oGf3jN1zBzzNGB7tAcXfOzHful5IT331q18V9tfFqNlMUDpn7969eDya5NkMLhloVVqUmyOq8vRZUh8Nyk7J+6FKCzChKkEwgPTCCy8I6Vh5vef6y+AW1zDa11u2bBFBuqkgNWhj8buUPcvoy5DQ4P473bhY+hqkFIJYj7SRg2T1WxHzrpJ6asgSq7R3509fchiz9DmH4sn2sDr2LVEBdBGCgW+SsfQnWKXBNYaJN+yZSFk9gsFyPzrhDwwL30Kjyk8pecnvp+1LG1auCuBx0UZm0g6rn0lETFTaKRaBQGJjoiChwOuV6obmyoQyxq3kqmwSGcqqDF6fyF4qvE/cH3he3Kv4GUrwWvIzlY3ZY4EJYdzj+KDfQp9kKva0RLEtfzn+0XoA3mDiU7ouDeuzw89vFHwNY45qnSBxKLGFvFzglX8C9iHJTud6fsudzEKb3hOZwZSAMQoWO/zqV7+6dEgNTrInn3xSZI3GQ4dzEMf7m0UgZ5GtCOuXXocBjxM5BhOea6vHPjZWjKjeSI/SsDsRVA8NoFUhgSHL3W/vbhekhlatxlcXrUfVYC+GvG7MsWQgJ45sUjRoVFZYtOvh8beJhnc6VR606gyA8QJjAmVhu58DTu0PGWrH35QqN9ZPsAmiuwlwKu6Bmz0n2NhbqnQZ9PShaigkmyPCuKqACGrK6KMERSSMs6TFyCF/XilgTi4otWrteuzY4cZI8fpRZnnBAj2qq6vFxklwcWagUxlwMqiNY3p/8DlCq9ZgTdZKEfjnWkiyYn/vMbQ6pXFUllaM00OSrnuaNQ1upxsGk1FUA7k9PjjhgSEJyRnC429HAFImpRyu8AV6RT8VDRLIWrCfRnFeGo6cbENBHoPHfqD/AHJzt4hNT2kc2N0j+MvZN+DQSTeIjPjbitcjg6TQxQaHHciMYdjkXlwakKOgRMuqe4CzO4CRAcCaJzX5ToQ40psAB4kNXzBgFgDSEqz20VsAS4EkcyUaywpLCMhKTKdUq05HzXEHVq9bMBqsNqhLoEGGMJpoFDILhNl6JMo4x2hEKY2lIfb0EISyClpVQKyNWpUKlebYhuCPak6jeSS0pnr9fgz63XAFgF+fOyvI4+8sDdd9J/7Reh7fOH0Y3qBcDivmVge0QrYhUQQCgwjAIQx3lUohq2TKZq22ZCgpMc49JJF6mUaDrZPIBuL1LCpdAItlNfQ2aexftqpCZDGJvjxqZvIfQiDgFdr3/kAtNKoF0KlLYNQYxsgvEkZ1aD1iTwSH14t0Xewm06w8YACfe2/67Fn4XvVRQeiL9VAbwGdyysXxRMuyY1YQS7Np/DMYE9eY7qiSGlaqVCjJy8Spc22odHlgKl8HGGKseytWA//7MOAcAYxp4r3Vp09LhmyycDjG9m9iJpTHE1XaTs5qYlCJzgdBI5qOWTQ9czkTjeMi8jrQYeH95OeMkhok8ykNyCzjvl1i/je39mDY0YKBPqc4dwbSOD8n06QwEnt7ukTygQz+a8jrwdH+Xmxh8/MomVQ3F67GeUcXhn1O5BnSkWuImONszsv9fbT3TQB9Xc2YvzG4nt15L1BYBJw8LjVo33YTkDtNTlTkvJYRJhsYA5QpG4MoY5z2R9AGmRRWbJAekwX7CNjWA0P7g1UbWsC6CtAm7uSJytGI5YU2qhJOvx0DHspaFOBtb3sbfvOb34ieFRcjeGx33XVXUnvGDC4RkMDIvgoYOAh4+oONwleKirFEITdRZQCQazzXcK7VtIMYROLfZVnAeE23Jwr585UgScis5gsBWX5qPHJD7jVySVRx6PTArAXA+epQZi9/znuLZrqmpUv9/fqapN8zSoEJxkOSBZUDOH4i7Ra5Iok2EQkNViIxGEtb62Lr13JJjOlxwJ5wbM4tE6O04XmtKdfNwLnbfxJ+MIFB8kF9gQ7o1ctSdu5MzmEPUfoQrMqQE0BpJ8sVBfQ9GLeJlJDluOBxsqKAa3My1RI8T8ZDeP7JgOs/1/epIDSU4PnTp5arMmQ5cf6M9LUJ/s55Qz8jWmUF3yOTJLHA68lrwjWdn0VSg34biY0LSWqUmnLw7vIr0TzSC51Kg1nm3OhqA72ngJ6jwtcYdBthZVyRYKXG938OPP805SeAhUuAq66Z9vOYwdThwx/+sIjzkitItWTalJAav//970WmaiT7qESToxd/aJC1MIH9vedwd+kazLNKk/GGgkps72xCm9M+2uviPbMWwxSll0YiYPbtWATCnuf3LE6fnJOkUZmh0UywadJZNvCM8DzPHps4qeEJSTGNwt04Smp0uzvCsoNF38yIfGH2qhAIMIu6iq6vkB5B2mLANHE5EzkwxA2amyM3aQZ9qKFIzVk6q9S85d+VmbSV5oU4MbB/tAcIq0v4nBIkNwiSFZfnrIIv4BNnRVQNtYqz06cZ4XKMCFKDGOzqQ2Fh0RiN6fEQENmTKhjVlOuSvoNkCiWpNKoESA3vELRaNbxeRYDB74BBrx/NTti/f7/YmJ85vgOnztVgydb1yCkrwLDXhZc7juHtpRuRcvQ3AwOtUjZQ/nxAk4TEQ90p4KnfSrrivAnKICOb3S8LL4O/qMDKjGW3Jf++ijXAqWATWdnBms0mrwmA46bkcuDIoyG5LvbSGe4DzOMv+AzUZlrmIddcJqSoNCoTWpuG0OauE8YWjR9mizCzh0YSy3iZMU4DUTZKC4xWUVXB/jIcx2pVSBowFhqG7WE9iEYNMJb5AnisqXEMqXFueAj/ferQaDiN7yfB8at5l8E0MpJQlZQvUA9/ICTVpAqUQ6sOZkTyGAqWAW0RPU7y40sakfihsZ0qp0N2/hjEZtUEqydWr2HZsRcOxt7FZeP/qmBUu1BpLkedvREDHvvoCrzINhumYAb94821+F3DaXG98g0mfG3xesyK0SODTg6N6f9+8lG4SnMluaHgbXqorQ6PbrpOZHfxflHqjw4DG5byfTxWlnozUygu2SAIDUmGyGjQIdNqQle/HeXMIoxFasj3Jy0UnOK6xuuetNPBHhqvvhr6nWsMZatYNUBy4+WXgM4uYOECYO260ZeRwFCSGHRA2IuE+xGdAtnBkPs6MQjFPYnXk9dLWeEStdTe1SZeW13XisK8DJQUZUNcRZId+mwRWEslqZEWQ2c41vOEyCYzh1erhIHBQ5dU/UiIoJ9IYmgCTKXSPdx4pfSYbrCHhi5TCnYqKmKRlkAWN4kYjj1WCslrFX/ccNPY17I5+Pe/T6YPoJ7xV77CLpC4YNDnAVk3AQE3QJnTJIMUGbpydLjCk4wouxkJX1Dqik4HiY1vfetbYm28mEC7iD4GE6dm8BYF16CszZP6CFbakeB/7rnnRINdZuuy4S7taWYWEwyusVqDleGpDnpGNiYWkr5T2FMpkcoR5Z4uHx8Df7RPGGCjvBD3vuuvv/7SkHW76b3Ay48CDVWAzghsvB6YkwJC+mIF+17lprYp83jgODly5IiYQ8rnOFbkpA7aPLSf6MfTf6dNR4KMkjwzSB24RjFgTRtSDlyzMoBrWHauDnqTXJEpzW1RtYFeaJBa8p+VIVwzuFZENqomucEEOvpBtOvpE3HtITHG9ZY2Nv8mEr0SXHO5dtNnTRasGuJjKojraIisyqDvTWkorqUkcThH5D2Ax8N1mf4HCW8+r+zNGq/iifef6zaJo2jnNV3nGws2nQmL2CMuFobOA92HR3+tr6M0rwoIrJZs2+wc4N0fwFsWLhf1U4HXXpN8Vkop33kn/lUwb948sYb84Q9/wGc/+9mLm9TgZPr1r3+Nr3/963Ff93rnGRG4UZp9r3ZUjZIabAT+veVXCmJj0OvGQls2lqYnz+gwS/XYQC98/gCW2jJxalCS7tFABbNWi+upc3yxIBphw2yUlCJ0xUkIRBe4kSkAFeZY5gEBZmNzAZK9YGreHgQClwNJ9I6IBANJZP3J6nOTo6PBskZuXnIGHp976aWXsG3bNinbSpeByzIvF7JZRI6hAGnBDC5/wC+ccq0qPJNZozjGFRlzcKS/FiaLCV2N7TBaTPDrDHAO2HHPxuQbnerUOQigLizfU7p61KxOYLxqbaIRaE6WFd09g8jJtonMtI7OzlGjhRsiN7j0OUUot6kw1N0nSA3Onp4EGjAljeYjQP2b0uZCx6f1GLDibkCbwFhkH5inHwo1SvUxJd0LLN8IVCwEZi+9OJuER6KvBehvl6o3CuaNLxtSwvPSAi00vEhSLJXelyhajo7NPK99HciL/xmcKzScKIVDo0gHiYRwOrtiBqVJdFBLWs405zgza/X4SOUa/Kr+IFzBrOdbixZgoS12IG++LR27erpGiQ2hWar4e7SmxWfE+hsO/n60vRkLssMlqqLB5+/DgOesIAX48WYNm4uehz+QC7UqKJNVslYi4yg5xX4jDNQ181quAHIWx7wmNC4nC647NFxZ9SDLXvAak6zp62uCJV0bJDRCcPrrYdPkY1v+RtQPN2PE50K2PgMlJmn+7+9tx4PnQgZ9l2sEXzm5G79bs030/ogG9uNxpRngdjigNaWNGrncSzlO6KAyu4dyZDxvJiHweWZZkUzm83FhyROERnNnH4adbhRm22Bj5lVagg2bg+A45BjmNUrKCKfeP3to/O53UnUGM+u/8x3p35/4uBSU5rXhnPrgB4EPB/siRICkDsEsRN4zEjrycZC0oOQCs58SJl1UGgwOOcR6nik3TxfPR79PvC8tI53o8wzCpElDhTk5Yv3qvEKUppnQSllNBs2gwkJbOi7LnEQTSetCwNE6+mt37xBysmzASJtEalxI8N7kbQa6dwGiV5kayFgGmKX7GBeUBPjJz6RG4cwAI26+DbgvKD0lo6EB+NjHpLJ3jp99+4B//3dJ8kzZ7JHVQgz8TZe0i8g6mVhmrk1XKMY1Jae4SJu0uWhyUMZLuRhRD1ki/BhwoA32xBNPiObhFxMoOcXsLjZZnsEM4oFE/Y033ij0umlDc29TVitwjHMN5t+pCZ9KcM/nPsq9WBmsuhAgkc8K3Wjg+dP+YXLDfffdJ8hC2pSXBKlBNYWbx+/bOYOJgeQEg9e005TzhkFVBm8j7SIGrhnY5bxj0JW2HQPZqWryPFGIBsTBngaXOhgIZ7xEmY3P5Js3dz+HtRvHJoUFAs6oBamTAWM39CFIppDUiAyic83jg3JUDNjTzuZay3WFCVUcOyQ+xuuTwco6Jh7JjdKTBe0EkgkkVaLJLk81ZF9bTt5iJRP9LrlKUP6dvnoy+wPnVqykcc5JEopTXZ0yKQy3hvUZdrrcMKpGAN+I1JvxrQ76qs9TojgYDfn2t1mOBFw/wQT2SxAf/vCH8bWvfU00Dk+lbZRyb+zNN98UTP0dd9wR93XDXveYgLrDF85Mpml0uL6wcsLH0uVy4r37d+DcsJSBW2hMw02FJSLDuDDNhE/MWYQ8ymBcLLhsC/D638KfWzkJzTGdFfBFyEfRJw9m++UZitAy0hDWONSqzUCePl0EVgrTimChxEGAFR/KUCRf7wqSG+M0KmN/glNvAoM9gDULWHxFWMksBzMN7uXLl+Pw4cMi04Oa5cq/R5ZDp2nMKDWFj4su13k0j0hOuk5lQKX5MphIGERgefocWLQmtIx0YfaaYtjbBhEY8aNIU4ZMf/IZiRqVTZAogPKY+f8Esr453imxH9Cisjwfuw9WY1NONvzpq1F1eD8WLZ0Du7drdDPvaFWjub0VWSV50Ru8pgKUIarfFV5x4OgHWo8DZQmUzvd1A56IDAOSSkYzMC95o+SCoP4AcPq10O/ZpcC6e8bvbVO4UHpMBC5KowTG3gtWbkR+r3cY6NqHvs5G+DsduGzT3WGkC0uDZUMuMluQRiLB+cRgLZ0TlqvS4COB8d2l29DtGka6zgjrOKXt/zF3IfZ2d+HEYCirWxmw/0DF2Equ7BjNwI0e77gNzngu/Z4quBUJ2oPeANK15MlIvGaEV2sM1jHdRXrO7wba9wPMHkkfqzlPhy1VTg+vK++BLHtBkKzd+eYBrN0YnUT3BUagV1swzzq2B8ux/i5BEMnkEdfrHrcT7c5hlJpil3DPs2ag0TuEgeo6Qdrali9GVpoJAx636GNCEkOZScdSUGbksWKOazJ7UNBximpwZFUAJZfB2fwi5pflS/JeC2+UMgmjgcbbQC9gMAKm8AoHOkWyLmzC4DF9+MMSYcH9Qh5Xzz4jERrydxIPPgjcfAtQFDubn/efAS8lKKfATChKtpHw4HURpbL93UDtMelFc5cBGQry2liK9PQM1Dc0Iy8n6FTocgBtiOyRS9OJQ31VqLY3jFbnkNTamrc2YWKDyR9/WX8Vfn62Co2OYcyz2vCxOQtjVKYmCH04MXW+pRvLF5ZL/UIuBtDpKbg2KEWlTq5qYfYc4PGngX5WwVmkccN59cIL7ILKBiuhXi3y+OG/SXRQtoySCu3twAMPsLGDRGhwDH7gA0lXT0w3rNoC8ZChU1lwznFMJIKooUG5aSmMlPoJ7g8f+tCH8Nvf/vaiIzUeeughcWxvBTmRGUw9uM9RbpGNKaNVyHGfm5AE4jhgUI+BLFY9cqyyovZCggE2BruYQMBAmgzua3ITcVZBMSt6so10Z/DWAKsxrrjiijBSgvEd2om0c2m3KcHxQ0KDYFCViSEManMuXMggq6hmv8jksCYKriW0mSkVK9vvvD/ZWUXo6KhHfn74eapVUzeX6ZPRp3z99ddFL43IHlxcR1gdJ+O1114Tx09ChLFC2tzxCC/a4NHkm5IBK00j/eDphhzHilxX6XuT8OEeQaRiH+KcY6LaRU1qKHwJmeQZlZ58q4N774svjk1i/ec//6VIjTvvvBMf//jHxTqQygSllI+gv/71r3j7298Ov9aHblcnjJo0KTAegVnmbPS47aNhPDr15aZxAuRJ4ttVx4SjL6PT5USn04U/r9+CixKL10mVGWwUzjm+aJ2U2T5RULrEoAXcwSAKu63ruYFQ1kgPk9aCZRnrcH64VjTYztBlodw8N6yyQUKsDWWcwAkDsi8/DPSGsj7RUgtc/0Epq10BLvZKBlsJBhtlSapoGPT0oHnkzOjvnoALdcOHsNi2GeqIcxElf5Zi8RAIxqM4saJpqycCFcwIIBTYlWAcn9A485Qk86FSQ6NTo6JkNk71OlCRdRRuVy0G/Xr0O84j1zAHOYbZuCJnIVrX9sLplwgU3qer8yYxPqLBI1iWiBNk05EEpXksUTZSLt7Rnr8YQXJBSWgQPU1A0wmgfApJGTYj7yN5GFoRwfUwktDgnGp9RRAbNXXnsWbZbKDtFaDkJkFiMhuR80hpSMYDg9Z0ck+ePCmMR6PBgBJTYvfKqtPhqSu2YF9PF0Z8bGA/gj81NsDl9+OukjL8++yxmSSrM3NxVW4h3uhqEz072FdjU04Bbp+3XGSt0DCOBT/ccAfGjkOXPwBjpN5271nA3R9lsWiMSmqkMkDGz6L0BbPU+FMOrCxefD2OHHoGC1eO/X5KhsULXEezyfl8PHzyiq048ejD6HV7kVZRBldPLwbydfjMsd34zeqrwippmEHF5rs8Xo4HOk0sm2aWlOwQkHSiE0LnUKzTpeugHrEAJflShUYEocGxyIyigYY65Lz+ODLkNWT1lcB1d49WLzCowvHH701a6obBeyVRxoAznaTIZqGUEVKSFjXVwJOPAyNOYOPlwLXbpED3738DnD8HFBYj8+3vQP25c2JeMFOMDkguM4n+9nOp9w2x90Xgro8BhUFySGNEo2MeSitJZHilpreWxaMBb5KIsnNNqTESGoRc49Tp6sV5RxsqzIn3Hco2GPFfixOb7wlLPFnmAvZace9dbi90bDpuiZHpxvHxt8eAfzwt/X7b7cBdb5/6IP9Eq0Q5ZrIUtsRPfwo88ohEUHCvYsZUtAknVy5+5jNAXZ30HInQX/6SzBxwww2YdtAW8PZIPVA0RoD9xdTR1xJ/oB/+AINQamhURbDqsrHUtkWQGhqVbswayKztL33pSyLom8rGn5MB5YRIGLNsfQYzSDbYQ9uI+w2DPgzeE9x3WLUqV7um+jsZqOL3XehMcZ7fzp07RXU89/ZoATTuv7fdNgH51Rm85cDxSrsnMujMsUxCI5HMd76XmeoMwJNQGy8zf6rAhBmSK/TxlU2cL1Ww0oExC5JF8v1ZvGgVtu88D4tlBGazlOyoU82DWtlzcAqI0r1794o1ZTx5KFaB0y+VSV4SIKyEVvY1oj9KW4OfO55vJlc/MOmI/gtlrmK9lja3kgS6YHjjFeCfz0j+yZZroVsk9RXgWszj43kozz0WYpE0cgNy2e+8aJExDxioFYlJdY2dqCjJBTKSlDm/VCEqriPGKX+/FBRMUgiOc/bFe/TRRy9eUoOb4N/+9jf83x9+jt09O0afL00rx1zLgrAFZ2v+QvS5Hagblpj+4rQM3FiYWi3M04P9YZrv/HfVEKsLphFeJzBQD/g8gKUIMI0jSTRvpfSYtKM7IAWNSGTolJOF9yCU3W/VpmNJ+ngZ+MygrQurRoBogh1js+Q1P7oLOLoTGOgCzAYgLbhY9bUBrWeB0nBG324fREDXgwHPMXhUrXB7HNAHNfk44Kn5zlJG6kdGwk6nXlHKRngDbjj9wzBpxt/QmQ3A7JKJOjQa1Tx4A4c4wuRnoFWNw7h3nwnpljMg4fajWOXBmZYBdHS2Y/WySuiNKrQ6A+hyncXx3fWYVT5bNGCqG+4QUlsV5jyhXZhSGKxScDLYOHr0+Cg3kwgsNmDTzcDOZ0c191FQAixLQcPUpjPAmT2SQTBrCTB/XeoDZyNBWRIleB5sHD6VYBXMUAfQe176nbJXi6MEydw9gNc+Os3UbH7BjGV7PWDwoensWRRmMSjHhuWJbZI0pGhIssycRrIsyZMImBG+KdgkuN3pRNXQsPhJMjGa2cV+HT9Yvh7Ptp5Hg8OOMpMFtxSWQatWoz0ycyFhWELSUzL6aDAFNaqUmIRcXjIgAUvjnYF9WcYhJ7sQ586tgN+tglofIlzSNPNEL6ZYuKFgFp5tPYdBj1TdyEqNmwpmIUsfnwCwmcxYO38xjj3/DNLKSuAbdoj3Vg/14/zwECo5V4OVAzve3AnvPBv2ddTgyZ17cOOGrVi/eHHYnk0HgkFFBoDkDC0GiboHWDlUA4z0SbJfeQvETzqP6TYbsrY/idN1dViQnY4s9jA6uB3ILQJWhTTT6fgy0MKfkwIDNZGEBp1YpTNTdRr41MelADbHyI43gI42YP9OoKNder6lCaaaKhR/9TuitwYdMBH8+uvPgc7uYN+YALQaDWa9/gRU7/iP0Y93+3UwFIw10nidKbUoZ7M5fFLPJCV4tSk/dqHgD3jR7zkHj0kLq7oM3Q1tKJ2zEii8Sqr8BLB7925h6zH7WZBQj/4V+N+fhD7kRz+UruE998b+Il539kRhtQOrX26/XdKWnW4we5qEBiEHHSkrRYJDrtags8FS/7lzWfcPBLPqRsG/7907/aSGbxgY2An4peq7UOJKKZC2MmztY8NQb+CEooFoI3RYA7XKEqw0HQsSm9S9/fvf/y4yqi4G8FgoSRqp4z2DGYwHuVE2g1+0dWRSg/YO13aSZQzOTUUFEBMGmPF+IUH/hvNmz549QhqL5I4MymRRnuqaa2Yass4gZO9FZpYLidkk9frpX5DcY7UEZZO4p1yI3jKc9/T1o8UQLkVQ3YKVNOyDR/CeXLHxNhw8uBtr1i2BCiaoVcYpJ0offPBBcQzxqiFoQ9MPIrnE5CCCVc/KJsH0JZj0w7VS7ifBgH+8CjeOK65jXM/i9UbiWs8xR/9FriSadrz2MvCzH4R+r6tF+bvux6HyOYLMoe9N30pJIvKa8G/Kqot40lIkNCZb2TIt0NuA8huBvip0jvRj7tprgHQpCZI+Eokyztd4fZkvWfDevO1trAAIJU/x579QTw0Z99xzj0ie+slPfhImb3jRkBpvvPGGmJDpy8MDNE0j55Gtz0G2IbSA6dVa3Fe2FkNepwjU2LTGlE/E0jQzWhwO+ILhNepNFyualE452Ey17hmAzVPlgF3Jhpi67qn5zl6gb5ckMUXozRL3MFrexazR6JkKrSPNOGuvgTfgRZY+G4ttS6Fj1iZ1nAPUm+VmRAeaHzg3pk449r8GbA9mbRLuYLBAJjY84cEcbobnO9+Ew9sIt18LS5YTZ1tfxoKym6BWacW44AZOg4hMO393eDvh9PdCDenvY6oLOLhjnGckyJDLjQMnAhUlqLAefkgEnRqUnRlHFsrL68jrFwrk8jyu3LgYSAsx9eyz3dbag8KSJWKDZyb70vTEg85Jg5UBi24CTj0rVZMQBYuA3PnRg8TRsPE6oKgcaDsPsJnxotVj+8Xws9obgaF+KcCZOQ7Z11QFbP9r6PfuJsDtBJZNQp4tGkwZUuBbSJvIx+oHrMn380kKlO9ZcqsUGGYWuJlVGtGWZ7mJmBcaDg4Z7no43X54XN3QajXA8H7AvD6h+8VGyJxXDCgz64WGVbysl2jodrlw+WuvosMlze1HmxpxuK8PD64eq1PNCoHbisc6FjRMabAxGB6tYbgaeujV2XD7SWKGYNYuin1gSkNbNCALJ1OTRc3QENqcTiyy2ZA7TkYNpfRonJGUla9lYUERhrq9KCmbBz+cgszQqOI01maWkd6In628Ck+21KHf48IiWxauL0jMMcssLIDKYIC97hwsldHfwyCHq9KMRlcXOs+3wjnkwMHuGhjVeqzMrAxzIJihN+b6nqE2KIk2znEfoD8HLL9Xatw5Mgw4B7GhJA+7G9uxrCAbVqMBaKwNIzV4feicUJN5PBmyuLhiE1PMpf4HMqHx31+XZIVk/P1v0n6sJNH+9EcOpNB88fsR6OtF22uvYNH7pIZ1dC7yzTpAlpXieOjsA+zhFUGxHHaeozJLMF1rETaJUv6R/8qiwX8B4A/40OY8ALdf6tPk0APH+npw61UfHK0Yo3PKah2eBzM26YjhiSfGftgTj8cnNdgk789/DpEH//gHdYUmRWzQjnD47MJ+MWus0EZdPyMwGIXE5v1jcK+nJ9QonNUZdDZ5fHLFhhKs7phu2I8B/ghizMsx3QRo0gFDSP7PG5D19OXj9sEXqIdaFT+R6N5778Vf/vKXi4bUYFbXO98Z0QNlBjNIAA6HQxDxtC0iA3Dce5h5Tl+AQU9fwI0hbxP8AQ8M6kyYNHkT9k+5TsaUcZxm8ByjZdizIpMBR/n6zGAGTMqJrPhmNTUToBh4TBR8D8c/A9gMLjNoTSJxuiXOaLNwfJNk4b8vBLGSSvD6kShQVlJKMrrslipVU081+B1XX321qMKI1diacQtWbHMssVqGBDIJ1MhKMd4PEg4TJR14LWQJ82hglQ7H9AUjNZ5XxMSCqH/0z1jyh8dG/YLIa0JSgza3ksTgdYpHICVS6XFRQJ+ODlQgb1E2kCH5lTwvEnXXX3+96L3ylsWnPkWHOtQo/D3vAS5w0sOFgJxEQmnQrVuT72k85aQGpae23nZlVI08u9ceRmoQUuPnqTOgvrBgGd61bzuGgk2LjRo1vrooXEKm1+3EQ+dOo9ExhHKzDe+ftQiZlFqIA1/Ah9qh0+h0dYhm22WmCpSaZo3dRLqOS4SGW1Hh0PAmYCoATNGllEZx7jTQWg+kWSRZKjZEU4KL2rkDQFuVFPyctQoomA/07wkRGgS/3zkHEE1nbUAMmZNuVxdODZ4I+/1Y/xGszlonPSEyiROU3zn4+tjn7E6J1GBwJDc8KO8NDCKvWAeP34rDB84gNy8TtTVnUZjdgEyF5AUXfDb0m7ciF/0eZksGyYyAVmQbetkQWLo4yNIVQa8ef2xRAzRpyZMoIImhQRJkg5n3I6jLLocaNMyyHEvE2GxW9J4bgDZKaZ7D60Kjo0tcinJTLtI0KdjQMoqBde8DhpkhoQIOvwoc+IY0ztgTZemV4wfLZy2QHtHAsfviX4Fju6Xf+VnX3Qcsj1PNUb1v7HNn9qae1NCnAStvAo48K5EZRMkSoGiCvTJkDHYD/axaygCyYxhVvA7jSfAZsjDk0uHQkSpsWMm5wVJGNQZHRtDS2YcF80pQd64d8HVJgWaN5DgEAn4M+3qEs25Up8OgsYQFq2VJHBpPdP5ZSizL7iSCR843CPkpZa3FnxrP4ysLF2FWgkFqZhHS2OV30kBmAF12PKSApQsWzRKMoBYuEpoqHazaudCrozSnzpwDDLdJ/5aHqqUMSJtYA2V+/yePHsXPqasvqis0eGz9etwcp08Dz4M9GWjwL2Mj66DONiUgaMQmk22cbUjDByuTLym+Oq8If7hiHZyUgTpRhcyVy1Dk9sLX0YXm/kHhEPE6P20/ItYgo9mEgtmlOHe0GoUbssJIjagYIYkuSShJCEh7Tnc1ULgcYDWJWgOV34f1pfk40NKF9eWFoq8GHQylY8lrTNthUqQG59B/fEbKeunsYp0+07TCX0NJysiqINHHKdwc4vVgvRGbh4/226DMlH1ArA0+Eh9sAGsyA/ueBwoqgPKFMR2OSGfEpDViY/YK7O45OkpsLE2fiwLjJJp8TwIjvp5RQoPHefxQNQrLc0XygEkt2W7MpGOmJYnQ0eBXtPONp2Hc0iIRGsrqCGbvPfsscNddEzp2p8+JXd17RPULh4BFo8WqzLWw6saR0uN9pexkf39oTPCYrr4a2BJFplR2QH7/e2mscfxyjbznntjf0d4EnD0pEfuL1wDWFEkx+kjIRLnO/oAkSaUgNcKrbCUEED0IoQTLwz/1qU+JTO4LJR0ig8fAbEw2L3+ro8/dg+qhU3D5XLBqrVhoW4a0f4UGmlMIJheQwGfyBoOb3H+UmaDcm1mFZssww2E4DV+AFXMqDKER6dpKZOgn1miW2bOpbkKeanAtZ0CUvaMuuETLDC442DOD1cbKJAzOFxITtM+Y/JQoOK6U/Sz4bxIdDHSLpIhpBIk72nP0+/nvWJLWU4Vkq1zGA2WG6E/wmsrBbCakMZmK1RPTEeDm95Gk4tpJcpT3mz4kxw4fTJKT5WVo37O/IMkHEmOp7HPCsclKHFYY0afl53M9U8Yjlf1apx2RFeQkptRqQVpwH4qWqc7rGdnrjyTRhbbFUgFW4HBNUVYw0sajP3oxJABMKXiv/+3fpMe/MDQaDe6++26JO7jYSA1KKzz51JP4xsNfivp3I/V+pxlzrTY8c8U1eLWzDf5AAFflFYZVaji8Hnzq6A50imCcJMtxor8bv1y1BcZgz4cmRy9e7ayC3etEmSkL2/KX4NzwGbQ7W8TfuUzVDVeLrMCitIiFxusAPFF0VJv2APNvjn7QI3bgzWeAqoOhwPGJ3cA9nwaMCqemdhdQtyf0+9FWYLkX0IyVtBBSQqr4msgdzrbRZqUEf/Z5euHxe6BLtkGorDceCWbuXvE2qWG4AgFIry8uzYMxzYDGhnas2bAIR4+cwJZNIUeCWR5O5wj2HH4eC5dWhBx6lQ8FxmK4/Rp4/W6YtOnIoQRDAmhubk5KbidlSC8H8lcAHUel39U6+CpyJWJjNIjL+IQG83I34mTj2TEbcq97CI837xntsaFTaXBr0RoUpaXAUKMEVXoh8NofgbazUpCKEmrHX5eItrkJNA2PhdrjIUJDSXJULgSsUQLUMQwC0V9iKkACI7MYGOwEDBYgPX9yMlfV+4F9z4XG64L1wJobJvSZPj9wqDUDm7dsg9rTD2jN6HarMNB/FgvnR4556fpQqqx55CBGfKHsqkLjUth00QPyJpNJOP40OGj0yzIN8TDg8QhpKa6zSvSLpvGxg9R8/SPn67C9qwMWrRYfqJiL+RlZYqzz+ynVw3H+fNsBDHlHBD+xJmseVmVKWf6+gBMevx1alQkqZeUYSQ3Oi66TUtUN+2gUTjyw8HhLyyihQTh9Ptyzdy9ab7kF6XE0emloU1ZJlr4gaMTRqdu/f78IdkylATfLbMPPLrsCP609geq2LixyB/DAui2w6fTiGjOAwWPUOjToaGqDKd0Co8WEwjllqDl4Eoe7TcKAVpaJh4FrwhioQs/TodhyG/DqE9BotchIM+JARz/SrMUoKygQWVtTgvJZ0iMa1q4D9u0N/c7A9PIVwEAnMDggBbfVaqj1Bsy/5Tac7VNIz225E+jtRKCrBUdaurC4OE+qrjq5C3ue+isy116DTFaqxQCdaTp5dLxYul9uLkS+MQuDnmFBclguYOCS0lPy3nNo72lUzi1BVk46/MF1RJkAwIoqZl8K3HQz8KtfhH8Yn4uFaHICvAesjJgAeLw7uneHZLsCwIDXi2MDh3B59pb484uEBHtqfPrTTKkONaCPRmjIYNUCgzH790vZVqwciCVpUXMM+Nuv5SMFdr0AvP8L41cmJgIS06JSIwqx4XOE/apCOgIYCHutGjH2WgUY+GE2FYkEkhsXEjwGHguP6a2MYa8dx/oPIRBMERj0DuBI/36sy94Upd/dDBIF91/uvQy+MZuXZIPcOFsGCdvnX/0z5qy0CV9Eni8D3nrYdOUikWIiuNizwpl08dhjj+Haa6+90IcygwsM2icMNssyTdxfacPydxIaTDhKZjwzsK2UYZV7QjCoyaoNJitO1/ygPyMTGZz/JPPo70xXAI9xslTJrMjgWkaSgGsXwUoEVp7Rv6AfN9FeocmA95hqGiLpdN488Z30L1gVw+OSbTDavQSlS9lPQ+5zwvGQaAJdIiQPK1dY5TBpOdtU4qprgId/HZaAlXfDzRiOURlHmS5W48jjU24mzrkUawzxtZy/PHf+e7pJw0TB5Cj6wNyPlfY5CUdKiM3gXwd33303br/9dvzyl7+MWhCRLFK2upIZpnzBsrUL4VMx+z70twxdJvIMF6bRYJ4xDfeVRc82PdjXiXZnyPkjsdHiHMbh/i5szC5El2sIf2rcIwJvPJ0TAy1odPRiocUjfPBIUmAMqWHMBgKSfmAYPKHm5WFoPgv847eAN5hBJwcIB3slYmONQu/0/JGx7286CcyiIa7MQmU24fgVC2EBwbB3TyDgtnAVcPTN8GzN9dcB67dFlazSqmxQQSvIjeycdAzbR9DS1IV069wxTkdhcT5OtgQw0DeE9Ey5fDUAFXwoTosjQxMDNKyYaTDt4EJevBbIWyKac6sMNqjVQ/AFGDj1ACorDOpiQcyxieeyZaYxDdTf6DoFV5DQIDwBH55s2Yd3lG1Gpj6+pE1CEE2pz44NmjRXT47U6GkP9duQwX/3dsYmNdhDo4uNtGWogNxSYP8/pAzY2auA9BQGO9Js0mOy6G4B9j07tsKkeK70SBJ0LlauXge1ohy159QBzK8ogNfrw8DgMOysiqL8mVqaH/2exjBCg2h3noJFmyfk3aKBhgadDRoZzCSJ18CbWJOVBZcvJKSjVXHtNWK+Nf41/F71SfyqXjLWKMXzQnsLHt+4BUvTMwURcPLUSezTNsITPEx+/v7eGmTqzDAH2tHRW4vc/EyoYUCWfhV0aktofuUskh4pAKW0dCoVPME1jf93+Hw4a7dj1TjZRnIWpPLa8nrqDVqcrj6BRfOXxFx/JwK334+TAwOiEfvi9HQsTc8WjcHbiucLpzQakbAyoxINZ88hq0iaQyQ3rt+2GTaVDp0tHaKR9+WXXz7W8WDFIasthaSgfPcDQLpiL9xwLZCVB5w7gwUGIzxL1+NwXQNam5thKysBmGgQTCKYFtx+J9OdgL89KpGlJDS++jV6XMAvfgw0ngfyC4CPfArIzoWvWxGEN1mBd34GA2erUHBsF04f34ti+FCQbkGOJQ1ze88AmbGz9uUGfj09PaPNao0aA5ocbdjTe0hUgRYa87A6k9KPYwNoJBZ4D6YiCGDUZAhZuyP7TmPh0krYMixQQQ1jsBKKjoggMgJ+OB3DoQpHVi5w/X76Ken32+8A3v2e2F/EYAkdOaczZCPwPkywueGgdyhqH5Jhr0tIUenGC0SyxwmrRDo6WKomERXxwLXljjukx3j451/D9znnCLD9WeD2+5EUokk/mpcDA9uB0QpVRVVahPylTrUEnsBhBCDZuyrkQKNiYsj4uOWWW/Dcc89dcFLj2Wefxa233oq3OnrcXaOEBkHvw+V3YsgziAx96jJb/xXBvZdBNvbPYPYwA39yMFD+++qNy7Br76tYtT7cdvAFPEmTGrTZU+GoTzVon5DQSCSBZQZvXTAYyux2pRwzs8XpI3O/Z1CadksygXnanMzcj7QdGcxm0FYmTJhdP51gMiMr/2iLkwiIlvzAHgycE6loLi5XoKea1OBnRhIX9DkYMGaAXyl/O5UgkcJrGitJlGOL91u+lpSjImHGqh/2TGUQnlUckwGv7ZYtW4TfSsKMZMl0kVbj4tY7ASb5Pf8Pyd69ehvwzvfB3Nsr+sVGVq3wvpIUYqX/0qVLxT1lclk8ZRFlVQdVDyYCztWpvGYkE1mltX79+piVTPHktWbw1sKGDRvE/eZaPJk2ADK0qXQ4Nl6zVhhwNOE0AakKnmvpYuvSi7KcyBOjMa38/KmBFnEOyuk14BlBjR2YZ9WIzGQZ6mhBqZwlQNsxEbQOgRIzudEDyM//HghKZYWBn+2QZCHgcUka3krNfxluO2BcDDgV0kwkNMzjN9spSSsVPTWUKDQWJ6ZLHYktt0uO/OlDUgbmZZtjEhoEHYV03UoMeo7DDxfKZxXj1IERqAPeMQ6BGjosXrIQe3cfxNrLl44+b4gmQXMBykETBQ1HSnjQ6WGASj4GHo/Vmiky1PVs+hlENJ3bfrd9TI4mw8q7e07jpsK1kz9I3i9ef2XlDY8zsj9GskjPCQ/0yMiIU2Eyb6009qv2SHMlIw/obgB6eY1UQP1R4Jr7gYwLQ55GxUA38OJDY5/nNWRW+ARIDToVSn1Njh8dK3NMJTh94CmUFqdLgVMzm6hLc8fjZyArvO8MgybegAv6GKSG0khinwMGtUlyxDLuf1pbG/YNJLW/tGChkGmKBa/fjwfP1YaNXZKoD9VW40Mmmwig6mxpeHr/QXhcbugMOhF0J/lRe+4ArOpeZGRaUXumURAbqqwjyNWHZ36kCiVpafBGMbSKEpCuo6FN8lTpePS7z8OVXi1IG1NJDwrTVoqg8mTR6HDg5jd3oDaYEbUuKwtPX75JVJMwe4nZUczeYbaaMjC+IqMSbdnN8KYZxV5WYc7CmaGTIigMC1C6pFBId2zevDn8+mp0wKJbgTP/BFyDouIMs68ErMEsfhnzl0uPYOeNdcNDaP75d7C7rR3rSguhuf4uqQmzywUsWQksDK3tKYfIxv8I8IEPAl4fRWil59MzgG/+cMzLed9Y8j1areJyImNkGAOtTbBoNRgYcQlSQ4BjxOUAoklYcs/2NwEBJ7IzrKiqHhDzWZ2nx9GB06MvaxlpF+TGFTnhlUU8BpbU09nh/eP8SCW5YR9woeGIB6vWLYOWSpHQIc+4DFq1NMaLiwpx+NFfo0jbB9Q2QuWsBzbeKUn23f9+6RH3C+xSY3DOmf/5H+CLX5SachOsjrj8cqQSXEu046xvo2CgIdXNpzkW7BE9O7jvDSauR47hDqB5B+CxAzoLULI5KF3JY7YCGdcA/a+E5KXkuakND3KoVEbosB4BsNqNYybxHnY333wzHnjgAeF4T7cOugwmt1B3l9lcb3Vwf4v6/EXoQ12K4JrJ9ZP2d7QAkUWfg8xsGzrbepBXmD3az0s7gaa7o0TwNINZ0gxqMUAVSawwuEj/QtkonIhZjTmDfxkwyzuyOS/tDc4TSvtwLLFSLpm+KyQ1aP9GjjeC/gT9FVmOKhlJ1smAsqsMpjOgRik6ObudVckM6HLe8Jjo/7DPDkHyYzL2FkmNqZA/4p5Muz6yQTT/zXnOjP/parhMuSuuq1zzIseI3LtRCR4jg/mrV68WPqayP8hkwPvGcUrSmt85XeMqLnhv7rpPeijA4yQBE0lqsPcRk8VJ9DAJiqQfbbBE5dL5eXIPkUTsNo5/HgfvIec5/cRJSQFHgLEtytoxfiE3t4+E1BPGK8icCX031RRYcc1E5Zn97JIAbZMbb7xR4hAuNlLjww+8d/R39rDlw6S2wDhJSQVOhp3dLTja3wmTRoebCitRmDb5ybY8IwdpGi1cPq/Ii6IzwSDcMgZdRwN0YwNZrgCzAwPI0IU2kOK0KOw0+0fMvx2o/keoOiMtEyiN0jvAYQcUVSNhYBC3oFzKkN/9uCTtwf4Uhojbp3MDTQeBgmUArw+zioylAJt9jwObLl1oUJ8brhOSU9mGHFSaJ6YhK4Le2+6RHglCp85Aln6TqNZg1UZR/lmxuUUubNwE89NWoaS0HW0tXSgszoVFwxI7D7pcu0Qow6QphUlTMq7TTgNjurOoGJRivwJu+tykGNTigyV38rnSAHzjjTfEczxGGlLRsquz9TYMj0jNyZUYZIP6VIDXb9HlwIntod9l+SSOVxJt6czUTrJsdMFKoOoQcDbUwwVX3ip9VrxjWbJZejBY9OT3FA1bg48ze4D1CWTPThf2Py9lZqgjxiGP2ZJ8SXC07AXZCHQHtLAV3YDMsvIxGb16NcdV+HsZ2KKTTmmq8/ZWDPQ6ka3PQLm5eMy84bhkGS+dZDq+kTq0fW43XmND3Yhm4If74uvukiTwRZyTp7sHHSMeLLj97WLcO31uZBVJzrZjwI622kZxKqXZFlQskwzVzCwbOtp6cKaqGqa5y2Axp0izXoH3V1Tg4YYGHOzrE8ElHvc3Fi9GYQLOHTOSWJbNec4sJoe3Bz1uKYumYm4J6mrPAXMDKDdvDqucYYXg9q4m1A/3I1ufhusKZiGNJEIc/Nuhg6gfDlUC8nj/88Rx/PyyVeK+FiyYh79Un0bv8UNYnZmN+cHKKDog2R4j1hWvg9fvw0sdr0mERpAOa/W2oXhWocj8Yul4mGFtzgFWvVval0iCjxd8Y0+L3/4IJdoAsgqysft8K6549i9QOdzSMH3lWeA9HwEuvxpTClaHJFAhwjEv60mjrxtV3/wcOjq7xHVZkp8FZ5YZQ06XcApgMAFmW3RCw7sPCDDILdF/C+cW42yDB53sS6J8KbOZuhqQ3W2BTkEg0zGRnVOOJTqBdNiUJOfpwX78z5njaB0ZwbKMTPznwuXIikKwyFlgDBZTgpFjg87Mti1SJrwfXq4QYWuBtv4w/HVHMVKeL61FlCXc8xRwZbiTFhXMGvvYx6TeFQQbkLI3Ah0QZgcnmSEcCHjhC1QhgG4Y1RpYtAbYKbOpwFxrhDYvK0NeeAJoaQJy84Eb7wQsU9iUnd9dUAK0N4dIfFGhGUOqKhK0Gc+/BPiDSQUkNvj73LcBuqBNTVlX2wbAzl5qwdfpCgDj2OpTVoOp4kgBxgKzdPl4+eWXcSd71VwA8Lvp5F+QqtppRq6hQNjh0vor7Y9WrQ0W7RSO1X8xcBwxAz2yETJh0hRg+YLL8fqbzwtSg4RGnvGyCVVTcm2N1bh2qsBGq6zoY/Y7s2K5BnK/lvt6MWBF2RcGaxn0ZHCXgbWLMfFwBtMLJjDF0utnoJ9698mCY4/EQTwwo56B26mWo6I/zSA/xzvHPkE/nOcly2yRfGCFhlzhwOAw7SXagCQ8eH0mMldkGa5UgzEMZvK/+eabgqRRVoLwPKeT1KA9ynNk0ibXIBm8XnyOAfZYiXEktxjE5zgbTxkgEfA6MEjKQDr/fSHI5URBwk+ZNPLMM8+I4+Xcod9B35tzIhn7h/ee7+UeEElqcA8gyaSMKfDfnHu8Vvy7LBElV5cnC5nIPHTokPhs+kecZ/H62PD7uX+xOiqpfjc8j+9+F3j8cel3jrFvfhNIUZ+GGUwtmDj1rW99C9/+9rcvDlKDxhGNpHfe8n706prQ7ZIatFq1mVhonbxB97fmGvypsWo0g+nVzvP40fKrUEht/yRBFnDXrl2i9CnHkIbvLb0c3685hNYRB4rTzPj8/FWjjcIX2Yqwt6dutM8EoQ4+MvX5sGrcUKs0okl4jiGG9I3RBiy9FxgO6jWbciSyY8zrzFKAJVo/ihWbgdI5wDM/DfURGOHmGABMaVLAxMqKjGCgqf0EsOBeQJtcZlGmPks8LhS48alEHi/iGk96tQXr5t2F3Xt3oGTORjh8LbB7Q3r3g94qkaVp0sbXE2zr64TfxD4cPmij3ZMpMBhZZsksAgajuXGzkTClHbjxMCOYCzl/MiOa2RfcjLjZcZGXNU5lXJW3BH86/wZ8ivHJCZ1jSKHzu2yLFJhvrJKqNpZfDVSfBHb9U/o7+7y87cNASYKbLcsuX/4bUBMkNEpmAVfdJo3vZDawyIomPueObzhPO/o6Qo1wCfZL4TUsXwyUzk/641iGGjkvGJRkFgqNBq5t0QLKGbpS2L1dcPhkzXqV6KnBleyljjdRPVSDguEinB1uRP1wM7bkrRtjtMtZP9HkqChzFAk+o40kcyJg1GiwISsX+/u64XW74WlthyYrA3etWz/q1Bg1eixPr8CxgXMwp1uhNxphSTMhdyR8ncwvzEZeQTa6O3rR0izNrYmUjNMQi4XHly3D96ur0T4ygusLC3F7QUHc1ytBY5SGKef9nOVZo4Ht7NwMNJ9vF0Fkt38YRk0oQP3LuqNiryNBRILjjc5GfHfZlaP9nmLJZCmJIv57f/AYGx123Lf3ddi9XjFMnuxuwg+Ki3BtfrFwJHjNeT5aqw6eYICUvL1JI93/QW07rCXZ4hwi1yIBJeHCY2g+BfS2AEYLULFKahpOnK8F/G5ApxGJDx5RbMUsCDXgCs7rx34PbNwyuV42KYQ8vzTP/RVWnxuWTCtKrGbsaGzH5WYjqrrZPJwpYvOi7+/+9iChIT4t+KMFPm8eNEHLRr5rrfUtGBlyoGDjNTEbKdIRp4NIYkJGs2MY9+3dDpfPJ/aEppFhnLUP4vENV0Mb0ZCdVTd03uk0RdOw1QT34TC01GB1ZRF2njkPK/XmeY9ba6NLI0Xiy1/mYhX6/dgx4C9/AT7xCUwEvsAJBCCtZ2qVD+syAzgxaEOvexg6lRYLrHNQZi4L33d++k2goU4iGHg9Th4Gvvw/gDHxrNOkcccHgD/+JFSdUTYX2Byn30hklYZMaMjg78PtQIbC6ddlA+nbAN+AlMjCNSTF8+amm24SdsqFIjX43TyGSwH0h0g6TlTP26AxYlXmetTZq+H0O2HTpqPSMi96NfgMJgQGJ+WgZiS412UZ5mNOngM2bxEyrLmTkoecrsQpruv0L7iuc6+inca1nYEpNmBlcJO+BfcUjk2SOgwi08eg/055yVRL48zg0gGD0fRRlRn2yszwiZJeib6Pvi+T96ZKjoqVJjw/zvtopAmPk1nxJD4ij5lziu/j++U+EMkGyfkZJDmnYj2QJW25T95www1hUl+pkM5KBvzuyF4ODJLTXuVaFE9iinE52qapIDVkcDzTv7mYwcREjivGg7iOM97D5DFZRovnICfAJtOHkOcdWdnBec7YEsdhLMKdf2OcioT3ROcaSRPOFxJtiVb48ty4N/P4KHuaMF59NURoEKyI+spXgNWrJUnZGVzUuO666/Cud71L2CST7XGcEgvmn//8p2BEszKzwP/mWCSZiVSAUlB/bToj/i0rtrNJ69OtdfjI7OS/h2yhCP4FjcB8H/Db1YpeFQrkG224vWglnmqlFrFUeWIMBngW2+Yg15DgZGH2qnWckjoak1ffBbz8aDDeFQBs2cCtHwCy8qXeBpGNkRnYY1ldGoO5SjmfgJTllySpcalAkozyiF4TGpUBDm/TmNc4fE0xSQ2+f3fPSZwaOIfGmjrUmLtwXcFa5BomL/8SC9zUWc3EzZ4bBh0I2bAiS8lMFgYUGbiWA8gsyZRBQ49OiTKona4z4e7SK/CP1n0Y8bnFsGEvjcuzE3SmOZ6GGwCfEzBkA2lRxui5E8Dh7aEgyat/BXoYwAuG4Fhd9PdfAf/+jeiSK5F45XHgACs/goGwxjrgzNHkSA1eN/bT6KaUiyLTPz8xjfBpgdsJDA+O7fJtywE2vz2mFFss0EGlYaPUPKbhQENxPMeBDnlJ2io4fL3wBdwicK5Xm1A71IABz1CYDFuXuxdNI+0oM0mfG0+Oitk/NGCtOh3uLS3FY01NwYo3Ce+bNf79+NnKdfjQi8/gYGc3bBXl+OTcRbijKHxT25C9EFl6K9qcvTBm6LE0vQKNdVXwB5gtz/uvEkNJp85H0awKscaTJOT8YgZYollf8XQ8uefct3cvdgWbGT9HrV29HlckUeLKLBRWbPi8JDWUGTLST65nMlpH7ILQIGSSonlkCDu6mrCtoGJUvqvB4YBNqxX9S4jCNCPsQ57R3YCESAmJbwA/P3sagx6PIOlJ+nLEfKvqqCA1aEjywWy08nSpRJxXjYSGEnvPvIl3bI3TL0HG8ZeB+oPBcR4Azh8Drnq/RGyc4vPBc+exsK+4Vg2t8vqz9wAD0ZFBFlahPP4oxY4BViW97W6p2fMUgU4E12XONWYSOvfshbevF9dUSBVNKwuycaqtG+osC5YU5gLWGEkBgbE9H4j8/HT0NHViQDUAoykN3a1dohJo24atgjyiYx/LKWUWFtcFmdh8rrkB9s5OqDLSoWJTSmYeDg7gzNAAlqRnCmedVYKcv2xSrqzwSAhaPbQaDZaXF0AjE5YkssYLWtDeCko4jIJVLRPU/A0ESNmENxU3aFRYk5kOrTqGhFVdNaCQuhPf39UBHDsIrNuEKQN7yXz0a0BXq1TBmluY+NofK9Ei2vtZjaueOvkYEgrveMc7LohcJ7+TPsaf//znKfsOrqX9HidsOgP0k0xwoS0nB6441xgoZhVUMjBpzViaEV2iYQbTg+zMXNgH3Mi0TY5Mmq6+Ggx+kVCjjUjfQQ5sssKJdhDlfWQfg4EDBo7lAALtOlZhRu2bNYN/CezevXuM/AfJsMn2OUgGUyFHxYoB+gMc4+N9Hvc2jn8GV2lXRdpIrFpn4Fluci435U703OTY01SA/iFJTGa6r1kTki7l2sPvvZCEJY+B/gWvLf2zWD4ZSbRUSh4R/L6kejQcPQTseEOKM2zdBiycumbj3BsoK8vkJPpe9DFop8sSXByzJAcY9+F4S4bQIHjNWXXE2BH3AFZfcJyQ2CYpx3nG5+NV3tBnIInHY2I/HZIksUgK+iyca5wT3EsmAs4vfkdS1Vpsos7xrZxfJDaammZIjUsAHC/ce2jn/9u//dukPkubqtJwMi1TgUGPSyxIbDxLsMcFQyHD0XpPJIAGlwMD8yuxZ6AHFS4fnnvmGdx7771i84pmeKrVAVgUMlMy0jTJOSkJYeEaILsAaKkHDGnA3OWAzhCq5BgDVmpkAYGIoD619PUXRvt4qhEIjMATOCIaXhotDeju0wNpYzeseFtYjb0JZ4YaodFq4Pf5RLPtF9r3451l1ySdDUdjgZsONyNCdvjJkJNpN3mHAOcANKZs3HNPbDkubjQ0uPjgBsdNiJ/Bz6OBQsOSz3GDYTBZDnSxKuPd5VvQ6eoXYcp8YwY0wV4KccGMz7ZXADezuIN5wpnLgYzFoaD8sVeBmgOSzJnbG9SL90gBodGewAGJ2OjrAvLjVMZwvj7xEHDqUOg5uWdI1WHg2ruQFNbfCex6DOhtlX5no/C5YzOOLxi6mqNXXbFKYwJZf2SwaQDxpxI0SORqAWXWdiQ4jsza8HLOXveA+KnV6+B1e0XPCqLb1ReT1FDKUdEIY3YVnegHV69BmcmMF9vbhNzNVxYtwtqI5nWR4Pm0nz2L3115LSw2mwiiRwuYCZLPVioeMvrcHQi4VUGSOQCvn3JWncjRSwYzjcBkS8ZFhmaMY/5WVRX28PoGs8dYWP3B6mp0ROjYjgc6HY0NDbDNYo8OktFSUzSrtgg6tSls3xtzfFBhkFVTVPMZGsLNO3eigVJOAD5UUYmfX3YZfrx8BW7b9WaQFw/ApNHgG4uX4pWONvzxfNMoQWKjMqFGhT63tL/yHHg/RABOY8B86xzU28+GndvQgF2MkSZnPTJIgsbCyKBEaBCy7M7wgERscI5Sui74uUatFhtK87GzuQOX69OgZ7UGjdmSWaIpe2dLS8gR5fV/4FPA+YbgZweAI4eA7/xAqoBKIaixzHJ52gUc43S0Wc4tzqHqiLQG8joa9Bjy+mAfcGDpLCOwMEREj8LZDfQeA3IiKwLUyMgswcas2chszsaJjioYnFqsXLAc+SOZKJxfKKp7aOTHAjPa6LQxu62qvgYBnw/uunPipy4vF5rMDBzetx+OjExxL/lZE86AXLgRaKlGltUUYuKWJEAI0NngvOrrUzB4GnpsEzuOGD0HYj8v9UFJ6vlUgcQ2xwudrblLoq/9nC/nD0u917JKgSLuESrAUgzo04HgOj2KvjOArWxC+8hEQYeDQR7aHxOVI5goSKAz2JEKvd1oONLXgf+tPYgRvxd6tRofqVyJy3PiV/nGg7IC4LXXXhO22jXXXCP8ixmJn0sH3He4rsaS40m0QpNBnsnIv9C+o49Be0YO9HDPZuBn+fJQYh99hFgBaI492jZ88Ji4tzHBgj/pu9P/YBCKKgYkRLhHzIzVfx1wPDH4SWJLCY4XJk4QDIhy7EzHuJD9msnKUTHIyr2D8yKZ45Z77sRK/JDnEq8ZHwz+jtdcmbY151UsHyNV4HErweA1r0Nk9cSFQqx7yTHIDP1oDaQnA957jqPIpFCC944B/tFqhp1vAD/4tuSDEK+8AHztO8CK1CcY0DYgSSE3/7722mujjiGSz5TlilW1HQ8c8yS15TgVxzV9GwaR+d2UQ2fiViyfQPb1ORf//ve/i2Qo7kOcU7yPjHGRSOfawPvHz6edNtk1Ih7REhVM7mQSnBI8BvpsM7gksG3bNsElXHBSg5Pl1VdfxRfZ9FFkPLnQ7aqD2z8Cg8aCHMNsaBJt1hgFT7XUhknSyzINS4N9L5LBky3n8K0qBsQlXJaejf+64w7BVnIzZ5kUG9goMyP7PcMimKSUoJL7FlimohIir1R6RCKzAJi1DGg4LjmzDA5kFwPzrwLadgEDkuEh/sZmkpq3ZraNJ3BcNLskhoYc0Bm6odPkK6R1JJg0sTMyOpyhRp0qBtEYw/d7xPOFbLqcIJjxJOs/Rhp73ES91duBvmA2KgexJQfIKAZKVwOG2IElZvXJgQPqZMqZXtyQWY7ITAyy+RyvhE6tRXFakvNhqC5IaBDBsd13DLBWAio98PojEmEg6QhJ42loBBh0jGWMOBZN4wTK3ngWOH14bOWCVpN8Tw4izQpsfb8kOUU5Hna2vZgQSyLIMrFqIBoRNHqUGZ+R2SfJGhKZ+nQ0OFpgNBnhGnGOkhoZuvEJUX4XnQ2ORzrtHP/fWrpUPBI9HxI0DACNl8XY5x7BWXuf6H+0wJojpHTyCjNRffYEKuYq10o/fAEvtBy/ipJxGm0TLRmXcZJNrMO+CehyudDLps1JZOEyw6W/dxCLFq3HgPu8aNZemW+AuzsDUPgcJSar6BXg8ofE5VipON+WJe7723bvQtNIqKT6N+fqsTg9HR9jc7mt1+DZ1lZo1GrcVVIi5MFu2LknTJZq0BOAXq0SGfzyuFE2w15gnQu9SoOmkRrpu/1+nD1ZjxUbl2LEO04ptyuKDBy/wx18X3Y+UFc1SniQ2Li8vBi7jtZgWVY6rMWlOLHuangPHxZZz8xWEse4bw9wLrjPyThxDDh1Ali2AqkEbQCu62OaFt50L9DSAPQH9xu9ARnz5sNgsgL3fASwZo4ljzvekOS2+v1AuinYC0gN6FYAKmnsLCyZh3S1BSiUsrtNOSbhNND5iGwyHy3oy/v2qRXL8Mqbr8CTnwt3dw/Ufj/KVBqsLC3D0jjESMLILQOuuR+o2S8RtiXzgYoEr/v/+3/A5z4XJMRpu2RLzcEnANEbIlCEAIKEdhBqVZwszIo5QJpJqgCS5bLouM5LMAuP8gU7dkh9OVhBmUiAgET/g/8j9Z7i973yBPDOTwIV88MJjT2PAHI/kNbTgL0HmH+lVOFbuAY4/4ri5Hks7YC9BbBGsRM5p6aA7OC+c+WVV+Kll16adlKD38nvTrbaIRH0uEbww5r98ATXIrffj5+fPYwykw2lpsnLeG7dulWsYY888ogIXFBW9KJoWjqDccF1eLLSLVy3aR9NBMxUp4/BpBEGliLH/759+yb0uaIvYX6+eHDPZ5CLZDftJfYRYFU4ZUdIxKSiae8MLn4wUMkA5tVXXx3Tv2DgfqolqCLnDu3licpRcd7J/TKSBX0S2rzxAr4EKzU4R+jLsCIknuztdJGEXCeYgCBn9ctVWNx3LmaikgkT9CmnQi6L6xsT3CjPx+9gEirJOo4xEruj5PCfHpZ+BpOWhN3210emhNTgfFOqccQCx9f27dsnXPkgE2okCniNWZ3HcU3w34xVxavGoq/O60Y5KKWUFSu7CBIaJDcuKG6+mRqlwNGjkl3P+8cefjOkxiUDxrd+8IMfTLqyddKkBrM96HyTDGBAqWF4HzwBZr8FMOzrhsPXh1mmtRPWJD0+EN6AlmuMQa3GtfmSREaiYIbrd84cDYvHHhnowe4CB+5etUqwmTt27BCsI/8tg7InkYQGSQ5K/0wreOLrbwcKKoH+DoDNcJmdzuApSYycxYB3BDBmAbrUlu9NNwKBIfgD0n1XqwqgUpkVshNDo4SGx+OD0aiHKkBV8ko4fK3BXhpsFB47u4rGmhzX0Ol1cDmc0KcZRbVDoqQGHQ4aCNH0yEl0dNadxIK0ZulLSArwJxvCOvuBnnrgsvsA3fha3pxX+/fvFxOdzcBooPDBTYbPy3Mv6UwWHwOMSiX3IJqOSs1ce1pCzwlDKCCVSUUrgSGhEWw4HBPnqsOlogj51w3bor+HwaA9T0tVDxzv624OJ/zEYjDN8zBR8DhzioGe1mAQTS1VW81aPOEFn9kaDO7IhulkDdS5lnKctZ+H1+RBf1cfLBlW2LQWzDInntEjy1HR8Ui0TJ3ZMXQA6KhzLrIHgCHGJlY12I0f1eyFOyi9N9uciS8s2Igsaz602irYB4dhsUnrg1ZlCJNwkkGnRFkyTiMx2ayXSrNZut7BMcwrb9FqkZGkVAPPW1QFqnTIMkj7TN5iCKeDxyWXiFu0enxx4Xp878w+OHxe8X3vKV+Mpem5GPB4RKWGEpz9u7u7BamxyJYuHjKea22BSzbSFWDT8e8tC5Wp09BVBjEqzBVoGTkrjNqWc22YNa9MaijN5sTxYMkEdEZAVJvI/SP8QFZwXG25Bag7DXS3S79rddC/65PYZMvCudpaNGh0WLhkichaomPKkmdBtgxLhvgYBA30VCJmub4tE/jEfwPnzkjrYcU8LDaaRGM/ny0bY0axZ1AiNIhhl9QLi2u1eZ4kTSS/zOMR1z8yo5dOMzOs9p+vxT9aajCsDmD5nHl4/6wVyNAbBcHNChJZhu73azfh66ePok2jRWFLO74yezFyU6kpS2KDj2SxaRNA+SAG4qjZzQZ+SZbSK6FRLYA/YIAf3VBBC7WqAmpVnD2IDcE/8WXgwZ8Avd2A2Qq899+BggSqRVhh8v73S6XsBOf8j3/Msqv473v1KWBEqqQS6wbXsGcfAT7xzdBrGlnF4QrfGxsOArPXA1r2LfFJCRGRoK2nhKMJ6DkA+F2ALgPI3QjoJnHf5eNR7DHcg0gwfOYzn8F0gtlb119//ZR8dt1w3yihAQV5XD3UmxJSQ5b1u//++8X+zSD1DKlx8YN2CUkDBvknA+6XE5WcoU3FrNdotj3t/8lUkMjgvkq7iL4ESQ1m4rKhOEkNfj8JD+5JokpxBm9ZMBDOuAeD8yQviFQGwJOS/4khR0XbWSm/Gwucb0xiYhB3MlJGJFKY3T+eX8PrxGsmy97K/S2mqtn5eGDFvlKmiMdB/2e84PWFAscGk+MYU5nKhAmuY3Izel4fkgS8V9yXRxGsfFcc3JT4FzznRJvG8/7xvpGUISEzUXAs0+dmwFhO7uP50x7hOk/Ql+f6L899jqXDhw+LZIzI8UyC46LpVUIi7Je/BF55BejuJosFxOgXMoOLE+zvwnFHTiFaXDVRqFPhcMil1UOeDniEpEZoA3P6BjDim1izGYIat5Fba47elPSG2+EaCctUlfXGm4NOJwNNLMPl5smSLOnYPShLy8UCayizit96dd5SmKejX8WIHXjlj8CfvwU89VOgvR6oWA6s3AbMWxfKBue1YKY+M/cucULDH+iFN7AffjSIhzewD/7AQESbduD0qQasXiMt8AHVEAyaIeQY5iLPuAkW7ay446PSIgWAOByK5paj8RSbwbMpbuLsII2/WCVyBw4cwBVLg5UbPAxuBuLfQXLA4wA6pQzo8UCHmBUZNM646cvgxkN2nI4NS8aThp5B3QhDk0G62j1AS7CJdySiXlMVUJ5AaT2DSJHvZ9CQjVRXRMhK8MYM9QLP/wZoPAM4BoHuZuCfDwKD4RU5Fy2oy33jB4GF6wESsOz/c9u/x5CRGx80IFh1IJeCpwIka7fkrsKy7AXIVWdiafp8XFdwhdD0TwYcm3R62QQv7vcFAsKwkrNGHm5oQMZTT8H4xBNY/tJLqI0I1PP1/9/Zg/AoegnVD/fh2bZaZOnLsGjOCnS0SeOBJEGZaWXcec+sHBr3dI5o2MoGGZ0Rkob8GQufX7AACxQ6otw7Hl6zRvxMBpzPzNyNtqHTuFZeQxIYD66+Af+7YiseXnsjbi2WMs7MGo2QSVGC9ywnRiZzZgzi5f8tXI7SOBVWvJbzrEtQd7IeGTnpyMrNEKvvLPM4mW+smtrAXheKPXLhZqAw+D5+50e+IlU23HE/8ImvA5ULoM3Jw9wNl2MVZfuCZdgsmWc/C4Ely8JlpgSpaQDmR2/4OlEwoMNy/ZjBR4MRWLACWLQSSJPILgZ8uQ6Pcd7Z6yByjfX6AE2I0GamE2WmOC5IbkRCm5eFx7wdcOTZMGAfwvG+Dnyvejf6BvpHGzDKWJWZg6cvvwb7t92Gb67eiPqjR8X5TDSokFLMng284x3AHXdMitAgmCSjUc+GTr0OWvUqqFUJSDpUzAW+9X/ATx8Bvv8bYMnKxL7s178GWhVVIbxH//Vf47+vrzskv0bwHsgNw2WICo1opEXQ4WWSSrS/K5MvXL1A1y6J0BDHNwB0vD62yXgi4DF6aoCRF4GRFwDXYSDgHS0PZ8Yg18/pAr+LyUb87qkAyeNosE5B5SdlNbimMCB9UczHGcQE7w8DqqnoL0HfkhURyYJBpGiBUSbe0eZiQkkqwEAZbUvZ71VqXPPBIMN0zvkZXBgwcDpqa11koL9Am2w8gpAVCgz+8lwm25uB85++Cm2sRCDL3jL7ncfAGIGUQBkQn8HHdKz7XLMi7Uj6Z7RTSdTG83OmEkyOilb1Qp+QCT1M2pxqUB6JxC1JJ9lXDFtjV6wKSU8RfM2qUOJXKgnzZAgmVpKQYCD5kyw47ujvckwwoTDa/kRCkw8SP0r5MhIatFui7UMkSBj/pX+hjE1dMDCexOSXd71rhtC4BEEOgZXNTJyaDCZdqcFBza7lRADRF38/s80miHtLF+Jrp3YJt47riz8QwLvKk2ebC40mEQhiebkMbyCACgZbg2BpFrPeDxw9jJoCLc4zyxLAmsxS3Fm0ASN+F3L0NmTop4E44HG++Dugr0NyjNm34MWHgVs/BmS9dUuCfQEG+5UbfwD+ACXIVotNSIM54jWz55TgfEM75i9g1qgLAbjgDTDA6YNGFT/TvCgtF+k6CwY8dqg1GlizM+DqHcasssSva6yGVjLZoTHHM144kD1SAGGkTwpssDcKMzOjgBsRs5W5KdGwU4JauCxhZECMQTVmIHDT5EbDbHRuaHRQSH6YzQwU0tjRQWUuB5xdwFCweWpABbT1SUE3SnIpstLF8fJ3pydU2jd6AGZgyx3jX7DNNwI1JEsU1+WWdwGLI8ovh/qA538H9AbPk1mqcu8NBmnOnwKWbsYlAQZ1N96aso9joFfpeMI9DHTVSRnA2bMAU+LVB0OeHtQNHxXVdby/5eZMLLLNnvCxMYOKWTAMzpI8kGWMZHDs8m+UqeKY3d7VhfsPHBj9+6nBQVy3cyfOXH/9aMB+xOfFoCzLokDbiF30AznU2AyvOQsDbguWpi9DmiaxPkLKknHOG84VZq4oS2sjka7TYf/WrfhHayv6PR5szs3FogkGZqM5Nry3V1xxBY4cOSI2dzlQzQoWSlEpQfmt7yxdhs8eOwqdSiWksGw6HT4TQ7d7XXYOrszNw46uTkHCcA9dnpGJGwpLxLGwGSSvQzSJo2xtHpbkXYaS2flQQY0CYzEs2gQywLNLgOs/ATgGpGoqfURVmt4ALF6VUAbhaEZTaRnwpa8CP/wuu/ECFqv0e3byUpTRwO/h9ef4lKX9EgXfQ6KZazAdyNGKC50FsMwG7HWh4LTWBFgqhfMgZ9UxQ40OO8ckHS4lDvW1iaXY5/Uh4PfDZbejRQW8dnAf7thy7Zhj4b0ksU7pBpIt3Ksu1iw9cWJ//xvwxz9I/S3WbQAe+MJo7xpprnQiAGbLGaFC4YSrfkeRrNwhiUZlYIPHxGwwBljiNd8sKgfaGkPEBo87L8I2ySoDmhVJBCJBJT0kT6m3AUVXAK1MXODnqICCdYBRQWo4Ix1VDpYRwNMPGJKcG97zgEfRUN3XBrhJHq4U44e2BPsMbNmyBdMBShzQjonXV2YyWGDNxrL0XJwY6BLEMNfGSnMGLsucmExhPJBE51xkAJnrDH2NGVycYBCH+7Dcb2oy4LzhnIm0ieKBNkmsZqxMfJiMFEk0kLigP8OKDSV47gw20jbimJX3Yu5VJDp4jPQxmNWuJNcvCFrPA08+BPR0ApnZwG3vA8pCqgszGB8Xspn0eOAewKRTBuUZgI0MtDLgy/GZCjvHH/Cj2VGNM0MH4RxqQ76xHHmGxOS3ZNlbZr2zYoRzWa6Ang45N9qfTJSJ7KHB60fygJLWrECbLikqfqccjFeqoMigTzuZzOxUYHSd/8gnAPsQcCjom15xJfDO96Xse+hr0e6nvZ+sWgD9C/YcoY/Af4/Xw4UEH6W1uE7z3nM/I4E03r2SCWz68yQ8osl+soqPn0+yhX4jyQ+lisQMZjAR0F/905/+hP9HueIJYlI7GBdtZpc+/LCkQ2fSZI+RtFFDC6MmPWyzYCNaX8CHbEMG9JGZjBFYnJ6D7y67Em90Noqy8I3ZxeK5ZGHR6vD1xavxnycPiIoN9v5mQOihc1Xi581Fs8SGSCb5tY5aDKutMGZKQasDfU3I1Jtwbf7Em70ljYEuoFfprFL6JwA0nLy0SA0Gop29UvNyZh3GW/S4scABdXD8BIJufAChMj2tqgwqpCEvpwc11a9iZMSFtLTQousNNIxLamhUatxStAH7eqrQ7RpA4dylMLd6YUqw+oaLfSydTY4hsdlkFQKFi4C201GlHJBeDFS/BHQHNxn2rFh8C2Ab6xjQWRjPYeiy98Jh9ECr82Dzig04eHovqCIUYG613YOdbz6P3IJBLF02i18GrWoJNDlrANs8wOcE6o9KWt3ycVL7nT00SHak2YAlVwL/+BOglgkNFcBg6/2fByxxgpw1J4EDO4DOFkmHfdQQVQHtUTL7X3oE6FNIzpFkEUTLFGyWPg9Qsxvob5POcd7lgCnOufA+Np4GOs5Lgdr5awHj9MlfcWyNZg6O9AOHHpOayxL1u4BltwOZ40tHef1unLUfYQeK4DMBtDnrYPcuhUWbfDMyZRaM3IiMDrzcfLCptxe7enuRXVqKPBVnL/DPtjbR64HEMsE1+dzwsKjWYG8Igj00zBodhnmfgqC8HCsPdvXsR3tvJ/LK8tHpGsLunkPYmrcJWlbIJABlyTh7EiTSmMyk1eLesglI70SA95DB7GjG4sqVK4UUFR2TeLqSn5w7F3MsFrzS0QGbTosPVlSiJIaRSyLjj+uuwEPnzqJ6aBCzzBbcm5MvpJ54HUh4Migf9b0aDUwqC+ZaJlAWzkpCa+I9imKitwf4zn8BjeeA/ELghz+VghYklVJU4k9bhgEdkhnxyK1RMABPOayMrNFjYMCXxBQDsWEyUjlcJ7IBV49UoWGbD49fahCvLCdnQEFulshsMhly5dRgYyts5cWCiCf0On3UDCo6GMywlQMUdHCnujnlhPHKy8Avfh76fddOwOsBvvVd8asf7EnVPGpXBtAGdeCyyRMbyVaYUDZLJvN5zRmcGC8AtPV2qf9K2/mQBBYrk5QomA84+oCzeySLJy0DuOz2cFshY7bUNNxjlypxtRFzVVSYRkmiSKLydBS+9pjPca1gJhV76E0XqcHv4ndOlbPMufWF+evxQkc9mh1DyDeacWNBJXQJ7iPJgMQ+5yEDB9Gq9WYwDRDSyFxTSJIyKWk+oIq+96WKfBLJWEGN/kQkabgHsJqHe0k0TEWmtbKXQizQJqGtREUDEnT0g2g7kODgnsWAMzPVkyFvUobhQeCRH1MvRSKRWSX3x/8FPvbfQPoF3Pt6GoH6Q5KvUTgPKFse3wd+i4JzINHxHwucQwyKM0DOwCv9bP5koJhzhuMuVdVLzY4z6HI3wQ+vkFNvHqkWvWFzDIlL85IkZDCZEqH0NaZLjorXgEkt0RqDM27BgDiPSWljphq8H3KfEbmvSaw9nBUxYb7tNIPjiOuZSFI1mYGvfkuSoaKNGcMvmgi4l5AkmIykIe8Z1RpYGTEeqUG7P7KfDJMP6Z/zWrOiSAk5wU9OIGHVVrR+NEyw5f2VG7qT1L5QUmszeGth69at+PjHPy788YlW2k2K1JCbD8mZhQaNGaWmlWgdOQlfwA2dKg3FpuXQBokLj9+DHV370B+sgNCrddiUsxYZ+viZn7MtGeIxWVybX4JF1gw8cHwvmhxDImDe63bhO2eOIEtvhN/jwaFDh+CfUwCjPtypqR7qnF5SI5bhkwKDiITS8f5atDq7YFDrsCR9LgqUmX+pgqsfOPciIDeWNRcA5ddKDTCjogVaFUs0Facp2jiEjw+NKlc81q3rxsGDh5GdbcOcufIGnlhVkFGjx5V5y0OxlKZdYlGPFdwj+HdmXhCxDAKWmYvFniew4FogfwEw0AJ0VQNuu0RezLkKsHeFCA1x2B6g6nlg7f1JN/o8P9yGnT1H4cl348DxPRjO98Jur0WZV7omelMfNl+djpMnerF/XxUuWzUP0B6HGhuhEnMvXSJTmk+GPpTVGrnZwFUfkaSUiPuygVf/Dgz0AQWlwLV3xyc0qo4Cj/wsourDHwwCBoBTB4Hr7w69nsGszihEB0kNrUoKkpbHCK7y+tGB8Ae1+yMzwyPB49n3d6C7UToWHmP7WWDLBwBDjMX06KvA8e3B+xMAag4Ct3x0wpJSyYIBEUoPCNTtAjwKSQBWa1S/Cqx/77ifM+KzKwiNEOzevkmRGjJo4NBwovNhmTMHt9TXi6baYHBVp8OOLVtEP4potUx8XgaN4H+bvQo/rd03Sn4UplmwIsOCGrtfGFb8LoY6Hb4R9HsGkGNIzoHl++MZhzu7uvDJo0fR7HBgTVYWfrN6NYonaeiyWqWq5rgo39Wq0sYY+zQqqc0b2V8hEjcWForHeKAzOdTXh2uhwTUWqfG0z2wTnx8vWEiHhMGViWqCpwQjDvh/+3+AQSvN7aFB4FtfAb7/fykjNDiOmKlK3diEGpS9+DTw1F+k48nIBD72BaAsVF3BbDxmYzF7VYDX2DpHegTR1tIYlajm2JD1bWVUmtNh06mgK8jEQEsr68Wht9nw5LnT2LxhI9J1hqiyCc2OYTx4rhZVhw7h1iuvwn2cLxdbQGXn9vD9gdd07x5RBREYrgds8n4grxb9CKATKkxjYscHPwgcOgRUVUm/c7349rfHfx8J7w98AWhtkAh9Vm4oJdkInvvsDcCsNVIPGhIXriFJwk2u1iCYcBEr6YJVlwOng71bgtfJmC/11kga6rjP0en41a9+hW9+U9EXZIpJjY9+9KNT+h2sfLu5cOozuun8M1OSQaVUZ9rPIAGIqlRm4Mq9nkgsDQCBjUCUXlxM0KDNTz+X1dDxfIPxQPJaJkhi7bmytjzXf1ZHRAsUMTuW5PmFAH19BsoYUKNNINtN3Gson8lrxSxwnqvcl2Ha0HgWcI5EyOi5gYZqYHlyVZcpA32L3X+RDwjorAdcw1Ly1EWKqSKPZdnVyUpCEawOIpFBW40BVsowpzqo2uNuHVNRzeeSITVkMCOfhHa0JKapgPCJ4shccf1INanOz+O9YLIW1wfeZ9q/4yUIMUBPf4RE/4VKvKG9znU3LIBPciOFIEHA/SOyCnsi4HWlJCfVBZId9xyLfET6GATHJ+c/bRRZnitaTIzzTklAMXGRfstMlcYMJguOO5KytCOYPDHtpIacRaWERZuLedYtCAT8Y7LpTg3WYiBIaBBuvwf7e49iW8GVmC5YdXqcZwNiBTRQYXtXK74UzIx55fRLGFLInqiCTVXHBWW2fH1SpgiDg1EM5YTB78vIA/qDWes0NriAVS7DZLGn5zjOO0JVIJ2d+7EtfwNyDCk2lpu2hze0HO4AOo8CBatjvUG4Gkq7Si2kj6IH9/S6Iqxb78S5c204W9ssiA01JtbQjk4LCQsu4jTemYHEzZabHVlDGglc3BkIjGaYMUuJ0gIMKI0u7vxJeQk+KjZIVSvMnuTzNa9GVDXRCB8B3I6wYAY3H5Zn0hDhcUUGYPfs24uX2/eKKiY2PR8ZcqC24RxyTWnimB32EZgtOvT2OlFQmAWz2Yi9e07jik1L0TfQhNrqPnG8VosF8wsXQc3KEoJSWCtvDxEaBImMd/5H4hd1+/PSOSoNLF8gFCOJ1K2mjji/T9FDYVQuhI23190E2KIYPnQUdv8ZGO4Nvj4N2HAfYIuTMTZIUilIEBA8RvcI0HwamB1FQ9PlkAgN8dpgptzIIFC9H1g+PVmrNGBG9V353ZG0gCuxhmYyyTzmeVVqMmWUhtvHzp5Fr6Ih2oDXi38/fBh/Xr8eP66pwaDXKwgLzoS3l5SgPGJuLc/Ix7eXXo3qoR6xBvP3Zkf03h2s4kgl2Iz72h074PH7BQH+ckcHrtm+Hce2bRvT0yJRkOwf1p/BmZad0OW3IdNagHzjyrAG58ymoVxQvOym8UCJCq5lfD/HDQ1ZVqPw35QBoyEf77PpnLy04zUsXLsShWz8faFQUwXLsB1DMMGqCxIbzKI6dgi4aqz00kTA7FNKFiREaJw4DDzxp9DvgwPAz74DfPv/RmWNOP5ZwcrM1miEGckiZkRFZkpFQ6fTjj81HoJBA+izbLBlWtHd2Y/2863QZKThk//4M351+7thlPtrBZ2OYw3n8L6aE+g734iAz4eDp46iZmgQ/51oH4kEwHHEIBv3RGbacdwmPV55zcJIb3ECQMcpYOAIYIsWRBgrSaesBG4Z6YDT50SmPj1pkjMqWJX50ENMtQMo/8jyfMopkLSKUbEZdi6lCcj6cd87vwvoCvbaop2w4Hogu2KsjdlzHKD9pjYA2cuAtFygcBvQfxLwOQB9NpC+eGIJMLoKwBWh/68Lree09z/4wQ+K+65sRjoVYHCEZGOkj3GpgkGbW2655UIfxr8wKN2p7A3BNYe2STfTJaK+g9nO3CtJbFBeY6LBU9rvJLFZDUi7nHIrtOWYzUw/gyQB106+huR65DrqCSbccU+PVcExETCYSL+Hfgsz3fn9yu/mPsUqDO5jfJ4EhtwXjdIj9I0YuKSPwuQJVggyS1yW86SkM1/DPYnBOBJFUwKtLrnnpwP1B4P/UOxttXuBuSTRLrLkghRWVEQDfWaOtVSRGsoK16nIEud1cA67YFSoQEzUv5B7XEwXqUFwHtOujdV4m2saiSHGOSYC2rBM3JGrgRkb4frBOU9bkJ89HqHBhClWLrAn74WEXL0QrSohFeD1YHwoVes2xzsr+lmRzbhVLP8v2bnGz2UlFPci+p/0Y2h/cU3n/qDcy/g813O+lv1juGelGlwvKHNFO5PHRXtwyvaPGVwUUFaDXzBS44EHHohxcFGyTDyDY7Jzh7xSwDiWM+z1M1wbSFk5eNTGrkK2P3S81+XPx99bjkt9PIIb2dV54yx4bNI4vBvwBwkTlQEwbwQS1HkPw9HtwP5/hg6OfT8yc4E11wPpkyvt9fi9YYSGjLrhptSSGgxSUHYq/EnA1wV4Wf6tBTQl0nVS/H3M3VHpoYpBDmlAh9+Ligo1Tp6ow/49Hagon4f8fE/YIpwIaHDQSCdolHNT4ALKhZ0bdrzsKhr0DALSgL/ttttif8ngOaDruNRPI+y85XNVA7qQIUBDiBnb1Jmjc8GNhgadMoPbA58I5Ij2HHYH0qwm9HX0QGU1YqhFus8deg1WVUqSbTXVTVizVmqse+bMOSxaIDlXzJhoyluE8tnrJXLFkh2zx0fCYHZ+1Hz8IC5XNP/saQee+IXUhJVVIvL14HUvnAVk5MSWeqp6Q5LwkOF1AkefAzbH0cJUSBqNgrc4VnNVV7TMFvYZGcYFgTVPqvYZvb4qwJKYLJ9RbUaWrhC9nrZRYs2gNiFTn/rsZ8pJUVpKBv9da7eLaoeD116Lb1dVod3pxIbsbDygcFSUyDdaxENGYVo+Tg/WjK7N/GnVmkUQM5V4qqVFEC6y2AP/fWZoCMf7+7F6gllFPa4quPyDWL1xMY4fqkFOXh90s9OQawxvkkcDm8ai0nlLBFyP6MwwUzJa+TlBY5QOSbysoWer92GXqg3HWv2CWL61eDmWpiefpTZ5qDDHakbVgB0rs5T3NzVBAa7zXNsTlsyoPiUFqmVykSQLiY2uDqAo5CCuWbNGEBuxMrITcTgYOH6x8bjUS6mmHnpTGvTmNOTmZ6FlBHD6vOgbHMDd//wrfnLVLagMVs0xG/hLr7yA7uMnoM7LhTZHcgT+2FiPT89bhEz2M0kB6GQwQ5eOB4NzPGcSOUnhttuB7a+HExt3vA1o2itZp6zSkxuqBaFC9GA698EdXfvR5Q7ZHcvSF2C+dfLZcaA9QQLjhz+kVpn0HCWoSHYke87R0HkmRGjI5EX1i8C690vVnTLadwNDoUaOQjKy7AZJ2jNHkgOYFDS5gGEt4OF3sKF9IaANkW8MgnDdeOONN3DrranrFxUNbErODMBY69gMZpAcYtmi8Zv3ypUI7ItBKRWOf/5MlsBlBiIftOVff/11Yctzn+YjHqHO9Z9BN76Pr03Wt4kH+iw8Hx4L+/VxXisrFuVqWFnyioEm+kP0eViVTmKb/gMDg9zTmJHMgKkc0KREFQOrPAcGwiYaqBgXs+YDuYVAd7AHJX0HSkPOmYBsZqrgU1TOyYhM2LrIQD9YlnZKJTg2mMl/qSDXUIaWpn3IzAnZnLmG0gmTGqN94aYJXCeYQc+MfkqqRq4ZTHCSlVaSJYX4uSRX6ZtEWwN5vgx2j9ePiGQnqxYvFqSif1IkuG5y35ClmlIFkkGco5xTXHujXVvaTolIhNGmY/yH4Dil7CJVBLg3kHzn8St7sPAnxxTJbq793C+mohcPiSDuHRxrlPTl8TKxYAZvbWzduhU/+MEPJvz+CY9EGjUc1Mlo65q1ZtFPgySFjDSNUUySTucgqobaRIBqsa0I6ToT/th4HDu6zotXr8jIx4crV8Gg1uKP56vxXNt50UP41qIK3FuaeEarWavDjQVl+Gd7o/hcLud8521FsxSNVcth1RlwfIC672qsyypHqWmcgL+zCvArMqUDbsBxBLAmyWC2NygIDfFBgMMO3PpRwDr5jFnltSdourIPc5ezA7VDZsyxVKRmYednUPtZWanBPg1cf33nggRHA6C/HFDJgXzqYYY3MlKJ52J9hQpa1TxoAnNx2bIto0EqOVNovEU9FhiciRdoouEuGwk07OWGT9u2bYttDA6eDzb7lOECbGZg0B6q2KjcHCbNxe/gsXBT4YIuGwtKbFyzHufqeqDWh97n9XhR+/o+rFk7HwUlUsCl2JYGvcaDiorC0cDQ/LmVopycGxIdkVDWRoxxJjsLiWLRSqBDkVUvenVkAgXFwIoNwDJFc7CX/wI4HVJgiwEdGloGHaDxA50NQNd5qUn4LR8HzBFBraGu8Exf/nuYTePjwJYHMFDOKg/le3Nj9FYwp499Pa9H3vSW2Y+WFc++HBjqCBIb9EbMwEIFSRQHnDezzEthcWdixDcEncoIi8UtNGNTiQG3G8PKBruiGoTru3T/KsxmIeeULLhnbM7dgBeah2AhmaFLx9L0BVCnWGdfSPVEKeOejISP098v5jqDBnMXlmP3G0dRVFSC3IikJs5HrjM7z5zCSIYFeUYTllqzxq0moCFI0jNeVhj/xiBDNCOexOnptga83H0GRYslSRamFfyj5RjKTdmwsQpqIuBcObMLOH9CykqfswaoTECrfP4iGAuK4BqoDvUzYGn4yuTHTSS49tFBi7fej4HZIgXaIxFRrs77JF/nSIeSAR86ktTxjbZf8PUMYgmtYa8HA22dyJtXAbVWg6GOHgz39EGfaYOzpR1pRQXoqK3HV4/sxB833Szez3tqKCyAybtwVLZNxrDXmxJSg86arBVPqRQGLUaryJLBsuXAD34MPPYXYNgBbLwcuOMO4NBDUHkA9dk2IQcqkxoqsMw9+v503tESRmgQxwfOoMxUJNaMSePrX5ca1Mvo6gJ++lMgFVJMw93S3ipXAY72IxsCzNmhIJmS0BAIAANnAeNapAwkNviIo78/HaQGA7+JaP3PYAaJgesGCUJlgJE2z/jZn8wWJUHNYBETArh3TDSYw0zXeOM6UhqEexSDO/S5Zb3zVIHSPcyY5l5Ee4B7E4NIcs9A+jYMYMmN02Ww4oRk9njNYWnHsBKFrxlXck3sVSSxJ2DHseLvfZ8DXnsa6GoFsvOBq28DDClY9yeKgrlAl2K95nXKn33RVmkQJDQS6imWJOSeMpcKioxz0KhuQrrZABU0olH4RJO+aMdx3ZhusFqDRCUz+qM14mYS565du0QmfqI9f/gaxkDGS7aSE6ciexVybeHaQWJ02iXq4oBkM9fZVPVkka8Vry+rKqaiSof3gNV70UgNVjawoo4ERTTwPpC04D2gz0iShIldjC/JFT70I/k37jncI+SkX4L7Q1J+0wTAPYnjhD4S91oSGzN46+Pqq6/Gu9/9bmHvyLHVZDDhKBZZXg56Gi2JYpFtLjqd3UL/nFBDjdWZy9Aw3I2/NO4bDbfv6TmLImPRKKFBHO/vxB8ajsGgseB3DUFtYwC/rD8lGj/fXZq4Ju4XFqxEgdGE/b2dsOn0eO+seZhnDSctFtkKxCNh+AYisjLY4Dhc5iomqKXsdgEmC9DdMqbZutT4rCMlpAb7mBQYstHh6hUNuQ0aqWGpJ+DBycFqeAJeLGLz6FSgaCPQ+FrwlwCQK1etyOfmkQgOrWyoc5Oj4SMHwrnBjE9MKI1qBub5YAYRM4OYtZqq8lR5I6AjoDTwSZ6QSIlbvjggMeEhsE8EtUo2S8EKWxGQXhRVsiCeM8DGyFcVrcHO7qOjhFWRJQ8b77wHA6pO4SjkGIpg0sxDQMWGag6oVCZoUI7sbA2ysxPI7u/tBJ7+ndTsm2N0293A/ASajF19MzAyDBzYLgUCl64B7nwvEC2o1hvMsApeGvj8kkauNU0hD+UEzh4Gll8V/l5zFjAonWuoifk4JCTL0jfcCxx8WiJF9CZg+XVARow5T3mXre8CXn0kVJ2xZBMwawoywdhYtmq/dD7zVgCl0nwM07dkRc+qeyRig86CLV+SrEsQIjtdkXk0rBqrsTkZvNjejg/s2oXOCP1n9sz45apVk/58m86Cy7KWYU7+1GmhUw7r66dPY8TnExUmrPJbkZ6OZcFG5hOBRqXHkH0AHW0S6bb5mlWoPdWEOVEqk+vybPjhM4/DOrcCXrsDK1UGfGThqtG1R3ZCeC8ZVJaN0kQMaBqz0ZwOGhJFy+ehpMMXtpuR2Ohy2SdGalDK8fQOoGZf6LnDz0vBiwrFOkL96+ZzUp+eJaulOUoH+6vfgfabX4XX64K2qAR4z4eA9MlVFdLgp6MZq3w7JjZtBba/JPUVIjj3ruS6MbZyh/sCK+2iORZ0+mLJXtGxYnCB2bNrM/Q4ow4lS1jystBW3wxPhhmmogIM1Z+HdcEcVB85iuH122AOSmBdkZsvKjNkcOwWp5lQmJa6DEzlvitn/kXrEzIuVqyUHkoYMwDnAFS9dqgP1wNGShCuhCo/BuksCJsRkRQTmbjh8DpTQ2o0N4eahRMkcRoiSYYJwmgLJzQEVIBeQZaN+fvogST3XbQ3ek8BniGA1W1Zi+P0ORsLBjInk0mVTKXG5z//+Sn/nhn8i4AV3wGu95RYpQ3HvWxR9KrpGOA+yyxWVjcwYMX1O5WNsZnIQCI8krwgqTIV2bBcw5XBqmiIVhnCfZPHOV4C3Hh9wQRo5+55Cdj3suilhNmLgRvfGbsyOxbom9z8Tlw0mHWZJFtbt0/aN0horLwJFzPkypx/dUj+URnm2CbvXzAZkVVW0wnKP9GGpJ1PkjRaI26uKaxAY5yEpAebh5PQ4joTKUPGcUEbb7wKbxn0QaheEZk4xSTQC1Z9yVjE6UNSrK1yIZAfOgYeD69DqkgNnjuTURl/miqZznhEIf9GsoNJvtGuNSt0GDAW0uPWkJoM7z33Ho4Zrt38DI4jjh3uTXzfdIHHphw7TFSOdT4zeOugoKBAkHLsqzERuVbtZByOZEuB6Fhek38FWkc6RbPqPGO2yLT9Tf124YjKrigDSAf6WsYEVY4PdKDLNbaE8fm2hqRIDUpNfaByoXgkC4/fjYbhKti9AzBo0jDLtAAmrRVQmwF/hM69OkoASA68ylnA+18Ejr4hvS89B1iyMXo5tGkCMlYxsCn3MuzrPYlOZ3vwUEILR529IXWkhq0MmH0LYG8JSkiQsFGCWfkKfWxxHNwwK1PCYjOjgBIgXLTpiEzWWOPCHikbIm/azFKNm0Ud1fintNKSiWUmKVBuLkSm3oZuVz8MGh0KjTkxstYrk1dtYfPuv/4csAdJO/ajefoh4D2fAwrG0YQnEXDrO4Gb75N+j3f9Sdj1BysueK1ED5nIg1VJfTcisfAqoLdZymoV36sDlt0w/rlZc6TG4IlWoOQUA3d9DrD3AQbT1DQIb6wBnv51SF7n1D7ghvcAc5cLOaKwIDQz3qMQYRMBjZloWeWRcPl8aHAMwqDWoNxkjerU/rKuDh89fJipFkxLDPvbuqwszB5Ph/4iwSyzGW9u2YLPHTuGJocD67Kz8eMVK8KkCpNFln4+TjWeElUaMszq4jHXvmXEjt+eOwVLZTmG6s5DZzXjdGkmenNs2JBdGLXBKAP1NAYSAZ0XGt40iplxybVSdlz6tNEFOayxGhXHAudy3Q6g9Xjw5C1A/3Co0uHckRCp8eYLwKtPhrLVD24H3vdZidjIzkH5l/8bDQMDCZ9fPHR2dgpDfcWKBIjZSFhswJe/C7z2PDA0AFTOAzZuiTmnojVsZKk3pQ0ZEOO1Z1aUch5xHMhjYWl6IW4tXIx/tp+BJ0CiSYN+cybc/QMwZGfBXFYMR3MbcubORtXxE1gdJAyvzS/CFxcsxQ+rT8LD+2q24JerNkSX35wAlJIkBI+fzlvCmZ5872uvSUQB1zRq8iqPbcENQNWzomm2yusHTBVAXvzgW7rOOobQoHSaRZsiIoc9NGpqQsQG9/sJVoOOQcFiSX6KJLWMyk1hcpTQGABjLuDsDu/FZUki45HVH00vAi6pYkzsM45WoPS6hO0Q2v3vete7hPwYM/ymAgwCMcgyIzcwg5RCRZtt8hmmTC5h9igrG2mXMRCUika33C+UnyPvH6x2mEjW4lSB63/Kso+P7wHefC70e91J4Pk/AXd+CFOCwV4pSdCSAUTYUikF97MFm4D5V0y8AmUaQR92QtWWKUaH04Fu1whKTVaRdBoPl0L1h6i4nUb5KfoCzKSXlRdYSXvixAlBYESCWfCMW3C/ZWwjGgnJNUj2E0jgJip/R0KF8kasTOP3y+9LOpEoFaCt/tB3gaF+aV6+9hTwtg8CC1ZGtWcnC1ajMBk1lVKB0RAtpkVfkkm99CtIRNDfiZSFjbdXKfvC0k9htQaJBN5D7kGplqaLBY5LJunJZD7JMMbzZkiNtz6uvPJKwTFMK6lBrb7/+I8kmgYHoVPrUG4OZ/vsXldkjcNofwXl85SeiuqST1M5JzWbTw3ux4iPWT4BOP0jODm4D8vTL4chbRFg71EE6TVAmsIJ93mBYy8CjSel461YCegzgKOvh14z2AOc2A0UzQZa66QgMBfaBWuA7NSVxbFaY1POSuzs2ofuCLmGyKDApED9UJ5DxmyAgQXxXRG9CdQpbk6uAIMsLK3khk0WngukXMkx0UVW1hpUakEyg37cLKqMucBgRFZn+pxgEB+Thk1nFo+Uo6sNGFL0qxBQAXWnxyc1ZMgbb0cLcHinRJQsWAHMVfQR2Ho38NSvADUbiSsarQfjLgIMdhZFCWqm2YAr3w901ktjLqdcei5RJONskKhJpq8Nj9nlxP/P3nvAR3pW18NnetOo9y7trrb33tzWvWFMM73nI6QB+UMCISEJSSCBEEijBUgwMeDYGHDvZddbvL0X7a5679L0+v3O886jeWc0I82MRrtro+PfWKvRlLc85d577j0XJsv06xQl5g68AJx6XZnz6tfue1qQGizHpEax7OFCY5NGR6Ly03RBg5aZ6zRAk6HDNYEvnXwdg6yYAbC+oBR/uXQzTCoyj8dEEkCcQ5yRyKucH5ctlCloqF0J42p1fj6ez6KOp0VXiGr7BuRF5mqOvhwFy4JTMvq7mVXEDdpqgdZogG9sArYqNkGe2gz+7MQEXnK5sCgvD4tTaXYdyQil88NgCQlgdUPB8nAIjaPFuOwcFEFhJhRsLmxAqTlNYr3nVJTQINgrJ88GjETOQQb8SZiS0BDPRcZMVytw9HVgo1KVxTHOazRbUoPjk9k+6VSYTkFuHnBfhKidBomCvgyAcR+ig7Bj2yYsrLPi8vn9WLB4g5JJnAA7Sxqxo7hBJHu80N+Gi47jCPQPw1TEBHs9TEUFeKCwGiXFxThw4IBwGrlXfbKxCR+tXwhXMDBjcCBdkLyIBx33lAJdXBe++EU2ZYvaOOxF9eUvR9c9SwGw5n2C1BA9JVhJNwOqLeVosNagxdUhfmfVxqbC1TCpe1LMBl/5CvCpT/HGKr8zq++P/zg7n81KiZX3A0OXAJ9bqbyzx41RXpuqG4DefUovDZ5X8RrAloZt6OwCvOr9nM24BgBXH2BLLcDHbEaZqX7nnXdiLsDPphObUeXPPOZxBcDAD4lpBsMYRGJSAZ8jWR6fFZ0qGLAh6S33DinBwM9m1u9bEs0qG2EyGeK0si9ku2rg3BvAa7+K2h4rdwBb757bGIL47GtEcuriCeClRxV/g5nqd3xQ6VcYkTmjz8wsf/oatCFIclCB4EpUb9A2+1HLGfy8Q+ktZdRq8VfLNk1J4pFgwHxWdtw04JzmNcgGeO0SJbfMFXjc6ooDErAMdCcDyY5nn302qbSdWmY0VfD7GCdh7IX3iHGYq1oBtOcpwDEeK3P3xM8UxYnI3Od1SiYJmw4mJibE+JlrQiMRGBegTBNJC0okVlXacP7cCRTk62AwptdkW1bqMNGNhBZjXpS7og8213YZv5u+BK+lJPPnq8h+t0iN73znOxm9NyNSg1mdR48eVbKowtSYp1xQENCUKI80UW0pQLOjfzKgTmd0dV4hXh/qF0EVWcVxX9USdLg8+EELy4ejuJeNhK8AnIExuIPqoFIYwXAAw74+VFjqgZwbgUCvEpgxlAJa1eJ46iWg9XiUprn4htLcUa2jzMV2fBC49/eAzmYlmFxYDjQwmz/7RlG1tXIKqVFlydJixSbhnS8AwUjwI28RULIOCBxS+kkQ2ipAmz1dxWC4H4HwOSFrpYEdBg0zYK0iUEvJKOL8xQt48Jmfw1xow7JFS7EkvxE6jU5oT5KRlnIu3ODVZXl0Mlg6SagZfW4idMBvuOGGmFJLbuoMHJIh58ZeUVEJTc1NwOAJpdrAGQKO7wOO7gEKa4D11H+dA1IiVfg9wFC7Ms4KawFDJDjFTOkpCCuNU9NBdxvwk29ExzoDlnd/AFgX0d2pbAAWrwYu0cFRGYAh2fjFCGy6E6hIUsXDjNaqZakdi2sUcAwCphwleDRXDs3Fk8DTP1NIDVZa3fMRIFFFGQPYD35Tme963dQKlUiTco4jSgVwjEkZKo7bc+fOCaIulebDyUAjiI7zdKTGP5w9iKEIoUEcGenHLzrO48P10evuC4WUPhp9fRR1jXk/j/uPs5BpT2eGJGI29U+vFLguWE35KDRG9yxy27yHalRZotUs5rISePsHhYNfbYklFv7i5En8g+q9a/Lzcfjmm6ft+8F1qq2tLamuN6u83lu7CafHuzHmc6PMnIuFORk0Qx5T9dMheEwGXVRdUVZpjCcozacDJCWeVNWEmTbzI2HHc2aQiNlo2ZQMSQYa5HIP4XGTWCcJye9vvXQKnRceRnVlHvyOTnZgBWzbY22GKf2jNLitrB7+UBAPjTnhnHCgqboWH1q1A7XQC6KP5BRloEhsMFuPVUW52uwSGgQdJzWxwXWIZE1K92b/foXQIORe+pvfAPfdx7TC2Eo0S+pJD/zu9QUrsCCnFp6gV1RuWNnXK1ugvOSjjwJHj7IMhymH9ISz9/k835IZqmQpo1WVeh+7KQj50nt+hkyquSI1MqkEn8c8rgboM8iAINdErvMMyJD4kwEZ7vsksvmT/gNJegaH1Osl/Q+ZKa8mNRT/oUIQJer9j2uu7LPHtf5KZdBmHbTt4/uXMTkj23Y57Ws1oUGc3ANUNwG10/cIeEugtx144r+j589+h7/6HvChPxf7mZRJIpnMf3Ncc7zSL2D15Wx7IDDpYTr7bd9Q7yShIV4fCuGrZw7iF1tun5KUwc9hALq2NsXEujRAn54kJQnLNyMSESj065Jp1EsJomz2uKA/Qzt3rqSX0sbY8FT5TvbwZLJxJMYh5WLTIW8kuG4z4ZDvp2++Y0cCPeE5gMPhmKxmYNNwkg+yQuT11/4XZUUlWLQghObmx7B46Y2ANrXKYu5dJKVkFTn3N95TEvZMCmMsbC6bvHPdoR9z9913izHLPZMS73Mx3+dx7YG2/4c//OEYH3pOSQ2WVjGjpKoyDwjvj0QeuZoyiLEE0KQ38O6sWCV6avR5FSa10pKPB2o2YGfJOF4daBUZipsKq7CuoEIs2Oyh8VRvW6TBdwPeXjV7uaJUEJ7peQYPjEnOvevs1E/wJ2gexQ2f0jbM3IyHzwk4hwGTfeaeASmg3lqNQCiAZkcLQuGgIDRW5WehER031a6XgKBKWmqsGTAXAXnXA6ExQMMKhYKsGa6h8DgC4WjGTxjj8IWPwIht0EQy8Sl51prTD+OyXIyNTOCx3Y8jX5eLtQXLUFZaii1btogFnAvqsWPHROCL2ucccyQokhk5dGSefvppEbCiISgzMxhopjYhN3YSH1yQaxvuBjpPAedV5dYjncChXwPbr5IerGMYOPBzwBfJTGFG9pb3ApY8oLgcqF8CtJ2PVJVoAbMFWJZmCSnlZVhFoTa4WAIqSQ1ZqRTTS4byZGHgto8Bl48Avc1K0L9+debjpus0cOrZ6PdUrQCW35rZ540NAm1nlWAUiUd1A/OhPuC3P44G7ZgZ9asfAJ/48tRG56cOAI5R1TnHVZ/VNolqIBJk8Y4rs2D4oD4/q4jSCfhyjEoDmGN4OlIkEAoJ2amY99N4HVcCz5ccDvzbxWYM+3yoN5vQHgwipKoaWJmXh++uW4dts6wqYWCam1w2pB4kUclHogoA7jv/cuECHu/uRq7BgC8uWTLr42cgIlF2GYMcNBpltUalxYb/r3EFvn/5lNDu1xgNcAW0eGD/Xiy25+Ff126EXqONITSIY6Oj+KtTp/B30xjmHEszZZKS2FiZN0vtUspVxQcr+E/uXYs2K9WKRGGp4liwgkuCa0V57PczY5DjPF2ng840g/AM9F9JR5UVMAwIsGSaewqPW5KGdWUjeOONPkFq6FjBwgpPz2nAOr00Cuf3PZULxYPkHudsUX5RzJimLAqDwvw5U2P5TCHJGYHLZ9H6+KPYtmoFMDIAFMxAGHV3J36eRGgGDmX89Slgn4i5wMQEcPCg0kuD0mXZJDSuFCxlkZo5lYOv0SmyVmmAjvOPf/xjzBU4Xz/xiU/M2efPYx5zAQZ/mZXMPYcBLgaZpAwh139pYzGLlkEbvpZ/Z6CINk18ZR/XWfoe1LKn7cPXyjVdBij5O7PsGQBgU++5WvPnDOuvB5qPx9oKm3Zln9SQ8rZq8DtGeq9NUoO+w+UT0eTG2iWzuyYiGVSlf8Eg79iQ0s+wtEpUCLG3glp1gMFR2jEM2HKMpZOhLX0L+VP2akiGZseokMek3S3hDQVFhfjyvNgsc9o5spl9NkE7jdW0nKuZJM/EQxKVV1LWK9Fx07+R602iatpsV1GQHMuEHJgzUFWC1V9q+XdWKKmSNjmeOK5SkWGOB4lsxovi+63ONVhlw0bwnGP06Xbu3Knc/9AwrJZR+Hz54m9m9qQLXQQ0pYBm5iAxz4G+aDyxQblF/uS4ZmwrG3LAicAxyspEKTUlJbTe8pW7p05x0WDTE2Dt2iumQnStQfALVVWCa9i1a9fckxqUOODmh3BrxDlSBy2a2a45rZth05vwsYadGPI5xJZbZMwRk6jJXiQeavD5B2oXicdsMOzz4PuXTuGyc1xoN36qcTlKZ2hMlqPPhUlrgTfkmTxnNjsvNJamJlsTD2suYHAojcKlkbFuV+Ls+P4LwLnnomxzzXqgMbaBNBevUX873MFR6DVGFBgbYNAm17fmtVxkbxAPb3AC/d4zaHPuhlFrQ6l5OYxJskZnBBvBB+JLNzWKzAHX03BL5PIZAd06QDP7IEQI1JmOFyzzIAyOKSWIzF4uo/5xMTbthbniQSwqWYwSUyHeGGnD0ZFOkeW8eVE9RluVgCcrk8jiJwKDkVyA47OuJGQ2OckRBi25Aa0Ld8QZl2GF2Aj602r2nNJ1CQehYb3TdPPx9LOAXyUL5nUAZ14C1r9dmcfUtt37jFJtkZsPbL9DaeabDliNEO9QsBG4GrlFwGC3KqNCA1hswGv/G7lUYaD7gtKoe2ny5ulJ4XUCp5+LHSNdp4DiBqA8zT4y3ZeBp34UIWpo1TwH3PcHQH4kONR5MU5+KazMc2ZLLVgxtRJDOnRskM6gk07KsNiBG+7HkUNHJquNEoEBXwZQGWikgyzLx2W/FwayE2XyqZtxcYwmQ4szIrkilqkQNLKBHMLYfeY07tv9GjwkMqi/qtGgsLJCzEjiI/X1+OH69bPqRSHBYEE2CA2CThoNt2SG2R8eOYLvXY42W36ypwe7b7wR22dBbDALLFHPAR4DnUqW99KY4z25v3oh1uSX4Ftv7MUTXZQbCsCoDePcxBjeve81fLwhsSO+Z0hpQh4PBj1olNKBmcssm0lUr1X2La5rYrkLK3tWTZyMBknSd34SeOQHSqNQYsN1wPINUwI8DOykq+VPOQVmGF0xTXJmf3W1QBsMYDHn1OKp90kT5v4YhsMh18AwEIr0BIoD9/VW1yCGvQ4UGG1osJWI8UGSnI4FrwtBwoTBMu45tM9kA8i5AAl73oslniHg6Yeg7RoA3H3A4deAj3xBIcMTgdUd//d/U5/nGjRHDtKssHcvcPq0Upnxy18Cg5FVjePv+9+/No95OhhzgarrgZ49QMgPaE1A5XWAIT1bj+PqD/7gDzIKAMwEfibXwR/+8IdZ/dx5zONKgQGmZHIusukpk51IeJMgpI+RSCucJAh9DFbozrTn0eZj8hSTW+Se8KZAzQLggT8CDr0C+H2KLO2aOch0Zt++eNAmSfT81QZ9h2f+R0makr7Byu3Ajrdl/pkiBpEgNVOvF1n8zDBPJqPM8URbg34EX3fixAnh9/LfTIjiI5l/IX/ONCaLjGbhPwgShBUdEX/BEggJ31keG//O75qLSm0SiCRxskFoMHOegfIrURUskUzmiteO1QNMFmCQOv5esJJ5tmA2PYPd9Ktiej9eC9h+G9B5CWg9r/zOuMI7pvbsYeIT7WaSw+kilQbq2Qb3hMRxASeqq0vQ2TkIt9uLpqbI3kJlnRRIDYJ2HeeZul8siQXGxHh/eZ24b81VNY5aEeVqyXldUXzve8B//Vf093e9C/jCF35niY3NmzcLruGKkRqU2wFYsh6/iJKRVgvhpwYGkktYgZAlHB4Zwl+dPoJut0tktn5t5Xo02JTP9wQD+JOjr4m/US/8smMcJ0cH8ZONu5Azjfa0VqPD8txNuOQ8DWeAzXLNaLAtg1mXgkPYtBU4+nTsc0t3AFsqgFN7FR3lqoXAgtWJKzTUhAbRcRjIqwKKooHIXs9pjAdkM24NxgO9qLdug56O6zQIhLzodB9ECEpAyR3yi9/rrNuh02QwRMT3xRMMNKoYAG5RnxgQPALors9CEzW+P9GGHmXNfXTiE4DPvz50GU/3RmXN2lzDeGfdGpw7dU4s2omyG7igM8jKgHIycDGmccNAJg0LZm8tqzPAHH95eP7MmMwSfCEnutzH4AuR1NGh1LQY+cYkvUREL5i4CgnKM0lwTlx/7+wOqIHVHhdiz7c+jkjYdLtCFrhlw289UFQCjPbEXqyzezIjNVwjU0tQeRziXNMkNfY8pmTsyuPyeYH9TwG3f1j53ZiETEz0PCWp9pNsiYDEBh86LYK3vR9HT54WBvx0mzrHJ4P9JDQ4LpmRLnVUZzIGxv2D6PNcxgVHL3INJagwL5isbpIY9XvhHxiE160QsNxnA043Shaa8IghDF9pqXBICL6TJvKJm25GmdmM0lQaB6cAOk5pN6cMBwFvMxAcBbQWwLRISPywl8R0TpY/FIohNMRHAfj9w4dx4rbbsuJ08F4x+EFDkWQHjWI6l8yy54ZOh9EXCuPZ8VGEVI0GTdowBrxeIcyYCLWqDHI6oXQIee1Y4TIbve+0QemgdQ8A3SeVBsieCaCXlSUaoDqqZStA6bnPfA3o71YI05LEGTk8fmacsQphWsfzYjPwj/8AtLchZLVB+7nPA1fCuSR5+4t/AwYi1QjWHCVgUxx3Plob9Do9+gZGUV7K6g0NoI1mHnqDXhwdPYVB7zAC7APi8sEZSfZblVeDjQWL8OvuZly8fBFbLWHcXblAVLBK54POiCQ0k2WOcSyOB3xC4tOexO4Rr/F3wBnsF/tIvrFO9IXhWnP61ClU7H4Y1nAQIRKawSC0HJP7ngXuiayD8XjkESUTKR7szzYNqXpVQNKCgXVev/hMS1Zt/MM/AHNYrTBnyKkBFj6gSE6xujhVp4mJKoP7AO8gluSwmb1eVE6tZUZZFkGZHq7zDC7NYx5vVXB/ZxIKE52SERZMVGHixXSZ1AwySakqrsvMbrzjjjvwpgJt4ETSrNkEE46YNHiE0ocRB6xuKVAfl2R0LaD1jEJoENJmPPm60l+zOMNg/tINwKGXFF+FfhDX/Zom9PnCOHvu+Ixyf0zsY+Y2g/SsNqbdmkoPAkp097ovYDjYg3GnCeXmRbDopo73nXkl+N/BcbR5HNCEwggEgri9rAahkbGYfnNzCfpM2bKPWd1ypfcwtcQUbbfjx4+LBCZKFNEPpN1MFQrGJNTHxvUj034STBqiL0N7k4lxV0p6KQZMxLz4GuAcUirBF1ynKE1I0L59/x8DvR3K+C+vUfpcxoFxGo5rVkG/uaWOrCgpyUNLSy9yc63CVhPQpHd/OR44LqQEEPcXxrC4BpBo5/iaq8bvspcH7wMTtlhB9ZZtEn7hQiyhQTD5ixLRG6ev3n+rYvPmzaJ3d7pIO2LNhZKkxp/92Z8BmkIgrAp+CkPBnoUA9ezQ4XLiowd3i8AUQ5gnxkbwoTd24+mdtyBHb8Cx0UF0RhqxEiQ22Px2/3Afbi6bvom0SWfBstwMJnHDOqWxY/tJxZioXwNURTbqrXelH4zltXYMTJIagZBHRWjIfh8+jPk7UWRKXGXgD/lxcuwMBrwD0MCPUrMWNr1i7AXDXriDI8jRl2SoDb0eGGD/DE1UCzqPwZtolYsCBuuYrTq7fhI6lCMIVg5FMn3FNxdAg+giXmzi79Fvl7Zij2cErw92TpX+Hm7FJ7duxVNPPSWCxOrgMMkKOt/MipoODChyI6D2ITd8BlPNS+qBwYtKlZM8iAWbstYULxwOodN1GP6wonkeRhB93jMwaK2w6RMEcq2FgL87tjTTliQbnkbB4w8Bw/2iZBlv+yBQmMIY2XGbUv59bJ/ye2UdcN9HY1/DjKl3fUYx6FkBUb0IOEyZrvDUTGghhZViMIavZTB14HKCv4Uyk3Jjc+OYCrVQbEN1VmMUlilyLPJ17BtSlSCDpW4xcOPbgVd+o3wOg2iV9cB29hBpgLNlz7TEmQQz+tjrSGYIppLZ4AiM4JLz8OTvbu+EIDlrbbGOnqulHRZ7DiylxZNn7e7swd0bNuM7zVODlHxNscmUNUKDoOGcVgYQ77uLcjFKLxwEycT0ArYbhFE/KZ+TAAwkJ0K7O666KE3QQOR5MFNTVtNwjVBX1dBYZKYy15x/vnwGWrNJnItaj1inCWMdewUVFOCNkei4s+l0+LdIkJEl4Myo27hx42QPlisOOhYkN9qjYwwXKQMRAmrXx76WsmwNuSnplzNrMOmcYCPnz3+W6Wci47FmfAznvvRnKHrkMaBsbhpLTuK1x4HB3ujvbhfw5IPAh78Q+zrzaixc0IfOrl60tvdjxfImaMzKnON93jd0WFQVin5i4TAqLVq0O4MYGXXi+dYD+KHnEEJWC9wjo2huO439J4/jE4vWxmQ3SY3gROPcEfDjK6ffwKGRfvH7zuIKfHnpBpjjKkpHfJcw4o+umy73ACotG2HRFWDzpk145D++ihKbBWsqinG6fxhLSvIx0dmOpLVUXV3R5uBqXA1HWILHwoqM3l6AwZMVK1jvrhAaRCLpCL6nrQ1vWnAd0aVBEHO+9jOTmhKEYWjDXmxc1SB8gWyTGvxMrllXtbHoPLKCPk8fLjguCJnbYlMxltqXQq/NKJfuLQkG0JjZzKSGeMgq8enmAW0I+hTc65n4QhuQAcx5JMHGW5XkwaEeICcfqF961eMVCSHkaBMkBvL5TEkNez7w3s8C+55W/Bf2It16G9yd3SKoPVN1Av9OcoEknMwOnykITtul3XkMjmCkejjkwCXHCBblbINJZ4uRZWUCzn/f934839chevflT7hw68JlV9R2zVZDbyY7XrHkIRVYGSJ9ByYyMSBM/4LBeq4xTKxkgJj+BslPeR8pFcU+IvQ90rlW/C4mh0np7qsCVoIfe0RJmuJ8cY8C433Axg8ovTYlOM8rZu4bQrtZyoW/aaEphEZbi1DoLAoL7ejtHUZ5xUZAM71/xTHCfUcmwXFf4Z5Csoz7FMkMmSTF+MJ0DehnCxKn/P6f/vSneO973yuqiXhcXCc4zt9SlRsdVG9JgPb232lS4xvf+EbafTTTti7JYJKBpkQGwAWD5IAMptsATeqL4lzglx0t+NfmM/CqnGZqNPZ7PTg5NoKtRaVCHz4RAlOIgyyjdoXySBdsaDwFYcCcE5MNMRXUp0xs4HKgvDF8GCO+kUmzqcMVQr1NC3NE+mZWW1ThMsCUB7h6lWxANgrXdlIPKcGLZ784aTRmGLAJwfAlhOGFFnnQaRpjJgMbh24pWov9Q8cirefDcAWpQ38Z3pAm4XiYCPkwtqwMX/i/76Opph631a9CTXmFYI1pADBAyY2ABoxa3oTZC1x8GbyUGeEMOInmqrmlwPYPApcPKk3DSxuA2kjT3CzAF3LBH44PvmrgDAwmJjVW3Kr01GCzcIJSFEtVTYQ5X5h57JgAfv5dpfE154pzAvjRN4A//lvAZJ6Z6Lr3w8Bt71YkZpjBnGihMtuUbCSJqsVAf6vqNDRAxcL0SvIu7wNaDkSyoTWxY7C4HqjIIAuoqBLoa4uSjTyekqro340m4H2fAd54ARgdUuRYNu5SrkMirL8BWLVNaV7G4G7EkeWrmV2zb98+oYcq5y7HUqIMbBoiieSNkmHQ2znFeRryd6E6vEz0VeB4ZWB049IV+HNXFf7lAsmykHjHOysXYrG9EDeVOvGT1iixQV3cRTk5okpDItMGz2pQY5HB7JSzn0IOhIMkbCePAgh5gYk98A62IFThgNa2EtBOXX8sOh3MWi08cXtFZQYkjT/kxURgCBpoYLIYxXrAdYPGIgnPeJ1Q/o3P7Tl6BK87+kRGkT4/D8GRUegLlTWmzmrDxqJi7N21C399+jR2Dwyg1mbDt1auRPv582J80Aic1Fi9muiK9jqaROfxqaRGGoEgSnUlJTVOn1Iy6SMwazTwct09chi4Y4YEgtmivys2AYH/VpMcEjo78irvxpmWZ7Fh/WqcbRnFsuVKgMAVdGPEH5V74/0bHx7DRM8IwvZcBEpKwbIN79AIQv4AHB3dOG42wVRahK4z5yfnGscZ96lE+PaF4zgiCFcFrw/24AeXT+OPF0XtN46hXk+LIPjMOmVeE2P+dkFqGM1m3Ly4Hg63GyU5Fjj9fpzoHYI1ZMSZPXuUHlLxjiFlHONJAgYtSjNoRJ8NcH6zxPuVV6LP/eEfAtNI/Qlw7b3WKkvmEgEnoBqTxObVjTiwfx8+9alPZfWrGGyZK9m0eVw5DHmHcGT0yOTvne5O+EI+rC/IbN1/+4nhpQABAABJREFUq4J+BAOKbITKQA4DzPQduH+TkGa1BqvFaS9Im4/BHspJMlmKgWa5xzOAmahn1zxUqGxUHtcyBHGRoP8He2vMBux3deeHYp5ish3HEceU7PnF5BqSafG2I4Pi6ci/0g+dJDQioO894u9GuW7RpK/M75MJS3cz+StSZcwAayrB5Wz4F/L8siGpw2Ph8V9p8HsZc2BMgusDZW3j7xd9Oq4ptKOFHPa6dcJnJEGaChjcZgyQDyYY8XOuKka7AM94nNSzCxhuBcoyq/DhNVPPh5nA8ZKuLO7cJ60sQ3mVD2FNGB6fDqMTxZjudFiFxfgBiQuOB64JJDPoc5Mop2xZd3d3DAHPmBdJ9WxJQseDexvvAz+f+6Qk6OjXcH0iGZdOvOOaRV1des//DmDdunUi+ZScQ10a1yFtUoOla2SBJ9lzzXIgzExAOqqmq6r/9avONvzV6aNJ/87GqsSq/GLkGYyY8PtFlQblF8w6HdYXXOXFORmY6VqzAeiQlQ9hIL8KKIlmYLL/hV5jRoANR6O1CImD2NzYQh4M+1SZ5RGM+xnA0EKvMYnAxaxgq1IeEkFTbECZp6JdAGiyk9Gg1Vih1UzfnKrWWoG9g2fhCLhjTEa7TgtfKBjz3DJ7Gf7t4h44Az7oV9Th9MgYBloOYt1JMxrrGyYzV7jJMxDJDVAy2tzo6YSos0yoQT4ZkM0rA9bejbmANolkGOXTEsJeDOz8ODBE8oDB+YZohgMbXP/y35UKDX8Q8AViA0Jjw0BbM9CUYlMwlnymoyC0aBPgcQDn9yvfV9YIrL459UoNVnW0vBH5JfIeknb51UDtWqCkMbNMrRvfBTz+g2iD78IKYEtcwJQ9eqgN/NoTQMs5wOUEdt6VsOx1skQ2gQwMxxlJM0p9cAOnsSyfo+HBaiEpy8QxmJ6REdcTST4bCiIYCgrjVzYK2261YnV+Cfo8LhQZLdD7/CK4/M7GRrQ4nfjqmdPwh8NYarfj4a3b0NfbKwxrGh8MkNLxoMGUiQFE54aZGak2YQuH/QhpzgE5yjjWeAPQ+PyirB1wQK/1QBvoBBwOwL4z4Vj61po1+PSRaFCGr/hOmlnJruA4LjoOTpLL3Y4BNBjrp604YYktz9dcXATfkb3QL1kMfVkpPOeaBamRo9PjN9tvhDFCfH11xQpxbZntdiqSOc3xcM0Ye4mSBWaZQMDxmNSRTZbBcyUy59h8kGulmuzMTbKPak2w5tbBHShARWWu6IdBh5RSnPEYGxxF5cJaDPrCGHYFodPrYS1TersE3B7ozCYEtBoxV6kLLcu06aTTIYnXymaFBm0fCR7twUjVBsFre2T0OHo8yrjlSKu1amHV87pHSQmzyYjT3f3onXBBx3nOhrhV5ahZs0YYpKxmjGkYed99wL59wKuvRu/V3/896/6TX1NKxwVOKzamrgjQZTG79sUXYwkN4t//HXjoIeWY6MAlyt5k0OPLX8YVB49l9Bww3qJcg/zFQO4V0K9OcL3XrajH099/OetfRR/jnnvuyfrnzuPKotvTLYh8JYFIQb+3X1RtzFdrxIJ+AYONtN3oxPPBaiXac1y7aT9JKSr2xWKGPLNV2dtIDQaArpRUzzzmECRdNtwCHHpe+Z02wQ3vBHLnJnjI4PTzzz8vguC0F/igbcWxx4e6p2Q61QzJXkv/gmDAlD5BomAwfWcG2pP1beKcYJBTVkSQGOGxkqRJt1kz38s5x/fSp5ktqcHPmqsmytOBgXUGmvlIRjbQL6CdyCA21xFeQ64Zsj9KMr+BwWy+h9eb/gvjgNdENWUyX2IWVTc8P14Xyt2mAlbJMe5zzZAaEdTVrxIJkezbROI8GUlD35GJdGrfnAQHxwXnsCToeJ706SU4xqkOMVekBr+bfaUoQ0SfluOTx0k/ieOVUsS33HIL3vTgWsHkIPbVkHjve9nkBb+rsFqtYl+ixNmckhqUs5jSZFRjmDHbfsI/gmFfv8j+LTFVpdaHIk080qnK6FaBGYYLbHaszlcmXq7BiH9ZvRP/eP4w2p0TqLDY8PnF61CSLNh4JcHA7YlXgIuRYNrijcCKnUDjNoXIoOQUKzdKWPkQ3bipgV9tWY8u95FIlr5G9FGw6YunLBLOIKWl3AmDQlroYdMVocS0NGlwfBLMuDzyGjDYo2SArL8+YVBWOa8hIMjAhPpgWNmTWBprLqE4WrGosJjQZK/GkVGlUfiWwnqYdUZMBKKstKUgF1zOV67fBovTL5wMZlPRuWC2icye50aQqOEaA5V0VOYabA5v11dgIsBeFARbheuQZ5hGj9BkBSqXRYmAsweUEuWW80CfWtYsEeaQyOT4XLULWH49cOxpoPMU8NIPgPwKYPO7lMqO6RBU9f3hYYqmexyPfqB0FkYnm5q/+0+BwYicSnG1kr2rBgmh//lGtEl6VwvQ3Qp84LPJKzaSgOOMhqp0NGjkc7xx4Vcb8MzOJvlBozxZ0z818o0VGPHHZpKPdnhxyXhZfC4JE/UakaM3IicnMseNJmHI0SH4/OIl+OyiJjiDQeRFHJDm7p4p0jck/2h489h4TjRYGIDld7Bkmw6F2lCmgc3PpwHD+UOjjHsQjanpSt9DoMwfM4uVYw+bDWJt1URIOeU72Jh9BAiOA/qpxujvL1iAWqsVv2hvF03OP9HQkHaT8A7X6UlCg/crEPajy30OjTnTkyN09ChsV1BRjtHzF2BcsQx+LWDQAN9YvQFFJpNYv9loVN3ETfaaeKq3F2fHx7EwJwdvq6ycNouNDd6/d+k8zk+MocZqw+8vWIwCVhplC+XLgEu7Y5+riKw1GYJzQWqITwFL6OsbRD8NsZ9qddAUFiG8acvcrFSc2+NjbGwDXHcP0N4MOCOZY5Rzuv19Sd/KgD8Ndt43VkTxHj79m6fRpe2DocyMstpyBPwB6I0GjAeUdWxncR0eVtk6RosF+UYTKi05MGp1Yk5K0AEgWcIqJzXsBgNG/N5Y4VB9dO9udbWjxxNdF+g2drhDaMqhRGXUWT4TMkNvMMATCGJjZRGC0OCJjlHkHTggtJW5L8aAa9I3vgGcOkUBaEXuqaws+bUNDgPefarfJwD9OGDYkp0Ems7OxD0zKGH29a8DlFmVmWls3Mf9m/ebzsbVcGBHTgODqsSd3ki1jb0OcHQCQS9gKQFMGUgqTgfa65ZKwB3pFQMNVi2tx+kzZxOSZpmCASzqKE/xMeYxj7c4aOswaMQH7RtmxTIjlUFE7vVMiuL+wLmWyMnna66JIOM8soONtwBNaxVZW/YDoVzWHIH2oSQPaKfKMcfnaaOrwcAtEyfibYpEYLKlWWuHJ+SY9MP4PW3NA5iwa0Ti33SBYFmplKg6m3YOSQi1H8C9iIF32j0kCAlWesjkQvofDIzKXmP8SZ+JQV0ShFJ5gfNuOnnamcDryM++0qBNnAqZwvtK347XiqCdxntOH0xNavB53muuKzwnXhPGO64pMC5GdQm/O9rPV2cACjOXj5JyXamC/jZJg2sNvG+cp/Qt+G/OaVYESgL81ltvFf+mT55MAYFjRT3H1PEG7kXZkmxLBI5H2oMkVXn8rOBlg3LujSTVeN0z7QVzzeETn1BkeNlzkElp6mSw31GsWrVKxHvuvffeuSU10g3MDnp70Ow4Phlg6na3YmXeVlj1c7840sR7e2Ut/t/iFZOZrURDTi6+t/5GXHMgocGHxNEXlSw5EhuFdcojCUy6HDTYdoqG3wxixzf79YeCeK7vMNpdiiNs1emRZwhOykow2L8sbyPyDHmpseP/913g8llF0ocL27mjwAc/pwRy4hHqTaAR6gQ01OTLYgAtBSzNrcOB4UgTtgiW59VjZV4D7qqMyoO9NnApkaopAuGgcC744ILOzY8bPRnxu+66a1oH/0pJwVSYV8Dst8MVGIVea0ShsUGQHTOChMYTPwD62qPa5xYD4PICeq3SAkWCf2cWcv3M/R5mjYv7FUJDYqwXOPJbYNt7p3+fwaJUOnlUPTB4Cxz9QH8zUDqLYyeBVzFNluzrTyvEhgTnSE8b0N0GVKdQ/j4+ArSeE/PJotUIo5vjZzotSf6dhic3/VTKtvMNpai1LEeP55JYN3L1JbCYDFjclJrEE4PKdABINjB7QhIaJCwSNfUmgUEjhPOG58NKExqDnDN0AmhYqecIX0sni/OL38Hzmklnmj1lgOH4JxFmszRfAP0DoygrVa9xyY2yuyoqxCNTeEPRku6BniGUVBTCG4r2c5oOVSUl+Mvl6/D3ptMYOnUa2kAQ/19NA7blKs3aWltbsX79+pjsG15XVpewyTmvEK/EbWVlWEHNZCZ/1NZinUomj9KMHzu4BweHB8VVIKH7Ql83frtjl+g/lRXUrAVCgYgMVRioWA7Ub5nVR3JsM2M1IalBQuZb/wr8+IcAm73XVAOr1yNss2Wf1BjoA/71a0BPpCfTrfcAH/1z4OJJJVDesESp3kgCjmPOG84XgmOfc2RD5UYc7z8FjVeLYnspCq35qK1YhAKjDcUmO4pM+fhRywn4QiGUma34wuLNgtCYcimMxoSyAp9sWIa/PP0GrQTxO+/9x6gvHsGYf3xKlnUwTFKzHnZ9NJix+U/+Aq9+9QvYnGfAxcFRrN66Hfe//ZNw+gOCcGOQgZldstFg5KRpraZ2ff1xiRBEYBidgW6MBbSoMuch3zgLh6axcSqhweNjBjRlXJ56SnEySGZeC00KR2LtFgFWbgyfA9yy0kYDVO4A8rIosSLkFXcAoycB7wCgNaNx0y3Q678k1n86mNkAP4t7QSPvyzze1KiyVAnJKTXKTGXzVRopBtU4B/jgGspAMDOHmQE+ne79VZeanEd2kVesPOYYHDdMpCEZwPV3Ovuafi+JhlQ/t97GZMtTcEZ8Ud14DpatWJpSJbGs8qa9S1k1NcFCuyKeWOGxM1jPv1GelN/POUMfnZ/Dc1Q3G6a9zGQpmbwl+wZMScZIE9NVPMw1Ul0DpK/Ia0sCSK4xbPxMkoe+Fn1N9su4pqE3AWveAZx/EXANK338mm4CjLPr05qOrBl912t17SXxR0lPzm0hfw6qq24S8Sr6HbzH9BOSVURdTTCGQCKD15eV34RsTE6ygwQLJdSuSnP6uQCTvOYrLWNIDfbtSQcZkRof//jH03pPq1M6YoqDzPbdHe6LWGzPrI+AKxDAuYkRGNg01F4gsmiJd9XU4zD16yPgEvOxhiZ8fnEGfSyuFi4lkM+6dEwhNVIAF1ZdkqqZA0Pn0BEhNAh3MAiT1oxikxYmrRFLcxfHEhpD7cDEIGDNi8j0qBbtjkvA5TPKv6WkFLPQm08CSxJlISdb8K/8RkDygt96bkIxXJbm1mJ5bj2cAS/G/B4UGq0w6wxYbC/FU71nhaSGwv9rRKZ6FYPk8ug1GmG8kEmmcTRddsaV0NhkOS8NOpZNksgoTEdxhZm7B55QCA1C9hOwGAGPX/ndagTCOqVCoqwauPt9ShBxrjEQV4VFgmAwcpzTwedSSDZN3FjjWB5qmx2pMR3Yd+TY68kbm80EVnVQ9suvsEgFrgCO+rworK2fkpFHA1SWGnP80XiPb65Ho5XPJWq6V2SqFg+ZveG0zRx0dwb8eLG/SzQcXltVDrvWIILMrFKiocHsnmQ9D9RZFSQrppNLUL+W84yfzfcwoJ/8faIkJ5as0GigERnmHUIy58Klbqxd1QidwQ7oZldqrgbvjfr+mLQ2IUHl9/ngnHChvKoURq0ezsAQrDpW3kyfWXnPmvWwO9zw1C3GeE8vmrwQ15mEEEuK4x2nN4aHBaFByKLsZ/v68EJ/v7gi325uxtM7d+LmSHb80ZEhHBhWjEVJcrS5nHiutxv3V2dJz5NzrX6T8sgSeN40yDnWEzrglA/4zJ9O/hrav39uslj/45+APpm9DuC5x4HySuC61EuiZcYgj0823istLkHDaA1AaeDOYZi0WiyyR/W0by6rx02ldfAEA7CqyCdeD/UeRCc/UTO/60qq8J01O/FCX4fYw+4or8UylbSFRRDgsWQf978S88IY502TW4gNf/nPeP7Xj2LD29YBK1aL+20zmkT1CUuHn3vuOTFeGZzbunVres5fOPFa+Zuuo+j2hkVCxgM1G7AsN0Ot8euuA97+duCxx5TfOUb+/M8VQoNgFmmKEgRXBgkIWOpH+xyxr+nZC9hrgWw2ZaZ8ZUH0WpBCo4wFfYJskRr8LFYvzWecv/lRaCzE+vz1aHY0wx/2o8RYgiW5v7vSSMFwCENep1izCo22lNdBKQFESTbu/cnAZI9sVUwlA4PDDCLR37nqWvrzyBo4FpmtzWqgRP4FEy+khCxfSz9BDf6tv78/YT8Xg9YkiA2J0+2n0wr4U4KENg0r0Jn0JOWmkklM8Xl1os900iU8F/U45rjeu3evOA/665k2Kecx0hdP2vdtFkhm85K8kf070zlOWVXC9YPXg0FEEjskVOdKVijrsBYAa9+Z1Y9kxQsD5yS8ZgL90ekkha8FyJ6UBOcxKwJ53Ex6kpJzqYzXuWwOnoyU4ZhUy14RtDm5DlH+6oknnhBznmOW5zWPtw6p8ZOf/CSt96RlAXHwk6GP0UiWCDP4xvIryi4wyNkEX1iDcX8n/OJvMS8WzVMzQbtrAn90ZA8GfQrjuNiej2+v2S4kpd5eVQd/KIT/br0oft5TWYNPL3iTGdGJglxZYoC7PEMxLjFD9a5gADeXKSVoMTj3CnBZ9iKgmN5SYPXd0WNRZ6CrQamdRNBWAaG47AdNedb6aaSCUDgEf8gHg9aIlfmN4iGxd7AFT/ScEteHvVfeU7MOK/Iq8NH6Tfi/zuMY93tRbrbjfbXrYE5UicKs1c2bsWfPHvFTGl7qjSBh2eZYB9B3BqBOedFCoDjzklduwHRqGAimIUqJlmeffRZ33HHHlOOZAq8LeOFHwFBUVz0GOq0ST+HP9/4RUHOF9UJFNm5coFr2/ZgOp54E3HFZ+xLZykRPhLFBIKIbO6W6o7yWafvA7qcUia/aRcCO22OP5+mHgEDUeKg1adG1/0Us2vX1KQYuH8y4kQ4Hf1KXnAEiGuyyxwazkKYz0JjFQamamRpxj/t9+OShV9DpdopqAN6RLy9dD2Nfn8isYMkoDeW5yFyhg8K5NF1pML9XE65FGG3qZ6HRNAI51cjVnsCiBToMjRtRWr9d7E+jvi6EwkHk6Ith02dmyPP60rHj+fM6cx7WWlfgouMNNDdfxuLl9bDoSDaMo9tzGGZtPqos65P3uon04dm8fr24nrsdu0W/jOnQlqTZnwh0t7ZCYzTiz06cwOGIDqmTlVlx4F1zBgIY8rpwarwfOo0Wa/LLBaHrDvrgDwWQozcLKclroTR1Jt3bjLLmKEv029+QJVaatd19N7Agbs1jj5zOtql79blTaZEasikfjXcGrbhWS91jgvOZJdeURlBnGbKqRhIafA2dWp6rOsud44bOSiKsyS8Wj0RozKlHl7sbzqBrsmJjZd4y6BKM1ZzcPFx3932iGmN8YmJSj5rzkPeGVTWUleNxMSswLa1pXSEQiMofcq3hqtrvC0+ScA93HMGXlt4qyMK0wfv1pS8pvT76+hRtW1nhxqy23l5qnV15qSlWFnqGAYMNsJZE7S57g1KZoYbeCvicsXsj7YmACzBmj7AVcA8BPQcBvxOwFGPl8mViDj7wwANZJTXm8dZAqblUPH7XMe734H9a96PfqwRmGm1FeF/tJpiS+BKJQHueAV4GcdhEMx7MsmYG7lyB/v+BiKwgA7+0LblfMblAZs/O480LJh7Q7ojv06IOdkofg5UMkmBj9QQJAAbU422UeHD/T0cfXU1U0JaRlXy0NeZCcobfwWQMyjLxXDIlJWjLpVrNkiro57Gqnb4Pr4G0DwlWVfB6pEtCCEncSBNo3lMGjjNNUKDvyHNm9XimZBCJX/Y7ZZItE0uvJjieeb1Tkddk0/Sr0UMlVcikKc5d2S9HPQ+5tjN2Nd145zpPH+NqEF3c75g0zOPkHJUVJSQybrzxRrEf0R9k3GOe1HgTgyobA8eBsVaRkLWyrkisKekQzGl5gWT5GJidMmiE3Ad7QEjdRMqLHESfhxriig64Py7BLNeQWRPqr545jGFfNKDVPDGG718+g88vVgIb765pEI8rCqEpx2PSzj5Iv3gTcPhZwKBT5H6I3LyINvjsgkgWnXGKnJLcOCjZEgxfRBgDwIQHhstxJT/dZ5WeC6WRHhgVdUoGvDooRse7KolsgDYP0G8CgmSKfYCmGNBduU2g30ON+ZMIIQi9Ro/luWtQaFICOu2uETzeE5U2CoRD+EXHYXzeuktUa3x56S0plSHy75Rmo+wGDX91NgWzIGIm5Wgv0HIA8KmyfEfbAPbwKE/PqedCz6AXg0YMgtI4ec973iN+LlpSid37H0XjwgbUlK+AXptE8u3cXsA5qpAW8aCsyaZbFD30xWuBomk00OcCg23AcJdKPioid7bshunfx7E5HtWXjwEDYFXJy/hnjRwGwRKIl627XiH+fvSPShUG1042Wu/vAt79qejrxoZiGp2ZdFrUaSGy5OjY0vlgoJABdGZREXJ88ieD3xyzHHckKWSFA50K2fRPNAALtsIT6hWBy/ZLPqxaNnNF2EPtzeh2K8FzrvwBpwtfef4JPH7nu1FaXCyOiZkVc5GlRNAoI3ky3ZzUgGuLSVnPoIcWddBo8pTWT3m7YLH40HP5MvIRxiXHXiG9xXcN+lpQaV6BAuPMesHxoFPBDCduwDKIbtHlIG+iEZubauDXtyE8WT8BeEKjGPW3odCYXGqFQWp+LvsuTOcsSqyMyEwJnVNm1LA6bGCAF00Ea8Pd3RhQEUIr8wpgZ3+TQEAcGd/LTNIaqxF/ceoleCPEXJ7ehNvKy9Ae0dS36y24o3wTCoxXT1+XwXOOBY7x6Ro7cuynpft65gzw6U9R0FX5ff8+4JH/A/75W8CmzdHXsUJNSvRJcDxa07smDCKw4R3Lw0mM0SlUg2OcQSM6HYnGANd/2maJ+syQ0JyR0E4Ag9aA60q2o9PdDX/IjyJTIQqNyW026eg8/fTTwhnkNec6Rb1sXntqPZPgaGlpSc/5M6wEQuNASAkG8i7+b09Y2JUS/nBQBA2L2WssE/CekexVE76HDgGf/zw1LpTfWRX2hS+kLps1GwydA7r2Rn8vWARU74hIQEWCmaJRuAbIZ9KODnDFJSOwHxrJjmyC1SCXnoyQ9WHAO46lpcDeLGpJM4jwlpESmMc8Inis6xgGvNFEsBbnEJ7vO4u7K9Oz9UlsMNiaqKcBkz3mSgKF85KZ/AyEvfDCC2KvIvgc13cmUTHQHN9/YR5vHjCY/fLLL4u9mzEe2g5M0lFXRMjxJYkP2l+0d6XULceJOonEH3JgInAegbAbQa8RYU1uRj0ZmFHO45ABd5LfcgxmGzynbEhHcV5kU4ZKBqVpR8mMewnawNNVvCcDfUjaYyQzSFjex+SOFCHvveyTSNuT42JKrCNFDHjH8Hj3QdHzldhQsABbCmPliK80eK0ZW5lJgutalZ6SYIyA8aBkPjPvHeMEJC0SyVYzbkAigWNM/X6SYonkbbMNWVFF/+j1118X44zrCJMHeQxcr7gmsEcPfQ3uk/N4E6L3EDAUlRyuCg/AarWI9S7VZKe0ouRkTKROYSxorNH5UwcOQrDp+HsY+UYNdKq3FBhKUW3JrEH0RccYQqrv4b/PU3v+aoFNuf2vA76XAd+LgP9EhOTJEMu2AdS2JqnB68wHg7qnVX02MsQmbhCq/4itRYqOdjB8FmG0AwEHdGywmggu9iWIIK8QuP8TgMEUDXxfdydQME35o7YAMGwEDNsB/WJFziAdUJfd0QGMX1YyBVOEM+DAmfHjgtAgAuEATo4dgTeoVPt0uKaOH2aA9rjHY8tz/W6cGutC80S/yChIBBow3Ahp+DOAwwWfhk1Ms7OBNuDlnwATbTGBa4HuSIP4aUCjgVlZL774otBKPHnyJLZt2ya+hxkDt912mwh+X247BnNBJyob9Bgau4BnX/0v+NkUORFc48qx6CktpQqCkbi6+X3AdXcD226/8oQGiZZ9DwNe1f1mhviGtwF1M5ASDDgmyia3FAAb3wdYIyXKw53AhT3A5QOx3zMbWO3ADZHmRvIYWKGx/Xbg2L4ooUGIfjTHgDFVRUlReeyxa7RoWL5SGKBs9MX7zgAnA4fJMic4ZhlUlE4JjQB1hYMreBnO4EUEww74Q+NwBpvhFb1voqARJMe6rAppGxiAf2ICIQbCWTXRNwB9dQUMuYpuPoOoNC7mAnTmpcEynSHJv2k1tdBp1kOnWQ2NJrbJIq8JMz/63Bcn1wW5f/V6zmbU/IzXmtks/G5eJwmPy4eqopoYQiNylPCp+ms0T/Tg4Y69+Hn7HhwZuSyOgY6DbP5Hg46ZasnA1xsHBvA5XhcGGtkEkNnmzNynw2O1ig3/upKSyfcUGk348cYdKDUrTohdb8C/r9uCF/ub4VNVGo0HvHihP1pp5wh48GzvwTltEjct9rwI/OknsO5H38KJv/oC4IgtTVYjmQRTUvzzN6OEhgQrXb71zfgPBt72nuhawweJjlvuTutUOF44Z+i407lIZJALber6euGExoMOJMdzPKFBJ4ZjR5KY6UKv1aPeVotF9gVTCA2Oy2eeeWbKeyjbQFk0ficDItz/qN3LterJJ58UTlNaDTRpI5h2AqbrAdM27HMtRrM7dsyxksiuz6J+tcMBfO5zUUKDOHdOaeRHsiMZOMZYpXPqqFLFkwlo13SpGqMTI83AeFv0epRuBBa+G1jwLqBoJVC4GLCpE420QNXO7EpPEWMtUUJDIIxFlXY0Nydfk9KF9DHmMY+3EuhjqPsT8V/t1H/PAHTuaQcxcYTBREpwJJVhzADc07kXMXjEQCd/0nZktizXbwbImCHLdZ0kBhNomMhCqUGu+fN482Lnzp1iz3711VeF7UCfdbr1WFZNSNDul9JUwbAXQ7434A0NIRh2oWfgErS5DKwm3v+FrxGRzyVJxoA57QxWANA+UleQk9CbC4lCfj8rULLR04nzRDbizhakL6e2u3m9Mw3i8vMYT2DsgPJJTJ6azqYn+cF1hwk2zIpnAidtPgb9mSmfadNm+pgkNFwRQoM4NHIJzY4kSYlXCNIef7Ovawz6sycF1+lEvS4JEtZc9xPdf/oYXOvVPjfnCpOx5qpKj/uL7KMhQSKTpAvVULj3EYyF8cFqd/r000k0vunR3wscfQNob8FbDuGw0htQBY63RfXVaVW9peX1MKBCmZN0wczPYiMXLg1qLDtg0lkyZjbLTFZ0uh2T5qEWGlRYrmJ2iP8YEFZJMYW6gKAN0GcWSBAkhmhqHIf2k8DKmzI/TgYczAV4Z/VOXHB0ij4RC3LKUW4uFFUaYfSIQaXrGoIm2aZmL47Ngm9vZicfQBMGcs1A52Fg6AJwwweA3GjgLCvgZtfxLOAbizr3VTcB1pm1tMf8cQ4Fm4P5A3isaw/MOhPyDYmJGLskbMhUOwbwi46DooqDqDLn40P1WxJKXtAB4ALLjUQaZzTUaOBw4xcEFa+xJK3iiZtpwDJTOhYMZjPQKcsiWaHBoCqNMW40t956K1zaCQq9KW8sy0d+gQ1HTjyPzWvfITYkGii8FsIgsiqZzqNON0acHnh9fvjCOoR2PADteBATzzwJd0crtm3dAuuyNQqJdSUw2Dr1mjCwkkppKkkB6vi37Ff+LVotaIGVdykanPycMy8CnWxgHKn+aDkEbPsgYMmCbMfmm5WKJvaasdmBpesViSkSGok60Ef6Zwjc9QHg5/8alXMrKBEkSa7VLhwPGpPc1NPVNZYN4Zh14Q5Gg9ROhxu5eTlwBzth1ikZUOcnuvDQa7+FxqxHvsGGG0pXwm6wokanh6urB4aCPHj7BpG3rAkFBhPyKK2lgjorhHKAR0ZGBFnIRtXmJHq404GEHT+PzgKNMxIImWQG8hrQUeJe1t57TKi7qEGSg+uFJH5TBQ1CVmrQUeDn0/GSZeE6DavkdAhPEihEGEatcvwXJ3rwRE+0Oq7XMwpfKIDldXWCiGKA+vrrrxcBBgaI+Zm8jyROJ53IYFDM/2++8534A6cTr506heULFuDzp07hFVZrULIpLw//Hidhta6gCHtuvAOuYBBWnU5c40c7j8RJFQIedoqe/D2MsYALnpBfVABeUZw4DPz3d8U/6dba+y/D8e1/QM6Xv5ZcjixVe6OnBziXxCgeShCEuusdQEk5cOY4YLECN94OlKbf34HHx/vHsZPsWJmdREOf6zyJTDqjJLq4B8i+HPzJvYakNufGTNJc6SAUdiAQpnHpwdlzA9Bo9FOCaVJvm300uK9QB5uVG9yzGPBgk9u0bT++Xqdkd24qzMWpsV60u5VEBH7S2ytXpSXjMiPYGDxR9hmJyv/+b3YqnPo3kmr//NdAV6TPkz0P+NOvAJWxUh4zwjueYGPQAN4ENuHkn3VAzS7A1afYSeYiwKhqzJ4tJLALm+rK0Nx8MStBVX4GM7Iy8THm8RYE7aYXHwaYtFZcAdz6XsUOehNg0DuKwyNnRZCuxJQvpBt9vsDkzOa6NRsilvYbqxNpA3HecX2lzZFqY9vp5iClPEigk6yQAUraWwwWUZrnwQcfFDYoA5nyuxhokhWHtEllljr3KO5VUi6En8/Mbto0fHDPkn3I+DuD14mkteZxZUBbgn4lSSvGe9JNiOB4lHKXnmA/wqICWoHb7UFFVRi+0BhMlJWMSLbSj+H3ikQkrVa8nzY+M+RpT0t5JDU4phjoSreHxExgcJTZ3zyOtJIvVOBY5lzhMWazcolzUCbnyHnDa0O/Q8QVMgDnLf0h2mw8Xga2SU5Ku5FBZa4t/B7OXa4H/C45n7lfqwmV+ISuVEHJKVmhAVVsr9szjCb71e2RQFKP5O50vrZsaH+t9gKjnygloePl5dTgveW5slqcc5JJVDyveB+K5AHlzklozEV1Hsc2xzrHdnyCJP9GCUa553H94HHwd65dapL1LYVXnwf+53tRO/zmu4D3fSxr7QmuDSTwMRoqp03mjIc+O1lUdDg5sOkIRs02lyoIwoaoOboCmCMl8cO+MVx0tCEQCqLSUoo6a2VKxtgXlqzB5469Dl/kxlKW5fcalGqDKw4eQzi2YZZAiMGPDEkNgUiAVfwzck3SrWpIgiKTHVtNSa5XMAQtG0Ib9EChHRhWZSo2bgaKVGVpL/8aOLqbjJXSMXLCoxwrF/UDvwFu+QSyiqGTgE9VZcBsj549wIKZm0PpNbEB8EFfCBPC1vKKjXTIN456qx2trgmxkZLiWZNfhUpz3uQiyiCfJDSIbs8oXh+8hBtLp/Yf4MZGQoOMuFq3kAa/cDpENUAYcPuiFTkCGqCgftpzIXnBZqtqkDTh59IA5BzlT9FI1xdLBuQX2NHV1iuy/Bn84uLP99KwXNLXCrfHh8OXe7CqrgxFORZ4iutxcGgYKzxj8P3mJ7CR9Go/CrBPzQf+ZG57UkgkyzZNVW+zbhNgzgWGWgEGX6tXAznFCqFx6BFgtCtKLrHhvd+tVGwsT10Tf1qwXwYfajStAvY9H/2dc4bOeqHKYS+pBH7vr4DOy0ynAaoXKP04IoYjHUrex3RJDWY70VChs0Iy88J5JQhntZkxNDiGynJl/PW4h/FC3zFBaJTUVYoA/wn9AN5fdz1+r6EB5zwu7O1qE9UZVoMRX1u1WWj8S0hDiBj0enHzq6/ieCSzYoHNhpdvuAE1aWb0kNSQAS8G9umAkNxLBzSUaJDJ913sBQx5fhiMyngaG3HANeJHUXnipocSdCrowEnZI2a10WiUDQrpDPBBclMec7l5BXo8JNAie5c2F/kGZU09Nto65TuOjrRgy8ImMU9lppC875y/fJDESFQ92WCzQV9ZiVK7HS9dfz063G64XS4429uRn0COiO+3qXRjqyy5aHWOTlZF8tNt6nLLSOPojPoYqMF5ePJVoOOMsp4s3Q7UxTqwU3B4f4zs09LcHJw4+AY2OB2ALVbaQPaXSdnR+tWjkX09QjorH6L8O1l5/6btymMW4FjiniGdxWSgZjrXeBp5DBiRvFbfe2bKcXwwMDSdJFe6CIc98IcPio4WvKaDQ5egNZjg9zPzKxqcI5EnJQ04bvlaHhODFDxm7o2zcfwMWh0+0bgN5yb64Az4UGstQDnX92xiuqzHuGaFk3jsIaCnM/q7cwL48b8BX/6n9L47IRkRnp6kcA8AffuVKg9TAVC+DXOCvDqg76hiewlo0Ni4QARbmDk+nbOcChjs5Gdlo+Em+6ddmGjBsH8MFp0JS+wLYNFlsZrnzQyuZxPDSmJSXvGVS1BJB5TffOQ/gaBfOV4SHI/8B/DhLyrVcNcwxv0OPNe3f7LC1Rlwo9ZqwbBPM9mDjH37bi7LvN8jA0m0NWRQl7YKf6d/QEIiU/D9tA3jpX24pjOwTH+B6zd9jHibQ2RVLlokKom57jO7lwleDPBSlpcBUQZM6ZvweElg8LUMpjKwesMNN4i9ax5XHwwkcizIBKh0guRUDqCtTvd5sH8UgwOjKCzKFT/LK4tQmBeNC3G953hL9B0c2/RvEsnbkABLlnGeKfg9tJlkNQTnGBP/0g2SMtDPc+LxccwzoCz3NM4LKRscL+UTDxINTGCRr+HnSr+FcQUGlTmHMkoUiYDnyrnM68nz5PfRZ2Fgm8fKccDKsFTHAI85k94S7KERD44SS4LnrzS43vE6JeutwcA6k1f590ykXq8UJCk2Xc8U7iFcwzmHmUhHf1PKW0swOZf+FAnuuZLd4ndz/iSa4/S9aWvyXGh3ch5xb+IeQh+DyX9vOQwNxBIaxAtPAivXAqveIkkAGg2QtwAYja3KWLR42dxVavCD2ZRl6sGwiTAvLDMcGbgyA5ommHQ98EeaPDKAU2pWNLGGvKN4sX/fZMZnh7sHnqAXS3JnLvmrMFuh1+rgCwaUhqbBIJ7r78LHG2INRC7Iz/d14/zEGKosNtxbWQP9nLCovITq4DEDpLNY2LrOAyMDUZ1uLUkDLbBoI+YKJJw04TKENT3Kd7GXR2kukG8FHF6gaAHQELdQnH6DEQb5AUq1hssL5FmBsTko1ROERhyLF3QrQbEZnLIiUwly9HY4AqxMQITQiEWBQYPC/CqYtQZUWfOxJr96csFmNrI7NFW+ZIga00lA458bHQ16OWe48NKI31RSD7hGAYdHub85nC8aoLARqL8u6WdyEU+0afJ7uMjL5uBkrvn9en0+AoJ0C0d7fqzfDmtcFRFJjcO/PQodgrhheT10kXliNwI2vRl7vvtPyOGQsFlgojxV+0Vg/4tKc+u5RvkiwJoHUApMBhdzS4GiFAMofH35UuWhRvcZhdCQryF43szOyZYEVTLULQLe/jHg2YeVSozKeuD+j08dx2YrsDBxgFcEoW02Mb6koSukodrahHHFjZ9B0vjApsyI4usmfAFU15aKKg2z2YiCQju6WnzIbwph/7mjGPeMTJaicp2eCLgx4nOi2JSL7+y4Bc8fO4K8qgqgux8r82IzptRB5M8eO4bT4+Mxzaw/cfAgbikvx0v9/Shi82oaJjM045XZ7DxnGjt0ONJ1uuhQqIn5rStvxmsnHkdJo0Gc69igCzetuR8dLT3TllLTsKMBSDKDc49zSG0s0olhtry6B0KOvgx11m3wBEeh1Rhg0xWLtZdIJGenllmkc8f5zWBAPKmZDLwuvD68VrVWK860tsacuycYFPei1GRCQdy68omGtfj6udeF7BRRZrKhMYcVLEGx7/LYKGdI6Z+pB+4G3EeB4IiyF5qXAYYkPUqOPAtcUAUyXn9EkbyrnibgE0emGnVaUQkkyL8EWrAM0PD6peQIM2gte/aoQcP+L/4SGYF7lMcFmG1J+2KR1JBzlmRjCD5owcyq2HOicyW1k0nOsWJLjgfOOQaF2JNAOl+OgB/fv9SMVpcTTXY7PtmwKKMqqSD6Ii26gbNn2jA26sCO6xphMHJ8RIPFPH5ea1aUkEBltqXMfOR6lZLWMnumvfxboLMFyC0Abro3RvaQY2557iz1tHl/X3wGePk55X7fdBtw463Kvzln778f+NWvYt/DvyXr99DBikLVHOa/u1UkR6pgj5qKjUozbtpinGxaE2BOQrT4HUDHcxEpQyZK9Cu/N9wLaDNIOlCTefEw5QGNtwFdEQLFWgxj1Q4RYKFvMFtSg5/BgOxsAwNcx/cPHUWXp2+SfO109eLWsp0wZbGqrNfjwtM9XWLtubG0AovsWSbX5gIBP7D7F0BvRMbOXgjc+EHAFivPeNXRcgYIqCpXOb4nRoHeNqD22q7kaXX2ikp4CdpOzqAL769bhw7XuEj+WJ1XjSJT5hmunCMM/MheSgwAM6DMdZckQaaENpMnEjUblxWElJ656667xFxNVFHBvTaeEKENxAAs7SnaRepkEf7OoC/tKe5ZmTSRnsfcgHYGK4NJSMlsbCZSMJAobe5EAWzaIazSGRweg18TwuJldWhv7cWGzctw8WwPalfniOA3iQ+SXvw3A+vx4LihH0u7KF4nn3atutdHNsDz4vmQXOf3crzTx0gncYrnzeOSdibnIYPesoqWmedM/OBncw4lq0rksRA8FhIk9OkZXJYxCdpRUhYpG4FlXmtW1fJ4uQdnSozyWNSB/24mUwWDqLfZhGJLMrC3K3toUHKKiaVcPa06E1YykeIaAP0m3q/4sUBbg2vw3XffLcbMtQra5JIEE3502C/8DKEgEHdfuJfwwUo9kluSxCYYN+Dcn8tqWsYvSKpIQi0elFrnWOXewfsi+8tw/+PeM9tqxaxhoB/4yQ+Aznagth74yCeB4gwrTXu7pvqk9CVZGf5WITWIyi1KHIxyu/R9S1Zi0UodXv2v/8KckBrTZoFo6NhGZS04pEpMhSgyLhZa4oqDrgy08xPUA1MLAgGnx5ux2N4w42D8RccleEIMrkRD3D9uOYcP1C6CSbXJ/fWZY3io/TL0Gg0C4TB+092OH23Ynl1ig8fK3hABqZsoPFBAl4CccU8AfZcjQdaFgClBsMzjBPY8EtEtjoDZ403UUJ5qaGYTOs0yhNxjgEkfXRQsRqXHQkWiSRNfSRL5yeqcnDlw7ky5gItNalWjRm9JKctMp9Fhbf5mdLhb4Qo40eKKDTZwrejzTWDYNwG73oTrSxfGZJ2T6OCD5IYahdM0ySWLTX1KGjI02BnwpBHC63rIl48NJDb6W4BxN2AoQmjT/Th44gzGWl4WGwaDQ2rnfsA7jENnj8JiNsEX8sOoCliQgachwc2Axqfsm5CjX4EJ/1EERc8bwKgphUXXkNgRWbkUcIzE3FdNTgFuWLkOodfqcGlkHKMeb3RsXDwNrNmW2r1mgOf4PmCwVwlOrdkavW9et0KKJsu+owTYzg8B53YDjmFF1mzpdbPPLvRMRMjYBBnceXPTgC4GKzcpj+mCSNPBMYoVA2fRerEZ54/vxuJ3fAQXW1pFYEkGDhn8T0RqcIzQsA6HG0RPjRwr+2hoYNHVwhqwiAqI8ooKXO4ZQn9bD0rrq2IypQmOgUZ7HhaWVuLiuCvGkGDwXe2kHBweFmuwBP/96uAgnu9neboiT/irri4cueUWLE6SFUXHgPOHJaasTshEt5WOEd+nzhTXanWosi1HXU41urq7sH3RVph0NuFI8PrxtXJu0fCnc0LHhM4PryOdPFn5Eg9WatAwVINyU1JySo0luVXo9kR7+/BKLs6Jll3TwGMWCjOpUm3CF99Lgg6abNL4Sn8/3r53L0b9fpE9+vVVq/B5lWZxhcWOr63chUvOYeFkNNmLxLpzZrwN3pAf1ZZi1NuU4ATvvSPYAlegU8yn4oAP2rBPcU3CHsB9RLEP9HFSARwTFxP0EOJz05Ea192s9NSQ5INGg5Lt12NgwoGSSG8QCcp/cS3muGEjOfZ7mFZ/eMNG4De/jh4fv4PVUL94WJFaTBcXjgEv/FIJ0JHUuONDQM3ChE4Hx+bIRDc03nMR2UANcvVLYNMnDhZzjpG0UQ41LBwQBpqkU0nS6v69r+Ls+HjkUoXx2kAffrHlumkdzOnQ2zuMrq5BLFxUhby8xEE5zgnODVmOL8ccz499NTiPWOauoV4uM3CYbUwnSVTLhYCH/h1ovRC5/lrg0mng019RCI5s4dkngJ/+MPr75YvKd998h/L7F7/IsjbgJz/hSSvHxiaaH/1o4s8rLgVa+RmRdY7JCkUZOjBsDD58CghFgrphP9D+LNB4HxAvWePgnIuVtEPACXiGUpLmnMREH3D2WUX61GQHFt8M5E9tSg/O+aZIM1Hen7FW1FeVCd9gtphtlrnERMA5SWgg4ml4Ql60ubrQZJ99FQhxyTGO9+5/FU6SBNDg3y6ewffXb8c2joOrAd4L9t5zjigyscmSPk6+DPSq9Jhp8+17DLg5ybi+WkjmpyUi0a85JJbvLTXlYHGWpFQ4T2SPLRINJCKYZMF19dlnn8Wdd945pUExA6R80CZgwDo+kMzn6UvE++C0G6S9xb2F+2c6chAMgjGbPBFIYjAYxeMnAZ5Jg+F5zA24h0s/lokS3M9pTzFZgfs67VsmVsSPIwb0lSb2VfCFajDuP4uGBiP0Gju2rNqGy5daxWczKMk1n2RXIlJDjkOOEbXMEe0Y2nNMlsi23A2/k/bLdNns030GA9/xUllqf4PfIeclyUcGmhkj4Gv4PM+R/hPnDM+bcQD6G5wX8VJbfC6bgXT6MNMRLTOB5yZtTxL9HzxwAL/sVGItS+x2PLNzJ+qmkSpiU/AiY66QnGKFBgkNq/7aqMrjfaFfnMgvlWOXfWj4b9rg10RQXQVW35AAaGtrRY/7JMYD3ZPJ5tWWddAzcSYOPAe19BP9Z45N9rOYK3AOkZhn7ItkajLIPpP06SS5zj2R6wkJkRn9vLmG0wl88bPA6Eg0wan5HPDt7ykyxemiKIFdyc+9WvbmXIHKD5VblUcE9fX9afkXKZMaDOqQPRaZFMzSCnmBsBdwnQfCPsBYBuQsBzSxH6mN+53wkyWM//ywQlSolwJXwI+nei9iwOtCjSUXt5U3Yjzgm0JYMWvUHQxMkhrnxscEoaF8rvLivUP9onLjjooEjtpsoGF2MRcEBpJZVbEY0MYF5kb7gFd/CvgjmoHM0Lnxw0COqsFv+1mghRJLHiXjVE0W8PVzvEgyI1THBpnxDXj53YmyBEsrgb72RB8EbEivUeq0mOhQGmVyAlO6jA67+B49UL4z5Y8JhP1wB6kB70GpyYb+SDa+jFu5ItUbjoAPL/efx9uqolrkvB73V63BLzsPT2ZUl5lzsaM4Gsgk6628VjFgaKjICg2WxXHDY1mc1MHtrNqJ0pW3QqfV4fiFy/AcPSkMKRoGNB7pqNAwpMHfPNGKh195DNYcG8obK/Fc7+u4pWybyDrka/n53LT4ejo0dECkc5Bn2IIQPNAEhqANXAL8LwE6am6vANSyXBvuAl57KJJtGgYMZmAVe5bYoTWZsahIg8vD4+h3ulFmswAXzwL//EXgE58HqqYJRPC6PPw94PxxhYggYXf2qFKZ8Nz/Am0RQ2HRGuCWBxJLWplzgDV3qG6mDxjpVBZAVm1k4ujaS2IJDbmolLIqaW4aXyVEJvOa1R2P/pv4WR8O4eCxc7g82gvbXR+NcQppGMfrfLLEmIaJ8tVaWHTFMIpKKx30mjJYS80iwMpG0KddnXCUTEx+RoOtDLkR+UA1mEFEZ5mECo1sjkV1BhezdC4yEz1yjXnGXlVWM5/3hUL4/qVL+FaSHgB0NGiU0RFglgYdh5lkbGhwcq7xmjDTiecen00sM6iMWgsC7jByq/Mm5zznoyRrOC+ZWUbQmZLGO+dzsqxIOm2pGrer8urgDQVwdOSyWGMY/Li+JNZp4/kzmMD5zcDwTJ9Nh1PdXFGey5jfj7e9/jocEe1j3okvnDiBtfn5uFlVamzVG7AyryyG0NrIpsRxcAZb4Qgo2TLsGqKjPRADDRDonUpqKEeV4nMq1DUCn/8b4MlfsRkMsGINGm57Gw4dOxbjHPP8GZChAcyMtttvv10YzNRfTRo82bUL6GgH/uuHylpYUwt845uZERpDPcCzP4uuLazWePxHwEe+JNbV+PtCecDXT7wCTWsIm7YtF0vDeOAsDNocGLVT92C+j3OTD1kZoR6LL/T14PR4pBdD5BD2Dg3iwNAgts2QMdTjHsPxsXYxFptyylBk1MEzPoGTxy/BYjWjaTHnkQUaIT0aC85RXvvnn39eaK/Hk2rc0w5973vY+FPaRBHS7Z57gL/8S2CgB2g5rzrJEOBxAycPAttvRVZJjUTPSVKDF/8d71AeDBzQJktWQcD7y9dIQoPgv9/+vsyOzdUbJTSUL1B6Zbh6gNy4oHzSNSCNfcXnBk78WpH6IbwO4NTjwIb3K9KNiSD6Tz0PjLWhrlDR9J8t+BnZyNQOhKeW4rJaI9HzmeJfLpyGKxgQa6e4P2Hgq2eO4enrsjhGUwXvxfGngXZKG0awYDOwPEFV/SD7aIVj3zsUqVi9lrBgFbDnKSXphWsAbbzCUqBi9qTXXKPWWo6TYxdVQswa5BtykGfIrta3rIrg+k+JRQY96affcccdooKPCRVMgqC9x8QLBnlouzCbnNnxDNIx+EM7inskA0SJyAf6/ZLU4Oe98sorMXbFbMG9j58ve8XFa6jP4+qBY4OEExUGSETIqkuC90nKHcfv/xxfJNmM2jwUm7bE/F09dmgjJ5MWITlC35ZQJ+fw9ZlIHE0H2vaygojHT8IwvuIoHiQAWS3FACznIMkZEgLxdjn/Rh8jPoOc149zVj7HOUjfic9LGVv+LZkcI+dzNucKbWJ+HpO5aKPN1CuBx6qukldfs6+fO4eHI4QG0exw4IH9+7GP9nUS8FzZP+Nq99BIBt5n3nOZbMoxKWWG6ZvxftB+Yb+Ha60vEO1x7getAyexZ/8bKCzOQ3V9ObyhCSGJXGNNTCBIJQLuIQwsJ6riyyaYmMWkXPU6kwiUxSLZSn9nkkhjgh7jFA0NQuaQe1s6Kg5ZxbHDwLASLxCgL8nKjRPHgM0ZyMOWVwJvew/wm19Gn1u7CVgfu7a+FcE5RRskmfxbxqSGLIer1HcAba8pT3IttpoUh8/tUOSA8qe/yJ6gG3kGG3o90a72NPpKTYUx2fHeYAB/e3Y3evi5CINiVafHB7AuvxLP9EYb3DKLtMZqi2lS20dDOA785F46x1mCaNIU6oQ+cEaEhZQjDwHBs4C2CNCqnP0jT8WWUvtcwLHngB0PRP7+PHDs5WjmeCAEmAzRTFTLlWp8kyBrPZnTvP1O4LHvxz7H5ph3fhrIzVLzrrEWoOOV2OdKVgCWYsBSBhhSYzz9IR8OjuyHTwQKwjDrwig0GOEJMcDnwYSfElPRrL4R/1QNz0X2MvzBguvR7hqBWafHAluJkEEjmTHoPQNHUGG+bboKFJuWQauSDaGRRKOdk1JqGrLkuqW1VSy6LLFjlsbLL78sAsR0OigpwoW5tb0Vr/a9gbLGSuQVK4aOK+jGBUcrVuY1iUWcr5X9bqixHt+4Vcd56T8ePRkeK6s5TJuj97esAdj1UeDEK4pEQd1KhXTj39/+UeCRH6DYasaZgRGYtVrkCYLCCzz8X8Bn/y75xW9rVggNQlYgXTwF/PbHQL+qj0DzcSAnD9h57/Q30zEIHHlUmUNEfhWw9u2p99iQKF0I1KwBOo4pv/N8mq4Hatdc+42XLh0HXFFt93U1pXj13GncdH/sy2ig0piJ18ScXL/CPfCFWGWmrDN+tMGiY28MK3L0Zry7Zgde8h3CqNcJU14uwmEDLjn7sTBH+TzZ4JFkAQkEuU+wkkKNb65eje0vvTQZRCeJ4knQdJZSgolAI0eOaf7k/Dl//rzIYqKTPVMPDhItvAaJjCQ6G5xvzO5I9FnS4eA8na7HRjKkWgrL12wqXCge072GDiGdJ853fjYNzkRVK7zvXHfkOdMBOeRw4NVz50QW1XjkXkiwonHv0FAMqZEqXMGuFOiIBAYqr0vDauDS0dh38rmZsHAJ8CdfmvxVF7lHvOd0FDkeSbQxwCObPtIgkj1JaPAmBI/pox8D3vd+pVk0nct01wOSjhfOAF2RaoOYLHof0NcBNEQzAUm8iWoGnQdbdixDX88Q2lp6UN9I504DX2gkIanB8UDHn0EmEjXx5Nq4yCKfijF/POkUi07XMB5sk/Kg7PfSgQU5WvS9fBjrF5Vjw8bFSuNAYS9wzuoUu6X5pKKFX1olxqnMJub6wCAH9zOuDQUkW//zP2OJoscfZ8MQYFUiqQeSYonPJWOoq2ElkjUGnSl7eLAfOBlXccQxI3psZJLVlgZRkVMLDBwFRBVpJC3ImAuYkwQ7gj7g8m5gJNJjqmYDQN1qPj+JMBAKAKOdQHmSjNWxVkFoELVlhWhrUSqGZgM6zdkgNXL1dqHRTXtPzj7adWWm7AWAut2uSZKe4EzoT+B3XBGwQkNNaBCXDgBVS4H8uD3LkjtVXo8VZNcabHbgvZ8BXv11ZE2pBq5npdIsezhdAeQb7bi5bDMODZ+ZbBS+uWhFjH+bTXB/IxnBwDPXXQaCafcwOMl1mnYNfQT+TnKDtgBfxwDkCy+8IOwaEs7JNNJZ1cFMeQZnKb/Cx1xkIsvPZDUAj+labbr7uwbal7TB2Tw4XvIpUa8ykhwcLzIwP9sgMqEOTkq7J5tg0ouUFuLYo/2YvIesAlZQMIAqm3YnS5ShHU7CkOcQX4monkdXNbNcdaw8DvpxtJ9lVX/8fOf5cr9WV8ucYEyjshLBCxfwTE9PjC/AvfKN4WEhyzdX6+Bcg+sg11jKH/F6kIBlZRwT7GT/E/rctHmlH3KtQBJrlQvyUVC/GEf2nxakBi0jV1CRmU60pvP8uEdwDMxlhQbBMcc5R59tJrKOc4n7FZMVmfBI30f2GOUc47Xn52Wj8jcjJPMlkj2fCu57D7BsJdDeChQVA6tpu7/198hKIckcFnYBydaZoE8ni6qqshx6l6p0masW+yjkWBRD2detZJglae7T6WrFJec5cYBWHYMhyvMlpgJsKYrN0D000oMuSjapcGp8APdVLhZSU//b3iy+nv0yvrFqa8yEbMrJnZSdUh/qijxl0xj2efCTlnPCMVmYk4sP1y+BNU1j2RduRTB0AYYpRACztkjY5MSWeKudCP6bUjoEz5GEhnheRtaZmRdS5J/ycoDLLwN9J4DltysZ5nOFnAZgJBLklbAlWRRqFwO3vhfY/Vslk5J9AW77AGDPojbvQJyjRox1AGXp9Rfp9/bBxwB8BNxUc41B3Ji/GT/vOAF3UCHOCI6i8kiD8HgUGG3iocaw78IkoUE4gz3Q+vQojmvGzslIFlrqa9JYkkYUjQPOL26WNCApKcIgK/9+9uI5NJQvgoEklwruoCfmdxImnFcJHQFxfDyzcHR8BQYB6qEL2TgyP15gz6+A0QFlLndfAsYHgfW3AkvXAp/+a9j/59uocrhwtKMfFr0OGytLoKWk1HRwqJq7q0FtZPZhmURYqdpIRGrweIc7FSLj0utKZqnEaDdweR+wKHkvkoTgOS69CahaAVzcCwy3ARdfV7JUF22/tmUOGOBTBSbYAyXHZOQgiHkZnYpnnnlGbAo0SrjRM/NCjhVfSJbTyvsQhD/UApNOMVRzDVZsql2Jb+/5FQr0XJtGcWysA3eWr8TagjrxmcxUit9o4scge2Wcvu02PNrVJYzbeyoq8K59+0SfDblG8+fdCTKj6NTQQVKXgzM4TQOTBlAiIoLnxixEnjdfO1OjP84zOiicl9kCj43XOZnzHwwHMOTtRiDsQ46+ALmG1A1grhEMWvA8ef1JyjAoziCFuqpE9l4gPvn44/gFDdvR0ZhgXPR4wijOUMue4W0JjhIPnU4ayZPPagFDEjmUDXcqPTQ6zirE5LIUGoUnATOR9u7dK5wMXn8SF/HXn8Yw58HAo4+g5KGfsQQIWLMW+NJfAAWqykk6zek4zu0twE+/D/R2KxnvRp3Sk6oowdiLOybeP+4BRaU5wmwoqyjCwX2nI6QGr6Nh2nNO1qBwU2FRjB3EbzVpdViTrzrPBNgzSNsqVh60ud+Bm5dVY/3aBapr6kUo3AJdeBHw6x8BF09Orktjy7fiqK1COCck6xmEIKEkghHnzkHLvj9GY1QGi/YXMzZvoexRETAeKduWB940VVt3Vth5E/CrX8Q+d91NmX2WN3YvFuD+kej5aT9nDHAPK/NAVKW6o0QFZadsCTIYKcFZewcwcEjpr2EqBEo3JJdnbH4JGGYVM20AD3DxJaAqSWUiKyGTQdVPrLaiEK+/pkpQyBC0gdSVPZmCySbXFW/C3qEjopcB5UfX5S9HEZuoZwmr8wtxfmJ8su8Rx/EylTzai32deKm/U8jd3ltZj/UFcygT4GTftARg37Z4UmPlDUDPpQiJFbEh1quqYK8lFJQA930Sb0aUmQtxV2WS/jtzANpcfHCtZVBWSjpJQp/7Ivc9kh+0G2gX3nTTTSIpgq+lTTEdUUGykX7JdEHebIB2Hm3UX//616Ky/VoKDP4ug7Y0ky/i7Qza58xMp50t9e9JnvH12UC8TR5feZ4N0PbnnFGPfyZBkfxLlh0sq7b5t5mywTmGaffQxsuWj8Hv5zzJtHfOdOB1kL0Aec9lJQ3XCSmdxaRMdcD47NgY3r9/PyYaGyftft4ldXQs32B40xIaBAke+l2UyeNPXnvpf7KSQVap0P/geps0ceoqgPOG8aeqlcr8LassRm/XAMqrSqCF0mczEThud+3aNUXKcC7A2Nj9998/Y4UQwT2MqgUkW0ls8Dg5Z2UFF/czElBXjdRYtRaw5QBul+LHcM1i4trKFBL2pkPTMuWRaSKXgzH6AGBLPSn8aoPrK/cT+gdZJzVqKktjg6OEaG6h1oVPnKvJJs0kNAhOoFyDBnZ9GGsLtiLPMDUQ7pKl8HFwhwL4g4Ur8JH6xaL8u8honrJQVlis+KdVG/FnJw7CH3HoP9e0HBsLi4UG7u8delVkVSnscR+OjQ7hP9btTLnfhhIQvCyqRBIjzqHMLQaGOqLEBh3evIiTIypR4iCaRpcDZr9CdPBtjiHgyCPAto8q0kBzAfuSSEO+ZuVYSXLkr0r++qUblUemfQFmAidfKs/N9DGJ+iaIDTeI+6vW4sG2/XBGMhUrzPm4sSR1TUlXcCDJc7GkBo0mLroM0HKCsokwN0ZuFmT2qR8oNw4u0DTcaEAatUbkWGzwquQoGG4qMMQaM1zEkzUSj4E/CIw6FdJM82ugaCOQuwC4eFQhNAQpFxmnp/cAS7cA1lzRC0PDBteXW7CwIBd2owGvdvRh5+qV0y8iFbUJeldolAXfpSY8KLOWYJHlhnD4MaBPkbcRMOiVJqqRq4HxJI3peR4+pzI2uYAnGqNdp4CBSJAHAaDlDUBvBBrnNithVqhpAvY/HdP/JGS0IFhQGrPy0PAkUUbji0F+Gtc0bpSGc3xFvIEdRihOOujZC4dgtFtjmla/1H9OkBo06GUjcYk2pxP/c+qU0PO/t7ISOyOl6dVWK/5E5RA/sWOHKEdmdUCOXo+vrVyJexI4QswWYlZhvNHF32WliDwGzhlmbdB54NxKms0V8gCBdkWrns269WUi44pODDVuM9HTVYPzmdc8UYVMKBwQBOtFxxF4Q67J/bTK0oRyc3p677wGcpNnpiWdD14DXjNmsUgcGx3FL9h7YfHiKYSGQTSNC4teJh+qr8f5iVF89cxRtLscaLDZ8VfL1mFBXN8cShI5Am6Y2GtIZ4RVV4PxQIQg02gwojegKJwHI+VeNCQHFgO6JP2HSGiQ2OBjluD14HinFBP1gdVjRv3vxS4n9v3NX6MkN0I4vHEA+Pz/A374o8z2sZEh4GtfVhpcy0C8VwPkmKc2dyPi9iPOS47b5nPtqFmSA2OOAyaTEX5/AGZjLiy65DIIcp9IBCZs/Oe6zfjMsYNwBYOw6w343vrNqJih8sAVjGa4S+itVmxYPJUMCYOB8ZPKQzwRRr/DhZanH8POv/wW3LZ8MbdiHJXSUpQZDOgPBFAhAwJ09ukUUn7ww58FHv0R0N0O5NiBu9+v7CPZxP3vUe7Vy88rv++6Dbj3nZl9FkvD2T+D40DefzoQK2L76UyLoQtA557omp5bpzTm9kX2ZPZYGT0LFK6aOkb5uurksg6T4DENx1dUaAD3CGArApzDURLFbAcKp3EILdGxUFteiPb2VzFbCB9DLQ/IClMX12km+FQACfyEZMg35uLOihvgDwWg1yhNjrOJzzWtwLmJMRwbVZKTqsxWfH3levHvX3ddxr80H5/0Dl4Z6MI/rtyKLUXpV/qlBHuSwG9OgufzSoA7PwW0nlDmXNVioOjalPuYR+oQlZ6bNolMVfoXDLKxyTMzbWXjYpnkwNcy0YPP0Yaj3zHT/LhSMh48FtoybLxLm1Vqp8/j6oJBblZHq6sJSGhwbJHMYHUQqx04/jhW0qnSmC7QH18ZQnsn3ueYLeg3s0IhEZFHX4K2pCQ36D9xn+IxpNN7gklHrGogMchA7GzJOtr4vO5zXc0kA/myjwnvOYPIPH61TfeHzzwDR1HRZK9bbeQnk2oIJtYkkxZ+M4FVSBwL9BPVlQvqhFIhJZufPynBdi2Aaz0JqraTbpQs06KypgwnDp0XpEaJafpxfCUIDSIdyS7ubUxG5DjkusP7Eu/vZXudSAu871/9J+C731H6aVTVAJ/+DGCfg37DqSDgBZqfADyRZHr25F14J2B7c/TkYJwjVYlbfTqBmsryJBdADiZDCRBpOBMMO+ELKcFGo7YUzsDUjG0OvEuObpwePwxP0I9ycwFuKl0Nm96MJfZiQRrIYBq/wajVocGmLBI2vUE8kuGeyhpsKypBq8uBCrMVlZHmLK8MdKOH2tYR0AU9NT4sHmvyUy1P5zGFxHsZYldvuxqYAW25KiPeC6y7A3jtfwGPI1oCvvrWqENCkkL021Bl0lc3AgNnY7/T7wHGeoDiBoTD1PNlljwbsuZDq5k++zIl8D7mLVce6b5PDVZFuC8pPw0FgKkus2BRbi0weEr9RcpzaaLIWAwNtKJhvYRBY0SuPg96rQF/uPBGdLvHBKlVbeG1TM1QmPCPo8/jE0E+u14Di145RzLficDxLjOdGHwj489gFh0PZruwRFwSIAzMyZLYElMZXhsgQacQOrXWSizIiZVpoMROUsdEVx0J4oaBEUdU+5sNRgf3A0a7UjEUL0sgPtihkBrEre+A+8A+NOkUI3RLXRX2N67FtPlobAz+tg8Dv/2pElThxn/X+xXW+pkHVRURYWDzLVPf33E8ltAg/AFlURa9INjQPsFGwXl37mlgPCKNk1cNLL5DISzU6KV+e9w595wHShYC7nGl6aZa/q3rItB+WsliXbReIR+vNJjJa80HRiJkTn4RVv7e/4d9h49OZqfTyWCwU53JQ5KDmf3Ka7TQIgchOGPOX6eNrXwYGBqEpTI225y9H2gwxOvOnh4bwz179sAfCeb/84ULeGjzZjyQgF0nybHnppsQYPUApWySjF0a0cl0FJnhzqwwGpl0fuhM0VlnpkzSuRByA+7dCqFB+FsA4zLA2CgcDxrqNP5SyRZJBjpIzDqh4SulGni9+r2XxIOITjPlH13uCyg2Vov1KBPwvPmg00QpIrWzc4JNtuIqWnh13l9biwKjEVUWCz69YIGQfPzkod1wBri3hHF6fASfPPQaHtt+66S846B3DC/0H4KbGv9M/M1rxLq8JnovcAUptxOGVV8Fg45k5iwCiVwrxPzWZkRsTPua116FUa8TfVyMvE50qs+dpWaX0hQ8XZw4olQrqsEb7PUra6064UN4erFkm+jZdP/9Ypyz99LKDfWorzFhtDuIFU2Ug8tccuWuiircVlaBYZ8PRSZTSg3CG20l6GHDaBXyDJQODcOkjSWINLABY+0xe0fL8DjWVpbg5MEDGLLkC4eKxKTU3g4XFKD8c5/D6X/6J1SQeCQRsGoV8La3KR9K7fxPfhFzClYyvPsDymO2oB362S8D//lNoLsDYLP693489YwqvzuW0CDG24BctUPMvfukIi2VSbNrVlawKiMROAdW3Q+0HQBcw4AlH6jbPHWvVMNeBZSuBvqPo6I4Dz19U5M70oXwMSSx7Z8A+p+PrNNsLnMSKN4JmKfXOY+HYbpqk1nAbjDgZ5uvx4WJMQRED6Q84Z8QD7YpPWGiPRWAn7c3zx2pwabgC7cAF/dHn1t6A5CbpKqbPtTyNCtb53HFwWSs8xPtGPU7YNdbsMReL6qQpoPMTGUQgPvJk08+iXe84x3Cl2DwVpITlMphHw5mGscHjpOBwdi5BoPI0n6TflGiZtTzuLKQEqfqe8EkGpkAxD2eZNTu3bsTJvMkA0mrdGSXsh2opJ2eTOqG80JWcFPylefI+UOCJ9NALwkS+gWzJTV4DLIi5krIVnE+kuThGkA/Mr7PR2t3N0KR6g6CkRa7Xo8/XrQIrkBAJKzdmIltfQ1C9jJSg/eTvTWkKgAJZMZ35lqyKVVwLZU2Vt+lLtQsyYNFM4gq81rkGN5c94VjkOsRY16UAOO+RllFXmvpm1xVQkOivhH4x+/gmkAvfdSR2OTw1peA5ZEWCNc4ON84dlNByha/KK+qXRDbrJkwRj6Cmd+2KvHPQGgUE4HDk8VnnuBlGLVTy1ZdgTDOjEflrLrcQ3iy5w28s3onaqy5+P0F6/FfLUfhDQVh0xvxxws3Is+QuiREkcksHmowgziu1kTg1f52PNh2RAR2FuYU4oN1q1FkSpzRyICgDvkIasbg1gLGcAg6ym1oC6HTU5PfoJR+n/wt4BpRHP4lGwErmxprgJLaqMPIYNHNHwBeeDDaSHzlTqCkJo7UiED0cQggEH6DV3Ay0zeMJug0Wc5mzASsKBh9WcmGJrytgH8UsGfA0peuU8rk2SicyG8EytNv4mzV27Amfz3Ojp+CN+SBTZeD5XmrJgOIZp0BjTnp6S2P+EZwcPjApEjHqD+MSrMWOQYN8o0NKTdgY9Njzi1Kc0jQGeEkZnBVBqXvqbwRY34HjFoD7HrblKAtyRJ+lsGiRbvzHLwhB3Jz8rCucSfySDaaNgHOE0AoNlglxg8bk3K8xVdTMFij7o9iz4P7XZ8AzFrR4NWyYAkM5xRt/2kzvVZvUeRDRoeAvELAGsnaNluBC0eVObFsU+ImkBNDCSo9ogmlMFiARoUMikHLbmA8KguGsS6gdTewMC6jNZGTyCbyL/0gchm0wNq7gBrKVB0FXv9VNNB64SBw+8eVa3clpace+x4woZKcGBuBzaxk4tGpYMZDfJNoOrnMsKERLJ1Yk24VPMGjCEMJyOo0pTBooveA97U2pxiXEOvM5huUyo34+/63Z86IBuDUTZX47PHjCUkNifjqOG5c/Fwep7opWyLwb1LCLWX4L0cCZaodwHcWMNSL+0rDnc4ajdRE2VupgM4PrzcdIam7O+bvnSQ0JAwawK86DEpR6aeRGpoJXEdIzIj+DCpYBwehKSuL2fP4799rbJyspCGe6unBhKp3ASs4Rvw+HBoewK6yKhFkIaHhiRAaxMkxXs9xmHUOIUNVYmqAVVebeWY098CX/g9ooZSRFli2Bdh+d3I5nUyg14uEiSml8JwXDgfw4guAwwmsX0/vZObPS0a8+AJKfyx95O9i769UpBrjIINNJBwZyNm48TpRwu6ocCM31yCI8x73kCC2S00FIvEj5dPValGahhO+s6QJY343TkUI4Vy9FmVhN06OaLC20Ej+KoI8aDUNQEkwhgz3BIJ4va0Hmz6yEStKK0Wwg/q2DIZwbj/++OPYce+98PF6c/7TwWcDySvV1I8VNZdOAewtUtukyF3NFuVVwN/+i/KZ3DfTGf++8QQWqUapVIh/js3C0yU1uHeeewJwJ5IpCgNly5TEmoXXp/e5VZuAwoWoyO+Ew/k3otItfu1JFXwv7Z3JYMH4CWCysXfk2owcBiruxrUCEoRLY4gnBZ64fi2i0Drgw2+6Loh9c2NBBaploki2sOwGpYeGc0Sp0EhGaMzjTQGuky/3H0Gnuz8i7xhGm7MXt1dsEXJqqYA2FCseWBXOgI+62oKyhc8+++xksCsVMHjK6gmZZCJlarLZ34B7BDMyZW+dNWvWCK30eVLj6oP34vDhwyJ4S/KMa72085gERIKChBTt3lQhmywnQ7Km27MJiMreTRy3zKZP1vSe1RD0y2eT4DQdIUF/LVM7mf4JHyRIWA0wFz1ukkn+8B7H790ry8rQFqn6lnvj2vx8/F3ca9+qoK/IhEFJ9nCMXol7kirknOHYo49doFuALSuKcPZYKzZuvPqkBn19PlKxH0nQc96S2OA+xnnMyg0J3geOUxJMcyFX96aEW1ZhS1DFZELxDa5lqfUI6BcwvpH1Sg3RFKjmTmD4GOC4xOYEyoOZ00REIscVZKaSOggZgg69KDVVoN9LtkUx0oJhKzRwTgaG+XPIN4GJgEs0E99SVIWNhRVwBPyw641Z0ePbUFAiFlwuvrJMjr8fGmmPZAwDx8f60HVuN/5uxU0wUR4jAcy6VXAHjyOkGYNPo4NBUwcTiRuZsXiCvSYiTiR/7zgELL8DKE2wgVc0Ag/8OTA2CFhyAFueUpVhylHkc8RGQTmLYtEYOQSW4UjDQbl2ofAFaFEJzTRZnZ6gD53u4Uhz9aL0s9gY8Hr+V8DZYwDJouvvBFbE9bfwtCnZ0Gp4WwBrE6BLU8ONgayq7UBlJGgtgtthwD2k6JZTAoHNLlNAobEI24vTdNqnwUWHojuuxpBPgwU5a2HVp+5QxhsIEiy55kItF2WD1oDiaTShuWktW74Up8f3oKSEWdm5GB0aw94Lz+OGJffAoisGzCSEHo97Z1ipeqhZAqy8Djj5WpRsu+G9QIREpKFKY7CEZXQqmSDKRbz22mvCOZo2Y8RiUx5qMKDEx3Sw5k0lNDgOFm4HjGagtAkwJiAfSWLEL+LiuTjUbwDORXraSDhVVWX87qNPAsV1wJHnos+JnxqlsfquD+KKYagXGI+UEKrRcgbFm24RQVE25GUgUW2s0wDggyXkElqNDRbdNrYKE/VmWk3sdaThcFPjauT4u3F8jFn4Soz2stOBzx17ChOuINZ0X8QfbN0lxmmvxzOlw9CQKrvvzPg4fnD58qQ01Z1x2S50IqQkAgk6khbJpAfUGqZpQchrxQcQI9JjUNYSOlmseJCVH5lC6lR3dnUBBcySiNLp8WqNOo0eRm3mZb68331jY3hJoxFG37aiIpEdxWqW+rw8/LCxEf/f4cOTTsffLF8eQ2gox5DYuJHZ/ZSckhUaEvxLr2cINVblvT2e8yLoUmRSEX38Tmc74BsDDOwboRBICfHaYwqhwfewkuzU6woJuj4FeZ1pwPvI6iVBVN15NwI/+clkafzkMf7sQeC11zgQow3Y/vpvgNtum/7D16xXZJJczqj8EO0iSuQ5AqyrBjxOoKQK2BbpIZIEHHNcy5l5RIOdzu+EcwK95X64jQrhRDmdm0s3oMIyN3rjHAdvq1qL28pXCIKyp6sDZ1vPoOuSEytv2AqTiUFbPTTIEwkeqF8MbL4ZOPCCeP+K8iLk3ftB6CsVMpPjUc5VOhy0I0nYLKPmcGNjyg41K4G8oVFoYUCOnlnLGcwXlwP46TeBkUhlAQmId/2+cg7ZQKSiKS0YEzlyERnIGJs3DExcBvxjQNl2IE6CMilIZlBiSo5LMbci37vgeqWKMVNYClFQly/GLdfkTKVi6F/wMyZtiIBr6jod10fsWsX2onI839cxuReSvPaFnHikU5Ho+3XXefzZkm1YRlnabCKvTHnM402PAe+oIDQI6WcM+sbQ4epHvS11u4dJUbICXA36FLQV2buPfTZSAXuoqXsM0OZjUJXreTYCeEwAYdBZnQXOz+V+yKxnVqDO4+qB94L3gOv8c889J3qyqMGAqawQzxbUjcj572Q9LlIBKy5kUJ5+EG0KymUlA33eGDnELIFzkteIx6Due5cJOFc4B+mzZPO6JwIJzURSwPz+795zD9r378fRUSXmVW2x4Ccb0+t9+mYGx6S6ku2aqBRIAiYCkijm2OGc5b9ZeZQOwZ1tUJmEhAbnxkx9m3jMHOvqijCOSTkuuY+Q6CPZNk9oRMA+wY6e2PphIcl+7RMaUr2GvnDWKzXYMAY6E1C4EvC2TXU6KDUkAuxTy1TD8GKJfSOKTWVwB52w6Gy45KDhRj37WFAYRe1gp1OdMRPqbHZ8beUW/MPZIxjxe0Ulh0HjnSQ0JAZ9bjQ7hrFC9r6IP0aNCTb9JlE1wSMWzr0EiQjpRErw78MdShA2EXiOxVWq383AxvcqjZFZ9cEG4Qu2KYF+VdPrWPiS3tIh7wT+r3Mf3JHeEXkGK95dsw05aWR84rcPAsf3R7MyH/6BctyLV8UFDRPUwojnM2xMI68tM+DaXgQmlACrGIsNtwnpE5AsYwaTpS598iQDqBuPS4TC2rQIjZnAsljKU6Wqy+iFE0FNNNM6vygP7Ze60NxxFvZwCcwmExx9GpRYHMjPJcHAwJs5mvm59mZg8WZFciq3cJLQkAFubibxQV5mgzFgtWfPHpHJk3IQuK8d2PuUQiBUNSrZ2CQp4lG3VpGDGpGEhAZYdRtQM8MGzPmjamaqPJeA/Khdq8g5dZ9RxhnnQ8fp2NeQxGBPG198UIUSM/HZtHOMhFlK4clAKR1AVmow243EQDxpRsNLnVHLdUvD7sRJsqgYpNrkteKVgQ75TQJ6bRhBA/DU5Qsora7CgjEHlo6P47XubiXbPSLLt3bBgsm+Dtteegn+iJPy/cuX8YP167EqLw+vDAzA3daGD61ciepIoH06gozOCRvlCZI9XegKgYCa3KLEEceFYUqZOJ2OZFlcqYCVDYO5E9h/9hAWBQtRkUvt6ujf5TLKur8FtrXQppiBGQ+h8XrpEj7c24vmiQmxl309HMZXli3DXS6XMFY3mEy4rawM5ycmUGezYWGCjJhtRaUoNZkFERUUDarZLN4gejMQJm2s02TRsgpOKURg1aVFpxiXI77eKKnBkxzYBzhaovuCow0ovz6xQdV6ZqoEXsvpWZMavI8kiSlN1cJqos1bgBPHot/FH79UNY6WzvQ//D1w881J5l0EzNb+i68BD/0I6O8FKqqB9RuVvkFNywFbesQY5y+DBnSMuAeE6q04+OSjqFuxAPbCPATCQbwycBQP1Oya02wwVjDyGLrbu3H39feItYOEKSsMp+D6e4EVm4CxYRRRZocVeRFwDWGZOK899xA2LKTsSToBgzH/OTiDyhrEceQK9qDMvBU6TZrExu4nlYpBiWAAeOKnwB/+Pa4aDDagcgvQvZ8Gb5RQC3O++SLzRPZP4lMjQM9LQPU9qVUwqeeZlGzkB5UvBwpm30iRjiMdzNmQGnxvTLNWYyHgH4l1wozXhj71TPjMojWiYvG1gW7BIS3KscARiBL+XHJ+2noSX19141U+0nlcq1D30ItPTMsWGEBKV1JKHVDmvKfePoOdTOCg7UnbjA8GzNLdm3gsiewtGUhnlcB6Vk/O46qC6/Q999wjktlYyaCWYqIUDCvuWEWQSkBxpuAvk1Doh/A7aUNwTNF/kVIk/A7aSzN9F/1XJlRQLoufMROZwMrSZLJU2QCvGX3l2VQ3Shk5nhPJmtn4KjOB/iTvQTxxwjnL61lht+PArl04NDIiJIXXFxTAmiH59GaFlB3mPaHvfa32AuJ4k/1iaIczRkAykioPJLqvNBEgK/NY/UVyg3vHdMQlx1u8xB2vORP7OKe4r3GsktyYRwTl64GJLsA7HvUJ6m7AmwW8ny+/HJd8nAQprzqcrJMDiUHjwi3A8H5VY8MVgFn5u16TB39YNjiMvEXDrD7KU0R1ZQ2aHJwca5msmqAJVGstTS/QngG2FZfjiZ13isX3yGgv/rX5YMLXpcK1JqyMYJA00aepNYopZXP4ecDnBioXANvvV+R41DDnAMunZomGYUpoGFx2ngY0NlRZ6mGOC+w/33dCEBrynMb9brzafxp3VaZoJFJ3/PiB2IATjdZje2NJDUMx4L4Q+16NMXmj2HTA/hqS0OBgoeHf8jRQYIo67o7zQPHNgD4L3zdD5YcjEA2as0S8wFgQ26vh/KtKxU1BFbDidsCUXhkry165UKdKarBvSDxqGith8phQU1ojDJDyze/BuTeeRH4JA7laIH+ZIh030aP0prDalUccGESe1LuO/16NRmyGDMQxGE3nhoYtNxqZacOMmElHZ7gPeOQ/FJJK9PnoF5UGWLQSWLQWKFcFWhis3/peoO8S4HMBBZVAbgrlknXbgDORqhTJsdVuSXTwQPVK5UF0ngba1X1cImBfjfJGoLs5dg5UKEH7KwYGDKsXAl2XIpm81L8zcbAAz/5YCcY2rELt4o1o7+gQRgKb0AsjIRTAyhUr0NrWJjL4ZzLaSVbRuHfnmafWNoTDCHk8cPUN4ufPP4V/3H4X/u7669FbVITfRByOxbm5+EpOjnBAvtbaKnoYqBtVf/bYMdHAWOPzITQ+jv8NhXCguBiFM2Qc0RGYLsNqWuhrgeCY0mOGYCNry8aEcjE0/lg9wkBsKqB27DcvnMHRkWFUWiy4u8KC8UAnimtKcb6lC6WraqHjSiG+K4xCYzXyjVWC4NdRsjAD8D4wg+EVk0kQGpxtUv7rb44fx7ZImb3sY8KHBF9HybD/vHRJ/Puj9fX44frr8DdnDuPY6JCIr4bCfnzqyCv43robUG3Nweq8hTg+dhE2nUb0WRASZNxPAtzDNbDp4yQDvEMRQkMcrfLD3Q24ugFbguvKPTKePMwk+72tBbhwTmnMtmEztHq9WH8YGKEBvZBk26mTZITiLmjc53g8CkkX15MkYbPoz/0lsgFePx6rdOjbJjqQV1qAwY4+hIIh5JUUwBPyCXLDMIt+G6mAGbJS4k2Oo9OnT4ugwBQZCK5NCfoG8HWNjY2C+CZZyD1h586dKR9DMOyDP9yBfKo6RZpOsu+LM9CBXMP0WV1TwAoNdeUf5wql/ES/pyxKnKWLkuUKWTGm6h8VYLVuPmDQK38TXERkM6MELGU9TUWpZWnZK5U9XlraPNfiNK/dDE5HquXhM/oXRN4q5fx8g7E+x5sADOb8zfJNwr9gdfkXTryEcdUywzswSrtwHvNIgkJjnkjuUzo3RlFqzq5+/nRNmlMBg5xSgoqfxbWe0qdMxmLALFXw9dP1KeD6Ql/opZdeEn4Rg+f8btoffLDKi0Hd2QSI55E6GPjcsWOHIBpoq7CKh+AaTq172gjT9rZLgdTg3zguaN/yc/k9lCFTBzw5JpiswmOZ7ru4v7CxdqrgOchzmivQnz5x4oQYt7Npxszz5lygTzcXTZ3p+/N+J0o0Y6WXvK4GrRZbZ9kr5M0M3kdWPTCwTvI1m7J82QbXbPr2tO1JwIR1QH55Pna/vhvX7bjuiklnURJb9vkkSAQxZsGExXTknzkG2SOKIKHKuXCt9DO5JmCwAIvvB8bbFV/HXqH00n2TIB3/ImWPmM51jK6ltRYwlQKBCcXh0EcDtlb9Ukz4XQghkrGrscOqn8qM5xtz8PaqbTg4fAGuoBcV5iJsKmxKaUIx45cNv3P1BuQzqJcBqDVdZ82bVHeS1f78yeqQRTkZNt9mI9CadUDHEeWDGTgJhIHxMWBiEBgfAXY/En19x3ngpZ8Bd3wyJS1mkhoMDqqbfvLjvaFhjPiHMeDtxpr8bZPEBo2DPs9onCCYIvWVdRjLAOsKwMVsd15QE5C7FchG8MXZpwQkJplkyigEAA8rNCJBQf7uOAPkJ8gmzSKa7IvhDrow4FVkLHINeVieFzGahjuBE09GXzzcDhx5DNjy/rS0tslgq3ttzASrLhc2XT6cQamdrRF9QxrLl0Cv1QtjcGJ8HHYd+5REslVH2oFRF+Ckk83eLzcq1RFxIItO5ls2IowHjR4GrFg6yMxxWTZO0DBl1jtlSERmjbcfegbfJ7Okmb09Bpx7Q+lTccuHgFrVesEATMUMMlXxyK8BVr4DGIz0YylpAnJSCIRXLgXaTwADrcq94nFS5uXF/1EqV/JLog26G1cDq7InaZYSSGLc+3Fg3zNAbxtgzwdqFwIHVLJi/W2iUXrtip2CuNj9wlPYUjoOi8YNjdaAhprtaB4amZHUoINIw6eirHhSso9wDo5grLMXY1oDStYuQ1lO3mTm02Pbt6PT7RYSU405OeJ9zAAZCARiCA3xORGHOjw4SAYFLU4n/vn8efz9DM4HHY6MdW55T82rgBCzmvyAJnkJJp0aOXZnyoISpMDBvdg/NCDWWd2oBi/0AZ9tMqLAqIXebMLrpzqxsqkaC+xVsOkLkG+onLXxePHiRTHv+pubxbVW9zNBezuKWV2ZBN84fx5/c+bM5O9s7M7PYMDcbtBMxvcZQP7+5dP46orNWJu/CAVGOy5OnEYI/pjjZ7WGTa9BsUnVQyWYRF95Sr+ACFiRsfsx5d9yM16bZkbzyy8A//7P0fVl8VLgb/5RGPKTBBUlNx5V7cHcU+gQspeG+jka1pG+RtkAA/t0QOnw0GjnQ8qUqUGjnKRwUUUxPJ1nUL1YWXcvHj4Li90KuzVHyFDNFWgzMFjAQIJ6nWCVBrWzDx48KPaThJIgoi8LezLpYwhSGqaUdUu2h0wBya0zr0Iz2oGCnBB8S6qgsRihRxg5epId0arElFFcAbSejxIbvO6sSryahIYE5dliQN3bcaBoPTDMv8UFgFK9/zzHxXcC7fuVEnSjDajerJAdWQLHCIMfmWKKf0FJzJIbFakt3itWaczheJ8LyH5Ri3IK0OdxCpubYG3kgpy5b+46jzcv2DPpxtJ1eHXgqCCvOWa2Fq9EoTF7exH3mGxI1sRXZzPQnG5AmNm1tDWnkxeiP3HddddNSr2og4YMaNEWom3IPX4uZIPmEQveB5IJJB0YnJRNw2m3cD9gYttMJBOTk+IJbY4BBst5L2l/3HXXXdOOPQY0SQ4kk1GbjWTVXII2H6uo2cOP558OCRgP2lQMTpMomW0T8vg1gntzIv+HPRCYoDIXRMqbEbyfVzuQzibuJJV5LBxP9C+YFJto/EvJJneuD52aHuj0OniKXHjpwMvYtSVWWm4uwDWDpOitt946+RxtQI5f+tysAGQyFP2GmSB9EUrGkVCdlypE4mT7giuchHsV/At9qg4uP3BKEIzSNXzEQasxItewGcGwEiDQadjYOHHgqNiUhzsq0tPeuzAxis8e24fBSEbn+2oX4o8XzpwVkAhlZhs+tWAdvn/5iNIiJAxUWHLwp01bYVZpXw95hzDsGxZB4mpLlehxMC0W7ACsBUDLEUA03NQoWeBdZ4Gc6tjmx/zZ1wZ4XYB55mCdFib4wkojV7XQEzMYiWA4iF5PB+ptykZ0bqIbgTiHmL+xb0nKYIB61WbghEp+ij/XbJv6WusiwNKgVFJQ9zobum2U86KuvyQ05L3mMXh8UVKDZxYXMOP4nQgMwxdyIxTWo8XZC0/Ii2JTIZbaF0CbwfFRN35dwQZ4gx6heWvSUlomckz9zdGAnDzGiX7AM6FUQ6QIOhzpNDri9y+yr0e3uxnOwLjQ6K+yLIJBGzX+ey4eRpMljszKsyjXkBUb516KVEOUTTEeyahzw7Dn5KC2+xKw53mF9V23DT3LNk4aN/EbKJ0VVgtII2nPD/8FW/0BmGQDXQmh9a1RKpjUpEamsJcpj3TAa73lPUD3WcA1DrSeAkZ7I+MsUpmz6/1AaV3iXh6J0HEZOPCiEqRjNcoGSu/MIphNcuW6t0V/f/q/pr7mzF5gxU5YzUbsLB/FnoMnsXF5HWw85LZXENZQumnRZGk2DWt11pRsyCcrdN5fuw4/Ofc6+ppbYbbnwLps8WSS++3lsRrINapqAILOzqr9+/GKaq1SkySRN4q1jITITKCRRsMtFakzVlrQYeYjBmJOROcFP4/GO/c4VpYw0KyuDOF1mM5ZOD8xjr1DAzFNtl1B4OEOP+6rMqCssgTjw2PoHfLj+rLMm+fR0ePx8f7QqaARyGPdUliIb6mup8blQg4bqU9DXP2sjRKSUYQjzy3Pi+0WxGBcr8cVrSKwVaDL1Qwn+xqpwD2+zroGeQYVeSiq1xLIEfaeU8i38lWAKQ+4fAhwTzBdCLj5fcCl40qgmY3CWZmU+gUCvvvt2GoqVmw88zhw7zuiz+26mYwQ8OBPldcyC+3r/wj88IcUeFVewzHA57KYtcQ5RWKA6yDJAfbO4L/l+qgmAeigjJSOIj/PhGE/e5ABDaub0HqiGZ++80Nzlk3FgBGdDQYnEgWnOP9IKnJuTWnA/ciPgJOHlGu2bjtw3weVvhWRsZNyST5loV59EBjvh5Z2zhBg7h+D96aV0Bh0QnFSjwwyjXYwuH8B6OuMrqVv+yiuCQhbOm6usGcYe9CMngZC/ujfzOWAIS89h6Yh9eqYdMF1k+tnppBrbwxEFeKbP/j/gboV6HI7cIlNvJk3YcnBJxrWXO3Dmsc1jmprKR6ouRnOoAcWnSn9HogzgKQAA77ZBKs11A3JM5GIZCCcUrbq/Y17EhNMpG+hJjS4jzIoLPs2Mcg+W+nQeaQOZnvTViGxIHX5GVyU1dS0VSmPxPsnIStsJEElCW0mztHPZFAy1cQl2iPs6yCqhhPYRPy82lpVok0KSLUnAm032uMkFaazxzjPeI04bnltZMKfDCzT/5pNIJafQXKHnzNbUoPnQ0JK+hvJ+n6y6pnSX/O4dsD7RUlA+rucb5SVIkHIypF4H5j+Y4+zDz2GfuiEWDRgzrfiTNcFrBpeiZLC7Empx4MENNdtkhbx84bxLtqC9Lvpl6dCakjQF473pebx5of0L5Kt8WqkZCVxcnBRTmexZIBDr8lOeYsnGMCJsQFRnbHEXojPHd+HYZVExUPtF7HQlgtHMIR9Q/3I0Rvw0fqFWBDRAp8JO0tqsDq/FP0eJ4pNVuTHafu3OdtwavyMkBhiuKfV2YrtxdtgjNMYjwEvfMVy4MgzsY0fRbPHaPArBilmC2o1hdCiCCEMCcmPcJhNesMYjcSYdAgjIHp9KOj2jEweuxprC6LByJRw7wcBixU4d0zpf3D9XbHSU2qwMmOahqhpo2ufQv7Q0VUPakEexL3WGB2nnAStzlMY9EV19Mf8IYwFgH7vEMb9E9halPnGbEpA6iW9j2lqFdrzctA+0IKS4hLk6BX5tpnAhsM1VkUuJOEh+J2AMS5wook0tSWpQYz1TSE1pFYuM/KbH30IOBLVt2t/9GfIGR1Fxbs+kvA71U4OiY/t978Hr//jF7GlrgJmg06l9c1/sOQoSXb3lQLvU/Vy5TiOPh/7N46/3stAdYqkS+dl4H++GSFswsDF08DEGHCTipSYLRgAnPJcRFbAMwJ92Iudaxfi0JlWmIwGrFxYDY23Txj7rAZiRgeN03g5Gd5vlvnTUHY5HHjA1Ii8u7aj2e3A60PdKDQCN5bW4A7Kck0D7hv3sVFzfj4eZM+NUAircnPR0tGBUQZGI2XNJALWqqTWOlwu/N3Zs+hyu7GhoAB/vmQJzDqdcJJSkU2gY0tHl8YRiTU6HgzUSrCckVmF/Cw6VRzbNALpHCRqhsfya/l8d3e3+GxJOHpJ7iWAZ8KJV084sKlQB23Qj+pZNIflPkyHg4Hh+Hv1zupq/L+mJnzzwgUxzmwdHfjtJz8JyzS9IIwJ1iOWkC/KycHp8ZGYzOKlubHBxQpLJS46IlVQEdRY65HPSr2YD7QDJVuAAZVUpcMDuOngUoruIkCOdWw4spaHgLrVwG0fyoxMGBmeKinF8+xVJNEmwc/+1O8D73sfLyxQxiCxAfjWvwCP/YpdEYHFS4BZNnKMBx19zidWtHH88ME5yKwktc4zxyMDAaMF4+JQCw3s4wSE9HqEcvJRaZ4bvWfOBVZWMSOWx5UInAPMsrr99ttj//D4z4FTh5V/c6078rrSV+SOd6d/IIPtyj4kv5NFny4vdL0jCNYo527WxlbeOQJePNVzAQNeFyotdtxR3hSTmKK8yQJ86P8B7c2A3wdUNwK27GU/zwolqwFnt0oeKwyUbQD0VqDqdmDkhNJA21QM5C9XkjdIzl4DVSZc42dTqTETafxmhk1vxF8v34lO94SwR6utdtErcB7zmAk6rQ652gyrUmfAXJDi3BdSlcuNB4MWlIakvRYfvOD+mKyRLdcONVnOIDuTBUhuSOnEecwtWFFBG5u2A+0Y3j/6DqygIWnB5+L7WPA1jCvx/lG2h6CNfv316Ve/055i8FaOEQZs+f0cN7SZ1c3nUwGPa6YAGpPBqD5AwoS2Gn0LNXkipWFpq9NPYABXyv6qk6akbU97i+/n+3jN6JvEVC8mAckS6Q+x6oWfkan0Ec+D3xnT3yoB6DvyddeyxNLvIkjkstcN5V25DvPBOcXnKPGkjsWQOH7x+MvQ1BtiYoPVS2tw7Oxx3LL95jk5RplASaKFYzcR6IdzL8hYZnoebynQN+CazDVxSoJqHPSpGhtk1Gf6sLnAqM+DL53ajR6PwhxbdHqM+vwxUkrM+v1RSzNOjo+JrgJcjB/vbsdj23ahMSc1YiXXYBKPeLDq4fS40nVdTnx30I0WZysW22eQxKFzGpfNqmhb5QHDJDZkcFkDNK5K3Cg5AXh+Zt0aBMJdCIUdOO/owog/GsjhvywqQ9jKbL8EHUJKTWneTy6Idz6gPK40RIObMHVeRCuIaMCLDSRV981UAeREDdkx/0AMoUFQDsYZDAnJrk53L9wiEyqLJZSVy4H2o5HgROT+li4ATHGluAyEdh4DHIOA2Q7UrJ9szu0KTGBfy4tYtLYBfeOXYNfnY2nuBkFazAZhUZ0TR6qxP4I/gL6hcRTlWmFkL5dIgIvBW7L/NMpolHJTNJ89inGvD7kmhdTzBIKoOX0ISEJqxGe9GKoasfnTX8QrP/pX3FBVALOB91CI8SuPymuzwdYk0vEF33g5SmhI7HseuPGe7FQwEQ0rgaG45td8johUlOl0Wmxe2YgJpwcHT7fAlaPFwpqwaN6bzHilMc5HvBYtZ9e9VenJgW3fuhWavXvx4cpKTLjdqCkoQFdNDT50/jzGjh+nwCzeXlODP4w4p/3MXn/hBQz52DsgjKd7enBweBhP7NghHAKOTY5LNUmhBh0ZGkSySoPBY2aB8X0cjyzLpQMen82XLLBGp4nGGAkV7oV8HX+X719iz0O1xYpuN2UXFYQcDpic/chZ2YTK0kLcWLoIOT6zyFKh08JjSqQbys2brymoqsTe8QF0dXZhqTkHDfb8pNmHvIffWL0an2lqwr6jR7F+5040xDVTi8cfLVqEjx6M7Sf1J4sW4V01lfjssT3o8ypVM032fPxeY2xT9kbbAtEIvcOlBL6rLNVYmJNEo9/eCFgqAP8EcPo3KhKO84JjlM1KVXOk7TjQtBWwZxDk5P2jo6VugEoJuZokmXq5ecpD4sc/Uqo1SAb99rfAM08D//ldls0hG6BUGDMaGQCQVRB0QlkBJ51aCVYiDXlGoDPrxP01sRE773VufkrSDpngyJEj4nspE8VxzmoMOuKEDFTQ4eD4n7JunDseu87x32ePZ0ZqJCBqxSdHiHctyqChtKUq8eWfzu/BkJfzL4xOdz/OTbSg0GDF5qIFWJOvuv+sHGlUpDKuKViKgYa7gdGLQDgI5NYBtoooOViq6A7D0w90Pg6EOMa1QPFGwJ5GWTnvS/shoOek8u/ypUD9llntRwzUMCCSKehjzNS89c0M9taotV4j5Nk85hEJ/GZblodBTtpVicDnaT8x0Cb3DgZzGYjl87SFuP/xuBiMlnJGtNmYtZ+sYp02KvcldcCOAWQG2NX77DzmFrzOrOpmMhHtbylJlcxG5xjgPWMAPZ1s7ERggJ17CMkR2duFCXSsMqAfQPuKOv3p2Gm075n1ngw8R0ma0S5nwI3nznHKecUxTTJFff7Jqk/oo9Cm47jn+zmPaHcx4SyZRBztMZ4vz1F+Lo+bgWL6EPx+Hlci/45kDOcZpdroi0jfiAHweMIlHpxrfH06fdHmcWXAcU/Jcla8MTGJ957jaevWreI53jOZEEf/IegLQhcXBubrjRn2eJwJrFIi4cx1QhJo9IfkmBOqKhMT4nXz6/Y8JLgOctxyjc8KqcEyIRocV6p5jBo/az+Dvoj0BeENBpFvBAZUMYtwKCwIDUK4vGS6QyE82HYRX1k+tT9AOvCH/FMqHBha8ARTaPTHDLrCamCkK9bZr1kGLL0BOP6ykg1euQhYk55uOCthDJoasQiM+eNkIKhrGoiWeq7Jr8fJsQ44Am5RsUGnf2vhIpgF2XGNgc0xPcOK7IK5KEpesK+Bz6EEqKTzzb/Zq4GqnUDYHakOscVk+HpCibP+GUcnqSGJq6wipwjY9ABwcZ/S3LqwBliwNfY1HA8nnwCGW6NR8oGLwIb3CqmIC47jsBVa0H6xE/VNtZgIjKLDdRH1ttk5/vr8OniCfpi9qiD4uBs+rx9t/SPw2ivgG/YjMHBGGJw0euKN0lK7DUO9UVKDoJZ/snxRLkbxAehhewlWfuarON56GZsdl5S+MkR1E7D1blwTMFmByialOfjkGhAG6pNUJyUCJVniS5lJZpGcy1aC7bKtgN8LnKU0HBuFrwQ2RrKoKe2TVweMKVJDdpsFW9YtA5a8AzBMb7xmCvbGODA0hAKjEbtKS4W2OMdSfAbWepabNzVh94oVGLx0Ce9evXpSh/yh9nYMeL1RggDAU729eLSzE++sqRHOAgOvJBskmOFHB4APOghSjkDdfFmCgdl0StJlI0o16DTQ8eHnDPT346HNO/Dhl55Fc28vDJowmvKMyM2xIBwKYV3BElRbSwCrUi5PMGBMx099XHSUeB55NVX4yJO/xNDwMMxFRbCUFePLVeWYviYGMDmdWGi1okF17snwkfp6jPn9+IezZ0UPFFbDfKy+HrlGIx7cdAuaHaMio3hRTt7kfYmVumsSj5SgtwCsbExUVZQomMp1ExmQGiYz8Nk/B77599GKjfUbgVuT6zJPoq9PITQIWQl06hTw1FPAffchIwjZOrdyXJFryGxC9qTguiideTrc1ESmQyrHA8dV99keBOtDYt8mKJVYqi2dEx1jVi7RueA453pNckNWlagbh69duzaxc85zZG+kSbB/TYoSffEoqlII/oBPXEMhtKnVIFxSC72mEnpNbOblibFeDHiVxJd8gwZ2YdmGMex34uneE9BrtFiRl6Ax/bUGNgIun6Z8PugFel8FJvuJhIDBA4oUVSrVO4LQOAi0snIqAhIcJEcaMm/ETf+Aa2qmkD7GPOYxjysDBq8o75FNcF9gFjeDVvHg+kC7TH6nlM9klYU6tiAbgKcqBcSMXgbI1KQo30M7i8dCAn5e9//KgPcxUVXGlUCyqhz2V6G9xTEyHUmhhiQS1PLPHFPcp2TD7HiZNdpL0keQyR/JCJ1EkJW78ZUePH7hE0QIDo5pEhAkPUhiMNAn7TFefxJ6cn7RpuRrZNCY58PPpN3JY6N9R1KRWfypxvcOHDgwL/FzDYP3lf1lSGIwaVGSZJS9JdHLPjiSzC4xFMER9MKn80+qudRZa+DTZ18tg+OZ83DXrl2CuOA84tjl+JZzimOWiVU8xnnMQ4Jrk/Qx5Po2a/mp2TQxmg3aXROTMhgE/20Sm0wIetEYFcgzGjHon6rF7siCXqhJaxIyUz72h4iAEz8vVT3jDfcBBx4BxiKd2xvWA40blMD7rYkz29MBjyWEYEIyRsKiM+KDdTtxYqwdLr8HVr0edoMJPe4hVJAsuFbg7AXaXohoR9NKqAVqb1SCXjXbFLKDFRvM1mTFwYI7AIuURUlcBmlOULotMk4pZwENbDor2p2jgGYM9dZiWPVZInoo37RumkDYRF+E0BBHpPxwjYh+HOHypXAHHRgbHseCpdGmqs5A4sbuJGU8QS/MOpPo9TEd6hsacO6sB0vrlgMXX1Ua1/sCMJuNKC7IQf2224Gi6Ru5dtctQVN/lBRpzLfjREk9liTJnGemDBcjybzTsOKmRsOXBhpu+wgQqcRKpafMFcXW+4HjLyiSU+yhsfIGoCiN4Nji1cCFE9HfxVheAPD6FVPyJgvjjZ+55iblMeVvGqB+F9B/HHAOKERG+do5IzQe7+7GO/ftg4/kI6X9iovx3HXXCdmoRMg3GnHP4sUILlwojGUaYXLtFkZ2nEP7rv378dfj4/jK8uVKk7PImGMJO8vBZdYex9t0+v3ZCKAxO5nfffToUVHNwqzAh7dchx8MnMNlpyL5Fwz4UT7ux9IEslMkDDkPWJUhs8tobDKI/K0LRxEqKYTO44apjFKDYXz7wjHsLJ4aLJDgeTMjjRk6qWDE58M3z5/HcKQa5tXBQdy6ezf23HgjTDodVuTNYm8IDQABVjh6AU0+oF+p9FfKrY70mFLdV/bzmQSbJRgA+yz0XDdtBf7jx8ClZqUKY8my1KT/+vsTJyYkej4VtF8Cfvk9YGJUqcJ8+0eA5aTyIJxCZijRthKOdjiMFYtsOHdqP04PHsDytTcjP78YNbnVCLnD0BXrxNpeY6lG20ibCA5l0yajg0wHV2bfJepXw4w+rt1Jta533Qs8/MPYflc3TkNQswKEshM+H0BtZvWcNNmAne8H3vgN4BiGhlnuG+6BKTfx3uRVkWVsIh7voJ8Y65g9qXH5MpvvMB2Skx9XBZ5BFaEhoVGqN2YiNUY7gfPPRXtDqdF3ZlakBsci17JMcTV9jHlkF86AG93uQbEMVFvKrs3kqXkI22lKH5ssgARCoipakvW0dWbqq8TAljpbXGa9J6u6YNAufr1nJjm/h74eg9nJpKvekmAi1VAfYLEB+deQf38VQXuL8lT0EVKVR+N4ldXYHEe0rUnCcQzTXpouY5iEh7r5eSbgmGalCwPBJCbo49DGZxBYTagkk30kAcIqDlYA03aT/hI/Uwa1SZYwAJ4qoUEZXgYVrzpJSHl13ykgSIlSHWBYBBjqru4xXUPgGOd9pewUqzRIaojei1u2CGKDBAft+M0bN2P367uxYP0ieMI+FBioOFCJN8KKHFw2QX9Hjj1JYpAkVO8HfA2Pex7zyNTHSJnUSKUpKxEWAXZKUpBkoDxCNTRCLygzVFtycMkxqtL3Vhrt/eHCJTgyMoQ8gxFvr6rHRw/uwQXH+GTzWf7cUTy7TYXgYr+hYB0ODh+GP+JMVpkrUWdNMcvXYgeu/wjgcys9JrIVNI+AmZt2fV6kMiMaKMo3xgbtSGxsKliAVweO49R4tEHsytxGbCq6BnRHmWHe/lKU0CAm2oGhs0DxcoXEYHa5s085TVup0vxyBuQZilFirMaAL1rNMh7QIhAOIs9gR/OEG0dGj4rnLToDPlC7FaXmKyATEPAmfZ5jbnzACZvdCrM1ajwkamje7e7D/uFjgtjQQouNhatQa00e+KScVBEdGZMdGIkE62TAjz9ZLTIDqaG12ZWxHAkk6VdtxJr3fxqXWloSOiyyD4Da4aCRRnBzU2e/ZAVsKO8aAiivVlA/u0a/nK/r78z8/Wu2KdnLe55RMsfZ5+fyOeC7XwV4HT/4J0DlHBtjDMyWZ7+h25jfh386dwxHRwdRaDThU43L8P4DB0TvI4nXBwfx7eZm0Q9jOjCgTyOcDi1/3l5ejq+cPp2oxTT++swZ3FtZiTULFoisI2bqsZxanalFZ+RKgIYhAwP8KbPA/qyoEM/1XRY9miosdtSPB4T8SCJwX+WDpKQ35BMkKzHodWOisxvW6uhcHg/4EAyHkuqxkxTavHlzyg7Kr7q6Yhqzc888MDwsHttnE+wIjQOBI9E7Fx4G/AcBw3ZgIRt0vwCMdypkXOVaoDAMnHpRCYIzO3/LOxUSgL+zAonPpTuHS0qVRzpg5Q4z9Bhkl+Aatzg9qTUBlwN48DuA7P3Fnw//APj9vwTKleA6Ay0kxGio2Q3dgPc8liy0oaNzACffeAgrN71PVHCwafeWxmjAmVl2HO+zDQLT0aWDTseWzjIdHjW4Tqsdc75mWqzdqlRmHNuv3NuNO4EFSWwLZvV/4hOA7NtBKa3vfpfpltHXFFYCt/++Mg5muP+L7cVChpQVg4ngC41gwHMA+cZlMGjT7PXGz/zGN4CHH1Z+Z0Dh7/4O2LULVxQBBzD8hqhYmTwucbqU5JzBrvROAGeeAELJEn1mV4XNNUzdCDZdpONjzOPaxZB3DM/17Rf2NWHSnsft5VuRK2RP53Etgf0OmKGdTTA5g/sZbbJMwb0tXo6IiR7cq5JBLU3FADTtSEmqMJD7O4OOy8DP/hVwR5LENt4A3P2+2flAbxHQlqJcU7ydMxMxwYpwgj4sg/l8jhnm8X3t5gr0jZmElSwRi74H5XoSyUbRF0g2F/keztdU912SJ7QZE1VhXXH4jgPB3qjguv8UQMkk/TVwbNcIeF+ZJMjKBznmOX6Z9EZiQ5IddTV1yPflxxDc2VLlYeyHigRSXlDdI4ZrNMef+rv4Oj7eLOBe0+5uRaerXSSYV5ir0GiLrTycR3aQqo+RMqmRSj8NhdBgKTvZFBmOYjPrNZPyCRwEHe4ODHoHodfoUW+rR64h+Wd/sG45To8PoT/SPNik0+OPFq5Dk70AN5ZGsza+t34bPn1kH06Pj4oKjj9cuBT3VtYgGygwFuCm0hswEXDAoDWIwFNag5avpZRNFhEOj08SR8ty1+Dk2GG4gkoGXrmpCtWWqYHpLvcALjlj+0ucHL+MBTmVKKJMzVwh2AUEzimbD7N2dYuBwKgyPgzlgNYCBNyKtMKUDESVpIFWD9jT00cVm7ptOYpMVfCF3LDocmDV54px+GjXYYz6RieDpu6AH0/1nsRH6hMb+nzPsdEO9HrGYDeYsaGgHuYUiJWEyImQMoIYkEegQTivCm8cOACNz4q6pdbJ0mv+v8c9iDqrE1a9bTIjbt/QkUnCL4QQ3hg+Dr3GhfFAO0LhAGy6IlRZVkIfCXowMCVKaxOMX7PRgO7+ERQ1eJM3ABsZRPDXPwPyVXrup49CM9ArdDwJGj50JJhNzNLv+I2Lm5nMNKGGp2zCnBW07wN6jkV/L2gAFt129Yx6fu/OO4Eddyj9NZ78eWzg86H/AD739bSbyE9B53ngELNwnUDlAmDz3YBphtJnrhdhD6DNUbLo0wDH5Z8e34fTY0pD6WGfF39ybD8m4qrjGMw/Pw27/p3mZvztmTNwBgLYPDyMx1auBE33DYWF+PmWLaLngytBU/BzExNYW1AgsqkOHz4sMk8kOP6uhMPBa7B7cBCvd3Tg+vx8bIsYhdyj7qFsWQTNDqUxWqI9g8+fGr+AM+OK42TUGrCjeD0W5eTjVa0GWr1usll3jTUnIaHBzyChQVIlnQwqN+dlAtIo0fVOC+H4ygZ+gxMIO5XeAEvvUSTYeC7ymtSvVqQYLbkKYdrbBjzzU4UQJMFx07uBhXOcbUmSgMHqv/gLLpTKcw88AFyXfuNKdLcDkZ4kUYSBlnOTpIYkNvbt3Ysdq6LGWk11CRxODzov70N10z0i60rdSJxkBgNSs2kIyUxDlqdz3iRyarlP7O27gD5fGKGeEJblVuGWsuWTkh7MFkyIpWuUx0z4j/8AulS2iMsFfOUrUeJAjRTW7lJzDn6/cTP+u+0IHAG/kJ9Sv21hjga+8BgGvAdRZt4Onaofx4x46aXY4+LY+PKXgfXrmRKHK4ahg5E+GhLsQUVGOAfImSGION4TJTQSVMChanbZcfQPZktqZKtnnyvgwkXHZXhDXhQa89Fga0iYEDKP7GP/8MkYOVdfyI/DI2dxY+mG+ct9jYBBJj6YcZ2sT0WmoG0/nW9Mv4BznXruyb47UVCLn0lfglI5DCqzElfK2vKn2n9g4pZaxoeYqenzWwL0JR/6d0Al142DrwDV9cDa7bOv/tj3W6DrouJXbLgVqLsG+1LNgHT9Ato7HHOE2rYmqZGsv92VBquXSPipZUJTAdeAVN/D82XFEwPhVx2iV60kNFQIdM+TGnFgrIdrLQkp2S+Sc4BkNu1/SkLTj+C/mRAniTESHGqfIxPQLyeRsW7duoTz7uiFU7iQ48AzzZeRq7fg1rJVonk4CRce15thve5wt+Gi43w0Ecyl+PELkvWXnMec+xhZJTWAwQihQUiniaVx1FnOnwzwXHIqN55ER4+nB1uLmMmT+PMLjGZ8e82NODbaLzKAV+SVoDBBQ+1KixW/3r5LBMcoTxWvAT5b6LV6FBivoAObBDTOQmBDxo7J54zaJmwo2A5fyCucN0OSrL0JEgdJni+itE6I94ra1exlkaVGQfzMgEp+h1m7vr2AR2Z9ngHsOxTHnI6naK6tgmH2zVC5ONoNNHoLYp7r94zHNJzniO1zjye97k/0HMeJsU4RYCQre3KsEx+r3ymCmGmD13vV24BTTwKUTiNhs2QXOkY9wkhZUb4c+4d2Q8f4A/drMZ1C6PX0oDFHqXoY8Y2K7OooJQLkGMIY8Svzi3AEh9DuPopG22bxOzcwNjMTjHnpYqBfLsgaVJXkY6xhjShZvOmmmxIbgJ0t0ISUZsuTmw7vWdtFVC9fL0pdZWkuDUKWjDNzi6W/dGLosKg18UxGA7znDgPjF5WqppU7AEuG99w1HEtoiIvUAoy0AoWxGuxXHLxWXa2xY5yBpfERhdxgBYcaI4PAgZcBtwtYuBRYsTF5cK+/HXjhwWjW7mVKTY0Dt38s+Xs8ZwCvzHzTAFY2qU89y6XH48LJsSjhyK82aLkWaeCjJmAEzJxelKSh8c/b2/GZY9H7tbuzE2979FH8ZNMmQZDVjo7ih5WVeD8lahj0JBlHMiwchm14GKFIvxcGdtXN9DgGZ5I4mC04/v/o6FH8B7O4+OjpwZ+tWIGvx8kcjPsduGTowu5Dh7CgvhHr8lfArspa7XL3TRIaMgi0Z/Aw3lNzPfblnUSLSqbry0unau1zPu3du1dkoaVLDN5aVgaDViv2Vd4xZroXGo3YOGuCMRFVEhecZvWQGgaz8iDolP/2h0qVhqxyePZBIP9zQPHMvUJmhetvAH7zW5YpKBJDmWacsr9EPDjf44hGrrF0LF/f/z/YvKEJ+giJtXRxDfYf7sV44Iwo1ybJIB0P/k7nknrJdAISgb3HxgN+FBlNk1VCan1oOuocM8my9J48vQ+XLE7oQ4odcHysXdy+25tWYc+ePclJjXSknFTkWbfHg/yWFracicI1rvQdc4wABeXA6huVqp0kWJ5Xii8tuQF7BlrR6x2CO+iEQevDslwdGmzKdQ0jAE9wEDZ9GskRrPriXqgm+0hsUDoxntTgPfZ6lDFrtVM7BVmDL5IIIiG6xhuAytsA7Qz2mjr5QgyHCLHBqtSqNW8ZUsMddGPP4F4EwgFho/V7+zHmn8C6ghSItnnMGuzbp175eQ8m/JGs8XlcE6B9JGU+sw3aY0xU4nckyhBnILitrU08KNmZCNxbEgVpGSBjZS59FxLrJC5kXwDagPxOBu5IrKhlqvh69vGgDO5bGvQlnHEJRLSzOltmT2q89BDQ1azsGdyXn/8ZcNcngIqZurxdO2Bgl4l2suKHUlTqald1XyfaSqwW4p7EigZ1k3Ha/okkz+YKPIbpZNt4HDP1nUmEVM+BJCH9eNqc10agWZPYx5hPXEgIjl0mvXEcS7udSaVr1iixHo5zVt8wUYqSyIwRMUbD99APl1JlM4FjUM4Ngu9V94tUwxv045m+47AbS4WNMOp34tGuA/hw3fXiPVzf0+l5mcqxseIqZg5xzrQcA9pOK+vkoo1AZXpxgx5PbJI40e3pnCc1rnVSgxtBotK2qfBP+3woHMJl5+XJZ2U4ts3VhpV5iQ0cKQu0lY0jheSJFw+2nceozysyV68rqRLSJxK2bDqRmSDoU3pDcNG1lackkZQehmMIDSKMC9CgGKaIdEky5CchCPL1rIh4VXX/zIBpq1JBMVuE+qZuQJRPmHwqALhOALk7gcptQNee6OspH0TpqTmCrB5SwxcOIhAKQh8XdBv2OQWhIU4pci58jsTGhsLp5ZqSIr8K2P5JhdTQmxDWaHH51VeFDqbSK0XpJ60+XnXTem8ogGDcvm7nvYzA7w+gp51Z013w5+TCoDOJRYHzWUieLLtFyYwebhMBRU39FrS29ovyxKQZLVY78k1GDLq9KIlIYwVDIZzv7oWxqFd8PjdMEhrMbKHMlJSaYlYVn4sxjHb/CoWXDmLfSQ82N1RBe+kY8PY/yqy3BiU2EsGXudZ3VhFPXBAcZ/HNdIcHgP/8WyU7iji8GxjsA268J/HnXiZpyGuqIkvYA8Q9AVCLPh7+PhWhId4AuA4D9sKUKzYSzR0GTz9cV4mftHaLHg3E5qIifDZJc77HurqEnKA4ahpEJhP2sDl9QwN0oZAwprfU1WG/yYR/27uXddiC1PiThQvwnHcYX//5D5GrN+BjC5YjPtw/18b3a4ODCqFBaQNmcBkM+Mfz5/Gumhqsj5AC3qAXLw/sE03YJlwTGPAO45WB/bit/DpRkUEM+kamzGsSGySo/9/iddBXlMITCqLBlgtLHHnKOcxsmA0bNohsnHTRZLfj8e3b8fFDh9DtdmOx3Y6HNm8WBMqsoK0AgpcjdzZyXhpekxTndH9nVLZJgh/DLMG5JjUIZjRFspoyRlW9Ir1EqTkBDVBQPNlTQ40yOs/6bXjt9Vdx43UrJ8fu5m27MOErFg42DXJmT5FQoD40JQdYkZQoA/W/W5rxzQunBOFdbrbgu+u2oiKswa9//Wth1HOsMPOO2VDJcG6kE/qK3JjL3+nqQY/HgB5HMyb8S2A3pC9RRsKbAShNfT0FmgVRIBoIut1YUFaG5f8/e+8B3dh5XQtv9MbeO4d9eq+aLo26JduSJcvdiUvcHfc48Uvei1/s/M8pjmM7TrPj3iTLsi3Lsvpoeud0znDYeydBopd/7e/iAhcgAAIgOEXiXgvDIQhc3HvxlXPOPmcff+B6WOXyh/8E7Kwm8wFDHcBwF3D3+2YTYgGwUfiXL+6HzRtqevhIhQ71GZo51664oBRKtOqlyCDZkVeAX3xXki+jjZNhBh55nyTLlQ7QfnNyT1CkMRjygUR6FuRUAZYCYGY0EBMgkZ0HrH1ESqiYJzgeaVekisR9jPjosfUECQ0ZTJyye5tg0iRmzx4aGcCpiWFkavV4sGwJchX+xSIwp48x7JwI3n/OtRz9dZBzXURCYHB/ThnBJEByXNa55rrNY09OTsbsu8TXc/+JlyEuH0e5t/G85QbjTJAiGS+TFLR/5CAaq8IjqxcZHGPiB/eedAbKbjqQRI+swuP/o/kdyYB7cQ8TKRXg57SeuaVIDZnMkxOgIokCViLIv3PsDQwMiAfHlDJpiuNwvv0ykgHnUyKEXLqrkXg8ORmRNudNA16jphrwyj1JA9C+huf2PMAxQfkpJrMqxzxjNEySkpU1ONblBFQSzpRzJokci3yOBJOuuL6TECGxHM8nvTbaC69eHbQTpMRdHzpswzDY7An3vYk2Znk9kYoFvA76UFz/g3O55Shw6g/yXQJ6W4DdbwfKE5cbjuZL3Ay032sRifoYCXkzQcmaORFtIDJsJW2qHMBKZ0N+jk5IIhh3OfG+Ey9h2GGHK3CYr15uFj01Pt+0NqZu+XUDm1i3PwN4AuWfzMituQ/QzxFw6jgvZVezyXPjRqC8XpLicDPbLzvMifdTxiMq+JnxnUI2BV+TXYfmyVBm8Ja85cjx83fld+AE3BcBQ/Qs0DnB8nNWC3hZoeMAVKIrd8RrFP/3Ba4ptwEw5kqkkMYAZC+ZOwNxHigwZGLENft+2r1uZEYETmY8yma2oQXNRhJrPuCY1UvfmyNQms2Ma5WaTcwzYPPOhM2ZIoNkTE26p3Fi7EKUc1IHSaEZqw3ZuZnIzstEraUCGpUFKnsfrp48ju5jJ7F89RagZjNQt11krXBDIpMfV9KkphFFhQXo7B9Ey9gk3F4fjFoNGs1aaOfIjp/VRNw6Dlw5icaSPJzrGYLT7YKJWUBXTgKrE2t2HP4BudEzOEzzbwqdFtx2F3D2KGCdlL53ltnf84jUGFmJw89LhIayDP/F3wI775n9WiLmuhfjeTEvI++TH/BNJ0xqMFi6IbcAp8dHBcknj7rPNa3A55pW4dDoKI6NjcHj8wmJqY8xmBpBOLN5eLAZ+MSEkP9htQDlA6HIPvrGunV4uKwMLx47hmVFRXh8oA0DRi38pUUgb/8vU/1YNTWOZVkSmZAuw56f//PubjRPjqPcZMafLqmBOXANrdPT0vczMsLUwuB7+LxMagw6RwRBMTowgrySfDGPHT4nhp2jKDdJmtFGtWHWnijdG73Yz2ozossCtrW1icA0Ccj5SEjcVVKC7je8QVTUpG3/VJkB7VbAeyWw/gckBxM9ftQgYqDfxs0E6xTw68elRuKUfigsAKpqgD37pMz+t38cOPgsMNgjNezceS+g0wPXTgETA9LeXr9JPGfK34oVK2fQ2taJhroKwFAPlaEGWUbpnrEig40quT6zDwezq9gLg86Dcr1+dXgA/1/LueDvQw47PnjyEP4akpYus1Y5XuRmfHTY6czLJDazYejYmIwmYVHIyNOrsCWfRH43ypdm4tcvfh9v2PMYcg2hjFjuHzyXWA4Js/147mwWWPvRjwKnTgkJqkmvF0uZMfmJT4jMMUG2dF+UMkJlcC0Y7QVGeoGi6M7rb/quwO5lQDuUMPN0vwcrszln/YHdUQuDJkky5v77gd/9jiL0kkwg5/2HPwwoAxtXLwI/+k7od2YjWGekxunFZenpm5S3ERh4XmqSSdA2ykuwVxLtmVUPAT0nANs4YMoBKjemhdAgmMgwn2Bp4j5GfMi9HGY9T7m7BJRHftJ1Fd9qPS/2IQ65J3ra8L1Ne5EfrfJqEbOwLX+16KlhD0jJZmrN2JR368nUvFbBPSRW9mwq4F6hDATLNtuzzz4rMoRlW4z7DIPKnOdy77F4YGCMlYiyTC0rMLhvxAMziqNlFfM57n3sp/CaJjW4Ru17CHjuCWm9ZzJAdi6w5faF+bwbHW9J8x7DccKgL5NIOKZZCRQpY0bQR5cT9RYatPN5DnMlLaWb0KBdyV5urKy6ngROwtAvAzx6wDMIqLSArhZI1q57nYHfJYkHJZHH4D8frFjig9UZ/Du/exJpiTRmJtrb24VvwnWaFXo8puyXsq8GfRWOUc4/Pt/d14msotlxGcorU/Lq+PHj4vXK/i2cl/wc7l/RfF7uMTxvxrLup82uAD9/x44d4u/cf0TPpouvKl4R8MEvH06K1Cg3VeKyNTwOV2F6De8xNxCJ+hhpJTVUyIAfzKy/FMjSpBexCipITrdGpUGuLhcT7lAmD1GoDzWPiYcneq5hREFoyHiytwP1Gdl4S8UNzhroOyT1hpDhtgH9R4DqfbHfc+U4cPDXgV9UQMc5YOlaYDhAPBgzgC2PAtkSU6+CKZqwByO3CZ3ixrylqM0og9VtQ7YuAzkkXOytswOcfqk/R9KgUes4AXilrJogtAH9dH6MVyn6RNZdEbgzFUiP64BKcx4uWwdmVQVZtKEgUZ99Ct/rOIEBx7REz2lVMAaqITiGr033YENuFSza1J1ezi86G2SY7777blFOTSNmbfEGXJ6+gEn3BPRqA5oylyFTlyUCpc8OHBVNgyPtGLvXiFw4xW02mY3o6RyAzueBw3wEOm8+DIOXUJ6nRlu3DZN955DlseHSeLHIAk5II5Qf6LCjOjtDcmICGfroiN3ELyYU2dg2l9xMOZClm2yFBgM1BgtQvgHoZV+fALIrgeyQhv0NBTOmPvzXwGnKr9mAmiagLoqjZmP/gYjn6KCQ6IhGatStBS4dCREVvI8so4wl4yUqsKLJAyVemUUD+v+t3op/uXoOp8alRuEfrV+BhsxsMS7+14UL+EV3tyAomDFOqakjd9wBk6IC6MN1dfhJV5dUrcHne3vxrspKdLa3i2MoG+PtLi7G7gceQMeMFX//+HGYKqVgqjT+gFeG+wSpkUoZtgyS6y6vHXq1EVq1Dh8/fQrf62iHLngNnXhh915BxiylbM/AAAVtw47BagfFXRKGmN1qQ35p9DWtNqMS12a6MO2xBbPLl2XWwkhSNwpOjY/jXT//OXr9fqxeuhT/NTMjKi7mi7QnBLAZszoGKc4AY+dhYLRVIvLL1wHFK0JOclElUFEP9AT2QD6fmTO7pwbnw1M/BVrOS3PrDey7kboGbFKYmQY+/TFgcCCwt4l6fmn/O3Uc+OxfSVJpe94Qeg//duRJoPtCQIbOD3SdB+74UzGvi2vvQduhQ2jI2hY1YMAgkdwHg5ricqM/JY6MDYs5J1dKcacdstswZTELWRASGLIsIOewLK1AUoNzhw47ndh7SjLwq96TweM2ZlB0UVyE+NeSacbPfv0DfPitXxS/s1ycJBvtRGrx0ungg84N9zdmgjG7lvsaz0Pcq5/+FDh0CMMdHSjYuRP5TU2YpNQcIXpNRYE3tlE74XIEqyiDX5PXD72qAF5MQ6syI0e3FBpVksFzfo/f+Q7w3HNM0wQYXAtkBwdxsXm2fKY/QG60X0kPqaHPBcruB+x9AcnAMiDB6gMBrR5YsjCyM/zemSmdSnCFa2RkNmyqKDQUom2mPSzxxKgxwqKduwqEkm3fuSY5qFzviQm3Ez/vbsVH6lfO+9xeD2BD8AfLdolqDY6CIkPerKrnRVx/MAuXfgUrJDjPmLnKAH+siopEIR+LWb2c9/Lc557CfYDPc1+g9A+DUYnOce4bzBDmupCO/mis0nhdgIlP7NnVcQUwZwDrtwOm+X3HQjKT/TO6LimqQPxAQ4KE+k0CVjyQtIi1P8myyLSNaBfFIxJSIRC4N3r8km+rVZnmPIZceTQXoSFLsaULtAGZfMLs/lR7ti04aGvpGqTHIhIGZfmogBCt2TuTTtnjj71WqBbCNTNRmVlWL9Ev4RzjeOSY5RziGCZZIlfmMcZFe6+8sgKDXQcw4rQKn5d2mlmjR31GiSDUKIPF6jpWVvFc+R722iCZSHJaJjVE1bdKJd5DAoZkRTQSjnOPycKs6mPFhiA1ovkYUZKX46HMKMWWum2d4mepqRxVUfoZL2L+oO2QNlJD2dh3LqhQCj8YgOfgMAQyx0NYl7MOZybOYMw9BjXUqMuoQ7kpMX3jcbdT8vUj4lb8hLMTo0mTGjMeNw6O9MHm9WBNTgFqLPNslu2M0Dzm/x3j8d/T/HL46w3aEKFBsPHvsceBfR8KaAYyOMZJKzWvIlSogUqVuOGSp88SD/GJfh/sfmYLe2CEX/RwkBpQptjXwDc1m9AQH8QglxZQZwNO6tDJ2YYmwDI/PedUsSlvCTpnRnFlWrqXerUGj1RsDAb4HF4Pvtl6SIwTguGKCY8f+dxP1VyEKQHlwItDzXigdA3gZ2Cf+a0sA65PuC8JG2WT3ZY18clUc4HubuvGusaNYkHnwh3UQ3eMiUw4rSKGLcOsycYS8xKMONswNn4J2Xo1srXSmHSrR6ExG4UIzKqmSnT1jWBk7DSWbPwTmDMT1NHnhxnNog+EcGLEc2rJgE4Wk8PCSO4cnUSu2QiDTisFhZIpaR5uAVpfDM07kc2tWCSmeoCpXiArzhrDQNl4j2Sw55YLKbC0gcQNF2LeH947Syaw4+7476ldCjSTpAiA97egKLZzUlAO3PVe4NRz0nrB+7f5vtiZVLpywNUFeAM9dMS60wBoknN+MrQ6/NWy2Y5N8+SkqHAg3AEn6OzkpCA53sOAZgDb8vPx0u7d+FpLC4bVauytqcGX9+4VWbKx4HY6odaHzyt+hNxAO9VspRFnH9pmzgUNLKO6ThAaymtonpjAz7u78J4lNdheUID31dTgv4UcjIS/XbECaxVZ6sWGAox1jqBkSZkiwGZAESVjAqAM1Z3F23FtugsOrwsFhhxUBKo4ItFrs2Hnf/wHHHl58OXkiGqYPS+/jEv33IPsBPfnmwIdB4GBUDUB2l6R9oaiACHBde4N7wNOvQSM9ANcmzbeITUMV+K73wAunJYGgKoP+Mb/BT73ZaAyTZmok5NS7weSa5H64K+8BAz0B8gMubdQYM05ehBouQQsjSAsJ4ckQkO81hf+XI2k+W8wGuGKk0TCpnuHDx8WWUfRgj2UY2PVjRI+uwOFZdlibpC4ZpCJ2ViRc4W/yxrTTZmleKh8A06Od4jjFRjYANaJgd5hjA5NoLSyEHUNS4IODY9Jh4gOMR0ZGp98UFKIJAr3Lkql8TXBLCvKDe3bh/yxMRH0ylVWppXUSdmm4rnAJsc+VPmx+/7UZOTgknU4aIGRhqkwZaLQuC76G0iKUdItkaAZM4DvvTf230UmfzRC1c/sAqQNDM5nLmy/oFQg+wepVFzIjkqiPkY8FBjysTp7JS5MXRINqzO0FmzIXZ9Qo3Crxx0kMyIrxBeROLinlZsSS1RbxPUBCQZl81Vm1DKRivuBHCxIdv7xPcyAZeA1EiRM+DcSKQyWpdpUOR2EBv0nZuhyz3xdoGGl9Egn9r4VOPr7UKPw9fuA4jQQ9dcRtEMS6bVHiRNlT5Z0wOt3Y9BxGk4fY0SAQZ2NYuN6aOLECFjZNFdVFYPEyQSf5wLtMK4VrGy6OfpnLCKd4LrMKohopAbBRMKOjg4Rf0qFKKP/wH1EEBdR5pCSJHukYisOjFzGsHMK2TozdhYshUmjx/6D+wXBeNdddwUJPfo8rFan78D9RPYxuPew+pzH5X7D6j42QY8GEhth+1zFMqCjOVyur3JZUtfLOcJqDT4WsbDg90YuIm2VGskYPCpRoRE9g8ygMWBL/hbhcJDUSGbhXJGVh6fYbDfy8+iMJylNwd4cnzqzH/2OmUDbIRW+uGwjdjBAmCpIFHgcEZrHcxAl7ogviY1CJaWEAPwAm1c7pkX/AxHc9tNgKYMfdlEdo6K8Rwrw+l3os5+Ay8dz1oAKd2VqLwzQA1eswIWvSVm1zMjYsE8KNs2JGBmWmlJAHzAospoAT6DJsJbBdA0wehYYuygFezKrgeItaZNGiAUGQh+t3IgBxxQcXjeKjVkwM5sxgG7bBKxRmFvGsHJ1gQZd8GOAxJX/aIDI45c3AfhpvGyJ37zKNgG0H4fz3BlYTDuAnNDrmdXKxZqZJfJiTPaam4as48d+GooWGsKZXJuzFGatERWaJoz5z6JuhaIUzg94DTrobFLWUlVZIHs8WS3rex8BnvheaDww+HS7IiN5LpAhP/A74Nwh8X1fGRzDvuU10ubC77wiwewLSoddeyk8mORzhxqhih8qYLI7NqnBeXXi54AjIHWitwAbHwHM82yWzGDcH38BnNov/V5cCTz6YSnjfC4wu2q4HzjwrPR7XiHwzo/HL/cuq5MeiYBjzLINcPcCnPuslNKlr4niiCLQL0Md4/mdhYXiceLECdG4LB6hITA6jtsal+L05HhQ9orNru8pkYwKbnrJOsIO7wyuzTDILut7+mHztqLarEKnLTS2SHYOK67hL5Yvx4fy8oTk1NKsrDBCg/A43NhdthUjmVOwemZEZdy6nBXQRUjqcd4uy5r7u/u3P/4RNmrrBgw9BuD6HQ4cGBnB/SlmalEe7NzkpCBsV9MgnYeUVcIYkvtMKDB8OURqEKxI2nxX7GNQwu38qdDvgtjwA0f3p4fUOHoU+MLnpZ4pxBvfBHzxi6E5OG2V/i84wQhmmZiSnNcwUFIyEqJ/ROh5Ot0MNq1bFz0QT4Oe2UaUaaIebmQfgkcqa/DjrjaMBQKxHCMPVy5BHmWvRCKHB74E+6+Q2OCDGHC0YNTVIUiNNZuYlatGplYKXHKPYlYWs/soX0VbkQ8GyyhJRQkF7pV00JXVVzJI5jP4xeMEHansAmDP24EjTwE2K5BVAOx4i0RsxMADpY3ompnEuSn2kQJy9Eb8WV1ERQUxPQn84t+B7mvSHkZZwNvfND85ja27gZd+L1XwKG254nJgZZRzuFGwjQEjbdIeUEi5zzhVXg4r0H0W8DiBghqgsOamJzWISnMlKkwV8MEnqsMTBasNiwwmjDjtcncqMX9WZd8k8pWLWEQKYNCTwSGlr83gEOWc2LBbrrDic9HI7lhg9R0rvGPp/c/VO+N6gZm5rA5MtOHtIqKAfvH2N92yt0ZODEzXsZLFmPNykNAgnL4pjDovoci4Ou7nzDVmKceTKmEYCSaksO8Cs+MXCY3XJrjGc1yRDItVhcPEKVbfMVEp0X4aSoWERHujkcC4szh8/I+Pj4u4F2NdMignNTQ0JJJ/eWz6QLQVOTdIatBf4k/GzOgTRQNjAuwVwjHO6kGBTfdJ8U25cr5xC7BsYSqZF3GTyU8tBJJxNmTcW1KFq9YJ/LynLdggWR3IGH5ndXLZa7/ovoJBSsAEQlkMZP3zldPYnl+W+oJetg1o/73ULJyghEhp9EkWRNUyoPVUiC0UAZLIF6lEI+fgb+L88ufdkGbM2QqX3M9CVCKoMOTPQGWHBjj0m9ALT70gkQ2b75n7oKzEoLyDP4IM0Cgyt/jd6xS/j18GRs6Efp8KNJMvjc64phO8l6Wm6MQTKzeigc8qx4gkqRMZsKUWIY2YGA4xiaojP4bTboN6og/6jsOAawpYGcriZwBIGQSidAiNjqLSYmRozZjx2EUWuZpy82oN7ireLggNcV3Qw+vxwuVikCEULFCFZVWpAFOpNE6TwZbdklbrhVOSNMeWPVLwJlG8+lvg7MFgEHldYT6OX+0BNGqROes6cCCxrCo7yaMIAzMywMiP0MQJllx5KbzBOCXjLj4vERvzwYmXQoQGMdQL/Oq/gPd8du738vzZa4ONwZ0OSVon3cFmbuL6hckuWJOTA7NGAzubAAee488dBbFl5ehYJ+J0cj58ymjErzNG0Tw5hgIRuFyOykClEEtnI3WT/X6eh0PIIKqovxqBaQ97jPhnfQUrs3XosrmCf2GAa3NgPtKhZ/BuY16eeEQDs12qiiuwPIUm3pHgvM/j/YuyqYseJCmAJNPd+/fjFPuZAFielYWvrV6Bb7ZewpDDgZXZufjqqvUoTWemORH1dJO8hmiNm4kUHM5ZIJHxF19gNCj03FO/Bkg0yNn6q9ZInyVOO2JuMvu/JgpJlVMsrUVKCSWuVwWh8UpSgJIgkU0sleDfadhHc0oYmH3ittvx/Y5WjDodWJuTh7dULMGJ48fx5Yvn8K3WFjGe6zMy8eMt27HEktjYLDLUw+2zQ6NpEXufSZONMuMK4SAxaMaSb5asRyKRbC+5MSzn9lr2+rh0HhgaBCqrgYc+I63xAbKfjhaDZZGNAAnugZ9s2CKkIl1+H8qMGeI58X7HFcA9JNkmLxwHegMSRfwOD/wByM4HNqbQx0lGTh7w+a8Azz4pSTEa9MDK9cDue2P0iLkBmOgBzj0VsjW7jgNr3wJYQpVjYfbJwe9LlYZcX9pPAMvvAJZskJIxuk5LCTy5FUDZyqA9NB/5v3SC56NJpImGAiSt/9/qbfhM8yGMBqQx31RWgzeULcoJLOLWBYM5cg+lyECPLLlDcB2n5Af3IFaLz0Uy8u8MNiXSxPhGgvso7wFlS2QwcJbO3iKLuLlBSRxloDQeIpvURyKV+JDDF6nYwf564/MmT+gzpYOAoP916tSpxQqN1wGYFHXgwAHs3Lkzps9N5RD6nIlCJPimoa8Lq6TkanEZ9IW4x1DeKta5zgVWplC+6pFHHglVigui9mHA/5B8EWLuj44OCX9mkdi7uSCPsbSQGjRemIl3M1zUJxvX4J3VTXh5uA/tM1Zk6/R4U/kSFLIkMgkMOuyzGrRShoqNJs3RdOsTAZtcNzwEWHslRzCDUjZz9FrY8gZJx43NwhkhKV8JzPQBLt5v9qDwAUt3SBMwzXD6GMwNvwcuvxNoiZJJe/l4YqQGg4bGLYDjJODnNfD31eF9MyJhjay+YZPNzrlJDQ7wqYuA0FDm/a4HMhvT1sCs0pyDOkse2mbGFJIWQKYutIDzk7bnM3BDnetIxDFKes8J8qu9fwT1csVE3wWgYYfUGyIKqCfI0sAMkwWrZyrRa57EuItzwIKNeey3EQo+qlQ6rFi2F+cuvIily+mU83xN0JubAMd5gI0czaVAforN4Jeulh6poOVU2LgryDChINMEGPVA4wYc0+vF5ka5EgbweN1Rm88aogTkqGEelIRRSVJSBXF09qdHwssP+f8ZyjLNEx0RBgHncW8b8NU/l5oG3/92oGqO7HxKmtyCDUoLDQY8tX073nL4MCZZ5adS4V/XrcOW/CjBs0CDYVl6bS5QNoGBzc80rRXVdsfH+tExM45cnR5FRrOo1FAaal7/EFy+c4G5qIJOtRxadXjprU4dPej4odqleGX4IqY9HjGk/u/KVdhVWBR0kubKQkx0E54LlPchifLuTZvw/z37LEZdLkGwkMxYYrFgZxSy6Pf9/fh0czMGHQ7clp+P/960CSURgeBPnjkjpMJktFitePTwYZQa1eJuHRodwruPvYqnd94JfTpJteKVQN/p8Odykwgw2G3A1DhQXQd0t4eIDN7rDdvmf379fUCkvcMxxX1RJjWWrQA+9ingO9+kRwhQOo9gRcQnPgsURmmuaDADOx4FDj1OHTUpUL/ubqAg3NlmIzsGYTgvYunDMgAVqyKp0GDEZ5vCCYbf93ThO7rQvtg+M413HT2I/XvvSsh4V6s0qDSvRaVpGk0Zm0RfCr6Pmb4MFjDDSnl+7PlB7V5mRjFwNhe5ITR0Dx7E2uMHYHiVcoIBvO09mLnzftFsUF4nSFzK189sY6UuvJSkEFF9YGuW5PZk7KgA+rqAYbkRuQpgL4X5kBpEfiHw9g/ihkKsN9wDo8zX1pfDkwBIrrUfAlY+MPu1bccAd6DqWF7DLr0MFNYCx38ivZdPD1wW/axcrL4N6N4mC2WVx40Ge0I9fttd6LXPIFOrR8EtuP8uYhFKJNqvhkQxg0ckASjjQZI63t5AYp1rLyVraJfdrOA1UPZQCRIc9DEoWcJqExIc6ZC6WsTNCUo5JSo/xvFPPzsa6UX/IlqT4rmgURng8Yf3dUm6t9cCgfYa5X2Y5Z7KtS3i1gLtLfbjiyfJx/WftnWioP0ea84kA/rukf2P5IRHrtWyHBUrTegT0Dfm/sb+L/FA/4PvZUxp06ZNYX+jvC6Pw+MxEZGyWdwfOC+4NyQiWbeIhQfX3kR6gCVMatwMDoeMfIMRD8+zKXiNJQuHRkOBaC7luXojTMyynA+0JiA3iUnAIMiex6QyKDrX3FSoi995GnDZpSzO0vSUF876aLVZlEEqA8w6NgueLylAAsNyO+DnNQUahMdDtKqdBDSQMXUBmCQZFMDE6UBT2UakA8zc+3DdVvxh4Aq67ZPI1hpQajTDIeS63MjXZ6A2owTFzJj1D0SQGHTU4xA5HjcmZxwYnJjG0ipF4EpU+USfuFy8aXRwoWWwaDkqUFQRO0vKqKlFhmEYelUeVCo9dKoyqMw6wByeyX7dEc1w4nNNG4HbHoSm+azQfWQmAbNVyNSz9FDOIAsGrY3ZQG41MC41aQqCQZjMSiHXhtJ1kqRULFBmyj6pIDZYvTLP3jri3MxRGsf6JamZQTvw/X8CPvo3QN7NkeXm87O5vBtqmMVq0DnTgVHXqJBGqrHUIVOXXDPqfcXFGHzgAfTY7Sg2GpERpwqDDgdLXhMFnc8+mxV/df5VTAXk4QxqDd5rKMGGqtC+4PPb4fKdVaxvfrj9F6D2Z0KtCl1PljYP2boCTLpHgs26HV4DOm1T+N6m5agwFaHMZEKBIjOe85BGD0tfo2WNE7JxRuPQBy/GXMPiZ46uAMYEGvzK+pHUuKXxSef84O234zPNzbhitWJNdjb+ae1amCPu7cnxcTx48KAw1njlzw4O4p79+3HyzjvD5L2OjY6Gacjz/w5vaBXj720z07g8NYnVOfOUY+NeMHoOmOkFKL+VUQxMDYSIyNaDQE7l3LJvLeeAH35Tkm3kuReXAlOTUq+aN74NqEtDo3AGaHiflIQUK0MChFYQ99wP3HEnS3Kk6oyJMek15jjrTXEt8OCnAdsUYLQEegDNBgNKzJqNRmpwHLAaiU3DEyEMiHaTEf7+XqgKC4Lf7ZVpKybcbuQmEPDiWKe8VHFBCbQKEpCOAJ1hkhssWeceJQeKOP75SKTZJOVBPM2noXvqhTB74dS3vwGn2oCmHTsFua0MstHZoZMSq+RcOnFPOKFB8BArKoGXA/1NeMy5kmI4lwcGABKI0SqvSKydPgYM9lNPiwLe0jioZG+Q6xQoGL0IDJ+WJBjNJUDFLqkHhwxnqCo31PNNUaWoBJNq/FHmcPfpAKGh+GPnSbjrK1KWkLqZSA25Qpf+wSIWcauDsp6RlavxICdrMWGDgS1KdcSroGXDVdrnNzOpEUu2h4E97rMMaHEf4d7F6+betYjXFkjqJZp5TXslWp8Y+txsIk4fNFnk6hswwERPxTw7MabCfm8zdhYsQYU53OeU5eBYCcVHLB+D55QqqchEQSaK0F6jHUmS8paGfQQYon1ilxI2i9YtuIT5rQqZwI5VacfKPq6JtM0iSYBYlRD0R+ZDalBmir4u5c9kMJmP50pSmpK7lKhubm4W1Rxcp/keyk7NBc59rveR1U+c50888YTwIbjXyb6KXMHIe0AfJtb8W8T1Q6JtMLTp7DoO/wzgvcgiIikwq1kOJNHAmgu51UOZEBUytFJX+4XCI5UNODc5gubJEfE7yYy/WrZ5/p9JZ2+iHZgZlKo0CpbNXa1BKKWOGOxo2jHboaQ0Do8VocmeKvL1DbB7x+BldYbw9dUoNK4AluqBIanRbxDL5pDRioZEJcZylwG2/vDn8hJgiafboj+XJlKDMGi0eGN5Ioz1esDPIAkDmRZAtSp+o/CCJdCqDyM/y6xowJ0lBepjgEw4Azvyosvflew14fG5MOrqEz1rsnQF0GnyYdDcZEzz2p3A4WfCn7vz7UCjpCPPzYWbCI06BrBlDcTJyUmxycgllCKzylIIjDNoFRGBIZlQvXNuQq1xD3D8Z4A7oJ3Piqhl++Z/jVvvAi4zwBRYE/jwKjLK2VfkcjNw2524biBhxjVEnxE09rjm2r2X4fb3iN8p0dQ5k4E+x2DgTSoMOgZxW8F2WLTJySgZNBrUKcbmuMslqgcOjYyg3GTC/1u9Wkg30ZEkaZWofifX6P9pP4dpT2hPGu/qwW8r1bgrP2SA+YUEXOS4YOPkV6H2soJsFaArFcdrzFiPYWcPrJ4pvDzUi7MT0/DBCj/6sDZnEu+v2RhmCDETnUFVOgI0CmMZVtw3z108i0F9Byx50j7AXlIrsjchSxc/gM9gBAPbzByR96X6jAxRBRMPT/T0yO3fpfP1+0VFBnt/NCnOs8JsRrvNFiQ2+B5tlPjrnH1OEsHgUWDqmvR/ft4Y+xApX+AGuk4AS++MX6EhExrihFXA1Cjwnk8Cy6VG2ynj0CHghz9g/TOwazfw4Y8A3/6W1ESahjDX3YcCZcqRSQl5AYcyomw6JkiAZMbX6ef3zaoEjrNofSi4/tOBYEZRLFKD/VJ+2NmG81MTGM20wHeJcn1+aIoKg98rZeISwdGjR0XGUmTAR1QrsmJoyRIhj8hMGgaKSHLQAL3jjjuQKOp1aokAUATM1+Zk4dDwYFAXnbIhiUov0IHxe91Q+XzhGYgkmyl1KN4bSCTZGuc8jxwBvvAFaWzwtZ/+NPDYY6G/83z/6xvAsQOAQQdkKRJDGlYA7/qEVOmzkJjqAAZD8iqwDQLdLwFL7gudS2YRMNEb3vMtK0pF0UR/qEF7ECrAnBNIvpkNt8OWshSGvM9fD1LD5fNg2uNEptYoSZMtYhGvYdBWidUUNhK0Z+T5S/ubyRhc04Ma5FEgr/+3Gu6///6g38Q9hQ85gYrXzMbL6Wq+vIibYx7wkWo1DjPDWXkaby7Eg0mTh3LTVkx7BtA1M45f9w1i1MneXyocHu3GZxp3oNoSUiMgeUJ/lz0EeM7xssUHBgZEMJh+VDLXR1uOfheJjb179+KWhnMC6HgmkETIZI1xwDUJVN6RNuWO1xo4XuhfRiM1GNwnucB4S6LBZI5RylqxaiLZ/kX0c7ju7t69O2w/kas/aCPyfJjsygQ/nhOri5j4mijhEM2P4nwhaSNXaXB+K+cQ906S3tH2AsZOZJKE53cr7oO3EhLt16dNW9dxvxvw0qlyB5whF+A9Cmh2SDrGc8DudeGnXcfRbZdkDOoshXi0cgP0C8S0Mhvr71ZtxxXruJCcqs/IQVagmea80H8CGGDFQMA5H7kELH0oMWIjFuig9rwkSQbRuSzeBOSltrkqoVUbUWm+DTbPMPzwwaTJh05tApZukjaH8wcl57ZxPbA2xU1PBHTHAL8dUDGYGkVGKKMCKL8dGL8kkTeZS4CchalOWTCo8gDVzsRfX7AElnX3YfrJ70u/U9d67QNRszqn3FZc6r4MtUYFP5vhKrQEaczIhtaIcwhXrc3wwyOkaVSqq4CLQcybjNTYeIcUDKQMFQMLa3YECY14jabIzpNRZ9k4g3lk3mFhkC2KxM9UDzDeDuTNUdHFQM229wAjlDDzA/nV0WWtkkVRGfCnX5T6akyMApfOSBnpSixEBu9wF9BGMsULVC4HKgJZRSMXgT42sw80Y6/aC2RVwu3vDRIahNvnQB8rxYKQsv27bd1YmpX6msOqgftefRXHx8dFEP3a9DR2vfwymu+8Ew05OcEs72hGOb9vGklyFgUNiWE3m7lK99Njd4ig4QSby4QhiiHG3s5iTXIC9pOAegegyYFapUaxsQqXhtpwZoJVKyGcmegXOv2yrA0zo5itxcohgk2OY5Ea3DvN1Wq4epwYvjKEipoyaHVaXLWew4a82HI3vEYaeSRNlLrXiSBWj43I5/9xzRrseuklOAKGGf9eblRDo/KL74hB7+VZ2WjKnGfWss8TIjRkzJqyfqkyMR5GBkKEhgyuH91t8yM1ThwHPv2pwGn42bwIePgtwLe+DZw/B7BK5Z57GOnB9QTHGEvElZlLStCI5pyJpmXL5z566hieGegVFYf83dzUAFvLVWiKi8T3+5nGZYJ4TARyhWBkEEw5XymbxXH73HPPiXNnVlVSKC0PrwAIVEvuuPc+eItLxb3gnJur8oP35MyZM8JmFQk5E0PweyaxYmklsphEwFtVuBJYrpd6lW3ZC5TECGCx38xnP8uohvQ758o//AO/HEC+vsvnJUKDyIwYI60XgUPPA7sSkO6cD6xMQlFSmX7APgz4XKGeWY37gLNPAo7JEMlRG5E803UGuPDcrO9BNBTf+JAk19inqI7luDPlwAXtvBp9J+RjzBPNEz34bf9ZeP0+6FQaPFSxDkszSxb0MxexiBsJrtuJ9Cvj/sAAp7IvEtd2Bv4jE6deC4h2PbxX3MP4YIULbbqosrevZzCR6NJBYGIQYBB+xU5JVvMmB8c1K4+i9ZYhKJ8pS+BGk4xta2tLqUJDCb06E3n6TPzdxd/DEfQF/aBL/9zgVby/NpSQxezwDRs2BP2feGC1FEk5+iEcr/w9EVDGl8HcZKrkb1pMtIYIDQE/MN0DeGyALvHE6tcT6DPQniZ5FtnHQgaD+SQW5pJaJlhVznHLcZXoGJRB4oBxrEg/hpVI8ryjLb99+3YR8yIBQb8okSrwuUDShA9WfbAanQmzMjifSHRHkhokAtlzRJZDYkUJ/aOmpqZFcmOBQP8gbZUaDDLO2VODgWsSGaEnJILDPwqo5pZH+F3/OfSw8W8AbTPDeG7wEu4vTSx7N1FMul0iwJaj0wvJqWVZ8TMmnV4XzkxcwphrEmatCWuylyJHH6PciY0TSWgQsuyMawYYvgiURmwczNRuPS3JUOSXAZVLozPKzK7ufkGSFJAOLGXkGXIBy/wdMo1Kh0xdRCYPz2P5VukxH9A48JwD/Ip+E5oGQFMXndjgIxlk1IbLT4nn5uhTcBNhIrMa2PQIukqK4PGrUBtFeqXX3o+X216FbcaGoopiTA4fxK7CbdCr9cFNicHPIc8gWsYuiefUahUyM02oLM1Dn+0qNvpvu7kWWp7L2l3SIwXIGwmDV/0zOvidGajQW0PXKBJwKeMW6hcQF3oTUJa4fmTCKCgB7npUmuv//hVgqC/U9JbNY5el2ZgcbAde/lGo53LXeWDjG4ASasgfCQ8yd74ILH0UHtVkWDAsVhcYn5CSSx6jTqcIoLLx9JEx7hESeDSXz4efd3fjS8uXiyoNNqqLLHUlO0/jgQYNjSb+pNOxxJKDHhurKfywD40iq6oc1ebw4LsaOVCjAD6MBPsTqXx+qOWKGcIzCJ/aAo9/SlSq2ZnZHZCgUoLPy6DxQ6KB2ejM4IpFwoXea0NuYTay8zPR094Hk9mIotJQxnk0sMSWhlaivUaUeGd1Nf7hyhX4mZ0WyMjfU1iI2gg9yvW5uTh39934ZXe3+N4fKi+Hz+/DVy+fRZ/djrU5ufirZWugnS/5ppRgIzhPdRrAHTGmssuAsX6p30RuCaCPCBBnRjG8GWTOmmfg4cknpXNS9ub49ZNSRr7CwI0Jvv6Zp4Bf/xJwOoB1m4APfjy+DFUCYJCF63usag2ClXuvvPKK0LRVyjNdnJrE7wd6g5U6Mtbn5GJ5ZTV2FRThzeXxM1GZ0T7omIRRrYPd44DL5xZydDJYPREpbcJzJhGXiCTWLKzdANxxN/DCs6Hn3v5eoKxCtHyms8FMLV4jHTHODQbelKQOy+m5Xqxbty4UuPKtgtd6HOeaT8Dp8qOmaScKGtZB3bQn/vnQ5v3Xfw1vGC9dJCdoiNQYk6p92cfJx/03bK9VAYPS9zBL3onST5Q6JLGSDJxjACvp+F1YqqWfsZJ/lNWygph4BzAzIu1BTKRQynyyh8bF5wPvC1wDh07tZqBxh0QgWvIA2ulth6Vxb84D1jwIW0v7nOvgvH2MeWDIMYWn+s4EV3W334vHe07iY3V7kaO/sUG5Lls/Lk61i+raanMplmfVRYyhRSwiNZCQIKlBaZBdu3ZF9QNkGU0GlSI19bmvsL8T11JltjvJa+43PHY6Aks3GxjoZeIU7VJmzbNaNhE979c0aB+98hNgsCPQt0kF9LDP2IdiymjeLKAdxYAr9xjlPsWxT1uCSVUkBhiYZMBSmUgkZwinw4dm7MkVUe1IX4OJtTLkecbALaswOIfnAuckg79MuCI5ybkZb17ymnjNt99++80VG0gVsXoXRvoeiwgDq/GYLESyIBpYxUF7mkF72vpzkbycW4mM10jICUhKcI9RVg/K4BxdiH2H18Z9jus+fXxe8549e8RP5XpBH4TrCatElOdGMoeVKrxnvFcLIefGz5el6V5vsEWs3fMiNfgFkjFbSHTOjIYFkvi/jnQ07A2Am8ZfnjuOl4f7BZmRrdOIjK0qcyb+1/KNaMzMwZTbgU7bJMwa6sgzmOTHK8PHMOGmDAmlsabxgnMc95bsFARHVFIjEhz0kc9zA/v9fwLD3SHd/VU7gU2BJqSRZXVBQiN4UMDOTImbPMvMPxxOaBDeq4C6WKramC+yVkj3QtkonI9bBCKrorQUg8PDIjgqG1IenwM27yCmrFN4+uJxqPVqQWgQNq8dV6fbsCJLYq9pbDu9DlztvYyq/FCQamJkEu1t/WBvWB980Iiw0GsH3EyYTSWkeSo349jL/y3smtxMMyqKc+HlhmgHdFZrQpqLQfAgrUeA1mNStUN5E7DyLkA7D8k3ys2851PAs78EejukRuF3PwJkz7NPQSQuHZht4F14Bci6MyKLNyBn5xiD2qwPe5rjxazhehkK7PNnoSG53h82jwdvO3oUv+mT5v/mGEFZ2eSkMcHviSXeslwCe23Q+JblyOh80DGhwf5erwdt0xPosVvFpeUajPhI/fpZY0SvXgOvvxc+/zjUrh5oPN4g58Mr88CDKfdB0U+EWJ9rwR8H6XgEjkFDTaNHGfuzBMBzYGYXjRZutHT848GszYTVMykMkaq6Clgnp9F3ZQh2iz2q4UOpHzpY8QgNkkW/6++Hx+/HPSUlQs5LRmNmJl7duxd/ee4c+h0O0Uj8/1u9OqrzUmOx4PMRGWjf35xExVki0OgljVsbe2gEBlteBjDJMRgwgIuXAq0XgP7fSr8z++/2d0mEvwzOm733Ay89LQVY6WSXVQIbE2sAGRNs9B3pFPHYiTZ5P/Ay8MP/Dv1+/LB0zM9+Kf77pnqBzv1S4oMpD6i9HTCGOw0rVqzAq6++OsuAlkGiz51hwsm2Flw+dgj3b5YI7InIihZB8qmwPjcff792bqKmbXoYv+g+IejHUr0P/ePtGOqbRp2lGutylguHg0Y8M5PEpUxNiXHL8yShwIynRLR4lejr74dt771wly9BjsuJkjXroKoN7eec/5xDdH7owNOp4ecyy4uOBB0jZnHNuldqPTTZ27F213bxGgap2o8fF+9j5mZUmRESGe9/P8uwZv+NY0M5NyurxQ+by42nLrahOsuCjWWF0IsqFm5KERrtXaeAK6+ESIeV9wDFCUpmsuJplBV3AUxcBMruAnKXzs5W5HORZAfnTWYUySlxzdOzxzydJ+6DslQT72vNFqB6o9RbQ2sQz/F7kCvXUsFC+xhMmIqczST7+h2TN5TU6LEN4tWR06Gvc9IqJLLW87tbxCLmCUp1kNDgmklyXKm9z+BId3e3yASnTRUtC1Juss0gEiFXfHD9J9lBGyWVxIubHQzMcT0icU8fgz9p6zF4zGQT2qO8Jww+0497XTQZH+2VEqdkcK+YHge6LwK1oUr7mxWsfFCSe/z+WL3B75eSObQNOBeYkKEMsNIPoSpCOkCyujGzAFesI8FKc2JpVijJiXYVCTTaKZTcpE+UKDi/mQDD7HLaR9H66XAcM/gai+S8JZFVDYwFeqQJUM47D9C9tirM0g2uWyQJSJ7Fqq6gPU2fl8F+VkfEq/qjP8vEomQh985QgkQ7/R95r6Jtz/Ogf8HKKb4nGeKA457XyZgCxz3tfuX7+Rl8Ddd0zhvuh1wH+Jly4hTPiWtBtF6HvI+8VyQWWTXV09Mj/BJWiaWrJwfXL65R9Lu4NryeMJWgj5E+UoPyOyBzRmeaizUXSx2gSqyBkUmjx4xokhw4HDOyqW+fJvzL1fPYP9wvjmvSSFnCXM+7bFZ86swB/O3KDfh+xyk4fVLQq86Si8eqlmLcHbpuXpXH70G3fQBNmVEa4rCCgzJTHkoVBDYsOpmWiAlwrVkiNOS/E+deBZo2A1kR90uWDgiDP8bzNxn8ttjPp4PU4BeYvUJ63KLgwsRFVmYg3b4ZDDiO4lprJ3RGPYprSsIMD5HVEUGS2b126KnlrUCmWQcNdPC5/HB6J2DW3kSN/JiJ/fKvgfbzUobPpn3AyuSqgmiEKlFheTfQeQBjkzPo6B+FJq8GWpsXtqstsLk8IvMqET0+dJwGLgUCTnK1A6fyuvsTPzn7jBQIMmeGMl7ZyPihP8WCwsVxERG6YXBTSN9FCdBqjdCrC+Dy9cEv1m1KlgFrsutxyTqKCfc4tCotGjObUGhMjtT4wrlz+J3CGD8xNoYcnQ5WOoLMNBBBVqk6QAYDldy0ud/IWdY0tGTDQ5klkq3R4Gtr9qDFOoYOYxv2rVkPkyaaU66GVlUJ+Cuknk+iakP8RWx/VtVYkNAQt0Q9g4/UleGbrQPw+H3I1hnxwdrNMJKYkm+p2y0MHRpFNHxYwh0vM73a3ACrexw2rxTAz83JxY6qO9Hb2Qt9hlYYQyaNJWyex5Ocapuexm0vvojBgCROplaLl/bswQZFcIH/f3ZXapVQC4LSXcDgYal3EjPLi9cCy2qkbHV+b5ePAf0Kh5lSVPt/Drw5IAsl4563AFX1QE+7VLlBQmO+spF33QW8/HJ4IHfP3sT7IBw7FN5YnEHvU8ckYiPWMRwTwNXfB97jB2wjQMvvgJVvle5HABwTNGAZQGJwKRLPD3bg29ekDPTRs5fQkW/GR+vWCtkwi0YLm9ejKMj3Y2/TUuGcEHTiuf/Q8KYRLmduMdmDWezMZl9iUWGyZxj5ZVJg/tpMJ7K0ZtQZ9VD7hgDfNKDOECQG5y3XWBIPnBfxytrpqNPoZ3CM2bAMMPB36vJqd+4RpOGxgQFYbI6gJAqPzXOmrJW8X5IApdQUnRXaqJQnjOeo8xgMSnG+0vmis8I1h3OQJfbBDKgXXpBkyKKBMlyUPpRRVQs8+h6Yf/F97M7NwXmHHS929GNNcR5K6xqBnXeFXnvlIHAlQD4zEKb1A+efAbJLpUqKeJjpDSc0CM+MRGzkrwdq7gNGzkvVvRllifUlU4LELceeoipNsl9zgSv7gZEOqaqxdiuQVxnWB+5mJzWi7Q0IENY3Elenu6I814l1OYsyBouYP7gOU6ecxLiS0GDwhoF6rreJNBKPDGTJ5DKTsV5r0lTyenQX7YIA5HvHfYoVutw7eE8YFGTGO+9hMg3Z5wU2sZ6ZAswZ80u2SsVvmwXVbEnQmxT8vhgkpa1Cf5DfHYONcp8u/j9a8JE2QrI9AuLhvUvW4z/ajqFtRpJa31WwBHcUhRQmmNxFe09O5uIcSwa0f2jLcD/l2IzsAyJLTr2mmh+bi4CKvcDgcUmmnb+XUfr+NULaLCAot0eSi+R0rOoHjhVWSp89ezauXJlccav0MVgFwgQiEhH0L6LZifQDoknoyvOOElg8B1YvESQ2WDERS5qX72XSK3/y8xhboJ/AecE9kc+zAoVzhNfF85ZUTtQizqCUmqP/wSblTF4k4RGN0FCCewUfPCbfQ+KU+0asa08GmzdvFmuD7PPw99dE1cZ4L9B6UPL786uABsqC6240qaEDNJsB7yXmOQQahS+bs5+Gx+dFr30Eq7OL8dLwtJD+kLG3MLm+Ct22GTwz0COyV+8sKkODQgv8yOiQyArWygo1gZ98bsrjxrdaT0Gv9sKgljKVx9wT+H7HETRkzs7AiKa5KEAnr+4e4NofQtUZxWuA3IgAlW0yVKGhxEgP0HFWCopULpOyVPVZQHYdMElNclWIPMm6BWSWYjWJT6J5/GsdzMpgQOm+++4Tv0+4W0VAx2IxorAkDxNTPngihltmRPaBWWsOk8wxqYHp0THUNFWjs60X/Y6TKDasRoZugSt7XE6gvUWKny9pAIwxWPTnfg60NksBPRIAL/wCMJiAhuh6pwmhZBWQVYY82xjyXNNAzwnAdlzMGUfJJrHBcTOSG63HRN/liCf80nOJkBrUm/39j4ArZ6Tfy2uBN70fMF2n8V7WCIz3h37nIldaB2QvAYYvAI7R0LqTUysyWZg5lKHdBpevJ9CLJRc6dSG25DcE17lUsnmeGxwMk7KS1l4V7ispETJUZUYj/nntWqyMCHqyTwoNCP5ktlLnwIDIjLBEcSrYF2lFZj4MGeMxg1Zh98K8CXC0AN5RQG2CX78UPp9CliuAYqMa/7T2PiE5ZdHMLju3ZmTg5/390ExMYIfPh501UQhuBXRqPdbk3IYp95iomsrUSuTMdP4QWq5eRoW2TDy3PGsDerp6ozZuU+Izzc0YUejP27xefPDECZy88zo2nU8WDB6W7Z79POV3CMpOhVUS+YGZCcBDUi7ChmD/jHg9NEgocpwnOm7v2Ad8YRL43veYMsTUJOBzn0fCiKbzSVsgnrE52RWx//sB9wxgGwYyw+UguW4xy1aWwZAx5rLj366xh5IElUaDF4Y6sTG3GFvySrE1vwAvDLE6RsLDFVW4e0VIM52g80G5BQa36DyzFH3CMYPpHBsMBj3UbjfGBkaxdHMocWDYfhX1Wg8yjP048up/YuuOxwTxSINfBo8VL4uK2UYkPEgm0PHmdcmNWmXIRIfcvI/OBjOhuJbTqeFr+Tf+n9lRJCoSLUmns8XX8t4yGMV9mE4S1zz+XjoxIc37SFuPz42OAh/4APDDHzICIT1/1wPA+i0oGx5Aid+PQ6++jNNuH0xvfQdyuLcRPReBy6+GywCygpLlcdPD8UkNEhVDivcG4Qe8geQRYz5QEWWOJQrOszVvAM78JtQQvKQJGO8BhlpDyUrjjwOb3wZkl9wypEZjZjEqTLnotY8LW4lZsvWWQlRRPusGQpmtKyNS+nARi0gVXM9efvnlWUkSDBIxWJNqpjblObgecx1/zWR7JwAG2SJ9CP7OgB3JcVYDLGiwuLMFePr7gNMuVYDvexRYnlxFZMpgPIJJaILECKxR/O6Lb52MYdobHLOUaKLtoJSWiQWlXZMOZOoM+EzTTtg8biHtSj9GCQZDGRTlnkg7KtX5xfdHZs1zPaBNlWyfvlumWoOPRSQFji8mibJpeLzqHc6VuWw8BtiVFdr0Lbgu0jaXSRHa9STuOP84HunbRKsSZDIX30vSRSbR5ZgEf48Zhw1cE6vt+Jn0cXgMrt3t7aHEOfob9FHof8j9a5gwxftAkPSnL87KJ/od9DeiJZbFg0wG8ZrlXiCc37y2VPYJ3gOeK+8n17B4PRdvGUwNAcd/EUrwmx4F7JPAujctLKnBrLs5oTIDWmlwJAK714nf9h3GJJ15+k8GLYqMRTBpjFiTU4ES4+wLYJbvgZE+DDnsorn3ukBp/+WpCbzt6CtweH0i9P+t1kv4r43bsS1fChCxh4acExVt0jp9Hhg1KkFoyH+3edlTwy+el6ePGmqUm+IEnSxFwPJHgdaXgbFOoKsZoO9atTkUZCmomE1o0EA5KjuTKuDCAWDP24DyRqB0O8DPZFBSa5aahM8VxLsZoCoA1JWAL1CVQmiaFkmNAMi0srRVKV/j8dsxMjyBukZJEmOJRY22aYZCJRTq81GfER5E1asNWJG1ChemzgmHOEevhlOrRVdHX7CCY8zVmh5SY6AHePy/AQbLOLcefi9QUQNMjAHf+SowNiy9LisX+PAXpdcowfEtExoyGPw7+YIUHKxmb5kUmWdzPqAzAad/JMkrCfhhHDyG21a9Ge0jdrGpxt2YIgzM4PklggO/B642h37v6wD++DPgje/DdcHyHQDlmNgonCtWSb3UU4MSJPX3A6OXQlI3uQ3B9Uit0sOomW3kzsdZLdDrwdxvedyK3rwGA36zI75UEI0VZnY89fvf4z88Hjwnst5P4a0VFfje5s0wKcr85eaWsgRONPj87OkxLa5R7fVC5emVmoT7JqFSZ0Cl1geqVIJXDbXKCI1KjQxKq0Tg8Ogo7j50CF6OCb8fX3M68UxJCe6cy9hTqZGjD8nQXJo6jRmvFSq1CmND40AR0D5zGaMDM7OqkCJxdXo6rE8C/982RzPBmx6W7NlEPx3oRPY59rFouQAwi+fFZ4FLF2j9AW9+K/DgWxIjNx56WHokAxJLzaeB0sD6rQyC3/OG+OuGstdB2PPR30MHgNm2NGblzJw++8ysoCglprptVky4/WGEBvFkbzc+37QSpQqygZk+StCIn3HY8fhPvw61QQdVlhY2qy1sLdCrJCmSVSuX4NjxFsB1FvDniCxIZnxR8oROBOdmrGbhJFHk/Y9GOo8fWe3E+c3eMlOTE8i1SJ+v0eeIKik6HnTMeU94Dcos5GRIDWWwgw9+Js/pgM+HepcLpZHOlvz9sgrtiSeA97439LeCIvFQ+f3Y0rBcnOPJs+fEeiYcmHbKeUWAfWV0WkA/B/HtYYZaDH1ofRrlX4rrgd0flBwNSsCZcoCXv614QYDY6L0QTmpMTiQn85iqj5EiuJ6/p3orjo13YMxlQ6EhAxtzq294QLbGXIYBh1w9KGGJueyGn9ciXhtgUIiEsbJKmQQws9PnO8aYffta7KeRCrgXsXKXaz6DWAvSf2N6EvjNf0vJU7KM9R9/ChSWAYWhZIcFA/eDPe8EDvxC8jNon219I5ATP3P5ZgOTpJilTbIvVi8BGUwwXKgxbk6gyoaBVGZ4pwpWulMyh5nnTAxhhSzH6uL+sgglSCpwjHBeKKsUlGBSVSINw5Vg0pCyskGu8jh58qSoNiDByP2ISYyR4Lzbu3evSDaSfQOeJ0k5OWYWrReHDB6TCcMk7+kzRfo6MjlPQloJkvX0XeRzI6lBf4U+TaogoSJXnMukKs891UoLzmveH0r+zhnTutnRR9m4AKEh4AeGrgFOm7TnKEiNRHyMhEgNliVxcVc2ZAyC/R4oq6E2AZrkmKdjo5cx5Q5JFKlVXhGAurskenkTgzdfPHsYh8cGgwrxf7pkGf6kZin+4coFQWjITr7KD/zfS814eoeUvfrhuuX4yClqpzPQRbdMuhYGAWosmbBo7VCrwkug+NrWaR8252XD7XPCpDViQ+6KWZnys9BxBBgJZRyi+7iUBVce0J2saATW7AGaXw4RGgWlwMRgiK3iFZ54RiI1eE65qU+oGwaet3YF4CsH/HZJckodY1AymMWxpNa/bkoGyRozgHn48GGx+FIWQ6/KClvkMrRqrM3JgkFdD61ai1xdTlSDpNRUDofXhqvjl9DdMyT0vPu6hrBmoxTw9flDjchSxsw08J//D3DYpGoiNkD9r68Bn/474KkfAxOj4Qb4E/8DfDAy41l08Q4tYFpmqmqkIMoffwAsWQHc+Y7UiQ3bqILQUHzm9CBqatYIDWFuprOCbTNjEkNMImBYIYND1MbIhBrsAg7/DpiZBIoqgd7ucLKGY7oriiZ7KqAj09su3ffyGqnReDRCZtMbgPX3SOehNJpJbBSuwvXCl1euxF379wuJKXnt/sqqxD6fhstvsrPxAktYWQXhdOIXHR0oMhjwf+vrRaYDDXQaBjQ2omV5EG7fMGzes8FgoM7rh8nvDNUCkugzNsCKnuB4VMMAsyZ2Nc9fnD0Lt0oFP7O11Wqoy8vx2eZmNCukChIB5aj4meVLSjE6OIbejn54CnzIMszdYHlVdjYuW61BYoONwFfMs6x1QcHznGoFpgLGY3Yj4PIDEz2SJn/ZSmDlTqD7EmCbCq3/dJjn2gsmx4GvfkkiWZ1uaXMnWLL/sx8AefnAztvTf03WKeALnwa6OmQxWWBpkyQ3tWEzcF94lskssHqz74RCqpKVRPmAObyBPG0uq2cKbr8bS1dK8mzcMzjmi40hg8/rdEJj0Av7p8RowZkJqxgXkeRX+4w1jNSIBovRhC0b1qNf7US2UY16gyqYFcVqq0aFmafTaeGmLMXIFWQZVwo5K1mDmjIPsSSoZBIjnmwbX7N21VKg/0VgQpJqgD4Hq1esh96UJQJHfD8zsUhqMFCXqL45MyCjNZ3jZ/Lcq9/1LjSPjMD5gx9gSbTjcY+emIh6bDoayjWJ2WpCkztWxiflnaIk8IQhlm2tYyVvmvsvGDOkB8Fy8GiQyUfnNND6HMbOPou8nNTXINnHWEho1Rrcln9zVTjXWMrh9ntwcaot0OOvBBtyw+VCFrGIVMB1h+sj5TKZtcqsbZLWzIxNNjglg/sAyQyZ0GBQaBESuOaT0CexT6I+TJaLkn4TfYEq6TLJ7kkWQz0hQiP0hUh+wfUgNYjCSuBNn5bslkA/pVsRTITiPGC2NiHbKUyYYGUGbQv+nc/TL7/e4HnQvyFRNh8CgmOQQWpmo/M6mH3OMbqIRUSC5AOJC8rrReshk2wPi3hgLzu5WjtqXDmAsHis3y9IESYykdTgHJb7+EUDE4kobRsP9Bci5bQ4X7h3MpGMn8/7QbKavg1fn0iz6njg2sLPZNUGr4XzMZmeTLwPTz31lDgPzu9Eqs1uaghfIqLna/D5wH8DFT0kmNJCajCDjYYRv4Sw8g9HHzBO+Y6As5a5CshI3MEad0/Pag4uV21Ew0tDvYLQkF9LfLfjEu4uqcSAwxaWtcj/DbHZYwBb8ovw/c278URPOybdDGz5Rf+MuoxsvL9mGS5bh/GDjlOzPtPtB5ZnLUdtRkSzx3gYiRLIHL4aIjWIDXcBjZukIE52AfDCDyKkDvyA4xbPvpWhptxKeAPUMFivAmO89z5AawEKd4ngxULA6/eKapsFyVTg9zd5Hphqka7FXAXkbZzdrFOhIcgHy8f4O40YqHyoZNA6kD2uURlQbFgDnTr+Qtpr7cXzp16EwWxAU3Up8k1aLKkPGbqmdPTU6GgBbNOK6/VJWdKtl4D+bingHrpAqaojWjCI/TPOUYeenkDEYt5xAei4CNSEy6QkDG20TZcBfikoRCORpBHL9oJ9NjpOhhq3EuXLpPJuyoOULQVqolSfTY4Av/0P6TVCKmeKjXrCs7UJReAxZcxYgR/9MzA6EKqCecenZjeglaHo/3CjsLeoCIduvx0/6OwUwdS3VVZiZ0C7NhG8YLXCx41+YEDcT//EBJ4aHcVflpUJw5yGgdywMRr8fi9s3nNh2c1uNTk0DfTB4KIKeq8bOUbKb41CBQ306iKoKaUYAwNsGsbMLY7vigpx9EHFPpMoWF3lDvSQyi/Ow/TUNM4dvYSPPnrvnO+lbNfJ8XFRsUHw/lJysYdNy+dpcC0IJlqAYUrBBWAfAiZmAEdg7vSdBTa8Dbj/I0DneUm7mbJpeXMTPPj594GRIek4MqEhg3Px5LFwUmNoEPjed4HBAaChEXjvn0qERLL40f8APYHaTz2blruByxeBD34cuHPu71BUlC17M9BzDHBZAXMBUL45rFLM5/fh7ORpjLqk6jeNSoOmNSsECU6Dm0H8d1evwA86L8Bjd0JrNGBzbgluKyjHmKsnjNAQt4M2l8uFu/Y/j167DSuycvCPazag0jw7q7S0oAAT1n5YPTwPPyxaqVp1rcWPDE1o73Q6XaItBDpPA8ds0JbuxKWOfuFEk2jgnhYtCythjJwAXIrAmWsSubqrgGVXMJDEbEtmVDEofuXKFbE2sGoknqMQL8NLxppPfQrN27ej3+9H6Re/yO6xoT2O/VKiOEw0vulksASfMlmUtZI1u1GxHLgo2a9BcP9zTgHNTwAbHotqKwjQLmLvsElFM0xtJlB6d+yqn3RAZ5T6Z4yFiF8x1yhLxZ9Xfg/YxjAyOYOC4tSDP/QxeK9ebxA9czKXiMciFpHupCllJigDVkyeSrWpNd/PGACzROUGrosIB+8tg2wkNkgcCdKexO+JXwL2wD7GqrwNj0hkdjIwxqj+SIePkQxoV3FfuMXBihoGRWmncI7QdmCQkFIx3I841ueTnT1fyPr/6RiTzBJ//vnnRZD3NaHBv4gFAWMi3CNYHUFiVjlW0hkzY+WCnOwU77gk9zgHSDTKFVOMlTG5ixKKJCBI2KezrxOvWZlsRYKHex6JfN4bfh4/N9nq8Eiw6oB+Aq+Fsl+Jgp/PXoK8hwn1ib3ZUdwEdAUk24O9kksBgyWsSkNel+dCQtEvDj4ujHQcg6SG1wGMHw4FjXgiviuAvVuSF9IvDwSzQ3B6bXD5nDBqLEJnPEdnwYhzMkhscGizCWWkkzjktIksJsotsLIiUnKBhMbanHzRQNUb+BuzFFfn5IYd55Xhfvy2v1O8okBvxNfXbsMyBggBbMorR7HBgl/0MIgwHfyc5ZnFWGJJcvBGczKjydpk5koPOQMiWKkRkKGgTNV1gD/AiLGp7nWHfQAYU8gyeGzA0EtA+YPJOeu8b95BwGeTqkG04QHUGc8MzkycwrRnWgSHlmUuR7k5+v3lWHum/wqaJweEVv89JQ1YnpVAo2Rri0RqBD+UWbwqoGBL1IWTk1Rmqfk7tVk5z7iIu/xWQRro1JlQz3Ef+gb68Fzbs6hslLI6Jn2AweNHJrW6RY+NfBQa0pD9F20ME3SSCouBiZFQ0IebYaT0lIzdbwZMGcCVk4BTavwUBNcR6zwyNpntnF8PjLYG5Gz8gKUAUGRoshyfjDulTfzU7T/7NMrys1BRGFivRq8BGx8F8uLMv7ZzIUKD4BxSBUgNuRaAz+18APPG848DYwptVOsk8PSPgHf+OW5mbMrLE49UkK3Toa+yEjhzRpAHqKpCcVmZMC7I1jNwGC9jwgdmL8zOjPaqVeFPq/TQqCyiUXci2FVYiGszM/DSwFKpROb6jgQ22kgssSzFxakTwZ1scsSK+3Y8KObvpakhnJnoE/1OtuZXodoc7vyWGI34zw0bsPcViYjjMUhy3PPqqzhz551Cpzcd4Np0eLQX56eGxDp4V3ENiuUM7mQwfinywIDFANgDsl/sP9VzBqjbIfWSevkJ4OjzUi+a294AVMdZu3o6w8lUJTgXldIBzCr9sw8AkxNS1vzpU8Cli8A/f0Naw5JBV+Bz9dpA8RntHx/w798ACgqBdRvnPoYhC6jbF/PP3fbOIKEhE/JXnZexY8ceUQXBwPybyxuwIqsAx65eQr7BjLuXrhHj5g1lFfhtXzeeHQz12PlQbSM+ceYEXD6vsNqOjI3g0cP78eKeO2GKIEKrzAW4Mt0P8hcsqnG5/aL1w7gvC361Diq/VLlLIknVGiBb3U6U2a6heM99gnhh0ICB/TBQ47m5WSKSWA4+l1HuZKA7MtljJGZzPlZuMbuSwQraqpRuiObE00lKpPnnms2bRSVI6de/DnzqU0zplP7wJ38C3C6RZVyLnn76aTz88MMio4ufyYAiCXQ2LpcxnteAC9MnsSPHIfWKIRlmZGUqKwzHpN4V+XGC23lrAGMB4BwFNCaAMpSxSJB0geN6zQPAxeeB0S5JcoRN/NjMj1KGrIzkljllR+OKxEnrSPC7U/ZTuZ6gtvmIy448vREZkf17FrGIWxT0JxiEkeU5afem0vuGQSTqgdP2UvZ0WkR0cL8hscGgG/dBTdthqCZ6sL6+HBqNGqA6xaXnJB8jGZRWATXLgfaLkn/F/ZcKD/XXrwL7tQRR7VpcLMY3KzfZm0u2CRjATLWaKR1gslY6CA0lmOwxV5PjRSyC4542NIlZrmM3UqqM+xfPh5UTMhnPc2IsmtW9tOOvB0lHQoXVIXzQ72HSFvdE2vo8j1TBGAbjGaxaiSdZyO+Cey/XBVbC07eiryH3ApHx/PPPi8qPBe3rlG4wzrb2QaBlP+AONApffmdYFSC/b67NaeupwUFNp4OZVMFSPA+b+imCCRlG6kfRswX8E4DzKGDYJclSiSbeLRhwSLIuFH2qy1iDTXlL0e8Yw7RHKp/RqrTYVRhqFsxGrf/UcgwXrZITm6E1zSI0SF5UmjNwR1EpftPXBW8gW7PUaMJXVoa+8OeGevFdZpoHMOZy4s/PHMbvdtwDXWBSVFly8KnG3Tg53o1R54zQ3V2fWymCBEk1zS1fC3RIzWaCKIvT2JRYd6eU/T0YkL6xZAFLt0iBkhiTlufkhzNwTw1JLz4+vxddtosYd/eJ33N1pagyr5gziJ5WOAYiSo9ITjgA91TiWtH8bhynAK+iSbK2BjCuCGa8nhg7BqfPGQwOnZ86B5PWhDz9bMLqZ93ncHBEIr94Zt9sHcWfN9yGxsw5gpczXZEnBtjYT2Q2qUFwsaYjLxtPytI+gypxx2NwfBCltSFmmec96PRjwu1FraUWZebYPQeSQh2b1xcB4yOhcZmZAzStAsqqgW//nVRVQOiNwJvfHf043Jw27QPqVgJPfStCsskP5M3D8OIcYKAwq0IKuBgygSISrOFLHTcQ0dBq8CrgqsLBC+0oysmAnvrmxMxofFIjGrRqYN+7gB7KRHmBpnVAVRoM4wHKWinWWv5/qBevZVC+6pHDh6HKyYHfZgMKC/G/A5nRzE6YK0OBMlKzoYJa7A+BdVKlB/TJZcj+45o1okLiFWrqc9nOycF3IgyLRJCjzxfNw0ecA+JsXE4tassbcGKsBz/sOiVIdeLIaBc+Wn8b6jPC16k/0MhTqUSFBsGfF6am0MoskjRJUT3VdwVP9F4Onsv+4S58eeVulCRNbESQDmHEX2Du05jhuH76u8BIQKaB1VLP/A/w0MckebdoKCkH+nqk9YjzzyMnWbBRuBq4W0EqvvQiMD4W+ky+p/kMcPUKsDRJ0re8ArhwLmDzKK6LaxurQxIhNebAtNsapXLUBTfcwphltRmzfRqzcqHKLBRZLLRVCI6N/9i4DS8PDaDPYRdVGSfHR0V1qrzaspKjy25D88Q4tuaHB6RXZ1dh3D2Dk+OSZFiW1oAdBfVozKwSlY7wjcN/7knYz3fhuE4HLYM1PLJ9SjgfzJxiaXVYv5uTJ4FPfpLpV9Lv/Nu//zs1EsIv8uxx4Lc/l6oCy/OB3Q2ASXbwKWkZIjP5GXQslFlTTMJh9SMrN0iqRNPSTQYMLpz2erHi17+GfniYNes42d4Ob6CxOLPJmM3F74OZncz45F5OgkUJvcGAPm0BuosLUDmtSH6QMUs2MQrM5dLjeoJZuWwgHgnFnjoyacNtKZC7Mjh2ef+uN46N9eHbrSfh9vvEOveeJauwbx4VJ/MB7VT2XlrEItIBVohRI125BidLaBCUmJKTSRaRGOiLM1Yi4iX+FsyMunCqtQebmqok+2M6hao0rg0P/CnQfAAY7Qey8oB1u8NlZheRNPG3f/9+0WdCmeQQrK68geOHZEu6Ara0hZjIt4hFJALa0KzqYcXGfO3n+YBxGvaMsNlswYpvzgmSAawc5H6WLkmsWHsfk193794dNjfpc8gVIyQY5nMOTH6in8I4ICswCPoUXJfkahbeB/b+oJ3MtYlJW6zWj4Rerxd2NM/3luqbU9wgPWKA3AN9vESuKeE0L95MstdBqBVMEB1a4dQq4QV8g4B6CSZcQ0FCg/DDh2vTzViTswcPV+xCj21YBJrLTAWwBGRiiJ91XcSlAKFBzLjtqDWb0cYgV6Ap5hea1sHu9eITZ47CE8jY5Jk4fT5YFJvUmfERkVUrB4FIjoy4HKLKg6RI8FJUamzOC+nJ0Wk9P3kFV6xtAb3qQmzOWwO9Oo4hQZkpjR4YviJlt5euAvJnN+INAzPg9r0H6LsKHH1KymA/+AsgvwLY9Xbp7wr4/W7Yvc3wghmTJIRyYdKshUqVeOZen/1qkNAgxt390Nh1qDRfR01f9tBI5vlo8A6FExqEpx3wVgKaLFGl4fCFS8SooMKIc2QWqcEqDZnQIOQQ6IGRzrlJjWhkUBwnlWz4fJprBnUOKWUSBQWGUlSa01g+yz4Of/ZF4JlfAoM9QGEpcO8jUvkzH5/5O+Aym4AzYLUKyIphRB17Djj4jBS4zM6hIHyI1Fq1Q+o5Mx9w4StKcAybpE2jsbwQ3WzQXlYQ9nxM1K4CTj4vkRecgPyec4uAhjVAU/SeQCkjp0Cq1JCJDVGe99p0MCl1M+3x4KHycjy3axe+W1CAwVOn8KdVVWiYno6azcuMdWYxKLX7VSodjOomOHwhIlujyoReXw54JkKEhjo5beMsnQ4v7d6NZ7Oz8Us2Mh8cxG0vvojPNTXhA7VzrPERsGjZy0ma//36MfHz6X6pqiHYGwrAswMtqK+/Ley9erU6UoFSwJCitEQkuJc+2dsSdi7M8P/DwDW8d0ko8SAhUFpl/GLod84XVmnY2bA9cBWUb5scA4aVknX8mxq4djY2qfHou4HWy1LPBdGfRw9U1QB5hcD9bwRqFYFll3O2PJzcfyNZvPO9wOmTwKSij5B8bWlqLGmM0keBhIIuII9GZ4MN92ij0OhnBo+8n9AIpuF9e3GI7G5m/5EooM0TCRqPewqXY3t+I9w+L0wafbhBqcmD2peF3StrhXFPWTcxWjOl6rxZPYt4X/7qr8Lv9dWrwHe/C3ziE6Hnrl0G/udfQ7+3OaReTg9tlr47PvI3BA1dOuq87mil4HIGFaVT55P1yAxO3t/mCxdEZtjasjIR4GPTTWUZOs+DcluxGsXyOWZ6dU5Mo0yjhoaN32QLg+dn7QSsHUBWJZDXcP30yt0OoPcs4LJJZd9FgT5uc4Ga6oVLgeHLGJmYmlc5/iz/4jpgxGnDN1tPBGXauM59r+Ms6jJyUWO5fgGgSbcVB0dOY9I9DYNaj015K1BpTkB67zohqYSuRdxU35vcM2A+uNFZ67c8zLmw2Cbg9crfhQowpbi+cJ9dHwqwLWJ+YICUezszpZmMEItE4Dy6nlJU6SQ0CAZE2cdgEYtIFLSf6VezUlnOlpf/z/HJoHq0fnnpBMlGBv0j7Xfa0nfeKfVLXkhcuHAhZtWD3Hz83LlzwuZPFbyHlJ9iZR9JEiYQ8JqZGMbr5H1mohiJDn4W1ysmbcX6ziwWi6gkof/3WoFMaiSChCPgLFtjCVBYg0JTLWBnJl98Y9fmnZrVCITEhsM7g0xdLmozohvwl62j4cIDVJPQePG9Tbdj2GnHEnMmSk0W/KjzGty+UA0HTYdhpwPnJsexLSCBk6M3zJbcFkGq+MHzazNduGQNBdIGHMM4PtaM7QVxBjGN/9KV0iMZ8H3nX5KCvLIDMdYLXHgFWBveiJbBOrd/HB450dU/Dh+akaFNPGt40jM0+zn3ECpxHUmNjFqpB4WooghcjGWJpCGdKHyxmlmS/MqCNgrZwBoXTbTn/bPa1YjfGdiZE1lNwHDEPc2K3WOGLDMNKQaiUtUEpIRVTkYO9BmNuDp9RZA1vLZMbRaWZa1KvyOamQ08+v7of8vIAjZGb9oUxJUzwIGnQ79PTUk9Iu54SJLkoCE3NgDklaR2fuNdwNBF6YssbAyTnYoKyoot2YiC9uNoGxiVSI3scuDMHwDnDJBbBqy7HzBFZLixD84DHwQOyY3Cq4Cdb4ot0TUf7HsY+EE7YA/02GFm1r1vw2sNv+zuxvtOnIDV40GFyYTHt23D93fuxFG9HtsVMi7RnHeSHZEGlkFTBY0qC17/JFQqPXSqYkliTxcjQJ4gOKd+0NGBn3s8wRqED548CbNGg3dEabCWCBh4JezszRCx9swEem/IoNQUZzVJeoJBObo/dxUXY0maempIEkXhKyHXFUq1JI2CdRIhN3Ut0BC7Eug6Gt4DY/AaYI4hVxcve7mgCPjy14GzpySCcfkaIDcG4beRgfF/CxEbXGtIqqYSsKG05Tf/E/jKXwOXAln3PB7tiX33IB2oMtdgyDkImzfUW2tZ1spgNjeNalFtFihPpqErVzDRAWF5MveW1atXi+fuKSnD/2u5gInePvizMqEzmdCYkYk1CpnOSOjUWvGIiqV3Qn3mCaipGU5QprM+RhNKkhmRPRMYcOsINFqXceaYdB/lYBx/9o8D+nogwwJYqgC9tBYzU4tzX85uigYG4+jQh1WMpABmhvFeU6aCjg61h1mdwf2bJfI8DzqAyoyuaOD3kVtVhQPNVmzPmYTGPQ2VOQvQe4AJ2tJ+6ad7GihJMzkei9A4/hPAYZXmBWXgrENA/Rz7uIya3YAxBwPjNuGMpYpZ/sV1QMfM5Ky+M8S16fHrRmp4fF68NHQMjsAa7/S5BMFxV4kZefqFDRgkIkvr9rXAC6kqVINS6NRLobqeldyLmLdsBonW+QQ4uM8s6vDPA427gcl+UQXucnugZwLYsjvmc8RFpBFMUIjXn4+Q9/jrRewyMMmeHvNNepTBoCgTIBM53ozHgyd7ezHldmNPURGWp6nyexG3HmjT8sGgMueH7EuQ1GCAnWOUFR1K35vPscKB70vHvpFuGbZkwPku++ax9lcmOsVKBEmlso9VGPxJf473kVUcskQ977/cWyQali9fHqzgZ4Pzqqqq10QyysDAQML+RcKkBg/IA4chez1gKATc44B/GFC5FbntGkAjycjoRFXH7C9ZN0eWbI7OiF67NfhOiYQwoD4jWzxCx4metapTBEMeqajFk73tGHE6hWIEKzbeu6QR2XOQGn32wVmBnX7H8MJscKLRNLPVIuR4xgeiOEOjQUJDhtM3BpPfDo0qsVIoNsaNhCaJSo+0gNmobHY5dUmSnTIUAJlJBpk0MTZqtUQUGDUmFBtKMOiU7iMD/5Q6KzfNlheiHj3HDeUIkgZ7dLDJORufU0rCUg1k1MWdU0VFRUJ3lUGXRJrgRGao9/T0iKZGJRklyNZlY8I9AYPagFJTWVTS5oaj60p40Ir3mdnOXZeAVjaLD2DTPcCqxJsnCYy1AZefCRGo/L1yE2ApknptGGOMk8ZdUBXUwG9/Saowaf5jIJvbD4x0Aod+Cux9/2zCorgaePNHseDILwY+8CXgylnpflG2KycNjd/TCJLKbEKcbzAI6Ztk0TwxgbcdPSoqNYg+ux33vvoqrt13nyD8GKSNxdRzHY5lRGjVOSKjnOXX2dk+aClRlGTFwumJCVH5tz4nB+ZA9d+vJybgY6BWcU7/09GRMqlBI4aBaVYMnZscCKvUWBbIfie+c+0aPnJKmid8Ra5Oh7qMDOwuLMTfrliRtj3JrNWh0pQl9l/5XPjv8qwUyvK5Dxdtkh7iQD7gzMHZr5vqByoagN7WQPWTSlorGtfFPjYNzid/Bhw7KBEKlDa6/Z7omeZ1dcD//Srwtb9nPT5QVQ38zd8CURplJwSWHP/vvweeehy4cBbIygYeeitQmqA8EGU3J9qlvSKzAjCGkws6tQ6bcrdhyDkAj9+DHF0usnTZaJ8ZwbBzGvl6C2otBcJ4pVb6pfY2dGYaRaB2W36xaKDNALycXUX8b40Z39cZMDg1jdoZJz6QU4SZyUnoY+jC9tiGMOKcgElrRJ2lHFrlGmjOAba8G5gakL7jrJLYpC4NccqX8L7Lc5XfbVVV+OtiOUE5y4DMcOeaY50OVDydaFZypKtXg9BH12iCn0cng5UayYANdllW7jFkY6hmG155+WW8aX0eTHZFI25i4DRQvG7hqzX6zkmEBj9b/l66TgJV66WGtnNBpYa/dC0GhsfmRWrI/sX1DBxlR1Q/y6CPcb0w4Z6CXVSqKqFCv334hpMaHn8bvAhVznnRB/g00GuWJiynRclX2tpcyxZxfcF5xJ4+/f39ItDB/yc7t1iN9loIitxQcJ/c9h5U5p1A16QV9Rt3Aan0JlvEgoAJCpTZob0UC/SxORdi9ZRhpQeDm6nIu8X6vPPnz4sgpdxLYD5go3BW9fL8WS0aC2Mul6g8b7FapSieSoVfbtuGNy320nld45VXXhEVAzLRRtuXlT8kN1588cUwUoOBeL6Wc4r2HOcE7eRbkRinfONjjz0W9zVy/EG5TzI2F498iAYSR5zzTKCSK7Xovyj78s0FjUYjqjzoE/IYlM5ihVmyfsrrgtTgIjuL1OCXaKqSHn4n4LoA+KcABtV1y6SfjMnpyzDs7MEMZT8CAccSYw2MmvhZpW+tXIa/vTgalk31zurZ1Q93FpfhX65exITLJRqFa6BCQ2YWVueEMjZz9Qb8ZMsd+GVPG8ZdTqzLKcCdxXMHH6Jl+UeTa0gLxP3MAOzW8OfkrDGR7XoVcE/Cn+mNWiDj9o1DwyaSccAJeHTsNCZckyg2aYLxI6LEOEdm+3yh/DAZ1MjOS16XPghNPqBrANxXQ8/pVwZJDS42q3PWoGMmKxj0Z6+JaPIezFD2RBAa0ohNkHlNUvOaCxcZWTodZKSTKedjWTizReWGyfmGAvG4qcFeG5G3koFmJaFBHP8DULkUyImSvc2GpVdeCVRSVEhZT3oT0CMfIxCg4ed0ygE9FdC4DyiKkbGbVwlD9So4p8ZhEG+UG4BT/3YMoAxe9g1sskYSd32C2bPXGb/t68Gnmk8IGcAcnQ7f2bAVO5lBnwReHBoK9AiSwBk47nbj1Pg4bl+9WgRm45UfMtuKRkEkMciGvSzbZAb7tWvXxOtIkiSyQTJT6e79+3FkTJKGqjSZ8MLu3WjIzISG+rQ9PVJQXacTa4TczyAV0Nno7u7GY5VrRC+pKwHN5fW55bi3RBqzJI0+fvp02PSZdLvxrupqfCLJaoPjY2M4NjYmmo6/sawsanPxTzVuwT9dOYIeu+Tg3Fdaj92FEUHolMDeE1rAq8yAoQSPAbjn3cDhp4G+doCykFvujV61ZZ0Cjr4CHHkVaLsGyNIOP/gPKYC+Q2riPAu3bQee/K3UKDwdUl08BokMPpKBywpc5XlQFlEF9J8EavZJ5IYCWrUWZQry/ff9F3BwtA0enx9OH1BsyMKjlaux9LatePsvfwhHbhZ0OdkwaTT4t/U7RRBdiQ1eLx5VXDcdcVZ0sLlcZPDq9PgVnJ1sDVb/XbF24d6SbeHEhkYH5CZQ+cRjf/nLUrNtV6DyiHrj73tf+Os27wQOvRCqpuHPFetmERoEs5bmyoji3+NlWiUDudx8vsdgo0M6HNzvq6qr8atnfouHtlaKLF6TUS/1daINwsdCJya47NEl2VyOxEiNgF437zF9hFTB99IRpEM8n8aLyaA+Ixdb8spwdKxPBG9IqDdm5mF9TurkTLJIpor4esPrn1054wUrked2kKfcUzg5fjIo+1pnqUNDRsNigPwGgHOLAZZU9NEZrF2UnkoD9CYUrNyBtmPHUL9IaNxUoG9AH5pVmLECkfQZmEAYbX7Qbmc/jqGhIZGkSDBDeq6+f/Eg6/azQiQdAUmeHyVBmeQSj9T46qVLoi8fQYuAsbf3Hj+O8bKyxbX7dYw3v/nNQvbo9OnTIl4lE220Z/ft2xd8nfDhqRygVgfHGUl1juP5VkvfCLz3ve+dc9zTjue9UUrO8vdk+2zw/bR95ysTV1VVJRKvuJaR4HjmmWdEDITHZXJossnTNwPIPSTqXyRVqUGjKCZUBsAQvVyecglLMzdj1NUHl88BiyYLOfrwoNcV6wD2j7Rg2u0UfTVWZJVjY141vrJqDw6O9Ihs0c15ZVHLwvP0Bvx86x7805UL6LbNYHl2Dj7TuELojitBYuODtclJKzVk1qDPMRgWSFqaWbdwC/zGNwAHfh7KFDdYgJW7pd8HXwXsLAVXQec3wp09O9uDTdjnwmVrK3rtEkHVb/ciU8uwhQors9YhR79AgVv2V/FcoLcMqDIB7RpAlcZsFUMToCsDfDaJzFBLC4zTa4XTNy2qhWostXMvUCo1srR6THtcwe+cvVsKDRZ0zIzgxHiX6P+yIqsMKylTlAbwnOhscH5t3bo14feNjY3Fld64KbF2J3Dm1VBDX6K8RpRnz8LkyGxSwzoMnPpVqL/E0FXAPglseTsQIdMTDj9w9QUgpyJmsIYbsT+WlN5iA8+oaLFO4aOnWWER+MrcbvzJ8UM4uPduFBtNSfWq8Md4Pl4lhoycvFwcO3cK5pwMmAN9mSjNJmt/8v3U5+SxEs3c/qvz53GcmeUB9DkceNexYzhyxx14f00Nvs7gLCVTSkvFufO5VCFrX5u1etEY3OZxiX3TyOC//Pl2e7AnlAwG49pnQvJEieBfr17FJ86cCQpC7iksxLO7dgX3yzMTIzg0MgCjRoNP1G8RvakMag0MinOZF7gGs0dIyyvyE9JztZskYmPXQ/Hfz94QX/0LYGpSkrAyaAGnJ0RsHHwlNqkhI5LQ4P0/ehQYGgSonbxsgfVIB04FegkRgb2+5yCwLDY50mObEISG2+fHZCBO32GbwtdaDsCozoK/qhye7h44+wZB4cW/GBzF3zatE2OLRi6JM2XWH+fE470deNw6iL//r2/i7soa3FvXJO0pBrUgNKSzk8bcmIuN6HuwNCu1aiQw4+jxx4HTp6nnBLDxX6TkQ3k18JEvAn/4FTA9BTSuBO59S8xDxtvPSfpfvnxZZCkmCt4TGtAMVLCJqJJI5frBag3u0fOx//gdyN8Ds0OrM53QOFpw5doQ+oYnsKq+ArWNTRLxZche2GqNnDKg+1QEuWicu6eUAnRa6ZClKqFJ8L0MLPFY14vU4Hf4sU8jUC0AAQAASURBVPqNWD3cJYjbIoMZe4uqoxK8C4VsXSZKDAUYcEokNu1w9utbYrkZ7DpNSv4FKzSUhAZxbeYaMnWZKDXePL1CXk9gMoccfJGToOYCq/+UzZMXMT8sVrzcvGByILOb4/XNYAyM1RpKn5sSmAzW8rulvcCfJDqYYDVf35w2gvBJ01S9SBm6aM2Fleiw2cJ8LX/Ap5vyeJB9A2WAFnFjwXFIG40EGyt+RB89rVYk4TIOxeqNJUuWiL1FOVb77DP4xnAXTh45iupTh/GexpWoLSkVc+NWWA/nOkfG7EheRO6plHpjlUU0kDxlBQjvH9cbeY/lcbg/K8nRVGEIkLP0Xx5++GGRLMSkIRIc9GPYAyTZSpIbCfoFiVasJFWpwQOnCgZoCg3RGeLOmRE80Xsi+LvN6cKLQ5M4Pd6DD9RtxyOVcxMRlWYL/nnt/LLooqHQkIfdhVvROt0RaGZeghrKDMXDVC/QdRhw24CMEmDJTiBGM+dZKK0H7v4gMNAmZbNWLAMMZsAxHCA0CD+M03Y4zEb4OCEC804NM/TquVk4mdAg7F4+pKBKk3+BDFjfJOA5HfrdPw24TwC6nenNRFRnSo8Axl0dGHFdCf6epS1HkWF53IWKf3vPkvX4zrVjwYqNIqMFSzPz8f3OI8FA4GUrtc7d2Jy3JC2nTgOGiwwXn1iLYST4ukiGOCb8dDAZ/DQCqhTlVtKBMZJbisxZfhcTo9Hb8mRFycwfaJktzzY1CNgmgPxaoOdk7M/m90kCJAapwc3GuHId0HkS8HpCUnp55cBcTeKvJ+zTwPgwYMkCspOUoRJjOhBETgP+ONAnvjpdINbBGDMrNponxnFXSeKkxiMVFfjKpUvotNlExiyPeXtRETbIzX61Gnzs8H6cd9pQajThL5etxqps6W/9jjE8O3AC0/oZPPPsEawtasTKrCViE5e1pJVzPtHmmaxkUFYJ8v+UySK+tno1MrVa/GhqShh2n25qwiOVqffqkDUzZZDciES1xSL6dtgUGp5uvx8r41R3XZyawodOnsRlqxVLMzPx1VWr8Odnzoi/yVf2yvAwvtfejj+rq8MfBrrwd5dOShJifuAX3a34j417UGVOj7ZvELWbJcJ+sFXa55ZsAHISDHg9+5RUqSEy+QNrgF7DhiTS38fHpDWGvWcSAcfDX/4l8NKLoec+/gngne/EgsHGIGYEUeeeAZzXAH01EEUGcswlkVc2Lk0K8CjTHit8GjVMS0KVNBMOl3BiaTR39fbiIz/7MY5OjiOnpASf2LgFkx47vtl6gV3ngZpy/MBrR4nbjukrVzA8MwZ3kRsaVg0EqhVpw3X2dKJp2Wyd1kjHm/vSLGmoZ54BfvxjqVLj7ruBXTHkBWsagQ//BeYLEhqsQOG58f8MSnCuxtr/GdQgmUHyZ8OGDYL8pPNBaTjav4WFhSIwyMqWoFTFq68ATz4OuF3Arj3Aw2+NLaEVAxWr9gL9mdio1eL05U60DYxjzHYGG92TQEYFUHX7wvRqIgrrgSVbgI6j0u8kNFY/KM3JBEG/YD5VGgS/E9nHuJ4NDlldt6coRZIuDeB17yzcgItTrRh1TcKsMWJldgOMmjgOJ22T9gPAWDug0QNVm6T+YWmGTl0Dl+9s2HNa1dzEvcPrCCM0ZLJmzDW2SGrcQHBeUZ87nsyOEpQNmY/PvwjMCnQlm727iOsDEunsjzWXv02ygnYBK1y5dvP7lO0J+ScrNJgZnQ7QFiFxEq+6IlGwmnKu8bcyKwu/UvxOS6bIaETWIrm5CJqLhYXiIScNTk5Oor6+HtPT03j65Al859J5TBp02NzQhM+vWYcPn3oVk24XvHWVuAgVvm4dwN/n5QupZdrVkeOaayQfJOGVkIk2eY7xd9rrJCNvFDnCecnzJJnJRCjGG0jW0N/iNUSqQTC+dObMGfF3kkOcjySIeP7s18efTJjic7y+eLK6ycBkMgXn/Rve8AZxfMqJMZE60VjjjUYyPkbCngvLWOjwxQW1obkMJjnIzk/1BiUOCL6dQZVB5xROjXdja37qGbDpIjb4SAi2UaDl6VAm+Xg74LQCKx5K/L5QtzxSu1w00g6BR8oenoC9qAYenQ4alRlmDTMF5v5KY5W2axbKcfaPRDSK50+HRG6o5sgI5GLGa+c5J6HL6/LNhBEaxJSnFxnaIli00r2dcM3g2Pg12DxOlJvysCG3RgRulmUV4a+X346r0yMwqrVYnl2Mn3dLVUrKMNSrI1fTRmoQLA/lYsNmo4ks1NwQLl26NHcQwN8H+C8oJJVqAXU9bgg6LkX01GAfmQlgzSagk+cYwLo7gNwoi3q8+1KxSZKkGrwUau0TCUPG3Pq3u94DXHwZsE8BeRXA8j0Lr22eKK6dA575YYB0YWXXHcD2++d+n2sSGDogpOug1gP5m4CM+QdzftXbGXabJR1WZqAmV3rNioyjd9yBr16+jM6ZGazJycHnmppE0IlkwlfG+nGhvR2aygpRjffWI6/gmR37UGYy4g8Dx+HyeaAz6FGxrBYj8EBfnI0mS3QZEW7wiTiYNRaLaMotExs08IudTlFKy/n5bqMRD65bJzUajOwNkCToBM2VOZGh1eLHW7bgsSNHRI8P4tGKCrx3SfQ1aNTpxO6XXhIyXrwG6uU+cOBAsLm5DK2i2uMbV6UglnzNJKh+0NGCLy3fiLSC86lipfRIFlMT4XI58tyU5Yo62oG//SLw119NjNh48YVwQoP45r8Cd9whqnDSjuEWyU5gtr5yXdGz4uQi4O4GLDtmERuFgbUrGiVHBT/aTPL3xv8vLSkNNr/8r6kx/DbTBJ9Jh76xUXzgFz9FtUmLGXkdEQf24UmVDj+58404dvYkTj7/AtbdfZsY6yM9g7CNjCMnfzkOjR8STszevXuF9BAbcRN09DmnaLzz/yQQgob5c8/B/6UvYcDjQTdJjXPn4L1wAQM7d+JNb3pTwo4Js8AJ7tNssvzbvos4PdEnsut3FdRid6FUjUVni+fIIB7nFrMp6XicOnUqalBPdpJ2KYgWuVz+4MGDQYOaTgCdOlaBFF65DHz1b0MHudZKNgd4T4Sk1lzgtZdtgqp0A9Zn/BrdXe3Yf6IFy2vLcPz4Cyism8LyXQ/HuCEeYOw84BwHtBYgfxWgTTJwVrsNqFwHuO1S36lYjeFjgH4B/YP5IiEf4zUIyrmtzklCmoEVpyOsouJcnwFa/iiRG2m0RQmNqhh69Vp4fEym8kOrZp+2uR3tWP0zdKrFTN8bCdoXzKYlUZuoFAjX83Q2LH49Ixld8EVcXyRqfzCAOxeY+MCAbypgwhVJEznBiXYJbSzaHfOphJSPNRc+v3QpXhoeFolOss/Bnhq3Qlb9Iq4vGJyXK5nH1Cr8r5kxOMuL4Zmexm/OnMTx5lOw+RTqGT4frCYjhpevx7p16/Dss8+KecI9iT8ZbGcyFP0GzgEen/1uaMPz73zQn6Fvwf+zQoSkhhL06+mP0PYnKLdE+cSFkFDk53BP5WfwJ6sr6F8wyZi/R/YQoXQXr1vp57P6gDYvH3JTbyZUMXEqXaRGJHnL+CLvKatMWMVOae8HH3zwpp7jyfgYCXsvHDzUFORgmlWS6rMCTmZIU/RAA+joWCVbejd7wSXRYfWkh/G+bhi7FqFP7Adsw4BjAjClUFbPY/WfB0auSeRIphEwSM6B2u+HRUPZpeSOuzKrCftHApl5ARjVBuTqFqoxISd3tA11joxGfvd9LwF2aYNFVj1QsjUhKSA3ZaiiwOWzgXn6U247ftR1QARDSaZdmxnEiNOKe0vXitflG8zIN4QClU5F8Ef+dL43neCiwkWGTkekjiabSfbbJYmCMlOByOTj68kMd3R0iI0hKvz2cEJDoA3w5wKq/Jujpwax5Q3A8q2AdRzIKQaKYixgJUuBjhMB0jDAXDDDm2SExwmM90rlAkpNdhnVWwFj1twGHwnFrY/gpgMrNJSEBnHiBaCsBqiJQWz5vFIAdYhSP26JdaChMczGyhZgHj1YeM+6bNNht5j/z9HqsDEv+bFVYDDgH6PoSbZYJ3G2tRWaQNUGv3mn14ffD/Ti0YrSWfOQcnGDjnHUxCA1GORMJGPu71auxPODg4IMkJvm/U119SwDiYYYs7w4H5lZlVDlVARorMmGWDywYV/rvffizMSEyKDalJsb0xh5YWgII3L/ggBRQYKDJIZSxkqu9uDfp5VVVOJekwyJbGR7g7GkDjh5OPw5kjyc95wb/P/li8CBV4A9Ib3XMNDp/O1vpL4ovT1k9EPyVQTvT1/fwpAaPccBN3tiCSZC/kCgIDNkT7l7pYoNBUpN2bizaCme7L0EUVypGO9NmQVi/+q0SWMoV2fAl5atD87Tn3R1SKulVgtNUSG0fJjUyFBKDfh8mJmYxNGjR9FpnsT6u28TE9rtdGG0ZxD1jQ0oVxUJG5AONscrJUpovHMMyhmKtA/lbCQZrl/8Ai9PTaHRaMR6sxlXHA6Mv/QSdn/2s1D9278BV68ClGtgj42ILC1xS/w+dMxcxLBL0rXO0RXh3IQGR8a6pO3EC/y2/6KQSNuWXy0co3vvvTfsGHQ6mFVFkiJSGoL3KJYGNt83ODgYdDI4/ylDVfC7p2bz5r97KnlSQwnXJCpL8vDYvVugVqtg0GsxNSpX6EaAe2DPi0Cw8lYFTHcDSx6QgtzJgBUafKQAOWMuXRIgi4gD7ucjit5xAipgqCV1UoPVq53HAVaCZZUCVRuClUEaVSE0mogEqwRIDfasa5tpEz6c/Fy1+cZVxCxCAu0Trn+UvUgkiMFAC3X4+XoGa+MnNLLfCm0x+haLjbAjwSz/dKyTi0g/mDWdKhERDakGCGVfPtIW6erqEtJX9FuYzJjK8RnQnEvahr3Y2Dfw8Oio6Cm4KY8JvbeOTM0ibgye7u8VPjk9KDXJt4wMWDVMbgyP1XmmZ3DxTDMySspF1YAc4Cehwb2GBDpJPEIe43yOPjV9ZM5T+hv0LxgrU84DksashGDPD5KDTPjlfpcIEZkK2BsvEkx+YlU4Y+WRnxvLx+B85p7M6yd4TfRfqNoSr7I8Veh0Omzbtk2QQ0xA4/Hp39yshDvXZVbFJLp3JkxqcHDwS2EZSBhjQmPGeUQKmAl4AfcZqUm4JrFg+6rsCpyd7A7GIPk5dNo5QSpSIQJuKGINwBQHZscRoEvuZaKiNhdQnC01Qy2ghEfy96fQmI9teetxeuICPH5qJWZhW/6GhWPq1GWAl9mcsqQPLyV/bsN34AAQCOQLTLUCugygYPWcH6lTR9eN1QeePzfZFSQ0ZFy09mBn4VJkBDT5lWjKLEafg03GmSUqH0uFGQ97wKRv02cTHy6KSjmPCZcVzw4egcsnzTGDWo+7S7YiW5chMji4IMUGM7CjsAj+qRtDaqzaCpzeDzBQKlczrdgsNcLmY64YYkY+sPER4MqrgUbh5UDTbmnh6DsLOKak18nBOv6o2SY569mxidZz584t2OaXNowNhRMaBAOjQz3RSQ3XNHDld4BjUvpdqwGyTAGih2tJ/7xIDY7PHJ0eY5RekZ8DsLuwWJIvShNEsZbDCX12iJDi4SlTZYwSvOOcNsWR70h0navLyMCFu+/GL3t64PR6sTszE6VRsp2op8nMR0HydHWJscQAqCrXgFG/FTq1Fg0ZFTHPye3zoGOmB4OqMVwdakNDUW3c86owm8VjLsRqXP6J+nr8a2urIDOId1RV4e1VVeL1TZk5uGqdhFeumhT78w1YJ+Jh731Ax7UQsWG2AH1D4aQEs2TGR6O/n/Jdn/kUcOKEVC1BZzbye+X701DyH/3zA/PFRZKRUnDcsLRSpYYAjaDoRNKeogbUZRTgBx3N6GIlmZDezMb7atbjQ3VaNE+Mwuv3YXVOPjICVSrSlc0et9laI8bd9uBfVGo1HttyG7ZWN6Cj60XpvgSyBzPzc2B3OLBq86qgYU6DmPrRcvNZGp+RgS/OCWYveUdGkKnRYNDjQb/bjSajEcvp/Pzv/w1QDo1EFL+LV18FfvIT4RQp0We/FiQ0iAn3EE6Mc/8Ox8mxHkFqKD/fB6u4fjUyxPlRT5bZSXQaeG2yHF1kU3UZ3Bf2798vggK8dpm8/E3LVex1e5AVkOiSvtuQNFzyUIkqOr/XiaPn2lBelIOG6hL84fQwlk1OCgcnDI4xBaEhrhbwzEhER0YlkLc0ffKeLptk6+szZlUtcs2THbL5gMfgsRZxHeGcBk78TOrvwzVwtAOwDgEr759XdWpjRiMytZlCckqv1gtCwxBPUmsR1w1cz5gJm2j2KoMdrHyL2WDUT7+EPuqMYv9aBSRQ1fN6AaVJ+IjM3l3EzUM4pUPiSUaqsRQGbaMFPuX9lfYWs6zpc5CckKWAEgHn+5EjR+bU66fvtuMWbCa8iBsH+tyRI97pZZhSAzdtbPihgQr5eXl4bNu+oG+i3GNoj9Nv5jiV+2hyXrK6gXEu2ul8TtkXkGDVNBOBSShwfb1w9iyycnKEXxL52oW5eCfgmwFURkBtFslcTPiijyH8j4CPwWuIti7wOfa5YPKA7INQNpjznPP99tvn6A+ZAjo7O0WlC+MWPC/ev5uV1CCZy/uYaI+ihEkNOoM8KJ2OMFLDR43x8OxOAQ/1XhMLuFeZ8/GW8k14fugCxl02ePwM9AA7C+qxNDN5w4g3oGV6CqNOBxozs1FoSC0DLSXk1wH9pwMxhEAmeUYRYFQ4pAxMnvgj0HkRoEzLmr1A7aooF+IDupU9AgLH8xUA1XcnLROgRJm5RDyuC9hEXrcN8LZKCwAlpzR1cztNNjrsEWELWx+AuUkNvdqCfH0DRl2hrLZMbRnMmoJgIFEkrUfKmseovtheUI8LU12iwkP52heHLuCBMikjNgwMlIy2S5IOWSVARuJGArUGmbHBn8SxsQth50Vy4/jYRewrloJJZLAZSOXCSEab4//kpQNwwIrG2lIURU3alDJ6udhyMyFjnkqGedLIzAXe8Rng+AuAfUZqEr5ud/LNTTe/NXrgRa7QCIIZ0PWxG586bRg99Sy8HR3IM28BsjcBmptUJoGkT7Q1ItrzRPtLIZKH8LBqwwlYuB7657V+yPhfy9fg083HhSHMu25Ua/DJhrl7ICWDxswsLMnKwaDLBb9eLyql+Hn3lJTDojViTXYtmifbRMY6jassnQXLsmKXKlJKgcZSTCddgWKjER9TkF2xdHc76GyMj6PAZMLSmhq0jHfglSMnUNlUI4bkxcl2PFC2Q5yvEm6fGy8MHYLVMwNPsR+/OvFbPLjjfixjVVqSGHA4RA+NMqMRS7OycCebQ5tM4nlWYbBCg9fz1ytW4JONjaI/SInRiI2Kao//s2IzPtN8ED2cmwB2FZbhndXp12ufF2iovu+TwIOPAQ4bYLIAn/xguDAT19/aiKCNLHV3/Lj0IOTsPHH9MuGuAj73eWAByn8FcqqA0YB8DM+JH5uhHBf+uLZTpTkXf7lsNybc0veapzcFCawt+bMdVv7tofIq/LKnM3iH+JEfrl+B9pkJPN3fJebTWyvr8LYqadxlas2Y9jjEfDKYjChvqEKRQQrqE8yCYuNJasFSMpGZP9Hk0/gaBgsy3/52nPjSl7DdYgkRnmvXAocPhxMCrI554QXgjW8MO864m1nA4VCpAvdOASWZ6vd74PCegg8SqauCEUbNBlFlonQU5mrEyb/x+phNxewsZokxa0j38MM4/Y2vo9xoQL3FLI2bPXeE3kjinnJuTgdQ3QDklyQgQ7UNtit/hNfrh9vjQ8fAJBo33I7m5uYwaSyBQJJDGDiebEPAzABg7Qaq70qosjVuZcC1F6QKZMKcDzTdH9aXij4Be5fMF7ynrIBZRBywgqKoCRi6Ei7nWpzintt/UapwVU4kVoXTbkiiWXwkRBWxqUw8FnFzgYEjSmgmSmpwzWfiFDNo5fcwaEOfg+si0KEgNBAYSxcAf5FY0ygJwmzb+TY/vZXBABfJ9EXcvHOC31FMxYMkwYSPVBp8MwhLnzyS/PL4fHh5eFg07d6yYgXGr10Tx2dGeKJkjNy3L9rxF7GI+eDukjJ8/eol+H1MIpLs8DpLJv5hzXp8reUM+uw21GVkierxSEKDiMzCZxXBT3/6UyHJzmQpVihFk0wkCUhfhLbxmVdfxYqf/ATlFy9y0wLe/37gPe9ZWOlwTx/gbg7ZT9oGQNcQttZH9gOJBsbWWfVOSSvG8bgOUJqKa9Jzzz2HO+64I61ztqenR8QXWZW/evVqPP7440KWd5YK000A+hdMIotbKapAUlfAgdfe3h6uSSyyNKJBoaUWA1a3DRenOkSQttRUgA/V7oXT58GIcwaZWgOy9ck31WL27pfOn8ITvVIZu16txtfXbsYdRfGN60m3E312qwgQFBvnEdw15QFLHwS6j0iNPzNKgert4RPr0FNAy8nQRHjxJ4D2PUDV0tkBSzmbPfSk9LY0BCSvK1RmQDs3GREGavMygyx0EECdeLZXnr4GZk0+XD4rtCoTTJpQ8G6JpQgnxtvDYiJalTpqlYY4FZVKSGAov0a+t31maLbxQgLizJPAhEI2YtmdQGn0TNBYJeIyqTHlmQmrKOH/pzi2IjYELu6c+D1Tl2Eq9mByaBzjLm1UUmN8fBoDQ5dEIIpMLdnQhdAdjIqcAuDOKKRE21ngwiGJ9KtfC6yImDdK0BHvOSdVa2SXAsUNAAnQ/nOKF6kAnSF2Hw23E7ZXvouL5y5jx4olwLUjwHgPsPEt8wsELRSYMb/hduDki9L5cW0oXQI0bQi9xuMCxrql4JY1CilI2ZtARjAy5m/Av6m8CiVGE14c6odRo8EjFUtQabYI4/v85CTy9Xo0ZWbOqwpMp1bj5295DH/+9FPozsgUn/fXy9egLkOS69mStxRFhhy0z/TC4ZtGrk6NtukzKNQXItdQCXVEtjIdGJITiZAakYhmWPyqp0f0uZArH+4vKcYu71WU1JRjqKsfRdWlcPjcODd5DVvzw9eAa9NdgtAgtAYtXA4XTg2dR62lCoYkJGSe6OnB248ehSuQdc5qjK+vXYsDe/fik6dP45LVimWZmfiXdeuQrdOJR1WUao8ykwU/3LwPfY4ZGNQaFBvnrgi5IeB4KlIEiT/xOeAbX5MahBMPPQasUZDNVivw138FnDwxm8mW8aEPMSUOqG/gorpw575kl1StMRGQ2mFlgUi4C2T5G5YBgb5PscD5lJuEffSVVetg0WqFZJtJo8XH65vE3AWq8KnG2fvytoIV+G3foaC0m1alwfaC0NhlJhHJQdqD3DdYPs6ybyXoPDPDlxUOR8fGsCsjI0Q68GdLS7QLA176IzDUCWzZAayWvkNNlF5hTZl6nJsMtzNvKwitaS5fa5DQIPxwwuk9D5N2c8RHqhIK7LGJHys2mNTDNaT0be9k9AJXf/YTGLV6VNxzL/CBD0lvICn4g3+QqusIrhv3vQPIoB2kB8rqopPnObWwrHwYSzPOoXdwDBnVDSipWCIyM2fBmC+t44LcCBBk4kJ9oaSQmX4goxwpo+9kiNAQxxwDrj0PLAuRTkzASEelhuxfLGIO1N8O0E4d65Bkxio3Arkp3n9voFosslKNzy/iNQu5KiqRect1ng9mxNJukm2gUJZ4KNFLSSi3tJyDVmcWASkSIjec1HDMAMd+D4z2AVn5wKZ7pZ8LDEqicI+U+1st4uaDPKbTFfBnrwESeVlZWSmfiwz2tbvzlVdwcHQ0KBH1X5TBzcoSGdcMACeakc6AKcnINVFkfhexiFRRn5GFn2zZif9zsRn9djvW5ebh71auQ7HRhB9tUST6JAiO57e+9a2iyptJSAzCc+xGBrY5lletWiX8j5xvfxvl7O9HH9jhAL75TUqfsDv2wnyxlHZXEhqE5yqgzgM0oX0l0dgHKzzOnz8vKjRIcnB/ZdIY+2vwPvA65+q7mShYxUK7nXsz7+ljjz12UxIaqfgX2lTKVsOgjtE9nV/sHITGU30H4BFanEDrTC8m3dPYkNuECurjp4jf9XcHCQ2CQZ5PnzmOg7ffF5UhJI6N9eFbrSfhCTiDD5Q24LGqOZovx0NmCbD8TdH/xgl35VREsFEFXDkxm9QgcZFTEQiOK16f5oaANx0cA8B4s9T9VAwPeVFQAfnJNZY1arLEIxKlxpyI5uUQ3/+5yR6sz41+f0l4zISRLFK1Rut0PxoyFaRZ7/lwQoO4/AJQWM/IZULnzUVNbo6To8uEw+sKEhvUKc6hDEQEGGjy+t2wTzEIoQOGxkWg1eeXlI25trrdHrS19cOSsUq8niApw1K0GwoSGi/+NPT7SK8gHUTD8GiExuEfA7aJgEPuA2o2Aw07AOtgiNhg8GjFG4L60LMO03sRx85cwM6VUnNZMRZICEwOSn06bga0ngMO/xFw2oG6lcDO+4HyWmCIzYSzgaUb2bFLei3laI78NFSdQeM4xyz1C5ChMwEZtUDuSkCTPGkcDVvzC8VDxqvDw3jg4EFBbBDvrq7G9zZtiimHlAiqcnLxmSUNYjOONGz43Zl5C1QjMGoAuw/oddhg9Yyi0DuIavMmqCJIqlQcF8oHRH62zePBu44dCxIaxItDg9ie6xONy0O9WvyiL04kHD6nmM/y3M4vL8T5g814oHpfwqTGiNOJdygIDeIbra14bnAQw04nGjIz8cRtt2FVpHxNDLDpcpU5emPQEecEBh0jQlJriaUcemVTWL8H8FwE/HS+tIC2EVBfJwmKrTuA5auAvh4grwAoivjcf/5H4PSp2EqQdArvvBOomH/D4znBdanpvlBAkfs8753PDqj0SRH3iYKE45dXrhWPRJCnz8QjFbvRaRsSY7PKXISMiAbUzBBkyTeNThrcQbD6pbtL4k4D2VS7HA5ouE7J8kycF9xzWB1ot0t2kfz8cDdwZBA49DLwgU8Ct+1GmakOLdYTYZ9/b8lyVJudOD3RK0iXnQU1WMMqvgB8flYRKyFLUYXAIAbXj0QcDzoAe/bsEZWNTAJgZpWttgF1//0DnDx1CuXKZnuHnwXGFdKZvL6nfwSUUm9eBZizgOWbgfImIFsxVkkiTXai0OBGYX0pUNKAlw4cFU4enbqwrEwR0N4HdD8XqtqIJOyirDlJYYqVsUrIZHloTeT3n46ECPoXPBbHS6JZWa9L0J6p3Sk95ov8JUCXcl6ppCQQ860m+7uIZMAgwauvvppUsIBBFqXcTSgWQF8kJIE7ODiO0VEb6up2wRAI5LPv2A0FE6We+S9gYkjaYyaGgYEO4KE/B0wL1/+DFTEkhBai6esi0gvuYZSACbNlUgSzy2P16EoW/9DSInpcyHB4vfh4Rwe25OYKyRg2Kk6UMGSCCbO/+b7FMbmIdGJTXgF+tyN5AiMWaAOyWoHjm8RGNJuQ5CHJ+caqKmSyL58StLMpZ7tQpIaPaidREuR8k2GkRjKkI/uEsC8hEwhI2MjN0el7nD17VpAcCWHwGjAxIO1t5SsATXion/dSbqDOz/vVr34l/Ave75utYXgyVaVJkxpsFkYmKQxqI6CtBzytEVn5UpZ5LLBCg4SGMgOdmaxrsuuhjRGEjMSk24XDo4OiOmNLXhHyDUZctk7OaoTq8HnRbZvBsqycqBUaMqFBB1yvBp4bvAqHz4V3V6+ZVyAuNmJkikbDsnuAS88CE92SM1O1CShZHpCtcLHzZ/p0k28GuMYDTY0pj8MvhAEfHWCuBHIaAWN8sixRsAG9cuwRlK+ZCFRAsDH4r/su4vLUEDK0BtxfuhRb8+rwVH8gMBaAQa3CgGM8nNSwT4Qy6WXw/6wqSJDUYJUGnQ4GMbbkrcSzg4eDQVFq82/Oi07u+AIkIeF2ueFwuPBcSx9WlmZBr9egv38MK1duhlqzLvg6Wb/whuIi+/JE4MLh6KQGKzRIaCizU9uPATnlUv+Myg2S7Bcd8xhSUrzeQ0dPYMvSJdAog/5KzfuFAM/38HPA4eclR2vFBuDuR4FohGtnC/Cr/wz9Pv6S1Cz8vndG76Fx8QXAqQjaMcBltQM5AaeNwdO6+6RqshTAdfa5wXa0WMeQpdML8jffEB7opH7mmw8dgjVAaBA/6OzE9oICfLA2fq+IuUBtTTodLEmNRLeNEgjhmPGQiJyA1TOELF249AuzqNh8Sna2+ZNlmPE2dBJ/eRFNjLvtdtgidPQ9fhWc3vDjcJRmRgSFiTx9tliHXHYnhnoGkZmbhdy8XFiivDYWrlitcCoIDRmXrVbxueNjY9jz8su4fM8982r6x0qYI6PNQRLm0lQb7i7ZDqOsle45A/jlQK4T8JwGtJsA9XXqyZGVLT2i4eTxUOBcEAmKADCN5b/6X9eH0FBCuTZxH9dEJ5JSARu+syJjPr1tzFojlmXFD3rRWGfz7SBGR4D/85dAT5e4xVublgNf+rJEXkTbY/72b4F//EdJdkqvA0pzpD1f/q6e/KkgNXJ0BViWuRlDzm4x9vL1pcjTF6PYCOwtii7VpgLnULjjoYIhzNl46aWXRALB8uWJJbGQDOUakGc0AP/wNXgO7Ee/SoV9735v+NoxMTr7eoXNFmgcZ5sCzr0MtB4CNj0MlDZINsK1Z4Hp/pDU6GQXNqzZh7MXW0RW5iypCY1Zqs6Lun+rANM8tbHFOhSe/KG0YVhZwQyvMEnaFMFj8Fg8Jn2NRVwH0GZadhdw5WXJ7rHkAyvvi5kIsojXDii1IzcmTgVcP0UTzx4WrE0hN9eH0dEpFBTkYfnyN0oa4wFwXvO1NywblET5+GCEP2aTJKCXhlfupQvsP0rpLdqVi0gMvXYrnu5vg93rwZrsQuwurLxuQTYGSGPJy6YiP0WJW84REv8MHDIBJJXqcErKKsGdeKy/H+a77kJWRgauXbs2J6lBf5eJGKyc3bdvn/CjFkmNRdwK4LzkI1Z1r1AqYSIViQOlL87ERVN6EjejQrG/xXqetiylY9/4xjcmvI6RxKAtLNvUXEeYfMVKjoRw6RXg6uFQDLLzLHDb22YRG8rPYy8PxjdIIKWyRi0kuCYns4cmZWGQLXnyySdn/0HHjMwcwDcu9U/QVEhOehzITY8jF2s2rtZiboO6xzaD9594GaPULRYBIx3+fcMulBnNQmdaCQ4llkFFAyWnZELDFPhYDr7Do10wa7R4tDK5yoA5wYlWvw5oZd8NhSZuQ5S+DITeDKx5s6RtzEHKieGxAhOHAa810LNjOWBJcMDf7LBFNIoUAWcvULAS0KYvoyZLZ4JGpRZNVWWwmVFeoALiex0ncXFqUIzJUZcN3752BB+r3wIji0cCX5uGsTHGDSIzqi15s2XDGFCOJYMUJ3ORBgt/Pli2C4OOscBYzheZ0tGgVRlEM3SXz468whyMDU/AqdbAa1wJndaEFStzodbkzZJ1uuHsLMf3rOdmB2oF6IxESibw9zO/lrKg174JyIuvM0rtwob122Bq/6PiOCopYJO1gFlVJ18Fnv9V6PdTB6TPf8M7Z7/23NHw6+TP88eBux+LvkFZh2f3E2HFTul6ae3IrwcMyZdDy/ivtma8NNwpVf1AhUMjvfj71XuEZJ+MXrsdo65wUogk88nxccwXzFaggxANXgWZJ0O+E54oTZflUlYGNDn2jx8/LjZPBmljZdZRRzryefasoMShskrCY7fDUl4aqI5SCXJR5XJjSN8Hn78J6kDVCHWqnVM26Cb8GPFNory+UlQ+VE4XQpMEUR2rabh8/dwPx1wuUbnBhuCpElrHx6SEBpkMJsl6caoN63OXSTKUQUJDBvs/9V8/UiMeKAMwMRGaH9xXViwDPvcFoKR0VmPqWxWdM9P48KnDuDo9Jcbl55pW4r1LUsuid3idaJvpFLZagT4X5abSufeJb39dqpaRcfUy8P3/lOTAfszqOpvkeIj+E3sA9onYvVsqF/+7vxBkSBjsNmHUy+QDAwOcu4lAr6mH3csKRLkflQoGdaj3AAN6NJZTkjz6+68A+1+B1ueDcD3+/d+k6qA775L+XlAKXGkOz1+hsaC8f2xsz/HY/IxEathGgGllZQSby00jyzca3rNiuguwdkprujEQzJglIaQCKnYD+tTXe4HyDYJYCe3PfqDqtrAsKton6ZDskAkyHnOR1LiOKFkGFC+VbNZFMuN1AwZNmDjFoFAq9j8Tr5glS5K3r3cpdDoLqqpUMLNXI+MANxOi+RfELHnn9IBBZPpu6eg19HoiNL5w9hWRGMV95sBID4acNjxaef1iG+lqLEwJaRIa9DFYscE9jQFDVnnyee6ZiX5WfUaGND9lbX4mnLA/XuB5jjU2FCZhqJSn4eewmkruo8Fz4oOvTZeMzSIWcVOAZPk73wl8//vS77RHOWcefTSht7/APn6cV2Yz1q9fn9j8UGcCmmrA2xlK/KGvqwklUA4MDAhbNlUpOiIpkmFmQiI0lHvbeC/QcwGoji05dzPL0XHtfPjhhxN+fVKeCL8cBn6iZnVrigBdE6BdMiehQbCHhjJTnkGybF0GDNQITgD/dKUZE4oA14zHg69cOiU03Vdn54Zd3BeaViFPH32QygE5XeDFSuPupaH2sKB32rD9TcCyrZJ8THYhsPtRoHqOTEE6G/LGNnEQ8EqNnsVEmr4AOBSBhEVEhcvnwrTHCq/fA71ai3tL1ohxJ6PWUoTV2ZWY9jhxIUBoBO6weNWZiQFszmuATs3giFpkwVKSY2V2hP56yQogVxFUZ/Bh+d1SwD0JMIDDRZHzTQQ7zcWoMBfHJDTER6lUWGLeAIPagtz8bJRWFmPz8j2wj+mRk1sPjTY/ap8KBouZXXLDUBe5qKqA+hgLLaWhlPNSeT3MWj3zG6kKIg54X0sbVgJrHghlnhozgY0PA7oF1L49fyz8d0FUhMuqxCd1FNUpMnitfC3JNOW94P+ZeVm+EWAz+ziEhtU9ij77VQw62uHxzSYOJlwOQWgEzkAQgDMeN14aCg9CFhgMgsSIOGMR/J8vmPUWVVueU844Wy6MUlSESTO7Qi87O1sci/OFGYTMBGEjMjobNPqp4cn5wP2O5IPsJEQiQ6vFf27YELaRbtRosKu+HNlatib2wj4wCK3HhfZr7Th3+TxaWlqC0g1MFHjTpvvx7g1vxb7i7bi/9Hbk6pNr0MreGP9nhdTvIF5m/nzCjkw2UBJHmVr23+Ca2oN+e3v0RPGbCR8M9DqgI8kH7xOfY/+MW5jQuDQ1ht/2teHQSJ8g1t534gDaZqRqLf7+d5fOin43qRAaLw0dxBVrGzpmunF8vBkXp65EJ5iPPQu8+DOgeT9w5XL4usX/t1wCSkqAH/wAuO8+gM1l/+zPgK98JbReUaZk9cbw9YuOyap1wqhnHzeSmnTOqS9LiaJZ50xiRAG1ygKTZhv06kbo1PUwabZCoyDYOK8pp8K9L6lKRWaF7X8l/Dp53i9KjpHAtrskmUDl3/OyZl+fuIcz0poerfG3IAYVz09eBfpekUiNqXZg6EioUld57LKdQFYaZEotBcDKtwCla4DilcDSB4CCxuD5XmlpSSsBIfsYi7jOENVri9UZrzfQ5qE9kgoYrCXBzJ58RpMJJnMpzJbqqIQG7S1Wxt4wFFZKkn+yDCnHO6ujKxamIoxkT2Tz20XEBys0SGjQt5B31id6rogm2bcamP1ssVjCJKhk/4IPSsuwaoIJG0r1k2g+xuebmrBSERClf/UPa0IqIsyw5rFozzAAyPnMn3JCJH0MfjYlfGQ7abG/yyJec/jYx4DPfx5gz+d9+4D//m8gwQpsNhpnHIANvg8fPizknyJBH2FWjEy3HNBvkNSKdKsB/abgHsO5TcKfMYbrFltzyHFhBXg+jnDZ3VsFvOf0B5LxMZLuqUHGSQQES+enOV9nKRPNjs9OtoqgV5bOgn1FG+JmjDBb1OXzwqjRoss2DYpXBf8GP7rtMzBoNPjRlt14ZqAHo04nVufkYmNubKaLTcEpo/LHQeqxhX+22Fz9fpGRn1bQmLrtQemRLKi77Q01iZagAlxDgDF+dvotAXM1MHU5QuO3ANDMo3k7szJn2nBtRnKWmQW9KnsdlmWVo8SYg0HHpKi2qDLnBzIfoh+DT2/Oa0SBIQv99nEYNXqszK4SP8MygjpfAFQTQBYDuVqg9o6U+6BQFuPixYuiPCxRGDRmNGZuF/011NCIfgJ2fbsICjMwFA3sr3H58mUhb8FM9euO5dukJr/nD0r3kCTHlvuiv5ZyI7VbgLaj0f/ucUh9JRjkjwIGrIPrFxuM83gkBiIloHg+p58Fui9JlTbLtwMN3LRSXBBs04DLMUvNIxjcisTyjcBlhdwZP7dxbeg8GVA89Cugv1XauOrWSQ1EKb1FkKxZeeecpzXs7EKP/VIw22DY2YmmzG3QKbT9bVEahwoVlYjnGeT/57Vr8fHTp0UQnZdZa7Hgk2nQXGePmVgl09XmWiFn2GPvFBJsJjWQrSXZsQwmTTZAoobrJuUSI3qJyNIIMuioU46K5drakhKcu3JFzB1mOXEu8ifXCTrqxLuXLMH6QDPkAr0e281mXBq7DJVRBa1ahbJaaV3OKczFstJl4X0oAqDclEXI5aSGv16+HNvz80VFTK5ejy9fvIi+yUl4OzqgaWpCkcGAuxlYThE6lRZmjRF2rwMZWqDQqBYGh0rlRZe9BXRFy7XFgF8h8SAyV6I0KeaY4RxfSPIwEjt3A//yTeDF56W5wqbOy+bRN+smwOM9V/Hf7ReCy0lTRh46beG2AR3gAyODuL0oOXutfaYLTl+ojxNxZboNDZm1ofHLKtlffxuYCug9t56RskOUVQNc2/ID9hfLqf/mb2J/6JvfKsk2HaL8JOBoXI72zbuROzAgbE4GCGiD0oFnSbdctcVMRI5FOusMJCkrOdQqI9Sq2cElOhwMxnEec0/ke2PtjbPAayIxFklqUD5Lhk4PvOOTQE+bdJ/UlB18MkS2s2rDSOlQFZBZKP2kVBSl3IT8oWKDUEpbjp4N/Efxd/YHc0xKfVnEsfXAwBFg8hpQth3QRSeCE4YpF6jaFvrd1gsMHxZrasupP6KxIbE+LYmAwRfaIYtYxCIWHpStYQA0Gd3vWFUbDKbGksiQZTuZzME1/LpDZwDu+wDw6uPA2ACQkQNsfzOQmR45YyW4F5HU2LkzDT1vXkewedyzJKEZg2HMh33eFhr83qIlS6QLchNyzjNWiNIGof3BxAoG7viTNghVBGibyHMmU6fDkTvuwNP9/aJP4Y6CAqgGpL5WQ0NDIuDHXhmJgtUj6bpOJphw7ifz+YtYxIJArsxIsDqDoKQr5yPtf85/zg0SHJyD3KsYF+CcJCnBv9PnYF+9YGUyP1NTLD0iQDv2tttuE+9jHxtlP6oFQ0aelJyirExkAnBO6r7/jQT9PipkJGMzJEVqkHlmeThZ5vmSGlzM1+c2YnV2ncgCZYVGPELj2YEO/GfbOTh9XtHEtMJkRo99Jig1pYEKtRYpGEvZhTeWJS6zwabgGpUfzw+xwXKov8LSrALoFNlLEy7qs1th0piRr5cYuITB86S22UgPYLQADZsBQwrOpkqX3PM3EsG+H8yMTXCo6XOAor3ARDPgdQCGQiCP8jmpM0tjrtEgoUEw4/jcxGlsL9iNXL1FPJTI0OrRlFmIq9YRYVRJQSM/NuZKwcy6jFLxiIrhc4C1WzpfneheLAUYcqtTugYaNjR45squ8Pl96LX3w+Z1IEeXhWIjJWxCY4K6ufGcDl4XiQ0u5lxICAaQUy2ZSxq8N2t2S49EXsum4FXrpF4aXWdm96mhbFsU0JjjNYY5HHLWViRO/B5obw4d++QzknNUk0KpXn8X8NNvAA6bJH3DuSHrmG3eG/099SuB+98FHHpWIkPqVgC3PwRYx4CTzwFDbVLTdHnjaj0JrLkDyKEx7AcKagBDfDJQGjdyEEk6H7ffhSFnB8pNTcHXFRksKNCbMOqyh8kaeaIkUe0tLES2ThdsFM5Rr5RnSgUkFZiRxKBXrPFbn9EoHj6/B26fA1q1QZoDrn5g6rgkY0eYlwHm0DygQx8pn+LW6fAnnZ145YRURbNzfBx/PzaGbevWCbKD1RwyqUGszM4WD2J8fBxl+mJMqWegEjuJ1IOiPqMmKqGRLtxRXCweBAmMd/3oR+gqLkaDSoXv7N0ryI5Uwfu7s3ADXho6hiydJ0BohNazQUcXynN2Ad4WwDci7UeaekCtcHY4Rs+/ALSflH7PLQc2PSTth9cDa9dJj3SgtQW42iL18Nh8m9SXQ4meVqD9krRerNgMZM6uFpoPhp12QWgQ8ny8aA01k5ThDxCNyYKERjS4fe7QGG47C0xGSI7VFwKnZgCV3K9ED7zrTxP7UK7BbAz+7g+hs6MD/ePjaFpSI0gMNtCTsx4575hRJc4nsMbQGaEDQuchEXkqOiybN0ta6myayeBAwpm1XCve/DDwi59Jv8skzhvfHPE69kBTkLlFlUB/G9B+HLCNSu+jJOXGNwWu3wDU3we0Pw+4rBLBUbUTMAWqS8Se4QwRRvL8I0nX8Ahg7QL6mBQQ+O5m+oCuPwK1D4YylOcL9xQw+KoINxFnL3Xg4/e/Jz3HBkST1m9961tpO94iFrGI+GCA9cyZM9iwYUPKt4q2ANdgBn1iNUimL8O/MdjDQBJfn6r0VUrIygfu/7MF/xj2K1iUz0seq3MKcXA0VM1Dy3mJJQvmaL7ZAuD06dNJJQ8mA9rLSh+D/1f2weD/WclEH59JhZyP21jRGoBRo8HDin5aF7u7xU8mGSfbE4cV6vRf5oupqSkxjxkPZCB4EWmEww4cO0THF1i+CqhcrPpKJ+jzsyJD7ltBEvHuu+8Ozk25oopggqOsEEESj7/LVU/xCEwmS8pznr9fFxjMwIY3AiefChEb9VuB4huQSJAGsDk6v4dYCh3RoE3FAOIH3XVXQDt4nmBT8Ll6aJydGMY3mQUYQI9tGrl6NwoNRgxw8tPR1evxl8ti9KVIAI9UrkCu3oin+6/A6fNgRVYR3rskFABpnb6GFmsoMF5uKsOa7PiNZcNw5o/A1WMB59IPdDQDd34geWKDAQUz9ZcVlSUkDMzza8IbBjrNoxeBkXOAzyPJGJRtlbLVE4XXBowcANwBLf2MRiB7bWKBfepEl8ydYZ4oJt3jwcBi8PTgxYxnBjlRDHB+p++r2YjHe86hZWoYZq0eD5QtQ11GAtrw9sjAEnWxZyQJicjeGwmCzgaDNZTgiBWYPjhyHCOuseB1NmTUYmV2KAAs63vOhdqiPKD1hJBxahvpwmRFo6jeuOE9N6KBAfu624CRDsA2HmqMRMIjShY4DbljR49iW2UmVBeeBUhmVW+IPgcFCXluNlnScS41UuOp7wLOQAUFITJ0M4GtdwFbb4/9vhWbpIcMuxX47bcBp4Oi8bPn02A7sCJA2FCfnTIl+kwgk32OZn+HrOaJzI7iNbt94eWSzJT6ZMMmfOn8/kAMzw+7D/hFTyu25pdhWVYo4+29x49jWlH5cG1mBl84exbf3aS4jiTAzzp69GjC+sRqlRYGTcDo4HVMUfIrZFQMdhyGFZOAPjSfIwnDT5w+jQMjoYDtoclJfMtgQGlHh8hMkuWjooFl4CQPy7xleLHfgxJzGQr0eagyR6laWCBQkupra9eKwO2hQ4dQmwaJJTY1f7BsL85PHYLLJzVYDwPJa22c6oe2EyFCg5joA079Rmpidivh2d8B3/23UDC7oQn467+nHof09wvHgD/+VAp+c2qdehl4+6eBHEXFKNcp7gnMqk9hbR0iORoBJnNsyMnGyYlxUSXFNdui0eKxyuRtA45XVmsoYVQbYNIo5onLPruXQ0Em8IXPA+090vXv2CP1LEkC4zYbhq1WbN26VfweLwtQuaexL05MbVie44WDwFVp/LmmtPCTGFGrxf525MiR5ORCPvxRekLAgVcBkxl429uB9XMEBZkdzN5p7Kk2NSRVbWQVhRPq5gJgxWOS3SWSQVSh8ULCgtWDMqQvGbCUS7YFiZDwiwacE4BzEjDmJmj3XQJsQwC/58KVQKDHWBCOoeBaSkftXEt3Whvh8lgMkshZrYtYxCIWFgyich1l0oicHZ4KEvExGAySE6uYqELpHSZKJhO0uJnBhCkS7YtNmJPH3sIqDDps+HXvVZFMWG3JwueatuB6gJU1TNplcDMduHTpUthciJTGjARtNdohJPwohcus7sjEIRkkP/hagvOVSVTJ6O6TiIgmc5Us2B+E41yWlmMF+yLSAOsU8DefBwb6JPuOj098XkqeWkRawHja2rVr5yQnCHlvYnIV500i72FisNJv4Tzl+5WJkAuG0kbgzo8C06OSpLolvQl11xPkGpL1L1ImNa4nTo0PCa1wuSqDG96oy4F/XL0bg06HkIhan1uATGYFzgP7iuvEIxLTnpkwQoPotfeh1FiKYrlRYzwwEElCg5B7AdisQNtpYFn0QHVcZKwCNJmAexhQ6QFLw7zlmcIw0QoMHAv/neddmQQbP3oIcE+Efp++IjX6zpi/DE2iYMa23TsOvz9cRkOGPk7/FpNGh3dVp0CS6fg9ROgLkQyaR4Y2jSMaDGxsGi0rgxUaJDQI+TqvTrdhiaUCGVppXNBAInMcF6wAePY/goGTWr8P0yYtWmZmRAkejT4uzjdVsIGZ0FvfAfRdAFw2IKccKIieuUIHaluODUbOO9lY7LsI3Pau6JUdgoCMMP5SuXbOnTEGgyJQVAZs25fcsdrPS1kc4nuOJINZcRIY033HpKohGTl1QNVucd1+vw9OXwvcfvZrkfoZkRhTGtAmri8RcPl9mPTIlUuhbKrmieEwUuPi1FRwrSb4/xeHhoQ00oYUypT5vTFYOef4jQYvA33hWRLWaQfqV+YA5tjZCy8PD8+6hgNTU6KBGAmNWI3EmMXBeUIwANyUVY/6XOlzpAoSOzQqY1gV1UKA1SSyjBzPlY52IqTmXGA/n1JjFTpt4RIxhYYEHJqhiIbMvL8jXdL8SFcm+VxoPgP86Ifs+g5suw14xzslKaFEMW0F/uffpf/L46P1CvD8M8B9b5See/lJ6Xk5O4fyQ8eeB+56TPp9shUYOgawR4nWDJTtAYzJNVMvN2WE2UQE//+R+kZ02hw4PjaCXL0B769pRBmD7kmi3FSCKU8dWqxS9apRbcRtBRuCTe4FSmsjOF9KMBmAVZuATQlU3MUJCLAyI1EwYEDijpWG8tybBRIaJ58V/51xupAzNA5Ny1FghWR/Jb2nccy8893SI1EoKyyyo8voBRGZQEIbbKot/DmfX1rX81eFjhsNiZJmvQeBMdq5gddPtAGNbwqXr1KsW+3dw3C53TGr51IBj8VSf9o6iTaEX8QiFjH/Cqn9+/eLjOtUE5i4hibzXvoUzIxnQLS9vV0ESJlckkjQ6GYE1y0mtMhk/CKSA8fO26qW4S0VTUJyynKdKjQYqCSpkc6m7rS1U5FZ43uozMCq01ikBpPzZAKBQVLOnWRIjXQkDPDc+LlMtOSxmFBy05Aa9CmuHZf6azLAWzM/pY/rjt88AQwNKNRO/MC/fwPYyMTimyj2couC6zQfyewzlJ1mVZRc3T0XuJ5s2RIiZK97zMxgTk0J6PVKajz5ZMBpv04wabRR+xzk6A1ozFp4LT+bJ0pWqmhOnmAJHzMaI8FFNtrziYDvNddQyRQLgsmI4BMjF2xKOboU+NV3gb5OICMLuP9twNIoesoM1rhmS2HAMXjdSA2Pz4Fe+zF4/BLppVep4KKREKhkKDdWwhwI+KcVRWuAyQ6pOkMO/1bsmPemSoOB5bHRSA1KTkVWohCS/r0luKjOmZ1x6YBkCCgmW8aVg1j6yBcxY7OJ0nLq7zKLZD4ZXWkHA/mUopoLjhkYJwJNEeVrdE4DPeekHh2zJK42AZcPhT9fn0KJPgOBXKesE4rAlhrIjdBY5N8vnpQyeCk1VRylRw7/JrMKHvYl0ErH5PF4zstuk6qFlIQGMXENyK0DsioDhEaPeNpNHVmfL6xvEOcL/LM3YYtGcjKUo4xjLrI8vMZiwWUSG4rnOm02bHz+eXx11Sr8RQwJtFigpmIimq0kDcZdrXD6rNCpTcjV1UHLHhoREFPR2QF4BgFjPaBXaNYHwB4UPTZbkA7h3SgyGlFWNvu1SoyOjoaRB3Q2SMrklRrgMnYGWyDm65chSyeVvqYbzz//vAjuyuXrDBaw8dlc554oig3VYowMOjuFc0NCo8IUx4Eb7wbGOgBXlCZmHGv2aamJZzoxMQH09wHFJUyTkZ67cAH46EcksoGfe+YMwIZwn/ls4scdHwvvpSDLDA0Heomw5JdScUqQtGESA2EfAgYPh/7msQO9LwA1b06Y+OY953z9RP0a/MvVZpHkQdxdXI2dBeXYpVLhXdXzKzemM708i3JuNUJyigRdGKFBFFYAex8F9j8hrUsmC3DnOwFjfGOaBBsdYmYbRmbo0ojlOE1G9pAZv5SQiiuJekWSkSM6RyZRU5ADXDkuSA3ez6QahScLjpcjvwfOH5LGXf1aYPdD0SUPY8HBpIXIZkxcSFaFCMHsWqm6ViTO8HW0E4sAfQLZYW5bgNBA6DModTXWAhQr9lZWm+myALcVZy93YfnShrSQpTIoT0NyiuNgkdRYxCKuD7jek1Cgj5BS8kiKoF9CCRAGWpnJygdJjnQSpdcLJGJZhXJTVrXfQtCp1eJxvUCflr0r04lUxwDnHve/eCCpQdJDlrfhgxUesaSlI8G5Np/+OUzqov22fv364Fpx04x5EhoHfhwwYfxSIpVzBlh2C8lj0ZeItEftNimZ0XydpHpvcbBfDUkIqioowRgWE6BiqZ7Eq0pif4xEwfmgnBPc1xrS0Ff09YazZ8/i4YcfTuo9SVsvLNkhazWXxn86sa+4Ck/1tWKG7HUgdLu7sAJFczjPiaLHZsUfBtrg8HqxPrcYtxWEM84WZlNGQQYrDxJt3kLWjCSGvFjR8SxMvO/HdUXUrFkV8MOvA5OBoM7UOPCz7wAf/CJQFinboA48lMEfVerVCuKeUc4m0Fhzzpf7Meg4+f+z9xXgjV3X1kssWWbmMYyHmTOTCUwYmyYNtE2bNuW/kKbcvr7klZlem/a1TSnFJA0nDfMwM9uesT1mtsX0f+scyboiSzKNPeOVTxlLlq+u7j24195rQQU7dCrAq1Kh0AjYvSkwqvOQRs8Jw+g8YWJCZwJmvVNmOVLbOrUYSIliEGS3SD+ElMyE2HcuHgLGqOHftc9phcVvbqBXUyqIFIcKaQrShouYuLp+lDQJn0xp6utxiyApHwwuc1EzqUiNBKGi3Es00EsnnNQg6E/BzGMahTOgP+cCoGSEmy16Yzzy66BRLEmOi64P/p6VHH/9iT8gqgI2PQ+888PSV0OJ0lky45i3ycP7SWJDB5TPA+asBnLLJKkWDX55ElZoBCAOIbK8FUQPVHAj9Fp1Oqx4uPEQ8gwaMU7aPCQRVMgxGHEpg5sKPLhiBS5/801YopBoXz1wADeXlGBWgmb0JAkSaWusPmm174LD2yeeO7y9sHk6UWJaC42xCrAzy9nvjiNIG5v07LHQa2NlBLHxg0WLcNVbb4lRLIDvL/RnRPvh9VegtDscWJqRgdT+fhFgZekpNx6UVWA5e2q6EW/u+weqZgWvU5fzCAzqDBg0YxvM53djBmSIHq/RKPRvxwpcrBWbKsUjLlqPAMf95twcv5TjC8dyEnONh4DZCiPi0eL5/wA//RFT8OTY+unPAu+4CXjisWDmUwCPPwZ85p5IT4xYyGOQ2CCrLwJgny7xE1QauqiXAJ0twapMoshPRltbw4LTfp8EygSZ4hvJtdgG8NMT29Bqt4gqqXeXV2Nmag5y9CbMTM0Y880l/TOG9YGpWQpUL5LSelyPDVNxw40G5w7OZSTnqRvNPhKQ6qCMAoNqyWhEs71z4Rs/y1JR0eL1QaOYcwPXjFm2JFwC0g5x0d0NUGaPBoDDXfc9rwP73go+P7GbEzpwsX+xbh8Ajr4I9LcAWiNQvR4omBOlAjQcNClUmJtTsooEv5AOpKdXBlC6IbGECs7zUV93RlaQFF0J9B3C7uMvYPGSkevwD7fHoHbxTTf5vUamODhPvNJWj5ODPcjQGXFdUTUy9ROzd5rGNBIFZW+Y/DBav8yRgIFZ7i0C+4upiICO+jSmFrjuSJQQSBRjlSTRaLViU2enMAtfk5KC7vZ2sb4PSG0yKYSBW+6T2HcTMSNmOx3NOpFrJEpiT8oYQO3OIKERwImtwJz1U6dao7wC2KFIfBKVvZlS5nQaw4JzB8llSskyQYa+GaycC7T3nTt3CnIilu9TNJBk59yYDML7F1UTSGzQmJz9dtKQgJMYjKWQa4gpKRwDSc/A3HCy5I1Z48rAyXgix2DCz5Zcgn83nUCfy4HZadm4qSRSJmokaLD048sH3oTbH/B9vaMBHQ4r3lESZNXMWjPmps3BkYGg5Ea5qQx5hgRL/hjoWP9uYOPDgJ3ZqipgwSVA8SxMSmTPkWbXShhKgZ6Noa+xXx4/EElqiEF4AdBHmbJA51UDaSMICDvbAAsnKhegMgCpKwBdlInbx4AtA3cGIRfmhWVoDlP7mP3BhwcV5rFdvEQFDT9zY2RbMNC193mgwS/hRlLjgtsl8TUMOAiyWoODolL7e3/fCRwbDN4ruxdgmGNNziIYFfrnzHaPS0LmzwDOHFN+KJBZEJFRKsxZ644Cz/1LVhdQs/2m94+5Ge6Yg/qCbk3QQCmAfn9mRPhEw8DX/IvkY7SonAN89L+BU8dkALVmEWBQBKQ2PicDpWIx7F+QvfxoJKmRVQBc/n5g05OAtR9gRrNeB1g7AEuPJDUMMe6DX1ed5tWBJR9tOcLpR36+QZ0Gl9ct5IYcHje+fWQTuhw2kRWuV6tgUKuwKrsM75sxL0L2b01ODg5ffTW+uG8fHm1qihB+Ozk4mDCpwYwgBrniweHtHyI0At/B43PC4m5DunkRoMsF3L2AvZamG6H3mlUbYaTGhvx8bLvsMvyjQfoKvKe8HMsU1SKcL2584w08f/iwIHnVKhV+f+WVuNufYcjMDsrokAjMKTYiLcOM/t5BZGSmDn10t/MICo2rQhY4XY5BnHH14XhnE2pySpJe/FCPPrzsnf2eRshnBXUbg+MeG5rTDWj8VUUkHQRfHSOgOhLwfv34h0FCgfP6L34KUM4oCiksfv+jHwHM3Lk4Ackkown47JeBn3xHnj+hVQNbXpf+Efz99XcBj/0f0C8lATFrCbDC75sjCIIoG95hpBCHTtXnw4+Pb0Wn35uHffHFtlqxHqo5m2MvK1VMqXG1xhsbG0UGLqsqAlVMmzZtEoEsbjKOHTsWUrKdCEiMUKoqbuZhzXJg98viR6vTBZNeC9RIs3GCwSgGNxgcoGnnsJsehwP4r/8C3nhDPmfb+vnPpcdGNNSGybVyjK87KEkNttMDT0lPKOG/ZQOOviQNxDMV5ErWHKCvDnD2BkmxnAVA85uA2wKfPhvNpw6jJFex8XX1AYMNQGYCGWKGNEmcsGJDSbgxKSMc9O/IXoptB1vGhXhgif/TTz+NcwW/q9uDNzsaBQlJbO5swvcWXYJ0SmdOYxqTBCSY3377bUEyj0Qug9njYwF+flNTU+Lk8iQBA8yUHw3Ifp4TYOb7/pdl3CKrGFh6DWCcmvJgsTDW0jBMzBhNJUQAr7a14fr//Af2AZmMtiA/H5tuvhnper0gTbimYvIUKzsYdOX6KRFSg2ubeB4fwwUamZwSr5rkrEEkZ4Str8V+31+5Opbo6QE6O4DiEiCW7OlIcP3NwOED8jG05/jK1CFlzgLYLpncRMKPJEZAWopjMV9nYDwguxZLOjrWcUmSJFPZQWmrcHK7vLxcVIiQPOUefSx96MYNPi/QdRAYOCWSmVpdeciZsWRMK7OHAzkG7g2TSXIbEanBIAs3njRunShSgygwmvFJlu2PMZ5qPiECVAEJB+JfjUdwY/HMkIBSVWolcg25GHQPwKQxIVOXmVzAKbsYuP4z0l9Db5JeAJMVNBUuv1w26IBRuDdK0J2bcxI2UY8xV/p82FukkXnqTJk5mAw8FmBwWzDk6nMAA1uBzMsBtSIg7GNQP0g4WdyhixTeJhIbatUkYLpplhsgNAhbH7D9MWDDR+L+KTMyOECzXDZQynakPzIrP0OXhdKU0GAEA0pxFyKzVgN9HdLrhSDRcuFtEW9ztjTB9eojgigSbeDEQeBPPwU+eX9y+vQTBVu/NCWmOWtmIdB7JvT3gezt8V40UG4qXHIqgH5KU4VV0pC0iAZWa9z+JWDn0zLL3emSfiK7n5WZuiVzgOI1QPPWUFm0VJmBp1OXw+mV2XDU5S9JUaPZRn1+GaTtdxnx2Bkp1zI7tRTZ+kJB9AYgaRcf5qZnCgnAWEbVd1VU4JEmKXMVAK9wooQGESizjoeArFPU13lf6fnAh7M+yn2OnlVFEkNJZCjxx1On8PzOnVytiDbPT//4gQO4paoKGTqdqNDgg31Vo0pHZnYa2lu6BKkRgNPXhwF3A9J1kqTc29uA/7TsR2PTcRxIs2CRox3vKF6a0DzDTQ6ztxgQCDep5GZ7LCs1Egb7FKvRAtD6jY/9VWUSKqBoDMtya09E9iPiEIPIlwIvy8B2CJ59BnjqSeDTnwHuvDP+ZyxdCWRnAIMW2aBJkjWcAl54CrjpDiAzD/jA14DeDoCEn8JvBulVQM9hKTsVaHep5YA+fsVOt9Mm+qGyNbD/HuzvwEquL8YbrGxy+qu8DIVAFGm3aGh+6CHY//1vXMyNxjXXAO9971B14ooVK0T2FPs4jWpZjZjMpoNkfUIZgwv8mXqUoUoZgGrF1UN+GgQzDwMbEpoIDlv58X//B7ylqLw4cgT41reAn/wk+vsDPkchr/k3BpZuwOonvwJgtUtXfSipwUrBymsBzvduO6BPA9o2D23Ye9rPYOe+41AvrEJRfoBcUQH2DjI68a8PP7PySuDUq3RRl8+LVgq5wmjgBpHX6Xvf+x7GGtxffP3rXz8nzMI77FZBaBCBPUaPy443OxpwQ/G0HME0Jg+4zli6dKnw1uDePplxmONmMtmvw4FBEyaFTEVSg/PROYP+DmDTP/1ymz6g7aR8fumHzhltf0rUxPThGiES3bPEW8/f9uSTcLBP+SunjqhU+P6xY/juwoWir5KEZCIGM8CZIJIokcKkxJGcH8kMZk5Hk+Fh3yfBMtbXMmnQQ6NN+sAJcM1XMHPsPfse/ifw29/IPQ7Hyfu+AawdgT9uNPCef+2bwMkTUnaqcqZMXpwA2D1unLH1w6jRotiYNiUqCnwdHdh+771YZ7VCy/34vfcCflKDewO2W65VhWxvkvGpkcg2h0tPEewXV1xxhfiZ+xyeUzI+OGcFHbvlXtWPk7s24/jJ07jo6ndNyMeTY+BeINk2OKKRN0BqBODzeeBFJ3w+N9SqTKhV0Qc2h8eBHlcP1FAjx5ADjersB0AtbleEF4HT64Xb54Uu7PzSdWniMaqMxqniRM/NrHJDy8G7ag5Qfyyo4c/BfNEwmZUp5fIxUrg7I3LIhdyOow7Q5QMaloSRmQ81rY0wd/YjzzAJsgu6QoO84loOdEovi2jBjzCQ4WX2+tatW0UghnRcBKIMAhzM42aQcKG6+kYpu8TzScmIunjVN5yAjoE8ZTZ0e7N8FI2PT8CI0VEP7H1KBn+ONQBLQiWEROAnr/LsL9JZ7dRyKtRzo2CYa8lr33Q4MiDfeFCSGnnzgfRSwN4rA2CmYOBPr6qASq2Fy9siqjZy9GXIN+TC4bXhrY7DOG1lv5M4NtiEPH10woASZ8PhmsJCfKSyEr+vD3r0/GjRIsxM0KArmTJpgzodGujhgTIbXwWTJmzhoOdGuSnKa8nhcHe3CLh5Ff2J/iSnLRYs8mdss7/x/CkzZdbmA77OiK5JmSxgBgbdDjzfcgBut0dIaWl0Whzqb0ZNagHmZwxvwMfFGjPembFOIiX8GjLr8ayAXza9EOhnINxPGmanAuTHrINSknH5NUD2GMpdZMcoE961E/jGt4GeLwAPPggM9Mtxi2bLnC84nv36V8DLLwKf/Ryw1O8jwOzTR/4J7NtDV0bg9vcCrDSgvi0rNIbgA1qbg0/ZLnJkRUIINAag/Fqg55AkNgzZQNbcuIQq7/GzLcdg1qqGnrMqj8NFit/rZlzh6gV63gR8/mxcFbP1Lwa0w2+2Tv/5z+j64Q+xLOCdcfy4rHT48IfFUwbOktGqjTYfMruYxxh2o87ru2A9PHPXwsO164K1MQNqcTORtm8P9VVhxQ4JzlhYejHwwkNhr10i/61TEM8BiKqmKPM0q3wCVRfdhxQZiEBX7yDmVhXDToI7vL0lClbyzb5FSk6xTQ0TCKCZKQOZ45FxxmMyQ46E8FTU1lfCEkXWi1V91rGsTpvGNMYIzGxlVir396wIp99FIuA4Hk0edyRg35/0AZ8YpMZZq4gdDzQfU/gz+feqJDoGOoCM0MSZqQrOMTSrH0uQNBhtNvOAy4VuEmQKKThW6h71V20og67JavUfOnQo6UoLtmtmrK9fvz5iT8b9BatvW1paRmSOPqaoWCI9NI5vkeujwpnAMoXM81hg/z7g/34dfM717DfuA/75aNC/b7Tg+m/WBKiKKNBg7cUvT2zFoF9ydFEG9+8roJ2o2AiTlr0d/u+fC6jiJ025LRZsuuUWLLFYoGW7PHOGcgXAo8F7MWvWyBVxWGGxa9cuETfjz4mA/jbD+cGR9EzGM/CswOcDAv6zfui0Gqhcfu/ICSQ1ksWISY0//elP4mcSGQ7vDvjgz1DwqaBXL4JGlR/yN/2uPuzopnGz3BSnaMxYnbMaevXEVCx4fD5RkWEIC+ouyszDjp6gxjxLxGemZkIXbVN5PoMDxrs/CbzO7PA66Qmw4UYgYxyN2lnhEfLcH3h31cmHJgswFUZUFaZqmQ0X+lqargIGzSQoC2Ygj9cyXHOzuxHIT0xSjRUbXESw01OlwpYVGtquSIkMEoZkPbo6AQerFVSAcQagDaugMZKUjJ1xYTaZYHO5YQoJ6kUnU84quCDf/1yo3BQrY7LLZaCVWvgkNBZcjbOOC68D2pqARr+eMPsXfThiQhXjNcXrhgz5CH+XSgUdSqBSs++qoFWliNdMmlQ02bojSF5KuRUYzCJLnNmmHCONGo3wHxoOPOZvly8XFRs0Cl+Qnj4U8E8E4VJrw0Gt0iJLX44Bd52ozvD5NMjSLYJeHdaOUxYCukb/2KL2G4UnTrwykEfdzjyLBV7KsLW2ysya7GzoVCpRoaLc3HCzz+uQo12AWu+hsKOpoGZwmHJVTkrm+dB2/BTyZ8rvzOvcFc1c2w8GEVjKygypaIQGwXNlWXp49caEYc5VwMGng9noRXOA2VcEje3HGosWB8dX5Rjb1grs3SN//5/ngfvvo6O63LCT0AiAet6f/Qzwl4eAikrg5z8GXn9FHovj5/atwC9+DZhJzlhCfGhQmCA5ozUBeUHpo0SwvbsJ2zhHDEEFoxh+Nbg8PwFvk9Gib1OQ0CB8TqBvD7C7FWg8IeWnOIaVBTfYJF7OPPoo1oaZgeORR4ZIjdGCUqis9mCVEn+OZ/jJDT37ynCIm83F6i22BSWxQcIrFioXANd+EDi0Vc5J9CGZtUz+ruu0TNxgmw20W/5bGMe4NGz9UFKQhZc3HcSaJQwq+Nsz5SezkjRA5WdTPjMOuPagUeh4lKIz65PHZuLGVCc1ik2pSNdSEtUxlH7Cvci8jKkXtJ3G+QH2aVaqce1ATXImT8Wrwhhp9nc0UHKCpGlApnAqXTchzXuuIOb6bJLt80aBZCtDJ4LUoKoC1/RZJhN6GKTl+eXkQG0wYFZYQhj39AGSIRH5N54b9zCJVlSQsOCDa6KVK1dGTTKjzC5Jj2QNfccFPL85FwKz/VUT47HHOHwocv1HQre+buxIjQkG1+q/rdsRkoSxv68Vr7bX4qrCCago9Q4CLib4BD5fC+jWAOrhY3aHn3oKywYGkBZYszPBqK9PVlKPkTQq5z/OhRs3bhQyVsPJCzLOxmo97kVigcmOY1XVOL7whTwzmwxoHbBM2Kdzj/GhD31o4kgNBpx4o2dUqYKEhoAPTu9BGNWXQKXI9DrQt3+I0CBsHiuODxzHgozwzOmx76y/OHEIfzp1Am6fD6uz8/DjxauQ7ZdNubqwSphuPtdCI1mgwpyOL8xeNa7nNGXBa3bVrRPzWV4GwU4DJoMcqKjFLoLyiknK0yMnk7A1iVnD9qWCgwmPKj1SteVI0yanyzZumLkGOHMYcClkWajvfeAZYNV7gbT4mpgEtQO58Th24jjeOLQVpposGI0mzE+vxMzUyOwqBofZZyuKdMDA9uB1tNcDGRcCusSNkMouuxa7X/sPMkhq+IMwmZXVyM2fAAmUZOC0hcrfBMCKmMs+PTGSU8n0rTs+BXS2+s1niyK8TELA856xGDjllwoT8AEV8U2V3F47Ohy74PbJCcqgzkauYYkgBvRqLdxhBt8pGj3+e95y/PnUfpy29CPfmIL3z1iILMroxQEXwetyczGSwlyWaAZkYYaDU3yfw1CrZCWEuKMqDzygTn1Yf1Jp4NbXwJcxe0SltZzzGDid5fPhbZUKL7a1MbUQqoYGPHDNNchULFYCJdnUr2VG2KJZF6PPc3xIE18NLTL841KGLgWWHukJYjDL60qSI0sfufnghp9l59zQMEDLsSAWuABj9tRwmSPj7mOz/N3SDJlShVG+z5iC9zQnV2rdKl87fgL4xMfl8zUXyMdLLwV/HwDHBG5WXn8deFcO8JpCroqvs2+8+DzwkXuAX/0w6AdCc7+rx8/YuMHWJ6SmGBBVnvKnq1cKz7FxhbtHSk+FY7AVOMhAvQ/o6wIe+w3wns/Bm1ss5pra2lpURlu8h40vY5EhS7kUZgqS3ODmOxYsAwNIp//Jrq2SjLziBmBxkGAS1VfKzWo0fPSjwK5dwZvA93/mM8P/zYy58hEOJs+4nNJvRhyOpTe5gCkO+ZtWBnTtHSI3UkxGpKbnoM5WioKsMul7kTFLGo+P8Xqa2aFPPPGE0C4eL/DYjz/+OO666y5MZejVGnxl7hr85Nh2dDltog+/b8YCLMwITfqaxjQmG7huoAQUx1RKcDCZKhYYxGVQlePDWEiWkCBRGobzmPz8ySxHRymjgJb7OYGSucCxTf752r9Xonxv+rlByLKtjkd7IknCOTJZc2Gira1NeOCxQurhrCzcuHEj7Lz+ra2YlZ2NL4dlnXOtwiAp1RvoBRbv+zJQGG9PRdKD2ekE+328eZ4BXB4zrmfnRGI89/UkLqKtEacooUHYvW4hb6sEr2CDVelTOY5wH+H/FC94APchQB/Z9gLrc1Zo29vbsShaEtIY7zECe2j6YrCyi8R7NHDOGk1lyKTqP2mVQH9Qym12ZSHefukMVvb1DUvajAXRzHvb0NAg/PUmhNTgF2KJODOpyquiBdE8/gYa3NBa3KEMD7OB6U+hRIdjEE3WHqRo9ZiZmgfNGOjg/auxDr+vZyBJYmdPJ764bzv+sHL9UCn43ZWL8N7yeXB4PUjT6iMWZZwMHF6XkFsxTITUw2RFdxuw5QVgsA8oqgAuuFpqho81vP2Ai4F3ZsfKQKQwUo0WoKbnR5TMEQYEhecDnEKCZtJoA1J+bPmNwK7HJRFK01xqzfNn6mgnSGoEMLtmFirKZwjDVC6m5hRXRP2uDHxysQJLoKRMYQZqPSKJjQShyi3A8vt/DLzwb6C/ByivxomaZRBL3TOnAYcdKC4DjGfZw4SB1CE5J/814fNANsJYtgmaAvd1Ahk5QHryi1l5PmogLwliaPGV0p+HZeIkamZdIMtto8FnBTyUIfKi29UDN5/74fB2o891Eln6OViZNRtvdu4fkpbi/5dlzRQExr30XBkjvNbejl+fPAmH14vbyspwZ3l5RLtNRFOdhMbRgc1I17qhD0v+t3qakK6LXGBwcUKSIZnFB8kJbhxIDopKF5UKz61fj1fa2tBmt2NJejqyrVZRekozMILnzk0u+2ZAG9LgMcPm6YYaGqRqS6H1+xLYevpR3W9Ay9wg+VCTmo/56ZHSUyQ0Ei3LDMjpjHU2WtLt2hS2CGpvlPMJ+0vRGFcafOYe4L7/lmNrgKRQZlBu20qNG+DW24BHH4n8+wDZGUtOg8dasBT49i+Ak0flODd/MS82xguZOqOQH1CCTb18IuQsvX7vEOXH81w6+oLjq/hXhYPPPYbemuVio33xxRdD09ER6jXB63r9GMsB+FHkl2qgwRzJvvBsSZIs5Uf2ANvfDL54/DDw6a8CC5eFrPeGBdvOQw8BzzwjCejLLmM618hOunwpUEtvDOlUJOAYiC9Hqc8ASq8A2rYBbitgzMX8S9bhwNFaeHOXjUuwhvITzc3NYh3BCrGRZFEliksuuQTPPvsszgVUmjPxv0uvwIDbCbNGN3FyDtOYxijBQCXlqEhSM9BA2ZpY0lA0KWYiSiJmxfEQnoRBU+S44/IkIDUmvaxIMqCn4vr3AQdflV6g2SXAIn+VbRh4b3xogc83IGRj1CiFahLIiw8HmrozIWKswWOyIoLJTMkE+hkQJZkXMMa9oqAAR66+Gps6O5Gm02GNyYTW+nr4CgqGfMToq8HqU37mcN5iXI/RV4B7k+GSoALzPPt5rMBttHufkK/ZuYJLNgBPPgEcPSJlZrkGvO56oPIsJY6NAQxqJjRq4FSoWjAGkKmfIKJKJFiGbDBkzCIM7FNMqGG7Z9VECtfiTE7r6pJEBtdW3OsmYeydKLim5lzIeXDhwoUR/YP9gKRkInGFKeEXV7BGKloMnhYxWF3+Aixe0yUI2/EgNXj9WA3D6/LGG2+I+zuSzxlxvSg3rGStbn/P2hiHDd1QpmhSYKHxs6LDmLXBrIYDfWfw78Y9Q2Z6M1KycVfFmhHJQNET4wdH9+PJMw1wiqB3EMx03NbdIaSolJsLg0YrHuGwe5x4sXUXmu1SPmNmajE25FNea3JP2GMOBq//+XOZVUgJBer/dzQDN39s7Flxb0sU2SneR17zMAZWzUXkQEjHsIWQ6CSkOqCLU8Y2oUhJB/RRAmDqkXVHBiwZ5KQZMIlGbjo4sCqDxENGfr4o5dFe58g8IO7+fPD5kcPAn38uTcOJlFTg7s8BJWexQoYTdLRqjIwxrijZvxHY9ExwUl5zbVAzfTzBsXH+JfIxHLwMkrG0U3YMpzjN0GviYNUT1YLSy2DS6lE32CpI5bnp5ciLImE1GrzY2opr3n57KE76bEsLuhwOfDZsMcD2ywk0UJXATEDqxyrlYTodjfCICkCFDNAQom+C2Q84Wba2tiYkcUCNZ27qw2VrmHV7lfLvs7NDsgsJZjqyPwb6okmTJx4BnDlzRmwimNV191W3oNXej1Z7H9J0RlSb8yKIHmajJ7OB4IaHWeyTBtZ+YNvzwNGdwduzcC1w4U1jN49ceBHwwG+AzZvk8z88GNkUjh8DvvcD4O67pdxUXZ0kP7gmYDD88sulP8fMGv/v/PMOF85r/Gue3Hz5mACsz63A1q5GNNsHhCwZ10k3Fc9FagI+TKMG504S766wuXdnaFuv7eyFPrda9E+SgIJUuOMOSQL961/y2l17LfDJT47bqZLY4Fixe/duIbkWCBBwE9HS3IwL94Z5WLDNvfFiCKnB/h7Xf4o61p/73OhPOCU7lDDizy470N0A5MfRp04pAGZcKxM7NAYUqlQoLBu/jTXL6jkeMXBHgndYM/VRIiB/w03iWZPOG0MweSpDdxaJ5WlMYxRgMgc1xSk1w0pRJjWGVyWQ0BjPLNXJTmpw/3UujFUhyCoC1t8Z9754fEfgAz3FpISiFy3QgnJFkzdOQgKBQVLOM4G2xcrmsZA9Y9D14MGDIqs7keAlSTvuE8IlZCvMZvEIID8rS5xvYA/ANQqJilhrlYBELUkWnksipBulpBLtx0xyOKeqkxIB4yg//1/guWd5sYDqmcBll2Oqr0/eU7YIfzm9R8RmSVGm6wy4iibrEwFVGuCzhy6EVaHtimtyyiFedtll2Ldvn2h7Qm72t78FvvUtKR1MU++vflX6Ho7HaapUwr+PyYv79+/HRRddNLRH52uJeNWwv7ISariK8kkBtQYoZPJkMIFyFPaHccHryD0Gx1+u+8kxjASjIjW+/vWvQ6v6Jby+LnhBw1NCLTw1woMxCzIWYWfPdnh8cmNs0BgxK1UOnCQeHm/aO0RoEA3WbmzvPoV1uYn5DCjx02MH8c8GaqsHQ1zK0zGoNSIglQje6NiPFrsM+BEnB5uRpjVhTc7EmvicdTAIRckkZWZmwzGgpwPITiKwQ0JEMLCsvkhCNoP3yzQPsB0YGvh8DLboZ8AH+kPIrFG714eBcGnJMVgLu70ebO46gdOWTlFJtDZnForiSUTEQmoekFkC9HIR6G+l3PAWjE4/mgsWMskcEEg48ucAGDgVhmKDbYDTb9wbgG70mVWmfVtRu2MLqjP95JHNCvz918CXfoizApqvn2AGLJ/4GXwGLfm1E/QuSQhdrcCmp0Nf2/ofoHQmkJe8AfW4wMMSwmBAkkHR8OJMjSoYcJmRUiAe44UfH5PVQkru8XtHj0aQGtxwcEMQWGBzUcOFg7LqoMV2Et2uVrSr6ZsjX6PZdlZWOsqLYksbcvPJTQcX8Mym4udw4c8MQeXx+ZnM8B6NmWC0bC2WVlI7N6cgG0tWLYRJmyrmzCJThngMt/lJ1FiZRCbJm0mj33nqIPDGw3IOMLLyziMD5Qc2A1ULgZIxXEDPnScfnKf+8Q+m5QV/x7Eg39++uTn81QPAz34GHDwI5OcBn/w0UOqX8Puf7wA//C5w6ADAzeUHPwIsn9jFaJfDin807sGAy4osnQFz0/OxMqsUc9JHP24nBHouce7F4eD8b5wFZLcAtnq/magKbQMWrL38erRrTEF9dc7b73+/fEwQ2Lf52LRpk+i/JDq4iZjPDZDSX2moci90wcC/ZRbncNq5Ywc/6R6+HPWvk2P/mQ/oOQB0+9dDujSg6FJAP7aZwhwfGcTk2MgNJIndRx55RAQ1E83kHAkYtGE23FtvvYVbb50gydNpTGMaMcH1CccAJpdwPCVhHAjic5wNvGc8QDkeeoPx38laDZFsZv65A6uf0IBiXzkIH1qhQmSl8WQB1/ncKy9dunSIFGCiE9tZPF8tkgX0e4r1PvYD7reZ1c31x1BSk8kkEp2U4D6A6/RonniJINr6nmt/Bn4JzqPxqjOUSVbsY4n2Y/ptjEQiZsqDctHvnAQeImOI1TllyDOacay/E0aNFquyS2GeiKQpQjcPcG5j4Mj/ggHQhSYRcr/OtsaqJJLsQ6b0ZWXA7343oWQo18EkVUiysKKA4wYD8omQGpy/JjtBP1FgshkT0DheMQZz9dVXi7GHFRvf/e53J5bUWL9+vcja6OjoQl7ecj+p4YIaGVBFca3P1GfiwtyL0O3sghpq5BryoPVnpg+6HHCLjXEQZAu7HLKyo81uwcbOJlFlsSq7CBXm4TOHn29tUorrBIkN//OPVSeup95k7Yowzm20dmLNCBVmpiyECVW4BgVXs0kYo3mtgHOHv9SMwaVCQL8kspxVXQR46hUvcNOfAWjLAXMO4OkGVDp4tWxzh4bOy+MzYoBSDGHQq0eXae7xefFU826cJCHg/7R6SyfuqrgQeYYRLLDZ9pbcBNRtAfrbpO581QUU08dYgBuN7u5uEQDmwottnYMuX89MXwr0bwXcfuNefRFgTsBMtOkksH+TbAc1i4E5y0OYwlKXBV0GHTqsduSlGGWgq6cTcDrkAmC8QcLC2islO/jZm/4GeFhVJNur1WKHwWQGltLcfoQBe7b1Q5uB3jYgNQtYcCHQHVZVFEBXy+QhNRCUbaPKSYrahwEx3LLf+aCCBhn68TcD40S+o6cHDVZugkJh9Xjwm9qT2NvbixKTCZ+eWSPM7JQLfW4gwsmFXGca6q17xc8mDf10VKJZ2jpT0FavRoc6NJvcJ6SIWuF09sFmVaFq9hpkZGaKzGP2E5KBXNQTnGQpKcAFzFhu1tkveeyixWnocp7G4YHT0KkMmJW2Aina4QOpzH5P9FxY4TLSjdKYw24F3nzEHwD3g9n/Hq9slL0dY0tqBMBr9ZWvAv9zv3zO8YDyGUqt/vQM4P7/if73rNb4/k/Omv+Oy+vBA7Wb0eO0iaQPp8eDvb1ncOVEZVAFYJoF6AsBzyCgSQU06cCNlcCr/xZzwwB0yLz+UqBoBiaLWwBJfc577G/UhRaVIyvWAtveDjXaXuEn/wcHgRdeoGkNvJdeCox3hRPnE0oWOmkU7gPoUcW1EOcw+mIMh8EGoHt/8LlrEGh5HSi/cUzbKSu9KAXFcZBjJNcQb7755oizqJIBP4OfNU1qTGMakwcM6jDL9LXXXhNZ2lynMUmDFW7jBY7dXPsx4DxZSQ0SO5NG5ngC4UO0Sn9me49AAWCCQak0JTHBLOFEKjVI7FHCdih5QwGfZxA+Twf6+weRk10iyA1WnPf29op/SWLwcxnQ42eTlBiJ/0YsMEDIfQyTn5KVnWUyRyx5uWiYDs6eW6gyZ4vHhENlAvQXAr4euTZXs4JZG0EaBwzuJ4NvBf1m2I+5Rua/yVRexPXtO0+wd+9eMT5yTueYFfDs456NHMOEkhoc+BjsofbVbbfdBg3idwSjxohiUyRzT6kNnUoDlyI7jURCriEVpyx9+K8DbwlJKeLfTcfwX3MvwOLM2FtnfVi5H7evmVodVufk4+K8QryjuDzh70lJKpcI6EtwyWI8H301quYD2xWmqdx8p2cB2QkGiD1WoP9NwOuQm3edBvDSFLkW0NVESkrpVgLuo4CPJppZgHa+3KyLgEoqfL5BP6FByACFRm2HXq2CU+hTKw6nyEJPFju7T+PZlgMiYBoAj0598wO9TdhQEIUQoBxE50GAFT46M5C3KNKwU3ggjF9ggIwxAzrMdGTG6bJly8RiKjNzHpBxEeC1y+vp1/QfFg3HgSd+6+eOfED9IcBmAZYpzj8zBznmFBzv7JGkBkED27HwXHHYgI5GaZxtMAJHNgF2C5BXDsxbL70zNv0LsPgrqrILI/xX9p9qxfIPfg3IGWHmEMefV/8OtAbINh9wZBuQliO1+xmcVSJ1kgSTCVW2WCy4fUC3j5sNQKtSiTZs0hYjXVsFrXp8/U84WX101y48WK8kKyVYNZen1+Pz+/aKn3l+jzQ24Il5C8TGIeBREQ2Z+gIUeWvQYj8Bm4cEJGWblsFYFlkSfezoUWicB+Fzd8Ks1aJmVhYaWzaio2M2Zs2eLYgGZh9zk8F/mZ3MTUcy2pfxFivMeuYx08p0aLTWDb3u8jlwcnAPFmasH7ONMbOomEUyKdDfGT1LXlRQeYDMMaw62LObLnJSW/X6G4ArrpTZPDt3UvAYuPwKpsskd8yzFKxosvWhyxkk6n1+kv1QfxsKSIbHAU0mu5x25BtM0I1Ww5VEBh8B0EvkuveLvr1/82aR8RgT7BcPPgj8+9/yvt94I/CJTzBChvFERJDivR+VhvW7t8q56ap3AOsuBXp7ZTVJSwt6LRbMfeIJ4P77x9b/gz4yTA4gyV+9ENj+GNDbItdSJPtIulDObC69kuKMx7aWsCQTH+AakOssrVlWetjb5XENuUKeKlkInXSfT2hxcywMyNgxmDnSLKpkQDKF1eDTmMY0Jhe4Jrr00kuFfxHHBpKdNPJmpndJyfhl53PtNBn1yJmle35WaXAWMkeRhmay1PiZyY4lARBXajIKSGaE70tY1djf2wi49wmB0MryNKSktGLfno0oLZ87FIglMccsc86t7DvJVFIzUWk4nXlWkDB7nPKN4Z5iiYD9KtGAK4md85HEm0YQbMOdTjt0KjUyR5u8ShJDlRez3SfbRycC7GNLlixJ6m/GLdmQ1gAn98rYXGE5UDyGiiTjBJJUJDAI7tM4/rz++uuiumykRO+odpSXX345Xn31VUFqjAb0zbildCkeadw1JEFVac7FquwKfP/oVkFoBF5nQtsf6vfjf5fG1rD7QEUNvn1Elt4F8NW5i3FDEmRGAKuz5+DV9r0hxrkrssY/q3nSgZ3k+g8Arz8OWAeBgjLgmjtlgCAePDag61VJUBCUHWEQ2KQHvP6KgXCocwD9cGY/kabhIkYWMseqoFWlQqMaWQXEaUsXnmqmabI8VuiRGVyKIhHBkzj9KmAJlOOqgIEGoPpGQDOxMjAcJDgwUFOTxAYlHaj7KjKdNElIf+16Xf6rzG7d+UooqXHJdcDh3VB39QYDgDffNfpgICshXvgj4LDKi24yyGMKo9oGoK8DcA7IKo0AWElBokEBtVoF3Wh0yDqbgNY6hWSJR1at8LxI0vHjyBrwdzOXjE/WeeCzG/YCvWdk4Ktypaz0GQ6aasA3gEFvR2AUFf2ED6+vb9wJDeKp5uaYhMZl+fl4s6NNLJDd/jZWZ7HgDZcTSwYGgm02BgqNVcg3zBDeGlqVPuZCW+Prw8wZ7INBT5VqTgnmPBlc91c5MfOCpMZINgWxPpsbBWpwsgycm5vaQVldooTDa/V/B92oM6MCG6aRfIdxQazqSn6fhevGbgH2wvPAN78hDfyIRx4GHvwjMGeufEwx0NcmcD8H3f6kfpJj/T3YEKck4pHGetx3cC9cPi8ydDo8sGwNLshJrI6C7dDhsUKr1kE/jEwkz4sZNtzgD2u6SWNtZYk4n7PPjaO/RlSQ6LrrE/KhxF/+ArS1ifaoZpvk43vfA66+OjHihe/v7ADcLiC/MNj+Aji+G3jlX8HEgINvACb/WBGoXqL3yJwrgLQECD51jLWEWgd4HEDbq4C7P/je/A2APjPpsWzDhg0hrzFoSWKWhMN4g0FTBoBY5s+suGlMYxqTBwwyrVixAtu2bROEBscLBnbHG5ON0AgEqs45P40EoVLpocEieHysHJR7YrWqBmomU01yMNlvx44dopJztOA+ZWYFJbqLQl5fskAH6IPVDyQl6D/DxOBkpWH5d0OyO2GgOgP3GKzQGOm6n8FWtuVEAopMOJsMGfPTODvodtpx794tOEi/XQBXFZTiG/NXjD55KgxMzmUl4KT3oEgQJL9Jpo45ofHEA1IhJJAktfZ6YPFFmMygkkw4yCmQWxgpRkVq0LDlnnvuwVhgfkYR7jFeiiZbr/AsqDLnCvMadhyl1wZ/6nHSUCY23lNehRSNFk82nxbHuK20EtcUjUwKZlZaCUwaPWoHW8SxxsM4Ny64CaZMAQOoWQWA/ixlhMxcJB9EYy2w/Q1JaixeA+QMs6ATutthMlXUUiexO4IMQglzhByWsN1Q18DlbYbX54BenYl03bwRZxPUWbqGTFkpfcahOnAsvjYrLXTxImDrVBAahE8G3ftPAVkTvwDg4obBTYKLbhIcZEGTuib0UgknBPzHHEJaBmwf+Qr0b78CZGUAM+dJM/HRgpI1TltQriYkqOsDzhyVVT/K7xMr7uu2y79vPAA07Jd/U74YKAvVbowKmrcOHT/KB3AiX7BaBmcr/VVF44GDLwFN3Dz4ddibjwAX3gUYhjFr4ySnWwaPd1NQ+s0PjzDnGn8c6u8X1SEB0iKAF9evh1GjweVvhW6GOdb2OJ1Yvny5CJrGM6VVqzTiMSx8MRYSrFpSgBq4LIFkSWkyYJZUtHJvBn6p789+F8jQ0IrqsbDxC2po4nwH9mcuiOKVlSdqgj6hpAYTEfa8EiQl03OBC94BlIxhRsn//kL+69f4BhePf/oj8M1vYbLD5rFjV88BdDp6YdQYsDRzLkpMOSgzZeBwfy8c/tg3W8zbXc1YknUGa2JUnu3r7cbXDuweal39Lhc+unML3rz0amTHyaiyuHtRa9kNj3/OztGXoswUfR7lxpY+NHE3wf/5T+hz3n++NtGkRiwwGOcfmwq1WuyyWLCUcz4lqeJlVbGN/fg7wM5tflOiSuC+70jpMoKZj288Ju9c4IZwDRtN57pusyQjWNk5YzWQFWMOzZgN9J8EvIF52AewCpMERtd2wD0QfC/f07UVKLo6qUvig6xQVim2CazS4Jg8nn4aAfAzGHTiZ9555/BmtdOYxjTODpgsxUAo/+W6hMHVgJHxWGOyynZwTTZe33kqQK3KgQoMoHEtzcSiSZJMk0CmMO9bS0uLWPePGtH2U2GvcV1Osj4ZmacAuAaLtg6jN8jp06eFZONoqid4Lbimi0WcKME+n4iHwDTOTdx/aBeODASTSV9qa0KpyYz/N3Pk/pPRxnsm0dCQ+1wBExsDUnTJxhhi4tAW6e+qTJLa8hwwe4WsqJ9CIKnxy1/+8uyQGmxoHEj5oHEL4fI60GI/DJunHzq1EUXGOTBpEiMBsg1m8SDsHjdeaquFVuWFQa2Cwy8pxCDzTOrZDwMO6u8snSEeY4GylDzxOCvgIu7tfwN1/soTEhqXvx8oGJvvNiIc2QM8+rtgWcTWV4C7vwwUxiCOwgmNoddVgDb65OnxDcDt6xCBPq2qEOownxb6tqh88+CjeelQ/nkV9JoK5Goqkv5K3c5BdDkGka4zocAo26tRrR3yU7GTg1GzyNYHs9aADfnzMMMcZVHijaEjmoz3yDiB/YLMKCeJaAxpTFQvAFpOwep0oaXfIiTwUTEXqpPSr8DtdKKkdh9a3ngBNdnpwNJ1QEHJGJF5HdFJBCUoB0fJrwBUwwT3T+0BDrwUfK2rUU4C5X6yLhYYPKRkGDNxY6FqwfiW/NkH/YQGggEyEj4kaWbG13/XqjLhDiE1ZCXTRKDabI4gNHibZqamIlOvF5nkAy7XkHk4pbEuzMsTWXnMACdhMBrDayFZoGO/VlT0BBA2P3Fhz2oN/ptMyWtArir8c7du3Sr0oJUlp0WmSnQ7W+D2OUUVIMeZ8pQ5UIX7C4WBGZHUth9Okos4depU3Oyzg31deLWtERq1Cpfnl2MO5QTHE0sulfNW5xkgJQ2oXAiox7CkmO2r35+drpw/u2NUA47l5x7cLBeW/Lw5K4GlF0d6RQ0Dr8+Ltzq2Y8BtEW3B5Xbh7c6duDx/HT5etQYf2/2C/6NIsMu/2djZEJPU2NHdGS5OBKvHjSP9fVhHiaOYX8WLOsueIUKD6HI2wazJQI4hdH7nxpz9MqFsvWj9iK/x2j39BPDc0/Ikr74WuPnWiZf74nz4kpwXZhgMyNLr8bZWi0VeL+L2iof/CuzaLn/maTeeBn7xI+Ab3w8mBbjD1gXOUHNy+bcqoL/Rv67qBw49Cyy6GUiPEmyhrGXZtUDvUbnmMBUAaVX+z+MYF0b+u8L6xTDwiWxbSntKotkHrnMWCHKDGw4mM00U+Fn8zGlSYxpTGW6vB2ds3SIZqsiYdU5JGNP4mHsKrpfKy8uFoSvXOslWVHBuY3DZYrGE7Fn4OuV+aEw+WUG992T9C841qERCztj4Qk4kqOlOicUxITVUmYCvXTH/siQ+MimCwUzKQQf8+0ZD6jE4WldXNyQPORokSohQbi3gbzCN8xO7ejpFsm8A/Gl7Twf+3xgdnx5NrAJkNeC5BsbODx8+LMYAyvaOWsZtsC8gvYEh8N5YB6YUqcG4BatyRkNijYrUoKQN3ehfeeUVfOhDHxIb89PWnXB4uSjxwe2xo96yHTNT10GfhMSJ0+vBd4+8jSZbvwj4GDTyflEzvdiUik/NXIbxBBdRRwba0WofQI7ejIUZDKqfJe3A4zuChAZB4+VX/wbc8ZWxDQglgxcfkUNYoAOx87z5DHD7JwCPGziwFejrAnILgXkrAX0BYD0ZKZNgWg+oI4Oqbm8HbN7Ad/bBgXqYNaugVshIeX02WNyn4INXxBF06jIY1JUj+jp7ek7hpbaDQ89XZFUK0mJJVik2ddWhX2Tp+2D3AvPTi3B72fLYg5AxV2ZLhpAbKsA8BgumEYKLD24U+C8Dw3E3G/y+xzZKHXxzFjB/ndDpO/WfxzE7PxMaVutc+e5gxdBb/0HjK0+jrXcAxToVUre+JgNWV906uhOnbwYXyoFsVMqWsVojAN4DelrMXgPse8EfRKQGmc7vc8HAkQ+nO3qRn18AZJcAe8MyhonaHfFJDaMZuOxO4M2HARv9XBB6HnoTkDPOEhlhPiFDn80KlASQppsFt7Mfbp/M4lWDZEICVSpjgFvLyvBwYyOebA5WMX1/4ULM8C+Mn1i7Drdv2YIOpwM6lQo/XbwUa3MkaVhdXY2TJ09i3rwEDO1jQEgxGbMBYzpgP+B/laVdiwBNqHwX5Q7/4HDg0d/+FlnV1fjq3Ll4d3l86UKSL5RKUYL6tqzQCJfP0quNWJCxDh2OJiH1k67LQYYufuYWDXuH09QNaN2SXIk1Rrm9Xnx5/xZs7ZZBS/aoJ5vq8P1F67Aie5xtnouq5GM8wO87fwFw+FCwUoOvJal5mjRIZmx8Kvh82/OSKF2eePCXZEa/O2xcodyPrRVz02uETJvL6xUSVIFt7ZauTrTYLCgyRW4uM3X6ofeFvz4cnF6HINoiz68bHfWDInjDB8ENeYh+OtcBJJAofRAuF3f77cC3vx352lOPA7/5VfC13/1GEkO33oFxAysr2DWUFSvveQ+wbx/w5pviaXpGBi76xS+w7fhxQSQOKy1ykFV//mo9YSgP4HBgjAnAX50U6JJOF5BaDAz6xwtWvKbowvUzgfZjgtRgMCMiS1OXCuRF2fBp0wAnJQEUGxz6bCQMrtWUlXOdpLDg880TBMOf/vQnTCSpwb0F1+TT+t3TmIqwuh14pGkLupxyfE/R6HFb6QXIMcT3RJoqmaeBOYFgcD/uHqPtJHCKezwfUDofKJkr1k48VjSpOQa4KK0zGY2JeU5dXV0icWUaUw+cVzi/jskco5sPuKxC7lcePA3QRu5bSADSlDsgQ5XMuSrBqigSGmMhnxXrM6KBOviLFsXZM0/jnEaaVgeHMyjBrk5gfzEcWL3AhD32xYB0MhUaSGifa2Af43zB/s8YAQnJUY09ucVy36SEzgCkJZ6oOBm8qri/WL16NVJTR55sO+rWcsUVVwyRGnZvPxze0I05g879rnbkGhLPstjd04JGm8wsC2TK69QqfKxqKS7IKYV2nC/8v5sOYGPXqaFMxyUZxfhAxTCB7NEGbre9ILNXKcex5prQhtjVHNRIE/BJGSoycCMwI2YAzeaxwaA2QBdLlzkeaESjBBeaA30ykPTYb4CmWkm40AC2/ghw3V1A2mJggBt9rzT7zlwr/40Cu/dIWKahGw5vLUwaOYly8WFx74UX8jz4Toe3EWpVOvSq5ALLvU5LCKFB7OypR1VqPirNefhE9Xps7KwVxEaJKROrcyqGbwdaA1BxBdDwOuC2ygB7yTrAdPZKkykNwsUPSQ0upIbNsOA92/QPoN8vx9F5CmivQ+/SW5AzcwU0JAfC+9/+7SjLSMWB9m6YdRxSfMD+baMjNfjZLz8UWoFBEs2nkWbhJF6YpbzmZiltw+qt1lppJl6xBHDZgP0vwTPQjQa7Hutv+xTQcABwkihRfo7f2D0RFFQAK64BNv7bb3Ds75Psn1d+QBqjjydSMqXMlEOSxgIcF3ISq9pSq3TI1q+Gy9cn/k6rzoCa5lwTAAZlH1u7Fs+3tqLJasXSrCysUpTrX5CTi7prr0Obw4EcvV5IUgXAID6zGuLC0wW4Tstroi0GdMGxYMhfwlAG6IoAL/umGVBHZtd9avdu4f/hy8hA08GDeA/7jlaLG+PoulNPOlyWhZ8Zyw9Epzag2JRcZQ+zGOJlZDFrcrjs+QfrjwwRGgSXpfRq+NOpw+NLanBs2fwCcHSP9ClYsQFYsGpsP+Mb3wQ+91mmfMjn1P6/6wOJ/31vD/DTHwIH9kpT8bs+DFx5zfB/c3hblNe2JkVqBDy7Il5X0f9GhQ15FXiyuS6EqGDyx89O7MIPF0VmtVxbVIoH60+gdnBAxMlZJbU0Mx1fPrBJZFddll+Kz9QshD4sMYIeGkq4nC7UHWuAz3IIG5bdjsyMSkGMR8yBNNr+2peBQ/65dMNlwFe/DgSqq266SRLdjz0mx00ahd9yC/DRD0Z+6eeeGR9Sw24D/vhLYO8O+Xz1euD9H5eG4WyPP/4xwOpDSk7V1ECVmgqGCnbv3i2IwiECh7//+9+p8Qawn3HdE7gegX85BnW0AXkFQMtpSchr+DtVcC7LnwVceKuswCPBsuuvoecr3irHeW58uOngOVDua1hkLgYc7dLLTBxHA+Ss9n+uDbDtA7zM7EoBjAsBTfg6sivKQbtEZQ43YevWDed1NrbgppbjatLVpdOYxiTBW51H0M11px82jwvPt+7FnTPW41xBUnvj5mPAjseD8putJ0TCjsWREtNknGQHqz+4j5ls4NqUCTfTpOvUBas0KNk66moNysrq1gK+wSCpEaNvsMKptrZWzOus3IjXfhj3YGVsuOwUJaDGqu1xfo8XUByJwfk0FHj9FeDPvwcGB4D5C4HPfikoVTqFcE/NAvz3oZ1ib08wGdHjs+GOrc8gz2DCJ6qXYB5jmnFAMpsVCwyoL168WLTlSeMFOc4gocnv+vbbb49OYmv2MqClDji6Uz6nqshV75N7mwTg8Xjw5JNPivMZjSfPaPHyyy+Pyk9jzEiNBx54IGpZnMvrwwDlfO3Hkaprxey0BTBrow+YfS4b6gY7oVGr0ee0hamNS6TrDMMSGof6enDGZsXM1DRUpabD7vHg20f24vmWJtHx3jujGp+eOW/YqosGa68gNKD4/L19zTgyUIZ56WNsBMZM8qd+C/RIg0p0NAPNtcDtnw+WDKWkR14JBlFHUFLU5WjH4f598PrNvKrNc1BoLBNzbjwt9xCUVQH1x4NECw8wowaoOygJjUAAizi+F1h2CVA8EzBVATTXDguchIN+GIriTXF4r0KXklrPXkRmtbq93dCrkyM1uhSbDSg+s9MxIEgNSk1dVZhkhnhKPjD7NllhwO96tqp8FFkhR44cEZm1zCZvamoCfG7A3SHfoM0BVP7Br5Pm221hJWy96DyyCzPXxwju+QPQq0vy0Wm1I89sSsxAfjhY+qQ5txLCA2MBsO4mf9ar4rrmV8rHEDKA9e/Hnp07sfSiOYBBDxx5LXicwHcjmCkW+O67nwOsfUBqNrDiRiAzzJeg+YT8e45DykBW+ggXJZQloz8JCZF47YQByJXvAnY9Adj65Dgw+2IgL/HMd8ob6VXjr4ceDRx3rxtm08CxvSSazrx/U0tCLqYZsbsTsG8NPve0Asw410synUHJoYwPtVE+ooCyV38+xQowZnLrAW6yz5zBH+vr45IafX19EWRCwM9mLEBCg5v+eJJYPI/hqlre5DwTBo7WfcI7Zxzx1jPAzteDz1/4uwwmzxnDykv6iPz170BLi+zzeUmQNBwPvnM/cIySPl5eSOB/f0Jxf2DlMJlw0bJHk0woTdOakavPQpezR/ypSPxXqVBmkm3ufRUL8Gr7GXQr7hFn3zpLdFmhFK0Wj15wCf586iSa7VZ4vC5s6W4Z+v3TzfXiMz4/O7SKRQ0NBk+pUN95BG6XG1qdFrPnV6AyOwVmjQUGbQxZwR98FzjKZAQ/Xn+NkQLgowpD7htukI941y6Gbnq9pRVbu47A5nGiyJiNi/MWIYVJBIni4T8B+3YFn2/fyJIM4Na75HOOvzU1EX9GDWyOPQI2G/DBDwKnT8v3P/ssMDcGMdnRLkmNwLgudMMU35f92JAiH0RWOdDTqCCsfUCeDORz7vZ63LB01KHRcgpl1fOAlBieOdoUoOgawNYs12jGAlmpwbWXZTPgI9nhk3MPn6deIgmO4AGiHFQnNhzr168XY/FEgZ/Fz+RnT5Ma05iK6HD0DyXnEfyZcrfnKrjOYuUGDVGj4sQW/w+KsfD4Fqgrrxj2uJS4VlaETAawgoRSPNNVGlMbbFuUuhkbCSruDaMnMoWDVeiUb2eWejyfKkqzUd4tfH6kQXmsxKmREHScb+MFoaNVU00jAezdDfz8h8HnB/YD374P+MmvznqcKFlcW1SOHL0Rr3WcgdqnwqGBZjTbBoXE4hnbIL5xeAt+sWQDCqlyoQDbK+NRDKQzbsy2RMWf85UUjqe8kPCYc8mtwJKLpZJIdqFUF0kQjCmQWGWVBP0/R+vNMxKwLbBA4jOf+cyojjPqkgc2Rppk7d27F0Z1OgxCToiMnQ+9TmYIMmDiRZ+rD3t6tsM1ZGwYRKO1Bz8/8Tr+fWYvHm7cjd199SEnJpQC1BrMSMmIyRx/78g+3LHtdXx+/zbctPkV/P30SXzv6D48deY07F4PLB43fld3DA+dDpNBCsP+Xr/ZShi6A2bFY4nWU0A3g2+KrGtWYJw6FKziKKgCzP5MuoBG9+rrJBOXBCgrcbif1Q3BcrFay1G80PYSnm15CTu7qaMd/N2weMcHgDzF5E9joEtukOceDYHXef5xCA2Xtwcun2w3fDCD2MPiDlXw3tNnIxKUZojcjFvdFhzu2yfaXu3g8YjvmKGL3KDzbmToRqlDJ5gi/aSYqFgOHsjw4EBl0Plg63gRsO2Uj8HXAc/A8N4fw1UzrNkgvmdOWgr6XG4pobF6w+hOOip5qQoyzwleV06cIvPEyZLgcHJQBWQUALPWSiJj878AS6/shwNdwMZ/yKooJUS/U5AZ4jzoDZMkicNz2fsa8PdvAQ9/D3j6V8BAAlloaXnAxR8BLvskcOVngcpzT28yGpiNRK3mmHBFGdddxyMrNZIFA48J3Fua5lGOMRzcJIVLUnl9LtjcZ2B1N8AtpBrjg+d/3C+FMxwoMxeT+PHDHIVwZCtemjnO0lP7Nke+tj8Q4BhD8J6VliZHaBD04zhyODSoTiJxy6bh/25ulGqTef7M+ATBcfnC3BWoTClDujYV+YZcXJp3AdLonSAqndSYnZ4tPMWG/oaZPpS9i4E0nQ6frpmL7y1cjr4wiTqOhK+2N0VoMzNjaHbJAmxYNxcXr1+A9RfMRVW2SRQZeDEM6bVnd1DyS3yAD9ilIBBi4aprI1+7+rqIl1rtPXi5bRf63Va4fG402jrwfOv25ORIDuxRVLz6z/HA7rh/xj7MgILAyy8D9fWyjfD78hj1DZF/xHZT5CeAiiuBDCYOqBVJKWagIox4nH01kD8H0JuBlBxg7nVAhgwelJeVonHPM5iTdgbNR99C/9GngU6FLGnE5+sBcwWQWhWUnvJwbrOGOa14ALciiUEgQJIrKktQhZdeekkkMU00+Jn87GlMYyoiU2cOqcTjT/TuO5fAoAgTRwh6X1AbOyai7TES9BxMxuNsIkBZ1AULJkbCdRrjB66/SMaRpJpoMKhLUiMemKwUvscgGTNsX0sCrFTh8eMFM5lVH06uTCNBbNsc6i/H5N/aE0B3tOrYyY/VOfn46pyluLqoGANupyA0CP7f5fVgDxO2/eBafceOHaK90ieDFQGsxGXl8flKaATAZMUzZ86M7iC8hlkF0tc1CUIjACYNUcqOHh+8TxONPXv2iDgHOYWzSmowULRhwwaRSaVWqTEjZQXStHnweHVhyYo+uHxO9LoiA3ePNe0VHSAAq9uJZVk50Po3gUaNFp+euQqZAQ3/MGzpasc/Gv0VAv4O9YNj+0WFRnjO3wutYdnfCrTbLXiu5UTU5MES09gw4SFQBgHCXz+wGfjd/cCjvwTa2qWkzpINwDUfBuYmr59ocQ/AG3Y1hAGbfyw5Y2/Fob5jiR0sLRP46H8Bn/omcM93gTs+KYPNhTOib+7zEzON5vkMuKmbGvo6yQ2dqizEkCzUP0PktUKvDp1o7R4bdvVsQZujVbS7BmsdDvbtCQmE5BrSsDYnNDtzdloRalITq8pxe53odjai03EaDk9iAcrxAr9XIkGeGQX9aGhsVPyhC7D7AyT0nRDZr4FJhlUJGvgyh8nMoM8LPSwCQX4SEor+OCKY0oCyOaHVECKTNrms7qHJ0pgGiI1k2ORZuUwGmdrrhzw4guaqdqA7bKKZs0a2aeUkPO8CaVaeDOiTs+/1INHS1ym9chIJ0gkPj5Sz56lzlog5bmiHMqbDwcqjiNc8SZEaW7racNeO17A6R4eZqWqpFkOkp+Nyt1tWOEUBswdZBh4ty4skBDcBAXh8DnQ5tqDffQgD7qPocm6Gw0PN+uFBI8NEdHMpERMvo/l9FZG/r0lLx/+buRDji7Okh83szkT6lZDOC4MwbIqTQLBwHbD2eiAjF0jPBlZdBSxPntTVqXVYnr0QVxVehIvyViFLH5rE8cGK+UIGLRBq5vroUzMXJ3bsKKblgbLxMwMd+MkLf8Zvtj0GS40Z5sxsmDVGZOrVSNdJ+StCq0gsiEBqGKHHOYBVEPHwrtuAuz8qK2wKCoG77gbueE/E2+otJBVUIdnOnc5+DLhDgxBMWog5B5rCyD5+ryh+JCFwu6HasQPG/fvhJbFB4iuccLc5gcUKcpm//3/30MwKsFpk+7nlk0DVfNlGymcBt34aSEmNJMxnXQas+iCw7N1AjmKNM9iA5dVmbNlzAisXVmLP4dOwt+wEnIkbgCcKlTAG5/cp8j+WweXMwhtvvIErr7wSEw1+5uuvvz6mVW/TmMZE4eK8ucJHIwCtSoMrCxIbt6cKGAxlxUJgzc0qjZhVFUXh6w8VPPnVUzKwxe8YL4lkGlMD9Iigb8tEg/sSzm0nTpyIub+hTFU0U3H2GSbtMbF4NGBSFvcpS+L4z0X19ppG4oi1B53icktMugoHV+FU3iGY2PfWW2+hpqZG9LPJRk6fbbBia9SkxihRUFAgSF2ORSRaDxwI9wUcX5BDIJcwWg8V7VhlUj322GP48pe/LHTCy1OWwqBuRq8rcoLocfYjVZsJkyYoG9DtsoaEO8j2qVQ+/HrZteh3OZChMw4rO1VrGYiQqxIdKmzc5VPDMIHAYwOdQnuasX+dQl1maUYJKs3j4IlQUA6YUmVFRkBOh1m0DOg+84fg+xhsPbgduOurMmgyAjjDvE4CCHh9E23UYUYwe9Dn88LhPQ2Pr5d27TBqKqBW+RdwvB/h+usFZcBl7wJee1xmRPK7XHNnwudMWSkfnFGT8F2+XmgQXDwa1NXiXCg5xQoNEhqawLkFvo+9Ge6wYGe3sxM2jxUpCuPM9XmzUWHORYdjQGRQVZvzE5q0nV4bage3DhmrtkKNCvNypGrDvi+Nw06/BVhaAa0JKF0DZCTmg5Ao9vfWYWfPCbh9HhSbcnBZ/pKQPqaEymcR1zhojObD0aNHoM2Qchb5869F+onX0dzUACt0UC+4HB7DMMGfra8M/cgKLYHj++By2KGj/8VIwPO69N3ArpeBlloZfFp6BZAVQ3IjCqjFzWB4ULrpZmD7v4OG26ULgTK/2VksuazaXcCeF6QR+PxLgKKZwHUfAw5tApx2oKgamL0y+e9H/UOlVw7/7euQYwHHhPMI3U4HNndKj4d1ufnIUhr4KsDF0MZNG7Fs7VK5mNemCSJdQFsIOJXZThxL88ViihsFVioNZ/Z7qK8bX9i3RQRL2YJzDWqYNWoc7vfAm5aGv7hcuECtRs+BA4KooC8Nz4GVQNyMxMrW43uYwRgw4bK4T4ZlvPvQ7zqEPM3FMc9NkM9a7bBat3zPQ6dP4WebNyGttwfvm1GBj8TIgLk4rxg/WbwWzzSfgsPrwaV5xbiuOHHPqxFjwRpgz1thrw2TlWEZBP7xV6DhFFBUDLzn/UBmEtJp3KB+7ascCFjjC9x3P8DS+pMngEOH5LHoDxBYRKWYgVUXANsV1SO8ftGqCZTgexZfJB/jiBJTKn697DJs7mwW4+yK7ALxWiK4pbQa+/pCs8HWuzT4z6sv4o2ugyieW4EcUx7qLW3odVlwc8kiWDx7mYoi3qtXFcKojtxUD+FjnwC+/Y1gFhqvyV1R/DLCwffd/m75GAbqGPk3JFzY9ussZ3Ck/zgcXrt477yMWZidFibLd8NtwIM/D5ISnKuuf1fsD2VQ7tOfZgoRTDYbrH/9K8yf/SxUStJEECMm4NOfB7o75aOkHHj7BeCLd8v3VM0GPvRZ4LokvF3C4bIgxWTEnKpibNlzEhcsmYmt+05ifeUgVPoEE27oncFKalEdFhA5YyVa5JyqAquDg14bW7e+JYJ3CxeON/EZfdznZ9NXZDjdYYfHiR5XH3QqLbL1mdOBl2lMCqTrUnBXxcWoG2yDx+dFhTlPvHYug5ncDMQyiBXQ6qeZNtdABlMZSsuXwFW3Gw0dvVAVzoTHOAOFCRgmc6wPrKW4rmJw7GwFWLn2GxgYmB5nzhHQI4IyTkxeot9FomAFOUm80ZBblK1l22af4bG4VwkkYTHYyfOKtf7ne0lIzJw5c8SfT4PmgJ9BvPfFqxY/7+GwAxs3AiSoFi/hYBi8JFdcAzz/jFx7stqX1/vCixNLAJrEmJ2WjRkp6Wi0MnnaJyrKUzVa9G7di025Z0TCH9du02TY5DbrXrp0qZDhYwUYYw4TKTXHaux3vWuY/dhEkhpXX3017r333hBtv2x9LvRqA5xeBnzlJtDt9WF3zwns7a3FhbnLUGySQfE8vRltjoEhUoIdotCYDp1ag5yA3rACFrcLD5w8iJ09HcjU6bEquzBqDugtpRX40ykpSxIgPe6qiD3wsyJEnKcv6P3IsWdVzjiV2lFH/8aPAq8+LH01aBB+8buAzqD29RBYydJxZkSkxoCrDT3OozCqacItX+P3osQzpZ6UmaJKWD0H4fYFysdUcLnbkKa9AGpVlEB1YKO/+EJg1lJgsFeeaxLmySrRHDXw+TxRiI3QFzg40hR8OA8NbiBY9q3UsyW8UWS2ylJyxCMZtNqOw80qBz988OKM7RBmp60PvS4nnwdsrFDiRXcCtS8Bs98BmJOTR+l09GNH9wlYPQ4Um7KxMqsGWrUGb3ccwt4+WYLKyptmWxdeatuNdxTHMBRWp6KkKBdnmrtQWpKLU6fbUVIyA2mFM2WAqK4OvlXvgaW8FTWzZwvmlgNdkAQJgzt4DTIMepzo6hPeBNojh+HQG8Via0SmYjT9Xh0noBgDZJm54WFAZAjZpcBlnwAGO2XVBn0zAiik50s6YB8IEozsDzQf532jPNWmh4FL3g/klgHr3hn0/ug4LY8l/G8SBKvORLq1v+oj0CETNHaKgLsfsJ6UFQv6AsBYPimkz+KBRsZ3bn8T3X55tBy9AX9ffREqzZFSTl6NF91pHXj5xAvIyssUpMbSzJXQU2pFVw34HICL/cAnCA0YF8FqPZ3QYv/l9ia/d0/wNZOWQVMfXF5gT28vrn/xRTy7YoUoAw9ISpE4o3H3cIs1/o4ZECRC3DTqDR+P4Ijdt/wb+XiZLX8+VY9Pvv0WfFwU9fZgd2+PICw+XRPdMHxNToF4TCguuUn26WO7JYm4cgMwLwYhyKzsr30RqK+Vi/99e4Ddu4D//T9p4B0PPT3APZ+RHggEM+y//CXgox8Ffv+74HzFzLSf/6/0T2lpBg7tlVVnQoJKJT1VZkwA4ZMgsvVGXF+cuIdOAJfml+BbWIXHztTB7fVifXouzIdP4pilFTNWzh4aK6Te+wB6XUC+YT08vkGoVDqoYRp+Q3LFldJ75M03ZNbZtdcBMyP9KUaKWWklONBfL+aVQDi+1JQnKko2dx5Ck+20SGLhObIi9WDfUZg1JpSmKKqnVq2TbWfb25LYWHspMNvvpxQNDz0E7JMVjJkaDbb19MD0wx9i3uc/j8wHHgA4ZnG9+4MfAJmZ8lE1E3j5aWDTq8HjnDoB/PU3wCe+PPILYCDB4ENudhoMei227D0Jr9cHj8ac+CKevmkpFwD2g4AnYBQ+Hz6VEV6fTUjhkXR1+QahURmRrp0HvUbOkf/5z3/EOv9sbLj4mVdddRWef/75mKRGp6Mbb3fuHEpiKTDkYl3u8uS84qYxjXGCSaPH/IxhSOEpjvD1C/tsoGKOwX9KG1JClOBa6JjNDu3Cark2U6nEej2RbHNmqzOwGvDw5GcwmJxMEHosQJnPnTt3jloqYxqTC2yjW7duTao9kYQYDaGgrPrhXptrfRIrTMRiP2J/CdnHRvHMpDTtaM6BBGEie3Tud5hVPo0YYLXaJz4G1NfJ50yY+vZ3gQv9MaGycuD7PwMe+Yf07Fu0BLh1+ISeqQCdWo1vzb8Qfz19CHWWXhQYzVjc5UT7wCnMXHPBsAmF05Bg8uWLL74o/DBJKpwNqFQqoQhBopZJCKOtmkgU5A42btyIBx98cNTHGpMz5iDHAZXlI7fccot4TafWY1nWapwcOIpeVy8G3A4MuOSGlMHmTZ178M6Sy0VQ9pbSpfjjqS2w+XU1s/UpuLJgTtTP4iLm6we3Y2d3h2AEm20WHOnvwYU5+djYJbN9iU9UzcEnqudiRkqakJxiedS7y6twaX5sI6iFGQUoMJjR4bDKahGfCuUp6ZiVOkIT4ERAQ5db7wl9LZY3RUpkoC8R9DhPicaaofPB4KXWHe+BGm0Oj58qkIH/2WnBSZHG3E5vKxxeGf/RqHzQq71wes/AqFFMag4b8MxDwPH9UoLngiuA9dfGl3WIApF9rZmHQfeBEB9oFfTQq+Nn8YQjx5CH09agDBIJDoPaiJQYZvUBsH2yvbCtuX1eQa5Fg9Or1KeWcHnDSq4d/YAtil5ib31SpEav04LHmjZLeQ2/xnivcxAZujTs7Kn3fz/Aq1JBp/ah1d4Np9cNvTpKFzfOh9nTixO1Z2Cx2oV6VMW8NUP3gP2Z+naBRRIzRmggxI0HDfEiAqzlNcARqU1emJYCkffJa7ZwEXxqjchkDzdQHg/wfnEDRVgajmLNjCzgWJeUbqP8lPgyBiArihyazghc8gHg0BvAYLckKer3hr6HDbLhgCQ1iKNbgT3U+vaH2VZcC9Qk6HFRvRQ4uSO0/WjVQG87kFuaPKHRRRN0bvR8gL0RoBRaapIG92cB3zy8F30KWZFelxPfOrwPf1x5YcR7j/Ufhj5Ti46WTkFqWNyDODZwGAszlsh7Y5gP6PmdOXgMH3xjFZrHZ4NapYdapQsXJRuCIDmsVni6u9GakwN7URFKFBmFNOrj5oP/DrgGxVxHkiXPkDtURcL+EzDX1anT4PKESjBqVLLqIxa4sQm061j4TW0tvK1tUFcGA/D/V3syJqlxVsBx4+Ib5SMeDu2XWrMBMIjR2gzs2ApcHEfaqbsb+P3vGXkIvsYJhZrfD/4+VIqKQeunngRuvQ3Y/LZ8Dz2BAmN+c5OsFKmc2I3cgMuB09Ye4SVWnZoTtbw7WVySXyIeBAND3fNr0OVNg00VqSPN9QBlHoeVnArHipXykSh4relR0dZKQVfggrUx35qlT8ONRRdgR8+xIaPwVdlz0O3sx5GB08jSR/afVnt7KKlBLFgqH8OBFRrf/CZTh4ZeKtXrxeOwzYau2bOR+cYbAHWws7MjPXeOhZVts+0ePxQky0eClGIgcy7QewRpqSZcvGouULAGMCWZ4ac2AinBOYoJJBb3Prh8HfK5//8enxU9rl3IUa2FVm3Gs88+i/vvvx9nC9dffz2+9a1v4Xvf+17UeX9z1+6Qqtw2RyeOD9Rjbvrog03TGB/wvtm5x/C0C5+8FG0ZdOpgddA0pg4CsjRKMLP86NGj6O7uFvuHALgWYvYntdVVfpKUQVsGculdNJxZMwPI4UFV7i+4PpoISRN+DtvtsQN7sTrLA2P9FiCnTEpqTYEkomnEx9kg7plIyP4zZ86ciH7E9j0cqEYQ2F+MBsMlVhET1cemNP76EHBa4XHCfdu3vwk8/1JwfKiuAb569tZS44U0nR7/b+bSEMnktTfdNKzCwDSCuOSSSwSByeD+2SI1AphonyhWabCqk/4qo4V2LDcd3PgESA3CpEnBwsxl2Nl9CGdsDSHhX8rkWD02pKtTUWzKwL01l+KUtVvoRFeZc2MGkilVsr07SF74/BIEs9PScOeMGjTbrahJTceSTElE3FpWKR6JgJUaX5m7Hs82H0O7w4JiYxquL549rPTVuKByPlBSDZyplcEVVmnULI7uWZHARMXMO4ITlknD+8JqGA2qUpehySozjstSSpBnCJI3DPhZ3MGQa8C4W68O064noXFsv5TP8TqAt54FmGW9PHkpDlZQWD0MxmuhVTGIx6BKFtJ0s0XgMZanRYezVpAJNKnPM1RC7TcMz9BlYn76EhwfOCz8XJjZzedDkjVhaLB24enmPRhw81hauLxeQWrk6FPxrtIVMGv1aLF3QQM1ik25MGnTYQvTtDZS3kGJmMGo5BbARwcaBdmi7EMnBlvh8oUa22sY2PeqRPwwoJkeAWZopl6ChSvmQKNR40RdL6BJjyhDU4KBWRIahw4dEtksIf4E73g/0HwK6PMHa/m5N31AZGMLt5Nx7D9kt2nWx39F9Y5eD1/Dfsy21AH1JNZ8wKndwMUflJUYw8GYCiy/Xv7stEWSGsqr390C7Hkx9Hc7nwPyZwAZefFPnBU74vaEeYa0n0qe1GCFRoDQCMByFDDTk+TslTIq8Vp7Gx6sq4PL58W7SstwW2kZ/tlYj109XUHJMr98WZ0lOqnb7+4N6TYMvPa7+kLfFDBuHwZOby+6nXuGpHVStdW4urAcjzbVDVX08d8epxeu/gFZRuzP3DKEtWVuwBsaGmAoMmBvb1BuMVvPoOsKkSnMzYDwJfBZYNZkw+Xthssnv6MKOmTo4ku6xPPK4TjFeUKl2HQor+uUQ6yszVg63QHQ9+RDd8tKjWhQmoATvF4BLVOlibQSE3wd6y3d+H3dNji8cq4tM2XiE9UXwBBLIm8EqO1sxmtohiHFhBx/gp7QSgaTH1KQZxgHD7FwQoOVNDQY5z3g5u+9dwKf/FTEW71CBpMEvh7XFq4K2XjTU8MXY0Ou8a8DksavfgW8EpRUVCJFp0MKg27MaszPj+3dEai+C8BoHF3Qi3+bvwLIqJZyloYMQDf6zaLdUz9EaIiPgfRZ41qPI6HD24nG0+3Cq+ds+GkEwM9+73vfKwKhNCJWwul1wSEqwkPR54qRHDSNSQGr5zQG3ceHntudrcjSs/JymtiYauD4q46yNmKGLgn08LE5IyNDyN0owex4khrJyv/k5+eLrFL+Ox7fi/I/fPBn7ntULjuMB56HsZCJUiqgfhdQsxaYO77yk9M498A2deTIETGnjVS+ihUeI1JCCOurrD6nYXEsRJt7pxGG5jOh6z7+zOoNPsJM3s9lBGQCpwmN5EByknPj+YZnn31WcAhjgTElNW699daommBmrSlCAoibZ6Xmv1lrwPz02BkaAYSGdUOPuC539CVOqVo97iifeN3gEHCT/86PAQe3SQPhnCJg7ooRbYq5KdWo3P5NahApmizkG3LFIxrsnr6IK011HBXMoQM2KzQCwSCeX24awOwVvQuYvVZ6ESSIXudB2LxS7sopIhVepOpLoFFFP4bH50a9ZRucPinpMoB2WDzdqExZiU5nB+oH6THhQq4hH9Xm2dApzJwZKKGMk1GtF9VC/S4bHmncLsg2fi2bMI2W6HZa8K+GzUjV8XrKQGiGzozri1bA6u6D3Ss3z1qVAaUpYW1HnwqklQADzcFwKQPNOcllUIugZRiU0mFD30vItwEL0iuGl15Q6aAxlslgkCYyUzcamKVBBpfEBllVZk0NSSl98huyLdisQHk1kFccYWA+1nqKzGChwRnJFkr7CJAAbHoZSDUF2yUNv2u3AwsuT/zgbLeUpGqrDS5S+G+5n8HuCSWThtDblhipwTGSlRnMCg90Lo9XSmN11AG5lYn3d9EmwxuDDz4GetR6qOIQG6wC2tp9AhY3Zc2ysCp7ZtTMcKfXg0cb63DaOiAq4G4tqxKZ5PHwQmsLbt68SVYScQJracEDJ4+hwdoHnZozQdDQnUQcSeloYJVVeDsyqKP7b8SC12tHt3NHyHw06K4VeqC/WLIO/1d7GD1OB+alZ+HXe49C7XDAW1AgzmtVdjaWUmJHAZZnOlwOHOsNzabqdnajwdqISnMF7JRA8h2mOq7om1k6NdyYBa8vFTp1RkzCNvxzWCIeqxz05uxcHAn0Rz8p857ys5vtMSrw3OmtQokf9mPRX7RSo3Y4/PoBKTUVbU6lGXVPtzymMrhe4U96oHQQEQhIs20zuFI+sRu5v53eJarsAmiy9eGV9hO4rkjKd4wWbEdbTh+Fdk6B8BDrdjLLSkV3BVSZ83Bp/sLxl+155WVJaBCBKqS//w244cYQDWK7ZxB1g7vg8kkyK0NXgBkpi4bGtGx9mmjrrCg1aoLkH8+/OnWE7Z96yOHklx8arxeWvXulLFksXH4jcGiPbEP+dQyuvRVjAkOWfIwR3L5Q8k+MrT4fbB6f9KTzAc8995yQfTqbm63MzEysX79enMsnP/nJkN/p1TpJHitkRTmrpNC/bBqTFha3X6JjCD5Y3aeg18cZ46cx6RAt6YJjSSCzO9HEJgZXKXGTTACVktdMbBprUoPBYsru0kNg5cqVwe9wbCNL0v37Av/3PrEZqF6Z1H53GtMgocHKoyHvxyjg79gWh/bbYSAJmJOTE2yPI0hkI5lBObXhSA1KyI1FJvU5DY5ZyoQW/pyRybI1nE9gEsw0ATYyUIZxPOJlkxXkDChvS1/uSUVqrF27VshwsOSImlxK1KTOQIO1Bd3OYFbt8qx5ER4OiSBXb8TSzFzs6+2SElH+BdXlBROrqTnuYFbm4nWjPozX54TeP8c5KSXFrHsVKwriyLOI8GMk1KqwwZlEASs0iBm5QAonZ48MIreeAC69G9DGzyKgHEyA0FDC6mmBURM9SDzgaofTRwmoIGyeXrTaG3B04OjQay32JuHtsihzmXh+xtaJV9p2iww/Vm2sz10Al08Nl39THCU8DJXKCYc3OMj0u6zY0V2LS/LWwCoIIC9MmozI7FAGnH16efF5ZHMWUHUpYEwuG63KXIh9lKwKnlRU8OWKlDyszUlMeoiZUVlhgdrhwIU9iQ1uIghml4iFELXy5y2P+jfcbHDxJiTQMjJGZTxE7T2WtHMg5HEiNG3d/iBo+EWhTFqyWHMzsPcloL1eylPRKDzXH3CL5Z+RqK8G+we1+wPg3o+L0Y5jQOdxoHA2sPTGxIgNQwHgaBp6ymZqT0uFx/e26Io6VRkM6tlRJ8kBlw1/a9gogqhs56esHWh39OPGouUh76cO/6d3b8KeXpKkKlEF8HZnC3619MK4lWw/PX5MnpfitZ09fUgTXYWydsHPoafGffOiBzVmpc1FZ38H3C4Z8KUhMF9LBjbvgRBCgxr9lJhyeLqxPGs2fr8iaNi93qHGn10unLJasSIrC99esCBq9ZPD6xA6/kowqGZxS/kjva6TzFrwd5S0U7EK7xKpc58ASGYEdKSj4Sp+j0s34M9NTeL7vX9GBb42d/LLj8XMdvrSvaFVGdwYfOGrQGGcsYNVF9Gkuphd/8EPys3F/fdJMoOgcTgzRE7XAz/6dnBDwn9nVAL//a1IeaFxhMvrQS9JWAV4P1ttY5d1vnfvXpQtrEGtv8qJ5Hi3zCLA/6tePjGVqe0dwQoNJTo6QkiNU5a9Q4QG0edqQ7vjFAqMcnOdrjNjXe5CbOo8IMYknZpERzrW5CxB+kgrGdhGwist/CjT67H5+HEUbtgQW/JhRjXwuW9KXw36TS1YDixOQpZrAqGCITLDjgFn/21RoQ/PPPMMrrvuOpxtMHGK5xJOanCeWpm1CNu6WX0nYdamYE7atPb3ZIVIdOHiJAxehYTYNKYWoq0vWaWaiFdG+H6BklX0CQiYjscK6AY+V0hCHTsmCHtK+IxUJofH4ecyWYrHYFwjpCqdcHLPGajpVb5uPy9IDcYU7J4G4QVHeUqDuuS8CcAlW0E9HDo7O5GXlzcsoUFwn8tq8FiEQlZmJjo7dgE+7rMY5MkFVAtF4mKiiEc6sl+JSt7z9D4njPe+D9i5Ezjgr9rnvf2mf19xnoBtheMnx+FpJA/G1yj/vmyZjFee69i2bZtoM/QmHQuM2W6dQZebbroJjzzySASpwUz4ywvWoNFK/VQncg1ZyBHGh8mDg+r3Fq7GT4/vw+6eTiFJ8PHq+ViQEWqgzWDVsy2NqBscQIU5FTcUl8eW4wn7u929reh12lFhzsBMhZkw5X9oYj6VBnadOl2cr0ED8QjMwQ5fFJ8HBYzqHPRBBq4DUEMPnVJeideBHhqUnCKZYTaGld11Ay3HgbLx0WfzIvoGqNPREbHo7HK2w+11C+LipVYaSnqGKjbe7NiPpdSrDnytKJyByFpUgFuyHteAyBg1a4chBU68AvScDh5xsAtwWKEseEkENAa/smApNnYehp2a4inZ6Hba0OuyhJxrviEd1xevjNtGmfnBsnCajIksjyTAY7NSI0AysHKD2UyxymdJZASyPANkSOjJWGVwK8UfTIoBloA3NjaKrKmYmxaSDySOrL2KoJQPyB4mszYWSMatiFESV1gFlM4Fmo5IMoJESsWioN9GOFhBwvtuSJEZ4C1h14Hfm7ofge/fegxoPS61euPBOEN6aFi4qPXBnpYCD6tA/HD5GkXicYomcpFxqL9piNAI4ORgK/rdNiFDE8C27nbs7u30Z/fK9+7q6cSOng5cEMdwetDN44eCz1k9xgIVBlVVPh/mpKXj4TWXIC18E+kH5eQuKtmAl069hGrzLOQbCpGiTQFYkULpJ2evlGTJWATw9fDPFOfdO/Rzk82HMzZ5ZqnaM1iXUz6U3dva2oqlM2bgCoV/RiyY9Wb43D6oeP+Gvp8PJpUJO7ZvRVkJic0QkyBJ/IJEW+qoN09CYtDpxH0rVuC+eJUMUwG//Jn0KwiA/aVmNrAkgUXenLlME4rMtKex+7e/DfzX14GHH5HvIZm7cJGsAnn0n9KcXPl33Z1AVui6YrxB+c1UjR4Wj3Ooz3DNkctxgzKE/b2otfSh0JiCZZm5I1qLMPlkQd4M1DbvHxJqIwk3w5w9cVKb9FgKJzRIHmk9wKkjQFEFvHo9HF6FL4ofVsrQKTA3vRylplz0uSxI1ZqQyerI0YBm8p//fHRigxVb116LTdu3i+qFmNe/tAK4/UOY7DBpq+BycVwP3os+RQnoqbajeO211/Db3/4WZxs333wzvvKVrwipmfA1S1lKEdK09MPrhk6tRampENpofmLTmBQQUqHqHDi93IcE25shRgLTNCY3oq1PmEFOQ+3Z9EtKEtyX8MHj0m+A8YXhdMYDnn0cG7gnSSZRS0nAbNmyRexthj3n7DKg3l9lKEA/t5TEE5qmMLw+F/pc2+AFEw1UcKAZbl8/UrVTNIFmGA+7RIixkfpMsF23tbUJlYF4YNuP5anH6onDh17H+gvZ9gJr107AdxBQxfEOSwJMSqTs9DTigOTrLx8A9u2Vnn7z5gMJ7CHPJZw+fVrEg6YxMnDuYrVisjKMUxWPPPKI4A7GypR8TFf9t99+Oz784Q/jxz/+cQTzy/LwCvMIAosxDGnun79y2Anj3r3b8VLbGWhVKhGE48+/WnqB1DaPAZIWPzm+DQf6gp4dd5TNx0W5JXi6eTea7T3QqTRYnzcHy7OmRqelMa0aKcL4MfDVA2aQw0GvyUCOfiG6nYdFRhUloHIN9KMIazI0BaeHxomdjE5HHoiZ8wmA5IBJUwybR3p8BJCiid1mqE8fTkGooYVXpL1H/36djv4hQkMJmmvn6lPR5SRJEPm3WrVOZJH5FEGgzHiZoJSw6lGYRvn/El0ngGxFaTU3BKx2UeuHLR2tSSsWjwA6HP34d9NWWOnPwGRkYybeVbpm2DYeABdUzPyIlyWSSOn3vLlzUL9/N9xuDyosHdDvY4UAfWAWAVfeIWVkYl2fl/8FHPNvEIoqgOvvluRGGJg9xWBcOGEaAX73VbcAWx8BbH4Zmoql8jGW4OdceCvQcAgY6AbSc4GyudFJmTNHgV3PAB4XoDUAK2+SlVgKiZmhYwafSGIm0XNJnQ9fyhy4vW/AE6XKyuZpgMOjRZY+1DiVJF80Es8Vdm59ruj9uJcu83FwQ1Ex9iiD1P7CFCJwpvz8O8qqYhIaAZh19GAqQYXZn7XEvtz2KhDw1nB2ArYWoPg60Z96e3uHSrcZSOH4pVe50GQPEhqExe3Elq5d2JC/Dvv37xcZVLkJLEY9Xg8O99TBnq+F2+kWEjgajxv9dX1oM2ZgwdxSpJuj3EehRJd43ysrKxOSDIHNu/hsnw87ujuw/+BBrKk4h8rCabRHEjAA/qw03xsOzOI+fEiSFkoEgi5//hNw441AUVjFR19PJBFCHdzRmDuPAGyj7y5fij+e2iHWI26vDylaAxZlluKhU8fxQO2hofdeX1SOr89dljSxwbXZvPRCbOs+LiQYCa1Ki3cUL8KEgeP4++8CHvqLfM4F7aVLgTcfls9T0qC6+eNQqzXwhmR0q6CLIjmXpksRjzHBRRcBv/41hV6B2lrgqL/qk2vaL34R2tmzMTsjQ8gfKvtjwmCb6joDWPuBzHw5d4wUPifg4prJAzAgrCYp2+yvDMsEVJHHdnh6ha8QZe9MmkKk6y6A09ssJEe7nB1S+tOPV57ehOUrlk8KKQGeA7PXHn/8cXzkIx+J+H2mPl08pjE1QC+pPtd+P7GhQoqmXDymMTURPg/R9HQkhEb4MSnPMzAwIAKrJDro0xEvIJ0suLfYtGmTqPwekrKNheI5QH87cGKLfE5CY/WtMvniHIfDe8ZPaBC+oddMvsqYMtFTDWxjrBTiHiAemGHMiotkgrisumD10syZoXuxZED/CyYlco9y0foqqFR+P8shdCa9dmW/iSZ743K5BFHIvf40EgDXsstXnLeXioH4w4cPo5Byv9MYEThvvv3220IFZSol0ScLqk+Q1PjjH/+IscKYkhqXXXaZCCBxcUD927OFHd2dgsRQZhW/3t6KLV3tw/pubOlqCiE0iH81HkKrvQk9TstQEPC19kNI15pQkzY1Oq1ZOwN9riMhSYcmdXz90RRtkdj0UpBAHUsmhR2OpuCUWHjpN1JuIRAiFd4RiW9SsnTzoIYOdm+HICfSdFUwamJXERg0qSg3LcEZ20F44IIGBri86Wh3tkudfsV78wwFInPPGEPyLEVjwJ0z1mJj5wl0Owdh1hikapHXjRJTJirN2fhPy/YhiSqz1ojV2XHK62INRkriwt4KdG+R2eYkjLJXA6bE2Fmaud5dcSnaHH3QqjQoNFKfP7FMW2Y6cuFGDdtRwToA1VO/R1XHGXi9Ppzu6oXK40NFdjpwbI98z/V3Rf/b7S8H30O0NgAv/xN4R2jAoq+vD4ODg4mX46XlApd9TAaNdAa56RgPCImaOFVIA13AjieCQVW3A9j2b2DmGqnNKw8k+4xSjorPU3OSPx81CbLQl4W0uw/oddfBpMmFUROskqs052N7d7BqhGRdutaIrLBsZ1bCsdKNlWyBYYTPwyvkoiFLr4VOIcDEkSQgiWdQ0VtJg49UzsL7ZiQWmA+Z5O0dQUIjABKEA7VAxlxRIUQyIFAKq1VVwqQ5Dosb6G7tgiktBSaz9Hzqdw+grqFeZOrFM+0bcDnwYP1u2Dxd0A60oCivBF6PB4cP1qNcn493rb1Zbo7ptxPgh5Q6q8hNqjzcnJWB+tOnhe4tDTZJr777kb9jX3srNBlpSHP246cmAy7NH2V/ngwoKQX6+4IkA4PJfC0RcOP1pz9LYuO++2SFhrK90N8kGuYvAg4fDN4jBijmzAv+LYmVLS8BtQcBgwlYcyUwYwQB7QQwJz0fX5p9CR4/cwwvtlLmwYrP7XsD/WEmSs+2NODS/GJcmJvcPads0lP122ANyEYKgsyDbd0ncG3RGJO/w+HjnwCuvwHoaAdO7QaajgWlA20WqF5+GCXvfBcarQeHxkjKO+b7pafGFStXygfR0iIN6MvKpC8LC/UKC1FbW5s8qcH2tfUpoDYw76mA1TcANdGlG4eF1wbYNgK+wH08Kuc6rTs4p/hqAFWQkBh0N6DPFZTmHHSfRp5hFUwaSjX1w6kwDSdeePxt3HH7+zFZcMcdd+Bf//pXVFJjGlMLJNWy9MvhE2tq9Tm9eT8fMZb3My0tTWSKM0nlwIEDYtyNlpBF8mM40iMWdu3aJaSmEkry4veaezFQvQpw2oCUjPOC0CB8Pq7iI9OgfJSNO0e6L4kCqgIkQmow4M8MY7Y7tlElGAtjJQWDvMq+wNeTWTew2imwH2EyIpMpuG9nexXH9e6N8lfJV9zynDZu3CjIFh6fZA2TCSm/xj3HNKaRCDiGJis5OI1IzJs3T0gqnssyXhs3bhQJBRs2bJicpAYHv1tuuQUPP/zwWSU1Ohz26DLOSo3uqL+3DunFB0BFEQa5lWDgr87SPnlIDZ4vdbgpvRNlIWnWlIrFyICb2a5eGNX5yNQnVi4qdBSH8qqHgcEMrL0D2PpvqTkqpHtuDM1C7DkDNPmNxfNrgMJZEdUamXpm9iSe3ZOmy8ds7aWCeGm1t+DIAAMgwSUXr0axsQwz0+TAkGvIwIyUApy2ton7yHdm6tJQaS4SMmlXFMQuB7297BLhx0ET5bKUPGFQOSy40M2bA3QEgwjizPL9ZZweK9DFoIQ/s4gLw67NQMHVgC6xrAiDRofylOQzPRlwpcTOqPHqo0Bni/hRrVahMjcTx1q7gu3yxP6QjBFmqXDSY+Y5Go6HLo7ZLppCZZmYObJv3z5ceOGFyZ0Xr33q2BmqjhjdTZESJgyQ0kyc/YNVHKzaYOl6w67ge8oWAfnJaYIL81xfOrQqViMpP5PGr/Inl3cwhNQoNWXjmsIlgqh1eF3IM6Th+qLlEUbh5Smp+PaClfifQzvh8HIM0eAb81egLEpVjRKH+nrxP4f2w6QF9F7JtwjuBcD3Fy3FByuqQozCEwE3EZwI9Xo94PetCAG/u71dkBo8bmpq6tCmQ6euEDryrXWvwphmhKVvEF0tnaKEnL4+8wqq4hIaxG9qd+J4dxuqnd0i24CExtFtB1G1eBbysgqD2X70IGK2tKfTv+nj/2h6nZgkX5fDhp+f2IZT1j4hQ3RNdjmcO3bgueYGHDPpkDJPjpUurxdf2r8DmzZcD/1ESQiNFz55j/TUGPT7SKSlAx//VHKZUosWA1dfIyszhogKNXBx0DMlBLfcAZxpAja9KZ+XlQP3fjn4+5ceAfZtlj+zrZ4+Drzns0Dp+ATYKc32YmtjCD+ZrlPB6fVJeyZ/S2qwhq5NEvFQMmSmYse+t1A0O5hdSFKvyRae7ZcAWEGza7usxlt/CbUGo7+P92D7dul5Qm3oJX6ZNJZX83Hk9VAvJP7c3YZsfQn06hQMuDpFYgWfR6vUGBfs2QP87ndSCm31auATnwj5dYTW+tHDwG5eCz1wyeVAbpTEkcajCkKD4HV5BiipSV6+xHlcVmooj8XkG1YDDs2rJwBfqUiYYKVpnyu0gsntG4TF3Yg0HcnedOQbatDuOCF+19HahV2bD+Dxf96GyYJbb70VX/ziF8XaZToT8NyAKkFfqWlMIzMzU2SMk1AOSOAqEZCfYoBWrA8TAAPYDGQnXbVO/4zzwENDCa06C/CGVs2qoINGNU6JY2cB3CewDSUC7kMoi8Y2pAw+cr/B1+gHs23fbvTDjXS9EVkaY1KG9lyvkRhhtRJl2EhwrFu3LnS/pJoB+EKTcaGqSrrCmImO3GezbzHYyOfUuZ8mm6eRDOh5SoyJ2TW9D7kn6+oCqmcCi88PnwmC/Y998VzGww8/LDiDiL3UKKAdj0wqylD99Kc/TXhREQ5mA1N6QTfC7Ie56ZkiaKYMCLBrzafZ6DAoTUkPITTk30XvlCM9tzFH60lg51OAyyEXWJS2yQ8theTAwqoHPsbd4PUwFzz+1PCsk0CRn7jobgB2Ktzt6RngulwGb0cJfj+a/fa5esX9CuSSS+tToNzM0ljN0HsvL1iGw/2n0e0cEBrcCzIqBaERD6zOmJWWpMZd1cWAPkX6amj0QOlyIN0vfeLsDhIaIYGJzoRJjZEi4cmGkkkthwGHBcgoBHLD2lBLfWgwisdVHpt6o/7nDP5ykcdxgRknBUZzpG45s6AVYLYIjdLiGZlNWpBojAb2VfbT8oXB1yqWAAOdgCkNyCgakeyNVsX+tBcqDAi/CgZH+13yX/F7IU8SinnppeJBf5nhKn0uLyjF+twidDrtyNUbYUhAS/ZgX2+wskMtC0mI7y9agrsrR2bkyk0EdTvFplaXEVoBEfhZE/yeLOGkl0sgk6qjDVhQsAZHdXUwpZnRaPVgY5cLNrcPRwYb8RVrMUpTQrOulLB5XDgx2IW+5lbkLilF08ETcLs8MKQYYTSbQucM4ZWyFPDUAb5+KTmlmQmo4s+NXBT+7MQ2NLLiSMxnPjzXfRofn7UMmnQDdM0NQ/MV/2/1uNHpsKPYNMU3mBWVwG//COzeJSfu5atiB8uHw4c/DAz0A08+Kas+rrwS+Oy90d/LRdXnvwp86OPcqQKZWUBjPdDVBhSXAfv8UhOEIGnVkuQYJ1Kjwdov7nd4e2CSRSCMzd/OSEkT9/z4QD8KjEZUpw4/b3CRvPnMEbjUQeP6ADTJZvft2AZ887+5YJNn84+/Aj9/AMgPy5Tl9frmN4Fnngm+RtN2peFzZh7Q1RqcS9hv0iUpnarNEo9xQ2cn3erkXLV2raz24caMJAbbDR/cXLBi4wc/iH6MTW8A//sjeQx+3+eeAL7zc6A4TD6TlcABD6YA+H5KGCZLagi/kTDCXNyKcNkJthiSGo6Q93PscHlVUKMXqVqvIMXzDFVI1xbA6bXihef+JLJBOf9OFnAs5zlxM3TPPfec7dOZxjSm4Sd4GeBVJoQk402QDBjojUVoMpmE68KDBw9i4ULF2noYMNFqrExKz3Xo1blI0cyC1SNN3FXQI1275JwiJpMJxLJSg0lTyjZOSSoSEIsWLcK+vhY8rm4Rc63X7sH6/GrMykzMT5YVHXxQDoskPnX2V6xYEXl+Kq6NVgG+BilDqSJpMrI5m8dmpcZopLGmcf6CZCDbPtvqaiYCjZbQ+MZXgUMHZEIa1+G3vRe44304HzCcj+a5AKfTiUcffVSs5ccSY05qXHzxxWKQf+aZZwQDk+xNfPzMUfyn5STcPi/mpuXi/81cgXRKyMQhQWweN8xayfZUpabhmwuW4f5De8RkwgzX++Ytxqy04QMjK7OKcHFuOd7sbBgy6Ly7cims7h7s65OviboFlRpLMmOblk0YBnuAbY8F9cdZCrvlUeCKj4+raVmgs4VMrn3dwAv/CA1Qb34BKK+Rj/odkRvw2i0RpIbb54DV3Sbea9LkQacePkDn8rpwoG8vukgE+BGe+a0LCyAycEsiI953pGFyt8uCbJ0ZM1MLR8Y6kywpXyMf4YglP5OELM1Iwe/HxVdcQmPHw8BAO80D5L01ZQDLbgLS/dkmDP7abUP3tmPAglyzInC+YkPIApDZUCwPZ5C5YPUVQIM0t5Yn5QXWhRpzMzMlvKx3SqGgGsgsBHrbglXbNBPPjTJ+UG4qWcmpcMNo3yC8vjKhb2tx0/C7KXh4TQmM6thyUYlIl5HIKDEl7nJfZAolUQJdaG76CILUfnCRP5TBYMgGmCXmswbHHrI5aUHCZKiqAxALLranWUUzcbLFguODjdjYRTEnFVRqFRptg7jv0Cb8ZtnlMLCCJoaZs9tmh9ZoQJtTDXNGGvRGPRxWWQk4Ky1Mco8bPm1kVmE8WD0unLaGSmvxDlEikRUy4YseVmjkjtIjZ8IxMABs3QjQm2XxcqC0TL5Og+7LrhjdsVmx8cUvAZ//gmwbiQRXSGZYBoFffANolnM+0jMBvQfQKv+eMm/xtbtZQShXDUlmzUXJAJXkffCe31g8AwMuN9a//jycfqmu98+oFmudWHPV3r17UbxmNmzuUJkhglrZVrcdKdoYRGw4/vencqMROCdKhv3tL8DnvhT6vq1bQwkN4k9/kiRTINv2wuuB5johZyjAtdxlE1AhQO+Vj31M+qcQlJz4wx+Ap5/2e135yQf+++qr1EIcIthI0ge/j99IO6Dnzqrgx/8JfOoLoZ+Xmh1KaARgjhPosDcDg4elTKWhCEhfFJ30Dk8qEMJ/8n5qVEZRdUuPNFb89DhlhU6vqx19ru2oSVsh5L0MGrOojvnTHx7Cl74Udi8nAe6++27h2/eZz3xmOot0GtOYBCCZQQ8NVlIEwP3FeGR5U4qW5GY0kEjhZ7KaQ7nuGw5jTbqc6zBpZsCoLoUXLqhhOCfHYLYjzu/xkunYxpRZxuwD3JuQULN4nPhHw+6hxCO1RoNNXadQk5aLhUxaiwPK5lJbn9eXklMkGpgFH5WsU9E/KzGyZBrTGC+w7XPcpxXBqMeFt16ThAYRWGs/8nfg0iuAgkmikjOOYIVWIr6eUxVPP/20iMWQM5jUpAYnAerd/u53v0ua1Hij4zSebpYZAMSxgS4h8/HlOeti/s0TZ07h+0f2we71oMxkxs+WrEFNWgZuKa0Q+uKNVgtKTCnINcTfqLMTfqhyCTbkV6DXZReVG/kGM7y+UqEx32jthFGjx6rsamSHac6fFXQ1RgZWaPBLyZuUxOSlkgHlWbzg/ZF+JfAVQo05MkuDEkThzCIHtfYmSWrQSyAcfoPrAFxeC1rt2+D1i9D3uk4g37AcRk3sLM0j/QcFocFk0UA2Ovya/WoVMDN1tvDSSO57+vB8614cGTgzVPkxN61EyPSM6QLOkAfoc2VlRiDoxcxz09hkRvJ71FnqcXKwDoc7D0PXY8DCjPnievB7kGQYFs00wW4H3Io2ZusDtv4DWP9BSXBceCPwzB+Gzr/b7sbs+QsAXvOaxcCSoGwUjc0ChmrCzK9wJnDHvcDhbZKVn7kImBEqPUaCtKenZ0Q6uZMCDIyvvxM4uR2w9Ei/j+qVMvNgDMF7PeDeB5dCE92snYMU7XLRr7SqFJg0ORO+AVmfm48bi0vxdHPTkLTfLSXlWJM98sk6tPRaDRRfAbRvARydgMYAFKwADEFyiBtbloNzY8BFQlaWHE/a7BacGPREmKR3Oe2ot/RjTnp2zCq99TnleKn3IE5bvdD3O2E/0QS9T4c12QtRnZpkNVcM6NWaKOrFKhg1WtxRNhMvt53Bof6gCbkaXjzf0oh3lJxlU9/+buk9QXP5/FKgaEZEBZZAZwfwlc8C3V1yrmBw4WvfAJaMwF9gOCTb1559BGgJkoEY7GfUBkhTZNhzrpvtl1CKAh9I9O4nayNqIHyYBRWiB2KUsHvc4r5Xp2bi6oIKvNB2SiRXsGpjRVYBbimZi1PWARQaTZhhSsO61/8zRGgQD52uxZqcPFxVGP2zli5dityaKnS174JJjSFTaINaBa3aJ6oXEyI1+P1535RzPs+jnQkJYaAfRTTw9QCpwb5255eA+kOSGOA8kDYB8oHf+Q5gtQafd3cDP/sZmdPI9YyCtGDwYihgxu8dkEoLgK/1RJHzmjEfOH0QaFJIUi67EkgdJiDhaAd6NgWfW09K3yBW5dBkWXmaOmWAjuueJUMeXlynZeuXoNu5F71OErlBWDx9aLXXocQkK2u3bNki5DMo9zTZwHNilcbWrVunM6ynMSHg+urYwHGcsp4WPxebirAgY/5QBfj5Dvo00ftOCa7ZGxsbRWXtWIGfEcuwmGu8QBDaaDSKhKhESI2RGIuf7+Bcwv/OVXCPQP8WJlANB+6hSWqwQoj7C+5N5s+fL/7tsFkilD+4lmu29SdEanCcCRArVCwgycFKjfMevKZ1B4HudkBvBMpmAVn0KDz3yLWpBvrHcC4Q8uKjBfeH3BOGj8/dnecFqcF42bnc33/3u98JrmCsVVjGnNQgPvjBD+J//ud/RFZsFfWTE8ReZjMrwI384f5OuL1eaKN88V3dnfifQ7uHnp+xWfGJ3Zvw7IVXwajhBs4gHsmAk1FVmBa/WqXCyuwq8RgzuGyAxw0YUkc+GMeqYIkleTNK+FDPSITilRZaiEODuUNSEaF/wIxp/+t5VUCf0sNBBeSGVkv0OI/Dy/LJoc/zott5BMWmtTHPqdPZKT5GSWgQLi+wOHMBShI03VaC2uIkNOQ5yAPz+cKMMpSNwL8iJhhsyLsYGKAUVz+gTQXo/TFGG6VGaxMO90sNbUoLNdmaxfdZlrVEGDnF1bFzMHtVFb2CY+8LQHohUDoXuP0e4OR+0Y49PjOO5xYOBY9FdrHXKxh8ylhwk0HzoyFd0bxi4OJ3Rv98Sx9yD7+Ok9t3oIbl4cuvBChZFQ4Se0ffBFqOysqYqlVA+ZLJs8ihv8ycJD1BkoTD2xJCaBAWz1Fk6S4SZMbZAu//b5evxvVFJai1DKImlZ4dJaMmV4SPRSCTSpsCFF827DksWBDpYZGlN0OjIqEYGbyMJ631gbmrobe7cLCtCWmZZbiibA5yKrWoThuDxZyCPLm+qAbPtJyQ1Wdg4FmNqwqqxPxWYjKiziKHWRK4WrUKPzi2F9cUlYnA+FlBWyPw6K8Aqw1w+klTnsuN7wcWrAp977/+CvT2yJ/5Jbh4feDnwO//irOKpjBJPQaoHU5g+Uqg/rD0j1h3LTBz4TDVGfRNCATLOacdYe0hVIi+Se5wDOL3dTvQYh8QlaA3Fs/FR6sWY1lWARqsA8g3pmBtTokgBuewckSsl7qFv40SWpVKEF2xSA2Oy7luvSDpz9hbRB6/EkZNglWC7L+UCaOnhtLQfWaUiiQ/kR2B8GCXMQWY6zfnnig0MjFEcQ3ZBk+dAm6/HXhMIZcZwPvfL4iPI5mZmPfjHwObNwK//DngYiWPOvT6zPZ7ZykhfF1uB86cBGz9QFYhkFMikz4oTxltXLRR0jOM3rQ3AmkXCE0/vqryBd7Cfk8pFX4nyjuGjgMGdTbyDGvRYn8r8mPcQX+W3/72t3jf+94nNqiTMSuc58ZznJaNmcZEgMlBtZa6oedNNpnwtCgzMYmjcx3sk+EefQzEjnUyEuV4WKlB+VFKUAV8MPhaU1PTkJk4H4maMTNblH/PBKqY6GkGDr4sx+yMAmDR1YBpfCWCp3H2QKNsqgnEIzVIPHAPEk2iMVMXOXcyppWVoA8L2+/hw4dFJRGTUfbs2TOUkHXegvuEF/8OHN0F2JxSQYKgDOwdn5RryHMFghDzS91OEeTl5Yl+Q3Q4enGgtw5OnwulpnzMT69Ibt9PeepwQoPV90VjkzQ4mREgNM/VKsLa2lq8+eabeOihh8b82ONCanCxccMNN+DBBx/Ed7/73YT/rs9lF/044IXA5q9XUe4pekfY0t0WYuzNCYMm4fWWAeGrMSnBDfSxV4HWw/K5OQdY9A7AmDYyaRsusPrbg/vdrCIgb3yksXwIM6MSYCB1rgxOL78E2PVGUDO6ah5Q45eXqlgF2C3SKJwnm1sBzAuVF3H7gjJGwdfsUSWnTllOwe6V7SWKmIMI3rsTkAeJhgE3zyMSr7UfwC2lFyA1UXmORKDSAumxzclHg2Z7cJMRkKpptrViaaZPEI5KY7OoIGkRLVuVr7XXAc21wNFNwPp3A2uvFb+a5/8sGvbxMzgwcyLjoo8kCrVuqXlrZBRm16uApR/IKwXmLA+dvCml9vzvAdsg1ANdQN1eoKMRuP4TUppEiSOvAacV5quHXpYVEqXnz4bTS/mlKHn9Hp8davo4nEWQFH5HydgF+wmWYjMziuXZI8WG/Fk42NeGVodVKFbBfwVrUrORyoqPON/pzmXrh55v374dleOQVXFr6VzkG8042NcBs0aHqwqrUETPFSFD1QVdwHXd3y057vW7XMg1nKXF0OuPSympAKFBcBx+6i+yYiNHEeRoaw0NKPPnrk7579n00MktkNJTgXPjNc7KAW66Wz7vbQV2PwX8ZyNgMAOLrwHylf4wnLMU2f/yIAzzUDwt4uMoKfWb2m3ocsi/oZ/YE2cOIc9gxsrsIvGIhoIo1adcCxUZY2+amcHK7EK9yqMYK+T6qtSUhxx9ErJwX/ov4GtfDFYkzJkHvPeuyPexX9x5J/C3vwVfox9CEgkv4wYSLof81SEENxHV1cAiv7xT+PznD9x5T5+Gjp4gev+YS1bRTL8cf7tdvhp45x3RP5PzXKk/4Ea/qjeekN5aXFfMvRzIS0zP2qdOh1dPkl9WvPKWqjWcRyODc04v18X7MejuEWkofCgTSNgGDBoZEGBl5COPPIIdOygZOjnx0Y9+VGg2/+xnP5sO9ExjzNHp6EC7ow1qqFGaUoYWe0vEe1rtbViE82eNORyYrMREKWVFBeeaYYmCEYAkCR/8LPryUf6H+w2Sr9zPkNhg5UWy68Jhg23WXmDrP/1zhA/oPAVs+Sdw8d1AokkA05hSYOUFK31GY3ZM8uLqwtl4ofXY0M6s2pyD5VmJBWUZ0AxITZ3LAc6k0HQSOLYbsCsIDeJMPfDiI8A7PoApD8bODrwK1O+WPxfPAZZdJ5MjpwDYTjsdfXi2ebPYj7Ldn7F1wuqxY1V2lESfWFixGrjhZuCZx4OExme/BCToSTOVwTHnXO7vDz74IG688caY3liTjtQgPvaxj+HOO+/EfffdJxY3RJ/LCofHJaScwo22d3SfQf1grwguKYPUJf4ATjSkanRRzVRS2fgnKxp3BwkNwtINHHoeWH7byKVtTmyR/hppOUDNGpkZOy7QDP/ahncClXOAjmYpJ0F5jkBwiv/OuwyYc4lf3zzyHunV6XB5lOaXKujV8v7zPju9DtDWcmv3Vlg8lhBT8GhQj7DiIc8Qo7zZZcVzzTtwW9mFU0JHlBuyiNdUaiGdwcyquN8hrxqoWAbURgluuANa6ipg38vAFR8d+hWPKzKCFXqA1Fk8cuSIyJhX0dT+378E+v2yM4e2AK2ngEsVUhdNxwG/QbIAP6u/E2g7BZSEZQM3HYo8v6aD5xWpoVExuBXeF0gIT75M27EA2xYrfljmGsiyU5pUDgXT3aESd0qk60z43KxLsKnzFN7oaIHV48XBPht+dqINPz7+DG4vLcVfVq1KyBA9kLE11mBfuiRvhniEQ5D9YZ7kfJqVZHVi0mBftHbLa5uWHzrfUHqKY0PE33iBltOhpEZVNXDkoAwWON0y2524673AqjXSZPmaa4AU89ic88svAq++JM/32uuB9TG0PK+7DTh5WEoKCaN3LXDb3UGyddvDAMcwwmEBdjwuAxxDfjjR2gv7ZvR21OuyoYPH8cPu8aHb4cN9B7cJGap7apahLIpxfZEpBffOmoefHT88lNyxNDMbN5fETmpgsKlgRjEae9tBcbNAAglbbp4+I7l5bUYF8Pu/ALUnAYMeqK6J7Vvy2c8CV18tJadIJJA4mGi89Bzw1KMAg28r1wAf+Bjw9a9LT40ef8UQM4s/9znZJocx6ktVqTDQ04PMnHTptUL9ywG7JDe+8R15/HjXsq8FOPSirPDgeykpdfg/wIIbgRyFhJypHLCdDvlTn6EEXhX1hkkeknwBfFr+E+neIv3BdsPmkZUYQTIjSIIb1CYUGSXJ9Oc//xnLli2LWt02WcBgD7NXea733nvv2T6daZwDcHspyeZFu70NhwcODvWkJlsjDGrziDzIzhewSkNZlcHno0k4iQdWaJSXl0cYfnMNmKwOuSD5h5Opaj0pVRVC1j+9MrkhZ2yTdaYxecD2Sw+yxYsXj1hG7YqCWag0Z6PJ2od0nRGLM4tEJW6yYNUTs+DPewz412lKQiPQJxtkhcCUx/EtoTGXM1Sg0AIrbsBUAAm4w/2nhvYWARzsq8eKrNmJz5tcE3/wo8CV10ipWyZGZp891YmJxrlqFG632/HHP/4Rf1MmuY0hxi36f/nllyMvPw9f+dW3cNV7bsSgx4MGm8zoS9HocXPJahQYg1mBO7qbxR6Le0MlTlv7cai/EwsyIgf0G4rL8dDpE+h1OYcMNK8sKEFpEka2E47ecH1pBmtbRp6dSgmqeZdgIqBGBbxCJzwIFSpCByFWZ/AR8yCxA4RZ+llw2gfg8kl9ao3KgBz9fPQ6e3Cwbw9cPqcgvVz+e60kNLQqLdw+ufDkZsSg1qPQWDBiUuPSvPl4vSMYLNf6/Tc7nf2weZxI0U5+Q94KcznaHO1D1RJEpblCZDOxWiIu+DezLgFyZwJ7npQBvYD2vdotDZl5D+xB2YpYgzPLaAWhwb8/vhvo65J/Gxi4j+4All4CZPr7uT9z1upwwahTDFPRqm+ixY6mAOk0ltCrC6H3tsPpC1ZTmTVzoVZNjeyOkYCZui+//LKQHGhvbw8Nwp3Zg+6Dr0FPGST7PmDOtYBivgkgRavHFYWzxOPGjRvxRkfXUNXGo01NqExNxXcXLBiWtLBardCeBSL9hqIZ+Htj6EL+6kKaxI9h2/faAXer7Ke6AsCrBXb/C7D79bNJHC96JxDQCM4vk4bR0RAemL/jfcCxI8Ah+m/4+zX9e06flg9+jyceA377IFPnRvc9nn0K+NUvgs937wS+8nXg0iiyZbn5wJe/D+zfKcehOYuAvAJ5DZqPAqyoUBoyk7DpPD1EaqhggA/MQFHKcbB9RPdKMig8n9xeH9rsgZnNhxMDvfivAxvxm+WXwxxeoQbgUzPnYmlmDg729SDPYMT1xZQeUw8rabB591bQ3kPJiXHOpGNW0iCRuNBfjRkPrAyMVx04Xtj0BvCn/ws+f/M16eP0yc9LEuNHP5Jm4Wlp0uSb8oirVgG7dkWWwFPW1+3GbCF9EtbXuIDNL0xs/ulqCFZ2DLUlH3DyDSBHkXFoKAQyLwAGDwFelzQKTyMptDP4HvHnJKk6oUKoRIXL54DNE+b5wXfpCpCmyxa+AHpVBnpcVhh9LlH9wMdkB8mMz3/+8/jUpz4VX0pzGtOIAUqz1g0eQodTSs5a/fHr4P7CF1X+uMocQ1bvPERLS4sgQolEDJbHGlz/cQ02EmNVVn0Mu36LNZafZ3uM8w05OTno7u4WMikkNih5piQ22M659o+Hmam54jEc4vUZ+mksWRLbw+28Qa5/DR1RRasCUkegdjIZ0Swlw4PwAa1Br+HJDlYYWKyWiIRjPmeclnk/SYFkxhgrPUx2MPmM1WLnIh566CEhP0+OYDwwbpEYBi+/8Pkv4Gvfug/VN66GU5E1zqDwk8078NHKy4aCrToO6DGIqU6/LEM4cgxG/HPNBvzp1HF0OuxYkJ6F95RXw+JxIzVKAGBSQOgshsnEsIR1CiyQVKp8qH1L4BVG4T6oUQSVKkniQGikUK4hcgLXqPQoMq6G09svBkBWblCKY3/fJngozxBGZCiRoklDqtYEm8cGszYFs9JqoFOPvA0sy6pEt7MfR/qbRKhAmcGqPVt69Uki35iHVdkrsKdxL4pzizEvfQ6qzBU42XYyuYzcgRZA7QGM/gA571+qEejzB/dyYpTTMpu5rwPHG5oxe/7i4KKNMmTRpD3sin5eXA2vWosdp05jdaU/AqdPAfKjZMvQP6Nue+hrZYtxPoH3M1W7CG5fD7w+B7TqdH/1xuRFu30ATzbvR6djEHmGVLyjeBHy/TJ8Hp8LrbaDsHgYpNMg11CNLH3ovedGlNm6LBMvKSkZMupLd3eja8+zyEw1obwwG7D1AkeeA5a8e9hx9vWODrgVbZIh3hdOnMD7tVohaUB/qPBqEFY9bdu2DRdeOL6eKdHw8er5wiT6aVZAwIdrC2fgc7MSDDAnAgZCLRsBn19Kyn4YaLAHCQ2C4/KBp4ALPy6fX/4uoLsNaD0TmqFQPQ+oDMvcNKUA3/kJ8NUvAPv2SmKfpMbQsX1AUyPw5OPAe983su/ATec//gY85S9hVuKxR6OTGgRNmNduCD5npubWx4EWxeZCr5WZ+USEDAWJfUpvMLOM42YlVIheNWXW6nFhbgXe7qiH1RM6w1FSs8flwLGBbuGvEQ3rcvPFIxGwatakNiBNCwy6bUPzKf8tMSV2jCmJzW+FrrtIRG15G9hwDXD//UGpsWPHgJtvBm66CfjmN4HvfQ/YupVOuEzpFYSH1eUSnjYqymexusJqkX/PDDQGHljBkggoJyDMcsLKrei3Js6RCQOnAXcPoDYCOZcC6sAcbEu4GihaxSahUxuQZygTWXXbut8Ur2198g3o9DrcxO8/yfHOd74TX/7yl/Hoo4/iPe95z9k+nWlMUZyx1Q0RGoExNxxalRorspbjtLVBkCAlpiKUjsCv71yFUh6HAZmJ1P7n+o8m4dUjqP5jgherfIfdDxXWAMfeBjxOOSbzvUxgyIxv9jyNqQ0m/3V0dAhvDcoyHj9+XHiwkAgjocGEqtGAyVLct7D/sFoomgctK9IzMjISMr0/55FfCqy/EXj9CempQYjsHDWw4WacE4gmMzWFZO64xyg1ZaHZ4q+q8SdNFRqzp0zs7GyDScCrmFQ13nAPAtY6gAnhxmKZQDWO4Dj3k5/8BF/72tfGTe1mXNNLudH4yte+iv2vbsecy9YMvc4l46DbDqvHAbPfn+CSvEps7myKSmyUpcQ25CowmvCVOTKA+cSZ01j56jOwejwoN5nxwLILMCc9CY3oicCMFUBHrWxEQjbAC1RfOCVIDUKlymWIcWR/fGw7sOslwOUE8sqAi28DzKH3R6VSw6AJaub1uShJFgx0qWOUaLU5etDl7MeVBeuRoo0tucP3Hxs4gzO2bhg1eizJrBhqg+FYnlWN2sFmIesRCP4sSJ8BvSKzdrKjwJiHK6o2YHfvblSnjjCzrL8t9LmQZPFPTun5wDLppxECykS98le0dXQiW6WCwdEGrLle/m1hZagRL/sBJXOyFEE1cwZ25S/DMq0JRo8NyMgH1r0TMES5t7MvkiarLUdkmWblCqB4rvyMtmOArQcwZQIFNGE/dyUDOEnoVMMb200WWNwO/L5+M+welwgiWK09eLB+Mz5bc6monmi27YfV0zlkvNzuOCpIz3Rd6EaSmoybN28WG1pupEk+DB48ijkVhYocagYHewGnBTDE1njO0ukwyOztAHp70a3XoKi6Cpl6Aw4cODCkcUvQN4aLj/Xr15+VSg1mj35u9mLxGBfYD/nnqQA8wEDYWEAwe5xGx6xeS80E3v8loOUUcGy/zIbPKwKWrIve9xgIKSwC9u+LntRAIrRTtoOkwSD0Fz8LnDjhN9wLm2M5DyWKY1tCCQ3x91wI6qUfVmGoKSllEiGqGBMLcK/JnoHX2ptgZeAkCkYiWRALLP++LH8V3u7cgy5nH/RqHVZmzUOBcWqMHSMCN1PhlkO8phs3RV97PfUUkJ4O/OQnwddaW+H75S+xc9curF27VvqCWAaBv/5ZttHZc4A770p8LVc4B6jfLPuY8m9S/eTVwB7AHjAJZ39sALI2ACJZg2uWPL+nWQBaqKJUA2nVeuToi9HlbA5+daiQZyhFp6MX27oPD62NXvz9Y/jivZ+bEnq+PMfPfe5z+NGPfoR3v/vdU0ISdBqTD32u0PmFLT+8Nitbn4MCY754TCMSTFji2ot9ktntFRUJErujBMcsZrHPn5+8LyGD0iQ11qwJxiaigobga98LHH4NsPUBGYXA/MvGUeZ5GpPNNLy5uXmINCOBxiqO0ZIMbLv0mKTMFY9FjxhWHDGLOfB7+lpxjxMutXZeY9klQPVCoO4I0NooYwLzVwAF5wjJTAn5zobQJJzZazFVQBlCSkKvypmD3T0nRGJysTEHl+QvPdunNiVA6UaSmOO+Bnf1A12vBGNx1logYzmQMn5eh08//bRIQuB6fbwwrpEYDtT33vNZPPjHv4aQGoRaSAQF2ceatGx8ec46/PHUPjRag3I2t5fNRXVq/KyP3T1d+PL+nUN71iabFR/c8TZeu+RqmKL4N5w10Bh85buBZr+UQE5lqH7yuQp6JGx9JvicBNarfwNu+MSwgWatShdh0KulCXgg4VJokPuNwX1unLKewbz0SKPNbscAmmydqLe045S1U7Q//u3h/ka8p3x9VGKD3i/vKr0Qe3prhRdMaUouFmVM7L1iVliLvQN2jwPZ+gxkJWPk6gcHRzKkIwYrJMIjQiQPLv8okJEbef/cLnFv3TYbeqx2zCnKBY5uldI01UuA0pnA2uuBLc/JzCca3l79gRDCgh4c3ow8pF15ffzz4+fXrJWPAHjcwy8AXbVB4/rOWmC+n1gJwN4P9PqlbrKq/N91asDlHYDT2yUqGYyagiGpKYfHjhODR4VsG0nCAmMxqszVI9aAdng8ODpAg1lgdnoW9GOwmau1dIYEcNl/LR6neH1+euEQoaHEgLstgtQgMjMzxUaa2VRs6xmZ2YC1MdJjJE62yw8WLcJ7tm2TTyhB43DAVFaMT+3ZjF/NXS4+R6kLSUKDFRrnbDDNa41yDVWAkucIQEn0skqyrEY+EsEttwGvvyYNj5UG4wRJkZFmw7H6g5n3hLhHYcTGRQnKNtI7pIH+BWHgpSldBMxZL2UgRwi314sfHdsKq9stZQ4VV53zVIkpFXPpUTVG4FyQpkvBtUXr/OXg50b79flIjzrpxAUVCasdzwEttYDOCKxYBOzcGloheOV1soommnYtX3v1VeAznwm+VliII+99L2Z9+tPQBgzuMjKAL/9X5N/3dQPdHUB2PpARY/2qNwEr3g3sfQxw+ysv6Oc153KZQSUIDXEy8h/6jZHYSKkWY47atxA+ULu4V0ieqVgNpIqeoDEjZb7wBRhwd0GrojRnFYyaVJyycvMscXjTXvS39eKDH/wgpgp4rvfffz9ee+01XHZZjKqraUxjGLA/KGHQcM2jgsff74qMxag0nwUPoCmE0tJSQRBQnic9PR39/f0TUq1RX1+PSno0jQD0+GOlb0JIzwPW3D6iz5nG1AYJui1btqC4WCYMsFJjLMAKjAChQXAPQ7P7APbv3y8+O0ByTEOBjBxg6cRXx08ICmcC694N1O+Rctul84Cy5EnbswVWMVEOdGFGGRakV0nPvnNkjzHeYNyL1WAXXXTR+H+Y5Yif0FDsf/r3jyup8eMf/xj33HPPuFadjXu0/+Mf/zi+9Z1vo27HQVStXCA26dx6Ls2sRIdjUMiNBLIQ56Tn4oeLLkOrfRCtNgsKjGaRUfZaW6PQk16WlS9lqqJgU2eb6DjMqif4GZ1OB04M9GMRg1xjhG6nRTyy9WbxGBFSsoCZ5+iAHAuNR4PBZYL/9rQCJLDMsStxUrVpyNXno9PZPmQMnqY1Y6Z5Ad7o3BoWcuP9D82xOmPrxEute4YCqMKjXGRiyb+0eVzY33caF+REmtq5vR64fG4szKgQPhsTbQxIQuPNjh1od9B/QmJ51nzMTE3esIxBEGZ+BAKwyp/jYsZKoOMk4LL7g0JeYPYGIDPGYsvSCzhs2HyyEXlpKXC6PdBT87qjSZIaxOKLgLmrAJsFSM2IMI4PLPhGjL4zktAQX9bf5rpPAT0NQLb/+g20AseeCfp0NO0A5r1TVnVMcjg87eh17RuakCzuOmQbVsPn02BnzxY4PA7/rfLhtOUkLC4LFmclr8naZrfi3r1vo8UvDTYjJQ0/XXIhsvXRg2eJItLONvB6IC85PK06towKNwKUgLrgggvkCwULgLbDgCcQIPcBhQtlJcEweHd5ObZ3teHvjafhcXqQmamHSQPsqTuJrRozrl4RLAfdvXu3+LxzltAg1OmA16K4DyqgdAZQ6+9XAeRWjy5rkXI9D/wWeOZJun3y4kozZ+Kmm4ErrhrZcW0KiR6xLvC3KVaF8bh3vDf+MTg2vPFXYED6gYU0W7anRVeNusqy3cE1BaWg5JVO0QBOr5SlouTU3ZULxoRIDGw4WB4ewIRvNurr5T0uK5VG8WMEt7cFDi8rDjjWa5Gy8RRUlGUTEk4WYKAL+MQngNffAhx2YOUFwDveBXR2AX/6E9DfH3lQxXUimH3GLKN584bxDCM2vwI896+gVMmNdwKrYxBo5mxg7YeAgXa/rGOenAtd/vYWAr+ZeOCZiqvpxDYgfG+RqQpFYe9npZzNLetQ//PAo/j0pz4VIbE3mcFzpafGd77znWlSYxojQompCr2ujiCRrALmp89Hpj5frPlHI2N7voDZ5MwqJ6lBX4uTJ0+OO6nBSlkaONM0PNkxi/sfzoX822lMYzhwjU99e1ZojBWhQbCyiRXelLBlO2Z/Cewnent7xb/ThMZ5ivxK+ZiCYFVTQDqJ7fkc3iGPOXbt2oXly5dPzId5uJcIS+ii1HRg3zLGoDcR1S6ee+45jCfGndRgGc3n7r0Xzzzwb7zv6Zth9bqwr7cVL7bVikexMR13V66BWRFwKjSmisfWrlZ86/B2uP1ByTlpWfjBonUwRqm8oIdGNLd48xjKgmzu5DnLUn3iioK5uJAmytMIRXcD0LhbBhUZ8CpbKrN3oyGO9wkHxfkZS3DG1oBBdz8MaiPKUiqFuaVZkwKLJ1QXvEhRHt7vsuK5lh1wKaoUAomaDI/yVZV/Yx+OAZcNT57Zin63DObm6tPxjpLVQrJqolBnaQohNIjdPYdRZiqCYQTnEVgwsVRcqYEbF5RXWfVeoPWo1JXNmgFkFg/zfkr8qFCUYcbMgmwca+1CQUYqssKNghkYjxEcpy7vSErKh+C0xn6d95uZ5afeCmqpE/xujVuAWddgTMBxa5yIsD4XxyGl/4MTFnctrO5MOL2S0FAuKtocLbB75sCoSY6M+MmxPWi3B4PDrKL75Yn9uH9+YnqPDo8b/W4nUjU6vNh2EicGu5ChNeKKgiqkaQ2wuJ2CgGZ4LlVrwMzUPBGAy9KVo8dFr4ggMvXRzcLYnsn8c3Ngova9MR1YdCvQvA9w24H0YqAgsbY0Jz0V5SkaeEzpcLS0wd7QBE2qGTWKDCou2lh+fjYkpyYUpgWApR/w+isnmQVetBZIWQic2ix9JnKrgBlx5BsSQfkM4JP3yJ8tFmkUnpkBFJeM/Jjz5svANAmSwNqAa4ffPAiUJWg811YPdPtle5Q8GzvYsmvGZPFnVGsFiaGk440aFd5ZUo2bS0dB7MaQ3KCG+FnBQ38BHngg+PwjHwU+/OFRH9bjG4DDezD4gssOVXOgysEP3iedE7j/+6GvMwvyL38B7rsPOHAgdJHwAYVZN/jrA/F1bptPA8/+M/icx3n6b0DFLKAgxpzJOSI9TMtWkwawSjXgZyMPBuhHKP0ZBZTgfKvjmGjSx7fsQ8OROtz7/L2YamDm189//nO8/vrruPTSS8/26UxjiiFNl4mFGRegzd4kpC6z9QXI0lPabRqJQpkkFUigGm8ws50ZrfQ6OHr0qEiCSjTJpKGhIcT0eRrTGA7cizLguHr16jG7ULNmzRLVGNyHL1iwICThcN++fULWdhrTmGoQMYdzOdlvnEDSlAlnE2YQbsgDnEo5ae6RssaF0OCY9t///d9CLpacwHhiQqIyn//c5/GrX/4Kg3sb0FqTiU5HMEjWah/A080H8e7yUHaKOmw/OLpT/BvA8YEePNZ0Eu+dEQwwBfCOknI8WH8cXU6HkFQgriig7MrYMOut9v4QQoN4ue0Iqsy5KI6R2e3zueD2yYw7rSobqjAppXMSPY3AvieCz/uaAZcVmLUSOL6DNzYYYJq5LLpHQhiYLVWWEin7dFHeKmzt2oNuVx8MavpjzEOuIZgdRLkpSnuEQ6lAwYBqqSkn4j2vte/HQEASgllBzgFs7DyMywuSz3YfKSxu61B1SqhMjy0mqcHqjk1dJ3G4v1mYG67KrsLCjNKQTUZA+zYpsCoprI/GBO/p8iug+s+/oFJrMKcoD41OFTrU2TCfOSNMnYcDdUUpJTQqMOM1PNufN75jD9D8NqDSyLYYwlT7AEeUjN1k4egBmt8CnH2A2gAUrgHSZoy5zErYq/B47fAKqi46PCH+CInhxGDfUFUTwU8+PiCziOLhxdZ6/KF+v6ie06lUSNHSnJZuAyoc7G/DZ2pWY0tXnazYM6TiuqIFMPolovIMs6FVGzDo7oAaWmTrK2HSxM78o9fFnj17gpsOYwZQlXwJ5+UFJfhDPQN9PhiKCqD2ejE7IxsV/nmExAklD9atW4dzCTSXt7lPwINBqGFGirYGapoTp14MeGj4xhI3Lni0QJYZyBpHKQYu6uJlwyeCnBzgez8Evv1NoLsLyDRLNvvLnwRuvAW4/f3xF3CsTgtAOVTMvRAoXzD6c6RKVl9HhIa7w+vDupxREDoxwLJwpzMJL5GxwvHjoYQG8fvfAayuGg15Ldpu0JBQIto9VcW+1yS4WK3x2mvAM36JzBtuADYEjeLZ73nt4s6bLZS9CwPnXmo/xyI1ooHZ4Rlrgb4tgI/3i+a0CwD92ElR7Og+KVcU9NL4xd/xpc9/MURib6qAGa5f+MIXxKbp7bffPq821D5fHx2poEIKVKpJ5h84hWDWpqMqdQzmnPMUrHoISEowIYkSVBMVPONegYEgEhsEvQ/iyVuQ1KB06DSmkQiYwMS5n0khY1XJKCR6Fi6MWFPs3btXkBxM1prGNKZxfuDQoUMTV6VBmGcDrgHA7k8e1aYBWX61izHGK6+8Ir7fM4H91VQnNcjMfPGLXxSbjhv+8A0RGAuAPzdYwzelzLJ3wspM0BCocMYW9NtQIltvwONrN+C3dcfQbrdjYUYW7q6sGbMNTju196O97hiISmp4fVZYPTuGgo/UeU7RrIRaNYIJ0eUA3noSqD8stbtXXg7MSyxTesJxZn9kMLlpL1C1Drj2Y8DBt6UcRFEVMG90gUGzNgWXFayLKaXEoL6SwIiG1dk1mJkaliVJ+01HXwSZ0G7vw0QiXZcacg6EzGiP3Ybe6DiKbd11Q8+fbdkr5N24yWBJ60QFLTzzL4R6gEa6XsBoRlnNMlGVwawqehHMnDkz6saDATdq3Y5aU5ASb7MuA0685q+YUAFpqYDL4r+hHn+VRkj6NZAyygw9+uQ0vuwv7eNzhyQ4ZlwLGCPJs5GAlQwalRken/+7+KFVpyNLmyOIMJJbwYw5+hcZYdIkP/YUGk1iLA6M2Wx/Rab4xzna34Xf1VEeS8Ll86HfRZ8aeZlZPXW4vxPvmxF9HBObVX2leCQCtiU+QkrEeS8GTskKHFMBYIqf5Vydmo7fLr8QPzl+AB0OO5ZkZuMrc5ZAo1KJjfvWrVvPuQwqn8+DAddOeEES1wcPBtDv6kGGjvJaOkA7dtnh4wa287ffApqaKIIMXLBW9vmFi4GHHwO+8RXgKH2sWBLhAZ54BMjJA664dvjj5pRJaa2ARB3B5xVjZ85eb+kT7SsgnSm+DgNFioSOaLB53KgbHESWXo/iBPokweAPJZQmGr66Oji9Xjh8Pri8PuTo/EvPurpRkxoqhCWL6DRwl+dC00C/Ib/sGK9tdRz9dJIYCiJDiZ07dwbl7YZDelZyrw8HfQ6Qey3gtQHU/Q+TwXF7nYL0ZXZ5qjYXOnX8BBElnF6XuDpH396NrlMtuPezn8VURaBa46WXXsJVV41Qrm6KweM7AUBuSEUr91VArZquHJ/GxIMVsgz4EjQ8rqlJ0E9rjEAZqblz54r1LqWvGHiOlTzF4AqJj/OJ/JzG6LF48eLE1wEJIpzQIDHH/QsrwacxjWkkDibr0uuS+3TOR0K1YYqAibzs9yQ6JwwqNZC1CvAulvEw7h/GsUqDHMB4V2kQE6af8elPfxo/+9nP0LRpLzLXzFWGEZFBI8cwpOtIAmjFxl251adpZiwUGE24b974ZNJn6ExJvW73HIEPQekA/mz3HEWKdlnyH/7qw0DtgaA+9GuPSKKjpBrIKuDMiEkDEfwJ12nzm9HwXNe/a8w/MtbitMJcgDStSUhJKcND+YYMXJa/GKk6E/RKg1sFUrVGOBSmtQwU81gTiRkpJWixtaPR1jp0DquyF0M/jMbvnt6g8WcAe3tP49aZ/5+9t4Bv7Dyzh4+YzUwzhpnxMDOFuYGmTVJuU8Yt7Ra+7G77L2x5t9xum3K6gbahNknDMMyMHjOzLYvp+533ii3Zkm0ZZnTyU8a+tqWrq3vf+77Pec4564Skdd26ddMiDSc5oZlfC0QtLNhVSWLl4sWL4t9wz1BaYu3Zs2fqsgqKFgO586UwcJ4BF6NYYgU7YZRS0ZvQ5QAVk5yw2gcAT1h3dwCWjikjNYhM1QoMOA8HSVOVLBtGZRVkMgVWZq3DqaFjcPmLVmqZFmtySKgm3/nzLwtWiUwNh7+oq1Mo8PHqFeP+3enh3mB+UgD8iuIYelYTrvBC8RRgxYoVQiIuLGL4mTY/AzjDyOiiLUDm+EWf1dl5+NPGSBsTTpR4brK773KznaKa0ItIuzYfHHB5e6FWjA5mn3XgePbV/wRefIFeZBJxccedwOf/TZqkMWz8THTQtww4emh8UsOQCWy7F9j7N0m1wbnKpjsB49T5hedpdEF1adjeITvGvCiAY4P9+NCh3RhgIDaAt5dX4itLV8+aQg0XGE1NTWKyTsj7+6G22aH1X/xn7TZsMhqh9IdvTgYKWQGpfvgwEiSp3Rs3Q6HvAdrrgN4hYNABPPInYPNVwNotST3/qVOnRBEsoeu+ejGwbC1w6nDoXFy5CZjvL/J1XQROvwg4LZLl1MpbAcMY5xLHbMVoObrTa0OjZR88QsXBdy1HhX4d9MrEz0vOj9ptA3juhw/hS1/44pT6hU83uO//9m//hgceeAA33HDDrLkOUqnQCBAawW0MjffRvjGt2Ehj+sHxkUGnLMj29vYiP3/6Lbx43ZNQCTRP0ZIqvHDc1tYm1hmB0Oc00kgULDgGrJtToaIgocFriA1/aaQxV8ExOFXXSDioCLx06ZKoNfE1ee2Q3KaFU09PD8rKylCeqM3wDIK1BV77M9YsKU9trhQzNOrr60X23XRg2qoz9HH+0pe+hF/96EHcteGb0oJPLEhluLV4dKceu8u/WLtOZGq4/B2Li0zZuLtsZgb8Cn0OVmeV4+hgS7C3e2VmGebrYxcqvQgPWCXYTzeBDkmqVepYkIkqRL/xpPRvdgFwx4cA4yyxDcivAfoawjbIgNyqlGULjAUW/99cugUH+i9gwGmGVqHB0ox5KNPnjlvg3ZG/DE+17xcd74RKrsCWvMWYTjDEdXPuaix0DsLucSBLnTGmSkMgBmHh80/IuOAgyJamWrUxFnHCGxD9RNnRRRl4RUWF+H1aR1B+N6XhfSQd+XDGUHix8FG4HDCVSl8bCiYXeEzE/Hsf4J5alY9KbkKeZhvcvmHIoIBSlhEs5GSrc7E9/1oxseB/yjjEXSKozcjGb9dfiz19nWLc25ZXgnzt+OSeSakepTIiAqUm/mxl1miF1JR1PQ2cAZzmyF/o2geYKpP+jGk9Q4UGCY1p7aSYJsT6nCK2s5jfdkwqxBoLAN6vZ2A8j4tDByVCgwjYDT75BHDLbZIKgOcFVWHhtkssrifayVNUDdz5ecBpA9RT183CvBkGhO/MK8NrPS2otwwFs57eN38ZTKrYFhpUOX3k8B4M+QkN4s8tDVielYO3lI22aQzHpKxBXC5g1ys0MweqFwCrJKk0lR8XLlwQ4zbJDAZq81qkZznt4MS4tGkT0N4GPPaY+Bub14uja9fBabMh99w51IZl1iQLErk6xXq4vE3wwQ65zAClogKydXJg36vAi/8b+uULpwC3C9i4M6HnptUcr/mEi2Cc1973EeDUIaC3C8gvApatk86ZoU7gCOdt/utquBM4+Biw/X4p6yUJdNsvwBOWt8HZZYf9NKqNiVuqrMuuwj/+9iQs3YPTtuBIJT7+8Y+Lxqm//vWveMtbpr55ZjbBF2ctIVlRpUmNNKYfJH5ZZCKRcP78+RkhNcKbp3ifoypjyZIlouDV2toqwsXZcZ9GGrMpL4AEHJXmaUIjjbkOrgGmktBgbYhFca4rqL4gYc1tHN+ZdROrXsTriPeiAwcOCPUGm3mnyjZuqo/V3r17hZ315diI4/F4RN2fj+nKcpzWltOPfexj+OlPf4q8Pc1YdfeNosC1LLMYeZrYb3ZjbhF+ve5anDH3iyDwNVkFUE4x+/dSVzt293XBoFDhvopKlOpih7TwhLujZCUWZxSj1zGCLJUOGUotOuxDKNJmjCqS05fcA0f4M0COCVxUPNGj3JwiMNgLvPSoRGzMBrA73mUDmg4BXgbJVgKLrp2x3dErtbiqYPzO8miU6HJwX/l2NFi6xGdfbSiGKY4qJ5Xga4fnhIyH5ZnlODwYGZK6IlNiqzn4k9UuKioSi45Ukhq8gTBQeSwUFhaio6NDkBuBwlHKukXVRiC7BhigjzghA5QaoGApoJrCm50mB9BkAo4wEoPXr7MN8DoB+dQFzctlSqhl8bNHOLHgf5NFkc6AN5dVJ/U32/PL8HT7JXTaLeLtM5cjV62GQuYWgeD3lC9DjXFqJdY8p/Py/FZJAZuxcJCgpB2YPLHP2+J24WenjmDfoYNYt2kTVvm8mLr+/NkDpSxLWPiEKwsBBVTyHMDtAI4+KqmdeC/qPAMMdwCLrk+JVHVC6OyMs71DIjW4n3fdCzzyR//9lA85cOudib8G/0YzdePEkYEO/OzSYaGAYmPHu+YtF+TkkMuBRaYcLMmIf2102W0iOyy4axx25DI829EscsQy45AhBK391q9fn/wOkxD66heAugtBO67+m27HuUXLxVjPwhHvLRxz4gbdfe7zwM6dQGMjdOXlWL9xkziu7J6lCmrLluQUFOGQcSxUxBijXvpH7G3jkBpcNFFNSEuVVauSVP9yjroihq1e96XIuRyJf9sQMNIHZBb6t3kA5znA2ycFhasWAorcoE2cMACUyeD0RjfNkOwKZYAlAqfDiT9+86f41je+OSsXfMmC593Xv/51odh405veNLXNEbMMzNDwxdmeRhozAWZbcMwMt6OayXElEMBMpR2zC3ifmcqg5zSuLLhcLgwPD0958ZEOCmw0nE/b1DTSmMMg8TBV8y424bJZiiQGCXMqL3hPIWGRCGnCv+GDz7N7925xL5hNtm5sBiPpQmeJaW2W9PQBjgtSVp8iD9DUShmzKcBvfvMb0RTK2v90YVpJDZ7s3/nOd8QbvPjO9yRUwGRBjY9U4LcNF/C9C6egpPc8gEdb6/HY5mtQoY9NsvBmtshUiEKNCQ8178Ugi/cAirWZeHvFpmDILaFV1MLqORSWqaES25IGCwiLNwBn9sf+OQt1XaMth2YMvOEzUDrRUOlZjCy1EatZDJ9DuLZwiSD+ooPCo8FBlAw2pXqpQiI2V5zMkdRgB/FkunUTwvyrACqrLN0ArcSKVk0toRE4/3m8B0YAj1fqCKd/vMwHeKxTSmokgosjHXij5zTsXheKtNm4oXAljNNgo6ZTqPCtFTvxj45L6HPYUGnMwvWF80UBN1Xg+cyJj4AgAn2jZZaKxM53t9eLdz35CM63t0G3qBptXS3YP9SHhzZeA4Nydqo13F4PDg7Uo8s+hHyNCWuyGa4+/vkml6lhUq6FxXNa5LTIoYNBuRRymRboOg4EsoQC13P3eaBivZRbMxsQT65fFVbkJqmRkwscOQhwzLvpdqBqZlSfVGf8uO4Q3H4VILM0ft94Al9behXm0e5qHGQJclDK4OBU1MjhRQacGOrFfXtfxIPrd6IkxpyJMmeO+xOyT3vtJeCSVLA6NTCIEbcb2Y/+GZv+8Bjk+ZKF4LiLGe7k+g3SIwwkIumDHgvc56NHj2ITlR4TQZiaZcxtYeC9iIUG2phQUThloGIu1i0xXKXhOAZ4O8OCVfbDo1mHAXc9XD5a6clhUtZAq8iAw0v1YcjIVSNPbq7yox/9SNx/3/ve9+JyAd/Lj3/8Y/He6OF7uUImywJ8FYw8Dts6L209lcaMIlDwpe0HLTWYc5EqsLN9vDUMi1/8PaqW00hjMuC8KW7DxgTAYh8zOlikLS6eAzavaaQxDWHXJEaOHTsmxnWSEmyYCmAihAmvWz7i3SfYDMl5MJt9pwsnT54UaxtaTk2rnbVnCLDtC60b3GbAZwN066b8pUgA0w72F7/4xbQ2GE27Ofhdd92FH/7wh/jWt76Fb3zjG5gpsGj1w4unpa/9xRqbx4PfNV7EfywZO1Dy6Y5jGKIlhx+d9mG81H0WtxaHFAG0QDAotsDt6xPfK2W5UujqRLDzzQCJFgaFjwwCjvCOPIYbz10v5LkOr88Nm6cDXjihlmVCQ+ZzBkHbtmsKlohHLJIhwHBXVlYGO5hmktSgDRZlaZSGTwnBwpyGrsPAUKNECOYvB7L9hSl2ZxdOg/RclQmoo4dWeqNPb9dau60fz3Ueifj+yfYDeFv5DmFtlmqw+H9PeYiosrkdONB/Bv2uYZiUeqzPWYxM1dSRhlVVVWhsbBSSVGTXArYuYKQlVFAsvWpc2yQuftltuKuhDpccVuQsXQCFXAaDAijR2vB46xHcXLwYuZoJWvikCDaPEw/WvwaLP6D+vLlTZOm8b/5O6KlIGgcKuQkZ8hiFYyo1eK5EX8vcPluweAnw4Y8Av/yF9D33918+w0Eu9DvcdtX10iMWnHbgyAtAbxtgyALWXg+MoZaYDBotg0FCIwAe3YsjfQmRGlStfm7hUnzn/CnolZEyzgGnAz+8eBLfXrFp1AKaeTMTtt7o7xUKhObhEajlMmzK9RNaQwOAn9SYKDjhZUcvJ/nhk1/6snPyP6kJ8ZrNwEtPh85fngdr/blJZ84AFy4AXMywg1cmE8eJhAYXG1MuBy9ZAtTvl/J+xP7QmrMCMPjVduycChAafvC3+l2n4EYgf8gLs/sCMlRLYfMMwSmIDd73VSjRJX4vZ9YJ599PPPHEqLDSuQy+lx/84AdinUGCYyYtcFINhWwhfL4Cv+WUXiI60rjs4PAMwubtgxxKGJTFUMimtzFmIuDYySJRS0tLynzNSWgkcm/gOoSFK9rcppHGZM7pqbDVYbGPilk+F5s1LkdL2zSuLHCMZQ2H5/RkivRcf5Po4/x7KuelVEMcOXIkQqnHfeZr0QL99ttvx3SBaxoqRmYk18ntr4eEw9MprT2meF7BGj+bGu68MwlHhLlIavDGwEUHT9oPfehDwnd5JkDbB1dUoYYn+WC473YckMQI9yHn1+22wVG/RxJDJZsC9o8X96abpUdvO/DXn0i+0FwUc9294y5MF7rsnThvPguX14lMVRaWZa6AVjH9tkyzhdDocx6A2xcKKDX5akRg8yjQOsJ8DnANSsVt0+KEu8anbH+93mChhv/SkzC6kDTVi47xwIH96aefFuGeU4KOfcDAhdD3bbsBmRLIivGZpAqGKsDWBjgCBSoZkLN+2lUawjoNsuBYxX/7nSMYdlmRpU6+44jj4z+7GvHPTsne7PrCebi5qDJm8Y/BxyeGWtFuG4JJqcWarHI837UPZrdV7IfZZcWzHftwZ+l2kXUzVf6NwYkQyYuSqwBHv1RE1GbDJ9egtaVFdILwwffDoHr+yyIfrwUSayT8arJXI/+sE2p5qPvQ5ZNh0NWDR1v6cFfZZqF8mS14rftckNAIYMTtxOGBBmzPn4QCKrM0itCgbZsa0Me3PZsRvOs9wFXXSLkNZeVAaWnif3tiF3DwGamELKypWoD2i8Bd/wLop568ov1aLLzU1YprC2JfT9H4YNUiLDJl4osn9gUzx+C3eWuyROYHmc1mQWjQdmqsTkPaK1Dqzd/v7+8XRAOVT7xWLAoVzD39UMllWJHlPybMKSmawMT8wkngyC7pvFq1BVi8SnQr0jeXk2DKxUm4837FLltBUk4Ut75FUmbsfUX6fvPVwM13A7//PfDjH4d+74Yb4PyP/xDZOSkhNAidCdjyLuDibsBuBrJKgAVbwmzcRjcB8JN1Q8rCCkEGt28QlYbNsHkGRJ6GTpEliI1E8ZWvfAVXX321eFyJI4moAAEAAElEQVRuuOaaa3DVVVfhP//zP/Gzn/0MlzNIZMiQJjMuV4y429HnPBVcXwy7GlGk2wSlbPZbqzHXghkWqQLtDjl3G2+sZpGN6rvwjt800pgoxjrnuOallz9/h/YynEPRZpnkHs9DBhiT7GM3+7R2aKeRRgrBeXNpaanIrhgLHIe5nuB9gettri9Yf2JzK8fzzs5OcW1MdaMNicNwtR7X+yQWeS2yBj1dxCIbJlljmBFCg4jXaMztU7jkaWpqwv/8z/9g165d054VMiOjKk/ae++9F5/97GdFqN9MgF3EC4wZqLeYhY0DwVN+fc74nfYs0rE4GBL+y5A5XXkLeSXA2z4HXKBNgQeoWg7kTY90cdA5gBNDR0PfuwZwZOAQNudum9MhN+xybrb2C5XDPH2uCAVPBFZPm5/QIKSzweyug05RFtlNxfOr9w3A0eXfIJOK3oU3TGuhm4N6eKcJJ1sc3FPVScXwM6ow2EEfnAjSj58Bqbxe8mtgtTuDN7ZJg8d58NLo7YN100tqsKCetx1wdEsZDqpsQDX9nf08n5PZPh6e6WzA/9afCH7/y/oTYux8U0mklz0/66faj+PoYAsd4AWJcXq4CTpFqOjObQ6vEy3WbiwwTc35R5KO9xNOUujpzGKhT5MjOr5bL5wVXdg81zj5YnGX5yPPf+4vC76cbAVClD1dLdAoIse0drsP8/Q+5Kh9ONB/AbeXzB5/5q7wDJcwRBMdSSOrFKjZCVx6Q7I6VGmBpbdKxMZ04Px54B//IGMFXH89MFa+AcexZMeyhtPAnqf8yipZaBxx2IGGk8DSrZhq1BizsdiUhzPDPcFtbh9wZLAf+/s7sSk3sfv5jvwi1JqycMY8EJzD0JZqgSlS7cFFNom6AKHB852FcHmYjyqzjWgBxXsBpdj8fV4P9CHnNZF/zQ0oG+iF/sVnQ4TGp78IGJNUiZ4+DDzyi1Ah/+xR4M33YyS3TBQASKiwe4qLIxIbJDm4PxMGrZ3ufrf0CKC1FfjJTyJ+zfHcc9iTm4stn/jE1BcbPG6g5ThgHQSYI7TiFil3YxTUAHNsvAPB+YRkjDoacihEjptBmbyaiB1rv/vd74Qi5XLFd7/7XZGF8sEPfhCrV4+tvE4jjdkIjtP9zrOB78T/PXBi2NWAHHWKrVqnSJmdiFp7omAR7OWXX8bOnTvFmB1vDcoCGjt100hjKhqneP8Mt9ghkcF5EtcSbMKgZSXPRa59mQHAQi39/Hm+8t/LSRmZRhoEm6E4Z483BvO6OXjwoFiDMHuJ1q68Vqju4DqDBDi/5/ZUXR/cB4LXL/eD1yLXRpO1y0oUVAvy+EyppW2yUJUA7qawDTJpzTHFKo3PfOYzuO+++7BmzRpMN2aMKv72t7+NRYsW4dlnn8XNN988I/vwo9Wb8NHDe9BolQrTby+vwr3l4xc/bypahodbDsDr75BUyxW4piB13qGjQFuMddMfvt3j6B7V+W3xjMDqscCgnFvZEwF02Yfxx6a9sHmkkNxctRHvmb8ZhgQsW7w+KhFGp7h7fY5IUsPVH0ZoEMxXsAC2VqmrP+JvnfD4hiGDEgpZ5pSTReHPx2JvKoOTFlRW4NLev6P3qAIupQFZRiOqPU2wOpzINunRcewVXPIUYmOZFjjTCeRVAQWLUrAnM0C4kTjQJqfSsnt6YfPwPJHBoCyFWh67mOf2umHz2qCVa6CKQ4pxbBp22eHydyfwCDDeo8pQCKNyYgqhZzrqR237R0f9KFKjz2kRhIbYD/+1MeiyQhdjshKueJssSNi95z3vEV3pgW4IEmskMlgcjWVvxk4NLrxf721Fw8ggckd0uKFwPhwilHc07F7pare5x1f0TSfy1CZ0BrIvwlCsnYIu3pIVQOESwG0H1PpxLbymDMeOAR/5SKi75NFHWa0Errpq6l6j8Uzs98MLxh3dIT81oPXb1flV2N/XLazNSEjY3NL2dluMgPsx8O9L1+Ajh95Av0sir0q0evzLgkgbIlpO0d+cj9LqTHQ7zsILNxQ+LfrrVHDafMjKyhIKhXCQzAiQfAIf/ATwpruBgT6gpAyw2YH2dsm+KVFbhtf9wd3hxa7Xn8HAVW8Vr8Vrl0UqLjZIZvz973/Hu971rqm9D7a1Rbw+r/99Viu2ZWVBNRa5brdR9iJls1D9se91YMQMLFgMLIqjJuH4e/AxoL9FOs84Z2Rg+No3hyk0wsPo1wHOk4CnVwSFy1W10HsHYfUGZOOcgcnR0+rDqyefwo033phUQwAbG5hp97nPfU4sIC9XcOHIpqmPfvSjIoR+KmxD0khjOuETurvoeYgPHrHumH1gZzoLudMFBitTUcguYXbasmhFVR//ZV4nr3neT2hBl77+Ewfvh3aPza8AlJp/0pCwZcsWYVfzyiuvCNKcX1PhynORzXvRx4qNGtxOTKevfBppTCeojGVXPq+P8POc4zPX4CQqaHUefX+YLqce3hNIPnJfWPNiYyP3l+ue/fv3i/1OJUh6ch9mXC2oyAW0awHHWcDnEkHhPs0ysR6U+eQRjW6vv/66aA5lw2cyeOaZZ/Dqq68KEmcmMGOkBm0//uu//guf+MQnREee6KydZjAQ/Olt16PbYYNeoUSGKj5b5fC4sauvDcMuBxaZcvCByh24ONIlOiNrTcXImC6lRorh87lh9VyExzcIGbTQK2qE3zrBzsBYiLd9LuCptmOw+wkNot9pwctd5/Cm0vG9x1XyDKa8RmyTQQGFLOpc8MYpjnlDr0u4vf2weI6JfqxADotesQqyFB1fdq5z0jUWfOgmx8y+KJa5ACyDDAmc6w4L1srPolE3hPbeIWyorYB5qANnuwZg0KpxvqUbOSY9ts/zAMK5jWqWOsBpAcomyO5yQsn8jP5Ad5sf2bO/eGN1t2PAFbAZkFRAeeq10CgibX66HV04PXQcXqErAxYZl6BMP9oreF/fRZweDvkn8iwt0GThpqI1wYm3x+eF2eWEUakW4fLjgZZSiWyz0u4pCk6PDAqZQpAtJDJYmqNipFSXP36X88VdQF+zVFBfsBXIit/Jzo49+may85yS1h07doy7KPv5pWN4qbtJjOV8P6/1tOD++bH96TOU3HOgTD+z2TnR2FlQiwZLT4Qyo8ZYgBWZU+QjzY53xTQT1z//uVQUDsiG+TnSMmgqSQ3e83kKh2cuCCmuHChPBcEqYcDlwLC4LQQ68jkZ82GeITlFV6UhA49tuR7HB/vE+bsmOw/a8OBpP5FNUu/UucN49vXnIFdI17rT4cKSZYuwpuTmiMnsmCgqBjKzAIYw72PgHADaQ/3wh0BWAgSaI4ZyyOnAihUrBOmyefNmUYhqb28X3VsMCpzyogrVPGE5MYesVqzQaqEaq8j/2P8Bf/ytdC6S1MjLAMyD0vNw233vA667dfTf9VySCA0iYBNGUmOgFciJoSqihZQmdP/rdzSiy9ECtdwnmmcMijxkqBagte+sWIjxWHG8S7S77cEHHxQdpV/+8pdxuYPv8aGHHsJvfvMbfOADH5jp3UkjjaQglymhlOngZpBnGNRcd8xCcL4VrqqLsANNAdjxy/kdO4DZwMLmFVqKsOOX2Wq0N2Hhebw1ThqRDVOnho5iwCXZhhmVJqzIXAfNFFnEXg5gNguLooGcFs5T0kjjSgbH2G3btmHv3r3B5kHayJJc3rp164wTo7wPcW3BugCv17/97W/i/sEmHzqWpBJUotCOKdXEScJQFksPMd7b0OU4AqfXLL7PVFUiW1UjPi9+jiRsaZmXaDYda4qf/OQnRW1/pvLsZtTUj9JwLjio2qDH70yA3ZFF2rEnPXaPG/9++g00W4eFnQrLcu+atxS3FddgToK2VezqpYWI2hjRoWF2HxWEhoQRDLsHkKHaLAr1xdoSNFkb4AnrYs5T50Mrn0JCZ6gFMLcBCjWQVwuoUjsh7Q2zESNYcO1yDCf0t1p5EfSKQVg9LUFCI1u1SixGIqDOlooVZEaDYFdmQcSxt3po7RM6tgyZd3qboFGEBd5OI3xg53fIbgjgcTkKHzaJbtG4IIlz5q8iTHh+cS6KcjJw8EwTtiyeh5yMME93caOLKoo3HRyb1BAFR1l8kih3gSRJGG6VwqHzlgOZ8zHbMeyq838VOh5md30EqcHuqVNDxyLUDedHziBDlSke4Thvbh/1GiNuB5R+a7Xz5h78uv4QrB4XVDI53jFvFdbnlI25j9cUVOBPzWdHbYtGvsYoCnAuLzsNJfCrNdlL0WxpxaDLDKNSj825y2BQjjN2HHsa6OKx8Qfr9jUBW98DmMYmFYqLE7PwabWaBaEh9tFf5GywDKHDbsGHKpfjVw0nA6+M5ZkKGJQyzDcUYmOOX0I60gNcfFkaT3VZwIJrAUPq1E/xQPXNB6uuwnlzB4ZdNsw35KNstuVeJIv+/hChEbSXG51dNSks3QycPQC43ICSY5oPUGmAq98G5ExBHlYMeLxe/KzuZMQ2nmO1GTlYmx0Zut1iteDrZ04Km8wlGVl4YMly5GsiFUdsxtieP/75XlyVBU3Z0lFjLsOmtYokLJ5ILB04EPr+3Dng618Hvve98f92yWpg9/ORJFKtZClWW1srLBrY4MLOL6o1UjIxpp/tv/4rfN/5Dg5aLChRqZB9991AlFIliAP7gN8/GPp+oB8Y7AfyM0L3okd/D2y7BtBGjWfOyIJkEA7ruLs54upBl0PqdnJ6+fDA7hlCtlp6DS7OqEigjVQiMm8uTr7whS/gD3/4w4w0Ec3EQvvHP/6xUO8xrDAvb3YR0WmkMR7yNavQZT8Mr2gqAnTyfGQoZ+d8ln7p052RSQKDRTOqNXh9Typ/KQ3Uj1wIEhqExT2Cc+aTWJk1tlf+lQYWSWe86zqNNCYJ1p0eaq7H421Nohb6jooq3Fk6sTGcajmSzLMVtJxidh8JBhIcVI1w7kziO1Wg9SHVIbOG0IhCl+OYWP8FMORqgEqmg0kl1YJ4nF588UVcT+vnBMPBeR+eySYi5UzfGH7+858L24N3vOMds1YO/0JXI1qswxF2Kn9qOo2r8itEl/OcgqUXOP8PwGWVfMQNRoncMJTBmzM/jNAIwAOntxM6RSX0SgPWZ2/GJctFOD0OZKmzUW1cMHUsbPdJoHVvyC6o5zRQ+2ZgAqHGiSJLpUdfVD4KLagSAd93pmoxDIp5YtGhlBkgjxXWSYug/J1A3x7AY5WCq7PXi6I/bJcATTZ8GgN8iFRuEB7f2HYktBh6uPkIGq390CvUuLV4CVZnj12cDh/kySLHl4zT8z2ceOC/Vv9jjGM02CgpLvzQalRQ+LuDxyUoSIjE2m7pBNp3Aa4RQGUCSrcD+rACoL0L6KHvv18VQzu4zBXxCRDxWlbAWS/lXihzANX8sX8/hXD7XLB7aC0g7YJWDnhlkefDiNsc065pyDU4itRQxlD3BLbxnPnFpQOCdCAYNPz7xiMo1ppQpo9f3Ly7bKEo/DMsnJOh6wvn460xutl1CjXeVr4BD7cchMPrFmfQ9YVLsCSjTDwShmME6LoYtsEndTu3nQJqp6Zbf9A12sqBxPWQy4E3ly3EtrwSdDmsKNYYoFJIP9MHrOmcVuDk44BQevkkgoPfr32HlBkzzdAoVFiRNUXKjNkA+mA3NYWIDXZ9jhNElzRIIrz5E8Cx1wC7FSirAZZvS9xOaQIYdDlhoQIpDHy1Im3kfW7A6cQdu19Fv9MhrruLI2YcG+zHczuui2nlRtg8bni8HrTYmLPhRaUhN2ilKBfTvdHjhyxRlUYAhw5Fkk30qj0aytoaE9fcCVgtwLE90q6s2Ajc+NaIIhW9bgkW3qnYoK0JCY8pxT334KBajRqPBzmcdy5fHn/sP3FMOvf8nrzi/sR993gBpf/Y8XiYh0eTGpkkm6LIe47DmeN3h1k8faP+1u2zw+m1iY5oFhHZeZaoRy8JDVp73XbbbbhSwPfK9QXfO1UqaaQxl6CWm1Cq2waX1yLUdFxjzHTXa6LKb67vAz7mqQa7bmkvklZlTA7MyQwH1xtcX6SRRhqXHx5suIhvnw81WHF9wdnmXRMkNuYCAsQ7lYWsfdEqiU1UU21RODw8LNRcVLDMxnu21+cOKjTCYfP0B0kNIlFLyQsXLogsuzfeeGNGc4NmlNQgGNJCVocP+hTORu/LPqdNsJiBTl6CXw26HHOL1OD+X3gWcNkgKnQGraQeoHf40AXIHA1AUXSXceSi2qQyYVVWCsJfWGBt2x/YUekferh3nwDKNiNVeFPJSvypaR9cfvWJSanBtYXJFVCUck7kx1GUqHOBotv8RXcF0LkbMDcEfyzLXwuYeO57o5RE8fMPaJXzm4b96LKbBdlmdjvwcMtRZKi0qDaO35XIELOxMzX8ncsxt48BkjVRKoz8bBMOXGzHuppSyGX+7cYCwNITea5lV4wuLrksQPMLQEAhRGKD39fcBSj1kkIjnNAghs9Kx1wfp4jutQMW/o2/IO1uBzzstp8a1t7rc8HjM0MGFRQy45g3Nd5crG5viNLyAS6PZD9wdGA3XF4nMlU5yNeWBv8mGLwuFr6jpeFrc6rxz85jo7YRLdYh0fUbDn4idSP9Y5IaHAPvq6gVj/FQZczDvy66AUMuGwxKtSA6kkZ44TQI2r0kvlDmceq0N6DH0SzeZa6mFCVayiulc7hCnwGNXCGOR+Bs5bVEi0EiV6MTj5hgbkiE1RYDpq3AxT1AZglQWB0K1Lb1A0PNAJUyDK1PIVGbErAIz32fqslZbw/w5z8D/X3AvPnA294ORGeefPKTUv7Brl3S9yw8f+lLYz/vhbPA2VOA3gBs2Unmdvx9YTD3tfeFvud4wowKZs+kYDKaqeL1oBQERDjKdJGTx5e7O9DDwHI/OP+ot4xgX18Pri6IVJGYXQ78/NIhnB+Ruiz1rJurAL1SjffN34xiXSZMqmIMuKi09I95/D1FHtSyJM9F3jMaGyNtwRKxniIYwn3ne4Db3yV9P8Z8j5ZZBLtwpxpcbBStWYOcijgkYHMT0FAPsLufk/pYgbeBc4P/8nzLjqGMysgHVtwEnPynRMjyGlp5G6Af/3hJis/Rr6uQKYV9Kwkf3sMTWXS88MIL+Mtf/iKsXq80/PCHPxRE2dve9jZcd911M707aaSRFDgOaJJR0s0QaOURvn6n/dN0gZkG9EYnwZtqO5HLGbSZsngiC13qOLl9aUgYco1gT+9J8a9JKNCXI0czOy3i0kgjHH9oqhu9rbHusiY1AmCeBsPNOY9uaWmZUpUhbZuOHj2akP31TEEm6njRbimyoNMM7+fMRElE4czfZQ2f7kvTFbw+a0kN4pvf/KYIkfnFL34hQgxnGyoNmRGEBk8D+lYXaKbZq1N0CPoXxhMB1RlOv9RIrRrVES+zuyD3eODlxDRsu0o2Dd5oLCQFPKfD4Y5j3zBFKNNn46M1V6HB0is8yRcYC6FVxFBbTAV4TKnkMDdHEBriRz2HoTPtgA2hznQ5DNDI4w+0gy4bOuyRVlnsJD893DmK1Ij2t2X3K7tixw4vY5cprXnCi8gs3IzThW4MBMcyEF26bqpK84CqZejJK0OhUSF1shvygMY9QNsx6VzMKgMWxSg6UKUREdzsk84XazeQMR9wj0QSGgIywNkXn9RwNYUIjeC2FkBTC8gnFqQdfBrvoLBx88EdvH5MyhVxs1Gsnn64o8gsgjJwq/9t9zo74PDaoJNnoNM+KPZaIfOhUJOJ/DAbswAWm0rR4xhGnblTKDTW51RjSabk4a5Xxj6/422fKFRyBfI0k8hh0JqAjELA3B0qKnKMKEqsM5nodjSh3R66pkhw8Nwo1UmqwAyVBv+6aAO+d/4A7F6PGNvfPW8plmYmYFUS/XnSwmjYBgz6rXkM2cD2dwOOfuDSc6G09s5DQMVOidyY7RjuA157BBjolGyZ1t8C1Kye+PPxc/zhfwN/eSxsG4C9e4Cf/UIqegdAkuN//gfo7ZUK6LQiGmuC+OrzwG9/Lo09fJ1/Pgl85buAMcEFJv+m8zDQ47fc02QC868HpniByvyaL9euxVfPHIDbf17XGLPw1vJIO0uXNxahHLJJC8eDjUdxcaQ/+L3VCygEMerC423H8LGanVDK1KjQbUK/sx5unwMaRQZyVJXJT7o5R/vQh0KfBffn059O7jmSaF6Z6kXBwMCAuP8tWuRXmXV2AkcOUyYCbN4CvPYK8MPvh8acnVcDWdnA4ID0ntl9XFUJWP1drVRnfPzfgHjjZ9lyoHAhYDcDOhMQUHqNgyxVOQacLREkVJaqDEq5BhqNT7wH3tcZpn7DDTeI+3ksUI3JxQZtXukBfqWBi1W+dx6DkydPTmuYcRppXKmor6+feoVdHHDsoyKLBHia1Jg4qgwLMOhkt7Z0v+G/Ncbp+QznIpweF57r2AeH1yWOldM5hOc69+HO0h3QsykmjTRmMdwxam6BNcmVAs6jqfSbSjDniVl3Yu3CteuhA9I6troGWDQ7xlOZTI4sVRUGXZcCWwTRkaGaF4ojMJvR3NwsbIHHsnak41JrayueffZZzDRmBanBRcavf/1r4Xt7yy23iK6L2YTteeU4O9yHV3qa/Z0LCnx24Xrxb3jXPJfeKWHlOMic2wWc3yPZ8+TPAzbcBWiS7LAUi+kAMxfDhoJKBbsDFo0GboUCcpkeemUtlP6g8JSCQWRqk590CbM7MqS+6yZTpcOqrBihnamCk0TE6DwJtVMDhX4T3N4ByGRKqGSFY1qD2KmwiQInVqoYf8PFfLiPdiAEaCwwENyHDQA46LHrit2lVWLoGxP6XKDyKqDxdUDGDm8VUH093N12KPQZQE5YwbhqG1C5xU/WxRiO3BbA0iApi3gduMNuwoHskpgkhG9scmIUCTLO9gQh5dIcDxIahMvXA7u3BTpFbILKJ8KzR0M6zqGf9DkH0O8MfU++aMg1OpSbeKHrDA4ONIjn4DmhHepAbUaZUFvM02djSUYBzg53B39O66lVWanJEJgQzP3A0WelMF6qHUhqcfxi8HXTJa4mgKLx7xN9jraY2wKkBrE6uxAPrr8Z3XYrZDIfmqxD2NPbipVZBUJlEhdUFdGqTlit0X4qysrKOgicex1QMlCY46//HObvtr4GqAzTMr5NGFTEvPgHwDLkP5EdwJ7HAWMWUDTBnJ8nn4giNPzn88mTkq3Rpk2j/yYRL3y3C/jjr/z77R8j+nqBZ54E7vGrAsbDQF2I0CCYrdT4ArDwzVOu2NiWX4LPLFyDb549CovHg339g3iyrRn3VYSIrp35BTAolIJsI5FB0j1HpcZ6BlVH4cxwT9AaM7j7HsCnBHodIStApVyLAu0kvaCpmPnjH4FnnpGONbvf/ZZRUw0qEQLBg1MBjs8nTpwIef8ePwZ84fOhAPOCfGBoMFKZQZLjs/8KdHVKP6tdAlx3I9DdAZjNQGm5pNQYCyQE+UgCKrkGlYbN6Hc2wu11CoL7+OAg9vS9Alv7MFYXLUb9vmPQ52biLy1HYIYLpbosXFdYG9GY8cUvfhGVlZX48Ic/nNTrX074yEc+gkcffRRf+tKXRM5GGmmkMXVgRycf4VAqldPepcrxPY2Jw6TKxPqcrei0t4t1Qb6mcJS17WWN7hagvR5gbln1KkA99j27y9EPuze0BuPZ5/K50W7vRY0xCavdNK4chOfJzTBuL6nAbxouRqwc7ii5shpfmHvBrI2pQmNjI0pKSqR1Cxugvv6fwL49oV94/4eBt9yL2YAsVRWUMh1sHub3Ah12D9ps55CjzoRMLhM1QqovaS0ZDw0NDWKN8eSTTwpL+5nGrCA1iGuvvRZvf/vbRTfV888/P6skOywEfrhqFd5UUiO81st1GTCppGIXfemfaj+BE4Nt4vc25MzHDUWLoYjTlT0hNJ0Azr4e+r63Gdj/OLDjnUm+ESVQsQlo3isVBaPVGhqVECSZuMBXFgCm1Nk+jQL3ofpGoO4ZSVFC5NYCeZdhGJeanb8xJt9qk7AqUigSI5EODTaPokb49doYmRosDoUHIlG1QcnYeJCBg9QKJI3chUB2ld/qTC/URb1n9sT2/+a1Eut68diB1uekzIuA8oPnicsrdVEb/EV4hk1nLJYspwJHRJkBGMfohFfmS3kaYe8UtPoSVmIThw9O8YiG2zfauzAAnSJHKGyii5LRZ4jD4wuSEAFYPDaYXSPIFOeUhG77sCA0pOeQfrfO0o0L5k7UZhT7x7MNeLWnHu02M3LVOlxTWA11LFIpVRgr8J3huq/+XsqsCEwAea4OdQPeZjLI0vYdbwaWjTNGxXiNWKQclXceePFfZ3cHbYGyVVr8x5LtyIunyCPJsuJuoGEXYO0H+qI+Y+4ju+f1Nn8IddT+9J6a3aTGcD8wEumxLK7TtosTJzWOHZWOwaiJvU/KJJgoLBbJRjEaDHVOFEOR6jnJTmwI8PitqKYQg04nvnL6GKyegO2ZD187ewyLTJlYnS2RFsU6PR7evB1fOHEEzVYLFhhN+P7KdcK+KhpauRIWke0SJQxkRlSyzQ+JgIuAT30KqQYzI7hAmAqw24iScGa3BeeX3/4vwBl23Hr7hTukAIl0rUo6kG+8DHz1W5FqjKJSIMU8sEquRaG2FqeH6nBk8ALc/svmUtsl2LNl2LRzLX7ywhMoKFwkxvqmwW6crL+Af91yJ5RyBV5//XX87ne/E4GIs9HadbrA987GKc6B7rnnHtHVnUYaaUwNuL4I2AUmntk3tejs7EyrNKYAzM6sMs7OfNOU4vwh4NW/+JeQPuD468BdHwfGcOSI1+A3buNfGlce2CTWvh8YoHOADMhZCJRsiF37mCZ8bqHUjPS31kBQeDXur7zyrv2pqje3tbUJxQJzNATeeDWS0CAe/CWw4yqgoHBWvG+TqgQqeS6e73oDTr/irHGwBXazGe9avUaQPuEqDdp10bqL+VVsImDNnpnY11xzDWYDZg2pQXznO98R3rdcfPBAzSbwwy/VMbQtsuD8TMcpHB9sFYUJqjX29NWLLrmrCxK3SBkXnVG+d7zh9jZJPueKJD/C4lWALhsYbgN8DsA3IhWNVW7AwE5+oTcBdKMDgFMOXQ6w9G1Shyw9+GfYd/7scAv2910QF3qFPh9XFayYGmsqYzmQUQUMhxXVC9bDx88AtK5h1yi7Y2ohQ/xOceYVRIOfXpZ6tD0UFxaU2YUzqbSeit42pWCRXGMKhiZlZCRp4zJC33Z/97u458jouQQY5gGFGyOVHVkrpQwNWk5RoUFCgwqReCBpp10G2M9IOSYkM3TrJz3BYIaGlDkSSRgpEL/jRylXo1y3Gs02WlaFIuttgQqWHzqFHlYGzUdBHrXP8c6L4TArN1rgXFc4dd0JCcPTCnjOk+YBZNmAkmHuUQXjrnogrLtcgAU5Kh2cVP7QAsYH7HoCWLRmzA7oAk0FmqynI7bla2N3ojzYcAyOsGBLEth/bj6FTy2gWmkMi6zFN0tf9/8csFLVEFawN+UBaivgilFcD+vwmpWIaadDwnASYyDHgHBSQzwln1MJLFs+8ec1ZUgWQeFd9iTCKhO0+GJuj7Uj9s9SQPZdHBkaFRaugAyHBnqDpAaxKisH/9wxfg7AnaW1eKj5ZATJbeLwK1fgztKVmKsoLy8XREQ+rcdigMQ8O6NYtI5W+LJzmOF1gWI+53ArVqwIKRZ5nlB9ES7B58KTNo0cawz+cYXna9054Nc/Az7yL5hucOFwerguSGhYhy1QqlVQqJQ4aGmGz6BBy7Gz/lNVgUu9A6iv3SRUG+9///vx9a9/HdXVUp7SlQx2433ta1/D/fffj2PHjs2KrrI00rgcUFpaKjItwn3Ji4uLRXbRdFlQsWN0UyylZxppjAfOxd54XJo9+cKsV0lsbLgp7p8VanNgVOphcdvE2o1khkauQqluGiy705hboO1w/7nQ931nANaTimYug0All+OLtSvE40oF1wXMf4pnxU4LpnPnzomsiPDGIFq/cjtrW2wS5jyd65StW7eG/rijXVpPhNUVgpa3s4DUCKDJ2gZHWD2i8fQlVC2vgUMm2U4dOnRIHB8eJ66fuObasmULfvWrX4l7/N/+9jfMFswqUiMzMxMPPvgg7r77bpFGz4662Y4zw52jOqrPDHdEkBpmlxlDrkFoFGrkqvNHFSHHBYtI0YUgPsdEO++y5kmPcLh6AVe7VJDVVACK5H3E+52DuGiuh8vnRIEmHzXGyoj3yoveCzfkGEOWTEsvki4zjEZLF15mSLkf9ZYuODuP4PbSjZN/cr73oq1A5gLJXkmdDZ+W5AWD0gMFFgZoc6K0IW7XR7E2A2fDzj9BaKh0MTvueS2dOXMmIsSHBSMOytEdVlMNDoBNTU2Rg30sOPsBFu0pd1aaJKu1GDZdyFki2ZVFg/kZMTI0JCuoTqmQjhzI4D+31ZWA8A/kDUc5JXJQ+hQaFIth8YQK6XLooFWMbZVkVOVjoeIqDLu7hB2VUZkLm8eBZitJNSeyVLko1M7HK9174Paxu1s6JoWaPBiVkcWZfI1plKKDXzF/Y0bh7QU8YUG1vgHAfRhQbkn82If/HovW1hEgMz6pkaeRzoceR4s4HrnqEhRoYtuAdTssEWoZft1pjyJXxsLa24G9DwNu/+SAuTa126Xslot/k4qn4ftvmkbLu3Awj8Z8GnANADx3TMsARYxuNNpMzV8GNPo/M+47CaSaNRN/7XvfBjz/T7Z2hmyiqDz45n+xCjLx5+W98FNfBL7/NcDiz43asBW49pbE/t7aKXXoR2fQZ1WmhNSIpbbg+ZYVY3siuKagEhlKDY4Odgqbqnl6I/I1elQacmFSzV1vZ3qlszi2e/duqFQqUTQLeKZ3dXWJCXVPTw9uuilUeBgaGhIWUwy4W79+fUSWVAR4PpeVA60toXOR59HiZUCzP4cn/Hp99UXgg5+QFinTCI5bHj/x4na50XzmEhZukDrsmMVUuGCeZCjq86HzzCWULl8ItU6Lz3zmM+LYffKTn5zW/Z3N+NSnPoXHH38cn/3sZ/HLX/5ypncnjSTg9XlwaeQS+p390MjVqDbWwKRKB/LOBnA9x4fb7Ra2U4FtJA5ZFDKZUmdhbLfbhb1ubm7urHJ4SGMOwWGTiI0IyIARv/VqHKjkStxUtAkH+89gwGVGptKA9TlLoGVTZhpphGOoMfa2GSQ10oBQ73K9QJKCzb/M2uNag98fOXJENP7ynhYgNDjPPnXqlNhOwn6srAlUzBtNaPB5Sktn1aF3e93BmlFbXQuyC3NF4xRrTdmmTKFs5vum8pKqb9by2DD2uc99ThAaSTcsXymkBsHAQ6o0aEXFhaxHIcfFkQHRxbjQlC3CZ2cTyHRGF0LCMw3abC04Mxwq5GWrcrAme31yxEb1eqDldEgWSSzcPLWyNVWe9JggBl1D2NW7L1hIpf+/1WPDqixp8T3i6keT9TjcPicUMiUq9MuRoRodcDxbUDfSEVFO5/tqsfUK1Ua/oxPt9hYxCBTrylGoKU1+Ms3f14cztfVR3f18ZdrZsKgaW769M78GTdYB1I30+Dv5VXjHvHUxf5fsKu036JHHATvQ5cptbq9H2Bhp5WpBvCULsrfd3d1iYOPgPzg4KB4c9Pl6DCfduXNn/CfgOT10BLBeCqv6rQX0xcDA8bBfpFJDC6iZ7ZEYJCsoql9CeQc+LIMs4BsirqGplX9qFSVQyAxw+wYggxIaeZHISBkPVGzkqEOFbo3CiCx1pL3S1QVbcHa4DjaPHbmaLCzOCLNS8SNLrcetxSvwTMeJYJF+e94CzDOM9uKfVjCcPXCD57gpHMXMfmVSWOG1oFKSfDvtEhHA80Pkqfj/VoyBMsnv1jg+UUNiI0BujIUSrQkNlsHgMaMlWFmUMm9M5JQC134E6G+ROnDy5vmVdDpg0VuBppcBe6+077lLgNwxJkOpAo9n/+sSocH36eoDHF1A/g2APAY5tO1uILsI6G4GdEZg+Q7AMAlyjJO53/4BePyvUh4BJ4Q33zo1heLqhcD3fwm0NUsZB8VliZNlnFfwd9XsquE55w91z06NmmmBMQO3FJXhmc5WKGUy4ag232DELcUTJ7rW5ZSIx+UG3j/44P2K4bN1dZJyleF+nGxT8s1gPnbpnj59Wiw+OOlOyG7py/8OfP7TTNOWvp9fCXz9v4D//i/gdPi9h/CPQ9MMzhULNbmo62/DpeMXUL16cfC9LTIWoM3WBI/Xi86z9TDkZqG4sAj7n30Zjz32mFiAxCV1rkDwWPzpT3/CypUrBRF21113zfQupZEgTgyeQJeDzSkSehw92JK3DYaopo40ZgYs7rA4tGZNqOmB6wEWQiZLapDAFlYZJpPw9x4YGBBrDK5fuJ6hAi9ep20aaYwLnQHQGgA7lfC+0Fw5b/xGG4NSi6sKJtHok8aVgVj5qGNkpqYxPeB9g81PBAl4KsNJaLB2xXsZG6uo7OW6g6QHi/m815FEHxdbtgM33AQ8/5z0PdeYn/oskDvxWmsqUKjNx+nhi+hoaIfX40VBWaFoHMlQhWqPVKRwPUFrLR4b1ug/9KEP4frrr8dsgsw3C5O1WCRlcvy2666B/b4b0MfiFhe3ehO+tnQbMpIMXEwlDvQ14umOk+LrQBH8bRXrsCSjGC6vC6/1vBTRMU3UmpagXB/VLcwgXJ8VkFGVEaOzcqADqDsgdQEXVgGVa2ZF0FAAxwdPocnaOuq93lp0g+iUPzf8Brxh7A8JgUWmrdAoJrcgYYCm2c3isRwZqhzIp+gmQZXGueHR7+f6wiq02JqC3ytlLFAtQYkuzNLG2Qk4eyT7I+38sQOr/fAJUiM84yGATZDFITXgtzzrsA/B4XGjRJc5pj0WfW/Pnj0bVGtcunQJ2kIjdg8cF+FmxMrMhViWWZP09frSSy8JiwsOdvTby87OFp57CZE99nagf9fo7YW3AbYuoGe/PyhaDxReDWiSITUuAGiJUnuwIHT1Ze97Ouyyod9pQYZKh5wZtnKDuxOwHQx9z/OCORMszqmukWylWk5J4xtJDXaXMyh8qEfqvLfYQ4QIu6p5nt/8PqBs6orObbZhfOPsbpj9SotCjQEPLN6GLPUUdrpTfRQvQ2Y64OwF+l4ZvV2QiAlaNV2OoHql8e+Ai8XtAGmWAcy7NSVKDYLh339pbcDZ4UEUanV417waGCdj7ZUqNNQDf30EMI8Aq9cAt981cZXoJHBppBlnzXXw+DwoUuVhbe4KkRsR6NY9fPiwKFYn7eFOy7JTpygLAVYyHFQNHNoHfPdrkePV5h3Av/wbphu8p76xdxfOO5qgnpcNObPQSIwZylBmzcCRhvN4qeU09PMKUV1agc0owNXrN+MXv/iFyI9IYzQeeeQRfOxjHxNFWFrnpDG74fDY8WpP5H2L87f5hkosNM2AVW4aMUELqtWrV4tCEMECUMwcvSSxZ88esaagEoQNWlxj8BFQhUwKwqWgU5qTqelSMD0ZIGnMMnQ2As/+VmqmIuYtAa5/57QrM9O4TNF3HmjbHbmtfCeQnbYGnW0gqRHdDERVOMH7WVJNzCyxnz8H9PZIdsil4zdYTjfq6+txtOEEBrOsyCjMEu4fW3LXQA+tIDJ4PNhUxsYxvvcvfOELIvt63759s66ZYFaSGgStctatX4913/oSctcuC3bO7swvw6cWzC65FjM1jvuDwtfnzMMiU2HQdmpfVLGWE3ESGotMYZY/3gHAdZizK38nYBGgXAgowyZXbgfQchiwmwF2XJetkootvAFfPADYR6RO4fn0qJ9EsZZFHeeA1BGvyUv4uY4MnECrrW2UFdctRdfB6u5Ho/XYqL8p0y1FbgId1PFgdZtx3nwAbtq7iLBUA2ozNkAVq+M4SXTaB/C31r0RpEatqRiQdY/6XYNCi025V0nf2C4CFipz/MdNpgayrgYUYTkXout8WCrUUz4vU8InFBnh9lMEu5vi20+Nh2ODbfh7+2lY3E5U6LNxX8VqXDh6UhCGRF1DHQ54z8Oninz+q/PXoyRJT9C9e/di8+YJBsubzwLmUzFsprYBjvOAl8fKf0ipZNCvTvi89IGEY1eMn+z051+kMS0wvwg4woK0SWgwiJefp7sKeOU3AK2eAmq09XcA5dK4D3M/cHqvJBHPyAXyy4DcEqm7aoox4nbivLlPWPgsNuVBk2xm0WyHoxvof2309oxVgGH22z2mFMyc6T0GOIck4jRvlXQfvJLR1Ah86iNSCHvAoomkxkcTtDTq6wT2vSDZxFUsANZfLalikkSLtR37+iPnEOW6YmzKXY2U4fWXgb89DNiswJoNwC23Alm5gClxUn2qCoWVlZXIzWd3lw9urxcKmRwXz18QXcrMigiAiw92TtGm67e//e207udcw3vf+160tLTghRdeuKJD1OcCbG4rXu99LcZaqgKLM5bM2H6lEQkWQFj0CeQWsbs1fHyaDLHL5163LrYSfcJwtgIWrr0D6wk5kLFjQvbLaVwGcFiBvg6AuZS5XO9f3o1vaUwzBi4BA3XSecWg8MyxbanTSCPV8Hg8gqAIWPjS6pbNYtz+2muviewMrTa0Dn755Zdx++23C3V8qq3rJ4JZW7FZsmQJvvud7+CL/+8r2P6b70GTnSlsQeotY3sczgRWZpWJRzR0Ci3kkMMbVqhmkdwQ3gnCwnaA0HB7pY5l0VneAmhqAP1SwOMCjjwK2AaDz4LBVmDRjcCLDwIWWokwzPIQ0NcGrEvQRzwaI01Az55QcdlQAeRvTejGXqwtRIutLWLBka3OgkqugjyO9Q5tqCaDBsvJIKFB2L1WtFjPo8o4+dCjIm027izdiEP9dbB7XZinz8d8QyZODY8mNeg7J8B/LYEshYCE1QnY6wDD8lCnNokup/95qOLI3gaZJgc+UMJ6wW/Hw8LJooQIDQ5CzNYwux0iHJQERoOlD//XfCT4O83WAfz81Ku4zRCyN3EqvLBYbdCrQn76fL1ex0DSpMakLC4EeReDW/WaJUJD2jH/TrcA2poxFx1enx0ub5OwnpLL3FDKfFHMumY2D31jIsBBzynvYHbBhxMaBMc6KpmUy4HTz0uLifCQPqo0ypb6w7ZzgE23TsuuGpVqLMswoct+Di3WC9AqTCjULoYqAbXVnIAqW7KZEqFggYPNHCW/HVsyGDEDTz4KdHUAJaXA7fdItk9zFUodUDRBYvZyxT+elBRSAUKDeOoJ4H0fBMImujEx0AP8/nsMgpCsHOrPSCTHLe9Mejearcz7ikSrrVOMhykbC3dcIz1a64E//wT4zXek7RuvBW5867QVPEqW1eDbT/8Rpqoi5GXn4NaS1dAOu4XyMjwji/jOd76D5uZmPPnkk9Oyb3MZP/7xj0VX+be//W186UtfmundSWMMaBU6GJUmWNwjwUYj/lugmT1hm5czeh0jaLT0CvvnWlMRNHEU4VTMBQiNqQTJW3aKTjmsAWvowFzIC9jOAcYNuFKR0nvqbAdtb0vSnfNppAhUZaSVGWnMIigUCmG/xcZkEhhKmURoUB3J7eGEBvML3/Wud+F73/verCQ0MNsre5SHP/fSizjy1f/B+u8/AIVCiULedOYIlHIVlmauwKmh48GJeL6mAKW6MALEZwspNAShEQZHHaDKBfoGABuJizAMNAPn3gBG/N7ogUnZpUPA4i2AIcluQha5evZGFpct9FEvAUzj25IU6wqxwrsU58wXROhMniYXa7JWip8ZldnQK7Jg9QRIGaoqjMhQJVA4F0VcX0y7FrsnOsTXB5vH7409BSjR5eL20pBv3gCDrGMgk4VC8fLuOMV5khR+jJwJERqExwZ0Pw/oFkLGrlOZNJn2+txw+0YggxNKmSHuJJNdm79v2od6S19w281FSzDscghlUyAfgP8OwIluc+gzyM/Oha3DCr0pdE3xPJ1IrkZ+fr7wNp+QlYO2DNCWAvYQKSbCi+lpHysonOdqHA7F53PC7tkPH68pEbAKeGUaaBTOsCFvxZyznmLuyas9p3HO3Cb2fHnmPGzLq00um2em4PETU6NgkK5rm1kqeoaDFlAM7ptmOx6314Em6wF4/Neyy22H3WJGlXHrlFnbzShIJOXsBAb3S2oxuQ7IWgcok/S8djiAb34Z6O6UCt5njgNnTwEPfIsVCLZWAl2dEsmRkzM1+07y5H9/BDTXAzl5wHs/Aiz2k8VpxIfIpWkEfLwONYC8CpAlqGYc4D3PJ43FIttGPCHgdIxPahzfEyI0Aji5H7jqTkCfnMVHysZrvidmsAwPAqXzgMyoeRPHoP/7md9r24/9LwEl84AVkuJxqkEPegbs8p5vcTvwl7aDcMq9Ivx7xO3Aj198BG+dvwmb10Z2LdMC8utf/zpef/31lAbzXi7gMaIN1Y4dO4R69ZprrpnpXUojDngtrM1eh5ODxzHgGhDNUotMtcjVzHBO2BWAOnM3Hmk5GFxLvK66gPsrmWUy+h6SymI48zOmHGw6i9wA+MLWa1cQ2mx9eKHrOEbcNmSpDLihcDUKtJPIT0sjjTTSSGNWwev1ilwqrjECyMzMDDYmDw0NiVwREhrhv0Prx/vuu0/kFX74wx/GbMWsJjU4Qfrz7/+A5WvX4Oyv/g8bP3E/3jPfb0kyR1CkLUaGMhNm9xDUcg2yVNmREz/aExFMCh0FGeAeAjxxJnMO2rX4iw0R263JkxouEgTRnTAywJW4MqbSUCEe0ZDJ5Kg2rkO3vQF27wjUcj0KtZXjFwltrcDQYWniqTAB2ZsBVWiSpVHookgMGbTy1JFemaosQWAMiZBdCSqZCotZgBcvrwb4+l5bRMQ4lGELr2hihJ8f69KDZwB1JpBRDad3CP3OI/7CPAmgQmSplovjGI2jgy0RhAbxXOcZbMypHPW7cqUCsIXOlTxjDkrkuUIXIhWNfDApDagylI3byXPy5ElUVVUF/cvz8vLEtgmRGjwG2VsARwfgsQKqLECdJ3ndjiKJFGOqNFw+WqBFXi8enwNe32r/+Wack7ZTb/SexZnhlpCQYbABarkSm3In71eccjAnKBZU/nMluxjouBD2+zJAnzXthAYx4u6BJ0z9JYgNnw1WzwCMytkV7jVhcAxlMDjvGxMtQpw8AnSGdc+T2GhpBM6dkux5/uNLkococevtkl3RZCxeSKL8178Dg/3Sa/G1v/NV4P99HyiPyqdKIwR+xh7aNvk/C47zni5AsSU094iH1hbg6CGJV+Z5wvsPpwhV1YApAXsOlyMmJw1X8sWpKmMF2uySjWDL+SbYLDasqVwBWZlMdBWRUGeYLMn1+fPnJ35s/vRLYI/fq5/+7B/4DLAyjCwY7AOsUSoz2mdRvZEIqcGmFU8r70KAPB+Qxy/CcjHBoPPW1la89a1vFV3PTY4+1J84C43JIBYjTYfOoaC6HPp5BRF/SxslLjioPohWb6QRHzxWP/rRj8SxYyZLeXlIyZrG7IJWocX63NQQiWnEx1Ptx4KEBjHksuP1ngu4uXh0QwFz9CwWS7AYMlF3a47lJHe5xgislwPFmClVgihzALefuA9uu0zmeUlg2GXFk+1s5pFqAIMuC55o34d3zbsaugk0uaWRRhppzEXwHkNrJd7LVqxYIfKhGJLd1NQkbBCpUEiFGjHV8Pl8IlOXayWukxYtWiRUGP39/Th16pRo7KFd5MDAgGj0ibZk/fd//3d0dHTgiSeemNVKPvlc6KZ69smn0P7E87ixyYIS3dwL8dIrWcQvRrY6Z/TJwMKCoiZOcYkdklogsyTMf8cPyn+LF0Z1OMukgF3TBCZlDGEe1Q3JgvzUHG8WlIt0NZhvWIUS3UIo4hU6AyBxMLg31ElD8qL/dcm+yY/5hmWQh7Xsq2RqlOlTFxrIrvjVWetQaahBgaYI8/SV2Jy7A0q5f9LHzzBjk2TvEoBmvhQWHoDI1gg7zsHuVxlg6xYDz4DzWJDQIOzeLlg8tCQbjQGXVSgywsGnqzLkQinnT6Sf8f9LMgphDJug8lxcZqrGltyVWGich5VZi3BT0RaoxgnGpb0FB3UGjVOyduDAATEYMkRowuCx05YAhhqJ0CBUeYBuGXxyGXxGLXyZevgy8+CTRymaIt48fyaLCFIfcvnQZjuBdtsZ2MPUQlOF6YglqhvpHFUb5LZUo8nShYebX8JvG57B0+17YHaFdSwnChKS6gBR5lffyA2Axl+MXrgZKArzXVbrgU13S1/z2PZ2Al2toaDwFGJWBkylCpOZmDj8YYrRsNuBrz4A9IcRrf94CvjnM5gUGuuA/t6QDZIo1ruBH3xVIjrmGDguvdDVgT82NeD44EDMnzNIfPKwhBEaBJ/TAfg6xv9ThoMzS0MQGv5zRSEHHvgqMDwUaUkVC9XLIn+HpEhuEZCRfCZFkTYfG3NWQW7zQeGR48bt12GBfr6QSB85ckQExtLLvbeXRHiCOLg7RGgQfK8P/lDK0AiAipLo64SfizEBUsdrAZy7AM8lwNMIuA74CY7R6O7uFguOQN7V7t27xcLqkWefQk55MRRqJdpO12HeqloYczIFoR2Aw+HAW97yFtx55524//77E3//aQi8//3vFx7BJJJ4LNNIIw0JLHJboprqqOYedMaeBxYUFIixLIDc3NyI7xPFuXPnBDHCIFJmCnGNEZ7VMWUwrAXkYWtcVTGgnQONQlOMVltfkNAIzhK8bnTYR89N0kgjjbkPunykMRrMbmI+7LJly8TagvcgNg2RYGej0VzMX/P5fGJNQTKDDch9fX146qmncOHCBUHWcN1BdQZtHqnQiH6Pjz/+OH7605/ib3/726xXgc9qpUYAZMZ+8+CD+MAHPoANq1ZhwYIF03oy/KOjGQf7e2BSqXBfeTXKkrROGBfKBYAsA/Be8Hfy+9sbFTlSkK5GDiy+ETj/olTUV+mAJbcAWaXAikHgxMvS72t0wLZ7AdUEOisUGiB3HdB3MLRNW5CQ9VRK4GBXZnibJ32E7IB7MFj0NiqzsDxrO4ZdvZBBjkxVvrD8SiUUMgWqWHiPB2UmkH2jRMKQ7Ij24jctBewdkZZULn+RXqGGF054hXYiEq5AtkQUCjUZEV1U0j7KUGnIxSdqtuPFrgswu+3i+2sLFuBw16EIz9SsrCxoXVpUMmQ+DpxeJyxuM1RytciDKSsrEwWXSZEYCcJHayoNCR0W1wJFusPwYTNkMYYvhSwHLlqt+K/dITcXZvyOdl5udNuPMhUZ2epKZKgm58ds89hw3G+HoJQpUWuqRbk+NZ2eyhgqHXofphK9jiG81H0oeHb1OAbxbOd+3F22UwTVJgyRi7EOsOdK1y8tj3TVkhVSoPN58z2AuVeyncrIB5RqyeLm4Z8BDeek38stBN75L1JYb4pANQaJUi87q6Wdh1Kmhk4xveHAsx6LlgJqtWQzFVB8aDRAYbFkOxUOTpDOnAJuvm3irxcvsN1iBv7v18BH/23CT+31cUFfx5GOSVBQyGohm2Te01ggWfHuA3vwPO20/He5byxbhQ9W1YiffevcSfyu8ZL4+sbCEnx35VoYJ6xaikUA8xXHIIYDiEVcsPBx/7sBixUwmoB//09gQ5zu6aolwI33Aq88IV3LReXAHffHtJIcD7wHXRq5gKGBHuQXZ6LL3ob8oqXYUr0l+DtvvPGG8IRNGK2NNJSNJEupIuntBsr9jQg6A3DVm4BXnpLGKb7/zBxg/VXjPz/JDDGOhN2f3WcBRUgJye5jKhwp7Q4QGrfccoso6jFg9+HTb8BTZ0ZWUR7mrZYCkXm5VRtDSo1Pf/rTQq1ClUYaE8NPfvITbNu2DZ/5zGfws5/9bFoPY6fdiodb6jDkdGJVVi5uL5k/qzvh0rhywHlelkqPIZc1OIrxzMzXxiZ1c3JyRNNTZWVl8Pvz588LsiMZZGRkiAeLSykF56IZVwNeq3Rf4vdXIOLN52OtO9JII425i7PDg/j88QNotI4gV63B/1u6BlcVFM/sTrGZhErpyWSzThFY0Gdxn4/wGhfJedbLNFznziH09fUJJcby5cvF/ZgKFK47qArne6Ryg7Uy5suF52cEQOLjve99L37zm9+gtrYWsx1zgtQg2EXFjo277rpLdOdxwpNqHB3oxWeO7YWFQd1C1iLD39ub8Iu129Elgm2B1Vn5yJgIiRANRSFgKATUXaHCH7ubA5OKgoVAfg3gskukRmDRs3grUL4YaPSHQnvidNAmgowFAD1qHX2AQgvoSydUgJgIfD4vehyNMLv7oZSpUOyTQx2rbzqq2KSWa5GnGdsuadrBY6aMc35S+VJwI9C3H2D4KSvufJskQLJqIRfWSDzmkcUkRRwP9BWZJagb6cGRwZbgOfqWstXQK9Xi8Y55kVYU7H5iwCgfZGwLCwtx5swZMVjHQp+jFyeGjgTD7ou1pcJuiwM/u2L5fBw0SXQkA5/PAY/vDHygvZkaCtlCyGWxFEZ9gMw9utMYVFyM/n2FPBdq1MLpPS/IHonQCP9rH9y+IbTZaMmyAhnszJoAeBM4PHAYI27J/oyEyanhU8IiIV+TXMh6OLrtQ2IBmaM2IlcTYsTXZlfjlZ5AqGFgW2oJx2ZrJLHIY2d2WzHoNCNXk6TXLscr3Rj7K1ROUcftlSeBxvOh7/t7gMd/C7zv80gVGAg+z7ABnfYzcHpt0MiNKNYthSKFRe6pgE8UTzn2a2KSfVOOvALgX74M/OpHklKCGRcf/jRQXDK6UExEZxUki/nV0qORheIowoTbXnwEMA8ABWXAhhsSJva9viF4fCQ6JfjQKezqFFiTssLiX1ubg4SG9JrA/3fqGN5UUorH25rxv/UXgz97rrMdKrkcP1o90eBSNmDwWIR325KESoAYXLocOLgvchttMq02P6E0Avz7l4E//BkojEMQr9oGrNwqkSOTWLDUjzTC5rHDMjSC7Hwpw+r08BmU6UtFowELZxUVFUFfWAHmeRx+WQonz8wF1l0rNX4EkJM/mrThZ54VlQGz41agsAxorpOUG2u2SWTHeBDe7NFzGLdEjMjkwqaFzQFcTNDTNuAbf+zYMVEU5D15+03X4oK1BwqZNCPgs+nkmqBSgwuNRx99VHSUxVqQpJEYeOz+8pe/CDuqdevWTZvipctuxXsOvAyLxy3Ique6WtBgNePGwjK0WkdQojNgKc/dNNKYIdxdtgYPNe2H3Sutg0t0WdiRH7uxkIo5Eqxms1l8HVBW0LaDBZRkGhnZIbt9+3bRSTpv3rzIsX0qwTFfkcB4fhmj0lAAk1KHEbddzPM588lRm1Cqm6I8tDTmNoSjhN0/dwyrP6UxpzDscuIDh3aJf4l+pwP/cmwf/rrlWtQkoj6eagwNAv/1NeDEMWl9cOfdwPs+ODmr4kmAdZ1YzhtU8LJOtnPnTswlnDlzRjRM0U4qsJ5l2Hd7e7toouI2rjXirXVpu8Wa+wc/+EGhBp8LmN2Vmih861vfEl1tb3/72/Hkk0+mbJLDk/q3jRfwk7rTorwc+LxZJLV5PPjEkdehkEsnfpZKg++v3D516g12j8frIGexnNYs0bka+x8GnOxglwHNR4CFO4CaCXbRa3KkxzSj1XoG/a5QUPSID1giV0MmJtL+QUZdKCkhEoCbYdIirF09baSMm3kOPhvkMhMUsqL4RTFaUOXvlILYWTimlVjmImEBxr/IVNZiyH0mWFBWyLQwKmP7hPM1bilaggyVCmaXHQuMBVieGV91QfkZBzUOdmScKbGLR2h4fB6cHD4aJDSIDnsbslU5QqLGQtLFixcFwcFsjXjPE/tYkYSz+j9bNzy+45BhPWRULE0SKnk5lLJSuL1mDGB/3N/rczROmNSwe+0wuyO91mn11e3onjCp8UbPGRwZbAh+vz1vMdb4iYsVWfNEIUsKCpdheWYFqoyTU5rEg9PrwpDLIsLJYxkyJaXSmAxaLkXmBbEY2C6pcFIJrSID8w2pVyFNFXzCXuiknwiVwYfFkIGWhSkGQ7p/8CvJtoddNgG89wPAg7+UJqn8/NiAcNckJ0R8/i98FXjgX6TwaqEMYWePHPA4gDMHpfOj5SLQ0QS8+aMJTYx9PqpKIoMffKDlAu8fGmHJcKD/PC6OtAnCeHlmJVZkxp8EJoIGywiUMhncYec2v2qxWvF8eE6Jf87xYhgBkjRIxinW+nM1SEYoAPkSQJbAWL2wNjJ3hTvp8ZfWxSFjYLgTOH0yPqkRLBpNbq7m8NrFvMxutUOtle7pLL64vC5R0Od9jYWvIDh2PfFLoIMB6f59qD8N3PdpQOVvENh6NXDgDaDhYiib7M3vjJ0Xsmil9BgL/HtXp6TSpOWePBvwhNth0cbLJOZxzM1gRzOLdoF5bGNjo+ia4r010A12TdFiNDT2w+X1ikPO8+Gm4qXiZwwE/+QnP4m///3vke89jQmBWSyPPfYY3vSmNwk1OD+bVOPxtgZBaIRbzT3ScgnPtF8KjjF3l9bgQ7RySyONGQBJjDtKV+DccAe0ChW25i2IsL+LBs9bjm9cZ7AzlEiG0AiQjGvWrBG2U9nZ2aKhMSkVXhpJQS1X4a1lW7C//wIGnRbRULUxZ5FoGEjjCgctna2HALffRk6RDeg3RNpspzEncGZ4EINhmXZiSu/zYW9f98yQGt/5JnDqhPQ1G+H++iiQXwDcftf07wsgLNWj59Js2qU1LO8/c0VB63K5RMMU30sg55YNU4cOHUJxcXFC91I2J7ztbW8TDcusvc8VzClSg50fjzzyiJAEffGLX8R3v/vdlLzOn1vq8T8XT4vuuOhzmItKu9cLg1z6ARnPH9Udw3dWbJvQa5ldAxhydYsQ6Fx1CbTJdow0HpYIDbEo8i+MLrwBzF8jWbhMIxweO9psLaJrPVudi3xNYpJjj88dQWgQbpkMnYZKFFMlI8KjswHj4nE7BPhcl0aOYUiETDPcOx/VxpUp7bRmkd7uOQyvUA9IxRGFrA8a+dL4gyC3G+dJjyjolWVQyo1wePshhxI6RTHkcTJIbB4nfte0S3T3s9h9fqRDeOBuyq0WP2dRblfvJREmzjyNrdmV6GttDao0WChiV1Us2D02QWxE7DZkMLuHUSwrFVI0kiNkc5OT5JHMIAkXCa+vG4pRpAbVGHzvgYwRHk++ltSt6/AMw+rhZEsOo7IIKn9QPK8npTwDGnmOOI5EgIF3B2z5o9QwySDeZF/hz3jx+rxweF3QyNWQ+88Bvn6ztVt8VtlqI8p0ecHzo9XaF0FoBMLB5xvyRccUUZtRKh6pRKe9Dy93HYZLZJNIVmac9Ii6oFDq5CJTNYX2e8P9QGsDoNYA80nshZ3npmxA1hyWGyQDDLPbz3G64RPqjBORNn2gAsoEGabpWIUTGsTd9wBl5cDxo4DRCKxYDRw+KNkVrd/ICkdiz3t4H3D+NGAwAlfdAGRmA599QMrRoJ0RQUugPFPoHOE13nYJ6GkDCuNYwfF3vK2AjyowGyCLn12xv/8cTg6Frst9/WehlCuwNGPiReSFpowIQiNwnc3TG6BXKEdla+sm27zBMVW5w583pEi8y+7kcen4BlQ3gX3WqEKqDadb+lwTAZULQqWgTbrTL0OVgaH+M8guCDVc2IfsuNBzQUy6OVGPAMnP9rDxlPs+0C0RG4vWSNuo5vnsV4AjeyWrrcoaoHqC8mo+v4VzsbDMDHUFoC4CBHFG6GC21+D4gV0oKioKLizYBcaQar6HrVu3RjxtgTYDH6nagWODreJevshUhHJ9NhoaGnD33XfjBz/4Aa6++uqJ7XMao3DNNdfg+9//Pt785jeLgmrARidVMLtdo5LsiMD9lvhrWx225hVPSLHB+XC7rUWQgkalCYWa0jmzME9jduDoYCNe7j4lSH3OBBusXXhXxXbolfHn/CQiaHPBwG+ShSwMMV8jGdCNgZYZ9PouKZmGJo0rHAalFtcUrJjp3UhjtsF2JkRoEMyltB0HDBNVD88SDA0Ar/xTUhzXLgPWzp1GtolCG2MtIdS/M2H7xHXFsaNRucAADh2YMVKD9ynavhK0aCIxz+ZdNrjMhXmTz+cT1rUM+6YCPKCU5HqBId9UIidaq/vCF74gSB4qJll7nyuYO3vqB7vBn376aSGdWbp0qfD6mmr8oTFk/xBYx4efz0pZJMlBmfhE0O/sQIOFBSnpCbvtjajN2AQdu/wShQhsiy6DsGPQMa2kht1jx4H+PXD5pEVai60JNYZFmGeoTIgUiAU3O22NkfZJ46HZcjZIaBBDrh60WM5hvjF1nW4eX7ef0EDwc/D4OuBFBRSYGPutlmeJx3g4PNAY9LuVSs/Aqz1nsTqrAhqFCn9rPY5jQ61BQuKMuRMrenyQO9xC4s0ApEBwUPSiQx2jE4OvoaE1mb+zlANk8tka8nGW8SE4PUMYcdOIzA2NXAaDMgcyet5DAau7B12OY8G/H3I1oES3AWq5dP3wJlSgWYUB5wVYPLSVccHlDV0ppklkaqjlapRqS9FmbwseWznkIlODtk2v9xyDy+eBSqbEVfmrUKrLx8vdx3FhJETeLcuYj+35UtdtnzM2sXRkoBGVhkJUGfJTflP1eD14pTtEaASQr86CT+ZDnjoD67MXT91+NJ0H/vq/kk0MQYuX+z4FaPw2KtfcDtSflXzuA0PczfdNzWtfNmDWTqyiPG3dZpAA2rhZeux6Dfji56TOeWLhIuBbP2Ar5th///RjwJOPhNQer70A/Md3gfJK4IHvAscOSNsVbuDkG5GKHoLZLPHguQB4G4LXrVB7hJ3TMrBwLt07L5hHBztz22RIjTtKyvB8Zwf+0tYcHA1/sHItCrRafLh6IXb3dYttgTaFj9cswpQgWWKfE+Jwe6boYyxkhSZgzTj3aBLjzuOAx684kWUC2nUSuZEg5uvn4YTzBGQZMrHYaD7VhCpNJeR58tGEBuGMY8PpsAEjQ4BWLxGonKxvGKcjv/MiUHdAyjMrqQWqN4wmZdy9kYSG2IdmQLMdLkUlevracOlCF4yGduETH1Bn0GqKHVS0PFIzoyYGstUGXF0QOgfYREA1AbuoPvzhD4+972kkjY985CPCb5jh4bS6TWUw4vrsfPytLUS+Bc6q6Dtsm20kaVKDDSlHB/bD4jGLcU4KeO7HItPyObFAT2PmQbXuq92nxdeB7D6L24FDA/XYkb84LqFBBXfgumGeBjtdkyU1OM6zs5TWGXMxnHW2w+llA2C3WKfM0+chk7bWaaQRjQi1KeEDPFKj4JwF1d5f/TwwYpbmci89C9z1NuBNc8NiZ6JYnpmDNVm5ODbYJ1o62UyVp9bi+sLUNkvGBMd0jRqw2yO36aPcaKYR4RZNbNilWoFZa7N9vsT9JJkxODgoVMas7REkN5inQcVGMkrH3/72t8LalgrJRB1YZgvmHKlB8EOj/y0XdmTVeNJNJVw+afrGNbw7isSo1Bsw7LIFy0jsXqnQT2zR02oN+MVLz8ZXbbddQrVxVeJPwrDwFr98S0AGaI2Adno9QlusjXD7JKuowLG5ZLmAcv08yMexq1HIVNApMmDzmCM6jjNUyYXLEcPuvlHbhmJsGwt1I13Y3VsHh9eNhcZCbM9fOKblji/CrzxsOztTUzwWjrgdwQVraH8Aq8cpzuMAoSFt98Ht9UK/qho7syoFITEyMoIlS5agrq5ODIzhgX4quQoLjLW4OMKgZqmqzG6/Mp3UAU3md2JBfjoRyCvZvAQgh1wWWZxyePow6Ar53Vs9bA5WIVMtFcP6nNyv8PftQb/zIoq0a0LPKlMiV7ME2b5adNnPwultF8crW12OPLWkZpkolmUug0FpQJ+zT5Ac1YZqkeHxSjczSKT9IkHwcvcRbMpdFkFoEKeGG7HIVIoCbRayVLGv14P9Tdjf3yTOw7vL1gVVH6nAiMcmFjrh4LHSK4EsNZU1FpwbGUCNcS10ikmqNTi4Pv17wBP2et1twP4XgB1vkr7PLwE++u/AyQOSxRHtX0pmwGaFBXl2/9utQGEFkBHHno/qgQvHpMJp+QKgYDomivGI64kGS08hSEZ9/9shQoOouwg8+Vfg3nfE/zu7DXjqUenrgEpgZBh4+Rngze8AcvOBa2+Vtve0Ayd3hcYBjtPMO8gvjV9g9xMahJx/5/XCI6d6QOEPCl8UnMQK0iMKsbYlA17DP1uzHu+dX4VOhx3LMjJR7Vc77MgvxEMbt+NPTfVwer24tbgMby6rSPo1fD4XfKB6TwNZEuRBBK6+Dvjbo8DgkNRNFc2d8RhFW4/FgutiiNAQOzcMOI4D2jgB4zHAz2NjyQacrjsDZ7cD9228F7lZYxTJiiokJQaD7MMjbvc8B7z0F2nxtOMOYPWOsV+4qw7Y/5fQ9wPtUq7Z4ihvXQbNxsCxxr0413gRpkwDqpeXI1erF4QG1Rm89/J9bdiQeMcj79G0X6WknCqNNFKD//7v/xaB7TzWTzzxRMqsbq8qKMVHq5bgfxvOCkVkgYa+9rZRi+iyCawxuu1U7UrNEoH5YZejHeX6ShiUacVjGuODa6DAXDYcFrd9zPV5VVWVGKtI2rJ7lGo6EoVcayRaIGJhqby8PE1opAAkpv7QtBuDLmswEPze8o2o0Kfze9KIgsjzjGrclU2vC8iU48V/SIRGeNPOEw8DN9wWaqq7DEES45drt+IX9edwfngIJTo9PlZTi8ypyAVOFrwPvO2dwG9/LX3NB+flb34rZgokz6lMoJ16eA7FbIXVbsHrR57CgLUT1QvmYVHNCuSq80VDgM1mE/fcZEmZN954A5/4xCeEeID38rmGOUlqBGTi3/ve93DnnXeKkzAgGZoK3FxUhj821YnoVZay3T5gsSkTP1q9Bc0WM/7f2QOwuF3iZ5lqDT65YByv5Thw+0YXw11ev61GoihbBgx3AU3+oHCNHlj35mkL+A7A6XOOmvpKocwuqOOEXAcgwmoMa9BkPQGLu18Uoou1C5A5AVKDBIkUmBsCg8cTRaOlF4+0HAx+3+MwCzunW4vjy3LlMXMgZCJbI9Uo0WbhKJrCXpUSQzUyVDoMu+zi+8BDun37YLXbhKSO9lEB8PrhtnBSg6jQz4dJmYEh16Ao3Bdqi4PWS7wBcMGS7MDP31dgJTy+OvYOisKbQlYNmSySobd5IkkAwu5tQ4aPagG5CPSNRqxtYl9lchH4zMdUgc9ZbawG/wugbqR11CLQAy+67OEETghmtw0FyEKFPg+LTaU4aw69Z6c3ZJB1YaQLZ4bbsCwzuUD2ZKCNkT8j+dZTDSCNJ06vA3UjR7AsY5JyTBIA0Qo3Eh39YTJnIisP2H5L5Da3A2ijgsMG5JQDOak7JkJF8vcHgXZ/QDXteG56NzB/SeTvkfB46L8lixvhnQ/g1ncDtSGCLTXI9Fu0sZsqIGfhtomH1U8ZhoborRO5jedMe2RuxChYraNVAXxvXIREg8TXbe8FXnwUsI0A2fnAze8C/MTnaEQFmPMj9fGRDShGj/FLM+fh8MDFyG2TUGlEFOlz+bmNxta8AvGYKDy+Hri9JyD3uiEXWQzZkKtXJaWMCIa7//fPgMf+D+jvA1pagOamyIWgP+B6THijmwpIJCXf6UfLJj4SAovAt38QePYPgNUs5WjQLsvutz3ke3j1cSC3GKgYY+JefzjGtkOjSQ1F6DgMDI7g/MU2WGVyyMuKsGaTVMgTnccnX4fG1oBsY6FoKli0KDkVzuc//3nRgDDXJOFzDeFWtzzmJDlShXfPX4S3VSwQawoWF37feBb/13Ih+PN7ymqwJB6RPgZcMdYXhNPrxJUdi5xGotAr1DAqtYLECLWb+VCkHbt7kypu2k+xOGSxWIRqI9lcDSo7mM2RxtTjjd7zGOL82Q9aG/694xg+Vn1t+nCnEQntYsCyO2yDD9BGrX/mGsRaImr9yjUH1x6XMalB6JVKfHbhLMnoest9ANdB+/cBtEV6053AgoUztjtUTM92cB1B5SNt4wc89ShZYESlTroe67uO4VjzGRiVBeKeS+vHZOo0XFswGJwWrKyxz0XM6VXRRz/6USFzZUfV3r17k5a3xsOnFyyD2+vDU+1Noqvy3vIqfLxmCV7racPXzx4KhvpVGTLw/RVbkTPBQdCgzMKIm7ZFoQKOSZXk4oUn7NLrgJrNUgehPksqvE0zslTZ6LRHFqu0ch1UCTL6KrkGNcb1EyqSh6NMtwAXR0jwBJ7DJ7Yliv19l0QqQjAc3gccH2zGTUXL4qo1FDJaRS2C0xtQ3sihkS+DfKIdsklgeWYZ2m0DODokWZlo5Cq8pWy92NdBp1XkwgSyXlm845m2OKsYnY3nBYvLATIA+u81NTWNCkrKVueIRzQCCxb+myxkMiWUsvH8y+N73XfaL8Ll9fhzb2TivLFQJuFzwOE9hVJtNTQMZJ+BwL1YyFLHPkbnzJ04Z+4W9lLXF65EbUYZGiw92NN7KSLxg4qwfmE1lzpoFGrM1+eh0RqSG6tlQI4m/Lz3wem1CUJWNQ5ZOSaYocGOelsgLN5/0bEoPRY4xu36A2AZCIX7Lr8RmJeEui0ZUAXQXh/6nqqDFx4C7v8qoAi7fR58CRgMHDf/hfbP/wMWrkoosHqikFRaLMZz7CVJxHO+TJihpQRUUTz8G+D4YclC6ra3SoHL8YriOr30NwGSguNN+TjKA8pdc/KAwf5QAZ3HvSbOeFG1DPjQMul3xz3WvD5N/mMVlkMii00wrM1aAKVMIYLCFZBjWeZ8LDDNgFQ7Qfh8Trh9J6Fwu6D0ePzvsA8+2+uQ6Xb6u+4SABUY5mEgKxv4yCelbSQ0Pv1xwGGXrj0e7498IoEn08yMkqi0Cnj/f0pWVLw2/xylbOC50npxbFKD551aCRi0ZLEBtwewBPKdwqDMgg3VOHbwHzAadNiwdgG6dUUw+yTi1ul04ej+M6hdVoV5BVXI1yS/cPvRj36Ehx56SMx155okfC6CNjrPPPOMIDbYec5Q9lRBJZcji/dEAO+tXILNecXC1rZUZ0RthpQfliwylKPPEVpkUm2bRhqJgHPrO0vW469t+0V+H8Hmm5VZ88dVWZCAZqYGs/u4RqBa48SJE8I2OhFClr/j5n0ojSlHn9MySt1PkmOy6+80LkMocwDjTsDZIp0pqhJp21zGglrgjZci54LZudKaJY3pA8eaa66XHmmMCY7N9fX1wiGF91CuAS7QwcA/jjddaodlxIrVK9dhvilxFXwAvb29uPnmm/Ge97xHWLDOVcxpUoOgWoOFWCo2XnjhBWjH8+pOcIHxpcUrxSMAm8eN/zp3OEhoEA2WYbzc04a3lE3MxqbSsAIXzYdh90oLXyoTirVVE9tpjUF6zBBKtGUwu4bRZm/xF9e1WJm1ZkJd/JNBlroAtaaN6HNKBAvD100MGU9w0Gi1RXaQso7BWvl4UMkroJQVwQsH5NBCloQ6ZDLg8bqpeAU259UIy6lctRFquXRZHxlslqgd/yEVCj/ezzMLMaRrxpo1kV3ktLQI+O+NBZfLhba2NjEIVlQkb42SKLSKIji8XRHbNPJCcb102+uDKRwyeOFijIz4nOywOtow4OzC8sxtMXNBUolSXR5y1ZnodzLTQEK+Jlt0d3fbzTg13CzKzUq5DE6vDCeHpPP0xFALrsqvxebcGmSrjNjdy0J66MSj+iNPM4UB3THg8rpg9/YhW8VuTuncz1XLBHEUDUWy/vzR4Mn4pvcAf/tVKFODXfebYkxuLGbg5Uel8N9cWuv5dygwFp96AShbCihScM0N9ITIk3BbJXZ/M8g8gCGOG1EDBX+PCg59aj83icBIoVolHL/9CXDskGRHxM/l9z+TPFBXx5hEsTPzC/8f8I2v+G2AyMKuBO4YJwSOpPynvgT88JvAgL/L/8bbgc1R3fFETxPQVS/lI8xbAejGyTDiZ6laA7iPAD5/t5aiBpAXxx1fV2VVi8dcAC2naLFFQgMR1L4LcDUB6gSK6Yf2Av/7I4m8ICn1sc8BK9YAFfOAn/8aePF56dzeuAVYnEDHnnoBYO+JJJHUsf3Ypxz8vDU6KUMjGrymqWwdC/nlkUoT5q9kGSSyw988wsLb8ePHxfxh3c4PQyV3AgojFO4WwDUC85AFZ07UYfXGJVCrVVBNoNnhqaeewpe//GW8+OKLqK6eG+fi5QAeax7766+/XnS90fJ2OrDIlC0ek0GWOgfVhlpcspzz37MVWJKxGqoYisw00oiHQm0mPlh5LfqdI9DIlchSj7/OzMnJQWZmJq6+OtTwQOUGiUL6fOfn549LitCeL01qpAb5ahNarP1BYoPzhCyVPk1opBEbigxgCl0OZhxsxGptBp5/WvqezTv/8uWUNqClkcZEQSKDKgo21wTiFgQBDTm8PjfOnayH3qDDkpU1UCmSn9/Z7XZRQ1+5ciW++93vzukPas6TGrTA+eMf/yikMu973/tEJ1sqQsV6HfSb947ypwsPCR902vFsZx2GXQ7UGHNwdcH8MT3w1XItlmRsEZ3PtNPh9ynDSC9gGwYMOZKaY4rB4k9txlJUGWvg9rqhVejGzdJIFUhiJEpkhMPiccDt80RkgLLukaXSjZmpEYBMpoYirsd9apGp0otHOLyjLFz826PO4wB43dASYyzQJ3f37t0iiIhs8URUGolCqyiEz7cUI+5LIi9DI89HhqpWhL+TaAq8C77NSFMZyfasz9GOYt34QfVTCRYObi7aiNPDDRhyWZCpMmJZRiV2917EoQFJTUPkyHUY8jCbJ/QZvd5zHhtzqmBSaXFz0XI823ki+NNlGaVYbIpdeA2AnXQH+xsw4rajWJeFVZkVSS1SaEtBaBQyaPxiL5eXfy/FFkvmRj6UaGsg91uQTQrza4H3/39A6yXJLqiyVipOh4Pn6pP/C/R2SIV0p0KSB0dcpNxuA3QpIDUy2cEfdR1xH6M9zknInAvlv4hlot4gFYUvF5B8Onogchs/h4O7Y5MaxIZNwK9+D1w4BxgzgOUrpPDveGisA15/VgqPu/VOgI0FBhPgz5yIQNNx4Mg//FaLPinM+er7AcM49zeZDlBtBXzsAmVI+NxfyEhKtVbY3C3IiDurGyM8PYCONuCn3wspZGw24EffAr79M0kmXlgEvOPdye2cnMHg2wA3rUS8gKIQUMRWxqQMDFlesh44czD0eRszgaXrx/47tSpyrBEEpwew9gPGfNGJTNvGtWvXwmCILPZlyebj1MWD6O8bxIZtK8RYLIcKGcrkCEiG5b7jHe/A73//e6EaSGN6wdwwHnvma7z66qvis54rKNPPR5G2VNhGUrkasA5NI41koJIrBLmRKKjSoHUUSYzo+1T0OBnLumpoaAglJSVYvXp1+oNKAZgT2WztQ49TsvRkI9ztJeljncYVAs7j7nsvcNvdgNUi5fSlKDcrjTQmCtbbOP+nKiM6G4NfG1GCl/c8g+pF5cjJk9a92erk7JFZD2TtnK/FWnoq6ufTiTlPagQ6QNhNxQXfAw88gG9+85tT/hp5Gh3UcnkEsUHVRpm/C5dExn+cfhVmdjAC2NPXikbLIN5fNfZEgSemRpHiwtf514HGQE6EDFhyHVAePyNiMmBn/HR3x08VaN0UHbrNMWShKUEf71mGpZklODUcaQnGIfHn9a+iyi4NZuEDmMi6GOfGTlUUCQ1KyqcDOmWpeETnpsSmZcIhgyeGf/50QCVXYlVWyNKk0z6EPX2Rvvz9/oC+cFCN4fJ6oVHIsTq7AhX6HHQ5hmFSalGmyx6ToLB7XPhd4y4hIedvHR9qQZt1ALeVJG7LpFNooZKp4PKF7FVoYFOgWQC5zAWPzwWTklZkU3g9ZOZIj3gY7AF6wrJVHC7AFE7+svNeC69aC5/PTuO3qe02W7ENaDwDdPlza3i9XHtfpPUUsfZqoKUOaDwXste6/f7LomAeBN9LtGqFx3+8SVBBofSIhcBz8XmbLwE/+6Y/lNoHXDglBYTfdm/svzvxgv9rbyhr5fweYM0tCb6fy2L6I2DxNGPIJdkfWumY5N8uC/+3ows486J07MqXA0uvHm1VefFsZGYG74VU2dRflEiNiUJumj51RjzccB9QNA/obAYMGcDanYB2nK5jQbIGsmrCoFAKKxXeL7dvH50vxHvr8SOnUJSxGgvW2+Dy2aGRG1GoWZ7UooH329tuuw1f/epXcffddyf1dtOYOvDYs9jKz4J5JuOpWWcTlHKVeKSRxnSBft7Dw7TGiASV3ew4HSuAlF2pJBLTSB10CjXeO38bmqx9opGvTJcDg3JurtvTSGNMBPy3Y8EYp2EqjTRmGLx/HjlyROR8xGoeplPKhVPduH7rnXAqBiGHQhAa+iSt4VgzP3DggLC1pQX9XMdls6pnwPGzzz6LLVu2CO/Oj33sY1P6/DqFEl+qXRuRqbE6Kx93lEid4K/1NAlCIzwk+PXeZtxVVoscdfInitfnwpDrEpxeM1QyPTLVNVBMxMO+rzmM0CB8UmEjdx6gT7zz5nICu4XsXqmorJWHJLfsRtqRvwiv9ZwTGQb8JHUKFTbmzE27hyUZxXhT8Qq81H1OWFPxXSplgNllwz/bLuIGXAV1lPc+w/xoLxUv1C8QDu7xMc9iejsbPD43Bpw9cHisCXi/+pClmuZu4CgwfI8kw9mh0YHnRGRpWIZ8jQmasGJ5rsYoHong5FArhlxW8ZyB5z053IoteQuQk4BdAEH1xersNTgycBhu0cUOVOgqUKYtgcx3DvD1A64uYJAhpmpAWw5oU5wvEP0ZD1ORoQaMfmJDqYZ19SYMOF8V3yqgRa6GFhtTNFFVqYE7Pwq0nJespFgUzYphnUCP6Ls/DHS2AA4bUFCWctupIBz9gLUdoO2cYR6gTNHEhITn9mslL1pxD/QXe7ddl/xzUYnx5/8Fju6XCKJrbwXM/dLzhpMmr/8TuOWto4kT2v+4o5QH/DtHVPj8FYIRd0gFZiUnRJsQrydI0cuGtMDxsHlAwyGJ0CCxEY545yxVR5cDKbdyq/RIFPmLgNajEmEWGFlz5sPsUgh/W6oV2eEU8Ih3OBwiq4r/LlmyRFiwRKPTNoCz5hbxbAuMJSjXx75PcdFy44034i1veQs+85nPTPBNpzFV+OxnP4uGhgbcdNNN2LVr15Rl+M0Uhlzd6HXwPPQhW12MHFVJ2n4mjSnBpUuXhF1bNDhnH6/pZK53ik43HB43jg42YcTtQIkuSyi6E2nsUcoVqDYWYFaB6w77BcA9DLDZU7sImKNNkmnMME4dBR76X2B4ECguB97/KaB4mmx600hjkti/fz+Ki4ths9kiSA1av7PBhuqNnTt3TmrO9tOf/hS//OUvsWfPHlFDvxxw2ZAaxKJFi/D3v/9d+N/yA+JicCpxTUEZFhgzcc48iEyVGmuzC4QFFWH1uIJdkeGwul1Jkxo+nxfd9kNwCc9vmkYMwm7vQ5F2M+TJZjWYe2K9AmDpmzJSQ/h1i7Ls7D+dXF4nTg0dwbAIaJfCFJdlroXK38m2KbdG5FKwg0XLjvvseTAqUx/4nSqsy5kHq8ch7I8ChFt/ayfcCqDN3IfKzMgOasrFBwcH43reqnLVeOjlh5FblovS0jKszV6FHM3kvJ8TAe0TTg3tg8NrC25Twifs3YTCxOeDUq6Fw2uHUqbCfP0SGGMEZU4X6Ff7SMtB2PzjgirGOm2xsRhnRzrE17lqA+4uXTfh16P1VLTKiLCLcMfEC5IMhN+ZfzWsHovw3tbREs9ziGcN4HQDIyzu+cHcGt9qIJUWXyQQSqqAjoZQx02vFdh+n/DJtxmAAd/Z4K97YEev4wiKtOyensDi2DYCHH8FGBkEcoqAFVdJ3drzE8gO4OsVT3MHr6UF6HrD/40PGDgFlN4EqFJEqNz3fsCUCRw/BGh1wM13AbXLkn+e//uVlN1AlQXDQJ/5KzC/anRHPMkLjxuI9oEnEZKRD5h7I0mQnNkb4p1aRB43m08GlywDhZq1Ur7T/t+N/pO206NJjZVrgfnVQFN9SJWzaAlQOzN+yv1OC84Mt8Pr86LWVIwC7TiZKROBuRu4tAuwm4HMYqBmh1B/CZBUXnUP0HwQcFoAUyFQvg4muQK33HKL6KaiJVFGRoYgN9gMQKIjXsdTi7UXT7fvD2pozg634IbC1VhgKon4PYvFIlQBy5cvxw9/+MN0sXkWgPMMfhb33nuv+GyYbzKelc5sxaCzCw3WY8Hvze4+eHVu5GvmjgIljdkLWvKxwXAiwdNcu1MNxQ7VRALFr2Q4vW6h0O5zjoj5P9d4bdmVuL5oDuYfcC5o3gN4BqTv3TKpiSrjKmCaMiqnBfUngPqTUlPJ4o1A8fRaJF8R6GgFfv7dkOq7owX44deAr/wQmILc3TQuD/D+1GFvx4h7BHqFHiW60hmzzY/GtddeKwiNs2fPigYqgmsM2jLy3jpZR4rHHnsMX/ziF0UWNWvnlwsuuxkDZasPP/ww7rvvPuTl5eGqq66a1PMNOh340cVjODPcjxy1Fh+uXobrC8tH/d4SUz6e6agLfs/LIkOlReF49gYx4PAOBgkNCeyMt8Pm6YFBGbn4Df2KDxiqk7p2VQYgix0OqvjBqeMFqiYAH9jNfZJyEP/3LIQvgwxSB7/X58GIe0hMtgzKTDFYsDhxfPAS2m290ChUWJlVg3zN9BWfL46cxbA7FOLMr+tGzmBxRigUfoGpSDwuF2gVLP+Hil5ZJQVQaTRoOHsRZ/sPi8EzkKVBK7d4QX4WtwXHzCexcP0itNa14Oj+I+goa8fdy++CQZ1aC7UW60VBWITD45OJMGueX2X6RSjUzheE4ISK2VO80Hi4+SDsXlcw28TtlcLBA1ibXYnrCpfiFq8bLq8HeoV6UjepCn0udodZXMn8dmq56vEVC06vFb2ORqEOo3QxW1WGDJWf8PSRxOj3MyQhW6ogGEKaSlJjuE+6S+n83VqmPOCqu4ViwufzwOneB5NfMeBgtIaXNl7MxrFBJUty7HXagad/DliGpMlwyzmgsxG46f2zN0Cu90BkQZu5KAMngYIU2TewyHDHfdJjMjh2IGQbFa7eCCcoeMxrFktqmVjY8GZgz8OA1T+ely4GFlyZmQN6RQnM7vrIbZwryP3F9WibqXjbqND78teB554GujuAkjLg+ttmxG+4wzaEPzTtDipjd/fW4b6KDag0jB0ymxRsg8CRxyTyjNeRbQiw9ANr7wlZx5FIWXjtqD9Vq9VijnndddeJxREx3hh+sP+C/2oNnef7+y9EkBpUSt5zzz2CGKHH7XiWkGlMH/hZ/OlPfxJqDZIbjz/+eFxV62xGt6Nx1LYue0Oa1EhjSsCAcGZqkNwgMUGCIlFUVlYKe1t2jzJwnFZvtLNKYzSOD7YIQkNSaEv3lAMDDdiQW4VM1RyzEnH3hwgNAQYnWgFnJ6AZXXOZDni8fXB4z3B1ATmM0CiWQ57suiIcZ/YBu56QVmicKlw6Dtx8P1AW344tjQng9LEQoUHQUnVoEGipBxYk0KCWxmUPztlPDB1Hp70j2BDaYe/A2ux1qSE2PC7AZQPonBFr7RVDscimmcC9cyINAvHwyiuviByNRx555LLL6ZullZrJgV1UP/7xj3HHHXfg2LFQN1Ky4GL6yyf3YG9fJwZcDtRbhvDlk3vRaBntFbo8qwDvqFgWVG7kqPX4/KLNwtIoWTAUOfb2OEkCgoneBXTtBQYvAj1HgebnAK8bKKiRHuHQ6AHFVCzEzgcJDQlUhUiFVYfHhhNDu3DWfABnzPtxamgPXF4HdvWexJHBC+h09KPZ2oW/t+9Fv3P08UwUvNBtHiscnsiCdzwMufqjulp9GHJJqo1okIDptPeg2doBizukEJhrWJ5ZBpNSJwZu/sfBcmP1UqxfulJYKISHg7PzlF2nsdDn6IfXfw6W1ZRj4ZpF4vsXXvd726cQdo91VDcyb0IrMq7CqqzrBKFBJEto8PwJFKSSgdllF5kV1mgLHNaZHSNBQkPaT8Dp43tg4d0n/pX7/fwZ0Ecv28nerOYb8nBdwRKoZTJo5YBBqcTdZesj7KziERqXRvZiwNWKIXcnOuxn0OWgvVQMxDpMfpuqlMDlAJ5mSDgVIV6/wqw/aJFj956FDFbRUM7jp1XIgoqYpBVtRCMzHAZCxXaeF50NQC8DjhNARxPw118Bf/ofYPc//YXSFIL7N2rc8wEcT80dUpjxBM7taUF0IDwJjKwc4JZ7pCB4fr9wGfCOMWwkTbnA9R8Brv0gcOPHgQ13JTRZvBxhUlbDqKyEXBgKqmFSVonvg6iKUVSqihOSzeN/x1uBD34KuPXNrN5jJvBK91lh4ecL++/5ztNT+yLdF0OEhoAPMHfFUbjGRyK2KoHso2g4w+8VPh8+8IEPCIn5E088AW26q3DWgZ/Jk08+iZaWFnzwgx+c0PxhpsGGo1HbEkgqSyONRMDmqIULF4q1xkS6QPn3O3bsEBZW9BVno9WVBN73mMXX4xgec3yxuB1iTRdr+9xDvPlyCtcYY8Dr4zruKHyw+6svZtg9h+GbzJrnyEv+L/w2q/xoj7+W2N96PMDr/wD+8N/AX34FdMW2NU7D35wTC/EapNK44sC6HwkNhBHC/c4+9DiSm/snhO6zwJHfAif+DBz9PTDUkvRTTBWhcfToUdx55534yU9+gltvvRWXGy47pUYAZKG6urpw8803Y/fu3aiqorVFcmixmnGJXbt++Pxd12/0tGE+gyajcENRNa4pqBRWVCblxDuv1fIsyMEw5PAFsBxaeZwAGMcgYG7wf+NfmDgGAHMTkFkNFC0A+uulNyCaml3AiaeADe+MH6CUEPribmuwnI7orLd5Lai3nMZFv90OEehYPG9uwebc5OSybq8HTq8dZ4aPweKRVC256gIsyVg1JsvKEHNaGRH8LRZBWQQm4aIK8+7k87/WcwC9TmkyLYcc2/LWoFhXMCdD4e6v3Ib9/fWiGE/f1bXZ83Fg/36sXx9Z2Ar3B4+Ggp794d8rFMgtzoPalvpCok5hxDA7ecLAPBSVQjPhRcPu3tM4Z24Vc8taUym25ZOUHJ8U2dt3CS92SZZHzF65o3QVlmWGbG+ougiHN/xf//rk6EAzrilYNMobd3dfHfocFuRpjNiaVy1Ij0Th9jlgVEnKFRKjJ4Yuoky3fszrod/ZDK9YTIQWTn3ORuRrqqEQxAvfC73D+wCVgq1LkU+gKU54/5JGTytgHY4q4nuA5vPAsi1w+7pGDV8qGaBWVEAhm8Dk1eWMHQpMcmU8dLcBv/ueVCDlfjael0LOb30nUga+eXUW4BwK7bPLDfQ0AS2XpO9zqoCFN8y+wPLr3wQ8+bB0uLOMgFYFMHumzQQ88N8hYmM8kMSgDVWSIGEtKN6pDJWfQfB9ZKoWiEdMMBicGUhNR0NB4RUrMJsx7LZHtR+koFgjCkYxikY9p4GMOOH2k0CFPh+DQ1JnLcGzr1wnZWqwePWFL3wBr7/+uuhSjpXHkcbsAD8bZvht3bpVSPi//e1vYy4hS10EG+3Wwreppv58T+PKBRVnvC9Fqyy4xkgU9BInseF0jm4eulwx7LLh/1r2CetFolSbjXvKN0AboxGxTJ8Nb1/o/iVZ3SqFne2cg4KODVxzhJMGMkA5M9mIHl9fjEY6hyA3FJig5bJYY0Q+o1CIJ4Infw+cYi4abXjlwIUTwIcfAHLT4/YorNksWdqOmCWVBo9XZQ1QkXwdMI3LEw5/HTAagfrglGGkG2gMIy5pCX7xn8DKdwDTrKa7dOmSqIl/+ctfxnvf+15cjrhsSQ2CC8Tu7m5hr8OFYnl5chJG+vXHQqzOiACUcjkyJhlspZCpkK9diz7HSbh9FihkWuSol0Epj2PxE1OlIAttH2z1d7CGdSOyi9dlByYQYh4CJ1nRN2lp4mXxsBgZWZKw+smHUbsfo2ssHpweF17rOYp2ey8KNTLomHztR5+zG42WOlQZF8b9+2rDIpwYOgSFjN2t0jaZzI6LI7tRbdgIjUKaDF4YaQgSGoEutr19x3Bn6fVxz4vZDKoBrilYPIqUiLa2oJ8tJeOUfUejUJMPg8IAK4O6/Z+tu9+NQk3qAzMr9AsEqWHzjARrUT1OCw4PHMaKzBXBTJQAmq2daLd1QylTYqGpAhlRGQO0ATk9HArXPWNugVKuxNa8saWprdaBIKFBsI/4idajGHE5hEpkniEHRdoMrM2qwOHB5jFGikiQRPtd4x502EMk6sWRbry/cmtcooUEa91IO4ZdVrGQOT5YL9ng+3/eZutFvaUTNcaSMcPXYz83g+CVUuFcsQrwngd0zC+wSUHYfBVNKWBchZQhZte9L6zYzX9DJAvPCaXcCKNigv6QJdWhHAGCX6s0QF4COQ2HX4+UOxNHdwPXvwVQx/BwZUHJ0itJUY35EyeXC7YBHS8BHpvfgtAuTeID6K2TvInzqoG8eYkp9E4ekDrCuNhauAK44a1T3+F0452AwQTsehZwSYt3gcYLwD8fAe58H1KBEbcVu3qOYMA1DJVMiTXZS1BlvELCA8uWSI85gnJdNvr91hqBeVeJbgLFBF4Xww2AfQCgHV9WTYjky68GGvZG/j5tAp2D/mvZ31gyEZI0BjblLoLFY0edv7mjXJ+PHflSJs3Xv/51/OEPfxBzVQYEpjG7QW/jf/7zn9i+fbso3D7wwAOYKyjUVMLjc6HX0Syur2xVMcp0tTO9W2lcRqBKI1boN6+VsRTh4fB6vWhubsbKlSF74Msd/+g4jgEnlekS2u0DeLX7LG4qHt2EUGMsxI68hXi9V1JXswnqrWXrhLXznAPrJqbNgOUQwOxE3nMNqwHFTFmPxWuqmUSDUEWtlKkRvk6YF7kujwmLGThFq1k/uNbgNP/ILuD6uye+P5crTBnAF74JPPUI0N8jkRm33TN7bYTTmHaYlBkxc0iD1tvJwDEM9DN+wAdkzgf0YTWxkc7RzZJ00bH2ApnTZ6vX3NwsauFvf/vb8W//9m+4XHFZkxrsEvn+978Pq9WKa665JunFYpnOiEWmbFw0DwZDlvnvC92XsDiT3e6py11QyzNQrNuamI8ag5rZhRlBDvgAnb+DVamJ0YDMYuVkJz7VAE6Enk+8gMSEa+RauEVIcej12Fmfr1Gg3zEMT1Cn4cM8feLHcU/fSXTYJTWIRhHPXio+stS5WJO9BY2Wg/6A81Bxt9N+AfM4iRLdMiOjDpnL54bD64RuguoAeM2Aj/unAOSFsyL8jF1T0cRGPA9vFv235W/CRfMlka8x2DqIclkxVq1PYWE7+NoqrMjcjAvms2i1tcDtV+52O7pxaugUVmdLn5vD48QbvUfQxWwZfyGsztKCm4u2IFMVmhxfClMMBUACYDxSo8MeaVXG69PlA/7ht0XhOXN32WrcXLwcpfpstNsGMeC04fxI16gA93DUjfSgPYzQINpsg6gf6cUC02h1EF/3uc7DaLB0BW/MfG1O2QLDBf8ZzzbNqMzDoCtcxiyDWq6HMryIR3JDsVSctsgM625Odfd/fhmQWwz0d/pDwuWARgfMXwqPzwo59PAi3LrOB7enCH2edugVJuiVSeYGZRcC17wdeOOvgNMGGDKBq98m2fWNB4Zdx9seXQ/tvgCcez5kc1W0RPLsnwixoc4Eym+XirBuF9D9eNhre4AROzB8Amg+ARhygE1vH5vIZvfX3x4MfX/4DcBuA+7+QMxfZ3HizJkz2LhxY3KqB/7u9uuA468D/WGkBo9J/TmkAlRnvNJ9IHhNcDzf338CBqUOhdrUE7OXJahM4nU5BUR/p30E9SP90CmUWJZZiGsLl6DXOYJWm9RcQPXarTEKO2OC40b7LmCIyiX/HX2oHpjnVy8ZcgEW19jRx99VyKX8HgaF2/cDnl7peRRFgHa1NM9KAEOuYdg8DmQojdArQ9ebUq7AjUVrcLXXLcbwQPHpe9/7ngihZug4bVvSmBvgZ8XAcGYIMAPlc5/7HOYCOFaX6hahRCuda5eLYi2N2QOuI6jWiKW+sNvt45IaHB85HlJNfiVlanCNEV5o41etJOTjYHv+QqzJnidUjNlqw4Qsr2cNlDlA5g1SLSPBe23KdkVWCCc4bwidw3JkQo5JnIvb7wJoWdx0VpozLd4ErEog95Vz+2S2X0Y4fPiwUGvRKjsp5OQB7/14qnYrjTkGrv9abT2iRsQc3yy1CcsyV+DU0IngeFtrWoLMZEkNkhMXngrZTXccAWpuBjLKQvXXWGpw5fQF1re3twtCgyoN1sQv5/neZU1qEPzwfvazn+H+++8XgY6cJMUKQY4FduR/Y9lm/LjuGHb1toslsVZBL3c3vn/hAL634mqU6FI72Uro5OPFUXo10P6qxAByTwvWAwGrpLJVQMcZIODdzMX7vPXAOH774+4bCuDDGpYj/AWDYsgghX7PNyzF2eEDfmsb1kMVIsTXoByBQSmHzc0sCwXW5dSiXJ+4pVObrSc4AHlZ52QxN+wYqeXjd1QalYwWDtBUAfjgZHeIHyblaPmuUqYQ4csTgqcLcB8NG9zqAPVmQDY5Vc9kF+QsSC5fvjy4jd61tJ+iCkAW4/zj8V2aKXWWHLh0AKs2pJ7QCEAuU2DQNSJIhHCQ2CBcXjee79yDYZG/IYHnCm9mZ4brsTl3ZURxKdbnK/7G5xPhew6vG/kaU4QFlDHqRuRPegh7PeCJtuNYmlGMlVnlWGAsFMQElTLN1n6xP6uzyrE1LzLnxhHmqx4OawQxGEKztUcQGoH3GHjtcPD7XBa9hRLEKeza1HKdIIgCyFQViVyNbofUZaCRG1ChXz32uCN+FufndiswMgxk5ky+u5/j060fAPY/B/S2ARk5wIab4NUpMeLeF5SpS3uiRpddg2F3KCy9XFeLAm0keTQu5i0BKhZLoV7KJPafiobje0Lfs2BaMg/QRY0jDAoLJzSITo7NDiC7DMhfDETZl40Lnp/aPH+BOYzctkbJaK0DwMVdwNLr4z/Xif2RahX+e/qQpJyIQXYys2rp0qXYv3+/KFRcdVUCC7Rw6A1Afzh9LBt9zKZQpcFHOEgIUtGVJjWSBJVGJ/8BDHVI51/1VmAe5wITw7HBDvyq/lDwrlyuy8RnF27Fu+dtEcQGx2SSGi6vSyxCLO4RGJVG1BgXQTNWkwEbIAShgdA5Zu0EzM1AhpTDhMrtQOPLoTGN53+OMURoEJ5OwHEW0Eqqinjgfh4fPIMGa3Pw/FqbvQLl+ki1XPg9hd623/jGN/DSSy9h2bKxnz+N2QfOn55//nmxaGTexsc/PncKKZfz4jaNmQeVFnwEFBscH5lFs2DB+MHIJESoGL8cCQ2HxyXuazqFCjnqSBU51wpOpztCoZgRRozHAv+Gj8sGM0xoiF2QqaFTbITTWwefzwa5LANqeU3SuY0RoGr7xvcAHrc0z0iUgMrIAgpLgZ6OkBKb/3LdcRmDxVhe//y3rq5OEBs1NVEZsWmkMQ48Xg/+2bUfPbTq98/0t+WtRJWxFHmaPNg8Nmjl2rHXEnFP0gNRuXzMLtgNLL1X+jqnGug4Adj6/a/sBbIrAf302Op1d3eL2ve2bdvw05/+9LKf8132pAbBCdWDDz6Id7zjHbj++utF8nt2dmI2BiaVGjcVVeA4F+9RIeKnh3tTTmokDEMJUH0P4LYACl1kYUyXCax/G9B6HKAndXY5UDg1UnMZaFM02qrIqMzEisytGHBJoTtDLnZchsJxaBs1z1CARaaKpF6PNjtuvyfrgJOFDpmou5GA4n/zDPFveE7vAAZdp+Dx2ZCllmPIJROfY+CdaBWhzqFFpkq02box4JK65/ncm3LHzuuIC76G+2RU2dkOeC4ByuTsQIZdveiy1wtlSaa6AEWaqglPsugLzU7rcDVQfWsjunJteK3pjLBnIelE+6bZAtpJRUPhnwC3WDthDiM0AmDR3ynIvhBWZ1Xjpe5jo7aRAHmi7SjOMmhZZHmocF/5RpFDQiwyFWK+IReNlj6Rp+GOEeLn9nlh8TjhcLrxv/V7xNdElkqHD1dtRVaMTvlyfey8nC6h3hhtjzPiHt+HdXVWDcr0eeh1tKHZetqv5pBhnn4pcmkd5Ue+pgp56vmCgKT13YRx8BXgpb9K57taA9z1AaBqgnY35n6JyNDqpe6mMNmw00PiIqRK4ydgdjmEB384WmznkKUuhFqeREeEwy6RMckQGvwb/v76a4CT+yTbpnkLgTveN7qD3TYUSWgE0N8IjLQAnUeAnBqAljTJWu1wgVS1A7j0SiAAKvLn/FxGxlayJdNx39vbK5SP7LjkQuPixRChlDCuugN46Eeh1+U+X3MnUoHAOBGPzJwoOH7avV7o4ijcLjvwPDr+FDDiD9Tj2HrxNYBzoYLxi1Wx8o1+13gkos2g1TaEF7vqcFtJrSCWCbfXjYMDe2H32CGX0c5yGP2uHmzO2RFB1EYgnlItfDuvN5UeGGyQyMjcRYDnUIwdDSM54qDD3hUkNAiOuYcHTqBAkxtzwcR5Kf1tWRRfs2bipFAaMwt+dszYuOGGG4Rig01UaaRxpaO2thbnzp3DkiVLgkQFiT+1evz5FR0WeC1dbmi29uGvrQeDa5IVmeW4qWhFcA12Q+EyPNoSsBryQSmT46r8tDXcTEAu00GrCDX9TRmSbSjlvORtnwAe/w3QfAnQ6iTbqeq5Yyc6ETQ1NQkVODN1Tpw4EdMWO400xsNZc1OQ0CC40tjdexIV+iLRqJtIM3RcCKtAX/z1BRuYltwJdJ0EHCMAaz0FS6ZE3T4e+vv7xZx0xYoV+PWvfx3TDvJywxVBagSksH/84x/x1re+FTfeeKPwwk2U2NDGuQFpkwjxnRZwf/yd2aPAouyCnckH3LQflrqLM8uAkrWJdxYIeyg9ihRSp3STZdeon/c7YwWNj41VWQuwt++U4DutHhZ9fVicUQSjUo8ibRn0SgMcHjuarHWi+GFSZaBCT5ssF/qdh9m3L55HLvMiU0ViRBqO2KFe7JfiB6yWri3chE57L5xeF/LU2TCpJtpBzNeM7sT3Sb6hScDs6sMly+Hg9wx6ZAd+uX7iE5tFixaJDA0uPlicu6TtQVtjJ3JK8oQ9C4+1XqFFWZSaZnBwMKa0PNWoNFSix+kvpvlRZZQsz9jFGyPmWSC6E3uhqVQUM8+ZW4QqJVeTKW5se3svBQkNwu5x4S+th/DJmmvFooOk1tsrNuLkYCsGXDb0O604Ptga8ZoauRJGpQYPNx+BjR3/fgy77Ph7xym8kyqpKGSr9MLdKdxAjrefLnu4vVII+ZrR0n2+99tLNotz3KjUIVNlEBkkTdZTEUW2Ris/00xoFCS0SJqoIJeVQzEZ1VDrJeDFv4S+Z7Dj334FfOz/Afokid/GU8Drj4WK/yU1wLXvDI49DEEPB89bq8cXh8i0JUZqsAPq4Z8DvZ2AUgXcdA+wfpzxkp1Sf/s9sO/V0LbcPODD/xE/vM9foB0FvlcWZqnY6TsH9F8EFt0B6JKUXDM3h2TIUCtw3k+wBM5OTqKM4ywKVm3xhxEi9DcrNsVUafC4U9k1MjIiChVcfCSNqlrg/n8FTuyTdnPZeqBiajqx7B4HLpgvwuKxwKSkleRClOuK0GLrDBLVHAOqjBP3Nf1nZwc+fPgg+pxOlOt0+N2GTdiQc5lbWXE+YJbUcRHnSW/DhEiNEbcTjoB0O/B0kKEnzFec6HP2io4qNd2uxBYp8+Ls8HEsz1oX35oz1l2ByqZwmEqkRwAWFeCLUsklQPgOucyjfHr5NRVC0aQG8zM+/elP4x//+Ac2bdo07nOnMbvBz/Dpp5/GbbfdJtSu7373u2d6l644sCmFVxxVvWnMPFiEPHv2bLBximRGIusG/v6FCxdQWVmJywnMznu89ZBQlQdwYqgFZbocLM+S5iGVhny8b/52XBzpEs16i00lyFInYIGaxuWNjGzgPZ/zW/Fe3t3WAZDUPHLkiBg3qqqqZh2p0WlvR4ddcnEp1ZUjX5MObZ+NMLsso+blzMm1euzIkE/SGcBYKOX1hbsNRLvP0Ga2ZHqblgYGBkStm/dQ1r7j2cpfbphlVfnUQqVS4ZFHHsG9994rMjbYHZeIFdUiU454XDDTPobFRhkKNHqsy0ldpsaMw9oHnHnCX1hkh28XwK7xmjGsS8ZALIVDrK778UDVgEauRpO1U0z4FhorUKANkVMurxNHBvfC6WVBwocBVy+GXYOoMZQECY3QPvFGVAO1PAc6ReaofWRnb6luCm5SYoGlhQ/2oGkPzyOZPLlib6+zNcY2TohrJ6zWyMvLE51UXGg4ZW7YVG7YrVZ43B4olApxI2i1dUeQGixinjp1Clu3bo35nFSRXDCfQa+jRxzDeYYqlOqmRu2Rq8nFhuwNaLY2i5tSobYQpVpJdVCgzZUss6JKWNWGMiwyjrYhqjIWoUCbhf9r3oOz/iIdQ7nD/16oANx2QU7o/d37/J1V2dL7cXu96LANocthDv6+TqEW3cfdDnNE9zG/7qJtSwxwwcfnt9Jv1Q+ejyRHYoH7vSV3Mfb0SaHl/Jx25i9DiS5y0md1R+Z0BGBxX4BSHpBD0qKrHUpsEpLrCaG1PtK2iO/b5QS624H5YcHdVFmxWyDepJx/s4tqj7Brtb0OOH9Q8p8V40YunGgJLn477D7YImuiQWjkCeZh/PGHwPBgyKf27w8BuQVA1RghfrteiCQ0iP4+4LEHgY98Oc4OGYCqbUB9FMnLw2ZzAnr/8ef77zoBzL8aScNUJD0MRcDBv0hWWoQ+G1iwbey/ZefXPR8B3nhGUqBQ3n5tbOUE752JWjmOidJK6TGFYFf/3r69sHnsYiI74BxAv7Mfm3M3IcucgV5HP7QKDZZm1IhMjYnggtmMt+3bE1RrtdlsuGv3Gzh+w83I01xGVhDRiNfMMcGMLpNSI3I07J6Q5QY/syKtcVTBUiGTRqxwCfWguxd2ysepUI0GrT1Kt0u5GoExpXA9oB/nvNXUAnY2EISNU+qwcSwO9ArdqOBBQqeIJFZ/+ctf4vOf/zyefPJJ7NixY9znTWNuYOfOneIzveOOO4Qd34c+9KGZ3qUrApwHNFnPosshqaQylXmoMa6Mr+BKY9pAFSeJjYBao6SkBI2NjcInPx4OHjwo/m62FTEni2G3DfYoq1muODrtg1iOUHNFgTZDPNJIYxSuEEKDWLVq+uytk0WbrQXnzFKWZqDpZlnGShRqE8/tTWN6wPyM6Hk5G9rYsDtplG4EmPtnkZrloM0E5iXZQJ4Cy6kbbrhB3GMffvhhUfu+UnBFkRqERqPBY489hne9611iAUIf4/HCw1nE/HLtZjzRfhEt1mFBaLy5dJGwpokGi5Isgmar9UHfZHZn0J9fr1DPHT+znnMhQiOAvjpg3nYpRDMJeHwuKGSjbYHKdaNtdRLBPEOReMRCt6MDTvrTh2HQ1Q+bN3b3LMkMrSJJm5cJwKeqAVynIuSkPkVpvGSC2M8Rw+qIGHR2I1szcYKNQXy7d+/Gmo1St2tRVRk6G9rgdXtQUF4MRUYkw9vR0SE8ceOdy2eHT6HHIQ3wbp9LEBwqmQoFU3SzJ7HBRzSy1RnYkrtahP+6fB5oZWpszluJYl38AtbL3acxxM5jPzz+whmDyAOg/DueWos3ym7HSARdNuCyYndvvfCAb7GGQv+4eMnXRBbqwnF94RI81X5c/J54Xbkc2/Ljdz6vzq5CjbEYZrdVqDIMMYKnlPLYxVW5nCqpcPrGCS9aoYCkekkaemMYoRG1nWDOxu9+Apw/JSkhbrwDuPGu0RN065DkNxsO/k7neaB2g7huVPJ8aH2LYPdexJDbA3sMNyeiQr8EqjjvPwL93cBQfyhc2+WWVCFnjo1NalwITWiD4DFoC9nPxEQ5OzZ8QMOeUM00cBw8XkDJ680HeKIyMZIFMzp2vB/ob5Xk7nnzEys8L14tPeYwmLNj9YSua16DZvcIBl2DWJY5NUqQN3q7BaEROOt5Gg673Tg00I+bii7jhQ3J3bKVkp2lAJkGhbRtAmBzwgcq1+EXlw7A5Sceqo25uK6ACssQctS5Yi4mHelIuIWqIg45lVkFGEsBpxmg2jIREktZDGg3Ae4O6RpVlgH+eQLnMzIoYjZqMDujxdaOHkdIhbokY2FEWPj//M//4Ctf+Qqee+65uI0BacxdsGGKVlS33nqrsNChGieN1KLD3hAkNIghdx/qLSex0JS2dJtpFBYWiq5R2lNy3UC7ShJ+VGLQDiOWR34gT+Nyg0GhGdV0xRmEUXX52WylkcbljCZL/eht1oY0qTELQZv7Vms32u2ShSxrLNvzV8XMVk0atPpf+CbAPijVTOmUMJncnUmivb1dZGisXLlSqMGvJELjiiQ1CH7IDz30ED7wgQ+ILjkSGxUVY3eSs6h5X/kYBS4Ae3vr8WwnvesZBKnAveVrMeAcwas950SXdqZKh7eUrp8bHRhRdhBBBEJok4DNY4ZC5kGmkoHIAbsnHlN5iuTno6GQZcALHTy+kB2LUmaARp56QkNA3gKolSFigoOerIF+Kwk/Rba6EENuKRw6HP2utkmRGiT6tmzZIkJ/ayrLUGdpRUl1uThM3Q3tWLgg0p6luroab7zxBgoKCkYRGzz+AUIjHF32jghSw+FwiO6tgBc/FzGrV6+e9ABcYShGmb5IWFGp5apxScRu+/AoBp8KHpkIoZfi5G8sWhY3S8XidsAT9ff8O1pT3VW6QmRqBCyoDEo1biuJ/3kzWDxDpcNFc5e42a7KKkeOemxppEmlE4+4P1fkIE9thFxmEd+b3T6oZTkwKkL+koG9hs8VN/97XCxeCxx6Fehuk85tjh/LNgL5fkuX3/4YuHhGKvpTjfH3x4DMbGBzlBJBnyERCuHjD/+GlmP1LwFV1wkCQKOogFpejkEnicIOyPiBhX0M1YZ1yFQnaAOk8ZNBDhcwEpbL8fpLwM5bAFMcSz+9Pn6oXwB8Hz0tkvojrwzQ+D8r+mryRItG+KlkCuWeTBhaE1Ay9n0Lw/3AiV2AwwaU1QAL18yZbjCvz41ux1mMuLshhwLZ6kpkqSoEORkL8bZPBAaFMqbVnVEpTauarRb8ur4OQ24XtucVYG1WNvb0d0MrV+D6whJkMLtlrmLR1YA+C+hvAVQaYN46wDDxItSSjAJ8dem1aLAMQK9UYYExQGCEQPumKsNCNFrPj1J86hTjSMhp/aRLTj3jU+TCI+fYqRQPWtnVjxyFzSup7Qo1VSjWMjg0dK3wPrEld53I1qBKKEuViTxN6LgwEPwHP/gBXnzxRaxbF8cyK405D4Yy8jOm/J/EBnNT0kgdBv3ZfSH4MOgaPwMnjekB7W3ZOMX5PsdLrh34uHTpEtxut7BriyZCGCheXj5xa8iGhgbxWrSZPnTokNiH0tIpmFNNAhqFClcVLMEr3WdEYY3ri2y1EWuyRivJ00gjjdkLT4zmGu8E6mNppB6cl19buB7d9n7YvU7kabKERfeUgWuAZDMwU5RBc+2112L79u0iQ+NKsZzClU5qEPywGdL4iU98QpwAL7/8sijWThRNln480xnq3HV6PXi4+SBUYecUPfUfbT2Aj1ZfM2rBnghsnh70Oy/A63NCI89CrmbJ5Hzwx0JOJdAd3oksAwz5UqBmkpD7TzOVXAZV2NtWTMB+ajxkq3Mhs0R656lkapiU2ZDJNsDsroPba4FKboRJSbVB/Ive4xuGy8t8BR9U8iIoZGHFyqRhkwa+iGLhaPXKWMhSkbiIDhyXOn0mC3pWMoxrS946ZKgN6LT1QatQY+38KjgHbUB+SGHA7qrFixcLC6rlyyND1KS49sjjL7aHne8MLzp58qQI1+TCg4sbdmW9+uqruP76idmbhcPuceKN3uPotPeLfIt1OYtRw07dGMhWGzDosgb3V+bPq1iSUS7yNOYb8mKGeLM4anbZoVWoxGtQiRUAFyqFGiOKtBn47MKrUTfSI47JQlO+sKYaC5WGPPGYKti9TdAreZ7JBDeg1chgVJRDJmMXe7hnPD2PJ3FTZoH2nZ8Fjr4BDA8AhWXA8o3S+U57Jyo0wsHtJ4+OJjVYIN16F/DGX0PnOS2Zso3AQL3kXem32OKCVaswAi7/Z+e/tGSQw6RK4lrNzAFWbAReeTFyu90GPP8kcLffG12QMX8FGuqArBxg41bg+MFIZQlVFncFft8BvPB7oNdvG8eA+OvfA+SWAJkl0lgqVELCjM7fEe6/TvKWSGHhqQYJjUf+G3D6VSFnDwKDPcDGmzAX0GU/gxGPRKJ64EGv87y4r+SqcyGHXFjUhRe/s5M5L8bBbSWlqDp3Bk206/P5QLO+dTk52JSTKwiNa157ESNut/hYH21pRJZKIcYZfto/rjuLRzZdhUIGPs5F8GKrWCM9pgjZap14xAMtOjpsbrg8uYCsV8wn+FkvNq2Z8rmEy2tBt+MI3D5J7ZOhrESXoxc270jwd7oc9dDIdcjVlI1aQJXqIpU6bGZ44IEHxELjlVdeEcF9aVzeoAKWcxp2zZHY+NrXvjZ3lNpzDLGuf1qfpjF7kJmZiaGhIWRlhe7B8+bNE81Ny5Yti7g2SH6wcaqoqCjpRieOtYcPHxZrCmZ9sWnq6quvFja7NpstpjJkOrEhpwqFmgy02vrFemBpRhk0yQZHp5FGGjOKAk0hWm2RqvyCSTSXppFaUBFelGxG5RxCXV2dIDSY6fbjH//4iggFj4Ur+k7KD/2nP/0p9Hq96KyiZHyiHn4ttoHRcZQynyiwBQqmkgWGHYNOK3LHsKGJBYdnCN2Oo8Hvbd5edNuPoEi7KTULpcxyoOoaoGUf4HYAGSVA9TUT6uDVKUwwKnMx4g5YMsigkmn8RfqphVHJgvQqXDCfhsvnhF5hwOIMysx4qiuRpVqa0PO4ff2weY4Ev3d5WqCT83km6iHPTtJQQURCcpka/Jxz1aXoi8rWyFFPTfcR1Urnzp7D8iVLsDzTT/DlQ3RMccCMBtUV7IBau3Zt8BzkvyW6crRF3exLwqzGOjs7xYKDyhCPxwOv15tQV5bFbUGjpQVunxsFmjwU64piLmhe7D6EfqekwLB5JYJDr9CgRDeaLLimYAkeaqaaQirwq+RK3FREX8w43fmiA7tfEJaW/5+9swCP4zrX/zvLqxWzbEu2ZMtMMUNiOwxNkyZNmxSTYm4p5d7eNv33Fu8tt7eQQsqQJqWkSRsGJ4bYMTOTZMliWMb5P++ZHWlJ0kpakX1+eTaSxguzs7Nnzvne73u/cEBkWy0pqMKODu6XFkCtzS7BqiKtRwB7YizOn5K2GNMW6BYVJsWW3Ix8rz3hsz2/60/nj9TDZrwMIXUP/xLbDEoNDEpCc6vBYrECK69J3s6LKzMG2E8jdmf66jtQswgIdwBndjMSDWTbeneeY1EMJdYqdAWb4Q7rlScKpmYtGHyj0OvfnCxqUAXqbO/9/YffAA7u1X7ne9qzA/jIp4Gdm4CmBqC0Arj69UBptDpl70tA2/ne5wv6gJcfAW77GMBeKQtvB068CHjaAVs+MH09wD4C3Pch9icYNHs3aYJGbAXDjueBJVdpQlVfsKqDFTVjWG3A77ornFy55gxdwGT7ZKwoXI69XftEc2mH0YHF+YuSmjUPB1ZkPL/+Knzr6GGccbsxNzcPn5o1W9jG/fzUcbhDISF2kDyzIoRQ/Tvd7PcJYeOr85cM+B7Pec7itPu0yAIrt1VgVu7sSy5gd9TZgCcbeT3WZlm09pxiV2E0BLGjYw+WFSxFvqXvMXsw8JjHChqkK3gqZd+e7lBbkqiRCK9x9913Hx599FFs3LhRZAxLLg0oXr388stisdnZ2Ykf/OAHl2T23EhTYatOqtZgvzzJ+IE9NfhdWL16tagOJ6zQmDVrlhA2mFgVC3tv8P5ckxcVFQ3aU5w2V4cOHRI/2aOD4/B48eif6igWN4lEMjGpzZ4tnCnYLBzRRuHTHENPjJZIhgpjaTfeeCPe9ra34Vvf+tYlnTxzSYsahB8+TwI2TGaPjX/84x/CEzfVQveMpwnNvk5kmWyYnVMJc4wfW7bRkpQvr8UzkrPo9ayM7qALR51nhFVOma0YNY4pfZ6MHhG8iW9hHFCdCKkemJUBrBeGSsks7TZM+J6qHUvQ4j8Nb7gbZsWGUlsNjCPUxK/YWiZuvOD0ZRs0EIHwyaTPzh853iNqRFQ/QpFz/BRgUPJhUiYNMJBQTGFQRm8Sx88svQtgINKJQKRdVLxMss0QNiudwSbx3sqs1Si0VGRM1GDlBMvE+bsuMiSKDW1tbSKLiqVu7EfDbKvYhsEzsmfDbLCg1d+sNQrPqkGhpThucdPU1ISamhohKDKL0eHo/xx2hdx4pWVzj33MOU8d5oZnY3p2fINhT9iPtkB8c2xWSZz1NKUUNVj6/e5p63HS3Sy+49WOkn7tnFi98cez2+GPNvtjVcaOjrN485Sl4vNg35zKrAKRFTAYWFXyZONrCKha1v/UrFJcW7Z0SBVd8SSPPyoYXM2BCWwaTZHABCWTmc6njgKnjwGOXGDJKk3suOb1wNOPav8u3pMCbOinGqBiNtB1NNm7MqEROsWL2uzl6Aq2wB1iY3oKyV5hS0TLmLRxZAMFRUBnR3yAf0q0mWVDHXCAIlCUSARwO4GTJ4C7+mgG23Ehvs8If3e2a5ZUvHbY84EFt2FM8aeoFhMWYf7UggXf8z9/CTSe0f5efDmw4XZN5BkDUlaFQduXImshrirdIL7XIzXJY0PwbyxMDpR0BoNxeyXO+Jh9oNhR59Es4RIJRsLRoh0jGrznccR5uOff6rznRPXJ/Lz4CrlRhf0rQgwgqoCxBEinb81wXk5V8XzTvp7UEMLKuNaAgjKbAf6IH6+1v4aryq7MiNgT5rU9RtDQSEpZEdsGqhChdzwXGgcPHsSWLVvEtVJyaVrv3HDDDbjzzjvxhz/8QWSQSzJHrrkQc3NXodmnjY+F5jIUWSd2X6Ow2oSwSgstE0xKJQzK4Kvkx1sSIW1ud+3aJcQMVmfwe0AxQ28iTpjkxEqLffv2iepx3mcwvYd4naXtW319vRATub7gc1yqmasSiSTzcL0/J3c+ZudoibKXciBZMnawfcLtt98uLE4/85nPXPLn4SUvauiD0Wc/+1lR6vr6178ev/rVr8TiI6yG0ehrFY2+6zztONB9tscH83D3Odw2eW2PsDEvbxK2tp1Go6+rx4t/dk4FLviZ8a8FQBl8uSyvCtkmmxA0nr6gBWm5/aynEa6QB4vy+xIR+howJ8ZAKgLwttFVsYcqaJBInDWPhhoVJFQ1AF/41Z6/oTYgonTDauzbu15BDlSsYbgrGuIq6Am+9Yc3dB7doYM9QRUDzmCSfSWmZKXO9qSQIwyghniBra6uFkGkxx57THwfVq1ahSlTejNRKWSwaoP/xjI3ZlJRCIkVNXjcqx0zxK0v6JmrM5CgQU66Tvd8V3SOdB9DjWNa3HvtSwToTxzIMlmxgJVJKTjU3SD8bylmVGYV4bL8qfBFBQ0djgnNfieuLB2aAMjj/UzTzh5Bg5z1NGN/12kszh/ed8ZqKIMvcj6hP5BW4aIdtwwHV15+BvjbbzXhguLAxqeAj30RuPnNmmXTgd0AAzpX3gRMjXlvzaeBtjrAmgVULQSyy4Gp64Bzm7Tnof/ljOu0CocYwqoXwXArfOFGBFQ26lLgjahwhRrE9yRtYYPH4n2fAH78P4A7WlFVOxe4ImqHFgikfoxu25SKnELgwukYkYS+mw5N0BgvTKkFjuzo/ZufW14RYO+jkvDJ3wMXYqqw9mwCcouAZQk2YqS7BTi7XxNJpswBCqMVLBmC5y/7Z3QEowJLlDxz/Hd5LBYblxeV4OG63iopngHGhDFjVkKvFo4x/2zYhdNuLeN4UV4VCi2JwXWg0dswdqJGxA14N2vChsAMZK0BDIOrOhwMHPdj7f10QpHea0FADcIVciHPPPxqjVRjBs+hPFMeukK9FWH8DEutfYsUzMy/9dZbRWCOQe3BZhtLLh6mTZuGTZs2iTkTA66cX8Xa8EiGT44pHznZF8cxDUZOI6SyOlq7doXV87AaOJ8ZoQS2UYICBtcUbGBKoZeJTfw71mKK1WwcMzl2sqKD3xXa3LLiIl1YDaULyEyckkgkkpFAihmSseLPf/4z3vOe9+AnP/kJ7r77bvlByEqNeO655x4RbH3zm9+M843nMfvOFegIdiNCm4vompphVdIWcOKosw7z87RMXoob76lZgx3tZ9EZ9KLclidsZ9hIeHvHKXhCfkyxF2JxvtaQ/JjzbFKQ9lD3SczPm5Ey2zDbVIHuEIMkvfe3GgphUkbJkzvkA+q2AQy4WPOAqpWANXdIAdxApF70qzAoNlgNDEq1oNVPSyUVRZYpKLQMVPUw8piUQgTV+CxmY7TnQFCtFxUasYTUephVWvj0nbWqMABEP6c0UdUIukN6lq72uUfghzt0Bjnm+AC6L+zFUeduuELdopJjmmMWKuzauUbaA23CvinLmIVC9h3p5/hyoXHTTTeJbCkuMHhf/s1FuC5A1NbWimwrVjdt3boVu3fvFo2+R4pgJJCUkc2MPH6HTDHfF/YBqc6qwGkPe6FoS0JWTczMTs8CKpZznjY81tBrQXbK3SLs4xLht5gVGkOFjat0+ysd7jetqIZLtmkWEGJmM/sOGGA3VsFmGHrzxX7xeoB//F77XQ/kN9YBm57VbJkoEOgiQSzHtgIHX+wVQk7tBNbfA5TMAYpmamMPq2cShKlgpB2u0G4hPnt7iiu0cySouuEKNSI3IcDdL1U1wJf+D3jlBeCRPwCvbgd2vAN49weBNVcABYVAV6dWpSFeSgUW9HPOL9oAnD8GuKOVQ8wUXDPGlRmJzFoCdDQDO1/Qjh0FjZvfndpmkJ9N3fH46hNy9kiyqNFWD7zwu97z4MhW4Iq7gIrMWoIUWWphVMxw9jQKnwqHaextHe6snIoTbif+7/hRcUbmm+1QEEY3G8YzCSI3H/fVxovgT13YhzPu3ua2e7vOYUl+srg0ptdG/wFAjRV1Q4BvvyZsjBCsWMk3O9AVdMddAWzG+ONgVsxDmo+EVL+ouNDFDP5kD43u0OmeoKKokrQvRF64Hc5gm7g/re9ET58UMEuY5eAMZj/88MMysCYR1eDMqmPCFPv4PfXUU2PeuFgy/uC8P6Se1P+K/uS2c7AofSdOTST4HaDIx4rtX//618IWatmyZaKawm63i3UGvy+E21ndwZ/Z2YOzbZZIJBKJ5GLj+9//Pr7whS/gr3/9q1hrSDRkpUYCPDm48LjpdTdh1ZENuPOz97CFbuLdRJYeBYtY6PO8pjg+szrHbMPVpb2ltTpBkXmYbA3DqpBUnrtsbF1uXY7O4HFhj2A1FKDAMnN0Ahy0Szn8OOBhTwwVYODFeR5YcKcWbBxEAMEbPoCgygCrIoJjbf46NMdkO7u9nQxVi4DBUOBrMJPeZjAP69hYDTMRCfvYelb8bUQ+bAZtQaGKoE4qOwp+ppmz4lBBE+9I8lbVn/SeD3XvgDesiTA8fqfch2A12lBoKcXR7kM45+3NGp5irxQlkwMJG7SWohctG/w98cQT8Pv9wue2oaFBNMEkPFdZpcGFCPtrcHHSHmqAK9QJs8GKclu1sKIaLkXWIjT6ev3zWY2SY84RAa9E1hYvQLA5KEQBq9GM1UXzkW8ZfCbxke7GnsosQlGl2d+NuTkVOOTU/k0PWC5Ks29GKqwGs6gk0a219HeYzeqEYaIoRuSY5yIHyWNQxumOCfjrMJDfEe1LkYqAVxM0iP7+adF0ehcwc7VW1WBJnZ3oCR3SJKXkYVQcv0hc8DVN/H7gT7/trcBgk/Nf/BComgZ86ovAA98F6s8C2bnAO94HVPcTpLfnAK//EHDuMMBg9qQaIHcMA+5saL7nWeDcQa3HycyVwKzVwOobgWVXaY3QWaHR57igaI3cAzHCHu9rS5EJue8F7boRO0buewZgdYKDjZ4zYwXBMazAUi1u4wnu1/1zFuDjtXNEs/ASqxXOUBD7OjtgNxqxKL8Q5gQ7jNOu5iThti2goiBh+KzKGkMLI9EoO3YfVUBNbaOVSV4/aSn+Vr9N2AuSHJMBhRZRkyiO2RT7ZGSZBpeR6w+7UO/diZCqnc9FlhoUWWZEK4BmiDmXP9wOg2JGjqkKJoMNRcbJoq9Vf9DHnVZD1113HX76058K8V8iIQzWsrfKvffeK3oLUNiItd6RSLQ5f5KZcYx17cSHFRi0hjp27Jiws3W5XMIhgVUVFDZiK7m5FuEYyiQrWlbJijeJRCKRXIrQnpHOQkwGYKx6xYoVY71L4wq52koBT5Itm7fgmhuuRfv5Frz32x9Nsj1hoLOUzV2HSLm9GGc8vbYwXJznmbNFk+C+sBrzUGZchlHH1QR4WuMn2EEv0HEaSCHY9EUE7qigEX0O2s7Tuz2BFv/ZIYkap93NopkorSoYKL6p4jJMc5QOORhsNy6OVmRQ1rL2iABGpVBkTcXdHxYoyGzVjAITDLCK6oxYzIb4CplAxAdvOD6wxPOpI9AKo2KJEzRIvbcOZbYKUbHRHxQqmFF49OhRYUtFb+iuri5hPRVbCs6eG1yYsHrj0Y1/hs/YielzqmEym9Dub8S8vDUwDVPYmJZVBVfQhTMe7bg7TA4sL0jOkqfAs6VtD1oDreIY+MMB7Oo4iOvK1qYUQPqDQkOqePlNFfMxPbsE572dyDXbRFNw2zAaO9Oua13xArzYsrdHKssx2bEorwbDItQNeA4AEQ9gKgCyFgAZEJj6pKAYsNlpJN+7KGdz8Mn9BGH9KQKi/J75nAO+XASaNY8hZQxehdU4hPH5zCnAn1yNg6MHgRtvBb78Xe09pdPslYLNke2A1wkUTQZyxth+Zs8zwImdvZ/N3ucAkwWYsUwTK3jrD34ua24EXvpHryjBbQtXA5v/ArTWATYHsPhawBcT/OYHVJwDmCPAnocBRwkw/5ZBCeLjlQavF18+uF9UZczJycMX5y0QfTZ0HCaTuJE8swVXlPQGa3QoZlIgZVJEgOdWFJ7WvKYsyp+DMzGNwqsdwxwXhgNtpsIx32/upWHks2eLrbl4d/WVaPE7YTEYwZF5f9cJ5i+L/kPVjmjvmzThdSJW0CBtgVOwGLKRa64Q13pWx/I2GJ577jnccccd+OhHP4r//u//HvOKU8n4gwHaBx98EF/84hdFrwBm2jHAK5EQ9jdTkA0VnBv1zkAN0CrFLyZmzpwpBA32z6ANFdcSFDooZMRaVrHJt74WYVPx6dOni6QriUQikUguBbxeL975zneKykX26GPMTRKPFDX6mWzt2r4TN9/6enzjLffjQz/7HCyFBT1TzCX501HtSA5QpMu0rEmih8bBrhMi0zDfnIMrSpaOz0VwXAZ5DMzK7jwNNGzXLGKyK4CqdYAptU+/GtMzoD/YF2KwdAU9eLxhR0+2Oxs5/7NhB+6etgF55qF5qvKzoJiRCJuFR1CLYOS4dj9YYTVeBiVD2cexr59vuQwdgZ09/TushlJkGauSmiUnwvOUNmaekCf14CiqOorS2geKGVSHuZigqBG74CDMrqqrq0PppCJMvawYXnc2jh04iUiYWfQqbAsLMKti0SDfffJ+LMifh9m5M4XdkNXQKzLF0h7oQoO3WfyuZz13BV2o815AtWNwVg8L86dgZ+cZYT/HZ+KrzcwuF9VXywqnYhkylzE9M2eKaFre4G2H1WBCTfYkEeQcMhEv4HwZUKPZ8gE3EO4GctdnLEs+CYsFeNdHgV9+t7cHxbK1wMp1fT8mK08LprNKQB9d+R3O7+2/wKbf/ki3cLG3GnJ7vmcGOIRQSnsxh1GFJ9wbAii0zITdGN9UfEBY8kHxItV2VmbocF/PMoBvAyoqU1c2sJrhXz8HXO3RqrTtWtB/1S0YM87sT84AZc8LihrpsmQ9kJ0HnDoImMzAwjXA3qeAzmhTdDYef/khoKIWcLZp2/IdgClmjGKl36lXgFnXYSLTFQzi+pdfQKPPJxp/7+nsxPb2Nrx05TWiGmMgXCE/Hjq3CyddrTApBizIK4XL0xStC9X+v6poBsptxSi3aX1wxhzrfMC7BdDFAMUCWBeOykubDbSAKkCjtwP/bNje04Pogi+AyfYpoiovXShmxAoaGgo84XYhagyFX/ziF/jYxz6GBx54QCw8JJK+4Nzly1/+MmbMmIFbbrlFWAm8733vkwdMIrAYFiMQ2R0VNjiXr4JRGXo18HiGllJsIM4eROyzwfVEYv8MVm40Nzdjzpw5Yk3BPn7btm3rqX5iBYdEMu5paQb+/Q/A2QXMnANcc9P46rEnkUjGJRcuXBB9pijy89qn2zNK4pGiRj/wpNn4wku4+1334Ou3fxp/+PtDWLhgsWgsnK41TCASwAnXKdHzINecKzIKGWwWQdq8WszLnS6CtOZ+KjTGHFY7UBhgdYYeFGMg3WIDTj/Xe7+uc8DJp4GZt6QM9BlFkztewHuzUbNNCjqC8YG2fEv6fSd0GAyOt+/RMmAZABmqqNEfFsM0mJVKITbEVnFkGlZlFFuvQEh1CV9vHsPE16K9U6l1Mpr9euUPw79szF6JUIoGqyTbNDg7JpaEz5s3TywoTp06JVRiZhzm5OSIrCnaUh09fhR1rgacP9MIW5YVVdWT0NLQii5fqxCqBtO4na/TEjgPV7BDVHmU26bCYrCJ7wn/64tAQhPvgbb3R4k1F2+vWo1XWo/BEw5gWlYxriieOejnSf/18sUtIwQagDgRUQXCXdqNVRsjxewFwBf/D2g8Bzhy+g766zAQufIO4NW/AKGoEFJ9GVA5T3sbERcu+ChWav9mNeSh3LZU+N47TPPhCu0S30GzQUGBwQGbcR5MhiwY49oyp8nDvwf++df4bdz3KVXAymjPgPNngZ9+A3BFK0nmLALezUq+hHPy5B7NRkuMl9Hx7dgOYN4VQM4gjr/w1lIzI0Ql2B0JUgiiAzJzsXYjFC46tP41GlH5L7sAKJkKNJ8BLKaEc0AFuvWKvfELbaM8UeuoVOP7s02NqPf2NvKmsHHM5cSm1mZcW9YbGA9GIvjTuSPY0tYAu9GEN02pxdriyfjD2Z04627XjEXUCHZ1XsCGkmqE1KAQ6hbnT8Vk+yhl53o6gRMvAd5OIKsQqN0A2FL0zDJkAVnrgXBb9PtLK7HRnbs83bQbwZixjTaD2zuO44riucNqBk5Y2ThYmEXMcnDapzz55JNYt64fEVciiYHiF/uu3HbbbTh+/Dj+93//V8y1JJc2BsUOq2E1Z0BizcTqjYsd9uyjuOF2u0WPPiZSMYDDvjMUNc6ePYsTJ9g8HSJTdeHChWL9QYFDIhn3tLUCX/yk1nuQ8/rXtgLnzgLv/fBY75lkDGCyZIvfh1yzJa0kqAkNz/f2g0CnNn6jYBZQMLv/2ICkh/379+Pmm28WawtW+dK+UZKai3+mNEx48jz0xz+JrKo3XnOraPpIv+R0CEaC2Ny6Bd6wT2SNN/ouoD3QjuUFy3qCJAz0DibYOybQomTOLcDJ5wFPO2DNBqo3AN1nEnpLqICnGQi6AUuKBqfhLjgCfnjMRqh8/ypQYCpEINImsqyJ3QjYFFqXDA7aTaXePnKnOC2qlKEETwcJAzAWpf9g94zs+cgyZaMr0C5Ejsn2atiNWUJDmpE9Eydcx3ruW+2Yjjzz0ILnPG8pYvBG4aG7u1uUjodCIRhMBnidfuTkZWPuounobGpD5dRSZBUGcca9HdMcy1NWlaTirOcwmvy0mtK+J63+85iftxYWQ/+DeYElVzQOD4kKhV5KrEMLDjIz+M7KlZhw9FXtNIQqqEGTnQPUaqJEWjD4fcOHtQC5JUsLiEdp8e/rETSIP9KFjsAJFFlnw2TIRa55LcJqp9bKVymA4m4BTv4L8HdrvThqrgJy06jQYc+Lx/+WvJ09Mz7/FcASPe9+/X+AO8Yy68g+4IV/Ade9IdlWS4xxCZURrGRIR9RgP4qzW4CWQ9pzFM0AajYAwxnPapcDB19O2DZcO8M+JqVGE7DhHcDR54GWI8n/bk5dzTdeFhv/7+A+/PTkcXFlm5ubiz+tXIvKrPjeLoFw6u9S4vafndqHpy6c6blKfv3Ia/jCHAWn3VFhIOZItgV8ePvUUbaYZLLCnr8AwaitlN8J7PkrsOxtSbab2o6aAdPYVI4wUcEZ6hWSCI9re2BwcwY2ly+0VKM90NsMnNsKzJWDeh4G4N72treJKkZmTzHzXiIZDFyovvrqq2LRSmHjD3/4g8g+l1zaaGvESy94wXNf9wkPBoOiClxPorLZbKJafP369UL8CwQCWLJkyVjvskQyMBuf0QSN2L6DrzwP3PE2IP/is5aT9M3Brk68c/sWNPi8MIr+e/PxH9NHLmFyzGk7ALTs6v27abv2s1DrUyvpG/Zdu/POO/GpT30K999///h08xlHSFEjDXgS0f+WllT0S/7617+Oj3zkIwOeXBQxPOH4BXiLvxXdoW7kmfMwoWDG6Pw74rd1x/eV6CGVSMNgqm8nTAgjJxDusfNxG1zIMbPpc+9dg+ga9O5NdZSgzJqHZn/vY0ut+aiif/s4QhVNABk8Yg+OzH39eC5SyOAtEYoYJdZSuENuZBmzkGPOzdhrson4smVaEI7CxnTfVNQFD8JkCMAxtTfw5Y10oT1Qh2LrwN7nwYg/KmgQLRQYVANo9tdhir3/oJHNaBU2bptbd4vqDFasLC+cj0LLBPu+DRdLBeA9HNNoPrpAPrYF8LmBvApg2mpNsBwPMMhdGC8+UDRjpUaWkT1GaAXH8wjw0UYrCpv4GpSS3uDs0X8B4WhVTsADHP03sPBOwDrAOU+7rEQBghmzFZMAe7TSi7022jRrs5idBOop7qYQatSN8dtY2ZaXZsno+Z1A84Hev9tOABRuJy3Sxlf2HDmzWROQs8uBmvWaINQf89Zrn/fZA5rowEbhlcNsUksBqrgKaKvTjgWvibxVzdd+5pUC7UepFMQ/blq08mUYsPrrcHcjOoNelFpzMCO7NCMTvt+cOYUHTmrWguSo04l3bt+KFzdcE3e/9aWlcBhN8IZD4ltGiTvPYsbqmLJgVm8803Q2zvSLe/hcU53oo8HeXL3btb4ao077uWgVZhRhI+YCOuuB4ukYDwQjYTx94QTqvF1o9dGWMQJTtJkOj1v+EKoxiy21ooeGN9wRFTS0ZuDpwmDbG97wBnEN3Lp1KwoLB2l3J5FEoTcyz6E3vvGNQuRgM3H2F5BILmXMZrOwu+WN80GfzycSDWU1k2TC4fWlzkxP1cNPMi5o9btw1NkkhIe5uZNED83h4g+H8dZtm9EW/dy5RvjSof2ozc7F1WXjxGY203QeTd7WcUyKGv3A690Pf/hDfO5znxPVGXfdddcIfkAXD1LUGARvectbRKn47bffjr179+InP/lJv2VAfVn/BPvYnvI5VD+8IS2QZjcWw2QYwPZKDQJhWnuEAUOR1thzpCiqBVoPRuPOUZkit1KzqkraLw7gwZgGqNpvphStmA392Av119T5jsrV2NF+Ep1BN/LNDiwvnC62jxdU0I5mHz/V6N+zoGB0Fq60mxqs5dRgYSZVUXYZHKEsnPRsSfhXBUHVK/ojdAbOIKh6REApzzw1qXojlKL3CgNX4TQtpOhBf9vkq+EL+2E1WoTd21hfnMhwA67hSBvCajugmGBWJkFR+sniM2YDOWsBz16tv4aSDRw5CvjZdF4FXC2AsxlYfMe4LQHl8coyMqM6LI6haN6rqLAYeis34nBeAMKx/6ZqPUW6G4CSAUQNZuGzKuPsqd5MKv5cGJMFyGoN0QjdGy985KUIZvq6tQbZscH8moUDN+PWaeutrOp5L62HgbbD2nPGPm/HGeBwN7DwTf3bVPFznr1Gu2UKPufldwJ7nwVazgG2bGDR1UBeVGgqmwu0Htc+G32XK5cD+VOGXU3xcN1rOOVuEWMDKyGXF0zDdeXD99Z+pbUZhhg5kIuOfV2dcIdCPU2/yWR7Fv6x9gp8eNcOnPW4UZudg58sXY5CvapnANaVTMdLLSd6r4kKsLZocA2vM0PyNXg8wc/6xye244izpWdbW5AViAax2Mwz27GicObQRHnzJHEbLJs2bRIBaHrc/uhHPxI2KRLJcKAo9vTTT+PDH/6wSBT5+9//LhqJSyQSbby229OzfZZIxh0LFgPPPB6/digqBopLx3KvJH1w2t2K35/d1tPj9aXmY3hvzeUoplPJMDjjcaM5QcgyKQq2tLVcvKKGOvHWHWOJ3+/HBz/4Qfz73//Gs88+i9WraUUpSQcpagwSnlw7duwQHrhXXnkl/va3v6GiInVjySJrIeBMOOCKCXlpZsoHhZ/8dkSiQXAlaESZbblolhtRPQhEDkCFS/R0MBtmw4hswM8mnnrQTQEsSwDj0Bua94u9CJhxM9C4o7dR+GStbDgJ4VUda1VFVJiNJYB6Ic7CKsc8tDI8ZrmuKZ6VtL3F34lWfxeyjDZUZpUKv/LRRhXeuHvj+okAR/lumWeKiwmL0S5sudS496rCqjhw3rtdZN/reEKtmGSnHVtvMNZqsMOsWBFU/TGPVpFrHrixuQ4t3bLS7HszUjAQf9J9Eqfcp8TEqMRagoV5C5P65/B+rYHT6AjUib8LLJUotlTHiSDByDkEIsx20CyNgjgHu3ElDEo/mSM8XnlXab/X7ewVNLRXBbobAVrgZI/fhlMUJ1W1q+dY8KdV8feIHHGw+iARBv9bWwH/SaCMgms/wcdPfA74/v8CJ48B9Di97U7g8g29/87Xe9O7gD880GstlZMHXJui+fex7YDRABhixrvGqJ/oQLCpO2/6a/bUtfUFbf/aAG8HkJX+dyRjsAJl+etT/xsrD+a9AWg/BQQ9WlVJzvCvR8ecF4SgoY8N5LWOM1icX4XSmF4QDV43XmtvhtlgwNricuSlISrlmy3auRVTuWMxGGBL4Xu7vLAI2665vs/nYtB9XfEUvNRSF/vNw5WllVhTVIF8ix1Hu5thNZpwRXENJmeNwbWgoAow2YCQPj5w3mAftvA0WLa0NuOF5kbYTSbcOWUapkTtvs55OnE4RtAgwQgwyTYZl+WXYaqjdFQrXPSG4N/5znfwH//xH6P2upKLH4pjP/vZz3DZZZfhuuuukw3EJRKJ5GKACVJvfx/w8G/oqwaUTwY++p/aWkMy7niiYb9Yt+vzdl8khGebDuMtVcuH9by5if0Xo7PuXPM47qs7XApqgZY98dvya8dqb8Y1jY2NInGePaUYa2ZPKUn6SFFjCPAke/nll/H+978fy5cvxz/+8Q/xMxFaTF2Wvwj7ug6IZuA2gxVLCpak3RS8PXC0R9AgDBK3Bw6h3Loc/sjOqI0Rt1Pg2A1buBiKqIjofQQC+wDbNSOXjZ1dBtS+buD7sdGddT7g39+7zZANo2U2StRqeMLnhTWTzVACK5uPZoj9XaewvZ02PBpT7CW4tmzZ6PYx4WcSPgaY4vs8aLAfwMQRNVQ1gpDqifYwsKesPmAPkEr7QtR590bttnghLxO9PgLBeO9zX6QTvnAnrMY8hFUvDIpVWIHMzlmGo65dCLDKgPUs9loUWCZWRku9tx7HXb02Ni3+Fuzr2oelBUvj7kdBo9nfez/99xJrTc8xD0T0zH19ihVEMHIWVmOyiAd3E9ByEGBFWN5UgFnMsT6ug+2xwf4OrRe0MaSoPHXD6RHCqFgR6TegH0POJC2oz74/PE6hCLDnLNAVtXHKLQDech9Q2Md5xKypr3wb8Pnoe5B6sbFkNVBcBhw7CFhtwJJVWkP0RPSAeOz3I9Heqi+6KG5FrZy0J+n90SNypGJ8VtzAYASKMzuB7U7oqxC7vRSaqLG3sxWf2rcZgei5X3TahgeWrEeZrX+rog9On4m/1dfBFwmLIxpSVXx29lwhUAyFj8xYjCyjCVvbG2EzGPHmyplYW6xVB6wumiZuYwptyxa/ETj+Uq8wNvPK1P00Roi/1J3BZ/bvFBlrPLt/c+YEHltzFWqyc+ALp6rcY/WhHTs76vB4437kmGy4vnw+qkZQ1KO/O8WMRx55RDYEl4wYnFN94AMfwNy5c4XdLavCv/e97wkrHolEIpFMUK69Cbj6es3ullXfknFLd9CbkIKropOJWcOkwm7HO6dW43dnT/esKYotVrytaozXASNJ0ULtZ2yj8MJhWh9fhGzfvl0kzF9zzTUiuYU9pCSDQ4oaQ4Qn229/+1uRScWKjQceeADveMc7ku43yT4JFbYKYanDKg09CMxgbSjih8WQBZMhdfawFjxO2BbxISLKPxJ9GFWokW5hxBEPLXs0x+8xxzwVMOQC4fZos9FJQuzgcck1ZL5JkjvkixM0SL23BSddDajNGaUsVFYbBLewzKaPz2DiLFR57jX7dyKoalnkNkMhSqyLhYiRSI65FLXGK+ALO8X5bTPkwhk6n/J5fZF2tAX2QI0KeHnmWuSaq7E4b52o1jCJvgnj4PwdJE2+pri/OSmisJFYZdAZTD4u3KaLGppdWeL3ms+WwobJdQE4+e+e+8BZB4Q8QFE1cHZbTGBdAZjR7hjAB97tBP78Q6A5uo8VU4E7PwTYR6eRqUGZBFWNz9JWUJHayovB89m3AOd3AN524MAJoDumqbezC3j8d8Ddn9IypVgK3lCnlX/fcGtv74yBJhJVNdqtP6oXAXuejd9Ws2iAd6u/wQFEo0RbK36W2SVa36NLBPZPSoQ9KkqsvQLTN47uQihGzOsM+PHzUwfxhbn9Z1rV5uTgxQ1X45enT8IVCuHq0nLcOnno1wur0YgPzliEDyLNz38scBRpwsYYwPHwq4dpy6gJSMQbDuPHJ4/gO4uWoyorX9jQecPBnlGQlmMnXefhCnE+pMIXDuJP57bhvdXrhm0PkIqWlhYRYO7u7hbZU1OnTs34a0gksbAZMs81Wpxde+21+Mtf/oKSkvHVI04ikUgkg4DrFClojHvK7Xmo93T09L3jnHOyLTMJqP+z4DLMz8vHzo52IWjcO70WxUySu1jher14kXaTpOT3v/+9SGb56le/io9+9KOyIfgQGT8NByYgDKx9/OMfF5UaPAnvu+8+BKjAp7gfqzP0QFyT7wSOOl/GSfc2HHFuRHcwoflsFAsFgLjsWzYSzRaDa0pS9dtQ7MB4CggbCwDLdMBcpVVvjCAuBnMT4LFzptg+YoTZ8DqoBZPDEe2nuPEfGRieOB6KbYEDohdGrBjRGezbUsdssCHHXAK7MU+c+zZ+9gnnrgIDXMEzPYIG/9UZOo72wB6EVR8sBtuEFDRIql4efX53k9BEC024MENB8nfbqKRofs4eN9HH99C8Twtazr8ZsOVplkBsFL7oNu33/njqIaClsffvC3XAc3/FaMEm4EaFfRIYqKStWRWMyuy+H8Ds8qlrgdmvB1zR711sVUpzvVZ58v2vAX//E7BtE/DE34H/+TwQ6LU7GzazVwMLrwLsuYA9B5h3BTA/xsqqP/Kron2J+jpXFM3aKqtYE6ZKZgJzbh63vVFGgqmOIqwrnhknaLyuYhHyYvo5Nfu8PX0xSBiqsKNKh+nZOfj6gsX4v8uWDUvQGBXYR4ZN99oOAj5WKU0s2LPEGQombWsTdnlAlsmMj9auQoFZGwNtBhPeVDlHVOXoC07+n1YBtCUbieyppUuXory8XPTSkIKGZLTgubZ582aUlZWJc5DnokQikUgkkpHjtsmLkRPTGLzClotryzNTXUAL9HdMrcH3Fy/D/XMXoORiFjQk/cKY8Uc+8hERQ2YsmdXgw+2/eikjKzUyALOodu7cKRpHbtiwQWRU9eWD5gy2opn+7lFoz3POsxezc9YnVWwUWmYj4HP2VGzQiqXIMk/00FCQCxXdcR+lYpoLqPuASEd0mxmwXAYEu4DuPUDIDZgLgLzLAGOGBlEGDTvrAV8XkFUI5A2+6ebwdyGC7lAnwmoI2aY8WAyabUau2dHTRLbnvlBRYBnZhtnx9DZHV4WooWjBRwMzPWtE74mJgj/SlVQx4A/r51ovqsrwYYgSXNzgbDE4UG5bhCbffmGlZoAJhZbp6A4d7TlGjNXylPJHmtEaYBbDKpgM/dvFjFemOqbigj8+yFbtiO+VQQrMlWjya8dAV5qrsnhevKxtUIphM86HL7wPKrRAn1GZBJOSosm8aKaeUNXBID4pnAasHGSJ6/nT8RZV/L3+FEYTg1IuboOGDbwpwuj7z+Oekw+cOAocjtrg6aJH/Tlgzw5gRYYas/K1KGTwNljY32DuG4BzWwFfJ8DsIAauXQ3av7Mio/oqwDF+e6GMBleUzMT8vMmiTLzQkh23ACHTHDk46eruCXxT+JiRnUIIHCfwOtbiPwtPuBtmxYJSWw3M0WtZn9CG68y/AWHrFx1XJq8DcqeNWdXFptYm1HndqHHkYFXRwJaBJoMBC/IKcLCb13A9K479SnrP72mOAvzPwmuFFZXVYESTvxtb244kPVcm1wJ8LywB/+QnP4mvfOUrIoFFLjYko43D4cCf//xnYUHFqnD2crn33nsvnXOR/aU667Qvd/5UIGGcl0gkEokkkxRaHPjIjCvR6OsSa4cKex6Mo2lbLrnoqa+vx5vf/GbRGJwx5Orq6rHepQmPFDUyBE9GZlR9+MMfxpIlS8QihAuQRLxhChHxvugUNnwRFxxKATzhBrhDF0QwPttciUm21SKYzGC81ZDXY/VjNSxBMHICEXSJLG6zYQYUxQFYVkVFjTBgyNN89VufBtSohU3YDYS6gJJrh1/BwQDE8ReAC4d6t1UuBarXYLRgr5JDXTvQHdKC6wYYMTdvKfLMhbAbrbiieCFeaWVvB43a7CmYljWK1RGGQiDCao1oyEnYxlgAw4wJVyhlVCwI9TSh17dZ44JA/sgpcSMGZCHLtBhGnpdRHKZSVDuuQgRBGGAWz6eLGnTVIfpancKHO3wOeYZ+MvP7gPvCZ4htQD7aFFoKsbJwJc64zwj7uTJbGarsVUn3K7JMFd/3jmC9+HuK3QSLQos5nVYoig124+Wifw6H7T4bhOdWAc5YOysFyJ0y9Ggfg8AeZ3yPCAoDE4H1twBnjwFej/blYy+QG+4COmOPbQzu+H4vY4o1F6hNaEBNYUNUaUwcy7qRpsDiELdUfH7OMnx8zyZ0BDUhsMaRi/fVzEv7uYORCHZ2tMETDmFxfiEKLSPXY4Lj1VnPPnQFdcs6BZ3BJszKWdOnPaWgdT8Q1KtPot/Rxi1AztRRr9zhe7j/wE482qBd78g7p07Hf84euOT8x5etxN2vbcKp6Hfw9RWVeH9NsiWlzajNf0qtOaiw5eGCr1vMjTh+mhUD5rCvTgbweDyiFPyZZ56R/TMkYw4FjE984hNYtmyZWARv3bpVWN5mZU3MhI+0cbcCB/4RvfYxT8sOLHijVnEqkUgkEskIYTYYUcVkXYkkw7zwwgu46667cMstt+BHP/qR7J+RIRRVi/5JMsiDDz4oSom++MUv4tOf/nRcRlVH4DzqvdHGtTHMzL4cgUgrOoO9GdukyLIYWaZhNEh2Hwe6didvL7oKsA4z07fjHLD/seTtS+7SPN5HgXOeE6jzxFsgMct1eeGVPce9O+hGe8AJh8mGPJMD57z18IcDKLDkY5K9bGR3UNhOnQAienWODTAtBQyjWS2SGTyhZrQE9vRkBDOQVG5bCUv0vQQiF+ANxzSCF8KGHdmmtf1mFbb7D8Edrocphf5gN1Qg37JgUNnObYEj0f4dKrKMZSixzkvZ92O8omJjT4VPL1lQsCa9861pF9C8X6tQoKBRuX7oTX/PHdd6aug9HNg8+43v1TLEKRJMmQ3YRqe/xpBwdQFHdgNsNjx9PlBcDnR2AJ/9EBAM9Io1fC9f+g4wOVl0kkxcXKEgDnW3i4D3/LwimNNscu8OhfC2ba9gd6dm55RrMuN3Ky7HZQUjs8Dxh9044tyUtH2SbRZKbP1UXdS/CDh7RYQeZr0VMIyu+PVqWzPesyP5PTy86krMzxu43wurNOo8btiNRpSl4TntDQfwbNMhNHg7kWu24erSuSijHdswOXHihKi6zc3NFU3BKyoqhv2cEkmmaGhoEMKG0+nE3//+d0yfPv3iPbj7/gq4aM8b0wusYCow53VjvGMSiUQikUgk6cOQ+ze/+U18+ctfxg9+8AO8973vlYcvg0ycSN8Egifp4sWLxcKYGVW//vWvkZ+vZTfnmSvQHqiHJ9zZU7HBTG2r0YFW/46k53KGzgxP1OhTs8qAluWlHVEKaEU1SqKGJ5ScXR1UA8KKig2mdRsq3oKREDa2bIUz5OqxpZqVMx1zczPfpLwHBvNNtYBaHQ1U2yas9z3Pw3JlJTzhZnH8HKZJMMdYQ4Ui7UlVSBF4hWWSwvfdBwWWObCE8uAKH+3praFjYaXLIOgMnoIzpFU8EE+4CW1+I0ps8xFRQ+gOnhN9QdibJtdUNaaVHH2iclgO9rZU4OGMnssDwnOrfClQtkR74HDfX1Ut8O7/0oQBPnfFFGD7o4DugW/8N1A9B5i2ECgfwe/RUGGlybKEXhb5BcB9nwUe+I5WncEM/Pd8eGQEDY6RtM/IKtCyTMcjvEaM0ZjEPgidQa1yJt+cA0OGv4/ZJjNWFA5euP7hicPYGxU0dHHkw7u3YfNVN2Ik4PUqGQXhhPEwCWtBgqihaP1YRlnQIBQkUnHe605L1DAqCqY50m/ybTdacMukxcgkjz32GO6++268613vEgsPs1lWRUnGF5MmTcKLL74oEqZYufGb3/xGNBO/KKH9YtxaRY1uk0gkEolEIpkYdHR04N3vfjd2796NjRs3ivmbJLNIUWOE4Mm6a9cuvPOd78Rll12Ghx9+GCtWrBBBm2rHcnQGzyMY8cNmzEFuVLRgLwKGMJgUzRCTSdG2DQvrJECJZm1r0VHAmAVYBg4yDEhfZXn0fB8lbMZUDZRN4pbIWU+dEDSI3mfjqPMkahxVsGWqx0hfiP2Z+F83qzFP3EhE9SJAqyk1AqOhBAoD7ym0MmWA980qDgokVqMXEfWM+HQCbD+CStiNg7MT8YRbUm5jBUej7zUEIs4e4cUTakO5bcn48qaO+AGfG7Dzy69v5P4NMhtTvKcMva/iCuDyaLbyvx7Qqh50+PuZg0DrSWDBdcDUyzAhmLsQ+MGvAGc3kJ2rVaBkWig4tQmoj1bJsSH73BuBonHkmRnsBNpe1ewIDTagYDlgH72eSL6wHy82b+8RNfLMObiqdAVsxpGzeUqXo93sxdELf6/3euAPh2HN9LkirmPZMAl7v6jNikBFjqmo/wcWLQA8zYCnUfubYsbkNBvSZ5janNRVEuytMRGa9X3mM58RCSi/+MUvRCa8RDJeodj2/e9/H2vWrBFrDC6Uv/GNb8Bi6ceqbiJiLwScF+IrNewDjIkSiUQikUgk44Tt27fjzjvvxNy5c0X/jKIiOY8ZCcZhmvLFA0/axx9/HB/84AdFfw0uQlh6RGGj0FKJMtsM5JnLRFCV20MwIqRqARRKGQGV2YhDrNKgGOLeDbheBCwmgIEiNh21lAJFG6JB9mGSPxmYkhDEZD8Nx+h9WafYa2A39trfsIKgNntBykA1g2j890T8kdhAkiQdIqob3vBWBCMnEVTPwBfeDiPs0cbnvQF1i2EalDTONRUMyp0RfTUMigKbUUGWMThowSFV43VFMYrqEk3Q0F6N+CJt8EfGWdZf4ByUoA9w+4FASLu5fVAi48Q729WRXP1FFZYd3rv2AA1PAs2vANFA9bjGYASYQT4CQWq0neoVNAh7Gx36NxDS+jskEXIDbTuB5q2A82Q/FXYZgg3lWzYCoe7o3z6gbZMmdIwSO9oPoks0uNboDrrEtsESUVX88ewx3LvzJXxszyZsa9P7UgydKodDVA7o8Lcii3VEBA1iUIyocSyFmeKSeD0DKu3z4TAVDHwOV10LTHsdUHUdMOONgH1sGsgvzi/Cf9TMjjtmn5o5H7U549v//vTp07j88svx8ssvi8WGFDQkEwWeqzxnmfXHc5jn8kXFjCvjKxytOUD15WO5RxKJRCKRSCQDwtju9773PRED/tCHPiRiwlLQGDkmfur4OMdgMIgy8bVr14qmMC+99JLIBiwoiA9W+CJdCKrxAS9RV6EM0bLEc0AESLWdYHRZBWyzAfssZJSay4GSmZrlFCs3RlHQICaDGYvyV6Pd3yyaMbNBeJYpG8GIF2E1CIvBIQJGpNCS31Oh0fN4xQQHK1ckgyIgeoToVUTaMQ2pp5BtWgV/5CQMcMKgWGFU0jsfImpyIFJFs7ggDEbYyDdXo8kf30Mm31wjzoVU9LV9zBCZ2gqUcATgLW57GtVEng7A3QbQWz5nGLZ1fZFfBrTWxQfdTUagphSwmrWgeLAL8LcAk24CRroCarzibE6yYkMkDLQcByrmx9835AHqnwR0cdV1Cgh0AUW0EBsh+DlRyEjEdwEwj04j+LZAZ9x4zN+5bbA8cPIA/lyn9VXiEd/Z0YLvLVqLZYVDP//vmzEHLzRfwDmPO1o1acB3Fo1sqbDdlIs5OeuETR57AKU97vF+6QoZrMrxngA47pnLAPbryGCl2kdq5+KG8smo87oxLSsHNdnju0qDPQmY5f72t78d3/72t2WzPsmEY8aMGdiyZQs+9alPYcmSJfjVr36F2267DRcFrPq+7K1Ad4M2uudNBozSEk4ikUgkEsn4pb29XVjZ0m7queeew+rVq8d6ly56pKgxSrBMnCf2PffcI+yo/vznP2PVqlU9/x7pI7jKAMeQCDak3pZpUYMweDqcACqzmEM+wGTXMk8HCa2mSmyabQqD4Bd8h9AVrBN/G2DGlKwlsBvzUWErw8zsGhxj0DAqaKwqWgITrWEkg0JVkwOiKgJQEILFoDd29EBFOyIqG3VXDKFobPDBtixTCcqVpXAG60WQNNtUDoepHIFIcu8VZkNbDcNvLJtRaDcT0JvKEwVQLIAhjWbc9XuAYy/1/l25BKhdl9n9W/0G4KmfA4Ho58/SmqJswBZre8FyMz/gOQ/kXARNTE/tAw5vByIRYOZSYOaSgQPBVvYGSFFt0XI4WdToPhoVNGLu33UYyJ8PGEfITiQq9MZD38ORqURIRZbRBm/YF2ssIrYNBo73f6s/1VOxQdu6sAp8dM9WvKd6Fu6eNiuu4iJdiqxWPHnF1XjmQiM84RDWFJWMSoCeQoYx3f45g4VVOZ0cH3jEVSDQCEQ8gGPekJ/yUHcrTrg6kG+2YXUReywZRWXGeK/O8Pv9Itnkd7/7HR588EHccccdY71LEsmQsdls+NGPfoQNGzaINQZ7bnzrW9+C1Tr2Vn7DxmQFCseRbaNEIpFkCtoN73gGaL8A5JUAy64DHONsXSoRa43nms9jV0crcs1m3DGlBiXWcdonUTLmvPrqq8JuatGiRdizZw8KCwfXH1YyNGQ0dxRhydE///lPfPe738VVV12FL3zhC8LH2Wg0iuAqg6xqnJM37aeG2p8iVXBq9AJWadN5Eji/WbPLoh945ZVAzuQhP113qKFH0CARBHHeuxvTHetFU+h5ebNQkz0V/rAf2SbHqAgaoYgbYdUDo5IFU2xwWmVj1QNM645m4c8B0qxsGA7BiA8N3v3whjtFAK3MNhu55vJBPYdRyUdEjW0Uz7M3FxHQ/iD+HFZxHED/ooZBmYKwGt8PwwA28h58QNJuLBK3WNgYvMS6AK3+g+I7RpuqUusimKJ2L+MGfg7WOYD/sPa3YgUcKwYONns64wUNUrcLKJoGFGawAXZuEXD124Gtf9IqSShq0HoqJfHnwYTkxB7g+Yd6/244CYQCwLzVWrWKs0WzlMopAcwx51L5HODkRk0I0eFxSiVeh/uoFqLQMVKiBqsxLMVAoK231xLtCe0j0Cy9D5YUzMXzza+KZuGEtozcNhi45xF+o1UV3nCvLOQJh/HDE4fgDIXw0doEEWkQTcZvnzJ6x2PE8Z7sFTR6th0HsnjdGbwT6WMNx/Gnc4dggCI+g2eaTuP/zV0LyxASE0aTw4cP4y1veQtMJpOw7pk+/SIQXiUSQIhzTJriYppJUw899BBmz+61hJNIJBLJOCEUBJ74OdDVoq0nWuqBxpPA7R8FLONsbXqJ8+DpI+KmJ0k9dv4MfrfiKpTapLAh6SUcDov+Zl/96lfxla98BZ/4xCfGV9/WixzZU2OU4cn9yU9+Uvg3s0z8mmuuQX19PUwGKybZLoMhqjMxRFxuXQCrcYjZobYZ/W/jBTTs1OwoGFSip7vrJOA6DYT78H0fLL5uoP0k4GxM7RHvawfqX9YEDd3n/dzzQNAz5Jf0hrtE3wxjzBgSVgMIxlQW2I025FvyRkXQ8IROoyO4Bd2hPeIn/xaICpydlGGiQSYvgN1RoWNksw3qPDvhCXeI4H5I9eO8dy88oY5BPY/ZMB0G9CrPCuywGhfQmCrFvfuxeOJxCNfDEO6GETOhgCJeHgxKLQxKZoNN2aYKTM26CpX2dZiadSWyTGPjPT8gtlog9yYg51rtZkzDDsjT3o8NUoYpqgQuuwnIztIqNFQGMZldrsRUl5hGten0iLH35dTbaCW155/A1t8Drz0CvPwg0HG+9z4cW8pmaNZcJgNgNgJmE5CbQrC1lyVUdSiAyQGYRtAWj0Hs4vVA9izAWgpkTQNKea6NXmZvkTUfN5ZfgYV5s8SNv3PbYGD/nXXFk0R1RqouJA+dOyHGPAnhNSfxWPDA6TaC6dMZ8AlBg1DQIKzYeLE5ank5DuF58POf/xzLly/H9ddfLyx7pKAhudjgOc1z+9prr8WyZctE43s5BkokEsk4o/EU0NncGx9hLMbVCdQdHes9k8TgCYXwy9NHxO9hVRW37lAQj9THuipILnXq6upw9dVX4ze/+Y2I8TLWKwWN0UVWaowRXGzQjuq+++7DwoUL8ctf/lL44NY4rhRBeGbQs7JgyNhqtMBioF4LklmnAZZotjyzhZ2vAiFm6TIqYQW8rt7gBrPXy68BTLRQieJtBdppkxIG8qYCuVP7f/2248CJ53uDKIU1wIxr4zNCPSkCrtyHhleA4vmAY3AVG1y4mZRuFFm1RtXiwhPkBYiVBSOU8dwPwUg33GHN612Hf5sNRTArDDAlikc8Vq0AHCO4Tx74k6yYFDhDzcgaqClt7CMUI2zGJVBBAYqVDw5xvkbUAqiI9cXnZ9FHoJJ9IgJbhU0VMbBYx7QYMPZf1RGJRIQabjYP3qaF+2hSJkAGDL+7aTRY78HWh93L6S2AyQJMXoiMMnkBUDYT8LsBWw4Q8QJt27ReEAzGF63QAvMTHb2KwmrSBArR6yQEnNsDNMd8t1m9QZFjw729Y1zNlZqtHkVdkjcFqFqT/BrZU4FgN9CxXxsDeNzKNwwpe35QUHjJX4SxJMfswNy84YmXn529BAe6O3DanSwI8xogicIeGn7OB2LFswKtQnKQtOv2cwkCU1uA4vz49LZ93/veh61bt+LRRx8VySQSycWKxWLBN7/5TVx33XV45zvfiaeeekqIG+PWAoHz/rZDgL9du/4VzQdME2CeJrkooT3hRWHdJhnfcC0xmO2SMcEVCqZMmuoKpkrilFyKsD/fe9/7XhHHfeKJJ5CdHRM/lYwaUtQYQ3jSs1rjhhtuEM0qn376aWFNlZWVoQxda5V201EpXBwDIh2A2a/1eRZFAt3xlRT0w+/YC5Ss7RUfTv2799+7TgKTVgOFKcraAy7g/Dag7YRWB6S7r7SfAlqPASUxj+krK9jTCNQ3AYXzgJKl/b9HZvqrHkCxwR9phqLQykmDL59rYtXGDNF3IxO4gt1o8TeJ4HiZtQL2frKpwzzeKbe7Ye6zAfzIBjL7EspYGTT452JNTHzgWkE1VDC4qAtWDhiQ4NkeYZPieu2nqFCJIbQfMJT32bOA3oSBQKDn+zN37uDsalLC7BiVAl8QUPIAJSEYr3q1/eUXRikBDCNvETZosouAqSuAs9sT/kEFTrwEOIqA/KHbuvXpdc0bYfPO8mu1Vxxkc/dxzfRFwOGXgSxr7xhJm6muCwmNwNmjwAMwqGuNnj88NnNuBYL8PiiAOavvXhwFC4C8uVHLKVtGmzdf7GSZTHhv9Rzcf2BH0sLjurIp4/ZcDEScCER4LciC1ThMD2WemwP2eanUxEcPM84imqCRu3JIL1duc8BiMCAQY69GAWlq1vjzgn7ppZdEI/ClS5di3759KC4epxV6EkmGoXi3d+9evOc97xHezn/4wx+wfv368XWcOXbVvwS4dMFVAZxngerXj5z9okSSApfLhddeew15eXlCCGePGtoUSiQjQvk0gH0ZmCSiz+G4lpokLTHHE8VWGybZsnDB5+2pTOZ8d0m+nEte6rjdbmEx9cgjj+BnP/sZ3vzmN4/1Ll3SSPupcQC/BAzWHjx4UCy8d+zYkfkXof2SyoBnq5YSbzECjmhAMimbVQVCveIAWvbFeHFH79u0O/k1mJV89FGg45TmtR/rt89gujfB4iinErAlBIhFkUU0ONN+UMv87ovIBSD4AhDaov2MnIWFsUO63+hNVw0KCixTkAna/a3Y0bEVZz2ncNp9HNvbN8PJDOs+MPQhXBhEpQCDP4kBIGbM0opm5GCVQrapJG4bpYl8y6SMiSZGZSEMuAIGrIUBK6HEVkZE2oDQq0CEVj2uFL3AqbTFZ6l0d3eL7wTL+crKyjBr1ixRqZGTk5MZQSO8E4jsAiL7gfBm7bzq+Xe3tk09DajngMiO6L6PQ6avAWZeqX1/9O+fEr11jfw++8M+HOjahlfbn8b29ufQ5IvNCh8BmM2UImM8o9Qu1gQNoh9LjnOpMtLZSyC2r4b+GEs2YHEMHHTm4032lPejUMQeNhG1BSpFNkkcN5ZX4p5ptbEGaLiyZBLun3vZuDxSnYFTOO/dihb/PjT4XkWb/1j/D+A12nsWcB4AvGe0cYt46oH6R4G6h4HGp7WKn77geZU1Cyi6Rbvlr9eqModAlsmM+2YsgzlGJL+6dCrWFGVYOB1mtu1nP/tZ3Hzzzfj85z8vKjSkoCG51CgpKcFjjz2Gz33uc3jd616H//qv/+pJDBkXsDqjR9AgKhB0Ad1nxnCnJJcKrPw+fvw4tm3bhgMHDmDdunWw2Wyw2+1S0JCMLDYHcON7gfxSbf6fUwTc+G4ge3A2rJKRhVXI31m0GqU2W8/64p1Ta3FDeaU89JcwjEvRdYd9+pg8IgWNsUdRpdnquCEUCokGM1/72tfEwoO3oWaJRNQwWv3H4Q23w2iwoNSSDYuSwu/aSXsUZqIneLpnTQWKV2l/nvpXslUUGxfPe2f8ttYjwLkUHvShaAfXaeuAssSs/SBw+inA16ZdKfRArM6UawBHioA7A3tBvlbvfvO3kMEIVdF+d4uXVVBovmp4Vl5RtrW9Ak843uKk0FKMRfnLUt6fXy1n6DD8MUFwm2Eysk1ztOxh0TSYnowMRPFiWQv0WcGRObRz44Toq2FUrCixzoBtqL1bBktwO6DG9H9IEtQsgOUqhCMRtLa2oqGhAadPnxbBKFYwUczgd4LNMA2GDGiykTNAJNG/1AAYr9LO8fB+QKV9UOx+0oro6r5tFIStG22ECgFl8NYu8DYB3WyurgKOKs2eKF3YW+O1PyRvn7EBmLQAcJ0B/K2A0Q7kztTETl+ddh9bJWAaWqY1z/V9XVvgCbvijtWcnGXIZ0PqTMKA7s5ngEO0LlOB4inAlW8B7CNwDreeA17+ffw2fndnrtH6BbFBu16xMf96YPLQmlIPdGzDKhvc62Ibq8/mwaCUZ/x1mv0n0S4sC4EiSyVKrDXjptLhpKsL3zq6C/VeF6bYs/GpWZdhRsLizxkKilLxUqu9p6HfeKzQoKCRSIVtOWzGFBaAHCO7tgH+873nmqUcyJoNND0X831jEoEdmPQ6bewaBdhb44ynG9vbGvBah3Z+XlM2DXdMmTWmx5/BKVZn8BrB7PSMVPRJJBOcQ4cOie8FA7n8Xsyfn/nr1aBxXwDOPZ2wUQFKl2g2VBLJCOB0OtHZ2SmSCYPBIEpLS2E0GoUYPnPmTCEGSiQSiQ6rM1r8XmSbzOImGR3qPd347dk9uOBziyrxu6ctxhR77pjGar/+9a/jf//3f3H//ffjP//zP8W1QzL2yLrKcQSDtcwovPHGG/GOd7wD//rXv/D73/8etbW1g36uRt9eeMKtPcnvXcF2FJtTJQErWrNfX0QTGMSO5AAFi3vvkj0lQdRQgOwUfQ/6azjKBrmx1lM69PMumgM0btYEjcR9s/TVj4GVJMkuhwa2v6aAoQJWgyoaTmdC0CAB2nKlyE4PRgI46jyAzmA7zIoZNdmzUGItF8HAHNMcWCOlCKseGJUsWAxFvUFCEfBOcUxGGINiRKltFsaGhKbhQtzRP0cTYF4Cp8uFnTt3Ytq0aWhraxONXSsrRygjgtZlcTZChFnQ/KyzANWvBdHjvjih1HYvER/gZqVHVPhSrIBjDTAYwch7AWh8vnef3KwO8WsCRDrYC4DSWUDzUa06ivtpywXKZgFtO4BuZoRH95u/swhBfxvuY0DBFcAQRIhAxAdP2JlUAdQeaM68qHH0NeDQlt6/2xqAl/8CXP9uZJycYsBoive45TEtmQrMWg00HtUqNwoqgfz+e8EMFYoZvYKGtoUihwKOJZmbWDf7TwlRQ6fJz54hCkrZn2mM6Qj48cm9r8AdConybwocn9y7Cb9Zfi0KLL02hjkms7iNZ2g51df2lKJGsDUqaKB3nApcANTE6ZsKhGmB1glYR8ciL99iw4nms3i2+WzPtr/WHxWCBoWN0YbB2u9973v4f//v/+HjH/+4+Mn+AhKJBELce/XVV/GlL30JK1euxFe+8hV87GMfy0yCyFCxFQIGi2a92IMKZGVWtJdIdGhDyCQO9uVjVcZVV10lrxMSiaRfOK8tt2XIHl6SFs6gH988uhnesLb2O+PuwjePbMbX5l+FHPPo9z06duyY6FNGUXzTpk1YsmTJqO+DpG+k/dQ4hF8SljWtXr1a/P7AAw+ICVi6hCL+XkEjCqsW4uDTRRhErgHyrwQmvR4oWQeUbAAqrtd83XVKFgAFMQEKRxkw+YrkF86ZktDclhY4ZqDmamD2zVp5pQ4FFAZsXacARzmQPysha18BKtZqPvQpSQ5UxIamGW82G3JgVgrgC9eLfhuqbtkxRHLNFFjiA9m5pnwc6NqFtkALwmoIvogXh7r3oCOgNWGngGE1FiPLVCV+jpes5+ESirjQHngVLf5n0eZ/BYFI/PnWJ0qKADd7VJhmAiZWJHiwf99eUQJOUcPhcIycoCH2h+dX4neL57AVCDcBgVYgGAYCoWg1Ez+/3NRWQr5DQERret7TCN27Z3D703FQf3DMtv2DeD8KMPtaoHYDUDYbmLocWHKnJsQIQUN/bgZAvUAoGGMrFwGctJobmlCWCJ/ROBIZ4w3HE14oAjSx4qYfUXWoWLOAlW/UfG515q4HSqu1nhmVC4HqFSMmaBCVFmhJPm2iGdKgnscZbEGD9xAu+I7An1BxRjoCyRZlHcHxYbW2t7NFVGHofrb8yYqMPZ0tKe/v9Y4Piy5v2C0SCih867CHRirMhj6udWEfQqEwNm07jGAw1pqP17MU84JRqtLQeak5uQJ0Y0u0+msUOXv2rAhOcb703HPP4atf/aoMVEkkCVDkYzX4s88+i5/85Ce4+uqrxXdnzGDfjKprNOtFwv53k64A7NKvXJJ5urq6hIjHHjNcjy1evFheJyQSSdqwsou3i4lz586JgP1447CzFe5w/NqPfx9xphlzyhCMwXJtwZjsmjVrROKtFDTGH7JSY5xCP09mHNIP+p577sE///lPPPjgg5g8eWDPajVFoCOoKugKlyDfxKCrD1ByAcM8ICvG7sjeR18FChWT1wAVKzThITbAF4stD5h+I3BuIxBwA7YCoPoqwF4Yf7+wD7jwLBBy9QZhSjcAxQuiNlQGLXurnybcWkPnckDVGvbyPat6H4EooYgF3si2nr9NSj5yTUuHXLkxO2c+9nTuiFrsAHnmfFQ5qvFaxyvxuwYFrf4mFFgGzpalEBKMeGFSrDAxW20CoKohdAV3IhKtuojAh67gHhSYV8FkyO7/wcbaaH+XxujBKgL4GPWEOLV27TqO4oJy7WNUI8jNMePM6ZOYVj1CjdOUKjaNYccUfQNgWKDZm/l3xgcNaaNGkc20KPVzhWkjFvvdo3DQj8d9KoQlWQKR+B4jA8Lze9LCZN/qlK+XMFakqEZKB7PBgmLLJLQGGvSdgAEGlNr66WfD8yB8LtqgvbDfBvFxWKJ9J2L3nZmmXhfgyEPGKZ8B3PRRwN3BQQQIdQJdxwEH7bqG1pOgryzz5uZmXLhwIc7z3GBsw6QpbpSUJFatpf/a7YE6NFJ0i4oj/LvGsSrOdk6zxEPSWDYeMPWRSWyKOV846WRwrr6+XoihzKRhlRevpYOBFnfnz58X9nfMbGYm52Dhvpx2H0Gj72zPt2FWziIUWctEU/A80zR0hXo943NMU2AzpKjS4OjUGcKh7UcQCoeFuGE2R6dtjhrA0xitsIx+cNYywDwC34F+MKa4nhoyfN7UeTrR5HOi2OrANEdh0rH+7W9/i49+9KN4y1vegieeeALZ2QNchySSSxwuzNnL75Of/CQWLlyIH/zgB7j77rvHJvHGXgLMeJNWrcFEqAxVV0sksbDym9aErFIidEFgr77LL79cChuSS6KpcWNjI9rb49eDubm5mDFjhuwhMwBMltq/f39PknFeXh5mzx6c2wYf63K5cOrUKRQUFKCqijGIsYPrzt27d4s151DWOqPRzyQVozlP4Xrwve99r7AqZCyWyVOS8YkUNcY5zKLiIMoF+7x58/D9739/wIUHA+RWQx78kdggqwqrcSrQR+AkLQxpnC60mZr/1tT2PDqd+4GQO962qnUrMOVWwOxIb1/43AwuR0qgquwR0ApFUcEcVb50OJINbyT+wh1SO0V/C5txaJn/VqMNywvXiL4aDPZlGR0IpQpCpzngdgdbUOfZi4hojk1P9dkotg6if8IoElYD6AgcQzDigoGZdPAnXGxUBCJtA4saXKyaFwHqvOi5GQDCm8Q/bdt2GDNnTkFhYQ6gHoPffxo52W3Ytesk8vPfjPyCOZl/Y9wf49Jon49AVCxzAMEzfWRBz4pWd6TA4AAiCbZo3DYYsqYA/rbYFwSyMtB815ybwuIh2pw69rXMQ7etmZ49H3avQ2SmU+SYYq+B3ejoW9AIbo5pCs/MbjdgnDHwC81dC5w9qFVm6MIGqzX+9RPg+vcCeSPghcwyV6sBqH+u12bPuAeovAGw5Ay7SWVHR4fIHiwrKxOT5NjJZSDgx7ETT6C5+QzmzZsmthmUmVAUS9qT6CZfbJWOJny3+k9jSlav+MUeGo2++P4yhZbMVkmdOXMG3d3dIog2GJbkl6DcmoVmv1dk6uiXl2POTqzILRYZRnzeqVOnigCF7ntKqxX9777g4/gZcFJPX1R+DkweoKDx2muv4YorUlQl9rPo4YIlbPf3CBokGAricOcurCy5Wnw3Cq0zkWUqQbe7FV3tPjQ6wzgX3C0+K9EUPtJbVcheQjMWXAd/+y7Y7VG/uJzFmoBRfh3QeUCznaLlVO689ITBDHJjRTV+eXp/0rZM8cyFo3jyQu95ua64GrdNWdCz2Hj/+98vFmV/+tOfRCNkiUSSHhT/fvazn+GWW27B+973PvzlL3/Bz3/+87SSpzIOxy3j6NtJSMYHvnAjPCH281JhM06C3Tg5o4ErzrE4T2AVuP687KlRVFQkbERkoEpyMcJznOc955RM9pk0aRKmT5/e8x3gfJPVS88//zyuv/56TGRYPbFx40bxHc+k7WhTU5MQIficrPCyWq09NnYUSjmG9JckxWQrJqvp6wt+Duzbc+TIEfF7f49PfC6uL/iZUVDRP0N+trzx+WPHTG7juMd95NqEayJ9uy7M8DkZW+R7YdXaeGNebgkKzXZ0Bn1i7ceEqXyzTWwfafSEKVp03nrrrXjooYeQn9+HJb5kXCAbhU8gmIHIBTybJA+08GAQusl3EF7RDNqCYmstsk1lo7q/qXesO1qlkRBcJVV3DSkgo4LB0V67EY7VqlqMzlCiNYkCm6ESDlNmvb4Pdu1Ga6Ap5lUULClYjex+mi4HI34cdb7MDiBx22scK+Ewja9Bk43FG32vIiT6T2gXQn5KNkO8eJNtmg37YAUjignh13D48Dnk5mZh8mRaDigIqxG8/PI+zJlTJUQOi4XZe6sBZQSaQTMgTmGMAXYlXxMtuLgK7E2+r3U1YEyoPNKhV75rk9aHQ2DUemqYCga3L207exuFU+QoXaNlLw4XXwtwYWNvNUb+PO34hzq0v7mfBZdr4sdIEzoCRM4mC0fmazT7iYHoaAKe/hUQ8GonI89D3ibVAle+bWT2+cw/gUBXQm+hSmDS+iE9HSsJWMK6YMGCASe1LpcT27Y/g8uvWAKzqRAGim+DmJgdcj6TtN1hLMI0x7K4+7UFzvXYUBVapghRIxOBBU6oOWnm+2xpacGqVasG/bwtPg/es+N50VeDZ03I54f79Dm8q3ou7l67Hjk5yWNDXV2deD2KRVxMcFLPBZzH4+mZ3DNLjYs8faGSWJLNxQjvx+en0JG431xkcNHo8/lEkJC3ww170eip0yoIVRVGkyYe1tjnwmHK6VlQsIqEDUm5D1ww6c3mEj3uaUeZl2OHSQnAFzQgrBrFYoRCTF/WlHyu4uJiIfSMJHz9Z5vO4PloXw02Cr+mdGpGzpsGL5vDb0za/sHpq7Hpr0+IvhlveMMbRFUrs94kEsnQYPCDi/fHHnssreQpiSRTeMON6A7GC+M5plnIEpa0mQl2btmyJU7QYJCSczBeH2WgSnKxwTnryZMnRVXG0qVLB6zC2L59u7B6rqgYOSvdkZyDcq7PpCnO9TmfHu77ENXWp0+joaFBPFd1dXXSvJz3oYjC9YM+d+dah0IS5+f8d443HGNKS0uTrqf8jLZt2yYe6/f7RWwvscqY9+E6hPvBz5BiBuFr6M/Hn3wO/TX1NQH3t7CwUKy7uNbg43lf/Rb7Pv7973+LtSj3g+sKvi7HTV0ISQWfs6amRggzI0mb34OH6w6iwefEJFsO7qqaj0I6NowgesIUq1mZ+EHXHMn4R4oaE4wJvfBglrjrBcDtAgIJFQ6mbGDy64f0tCqeS34p1YRO4T8eH/BxGOfCZpyc8aD/afdx0UeDWbjTHDOQZ+4/wOIMtuKMh/ZG8VTYZqHYqmVjjyQRNQRn6LTojWFU7Mgx1wjxKxXecCua/buStltYLCN8oijjmFFoWQ1DmtnjPVAACG/Eli0HsWbN3J7N2147ivnz2VMjphxSmQMoGe6voYaEqAJ0x9hPLQaUAsD3ilZRoJ9DhgKo1pVQ+vOs5zkeosAVAUylgGGIF17R/0XNvD8+qwxYJWWwalmRoqyJ1SXMksweUFQMRnzifLcY7EO2cROE9gORhhSixgZASbME9uGvA8EEu6z8MuDmD2JEOP6Qdr7EYi0ApqY32eEklJk5RM/WYcl3qmB6X6XjDG5z0ssJcqpeM5yM8n56gJe9HM64T6E1cB4Kgsg29QqRpdZalFiH1wRcVIE0NYkFhV5dkOp6xID9rFmzxCSY9lC8DdScls/Hx+mTalcogE/v2dzzugaLGTnTqnDdlGr815xecSYRZilRcBCPMRjEooDVD4O9btIajIGQ2P3mPvK5mHUVW11zwVeHky69P04vywrWw2oc2phAOyy+B54vXJxwP/h7X++DC5JnnnkG11577YS11tjT2YDfntkRt83Z1IrD3/4Dzhw6KpI7ZHWGRDI2yVMSSSZo8zNpKt6q1QAbSmzrMvL8DO5yvlVerjWfp5jBuRjtKSXjh7AahifkhdVogWWC2DGPFzgXZXINRQxdwKC1kX7Op8OhQ4dEjIkiH4P4nNummk9zDj2QSJIpGGQ/ceKESB7qa67L987r1JQpU8R6gQlUDPD3B/+d65FYq18SW8HAvp6sbOkPPp7HRH89PudQjg/n6xyTmHCVWG3Bz5HCykjG+vT3wXWM2WwWIgnfA39PBY8Trcwo/KxduxYXC3xfv/nNb2TC1ARF2k9NMBisYjkUFx733nsvHnnkEaEijmgz5YQvfHeoAf5wN0wGG/LNlVE7ojQIt2vNk21meoNEGy9rPTXUYk4uabvDQTtfeJCnv1NZUMELQe8+RtQgHMYKuMPsuaG9jkUphdXQ/wVqqE2Sp2cPzlexr/4ZfQkLmYQN09v8OxFQ9axzBb5IK0qtq1J+ln01WDcbimBSIjAodjhM0wcvaIiXtgKGhSgoqENdXQsqK0sApQKRyOF4QUN7RWQclTZTsYspFYjsB4xXAba1QPA4EPFCZaWE2QUoL7I4nkWRUJBCuOLnmgnLnpHylKZIQiuqnr+pTPVdURR7DpzzHEBnUOuFQlGjxrEU1r7spfoj0qn1AWAQXK+yEDDYOwj7icIKgE2K9fOTz1M0gllG1nyt50+PEKMA1tRVOxQWaGnErB09g4bZQ1xE6xk9g4WL8vXr14tJO7NIXnnlFcyfP19MoBnw5uSSk15WFHBxE4qEcMR9AKYcM+wOu6gWyM+xoSzbggLLFBRbpg1rAsxAASfBXEiwYVq674v3520g9EZ8+qTaHw4jL9ASJ4MZocBh6n9cSLe0eyAoJPGW1n2tk9Hsq4czxDFWNAhCpX36kAUNwqqLwbB3717xuYyGoMFr7msdp7Ct7QSCahgzsstwfdlCWPvqv5UmJVZHfMXRY89j03d/jTe/8Y144pG/yeoMiSTDMCOR3tGsgqItxXe/+128613vmjjJU5IJR2LFukbqdcdQYFBw8+bN4vrNZICjR4+OS6uVS5n2QDt2tO9CMGrpXJs9AzNzasd6t8Z1ZQKz92MtiJhcM9geD7GwEpnPzfULbc/1Kma9akG3WmJyj14RQOGDcSm9EkCvJBju++NrcQ3F+St733Bf0oHrBVampAPXZwziD+faxv3LRHUL95uVEmPFUN4HbV9vvPFGXCzwO8W4KtdOf/zjH2XC1AREVmpMYKiof+ITn8Df/vY3fP3rX8cHPvCBnsCSPxxEW8AJi8GMIkt2RhYkvNBc8B1Ed+h8T6DGashBVdZKEdgfkGAz4HlVfzKt8TKtonLmALZmrWGwgDYiS0T2f3r71QEVO3ov7qoKf5gxThtMynzR0NoAC0xKrwfhWMNjWe/dHw0Sa8fSbswV9lOGEW6S6A+3ozUQn/1KCszzkWVKFn3CahAN3k09jcGJAiMm2dYKYSsjqH68+upLWLRoGez2Quzd8zAcDi9mzGDwk2HMXEBZkflgf3hfb9PyWIysGNAC7CpYDbCFd465A/djDQ3NcCnQ7DuNxp6+DLqVWzZm5a4Z3BOFGoDgnp5zXsBqH1p+mZcCyiAa/Lo6gGd/A7g7e0WOq98JWPvoeTJcaD1V9ywQjlrdWfKBymsBo00E4GlTxKoFig6c6HMintikmuJQR+Ac3OEO0fuoxDqjT4FzICiUcNLPDCaWGHPhHpsd1OCtx76OPXB1uuBxecWip7ujG2+7+u0osgzej5SLDAoZegUFs5j4uqPJz08ewEN1x8XZQ29Vs8GAny27ElVZI2BLN0wiagQt/gYEIn5km/JQYBlAlBC9WoSnX8Yy3Jixxs+LVTIjabGxv+scnrqwr+dv1u5R2HjD5L4raNLlqcYj+PP2l/Di1x6At74Zv/7Fg3KxIZGMAv/617/EIp/jB5OnWFkokWQad+gUXKETcdvsxirkmoceoE2E1RkUM5YtWyZ+37Vrl2gYPh6b415qMAHn+eYXEUqohF5WsBRltvQSSS5m9L4XFDHYA44wkZVVBIkxDX/YCWeoUVif5pjKYTMOXWRgshSrqgnn+6nmkBRAWIVOgYDrkaEGubmeYaUCv5t8TxRP+JqjVREiGTy0xaIQQLGYlT0T9bPiufeTn/wEn//85/HGN75RJHJIO9uJiRQ1LgLY4IkLD2Zy/uIXv0BJbSUeO78dPmZDs4Gvoww3ViyBMUVAmBfLFn8dWv2aUFFqq0SxNXW5uT/swhmPZv8RS7ltHvLMA2feiqCNi57+7mhAk8FMK1R6oSsxjcMFU6AgvQkt34Mv8jwM0SCpMO6hWCJ+N8FhpC3S+Ju4cr87gw3whik+2YR/fVri0DDxhVvQFtidtD3fPAcOU+oqg0DEiVb/AYRUN0yKHUWWebAa84d9IWHgTS9xZSkjM8CFBzwrA869irpzp5BfUIIZM66A1TYCwerISSByIrmAjZUaeiM10E4q3u9Xg9UaE89/dCicdu1Cd1KPGmBB3jXpn7P8UvqejWkOjqjtVYXWPH4owdxQEGhvpJ+TJmrENT4fxNOEQsLaiVCgWLFiRWpbqHBAVGtwbDnX7EXDhSYhJDPLhl6unNz1NbHj9/2cZyc84bae/ua08JruuByWYWTw98U5zxkcdR6O23Zk11G89aq3ociavqjBRQszcnh9YSbYQLZRIwmP4b8az2BXRwuyTWbcMWUGqhzjT9AYFBEf4N2pVTJS1LDOBCwzMiZu8HymGMVFsb4ApiUBBTd+thx3KXysXr16yJ/t3+q345SbiQmIEzY+MfMmGIbxPrjv3/rWt/C1r31NZIszeSPdjD2JRDJ8GETjYv9Xv/oV7r//fnzqU5/q05JCIhkKIjs8dBKe8Dnxt81YIXpqDMviNNrvihWu+nWP/vUUMgjXGvRL52szeJrK814yOnQFu7GpdXPS/KHGUY3ZuZntgTlWsLpabzbNOcycOXP6vT/nZhTheJ7qtqkUMVL1jtPxhjtR76Wdso6KSbbL4DCNfFNlvS8H106DhWIGk3RZKZKJSg/J6MK+hRQ4dKtgxnT0qjiKYjyXGdcZLUeZwcDEr/e9730iaY+JG1dfffVY75JkGExMWU0SB7+ELBX88pe/LCZsV97zBlz5gTtgtmlBuZPuJuztPIMlBcn+6U3+s6jzaD7v5LS7Uyj8JdYpKZuPpyLEPgLpwOCnYy3gO6w1DKd1jW0uoDALPhH6/Kf5tIoCszIHQfVQT7Cwt3g5hKDaAKsyPO/4kYD7XWCZnMrEaEQxG/KgwAQ1IbhsMfSddW0x5GCSfXXG9oEZJ5zg8eLHgBrLRelZz+x2bXcMqJq6Rty4qD5w8LCY3C1cuHBQTanYO6QreAj+CANuRmSbpsFhnNa7eFFowcMm4dFsfwYVDQsTAop9LaxGXoAaL2jVBDHVFeKo0PxnMItOfiMTm45RfWSD9iEuJmk9VFqF4cDzitYEHDt5PlJoe+2113qaqulZKCQYUbB9zykR1KGF0mAaXnPBQUGD9D4kgnrvXtRkr0KmKTDHf5+b65tRUFSAHHN6iwYu9pl5RY9eBrwHa5vVHvDi343H0RX0ocZRgOvKp6cU1gcDj/XNk6rF7aLBuwMI6+NPBPAfARQ7YEkjUSANeK7GWhJwvGVVEQM+zFLlOcxMu1dffVXYVQ0lc5Wfa/zowAIshiWGDveHiw09cYP7KZFIRhcG4H74wx/ibW97m/g+PvTQQyJ5Sg8OSyTDhdf1bPMMccsUtOdkZjmDpbz+lZSU9PjlEwaXGYBlctXZs2exdetWkR3OqiTJ6GJNUa3MOMTF0leDPSHY1+2KK64Qf9Melo3rucbgOcn+RbpNKO/LCgmuRRjkH8x8rM1/PKlPYYv/2KiIGnwfrNYYDOwfwYoprq+GY5slGVs4tvKmw7UFz2GuNfi5MoGK4yvXIrrYMdbwXP3KV74iehOzT/EXvvCFJGcFycRDVmpcZFB1vOc970JDaxPe+KUPYubqRSKwUJs9CTdWXJZ0/72dGxGIxF+I7MYczM9bm1K8OOXemOR/WmlfjizT0G1IVDBDI/FiWA4F8xPu52JoLvpXGd3U4/49GKHVx0FxSe+9rCuwKFNhNUpvzlgCkU60+/chDB8MMKPAMh824+hkc+iZGVxA8ELIDGJmTLHcj56OsRfHWLj4YCYIJ0DpNj/rCOyDL8K+Kr3kmeYiyzRFWAG1B07CGWqAAhV5piLkmadDMcRXhKjCdmo7c2eiW/iN4sVvpbDhGu+ccjfhnKcFZsWEBXks6R98xYs/7MFx11bRyE9DxRT7XBRZ08u84GfHiYzifxlQE6qyTLWAubbPiTJFBgoPnCBxEcDJ/nAmRXw+Zo8wWM/9YrCeYllfDbuZ5U5bKd6PmSi0L0i3uXcszmAT6r17Et4fg79mzM69CiNBk68ROxq3o/7MeSCo4s4Nb0W+JV5GZbk3RUZO8pgZry/8+VNvUDdYKGTcf+BFuEOa4M1nXFE4Cf9Rs0xmQ8ZCQc/5ZPIBNE0GspZgNOE4zOAOF98UOdatW5f2gvqcpxUP170aJ2ysLJyBdSWDX6jKzHCJZHzC6/C3v/1tUTn1nve8B1/96lcvmsopzgf9kUZEVA+MigMWw8g2ZpWMLBQ1GETmPIaVpjxPKeZffvnlfT6G8yBeAweTrCLJDAe7DuGM56yo0KCgkWXMwuXFa2BmP8M00Ofyg4Vzes55+JPrCs7thzK/1+H5xoAuKzM4XvJ52cRaOBD0sR5hlTgDvlzj0NKVFdFD4Yx7M4IJ6yuu76dnX4mRgvvM90oxhkkxiZUW/Hd+rygu8njoDbV5nCjksI/EUGyLgpEwznudMCkKJtlzh1URLBl5+HkzkZXnAc8JflfT7X0yUu42jEExQYPrf8nFgRQ1LkJ40fjxj3+Mz93/ecxZtxSv/89346o5y3F5cXK5456OFxFU2TOgF5sxGwvyUk/83KFWNHj3IBLtL1BinYXCYTScJSpou8Fgny6WMGNhRVy/AhUdAHbFhEwY1FwKBXlxixJ3eAvUBIHEblgKUz9VCJcyatTHfbQn8LzAvfzyy2LRwYkkJzvMzOUF75prrhHVGPw8W/3n4I04YVHsKLFOhdFgxs6dO4XwwYDrQK9xwf9cUuaKRSlCkXWpyGrpDJ6O+7di6xzkmZMD9aroJ3IyKmxQTKuhXIbRgO+j0deG7qAbuWYHKmxFaX9eezpP45XWQ6L/AI+C2WDEXZWXI888+AbfFD/b/ecRQQg5phLkmAduwMwJPSe7LE/l52wy+OFz7sHC+ZNRUJADGErQ6anBkaPHxXtSwj64nR0om1SNTqdbTHYZWOXkl4/n87GUVW9SRyGszwUA79N9GK6WE2hpd6EjWISAIVc8H8tgOaFJ9zj2iDLDsdKJeHHC9bIQMvg0etKg1ZiH6SNQqcEFPAWZ7OxsTK2Zirzs+J5CnFgyS4qLKX6X+J3j75kYC759ZAsOdLckFeB8Y8E1KLUNobn8xUaoDfCfANgUM8JrWywKwDHIvmiMdg44fPiwWOgzgzBdzrpbsaPjlFhssp/G0oLqQZ1L/D7/6U9/wqc//WmR3cVS8J7KPYlEMm5gBR+DAkxOoT3cW9/61gkdBObY4wztQVBl1a4mzVqUUmSbFk7o93UpQ693Boz1PjBsdHzgwAEx11m0aFG/4j4bxa5du3ZIQXLJcNY5jegMdonKjaqsqrQEDa4ZDx06JL6n+hxdD5jydz7vwYMHRfKO/nlyO9cS/Js3rgl0UYHzHq5HCX+yIq0/uz0+D0UM2tfwNfh6zEankDGYKgs+z3B7EjT7DqMrVBezRYHDWIJJ9sXINFwTMRmRx0hPfkpMNuO6j58Nm41TrOE6jsd7uGNqi9+N7x7ditaAR/xdm12I+2pXwWaU5jMTAZ7rGzduxOLFi8U6fLRgMiPXF+wVxsSMD37wg3KMv8iQjfm3vQABAABJREFUosZFDNXxj33y43ji8Sfw+fs/j09/4lM9JY46dZ6juOCLD+xOttdikn16n88bUcMiQGdULGL+b+B/yvAuJip4caK4wYtdaVKTcBXbUlhS5UNBfCPSiOqGN7wPEVHVYYTNMAtmQ+oeIZKxhZNRnqPz5s0Tf3MyyIwVZkpxQeHLuYDuUHNMU3oHanNWwaiYhNcoH798+fI+L0p8vib/C9FKi16shhIUWi7DGddLCCPeOs1qyMWUrPFjc8L38GrbQRxzaV7DZFZOFVYVxVcxpSKiqvjpqacQVnsrq5gFNS+3EleWLhiR/dWrIDiZ1RcZNTU1cRMXNRLEtldfRF5eIdweFaFwGCtXrIDSthvoos1YCJ0uP0rn3wJkVQwYXGEVhb7oiJssdx8D3KeR7bChpDAXBXkOmCZfC9jKMFa0+k6j2X+sV9RQDKhxrBZCcjq4Ql7Ue1qE3U9lVilsxr6Ftccff1wcd2ZCJfrwcgFGy0JmVg0109YbDuKYk/1FVMzMLkYWrcAYEO9uwTeOaJaCiWuXL83bgKqsS8MzN6I6oYpKOHp6x1RHhdoBz5YksVWDB0wBHFcAxrHNgGbAkgEefre4gOVPLtK58Of3mmIZxeXhZDXGVph++MMfFiLcd77zHdx5550ymCiRjGM4N3n44YfxyU9+UgStfvSjH03YjMdApBXOUHKfuVzTUphlQtSEhbaia9as6bmWsALxueeeE/3PuMboq+I31haHwWnJ+BlzuO47c+aMqComrAzg+BMrPHD9QYsnJj1RcGCSBIWG2IQf9ogbSETgeoZ9WPQ5TqpgPNeffC7OtWP7t4wFtFtu8O2BV/Rn09azk+3sp5peAh6Pb723Bc6QF4WWHJTb+k4GpUjI9Rf7fMyfH78e5XyRAiLhNSHTx+QbhzfhlLsDkegcms9+VWkN7qoaeF0sGR/wXHvhhRfiKnto/8QxmWsM/k77QH6Hh3v+UHj73ve+JypLb731VpGIMRQHAsn4R4oalwDMiGfAgF9seuNee+21Pf/GbPh673G0+uu1RuHWSkyyzxhwEAlG/Kjz7oY33CX+LjBXosI2Z8Qu6Cpe5tCUsNUOBWtT318EchUZGBlB/OF2BNUuGGCB3chS/fQsgTjh5IRIXzxs2LAh7t8ZbD1TfxKB/CbMmBNfBTTFPg9F0X4vzNhn74P169f3+Tk7gyfgCp+K21ZoXgKrsRhn3C8l9YkZb6JGk68dT114NWn7jeWrUWrrvxtLIBLCz049nbR9hqMcax21QgzgxIGTUk4kGMRksJuTDWZIc+HHySkXBbzxeBMuBJhhwfGEVRj6dmKxmFExuRAlxeUwGvrPUuJzcsIiRCmKNhf4HY+BQmn1G4E0y8/joGJw7pGY6q8oWZVASd/2A6OBL+xCV6BR2E7lWypgNqQXFG72deBfjdsQilqAZRmtmJ1bjQNddQiJ7PhyrC2eDVO0WTrFQZaFsx+G/v3gZ86FyHCbfrf5PfjW0c3oCGpVcXlmKz45cy3KbNn4d+MJPFJ3sEe40X+ysff3Fl8vqoXGElfIh/aACzkmOwosma8a4fcnpB5HBL1CpEmZDaMS7ZHh3Q0Eeb1NwFimneuW6WMuaMS+F93Wgd93LvL1RQezEjmW8ycbrbLHzGCv/wwy/b//9/9ECfh9990nfG0plkgkkokBv/8MFvzf//0f3v/+9+NLX/qSCEZMJHzh83CHDyVtzzYugNWYntWpZHzA6xWzw2mnyfkObRRjA2fc/uKLL4q5L5Oi+rveMDDLf+f1TTK4eQOFB8JgPxNnOH/YtGmTOJ6cP7ACgvMFrh/4OXEuoDcaZiU1LYYbGxvFHJb37+lDWVAgrJwSEzQT4dqS50J/jbUvRrT5p09ETUyKPe05mQgyN+/GKXdjz7Yl+bVYWpi6Ep6fJ5MQKfzpAWJ+fkxQ4b9RSBqp68AHdz6BYEyyHql25ONzc9aNyOtJRg5+R/WKKo7N/Mm/WTFFAZNxAgqWFM6G0u/vmWeewUc+8hEhTDLxgtcDycWLFDUuEXix+clPfiICCGws/t3vfrdPj8d0OO1+DZ5wR1y2aZl1JoqtmW/gGlF9ULELCjwJmb/JfTfGC6GIH63+w/BHusXEotg6C9YRDlRxcUnPQk7geeOknQN5JoSmUMQJf+QsImoQZkMRgpEgXGHaMWmYlVwUWZZDYTP4fmD2DC9WzB7vb7+e3fhvKGWdaKxrxoJls3vuW2GbhVJb7wKDE2G+Z2acp4IXSE/4HLzhJrFvDuNU2IzF4t/YT6Mj0PseSIl1HnLN4ycz66SrHpta9yVtv7x4EaZnD7yffzr3sgjgMpteZ2qnHVPNRWLhwIk/s6N5rugCE48ZhSV+TgxgMgDOm56FxCZg7G3CCSsD4/qiMKy64A7tFtnpxKxMht2YptDZuhvoPJScvV55E2AdgnUcJ7xC1Eh4PvskoHQ9JiJ/rXsZnUFm/2uE2VM64e3Nz63E5fmzhNjHDEO9wTknjsw85GfFxcZwx4Qfn9iGA13NPZlStDebmVOEj89cg62t9fjpqZ29dQdRUeO+2uVYUjAJY8mh7jo829T7fZpiL8TtkzPrnx1R2xBUk7N+LcpaKGwA7tkJhBqSH5hzEzDA+Dke4XhB/3I2teQ4kQ4MUvz2t7/Ff/7nfwqBlAFR2ShSIpm4MCmCwQNa93zjG9/A3XffPS4agqY7v+0KJSeP5JvXiP4aEm2cZ0IEx27O/Tgf5LxxuJY5ehKaL3IOwUgHDAqTpKYN+bg//fTTIpGjvwpUBsooyLOvGhuD93fdYoNb2jAOJaB2KcK5JhMpeT3nuUEbMAa52SuLx5HVnTz2FD14TrFHHqsoKFYweMlttDVigJNzWCZLSBuwkeesuwnPNO1I2v6mKeuRb8lO2WeQFl96FQv/5jWAPQc5Nowk/7XvObQFPDFm5AouKyjHf0xfPqKvKxkbGNuiPXlsQvZAMKnvE5/4hOifwYbgH/jABzJyrZKMb+QnfInALzMzIWnr8NnPfhZz5swRf//Xf/1XUmOngdACxVp5YyyuUFvGRQ0KGt4wFxshWI00lNLhhHUWxiOcoDd4dyCo0lJLy5o4730NVVlrYRoge304MKOFk3XCjFr9pjf+1bdzEh9bijsQ4YgLzhAbZWuZEaFwK0Ixz0mCajc84fNwmPrvc8EJKgOuzPDnZIiTWwbJKUowo4aBdE6CXR1eTF9YIiazp4/VoWaW9rzZpvjqBC6uGKilHRXfVyIMVjpMU8UtkQIz+2IY4Aw2iPvlmavGlaBB8syps8jy+9ieyOsqluKfDa+hM6hVU+S3qphqL+rxFKbPKT+TWHgs+rOR4PG+6qqrkseE0B6o6O3PE1TPwxjJgdWYRjNxkz21HY9xiN8XVg3ZJwPe8/HPy0qNCYoz1DuJJ8EUh+uw8zyWWqtEaT6rbij68XcuNCkkZioLng36dEGD8PcGr1PYUfkjIVRl5eKcp1uIGhEVuKa0eswFDWfQGydokHpvO15qPoAryzJnx6ZZHyajwg0FdsBckSBqKACF1gkmaPCc4jjOaw6DD+l647LknL62fNxPf/pT3HbbbbKiUiKZ4DCIyazIf/zjH/j4xz8usiJp85A4VxiPmAw5cBhnwx0+EuNFP1cKGgkwmEmxgPM9CgJ6g2Ud/s7gP+eXgwlEu0KHEFCjGeIqq4ybkWem1SznhYODlcdcD3Bey/kPg+lca1A85343NzeLCgDuK62pKMj3l73LIC0rDCiUSGGjf3g+MPi4YsWKnsA2RYxEuC3Vdn39MZheXpLMwJ6NKbeHPEmiBm1J9ep+xhQ4F+Rakt+j0bDfeuvUBfjR8e1RY2oVVqMRb5ic3DNWMrFhpYYupF9+eXoOCxzz/+d//ke40jDeyWsBXScklwayUuMShVm7DCwwq4qWD1QxByrn1OHE8Ijz+Z5m4Tq5pnJUZmW2uak/fBQhlY2ver0Tid14JZRh9vEYKbzhDjR4X0vaXmyZjTxL1ci8ptcrMoqY7cILASfyDGbyYsBFCIUrXaXmv9FPlllWA7F7924EFYoKVhiNhrhzID5LnIvAacg1p9fYlQEt7p+etUNVnZMhLj7YdIzvwx3uwI5TL6C5qQWzF8zAFPtcFFon91kBwvfH/g0XG3s7j2NP5/Gevy/Ln4mF+Vrzw3Rwud3Yf+QgfB4vaqunJ4kYibhDLrT6W2BQDCizlcOShj2SqgbRHXopabtZKUeWKY2AcSQE1D8FBDp7eqigYAFQNIzxJBIE2rYB3gYtYJw3F8iZndzoYYLw6PlNaPV391Td+MJJ5lqi18aHpt8gvktcaPCW7rg+GP7v+Kuid0ZspYbdaEFbQKvSIQtySzA9uxCVWblYWkB7urE97mfdLXi0geJsPDkmK95dfU3GXiesNiOkJldXmZXVMOjZp4HTgP8YoIYAUynARo7KEGzWxgAGszje8vOkTR0Xs+lAK4/PfOYz2LJlCz73uc+JpAoZJJJILj44H2X11de//nURjPjmN7/Z0zttPBNRA4ioXhgUu6gYkMSP30xAoljB+TsrezmH5xjONYZeycusfM7f0/m8+RybNr+EmgVeFBTGVlYosBumIcuU/jw3Fu4X+/VxP5gsRZscrll4Y4CLdjlca/B+FCxoYdtfVRHvx/4cV1xxhawaSIDHlAl1TEbTrWn7a6wtGZ+wV9+TF5Lnx3dWXolcc0xPuNgYgN+fMTeIwVLv6cLeziaYDAYsL5yMQsvgBVDJ+INrVgoRFCc4fnONkU6FBa87DzzwgKjKoKjOhIq+HDwkFy9S1LiE4UXpySefFIEGZlhQ3bzjjjvSukC1Bc7igq83q4n/VTtWwp5hiyV/+ABC6oWkLO4sIzMCht+gNBPw2LGUltmqnEDHihrdnS4YTUZksVmxdc6IiRrk3LlzInOWGdn8bPXJhu5LyIk5J5v899hAZyQSREtbHRobWhHwa1U9eskv/TFnLrDi0JEdcedFOBxBYXkRJleW9GzPNy+EPeo/zGwo7g8rgrjYobjC+/V1bum+qgyY8b56dUlBQT6mTa+CyWAVQfb+OHz4sHhfbBp3sdERcMIZdCPH7ECBJSetiQEFS37mFK94TNIpCW7zt2J3546ewLlZsWBl0WrYjcmT2sTqpO7QCwnfUwUWQyXsxjQrqihsdJ8Ewl7AVgw4+hdfLjV4DjzR8Cp8Ea0PjNVgRUewtzKGLMqbhg2lWjDBEwrgmKtVSES1opF35oI0zT4Xvnl0E5whbV8sihHdMRmbOv9v7jpMz+6/98to0R5w4vdnX05RsZWFd067MsOexvsRQXPPNiOqYTJM3HGJWa0UMrjQoIjBhUa6mbgMLtH28o9//CPuvfde3H///WnbVEkkkokL550MMvz85z/H29/+dnz5y1+WDTqHAJN+OC9nn4HYcZfzPFZkckweaWsNziXZNJmBJgoDehIShQnuA5OleO3jOojWprEw+MnAN+f3elY3E5p4PensboZX3Q+n092zPuD0P8s8CUsX3tRn8hUrvvkcuo0y1w19XZP4mkye0sUYHQoZtFDtz6pKh4/j+x9IALlU4HqN1r88FvzM9d58kokJv7uvth3CgW6tFwpZUzQP8/JkPxnJyMNrA6syCF030u3HwvP2r3/9q3CdocDOBIobb7xxzJPoJGODFDUkImDxm9/8RgQeOEGkwplOqVd3sAnOUDMUGFFoqYTNmPmGXMFIPQKRw/EnLeywG9eOm0GLAzHLslnSzOC8zW5D2N6EuZdNg9/nx2tbDqCktAhzK6+AUbGKQZgLBGb9pIILBIoC6SrUiQILs5JoL8XHJx4j/jv7JeiiQVh1whc+ioLCLEyaXIycrBoYQtNFIIoLAV5YcgtCcIf3xzwLTfJtOFVXh6bGFrHFZiiFw6RZj3Ghw8UK/TY56dWby/L5+LpcQHDfmM3HhQ5FF4pBXLBxsTMc/1RmAbOkfCLAhsVhNYJcU/rN3AZzHCheDbZJ3qbWl+ClqNCDgjJrORbmLx7wsf7wOfgiR3sqLRSYkW1aBYMivYgzhT8cRJO/HQYYUG4rxEl3E3Z2nEQwEsbMnAqsLKwV4l+L34UfndgMV1R0YDXCh2esRbE1c163fO5D3c0iANEd9OMP5w4k3efemiVYUzy6ll9Nvg4hYGSbbJhi7xVdyV/rtuC8j72germ+bCFm52Z2HznORUArQB8UZMOgjA9hZzAethSleT3TG/dxzGZAK10Y5Pr2t78t+nfddNNNImniYhScJRJJ/zDZhkEHJlF98pOfxKc+9alLroHvcKCtF8fkCxcuiDUB+xBQOOBc97HHHhNzbK7dOPfmeM0APoPvqY4x//3gwYNCXErXMjCVnz4tSlPZWTLBi/sZG+BmMhUTpfh6vJYwgEWBgYIFBRKPsgMReOOSYsyhuTh1rEVk4MbuOx/P1+V75mNZIcBt+hqDUKjgc3MdQkGIj+P6giIIj91Q59t8XxQ3LsaK8MHAih0mTfH8Gy/rcElmaPV3wRnyosCcnWQ7JZFkAgrQHLcZ62L8URe6Z8yYMaj4D6vs6DjDMZ4JE/fcc4/sm3GJI0UNSQ8MYHznO98Rogb9Q7/4xS8Km6KxhJPRQOQYQuo58bcCG2zGy2BQRv5iS99ITmKZ4c5APCez3MaFAgdgq8WCcKgJ3V1N6OgMwmavgM1uF5Przu5WtPqOoWRKNpSIGZ4LDnS3+3psYRjc4e9c7CVmt7DaYNq0aT2qNYPTnKBTCOGCJp1JpJ5FwwsEjyEn82waHPtYZte7wsxcDsY91maYC7Mh3ubJFz4Dr2gMHoFRyUe2aaFYfoQibiHUKAo/pzqoagjhYDbUYFGf2U8UMbhvPK5caKSryA8EFzm0y1q1atWgH+sJ+bC1bS9aA52wGixYWjAXlVkj48MYioTxeONuHHOyAgkotebiTZUrRBA2U3DCQL/T5cuXIzs3W4gnFoN5wHPnuaan4pqKkzxTPlYUrU7rdYMR9ltpgwITLIYpMIyTaqpLjZ+e3IqTrrY4e6janGK8v6b3u7G38wIeazgMdyiA2bkluKtyAezGodkGnHC14yuHXkna/t9z16F6FCs19nScwGsdFNY0qh0VuLr0spgMUBUvtRzAaXczzArL1msxO/fSrgjiMeF1joEwit5ccDBoxGuQbikyGChm0M+WcwlakHA+MZQxWSKRXFzQb59BiEOHDglx48Mf/nBaWfIXI1wH6AF3HgMG/hns599cX9AyVqx/AgEhADD4w/GY9+MYzcQjVkJzTcDg/enTp8UagXDeTwsOJjDpogDHcVFFGAqJoD9fi2M+s+w51hP+WzrWQXwdfoa8XuiCwty5c4VgMKRjoXrgDO0TSVasa3QYZ8Jm7L0uc799kTqE1E4oqhne7jwUFZSnrAzgcWVVId83jyHFn0w0muY+cH1Bcf9SF+S41qIwxp+8tvsjAZgNJhgnWF8wiUQy8jB2xusVYz+8dhHajDMuNZQKQ1oB/vd//7eonGOCBOcSI92cXjIxkKKGJAmqp9/73veEJ+7KlSvF4DEkcYMpvCEvYLQAhuGVRqtqACrCUMAA+siVuHIizEwkTvgZbOeEnwMyAzWcsHPxwSyV9rY2+D2HYDS0ISfbjoKCbPiDpXD7tfvHZhf1BSfGbK7YX9CIk0baKvE5YyfwLLPj4zlp17OfdHjRYBUELxZcoPBxvKAwg4riCDOsOMmnd7A7vCnhFRWYlcmwGZObbmnVHWrS8Q+rLrhC26JZVloQ1Wqogc3Yf1ZuRA3BFTqLkOqBWclGtmnqkD5bfh68uPHzmj9/vshWGwwR2rBd2ISuoCsuoH992RoUWzMjuMTycssRbG070fM3rdumZhXhzqrMBP64qONiVlQNzc7BGW+92F5gzsOa4mWwGfs+PtvatqA71B2XMVdpn4rZuXMzsm+S0eHLh55FV7C3vwUpMNtx/1ytd8QJVxu+e2xzz6dM0WNmTjHum7FqyJl3j5w7iH9d6D2vb500E7dPmTOqjQ4frkvu63JN6RJUZ1eM2n5MBPRsXl4TOF7QOoLVfen0WUpHzGCA60tf+pJoEiwzOSUSSezY8/zzz4t1hS5ufOQjHxmauBEOan2JjLYJ0SuL753rKyYzESYacS7OKgyuGShm6JaxTKDSk5xo18e5POd0vHF+r1u66lXXsejbub4Y6LiyWppVeRQquMahsME1A9cXvHF9kXhd4LqEr881Bm98LHtucDurGIbamJWJVmJGnPBZuoIH4Vcb9HcnkmbyzatHLWlm586d4rgwCUu3u7qUoZMAk+5yy/Nw3tEkRA3OIRfkzcU0x8hZLEskkomBHkuj4M7rB0UMxmiGY1HHygyuKxjv+ehHP4qPf/zjaff0k1waSFFD0icM7FPc+MEPfoAVK1aIRUg6tlQCTytw5jkgRP9SBahYAZSMbaNAVgdwMsYsVJa5ceGgN6jWJ9F6s+kBqwcibUAouRk4TKsAQ+YD4Ylw8cCFEBc+XCTpZdk6XJwwWKU3C9fhfSlqUKxhVYUr/GLSc1sMNbAOwv/dEzqEoHo+YauCXBMDWikyqVQvQpFudAaPC0FDtyuyGopQZFkyqCAYBY3t27cL0W2wYoZOd9CFxxvjvfa5bJqTW43L8mcj0/zhzOYkCxw2eP7UrJuS7usKeXHB1waTYsRke4nIhuov25qLU37erO45H2nCge6jce+p2FqIdSUr+20SvrNjO1T4YVYoTtkxP28VrAwaSCZ0pQZFi/dFKzUeOrcPm1rP9vy7zjcXXI8c89ADBaddHbjgc6Pcno1qx8iPgwM1OuRIsrxwNhblS9sjXguYzcvrOmGWFDN0h5vFymsQxQzaTLEyg/MEKWZIJJL+4JzlhRdeEBXhFDc+8YlP4L777ktP3GAgv2k70BHt62ctACqvBsxjl63JxCO+D867GfimIMD1BYM6sdmoDMLoYsZ4hXNIiidcY+gVfLHVjpxrc/95TWGwX4f/xusLPc0zti+qDx3B5CpQu3E6sowjawPF90MBiseCDbAvZXgO8FjwnKANZU3tdDzXvBFBNb7S//KilSiyykCjRHKpwfgSE6V4XdD7mzLxdri88sorQsxgrEeKGZL+GNnOYpIJDSffbPJHNZTiBn2xKW58/vOfx4YNG/oOPrPh75lngJDexFYFGrcBtnwgJ97WaLQnZVxcMCOV1jx8f8uWLRuaB5/q7Wf7yAfzuKjgjRlcQ/UpVxQTrIZa+CPHY/og2GFRBptpk9wgWMv0p5gSL2p4w/VwhbQeKQaFheYUObTAqj/ShkCkE1ZjQb9iDgNzehYbK1bYmySdkvm+6KsBOfsWDIWwGkZHoFuICAWW3KTnZ8Nm7Wj3ksr254KvHc81bRfWUSTHlIWbKlb3VFpwcUGbKf7UPYNpN6VnQjS3HIx7PlahtPo1e4O+vrsOUzZm5VSh2U+rMeLHWc9OzMheCYMiLxcThTdOWYAfn9gCZ3QMzjZZcNvkBT3/3tfQPdyselpNjabdVCx5KQJa/I7lmy9dX2B+15mJy3GC1zleK2g9kgkYvPrxj38s5gYUM9is78orr5SVGRKJZEB4rbn66quFAEpxg2IohVGuNz70oQ/1n4FJMUMXNIi/E6h/Eai+ecyOPOddnItRKKbAQTGA2amsWptoxFZqsIpvLImkXF+wEiXV9sxUMOrJQbxRgKLl1KUGxTkmSun9FbneZDKgbr3VHuhIEjS45mkNtEtRQyK5RGBcjW4irB5kPIpJs8OJx+gIq+CXXsLXvvY1IWZ87GMfwyOPPCIrMyT9IqNUkkGJG9///vfxxje+UVQz0MvujjvuSBYF/N1AKN76RATNXY39ixptx4G67UA4AORNAaatA0yZKy/m4oID8J49e0RwnIGYIQkapK+eHqPQ6yOTWAzTYEAOwmoHFMUMszJJ/BwMJkMRguGmmC1sHZ8jRJNE31xd0NAxKoqwf1L7XcBoATRmwdE3kYtGltUPhq5gK855jiIYCSDHVIBpDvYNsYh/cxjtqLAV44KvVewHw7oUImocgxfgPCEvXmzZBpeoUGJANQcbSlbCSgu2KGuLZ+K0u0WIOSaFIgewMK9MNOh2h7yo9zZCjUSwq/44Wtva4Ol2a5mJUNDhaMSCvOnigs/FBf2C+/L3NRuSP0dWfPQXuA5FAjGChoY/4kJboB4lVs13WTL+KbFm4zOzNuC4q1Wcz7U5JXHC2arCSrzScrZHXOPP+bllQvwYDwQiQTT7umAyGFFqzetTeIwlx5yFtUXzsLmtV8ybnVOFqqxSXGowCMGFBi2h2KCVvteZsoGiqEwh41e/+pUQUaWYIZFIMiFuvPjii2Kt8b//+794z3veI9YcDCon4db6kfWiAr42IBIGDGNTAcEEGyZ71dfXi34DXG9kIkv1UseILBhgRQR6khxRYTYUjojFFCsYWZUxmLVhKBLCUedhtPhbYFJMmJFTi3Lb+La85FqYvVq4tmJAMnF+QGGLiVJLlixJWVVkTrFO5Eou1bpDIpFcXHDcYHIwxU5e64ZjXRsLx2CuKb797W8LdxX23ZJihiRdpP2UZNDQ8ufXv/61yKpicJULDy5AaOskCLiAI48knmpA+VKgdGHqJ+2sA44+EX9/Chuzb+4zaPPUU0+JoAonobETMg6KzLBh+Vss3Ff6vuqKMgPCw1KU2Tg7rDXzFhhnAcYUC7CLHB5Xf+Qk/JHT4m+KJA7TYhiUeMsif7gF3aE9SY/d/Mp++PwB5ObmoMh2GQzobWitVxXwtnTp0iEF5tyhLhzofjWmNkJBtikPc3NW9jwfm3fv7TqKZl8H7EYrFubPRKFl8B7PG5u3o8nf1tObg5lLVVkVWFUUX7re5ndhT+dJdIcae/bLCAM8Xg9O7T+pLahyspFdkAN7jqOn+qLcVoTry/u2j4qlM9CFF5u39uwLfy7On4fp2X17AvvCThx3bUnYqqDYUoUKe+atuCRjx+HuFjzeeERrFJ5Tgtsnz4XVOPw8hxOuFrzUchy+cBCzc8pwZelMYa+WLm3+bjzWsA1eitsAymz5uGXSSljS7MvUGXChPeBEtsmOUlYHXkLwuqf30+FCY0AbxUHw2muviYXGo48+ittvv1144bPSUSKRSDLJjh07RG+ev//973jDG94gEqg41++hYTPQdTK+3pVNime9LWO9NZj8xHkXK9soWOhwTso1BIM4ifNRjrtshs7qBq4xuDaRDJ+Q6oIzuAcRsBJeQZZxJuzG9CrKaTP87LPP9jRDTwU/R1ryDrUqZXfHLjT7YxO7gKUFy1BsHV+fP89dNlGnTQwTxHh+0kpqKAFJPtfOjj0477sg1jnEbrRhQ8laKWxIJBcpHDuYMMVeS7NmzRpWj4zEuOIvf/lLkTDF8Zh2lO9+97tlA3DJoJCihmTIUDzgouNb3/qW8Nr8wAc+IBr+VVRUAHWbgI5j0RxgpnXYgdo3AKY+vPlPvQi00P8/oend0nf3Wa3BjNHNmzcL/z5m7uvZJPzJgZbVGAwOc4DkvlIIYdaX2L/hEHQB3gvaIiorH1CCgJI14ao0Mo3W5C/cZ6VHKOJER5DiQjz+UBjHjzTAFKiE1ZgnJsuccLPke6h9MmJhhUaj70zSubUobx1sxixkkn+cf1ZkmceSY3Lgpor1Sffd1Lpd2EGFwiFcONMIV6cTJosZ0+ZUw2gywhmMbVuuCSS0hlpZlH5vmq6gE6dd54QlVoW9DJPsZQM2bz/c/RIiCMdtr7QvRL5lfGeeScaeM+42PHh6S9x5u6ygCrdNXpT2czx0biM6Au4YYRBYmF+Ny4snno3HaMLr4JYtW0SWaaYyhCmS/Pvf/xbXeGayvu997xOetv0FiCQSiSQT0J+b1eEPPvigEFApbtAG1xDsBk7/C1D1eYoKlC4DBjE3GgiuH+jlzbUNx7tYOyyKHLSY0n9nU1Q9kYr2G0OuAJf0CdcFKkKiDjxVr76BAnHM+hXPoapifcgKxmGvBaN2s881PZO0fZJtEhbkpz/vGUko7LCxN9fAPJcz8b4Jj+Up91l0BbuFLe6M7GpYohXwEonk4oK2fOxLO9QE01Q0NjaKnnwPPPCAiPl8+tOfxm233SavoZIhIUUNSUYmNpz8M4vzmWeewV133YWPfPjDWFqdDXhaNCGjZD5g6icb5NRLQMuRZFFj2XuBhH4DiT0BOGFjGRyrLmiLxcyTngz8UEjcPxMefwJvE9DwQu9iyuQAptzQ/3sb56hqGBGchao6RU8NoAxB9ShU8G8rzIY5MCpFGXktV/AYvJGzPX9blBI4TLNgVKxioUK7FC4Q+bnt27dPZE/RJmwkRI3FeetgTVPU0DOcuH9cvOrVIyzP5uJIP9+evrAJnVxwxwgRpdYibChd0bONi2EulF+s3wRf2A+D0YCyqgrkFMTbSIUjKjzh3r1mb4AbylfDmqL/RiZxhdpw1r27R9goNFdikn2O9MqXDMhf6ndjb+f5HkGC8Jvx33NfB1MaGT2+kB+/PPNc0vbJ9iK8YbLW5HxCQ3sU+r/TD97TBLA/TsliIHt4vaZoI0F7PmYzZ6IMnFlTv//978Vig41u2cD33nvvFddWiUQiGU04x//Zz36GH/zgB2IMYvLUO958C7KD57Uefhw/c/uuQB0MnOsRfU7H+R7XF9yHsrIy0QQ8dj1B8YNiRqaCPJKRg0F9ChwUnRicY+U+K4GGI0KlEjU4768QokYfzgQp4PnFoCFtocRzKIoQzNjHIitrcMlX7KHFYKF+LjPJgc8jxTaJRDJYOI5s27ZNVHYNtYdrIkyS4vriz3/+M6677jqRsMD+qPI6KhkOUtSQZBSWpf3oRz/Cb3/7WyxYsED44b3pTW9KsoJKwnkBOPRofOC5aAYw49qku9L+goMss030AZA2G2wAziZvnBwS/htLwBl07vP1uSBq3Am4mwA2mq1YqjU074szjwIhV8wGBcitAUpXYyLC4xhWd0NFe8w21luI33q2KSiExTADBmX4GcCBSBtCEReMih0WQ6912JNPPikaKzIox4k9K25Y+j/ci5wn5MSBbmaP976fHFMh5uQsT/u5eV5zYcFzSa8I4mKXTfToocx9ZoZeW6ATL7Vs62nuTX/dq0tXI9/SK1iw+dWKFSuw33sEjb7muP1KZIp9MkqtpaIXRrmtEEaDUVTEnPeeQXewAyaDGVPsNcgyZbZKKKwG4Qu7YVIsaQs/EsnDdbuwv4uiRjz/PfcmmAfwOt/beQz7uk70UaE0GVeXjY+sxyHjawfqnwcC7oR/UIBpNwBZ/VdR9QcDGLt37xbBtmuvTb5mpsvRo0fxk5/8BL/5zW9E1hSDh0xSyETFnEQikQwHCggMgjAYwszze+65RzQV5zwxU7A58uOPPy6CwKwY5hyZwWCuNzj343yPSTd60JmJN6zikMGY8Q0/1127dmHOnDmiqpEWYhSoMmHRuLdzDy74aCXby/KCFSi0FqUttmzatAkrV66MszpjAhSTqWhrxuSudPb1/PnzQriZP39+xqxhJBLJpQtjHRQhmDh18803C+upoV6/2S+DMULa5N59990iRsgxWSLJBFLUkIwIXV1dQtjg4MXs9ve///0i05MLgL4fVA+c3wGE/EB+JTBlBZDCR10PJvPG3xlQzle70FV/FJ5AGErRDNQuWCImgJzcMfuFk1h9gsefnDhm2e3IatoKa7ANZpMBJqMJVvrkzrkDsKQIEjPaf/JPydUktlJgynWYiETULoTV1+K2cRHHkHxyqF2B1bASBiV1Y+rhwoxgXugohmXSC550B9tR5z0mGoXnmgpQlTVbCALpwOOxdetWrFmzps/7MOvr5MmTWL16NZxBNxp89NdVUGkvR1ZCFQ8nB/SiNNpNeKVlG9xhj1jUKD4VprARrd4OWO1WTC+txvKCRTAlfAeOO/ej2X8++pcCAwxYXLAWdik+ZJSuoBc7O87CHw5henYJZuYMPeh8qXCo+wL+eK53PKFkOCunDO+Y2luplIpzngvY2LJL/B6KVijpOIxW3FG5VvTImLDw2nHqb0DQwwtYwj8qQP4MYNLalA9lAO3s2bNifOQ4Ef+0vT2HmI1JIWKwwTU+Jy2meK3euHGjSELgQoPCqwzUSSSS8Zo5yjGLQRI26OaYdeONN6ZsajyUSjVaX/Enx0BmqPI1WRHHn7m5uUJI4bqCIgfXGfo4TCguMwmGN65P+DdvTK6S2fJjB88ZJrpRzMjk58BqjWPOo2iNNgqfnl2LUq4L04RV6bRGzslJvbbiOcdeLby+c//7g/MF3vfyyy+PezwTxSiS6FbMrDKXoodEItGrxOiSoVd3xY4dvK7x2sUxaijWtrxGstLy5z//uYjt8Fr9zne+M2M2uRKJjhQ1JCMKRYfnnntOLD6efvpp3HrrrSKzat26dRkLmHhObsHZ7U/B6fFHA/IKGrLmYNX6a1OKKNwnMcFrb4B7718RCIUQDIURCkXgC4SA4jlQC2vFhI+l7pz89TRBP/tPIOiMazqNvFqgpP+g3XgloraJSo3+RA1e43rfrRU243IYlJEJMPJz4WKSF1kuHKngZ3LizWwt3igqpGPTwmPB7C42lWclUH80NTWJRe9Adlk8/5hVzYUFF0PdQW3hPKmgAlk2OyKGCE6fOoOVy1aIYxALRZnt7S8kPKOCyfZpmOaYhUuVREu64dIZ8OCnp14WggaJQMUN5fOwuqgmY69xsbKj4xxeaD4KfziMWTmluGXSAtgGsEzb2XEYh7vP9FQthVUVoQiwKL8WC/KmwWac4D7NIQ9w8q/aYBpRU4ga04FJvUEIBstoecIMTQbpdE93joWZOs8ZiPv1r38tKjO4mPmP//gP0TODFisSiUQyEeC86xe/+IXw5GZF2Qc/+EG8613vGnI2aaq5BSvheNNFZQrMHIevueaapPGY92fgmAFkjt+sCOB4zm0cZ/m7DpOrOK/kLRNijGTgz5IJSAyy8XNhAlWmA2tscs81I9ee6VyrmfRHUSNWhOjP5pnJVQMJMnoFeSw811h5xMfyHOT1XyYuSCSXJhwH2ZeW4wqFBq4xKFxkKt7C53355Zfx4x//GI899hiuv/56IWbwminFVMlIIUUNyajBAZQBlF/96lci84kBFJafDZR50i8MEm37eUzDQKIAJbOAGVf1/1hPK3D0HwkbFaD8MmFDxeAzg+tczDidTlF+Pq08B2h4HogEtLtb8oHJ12re6BMQVQ0ipG5m1C3BfkpNEjR02HfDblwNhY3SRxAKBDxnYntYUIhgoJ/nTH8+s8ze40JRL+XWMw34eXIiT6GCzeX7OvdYZsksPcLsBJ6vfV24uSjhAoLnCLPx2ERruHDxTDGQmVt6hYg75MTBrt0IqlqDylh7nnJbJWqyR7eRcrO/FQe7jsAfCaDIUoBF+fNGvUmgK+TD0xd2o9HXAavBhLXFczA3t3LYz/uvxv3Y0X5WiBk6RsWAz8+5UfzMBHWeTjR4u1FgsaM2u/iSzoo/2HUSuzvZxyeeO6ZcDXvM2OoOdaM9cEH8XmSpQJZpZKrGMg5tDo8/pEnF4cRKDQBV1yFsLxMVGQzSMcBFQbevzM2hwvGK1RjMmPr73/+OVatWiYUGkw0y1ndKIpFIRhkGamlNywQqZuTffvvtokJ8/fr14/baysA6LXO5xuA8lz2RZCXH6MA5NhMH9Hk+zxFeAxn4ZwIBE9r6EprYE2/v3r0i2UmHnxtFKwoafByv5X0JFRS4KFDolTz9WUXxvjw/mIzFpC8mTcU2rR8q7LvBig4GGWWvLInk4oexCo5dtIFi7IP9MTItMDBuQ5cWJhrw2vbud79bJBowjiKRjDRS1JCMOpyY/eMf/xCBFdr63HbbbULgYAn5oAdYZlJv/3ny9sIaYNYN/T5UDYdw+tkfI8cSEfZTBkWB2xuAq/wKGBzFIijOCg198ks4+UTYB/haAQb17aXazwmMZkG1j46HDN3CgFoE1ONC6EglahCbcRmMyug2jeWij4tAXpiZZcQsOB0G//h56dnMvB/7YHCRoS9o+TsXG1x86Nl0a9emtnzhIoLlmIQLHT6GN54LfO5YKLKw10ZPNc8A8LW57+3t7eJ9xGbu6XCfmWk4adIkIcyE1RC2tb2MoBqAfrbFrtPn5i5FgSW18DISdAW7sbGlt08JhZV8cy6uKF49agEEHsc/172CtoArri/JrZNWoCpreMfiL3U7cbC7Ienc/+zsG2DPQKP2F5pP4F+Nh3v+XpI/GW+tumzcBl9GmkAkiH81bII7zO+WIj7PubnVWFrQ67XaFWzDUeeO2Bo5zMlZgRzzBGle3X4QaNmpDajCgkoBzNloMU7D0QavEF1pi8FxLNPnAbOy9IUGxx160b/3ve8Vwq5EIpFcTHC+/uCDD4reQJxHcaxjAtVA1baZhmMt53oMlHPuyfkjk184h2R2LANLnFfyfnv27BHzUdm/aOzgXJwiB5Of+JnolTn8jPhZ8fyhCMG5J9cHXCcQrjl4Xz5eT6Tiv3Nt25cwwooOPoaP1dcYvC8rfGJtJrmNCXWsoEy3modrG74Hri+4XkoFzzNWk/D7canOOyWSS4HXXntNjCmMk7BXVKavMYzNsFcoY3pMLKANNxMKOP7F9giSSEYaKWpIxhQ2JtUXH5zk69Ubg7LA2PdXwN0S3wVi2hVAxYKBy+OeexIXDryIoKcDYZiQX70Uc5atF4FqTmp1T11eCC7mbBbNR5ETaSPCagOC6qHo9r5EjeUwKpntezGcfefnxMAdP7NET0jCxQA/U36GvPEz5YV4oKw4XfzghIA/uQDhooOLUv4bb3qPFt644NGfk5lVtNLSq0x40/dNzwbjgqLPJvYxdAbasbdre+/7if40KAbUZM8RlRqjyaHuozjhOp3U5Pzq0nXINjlGZR+cQS9+czbeioviyvy8KmwomT+s597Wdhr/vnAg7nkLLVn4yIwrh70AbPW78T9HEi3EgHumLcOCvApcqvjDQRxznYUvHECxNR/TsirijvX+rs3whGn910u2KR/zcldhwuA6D7gplqpAfi1gLRENbzm2ZLpZHse3F154QQgZXGgwa5QLjTe84Q0ycCaRSC56mJHKBCqOgWzEzLGPa4yrrrpqVCwwaAO0ffv2nl4GupUgs1YZAGdWK39yTso1hrSgGp/ws+NnxTUGk/JSwaoLWllxTs91AB/DtcBA80Vep3lffZ3Bc4CCBH/X1wtcI+hrDN709QuFMJ4/+mvoaww+B88p7gvXPVK0kEguTTiuPPPMM6LfVKbHAVaVM3bHGB4tGWn7yAQC9puSSMYCKWpIxs3ig757VHrpw3fDDTcIcePmm28eOADjdwJH/g142rS/yxcC09bGp7LrqBEg1A3QQsaYk3Qf7sf58+d7JpV6I3IGnNIJPl8MBCNnEBKVGqlEDYZ3HbAbV0LJkA3PaF3YmSGXmIFFKKZxkZn4+fKzpzDBhal+f04KuFhgVYYuVPDfeN5wsUOxg69FmH21ePHijEwkuoNd2N25NWn7DMccTM6aitHmUPcxnHCdShI1rildD4epb1uwjDUJbT+BA9116Ap6YFQAnoni84CCxfnVuLx4eAHiiKriicZ92NmhVevkm+14x9RVKLamV43TH0edzfj5qW1x23iGvK5iDq4snYHxRigSxta2gzjlbhRN6Rfk12BRXs2oL5R3dbyIoMpqsl6sBjsW56/HhMF7AnDv7/3bPgsv72wVgkOmgmwsL2dVxu9+9zsxJukLDQbNJBKJ5FKE4rGeQMU5PRuV8jaa4yLnLsyeZzBIrzTm/JFCB7PxJRMTfq6c+3d1dQnxQ096IlxXsCIiVZUQzwFWdLCaQhckuN7VK3hiq8z19QXXGrwffy5cuFA225VIJH3C+AVjH+wjmgk47jzxxBPiOso+ueyPe++99woL20slRiYZv0hRQzLuYHb773//ezFoUv19y1veIgSOZcuW9R1I4wQy5AUMZqAve5iwB+h4BQhrHqowFwP5awFD/9n6nHiysTMXHrQZutgJq50IRF7r+VsXNthLw6DkwWqYBUW5OC5eXBzwHDt16pS4WNP+hdlYum0V/XFpATXWPsfcT1ZqdAU7olsUWAxmLCu4HOZR7mNBuoNObGzZ3NNzgt/KQksB1hatHPFg95a2Y9jadixuG4UNk6KIfhd3VV6BAsvwxQfiCvlFs/B8iz1jvTTa/B78z5Hnkyqg3lO9AnNzx1+T5peb9+GYqy5ufy8vno85uaMrph137kF7sCmuIq/YMhnTs/uvyBs3UEzvfD5ps9NwGfYcrBN9e3iNGUq5NoMpjzzyiBAzWGrOZABaTDE5QPbKkEgkEg1mtj/55JNirGRwhn0sOFa+6U1vGpMAMed2FKIZDJc9NS4+uJZg/wquKyimsXqCiXOcJ7O6o6qqSgoTEolkxDh48KAQNmpqasQ6Y7BrdF6jaJfHa+ZDDz0kEkF5zXzHO94h1iwSyXhBihqScQsHUpaMcyBlwIZN2ShuUOSIbdDW12O5eGFmC9VjTibRvhEIspojxpXdXgPkLk5rf7jwYOY+s/T5fHqTNw7wYx30zjShSB2C6tHosTLBYlgEozL85nTjGZ4zbO7HjKr+mpCPFeyrccZ9Aq5QN2xGO6ZmzRA/x4o2f7uwodIbhc/PmwMzRcUR5qcnn4U7HJ+xbzEYUZtdhmUFM1BszcV455WWU3i04WDP3ysLq/CmKQvHpU3Ar08/hZDaW9lEKmyFuHnS6lHdj2AkgKPOnXCHu3qsp2ZlL4VpFM65jOA/Dzh7LeR6cCwG7NUig/dvf/ubKBOneD7QucDr2/PPPy8qMmixwn5P+vWRgROJRCKR9A2FhD//+c8igYqBH3qAs3qDzZMHmtOzkpfZ8xyH6VU+3Eo7rlVoJ8Tn0dcYvPF3vTJYMrGhTS4rxbl+lZ+nRCIZLXidohUiY1i33HJLWslOFGMpYvD6WF9fjze/+c1CzGDvJzl+ScYjUtSQTAjoScvADQWOF198EQsWLBCWHTfddJMo1+Xkn4sBBqaZtUqhQRczdGsgte0lKGCDViDbYUNOth1ZOSWwT75BZMemsyjh8zPzhgsQ/UZLI1oOxQ7yep8FXjhieyrwxsx/IbKMc1Q1BBVBKLBOKKspydjRHXSjLdANm8GCclvhiEx8UokapdZcvGPqOkwkGr3daPQ5UWCxY1qW1udlPPLbM08jENEs1XQm24txU8XKQT9XKBLCOc8peMMeZJmyUZVVDaOSXvNLImwXIh4hSNN6arwes5SEOoHOF5O3564BLGUiSMaFAzOqWDnGnjuxCw++d96HdhVsyvfXv/5VXOPe9ra3CTFj3rx5o/t+JBKJ5CKBogbXF3/84x/FHH/p0qVC5Fi0aJGo1qZwofcraGlpERmv+hyf9kF6jzVekyiIcA2iJz7xZ7oVc7Sj0q2GeOP1gDcSe73j2M+1D9ca3K7/5OtxjSGRSCQSiQ5jY6wYo7h68uRJUSUWC69fjLXt3LlT2MG/+uqruPLKK4WQwX5U4zHZUyKJRYoakgkH/Wj/8pe/iAyrbdu2iUGX2alcgLB8nIp0Sm+/1qeAsFtrLO32wenywRvJg9c8RywiGDDqCy5kFHMYRhMQ8AIJictJYoa+iOHvXCAxI4yZuBRYeGHhBWIoNiMSyXjltLsRG1v29PTZmGIvwdWlS0Uz80yypfUotrZrPV90ri6dj8X5WhlsWI2gztMkGk6XWPNRZB19S4mLiZ3tx7CrM/54X1e2DFMdg7PKCqth7Gx/Fe6YZt955gIszl+e8XNk3OLaB/hO9v5tmQLkLEvd/ymh9JsZU6xY5LXqjjvuwFvf+taM9uKQSCSSSx2uA1555RUx3lI45jydGapcY9ACl6IDxYP+mnpzDUJbU4oSDBLxJ7f1BZ9PFymYIMXH9AXXFQ6HQ9yXv/NxTKzi+oJrCwostbW1op+bRCKRSCT9wWvV3//+dxFTY9LwypUrxfWO64yysvFniSyR9IUUNSQTGmatMtDDwXj//v2ibJyCwetf/3rRDyEOfyMQ22yZGcKFVwKm3AEXOefde9DuqUc4FIbDkY2qnOWwG/P7zLKiEs4bM6y48KAdCBtMD9j0XCKZgAQjIfzp3HOIRCuhdFYXzsPsDPde0BuFH+yuizYGn4bL8qdpTdsjYTzTtB3Nfr33CLC6aD5m5sRnpEgGd7wPdJ3GSXcjTIoB8/OqMc2RMLamQbPvAg5270naTlGjwHIJ2SUFmgEKO0YHYC5LKWjwOrJlyxY8+uijokKRAavbb78dd911F6666irZJ0MikUhGGN3ij+sLjsOcxzN5imuMNWvW9CtsDAauMZj8xBufk0lRqaoQdVtdVo7oawyuN1gVwsq+TNhgSSQSieTihjZUjz/+uFhjPPfcc8L9hEIGBfyB7N0lkvGKFDUkFw3Hjx8XAzRvrOCg2szFB2/MXBIEOwF/A8DMYFulFlgagM7AObQEjsRtMyoWVGetn1j2JxLJCNEZcOIfDa/EbTNAwaycqVhVNHfUjvuh7tN4rf1w3DYKH3dVXgOLcYL0XrhIafDW4aizt4+Izvy8y1BildlAzObl4oLXr3/+85/i2kJx/tZbb8V1110nK/skEolkjKB48MwzzwhbDo7PhN7kXF8wmWoiWMpKJBKJ5NKkrxgZhfoZM2aM9e5JJMNGihqSS0KFrq6uFoEh3jZs2CAa76VLk+8AukONMQ3GNaqz1sFkkBZSEkkgEsRDolIj/juysnAu5uZqtlCjwfa2QzjqPJu0H7dOugL5lhz5QY0hnpAL29s399iTEQMMWFW0DlbjpTeOMuv2yJEjIlD27LPPirJvVhfqmcCrV6/OWCawRCKRSDJDbCUdb1xv0Ab32muvFWuM2bNny4QniUQikYwZdArZuHGjWGPwdvr0aXGN0t1MpLWU5GJDihqSS2JgZwk5A0cc2M+ePSsCRhzcp04d2BrHFWyCK9ycsFVBqXXupeMFL5EMwAVfO44563r+zjM7sCCvZlS/I43eNpxw1cdtMygKVhbOg8kgA8RjTXewS1Rs0KbMCCOmZE2Fw5S+wHwxwL5KDIjxWtTe3o5169aJa9H1118vmn3L6j+JRCKZOOI0m4w//fTTYo3BIBJtqihu0KJKWs5KJBKJZLRgjIvXIq4zpk2bJq5FXGNcffXVwqJQIrlYkaKG5JKDajUHfFZwtLS0pPWYiBqGinCPnY2imMRPiUTSC7PwVVVrEzBW3w82pA6p7O2hQoEBZsUoA8WScQM9z5cvXy4WGWvXrpW2UhKJRHIR2VRt3rxZiNY7duwQ/TIkEolEIhkNSkpKhCUi1xh0KZFILhWkqCGRSCQSiUQikUgkEolEIpFIJBKJZEIgvXMkEolEIpFIJBKJRCKRSCQSiUQikUwIpKghkUgkEolEIpFIJBKJRCKRSCQSiWRCIEUNiUQikUgkEolEIpFIJBKJRCKRSCQTAilqSCQSiUQikUgkEolEIpFIJBKJRCKZEEhRQyKRSCQSiUQikUgkEolEIpFIJBLJhECKGhKJRCKRSCQSiUQikUgkEolEIpFIJgRS1JBIJBKJRCKRSCQSiUQikUgkEolEMiGQooZEIpFIJBKJRCKRSCQSiUQikUgkkgmBFDUkEolEIpFIJBKJRCKRSCQSiUQikUwIpKghkUgkEolEIpFIJBKJRCKRSCQSiWRCIEUNiUQikUgkEolEIpFIJBKJRCKRSCQTAilqSCQSiUQikUgkEolEIpFIJBKJRCKZEEhRQyKRSCQSiUQikUgkEolEIpFIJBLJhECKGhKJRCKRSCQSiUQikUgkEolEIpFIJgRS1JBIJBKJRCKRSCQSiUQikUgkEolEMiGQooZEIpFIJBKJRCKRSCQSiUQikUgkkgmBFDUkEolEIpFIJBKJRCKRSCQSiUQikUwIpKghkUgkEolEIpFIJBKJ5P+zdxZwclVnH35nJRt3AxIcgie4aygJrgUKpUjRUkpLKfDRAhVKWyilhUKhaHF3d3cPbgmBCCFuu1m73+85m7O5e3dmdlx2/w+/IbuzM1fOPfL6MSGEEEKIckBODSGEEEIIIYQQQgghhBBClAVyagghhBBCCCGEEEIIIYQQoiyQU0MIIYQQQgghhBBCCCGEEGWBnBpCCCGEEEIIIYQQQgghhCgLqop9ASJ31NXVWX19vZpUCCGEEJ2Gbt26Wffu3Yt9GUJ0SaRfCCGEEKKzIf2icyCnRidSOHost5zZ3LnFvhQhhBBCiJwxfPhwmzhxohwbQhQY6RdCCCGE6IxIv+gcyKnRSXAZGjg0/vMfi/Xs0aa2WEXM/7v0h7h/i/8+xNr9LdbuM2kdz2JJv5Pa8WJJri+V48W/hvSPF0vhfpNfQ2r3m8fnEcvx80jxO7l6Hm2P1/Hz8D/H0rq+JMeL3neCa0j5eEu/n931xbK838TPo/V4QdDyg/+3OfKv+5u1fS/62WR/ix4/7mebE3+mo+O69/x1pvLZRMf1B0nWFvHaJsFnQx9JeA/xri9oTuF5dNA2ST8beT9ZW2TynOO9l9LzsPSfR7y+lUobp/M8Ej37TI6brC3itXXC52GpP4/mFO4lnfGR9LOR645cx/ymJhs5YYKTc5StIUTp6Bfpyg3Rv0Xlu3TluHbHi+gXba8vneMtlcOylONSu9/k15BMFo/K222Pl75sn8nzSH689J9HMl0r+f2mr2vl+nlEn0vm99v291iOnkci/SL947W9hvSOl/h5JNQvkskNKcnVSeSkpHJmB3J1Ujkucp5k50oq8zVnLpvFlcETXG+y+4vqF8k+m/HzSPCdTGT7rHXLODJzR88jWd/KRCdK5bOWqe6bg/ER93kkuN5k50p2L9mMj2R9S/pFp0ROjc5Gjx4W69nT/ejFhlhEuIz/t/jvZyvYpSPsZ2pEz0awS0dZytSpkY1gl47yVS7PI1fKcLZOjY76RPL7TXa8+NeQq+eRqVOjoihOjSyEtZQ+m61TIxefTUPQTkfYT/b9TJSvZPeXs2eXI6dGV3geuXJqZKsM52KMJru/TMZo+L1Y6D0hREnpF/F0jHh6RCIdQ3JcbozouXJqZPI80tG1kp0rE10rteOlrmt1tedRqrpvak6NDOS4tGU+yXFx26EYzyNXTo2sZfAc6Fqlrvt2lech/aJTEV4nhRBCCCGEEEIIIYQQQgghShY5NYQQQgghhBBCCCGEEEIIURbIqSGEEEIIIYQQQgghhBBCiLJATg0hhBBCCCGEEEIIIYQQQpQFcmoIIYQQQgghhBBCCCGEEKIskFNDCCGEEEIIIYQQQgghhBBlgZwaQgghhBBCCCGEEEIIIYQoC+TUEEIIIYQQQgghhBBCCCFEWSCnhhBCCCGEEEIIIYQQQgghygI5NYQQQgghhBBCCCGEEEIIURbIqSGEEEIIIYQQQgghhBBCiLJATg0hhBBCCCGEEEIIIYQQQpQFcmoIIYQQQgghhBBCCCGEEKIskFNDCCGEEEIIIYQQQgghhBBlgZwaQgghhBBCCCGEEEIIIYQoC+TUEEIIIYQQQgghhBBCCCFEWSCnhhBCCCGEEEIIIYQQQgghygI5NYQQQgghhBBCCCGEEEIIURbIqSGEEEIIIYQQQgghhBBCiLJATg0hhBBCCCGEEEIIIYQQQpQFcmoIIYQQQgghhBBCCCGEEKIskFNDCCGEEEIIIYQQQgghhBBlgZwaQgghhBBCCCGEEEIIIYQoC+TUEEIIIYQQQgghhBBCCCFEWVBV7AsQOaa21oJYy4/B0rdaf48t/SHu3+K/D82hnx3+ONH3w++1+86yH4OlvyS6htSuL9bu+ryHrsL/Hjpe+7/F4r4fvr3Ujrf0OAm+E771RNeQ7FypXV/ba0j/ePHbIr3jxVK437bv5+p5tD1ex8/D/xxL6/qSHC963wmuIeXjLf1+dtcXy/J+Ez+P1uMFS0ej/7c58q/7m7V9L/rZZH+LHj/uZ5sTf6aj47r3/HWm8tlEx/UHSdYW8domwWdDH0l4D/GuL2hO4Xl00DZJPxt5P1lbZPKc472X0vOw9J9HvL6VShun8zwSPftMjpusLeK1dcLnYak/j+YU7iWd8ZH0s5HrjlzH/Kam0IULIUpFv4inY8TTIxLpGO30i2Q6Rvj3BH+L6hdtry/+NcT/WyyufpGuHBf9Wyyt43Us20fl7bbHS1+2j2UgVyc/XiyN+012vIhcnNbxEutauX4e0eeS+f22/T2Wo+eRSL9I/3htryG94yV+Hgn1i2RyQ0pydRI5Kamc2YFcnVSOi5wn2bmSynzNmctmcWXwBNeb7P6i+kWyz2b8PBJ8JxPZPmvdMo7M3NHzSNa3MtGJUvmsZar75mB8xH0eCa432bmS3Us24yNZ35J+0SmJBUG4x4lypa6uzlZZZRWbPn16sS9FCCGEECJnDB8+3CZOnGjdu3dXqwpRQKRfCCGEEKIzIv2icyCnRidTPOrr64t9GSXL/PnzbeTIkfbNN99Y3759i305nRq1tdq5s6E+rXbubJRTn+7WrZscGkIUCekXnWs+LWfUzmrrzob6tNq6s1FOfVr6RedA5ac6EUQwKoqxY5hcS32C7SyordXOnQ31abVzZ0N9WgiRDOkXmk9LDa1bauvOhvq02rqzoT4tCoU2ChdCCCGEEEIIIYQQQgghRFkgp4YQQgghhBBCCCGEEEIIIcoCOTVEl6GmpsbOOecc969QW3cG1KfV1p0N9Wm1tRBClBtau9TOnQ31abVzZ0N9Wu0sOifaKFwIIYQQQgghhBBCCCGEEGWBMjWEEEIIIYQQQgghhBBCCFEWyKkhhBBCCCGEEEIIIYQQQoiyQE4NIYQQQgghhBBCCCGEEEKUBXJqCCGEEEIIIYQQQgghhBCiLJBTQ3RaTjzxRBs6dKhtsskmbd7/8ssv3Xurr766HX/88RYEgXt/5syZtuOOO9oaa6xh++23n9XV1RXpysublVde2TbYYAMbM2aM7bbbbh22u8icBx980EaNGuX67FVXXaWmzCFVVVWuD/M6+uij3Xuvv/66rbvuuq4P//GPf1R7Z8i+++5rAwYMsAMOOKD1vURtq3kjt+2s+VkIIbJHOkbh0fpVOKRf5BfpGPlB+kXhkI4hSopAiE7Kiy++GLz55pvBxhtv3Ob9/fbbL3jggQfcz/vss0/rz6ecckpwySWXuJ9/+ctftv4s0mOllVYKFixY0O79RO0uMqOhoSFYY401gm+//TaYP39+sPrqqwezZs1Sc+aIQYMGtXtvk002Cd577z3X9vw8YcIEtXcGPP3008H9998f7L///h22reaN3Laz5mchhMge6RiFR+tXYZB+kX+kY+QH6ReFQzqGKCWUqSE6LVtvvbUNGjSozXtkB7zyyiu2++67u99/8pOf2AMPPOB+5t/DDjus3fsie5K1u8gMH9m+wgorWJ8+fVxWzGOPPabmzBNTp061xsZGl4VEhNUhhxyiPpwhZMTRZztqW80buW3nRKidhRAiPaRjlAZav3KP9IvCIx0jN0i/KBzSMUQpIaeG6FLMmjXLBg4caLFYzP0+YsQImzJlivt53rx51q9fv3bvi/SgbbfbbjvbbLPN7K677uqw3UXmAjAODY/aNLfMnz/fNt54Y9tmm23sueeeU3sXoS9r3sg9mp+FECI/SMfIL1q/CoP0i/wjHaMwSL8oLJqjRbGoKtqZhcgBGB2XLFnS7v3HH3/cll9++Xbvx9vHwRva/b/R90V67f7SSy+5tv/2229tp512stGjR7c6i9S+uSNZXxbZM2nSJNePP/jgA5dhdP3116u9C9yX1cdzj+ZnIYRIDekYhUf6RfGR7JV/pGMUBukXhUU6higWcmqIsuatt95K6/ODBw+22bNnu0UOoxmG9+WWW879rW/fvq3ZGuH3RWbtTrT12LFj7d1337X9998/YbuLzCCyPZztQptuvvnmas4c4Z2i6623nq2zzjqu30bbW304f32Ztk02X4vs+rXmZyGESI50jMIj/aL4SL/IP9IxCoP0i8IiHUMUC5WfEl0KDGNbbLGFPfTQQ+53oq/33HNP9/Mee+xhN9xwQ7v3ReosWrTIFixY4H6eO3euPf/887b22msnbXeRGZT3IosAYzBt/vDDD9u4cePUnDlgzpw5rZlIGNI/+ugj59yorKy0999/3+3/cMstt6gP51AIjte2mjdyi+ZnIYTIH9Ix8ofWr8Ih/SK/SMcoHNIvCofmaFFUir1TuRD54qc//WkwfPjwoLq6OlhhhRWCu+++273/2WefBRtttFGw6qqrBsccc0zQ1NTk3p8xY0aw3XbbBauttlqw9957B4sXL9bDSZMvv/wy2GCDDdxrvfXWCy6//PLWvyVqd5E59913X7DGGmu4PnvFFVeoKXPESy+95Pov/Xj06NHBPffc495/5ZVXgnXWWcf14XPOOUftnSG77LJLMHjw4KBHjx5ubn799dcTtq3mjdy186uvvqr5WQghcoB0jMIi/aKwSL/IH9Ix8of0i8IhHUOUEjH+V1y3ihBCCCGEEEIIIYQQQgghRMeo/JQQQgghhBBCCCGEEEIIIcoCOTWEEEIIIYQQQgghhBBCCFEWyKkhhBBCCCGEEEIIIYQQQoiyQE4NIYQQQgghhBBCCCGEEEKUBXJqCCGEEEIIIYQQQgghhBCiLJBTQwghhBBCCCGEEEIIIYQQZYGcGkIIIYQQQgghhBBCCCGEKAvk1BBCiE7A73//exszZkzr70cccYTts88+WR0zF8coBJMmTbJYLGbvvvtusS9FCCGEEEKIToH0C+kXQghRysipIYQQeQKnAMZ2XtXV1bbqqqvaqaeeaosWLcp7m//rX/+y6667LiunQDrHyITvvvvOtcuNN94Y9+/HHXecbbDBBnk7vxBCCCGEEOWE9IvkSL8QQoiug5waQgiRR8aPH2/Tpk2zr776ys4991y77LLLnGMjHg0NDTk7b79+/ax///5FP0Yyhg0bZrvvvrtde+217f5WW1trt956q/30pz/N2/mFEEIIIYQoN6RfJEb6hRBCdB3k1BBCiDxSU1Njw4cPt5EjR9ohhxxihx56qN17771tUrqvueYal8XBZ4MgsHnz5tmxxx5rQ4cOtb59+9pOO+1k7733Xpvj/vWvf3VCe58+fZzhv66uLmnpqObmZvvb3/5mq6++ujvPiiuuaH/+85/d31ZZZRX374YbbugyNnbYYYe4x1iyZIn94he/cNfVvXt322abbeyNN95o/fuzzz7rvv/UU0/ZJptsYj179rStttrKPv3004Ttw7U/88wzLlskzJ133unu6cc//rE9+uij7lw4WAYNGmR77LGHffnllwmPSXZJ1BlDm3NtYR544AHbeOON3b3Q/n/4wx+ssbEx4XGFEEIIIYQoNtIvpF8IIYSQU0MIIQpKjx492mRkfPHFF3b77bfbXXfd1Vr+ieyF6dOn28MPP2xvvfWWbbTRRjZ27FibPXu2+zufP+ecc5xT4s0337TlllvOZYAk4//+7/+cU+Oss86yjz76yG6++WbnFIHXX3/d/fvkk0+6rJK777477jFOO+00d53/+9//7O2333YOknHjxrVel+e3v/2tXXjhhe7aqqqq7Kijjkp4Xbvttptz+kTLXOHowaGCE4NyXaeccopzoOAwqaiosH333dc5ajLlsccecw4TnDS0xxVXXOGuwTt6hBBCCCGEKAekX7RF+oUQQnQRAiGEEHnh8MMPD/bee+/W31977bVg0KBBwYEHHuh+P+ecc4Lq6upgxowZrZ956qmngr59+wZ1dXVtjrXaaqsFV1xxhft5yy23DI4//vg2f998882D0aNHxz33/Pnzg5qamuDKK6+Me50TJ04MWA7eeeedhNe/cOFCd6033XRT69/r6+uD5ZdfPjj//PPd788884w7zpNPPtn6mYceesi9V1tbm7CdTj/99GCllVYKmpub3e9fffVVEIvFgsceeyzu52kvjjlhwoS413/ttdcG/fr1a/Ode+65x33Gs+222wbnnXdem8/ccMMNwXLLLZfwOoUQQgghhCgm0i+kXwghhGhB5aeEECKPPPjgg9a7d29X4mjLLbe07bbbzi655JLWv6+00ko2ZMiQ1t/JzFi4cKHLUOB7/jVx4sTWkksff/yxO1aY6O9h+Dylo8j2yBTOTYbJ1ltv3foem3xvttlm7vhhwpt7k0UCM2bMSFqC6uuvv7ann366NUtjxIgRtvPOO7eem9JdlIiiHJcvlzV58uSM74d2/uMf/9imjY855hiXqbJ48eKMjyuEEEIIIUQ+kX4h/UIIIYRZlRpBCCHyx4477mj/+c9/nANg+eWXd/+G6dWrV5vfKamEI4D9KaJkumk3KenZwl4fEN2Xgvej74Xv0f8tWamoNdZYw7bddlu3YTjtRXmrI4880pWZgj333NPtSXLllVe6NuRY6623ntXX18c9Ht/z15toE3aOwR4a++23X7vv44ASQgghhBCiFJF+If1CCCGE9tQQQoi8gtOCvSfIyIg6NOLB/hnsp8FeFHwv/Bo8eLD7zNprr22vvvpqm+9Ff486DXBssB9FPLp16+b+bWpqSngMzs/nXnzxxTaOAvbN4HqyhWwN9vJgz45vv/3WOTVg1qxZLhPkd7/7ncs04Vxz5sxJeiwyXxYsWOD24vD4/UrC7cwG5tE25uWdKUIIIYQQQpQa0i9SQ/qFEEJ0bpSpIYQQJQQllyglxSbZbOw9atQomzp1qts0nPc22WQTO/nkk+3www93P2+zzTZ200032YcffujKM8WDzIPTTz/dbfSNY4ISUt9//737DsL+0KFDndPj0UcfdWWf+Hy/fv3aKU8nnHCC/eY3v7GBAwfaiiuuaOeff74r1cQxsuWHP/yh27T7uOOOc86LlVde2b0/YMAAV4rrv//9r8tgoeTUGWeckfRYm2++ufXs2dPOPPNMO+mkk9xG6NGNyM8++2zbY489XAYI58aR8f7779uECRPs3HPPzfp+hBBCCCGEKAWkX0i/EEKIzojCUYUQooSgXBMODPbeOOqoo2zNNde0gw8+2CZNmmTDhg1znznooIOcUR5HxcYbb+z2o8DhkIyzzjrLfv3rX7vvke3AMfw+F2SFXHzxxXbFFVe48k5777133GP89a9/tf33398OO+wwl+nwxRdf2GOPPeYcD9mCE4L7JAuD+/bgbLj11lvdHhiUnPrVr35lF1xwQdJj4XS58cYbXTuuv/76dsstt9jvf//7Np8ZN26cq0f8xBNP2KabbmpbbLGF/eMf/3AZNUIIIYQQQnQWpF9IvxBCiM5IjN3Ci30RQgghhBBCCCGEEEIIIYQQHaFMDSGEEEIIIYQQQgghhBBClAVyagghhBBCCCGEEEIIIYQQoiyQU0MIIYQQQgghhBBCCCGEEGWBnBpCCCGEEEIIIYQQQgghhCgL5NQQQgghhBBCCCGEEEIIIURZIKeGEEIIIYQQQgghhBBCCCHKAjk1hBBCCCGEEEIIIYQQQghRFsipIYQQQgghhBBCCCGEEEKIskBODSGEEEIIIYQQQgghhBBClAVyagghhBBCCCGEEEIIIYQQoiyQU0MIIYQQQgghhBBCCCGEEGWBnBpCCCGEEEIIIYQQQgghhCgL5NQQQgghhBBCCCGEEEIIIURZIKeGEEIIIYQQQgghhBBCCCHKAjk1hBBCCCGEEEIIIYQQQghRFsipIYQQQgghhBBCCCGEEEKIskBODSGEEEIIIYQQQgghhBBClAVyagghhBBCCCGEEEIIIYQQoiyQU0MIIYQQQgghhBBCCCGEEGWBnBpCCCGEEEIIIYQQQgghhCgL5NQQQgghhBBCCCGEEEIIIURZIKeGEEIIIYQQQgghhBBCCCHKAjk1hBBCCCGEEEIIIYQQQghRFsipIYQQQgghhBBCCCGEEEKIskBODSGEEEIIIYQQQgghhBBClAVyagghhBBCCCGEEEIIIYQQoiyQU0MIkTcOO+wwO++88/Jy7JVXXtn++c9/WmflwQcftA033NCam5uTfm7SpEkWi8UKdl2ipe89++yzagohhBBCiDR4+umnba211upQvs2E3//+9zZmzJhO+zyWLFliK664or311lsdfnaHHXaw6667riDXJVr63hFHHKGmEEKIAiOnhhAFBoEHIzSv6upqGzZsmP3gBz+wa665pp2AHzXcB0Fgv/71r61Pnz5OKeiIr776yn70ox/Z8ssvb927d7cRI0bY3nvvbZ999llK18o13nvvvXHvYZ999kn63ffff98eeughO+mkk6wz8d///tcpCn379nXtM3fu3HafmTNnjnPo9OvXz734Ofq5yZMn25577mm9evWywYMH2y9+8Qurr69v/fsee+zhjn/zzTdndb30Id/f4r24l0zhu7/85S+t0OBQoB8vt9xyrv1QYG+66aZ2n4l3v5988kmbz9111122zjrrWE1Njfv3nnvuycn1hc85aNAg22mnneyll15qpwDx9/Hjx7c7xvnnn9/u+SxatMhOP/10W3XVVd14HjJkiPs7DjAPv8e77+OPPz6je6mrq3Pjff3117eqqqqE4/65556zjTfe2F0X13f55Zd3eOyOxsCnn35qO+64o5sj/XF/97vfWUNDQzsl/7e//a2ttNJK7jmuttpqbj71fPjhh7b//vu3joXO7AwVQgghci3XoQuEA2gwlvfv37/NZz7++GOnZ+y3335uXU7Eaaed5tbsiorOY4ZIVc647LLLbJVVVnEyDTLTCy+80Obv6HnIhuhtPXr0cM+DY3uQcU499VQnC2aDlz+TvQiayoR4faMQzJ492+mco0aNsp49ezrnD3LlvHnzOtSLzjjjjLTk00wJn5vni3PvggsucM89GqyGzD1lypQ23582bZp7P/p80GU233xzp3NiI1h33XWdvSD8TOI9Y/phpsTTNw4++OC4n2U+QFfjM++++27CYyLf07fROWh7xsFPfvITmzp1arvPvvLKK0634nP0N66ntrY27XPTNhtssIFri+HDh9vPf/7ztNtCCFFcOo80IUQZgRETwQSB5JFHHnGGu5NPPtkZshsbG+N+p6mpyX7605/a9ddf7xwaLOTJQPjCWTJ//ny7++67nYHwtttus/XWW6+dgJcP/v3vf9sPf/hDJ1xlCvecj0guiBpGU2Xx4sXu+Z155pkJP3PIIYc4wenRRx91L37GsRG+r913390ZqV988UW79dZbnUAaFkDhyCOPtEsuucSy4Y033nB9jRfnAPqCf4++UW68/PLLTgDlfnCeHXXUUU7ofeCBB9p9NnyvvNZYY402AvFBBx3kns17773n/j3wwAPttddey8l1+nPj5MABwTOfMWNGm8/gmHnmmWfs22+/bfP+tdde6xSyMDgmMCwwtnDO0LdQomfNmtXmc8ccc0ybe+aFkyQT6KsoXih0O++8c9zPTJw40XbbbTfbdttt7Z133nFjg8/7/pbouB2NAZy+PNfHH3/ctSVGgiuvvNLOOeecNsfimT311FN29dVXu8/dcsstTlEMj1kcIn/961+dwiKEEEKI3IGsiQwwbtw4u+OOO5zxPZH89vnnnzv9oBgyfL6OnYqcgQ6GwwiHDrIS7bXrrrs6A7oHWe0f//iHk/NoU46FLrdgwYLWzxx66KHOGYITKVNwjIRlRJxRf/zjH9u8N3LkSCsnMHzz+vvf/24TJkxwxmrkZHTnKNF7JWAmXR0tU/y5eX48B2RmAuaiYNBH5w/zv//9z1ZYYYU27z355JPOmXDAAQfY66+/7rJ4/vznP7dzwhCMF9UNvv7666zuJapvXHHFFQkdmdxPKuPo7bfftrPOOsv9i45KIOZee+3V5nPob+jiu+yyi7tnxgrOiHiO0mTnZqwxHnFq4TxEl2AOE0KUGYEQoqAcfvjhwd57793u/aeeeoowjeDKK69sfW+llVYKLrrooqCuri7Yd999gxEjRgQfffRRSud555133PEmTZqU8bXy/XvuuSfle/A0NTUF/fv3Dx588ME278+ePTs47LDD3N969OgRjB8/Pvjss89a/37ttdcG/fr1Cx544IFg7bXXDiorK4Ovvvoq+O6774I99tgj6N69e7DyyisHN954Y2vbeObOnRscc8wxwZAhQ4I+ffoEO+64Y/Duu++2/v2cc84JRo8eHVx99dXBKqusEsRisaC5uTnjtnnmmWdc+8yZM6fN+zwf3n/11Vdb33vllVfce5988on7/eGHHw4qKiqCKVOmtH7mlltuCWpqaoJ58+a1vsez43tffvllwuuYOHGi+0ym1/zSSy8F2267rWtb+tdJJ50ULFy4sPXvl156abD66qu7axs6dGiw//77t/YBjhV+cS3x4Fn9+c9/Do488sigd+/ewciRI4MrrrgiyCW77babO36ye41y4IEHuj4YZty4ccHBBx+c9FzcD8dPRLxzv//+++69+++/v12fpG+fe+65bZ7J4MGDgxNOOCHYfvvtW99nbFx33XVJr43Pn3zyyUE+SDTuTzvttGCttdZq895xxx0XbLHFFgmPleoYiPKrX/0q2GabbVp/f+SRR1y7zJo1K6V7iM4bQgghhEguQ6ALhGVNL697/QXZ7tRTT+2wGZExDzjggHbvX3bZZcGqq64aVFdXB2uuuWZw/fXXt/k75/7Pf/4T7LXXXkHPnj2Ds88+273/l7/8xcmmnP+oo44KTj/9dCdXhbnmmmucjIJ8MWrUKCfXRmXo2267zd07n+Hz2ZBIzthss82C448/vs17XNcZZ5zhfkYnGT58ePDXv/619e/of7Tz5Zdf3uZ7O+ywQ3DWWWclvQ7uh+eUyTV3pFPxM9dAu/P3jTbaKHjjjTda5d/wC1k3Hl4G5llz/r59+wYHHXRQMH/+/CBX3H777UG3bt2ChoaGhPeaK/mU+0FOTka8c9N2++23X7s++bvf/S5YY4012nyW/stzD+tcjFeeRTLC4zVXpKpv0J708w8//NBdN/aJdHj99dfd977++uvW9zbffHPXPtmcG5sEtognn3wyresRQpQeytQQokQg82L06NHtIucXLlzoIkaIIKB8zdprr53S8YgMJ2LhzjvvdFEnhYToecotbbLJJm3ep4zNm2++affff7+LskBPIcI7HBVFlMZf/vIXu+qqq9w9Dx061H2PrBYyVLgf0rfDEe8chzaaPn26Pfzwwy5KZaONNrKxY8e6dGTPF198YbfffruLuPEpqOz50bt376SvaHp4Mrgv0n9JA/ZsscUW7j0i1PxnyJgJR44QGUKKbLhOLuV0uP90zp8ORDJxXkoF8MyIIiMqyafe8qyIuCeqiAh4Ip62224797d//etftuWWW7aJ0kkW1XXhhRe6/kB02s9+9jM74YQT2pSCIlU62TPg78kg+2jgwIHt3mdfErIh6AtkRIThORDlE4b28M8pV9Cnybzw2QdRyDQJ1z2mdBKReN26dWvzOSL26N/hiL1MyLatoyRqR/pPoojHVMdAGMYvfXD77bdvfY+5hH5FdCPRa2uuuaaLfIuXgi6EEEKI3EHJTuRvop0po9MRzz//fDvdgGOQrU4k/AcffGDHHXecy1SOymxkaVJ6FNkVuQl5nveISkfeQNZDPwhDdifXxmeIjEfmJwqciPcwlLxB3uUzyCJkT3SkG6RT1pOoeWSbqKzE717mJOsVPSb8GTJekHmiculmm22WN90gFZ0KGZXsDiLk+TuR7si3W221lcuqDWcFIJMl4ssvv3QZyJRR5UUpU7JdPNnqaOgGXAslm8L87W9/c6VhKUsUzWrIRD7NtJ3J5KbPxdMNyE6gnDF6GfAv7U9ZrKhugL7M2MmGTNqa0r+U50Jv4DlH9ZPvvvvO6Yk33HCDKwmWCTxDSkf5kmbo/2TUox/T3yhRyxjx7ZTquZ944glXDYISX9hW6M9kfn/zzTcZXacQoni0neGFEEWFkikYl8P86U9/ciWcPvroI7eApwoGvosvvtilXf7hD39wSgRlrhBESZFOFfbkqKysbPMegh0CbyJwQPCd8PWSbo4BEscMQogXhjCEI9D6VHSMoCglOHiAtFNKdL366qutjgLKzISdOyg+KDkIOj7lnfRjjosT5Nhjj3XvIbQi3ODw8aCUIMR01JapghIQ7znxHn/zn0EICzNgwABnxPafCZ8707q2HYECSqksXz+Z0kz0GYTD//znP06po1YpZdHogzhZcBIAThquF0ExlZI+OK9wZnjl8aKLLnLCvC8ThOKULOU/nsDv4RmjWIXTnlFuSeemZjH9leeOQsY5vWMm3nPg9+gzyBQEZO/UQHnhWriGKLQv/RBln8+gqCOch/eFAO6H8YsixvjYZpttXLr51ltv3eZzjB+cgmEuvfRSO/zww7Nu63gkakdK6c2cOdM9i1S+k2gMMF+Qhs5zZCzjZAvvG0RbUQsXwwjno5+h+EXbTwghhBC5gaArZHfK50T3JEgE8my0FAzyOsFLXkY85ZRTnMzP++gtHuRVnBlh/YTfjz76aPf7ueee60rxsBdYWIciqIbgHWA/C/Qp5EUvEwFysP8MIL8kq/0PGMtTBdmEALNkMqf/N95nomWC8qkbpKJToR/85je/aZXhw6Vd0Q8wQqeiG2BUJqjHlyqmDCwlgHA0ZKujUZqV54+TLAwONJw0yJyULvq///s/51DycnM68mkmoANR7gqdFFkc+RWHWjxZ/Mc//rGTZZH3+ZffozI6+4jgbGAfCvQ0AulwjKEvhMvA4RzAMRGVrynxmklbc3zGE88ZhwrtSClfnAWA3sO45rjYIDLpr4xl5hbGvh9vyP1+Xxj6JY4pynShX3Ed9MVUzs1x6H84cwjUo9/yXCj3hi0mGlgmhChd5NQQooRgEQ5vxAcIJgjpLLrpbnB74oknupr0CKhENVDnluPgXGDRTgWMz9Fa+ghkybI/iJJGkArfC5EoRMqEMxgwzrKhW7guLEIE+yVEvxeO7EKIDm9CR+QMyhXHi14HUUAehL2wQwOI7o8X4Z8N0WcY79mm8hlgPwOM4vmAdiP6PbzJNteAkIeATx+hzXCCUbuU17777ptRtE34mXplJ5xtw3kyAScFgivReOEMA/oVLw9ZJUTfIAB7p4a/lo6eQaagZOAUIjuFMYPiFs9h4BUXsjkQssk2CLeXh+vm7yj7OAfJXEIQx2lJ5GFY0SAyMUzY0ZZpWycjXjvGez/Zd/z3ou+TQUT0F8oSSjTPEGct0Ff5PH0YhcTXyMXZgyOH8SOEEEKI3ML6irEV+QsHQyqZ5Mjl0c2JkfN98JGHYA3kmzDRDA++F82WQNbzGR7ff/+9k/vYU4GI7bDDwssLiY6N3rH66qtbrklF5kzlM/nWDTrSqXA84UwiYAgdEefWaqutltHG2eG9FwmCCesGmepo7CdJ8N0666zTbh+2X/3qV60/I2vjsEBm9Nkb6cinmYAci95C/0RWp1KDD/aLQt+lT6O7o8OTRRLdexM946GHHnLPhr6PjkDWE+OHz3udjXYmQChMWEZOt63DY4rMFpwJjCPOgdOIPSF5Djg7MgGHD3uFIOeHM7D8Xps+owsIuMMZhuOHag+pnJvjcA6C+Xx2FHvyoZ/SjtpbQ4jyQU4NIUoIBHSiHsIQeUAEBynXOBLS3TgaIYYUVl5EMbFI82+qTg0W96hgzzEpL5UIUlERtolC8ZEO3sjZkZCIgBX+PRXjKIIJgjAG7ihh5weCXxQERV7JIFOEDf1SbS9SXqMgvPrIHz4T3YyaFGOEq2h0EBHnUUdMrqDdEArjRQixSTXPDuGUdiWS5+yzz3aRMWRFhNs1FaLGfJ5neBN4HBLJNqzDEE96dRjS1EnDxoiN864jiF668cYbW3/nOUSjrlCmos8gUxjLtBNOCqKNcAgRRRRvA02iDXH48fdwJGK8dqQv8iJ6ibFM5gJOEz/WUNaTKeOZtHUyErUjRoGoUhz+TqpjwJc1QzllDsT4gcJGNhjjnsixsIECwwrzBpuvh6MHhRBCCJEYoqGJ6I6CzB/NTGANJnp///33dxkVBFqwTicD/YC1Pkoqhvx4MnwyvIyJ0yUcUOWvPdmxyUTo6F4IRrn88stTuhbum3Mmkzl9ZgOfCWe4xpNL860bdKRToQsQPY8xHR0JxwEbaiPn5lI3yERHIwiGICyyEsjg7Sj7GN0ACPJCZk1HPs0E+gIyOi/KIfMv1xANIPTOAgL5vNOQ3xNlEOFU4oWzCWcJugdBQd7wT0nqZLpBtvowjgzamsoM/Mx8gIMlqvPg+CD4KloCLgxtTdYIAXYcJzz3+LERHZ+0D+MWUjl3vOMwpng+/jhCiPJATg0hSgQWYNJ9wxEkHhwQ1BrFgIuw9+9//zujaBG+g3CU6z0DopAKCqR4+58RGoguQVD0ESmkBlNeKll0F3/je9TLpYYssL9D2KmC8IQSgBGVqJ90yHX5KSJqUAhJafbXyz3znr9vPkNqNbVmvVCF0wDhi/JDHgzhRN74kk+5hnbDeJ1MyKVNEbR5obSg0NBXSdPHiJ6r/VrSLYmEskXZJiKrohF+iSBjIqwo8hxIkw6POZ5DooipbCClHucD0UbxxjiOBl6kPKMopoofV/SVVFOlc11+inZ84IEH2rxHO6I8JDpWqmMgCoYOrt07O4nmJHqNqEKfVs+cgvLmy38JIYQQomPQETBcRiGYJZz96mHNZi9AIt1xbBAtjeE1Eciz6AZROZ8ykuHgFPSUjjI/+DuGy/D3+N2DARr5nQxXDJnpQImsXJafQj5DtkHmDBv++Z2gNfClfHjPy/0EhxHAg6wbhgCYfOoGqehUGM15IdNidCfbmHvLpW6Qro5GdD7Be/RLqhJEs4IS6QbgZdFM5dNMIEuE8lHsR8F1xNPtCXSiNBtlgVOF50aGxqJFiwqmD6NPIp/7NiMDgsArz9SpU92zwdESdTLGc2jgHCFjIhocxb0xPrEFhEH233XXXVM+ty/dy3G8voCzkFJx+choF0LkDzk1hCgC1IZHYEToI6qfzW9Jl8RImyjinPRUImL4DAY9Sqskc2wgjGOExpiK4RMhE8GY1EyiuvMJkQ4IxSgp3qlBxDSCO+mq1LIl24NIcwQkL9DHAyWKiBu+x54CCNnUvg2nzGJwRwjdZ599nODPdxBgMN7yXjStPEy66bY8N15E9ACOKO6FzAaOg5Llr9fv8YDRnefmFULSXHkmPBv2tUCIQqDlO2ElyUeZcG/5gH5AdBBlyjg3kWpkC6FQkRGEIw1lkLJHCN60J041fx8IljhsqFWKQZn7x5icCekIkDg0SCunLi4Rgj7yjT7unyWl2rg+HAUohWRoEBHFy8P3uTf6DH3wvvvuc6XeopvN5QLahX6LkE12TLwSXjiLEOYTZcHssMMOTnGkPyPkYxigljWGhHC/IUsqGg1IP+IZQrrCOuehDemnRMB5Rd+PbRQhHK2UI6Afke7OvjekcXuIliMN3G8On8oYoKQUThHqBHP9lETgGAcddFDrpo84gKiZTCQaZbhQRkjtRwn0cwTX7o0o/MymgNwDfTYf5SWEEEKIcgTjKes5ciGyK+soMiFrOqWG4oHshWyFIRJdBccG63Y8MCxGI7RZs/mu34yaIAkcJchjyUCGY18MZCLKYCEzYFgN7xtIRgHZyMgVGDzRvwiSIvIemSUR6ZafSkXO4HzIPFwvcj06DRHhvoQWOh1yItHy6Ey8+Bl5MRrsQnlTZJ980JFOhVzNM8ORhSOGrFicXsjjgOxNoAn9gP3fuP5MN4lOR0dDPkW2RAZG5sfBwcvrpWTKIJ+iWyE3k+HLdeOUoZoBelw6OlquYKzRzowh2jQK56W8VyLdgD7OPbN3IfI9QX8Y9dEnwlUZsB3E2xOE8rToKOm0NQF3jDfOSWYDfZ8Mahxt3lng29PjA4/IJgkHHeFIxQaCQ4wgLdqAKgHooNhJ/DVzbcw1jBP6HzYO+he6CHMK+gV7vqR6bhxy6H7MI4xFni06BtcT3stHCFEGBEKIgnL44YcTYuxeVVVVwZAhQ4Kdd945uOaaa4KmpqY2n11ppZWCiy66qM17zz33XNC7d+/guOOOC5qbmxOe5/vvvw9+8YtfBOutt577fJ8+fYL1118/+Pvf/97uPIngGu+5556497D33nsn/e7ll18ebLHFFm3emz17dnDYYYcF/fr1C3r06BGMGzcu+Oyzz1r/fu2117q/RZk2bVqw++67BzU1NcGKK64YXH/99e3aZv78+cFJJ50ULL/88kF1dXUwcuTI4NBDDw0mT57s/n7OOecEo0ePDrKF4/jnF35x7Z5Zs2a5c9PmvPh5zpw5bY7z9ddfu3uiHQYOHBj8/Oc/D+rq6tp85thjj3XPORkTJ05050+FZ555xn02fC2vv/568IMf/MD1kV69egUbbLBB8Oc//9n97YUXXgi23377YMCAAe46+dttt93W+t1PP/3UPWP+xnG5lnjE68c8C9oy2zEUfnGtnr/97W/BaqutFnTv3t1d/zbbbBM89NBD7Y51xx13BKNGjXJ9Zq211gruuuuuDs/P/dCW6bQzLFy40F0L15ZKnzz55JPb3NN5550XbLnllq6/cF+rrrqqG+MzZ85s/Qyfj9c2jLVM4X7jHTPMs88+G2y44YZBt27dgpVXXjn4z3/+0+bvjI/odzoaA7feemuw0UYbtfbNddZZx7VBbW1tm+N8/PHHbg7lOCNGjAhOOeWUYPHixe3GSLL+IoQQQoggePPNN53MMHTo0KBv377BJptsEtxyyy3t1vSovF5fXx/sv//+weDBg4P33nsvblOiB7BWf/LJJ23ev+yyy5xMgyy25pprOjk/FX0EeZXzIScgG5522mnt5KqbbropGDNmjJNPkMG222674O67724jH7zzzjtZPfpU5YxLL73UyVRcC/INOl0Y9Dpkw+HDhzudh2udMGFCm8+8/PLLQf/+/dvIOfHg3GHdJBnp6FRLliwJDj74YPce98FnkN/Cstnxxx8fDBo0yLVBIlk/ngzMNXAtmeBl73gvr5+89dZbweabb+76LnI08j/XsWjRorR1tHj3Qx9MRjx9CI455phg3XXXdfp5R32S98P39PTTT7tx55/HsGHDgvHjxzsdLiqDx3uhY6cL/YC+SdtwTvQt9BH030Qkuq+wDp1oHPGK6l1/+ctfnMzfs2dPpxuF7zfVc8+bNy846qij3HjiXvbdd99Wu4EQonyI8b9iO1aEEJ0PyuEQ3UON1XxlGnRm2IODaBEiyqL7rIQhS4K/ayovHEShsek3mRNCCCGEECI1TjvtNFeS1Wczi/Qgap+IeDJ1k4GMyobUvET+IWMCnQz9QAghROFQ+SnRJSElkz0PMByL/EHKLOmj8TbOFsmhNigldd577z33SgQbCAKbNYrCQJo3JarC+7oIkQqk+FOTOZ19eoQQQpQPlB6iXGJ4w2OxDErGsG8H5XaiG3aLjvVXSvBQRqcjuZ9ynJTxSVS2SOQWyh+hk0kfE8WCMmvs55nu3ohClDvK1BBdAqLY2QiKurC82HiK+p4jR460coXaoX5fh3h0tIEcG6DFq63pa09Sy1WUPtQHZsPAXG9eJxLDPipka7CXihDpjlf2rSGLjbrN1Dvefvvt3X42Qgghyg8242XPOjYTRsdA32B/NfaCEqJY0A/Zf409D0T+Yd8R9lNJtrm6EPnkm2++cYF37AmCfoGewd4hyfZgFaIzIKeG6LQgWOC8IGKCDbbJyth2221bDUlsYpfppsalQG1trYsGS0RHG9yx8RmveLAxoCKJywOyBdgUm7RnURhobzZLlOIiMoENSlmbvAEMJYRNTtmwkJf6lRBClDaUmbnvvvvci8xNgqS8fsFm2YqOF8WGMkhsosxL5J9nn33W6WToB0IUA7IDCbzz+sXzzz9vw4YNcxu60y9xdpDpJURnQ04N0amYP3++S2nGkfHwww9b37593SS+5557OocGxnohhBCiVPjyyy/duoVxDKUYhzvrFi9+VoSVEEIUP+P7/fffd/oFLzJk2bMAR/Suu+7qygEJIYQQpRQA+8ILL9gDDzzg1i3sZLvvvrvTL8aPH+/sZEJ0BuTUEGUPEzQTNRtSP/XUUy7NzhuENtpoIxmEhBBClE0WBw551jQcHUOHDrUDDjjADj74YFdSUA4OIYQonCPjnXfecfrFnXfe6erl48BAvyDydcCAAXoUQgghymI9Y48d75hn786xY8c6/YI1TQ4OUc7IqSHKEuoFUlLqlltucQYgatcedNBBzvjTUdklIYQQotSpq6tz6eO33367U0CWX355p3zwYs0TQgiRe9j3CEcGL+rkY/A58MADXWmp7t27q8mFEEKUNezLirP+tttuc2seGRzoF/zLvrNClBNyaoiyoaGhwdUIvPnmm12ZDvZ8+NGPfuQm4LXWWqvYlyeEEELkzZGPAx8jGw59Nhpn7TvkkENsxRVXVKsLIUQWfP311y5QihcRrGEDj0rXCiGE6Kzg1MC5wfqHI5+yitjY2Cequrq62JcnRIfIqSFKHmrY/u9//7Mbb7zRbW6EEYeJdvTo0SrF0UUIgmab1/C11TfPt8pYjfWrXsWqKmqs3FiyZIlNnjzZ5s2b5zbzSlRKhhRRT2VlpdvQnheCBZtPDho0yGpqyu/+hRC5KbmIYx/l48knn3T7RR1xxBG23377Wa9evdTEQgiRAosWLbK77rrL6RjUHd95552dfoFBR6U4ug7f1c2zt+dMsobmJlu9zzBbp+8KVm6gN8ycOdO+/fZba2xsjPt3QO/wP3vdwr+QH9Av6Pv8LoToWjA3vPvuuy6AiiDi+vp6+/GPf2yHH364bbDBBsW+PCESIqeGKEm+//57N5miaHzyySe2//77O6PNjjvuKEGrE4GRnxfp/DisqCdPOmTYqM/Ps+s/tyXNs5e+E7OKWLUNqVnfKmOZRQ8MHjzYZfrkyjHAok9kAwoFzgpP2GnBfeCUWGmllZxjIhWFge9wPP8iW4k2mj17tmu3MH369HGl1xRRIUTXYfr06XbTTTfZddddZ5MmTbIf/vCHTvnA0SGjhBBCtAVZCgcGcyalN1ZeeWWnXxx66KE2fPhwNVcnAfl5wYIFLjCIUio8988//9wFBYSZtWSBPTL9PXNqR4z+EdjGA1ex9fqNyOic6DOUihwyZEjO7mXWrFlurV+4cGEbvSKqY+CQGDlypNMDUtl/K6xf8OL46BcEXoX1sKqqKqczDRs2LGf3JIQobZqamuyZZ55xtjic/1RFQb8guDiX85sQuUBODVEyEFlCeY1rrrnG/bvllls6RYN9MjDYivICARnlYe7cua50Cs+XGvHghWUcGaT1Ey2HkZ5NF9noHSXE09C8yL6tfand8Qd2W9P6Va+c8sLMtbDJI04B/l1ttdWsX79+7pp4cX4+FwWFF+MgTova2to2f+N6cYywoS+bRyL0I/xnw6uvvmpTpkxx7YUTg395MQY4H9eMssL1ch+8z3v8jXsKt13YMRJ9XwjR+Ta0xVBHQADzAuvnkUceqfJUQoguD1my1157rZsjMXbjxMBAs+GGGyrruwxBbke/QLZHVkYm9hkKXscg4wD5FyM9MjD6RXRz9/unvm2fzJ9qy0z4ZtWxSvvVmuNT6heci3MT1IRMzvXQvzbffPNW3YYXwU9hRwGg/6y66qruewTzhf+OfjRw4EBXFgYdBJ24d+/eWfVV2uyee+5xeorXMXjRNrQL50On4TpoW9qN8pZcCwFZOE2ioDdxfdlclxCitGFeIwgAB8crr7xiu+22m/30pz91to9s7R5C5AI5NURJ1LG9+uqr3QvBCiMMigaCnigfvPEcwRYB9/7773dGdrIiiJJi0SOCKV3Bd0nTPJta91rk3Zj1r17FBnSLvyk8Rj0imzjXeuut5xQB0qoR1onUQnDnPRQelAre5xVdmLmfL7/80vXL5ZZbrk1dZS/047DB4fH222+76Kw999wzrftrd79LlrhIKSKtuB7+5fzhCCqui+vlnl588UXbaqut3N/jOWWA74czSPiu0khFutDnpbiWPhhPfIDAo48+6ja3PfbYY11teCkfQoiuAoZb9iD673//a0888YSNHz/ejjrqKGeQwVArygefTcAaRoklSqSsv/76To7nWcaT4VPhjm9et68WzWj3/m9G7WYVsfYZ1WSTE8gEPhCL8xNIQECSzzwn2AgZnd+5Nt6Lyk8YCtGByRDCYRDOrkQXQMfAMcK9cs/s8TJ06FDLBnQjdAWvX/Avv0ezwMkoR7dBx8GhwXUk0y+8QwbHycYbb6xSmCJtpGOUB1999ZVzbqBjMPZxbvBinhCiWMipIYoCkSEPPvigUzSeeuopp2BgdBk3bpwiyssEhHEM/jxLBGEcBQi3COGbbrqpE87JOuBnhPtMaQ6a7NvFL1iT1bd5f7num1r3ygGtRjyinIh2QiBHqOb6cEKgaHAtPgV9lVVWcUpGZ+DNN99090gaKO0fT6GjLaZNm9amNBYOnXXXXdfKUeDlWRd6PxGf6YNDif6OYooCimDHNaHYEs3WGUt/oaSjdPu+Fa7FjCJOdpLKHJUuGEKITr7qqqucgY+ggaOPPtploAkhRGdk4sSJLlAKowvrMnMec9+IEemXFBKFB5kLuQO51esXyF28jxy/9tpru/0WcRqsscYaWemNb86eaE/N+LD195jFbMWeA+3gFbdslXmQ/XymN+sovwO6DbIR14Zc6kvbdga4X0paol/woq2jThnaBicJOgbOD0Ae5JngFCk3fNZKIWVa2tBnHeE48joF/Z7+hr5G2S/6VmeD9n7vvffcuPZ9y+sY9DfkVO5flCY8t8cee8zZ8gikYk+qY445xvbYY49OqQ+L0kZODVFQEJCY/DCyIAAy+aFoEOEuygNSoRG+EObJxIgamHEqYOwl/RqBxTsaEND8ZnQoJf6zo0aNsm222SbpOZc0LbAZS961xqCWHTVsULe1rVflci5iCoHaR2mhAOGwQAgis8IrQl0BhGGix1C4EMpRrLh3nB6MLwTiUjM+h6NyuG4cFmScvPHGG+56o8+Oz9B3+Fz4GETqYazIV5k6Ijzpx35fFBx1COK+/Bh9cKeddmrt152Fl19+2b755ht3v74MW1iYpcYzRnP/DOLNB6I04Hk9/vjjbv0lcpn+SiDBXnvtpewNIUTZg3xAhvAVV1zh6oBjWGGOI1NN5TfLA4JHPvroIyf7EYCEMT0qB2JAJ4OANQ25jGeLzsHn+JkX+gk6B3Ik+kWyvSCQX579/mN7Y/ZXrgTViB4DbZ8VNrbGRXX26aefuvMg6yHnAfoG2RIEdXQleYcgKh/Ig7yHfsXPPCP0rVKTf6Mbo/vyZDxTnFJRY7nPAvI/+2PQn3je7FWSD32SfkpJMJxA2EjIqKWv8T79Ft2O9ib7pbONdfZpIMAOO0A0+BFdEOc094/uQRYAz6Gr6PTlGADnA6gYa9j2WH8VQCUKhZwaIu8gFJCNcckll7hSGCzYxx13nFM0Ss3IKjp+liiLGMRShWdPFApp/4CC4NOYEeI6ElAQLvk+UVvz5s+xmFW67/A+G2P76BUEJAQgnCnsfUH/6krQrmSrIBTTvttvv33JGSuJOvrkk09aDQw4J7wSigCLwMpzRThCaUVZQphHmMU541/+uz4rZ8KECc6JQymCfOJLjqFUI4DjLOrsAjb9CmchWVlETqGAxOtXKIl8hmg9Spvx3ERpQv9F+cDBwfM94YQTXCRztmUthBCi0GDYvvLKK+3yyy93sgSGFAwqGFpFefH666+7LIxUA1SQJ5H7x4wZ4wzAyI8EUyGPYHTvqMQYMh2GbmTTSZO/tobGBquuqHLH4RowtiLv8DkM+n7DbgI9ulr/8vuXsHfXRhtt5Iz8pQbBRr58FqALojOgX+CYoj/4KgPI7jxDZHn6Dp/zpcL8Hi3oGchLlC3eb7/98m6z4HzoPpybQC1k7s4Oz4jnwZglwyeeHOozt5jrcZ6pfHLpwrN68sknXXABAVTsufHzn//cxo4d2+n1ZVFc5NQQeQPB4frrr7dLL73UCYIoGjgzSlEQ6kwgfPt6qYkgAuSDDz5oU4YJAzKGYa8E+Mh5fg8fCwHv6aefdsojgj3R2VH4LooGwhmfR0Fg34dUPfa33HKLi6bx0e8ImfQbn87MPdK/fHqu3xwQuC4Mr52lxFSq0MaU+6KdSnWM8ZxeeOEF23rrrTtUNrkflEecNL7sk4+U4vn7fVB4oZgoEjP/4DD0kYvg60bjwOBfslh4bs8//7wbg3yWz/iIOX7mWaG0yKFdfHiOlIH897//7cblgQceaCeddJKb14UQotQN4Mxdt99+u2233XbOcELQlGSB/OKzgZMZqNhjDv2BdZ71H3kcI204expjpi/b5N9jTXrttddcaSkyNdAB4pUxQe7H0My/BMGgv5CZk4rRDH2UIDv0ER+kgfxCCVGv63Dt6Bd8NrqXBPJLV4w+RpfDsbPZZpuVbGkZjOP0T5xRHcFzRb9Az8DpgRxLP8KhgKzqKwCgd5ZjKa1yg3mC5+czohjLOBa9fuHLn2FXoB+y7ypzQFjXZ34h2KwrOIPKATL9CTYggIoAVNbon/zkJ3mrqiC6NnJqiJxD5AyKBpsIrbPOOm4Sw1jSldJ0iwHCAOViWPQRyog6QciPRkxjmHzppZfcYkPpEYQ4IBr7rbfecl51ot9feeUV9zeEe4zKGLpI1+XYzz33nG2xxRZOcPCbxPE+kRSfffaZE34R+hEuEDI4TqqbMnI+zo2ywSLI9XLM8CbYXthhXwOib0otI6EYULKJCLdSrz/K86Wf0T/JrEhmgPBODZ63E2h7mX0660Or7B6z1UauYWv2Wde6VWizz2Lh5wYcjLxwPAFjHQcpisfmm2/e+nnmCJ4p0VYonltu2VKzWpRGWcHLLrvMrrvuOq3bQoiSBNkUJwY6BnPWEUccYSeeeKKttdZaxb60Tm9wvPfee1sN/BiBN9lkE/eKgqGRrHzWd54LMikODHQOvkeUPyWmyOpEVuCYyHc++hr9BIcGuoPXG5EXyBTg2Dx3vz8eOoLXA1K9DzKaye4lcthfL86RMFwX5+cVby+JrgayHc6mjkoFlwIE1OHowrHh9dtkJbXQLV3JsgH9bHrDZJsye7KtvPrKtmrfUTa0u0pjFwvsCswNXsfw+434EtYESKFPoPcCf2Mc4/RAxyB7i/ErSmPdvuOOO1rX7cMPP1zrtsg5cmqInJaY+vvf/27PPvusHXTQQW7CIqJD5A+UAgRyFgxfjgfnBErDmmuuGfc7fJaFH0EOQfW7775zAjsv/kbEG4ZkBAMUEF44DXiW1JAFIikQGhAyEDwQMvg+Sof/negJIvI7gutgkfObzHEusj+IzKBf3XbbbU5xQoFRBF5iUDjCBuRSB2cViiXPFMUjHCXnlVicdPRHeOmNF+3pDx+39bdYz+bOnGuLF9Ra46JmO2r8MSXvyOmqUAqAOSVqEOB3nhkGj65uLCg1UB5vuOEGp3wwzxOU8LOf/axTblIphCgPkGtxujIvYcRmXjrssMNU5jCPIH+zZxa6AOs0awN6Bg6KfffdN2G2Jc+KzyHb+82P+Sy6BIFu6BHoi8j4ODr4HPLfhhtu6DIA+B0dhM/7UkK8j3GT43A8ficoJtleGeHr8XtBAPImJU2RPX35KnQVlcxMDAFrPtO2HOBZkylMtg0GcJ531PFF8B39E6M4cuoFV/7F1txyZevTv7dN+nSyO8a6QzeyHTZLvdyyKBw8Hzanjue4Yn5gTmGtEKWXYUkFF2w7O+ywg5166qkqTSVygpwaIiswijMx4cwgMgJHBgYQ1ebOrk0R9IkmChv8ECopv0OGhAdhHGE9nMpH2yPAZWIs9Bs3+30siGLxjguMzGRxxMu44HsY1VFK+D4KyeTJk11UT7wyUDgwuB+UJI5NVkYihYJj43jheBjCEVL5HtfDC+VKG82by57B+VNuabcorTfffLPtueeerYZT+gSbGuOk8YrIy5++YG99/oZVVlXSKVy/6N2/j+294f7Wv0dqUXqi+DCffPjhh+4Z+zkK4wWOzK5Y0qFUYXwxBi+88EJ78cUXXY36X/3qV24fIyGEKAQE7Vx00UUugwx5EgMI+6XJGZ7dvmDI38hevh19pjXZ214WR76mlCRZtT6giMATghEy2RTa6xeAfoGOQ+ADjgrOReBSon3RfOlLNkvG2c61ci2jR4+Oex5f+hZ9itJByQIouBYy3WkXAr548R7XhG6x9957W1cHnZR2Kce9DNBj6TPjxo1rfY99QejD7OFA/2tqbrIrHvmnVVZXOf0CKunrK69j26wmp0a5rRk4Mhnvfm5hfiNAToGRpQOBsT5QAdsWaztVXUq1tJ0ofeTUEBmBUZmN+f75z386o/Wvf/1rVycvE0G3q4MAzuSOkM7PCOpM8EQf8DvGfARKog5wVsQDwR1Hgt97IlWFj8We7+KoYNHn+SHsIdD7+vhEXPG+d2ZQVgZDM8ZJPuvfp15uqs4FIvBxVHCeAw44IOkiRtTWXXfd5e4d5w3ppChdfIe26qrKrXf24EykjXyqrk+pLydQOFAquQ8i+InIo7/z8kxc9IVNWvQld97mu5sN3MZ6VXX+TA2UeoR1v38M4w5nYLkJgBjIKUvhlQueOeXm/Ibx/M4cQ4SVV0z8C4cdBopUx3xzEFhdU50rUVZVkbjMmUjOu+++65wblH2hpCHKh0qHCSHyBWvCBRdc4DYaJfMbHSOeAVt0DLI98hUyBLCGIk+juxFQQHlXPoPDIFGZWNZiXqzN6dRDJ6sDOZ3jI9thGCdDF50CmRXQMfyeBZwfQzNrPMEPvtyVz8xIpdQs+ikOeWRknPAEWyWD86B/kUGCbsMLRw7n6spGUJ4d+ifOHp4HcjpVAFLJjiklkJmRnbkP+hF9jL7Mfi2eIGi2F2c93u67g7sNt7X7jrHODmORIEm/nwVyOHtWlNuzZr6g8kO4LB5jm34cLoFOaSr6AH3D6xc4QAisS7VUNtQ3N1hT0GTdK5btCyTSgyBXytX/4x//cDahX/7yl3bMMccoc06kjZwaIi0woOLIYNMfDGq/+c1vXGRPVxb8MsVvUAYIDmRYEFnkhQrw2RoY+PiXduazOBD8Asr+BECUMxtFjx8/Pu7zQMBHmWDR8GnYfI6FHuEV5wYp2F988YVb7P0Gf6SLewcHAgPn4xwYujhWKhv+ch6UFoRKb5RFMcJonUrpIL7H+ehzHIdr9Q4Zb9D3CpLf4M3v99FZ8Xub4OAheqozOBTpb/RF+lvUWL+ocaG9Oftla7aW52wWs75V/WyjAZt3GmHSRxgyL4T3kKFvM04oKUffpu8zTpkvdtxxRyul62dMYqTgX8pL8Gz8nMJ4ZCyzP0+yGtgoV2RkYUDxx+WFAYbv+/eYC1C84jl2Zi2Zay/OfMvqmpdYzGI2pv/atmaf/GaC8MwwqDAn8vLza2eZh3Cu/+tf/3LrPwYmnBtEsWrTdyFEtrDO3XfffS7zG1n1uOOOs1/84hcuUEekB2sl6y4yOjIDmZDIjMgXPkODfwmowpDnM6qjcjkBJgQvsRYjd/jM6niwZmM85thex8A5wLrI+z/84Q/d9fjsDH8+5BpfZoqoehwRBC+RJZLKBs0ua7d3b+eE8PIBay6lknh1tD7xffQLrpt74x6RX7x+EdYxOIffQLozyNzJ4JnRhyjP1FmyaP0z5RlG+8VnCybYd0umtHlvvb6b2IBunaf0JvoV1R28s9BD3yZTCjsE44BxT4m4XXbZpaTK+/r9O9ExGIvo+dzLBx980CbrbNttt014DOYgxjtOTOY9P1cxv6Gn+L0BmUNw/sYrvUrA1FuzP7AvFn3jfu9f3ce2H7KJ9azqYfmEe+XefRCYn486gw7MuGT9J5iBPZeOPfZY5+DweqAQHSGnhkjZmPHXv/7VrrnmGmfQxpmx1VZbqfWy4Mknn3QCvV+MWFzJdOA9v8cEzgteXvhi0UUpoc4tkbJ8l/qE7HeBEvPEE0+4BYCoBEpF+VJECALPPPOMOxZOkbCiwAKPM4QoLc7NcfxGXCyelJXyAgKOGKIeUEIQdLhm7/BACMIhw3UjVHhBwde25HeMnJlGlvN9hGxfT5fF3EdS8fJtxDXzwrjIIolSk2k5rnKAtsbR1BXG47yGufblwk9tSXOd9asaYGv0WduqK8orUyEKfRQjvs9KYA7gFTaEM7YY037fGf6GQM7nimlQ9mMeRYkXTm/GGkI2Y5O/+7ISfj+eXOE3BURBY7zjKPFzS2Nzoz0w7RkXRRVmhyGb2bDu+VFQeX5koeAE5tqYE3m2zK/e4Qo8L+ZfHNXpZJ2UEjxXMjUpC4Px6ZxzzrH99ttPzg0hRNowP5KN+8c//tEZxilzR6Qm8qLIDGR1Xt7wzjqDcQ55mHUTmQPDPGWZwiVL/V5nOBYwcLLnBHIGwTPoEHzWBzOFN41m7SfjgQAbzhPOqEBuR3Yh08bvs+Fr3bMpOLoHuoTP2kS3wIiOHMH7nItzImvwXWReHxzFfaG3oPfwfR9IkQnoDFwr1x7WMXyAgi9l4/cVQRehDdmQutzKvqYDOuB6663Xqe8RmoNmm7ToM5tdP8MqY1U2sueqNrgmt3JrseQ1nFMY63EyEgjEGPPjxNsVyPz3sirjHXtBOllZuYZrYV5h7mC8YXtgnDPmmBu4H+wV/E7/zHXWuq+AgWzPXIhj2PPx/K/s3bmftP5O4NSgbv3sB8Pzp4c/+uijbv5mTvfOVl9ay4Puwd+ZX3mFs1PKCap54Nzgnn/605/aGWec4fqjEMmQU0Ok7Myg5v1ZZ52VsOapyE0NcwQNhH8fke0zEviXBYpFHcEe4ZvvYCz0ZXqIuuJ9PhOOrEEhwPhHTVqeHwqL30iNY+HoIOobwR0hhuOwgRMLC6nbCELh60QhYiHluFwb14wywSKK4OujYPisr8tLPctCl8rh/Bj8UbI6S6RRVFjlWSDwqRxMeYHxhrIPgLBMHw0r44xXxiHvoWAwxktBqWSeoL/xL9eFkM38gPGhWBkJGGmI/mTOgtn1c+2J715u8xmUjrX7rmbr91szb9dBtBhzIkojmSjxjCveqEMWHgYklDHKOlFu0GfkAcYU2henAa9ShHtFNjjvvPPc/Z599tm2//77y7khhEjZmfGHP/zBzYlnnnmmM2CUqyGmHPQ5DNQY4ZEnovoFL3QH1nICqzAYsl6xjx/rEn/3JaEIpAo7Lgi6YE3jX0oUIuvzubfffts5RVijORYyAn/DqMqagdGQkkZh6AsYWHEe+Mxy+oQPBPCyht//z5+XwIZCgx6E3oZ+3BlBn0RP3HnnnePujyhKE8YFYxgdEd2ckm7hskrI8MirfI6xxBjFllDsrFvmDJwIXDfyMXo7eg+GeuaNYgQBedsMwZ3eSfzsjDdsWt337T570MjxVhHLTxvyzAhA9aWZ49lTfLUDX9IZhzFZD/zM3B625dCu6BjYf9IpuVVIcLD/6U9/sgceeMCOOuoo59xQ5qZIhJwaIqHw+5e//MWuvfZaV14KZwbGF5H/BZ2ICZQHFAaipFA4WHxwNrDwIISEN9wLg5KAwyLe3/gOhj8UBQ8LI+ehjBEpxizYLIZ8n+dN5geCDlEKvBIpmxzbOzlYaDkOPyOc+BJbxVA4gPtBEcNR1Nki/4gsQ2ncfvvtyzLiuyvD2GbcMO7CMF5QJDGMl1raLeOZaCnmDQwMxVaAcPoQgca8xPj2RpaFjYvtoWnPtvs8JahG9Vklr9fEPIiBBaeVj5ryhiPv6PVgjCHDCkWDeZ+5KpwKz/hG4WRj3FKGe7766qudcwOjE5kbcm4IIeKBDHvnnXe6zAyMVzgzMFjImZH/dmfNZG1iXcFoSPAEegWOc1+61QcqRWH9wiGSKHqb9Yzj+e9yLIKo0C94tr60DWsd+oXfRw25HD2DfxPJFKyHPogKQxzHwVHC/fAvgSHFqv9PeS7asxw30e4InGAYlsttb4WuDuPk6aefdvuGhI3fjCPsAMiZyMylticfmVuMZZyexS57RRviGGKeRB8jgMvzysz37OvFU9rs8FgVq7QDRuySV12c54Yeho7hs9WYD1lHmTvDpf/4lzmTgFLmZnRLHB7A57A38d64ceNKNnDKI+eGSAU5NUQbSPX785//3CmcGUHQZHXNH1tj8J2L0+0WW8m6VaxaMOMvpUii3m9fG95vTAW+jmu0Pqs3WPv3USRQCnBc5NqYyMJHNA6RWSyQCBNEDvMvCzsLIsqDd4iEF07/O/eKcY7PkC5KVA/R5xyn2IITihNtWSzHSj6MiGTR4OxCoSMKB6FLlAd+bxoMBGFlkfcY536jzFIDARgnAuXnmIuK7RTitfnmm7t5xs8xfn59fdb7NnHxty5DA3pWdrddhm9j3Qpcroy50O+tkS5+ji3FvpBoXvKZGxiofv/73zvnRrlcvxAifzCfkZnBvIB8+3//93/l7cxonmnW+CEStFmst1nV6JZ/CwAZusjlqawZ6AzIFFGQN/zeXaxRyOqs67mOyPd7hREsB6zXyK6cC10GoyGZn/SJsF7hv+v3E/Sbd/N5HDTcF9dcCsFKlAFmc+JiB3rkCmQrjKfodch92ANE+eCN8eE+SQbU+++/76L3SyHrO5ETjfkHm0g4E6zQMO/ccsstzilLEJd3pPr5aF79Anvsu5ddyTL3eQts4wHr5H3fvijMgzxnf33pkihAtlQhu4jMjfvvv9+OPPJI+93vfueyjIQAOTWEg0nx/PPPt3/84x+22267OaUj0WZw5UJt0wfWGExr815NxSjrVtFSqimfIATyGjNmTIefRal44YUXXIp3OsoEixmKAs+OY/hNhMP4MlU+7Ru80kD6ON5+FA0cJ9Qr9JkgODJwcpAazuZ+XBfRXUQSo3jg7ccRMnbs2Nba9qSLcz6cYJyP0jphxw219otpCCVtkzbuLPDsUGzJ0hDlA+PmqquuckqiLxsHPoKSGtfFhLFP5GEiYdenN5OKXcwNsJmncMIy9zAvcd2+3B1GMvbxeOmT12x27VzrXlVjK/ZY3rpVVru5jBdtH8+YRqQY8yBzIk5df4/8nMqmpanAdRMthyOGa+Z6mH+JlionBaMj5wbKB+3Mxr/h+utCiK4FQT6nnnqqkzcxRJS1MwOChWYNLzlzVgvM29Vm1duZxfJrjGOto6Qr8neqwWo4QFLRR6LrFDoGgUo4EKKZo8j5ZGb4/cDCJXN9CUbWOdZqZH+cGX6vKfQI9B6uiQwH7gmdCR0C+QJZCB2UUiPITOgX3ANrJToGkbs+6hi8IbRYMgn7HEJnqfvOM6fkDiU9ix0xL9KDDb4ZM0Tghx0DzMFkBhfb8Ua2N+M5EVw74x4ZvthjgPmIucg7XP1eFsxvM+Z9bxOmfmJNQbMN6T7QhtYMdDI9cxFzX7x9BJkn0N1x0DKufDko5k8C3HLlyMH4T0CVt/cw/zJ/l2qZqXSh+gZ2ykceecROOeUUt89vMfd/EaWBnBpdHARJDGxMDkQNszEPka+dgQWNTyN2t3mvwvpZr6rN8nI+v5F1eFOpRAYqhH6UO6KeMWayiONtJh2Uf6MGRf87Ed44G3hu3vmAM4MFDCGAZ+jBMInSSIQWCy0LMcIEigPKBYs11xnea4LzcF28MLAhVHBszkO5Gb7H/WFIZNFGyeDYfJYFGUPgG2+84a6TtFd/DxyXyB+UpGJlSxCVRtRFuS7qpOqjQIaj0rifcI1MUfowltgMEwXcb5bJ+PD7zuTCqM3cgCBO9E66/Z1rQfmhznWpRnN1BMYOxjtrWThLjHbxm3wyj9I23COf8aU3vHOJ+2ce85GsKDXMmYBzg3k1UwMKRiYckv7ZMP+j6PC8qM3dGRwbQPuxmfjf/vY3V5Ob/bnCa5QQonPDPEsd7CeffNJOP/10twl4ua4rbWiaZNa0bKPYVqo2NatoWddzDQFM1EdnfVhnnXWSGpspR4I+ghGN9QxdAENcOLAnLJ/7n8NOCvQAjNr8y5qFkTQcHPXwww+3Zkr6NZJgJmQcgpjQb/h72HnFWodOgQ5EAAf3xDWyBmN0457QXTAIct1cB0ZC9AvOgzMD3YP7Q18Kt827777r1pdiBE8hW6D7lKv+zPN86KGH2rQd8ml4c2RRHhCMiG5OloYf1+j+9NFcOQrQ5Tke4zVdJwkODZyl7NdZrvjSedGsel8mj3sk+8yX9fM6BvMnVSPQ3bGXeMcsx+I7fJ/PMFd7/TATCM4KZ6/THzg+pWzDwXTlDpk9BEuw7rA319FHH13UDB9RXOTU6KIwyZG+hZIBGB2IHO4sxhRY0EhN9YY271XaQOtZtXHO25IJFUUABwPp0PE+gzMBhYEXCxjCIwsXzggUBgydOBgwUrPQEfGDoM7PGIe23nrrNoY6FkwiMjCOErnEe94hwc8IDTgWvPfaG06pC+nLoXAOFBAUBV9Hl7/zPkY3zkGqajiDhLJHXD9KS7zFkfq5LNh40jknAhb/+g2/ELSKAUIYjpVinT9TeC48SxQ+lDb+Ddf2FOXFM8884zZ1D48pohmZB3JV15RzcDz6vC8ZRz/yYxmjAkZ0orbiZR/47DHmms7sNPP7ANFGtA//+hfKSqJIYowtzCW0HXuiZLpuMjcyx0adI8zfzJV+g0TveCnX9RllCoWDspZEaLPnRrHLlwkh8gfOYYKl/Jg/++yzO1dd/qbJZk0ftX+/anOzigE5PRXrODI6a0GisiysYzgRkNtpe4yNyPHoGMgVjz32mPuMD2JCr8Bgjb6AfM4ag1yJ8QuHCTLAo48+6tYmjI84GTBiYkRq3btq4ULbcccdW68B5wSOFBwnb775psvOY81ijWOtw/iJYc+vmXyGc4ZLp2LUu/fee538wnmjQRk4TrhPrtlnd5CpyT1yjRyrWP0MHY5rLrcNtVmfuWaeOU4zZVWWL4x9xjSyvgd5FocbNoRcgL2CsU4QDnOAL6tNwB2OSvrRPffc42RosrDiya3MB9g1yr0iSDKYL8mW8DpFWMdgfk604TVzIPMoega2FNo5E3gOzEnROZTnwVridQte6HnFzuDJlK5gzxSpIadGFwTDONFSRFChdODZLPaeB/lgSfNEq2/+os17PSrGWFVFe6dDNpkZCBGJasyjOFDb0qcvomTggIjunxEPFn2OyWdRJHCWcE4fOYPygSMlvNEwwj5ZFEQZx4sixhAHCD0oLigELKA+aiBcHxbFAMdFFL9fRrwFkD6F4Z1oEF9WBSGHxYVz0wacq9A1cHkOCHU8p3gpoaUMTiKUNgQTjKgogYqeKk9wUj777LMuaj3sLOC9XG72zvilvFwYxjkZGEReMjbZRJA+5ccnc0d4TGPwJ7qLaEjmLAmIiTdODyuQ6T4nHCe0b3i+9vsSofTx8opRFJ4jjuVycXKyBhC1Td/jX9LGy80AJIRIDIbzCy+80BkWKI/UabOzgnqzhheXBk4tLUEV69fi1IhV5NSZgazAGhF1ZoQzq5HlMX4h47ImdLRec2z0EgxrrPWch3UmnC0aLVfDGoSRjOcZT47m2fP31Vdf3ekvBOAQkOMdF/64rJvoCmRtx8skZc2Ll82DDHPbbbc5xww6K0ZDv/5hmMehw/fQMQotrxDYRiZnue2rgb5IcBwGUPRT+gLBcKL88DYJxgLlYcNZBYyXXEXoY3tg/ohmfXBuxjN2AxyZ2D6Q79BFkP0Y14wN7wThM/yOY1Slg9rDmEQ3DFe9SAfmdwLmWDvCwbbMmz5Y1usZ4VJVYdBPsDGVQ4WLcOUZ9FrK6Weqm4nyRE6NLgSG0TPPPNNuuOEGO/HEE13kVGdeSFwkTzDFGpunu8JT3SpG5syhASgSLBgYI6PtyLlxHjHJsmCwoEQNV+lursj3USb84kbWBYJLdLHjfDhBiLbxe2R4gR/lCMUD4yoKCgpNOIUb+BzXmYkAxELpUyzjwbVgpPcGV5wc+TbIIYDxLIhSQWD3qfLlBH0AJZDnxiJdTkpTV6U5qLcgaLKKWPfWMYpywRiIbtqJYxTln7GYrTKOcIpjEWNEFOYFsi9QPhh7RBUSjclcQGRLtF+xZqCMEFUUnSdEC2SkUQoDgwDzqX9+pH8zD+KI9M5hsvQwzLAeMA+htJCKjmMEJ3O6z595gfkagxZzO8YezlfqDiiygAiswAj0r3/9y3bfffdiX5IQIksefPBB+8UvfuHk4Ysvvrjz7/cV1Jo1fW4WLDaL9TWrXMMslrsAMcoBYTiMlw3I2kzGJe8jU6AfZCrbosdgAMMg73UZDI4YLqOyipdFOSdrlpc5yPzkO5SG8vtioP/ssssubRzXfJ/5P57u0hHeGJesfBntwvUh52CwxyiXbz0XpwCBRshJnKsc94pBb0RPwhioLMoygA2qmX9i3VrnHMYWZf4wgkd1cMYc4zsXmdfoF8i70fkGHQaZFvkWXYM5iawOAqZwYEYzE5gfkImRf9HPO0VZwhyDboGOwZzCfOufH/obTmfGKs+Cf7Hz0N4EjfIM+BfnMvM7/QGbEPNzuvMCzil0RF/Fo9SDoLGTEFSBU+Owww6z8847L6tSXqJ8kFOjC8Aig/cShwZpwSgdCH4YlomoKSQYdBB+MeIh0LIIE/ET3tehnOB+EBZYbFAAaFcvfBMxhYDLgkQUD3/jWRAJRUpmNiC0E5WVyCHAQuQXMtqZhQyBAyEEBckbLxFi+T2cpYGRExDSowpNLmCBZNHhnFwbi3U++6EvD8aizGJMlBjCXalHCBM5h3JIqSIfoYfSiAKpTI348Gx53rQXY9NvbI+ySaRgIZ4551/Q8LHVNrdsHFkZ62H9qzeyqooWgZ2+iLJN3+c6cSzwM8IoAijjmjGZSV1QlASM6Yk28caA7jf6Zm5C6cDRgpOD84aju0R6z5znx9rGc2NfJMCBzJyL0gb0QeYgn2XH34kq5XmwfqD0ZVoP1u/LQd8CHzHH3O+NR6xLfM6X3PKp58VwkrIW/t///Z9dffXVzgH3z3/+U/OaEGUIa8gvf/lLV5r0tNNOc3oG8ubBBx9ccAcr8x/rKAEs3uCPrlOsDaSzhbUCmZmAJpzX3BfrDcYt1g7+zh4UtDdyP+9T7iubdqfNMJihq3Qkx3MtyPK+ljxGcW84Y41B5gjv48EahW6C3E+Edq4NZF4GQr7BwU+ZnFztVZYIv5cI941u6/dULHXuvvtu5/zxG8DzLBnDuSpT1Nmgf6OT0WeRmXjuXq/H2IstoyDzXdNcs0WvmQVLWn6vGWXWvSUbjjHpnY686IcYuBkX77//fmtp1UwdoAQ5MXbj7c2BYxR7B2MPYzz6BXoF2VuM+z322CNuuVuRWt9jzuVf2hfZHucScyu6B/MO1TUYy/RL1gWeAWX5mI/oF+gE8cqjpwJzA+dgPmWuB45Fv/dZHHyGv6HP8EKXQffm78UItKIvnnDCCe5fHBtUpSlXOUCkhpwanZxXX33Vfv7zn7sJ7uSTT3aeWjyWGMNTKYGUKRheEbK9kR+Y1BAEmIxZUL1T44EHHmitgUo9zx/+8IduEmQSZvIuZtobQgHXmGwiRNHAQIVAwwISNhBhzGLhwWlDu/uNapn4aYPo51OF47KQUWaqI0Otd7wgqEbbkihfFh2/0PFZosM4LotVIRYios1QxjgXC7IXsHOJT9FnkUW4Y5GjDA+Op1IEIRnFDOETZYnr9EZ6+hDPRyyDMUopHyJTELhpK541Y445hLmEvo5ASD+jbzMH5qN/L26cbAsawxuIxqwy1tMG12wdVwlgzguPSwRSopdQALxxPFUweDDnJ1PiMZ7jzMSxyrlpi3hl5kTmY5c1wUethQ06hYS+zpznN1sF1hrGB4YvFHOfeu4VYGQC5ppCOXwxQBLRd+WVV9ojjzziNvyjLFU+ZRMhRG5g/qC81N///nfbbbfd7LjjjnMGFNZW5Nt8OUuZq9BpmGN9WT6vT2A0Q9720fIY/JkLkeF9KSSyXTG6ICcUO7PNyyrJQIfgOtExonMz2TG+LCl/8zXbuf94n0/1mpAjUi11SiQ2a128MmOUfSVK24NDg34zZsyYguzXhREaQ6yvI08AVa7Py70jf9LH6IMY/jgfGYilmrXB+EG3QEbmeZCZz3pMW+25557FvrySbC+cBQSeMD545hjs0eF57jxz5D6ePzoHASx56d9Bk9mCJ5c5NDw9NzGrbqsvcC04FZkv/e++RBVzBjpGujB+mVeS7VvDfEW7oGvQpwjMU3WB3IE+i5OIzAzm0VztxZgOfvNxdAzGgge9gn7v9+ihD6CX+j7oywUWYs3lnDiC0C0uueQSZ+f697//XTSdTOQfOTU6KRjZiZii/ijRkBgL8mWo8JOb3xAamNQwTjPZpjp58V2um4kawzMCA55mv4E1IPyzoLJAslj6jIhso304Dsfj/AjBRHbhlUaI4fwI5VyP3/jaKxAdgeKE0Z7jIjhynQi5XD/v4VCI1tdPFRQXBAxgYfNtgMBF5ByRFBhLqcnIJB69Xu4F4YP2DGd84DDhGfjPEMmL4RODcbJnyb2y0PI8MlmweAYYnjGyZZvJEgZHANdFRLyH+0LRLcVNkLl/NkrkmdA/gOfJc0A4zDTSorPAeOR5Mn58P6PvUbonGgXEGEHZwMDiN1ujVADPn+jwfBhP59a/b0tcybu2DKnZ0SpSLE3B9WUq9OEUwZCDEzORsYQ+xrxElF68/TdE1wQFBIcv63Y2WXooMTjdUe6Zw1h7OurPbBpLAAaGAUpS7b333hmfXwiRX+677z4XKIX8eOmll+a1djXrOIZz1jZkZ+YS1nSc/ukYjZH5yHbF0cvajzHSZ7ZxTF7IWRjsMByhC3COXJTjQUZBZuGFDEe7Ib8jz4A3tLA2I7OmUv4VOQF5HZ2Ee/IbcfPifNwDclGmm/FisOL4OInQATzoezwTdDzkK9qJqOwo3AtODQKIPNwv8om/fq6Z9qY9kgWwed2LbIhMo719iSUytXMp+5HdQDuHy+fQ19DJSjEyGEM3xj7WePo8Y4A+hyEeXberG6AZS/Q1dGmf7UXfRM8OyzHe9sFYw9lBuzEe6N84z8L7XeaMpgVmC5+JvBkz67aKWY/2YzAe3k6TqY7B/TEG42VreHz5KXQ15o7OXOpcpAbjiP7AmoLumY1NknUcp4nXMTqaZ5mPL7jgAheEQQYp5anKsRS5SI6cGp0MFqsbb7zR1avGqEVd23xF4fraqghDvoRUvgU4hAyirQDh1l8DDgMWaBQBlJB4wjETKo4KBHBvoEcAYWLkZww5THIINBj1+RnBBOGdBZoJmL9hsEHwQ+jBaMg1cC1eQAzDvhc+S8OXW8oFfmMnJmqiLlCI+NlHTHHdRHtzD37TvXDEFfdx//33O2UHB0IyxRCnCIIbx/GRHghw0c2+EeBR/li0+BnFIRN83f9cQZaKL+Hky8Tw3HnGpW44QwEkcgrl1PfHrqpw4OCkJBfjMJztlQjajc8iUNN/MZailPAzGQoIQr5EBe2aqzrC8yk91UTpqfCmazEbWjPWYlluIIrBmXEIvgSFj+AMz3nMBbRVOEIyCg5N5gScG4wPITwoHRi0WLcwXDGf43BGeWd9YQ5CmcBR6NcB1iPmK8YRfdPXg/f9MRm+RCLnoyQGARnU4ye6Kh+Ze0KIzEB+Oumkk5xse/7559sRRxyRN5kEuYd1zJcojcqceSkduWCBkw05Fy/mQRwRfv835qNwSb8wzI/IlsiazI3IGugbfBZZxO+lRBYE+gCZDcyj/I7+4feDIKPe732HToXuQqmpaM15nNB8F7kGOSDT0oVRmOORt5CPfElPSuwSXMPv3APyBm2D4ZKyNtGSl+ydh+EJhwZOi0SyGsejLyHX+LImPG9kk/B3aAP0Do7J51hbkkWLJ4LnSB/OlcGZdY8+6nUWfkcnQw9Ctyr1LFh0Yp4rz47+mO8xVqrQx3mO9EPkEJ5bMr2Yfst4Rd5BDmcs8HnmB2Rr+qh3iqBr0N9yUmqteYnZgscib8aWlqDKrvIA451SUd7hx7zOPEZ7RANUKU/HPJUI7h2HJnMm7ZLvvTNF+YCeQDCdzyZkHvc6OmsPY5G/MRfR/1j3WA9ZX+mbvIdTlrUW/d5nmyfDr7+smQR544i+6KKL7NBDDy35PQhF6sip0YlAMDn++OOd4YvIqX333Tfn52DiYCLxXtJcGgMzBcGCF0IwggbGFxZhro2FlRRblAMWaK4VhQTB1tfBTEURQGBhUUZYZXJlgfeRGUB74OTw5/CbKRF96kt78D6CeEcTKBM3k7vfzNvXLQwrDA8//LATkpioUTC4b8oVke7M9fmUer8hHxM4jo+w8ulrbPqNvVlAogYkjL/R932GB+8T2ZNu3c1ksOCgDFHKIFcLTTj9HHB68R7PIxOlKN/QvvQ3FC/6JjWKu3KUC+1BP2W8orzThxnvzEP0f79PQRjGPYbZsCMP4z3CkY+M9E5Mjo8zkDHkxxh9F8EJgSvdbJ6m5sU2q/5VC8yn5AbWu2pN61WV/b5BXCcGF1+rlrFOlB3zBXMFfRwnKn2bKFBf6g7nRrSNaE8ciN7p4ze6zJVhRJQ/3gBEP2Iu9/tveOMfhi8+A76klVdMGF/0TxQOX2M80ZzuMyXph75ONQ4N1rS//OUvTq7pqs5cIUoBxujll1/uMr8JBrnwwgvzkjWKDIgugxzM+o4xv5jR7sx1yI/IjcxvyB0EVvnsZ+YsAoh8kAF6gXcKoIeksjErcykGReZXouh9iWBvYGYN91kdnJM13v+N4CQ+y/yIrOize5M9R+ZX5B1f95zj+UxWwCCPPsOcz/G4Bx8ExVxOO3AdGMQJkKJt+Hw4w5r2YC4nMxbHht8sOLwGsK5gRI2Wy0Q+px3YcyPemsE9PP744zZ+/PgO2zb6PdYzXrkqQcIx6R88b56BD7pBZk22J0kxwfGC84znz/NEhu7K6yv9jfGFXM3YYywyBuif/I4eEIX2C5dbo78jT/N5n4Hk25XxjRHWw3t+XNH+aeu6tR+a1X/Z4syAWI1Z7+3NKrIrdYa8xrjDfoB9gb7NGGauYJxzX1w31Rjo79haaCvmiXAml4cxwHzIv3yHOUulRYWHdQ19Fp0BWYJ1jLWe8UFfpO/Rz5ivmFdZXxlLvI/din7K+st36K/J1j7mOr+XIeOaYF2cGqxZV1xxRUJbligv5NToBCDoMjj/8Ic/uJIqd955Z06iLViscJAwAYSVCgzchaqJ1xEoGD5zIAxGYRwNTI5kUBQq+iTeZkq0I1FOvIdxmsnZe515+VRQJm6Eba4ZYYfP4Zih7Xnfe6J53jyD6ObqKCoIDzwbL4DwMxM/z5HMnbDgivJ41113ufZBGcKBgaDvjZpesYyXVk5ECwbheNEXLDpsNs4zYaFIxemFMEg7cd98JxqRlstnw33hzCiljfy4NhZ3FEOg7VAUS2GMlYqyEd5wjH7OmGbMIOB4RQK8ExEhOjxv0WdT3YCbvogwhfGCseoF+WTlEbgO+i3nbApqbXHjtxZYo3WrGGTdKxOPAcYtCr+PTgmXSQuXBGLcMx8w7lDocfxF4b7pQ/QlxjpzBHMO9859sxdLuE8xr6CYMEdwb8yZRMgLkYu5lr5In6QPY6BKx2HG926//Xa3xwbr3X//+9+My6gIITKH9eeYY45xgQKUbzjssMNy0pysPxzTO9z9ptPIgKVg/EL3wWCNfBxe+31AEDI+skchjcLII7SZzwDxcg8BHwQx4QTiM7x89hzwOb+ZOHKKNzCim/AckF+8bIBThnJi4Xvm+5Ru8mW6OD7zObIZ/QNdIGrY5Ho4LkZ+DMPIF1yfl7EItkJnjUbEo3cQ5Z3IKYC+gIEKeYi272jfRdrKZ8N4/TUfz4s2QZ7i2tClirkfZLy+7AP8fJZQofbOKvWAKWQNxoHfAJwxj97qgywYLzxLP9YYUxhhw05djkHgFP27I73Ny0TI/sjqfB7HQDJdmfHCuMSw647eMMWsabZZrFtL6akEDg0f2OkNxDgYw+PNB3Uhr3FsjLzcB/Na1K4CHIfgTvoTjlDsB97ego4Uvgfuk3HM/aFjYLxmLi12IKwof8LzGfMufZX1IB3os2RtUO77nHPOcRVucpJNJYqGnBplDpkARx99tFtkUfpZlFic0o1giYIwjMF7r732Ksl0VL/ZFQsvr1IF5wJRXAgwRCkxiSJI8kKw5F+EKK8ssOD79wChHyGJCTzZpukIITwzhAwiXjHC+owEroFSUFFBC2GKc/F8EXYQ7nB8eEEEwT/qOOF4GEgTRWagMBHBggDGvSC0cc8oPski1vzeI5wbw3M4aiwXcGyeAc4MSpQVI/KPe/ObB7MI0+bh/oygh/LXlSOmwm1FNhB9FmWdZ8c48s4uX6oG6GMoHygY3rkRT6nwEef06XSdRV6QZ57l3N7ZGFW0uUbvLOM6vDIfhbGK4sQ44Zr5DmOd+2PsMja94sEY9c4YlA8iyBjPHc3Lfo8a2ob7pQ132mmndvfua1UjHHJdKLupRJcKUQhYUwjYYIO/X//61/a73/2uZDdfFaIzwfp07rnnuqwMSk6dddZZdv3119tRRx2VtUGU9c7vbVWKwRus+QQisfaW4vV5gyUyB+s3chJyJeu+j2r12XPIPQRCIDcQFIEs4WUCIsiR1TlWonXfOyD8hrToFD4rg2AonM3xNqtFXvEZeug4yEI4FXwwBXt5RXUa9BDOF94nMAzfQ8dFH0JHwbDF/XI9iZ6Tl3GQv9EB0F9yqQPQjuwzgOyGk6VYOjN91u81ia7mHVrAM8cA3ZWzvsMQsY0zDv2avsvPjBfajv7i9xRDX2OMAc/WbwYfr68RgMVn0m3jsKzO8dEvomORZ3vPPfe4vuvnXp6pz16Pl+3vS+HhbEBmeuCBB1yZNMYfY8s7aXx2N7IW+j1/S2WPpLCDg2vi2uPth4l+78tAc3z+LdU5VXQ9yAgnaIO16Kqrrsq4dLooPnJqlCkIryj6bKhJOvjpp5/eKhxikGahzlbpYCFikc5Vmm6uQMhAEC+VaK6ONmNDQOFZsPB7IQJ8ujp/RyDGuIlyEQbhHicB94kiARj8MUCGj8VxfAQUji6yEYj45nvxnA9RnnnmGRfBgfCGE4KI7nhti5KCwB6v7AAKA4oDCwIKEkZpFFauE4UIh0lHIISjpKAkcAyEn2yM/Jyb9sDBU6za7AisjCMfTeBT93mOEuzag2EdBwF9kHZiDPk9YZL1BRRd2jjZBpvMaUQ50vb0iUxgHCLIM9+yOWT4mrhuhHrGI+diXDGmmavoz3wPZZO/ewdmeF5DSeAecl1/lmOjsHhnYWNzvTUEddatoodVLt28nGvHyMB48c4/IUoF1kgCOBgf1113XdL9YoQQ2UHN6yOPPNKtVSj6GM+8TIrhORfjj9JDyKmlFjFORDHrd7rlUwsJMjL7FrGmI9siO7POh2VfZI2xY8c6IybPbI899mgncyIP+T0JMfRi4EVX4RU+F/XzkXmYf2+++WYXqOQzHzoq0YlMz2dwqCALY7ANl+6J9ol4e3whR/M3AkU4JzX9kVcw1mIQ5tpSkfGREwkMoa2Qc6KBW+mCE4GgqWgmfCHBGE4be92M6+DnUsoUKSVwYNCf6MPotPT9RHqtB/md7yHzJwNHAnI8joRMSrlyHq6JZ0r2SDhokzGOnM5Y9JsuIxdRDtCX5eW7/M3vL+rHO/OBD6bD6ZLragjhTHXOVdu0qGUvoYqWvUQZ97SJtyuRuStEqYBcw+bhZKL+8pe/dJkbCp4qP1Q8u4yVDYzO/BwtD4RRjYUuW2dEqSkagPCN4Z3oqWLW2U0FjLK8EEQQMnzpnDA8J4RRoiLitTeTajjyAUUDoQDhIJ4AjYCNw+OAAw5Iq324NpQEjK+JNv9CUEHQSrQPBZHn9EWM0ChBKBz+mjvaxMmDwd+XGPGlh6LleNIBAdCX7CkGPCuugXsoxYynUsPv88A+MCgE9CWUiFScP0Qa4TRIBoo1/Zd5JFNQ5Il2QjmgpAGOO4+vBcp5iP7AEOT7HveFkpJog0rGa74yJDi2d2jMrv/GptRS3zewmFXYiB7rWcO8qtZ9eBir+aiVLkQ2MIcy3i644AJXIu3kk092ikcpyilClCusX7///e/t4osvdpkZv/nNb9oY55BJWacxAmSr9KPDhINzSgEM5KzfpezQALICDjrooFZjJbJz1IiKjIIcggwcz6EB0Y2s+Q5zaqJnS8bFrrvumlbgBZ+lhjmZGug6iUp8YJRNZAhGT0J2884NHBj+Gn0WdiogJ/ryNwRb+Y2MMwEnEDInOlOxApTQu3j+pZrxVGoQ8MfzZkwQ6IPum8pm7vRL5POO5j2CCHF+ZGqf8Pso4vQjQBW8Y4Nj8qz9fjXcC8GI6B1cF4GE/B7v3PQNn72VD7x+Ud+8xD6a95YtamrZa61v1QBbu+9GbowyrzB+O9r3R4hCw5g+++yzbb/99rMjjjjC7rvvPrv22mtb7ViiPFCmRidSNnwU8WOPPeYGaDhCn0Uv3ZQqFs7nn3/eRWTFzYgIms1iFQXfEJy00HwLb9w7/1UU8P6KCUoqfSReuxLZ5DdCxJmSqNwXESIIUxhEEVr4PFkmOFpw7mSiOCCwI7zhGEHwjFfjMxm+/+ZLkOsoYocoOAzg2ny5Y+gvKOAYL4n4QblGEE4nwwYF06eNJ4KoukROUZx23nGH86KjeQblOty3uQccccyXOAlQxFGauRcU4F122cWKSW3TfPti4SuRd2M2e0KVbbf1js4Bi/Gh2NcpREdjGMUDp7EUDyFyA0FSjCvWX8ZVNGAKgxSBOKx76BM+sAb5DENcusEjyEjhUkZhvLOjUIZazkcQBXpTrsufxj8fWRXxZe7OqmPEk7nCtdHpd4n2JECPQP9DlvcR6Bil6Y88s0wyLjg3wSbIb+jX6W7sjW7CWhQObCkUyGpk1XLv6epFXRVke8oyIbODL72W6hikv1DJIJkTBJmEDIp4mUh8n2oGOFJScaaRtUEgRzjQkHGAjkGfI/uKMcW48WWni73v2Mfz37bZ9d+7oClPj7q+1m1uX7ee0P5ca7w5X4hSgHF3/vnnu9KbCp4qL5SpUSYgeJGdweIVLzvDw2KHUBj9O99PJGhitGah9vU3McQSVT5u3DhnmGsXSVX7ndn3r5g1LTar6mU2ZGuz7vmNhGchRPDAqJ5PuNcvF31iU2snO6fGwG5DbO0+G1hVRefePAjBCIMwJaToY86pEwSufxDVhLe6o9Tq8CZNGKMpv8XzipbUCkO/85uqE8UezWbgu1wD9TjTLclDND73VUiHBgKtj5zCMJ5Kya1kEImDIgekDIcFcEok8DPPh/NR25gxzHPC+OAVxHKBKCP6i0+Lpi8mympIBMoCzz1ZWTqOSSQV/S3qbKK9UZRpP372cx/zIIq0r5WLAs544Xf6J84Yog/5HhGezKHcCw4+FCmUzlIoBVDbtGwfF09Dfb19M+1bN8fSd+OVfhBFBONXU51ZZXezWGlnJxYK5BuftUFk4i9+8QsX8KGsDSHyEzAFOOdZ27wuEjbWsabGc2ogC7KWY8jyn2WdYb8+AhZYd6LG/iXNH1tTMN39XhVbwbpVjLJYHgOMEm0Ing8amufb/Ib3rdlqLWZV1qdqHaupTC3LoJxBxicbHJkc46vP4PY1/ztyJBElH85kpb8hbyOvJIqK9yWDiGIPl+j18KzJCiZoK1y6K1UI5Mq3ThoGeRR9iRfyJlnM2ZRgZmziUKKduH/ax0fR83wYmzhNaKfwHg1AkA4ZO+XilPP72HkHAXoVAXrpXL/XL5JBP6MvU/ItGpCF3kBfox8zH/oSb7Qp+i06I32ZZ4FuTP/CeeyDA7nW8F6c/A19heMUItgzFeY3zG3j0IC33n3Ltl51rBv/6FGJ7Fei8DCnLG5aYjGLWc8q7VXnx+OZZ57pZBRkHZ+1QYCqKG2UqVHisFixd8Y//vEPV1Jon332sd133z1l5R1hhXRqFtKdd97ZLXostiyYTGYsjBhFWVBZsH1kgBfsUVzaeP4bFppNeajF0OKhJvvIPcwq82M8ZmEPbwycTyYv/somLvos9E7MBncbauv2y7wEUqmDIZzIOxwKRE/Q5xCYM60Pi+LLZmBEkiQ7BuWlMAz7DcnIqiBSKvwdFGiiXvhMusI7x0bATGVPkUxAIUOw9xtQA+2G0TwXjhS/zwoGccYtm0cjlJNBgHBMKr9/n/GKkd7PCwjBjBmfZl/qMMbDjjEUKBSDVEt2obTihPAbJdI2tEeiSCieHRF28aICORb9F4cF0VbMk/RpnjNKIPA3atZ6Yw39E0GI43IvtDuOkFJjfsMM+3rxO+3eH1E9xurmNbv2LuV9iroctV+bzX2THmsWqzLrv7lZ98z2gumsYLTyWRs33HBDVuUKhehqIGcceuihbh3kX3QM1s5UYf1j7UUGQX5ENiEa2Rv/0CEw7vnyhugiBFJ5OQ+jcHitXNL0sTUG37Y5R3VsFetWuSxoJpewpiNjFmJD8OagwWbXv2SBLdvAGQZUb2FVFZ13A2fkOeQt5HtkY3SObEqXUQILnbWjfdHQazCgIq+hByPLUWLXg9xO8FWyPT5S2YswX/2GNc1nM3EOjN3oM7yyPSfjlHtHT0Nv4djMBYxL2gXjOhkJnB+ZGD2EcezPi4E6UaniUgO7BoFx9D/uCzmdLI10shoIIvPPAj0LXSVZ/+PznCfeZxgPzJm0PfMm44HPo8v5qgn0b/olz4j9BP1x0HHQRdIN+CoU78x50RY3tehJnj6V/W3F2FquP+d6v0CROXVN9fbo9DdtWl1LwMFKPYfaD4ZtaNUVincPzx0ET/3pT3+yX//6165EVaLyiaL4yKlRwhB5fdhhh7lFDGUdRQBjHYIZg8qnUIZBYcDQ6kEAIXLYR1CxmBKhgPKRkdF6wZdmM+NkfQzd1qxXblNguU+MuAhwPkI637wz51Wb30ikwTIqrMK2HdI5y7HQX1BA/SaDRIoQ/dNRuSSeDd/z0Va8EFj4l/5KyaeODNI41hAQvWCJ4Zh0WiKviBwkch8hOpvaxgiyeNdzFX3nN/3mvhlbjKtwbd9cwbOg3q8/LhH/tAPnRwnvKKqN9kOh495L3UiNooBhxEfRoWwxR6VaToC+S6YE86FvFwwqOCYQoBNteE2EWrLoJtqPfYkS/R0lnXk4E2W4WARBs01cRL3b2c5hS0RV36qhtmLPMSUR5VUsapsa7fMF86x7ZaWt3rufVZRCWzTMM5v5eOTNCrOhu5pVlvaYLjTMAeedd57b6I9I89NOO63k99wSotjKOuPlz3/+s9MzKLOATIaszd9wNkQzLzBuIv+E5UDkPIxuXtF/6qmn3LqZ6Ua0ixufs8CWbVINMetlPatyX+IHuQN9qaOSlbmivnm2zWt4q937vSrXsJ5V2W1YXYrQX3BAoBOguxJoRPZyR84Irw9g5EXmpZ9xLB88xHup7BlJoBSf8/I/FQuQB7kWdEsCqzItjQtk4WKMzmVGNMckMAb9HOM5+m8qpVDThXPQ9zkP7Us7MIaRv9EZOlo/aT+eQzoO0GLANaIHUm3AB5vh0GDMp1oWmCAxjuO/Q3v5gLtEJcuYQ/kMc2M80OPQv5OVYSJCnCDWcipfPLd+pn00/63WXA0yANbvt5n1qS69IK9C8v2ShTavYYkNq+ltfapLIyPi8elv21eLpruqJMAMs27flWzbIcqkiYJtCjmJsYg9ttTnva5K+cyUXQgWT9LAf/vb37qN4P71r3+1MepjBIymbLOAUpYKYTHZxjZERCHIJ3JoYExFmGTgxjWcEi0ajxyXxaANcOoQDVHIkhJVce6vohOW/KB9qcfqo+d22mkn99zBbyyYaC8DDOYoBwh4XthGAOY5pWNIwuCMYMdmaH4jbQRr+jFKRrK08lRBoCSaPpN6u1FI8+Vaw8bzfOH3JkHpQCGjzYFxm8q5eRY8PxSPeM7PUsErhX6/H5QFFIV0Iqh4JvSVsGJKm9FWyVLFMYQmUxRRJuk7tKN3EvmN9hgfCDVEq9GPC1GDOxdQwmPlXhvbnPopVt+82Goqe9mA6mXRd12RrxbOt5Peeclm1reMsY0HDLZ/jN7SulcWWTxqaCk715Zms4Y5cmpEwBjDpuG77babizZ/8MEH7frrr09oUBCiK4NB8yc/+YlzYtx1112ujAx4Rf2hhx5qFwyB3kEgC/pFsiARZI9kDg3WZIzcBNDEX3fibHIb571cyB7IdIVyaCS7j1givaqMoX0xHqNjkLlKhgROMhwJRJqPGTMmYcSr32OD+Zs+gixHn+OVjqyCQwO5jWfsdWMcK/R/glGyjXZHRud4ucw6RAdPFkyTK3DsIHvzDHxwGqQaQEiQ1ZNPPulk4VIorRoP9AmeP/qld2gg0zP3pOooIFgPm0nYicazCbdbPHsKNppkWQmU+QrrJ9g7GBvofvRV9A8ywDGmlrIOF6V/t8G2Qf8tbdYSygfGbEjNctazqutuDE4fvHfKh/b091+536tiFXbkyhvbBv2Ln7EypXZWq0MDgqXvifbgfMQ2RaYGQbsEg1D2NtOKJiI/dD5JqszBoEcNNzz4jz76qKv3GYUFDwM0AiMCBYoGC3U4EiHZAo0hGaO0F5p8DX4cI6SfYqDjvHENdT2XN2OBaly0dAqMmXXrZ9YjdzVhuQ4WeATOQqd5jey5is2etyzTBVbq2XkMIz7tFuEJwzHZEERO0c4+Km/77bd3qbFEOWFsjiq3TzzxhNtImP5Df0Iw418UBYT8dLKAUDb4PufivCjDudx0D+UpVwIhkfmMi0JFAJPuTXSab+NUQVFHEEagLmVhGKUXhTC8h4PfyyUd6DM4HbxTg+g+Iqs62iAepY4+G94LJgxKxdNPP+1SwJmT6O8o4r7EFO2MYuT3IioXKmIVNqhGG0t6fvvBGza7fknr7+/MmWlXT/zUTly9uBsuWqxbeu93YRiDrEvMA5dddpnde++9bqxStvPoo4/u0k47ITysrVdeeaUro4Ce8de//jVuJidlZTAGIu8gfyCPE8CSSrkZ1mFK2HoZkLUTAx7rJfIYBkJkEyKQ41FdsYrVN38UeS+3WQzoS6zjuQh2SYeqWF+rjg20hoBMSYhZhXWzmorOs6cGugXGeYKUMHzjnPAyli8FizxLWZ14GxtjDCaDG32WvudfOOD4l3JSqZYixthOJD1ypt8zhQykXO2DQd/OlY6KgZz78wE++Qa5mfJSjEl0GhyN6G8dwbjB5sDz+MEPflCyDg2uEzsHzquoLcM7cFIBJ21Yxg8HBFLSOxHMLeja6HGJHEUcG2cxgVOcAx0cnZhnggGVcxVyT8hc0buqr3sJswnzprc6NKAxaLZrJ71l5663i/WqKu7Y6V5ZbXXNbbMiu1eW5nguNqwhrFnYvpijyQZ/4IEH7LrrrnNjXJQGKj9VQtx66612/PHHu70zLrroog4jJlgwEYIYUKkII4lAwEQwQ+ngmB1uFMumpXMmmDUsaHFoDFjfrCI3EyFGXKJ0EHSL5QGd1zDHbRTeHDTbkJphNrSMa5jTnigYCN/Avwj2KLIITTggfD1j/kbJHgQr+hQGXRRUnEsotOH+wjERZhHKfPQ6x6EPcQyUmXT2suB4CIGZlixIBEJtLjZ34r5wahR6oyiMdI888ohrSxwU8RwqGCooTUeqM0IwzxZBPtclsXIF10sfgfCeFjh0fVmvVCOs/UbqCBl8D6cs903EaSqGTBwrXE+ivUd8ijh93m++Tnk20TloCgLb+ul7I9samm02YIhdslGR60UHzWaznjFrWFYqzGqGmQ3YllDB4l5bCcI4JvIUgwvyDM7NY4891s2b11xzTVYykhDlDmvdUUcd5RRzFHEMksnA+YBMwXqKUTpT4y3jEoMpRu5nn33WyVAdGeoam7+zxuapbt6rqljBqipyN3aRM5BdCWooBmyEvrhpkjU2L7CKWHfrVbWKVcRKU1ZLBYyyGGGRt7yMhP5GOzMHh2VmficghEAqdFeMQsiq6CFheQ0DEn0mrF9wXHRi5HDkMV8uKZ2MXgJdcgnyIetNLjY+5vrQzQpVatmDw5IX2fphPS/RnnU8J/TDVEqIFQuCPtEFwln16FK+1DKOg1QqQHDf6JA8F3QEdA36HxkUfkP1jqBtE5WqYbwwdryTlePvsccead6tKGUemvaJPT79c2uOaBmnrLmNrdIr/ngrFBMXTbdHp7/lSoQB/99z+c1t+R7537+2HGE+wDnOvM9c+fe//91lul5++eV28MEHF/vyhJwapQGRLCeddJIT8K6++mq3UV+qEGnPooghGuUBg1uqnn0WfgRMBHyMtvxLmmaxohq5BoSOVA2SpUxTMMeaApSywCpjw60y1rY2cSHAQcXki8Ad7hMYlBGgiZbD68xncCSR9sxn6VMoskRDoQjzLJKVNIsHhngEStJ8w1DbFOU2ukEzgiLp6bne/4H6zxw3G0WB9iEyp5BZGh6/STXCLkYGnhHQdn7jQEDgJuqq1OvI+80JGeNhIyOKBkpwqs4MnB88ExQrnGG0E2XTUKbJrEgVNo9EOEllQ3KUIq6z0EqnyC/jn3/Y5jYsaVU5Ki1muy23ov1unRLIcgqazBZ9bta40Ky6nxlZgzGlO3cEBjOy1TB+XXLJJS7qnHJUHRlyheiMPP7443b44Ye7TIsrrrgiofEyXtQ9siJyBfoBxkxeqQQc+YxGxiLrNbIKwS5RmbCQYJgl+CbVSP9SpSlosqm1E622aaHVVPSwFXqsalUVhc1q92VokTt5eXme5035P/oMxmBetDuBN8ipOCJwPmMU5jM4RXB8pFPCk7KsnDua7YOsTNY3snp483kgiCtdPSYVkDtx0mQKsitrFddLOxYaxib6BW3Ks+N6wroZ45ixj2yeipxcTLh29F7v9ArPUx3toRcFfYSXL7dH/8U+gWMsHfsEOmjUaZfo2nkO0X2MRHnz0syv7dZvlu1D5fnDujvbwG7F3xtvWu1s+3LRNPfzqD4jbEhNaY/xUgHbAbZX5pXf/OY3ttdee7ltA2QfKC7K1CgyCFqHHHKI27yMzWcS7WOQCBQOFm4vjLCgc7xoFAERFkQM8FkftY8QSZQMQk2uI+TTBWEUQbecNt1NRFMw0+qb31kWXUv6fGw9q6pYrmDeZCLs6A/J2vO5555zwhr9AGHPp9iidKBsIGCh/GViKKdf892okRphk2g9zolySwQgyi7ODspP5To7x28SRwp6uo4ylDYEUtoIJ0ypbdZGu5W6EwPohyiuKAgs+Dg0om2J0otzIpX7IXOC1HeipXy0FY445tBUjTXRcgc4rjh/OaZ6i+x47vupdsb7r7ufqS/bv7rG/rfZDjase/EVDpEdrDcYjO6//36ncJxwwgmuFm6plswQIteyIHvz4cj45z//6TI10pGDiCBmrcW5gbyGfsEaO3bs2HZrPBGMBC0gc/k688h36CKs98WUn7g+jA/ltP9VIoKg2T6c/7otaJzb+l6Pyt62fr8trbIA+//xbHnORMLjJEv0XOl7zLvIZQQWYRBH16RfIGcRxIYuGnU+pIKPmGefg2hGMroH50GWRG6n/5H5we9cR66hXxGMlU5mugcjOfInelqibOFi4UvBlkPNeHQ1sv3pc+iy8bIyeU6plB3jWGxuTyaX11/pTzjR6O/p6pHYZnjO9EOVqel6NDQ32cWfv2yTFs+xCou5jI3xw9e03Zdbq9iXJrKE+RHbBbbV8847z61LN998c84zAkXqyKlRRIPkBRdcYH/84x/dBpennnpqVgZKX0Iqmo6L8Mlij/EPAa8UjaBEiOB0yYfAWQzqml63wFo23fbErKd1r8w8mieV/kQGBv0AAZtoj46EbB+Rg8EZrzPKHkorgly8SRkFBCWXY0czKuJtloawzgslGGHS/x2HC8InSg7CHkJfKiUJst2Mmv6fKlwjqfJkPpVqGadygEhNIpzoiwj0iRQCPuc3hUwGTiYyMVCUw/345ZdfzjpaDiU5nT4iOg+fLZhrr86aYTWVlfaDYSNsYDeN+ULBWoAywNhPtrFmJrAuMf9gUKCc4hlnnOHWGRSPzhBAIUQi2FfqRz/6kfv5lltuyaq/sz6iSxCUEF6jeZ+1G/mKMqWlGGXMNWJ0IEsk15nAxWBewyz7aP4b7d5fs/cYG1QzPK96Go4r5GH6ATJYR+1J2xMshdwGyPoEpVCqI175H/bSwICMgT+sTzCP++xkD5/juOg+9D2fRcBnydbA4YEhG+cJ15uLElGJIHCK86VTDotrpE/K0J05OCBwrhGkQLBUMh0y2f55Hvqqz/QJBz6QXU4fy0ZHRRdGDkknk1x0HsfGW3Om2LyGOluxZ39bu29pOTA7M6xBrCvIL+gXubbnYLvENsbcwSbiBKdj0yV7oxwcwp0NOTWKAIbhH//4x26Ry5dXD0M1hlmEuWJsuJ0qKEMIp2EjZblT1/SyBcZG6mG6WY/K7XN+Lh8hx6SNQTaTKDRqAlLGB4UBwZBjICxS3gdj05577umi9ehPRLOj3OA88QqGj8wj4iq66R9wDCKl+DxRXSwsvq5uoZxsGL3T2YAcBxECcMlH7jc3mdXNMSNCr3v/kqq1TxuSAZaKs5I5kf7TkZLMBqQoj/GUSvpTpkIEhk+U41wbVYUQyWH98vXTmQNyJQuwThHQwfEwbLFmsU4+/PDDdu2112YUuS5EqYNMyB4yJ598sstMOvfcc3OuyOOIJAiAf5GTSi3K3MOazhxACaTOkp01a8l39tlCMsHbsmqvdW1Y99xvWIoxiGw3gpNox3TnS4zOHANZ32+EjJyGToBegYxI1u2LL77oIuxZBwgo8kFXflNn+holhaLlPXjGBGMxxwPODbLEfamgQoCzhnWM8s2pkq9yWDmnfqFZw2IzytJUlU6wB8+bShXJsoXSdWoQdY3+G830wE6BjJLpBvMKmhKiODD27rvvPrdXMOsEVTty4WxgXaJCBMfCWUm1GZwbrAN/+tOfXClBHBylvPdQZ6S06ql0AR577DHn0Nhtt93cQMt1/TXq7qNsEBmNEbeUFXacOggjncmhARU22JoiTo1Ky30NX0rmkCaL04pXpuy///5xhTsmbYRAJm0cGiwK9KdEkzSGaQT1aM1RFAxfwxhvdriebqHAWI7zLFXFFiWllMeOo26e2RcPtSgd0HeE2aq7mFUUf1r3kZ2pZj6gMFOfMtmeGii1iYwzROERjZlJNB4ODZx4nW0eEqJUYK1ifCOfsH6ExxqGMqIjMWzhSMexkU2JmHCpu3hlB8ePH+824zzssMNc9gab/KkOrugMsOYef/zx9tRTT9ndd9/t9kzLJciEfqPmMWPGpLTZbrFA1sSIQaBNKWaoZ0qf6n5WYRXWbC3GfmCj177VA/OS4YJsRCBJpm2YyNCPkZn+g+6CAxpDss8mire3BH2P4CQCr8JlRrkuL/f5TelZAzIpB5UpBD/58r2dRr+AaW+aTV/qQCNwapWdzfqVhpzMPJTOPocYHHklc4CQ1R8vCItSVPTXdPbkCPdb71gVQuQeHNvo8TjJ+ZkKG97Ww3jFdkVwLWsRjlAqg2QK87zfr4+9cqL7DHHs/fbbz0488UQnI+HYGDduXNb3KFJDuTEFgsX0zDPPdAZkyk7973//y4sijeeQyAXSWvMhNDFhkNLLBJENlDtCoO2MkdHVFatbhS1LA6+wIVZdsVbOSwugsPGs8xElhyJIFBH/oigTTdVRf8JYxQTPs8WIFU/IJ2OIdGsirgoJe9XQZqmA8Q2jWikr7I5JT5nVh5xn86eYTXvbSgGflZMqtDWOLoSSZApHImMnKd0IGemCI45+KoeGEPnhySefdFGz1ORH4Pf7HGF8Cs8XOM9xiFPqwe/7lS4YtXBooMTgUE20ZmHsRVZivtlkk02ckiJEOUMpHvoyQSP07Vw7NAAZimh7jNz5ko/YvwMjYDZgNOZaS7XkbjZ0q+hua/Xd2KpiLdn3lbEqW7PPhtajMnf7IiJLMUfzrHE256MNd955Z+d0JuCIrA0MxslgfSAbl7maPoKzxWdxeJjv0UHQWwqtWyJHEniTyhpF8Fc6WR1FYd43yxwaEDSZTXzSrLHOSgHsJ4zzVCHLAp05GQS+JSKTbDfkGJwvpVwtQ4hyhrUDRwXzL+s9jgZ0CGQI5lpgnWD8sTbgoMSGmSnMIcwlOFSjDo3w3HT99dfb+eef72y+7GuWqU4j0qP4Ib1dAKJGqG1L6QOEGSJN8jW4MRzkw5mBoYBoSu6BSIYddtjBvY9QQcYFEZhEWKZSt5cJBUN8JhvElQOxWIXVVK5vQdDynGM53LyPyfmVV15xZaDyXRPcK62k0+FxTgX6Bn2FF8LcPvvsUxJp/0SBYUQj0itsbEexCAubKEo4ZXAWlTQs1otnRt80WzTDyhXmBDKEkjmmKD9F1k+8uSNdoYE+St9EyBFC5AcckeEMKtYunIi+HjqKAe/5mulEvUaNVaniS0ykIgNh9HriiSdceR6cIBdddJEdc8wx5RFBK8RSUNz/+9//2imnnGKnn366U6DzYYTGYEtZIAKmcg1yF7oRBmuCGzCke3A88uIzyGsdlQQlCIfAFAzbnXUs96seZJsM2Mmagkbn1MjlfdJ2yEZk+udzc3cvd2NI5udUSoL4Ern0Q+R5+gTGpVKAIDB0s7CzD5mWtS0M6x7rYT7bNics/t7lADm9IuzYqJtr1rv4MjPZOgRXMl+kgt+gPhk8F+aheJHczLPoGKk6J+jX6M4cU7X1hcgP2CCjWaMEXZD9h2MeZzm2Ub9OYAvqyLmZyt5iqXDEEUe4QK0DDzzQXQvbDRQyg7ArUuKravnzyCOPuDIHe++9t11yySU526iOSHiijIlY4Zik6/L79tvndt8GBFxq4zNBjB071hkAvQDNZPLaa6+5VC8mDgRhjMYs4okiqvk8UdVdodxDLp0ZgED10ksvuWg8MicKhfdypwKfo4QQfYNor2QODfoWhupCwXWFyxuhEFHHF+WNKBwfQYWBq+RhDFb1MGsMRyrFzLoVrl/kGua0ZPMC8w7CCs8IhSYa2cfcxPyDEhkukUb/ZW7kFR5LnC/X86UQomNQ8jFQUhfbl3UA1gzWkFQc4WR/+BKWZP+xlhCl3lHEbxiMv2zqh8Jz6KGH2jPPPGNXXHFFVuWvhCgUGHaPPfZYe+655+zBBx+0HXfcMSfHZc3EkUB2JBGQBKtgjM11hobfRJoyR5zjuOOOayNr8jf0G28YJKgBo4TflyEK+hBZKswtndWh0SYbdmm2Rq4goAejTSFlYIL+4pWbSgSOAl4d7UnhdRBf+jbf0G/JPAmD8YwgPr8XA84Y1qtCXVNWVPds69Bo837xQQcgGjudvpPKMdEfMECiZ4edIMxB7A8D/C08/9DPCMgK7zXJ3CmHhhD5xWdjRCHLELme+Tcc4IA9M5VKIcgm2CvREZgHCCKmDHu6++pwbtaqk046qbUc1a677prWMUTqyKmRJ4hAPPvss+3iiy+2yy67zDk2cgnCvi+Rw0KKMZDBhxDlywXF27Q5FTD44dkk+4LNbxDAfvCDH7hFHCUKwwHvYxQOb9KFcIFhwW/a5hd92gJHBooIEVhEfPEZDMzRyAk+29nSxXM1cWOwJYKkkGWRmNgzUQ5RUhPtp4CyQe10/kWILNZm3LQj6excC32YNqbuadmkCY/c2mziU8t+r+xmttyyCMdiQ5t2VMM2DE6HjjYVpy8yBpj/MLqESwzwXV6knnIs5hIPTo6oIsL8xb5GpZJNJIpDQ3Oz/fuLT+2lmd/bwG7d7OQ11rJ1+3XOLMJSAzmFoAgPskW4FBxzMrII8g3zMmnm3iDE2oHSwmcwjKGA8K/fYDYddtppJ2cMZb8z5gn2I8hkfx4hCgXKOaUNkLvpu2Rp5wqijJGLGAMEKyGTU9aB8yA3+ejITINriLDGmclx0AcolUVQAvImwS4Yf3kfB0Z4c1/0DQKscG54uQIHDOs6+gUZnBjkySjnHFF5grmBz0vHaA8yE3obz7mQ8Kyj2QwdwTPklagkEIYs9FeedzEdCJQdop+zNvmSrCVfdsozcHWzmR+b1ZIRjtzcbDZ0fbOa0nD4I7Onu49JKjCPIltQmYJATg/OVZxo2Ff4GxkiXsdAh2WuiWaNoKdQ679QG9aL0mTSorn2wNTPbWFjva3bb4jtvtzqVhlT9f98w5wbrWjCe2EbD85HAhzJGGfd8NnjyBDYGlhjsLFSzhKbA7IRckk6ICddc801riQVWRsnn3yy/eEPf5AckgdiQSI3l8gYBskhhxziFOy77rorL+WmeGx4DfFG+oWUxTYcNUAEerrC5VdffeUGGt5Ijs9gRGHgb0wGKA0oHwz6dAwHTBAIdwh1eEkxdqIsIQjgIPHHJ9JKAkB7SMvHMMvzLqSSQz9CkEu3nihKJ1F9UVA0UMbpmzxrnn2yzaFFB1Buav43LZv4DVyjpDI1iKJm7kiWjUNkIFHWjH/6WKr9mwwbHFKJ5iAcFhwPhYJ5BgXYf5Z/mT8xetC3yyJqTuSNX7zzht357WQXk1hpMauuiNlj2421NfuUhvJeCmBgRK7AoMkrPO5wInrnN+MKA1XYwEoZQoIhMPAQ3JAMIiExpIaVDjY9JmUbmQEFBaWD88UrO5GJQyMMx/7jH/9o//jHP1zGBnKcEKXGTTfd5DYEp+QUwVP5MNIjnzHmkfdZxxlbOBQIpnrrrbecEyEd5Z7xS2Q1x8RoiFyA4wT9CF2JOQZDJXMHkfjpBBpwbPQLro95hjXd79PA2o8MwJyB8QI5OpcOoM4AhhqiSQtZdpVnxvOnD6WrAyBbMs9HS3nQR+mb9B+c4zgTstkUtsvT3Gg26zOzhsVmPQeb9VupJUu8ROjo+TLXoHMS/MTcg2yRinyA3sD3EpV4Zrww59KH0ZGZV8Cb05gvfZkagiQ6e9aYSMw3i+fbOR8+Z01BM25Bx3aDV7RjVi0T52YBYI1mvDBu0C/C8j+6AzqGr3LjHcN+TGFDQsfANsna31Gpt2iGHzIDx6esJvZO5APslegq8cZttjoGAb9sJM76RDmqQtr0ugLK1MgxDJgDDjjALbRE++WrzBKDKhrdwrnwNKIc4JRAYCTyPKz8E63AoI9npGZxJsqJY2MUZGFH4EQgwECdzUBGiWEiQdHwEdZEZaJooCT5lC7+LtqC8ZVJNl97scRbYJjk6U+Z1KvlmfpN/MILDBF4KK8ck76E4okiKqdGFvQa2vIqQbzRIp5Tg76BQsL8Qqo3hhMEko5AoKDsDN9LNh8hGFEvnyhPnKhho4+vjct8J4dG12ZOfb3d8e3k1t+bcG0EZjdNnmh/WHdZBkFXgrFJRkR4fCEvEI2IbMHc7pV3/kUJwDHpYczzPoZD5A2UDb738MMPu/Ic4c/GWzuimXLs3xUuC4hSk8jJnq3xgHmCCCrkN7I2cM7//e9/VyaXKAkwpv361792To3bbrvNdtttt7ydi3Eb3rvKb8JMCVSyNJDfMUQwnv2YZdxjhIi3gSb6BLK+z8wiipl1GqMEsm02GcjMCeg6nJ+AB2RMHJR+Q1CyWZgzkKMz3a+nM0P5MrLoszXYpAp9gf0GMPhmUpKZgLtoxgPPlWx2+oE3FLFmYXBOdd8FEaGiymxIYfTOTNfreDIDYGcgSBNDJcFT2B9S6dvINxgek5WmxeHKsQnGoiJGuFyll43QL9B95NDo2jz7/dfWRIZg6L3nZ062Q1dcz3pWlUlViBxDkAHyg4cxgr2JsYPOEN4fk/fQG7wtiWALZBCCGvkc6zzzPXYBxtyee+6Z1pjjuzjBsUn6dQJ7ZSKyHc/IOugzRx11lLNN3HnnnUnLKIr0kFMjRzDwiOxD4fjTn/5kv/rVrwq+mKFMUNaJAeozI1jMWVhZqDEq430Mp1R6TyWpVWFDIQoGJadyDUZGUjc5F0J01DiBAMKk1Vk3Ec8EJntf5xMHQLg8R74yNFA0Mt2IHAWTDBwibHfeeWcn+NEPUXTDUTV+Y9io80MUF54XAoKPzvQ1rdOF76NwYLQIGzmInmJRR6FFiECA4HwdwRzmN/5Kpa4lx0ZgiUax0ucQftKtjSk6H0ual5Uo87ACLmnquoYvHM8YKsP70qQD45qoSKJpMSqxllOKMLz5byJ8tkdYdmL8Fnp9wFiMc4byPjhV7rjjjoLu/yREvHFJ6QLWQaLR87Fhd0cQiIAB0UcnY5jAwOwdBch5GCDC6z1GB+YDHCBhQ3S47FyuYN5APub6kEG51vBcgqyBwVLRkW3BuIIRmDmPUmD5BocWukGmOjL9iAA49FSesy+7jLMt7MBAxuNvZVPyqQvA+k5GJvMCPzOfUZ0hkzLEvtws3w/LCIxxjo/hE9grK5VstvCeGR31TWwY6ErRwFUvw6D7FKu0sigd6uPoGNAQdF0dAxtTvEoeqeD1CWQRnIuMPwKomOszXU+QGVj/0tmPLxtwgqJTkBHOPmgXXnih209MDtDsUfmpHIBn8YQTTrBHH33Ubr/99nabhRUTlA4GEEZEIqF92jULPoZE/o4CgvewUIYD6raSLuaN2j6yASEB431441CxDIQ3jMuFqP/vNyRPt+xUtAwbwiUGMhTweBM2/Q+hNt2auiJ/cxkGE8oQYKighjZRjplmNDDP0AdwuKKAElnBPBPNnvD7xSRTPMhYwuGZamYP50Z5xmkbVdRRgHllargVnQPWnnHPP20fLZjnoqk8N2++te04dLh1RVAWUo1qzCU+0pZ5IBp5ibzAHBTeQ6dQ8+GJJ55oDz30kIuMx8EhRKF59tln7aCDDrI99tjD/v3vf5eMscyXXcHhws8Yj/0aTjADhmeyS1IpPZdL0CGQP/1eaR6M3ug9CmhoD+3E3J+psSkdKO2DM4znkQ1cL/Ik/StRNkaiUriiOOA4QCfEwcj6io7hnQ+ZgF5AUCZ6sXeUUJYsXJqMuYj+1tF+MWSoplP+mowj9HKyNaIZqFxTpoGBovPw7tzpduFnr7X+XmExW7lXP/v9Ott1SSM29p5iOZoJqMQm4LO+PcwZOD6R7wsdQEWWJLLd+PHj7T//+U/JyHblipwaWYIwz0azGH/xvJWakQyPKNFTGAqIHODneXPn2tx581q9ngj+OBqKqbAjmBD1gGMDg4YyNdpTyPqwfvOkfEcDspjgcFPd29IAZxbPgjmBuYISAUQ18pwQ2jM1KvqST1FjJRllOCowWJKSmmhzeSDTDDqKmMaJwbF83c0nn3zSOWa4BgQWort4H2cLyojo2kyvq7UT337dXps9y/pWVdtv117PDl2p8FHQpUIxjECUicHYiBGAqKl4yh6ODeQUv09PoWQt5o0rr7zSZd+ef/759rOf/axLKqOi8ND3Lr30Ujv99NPtn//8px1zzDEl9RhY0zEEeiMiMjxrL8YDDIk4MtCN/F55yco6FEK2YS83BU3Fh2fHcytUBlC0tnm+wGhOSapsypuJ3On5REQTnEcQw/333+/Wex/sRCBdpuA8jWZm+aAqnj/zVEeBU6n0SZ955ve4xLGBkZbfuSdsGMxzlKdCX5KRUjw9Y5Ld/s1HVtvUaGv1GWQnrLax9e/WNecjxg62hHDJtkLIUffee68LiET/j+cAJ8iRvWuxfTBmmTMKEUDs7bRsW4CN9p577mm3V5RIHZWfylJIpjwB0VMoHtlEtecSBAQMAERBsMBiuHvllVesT43ZSt2/tRkTP7C1Ro6wYWvvYp98M895ThnAxYSoBgwppI0rSyM+PCcEJQzBTLpEm8WrJZoLKDmEsJZvZBwqHZg3WMRZ1IGFHyUAByMOLhy4mTo1eM6+r3Ie+jBGTM5BFgeCBudAMUkkSND/49XqDoMCw7lwYnjjLMKTn1MQGhhDnIc6/yp9JoZ372F3bbV9weqJlzJ+g7xCw/jEacm4TYQvgceYxQmC0TSVfXiyhT5x7LHHOjkKeQ8jGdHypSLvic4J6xNZQg8++KDbHwqDfKlAhi1BDxgICURAbqfcXPWgJfZ91WSb1zTPxmyxifVaspxN+XaaK4nb0dqd78wA5JiuPr93FMhE+V90R+/cSGcj+FIF2RKZr1B7EorE8BzI0vbrOCUp6WM4nAjay4aw3oCBEkcqDkyOjS2E9XvChAlZZQfh0CBrDgctuoZ/jz7mMzVwDnJe9BrGk5waYqehK7uXdIyWsVlIhwaw7pMNxhqXKKOPMlY+Y4zgKey7ZHQUwhmOQ5RMEeQ9HLvsx1xK8l45oUL2GXLVVVe5kiZnnXWWi+IrtoLLZInREecF5WNYYFEiiFrGeLj1lpvZmCHf2ZyZ050wwcL76cu32dqrLe8GDxv1FhOiNLSvQscTHxMvEeh4cjGuUK7DlwDIJUTbFarUB8p7eGMo0TFB0GyLGr+yOfVv27yGD62xeXHWzUZ6dni/Fh9xxLikjESuSoThbEBowJEaNnag7FDTOREYMaP1a6OgyGDoJPMDpx+RV2EBCscK+/qg4PI55kwhQAavFqMDCnox2h5HerhUTCKYj5g7kLkwwBUKFB6iPSmfsdNOO7XZ6FCIXELfotYypRJYE0tBwcUwyPWwfmOY8BH9RDcij6658fLWfXi9TZ74rTUHzfb2hNdtet3nzjAQLktVLDg/hsaOZIiurF8wt/JcMawgS6FPEoWeyrycSeZuodYWnDQiTZrnmTW8Z1b/llnT1zy0rJuQZ+7nAYKnsFNgNCTYKFd9DMMlcgF9mc2+cTow5tEzOAf9Oh7YRTq6Br+5PToFAV8EYqA3hTPQKPGNjoE+g84ihKer6xg+I6sYYM9I1VaGM3Lbbbd1sk6h1g70GWzJv/vd79x+xldffXVBztvZkFMjTVh8f/7zn9sZZ5zh6izjWSvmROU33WXwsSizoPIvRmkUDSKViUyoqJ9j1lRnq4wcbFtttIZtseHqtuG6K1qPYI6VAggJCBtEUPMS8aNZmZgRChHQiBZBsEKAw0mVK1BYidgtRKkAUhExlvvsAJEa8xom2MLGL6y+eabVNU212fWvWlNQl7VSi1EznoEFZwBGx1wINSgxKM6MeaLCyQgi/ZJFnfmLucuDkuEVDZxfHTmP6UfM0ThyMcJwHhwc3BeOlDBEUCHk5FphF6JcYezkK/uvIzAE4KhPFZwvGCswtEYdvk1BfgxmGGFeeOGFVsNfMiesEJmA44y+RamE559/vqilCFgbycLAuE02tQ96oIwM6yuR1jhcWGPnNbTIoJtus4FtsMlaNnrTta33sIqSMOQgZ5Ctwfwm/SI+yPtkymHQwfCEvoFDivfRL3MJ83yqe6NlK2+SAeCzA0Q6Do1XzJqnmgUzzBo/Mmv6LOvmI5si6lRgjmF+yVWNfWR97B6UqKQfs2YTdQ3YQpAXwjK//5nvdVR6DXsLeosvcUtQFuOEuYVMtSjoMwqcEqIFMpiQ84sBcgjrAXJLKmBLwLGBPBZ1hixparSmPGz0zjViXyY7l5KjJ510kpNZROrIkpgGGHqpe4ZAjzJbjM2NMTiz+PrSKRgg2PzKRwwglEY3wXFUVLYOmu41ofIusdLoArQp0ZcY6zF0Y7DIhRG1s4ODA8cGhha/B0Y2nnAMzETlx+1DeQDBEyGT0gCFTkmE6XVz7P15X1tjc5Ot2muYjeqzQkko4cloCmptSXPYiRVYYI1W2zTFeldlrihi5I9GUyLws5EVgghOARTebOD4zFse6usylwEZZsxvKA7eeEJUNIs6zyS6EV88UDZQIuhXnIdjcw/Mi8yPGDTISqO/oYgg5DDfoGz5qLFSf/5C5AMiHItZbgTDqDcWhDPGkkEZKuYtxjFKyIwlX9vU2k/djNi9opet0mtD616Z3ZwVhfPdcMMNduGFF7pIUCKqDj744JyeQ3RNbrnlFrdvxh/+8Ac75ZRTCr4WsWayTiKP8zNrL7I4WY0YJIhUxpERr0RkhbXIDjXdl/0tFiuNuDnkDIzzzDGAo6YQRvXOAIZZ5CYcG2S4ZiOn06cIwkMe47j5BnmTfQ+Q8ShfWOjxhF7x2uwvbXrdPOtT1d22HLSG9akug1r6ZGa0e2+iWeUaZlmM6dGjR7vMrrCTiTkF3QK5PRdjkmfsnzOyP2XyGPvMX8gX6Li86H/Mb+y5x7zAZzsyuKIfsDE4ejZBYOgXjA2+z8/ozpyb++B4nI9sDXQa7BnoJdIvRFcFnbuYpe6xlb344otObk9lHGLzJbuToGHKVs1rqLXbvnnDptXNdxu+bzdkDdtu8Bo5H9Nk6eJMYb/mcePG2Z133lnU/cjKidKwaJcBLIi77767G5BsbpWoLls+IH2SxdLvpYAjA+GQgcSijJCAg2WbbbZJPLhqBpl1H2xWN8sZQc1iZpXdzXqnZjzIJzhpiEbDGO8js7WpW+rwzLfbbjsnqBHxguCUrsMN7zXRU0S+EnlXKMEL4zLecCJpiNQp5LiaVjvb7ppCBBomMLMvF023xU1LbKMBpa3sBkFTnHdjCd5PD4yDZKBhxPAbah900EHufZRa5phs4HjMZ/5cZFRQxo+a4QgazG3XXnutM7B6p0Q6DjaUCxRm79gLzyO+rA5ZIUSdMkZwCANzjldIIGwAKUvmTDP77nOziiqzEeua9cyunrlPzS92CZFMeG3WZHt0+me2pLnJ1u83zPYfsZ51o11Eu3U4m406cwEbhbOGMf5SWQuIvEThwKExr+F7m1L7Sevf6poX25eL3rJ1+myTc+Mq88Spp57qjHw4NJAPf/vb38pgITKCufXcc8+1Cy64wG6//XbbbbfdCm5swEHIdfhykPzMuGKN5j3WymQbZw6uWckWLka/WMaQmsJsOt0RlFDCUADSL9KHgA+MwMhIftP3dDdRZX1B9qIfFSqAiXnarxEYo3HIFQr62T1T3rSJi75fqnHH7POF0+3Ilbe3nlWF2YA2c5DRoxnM/J5dVjNyOQ4mdNXx48e799A1sKvwPk4AnFDZwJzls7rZQ4M9fziuN2qiExAshazDnIf+mareSUlbrtMbGMNZrdhm0GdxcuC442d0Dv8ZrgE5wVPWe4c2NZpNft+sdr5Zv2Fmy6/FYMvqkD4wpdxY2LjE7pnynk1ePMf6VNXY7suta6v1Lv+9iHINY7JQG28ngv7FekBQOmtYRxDkRZYnQVbM57dOfsO+W7LA/a3ZAnv2+89sQHVP26B/7rNpcZxiFzv00EOdDQS7TLGyXMqJWKDaGx1CFPy+++5rRxxxhP3tb38riGGHyAUWXCYCvPw8JqKkGZQY4zC4pW14bm4wm/We2ZLZZtW9zQaONqvObRRjJiAE0MYIGURrIAhoc63MQQmNbvKNMBk1WNGnMB4TycLfiaLJZNFpaF5iXy183xY0zrGqWLWN7DnKBtUsn7bRFKM5Y8unwed7AXxoGgrHd23E9G6xKjtutXFWylBeZVb9S0vLTS27+gHdNrFuFel581EyUfgZbxjomG+IlMa4GFU8UUp5JtmWwyAjgzGO0E95DZwZRIdyLUQ/Mfcxz3Eu9nVJZb8i+g9zB84KlCKcczj34s0jbDjO3OodGvHg7ygwZcm0z8xev3upkhGYVVabbXe4WZ/MlEWMGGQC0pY+y8avQ6XOe3On2VUT32j9nRYZ0395O2qV4hrvSw3WAubfdg5ESjPUTjRbQmmFCrOeq5rVrJDXa8HpyV5RzEepOBaJ9CSaavCaNTbPvmtneFmnz7ZWU5m/rE+CSvbYYw/bYYcdSmJ/NVFesOYeffTRLiOSsgPJ1qVcjnfWeTIY+NnvV+XlL+b2TOSvBQ0zbVb9ZCej9Os23AZUl0ZkMjIu6z73xNqFfCky768Yi8MZt8hsRN9HN4NHt8OQS0AefSpT2fHdOZPt6Rmf2JLmRlu512Dba/nR1quqJm29iFKqPis331mJ39fNt2smPd/u/bFD17VNBpaGsy8hTVPMGt8PvREziw0w67Z5eodpanKR0ciOrIs4GHxViWiAFPMQn8XJkA2Mc4yW7IGFA4ISUb6EGsZJ5j0MhnyOOY+/pwJ9HKceDjL0F+wx6Cfx5jf6POdMFF3NvIsjpSz398Gh8fLNZnOmtmTtUIZnpQ3NRmemN9NH0C3D7YiDgzGabWWAfNMcBHbpl8/bjLoFzsgdW+q8PHH17Wx498JXnihlWDMYd1EH4qLGWvtw3ie2sHGx9e/W19brN8q6VeTX9kOpOMYgjo2O5BPmCZyUy600wm5e9FGbv/Gs1++3vO27Qm5K5yUaH6eddpr973//s3vuuSfr+bGzI6dGB1Bm4Pjjj7eLLrrIjj322Lw+DBZ1Bj6LPqWEiIzBy8/Ch1e/MyvLCBwYMMo6eqFEQRBDqAwrFGRlkJ2BQIeykamjjj770fxXbHHTgjbGpFF9NrW+1YMyOh5Rg+wTgpKUz8yNe6e8at/Uto0sJKXwZ6vtmnChm1o716bUzrXeVTU2qs8wqyhSeYXG5kU2r+E9awwWWswqrU/VWtajaoW02hljnHceEAGAs4ExyDyD4E8bILyHlT8ML36D70yNFWSXkZnBWEeYJXoKBwfCA9eE8y1eCTWuGaUUhQg4P44XIr2IaKAf4xThfT5LNFa8WsqUO8NwlOz6mYeLUa4gJzz+b7PalmgSB/ew3CizTfdN6esobRiJW74ac2OQkl1hfCZNIUpIZMM1E9+0d+dObWPm5oleNGYPqyyR0iilAEZNao8zDlkLUD6cQr7oM7NFkc24+25u1j2/jg3GL5G1XENHda75LEaMuc3TbPCo7u2cGuv13dGq86wkoSDtvffeLgr07rvvzjraVHQNiN4lYApDMRng0Xk212BcZm1j/kYm9MFRGPs4d1mudylA+1JmZuzYscoCzzHIAQSlkS3u+w+BEPQzSu4gW0adHenw+YLv7NZvwoEJMRvRo78dvnJmGeU4WnwJZwK58sWU2jl249ctezmEr33bwWvaloPj67nUav90wXSrb26ylXsNssE1hctcbxfM0PRly8uazWIDzarHmMVq0pIpkMFZw5GlmXs++ugjt9+F3zsL3YNn4J8jgU3YP5DPs7F3PPvss07f9fuLEnCAruMDsxKVgaJPEBiFAwMdBIMikd30Ga+b+LLYlMrlegkAC0NpO3SmZNlIfg/MUpef4/LNBLN3Hmr//o5HpxQ4hf7ndThkN9qZ5xGtzoENYN111y3pNQlnxr++eLadDWH7IavbzsOKV2apFCFQyWfioFNiR2gIGu3J716wJc31rl4G82Pf6j6209Ct8m5bwc7EGkUgV0f7CDKXPfnUU/beqjGr7Fbd5llvPGBF22259S3fXHHFFa4kKf/++Mc/zvv5ypXyy/UqEEy255xzjl188cV27733ut3o8w0LKMYk6jFiNEQQLOUJPRO4P7zw4De8RjhgspNDI7f9d27DTKutr7Vvpk+2nbYd2+7vRM1k6zRoCJbY4qYWA+gyYjan/ruMnBr0d4RgFjzqOufTqbFyr2FtnBosqCv2HJJwzL0xe5I9PH2ZgW/lnoPsxyttXhTjaFVFLxtUs5WLiHRXnsY8gcCOERCBkdRKnF70B+YdFACUDJwDvIehE+XCC+hEVyG0o6DgTMgEBAjGPeOdiEmOh/LD8VBCEH78RpUoBzg7yCbhXwwvOEH8/RKBSRQFx8RJx7E8HIsobpwdCMw+OhVFoqP28mnmRItns0dNUViyOE60fcjJsRTmYYQ1Xj7qkjaiT3Q0F/NcUP6Yz32yJ8oe/SbcXgiOOMuILimGUz7eU+Zqn5j+mW02cEUbWKN9m4DxSBk4L4fQ94lsHTNiCvkZ9sXEaTZ7zkKrrKywtdfpbT1XaHFqMEcTkLDTTjvlNLuS8UmpEDKmMCYki15nvqC/jh68nn2y4JWlm4S39MlB3Ubk3aEBRGxiSPnJT37ijDakikeNHUKEYf6kpC1lN4nCK8QecqzbrOUYmpnDi10OIh9gFGU+IzAMPYr1iPrUzG8dGS9E6sxrWGxTa+fYFxM+tm03bBvxSoQ1bZ2Lck+fLJjuZPOWIrEtxWK/qZ1jtU0NGZVxos+jX9An8smQmj7Wq7KbLW7CYOcJbOVe8TNEFjfW25UTX7TZ9YtaDWY/WnFTW7NPEQzfPMuq1c0qKcUbpL2PBvI6FSZwdKFfIF/i6Efnp59sttlmrc4vnoP/HfmRvkPGBjJFpvYPxj0OW2RZAjXQEdArOR5R2ugB6JbI91wTcwbv+77hMwRwZuCA4V+uHydGGK6fDHEfMIhu5TcTTwY6CPJVsfcxy4i6RS39I7TZesv7C9s4NdALaAvaKLw5M30Bea2jZ4s+SgCaXxf5vO8f4WOh86Fn+D5USOLdAvPTlwtn2pCaKbZBv5YgO2GurHmrbWruXOcI77PKAKurWGIN9Q321YQv3Frdd2Bf26Df2ja0e4v9iGoOjJFM7Q2JYG7AlkBAJOtUoqwpxj46xr777GP9vv/UXpr15VK9MuZsP5sVKOvuuOOOc0GmP/zhD51exL5r6lvtUaZGAk8y6eAMJpRTFj6RPT6NiwUdowlRD75WHZMG6aIie5qDZvtw/ls2t2GWTZ00zXr06mlbrrqdDalZro1CzXNA4MuGhuZ6e3fu05F3Yzas+0q2Ys+1sopgRDBmscmXsu/63axP7N25E13q6Io9hti44WOse+UyRamhuckqYzGra2q0v3/2RKti5dl9+Pq2ycDyKWWAwfKpp55yEYsI8xi2EcqZ80iZps5teKH0ZcG8QOJhvEbf6wgEGSKUEEo5F9FTHq6BayFiB2WYZ8Pz532EDyK9MjGa+rI6CEXcC8pHqv2JNkGoRgDPJtKw4Lxwg9mcKSGlI2a22qZm641tvS/mX+6J8Y8ClkmmFm1Lm/rvotDhZOJZ+X2gODYKJooHjiXm+0IKYp/M/94u/fIVJ4SGWsOqYjHrVlFpv1pzWxvWvQxLABQAnF0fvfRfq4g12cJFdTZ2uw2svr7BJnw215p6b9harhClH+fn/vvvnxcFnewsIixxFsRzMKIwk9WB/LCkqdZmLJlojUGD9a7qb4O7Fba/MR7OPPNMu+qqq1wpIa5ZiCjsG0PJMjYFP++888rPcV6iMBexvmPkYt7Yc889Xdsi72LULDsDYokycdEMu2/qm9YUNNvX731qm2+2uR04cgurDu1VxZqA0ShbHpk2wd6eM9nJ6GFOGzXeaiozj8sk6xjDaT43b55RN9/unfKmzWlYbDUVVbbL8PVtnb4rtClfg47BfTwy7QN7bfakNjpGz8pqO23UuLIyXrEW4+Tn5XU5Ahf5Fxke+TAMNgEcu2GHI84DjInplDhFHsVRQLYFugTyLSWMAHmULBHkf2Re2pNr4tx+Y+9MZVN0FJwTOEkI3OGcqR4HZwpyFgEQZbNf3cyvzV6+pe17FZVmO//MrHuLwwF5DVkIXY/2RvbPpG15br5dOB7H9c4q2pw+xbNDtyTAhiC5QgQHhMfvlRNfsm8Wz13mdMUH2JLfZJsNXMn2XSH/5STLEcbro288Ye9+94HVLay1FVYfaQOHD7I53822oXP724ZrjnZjkmdLFivjCptBrudC+hVrFXONn7OiEHTJ+el778z9xr5aNNO6V1bZFgNXLXg2HfMYe675crcK1GiLnBoRiO7BE4ZA/PDDDyfs5CJ9iMgg4tLXuQyXQ8KBRFSHyJ4ptV/bV4s+dj9/9NYntvZGo6wyVmlbDNrJKmMtSgCTOJENuVggvlzwrs1umN76e8wqbN1+W1mPyuwme4ROBGT2XMi3YBJYs2uj1nM31Nkd377pSk0RMTW6/0h7a+7kNt/j/S0GrWI/GLZO2RlU4m2+jYDvo10Yjz67AQEyur8ETgkiG5KlTyMs8DmETQRThBOUXKKkiLpIZVM4DOOUn8lmAzkM6ggkmUSl+pJXrAtlk0m2eK7Zy7eaLZrT8vuQlc02O8CsqrrVqUEGCxFpuRaIcFoxboHnFlbUcHqQdo4RoZBOognzptvj0z+3yUsVD8yHTHtEf47ut5wdXib7a9AXUaAJDvDzts+S8bWhc24cnf+OWd1Ed57WtaL3+rakckXn8OP5kj3F2MaYwFzRUbmoTGD8kV2GkyBapsCXf2KeiRpMisWll15qZ5xxht1yyy3OeC2EB2fXj370I7c/389+9jM1TI7wwVFkIOPcIKvUz4c4OZg7+ZvIvp0v/fIxt79Fw5J6m/HVtzZi7VVti4Fr2FaDW7LTWAswvmCszpbpdfPs6q9edGu3N/eP7jfC9lphTFbHxWCKbMgahmE0n+C4qIpVtNG3Xp81yR6Z/qE1Bs02pKa322R40uK25XDht2vvat1CzqJSB5nfB7OEwXjPOo3cSbtjkKY9WNvJyohCBHVHgY4cE4clfZKsYYzanJv+l8q+Oego6BjZZFVybq6B+8oE5DlsIgSJlM1G2Z+/Yvbxcy0/0zc32dts+DL9iLkWst1/MR4Eu6G3RPuYz8RHv2E8F8oRSMbYI9M+tE8XzrAFDUsirlezX62xgw0tk8ApxiL6blje52fmSHT9XO9xsrBxkT0x/QVrCprc+fiP/TTGDd/Opn4z1T1PniXzBcHllCQjmDIfhnxKUWEjiDcX0AZklqB/lEJ2K/Porrvu6jKX7rjjjpLfe6aQlMkMWhjw/JIOjoGAKI6OUghFetCeGE5JfQ87i0j7xNAlckNt06Jl6dosFLGYNVuzLWleYj1DkU25WvRX6b2B1dT2tAWNs60q1s2W77Fa1g4NYBwWomRNhWuHtlEyODSm1s5zPxMh9s7cyc6JEY4W42dSzMsNBAKioMKLM4s2wjVglCYigfJUVjvf7LPnzV5+tSW1eM3tzWp6OUHj6aefdopENCXYK4z8HaXWO0RQHjBmp2NYQEDNtp+Sssm509342xtJEJy5x6JTv9hswRQznG/9RrZsAB6Pnv3NdjrGbMGslgiq3gPb5Enz/BHcuLdcOwwRfBM5uugjCKZ+09ZC1RNev99w9zr1vQesKaRxMD/Oa2zp8+UA7UZfjFeSj3HI3xl3fv+kpOMGZ8hnr5pN/siMNWHNzcxGxEnv7kOUWWCxJSipMbMeq5n1WN1qlpaQoLwAwj7GAYRs0qJRDnJtPOSeMVJ6oyXOEw+Ob+aJRJtyFoMTTzzR9e+DDz7YlTA96qijin1JogS4+uqr7eSTT3blpshsErmD+Q4nBs5Pohi9Q8PvH6agqdxQ29zgHBpQv6jWuvduiYye09BSNskbNX2UfLYM797P7Z/x4swvrLap3lbtPcS2Gbx61sf1DnlvgM0n1chhIb5Y+L09MG1C6+8zlyyy+Q11LemkS5dt/ulT1b2sHBpAUANjMOrUwLHos62xtWAgZGzWN0+x+Q2vuTuuqRhhNZUtsjY2AuwFRGuTYRXNZMDACeybhzOAY+FQSXdfIl8OO5t5h2sLZxWkCvIS1839Fd2hgUy4YKrZkoVmPQeZ9UqyP8YaW5qNXL+l5FSv/mbVbQNNkD9xPmDbydU84ElUhojngCMLmbSQpYN7VFbbfiPG2HPff2GPTf+k3Z5uCxqX2FArfTsBMjxBZ7RbVHdgjBCwRmYRYK/ryJA+v2GhvTv3U1vUuNgGdutnY/qvZTWhKhjQu6qXbTNkM3t7zgRb3FhrfUPGqFcAAQAASURBVKt726YDR1t1RbV7lox9HBpkYPHcmTNwdlLlJdelydErOA9zFxVCfBvgOCVIgrkLPaMUnBrMjdio99tvP9txxx1dRSFlobagnOelkNaGBxBjHRkacmjkHl9XMZr9gjBA1AYThsieHpW92pVJqrAKq6moaTWA5dJZwIZOI3quaWv33cLW6LOR9arKTRS2j0IuNGzWR4ZGuA1Z3ticEMeGZ92+y7mIsXKCNiUaIypAk17p69YTCc14DOprzV6/1WzG52YLZ5pN/7Tl98YWRwPRVTg/qCUffVYItBg9wxvmZuKcYD5mbs4GBOs20d3NTWZfvmT28jVmr11vNu3DuN/jepmrEKyKHgmxcIbZhFvMvnzK7IvHzT64vcXJkQiU6H5DzfoMilv4leeNkbhYSq8XoAvJij0HtBm//LRyr2XG8VKHOZu+jEMyCvIKYwXHHWMShZ9oxYR8+JzZe0+azZlqNnOy2ct3mk1BIYuAA63vRmZD9jIbsqdZ73Xa9CdS/alVz7ko88b5cS5gRMw1zFnszYKDknbwcD7mmVLLpDrggAOcssHmfueee27R1jNRfHj29IFTTz3V6RdyaOQHjFgYO8LGLB916g0yIjt6VFRbTcXSgIpYrHVeG1Ddq035INahXDGi5wA7eMVN7chVtrbth6yZs33sMJgR9FJovlgwo40sgq6Bo2hY92WBlDgzfjgy+z1JCg3jLPrs0f3pEz57AnmF35c0T7bFTR9bUzDfmoJ5trjpQ1vS1DJOeS5kLyBb4NwI4zMzyMz0ugzGx3TLKTNP8P1sbA9cH1nI2DFaWfi92Qf3mL11vdmnjyaU1QnwBHSMosIY/vJps4/vN/vqabMP7jCb9l7y73TvbdZ/eDuHBvBsaI9cOzRSAZmUrBkcG0ll4ByDfSBqdyE7q1zK2+K4wCkX3TcGGCM4MpDvkbNxSoZl8Ci1TXX2xHev2NTaGTa3YYFNXDTFnpnxuiuNHmVIzUAbN3x723fEeBs7bBu3UbiHdZsxQrlsSlAx7glOYJ/G8D4tuYISVOin7OnjxzPBtZwXR0rR7QAhCBBFlmSeJKMtWztJZ0FODTNXRgGHBmUCbrrppqJsaNoVYOFm8Y83kTB5IZSI7Fmu+0jrv3ST7qbGJmtqarY1+6zvSk8RIY1BE2GxlMHoSQRuPtJXO6KqAhdQW0MwmS9kZZy0+o520MhN7Kcrb237r7BRWdW6BaKoUQS80k8aI9HPfn8FBBvmQ/5eP/Xzlqgdb4xzG07PM5vTIsxw70RMs9CH2wEBgLEcLjHk919IF+biTL4XBkGtjZP6ixfMvn7DrG6+2aLZZh8/YfbdpwkFcyI3aDeiwDCoUlah4AbKr55hMC/7nefyLdFt6YPDCqWzmDV8cRbxTHzpgELw45U2skGhjcFH9Rli44e3LxlARFIpGqBRKlDWiO7rSKljjJO5wcbAcfk8ziapn7+Z0XUx9qldz/l8eTGciPyea/wmo2STMQ69/ECfxllbapAJRSbLf/7zH/v5z3+e9Vwmyg+eOc/+8ssvV5nVPIPxJVraEFmGaG4M2CI3c/Duy23oHAuV1VVWv7jOhtX0t00HtpRwQlbCMFTqsjFrIzJevHKG+aZbZVW7MjXAxuBHrLylHbLipvaLNXayFXuWTvZhKuAcoGSMN9b7jbYxQm666aatTg/KVbKGL2lqW9IXljR/00b+J0I6uokvcng0iAEZIJNsTa6VCO1MIXLbvxzoFR/eY7Zguln9QrPZE80+uq8lmCoCbYWBmCAQ7gkdA2M8pTQLypxJZrMicuXkl1vuJU2QnTF4p1L+K5/rgJeBC2VXWq33YBs/fFkWSXWs0g5ZcRPrXdXWnki/L0VZlf6LfsG8zVhKBH9H5iZ4gDLG8XSlbxZPt/rmhmX7jFhgcxrm2+z6zJ4FlRJwKjz22GPud4z4BFG1cSTmCHRjMjUowU3WD/IDv2MHKDWYH2+++Wa3xwY27Pfe68AR2QUor7zGPEAEAJvjnnbaaXb66aeXvCBWziBw0NZRWMxJGQ1HdYvsMifW67uJzW2YaSM2X90mfTzZhmyznBMwWQiI0C7Vfs4CyUKJkMmiWYySIihrWw1e3V6c+XmrQ4MSVZsNXMX6d+vpXuUKizQefV44MBh37K8Rrp+JEEgKalC7bJ+UNoSiLVBgo2WdGM9hgRYBFyEu04g4hK1ouax0wMhKv8eITuml2BvPmiFwLZXFKipiVl33rA3eYaQ7j4/2ImIFIS+qPBEhgmE5mzq8abMEYTCwb76bbUvqW5wbwcwG6169eselhiL4VHmeS7rfzSU4xBDKUOJILc63k2Vgt552+lo72oy6hc5xObhbW2dcOGsQYwdjAeWIOQhDWSnMmb7UQSqfo+/yjLmXdhtuBnGUgThRVKnCNTG+Udh4npSuw5iRj/IDwPzEsRnXODkImOC55TolPVdp7TiOd955ZzvyyCNdCaKil5oQBYE1ltJj9FP6QNEjcjs5GB/igUFC5adyxyq9htqRK+9gU2vn2KcLJti2fddxm4QjF/nyh6UK6yFGZMohsY4Ug00GrGivzprossL9XiFj+o+wAd16ule54vfJwK6CLMU6RxuHI5wJGqE0FcbDpuZpFk26CSJyCEF4ZINGS+GE9Q4+Q7nHTGRIvpNNsIHbCyAWc32fe66e/YXZ1GltjL3VVTOte+8Prc9yqzkdBF0G/QIZON5m6MhOucx06pC6uU7Tra1bYt/MmLO0JLNZc9W7tvyojdKWq5DruT9kv0Ju3B1PBi5kudvth6xuGw8Y6UrJoW90j1MimD0raBPah2tkbGD7KoWyRv7ZpVKSjb7LfSDv48QOj3H2KY1HovdTAX0b/QJnAw5S5H4yKrBn5Vp35L6QF5DZmGfoO6UakITT5aKLLnLXuOOOO9qjjz7aujdqV6RLbxROBB3ZGaSFn3TSScW+nC4Lm1azgbDID0SDEKWGYIKBpRiRSR0pGTgxMBbzYuFC+Z8xY4aLdC3GYs+0+O7cb+zLRd9bTUWVbT5wFRsaSg3vzPAMMAJM+vwT26r6W1ttGEb+pcV+STXe5giz6pbauDwrFESEDJwZRL8hQCKou3q59fUuygJhP9Oaj6QQc+xsNgEmqhuhyAk/z17qnBoehJWGPsvbd8tt1Xo+LyzE20CSzxMRwXjCkFuIjIfg/Vvtk48/seWH9LN+vWn7mNngUbZ4+GYu8o22xvCOshjP+O6X+fDfyJrDMMx3irkfAQI05cpoazI3UOZQhsKOtkI5FMhcIirQK3FcG8YPIguhFAw2RPP5SMhUwKHI/IqC12pMf/NBs6/ebVv/d+PdzFbLvNwFzmjGOOMHpyn1b3mejI9MN9HsCOYfokAZk6wVxcw+SqVv4dhgHrvxxhvzstmhKB1Y+3784x+7tefJJ59Mu9a7yA3M3RhBCxqE0IVgfaYEKXI6a2MxI7TjgeyJjOQ3wGUexoiI8Y51PlGN/nwzp36x2ydkUWO9K425xaBVW43JnRn/DNhzr3vvRbbDuJXarNvdK1e3HpWrtP7+1FNPOcMlchiZoDhO0A/RY8kM4VkSiJVp+UlkPORg5M5My4778lpOX5j6ntnXL7eRrRoam6x21fE2p7ElQAt5lgAzDKbx9HHkNa4r3obreWH2RJv95l02c95CW2PkMh0i2OAQmzp7odMV0IeQyxNVMonK6T5jA30w3f0Mcw39jXbnmpBPmafCRvhC6hjhLCPOSz+gT3N9zFXolMV0cKBfpLPROveA4wjdyJeWX9CwyB6e9oLby9UHhvaorLHdl9veqiL7C6UDTgZshThNCVZk7GM/JDAzH3Yt7o1SvjwXxiPZIqXMxRdfbGeddZY9+OCDrlRvV6TLOjVQMvbdd1/n4Tr66KOLfTldDrodhg8UPbytGECiaeOic4NASokpFkOMdCzufi+GV155xfWJUnPAdAVwUiDwIdgE86bbojcfsN62xJZU97ZpAzew2UtL+vuFnqgGfuaFAB5Wavk7Qhz/prt5MIKeT8NGwOIYKKEIM+kaL/ku840Trj971uxbDLoh1hlvFqcUUTK4Xwy5bkP1PIKw++k7L9uo4DPrVrE0WqSmr9k6+5hVL4uCQrFDcQeESwR5v3kqYPjlRfuR8s7P3AOKU3Sfo2KsB2T9YHjimvzmnWHHBv0sE4eC378jFaWV/oYClyiimvblmgq1yXkunBq+DTCuojC5yDlKmb37uNk3H5qxEemaW5iN2iLu/iupwjNi8zqEaZ8VSLQTii3tSgmYfCiO/vkSLVbqMO6IOsU4cPvtt2t966RgyDnwwAPdPPb4448rC7lIzgwCZpArkTVZpwuxcawoHRh/GNyQZVmzcYAj69AfGKPFytToyhBkQdAU5VKqq6ts5vz3rbJmtpMNFs3tbXNmdG81/PNZ1nX0QmQ/ZDMCJMLlqJDJCJ4gqCEdkDPpG+gGrtxufb1bjwnMyMQWgVzmjP7BErP3bl1abmppMFiPfmYbHNSy112a/Rd5Ld9BR9+yP8LXL9mI6jnL3lxxK7PllpWp9tndOAXQ2Wkr5LpwcAafwelB+3ndjfnX79dYTHCKcU3MBbSrLy3kYU5Ars7EoYDuxXrD8+9ojSGzgfPEcw55IzqBL8XKDGdMoP+kq2MThMr49HL4jLrZ9sacD9wG4AO69bXNB25gfUL7LmUCcr5/TujeBGkRQImzA/sC2SP5gHuLVwavFLnyyivdPn733HOPC6LqanRJpwZerIMPPtiuuOIKO/TQQ4t9OZ2KVD3epGqyGM6eN9ve+vZTmzJ1ig3pM8B+tMu+hU27FDmBxQwFkogV3wd4vgg3LAThPoHwiHCEMIqhi8WC75NCjKBLeiHeeN4XhYPnRgQEyh8CHwICDgkcHAj+CGEohOHxiVCB8RIHlI+cJmIpHBWJ0dErlqlAX0DojGdsRyhFwEB4pm8hRDYGtbakaYb7e03FUKuq6BHXQcLLRYyjbEx8xey7z1oMuitubLZ8Zo4JrscL9rQXRstcZW8QrYUyg3MCZ0yssdZs/tQWxajvCLM4qc3hSLh46fj8jeeDopQvATBf0M48Q/pRKuV7uFfakPkGwZcsIpRk5iL6JP043lrTkWODzDcMIpQp89lHHBulhnPy8hF26SomfrO+ZN9Dwfe1b9OF7+Zz43vaF6WM+YA29PuV0aYoc0RUdfUMBZTrXXfd1fWRe++9t2jlGUR+YO3cZ5993Fr4yCOPpL15rciNjoEcyVrxxaQPbPrsr23u7Hm2/qjNbKst0jN+iuKDvMe8yZriS6PQBwhyYXyFDZH0D18GkfXZBwAQQMXah47CWp1ukI3IHuQ3jJE8B56jL/cJyD3Iu8j8GMx5jhiIybLhX4JxAAcG2d9hA2O6gR7IcMgqrdnbS0EuJLgG+Yjzc55erM9zJ5rVzTPr3s+s/yrtgj+4D2QrArrcdS2cYTbpRbMlC8x6DTVbdTuzbpnJXH4PNdqGvusdL9mCDEs7kDVAm66FzrZgWss+ID0HtbySrHG84pUM57job+kG3hQbnjfPEL0pVRsU8wz9E10UfcoHY/njsf5EnRy8j1w8ZsyYuMek/QjAQ99Gv/Xll8k29FUE6LMEBGdiI2HcJdOfcNAwNvx4SwfmVcZzPp/9G2+84fRh2hwnNfo2awCOUvpjOQQ35Zsbb7zRjj/+eLvtttts9913t65El3Nq3HfffXbIIYfY9ddfb/vvv3+xL6fTEAT11hB8aIGxMWmlVcZWtapY4vrBGLCbY4F90ON7q61qtKA5sC/e+dhWqBpgJ+99ZEmXkRDtI6xZKBG0WGR9mrCvExndFAujFoIDAiPwXbJ1iPClHqDf7NEbykVhQEAjlZNnSFQjTir/TOOVL0J4QeBGuArvl8HzxomBUIZghKDH91MtQYPDCwExUR1XhGn6COeft/A7a+j2hQ0Z3hJZFbNKG9htE+tW0a+NkMYrn/XMiR6h36N4oCBw77zo45nMZbQXDiXann+plVkKezoUG4R7+hPCPQopCgNtHzWUM6egPLdmJsQBJQEhnOdDe6MsMi/h9MEh0ZGxme/ixAKeO32W6+FYjB2ctj6SKJU+4MtEcR1cv88Goe8SpegVEcYcin+mUUO0C8fL1/4TftNN7ptnxbxCiQ+eGQ5QovbKzaGWa2gTSp/SXx566CE5NjoJrE1s2ggPPPBAWUT2lQuNzdNsSfOnrLZWYf2spnJ9q4j1SBq8tvq6w6zHMKKPYzZj+mz76tNvbMya42zUajJolwuspTgkMPBhbGatx2HB3Mm6y7qCDODxdeoxKPpoaGQo1mDWPByNrE3Io8XMuOyKYITEMYX8gVOfZ+eNp1FnpZfhCBih/FTYOEw5ZZ458jGyms8KTVXWxnidqOyYl9+4vg8/+MCqv3vbVu+zeOmxA7MBq5mtOraNY6NNeds8QWAIMinyILoG44B7z7RkFo4g9BNf6qoUsilKAWR6dEscG7Svz3CPOgLQE3zAVDxoU9o4/D2OiW0DGb4jfZjvIz97/QY921eu4D3Oj5MrnfLBzIOMMbf3y9L+g97BNXoHlXeqZJrFRv8k8DDTcnAdwbNg6wC/RxbBCy1ZX9WuvRm7G264YZfXl++66y77yU9+Yrfccovttdde1lXoUk4NDKY4Mm666SYXSSVyR33zWxYYdceXdaeq2HpWGUtcR/iNqR/bTU/fa6O2bFlMJ77ziQ1fbYStPXeAjd/hB51uUmIhCZeDKXcQNonKZXEO1/1E+GKjM5QQL4ghMCWq9Y9Dg1qFLNgsiCyqfJ9sDb+4KmujuCBg3XHHHW7vDPoxzxUBDcElWX9GYEYwQ8lE0EgF+gQlsBL1mTAzl7xu7094x5qamm3Y8IE2aEg/a67vYcN6bumEG64RBTib/TgyBYGTjAnv8EvHkItQ6LM/EOI6y5yRK3w5MdqFF7/TF+k7OCtQ+piX0mk35h2+S7t7h2u2oHyggKPIxCvxxbP1WR78HK49TB9AScbJh+LKPXI8ri1bpwDj0huCGGNcWy6zN5jTt956a3dsAhiIniKaiueDYYNx2dUjqjDukLHB2nb//fcnrFUtygPmD5RH/sVwmq9sqK5IUzDH6preDL3jqnRbj8otLRbdaXgpzKe3P3qRbbjFKOvevZs1NjbZB+9+br27DbX1V92pMLXqC0w8B3+54st7Mk+iH4TXcjKKMcSxPiLnId8lWhNZ0wkwGD16tNMvcDTiSCZrkL8xTomw72z6ZrmBcZJnSpQ3QVGADJesFJTPBkU22njjjVOec5HJGP8dOp0XTLWGD+6xZ9/+3FYcNsBWHznU6hsarWa9fWxOU0/nJMMI7jOGComXdQmQoO+mGx2PYd3Lm9Ix2sOcgcGfeYc1HecbfRG9zpezTSdjhueF3oy8jyMkV/tm0Ac4LvpPvLmf66WP0k+wyXgnGPeEnsSYwcnn50/m3UwzwT2cy5dCBmRc2i9XcyzH5hzcM/eBjsF8DsoKXwYlqNjX7e6777Zx48ZZV6DLODWeeeYZ23PPPe2qq65ypadE7giCJqsPnmn3foUNs+qKxBFRb8353B6d8JLVLVpsy6+5ktXX1tmk9z6zn2y5ly2eNjdto1SpgxGOBSVfHuxCpmiiHLAIslj5OvPA80K4RLnC4UEkDkoHUQYoFdFjEWFDe0TTWImU4LgIamwUN3bsWNUfLzJkcfh02Hhpx//P3nuASVaX2f+nqnP35JxnyJkZchJJQwYJgqirYFxYdVVcw65/05pXV11lf4oKu4oJXJScc84ZhiENTM6hZzqHqv/zudVv9+3bt/Kt2Pfw1MN0dXXd9A3nvFFJgOCAVDEOEKG80tXshIgRVcIawPEg3ZYJ4SZGL759nbp72zVj1mRt3bxdra1tamkap0l1hziCBcMwES2Mn1KBOYBzI1lEjx8wcnO9o93wmykYLxBb1h2cZ7lGrxUKJmYYuxaRyJjm/8wlBHEywp9LD41swHkwRxBwOEyCuHes38x5oiH5fnok4eS46667nO/HaIEhyYRIydG6Wlr2kNTTLo2dKe16rFRf+LJQrIn02CBi+Nprr60ag+RoAxGb5513nrPfMMbDDI2A72//6+qNrxgWNAWaao5UNOJvyGSdfWv7bXrioRd15LGLHG76xqvL1VA7QTXtsx0uU6hstVLtMXBmotorGayJRL7zvOCJcCdzwDPP2FMwKBIsg8PfnPQf+MAHfO8JmbNE8rphWQDsdxjGMVJW+n2rdPA84ELGezMpMerOkGMsmKZgfKQL/OCZW/NrAP/h790BdPFNr+n5O67SHvOmq6G+Tq+vWK+Wpgb1TDtYE3c50BmrOEjgnKUsm+12UGQKAsfIyA+rYmQGssVYKzDOsy5lMz4LDTg29iXgNunyPkGlOPBSBc0UWmPY/ETLo4ODcG4QOMW6zhwmcJHALNZ1ylOhMZiXBK+XQ/nPvli/ntiyRCs61qs2UqtFE3bVbmOz7w+ZC/7yl7/ok5/8pJO5euyxx6raMSqcGo888ogTEXfZZZfpoosuKvXpVB3i8Zh64vd63o0oqpmqiyZPYVvTuVk3rHlcbzz5siZMn6wp82Zo6/L1+vjCM7X05SUOeS2njSNfsPhSwxLihhHDuTamX88WKdYnNUyUosF47wsBNgycEJR0QSzgKbcN3jYprg9hRbPvVDWQIYMYIXF0pNp0EDFEVRGpZeQzRGHBPSea2utQ5LmeddZZgYgXCFS6uU3ECMSF8cVYQ4BiLIWEQ5Igad3RZZo2D0OgbWMR9XY0a3zNvk5UDcIFR0gpjUyQK64hG4NlLo6Q0QyM8kQcIToYV9UUhZtr476MEIuxMQ3+SH1g1mYEcr7Hg3dRQpDvQTQx/nE+Ed3Gmo/TA2d3IOjvlPrbpZoWqSbLOsPtm6Xnr5HisYE3IlLzJOmAC6QkUeBBAiPO8ccf7zj3//znP1cV5xkNgBdhUGWdv/fee8tCRFcbevrfUm/8bR+nxtGKRhIlOfywrutJvfn2a9q0Yav22HuBmlsa9dYL7Tr64DOcZwWvrBawbsAZMBxhYMWwA+/u7O/W1p4daqpp0IS6MWWdkYDhi70cozZ7BhzPnamNlqCsG3re+JSfvrCyuPw/XcYvWR9wXjOQhSg8WCutl4YBTg8fSFYWKlPgAMPA6+7rl8qwDz9hnNl4gnszbjif5ki35ux40nFkuA3FG6ceoynzEuWmCPILgi/lAxx3mVxvvo6Q0QrGg93jUjfyLgQK5dTwlq0mqwJNjjbPl/vDm9ErPAuO88ADDzglqbBRsZbjpGSuBhGkhY2zK7bD+XdjlB6x2e0T9294Tm+1rxn23gnTDtKCloD0Txr87ne/02c/+1mnWpHXwV9tqHr1RPMYGqX86Ec/Ch0aBQITvCY+V/1KRM0YaiKpPZGzmibrqMl7K35wXC/d/5TefmqJTlt4rKZOnKx3BuqbVxsQvBh2IO89XR1qantBkZ7N6uru1fRpUzRutzMUq5vgRCGxEZSLIR+jMh5wiB/nDvH0bhaQJCLMydRww2/zh9Rag6d0BJUyVByzXO5FtYN7zrMliscNCEK+sOZ/mRjuII9EcuPcIKqbv7MMEQimQ8KiO2lzz9Pqi7Vp88ZWtW3v06SGhWoYlyglREYRYiOZUwMBDTFiHHIcKxMVpLjl3IkkzIY0QtgyLkcT75c61kmxXqlxilRXPdGnmQCjhKWG8+KZIjSpqR1UincpwTUgtANtKL1+hXTnn6TWzVLLeOnE90tzdnPuI2KDyCeMSe55yr5ljRA5n3Ql3XDIMbdwyDD22T9Y88nAwtkRmEOj4y1px/NDP49dJDXvkvnfb34rEVwwiLjUsVnq2Cq1FD76kvWG6H6iqD72sY85AiQ0rlUG2DM++tGPOo56ssFDh0ZhUBudpd7+5dzxwfdqNDWlQwNMqd9fsQV9isff1M1/e0CTJ8zW8Yef4+zH8NRSRlcXAux/Vh4UQ9WqtvV6Nb5K7TvaVVNbo0N3O0BHTd3fMfiwT8KpA91X8gBOCM4bjYGOYI9wr4MYmdHzlEB1B4gkK2tLgBV9i9KBCF8CB6rJSFnu4Dmj67zPLIgYW+Z2JtUQ0Aes18wBuI13Hjh9MvY+QFo/Rlr5iHr7+rV87WZ1TNhL83af5YwZ5hpzCc3k59TAiIsuZhwzZnnBsYIuNWl9ErJxrHBuGf8NXGj7eqm+SZo4tyjBHuUCnjNOTytRh2OD/Z6x41dSNoQUi8f0+o6lWtWJTTCuGY0ztde4fZ1qG9w/nO9oA28zcvYkAtOYT2jxVOW94M3YAQDPhf2cahLMY74jqCzMvli3VnQ8re5YojdsQ3SM5jUfrNpoQ8b3Yln72hHvv9W2umhOjY985CPOGke/t7vvvruqgjlGlVODheeUU07RN7/5TacTfIjCoSaCx79RsfhmR27URhYoGknvId1/wk6a2tukI941T3vuvJsevP9Bx3BSTZG2brD4sug6ZWW2vqiOtd2KjBmrxoY6rdvYqvVPXKPIrBOdRmpEAGJcwihUykhzNh8MzBAy0mz9NguMsJAkd4ZGKtIJmUpXtx5HCtdNBLYZ05KhP96vJa0va13XOkUjUS1o2Uk7t+wSCpUcQFQc5AIi7jYMB2Fog2zwTBGo/NuPtFhZHuaJn1Ga3zMWyfTBKNHXN1Vbt/dr/oI9tM+cuYpGEyQdp6iVOMO5wXfxvcwrM9ZCTDG4EjVGqSsrV8DYTFXLN1MgrCHE2UZxYfAlMwbnH/cIMuhroCfDa/VdUvfmgQUmKs04VmoJpidEJYDn5F5L+Df3z9YZnjnvlYvxJluwF+Kk4ToZu2QeMTYYU4xjxFZW46uzTbrhN1Jvd+Lnju3STVdKH/ySNH6y8104LNh7bK1nznFsIvu4nzgm/CJkhx2ms3NYA0OINHWzmbvsF4E0C+9rHe7QAPxch3Mv0/lbemMWzlrEBpFmn/vc5/SLX/wi3LvKHIx/nhVCmnGdTUnGENmBhuBNNYeqJ/a24vFu1UQmqC66c9q/q402ambjEXq7tVdfuPgCbVy/1dlTeXaVXAY2FVi/2TMmTJmox1e8KbVGNGXudPX19OrRJU+pZ+IO7TVtF2ctt94F6JFSGfXZo+FoGIfRPd5gGgN8jyjTTAJiaCSL8yMV4GXsaxi1MXDz71T76KqOjXp400tq7+vSpPpxOm7aIk2oH10BJEEBXkEwCuPONADjL4gxaH0r0TCsyX68mTEHP7E6/15uSNAGjjVsSE1NY7Wlfx9NbKnVgpP2UG1LgreYAxudyh6AgZvjWn8KCwJjPOPY4FhwHyunFsT6w5zh+6z3QzbgvOCVcGPmVNKI9vWvS0tuGwr8mDhPWniWNKCzqh2sSeg4yxZjPBHUCYeFBwNsFDz/SnWM4mQju4l5wXUyTtHE/D/bfpDg7fa3tLKTIIQE1natUU2kxnFsAO4Vay7zyxx82ANYi03LcW/T9Sxxj3nsCmghvpdKE0cffXQgz2Nd16vqjrUP/sy/eW9O86IMv2HkOZRilPzTP/2Ts+ZhEyeLPtvMrkpB1To1MMTSGOXiiy/WpZdeWurTqXqweNRqvhSZn/XfblizXnvstpuaGpocokB6GgtSVaJrs9S5VorWOobIZlda68xpA1kL8xckfj9APNhQqdVMlGsxN02EHw27cSiwedCXgI3HD5QsydQRxaYJ4WPj8R4PwxnXyTVzXDzLkAg221TAobGma82gZ/zNtjdUG6nT/Jbsx+NoB8/6xBNPdEg90XPcewRivmMPIzPOBYgSTg0MnkRDMaZ4xhBDM5TaWPCDNT7DgWYOiojjVB0JxMSiRYuG1cll7HFN7mg/3mO8QWAhcIx7HHj5XDPGE4zrHDsXhxB/y31CsHBuELYRjRC3LZG6twz9TAmd9Q9LO53PoqxqBM8fJ489G0gwAtYd9cP9Zh2xZ8t6YjW6Kw2MU/YBhDDzhN4cXBtigPeIyIXQZ9zcfN1yqadr6GfEaj/OsTcdpwbgXuHYcK+7bmMSYxontnte+e0J7vJpPC+ib1n32eN5UX4wr3Wlt9X//b5tmTs1pu4qrXo6UYrLKW8TkcZMS5SgKiKY73feeadjuEP0ffWrXy3q8UNkh+9///tOA0b6xQSWdRQiKaKRMWqsSd6jLxVqIg3OiyAI1p9qLbtC7e5l7avV1d+thmi9YoqrZXwiIKq+sUEzF8zWuHFTNHNSIsKYfYT9FOOmZTsWExgGyeBjLyG4BCM3zgY/QzT7YKal+dgP2fPZY9wwgzM8wKJ8jznmGOe9VNmQ23radMe6p5z7CTb3bNctax/X++Yeq7oBrRYic5AFCrfAqcbYw0icTdNlP/BsaRqOdoATYUvADoRxlvEAVzLtifMKZwTrgV9ZWMpSwX/s7+DefmCsws0JRnDzGO8Y5voIDOS64URogyACOzBEc5656AsLXkFLsQZgDB7haOnvlV69Y3gm69YV0pqXpDmZGnUrC4wPnh8aAzA+/LQoz86eHxoNrYyhOOgsnGIAZwJa03g9mtycy4yLbDPf13cNNQk3bOher700lN1N0CFOR8vOYgznGziJBuJ8ma+Uo0Jf5DvHOvvRGMMzuRPvZYZoJKLdx8zRa21DlWz4tt3HFr/yyBe+8AVnPaSP36OPPjoiU6YaUJW7MZsF3qjFixfru9/9bqlPJ0QaOLUrB8gkxGC//farWI93SrS9I214ZMBPG5citUP/NkTrpMjwCAgIOBsNBmCMrsUQHjyT2267zdnEiKqBlEHE2Og5F29pMDZ1PpesTicGcgQJn+Fv/UqLEXFChBVkE8MORIJNlpRzxkQqkKHhxfqutaFTIw8gCkjpZD2ltwWNFHOdlzxTxIG3WTywiCpEjaV087M7yttrLCWrgnNJ1aeC8eNt/JeMPCFuKHEGwec8IKf8nIsRHEKIgMGBAxmEJL/nPe9RLmBdtEZvkGb3PGAevvjEY5pc36YFc6YOZrjU1vZIsW6pJnVpjkoFETzmALOxlWp94FkjJK0kRyEb4hUKjGOLInVHhDP+GavMUfYHRHNa1CaZMzXD30+Mpdqk9zSZ09HgzogyIPg4f/YB9hIi3NP1VUqJZGM8m74aTROl/c6V3n4k0Sh83Exp53eVxCnIOKXuLUYS5vwnPvGJop9DiPS44oor9OMf/9gZv8kMXiHKA+4yF/Ba1p9K3AMycWjcvu4xbe3drogiivmU8nGcHLXD10z4EfuG8Z9i3Rv2K5rv8jyYQ+gaDC7wpmSlDXFE4PjwGtfgAu7AhgMPPHDE3/IZSsQRFACn4rg4jzHowk1TZXKu6NiguEun8e+O/m5t6m7VzKbqKmFWLMBnGGsYS3E0whfyKecDt8Mo764kwDNGm5KFwAtjNTwQrkywUzLY50EqQy56Bf7l1UV+f4NmIfuVsc01WwmjXDSVlXpmvuIUPPPMM3OubmHOJOYdTkD3fvb20pe1Zek72m32VI1pqldvX0wNDfVS+1ZVK7gPrIWsi/Zs0vVDZMyhQdB7OAhKWVkjV9j6x3W7NTO2GsYW9h/W6Eycj2RleBHVyPdSZcalK0VnetcL9AT2CpzVODlxbqfrq5QKddFG9fV3ed7LznF1xJR91VBTr+Ud61QXqdXCCbtqbnNpqtF873vfcxw/2Mixt1VbydSqc2pAUDAgQXx+/etfV6dxvIoAwXBPqkzKF1UkWKA3Pmk/DPyvP5GRETfjUESacpivMQUDEJ5zSBGEKVWEbBDAcEzNU/6PYZdNm2hENgg/pwobEOSK88QzzqbOpsNGiYi4+eabde6556Y8Jn9DDwWECQSXvyetF3KZrj4iJafI0Bj+3uhIjy00eJ6IXtL//cRiJoAk+hlcmfsYqPluDLMY9RhDRCCxNrB+8+z5nJUgw2mAUTWdowuRk01EJmQU0Qxp47gIIAgucwCHGwTM2y/GwLkjUvgMf4uAgVgheBAwQYB5wFxibiCmuAeLFh2kbSue1LOvvKPamqh2tHfp3YfvJ2VJuioJrCm53FMcVzhAkkWCVhIsGtHNb4g0xTiTtiH9rJ2lqbOlTWsS+xIly8ZOkHbKwCEyAIwPCOtUKczMa+aOt6wBc5K5gbhnLhMxRKNsi4zLCnVTpYY5UveqoQABfub9bDB2urR/6v2pWECA3XDDDU4vOMRkrg7REIUBz+bzn/+8br31VudZhShvYKiz5phwV28t/2rBG20rHYeGGd2drSE+mHvm/H9K/XjtPmZuUv4D74LL5GpszRSWkcu6j9HZzdPQ8H6AD7LH8X8zSFv2BqWM2GvSRZ4SnIV24vrYv+B7fCcBO6lQG4l6WtQnUDOKegsUCowDSlNSEoX5mUuZUHQpnMSrExln8AzGBg407EI4PnAIWOkgODvOD3gh/Jrzobwnxv1UnAQHKTwy0+hyxinjnIx1IsqZb+h5xiROHetT5hdZznUQKMXcgP9zHcxTAr8Yu0EEOvIdrJUcg3Nkni2YM0v77TxHb65ar47uHnV092r3OVM1Y9fqMoR6eSvrQ7YBbYwbNC7rSiU6NbxgLuBkdo9v5lEmTo2dWnbWC63PjXgvG7C2MyeSZb5wXn4VPHgfXUK2Fb9HY+Rju5jeuKeWtz+puBL2pYiimt64V1bfwT5xyKQ9nVepEYlEHNv4Oeec42iLO+64o+D2xGKiqpwaLPYf+MAHnA3g6quvzjhVNUTpgCHQBEdVAwdGvNf7ptQwVWqZm/hd4wypIXWqHCSGBdrbLC9o4MCg5JVF1bqjw/2InrssDgQTTzCbC+eJcMAhA2lMBY5jx4IsZlOCjB4alJxyIyw9FRwwCENyvZE82cCdacDYgjhS5gkSz3e6S3i4SwfxGUsBh6RgzMVQiiDgPRszECCcJ4xR5gZzJdt0VgQO+wcig/Ox72SNQgThZAN8N+SAa2Df4b5gLHEfD4HNXAmq6S/XSmQwkTOIQCfKJdaraX0bNG1ywnC8dNk6banbR5NCZ74vuHeMPTLeKhmMRRyAOTlnamqlsy+RnrxT2rJOGj9FOvQk6pNkXbfaynn4gfFP9KxfrWac1/fcc49jiGBu4+QgszZrMM7HHyp1z5L62qTaMQmnRoWPf6LMrrrqKn3wgx90MjfSGd1CFAeM03/4h3/QH//4RyebJkR5A6OM7dXVjs7+LidDw51RwGUfMWk/dcd61VTT4DQm9YuiNbAvEpyE8TXfckCpwPPAmAuHc/fQwGiWzCAIp4PbYeQiy4O/pcQ03AsjL0bfVH2eeN+dCZBNwMtOY2bqma1vOPfRcRjhIGqYoCkNxS3XVc2AP7O+sq5my5kZE+YsQKvAKQhIYv4bp6f0pfEleLsFUzKWMOTzOXM6MA8IouI9eJaNKYKbMJLyecuSzfY8TcOYQw4uR8YKPIneMtZQPNEvsM8xrlPiEO3kdnhw/gSEBFm5geuhQsOxxx7rzE8Hjadq79o7Eq5SehSu7NCM2dXrzOfep7NVpEI17DWMO2w4g2MgS0xrnKFFkYO0umOls17ObJrtNAvPVu+SHZJL7xl0Hs5yHJmMZTL02CNyKUXVVDNeO485Sjt61zs/j62brvpoZfZnNLDuXHPNNU6ZcTTGtddem3Xfz3JFbbU17cNLysZYqU1BRxNGk+BwMjJqxyYML+6Yn8Yp0rjM070h5Yx1DHOFrIdHFArRLG7in0owsGHg7cWBAWGDPHJ+CA4MWBBFPOdsMIUATcHrInVa171OUUU1v3m+pjZUZ7P5UoGxAOnOxalhESyMAYg9BJ6yEERPJHM+40SBWAFr6Md3IADMsYYDDcMpIgaxgbEf4UsmQy7p2GzsHIOMMc4T4WzlGIxcWfNxDLqQJM7BjLxucD7JyihkCzIMiKK0KJRh5ermnCx1rJViPZoxvk7rt3SouN0AKgesUcn6AlUKENSMvbyyTRqapKPPyus8ENUYoZKlLzO/k0W7Mc8wXvAsEO/pUs1Tgj2psfj1aQsNMhvZ58866yzHeFGtjf0qBXAbItt+8pOf6Oyzzy716YTI8JmNiqApAi3qxw9zaIDaSI0WtMzMuO8Dewr3i6AOApoKqc3MMe8GEfLJjFhoB8qJoBvRFfwtnAheSmALhjg4o7s8Y1DAIXTW7KP09JbXtKOvQ1MbJujgSbs7GeIhggE6gEAinBHZGh+tpDG2H3g//AhegmMOfuEHPsP6QMaDlTTj2O4m3vAT9IBlb5CZhwZn3KFhcpkfODUsa4Mxi8OCzG5gwQuc04033qj3ve99jnMFHuB1XsADcy055QX8C43OsdA+wzjdjD2lsdOk7esVqW9UTcv6qm4SHsSaxzOuVCMxY4F5kW8je+wv+dhgMnFspnpWZPNjJ2ANOP/885UPcGJMbthJ1YTm5mbddNNNzpqD7fyyyy6rClts1Tg1fvazn+lvf/ubY2xKVos9RHktnBgMR1W02/SjpXX3Slafr3mWNCHzkh8GolEgIBh8/XpT5OvMwBjlF8nEggfhI3od0oUnnM2bF2TUXt46txBNBEihHBp2bvNa5juvEIVDPlkHCAfEQCbGObI6LLKOiAIECMIVYk8kDeMQUk8Kt23EONMYv3wOJwhZYDgkssnYs2wIwHcxtr3CgQhzjLU2PxBNrGXsPTbmmaO8gsgW5NqJOEFs+KWJ9vT2a8kbm5x5yb1IRUxw+CBc+E7OlRdRaenKu5UDOFfWPJ5LPmAMIVR5PmSYVQqRw2CDQYcxUFADN1FqTz8stW6V5i6Q9jlwWOYDmUvMQe4dRoBkTg32h1Rp+AgO5jnRWJR1q1QRWEhccskljoP2tNNOc9aXQkZPh0gOapfzDP7xH/9RF198cXirKgDsc+zdlbK+54v5zTO0Ycx8vdaWaI5NRsYxUw/MupG1lVEh8yFdic9cnwt7A1rBneENMBSzx2FgJqiKiGkMvxbM4S3/aU3O+Qz6opBlJcfVNev46QcU7PtDJPipd0xkA7g4xsx0XILjUO7qgAMOcLg1PN+cFDgu+NmbjWHZG/AdMsnRFyBbJxpjmWAQDMc457wVCRj3aIz3v//9TmAVuonrwvnB+XAO6H7GfFD2E84FPpasb+LqbZ1atYrSdtu1biDQLBVH5/oAOoPvRVdVwjrMuCBIzS+7OFMwNtCDPCOeW6VUjLGStvyf51XItbQ31qru2CanlFNjzSzVRIZKTOFIxImXzBmZSVafAfvBX//6V2dvCMoBWE2YNGmSU0aVLDnWzUsvvVSVjsqYbWnw97//Xd/85jd17733joj8CFGeIGKbKOZK2OgCA6Wl5p4l9WxLZG7Ujc+5TAYEC+NStsDwCZFKZpyGhOChT/VcqF+LURdPL6m7GNmsxj3GZoww1GMEGA7zaTAdonoAUcmUWGC4ZqNlPDLWEBhkKxx33HFOFCFjDtLDdwI+B5lGEFkPDv4PSbMoqEzAvIAAQWwR137RKogQDLH333//oKOO4yLUAdeIGGfsBwEIMkIrWd1LHIwcnzrTCB2/OqOcD+Kf+4JIMtEHgcWJiVDCEVSOtWDdIgmhkG8de4wpjB2eL+UEDNwL3k9HposBniFlBi06DzHLGlrwaP3eHum/vyutWs5kkGL90nGnS2de4Pya9d6yL7h/yYzs5jBLB5yOROySiYAYLGT2YaXiBz/4gbOnkrGBsaOa6t9WUp8+sgC///3vl/p0QmTIcwn8GU1l29gfDp28j/Yat0Cd/T0aX9fiNCfNBXD7XNcZgjowTPlxfvgJe3iy8o/szfSsgbPBVTDw8n3uElVoC4Jj+C4MpuyJ+RjCQ5QP4Kn5lFVGB2QSHMFnyAbBqAdvhu9jxGX8wQnRFRYca8FCnBvObQtmYowSHIMBPBsDMJoCnYwjwduUGcD50BiUgYNr8eL7eZkeIsAF7hRUVgHfd9RRRyX9jFVcAMmCiuAoXJc3uJH9k2vFQeMu5VUugKeijRh3nGMmTrFU4G9xBqNXsdGwTtk183/uQTkE8JgDAzC2OF/GdqH7DXb1r1Nr74uDnZ7a+97R5PrDVBNtdsYW44i5xZhM1dOGz6XLJkHPs9egVfg8lSEKWba9ErFgwQInY4P+VvybXhuVjIp3alDj/MILL9Sf/vQnp35/iPJBb6xPL2xbog3dm5zSQPuM312zmqY7CwwoB+NR0YEzg5JTeQLSlW2WBsSEDTad08IPbDZEbgHIDWngECzIF0SA1FyEBhsiRtNs6tWGqAxk1IQ4BZjvGKgttdyM734Ej99BuMzJgGjFkcAYw1lBhgbC2z0HiJhCgBiIkEEY4xRAZJPpkCkw9CN2/MQy451IRq4FooRDgMgWdwNSBBANuBDgJ5xwQl49HDhOsjJWRJtx/+y5cM1+kUH8nvvlLR3GOmDnxjXzwhmTT6RS0OAZ8Pysz0oQ4H6462u711Ui8BhLhShjkSkQGIxxxjfzgPW3KIabpx+RVr2T+DcODXDfLdJRx0uTpjpjg/0b8psquyeVw8MN1gLmCsZH5jfjmTq+ZlBgj+FVKdFuhQBr4O9//3un5whcl8y1oPr0hEgNxt6HP/zhwWcQ3vfyQm9skzr731As3qvayCS11O6hSKTOMQhms99XE8bWtTivYsMMUvCJbAM62OP4WxwYZGPAsTCEYsBduHChwz3prcYx2Jd5LzROVSfyMXp7xwTcws9Iy/u2lsPl4cXw4wceeMDJ3oDbeEtn+gVpYADHsYHhHkN+puOeDFe0uJ/Tles33ofewbHH93Ne7mthvlxxxRWO3qYHV64cCYdOsmAz5iPngIHZnksyHprMtoDTBiM1WRDcK54R51wuzg0rHRxkdgLfiYby6ijuETyX36NnSuncQGNbkBS6GT1UaIcG2N776sC/EvMprj619S3T+Pp9nTlovfrS2beYE5m0GeC5cn2MOQJy0Rxct937ZGvEaMKhhx7q9In70Ic+5AROuW0ZlYZIPK9ixqUFizo3/2tf+5o++9nPlvp0Qnjw8KantKFr47Aqr0dNOkSvPbXESZssl02tEoGX3Wr9pwMR5NbgOZsyM2x6EBoMVHjMiZayuqOQHYxPRFDj4IDMsUmHz7R6QUaAO1IpFxCVDclFpOIIAwhYq4ts4DiQbSIr3GPq5ptv1qmnnupE1jAe3b/jO3mfKBnvOKR0C5/3y0SA8FhfDpwgEHyMIumc5BAvorogg8mMXYhwGu8hCjBIpoo8SQZEDQ5FIqm814UBHgM4hoB0BjfufSZ1Uolw5Tzd0ValBmPFxF4xwJrHeGdsJusLUYy6tkV3MN1xnXTXDQzu4e9/9uvSgt0GnWycX6rx8fTTTztOwUyENo405iD7B3sO49TuPfsLx0K0j3aDMmOSgAIiqX70ox+V+nRGBb70pS/p+uuvd0p/lNLJGWIk+mLbtKPvqWHv1UYmas2ysY7hLZ9AgtEO1lyMb5lqDLQ4yDYSG27Deo+xD31iRmH4Go2drUwxGobAjkookxkivyDVfAxqBBHB5eG5GDDhsRgtCRBhPXBzCMoiw1HcY4oxj/EdnumXbU0Ak/X+cAOeQhYrY9YLxjD8H07Dv+E5BM+gH9KtUXyv9QdJBuYHjZC5b7kG9qJ3uEfe6+JecB9ZB9I5bJjLaKh0wSx8J3MeZ1I5ZCwAngsaq1jVQ1jvsMlgmMfAXgoHLXOFcZlv74xs4PSD7b5rxPv1kUma2HBwxs+COYHGyDRwgblu+pl5iHON42APwJaGw7AUOq/c8POf/1zf+973nHUYJ1AlomKdGiz2CDxSZmhwEqK80BPr1U1rhi9eEUW0Y8lmvffIs0NymieSGSjZJDG02rRm8QfZbFwQLsgdhIOFzUtSKIsCEbSeAxDD0W5wqjRAqBgnOKl4vjxrxgwigChpPzIBMYBE5BPVQMQ9ZQIh9lZSgDFLhDZruXscYciGfODYMOGBAZ/xB4Hnd0QAuQkh7/lFC2H857vdacCA8cvfQ+j5P4ZDiB4i/ZRTTlGQBJJsQspnQeaznS8IBq7BTzSxF7IeeOtNewF5475ncmzuPYZmxgYOJsSKgXvHulDsOc+a5FeHu5qcKV5Q75nxXzQseV664qfD36url77xX1LLkPjHecjcSyY6zCiVKdyiw8D4Y21gbULkWPmD0QzmOWsfzao/+tGPlvp0qhpXXnml49RA4BVT+IfIDB19S9UdWzUY8Qm2t7Zr7bIpOvyw0VN2qhBIZqCEI7InukvQ8G+cGZmWruQ7yLrAuEmpHeaW16hHIAhGLYKq4ImhQ7GywN5NsBvPGoO8e2ygL/yCe+DeOCTyLS9KTzyOaz3orM8d49bNka3EJ59xlzfjPRv38BF3SUz0NDzUux/AwQnCIhDD/s6Ac4TAJ+4Df8+Y5kU5wyCrVZAZzjVaL45sYc4Lv560OPUx/KYLyso0cIp7jxOITBc0IYGS3jKwxe6NayULC17itYTOFL85x/pdTIP+5u7H1BdvG7Zvt9TspDF1Q+MGJzaaOdl54VwEmQYuuAOn3PeeYzAv0VmMxbAfs/TP//zPjpOUksDlVLGhqp0aLIjUuMXbecstt4zq0gSV5NRY/uo72nvW7jpln8UlO69qAEZGxr63fApTmX4DkA93w+BsNkvIHy8IVzJA0tgMMS5azfcQlQEIJKQdoo5h2p6lOb8QmtYPxTu2MC6mGhf2OZwlkA731sI4YZ3GYYZRzhuhw99wLt7ySJwXDg93Mz0IjzUSRwyTmYGjBaM9cwNiDinmHIwUYRyFsJJdlA5EgEDMgx7bXMuNN97oCAO3c4H/m1MlnehIFplCjyIiqVKJf+4b4iubFHnGC9/pdmRh7EDYkcabq7GfsYFjzcpfZRqxZb2YiulQYdzgVCu2YYX7A6m0cm1Fw23XSnfdOOTQuOifpb0XDvsIQoi5xrMjUhCjgD1DxD1jDeddOuCkxMDFmGJd8HNYwfeYkzwDt/FhtAKn8Jlnnqnbb799RJPREMEAwxgZgVZrOET5oaPvNXXH6CuX4BkdHV16/pnXdcpxn1ZNNPuMyBDpDW04s1nzWafdv8tUYxifg7Olij4nmpY9hGOF2d+VNXYILEJDEIxhmf3s8zxHMySiM7zPlfdZa9MFTcE9cAowPuw74M44IuhhgIbw682VLNCC88Xp4tbTBBBxPjg8rA8dxyN7Fp7D3MDxx++Ns8CJ4YjpdAOcGv2STdBHpkC74xBEp7m5FNeQjr+iv+By2A+8gJ9x/9L1x8zUqQEYCzwvOC73zP29OGe4v2i5XO17PC+M2TzXTI2zPHOeD3qsWOC+M67gtsXUNazFPO9iO1T6Yu3a2vOMYupyfq6PTtaEukWKRIZrQAuc4hm6s6OwfcHPKEudyf1CM+LQYN4mCxBDF8Onzz///FG/3/T19em0005z1mH6W5VLNlVVOzW++MUvOmVIWGRTEaMQpcWjm57R2q71zr/XLFutmtoaXXDQOZpMw+wQOQGCiBHaGnMbLBqF/+eadm8N+NIZrkNULojmQTjkku6KQwKiySZnhmgEhpsw4lRDXCAq3M2oIQ2QRbdBnfeJ5rJmj9SzpcyMl2Bh3IWAedMhIYMY+vlenBUQFwg1xlZEA+/xb4yrfBbHiXfeeME5EZlbCMHhJuxW4ona0Fwb14hwsEbV1k/ES/Ig/35OCT7PNfM8qDPqV4Irmeiw5ozZElsyWhAOXkeUH3hG3H/OH3HFPSCajHOFtHIP/K7Zrg1hwnkyBhkrxTZ0cI4cn0gejOyIWZ5foc6DcYshIMg+Illh62Zp+zZp2gypabijgTnLczRjBaXjMAwgopmDVrM2EzB+cIKYA4Rn7b6nZEwxVogERtSEBq4ELr/8cn3961931r9KTRMvVzDmcB5/97vf1cUXX1zq0wmRBH2x1oHyU3H19vbpiUdf0dFHH6PxDQeH60SOYP3FoMt6621gjCEa4xucJVcQ8Y0RLdOsjhCVBfgZXDaXMkhwQPgpezx2HXQEHAN9YbyQ8XnnnXfqyCOPHDaG4M5wJv7OXWcfvcKYw0GNARfu6Q3E4TsfeughJ0DAT3vAq+HcGPkYuxyL7+J8CbTg/OAmzA324nROGYI44Efe+RWkUZLzQ3NxvvAxtD33B03G3OZ++nFtIrQxFvvxLPg+zwfA+fyi2nHs+DXChhdmmz3C/eXeZ7Le8FkrIYxWgJuTCYIGpFwqPDWZvgDoHwLg+Bu0bbGDNa1xPLrZ7j3ju1AGZRsj8O5SlL6Kx/vVF29XRFHVRIY7yC3A0QJnrXcG44/5xlgmACrT+eMOBjRzt/t4999/v1NymnGbSY+O0YCtW7c6mfEET/34xz9WJaHinBr/+7//q3/5l38JU8IrAH2xPr3UulTruzbpredf13uPPVszmvwbUoVIDxZzSJoZupi6/AxZ4d8Qr3zEAsZcyGhYSqp6gYEQA3M6434qYNQlCwKiCumAMEIGICJkS2RatgnCisCw3kjWOwLB4h2DCAHGtt95c038HiLLRoxjBcJDxBFkl8wUfsffpkufRrhzDsVw7EHSEE3u6BEIHffTmqUhSNwlIIhYtwbSyQCJh5QQ5QJ5BRbt5HVqMBZwsJhTI9tmeZlGZnFPiUQypyxjBwKfaVAC58izQaiVqgEs94oXQoNzgWwnA+sx9xGhlMoQzzPGYeKOXGR+Ib6DLE0QFDhP5ivnyrxCAFuEI79zN63PBNwnSk9ZxgECg/vGtTOumNeUowizcf3TxLlfpImHRsJgwPrC/oPj/xe/+EVA3xqiUOiNbVZn/5t647W3NG3qAs2ZdqgikTBzPxewFhM44TbusE9jqGavs/5SuTqW2R8wGha1nGKIooNMHEpJ5gO0AOMOfUHgA/s/3AL+xVjMhJ/DrYjohkugmfkuDJyMYW/vCHgpfIa138t/MTijVXD2USHEqhmYYZVIcJwZ8KJMSmfhZIHDFjoCmvnM9XNNxsngltasm99zj3BQ2JzmPnCtfv37vE4g+qJ6HRY8G/i9O9MBjYCeQYvgLMimVx/ajrUoXaaFlablPHg2nBPXigbKBIwrgmv4W66rNIb+uGPL4V7xb3SgVVDwA7/D5pOuzxDfY2V07Tgcw8/5VGpYoCPzlbHLXDzxxBOdc+d5Mhd5xtnsQXwf18rYY1yQfYDOZWzwXqoKCKMZbwyUuv3pT3+qj3zkI6oUVJRTg83ypJNOClPCKwwsJHiFC01mu/q7FIv3q6mmueoitTAYQ5qI8GDjRXxD9DAKQtoyvd6eWJ+e3/aOdvR2akrDOO03fp6iA3+LU4NNhMW+UFEkIUoL0n0hgBAhhAFkjnGUSw1Wr2DlO2g4lylBINOOTdOdGcG4pr4lzgnvOeEwwRHAmPdLKcYhwB5BI12IHOQd0gIRwuiHOEpWvgYyzr1hHmGAP/3009Ma97l3nBPrmwEnBCnTmc5H7gHPIZnR1hpwQsLM0WPR2cka8iFMMAZzXtxPi3TivHAOEIkEaWcd4RrMKWGRUdyjTI3IEB8+myxa3Mp+4WhBLOTjMHU7ayoBNqYATgoEBC/mHmOSscNzwLlT7uutOSwRmBgFuC7mCU6NfHucMD4QvIxPi5Sz4+Hwee973xvYdVRjmjiOWppZh8EI+YH17+yzz3bWzbCsbWXBuEQheX9frF8d/Z1qrGlQfbT4Rq9Cg/UWIxl7FGsy9xIORgBFumAQNzZ0bdbqrnWKKqqdWuZqXF3C6Ab/gpOB0LFR3dngGLXhBezlZA/z73z2J/iSzW2MlOn6xxn/IvodfcyxrSQV3BgOBt9wA85BgADGcbg/nNa9nmBURV9wLYxfvofP8T5ZC3BtOLkfL8fMBpfhXvDis+YUSLUfETyGQdrA+ZB9kM6QbeBY3AO/klKAvY45Caez7AQcSthrcIYkW0+5T3BX0xPoHqLduQ8W6MR3U/nB+onYd2O4z7RElTWExu6RLHsCZwTfa7aLXGHOK7IXslnvSgWrzsG6in6w+wNHN33H79C+lZDlzFhHj5q2v+eee5xryjfA0JybBE6xt2FvYP1gXqCV0a+hUyN1qdu77rrLWQ8qARUT0sLCTr2zH/7wh2GN2wqDGcoKhVg8phe3vaD13YnNv6V2jA6aeLCaasrbUJQNWIwhT0SLsOhnWhffjd5Yv65Z8Yg29+xwmrbHFNeqjk06edpCx1jIpkfkZzEbZYUoLhhDkHnIAiIWIkgGEGnK+URDQwJZo7PxkTOOIRbu40Jm2DwxUkBQIcoGzhthAllGXJBS7v0+IowQPYgOvtc9ls2IjyhBVHAvrF8BmzfH5We+h2gqd4STGxBo1jTuIVFOboM0hnf+FiIJOU4XCcP9gvib6PIatxFWkD0cGRBTIk74XtLEMeK474+BY+Pg4f4hZvg350kUJtkqiCvWDwidu8cI58A6TfZIppk8iBO+h3vrXZNwQPHifa4rV1HL/WZsca2V4tAAXDP3nWfMfbBII0QYay1js9wN0YhWjAPcewQy44VzZ0xhUA9CKDGmcYYSKYkI457wnHEEVVDMTdHB+nbNNdc4ou/73/++vva1r5X6lCoa3/ve9xynGmMxzAyqHMAHWDMKabRZ17VJD258Vr3xRObjgRP20l7jqqvsG8YwuAach309l71pRccaPbHleUdfgDfbluv4aUeotjvqBL6wxhezKW2I4gODOxwVHgm3hdMbB80VjEmMk+iCTL8H/gXPh7fAlQ2LFi1yArvg1OydNs7RQjgaCIjCqM98cGcVwOXRFxhbrZ+fN/sbTYVTw5wYcF++HwcIf8OxAdcBp/XLWrBodeYLziGqJ9jaBn9kj0LHcB/S9cnAKYkm4Xo5T2+vEX5PqVp0v2XH4oDgs2TQJuuhwbUT2EIAGXqHyHrWDvdn0Xbe/nd8Nxk43jKjycDfYnDG7uF1alipPO4xvDFTR4kfeG72fMqdkxvcTezh6NwDgNOIscPYymfOFQs4rZgzjG3WCl48Wwu8zBc8T76HMY7TjfsDvzObZKgxkoOMZWzu2N6pgJFNllWpUBGZGgz2xYsXOwv8H/7wh7L3OIYYAostEZ251NjMFG/ueENvtb85+DOEenzdBB02uXB18SsRr7Su1J3rX1Dn9jatfX252ra0aty0STp99kHadeZ8h+Bg0K2UTT1E9iAaiXRpN1kwYphpMzU3IE8IFogq48dIeyZg6yHihywEbx8Yfgd5R5RAoDHOu3t0cM4YiiGy7lJMlrYOmYNUYzjm8xBufocwQuxAmBjnGOTteyHynIeVdePcLOqK3+EMQSBxD3GapNqH+F6cCAAigJC3Jon8H8HEuogDgnPkHBCB3E/rTYCjkc9xvhAxHBxcM78j+wJRZk3R/YzRpIHznBGWfBcGaTNSIxAs9dwLjO6cB/cHQzzPJ13kEs8KUcg94Z5xXVy/9TPhHNyAjKcqmcM94py5j5B1hFeI4oMoPuaYjTvGMmOVsYVYDipYgecNz8PoZZS00JHX1QIMIBhb/va3v+nkk08u9elUJGgSed555znO8nx6BoQoPtgHKftSqOja7livrl99r/ri/cPeXzztME1vLL8SgaXETWvuUUdvh9a9s1brlq9VTTSqeVPn6V1zD3GeD87xTKPMQ1TufLSgIgCHgyvkYmC1gBCCZuDORFhnU2oRBwU83m9f5HfsnfBW9I9bg6A7cGwwZjHMu4ObrFwNWdTwWP4ejgpH54Xh1EoDoTu4D3BuvssM7/Bq+DHXhe7i++H6vODoHDNV+VG+H24GR+Z70QHcF/QJ2sV6D3B8OBrOBBwhOCLQE/w9L7QSx7GgSe43fN0yttBZZM37PRf4Bnsmn8exwRrMseGGHA++iHbwPi/uBVqH+8L3cD7p+oAyhvgb7g/3G1sgjggrl4sG5djGF7kn/C4Vf0TjcI/4bpxDyTLfQxQOzBvuP3OG/2NDsEBDnGqMvaBKZJHZzDhhDjBmmWPpnIIh5MzRD33oQ85acvfdd5ekNFvVOTXooUFzKLzb+ZY6CFE8+NV0LASe2vKEtvRs8bwb0UnTTw6NIi7c/87zuuWFB7VxxVr1dnarrqlB8xfuoY8ffIbmNYeLe7GxrmutVnWscv49u2mOZjYlatMXGt4akpBDbyp2JrCmfTgcIAfZOMMg05SZIkqbiCVLDfUDJJi1HwM5othdG5Rz5+85B4g8AghySwQVAgMiDKmGNEN0OQZECoeHpbTzgvDTpJzvIn0aoz+f57swrkLQ+QzXaKQr03vEd0Kk+FvIG8dDUOHM8JbYskgsDGuIDBwfREFxfG+ZOatty/n5lQ9DsEHkcFSSWYLQzJa4cwzEHcIoEwOzNXtEaCBsIKpcE/eOa7Lv4H4gwJJlheFY4f5zv8Ko6eDBPGFMMC+SzX2i+6yxuxeMK7KbKAdaiLUJYYzRoJDBENWEq666SpdeeqkTTcWaGiJzMM4wlv385z/Xhz/84fDWVRAwCrJHww0KhQ1dW3TXhseHvUfg1P7jd9O+47PnTdWMXz7yv1q/ap22bdzq/Dxu0ngdt/g4HT+tMkpXVBX6uqRNz0s926T68dKUA6TawpfVIWgK471lHeeqLwD7mZWbzbaJL9nM8GI4JLw/VSQ/XAiu6+XkaAU4OI4HW2OMo1i/PnQH12qOADgu3An9YXzXsi7Ym3Gc83n4Lc4eAhLguPBheDIcPRvOy3dzj/lb7hH3i/PGVuaXFQWfR1NwDmgTjssx0Sde+xpGYLgiBmAvOB73guuCD/JdlCPNFnA9/j6TKHCeEeeLgdX6iHIPAc+Nc+Gec0/QlzynZPeSZ8594J6FwTPBgrlBdhJjBA7vF3Bg/RyZV373H6cd8ymf/p9eMCZwvvG9OOLQtcVuCl+JaG9vd0qE4xz+z//8T5Uzyr781F//+lddeeWVTkp46NCozLJThfbs1Ueofc+iOOSfq4smGt+GGMIr9z+l/nExzd5rZ02YMUW19XWqi9RoakP2Efoh8sOaztV6qfXFwZ8392xy+sHMbk4dsZIvrP6mAYNzrpk5OBrY6HIhBRhKcQ5QZgbDKmtFsiwPzheHA8QdoWAEGyIMoUVwQKAg8IgfiBIw0mtlkawXDZ+xmrbmSIHg4OggowCCTyQHG7n1x4CU5RIJyt9yPGumnA4WsQU4NyLBuEaInXc942fuP8IPp4e3ZwXvcU+57hNOOCEn5wDH4NgY/hAA6cDxicjjOfFciIRCTOLosGajjDmvIQoHDkLQjsm9zybrJ4SycigyR5hTOI8wQngbXPI+8yBZ9hZRhu6SDkGBMYsgYn1gDjKvw3GQHhdeeKGzHhM5SYRbJdSELgdg/OCefeADHwgdGhUGywCkREoh0VgzsrdWXHE1+Lw/muEEbzz7tqbtM1vz9lqgsRPHJTI3G8JslqIj1ietuF3q3ZHQxZ0bpY510oIzpAL3g8Ho7N5/UjU7TgW4I7wwV6Mmxn2ykzkXHAmpnBpkAhNoxOfg0caVMerDaeElBBDhgEBXWLNl+DoawXQEf092NBkU6A5zKsB30S+mTeDB3CccInavrPROtrBskkyBpjAdiCMBJwfX42dfQ7ewRxKhzb10l6/i2Vi/DP4+19KwcD0yQvh/Oo3CesJ5ch5WBpXnyvHhpDwnK4WMXnF/H7yX52IaCd0a2hSDh1VgYE6hZXEuoj/dQYt8Bk2ZbNyyZqAhmW9BgvHOHGTOkgXCnCagJRwHqcH9ITMLBxW6j3JU5YqyrjODp+4Tn/iEfv/73+dVLy9EaYARiyiJQmOXMbuqJhJ1oqesluueYyun9noxAAFZtNveuuj4czRt3izHoVEfrdV7Zh+iplCcFR1vty/L6L2ggRHALRLY4HOJ7MUBwN/lGuXgroENwU+XMGjGckQENTgBZByDJwTFehXwe15Wnsrd5wFHBuKCyCvA52PxuHb0dmn/RQud9YpSVkT38IJQuUtbFQusmex9OCMg+jhvmL9+ICOCa+I6vfcQQm91MPPJdkD8cBxqBGcCnisZNQgIxgfnYFl7jDe+BzJrJJfnwnhiXPKM2etDQ3ZhgEBlbLnrNLv3aJ6TpWj7CWxrlsmzK0SEE9k8RFMBxjSC2ZrLhkiNn/3sZ848//znPx/eqgzxuc99ztkvuHchKgusX94eVIXA2NoW7dKSCDYxhTG+box2as6+r101AyPUZz/wKe27zz5OhgbPZ07TDO09LsxmKTra10i9212BfvGEg6N9bcEPbdnPhlyKgfA3+ZatJmgKbgkyCawko5sgIQyyZA/AdayEK+sMegJnAMZ9uKyVWXIba+Hq/D1GWTSIc+3dXZpQG9XkSZMGe0TYqxCBIekAryODmvNDNxFcBN9LBpw9ZIl7AyXQBPwdzqd8SzfB+dFzOCUyAc4gDK3cZ4zjOIw4TwJ0LNPenfnB7wGZOByLV9jbpzDgGVhjd+Ydz8k9R9AgBN54+7sY0IgE6HgDrYIAmoJ5S9AcaxQan2yjZPo6xBDQ5mSEf/zjH3fmWLmitpyjcD74wQ86To2zzjqr1KcTIgcgsNk0/UqjBIkxdWN15OSjtLpztfrj/ZrWME2TwuggZw5x//GWE4mOwQjitte42ero69aY2kbVRoOpVxgiOzBOvYgpt4imbMYDBNQyASztN5dMDb4nqIbynBcEORNwTCL6MYxDlijVRFklyldBVABkCFLkbaINkSUygwh1CM2Kjq36/qt3aUdft+K9/dp5TY8uPqP0EQgICOYpzhuuj0wW/k9UFOIBYYXDA2cOz47P+9Ur5r4QvcT9zTdym3PAecJ5UKs2VWQL4g+HBueJcwKSynlaU0Wuw11CDOGJAA2qdmqI5EDIItLt/jNnzDmBGMGxhxjkebNn8NwYR2YUYM3A0RbU3PcT3AhW5jeiB8cp58E+Vu61XEsNeBaNw9nnSROniWeI5LjuuuucTHCrqx6issB+AX8pNFgrD5u0n6Y1TNKWnlY11TZq9zHzVRstW/lcNMAJrPkvPGPmlBmaEZ+uzv4uRSNRNdYUVvuFSAIffZHy/YBAAILbmAxfzKW5LPw2XU+EdIBrYkDNBmgjtARGVxwO8A6CPMhCQE9YoIc1Hj799NOH/T3cCD7uXDMZKtdcKT16r/O7tvqx2vPiL5S8pwycDu0Ax7rtttucfzN3eXaW3WD9K6zpuWW8uMHPJ554omMUDmItR7eh7Th+umePUdV6E3LufNaCczBcu/UJewQOj1xLoIXIHDwTNIQ5uXA64ajiffQg5dqslBuOJn5P4KA7sBJHGXOwUMBRyvy2Sg3M5VSVIkIMAVs8Tg1s8wS3lSNvLttMjW984xvOYvSDH/yg1KcSIkew2RTaoWForm3RbmN3157j9hr1Dg02FWuAdeONNzrki8hoa9hFhsaE+pbQoVFCzGgcWY5oemP25D8bIDDcvTTyMSQHEdnA+g55prRgNs3/MI5Dfvk/IgJDK0TYHTmOIRZh5AbXijHWKdfU36f/efsJtfUlIoPiEenBTW/phdWFz5bJ1FjAudIcnHvDOmpN+ChHxfsQe0hhqkg4BBQRUBDKfGHp/NarJBlI/cZxYSQR0oigwKnEd5AtBKHlxXM0Z1SIwoExgPj0Orh4nkREPvHEE844Q8gjKHmRns3v3ZkSCBCeH2OgUMCAQM1kA0Z6REeI9OC5XX755U4wUKaZVaMR3BvuEfcqjNisTOSaZZoLMJrtPGaODp60j/YZt4vqRrlDw7gVDud77rnHMURRxsPuVXNtU+jQKCWaZwyUmTLDcESK1ErNhc08JqCFPcgAb82G2xtwkuXbxBeDNzwFboMmzhSMX/gq6wuGTzK6vRmtGM3p3wAfd4Ny22SaOvronpsHHRpg7Jb1Wv2bn+eUuVIIYOjHKYEOIxMFzs61whW59zwDAGdMFVDC783JkA/svmMQ5xySPTPuLYFQ6AmCb3DEmL5AE6KTOB/TGKxNYSnywgINh5GbYEL3/Lc+kvB3dDelItGFOP6slBFOQzcYe4NzqADADoDtwxyerE+cf7nMy3LHD37wA2fOf/Ob31Q5oiydGtRg++///m/9+c9/LppRPETwCDeS0gCRASHAI26bB8bEEOWDXcfspnlN8xQd+G9u0zztNmZktH2QYBN3l2tgc/fbyDd1d+ihjcv1xOZV6ur3j4Tkb7MRCn7A2A2ZoLlctoYl9gXIKi/GuBdEEEByiT53A6LEua/r2q7O/t7B5PxobY1m77uHHn3leZUDiApDMEHSER04KREc1OHlPQQJwgqHEEIqGRBezH8IZVBrOoYkSCFZIF5RB4jCQVhg/PaCc0aAuF84b3DAJo26fehB6SMXSuefK/3kxwzkQK5ltIA5jrHBSh541wTGGo4DHGVeQPj5WzdOOeWUQWNBoSKlveUrwiyNzPH+979fZ555pi666KKc65lXM7gn9CB5z3veowsuuKDUpxMiR5ANWIzytiGGAy6CAQvAvdg3zKERokxQ2yTNWSzV4VCISHVjpLknSrXZNdvOdl31Zn0TbDTCOInmiG+U4qukeKI8lBcE4RHVnQ/Qv6wPBEmQnZ0NCJCCk2KkTeZcoWcAHNibDTKYIfJ6osytYcGYZnUvf0vxMil1Y1oQDg7HQiMQTIJustJO6A0MmKmC3zBUw8+sJ16+4NhoGhynOCe844dzIdiGDEsyadxg/JHhyzWYvmB94kUgg6+hvKNduvIy6SuXSN/9irR0+HMLkR44oQgkJBDK2z+SccYcYg76VYXg98w1A8+W50YZN29gYlBAv8If3OtUaK/MDKxvf/nLX3TZZZc5tvpyQ9k5NRDQH/7wh50O6+5GoiFChEgPSAkbgdUTJRWz1OmuZYG+bVL3O1LPugSpLjFIzd9r/D5aPP0k57X3+H2c9woJjNGkXRoJh7B60wdf3b5RX33pHv3PO8/r8mXP6Juv3KftvcPrnBJZgVMh33rWHDvbNZ5z5/gYUxnrRFIlM3gSVc7v3JEgRPZgxH3xiWfU3zvcILtx2QrtVaw9hzEY2yTF1khx/ywKSBbCwqLmabZmzh/EGr8jKyKdwRfCFnTtcQQj5BMxRD8MMjfMgMp5Iyz42Ss6KH/E573igufCd0COh4nEZ5+R/u1fpbfeZOBJN1wvfe87gV5LtcN6nHDvvWBcHHDAAUnLk/G3XkGLM5O5y7OngWSmdZBzBfPX3RsnRHogOIg0/clPfhLeLg/QFqw1v/jFL8J7U8FgX0yVMRgieBDEAO/C8Mj6D4f0c4aPOvT1SGtflVa9JHVsU1mgaaq089nS7h+Sdj4n8XMBgcESXu4OZiFDeFhgqqO9nh94vSrpCSmeyIRwg4CJIGrqMzaz6f0FZ8XYiUOFjAF0dLJ+rnAjenAQWOQGQVoEUy3v6JZcmq4fp09Dg6J59LfLCr3t0va3pbZVUmykMR8tQVAYGS1wec4bPWWakJK2ZDpkUsILfkagVVDg3hK8xfNjPJEt7DZ8c844lein4QbBVGgIyzIxMAbQHHzemrUPjsf//g/piYekzZuk5cukn31HWlEeGfuVAiv57DdWCFpjfCRzGqARvfqUsUQJN+ZhIXrqEeTLWAmRG7Db/PjHP3Zs9QQ5lBPKLof205/+tBP1cfHFF5f6VELkCbyzGNgrySCxtadNG7pb1VzToDlNkyvOe0v0AgYsPKgQMoyhox5dy6ROV/mS2qnSmMOHEc5SoZjjyxpGE7ECYYQYerMtrnj7OfXFh6J7N3V36vrVS3XhgoWD75ExUIwGd5BPjBZuwoNA4vwRT0Rgpbt/fAaB5I4eQZjvPX9nvbn2Fa2rw7MfcTI2Zk6boZpVWxWfEy/sc+H+9hOF5spyiO4nRYevk+mirBElzHXmvDcC3wuux7JU+D8RUIhQDBP2fwREthHxCD9eGLYRHyZGLIoT4mj1VTku349Dhs+6xSKOV148V8YXKeUObr8ND2CiPnHipkj33YtlHYt8Vuc6msE8YD4xF3hGcCxzVuAYs/vvBz6PUOT5WZNJRAzRVAgSonbJBkrVZyUbMJbczjDWKcaRX0ZWCH+QYUOm83HHHef01yhE08VKBMamf//3f3f4US5lUUKUD1gPKKlZSbXS6aW2unOTemP9mt44UWOIqK8gsAfcfvvtjq649957nVIilaaRAkdPh/TEX4acGfQpPPAcafJQGZaSoojPxxrvGn+D6w3PqCLAyJvd+5oUnyZFGgcNmmhX619RSBCU4T4OYxkeQ9YBGQrpMjzgsxjMyTg3/kN5WPTG2t0Xaf7ad2iWmPjueFw9hx7jBO4WnMt0rJVW3zfUQ6VhojTnJKmmfoQmSAa0FrwBpw06MVVgFI4Q7pcBTolDCz6P5kTLcJ8INMsG/C06AX3CfcWeRAAUe7df9ggZxGR5sC/AG92c1gLCMJLzfc66tWWT9PoSTxZRXHriEWnegAYJkRYERZGxQ9kogM2PKgGAMYYjnPvvt1fwWcYYcwYnOeMEDWAN3ZmLlK/CiRUU+E53mSz0xuCYCJERLrnkEocLYLMnc6NcUFtujfvuuusuZ0EKB1flg8XJneJV7li6fZXu3vDCYEmaBc3TdPrMgwoeQR8kMGLQRwNieNppp4XzKNY13KEB+jZKPSulhjIRHUUEJNwMhhANN8mMxePa0jPcyRFTXOu7Ez0PmMtE2QctNiC+fhHhkCGIKanobkIN8cnUWWf7iBn0IddEJ0Gk3r/roVpW26lVnds0vrZRR++9i3p2tDsNujHSFgzx1SOFXexlKTJVff0JoYGgwDkAAccgnYpMIqYwVvMskzXYw/DDs+O7SJknMst9v7kvRDHZfUI4ZFP6EUKIsZv/E63D3yLeELe8xz23UkdEybjLVvF7axDI+GRc4mAJOUDwmVq8eE6ICMa4Rd5y3xHmPDeevXtdOPLII51sCUQrn3eTf/6Opn44NihhlU1UZDJwbm4nHXOVDDPGUjgmMgf38NJLL9XHPvYxJ4qO+TWawT7z0Y9+VF/4whfSOoFDlD9YCyppTPfE+nTr2se0uSdRcqcmEtVJ0w/RrKb8egcUE+wLrPeUqjnnnHNCRzN48zGpcyiK3Am8eOk26dhLNJoBp4QDwt+HgL6AO3iz5bsUjzc4wRNE/gbNv+Eufk1tCehhTLsNrnBuGoBn6ngwbk3GK+B7sGFNnzNP+uJ3pUfv4QQU3e8gHb3vgU5ZHZyBQQWBjABG+bUPDW8K371N2vKyNPVAx0EBz+Pa+b/bIeMHeB38DkcTmiRZMADaAW4IR0Rjep3NPFeyhVmz+Y5sHBzcUypPoHcIzkG/WMkgjslYY+ygFwG/J8vEAqc4NvqV6+VvTAuFCA5oAQt2ZIzzjNED/BsHB/2X0JY8QxwUVoqKrH44PlkZzEOcou5G4Yw5NAp7ThBlDhmfHJvxbGCcMO/JKgmRGZhHv/71rx0n4vXXX6+zzz5b5YCyYYQsOv/0T//kpIRn680NUZ6ASBRs4w4Y3f29umfDi8Oo1jsdG7Rk+yrtOz67ev+lBFHObCYnnXRSaAACMb++D0R+d2g0grRciB2kE9JHFL012Y5SpqahRRu724d6TSii2U1jncgJGgoTjZGLYZFIa4ikZQVAalgfeI+fITu8EBKQDcYwZAYDq5UkIvPL6r9mAwguIoNjPv/88873Ql44F2+79paJ9Y4DpaDRVHGcRJBxHCwb1dPT51xTWxdR9GOc43K/uT8QeTJTIH7JAKng74mOevDBBx1BaOTewPPFsMmzh8B5HUjcC8uwQJyQSYHzwbIsUoHPMp64v9w3oqV4XpBHDIkIDo5LOruBn4nk4jzIGrGsG66Z43KuGB5rTjlVuvWWRLQhzx0i/O5jwiyNPMBYYAzgvOO509vG7fzDqUCEm3vvtkwnnGY4qPiM/R3PEBHC2MMBkkv/i1tuucX5bv4Wx4vXAMEcDoVo9vj617+uv//9707JpX/913/VaAbp8qxLX/va10p9KiECQiU5NV7Y9qa2DDg0QH88pvs2PKcPzltcUVwdpzg8MBUnGVXo3OYpaRuXCATCueFTQ77aAReE7zOm4QpweXh8gnPCKbz8nbHf7OgLgl6GO0EyAxwTg7k1/EUrcEyMoQRkYEhF+1BeiZ/N4H788cc7n8HwatwY7gPvzpT/W8Y758A1Y5Qlw2MwwOO8jwy7Ugy/BCGR1VIQxPuk/kRJ0B1tnVq3qTVh/N8Q0frXOhzDMc+I84XvwQO5L8nWILgYwZLsnWgxeKGfQwDezrXD3yxK3w2ydSxjBwcDmozv8euz4AbPgmfGOEIboi8YV1yDOVDQDXBEC8TiWtAejAX0E58hKIZrYGwS2MUYmTJ5irT73tKbSxPzlXvA67ACPZtRArQbTgKqCKBPudeMCwt+I6MD/WDgedGTA5BZjBONdcDGC3MZWEBetuBcGDMcgzHMeHcDm3NQPWFGE2bMmKGf//znju2ee1oOPc4i8TJp+U5TQxwbRJlXEsELkRxsOGxIg6VEyhgbu7fr6pUPDXsPg+7CCQv0rimV09uFDA0WbWp9hoDtdkutd4wk0i0HSfWJjXI0AcM3hBKDMuTWmk1jlMRAsKxtq7714A3asmGj8/kZjWN00fxF6unocD7jNTayfZASTGqwX9Ngfs/7ZHdAbiCwEFAEDOsDdVztO++8806HZBPxxGbp/R4M5pAa/u0lJekAieJaIVaksroJld85U1rBIpIQOhbVQZQQ4sia1XmdB1wX9xYSh2MC4c/f2OccB05kg+L9bzj/njt3qpqaGhxNvOS16dpn30XDvs9KReEQSEf+AfeaEiv2LGx7528xGiMaMwVCgqyYdBHyOMeSlQBJltJr2Rl8P4LTro33OU9+Nqfb5Fdelv7nChSaRIbOZz5LikrG1xEiMzBOWR94XojNZFkXjEcMATgw3Nk8CEairXIpeYgYxqnG8ZM1vuczYYR99sCAgvEG40UugrAawL5BlB9lp4pROjFEcVBJa8Ld65/W8o71I96/cP7JqotWjnOGkj309wp1+gBeu19651mXxohITWOld39SoxEEDsELMPQT6IIRGo3gcG5KMPU8p5deekz9/QknQDy+QP2xyYMawQu+hyxieLUf4MgY6NnbzKiGARMuAY+06G4Mm3B3eMrixYtHfA+Gb/YJeNAwp0QGwFAO7+ZY6G80TKrgDrgv/Brtgy6hRj33gnOAQ/FvnCpe2wl8mX0cxwtOBAKwODbXaQFjsO3IytvV39ulMS0NmjVtoiKRqLZG5qizZfdh5cC5d9wPnhHaKBPgJOBlMI2BxuE6UjUVdwONgiMKvp+qFCTPEt3ilzmeqmQQtifuM+fFvbGsDu4dQTpoVu7dHnPnSH++MlGGauw46bwPSXuF5TqDBmMbHc4z4Hkn4/nWqw/7hDdzgr9HJ2SbEc68Ztxb2WO/UrvYQZi/mejsEMOf15lnnunYPH73u9+p1CgLJnXrrbfqhhtucDaDkChVD9iwC91ENCiMrW1ynBiU2zHw7wl1mWWaOMbb+EbF1a2ayFjVRCaoFGADr6TotYIj2iA1HyB10MNg4NnWzU68RiFwFngdBmzkRFTw/y1L39IXdj5UsSMmqi5aoz3HTnb+7wfWa4gjhnvES6YR2qwLRExgHHU7SU488UQnQsN7foB9gU0Tx0qqOrDJwPEQOQiGdOfJsRDtNq8RK5BjRBeOGIy5EDTEBecKQUNwIZgQIpA1HDD2e+7PsIyH+G5SP06OoRJMLy+p1S67DWUyGBAIkDgM/O5Mh2Rg7lvES76wGqdcuwlUP2HibQbuRrL9nPdNuHEcPyJppbImHf1uRcjOyAVWIzckqmnB/MjEGU4UHp/1OjhxTiGymQvJ+nOkAmMhVXQkztCw7m32wIhPJNXHP/5xPfTQQxkbHaoF7BeU4KL2b+jQCFEqjKtrGVF4pzFar9pIZvOxra9DG7o2OeVwZzdNV100+4y4IAAPCnW6C7scIW1ZKW0faE5cWyftf4ZGKyit5AbGRCoIwCF5vfzSDh166HvV2MhsGCNFWpIG6MG90bQEzcApMh13cBRviRn4M1HiyUpboQvoIQF/z3aPxMGALuB84cPpMj3g9JYZTZAZ+7LdJ3QYc4zAEfQQ5wMPJwuF9wjognfj0OW+cJ1c27B7M3ustGaop8bGtojaxs3UTp7+plwnThUrL+pXossL9EwQWVpoFaLxuSaum2h5NJ4XOF2SxV6nGg9cC9fHWPAGnwGOx7Wv2LRZ8z7x2fx6JFZQifJSAR3JOHXPyWTPlGfDnPIrhYbzIZegXeaJlYjzAw5RdE0l9QAuB0QiEV1++eXOXL7tttt06qmnju5MDQQw3t1vfOMb+sQnPlHKUwkRMIieYOOtlKyBV7evHFaCan7zVJ0+82Cn9m0qMIU6+59TvzYPvlcf3U0N0cwiH4IExlRIUiU1TywK+tul/lYp2ijVTCxq87xKABkGOCkg1KnGDuOLKB0nwmWPPfIqFegX4X/PPfc4UcWFFs0Yuix6JxMgKHDQQsjcf2Np0JBjnBtu8kwNUMQHkaQjjuMY21kveqTIOL3x5tphjbO9QHRwDkRulSKSBEJKJJeJPUQWYgSYsMoWmTR/455TdohxRrRWxuMCx9cfrpRuvTGRVn7Uu6VPfV5qCDM8/PYvovnpY5LpfCayj3XAa8CwaKxMjMcYOHieOEMyibjGaWiNIMHKjlVa0bHSMRPOaZqjec1zQ2NbinuNweZzn/ucPvOZz2g04bLLLnPK2mJwStXsNERlRoVjKPEzWpUbemK9umXNY9rSu8P5GV1x4vSDNbspfYnHDV2b9dCmpxQb6DrcXNOo46cdqaaa4u9nlZQdUzTE+qWtq6T+Xmn8LKmh/MdjMQEPpgoHPPGss85Kuk9jWIdXsM9jP2Cc5cp32fPQK1a6BhC8hdEyk7Kq+cL602XKweA36Au3YwGDPpkjVg4UZ4d9J9dHRjn2FV8HSm+71LlBitaqMzJRm7Zs9S0LZcdH/3FvSlVChiA3twayPnyZlsH1wkqf+RnH3SB6n+vPto+gNqySbv+jtG2j1DJeWnyBND/syeB7qzZscPQ+jr9MwPPAoYc9wsvZ0BdkZaXjcqwfOEaZM2QEWYP5VMekhC6lFZ05tn2rdPM10vrV0tSZ0hkXSBMKVJK6CnDFFVfo29/+thOIWMq2AyV3anzlK19x6hk+8MADYdpPFQJjCWmnlRIduKVnh9Z3taq5pkFzm6c4fQbSoTe2Vl00+vWgpeZdikaKK6KJDsfYnGrxDhwdm6T29RICa8ICKUlkf4jyBZFCGKeXL1/ukFrmK6QcEgx5gFwytiCXySJf8iWxgA2R7IZ00Rz5gmgPDPMYyhEHXA9p0NZALxtQNpEMDpyJCDJSMYn6wmiPoTiTqHW/e+EFBmOcC6VocIc4hJRyv4jSwgmGQTsfBwv3jWvKxJCOEw2hyz1mfUubfnztX6Q/uVJhOc/jTpQ+84Wcz7dakWtKd7K9nX4ofoEMUM1nrv6L+m+4jjBMNe27n+LnnqfuSMSZd0RhpQJ/f99j96l5j2a19bU5RkI3ed173F7aqaX4gQSVAsr7nX/++Y6T0C8brhqBM5j18tprr3UyAUNUF+Al1GYPKjux0OiL9Wt15yb1xvs0vWGixtZlxqNuWXuvOvq7Bn+OKKIFzbN18KTilkmBK2EYLmqgGj0CulZLsV6pfqpUl90+FaL0wDBvpUYpRUVpGYJ04PnwP5wccFuMn3ARIvaDCN7x8mo4BPwklxKZ2QDtRPNcgpDgR9bng303W1sIfwv35jvZu3HiWhlJmi9nei2ZaAy+H82SScZGITQZBmjukfUIzFfrZNqDAacb2o0gKsapN1hnBLo6pKt+IHV3DvTTiZD2In3wi9LEwjvMKgk4F7AvpOrbkuyZoE28DnSyrXhWBMN7sXbFcq24/k/SqmWKNjZrzLtO0o4J0xznBj1sWH9S4anHH9NBm1cq+sKTUuuWobxK1qKx46VLvys1hQ7rZM+ZZ4xT6Ic//KFKhZLWqcEzTBQV4jisY1adwACGsHRHSwQJNr9NPdvVG+vT5IZxasgzJXtS/VjnlQ1iohm1N7Gc97sUVfGcGog7IjaK6tDYtFRa8eDQz81Tpd3PdKJDQlQGiLhnflIGyGqNkhoMAYEIYzjAqE0d2lyaAGcDSGUxvPwQZ0t7Zo2C9PzmN79xskTSZQ94oxa5X4gzvu+0004bvEfZELhMPsv35lJ6KwjgfIBEInYQpfw73z2bCCrLwsjksxBSiBPODdb9lBFYjz8y/GeyNZ54NHRqJHm22To0/PZ2vseaxvth2b33aM6v/59mMD8Qgs89LUUj0vf/I23mnJP1+dyTWlm7UtN6h/rCuHfdd9qXh06NFDjppJN0yimn6Itf/KL++Mc/ajSAayUdPnRoVCfYE4juLiQ6+7u1rWeHGmsaNDFLbeBFbbRG81uyy25lr3M7NJz3FFdbf4eKCXggWh2jRdGAI2PzvVKfNViPSBMOl5pGXz+8Sgbc2MYNGgPDJPyNIBWM2VTsgE8E6WxgvHoj7+HZxQiwZM7iuLGG6WRAPPzww47OyKYiCYE/3B/0mDka3Eb6TDk4/CmTz8LF4ddBlJfKFmgoAutwZvCMvP0UcgHXjHE8XUlufk9QG84jgvZwhuBQSarL1q1IODYGEZf6+6SVb4RODQ8I+GMuZFt9gWfijbnHDoGdK1kp5mVX/T8dFWmj1iNuEempW6WLvijNSZ9Z6PQavesmRVvXDLflOecdk1q3Sq+/JC0Me7Ilm2u//OUvnSz9Cy+80NfpVAyUzPLIYP3Upz7lvLIxIpUTNnfv0JLtK9Qfj2nnMTM0D4NuiBGbMp7/QkU93b7uaa3uStSmx6Fx+szDNLWhuJE8UWGE9SY8RRRV4T26EDdS6/g/htpMm30Fgv4eaeXDw9/r2ChtXCJNDxttVQJYh2nCR68Kg9vIbKnIRFpBDoNyakCyeXkdGhCLYkQJQVx5cQ4YZhFa1IRMF8nhBU5Euxb+3p2VwXciSNjkg8hsIYsGclgKsL4QaQbRDGqNwQDuV+rMMjIgwYhSnFyIQyL74AoW1cfYTZrR44hZj6O5BNFnlQDGMIIbEZlNmj9CnSxbBDDPy8qJ+YlRnt3qy3+ld2NQGBAq8f5+bX/sEW169hmNn79gWD1lni/cwYDhcsyCMZpW43JoDGgk0z3xgdIsIZLjpz/9qWMUoWH2cccdV9W3itIcN910k8OPKhGxeEzL2ldoa892Ndc2afcxC0rWR6Gc4eURQWJlx3o9sPE5R+OB3cbM1RGT9y1qmTuONba2RTv62ofekzS+bmzRDNJWahLDdFF79rUtlfoS5boSiEutT0qNs8MSshUCeJ6bM5L5baVjiMzHmAyXI8AlSBDN7S23xDGKUXqKOWK9O+CuaBuc69nqAAvm4X6hv4iEdgNnENeZzglBjw/udTowz9ExpUSQWaTsDd71ysaavU9Aj2VooDXgw9xvy/LwXevpm+OHsJ/pCDDu4PM4Mhlb2ewf/K31qiRDkLlAlqCfHeLF++/Rgu1rpXGmwePqjcW16f7b1HrwCY4usWdJxiF6xUCwINr9GOxXXlueZeKAAnKNasD+++8/aNdHY5Si91bJnBpEijHQb775ZlUiNnS16u+rH1VsQFG/sn2Fjp+2v/Ya51+zcLQCjyqLBxsyZCLImvDPt7416NAAlKO4a/0z+uC841VM1EamqTYyW33x1YPvNUb3VjSSRX3GHIDxD8FOCZCS1Iru7Ug0yXKD/iM9FlUVotzB+MkkIgZjOo4N5m8QgDB6oy3IeihqFOBAdEE+UUmkyiPY2Ly96xr3CwKG0yhZ1AIEm89lMn+JgC9qFpYLmUQ7ZQucV5BJL0FlXbP7hdhAuLn7edh9J2IHQeK7n5x9vrTkZVf/koH3775dWnxKoNdR6cCxxP18/fXXHVGbqpmeG9x3/g7HHYIgWXo5z++5q67SUWvXSA112tHXp+d2tKm9v1+bevs0Z+VK1bR3OI5V/h4HCU4Qb0PPt9vf1podRFH5Y3ZT8aMLKw2sdf/+7//uiA5EYinKTBQDOMFoDE6N30ps/Oj0udnyvFZ1rnNKDbGArexYo8XTjlJtmAU7DAQPwB145qwZWdVFTwH0xIMbnx90aIA32lZqRuMk7TymuGvNYZMW6cFNTzrnBMbXjdM+4/yjVYPutUTJSQIzStIcnF54I4xM/VKcXmSF1Vch8gf8jszedLyesRUkvyQgAl7p3t/gFRiug8gAyAbpejqk41jHHHOML/9GD8C7CDjilSwgi+wQeF26Uk7M92yDusodflk5ZNazphGwxzXjdMKZ4a4mgvMJrYuN0rfH5MwF0rS50sZVCUN3LJ5wsr7xojRvD2lMWCLPwJgiA4txSBkqykllWo2BMUtvSvqjEBydrN/Lc88+q2mP36HZ48Y4z/TVDVu1tatbb2zZrgldjZqz52HDnqX1/Rux5iTb47Br0Y9x19JkH1QSvvWtbznP7U9/+pM+9KEPFf34xe84OrDIfvnLX3aixkplpMkXT299w4mkig/8Bx7bXJkRYYVe0BAaLGpsMHfccUdgJVQ2dw83nvMUdvR1qpembUWE0+w0upeaaw5TY3ShWmqOUl10VsFS4Z974yXd//CDg2QxCIdGotn5MrX2PKLWnsfU3T/koEmK+jEjy0whABtL02gsROaAJGMIYOxC8DIh10HNW8YahgevMRuCTq8GCHolgXXNa1jnGjGykAWSzGlCKq31EMnUsVLoUhvFAAILUoko8xNRcAI4AmCcEKXjJ05mNDVp6x/+IF19NR6k4b885HDp69+VZs0dsols2iRd9lPp7jsKc2EVDNYBxD5iAwGRjUERkYITKpnRixJ2k5Yt0z2bt+rJ7Tu0pK1d2/r6Na6mRh9699E68NjjnMgpxAuRdckayE9vmK6ayMhxUBep1S4tO2u3MT7iM8QI/PM//7Mj5v77v/+7au8OZW3ZXyq1KXpr7w7HoQFMYRCpv6JzbalPreyAsYM1iDro1JgniCAItPV2qg8DugtRRZy+e8XGxPrxOnXGMXrX5IN1zJTDdMK0I1VfgKwdNO3mjs1O76KHH3nYMU5g3AvEoRFvk+JPS/GHpPgLUjwDnufXPyPaIEWq0xlbTYD70uw30343QbZ3xanhzWomAIbSRmQ8Vxr8HD4E9WCkRRP48Wi4FIEqOHMyiZDn/qMLS4GgW/uSeUwpKb+sHCoR4NgArGtke/tl0Dj3dMkr0rXXSPffmygvZUCPnHOJtPdhUjySCJzCubFsiXTNZVJv5eu0oIGuI/CJIKhMnzfPh6BdGoYnc2jwXV3bt+n5l1/SEyvW6bEV67Rqe7vqolEdu2CmzvrEJU5wHKWmWRfIbGIM+M6HQ6hY4dnrcIzOnCv945cTfTVCpH3OP/vZz/SlL33JCWgbFU6NH/3oR85iTNPCSkVXf8+IgkM9sdJsCJUkPvCO4q0NAmNqmwai2IZQH61VLV7VIsOp1RkZp7roNEUjhSk79dqOFfrpvb/THase1ztz2tWw88TAoqe6+t9UV/9biqlDMbWpo3+JuvuTR8U6wKGx0wmS29A0foE0pfiNjENkBogujgMcGvvss09Jmk4zZv1KRmCU4Hyef/55VUMdUUgYgsPbrwDhwDVan4pMnZIWTR+0AMgElAZi3AQBIqJwUnA9fqBkQFrD+rp1mnDJJdrwox9JP/mJ9IEP0KF6+GcOPETqGCrZMYjbbsrn9KsaZE9B/olozBeM76efftoZr3svWKBZtXWKdPZoWm2dDmxp0cFNzXpk8SlasXKlIzLghIhR/u+3rzXXNuvQSYdqQt0ENUQbNK1hmo6bepxOmnGi9hy3h6KZ7vtvviH95U/SdX+TWrdptAExh+j4zne+46xT1QauiWsjaKqopXIChEXku8GM6ImFxpJkICiCbC8MF1YuKR80147MBCCMraW2NNHM9dF6zWyapmmNkzNf67JAT6xbD697UFfc+hu1zdyumj0jahobUAa448B4imI8qA3yMSU9k8i6SIWW3aV6V9BNpFaacERYeqqMQeQ7+zgGLYyYmZZcgifzt0HBG4RF4NHJJ5/slLMhE7TSQSAIxnm/zBOyv9EK2FwyLVnL/eEZlIoTMF6CMoJa/z+/8rRwS7QZQWUp8YffSf/xPXX/5lfS978tffUrwx0bRO7PmCe5g2gJ6mzdJK1+K5DrqDag+yhRRFBbEMBBgeN0tz330tHzZ6m7r187TRyrsfW1OnTudMXm7KJHVm9yNDcZTzhamRu+2TfgPR+QjjtNmjRFmjZLet/Hpe//Vvrct6TZ8zM7qb5e6emHpXtvll5/RaMR559/vqPvsfUXG0Vn/AhmxMb9999fmnTWgDCneYrWdm0d/Bnj+qwwQj0t2EyI/MFo5a13mS0OmLCr3ulYp7a+rkHXxjFT9h82rmggTrRVY7S+osfb5q5WXf3QDaqpq9P0nWY7sXuPbX5Z0xsm5t28EHTHVvu+11CTJuNk/Hxpn/dLnZskxF7ztOIKjm2rpTUvSTgUp+4qTQ8dKqnw+OOPO46DXOumYqAMYh7hzSdiinq6Br4XAz/rwi233OJEgWNkxThKbc18GvxZBFKxjFzUbOUa/UorQaaJAsq2diz3h7XzlVdecWq9FqPhoYHzdfcLyQdE0qa7dhwfKfeIyy9XdNs2Tamt1dqeHs3kve9+V/KWs/SrgRrWRU0JSqYReEB2ZT6lXFgryLhxylLRs+baa9Xe1qant25XXzyutpNP1u5HH+3Mb7JxGGPemtFe4NA4YnIezUQfelD61tftBKU/XiVd/ltpavpstWrC4sWLddRRRzmlqMhqqLb0d4zbXGOlYkL9WNVGatUXHzKi4MqeWh9mwaYCeyJRnRg63OVEcgGNwQ+auKee2bp0sDvTlPrxTl+N4RnOPWqoqfXNIqskPPbOY3ry+Se198F7q76xXm19bXp1+xItmphZOcLUwInhDvrjbuI4b0UVJv8z7umkd0u9mxNNw+smSjVFdCphoHrhfmnLGqllgrToOKk5fW+C0Qq4Lcb0I488Musy00TVE12fT7kmA8Z8Mhl22223YVkMnBPBU9R7h5+jMQioQB/kWzWEaHAM6cWwM3C+aAm4k/s+48jh/qNz4FTZ9kDkfvAMgLvHWVGqXQSkMbgnaIdUjYp55owPnB6+43TtWsepsUtzk17Z0aZ9x46RnntGuu9eafFJQ59LFmBWgsCzSrIB0l/DepbkA74HjThlxkzp2NN09KN36uk1m9TRG9Oati5tPPgwJ5AaZwbcgEDOlKiplU5/X+KVC3p7pV//UFq5jMUmoTUXnyWdeLZGEyKRiGPnp2/fJz/5ybxtvWXt1PjqV7+qc889d0S95ErDQRN3VWtPh15vSxiDpzaM0+Lpi0p9WhUBNn6MfpAAAymSRBxk0szKHUl13ux36632tY7zYk7TFE1uGDcoNh7dvEQvb0+UJZlcP06nzDjYye6oNFBm6sGXHteUOdPVPNgEKYHNPdsDcWqMbHSe7D0f1LckXsXG1pXSi9cP/BCXNi+TerukOeE89APG8FR1KdOBjQnCi1DIF5BnIoJIB/cauCnFRBQSx4Ogs15QDxOySiQRjo5MnRNEnFPWBmcJGy2kn++hb0AQzbuTAQJFWS/On+tEYPBvBBtOJSJJiRbLtLaogXPm72m+CHByFMO5gXE7qLJg3H93M3o/sA/Q3BAHiPUsGYbVq9Xd26uNfX3qjsU0E/G2YUNCTLg/e/xJ0nV/HS4yjj8xkOuoVjCeMHgTbUl5w2yFsYF5S1mYBx980JlvE/7wB7VcdZWaXn1V+x53nJ7ffXfNamzMqPxdYPjJj4Y7tVpbpd//r/TFr2i04cc//rFTGoTeE6XI2CsEEMpXXnmlnnvuOVUyiMp/15SD9ejmZ5ysDYKmDpywjyY3jIw8DeG/75txzkAUMAbNbLDv+J01pWGCNnVvU1NNvRa0zBx0XmzubtXdG55RRz9BVREdOmkv7T1uqCFypQADKZGzyzre0n5HDDVBJ3CqtbcMMtk4n/riGViHRV3f9Xtp3dsDvCIqrVwqnf1ZqaHydGQxxhHZvMl6a6UDf0NmM1nc+fbdhMMQuIXm8QZwwWfgzUQSoz+wRfAe6wN2A/QNfD2bPYcMEzSGlYfFQZKvwTadTQB7CVlprHNwZPgy12P6CL2RS1N0otjRLPRbxLFRLOcG9491O19dRuA0zzydLuI549jg3o1wpmxMlKda0dWttv5+dfT1qZkAnw2J9wexYC+pvlHq7R5aI5rHSLOC6T1ZrcAOwfhk7CbNmsgAOK7gemjpPY8/W9GJUzT32SfUHa1V98Ij1VDX6MztTLOV8sYzDyccGsB0xt03SIe8W5owugJSDj30UJ1zzjmOzf8PlIkuEiLxItayoOQJnhsWy2J6bgqJrv5e9cf71VzTUNGZAMUEXlMIkHsM8DNpYRgdibDK1tjnxcutb+uRzUsGf0Z0TG0Yr3NmH6VKAvXFISjjF0zVHeufHPH7k6cfqllN+ZOOjr7X1R0bXmu0uWZvNdSUcfPVl26Qtnjqo5ItctQ/luqMyho0fSRqIZf5CmlAbOBUSGeQzhRsPTgr+E43+caQDcn0I+SIB8gQoiNZ+SIDjhFrPu4muKw1GFoh65CiQmRv3H777Q45J9odsUFUFULKGhdy7WTN0Dsg1+gk1koEWaqIpCBB9BMOsXzXZnosZOoYY9xBfBcuXDj8Fz/5ieJXX63XOzq0U0OD6jG808T+L38Z/jlKEPzpd9J9dyciZ04/Szrn/LB8RQbAiUWNesZwPg0kGafsYziqmAPMSQhv0UHpgMXHjXz/0MOk//hPjdb+Gqy3N3sznCoUZ5xxhjPGfvGLX6gaQI+Drv5uNdTUV3wmQDHht8ZgqCOS3LJB89FrfbF+/d+q+9QdG16GOCg+XixgyKVEIPfqxc4XRjgxxtdN0OH5ZMYNKz/1KIvwwBvce/YUSkmV6bjetEq68Zcj3z/yLGnP7Hl0tYN9BM6brT6AZ1BGBk5MwEuQzgC4NwFEfKfNdzgltga/qG00DvwU50AmzpkXXnjB0SLerDBsXDgcMNiiY4K2DcGnuFd8LxqCrHfWtVmzhior4NCBcxEklquTCM6PgyaI7Jl04Pmj7fJt5I5egANkes04dBkfw8oAb9ksfegCvb1jhybX1GpsXW3iGf7796Ujjhz+BRtWSXf/Vdq2WZoyUzrxAmli9s6k0QjmCcg3qIZ9nbl4/PHHO9kbzN98x1FOuOPv0v23Di9JBj7zdWnu6HN0rVy50nm2VGYqViJDUTM1/u3f/k2f//znq8ahARpriGIMvmFboRGPxxTTWsXjnYpExiiq6UVzymBEdEf9krHBRmTGOQgORMTSQ5OdF4ZCFjAD5MJqKK7uHF4vkaijDd3bHAdUpYhD6u5DiCAlbPjzm2doecc6x0HD9cxvnq6ZjZlHlKRCU82uiiiqnhiNKCNqqJmv+gI1Ow8MfT71pcO+NknFa7YN5XEgMBchyszDoMG8hvS7G7rh5GAeJyPRvI8Ap7xEugaWRI1hkPVG7LD+QH4QPBAhnBw4Flh/zMefT6QY943MBs4VxwrXw4vvJsLK6sYiRjgHnEaIKa4nm6h4rou1gbUy31IbmYBj5Jqpw7Uj8ixTJhuh7CtyL75YkZdeUt3TTyfGAH1LKD/lBc/+wo8nXiGyAo4Myvgw1yjpkqvzjXGKU4oGgYzxjMA8JFI2GuBeTWr5vPnSqpVDUVTM891212jFN7/5TeeZWFZOJYOSabx+97vfqVpA74TmCswudtCxWdr2TsKBPGlXaSCLuhTAUImxFUMH0cD8DIjgTOawZU9mvzMuwD5jgRTb+9rV5elvAi9f27W5Ypwa3AcMehhvucY9a/fSU1ueEMoQRBXVnmMDyuCKNErxgzFjEW5CLLukvcrXoQH8mv0ylsMmwL7AkEjpyoxvb2+vw30Zh2QX5Jud4Qcz6uPQRGPAx3EAJCtDwzmwRsDXMbimcrBw/tgx/Lg3hjw4L+sMHJbjUpILfs+6km92NWU9+S40BnrG1iWOg2bjGFw718Hx0Th8NluHE7Y6nCMEnhXaPsT3c/9zLXGMHYnnzLVzHzIJvuLzBLeN0MaTJktf/Fct+NH39caONo2rr5POOU863MfBO22O9MEvZH2+IRLzBAfjiy++6PTayBWMc/QJz55nyfNPCwLeWHOCHNezPT1WQG2dNDmYQNBKw9y5cx2bP7b/u+++u7qcGtQwpAzItddeW6xDhkjh0OiNP6e46AkScQwIUW1WrfYuimODaA4aXBlIH0NcELnM8TFasrlh8MNzb5udF2zUbg8vJISFjc2+Plo3aPw31ESg6cVvIp4LICIQCQgZ4B4cO/UAvd2+Rq297Rpf16KdWmYF9rwikaiaandVk3JPBSw6puwsbccJY4hIk3cq4QmVL0iRzrb0gqXxFkJsuAGJ5cU5IhAyKUGHCCLbAgECUfebB6wD3maBbnAcjLUAYxhGV64VYsSLdYrjmNGDc2QP43fm/EAoQajczcAxhFBSh4aElvrO5zEcUobHHU1lhmK+k5JSfC6bklIcF+duMcA9JtOENZl77m2A7gdEH44czhFnCM8kmwganEOW3TIM3LcrrtD8F1/Uq6+9pl1PPFGNRawBPFqAYMbYzbjPJ7uCsYPxAuGZVnCsfEV67napp1MaP006/L3S2GCc9/rWt6V/uVTauiXx86IDpA9fpNEKBP2ll16qr33taw5Hr9RsY9ZNroFrKWYt8BBJ0LpSeuO2oZ/XPi/teZbUHNA8zgIEEaAliNAmCApDJfs6ey4chzXJb7/FcMheZeVQ+B54OaUz0BcjEU/yfnnOF/gOa7vxuwn1E3TklHdpfdc65+fpjTPUUhtgWdkIvK4EGXq5YvIsqaE5sQ8NFrSISLMrSCMVCRj44QrZ7B/wXXR/tsFW2QKeT7AU52bzPx3YQ8iCxqZA3z+/c+R6UxU64XisM4DAI7JeWUt433r88d2U37X7ZmWf4LyWuUJACMe3jHL+lhefgVMZuEb0DoFibs1mDg+Civhu0yyZIp8s3WyBNsIRhE5gnc0EOMYoXw6slFSm2eT8XdLyR8cvVmS//dX85JNaOXGi5h5ZWVU+KgU8M7IFGeu5lroFaG7GDv9PabNo2yH95ufSy88lHA6nnyu9J6Ds/X0Oko46UXrkrsTPfP8//JPUXILy7GWCL37xi85cJlvj2GOPrY7yUxyCiL+TTz5ZX//6QJPGECVDf3y9+uIvjXi/LnKoog7xLPx4ID3cSuG4U8WpcYjBLJtN1/29RGcTcb29v0N/X/2I+on2HMjUOHzSXlo4oTJSwB599FEnsiNECrB0LXtYWv1CIqoXh8aeJ0m1uTe3rVZkW3oKwyPG6HQlnoIABm/IjBm+MwXEHrFAhJhFBUCKTSBYU+50qa0Y3vkOyJUbGDEw4FsDQO4JEV6WDQYwBEKgrQ4w54TQOOGEE5wIFM7JxAgEmiyOVGSdv+eYZMa4j5MKGJxxnuTT1LlQJaSsqTnPJBeDKcIynROEdR+BjGEKkViphtlyBfeXcU52Uz6g3jR7PSIyaaP4zauk+1yR9jzLxrHSKZ9KZFoEgc4OPI+odWmXXRPRWhn/bSf1K+h2mN3flTFsTSLgiHWrEkEU2Pve977BYJAQJcZLV0vdQ1nUjjF43Bxp99OKcniMJAQQsCfAY9ifCZiw4AL0aC6AU8AH+K6HN72oN9pWOcFTgJ4bZ806Wo01Pk74MgPR3HAN9ssQKbB5jXTvn6UdWxJ9NI46V1qQptnsKARBivBjL4dOBuYhWj9t494A+SrODQz02ewP7I1kdlivB7io28ifaYYjzgSvDjGnqjUxtkAqMriNw3L8Bx54wNE1/J73uc8YbuHV7p4Edk/5OZUW4FnxvThcMuHKnCPaphjZ4G4HDWMpXXAX+g5eic0ol+A7dB8Oo3RjAqc4pXQ4p3zL74YYCQsuyLdfJ5qb+UTGWNKx/dPvSK+8MLy33kWXSMe6GsDni43rpB3bpGmzpDFZ2FSxY7XvSPRpqa8eO9a3v/1t3XXXXU4gaqH1eVEyNe68804nCvfWW28txuFCpMVQg2434kneDxreQe32q2FIzLVGPN/L30JgIBDnzn6Xlmxf7jQRn9c8TTuPCb6ETjJwTSzSkAvOi58hI9awOBUgL/l4rEcNuI+7HC3t/K7gS5VUGTLdSCCJRAsxTjMuFZMniIqy2prZAGcBJJMX8wtiRKkcyDck15oWpgMixRr8uUHED410zfHhF7FEjygviAjF2ULUlLtfB04KDAmIpGTGBD7PGgaBhkhnYnTAoYFQKVYzNNanVBkwBiJvEGm5ZvowFjMRoFYWhPuOE4SyhWS4ca8L0S9ltAGnWRCNpHEMpjUArHsr0WxxIBjBcVx3bpe2b5QmBrR/NzVL+2WZ6s55/OpX0v/+b+LfzLX/+q/E/ysclKUgmoqAIxxXleYUZO3n3LmG0KFRJugdiiBOIC71thd1TGO4IziKfcSCMxjb+Tj/iXLGcIKR88jJ+2ly/XiniXhjTYP2Hb9TUR0a7G+MfdMKFsGdSWQ1/KQkfY0qDWRrnP/FRD8m9EWFrY3FnG8YE9PBSjKBYjvUyNLIdn/guqwULvySIB3mGNkTBNK4s65Twa+sEu+ZfrHMDS9f5dhnnXXWiO+D5yazf1BSDt2SbB9HG/G96COMyOnWC3SFPbNigXuDJkuXxcPabtkwuYDslUwyd3CIcW/RmKydPDP4bBClxEY7uIf0iDn11FPz/i6bT0mBbn35eVfm3QCefzpYp8bUGYlXNti0Vvrrr6RtmxL7zLtOl44uThBIoUEJqp///OeOY+OkkwK8zz4ouOJnIf/GN76hr3zlKxmVFQlReETkt7HTUYE6p8PR0bddm3pWO6JkYt0Mja2bVJC0VQMEnc3Mt+RIBmCDsU1mYv0YHTWluFE1kBNK2eDNRzxxLea0YYMmWpvoFMQW184GCblzG/7CjTJLsAGUc23eMgBzIp2RFyccJK/YDbYg3xggcmli7v4O5hEvHAIQdqIxMyGcFoUVVAo2c5957Xds7m8m0WxkeCDCEFEYlJNdB45SnhvrCp+H5HMP+DfCqxAGSjJJkqZsZ1H+KxVYM3HUZOPg5r5zr7h2IuisTwvPtpr6eBUbjCP2NAwCBY9SI13bL3m4tsTRzwTk/M//DP28ejVMXfrb36rC0PXZz35W//Vf/6Xbb789EHFZTHDORKxyDSHKBC3TpB1rEs4MBxFpjI/Ij/dL7W9KfdulmhapZTcpgBJOrP0YWeHZmezv2YC9BMcGjpK9xmGYLa5xlgAQ+BL7K7rBelQZR2DPt2AM9lDr7+VGrpmToxZBZQlWKUzrpgPrtBmEi31+cNF8SinBfShXy3XSiw+HhpWHzgSp5lu2wTfmXPUa/dF4VuIq3f0gGApnBWsDmR/JgKbg2ll3yHqDBxI4xP+z7dGRCViv4f+ZlCXLpzQy15KqZ2syjWmOObQcAXmMK/aEIPeY0QSeIZnC3NNMs4fyOBiTDcPj0HsEURWxxJovyBq55pfS9kQZNUcDPXSzNGWGtFdiL69kjBs3Tl/+8ped4KMTTzyxoM+44Dv1vffe66SSffrTny70oUJkiGhkvGq0m/rj5n2PqDayjyI0c3OhrW+rXtvx1ODPG7tXaueWhZpYn6UH0gcQcDYDiLm7aThlKRAj+URxWGaETRyyP9jAQKa14L1AOBCZgZjgu1mE+T/RG+6GxfwfApAsCoq/IZ3UStawGVIaiIbGXgN0iAoFG1IZCUYbp4w37/tmCGcuMu4yMVYXApD0oCohMqeyMWIjKLJpXu0FJU8QOm5nEKTf3TeINY11CCNLppGiZkQmy5H1kO/0Ozb3DucHxz/mmGOcY/Ee0VxBRNi7wbpkzdzNaeAHrpUSF37nnAlYr3PNPGE/IQLNBBe10bk/Yep4bmCPJuOJki7cw6Slo4LAvP2k1x6TersGnBsRacYu0phggymyxtNPJ5rOm5OO/69YgZeSLoWqdDBPyXT4wQ9+UHFOje9///vOuefayD5EAbDTsdLrt0hd2xI/j5kuzfEELTC/tzwidVM+Er4Ul7pWSVNOyDtIBRFt2Z8YntgTMWABLw/KFvAkL1ch+xIOYQaabEU73wcP43vMMAmHwXA2adKkYU4JCwBJdgyiiSlfy3eyB2JIpqSNN6gw14a8IUoLG3vl9OzoH+fnMGBO8DszVMMZi+3QAMyloLINOP9DDjlExXbcU77dnjlrwvPPPz8s85VALhwBmWYuWM8PSjCxRuCk9Y4pyu3Bp+HycPLFixc76x/vUeqK5xl0z0Wui+tLFdzKWCJ4LaOG0Ck0SqaZNm5wjwgks96vnAs6jPfhx+U0LysFBK8xDtm3jjqqgL1LeDannSPd8NeBn2kULunE01VSUKqqdfPw95hX77xWFU4N8JnPfEY/+tGPAillXFKnxo9//GPHoRHWoSsv1Ebmq0YzFFeXIsKzPzI6ak3nm65IqwRWd76ecGr0d0mdqxJlIppmSrXZGa/YTImmhZxjvMJQxqaKwYS62/k4Ndio2HA5BgKBzc9SDNl8EDWZ1qtnwyKdk/9zfhgO+Xu+B2Momy4N94iMwnhG9ESq6HA2PIgI32EbNlHWlPyxBpfhplihaF0rvXy71LlNahov7XOyNCHzSJ5Clo+BNLgJIgQVhwbGaUR4oZuBZyI6IM7FKqHkBvclH6cGxP73v//9sCgT9juclRgLERqIulzqhbLGEFHFeoMxA8MM6wvHwWli/Yd4hpZybkSdxoT5ZL0lOx+OgwHGmvMZsQdWYs/EVa5rGU64bKLg3KBsl/te8wwsdZxxT1Ra0GVqWM/NcGb1d3lu5biWv92+QfdteFntfd2a3jBeJ89cpPF1zWmfO1GKCOcZOLNuuk564VlpzFjp3PdJOwXUe6dprHTCx6VXH5I626RJs6Q9jyq9k9gvy5g1s4rqK1988cX67ne/m3X/pVLi8ccfd6Jmb7755lKfSgg36sdIe5+XcGo4fXEmjpzDPZsHHBoa0hm926Su1VLTPCm2SYptlyJNUnRGVmsA6y57EI599iEyzawhL8bXZOUkMwV7sK33aBe4O3sK+1+2wQQ4Lii/wX5HVhxrLdzMMk1Yc9EvODbIkuTzqQCH4nwschhuABdwG0C5N9yDQjdpDhEcYvGYnt36qpa1r3T8gTu3zNaBk/ZRDYa5EgLdi8PA3QMS3kkQIAERzJFcuVxQKHVmEjwdo22uASGsKzge3LyWn+H6zHN+nyzwKR04J/QJ6ww2DtZF04PYJVh7KOkLLzDHKPqGtYq9l95FQYJ+CDg2yHbHfuN+blwn14ve4Jxy1Yv5ZO8RAMj12z3i/AhsRbOwPjPmC1G6Gc1FQBzH5jjYsFJl2JQKfbE+vbrjFW3sXq+oarRTy86a15ze2cM4RKM56NgirX5W6uuUxs2WZi1KOCCCwFkXSBMmSc8/JTU2SYtPl3YpbnWKEaBfkxfxgVK5VYKWlhbHF4BPoGKdGi+++KLTGOQPf/hDIQ8TIkdEIg2KKHnUcF9sZDppX7xP6muTNtwtxQZ6cLS+IE09RmrIfIFlc4QE4cDAM4uDw4yr+UZSsVmxEWMIZFN0CwyOYRtCOkD62ciJciJywMA5uhsoIwwY65ABNtp0EYPeVFMMbhzHnBog33tQNoj1S2+9kmh+NGuBNK30Rv6CoLtdevbvUv+AcZw68M9dJx1xUaLRbSlOqbt7MLraxhaGcRPjuUSplDp9vVDIx6nDfOd+ukUHho1FixY56wQRQfmKOtYtM5ZbSScj5hg5EDSISPe6hNjAIYtICBJcK8fm/4VIubaI1yBhqePAIniDcmwg6iHjCCyeAWMZ5zfHyaRebzGxsXu7blj9lOIDRsQ1XVv191WP68Pzj1Ftmp5EiDXH+XfFr6Rbbki8yXN69GHpp/8tzd8pmJNsmSAdfKbKCu9/v3TjjUy6xM/MwY99rPRp6wECo+k//uM/OqKDpuGVAM6Vcw57aZQhWE+aU2QxxZPs9+iO3qVS7O2h9yJTpbqDsnJs7L///o7RDec7HNsaE1uJ23ycGuzHfC/fiTHGxh9agO+1fnrpgDGY/YNMOLfRh++z74S7cRz0DMdlnqYzELn3ZRwwfBfHMaMnezdGw3wbs5YF2rZIm1cmShTO2FWqqc5+hC+1vqE32pYP/vxm+0rVRGp04KTcelAGAfgOwTVUGrAxCQ+2LIBSB0u5UUqnBvOPrIdcwfpw/fXXO5n0XAf2AdYYeKaVuM5nPWO9YG0ksMy+jxf/xl5C4Jb18LH7iA2H83CvK0GAMWOBXIUqhYyOyrUXB+u+t5yfGW05X/Qe2fV8f1D9NnCc84x45tbTAydZLn1iCo1Xtr+kDQPBCv3q1+ttS1UXrdesptmZVU3YsUl1S66TYlQriUvbViacHLstDuYEGb/0zwiyh0a+wLly+InS43clNFV84L2DjlE14TOf+YwzdgniwK5acU6N//zP/9RFF12UskxFiPLFuLop6uxuc70T0djaSQknxjCHR0za+rQ0I7umNogLFmQ2CQY6hkCMQEQWszHkU07A63jIBnjciVpg88bxkq5pN6LD7ZDItVyWGxAGjJgV3Yemr1e65v9JK4bKnOm0D0oLhyJ6qgbbVkv97jkRTzg4tq6SZhbfsAn5sVROd6S+Rd6UG7y9dSoNiDpSxFlzWHu4/xB95nZQhJ+1wG894JnywkDiLoWHyCHSEwIfdE8JjsPxWBuDTrnme/Mpm8Wewl6CCMDJ7D03hAclERDjjLlcyoUYcBJy7xGEwP2sEYRW1q1cSn0saxsqiQZwbmzr7dDmnh2a3jhSqI1Ad5d0y41DP+N8j/RLt90kXVLFPQ0oXfPnP0vXXEMqkET5iVNOUbWBhn4YOjFMlaoUYTbrxC233FL0JqYhAkIdgUUYfTx9l2qbpNiLw9+Lb5Ri66Wa7CKdcWw88sgjTpYCgUfsK4zvIJz9BEb5BUeZczvZ/GEvwEjF/sNn0zkW2Ddsf8knUpw9z/Yn9Io38rsise4N6cm/JyoGgLFTpaM/JNVVj7PZsLJjZCPulZ3rdKBK49QgaANu4y6dbMhVexcSQZW3LQXg8RjSLQsMYzaBlqwNmZazTQd0SzKdgJEesG651zUCt8iCo+xtkPwWzYrdkDFG8F2QdhDuXSZ9R5KB54DDBSeVXy8N7FZoCqp2oGvzPX/uPfuEVbuxYEQc6NjJzKlRDhqDcyBDw4v1XWszcmqgJ/vWvKw6c2gYNr0uLThSSpNRXtE47mxpysyEvaypRTrkOGlsBpqsgjBt2jRdeOGFjm+A6haFQMGsSHgT//rXvzoemRCViVlNu6o71qFtvRucn8fUTtD85r2l9vtHlKVS/0AEYw51ZImiYqMgRdo2SoQIBtmgPN1ucCwcKX6CBKLP7yixks6ZERRIqfSWiKmKZuHPPyKtoISZIS7d/hdpjwMSXuhqQrQ2u/cLAOYTJJDIGYzqCPlijeF8gXHYL/qlmMiHFBL5yIvMRKKqWENwjhbDMWlOVQwWEGkItQGjBWspAiGfSC4vLBKPcWYRsLmCtc6EBvsBka/5RPhBnHhB+JkPiD93Q0POnZ95kY1HJBrHY65Y2Q4+Q31zHDYYqHBQ8Ble/B33mvfIekrmJORv+Q5KXrHXcB5W171UiDrje6S4T1fCgrmBk3SSs257/h5jgasvVtWCZ0dz8CoGIv3888/XT3/6U/3yl79UOeNnP/uZ3ve+9wXusA1RJNQ0SZOOkrY+JsXJcI1KEw6R6uolv2qQ8ezrp7MPW3AU+wr7MWsxXIOo5EKUxWGPsGxKL9gz0Dk41ovpNMQIZ83DDeUURZ8T2HeevXnIoQHaNkmvPybtc5yqDWRlZPJeoXk6TmSi9uGU5e74riZQsoVsDdYvHKJwUPht0H3z/GAcl4oa7t4b2ChwHFMuKugyVPBlXmgaxpu7Wka2QItZ83Fsk/noFQKh0FV8HxnaaAiCvNxaG61ljj32GbIquGfsOzghsPeg0608MIFnfK85qeA0/BvtQWCWX/l+HF04qpmPfD86k2dRasfGYH8s9zsZlI7iOVN1oGYGgVKRgZ56LvT3SZVhzsgNXPP+hydeVYwvfOELzjilF14h+FfBLG5XXHGF0+W84iNBRjGikRrtMuYApwwVEZ21kfrEgkmEFbVvBxcu3svOIEnUFIYsNhcj1xi32Chso7z77rsdx0bQDSAxeNE41pvGjUMDFLsJGJuet4fIYKmPSsa2TYlUOkpQuSN7d2ytPqfGpHlSyySpnT4DA43CG8dLUxYU5fBmdMTQnCoTw/oglBsgbcnSeosBiCUEM5+oJ5wZOBBuu+02JwoU0kkUE++7DQhxx4DDEAmWoUEQcKpA/t0pyTS0owwZax0iNJ+sMi+4X/lGiiHMEMgINda9fMSLG+wbzAWIMsfgZ+6Re81HfFiKO+stWRecB84KnEP8nTcTDwFCpC/flUpQWgkRSnVxDP4un7rKQWCPsbP15JY31Rvrd/Z07sSspkmaXJ86m4jzRlzNp4bx7ntKb76eWMsB/z90ZLRmiMrE5z73OR133HH6j//4j0DLSgQJDAJXXXWVHnjggVKfSoh80DhDmnGWFOuSog2JBuHJnBfRzMciRrB77rnH2UsOPvhg5z3GMkZZjIKs20TA4nR29wIICuwX3rUefWPBWsVulgw/9Aa4VHLkuoO+bqnX40znkjoGmtNXGfYat7Me2/zCiPeKBcYz8wV9nMwhRiBJKcvIVjNYt7CXwEmZy2eccYbzPDB8u3s49MVi2tTToyn19aoN2HEJJ37uueccB4a7aTm2HJy1cGyCVIMMyMQ2QoZIrroArkBgEUZ/nABBOeKspC3rOs8ARzYZFd4+RWa4Zb21XkkEg1nZLv6Oe+YOcqVPK3sEzo1kHAwnCFkb6GY+y15DhRH2tlIFM3JP5jXP1/KOt4cHy9AjK4P1hYwfbVshbVnq/lapaYLUEKwdMERpgI1q8eLFjo/gm9/8ZuDfX5BQDQjllVde6TQeDFH5qI3Wqy7aMGQMmrD/cCdGtFGamF1jSRZjNmm3sYgFnM3HOcSECc7gxyBUCPD9bB4GNhyMWaWIPMHJwrHdwMiXTw3OssDkGcMdGqCmVhoXjNGyrMB1HfQ+ac7+0oQ50qx9pUMuKFp9Xys9lKrUH2OcKBWEfbmBSJqgnZfZwOpg5wMi/FlXTjjhBGdOQ+yJjqRRO4jH+9Xe97y2993vvNr7nlWcHkUBAoMJxBbybIDgUiIA5waEGoduMrDeJosyTQbEAtkOufQBwqELGWfcIggKkcmAGILo42DgPHFe80Iouc/ZmloSYUU2BnsUIsjrBGKfwlBmkVXJgJOE43AMK3PFGEP4lwrj6pr0/rlHaeeW6U6T8IUTFujs2YeOvA4MXjvelDY9Km15WivfXpqIiOdzX/2WtPe+A47bJukT/yQdeXSpLilEwGBsw72uvvrqsr23f/nLX5w5TfP6EBUOojhrmhMODefnJqnWU2+5ZmcpmrkznvWWQAm38Y31i/3PgMO7UCUvCWjAaOUG+y7jtdgOjapFbYNUTzkSz941JkUflwrGgpbZeteUAzWzcarzOnLyIu0ypjhZamgHNMZhhx2WMsOHwA10azk6zEofwZ4/aM6NzsARy3OAU8JR0U/gtrVrNeXGGzXzpps08YYbdP3q1YEeHwM9hvzHH3982PucBxoDDk8gl52PF3DhZL9L9dzg0rnYQzgejh+CiuD2aOSgynUZeA7YjeBMljlhGoOAKvd1oA+5f3wWZwVZHu4scoBTBK2GHmEfSQXrqwG4LpxeFpxbKuw2Zg/nNb52gibVT9YBEw7W5IaRe3dcHYprqeJ6Sb1976imdmD/nzBP2vlYqWZgnxwzTdrrjOAahYcoOfAN4NTI1taQCQrC6IhUBadUYc3hEKziDdK0xVLP5kTqb/0kKZqd8RZDEYsx0eVkZWDQRHQQacwib15oGmhj8AqakLABYGCyeogYekvV+8X6Z7jB5mebE0TRvPts0JSHCHpjLggWHpFoEv7GQH1kyPCZF0kN1Vfv1kF9k7Tn8UU/LBsDhM/d+Iy55RYXkELENCVxENv83psdVEpwfoVoOp3tepAvWMvIQrNsL+Y2xnTm8OSZbeqjNvgA+uKb1dm/VM21+yooWI1joqYQIDxvN/mmOReOBCJGacZnGR2sNRByDEGMDX6XaWQUDhyuE4cZoiYbQMq9ZTEKBUSEu/EgYw5DE/PGu56SlcD1IFaoY4wDnnnjFuupnDjcSyKocJC4DWnscanqrRcDkxvG6j2zE9HLSbHtOWnH6wM/RBTbuEzR2CFSdIw0abL0/Z8kmmWzpleBsWAEKKd1880SQprMNyLIqvE6k4Dm27/+9a/1yU9+UuUIzi0Mmqpi1MyRopOlOA7gpsS6kyVwIDz55JOO1mC9Zf9DU2B0sgoCGJAKlT3n1SwEDJSqRx7ahj2JvdovCJHyMexnZriuiLJC3N9DzpYe/79E/zowcZa0W/WW75jbPMN5FRuUGHVHk1tJM7QH48YMz8wxeCeBNcyxIEuehkjoFIBWsX+jNXAk7HTIITr30UfVPcBL2/v69L7HHtMrJ5+s3QLMuMTQDj/GdkMAhNtJi544+uijHacHaw1OBDQJAaSsMYwPxgTZPNlUxMABbQ3LswEljawHRaFhmRtuoLXQfn69/QCBGegB7E/WI8M+53WKu8G8Q5ewb6GbWcMB97fUZcs5/wUtOzuvZMChIT2R6MeruN5551ktWOBqCj99b2naXgNVL6rUmbH8TemV56X6BumQd0njR5bDr1aceuqpjpbGV0DGWdk7NX7zm9/oE5/4REU3fg2RBiw0Dfk5ATD4YDjDCMjGgwEOAxfplRjV2LT5//33369jjz02UMcGx8WQZ301rLxGqY2qRlTY0EwAQRA5XzYyiAIGS+5H2SNaI733k9KqZVL7DmnGXGlCcKVvQiTGCaVwvIZhxrPbgGtgzGNYxXCNQRdSVWoSBCDGiH6/2qHFAPPO61jM9XvYrN1zGaM5tWjHTOkb0WKlLz4UyRMUILZkbJhjw5uKzHuIA8YCJTgQo0RaEf3FuEB0MKb8GkB6AZlGOLA25eIkQ6wwDlnniw3uC8fFUewu9efeZxiPzCMcFOxPmTT95rOIOJt/7pr/PJtyjGIchliPy6EhdXZ1q6mhJpG5MXHR0OfKYN0oCHBuYsxfujRxjQjGD3+YukwaLfjgBz+of/mXf3GyzMotG4JzwsDxgQ98oNSnEqKQIGODV47ASMSehp7A6MeeyBrOvmhODZz8lFMhejjo5sYYe9kXjV+VMlKc/Z2SJn5ODbLECYCxjEb0ltWzL3tMmS+dcLG0dY1U1yBNnpvQHSECAVwFjgjPc0eNw9Ut+9QN3sdAy1yCX2Lk9htzpYAZfisdrFn0qrWyelau+8pHHlGXK9AGltlLWeLNmwN1ahin5bmyduIEdYPzYa3FiYqO4L7Dey27mb55BAyx5mai91ibyHjIJZMfp4r15CjFOERroanICnfzfvYC3seJwZ7E/UEzWqY6n03lWGZOslb72Vit6kB5B76uGnRogM2bt2uXXXYorj5FzCzt7JdVGkj03BPS734x0DtE0t03SV/8jjRleNZOtaK2ttbxEeArKHunBovVHXfcUfZNBkOUB3AqIDyIBCHyA8OYuywUxJrFOWhBwCZCNgSCh3PAqFVK4y6GTzzvkBObR2YkJOPJrv/ee+9Nm5JYds6vuRUQ9VWBQDDTK4GeDe6xyzxiPOPwsObG/N5tUEVsQBIZ/xDUUkUQGohmh6yXCqwxQdUCZg6/8MILTgqxYdGiRXr+pb9pvwOGRw0NErgCgPUNkck4wNDuvr84kbyRdxD/W2+91REpkA5rYpcK1iQ817XTSnZxnqXI1HGiilzZLMCcFpbthIDgPnB+XGe6vYi5VK69azJCbLj4f3vFBu00d+pAM99RgFtuSTg0WCvNEPKHP0jnnYdK1WgAWU04Nn7729+WnVMDIcS5lWu/jxDlBYw/cGkcGwQDefcqol3dmiMoYFDDmGURvKV0ZsP9rGehd/8ioIwX77MPY4i0YK+KQNNYqWlkAE+I/IHxHGOwO5MJXojuoBQSRlm3vrBsDcYSjkMcZmT/YKAtpVOPIK+K0s0pgNGaeezmzNhJ6gnMga94DN0TCtRfwYzmrCtkZhAg5NaRjBuvI4FxQ4lxbBpopHTZGgQcoVP9AvQyBfyeMVgq55r11jPYPsB8IDgDrcW8QQcZp0k3V7j3yYLGyUxEt5QySDc9hmuMoX2JckSjIBj+//43oS+ME3R1SLdfJ33oEo0WfOxjH3OahWPrDHKsBp7Xc8011zjNXtzRiSFCpAMGNhZiCJG3zhokqhCCgE0OwcMGgSG4lJEcbNx412lUC4h2scgE9wZHRH6q5rQhRg/I4CH6xS3SmSc4KijhBunFMEtkDKSeNFg3ueLvMEgTBcPvSglIeSkNwcyxoNYYRAdiw10Dlrnc3LCTVq3cMBB9kpjTjTW7FvSe8twhDRjliZxKBUr9WZ8j1mOECu8hRHA6ecstcb+Ihs3XGYxBhfW3LPDqS4rcfZv0wjPq7Ohwro9sEiISV6xYkdFXMI7d88y7lxWijmigqKGm/djBMbpte4fGjW1KNPUdDWAP9qsbPrA3jxZcdNFF+r//+7+yavzKuXBOF154YalPJUQFgXWcNZm1lww9d6lJ9jZv36QggNGJYCWcGxjnSu3oRjdQjgtgbLZMDLiPaQzu0znnnFMV/QdC5Af4HU4Lb2k2eBD81vqDMa7hlpTaQWu4nQfoEAxWaJUgyrvmo/XT8d9KAr2CcD668dFjjtG+a9cqEo+rJhJxXodMnKhTC9CnzoAxnkwIHAc4wFKBscH6ixbhhd2F9QiNQeCQX68N9vsgnFFolGx7eRQEHW2KLHlSkZcfl3Zsc3pqYCvFaYNWz1SDpvocmq/8ndI808Q1bN26Q+PHk7GDg24U9JtCR1O5xPvetuCrNpQzKFWNr+Cvf/1roN8buEuM5oKXXDJ6vE0hggGbnTUgY8F2RxRhYKPXBhGDhWgmzOZK2l+po9VxWNxzzz2DqZ1u4NGHXOLNDxECIBK80apEwWAkzqb2KIQUYQKxhFylagRYyDRd611QDcBZRMkp93M4cOG7df+Dt2nB3BmKRKKqj85QbbRwJR4QlAhQhCeGeYw3yaKVKAGI4Z5nj5OLMUEGEMDZy7jCuWHAMMRaTIQraxOlu2zdTjZWSUn3G1u8x5pHuYKS1vK++vfSbdcP/jjjyGO0+b0fcsYl84Jr4xxxWlnGBvfTe03cP3d/GwN/z71ibJQ12Henvlva9LDatq1Xc1ODNH5/qXnusFraFVF3PRfwfLyOJ6ISPRk91Q7Kz2G8uvvuu3XaaaepHHDXXXc5604mpfFChHDDyqAQQc46bJmLRJxT7x2Dvjd7MQiwVxCkZPtpqWDcCk7APnbccccN/o59nNru7E2FcPCEqDxgcPXLhmPvZyxlGvnOvCKKnzmHgboU48v66eDM8zZmrkRYz1F3lPO4MWN024c+pB8//ri2zpih3caM0Rd23131BdRzZKD/6U9/0kc+8hHnXJKB9YUys/TbwIkBrCoFwKDP+kMgntl9sHWgS3EI83vGY6rvZ11P1j8DxzI8nntVqhLH2rpRuuYXUmfbgIhq0L7v/Sc99+KLzr3g3HH+cX7wLoLIsEl5bV7ch2SlpQik429Ldo0ZIqLpA301lmnp0pU69FCygRcpooizvjBGcNyxdlQdmI+z50trViScGYAxv6BK9VQKXHDBBU7m9Re+8AWVpVODVFtSyoj0CBEiGxDlYWQD0oNX38gP5AkyhFffxGw8HlNvvF1R1ak2ml8zMiIM2JxLDWvyy8buJl5WQgZh5C5pE2L0AgOpX2YRxuVsCQ0RP5AnyCR1lxG2hXAepoNlaZVDj498wb3E6OttsDhj2k7q3jGtaPWqIYaXX365Y2RPlj1AajZjBlLN/k1Em7skEwKKqAp3E3AiqHCS8DsELjWUUz03xiVOnne9612+jg3GH8ct2fNf/vYwh4aDRx/QuqmztctpZzo/mqMCAYXg4MV8YU12R7YmcwpybdznioiCrRsrzTxVS5Y/ogNO+ECimR3NJ9vbHeHE+sDztxrAVYWjjpI+8QnpiisSP9Ps9Ic/pI6ARhMYx4gOsq/LxanBuXBOpXC8h6gOpwZrlztql/2GgCn2Pni2GXK7+7vUF+9VY02zaiK570kWoV6K8opeUO6FAAX2Yfc+xLXDKauFf4XIHxiTCTZyg/lBUEc22abW04BxhTMNA3XQ/WsyAUZyHCvV4NQwQz3BnlyP9c2bM22a3j91qo4okp2AtZRgKJ5rsoAmnj1Z3zg0sPMwhhgDbj3Cv93lcfku7B38HwO+NZ1PxZ0tcyWZYwP+jtbxCzgqCh64IVFmyNDXo83X/05TT/+o8/x4cQ+YW2gM9iruE+u1+5zRXsmM/dwz6xdV7ohoJ3V1z1BdXYtqaw4f1jMNzYy9j0j+qsRH/ln6fz+Qtg5kf++5n3Ty2RptOPfcc/WpT33KcWiyngWBaNCC4+STT66MBmN5YmvPVr22Y6ne3PGGOvpcC1WInEAWAiSKSArgTcWDEGH4wpDSE9uhlZ0PaXXnY1rZ+aA2dr2Sc+kYa+aXrnZ8sYCg8pIuohXYxMq+wWyIooBxQCSDu0mflZ3yipBMwLzCAE95AvopYLSEfBYbXA8RkzgCIGeVDgy+pGe7y1Bxn8mIKdZcxulAqRbL2PAC5zEiw0QmRvdMSi2xXlInnM8iFtMZQhhjEHaitQyU2kMM2QvjT6YlngLHxsS+40Z/LK7OdWtHGFBZi7lPrNVwHa/QSvZseRZlX3rKBfbi+sYW1Q04NCyjBwMg117V+xHZxvTW+N3vpNtuk971Lo1GvP/979d1112XMkqyWOAcrr/+euecqh398X6t7nxHb7ct1YauNdU914q4H2Nc416y13j7aBDMgUEFTbCsbYme3nq/nt/2iJ7ecr929ObOR+BlhcgAyQXsZUQre/UOcwvdYforxOgG44CMU7ctB06O/kZjZONUxqgN7+V1+OGHO1qfiHS4cTEBT4ODMR8xblfDmkpAjWU+GHCeFrPU3YknnuhwYmw2fiXGOD+eO/eedeaEE05wdF6q+8+zIliKDGnGC9+fLhiIShcYR61MLlzbrS8IukbvlAytm6S4q4RvPK7W9etGrMVoKadccXPzYM9BNwhyxcHjh4oImBoAz//JJ57SvvssHHyPMcS1cc1VmaVhmDZT+tp/Sl/6nvT//Vi65CtSXXnYIIsJ9peTTjrJ8R0EhUCdGtTGIoqq2rGuc62e3PK4lre/o2Xtb+nRzQ+rrddTIy1EVmBhJ3oIY6o1qcX45xUdOD6Wrr5f/fGhzbOtf7V29K3Mmbwl8+yXCyCRRDpX0oYVonBAeOOAsChpSBzGYqLrgyihhqEaYsH88/ZRKCSY9xhMMQBggEdAVTpIK8ZJY1k1XCORNGQtFMuAwJhgbUVIIDCIgLEXIoBsDhOtiBLEJ+SfMRWk8ESksI5ZpBbRsiZ2ebG+ux11RcWskY3KaqIRHXjc8Y4g8gPi2J3RxHMmBT+Vg4d7zPeVTQ+RJKDHDtfizmBkLWAPsvrEpcjmKioILth3XyINNFrBekym7O23317qU3HOgXPBeFHtDo2Xtj2ud9qXam3Xcr3R9qLeahtyBofIDewtlGFk74U/4eBw8xv2Zta72x68WWs6lw++T7bGq9ufVcxtkMoCGPLKvRwI87xaDL0h8gPcG1545JFHDr4HV4W7EDFuPCBT8Fn3PGMuwPVKYWQmeIe5T9YxFRqKOd699yEIoNPgzu5gIYKHyI6B3xfj+rguoqwJcmKPdmsM/s3vzEAN/0djUPKPLBOcL0GeI+OKMrncZ8pNsea7NUZJy1ZOmSVFXCbXSFS77r2Po4f8nEHWp8ZKGNMrgzWaILlkOp97ydy1ksDlChxODz30kDMO3M4L1oR8msJXFHBizN1Jmj47UX5qlOKCgWzwsnNqEGHJwnrGGWeo2rF0R8LYHh/4DxHyZpu/4SNEdkCwsngTTQSJ8ka2HnzoIi155bUR73f1t+Z0qzGclrtTA3COYT+NEIxXCI17LGBchagni97IBZR7s/JJpWiwxvxPV0e1EmDRYW5DN4Y5UrERHsXMSMF5QWN5orvsRTkKE6iQZev3wGeJkCIy/7HHHkvbBDBTcEwc0wiaTGsyF82p8cGPDX/vzPcqute+g6n9biBESJnHAGARvzjmeQ9xlwwITutFUcrGmamA04VxiaEr2V7J9Ve9UyOEszacffbZuummm0p+N2688cZR0cR4U/datfcngqTQF2B99yp19I3MtAuRHazMCXoVrUHEuBvwqinzxuud14ZnDPbGe9Qdy56LVIqTgMw7ooLDfhohnnvuOSdj20DGBsEtVpKUMZ1NpgZODKLL3WANR7PA7zHCFhuUNMJ4iuG7WIAfEqwZNNB97nWGZwN/5/q8WRyFBA4vnMaMHdMX/NtdopR1BiM26yyBVjjLcH6gMYLQQgROMU5xXMPNy6qU3rFnS+NdTc+bx0gnXuCco995cj+4dwQYAeYQ15ZKX+DE4fc8C+xo5Qh0Dw4N9IW3JDLzI1m/kBDViTPOOMPhYUFVaQisp8Ytt9ziNAIdDYbXntjIjak7Vp4GikoEGyG1ylnwiORwG79qIvXa94DdteTFZdrvAKsdGHHezwWQNT+jVTEAocOQSM1EFnLICQZQNiXvObnr2YcYvfBGUEFmGSuMG0u9DYoU8L00+ON7GZvFdP5RmojsBgidiSgcHea4gfxgfEUcYVyl0XS5IhmxxqEAmWffLAZYV3AoJKtd6XUU49jiBYgYInMH0RLEmELQIrgg6bmUTCsITj5T2v9Aae0qaeoMaW5CPDD2GG/uNHGEGdkWzDXImEUX+TXWdMPmJs8CRxH3IlPhxfPBgYnxqRAZLcwzssBICXbXNzZwbUY8szVshKhcnH766frgBz/o7C2leuYc+9Zbb9XVV1+takevj75w3o8HbxAbjUCjsn7BJfwMaTNnzNbzrz+rnu4e1TcMrfl1kex1grd2fLHnDMeHQ2FIYt+AJ7K+W/SvGwRahBjdgHejt93rPBqcQAyMxIwla1KdjVMjWWYn3I/vxagFhyqWFmduwN+4TqLajd8QyGNOc3Q56wM/w1PzLYfDtWFvCDL4DPB9fhnVrDvcc7IhMPYXGnbfWFv9Ag/IPHCXM4P3ogPsbwh0QtvmOwYYv7wYq48++qhjTyoL50bLOOnDX5RWv50oQzVzJ6mhUfPHTXK0rDdDgWfHvGCO8Hx5ngQTpSrvz9w08Ayy3X9wMvGc0IiFyC7EQYru4Tn7lXxnHPAqpV0uRHHB2oQNBH5/CWV/y8WpcfPNN4+KLA0wvm6CWnu3DUZRgYn1hd80RgsgGrZJe6N5I5Go5k5aqGVaqY72LjW3NKomUqfxdUONbTOFLaDFBMcjOoTFneuEXLGBsPFwzRjPiJRhQScVvtqjEkNkB0iLe0xAuhHpOAAYWxYFHiQYn4gdDKoQr0KOSQy3pNhyTRh8DcwT3kdMMXdwBhLpwblA9DC4Y4Avq+j/AUDc/Qg785/r9NYuLhQ4NgKI+2iAuHLPGFepUuMh1ohbhF4Qzi2OB5GxTKCSNe/zYubsxMsFa4Ro50hZJl6MPRxt3BfGbTb9Mnj2jAvWerJn/IC4IDrLmpKzP/A3vMcz5Hg4R/IR26wbZAxZWTCcGcnGIhli1rwxa5CV8vOfS/fdh0dI+tjHCNHJ+bxDFA9EVDLmaMSZbKwWGjjbCAIplgO4lBhbNzIwLKqommvCzKigQBAEe7LfeJrZOE/7LdxXL7/4ivY9aC9H581r3k210eyNLBjWgmqAmSlYxzGGsccwZ4j2ZZ/AsMeewb6CIRcuF2ZmhHADfufXaBiuw+/gH9nyf5xp7r5yXmC8peQsupi+LoXmwox/OBvX6TYCYwBmXhBACC8jgAeuC9+BI6HNKV/l/ptyytTASeMNduG+FjNwinvnLtfKGoQDFYcRGjJZUBRjCueDnWsQTgjWeMYS6zwcpiyM5LX10vzhzgvL1ICL29iiNKLpQxxqlGUiICqb9RpbAGOd+249E91gLDKnAdoCjYEdCt1N8JIF2+YbUMvxyRrBecn1vfvd7066hrBXoa2sr0ZWWPO29OiNUvt2ado86ZhzpebRWzq2koDvAB9C2Tg1MErcc889+slPfqJKgJVOyNU4t/+EhXpmy1Nq70+UZZnaME27jAnemDiawSaUDOPr5uvdB56jZ154TAcdtK/G1s1WTST76HQMNMWIYHCD6GQ2GIyJJnioQ0ldOauTyMYFCSRFj8WdKCtSOcO0vNENyIE32olxAjnPts5ttmBMMj4pMQhxLkQUB1EiRBVxPV5SC8FDhEOYET6QMLteCDNkmfuDcwMhUqroSD9AziDsjzzyiGMUdD9DzpsasMzvYsDrkGDvZh3iflJGyU2s/fqD0JeDz6ZKgc4GiK4777zTeebl6sD1RqdzntxHnGiU5+KZkgGTrbGf9dyPuDMPLHILp4n1sTAw3nkhWBAmuTg1cCZZWQkcGQinTMQ655RTLeof/EC69Va8k4mfv/WthHPjhBOyPvcQxQXGgFNOOcURHaVyanBszqEsDBNpwBrKvMrV2Ti+bpIWNO+hdzoSpVGiqtGe4xapLjr6GkkWChhP3GVR3OA+HzL9GG1pbtfU2tma3DxNk+pzC5Zgfy1mPw2c0+xJ8Amy3dk/MFBhsCUzFMCNMHihRcjCxdEBv/MzZocYXWD8uHWmO/APHpQLR4O7pOslwXfDhckIZkwWwhHIukwwCLzNz7jOvDDnDYZ4sw3wHpqHecK5cT38nO29wKmBYbcQoJQeDhm4mTvLlvuaqxMmF2CvcK8j3DN0GVyV+4XjJdmz5W8J5HzggQd0/PHHB3I+rL0Y6bkv5dyLC8cBxnx3iTfWcII5WLtN32fTk4XP49Sx8rjeecD3WolZb7Ck6btk/QTTAV1y3333OTYDngE2L5yb6eYM14/di7ni7ueXFlvXSzf9JpEBw3q1/FXppt9K539OipZBlk6ItE6Nb3zjG07wFOMkHwSy2jF4ieSshAYv3LTf/va3+uQnP5m2VEQyNNU06cgp71Jnf4eikRo1RhvL1iBTrZjQPEtja+ZqQn3u5MccBsUAm/tdd93lLPIYBtnYrYzQqaeeOlizHjHMYg6pshRx3qNG5OGHH553CmyIygQEBEOku0SART6Y0bPQTb0hGhBEokYYh0GVo2J8Q7IY75aOnAyWieLXZBthzgthhDCxhufZAENvocC9u/fee3XmmWcO7helLuED4bToLtYonm26rIkgozsR0YylrPZPGsivW0vjFWlqcTJzIOmQbZ4X53zsscc6502WC4INZ1wuEU3MXxwi/C33gLnA9+MEQ6Smutc4RIhuxDGUaWQbAgfHJM8dkcffZeMsZ47SWyOrjCgyWG67bcihAXjeODlCp0bFlKC67LLL9C2cUSUA5W0/97nPqRLAOgGvxAmTK2Y376RpjbPVE+tWY02TaiLFM0yFSDg29p69v8b2jtWk+pGlmjJFMXUhBlcyNNAYL7zwgrO+s0dhKOJ9axxsQSDoCQPGK/YF66kVYvSB7ATGipuTwi+KmWlUqHJUcBZ4Fdw2FVdi7vCykm1ubsTfYbDnfe4LwUk4DTMF1wJX9Cu7EwRM+6Cl3I4F1qBkJaEKDe4ZQWi8AJw2VRlLeGm+Rk0vGEvuHjFpgU1o6VLEGVFnkHQVGowJKiFYoJI5oljDsf0QTML8zFZzo+kY92Ys5t7zHsfDqc/7qQIA+RxzJ1kAgBeMM5zoOK/QSOiNbDMueFbo5KyCeJe9nHBmWLAVzo0t66Qt6xMN2kOUNbD7MMbwJaA18kEgs5VISwh8oRbNp7as1d3rlyumuN49Za6Onpr5RuIFE/uzn/2ss2iQgogAwUuUrTc7GomqpTZMBy81vLXOswGEP4jICRZysj5Y/NnE+Zm074ULFw4zXhLpzM/uup3AvenjRWcTZjPguzAeQTQxZhPpzSaXqzMuROWCDASMl5AeDJ+8MMB7m/nmAm/qazoQdcHaCQlDJORjmCctFYcNm1pQBn6EEeSPlHHuE6KsmNFKycD1EQHjbrhcTs1EGQepngHnCQkOMvIUAcnzYs1zlxtLiqv/JP3PbxP/5lw/+wXptODLGDG3EIe2TjOOyB5kXUcUmHOZsUXdXtZkvzrl6cDaTjSvNazkuHwn99mibJM5S9j3WBMY5+wXzCXLAkJI8D18hmwunIBkqPIzkXz5ZP2xj2UF7qEfNwwDQSoGJ510kj760Y86UZfFLlnD+EXQcw6FQFtfj/7wzlK907Fds5rG6ML5e2pife51zxHzFmAAZ7OSDrkY1sPsjNKBPZp9KZd13UCkLOM3n++w/YdIXtZ51nDWcvZNt3MZo4DpC3fwE/s6WsTAXmANhNnLCK4iWATnOgFXWUXIhqgaYPykZA/8FF1t/R8KZYTPpBwV49v6uuUCxreVtcom6NaqJfg5LeBO6BXuF7qMz2Yyv62nRiHBmoDjwO3UYP8pRVUKP6AvUmkMnlXQwdHYTnAMkPmQ1mFCc+2LL6aJYOJn7uOvfgWhVtAgkBWe765EwJ5jTijAvxlf1usm27JczF/4PjqCvcO+m+9Bi+LIRoMls92iPdD6aCH0hWlE+3vOCScfQYScI+sGfwMHArnoDNab7ANCOH8fHR1qjIoA449nji+hbJwaP/zhD1UIPL55jX782pPOkAXPbl2vzv4+nTQj+x4KBjNwscCx4FO/jkiV888/v6ipwiHyA4sxBqF8SFc+xk6ic9mEmZAQBgihiVmEA4u8kR0rWZLJ+bhFBWPTSoOwOZNSzqaEcLFUxRDVD6tJC0mAHPNi/LvTQ618WbZgzYNcZfP3CA0+T/YI49CvCWAqMIbJCsAYnAuJ5ZxTRZC4U8Y5DiQMI286x3uh6/myTmBQcK8FnJM3KqwUQKSlcphS77YQkZw8FwwqRJea8QVnM6Sf9X3x4sWJD776ypBDA7DW/vwn0sJF0uzcAx38gPMQ4eqNYvJmyvHsIPms9bmCscyxcJhgrMKxwXhANCAkUjkcGdeIHoy+1Kq1vRBHB5G7fAfCG+dnybgNIvass6S//90VSRVPvBeiIgDfwDBPmVlKZRYTHBNhboEfQaI3FtOXX3hEb7dvd4KmXty2Wc9u3aBfHXicmmtzixJ2l4tjbrKuXnXVVc4+h2ElzOquDLAf52uEZG9jbc7FqYGWIHCEoA/WdQysfI8Zjihb6XZqsDd5a+r7wR1EBX80ncJeAa/ESc//mXNBNzUOUZ5gTMEX4H9wDV6MAbiHaQzr21eM9SuIclRwKYy5cKtsjcHMJYy57HvJuJdldWRa9pZr4veF1hjMWQzYxlWxS6DTSlU6MlNYD7mgnS9mOyGA2cr/Mo4x1OMoZmwN2lK+852EY8OwbJn0059K3/62ggbXmkkGCed200036bjjjsvpOMxXKydFBoX1cwU4v9DHqXpxMl6ZR4xv683BWnD//fc72oxea7zPnlSyhuy77i89e4/U35fQFqxRU2ZLE4PnjCEKgxNPPFFf/epX8/6evJ0apOmx6eU64dLh+tWJpqZuH9x1q1/Py6lhYNHnvK2m6HXXXecsMmFt0coAjoMgvPq5EDVIIFFcNLXyRh7wfRjo/JozZQtEC9F+GJG5VtsE77jjDsfQzWZUDhEYIQoLIpcgqrlEfKYDxk6Ec7ZOEUQP54PowHCTaWoshAixDpnMNdUYZwbf4c168gKSRTZJrinjQcMysNzAiYmzstRNcFlPkhlFIMKsM4XqVWKNTHH4YMxhb6YGuGWtOQLtLZ/6rhDYt5cF7tSwCNtMrpfMBUoL5gMTWYgu93hmDyGainlizmy344k9EKFyzDHHDPs7zhuHEBF6iKKSB2t86Us0AxlqFP6RjxBCV9pzCpEVyJRgnBfbqcExC5Wl8WLrJr3V3jr4M46NdV0demLLeh03Lf81xfgpBuJbb73V0Rjnnntu3t8bovDAEJjK2JMJ8ikLiobAIAn38wKDaxBrOvs9Ucx8F7oXDseLgBFKgLAvc/zQEVfdwPDLWMORUE4ljnMtR0XkOJw/nyAc5j7fk66saBBlb4M2lrsdMXBH7hscvtAOlXyAHfGII44oyHezDhP0Q48K1mM0KOsdLzTHoFODslOUSzXw7yVLCnJO2B0zAfOR8ZRv0BvjE43v1ng4yxkb5rhk3LiDZdEcBNaiQ9wOdD5nmsQqipQU46dIZ10iPXaL1LZNmjFfOuqsREBViIoAtnhzNOZjo4kGITgwtBbK2NEd6/N5L7PFIFMwKdkw8eYywculHEiI9PU/8yXbEBfrZ5ENmHjJSuaQwp1LdIgfrPcGx8G4ZTj55JMdAxgedIy7IaobkArIGM6HoAFp8jYTy3aM8vc4+tKB8yc9GqGcb+1U5j/flQksZZx9iqgqooJKAcipd3+BWCLeiLwsdHp6KnBefusZpB9Rma7XRr6AyMAlEDY4eiDOEHCyOBwk659RgL4aOMAyzYTDIIRjI9Ox6AUCi7XdrxEnewjfzT6FAGPfMbAecExqo3v/DgcUIpa9rSyc3ojsz3xG+tvfpD/+UbLsmxAVFUkF3y8mP+ZYHJNjFwI9SbRENxF/Aa/7ND/FuWElJ0KUL1hbyXYLwgho5UKzBXzKr9Qbzmo4TNZlAH3A/oJD/IADDnAib91BI6eddprj4OD9YvUeDFEawD0oGYOuLjdYOSqCpzLZL+BE2HEyyVpKx+vI9sjU+AyHR2PgEIQ/wutKAcu0cYO14sUXX/TtRVgssAb6Zb3AU1ljWOsK6TxljBMohcYggAxDPMcbdkwy2NwaiH9n2E8iG6A/s7GZUpKHjIhc9SHHQ0f5OcJxXKMveMFTqEBiYC4xj/ycFuhBsmgznR8Fx7S5CcfGP/yrdMIHpMZge7OEKCwomUwmc74BgoE4NQolOMDhk4ZH/0Z83gsCtrhhNCByJXRslDdYnIOIVEK0IB6yFZpE8foJDqJKGEdBeq7ZkBiTnCPN/ngR2c2LkiVmKMWZQiQCjg4iRvgc0V6lIlcjsGa5dOs10q1XSyvfKvXZVBwgEURlF4Ls5bveQXwQHRCnZN9FpAiECYGSb/+M/ni/3t6xXKv712pN59qMz9+aZHJ8DAM4WXAKEqmSj2Mn33uNAR1BxDwv9nzlfHhufoIC4w7vE+VUitRiIovIAnJwyGHSke9K/NvGz+lnSrsHV4OXCNgHH3zQeRbZlCbEsYfgwPmTjQGIzzL+GJNkYSCI0zkgTXSwtvsZtniejG3OH+GEswzhGCJEPiDoB6NIrs67XMCx4D/0sykE9ho7SS01tYNCiBWwPhrVogn59UDwA3ORdZY1wu2cDFF+YJ9GGwSx59EHD26eDeAAyTgSjmoc/0EaAG+//XYnuMKtL/g/889KnqJ5eA8HPBmAXBM/UyKxHBCPx7Sjd7m29CzR9t53FI+XibGtQgBfCKqvXdDgvAjOgNfg1POD8S8yTvLtYQO6+7erPb5G2/uXqzeWmTawsrcYiNFqlPZhz0Rf8CpGFgzGQbdh2s4L5yVz2F2yuFjAOcSzg+N6wfmgLwodNJUMw9bRL3+ZEgQJfcH7BN59/vOBHQs9xbqJjcYvAy8Z2IdY89GH8KFM9S6fwzkP+Pt0jkHKEloQGXYxK8HmBbqZ78IGx7mRzREiRL7Al0A7i5KVn2KC4qn79Kc/rULhvLl7qL2/V3eue8cpQXX0lDn6yE7Bl2AxoxviCdLGRuQ3mUMUFvF4t+KiPnlcEeG590/jhDRQ+zUIsctmykKOYSkT4Jk2Y58XiNVMy/BkCpwnF1100TCBxfHdBBSRAWGxdFsiRUjnggDi6ECwEP1csjTyZUul3/1sqJDcY/dIH/6stPt+KinatkjbN0iNY6SJs8u6sRREAsJajmAsYgjGMWBNzY1UsZYyPhnHRDPli1g8psc2PanX3n5Nk6ZN1jNbn9f85rnaf0Lm+4KljOP845yJVCwWuCd+Je+ISIN4sq5B8gsNnAUY0TkPUuz9Uua960yxMewecR7f+Lb00APUF8ATJB1+ZCBzlueBgYZngPE2l3WStRfnAb1HrFFequNhOEJ883ccjxKDREoyHv1qSLO38CxsDUB0+EV7YfTie20/Q+Aiosq59ECI8gcGGSIc4fxBN/NMhrvvvtvheIUyBk2ob9AP9z9KP1z6tNZ0tmtKQ5O+uMeBmtEUfLk21gbWWbjrAw88UNIyiKMVrLvdsc3qi7erNtKshmjy6GDW2SCafDN2sy1BhQaFTyUz3gYdYEB5N4zadi/cjWoNGGhxbjCG+Td7JRnEGCXZc5incIhSaAzOd2P3c86ztaaxnf0bNK2Bco4lNNT39kivLUn8f5c9pHGFqWgRFArJ9fLtx0GgIHORsnCMO+OrBIZQBh1dTjncIMZfe98Gret6Xjv612hLT1xbe97WnObDVB/NrHcg85PMWs6N8tFo8WLNC5w66H8v3+P43B/WFmxdQWixVGDNg3fCU1kryFJMdg9KWd6ONY1zdPrR0c/0mmukBx7gIVITJ5G9EQBw/lJeh0CkXCrb4FxmjcWeA9IFzprzijljx8MJwftoX68tgflJNo85l3Ba+DleGNN8jgoitg/BZ4rVbydE9WLx4sW6/PLL8xpLeTk18IoTPU7KSKFQE4nqYzvtr48u2M8xh0YLOGlY2DBIEM1biKaEIVIjHm9XTE8Rp5T4WTWKxg9SJDLOl3xh+IEwkFKYT7NwooFZpDMBmRhsKkRf+QGDVLZNk9PBT8B4yScbFMY4CAtNYS2ahU2MdEtEyP/93/855CqIKJascff1Ujw21CiWeXzHtaV1aix/QXrh9iFHy5x9pQNOL1vHBs+4XKLiUgl41lGIE9E5jD/W0iAdxGRmbO3dpr7eftXWJ7aw5R0rtaBlvsbVJW907QcckMUuB4KYwJniJyoQapR+oPmgXyZYvmB94thEgrLuQWBTkQc+X+rSE8MMQqx7xwTfv4v1H2MjjoV8gJDkGTKmUpVXIwKK43mFJ861ZJF0PAd3TelkhjKeGWPIgJOxULWKQ4wunHDCCY5T41Of+lRRjsexOGYhsfvYifqfQ05UjPJ7Bdz7MboRPIOhvJhO9BBDaO1dqvb+lYM/N0VnamK9vyEUpwJ6EC6daSnCZGCdJ7gjna60LDvOx907yT2GMu0rkA283+l3PyhzyP1A38AbcJbzOYy3GFLRRex7RITno8dyQXds64BDQ4N8vie2TV39m9RUW6J67+1t0k++Ja1ekfi5iajvr0kL8uvTUkgUyiiJQRZdkG9fAM4P/QpHgmcR6AVnIzsiSEffpu6lw8ZSXP3a0v2mZjQtyup7uF7sFPDtQszbZBqMCg7JQBAN87gQTg04KfsbQWzYKFgXUvUXwUHLPYIPF0LvZAIr1TqoUQk2+Id/CPQYcHfGLDaafMAYwtGAcySdU4O9xNsf2BqGcy5epwa6wT3/0e9+PaXQpuyHNt+4d1bKK0SIfICt0noo4QTNBdF8BQcRpcUgMEyYQgoOgBcbsRGUtz9EdojptUGHRgL9imlpSoMkfSXYoAdLlOQIFmlS6DBuuXsDsDFA3kkZ5PdsJnjLvYKDz0Fc2Bjy7RXg/d5UvQqIhOCcIHeQA8gEm463dAqRLWeeeaaTuohosu9GhECACl5urX37kEMjcXCpfYdKhq426UWXQwOsellayxgsT/B8S21gzgRkBEGGmCOI36D7LXXHurN6v9xAhK6lBPsBEcSczgcYP4i0sbR3e0FAMdIQaY3hO9k+Zw3iSKEvVVq4AfJcyJJciDDLlAgCONnJwki1prIOZ9vrgjWccyQKj+fIGs96nwqsFyPqBgcEnkkpe8CEKD5wMNx3331FqaPMMThWoZ0ahkLrC4w2aAuyXco147Ka0RNrHebQAJ2xteqO+ZfmY80kGhVDHfXM84GVo6E/lbfEIGsofIDf8cIB4ldWkPWWtT9ZBkeu4PjJHOT8jjKHnDM6BN5ARiPX4nbQYLjkXi1cuNAJNDPNwv/RRIUuqRmL+2ukfpVwf7rxr9JaV5k5DM1XXqZyhmURBw2Cm5KVjcoWGFBZR5lTaAu3gTUo9Md71Nvbp9raoe/ti+emL7CPFZsnoRUxDqb6fb7ldnFEePUF6xjGcNYwDOqpHBoE6LHe4YQpZd83HC9k+hQS6IGggr8JRsOxnKqEJeMt23mM3QpDMnqIsoPsM5y33/zjMwYCZrmHQcNsX9lmOYaoXLBW4lPAt5Arass9iqqYCMqoESJX+G2yySMObHPG+02jKTzYuUaEY2jkBfGi/AtkCWM/ZIl/s8An26BZdEm/IwX7ve99r4ICQoD6chgfrdkgmw6bmgHjJ+f20EMPOZ83wzfnDclgs2ahwADGefI7izrDUWNkEMcNxkt3hG+g2GlPactGV6ZGVNo5WHGWFdq3Dney2DntoPRZiHLG+LqRTpKoohpbm12WRqmAsYQ5iPiH6HojuJiTEEzmZC5kH0cnaxfOEb++EKwH6TJBMJYjTErt0ABE4SGW/KKG8gWkmXIGQZb74vkhunGQY+TxAgc0zzwbRwMig88TacV6z7rPMTAwYUxyjyErzcjnWfMLlUmLwZkxgjjGSUM9VL/o4hDVA7JAcTbAHfh3IUEpNoRtoY9TLBC5WjB+FSIt+pLUxe+PpzbusY+y/jIeiXLOJYiPtZhMB9ZjohDJ2mDdZEywdhOgxTqdbE9gT0dvo2+yqcWeDjhrmGOcF/qB9Zt9xcB5Mt9xatieYqXn+Dt0F7oIXYFxEo3CZ0xX3HvvvY6u4vcEUOUbqZwM9dHxg2Wn3Ghw3i8R1q5EHA79TKb6xnUJ3VGmQZOM7SAyKvycGoylSimB2RAdp03b3tH4iUN8prFmQl7ljYIMdkwH7AQYpFlXLDrf7/dwx2zBugUfRz8k4+T0iUtXEYKsxUL1ysoGrFWFDNJAX6Bhggz+Zg1GP+Jg8PtebFEEF2YKnBToEuYp44VnjNOEtZtn6XakM57Zt9gP2DP4fNDVSQD2K3Qq+4nZ4gq1f4Qov2zwz+fYyyZnpwaDGUPud7/73Vy/IkQID8YOODGMmEL80i+WLLCUVSJ6iOhiNigybnIhESzqRNqygPo5MSDt1kgJ4ASBsLPok84ZZEon52BOGjYvSCFNX48//vjBz0AKMJ4hmFj4ERYIECJ5uRbKjvA9ZvTi3pjg4LOW4cLvCxotcer7pG2bpTdfSfw8bxfpzGBTPLNCs4/YQXS0lC5iJERmmNIwWXuN3V1rtGbQoXHgxEVqrAlWiBUSrBMIvOuvv17nnXfeCGMGkZrsr5C4bKLQIIGsW9S9TgbEBkTVz6lBlBUkHAME62A5gPOE4Abt1GAtRBgg7ILOZCBAAkOYX21QnOTUNE5WN5T3EBVEQFnzTtZ//g6DGHsN94J/Ywgj2s0tDjk21wRHy6bZeTbg3HGuY3Dm3zwf9sHQqVHdYDzRLwbRUWhnAwZRjhV0FG6I0Ym6qH+flLoM6uTj3CfyGWMR+wbrXKY9+NxgLWdvJ8qZ/d9v/Yfnsw8b4PLs2RiycjFCpgLGNjIxyALEsEV2J1G3ll2LUQuDLHsJZaVY41kD2HO4H+gOfofW4FrMQWJzFl0Bl+B36cql5IPaaJMm1y/Ulp4XFReOhKgm1e+T0bMtGKbNlN54dcixwbOeNKVsHRrFcJZUCqY17qdlbW9r0pSEDaExOkmT6nepmGtnjqL9CXbEZuHlzsxL5iPOiWxL6/E3cNBUnJm1jTXOL1CYShGsb8Uqx5UJMMpzTkHzV2xS3KdClPoiYAod55fVhyMeO1Cq4xLwiv3HnBSMEfYAHOjoRwueZf/x9pbCYeJd64MGgXWUI+LcOI5f1kiI6nRqfPvb385Zv+aseIla4YB+EypEiFwQ1R6KCTJvkVMNiioz4cDGwUJu9SsffvhhZ2zmEhkCQU+VMgnRN4cHhicim4h4CrpOMtdE7w4cJ0Rts8B7QUSN1/jINXM+bDzeDcf9c8Zp7Bj7X3hMWrtcGjNeOuQ4qTFLh1FDo3TR56UdrQmn1dgJpSX3TeOkfU6QXnGluU3fTZqdvVAtFhDTpWzaXE7Ydewu6p7SpdlT5qiltll10fIhyJkAgwBrB3MXQwJGBe/cx2CNYwPDHg5LPk/ZEitBhuEB0eAeEzg1MpnXVkfWuz6yr2MgL8k4I4Kxs11qaEo06Rvx6+Aa0XE/cYCzpgYdlWggKpv9wi8620qM+WXC4MRBOFoDcTd4z+qN8rc42Enzx9jmrUFaKIeGrUX2/Sba6OdUqrrIIYoHc2p8+ctfLuhxWPvI/gkRIgjURcdqfN0eau0dKjE6tnYX1UcnZGz4Mkcee7Zx/lz2pFRVAdy1zK0cEM6HQvRTgwfgrCAqFyeNX69A9iovMJjCYbyBX+4sDZBxJYfuVmnr61K8VxozTxo7J+traa6dpsaa4xSLdysaaVA0UmJn6HsukF59Sdq0PvFzbZ30kU+rnFGILA1QaeW866JNmtF4gOY2U6I1qrpIc87XgFMjVSmoQgAjMEZtMjX4P/rCe/6sMTgY4KFwSAJT3CWO0QDwOvd4ICM3k/4JfAaDu3edo58f60Mh+/CmRF9v4uWxX3B/CELyy6zOFZQKxBYFZy8E2I+SlXRDJ+KIIDiK4FUvOC+cFN6yUbyHU50xg25hTDA2CKbFyeEeC961Pmiw75kO5f8W7OV3PSGqB/vvv78zrsgGp71AtshZ9VoUaWhkCxEUIhDR+OEU6BjI1pigSA7EFGMLRJ0IJAyBTJIgiJoZIt0bgXmqCxkVi5MllaPFCxb/wKIoMTTe+mfp5ScSTXp5LEuelj7y5YTxMRtAhMaVUS3pXQ6RJs+VWtdLjWOkaTuXdRQVhLBQ0W5WqqaS1vOGmgZNqC9heYE8yC4RQRihud84YDEceIUCmWYYrjFws75gdCDqykgdTgkigSB/GEIwMGRafo962IgOKx+EQRrxxTpTkjGwfpX0119JrVukmlrppPOkg44ZRrZx2KRLac90HccghfOmkIZ/7jGC0evUwHDAvcYBYFFRbuCsStaHhmfDMybCis9glGJs8L4JkWKA++ZO2besSLu2ENULSrV961vfGpb1GTT4btbF73znOwX5/hCjE2Nq56sxOkV98Q7VRAiGyJxXu8F+TaARkdBEOqfKjMwGRKO6y8UYJ2B/L0SJDztGtjXR2YeD2IsddG2V3r5Fig/sJzg3ZhwuTcq+PC2OjGikeGV+UmLceOnrP5Jefo6NUdpjH2lyQPesQCh075NKAs6Mhpr8I/et/FSxAL9FM+AIhc+jC9AJfs5JNAX7LA4O9B980rKu0BrWS4f9GGcHdhQrP5cOGNaJtuf/OIE5L34O0nGQMQjMvOc66al7EzaNmfOl8y5OBFcOVK3It8eIG9ieuG6/0l9BIplWI2iLZ4qu43l5s2JSBb6ZbjFnD99B8BfZedh8i+mgdAeyEUBA0F3JHGIhigI0BdocH0PRnRpEbIUIESQSToz8626yEJKpwOJOeSZ3yniuRjtEBX9P6RB3Wl/Bm2yXEls3JhwawNKoWzdLLz85zOgYCLiPLz8ivfgQVg1pl/2lw09LGDkLhQkzEq9ywbIl0lsvS3UN0sIjpYlTh6WLEiHHeOPfjGkEdRAkA6MkZQJSCee+WEx/WL5UD21ao8aaWl0wdzcdMzV5emshm5pz7ZXaQIxNmwgqA2tJsoh+ssIwnCA2WLfcUSo4JCzTwu5FpmsbY4j0cOqiYqBGaPC83P16iobeHunPl0kdAyU3+vuk266WJs+QFiQEFOKAyI0gDCk4BDBCFdKhYcfx3k/m2GOPPeY8N0g6+1OyNP1kwBmCSMWpwf1AMHI9ZHAwtgpZ5iMVCh25FaI8YLWaEe5+kd1BgLnOeCqJASREVaM22qJa5ebMcIM9GQcf2XK2j2IEyqc3I/OJdZx13R2ZiuaAC1QlNr004NBw6agNz0gT9wg+yKijVXr5dmn7hkS29t4nShOHuFjgaGySDg62ZFheIGPgjpukbVulXXaXjjlh8B67S8zgRCNiG66BcbZYWNvZqjvXL9H2vi7NbpqgU6bvo+Za/yAJzrGQARRBOXgK3bPBC7SEuy8O+gLO6efUAARXEeSDBnTrPwzhbmdttoGbOEewE7JOolMJxuHvSxL08sxD0pOuqgzrVkrXXSld+C+Db1mZoyCCusigKAZ38dP+7B84tU466SSn/xLZOLlmi/D9BNYRzMt3MY4oOViMwDfGD/fRxmQ5lSsLUVgQlM7a8S//MjQ/M0VOI5MFGkMLBw4RopxBlAIlXliI2dypoc6C73VEmKEYoW59Jry/Z8OD9BGh5X6f7/SWj6kadHWMfI+NlDIxQWPpU9KjN0lt26TOHdLLj0qP3qxRg+celv7vV9Lzj0hP3iv9739Im4dKDhDpYunCGC4Zz/xMBke+wECKqEmFX731kv6y8nWt7GzTG23b9N1Xn9Jjm9f6fpYIn0yzBrIFQp/rrsQ55+cAxWBPxH4yEHFjTUWTgd9lQzTZvyGr1Ogm8oUIrJI4NMDm9VL79oRT08C1vL108EfEUL4ik8gzjKWs84WoceuFZVMYyDR55plnnGCQ/r5etbc+pVlTXtH6VddJfa8Nv/4U4DnjgOJ6AGIVIUtEFoYIa7BcSJCOTtSdgb2R6w2dGqMrkqpQ4Ls5RjieQpQ72L/ZR8mOZh2mrCHOai8w0MBbKOnhF5BhUalmRLJyL+htNEdBe96VEv3uPooDiLG3BbyH9fdKT14tbVkp9XZJ2zdKT14jdQztY1WN9jbpa5dKf79auu9O6dc/l6785bCPMO7QFzQNhncyZsnoDSJAiYj/VLxka0+7/vedR7W8Y7O29LTr5dY1+uOKJxRL8jd+Wa5BARtBUPOtHEpvperrYdqPMkOpkI1DAycs4+aUU07RUUcd5fBtnCYly+J9+9WRmRur3kqUohoATmO0QT6Af6Otss18CwLMLTL90HGLFy/Ww089oBdaH9a6ltd0xyt/V3vfSLtWMuCYN03KM+PeMEZwjjz44IPDbGCFAHqXYGR3byn2Qr8+tyGqD8ccc4wzj3JxBucUqkg6EgeziK0QISoBRJyQPkf9WFLpzCCEsYlSLkQTmKDAYIMzxMpWsUHbv92RWBiUIX5shogVNn5rwA2ZMe8/7zNfKq40B9HSRBt1dw0Z3RBkc4Nt2uvgjWc9b8QT7x19tsoWPV1SJCrV5flcubf3X5/4twleCNfjd0mnf9ghLEQuEC3hBj8jQKgH6lefP1NAFlJtIBz/1nXLh8lMqPpt65briMkjMwyYE4UohcO8hDj69RvIFuaoLCZYZ7yRnOaMwGnllynDc7fyPkGAa+Z5B1Y+Il/4lbFjPrDu+IivbM+b5wwRZ11m3GRTyi8f8JwxYnGv7RljqHUi0Pdt1IsvPKCDDtxNdbURKfa2RJZiTWbrKunjRGRxPRh+rSYy+xmCAOdJLqm72dxT91qEIa+Qxysb2B5YBoaKUoIIdcpWXHrppQX5fr6bY4QIUSlgHWY9xqGBXmAdJiOPfQsjKfqBQAzr50SZDwvMsNK21qjWsjLYK4iihks9+eSTjmMfLgBXI+uP6FUzFJP9WehyJwVBy0yp3R2YE5GapiZ4dZBoXSd1ug17cSnWJ21YJi0oTMZZ3oCTt+2Qxo5LBHrkgwfukTasHx48cc/t0lnnSVOnO0ZDgv/cmRkYo+GL8Bi4RT717BmrqTj7K9vXqi/eP6gx4oprbVerNnRv14zG4eU4bR4EDeYS5WEJyAoiQ4XzLAfHPA4qAja9/TcNrFFBZi7j1OBY5eDQcYCWYD3BmWGgx42rCgRrLLwjlzUURyAZ12Q2GccvBrAnkT2BAw5djI2JZ9nd36m3219VY/9uamxp1OoNa7Vk+1NaNOFo1UXT2yrYtwiasqok7EuUM8PRgcGZzEQyUQpVEhHNxL20/RDdT3b6qCg9xfpcLvOmRKCKAWvnkiVLRvSKTIecVjEMwixYhS7fECJEIYBg4MWkQWwgBtwGGlI3iWogAwNhbYZIv7Ie/B7Rwe/wzkMAiWphUXZvbLzHRsB3V1QzVRp8n/dP0t9/mygPA7E+/lxpfu4G9KTwW8jLtcdDd6d061XSioGmk3sdLJ3wvtxLZWFc7/FEQ0HAOhIN5ljck9UyhcQglM3gi0MNgUIUE8ZUnB78nCp9k/GOUy8ZEkJjZMRULJ4Q5BzbnCIIg0I57yBumdZ0zYSIsocVk3gT4eKXYcJ+ynqDUcTbvNvdlDmI4zOWIKhlg4lTpP0Ok156IrEG8Goemyi/5jHk45zItpeXzYViXzPPkQhe5hbz050JU1ezWX19Q/PFQf+6jJ0aXD9jBUOYOTOt2SHzgzWA3xUqm4l1xi3WWVtKlulTDJAldNll0t/+llirTztN+tKXpAI1mS93YPy6jPtRIKAxPv/5zxfs+0OEKBTg/hbwR8AUAR44fG0PZ51EL8A/rEkvxiK/ciXsW6yz8Cv2P74bLcH67+VzrP04szEIVFJvNE3eR+reLrUm6rirYaI0pxBVIJLwvHI1ID1wn/Sf/0HYMhF50je+Le2fR0kbnCOMC2/w0vbt6p80xeGGfmVqGEvoY4JZLZgI3YyDA8cc+oJXur4R6ThsshyOWCzuBA/ixHMHIxWiHBvzjGsKwqHB/eCeYZj1cvpiA42I0Z5IftYir+YJSgPxXNAX/L9sHBrgsMXSq89KsYGBhrY++vRhc5/zRSPYmpwNsBHBiYoN9hnmg2VW2Rxr7d2s8ZPHauumVsX6Y5o0bYL64r3a0bdNk+qnZdV30TII2XusPwzXSiQ9WqwQTjuOx7wxpwkBAtjpympMBY31K6U7/iRt2yiNnSid9AFpduVVowgCjGPWKXRA0ZwaNI4JEaLSJw4bgR8wyjLG8YLj0KCXhhcs7iz6iBETGN669waitDBwXXvttTr33HMrK41u9k7Sp78jtbVKTWPyz0pIhr2PkNYs87xXfKKQEe75q7Ty9aGfX31GGjNBOvK03L4PYjBznrRu1fBoknm7OQZKHAep6mJacy9IAIZ/jJkID2v+xjhP5tToj/dpa88G7YhvUqR/kppqRkZfRCMRHTtltm564WnFenoVbWhQPNavvRomO6WmIIOFdnJzLUERKOYu35WszmwxGp9h7GAd4DxYN4jwoTwShhB31g1iLp/0f/4WgwdjCIMKfVmKbvToaZc2Mrfj0uSdpEZPA8YzL0yM/9XvSGPGSYefKDUPH4fcNww2OJGzyQrgfhaqTEEmYC8Z6eRL3P8331yj6dMHyhxkGZlq48ZgmYGAcc3eVSinBvcTJ71lYxWzZnRJ8D//I/35z0NRrjfcwIOVvvxljUZQaod139tfLAiQScsexjFChKhkEMCULIgJwxmlAjGkJjPWwOHcThJgGeNewBmIMr/nnnt04oknqmLAvjf7KGnGwVKsX6olqroAxqsJM6UxU6T2zUPRsDX10vTsDJhFwbK3pO99OxE1BFpbpf/vK9JVf6GZS27fuefe0nWufdoJHmlRfNZsJwsIbpUMjE84BdmgFskOp2G84QCAV6ZyBLT2tmpb7zat61qnaQ3TFPXhOnuNnaHb33xGO7Zsc3hybUO9xqle63vf0S477exwjUIbNTEOZ2vQTgYiy7ENFJNroxXc9gd3XzecQGQpExhExLv7c0TCu7VJNuDv2K/pLcS/Caopeqk8h5dtwXNHh0hW3uFryLTZ0ke/Ij3zQCJ4cJd9pX1GagjuEVl0/D+frKRigmfmnXs0uZ8+e6reWvKO8/MeCxN2rmgyx24SYE8gIMvuBXOfHn7YExjb6Eo/21gQIBPLGs1ju0AvVy0626TrLk9U/mAsb98qXf8b6cNfkcaV1iFaKlhz+k984hPFcWp85zvfyeVPQ5QZtvZsUWvvVtVF6zSjcZZqImH2jXtRheAQ/eS3cCNGiG5PFQGPQMeLjxDB6IvxN5koKWtEawq/uNIYHFHz0sOJaKJdF0qLyrRvz4rXPTXw49I7r+bu1ABnfTzRU8P6aOx3mHoWHqXbb7zJcYSlA0SDSByMTBDpTAz2fbFevbL9cXXFEoQhooh2G3OAJnqiOSivdpqa1bjwYD3Wukm1ff06ur9BHzy0eKm2yZpp5wIiI0udrcA9JcWX9YD/IzgwFlszaTZ1niPPE8GQLRAYiFXWHQwelspbdLRvkZ75q9Q3UGe8pk464Dxp3PShzyD8Dj0+7Vch0LhfrKmZRr9RSoCxU7Lr90PNAs2YnujttH79Vo0f3yJFk2dKJYM5E8hGQUi65yLZFLmU68oEjEv2vhtvvNEROgiPbJ5JxeGee4av9/yb90apUwOxS9YpWiCTvSkb8J18d7HKxIUoIOiN0LNSivdJdVOk2ipdH3IEzoo77rhjWGNfA3sDXCBVBi3rP4EQGJ3Y59kfy6asZLaoaZBqCqxhDr1AevVeaft6qWm8tNdxUmNhSqjkhZdeHNhvBvYc/k3GxuuvSYflGOi1/4HSP3xU+vPvE4FTY8ZKX/y6nnrpZWf/TrfeoofhGFbaEt2bifF6RccKvbL9lcGfJ9ZN1CGTDlEN5TYHQCT2xndW6tzpC/XMtA3a2tmu5u29es/OB2v+9AI2ci8QzEFQ7OAhdITZI1g7CHRjbeA8OB/+jUODwCD0oekpHFoYq7MNgoF3EthJlQqyzUuWIRan/567HwbXte9wx8bUWdIpH0j7VYxtdBNBqJWKiXVT1dwwRj09vZowaZx6uns1vnmixtVNynoc86KqCfweB71pDOY+z75QYE8kGwRH4/HHH++MtWRByBWPte8kqn8MIp4oP77yDWmf0ZlAcNhhh+mb3/xm1n+XtQWbxi2kl4WZGpWPlR3v6M22pY4hk/qVqzqW66CJR6g2Gjo23BHwpN5CAtj4bUHHSEakIvMBY5nXsGspgURJ77///oPvk0Zb1Sl0+WK3AxKvckd9o2cTikiNedZ4HT9J+vi/STu2SbX1TqQ68d1E3b300kspI6mcM4hEHKNiNpGza7reGnRoANaBt9pf1EF1JwyOU8Y5UeH77bW3SAT83MBnIczFHMsQZrINgjB2Qf5LMQ8Rb9w3QAQmUU1koHBtGCiITCEihWdIJDxN9jhPrpm/zSR6yIgo5axI3QwilT4vvPGA1OfKNOnvk16/Vzo4vcDwA+sxwjrT+qrMCSIMywrReZq30wl64vE7tPNOc7V24wzNnD0r5XjlGtwOdPYWMo5McLAXuYk/TjsaehfKyEU0G/ORcYnzH0cchjXGM/ecsV01e51fIEIlBicUIJKqEE6NUF9UAWLd0o4HpRhlYyJSV1xqPkBqKF3WXLmB9dFp6vrww46j0PZquABZHPweHUH5GDesZCGRsuyHxgvYB8Ky0CnQ0CwtOkNlDziuX4PsfGvYn3GudMIpTskpTcbJWKtD4nHHeMgenm7sYNSEo6YK5HOjN9arJduXDHtva+9WrepYpfkt8we5DZwYrsp4P0h7DmbYFrsfBVHpxsfzAdysFPMQWwQNllkz4GYHHnjgYIY7/+deE0xF+TuyLHmff/MisyQTp4atPdgy0IaUICopz4u3ehwaYC1dhqhtl/XX8dzIQnI7iNLByjKVS8/U2mid9ht/uFaP26jpc6Zoy8ptOvz/Z+8/4Ny6rzNv/AGm98bei9gpSpREkVTv3XZkuTt2krUTJ5tN4jd9E8fZfZNs/pvsu7vZxE68GyfZJK5JbMuWZXWJauxip0hK7HVITuf0GeD/+V7Mb+YO5gK4AC7azH30gTiDAS4u7v2V85zznHNuvkVBWyAxGowHbHk7EEUxpvgbQQx8v3ZRrmlAn4nvzRyE/z777LPW8fHFYRuaPphUrpg0JW8R+jmBvi9TFBs3brTK98XqNRoLSa+6qEJQPmaiCayP7AGFNgEN48gEPcPdOt97RgursluSJd+BUorFk+g9wGCASBDRxwjC4cxGT/ACYwZVNSoJNgG7kWAi3T4mATY/Ir3wLVvJmLC04YH0j8vxojJiGEc06jObOUYp4wt1DE5MnJfGqMS4Ygy6zQbqGx5vxJhyVMMaVrGKrfRynKJOCmyItEkPdQLGDnOBc+PceZ1TXxq3YN/hfDBu0gWO4FwY4qjdotUmhrhhTJtyFTgrUMDjlEYBhYOYDT5RSRZeQ21kjECCYJlq5JYUeiEdUVlN4xp2JgdD0NwiLx3rgYACxfOk4jWaPmejRSiZ07GaX9IfgzEQTbZNCUT+xtxnTppAu7lOqZYVcAPq3BLwh+By7sxNAnI4JAjK2dWABY3Pflb63d+NKP+Ms4nnpjBYl7797W97flwClp/4xCc8P66PLKPvfSlk7IuROdOzTyqd530T6AIG+z69+di72fcJaMAreI41nLWf8i6s7dh8OJhoSmtKvdhL2bIXUF7SR4Hj9julBQulcyPOWvacG2+SVnpQ6qWiMvIYAbYBGUOmKSuOTcqeIaTBjmCcUYLZIBnbq2+4b9S/MPp5CqhnuGeUE2MroMqOtlH4TM4pXjYITnVEQMbRnqhyQiLQt4bvnm5ZWnhRLjKDcQCSlW3nSIZfYDvCC+Ef3GPWGYQyrB/4N7j+8KJ4wRhsVBzL2HrwuWTKwGYOvUk+nxhwasaB21Jk2MHYwPGy6rKN0mC5Hlz/hMWZH7jpDp04fiJmP0r2G/wG0dyU35ljjBvDTTmeAdeHgGS80tjpgLEI76eyAuOVsWzGHPsl43hSCGDondE0W2ptjmTRYR/V1EuLxnr9TjXMmTPHuveIOwicZiyoQRTYqaGZj8LCQGhiUy+Mjf7QSIkQH+OAU9csniz+TDQWWsrFYAiiisY4w6GEM9KpZwabZLJNb3zkKVZtkMqrpPf2RUrnkCI4O3MGDYSWTZx/MSzZ7FmLMeAxYE32Ag5FjAy3dS4ri6rVNtg87rme9j69fzmibGdTiRW0QKkN6UBF6OQ0hYxDEszfcLYTAOQ5jBNKs3EMu6OW16CQgRRgSEHiMVxw+pqm3ny/dMkLx86Fs3u0KXQCYCzi4IAYcY0Jojr1LSBohMPDNE/kumTKwEwZ1dOlvg6b8jAQqW2dBrh/yQTv8jKwwTKyapXlxEVRZ9L/2UuYH4wBSIRRjDmpBwle2UuXEECAqBpCTUASB1imgumMUc6LeX3//fdbvzM/Ib1k0hw8eHByBDUeeED6b/9N+v73xxqFP/qopjIQcfze7/2ep0EzjrVv3z796Z/+qSfH85FDhOES0ftdSAoPSoGpneUUDdZQOzdgDWdfYB8w+/mWLVss+446+fbsbzt4fb7udT6SAJk3/+sr0ne+RVkAafES6SMfi/TeywBwahthAnYVYwi7AaU2whh7UIO/4QR10xeyvKhcQQUVsjo0RxAKh9R8ollFlZGxau8fZwd/w/5HxMW/0TB9IIwjFvsYjo2tBC83f7cHRRCBIEokEIJdhcgK/oLtRUAA+xl7musA90kVBAdyEdTgnrlpoA6PNBn9+DJo/Mx9js4w4T7DP/g+8AuurQm25g9iCbdSF3Qlm93N/MAGzqeghrnPJmBh9gW4NPOae2mqPzBvYgU8ou13Ow9h3WDeZAqcu1mHWCfgS2SV8Zn33HOPNTYnBZhzH/llaetPpJaLUv10afOjUlkB9d7NALBzmFcZD2rEMqh8FA7KispVHCjWELVuR4CioqZ4kqRzZRA40jACMJgIbNDHgIX+mWeesZr9xnK0YRik44j1kWdYvDryyAJw9mOoM+ZwdmKsmGbXdgMTgxQDiwCbG0fm7Iol6hhq0bWhduv3oIpU1ztHK9dEUsATAcMdghCdEWA1+hsJQhhALiAQxljEqOZnXgO5QBHCa5g/fEe+H3/n++G4hWwwfzBwklGLRYOgkBtC5jUgU27TZbkmJjuDn1Fgsn7YYf6GYjqvHRnL75GuXZF6I2NMZdXSyvSymggcs/a6VVKZbKGc1fyNAeYD49s0guc8CVTw/ZhXzGHmkdtG3LzfPhaYl4z3TIExytgk6GZKSFCakXMnu4wx79YBkve4557Iw4cFnK0QTtbldBxAdjB2UO/mXWDWR/IoomTSufHPBcqlQH6U6MhnGEctazfrK/sDWbmUqYqVrYnNlW/7m480QM+Lz/1CVi6hKU0EEDEZR6bp+RY9NhmTboRT9Oq8of4G7W3fO5qxUdFTobXz1rriJybY4hQ4hwfYleXYUYhCmCfYIgi9mA+cK7yC74g9wl7FA5sEzkHmE0ETsjOwWRBj8G86e5opIZttJONjgLvBFblOZNWSncD1MNcUgSa+DXwa+VJWyREBuOcKKTyWQaDAUimQetndZPkU19zMn3wD45kqI3BseATzxjxnkMy+EX1tMsk9maMEmOD/VC2A7+F7Y37xO+sU1YPINCt4UL783qdyfRZ5BWINxBySQUpBjV/5lV9J9m0+8gw06Vpbt14HOt7RcDjiMJlVPtd6+HAHFnOcTzxw3LCpYVTl0sGIqh0DMFYZEx+FCRTXjCvuLcoluxFi+kyQrofhDglxG9RgHVhdc6u6htqsdaAyWKPTV6Prk8YGn0nGBcawccYaNQhBmGhw3nZlFj9jiBO0Ye7YDXK+h/21ZKSg/MaRRlDH6fhuwGdhFGUbkCe3GTQGXBOUyzg1uKf0LTDXkeuRb8ogR5RVSbf+tNRBnduwVDsr0jMmDZj6rm6DGhjz+erwYa0mgGcPXBDIIAsHJy9zO5lguH3/4X2ZHOsQY+4F892UqDPOAMYrQUnIE+SENQzi7GNygIAZ6xOcwKugBsfCqeI3CZ8EKFsiDbVKgxdHFqYSqfrW8c1bfSTcG4zDxjRqjjc33GaCpgPsJ4Lx+bqf+kge3EvTtwV7gcwFA35n7LGHY+8TGEhmnM0qn6W7p9+tzsFOlQZL1dzePC7zI+H7Z82y7Ai4LeIJbCM+H3vJiWubHhH295O1wWujVef8bn+O97322msWd0onAxF/APZPpvqZOYEAaLKiSc6PoA//YqdxXxAVmPK8qOELYp4H4J3wXQQ8FSOBjvTAfHDLowkUJDOmswnun+GLBAjt4J4zj5jXBAPdlHbOtn+LclOvv/76qDCKf3mQMUSWEbya+cZ3ZK/M6wCcj6SDGl/5ylcyF9TAYUYqiJ+pMTnQUNqkzU33qHvomqWoqCyqym/FbwbR0t+t55oPW//OqajTo7NWq6rYfYo8iywlRFBSOTXOwkCKbsKUCaOGTQqjD+cpm6yptz5V7+tkAUYH6iFUM9FrMgY/jiA2d4CRws/cc9RHKGkhoqakTTQCgaBqS5pG1f+x0lCdwDFNIINaqxgXRhXltjQQ49Wt8c+4pqYnxCpVoKDiM+P1A0kFfGcMQ5Mqa//+lOLic5Odh1xP0skxNilHkU79UK4Zn5+T5mpFxVLjxBICqYLvAVnFSGduxAMELb/S5ceDscg5Qq4JEPLdWLsx2gEkE6JNEC9eo3iC6jgj7OUWOHamFWSsPaimovvlQAZpIk0gkoAHc4N1KF6NbB95Dur9dpyWBruliiaLCxB0feihhzw5vJ8JPolAXeiqDVKoK1Jyqqg2EtiYgsD+bxk4o/aBC5a91VS6QPWlyZXlIwuOucb+4KQC529ug/ypwnw+diK2KGpu7Ao3JW985DcQzmBLRQvisLcZV/BL7Fj2b2xnuCWvxybB7sDOwEZxsi8riiqsB7YMx0/GDsZG54FwgnMxnCMZ4Z5bOx+7Cx6PmIRMYMNtkgUl2inF6XVQA/EL94DApl3Uxb3hfCntmwpv4X0ITjh2qv49jgPXhKvkJBASINgbO+CbLODBxp+TCJRvYtzkMxgzCCLZi3iQrWHuGzY68ytRUIP7a7LKswXmO+fKnmMHY4ysRbI48D3AjeEht912W1bPz4fHGOqS+pulQJHWrV1lcYJkqiwktfIwIXCyxKqB6KPwQDCjvrRBVcXVU9bx3T3Ur6+deFNHOpvV3N+lfe3n9bcn39ZQyF3JD7tqFQePvZGS3ehwarbsFTAqIRzMTZT7qCiZr2xC/OujsIGxjQonOjCGExRlAnMXYskDQxzCiZMRw5q/YbRi1OBUtGMo1KPuoTPqGTqn944fsZT/8RrFxQLH5fgYgpAbHJiZMnD4vm4DJrHA/PC6FiiEAFUb64B9A+Y57p1RwiUDnPY4MGjGHM+hHQ/U4EbRwvklm8ppx/meNn3nzE7931Nv640r71m1kXMJVOIE9BKVImM85nP5IxwDrN84A/g3mliwbzzxxBPW+u4ECAr31jQ2tAcXGIfRZcsypaZi3L/wwgvWueDoMvcF0sF9wilHWQgfBQrm+4kXpNOvSBe2S8ef1ZqFTVaw3SuY/kE+JgngFAQzipumbEADXOk/qYt9R9Qb6lTPcLvO9u5X+8BIBkuSzlLWV6dANc6mTAWMOTa9nrCb7GVFsUvIKPRR+ECVj51oB7arsZMJIsAvcdTTjB5bH7sfmwW7g30+mmtim5zvvaRjXSd1/toli4+mkmFtsrnh1zw4z0wpsplD8Ix0xSBeZ01xvG3btln8juCGHakGNAjeYG/CK1HDpxLEQUTHfef9/IuoKxUMhIb118cP6ed3btFv79umY10j5WpzXInDnrUU797ks3CK78I9hoeaLBw7RyCQZUpJO4EgJnYea3+0fZZOKWi3wLdAYIO9BoEXwVFTVhdfAGsPfjd+jp4bPgoIBDOuvCB17pE6dmlF42lr3OLncoukgho4yIjm+uk9PiYTDndeUvfwgEIjNT/593L/NZ3pGe8AduugMk4muzMJp45Ts7N0QOQaJwD1MI0KH8MCxzQbDQYGjj8co5Stwenlo3CBKoFMObJxKPfEvSfY4VSCCEMFAgoJMU5OUyrKYCDUpisDb6tz6Ig6hg7rSs8OlVUkH9jEUYkhYeqg44TPVGNigurMK75fOqQBIwnDyEviAdnieJyfPTDENU917pt0cggkawrGZTzw+bwG5cqLL75okSDWAdYGlESp7t0Xezv0D6fe1nvXmnWmp1WvXTmqZy8eVC6BYY6TJ5az34CABuMmX0GwCgIN4Tep4NEwgTynQBxGH+s999geTGNOmjq0XoNx5tRIkcAMzg/GKg9eBwnhXFi7sq3y8uEh2o5L1yLZgAbLp4V07NhEEUeqgGMkkynow0choGVgouOfzI1UgCKVAIO9VxLrrNdrK05kghaoeMkSxamEgw8+gRMMu4Z9B9sGm5Rzctv7yUf+AVsTURSNeBElIYCh1KtTDxfTuwunL+u1sTuw/U1ggzH5dstuvd3yjvZ1vKuXzryh5uKWpM+LMYXtgDMVGwjbGj9UpkoUOglLUgGZt6jfvQLX2ymTBtsqVZEX9iRBHLgidrIbHwGfB+czHAMbk/tPRgPHSpVj/OHBXfr7k0e1t6NFW65c0L/buUWnuruUSxCAwy7PZ/7gBvAk/EXwjFiixXj9C6j8AI+ILrHFOPCy2oGTiN4O1hfGl70UMwIq/F7sS/aenT4KEO07LQ+sQWnxsBYvmmfxArdISpLLAM50equPAkLzWem9A8inpVW3SPX5WVPQCW0DXbrYe1XFwSL7ot0sAAEAAElEQVQN2pql22GCHMkCI+9b3/rWqIMTwx9DABKS7gbA8XBUsrBj4LCI40xl8cfRzfxEfYGja/369ZbDzKSGs2Hxt1TqX6O+YGMjIp6Kkt9H+uC6QyxxWm/ZskWPPvpo0gakqYeLgdwxeHh0A+nt7VdJaVBdQ++pofSG0ddjlNuJqqlli/ofBTnEF0PDCxKQCBhkGCwET3CYpgrmI3sZ85GHV+SIUgzcD8gB14e5yXxNpzYvwUrOlbn9+OOPW0TCng7NsZn7jAmMQO4NxIIABuotyo95kYH3TvtpazW0r4h72s/o4VlrVBLMnUKJa4wzJ156qulFk8/gPnMPIayx1lcyHUiHv/vuu0fvKYY8Yxgjn3tt1GLMS4I9t99+e0ZEKOwjqCXtjToB9wAyT8CVPYMH6i/UnThKmGvM42RrP+c9GF/PfF96+t9YqKQ775F+5vNSySSq7ztAQI1xNzaXli2Yqffe+5FnH+FzDB+jsDIBCaLh2KBG+qyC6ccRDofUPdysULhfpcHa0SbJ416TIr/Agck6ij0PnzAlJb3KPMVu4MHn4AyDS7DPMjfhFOxB7C04tFH38/k4/9iHcDRFl0h1A/gRzmScq34pq9yBe8q95B4jTEo2qwK7wDg+L/Q160LfWMZ2+5U2VSypVOdgl2pLaqznsJN5j912M70yTHlY/mXc2W0G7AlU26jLvQTHZKzzSCfbnPdy3sxJL/uImcCl3Z4lEJFqmSwEb3ApOAQ/cx+i+0gYvsd3IsgFv4Hz4WswpY8NUrXtrvb36aXLY4IJVv6hcEg/unBav7JsrXIJ+jSQpcAYjId0eF6mgR3OfYOPRpeJNeC+wR3x85jyhow39gL+xr022UB8V/YA+EoqGUJuQOYF486IJQHciAc+LIJpnIPZi/hujONJ65+61iZte1pquyRV1UkbHpemp9ZXNC/BmhaKLtEf1rLFcyzb44EHHnB1mKTuPtESr0pPUbqidaBdQ+FhNZbWqzQ4yUjuZMfxQ9IP/nbs9x0vS5/6ojQ9fn3zfMCZnma9dvmdUVJRESxTebBI/aFh65mAAqouLtX8itRSuTEMUC+ZzR6DnU0FgylR/fdEQBGFcsZeigaHHqoplCw4mljUnTZgnGLPP//8uDJYEBaOB5kABEyMgWnUtcx5DBs2Qza5SbtpFAgw9B9++GFLQZdsHU/GHxs/42c43KeWK+0aGBhSfWONFAhrKDym/gPc92jHJWAsQ14Ye7HGtNdGHgEWDByOyfzidxy5yQKHK4YY4xxj1W402YlDMuonDCozB7lmqGL4mc8iEJgqzPfj+DinmYsQTpwYRqmJYcmYiCZ+pleDeY57lWqq8FCM9w2HQypRbtOuIVcYPbEU3iawlM+Izu5xAn9HTYXjyCglGWPMPzNH+Z6s2YxBe/DDazD/mYOsJfbSXozFV1991fo+ZJAxJ3BAMAZ5HUHZSRfQAC/+RPq7r439/uwPWTylL/yKJg0se2j8PCKogbODYHkq5fXswAmEneSVcOraUK+u9nepqrhc08ty0EfIR5oBjd2MClsgrVUKr877wAYBjeb+XeoPjWVV1hTXqX1wfJZlfUlyPTXsIHCMnUImNmAO4pAigJyOSIPsVZya9sAEdgfrPcIVnMgEzHEwGM5gX/tZA3bu3Dm63/Ivtqap08/x9+7dO/peHLJ8D95HDykcWX5QI7fAZkA4g52RrA2PHYKTnD2/e6hXg/2DarlwRdPmzRg9Fs+boAZjCae/k02A7QxXNaV37cB297rsjREfmqCMsZeT7Q/BHoZzH1sdh3D03sh1INiRbEDG2FxmriF0MiKmVDM14BE8sJ9N6WEyrjhfbDuOj1CG37mnibIouX6p9NOg9FQyz2cTrF/4SWLNBfM89zxfq9gwJrmP3J949xBejA8Ju53vbPxY9u/NeGYNx5+UqZLq7GOmd080yJzhmsON4dnsF6xXZJgxZimFO+kwNCC9/A9SD/3JQlJ7v/TKP0qP/XupJnNl7bMKxlhxbaSnxijPCGj5ssWZzdR47LHHlC6GQkN6/eoOtQxEBiwBjbunbVR9qbfEA0fM1YE+NZaWqTSHatKk0Ncj7XhWajkvVTdIGx6R6p0jqznFK/82Qjxsk+7NH0tP/rzyHW9fPTBOJdUXGtDmppl6t7NLbYO9mlleo6fm3qgymtumCIwwNjkIAgSEyDebgT0KnixYyDEYomvrmzqlkANTE9FpA+b3Rx55ZNxzGG2QEP7GhsdxjDqHDYLPw9DBgcpGl2oTMR/eAgchhjH3xZ6OjDMRosj9hCxEG5iMRdT+OELbh9oUKB7U4OCAzp3psQLlHVeHNN02PGM5g1FG0CgQo8apWXOmVCscE2KMEY9qiPNIppYpY5jxbRzI/AxZMEoniBSE2yhEYn0HVIVce5wKzEeInDEWOR8CGRhckAEvmuaxnjC/cSxg7EE2EvXYMGuQIYTpND5cWTtL+zvGSpcR+J1f2aDyotw7qCGhsYwejGJKZ+T7uoVTivmaqLEk841GeMwvlErMBXtjTlRLzPFMZk4x7yBJkIroXiUEWZgzBN64L5wnc5U1irmVV2hvlfa/EzGm190k1aVRj37Ly+N/Z93kuckU1KhdKDWtlFqOjDwRUOP1j6mp6f+15t+GDRvSOjz8AoWoF30BjnVd0IvNe0ezbdfWLtA909d6vid1Dw1oOBxWTXGkr1VBoPesdO2YFB6WKuZL1SvzMFCASto4NIwNcoEdmxCB8hnXhs6PC2iAkDrUWDJXHUOXrb2zqXSh1Sw8VbCvsZdjz7DeIzhi3lCKluBAqmMR29FJhAE3YJ8hO4TsDfaA6KAGwNEVDc7RBOLZD3BAGZsE24jv8corr1g21KQMeBcocGiyZ0eLfrBVsI8Zb9HlaLCr4aCMk54iqgec16xFc3Tq0AlVVFfo3HtnVDvrntHXYxvAM53uu+ltwfjh5+jSml47kE2ZWM6fz0IoBMdJpscT4xvOv2rVqlF+AQ9g7hjuQmaK+d6x7D2yvrHXmWPMD2x/uJ2x9fAhsF9i33vRg4qABpwGDkN2L9zGlPpKBnynZBq4G8wqr9Ty6jod7+609lNWL/69J09EsmQG4GdxqnLB2gbPy2exJ/sD4wV7PdEaawSxrPGMXcaweQ+BDjgGa3gmm8FT9pCx5NSkHR80wVDWIYLviBs5Fx7R61FOAQc49x5OFal+ujT3utTtrJYLUneH/eAR0dSFY9KKTZo0qN8otWyRwiN9UUoatGz1Rv3kuRdcHyKpWZiqMjYahzrfs7I0DAZCg9rWukePzLpbXmFry2X92t7t6hgcVFkwqP93zU36qbk5TtVBWcCgjjWwiUo//3WprTkyITquSJdPS0/+mlSZZ0ozIoZ2cL45buzkBiiL+0PjGwlFAhwh/cqyMWMrXeDkffnll62F9+Mf/7hlIGB8sAFijDkZM7yWIEOs8lAQGafIOAYQxAbVOcYURhMBFDelrtgIqNHrBDZAnLYQHTYzXpfJjcxHcsC5CYklPRZjF+UOYwFlBU5v0kWjM+sgI7yesTEUmquWgV0KKZKZUByoVuvpOsuQwYjBQI+nnMWIw1jC8LcHNTDAvR4nkBjOiTmCwc3P0QqSRGBuEQiyKwFRSzFfANeO68J3YbxjBPJarivXlzlregmghsQJbep3YtTagyt8fwiMV5mNzEPuCw4Ht2SO+2Nvdsi9NPWRuwYH9QeHDmlbS4sWVFbqj9eu1fI4yrEVNbP02Ozr9drlo+oPDWlRZZM+NPdG5QtM34xoJztrIiUyWA9R8mAE56PzkXnEnC0rL1XbwDENhDpVFChXfclSFQcnBq/4DjiRUdWZnjqUH+P7ZzKgAbGGcEAeop0dZtwzzozDA2c36xTgvAjEsEdlqh62a5w9Jf3Zl6Xekcy0yirpd/9YmpNi3ysnAUShCGncgnkz7/ZIYGOwRypvkEqrLU6AjZBuUMMrftE7PDAuoAEOdp7R/Mppuq46dXW8HYOhYf3j6T3a1YajXbquqlFfWHqrleGbUyA0CsTZe3vPSW2RWtQWsNkpv1o70RmdW/THeT6/gxpD4b4JZdrAtLL5mlvpTSkV7Aq4BJyAdXXTpk2WTUTwnj3BqQQUtgCOW14fy/HopDbGbjJ7JjYSTk8+w61AAhsqVvYFtijZGQQzsfH4Hj7yA9wz7juiN9Z2bF9sUMYPtje2L0736CwB08sPVM2r1ZGuE7ruxsjvywILdOa909aYYpzBFeAkTlkLHAdbH3uBgJedh+Dwd+olmA6w+bGfsBn5bGyUZEVA7IPR58VcMxnT2EYmCAG/4HNwHjPvTL8AeAPiRwKEXBvOB0SX+cG+8qp/AOcGx+FcOG4q9pkRXRoc6Lis5y6+b2Vb3NwwWw/NWqpgDNub5//n+tv05YO7tK+9RbUlJfq1Zdfr5sb4Ip9sgXvKPIj2zbAecq2whVkTCQplqp9kuveXc4cHtfa36HTPaQ2HhzWzfJbmVUTmeTQYB8xzeAV2GfcXQSR7Syb9QMwL5sC99947gc8BeDZVKshEJ+jKHgIIgDC/TJWEnPI8/KGvf086So+IEazeJN3xU6kdz/F6h+PbeoWIknppxqPSYKsUKJZKGrVkab9Onfrf3gc1GNB2RWs6aB/snFBNtGuo2zN1b0t/n37pna3qG6kF3x8K6T8e2KXlNbVaXZuZpjZxMTQo7XxGOn0wYusuuUm6+ZGJpPfqeak14mAbnRgD/dKpQ9LqzcorzFoonTs+lq1hEd70CWmmURQIqqa4UteGemwJTlKjx1lCzBM2BRZgUvVwlrLhsUjzOwuyIQ84WlmcMX4wrDDYMIJwQBqDEccqBoxZwKN7HBjHJ8eGHHih3gAYsDz4HEpTcf5e1zH1kRpwWmNg4KzdvHmzRXCNQsgYHdHp0zhPcTQyHouDVZpedrsGQx3W/C0N1Gv68uC4ddiN8YIxbFKseS8k1Umxlw6MiggjH6KTbLYTRpLpOWMHc8d8R5Q4xqlGFgRGIKorghf8jfnH9eQamoBhvFIpGLuppoVHw2TeEEDiOrjZJ7lOdhW/GQuhcFiPv/mm3m5psdRQu9ra9FJzs/Y/9JDmxVFZ3dyw0HrkIyDRjOvojAzWQpR3OHNYHyEeOGXyLbDBXCZg/fTLX9eCZfWavxDyFFDv8FXNqdisosDEccRcM4E0iAcGfbLl6JIBc4UMDD433udA6iDijNVoZxbknb/xGq+dEknhm1+X+m1NCPt6pW/9nfQbf5ja8R55Qjqwd/xzj31QkxIVTZGHzdaBG6QLz/jFQPeEfmhBBaxSVF4FNX588ah2jwQ0wInuNv3z6b36xaW3KicItUhD+0ec/hVS8Q1S0IHr9Dj0oup+Lw+DGk6lzFizJ2YH5BtKg9jH0Qw3qOJgpXefUVpqZWSwt2Gj0CiV0n7GQYxjxwSdWa8JGMIrcIwSDIFrYMeY8lW8BqedkzMTh7KZl0ZYkm6pOfu+x3nywIakvG4skZWP7AM7mz3bNLxmHBj+h63sJJxijJhMiuvrVmph5dyRklPVqiquVHhOZG4YGwwnphOwbUxZTv41wBbHrvUyUwPebbfV4bzJHB/bmnnCvIrOoLaXgLXzKRzGBEGYh3wW7zdqf8OfTHkoJxAQ8fIaIIzkXnDd3Qgi7b4HgHreZFke6ris/+9oJHjO3T52rVVdQwP66PzYfonpZRX665snKvPzAdw3vm+0j5Lvz7oFuHfYMIz9dMuMew3DfV/b/preeXuXZi+crfLKcrUMXNVAqF9LqyeWmAYE18jc4HvCnfg5kxkp7FF8JhlTscY9gAOTDRbdc8PqFTrSODydjMW0cfHk+IAGOLxNWrZempmCjds4J1KxB6E7fmG+V0mZND/i75lUCJZKZbMm8Au38YFgMk4hnCTJNo5yQnVxpZWCa0d5sMyzAXi4s0O9w5H+CAb8vKv1qnKCPS9Ipw5EAgBka7y/Szq4ZeLr7OWc3DyfSzz6aanBFkWfd510xxMqBNw9ff24Hi4zyhq0rs55UU8HGB0YOBjpbPhsdhgMpi66eWBIEKzAeOQ1GJIoY1DhmwbgLPREop0CChhSOH0xcDAqICpeL+YcDyMMAuUjv4wtDGDq2EfXyiQAZfpD2O+j3bAOBopVVtSksmCjAiNRf/vYgeSakmaxgAFnmurhXMV48nr8MW84JmSac8Jhaic6TqA2KKSL+cW8wviJJgFcCwgND1NuzQCjiv3OND9m/EPg3Bj8wO3r3ILvjSOY70PgKN59MWuJUbpY9YFJje/v1772dr1x9aoV0AD8S0bjd86eVaGCa4Mxa+oscy9fe+01i5zyN+4v45Q5km9lkJijEOqSimFtuGupOtquqbODextWSAPqHrIJHWwwdXwJ5mDcZzKgAXA4YQOyNzkpqOznRXYG98LUgGYscj8wTKl5i2Mip7hyKWKLGfDzZefr7Aq33Sn9xn+Ulq+UFi+VPvPvpE98RlMBrJGQ0XTBMbzgFzUlEzObCHLUOjyfKt7tujKOX3D8o105so3ogzVE/wmz1/dKQzulcL9LLpGH/YYC7J12Zyn78vVSIPa6ky+oLJqp6iK76CKg6aXXqyjgbWkl1lk4BjYB6y32DnyDPY91+qWXXrJKhSB6gSNgJ5pm3KzhOKqwI1iTCVazNzqVnuJ4OJAANhSZrZloDottl29iAx8atTkZQ3b+aRy90UDIAF81oH/G7IoZVkADmH5gbnsZALsjFTsvVjWDVAHHNrYKY5sAQyIbhf2K6wLH4MF1chJbce4IF3m93VFrhErYpwQ9+Fw4hlMPQyfAZZxKwKUDuBtrB+sC5xuvFx3f2d4EnQDNrHlz1Dc8pJeaI0Ew+7tfaD6R973t4oHxwbgwIHCM7c26amx4hKissYm4abZh+mhOv36aFdA4cThyf8DJ7rGfneYyXJs9BLvdq2B2rHPEH8Yak6gpO3ycc7GLJ7kf3B/OmXlI1lHO0NUa4/m21DPB7/sZaeE6qXa6NPs66cHPSRWTX2C8YMECy9eBD9UNXIfcWOBQEnqxiK6uXaaLfVfUOxxRyhHg2NDoXc3rOoeacSyltSVjDq1jXe261NetRVW1WlCZ4YFxjhrEUYv52SPSuvvGP9c0N9JHg9ppFvkI4M2T5ntvQKaN2gbpZ35Ham2OnGPDjDysy+uMprI6fXjuPWoZ6FBxsFhNpXUx0yK9Ak4geg/gnGUekalh1CFsgCzIBCRYzAlmYOhAPHAEsZnGan6EYUQk0zjqvGqy6QTOD5UtawF1db122vpIDYyn6JqzgHGDYoGMIQirUVDhAHUb9UZRjcHvpODjObJDGAfmWBh3iXo9JAOT+WEnC5SB4vsyDmOVK0G1znVJpPw184csJC9LH0C8SE32urcI6wiBFY4LcSQDgfvA9bF/Dsa2ydoB3965U7967Jhazp5VpUMPEt5LRmMhg7UT5wzBX9YpgsnRY5Gxg+GbiZrMqQLCC4aGaQZfolXXL9beXUd188aIqo3QRizghMLY86rMWSxAdFHLfOhDH3I1vyHyXN9nn33W2ruYwwTz7eUTM9V3xxXmLZQ6O8YCG6yd89PMHLnjnshjisHUxE8XrOc0IE4X1cXluq1ppd4e7f0hza1o0oqa1HqZOX5GUemEAkMVtv5CfcODOtvTamUGz69sVEkmS5GFINDRa8SwFG6XAlGl6CoWSgNXJj6XjwgslMJk1sATKySPgwKZAmtaU9ka1YQWaDjcr5JgjYodMu0ysY9gj7HHseYSVCbojc2A6ISSHahXWcvhGGT5mhJWrM2xHFY4YnFeEszAmZzJ/lScA7YYvAihlx/kyA/gRHQS1BnuyhgxQQHGWjLlaWLdYyPasL8mlcbdicD4Nw2xzWdhO2NDxyrlyXlgd2F/Jerpx9whuMiY9iJob647HCcTMFkGrBMEbbDTuLf2knV8d343NjT9a79/ar+uKtJ3ryJYMiFUTulvnisML9FEwLEIHMOHGRfwO8MZsY1NoA0ewv02pX7zBRbvD4StDI3SslL19fRZP4fCwzFtceY9QlaC5pnmS5S2Yj45lbV1AufG/mUC+Xw/KlYYPwQ+sZyVAmuIUQIYP2mqKK+SbntSUw01NTWj/h43jeldBzWYtGbTGoWVedAfSRcJuDfaK4rK9dDMO3W+95JV121G2TQrNdErrK1r0D3TZ2nLlUuWs5rg8NLqGj08K7JY/9V7+/Uv58cirr963To9NS+ShpsRFDsY47YAy7jXPfI56a3vRRrDVNVJmz8o1eZR8xs72MzzpJFTsigtKtHsiuwteBjoqNhpWouxQFSadHEMNBZm/m5IheljQeCAn90EKnBsGrVApoCjinPGQYXBR4DTqzJXPtKDPQ3Y7lhnszeleVAC8RyBALdk0SjfncCxUaXYg1tek1CINEZ2NKGK7hcR/R6uh9tSJhybvgs4u/k53Z4EBCtRecVrNJ4uOK4p7YOKwTTKhmBB0lgzDPm73Nenz7/6qvpHSk30jIwVdmx+Cow8npjtTWmWXIH1iDWTXkYYP7Gc7zhlcPBkOrPBLVi7Kf3R0dmuJTeXSkWDo01VyZ6qKIq9T2HsedFYORHYVz760Y8mJPCAvY3vw/x79NFHLacHASb73sReBwnJpPIrLj7zhUhPjZYRx0DjNOlTn1fO0N8lnX5N6r0qFVdI82+XalPs75Fl4KjxqvzURI7BGo8rpCQp0czNDUs1u7xBl/s7VFVUpqXVsxT0sP7wY7NX6Oi1qxa3ALhrfmpuJIhMmat/Or1VPcOR3m1NpVX66YW3qbo4U7ZZkfvnKxdHemhcOxoJhJTPk+rypzfSBATgSfkRfE6tDFV2lJTYQwQsvv/971tKb2wj7DKcs6YuvynHwX6DUxQbCVsHezCRoxhhDA5OrxXy0UB8wzqAwxBBDo5Br9XoPpIHJZKwq6NFRIwzxhiCPXgtjm7s0WREFk7cxSC61E0m7Gls9egsd/Od+Vs0H4ATYT9i37uxhwB2KfMQWwge5vZ98bIksP8zOR+5l0ZAhe8CXmOVrw2FrJ/tpbC//e4OvR/uVr0iTse+0KBKg/TLHSv/SF+NTItHMw0CFmQss57agxbwauOnYX1lXpjSafkA5hgCo6UblkpV0sIVC3TqyGktW3edZpTNjDuvvC4lHQsEThI1MTeA7xJY4z0EXRijJqAB4BoI13KGGfOlWx6Udr049tytj0hNOfKXhsPSkW3SwTek4cFI2apbH4+UsCogjhFdft8Jrmccm9a4+sj9V6TWt0a6lAel+lukSvdKN8r/LK7KDGlj4fyr9Zv0jTPHdbSrU3MrKvWzi65TRVGxdrVeHhfQAH/5/n5tbJypeZUZMp7W3Clt/+HILyP6rlW3O7+2piES2PCR92AhvUYvGIVVXVyVkDRD2D/72c9aP2MAYrTjeMPIwWgwDh6MCYwe0v7cNuzKlvIYYoNTkE2QwAvGldt0WR+ZA45/+73AoGLNNkEnxhMlBLl/yThz2UhiERSMiUwrISACKDKcgAFEIA/iYQwYDG7mUbI9N0x5NdQd6QY1OGeOh9GVyabNBqwRhpTxmRh0dmfx2xcvqjfKaOW3BVVVutjbq9nl5frKTTdp3STIvOJ7kwUXbRzbjXbWVxzvmVD9pQKUtA8++KDlhNq/d7euu6lKjU216mjp07I5m0ecY7lFMiScfQvCx95GjyfmaPT9YL0i0yhnQY2m6dJ//u/ScZy7Aem6lTCh3JxLaFh6/1lpoCtiG/Lv8eellU+O612Rr2CtZa9JF+M4BoKp8CEKg4z8lbXpxhEntzvMqWi0HpnA0upG/c6Ku/RWCw03w7qpfrZW1UZUeD+6sE+9EMcRtA706JXmw/rg3PglFVJGcLo0jIqWUi0jUZZArRRw+O6sg9XLIw8feY/+4QF1DfWoqrhCFUXx1yf2/U984hOjv6Mq/ta3vqWPf/zjo/XvjZONtRl70GQJukE2SscYHkRAnP0ZjkTmZbpOYB/pATt2y5Yt1n0x9wL+R5CDcYdznXIv8IVYYiMn8NpkHL9eBzWwA2OV0sQeQ8CHQIYgBoE9Xg/gVck6rMlmgevD05yCKMmAuUGWNtws09lMHN+eYUJgJtrR/eaBd1S7dMxhy0pRUVSk0mCRBkPDuqlhln52UR4Hz5MAFTTsfVIAwiLEZEZIRcALLpnJrLZkwLhjzBKMaSqZprbSNoWGEJTP1Nq6iSUHcwG3AQ32IfYuhFLMR/a5fMuKsXDT/dLitVJHi1Q/LdITI1c4sU/a/dzY76f2R3jHnR9VISAZjuF6VSblajQqTHZG6xsRxU/kCal9h1RcK5VmhkQki5JgUD+7aKLC/VRP54S0cX4+2d2hsqKg6krKrIXYUyxZL6HSYiCxAS29SZqTuTJBPjKPwdCgtrXsUutgu/V7TXG1NjdtsLKQ3ADHGo4syjbgBLKn3KEWxzmJM9dtuqpJz8LojCYwXgInoDGiqCFKuRc/qJF7cN9JeSVzAqJBYz+74cx4Iy2c0gTx6uFHI179W9R09pqqXiNReRoMG4x7o8qHXKXrpGZMY7SjPkv1WBBAHhB0ztFNyqQXMAGe6HrXbTR5jGrYzJ73R2vW6NMeNObNN7gxjgkE2XuO5BqmafayJavVeaZTm5Z/yirDUTHf1reqQICKj6wn1gf2MJwDZChCBgFkjz2Dv+cU5RXSmjwg2n2t0kDnxOc7zhREUANeQCmMdJSJOLc4xijHCJ+wBTQAJVkPSoH8yK4C8yvr9InKiU6LqwNdltDFgJ+b+zvVO9xv9TWqLPKuf6CFQLFUskkafj/SXyNQLRUtlTzMTPGRfZy4dkFvXN1nZQExWm5tXK3VtYuSUhXjiKUeOvaR3Z7BNiRD/PHHH09qXWeesr/CL+xcwCtgz5k1hOOTGU5gPC+dVlMM7N+MJRTRCOiwN02PCH5HrY4NnozQCc5qbB+348PLMYeNHou/8n1wVPManNM4UN2KDGOBgBDXir0OLpYqcFCz3+Kkdluuxwtg19kDW4C1oLyoWANFReP8ajUlZfqTtQ9qsoHxF80xWGe5NkaAhw/H9IPJB5hsbgIBBIrvv/MBzZo/W9P6pqm4oTDKOhoYMRR9RJmPDz30kBVwhXNzb5inBFz5OefCNcpQxSpFlU2cPjj+dwQKZw5HxEMFYCfCC4hBuIFrBmLqaVrAkTsa0LCBWq15EtSIhTnlVRNq/ZUHpX84vUcDoWGVBIL6/OL1um3aeCdQ2liwOvLwMSlwqPPoaEADkLGxt/2AFdhIBvfdF9VXZQTUu8WAdNsjA0MS4wIVCMDoIA0YB7e9FmaqYHPAuIsuR4RR+84771gEyq9/m1uQ4oxSypBYu+FpMhdMmaJkmovFM5Rw2jP2vFDxYQQyfjFCMBI59rjswKjXQgrclphKNuWawAb1PdMZ02RmHThwwJojmU5D5vpzb6MbffL8dWVlum/ePL12+XKknm0goOXV1fqpJLNZChmMKbvTNdOl+lIFpIhsKtZ95rMx1glU5Wp9Jatw165dVnDOqZGs0xxibTBqPuaScRyQ3YWzAEdVvgSUco5YpKIAyAZgfLLOEDQ39biTBe81JT8iiG7yyP4So/linqG+pFJX+glsaLRnYFFgQP94+iXr99nljXp45i0qs/XgSBtksBT7/GKy4NpQr16/um80OMb/t7ce1vSyeuvhFoiinIRRrOXYOai9Y9lY0WA9Z2/CrjRlaNiTKJHrBchwxeYke8SUnDJinHH+Bx85AfeCci8IFLhH/GzAmMC5jlAERa1bhz1jyE0WjuEX6Yr14Bd8JsfBLo8XmGPM4QQ2SDegYYAQDC7NuB7b75IH18H0PIyVze4lEEwxL6PLwbEmfOLWe/RPLUdH+2UQiP3g7DzsBZvBuYGdbEc++kNMc3qc/qznlIklSMAano0ytrEAT4YX4EdKNM8Y88xjMvLN2oGfwlxvuApznOBhPmTi5wWKuE5Rcn6LX+TfGHUCez/rsRu4vuMccHThjJUCTm+NPMfmplm6f8aYEVcUkGaWF1kBDTAYDulrJ97RuR4H5ZwPHyNoHWgbdy0gH20DY0GOdMFizCYTvVEmci7gPOLBxoWRiWLf7nCO1yMhHnBGYcxEp52iYOHzaDyYjfR0H/GBk5+AGOQClVE0kkkNZ82PVz4JYkzgAXDveTBeUWBDUPl8HMluABEiIGMahWGwky1hlGDRICPFq6Z70UCFA+FHeZMuUFTh1M303CBNHvVltCGN8bds6VL9+I479Edr1+pj8+frt1es0Nv33aeqPKn3mg0QFNi/f7/1M4SWgG8+kg6AYf/MM89YcwJSjRFPE71cEg7UmajP3ARFCcbYHWXMU0g8IJABAUxWoTmpUd4oVbHOmvEYkIIlUkMG+7x5CMgjQWi3pMMJvJdxPlZC08nhXxiKwsdmrxvXGLyuJKhBq0xvBJf62vTG1QM5OjsfhYDWgc5x2T4GV/s7PPsMAuc4tJLhJNgY2PuIpfiXPcEEJNPlGOx32F3RwQv6MhknsI/cgvUZW4AStti10eA+eRl8ggfAZcj2hGsQ9OIBx8AJ6rZ8GjyEElImmMf4hc/GCsjBY9yWw0lV8MT3iddPxA3gZ5wn9lQmwfXGZo7mg/AafAObFi7Xb6y4XZua5uvWxnn65aUbtbGpMHqCeQWuhSmDzFqWr72AsMdZS9966y1rrsI3qBZCdZBcgLnGdWNdoV9JIq4Mh6AHpj0Yyt5k3sfz+L9YH3yMYPmtUfWJIMUbk+pTl0sQe3DLL1IrP1VSL5XNkfqpcTVyUYqrpfL8X8RwZPzBqlv08MwFutTXo8HQgJ6+eGTca4gyv3etVfMqnR1qPnyUF5Wra2i8QVWWoOZtsiCowcKcaqYFYx3HERuW3UBjA0m2wTcGWKw0XRzPOKBffPFFy3nIOmHKj7DBcA4Yk/nqRJxs4DpjIBBowni3q3i4N5ARyGkixZPpWRGvVwsZE5TJgVwQxOC1qP8A9x5FNkGWeJ8FCeb99rJJ8VLYMa4Zc5kcT6j4IRzpNnvjGuA8MKXhMgGODdmIvk8YeairTLbX762KNLKdioBgMM4oucG4oaxFvgJHEWsmgQHWUQJ9KKvI2rj99tuz2nwQuw/lFGsB45eUb84tmf5NfB+CqZSdYp9grj/yyCN5S/qyDqsk6SPSxV3StWaptFqasyHyb4EgmfTwhPwCBJZIYXu2RlgKFEbvrrkVDfrCknt0ovuKigJBHep4Tx1DY+IUnNXne6MzUXz4GEOs/hmJ+moki3RtKJTmCKdQa5tj4aRij4glSHECewI2V6zzueWWWyx7FmchTjmcWAg2sNHgFnxeJp3QPsaDIDbcAHsKW8o4GOEb3BeU024ygBIFwHD+Y/cYJTxjimPDMRgDcEx4cqJsIewn+IWxnRhr8TJ1Cc4k0+w8FXCtEGe5rcYQC3AlOH6icr2pAg4Ex3Aqc4V9avwCS6oarcdUBU2MKSdOAI4xmayPJZtgziKU4lwZ6yYrHL8A4zJbYMySLULWF2sImb6IG5MVPDFXqVDB9yCggY8AruRjBLOXSvf9tHT4bWloINIofPVYpt2ULD9FNHY0pZCFs/E2qfs9abBDKqqMNJ4LFobyk4V/Y1Mk4vxu51U97XCtqovzP+vER+6wuna53rzaplAYpUXEkLi+1ttNjDmXbr8KHFI4kuxGZCoKdIxKNopYjV0hFhigEAuc5jhZ+VycWRhuRhWAAZfLNMepAtY4NnUcidRvNQ3LICNcf0hoomZ1GBgos6NfhyGCkQuxgGRQ7zgWoYSAQnxo9hgLHCuZrAsT1Mg0mHsQNMZ8OuozM3fSDZA4geaMJqsrGsw7r0pDTAaYuty5DK4yFyHTOPrjnQfzib9DWF/e+4r66wZVObtSP37tWX3w/g9k5Tswtpib9hIM/Eygkn0EIuG2XwzXnuOxDzAf/IBGFIpKpXmFQzKiATfAXkkV4/gFCFBiZ6MURklO3d+ZUsB9rfZco7akQjfWR/a0E91nxgU1QDn324ePGJhWWqclVXN0ovuCggpYQrvZ5U1aUOlds1HETekqxUF0XzVsy2TXdxzV8WrQG4EW9hNrBc5ssgV4HsHVSy+9ZNlp2LfYbX5j8cwDfsA1x5nI/m5sGjgeHNNNiU/+7tSsm7EAj2SMUh0gXpkmMjAIdsUqfZhKj0nGT6ZtLIQhOJXhaHCsdMYs/MkeYPASBEzsPgT7dUU9jWjNRySD4OGHH84pv4BnMp9Yf+PNPTgj4405++7Jo3qreZ8abpimbcf3qai0WAvnZaYKQvT4QRBJIML4D+AUBMnhCXBat/OCdZ9xSFY52YcPPPBAUsKrKYE5ywq2l3My/CKpoMY49Sz1uKrjO8UKAStqmnR93Qwd7Lg8ajwuqWrQ+nq/fqeP2KgvqdO902/Xud6L1uI8u2Km6kq8dbQSEPDa+cOGkUrZD4wmemcQJMHBGx3c4BpgNJIVYM8MYLNiUyKFnM0eAw5neHQZKx+ZAWoRDH7qTKJ2A051ZCEROFxZ53G+43jEQI5+HY5JSATOctQViQw4DD0CXBP2DxuSbdiMsWLSfBPBrXqJ15lazqYvDe9j7FLaiXNPJyCBsQY5YO55NacxXrkfTooynM7c93jBpKmGfMgUox41GQ+MNVOyjWBwNCHnXHEKtFV2qGdGv/Vcl66pa2aP3tr3tu64MbMqJAgyBOFDH/rQuOdZF1B54SRAlfbgg+4bQRri4WPygfUx3aDGhP0hUBt5FDg2NKzQMxe3j8vU2NhY+NzJR+bA+n/XtBs0r2K62gevqbq4Qsuq5ynoYZ8dbBuvsxuMAznZWuacC33fUO2yTziphXHYYetEl8CBi2CP8jfsIRrhskd51QPBR2xwje+66y7LCQmnw0kPGFf2LAx+JvgExyAgxfgmkwPbx56twPjZs2eP9S8Zqm54Aa8jiydWUIPPjFdG1wnJlIt1yzHINIG7UJqXcQp4Hw84Fc7dVGH6mNE43CkAkSrgQsxFJ8cywUtfNJVfHAOOyVgg+8LOkTds2DDh3PAvdQ/16kR9q8J1JWoZ7FBgQZn+dfez+rVZn89oRjjchkwR1g+zZgDOkfHGg3mLgMqI0RKBPYc5lM488lH4/KLYrfMEgyJeSZBCRTAQ0K8v26iXLp/U+d4uzSyr0kOzlqjYbzDjIwGqiqu0oiZzJRFQo2MAxcqOSAUYYKkSGTYfAhpsmPZzwkBjA4qVtohBaz6TjAHej3PP7WblIz1g7GOgGEUVRo+pQUlaNsoGFBuooSAm3CuIBwaSMcB5P8EOxiQkJhnjjeDIm2++ab3PDj6fIFsyfT4AxMBNLV1KL5lMBsZrdDM9DCvTA4R/CTZwLSBahhBzjigRyVhJhSzwfq4b14v7wPVMNajBsbgnfH9+5nvFSl2HGJIO7SO/wBrK+LKPAdRK/G7PPkKhxHztum68erWuqU5H9h3NaFBj37591rngLHCa54y9p59+2rEcgY+piYwENSYJ5lQ06cm5t+tY1zkroHFd9RzNoo+KDx9xYDXhrp6bsWuEo9KLTA07sKNS5Svsgdhd2IPRjmKEJdioTqBciXGuEhAxTnacW15yJx/OwA5FrICT0mTK4FAl+4bxgMMeDojalsAV99FkeyO2wr9EwIP7zesoE2V3dLoB99lpD8Fvhc0czT28CGowdxiXRiQGT4hWh8M/4FMIjOBdppyv3W431wIeEmuMJzoPjsE1S7fvDPcB3mT4OvfTqfy1uWeZLtHlIzmwDiJIsvtWKAtNsDi65C58eNuxdzRUNTTabYF/a66bpm37duiOmzOTOcyaQO8MSmSbXnvRQEDLGIOD+PAxbdo0y+fjJvvPVVDDRJXdlhooNBDAeGSWX6bDR34hUT+DbAEjh94epp4pmxKGlFFvsGlipMVKE4xWbeHUAzi0o1PXfWQGBCxQ8tAsmXHFmo4BTOaAk3oa49ruFIdophoMM853CAykhvHEOUBgOK9kG5QxBnkPx4oVXGGMQmhMXVNeT5YQ5IcNko0RhRg/s6/FKn9lsjUgZJAjfuf7cP6ce7y0eIgGanc+k+/K7xhqnJsxQGOdv/168zPvgfhAIhJt6txb1o1UCJKPzALFFEpS5py5v5R04jmMfLPeMy6scT7cpWDR+PWzrKLMlXGXCnAQsZ6jbCTAZ1cVoppkvOM8gOwmKl/nY+qA8ZCOQ4Ux5mWD2XzD9LI66+HDR77AZAp6CeybZPclHODYNog/2FNYR9h77D0ZjCjECUbgYYC9RoYqQho/UzU74P5s2rTJqnvOdYcvch+xYQguRY8JU07MVA0wwbVUSzBh5yMCwaZmPGDHcCw+H5sK0d248oYJgM1OaaV4exKZCtjjCL7sZZ3hFAQYuAYEKvjujOtYtj7Pk70LP+FYJriDjWfKBseCya7ne/Ie3g9PYT7wfWOVejZiK2OD4jCEI3EMN+ItuJy9LKmP/ADjDluMMWB6VPIcAQzGl73PB76Xt19+R1qGgG9sH6iorlRvW+xSgOmAcff8889bjb4RVMIpWPuZ/6bcLs+xjyCSzUaZaR/5DxN7gCfEsgOSDmqwyGezQaUPH1MdGPs4ZjFYvEIsEkP5IZQ2xlGMQYSBahy6dkUGCl02JKOOwjiKZ4zy92jlFcYWvRYw3OwNon1kDhiwRsGRbFO5dMsUYFRh3GM8Y0zZFXQ47VFsmGBXInDeqMEoYxbrPZBie31ZxhsPggMmqIKx55boYGxhLNoJNNkbfCcnNSBjHiOS1zs1COfzqRnMHLLPb4w55hafwXUxZYiYl5CNRHswn0sg5e6773b1vXxkF6yTlG1DSYrDxdRuJqBBCQUcA6bkAgGEy+0tCjWNX7NnVc7wXGFrn4es78wT1gqIu8nAY4/gvE22lg8fBqyNrHepAo7hZ/748JE9YGvw8Nr5gHPXSQBJBiD7neklwH4Cx8GmwSY0HAJbEaecAfZPPPvTBOCjgyDso/TauP/++3NeFmaqwJQfNjzT7XVPtweKsaGw7flse2lcfqcJMj0G3fIYxhT2fbygBuds7HEj3MIuIzhA2SbGdjJ7Gg5nggqGXxAUIRME7u10HeHncCD+7sRB+BvHiC43zfPwMFNBAeEK35djuHEic05wGq9L1/nwBtxvuKW9VyU8GVveXp7MCiKU1OmqhkbfG1BAVcUVqi7xXjAFyNqC98BxCXaaEsqcJ/OUQOgLL7yghx56yO+752MUrDWsTQRwPQlqsDAmmw7oI3/B4hFSSEWB9AwJH5l3FHhd9o0FwSlDAscqxqgxzDCYYqlLcHzZCQSOYYzJWHVL2VCjHdBsuDRzovSKj+wjFyQPoutEdhlnkACc8W57rTBeUUEZYKDjVIMwQwogFk7fkVTqZJqS22Hq3xqYpmacO6UTzN9MOjqps6RoY6hFZ3QQDIHwGHUX5w+pQHnF8+ZYfCeMP7c1qknrJc045yQ+PCQNnJbC/VJRvVQ8mwuY23PKE3DvTcYGajfGJOMW0k3QgN43jCer+eNrx1U5vUat4TYFFdSymiVqC7ZmhFCyFnNe0XWsjcOB0iCMQ7+smY9owA9wjqQKn2NMMoRH6ul72APCh/f7kNd2AsdDWMEjWoCB6hxHMXYT9hn7jJMTNVp4xXkS2IgFMlIJatjB8eEoPFDtp9JH0EfqyIX9iU1k56X2c8Gmwt5CIe42gAJXsdvucGL2Kd5vz3Kwg7/B11Pl7HY7Hz6AXUipZmxEI4AxD0Ra2Gs4qp2CGlwL9mSTucGx4BnMOXOd+Dvz0Om6OYF5RUAkH7J0z/S06Pi1yyoJFmld3XzVlngn/ix0wCEJINizM/DtUJ2Aag0mA2jdsjU6cvF9tc8cVu9wv5rKanV70406fO6A5+cEt8W3ZJ8fZEMRIDNzkkA3wi6ve8n6mDocw1VQgwP5aUCTA819V/RO+34NhAZUESzXLY03qrHUOUXRR+6AyoMFH8WJl8C4iaXyNRsL/8YzciAd9nRijD8i75AHJ1U6m5k9usrvGIoYZoawZKKUSl7i+LvSi09Lvd3S8rXSQ09KJbkvMZZLMA4wuMjccSLDsWBStsn0wbDnX/Ne0+w70yA4yHyyZ4XYgfGGQsapTJXJOLE3KrersiBRBArdBjRwGDBvnWrgZj2g0f2GFKLvCd8lLJUukcr9HgzRDTYh2mTVcI8hyTzHPEBNxbp6x+13WP1RHtv40Oh7L86tsMZGqsG5cbcqHLaUUowd5qA9oEHQ0P47hLbQapRb82qoWQOhayoJVqqmeJYCvqM1Y/2/UoXPMSYJWPuv7ZEGcTIHpLKFUuU6P7iRh8C5lYmmqoZjRNtxppQniFUWByAEibaXyFRlH1y/fn3Mz7QDkRX2IOs/Qo8pE9QYGpS2/UQ6/z51ZKRbH5JmTZHvHgc4SRH7IJxyK8rAdscJbERKpt8HoERnsr0AUwHiLZx5sfgFMMEKJ57Avmx8d5wvTmPzO3PDZOa6AXOabKt8yALf33FWP764T0EFrMJJO1tP6mcX3aGG0kgvRB+yRHWIR8msMUEosq7hDkb8hl+Gfi+bm9aMqxjgZVCSbAw+k7EWXQ4Q3489yEhwuuBKBna2Su/vY0JJi9dIjc7iXh/Z4RjBXBCO/uF+NfddVkt/q0JG0eMj47g21K3trbutgAboDfVpa8su6374yB+wAVAf9KabbvL82Dh/nQIPiQIeiUpYQRpiqakI0Nh7g0BaTKSe9xEMmRI4e0L6P38unTwqXTwrvf6c9N2ve3PswX7pyA5p/2vSpZMqRODUTcaYwjGMUYYSCee/IdGMz0yU50kVbupW871RHNrnCaowsrXcAMOUkkFeOLrTxsCZkYAGGPnuAyekUGbqtBYqMOZx0qCosq/BBLIh36anEmMZMmrAmICspANIMOWuIDeMM4i+vdQCY3b/+4d1pqJbb1w5rIu9bVaQBVvQ65IlmQLfobn/oC7171fr4Enr5wt9ez2vI+8jv4Ia3N+OwXZd7mtW37C/5mQV3QdGAhrWnZD6T0m9Y6WEfOQe7CuIQdhXvK7AgDAD52k6IiWC59HnhW3E/uO090TzC7LbjdI35xmr2caL35AOviW1XpIuHJee/pp09YI3x24+HeEXR7ZLA4VhA0SPIcZKMsAJzHhEiEdgLR8FHpxXrIbLTiVcDOwlpN3Yi2QRkxmS6znF/v5i86HIeSls/dcfGtLWluwI2QoJ8EFES4gFDajIQdUMwzsImEWXDuUe2yshpAI4ChwDfg5Xp4+gfeycOn1avbUhbW85pEMdJzQUGrYCMQTOCgZXz0vf+f+k7T+Rdjwn/cv/kC6cyPVZTWmOkfVMjdb+Nm1v3aUhFD2ovEsbtLFxg4qDfimkTONqf4u1AdjBfWgbbNesIj+6mA/AWCJajWI3EyVGcF6xydgbRtk3O8rhQBzYXGIpxDFwnIBCHAcZDxPggOBgSFKmB3UWm6s9tRCigkp4SmDnG5F/jVONf/dtlz78M1JFGup6CMazX5M6WyKKSALFGx6TVm1SIYFxwzgBpqk2Y8rUUmQ+QJYhsJQ8Y3zyt3feeWfceI7XayMTQLUVSykFOEe+W7IkyG3GCuSEc8ibskCUnDIZGhOe91PE7cB5w/2z95Qx9aHJ2GAfQFFF8AGCQiYO45/ANGu1W1IaDeYT6310GUKDw+eO663QaZW3Rdbqd9pP6onZN4+eC6WyvIRjjx/Wx0tnpK5WqWmW1DQ7qWP2hTrUNXTRHMz6f8/wVXUPX1V1sfumoT4KJ6jBODrQsVfN/ZdGa0RfX3ejZpZP3ibkeYXBiw7PcS8m2ps+cgPK2WDf4+T1Gtj27GnwGKdsDOw3smj5N5aNxh7HfhgtvmL/Q5SFrbd3717LUcZ8J6Od78Nx2Q9R1/O7AQpkBB85z2DNNHq6pFM256TFM8LS0d3StPh1yBPi2C5p2w/H+MXht6XHviCVF841JdiFg9dkcZuscMYFAT4CcvwMz8BWZ3yZvSl6LEYH0jINhIBOpXsB2bTxyrPFAt85utSoE7hOBDQQWZprkksQyBgIjTnpAX6ta0O+ONcJZONx/7DZDafE9meNhiuz3pIZRwAC+57X0E8PYW06WTmIc2NVGmHdfuPCOwrNLlNwMGDd01PdF/XI7E0qOl9kzVO3gr60OEZfj9R8CoWZNHNx8lUz3v5xJDvO+HSGQ9IbP5A+/uvenbgP7zM1iNh5UeOMQbWrbc9oQAO0DrTp+LXkIlutA3061d2pgVD+qHELAcUBZydZUYznfWQfbDSZbMLFJoaBb4/c241/1MMYdvHUGBCS6NRblCyoiXEws5mhHqGcCQSEn3FC40gzTY/twCFMuvikR6z1Kt1stSPbIo4/+7F2PRfJ3iggYDCjGuHBHGDPwemKkcWYM+MIQxyHLsEwat5Gk2MTDMkWOJ94mSFkkWRqfKO2Yd7lTUADFEG+otXwxVLQr5PqBO4da7JdoQRhhowQQGBNZb1mb8CZAxjzyYwp1thnn33WchYxVpk7sXoggS2n9qq8tmpEBxe5l69fedfal9yWQ0sGP/zhD8dn7EESXvs36V/+l/TcP0vf+G/SnteTOuawFURzer7wVKb5Dhyk6Sj7vOIYF/rOjQY0AGP3YMc+DUU5QeJhOBzS1f5rujbkj5Ok4cQlfH6RN4CDY+PjIMiE4hpegEMKPuAEPjda2ORkT0Ur6tm/KOPJOsEeaEqWUq4TxxkOaZy+OKx53v7dsCF37dqVtEq/4BBDbJY2vxgeknb8ePyxutulw2+pkIBynLECvyD4Zcq/EsCgcgDzAmcvQTlEQthIjCfGo71HBrZaNmv+w3k4n0TIRAYq8+rNN9+0nN2ZCIKmgqJAUDPKaizBgh1zK/wy6rF4NfeP+2h3CuObQWwHx4AXcH/Na7DxCSJTAcAtCCa/+uqrlo+H48YSv4LLva26NBDxWRDQAFcHOnS6uznpig1uwH707W9/e/yTbZekp/+XtOXb0ivfkJ75qtSbpA17rX0soGEhLHV3eHLOPlLjGK682RgDXtS8Hw4Pqz80kWh2DrkbSCzaf/n+Af3LuUhz1cbSMv3Zutu0osZvYu4Gs8pnqKqoUj3DvRbZY9moL6lTk99TIy9glfzYv1+PPfaYJ8fDEMJRZFdXYLAxl8kGiVaf4DRjM4ul3jXAuUy2B3XfUZDwPsgKZIJHtIoFowzEqgtKYyg2U3NukxY3bJR22BxzqJ6WrJAq0zSQezonKuMhH6gQSgrjemI824mCqWPLvyj+MHQgyxBXXmeyHjCeoglGNtOjUQ4yX+IFIdmIvVY44TygsTOkPp5zOicomSkNL5cGTAZWsVS5wXduxQE1bw8dOjRO4cp6SCNxwJqK2opyfSiqWDNZK90qBlHnQthxKBEkYZ02gT8MfuYRpdx4jmP2DPSpJCqrpm+kbCXB71j9k+KBYArfgX3D7AkAEsRnc0zUlFbW1emj0oGt4w/wxtPS4tVSvbsmnKVBZxJeFvSulKqPkWtdWpqW09ArjnFt6Jrl7LBnJIcUUu9wr2pijAc7mvs69a0z29U1ovq8pWGhHpm1NuclNwoGFSuk7j3jnytfnquz8REF1v5Umxg7gTKJ9lrscBhK9vA52CZ24OQiK9FNHw/2ITgKtg3OMt6LI5pj8hl2URaiF1N200nNjjOEMo8ENrzuUZhXqKqVZi6ULp8dCz7gcFsSaQqcMgZ6nQVZZIYUEHDWYntE93cxtrk929oI77BZogN0ZAZlq0cL9iAcO1FGrqMKPU0wVxHOkC2cCSFLOnhy7s369tnt6hiMlJdcWTNbm5pSy1qeCmCNJuuC7G/7Gmjvp0LQmOwIhHKs4YxxAh72fqjxOC7zBLuePYE5Zcqnw1UJmmDfmWMdfe+YGmZN3If6QwOaN3eutX/YM0vcgs9BuEumux3sIYjH4B/8a835t34QWdvsAYrdL0h3POX+A1lvO1rG1lt8OjPyoAT0FOYYrkYMg9KLVLuiQJGVLWDP1ICAVBa5U9U+d+nMaEADtA/063f3b9W/bH5YxXm26OYjioPFumv6Zh3tel/dQz2qLanR8uqlCvqNM3MOk+WAcU4JHpxO6RoSxilMeqE9zZT5jAOJz+I1RONxKuMoThTQADhw2Qw5TzYH04TKINm1gnPEgQdRmdRBjWVrpE/9ovTcv0m9PZHfn/qZ9I/bOEcK77I9EZBKy6XK/FDWuIHJ9DGp4RAM+1ggcEGQDiOff40xlsu6tswb+hAkClhAzk2DQS9AcAcHOA7vvJ0v5Suk0kWRklPBSj+g4QKMkZ07d1oBi2iwpkIuCWjgFCKgBSl3Y/RDeE2TS9bsW265xdoDeDCGITyQGMYphID1ff3qdTp07eyoaxg7bU55JNgCKWD8oWQ0jisTdGE8spaTzs7c5Fh8PsEKnEqcb3QA0DQ9x4EFibLQ1uxcwqz9ShJBjUrNLFuj5v7Do8eZVrpC5UX5UQt7MoHxyXhKxbnCe7ziGBVFlRNKrDKOyovKXJ3Ht8/uHFfGYlfbac0sr9VNDckF8KYsaAweKJH6z0YIPr+XjG/67CP7YGzj2MHGYv0la9qL4IbpBRYtrMCmxyHK88xtMmvZr4xTORFwKptSQXAhezNZPjPZTHbsx3xzzHoO1t3HfjaS4Ug/DUpDbXpMmpOY08VFeVWk6Xhv99h+TLCkKc2SVlkGwg3DL8zvdmCD4BQl05vxh1CPcWfvNQbilZr1es5iGzmVirbDlM3y8nMRwSCuMaKafENjabW+sORetQ10qyRYpLqSwimDliuwbjJOsMWdOCvrLL01EJ9SAYHMJjcBDSPuo98KgTDew9hhr0GwRLCEdZ/f+ZnzWDJ7oc70dGhY47M5ppfVq6asxsosofwVgQ1jF8I3mA/4spif8Ay+B8djTiK0jdfvj3MgsMn3svahzivjsywITLRfVlK4/QOR/kVXzkV+r5sm3fOR5I7hIymO4VmmhhflcBh86xvWaVfrnlHiQUBjWc0SV+8/2NmqokBAwyMDkelwdaDPeswqoNqOuURpsFTX1/n1bfMJGPyoMdgUmLg491n8cQpFG1TJgGMxb9lo7E5VFLsQGhxTbBA4taJVVYmAUZdqPfdosBFhYE76mrfgxk2Rh5dYdpN0+Yx0IlKaxqoLee8npaLCKSvHeETtESubBxCcw+Am/dvrFOz+4X69f+2ouoevqaqoWtdVL1dZUfx6s5xvIqMP45Dv5pWKCqMO4nXnnXfmv3o4iCMxT4MueQjjuInlGObvqKcIACajODXl2EzwAgLCz3wGe4zJDLGXcZsXGta1UJ9O9URUitPLavTAzBtGgxO839iEGJqmZi97Dms5DinGKX/jc3ge0kRAMroJLIGOCeUQ68nkcJjLkIYkUFsyV5VF0zQY7lVJoFzFwcQ1pH0kDxO8QtmarLrO3HsvOMa8inlq7ruo9sG20edW1qxWSTBxwIRgRueI8tMgqIDO9bT5QY1kUDon8vCRN6AsCOIjmrUatTn2v12pmyyY7+wdR48enRDU4HMQTsFj8B/gRErWEYxq2Ku66mTAuxFsFTwIQDzyWW+PSXDynk9KL//zmLJ50Vppxa0qJBB8M2WnnICNxBhhzGai1NIrl8/ohUuREpsPzFyg+2dERIWxQFAwulSzE5hn6cxjO3AOI5whkBKrh0e+gDJU08oKR7iXD8APhPM/Vrli1mmCCazfya7XvB5+wXgkaG6yvhmbjHMCy3Y/0909RXr9yl4NhYct0dTGpjWaVlZvrdWGs8AlyP5gTrLfkG2CvWiCLgT0zGczV3hdPG7E60Z9BDWNkSCG+Z25WJtkoB+/81O/IrVejByncXakP4cPzwE/8DRTw6sa/7PKZ+ru6XeoZaBVxYEi63cyCNygoaRsfPmyES1fbXFm+g/48JEN4Bxl4TcRaZwCEBCUVShLWMSZf2wYyTgyCWZwHKf3cDx7M71cYvv27ZZDLO+dtPkKSMftT0rX3yX190j1MyKZGgUEDCE3pBM1eSJDP9nAxnB4SLvbtqk3hMqDhnNdah9s1cbGO+LuTXb1YCyCgHPXrToxEVDR49BGxeJjcoIgGWOcAJ4TCDzgjCKrD2LihnigljJ7C0HsRMo/gPrtg3M2WH0FqHlbWxwJguzcs99K7bar6iEhKKb27NkzYQ03cxGVFgE5GhBGg8A9/UTIGhlVTy5aJa28WTqye+yFmx+VGpJv8F0cLFOxH1zLKOwBrmSDGkZ95QXHCAaKdHPDrbrS36yB0IBqS+pVV+IuM6e8qGRC6Sp+qiz2A7M+CheswQSh7XYTay7PoX5lzcZZgFI9GWcuvAUbB4GFE5ItT5gpsJ+yX+Vdmc5CwvT50of/H6njslRSEREXFBhfY5y75ZjxXpeKcOqFS6f0l++PiM4kHeps0UAopMdmx+Y8OGwTiRrhTV6VwuJ7UQaaUm350j/Dh7fA/scWjzfuKRtFYAP/UCpBZbfr/vzKmfr4/AfUPdSriuIylQYjTmv4ip3jYh/iByPIhq/IPjfNXERMQxY6/q5Y9idBQrjTqJ/htielF/5+rPdoRa1080NJf1/BwaYlDj76SA/wA88yNRgwiRw4yaCmpNp6JIun5i3VsxdPq2WgX8GANBQO6/OLV6vSD2r4KFCwKMdKHyWIYYBCFucRCzrRdjelGpizhZByjXIXJx2AaPF7dPNnHwnARp+kijmfwBxwY0gb5XcsoEBMNrupdaBFvaExQw+nVl+oTy0DVzWzfFbKxAY1SSzndLLAgUBQw6k0kY/JA9Y9CAUGfKzMNQLgZEu4bVjJvLKXXUiqtAgODBtwYkXvPZAk1m2ngIV5TzyHAiQEMsO/Dz74oPlw6cFPSqtukTrbpGmzpZl+rdp8hSGSEzJuXPIL4BXHoJzqzPLZSb+PQN59M1bo5ctHrAwNVviq4lJtbJwCCm8fkxYELpyyqsnSs5fvRASFkIrMUjcKcZCJpq5eg+/I/ocNxRrDnold5nWfs0kPhFLTC3MPdsoQjYVEe1gqQY2nKQcWhR9eeD9uUMMNsJu8GsfYlJQ29QMakxsEHRARxeptxPpIoJrs61gBa69QTOmw0uq42Ums3azbBDpi7TWJSsKRUbh3716LX4y+jvJ5H/pV6dKJiDB07rKCE4NOJRQ7ZfQ7vc7twYie5RoNpWX6+ob79KMLp9Qx2K8b66fpzul+mrOPwgXqVNPkLh5M7wocpSzybEyJjBnqmhO9zve0a7O5YiySdkg0HtLBueOgnhJlqaYw2FvcOrRQJVE+J5ba3I26KRqhGCQlHFXvM1oh5VXAIhHYyKlxS08FH5MbGO1krRHYICPCqWcK6yRk1k1AAzC33Chc3J6fHfS4gSQ4rdEQDdLQ2acS7VU4x8hCGld6i3/nLx9HeCh1QlCFz8NR5zum8gPpZFukExDxGrdNu84qaXG6u0VlRcVW2alqP1PDRwGDNTq6710s2wpugZqX4IZdVOUEnJ8ILXAY5W1vrxEnHWVLzBqDjUgzXMRhBDpwouV7YMZHeqAsjZsG9QBBCRnWsVTqqYyVodBELjEYih8ccbMfejVuuT6U+cllj0If2csGhz/Ao2NVETB9MNwCO5x+FfSh8dpxTfZ2LDsfYRd/p6R6POAvo18h/rZxvJ2+o0tuGBXX7Nm1y7Jl4TRcJ/aGQhAGTwUMDg6O6w2cVlDDbdpHtgIbn12U2EDz4SPfwZyiMdN9993n6vUQBxzAbDak0eHYjbfgokzBUVQowEBD8csGRmCDjYrNFSUwTiy+t1dl8HzkD5gD119/vavXmvr8sdRYyaSYGzSUNqo4UKyh8JgxFVSRrvRL5cE+1TuoNzC82GAxDimrYz6X543ymPPBKEpV+WSOg4KKhn0Fb1z1d0g9zVJRqVQ9T3JZdnKqgSAEAQ0y8+izFGvvwPGEse4mIMi6ijPHbSAklnPMjEkDzoHG44xRSA1BCcgSc4HX4xxA7ZUo+EApreeee84KanOuBjjZCIxwPJxnppcMZIa/MSfypYziVEY6QQ176ap8wPKamdbDh49CB32N3DgCDFi/ceYyFxFQIYyKBdZhgiVkeHjVXy/TwGkGL8I2I7CBoAyuARB/+SWqJiewy91UNwCMAWyL6KAGc4OsJ7fNk+24e/o8fevs0XHPratr0qnuNi2orFfQoWynCfbD4bF3sHUQitj3Sf4Wr09IPGA34k/A5iK4R9mhQkY4HFLn4BUNhQdUWVyviiK/hFa8ADZOfu6/U0CaPYNer9hmbgJd7ANwgHQqCTDm6ZNh76tpsrzx/XB8+BC+L9NLg3kKL0gU1OA7UlYNPmIPasBnyEjh78wHen4QmOFcCIC89NJLeuCBBwqfe08CuG2DUexl13EfPny4BxFmNhe3KnUWWRZvFnkW3wMHDljO4HgLbiEqkFDas6miHDH1GdnESB/EOOW7e9VA0EfugdMfkuk2w4IxHZ1uigOVY8RrNB4LpcFS3VR/qw53HVDPULf6Q0E9f6lHrQM7VRwI6heX3qwNjeOJDJ+FapxyVybgwDmx6drnM04FiBBNkpOdiziCUadzTLujtyDReVo699pY8+eyBmnRo5EAh48JwMg2BDbakGMcoTxlPzABr0TA8f/6669bQYFUHM8EL1pbWyeQFs4P+5DnOT7HplEfpIBskw984AOusikg8E888cSE5ymFwhyAkNizQfh53bp1ow4pH7mFUbelUkKK9zCmfY7hw4e3IJOCALlbsOcAbDH2F94fLyOVuZtKOZ5cg2ANgXTWHUo+8h3Y43Dk4cQji8PLkts+cgszrtMBYg2yl5IJEhp8fMFKDYRDev7SKSszvKJIerfrnI4cPa/rqhr1y9dtUnnRmDsOIR/cgrLTxqlrHLz2km/MTc4LvwDO3WRgst4Ro8QSzxQKQuFhHb+2Sz3D7aPPza9Yq8Yyv9dBPE5AYNepbCx+F/wv2PAPPfRQQu6K/Y5NDt9NpXQ4th8N6qP7eJj+gmRPMReeffZZK/hGCV4Er1QvcJtd5FTdgTWe+cQ5s+cZnwLfF0EiokUCh36wO/cwXDMRgl52HU8X+9vP6Z9Pb9M3Tm/Xu50XM/55PnzkEizUOEfdgPmH8WGUtkxuDB1KNE02sMGwodrB98bwwoEH0YKA+HAASuorp6XmE9Jg5tdsL4DSKJma/xBQe0ADY59siXTKrNWU1Gpj4+2qK7le3zrTrdaBCFEfCof0N8d3q9M0ExsJRkIiCFTgMDCNKCE70USYsWzIA4rzZIBihMZ9brNY8hbhkHThzbGABuhvl1oO5vKs8h4Y+KiSYgHFoFuHEqSDYEOqQQAMezIyopv0md+ZjwRa+AzOedu2bdbrGcPpOIdQ1TL+WR+2bNni+vsyR7du3aodO3ZY/6I69pE5YJ+kmkVpHDWZ5hg9Q3166+p+/eTiVm1tOaj+4cLYH334SBXJBB0oqWl33rC/4EjywiGcbyBIbt/L+Bk7lP0LZxYB+sn4vT1BV6t05ojU1qxCgQnSpQqykcjqSSWgAYoCAf3sojX61qbHtbauSlXFY7bT8e5W/fji0QlzEaEIJbPgFuyPCF342T5uOR+ctQRBEFklO2YRqsCtCz2A19J/dlxAA5zrPaRhW/a9j/FAKIfNFZ19bUCQgiAe2Q1ugMgIO5sxlSwY7wQqosWqCJqM2BEu/eijj1pjngAIY53n0i15hU8JhzmchfOwg0Chk4+OPRWeA7/gYQS3PnLPMVxlarCYkqKWSexqPaWfXBpzcpzovqIn567X2rrcR1qv9LeqbaBTlUXlmlMxc0KqoA8fqYCUUowJDBI2llilQfjbkSNHJjg3MXBYSHlwHKLkRK35HWdQoRsqTsB5hqGHggBnsZ8WaEN/j/TqP0kdI2Sjoka697NSTX5ntRhVBKmnTv1lCNwxB0wWRrTxgCHiVfmD0z0dVpPYkM0BT2DjUt811ZaUjWaFQP4Ze2yyiZwGzFOIBwYa85jvYVLLYwECRrDkySefLPwxjvMw5JDpOdCVi7MpGJDhwN7AeI81XngNJIIAeSJABtyWYIiGk1ILsmNvvmkyqAje3XPPPfIKkBYIE8pFAhuGxBD4jj4vrtWePXuseWkyqJifqLog/QgByP7w4S1ilTFwi0xzjIHQoJ69uFU9w30KK6wr/e263NeqJ2bfrqJgbu2kodCgWgaaFQqHVF86TRVFfg8xH94AZw1rMjZLvHUPe4P9IbqZMkIRshcQcMBXsGGwfVhnWYexywrePokC14CAPI6qQi/J4zne3SG98T28epHfb7xH2vio8h04Z+GMptJBNHCSwiuxpxBw2OcB9gP7WzqlOw3I0rjY12WX91g/n+3pGPd5wNh8bng8AUjmJDYOjuF4ZeMMKLtDRtJksIf6Qz1YoOOEU+zzgyH6NfplbmOBQALZGrH6zTBvcPYzptxUGmAcUlXDDR8Zd//6+x2rEeDIto9/zgE/F/tRtPA1VbB/kZnCA35hgjh8FrwdMbEdCCjhE/zNzB3Kt1HOi+8AV5lse2IhcQxXsx3jhZuWSWxtOT7huW0tJ3Ie1Djc+b4OdBwb/X1W+XTdOe0WP7DhI21AIlBCocbGYHJKE+fvqFTZfJw2FZy5OIMxaHAMM09xXFHyxo1hU8hkjQh5tGN8Sjf92/eS1GlTJPddk7Y/LT3w75TvgDwzDyDMZuNiLOPYx7jHgMGQwOCPLlOF4eNVo8qm0opxAQ2DhtKKcZ/HOdG8GwPObXkHvhtGEPOZPTVWHVCykHDgohqcFCgqizyGx7JdLJSNd6D4mAgM5H379jmmiAP2BXpv8CCzIxMElTnnFLgj+4im5nZjn3lKsBllI/96CeYPjm9IDUQHR5zpM2XmP5kolBWxl7wy5RohQgQVcdLxfXgN18yr/QIHH5/P906l7nYhI90mkZnmGOd6rqh7uHecw6N98Jou9bdqboU35DgV9A/3aX/HVg2EImtjoDuo1bU3q740v4UIPgoDlO7AUYPa9MEHH5zQ38vUMWc9dVqz4BI4qLC9+BnbyyhxWX8na3NhnFLsYfbsMRxW7CH2MohTLkPDHtAAe1+T5i6T5iVf9jWbYI83tpLdgYutzb6N2IGAnSnxZBdJYYsnqtnvFghiEUeR+W2uIiKqRhu/YB9EIc6cM0FJN4CT8D0IymCbIZ5yUjbjK8BRjZ0yGQIaoLyIgNN4GzWgoEqDqWXWTBVgdyGcYp1zEjyxLzCOcNgzTxJxXdZG5kuywK6P5vAcx565wVxg7zGldCk95bWvx/Ts4HrwefB7O/ch4IFPwvT2M2Bf5dqQIc61AhwDLpJqT00nsFebcryJhJFTlWPkTVADNezE55zTorKF7qGecQENcKnvis70XNCiqtxnkPgobLAo3XHHHdZCiaPGDgwT1FWmdnisxZtjEE03MM4clCYENibrwnfrrbda39VcFzYeNhsT6DDPszGxKU+Jmohtl8YTDn42WRsFck8xto3BQJkxGpAZ1UMs8uxlRtJtTfP15tWzOtrVMpqx8VNzVmh62RiRZb7iSMVgYczFMghjnSvfCeOMBumMzej3onSfNAENwFycd4905iXJ7OmVM6SmNbk+s7wHaxxrOGPCiYAyNxiHrH8Y+pSAigeIA453xhzjFoJPcB3CwlrpVF6B8R6dFs7rIcxmbjJvMbZJD4cso4rEoRZr/4GoQ2SYR+xVHO+1114bNVqNc4H5YT6bYCBG/X333Te6vmPo8tkEfVgvUFvF6uFhalMDvivBIkpTIShAdZXuXml6QnA+fHfUvpNGsdV1RWo9J5WUSzOvk4rGO0vgB/kc1BiOwSWGHXhHNnGm5z0NhMYcp2GF9P61g7qlMf489uHDDVgPCUS8884745xGpukxayJrVTxHPeuvff1HOGTWY/YGO/+YLOBaPfLII+OuC5kqfGfTtNmAn3EyTcbM+HFouzyeX1gISK0X8z6oARjDBOLs2Ubs2djjIFZPCoSFXgU1wKcX3Ki/Ob5jJLQuVReX6ok5K0f/jpOZ7HOCjNg/CDGSAYFGvitjFVvIzFe7b4F1IV6vnEJDU+k8dQ1eVefQFev3gAJaWLlOwcAkn5MegLULR7xdoGQHc4UHtjdBwHjrPeslYxdbjr0Fzm4qLLBuEhh28mU5ZaMTVDa9XhizzAPWWs6FACXBBwIcsQC/gWOYnpT0oGVu2YVazA+OZTK6n3nmGasxuD3TBF4BVzd9bOFYsfxxfF/T45Dz5XqxfniVWcJ3QqDwk5/8xPqcSSNcHh6SLhyVBnqlpvlS/UR/nVuO4TqoEZ2C4zVW18zWzrZT42Ktq2tzq3brHhpTdhmwWPbYFF8+fKQDFlKavdKMyb7A4/Q0xlYqYDFFbRJdI3CyIDoV2JCzaEON68vmwgZJKnmqdb9d48Rhac+bUmhYWn2LtCaLDdiqGyJBjFHiEZAqCkdFh6MVAwMDiwAH2RuM41Saf6eK4mBQv73iNm1vPa/2gT4tqqrTmrrxhgNkA0cBJNbsjcmmpmM0QaJQjdu/XzRhnjSomi1d95TUeyXSHLxyphSYhN8zAyCoTZAAIzZWRhLrn5t6zwQxWBMh87yHQIKpqUxggTlIkAHiYgx3yEw0eeD17FM4CZgPjFmIvwnQcQxUTtHlTAzIcuI9fB8C+tFkh79BzFEcEvgHEANeZ8iMKYHC33Ha8dmUU3ADgimQfc6Z+cv5cF343qkSBa4/wUjqClMCC4U0BKjg5/OFd6V9Px5TQtZMlzZ9SioeC8ZyDdNRpGWaY8yumKaiQNFocIORVhIs0Yyy5Jqreo2+YcpmjHcU9od6x/Wr8eEjHSC8YI23iyewqwg+p5rhyl5B4Jm1bTIKp/hO0d/LCMyiwT5H+SLs1Yw7igf6pJ0vSi0XpdpGacNDUlV6NeVdo9ppLw9HeEeBALsBIQN7tSnVnG1cXzdTv7/qbh3uvKySYJFubpij6uKxeYjTFsW3abjstieOHcxLnLBOvQqx53BkTyYEAkEtqlqv7uF2DYcGVFFcq9LgxHJGPiaCPQC7lzJU8YIEplpCPOBfwZfF/mD6TiDy4WfmHI5pxFlwBsMNYo1v7H2Tec0aiz/M+LPgBpRaiwUToEDERFDi/vvvt95rbHtsK9Z3ghrGx8BzH/7why1ORGDDBFrgTGSNvPLKKxbXcGuX8V74iBEQcO2wc/keqfqhOE/8elxTyiNyLAKUBY2hAem1f5TaTYZPQNrwAWnh+L3WLcdwZY1woEwHNe6fucrK1jjQcd4iHDc3LtQd05Ypl6gpqbKCGJF4egT8XFfiXTqRj6kLFk1K7rBZ2B0f0Y7OVEA019T9n8ow6lwi9Biz9CWJpchJG+8dkL73v8dqexLg6OuRbs6S6vKG+yNNwumtAVCObXhchQSMFdQVBPowthI5KSl5E8txmk5g4/ZpEULhBByrOEUxWnDqovJOdWwSxMAANCo/1ONGnTLpUFIplRS4AZYDME5oGhlPTZXs8YyDi94XdgObcc2cwgiH9LMXYdA71RZnnGJcm7IlJkiH0c0x4/VIgDxhlGNXQhoI8kFiDCACzC3OxZ4pgtOIIAgEAXKOIwlCluycsWdXQTQIGOGkg+DwHfgbZCoV9S3EieAGmTM4u7hnBeugJjh/4Lnxjveuq9LJndKy2z0rP5VpjlFdXKEHZ26wGoVfG+pVXUmV7ph2g8oJsOYQVcU16hxqG/dcZVF14Y4XH3kFxDyoXO2BWtZ4xlc6JTtZl7F//GbaEYEV2cXsG9itZA1mZP7S0PeH/1u6ej4iXLoQlM4ekz72/0hlWXDgNs6Sbrhb2rclkn3LOSxcLS1Ozf7NBbgvlIlhb0aYlIg7mIxSrzGnotZ6xILh7cxfE9xIBQTiTGYKYI9Np7davt/b6uLCCbDlExD3ME6oDhLLP0JwPDrrJxaM3UwQGD+X4RjMJzIXECExD8mAINgQ3S8WYPPjtOc17F8ch6AIgiwjwoo3FuAYZLgzx/n9iSeeGPcak63N/mj8bexp8B7+9uqrr1rzB4Ela0V02Wu34LONQJn5x2dyfEQFqWSAmTWM48Ch8EcUdMbG+zuldntFkbC0+8fSvFXjMsLdcgxXHk8GBYM9kygOFunxOev0u6se1e+selQPzFyd874VFUXl2tB4veWiNFhatUBzygt4APnIG6AOZcHEyLArRjBo0kllxghj8r/00ktpORomE1j4IR4of00jKM+x4+UJzcq07UVlDdWN0iO/JN3yuHTTo9IjvyhNLzwnMoYTRhCOzniKBpytGDbZrgvLOWGUMJbSyaYCZKZwHFO70yhIfPiwAxKK4Xry5EnHC2MPjCWD6PmFowtjG+LBA+c866YTeC1Gv3FsmWayOLyYFwRGYjm9WI8xzO+9917HpoKmR9JHPvKRCX/HAY5jDvUVryOwEo/gOMGpZBz7LuSG8yIQjuODz0kWOLeY09jNfH/IYsGCdPBQlJqVLa53rLEpgB+kU1s/GxxjZnmjPjzvHn120aP60Ny71FSW+yzGBZXLVFU0ZqMVB0q0vGaiGtyHj2SBkIe1h3lpd56YMn3pgHWX+YrT1UcEBPO5rqiLU1HXJ0TzKenKubFMbErnXWuXTh7K3i3Y9Jj0+OelTY9LD35GevgzBZdxy9hFaGBKPMUCNgXq9XRt/FTB/MImSScD0nw/bBKA/UgGhw8f0SAAgZM8VvYS1UOSbQDOXLNzDDOeCWIQ2IAzwC8WLFjg+H4CGyYjkDWVeYs4ld9Zb+EYscA4h78geHIKMnNelIilzKAdiBT5DHxzPChpjsDKC+CP4zPhGFzPeOcfC6bUMFyL+2HmdsGiuz0SJI8WU/V1p8QxXHlPGFg4Xdx2H59MWFw1T9PLGtU52GUFORpKc0+EfBQ+TE0/nEI4eDCwTO2/ZEvZ2J28RLJZ8NksULFmvNxSAcFEuHGCcX1GneHXWqSDz0vdLZFyTWsekupSiMoP4gCLIjNDkTrrWUN5lbTUualwIQH1NiV3yGIihZpxzIZGBhJzhHlDYCEdFVOqYA+kvEyqSm47eD+KEgwTHhhRPnw4gTULI9hJDZpuT4No2DMz4qmAOAd7QME07oYIEFxHTZROqQMnMmJPkWeP4/y4Jua1kLNEpbgIasQjaKwvpJqTScm/yeyjkBWIC9eAbJKCdiKUVkqUxhiyBXdwqlWPDyQjFEgnuAzHyJjYII9RHCzRuvpN6hpsU0gh1RTXW8/58JEuKM+B84h1mOAGthMOG4IdqdgtOLuwUeAXzHXWdT9TYzywV02gnexKa09ivRx6XxocCQAVz5VKViYfDBjp2TTxxmSZY8yjMXhuq2ikC4RD2O+owBnLcGecpGSBYhtgQ/A3REe56JWCKhnRRSxnbzIgS4MyO2SNckz4kw8f0WBdxz/y1ltvWTZ2dB894FUGGrY36yOI1QMPsL/gJ+Nfxi78gvMkOAe3gPMgwkon8yj6O3E+NCE3wP5nL8Weh+sgEOAz0wVrCxnncLpkOIIpNcyDdcL08itY1E6PBOjtgHOUj/lBsWEQ2bnhGK52VTO4pyLpANXFlZpTMdMPaPjwFMYZRPkMDCqMJxaoZFPSOA7KUAw1HF8cKzpC7mN8KRdSKTHyjh06oDPP/I0GW85HavtRWmPnv0h9KTQtXX7j+N/ZLJff4F/6FIFj8LnnnrOclqgScJaazd9eczbbgBQYouAVUCqj8vO6lJaPyQXGP9kE9GuAfBvgqMoFWWW/su8znJtp+AxByobBzTykfBQBQYKhNPODgMTLsuDaJdof+TtKLwKY8QDhQuEGIFvsx4iAOK/HH398NG2dz2TfKShQBmP9B6WgTf/UtFBaND5wHquRvVvwXo4xFREMBFVX2qSG0ul+QMOHZ8DRgzMABxK2BY4qeILbvkN20CeIgAb8ggclSiZrP410wVqGuh+1L3vHiWNb1HJpFztA5DF0UhqMXQ8+JmYukMoqbarWgBQsKogm3fkIeAW2CiIEHISUnkQkgY1FLwDDyXMBnLVeBDQMEEoyX+FUPnzEAvMAh74p8ZprcB7wfHsjceaoaRiOryyVbOpkwXoOv4DTsCYgdkKwlC7YS/GrJ+rtA68gUwE+hdiTtYG1C/EZ52Rw6tSpzGQJZhKIcGfZ9jC4xqYPS0VjtoWJPbjJFHIV1IDcscjmgnRsbTmnPz78hv7zodf1YvOJwrthPnzEcK6jPLeXXKDcRrx02Fgwkd5sl+Ep5GuP84vNYHrpkErDA3rzkFlbwtLwgHQ1hbT6TQ9KG+6LLMaQjVU3Sw98JBNfYUrA1IpkQ+N+mSAG/xIIzBUyUS/Ziz46PqYGCHyxduE0MfYQQQ03jcK9BM58go18tgleQDZQB/McZagyHWiB9NgJAUou0s1RWqLkilWuy+085nh8Fx7Rn8vxSUsnS4T5S88Tghn0/SCogiranj2DqoqSkJmo0Z1RTFsk3f3z0s0fljZ+Utrwkcj+ZgPcwEnZ5xa8Nxf8onuoX89e2Ku/P/m6nj6/Wx0DI/2ofPgocBC8OHLkyOjvOGNYF5MNRJhShDh2pnqPPrfA+UJAnDKGtZVdOn+hRSdPXbJlV6QQ3C6vlD7wealuhOfRIPyxn5Pqp3t34lMMpgkxc8JkeGKzEAAkWzNXwDbxMgsK+8wPQPpwO/aYF4hwKFWYS5BhiDiILEMTYMRPZrI2EAllkmMgUIoOXuB/IHsD7oW4LF2fNN+HTBD7cZj7cAh4Bd8RHx/Zf5SdYm1CZAAPZI2yi7MOHDhglcwrKMAlbv+4dM/PSLd9THr030uzlo57CdwAf6kbobZr6waHabYzNd6+ek5/fWL36O/vd7dpIDSsx2dnN/WR0lMnus9qODxs9dOYXRFpuuTDR7qLGZtGOo2rWQjT7cExlTdv69o31qqnf1BXOq5pRv1I/dJU6sRC+O57Urr3pyLBkQKrNZuPIMiHUYEqg/3HyYDgOdJBs5mZ5HVwHSPGn8M+3AKDHuLxyiuvWDVqMfrICKDpHX9jb8l0kMMEMDC4CTraQTYcAftMll6CdPHZfA4OCTv47iirSKVnbUgnEMl3QblJMILAI4FWghk44iE9qK34/nwGSmiCFzRgj57Pphk6vbS8aPieVZAKbksHj0a65adywS+GQsP61pmtah3oVlhhXenv0tmeVn1uyd2qyGIDcfaSi31ndG2oU6XBMs2tWKSS4ORr5Ooju2B9TrbXkBPYU1JpaOojsg+VT2vQtKagtu88qsWLRsraprofTZ8nffK3IuU6fH7hyf1hfGMjIMIgeBcr0yHVRsGpwPQR8Aqo7rE9fPhIpjwS/YFMPwnsbJzm8AvEOtmYD2QW7d+/37Kd7VUM2I/Y2+AfmQzWIVwik8spa4rsR9aPdAWJHJvABSI11hnEUHANMjEIoPA96T2IeAvOQy8SuI1TmfqZM2daa0cq/U9yCvbDafM94RfFydSKzHbDwxeaT0x47vlLJ7Ia1Ggf6NSrl9+2SA841X1W6+vXakm1d6mBqSIcDmkwfEbD4W4FA5UqDSxQIOA7twsFqD3TjTKzePkZGmmgYa5U2aD5M0Laf+KCZjTUSiWV0vQ0MgEswuK9mn+qBjUwpFAaQTycHP/8jeAgaamFGtRIlsD0DQ+rLBjMSNaIj8IAyhWaZQPqP/M7DwxiiABBPohsqj2a3AAHv1PPDYz9TDvCCGhAJqKzKOwgawJnRSoZkAaQONK8mfMmaEG5K7JA2MP5rvyN6446OlbGgpnjXK9k6+jmO1COwRFSRS74xbneVrUMXBv9HRu/e7hf73c16/r67JQ2ZNwc7dqvKwMXR2wGgisXtb7+tvwoRXXtstRCM8uw1HidVOM7twsFODdQobMupRPgxpGS7SzASYWSRdLAvtH5btlsxWnWZPcDGp4BDs4cwWkay2YhKJDNoAY8x15yJ11gIyVjB4bCYQ2GQirzxZJTGti5gLFIuW7sbco94WSmDBKOfWzsTGXwcVzEW07gczMN9r54PjqCCJStS7fKAiI0HgQyyMa47777rEw/fB8mSGH6Jt59990xBZArVqywSlARjEE4lU6vkULlF65XTBZ7millE0PRzUNiPJdJHO06rrBC49r/Huw4kvOgBsZR7/A7GhbliyLNyIZ0WZVFGxTwDZ6CAOpSnCJsFqhBU9kY2GwKvlFQLlFUIt36cZUc3aLBs53S9OukFXdLpX4ztXwAmzdKdDJqYm3kOG+zPQeYq173McA4SlRW672uLj319ts60NmpqqIi/fcbb9Qv5LAUl4/cAyc7ZaBQ+ADIK4FuVIeof1BcmcZyjNvXXnvNCn7wO3WjmV+FCIx9MigeffTRhEHRdIIaBiiycBKafZogKg/T28QNgWDPJz2c9Yp7gCM/OsukEEHgKJ0gVi74xXAMLjGs7HGM3uHukYAGiLCMvlCvLvdf0JyKhcopOs9Jx54d+/3KYWnpw1JD+k0yfWQerFMPPPCAVRYPQUgqayAq0XhBYx8uUBxxvs2Y2aPLV0OaOWedVJzjue1jFAgRKG8Tr0cfJXBwKmbLVsJhi72Bk9ILoAbHFqQ0JyKNeH6lP3n3Xf3xu++qPxTSHU1N+u7mzZrtNxef0qDEKlkT8F0eBADhqqdPn7bGFQId+ARjC5vc9LnArmNeFWrZQjh5oqxqJ78EfrlkKy/A4SgXSYDVBFPIyIDn8Dv8ItF1rKurs14LFyTLhnWLNcTL/p/5zi9cjzSi1NkmHbc1jY/EBRyeyzQGQgPjAhpgKDyc894ew+HWkYAGiJxLSJ0aCrurS9w+0KHdre9oW8t2vX/teM6/z1QFCz4pdES9k00VxyHCe7IRsZ7UKKuS1j2mwI1PKHzjB6TKulyfkY8RoGrDmMIxGQ8ENuyNkw1QOKBiR7nuZc12nMHvv/++Z+smxotJ840FlFMPv/GGDo80Yu4eHtYXdu/Wy1lWOPvIL+AgZxw+//zz44J7GLIY5DzH2Ge8Mo9Q+hBEx9iFlFCrtRDBvsf3i5etxLqQqAmfW6BOY55GN9uEbLhVRBFwwsFItgbEEAJI7dxCBmSMwFo6Slbe29HRkVUH6tyKRlUVlSowklXJ/0sCRVpSNTHzKFMYCjsF4wMaCnszZtPC+Z0j3MI8eM7lWA0PS1f3SWeely5skfrHesf5yB5Y/1B8onRkrU8W2E7U8PaRJornad7Sn9LZK3MimRt+hm3eAGcZpXXi2fK8BsdaNHA44viEX3hZyx57AmGKVz0NUDlju+zduzfu6/7p9Gn9AU7pkX4e21pb9dTWrZ6cg4/CBVyC8W0fP9jd2LJkgyP2Yaxiz5JNAL8gy4P9h8yDbJcW9QrsnWSjxAPZVNGiSuzYZHvymGyxxx57bByfIJBhBGlucMstt1jlrNavX28FVp555hkraFvIIPbgll8E81lJ9cispfrw3JWqKS5VVVGJHpy5RJ+Yvyar5zCjbHwdLwhQU2lDzst+kJfhjMSK5c7BTr3dslWX+pvVMtCqo13HdLDjkOfn6MMdUH+wAEEgkgGGWDolH3xMvA9szj7yCxgHRvkRCxgekItoQOZp1oiShPmC+gkCki5Y/1GqYDx4hURGy/Fr13Syu1vDNvJVHAjoJ1nel33kFyh3hAIPIoE6B1UVthokHaMWhS7Eg+A5Bq8pJYISaPp1S/V2V5v+buc2XekurLWPmrEok2IBosG1SEVd6eRc5zhezHeMcwJKqNqeeOIJi/ARFEDxRg+QQsu8RFzBOEu3pwbHyGYJqvKiEn1iwWbNKq+zghlNpTX6+IJNqi3JXpZmZVGNigPRCfNh1ZXkQT3kIYc9dzj+PjyKi29JLfuk3map64x0+llpoNPzU/ThDpTwYO1KpvkwwUr2CL/XlzfAwedFjxMf3oPSL/H8W9j7OBqZE3YQKOS9ZFvikIRfwOPhHV7YddGflw5wOjv1BrDj2UuXxjkFh8JhbW1p0TWPhCE+ChNwB4JizANEUJRhxUaGY2AXE9yAZ+PHQvAHeC1+49nrV+q7b72og5dOFZx4mu8VD+yp2O7R+6rZO2MBGz9abEU1Ciq3JLNHOyEQCFjlchFQ0euPTH2TAfKv//qv1mcUGlib3WZqFCdDxCBc2UQwENCTc1dYj1xhWc1iXRvq1qmeSMS8vqRWtzY513gbxfCgdPFdqb9Hqp8tNXmfaloUqB2twTuGgIKBsWY6sXC6Z+KgPtN7VqtqV6o4mLmmOz5ig+i2WRzdRmT9BuHegoBGJuvP+0gdEOt4c4PnIRXGKYYygU2c8jBs8jhyTaM86uN6pV7xKqgB2U3U6Lzaob4uq39NBhul+Sgc4OC/4447LGMZAksGgKnJTBbT+f4+nWuoV0NdnT44Z54u9Hbr87veUPfwgAbfPa2/3rtDX15xgxqLii2CUlCN5hyAqgyD3q1iijlIJgtkjfWCf9mXKU/BdSSlm4Ao64ohbukCQmjKZ0FAULXx4Pp7UTIrG0DBSm3hdEoMsL6TvcKxEhFJLzGtrEafXXSHcgXs7TW1N+tw5x4Nhgcs0dTSqlWqK2mI+75wuENhtXLlFNAsBQIZqJ1cO1e60mnjGAGpZm7i9w31Sl2n7GcbaWzc8Z40/Wbvz9OHK2D/0MuH9cwNJks97nwB+4sfIMrfrE8a8MZznCGcImDBvwQtuJ84J005J1MxAbvBq6xw7AP8Atgh6YKG6KZHQjyOgd+NnhoGRYGA1b/Phw9sVMOv2UtMCWarZNLQoFrKpaq5M7WoqkHXVTfpX8/t0f6O8+qbFtb+Hc9qdUmTNjQstN5DoKRQy1IBvjM865577pnwN66LU2895iD+B7g+AR7WEPpxIE7GT0HgCPufDPRE/gC3WGvLtGTvJ5sGvwWfVSjXH14At3UD194QFmyvUuEKCcFAUDc3rtO6+lVWDd6yYGn8LI2hQWnHt6VrVyIppmwO190uLdno8XlVqCJ4g3pDlGVB/RFUeXCtigKJa6eFSA2fUFQr0ijRR+6A2gNHFIpbN8C4QimSzQbJkx25zsDy4QwcfGzE8ereEnhn88OJiRI6FonEWEi1eSaBFcpE8X7Az+yNyaaaOh03EeGdV1mpn16wQN84c2aUbBDQ+Jw//33YgAMex7DdOfzCpYv6pTe3aGDfOwr3D+g/lZZpUVWp2oYGFC4KKjw8rL7GBr1cU6r/ceMmK9Wcx5133mmtiQRFINfsTwQP+QyyQyD1kBv+xufh3GYOkjUF2Xci0QSPyZriwbiHKHm97qKUwvGA+omyRvQX4XyYp5CpaEAwXn75ZW3evHlcZgffi5r0PG/fo5nz8WpTpwLOjcbvHB9i89RTT1kECGd/OlkQmQa8ALVqupiqHKO2pEEbG+/VQKhfJcESBQPx94FQ+JLCOjgqagrrlILhjQoEvB2PmrdJGuiWOkbKFtXOkRbEry9tweIXTifuq9RzCRwnNBFl3WVdTATWeGyo0ebWPtICju58XsenMoxoKh6YA3BtbAJ4CPPDSXmOwzadDAt6d2C7cGzOiTlr7I904GYO/9qyZfrG6dOWJ4jABv/+9ooVKikQ56eP7AAOwMNgKBTSX76/VftPH1fXwUi5shvqZ+hUX4uKS0sVRpBYVKSLs0q1aNVqlfeF9Morr1iiIxNIZF5h72KvIy41zbMRKjIHmG9wBbjI0aNHrdd+9KMfnTCu2bNYa5mDvJegQSbWXbLi4TvwH+xWRI7MVwRJCMuMoMyA7BbAXDbnzOs5DmsG35kH/j8EWfArr7F27VrrM7h+ZLET5ICrxWrGXogcw3VQg7S1M2fOTFkDB7LhKm52/kAkoAHMhvf+W9Lc66WyFB1fAz1SV3OkqXHtbCkYIT3FwemqDtyjsFB4EWxxt/HMLJ+pc71jCmPUYXUlddZ39JE74ExBbUs5CqcobzRwqjAfXSs5LLIZ9Ou5+ig4EIigP0Yi2JVWsYIEGD2pqhBRaqHSNqml/Hzy5EnLiEk3uOGm9v/fb9ig6+vqrJTw6WVl+t2VK61ghw8f8fCre3ZrGBXeSH3YrkBAHRUBlQwMRpyNRUUavNKiPdu2a2tvxMZjbPMve8zbb79tGZX33nuvRRhMiQUCGMwFyAYBPgIDBDownCET0YYpqc/sc+xXkHcCi5mwJ1kvIAUQC4KdJgOPwCjZGNGp4ZwDJC26VBXnybEof2eCGARFUDuR4p2J80alBYmD8HCN2N/z2RkGL0hUdzgZjjEVwfgrK0ocZGcuhhUhx2PCpEGFdVIBuVPgOxxU6r4sDfZIldOkspE5AN9Y9og01Bd5TXG5O9uxuEoqq5f6O2znGJaqYwsSfGQHlB9kLU/U/NSAQDWlK3DUJITFd0NSgqDcVAUlNLxq/OzDeyQqcQuw7+02fizbJVUFNLYVAgyCJ+YY2AA4RLFFEHelai9xPLsd4wS4xbb779f/QKgyOKiHZ83SLyxZktLn+Zg62NF6Vu9fa1FlU731AG1BaVagTkP9gwoWBTU8NKTmoyf1ytUtWlw9zbJpTTY44lyyGAgWwjtYKxFVwTlwvuPoxzam9BVBeeYCe5N9LmAb8XeT9Yt4CvFhprIR4DymITjz1WRfIEhysteZx2RH2MG5sSfv2bNn9G9wInwUToERL7Bo0aLRHkEEiOAaZHHmazNxrmkyHCOpoAaDhLQjNyqPKYv+axLBBdKt7UDxlEpQo+OidPDpSEkrUDNLWvchqSjilCOQEVByiuNZ5TO1tna1jna9ZzUkbCpt1I31kbQyH7kFRi8LMw2K3IC6/jhYiHjHRLhHGtojhWkwXCQVrZCK4tfWnGrAQWdXHvjIL6DKcDsnEiGZEm92oPwg6Gh3iJreGiaDAwMBJ2qypXuMQZTIkCkOBvXbLstH+PAB6MHS3B/JLLI/V4yIoSww2qOlcu5sbZ45T5uvv2UcWYBEYGQTVMTBjjrpvvvus8asKflGYI/5wVyAeNuJP+/DMYbtyBxGJQRxhxRkqtwf5waxiQYkifNkz+S8mMvYtqitYq3/7K27d+8eXX94D9cD8gIhSTdLywmIGgi8Qt5o8J7qmpUNQDgS1ep2g6kc1HAPeEV0xgOBDpe9LqLB3D/xstR2PPI7837xfVKjLSBJMCMZcIy5D0gXX5d6r0S4yvRbpCp3NZF9ZA6s69gwrMVu7F0coGR4sMaz7scOih2RuugJGJJKmqSGzVJR9nrTFAJMEN9HfvI/tzXbMwkEjdGBL4QWBBXxv2Gn4ETFMZmswxM7xo0Y44b6ev1DlPPVh494aB3oFZ7IkK3ay3BYChYXqbRyRIRdVqo5a5fpgevu0Yzy8eIhxEaMa/gzNi9z0cwDsr2x1Vk/cbzjgGdPMvYwtjhBPzI8sM1NWWjs/7vvvjtjInyn/ZPPQjDwgx/8wOI4/A7nQOjF+TslBcBVmMv2DEpK3W7ZssX6Dk6Z5ekiHA5bPI3P5prhX8nXbA2uC9fSLcdwvSoy0LiJRNT8oEYc1MyYGNBA8VQRu6FlXLz7nDRsU/CSsXFml7Q4PQffwqqF1mOqZt7kK1ioWZSTqaPJoo6xw2Jv7iXvZyFtamyQBndR5c+8Who+LAUqpKDfZBzgMMLpRqkVH/kJDB6v1iqOkYoKAoIfS83FvCVTg3Ok9xR7JQQ2Wq0eD7yWRoO+ms+Hl6BM2fLqGh3vvjYawIAO3D5tjva3N+vqQGRMz6uo0m+uuH7CHCNAR1kkOwhiEMQD2IP2uq12QEYwmFFavfqDH+jCn/2Zbi4uVjEGdAbSqyeAOv5tRyJZijWLpabrrX3S9NfBUYBNS1mpWApGjH5UQmR4GIJBFghKMgI0y5cv91TlhLqMNYSAj1GjUe+bAEo+gusXPT5SAdf41Vdf9eScJisCqODDOIvHN7IPaLyTwDVajo0FNADrw8nXpNr5eCFSP9GSSmnBI5Hj+fwir4DDJBnhFGpO1klsHJxJADuHeW+VOOw7K3VRDm0Eg61S21vStAcy9RUKDvAzv0xw/oLAgVf98UCqzX7Z62MJGLAH4AYEYN544w3L4cme6VYYgh3jJhvFh49kMa+idlxAA4QV1K0NC7SjbaR8paRHZ62ZENAA0XsRPi3GOMIe9h8CccZmjwZ7GQE7xIQ7n/uebp1bqxVkSsxuyrzt0d4q/cs/SGdPSU3TpY98VsVzF+ojH/mI9WfmMkId/BfxbGQarCOSIluDeYrvj9c/++yzli8B34KXfto9e/ZY15b1gIAppcDgNvnY88nEHKKz6GMhKekXkRI+wEcczFohzb9xfEDjhg9IxSmUOyE7Y+BaVP+LsNSTuAyLW/gBjfyDUVS4BQ4ZjBui1SiwMM5IYyXCGSG/PRMayoeHr1gKUPOgrl7O0dcjHdstvbtDutaelY80pVXYNP25kL+APONI9QKoyRnzySLWhn+5r0+f3Pamlj77A9368nNqW7zQcpBCNpJpGIg6202JLR8+ksXfbtiohpIxG+SWxib90dob9YPbH9Jfrb9NX73pdn138/2aVuZOSQqBACiqUP7G2q9w/GOo73vzTa37yle0+uWXVfz889J/+S/Sn/5pZm9k5wmpeZs00C4Ndkmt+6Wre8e9hDnKfpuoPwZqKzIyTF1cQCAHpRM1cZ1qa6cKjokRD+lgnSLYng8q0mxkavj8IjGCWscosT0zTQGl2Fy9ty2SWW4HAcABsno9gB/QyDvgMMVxQqDCDVB0EriFW2DPIJhivedfC30XR/q7GISlwTa1XLlkBbRZwyghkhdoOy+d2StdOTFWHjrDoKwKKl0v+g75yAyYDzT39Qrs3akcj7npxEOPdZ3Vt868pH889Zz2h89o5pxZljMy2f0SgQrZID58eIkb6mfrzmmLxgmpfmbRen1g7jr9+6V36ZMLbtGvLrtHt01b4pprs14SFEDMw/7hVJ4ZnwAlq7CZj7z0b3qk+qqauk5LJ3dLr/2d1JXBsT44IP2vP5YO7JZar0jHj0j/4z9HAh22+YzvIl4vUHuGBwEae+DxkUcesQIO7B9eYu7cudb1ZE8nWEA2zGTJBC9O1hmEktRHHLAhrbpPWrhe6u+RqhulkhTTcIPFkdRvatqOfYBUnmLWh4+CAApyjCLKZLgtbYFTBiUQRAXVKItWxHE7cYp3dnbrzLmTWr76QetzcMjkfF53tUk/+IrUM7J4EwR84vPSzPRrdSeKWKN+yVQZFB/eACfqm2++adXbTBds3qnUqsTAiiYcNNP76e1v6XBXh6WC7+3t0Rfe2aH/NWuePrJsmaXQS6YePiUe+J533HFH0ufnw0csXF9Xr10PPqJ97W0qLyrSTfUNVikzcOf0SIAiWScARjgGJw+nUk8YzaSE4/Sfv3u3pmOY2xWM3/++9PM/Lzm817MsDafnpqfWC4O1h+/K9zEgG4vgxvPPP6+HH37YfWB8sD+SgUtJ0qj3QOjsDdYJurOnc40hb/kEbAfsDLhBujD8ws8ejo9AoEbBMD0RcEqzj1WlLsgoq52YWQ7HKMnP+so+vAFrFiX1yKBLJpMUoRTOWhqMUipklKfawPw9cbpZZfP6R9W1qYhIPMf7b0snto39PnO5tO7xjAbe4GNk87ntYeIjd0BURADKZCOlA+yCdLI17DjT06w3ru63/X5ZnUUVamiut4IUyVR1YA7/6Ec/0kMPPZSRspk+piYYs59YsE53TV+kjsE+zS6vUX1pxO85u6LOeqRibxMMoGk2NrGTqJAAHTyj+dJFbS5vi8wdY8+EwtLx7dKNjykjOPW+dJmA/giY73290v5d0l0PJX04fBLw/n379lmZKvbABqJjbP+4ZebtCIWk/m6Je1A00ddBkMAECtijsLtZ/5x4XK6RLL9IyrODWgPlhQ8XqGyIPNIBE3Tlg9KhH49NVBaHBflZhsCHdyD7IlnSgaMpugGqAmVSYI4UjpQKOX78osrLK7T2hkelwJhy10ulaUrY/qzU2z0+S2nLv0kf+/WMfSRkA0WsF0bsVEHHYL/O9nSqprhUCyprs5rdgoEDkbZKHngEDCKChwS1EikVcNCiSrfjXG+PDnSOZRUxi4oDAb126ZJIQE1G/QA5yWRjMx9TG3UlJbprurdGq904djLSP/CBD0R+2bGDyTA+qAFQ+2bKkHbc08Y/h+IYQ97tHhD9fXGCYHS7buDJ99/9rHRyT+T3htnSHZ+QKmIH1cnSgNS88MILevzxx5VPoNY+pCiZMnuxwDFQpFFeI2b9fh8WAgGomwc9wKatiJSf6orYhxYW3J66EMtHQQAHCYIm5q5bQQ/rm2OvsMrrpB4U4yF1XevR2QstWrr6DpVNXzi+wX0uSx13t44PaIDmY9KVldKM9NcuJ1D+F4UxmXZ+Frg7IBI60d2hnqFBLamuU3UqFS5SBP4tSt54wQftDbnZ0/iZ+ZYKTnZftPKgwrYOSmc6LmpT7UrNaJyus2fPugpqWMHGEyes2vk4MTNRq9/H1Macilrr4RWYM9HNte0YLck6NChdfGWi/T8wvpegp3CiF1ZQZexX+DxBGbd+PPZle5lZ5iylZ/FTuM5WaDknvfndSFADzrX+EWnpzTFfzv6P3/Cll16yhFkEN/IJxBxYmzMW1PjmN7+Zynn5SBWNi6RbPi21n4s03GtaNNok3MfkhWng4+RIdQsWQpTi589d1bw5pQqEu9TYtFyN09dHemqMAIM750Z3x9XxikE2JLI3MvVxHR1W8yG/f4F77G1v1v84tlMDoYiK4M5p8/WLS9crmKWxw6YOSfQiqAHhxBlJAAHFEimeGPv8zKbupAohVTPasCiJLt0BMRseVll0cDHG/ET5iOGD+hE1NvWud+AA9uFjMgFD/R//cex3jG0a7SVIywas05R9guAwP1xnWdVdJ/VdmficLSBhshRTdWTwvqTee3TrWEADtDdL278v3fOZmG8hAxMnfz4GO7EvqPfrheqT9Y9jcUw/qJElBIuk5Y9L7aelwV6panrk4WNKCKewNdz21ogGjlTmKvZLV1uVZtZ0S6Eyrb7lg1LleGUlTl1qoycq85cx9HQ4PBmQep2e9wbYqps3b87LOuX5iMHQsP7Lu9v1Tvtl6/eqohL94ZrNWlHjEEjLAODAXjVyZz805XJxGuLY5NhknBMYTKYHV9CqEm8PazCcr2nG+hkqDhaNU3U78VzmXaQUdUQYhh3B8z58TBoUl0h1s6TO5vFipmnuKn3AARDTkB3iOlth0XXStJmR0lOIlfAFELi8fiwTHGEyex78foLg2OWadPPNsQMSjhngr38r8q9dRFU7XZoeOyhy1113WeuEV+ufl8DG+PSnP52ZoAappH6mRg5QUR95+JhSQEmxdevWlFOXSRE3Sk+MGBwGscCCllM0zpZaLo0FNtggGjKXCnf48OG4CgAf49E/PKS/sAU0wBtXz2pN7TTdPSP9eupuQBDAK4JIPVoD0sRJ62SeQNDpAwDhx6BAZYUTFYME8hD9+bPKy/XwzNl6sfmiGLnQj2Bbu37utrutv3MMHLLGEWtIPfONB2XijFLCh49JCxxnX/yi9Jd/SRdAiZJs//2/MyESvpU5xFzBUUBpNox8V03japdG+gO0vRvZV2ppFH7D6J/J+iKQSQnCrKE5qi8Q53XldMKGyvlaKiJZFZVbjoG62UeWYNlai/3LPcWATUJggj5ejhkYCYDzxziA3n23REtXrowpjiJ4jP2Ws6BGlVPVhLBUlRlVKhkw2HQ5+74FiB+cf197RgIaoGd4UP/1yA59/ZYkyjrmCexzw4wH+l7CNS5dumTxcbI7mRf0qYknjFhZu0DvXTs3LqyxqGqWFdAApkeGqbbAnCbrG76CnUQAhX3VXEPEWT7f8DHpcOuHpe3/InWOCJmW3CItdldqlnlpRIb0pKVxd0IQwPi1P5C+/XXp3Eij8I/+rNQYKTdtgvjwe0SUWZlzHVekwajsFOY9HCNOUAMRdb6Vts1apgapbsnU+vfhw0dqQFEB2Ui3zieGzblz5+Kmf+NcPXLkiJW5kBMDctNj0uWzkjFqyyulez+WkY8iao7q11dQuceV/h712QIaphnYqZ4ORdz3mQfkux51t8dgXjDXMEB4MAdM8N40EGTvgxzQmA9yYsBc+eubN+rPjx7S25CTsjL97PI1WlgeMWAgFvYACkETPi8bBg6fA+FJpqeHDx8Zw0//tPSRj1CPgY6VNI9wNYaxNTds2GAFH1AU8a8JwjMXTUNtHAcoY0dVkOxj9SsijyhAMpibGPLMSY6XanmIpFBWMZKiblOTFVMisrCcNnYVlamb7wVYZykL6cOHj8yDzDd69qTbw4sMVvoN2W2j6L+bDKxM2HAJUVkvrbxPOmIrUbJgvdSUmZ59lDW86abUejdNVZzsHp89wA7ZMtCn7uHBrJWhSrUPhhs7xtgXZJrjOMVmwZGKHUIZZIKM+NaiVd3Ty+r16KyN2tdxXP3DA5pfOUPVIz2TsV0QY5kMcj6HgAmlHGPxeL6jV1mfcDIqSfhc2kfOUVkn3fO5SC9j+kiUJA4oG58Y84VMDbg/QUfExDzPnERcjE0Kv4CLIIQaRUOT9Eu/7XhsxJHsr8xR5npW9oNSh0wLuEZJ/mVguAFZ+vguk+EYSQU1MEhwsqJyttf98uHDR2ZAtsXrr79uOXPSCTZgSKFMpZSFEzBMMLpYiFGQYGRltdwFNcWf+lXp4knq90izFkUcQBkANQpxkvlwj/rS8qgE6Ej922kZukdOxkemGi4yzu0p3KRg2pUBKKkIcuAApTQVhBUj3mRCVRQV6cur142+HgcppIVgYvSczWZ654EDBywDDYeFL0LIAPr6ImqdPCwLlLdg/LuYA9SkJnXblEZkbyKbirlKpoap0U7aOCSerDvmME283TjomBtkYwHmNMQjK0GNlbdL549Z9edHMzWuv1eFCuyFRx991LPjcY9puu7Dh4/Mg/UUpyhiRUrTpAoCyRwjlnCK53AYUZIHNSxleLKuFl9wYySI0d0ilddKtZnJBEdIgp2Xr8rXfMX0skoFFLB6RhiUB4tUWZSd63jx4sWMBdwY//ZgAiJCA+wWuDnBDDgFTjz4gz1bcXZFk/UwOHYFG0LWa+1NdPmcRN8BLsBcTRcIQxCYkBni8+kMgADb8JBU4pebdw32nnJ3pd3Yi5g/rNPMG/ZBwN7Eg/lqemIgTGSP42eqNyTKFDfvZa7xIKCfFdQ0SfPXSGcPRTJwWUsJ9iwszP45xBrgZ8mUo00qqMGNhwju37/fD2r48JEFMOdYUJncEP5UgZGdqMQUJGPt2rVWeqoxsvjsrAU3qIs437tSFk5gc0HVkhUH1iQCSqnPLlyr/3v64GhwY1FVnR6Y4V3T7nhgz8Hg90IRhLHBGHciG04g04EHRANlFfMQ5UUs0IzPNM7NZdo83/O+++6zaju7bVTmwwXOn5X+6x9J585Egho/9wXp4fxq4FzIYD6iHiaAGWudNsEOu4IHpbBbxxyKSFMaBGcbge6slCNsmCU9+Dnp+DvS8KA0d4U0J7N7XqbX5d/5nd/x7Hjwiz//8z/37Hg+fPiID+qI0yAZp47rfkUOwDmEfRTPmY+oioA12ecoXxFPZbV/DmWoHEtReQMc1AR6EaH5SA5PzVumrS0X1NzfY3EM8MvXZadnn1FTp5uxZBdCMReMncJYj8Ux4DTMQQKClKpF3R0vY4RjGbsIRyu2DCVu3AK7hzmYLoyynX9RVCfTJ8RHHKCsf/NZ6e3nIwLPOYulD39eqvFL0HsFAhr4w+KVOTX9Njdu3DhyW8LWOHcz1zi2fb6bjPCMixoDAWnjT0nT5kltF6WKGmn5JucMjgLhF6YUuFskbcGYoIYPHz6yA2pzEmRIp+yb25p+LB4QDR6mmTKLOUZXOoQnHwDZ4Duko0ibynhk9lItrq7Xe11tqi0p1aamuSodqeuaSWA0c99cN/BKAFRKEHhTg5Z/3ZRoQl1FcBElXqwAIWSEc831XOH8zLkwp+OVnss6Oq9KbZciBhd1PvPlvNxgaEj6oy9JV0fqtjIOvvaX0qzZ0g1+uQkvALEn5dtt4JlxTiYVdaTdKAZN2SkTIM36XK2bId30iAodlJ5A6ca98goci/UZNTc2iA8fPjIPSmOguk5HcY2N4SY7Aaeq6e9HJilCIzLFsxrcyAD4/i+++KLlGM8bW6uAUFtSpv9547168+p59Q4P6fq6aVpSnR1HrhH+eHXfmpubrVJsJqPUTfkUoxaHp2LLkIHhFAi5cOHC6PFQMZNhkkxQwysuwHcko53S2PAiV30IsoHQkHTtYuTfqplSSYGVyj+wLRLUMLh4Wvre30o/85u5PKtJBWzMZALP2KMmu9vN3GEO2xt8k82ESDgrlRqCQWnZ5OgXa4IaySCloMY3vvGNZN/mw4ePNEnHzp07rZrhqcAo0pMBBAWjhfdR5oNj4BAq1AAHChdfsZ4eVtQ0WY9sgsBaquPeCYzjZEiAHaioEhlLlG+zE3jKZjGPskV0yRTB2WjGOsQnb3prHN8j7fjRWCGzBWuk254qnMDGpYvS5ebxz+Ec37PbD2okgVjEGgUvBCKZGqqQBTKv3GZaRNd+N44HH8kBkodAwMvgA+VtcXhybF/t7MNHdoDKGmU5j2za9qbUB+s+dp7pL+CVgCWbwLmMfZiqbelDqiwu0UOUHs4iEE0RUPOyXJhpEJwsjKAwHuDjxnZC+GEaf7udM7w/nYx3uASZKFwv8x0TVYHIGob6pRPPSn1tkd+DJdLih6WqAlpPTrw7vu8aJUovnJQG+gpWcZ9PHAM/mgmquwVBO/iF26BEdMYivifT98ZHckGNz3zmM0m8QwqmEtTYt2/fpCKB1IY/3d2hY10t6qUkgA8feYZ01RUQFRbaVIBaBCcTJXV4kLpXiMhqjxAfnoA0aQhCISjfmKPMszfffNOq8w9w+qG4Mo3H3SDd70pQ44YbbhhVulP/k7TZnKOvW9ppC2iAM4ek0wdVMHDKdsMWynZ98AIFhJgmfDwoMWXvZcPPZGnEKzvlBOYWZRDczhsIBspgp/mb6h45FQEPSFZF5QYck9rFkwndQ/06ce2qrvRfy/Wp+PARt3xULoDaG27BOk4GWCEib4QjPpICfBahXiGA4AGiD8plGmDrwzfclpTCzko3qMF5UKraIG/42eU9Ul+ktLAFsjXOblFBoZSyqFHXE99FlnrLTAa7FH5BL1qymuxAYEjFBXsfmkRgXjG+k8myiPY1wS14juonPtyBa5YKx0gpUwNSiOOkUDaCeBgIDesvjm3Xwc5IOYna4lL91srbtIDmKj585BHhwGGKKjXrDfZswBiyO6J85BhtF6TmExGDhwZR5ZNLJYZ6LxnVdjbxxpXz+up7+9R84aIWlVTo55es0Yq58/XQQw9ZmU0YVChCCCowbzBo3KrBUiUeEB5UZ3anLcaY6SGSU1xrHVMfGdDMjHJUhYKmadLd90tbXo6oqTh/SgI+UPjlhDIJxjN12xnTpjwHgTYCG6auLaocMhKTIciUL2FfTKbBJ4Tcrkbm89hXOT9+jldnN6+Agu/aWWmoRyprVLhiRladCzt27MhIg1COiZpusuBIZ7O+cWaXhrhfkjY1LtIH56zNH0eQDx8jpd9wIqTaW8groSP2kptmrPkGSgOzj6RaIjgvgcjzwrsSwdi6WdJ0987AQoFxOHp5PC/W9sHQoI50HtC5trNqu9yueeULtKBusbU/4mglm5EMK/rU4Isz4o5EwPZJJ7OCrCr6DOTl/mUFNOzrUFga6IrYSlbj5ALAhnulQzsl3CzWmhqWNj0UyQj3ERNwXAIZ7GME+gAlFeEccGIylBi7buaIfS5j55q+Gm7g5B/DD0BpQtYZylIRxC8IXGuVrpySgsUKz16mQGn2fI/4UOCIyZa3TTqogWr2xhtv1Pbt2ydFUOOZC+/p0EhAA1wbGtBfvbdTf3bDAzk9Lx8+osFCjQqcsjLJBjZYILxKf8MoStRc2UcWcO6wtPPpiKgD2+fYVunen5MmUUCWADr1KPMNhztb9R9/8j0FiotVNq1Rl8rL9Xf9V/U3DZEN2OyNKEMwojBqKE/gZg6yx0KOk01fJ42ez4tudgjJ5jrmHFU4ns1gHQFko6bR28+hzN63vim99GKkkffHPyHdd793x/8Pvy4tWiIdfVciePTkRyPBDh+OGGhu1htf+5puXLBATQ8/PFpqDELOPNmyZYs1RqnRbFf/ud3XyOxIBqanhh2QFoJ/58+f16FDh7RmzZr8vpvMm3MvSz0XrV97+wb0o13tevwTv5S1hp1wgJ/+6Z/2/Ljci29+85uaDOgbHtQ3z+weDWiAba2ntLiqSevqx8oU+vCRa2B3EBwmW27VqlVJvZcMD6+CGihp6bNRiEEN+h5S7nPSBDR2fEfquhxxCLOGLdkoXZfcfpvPwBbwutxaMuKleHjz+Ks6e/WsKqrKNWvRDA0H+lVZWzbasw9nHzY/QibKNrrl47wulYws5jd7/qJFiyZcM/6WFz6BstpIP41RjhGI9NTwOqBx+rT01b+iwYm0YqX0K78a4QJeYPoc6Wd+S9r5aqTk1OJV0g23eXPsSYoDW55ToKddj62/QcXzxsrX4atGrETJWfa3ZEE5aUrVJpM5zvyPzuogiHHfffdZ50DgxfCNvMblk9L2f4k0q2c9+tdm1d/1CV2/IVLWOtNgraFPT7L3LaXVnBvCB37qU59SoeN0T/u4uC7Uo7m/28rgyEYTXB8+3AJDCRUpqXXU6E+msV66dTSjI+KTqfxcwWLv8xHjzdyKwV7pyFvSTY9psiDZPjDZwtaWiyoqKVH53FnW78MK60xPl873dmtB5UQyztxz+10IQCQb0Dh16pQ1L52aVEI08qLmLY3Bb35E2v2TsefmLpcWetdo2MLX/1b6u6+P/b5/v/Sn/1W65x5vjs86+qGnlG1QHvO7Zw+pc6hfy6ub9KkF11s1qLOKJBVvAydO6K2PfUx3hEIqg/B+/evSV7+KLNj6O/1neDA3IOfJ7lE44QhsJCOwcUoBNyIBgo8QoLxH58nRgAZovtqhtbMGdenscS1d6X1JKKcSFFz3VFXd8cAxydCjDA3OmkJGy0C3BsPjlXtBBXShr0Pr5Ac1fOQX4BWsf/D7W265Jan12Msmy172N8hmUIPMwUmDC4cjAQ1ggrIntkvz1knlhRVwigX2MJz0XsKLvjTD4WFdaDuvuYtnjwsUXOlv1ozy2aO/9/X1jaq+3c4/5neyAkeCPwgqcRJHl+7kbzh+4SDJlPXJCGbeJHVdkAY6I79jq8533xDaFa5elX7h5yVK+uLwpRz3e8ekr/89ik9vPmPGXOlx7wUj8TAYGtZzl97V0a5mlQdLdN/M5VpdG+G32YLxKyWzl7z/7D+p+PhurZo3Q9p1Qrq4UtrwpCWe4jimNxtCwWTLPzG32Atnz57tek+Cx0QLi+zlq/DbEfzO+6DG3mcjAsERlIUH1Xtkq5TFoEYyGTIGwXSCGpMBjaUVFsmwo7KoRCWFkqrmY0oBYwnHJU4F0uLcloLyUkWBUZOXqadTCRhTBDHswCDoGzHmJgHy1amFmrvt9FkNXJvYp6LUtm9AOEwpAuaem6BGdKPxRKBMw5EjR9TZ2WkRjuh5Ccn+yU9sQYRcY/mt0sO/IN36QenuT0l3fiJSL9ZLfPe7E5/7t39VIeNsT6f+7OhbOtHdpiv9Pdrack5/8d627AWXh1ul7pek7mel7helIXclw7b93u/p9kAgEtAA1H3+kz+Z8DrmBwrXZPcoMirYA7FHIS2JQEk4GtPGAsdKRdGVdQySeTU218tKS3TkxEUVKzvBS2wPauBnYn1mHBCk4jMKHbXFE4lrWGHH5334yAfg8GRdfeutt1w76b0quWMcSYXY24jAOM6qSQNKTjn5QSZRX6BM9EFJN1OD0lLHjh5Te0uHervH2zTBqPvBa5Ot2kBQg2woN4CzkGFOQIOyV069yJ5//nkru8vpb1kH++ryD0kL7pXm3ymteEqqHgsCeYItr0nXukYV7Jbjl56J7x5WIePfzu3V1paTahno0fm+Dv3T6Z06fi07pYHD4ZCu9h/Smd6XdKb3RV3tP6BwlBjECecO79XQ8XciAQ2D80ekSxN7WDJPkq36wHs2b96sbdu2ue4lS6A0XnAPrp6Pfo1xgFf24ksa45fdff1qaR4TUk26oAa1ykwz1ELGB+csV21JmUUPuRj8+3OLb/Cdtj7yFpCH1atXW8SDmuTRzZBiKVq9qquPmvXcuXOeHMtHiiCLrGbaaCmXCAJSw+RRf54+fdpzFVW62R/0kiJQ8emNd6pu5nT1nruk7jPnNdDSqlsbZ2pmeeW4AKQpy0BZqERlCXCoYjgl+s7UmUQVxdznfBoaGmI208LRi5MgE9cxZTTOlpaul+Ysixq/HsGQDTsK0Elix/bWc5adaUzMkMI6dq3VCnBkHKE+qXe7FO6L/B7ul/p2SKHYn41zhzFa29amUnvghbl38aLn6mLSlGmeCQGPh0RqQoIqBdEzqqxhHOGYPaNeM5vqNKjsOMsJlKZCONyCY//whz9UoaOmpFwPzYzUcDbiqTkVddrQ6E0pUB8+MgEyRVG4whkILiYKMuDE9aoJKmtwIWaC42DOe/VtMqidOZahYRAslirZeyYHMlEuKR37AXsdp+jKFSt1z8Z71Xq5TedOnNfJI6et486pGL9vmEAivQLcgAzIRFyAuUe2lGm4TJY32ZOxesUQFEIIkjdl14IlUv1iqWGZVJqBHpNDw8k9XwAgS2Nfx4VxVWsCCmh329msfH774PvqHj4/atN2D19U68CxhALA0+8d0Yq5DkHJHu9804xtKqTA5wn0x/MfECSFbxe86DcQkKqZz2Pf4841i9URipSez4agde/evSlxjJRypVBR4UhhwXvkkcJukNlQWqE/WXuvtrWeU//wsNbUTdciq/a3Dx/5DVLcWGypP8tcpOdGLMMDdSqKbuZtuiB6zaKNgWSA4UOgxUcWceuT0lvflvq6Ir/PXCwtd669GSGJOCXZkLh/+Z+JBknOhGqaOYDRnkz5Nq4fZAPSaubQ39z/U/qn00fUOtCn+QNhPRisG6f8wggyAQiIRDxFFcfHYKKJWCLs3r3bcuSiIElkPHGuzE3SZ6cMHnpYevoH45uS08uhgJFTJ0+o1SqwFvVkJHsjOH6/wQnG3ELhO2PGDC1nPL/8Mkw/8gLKmWSgFxs1b+mtQYNpfnYi2GQ1sWfGmzMFQ0aq50t1y6UOQ/wCql10m3bvO6Qly1dnxFlDoOrw4cPWuoyK6nOf+5wyBeyar1OqbBLg3hnLNb+yQed62lVdXKYb6ueqxC9t66MAsHLlSmveM99Zz8nOirVGYmtQesMLjoGS3M4vsKtuuummvC5LhQI3H/u/pYzpS6VFt0indo0FNG74gFTiHLgJhYc0GOpVcbBMRQH3NehzBeziZGrluwXjn75c8IBk7AnmDmJB+opZwsWGdapeV6OWgSsKhAIauhBWc9dl1VxXO7q/81k4d5mjiRogI36ktC1zONHr6NUBx3DTnwvb9LbbplDPh9tvk/76q9LQYESkg01LCbAk+xDlP8gpzQ7v6BkeKXNnQ2+I5yZeU4Ls8FmLa89frMDpExMPWOt9I+758+db2UhkbcTi3ogX3TjivcxszBhu+ZD09rekgUi2WLB+lkoWVlnBBvbiTABRJ/yRewyHS6WcXUpBDW7GPffcY31woQc1QHVJqR6YmeNagD58pAiMD5RUKCtYLAluRBv/Xi+iLPB2vEf6pY/sgo37oV+UOq9IRSUOmRsRkMYZ0gGKgY48U6lgeL0CgeTSlnMFjH0MGLKNvEgVR0mB8QEhcEtqCAhSN9YeNFxUVas/WD2+pjxOP/s5EuiDnMcKaDAv+TsbOeWjEpEI1ICcQ7wSOtHZLsnWzy14fPH/iZS0Mo3CP/kp6UM/pULGrY1z9XzzcQVGsjVQfS+uqte0MucgtreIUVc9MP55iPX+/futOUVAz2o2+1u/hTyQFKfIi0i7/vKXM3am1IHftWuXFehnPtnnHee2aVPierAQ/7wnHZzbrE1S/XJpqEcqq9eaZZW6bn1/xpp1Qma4LgQ1sDXuvvtuZQoc+4tf/KK13hVEObAEuK56uvXw4aPQgL1BwNiUoUHU6FQi08sm2dhBducr604+BzRMUMNtWZ+CAHvM8ruk+TdI/d1SVWPMgEb30FVd7Nun8Ij4oal0mRpLFyvfgxrsLXBnggkokLGV0+2Hgd2AMw4RVKIAggFVT+A4NP+2H2dh1RLrYaEpIvSCL8D5raeamqzvEY8PYM8cPHjQmlPYRIlAkIRSU2766fDZBZHZ6iXmzZf+4i+kP/8zGgBJy5ZLv/8lqYCztBBZrK2drUOdF0fDGPy7vn5eVj4/4MAxAg7uaePjQijI3LLEevXF0j76i45g2WZpemYqExC05nMRICKKXLZs2ejfmL8EPRLNG/wDnP/ixfm9PqpupnT/F6T2i1ZAO9g4V4/2JV/uzi24r4gn8LPgR4EDpMLBUl69+cB//dfCrlPtw8dkAYYYKm+IBU4djB1j+ACMNspGZQoYW6TOZmrB8xEDBDMSlJwKi1qQ9tqYPQrpoIq0Ia8vKz0pCD5QfxIijXqI4EIi0sHmCKmIZ1wwNzD0+RdlEoZ5TU2NY/YGDlKCeLGyoOyIDpLgYLTPQwMIFN+FgA1Gkt04igfKPlJqxw34DDJFvFBOFhRwgv7Wb0cekwQLq+r168s36zs0Ch+MNAr/zKJ1CmbD8V40TQrWSqGuEaoTkAJVUpGtjq1k7Ts4vzBIcUZbYOz98z/DRiLZGmvXUtskY6eKEQwhZz+CeFBChTlIHxz2RDdEnaAna09B7GXl1OaN1OcllOH1ObOGkHXD+ki5SxwjlL0jwOy0rnkFVKcQRIRTZG348OEjt8CBhPMUZy3BDfiGfb1Jt5dAIkcQpTZz3og4DrC1KNs16VBRF3nEwHB4QBf79ipsZYFH0DLwnsqDdaoszt/a8exljGUCGwQzsBPoDYGdn6jECnuTadDtBI6BAxRuwfEpp2IFKRYunDBHGDeImtzsp7w3WrQQKwubrFlsMc4FsYebYA3ZrHAYN3aSETpw7CmHG26U/vmbmkz46PwbVXmxVEc6m1VeVKwHZq7Q8prxNn6mUFeyWFcH9k94LjrYxpwlk+nFF18cG6NLbpZmLJG6rkpVdVJtZs+ZjEUelLu9evWqtTdhHx84cMCV0Ic9DLs274MaoLQicm1H4MYHkixYGwGc6/HHH7f8ML//+7+vj33sYykdL62gxm/8xm9kLIXPhw8fyYNFxzQ2sqsciQxnUkWEKhcDCgdtPiorvWyUXmgIy6mXSv43FIcwY+ibaD0BBxz0GPbxIvgYGKZxHfOB41A2yjTrvnDhnLp7OhQMRkqo8RkY/BglkBxIAoSEYArOUIwWL8kqxAEHMBlVyZZs4zu4nV+ov7xugugjd1hdO13/ec092f9gMjIqNksD70UCG8FqqZR+KEXjHFoQDoiGXW1oAftwQ3YDqKwR9JlB+cN6wDxIVJ7BgLXCD9BH8Prrr1sOH+4vSm1qa2/ZsiVlFZVbcGwCUnyWH9Tw4SM/wLzExkcgReYb64Gp6Y8vIFPA7sNGQ2CSyWBqOpiqHKM/dG1cQCOCgPpC7aocCbjn61iGUyCYMiBwz16XyKdFoN+Uc8ZegGMYXoJ9f+7iWUtc1Xutzyo9i2iJjAn2UwKDvI8HtjwlV3DUegnET5wPmanJjEn2+GSdrb7/b3KgNFisJ+euk3KQbFZVPEuBQJGuDdJXQ6ounqPK4vHBCQKE7DfMsQkVgqobIo8sgqwn/AwILzkngvx5nd2dh6CSBEEN1kHWnnvvvdfyW1JO/ytf+Up2gxo4MXH0EHG64447Uj2MDx8+MgDTOBXHLMYTSnMWX6skSAbAYs6xcQq7VZ1nE6Z80NSEk4Ge36n8ACcpaiN7poEbAxrHKiQBxyQPjoEKgPIA13ovSKXnVFlRKgWCCg/M1/HjRZbhD7lBMQXp4H0EPFANMIe86n2AKgvCgVMw0wQY4uVUJsKHj6QRKJHKYgfgCJpHlyTMNZi3BPiTBUZ2RhTBrA193VJJqVSc/0Ig1jLWTex8BAsmQ+y1117TU089lfHPZ438/ve/ry996UsZ/ywfPny4B45c1KpkbBA8xiGBEASHLjZTJsBx+Zx8DB4QOM9kUCefURxwEtmEC6KvRnRze3isG44BR0AoYecYVElo72zXuZ6jUuVgRIAUqlLl1QqLVzA/cNpRqhnBFYFA9lf61rgFYyxRxgXZE8zNVESMnHMy/duy0TTYx9RAZdF06+EExiRrbD4FDZgrBEGTBU57t5lQSWN4SBrslyhLnEfXKhbYzwn6snYiQOWaElNgr09mXfQkqMHguu+++/Tyyy/7QQ0fPvIMRD5x6Jw5c8ZK20axgSIkkyniLEj5tOnYgUpmqjYyD2qpQmoZafbL/QkrIHeq5VyCTe2NN96wFLupjCvUU/byCLv3bNXC5YOqqVk0ery+vl7Vl81RWWmkwSOqZOYLasBkghluQECDDA2Ux6mS8mQIB0QrU0FMH/mP5r4efe3Euzrf262VNQ36wpJVqkyzXnSsgAYBQ7f1owsBnpOOzlbpR1+Xrl6MrME33yvd/nheEw/WSOwGgjz8azJYKOv11a9+NeOff//99+s3f/M3LccPn+3Dh4/8Ac4IMjaee+45Pfroo9aaicM2U0GNfOYYOK2nqmiqNFil2uK56hw6P8ovyoI1qil2LouUTyArG/uFsZwsCC4w1s14RzRYNntIc0oaVVo2FhgJDVzTmlljvfcYv6bkVbKgjHS895E9BW9JtSqD2ePdiqYInviYmoCLbm05qz3t51UcKNJd0xdpVQZKP+GzouQpwfPJAPwABDg9x7tvS3tfksIhqapeuvuTUv3Ectr5BErXsaaxhpoMMWIK2P6p7vNpyR34YE7Ahw8f+QkMILM4YOhQlzBTSMbh6iN7CASqFNQmWgsroAUK6hYFA/m92RkCS4onGUdeoKIqpNraynGbZXl5iQLFPeOICo/RngBJANVSLGcogZJDhw5ZAoB0VYZu5hnKh4LoCeAjI2gf6Ndnd7ymZy6e0a62q/rmmff1y3ve1JDHyjrT4HIypV4z//lenuKZv5damkd+CUu7X5EObVe+g94ZZK2YoALkEoVqNkrAUC4MBw2f6cOHj/xcKwlsGMU7atpM8gCOnY/7DPbiVA68zihbrZll16u+ZIGmla7QvIpbFbSVp8xXsLehFnbryI8HbP9gzdC4gAYYLh9/bMQfVDRIBXAMp0wNmp1T2oo+H6kESwwox0Wwwg1SDQb5mBx46fJxfePMXh3uvKIDHZf0V+9v06GOy55/zs6dO7Vx48ZJ0xuS74Ej39N98vwxac8LkYAG6OmQXvtGJHMjj8FaxhrCumX2dRPUSBVpBzWo3U/kyYcPH/kN0mp5YMSlChZiUsxx0lKehwe1Dk19vEw2I/eROgKBCgUDSxUMLFMgUF8wl5I0bQz2dA0AiwzHSEwMRJXiYpNNhXSwDzo10qIvB5lC1ANNVwFOYBKDKBFQjaWavumj8PHy5fNqGejT8Mi8CSms/R2terfLqb9OasARQMDRbeP6QgHfh/2NMnGeEI/+PunK+THCATDgzx5ToSFdFVUy4DN84ZQPH/kNyg4asRROYnqTpVOWBnsPTmH4BdkfRmSSr+VuyCbLx16C2QJrdW3JbE0vW6GG0oUFEdAwuOmmm6zyxF6gODixdFVxVBkuRE3wcIRHXgAbhezJDRs2xGwa7hZUcXBTRo3gJci3MnA+socXL70/+jNWMhbhq5dTC9bFAvOSQNtkChizVpJ1QhUKBGGe4NIJq5z2KOAtPZ3StTYVEvCTEFPIWVADAwYnCzfHh49CRetAt/7+1Nv6/x15Tn9zfIvO9hTWQuAmGgpRAAQdWDgSgoj7+XeltrEACOQCkoExRqo1ihMe9D6g5jYlSBLV+8wFON98PC8f7kD92nQCcYDxX1E6Q0WqG3km4pQrCjSoODBeAYKhzmdSui0ZMA+i5xbBBQwXr1JnIc7RdYCd4JdsmdroD4VGRnjU8yNkNFXyzIPxTBk1ajenU0otX8H3IbBBOjS2rSHwKaO4ZDzhsBCQSgsvkypdFVWy8IMaPgodrJnne09qV+ur2tn6sk5eO6yQPcBZ4CCbihJ1AGctwo6EQc+BfunkYen4QakvomRHLEU2KwESxCyGX/BAyEH53Hzda8jscxK0+Mh/YFOnkpnthHkVpp8k43+kQkLlxB6TCKcQThhe7hbRQT3Oe8uWLZa94lVmtps5hh8gH3tn+sgehsLj7WICGwOh4bT3SvgtvJlAHZyaXpeTDU1NTRZ3ItvJjUgxIUoJ+jgIsKznCwfwLUQSphRVKkjbQnjggQf04osvpnsYHz5ygoHQkP7vqa0619Om/tCQLvd36Z9Ob1P7gDcqinxZQA3pcNVT4+jb0sv/R9r+PenVv5MOvGwZXxg7GDJEzu2KcwgI/SoIbCRrpGUDqIknSz3GqQjSqZMNMESDcV9aWqaq4ptVHlymksBs69+qopsUmOBwjBB1AmGXL7tPp2XsR88tyK6XYw/CT8AlHkgfZ476SBFvvyn9zV9K//wPUsvVgryMmxpnKBgIjAY2ggqoqbRU+zua9Q+nDuloV2Q/cAM7ySAVnL2EsnD0bJrMwWJKL1FWC0M7rSaw7JUbH4r8zFrDg0DHTXe7PgTXHwWTV86XVNDW1qbdu3dnPahBAI3+QD58FCKa+8/qTM9RDYb7NRQe1KX+Mzrd7Y0yPB9AACM6iBE3qHGtXfqnP5N+8H+kH35d+r9/KrVdtoQr8AgcGjiz7MfCsUVmoNvSONkEQf7q6upJvRdOdqAE9yJzor50hlbVbNSMsvnWY1XtJtWVTJvwOsY0Y/3dd99NKhs0el6R0cRxvKrR75a/YwPiV/CRAjrape9/W/rn/yPtfDuiqi9ArKubNUE4Nb28Qj++eFBvXz2hQZcBDsY/PBk7G35Bph/liG6//XbLwT1ZwVyGX2BXp1qObhTX3SyV0hwcfhEYe67CXX8rhFsERwlW5hIvvfRS2vwi7V34wQcf1B//8R+nexgfPnKCcz3t6hzqG/2d7WUwPKxj1y7r1sbJUS8SBy0lcGjqlVB12nlFOvTq+Ofe26Yz/WVaeOOmuG+lvE6+gZJYZJP5hKNwYQx5VEqpKvVMMC8QKFJZkbt5PWfOHGvesNG7aYKMYRJdaz5tlbcNkC6Ol0gRiPqDGqQ+UsB3vyn9499FHNGQjZ88I/3F30jTJhLTfMaS6lr99xs2648Ov6OrA31aUFmhcHhAT58/btm83zv3nn5v1UZtaprtOFdYN5kvjH/+JcOPHgdTDTjXKOPGGoDzIGXc+qBU1ySdOSaVlkk33CE1uG+qiEOP609/idtuuy0npU5eeeUV6xxSbUKaChh3rKl89oc//OGsfa4PH17hSv+Fic8NXNBipbGe5DFMX42YgY1Xvy/ZyyD2dksvfleB9Q/HDYawDh87ll8l+/ieBw8e1F133ZXrU/GRBhhbBBi8KKVZXdJgPRIBLsPexvih6ok9kBervG00j+U5elx5KQC8/vrrE56Hn5WURkDjD39D6uqIOJ9f/on0wY9JT35ChYZPLLjBKmu7t/2iJZqaX1mlI10XrJ/DVrnb8/r84ttV7MDZr169agWDWT/5mTFM0+ip2AeS+YZ9m1ZGCsGLR78gHdkq9XVL0+dHghouwVoU8Y8ELHFyWlwnDbzwwgv68pe/nNYx0s7UIKpCymg+Kih8+EgE1KzJPF+IqKmpsVK7jSKF0jQx1SFdzsrkUGdLwsAACitPUuk8BOeTTiqbj/wAGz7lz9JRIKUS2MKB50ZJBZHHKIjumUE5Bi+aEAL22USki8CPk3LShwuggic7AxCMItWfdfOH/1aQl+/2abP03F2Pacf9T2pdXZ2GwyGLhJg+G3974sCEsQOp5UEzO8YQwTEvajUXMshETLtvHPNx5c3SQ5+U7vlwUgENsHbtWots3HHHHdq6dWtGm/HGAhnZiJiyDT7Tzwb3UagYy5eL/1yhw6xJZNYSFI+JqxfH9xfi55ZLCW0WHC/5Jk6iJBaOad/emhyZGtneVxEnUIqK0s2JgMAkujE3Yw+7wAvw3bEBEwUs/H59aeDlZyMBDbiFEbz96F+k3sKrDFJeVKx/t/gW/cWNT+g3V9yu1sFI6WU4BrPobG+7DnWOLxt95coVvfnmm1amD4EMHti0BBWnYkDDgEy/tP0ElbXSTQ9Lt31YWrbBoeRtbLB/cQ4IoPk5F348MjVZy+67777cBjVIQaPREmkjPnwUGuZV1Gt6Wc0oyeDfiqISrayJX+KlkBBtcGMYoeZ2RJWzwiRcmTiNLR8N+4SltnwUBEhHxSBKlXS4KrsWAwQmEpU/oaFZNOEwKgwvSAfH4DwSkXrq9KL68pECKD/g1IjUBeHMZxCgbxvok/2bMYs6h8ZKKmFQv/7661ZGEoEM7DqcU+k2tp8MgGwRFM1FIMGAeU/mGM5CSn9t37496+dAYOGhh0bKaGURfKYf1PBRqJhVvsDhuYWaTMAhYvqJsV7GFU41TB/vcAkEFa5ryun6miooHUIGvI/CB6WVc5EJhOgQ1Xq8rG7GGYGXaI4NJzDBiHRAec3XXnttQqa5EyiBmYtM0UmBa11j5YEMWPe6XfQ5zVMwJruHJ5Zn5Vt2D0XKpTJGCYZRLo2yUowzOIaXWUaFDGx6RIu5xNq1a7Vv3z7r3hBAZc3JJoghUI4r3bJ2Qa+UVKSN+PBRaCgOFulnFm7S9XVzNaOsRsurZ+hzi25XdfHk3bQxjqKbDTc3N1v9A0K1M6Tlm8e/YdGNUuM8FRripsD7KDisWrXKqreZDCDalI5BeQABSAU4d3H6kp7u1GOD4xPQcAo48Jle9JlByXXDDTckfB2BHwJAPhKAexLtRKmrk2bOQhI69hx1YVevLfjLuaZu2jhjjxTxxcEyq0cLJY0Y25ANr2ozTzbgqIveM7MNMg7JGCHrkvUm2bUwHVBaj7Fy993u+4B4BT6TnkoEbH34KDRMK5utZdU3qLq4XlVFtVpYuULzKiZX81NsH7tTFpvJrvbEBmL+WmrUe56UKmxq8NIyXdv8mOXcLTTwnf3A/+QAwSlK4STbt4q9CQ6Qjp1PJjpCQ8RL0QEK5gy8gznlBHrsJdP7L1bGEY5VlNrxgMI+0Wt8jAQqhh3Gw7KVYxkaAK7R0Cg1FHZ/ktnldSqKygyAXQ1daLXWfTgG6zsc1vfJyNFPkG5gMl0Eg0GrhDyZNAQX4Bdp9RJMEsQQvMgE9ySX8+GHH9bHPvaxuDXPL/Re1p72I+ofHtCMskZtaFyrsqLUnEw+fHiJquIyPTn3xil1UdlgcJTixKJJE4YcZTbYgPoDM7Xqjk9r/46tqp4+S6GqOZYxs8zFcTHsMPBYC3ig4KIudraB8oVapV7USPWRHyCCj1KZQAXjyg0oi+imH0Y8YIRRfodgAc16jYKB50lZp5lZvHq4OCIhJqmm1zKP3BBnDBA/MykBzp+X/uBL0tEjUmWV9MUvSo8/EfkbtsuX/1j6T78nXRkhiR/6sPTgIyp0fGbhKp3t6dKe9sj3ml9RrRvPdOj191/XRz7yEV95lwCUZKCRIWqmmTNnKleAFKLoYk0gyECgNxslWZ577jkr6JWo7ncmwFrPZ//kJz/RL//yLzu+pn94SC82H9SJ7ssqDZborunLtbo2e70/fPhIFNjgMVkRLSBivbQHNVDAY4fhfMVmqnnwZzV08l319vUpOH+pTh0/q7vuSlwmFr5iypASUOAzcfZmu3QJ6+/evXundMmUyQhKbVJ+c9Om+P0jo+3udDkG44jABgrpHTt2WIED5hT+NEQM8XgspUJxQBLcSBVkorvpmQa3x+HpI04w4zvfkv7v30fK2d64XvrSH9LYNPL3TXdJZ09LP/lB5Pe6eumLvx/p4VfAqC0p1yfn36zvnH3H6klLxZNbw416b+c+1d58s2W/+YgPfGxkYOeyH2ZlZaW1BhLYwG+X7FqYzn5KUOO73/1u2sfyhA3RuBCn6K5duxybBbf0t+v1K7usyB0419usnit9enDmZj9ql+cYDHUqFO5XcbBaRQHfgJsswACCHJC1gaN43bp11vMENlCkvrNvn+bcdLdV8sLUu8OJggM3HqiNaCc5BExQssRSmWQK7x48oLtuXKVAceGltPuIjYULF3oSqEgGjGXGMHvc5s2bxwUYUDBDKuKhvr4+Lccj88hNaQYIByn0PmIAJd2vf1G6cGEs5ftP/jiSnXHLLZHXLFwk/e0/SZebI0EPsjcmAah/+5/XbFZzf49C4bAGr7apo6TNIs9+KYHEIDOCPQxj360DIFNYs2aNFbSHdGSrxvwzzzyjJ54YCf7lAI8//rh+/OMfxwxq/PDCHh3vbrY4BqUQnr6wR+VFpVpS5Wet5TWG+6Xeq1KgSKqckVQdaB/5nRWNQ5bAA7YTfAM1qhE4oQxva5hjla4Gi4eHrWAtQdp4wgzU9Dzs9hDvY03MpgIY8dfsmirNrquWBgekEl+gORnAGM2FkpxAH5kajO1oJyI8Pd45wdlTzUI3cPud/cykBHj5Jelrfz32+7590h9+SfrLr5oLLX3ss9JjT0b4R9N00tw0GbCqdpb+48qH1D7Yq9qSMu3ZvktLN23KWdPpQgN+dPxlTz/9tD70oQ/lNLBx1113WX7BbAVYdu7caQWHvQh+eTKbMEIeeeQRi3Q4BTXO9NAshkUzYojQRqZloF3dw72qLo7flMhHboDReG3oiPpCRm0TUE3xapUXRZzcPgobGDGoLlGGRJMIfodc2BuGoVSntA2Gl1PvAKfjG4Mr3dRYN9khECc+ByIV6G7TjONvKtC+NfKC+ddL6x/zCfMkAA7YbKZEoiAgJZzSL04qZcg6QRYT/PO6n4eZS25SU8m88g3IOCCYcfbs+OcIUG19eyyoYZ6bPfn2OcbRrPLIGH6v76Lmzp2bdv3SqQLjNGBfzAdk876Rafbqq6/qf/7P/6lcgYDKl770JetcotfhgdCQ3u9uHvccSsHDHef9oEY+o69VOvNCJLABypukBQ9Jfgb/pAhqEATGIUtmLWIUO7Cn7GUyEYrQf4wABYIVxFbxYP8s6rLzGZksX0Ugm0AMtqdVK/6N53TP0JWIk7K8UvrIL0pzE2ea+Mh/ZDuoAW8lGwPbIpXP9qIXDVyfc4g3hxBS+PZiAsAlyPg2fI3ytQcPRBqB20vuVddEHpMMZUXFmllUM7qmE2z24Q5kIfJAQJxrBINBa4/OFogdEEPwQqQV9JJ0oOZygmnC7PZ5H7nHQOiKLaABwuoaOmxlbfiYHMCpxcLl5HDFcRBtYEFCWHRRhOeDkUgzwrfeekt79uyxSlQQVb51wwZtGDqu5dNtjo+zB6ST72TkHHxM3j4pBMsoYUb2UayyKzyPoy0e4jUA9Iq88BmxSj/6GEEsR8kULCFBgNqvjZw8cJ5NtZIjL7/8spWh6aaJaKZAZgwq71deecU9v/D7aeU3zr8h2RuMEuS4sjeXZ+TDQ7uMQDC2E5mqTrZJdAlR3o8TjCwI03TcDQhq0AshE4DrbN261XI0IRixOEZ1UPcOXx37vv290r99TRrKbc8lH4UHGicjeqL0VKz9KpGoCUV1uiWeCSQm4vXMyyVLlqT1OZMe5WUTG4Gz9hWnLmorRJBxkItSpZMBXvkLCgleZoJ75gUhykLndJTf0VhUNWcc7eDnmWVNqiyKr8bwkTsMhTEqozfZsIbCPTk6Ix+ZCgwkU34ERxgpsjh7IRJuVCIcn9diNPF56QJFOrX+du/ebWWGUQOQc7KMwsFeqadjNCssgoDUNnFdmqwIh4c1HO7QcPiaJyqefEI2vw+lpaihn656oH+gT2GdUVgHFdb7Civ5TBOUi/EyVDhXn3AkwIwZ0gMPjJEOyAaBDtNTY4oAxSkZRr7TN3kwx9j7Jtu66oZw5HK88Nmcw49+9KMJfysJFml1zXiOQTb4urr4pTJ95BDMn4FoOy0s9bfm8KR85FpswjEIHuBHYJ9ywxewzyjfg+Ldyf+QilOJz6bUIMem5Cgq+lGedOGUFCwaP5Z7u6XOSK+1qYC+4R51DbZrKOQHclIF9jy99hAXxgO2WnPz+ExEO6iGcOTsCb1w6YCevbhP73VdSolfEFxJJPLKVrnLgsWTT0V4hT2I+5GPUf5CUwkITfMlq7nQkC/ZGtkCe/b+/futGEJeBTVQS1AP6/vf//6Ev9WX1uq+mRs1vaxBNcVVWlo1X3dOv9kn1XmMSP+MicS9KOAHoiYLMJQw3iktleyii6KK9MIjR44kJBKoSCAKpKDzeakCowqiQW8PPp/1ZkKWSTFKiahlDa5VNjVUAwQyuoffVM/wDvUMb1Xv8F6Fw4lLFxUKUPclo+JLFfTPwIB3k/3Aa2Ipqa62XFV1NYTkGG3LJZ2StENhJUcGTdPAWCBoyB7sIwG+/J+kn/8FadNm6ZFHpb/7e2rrTanLRrk0PwCWGkxpRozwqQD23B/84Af64Ac/mOtTseoMU2+Yc4rG47Nv0IaGxWoqrdbc8gZ9bN6tml850pzTR/4BB/iE0sMBqWTyleSYCnAK8jJPjx49mpJil8wsnL1wFMp/Jio5SsY59ho8Ix37kDK2ZGZQjgd+4Vhqt6pGmmBTB6TK8Zknk/U+n+p+V3vaX9fBzm3a3faa2gcykyWTS7gVLWD3p5ohTeaDmxIv8TgP5/nGnh3aWtKsve1ndLDjnL53fpfeaYNneAc4frJ+gimJ65ZJf/XX0r33RzjGr/ya9IVf0lQCazalB+29J324B74tRIpeCIALAd/73vesvdar0naehl0//vGP61vf+pb+w3/4DxP+Nr2sUQ/M3Ozlx/nIIMqCM9UXuKjBcMvoc1VF1/nNwicJCAygzkg1mo6iigbJPNra2qxauKTQOtXC5bUzZ860fk61twBKrB07dljp33HLf6CgWvuAdOCFSHAD47S0UlqanYZHuUbv8L5xmQDDuqqB8CmVBSZH2jAEIhmVdCL1kRMIvKGgSmZuRJ8TJARFd1l5WDffHB1s6BsJcLhXEkNsSOl1Cly0trZaJR58uABKs5/9uSl9qaZSlkEmQBlGykZQZ32yz7vXXnvNIqc0Dsw1OAdsiS1btuj+++8f97fiYJHun7lG45/1kdeYc7t09qUxB3FxhTR9fa7PykeKiM7UgBOQ6Zqqc4vyVQih2K/I8oZbIJByygghCMGDz8J2iy5t5QacL/blHXfcEf+FN90l7d8mdbZGOAZ18+98LNJbY5KjdaBZF/tOj/4e0rCOdu3RLY33qigwOVT8jB1KyroZQ6nwC+x4HL+xyrK5AXOCfjWUEe2YUaKS4jIrO9Hg9StHdFND4t6XdiQqcUXGkg8XWL5C+tKXp+ylctsD0kds4OeieTbNwyc7vvOd7+jTn/60Z8fzdBd66qmn9Gu/9muWw5QavD4KF4FAUHUl6zUQuqyQ+lUcqFFJsCHXp+XDI0AAvConQWADAw2VE9FlnD5e1mtng9y+fbvl1HBFkJbcLFU3SldOSSXl0sJ1UyJTg7JTYU0sDzcc6vAwJ6+wwHgkSOG26RWEg7mRTJMsxqcZlwQzDvz/2TsPMLnK6o2/U7e3bHrvBVJJD6F3UDoICih/BVRQECl2URR7QUUBRQEVaaI0UUAhlARCKimQ3nu299kp/+f9Zu9mtmZmd/q8P55hd25mZ+7cuXO/c857ypo1RnibNWsWHE5mWC1r9xf83kXmDDGznsN62WqN5eaW0PfBBx8YB4yZDkKEA4Xl3mQYCpihtosXLz56ACzFefzxx3HZZZclRdYd94H7wn1qL2qIFCRvEDD6AqBuL8CAaMEwwBF+K1SR3LBCIxrXDfop48aNM4OMN27caJ6zqxkEPRU0+Lz8u/YDzTuF4sWn7gBWLwEaaoGhY4Cxk5EJ1HqrzAyj0AA6hQ22o8pzFiIdYOIQxYJwziNWI23fvt1U9IRrT1HQiGQ+FV/D+h5ZYgZbrfE5eNu18x0E6tv6fR6/L+KWcPQrKOyx/Rv/jn9PMYOtcFrbOwtxFBj/oc/NOS2i5z4aY2rp3oFh165dJrbHao2kFDWYjX3yySfjqaeewq233hrNpxYJgItYliOYYS/SC14wabBES3zguWI5BCw57+p52daHQdhIStLZn3HmzJmROUj9RwVvGYW95ZIe2p7DBnsatowL12AvKCgw2VTtg7hswcNMaz4P2xywpRofx5Lw6dOn92if2CaBIgqFB6v3bAB0jFztRAw6hJF973jun3rqqUYwoePO+3w/zISMZCaOyGx4jjPhhAJ0p601ekJdXbACJoPOQ373elp1mCqw5QudDc7USBauuOIK0wrrvvvuM5ncIsVxFwZvQoRhy7EtFZM5du/e3WniJJNKIr0u0GYrKyuLLBOdwsbczBNW3fa2FQEWLnv6XIvZBoX2UTiJTVwjWdHNIC6FNgv6G0w4oo1AW51JSfRXKJZEen4ysMlAMe1+BgD5eqGiyKi8vthRf6QFGEWn4bl9IhYh+Lz8HrDFM79rFBG53/PmzZOgISLyMaJaDc5KTr8HsHcyhD2N4feR1RrpLGo8+eSTOOWUU4xoGi2iXi/IFlQPPPCARA0hkhgaWvEuEeRCx1ZVNLZoAIYTjKUAwgASg86ie3hcs+0T0ehf21INEIANLrjt6SXucAFkthMH6IUDnRNmU1lzBNh3mRUZEydOxJKynXho+Uuob6jHsYUD8em5p4VtwFuVSRRH6GjzxuqMUGxwIgCKJKvpArV8LhNhQ+Rta/g9mDZtmnGk+N3trNWbEN3BTDyer6wk6rWoUVUJ/OR7wIfrgvfPOg/4v89xccmIDyHdMxdfeeUVk63KoEaywMAjK+m4b+edd16id0cI0WKb0K6K1QDxUGj7dPWcDMTSLuIaF+7cqNWrV3ew20Tn9M8ehoNNe1Dvq2mt2BiaMxZue3Za+cadnctdBXCZoFddXd1mO3vis7rI5gxgc9ly/HvVEjjgxqQhszByZHjnJb87q1atMq/BGQVMuGJlaPth3bP7jEGFpx6rq3aa+4Oyi/DRwcehp4LOCSecYN6PfG7REziDjcJdVKrBqzYDB5cCAV9wBtfgk4Ds9A3yZ5J/QVh1/dnPfhbRJOr9B9iCir3EeRNCJC+xEDVYidGVWMEyWmZXUYFmkDkcKIJY8zjE0XHZByHXMQdu+2hk2ccj1zE/7So12Ff5aMPp2xsHloFAY52VRCyNXVGxB4/tXIWm4hw4BpViQ14zntm7Pqzn5LlMEYMZU2y/Rkeasy06fX2wXdQJLbdTYMMQ9AYajBI0RE9YtmyZcVx5zvaaX/0E2PjBkfv/eRF4PnplxMlOuvcN/uMf/2h63SZTmzLuy1VXXYU//elPid4VIUQLFMnbDzbtSujoLbT9WF3bGcwwp33I1w33tTOh6i5aOGwOTC6ah1F5x2BIzmhMLJiJYbmZ22aGCUZWNYYF20OxlZPL5cSW2vfgcdVi8Kh+GDCqCBXuLWj01YX13Kz+YGUSK78p1rFag90V2mO32XD2oKn40viz8cVxZ+KqEccjz9m7qlkJGqInMO7Lc4fX4V7bjQ2HgANLgoIG8TYAu/8L+I7MDE1nGF9IZz9/zZo1JsmOmkE0ibq3Qmf58ssvx4MPPhjtpxaiS/wBP3bU7cDaqrXYUrsFXn9oCx7RWRYIDbJoQ6OLWSVdldFywWOAOdxWUnRMMkGxjiYOWxGy7KPhto+A3ZY+ZeEWzFSK1FmmmMFhk6zwYM9Ynn9Ly3e3eQwLZpdW7AqrdJaVHnQyLIGDmVndwaw2G7LYDAwZgacJaM4M4zOVYHUTq+Qi6encKfyOrFnNyH7b7atXIBNI92Hr7KP9/PPP47rrrkOy8ZnPfMbsG1sIChEv6r012FH/IbbXfYDq5s4TGDJZ1GAQpr2d1n5bb7HmC3TmE/CabA1uZlAt3NdOd3E6FsLGwOzhGJY7DiXuoA2cbrAaMJzzh+eblZXOym0GdQcNGmSqyRt8NWjyU8A4YisE4EdV84Gwz3Vr9iV9Z966E9/cdidyHO7M8JfZkqihLvhTJA1MaGUSalRmntXTvgs9lwPBNlSeSmQCjCmEW22Yijz44INGK2Ar/GgS9fZT5IYbbjCl4T/84Q/NRVmIWEJjdmXlShxsOthaEru/cT/mlc4zBpjoCA2fWBjzLPumode+WoPbmclFg48Z7eGKGsy6Wr58ufm7bqk6ABzcAtgdwOBJQI7aVaUzkZy7dK4ZxOU5GZo90pnpz+tHOHBmDB0Ynsd0Ptjiiv2cMx6KGf/6C/DhKqaxAe4cYMyxwNmXAQXRNV5E5PA6al17q5rrcLipCjmOLAzKjrAHMx/LLKL6kKxDfrfyIh/SmqoORzg9t1OVhx9+2PS6TUaniqLcSSedZPbxK1/5SqJ3R2QAtd5KrK9+tzU8eaBpB8bmT0epO7wWmOkO/Xza+KFQWGDyRzSzvpk0RQGls7ZWnGHAawP/jbMBwg2W0D5kgle3sw6YRLN9NVBfBZQMAoZMyKj+7pkGA7MUzY+2/jG5yhpcb7WJOkLn50e4Zw19DJ7v9HW4H+xuQH8449m8Fnj2T0Hbkxfk0oHAgjOBGQsy/tAkGp6nrAa3Eo33NZah2e9F/6wS5DojrDqwU8DrJHkojeb3dAers9j+NR2pr6/Hn//8Z7z00ktRf25nrPresjz06aefxjXXXBOLlxAZTqOvAY3+RuQ68lDvrTeCBrGGmFV7q3Gg8QAG5wxO8J4mJ6EzBqIJB3pziF/7KhBmmDAIxNJxGmnhBoTCEj8ObAGW/T34Oz/+TUuAhVcD+aU9eg8i+aHDSseVrXTCgY5we+aXDsfa6gNtnA1uCye4y8ew4oPZU8cee6wppWSpeMbz2j+AjauBhqZgNr+3DljzHrB7K/D5bwNZaVBO6/cBW5cB1YeA3CJgzBzAlZUylRoHDx5EU4kD/zu4qnW9HJE7AGcOmGlaGYTNldcAD/0uKGbw22O3ARdeikyAwnzoYNB0guvz73//e/zkJz9BsnL99dfjzjvvxB133JFU7bFEeuAP+NDgqzWVlTmOfOyu39xhQPKu+g0SNbqo1GCwl0FhtpqNJrzmUihhi59QO43XLAaUuY3JJlYSS7j7zra5XYoaFDRe/zNweCdgswezw8fOBmaeE623JZIMVmGzsjtcH5l+antfldeNHHsBGvxMdgpeO3g9KXKFJ4RyQDCfk+cxRUO2o8r4ta7yMPDM79nnGmhuqdY/sAf4x5+CdvlMtvhNA2oPAgc/CF5rSscCxcOQCljfAQoZL+9/F4c9VcHtNgdO7z8LA3MiiMkUjgIq1gNea10JAPnDATfbOac3FObDTfxNRZ566inTij4W8/piImrQsKDTcf/990vUEFFna91mbKvbHDzXYMewnBGdPs7DUjXRIbPdaj0VCxWYF+IRI0Z0md3K9nTMQIkqa18OBlAtvB5gw5vAzAuj+zoiaaDT+tZbb5mhdqQnJddTiwfhUyNn4tUDm+HxezGjeDDOGTQhonPdmvfCKiRVJfLiTCPU1/b7SGO04jCw9QNg0gykNHxf7/49WBVmnXN7NwAnfhJwJn9fbrZh6z94AP5zkFnHRz6jHfUHsKFmFyYVdt46sFPOOR8oKQWWLmHdOXDmucCo9Az0t4fDQevq6tIyk+rVV181Acrzzz8fyQr37cYbbzT7euaZZyZ6d0Qa0eSrx0b2wvcHZ0TkO0vgCQTbGoXi9XfclqnwWhhq/zBpioOSYwHnFfDWHlbK0seYPHlyRPag1UKoS3atCwoaxGp3s/k9YOwsoCg92y9lOjx/mDjFdrW08Xsy8N5ms2N0/izsafgQ9b5KuGzZGJwzEVmO8LuXWLPPmLXNyqeMZ9cWwOcN+hjtWfxKeoga1XuBdc8e8aEOrAfGnQH062XL2DjB78maqi0oaxE0iC/gw6JDK3H5sNPC/x45soDh5wIV64LCRlYpUDIxIyrkQueApiMPPPCA0Qhi8R5jImqQT37yk/jGN76Bd955JyZqjMgceEHc17AFdd5q+GDDvsZDbXpU7qzfBjvs8KNtS5oSdxSGoaYJoRcP9v7sau5FrGAZLbPqI+2fF9ZMjab2g9cCQENN5DspUgZmLDELcOXKleZ3Ztu1r5QIp+/9zJIh5tYb+Drqy9xCVk47QSMEOiOpTsXeoKBBrPdZcwjYtwEYNhnJDluBbN2zA77stmulHTZUNvegfdq844O3DIMiJtuWpCM/+9nPTAvZZB6eyyDkZz/7Wfz85z+XqCF6TZ23HGWeXaZCo9ZbC4+/qfXfar0VyLK3DyjaUOCSfxHqU4wde2RgNFtyxvP6SGGCPgarZiOl2yoNQl+CPkh7u6axRqJGGkP/4u233zZiGW9M1mvvvx7Nx3Da3RiRN7XX+7Jr1y7T/STjye5YcZ9W/gXZ9V7LtSbk3Nq5JGVEDV5Pa5qqOzSOavR74A344LJFsC6wZVW/mcg0uH6m69y+JUuWmGrKWHVxilndNjMpOGTwpz/9aaxeQmQA/GJvqlmOfY1bUe09jHLPEUGjFRswLn8snC0XS/bFn1w4GUWu9C9T60lvPlZpZLMfepxgtgtLaHsyEOjDDz/ssvKjlaKB7dR7G1CitmOZUCJOZ5Z9Znk+06mlMUDY33n9+vW97kHL6w/L0HnryshYtWpVzLISU46F57KEpZP5C7nAyDRoz9XcGNn2JIOOcUNFnRExQvEjgEJXXsL2K5WggEnHLS5rKLPBPeWAryH2rwXg/fffx5tvvombbroJyQ4rNRYtWmT2WYieUustw9a691DVvB813kNo8vO71natd8COElf/1vt5jkKMzpuig95CQ0NDa6UGq7xYyRYvaJfRT+hJqyv6Qrx1mzjVZ3BHQYOz+wpVpZHO8JxgQu7WrVsxbdo00/6poqKijV/QWVvbSOFz0ldhFW1n8PvEx0R7oG5KMmoSMGBYRx+DTE2T5GkvfYl21xt2n0gRWF3k9rT3MIAsuwtOzbgNC86HmjhxImJOIAA0VwOeCiDQSfVTDKAmwCqNzqoto0FMUyluueUWE+zhxT80i0OIcKn3VaPGW956vyvTc0D2AIzIG4kmXxPcdjec9vTMouwJbJPDiodEwOAPjbWeBH1pRHLf+/c/4kx2yvSPAO88DjS0lDuWDgUmpEEZqjgqs2bNwnvvvWecDzqn1mA9OtU9ydprf+5u3LjRVIBQPLF+t+DrLF261ASK2f9WsC/YNODKm4Cl/wO2bAj2vu03CLjoU0B+9AaGJgwKqA5XS1aY5XjYgNLU6HlLspwunNB3ChYdPhIMHpxdiokFqfMeEsm6deviMz+ncT9QufiIs5E/CSiYHHOHg1XWFIyTHdoF3FdWljzyyCOJ3h2Rohxu2n6UR9iQ5cjB6PxpaPY3GQHYbctK6/YQkRKa8ME5Y0e12aMI4wtsRxrpvAHab8zEX7DgKAOGB4wCJp8MrH39iKAx72IgR+2A0h1WG9G2p7Awffp0c27TD+B3n90Owp3b0l0FhjWfj3NieA6HJiBy/hlfT91OrA/EBVx1C/D2f4B1K4DyQ8Fts04ATjoPaUHxcKDucMgGG1CUOgPi6XsXu0pQ7q9rrf5mJ5WFfadpzQwDxjEYM4tV0L8V+hWH3gYa9wbvsy1e/5MBV+z8dF7jXnzxRfMzVsQ08stBIJdddpkpEf/tb38by5cSadx6KhSXDWAnW19LRQb7gnOmRp4zaAjkOsPvV5kpMIMqdIhfPKHDQYOtJ+zbt+/oDgfJKwZO/gxQczjocBT0zYi+iyIo2NERsPoiR2t4L510OhNWf2Y6N+3n0ixevBhTp041LX1ECCMmBG/pSHYeMPdS4L1/BKszKJ5PPwcoCs5WSQV47k4sHIZ+2UU41FiFHIcbw3L7wc4hqOKoA8KZIMBWijGF88BCBQ1S+wHgKgWyB8XkJRlgefLJJ7FmzRqkCl/+8pcxZcoUfP/73+91VZ7ITHyBtm1LnDbAG5IoyyGng3OCSXkue++CmOkKs3N5beS8PK4v8RpozAx2BtF6Ely25m+E9bfHngiMmgbU1wAFfYAs+ZmZAv1XrolMoKLYHy3Bnx0M6LNYc/nY7jFUKD106JBJ0jr++OMVDA7FnQ2cckHwlo4MmwM01QCHWwK/hYOAMacgVWBFEcW488Ydjz0NB9Hs96F/dokqwcOESZq81sScqvVHBA3CavDDi4FBZ8fsJakFXH755UYbiBUxT2e/7bbbMH/+fHz7299uvXgLES55zkI4bW54A8HyO675uQ6gX/ZYYzwXuArR1x2/rKBUpifDznobJGHGVk9ek4LGwIEDw/8DhxMojuDxIq2cDpZvs0Q8WuzevdssvDx3rfYGodVGLA/lfQkaGUi/kcDZNwOeesCdExRSU4TQGUWl7kJzE+HB6wCDGyeeeGLsD5m3ppNycGZ0lMdM1PjFL36B8847L6Va6XFfzz33XLPvrNgQIlIKXQPMIF8Luw0odvRBrrMP7DYHStwD4bbHr11rKjJy5EhTwUZRg5WrtP1HjRoV09dkkgnttJ60nbKCxhFd63KLgjeRUVD0YtIUz7doinUM/FKQt9q3MUPbattGO40+xgknnCBBI9OgPzH+TGD0ScH2QM6slErSZLs2ntdOuwMj8mJjq6Yr7E7C9bO3FWBh4Slrt4GtqCqBgB+IQYIbRVxWVHPOdiyJeToFA02nn346fvSjH8X6pUQa4rA5MaFgFrLtwcwYp82FcQUzMCpvLEbnj0O/rAFa9MMg1Bhjdrs1eyDWwwM5z4MB4UiHHm3fvj3mTpFID1iuXVdXF9XBWsz6pcNMZ2b16tVG4LAG51rloRLpMxheT7PzU0rQIPyexLysOU3hcDtWZsUlMaDTIGqgi+29h0kE999/P772ta8h1fj6179u9n3//v2J3hWRgvR1j0Bf90hT+U0KnP0xMn8GBuWMwYDskRI0woBBGMunYFVrPPwLVtLSJmMrC1aJRALtt4ICtY8S4cH+9hQZogmT9rjuMghMgS20RTt9jhkzZii2kclQzHBlp5SgYQlyrEASkR+3PXv2mASBuODgPKB255bdHRNBg/zwhz/EGWecYXyoWBKXwQPf/e53TRsZVm0MHqwBviIycp2FmFJ8IvwBv1pk9AIrGEOHg45HrGHQd/To0WY2AQNCDD4zOHy012bQmI9Rz2IRLsy4az/zojfw3GOfZjocXIQtUZDCybvvvou5c+fqwxEpR7SzDTMFfu8p0LPNSlxw5gF544G6jS2OBzP2ioHc2Dg8P/jBD3D22Wdj5syZSDW4z2eeeaZ5D/fee2+id0ekGFzrB+VMwMDs8eZ7ZlMbvoihjW8FspgIwsSpWEMfwQoEszKEgWFWioTTHoiJVgwaCxEOXPdZiRTNbgc8T5k4xWrv0Jl8VlsqiW5CZA6MX/S0VXuPKDoWaNgD+NnQnwSAkti0vaJY88ADD2DJkiWINXHxbjlgiSXi99xzDzIGlvB4PwA8rwRv/J3bRI9Rz+9eHLuWuQMkHmJBqPHHLC72rmX2O2ds0GjrjuXLl/d6yLPILOgg0KkNnXvRW+hYUCQJDQKvXLnSbFMmikhFKCwzOC8ig6I8Rc64UjAVKFkA5E0ACmcAfU8BbNEPFjIg+Pvf/x7f+c53kKpw3x988EHzXjKFOm8lPqh+C6sqXzY/671Vid6llIb2qgSNngsMTEYKbd0ZT/h6lsDBjPruKkU4h4Oii1V5K0SiqjXoE4cKGrW1tabySP6vEJkDY3NlZWVGlI8bznxg4NlA8VSg8BhgwOlA3vCYvBRj/2xtG80W4Qmt1LCcDg4/uf322zFixAikPb5NgH/HkfvmdwfgjLNjLERLJjuNJSrB0Qz8dgUdnFCngQ4j79MwPHz4sDEO2weMCbfTQeFwcyEigcIZZ2vwZyygoEHxhHNihEjV4BPLnOMBhW1e69lKMLQ1HANOdOaHD4+NAR1tGASj0xEafIgLTArIHhK8xZDvfe97uOCCC1r7e6cirKY7//zzzcBwtqJKd5r9Tdhcuwx+BL/Ljf46bKpdhmMKF2qYtYg7FAlC15V4+Bid7QPts9LSUpP1yp/tqzZ4HX///ffjMxdJpBVc/zds2BCz2ZRMNlmxYoXmaIiUht+NeFWEM87EbgpVVVVtvpNci2bPnh2XjiTRYOnSpfEZDt4eZy5Q2LOZVOGyY8cOPPTQQyZZOR7E7ROn8nzRRRcZceOPf/wj0h7//i62SdQQ8Yd91BnwDZ2pEcsLPp2HrrLZaRwWFxcbAYP9A63haHREuDAx4CVEpPCc4jkUC+hssJ0ah3gJkaq0F5ujheXo05mgiMGqKcKgEtt8tF9rGBzgwDj+DQPSOTns75p8cIBnOgfBWDnJ4X2rVq1CqnPXXXeZc42JU2PGjEE6U+MtbxU0LPzwotZbYQZbCxFvmOzB4cdMWuR1Je6VbS0wmMbkKc7YYcCLLXCtte/tt982rbDV2lb0BPq0sRA1KGgwaYqDwdUeVKQy/H5E+xy2kqL4vaOAsWXLFnM95/eRs1d5vW9vt9O/4L+z+iF0Xk2yQf+C7yE7Ozbz8hINY/6XXHJJ3KrP4ipjMSOMWbQ333xzXMpQEktnX2r1shaJwxrgRyGBgadYXuhZaUEHp7t94UWOfUpZRUJln4sV1XUhegKHP0bb2aAxtWzZMiNmaB6USHVYrUcDOlpw0CUdDAolwfYtNhPUYiCpu++iNfuG6xGzlJKxcoPCPOfnLFy4ML5BMF814CsHbC7AOTAmLacs7rzzTlxzzTUdnMJUZNKkSbj66qvNe3r66aeRzti78CXUPkkkCq4rtJWsStZEz2/iIGZW2dHXoc/DoPHxxx+v1qGix74AfYxon9M8R9neknaGBA2RyvCa313rv57Y4GvWrEFjY6PxMfj8nEHDOHJ3LaCZJMXvE2FLUorZc+bMSbqWgxT/ua9xjS0EfICPLeA9gL0PYC+M2UsxWerxxx8317d4EVdRg47uTTfdhFtvvRWvvvpqemdLOEYBvnYfpCNOU+2F6ARe0JlJyxkX/BmrMlpC4yycEnQGIlixwQUq3bMrRWzhwknDJVowE4RBTQb8whk+KUQyw+t9ZWVlrzNm+BwUR+i8MIDFQFFP1xGK2xz0TKejK1GDLazi2fqJx4mDZPk+582bF98S9ubdQFNI1URzIZCzALBFfx8WLVqEV155xXyW6cLdd99tkiTeeOONtK2uIQWuUrjtOfD4G4MDHmFDlj0HBc449mQWIoTQgCyF7Z07dxoxIdYtr7obSs4BzxTerdZYmoUmegrtnWi3RabgxhmTcU+cECIGMJbDmE5v7W9WctP+5ppCf8Xq5tET2M587969Zl4N14P2UDDhd5vzBuMFK7NWr15txAyrkjAuBLxA0xIgEDJX0TUNcEa/AwU/R8b6v/CFL8T1PcY9jeLrX/+6Kbd54YUXkNY4hgKOqYCtD2ArDf7ObSLt8Ae88AUa2/QNT/ayQGbGxjqYEU7vdjo+zPBiqXhnC44Q4cLKI/ZvjMZ5ywqi9957z4gkEjREOsDvRm+rIegAMHOKbX7mz59vhOjeOuMMNDEw1X795H1WcVBYjCcsW2c/dr6/uJaEM4Oq6f222/zVQPO2qL8UEw7ocHz1q181bfXSBWZn8z3xvSWir3+8cNicGJ8/F6XuwchzFJmf4/LnmO0iveB1sNHXiGZ/M1IFJk5Z1dexgs8f7necQaRkbXEoUgOrGjUalJeX46233jLn79y5cyVoiLRYp1h11Ns4DrP7+RxMKKL/3RtBw4LiOmNM7WGb3KeeespUhMQLfvcZA6d/EVdBg3i3txU0SPOaoO8RZZ5//nmTaPq1r30N8cSZiN7+3/3ud3Hbbbfh7LPPTrpyoKjiGBy8ibSlpnkbqr1BccCOLJRmzYA7huVcvSG0HJwLBX+PZbVGdxlUFhRXWLXFckJlqojeQKe1rKwsrMey5ybPfWZe8Wd9fb0xyPbs2WPuszojXj0ghYglFOkWL15sHAVeZ8OhqanJ2Gah2bcUNJYsWWKyCqNZvcDrPp0OZtRaZdj8PlLQYD/c3mZ+RepQ0dGwWqjElYDHTEZoiw3w10f9pf7yl7+Ya+WXvvQlpBt8Tw888AD++te/mnZU6YrLnoXhueF9n0VqUuetx5Ky5ajx1pr7o/OGY2rRMSlhK/M6ysQp+vyxgHZaOPEDHisOKT311FNjsh8icwi3PRTPTQp6bJXDv6E9w+QNzuajPcb5f5ztonZTIh1gC1q2G58yZUrY3w92QqD4HQorpBmbinaiDZ9v27ZtHapB6urqTFVhvBJqeR1gwmTCKrMC9CX4uqEJZP6g72GLnujPz5Yxfsb6Y7X+d0VC0nquu+46/OY3vzG3dHSqRGbQ6DvUKmgQP5pQ1rQCA7NPgC2GfbB7Svset8xA50IUq0xN9rBl6V/7hSsU7g8NPw0HF72FgVarXVR7g4GZGOvXrzdGDMU2ZmBbzga/FzSkrKwpIdKJjRs3GoGOAkFX8Nxn4MeqlqADTsM0VPTm94WDLGPRjomVERzqOmjQILMffE3OsaGQEq9sJpaD07lJWOWCjeskj21oT+IAYC+I6sswa5nVDD/72c/Scjghxe0f/vCHxqm68MILUVAQ3eMnRLx4t3wlar11rfe31u1EgTMfo/NHZPyHwCQUBsGONg+IAr2y4UU0YOIFb7RT2sM2Ugzu0k6y/FqKelbLZ56H48eP79YOEyIVYRUE29Ae7TG08S3/gT4GvxOhvjq/V4wZxQKrqo8xL/pEVvVGvOa4sv0VZ07Rh0pYUoK9APC17yjjbPE9ogdj+0w4+MxnPoN4kxBRgyc13/QFF1yAyy+/3DivQqQaTf7yDqqnHx54Aw1w2eLXny9c2ldlhLajigXMRmGvdPYqZCCZKn5oT1suMszGpaEnRDSCWeecc47pp85zjQFKChkM4nHNofMbz76ZQiRLSx4G7GfNmtXh/GfFEkuEKfZ19u/xgvswbdo0Y/SzpRXXCX53Yz37yYLZUxRWEiqu2+xA9kyg8b0jFRuOfoCrY196DizlMWOJfqRr+F133WXW3I997GNIV6644gpTrfGd73wHP/3pTxO9O0JEjNfvRVVzdYfth5rKk1LUYIAqnjMraN9xJgErzuhf8Nrd/vrN9iJc42T3iWjw0Y9+1PisPK+mTp1qfAu24+T5x7lfbCmTClVUQkQTJo6wpRK/E+2hkEEhkBn7rE5KBPQjODeDdjOFR1ZK8HvM+WtsgR7ranAmiNG34Zy3hFZnOUYAvsOA/1DLBjvgnhH0PTqZj8KYSqRzsdjtgj7Gc889F995hC3YAgkcBPCJT3zCnGycji5EqlHTvBXV3s0dtg/MPhEOW/JlQPIiHjpImVkknEMQKyOMWbacS8AgFbNTuOjx+87X443/zt7szGgR4eHxe7C5dgNqvNXIdeRibP4E5DiiO7wu1eGSxvOORgzP74S0khEiiaCA/Oabbxojnhk0zByks8EKJooJiR6gysATBfAJEyaY9YBVftxPDgs877zzYvraHCLIrN+kaTfH4c/+KsDmRjPcqPRuhy/gQba9GEWuEbDZ7KZNFlvnMVhntewKB67BzFpmNcwxxxyDdIZCFbPwWL0XblsEIZLJjnlu78vwh7Sks8GGEblDMaMk+dqOMQOW4jhn5Fn7v3nzZrPmxAo+/+HDh40gv3v3bhNQseYzcc1jII3XdQWaw2dd1V4sq+Ca48eUoiGYVTJSx6+Tqgyee6x0pA+rNlIi0+H1l98Lzu3jNZi+BVs8sdI6GTpxMEjP/aOwQDuY+8cWcazMjqV9yLWIiZYUPBPtZxkY8vdXsn8FArYCVPsOo95bZuax0b/IchS0xgsZQ4lUqGWyFH03trhNBAkVNajeMXv26aefxhlnnJGo3RCiRzDQcLBxiWk7ZVVs5DqGosSdnMGCzkSNWDocFiwR52UmVA2nuEFDUM5G+PgDfiwtX4x6Xx0CCBgH12VzYW7pQrjtSbBYCiGibxAfPoCd9XUYl1+IeaX9evxcDPIwK4k/mVXLYHi8+52Gk9H04osvmjJ0Zj5S1GDAKpaBOPbaTca2c15/I/Y0LIYfHOIXNNPzHIPQP3uKWct5XCj+UAQKZx3n587Sd97YnikTuPPOO41YRqdSgSeRamyq2Yq11RuMrUccNjtO7nc8Cly9H54abehPsNrNaq/DazkF40gzPSOFLUTfeecdc10LnbFBHyOcuX7iCGur9uAfe1a2OSSn9p+I4/uO1WESIg2p9DRgQ80hMG59TOEA5DuzejUEm9UQFAwY22FCa7LFeFjhTPGbHYK4rxQ0YtWilP4b5xAyzp2MbefKmjahsvnIrBEb7BiSM9cIG/QxOG+Ex+u0004L6/lefvllXHbZZUbMYoeARJCQ9lMWdFy/973v4cYbbzQlfN313hci2XDY3OifPQ+13p3wBzxw24uQ60idVmp08uNh+POCzgtkKHI2IqeyuQJ1vuDASEJhwxPw4HDTQQzOSXwmhBAiugbxbauX4ek9O1u33TB6PL42aUqPr/exDjD1FmYyXXTRReb3//znP6aFIasoYjHIj5llbCMRKvQnEzXe3fC3ma8B1Pn2wesfZ6o0WN3CKrRwncZHHnkEu3btwje/+U1kCnyvrEh59NFH8alPfSrRuyNERIwrGI1cZy4ONh6G0+7AqLzhyHcmn6BB2IondH3htZzBrVjDayEFel4PQ0UN+RiR81759g7blpZvk6ghRBqyq74S921ejCZ/0M7Md7rxxXEL0S+rZ21oGbhPxuB9KJMnTzY32sIU3Xfs2GHux8J/Y3U8K+GTLXmMBAL+NoKG2YYAqpp3or8jWLXOKhZW9IcDO68wlv/9738/YYIGSWBzryCf+9znTAk9D4QQqYbDloUi1ziUuI9FnnNo0qnS3cF+eXRE4gHFEwaQRO8WoU63h8x0ER3x+OtxqGkLDjZtQWOIKCREMvPm4YNtBA3ywNaNWFfF0uH056yzzjKCA8vGow0ztChqsHVEsq7Z/gArNDruWwA+4ziyl7yVcXY0WHZ/++2341e/+hXy8pIzKBoL6Fvce++9Zmg4q3KESDWG5Aw07aamFE1KWkGDsFVF+x7a3BYPWNXHGUzWMFjRM9hyqj3+xDXzSAkYvPT698Hj24xm/54u/TQhko2ndr0PT4ugQeq9zXh2zzpkAhxIfuaZZxohPBaxKbbATlZBgwRC2lq2jSb5Wq9rrIJk0kA43HPPPaZqnDH9RJJwUYNG0EMPPWSG+XGYpRAi+rQftmoNTWImbDzgQFP2IOVrip5R5Cru0GbKDjv6uEt1SLugwVeFzbVv42DTZiNsbK1bjDpvuY6XSHrYcqozdnSxPR3hmhXtYXMU2FesWNG2QsPnC/aaTSJynX1b204FscFpyzE3ZkJR0GD1DQcfHo0vfOELOOWUU3DhhRci02Dlz8knn2yOgRAidoT6GGzrx8BRvEQNVmS1rwgXkTG5qK1Azk/z2MLwZzZlol/d5F+DJv9aNAe2w+Nfj0bfKrNdiGSnzMNW1kfwI4BDTZmV+Mckn2hXFLIVI6sc2gga/qBYkCzYbU5k2Ys6JE7lOvq2XtuYOEUfg23FuoMz/hjDZyw/0RWSCRc1CLPlvvzlL+Paa6+NS7mqEJkGe6iHDiliv1tmMcbT2aGDI1Gj5zjtLhxXPAeFziI44ECeIx/HlczOrEHhPi/QWB92APJA48aQjARmIQSwrzH6md9CRJtx+R37vNL8HNvJ9nQlFpm3HJLNeRRmxkJtLXDn7cBJJwCnnATc/7ukETdyHKUodR9j+twaAjkIBEbiYFMl9u3fZ9q3soSeA7Fr+T664O9//zv+97//4Te/+Q0ylfvuuw+vvvoqnnnmmUTvihAZAROn4uljMEtU9I65fUbh5H4TkOtwI9vuMkPCzxiYnDMiY4EZMB9oDrvawo8q+AIHrL9u2VYGX+BwDPdSiOgwMLsQ9pCgNn8fnJOclQWxglXM/fr1fFZhe+rq6owIwHkUhh3rgMe+DzzyLeCZXwIV1vUi8QzMno5sI2zw8mWD2z4U9V4HGnwNZhPj8Ww/RR+jK/iY//u//zPV0NOnT0eiSehMjVC+8Y1vGIeDas9Xv/rVRO+OEGkFB/gwW9GCJWXM9IwnbPcxe/bsuL5mupHnzMfsPvORkax8HVj6b0Y6geJ+wDmfCv7shuZAY4dt3kBTDHdSpCzMpKnaADTXAO5CoGg8YEtc1snc0n743Ojx+N3WjeY+XQ/O0xhfkDnBGwbto5ntS2eD4n5rsO373wPefjt4TfF4gEcfAUpLgcsuRzJQ6BqKAucQHG6qwP8OLYPHv8Zsr9lSho/PPt84Efv37+8yeMhMK/a5ZdspZo5lKnzvPAaf//znjR2U7H2fhUglOBi0fRs/Zmx21pIqVnCmRmjilogcfoYn9BtnbpmGL9CA2uZV8COYIJDtGI1s++hu21MGuvAlApCPITqh7iBQ2TKjoGQskBvMik8UHxs2DfdtfhvV3uD5WuLOwYVDoj9fIplhUD50FlNvRVEmTR1//PHBDWV7gdf+diRRqvow8J8/AZfcCrgSv1Y57VkYkjsHPr8Xa6vWY3ftDqowRp/1N/kwtnAshg4dam5d8ZOf/MQkSX/9619HMpA0ogaHhP/pT38yJfIXXHCBKSUVQkTPqW8f+GAWbPu2VLGCLT/4OokuTRMpyta1wDv/OnK/qgx48Y/AlbdzAnKXf5ZrLzYzNY5gQ44jPi3XRArBzLy9rwKN7K3K62EAqNsDDD4VsCWuoPUrk6bg/CHDsKOuzlRojMsgQYMUFBSYfresSui2BURTE7Kzszv8G/+WwgiDa7yx1Hzq1KnWHwKLWwSNUN58I2lEDcJ1862y99Hc0vuY6/a+sn3Y6t2HaSPHYfv27SbbjDY0+8pTuKEdzff6pS99ybTZuvLKK5HpfPzjH8fjjz+OW265xQwOF0JEh1GjRmHNmqDgasFZP7z28t/iAWcvKW4gegJtiNrmlfDjiK/Q6NsKO3KR5eja9rDbOq+atdsyy04TYVC9C9j2ypH7h9cDY84G8hPX3q1/dj6+OulUbK0rhw02jMnvA7c9acLCSQOHYNO+bh8ro0/BVlOslKZdToHk2GOPPRLn2rOp7RPR52ioAcr3AQNaKjmSgANNB7G78UiS867NO1HYpwiFfQpRUF5gEqfoi3Go+uHDh40IxJby9Dc4D/v11183xycZSKqzl87XTTfdhGuuuQaLFy9W1kWK4Q/4UeE5DG/Ai0JXcWa1xUly6Fiwx+2YMWNat40cOdJsGz16dExfmxd79rqdNGlSTF9HpDG7NwXFCysAySB0dRlQWwkUdp11OzBnIprq68xsDZJlz8Pg7GPjtdciVajf2yJokJasmob9QMMBILdrpzYeHFNYbG6ZCDPqN27caAJmY8eORU5OTptAxPvvv48PP/zQtJMKXdvoaFDQYK91ihh0MjqI97zPzN7QlqfcFvIayWJX1XqPBFu2vb8JwyaNRoWnxrwvChhcx1mVwTWd4g4FDbadev75503peLIOQ48nPAb3338/Jk+ebI7NJZdckuhdEpHirQG8FYAtC3D3D35fRcJhhQSzNUNhkIPbaP+bVn8xpLKy0gSeOhO2hTgaATTDj/azymzwBsqQhe5EjVy47ceaWRqW3ei2T4RDooZoz7732s1I47blwLjEzqzJdrhwTGHmVvGyEpzxZsbB+vfv32atqqioML4Hu4yEJgaxxRS387ETJkwwAf9O1zinu8NHHtwencqQaFHdXGtELbbnrq+pR31tA4aNG456bz3Gjx9v1lfO7mNyGUUN2tBc26+++moTs0+mDixJJWqQ7373u6ZVzje/+U386Ec/SvTuiDDxBbxYXfkearzB4CH7QB9bOAOlWdHrVSd6DnsGskQ8NPBDp6O+PjSLPXow4MRycF4IOUeDgka8hpKLNMSd3blxwO3d4LC5MCp3Lpr8ta2ihi2BmfciSfE1RbZdxA1mBHENsTKiSkpKjFjBLCEKFlzDmDFF+DgOrRs3bpy5HZWrPwnc/9tgcNQKkF6RXFUNdpsd2XY3Gv0e7Nm0E8X9+yC3IA/5zqD4QmeK6zpFDQpA8+fPNxnS1113HX7/+99j8GANeg3NHn/wwQfNsZk7d263ZfUiyWjcDVSHBIZc/YDi4xNaSSeOYM34CW0XyN/bJ1NFs/qbAZbNmzebeRpcJ4ToCTZ03kHAhqMHH132wXDa+sKPBtiRDRsFVyHa09Liqe224OwCkVibkGsXqxDee+89I8JPnDjRrFtMGmLsiv4F41lMqqLfQVGDiVRHba04eirw/ustc0CZkGkDBo0G+gxEMpHrzDGCRrOnGZvXbMbkecEWZNmOYHyFsTsOPV+yZImpRGHXlzvuuMPE+e6++24kE7YA9yrJsDLvnnvuOZx66qmJ3h0RBtvrNmNH/eY22xw2J44vPU1ZgkmC1YbDKgfft2+fuSj3dkgSA0mvvfZahxYhnNtBp4YXw17hbwL8jYA9D1BpZGbCiownfwF4aBgGgmWck48HTrgg0Xsm0gFPFbDzhXaZVHZgxPmAK37DTsXRYfD+4MGDxvGwKhDogPDGyg4KHWFnBvM68uILwKLXgaxs4NLLgCQYdteepRtX4MW1r6GwXzH6Dx+EPEcOzhm0AG44jajDY8LKFNrNdMpOP/10k3n20EMPJXrXkxIONmTbrldeeUUtMVOBgBc4xOtzu1Zx+VOB3LGJ2ivRjjfffBMLFy5svS6vXbvWBEF6WylG/4LVZ6Ew4ESBm9VpvakEYSVcg6++RTzOkb+aoTR4N6HRv73lHs9XOwpd8+CwqeOEiAI7Xm+Zp2H5GDagz3hg2EId3iSCYjlbGQ4fPtyI5da2N954w8TLKHIwqSqi2MXq14C6KqB0MDDtlKSr1KhvqMeTi5/C4foyjJ06Fi63C5MKJmJ0/ijjX9CnWLlypVnL6WP997//NWMili1bZvywZCIpRQ3ywAMPmKoNthco5eBGkdR8UL0aB5v2ddg+v/RUuO2JH4gjglBpZSYnYRCIokM0Bvm9/fbbR4YjRZO6jUDd2pY7DqBoLpCVXCq3iBM1FcDqN4DGOmDwaGDSXLWfEFE8v7YDB5cAAV9wQPiA44H84TrCKUK85kPFG1ZfMGjXd/gAHGgqh9PmwLDcAairqjUl8ExKYAYZg3vkhz/8If74xz9ixYoVXQ4Qz3RY8TNjxgx85jOfwZ133pno3RFHw1sLlL/cbiNbxY0CCpJPhMxU2OqO1U9WIhOrKNg2sLdQsOWsILb6iCaNvgasqlyGBl+w9VCpux+OLZoOB9d/kXH2g8e/F94A5wu4kOUYLkFDRA9WfW97FajbH7yfPwQYeRrgSK4At8gs/8Lv92PRokWYN38eKgOVaPQ3odhVhBJ3ifEhrK4uTIZmojITqJg49u1vfxvXX389ko2kaz9lwYP10ksvmTJx9r9Nx5MpnchxtM2isSo1XDZdsON1wfUG/HDZuzbG2QMvdJiP1Qc3GqIGBwdFHc+hEEGD+ICqd4G+ZwN2lfhmHAUlwEJVZohYnV8jgbyhgK8B4Dyobq6lIvlIVxuRAUK2jmQCwoSCEa2OCBN+TjzxxDbvm+XzTAaikyJBo2t4bB577DGcdNJJpho8mXoCi04wbRDs7So1AoBDol288AV8sMPe7XWWYqGV3RpNKNwyCSvarK9abao0LMo8h0zXgTH50RVPRPLD8zrLMQRZGJLoXRHpiCMLGHMuYOaj2QC2D01TmzUdSVf/wmazmSrIPbv3tElAYDsu+h6hrSMZZ2QiEFu3MjafjDiT+UD/4Q9/wPTp03HvvffilltuSfQuiW4YljsS5Z5DbWZqTCqYlrYXgmRiWfkuPLNnDZr8PvTLysOnRs7GwOyCDo9j66lQ8YGOPZ0Qqq+9hc9r9RyMGs3lLWXAocVkPsBbDbgzb1bLnoZybKzZawY6HVM4FP2ze9nWSwjRFra3s3e8dgqRKAYMGGCyo5qamkxSAts9MoNq5syZbewrPubyyy/HXXfdpSB9GFDIYLYZj9ny5ctNWb1IUmxOoHBWx5kaOaMTvWdpT5OvCSsrV6HMwwx2G0bnjcaEgnFd+nax8vnYAiTaVBt/tW2ziqrmCmQiHr8Xbx7ahsOeeuM/Luw7Eg7NqxEievDa6OqYACxEorDZbJgzZw7eeecdI2pQuGDCFNs7ciB4KL/85S9NyynOLUzW2G5ST1hj37Knn34aX//6102vTpG8sCpjevFcTC48DhMLpmJ2n4UaEh4HttWV42+7VhlBg5Q11ePBLe+gueV+KBQvKDxYFBQUoKamJioOBnvtWaVqUcNUY3TSHS8DqzS21O7Hk7sWY3XlDqyq3I6/7XwLu+vLEr1bQggRVyoqKrBnzx5TrRBKc3OzMchTGb6v559/vs37YNsVDsRlBvSWLVvMzDlWaHD9Dl2PP/GJT5gkoNtvvz1Be5968FhNmzbNHLv255NIMrKHAn1OD4obRccDxQs1JDwOrKxcjXJPMNDPYaJb6rZgZ/2uTh/Lqm8mT/UWfhfbfx/Zgi/Uf4kGrg6tkW1w24PDUTMJr9+P+zYvxov7PsDSsp345561+MPWd+FP8fVUCCEigbY3qwKZ8BsK16NYCOvxxOPxYOnSpaZtbej7ZVIPB6YzcYqxds7HbS9ocKbIN77xDROTT+aREElbqWExb948/PSnPzXZVAyath9GLJIHDlorzeqf6N3IKDbUHGRBOPwtwX/+rPI24kBjDYbmFnf7t3QSenKR5pBWXhRZlcHsRl4Iq6qqcMwxxyCqZA8DGrYC3sojFRvZIwFn9Mvbk503D33Q6lRaLC7bgMtzFyRwr4QQIn4wYMa1h0OwFy9ebIR6ihmEmUUUAM4888yU+0i4DnPe1eDBg414waF8DBCyRSQH9XFeFTOpBg4caLKq2sOWU1u3bjXtp5I1gyoZoQ30yCOPmAHrPIaschFJjLMgeBNxgUO0yzwdk2cONh3EiLzhnSYiUoDldcqq4G5sbER2dnbEM/p4/eOtuLjYCNm8rkX6PEdjXP4krKteZSpQaFmzMmFUXuYNnl9XvR876yvb+Bgf1hwySXNj8pM3gCWEENFk/fr1xq9gAhFtb65BFDS4/jDp6JRTTjFrUqqxY8cO7N2717SU4pr8r3/9CyeffLIROZjYw230Hxhzp98RCv+OMfif/exnpvVUMpP0ogb57Gc/axy+yy67DK+99lps+vd3Q02zBxtrq1DgdGFcfpGcRpE0ZNmdbQLdrdsdnX+122ey0qlnoCiSuRq84NN54YWQzgbvh87qiBoc1ldyEtCwDfA3AM5iIGsoMpFGfzBwZ8FPscHnSdj+CCFiw7qqSjy9exuaA36cNWAITugXDBAJ4MCBA0bQ4DBa3kIDZqtXr+404J/s8D1QsOC+M1Fg//79pgqD6zIdK2ZQvfvuu2a9Ze/b9rzwwgv4+c9/bmxka0CvCB8es2eeeQbz5883LanOO++8uB4+2mT7GmtQ7/NgUHYh8pzts8eFSAwM9nOOhr/NLBPAyXZgndC/f39s2rSpVdQYPny4uT9+/PiIXpf+CIMnvAYyY5ZtMWIh1vbPHogsx1wcbjpkkvIGZg9GDudpZRj1vrb+hUWdVz6GEOmEL+DHW4e3YW9DNUrcOTip3xjkaFh5K3V1dabziFXZwGQp3pg8xfUoFQWNzZs3G4GGNi6Tn+hncF2lgEP794MPPkBJSQkWLlzY4W95DBh7P+uss3DDDTcg2UkJUYPGzP33328+kNtuu83M2IgXqyvLcMuqxaj1Bhf9k/sNxg+mzIGjxcBSVpxIJLNKhuH1Q1tQ721urdaYUjQQfd15XToLvDhbwuDIkSOxffv2NgOCjgZncfDCT3ghjGnLDwobuZmXOdWeYTml2FS7v1XA4tVneE7fRO+WECKKrKwow8ffXdQqUz+xazt+PHUWLhoSHBAdd8rLgCf+Fvw5fgJw0aVcRBKzL0wwqakx1QwWlqDB9YjrWqo5HOvWrTPOxoIFC1qzozgQfN++fUbk4DrN99vV3CuKHldffTUefPDBDuXiInymTJmCBx54AFdddZXpGRw6HDGWsL3LX3auwPKKPeZ+lt2B60bNxbiCvsaukn8hEgnPv7H5Y7CxdlPwvrE8gVF5ozp9PIMloS2irPOXma5MoAoXfv94bRs3bpzxMWL5PShylZhbJjMyt6TD9EKnzY5hR6n2F0KkDrQpHtm+DOuqD5gOH4wnrK7ch1vGndBlImzM8e0F/AdZJwc4RgD25OnEEVqxwE5Bxx13HFKJurq6VnvWivEx0YBJVPQX6F+wspJta7taYxlz5/P87ne/Swl71BZIoSbENHLo6P34xz/Gpz/96bg4HOe8+S9Ueppa81T4kc4q6YudDcGB2GcNGI7PjpkCVwQGmxDRpMLTgJcPbECVpxGj8vvg1P5juxzwxhI0VlWwvQWzqShqsHUUf0YCL4QbNmwwgSTeTjvttIicFhEZjT4Pntu7zAwLJ6Py+uO8QcfBxcHGIrPxe4CaNUBzFeDMBwqmAI6cRO+V6AGfWfYW3jx0oE1e7ICsbLx1anyzxw1VlcBnP8MhFvSGgiGPhScC37grOPAwAbAtCZNbQtcalkbTNuT2SCoOE91uitUXXHctkYZZYNbAagbzjgbXbb5nZlD94he/iMNepz+33HILXnnlFVP1wjZgsWbx4e14Yvf7rff5rXLbnejvzkJ5cz36uPNw2dDpGJGnIeYiMTBEsLt+N/Y17ofT7sKY/FEocnVdEcbrGrM/WVU3adIk06524sSJEV+b6aNQIOF1bsKECaZCT8SO5RW78fjOVfAG/EZcvXrELBxbNECHXAB1u4BqCpsBIH8kkD86YTag6Dm76ivxy00d5xN/bNh0zOkzLP6H1rsV8G1oudNyPrnmA/bEVBxXVlZi9+7dbRKEaKuzLZMVL0sV9u7daxKWWfHISpNQX4ntVpmAcDT+8Ic/4M477zQtquKV6NNbUsMDbIEHlUNKPvKRjxinj8MSY0mFpwnlnqYO21dXHUauM+hUP79vG7IdDlw3umdZcoGAH43+elPi67bnpIQSJpLL4VhZuQMf1uw29+t8tZhaNBADsjt3yNk2ikOQOOiH2Z/MoOJMjEjh8/BGh4OBGIocLD0XsSHb4cZlQ+ejztdksuXynJk3LF10QsAHlL0GeGuCDkdzOdB0EOh3JtBhCGaKwbV3/14gLx8o7YdMoMLjadfoA6huqRKNO6++EqzQCM17eXMRsHsXMKxjP/VYw2whVgmGChqcoUFD/YQTTkCqsHPnTpNcwKwvq50Ue/WyLHzmzJldVmWEQkfryiuvxIgRI/CTn/wkDnudGXB+H/0LHtvnnnuu1RmMFbsbqtrMROP/m/xeHPZ4jS9Q7qnDH7e/g1vHn4IiV8+E6ma/Bx5/E7IdOXB00TZIiK6o9dbhw9otqPcFKzDc9iwj+HXnqzJgQiHC8jV6kjs5Y8YMc51jEIbXR4kasWVmyVBMKRqEmuZGFLmy4bTH9tonUoTaHcCht4/cbzwA+L1A0QSkPDUVQFM9UNwfcMa3rX2yNAk1egAAiLtJREFUtJmzJaqVNdcE3+bQDcEfvm2AfXr894dzhD780LQgbS/ST5061fgeqYDH4zEJAVyjWQEemjDFqsdwfaVFixbh5ptvxosvvpgyggZJOQuXQ1qYlXbxxRcb9SiWhk6hyw233Q6P39/maxdqyvH+okN7eyRqNPkasLFmGRr9wVY+Ra6+GJs/A3a23BEiDNZV78Obh48sDDXeRjy2cyluHneq6RHbHgZRGAhiWR2dDSrTVr/ASGfVsG0G+5hTXEyVDNlUg9Vi9pBWd/nO6A5KFCmO5zDgrQ7ZEAjOn2ncB+QmqGVRNNi2GfjF94Calvd28pnA1ddzCBDSmYV9B2BNVUVrGwi2uZxfmiCxuL4ueLx9vnbb6xOyO2VlZRgwIJg5yllOzKbnOsYqwVSALaUYoOMsEMuxYF/b999/3yQIRCLM3H777SYLi8dAa2/04LF84oknzLDEO+64wwxGjCXFrpxOZ6JZ8F88fh+21ZVhenHk88R212/DtvpgJqQdDkwqnI4+7swQiEXvoRjxbvlyNLQIGmRL3XYUuPIxIrfzzF76E/QlrOHe9NHZ0zvSuRqE/sWgQYN69LcivM+X1xjLx3DbHSjN6rx1schQqj7sZNsHqS1qBPzAor8D65cG7+fkAed9Buif3jM7h+QUmjmsHr+3jdUxOq80QXvUzrcggQQlcbXMmOX6xesi1x6uW6yGTgVBgzG8tWvXmiRlJgSwIwvfx8aNG03SMZOowqnOIEywuuSSS/DLX/7SDBNPJVIyEnn99debXsQf/ehHY1omzpZSd06cjrvXr2jtP0eRI8vR1gnhtp6wte59U6VhUdV8GHsaNmNYbgovFiKu7Kgv65DpV+1tRFVzI0rcnWd8ssqJmU/sP86MUV4AeTHkcKRIMhOpBlMJVlAl+hxuqsE/9qzAwaYa5DrcOGfgFEwsHBSDVxIpX6nRKe3z/VMIBtHvvYcR3yPbXn8ZGDkGOOkMpDM3jp2EPQ31eHbvTnN/enEf/GDKzMTszHEzgT8/fOQ+7ZzCImD4iIQN1GaVw65du8yac/bZZ5tKw2SubvX6PXh/49vYs2c3hg0ZiQULjm+tNGEWMnvb0mmIZA196KGH8Mgjj5ikHg0Gjz48ps8//7wp2z/mmGNi2ur2xH6jTNuX/U21rXacy9ZxVp+zB4lOlc3lrYIG8cOHD6pXYnafk+FO9So+ERc8fg/qfG1FbFYKlzVVdClqcD4NAylMmuIMJF7vGExhOypLlA4HiiIURyhqiOjCYNcrBzaaeYwcHDypsD8+NmyGBgaLMAPPKexfEIoZlqBBGuuBf/0JuObraZ04le/MwqdHzTZzNep8zWZuzmVDpyZmdg5tHFspEGBL7ZCYqj1xSRdM+KWYwWRfrmOMiUWa7BtvtpfvwOKVS0yy/SkzT8HA4gFt5oAwiYpVk+FSXV1tYuucL3fdddch1UhJUYMwg4pl4ldccYUpE49VYPX8wSMxOq8QKyoOI9/pgssO/Grz6jaPuXzY0fsfd0adt7LdaC6W+lb0an9FZsGAd2d5fjmOri/E7A3Yr18/851hYIVOBy/edETYAzdc+PcSNKJPs9+Hx3a+i1pvsC1Yvc+DZ/Ysx7WuEzAoJzG9JkWS4u4L2Nwt2S0tVwIGwNwp3AqusiI4zyEUtkLYuintRQ0mSPx02mx8+5jpaA74UeJyJy5of+wU4NY7gN/cG2wF1rcf8J3vsbdIQnaH2bosg2YmktWiKVnnODEzauu2zdhdvRIDhxfjmLkDWeSP/Q3vo3KH2wT7uPbScYpkDWVJ+Be/+MWUKwlPNThU0Wp1y99POumkmLxOtsOFW8efiBWVe1Dn9WBYbhFe2LsW5Z56I3AwgNzHnYvxBZE7+jXN7a6hRtjwo95bA7c7UZmZIpVw2p3mHGxfTeS2d+1fMEOUMzQYDKKQwXYYw4YNw/r1643fEe41m1V4ySxYpzJLynfglYMbW++vrz6IJ3atxKdGzknofokkJG8E4Gm3luTFv/1oVDmwE2AnC0ucYSuk+mqgvgbIT28fe0x+X9x17Fmo8TYhz+GGM5E2tGsa0LwSCLTEPR0jg8PCEwTn03GOE0X4ZF57uKayUnvtrnXY6duDEeNHwOV2YXnDKhzbNAEHth9o3f9IEgnYpooxda7XbMWaijhTvUycmeJ08u67776YnYSTi/qYW2hbqn/v32F+P3PAcJzSw5I1p82N5kDbeQYum3rli/CZ3WckVlTuNA4xz346wgv7jjXOclcwKMRMRCq4DK4QqwUVZ2SEm/1Jp4MXV/4U0eNgU7VpI9YWG7bWHZSoIdrCjNvSk4DKpcE2VI48oHgW4EzhFgIs9aWhHdL20Qg2RQnIJkoQBcmSHXT2ucAZZwEN9cHZJgk29LlOhTNzIlEwiMfkACYOjD62P0r9lvAQwJYNO1Fd9QFOmHaJqQCIlA8++AAXXXQR7r333pQrCU9F2OqWx5rHfPHixSZQGwuyHE7MLz3iyA8dczxePrABBxtr0C8rH2cOnGiGh0eKq4tqjK62C9Eeh82BiQXj8EHNRiNumPPH5sQYDgvuhrfeegvZ2dkm83Xw4MEmsEK/gjM2whVj2SqXc5RE9FlXtb/NfYpWH1QfbNPuVghD0STA3wxUb2wZFD4KKJ2R2gcnt6DjNooc2clrW0YTfsc5NyfhMN7pngcEvKZBpvkMErk7NltS+xcUHdiulrE6dlzJLSjAmEBwPW1qbMLGVRvQOLgeF84+P+J5cKze+8IXvmCq4d9+++2UTVhOzb1ugUbSv/71L9P/lobSl7/85bi87kn9hphbbxmeNwlbale1TungsPAhOWOjsIciU2A54Q2jT8DS8h1m2NOI3D44tnBwt39DZ2P48OGmvQJbelhwGBJL76ZNmxbWa/PiT1VbokZ06bzVRKBHLShEBuAqDg4GTxeysoHLrgaeeIRR7KC4UdIHOOMjid6zzISfQX4nTqBoU7K9Zs0aMxtj4cKFxjmq8GwFPDYEAn6sW7UZxX0KMGPCcBSyf3MPxJJzzz0Xn/vc5/CZz3xGRz5O8FizvzCPPVuF9e8f+wq4PGcWLhoytdfP0z9rEPY27ESdr7o1235A1lDkOfVdFuEzvmAM8p15ONRUBpfdhVF5w5Dj6L5ar0+fPqblBQMrVmXGkCFDzHVs//79RvQNh2TOlk1lXHaHiTqE1t8wY1tHW3SAgeY+04O3dGHaCcDGFUBdVTBRhz7GgvMyYlh4UmJL6VB0zKHgwKQmtsWyhpZzm3cfxSCgtqoW29dvw6TZx6B/br+IBQ2r+9E///lPY+emclvblD+TRowYYbLOmVXF3y+99FKkCn3cA+EumIuK5oNmOHhf92BkOZJXJRTJCYdHn9q/Z3NYKHBYsB1GOAIF59lY7TM4BFBEl/5ZBRiV2xfb6w8bp4MBCbYTO7ao90KqECnB2RcAQ4YDH64LVm6ccJoC6yLp4EwP9q1lqxUOFAxtrZLtKDaVjKuWfoixE4ejT98i2OGE2x7Z0MH6+nqcf/755vnvvvvuGLwL0R3f+973TIY5P4P//e9/SZ3JFwp9imnFc7G/cRea/I3IdxSiX5bmE4jIGZwz0NzChaIFxYuRI0d2aMt3tBa3TLRiYIXfM4ojIvqc2Hc01lfvbyNsnNxvrEQkkRnk5AOXfwn4YCnQ1AAMHg0M1yxbkXwcOnTICBqs7g6t8Kbg38ddgvWb1qOqvArHzptstvXNiry16FNPPYXvfOc7eP31100cPZWxBSj3pAEUNq688kq88sorxvkTQnTNu+++izlz5nQwYjds2NDtUCFmWpWVlfWofYaIbK7GW4c3YV9jJQqcOTix3zgUuVIjmCKEEOkOWzWuWrUKM2bMQGFhYYd/5zr5zsr/YtSMPGRluWCHCwNzpiPHURL2azBx4LLLLjPBQNq27Fcv4g8DrWeccYaZCUAHsCeZcEJkCrxuUeydPXt2h9a3+/bt6yB2tG9dxe4Lqdr+IlXYWluGtw5vgyfgxeTCQZjbZ7hEDSGESAIYmmf1N2N0kycHBYv2/7546WJsxy4UDgtWVgzNGYxpxVNgj6CNF1urnnnmmfjb3/5mBoSnOmljNfDD+OEPf2iyqdgPjEMlUx2PvxrN/jo4bTnIcmROP3ERH6eDF8X2F0o6Esw+7WqYH7NG6diLCI51wI8KTz3cdgcKXTlhl4ef0j82Pbx7RcAH+MqD+V0MztlUrit6yIdrgZVLAZcTOP5UYFDPZlMJkYjetitXrsSJJ57YYa1kH3hWM7Lq8dzTLkMAPvgCzXDasmCLwNng+nzrrbea56LjIUEjcbCilaX5TJjiZ/LLX/4y5QOAvoAXVc1l5jwrdPXRrA0RNVjJHVoFbsFrGK+d3cHrpgSNyGj0edDoa0K+MxdOe3iC6+j8UnNLOjzVgKcKcOUDWeEnAAjRhuYmYP1i9uYBSgcB42cHZ/UJkQIwYYoDu9nSNhTaa2yJunfvXiN2LChegEZ/Exw2O9wRzkvj/L8LLrgAP/rRj9JC0EgrUYPcdNNN2LVrl5lgTydw0KDULbWuat6GyuZNrffzHcPQxz0x5R0pkRwZpgUFBZ0KF9xOh6SrnnrFxcVYv349SktLuxQ+xBHKPXX48453zU8ypWgwLhoywyxAKYe/CWhYDATqjgz5ypkPRNhOJek5vBeoKgdKBwLFbQ0KESXeeQP4028A44AHgNf+A9x5NzBc7exEWwPe428y8wCy7NlJY/9wUB8H2bLisaSkxLRhZL/bzZs3m7YprN5gSypigxP2HvQMpqPx5JNPmiQdrrcisfAz+Pe//40FCxaY4cd33nlnyn4kTb5GrKt+F03+BnPfaXPh2MI5yNW8DREF6COw2qKn13xWhHO4uDg6qyo3Y3nFBvO72+7C6f1nYlBOiq4XlR8Ch5cduV98DND3OKQVnkZg/47grLKBIwFHWoXhkoNmD/DC/UDVoeDM2o1+YP824KSPBWdoCBGaqOmtBzgnyp4830XG4iherF271sy/ZbyNnVRqa2uNv3HCCSe0PjbHEfnQ971795oKjf/7v//DjTfeiHQhbdpPWfDtXHvttUblWrRoUUoOPGn212Jv4+IO2/tnzUSOI0WNFZE0sJUFgy8MEDEjioOHrAAMe4CzPLy7vnq8qC5fvtyoxAq2dM/9W97AgcZq+ENG8p3Wf6JpJ5VyNK4EvHtDuvDaAHsxkHt8wnap2V+OZj8NVzuyHIPhsEU+hLcNi54FVrwe/J3G76mXAlMXRGVfRQi3Xw9UVx65T5Fv2kzgc7frMInWTPL3K1fisOewuV/kKsb04pkRZyPFmu3btxtBg8P7xowZExXh5U9/+hO+9KUvmR6306en0YDONIC+xUknnYR7770Xn/rUp5CKbKxZhTLPgTZrOWduTClW614Rne8IW01ZA8KHDj1ShUnfY+zYsd368BRF6ItMmzZNyVPdsKv+IF4+8F6bbS6bE1cMPw3uJArQhV2hsfO5jtsHnw7khj/PJap4PcDW5UB9FVA8EBgxrXdB8YoDwHMPAvU1wft9BgLn3wDk9NJvEW3ZuAx4+x8dj8oFXwgecyFI/R6gbAkQYPWgHegzG8gflVTHht1RNm3aZDqocN2k2BGNxOYTTzzRJF/R10iWZLFokGKr3tHhh/P73/8eF154obm99NJLnZbBJjPN/vouttdJ1BC9huVsvNF5oPAXWnFBR+JobS4YvOEFkcOL6KDw/rHHHqtPppO2U/saqzocl531bN+UgviqQ4IgJAD4W4zzBODx7UOdb21QXGH2qX8nCpxz4LT3cNHfseGIoEGo9//vaWDEBKAoDDHZ5wW2fgg01gNDxwAlEqC7pL6l2qf1WPuBGp5fQgTZVLOhVdAg1c1V+KB6LaYVRzlzk9/zgx8AZZuDlUMDJgMl4Q/L664/fE944YUXTNXxiy++KEEjCaHIxFZUH/nIR0wrzvPOOw+pRr2vtsNa3uBvd00WoodYQuzOnTvR0BCsBorEh6c/QaF42bJlZp4NZ3Pk5ITXujWTONhUARtsppLRotm0latFv6wUa1nd3NFXMrAVVSJEDW8zsOhRoPpQUMigjXpoBzDr/J4LG/97EmjgtbeFioPAOy8Cp1we3t9XHgYO7ASy84Bh49ROqSs4/Nt8Zu1ytps6j62JDMRbBxx+my0oWjb4gfJ3AXcJ4I7utbPZ34CDTZvh8Tcg21GA/lnj4AizepuV3xT3o0VjY6NpOcVEA8bK00nQSEtRgzDrnGX7p512Gq666io88cQTKTXYz2nvfCCwq4vtQvQEDiFilUbod4OORJ8+fY76t7wQWsPCly5dmlEfwOGmWvx7/zrTUmpgdiHOGTgZBa6OwqkdNmTZnWjyH+khTAckz5miw17teUD7YEgCr0n1vo0tv1n7E0Cjbwvy7T3MbD68r6MhzN/LDx5d1GC588M/B3ZsDt5nSfnHPw9MmNqzfUl3xk0CNqwF/C0GJY/7BAmj4gjlnrI2h4OBmwpPDAThfauBHSGVsRU7gInnAiXRFSvCgW1Tr7jiCjzyyCM4+eST4/76IjxOOeUUPPzww+azevnll82sjVQix5GHBh9FjCNrXbb8CxHluX07duxo0yaDhNscgq1u58yZg23btpnq8EwRNXh89jVuw2HPXthgx8Ds4eiX1fm8sWy7u42gEbo95XB20cbWlaAqhj3rgeqDwd+tc3b3OmD8fKCof8+es3x/O//CH/Q7wmHz+8CLjwJ+X/A+RY2Lrlf7qs4YMKKdoGEDnC6gRFUaogVPRYigEULT4aiKGl6/B1vr3oU34DH2Vr2vAg3eSozKmxvRjL1orcmf+MQnTBUlk6asDi3pRAo2dg8P9jvmh8YhizfccIMp3UkV3PZ8FLnGtNmW7xiCbLsyfwWiqti2FzAGDhyI3bt3h/0c/F6xuiNTqPU24Q/b3sLm2kMo89Thg+r9+OP2xWi2DM12ws/ZA49tFTj4H4eFn9i369L7pCZrUrvB4A4gK3FB+wCaO2zzoxfnYlGfjpk9pPDoIh/efhnYuaVt1caTD9KK6Pn+pDPX3gQMCwkaz5wPnHdpIvdIJBlue0fx19XTYA2F5aZqwNfxmoE9Kztu27saiWjZwuz/H//4x7j0Un0Xkp3LLrvMzD1hpcbq1fE/X3rDyNyJbdq4MWtwTP7khO6TSL/gPP3wrmYShQv9i1Ty33vLnsYt2NWwEQ2+WtT7qrG1bi0ONe3p9LHjCoahyJVnapXpY5BjCkaiwJWCCZAcCl7cLrElfySQOyQx+8Os/s6ymD29yPYvKGn7nAxqhlMFzjZYL/3liKBBdm0GVr/V831JZ/oPB+ZfEDy+xJ0FnH41kJ2C3wsRGzrxLwyOniWd+gJN8PobO4j21d4D8AbYivHI9gZ/NRp8XVSmxQi/34/rr7/etHZkNXhXa3Oqk5aVGhbs9//qq6+aVjk333wzfvWrX6VMqU2xawxy7H3RHKiF05aDLHtJyuy7SF04YyPcAeDMnmJ5+MyZM5EpbKg5gIaQwBhnZbBigy2lxuT36/D440qGo9iVg421B42gcVzxcBS7c1O3UiP3ZMDb0ovb0Q+wJy57zmkrhjdQ2cZYcNnCECC6YsxkYOzUYEaUxZwzgNIwBlYePtCxyqOpEaivBQpSb65TJIYSe36yBV1EFBUDX/0BUFkezKAqKIzVLooUwh8ImNAMbZ0x+eOwvOJIFSAzUsflT4j8Sat2Atv+C/ibg07u8BOA0gltBwV22JEj1XXxgI7GGWecYYZPf/7zn4/ra4uew8+quroap59+umnlaVWvJjtZjhxMK1qIquYyBOBHoau0UxFRiJ7CCnCvt+N1lIlTZWVl5ufRYFIibYz+/XuYGZ+CHGzc2WHbgcad6JfVMbjPuRnnDz4eH1TvRIOvEX2zijEmbzBSlr4zAO5/UyXgygdyBydusHPp0I5JTg4XUNiLc/GkS4EXfh9sbUUYZJ937tH/rrb6yN9Y2G3BKvI0p6amxvgXEce/Js4BxkwHGmuB3EJVtIgg/E7zXMrqC2QPAhpZKcVzKwC4+wA5kV0//QEfDje9jwYz25PX5CL0z5oBhy2YNEL7qtO/62J7LAgEAvjiF79oZvS98cYbaT0LN61FDWtI2X//+18jbLB8lZlVqSIOZDmKkIX0DYiJxMILG4eCDxo0qEeVF3Q4+L0KVwRJB7oqne+sBNxidH4/c0sLuFC7hiEZyHNORq13FXyB4FwPl60/sh2je/6EDHh+5JPAVpadlwP9BgNDw6yqKe3f0QFidlBuhMH+FIMZygzqcQ4P5/R0NwC0A1yHNXdEsJWctxFvHFqFg03lcNqcmFEyHpMKR2JunwXY27jHXHcHZg9CMfvdRoKnDtj6yhHhgu0ediwCckqB3L7BbX1GAYfYyi7k+1vatlI2lnAIIFul3njjjUbUEKnFV77yFTM3gMIGHcaIroEJxGl3oTRL7ThEbKCf3VlFBlvcFhYWhtUqg4lTc+fORSbRuS/RtX/htrswrTh+61XMyRkQvCWa0mHAtDOB918N2g1sGzz3YiCrF0lpg0YCH/sysGtjcH7XqGOC8zGORn5LUJ4V4Bb0N0rSxK/sArbJefvtt81cAQ5IHjNmTFjXjlZc9Fd7kegm0gffLsD7IcueAFsx4JoO9DsBqN0cnOfD9nf54wBbZKMKKj2bWgUN4vFXo6xpHfpnzzD38x19O8w9ouCR44hPIl8gEMAdd9yB5557Dm+++aaJiaczaS9qWIMcKWycdNJJRtj4zne+k+hdEiLhjB492vTwDhU1NmzYgPHjx4fttKSjoLG59iDePLQRjb5mI0ac2n8SXDRATbl3f7gPONHs95oliiXf+c4sDMuR4RRv7LZsFDjnwo9G03vYbotCpimFDVZsRMrxZwIfrgb2bG/ZOQdw2WeYroh0djiYicl1lWIoqyJTJaAnkgca3a8dXI4yT7W5pnLQ6dLy9ch1ZGNE3kBMcPXC+G843HklRt2BI6LGqBODAYKyLcHv/6ApwKDoDebrju3btxtB4+qrr8a3v/3tuLymiD533XWXqVg79dRTjbAR7eHxQqQi/fr1w8GDB1srLZgwVVdXh2HDjp4YQ6GQgcx0wx/wotKzAU3+cthtbhS5xiHbccR/YEXGvsYWO7KFvp1UaYg4MHoWMGwK0FQH5EQp25/tbI+dF9nfON3AWR8PtqCiwEIGjwKmLUS6J01xTXW73cZW2rt3b2SihhCEooN37ZFjEagCmpcBroVAQXjxrq5o9Lef8xdAo5/zOoJkOfIwPPc47G1Yh+ZAI7Ls+RiaMxWONq28Y8e3v/1t/PWvfzV26YgRI5DuZISoQcaNG2eCLgzAZGdn46tf/Wqid0mIhEJRgjM1WApulaMVFRUZ5/xohgMDUZH0xU0VdtWX44ldR1qeVFTUoc7bhIuHBltsFblycO3I+Xhh3xpUeOoxIKsA5w+ZhqxoGLuiR+ewA0kwQJJVGdd9Bdi0FmisB4aPCa9tVQqzZ88ec61YunSpCVZwqGeyUdVciQZfPfIc+SjoTXBcxIwmfzMOe9r2l2Vm0+6Gg0bU6BWO7KNvZ0uJcacDY09refH4VPJydhUd9gsvvDClKohFR/jZcRYK55RRpGIrqqFDOx/uK0Qm+d1LlixpFTUYnAyXqqqqtBsOTr/pcNMqI2gENzTgUNNyDMieB7c9KOAMy2GQzYbDTXvNdWVg1ggMyBqe2B3PZFxZwVuimTAD6DcE2LcdyMkDRkxM66Qpq6qLs8YIhxrPmBHMfk8W/IFmeMx3mfMy+8BuUxwgKfEdPNJiyhAAArWUJFga1qunZtVFc/sude0Ei3xnX4wvOMlc/+Np5//gBz/A/fffb+zRTEk4zKhvIPvdUtig08EM02984xtyJOOEP+DHoabtaPDVwG3PRv+sUXD2dOiniBoTJkzAO++8gwULFpj7gwcPxocfftitqMGycJaETpw4Me0+idWVu9qUCvL/H9Tsg8fvNf1ryeCcYlw/+oQE76lIOpxOYNJ0ZFKlF2/JysaaD7CrYUfr/dF5YzEqLzMMu1TCYQ1zDHN7ROT1B4pGAFU8D/h8ASC3FCjuJJM+js4Gsw4paHCOxi9/+UvZoWkAndV7773XVLAxeYrV4arYiB9efxUa/btND2u3vT+y7OmdVJAq3wkmSjE4WVxcbLZxQCnbSnU3h2vbtm04fPhw2s3r4zDZVkEjhHrvXrjdwTlPNpsdw3MnmJsQbejTP3jLEM4880wkK15/Hco97yGAYLtuO7LRJ2s2HLb0EmLTgi5bSvXexyh2jcX+pvdCtgRQ4uq8+iNegkYgEMDdd99t5kjTDp00aRIyhfTrHXMUpk2bZoal3Hffffja177WZY98ET14jLfXrcT+xk2oat6PQ007sKn2HfgC6Zfpn2qwfRR77K1Zs6ZNyTiFDfb6ZluZ9uzcudOIIek4vK+rq4EuE0KkDhWe8jaCBtlatxk13uD8FZE8uOxOTMg/kolKs99us2FCQRRKpelEjD4DGLoA6DsBGDwbGH9+sD1cgti4cSNOOOEEfOQjH8Hvfve7tGzhmKnws2Rm3LnnnmvmjdGGErGn2V+JKu97aPIzAWU/ar3vo5E9tEXCYUCF2das6iSsYKIPsWXLFhw6dKQXeSgHDhzA7Nmz0/Da2NVMPiFEKlHdvK5V0CB+NKGmmTMbRNLhYLvDdmuJfTAQhZbVWY5iDMqehwLncBQ4h2FA1mzkOgckNN761a9+Fb/97W9NrJsx70wi3SyGsJg8ebIpx/nzn/+MW265pdPArYgerM6o8R4O2RKAx9+ASs9+HeYkgH32mE3FeRqErahYNs6+t3Q+2sOBwGyd0VtY8UHYc7ei4kgPwkQypWhIm4FODLCNyx+g9lJCpBD1vrrOt3s73y4Sy5zSYzGzZCIGZ/fFyLxBOHfgApS4o9RPnRUf/ScDw08ABk4HWiruEsHatWtNsPsTn/iEyepPv6Cd4GfKDLkrr7zSfNbr1q3TQYkxDb5tLaHhI7ZbvW+LjnuSfB8o4rIi3MpWZdcE+h3l5R2rFkJnavTWv2CAh7fNmzcnhZ/vsGXDbWfFSmjGbgC5jl62WRRCxBVvoL0vEehkm0gKbHmAa36LkNEXcIwHnFOi9vRsHdjHPQF93BOR7ShBovD7/bj55ptbZ2gw1p1pZFT7qVCYac5J8GwBwAwSZlc50rw/YSIHo3WGr4vtIv4MHz7c9Ma34HeBNzoWdA5Cvxsc3sf+lqGzOCKBTsayZctMCzjL6cnNzTWZW7NmzUIiGZnXFxcPOQ5vcFC4vxlj8vrhzIGZtzAIkcrkOHI73Z7bxXaRWFiZMblotLmlKytWrDDtFL74xS/im9/8plpOpTEM3P7whz80dg1bUb3yyitJ1w88nQigoy8RQDBpRiQe+g9saRvqSzidTgwcOBC7du3qMDicbafeffddLFzYs0HI9E1Yfc75mfTv2SbTapnLSvREXhf6Zs1AhecDNPnKTT/2Ivd4ZDmKErZPQojIcdpy0RyoDhHSbXAweC6SE84ssqdv1QLX1s9+9rOm3RQFjVGjRiETyeg0MX7oFDZ4AlxzzTVpOfg4GchxFMDRyQClfGfkAXEROyg2cDhfKOPHjzeZhvv27TN9cdkvmo+bMmWK2U7nIVKYsUVnZtCgQaa/LoUMOv+9zcyKFpMKB+OGMSfj5nFn4CODp7fO0hBCpAYlrj4Ymt12uObI3DEaFi4SAgNqnOV255134lvf+pYEjQyAAcxvf/vbuOOOO0zyFM8BERvc9r4dtrls8i+SCVZ/L1++vM02VohT3GCbNs7QqKurM8EZJk2xkoOPj7RFNB//2muv4fjjjzfPRYGkpKTEiClWIlUi4RDZvllTMST3ZAzMWYAcR8dzVwiR3BS6joUtJC/cDjcKnJqDI+KPx+MxMew33ngjowUNYgtoqAT279+Pc845BwMGDMDTTz/d7QAz0TPqvZXYVrcK3kAT7HBgaO4xKHEPTunD6Q/Um56KduTBZnMh1eGlgIKD1X7KGmrE7XQ2KDo0NjYaYYOCBu/zxt7gkWQ/sUSOVSEcHMhWVqzQ4EWYzxGvQUpCiPSG163K5go0+OqR58xHkSs4qFSIePLss8+adlM//elPTSaVyDw4O+X222/HY489hvPPPz/Ru5N2BAJ+1Pk2oMkfbIvqtJWgwDkN9hS2y71+L6q9NXDanChw5qeFbcy2tazMYFsMVnyHBmVYUWH5GEyW4uwh+gr0y88444yIXodJWJwLOG/ePNNW1+12Y+zYseanEEJEA3/AgyZ/GWywwW0vTen1RqQmNTU1uPTSS818qpdeesmsl5mMRI0WqqurcfHFF5tM9RdffDEthyAnQ5CJrajsNmdKG+h8Hx7/B/AG9rRscSDbPg0Oe3pkhq1fv94IDSNHjsSxxx7bpu83L6AsC6fjwUoLztxgv1yKE0IIIYQI8uCDD+LWW2/Fo48+auxLkbk888wzJpvuF7/4Ba677rpE707aihsc2WrrpDI8lahsrsKSsmXw+IODaAdm98fskhmwcz5QisNqiRdeeMFUT0ydOtX8bD93iP4HfQpWcXDuxtlnn53SPqMQQggRTQ4cOIDzzjvPJAjTviwsLMz4A5z6FlKU4Mnwr3/9y2SoL1iwwBhVIrrQKHXYXSlvnPoC+0MEDbMFjf7VCATSo4cv+9tS9V25ciUefvjhNt8FDvVmq6j58+ebsm621FiyZIl5vBBCCJHpMPHhO9/5Dr7yla+Y7CkJGoLnAM8FtqP67ne/G3FbHXF0bDZ7ygsaPC+Wlq9oFTTI/saD2FLLYeipD4UKJkRRvPjnP/9p/G5Wf4cmTjGpkO2j5s6da0QP+hj6vgghhBAwcTmukZwPzTVUgkYQVWq0g+WuLBP/y1/+Yk4UBm6FCKXJtwHewK6QAVFBchzzYbelT+syVmxQxGCmFOdpcMgeZ2m8+uqr5mJqVTOxzJuPmTRpUoesKyGEECJTYCbyjTfeaOzHf//736baUQgLBnOZec62nffdd1/r4GQhSLO/Gf/a/2qHgzE4eyBm90mfYfO1tbVYtmyZ+Z2tpliZQX+b2adsR2UNCWcrKs7boA9CkUMIIYTIVDhr6txzz8XVV1+NH//4x226qWQ6OhLtD4jdjp/97Gcmm+qUU04xraiEaHOO2LI6CBrEhvTq13rMMcdgxowZZtYFHQz2o2V/cDojoTM0OOyPj922bZtpTcX5G0IIIUQmwSzjiy66CG+99RYWL14sQUN0gPMEmHn+5ptv4sILLzT2lBAWnKHhsLUVutizPdtBvyN94OzKk046CX369MH06dONr/H+++8b0S903gZn/JHRo0fj7bffNhmqqtoQQgiRabB1I2PTjFFzTp8EjbaoUqMbnnrqKVx77bW455578IUvfCHl2yaJ6BAIeNHgew8B0BnlORGAyz4WbvuotD3EnJ3B29ChQzFixIgO3wUO42MZXHNzs3FKOHMjJycHgwcPNhlYuvCKlKC6Cti+BcjJBcaMp8qd6D0SQqQAHID70Y9+1Kx3Tz/9tOlzK0RXMPOcAx6Zif78888b20oIsrN+D1ZWvm/EjAACyLZn46R+C9JO2LBg+ykKGpboR98hlB07dpjKcG7nEPCtW7fC5XIZwYM+RvvHC5GUsOXg1s1BP2PkaKCkT6L3SAiRAlDI/9WvfoWvf/3r+NOf/oTLLrss0buUlEjUOArMPD///PON83HvvfeafqBCcH6GN7AXATTDgaK0GRJ+NPbs2YPt27eb6gwOEs/LyzMCx+7du42QQSoqKjBgwADjhKxbt844HnysEEnNhvXAj74DNNQH70+ZDtz+LcDlSvSeCRF7Z7t2F9BcA7gLgbyhbFCvox5BOTgFDaulEANuQhwNJoF8/vOfNxXhFDbU7lZYlHsqcKipzFRuDMsdDLc9vSrBuxI36DPwJ30GVoTT52ZAh8lSlnixf/9+zJkzB9XV1VixYgXOPPPMRO+6EN3j8wH3/hhY/EbwvtsN3PZ14Lg5OnIi7anz1mNfw0GTBzw4ewBynRKiI2lpe/PNN+Pvf/+76ZaiNoxdI1EjDBjEpbPKAcpPPPGEBrKIjKeqqsoIHGy34fP5TPUGhQxefNlWgaXiHB7OGTWzZ89WkEckf1D3c9cEM6isAa4M6l5+FXDh5YneOxFnVlWW43dbNqHG24zT+g/E/40ck76Vmjzf978F1GxvrTxE4VhgwDwJG2Hwj3/8A9dccw3uuusu3Hrrrel7noiYwIAtW95ysPyf//xn05JKiEyGfgMr3zhLgwIHk6fGjBljfrLNLROnKAiyApxJU/w3IZKaV/8N3H/vkfu0E9xZwB//BmRlJ3LPRJzx+v144/Bm7KgrR4ErG6f0G4fSrLy0/RzKPZV449C78AV85j6F+pP7z0ORqzDRu5YSsbaPfexjJt7G1lOMtYmukajRgxOLGVXKPBfiiFNOB4QiBuEwcc7fECJlqK0Brvt42202OzBvIfDF2xO1V+Io+AJ+NPiaketwwx6lYPLqygqc8+b/4A8EEDTBgS+MHY9vHTM1PT+Pur3Anv923D7sLCCnfyL2KGXWPfa0/e53v6tgtIiaOPatb30Lt912m8QxIVrg3Bm2v+XPQYMGmQRDIVKKh34HvPwvwOdtu/3e3wND1HowWfH6m81a7LA5o2Y3PrZzOdZV7zOTWe2cxmp34OZxJ6HYnYt05H8H3kZlc1XrJFq2Veyb1Qcn9pub4D1LbpRQHznqpRQmbLdDleyWW24xmefsmcwhZ0JkOlzwhw8fbm5CpCS5ucFsqaZgCzUDY+R9+yVyr0Q3rK7cg7/vXo3mgA95Djc+PnwmRuf37fUx+8O2zfBTMAnZ9tstm/C1iZPhTMcZK966Lra3tGETHWhoaMANN9yA//73v3j99dfVNkj0Gg6Yf+2113DBBReYVjsPPPAAsrOVwSsEh4ofc8wxOhAidenXH/CHWpWMaDuA4pJE7ZE4ipixoWYlqr3l5n5f92CMyZ8MO5PdekFlcwPWVu9rve9HAE1+H5ZX7MZpA8an5WdS72toFTQI50TVexsSuEfJz6JFi8zYAybT//KXv9TogzBJQw89drC3529+8xt8//vfx7nnnovf/e53RnUVQgiRwtC5uOGLwcHgVsb/gIHA+Zckes9EJ+xrqMITu1YYQYPU+zx4dMdS1Hqben28Gny+Duu6LxCAx0+pIw3J6mJYpVuDrjuD1bpMaNm8eTOWLVsmQUNEjVmzZplzauPGjTjxxBPNuSaEECLFOes8YPTY4O+Wj/HpzwF56dt2KJXZUrcW1d6K1vuHPXuxu2Fzr5+3ub2w1ZI/5/G3q+BJI4rdRaY6w4K/F3N2n+gAfc/f/va3JsZ8zz33mJizZjmHjyo1esD1119vskYuueQSrFq1Cr/+9a/VbkcIIVKZ+ScAg4cCH6wBcvKAuQuAbA0zS0a21QWzpywoQTDbaU9DFSYU9K5l0pkDBuHZvbtb7ztsNsztU4pcZ5qaS9mlQN/jgMMrjmzrNxvIkqjRniVLluDiiy82Dgcdj6ysrPh+ViLtYXsdVv987nOfMyLHM888g/nz5yd6t4QQQvQUVoJ/76fAEs4vqwbGTwLGTdDxTFKqmstaPIsjVHoOY3hu76opODujjzsXlZ4GU6VB+LO3fksyc1zxZDNTo84XrP7Od+ZhevGxid6tpMPj8eCmm24yw8BffvllHH/88YnepZRDMzV6AecIcKhfTk6OmUrPQclCCCGEiB0rKnbhqd2rOmz/3JiFGJ5b0utMmV9t3oCfbfwAjT4fTujbHw/MnIu+6R7A9tQAzTUAM6hc+Ynem6Tjj3/8I77whS/gRz/6EW688UbNPBAxhdchZul95StfMT+vvfZaHXEhhBAixiyveB0ef2PbNvSuUhxTOLvXz13WVGfmauxtrEK23YmPDp6M40rSe04Qh4RXeKrM7yXuIjhsjkTvUlJx4MABkyjf2Nho5qtpblTPkKjRS+rr63HdddfhjTfeMMLGnDlzevuUQgghhOgClmr/ZvObONzEeRDBbKex+f3wqZFzozYwnEFFtp1KyzkaIqLsqVtvvRWPP/44nnzySZx66qk6eiJu/O9//8Nll12GK6+8Ej//+c9VFS6EEELEkENNe7C5dk1Lc6ggxxbORqGri3atPcAX8JtB4ZxLKjKXd99918zPYMvRP/zhDyZRXvQMiRpRCn7Q2fjWt76Fn/3sZ2aAZMZdpAJ+oJcDlIQQQohwaPQ1441DW1DRXI+B2YU4vnS0BAgRVXbv3m0Cyk1NTSZpZdSoUTrCIu5s27bNZPFxcDiFtaFDh2bUp2DNOMo4v0oIIURCqPAcQplnv5kBMSB7GPKdRfokRFTtmvvvvx+33XYbvvvd75rkKdk4vUOiRhRhtcbll1+Os846ywwRz83NRdpTuQvY+CrgqQOyC4EJZwEFasMlhBBCiNTNkL/iiivw0Y9+1LT/UfaUSCQNDQ2m3/Lzzz+PJ554AqecckrafyD+gA8Hm9ajxrvf3C9yDkW/rAmwKYFKCCGEECna5eezn/0sXnnlFWPPsUpD9B6l1kcRnpQrVqzAli1bzGA//kxrGquAdS8EBQ1zvwZY+yzQ3JDoPRNCCCGEiDh7inMzKGbcc889eOihhyRoiIRDUY3nIs/Jj3zkI/jxj3/cWsGQrhxq2oAa776WFoMBVHl3odyzNdG7JYQQQggRMZs3bzYxYlbgMmYsQSN6SNSIMoMHD8Zrr71m+i7PmjULzz33HNKWyt1AwBeyIQD4PEB1MKtKCCGEECIVqKqqwsUXX2wqbVl5+5nPfCbRuyREG3hOLlq0CL/97W/NucpzNl2p8x7ssK22k21CCCGEEMkMY8KMDZ922mmmGnzQoEGJ3qW0QqJGDHC5XPjFL35heqVdddVVuP3229Hc3Iy0w+HqYrsz3nsihBBCCNEjli9fjuOOOw6NjY3m95kzZ+pIiqSETjHPUZ6rPGf5ezpisznC2iaEEEIIkYwwBszZGYwJP/DAA2YOM2PFIrpI1IghH/vYx7Bs2TLTM43lRTt27EBa0WdkcI4GrOF9NiCvH1A4OME7JoQQQgjRPWzhw5kZtNGuu+46vPjiiygtLdVhE0kNz1Geq6zc4LnLczjd2lH1cY/qZNvIhOyLEEIIIUQkMPZLG+3VV181MWHGhkVs0KDwOA34+9KXvoQnn3wSDz/8MM4//3ykDZyfsXMp0FAJ5PYBhs8BnFmJ3ishhBBCiC6prKzEpz/9abz77rt4/PHHsXDhQh0tkXK89dZbZqj9vHnz8Ic//AHFxcVIF2qa97fM1bChyDUEec5+id4lIYQQQohuefbZZ3HttdcaIYMdfLKzs3XEYohEjThCp/mGG24wmVU/+MEP4Ha74/nyQgghIqWpAdi1kSndwNBxQE6ejmGSsru+DtVeD0blFSBHbRBFN7z33nvG0Zg0aRIeeeQR9O3bV8dLpCyHDx/GNddcgw8//NAkULFFlRBCiCRnzzbg8H6gpB8wfGyi90Z0QbPfizJPLbLtLhS75QeKrvF4PPjKV76Chx56CA8++KCqM+KERI04s2nTJlx++eVG0HjssccwZsyYeO+CEEKIcKguB565D6ivDt7PzgUu/DzQZ4COXxLhDwRw9/oVeG5fsMVjkcuNe6cvwJSiPoneNZFk+P1+/PKXv8Q3v/lN3HXXXfjyl78Mu12dWEV6nNs//elP8Z3vfAd33303brnlFp3bQgiRrLz2HPDmv47cn30ycM4Vidwj0Qn7GirwzJ6laPQH5+MeWzgUZw2YBpvNar8uRJDNmzfj4x//OLxer0kwGTtWQmW8kCcXZ8aNG4clS5aYMvEZM2bgz3/+c9r1wRVCiLTgrWeBhtoj95sagdefTuQeiU74557trYIGqWn24Murl8Dr9+t4iVb279+Pc845B7/+9a9Nf9vbb79dQV+RNlCcu+OOO8y5zXP83HPPNee8EEKIJGP/rraCBnnvdWD7hkTtkegiaerZvcvQ1CJokHXVu/F+1U4dL9EKY7mPPvoojjvuOCxYsACLFy+WoBFnJGokAPZUu/fee/G3v/3NZAleddVVqKqqQlIT8AP1B4G6fUDIhV0IIdKWioPBa58Ff688lMg9Ep2wtrocjpCMKX5iZZ4mHPY06ngJA4cqT5061bSZWrVqFebPn68jI9ISnts8x/v06WPO+X/9q13gLAnx+htR5y2Dx1+X6F0RQojYU36w8+1lXWwXCaHO24g6XxNC04/tsGF/Y6U+EWFgDPcTn/gEbrvtNjNqgNXgmp8RfyRqJJDzzjsP77//vumFO336dKPqJSU+D7DtX8D2fwE7/gNsfgZo0sVcxJ+ysjIz1JXVTuXl5foIRGwpGQDYQpZJ/l7SX0c9yeiblW1GnoRCkYNtqERm09jYiC9+8Yu48sor8fOf/xx//etfUVRUlOjdEiKm8Bznuf6zn/3MDBHnd4DfhWSkunkfttW/ib2Ny7Gj/m0cbtqY6F0SGZppS5/8nXfewZo1a9RFQcSWrtrY9h2oI59EZDvcsKFtmym6G3nOrITtk0geGLtlDJcxKa4frJAViUGiRoIZOHAgXnrpJeNwnH766aYXLvuwJRUHVwCNZUfuexuBPW8mco9EhtHU1IT169dj69atmDNnDkaOHIktW7YkerdEunPCBUBuwZH72TnASZckco9EJ3x82FgMzM4xbodVsXHLuCkaFp7hrF27FrNnzzZC+MqVK01VrBCZAvt9X3311ebc53eAthO/E8lWoXGgift0RJWuaN6OOu/hhO6XyCwxg8mFb731FgYNGoSZM2di37598Pl8id41kc4MHAqc9NG22+aeBowYl6g9Ep3gsjtwcr9jWis06GEUOLMxs2S0jlcGw1gtY7ZnnHEGbr75ZlMRy5iuSBwaFJ5E0PFg+VJhYaGZtcH5G0nBtheBhnYtV5ixPOmaRO2RyBBqamqwYsUKbNu2zSwWJSUlJttw6NChGD16tIZ0idjDFka7NgWDLkPGBoeFi6SjutmDF/btRFWzBzNL+mJOH1XUxIu9DTV4r3yv+X1On8EYlBMiBCZoYPIvfvELMwz8S1/6khkI7nK5ErpPQiSS5uZm8z1gW4RkGiJe7y3DnsblHbaXusehj3tUQvZJZA4U+TjYtb6+3iRLUQikyDFt2jTk5eUlevdEJrB3B1C2HyjpBwxVoDxZ2VF/GLvry5DtcJlB4azgEPGZabKtbg+qmmtR5MrHqLwhsCd4QPvGjRtxzTXXoLq62lTEckaySDwSNZKMhoYGfPWrX8Uf/vAH/OQnP8FnP/vZxAdudy8Cqre3yaSCKx8Yd2ki90qkKXQo2A/a4/GYIZfsC83BS3Qw+G8MWDkcjkTvphBCZDybasrw4w2LjeNB6GzcMWEBxhWUJuTY7NixA5/85Cexa9cukxzCgX1CiCBvv/22ccaHDx+Ohx9+GCNGjEjooeEMDbacas+ArCkodA1KyD6J9KaiogLr1q2D0+nEnj17MHnyZIwfP9742qzOkH8hhBCJhzGf1w8tx+6Gg6ZGJoAAhub0x8n9ZiYkNsr9uf/++3H77bfjuuuuwz333IOcnJy474fonMSn6Yg28MvBTKp//vOf5svCuRssg00o/Y8DHFltqzQGKVAgYgMFjWHDhmHu3LkoLS3FCSec0JoxxUVMDocQIhMDMcnI47vWwRfww4+AufF3bkuEs/HII4+YwcgMUK1evVqChhDtOP74442NxUpwflceffTRhM4OcNvzUOJqW5GR4yhFgbOLfvNC9LKVLedl8HtQUFCAU089FRMmTGgNkMm/EEJkYiVnbW0tko39jWVG0CAUNAjvc3u82bt3r5mXwdjss88+a6rBJWgkFxI1khTO16DhxSx1ZpE89dRTidsZdwEw5gJg4DxgwGxg9PlA/uDE7Y9I68Adq5X69u1r7rM9AhdbITIFZgpWVlYaA4rfB/V1Dh9WcX3wwQdmcNuGDRt69Tkky3WHAUe2pvzvf/9r2vElG5WehtAaTvN7ZXN8BxKzH/qll16KO++805SCP/jgg8jPz4/rPgiRKjCYy+8IvyvMOOR3h9+hRNE3axwGZ89EX/c4DMyagiHZM2Bj8pQQMagCP+aYY4yIMXjwYFPRJ0QmQR/7wIED5sa2a4kUtVONqqoqLFmyxNzq6up6NY8hWY47/c0333wT7733HpKNBl9TRNtjBWOwU6ZMMbEpxmZPO+20uL6+CA+1n0oBnnzySXzuc5/DmWeeiV//+tetAV8h4onHXwmvvxYOWy7c9pKol/7t3LkThw4dMq2mrOfm/IxFixZh3rx5KCoqiurrCZEsgXjOjOG5b1Uica5Sbm6ucTjYs5PfA2af9++f2nMiKBTQgO7Xr1/Un3f9+vXGyWDWJSu8du/ebRyQY4899qh/z+NMEYTPQ0eDN34OzMJhUkE8sF6XN2ZMcd8pYjDYOHbsWPN+WL2WbPxu8zIsq9hrqjSsQYqzSgbjc2NnxeX1//73vxv7aOHChXjggQeifm4Jkc5w3bnhhhtMW6rf/e53uPjiixO9SyID8Qea0eijsBZAlqMUDltWdJ/f7zeBO1YncTZfaG/08vJy42MIkY7Qh+DcGCtRh3YtfQxCG5N2M+3d2bNnI9VhZxPO34x2fOLgwYPYtGmTOW4URckbb7yBE0888ajVXbTpKZ5y37hfVgttfi7sROF2x2c2B1+XsLU3/Qu+Pq99fH2eG3xfyZYMVOmpwfP73uyw/aODTkAxE65jDP2vm266Ca+88oqxjy6//PKYv6boORI1UgTOFuB8DarDcjxEvKlu3oQ637bW+zn2IShyBbOdogEz0ml0hRpV77//vgnmsvWUlVklRDrBc55GE4feMxjb1TnOIDeD7jNnzkSqUlZWZvpY05hn64doXTc+/PBD0xt74sSJJgM5FGYeUeSwHDjLwaCIQWOeAT0a+NnZ2ebv+TOUpUuXmgHTzKqiOEKhqavMN1aI8HHWa7DKLCsry/xb6OfK6xn3l+IOCf03a3Av3wdFXP7kay5fvtxkCfH5QqEjwmuk9Zqs6uHzcZ/5XuLRSqOmuQk/27gEO+qrzP2RucW4dfw8FLiiG5RqDz+7L3zhC8bZYLLHlVdeqTVCiB7Aa8djjz1mvk9MnvrNb36j5CkRN7z+ehxqWgo/POa+DU70y5oNlz16QSv6zlxDraAdbQDaB1wnaSMUFxdH7bWESAZoj/K8Z8cPthvsLnhOwY92uWWDpuIaRjuZdv2sWbPMe46GCMBkKQb/6Z8xuSjUXqcYtHbt2g7JRrTD6ZvQ57HaxrKlNivDQv+efh19DNr69FH4GXX13pjUxPk//HysCg9eu+gDcD/5vFYyFp+P+8x/C309/m75B3yM5WPwb3ietJ8/x+ej/2J1C+Dr8Hf+Pc8l3uIRl9lUswvvlq817ac4V2Ne6WSMzR8Wt4QpHhfO0aBYJpIbiRopBC8wf/vb34zjccYZZ8jxEHGh2V+Nw553Omzv45ppMqqiAbMEuXBYC+TWrVvNAkojQoh0gYFYntvWQEpW3XVlyLa/9tN45neCwXUGxZMJBggoztAwpyNFQ55Be8v45nul8cx9X7FihXkfDCLwscwU4/th8J4GO41xMnLkSPMYqwUXxQe+Bo8dAxM8ljTKJ02a1KUjxtdh4KK94c3XYjUHb905etx/vi6NfjpMfB46S3wv7M3N98j3TrGBokhof1W+Nh8Tuo3PR0eI75v7Hq5DwGNAUct6n5YDQ+eCr205K9xPa58pdtDBY8/wWJ8vHBK+rzHYGmtQdoEZFh4PZ4NOOJM85GwIEb3kKbbv4/fqkksu0WEVMaesaSUa/cEqDQuXrQj9s6NTmcg2O7QzLH+CazMrwMPJshYiVaBdySA8bT+e47QFp0+fHlbff2bus83ptGnTjN2dTEmEfC+slLBsd+4rbeLQhB4+hr4Af7I9EG17vm8+lseFf0dbn7YxbXb6EBQzGbSnuMDnsKoXWMnFn7TfmVDZneDJCgyum6GtpHhN4d9wH45m51vCA6s4+Fz0Lfi3tO25XxRG+J4oigwZMqTNc/H983Gh1zC+V/olfF3+WzhY/iWPS3sRhMfQen76H7zxNXgceXzof/CciTWNPg/qvA3Ic+Yg2xHbyhb6lqzOePXVV02c9Yorrkiq74PoGokaKQgvoHTorXJxOR4iljT4DqCyeXWH7YXOSchzRkctf+utt0yVhpWJzPtsJyKSh7KmMmyp2wJfwIf+Wf0xOm+0FvpuoNG3ZcsWY7TSuKQRbokYPTWQaOBu377dGLM0cBn4t6AhS4eGz03jna2qeGuf3R8taNjSebCqqZjJREeA75VGOW9WVlH798u/5WMZbKcRz/s0qGm8W1UVLPWmAEARgY/jjcePj2GAgg5DvA1NiitWWygeYzoOXVVvJBqKZzxfWN2Tqtl37ZGzIURs4fX68ccfN049k6dYBaWWbiKWHGh8C95AfZttdrgwKOeUqK0b9JtZqUFotwwfPlwtbZMIf8CLWu8WePwVsNuyUOAcG9VKnXS8TvO8ZiCcdihtPcIgfPuK5XChbc3KZwbTaZOz4sFK+rHm1dH+pd3NwP+AAQNiKoBQjKQPxaA6X4t2LH0Cvl++viXWtPcxrAoD+hT0J+iT8Hf6ZHw/tNnpfzBRiOIGb/x7PpY3+mp8TPvK7XjA/aQvx+NPv4rvMxntdx7j119/3Zxv/GzShaeffhqf//znlTCVokjUSAPH45RTTjGOx6BBgxK9WyINafbX4LBnSYftfdyzkGXvfYknobFBke6kk04yCzgXy/nz58csICsio9xTjnfL322zbUTuCBxTGOwtKoIwwM9gPJ0CGs9jxowx5zC3W32cfYFmNPsb4LRlw2nvecYJxQ1m91jCAR0Zq2qBQgodHmY3MZvGyuTnY1hR0FPoCHD+B98Pn4+VFzS8RfLBQfP8rPh5UwBKh/Y4t9xyi+lBzGSOdHKkhEjm5Klf/vKXau8mYkZZ0yo0+g+FVGrY4LYXoV/WnKi9BgOYtMlGjRplbCf6HJxTJpJjfS/3LIcnUN66zQYH+rrnw2lPzqSRRB0nK6OfIgPF5hEjRhihoTUz31QMUCDkzzym2/foteg3rFq1qrUqmDAhi0lGvE9fgKKD1UaV0O9hZXBvkobYZolrD6HP1JskMBFb3nnnndb24Kle8cbvFbvgvPbaa6rOSGEkaqQ4XFS+9KUv4V//+hd+9KMf4brrrktKVVekNrXerajxbm69n+sYgSLXhKi+Bg01nrvMQKfDwbYxDMbRqEn1BTPVWVW5Cvsa97XZxt6WZw04K20MTgoAzAqi0U5n92jvi4Y9e5wyeGwZ/RQw2OKgq2Fr1c37sbdhDQIIDmwbmH0MStyx7w1qwf6tc+bM6VHWP40+ZmVx/kc45ewi8dDxZTAnNLuOfX2t89NykimC0TlORijMMLi6evVq/OpXv8Kll16aNtccIZIZXh+YuUhnn21MKCYyKCxENPEFGnGocSl8CLaTscONvmamRnQTJt59993W/vdcV7j2MTGDAWGROLz+OhzyvN1he75zDAqcY5AOMGDPCiH+ZLJTODNcWG3AJCkmEVnVCEzmYCuiTuM8AS/gXQ4EgnMcYCsAnLMAW1ZS+xd8n5yhSVhBFYtB3yI20LdgBY/VIYDnJ29WzIbnLmdH0qeO10D0SH2k3//+97jzzjtx3nnn4ec//7kSplIYiRppwksvvWQcfy52Dz74oMnYFSLaFRveQB0cthyTRdUbWEJKI4YGHlV+BpIZUGbpZ2irA/bQZ0CVpZhstcOAnIg/KypW4EDTgQ7bKWrYbakvorJtkzWvgplCvE2dOtVkKzEAHGqM0RmmAMLHDx061NzCMcA9/gZsqX2zTd9oMipvPrIdRwZZxwoalxzUHcmwcUtcpKPBtUWkLnSKmVVHIY7XX56zPIdZ4cmsO7b2SqZqT17zmSF+11134aqrrsIPf/hDDXMVIgGwHcdXvvIV/PWvf8V3vvMd3HzzzUk310mkfvuhJn8wUz/LXgK7Lbx+8F1BMZ8zpdhGhm2nGARmZvG8efParDF8HNc/tpqh/ZeMgbfM7AZgQ75jFApcqT9XkUF7nnts58TzjEOZmfxkzaALFTj4WNrp3M4kKc59CLvFqXct4N8dssEG2PoCrvBt/t7Aqj7OOYuEnTt3Gn8rtNWVSE3oL7LKxqq0seb+MRGCgh6rrJNJrKLff/311xufiAkbZ599dqJ3SfQSiRppBAMV3/72t/Hb3/4Wd9xxB7761a+mVfseXiADaGrJEXcn1cVRRAazbmnU0VhjiSvb4tBx5sCpzgwbfvYUN7hYMtNKDnV82duwF6ur2s5V4VyNmSXxMZZjAQULimY7duwwwdzQDFReS3m+sSKBBg8dCzoj7DfLLJSeZKtWNx/AnoZVHbbHo1qDDhIdjgULFnQ5PI7fMWvIN48L50ZQdDzuuOMS0ltWxA9+9rwOs2KDv/MaPHny5LAHDUYbDmVn1SkdfCZp0BkSQiSWN9980wQBuC4yuzESgTwVCAR8xsewIQs2m6qDUxkOu6e9QyGfaxvb2a5YscL87KqXPR/H5A0lcMSXQMCPQ57F8AUa2iT99HXPg8se+4SfWGDNfqN/wZ+hfittLG7nOUcbm3YOk6hYOcTtFAZ6ZHt5mDRV126jC3CfhljD/eb76K69Ld83W1RRbKSPwUQr+l6s/hbpDWe+UEC2vgPsyMFbonz/e+65Bz/5yU/M/AwmaqiNcnogUSMNYUCAjgcDc7/5zW9w+umnI9XxB5pR510Nb0tZpdNWinznVNhsyhZLRaxyU6sUnAFkZmuwZLG7TA/+HUvIe2z0iR6zo24HNtdtNoPC+2X1w+TCyXDZj/4ZMFDOaxID4wyGWAa8JUrS0GUbPWbsED6O1Ttr165tnQfRGfz82Zqsq1ZPne0Hy2Dp5PI5+Tpsx8NKi+5a9nH/WEVkVQv1NMBf763AjvqlHbYPzZmOAlf05wNwv/l+6TzQkGT1XvtjRQGDWWGWkGgN+WbbNwqNPRWOd9dX47Gda3GoqQ4j84px1YgpKHJJGEml4CW/g/GexcHvJhMzrHJwZoenU2KGEKkO12RWTVntbhkQCKeVSrLj9e9Hk38dvQ3TACnLPgVOe/9E75boIcuWLTPJKLRjmEFMv4G2J32O7tpN0e5kspUCrfFvQVbpWQNPoAoOuFHomohsR3jfPyYesYKalfysumGwNLTin9esjRs3ms+fsP0Tzwn6HNa8OauFrAW3sYMAHxsO/Hva07S5+Tt9ClZicJ+ONribsRoGfLnf/fv373nCZvNSIGQuSZB8wL0QsYAChVXlzkpfJiqGwuNAP9+awccbg8c8rnx8T5MT/YEAPqjejJ31e2G3OTChYBRG5iUmQC4ih8lTr776Ks4666y4t8t/5ZVXTDtNnof0M5i0J9IHiRppCgNw9913H771rW/hjDPOMH3i2EIkValtXoPmQLCkzcJtH4I8pwYVpyrMemdQ2Rr4SmOUCx0DaczM7Wqxo4HKUl5mhGhYbPJBQ5btmZh1RGOWUMSg4UtjhoYtDV0KU7zP9kYUFtjTn585q3EoIrCHd3fZE3RKeB7QkGbGEx2K9o5JKBTMWGFBgzoRmBkcDatR42UbLzotAeQ6SjA8dxZsEbTw8gf8plbtaI7P66+/bjIOKcR0NpOGfVD5+fA4R7PqrdzTgK+t+R+afD74EYAdNgzMzsN3J58Ml13Zr6kAswcXLVqEGTNmmIAl7/McilV1JK8Njz76qBEyeK349a9/3auB9kKI2NtvDA5wLf/xj3+Mq6++OmXn+fkDdWjwLW631YYcx/Gw2zQ/KhXhmsJqjYULF7apRuSMAvoX3bVaZACc/gjXP83zSz7oA1KQoM3Pz5k+I/0A+h20VyhMURygX2G1N6JdYYkLbP9kBeG7smn4vPxbJgXRjqY/Q5+jPdasC0KxjCJIwjoJ+KsA77ttW9w6ZwL2vpE9T8APHMUnOXTokLn2c4ZGZ6KNlYDI4x5tn2t15Yf4sGZrm23zS6djeK7aU6cKrNZhSypW0/Eay+9RLK+1/C7feuutJsbERIwbb7xRHT/SEIkaaQ4DhMx25LC/r33ta/jyl7+ckpmPlZ43WlpPHcGOHBTFKANBxBartQ2zPLioWdBo5KJDo5Dbu8rC5wLIDHM60cwoFr2HThyPKY99Z23AGACnWMpKBctop+FPo5/OBIUFaxsdCmYddXWtsUr9mRHHaoueln7yPGCGFp0JGs7J3pKO+1vVvBdN/lq47TkodnEeR3iBoGa/F4sOrcb2uv1G1DimcCTmlk6CvYv3zMwvVsicf/75bQZDM5OMDglFpFiU/756YCv+soPD0Nvy9UkLMb4gvpn/oufwWszvFq8LvM7yek1Csxr5k989ZsT29Lu3cuVK42Aw8PCLX/wCF198cdJ/j4UQwe//3//+dxMsYOCPleEMBKcazf698JgqjbYEqzUGJmSfRM+hncp1izYQkzZYIWxB+4d2LgPanKHRXRY6E244gyP070XPodhA25/JaLQ/Q6HfwIoFVgvTb7BsAAbHKSgwCMrHWO0x+fdMiuvKVqDoynOAvggTe3ozCJniCUWwlIidBGoB376gsMFrVyTtu5r3A3UrgYAHsOcCebMBZ3GXx4X+BT8LSzi0vjeWb861IBYCzzO7X0Yzh6KH0C+rD07tf2Rejkh+6Ifu2rXLfK8Jf7b3LwhjPEerdupOAP3pT3+KH/zgB7j00ktNhamSYdMXiRoZAvup33TTTWbB+dWvfoVzzjkHqUSVZwn8qG2zzWErQqFrTsL2KRkJzRqJ9vM2ByoRCHhNj1O7LavH7UWY3cHMGhqvDGozm6azDHz+Ox3l9v/e3nC1SnxFeNDQ52dAY5SBSh4/HmcGF5nBZh1/fjaWUWG1a2ImBT9D/i2rLGiwssybjoN6UsaW1w6uwpbaPW3EglklEzCjpOMgRRqK7HFLccoyBumY8XOPdYXTK/u34q87O4oaX514PCYWRpgxJpIeCpp0YpmxF8k1gEGKb3zjG3j44YdNUJQzwHQNESL1YDCSPaopSl577bW4++67E1YR2RO8/kNo8necd5VtPw4Ou4T4WPsXVvuhZn817DY3XLaiHr8O5/UxAYfnH8UNtpFqH4xmcN0KzFK06CpD2JpFduKJJ0pojwDaAzy29A8YVGRQkols9C3Gjx9vjj/FCjMns+WcYhCc6z8fQ9uAfgl/5zYKCvw8VTUTQ3w1QPVrbas8bC6g8HTA3jbBjb4fBQ1+RmxrawWimahGpkyZEtMs+L/vfhne9qKGuwSnDuh8Xo5IXRijYEUHzzH6s5Hw0ksv4Ytf/KLxgZlwEZpAK9ITDSTIENjqhT1GH3jgAXz84x83wUsqllyQUoFc5zjUele2tG0JkuPoGMzLZJhVxBK79kPuaGwwIE3jkhd3BjUjcRg4PLGqeSWaW+aZAA4UuabDbY/caWVAnNk1PO9oxNI4YhYORQ6rMoA/aRDR2WCgnfvOUuHO4Hth/3eWHDMIL7qH5wCHJZ500kmt5wCzJfbu3WvKpvmZ8HiffPLJR/0cmaGtjOr4saPuQAehYEf9gQ6iBj9Pin2nnHKK+XwYdN6+fbtx7OPhnE8vGYindq9Hs9/f2n6qb1YORueXxPR1RfxhZhWvBRSgw209w2s+h39zdgaFEAahWK0lhEhNGHj8/ve/j0996lMmiMDKrbvuusvM9kuF2WcOWynsKIYfla2tIe3oA7stdYSZWEM/gj3IGaBuX8XLaz8DzgxC9ySrttF3EBXNq1sDqln2fihxTYuoLWfoQNrZs2ebfeA+M5jORJ7QGQv0G2gHs4qDCTxdzfGzZpFxjeJjxdFhdQx9MasKhsIQKz55bljr/OTJk7t9DqttbU/n14ke0HyoraBBAs2ArxJoN1uIcwlou9FvpzhF/5F+PD/zeIjZo/KGYFPtjjbbRuZ1nfwoUjthgtcDxo3Cha2V77jjDrz11lvGLrnhhhskiGYIqtTIQBj0Yk+5hx56CJ/85CeN8zFwYPKXWHv9VfD4g3M13PZBcEZSVpnmMFBE453ZMRy6bA3lsuCiwKAmDX4+5sILLwyrpJoBUXdBBZrtwSHOFja4UOo+EhiPBD4njVz+LZ0G7rdVCWCVH9JJpoFEp2Tp0qVG4OhOyaewQSNLwkbn0LFgqy9Was2aNauDUyqSn7/seBUNvrYt+AZnl+K8wR2/G5aTwe8PnYzuWoHFgm21FXh0x/s43NSAEXlFuHbkdJRmqYVDOtkQbOPBYBYzYUMDRl3Bc/Ef//iHaYfJABj78H/kIx+RMCpEGsHv+QsvvGCCCrTnOFSc9mayJ0Aweccb2AV/oB52Wx6ctmE9CqqnK6z05PWeMyloc4d+nrzPG+15BpRmzpxpeumHY5cePnwQKF6PgBnQfoRC5yTkOdsmaIXrCzGpg8Ew7iOTb3if1dxWZQDXH65bDMpyvgKHh3cnyrPyldUDkWYKZ5pNYA275uw6kWI07QDqO1aroeAEwNlWqKBAxbkzbCXGOALjR+HYgNGCcwXXVG3Ejvq9cMCO8QWjMDZ/eNKvMSI8uC5wHWG8guI0kyTCSY5gu33GMx955BF8+tOfNolT8TwvReKRqJHBcFFigIGq++23327mbaj9Q2pCQ/7f//63WQw665NPQ54BfxryXX3GVlYTjRRrAaHS3ezYiQbPIRSX5KOkTyGaGoPD0qaMvhQOe1aXFQF0aCPtRWv1wqUDQSeKxlPogLfu/o77Smeqp70X0w2KVxy4zWNIJ46GAcuFRWqyrmo7Fpe17ft99sA5GJYro03EHl5jGbTgdcUashludQYDR7QxKIQwoYIOR8KGaQoh4nK9YOIUAwu8VvzkJz/B/PlqD5KqsNqT8+6YbER7srPe5/Q9eOsquEhblAlWtNH5GPobb771PzRnbUV2jht9SouQne1GbW0DRg6ZgkFFx3W5P1yHaM9GVnUeMJnlrC7kLDgKH3yO7mZrWNCW5uPDEWsyAR5LHhMeR0KByKr2FikIqzLYfsrfeKRiw9EHKDj+qEPDhYgGTLqlaM5rCKvpwo1XUPz42c9+ZmZnnHnmmWZ+BtvcicxDooYwwWAGHGi0fve73zU9cRVwCB/2B3399ddN30/2qrcMe2bDs++k1R+Ux5Rtkqwgc1cXXSubqCfGoRV0YnC/ff9RGuRcMJhdwcHBzNymgU8BwlwMbDbjbPDf6ZBafU4nTS9GnXcraqrrUV5WbZwOvw+oPTDMvCerKoRiCbPCqYxbA+GsrCj+O0UOlhDS8LXOL2Z3MdDFY8TH8cbMKj6Ot0iOAZ+fwbOuSskzCTobdBg7axUgUpfNtXuwtXYfHDY7JhYOx5AczagQsYHXbgag6GgwU5XXYgqjXMPCPl83bzaJExTcb7vtNpM4IdFZiMyBdgiDDQw6cJYfAw4UOUT4sO0TE40sH2P48OHGPmYy0z//+U9zn9dnJi7RtmfGKof0dlWdSbu7J/MJaNOzjTHbCHFf2tvfrBDlLC++LveF+8QkJUv0oC3Kf7Oqs7l92PAhsJV8YN5L2aEqeDzNyMvPQfX+PNiaS1r3k++fvgHnt/G5uaZYg6L5Onwu+hUcCs0AuwX9IVZqED6Wf8s1jI+JNOmK2cN8/dDnz0T4WS9atMiIQfT3lCGfJlDQaNwA+BoAZyGQPR6wKflExAauQ1wfdu/ebYQJxo+4toQb92Gc6k9/+hO+9a1vmeowJk4o/pPZSNQQBhqEf//7300AgoYns6suvfRS9aELE15c2VaJrZ/27NljbjT4mJnGtj8UFGgI0sCmcU7HhH1F6QDw7xjoobFMkYGBIBrbfDzV6kh6CRK+FgcC08DnIkEjnq9nLRR8XgorFDPY8ulobWn8gWZUNr8HX6DOumyg0DkFWY4jw4bNe6vZi7LyMpQfbkBOdp4ZFtb+HGP1Bd+fNRyOPylydDUzI1IWL16sYVCAadlF+BlE6rgJITIPa33iXCauQ1wveO3g+sPWHZEIzFzXGLxkGfjVV19tqjPaB8GEEJkDA970K/7yl7+Y2Rtf/epXTTBeHB2ripotJekv8BpNW/q6664zQSG2c+X1myI07X/6cJybSDufyWoUDBh8pp/Bv7X8EGvQb6QJVKzyZ7shrg0UUfgzVKzm61nV2lZVRnfUe/egynukCpWDwkvds2CzHRFeGppqcLhirxE+6qqbjW3bPpOXr8tjQSGe/g9fn/tHMT4aiXoU9/lZZPp5y0oXziQZMWKE8d+EEOJocE1gjIy2ANcECta8htO/4HU6EjHkqaeeMknYXOPY4vKSSy6RuCokaoi28ALxxz/+Effcc4+5yFABveyyyzJK3OAFk0Y7Dbdov28axFbvWV7gmbFGwYEqNZ0VigwM/vB1aZBTEKEzQ6eBhjodES4A4QSZ+Fnyb+nkUAnnc9OB6UmQm/2Gm/yHEEAzXLYSOO1HFqBAwI9a7yp4A2Xmvg1O5DuPg9NehHjA48mKGApLNLI7a7+VaTCjjgMQJ86ciEOBg/AFfOif1R9DcoZq4RdCGBjc4lrHayjXE2aw8vrZ0zaUlpjx8MMP46KLLsI3v/lNUy0mhBCENu3dd99t5utY4gbttkyCggSvu0eryA5t8RTOdkK/wXoM/QUmRvEn2wRRAOH13QogcT9YfcB/Z3IT7UZe+7trU9teaOHfUODgczPYP3Roz2zMZn81PP4q2G0uZNv7t5ln0ug7hDIPB4kH31uOYzBKXMfGzZalEERBiceW8/0yvTKBnz3FsoOHD6JwQgkqm6uR48jGhIIxyHEoiUoIEVyLrBZ1XOe4xjB5lzGunlxDQ8UMxsy+9rWv4f/+7//UjUK0okoN0SkMuDMw8f3vf79n4gb7Ex1+H6g/BDhzgH7TgKzChBtiFAYYuLGydigk0DgLfQxha6hIKyRiibXfXAhChwBb+8vPiO+JmVq8tV8w+G8UNZjFtGDBgqi+twbfVjT6trTZZkMWilwnxMX4p1PGLLRoVXukMhTi2IKMn/fQCUOwqnZlm38flz8eo/OVWSVEJsL1wsr0pYPALFqudeEM4TuamMFECFZmXHzxxfjGN74hMUMIETNxgwHwOu8OBOBFlr0vch3DEh5spu1N34ltkggTbfg+uT000MN/53U3WZLFWOHAIBH9AiZB0U+gWEEs4cVqncvtfA/tjzVFEP4bq/xOPPHEqO2bP+DF/sZFCMDXZnuJazJynYMRa7hOMkFIbU2Cx2Lr1q2mvRmFr53Ze3GwKThTwwYb3HYXTum3EFmO7qv/hRDpCdc6rh9MmOK6wbZQvR3WzevOk08+acQMrjFf//rXjc2g1tqiPRI1RLfQiLXEDbYxorhx+eWXd2+MM9C+6zWgeod1mgEOFzD2QsDVswzQaMAerKyKoKPBiySNchrx7OGXaGeot9Ah4fuicMFbZ04LA96cXRGaqRWV125ejebAwQ7bi12nwBaDfpyWOEXDmtlhFDMydSiU1ceY7c54XOh4shycQcqVFStwsOlAm8c7bA6c1v+MlD/fhRDhwesCrw/WXAxmSjGjNhoBtfZiBiszWA0ohBDhwKD/9773PTzzzDP45Cc/abIvjyZuUNAo87DF5pFqhTzHKBS6xiXsoNO2fuWVV0xbJCugQzuLlWrRtLcTAf0lvicGkVjp3X7t4BpDMYTHgH4IA97Rotlfg4NNS9pttSHPMRzF7gmIVWCO/gUTAPjeORy8p9WLqQ4/VwoZ/MnPnTNLGKSsbq7Ba4fe6vD4yYWTMCZ/ZEL2VQgRfyhkc24Rr5tcHyhkWMJ+b5CYISJFooaISNxgAIMB01tvvdU4IBQ6Oj64Btj4dPtTDeg/A+g/LWFHnBfINWvWGOOMQfDeqsciSL13A5r8u9o4mICjRdSwRe2z45wQnoeEQgYFqnCdRV/Ai70Ne9Hs96DIVYzSrNSp6uB7ZpsAClP8nUIOf1pzSVjKSaGqfRuDZeVLUeYJtgSzYDbVGQPOkqghRJrDjGEGDOlwsCXIsGHRy2RetWqVGfzLUnD2smVlhsQMIURvKm5ZuUFxg1Xht912G6ZN69xfqPSsQYN/X7utNgzMOj2htg2D4JwZR7+I4kayVGKkMpzpt6/xtQ7bi1wTkO+MXtsyihgU6WlXW22A6WeE+xlWeiqMve20OTEoZzDcdjdSAb5fClbMgKaPQXuBbcWsNmY8lxmkbO9rlXsq8Obhdzr4FxMLxmF8garBhUh3mCzFa2ZvWpt3Bq9FTJT6+c9/bq5JrMxgvFGVGeJoSNQQEcELzBNPPIGf/vSn5oJ24403mlsbgaCxHNj8bPtTDeg3BRgwM2pHnEZXpAPuaKwxOM6MotNOO61zUUZE9jkEPKhpXgo/Glq35TmmwO0YGNbfs12SVerO86urz23OnDlthhGGi9fvxdLyJajz1RqjO4AAxuVPwMi86GWTRROWbtI5JnTQuZCzAoM/reGORxvuTnbU7cCHNevbbOuX1R/HlUTvOyiESC4YlKB4z2vH5MmTo+ZoMPjBTOSf/OQnePvtt03595e+9CVT6SiEENFg06ZN+MUvfmGSqBYuXGjEjTPOaFtdWu5ZhSZ/x+rgoKgRmU8QTf+CrF271thvffr0wfTp06OyL5lOrXcHqpo3tN532QrRL2t2m0HiXcEkIPZ1t5KjrGB9+9kk/KxnzpzZI1FsT8NurK1a0+pfZNuzMa90QVK2YWKyA31g6zjwfdOnYCUK/QsGKOlnHe3c9/p9ePXgInj8HvOeLU7quwDF7vjMUxRCJGaeDjtEcLYrK7eilUjA2Uz33XefuTFRk2v/xz72sV63xxWZg0QN0eML26uvvmrEjTfffNOoqKzeMAEOztPY9AzQXNc2e3/UuUDegKgccRqnDz74YGvgxur3ytJna9YEjTQO4mYAmO2ZaNiyPG7u3Lk9clZE1wQCzfD4D5i+t05bHzjt4YsPnA9CYYMLFwNwAwcONLdofUbb67ZiU+0Rh8ji5H6nwZUk2VR0MFjifeDAAbOY01DoLfwObK7dhO112+CHH33d/TC1eBpcdhkIQqQjzBJmVRcznKOV1cR11Upk4LX6pptuwuc+9zlVOgohYgYDHL/97W/xm9/8xgRPrAAHr2v13j2o8q4LebQNblsJSrNmRe31ly1bZnwbZqmzys0apE2sQDgr4LhvvEZSyGDWKh9PG05ElyZfhWk75rC5keMYEJagYfmK7733nvlJn4Ith/l5RqudlPGFD7xsbGwLihvDc0dgYmHytGKkuMPh9EwcmzVrVlQChWxB9V7FKtR6a+GyuTCt+FgMyRkUlf0VQiQXjK8xoYktrrn2RQtel1iV8eijj5p5TFzrmXSsNtkiUiRqiF7DrA+2omDg49xzzzXZmwtnT4Ft96Jg1QYDx4PnAcXRL0mlk8HMKLaUOu6449r08bOEDBpxNGDZp1eVGckNPysGzlgKzgWUbcJ6OwB8Q8167Krf2SabiMwvPQH5zvyIs6C5fxzEzd70nc0vCQc6WBQw+Fx8n4TOMJ8z2hiRDwHYo5TBKIRILiiKMghHcT9a84UojvzhD3/Avffea9bNL3/5y7jmmmuiVvkhhBDh2FwMdtDHoL1/880349Of/jTchZWo8W7l1Q9ueylKXFNgt7mjbjsx2WT37t2m8oIJVFaghddcbqetSsGjtLTUiBkKxCR/qyXOl6J/yLWM8zJ6k0DV7G/G/w6+2mH7wOyBmFY8I6Ln4jnFeYFce3k+UYDp6b7x/fH85Pvl+2b1BW2DWGQ9+wN++RdCpDFc5yg+sGNGT+MeofCa9NZbbxkx46WXXjIJC/QxeD0WoqdI1BBRgwbUr371K/z+9783AgIzOj9+5RXIzYvukDwqxZZhxmALHQmKFuzJSweIRiDbYTHbXwGY1MGad2IZ8RQ4mBHAz7E37G3Yg3XV77fZxr63J/Y71QzODgc6GcuXLzfnFTPzKEQwM49ONnvuTpw40ZRwhwODj1zQrYoUlVYKIXoDZ2fQ6aCoQXG/N6xevdqUf//lL38xGZ10ND760Y+qulEIkTAY8H3++eeNuEFb7BOf+IRpfTt16pSotZwivI6yctgaiM2kGs5XYMtatt2w/A5uY+BZVd+pAW14DrO1BrjTBrdaTvXmM+TzvHHodTT6G9tsH58/AaPyw29xu2TJktbzjes4zzfemABFQY2CRDiCGd8nEw15bjKbmol+EtqEED2F16AXXnjBXFOOOeYYcz3qKYyZPPbYY6YCk5WN1113Hb74xS9GtfJDZC4SNUTU4UXrr3/9q7loMSPm//7v//D5z38eo0ePjprTEepcMCjM0lrC3ymo8CLMx1HkoNHJgDMDyMyoosqsAX7JBytuaICzNDya8PP/oGYd9jRwmDmMkDG9eCb6uEvDfg62ITj++OM7dX4ocKxYscI4ueHsO52XefPmydEQQkQU1OO6xusN1zsGLzhryGqFQhF4woQJPaps49/+4x//MGs2RderrrrKBAy7GtQrhBCJgsIrr1X0Myi8fuELX8CFF14YlQQRXk9pz/E6S3uPGe7WEGXCwM6AAQNMeyxm1VttjRiU4TBl+hhskaVAcnLB1sPvvPOOaW8SbSGqurkKyyqWodnvMff7Zw3AtOLpYVcvcD4l949JXJ3BBCoKMvRBjua70iZgsJBVRUIIEe66Z/kYjJtVVlYaH4OtFS3YoYJzonpy/WTFI9tJPvTQQxg+fLhZsz/+8Y+re4qIKhI1RMywysvofPzzn//EmWeeaao3OPQvGkYln//w4cNG4LCGntGZoLPB7SeddFJrb1uW4NIw5JA03njh7gpeuBkYYla+Kj3iCw13LqY03KM55JbUe+tQ11SHHHsu8nPCrx6i48pWUcxQ6A5mD3IWBp3eoz0fBRyrFZoVlAyFDvWCBQvC3kchRHrBINq2bdtMtibhNZE3rk8U6dmaIpyBnt1B4Z+Vlffff78JxlHIYBICn1sIIZIZXhv/9Kc/mcoy2vWf/exnTeZnb6t7LRjQYTCGNilFCiugzDYc9A9OP52DyW3Gn+DgVFYaWz5GV1hJVmw12pv2QqJnLZloe/Mz4+cXrUQ74vV7UeutQVNDE/oV9I/oc6WfTMGiOyGM5xbFvHD8gqVLl3YYhB4Kz0/Ov4zW90QIkZrrJ9c3xht4raBvwTWOib9cm1gxRr+gpzAu98orr+DXv/61+cnEA8YAFy5cKNFfxASJGiIuMBPlgQceMDca9HQ8PvWpT0V9hgCNVmaq8DUYNO5JthSDSRRFKI4we4YXZj4XWwxFa/iqOPpnsHLlSnPsubCyCsKqygkHfn6hQUB+bvzJKgn+zkyE2bNnd7lgM0OB2U5WlgKzAbtzUug48Bxn2TfnyvQWKzOLwk5vSj2FEKkDryNWJSKvXczcPJpIGim8pr722mt48MEH8eyzz+KUU04xjsbZZ5+tCkYhRMpBUYF9uZlA9frrr5vgCX0MXtuiKRrwdWhb0g6loNyTyhBe4+mj0MajnWllyDIhRkHm+MEuArzRH2DyG6vEmUQVjs/Iyh36EJaPweew/Aq2R2bFDtftruZb8TPftGmTEcvCbbPL12TF+IwZM3rtN/P1KXzQpzr22GMlrAmRITCmRVGesTJeoyjsRrsFNte2hx9+2CRMca274YYbcP3117cmGQsRKyRqiLjCDBG2ueDFjtkpF1xwgXE+TjvttKQ2rOjIcOYDnSQRX2j4s1KCmUrWUG3LMLdmqjCrINQZoYPLhZWzVugs0HHgYk5ngI4GncidO3diypQpnb6m1X6A2UwUVazn5mvSueA+8ZzgcxL+O4UX7ksk5zH3g61k+N6sG/fVcnT5/tQCRoj0hYEyCqi8XhG2NmGAK9rtSyiW0NHg8G9ec5hUwLWX1zghhEgHGCymf8EKDgaqrQQqXleTFdp6//nPf0xrJNqsIn7QzmdCEoNvVrtiC6uig3P0QhPaWJXz6quvGlufPgU/M8t25yBdCl5MyuqqzSxf88UXXzT+B1vWhgYV+W/sLEDBizerCwEFF4ofkZ4ffE98b/RbuI/8yffIG/eXiVP0cYQQ6QmvJ1u2bDHxNyZyMgZC4TWa8Dr13//+1yRLPffcczjhhBPM2nvRRRcpGVjEDYkaImFs3rzZBFjofNBQ4wXw2muvjVu2Eo06Bnms4dRsOcWgNA1M/ptljPJizWwcBpdVqZFc8LOjg2G1aLE+M35OdAL4OTKAZ/U65udMIYKGPT9vzl/pCj6OwgcNglBHh0IDnQD+Pcs0u4J/QzGGDhPPofZl4NagQjrePP/5vNxPDQ4XInNgtijbTNEJiLaQYZV/M8hHR4MtGZkxxWQCrWVCiHSFVbasRGOQ5Y033sBHP/pRc+1jy6h4JVCtW7fOVMWR0AQcYvkYVgIL/429xkXywM+FyQb79u0z55O1PvP8oe1Pn4IZz/wMLdudogSFBN5Y4d3VDAz+DbsB7N69u02yVmh7yaO1J6OPQtuBfpAlfoQ+P6EfRJGFPoblZ2jeixCZw9NPP23Wv960kuoKXhutqgxehxjD+8xnPoOxY8dG/bWEOBoSNUTCobHIgAudD5aOn3feeSazim184hXgtcrK2xuYNAKZyRqLxUDEBisDyXIsmKVEYYOfsWXo01Fg6SWzrI6WscDsBs7B4PnB5yNHcwroyFAMYWknB0jyvhwJIUR7GHCbP39+VNc6BjoeffRR42zwmsU5GZ/+9Ke7HEQqhBDpCrNUrQQqBnnpX1xzzTUYNWpU3PaBiTT0L5h9b9mC/En7kFW+InWggEBBgWsrA3m09UOrPPi58jyjf8Hkp+6Ge/Nv+Pf0L0IrM8LxF+jTsH0URYtw/0YIkTkwsZLiKSuyohmzYzcM+hesOGMHEyYMUDhRspRIJBI1RFLBoUW8UDIgQ2PxE5/4BD75yU9i+vTpCTHYmIWzYcMGYzzOnDlTPcfTBH6erO5gFhY/YzoSLDFnppyV/UQxhHCRZgk6RZBwB5dTGONzaoEXQnQH1xcGvKZOndqr9YXXK2ZkPfLII1i8eDHOOeccs3bS0VD1lxAi02Gyy/PPP298jH//+99m6DKvkZdeemlrYDie0EZk60GKHcxslbiRPnBNp39BsYL+AH0BzmWkD8HsZquCm34tq7XpY7ACPJwqIgoh9FPC9UeEEJkJBQj6AxQ1KLD2FF5z2FKP/sVjjz1mEn6ZGMAEASZvCpEMSNQQSQmNvUWLFpkLKAM1vGjy4kmRIxG9cRn45gWdWVWx6HcuEos16JsOJh0FOpgsARdCiFjDSrIPPvjArDMcBNqnT5+Ihn4zSPfMM8+YXrkM0n384x/v9TBRIYRIVxhwZnCG107O4bjkkkvMtTPaw8XDZf369UaYpritYHX6QRGCnzErOuhHWhXcQggR6/gGk6dYCca1be7cuRHN4vvrX/9qYnFMOmYCAGNxnP+UzHNwRWYiUUMkPQz0/P3vfzcXVQ4XP/XUU43jwd64NAx5Yd2+fbsJBDHbhQEizlloD1tIMduej6PT0BNhghlV1nNb/XB5YxYOg0h8XqvXqgxWIYQQ4VaPvfzyy2aNYpbnMccc09qfm/e5nnC9Y0bx8uXL8fjjj+OJJ54wmVhWRSPnPgkhhAif1atXG//iL3/5i7HdOdyUVRwc9My5Bpx7RAGZ/gWzXZng1B4r457Xb2bc98T+57WcgW/+tLBaGvF5mWhD/4I3Vt8puUoIIUQ4bNy40SRvssqC6wf9CfoW9DEounJtY7UgY27/+Mc/jI/xv//9DwsXLjRCxsUXXxz1AeNCRBOJGiKlYCY9Azl/+9vfTGbrySefbMQN9vOjI8BSX16c2a6qPdzOlkO88ULeFbzgM3DEgWoUK+jk8KflQDD4ROGEz8GFgTc6POyLyn/jDAa+VmeD25iJxUAVRRnN6RBCCNEeriXshcs1iEI5f65ZswZ//OMfTS9bZlxdeOGFuOKKK3DGGWeovZQQQkShPdUrr7xigjkM6lDEOPvss/Gxj30Ms2fPNv4FbwwGta+Es3wA+hehsxHaw0HQ9C94ow9g+RfcbvkJDDLRx+Dvlo9B34F+Bf0H/gwVPiy4jfPfKIhzFqAQQgjR2ZwpJgRbPgbXFcbWnnrqKdOuimvIlVdeicsvvxwjRozQARQpgUQNkbKwnM4SOJhJdf7555vSuLPOOssIEz2BTgTFCjoQ/EmBgo4Cb1bGFDOwmDHFxYALAf+NzpB160zMoCDCGx0ZZnOxhZZK94QQQnS1FlG4Z3CN6xxbpHA+BoUMzstQixIhhIgNFBYoIFPg4BwOigQUN1jFMWnSpB5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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-06 15:53:43\n", - "\n", - "✅ Completed Repeat 1/1\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------|------------|------------|------------|\n", - "| OLS_wendland | 8229.30±0.00 | 90.72±0.00 | 61.07±0.00 | -0.24±0.00 |\n", - "| OLS_sphere | 268.97±0.00 | 16.40±0.00 | 8.36±0.00 | 0.96±0.00 |\n", - "| DeepKriging_wendland | 3928.12±0.00 | 62.67±0.00 | 48.07±0.00 | 0.41±0.00 |\n", - "| DeepKriging_wendland* | 3921.94±0.00 | 62.63±0.00 | 45.24±0.00 | 0.41±0.00 |\n", - "| DeepKriging_mrts | 230.57±0.00 | 15.18±0.00 | 8.54±0.00 | 0.97±0.00 |\n", - "| DeepKriging_sphere | 58.58±0.00 | 7.65±0.00 | 4.50±0.00 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 100.72±0.00 | 10.04±0.00 | 5.49±0.00 | 0.98±0.00 |\n", - "| UniversalKriging | 177.90±0.00 | 13.34±0.00 | 6.63±0.00 | 0.97±0.00 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 7.65±0.00)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg12.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg12.ipynb deleted file mode 100644 index 8fd1fbe..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg12.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 11:37:37.907727: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770694657.923417 194036 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770694657.927937 194036 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770694657.940403 194036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770694657.940427 194036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770694657.940428 194036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770694657.940429 194036 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 12.0 * x_cart\n", - " y = 12.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.6440 (0.5117), Variance: 654.6295, Range: [-63.4451, 31.6165]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.3058 0.2476 \n", - " 100 0.2204 0.1855 \n", - " 200 0.1567 0.1166 \n", - " 500 0.1234 0.0946 *\n", - " 700 0.1302 0.1456 \n", - " 1000 0.2766 0.1993 \n", - " 1400 2.6666 60.9620 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770694684.421742 194036 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770694686.785327 1355052 service.cc:152] XLA service 0x58759803b770 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770694686.785388 1355052 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770694687.172326 1355052 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770694689.726507 1355052 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72925c26dcf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72925437fc70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.6895 1.4211 \n", - " 100 1.0954 0.8381 *\n", - " 200 1.4799 1.6814 \n", - " 500 3.7373 4.4274 \n", - " 700 2.6935 2.5509 \n", - " 1000 1.3269 1.7319 \n", - " 1400 3.0752 3.7770 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 133.3905 122.0624 \n", - " 100 146.1122 145.6155 \n", - " 200 0.8925 0.9559 \n", - " 500 0.8789 0.9643 *\n", - " 700 16.9419 29.9878 \n", - " 1000 97.4112 101.0457 \n", - " 1400 370.1122 344.7411 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0875 0.1536 \n", - " 100 0.0745 0.1652 *\n", - " 200 0.2185 0.4663 \n", - " 500 0.3672 0.6959 \n", - " 700 0.5436 5.3829 \n", - " 1000 3.3739 86.5514 \n", - " 1400 12.0043 308.0277 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1471, sigma²=0.2879, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0980, sigma²=0.2033, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0514, sigma²=0.1249, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=379.0009, sigma²=0.0000, nugget=0.0657\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.8738, sigma²=0.0000, nugget=0.0463\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5983, sigma²=0.0000, nugget=0.0293\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7775, sigma²=0.0000, nugget=0.0172\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.1048 0.0740 \n", - " 100 0.1037 0.0756 *\n", - " 200 0.1081 0.0732 \n", - " 500 0.1234 0.0946 \n", - " 700 0.1302 0.1456 \n", - " 1000 0.2766 0.1993 \n", - " 1400 2.6668 60.9608 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0980, sigma²=0.2033, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 599.768 | 24.4902 | 17.193 | 0.1004 | 0.41s |\n", - "| OLS_sphere | 500 | 0.0946 | 0.3077 | 0.2469 | 0.9999 | 0.06s |\n", - "| DeepKriging_wendland | -- | 300.253 | 17.3278 | 11.3897 | 0.5497 | 49.25s |\n", - "| DeepKriging_wendland* | -- | 301.987 | 17.3778 | 11.4174 | 0.5471 | 19.49s |\n", - "| DeepKriging_mrts | 100 | 1.1963 | 1.0937 | 0.8791 | 0.9982 | 26.50s |\n", - "| DeepKriging_sphere | 500 | 1.1077 | 1.0525 | 0.7231 | 0.9983 | 41.58s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.5343 | 0.731 | 0.471 | 0.9992 | 27.93s |\n", - "| UniversalKriging | 100 | 0.0756 | 0.2749 | 0.2146 | 0.9999 | 3.94s |\n" - ] - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:52:45\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.0863 (0.5074), Variance: 643.6517, Range: [-60.9934, 31.4891]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0905, sigma²=0.1900, nugget=0.0011\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3576.55 | 59.8042 | 20.0339 | -4.5992 | 0.44s |\n", - "| OLS_sphere | 500 | 0.1255 | 0.3543 | 0.2729 | 0.9998 | 0.07s |\n", - "| DeepKriging_wendland | -- | 285.6 | 16.8997 | 11.5218 | 0.5529 | 46.38s |\n", - "| DeepKriging_wendland* | -- | 281.228 | 16.7698 | 11.597 | 0.5597 | 20.05s |\n", - "| DeepKriging_mrts | 100 | 9.1242 | 3.0206 | 2.499 | 0.9857 | 20.38s |\n", - "| DeepKriging_sphere | 500 | 7.7573 | 2.7852 | 1.072 | 0.9879 | 38.74s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2272 | 0.4766 | 0.3797 | 0.9996 | 28.29s |\n", - "| UniversalKriging | 100 | 0.1039 | 0.3223 | 0.2529 | 0.9998 | 2.19s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:55:56\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.8603 (0.5059), Variance: 639.7552, Range: [-62.0271, 31.0718]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1000, sigma²=0.2120, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 491.58 | 22.1716 | 16.1497 | 0.2654 | 0.40s |\n", - "| OLS_sphere | 500 | 0.1039 | 0.3224 | 0.2525 | 0.9998 | 0.06s |\n", - "| DeepKriging_wendland | -- | 258.238 | 16.0698 | 10.5962 | 0.6141 | 46.65s |\n", - "| DeepKriging_wendland* | -- | 251.02 | 15.8436 | 9.7644 | 0.6249 | 31.49s |\n", - "| DeepKriging_mrts | 100 | 0.2475 | 0.4975 | 0.3943 | 0.9996 | 57.81s |\n", - "| DeepKriging_sphere | 500 | 1.3729 | 1.1717 | 0.7295 | 0.9979 | 45.80s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1943 | 0.4407 | 0.358 | 0.9997 | 28.60s |\n", - "| UniversalKriging | 100 | 0.0817 | 0.2859 | 0.2227 | 0.9999 | 3.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:00:06\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.6809 (0.5058), Variance: 639.6840, Range: [-61.0309, 32.2772]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0836, sigma²=0.1777, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 423.126 | 20.57 | 14.9907 | 0.2751 | 0.37s |\n", - "| OLS_sphere | 500 | 0.1372 | 0.3704 | 0.294 | 0.9998 | 0.06s |\n", - "| DeepKriging_wendland | -- | 200.401 | 14.1563 | 9.374 | 0.6567 | 45.82s |\n", - "| DeepKriging_wendland* | -- | 192.263 | 13.8659 | 8.2723 | 0.6706 | 26.10s |\n", - "| DeepKriging_mrts | 100 | 2.0322 | 1.4256 | 1.2104 | 0.9965 | 23.44s |\n", - "| DeepKriging_sphere | 500 | 3.042 | 1.7441 | 0.9556 | 0.9948 | 46.08s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1347 | 0.3671 | 0.2944 | 0.9998 | 52.03s |\n", - "| UniversalKriging | 100 | 0.1107 | 0.3328 | 0.2623 | 0.9998 | 4.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:04:05\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.3889 (0.5060), Variance: 639.9807, Range: [-59.8750, 30.7408]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1013, sigma²=0.2162, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 425.115 | 20.6183 | 15.4036 | 0.3321 | 0.37s |\n", - "| OLS_sphere | 500 | 0.1473 | 0.3839 | 0.3039 | 0.9998 | 0.06s |\n", - "| DeepKriging_wendland | -- | 209.468 | 14.473 | 9.5203 | 0.6709 | 46.73s |\n", - "| DeepKriging_wendland* | -- | 186.508 | 13.6568 | 8.3004 | 0.707 | 21.24s |\n", - "| DeepKriging_mrts | 100 | 5.3998 | 2.3237 | 1.6889 | 0.9915 | 25.36s |\n", - "| DeepKriging_sphere | 500 | 100.16 | 10.008 | 7.4798 | 0.8426 | 14.70s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2546 | 0.5046 | 0.4052 | 0.9996 | 32.19s |\n", - "| UniversalKriging | 100 | 0.1035 | 0.3217 | 0.2522 | 0.9998 | 3.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:07:12\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.8360 (0.5057), Variance: 639.3803, Range: [-60.8009, 29.8165]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0944, sigma²=0.1912, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 618 | 24.8596 | 16.6026 | 0.047 | 0.33s |\n", - "| OLS_sphere | 500 | 0.11 | 0.3317 | 0.2543 | 0.9998 | 0.05s |\n", - "| DeepKriging_wendland | -- | 260.498 | 16.1399 | 10.3513 | 0.5983 | 50.91s |\n", - "| DeepKriging_wendland* | -- | 272.179 | 16.4978 | 11.1468 | 0.5803 | 20.32s |\n", - "| DeepKriging_mrts | 100 | 0.4201 | 0.6482 | 0.4998 | 0.9994 | 44.75s |\n", - "| DeepKriging_sphere | 500 | 1.7795 | 1.334 | 0.9241 | 0.9973 | 35.58s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1453 | 0.3812 | 0.2921 | 0.9998 | 46.09s |\n", - "| UniversalKriging | 100 | 0.0836 | 0.2892 | 0.2226 | 0.9999 | 3.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:11:21\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -20.3817 (0.5060), Variance: 640.0265, Range: [-61.1596, 29.7993]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1002, sigma²=0.2037, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 592.514 | 24.3416 | 16.8218 | 0.0709 | 0.43s |\n", - "| OLS_sphere | 500 | 0.1276 | 0.3573 | 0.2855 | 0.9998 | 0.04s |\n", - "| DeepKriging_wendland | -- | 271.918 | 16.4899 | 10.6128 | 0.5736 | 59.85s |\n", - "| DeepKriging_wendland* | -- | 254.619 | 15.9568 | 9.7107 | 0.6007 | 29.52s |\n", - "| DeepKriging_mrts | 100 | 0.3435 | 0.5861 | 0.4597 | 0.9995 | 81.16s |\n", - "| DeepKriging_sphere | 500 | 4.1083 | 2.0269 | 1.2492 | 0.9936 | 48.02s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1652 | 0.4065 | 0.3238 | 0.9997 | 52.79s |\n", - "| UniversalKriging | 100 | 0.0892 | 0.2986 | 0.2387 | 0.9999 | 2.88s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:16:43\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.5552 (0.5085), Variance: 646.3675, Range: [-62.7141, 31.2398]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0851, sigma²=0.1909, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 393.454 | 19.8357 | 15.5211 | 0.3908 | 0.42s |\n", - "| OLS_sphere | 500 | 0.1296 | 0.36 | 0.2841 | 0.9998 | 0.06s |\n", - "| DeepKriging_wendland | -- | 279.84 | 16.7284 | 11.097 | 0.5667 | 66.45s |\n", - "| DeepKriging_wendland* | -- | 279.892 | 16.73 | 11.6268 | 0.5666 | 27.90s |\n", - "| DeepKriging_mrts | 100 | 0.6694 | 0.8182 | 0.6677 | 0.999 | 69.21s |\n", - "| DeepKriging_sphere | 500 | 4.1619 | 2.0401 | 0.9115 | 0.9936 | 58.52s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.253 | 0.503 | 0.4086 | 0.9996 | 42.80s |\n", - "| UniversalKriging | 100 | 0.1045 | 0.3233 | 0.2597 | 0.9998 | 4.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:22:03\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -19.7190 (0.5125), Variance: 656.5314, Range: [-60.4699, 31.3241]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0831, sigma²=0.1887, nugget=0.0006\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 381.896 | 19.5422 | 14.9299 | 0.4087 | 0.38s |\n", - "| OLS_sphere | 500 | 0.1243 | 0.3525 | 0.2755 | 0.9998 | 0.06s |\n", - "| DeepKriging_wendland | -- | 264.683 | 16.2691 | 10.7545 | 0.5902 | 75.97s |\n", - "| DeepKriging_wendland* | -- | 254.147 | 15.942 | 10.4183 | 0.6065 | 27.23s |\n", - "| DeepKriging_mrts | 100 | 6.0644 | 2.4626 | 1.8715 | 0.9906 | 28.27s |\n", - "| DeepKriging_sphere | 500 | 10.7598 | 3.2802 | 1.2177 | 0.9833 | 69.22s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.2352 | 0.485 | 0.3835 | 0.9996 | 43.51s |\n", - "| UniversalKriging | 100 | 0.0927 | 0.3044 | 0.236 | 0.9999 | 3.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:27:05\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -18.3749 (0.5066), Variance: 641.6500, Range: [-61.1502, 31.7570]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0876, sigma²=0.1827, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 342.792 | 18.5146 | 14.3709 | 0.4134 | 0.46s |\n", - "| OLS_sphere | 500 | 0.1292 | 0.3595 | 0.286 | 0.9998 | 0.07s |\n", - "| DeepKriging_wendland | -- | 206.859 | 14.3826 | 9.5092 | 0.646 | 76.83s |\n", - "| DeepKriging_wendland* | -- | 189.553 | 13.7678 | 9.0296 | 0.6756 | 29.06s |\n", - "| DeepKriging_mrts | 100 | 7.8908 | 2.8091 | 2.1935 | 0.9865 | 30.22s |\n", - "| DeepKriging_sphere | 500 | 1.2666 | 1.1254 | 0.7599 | 0.9978 | 69.98s |\n", - "| DeepKriging_sphere_Huber | 100 | 0.1945 | 0.441 | 0.337 | 0.9997 | 51.84s |\n", - "| UniversalKriging | 100 | 0.1066 | 0.3265 | 0.2555 | 0.9998 | 3.73s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 12:32:25\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_mrts': 100, 'DeepKriging_sphere': 500, 'DeepKriging_sphere_Huber': 100, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------|-------------|------------|------------|\n", - "| OLS_wendland | 784.48±935.41 | 25.47±11.64 | 16.20±1.54 | -0.23±1.46 |\n", - "| OLS_sphere | 0.12±0.01 | 0.35±0.02 | 0.28±0.02 | 1.00±0.00 |\n", - "| DeepKriging_wendland | 253.78±33.73 | 15.89±1.08 | 10.47±0.74 | 0.60±0.04 |\n", - "| DeepKriging_wendland* | 246.34±39.98 | 15.64±1.31 | 10.13±1.24 | 0.61±0.05 |\n", - "| DeepKriging_mrts | 3.34±3.26 | 1.57±0.94 | 1.24±0.74 | 0.99±0.01 |\n", - "| DeepKriging_sphere | 13.55±29.02 | 2.66±2.55 | 1.60±1.97 | 0.98±0.05 |\n", - "| DeepKriging_sphere_Huber | 0.23±0.11 | 0.47±0.10 | 0.37±0.05 | 1.00±0.00 |\n", - "| UniversalKriging | 0.10±0.01 | 0.31±0.02 | 0.24±0.02 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 0.31±0.02)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg128.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg128.ipynb deleted file mode 100644 index d7b3ca9..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg128.ipynb +++ /dev/null @@ -1,3546 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-09 16:37:50.443201: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770626270.457240 421277 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770626270.461202 421277 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770626270.472479 421277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770626270.472502 421277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770626270.472504 421277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770626270.472505 421277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/50, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.8990 (1.5865), Variance: 6292.0918, Range: [-217.1357, 204.6192]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3124.5784 3294.3484 \n", - " 100 2143.3491 2319.1880 \n", - " 200 1457.7478 1169.2509 \n", - " 500 357.0222 405.1201 \n", - " 700 255.1198 372.1026 *\n", - " 1000 356.5432 514.3911 \n", - " 1400 23393.5117 2737.7085 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770626301.523409 421277 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770626302.363971 421583 service.cc:152] XLA service 0x60ad1975c6c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770626302.364049 421583 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770626302.726168 421583 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770626305.306866 421583 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72a120579630> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x72a11849ef80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2380.5034 2516.7395 \n", - " 100 2125.7830 2437.3875 \n", - " 200 2274.1343 2342.8804 \n", - " 500 203.1633 214.8340 \n", - " 700 186.3717 217.5690 *\n", - " 1000 318.6537 337.3055 \n", - " 1400 900.8218 1550.0637 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 53.8752 96.4880 *\n", - " 100 54.2793 92.4967 \n", - " 200 102.8511 140.5747 \n", - " 500 150.8710 189.4033 \n", - " 700 212.3795 289.6274 \n", - " 1000 501.6202 682.6497 \n", - " 1400 2007.5522 2012.3313 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 4.7668 65.6266 *\n", - " 100 5.6655 87.3264 \n", - " 200 6.9839 149.9185 \n", - " 500 10.0963 267.7331 \n", - " 700 12.6768 353.4837 \n", - " 1000 18.9904 586.7411 \n", - " 1400 35.0696 2599.4995 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0276, sigma²=7154.9680, nugget=0.0036\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5087, sigma²=3344.7074, nugget=0.0012\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2622, sigma²=1469.3274, nugget=0.0063\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0675, sigma²=200.2165, nugget=0.0026\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0259, sigma²=0.0545, nugget=98.6615\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=49.4223, sigma²=0.0002, nugget=56.0579\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3240, sigma²=0.0001, nugget=20.4257\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 181.2636 172.5062 \n", - " 100 180.7028 178.4778 \n", - " 200 191.8894 165.7470 \n", - " 500 173.0663 207.4455 *\n", - " 700 255.0695 372.0506 \n", - " 1000 356.5422 514.3957 \n", - " 1400 23393.4045 2737.4504 \n", - " Best order: 500\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0675, sigma²=200.2165, nugget=0.0026\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8229.3 | 90.7155 | 61.0671 | -0.2378 | 0.46s |\n", - "| OLS_sphere | 700 | 372.103 | 19.29 | 10.0156 | 0.944 | 0.14s |\n", - "| DeepKriging_wendland | -- | 3955.64 | 62.8939 | 48.2948 | 0.405 | 71.65s |\n", - "| DeepKriging_wendland* | -- | 3637.09 | 60.3083 | 43.7626 | 0.4529 | 35.88s |\n", - "| DeepKriging_mrts | 700 | 215.115 | 14.6668 | 8.5334 | 0.9676 | 57.01s |\n", - "| DeepKriging_sphere | 50 | 59.8546 | 7.7366 | 4.1529 | 0.991 | 63.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 76.4188 | 8.7418 | 4.6616 | 0.9885 | 50.20s |\n", - "| UniversalKriging | 500 | 207.446 | 14.403 | 7.4402 | 0.9688 | 5.62s |\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 16:54:42\n", - "\n", - "✅ Completed Repeat 1/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/50, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5511 (1.5780), Variance: 6225.4644, Range: [-207.2308, 204.4013]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0647, sigma²=210.4715, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7810 | 88.3742 | 58.7111 | -0.2344 | 0.43s |\n", - "| OLS_sphere | 700 | 266.544 | 16.3262 | 9.6716 | 0.9579 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3594.46 | 59.9538 | 45.5213 | 0.4319 | 45.72s |\n", - "| DeepKriging_wendland* | -- | 3541.53 | 59.5107 | 45.0434 | 0.4402 | 21.22s |\n", - "| DeepKriging_mrts | 700 | 180.89 | 13.4495 | 7.9398 | 0.9714 | 45.83s |\n", - "| DeepKriging_sphere | 50 | 104.814 | 10.2379 | 5.0858 | 0.9834 | 39.42s |\n", - "| DeepKriging_sphere_Huber | 50 | 136.72 | 11.6927 | 5.5 | 0.9784 | 41.63s |\n", - "| UniversalKriging | 500 | 164.196 | 12.8139 | 7.3171 | 0.974 | 7.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 16:58:36\n", - "\n", - "✅ Completed Repeat 2/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/50, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.5109 (1.5935), Variance: 6347.7271, Range: [-215.1664, 203.1886]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0720, sigma²=202.7150, nugget=0.0027\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8445.34 | 91.8985 | 60.2661 | -0.1795 | 0.41s |\n", - "| OLS_sphere | 700 | 289.061 | 17.0018 | 9.3128 | 0.9596 | 0.08s |\n", - "| DeepKriging_wendland | -- | 4183.32 | 64.6786 | 47.9455 | 0.4157 | 56.96s |\n", - "| DeepKriging_wendland* | -- | 3852.57 | 62.0691 | 44.2907 | 0.4619 | 22.14s |\n", - "| DeepKriging_mrts | 700 | 185.709 | 13.6275 | 8.1413 | 0.9741 | 42.46s |\n", - "| DeepKriging_sphere | 50 | 47.4499 | 6.8884 | 4.3926 | 0.9934 | 47.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 118.761 | 10.8977 | 6.4961 | 0.9834 | 30.03s |\n", - "| UniversalKriging | 500 | 157.706 | 12.5581 | 6.7232 | 0.978 | 4.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:02:37\n", - "\n", - "✅ Completed Repeat 3/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/50, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4385 (1.6090), Variance: 6472.2598, Range: [-209.0001, 205.4681]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0534, sigma²=155.8935, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6465.32 | 80.4072 | 59.4873 | 0.0131 | 0.41s |\n", - "| OLS_sphere | 700 | 365.25 | 19.1115 | 10.1717 | 0.9442 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4323.2 | 65.751 | 49.1865 | 0.3401 | 57.14s |\n", - "| DeepKriging_wendland* | -- | 4368.12 | 66.0917 | 48.4286 | 0.3332 | 22.64s |\n", - "| DeepKriging_mrts | 700 | 290.875 | 17.0551 | 10.4235 | 0.9556 | 41.85s |\n", - "| DeepKriging_sphere | 50 | 92.6213 | 9.624 | 6.3176 | 0.9859 | 30.50s |\n", - "| DeepKriging_sphere_Huber | 50 | 95.8645 | 9.791 | 5.7067 | 0.9854 | 34.72s |\n", - "| UniversalKriging | 500 | 249.678 | 15.8012 | 8.319 | 0.9619 | 6.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:06:32\n", - "\n", - "✅ Completed Repeat 4/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/50, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6026 (1.6158), Variance: 6527.0991, Range: [-209.7779, 205.1678]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0576, sigma²=194.4187, nugget=0.0018\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25831.4 | 160.721 | 65.9493 | -2.9591 | 0.36s |\n", - "| OLS_sphere | 700 | 333.92 | 18.2735 | 10.4708 | 0.9488 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3607.77 | 60.0647 | 46.6701 | 0.447 | 51.33s |\n", - "| DeepKriging_wendland* | -- | 3427.77 | 58.5472 | 44.2878 | 0.4746 | 21.36s |\n", - "| DeepKriging_mrts | 700 | 2165.04 | 46.53 | 36.82 | 0.6682 | 17.44s |\n", - "| DeepKriging_sphere | 50 | 84.3958 | 9.1867 | 6.0046 | 0.9871 | 32.86s |\n", - "| DeepKriging_sphere_Huber | 50 | 86.3555 | 9.2928 | 5.3865 | 0.9868 | 33.35s |\n", - "| UniversalKriging | 500 | 306.14 | 17.4969 | 9.3076 | 0.9531 | 4.12s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:09:54\n", - "\n", - "✅ Completed Repeat 5/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/50, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.9735 (1.6405), Variance: 6728.1138, Range: [-206.4226, 204.7059]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0618, sigma²=185.6695, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25664 | 160.2 | 69.8779 | -2.7327 | 0.41s |\n", - "| OLS_sphere | 700 | 209.73 | 14.482 | 8.7416 | 0.9695 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3733.06 | 61.0988 | 45.9169 | 0.457 | 56.60s |\n", - "| DeepKriging_wendland* | -- | 3515.27 | 59.2897 | 42.9812 | 0.4887 | 24.43s |\n", - "| DeepKriging_mrts | 700 | 1328.19 | 36.4443 | 26.1241 | 0.8068 | 18.61s |\n", - "| DeepKriging_sphere | 50 | 28.0282 | 5.2942 | 3.5483 | 0.9959 | 37.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.2095 | 7.6948 | 4.1885 | 0.9914 | 35.62s |\n", - "| UniversalKriging | 500 | 133.161 | 11.5395 | 6.3248 | 0.9806 | 5.32s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:13:40\n", - "\n", - "✅ Completed Repeat 6/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/50, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.0090 (1.6395), Variance: 6719.9087, Range: [-213.9856, 205.0166]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0718, sigma²=239.4600, nugget=0.0964\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9379.86 | 96.8497 | 61.0687 | -0.4079 | 0.40s |\n", - "| OLS_sphere | 700 | 232.342 | 15.2428 | 8.6253 | 0.9651 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3818.86 | 61.7969 | 45.8606 | 0.4268 | 54.44s |\n", - "| DeepKriging_wendland* | -- | 3306.43 | 57.5016 | 40.0348 | 0.5037 | 27.07s |\n", - "| DeepKriging_mrts | 700 | 245.568 | 15.6706 | 9.1388 | 0.9631 | 37.06s |\n", - "| DeepKriging_sphere | 50 | 5631.06 | 75.0404 | 61.6307 | 0.1548 | 15.89s |\n", - "| DeepKriging_sphere_Huber | 50 | 79.0008 | 8.8882 | 4.6676 | 0.9881 | 38.39s |\n", - "| UniversalKriging | 500 | 112.557 | 10.6093 | 5.6806 | 0.9831 | 2.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:17:20\n", - "\n", - "✅ Completed Repeat 7/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/50, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.2623 (1.5935), Variance: 6347.8706, Range: [-193.8428, 206.9386]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0591, sigma²=159.8777, nugget=0.0145\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4527.34 | 67.2855 | 52.8866 | 0.2363 | 0.41s |\n", - "| OLS_sphere | 700 | 316.082 | 17.7787 | 9.5382 | 0.9467 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3138.47 | 56.0221 | 40.3933 | 0.4706 | 54.53s |\n", - "| DeepKriging_wendland* | -- | 3171.98 | 56.3204 | 40.686 | 0.4649 | 22.23s |\n", - "| DeepKriging_mrts | 700 | 240.262 | 15.5004 | 9.4166 | 0.9595 | 36.75s |\n", - "| DeepKriging_sphere | 50 | 76.7182 | 8.7589 | 4.8495 | 0.9871 | 39.16s |\n", - "| DeepKriging_sphere_Huber | 50 | 83.5463 | 9.1404 | 5.5057 | 0.9859 | 45.82s |\n", - "| UniversalKriging | 500 | 243.618 | 15.6083 | 8.5868 | 0.9589 | 4.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:21:36\n", - "\n", - "✅ Completed Repeat 8/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/50, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.8492 (1.6373), Variance: 6702.1196, Range: [-213.2899, 206.2633]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0507, sigma²=161.0195, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4703.99 | 68.5857 | 54.8268 | 0.1706 | 0.41s |\n", - "| OLS_sphere | 700 | 243.912 | 15.6177 | 9.2997 | 0.957 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3376.12 | 58.1044 | 43.2181 | 0.4047 | 52.55s |\n", - "| DeepKriging_wendland* | -- | 3246.72 | 56.98 | 41.5936 | 0.4275 | 25.16s |\n", - "| DeepKriging_mrts | 700 | 239.125 | 15.4637 | 10.0639 | 0.9578 | 40.24s |\n", - "| DeepKriging_sphere | 50 | 79.8991 | 8.9386 | 5.3517 | 0.9859 | 30.54s |\n", - "| DeepKriging_sphere_Huber | 50 | 92.1451 | 9.5992 | 4.8574 | 0.9838 | 34.99s |\n", - "| UniversalKriging | 500 | 170.786 | 13.0685 | 7.6241 | 0.9699 | 5.76s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:25:42\n", - "\n", - "✅ Completed Repeat 9/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/50, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2267 (1.5728), Variance: 6184.3008, Range: [-213.3326, 204.9748]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0692, sigma²=187.1184, nugget=0.0024\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4528.93 | 67.2973 | 52.9142 | 0.2262 | 0.42s |\n", - "| OLS_sphere | 700 | 255.636 | 15.9886 | 9.1326 | 0.9563 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3505.49 | 59.2072 | 44.5064 | 0.401 | 52.62s |\n", - "| DeepKriging_wendland* | -- | 3200.98 | 56.5772 | 41.1444 | 0.4531 | 24.41s |\n", - "| DeepKriging_mrts | 700 | 138.394 | 11.7641 | 7.9154 | 0.9764 | 39.64s |\n", - "| DeepKriging_sphere | 50 | 46.6894 | 6.833 | 4.0763 | 0.992 | 51.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 69.4924 | 8.3362 | 5.09 | 0.9881 | 34.04s |\n", - "| UniversalKriging | 500 | 144.241 | 12.0101 | 7.0843 | 0.9754 | 6.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:30:10\n", - "\n", - "✅ Completed Repeat 10/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 11/50, Seed=56\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.7272 (1.6331), Variance: 6667.9258, Range: [-209.9437, 203.9548]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0589, sigma²=224.6836, nugget=0.0019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6210.47 | 78.8065 | 54.9741 | 0.069 | 0.43s |\n", - "| OLS_sphere | 700 | 221.411 | 14.8799 | 7.8048 | 0.9668 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3171.09 | 56.3124 | 41.206 | 0.5246 | 53.90s |\n", - "| DeepKriging_wendland* | -- | 3026.56 | 55.0142 | 38.4171 | 0.5463 | 22.07s |\n", - "| DeepKriging_mrts | 700 | 130.388 | 11.4188 | 6.6809 | 0.9805 | 45.57s |\n", - "| DeepKriging_sphere | 50 | 52.9704 | 7.2781 | 4.8198 | 0.9921 | 35.76s |\n", - "| DeepKriging_sphere_Huber | 50 | 45.6037 | 6.7531 | 4.489 | 0.9932 | 43.25s |\n", - "| UniversalKriging | 500 | 109.501 | 10.4643 | 6.2391 | 0.9836 | 8.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:34:40\n", - "\n", - "✅ Completed Repeat 11/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 12/50, Seed=67\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.8132 (1.6021), Variance: 6417.1538, Range: [-213.4412, 202.3088]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0786, sigma²=256.2824, nugget=0.0008\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10875.4 | 104.285 | 60.2194 | -0.7062 | 0.42s |\n", - "| OLS_sphere | 700 | 422.314 | 20.5503 | 10.6618 | 0.9337 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3335.69 | 57.7555 | 41.2087 | 0.4767 | 49.17s |\n", - "| DeepKriging_wendland* | -- | 3099.01 | 55.6687 | 39.6856 | 0.5138 | 28.65s |\n", - "| DeepKriging_mrts | 700 | 161.571 | 12.7111 | 7.8236 | 0.9747 | 35.73s |\n", - "| DeepKriging_sphere | 50 | 43.3733 | 6.5858 | 4.2561 | 0.9932 | 31.21s |\n", - "| DeepKriging_sphere_Huber | 50 | 69.0029 | 8.3068 | 4.8583 | 0.9892 | 32.04s |\n", - "| UniversalKriging | 500 | 224.294 | 14.9765 | 7.8634 | 0.9648 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:38:45\n", - "\n", - "✅ Completed Repeat 12/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 13/50, Seed=79\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.7703 (1.5807), Variance: 6246.6929, Range: [-211.4745, 202.6139]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0707, sigma²=220.6349, nugget=0.0015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|----------|-----------|--------|\n", - "| OLS_wendland | -- | 642488 | 801.554 | 145.701 | -103.765 | 0.42s |\n", - "| OLS_sphere | 700 | 274.613 | 16.5715 | 9.4262 | 0.9552 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3762.95 | 61.3429 | 46.8875 | 0.3864 | 50.05s |\n", - "| DeepKriging_wendland* | -- | 3413.15 | 58.4222 | 43.8062 | 0.4434 | 21.80s |\n", - "| DeepKriging_mrts | 700 | 248.642 | 15.7684 | 9.6029 | 0.9595 | 51.52s |\n", - "| DeepKriging_sphere | 50 | 81.0099 | 9.0005 | 5.2588 | 0.9868 | 46.68s |\n", - "| DeepKriging_sphere_Huber | 50 | 88.0766 | 9.3849 | 4.9694 | 0.9856 | 40.23s |\n", - "| UniversalKriging | 500 | 196.804 | 14.0287 | 7.9331 | 0.9679 | 5.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:43:27\n", - "\n", - "✅ Completed Repeat 13/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 14/50, Seed=92\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.8304 (1.6294), Variance: 6637.7637, Range: [-205.1611, 204.2837]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0584, sigma²=208.3281, nugget=0.0036\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 335291 | 579.043 | 95.8586 | -48.475 | 0.44s |\n", - "| OLS_sphere | 700 | 147.194 | 12.1324 | 6.9639 | 0.9783 | 0.16s |\n", - "| DeepKriging_wendland | -- | 3725.48 | 61.0367 | 45.4947 | 0.4503 | 53.81s |\n", - "| DeepKriging_wendland* | -- | 3479.92 | 58.9908 | 42.3482 | 0.4865 | 20.13s |\n", - "| DeepKriging_mrts | 700 | 180.163 | 13.4225 | 8.1331 | 0.9734 | 46.64s |\n", - "| DeepKriging_sphere | 50 | 84.593 | 9.1974 | 6.1166 | 0.9875 | 32.44s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.4999 | 8.0312 | 4.4248 | 0.9905 | 35.00s |\n", - "| UniversalKriging | 500 | 115.684 | 10.7557 | 6.3541 | 0.9829 | 7.60s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:47:53\n", - "\n", - "✅ Completed Repeat 14/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 15/50, Seed=106\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7905 (1.5942), Variance: 6354.0728, Range: [-212.3665, 204.4863]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0600, sigma²=209.4348, nugget=0.2305\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 15061 | 122.723 | 65.5612 | -1.6209 | 0.45s |\n", - "| OLS_sphere | 700 | 273.722 | 16.5446 | 9.4747 | 0.9524 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3131.86 | 55.9631 | 42.1811 | 0.455 | 50.62s |\n", - "| DeepKriging_wendland* | -- | 3088.02 | 55.57 | 42.4003 | 0.4626 | 22.28s |\n", - "| DeepKriging_mrts | 700 | 191.488 | 13.8379 | 8.0498 | 0.9667 | 43.47s |\n", - "| DeepKriging_sphere | 50 | 54.8997 | 7.4094 | 4.7259 | 0.9904 | 34.84s |\n", - "| DeepKriging_sphere_Huber | 50 | 81.128 | 9.0071 | 5.002 | 0.9859 | 32.68s |\n", - "| UniversalKriging | 500 | 129.123 | 11.3632 | 6.6678 | 0.9775 | 6.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:52:14\n", - "\n", - "✅ Completed Repeat 15/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 16/50, Seed=121\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.3907 (1.6051), Variance: 6440.8555, Range: [-213.6481, 206.6171]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0645, sigma²=216.4751, nugget=0.0012\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5800.87 | 76.1634 | 57.4941 | -0.0138 | 0.38s |\n", - "| OLS_sphere | 700 | 422.673 | 20.559 | 10.4138 | 0.9261 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3653.92 | 60.4477 | 45.5682 | 0.3614 | 46.36s |\n", - "| DeepKriging_wendland* | -- | 3562.31 | 59.6851 | 42.8732 | 0.3774 | 19.29s |\n", - "| DeepKriging_mrts | 700 | 424.406 | 20.6011 | 10.9873 | 0.9258 | 49.47s |\n", - "| DeepKriging_sphere | 50 | 78.5417 | 8.8624 | 4.8936 | 0.9863 | 39.87s |\n", - "| DeepKriging_sphere_Huber | 50 | 165.573 | 12.8675 | 6.6287 | 0.9711 | 35.00s |\n", - "| UniversalKriging | 500 | 320.252 | 17.8956 | 9.6839 | 0.944 | 6.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 17:56:49\n", - "\n", - "✅ Completed Repeat 16/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 17/50, Seed=137\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.4960 (1.6194), Variance: 6555.7754, Range: [-213.5275, 204.3283]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0702, sigma²=206.5751, nugget=0.0011\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 38454.3 | 196.098 | 75.031 | -4.1858 | 0.42s |\n", - "| OLS_sphere | 700 | 354.28 | 18.8223 | 9.9707 | 0.9522 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4105.53 | 64.0744 | 46.884 | 0.4463 | 52.09s |\n", - "| DeepKriging_wendland* | -- | 3949.16 | 62.8423 | 47.0989 | 0.4674 | 24.82s |\n", - "| DeepKriging_mrts | 700 | 242.468 | 15.5714 | 8.6525 | 0.9673 | 40.27s |\n", - "| DeepKriging_sphere | 50 | 170.836 | 13.0704 | 6.7523 | 0.977 | 28.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 138.434 | 11.7658 | 5.6271 | 0.9813 | 34.58s |\n", - "| UniversalKriging | 500 | 222.47 | 14.9154 | 8.3501 | 0.97 | 4.43s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:01:17\n", - "\n", - "✅ Completed Repeat 17/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 18/50, Seed=154\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.2426 (1.6400), Variance: 6724.3662, Range: [-208.5981, 203.6823]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0640, sigma²=198.5597, nugget=0.0013\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 14164.4 | 119.014 | 68.6117 | -1.3176 | 0.50s |\n", - "| OLS_sphere | 700 | 185.295 | 13.6123 | 8.1552 | 0.9697 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3722.04 | 61.0085 | 45.227 | 0.391 | 51.56s |\n", - "| DeepKriging_wendland* | -- | 3548.79 | 59.5718 | 43.0317 | 0.4194 | 22.06s |\n", - "| DeepKriging_mrts | 700 | 234.13 | 15.3013 | 8.1975 | 0.9617 | 57.69s |\n", - "| DeepKriging_sphere | 50 | 88.8003 | 9.4234 | 5.9294 | 0.9855 | 35.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 70.9775 | 8.4248 | 4.7597 | 0.9884 | 35.91s |\n", - "| UniversalKriging | 500 | 183.158 | 13.5336 | 7.3662 | 0.97 | 7.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:06:12\n", - "\n", - "✅ Completed Repeat 18/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 19/50, Seed=172\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.2746 (1.6210), Variance: 6569.3911, Range: [-215.0105, 203.4060]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0716, sigma²=206.5365, nugget=0.0018\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5500.22 | 74.1635 | 55.8497 | 0.2002 | 0.45s |\n", - "| OLS_sphere | 700 | 410.772 | 20.2675 | 9.9058 | 0.9403 | -1.67s |\n", - "| DeepKriging_wendland | -- | 3820.89 | 61.8134 | 46.2513 | 0.4444 | 57.35s |\n", - "| DeepKriging_wendland* | -- | 3786.19 | 61.532 | 41.6939 | 0.4494 | 24.67s |\n", - "| DeepKriging_mrts | 700 | 265.161 | 16.2838 | 10.3011 | 0.9614 | 42.48s |\n", - "| DeepKriging_sphere | 50 | 39.3317 | 6.2715 | 4.4472 | 0.9943 | 36.01s |\n", - "| DeepKriging_sphere_Huber | 50 | 56.9154 | 7.5442 | 4.9192 | 0.9917 | 33.54s |\n", - "| UniversalKriging | 500 | 174.802 | 13.2213 | 7.1044 | 0.9746 | 5.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:10:56\n", - "\n", - "✅ Completed Repeat 19/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 20/50, Seed=191\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.1637 (1.6099), Variance: 6479.7686, Range: [-213.2204, 205.0539]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0545, sigma²=213.8692, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10518 | 102.558 | 59.6725 | -0.672 | 0.41s |\n", - "| OLS_sphere | 700 | 147.438 | 12.1424 | 7.2927 | 0.9766 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3795.59 | 61.6083 | 43.8388 | 0.3966 | 59.21s |\n", - "| DeepKriging_wendland* | -- | 3589.95 | 59.9162 | 41.4525 | 0.4293 | 24.75s |\n", - "| DeepKriging_mrts | 700 | 149.158 | 12.213 | 7.7507 | 0.9763 | 38.96s |\n", - "| DeepKriging_sphere | 50 | 33.2917 | 5.7699 | 3.5199 | 0.9947 | 43.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 43.3308 | 6.5826 | 3.8439 | 0.9931 | 42.37s |\n", - "| UniversalKriging | 500 | 102.61 | 10.1296 | 6.0014 | 0.9837 | 6.40s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:16:01\n", - "\n", - "✅ Completed Repeat 20/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 21/50, Seed=211\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.6205 (1.6380), Variance: 6707.5342, Range: [-214.6801, 205.8385]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0625, sigma²=180.5292, nugget=0.0023\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 40706.6 | 201.759 | 63.9408 | -5.4821 | 0.47s |\n", - "| OLS_sphere | 700 | 363.357 | 19.0619 | 8.9163 | 0.9421 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3333.87 | 57.7397 | 41.8489 | 0.4691 | 75.50s |\n", - "| DeepKriging_wendland* | -- | 3383.1 | 58.1644 | 42.1211 | 0.4613 | 31.38s |\n", - "| DeepKriging_mrts | 700 | 242.215 | 15.5633 | 8.8988 | 0.9614 | 60.00s |\n", - "| DeepKriging_sphere | 50 | 123.183 | 11.0988 | 6.1423 | 0.9804 | 38.40s |\n", - "| DeepKriging_sphere_Huber | 50 | 147.531 | 12.1462 | 5.7595 | 0.9765 | 49.36s |\n", - "| UniversalKriging | 500 | 272.662 | 16.5125 | 8.2385 | 0.9566 | 6.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:21:55\n", - "\n", - "✅ Completed Repeat 21/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 22/50, Seed=232\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.1542 (1.6252), Variance: 6603.5010, Range: [-212.9713, 206.5289]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0693, sigma²=208.2328, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 17842 | 133.574 | 67.9287 | -1.6909 | 0.45s |\n", - "| OLS_sphere | 700 | 182.052 | 13.4927 | 7.9316 | 0.9725 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3988.07 | 63.1512 | 47.1166 | 0.3985 | 68.32s |\n", - "| DeepKriging_wendland* | -- | 3647.79 | 60.397 | 44.8164 | 0.4498 | 29.49s |\n", - "| DeepKriging_mrts | 700 | 140.832 | 11.8673 | 7.5988 | 0.9788 | 69.15s |\n", - "| DeepKriging_sphere | 50 | 62.6636 | 7.916 | 4.4636 | 0.9905 | 66.17s |\n", - "| DeepKriging_sphere_Huber | 50 | 45.4374 | 6.7407 | 4.2171 | 0.9931 | 59.65s |\n", - "| UniversalKriging | 500 | 130.546 | 11.4257 | 6.7796 | 0.9803 | 6.54s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:28:30\n", - "\n", - "✅ Completed Repeat 22/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 23/50, Seed=254\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.0980 (1.6040), Variance: 6432.1392, Range: [-204.2614, 206.8488]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=128.5155, nugget=39.4268\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5040.1 | 70.9937 | 56.4284 | 0.2036 | 0.48s |\n", - "| OLS_sphere | 700 | 216.581 | 14.7167 | 8.7802 | 0.9658 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3853.23 | 62.0744 | 47.3402 | 0.3912 | 67.90s |\n", - "| DeepKriging_wendland* | -- | 3793.72 | 61.5931 | 46.5964 | 0.4006 | 23.19s |\n", - "| DeepKriging_mrts | 700 | 2810.12 | 53.0106 | 43.1119 | 0.556 | 21.87s |\n", - "| DeepKriging_sphere | 50 | 74.5152 | 8.6322 | 5.2712 | 0.9882 | 41.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 202.647 | 14.2354 | 5.9652 | 0.968 | 41.92s |\n", - "| UniversalKriging | 500 | 247.178 | 15.7219 | 10.6253 | 0.9609 | 1.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:33:31\n", - "\n", - "✅ Completed Repeat 23/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 24/50, Seed=277\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.3593 (1.6087), Variance: 6470.1689, Range: [-205.0350, 202.1178]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0619, sigma²=201.5664, nugget=0.0026\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 73552.1 | 271.205 | 78.697 | -10.77 | 0.36s |\n", - "| OLS_sphere | 700 | 207.506 | 14.4051 | 7.9204 | 0.9668 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3573.32 | 59.7772 | 44.1946 | 0.4282 | 71.49s |\n", - "| DeepKriging_wendland* | -- | 3339.66 | 57.7898 | 41.7942 | 0.4656 | 30.02s |\n", - "| DeepKriging_mrts | 700 | 1560.69 | 39.5056 | 31.4807 | 0.7503 | 22.89s |\n", - "| DeepKriging_sphere | 50 | 35.3331 | 5.9442 | 3.566 | 0.9943 | 61.55s |\n", - "| DeepKriging_sphere_Huber | 50 | 41.0232 | 6.4049 | 3.933 | 0.9934 | 43.31s |\n", - "| UniversalKriging | 500 | 122.689 | 11.0765 | 6.0139 | 0.9804 | 7.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:39:13\n", - "\n", - "✅ Completed Repeat 24/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 25/50, Seed=301\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.7417 (1.6056), Variance: 6444.6445, Range: [-213.0987, 203.9253]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0554, sigma²=189.5161, nugget=0.0029\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5247.18 | 72.4374 | 57.2559 | 0.2369 | 0.41s |\n", - "| OLS_sphere | 700 | 352.485 | 18.7746 | 9.7588 | 0.9487 | 0.12s |\n", - "| DeepKriging_wendland | -- | 4232.92 | 65.0609 | 49.2949 | 0.3844 | 65.53s |\n", - "| DeepKriging_wendland* | -- | 4313.66 | 65.6784 | 48.9384 | 0.3727 | 23.42s |\n", - "| DeepKriging_mrts | 700 | 216.804 | 14.7243 | 8.8276 | 0.9685 | 62.68s |\n", - "| DeepKriging_sphere | 50 | 153.061 | 12.3718 | 5.3214 | 0.9777 | 52.58s |\n", - "| DeepKriging_sphere_Huber | 50 | 144.03 | 12.0013 | 5.7693 | 0.9791 | 47.50s |\n", - "| UniversalKriging | 500 | 268.092 | 16.3735 | 8.5403 | 0.961 | 6.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:45:17\n", - "\n", - "✅ Completed Repeat 25/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 26/50, Seed=326\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6457 (1.6446), Variance: 6761.6753, Range: [-211.8240, 206.1550]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0501, sigma²=162.1723, nugget=0.0030\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9893.75 | 99.4673 | 61.9286 | -0.3582 | 0.49s |\n", - "| OLS_sphere | 700 | 230.486 | 15.1818 | 9.426 | 0.9684 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3804.94 | 61.6842 | 45.9186 | 0.4777 | 76.48s |\n", - "| DeepKriging_wendland* | -- | 4120.14 | 64.1883 | 48.6176 | 0.4344 | 24.35s |\n", - "| DeepKriging_mrts | 700 | 203.016 | 14.2484 | 8.3749 | 0.9721 | 63.15s |\n", - "| DeepKriging_sphere | 50 | 76.4739 | 8.7449 | 5.6767 | 0.9895 | 42.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 76.3504 | 8.7379 | 5.316 | 0.9895 | 44.91s |\n", - "| UniversalKriging | 500 | 168.4 | 12.9769 | 7.4475 | 0.9769 | 7.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:51:23\n", - "\n", - "✅ Completed Repeat 26/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 27/50, Seed=352\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.3714 (1.6160), Variance: 6528.3906, Range: [-208.2301, 205.0020]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0603, sigma²=173.2467, nugget=0.0124\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5434.04 | 73.716 | 57.5518 | 0.2049 | 0.43s |\n", - "| OLS_sphere | 700 | 227.018 | 15.0671 | 7.3633 | 0.9668 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3750.93 | 61.2448 | 45.392 | 0.4511 | 79.56s |\n", - "| DeepKriging_wendland* | -- | 3707.19 | 60.8867 | 43.0444 | 0.4575 | 24.50s |\n", - "| DeepKriging_mrts | 700 | 219.131 | 14.8031 | 7.7888 | 0.9679 | 65.38s |\n", - "| DeepKriging_sphere | 50 | 113.647 | 10.6605 | 4.9082 | 0.9834 | 38.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 68.0273 | 8.2479 | 4.3644 | 0.99 | 57.24s |\n", - "| UniversalKriging | 500 | 209.4 | 14.4707 | 7.1978 | 0.9694 | 2.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 18:57:47\n", - "\n", - "✅ Completed Repeat 27/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 28/50, Seed=379\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.0608 (1.5787), Variance: 6230.5850, Range: [-209.3517, 203.6188]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0719, sigma²=231.5229, nugget=0.0031\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5829.8 | 76.3531 | 56.0117 | 0.1016 | 0.39s |\n", - "| OLS_sphere | 700 | 298.002 | 17.2627 | 9.6384 | 0.9541 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3525.89 | 59.3792 | 43.5843 | 0.4566 | 70.74s |\n", - "| DeepKriging_wendland* | -- | 3231.45 | 56.8459 | 40.751 | 0.502 | 28.76s |\n", - "| DeepKriging_mrts | 700 | 180.689 | 13.4421 | 8.0114 | 0.9722 | 65.98s |\n", - "| DeepKriging_sphere | 50 | 106.006 | 10.2959 | 6.3047 | 0.9837 | 45.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 117.996 | 10.8626 | 6.1515 | 0.9818 | 43.12s |\n", - "| UniversalKriging | 500 | 209.011 | 14.4572 | 8.0018 | 0.9678 | 6.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:03:59\n", - "\n", - "✅ Completed Repeat 28/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 29/50, Seed=407\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.3726 (1.6330), Variance: 6666.4644, Range: [-212.9247, 204.4900]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0559, sigma²=202.9188, nugget=0.0024\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10893.3 | 104.371 | 63.8238 | -0.688 | 0.42s |\n", - "| OLS_sphere | 700 | 225.389 | 15.013 | 9.34 | 0.9651 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4088.09 | 63.9382 | 47.7303 | 0.3665 | 72.71s |\n", - "| DeepKriging_wendland* | -- | 4032.38 | 63.5011 | 47.1804 | 0.3751 | 30.55s |\n", - "| DeepKriging_mrts | 700 | 219.906 | 14.8292 | 9.661 | 0.9659 | 54.51s |\n", - "| DeepKriging_sphere | 50 | 67.295 | 8.2034 | 5.0539 | 0.9896 | 49.13s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.5871 | 7.3883 | 4.479 | 0.9915 | 69.27s |\n", - "| UniversalKriging | 500 | 168.652 | 12.9866 | 7.6958 | 0.9739 | 4.71s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:10:35\n", - "\n", - "✅ Completed Repeat 29/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 30/50, Seed=436\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8556 (1.6363), Variance: 6693.4346, Range: [-213.1151, 205.5428]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0670, sigma²=205.7629, nugget=0.0026\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 7573.81 | 87.0276 | 61.6513 | 0.0162 | 0.44s |\n", - "| OLS_sphere | 700 | 217.067 | 14.7332 | 8.3381 | 0.9718 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3719.6 | 60.9886 | 45.1864 | 0.5168 | 70.69s |\n", - "| DeepKriging_wendland* | -- | 3184.53 | 56.4317 | 41.5257 | 0.5863 | 29.51s |\n", - "| DeepKriging_mrts | 700 | 230.701 | 15.1889 | 8.2681 | 0.97 | 57.59s |\n", - "| DeepKriging_sphere | 50 | 55.5055 | 7.4502 | 4.5029 | 0.9928 | 46.45s |\n", - "| DeepKriging_sphere_Huber | 50 | 52.9629 | 7.2776 | 3.7989 | 0.9931 | 57.79s |\n", - "| UniversalKriging | 500 | 149.179 | 12.2139 | 7.055 | 0.9806 | 6.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:17:02\n", - "\n", - "✅ Completed Repeat 30/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 31/50, Seed=466\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5201 (1.6337), Variance: 6672.3442, Range: [-207.8571, 205.5716]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0575, sigma²=160.8946, nugget=0.0031\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6089.48 | 78.0351 | 60.8516 | 0.1852 | 0.39s |\n", - "| OLS_sphere | 700 | 257.262 | 16.0394 | 8.0955 | 0.9656 | 0.12s |\n", - "| DeepKriging_wendland | -- | 3843.55 | 61.9963 | 45.9997 | 0.4857 | 74.89s |\n", - "| DeepKriging_wendland* | -- | 3407.6 | 58.3747 | 42.5007 | 0.544 | 33.91s |\n", - "| DeepKriging_mrts | 700 | 304.862 | 17.4603 | 10.8962 | 0.9592 | 43.63s |\n", - "| DeepKriging_sphere | 50 | 77.4053 | 8.798 | 4.4913 | 0.9896 | 50.04s |\n", - "| DeepKriging_sphere_Huber | 50 | 69.0656 | 8.3106 | 5.2419 | 0.9908 | 42.77s |\n", - "| UniversalKriging | 500 | 167.831 | 12.955 | 6.7347 | 0.9775 | 6.61s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:23:18\n", - "\n", - "✅ Completed Repeat 31/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 32/50, Seed=497\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.3348 (1.6258), Variance: 6607.7144, Range: [-213.9744, 205.6017]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0599, sigma²=172.6595, nugget=0.0020\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4774.43 | 69.0973 | 53.9581 | 0.2698 | 0.42s |\n", - "| OLS_sphere | 700 | 310.223 | 17.6131 | 8.4888 | 0.9526 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3534.62 | 59.4527 | 44.7084 | 0.4594 | 75.75s |\n", - "| DeepKriging_wendland* | -- | 3722.81 | 61.0149 | 44.1181 | 0.4306 | 34.48s |\n", - "| DeepKriging_mrts | 700 | 314.287 | 17.7281 | 9.8425 | 0.9519 | 45.96s |\n", - "| DeepKriging_sphere | 50 | 98.7377 | 9.9367 | 5.0934 | 0.9849 | 50.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 195.246 | 13.973 | 6.485 | 0.9701 | 45.84s |\n", - "| UniversalKriging | 500 | 265.529 | 16.295 | 8.1906 | 0.9594 | 6.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:29:43\n", - "\n", - "✅ Completed Repeat 32/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 33/50, Seed=529\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5563 (1.5943), Variance: 6354.5479, Range: [-195.4401, 205.5299]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=141.9134, nugget=37.1356\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5554.61 | 74.5292 | 56.3306 | 0.1914 | 0.44s |\n", - "| OLS_sphere | 700 | 176.602 | 13.2892 | 7.3644 | 0.9743 | 0.12s |\n", - "| DeepKriging_wendland | -- | 4144.5 | 64.3778 | 45.4344 | 0.3967 | 71.55s |\n", - "| DeepKriging_wendland* | -- | 3997.52 | 63.2259 | 44.5033 | 0.4181 | 25.70s |\n", - "| DeepKriging_mrts | 700 | 1839.9 | 42.8941 | 32.9055 | 0.7322 | 23.37s |\n", - "| DeepKriging_sphere | 50 | 41.135 | 6.4137 | 4.0236 | 0.994 | 45.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 47.8441 | 6.9169 | 3.9208 | 0.993 | 45.83s |\n", - "| UniversalKriging | 500 | 274.155 | 16.5576 | 10.6247 | 0.9601 | 1.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:35:25\n", - "\n", - "✅ Completed Repeat 33/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 34/50, Seed=562\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4604 (1.6178), Variance: 6543.4736, Range: [-203.1499, 204.4040]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0726, sigma²=199.5090, nugget=0.0017\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5323.36 | 72.9613 | 56.8765 | 0.1144 | 0.45s |\n", - "| OLS_sphere | 700 | 246.845 | 15.7113 | 9.1342 | 0.9589 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3377 | 58.112 | 43.971 | 0.4382 | 69.63s |\n", - "| DeepKriging_wendland* | -- | 3104.74 | 55.7202 | 40.7857 | 0.4835 | 29.74s |\n", - "| DeepKriging_mrts | 700 | 286.888 | 16.9378 | 11.7184 | 0.9523 | 42.35s |\n", - "| DeepKriging_sphere | 50 | 4963.46 | 70.4518 | 58.6337 | 0.1743 | 15.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 51.0917 | 7.1478 | 4.475 | 0.9915 | 62.48s |\n", - "| UniversalKriging | 500 | 140.301 | 11.8449 | 7.0454 | 0.9767 | 5.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:41:21\n", - "\n", - "✅ Completed Repeat 34/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 35/50, Seed=596\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4456 (1.6400), Variance: 6723.6465, Range: [-212.8170, 205.7791]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0605, sigma²=200.6093, nugget=0.0017\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4217.42 | 64.9417 | 50.1495 | 0.3164 | 0.46s |\n", - "| OLS_sphere | 700 | 174.187 | 13.198 | 7.7949 | 0.9718 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3309.83 | 57.5311 | 41.6425 | 0.4635 | 78.09s |\n", - "| DeepKriging_wendland* | -- | 3191.29 | 56.4915 | 41.5799 | 0.4828 | 26.58s |\n", - "| DeepKriging_mrts | 700 | 1756.61 | 41.9119 | 34.1191 | 0.7153 | 24.07s |\n", - "| DeepKriging_sphere | 50 | 28.0773 | 5.2988 | 3.6803 | 0.9954 | 44.37s |\n", - "| DeepKriging_sphere_Huber | 50 | 32.6622 | 5.7151 | 3.3591 | 0.9947 | 49.06s |\n", - "| UniversalKriging | 500 | 92.2026 | 9.6022 | 5.5796 | 0.9851 | 6.54s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:47:24\n", - "\n", - "✅ Completed Repeat 35/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 36/50, Seed=631\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.6565 (1.6100), Variance: 6480.2925, Range: [-214.8167, 205.1297]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0698, sigma²=169.3822, nugget=0.0007\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 148553 | 385.426 | 84.2516 | -20.8093 | 0.41s |\n", - "| OLS_sphere | 700 | 230.695 | 15.1887 | 9.0445 | 0.9661 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4397.18 | 66.3112 | 48.7544 | 0.3544 | 81.72s |\n", - "| DeepKriging_wendland* | -- | 4117.69 | 64.1692 | 47.5046 | 0.3955 | 30.04s |\n", - "| DeepKriging_mrts | 700 | 333.446 | 18.2605 | 11.0934 | 0.951 | 53.43s |\n", - "| DeepKriging_sphere | 50 | 48.7066 | 6.979 | 4.4749 | 0.9928 | 62.71s |\n", - "| DeepKriging_sphere_Huber | 50 | 54.4926 | 7.3819 | 5.0107 | 0.992 | 43.10s |\n", - "| UniversalKriging | 500 | 200.847 | 14.1721 | 7.4482 | 0.9705 | 6.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 19:54:19\n", - "\n", - "✅ Completed Repeat 36/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 37/50, Seed=667\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.7556 (1.6259), Variance: 6608.6699, Range: [-200.4348, 205.8158]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0455, sigma²=192.3739, nugget=0.0044\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5572.17 | 74.647 | 58.7928 | 0.1692 | 0.41s |\n", - "| OLS_sphere | 700 | 244.995 | 15.6523 | 8.7082 | 0.9635 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4394.91 | 66.2941 | 49.5675 | 0.3447 | 74.95s |\n", - "| DeepKriging_wendland* | -- | 4320.52 | 65.7306 | 47.5141 | 0.3558 | 29.29s |\n", - "| DeepKriging_mrts | 700 | 191.465 | 13.8371 | 8.0101 | 0.9715 | 62.17s |\n", - "| DeepKriging_sphere | 50 | 79.796 | 8.9329 | 5.6749 | 0.9881 | 47.06s |\n", - "| DeepKriging_sphere_Huber | 50 | 95.83 | 9.7893 | 6.2839 | 0.9857 | 43.70s |\n", - "| UniversalKriging | 500 | 242.737 | 15.58 | 8.3527 | 0.9638 | 6.65s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:01:02\n", - "\n", - "✅ Completed Repeat 37/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 38/50, Seed=704\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.6789 (1.5863), Variance: 6290.7720, Range: [-212.4979, 204.6321]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0695, sigma²=216.3590, nugget=0.0018\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 119066 | 345.06 | 77.048 | -19.3072 | 0.44s |\n", - "| OLS_sphere | 700 | 306.102 | 17.4958 | 9.3821 | 0.9478 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3495.26 | 59.1207 | 43.7962 | 0.4039 | 80.16s |\n", - "| DeepKriging_wendland* | -- | 3487.71 | 59.0569 | 41.9893 | 0.4052 | 34.17s |\n", - "| DeepKriging_mrts | 700 | 248.29 | 15.7572 | 8.4115 | 0.9577 | 59.83s |\n", - "| DeepKriging_sphere | 50 | 59.4368 | 7.7095 | 4.7459 | 0.9899 | 42.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 91.6059 | 9.5711 | 4.6104 | 0.9844 | 47.76s |\n", - "| UniversalKriging | 500 | 197.936 | 14.069 | 7.4566 | 0.9662 | 6.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:07:57\n", - "\n", - "✅ Completed Repeat 38/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 39/50, Seed=742\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.8602 (1.6001), Variance: 6400.4038, Range: [-212.9555, 202.7715]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0634, sigma²=185.0820, nugget=0.0011\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4335.96 | 65.848 | 50.9024 | 0.2426 | 0.41s |\n", - "| OLS_sphere | 700 | 261.059 | 16.1573 | 8.5081 | 0.9544 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3055.45 | 55.2761 | 40.8275 | 0.4663 | 76.34s |\n", - "| DeepKriging_wendland* | -- | 3075.57 | 55.4578 | 38.7669 | 0.4628 | 55.74s |\n", - "| DeepKriging_mrts | 700 | 250.515 | 15.8277 | 11.2121 | 0.9562 | 40.44s |\n", - "| DeepKriging_sphere | 50 | 66.6628 | 8.1647 | 4.9106 | 0.9884 | 43.18s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.2029 | 8.0127 | 4.3148 | 0.9888 | 66.58s |\n", - "| UniversalKriging | 500 | 165.693 | 12.8722 | 6.9049 | 0.9711 | 8.63s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:15:12\n", - "\n", - "✅ Completed Repeat 39/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 40/50, Seed=781\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.4344 (1.6370), Variance: 6699.2339, Range: [-209.6362, 207.2669]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0016, sigma²=43.4824, nugget=142.2412\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4980.13 | 70.5701 | 56.1839 | 0.2549 | 0.44s |\n", - "| OLS_sphere | 700 | 270.27 | 16.4399 | 9.1717 | 0.9596 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3898.07 | 62.4345 | 46.6399 | 0.4168 | 68.30s |\n", - "| DeepKriging_wendland* | -- | 4032.67 | 63.5033 | 47.1267 | 0.3966 | 31.77s |\n", - "| DeepKriging_mrts | 700 | 357.893 | 18.9181 | 12.6349 | 0.9465 | 44.20s |\n", - "| DeepKriging_sphere | 50 | 91.2765 | 9.5539 | 6.139 | 0.9863 | 41.25s |\n", - "| DeepKriging_sphere_Huber | 50 | 117.549 | 10.842 | 5.6743 | 0.9824 | 41.69s |\n", - "| UniversalKriging | 500 | 293.229 | 17.1239 | 10.6022 | 0.9561 | 1.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:21:30\n", - "\n", - "✅ Completed Repeat 40/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 41/50, Seed=821\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.9745 (1.6115), Variance: 6492.3730, Range: [-214.9372, 204.2731]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0562, sigma²=180.5594, nugget=0.0025\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 11104.3 | 105.377 | 62.8848 | -0.5467 | 0.46s |\n", - "| OLS_sphere | 700 | 127.918 | 11.3101 | 7.1709 | 0.9822 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3742.23 | 61.1738 | 45.2423 | 0.4787 | 72.78s |\n", - "| DeepKriging_wendland* | -- | 3932.9 | 62.7128 | 45.3078 | 0.4522 | 23.31s |\n", - "| DeepKriging_mrts | 700 | 138.825 | 11.7824 | 7.8091 | 0.9807 | 54.61s |\n", - "| DeepKriging_sphere | 50 | 76.1055 | 8.7238 | 6.3328 | 0.9894 | 33.84s |\n", - "| DeepKriging_sphere_Huber | 50 | 24.3004 | 4.9295 | 3.3268 | 0.9966 | 46.37s |\n", - "| UniversalKriging | 500 | 97.2506 | 9.8616 | 5.6591 | 0.9865 | 7.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:28:05\n", - "\n", - "✅ Completed Repeat 41/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 42/50, Seed=862\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.5372 (1.5995), Variance: 6395.8828, Range: [-207.5092, 203.6378]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0537, sigma²=193.6605, nugget=0.0044\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7964.71 | 89.2452 | 64.9756 | -0.2982 | 0.51s |\n", - "| OLS_sphere | 700 | 136.273 | 11.6736 | 6.9178 | 0.9778 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4550.18 | 67.455 | 50.8478 | 0.2583 | 72.00s |\n", - "| DeepKriging_wendland* | -- | 4080.56 | 63.8793 | 46.9541 | 0.3349 | 32.57s |\n", - "| DeepKriging_mrts | 700 | 2536.62 | 50.3649 | 37.6748 | 0.5865 | 21.63s |\n", - "| DeepKriging_sphere | 50 | 70.4757 | 8.395 | 5.4343 | 0.9885 | 42.54s |\n", - "| DeepKriging_sphere_Huber | 50 | 47.7202 | 6.908 | 4.1678 | 0.9922 | 50.66s |\n", - "| UniversalKriging | 500 | 149.761 | 12.2377 | 7.0635 | 0.9756 | 6.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:34:27\n", - "\n", - "✅ Completed Repeat 42/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 43/50, Seed=904\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.1112 (1.6093), Variance: 6474.6240, Range: [-209.5871, 204.0871]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0593, sigma²=192.4807, nugget=0.0019\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 7309.68 | 85.4967 | 62.9699 | -0.0558 | 0.41s |\n", - "| OLS_sphere | 700 | 345.211 | 18.5798 | 9.2509 | 0.9501 | 0.10s |\n", - "| DeepKriging_wendland | -- | 4889.75 | 69.9267 | 52.6586 | 0.2937 | 73.03s |\n", - "| DeepKriging_wendland* | -- | 4635.41 | 68.0838 | 49.8636 | 0.3304 | 30.33s |\n", - "| DeepKriging_mrts | 700 | 248.185 | 15.7539 | 9.1617 | 0.9642 | 54.90s |\n", - "| DeepKriging_sphere | 50 | 97.5705 | 9.8778 | 5.5125 | 0.9859 | 37.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 89.829 | 9.4778 | 5.1044 | 0.987 | 36.13s |\n", - "| UniversalKriging | 500 | 318.259 | 17.8398 | 7.8336 | 0.954 | 4.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:41:06\n", - "\n", - "✅ Completed Repeat 43/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 44/50, Seed=947\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.6344 (1.6029), Variance: 6423.3857, Range: [-206.6412, 207.5016]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0644, sigma²=205.6019, nugget=0.0027\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5553.51 | 74.5219 | 58.7567 | 0.1797 | 0.42s |\n", - "| OLS_sphere | 700 | 373.384 | 19.3231 | 10.032 | 0.9448 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3609 | 60.075 | 44.0079 | 0.4669 | 80.04s |\n", - "| DeepKriging_wendland* | -- | 3146.3 | 56.0919 | 39.0253 | 0.5352 | 55.35s |\n", - "| DeepKriging_mrts | 700 | 302.738 | 17.3994 | 9.3876 | 0.9553 | 58.08s |\n", - "| DeepKriging_sphere | 50 | 79.8302 | 8.9348 | 4.9178 | 0.9882 | 47.48s |\n", - "| DeepKriging_sphere_Huber | 50 | 86.2295 | 9.286 | 4.5813 | 0.9873 | 68.45s |\n", - "| UniversalKriging | 500 | 279.728 | 16.7251 | 8.3136 | 0.9587 | 8.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:49:09\n", - "\n", - "✅ Completed Repeat 44/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 45/50, Seed=991\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4507 (1.6115), Variance: 6492.4214, Range: [-213.9150, 204.9209]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0589, sigma²=201.6627, nugget=0.0027\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5397.99 | 73.471 | 55.9696 | 0.1896 | 0.42s |\n", - "| OLS_sphere | 700 | 224.792 | 14.9931 | 8.7359 | 0.9663 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4327.22 | 65.7816 | 47.1021 | 0.3504 | 77.10s |\n", - "| DeepKriging_wendland* | -- | 3765.8 | 61.3661 | 42.0396 | 0.4347 | 39.06s |\n", - "| DeepKriging_mrts | 700 | 144.207 | 12.0086 | 7.7272 | 0.9784 | 72.93s |\n", - "| DeepKriging_sphere | 50 | 43.4787 | 6.5938 | 4.0292 | 0.9935 | 52.64s |\n", - "| DeepKriging_sphere_Huber | 50 | 41.471 | 6.4398 | 3.8684 | 0.9938 | 66.03s |\n", - "| UniversalKriging | 500 | 170.59 | 13.061 | 7.0713 | 0.9744 | 7.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 20:57:08\n", - "\n", - "✅ Completed Repeat 45/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 46/50, Seed=1036\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4625 (1.6287), Variance: 6631.7808, Range: [-211.6098, 205.0226]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0645, sigma²=180.6392, nugget=0.0015\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5465.92 | 73.9319 | 58.7004 | 0.0968 | 0.40s |\n", - "| OLS_sphere | 700 | 193.916 | 13.9254 | 8.0358 | 0.968 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3976.48 | 63.0593 | 45.5814 | 0.3429 | 71.08s |\n", - "| DeepKriging_wendland* | -- | 3601.92 | 60.016 | 43.2713 | 0.4048 | 26.15s |\n", - "| DeepKriging_mrts | 700 | 150.102 | 12.2516 | 7.6391 | 0.9752 | 63.71s |\n", - "| DeepKriging_sphere | 50 | 53.9606 | 7.3458 | 5.0651 | 0.9911 | 40.11s |\n", - "| DeepKriging_sphere_Huber | 50 | 62.0025 | 7.8742 | 5.1658 | 0.9898 | 38.75s |\n", - "| UniversalKriging | 500 | 112.478 | 10.6056 | 5.9531 | 0.9814 | 4.06s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 21:04:06\n", - "\n", - "✅ Completed Repeat 46/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 47/50, Seed=1082\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.8704 (1.6016), Variance: 6412.9419, Range: [-209.6654, 202.2478]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0593, sigma²=186.1957, nugget=0.0054\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5736.04 | 75.7366 | 58.8934 | 0.1226 | 0.45s |\n", - "| OLS_sphere | 700 | 173.535 | 13.1733 | 7.6818 | 0.9735 | 0.13s |\n", - "| DeepKriging_wendland | -- | 4016.45 | 63.3755 | 47.9296 | 0.3857 | 69.87s |\n", - "| DeepKriging_wendland* | -- | 3878.74 | 62.2795 | 45.5281 | 0.4067 | 29.29s |\n", - "| DeepKriging_mrts | 700 | 144.291 | 12.0121 | 6.9163 | 0.9779 | 72.23s |\n", - "| DeepKriging_sphere | 50 | 60.7086 | 7.7916 | 4.9939 | 0.9907 | 43.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 59.6101 | 7.7208 | 4.0784 | 0.9909 | 72.44s |\n", - "| UniversalKriging | 500 | 154.897 | 12.4458 | 6.6576 | 0.9763 | 4.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 21:11:54\n", - "\n", - "✅ Completed Repeat 47/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 48/50, Seed=1129\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.5026 (1.5923), Variance: 6338.6567, Range: [-199.4267, 204.6384]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0691, sigma²=184.8841, nugget=0.0025\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4642.66 | 68.1371 | 50.7493 | 0.2294 | 0.44s |\n", - "| OLS_sphere | 700 | 315.827 | 17.7715 | 9.3778 | 0.9476 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3229.74 | 56.8308 | 40.154 | 0.4639 | 72.48s |\n", - "| DeepKriging_wendland* | -- | 3447.19 | 58.7128 | 41.3854 | 0.4278 | 35.57s |\n", - "| DeepKriging_mrts | 700 | 142.364 | 11.9316 | 7.5775 | 0.9764 | 58.37s |\n", - "| DeepKriging_sphere | 50 | 30.2365 | 5.4988 | 3.679 | 0.995 | 51.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 31.0193 | 5.5695 | 3.6077 | 0.9949 | 40.39s |\n", - "| UniversalKriging | 500 | 104.821 | 10.2382 | 6.3246 | 0.9826 | 4.40s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 21:19:13\n", - "\n", - "✅ Completed Repeat 48/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 49/50, Seed=1177\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9949 (1.6361), Variance: 6691.7002, Range: [-212.6875, 203.7853]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0013, sigma²=127.1519, nugget=50.1937\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4702.16 | 68.5723 | 54.0046 | 0.2663 | 0.35s |\n", - "| OLS_sphere | 700 | 305.438 | 17.4768 | 10.0094 | 0.9523 | 0.14s |\n", - "| DeepKriging_wendland | -- | 3500.64 | 59.1662 | 45.6324 | 0.4538 | 69.07s |\n", - "| DeepKriging_wendland* | -- | 2987.5 | 54.658 | 40.4587 | 0.5339 | 28.07s |\n", - "| DeepKriging_mrts | 700 | 239.429 | 15.4735 | 9.0313 | 0.9626 | 68.59s |\n", - "| DeepKriging_sphere | 50 | 37.7099 | 6.1408 | 3.7382 | 0.9941 | 72.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 55.6499 | 7.4599 | 4.2561 | 0.9913 | 52.85s |\n", - "| UniversalKriging | 500 | 400.678 | 20.017 | 11.75 | 0.9375 | 1.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 21:27:05\n", - "\n", - "✅ Completed Repeat 49/50\n", - "\n", - "================================================================================\n", - "🏃 Repeat 50/50, Seed=1226\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.0375 (1.5918), Variance: 6334.2856, Range: [-211.4633, 206.7199]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0669, sigma²=200.2051, nugget=0.0018\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 5068.97 | 71.1967 | 55.7884 | 0.1457 | 0.41s |\n", - "| OLS_sphere | 700 | 227.295 | 15.0763 | 8.6554 | 0.9617 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3592.94 | 59.9411 | 44.9367 | 0.3945 | 73.50s |\n", - "| DeepKriging_wendland* | -- | 3213.59 | 56.6886 | 40.9014 | 0.4584 | 29.50s |\n", - "| DeepKriging_mrts | 700 | 157.674 | 12.5568 | 7.4722 | 0.9734 | 58.40s |\n", - "| DeepKriging_sphere | 50 | 47.3022 | 6.8777 | 4.3475 | 0.992 | 45.39s |\n", - "| DeepKriging_sphere_Huber | 50 | 41.1627 | 6.4158 | 4.1851 | 0.9931 | 41.09s |\n", - "| UniversalKriging | 500 | 168.607 | 12.9849 | 6.9894 | 0.9716 | 2.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-09 21:34:19\n", - "\n", - "✅ Completed Repeat 50/50\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 500}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------------|---------------|-------------|-------------|\n", - "| OLS_wendland | 34867.49±101296.07 | 130.09±133.96 | 63.01±14.60 | -4.46±16.20 |\n", - "| OLS_sphere | 263.32±75.15 | 16.06±2.32 | 8.88±0.96 | 0.96±0.01 |\n", - "| DeepKriging_wendland | 3764.35±389.81 | 61.27±3.15 | 45.51±2.65 | 0.42±0.05 |\n", - "| DeepKriging_wendland* | 3594.26±397.43 | 59.86±3.28 | 43.43±2.84 | 0.45±0.06 |\n", - "| DeepKriging_mrts | 471.39±646.44 | 18.91±10.67 | 12.48±9.20 | 0.93±0.10 |\n", - "| DeepKriging_sphere | 279.98±1026.75 | 10.82±12.76 | 7.15±10.85 | 0.96±0.16 |\n", - "| DeepKriging_sphere_Huber | 80.60±40.16 | 8.73±2.09 | 4.86±0.82 | 0.99±0.01 |\n", - "| UniversalKriging | 192.23±68.96 | 13.65±2.43 | 7.56±1.34 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 8.73±2.09)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg256.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg256.ipynb deleted file mode 100644 index 84218db..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg256.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 00:46:13.783739: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770655573.798167 3769727 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770655573.802340 3769727 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770655573.813920 3769727 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770655573.813944 3769727 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770655573.813947 3769727 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770655573.813948 3769727 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 256.0 * x_cart\n", - " y = 256.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6847 (3.0726), Variance: 23602.4551, Range: [-304.3790, 400.9495]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 17451.9863 19489.6562 \n", - " 100 13758.9014 13980.5996 \n", - " 200 8412.2666 9090.4922 \n", - " 500 5612.0068 6926.3271 \n", - " 700 5082.3970 6215.3291 *\n", - " 1000 13091.7373 11452.3223 \n", - " 1400 222713.0312 120649.5078 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770655600.137369 3769727 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770655602.454043 3770167 service.cc:152] XLA service 0x7eb8a8008da0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770655602.454108 3770167 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770655602.870265 3770167 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770655605.543883 3770167 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7eb8d055a320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7eb8c8683250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 16511.6191 17083.8320 \n", - " 100 17333.6484 16845.9043 \n", - " 200 3722.1775 4382.3550 \n", - " 500 3561.2192 5291.1768 \n", - " 700 3064.3235 4912.3911 *\n", - " 1000 4306.9038 5409.7959 \n", - " 1400 7423.6611 7263.7339 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2289.7634 2394.4905 *\n", - " 100 2948.8669 2737.2129 \n", - " 200 2761.8706 2528.0474 \n", - " 500 3558.1060 3393.7280 \n", - " 700 3819.1941 4151.5532 \n", - " 1000 5673.4678 6209.2876 \n", - " 1400 10493.5645 9339.1562 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 30.2272 2833.3018 *\n", - " 100 32.1246 2763.5896 \n", - " 200 40.7788 3348.4856 \n", - " 500 41.9496 4090.1970 \n", - " 700 50.4980 5253.6890 \n", - " 1000 61.3065 7060.8999 \n", - " 1400 95.5915 13660.5820 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2758, sigma²=18480.5767, nugget=0.0737\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1963, sigma²=13000.3652, nugget=0.0523\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1010, sigma²=6838.9144, nugget=0.0383\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4210, sigma²=0.0229, nugget=2585.5952\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=204.9335, sigma²=0.0151, nugget=1783.4826\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.5849, sigma²=0.0075, nugget=981.0424\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.5262, sigma²=0.0047, nugget=528.8720\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3317.2339 3496.6024 \n", - " 100 3338.4761 3487.3148 \n", - " 200 3281.5373 3527.5422 *\n", - " 500 5611.9912 6926.3048 \n", - " 700 5082.4086 6215.3234 \n", - " 1000 13091.7648 11452.3556 \n", - " 1400 222709.5714 120648.9856 \n", - " Best order: 200\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1010, sigma²=6838.9144, nugget=0.0383\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 21252.7 | 145.783 | 120.035 | 0.1129 | 0.43s |\n", - "| OLS_sphere | 700 | 6215.33 | 78.8374 | 46.1107 | 0.7406 | 0.09s |\n", - "| DeepKriging_wendland | -- | 15665 | 125.16 | 99.9094 | 0.3462 | 76.10s |\n", - "| DeepKriging_wendland* | -- | 16460.5 | 128.298 | 97.6997 | 0.313 | 27.25s |\n", - "| DeepKriging_mrts | 700 | 5043.37 | 71.0167 | 37.1848 | 0.7895 | 52.25s |\n", - "| DeepKriging_sphere | 50 | 2385.17 | 48.8382 | 25.8536 | 0.9004 | 38.14s |\n", - "| DeepKriging_sphere_Huber | 50 | 2764.82 | 52.5815 | 25.6871 | 0.8846 | 43.01s |\n", - "| UniversalKriging | 200 | 3527.54 | 59.3931 | 34.5881 | 0.8528 | 3.88s |\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:05:07\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 17.5563 (3.1046), Variance: 24096.7520, Range: [-300.8167, 399.5595]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1070, sigma²=6728.9098, nugget=4.3144\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------------|-----------|----------|----------|--------|\n", - "| OLS_wendland | -- | 1.3481e+06 | 1161.08 | 203.802 | -54.6066 | 0.45s |\n", - "| OLS_sphere | 700 | 5790.9 | 76.098 | 46.6806 | 0.7611 | 0.10s |\n", - "| DeepKriging_wendland | -- | 17904.6 | 133.808 | 108.932 | 0.2615 | 68.53s |\n", - "| DeepKriging_wendland* | -- | 18984.9 | 137.786 | 107.266 | 0.2169 | 28.07s |\n", - "| DeepKriging_mrts | 700 | 4046.7 | 63.6137 | 34.6587 | 0.8331 | 53.03s |\n", - "| DeepKriging_sphere | 50 | 2559.36 | 50.5901 | 24.7483 | 0.8944 | 59.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 2800 | 52.915 | 27.01 | 0.8845 | 54.23s |\n", - "| UniversalKriging | 200 | 3286.97 | 57.3321 | 34.6744 | 0.8644 | 2.62s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:10:08\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.3929 (3.0664), Variance: 23506.5137, Range: [-305.7543, 391.9998]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1023, sigma²=6551.4797, nugget=4.7647\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 22275.2 | 149.249 | 118.216 | 0.0521 | 0.40s |\n", - "| OLS_sphere | 700 | 4701.52 | 68.5676 | 38.4573 | 0.7999 | 0.09s |\n", - "| DeepKriging_wendland | -- | 16299.3 | 127.669 | 100.971 | 0.3064 | 75.16s |\n", - "| DeepKriging_wendland* | -- | 16647.7 | 129.026 | 100.085 | 0.2916 | 30.89s |\n", - "| DeepKriging_mrts | 700 | 10403.3 | 101.996 | 80.8477 | 0.5573 | 24.24s |\n", - "| DeepKriging_sphere | 50 | 1633.8 | 40.4203 | 20.5225 | 0.9305 | 45.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 2147.05 | 46.3363 | 22.7899 | 0.9086 | 52.72s |\n", - "| UniversalKriging | 200 | 2442.22 | 49.4189 | 27.9712 | 0.8961 | 2.72s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:14:34\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.7112 (3.0938), Variance: 23928.5000, Range: [-306.8849, 399.8038]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1029, sigma²=6871.1481, nugget=0.0424\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|---------|--------|\n", - "| OLS_wendland | -- | 23154.3 | 152.165 | 119.978 | -0.009 | 0.39s |\n", - "| OLS_sphere | 700 | 5125.99 | 71.596 | 44.3448 | 0.7766 | 0.10s |\n", - "| DeepKriging_wendland | -- | 17403.8 | 131.924 | 104.653 | 0.2416 | 72.19s |\n", - "| DeepKriging_wendland* | -- | 17917.6 | 133.857 | 101.889 | 0.2192 | 31.56s |\n", - "| DeepKriging_mrts | 700 | 4818.87 | 69.4181 | 48.2515 | 0.79 | 36.69s |\n", - "| DeepKriging_sphere | 50 | 1539.71 | 39.2391 | 23.0624 | 0.9329 | 48.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 2312.26 | 48.086 | 23.4681 | 0.8992 | 45.78s |\n", - "| UniversalKriging | 200 | 3025.51 | 55.0046 | 34.7676 | 0.8682 | 1.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:19:10\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 22.0314 (3.1108), Variance: 24192.9062, Range: [-300.8152, 399.5305]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1060, sigma²=7318.3897, nugget=0.0795\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|----------|---------|--------|\n", - "| OLS_wendland | -- | 261028 | 510.909 | 146.322 | -9.5018 | 0.45s |\n", - "| OLS_sphere | 700 | 6021.42 | 77.5978 | 44.2605 | 0.7577 | 0.10s |\n", - "| DeepKriging_wendland | -- | 14098.1 | 118.735 | 91.4569 | 0.4328 | 49.44s |\n", - "| DeepKriging_wendland* | -- | 15039.1 | 122.634 | 93.5388 | 0.3949 | 19.23s |\n", - "| DeepKriging_mrts | 700 | 3712.51 | 60.9304 | 34.3306 | 0.8506 | 30.69s |\n", - "| DeepKriging_sphere | 50 | 1834.25 | 42.8281 | 21.4092 | 0.9262 | 32.82s |\n", - "| DeepKriging_sphere_Huber | 50 | 2565.71 | 50.6529 | 24.5239 | 0.8968 | 32.00s |\n", - "| UniversalKriging | 200 | 2722.12 | 52.1739 | 30.7644 | 0.8905 | 5.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:22:44\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.3624 (3.1190), Variance: 24319.8379, Range: [-305.1224, 399.7820]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1238, sigma²=7421.2330, nugget=0.0278\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|---------|--------|\n", - "| OLS_wendland | -- | 42778.9 | 206.831 | 127.174 | -0.7091 | 0.42s |\n", - "| OLS_sphere | 700 | 5008.06 | 70.7676 | 40.2215 | 0.7999 | 0.09s |\n", - "| DeepKriging_wendland | -- | 24038.7 | 155.044 | 104.169 | 0.0396 | 42.26s |\n", - "| DeepKriging_wendland* | -- | 16565.1 | 128.705 | 101.779 | 0.3382 | 16.19s |\n", - "| DeepKriging_mrts | 700 | 2258.25 | 47.5211 | 29.0141 | 0.9098 | 50.69s |\n", - "| DeepKriging_sphere | 50 | 1477.79 | 38.4421 | 22.1649 | 0.941 | 35.39s |\n", - "| DeepKriging_sphere_Huber | 50 | 1376.66 | 37.1034 | 20.756 | 0.945 | 47.43s |\n", - "| UniversalKriging | 200 | 2551.46 | 50.5119 | 30.6069 | 0.8981 | 3.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:26:48\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 18.1780 (3.1251), Variance: 24415.6855, Range: [-300.9675, 396.4040]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1138, sigma²=7262.1726, nugget=0.0604\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 23002 | 151.664 | 121.331 | 0.0247 | 0.45s |\n", - "| OLS_sphere | 700 | 5823.12 | 76.3094 | 42.6871 | 0.7531 | 0.09s |\n", - "| DeepKriging_wendland | -- | 16472.2 | 128.344 | 101.003 | 0.3015 | 46.87s |\n", - "| DeepKriging_wendland* | -- | 14367.6 | 119.865 | 89.834 | 0.3908 | 19.15s |\n", - "| DeepKriging_mrts | 700 | 5369.46 | 73.2766 | 40.9285 | 0.7723 | 40.40s |\n", - "| DeepKriging_sphere | 50 | 2219.24 | 47.1089 | 25.285 | 0.9059 | 29.83s |\n", - "| DeepKriging_sphere_Huber | 50 | 2291.11 | 47.8656 | 23.954 | 0.9029 | 41.71s |\n", - "| UniversalKriging | 200 | 3343.38 | 57.822 | 32.4864 | 0.8582 | 0.69s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:30:36\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 14.2095 (3.0754), Variance: 23645.5684, Range: [-304.5895, 394.7179]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1046, sigma²=6819.3863, nugget=0.0578\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 22012.8 | 148.367 | 120.304 | 0.0432 | 0.41s |\n", - "| OLS_sphere | 700 | 4403.24 | 66.3569 | 38.0962 | 0.8086 | 0.09s |\n", - "| DeepKriging_wendland | -- | 14307.3 | 119.613 | 94.0246 | 0.3781 | 45.07s |\n", - "| DeepKriging_wendland* | -- | 13462.5 | 116.028 | 89.3453 | 0.4148 | 19.49s |\n", - "| DeepKriging_mrts | 700 | 2751.96 | 52.4592 | 32.2116 | 0.8804 | 28.08s |\n", - "| DeepKriging_sphere | 50 | 21405.4 | 146.306 | 122.271 | 0.0696 | 13.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 1729.54 | 41.5877 | 20.7109 | 0.9248 | 46.41s |\n", - "| UniversalKriging | 200 | 2640.02 | 51.3811 | 30.1328 | 0.8852 | 3.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:34:06\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 16.7044 (3.1520), Variance: 24837.3184, Range: [-300.7209, 396.1819]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1062, sigma²=6441.6866, nugget=0.0379\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 21438.5 | 146.419 | 117.09 | 0.1059 | 0.41s |\n", - "| OLS_sphere | 700 | 4533.13 | 67.3285 | 39.8169 | 0.811 | 0.09s |\n", - "| DeepKriging_wendland | -- | 16318 | 127.742 | 103.125 | 0.3195 | 45.18s |\n", - "| DeepKriging_wendland* | -- | 14537 | 120.57 | 94.6781 | 0.3938 | 23.56s |\n", - "| DeepKriging_mrts | 700 | 2834.24 | 53.2376 | 29.5084 | 0.8818 | 38.76s |\n", - "| DeepKriging_sphere | 50 | 1787.51 | 42.279 | 22.7802 | 0.9255 | 31.93s |\n", - "| DeepKriging_sphere_Huber | 50 | 1806.03 | 42.4975 | 20.975 | 0.9247 | 51.16s |\n", - "| UniversalKriging | 200 | 2071.06 | 45.5089 | 27.0734 | 0.9136 | 3.70s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:38:19\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 19.6087 (3.0578), Variance: 23375.7559, Range: [-301.2854, 399.4953]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0962, sigma²=6658.5113, nugget=0.0535\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|----------|----------|--------|--------|\n", - "| OLS_wendland | -- | 21448.7 | 146.454 | 117.689 | 0.0708 | 0.42s |\n", - "| OLS_sphere | 700 | 4414.84 | 66.4442 | 40.1667 | 0.8087 | 0.10s |\n", - "| DeepKriging_wendland | -- | 15831.3 | 125.822 | 98.2968 | 0.3142 | 47.84s |\n", - "| DeepKriging_wendland* | -- | 14785.7 | 121.596 | 94.198 | 0.3595 | 25.09s |\n", - "| DeepKriging_mrts | 700 | 2847.2 | 53.3592 | 32.1897 | 0.8767 | 36.26s |\n", - "| DeepKriging_sphere | 50 | 1436.56 | 37.9019 | 20.2682 | 0.9378 | 34.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 1453.46 | 38.1243 | 20.344 | 0.937 | 36.24s |\n", - "| UniversalKriging | 200 | 2276.27 | 47.7103 | 29.0004 | 0.9014 | 3.91s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 01:42:19\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------------|---------------|--------------|-------------|\n", - "| OLS_wendland | 180649.12±395526.89 | 291.89±308.95 | 131.19±25.56 | -6.44±16.30 |\n", - "| OLS_sphere | 5203.75±665.56 | 71.99±4.60 | 42.08±2.98 | 0.78±0.03 |\n", - "| DeepKriging_wendland | 16833.82±2651.48 | 129.39±9.65 | 100.65±4.88 | 0.29±0.10 |\n", - "| DeepKriging_wendland* | 15876.77±1644.02 | 125.84±6.48 | 97.03±5.45 | 0.33±0.07 |\n", - "| DeepKriging_mrts | 4408.58±2243.46 | 64.68±14.99 | 39.91±14.69 | 0.81±0.10 |\n", - "| DeepKriging_sphere | 3827.88±5870.91 | 53.40±31.25 | 32.84±29.87 | 0.84±0.26 |\n", - "| DeepKriging_sphere_Huber | 2124.67±489.03 | 45.78±5.41 | 23.02±2.20 | 0.91±0.02 |\n", - "| UniversalKriging | 2788.66±462.43 | 52.63±4.38 | 31.21±2.68 | 0.88±0.02 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere_Huber (RMSE: 45.78±5.41)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg60.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg60.ipynb deleted file mode 100644 index 01aa98b..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg60.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 23:46:18.507132: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770738378.521089 2634695 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770738378.525071 2634695 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770738378.536387 2634695 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770738378.536412 2634695 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770738378.536413 2634695 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770738378.536414 2634695 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 60.0 * x_cart\n", - " y = 60.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -3.8718 (0.9441), Variance: 2228.3479, Range: [-121.8139, 121.3869]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 374.6077 332.5887 \n", - " 100 84.0011 78.7625 \n", - " 200 17.6920 10.4213 \n", - " 500 7.7963 4.1819 *\n", - " 700 8.9862 4.1580 \n", - " 1000 40.5052 14.9377 \n", - " 1400 828.9690 115.4321 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770738408.494782 2634695 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770738410.987875 2635170 service.cc:152] XLA service 0x7e05cc00b010 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770738410.987943 2635170 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770738411.418124 2635170 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770738414.230734 2635170 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7e05dc4eedd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7e05d4573130> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 498.1439 467.6924 \n", - " 100 423.4491 473.0262 \n", - " 200 256.0278 244.9088 \n", - " 500 438.8108 502.5382 \n", - " 700 17.7870 14.8871 *\n", - " 1000 380.8632 486.8907 \n", - " 1400 385.9572 428.0511 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 13.8392 11.5615 \n", - " 100 8.4693 6.6868 *\n", - " 200 8.5435 6.1397 \n", - " 500 390.7777 401.9280 \n", - " 700 525.5779 594.8137 \n", - " 1000 177.9384 315.3433 \n", - " 1400 1361.4075 1484.5695 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.3453 6.9110 *\n", - " 100 13.5407 216.8084 \n", - " 200 8.9796 77.5947 \n", - " 500 2.0747 11.2568 \n", - " 700 3.7102 34.6495 \n", - " 1000 7.1988 343.6040 \n", - " 1400 18.1856 926.8869 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.8429, sigma²=1382.9867, nugget=0.0001\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6439, sigma²=147.5788, nugget=0.0001\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0767, sigma²=14.6923, nugget=0.0013\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=22.2817, sigma²=0.0000, nugget=5.6694\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3461, sigma²=0.0000, nugget=4.4448\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.4422, sigma²=0.0000, nugget=2.8991\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9590, sigma²=0.0000, nugget=1.7598\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 8.7034 3.3165 \n", - " 100 8.5953 3.0470 \n", - " 200 8.1866 2.9736 \n", - " 500 7.7964 4.1820 *\n", - " 700 8.9863 4.1580 \n", - " 1000 40.5053 14.9376 \n", - " 1400 829.0155 115.4265 \n", - " Best order: 500\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=22.2817, sigma²=0.0000, nugget=5.6694\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1663.58 | 40.787 | 30.741 | 0.3353 | 0.38s |\n", - "| OLS_sphere | 500 | 4.1819 | 2.045 | 1.0979 | 0.9983 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1077.25 | 32.8216 | 22.9619 | 0.5696 | 49.74s |\n", - "| DeepKriging_wendland* | -- | 1185.5 | 34.4311 | 22.684 | 0.5263 | 18.93s |\n", - "| DeepKriging_mrts | 700 | 18.2668 | 4.274 | 2.524 | 0.9927 | 37.60s |\n", - "| DeepKriging_sphere | 100 | 3.7795 | 1.9441 | 1.3403 | 0.9985 | 30.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 6.7496 | 2.598 | 1.6216 | 0.9973 | 30.90s |\n", - "| UniversalKriging | 500 | 4.182 | 2.045 | 1.0979 | 0.9983 | 2.76s |\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 23:59:49\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -4.0004 (0.9441), Variance: 2228.3379, Range: [-123.6285, 124.2964]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=326.4118, sigma²=0.0000, nugget=6.6202\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8120.53 | 90.114 | 35.5147 | -2.4688 | 0.41s |\n", - "| OLS_sphere | 500 | 5.2229 | 2.2854 | 1.2811 | 0.9978 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1092.21 | 33.0486 | 23.582 | 0.5334 | 44.85s |\n", - "| DeepKriging_wendland* | -- | 982.848 | 31.3504 | 21.1349 | 0.5802 | 20.46s |\n", - "| DeepKriging_mrts | 700 | 24.4242 | 4.9421 | 3.4868 | 0.9896 | 36.03s |\n", - "| DeepKriging_sphere | 100 | 3.8505 | 1.9623 | 1.3015 | 0.9984 | 33.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 6.4167 | 2.5331 | 1.5382 | 0.9973 | 30.60s |\n", - "| UniversalKriging | 500 | 5.223 | 2.2854 | 1.2811 | 0.9978 | 2.66s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:03:13\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -4.4462 (0.9814), Variance: 2407.7671, Range: [-123.6106, 120.7751]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.4059, sigma²=0.0000, nugget=6.0198\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2945.54 | 54.2728 | 30.1136 | -0.2791 | 0.39s |\n", - "| OLS_sphere | 500 | 17.287 | 4.1578 | 1.5368 | 0.9925 | 0.06s |\n", - "| DeepKriging_wendland | -- | 782.794 | 27.9785 | 20.3653 | 0.6601 | 47.00s |\n", - "| DeepKriging_wendland* | -- | 704.578 | 26.5439 | 18.3154 | 0.694 | 19.04s |\n", - "| DeepKriging_mrts | 700 | 184.369 | 13.5783 | 11.1171 | 0.9199 | 20.58s |\n", - "| DeepKriging_sphere | 100 | 14.6577 | 3.8285 | 1.6835 | 0.9936 | 35.84s |\n", - "| DeepKriging_sphere_Huber | 50 | 405.942 | 20.148 | 14.6167 | 0.8237 | 14.97s |\n", - "| UniversalKriging | 500 | 17.287 | 4.1578 | 1.5368 | 0.9925 | 2.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:06:11\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -3.7889 (0.9492), Variance: 2252.6167, Range: [-123.4513, 126.5592]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=56.6056, sigma²=0.0000, nugget=4.9403\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2479.08 | 49.7904 | 30.5509 | -0.082 | 0.38s |\n", - "| OLS_sphere | 500 | 8.0993 | 2.8459 | 1.3324 | 0.9965 | 0.07s |\n", - "| DeepKriging_wendland | -- | 802.421 | 28.327 | 19.9722 | 0.6498 | 50.14s |\n", - "| DeepKriging_wendland* | -- | 724.422 | 26.9151 | 17.7717 | 0.6838 | 17.83s |\n", - "| DeepKriging_mrts | 700 | 365.819 | 19.1264 | 14.1677 | 0.8403 | 18.15s |\n", - "| DeepKriging_sphere | 100 | 6.057 | 2.4611 | 1.2088 | 0.9974 | 38.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 5.8032 | 2.409 | 1.39 | 0.9975 | 29.54s |\n", - "| UniversalKriging | 500 | 8.0992 | 2.8459 | 1.3324 | 0.9965 | 0.58s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:09:27\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -3.1378 (0.9723), Variance: 2363.5488, Range: [-123.0478, 125.4565]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.9285, sigma²=0.0000, nugget=4.4403\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8790.51 | 93.7577 | 33.0112 | -2.7275 | 0.38s |\n", - "| OLS_sphere | 500 | 9.4792 | 3.0788 | 1.4352 | 0.996 | 0.06s |\n", - "| DeepKriging_wendland | -- | 791.557 | 28.1346 | 20.1814 | 0.6644 | 44.62s |\n", - "| DeepKriging_wendland* | -- | 673.899 | 25.9596 | 17.2657 | 0.7142 | 19.71s |\n", - "| DeepKriging_mrts | 700 | 260.822 | 16.15 | 10.7047 | 0.8894 | 21.40s |\n", - "| DeepKriging_sphere | 100 | 25.6013 | 5.0598 | 3.6686 | 0.9891 | 25.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.4736 | 3.0779 | 2.0721 | 0.996 | 26.54s |\n", - "| UniversalKriging | 500 | 9.4792 | 3.0788 | 1.4352 | 0.996 | 3.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:12:29\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -3.6918 (0.9412), Variance: 2214.7603, Range: [-124.2898, 125.2375]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6541, sigma²=0.0000, nugget=6.3411\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2545.02 | 50.4482 | 30.4054 | -0.2518 | 0.43s |\n", - "| OLS_sphere | 500 | 11.4156 | 3.3787 | 1.7377 | 0.9944 | 0.07s |\n", - "| DeepKriging_wendland | -- | 764.358 | 27.647 | 19.5289 | 0.624 | 47.74s |\n", - "| DeepKriging_wendland* | -- | 766.878 | 27.6926 | 18.5805 | 0.6228 | 18.57s |\n", - "| DeepKriging_mrts | 700 | 47.5208 | 6.8935 | 3.6895 | 0.9766 | 42.11s |\n", - "| DeepKriging_sphere | 100 | 6.5172 | 2.5529 | 1.4966 | 0.9968 | 38.36s |\n", - "| DeepKriging_sphere_Huber | 50 | 8.8158 | 2.9691 | 1.7214 | 0.9957 | 32.09s |\n", - "| UniversalKriging | 500 | 11.4156 | 3.3787 | 1.7377 | 0.9944 | 2.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:16:19\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.6715 (0.9425), Variance: 2220.7517, Range: [-119.4448, 124.5189]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=147.0279, sigma²=0.0000, nugget=3.5193\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1759.94 | 41.9516 | 31.2688 | 0.2361 | 0.39s |\n", - "| OLS_sphere | 500 | 8.7253 | 2.9539 | 1.2894 | 0.9962 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1073.85 | 32.7696 | 22.3753 | 0.5339 | 45.69s |\n", - "| DeepKriging_wendland* | -- | 1037.31 | 32.2073 | 21.125 | 0.5498 | 22.07s |\n", - "| DeepKriging_mrts | 700 | 36.0109 | 6.0009 | 4.1413 | 0.9844 | 28.98s |\n", - "| DeepKriging_sphere | 100 | 10.0178 | 3.1651 | 1.4459 | 0.9957 | 37.52s |\n", - "| DeepKriging_sphere_Huber | 50 | 471.617 | 21.7167 | 15.8962 | 0.7953 | 14.42s |\n", - "| UniversalKriging | 500 | 8.7253 | 2.9539 | 1.2894 | 0.9962 | 3.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:19:40\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -3.4855 (0.9332), Variance: 2176.9329, Range: [-122.7833, 123.7954]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0938, sigma²=0.0000, nugget=6.4985\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1632.17 | 40.4001 | 31.0687 | 0.3307 | 0.42s |\n", - "| OLS_sphere | 500 | 17.503 | 4.1837 | 1.7663 | 0.9928 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1172.08 | 34.2357 | 25.2925 | 0.5194 | 47.48s |\n", - "| DeepKriging_wendland* | -- | 1132.05 | 33.6459 | 24.1026 | 0.5358 | 17.35s |\n", - "| DeepKriging_mrts | 700 | 332.793 | 18.2426 | 14.513 | 0.8635 | 17.52s |\n", - "| DeepKriging_sphere | 100 | 15.2001 | 3.8987 | 1.9447 | 0.9938 | 34.79s |\n", - "| DeepKriging_sphere_Huber | 50 | 456.142 | 21.3575 | 15.9503 | 0.813 | 14.98s |\n", - "| UniversalKriging | 500 | 17.503 | 4.1837 | 1.7663 | 0.9928 | 2.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:22:48\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.9200 (0.9667), Variance: 2336.4556, Range: [-122.2650, 125.5591]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.6719, sigma²=0.0000, nugget=5.7331\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1287.36 | 35.8798 | 26.8336 | 0.4008 | 0.39s |\n", - "| OLS_sphere | 500 | 12.0854 | 3.4764 | 1.4065 | 0.9944 | 0.08s |\n", - "| DeepKriging_wendland | -- | 886.259 | 29.7701 | 21.563 | 0.5875 | 46.21s |\n", - "| DeepKriging_wendland* | -- | 886.595 | 29.7757 | 19.4601 | 0.5873 | 15.40s |\n", - "| DeepKriging_mrts | 700 | 54.8258 | 7.4044 | 3.9297 | 0.9745 | 46.63s |\n", - "| DeepKriging_sphere | 100 | 13.0623 | 3.6142 | 1.6121 | 0.9939 | 37.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 312.722 | 17.684 | 13.501 | 0.8544 | 15.07s |\n", - "| UniversalKriging | 500 | 12.0853 | 3.4764 | 1.4065 | 0.9944 | 3.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:26:30\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -4.1160 (0.9520), Variance: 2265.5601, Range: [-125.0190, 125.7955]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.3895, sigma²=0.0000, nugget=6.1505\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1392.92 | 37.3219 | 28.0346 | 0.4016 | 0.40s |\n", - "| OLS_sphere | 500 | 8.95 | 2.9917 | 1.3006 | 0.9962 | 0.07s |\n", - "| DeepKriging_wendland | -- | 900.198 | 30.0033 | 21.7432 | 0.6133 | 45.51s |\n", - "| DeepKriging_wendland* | -- | 812.009 | 28.4958 | 19.9742 | 0.6511 | 20.61s |\n", - "| DeepKriging_mrts | 700 | 669.485 | 25.8744 | 21.2031 | 0.7124 | 19.27s |\n", - "| DeepKriging_sphere | 100 | 5.6279 | 2.3723 | 1.3344 | 0.9976 | 34.08s |\n", - "| DeepKriging_sphere_Huber | 50 | 13.0188 | 3.6082 | 1.9597 | 0.9944 | 29.43s |\n", - "| UniversalKriging | 500 | 8.95 | 2.9917 | 1.3006 | 0.9962 | 2.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 00:29:58\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 100, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 500}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------------|-------------|------------|------------|\n", - "| OLS_wendland | 3261.67±2650.60 | 53.47±20.06 | 30.75±2.27 | -0.41±1.12 |\n", - "| OLS_sphere | 10.29±4.22 | 3.14±0.66 | 1.42±0.20 | 1.00±0.00 |\n", - "| DeepKriging_wendland | 934.30±146.44 | 30.47±2.38 | 21.76±1.74 | 0.60±0.05 |\n", - "| DeepKriging_wendland* | 890.61±174.92 | 29.70±2.90 | 20.04±2.10 | 0.61±0.07 |\n", - "| DeepKriging_mrts | 199.43±201.14 | 12.25±7.03 | 8.95±6.03 | 0.91±0.09 |\n", - "| DeepKriging_sphere | 10.44±6.50 | 3.09±0.96 | 1.70±0.69 | 1.00±0.00 |\n", - "| DeepKriging_sphere_Huber | 169.67±201.41 | 9.81±8.57 | 7.03±6.54 | 0.93±0.09 |\n", - "| UniversalKriging | 10.29±4.22 | 3.14±0.66 | 1.42±0.20 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 3.09±0.96)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64.ipynb deleted file mode 100644 index d01bcb1..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 10:44:16.750562: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770691456.766388 778491 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770691456.770929 778491 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770691456.783804 778491 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770691456.783831 778491 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770691456.783833 778491 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770691456.783835 778491 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 64.0 * x_cart\n", - " y = 64.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.0445 (0.9944), Variance: 2472.0532, Range: [-125.8477, 131.2870]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 562.2104 474.2316 \n", - " 100 145.1374 126.1519 \n", - " 200 24.6962 24.1966 \n", - " 500 15.3794 11.9683 *\n", - " 700 18.2700 9.9499 \n", - " 1000 23.7605 11.1025 \n", - " 1400 203.7644 592.1235 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770691486.666490 778491 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770691489.102096 778776 service.cc:152] XLA service 0x7ccaf40063b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770691489.102177 778776 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770691489.442756 778776 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770691491.975369 778776 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ccb6012b250> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7ccb4837beb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 606.5167 571.4130 \n", - " 100 13.6376 12.1703 *\n", - " 200 562.9031 514.3455 \n", - " 500 19.8132 14.8401 \n", - " 700 32.9488 20.5982 \n", - " 1000 574.2417 622.2029 \n", - " 1400 382.8658 343.9546 \n", - " Best order: 100\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 11.2315 8.7679 *\n", - " 100 11.4819 8.2311 \n", - " 200 13.0449 10.3679 \n", - " 500 518.2028 520.6010 \n", - " 700 621.3844 635.2644 \n", - " 1000 889.4126 1022.6111 \n", - " 1400 1456.2412 1448.6464 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.7800 9.6613 *\n", - " 100 1.7994 11.3984 \n", - " 200 10.4619 115.1547 \n", - " 500 3.1159 14.9992 \n", - " 700 4.9320 51.5736 \n", - " 1000 8.9221 335.3613 \n", - " 1400 22.6214 857.4859 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.5853, sigma²=1904.8614, nugget=0.0002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6890, sigma²=238.1127, nugget=0.0002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0727, sigma²=19.8430, nugget=0.0457\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=14.9544, sigma²=0.0000, nugget=7.1573\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9850, sigma²=0.0000, nugget=5.2968\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9970, sigma²=0.0000, nugget=3.2503\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=8.3128, sigma²=0.0000, nugget=0.9992\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 13.7642 9.1952 \n", - " 100 12.8133 8.5630 \n", - " 200 12.5713 9.4381 *\n", - " 500 15.3794 11.9684 \n", - " 700 18.2700 9.9499 \n", - " 1000 23.7606 11.1025 \n", - " 1400 203.7710 592.0956 \n", - " Best order: 200\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0727, sigma²=19.8430, nugget=0.0457\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1880.43 | 43.3639 | 32.6326 | 0.3062 | 0.35s |\n", - "| OLS_sphere | 500 | 11.9683 | 3.4595 | 1.5388 | 0.9956 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1182.44 | 34.3866 | 23.9861 | 0.5638 | 72.76s |\n", - "| DeepKriging_wendland* | -- | 1200.88 | 34.6537 | 23.023 | 0.557 | 28.17s |\n", - "| DeepKriging_mrts | 100 | 445.931 | 21.1171 | 16.6197 | 0.8355 | 21.48s |\n", - "| DeepKriging_sphere | 50 | 14.6055 | 3.8217 | 2.3921 | 0.9946 | 45.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 386.825 | 19.6679 | 13.764 | 0.8573 | 20.78s |\n", - "| UniversalKriging | 200 | 9.4381 | 3.0721 | 1.3797 | 0.9965 | 2.80s |\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:03:26\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.3284 (1.0020), Variance: 2509.9199, Range: [-125.8185, 133.6527]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1216, sigma²=25.7954, nugget=0.0293\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 33621 | 183.36 | 43.4911 | -11.6497 | 0.38s |\n", - "| OLS_sphere | 500 | 11.1845 | 3.3443 | 1.6294 | 0.9958 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1206.76 | 34.7385 | 25.5118 | 0.546 | 65.07s |\n", - "| DeepKriging_wendland* | -- | 1049.16 | 32.3907 | 22.2865 | 0.6053 | 26.98s |\n", - "| DeepKriging_mrts | 100 | 10.403 | 3.2254 | 2.129 | 0.9961 | 51.71s |\n", - "| DeepKriging_sphere | 50 | 9.7556 | 3.1234 | 2.2056 | 0.9963 | 33.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 10.7113 | 3.2728 | 1.7636 | 0.996 | 31.82s |\n", - "| UniversalKriging | 200 | 7.971 | 2.8233 | 1.4801 | 0.997 | 2.65s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:07:32\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.7836 (1.0343), Variance: 2674.4414, Range: [-125.9060, 131.4306]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1046, sigma²=24.8325, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3168.25 | 56.2872 | 32.27 | -0.2258 | 0.36s |\n", - "| OLS_sphere | 500 | 11.3863 | 3.3744 | 1.8069 | 0.9956 | 0.06s |\n", - "| DeepKriging_wendland | -- | 882.171 | 29.7014 | 21.3941 | 0.6587 | 51.00s |\n", - "| DeepKriging_wendland* | -- | 771.989 | 27.7847 | 19.7041 | 0.7013 | 21.04s |\n", - "| DeepKriging_mrts | 100 | 252.435 | 15.8882 | 12.2257 | 0.9023 | 19.46s |\n", - "| DeepKriging_sphere | 50 | 13.5682 | 3.6835 | 2.5574 | 0.9948 | 32.35s |\n", - "| DeepKriging_sphere_Huber | 50 | 11.8392 | 3.4408 | 1.8495 | 0.9954 | 30.29s |\n", - "| UniversalKriging | 200 | 10.8277 | 3.2906 | 1.5394 | 0.9958 | 2.71s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:10:46\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.6009 (1.0016), Variance: 2507.9319, Range: [-125.3122, 135.4222]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0769, sigma²=26.3225, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2762.11 | 52.5558 | 32.241 | -0.0455 | 0.39s |\n", - "| OLS_sphere | 500 | 7.1346 | 2.6711 | 1.4577 | 0.9973 | 0.06s |\n", - "| DeepKriging_wendland | -- | 895.684 | 29.928 | 20.7313 | 0.661 | 52.94s |\n", - "| DeepKriging_wendland* | -- | 837.482 | 28.9393 | 19.0372 | 0.683 | 19.58s |\n", - "| DeepKriging_mrts | 100 | 604.794 | 24.5926 | 17.6971 | 0.7711 | 16.62s |\n", - "| DeepKriging_sphere | 50 | 12.5414 | 3.5414 | 2.5182 | 0.9953 | 29.07s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.8001 | 3.1305 | 1.7318 | 0.9963 | 31.93s |\n", - "| UniversalKriging | 200 | 7.5878 | 2.7546 | 1.5335 | 0.9971 | 3.96s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:14:00\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4531 (1.0167), Variance: 2584.2148, Range: [-125.2583, 135.1093]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1263, sigma²=21.8790, nugget=0.0085\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10093.1 | 100.465 | 35.3579 | -2.9012 | 0.41s |\n", - "| OLS_sphere | 500 | 21.1391 | 4.5977 | 1.8438 | 0.9918 | 0.05s |\n", - "| DeepKriging_wendland | -- | 920.034 | 30.3321 | 21.5993 | 0.6444 | 46.27s |\n", - "| DeepKriging_wendland* | -- | 799.817 | 28.281 | 18.6048 | 0.6909 | 22.51s |\n", - "| DeepKriging_mrts | 100 | 25.5354 | 5.0533 | 3.2514 | 0.9901 | 38.72s |\n", - "| DeepKriging_sphere | 50 | 20.2046 | 4.495 | 1.9311 | 0.9922 | 36.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 435.194 | 20.8613 | 15.3962 | 0.8318 | 15.71s |\n", - "| UniversalKriging | 200 | 19.1983 | 4.3816 | 1.7163 | 0.9926 | 2.83s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:17:26\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.9713 (0.9865), Variance: 2433.0056, Range: [-125.3531, 133.4561]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0812, sigma²=28.6316, nugget=0.0008\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3069.45 | 55.4026 | 33.6286 | -0.3219 | 0.38s |\n", - "| OLS_sphere | 500 | 14.1891 | 3.7668 | 1.8791 | 0.9939 | 0.05s |\n", - "| DeepKriging_wendland | -- | 836.99 | 28.9308 | 20.0841 | 0.6395 | 53.59s |\n", - "| DeepKriging_wendland* | -- | 872.784 | 29.5429 | 20.5425 | 0.6241 | 20.07s |\n", - "| DeepKriging_mrts | 100 | 86.2822 | 9.2888 | 6.9962 | 0.9628 | 27.12s |\n", - "| DeepKriging_sphere | 50 | 13.0507 | 3.6126 | 2.1933 | 0.9944 | 36.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 7.7702 | 2.7875 | 1.4459 | 0.9967 | 36.58s |\n", - "| UniversalKriging | 200 | 8.9674 | 2.9946 | 1.4102 | 0.9961 | 3.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:21:11\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.1842 (0.9952), Variance: 2475.8503, Range: [-124.8824, 133.4447]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1612, sigma²=21.0826, nugget=0.0001\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 2491.28 | 49.9128 | 34.3867 | 0.0719 | 0.38s |\n", - "| OLS_sphere | 500 | 5.6705 | 2.3813 | 1.2131 | 0.9979 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1261.99 | 35.5245 | 24.8813 | 0.5299 | 47.68s |\n", - "| DeepKriging_wendland* | -- | 1200.09 | 34.6423 | 23.6164 | 0.5529 | 20.96s |\n", - "| DeepKriging_mrts | 100 | 10.2062 | 3.1947 | 2.3142 | 0.9962 | 45.52s |\n", - "| DeepKriging_sphere | 50 | 4.6173 | 2.1488 | 1.3362 | 0.9983 | 46.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 520.926 | 22.8238 | 16.2958 | 0.8059 | 16.89s |\n", - "| UniversalKriging | 200 | 4.9664 | 2.2285 | 1.0416 | 0.9981 | 4.14s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:25:03\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.7955 (0.9879), Variance: 2440.0864, Range: [-124.3024, 131.4163]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0673, sigma²=19.4959, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1894.33 | 43.5239 | 33.8773 | 0.3276 | 0.41s |\n", - "| OLS_sphere | 500 | 6.1999 | 2.49 | 1.4727 | 0.9978 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1234.52 | 35.1357 | 25.3437 | 0.5618 | 50.64s |\n", - "| DeepKriging_wendland* | -- | 1183.6 | 34.4035 | 23.6738 | 0.5799 | 20.34s |\n", - "| DeepKriging_mrts | 100 | 11.2031 | 3.3471 | 2.133 | 0.996 | 58.06s |\n", - "| DeepKriging_sphere | 50 | 12.0992 | 3.4784 | 1.7673 | 0.9957 | 41.44s |\n", - "| DeepKriging_sphere_Huber | 50 | 6.8941 | 2.6257 | 1.7737 | 0.9976 | 31.55s |\n", - "| UniversalKriging | 200 | 7.8343 | 2.799 | 1.5376 | 0.9972 | 3.29s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:29:20\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.0594 (1.0196), Variance: 2598.7717, Range: [-125.0845, 134.9061]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1069, sigma²=27.2416, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1503.82 | 38.7791 | 29.2469 | 0.3636 | 0.36s |\n", - "| OLS_sphere | 500 | 17.0747 | 4.1322 | 1.8227 | 0.9928 | 0.06s |\n", - "| DeepKriging_wendland | -- | 954.14 | 30.8891 | 21.6446 | 0.5962 | 50.39s |\n", - "| DeepKriging_wendland* | -- | 979.169 | 31.2917 | 20.3442 | 0.5856 | 23.08s |\n", - "| DeepKriging_mrts | 100 | 15.4095 | 3.9255 | 2.4312 | 0.9935 | 49.17s |\n", - "| DeepKriging_sphere | 50 | 10.1481 | 3.1856 | 1.7712 | 0.9957 | 33.51s |\n", - "| DeepKriging_sphere_Huber | 50 | 12.001 | 3.4642 | 2.0846 | 0.9949 | 28.52s |\n", - "| UniversalKriging | 200 | 12.266 | 3.5023 | 1.5295 | 0.9948 | 4.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:33:26\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.2916 (1.0037), Variance: 2518.4224, Range: [-127.1091, 134.9120]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0729, sigma²=25.3969, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1614.76 | 40.184 | 30.8272 | 0.4071 | 0.39s |\n", - "| OLS_sphere | 500 | 18.189 | 4.2649 | 1.8338 | 0.9933 | 0.07s |\n", - "| DeepKriging_wendland | -- | 989.567 | 31.4574 | 22.5809 | 0.6367 | 51.34s |\n", - "| DeepKriging_wendland* | -- | 881.245 | 29.6858 | 20.8411 | 0.6764 | 20.70s |\n", - "| DeepKriging_mrts | 100 | 510.32 | 22.5903 | 18.1868 | 0.8126 | 16.69s |\n", - "| DeepKriging_sphere | 50 | 14.1358 | 3.7598 | 2.294 | 0.9948 | 32.58s |\n", - "| DeepKriging_sphere_Huber | 50 | 629.88 | 25.0974 | 17.6656 | 0.7687 | 15.04s |\n", - "| UniversalKriging | 200 | 14.5817 | 3.8186 | 1.7092 | 0.9946 | 2.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 11:36:44\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_mrts': 100, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------------|-------------|------------|------------|\n", - "| OLS_wendland | 6209.86±9444.10 | 66.38±42.46 | 33.80±3.64 | -1.37±3.55 |\n", - "| OLS_sphere | 12.41±4.99 | 3.45±0.72 | 1.65±0.21 | 1.00±0.00 |\n", - "| DeepKriging_wendland | 1036.43±156.95 | 32.10±2.42 | 22.78±1.90 | 0.60±0.05 |\n", - "| DeepKriging_wendland* | 977.62±161.46 | 31.16±2.56 | 21.17±1.77 | 0.63±0.05 |\n", - "| DeepKriging_mrts | 197.25±225.47 | 11.22±8.44 | 8.40±6.66 | 0.93±0.08 |\n", - "| DeepKriging_sphere | 12.47±3.79 | 3.49±0.57 | 2.10±0.37 | 1.00±0.00 |\n", - "| DeepKriging_sphere_Huber | 203.18±243.91 | 10.72±9.40 | 7.38±6.92 | 0.92±0.09 |\n", - "| UniversalKriging | 10.36±3.89 | 3.17±0.58 | 1.49±0.18 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 3.17±0.58)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64_norm.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64_norm.ipynb deleted file mode 100644 index 15a71d0..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg64_norm.ipynb +++ /dev/null @@ -1,2072 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-11 13:00:18.610783: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770786018.625452 3774141 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770786018.629969 3774141 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770786018.642223 3774141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770786018.642251 3774141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770786018.642252 3774141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770786018.642253 3774141 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 64.0 * x_cart\n", - " y = 64.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - "\n", - " y_mean = np.mean(y_train_flat)\n", - " y_std = np.std(y_train_flat)\n", - "\n", - " y_train_scaled = (y_train_flat - y_mean) / y_std\n", - " y_val_scaled = (y_val_flat - y_mean) / y_std\n", - " y_test_scaled = (y_test_flat - y_mean) / y_std\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_scaled,\n", - " X_val_cont_flat, X_val_cat, y_val_scaled,\n", - " X_test_cont_flat, X_test_cat, y_test_scaled,\n", - " y_train_flat, y_val_flat, y_test_flat,\n", - " y_mean, y_std)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train_scaled,\n", - " X_val, X_val_cat, y_val_scaled,\n", - " X_test, X_test_cat, y_test_scaled,\n", - " y_train_orig, y_val_orig, y_test_orig,\n", - " y_mean, y_std):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train_scaled)\n", - " \n", - " y_pred_val_scaled = model.predict(X_val)\n", - " y_pred_test_scaled = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " \n", - " y_pred_val = y_pred_val_scaled * y_std + y_mean\n", - " y_pred_test = y_pred_test_scaled * y_std + y_mean\n", - " \n", - " val_loss = float(mean_squared_error(y_val_orig, y_pred_val))\n", - " \n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test_orig, y_pred_test)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_orig, y_pred_test)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_orig, y_pred_test)),\n", - " \"R2\": float(r2_score(y_test_orig, y_pred_test)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train_scaled\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val_scaled\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " y_pred_test_scaled = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " \n", - " y_pred_test = y_pred_test_scaled * y_std + y_mean\n", - " \n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(np.min(history.history[\"val_loss\"])),\n", - " \"MSPE\": float(mean_squared_error(y_test_orig, y_pred_test)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test_orig, y_pred_test)))),\n", - " \"MAE\": float(mean_absolute_error(y_test_orig, y_pred_test)),\n", - " \"R2\": float(r2_score(y_test_orig, y_pred_test)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " y_train = y_combined[train_idx].reshape(-1)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " # ========== 計算訓練集的統計量用於標準化 ==========\n", - " y_mean = np.mean(y_train)\n", - " y_std = np.std(y_train)\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred_scaled = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " y_pred = y_pred_scaled * y_std + y_mean\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred_scaled = model.predict(coords_test, X_test, return_centered=False)\n", - " y_pred = y_pred_scaled * y_std + y_mean\n", - " else:\n", - " y_pred_scaled = model.predict(X_test).reshape(-1)\n", - " y_pred = y_pred_scaled * y_std + y_mean\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.0445 (0.9944), Variance: 2472.0532, Range: [-125.8477, 131.2870]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 527.7554 427.5643 \n", - " 100 142.7206 127.6657 \n", - " 200 25.3092 28.5915 \n", - " 500 12.5815 12.8336 *\n", - " 700 13.7047 11.9684 \n", - " 1000 30.1250 19.1419 \n", - " 1400 452.0988 379.4556 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770786047.593002 3774141 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770786049.940446 3774469 service.cc:152] XLA service 0x654538d97dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770786049.940485 3774469 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770786050.303381 3774469 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770786051.862016 3774469 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d468039a3b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d467845beb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0434 75.3489 \n", - " 100 0.1950 414.5157 \n", - " 200 0.1998 524.1736 \n", - " 500 0.2625 705.1730 \n", - " 700 0.0128 21.6056 *\n", - " 1000 0.1113 203.7987 \n", - " 1400 0.0692 224.1712 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0052 10.7891 *\n", - " 100 0.0062 11.3449 \n", - " 200 0.0081 16.0055 \n", - " 500 0.0211 42.2025 \n", - " 700 0.0404 104.2109 \n", - " 1000 0.1022 223.2793 \n", - " 1400 0.3113 952.1674 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 0.0027 11.6384 *\n", - " 100 0.0032 12.7045 \n", - " 200 0.0040 15.2114 \n", - " 500 0.0095 47.7557 \n", - " 700 0.0196 67.1426 \n", - " 1000 0.0536 256.0339 \n", - " 1400 0.1486 947.5157 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=2.9540, sigma²=0.6392, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6979, sigma²=0.1014, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0862, sigma²=0.0098, nugget=0.0000\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=26.7015, sigma²=0.0000, nugget=0.0030\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7154, sigma²=0.0000, nugget=0.0022\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.6326, sigma²=0.0000, nugget=0.0014\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7246, sigma²=0.0000, nugget=0.0009\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 14.0060 9.0990 \n", - " 100 12.7906 8.4065 \n", - " 200 12.5613 9.4386 *\n", - " 500 12.5815 12.8335 \n", - " 700 13.7048 11.9683 \n", - " 1000 30.1251 19.1418 \n", - " 1400 452.0067 379.4246 \n", - " Best order: 200\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0862, sigma²=0.0098, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1880.43 | 43.3639 | 32.6326 | 0.3062 | 0.41s |\n", - "| OLS_sphere | 500 | 12.8336 | 3.5824 | 1.6783 | 0.9953 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1278.95 | 35.7625 | 24.5144 | 0.5281 | 53.08s |\n", - "| DeepKriging_wendland* | -- | 1097.16 | 33.1234 | 22.3346 | 0.5952 | 28.64s |\n", - "| DeepKriging_mrts | 700 | 431.292 | 20.7676 | 16.8456 | 0.8409 | 21.92s |\n", - "| DeepKriging_sphere | 50 | 12.387 | 3.5195 | 1.9277 | 0.9954 | 32.26s |\n", - "| DeepKriging_sphere_Huber | 50 | 11.887 | 3.4478 | 1.8476 | 0.9956 | 40.85s |\n", - "| UniversalKriging | 200 | 9.4386 | 3.0722 | 1.4103 | 0.9965 | 2.64s |\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:23:30\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.3284 (1.0020), Variance: 2509.9199, Range: [-125.8185, 133.6527]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1133, sigma²=0.0096, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 33620.9 | 183.36 | 43.4911 | -11.6497 | 0.39s |\n", - "| OLS_sphere | 500 | 8.3728 | 2.8936 | 1.4643 | 0.9968 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1261.42 | 35.5164 | 25.9788 | 0.5254 | 58.74s |\n", - "| DeepKriging_wendland* | -- | 1192.5 | 34.5326 | 24.3393 | 0.5513 | 27.49s |\n", - "| DeepKriging_mrts | 700 | 29.584 | 5.4391 | 3.3325 | 0.9889 | 67.98s |\n", - "| DeepKriging_sphere | 50 | 12.888 | 3.59 | 2.3416 | 0.9952 | 46.81s |\n", - "| DeepKriging_sphere_Huber | 50 | 1679.06 | 40.9763 | 31.6689 | 0.3683 | 16.33s |\n", - "| UniversalKriging | 200 | 7.0368 | 2.6527 | 1.3098 | 0.9974 | 2.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:27:45\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.7836 (1.0343), Variance: 2674.4414, Range: [-125.9060, 131.4306]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0929, sigma²=0.0081, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3168.25 | 56.2872 | 32.27 | -0.2258 | 0.39s |\n", - "| OLS_sphere | 500 | 10.5332 | 3.2455 | 1.6537 | 0.9959 | 0.07s |\n", - "| DeepKriging_wendland | -- | 951.427 | 30.8452 | 22.6549 | 0.6319 | 68.24s |\n", - "| DeepKriging_wendland* | -- | 797.103 | 28.233 | 19.4939 | 0.6916 | 32.28s |\n", - "| DeepKriging_mrts | 700 | 85.8161 | 9.2637 | 6.7693 | 0.9668 | 41.78s |\n", - "| DeepKriging_sphere | 50 | 1798.93 | 42.4138 | 33.7177 | 0.304 | 16.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 13.5859 | 3.6859 | 1.9559 | 0.9947 | 43.37s |\n", - "| UniversalKriging | 200 | 10.3061 | 3.2103 | 1.5066 | 0.996 | 2.26s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:31:46\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.6009 (1.0016), Variance: 2507.9319, Range: [-125.3122, 135.4222]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0741, sigma²=0.0100, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 2762.07 | 52.5554 | 32.2409 | -0.0455 | 0.41s |\n", - "| OLS_sphere | 500 | 7.9764 | 2.8242 | 1.512 | 0.997 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1001.38 | 31.6446 | 22.2755 | 0.621 | 73.30s |\n", - "| DeepKriging_wendland* | -- | 877.506 | 29.6227 | 19.8007 | 0.6679 | 31.10s |\n", - "| DeepKriging_mrts | 700 | 323.115 | 17.9754 | 14.5922 | 0.8777 | 25.05s |\n", - "| DeepKriging_sphere | 50 | 9.9111 | 3.1482 | 1.9336 | 0.9962 | 35.24s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.8865 | 3.1443 | 1.8684 | 0.9963 | 52.96s |\n", - "| UniversalKriging | 200 | 7.0555 | 2.6562 | 1.5247 | 0.9973 | 4.99s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:36:08\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.4531 (1.0167), Variance: 2584.2148, Range: [-125.2583, 135.1093]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1331, sigma²=0.0089, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 10093.1 | 100.465 | 35.3579 | -2.9012 | 0.40s |\n", - "| OLS_sphere | 500 | 23.2769 | 4.8246 | 2.0322 | 0.991 | 0.09s |\n", - "| DeepKriging_wendland | -- | 968.144 | 31.115 | 22.4049 | 0.6258 | 70.43s |\n", - "| DeepKriging_wendland* | -- | 931.125 | 30.5143 | 20.7801 | 0.6401 | 33.24s |\n", - "| DeepKriging_mrts | 700 | 45.1458 | 6.7191 | 3.7943 | 0.9826 | 77.74s |\n", - "| DeepKriging_sphere | 50 | 22.5286 | 4.7464 | 2.177 | 0.9913 | 55.82s |\n", - "| DeepKriging_sphere_Huber | 50 | 24.3252 | 4.9321 | 2.1295 | 0.9906 | 53.63s |\n", - "| UniversalKriging | 200 | 19.4094 | 4.4056 | 1.7799 | 0.9925 | 4.10s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:41:47\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.9713 (0.9865), Variance: 2433.0056, Range: [-125.3531, 133.4561]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0722, sigma²=0.0106, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3069.46 | 55.4027 | 33.6285 | -0.3219 | 0.40s |\n", - "| OLS_sphere | 500 | 14.1267 | 3.7585 | 1.8978 | 0.9939 | 0.09s |\n", - "| DeepKriging_wendland | -- | 928.505 | 30.4714 | 20.9829 | 0.6001 | 71.22s |\n", - "| DeepKriging_wendland* | -- | 893.957 | 29.8991 | 21.0737 | 0.615 | 33.94s |\n", - "| DeepKriging_mrts | 700 | 38.9123 | 6.238 | 3.3115 | 0.9832 | 72.49s |\n", - "| DeepKriging_sphere | 50 | 1520.51 | 38.9937 | 29.2243 | 0.3452 | 15.78s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.4064 | 3.067 | 1.7608 | 0.9959 | 47.76s |\n", - "| UniversalKriging | 200 | 9.2202 | 3.0365 | 1.5342 | 0.996 | 4.95s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:46:41\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.1842 (0.9952), Variance: 2475.8503, Range: [-124.8824, 133.4447]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1533, sigma²=0.0083, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 2491.28 | 49.9127 | 34.3867 | 0.0719 | 0.38s |\n", - "| OLS_sphere | 500 | 6.5575 | 2.5608 | 1.2733 | 0.9976 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1328.21 | 36.4446 | 24.9385 | 0.5052 | 76.33s |\n", - "| DeepKriging_wendland* | -- | 1271.61 | 35.6597 | 24.1955 | 0.5263 | 30.47s |\n", - "| DeepKriging_mrts | 700 | 265.51 | 16.2945 | 12.7371 | 0.9011 | 24.30s |\n", - "| DeepKriging_sphere | 50 | 5.9518 | 2.4396 | 1.7278 | 0.9978 | 44.15s |\n", - "| DeepKriging_sphere_Huber | 50 | 5.664 | 2.3799 | 1.5236 | 0.9979 | 46.35s |\n", - "| UniversalKriging | 200 | 5.4661 | 2.338 | 1.0433 | 0.998 | 3.85s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:51:16\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.7955 (0.9879), Variance: 2440.0864, Range: [-124.3024, 131.4163]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0819, sigma²=0.0095, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1894.33 | 43.5239 | 33.8773 | 0.3276 | 0.41s |\n", - "| OLS_sphere | 500 | 5.8887 | 2.4267 | 1.3153 | 0.9979 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1341.59 | 36.6278 | 26.2608 | 0.5238 | 54.24s |\n", - "| DeepKriging_wendland* | -- | 1203.51 | 34.6916 | 23.8172 | 0.5728 | 33.26s |\n", - "| DeepKriging_mrts | 700 | 47.6672 | 6.9041 | 5.0639 | 0.9831 | 40.43s |\n", - "| DeepKriging_sphere | 50 | 9.2811 | 3.0465 | 1.8658 | 0.9967 | 47.69s |\n", - "| DeepKriging_sphere_Huber | 50 | 8.6139 | 2.9349 | 1.7601 | 0.9969 | 45.43s |\n", - "| UniversalKriging | 200 | 6.9702 | 2.6401 | 1.443 | 0.9975 | 4.18s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 13:55:54\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.0594 (1.0196), Variance: 2598.7717, Range: [-125.0845, 134.9061]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1070, sigma²=0.0104, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1503.82 | 38.7791 | 29.2469 | 0.3636 | 0.41s |\n", - "| OLS_sphere | 500 | 15.9243 | 3.9905 | 1.8931 | 0.9933 | 0.05s |\n", - "| DeepKriging_wendland | -- | 1018.19 | 31.9091 | 23.3994 | 0.5691 | 58.06s |\n", - "| DeepKriging_wendland* | -- | 966.645 | 31.0909 | 20.5481 | 0.5909 | 33.01s |\n", - "| DeepKriging_mrts | 700 | 50.803 | 7.1276 | 4.5259 | 0.9785 | 53.29s |\n", - "| DeepKriging_sphere | 50 | 16.3726 | 4.0463 | 2.4596 | 0.9931 | 40.04s |\n", - "| DeepKriging_sphere_Huber | 50 | 13.7617 | 3.7097 | 2.0155 | 0.9942 | 51.46s |\n", - "| UniversalKriging | 200 | 11.2676 | 3.3567 | 1.4613 | 0.9952 | 5.30s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 14:00:51\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -2.2916 (1.0037), Variance: 2518.4224, Range: [-127.1091, 134.9120]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0787, sigma²=0.0111, nugget=0.0000\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1614.76 | 40.184 | 30.8273 | 0.4071 | 0.35s |\n", - "| OLS_sphere | 500 | 18.1593 | 4.2614 | 1.8031 | 0.9933 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1114.94 | 33.3907 | 24.1233 | 0.5906 | 58.35s |\n", - "| DeepKriging_wendland* | -- | 967.834 | 31.11 | 22.6636 | 0.6446 | 32.49s |\n", - "| DeepKriging_mrts | 700 | 91.5148 | 9.5663 | 7.3247 | 0.9664 | 38.35s |\n", - "| DeepKriging_sphere | 50 | 16.0586 | 4.0073 | 2.1222 | 0.9941 | 45.67s |\n", - "| DeepKriging_sphere_Huber | 50 | 16.1983 | 4.0247 | 1.9284 | 0.9941 | 52.58s |\n", - "| UniversalKriging | 200 | 15.4731 | 3.9336 | 1.7685 | 0.9943 | 2.52s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 14:05:42\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # ========== 標準化 y (使用訓練集的統計量) ==========\n", - " y_mean = np.mean(y_train)\n", - " y_std = np.std(y_train)\n", - " y_train_scaled = (y_train - y_mean) / y_std\n", - " y_val_scaled = (y_val - y_mean) / y_std\n", - " \n", - " # Train UniversalKriging (在標準化尺度)\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train_scaled, center_y=True)\n", - " \n", - " # Predict on validation set (標準化尺度)\n", - " y_pred_val_scaled = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " \n", - " # 反標準化\n", - " y_pred_val = y_pred_val_scaled * y_std + y_mean + uk_model.y_mean * y_std\n", - " \n", - " # Validation loss (原始尺度)\n", - " val_loss = mean_squared_error(y_val, y_pred_val)\n", - " \n", - " # Predict on test set (標準化尺度 -> 反標準化)\n", - " y_pred_test_scaled = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " y_pred_test = y_pred_test_scaled * y_std + y_mean\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " y_mean = np.mean(y_train)\n", - " y_std = np.std(y_train)\n", - " y_train_scaled = (y_train - y_mean) / y_std\n", - "\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train_scaled, center_y=True)\n", - "\n", - " y_pred_test_scaled = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " y_pred_test = y_pred_test_scaled * y_std + y_mean\n", - "\n", - " # 在原始尺度計算指標\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_mrts': 700, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-----------------|-------------|------------|------------|\n", - "| OLS_wendland | 6209.85±9444.08 | 66.38±42.46 | 33.80±3.64 | -1.37±3.55 |\n", - "| OLS_sphere | 12.36±5.32 | 3.44±0.74 | 1.65±0.24 | 1.00±0.00 |\n", - "| DeepKriging_wendland | 1119.28±158.22 | 33.37±2.35 | 23.75±1.62 | 0.57±0.05 |\n", - "| DeepKriging_wendland* | 1019.89±152.33 | 31.85±2.37 | 21.90±1.72 | 0.61±0.05 |\n", - "| DeepKriging_mrts | 140.94±136.86 | 10.63±5.29 | 7.83±4.77 | 0.95±0.05 |\n", - "| DeepKriging_sphere | 342.48±661.57 | 11.00±14.89 | 7.95±11.81 | 0.86±0.27 |\n", - "| DeepKriging_sphere_Huber | 179.24±499.96 | 7.23±11.27 | 4.85±8.94 | 0.93±0.19 |\n", - "| UniversalKriging | 10.16±4.10 | 3.13±0.61 | 1.48±0.20 | 1.00±0.00 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 3.13±0.61)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg70.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg70.ipynb deleted file mode 100644 index 1d24b69..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_all_model_egg70.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 17:03:39.517527: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770714219.534458 2020097 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770714219.538576 2020097 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770714219.550166 2020097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770714219.550196 2020097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770714219.550197 2020097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770714219.550199 2020097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 70.0 * x_cart\n", - " y = 70.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.4362 (1.0576), Variance: 2796.4089, Range: [-131.2211, 139.0561]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 819.4460 734.8011 \n", - " 100 246.3321 240.5580 \n", - " 200 41.9053 38.5672 \n", - " 500 14.4906 7.6482 *\n", - " 700 14.7269 9.4631 \n", - " 1000 35.6742 17.0987 \n", - " 1400 248.2077 529.8781 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770714249.207062 2020097 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770714251.733684 2020391 service.cc:152] XLA service 0x619fc4e33140 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770714251.733775 2020391 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770714252.111472 2020391 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770714254.866024 2020391 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb80c176680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7fb7fc12f520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 20.0439 11.6343 \n", - " 100 132.2263 153.6839 \n", - " 200 10.9601 7.9544 *\n", - " 500 102.2028 101.7754 \n", - " 700 301.5422 362.8962 \n", - " 1000 379.9594 368.8171 \n", - " 1400 347.2411 290.8416 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 8.1126 7.1048 *\n", - " 100 10.1923 6.3539 \n", - " 200 13.2164 7.6933 \n", - " 500 19.2212 21.5466 \n", - " 700 571.2189 596.8360 \n", - " 1000 297.1174 277.6819 \n", - " 1400 1061.5692 926.6975 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.6679 9.0172 *\n", - " 100 2.2262 8.8170 \n", - " 200 10.9707 155.0656 \n", - " 500 4.4678 24.5065 \n", - " 700 3.9590 40.3343 \n", - " 1000 10.3899 267.6801 \n", - " 1400 25.3751 897.4371 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.0725, sigma²=3090.6213, nugget=0.0002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0528, sigma²=500.3719, nugget=0.0002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1509, sigma²=42.6187, nugget=0.0002\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=93.7603, sigma²=0.0000, nugget=8.8125\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=11.8266, sigma²=0.0000, nugget=6.7960\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.3044, sigma²=0.0000, nugget=4.5774\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.9974, sigma²=0.0000, nugget=2.4322\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 11.9750 7.5754 \n", - " 100 10.7968 6.8271 \n", - " 200 9.4603 5.8798 *\n", - " 500 14.4907 7.6483 \n", - " 700 14.7269 9.4631 \n", - " 1000 35.6738 17.0988 \n", - " 1400 248.1934 529.8449 \n", - " Best order: 200\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1509, sigma²=42.6187, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 2240.85 | 47.3377 | 35.5599 | 0.2699 | 0.41s |\n", - "| OLS_sphere | 500 | 7.6482 | 2.7655 | 1.5296 | 0.9975 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1269.91 | 35.6358 | 24.7481 | 0.5863 | 72.08s |\n", - "| DeepKriging_wendland* | -- | 1222.52 | 34.9645 | 23.789 | 0.6017 | 25.94s |\n", - "| DeepKriging_mrts | 200 | 9.281 | 3.0465 | 2.1291 | 0.997 | 70.77s |\n", - "| DeepKriging_sphere | 50 | 7.3328 | 2.7079 | 1.9574 | 0.9976 | 42.12s |\n", - "| DeepKriging_sphere_Huber | 50 | 8.2215 | 2.8673 | 1.8374 | 0.9973 | 41.97s |\n", - "| UniversalKriging | 200 | 5.8798 | 2.4248 | 1.3356 | 0.9981 | 3.80s |\n" - ] - }, - { - "data": { - "image/png": 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", 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norNSmSvb2toiv/zlL+0YHEYLvfPOOxnVQkHbifdKZVwKGhteeeWVyOabbx5zP8ypsWxUmX5uyOW5MSz19fVJXVeehWI93wfdQxzf97///bjn8MILL8Q8vv/+978xx2zujT//+c+B+01l3Eq39ktmbI9nuwzaTryX97uJxj1HMm3INv3Pk0EvnucefPDBmG2cijZPp/2zpaWl17PM2WefHfPzQoRBjj+RccffzJkzA0UMDsEgMKgnM4ngSPE7XxBH8Qwx6XT88cAWxlGE8y/eBJgLjj/3QqwEEaY9cMYms69UHX9BInzPPfeM9AUEerKOAoT90qVLQx1fIsNxdXV15Nprr034MEWf58ErTN9ZbbXVEp4Dxoo5c+b0yfHnXnfeeWdg2/o/h2iNZXRIxvH38ccfJ2X05hX0YIDTNazB0b3OOOOMUOfKwwOvRNvDkJULjj8M2bEM2Jdffnnkvffei/kAmi3HX6yxqz8cfzgn4jkbgl4Yi8K2Vxhn9G9/+9s+O6t4rbrqqpElS5b02k7Qw9348eMDt+HmtaOOOirqfe6vTz/9tNe2v/7661733pFHHhlJliDjGg++sbRGsqSrLdM5x6dj/Dv22GOT+n5/Of54MUcE9Zmgthk5cmTC7aE9YzlkwxB0f4aZYwkMCwrsSGVehEsuuSSpduSFUzSIRx55JKk+5Hf8cX+FMbR4X7/4xS8CHRHJbCPI8ZcO/YlhMxnjTSwj+XXXXZfUNuT4+5Zbb701YXsxZwTdj0HjTpj7heC1IOdSGMcf34vnRHGvxx57LPAe/MlPfpJ03w8yWoZ9LmTeCKNJg55bg4zD6bpnIOiaxtIayRrQGav8Wu3MM8+M+XmCyYICsbwQoOL/zN133x31mV133TXq7xiPvRq6vx1/tHEsp3qiuZJ5bO+9907qeHi2feONNwrO8RdGm8fSspl+bsjluTFTjj9eG2+8caB9JOgeCnP9aNvW1tZe23v22WdDOb7XXXfdtDr+0qX9CtHxh72Z4Iywx8IYeNddd4U65zDaPN32T39QAdsXoi9ojT+RUUi7/9Of/tTrfdLggxbSppQWKdB+KGm26aab2hRqSmZ4SwhQooUSRHfffXfPe5RhpPSVF0oRuFKHlEYjBZ1yMYnK9iWC8mVPPPFEr/cpncH+SLV/+OGHzQcffGCyCanulEihFAUv0t4pnUKJSUr7eaHUDG1KOYVkoNyjPxWdkmGUSKSEAteP0jKU++JnXwgqoxBUriUZKO3gL6lBuVrKWVJihXai/3Vrgm4oMUgZp9tvvz3h9ildSNo/JQAoD3LvvfdGlYSjtAylCWDcuHG25AqL+/rLrtDnKWtBaYpE8H1KEXAOlB2gFBelYbzQDyjnGescuHaULuIeou/QJtw33Gf+Ep+UN6FEQaI65N4SFJQzoRwDx+FfeyMRrMvlL6VGmRnKjdDWlHPgutHngsr5uvakVIf3ugLnyzVgrHr66adtWRsvjG2UcaLURjzcfrkXKMFBORDKPPhLTVxxxRX2vss2tB/lQihp4eWtt96yL6DEBWMy127vvfe27R1U6oN25bpSFobx3Ysr3+nF/3tfx6507D8eXENKcPlLO3Kt6ReU+qK8Iv3P328pHUQ54UQsWLDA9hnamfNn/UXe80L5RMrrBJWy5LrQfowpfJ9jYiymTBFl6LxlXbnmlNg+55xzEh6Xu/dpa8YXts295EqjUULtv//9b8/nub8oVUYZUC+UD/Tfe5Q5TpbXX3+913uUoArSGqmSqbZMlb6Of3yXclxe6GvMpZSZZp6i/Bylp8OUaQwLpbAo2UQbcn9T7or+RBt6x0X+T3+h3yTCaTmOm/7IfEvpSm/fYj/cK7HKuaYCcyxt7e53NJC/bONLL71k95vofg8zL7Kt3/72t72+y5jNmIbuZu1jfzl9yhNtu+22tjSWt3wQZWCDypHyOUprMqZw7TkGNIqf0047zY6vXvgOx8IxoanQyN6Sb7Q/upBS/g7vWOHV/pQJ5R6mLBKlt+jLQaV006U/KRvmL6lOqVH6FKXMOAZ/OTrKmKIFmBPjnQ/3JXMM7UP/pEwYmiyXSn2nE8ZkyrbGgrJ4lKv23wOUkPNDH0A/MV4888wzdlxLVAIP6LuUWPdDSUK2x3YfffRR2ycSLUERC/c9ni+Zp+lraDx/WU40Hv3IC2X+rr766sAxkj7FuM1yFEuXLu35G/3le9/7nm0DSiwmC+OQfz5gbuNe4dmVfslzQtjn1nTdM4m0BmMBL8pAB833iUC/+seOWMtCABqNEs5eGH9dOU/m/aCxhLHSlWrjWvnHR8b2kpKS0MdNSWDGb+bD66+/PupvaBE0qBdKRsbD3TepzJV//OMfrU3DD9eR8swsH0F/9c4V/P/ggw+2/SCdeszBOTD30k+8tiCgvJ7/nuM40wE6nGfdPffc0851PKv7dRL6inVOuQ/687khl+fGZKHsPFqAUo1oRp456FMcC+1EX3WwjAtjKvacRLjnKHQOL8qM+5dx4V7B3uItvYjGYukA/7zNcWIz4J7k+ZQX83u6SKf2SxfoGbYPlLb39zGewb0wf2cCzp1r5F++hv3Rz5nv6Rte2yHfodQ64yvXrC/aPBP2T0rbepcBwobBMWudP5EyfXIbigFNUAQGkW+UB+FFFPk666zT6zMTJ06MzJ49O3CbZGz5P/+vf/2rVwSQv1SHPwqZ9Hvv34jEJFIpCCLaiLoIKp0SJooqKIJ9//33j4o4IqolKJK0PzL+Pvjgg8jrr78es4wgGYtcL/+27rvvvqTbI6hEYKzoeqL4iQS7/fbbI+mKArv33nuT3la8Yyfi8fHHH4/63D333NMrO4XfKUWTKDqQiCGuh+Oqq64KPI+NNtoosmLFip7P/fSnPw1VNiio73BslDDyQmaZ/3NErvnvS/qN/7y8tLe3Rw4++OBe2woq6xIrwvrRRx+N+hxRsE1NTaH7+A9/+MOov5N5EJSNxv34/PPP29KECxYsiLsNFzXozdbh+4ceemivz22xxRahzpXyTLNmzer5zFtvvRUY7RmUIdTfGX+x+kiicj7xymakUu4knWNXKvsPMxYHlRwm0+vLL7+MOs6gMmP0Ce6hRGMsZcjefvvtqPkkKKoxqAQf936suc+VJvGXLqHv+4kV1UmJSP/9xPjs2HTTTXuds//+9M/nzJVhM0q9BJVcilVeLWjcShT5mq62TOcc39fxj0xv/z4eeuihwPMjM5NxIah0athzIuOF7cSCY/OXvho6dGivMsNBbcOLzAo3f8TKGmZ+TZWg+5MSzp999lnPZ2j/o48+OrA6gJ9U5sWgChOU8/ZmtnIPBpWfZw7zcvLJJ/f6DJlA999/f69jXbZsWeT3v/99VMYLmsGfLcj4520PoCQa5xXv3vCfl/9YHbQD/Yi+n279GVQumP7iv++DdOABBxwQ9Zk111wz6u+nnXZa4LFQ0hjtGuvv6SDRfZkOks264cV46YfshCDt/O6770aNZ7HGcP/5nX766YHPqt7M54aGBlsmLmh7YTL+eK2//vpRJX3RQ0EZ6H6CSuyecMIJUeM4pSODPse5JTtnoOmDjt9/L8TSgP65MZ33TKwxlu8F6TWuWzLccccdvbb95JNPxvw8JYz94xvXxkGloKA28j4XUNLT//dLL700aj9hM1/Cfi5TcyXtzXycqE9QNSQoA9+feZRue0cypW6TJeiZnudmMuYd3P/+cT/INtEfzw25PDeGhXEeLRKv7D/3lz97NWh/sbJmzzrrrJ7P8KwZ9Jzvf44IGtt5nveXBaUSS9A+U834S6f2S1fGXyqfy0TGHyVN/Z/Zfffde80R/+///b9en6NscaJzSaTNM2H/DMo4j/cMKkQilPEn0gpRDP6IDwcRCgcccID9O4vf+iECyr+gOFEVRNV4IbKaxaT9WTZEebnIPaIsvBCVQwQMGTx+iNrglQpEmLsMGO95EtXpzbxgUdj/+7//sxkQ/Y3LYKENiIwigpPrRLSni/L2Z/bA22+/HbiQeTz87e6iYILal6hSssxSXdQ47P7DQnSun2OOOcZGUXkhy452ITrSgUZ44IEHzPrrrx93H2QGejOK9thjj8DPkeHgPRcWtb7qqquiPuOyahJBBJQ3qh7IiPr3v/8dFTFL/yBa8Qc/+EHPe1tssYX9SaQSfYcoYLJt6Tsuwi7oWtB3iM5LBJFr/jYgCpYo8LD4rznjCK+hQ4dGvc/9yHjCywvXznstHYxT3m3w/SuvvNJmW3mjNIke435KFC3GuZLh4iD6kfYlgs8fXeg/9mxAHyFrmujeMJDhQj+77LLLUr6vszl2pXPcIOuQSGpvJP0ll1xibrnllqhsdbJtuAfJAkk0bhD5551PuG/8GSWMCf7odXfvM/8RdU6GAPcs0bIuupuIXe99TEQi9zfRq/HgXiWyliwH/xztYM7zjimcM9kVZEUA2WRcWy9HH310UtHwjqCF0r3H0lcy2Zap0tfxL2jOjBWNyhjX14xkF3lPpCzXnYx5/k8bEsnqjtULmS70bbLuE8H4450/0I9kHHkjs8POnWFB25FN76DvkiVBpL8344dzJWsc7ZPqvEjfIvvcC+MLmRneTAr6PfOVfzwgE4P5i6xp8GdHwJ///GdblcAPEdNnnXVW1HsPPvhgr2zB8847L6o9gIhqtknGgoPoa297+Pui6xdkl3uhHehH/iyOdOjPoPagT/mfHdCBzN9eDeWyXNzx+o+H7ALayt+/yV7g2YhXqtBWF1xwQajP8nwSpHmATLtE2UKZhnb0w7jjzVagDS+99FJ7HomyJYO2R2UK73Mo4zZaZ/vtt0/5uMlc9GYzkE3ImOkdT5mjeJHRA2RSMy54od+gP71zINk0VJjw9xHOP6iqTjweeeSRXu9xL5H5m+g5IYh03jPxMo14+eG6JYO/UkKiDBSOi2P2ahSvZvf+n+oC7vdp06b1nFdQFROytrJNKnMlmbbezFNgvPBXNaDqAHOFP9uX/nrSSSeZQoH7kWw/b1/iPvJXtfC3Y388N+Ty3BgWxnmnRXgeJfsJzYDGpbqG091slyxg7zNgGMjE8vZd7HeMe/4qGP7rFzSGMj6RVeeFOfnGG2+016yvpFv7FRpB/RO7mX+OIDuRLG7vXEDVmb/+9a8J9xFPm2fC/kl2qx+OO8iWLUQY5PgT/QZOEURgkNMPcKB50/WBsk9B5eOC8Kbnu/JeruwWkx2OCMQoZR+mTJliSxdutdVW1rgcdh9+MFz4YZAPOkevOOxPaFMeaJnUKOcRFn+6fBhoS0oxYMz1njeGav7m2h1RyPVIxbjrDFBBeIVfOkqHOuN00Pt+w0nQ9/34DVW0lR+Ep79kKeLUj79MZCwoOxEE4sX/QO/vz7QnJZL+9a9/JdW2YftO0IN8stBWPHw4KKtBu3IfUo6HF32PMSHIOYeg95f7RcD5DeSAgyPIWcfDSCLHH2WZ/PTluvYHPHTy0IVIxlkTphzZ6aefbu8P7vV8GrtSPT4MPGHGDR62KPnhd9YxbiR6gO9L3+Gepnw2Bht/Oc1YcJ0x7vgden4wkvoN/H6OPPJI2ycwTDswJro24mHNOXwcYUr0hJ0X+jIn9GdbZmv8Y6xjTPPOXziaKRvLNtycTaACpZD6ajTAeEN/IIAimfKG3NOJHH+UYOXlL1uKE9Q7JqR7jA2aY9kn5cTQsP4+lMjxF29exKDlbzeukzeoxIHG8jseCJoggIe/EWTi1WquvZKZl/3lkwFHv9fZn0i3u/agL3sNopRV577h2cH1Zf5PXw4yfKRDfwadT9gS8jxncH2c8Y/veQ2Q//nPf2y/954P/ZXxP5amDQt9OlbQZRgDmdeQnW3HX9BzVVBZe66rvxRW0DWhv4fZHpqPfpWKfuD6Bc37zNP+QAqulXP8BT03UBYvKGAFzU6f9d7/BJ94HYmptm+sIMSg54RM3jOZfFaIFRyUKGAUJ53X8Uc5Q/oIfcXr1PvJT35iHbkY6NGGBAex3Iff8UdfiVdetD9Ida4M6q+MsUGl57gf/I6/MM/J+UQq2ry/nhtyeW5MBvQ25ULjjfN+wo7hlFv1a9owz1Zhx1AC/mijm266KdTxxCOd2q8QCeqfibS210HHK5Z9Osw8lAn7Z9ByS0FzmBBhkeNP9Busp7fTTjtZAUMGk5++rrXnjYgi6gpj9a9//eue9zDUEVnpj65k0kSc/vznP0/aERVkjI41cSSaUDLFEUccEfdBPxZBa7kkgvZjTSP26Y8a5OWNvCWCFQMjETTJZHgBD8V8h7UavfhrzydDUP+jbnoQQe8HRZIm+l7QWgcYuvzvBxlb/cbysPuM1x+9YpnIdLId/WtTpKvv8PDrjW5MFTJ/MVZ5HbE8ZPBg5X+4whBLdJ/3ASvoutM2sYIBUrn29NegqOKg6x/2uvYXPFyyvgFikwcwnJ4YQXgIC1oXivueh5wLL7wwr8auVMCZ5b9ejEtBUXp9GTeCHuzC9B0ehDA8pdIeYb4TZr0I2oM1FP7yl7/0vEd/4ryZA4i09ML6PYkyp2PhXUPFEWtdOrIx3BgYtP6jn0y3ZbbGP2D9GYxL3vmUCGVe3vWLGbNZe5GMrlSyknH6kfmVitYL04ZB90nQvRLWaRuWVOfYVObFZHSK+5vf8eDGHK9m9rZhMnosnbqdtWBwjnnvRa47jgev84G5mWcJsiroT+nUn+k8H4yVODK9awPjFGAO9QYPYRxkPOKZhcDEQoLx4oYbbujzc1W8eyyeQZigC/+8SHsHGXfd9lJx/HF8QZox0TydzP3Mtng+8Dvr2UYyjr90P7em856JRbrWpgpqp0SBIEHr/KGBCXzyroGI43jrrbe2QXKAww/N4A8UTHZ9v0yQ6lzZ1+dkniMKKesoFW3eX88NXnJtbgwLuhbnXLJr4IbV3Kk+W2XD9pdO7VeIpKN/xrtWibR5JuyfQRW1kpnrhfDTO0RHiD5ApCJCkUkX0YsI9sJgSCRwXxw0sfBH9pNGjdGKKJx4IpOoZz77i1/8Ii3HkWr2YDyCnAFhMmBYgDsVw3lfjGOINAypLOQdr6wDAgTngH9R8rAELc5NhHiqBJ1vuq+lX1CGNRT0hVjnkOh8yfJLxekXa9t++hrh7oU+TrkLor/jXTOMh5Tr85Yb64/rHuuBLtsP/8mA2CRKnvJaGD0Yf4LKaIB/QfpUyMbYlY79ZGL8D+o/YfoOZdtSdTql8x7+6U9/GtUuOIxxDvOg5s+ISjXbL9acgNEtyEFN5CYOL15hsvEz3ZapzvF9Hf+cc58IZoz08a4pD6GUsMSYGdSmiTjzzDNTfjgP04a5Ns6mMj4kuqcyPeZkYvwKq9vRPs8995zN8iYbLl4bUPKK5w2/Ub2/9GeY8yEjiEomLE0Qz2BEBggGTp6XMvFsVAjEMo4FZRolul94Hoz1vUTbS/fY018aIp3PCekmTFZ+up4XgjKFvdUIgiDD2F+e1wXAufYhuwYnAp91YAPB6U9p8Fwr89kf/TXZ99OhhfqbVLR5tu75XJobw8C8yLNDsk6//ni2ysYY2h/9Jp/uvXSTqH+GmYPSfY8FzU2Zqh4jBgbK+BMZe0hDABP5RqQezjVvujnrK/gj/YOi9Yn8D1tKICijhih2Xhjs3n33XbsOFQ/WRMKznqDXeHX11Vfb6ItkonOCREPQmlNAGnkYgh48/Zlt4G3TWFBWyA8R0hhWSDV3E9ljjz2W1lKklATj+hLZhxOA8gK0O1mf9Anv+RAFQ7knv5M4TNlMjEReOA8yOlMpM0j/o394mT17ds86d4mucVD/zQU4hyCC1nHy9uegvkOmCNkeGOVceRwMzpS4SZZ0ClZXl58X0doYsekHlEHi4dybLYB4Pvvss3vWMwm6blxfPhd0jPl07TMJ15/rjjD1r/sUNF4lS7bGrmTgfqHveR+WmGt4UAo7N2Sq75Ct5c+EINOBLBkiEsl4cMYsxt5UnPxh72HKrXCNvOtiUO6TTFjvQz3HQ2nQVGFOoESlF64F6xD2xaGY7rZM5xzf1/HPwZxJZg7XgywGSoZitOT/XDdveRm2z/yezLXCiIMz3w9ZUawtRHQypcaA7d52220mX+C+DioplGiOTeWeChovYs3x7thibSNoW0Qm0xfDZv0FbYO+FSujyg/l9r3QB+gTvGg/NCP9mPJ66D1vhg36kjV0mAPSpT85H39pyBNPPDH0WmL+jD3mKcYJXmQfczwcC/sgUM2bkYxjnfUV0VTJQjmpeAZGb78icz8XnA/xnDP+5yX6QpDBK9bzlve5MGiOjlUeM9H20k0y9zPrhQYFTiRrCEzmuTXWeq9e0n3PZPJ5YezYsb3eSxSMErTOH04977q9roSht5QhOsD/jAq5fO+ls78Gvc8953WiplsL5QPZfG7IpbkxEehWf3Yz+yJYD1sEx+OcdOgN/2czSazrFGQvCjOGhiGd2q8Q7z3OzXus9A0CNsOSaLmWsHNQOu2f/gxNgpYSPUMIEQ85/kRGoT4xC57766AToY7zjQh1B//3r1/AgyzrO4UZcONFBSHcGVi9g+u1115rDU4ORBjGvWQcf0F1+jFMMNH7JxG/cSIWQWs7BAmHMIaxIFFwxRVX2HJqXhKVOUsVJin25d0fDldKSPr3n6zjj8xRjCneLAyuIQZeosETLSLNdeJaOwcCgu3555+P+gyTMpE7frwp+44gwZcLIDSC1tt5/PHH4/bnoL7DItV+0ZGpvtMXo5Fz+DuOPvroqDUSMIo7ww8GMwwn3of/+vp6+2DvX+cPo3rQGhUIvUKC86fkGg91zhifzMNIUBBGUBRlvDE73WNXsvsPA4Yf5i2yOvzjwzHHHBP1Hg8BjH39NW4EtR/ONwzqXgjEYV2aTMMDmNfxx8PQ73//+15lK/sSzUjmGvei30lHyW/u5URrxPVXW6Zzju/r+Bd0nxCs5S2thuPFH0zDfZeM44+x029UILALR74Xp8PyCebYk08+Oeo92jdobay+ru3EWot+nYyWweDhL1uFocHfrzCcOQMcn/evSYJzgXk+bDAP4xef98I9yDqOieAc4kXXo6F5uTUUeR6gvJ53zax4438q+pPz8RvrCSg49NBD+3w+jD/eMYg5gTVfvM6/XNNT2YB7xO/4o3qL/95hXPauoRirD7C+jn9cZnv+a0oWV1/LhSVL0PzPcwhzid+gzjOkX7Owbn2ypb9ox1tuuaXX84B3aQzv+4nI5D2TbhgLcDx5g34JZEk0l/nX+UPzea+Fy/QjkMFtHx39j3/8I63r+2VCx/a1vzL/MW/7HQlhnpPTrYWy3T758tyQa3NjWN3NsgE8l/o/159OP+Ae9o4Hbqw86KCDegW8YY9KB+nUfsncewROhsXv0O7vscnr+GPfRx11VKh7JRPzUDrsn/7lGphfUq1KIASo94iMw3p+QYvF+2vmI4j92X1MapTgjJVBwsMhwpqB1VsyDCHAeiFEtsYqSRVUbxyjRzJgpPQbopn0WC/Qu1+Ox2/kjEWQcfL66683LS0tPb9fd911oRbJDipxSuajF1daKR0weSICieyPdc3S0e6wyiqrBD6oMomy/gvCJwiMmBihEd6s/+HwCzbA+eEX3ffee2/UekqAY3q//fYzuQhOdtbU8kK2jd/AhOjxCpIwfef222/vZfTrb8gMI+Ms1vXmfgxas8X1Oa4da2T5YdzxZrlwP5900kn2QcwLojJRpFi+QdtwjzAW0bZk/wRBu+KM8+MfE2M9ZMTabibGrmT3H5agcYM280ZbYrAmy90/9hGlGlSeMh0EtR8OH++4TNAED9LJluBJBRxlkydPjnrPb9yl1GRfIdDIHyhEJDVOQcbuXGjLdM7xfR3/gOOm/GfQehKx1gVJds4OakMyKb3HhpGEOT3fyh1Ssof1C706iHWj/W2EkT4oMzAZ0MmUWvXC+IKjztt/cBz4nZHOue6uBfdJUGDTaaedZh588MFe77PNP/3pTzb7zkHpWG/mC5B169a5CuqPZMKceuqpvbL9rrnmGnv/otGCoD39fdTbxunQn0HjOXNhLGc0uoDnDLJt/TqCZxzWfImVTcVYQZvGOpaBCn3UD5mQ3nGBsYJ+GsawGLS9//u//4ty8jF+M0f3NwQ/+IMqWHPulFNOiTo3xuAgZ3qQdk2Ec6T7nYr+0upBzwlBpPOeyTQEhPr1aZh51p+lx33qbRvn+CNT2hvQ7Hfu9HV9vyAdy32RSuntVODZ2r++L/v3r+nNGB5k8/Bf73RqoUzq/HTT388NuTY3pqoZCeDwOpbQ9n5naX8QNIZSMcPvDEQLpSvjL53aL9a9h27zBiIRFJFMBQL//UefSlRKOV0E9U/6RqznCe4D7geCPrDt5Jr9k2vqf66jvL0QfUEZfyLjEJ3AAwsLmXph0XtKKxLx6p0kcbR4J3YMy0SrI5gp08EDHw9BOAUpRRUExnnEIy+iXDbYYANrcGHi5G8IQdKr/aRSIhIHgd9giYGRKFMmaR7ucbwkWkA83sCOoQQHA22A0SVsBBEPOH4DDiKMBzxKa2G8fPbZZ9Nag5wUd15kCvFQi7GLBwWuKRNwUOmTVNodKDvJA4E/m5JIe8oB8gBG5gJ9AIFI9AznHASGYaLXvBGu9DXWiOR9jNY8zNA//e3FZM+55iIcK05JzgOhR6YN19wP67d516Gh7/jXaqO8BuKRrD8ESS5Ep2O8wcDGC0c89zrngQAl4pb73H/NiZD2ZhZR4pc1x7ziGcFL3+V86cvcc0HjDX2wUGFtEte2ZIbQJ3C4E9GM04Y28Qtc/hZkTKYt/bBeGG3K9WKeoJ2dIy/dY1ey+w8LD76UnvEaEXl4X2+99ez9Ql/jYTDIMUPJRb/RPF0w/2CE8l4f2oyITwJs6Ou0H86X/oD2Zb2OWJlAZInSXn2FTDd0xDnnnBP1PhHBPBjipGes5/6nDbj+/of1TLdlOuf4dIx/ZL9gtKAvcq7Mx64cFRG0QceS7JzN9SW7mlKSDgwC3Cc4hXFCuTWR8g0e5tEb7n4nY8dfEgsw5qeDc889t5cOwWDDNUGroLXQRP7+yPXF4eGFuY/gHa/DGsMRmoFzInuPMZ1+wPWhT7EPB32Nyhms9+r9PoYxri39kb7E9rn2jIMu4Mofpc4+qOLAmnhUY+D73K8Y6/kO2tEfLODth+nQn0Rec+ze7GSeN7g3eVHmnOcIHJD0VTSSazv/fc1zBs8uPCOgH7m3mEc5Nu5b7iu/Uz5VLZyroMVxgseDwFBvSS4ylJkbvWsL0V5kWeyzzz72HsOgHEvL+2Heufzyy6MMbHyXZ0/uWa4H90/Y5RjSDRrSXxUHpxv3G30KpyTGRG+goiu3jgM9WbgvcODQhv7nGIL/eFbm2dz/91ik857pDzh3bzluHBc868XTYm6dvyAHG3MtY6WDcp9B2d7pKPPJWEZWv3fcQNvQzpTCd2vEE8zjPaZ0wbM0Go4x2t+HH3jgAeuQ4r6lv/rXRGauICA7U1rIjSVoCe/cSFYw14/2cI4P7hueI7JFfz835NrcGIagAFJ0NpqC60lgLuWy+8ux5IV2oK95nUrML9zf2AywVTKuhHVYZ0P70QfQ5d72Y45hHNl///3t/7Fdeu0iiaBPeYMdGC8J8KJdXAY722YMzkSSCc9B3ux+xg/6Jf0FDcYxcF7Y8vicO7d0BJ2m2/7JuOWfb/yOXyGSJiJEipx77rnMPFGvHXbYIfCzLS0tkTFjxvT6/LHHHtvrs5dddlmvz4V5PfPMMz3bmDlzZtLfnzp1aq9jmTBhQq/P+enq6orsvPPOCbe/6qqr9nqP7Qex9dZbJ9zeiBEjer133HHHRW3nq6++ilRVVSXc1h577JFwW2Hao729Pel2nzhxYqSpqSmSKitWrIgccMABKfWZL774Impb8+bNCzzHeK8pU6ZEli5d2uu4uBcS7Q/C9Am+F+Ze45r5P7fuuusmPIehQ4favuLljTfeiBQXFyf87u67797rPcaGVM7TD/d0on55zTXXJH3dzzzzzF77uuWWWyJFRUVJbeeMM84IPO6w5xp0vbzjWF/G5KD7NywLFy5M6X7idc4558S8T6urq+N+t6amJmNjV7L7T+b6PPfcc5Hy8vKk2umwww6zc0cqc06s+ff666+P+szPf/7zhMcxfvz4yHrrrZdwrGLbYe7zeCxevDjmNT355JMj6eT8888PNX7FevnPLZ1tmc45Ph3j37hx45L6/uDBgyNz585N+l75y1/+knDbgwYNCmwb/7bCzA3J3lNhCNpWmDkWfdnR0dFre6nMi3DJJZckfd2vvPLKwG099NBDkdLS0tDbmTZtWtT30W9bbbVV0sfjP9ezzjor6W1cddVVadefCxYsiKy++upJb8uvyY466qikt/HII49EMkG8eyldBI0BqbQb3HrrrQm/R59Ff/vfDzq/MPdLbW1t4HOaf/wOq8eTeQ74yU9+klSblZSURB544IFe2wk7Ls6YMSOhHuIV1B5B83667pl0j9dBvPfee722/+ijjyb83ne+853Ac9h1112jPnfPPffEPN8333wzcNvJ9KljjjkmYbveeeedGZsrmcf23nvvpK4zfY1nyiDSpYUc2223XcLtxTqWeIS9l8Nq5f58bsi1uTEs++67b8Jtb7TRRpFVVlklYb8N88yUzL349NNPh3rGCBpDg/YblnRqv9/+9rcp3XuxdOrZZ5+dcHt//vOfk27rsNfuk08+iQwfPjzp9gkaS8Kec6bsn6eeemrUZ7GhBz1DCJEMKvUp+gWiH371q1/1ep91BrwR4MDnqOeezAKmRAB5F+1OdiFwIkkpdZUK7IssFKJkYkFkM1H1YSFTkcyaeFkNlL9IBFHTN998c9x1uoiGTVeJm2TbnQwAogTJpkgVol7JsKTkazLrM5KtwXe90OZEG3rXR4oHkXlE5SW7xkZ/QlRhUAaWg/uMiDF/2xHpz3dj1RPnWhNdlcw6T5kg2T5HZDUZQX44D6LbwiyaTn9l7dF0lcjNNRgvvGt8hYFoaNo1qG2Bey1sueNMjF3J7j8ZWHeKUtNhyvgReXnmmWfauS/Zvpss9E+OLRaUDGL8JYK8PyC69Igjjgj8WzoiLr2Q8cecm+xaKETvE5XuP850t2W65vh0jH/JbIPzY74lcylZyPwhKjfemtB33nln3mU9MX+SzRULKg+QvZzONUQYQ+gffg0TBPqEdVpilTNCx5ClGFSSPwzMh0Tes/ZO2PVHmC/80cvJ9EM+S1lYbyWRdOlPsmHRgcmUJyMr0b8mcDLHQ3v85S9/sdmvwtjx97LLLovZn8hsIks5qOSdy3ry3y9Bz6Dee4QS/v4s1FjbSzc8v6CnE62pDDzrUpmEMrupQrYjZaLjjR9kXpJhEoZ03TP9AfYCf5nhMGvIxcrWc2U+Y/2ervX9HFwTf7nN/oR5DA1ABnuYOY2+xnNyrLXQ06WFvFqtP+7ZfHpuyLW5MSxkPsfTVmRzMY5l43qTtcYcFFSS1ME182e55pL2Iwsw1vpyQJWooCy1eBqfvpMtyLAj05Ks67Awlvrng2zbP/E7+m3SZHH353q4okBJyk0oRIoZfy7jYsiQIb2+89Of/jTw842NjTaa/ZBDDolMmjQpUldXZ6MciQhfe+21bZYXkSNE7wXx2WefRa6++urI97//fRttTZQS33XbWGeddSJHHnlk5I477ogZRZFM5CHRHldccUVkiy22sJGjvDbYYAPbTpx7shEkc+bMsVkQRIQQFUYUyy677BK5+eabbcRXMlF877//vo0+JmKkrKzMRkfttddekfvvv9/+Pey2wrTHN998E7nhhhsiP/7xjyPbbLNNZM0117QZZbQ7bcLvBx54YOTf//63zQRNJ21tbZG77rrL9qmNN944Mnr0aNt2FRUV9tx33HHHyG9+85vIiy++mHBbRONxDuuvv749fqKL+bnhhhtGTjrppMjLL78c9/u5kvHnop/JaKP/EL1Fe6yxxhqRX//61zaCLx60FffayJEjbd8hO4R7kvZJJrox2eipsP2Se4HozT/+8Y/2uDbddNPIaqutZjOLON5hw4ZFNttsM3vNwlx3oq/++c9/Rvbff3+7HSJVXf8hs/eiiy5K2Gb5nvHn+PLLL+29zP1Ef6M9uIe5l2kX+sJuu+0WufDCC+1nw0CU+ne/+93IqFGjemWZ+DPu0jl2pbL/ZK8Pc8B///vfyOGHHx6ZPHmybSuOmX0xFpLRMmvWrLjtk86MPzcmXn755ZHNN9/cXjPOkfmTrK/58+eHHqvSkfEHb731Vq/tMBdnkueff95Gtm677bY2+pZ24LownjMO0h9oj8ceeyxu9nm62jKdc3w6xj8yMW+77bbIL37xC3v8nBPH4u5zjo82+tvf/hZZvnx54DbC3isc74033mij8tF0lZWVVpvRDmi2sNvKpYw/oN8Qib3JJpvY8+Le51pQwYJ+E4tU5kUvVBsgk3LPPfe04zHtyYvsU64Z/dXpz0Sgg++++27bjvQB9DpjJOMXUfU/+MEPrF5ubm6OuQ2qbTDObb/99lZ/MXfSt9EPW265pd0G/XvRokWB4yf36gUXXBDZb7/9rIbjnLzbQMsTCR1L96dbf5LdiE4io5F24DhoX86N7XNf0Wb19fW9vks7MabQHsxZPA+4eYxzYhu0E1Hyn3/+eSSTpEtj9FfGn+O1116LHHrooT1anrHt+OOPj0yfPt3+faedduq1PfpgLLgeZCs5Hcw8/bOf/Swye/Zs+3eeNf3b89+/mcj4c5BJjZ7iWcWdM2Mw48JBBx2UsN8mq4c4b/qwm4O4x6jiwXNUqvN+X+6Z/sj4g9tvv71XFnu8cQ2eeOKJwP77+OOP9/os44z/c/vss0/MbSfTp9zn0eXsJ6iKQiYz/rx8+umnVluRteeeEZn/mNOxr9DOYbJU0mnvAOYHMiPZHvd5Lmb89fdzQy7NjcnA99EE2GLYPvY79Mjvf//7nm2HOf90Z/w5PvjgA9sX0Sq0AedPO5IRmMx+kyVd2g/tevHFF1vbFmOJ0668hx02WZ2KfeT000+314tnpP7M+POCjYo+iCZnPEHLcn60FfMrffd///tfzL6eijZP1z32wgsvRO2bfsUYKURfKeKfbDsfhRBC9J3vf//7dr0eL6zT0dd1JUR+wDobLpuHDCqiEYXIJVhfgaxa1tRxXHzxxWnLOhcikxCly1p0XvQYJUR21tbkfvSuQURWFeufpbJ2Lutp+ddFY80t75pBojBgzSWqWnzwwQc971199dVRGcRCCCFEf3PYYYfZ6icOMjavvPJKXQjRZ1TqUwghhBBCZBzK2XmdfhhojznmGLW8EEIIy9y5c81pp51mPvnkk8AWmTdvnjWOeZ1+sNdeewU6/aZNm2YuvPBC8/XXXwdub+bMmYFl6/tSTlPkLpSQveiii6Le+9Of/mQ6OzuzdkxCCCEGNmgRlo9y1NTUmLPOOiurxyQKh+RD4oQQQgghhEjAp59+aq644grT1tZmPvzww17rRRx00EFm3LhxakchhBCW1tZWGyTCizU/WRuN9aXa29vNZ599ZtfGYk7xwvo3Z599dmALLl++3K77yvpo66+/vl3njbV92M+MGTPMK6+8YrPA/GuOso6kKEz2339/s9tuu9m1gOHzzz+3a/0dddRR2T40IYQQAxDWKPVqkd/97nd2XV8h0oEcf0IIIYQQIu2QYXH55ZcH/q2iosJccMEFanUhhBCBkPUXK/PPC6Ua11133bifoSzv+++/b1/xIGvw9ttvt85GUbg8/vjj2T4EIYQQwnLttdfalxCZQKU+hRBCCCFEv1FUVGSuuuoqs9Zaa6nVhRBCpATR8A8++KA58cQT09KCZBg+//zzZs8999QVEUIIIYQQeY8y/oQQQgghRMadfcOGDTNbb721OeOMM8x2222nFhdCCBHFhAkTzFtvvWUeffRR8/TTT5vZs2ebBQsWmMbGRluCc8yYMWazzTaza/pRLrq8vDxuCzLXvPjii3Z7lJv+5ptv7PZYI3Dw4MFm/PjxZosttjD77bef+e53v2vXgBNCCCGEEKIQKIpQ90IIIYQQec15551nzj//fPv/4447ztxwww3ZPiQhhBBCCCGEEEIIIUQ/I8efEEIIIYQQQgghhBBCCCGEEAWAalkIIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeEEEIIIYQQQgghhBBCCCFEASDHnxBCCCGEEEIIIYQQQgghhBAFgBx/QgghhBBCCCGEEEIIIYQQQhQAcvwJIYQQQgghhBBCCCGEEEIIUQDI8SeESJmioiJz3333xfz7rFmz7GfeeeedtLbyxIkTzf/7f/+vT9vYfvvtzS233GKy0S65zEMPPWQ22WQT09XVle1DEUIIIQY83//+980BBxwQtx123HFH88tf/jKtbXXeeeeZjTfeuE/b+Pe//2123313k612yVVaW1vNaqutZt56661sH4oQQgghhBCiQJHjT4g+GBxw8PAqKyszq6yyitltt93Mdddd18tp4ndURSIRc+qpp5q6ujrz9NNPJ9zX559/bo488kgzduxYU1lZacaPH2/2339/88knn4Q6vtLSUmtg+OlPf2qWLl2atms+d+5cs9dee5l8dG7NmzfPHHHEEaaQoJ+5a+5ev/nNb6I+M3v2bLPvvvuampoaM2LECPOLX/zCtLW19fx9n332sd/LlFNUCCHEwCDXdVKYgJ1EzqVnn302as4dPny42Xnnnc1LL71k0sXll19ubrjhBpOPzq1zzjnHnH322aaQwCHq11qjR4+O+gz9l8/RH6uqqqxj9oMPPuj5e0VFhTnttNPMmWeemYUzEEIIIXIXp62WLVsW8zPooiFDhqR1v+kIWl+8eLEZNWqU3VY22iXfOeSQQ8xll12W7cMQoqCQ40+IPrDnnnta5xcT+yOPPGJ22mknc8opp1jnSUdHR+B3Ojs7zQ9+8APzn//8xxqzMBDFA6cMhrIVK1aYe+65x3z88cfm9ttvN+uvv75Zvnx56OO79tprzYMPPmhOOumktF1zDB0YL/KNv/3tb+b44483xcWpD4Ht7e0mU3gdcclywQUX2GvuXv/3f/8X1ff23ntv09jYaF588UVz2223mbvvvtsaV73QNn//+9/7dA5CCCFEruukdME+OU+MMiNHjrRz7YIFC9Ky7cGDB6fduNUfoC9qa2vNdttt16ftZEpv4ZyL1QcTsd5660Vprffffz/q73/605+s4eqKK64wb7zxhtXL9NH6+vqezxx11FHmhRdeMDNmzOjzuQghhBCJqgAQ4ITjKJ7zjDmJ4KmDDjrIBvAkCjYmuGXttdc2f/7zn+28mg6+853v2LkV/ZNvXHzxxTbImjYqFNC2BNONGTPGBo9TDeK///1vr88999xzZrPNNrMBeJMnTzZXX311oDZcd911rQ2Rn/fee2/U3wkYu+iii6ymF0KkBzn+hOgDTFg8zI8bN85suumm5ne/+525//77rXErKDob8XTooYeaJ554wjz//PNmiy22SLiPDz/80Eay/+Mf/zBTp041EyZMMNtss42dEBN93x0f4o1SS4cffrh5/PHHoz5z/fXXm3XWWcdO0Ig29uM1pv3sZz+zkzx/R8AgZmJFyL/++uu2TCSf3Xzzzc20adOi9hUkLv0C9LPPPrPCgswADEac45NPPhn3PImqJqOR8yW6miy2WCxatMhub7/99uuVCcd+2eegQYPMYYcdZubPnx+1D0QOmQoIGfaFuJ05c6YtG8o5I164tn7mzJlj237o0KE2G4D9eKPAXEYBbcvxT5kyxaQK2RFcc/fifBxce/rTzTffbK/Trrvuav7yl7+Ya665Jkpc0TZcS/qdEEIIUag6KV0Q3c15brDBBjbgBofja6+9FnWM3/3ud+2cjL455phjrB5x3HXXXfa7GNDQCczPBOkEZR3y/rHHHmu3hT5jHg+TwYj+8rY52WbojerqaqtryMyL52TD8LPllltaow/boo2//PLLmJ8nuMivtcj0JEAJXUrfQFc9+uijvaLt77jjDmu4RFuhWXAG//rXv7b7pX3OOOOMXgZGfsfhxrnQjhtttJFtV+/xs+3HHnvMalT2j+MtFaik4dVaOHu9x0H26llnnWUNpzigb7zxRtPU1BRVTYHzwLh56623pnQMQgghRDohUIVgnT322MPceeedcQO8XbAxjkIy2NF3//rXv9JyHOXl5XZu9dqI8oHm5mZb4vzEE0/MWhB4Jrb98ssvmw033NA67d577z1zwgknWB1KUoHjiy++sDqX/oMNkP6ATY7vOF555RVrE0MDv/vuu/YnNjevXmY/2ByDHItCiNSQ40+INENkOsYGos69NDQ02AhwSv1QAgpnWxgwJpCZhvECw0eqYBTDuEK5LQcOHwwTGMcQbX/4wx+s4QcDhcuMe+CBB6wBhmh2jC+xopcwRBHBv9Zaa9k1S3CUIQKThXZCNOCcQzQgPImawjEXBO3y17/+1fzzn/+0TjgMXRjPYkGmG0Yub/tjpMGotmTJEhuphMERByTCxMunn35q2wIBQwkIDFgYdUpKSsyrr75qo5r8ZZsw9JDhgIEOIyb75/9kQXiF11NPPWWvAfumFCn85Cc/sZ+N9/K3yx//+EdrTMKYxnX17gOxhQEK56KD9sXQ6l1nBqMpRsxUDWJCCCFEvumkdMCcT0AVOL2FYWyHHXaw8/Kbb75ptRiBRRg73N8pU4ohBR2AgwptESty/vTTTzfPPPOMjZImoIfPp7JWHIFCOAJxSlJOFE2IngqCzDh0EueB0Qc98aMf/SiuUQ4NgYPNC/vBUXnppZfa7aBBcA6i37ygpTAY0R58hu8QeIVBDR2FXvNHieNwpe2vuuoq24d+9atfmaOPPtrqOi84DQm0YtsYmDjORFoLfeyF40VLTZo0yZaN9wZKYfyinLx3bUOMp7QdxjMvOFKltYQQQmQbV2GByj/Mtdg3wgQbYxvC0cV86g0wxwbBfEvgFwFDW221ldUrDgKHsPEQGM3fyaT/3//+F7OkJXqFQG/sOAceeKAtqZmoNDtZjwQROdBf2267bU8QEbYrbD6xYIkcsvPRmQQUrbnmmj0aLwiC2ggM2nrrraPeR4cw36MFCNhiKRZvxQGOkWB7ApxYioUKAUB7EKDFvrEnBZUPRVcQhM5nVl11VaudXOAYcH1+//vf2/Yhg/KHP/yhSRaceBdeeKENVlp99dXtPrBleXUYdjCuD4FP6Hf6BLoWvefgb5zbb3/7W5twwM9ddtklqtQ/oAsVFCVE+ihN47aEECthIsOg4YXJEoGEgQWnSlgQSzjgEE7nn3++NaIw8SNCiGqOBw4kDBYYwlpaWux73prZHBPGFAxMgAGD48OJdtxxx1mnEgIHgYT4wiEUC6Jy2A+GGQQZ4u3rr7+26womA8ZAXg6ECqICBySCyA/HiOgkOh4jG4IDYRULBBPR9t4ynzgZuV4YaxBMcNNNN9lzIPLNZQwgYHnfRXYjbjEcsU2i1wHjkHfdQyLe2RelVp2BDMGI4ETUOsMQgpfPEOHmjaRL5Dz1OvEon0ZGBQKajD3EFOfEdgFDFOfuhc+yT/7m73eZqE0vhBBC5IpO8oLzzW/oIjAGZ2QinAbA8YfDjlJHGDMARxRzs9d5hFZCb7AGIQ5PDEBoMaezYgUw8VkMcpRBdYYhgrXc/pPBWwocwxBlvymRSjv6oSoAWYwYyTD6QDzHLMY6Xl6NAhiAcOq5NZYJVsKJidHnyiuvjDLWOW0K/B1Nc/DBB/cYmMjcc2DkQt9iuHQGN649TkI0LU43r7ZybQf0l0Tr+QwbNqzn/xgvaX+McThw0akYw3A2Ykh0esqvt/jdnyEprSWEECLbYGv53ve+Z84991zrlEoGNA+OLWwi2I0cOBCxJWALQQuwD5xFlMbmcyeffLK1rRAYjR0E7eetVOSFjDCcSOgotAEOPI41WdAKONfQWPyfspI4EdEAQUvAEBDPceHQwyFHEDhZfbHgXPwBT1R+Iqgdxxva4aOPPrLONyoaECjvQMthNyPwjTb96quv7LkSCM77BI75l2ehLQmOQj+jDRcuXGjtZby8DkrKsHIuXt2HnSte1Qb0qHdtYj9oQq8OJCDMG/AEHBvHRTUJ7HR8hqAs/2f8jj9seQRoocHzcVkhIXINOf6EyABM1v4oaCZCHEwIFv/klgiEEen0GEcQPpReYDs4w7zGCz8YvjA4YYjC+YOB6ec//7n9G8IAQcE6Ot7IH4xPrp46AoXtk8WHUMPg45/QHYg9HHY4/Rz+aKcwIMIw3OG0/Oabb+zxILBiZfxREoz2xMDDMSKsiB4j2ioItoXQ8h87Bjjn9APKduKc42/O8YcA8pZz4m84Gr0GN/85E4WPSMSY6QVHrDfCDAHqdfoBhs9kjJ9eIUXUHU49Fkh2WYAQFJ0f1F+JGqPfCCGEEIWqk7yQ7UYQkRecVGGyCMnawnBFpQK+Q2S6y/hDB3BcQQYtdADnjZMQHYABhN+Zu5nDgz6PocyrNXBKodOShQxJ2hmN4pyPlDoPgn2gCTk+2pN2ImORyPUgnGHMq7dwHqLrKBHqhd8p+eTFazjDuERWpPec0Xh8xmVFYphDV/mvNW1FafNY23Z6Z4011jBh8QZ3cc04LpyhGO0wKDr8/VtaSwghRK7B/I89hayuZJx+aB0cScyzOHaY791yK2gVMrYIAncBQAQz47DDIYU+w7ZDMI8LdIoXqEW1APSHOz4Cb8h085YKD4MLHnLglMLWgoagKpIfjhEN4XRDonX7cHT6A54oQ4+NiTV/0QUEvqGFaD8cj87hiA6hXLmD60GboE35HjoPRx92Ha9DD4etW88RhyqBcAQ7YQN0GoxMTn8wOdmE8cq7e6uEBelHguMJrHIEBZjzO9qS0vboxVifCQpAx+nH+/ESD4QQ4VCpTyEyAA4hsue8YNTBAEXtc+d8SwYcR6S9U74RAwn1s4kyjgdGKEQETiBEABMoTjWgTCVQ2okoJ/eaPn26LVsJRKiTMUYUEUYcjDwYo4IIs5gzwsb/Ob/goIQVpTQ5TwxpHBOCMFY9coQUZUiJFMd4c9JJJ9lyB7GEDNFalG3wH3sYhxjtmeic/duhnYn897YxL5ywCLVY20611KcX1joCjHpAZqRfWNEWtJVfhFFGy+vkFEIIIQpNJ3lhjkQzeV/+oJ1YcC4YoigRjs4ighzN5XQAAUl+HeDWCCbLkDLfRJQTdPT3v//dGnjQX6loLadF4uktdB5ZdzixCLTCYUnp93hrv2CsI1qb7DYyAzlfpxf9EGzEMfj1ljs2/zn53wvSRPFwmvbhhx+OamOMed51/oK2nUqpT//20KmuXCn9CPx6a8GCBdJaQgghcgrsJwTNYBNCm4UFmw3zLNl+BJujIdAH8Pbbb9u5HZ3gnUv5rAt8xkmIRiP4h+w9fxUILxyXP7g6lQBz9o39BYcagU5Oh8ayp5BpR8YipdqphuAv1x02wJxj9eoczhmHK47RWEFJfA9bjvd7QQHmBJp52xgHKZrIqyH92wYcan7N633FcrhRsYpAMPoLWYOJ9J3//bBBUaAgdCHSgxx/QqQZygwRjeOPKAJEFQYWSjwRnR7WgOPHRQt563eHAVFFmSWijHD0EE3DuiT+id5rjEMUYchicsfQg1MOp5AfjFUY2rzlD/wGIRxJ9fX1UcftL6+EAQYxgdEMQwoGlEQlJxEHGPtwbiJGMExxDYIgagtjjNcYxbEj+MiAdGAs8pcwCDpnvkd7Oti3F5ynGIOIJvO3s8usjAXlqPyGQv/LH1XmBUMeuIh8xCKOXSLnHZQrpYQCzkl/NqI/Sl4IIYQoZJ2UDo455hhrdCHK2+kAyiURKe7XAc4JxfFiCMJpyNxNBQD/GnbAd4jC9uor9AzBRH695Z3r0SFeAwqlpDDqYKjDIESUeLySTw50ASU3MX4RHX/LLbcEfo7jRyOhpbx6Es1C+U0vbCue1kIroWO850wEuXddQ/aFlkGT+dvYW80hCFfqM96LQKxY4ODFQOe0Fhoa7Yoz14FDFYOnM4o60GTSWkIIIdIJ8y12DD+U4PZn9hN8dN9991lbAA4877wdD4KpmWOxL2AfIjONqg2ABmK7zNPeuZS5kuw9YA047FBoJjQhczGBT5kMMCcIi7UBsWtRHYIXxAp6IjgKbURGHfYeAtTiLcMSNsA8yCEWJsDcD+384x//OKqNsceh+VxZ9qBtA067eAFPfqceoGNoQ0qrU2XDS1CAOQFPVGhwladifSYoAB0UhC5EelCpTyH6gEtBpwwU63xQboB61JTE9E+GDlLtiUjmM0zoZKoFZZs5mMBx2CGKMGxgTGHSxShGiYBkYOFgJnEilyk3QF1xoq0QgAgbzof64QgWyhUh4DBkEOWEmKJ0FhM2JTD9ED2FAYnSoZR9wFnnXczXrYtCKVBKFxDNzzp0RCl5QUDec889VlTQLtQjd5HcQfB92t9tmzX4cATGilLCwIKIwOjFNQBKVpEVyXpAlL3CoETmIGUSgiKkHHyPqHyuNWslUsaKNvDCNinDsP/++1tHHmVBMUxxjkTKxVuXJ5lSnzgcMYoh2DGSUX6B0p84RClHCpQPow/RlzgmRBXilVKv3ocAtoMBLZVIOiGEECJfdVI6QC9hJCKSHYMMDkyMTKwhyLzv1okhipz30V1PPfWUnaOZ8zFEUY49yBmGMQadxXYwpGAsQXf416ahDdF5RIujoWgHb9kmtBZahGOgnDntHeRodBA5TiYmmgLnHZUWcDbGuoZA1DlOPleCCjhurhUGKbQlWYRcP9aJjgdrGF9yySXWQUm7YHTCgOkgMxM9g+7hfFmbGk2GU5E2Y93qWCRb6pP9oFHRVhisuM7sy+2Dvso5o7U5Xl78H43qrfTggt2oqiGEEEKkCwKfqCLgB/tAUGlwnvuxTVDZCVsCmiSo9GUsKE2ObYf5keAl7C3oPuZIqi/EgsAcAmt4EVSEJgqq+IC28weUBwWYE0zjBX3htA8OPxyPlKd0x+QPRAqC7RKUzovvoWP8Ni4H533zzTf3OnYco14HINoE3UIQfiz4Hg7ZeOfsAsuS0TCplvokuB5dTqnRH/3oR70+j93owQcfjHqPAHNsaW5bfIagKO/yNHwmKCgKGxl6WQiRBiJCiJQ47rjjCMOxr9LS0sjIkSMju+66a+S6666LdHZ2Rn12woQJkb/+9a9R7z333HOR2trayI9//ONIV1dXzP0sXLgw8otf/CKy/vrr28/X1dVFNthgg8ill17aaz/+49t///17vf/f//43Ul5eHpk9e3bP7xtvvLF9b+jQoZHtt98+cs8999i//etf/7J/q6mpiQwaNCiyyy67RN5+++2ebXHu9957b8/vr7zySmSjjTay2+J7d999t/3MtGnTej7D59dYY41IZWVlZJ999rH78A5FX3zxRWSnnXaKVFVVRVZdddXIFVdcEdlhhx0ip5xySmB7sr2tttrKHh/HOXXq1MiTTz4ZicdvfvObyBFHHBH13pdffhnZb7/97DZo40MPPTQyb968nr+fe+659tz8fPzxx5Ftt93WnvOUKVMijz76aK92mTt3buTYY4+NjBgxIlJRURGZPHly5Ic//GFk+fLlca9VMrz11lu2HQYPHmzbdq211rLH3NjY2Os89957b9u+w4YNi/zsZz+LtLS0RH3mRz/6ke2XQgghRKHqJC/+edt7DvHm52eeecZ+d+nSpVHvNzQ0WE31xz/+0f7+ySefRA488MDIkCFD7Py79tprR375y1/a8/rwww8je+yxh20fNAJa4u9//3vMY6ivr48cffTRkerq6sgqq6wS+dOf/tRLJ82ZMyey++67W02z5pprRv73v/9ZfXD99df3fOb000+PDB8+3LbZ4YcfbtufzwTpHvTQAQccEBkzZozVO1yvc845J277zpgxw57rsmXLet7j8+eff35k3LhxkbKyMrv9Rx55JEoD+nUjtLe32/ND69GGv/71r62u8rYLbXn55Zdb/cO2aU/alX4U71olC21FO7CPsWPHRg466KDIBx98EPUZjoX2Gz16tL2maOv3338/6jMvv/yyPZempqY+HY8QQgjhhbmU+fekk06KvPPOO9ZegU2F+eiOO+7o+RyawDvvt7W12bme+fO9996L2ahBmm3BggXWBnHnnXfa34866qjIxIkTrT3o888/j7z++uuRSy65JPLwww/bvzOnYzfhb9gxttxyy8hhhx0WOF9jYyoqKrKainNBIzF/eo+dbfGZG2+80WouNAqaAX3k9AeaB/00c+bMyFNPPRXZYostovSfX4OcffbZkfvuu89+fvr06dZ2xXHGgjZD7y5ZsqTnva+//trqtZNPPtnqIraHTQiN4PBrOGezQW/96le/inz00UfWZoem8LbLu+++23OdOWbO+/7777f2nXjXKlm4HpzDb3/7W2vXcq/Fixf3fIbryGc4XnTtv//9b6uT7rrrrp7PvPTSS5GSkhLbD2gLftJer776atT+0L0nnHBCn45ZCPEtcvwJIQYcGLAQfrNmzcr2oeQciHYcgog3IYQQQohUIYjqD3/4gxowgEMOOSRy0UUXqW2EEEKknTfffNMGv4waNco6wDbffPPIrbfeGvUZv+PPOf8OPvhg65zCsRRELGcSgc3rrbeedbKxHZxvOP9wAOG0IgDKORRxTq2++urWGYmj8ZhjjoksWrQoZqAOjqTx48dbR9e+++5rg7v8x87+CIjifRxQ7MM5/uCJJ56IrLPOOnafG264YeTZZ5+N6/i78MIL7eddwDTBRolsJAShX3311VHvsR+cjDjyaIczzzzTBjTFc/zBgw8+aAPmOd7tttvOBs752wWH6m677WaDuAj24ry82iIdjj9vIJ/35W1bd56bbLKJPU+u+1VXXdVrWziGXYAWQXA4hr00Nzfb/oqzVwiRHor4Jx2Zg0IIkU/cf//9ZtiwYXHLTwxEKL9KSS/WdRRCCCGESBXWxnnggQcCS3cN9BK4lFw/9dRTbalRIYQQQuQ/lNCk5CnlKv1l2EViKO+PnY4SoEKI9CDHn+gzc+bMMY2NjWrJFGFtF9ZYiwV10oUQ/QfrAbGGkRBC9BfSUpnTSVdffbVd0yWIzTbbzFx77bWhr5MQIhzSUkKI/kZaSuQCN954o123ecyYMdk+lLzj9ttvt2tPT548OduHkhNIS4l0IMefSJrly5ebZ555xkZhsDjr559/bhcEFqmxsuRuzL8rUkiI/o/ER2zutttuVrSzyPngwYN1GYQQaUNaqv90UrzvFxUV2ZcQIr1ISwkhMo20lBCikJGWEulAjj8RitmzZ9uU6/vuu888//zzZo011rAGcQzjO+ywg6mrq1NLCiEKgvr6evPcc8/ZwAYCHGbOnGnHuQMOOMC+Vl111WwfohAiD5GWEkIMFKSlhBCZQFpKCDFQkJYS6UCOPxETjN2kWt97773mvffeM9tvv701eu+3335mwoQJajkhxIBao8gFPmy00UbmwAMPNIcddphZc801s314QogcRlpKCCGkpYQQ0lJCCNEXZJcSqSDHn+gVQXXHHXeYW2+91S5Iu+eee5pDDjnE7L333mbYsGFqLSHEgGbJkiXm4YcfNnfddZd59NFHzQYbbGCOOOIIc/jhhysTUAhhkZYSQghpKSFE6khLCSGEtJToO3L8CbNs2TKb2XfTTTeZ1157zey8887WkE1Gy5AhQ9RCGYQ1Zzo6Ok1nZ6ddY6a0tMSUlJSozYXIk7GTjOjbbrvNPP3002bq1Knm6KOPtk5AjZ1CDCykpbKrpTo7Onq0VElpqbSUEHmCtJQQwjseyC6VRbtUa6vpbO9gAWRTVlFhSspK1TmFyAOkpUQ85PgboHR0dNj1q2688UZbvm7DDTc0xx57rC1dN2rUqGwfXkELqnnz5tmBuampybS0tJjOzq6evxUXF5tSa7AqDvw+fysrKzNVVVV2XcURI0akbNzCQEb20vz5801jY6M1ljm6urrs7973+D/H6P+/H85h6NChZvjw4Wbw4MFR2xCiUFmwYIHNlv7Pf/5j3n//fbP//vub4447zq6Dyn0rhCg8pKVyR0t1dXb20lLFMfSRtJQQuYm0lBADD2mp3NBSzY2Npgunn1dLVVbYYKogpKWEyE2kpYQfOf4GGJ988on597//bY3TTObHHHOMdfitu+662T60vAZxtHjxYutIwwDV2tpq3/M6ynC08fvYsWNt2VScd2T7ebcBZP2Vl5cH7gMHHfv4+uuvzaxZs6xII0OzubnZ7hPh7GXMmDGmoaHBrFixoue96upqu41PP/3U7Ljjjmb06NGmpqYm9Lnef//9dpHZ9vZ2uz9EH+eDs49jQEBybjj+ttpqK7s/4PMcP5/n78psFIXKhx9+aIMqbr75ZnvfMs6eeOKJWg9QiAJBWiq3tBTZft5tAIYqaSkh8hdpKSEKG2mp3NJS7Y1NUduA0vJyU1FTHbgP2aWEyH2kpQTI8TcAYLK/5557zDXXXGNeeuklm4nygx/8wOy6665yvqQIIuqjjz6yooesNsr6Pfvss2annXaywgljE47VePDdlpbWXu+T7ec1Vi1atMhcccUVdntbbLFFdxmrkhL7+8SJE82gQYPsPisqKqxTzWXYsX0chBwf2YHufZyFOAPJSuL/++67b1LnjshzDj+yD3HoISx5VVZWmi+++MLMmTPHrn3mhc/iBOS733zzjf0M60fW1tYmtX/RP9B/uKYLFy60fW3cuHFJOYhFd1btk08+aYMtcJhvs8025kc/+pEto8z9KoTIH6SlcldLtba09HqfbD9pKZFtpKX6jrSUEIWDtFTuaqnm5d8GijtKysqiHH+yS4lsIC3Vd6SlBjZy/BUwH3/8sfnXv/5lM0/IxsLgTOk5lfJMDcog0KZMPDi9JkyYYMXVl19+aZYuXWpTqnFqIa4YWHHO4Shpa2uz2W+bbrppr222trbZsppeuro6zcyZM62Iw+lCth5ZeTjviMhyGXq5WkITZyIi0y3KjWOQc+Q8aBegPxJhlg2n32effWavF8fkriXXyGUgcuwu+q2vbfziiy+ad955x/aLLbfc0sydO9f2E/ZLBlhQNkIuQJTgCy+8YDPUVl11VXvdcCJzbTl2SszidFYJy/Bw3RmLGZPpf4zFjMlrrbVWBq+kEKKvSEvlvpZqa23tpaU6u7qkpTKItFRipKXSj7SUEPmJtFTua6mWhkbT5ase1VVcbD79/DPZpTKEtFRipKXSj7TUwEOOvwKDif3hhx+2GWLPP/+8zSz58Y9/bHbYYYecdRRlGwRTmLZ55JFHzNSpU23EFMIqaDt8hrKbm2++ub0WZNbh5MJZR8abA6ceDjHE1+LFS2x5TCguLrJZcWuvvbb9/KuvvmqWL19u1ltvPTNp0iSTT3BuDz74oI1A22+//ayDo78cXQhhBDHXlchCsqvIVMTRiAMLAYEDnFKoLiOS9uaYEcvvvfeeLX+70UYbpeV4cIByjdn/lClTct5hRjsgCFj/kfbjmHlIcGs70n5kdvK5jTfeuCeT05txKoKh/YjCxAF47733mu2339787Gc/M3vvvbcysIXIEaSl8k9LLVm8uEdLFRUXS0ulAWmpviEtlTmkpYTIfaSl8k9LLV6wwMz7Zi71QE1xaYmprq2VXaqPSEv1DWmpzCEtNXCQ469AwBBPObl//OMf1gD/05/+1GYUrbLKKmYgw0SBowfnGZFQZHF5efPNN21WHU4NV8t8ww03tA6qd99910aQT5482X4PRwjl+nAW7bPPPj3bIJPt9ddft6UraXuyxfbYY4+YJRXYz/nnn28dS0Rb4TQhywyHEy+24QQfPzk2nH755lChzWlD1vnr77KGlKFAZJFl5xW2Dq4rjiuy2rgeU6dubX9y3YmGGz58WM5m4/U3PCh8/vnntuwnuH5IWVf+hpOUzEB+56HBwUMIzt5867f9CY5VSjBfffXV1ml60kkn2TLM3PNCiP5HWioYaanskS9aqisSMVttNdV0dUVMRUW5qatFSw2XllqJtFTmkJYSIreQlgpGWip75IuWam7tMMMnrmcaWzpMbXWl2XD1Vcyk8aOlpVYiLZU5pKUKGzn+8pwPPvjAXHbZZeaWW26xUT9kjrCGX65nFPXH5M4E+vTTT9vfcbLhnFh99dV7PkNGGGse4rDA+UY99Pr6euvoo9TmyJEjrXD96quvzMEHH2wnGhx83rZlG/yd91grz+9YdLj19hhQcezhXFp//fVtJBaRVxhH+C7HUCjX7rXXXrMCJ1uOHyLccErRnpSl9Jbu5HrQN3DyfvTRxzYCjs/R/jh2x44dk7AW/kDGrqnU2r0+JeL5008/tdGC3etWdq+1xL1DNiDtKeKDw5SgAjK1uW+OOuoo8+tf/9qOW0KIzCMtFYy0VPbJFy314YyPTEN9gykpLTVDhwwxo8eMMauOHystFQdpqfQiLSVEdpGWCkZaKvvkg5Zad731zd1Pvm7q0VIlJaZ20GAzcuRos/e265rqit4OQ9GNtFR6kZYqTOT4y+OU3EsvvdROEhiJTznlFLPBBhuYgQBOmlmzZtnSjUyYONJwqpGFhwMPx8NDDz1kSzni1MOBgwGdv8VrU5x+fJfvffPNN2batGm27jblGWnbLbbYoufzOAFxbHzyySdmtdVWs5/nczjz/CUeWdeNYyTjkNIKXsce26AUJj/JqOKc4IADDjDjxo0z+QxO0jXWWCOmM7S/4Jredddd9vqRgca1fvzxx634GzRokJk+/QObzUmGH2vYUfZz6tStCsYB2x9wP9B/uQe454hswxnIvcff6NcsMJ5qm3LNIl1dtnxboWcQvv/+++byyy83N998s9lll13M6aefrlLNQmQAaSlpqXwgH7RUXd0g8977062WItuvqbHJ1A2qM1tPnWrKyqSlwiItlT6kpYToH6SlpKXygXzQUu1Fleb+J18xS5csMmXl5aaludlU19SaA/fa0aw2si6rx51P9IuWWlmSV3YpkS/I8ZdH4DxiosDhR+k9ysKR4YczqZBx2XKU0gQceAzkDNwM5AzsW2+9ddREzoBOSj3RNZTJpPymf5uk1ePoYbuU6+T/TLpvv/22bWu2x0/KMjBR4BjCAcgaikwUZDLh6OM4mMTJgCKTj5fLBvzPf/5j/37CCSfYbDI/7Pe+++6z63yNGDHCfjefJxDaC6c014c243xYnzDbcA3J4sS5C6uuuqp1rHJt2tq+LU/poAxDaWmJyeV2pr9SGhanM1Fhrk9Txz+ojER/wH1Av+ceotwtx8U9yHjFT64Dx0a7ezMq6SdcD8qD+vt/B2sONDf3/F5eVWVKs3R+/QlrVZEBSPlmspBxAJJ5LIe0EH1DWkpaKtfJNy3VYXVwW6/PV5SX5bTjb0BpqbY209bk0VLVVaZ0AJS0l5YSIjNIS0lL5Tr5pqUWr2g2r3+yoNfnN5g43IwfUWtylQGlpdrbTfvKqldQVlEhu5TIC+T4ywMYlHAg/eEPf7CDKCXgcCQ5B1Mhw0Ry5ZVX9kyI4LL8cJrhTMPx6Rx3lM0keubVV181m2yyiZ3sXabftttua79L+rIr8Ul21/jx4+1P3uM7vP/ll1/aCYHJgu+xfcpykolHdh7bYH84HjkWJgsmE/5P6UN+51jYdrxMQ+ekpOZ4PIM+152JiXPL9etOOVPOZ+edd855J0V3aYC2nvUdHZWVFTnlgKUfkn1Kv6Nv0a70K/oCooooe37yNwRWpo6dfdBfuTcopestUYFooo+yriif457lnvDDd7nHvMeI8EUocp58F2c993UXxsSVWbBeKmtqTHHAtsOCk/6VV16xbchxeK8/Dwc4/XnlwjqPZDhff/31tqQzx/q73/3OHHfccVkT0ULkK9JS0lLSUpmBdm1uabXr+zmY4qurouf6bDOgtVR9Q69tVNbVSktJSwmRFNJS0lLSUpkBDfXyjLmmobndoKaY3cvLSsx2640xZTkUkD5gtVRXl2ltauq1jYrq6j6VtZddSvQHcvzluLC68cYbzUUXXWQHpLPPPtscfvjhWXWm2NRmY0xxlh7kidykvCmDN1l8ONrcC6cYf2eyYfBde+21e8qCYsSfMmWK3QZ/p843Rv3tttuuJ/0bhx0TAt9lImP7nC/ZNi5qhNIxzknATyY5nIbuu3yG/ZHBl27Y9qOPPmr22muvfl+UOBmYKN966y17nLnu+HPXsb29Y6VAKbIiIVcMVayVwLoAOJFZn5I+6/ohx0v70k8pTxskZtIFkVFEcbFv7qWqqiobBcVxpRvOi1K9e++9t4mQkblyzUAv6cj6e/PNN+29Tp19zsM5ABGQOPd5Ma5wj9OfEXzufLn/KN0blMWbKTiW22+/3Vx44YVWIJ511lnm2GOPzQnnpBC5TK5qKcjWXCMtJS2VkUCqtvYeY095WZnVVLmAtFRnVLZfOrP+pKWEGBhIS/VGWkpaKt20d3SaT+YsMyua201NRalZc9wQU1WeG/a0Aa+lurqisv3SmfUnLSUyjRx/OS6sMLCfc8455rDDDsuoYT9UNG9ru2lu7S7lU1JcbOpqKu3PdG0fQ7srmclPDHSU08Swzbk7wzzZd0RnkCLPIrms00aZTLdILi8MehjnaT/W4PO3HZEcODN4HwcG23jnnXfs5zGw41jEaci+gO2RCcS2MWpwjThOV+OZvxNZwovvkumXCWij6dOn2zW/chEcpohgnJ84VUXq0K9wgO2+++62L9Pv6KP0sY022qjfHD5ktiJGpk6dmtF9cr6cH/c+TnucbJ3t7abVU+bTG1lVkgajPfcw63BSLtjhNcTzd8oI0wb0ayK/gN8JNGB86m+4/jgAL7jgAttWOADJAJQDUIj80FKdK4OV3HjDOh59iRT1b19aKjHSUgMHaamVWqoxIEq9ptqUpCHjTVpKiMJFWkp2qVhISw0cpKWG2ue3wID0ykrZpWSXynnk+MuxAfWOO+6wpdww4uaCkcpBFG9Dc3SEA06/wbVVaYlYX7hwoV2Tj+w6nBy8cO5hbMeZhLGbB8ttttmmZ3+uNjOfxUno4LNkJwEOQW/7sp4f2yTtG6M57UwJUZxp/I7xzTkYcQZsuummZuTIkTbrikw2fg4fPtw69/gM2YM4KDgWzoFjIc2dLKxMRfLjHMEx4j3nXOHll1+27US7uUxJkTz0rZtvvtk6eHFcA/XJadNMZplhjKZ0A/0c3L3Ae+x7q622yti+uc9xMLq68Lw4njUmTbJZeNzTONirqqvN4KFDrbPeW0aX+57jxEHPi+3wSvU++eijj2w7EBjA/Z5rJXY5X+YLhBbj4MUXX2wOPfTQnMlWFSJb5LKW4qGRsc4L92x5RXrKS0tLhUdaqvCRlvJoqYkTTWODR0vV1JjBw6SlpKWEiD1+SkvJLpUIaanCR1rqWy215uTJpnHleoalJSVWSw1amUQiu5TsUrmMHH85wgsvvGBOO+008/XXX9tSbmRw5IKRytHQ1GJa27sj1L0MravpUykfymx+8sknNhp/gw02iCq9xQCLEYusvrlz51rnGk461o7DCYgDhCwosm8wyuMYcY4LyiKS0Ue5Sbcw6xNPPNGTpTNjxgzz1Vdf2f+T1Uf9aZwF7IP1/Yhgcg4/tsk2eI99caw4E3jhQGR/ZAeyFiCf5/9kEfF7Jkpysg8cmOuuu67JBbhGr7/+um0vrtmaa65psy9F6uDIob/Sh+h79CkcUfTTZMGB7RxYiBJ3j7mFjL3GZgzSTz31lNltt92yPv445x8PFGWlpWbc2LGmC9G1MgiA+9HVZce4T4lWSnJSuhPnPOfHvctnAGe8c6KGgXZgTCDLj3GZ9jvkkENMrsF53nDDDbZ8IQ7KSy+9VNm2YsCS61qKsZ31tvxU+NaXSBZpqeSRlip8pKUCtNToMabLREwHa9VIS/UgLSXEt0hLyS4VFmmpwkdaKlpLlbPUzrhxxpb+lF0qCmmp3EWOvyyDMf/MM880zzzzjP35y1/+MucyS6CxudW0tEVHqcOwQTUpGatwRnz44YfWQE/2mtsGjjzW26NdyMwjYw8HF8Z8nGk46/gOAy/OEAz+bAuDPmtw8bfHH3/crnm24YYbWkcdDhQchhj/Nttss579s84fxnyymHA+8h7r9VGmk1KgGA75vMsYYl01HCOUWXSGRI4XBwH7c+uuAQ6WfffdNyMGRx7Un3vuOVsCMptwri+99JJ1buKE+c53vmPbU/QdnNgsJOzWlOMBjPZNpT8xtqy//vpWmPMie47M2RdffNFeN5x8OLjIXkXYub6P09EbuZSrIDA4bl7cn4wJ3H9eGC8YO3Dqb7LJJvZ8w8B6ou6zLvvRC++5xaz5yX6zlYnLdf3rX/9q/vjHP5pddtnFXHLJJXZsFWIgkC9aqr2tzY5Z6XL8SUuljrRU4SMtFR5pqW6kpcRARlpKdqlkkZYqfKSlwiMt1Y20VO4hx1+WIMvsvPPOM1dddZU54YQTzLnnnmudR7lKZ2eXWdYQvT5EZUWZqalMPqPtjTfesFk4OBbcujYY1N99911rmOc9nHY488IYwu68806bycd3GGxfffVV69hwUEIUJ6Fz+tH27IsMIRap5ed3v/vdnrKgRM7j+Nh4440T7hvHCY5Kst1wPDpHictU3HHHHa1DAmdAOsp4UUaTDEOODSdlNuGacb1wyJJZRVnKXMqsyAfsek+dnd0ZICvXpuS9+++/32arkl3q7hn6uN+hlQiuCw5u7/2Ak5u+jsGY+4I+hRN3vfXW63FacU15bbnllmbChAmmUOBexIlKVirZcdyT3Ou0BcEF3FPz58+3YxBBBDhDX3nlFdv2BCD472HajbEMcUOQAdmB/OS7lEZlbOlvOP7zzz/fXH/99ebkk0+2c0u2xwohMkW+aSm7RrBvYXjWK01lrJCWSh1pqcLCrUPe1t5piouKTFVlmaEYibRUZpCWEqKwkJaSXSoVpKUKT0u1tXeY9o4Oa/OoKC8zWD6kpTKDtJToT+T4y8KA+t///tecccYZ1vB80EEHmS222MKWr0wXGKLd+nNkzTkHAKU03ZpX3pKayTj/mtvaDEkvZaUlpqKsNCVnFseGU4zMIo7FOj66uqxxHacEf2MgpH3233//hNvjs2T5ACUnMdD7M3ow7iNO2DeGeX7SHjjsMPpTShTHBzz55JO2DbkmfckYIKORc+TYOEccj311/iHM2S6wTUo/ZqKcaCJoS5ymODYxWLI22zrrrJOTGRapQj+ZNWuWvVfIJMO5A2SEUiI2HY7coOwP1nqiv9In6ac43igpS7QVx8F9EraU6vPPP2/HF+dABLaNwxYn1ZQpU+x5TJs2zey0005Ra2IWKrQpbUAGINcWZz1tgbOO+4u25jM49Rk3udbcx5QP9rZjPPgubezW8qJd2S5jFWML64kSIcn2GKsoW5xuJyEZyJQ5ZA6g/OeRRx6p9f9EwZDPWgq9w1p/5A8TZMC2pKViIy2V3/SHlmpoarWOPy9D66rMN9/MkZbKENJSQuQ/0lKyS/UF2aUKS0s1t7T2WtqprrrKzJkju1SmkJYS/YUcf/0Ihtif/exnZubMmebXv/61XS+Kgbovjhu3Jh0TrytDhzEKYxJOLYzNTASsCUcWGk4GHAiUqwSMzWTWkRHC/73l7FKdQDBq8V0mJZx4OBdcphLbdn9z6+h5s8QeeeQRawhnGxjGnVENZwflOsNku5DhR4m7oKh/jHc4rTbddNOo93EYkt0EDz30kF0PjP1xfC4rMQh3Pomg/XE20P6cC06GVOA6s44ZjgImfEo37rnnnv2aacditmSRkTXFOopcL4yh9KlkM9JyDdqTa4RjxmVW8jv9EKemm5y552gHymSGdQb5YVutLS293udalq10ZAMOVifuuC9wjjN2hCnDiUMWQUg2qhfuP8q0kpVG39x6663NQIT2ZMygfddYY42M7IPrTBYxzgPGerdGKP9n/CbrkrGQa4KTl7EnXTDm//Of/zT/+Mc/7P16xRVXWKeFEPmMtJS0lLRUbtPfWmrRsm6N5KWyvNTU1XxbeltaKnNISwmRf0hLSUtJS+U2/a2llvuqu0F5Wamp9lR4k5bKHNJSIpPI8ddPERoXXHCB+dvf/mYdf2effXafSq8xMFNeEmMxBmTWAsPhkoqjjomECQPjM442YDtMKuyH7eIc8DsZXEaMc8wRgcL3+S5OICYq1ifDGYTzge3MmDHDZqi5SQxnBE5HB9+nDB+fZdLCMM62+B0HXBgHKZ/HmP7ss8/aNdHcmoH85PsY1zkfHKKA04PjIxsPB4k7N2eMJxOKiZTz4pjILCCDzy3wyjHjGMRgz98SwXdefvnlqNKLyYCzhvPieDDqM/nixOxPhxsODLIkyV5i/5wTZSndenT5hjdajTUKnQOcfuLuKcSWy8zj7/R7vkP/JVssmSy8RI6/4pKSngzWMNAHcebzHRzCvLgWzmFN6VuyWf1jDvcJ14/zYp1LkX3oU4w99D36E/ca14nxC4HvxkbGbeCa33777fbzQQEKbgznOzj9cAD+4he/MOecc05erN8ohBdpKWkph7RU7pEtLdXF80iA46+ivNQM8jj+EiEtVThISwkRG2kpaSmHtFTukU27VJDjjypvNVXSUgMRaanCQo6/DPPEE0+Yn/zkJzb77JprrjHrr79+Ut93pSkZ3JkIyFbj/zj7/Fk86QTDMplF7IMsOUrdkblH5gqOO7cGFo42V44wVoQJTkEmH9bVwyFB5glOuC+++MJumwwn9oNzj4w/siApjcfn/OC4wxhOlg775ji8mXnUoCbbj2Nj4sPZyHEzEeIAxOHHeW2//fY9D/pkrTGBOkelKxPKPnixdqBzBpLpxtqEbi004Dj+97//mX333Teu8xXjPeUbyYLEUZYKtCXb4FxxBHA9KLfZX7jIIl5cN4QFoiQf4VymT59u+xQRVPQr+gL3KmUdgevJ/cffWAMO6AP0FRyfONnI0CLL1N0XziGDQEvkkMXx5zJsHQg41nzyH6s7Hn+fIlsNB7XL3KXf47Tmb/RT7g/Oi3uavux10LJGFPct9zf3nTtHkT24TgQkcA3pi9znrh/Sx/g715Xry3uMYc4xyPjlHZvoN1xbggQIYGDs++Mf/2jfIxOQyEAh8gFpKWkph7RUbpELWmrpiibT0dkV9V5dTYWpLI8uoS0tNXCQlhKiN9JS0lIOaancIhe0VH1js+nsitZSZPuR9ec/Vnc8XmSXKjykpQoHOf4yBIbWU0891TqiMK7+5z//STrDAkcZRn0cKzhZMOpjnI9XejIdYCDG4YVTxw3o7qbHgZfuzC6X1syL/2OUdtErXtxafDgFndORY+TYMJIz4bHOnHNEcQ1wiLAtnBt8h+wZDOM4Ax04G5lUcTRS9oJsPBxsTKqsGeTagPPHWcnfvRMdjpZ33nnHbhcHZpDzhAnyzjvvtFmBZFf15RrOnj3bOoCBTMV0rw3mhTam3WlfBATnQWYkr3TUEs8WXDPuL9aDpJ8giBBSOLDd+nfca8C9h6PdnS9iin4XD5wxOJpx1tMv+Lz3/qcdWX8PZw2O5uHDhtn7mz5f6rme3IuUBsbZT987+OCDrfOGPu/WxuTFd9mnK1froL/zec7NradJf3GlfvmdPs+15TzpT6mWiBDZgb5AH+Fak/2baM0xrvnll19uzjvvPHPAAQeYv/zlLzZwQohcRFoqPNJSySEtVThaivfKK2vMoMFDTW1dnampKjfVld9WTpCWEomQlhKFjLRUeKSlkkNaqpC0VI2pqqkxg4cMtbalyoryqAAqaSmRCGmp3EWOvzTjHDwnn3yydRBRXg3jPiX1yBRLho8++sj+JIOtv8ChhLPHW4Kzv3jyySetc401qHCGMEkxeGDQZg2yp556ypYKxfHnLU+AoRuRhlONSZLMPBcRQ1lMBAnbJtsSh1WstQLZPk45HCHe6BpgW6ytt9122/W8x/4psYghHecYf8NBxuRMZpUfPs+x4LwEJt1knag4MnFseif8dMJ5cv7A9mlbnL04Tbkm+ezscxBNRR/H2YGTmcgoIqC86zXicCcLFEey37HKd+inXAcnwuJB/0WUsU2irdx36Afcb9xr7Iu2pn+7z/OT/sHac/yNvsY9wXjAvr3OY4QYUV7xrg/XFiegzajFcd7eYYjXKi0tMV+tzODEuUiUWb5mcYrwkLVL6WnKB1955ZXmsMMOU/OJnEFaKnWkpRIjLdV3pKXGmKHDhpvWtnarpcpLS83cb6SlBhrSUiKXkZZKHWmpxEhL9R1pqTFm+JChpm3lMial5eVmzry5sksNMKSl+oGISBvz5s2LHHzwwZERI0ZE7rjjjqi/Pfroo5HZs2dHli1bFmlrawu1vY6Ojsirr74a9V5XV1eks7PT/kw3M2fOjCxYsKDP27HH2NFhX8kcJ59dsWKFbcelS5dGmpqa7LnSZi+88EJk4cKFcdvqq6++st9rb2+3bez2/dxzz9ltJeLpp5+OvPbaa5HW1tao9zmGd999NzJr1qyo97/88svIE088Ya/RtGnT7Htz586NvP3226HO9dlnn7XbDssbb7xht58p5syZE3nppZds+xUqM2bMsNeN/vLRRx/F/Wxzc7P9bBD0kY8//jipfXPN33vvPdtPgWv/+OOP25/33Xdfz7XlvcbGxkim6GhvjzQ1NEa9Xn7p5UhLS0vk9ddfz+i+RW5Bn7z99tvtnHXIIYdE5s+fn+1DEqLftFRHh7RUUFtJS/UNaamBoaVa29ojS5Y3RL2ef/ElaakBiLSUGLBaqrMz0oHNJQl7RtbsUu3tskvFaSPZpfof2aUikfaW1kj9osVRr5eef15aagAiLZVZ4tcEE2Gdp+aOO+6wWX4777yzze4jY8zx9NNP2+we0rdZ047/k0nEenP+7ZAxRhYQWUWbbLKJzV5zkFnW1tbe83tZWWnCsm5hIFuOxTspUenWt0sVzqG1sdF0dXQvOFtUXGwqa2vsz0SQrURaOS8vZB8lypYkW4lsviDcen6J2GmnnWw6PZlW1Ndm/TyuBZlVZP+5TEMHmWJkw1EakWvzyiuv2N/JmPJDphalSN25ca5c3zfffNOWFQ2TbcP3+3p9giBbjAwzst7IkAyLWz8MgmqGc7xkmAFtl8k1KZPNZuW4vBmdQVAKgesWBJmo9DkvtAX3OudJiQYy6xgHXGYe15z+QQkHsgjpZ5TWpN+Q6Ucdd/of2ZzJlgVOBjL9vNB3WWuQ0rZkJWZy34VCZ0eH6WQsLiIyrcIUl2S2/HKmoE8y9rBOKPMXc5TL/iuE7F6RX/SXlmpr7zCNzd+O7ZQErPCtBZYK0lLdSEtJSw0ELUWmn19LNTdLSyVDV2en1VNQUlaW8aUsMoW0lBiIWqqdqkxLl/FB+3vl4MGmvKY657RU8/IVpqu9e7wuLimxxxnmuU12qfjILtU3ZJda2Y9WZvpFaamV6xjKLhWOzvZ209HS2l2trarSjnP5iLRUZlGpzz6Cc+gnP/mJeeaZZ8yhhx5qrrrqql4GU8pAUrKP93EozZgxwz7MUsKPtFbWtrMXo6jIllTkb2+88YZ1ZPFQi9BCuLS09H5wrqgo79ODEg/jOEEQfOlwIrY2NZvOtrao94pLS63zL5dxjjvan/8D/8fwkMgATr1sSh1MnTrVVFRU9Po75UgxPlBqkbUFnQMREU35RtZbo0xjPHBYsZ2wTsxEx0u/Q7yzXfomx+U3vsSDhwTWFeNBwjk1Me5wTvRH+it919Ucp0wqzoVswTnz4EPJWP5POdZ45WzpA1999ZV57733rPEo6P5gGzwQeR2IPEBRQ52a7Dy4IFr46cBBynp6tDnXnNKvOLa9a05mmpbmFhPxHBPXb/oHH5jautpAp3U+EOnioTPCIJpxh1VHW5tpqf/2wZd9Vg8elLciywtlqk866SR7r1599dV2DBSikLQU4/HyhuZe+6+rqTSlfbiHpaW6kZaSlhooWmpFY7Pp7IzWUh9+8IEZNqQuf7UUBvxI/2gpDFWtzdFjcWVNjbSUEPmgpTo7TcP8Bb32XzNiuCkp/3Z91WxrKZ7XOnyOBYIMqobk9pIW0lLSUgNFSzXhmF8ZAOTGgA9mzDCDhgcnU+QD2NnsEkLFxZm3S7W0dgdgOIqKTM2IYdb+nu/ILpVe5PjrA4888og5/vjj7ZpbZ555ph0oGaxY380ZTBlccQo5Z5LLHmOAJdsLxwlr0yUaFPzZfunI+iOKgqhYJo90DUrNK+qjnAqO6hwWWETcvPPOO7YNmDAxesfKHmQCZVL0grOLjMQgpx8gvln/D8faiy++aNZcc027TyYEJrSwDjecZ4jyVNZf4xgRC/QVsg45v6AsvbCwLe/6izgQeQVtk/PEqcqxeyMO+wuO8fXXX+9Zn5EHnPXWWy/m57lnWeCY+5rjxUHKAw/n6Y8e5+EkSBzhpHXZk3yfdT7pX6wXsOWWW/ap7ftKR3u7afeNJSWlJaY8Rv/NZWyGcUOjdcYBIqeqrjZUhnGqNC5d1muMKykvM1W+TOV8hYeCH//4x+bVV1811113ndlrr72yfUiiwOlPLdXe3mEaPNl+jqrK6AXsk0FaqhtpqeSRlspfLdXS1m6aW6IDHcvLSk1NVZ5qqcamnsDN4tISU1mbWS3VXF/f7Wj0UFJaaioKpOqEtJQoaC2FDWDJ0l7vVw4eZMpranJGSzUtWWqdlFEUFZnaEbkb2CgtlTzSUvmrpdqaW0xbU1PUe6UV5VaD5Bs2w3jREtPe2Gh/L6koN9WjRmY0oKlh/sJedqnSygpTNTR+Ukm+IC2VPvLfFZwFECannXaa+e9//2suuugi87Of/axHoLz00ks2owiBRQnFd99915ag8ZevdIMDkVZEWjAI8x2cSgza/H7kkUf2fDa2AEpNGBEVwo1EmYd0wkOif/DJ5INjX6DtuT5cBya8bbbZxjqvKBPEBEnbeDPxyBjjs0yQgOOLUkPu91iQCfjaa6/ZaBschGTK4QRLdpKlf5A1mKzjj76EY5PzS1cZHdrliSeesGVBcFwSDRivnXnQoG37G/ZN29PunHuYEp/cGzz4uNKk3CeUcw1y0OJU5Nz87epKW7Etsjp5oALaCyfoHnvsYbIFhhVsLTgAu38vMWV9iM7MJu3NLT1OPyBirKWh0VQNypwTLiiwIei9fIUSahdffLG59957bcnPo48+2lx66aX2IUGIvNdSxcGaqThFI5O0lLRUX5CWyl8tVVGGlorYkp9oKpx+lA3ORzDie6u1sFxDS2NjRgOa/E6/WO/lK9JSopC1VCx7Qqo2n0xqKeNz/OXqUgayS6WOtFT+aqmyygpbacCV/CwtLzMVeWpzaF2+osfpB52tbaZ54WJTMzpziQ9BNqhewQ55jLRU+lDGX5KQOXTUUUfZ9d1uvPFGW3/cz/vvv28dNBhKySyKJTAYmCmpgAOJTCKyyUaOHGk++ugjs9tuu0UN2ogxMv686dlsl1KfyQoYykAgAidPnmzSjX1Y9NR/h4qaaltWIe73Oju7B6miIuuYyIQoow1ZTw9nHe3IPrh+1JHfYYcdbHvTNojbcePG2fr4u+66a0/NexyCfNd7bAxGXDfKdQaJaC9k0OAETBUcWDgZk20bvkdmYayMxFShD+EcQCz41++j9CfthcCgXWmfdO8/ETxAcC9ttdVWdt+UJ+X64YCN5TyljyCIKMfKPc79TuRjrLKgODN5IPI/qCC8eLgC2uGII46w143yKojTZMqqivDlIRy1w4dlrNmali/vWcPUK1rzVaQG3QP0WTJj6bOXXHKJLfuDQWGLLbbI9uGJAiGbWor1/do993BJSbGpq66UlgqJtJS0lLRUYdG8Ai3V21BUM6zbOJgJcCz6jVOl5eWmvLLSFALSUqLQtRTl5bxlNLH1VI8YnlNaqrO9wzQvWxbtBBk0yGYUxQONyIugsIpy2aWCkF0qOWSXKnwa5s6zzj4/gyfGXl6orzQuWtKzhqmjrKbaVGYwCL4/kZZKH3L8hYSSCH/+85/NhRdeaM455xwbWRXPeP/AAw/Y8oqUVEQ0BZUtILKHbWBkRfQAoopIrKBt0/E5jq6uiCkuLrKfSVZc4fzAWYEzI1PwINexspRgSVmpdeQlLD3Y2hoVnVVRVZVW5x9RLmTxsebcmDFj7LZxAuKMQ2jijMGQQXQbThscVwhfMtko8RgPriMOMGrpu2P+7LPP7HUl04xssXQ4/tgeDiyckmFAwJPpx7llQkxz3pSjwJnF/x1ufT9qkLuIov6Ea81DDm21/vrr22tCn+d6k2kZC5yUtBdOUh6OKMvKtSdKKh44+Li/Y2U98uCE8zhXIwzzGUoLs06MF9o5k8YqxjdvSWNbXnRQXUFeX5ctfNddd5lrrrnGnH322eb000+X41rkvZZqa+8wnUTFFhWnZNSRlpKWShfSUt1IS2WPlnq0lC+ICi2VwVJRPKO0Njb2ZPlRCosyn9JSQuSPlmpvajZdHe2miOo/K8sH5pyWwonX6rKJyhMGo1PCub7pW7tUaWmxGVqX3rFJdinZpWSXKjwa5y80Hb61i7FrD1oteAmpdEAAPCWNIyvXnLbLzwwdkrMV9/qC7FJ9Q46/EBD9dOyxx9pFVW+99Vaz+eabJ/wOTgSyJCirwMMNDgQEAyKJyCmcJTiEmPhxyrisskxCuQYcMji+cgXag6hPP5QeRJz1FWrFcx1w4nENOH///imDwbp7fM45/VgPzi7KGkLk8bnp06fbfeBo4rry3tNPP20j5tgGghmhHRSJlwysPUeN7kTrOpKFhiOTyD5vudJChvUZOW+uMeftHK4wZ84c2w6xyhVSG577lMxErlcyWZJ874svvrCfjbU2pMgMNpJzxYpeGcZlGY4Y5/52keoYqwrRUOWFSNx77rnHnHvuubZU7k033RQ6AEEIh7RU5pCWSg5pqdhISw08OimTvqI+6r3yarRURea1FBVYVhrHpKWESIy0VOYguH7RsujKVVBbXZGWUs6yS8kuBbJLFSYdra2mce78qPeqhg8z5XW1mddSBG8VdQekS0uJIOT4S8B9991nfvCDH5j999/f/O1vfzO1KS40ioOPNeJwDuFkwgnUnxC9xZpylDDMJWzEp29BVyAaq7wP5ZRw4uEEoqxjrDKNXh566CHrKMJRx6LYDKBkuuDIw4mLIcSVuOAn0WlE222yySY9ziSuLdeYiDmcQLQ1zlYcjkTZJbs2XxCU6sBxvOmmm8b9XF+zC1Olo7PLNDW32kwKyqfVVFWYkn6IOOFaO8dr0LoDtD9rIPhLkgKOXhy33jbFGUz7hS3JSR9hH5mMWBSxDVYdLa0msrIufDoCBgoFxiKifBl7WOPT6wxPxsDgHNuMg1deeaVdD+C6666z86IQYZCWyizSUoWlpchQaG2iFGJXdyZUTbX9mWmkpQa4lrIlqiKmpIzgy/hZMQMJaSmRK0hLZZaOzk6zZHlvu1RVRZmpq0k9qFR2qexoqaUNreaVj+aZ5Y3tZlhdhdlmndGmtirzc5u01MCF9ZLb6hu6K6ARQFWdvO2lUJGWyi7x04YGMDjqzjjjDFsv/Z///Kc5/PDDU94WziBSUzfccMOUDK/pmHxGjRqVk5lfsSISYi0anQiccW+99ZZdb48ym2EjHvbcc0/702XSkfHF/2mzTz/91GbZOVxZGgxtlA8lSwynKiUhKYfh+g9lMKm5z7mkw+kHOCZxOsaDvubPbOwPaI8VjU2sz9v9e0enWdHQbIakuTxGEJQk8V4jPzjsibILcvzxPg5iL1xTnIH8TARZvWTwkgkl+h9KCZfUDuypDCFFyRD6IuMDJW0Z8xkHCHyYMmWKHdO233770GMr9xRzB0EqZEA79tprL3P77bfbLPjjjz/e/OlPf4pZ5lYIaan+QVqqsLRUE5nsK8UUgVSUl64eMlhaSmRWSyWoJlLoSEuJXEVaqn+IFaxcWiK7VL5pqebWDvPg61+atvZOGxzc2Npufz9km8mmrDSzQemySw1cSsrLbZbfQEZaKjcZ2Ao/TnbcYYcdZv+PE6kvBn2yicgGItsiVXA0kRJOFhLOqIMPPti+TwYTk20spxLf+/DDD215yVglDnPBWMVC7m2exaGJak7l4ZNsFJw1ON+Szcz0l87caqut7M+ZM2f2WhfOGdjIBiN6iZJROBqHDRvW81myY1jjLhNwLWfMmNHjyMUw746fa06f9Rrq+4u2js4ep5+ji/r/HZ2mvCyzQ02i0qdcMzIwcYz41x1EjOK4Y31HB/2Hh6wwwo5MPzn9RDbBQcd44MYfMllYZ5SMZDJhCUwg8IS1L8M4s902XYlqPwTCUPKaeZK5DUdgJtYRFfmNtFT/IS1VOFqqkzWyfWKKNWVZzzbTGe3SUmIgIy0lchFpqf7VUoNrK83yhm/tUhVlpaayInnHlexS2dVSXy1qMK3t3ctyALKqoaXdzFvaZFYdmdnSi9JSYiAjLZWbFN6qj33krrvusqV/yB7C0ZasQZ+MM0qjsUj9ggULzDPPPGO22GKLPom966+/3jqXmDQPOuggW6KI9eMov8aNhbOL/QatXUfGWa46/Rw4+Sqrq60DsKKqyv5MNkOM7DraioyWVMuxBoFDiMyZWOA02mWXXazzFeO6H5xHrI+VTli/DicjTl8cyy+++KL9CW+//bY17KeaMVnIcC2C7gUc47Ql6/y5bE4XdT9t2rSe9/jJuokI2Ndff92WO+Q+7+u6jaJvsNZeG2tzNjf3rLs3EMF4zbjJa5111rEBEPRPHHK8F2sRcde/mUvIYGZeIViFcTTeOMzcyOdxDjJn3n333Rk9P5FfSEv1P9JSySEtlRrSUoUJmre9rc2++P9ARVpK5BLSUv1PRXmZGT6kxgyurTJDB1WbQbWyS+WjlvLFoucc0lKFCQkPS+ub7Yts04GKtFTuoYw/T0oqpT1xsrF2EQ62VAdxHAJkFTEJEkGM86C5udlmSIRxSpGhQcbG8uXLzbx588wee+xhI0dwKH711Vd2Yt1hhx161h/DSPvmm29aI61bKB1nFRMtGU5sb8yYMfazOKH4PseWS7Coe6prwXH+tM2WW26Z1mPCwRr24Zd2XmuttXqVkMQhidhxGYTpYpVVVun5P9cWYcV+eD9bJV3LS0tMU1F0oHpxUZEpK838ujT0fa5VLGHJfUFmnr+kpwNHCZH/9CO3Th/ZnDh+n3vuOVvKgm1w37BGJxme7JMMKu69vjj3Rd/WpGlZUd/ze3tzi6msqzMlGc4wzSaMNZSMJqvYOeb4SQa417FN+U3vuMO47/7O2DR37lw7LzG3cO+QoYwTjzmHfh0UyOCHY2DtW+Yj1sLlYY/Sn9ko6SJyA2mp7CItlf9aqoS11Xxiqqi4yK59nWmkpQYmBE21Njf3/N7R1mbKq6pCr3Odj0hLiVxGWiq7YJMqKZddKp+11Kojak1FabFp6+yycopH5uqKUrPK0MwvuyQtNTBpaeswcxfV9zidl9a3mLHD60xlhexSsktln6KIN8VlgIIBlJJlOMXIWgjKikgGvwOCzL+PP/7YRsTgEIyXRUHWEQbatdde2zzyyCM96zQxcY4fPz60QdXtkyxBnIg8vHFcZKbhAMF5UQhwTmRDYnhO5zpyZA/ShjhrE6Xr41jFaURZPQclVukDXMdXs7SgcbYWxW5sbjWdXRFbD7+mqiJlh24y4BCnvceNGxf1Pg4OyrVy77DWGQ68WOC0oGxhUD9CwBK9xn3EWo44hQFny84775yBMxJhYN2jLt/aApQKrhqc2GmVi7AOAo43ghm8QQcuqIPAElfamc+QzUffJuP7nXfesYvHMxYGZbaSpeqCI3D4EZBCViDOu3TAWqiUocZheMcdd/QEm4iBg7RU/iItlXtBLa2smdzZaYpLS0xFTY2d2zKNtNTApDUg0JEgAqqx5CPSUiKfkZbKX6Slcosl9S3mpRnzzIqmdjOstsJss+5oM6g68+vSS0sNTL5ZVG+df17Ky0rM+JGyS/mRXar/KVz3c0hwGuH0I6vuqquuiusYCIvX6YdT6Mknn7TZFPfee6+NeuGF4RUnAvvl8wgFHHU8rFBmFA488MCUU+NxGOLgw/HBOeEQcZGbRDkWCrQv2S/O+ZcuwU0bhXXWYZDnelIij7bGAE7WGI4m4D2uazpLkOYqpSUlZnBt3+4hV3KTa0smXpjMI/o3mXmjR4+2/Zzv4wjBAUL5V0ru4oQNAmcJ1wyHfyznsbsv2bb7DM5h1k0T2YN1j8K8ly/gYCZAhPnCH2nvsrm975OZR1axi6Qi+IDxMBF8hu3wnXTB/UO2+09+8hNb+hPnXzbWdBDZQVoqv5GWyr2yrdV9DGCRlhJhCYoBzue4YGkpka9IS+U30lK5xbC6SrPvln1bkkVaSoSlo7O3Daoz4L18QVqqsBiwGX+c9uWXX27OOuss85e//MX8+Mc/TmvGWKx9skYYDoPhw4eb1SdPtgv/fv7FF9b5gNOCkoHpOg72hzORkqFum2Qr8SLjL9Pn25+kM6uOdnvhhReswygZ3njjDVsK0q2pFS8jUASDI4PyuDj7XnvtNevMDev8pp/Tzt41yNgW8D7OcO67IEHH/RDPYUJmHw78DTbYwF5jnPe8RylQVxpU9D8tDQ2ms6096j3KoVXW5Z+TneABxuuNN9447eWCXGlPryOOaEQCHNZff/20rr3A+Hn11Veb0047zVx00UXmlFNOKai5RuSelpowcbJZtHixmT3rC7NggbRUX5CWKgykpUQytLa0BFZPYN31fENaSuQjuaClxq460SxcuMjM/fpLs3DhAtml+oC0VGEgLSWSYcHSBtPQHG2Xqq4sM6OHyS7lkF0qewzIjD8cMWQlPP744+app57qlzKMlO/85JNPzKJFi+w6Y7M++8x8OfNTM+ebb2y20qiRI61hlpshXVkYCEZvltOcOXNsqTjKwhWSIZYSjLQZGY5ksfQ1a5O2YRvx1owLItY6bzhaafeBAG1GxhVtmGxZKr6LMw3nGo462p718+ivrD2WCEog+u9lsqHeeust60DEwbftttv2+h7XJla5Q76Dc37KlCn2xXnxeRw0HC/3qxx/2aOiutq0dDbY9WmgqKTYVNTkZ2mqzz77zDqr0w3ZqrNmzTK77LJL1Pv0W/o99wdzItmrroxoX+Ae+elPf2oDHVgrl8xbHIGMg6KwyAUtNf2jz8xb02ea+XO/MWPGjTNjpaVSRloqd0BfUDKddZJLSpILzJCWEslSXlFhWlfqd1fmk/fyEWkpkW/kgpZ68e2PzOKXppuFC+aa0aPHmnUnjZJdKkWkpXKHuUubzRfz683YYdVm4qjknC/SUiJZhg+uNm0dDaatvdsuVV5abEYMll3Ki+xS2WPAOf5wBFBCE+MkToWxY8dmdH8ukgqBRUYRDqqF8+ebzTba2Ex75x0bTVVcXGRqyivMvvvum9bMCz+sHYVTheyPQoK2JcKTDBYcMay/xrpXfXHIsM10JsMOBMdfR1ubaWtp6fm9tLzclCdh7J89e3ZPeVRKfPIio45+myxkMuFw4P4m0497j/7AvUhGphcci0H3HWuo4UjeaaedrAPQlX/FWeIWsWb9zGQdxCJ92DVoBtWZCAt3m4h1NudrUAOBC3V1dWnfLuvKst2gduHe4MVY9/LLL9t7joxW8I5/qbQp9wpzLPPtjjvuaO65556Mz7diYGmp2d/MN1PW28hMf+8dM3KV0aaoqNi0m3JpqRSRlsoNWtvaTVNLW8/vleVlpqoyfECgtJRIFsZxsvu8jj9pqWikpUShaqmPPp9jRk9a16z48D0zfMQo02WKzPyGiDlMdqmUkJbKDe599Utz7q3vmq6Vz7M/2WOK+dne64T+vrSUSJaS4mIzbkSdae/otkuVl8oulVYtxauoSHapFBlQ1mpKB26++ea2Ez377LMZE1c4AkgNp8MisBAACCsy+7baaiuzwbrr2Q67wfrrm6lbbmm22mJLM3nSJJNpczXni3OMrCqOqVAgO2+bbbYx6667rtloo41sdhjRc2RlpQKOUdbj86+xlSpkWmZqfT8GwYamVrNwWaNZvLzRtPoWlO0v6PNep59zBHYm0c9wpn399dc9v+PE5T7ifk2WmTNn2iy/tdZay04wlNzAqUjmE44+YF/8HsvBiwOZ82Jbbp1M7h/6B+8DGVo4GN3vInNwnehP/ra22aWlJXZNpHw1VBEYkM719oLG/njjIe2Go451Mslw5d5tXLrMNC5ZapqWLjOd7e0p75dtknnO3MscLPKfXNFSU9Zat7uywbrrm00339JsstkWZtUJk0xXhgvYS0sVppZa0dhq5i9psHqqJUtaqrOrK8rpBy1t7aa9o1u3hEFaSiTUUp1dpss3ULpKHfkcQJVrWqqzo9O0NDV3v5qbe54/UtmvtFThkStaatXV17Z9d/Up65r1NtrcrLfRZmbU2AmmozOzYkpaqjC1VNP8BWbpJ5+aZZ99YdrqG0w2mLO4KcrpB1c/9ol5feai0NuQlhLxQEPxrOBf14+xtLysxFSUyS6VLi3FOLLkw0/M4ukfmaUzZpr2xqaUOufYAa6lBozj7/bbb7elzs444wxz/fXXp7XsGJMc0VOvvPKKeeKJJ8x//vMfs2LFCpsFhJONjkUZSCYQOnJRcfcDVVlZmSkt9SRdZvhBCwfGpEmTbGenvEMhw5ptCF1EbtiMMYQw0XY4fFj3KtFgj3GmobnVtLV3JBxkyBIjOyzdrGhqtS+MQq3tnWbxiibTmuB4MkEkxsOsK8EYBiIPuV94OAHuIdqNhWWThXuL60m5Da4/DzgM7kweH3zwgTUO4MRjnziLg2C9Az5PX1pjjTV6XrzPvTR9+nRbUojry/3P77xPCnuu0IXRob7BNC1fYdfD68rTBYY7WltNw8JFpmnxEtO4cJFpbWw0hQTjMU5q75zS0dFp2nF0piHzmGxXsqIp6RML9sl9U11VZfuMjapa+X7zivqUndvMtcy5p59+up2DmYtF/pJLWsqVQPRrqZKVGitTSEulT0txzclwa2lts2NetrTU8oYWs7yxxbQxZ7Z1ZM35F2uOxlETFmmp9MP819LSapqbW6x+zNdgLzTFsvoms6Kx2SxvaLL3XcFrqc5OO7ZE+ltLVVebNm+1l4gxbS2tJpJiZIq0VGGRS1qKrBQoLSuzQZTOJFWaZJnpZJGWSp+WImho0fJmM39po2lsac+almr8Zq5p/Gae6WhsMu0r6s3yTz83bVmwi3w6b0Wv52f69MdzlofehrRU+sFeuXhZg5m/pN4sWd7Yy2mWLzQ0t5lPvl5sPp+7zHzy9RKzaHlqjqh80VLYveub2myAZDquWTJaqqqs3KyY9VWPrZn1qJd/Mdt0pWjvrhzAdqmiSDrrGeYgnN5FF11k/vSnP5nbbrvNfPe7303LdpmMMfpTihAHgiv/xxpJrsRkrGgaSqlgSHUlVaC8utqUVWZ2PYUXXnjB1nHHMUF0Wb5GdCbLhx9+GOWM4bqQHegtsYf4JEsQJ1BNTU3c7TH4LV7eYNdfcQyqqTTVCUoxEdlAuUgy0MgUi3L6pti35y6u7/V+ZXmpGTaoupcg5GGTsrKZKEuJg68lwBlTXlVlHyTiQRlNBv8xY8bYB5L58+fb0oAOHGqIVJfqHQbKdH766ae2rCeRciNGjOhZq4B2p++zppn3QYv2dPcE9/cXX3xhrxkTH1GR3rUjMfq8++67dh0zL2yD73jX1syqoWpFdP/g/CiNmeje5zwY27gWLnqatHxX4rQ/YZxsQBj4ZqqqoUNsOdlCgIdzt44lbd/Y3BIVjV9TVZn0Ok9+2C77wZE9cuTImFGgn8/81Kyz5pq9jBCVdbV9bu+HH37YHHnkkebMM880v/vd7wbMHFQI5KKWwhny1cIVUQ8hI4dUm8E1mV1PUloqTVoqEjH1Dc1Rxhl0VEV5Wb9rqa8Xruj1flVFWa+1OXDMRSJdthRiJrQUfRqnjJ+aqgpTXhb/PKWlMgP9A6efn8rKirzSUtxny+t7G6dqqytN2UrDf6FpqWYcbZ7xhWtGKa5+0VKff27WX3e9XlqqvKK8x7mSKtJS+UsuaikCXt78ZIFpae+01ae4Y9YaP8SMG56ZrDCHtFR6tBQa+OOvlpi2jm+18Gqj6szwQVX9rqUWvTu9J3DUUTFksBk0aUL0Mbe0mM62dlNSUW5KM7Ce7CffrDAHXfJMr/f/+oMtzG4bxc+slZbKDOjbRcsaosw52CVHDKm161nni5biPD6Zs8Tfzc2EUYNMTVXh2aUYX76Yt8wmmLhrNmmVwfYZqT+01Mzp080aw0aZSt84UTdhvKkYPKhPx/DwALNLFbTjj6jMH/7wh7Z8wkMPPWQN/ekAzzClEpgkmSwpIYjTIpnOgjGbcmo0Pw8AJQmcI+mATCoGS68Hf6BAxCcGmw8//MCuq8gLnnnmGTvYkMkVNtquvqnFNDb3jpJdZVg4hwoii2tBKcpMOP5ILWdxWUdrW3QkPcYjIijSTStlbDwlAYtLS+2aIbHaBCfcG2+8YR1qOKQXLFjQHf3f2mrFLtcFxx3vPf/882bixIn2c2F5+umn7b3JgxDXl2uOM5AHIG+9aCYVHpJsNm5RkX2PYyG6ju+7EimbbbZZlFjm2ILakb9xnOmM3kyFjtY209bc24BYUVMdON7w0OgtYzpkyJCoiZh2ZJzD2MkDpf+60q/5TronTcbJ5qXLer1P/6oaMtiWpcpnXP8mspC2X3OttU1ZWbRwRBDX1lTZDFZKvaRa7oV9UZp20KBBdv4KuidfeO45s8rwEWbCyvU2HTiMEznxw4DDfJ999jE777yz+de//mX7kshtcllLEdRCFCKO8qqK0j4/iIRhIGspyi3z+nDGjG4tNWZMylqquaXNlrH0M6Suut+1VJDjjyCqkUO+Nbi1tUaXLy8tL8uIlmpqbo2q3FBaWmJqq2I7maSlMgvaqD0gsri8vCxwLs5VLUWkfUNTdEl+IKiI/pXva1X7tdRaa69tysuj9QVtWl1V2S9a6sUXXzRjVhnd67mlHOdjGnSrtFT+kctaijWp5i1tskbeobUVZkht5rX5gNZS2KW6uswHH3xoRo9epU92qW8WN5j5S6ODOrj0G04eGcqhkk4tteid7qpNXsoHDzKDJ0/s+b1x/gLTsnhpz+/Vq4w0VcPDB3eH5Y/3vG9uevZz2wYEvuyw3irmbz/cKmZVEGmpzEKltBWNvTXI0LpqU1FemjdaiozaL+f3zhytrig140cOynimdH9rqZHjJpnmzuhzopTplHHD+kVLPff4E2ZYcZlZbdy4qL8NmrSaKa/re3DKuwPILlWwjj8yfg444ACbHn///ffbgSFdEIlDdE4ue4W5ESlzyeVFBG688cb2IYS18HL5uDMBD+xt7e22LV5//XUzdepWpqqy0raDN6IhLMsamk1La29j1cihtaEjSVlzjjIbfX34s6U9feWohtR2R+8yYUZWZuP5r3lVZfof8mlf0q+ZJIiGj7fmmhOZtAERPGT6seYeGXT0V6C8BQ8DRPQk02bdJZlabInQLbfc0hrm2Bf7IPKR8+YBCTHAvTx+/HjrDHTHSnaoKxPK+MH7/M4xMzFxnkTf8b1Y7UCKPIvTunPJLcdfjV0Tj2P0tiX/Z5yM9aDhzgshwMMrbULmMD+JTCRrkzalnHA6xxj6VOPiJf6DsSGp9LPqEcNNcZ6LLAdt+eJLL5uIKbLrwdB3affaulqz5eab2X780ksvme9///sJI0DjgQOcfk4kqfdacY8ggDZaZ92ojHScq1WDB6XtupJ9u99++9lzuO+++2y/EbmJtJS0lKO9tdW+rJZ64w0zdautTGVNjR2HU9FSjTFKpQ+urQqtT9KlpYJKew4bVGUqy0qsprDrogUca0VVZUa0FMFaOLXZNnpOWmpoTjr+uD75oqViZZO6CO66au67wng+pC1fevkVa/ymPGuPlqqtNZtvtmm/aalNN94kKuOQsbIiRKZoWKSl8gdpKWkp/5zSbZd6zWy11dSeDPJUtNSX81eYJfW9HSrrTxweOps7XVpq+eezTNvy6ECqugmr2mf21sVLuhMf2ttNkW8/g1efmPbMP9r3xRkLzMy5K8zYodVmt43HxnT6yS6VXccfjqR80VJkvn32zbeO657jLSoyZaXFZrVVBue9889BWz7w2LM2mxjbaimVRyIRU11Ta/bfbdv+0VLvvGPWGbGK6fSUpi+tqjSD10jfdZ07QOxSBen4w4Gw11572QwhoqC8JfoGCjhNOH/WM9t2223t4PnWW2/ZEoR9uTHzDbvIcHP3JMN6bUzsRFGVlZbah3bWfWNh62SNVfVN0WV/iCbC8Rd2AOJYuB477bRTnyLGyXLAEYnzj33XVpebspJi0+xbt6O0uDubzVFRXm6jyLMFjjcegkjvdrhrwWTNdfriq7lmvU22MlVVlWbssFrT2txgs/PWW2+9XttjMpoxY4Z11lEvmsg5HvBjRREykRFhwjoHYcFJyBoJlP0M0+9mzZplI1WmTJliMoU1RtrSYxEbte01QNqSwp612pzRgZKNCCQmWdoy1UkThy1tzffJfmSMQWRx3jg9WQsxXTSvWGE6uI+909XK/1bU1Zry2uyMabQ70Wj0JwftQemJeAun29Jhixab9qZmm31ZNWqEzWAESlPx4h5xASvLly83s2d9bjNVuQcQwfTtWKURwkBWK4KZB0yv0Ob6tba0mImrrmaDBnASU7I33QEjONUPP/xwu79HH3001H0l+hdpKWkphy2bV99dYWDxkiXdWorSUOXlpryyMiUtRQCVX6swzAyuTZzxlwkttaSeNc+6tdSgmgpTVV5iDVQO+7jke2JKR9m+TGupOXPnmy2mbmPnjcE1FaaxoV5aKhktFVDqkz5SUVGed1oqlrOdQ6+sKDeVCcrs5qKWck5yu1ZZ2bcBh62tbaalFS21wIwZM7ZHS33x+Wf9p6VaW83kiZNstglOVaomSEsNPKSlpKWC5hOvXQqbDBomFS21YFmTmbMoeh290pIis/7EEf2vpTo7Tf3sr03bihWmqKjYVI8eZSJt7WbZRzO7Jxp0FGP1yOE9z71QO36sqRjUHeidq1rqsdc+NvU1a5jBdTXm6B0mm8HlHdJSQUkAdqkS5rziqP5n16Fc1hgVDFOystRnvmmpbxbXm2UNrYG21hGDq8zQuvhldnNRS61oajWtbZ3WaTm49tuy6HMW1ZuFyxrMksWLzKhVujOTmxvrTduyuf2mpVqams34IUNN18rywNWjRpqiNDtXmwaAXaow3NG+9dy+853v2IH63nvvHZBOP+CGmTZtmtluu+3s/ykpgNNiIDn9wOsnIJKAjK3u9yMpL/Ru16DxrLXC/DSkLjnDOJMP2WB9fQDkQZL1/MaMGGRGD68zNZXlvQxp0GuB4yxH9bLmHgO8P0sPeH/MpHXM2LU2N0ubOs03ixvNtM8WmPKqGvsZhBg1vh30baJNcGpz3+NooyxnvNIh1LF3NcLDQuYh95A7XtZhW9HQZBoam6NKqToB6RyQmYL+S9kmSs82NLea5Q3NtpRTlJOvluy+0u4MzLJS+7td56+y0rYBJU5ThcmetkYkuwmaqCuirZg8EQLporKuzpS7satbT/aQzdgVRCr3MQ9t7kXABU5ixAvrRPqPj9/rv/zKNHz9jWldstQ0zV9gln4803StvHY45Xno8mapjxk9ymZrc99Q2oK1ILh2iLtUQQSTYcsaGwhjF/HEYu9Dhg61JWGrBtWZiurwRvhkYG5mjiYrF5GH417kDtJS3UhLdeMdxxrq69OipSjt441GZ5SprequxpANLTVicI0ZP2qwGTdykKmtKo9y+kUdpPfXouKc1lJT1tnQbLTFtqats8isaGy1JU2ra+tyRktRVm7+0gYze8EyM2fRCtPsq6aRE1pqpZPPGbG6y0qV56WW4vml0lcO2d06+ailcO61trVbDU4GDWVy3efKVpbidU4/WGXUyP7VUkOGmLKKcpvlV1be3WfSjbRUbiMt1Y20VG+i7VKpj8EjB1eZwTXlUc6USaOHZEdLlZSYwZMmmJEbbWBGbLieqRo5wiz/5LPuP7rzI9CmoTHqeyVZCjoJq6WmLR1s/vNhjXngrXnmpmc/M/v94WmztLUkp7TU3EX1ZtbcZearBctNU0t2tFRbW7sNwucnDm7awoEjiaWIyO7j/zwDDBucn3apMcNqzagh1QZzKskf3gSLbsdnfmkpqp4sXt5sGprbzLKGFjNnYb111MKoITWmqrK8x+nHaa67+vh+1VJDhw8ztWNHm0ETVzU1Y1ZJu9NvoGipgnL8UdqS7DbSTa+55po+L1Kbz7jJw0Wt8pMJgsXGBxIMTk7CMCATiQSUBqQMCxEhyW+zyDr6hg+usU63kUPqbJRpMhCJQXRLf/VR7xhPVFlfF7fvK9OnT+8RQAhfaklTjpYMOUTUG+99FPX5zq6IjWZDOPHyCgMGZjL3mNwhrHAeO3asFUFhIVKY+6g7ixQx09XjVG2y4ubb0og87HONEVpMwky26TaqkOWJ0PPS2NQStR8EOM4+68BZWZINEGX0P9aByASUTmUMQgykAxvJXYMDqne/JfLH4erA9xeMH/PmzbNRgv7SFIhPIqzIuvZGXlGqoNW3ZiERTK1Ll/YYoGurK00V0fcV5fb/pSsFLJFUhx12mLnzzjvtGpREt3kfVJKFQBDmTNbaZFs8EHAfuTUwMw3j37XXXmuOO+44exzM4SL7SEt9i7RUN/aBduVDLf93D/OMd33RUjVVFWZQTZUd5wbVVSddiaA/tZTfIEamX7bLTCfSUh98/GnU55EHyxtackZLLVjaYEussiv0zIJljaatvTP3tNRKZx/l2JzTL1+1FFl96AzPLW0p80RY54OWstl+vuvGe85pizGOZQ0qystshRf+74yB0lKiP5CW+hZpqd4w/jotxZjcFy01afRgs/aqw8ya44aYdScMN7VVZTmhpag+5F06wvu+o3L4UFMacj3DbGgpKoddeusLPfYoXpR7/PeTM3NGS81dXG+a2zqsTcqu17mkIWqtaLQUfS2TWooAHL92wAHo3Q/2jGGDamyVNEp8OntkPmoplgMotzbV6Kpq1ZVleaWl2ju7TENztKMYbdXQ1J1IQvnSNcYONeNH1Jlxw+vMmuOG2UQTkJbKLwrGM/bUU0/ZNf3++Mc/mpNOOskMdPC8E+HgBW//pptuagYSNlK3ssJGnTChIsJJ3f1oxgy73luqKclsN2zddC9MANRDJgIQoZBuuqORi3pFm5SVldqHev6WjkXl+wrOCqI5KJ9AhAVZukzI/F5aWmZKImV2UvIK0I7O7nNirUpXBgOhwORGX0dYvPfeezHX3vNDlArCh59hoL98/PHHdiFuJknLSjGDQ23UyBFm8KA660xB9DHBcm6u9jgRK/57si+4SBzu6wUL5psiU2QiJmKmrD6pJ4IwFrQZi9dSuiJT6xBybxHBRV/nvPsaRcj3q4YNMc1Ll5nIyvavGDzIlpoDjJw8NHE+CGFELhGMmVzTlHuJxYiJoAqqB871Zyzm2iO6OLaIJ/rNi8v4c+fKPRsE26AUBmMZ4zkPK9wnqTrr+C6Z4ZSH5l4gOoyoOCLE+gPO9cILL7ROxz322MOuycsCyyI7SEtFIy3lcRhUV5uWpqZuLfXKK2bc+PFmxscf91lLlZQUmZIc1FK8/IaRkpLSbqcJWfQ5rqXKyrqzx/1ayunDbGup997/wCxbaVhw7czc3dkyyqwyYoi0VAa1FJm1VK1w1UAINHJO97zRUrFslt7y9kTixzBkS0uJTCItFY20lL9MdJsZO3bcSrvUWGs36KuWqqpI3qybaS1FAHJpTbXpaGyKer9q1EhTOWKYKamoMGU11TmtpUxRiemIFJuSrk5TVNw9TzJ3rmhqzxEtNd0sru+tpSJto8zokUN7tBSZcJm0S0Ui39qlyIB0GnrixAk2+aTQ7FIEF40bUWe+WdxgHWVsbiTZcSurKuSLloqVoeitFoeDdkhtsHNeWip/KIg1/v73v//ZLIh//OMf5thjj8324eQkDMCkObPA6UDElaNiYkHg4JTxltLLJBhdmPiJGiKrBpGFMYYFTIm8SDc4hIhWdgM2DsrqNC4mn2moMf/i2zPM5LU2NEOGfVuPe/Uxg83oodXm2WeftaKIqCVXtxqxwATL70FrAMa6J4h6mTp1auiSwLNnzzZ1gwaZktLy3uUbVyw3nR3t9jrjeCNl3QtijmgrIsq868ekSnNLm5n99demqbHJTFx5X9t1mBqW28Vw2QeRUwhXIshc9A9CwIlK2oDPphJhGBZEMAKD+wDxyfXimEjf5zgQrsnQvc5S9xoB3j6NyMGpz7bdvc6aBVwPym1kqv/zoMB9nagWOGt8cO+PHzfOLPngo14OwMFrTDbldbHrr3txJaTojwhZV76DiLa+QCQcJaIJoslGxvyNN95oTj75ZHPHHXeY7373u/2+/4GOtFRipKUiNlJ72fLlBa+l2H57W1uUISVT5foypaWmf/S5WXfDjc3w4d8akkYMrrbZANnWUpQcbWwv6r3WXnODKS3ukpbqDy21knzUUugdnnX8UJIqrFNeWkpkAmmpxEhLObsUWuq9gtZS7Y1NZvG0903nytKZVaNHmaHrrp31ZWeS0VInXf6oWVw6zhRVfBtYfd4RG5sjt5uYfS1VU2dWtAastdfWYMqLI/2mpdjPnDnfRNmbOY6lS5cUvF3KruObr3ap8aua2fOX91oWaszw2h4nZiKkpfKDvHf83X333dbZd8MNN5hDDz3UDCRsSZPmZtPV3mGKWRQ4YC0mJp3nn3/BrkW2dNkyu9bBjjtsZ8sqib63PxEzTJquPrV/4nT1jsnMYcAn/ZrPYqTPpMBzi+vSHdIxmfcXRMbwwL7axMnm/sdfsGuuEGXCQrmkmAMP+wgDfjJJE7WDk4LJC3G2xRZbxN0H0UTffPONnejJkkI0EZ2EAwWhVV5RaUtPAWs5sh6Ku68QCUxukyav0ZNx5+CeStTWTPYYS8mqYl8cc7I13V15B4RVfVOzGTnyW5FO2TRXdpZ+ybnRR4kwclGEiC2EB2MDgoc2yHRpR1crn3Pm2tGO3ANcP/6fKLuM60MWmsvCoJ39kZG0LRFtlMD0Xgf2i7glAsqtcUqb0IYsdNzXdWDZL/d5GJGKEOM1YcxYs+LzWaZrZakFFjVnHYRk7m9qoPNwwDkzlnBdeT/sA0a8a5XNMQOn3/HHH29uuukmc9BBB2XtOAYaA11LdeDgYS4vLjGlnlJ+fi21rKHJLFu6zIweM9bsvKO01EDQUrzc3JNvWmrS5NXNMy++ZpqbW+zvNVXlPVG7mdZSlWVlpr2l1QbqlFZWmPKqql5aasTYCbZkloM/jxlWl7CihrSUtBRaatKkyaallXX9uvsFZT1jVUsIQlpKpJuBrqXaGhpNV3u7XV+eYMYgLcXzi30Os3apMWabbbe1jhJRwFqqq8t0NLfYDMCSyoq801LVw8eZY867xXy5YLkpLio23918vPn+zmtazZJpLdX89vtm0ctv2Iy6YZttZMbstlPPGmdOSw1dZbVeWmr8yEH9rqXIZPU6j3Eou2oCskvlrl1q/GoTzfwlDbaMLQy364aG9xVIS+UHee34Y50j1vO77bbbzL777msGEjazZ+ky07EyegZKystN9fBhUSKLieSNt981G268qWlsaDDvTHvLDB8x0uyy43ZJr0snvm17JklqgDNgM2EjnJiscUZ4259Jm6gXFt8liocoFsQBC4eSci++hTZ67rnnrPBEqOIMqq0bbNrb20xNdZVtd6JTKLvAoquUKkCg4PhAWBDJxHWhbGG8+wYRR912L9aJVl9vFi5abNfrmzx59Z6IYdZDIYKYfVFW0Qm6ltY2u64fEWuV5UQYh3OWcAz0ASJyiDqivzDJc95hopTpd6zViUCgLEdpWbmN0qFuemmW1xtK9bpz/3A9EYsIIm+pUsQLbT9hwoSo7zkHpmszIsYo1RFUvoE2ZyzkOgMPmNyrtCUv+hv3aawsN64VNfH5HoKYzyGs6KMIWfrgBhtsEOp8uW5cf0Q2jgYegtzai8mASOSBgnuBh2jKjNAes2bNsv/PhTJ0qfLAAw+YI488ckAaTrLBQNdSrQ2NprP92/UNMFhV+gxWjB+vveXRUu+8ZYYPH2l220laqi9tLy3VP1pq0OAhNnuxqqqyX7TUogULTWtjo1l98uSeuaisstJU1FRHaSlKpy5taLbr7KGhhtZWmfKycHOXtFTv6z5QtRSruXevWZi8c15aSqSLga6lmhYustldDhw8taNX6aWlPnh/un1+Za7gOWbEyJFm2223sc9DIrW2l5bqHy3VWVJtik2HGT64tl+01Kynnzdzn3rBrDp0WM96eKN23MaM3WuXXlpq8YomG7iOLYj15whez4aWKi/HsRuxbZZPyQeOgayl8PuxnAHZi8kiLZX75K3jj3WAvve979nsgL333tsMNIhOb1q0uNf7VUOHmrKqbz30LHL+xVffmA+mTzdTt/6OefedaaasvMwMqas1m22ykclVKDlHJEQuwSBP2jYDNVEURGUwuDK5v/rqq3aQZrBnQV/+5mAgfumll8z2229vJw8mAQZrIrGYSDbccMOsnleuc88991jxRPtSJ52IqqAJlJrdTFy0f7w17uhbTK5BNdRXNDSbr+fMMbO++NyMHDWqO8rfFJna6u6SDWRTZaIEIhM4/Ypt+4WEHyK7cBrns2MnCCIPER+0AaKL8+OeQ3QlqvfuSijwnUTrGwZBxBz3JPcyL2rRsx2i7rjH2T6CiP5APwDq6iOM+TslWpIRtwQAcL3dsXOOyZaWAIQd/Rhx+frrr9s1CbgPyI7cZptteiLJMhnByTzU3tDYs+Zi5bChacmMefjhh20J71tvvdXst99+aThaEcRA11Kd7R2mpb6+1/sVtTU964c6LfXZ7G/Mh9Onm622/o55791pdqxAS22+qbRUMkhLFb6Walq23Mz5+mvz+axZZtTIkT0Zk5WD6qSlMoy0VH5qqfrZc0zrkqU2VaNq5HBTM26MtFQeMdC1VEdLq2mYO6/X+9WjRppyzxpuzP8L5s23DhHurbenTTPlZWWmqrrKbJDD9hDZpUQ2tNTHl//LfPnxJ+brZUvM8Bpsi0WmtLbarPGT70tLZRhpqTzUUlRDbG+3iRlAtil2Rdml8tjx99hjj9kSYDfffLM58MADzUCkvbnFNC9d2uv9ysGDTLnnpmKx0UXLGszHH82wUQETJk4yr77yshlcV9P9SuGm7g+ok030i42e6WStD9ZZYdHSYlNa9m3pxf6CKA4y9nDW4SSgLXEOEBkDlBckOoQoECI73CK2RH4QjUNJPm8ZC4Qv5/Doo4+anXbayW5XdINjlDIUruY8bb/77ruHbh4cKjhWaN/uSODekBVFJBUOJfdZrsHCRUtt9tWkyZPttcWRhLAbOqjGLtTOpEWfzAQcB9mjiRaBpk/RB+PV4ceIAKlkkuUCPBBy7XGCZqq9g0As0O/oGzghEfOxSl7gKIzVv0IJk5ZWe50o07xg0SK7T+qxJ1oA278dFhUnmhCxRdbx22+/bR9I6L9EICYDZaNbli0zXR2dpqSi3FQOGRy3DxHd27Yi2mmC469yaO8FpVN9uDrmmGPszz322CMt2xTfIi3VHURFxp+f8upqU+YpR4SWWrA0Wku9hpYaVGOGSEuFRlpqYGipJfMX2IjhyZMmRWmp2uHDpKX6kYGgpWyZZtbXKS428xcuzA0t1dVlWttog4h9bqUEabzn1vrZX5uWhdHBvNVjR5uaMelZb0taKrNISxnT1thkmhYs7NU2VcOHmYpBddH3RnOLNQYzV5DVQnB0VXW1df7lh12qs2f9X+Y2gsRklypcsqmlPvjnjaZj4WKb8dfe2WHLjJYPqjPr/9+v80xLRfBZ5lXJ/IGmpXLRLtXZ2moav55rOtvaTGlNtQ2IipcZzj6c08+B4y+ZEvCFrKXyrtYjC6gefPDB5tprrx2wTj8oidGBS8qiB4OS4iJbNmfKWmubF55/zrz04gtmrbXXtunfm2yySc6mYDOwugVemxub7CBHZg1rlQ0aPNjMXzDfZtVlYhHioIGUQYvBj8nPLZZKKjYlKtZdd107kZES762PzuD3/PPPmwMOOKDXREe7kxJN5IPLIOw1ADc1mfamZvt7WU21NUQOBGhvV6rQLTydDGPHju1ZUDcWTJ5kadG/uKa0N6Uaxo0bYyqqauy1mTd3rmlsbDCDB9eZRfNL7cTH5OtdA40JBocw/ZPjZWJLdb08hAQRMYkgGy7WPmwG1uIlprO12yFdWlVpqtKUgdVfcE9RqoBzpC/QtiwG3R/ngKgDDPtBkXde/DEzYdfGs2Wal6/occ6aVmNGDB5i+yvnyzhHpJTrpzxwIL544PRvnzbhIZl+zELRPDzzXf6fbFQVD7JE67rjQmzxqvGV6fHiMv28sL5Huhx/BPiQYcmc/9BDD5kdd9wxLdsV0lKOWA8QPPj4tVTFSi314kotxX0mLZX7WqqD6M+VpVwJHPNmchYy2dRSY8ePN9Xl5fbazJ03z/69bugQU7pkcd5oqealy62xAVij0AbCSEvllJZqaWz8VksZY0YMG5Z9LdUVMY3NLT3rDrIeOIao6qqKmP2ndemywPfS5fiTlsocskt9u9xMEAQRerHrtJcU2yBp2u65558366y9tn0vL+xSDQ2mubGxt11q4cKCt0s1zZlrmubOt7/XjB9jqseMNgOBbGqpNXfczjQ8+bxp6+gwCxvqTVN7m1l1o7Xt3/JDS0VMa1OjDSiGkrIyU1797XrP+YDsUlnSUtxrH3xsIh0d9vf2+gZrdxq6zpSY/cfv9Ot+j+p86XF55buWyquMv2nTptkG/utf/2pOOOEEM9Bpb2626/zFyvZzcIkbmlvNK6+8ajbddDMz7c1Xbco5k1iu0tnR0f1aeQMTScIERpQN9ZBXX3ONnsGH6IZUFqJNBO1GmjITPpELOB+8vPbaazZ9P1bJRforIszVBmegQBQSrUHkEBEI/I6QXGuttaK+SwZC64oVUe9hdBgIzj9KHnBdETSI12QfAhA97733nv0+15CfCBL6DX/jmlBa019iFRENFRWVpqW13aw2YTVTV1trKsq7U8SpX464YXtMIggtrj0laYlcZFIjMortE8GYDNSI55iYFBGIiSAyjHOh/3g/j9OPxbO9sLg6Y0O+QAlN7/3AdXPCEzGBiM0FGIuIAHV11d04wP0er6QHEVVtK2u6e6kaPMg6IXg4oH8B28YJSr+jfwQ9UL755pv2YZn9872nn37a3jNEASaTSdy6ot60UGbKR+2Y0b0e3B3LZ8226xR6KSEbenziPpwM1113nfnVr35ljQScq+gb0lLxs/782X4O5pP6plbzyqvdWuqdfNBS7e3W8bVyvfYBp6XaW1vty0t5ZeWAcP5lW0tVlleYtuZmM2G11UzNoDq7xl++aKnuNdR9/aamOipzJdcpeC3V1mbaPevcOyprarKqpdra2k1L27drxjpqqipjrgW++L0PrZHLC9HtQ9dO71rw0lLpRVoqmrbGRtO0YFHMbD9/QM6rr7xq1wR7+dVX7HyQ81qKLN5IDLvUlCkFraUaZn9tVsz8POq9IetMsZnJhU62tVTHF1+ZxW+9a8YNG2ZGb7mZGbnd1LzRUqz1zJIKXkpZl86zLFWuU/BaKkftUk3zFthxx8/Q9da2CTFBNPtsoNCdPdv7mX4gaqnSfBp099prL3PWWWfJ6beSsqoqO3iSqcGNGaskGx2+q73VjB8zyowYWmcjVai5m6vg8GvreZjr9ui7ddVGjhzZ/XtJiY0iJdIKAURUSqJU9GRgkqQMJ4M7Ao7BjVKeo0ePjmpXIhr8gynvMVGyqCrbcdE4HDuLBBP9wLHyQmj5hZsTz73ea2gcEI4/BAsvamsTmbbtttuGXlfPLXCNwKFcAhMNIpltMTFzPZikvdfRgVgh2ioIxDx9zB8B5yByEZFFyVf2RSRMmNR4jhfhRjo8E+See+4Z6jyZdAEhivhwUWSLFyw0ba2tZsTw4T3RMB3cSznu+KPtXIQ49483eo119FjTELgHuWcSrYPYHzD+BEWyU+6XhyZ3zH4iKx8ae72PZb7EBIpzouYR70E4oQ/83HTTTe3ahDzg0edDr5UaIwYoXmxQ5dDBpnlRdORjxZD0l+kh0AcxiwZ4+eWXY96HIjHSUr3BCVQypMx0dXXacSeelop0tJpVx4wyI/NAS2FYw/FiKSq2a0cNNC2FUzfIYTAQHH/SUn3QUpSxbm0xI4aP+FZLtbaZ9JoO0s9A0lKJNEu2tFQsxRSJ+RdjqkePNA1ffRP93irdY3Q6kZZKH9JSvSH4vGxClV02oKi0JGZFBcbUpuZmM3zkCFNdW2NLBSZbAq4/weHXYxgvLhqQWqrRNz7ZY/pqzoBw/GVdS226qTEH75+Xdqklixeb1tY2M3LEt1rKVuDIccffQNJSuWqXimV/8lZ58ENmX7vP0ZyubL9C0FJ54fijs1JHlUWTTz/99GwfTk6BgaokROQJ0QrOI3344YebXAajzLdQE5rz4+bvnjCsUW7l5MEkwkDG+c2dOzeqpEFfoMwBwoeoFYSQLVc0d27UZ5is77zzTmv4YzIllZnJ9ZVXXjGHHnqoFQUuNRqIemCbDKBMFPRrhBhCoBf5k4ibMWg7ov+IYCNKJAyUPvCmkzNpIJqYbPg/EzNiiReTEPtgQgd+Z+AOEnOIGKK8YsHExn6IiiGK65NPPrHXmZ8IOgSYfxKmjxExyPXn2NZbb72kSx8wgbKGJFF79Kei5lZTVVFhvvxqtllR32A223hjMyxBmaVcgMhIHmTcoryxoukQxrQx96N3zcxsw3FzXIgdNzYQ+RYU8VlCJFbANihzEwvOlWschCs94gQqD530ZR4YeLjg4RDhnwjKwhqfhisqKTEl5d3bDaKch8uiYtPe0GDH54rBdaYsQws3n3HGGXbcRAvwkJorEXb5hLRUbIqKi0xJcWlhaSlvplsk0q2d0BZFA0hLCWmpFLRUcWu7qaqsNLNmf2UNZpttsokZNmJ4zvemgaSlYpZpjrP+S79oqZIS0+pTeQyz8Z7VK0eOMEXFJd1VF4qKTNXI4RkJogJpqb4jLZXALhWjSkjeainvmDFAtVSgc2CA2apkl0peS5VGuh1lOMdW1NebzTfd1AxNsfRofzKQtFSu2qUqBg8yjV/PiYqmKi4rM2XVsddYdA7H7gxFAjRKYmZAD0QtlfOlPumolPckxfamm27K2drfuQw3GPWeSf/PB+yaDf5uaQVVkb3+5ZW910ngBic6hQE40XoSYcAhQ8QWTiTg/wgj7+BBm5K6z4SGIwnHEIMcRgKiqvqyQHXLihU2w89LRV2dqairNQMNhC3t665FmAWYcaLxPcYPxC/XyB9NhVMO8UzaOf8n8o00eSYzBm+2Q38jomOjjTYKFSnFdtinm+RwWNJ3iIpxafdE4nGcO+20U8/3nnjiCbPbbrul1D5knbBfnKQdlP9dsqznnnjnvffMdrvtatenyWWSWZAYocE1oTRJrsD15IEqbB399uYWTyaOMZW1tbbuPTD2BTmAcWrTRv6/Me7xYOl9mGMbjz32mB2T6IdEJ4Y6rqZm07x4iS3fWVxeZqpHjIjr+OtvELBHH3207e+UV0BEinBISw08LdXc0BAdGVlUZIpWZv4NFC1F9Qh/1l9ZRYV9DTSkpZLQUi0tpmXZip57Ytp775rtdtnFlIYwaGeTAaelfKV8K6qrrREr61qqo9O0tLbZ7xcXF5mqyopQQbr9hbRU6khLDUAttaK+t5ZamflHoEF5RUWvShGFpqVWfPqFafjyq6j36lafaOom9s4OLHSkpcJrKbL72pqav9VS77xjttl+e1OSgSysdDLgtFSO2qValy039V/MtqXQS6urzKDVJ3UHqucIXXlml8rpu45J7ogjjrAT1fXXXy+nX4q88847toZ6vlBcWtqdBu4BYcO6LLHg70RYuQwrF2WQLAxMlJxgUsd55HC1jckOI6qDvsn+cBwx8JHRxc2O5x/HEaKrL+DkI8KBdRyhrLralNdmJpMm10E0U2s8DDjsuDaUYGEyogyCHybnjz76yIpj7guuGxFuOOuYILm+XEciY6i3zt+47mEcf/QdPs9PV0OfTEC2x0SI8ObvZBCSych2qY8dVFYjDC+++KLtazw8MfHST3CZfzHzU1tGpaimOtDphyOSdnBZkMnU3E43OEQpXRIWjtWVyMh2dBV9hLr2LsIzLGVVldaAaMUUpQWLikxHa6tpXra82+lWWmqqhgyxTjfGHtqH68zDsn9s4eHOn1HK9ijP8cYbb5gtttgi/HFVV5my6nExRV624X664YYb7LkdeeSR5r777stYJFchIS01MLUU90aH11iFEbq02FQEzIuFqqWcg4+yp1BaVjYgynwGIS2VhJaqqurRUs0tLabYLq3Qu99IS2VZS1VU2PsZo7zTUna5iKYmW6aK93AGFpeW9K+WKi0xZaVV0lIFhrTUANVSpaXRAURoqZJSO7YMFC1VN3miPZbmufPt79Xjx5jaCbnj6OhPpKXCayk7P6/MPmtuajaRkuJAp5+0VO7ZpTpZr7i+O+iBClCVdXX22vWnlqLyQcUmG0hLFbrjj453yimn2IGC9MlMLJJbaLS2d5gVja2mqytiKstLzaCaCltSkEijfGq/Mo41ErEPb4ARPEx0NsIGx403vTgWfIa+5c8gZcDEoYCDxgvOmXvvvdc6cVzt9h122MEKOyIqiG5hoVMisdLh7bcLkQ4eZF8DHSJpknFMcQ0p2Rnk9AOcbltuuaV19FHHnAmMLDweRLh2LtuUaBm2hYhGOFNmA2dePIcIaxRQ3oN9UNPawfYR/jgkKW3CNukvnBv90Jv9FxaOnWg/V0fd0dDSYla0tZq11l3HPgx44cGByD/KeDhBQGYjx4EDMFabZQLGJvbL/ROvjGoQPPzwfe5j4Jpxz3MOmXZYUffdjaccfyolWsEKq5X/7+roME2Lv10rj98bFy82taNGmXfffdf2O6KMk40aRZi99dZb9nux1q8MPLYcdPo5aPt77rnHZtOiEf7+97/n9PFmG2mp5Glr7zD1Td1aqqK81NRV56mWqqy0zgsXSIXxKl4AVaFqKc45zHkXOtJSSWqp5mazorXFrLX2WtJSuaylyL5ZGQDU1dllWj3VUjBYtTQ2mKq6OmkpH9JSySEtlTydbW2mdWW2XElFhakYVJefWqqq0l7/Hi1VVmbKQzwvF5SWKi4yg9ecbF8DHWmp5LRUfWOjWV5fb6v3yS6VJ3apzk7TvHx5z98iK3+vGTZUWiqPtVTOOv4uu+wy24ivvvpqVKqoCKato9MsWNoY9XtjU7MVWGHXR8sVnKHGOWCSuXmIPPBPOH5w4OD8wPGRqHQsUVZETiHYdt99dxupsOuuu9r9sG4cWWBMZP3pMBlouEWpw4LIpq44TiDqjONwc4sOOyGOE4VoFK4d15cSn0SseNP66XeuNjbRXTjIHn/8cdsPgvqkq51NpAuf8cK2cQQSCUUgA0LcObuI0rv//vttX0O0c0xhYD/+tQM4F7IZqZ3tX9zbnTt92etAom2410i9RzDwENEfExbXBaer99qEhfvWv5Ayx05kJX/jeoXJ0EwFjtm7MHE62spfis5iHzLb7HoOENQviJCLNz/SD7gXeOij3+Hspo/TTvTBfC2dzTk//PDDZurUqfYh49RTT832IeUs0lLJQYm2hcsKR0uR3RdZ6fSSlhrYSEsFIy1VOFqKoKleRLr3JS3VG2mp8EhLJQdOssYFi779va3dNDc25a+WqqmWXUpYpKWCkZYqHC3FeN2LlcEP0lL5q6Vyco0/jOBHHXWUrZWaL/W/s83S+mbT0Pyt8ZjL+vprr5h9dt/ZVOb4+l6ZrsmMkwfnCpC5RZRU0MLF3gmdOukMpDiR2B4RVERU4EwhmwunBYb4XPXo5wNEt5Edx2DJiz5LZp8/Kg4HKxNakCMrFgQM4Nwls4+INxxiLHbN9tkPf2dNP64t1/n555+3Di8ikcgWBD6HE82brk72FdccRy8/mcxx+NE/ePF/Ivb22muvpNsDJyPBDvw85JBDQjllcETjAHMLynLM9F3OM6g2OX/nHomV/s/xu3OmLWg7fuLQTORQTwWc8FxvrkW64BwRWpRPzYRDnvagP4VZmDgsdm29Zd1rM3qpHjbMlmrlPuEeIIrTy9tvv20ju+KVPMXhRyYq/YkIUK4/jnTKpXJP5PMY9uabb9o1gG+55Raz3377Zftwcg5pqeRZ1tBsGgO01Hd3k5aSlspNpKWikZYauFqKICq3npAXjPZk6UhLBSMtFR9pqeRpWbbctHmzbyMR88rrr5ud9t7LVAygDHzZpfIHaalopKUGrpZqb2kxrfUNvd6nEh3lW6Wl8lNL5ZzjD8M6Cz6yjs/BBx+c7cPJG5asaDaNLd8aq959Z5oZP35Vs8Hak3JqQfFMglGb0naUUXQw0BJh4ZxJOI/ilZfA0UHJRwzifiM7Ri+yovDkK8Ovb1DyEicspStw0BEl5KJjnJMWhwTDE+9tv/32gdshU4/SAt41yWzGaHm53TbODoQLzlwy9ryTIg4yMt9wBOP8oySiK6fI2n78HefIRhttZI8VxwvboVymc5y5jEDnpCOSkWzQZMoq+mHyfuGFF+zx46Dm3HBAk60VxOuvv24dnF5efvllm9EVtKgv249X95tzok1xDnG/4FTivmKdRe6tsA5YMhh54dSL52h///337bGmuv5BELQZjvp4JV/74qxkbHDO1nRAGZyGhYtsKQUHJY5rRo7orrPe2WnvGc6FBxPOi76I42+77baLuV0yXrlH6M/evkAfZY1Jorb60ldzgbvuusuccMIJNpuW8xTdSEulHkTVFKCl1ltLWkpaKveQloqNtFTfyTstxTODXZMmElW+qrKuVloqAdJSwUhLpUbz0mWmvfHb9dvffvdds9r48Wbi+uua4gGyNrfsUvmDtFRspKUGoJbq6jJNS5fZnw7G7aqhQ6Sl8lhL5ZTjjzW1WPDxxBNPNGeffXa2DyevaG5tN4uWdwuszz/rrvU9YbXVzKihNXmd0REWHDQYuF2WE4ZyHD04kWIZtm3X58X6ECvb6LnnnrOG9HwtgZcvkM2LMy9MO+PgIMvSOaXIrMP5yiTHoEo2E++5a0rki1sTEIcd15QggldeecUuJu4WoeWz/I3vu4wpMt5wuuEApi/hGHGONZy+q666qu1bOAuDYB9sr6/g3EGEUsqRtHHnRAPaDGeaK/FI9iKOM2/JR44RoUaZR0qMessLEH2I8PA6KwGnXqKxAtESbz0+/u4ceDhRWc9w4cKF9jz815priXORcZ/PpRuuL23g3S/nR9lLnMm0MQ5iyi/ECwbgOHGUMZYwxtDXUilPmgjqqbfW19tSVcWlZaZyUJ01WOFopu/RjmRd4pjGCctx0J9d3w8C5x59l/PGCYjDl77O/Ue/SCaLNpe54IILzHXXXWfv1XQK33xFWip1WtrazWKPlqpcqaVGDpGWiqul/j97fwIlx5Wd56K7Kuep5sI8zyABAiQ4gfPYzaa61d1SS7IsW5ZkSV6SZU3rWX56upZtrXf9/OzlJ11LV77rSvJwZbWstltX3Zpa6mY32ZxnkCAJkARATIVCATXnPFW99Z2syIqMiszKeTz/WomqSmRGRkacOOeP/e/97xVoLtVcaC5VGppL9R6XIlCVSiRkObukAlX0OqU/leZS60NzqUJoLlU9MomExKZzvcvPXcglLu7YuVP8KwmN3Y5q41LMX6rXlo5LNRWaS5WG5lK9x6XomZyKRVV8yuFwijvg13GpDudSbSP8EXB/7LHHVGCd8sheIAX1BvZU89GEvPHaa3LyvpMyOuCrutpPNTHOZCW7Un3ldLnE4WzPDC2EIbIdGDuQKiZUxg8WiMWqiFKxWL6Euc/RLz5Ek74++da3vqUqvJh0NRoHBDIEOIQIRLpyhVbGJQvexYsX1e+IVo8++mjJz6EPHp+BWIbtIVV/RvUTiyzVcQiJCGeGEILwh40mizETNmOMB6IJIprZS9sg9IgzZ86cUfbExSZ5xDDGKq9njCLAlVrcETyZG9k/43M4Vgg//G70ueN7kVViFXL4PL4HhAIR0/AYJwsREQlxkfcgwLH/HKtSPeO4vrh5s34/g8xQvYkYxd9UCa7XmxFBkEpL+mY2A+wXx4TzyHFHJOZ8cIz53kbvQ0gUQSJey/HmOyM0U7lYz8rE9UDlJfvGef693/s9+Zmf+ZmKPp/ji/UsIh83NZx7vjNksVgVbSeC8/rDP/zD6rr49re/XfKa6nZoLlU7cE9YjCTk9ddfk/tOnpThGrkUWe/peFxxDE8wIM42tbmqhksxl6TTq1X6brdLfWfNpZoDzaU0lwKaS5WG5lLlQXOpVWguVTtS0ZgkFxbltTdel3tP3ie+kaGqq/1UYm86I8lUWnENr9slblehM1Mnc6nE3ILEb95UPUn7XU4Jbt0s4nRqLtUkaC6luRTQXKo0NJfqbC7VNsLfz/3cz6kAPAFsbaNYPcgAwKKw1t6I6VRaMunCxp5uj6ctxT9EGggW4g6kaj3ROJNMFfTT4hKYmZuTaDYjzzzzjDz55JMlq5o0agc99WjujViEyIKIRdVaJeC9BB3NPfis4FqAdHNOIduca4QmxC4j447nmJjJyvj85z+ft3hFYPzGN74hTz/9tLJURDwxPhfBEmGIsYaY9s1vflMJxthxMsFz/ZntLbEyfffdd5W4RGUdn8F3xoqWz0eMtBPJEJ4QbNhXhDuESEOwNLbJWEVE/c53vqN8pek/aYDj87u/+7uqZyCfxXYMUY7jwWvN+0l/v1LnwRBb2VfDepSbY7y+2RbbRBQ0bnJKgWsWoRDxs5liWqnsTAgf5wVxjPNazF610aBykuuCcUlCDGMY0mA0VK4EnFO+F4/HH388L3gaVbHdtPYxp/D47d/+belVaC7VXlwquRhWDzN8oyPi8nk7nksxV6ZMDeBZH8g+XVxc0FyqSdBcSnMpzaWKQ3OpyqG5VA6aS7UXl4onUwU27CDk97al+FcplyI5LHL1WgGXml1ckITPK898W8elmgHNpTSX0lyqODSX6g4u1RbC3x/+4R/KL/7iL6qAfCNKVXsJCKfYpdaqLMdNvuwGCOZ72jBYVSmo9IuHw/JHf/LfZW5uXgXYn37qKdl18ICEBga0zecKmBog2plMVqCsZPJDsOtRjYt4xHii6gwLQ4QIc2/GYkCMQ6BjHxCYjEo4OyCOIXog1CHemYUOBBUWeASn48ePq+0hsmGHyHOIeIhzhs0nAhufhziE0IXQhohHlRzv47WId/w/20VkI9PPEPP4rgiHdpWN7CcLKgsEY5HvaPR1ozKNfeF5jhnWpQh17D+iFJV8fA4iIKIexPWee+4p+K5k5zAfIMwxP/B9Cc7yGsQtQ0wEiEP8H+A4FJuPOabsIw/2FysT9oHvzXFh7PBded46XvgenG/2iUbHvQqEY6pQrb1EyRTFopWxzRjg/znGnLtS/fxKgXPDWGY8IRQblY3dBqobqTj9D//hP8jf+3t/T3oNmku1H5cKX5ss6DcFHB6PBMZbk1RQT7B2JZMpNe7m5mbVOvXZz35Otm/fptYVbZluqjSPJyWVzlD0KT6PW1Ur9CqXYt1jTdJcSnOpekBzqfpDcykdl2o3LjW7GFHdWcxwORwyEKxvv6xWIHZzWmI3Z+TL//efytzCvDj6HfL044/L3juOS2h4WHOpFdAKY+b1UxK9ek25dQ0dPiADh/ZrLqXjUjouVQdoLtX9XKrlwh9Nk1FCaYT41FNPtXJXOh7clEOsDPu/egt/qkF6Fwh/CzMz8jv/4bfl7jvvVALO9m3bZHhoSAJjoz3TcLocxOJJyWSzBc95Pe66ZNchECFc8WDc2glEdjh9+rTqr1eOXzKiCT0A77vvPmV3yU+7/g1kInJTQqCSCjQCXPTTIzsRK0yENIJrVM8hhBHoQnz7+te/riqo+BzmMa47tsVP3o99ZaWVbGyrVM82o9rQqFxkMTEfNwJ09PzjuJqrVr/2ta+pY8axo8KuXALAfFKq6s2okqOCEDGJ3w8ePKiCj+zrrbfeqm5YOK5sj9/Zx1JVmr0AKkgRhQmY0sPRqHLnuDHmrBXHZI9iI8tYrAaIiZwPgrDYmXBezJWh3YS//uu/lh/4gR9QDgLt1lS5kdBcqj251OLEZK6XsAkOt0sCG8al0xEOR+R3fue31bzk9wdUIgdrIwkG/f3arj9/nGJxSaULuVTA51HiXy9yKcOmXHOpVWguVR00l2ocNJfScal24lIzC7n2LGY4Hf0yGPRLp2Nu4pr89v/2H+Su22+XgM8n27dslaHBQRncs1P628ARp11w46XXJHr5qrJDNTB29x0S2lt70YjmUvbQcanegOZSvcGlWlofz40hFnS/8iu/okW/OoCge61WCgYM+0UznG1o81kNFiJR+d7Pfo/s2rFKRJ1ejxb9TEBYsop+IJ3J1EX4Y6waVptGhdt6oC8d+1Vuk1TGMIIK4pu1qsoA2yJgRkYiYguWlUePHlVBMX5nH5moCWYaYhWBK4JYBM0WI1EZGdsgtx67Q1596UXVN43nq0Up0Q+wbfYLwZp9QhAyCzh8V46ndTsE4rCJrOTmi35/EGFQTPyjuo8Hr0PEQtQjMIgISAUk5xXBj2y0Ys3Me/FGmONDkJzrgJtixjXH79SpU/LAAw+syRai0pNqiWqByMi5oRLj5MmTqjKU4G07WKzWG5/5zGcUp/j+7/9+efvttwsqWrsVmku1L5dy+rySicUtz3V+hjqYn5+Tz3/+CwXVYg5Hvxb9TGBut4p+IJXK1EX46xYuFYnGZHzDRjl+x53y0ovPay6ludS60FyqsdBcSiejtxOXIvZA1bz1uW5AOJ2S733qM7Jz69b8c+6BkBb9TICzRK9cKxD9QOTy1boIf93ApTaMjcm5//zH0n/6rHiyGXlpy6h87p/+kmzcvKmiY2Hd51LQcanOh+ZSvcOl1vrONRE///M/r2zq/pf/5X9p5W50DVio6gWXx10ghDldTnEUWaTaHSzKHJvnnntO9cqauDYht95xh7j8PiX4eUJB8Q4MtHo3ewbYUSFcGUSrXCAuUalUKcikJohSitRgoYjdJhV9/EQUITsdSyrsMM0iOAQM0nbPffcr0c8YY16/X0KDq/3yGgF6EyLisEhz7CCBCGtYjmKtRcUYpJKqOzP4fghLlQKCSaUjmfvWRADDRpR9gfixkEFEEQJ5DyT03Llz6jWIiFZwTSIkIzC3geN0w8EY5DxxHgyLUyzTqIhgbuJ4fOpTn1KvoV+jAc4z43498r0eqA7FZo2bGaoyqBDrVvzar/2asruDY/QCNJdqXy7lGxpcFfr6+sQdCoo7uLafaydyKSq2Dh8+pOYmBD+Xy9mVyQQNQR0KIruFS913/wNK9DPGmN8flKHhEWkkuopLZbOSSSQlm0ppLqW5VF2huZRGu3CpoM9TIPT5PK66JM+0RVxqclJue+h+8QwPiisUFN+GMfFvKk9M6iXYiXG4kdWKbuFSu6/cFHnztCwlU5JNJCX55mlJvfi6NBJdxaXSaUmFI5KOxzWX0lyqK7lUy5Scr3zlK8oqj+Cj7gNSP9TrWLK4erwe6QZgbUeAnWPDgvTkk0+q7+cNhaQTyaIhkhg91hoBtmuXXeepQ1CPzCRICmBBxiIK8L2SqbQkkmmV0OVyOpQdlvEdR0ZG1GvtFutioLKKRd5qnWgFnwF5gYwxTvjbyMbCGpMxRFVNKBRSD/rTxBOp/PUGucDebHZuTvybq6/4W3NjkM3mzvPKPhmfBdGk2g/ywn7dfvvtqqqO3+0EIqrKIEFU4yH6VALex3GESPHd6SVkgL/pRwfpw4LUvO+AfWahm5iYUOTV+A4Ifhw/s12Lz7t6rrsRZPlwLMyVewZZ5pwY/SCNzD7GGNWcnGvWScYXFgHVHiPGqhGU5wbHjjB3C7gG6DvG8SLTCouFboXmUm3Opfr7xT9KQkhjk0JayaXo/9uZgbfcOoUtaSO5FAHKeLIwyFOPgGUpLpWKxiQVifKXOD1e8Q4OSF9/e3KpZAou5SjgUvTf3rRxvL7nuk+k38Sbu4FLIfjF5+YL+of6hgc1l9Jcqi7QXEqjneJSIX/nt5spxaX8HWgBr+JSKwKvOVZRbyhucXCvLHyQcyIyMLC/vPYl1XKpi//5j+XS//UnspRKy4bHHpTDv/aL4lhJ5ms3LtX/wqv51gJ+p0v1wLz87Iuy/Uufk7pxKQTLvj7pdzq7iksh+EWurbZmcAUDEty6RXMpzaW6iku1pOKPLIB/9I/+kfze7/1eWU3oNcpHqytnsum0xKdn1CObKp7N0iywuL7xxhuqdJ5sD4NcdSJUMCeZLHg08nx73C71cPT3qwfCTD3sXqnyxaYAxOPxvBiByEhwzPhG6UxWovGk+p3vySJdqt+cHSBH2IxA5Mzgc7FPhKSQcURvGjKUIFh2lopkWSHcGHjkkUfklZdeVNsxAMEJ1klMRvCLzS9IfGFR/UyEaWqeOzJkLkHwIDSf/vSnVZ9ByCd2a2QOMs6LHQvreCETy/wdSi1Yhw4dUoSKmxSuK74vmWc8jx2lAX6nH51xnXFzA8HjfYYthln0A5lsrvqvm0HvJQix2d6Dc0iPW0P0M8DxMrIBCYIy7hmrWHTWkkHLe41M006dB8sF3AKO8dM//dOKc3QjNJfqXi7FGpCKRtWD31uNbuNScQQT4xFPylIDz7fP4xa/160SXHgQvKyHRVkxLkW2MkEMFcBYRhxKSGJhsW251EsvvtQwLpVdWpJwNK76LPITTtlVXGp+ofD7JpOStlgLdxs0l2ouNJfSqAWt5lKIUpl0Wj0MgaqV6DYulYzHCx6NPMbDt90qI8ePimdsRLwbx2Xjw/eJf0v1NpbrcamJP/1LOffbvy/phbBk4wmZ/Otn5IP/9bfalku9OzMlyewqb8ksZSVYJ4cR4rvhqxMSnpiU8NVrEr1xs3u41NJSgegH0pGoJC38qtuguVTvcammC39c4D/5kz8p3/d936ceGvUDJALrpVYhE4/L3JkPJXJlQj343drbptnAUo9F4E/+5E+U1V2nkiuQttj48DvPNQqq6tPtkoDfqx5U4NUDnA+ymFig+QzjO1mrCw3xj/9nQWfCZKGuB+ilRr8zSBuVT4gxbLtYHzqyrSAxEDKDRKRSCZmaup5/za7de+S9d07VZf+U0GcizxCudDyR/xtRyEqWIEGMcb4PBMwOhuiDrQUkCFKJ1SSWCcwd692gcd6wa4DoUoVmHDsDbDcWi6l+gohcBvgbIsb/FwuwZleqL7oVjB8y4LDbfO2119Rz09PTyuqFY8X1YJ63OCdf+9rXVFNghEFsMbhZQPwrZRFSDJBpyLdRKczf3Q781L/4xS/KT/3UT7U8+FBvaC7VvVyK+T5y46YSa3jwO8+1Et3EpRLJVL7aD7AmJZON5VKIf4NBv3rUqy9RMS6VMXEFA4h/7cqlkolcwMvA7j175N1Tb9Vl/xD6zJwDa3GcJbqBSy2TEGDzOUtdnkSluVTzobmURidyKeboRDwh6VRaPfh9Kdta8a+buFQKXmGKVfA7SemNAsdq8PB+2fLkI7L5sQfrIvqV4lLX//bZwhcuLcmNbz7XtlzKd8dtMm0SzbYGBmRqX30KbBD6ljKrSYjEd83CWCdzKcWZbD6HRKpuhuZSvcel+pab/Kl/8Ad/IP/iX/wLlcmAjZlG/ZtzWrNImoX5j8+rDAkznAG/DB/YJ60Ci8lv/dZvqZJaMno6GYlEwnZh8hr9gzoIr776qrIFYB7Aq5wyfar9qPwyg+npvXfeUllBvGY9ggxpgzhAQCDX+IrzN+OAjCTmHCwZIAF8NgSB10GcuEFhESwGtvnWW2+pLKu/+Zu/kePHj8vA0LCk0thx5iokpyavqW2S5VRTA+vZnMc84szEtWvquXA0KhtXbAcYC4g47IPdMcDHHDKDlQRzggEIJcSH/bNWW0M4Cb7xfkhUsZ5yZKKRgWbsKwQLqwtIFyQV6wSeI5OKbCI88A0SxmfTjHpoZHWfrBWmvQDIrJHtc/LkSXXcOTdkoR05cqTgtXjmY/1JZSDnhDGBxcfDDz9c1g0jRBhizPbJGjRuUvibc13LWO0EcLPBXPMbv/Eb8hM/8RPSLdBcqnu5VFQ5JhQKUQ6ScExzebPRTVwqGsuJYGYwlQb83cGlUothyVqETL7tqY8/bFsuRU8/RDmAMDp5rT5caiESU7+zbl6bmFDPRcKLsnnTxs7nUocOybB7rfWdp4P7iFYKzaWaB82lNDqNSyUR+iwVaAgmHl/rLEO7iUslotG1wWsSnSwuNp3KpU7/0q/L9Iur9pmg3+8T1//nn7Utl5IPPpabz7+ibFc3PfmwRDeP14dLXby8yqWuX89xqWRCNu/d0/lc6uAhCSbWiny+8THxjTa233S7QHOp3uBSTRX+uLj5ol/+8pfl6aefbtbH9gwIJDPBsWC1AjPvncl5P5vQ73LJ6JHDtq9PpDIyF0nSdkOGQ17xuOpTUWYGw5uKGkrgOx3JlYzttb0YO8/z/uLFi8r3G6EDb3CsAE7ceadcu35Tzp49s7q4Z7Ny7NhRlY0F+P4sTizyVrJl2Bfi0852ueEgA+qxxx5T18V3vvMdNdl+4QtfKHgPnw3ZwpoAQlYKkAbIGWLMQw89VJBVZOCVV17J+7EfO3ZMPUeFF6/FBsGwhcBegOov4xhAzPh8Zd+0GFZEE7uJ7du2yWI4LIuRsNxy22154kVWFOSJ99qB7dAQOmoi5mwP4a3U92Q/yLSiDxw+6kbDawNkuUGczIDsWY8FpJUMNr4vdpXsg0FuZ2bnZOu21Rsejpe5n6P1e+SqAZeV5WwnZ0cWAySU8QDBt2b38f2p/GMO4yYBQHC5huwIthWMPdYE69rAdvHWNyxEzHNLtx3jv/zLv5Qf+ZEfUde5uRdAp0Jzqe7mUuGpG7lqHhO4iQ9tsu8fS2V8QlUx5frJYSdZb3QTl4qpYGAhl6LPn7+FwcB6cqk7j98uU5evyJkPPxTHylhYcvTLbXfc0XNcSom8K9xn2/btipdEwgtyZKW/Tsdzqakbsm1stS9Uv8sp/tGRolyqGX0tWwnNpRoLzaU0OolLJWJxe2GqSJJPJJ6W63MxlQi0ecQvfk/9k1G7iUslYrE11p5wVa9lretULrUv2y8v/Oz/Qy4lYtK/MozGP/cpefz/+Uu9x6Umr8vyEnEpp2zbslXFpSKppBy5+66u4FLTE9dkk291uw6vVwZ2bFPj2e57qBYMy8sFvQ67CW3DpXiYenN3C/6yRXGppgl/fMznP/95lfXzX//rf23GR/YcsI3jwrNOfs3CwoWLklrpI2LAPRCSwb1rs0zC8ZR8PDGXT6Lp7+uTg9uGxe+tP8ligrHzx67HmG7mRLRkY6HApNlfJAOmk/Dee+8pT28W3+N33KkIisvpFI87t6ByrCE1EJK9e/eqrKByjj0ky2juCyGBmJD5UwsghmQgkZ1kR7AMkP2E/zrnjYUQokL1Fc8bBAzrRjK2sG9kv4xrN51MStJUPXv2ww/ljnvuXnOuqQQje6pYFlQtYL/IhCLLyvgbcst+l3MMOd7sFxlrVpCJlc0uyejYqPQJDasdtucTi66FcFwFtgFBzKGQTwmA3QLGPePkvvvuK0nsIcVkARqg6o8sP8aSGVR6UC1E0oXT6yk5/z3//PNy9513Sd/SkqQNy5a+PvEOhMRZJ/uSdsGP/uiPqhssyGonQ3Op7udSsZlZyVjWeofHIwGbzNNkKiPTC6trBbPo2FBQ3A1IpOoWLsXaQ28/M3xed0PW0ZZxqXBEThy9TXFrp88rLr+vJ7lUKp2WmKmn8Idnz8idJ+5YwyE6mkul0zI6NKwCVKz5xUS/SCyRd9VA+KPXpJU/dDLqzqVSaZXMSmDP4XGvz6Xuvltdb6pFwcrzHg+9PTt/XjFDcymNTuFSJExbrT37Hf22CdOz4YS89uGUGDlBjv4+uffQJhkM1P9eqFu4FOIHff3McPt8XcWlbrz+tmw8/bEsJ1Oy8YmHZfsPf7E3uVQ4IrHpmfy2Pjx/Tu58+CFxrPRD7AYuhSX+SGhA+hwO5ZpgK/otLUlsdi7vygI/INmqG2KxjeJSyXRGkumsuJ0O8bqd63Kpu06ckOV0WpKzC7JMixpHvwQ2bhBXoPMSCtqNS9Wn0UQZ+PrXv64GBIq9RmNABgMKfT0IFgvacnZJXWzlkojgtq0yD8lasRjqd7sluN1exb5yM1zgWkmA/+p0WA5sq29JNUJSvW9syTxJrfQI4diQrWJkVTcSfA+3x5PLMlHk1dE1N+1k3mABwIIcDS+oxZwsEMYhiw9l/iQNkM1U7XdmoX/xxRdrJlg0EjaaCUO2sGlgv8nY4HsYIGMKG0czIFp8Nyp2mAvffPNNRbJ4P8TRgMvjUd+TZuQIY6ERe1KBPzmkkwyueo8FxjWE1PCxZ38hc+X62ZMNB0GzI1g8R3bcxU8+UdcolhnYUVoRiSbyop8RsA1HEzIU6o7Fn/FNQ3FsO0sBkvvCCy8oWxhjPma8kDloHjeL12/IGy+8KEvLuRtdVzAoGXfxZX77li1y6dy5XLYR2+WxvKz6ivlHhruKyP7mb/6mOo5wke/93u+VToXmUh3Ipcj6raBa2Ts0qOw+jao/bkJ9Q/bW+AuRwqALtGohGpfxIfuM27biUplMLmi0EqxSAaMSQYt6Ab7m93kks7K2kHjSdVzK6ZDFdDLHpUKBnuVSbpdL+vv6JZ3NKlF8aCBkmzjU8Vxq4mpJLoX4abbSp/Ivmkgp8a8bUG8uFb45LW++/EreJtDh90mmxH0ewa8rly+r89vfxz1tbi6md2i/N8fnuwWaS2m0hEstLeW4lMM+UdQOzLHJRDJfXaRiNkXm3Q8uz+ZFP4DTzNkrs3LPoU1tz6W4T6afLd+T5AMcdOA1jQb3iB6/X3E5AH/rNi4VOnJItnzhs4pvbOzhuJQ7FFSOAulYXN3PDG3dskb06wYudfnmjZJcKrG4WNCKgf6AifkFJf51A+rNpSanF+SFV17Pu00M+Fzi7i90tDFj68ZN8skHZ2Trli3S73HJUn+fLKfSEpm8LgM7tqu2F92C32xBXKopwh8lvT//8z8v//bf/tt8OXErwQKV4aJdFnG6XbYTVyeCC68ekyyVH8lwJP+3qv4wLRzFwMU4cuiApKO5nhoo83bZEuozTMF8A6lMfRsus/ixgD344IP1rbpbEf2MCZLF31sky7be4Px2C6kyH8OpqSl59NFH1YJO1g1EhfJnMpjuuuuuvKVCLYDEmAlQraC8XZGEy5dVhhWNuv/ZP/tnJc8PfuV/8Rd/oUgZ3/nTn/60ep6sK+v4YV4y5iaCeMVIEMST7doRlFrBfkFI+Z6QuUqaWHNMsFoww3zzRWNlw/aBLCyeg7iakbZpwk5PxW6Bcd5KZegZwN6ChtfYaHBdcDMN4TWQjsflzRdfkkP794vPlM1aLPliYX5e3j/9ntxzV86mAzBfG2ID2e79vu4R/uAecBC4CPYpgQ7sQdF2XCqbza/lVDU0I9DQUVwqmZTUCh8CnmCgrEpaginBDeP5m0uHyy19RJNtkLNBXv+5duRSydjqsWEt4G9fMFiUN9Y9kcqtuVQvcCnmJWNuKpaF3u1cyuifaH5vJl24zU5GXblUIiFvvfKqHDpwoIBLBTatDRYCMrbPfPCBynQ3wHSdXZmGs0tLXXXfprlU/ZFKZ1Sfey5Nnwe3Gx2XMoP7mwQtMFbgHRwUl3f9NahvpZ+fIeAbVU12SKTW3lvGbZ5rNy7F/EI1tzmRPhxLyGDQ15R5R8WluswhRselinApr1c9gGP6Zk9yKWIjVihNoUtQTy4VT6TlxVffkH0HDonXu2rlumOD/bhYmJuT90+dkrtPnMg/h9hMrEGyS5KJx7tK+BttQVyqKVafv/qrv6pKNykbbjX5xRIlaQrEALff1zW2ZnjjckopI6+mmTKiaHwu1yDVjHpXf5ybmJOFWOFEORLyyu5N9lntlYAgANkgTFz4RpczeeV7iSVT6icTi933ZcFI29wsuxGQu6g6ppmgyTHCnrnZrwEme7JHqskWxMsbG4adO3eqv7EzYJK1Ng9utrUGmVDYKuD5bnia4/OOzzgkxm7MYQ3BWLYDC63Vf7tesPNNr+S4sG+GJQOATBkkLddvZkkROIKIfEdIBCTLOJ5zizHbJAHsPt2uphWsNwwcAxpeQ5iOHDmS90q3A8fq29/+tvLO5xhBrszj7q3vPCeJaEwO798vM3Oz8t7ZM6qX1fj2bfLE9zytXmusD4ZQs2/HzoJ5K++nvrwsnlCorBvrTgLHkBtuMtn+9b/+19JpaCcuRbVUYqW634DH41IWzd2AWrkUWaCqV6sFvsFBZTVVL8wsRFW/5ILP8LhkZMDfWi5Fv+eV/he2XCqVklRiNVhlgKo/Z5ck4zUbmktpLlWMS4WjcVXxpyqQTWNmIODtijm7rlzq+eclFU/K4QMHZGZ2Vk5/8IHEE3EZ3bJFnvjMU2u4FIHOA/v3W7hUruKPByJOuXNnp0BzqfoBu+65cGFcCtGGdbwbUHNcKp2R2OzsmucDY6N1jUu9/tGUTC/kesICZoQtowE5tmdtbKKZXCoTT8jy8pJKwIdPWZFMFdpZG6Dqrxvuk1sBzaU0lyrGpaIzMypWLCQRGP39lpfFOzjQFfcu9eRS33r+dYklU7L/wCGZm52Rsx+8L/F4THZs2SCffeqJNVzK0dcnezZtXhOXouJvOZNVyVdUnnYTlpocl2r4ikDg97d+67fk1VdfbXmgCqQtfT2M57pF+KM6yihDp5z5hEk1LwdLRTJACWLVk2Dt2DggH1+dk8RK9Y7f45Tt44VZFeUC68vY1E2ZgVhd/EQGN2+S+++/v6JtGJ7N+e/fJ+IbHhanp3BcdFtz0VaCcQq5wmLATvQDtYipZOuYRSdK90stYNWi0jFx2223FfyNCEgGk8poseDKlSvKa9v8PawwMhkbgWrzQmjwy/eiKbAZrAHYJ1jXAj5ny7Yd8v6ZM7J37z5xUfHi90rQ71HinxXz4biMDgU6vtcfYwdhFVsExJwHHnig6JjnmD3xxBNFEzY+PndOAl6vvPb223L12oSMj43JbbfcImcnJ5TYjI0F1RGGSMy8Gbf0ZFV9C1asdKxznwGChwgNvJZ5u5PmRI7h7/7u78q9994rP/ZjP9YwwbwXuBS9s9Y8l8p0RRC5Hlwqa5OwAJaycKn68c2hoE/1+DNs/FzOfhkMVmffpxwM4nGZnZmRjz7+WEKDg1VxqfjCgiyZvr+6IbZy7CLzRifNJ+0CzaVy0FwqNxZ2YoH1wRnZt3ev9DtzVmz0sFyMxAtEP7AYTchwyN8Wa0q7cKlzH5+TgD8gr731plyZmJANcKlbj8gHly/acymb/utMYxRes1/F9oOktkgiraz5Qn63+tkp0FyqfojE18alqODqFuGvZi5lwzVzz9c3LnV016i89uENdU0CrsnDOyoXKo2Y2eInl2V6YkLOX70iIzt3yP0PPlAxl4pMTq3aCmLhSZ8rn7csztRB00nbQHOpHDSXyo2F7Rs3yZnT78m+PXvE4XaLb3hQvKEBiaZmlOiXR1+fqkj2Dw9pLmVKDv743McSCATl1Juvy7WJqzI6Ni63HLlNrl88a8ulmNOjN2+umd+oYu53uYr2+COelYWD0dPc6+2o+8j+JselGs70f+VXfkV+/Md/fE2wu1WwC2Q3oeixqWDA33HHHSqzCL/nit5bxE4KuwQurEwyqUhYrceMBp+37ByVQ9tH5PCOEfXTWUUWPPsRnZiUqctX5OyHH8nth2+VvcNjqkFrJUhFooWi57Ioz2br97SziaiXLVg+8BaLSWxuTmJz85KxVFR0CxDl8IZG8MMHvBRqGWtmMY1GsrOzs3Lp0qW2uuYRySitN4t7/E2AH6GSRaDU+CKDEnvURoCABo3ZKwWLOYKu1UedoAyZRFYkUmn1wHqKz6QfTySeEJfTIX5fEQGqSGC9E8Ecws3xK6+8UvHYZEx/9/nn5d4771RCK/2rtmzaJPt27ZaLExMytmmT6pfJuDcaigNumK03kMDp9Yh/aNCWOCH4Xbq+IJMzEbk2HZYrNxbrbivYaDDfQK6wkusktB+Xsnuus8ZCS7hU3wqXStSHS3G9bxgOqp5+4ys/q0mIMKw2WUvOfvih3HnihLINNnoKl4tULF4g+oHkYnjN96QXjNXSk7/rFcjj88iCn12IquQRqiq6EZpLrUJzKVH3DOqRTisuxbWYjMVVhb+3RDJPt6AeXOqeO++UUbhUv0O2bNos+/bslU+uXJaxjRvtuVR/v20FD4Kfr0gbiEg8Je9dnJbz1+bl44k5OXt5RvWw7iRoLlUf2I3TDqPVLYpL9Uk2kZD4jWlJLSzWzKW8bqc8cOtmuf+WTfmfxKoqBfsx/+E5mbxwQT78+GM5tne/7HB5JR2JVrQdYlDmXmKQ7tjNm2u+J/fJ/ZZjBAdkzq8H+LzFWFKuz4Zlai6sLGm7EZpLrUJzKVFtq3gYXIprMT6/oBzhPIMDak6zru0I/t2CWrnU889/V24/cY8MDY+o+7qNmzbL7r17ZeLyJ7J547gtl3K4nOKxqehzU1G4fYttGwh0ifDVaxK7Ma2KgOgFSNJEJ+FYE+NSDU3Nxr6OB6Wy7QJ6ZjFICp7rkgx1K7iYaNBJpRO9gRATEFqsfsVmkNGAlYB58mKS4+YxsThXEBz2DuQmvmrBewPe2jLasIt66803VdPQu44fz+9PajGS96EuB9ZAFVATB5Od6TuyfY/Hrew+sV4giOdy1a/qhX5AqegqOYynUuIbGipafdOJQEiikq1cj3uq4XhPpdV6ZP/SJ/DWW29VN+A8aGJMI2W8wY1FmxsRxLX1qguxDaXqBmDRyfVlWHXWAsrpsRRAKCN4QJ8bBEu+b6lr1QA2BBxP9m337t3Kdqhe4JhQwl8NacQiwAr2jWPGd2bRN/e3AFu3bZNPLlyQvfv2qWxoyIazSBC7kzJ6yh3njCmjsXa5MDz0B8bHZHBgQC5evizXrk/Ky2+9KT/yk/9Q+t0uZa/KOLGz1WNNJLiv+jQYthVFgOBH5pWBZDors4txGR+q3VawmfhX/+pfqfkBfkJv0XZHO3IpemZZba/rFWjoCi7lcqlqGzO34KZmKZOWxPxqpS08xTtUO5dyu2o79qw5b731luI9d991V35/uOl1V3Be7YRCu5tGtu/1+1UfRG6qVSKCp369kqPxVEElRTKdUZVNHnf38H3NpQqhuRQVa7mgLJb2VNewznE/p1oYFE1GkK5CzVxqbEyGBgbl4pXLcm3yurz8+mvywz/+YyrjvBiXwuKLOYy5zEgGLTWXfXJ9oSBpKpbMyLXZiGwfr39PpEZCc6na4XE5JZO12KbXuJ53E5dSFpdwJxPfJFaVmpmT2VPv5bPQ/Fs3y/BthVZzlQIBbTBQW4sDEs/feuNNWVpekhNHjub3JzEzK65g+T2clmwqHZez9nGpkN+nrPfp90cymM/jrhuXWogmZD6ymkwfT0Zk43CwaypSgeZShdBcKicogW1bt8qFixdV1Z/RDqpogmKXkalaudSmsUEZGByUq1cuyfXJa/LGqy/LT//431PrWzEuRZsZh8ej5nvuoZnrS81l8ZszBZnISxQSzC+Ib6S6au1u51INuwMmiPDLv/zL8mu/9mtrqj5aCXoWEdgwrAMIdNLjr1VQvVDwrl3KqpuKeouQNBJmQePiJdBOvzR+p++Z3YXEc76hQUnjKU4weOWii07PFLyObPWMO2lbMdIsqKqol1+WfTt2yECwOptQA31kdSVtssyKHCN6+jUCVPvZPddNwt/Zs2flnnvuWfd19LZD0Eomk1X1x0C0YsHi8wzfaWwzuckw99JD0HvjjTdUU1U7scrIBLt69aqqGjS28/LLL8uOHTtUj7pa+nfQi5B9ogcA2926dataNNmv06dPq31fT5Tcvn27mnOxTmXh8FYgepcCQiIVepWWniPwFVuoCUjxXTkvxnkwXvvh2bNK/FPPGUK72ymORH9BNjQZjjy6DayVjHs8zyFZ5dy4Mfaw46OHZcDpksGREWWH8OQXPi8uG2Ha2pOSNaecdUf1mrDJSDdE2047znATOAqEtp1tztqVS9E7BC5ljAmCDfT4axUYnyQLEEx1OajAcLScS3lDIcWVcsJWvxIDozem1wSJMnG3uFrIQ40K81sPHszfsFULbojXSH/FrKj6+1XyQSMQtbH157luEv40lyqE5lIgd62dOXtWtpv6WeeSA5ziSKZVcNgATivdmLBRM5dyuWRgeEhcXp888bnP2t7rWrmUSp4qg0uo6uqVNhdmxJOaS/Uil6KlAdek0asXIXAg2Nq4VHoxrOJkLr9fnHXmJtVwKf/wsKRxEzDiUk6XTL76VkHANzYxKZ6xEQls3Syt5FKvvPii7Nq2VUKB2npRERMUiRc8pypebI4RgqXf15ie7Ah/YmMR3U3Cn+ZShdBcSl1subHx0YeybesKl1opGKB1QdqRKEh0RE/oxkKiWrmUuHwyMjQoQa9bvv+zn5aAjYuXlUup1hBltGDjfXZVlugqnYYNTYpLNSza9ZWvfEVmZmbk53/+56WdoALJAb/4BgfUwxsMtKxyhAFLL5T4/LzyBo7NzCqbpHoCG8AtW7YoD12yuVHYx8fHVeUAFUIIDVZQcXT52oRcmbouN+fmipbMtrKkmf3mwnjkscdkZHwtgXcPVCYEeoKBNU2TvUXs7lrgnybdAqMf3XrH1ahgQ9BCHLLLCikHVJchNGFfxs0GZeXWfnlcIwiRVNxB2u3AjQkl78Z+s93HHntMZSmysIQ11p0AAQAASURBVD3zzDOKOCK8QZisQMTjs9kHq486CyrbQ0Bk35g3uT6np6fVc1TPsd31LDcRB6luZF/rBbapbgKL9FiwO2/s+3q2AHwXREUDZCcCRE/fimhJTxrAMR8e8Ivf61ZBW0jDUMjXdRV/BpivOTaI0eWCeZ3xGU4lZWzPLukPBdeIfqlwROYvXJT5c5/I4uWJon0zikH1qrGpGOhUAfYXfuEX1LUGV2lntDOX8no9EvB71aOYpVkzwHyzEImr3p/haEJmF2MST6TagktdmrgqVyavyc3Z2fbmUo88IkPDazMkK21WTyKdNRvWOxBq+tiwXYK6h0ppLqW5lC2XIrkVbNu2VbwrYpVrhVMph4ugTwVsEQHhXQMBzaVsuVQyKWMIEUH/GtEPASJ87bqEJyYlMnWz4vmb80ByihUIPp0IzaVqA+NhKOSXjSMh2TASUvc7/a3kUh9fUFaV4QuXZPa9M8pKsy3iUpPX5OqNKZmen5csr7Hyqb4+SYft7+GbyaUefeIJGdlsER/hy2OjFW3PMzi4Iv4Z2xDxj4+1BZfqIiqluZTmUrZcyhPMORlt3bI1n0xPrDhfJDMwoLgBIhUJjL4W3Od0QlxqKRmVgzs3ynAgF78zA9vgG1gIz4ZlZj5Sse28cm6zSVzDXacT8QtNiEs15MgQyP6X//Jfyq//+q/XrfKk3miHi5NMb8qGzUiGw8pGs95KLyIC4gm2fVy4Dz/8sCJ4BOpTZg9xss+CQZV5ZZCtmbk5scv36quiJ1+9cOrUKZVNoKwIt26W2I2bkonH1QTgGx9Tx7ASkEXlHxvJlXYvLa9YnjY/oI2lhdWKttLv0s5A/ELMWg9U+VHpVg9wc4Hwd/PmzaIVfeDo0aMqowWMjo6qfnRGpR0VbMxr1so+rikeiH8sjIBrCvGPRY/riBsZqiio6jMEPLbP44MPPlDezgiDhjDKe+njZcxRhgUK9qQ8qL4rVmGomuDS14UKkzrNIXv27FHzABWRZntO6/yCpzf2pIi164F9w97ULB4RgPLs2C7nL5yXbDopo/v2rr6+r09lxfYKGCsIuAiulVi3st7ix04vDTMy8YREr6/2gaQJcmRiUgZ2bq9oLdw0ElS9/YybPoJXIwP22cCsLzOLCYkl08rWYXzQv6YPRSvBsfrn//yfK67ypS99qaaq3UZBc6nyQKZ8ylJBEY7lqrvaikvNFuNSjrbgUv1+v6QSCRXIhhNhvVlpzz3e5xseUv0wlL3gigVes0G/HqOCIv9cF2Woay6luZQdlyLjnODUtu075PyFC5JIp2XPvn0FXMrfRfcUTedSyaTEZ+cK7PiiN2ckuGlDRVxq9+ZB1dvPCKrDkbaM2lcIMY9+NLEgs5GkBL1OObx9WFVqtgs0l6oPVBBTWgvsKFPzCwXPhS9eFs+wRYRqMZeas0vSpTVEgxwEKuVSQwf2SfjSZUkthtVxC+3YppxYKgEuEaEtm5QDFyKnw+tRfKrZ8HtdEkuk1zzXLdBcSnMpWy7ldot/dES2+7zK+SrVtyx7N28scIXz+DurzUk7cSncokjYNZDOLsncYlTGhoIVcSn/2KhEp1bjWxTxeIYGbV+7tLwsz713XS7djMjYgFeePLalrWy1m8GlGhLp+qM/+iMVsPoH/+AfNGLzXQO7XigAi03p789VBCbT6mKA5Ps9xe3z1s3gWlhQWVaIDSxygG1hVVgK/D+NmIObN0sW8rEC/NbtbOSqBSSP6ifIHXYcZE6VspxSpdYrFwQCZMA0GVcLlYXZYqGazPjE4oq3NNY8frJNW0dk6w3EMOaGYsBSk+wjsv/qCcbUegFgxtPJkyfzNyTciPAeqvCoBCz1fir2jPFK1Z0xplkkKY3newOEM74f5AIB8IEHHlCfZfQvZB/4PARBYzsGEBZ5P9fj5s2bi/Y8RHjDlnS9a7tcsMCzLWPesAMZKtZKymIoVkFo2HceO3KrOkYcA4jGyMhIrtqsC+2oSh0jxOpKRQtEZMa69VjRoNoK/NPxQndUYCPMzd7OTYOqHw0aXsDrthXz2H962MxFVpMYZsMJObhtpK3EP5op/5t/82/ky1/+svzoj/6otBs0lyoPZhvggueXlqFSK9ZqGclkcv1PEIVaxqU2bZasydmBoFA9bT5r4lI4YtSBb6htmm6gWwFllRaJK/GPMx3webrKmkpzKc2linEpw777lqNHNJeqM5fCutnunp1kiUqC8gN+jxzZOSaL8ZQSY4eCHnHY7CP7/+zpa3LhelhxLtoC8vv33LWjrcQ/zaW6A1nGd9/aki6S1OEqRnuapWxGJfSs13+pkVxqw/49Ev34Qv559/CgBLblEnBbzaVIHB/cu1o9VC2UHXqFgmG9MTYQkGmJKvGPUz0Y8EqoQbairYDmUppLFeVSbpf43INy5MQdmkvVmUtxT253z879fCWtOpw+r4S2blHFVExQ3E8rS2Sb/f/f/+qMvHT2hnKvQgR86eyU/Mu/c7u428i5qtFcqu7CH0H93/iN35B/8S/+RUWKcC+iWBY02d+5aomYROKpgqDr+GBl1qRU6dBAGZHByJaqFLyPi30rAatMWl1QBHUqJXvFqpDIBEAIQelGYIE4MXYI/lMm/P777+cbjBpo555MtUBlyw8NrvE7bqT4jP0YY9Fuoqw3EKvInMF+0wwjy49zX2k/uXJQaQUhghMPiD43GAh0lY45yKSdGMZNBATD6DGB2GksiDxPVR+vsQP7cPjw4XxVofUGyXg/18y7776rjjfHFCvTWsYTx6DU8atk24ihVBGWAlWOfB4C5muvvaaOFfMH/Qu74dpnvBvXeLGeFsy7nEcqUfnbsE9d71jbCqTF3lOFCIc4O7gOSaJXjVn0A4iFc5GEjBapEGwFWGdwJqCp8t/9u3+3rar+NJcqH4h5ts/3566bhUiu8tQAwt9whXbB9eVSmySbzlXVUdGvuVT9oYLpIX/TuBQWNUtLyyoY34zkBs2lNJfSXIpA0ZIKFLEG2AlndlyKgI/RQ7pSLlXsPdXMMVSkj6/Tc3QmnFRCH0D0A9OLCbk4FZZ9W+yz2lsBzaW6A1SV2fk4kiCoEqgikQLxm+fpZdwqLrXx5F2SWlhQAqSPqtsK7w91XGp9wGc2DAWbxqXg6qnMkgQ8LnE5dVzKgI5LFULHpeoL5sLl7JIqpukvl0stMSdUyaWK1bdXMcdQjOR2le6pevFGRIl+hsAIPpmKyKsf3ZQHb7F3M+tGLlX3KNf//J//Uw0edlZjnYPv9YozmSqwdvSEQuqCI0vZLPoBsm0WHHF1oRm2RWQwl7rg6JnFRUr10OnTp1WpLRegnW1hMSDIkZmFyDC3uJBX/CFv69n6kS2F/QHgPWbVH1EPQYZ9uffee9e89/r166rqCnvGCxcuKDtEssPaGSo4DzmqUZxoNLliP1PRmGRMdhreULChNhJ8Jn7+t99+e/5vsvyMLBuq2IoJXrUCwUgFXLdureh9VPIdP3583det19PODMREKnl+6qd+Sv2N6I0dKGIYfQLpabhe0gTXHTdNVMXxemOR5jpBIJ+ampL77rtPbYfriO2XW5FnB8RExiTXPNcv17Vxw8TPSq5LrvdyLaBJBKAKk8/mfRA93stcVEsFoHEDwzHkHJvtHRoNvkfGlFlGDy27uZh5j8De5StXJTAwnPc+D/k9qnqlEnioJKZHpBE5glwE/Gv6mtYLxXzaK/VvbwZ+5Ed+RNkqfPWrX5Uf+qEfknaB5lLlAyEvlXIWZBBynTA/YSdiFv0A/CoaT0nfSoTL5aLa2NlkLrWouVQJlJvo0A5cKhJLFtiKDga9qodaIz9TcynNpXqdSzGHL0QTBdcdLgSluJQnOCTpTI6HDAW9ap2oBGSTJ+kjZuL8KnmjQY4UiZS9M1DCYm3dDtBcqvPhHR2R1NyCJOdWe8qHdu1Q1X70BbdWvFIJuLgclYVEVpZlWVWycl01k0uFs+kcl1qc13GpDo9LfTQxL9fnYnnB8dYdIzISapwTl+ZSOei4VG9zqXQiIUmTM5QnEMj3i7bjUp9cuiIZR1DiOKr0iWweCcr4UGWVyegX0USyoIcofY5J2G0EwvG1FZ3su93z3cyl6npnyqD9d//u38kv//Ivt1XmfLuCC9w7OKDIFCo7ijX2LKWsq6iaMJqCc9MD/DY3OuasYCYjKoQQ3WKxmLIqgDBhr1AuDFGCicio/KERc6leYggO586dU9VGViEDa0AqeRgnxSwLsTU0eoox2bz66qt5K0Y+t52gMuEWFiUViaq/VfbZ6HBLetuUAwQ/s+gHEuGI+IeHGkLuEHg531SrIe4hTCHkkj3CAtdoIFhRWdcoVFKFRvUqr8fikwwuriMq3BDt6CtYbqU01w3HkgpK45xRJcs1x2dwvbMtriG2jW2JYTlaKZ588kklMiLOMtaZByA8lVZ1c87puVguOE7Gd2OuYPzwPbhppDK0mh6yExMTqloSgsb3oF+Ekb3UaDBvmUU/wN98T7sxRP+L1956Vw4dHizoXZazKyz/2LO2DGzfKonZeWVJhTWCd3i4Yd/Z50FEWdsUPtCGfSEYV7/0S7+kuMsP/uAPtkX/X82lKgPnbCDolXQmm6u6cvaLc2XtNTL7rEAQdK9kE6cQbZap0i4+PjWXag6U5Q4JYSv8BA6F/WgzHAmqQTKVWdNLcJHK5qHK3DnK/jzNpfLQXKp3uRRzvVn0A1R2Y9mEK4Edl3r5jXfkwKFVq775SEJV6FZiAUyyVGDDuKTCYeWYwr0eiVWN+s6jISxA+9asYxsG28c5wYDmUl3CpfbtlnQ4oloBOAO+fN+8pSJxKRJfUku58T+zmLMxx1GhGDSXah6Xik3dlMRKT1Kn3yehbVsalvBZK27Mx/OiH4DLf3B5Vk4e3mRbzV0rNJdaheZSvculsplMgegHktGo0iOwLLbjUt98/g3Zte+Q+ps4z7WZiLhdDhkMlC9WEscaHQxIJJZSsTEScIO+yh1wysWO8aCqIDYSv4x937+5uH1zN3Kpus6kzz77rFy8eFF+4id+op6b7WooT3C3W1w+b170A1wAdrAK4fQAXA8E62dnZ9Ukgni2e/dulaFQqXBCZRHvtfYSs4LMqxdffDHfw8xOHEB0RBA8ceKEEvXWgzUozuSIsNEuQPAzRD+QTaUkPruaMdduWMoU6S/ZAEGV6rCXXnpJnW+ELkOgYjw1Q/QDLCqNXEAr3fZTTz0lX/va1xQx51ggzlOVCPmqdHGgwo/38qCfIDaqEAeqZQ1wnb3++utSy/fj2kdAJGlA9cOsUPTju1I5CGkoF4iEzFUkERhAvCRTlHnmzEpTajuQ5ICdjBmQKY4NRA2ChvDM9hBPefDdGoliCQvFrjsSQLh+7ILNlYIAVWDTBnXj5xsdUY2pGwUCb3s3Dym7PQPbx0MS9FUnPDcacBbG9nPPPSftAM2lKgdzElVWOCEYoh8o1gfJ+jSB5PWguVTzk5IIrifjqz0R2w12VczLJQTnWqC51FpoLtWbXKrYfJ0u4TYQt+nPR8Z6pXC4nOIbGZbA+JhK3m3kvQVJVI8f2ypOR+4z+PfkoY2yYaj9hD+guVSXcKmBkHjHRvKiH3AUsfjPWJa6heja68wKzaUaDwQ/Q/QDmVhcIhONS4CuFWHlwlEIeFSxqudaoLnUWmgu1Ztcqlg8OJu150apTFai8bVxqXCssJikHHCvPhTyychgQEL+0pXitWIo4JZf/t5bxbuirxAC+/HH98v+NrJMbwaXqmvax2/+5m/Kz/zMzyiRSKM2kLU4EvLJbHg14OFxOirOeiGDgCwEBBeyEBBajCogxDnsAKu90IyefGYg/DJJ1bJdO/A55nGFiNBOvb7sGr5nk8mmeaJXimLHrp77SgYLJeGIWVYBmGqxZqJUZWo9gABXiU0JVifc+HznO99R45pqWqr3IBQEOrH/JROFxsJYQVYCtnfbbbcpoQ+SwfGHVOzatUsJsFTsFquyLQW2AzGhQrES8Y5qYzLrINrmPp3lgM/hQaUgxwhLUf5G+Df6G1I9yj4BwyaBCkrAMUVs5DgwX/G8IT4bQIDmOn3zzTeV7azRb7QRKHp9mZ5nrObsnJfVHH3gltvWvNwsqLUryPw6tmdckUSq1Iv1YWsHME5+9md/VnGYRx55pNW7o7lUHaF6Uga8BdUhHpfmUu3Kpcg+te1F3LZcyn6f6jlHay5VJZeiwj6Ts6CDM5gTAspBr3Mp7uXgTFx77calis1ZDtN1hzhIdjfU48UXX5D9B+24lLQ9to8H5Uce2S+RRFr8bnrZtKeTDNBcqntBpZg7GChIco4t9Uu6whwXHZdqDtKm85R/LhprWy4FL7cbSoYzRz2guVR1XGpyLibPnr6uEmtu3zMiR3eOVHTcNZdqXy5VzE3F/DyJU5lsVt3XvPzii7Jn79rWQc3obV4rju8elf/jZ+5TfZKHgx7xe9qz+rmRXKpu35jKr2984xvyO7/zO/XaZM9jIOBVFiRccGStcxNj2HsaKMfujcnD6O9FFgHZBlh3EiBnQqnW/g9w00vFEhMXN9ksqvfff3/dzx37aPSCA9iVQh4rsQ1sJGyrZ9qQWJl7UqRTSWUxa8BdRzstyt7p34fgZwWBi7fffluNwVrGXjsJfyzKBGMq6VH48MMPq+PAT0j4d7/7XXngvvtV83QDSgAM+MVVhdc3vRRffvlllYU0MjKiSN2GDRtUphJ2oFQBmsk/JITjtN53IHmAANR6/T0BQSY+g2rEWkCQigfbY/8JVjGvbdmyJR/QMkDgClLFcTVg+LDzeiuoHCZhge/FPPnuu++qYF8jbowMS09z5Z/Z5jOZSquHsaYeOHhQRkeGCiq72S1fCXvnVoJK59RiOJcxPDigAgX0X+sE/PRP/7Qap6xhlfYCrSc0l6o/Aj63eNzOPJdaWsrm7D1NsLOHs0JzqcajHQNSpQBHTyQzkjXN6Yy3et0Eay5VJZd64IH8WgqUAOh2qV6elUJzqfbjUgSJ6Qdj7uvqNYlic+F43nZw8tqE7NyzX8ZHhySaKORSWEu1I3CBMFxtcANi3RqqwEarldBcqntBnACXKpJxsOFOU+Wxcp0ZoM/fetBcqvGwi+e0q2U62DIakKn5mEQTq3P6nk0DZXHzcqC5VHVcavctJ+Q3/uQdyWSXVcn5N09dk3/45H555Mjmis+B5lLtx6VwMHCs9HDNP+d25V0IF2NJZaMOrk1clT37D4h/YFRmw6vJtAiCowPt6UIQS6Zlcjamjt3W0YDijvzsVS7Vt6y6vtaO3/iN31A9vP7iL/6iHpvTsAGnigCwEQRG9PN7XetOBAQSKT0m0I9Ahxjw0EMPqSo6JniEumpFEUQ/hEQqCCl7RtShTLkRYL+NHn8E9pk0Wcj5XCZFrEwffPBBaVXAO3pjuuA5z+CAeELlC0GtGE+ZZEqWl5fUBM/EX89zxQJPhgrnifHH73wmlX+MPX6Oj49X1GuyWjAuKcEvR6yqBljp8t0q/S4cp7vuuktVCiJkv/v223L7UUtWcl+fBIYrzyoHCExkq5PFZWR1AwQ0mqpz/HlwfhDTES95Ha8vtU3mDeaT9fDRRx8psbGarPhygMjHcWff+RzmMZ5DJKSPKHOjMbeROECWvRkcB+YPFlaaW7Md85zWCLC/3DQvUWnD/jkcaj+x9TTbJ7zx+uty4s47JeDzquAWlXO8nqBypZULzUA6FpOFjy/kbUv7nA4ZPrBPHE1sUF0rvud7vkdZEv/6r/96y/ZBc6km9ZFLZ/J2cQQWXC76Umou1XIuhbWnxQqHxJdqkl+aOZ7o80elNj0ksJytFzSXqpJLnX5PcVArAv7qghOaS7Unl+J+2EjoQIRnDscK/crNxfzrTr31hhy7/YRs3zCgKgDhU3CpkN9Tt6ByPbHG3rivT/U5bafK7PWguVRvgGuQXpmGvSei33Bofcs2HZdqPNLxuCx+crngOf/GcdXqoV1BAhW9/lKZJRn0u2UoWD/ep7lUdVzqf/1P35DrsknM7vUBj1P+j5+9r6rzoLlUe3IpWiwYCR0kdzCHp9JZmZpbLUZ4+8035PgdJ2TjSFAWoymVSEUP4g1DfpVc225AnHzmnas50XolYezJ27dJqE3bzjSDS/XX60b993//95UyqdE4cBH6vW7VDJMHAeBy1H96fj322GMqCI9AYQgwVPZQdUU5d7VASJyenlZBdiYrAu6NAp/FIgRu3rypBAsqrSgh/8xnPlNx38J6QvXP2jAmLr9feeL7RobaWvQDqk+b16My+Ool+jGuXnjhBWWFZJSlc14oW8eaCP9qesWRxcJzCE3YgTYaiH4sno0C3xVxsVLQ3/K9995Tv7N/HrdH5q1+3svL6rhWA8gFC8bk5KQiEFz/gOsU8Z9MI65fxH+uI66nUucDMkK1YDl9OQHnm89uFBhTPLDOwgv77IcfKUGZTCme27t3b77/oZVcGfMIz/MeY3wwz1gtjOt93SG085n8NOZwc8UIMOxY+BnwcTPtl8Ggry1FPxC5PFHQq3A5k5XI1WvSSYDDwGVa1T9Wc6nmQFWkul0S8HvVg981l2oTLuVwiDcQUJyE+RF+0s6iH2DsIDrAyesl+mkuVSOX8nhs+6xoLtVdXIp7Ytxx+GnM4cl01pZLIfoh9o0NBmRkwN+Woh9IWe8lSFSp4v6ildBcqjfAdTUc8smuTUPqMTLg01yqTbiUy+eTwd07xTM0qHo2BrdubmvRD9DOaPNIQHZuCNVN9NNcqjYulVxySCIWLnhNjEQ3HZcqi0t99OGHHcGluM/y+HMOYwaXsvZSNrgUyVbjQ37ZtWlQJVS1o+gHXvvohmRXRD+QSmfl7fOFRTq9xqXqIvzReJCg99NPP12PzWk0KJhy77335kuHsfUDCHZUytQCFgkmAya4RgZMKXem0ggwAfI9qOAiq5cS2EcffVRaCcQ/BD//6LASAHsRjC/EZKNiDKEJy08eCEbGw7CT5LzhbU0fykb2/YOIVyPMVbJ9RMxKwWJOxo+xoB+97ai88+67BcfCqAirBXwG1w7VHObAF1a5CLDMB++ufC6iINXbb7zxhrL0NF/T7CdiHt/X7jPsgmoI9I0U/zg2fU6PuEJj4hvaIMuekPT1O9T3oKqxFOz2l742jfJSLwVzTyg1v61UJtjaCLchqHou57l2z6xinsDepBXQXKr9oblU48GaR4ULN6GV9rjtFmguVSOXOnpE3nnnnUIu1d+nuVQPcCmnqSdUNMq9Yu5+jKrAToA5garUc+0MzaU01oPmUo2H0+eV4JZNEtq2RblQ9SI0l6qNSz108g6Zu/qxLGVzXIqQxP7NAzX3se72uJTP5ZJNg0OyZWRUhr0+cfb3dxyXcjisXCoXl+K7dALoi2w+mss4FVpapvUal6qLRPvf//t/lx/4gR9QZcEa7QvKq41+W2QhHT9+XIl23BxTRkwlVjocUc1/+10u8QwPlR10xsrP6MPVKJD9gKD00ksvFXgiEyy94447VBaFRmtBZd3dd9+d/5tqMhbkUj3eWMzoQcd55XV2fte1AnHbsJ1s1A2MuW9bJUC4fvXVV1XVHYHOu++5Wz44c0ZuO3pU+hz9dakcPXbsmPzhH/6hqv6DMJnnauxyIVNkJ9JI+M4771TP8zoq/CBm/M7cMTU1la8atIJtYkVAbx2zUImwC1FrVEUwFmuz4VVbpP5+h2T63DIzM6sSAxCazftINerqa/ttLWjoi9gKguV0OlQvorNnzsix48dVRnosGpVXXnlFXVeNskytBxzY+los+rjp7CQwPr70pS8pTtOKRBLNpbqHS9HrEj7V73aJd3Sk7N4qmktp1INLkXFcyrK767mU2y13332XfPD++3LbsWPqesVRoVZoLtX+XMrndkrQ55ZIPCUff3hWbj16TEJ+t6STcXn+ue+0PZey9oFu995cdtBcSqNeXCoxPSup2Tl1j+HftllzKY2KoLlUbVzqe+/eKZevPyjffPGUjGzdr/qj/eOn11aqVYpu5lLZVFqSkWihk4nDKbMzMx0Vl8Ia0+9xqT55H509K0duO6ZajCUTMXnu2faPSw0F3DK9kMiLf33qufZ2kGk0l6pZqUNN/+pXvypf+9rXpCP6uiQSkqKP0vKyal7pDQYb0iyzHYFibPRWM6qfmGCY4LFnHHN7JX7tev71rmBAhg7uK4tkQdiaMSkR+OBhzoLg+2BPSONTjdaB7BzmA+N6opqUDByeKwUWbbfbrcRbgltUBzaiTyRBsUb3nKt2UocIsG+M7bFNm+TMRx+Jb3BQCe/1mJ/I4OI6J8OJ42yeE/Fzv+++++T8+fOKSHEesSOAqFCRWUlDWax46SkIGVD9V1b6lND7kPPaKOHPiumZGTl+5KB4XM6Cvosc4/VglzXWPLs4tywkF5X14EAooGw933j9NdVvy7gpbleEdmyThY/Py9JKhYXD45bg1vqL+I3G3/k7f0e++MUvyu/8zu+otaVZ6DQuleTGJpXLpkOg9ntX7UF6nUuNOJwSu3Q1/3rXQEhGj96iuZRGWdBcqk5canRUzmQ+EN/K3KS5VO9wqY3DAelbSstQ0Cs7Ng6rYBVzcydwKRetA+DOK5n/ypq6ze2O7aC5VHlcKpvJ5CuT4RGulf5KvYD1uNRgPC2L753Jv94zNiKbHr5fJcWuBx2X0tBcqj5c6he/eEIOhxblxN13y0jIU3O1X7fHpbI2cc+ZmWnZf8th1cagk7gU9s1Lcyllk755bFC8bmfHcKl7DmyUb71zVRKpXIVo0OeSO/aNSy9zqZpTyL797W+r0k8u0HZHJpmSVGyVTKPIJxromdtu4DydPHlSXbAMHDKqDNx+223yzF/8ZUGWYToSVZlW5YDMLMqzmw0+k0WhkTaOGuuDmxbK8LGTBSzkLNTlCHh44UP0sf8k64VFGOvPetvG7tixQ+0PNxnXrrVX7zH2DRHb6GF56PBheeHFF/J/1wNc+xCfP/7jP1aZXJwvKgPIrkR4JSuKeZznL1++XJX1Kn7kzC0TExPy8ccfqyws+uzwaET1gWHfZcXOXbvkwvnzBc+xTwZ5Invqgw8+sJ2z2GfIDMej2eB8v/vOO3Ly3nvE5XTmGzszv0F62xlOr0eGbzkoA3t2yeDe3TJ86ICqHO80kOFIkgDcppnoJC6VSmcksSL6GX0AYoneWYNLcanjR2+TZ77254VcajEssambZW1bc6nehuZSdeZShw6p61Rzqd7jUh9+cFoefeh+1Xuzk7gU90Nev1/cXq964ATSaRV/QHOp9cF9rvleC96Q7jCL/IZxqVuPyN9+5X8UcKnk9KxELl4ua9uaS/U2NJeqL5e649gR+eDUaxLWcan1YcOldu/aLefPX+hILnXmvXfloQdOql7mncSlcHr47F075eEjW+SRo1vkqRM7lCNEL3Opmpnk17/+dfnCF75gW5rabsik1gamEP+qbfbeqSSLsmrKqLl4DcLpdjjlyIED8v7HpsyDPpFssjwC2ujstFLnCIsIcxWgRvNB2T3jiiwhFgMWp40bNyqBbT3bJoQ/82LGgkKwhkUOwaieYIHdtm2bWmDJ9LGKi3xePQNElYBjhwBn+I+TTXPq1Kmqba/sQLXjiRMn1LlCBGQxIfOI6wuywY3Xww8/rLKq+GysFioF2T9UL5Gphe0Y1q08GlVlGfS6VUNwMzaOj0pfgbN37txi64DwSz9GrNA4zlbcddddaj5hjJQDtlePNQSxHNLHeef4A/aB8Qr5Npo8l0JOgElLOrPUut5cgwOqkXwnBqoAXObzn/+8/Pmf/3lTP7eTuFTaJimAsae5lIinv19u3bdf3j/38erB6euTbJk9YDWX6m10GpcicKG5lOZS3calstklSaYy6mcrwDrgcDrVo1OrvzSXWh9LNgmuzPOaS4m4lkVu2bVbzly6uHpw+vokE42VNf40l+ptaC5VO3Rcqjq4PJ41MZCRsVFZ6sG4VDKdlYVoUv1sBXAk2jIakM0juGi1f3yl0VyqJtmTQfWXf/mX8h//43+UzkBnkud6w2jOifBnBBmxZRsIDUg2e0Viibj4vT7VBdPpX79HExc/Jdv1Bvv33nvvqeAHRNhobIpYSZm3UU3WqEoijfKBgGaMKxYKMoUQfjhH650fxC6rHSgLzC233KIqzxDi2F49wMLKfrG4kqlC02c+m79Z3BCqmNcIZu3Zs6ep5fWIY2fOnFHWB8ZNC/a1PFdPG12uVfzFufYhGPyNZzrBReNzDSsFziXXIFWB5YL3U7mJTQTVg/zeyGuULPXNo0FZjCUlm11WnuT0d1m46VRzBcSVoCljlDmEc8s+FiuXN24WITSGzYUZbIcxw1iid4ARoGV7jPdKyvDZP4gfRIoxT+arGZArgrZ8RqneToA+h9dnV4O+kJzhUO6a1Ki8mfI//sf/WH77t3+7KUE3zaW6iEv5vDIQCinOEkskxA9vWV4WV2D9zEjNpTQ6jUuxLmkupblUN3GpcCwp85HVRI3hkFeCvs6z2mwHaC6lUS2XcgYDMhgMycXJSYknk+LjfhwuNbh+sFlzKQ3NpWqHjktVB0Q///CQpOJxWV5aUgk8Tq9X8Y9eiktNzETk3LWF/N/7twwpEU6jdVyqJuEPO76bN2/WpdlgtcEyBrgRsF4PLp93je8uz3VqNl0tIHuA720QrH6nU1m03ZpJy3sffii333KreEdHxDOyvvcx4km9K+6YdKg2okKJCQ3bQCzQaPTMPp89e1ZeeeUVNdEhSjTCo1mjPBBMMkg7YLEhAMVCU2sZuGE1wFxD5rqRcVINCHxR6UoQzLjm9+7dqxZKFjH21/ANZ265cOGCGnu8ppzPZcwWazBcDvh8CMA3vvEN+fSnP632kXHNvtWbyAG+G/M3PRapEuBcWbN3CDRyfE6fPi1Hjx6t6HM4Zli/fvOb3ywZsORYLy2RmbSsru1q5mMq/oaDhSIXZIfmzcwZnEvmKI4l+4IN7XrgdVZbBeYdzjPEh3OFOGw0pCZri/+zy9aygl6WWNrwfRnj7KOdvQPnhDmOcVVKfKU3j1n0A5OzUdWU2dOBtgatBpyG64JkgGb0ru00LuVxuyQTz655TnOpHJcaOrRfbslk5P2PPpTjh28R34Zx8Y6PrXtcNZfqbWgulYPmUtVzqVQ6K8uyLG6n09a6s124FAltcPt241KpTLZA9FP7E06oHodkjmtUBs2lSoOA8JLF2rOTqzzrGZdyuF0ydu8JOZxJywcXLsixffslsGu7BHas399Lc6nehuZSOWguVQOXmpuX5eySuIcGqmpbgvjnCQR6lktF4ukC0Q98fG1eBgNuCXg7rw1Mt3CpmiKCf/VXfyWPP/54vhKrGixlMhK7MS2ZRFIFTPzjo+L0lbc9Bt53v/td+YEf+AEVtGJglyJLTgLroZCkEwl1UTsRJ7y9mcVnJ2R4R4Zk0x3H5Hx4QfVqcvp9ZZFPJgEmLLP4Uyk4H8ZnsS3KWakU2rRpkxL/aPwKkXvzzTfz5dBMoIw9BECyDsqZ2DTqC84bTXONgPXMzExZpd+VgIWH/n8sjtVYRjKePvnkE7WQ2VWmMoYQA6mCM8BYRPAj84XnETIZj6Wuh3qU1RN4Z38Q2qj2A42y/mMO4PoyUExgpHKl2muL48f+sw2778ExS6iK3tVj5/N6avrOfBYEBrKDoIINGeePeYRsUiMzqhwwpxmkjLHNfmGNSmWoVdRmnCIM8hkItpAihGSDgBljkQpKjj2Wqmxvve965513KmJXauwh/Nk+n85o4a8KcN7hNjgaNEP4qweXIqswmUgo6ygar7u8XhVAagSXIgAa8HklmUop4xL6URIY7UXYcSnf2KhsPXmXfJKMydjtt4kz4NdcSqMkNJcqPBa1ote4FBzqxnwkb6fEGrBxOChul6MtuRTHrB25VLqIHRWCoBb+eoNLLWGZPz0tWRWXcohvtHFxKcUf3G7JZjKKS/F3LQmunQy77x3YtkV2f+9nZOLb35HN998n7qFBzaU0SkJzqcJjUSt6jktlMnLjxdclNTun/u6H8zxwt7jLqDQuhl7kUpGEfauwSCKthb8Wcqm+5RpmhSeeeEL1pPm5n/u5qt7PRy9enijMduoTGdixTRwrva7Kxe///u+r4D0TFAF7jeqBkEb/r3Indiast956S2Vrld10/cMP1URoTBpMXo888khZ4iHvY9JEkKEEmb4mVAcyng4ePGiboaDRGNA8+3/8j/+Rt10Fxu8sYocPHy5rO5zL9UrGjao9zrP580oBqywWNTJg1hOx2TZjx278sGgiPCNqGlWB1X6Hcq9BspIAYjeTfC03suXAbv+p3nz11VfloYceqnq7eI3zHR544AFFLMhAgvjQXzCdzqzpF8Z58pd5k13sxp9xh0DM3GD0TTTAeGV+K3cMMd8wjhCPmROZcyA8rDd2YG5jLJEJZdg3jI2NKVsHSBn7Zt2nWhGJp+TyjbW9KXduHNQEq0pgp0DfPTIDG416cKlENKrEPzO8gYDqu1gJNJeqHzSX0igXmkutQnOpyrkUVWrYnZvhcvTLlrGBtuNSOLiwrXbkUiRR3ZwvzKgH40MB8Wr3hJ7gUuGrxKVMQdg+kdC2rTou1UJoLqVRLjSXWoXmUlVwqfc/lPDHF1Zf3IftcFC2PFF9HKwXudRcJCHvfjKz5vlju8dkKNibRVftwKWqTtEmmP7888/L7/7u71b94dlUao3FASlPqUhUfCOVDcCf/MmfVFZZXFyUQmILqVFlADGRqCibg0mTSajcyfbdd99VooY54+C5554ru58a+0YJMtkOkEGyHMg+QID8m7/5G5UtQlZKJZ7GGtWBY0x5ORV/jcoAsrP+pNSZhaqYmMc4hvAg4pWbCMC2WfgR96zbRZDm8/hsFlcyjVg0GwUykgwwlumVByFpJriejGu1FlCtCaFBQOS4cnxJ0EDsDwRDtufOXAFcKSBWzA+l/r8SEZVxzbxFYgPEiP1i/yFSzH2MA6MKlcwq/v/JJ59UfzMXvfzyy2pN4jg2yr4H24SQ3y3h2Op6OuB3i9/Tm1VY9cCnPvUp+af/9J8q0b+WavZmcCkEP6voB8gir1T401yqPtBcSqMSaC7VGPQKl8r2r+U06exSW3IpglTtyqXoEe3zOCWeXB03/M3zGt3PpXJxKUvlxbJIOhqrWPjTXKo+0FxKoxJoLtUY9AyXml9YM/9nwhHNpSrEUMAjYwNemV5MoJ2qivbxQZ+y+tRoHZeqOlIPuUJ4wXKxXUAw/od+6IdUHy9KajUqB01DsTSsFNy0cUO3Hqiauv322wtEP0qM6d9WqXDEDSdlyZQuY6lBhdnTTz+tBJy//du/Va8xBElDTGgGspmsJOMJScQTkk6lm/a5rQDnABtWxk2zgJhHBR+BAbtjyzhgTCFIUkZfLlILi+JejMq5l1+T+I1p223z2QiAjCsEQDLLmG/IFqI6rl4wi9ZGc96XXnqpgHjVExwzq80Kn0cQph5ZQCxQJGMQ8KF6kTmG7CPsqKzgmWqCOpwvzrvRw7BURjCBpnL81NkmY5u5DcJp7Be2ClQXUyFJ1hVgLOC9zvP577LSrJltkJjSKPA528ZCsnUsJOODfvWTRy/2CakX8N7nRgCu00hoLtWd0FyqdmSyWYknkhJPJCSV1lyq3tBcqnu4lNO59v7J0d/XdlwKq6t251KjA34ZGfDJQMAjowM+9bfmUr3BperNmHVcqnZoLlU7cNYJR+OyGIlLPJnScalO4VLpjLh9ATn3yWWJJ+zPm45L1ZlLYZdp4U39Xo/mUhUCznTLjhE5vH1YdmwIqZ88NJdqLZeqWvj71re+pUh/LSeQ7CmHx3Ij1Ncn7mCw6m2i1mPFhxdxNwsu1YLG77MLUZmei8h8OCbZbGGVABMfIlqlQMyjCqscm0/KnM3gRpCBXA0IyCM+Xb9+XZU802+Om0KsCREZ/9t/+28qo4NKUP7vxRdfVDahjQI3tCnVsyxXgZFJpyVtrWqt6+ctKTK3EI5JOJaQbJmVl/UEx79RglSpz9y+fbsiUmQ+AK73q1evqnHAIl5JNgSi3/yH52T22qQMOJwSvnhZYpNTRV8PcTMqAF944QV1vhGc6wUCHAiKBiCLCKzGZ9ULiOYEbrgmrPtPkKpRiR18P64VflpXELd1TShz3mchJGN8vcQF1iyy1Bg7kPRiYP7g2CC6cux5rbWpMgEqxj5jj2rR48eP5/+P5zhfZJZ9//d/v/r/RoLvNRjwyPiQX/3U5Kr24wnHges0EvXgUjQR52FFuT3+7KC5VGlQTRlfDEtsfkES4YgsaS5VV7A+JJMptd7Rvwxb6JS1EqOOoDcmdrnxcFgSsVhd19lyobmU5lLVcqnBgFcJfWYgWLUblyLLvhO4VMDrVsfU781VJmr0BpeinxOPAiA6W+IWlUBzqdJIzs7J9RdelYlvPSc3XntLMrHcPb0BHZeqDelMViKxpGSySypGlEimJVak/1Y9kInHZe79s3LzzVMyd+Yj1Suz2egKLpXOyHw4LrOz8zIwOCzhWLLkedNxqTpxqcP7C7UJkoFuP1rx9jSXyq39G4b8smvjgPqpuVTruVTVUaFnnnlGlRvW+gWCWzdL7IbRRNkpvvERcbirt2jEpvLChQsq60sPsEKw6C9EVglVOrMk85G4jJiyGbHbJDP17bffVjde5R5DMlzJaCnWLJUF+NSpU2v6o/F8LZacoVCoaMk3/4eXMTagjAlDxEAUxiOXktl6903LpjO2FYDL7urtdoqBYFg4FhdD36YheTiakMGgr6ljn2NYblPaeoIgAGIVhIrP53wzvhDJKkXs+qrYbRw7hL/AltUmw3bgs5hzyFquJ7AnxarU7NfNcaYyFpGulP1SKUBqjApZrjua/Zqzqs2ExRqYqTdyc0Wf+HwQ9Kx6zuFYv6mwFVRaknTw4IMPVjRfYd2JxTDf3+59jCmOtwFIO33+zM+RtGAkMvB9zJn+jAv+D4GYfWR8anQWaKT87//9v2/oZ9SLS3n9fkklEkrAQAR0eb0V23yaoblUcXCMk5Fo4d/RiHhDq1W2mkvVHqyyqwB012BdWAwkaSH2GWRqOZORZDQq3mBQc6kKoblUa7iU09Evm0dDEkvkKmO9Hpe4nZXN/5pLaTQKncSlgls2SXx6RjIqLuUQ3+iojks1CFio3nzjHSYx9Xdqbl5uvv62bHrgHulb4a+aS9WGpE3CFKJSI5IaltJpmT19RnEo9TnJlMxGY0o4qeV+pCfjUiaRLx+XSqQk4CvdFknHpWrkUj6vbHrsQYlduy7L2ax4N4yJe6Cy+I3mUhrtyqWqEv64kXnnnXfksccek1rBQhDcXF21lx0IiDPJ1lvQ6Qaw0NuJRwiCLtMNIqWkBLTpp1BJr0Q8n6mqAydOnFBBciwQjYA4AXNz8JuFkYD9I488UtX3YR+xgOBcI5QYAXgq/sguNaqmEPrwb8b+FQJJxg37SlYr/SPIzsGqhn0jSF8LlpWLcXNAYMxa1MoNP8+7Xc3r7YVta62ZVYwPtmG2gC0HWBxwLmttYGzXH8vuuWaB74VQzrik8a4BKmOpWi1X+OPaQ/Q2qp85zvTE5NrgmrFaUgFez+fec889dfxGxfcPoc9Vw3hlzmFfK72B4fU0OsYv/tixYwX/R8Njq1DH3GEenxxTCDYEjzmEc0MmuyEMMs8w53AsWS/tBNZKwJxGAJGkFpIzGtkrRWOVYP39v//3lVjOdVNv1JNLIfZ5THNFrdBcqrIEn+WlZSUAmqssNZeqAU107KB60/p5zO8872xir2jNpeqPXuJSjv5+CfnL65VuB82lNBqFTuJSxKUCG8u341sPmksVR+LGdF70U1gWycYTkloMi2c41+8TaC7VGUjOL+RFPwNLqZSkFxbFMzLctP3oCi5lQ4FbaWTXU1zK45bQ7updvDSX0mhXLlWV1ed3vvMdVf1QrT1jI0EQWYt+tYPAMhMw4h9WmeX0TCRQzqQMQXvjjTdUNRFVNVTk8ZNAO+ScB4IfoiCvYTGpBoaQx75+9atfzfeZICBu9PTjd55n0SBwTmCez6TyCXESGNafeDvzvBlsgyxzKpIQSdeDnbVav6O/QVnj7WNlW6utLqJto7OiS8F8g1HqOTtwbhthC4aFEvZGXC/mz8IuCUJZbp8Wss+oduWBBz3XCz8R5q2vR0jnWkEQs1ry1htYYmCBUQuoTIZYV9s7BzsrhDuzrSrgfJqJrRlXrlxRcwmfff78eTVvcLyw9qBKymyVTBUicw7rZa3BDmxlqQDl87HO0mg84DgcczhPI6C5VIeiguVccymTkJbNKjENkXQ9OG2qlRA2ut3JQ3MpzaUqheZSlUFzqeZDcymdjG6LYuu5zfOaSxUmJeW41PrrpV0iOMn+DeFS7ROW6nwu5XaW9ZwddFyqOmguVRk0l+o8LlVVmcWzzz5bdZWWRuvAgkGZuHktxB6GR7HBxYPFk+wFBLRy+pghrFntNwmuIygwqbI9QyippKLQCgL+iIdU+H3f931fvgKGUnsC7gb4vLfeekv16TI3lDfwPd/zPbbb533xBP36Vg+Y1+sWZwm7ApUZRNUdFgPLucxBa88yg4zUSrpcTqf0SaqAZ7FNc/VmM8C5rPW7cM6o4CwlAnPcGhX0820clyWywyauqhsORL9ys33IUuJ8I0LXC3xXqs64XhCOEAEN8Bw9ThDuGPNcA+wD72EfOIYIYVwXiPd2ohjjNNc7adWal21yXdAfs55YWl5WFrRUHPdJnwR8bvF53apy7bXXXquqNyLf9c0331Se9ps2lbZjXQ9U4lltVankQzTlOav1KAkOnANz31KEOOYjREDOF+eAc0ESCvNOvcD26KdKYoVGcwDXgfPQW6je0FyqM+Fgzk8kCoIcVFwWszLSXGpZkolkQZDK7fXYZvaa1yioEmvr8ooNtMfdGC5l2wuzr6+mHpnVQHMpzaVKgfEejSdz1uh9feLzuMTjdmkuVQU0l2o+NJfSsMK/aYMsnvtElrNU3eeSqlyhUFFrPc2lliUZj1u4lLckV0H44z6c3n6QKpKqrHaROH/Bo6x9YiuFZ2hQ+pwOWTZZtfe7XeIaGJCu5FJ8ToXtScoF67s5/uhxOSUUKC+BQMelSgMLz8jEpKQWFqXP0S++jRvENzqiuVQV0Fyqs7hUVbMVfZEefvjhat6q0UKQLT0U8qu+D7kgirOsfnD8Pw3ZCcBQzj05OVlxJg0lqUwOiII8+J3s70pL6K1gW1QYskAX+x48T3VfpSQAocJa5ZdUwmnp7441lM/vF1/ALx6vJ/+5SkiMJyUaS6hHooxtlQL90UIBnxJu+Qx+QgoaJY5BpBBC2fecIJojnghQ1fiXm4GAZTRDtmJ6MS5vfHxdXv3wurz7yU2Jp+wsa5dq7+uwbYuMHDks43cel8F9u8v2o0dYq7eXPELU+Pi4sp4lyxFxzwwqyBCZIKVYSyI6UdGHEEb1GlVhoFTVHrYMVN5idUllbzgcVjdV1VbPFUM4kpBkKqOCuEoEjCUlkUqrY17t+Mdugv2vRjS0AiHPqBY2xhLVfPv37ZN33npb9fkw9pPjzPkwAxth7ECY0/g/GtG/8sorNWcbliL0tRw7jcoA14HzNAKaS3UmlLNEMKj6UnPTjxDoDQY0lyqCDOKdZY1OmebVYsAC2u/3ScDvE6+nkEulkklJJhLqwZpXy3zIOfQGArk1v69P/eTvRnGpbHZJ5sMxmZmPqJ/83QwutRBNyodXZuTM5Rk5f21Okum1fRQ1l2pfLoXopyz+V64BEjlVQlWbc6k9e/fJq2+cknAspTgg0Fyq96C5lIYVDq9HNtx7Qrxjo+IKBcS/ZbO6B19PTOnVuFQGrmPlUsn1uZTX7VLxv6GBgAT9q3Ei1pMX3r8mf/byJ/J/v3RB3vj4RlnuVsWAyDdy5BZxDYSk3+US9+CADB85rHplNgKxKxPy+k/8gnznoe+VN37qlyQ+cb0pXGru1Hty+l/9O3nnV//f8uH/9n9KcnrVmamuXMrvkZHBgIwPB2Uw5JP+MseLjkuVRvjKhCTn5tW1tJTOSPTqNWVT2+5cav/+A/Luu6fVPKXjUr2Lh2uIS/VXY69IZUi9q0I0mgPEIRaPkQG/DAS8SjwqF1QPUaFH9R43t9zkWhc2xgeVRlZQ3UPAmrL5SCSiLPH4u51ht2jXEmpPJFOSNW0TC1G7vouVAAEX8Q9Cx0/E3UbAqH4kQJWz7MqJgEZlGgIU9of0bTxz5swa68T1wGJrJ56F4yk5d21eMtnckY8lM3L2ymwBMa30s4qBilQq3iol4hCseu2DGUYFq1GRZgXPT01NFewvNy4Ihtx0rEc+qOLlOsQ69OTJk+phrW6rFYyPpM0YV5mHK+SGirlKQCCO8VavPiEE6Yx+fpxHen96XC754M23ZdPQsJx9912JTc+WtFShuo/tECw0ev+xKFf63YqB84ylggGExm63vGsXwHWo/jRb7tYDmkt1NpQ4FAyIbyAknoC/oqzf3uNS9U1SSKdSBceL3oq19nMxxD5/KJQTARvEpTgWc4sxSaWzkl1aVj/nwjEliDSSS8WSaZmYDqvPBIh+l6cW8kIM0FyqvbkUQVorjHuIduVS4nDJc6+9I87giLzy1ntycXK+4D7ICs2luheaS2nYwRUMyNiJY7Lx/ntk5OhhJR6Vi97jUjZzZw1JT2+dm5apuVVx6+JUWM5cKUw0rhROvy+XRH3X7TJ86yFxNsjmNjW/KC9+/kflxre+K9HzF2Xqb56Vl774DyQTiTaUS0UvXZFL//1PJRvLHbf49Sk5//v/LecaZeybjku1NZei0s8KhL925lIcR9rxbN26VT7++Jwkk6UTHjWX6l48VENcquK0lpdeekkFnLmINXoPLIRUtPAgW4pJ2uiHZWS58hqyX48fP65K5Fl4sXRigmLionoJ8ZjqGMaT0UDXeA0P3lusx1azwGJjZGLXA3bbyvUUbA7RJMCzEI5LnGoryWUSBX3usgQERErr+sLfhhBI1ZmxHcYD5x9CbohX5cDO9ms+klzzHAErqv4C3txxo08cn1ULGKPcBFC9VQkgBwgylXzPaptUc3NizjpnX8ksrAYQK64vrrNWwBgrnDdu1MjcXA8QecMu+NFHH63bvnCjx00hpI3jQp/SzMKivHrxsuzbs0euT00pQp+KRMVTxH4GMovFJz8hrPQNpbn3O++8o6w+a+2XyM2HMTZZ7Mttot1K5ALaccksLSn74ZFQrjq504CYyw0La9XnPve5um1Xc6neRm9xqT5ZWqtZ1DX4hfgnTQraKREmnc595soajdVWOVxKOUlYyBRiYDqdaSiXisTXBr3S2SU1T3tX+sZoLtV5XMpAu3KpSzejMn/+ddm5a69M35iSRDor0wtx2Thsz4k0l1oLHDNuzEclnVkSj8shG4YDTW/pUA9oLqXRCPQWl+rP8456YHIuuiah/fpcTG7dWR8BYz1kaQP0+38kM6+8IX0ul2z9/FOy9XufKotLTT/3kiSnbhbYN8YnJmX6hVdl2e9sGJdaPHsOmwh8PnNPkMA1tyDJmzPi27xRPaW5VAdyqTaPS3Hb8Nprryq3hOvXr6s5iTmqWLKC5lJrcWMhLs+8c00WoikZH/TKE8e2ymCgvs4c7c6lKhb+Xn311TX92zRqR716lTQTZDYwGbGgGhO2ASZOMhSOHj2qsg4gZAbIYqB3FsFrrBDsxDAyt9gGYkyrgC96NpMtCNJ4PeUJZXbgbVbxDAGuWZhH9FuptFqWZVmMJpRtAD3XqgX2GgapNhNTrCgRKI4cOaIyrSBPvIbMFUi2GQSers1EJDg4Iq6pRdkxHspXoho/U6mkIrten19i0ah8/NGMsqvIB93KtOW0A1YOEMFKRT+uWcgd1V21CjvW8W/9PhAQrgksqDjegGuu0qwy3oNAhYXAsWPHpJ6gR8B8JK5+4kU/GPSq8eX1uPIVfmbvesBYYD6AxKzXq4+MKnod1nuOZN564IEHVjOnCOouLatq2qkbNyS4cm6XbLLtzeAmg8xCzhHiHzeWZKJiQ1NrhTznnGsNcO64vtoZjIEb85H8fKcCV3MR2TQaKtuqpJ0A54H71FP401yqMdBcqv24FPbncClzdqrbZN1ZFzRxXrFWHKpqQ+zWa7AIu1YHLpVIZeTi1KL4B0Zk6eqc7NsymHeBMOZdbMGyS1nx+fwSjUbkow+nxL/S70dzqfbgUoytZCqt7j04f/Tx45xzT2J1CeH/2pVLqeTApYiqpr15Y0r8K1wKzl8KmksVcqmrN8P5+1BcT/h758bBihx72gWaS3UONJdqQy7lduds/iw9/qqdy12OfpXYbX2uWTj/f/5fcvP5V5R4JvGEXPrD/yFOv182Pbl+O6llQ3iz4Pr0Tdl41/GauBTzLm1JhkdGVVxj0NRGR1WkUjWWyUh2eUl8LrfEUkn5+OIn4o+G1Ws0l2oPLoW7QCyO2xq9Lfsl4M3FcL2jI5KYmS14rXd0uG25FEgkktLf71D7ZcQc13NT0VxqFeF4Wv7k+Qsq4ZHDGrsZka+8cEF+7PED4nL29wyXqkr4+9KXvlTp2zRKgBs5bCABRN7v9TTMZqgRYF+tWVBMgpRo/9mf/Zk89dRTBf9HdgQLr3WiZIJjUUYoRIRhYW4lwWL/fD5PvqoNIaCW8+J2udTNvBmulUzrZsAQ/QqfS5Ul/OWEqML3z83OygcfvC+f+tSnbI8d2QhvvPGGyoQxxCrrOce66M1zN8QzFFRbv3IzrDLTb92Zs9z0O5bkyifnxOkmMNUnN66/J/v27JRjdxyr20JLxo5ZmC4XExMTSuCpp+hnVBEaIiTHj6o+AkH0++MzyQLi+iFrsVzhj3JwhCgETs5LvfcZcjU5s5i3ESMAyWPTSFBCfo9qGI74o86p160CWGZiQpIAGSylri+V6bi0VJPIWwr5/lGq51OfbN28RS5duSyHDhzMfX4ZGdb5SpCV/eSBfSxVoaUahK8HMrrY1ne/+101JyI4c121Kzj31iSHnK1dRvWa6DSQ4PKnf/qndd2m5lL1RzqZlBT2O8vLKtjsCa70besQdDOX8vi8KnmHeaG/Ri6FkEjfwILnauzLU2vFYbm9gdwux5oksPn5Obnw0Rn59Ker51KIKc+9d036Al6JZUU+vragHBPuPpgTE52SkYsXzonLlQt8fHDttOzZvV3uPHFUc6k24lIIPNF4Ij8+uP/ggTgLd+IeMZ3OqjFEUpW58qvduBRsCu63afNmuXb1iuzdfzB/DawHzaVyiMZX+yKag9Jm15NOguZSnYHE7LxEr11XApPD55WBHdvE4emcyoju5lK+FS6V47m1cKlbdozIm+dyVXN8c2aaQ9sL+9g3EjMvvZET/UyYfum1soS/sQfuEdfQgGTCUVXt1+dwSDzokwmfW54+cKBqLkU849pMWHxDy/neyDhNbRjK9X12H9wrl7/6Z+LExWtZ5Gzkuuw9fkyO3nuP5lLtxKWWlmV+MZ5fP4k3ZjJZGQz6JLB1s/S7nJJcWFRimm/DmLhNhSjtxqUMARCbz8uXL8mhQ4dXPn/9OKjmUjl8MhWWVGb13o1DuhhPy8RsVHZtsHfz6kYuVdFqweCmhJ4P06gPmIQM0S93jLnpW79RbyeAYPWnP/1pefPNN9UET0D84sWLyrIQscW8OOCvjncxQgoLAAsyE1yroQImToe4XM6axVi2QcWg0+FQ2/R5c783C/Y6WXniWf+KCGquxNu6dbOq2Cy2yEOUEa3ImOE42gl1lF1jX2PGXCSpslrVOJm4Ip97/KQcPXxADu3fI4f37ZBH76mf6GeMU+xAKgXvaYTFJ+SHmwvjetiyZYv6aRxTjjc2BIawVKqBNL0N6A/Igxue22+/ve7kCkQTuYwqM+jtl8oQoKKqNNegenjALx6L2M3/k0VJ5lQpMI7odddoqGywoUHZqPr1UYlxTbLLy+IOrj1uRv8Ao4cAoi3nzegvAKgA5P9rBecOOxrmRYS/V155RZX581znoPMy1AGc57XXXqu5WbsBzaXqj2w6LaloLK+oEBhJhKk61VyqXbgUdpjOOnApBDaX250Lejkcqs9uo2686z2z8d2HQ35lewyN4eeenVslFKqNS03MRFVwyjzaby4mVJYrXGri6hV54oG75JZD+2X/vj2yf882OVlH0Q9oLlU7qB5dmzSzpO4NVTKixy0DQZ/q6e1ytj+X2joWkvENG9U8fP36NemXJRkbXMubNZcqdhArerrtoblU+4O2BpGr1/JVZdl4QhY+uaS5VNtxKVfNXGr3pgG57/Am2T4elB0bQvLIbVtlw1Dz7Ez7rAm1faIEmXLgGRuVk//zP8nQ8SPiGh6UoTtuk09/7b/J0KYNNXGpaILk3eU1yazEqlRcam5WPv2v/l9y66MPy8E775ADjz8sD/ziz2gu1WZxKZLNrUkznEMSZ1QS+sYNMnxgnwzu2y1uSxuXduRSbrdLfR5ciiIAuKJdsqHmUhUeW+nrKS5VUYrsJ598omzxyIrRqA+sJfaAi1pZvHSgJZoVZEhRrkw21fnz55X/OpM8kxiCIKWqBMwJGOBHbAgpjLVuFJgR/Hi0AkGfR1kXFD5XfgYflkN+X6EdAv3LnnvuOdmzZ49a/DjflO3fdddd+dcwKRUjp3Zl6rFoRGZmnRJdmFWWsIyVfVtyGWgbg30yOXltDflGCOFz1bWztKR6kBZrsktmPvvDdqmeY8xBRPD2rxSNqMylOgyLXOv+UDnGg+/KA9KEXS7XFp7fBpijESW53jgfvMf8/41Asdh6uUF3xE0WMEPgtANZlvTM47WNhtPjkeCGcQkMDcjBW2+VazduSGjjuExPTSmxG5IKIWRfjAbMxthC/OPGwsiyIiht1yC8UjBeufF85pln1Phm3HJuERoZB2SmtQt8bqfM9/UVnH9nf39Zmf7tCK5HrisSV5jraoXmUo0R/qwgcMWDTOBOh+ZShTACDK0AQbcs9p6W58oFHJBEmHpyKcQhI2PfzKVmZ2YlHpnLcymEGBBybZfJa5pLdQyXWtOJqTO4FPcY+7aOyNTFkNxy6y0yOz0lsjyqxrjmUusDa7L+vtWqBcOKz+jL2WnQXKr9kV6MrHluKZWWbDIlTm/OFrqToblUIbaMBtSjFdj89BNy9at/sfrEssimpx4v+/0Dhw/I/V//w4Ln7ti2uSYuZRe3iETCMteXkdmZm3kutfMHP6/+b3h2VianpnRcqt24VBHOVG4uaLtxKe53sCH1eLyyf/8+mZi4qhLLiV9qLrU+9m4KyfMuhyRV24lcMcyg3y1bRlrbt7XZXKoi5sjgZsIr1khSo37ofMlvFWQkUMUHyMK5fPmyqgBkkfjKV76ihEGrkGMIMxr1A5aLVO5h+cmhpQoLq6BaAHlC7ODBYg+5YkE3YPQ6w6bSDsNBj1w0JcuQwTI9dU0c20O270HMY3sQM/P4YPJjATRw6dIltRiyXyrYtXWrEvnYvlGaz/8hriG0kQV2+vRpNZGWi0aNTyq4zN/FCnoKIiRBaPkeZKABfscKkmMECbjzzjubNlf73C6Zk3jBc1SFussMhpZzLJlHVC+lJgFrz7tOnlTED1ENX3X67CWTSeXpzlg3i34GMUP0A/wfnvVYX9QrQM04x5aW7bFdRD9ESMZ7Owl/2CJvHA7IXDjX89HtdMhQyNeR/f0A1xHzERyoHsKf5lLNLI3ozDFnB82l2uc8sGZlTb39ap3ja+VS44M+OXt1fnV7mYzMT18X54ERzaU6iEvhApK02OozhRm9GjuRS7H+P3j/vZpLVQEqgrdtGJCb81FVreBxOWTDcKAj+/sBzaU6AEXGVl8HtaBZD5pLtQd2/NAXxBkMyszLr0u/xy1bvudJGbnjtpZyKWtSBWvl5MRVGd6/q+lc6oMPPlBx93Kh41KrINE4GrdzcetcLsW6f9ddd2ouVQWwRv87D+6Rb5+elPloUsYHvPL4sa0d2d+vFi5VkfBHGe9tt9U2IWsUwq5ZOzd+ndTjz2xbml6ZAA07S+vESfULWR48WMyo+rNmSrB41msiRWgkwwS/5l4XEvn+Qb9HPeoFqo6MzClEDgBhuUGFVCiknqcCygCvNTdSJhv48PYR+fjanGSyyxKZuymfeuAOGR8ZLJlhQ1aRqgTct099lpnUAXOGTs5iKNek1+r5D8bGxuRb3/qWqqIqF2yT6tVSWWPVgGNjJY92QPyBMDK2CVYhRvE+qmlr6SVXDOvtEwQL//vpxZiq4iQjeXwoWFFwgvFCQoD1XBrge9qdv0YCUmcEdY1KUsNmk3PF38UqRTle5hL8cs7renjwwQdVJiM3Gtw08KDykJ4F3GRw7Ghg3g6g99CG4VXP/E4E1WLpRFKlCB49ckRxoC9+8Ys1b1dzqcZU6dLjz5zO6aiDFVIrACdMpdMqX5XkCXii5lLtg1zwILc2tAuXGgx45MS+cXn34owSCGKLM/K9j94pm8aKOxloLtV+XIqkGdoA0AJC9cPE3tPrqYg7aC7VXVwKsW/beK7vd6eCisX0SqyDBEvNpdoX3pFhSUzP5q0+gXtwQBwd2J87k0pJKp5Qv7toteJZO5fquFTrgJi89XOfUo924VIel1PGBv0yu9IfbubGdbnz+BEZsthBNoNL8f/lQselCkEceiDgVU5nHBv6DQ8EvRUlIGsu1V1canTAKz9w/27pZODuEseOeHlZOXBWyqUqumulIgY/Xo36gQUo6PdKMpU7iUxUBHk6UfRLmjy1U1hsybK4S2TJMqEeP35ciX9k1Bog+M+iVyvoG0hPSgLz+CEj1BgKuUb9xi/nEUIDeQb0NyNLGjJD9hKiCc2xCZIZ9ocsAjyPb/pMOK6sAZfTcdm4fbik6AewCeDBQsO2qJC77777ir4eIlaqGTdj4oknnpBnn322bCGPbTKOGFPVjlX6vmF9YO4bQAVXOQskGWx8Lg2qGdvm66cUIKOci4cffrisCgWqQ+fDOfJLVszIAL2J7N/n97plh9ddtcDF8XzhhRcUibAD5exk7zUbjGFsiL/97W+rYCDVfoDMPTJtOJZm8P2xciR5wbCyYIzxd61VA1xDd999t9oWAiQVC4wBxg8VCuwrY7KYza1G+aBHXHx+XpZX7IgP7dsvb7/7bl0OoeZS9Uc/AfOBkKTjiVyzdZdTXCs38p0m+sUQm1cQz+aSQegPXAyaS3U+auVS9EWenIuJ3+OSfmdC9u8dLSn6Ac2lmsulFiKJHJdy9Kt+x1Rz2YHefTw0l9JcqhugqmnjtGLI/X3w0GE59fZbddm25lL1BwLf0P49ErtxU1WOu/x+8W2oPSbTbGSSKUlEVm1Lk6p/6rK4V1rK2EFzqc5HrVxqLpKUb7x1VaYXkzLkzcqJbd6Soh/QXKp5XOrqdETePj+teloPBdxy76FNEvTZx1Y8bqd6aC6luVQ3IJtdkhvzkXybrN37Dsk7756qaBsVpUJTynzw4MHK9lJj/ZPQT4anRwI+r3jcrnWD5qtl7Estb7bM4KNPIdnpVhjZfaWAKMcCjeBhVMeQBVNJ9ZUZHBe2xc0AJfIcWwQWPofFZX5+Xl5++WW1QGnUBydOnFDHlAwPjjmCKwQLQoVVAaIJWQnMHYg7VHqePXtWZaW/c+Gm3JiPSzielg8+viQRCdn2/TODMU+GA6Ix26tHFRhjg8pTslPKBeOW91ULvgfNgw0xiHGPkFOOaMaxRgTDOqIUyHZ76623lKjJOUKswoqynGawBKBnqeBbmWM4X9Pz0XXnnGqr2ngf5epcv3bgWm7FfMdxpg8px9KcwQfBpSIA8dYMxiZjCXtj9hmRDnG61sojxocx7pjPsCCl8pBriUoFPoN5jazDdgbX93wkIdMLMVmI5vpytiPSsXhe9AP79+1THKge0FyqMeh3OMQTDCgBkOBOOVyKpCV4Q6vHoeJ1mUxBApWBZBk9QjWX6l0ulcpk5YX3J2ViOqqCVu+evSDXE37NpdqGS2WV7XWeS2WXZGZBcykDmktVn/l9cz4mE9NhxadavYYVQzKN4LL69/79BzSXanM4PG4Jbd8qg7t3in/j+LpcirHIPSN8qtXjUPG6VFqSNvfzRvVfKWgu1btcigSq3/7LD+TtCzNy6WZEnn39rPzNh0nVsqIUdFyqOVxqNpyQV85OKdEPLERT8t33rkl2nbihjkvpuFQpxFMZ+fprl+UPvvmREv3Xu95bBapXzTHyvfv2yUcVxqXKLi3jgsQKxejVVgkyWBbhadzvUFnY3WC5aBCbZn8XZadpCgIRTHaXIRY2al/sglSVgoX3y1/+svqdcnsWjVIVWsXGJ0FwskVY0BEOCYYTdGfc0miWSiIWfI7fSy+9pLJLjL4eEALVs2WlYga7H97L/1OBCBEs1Xetl0GPMY4lx5/jVGyOMMYoxAubBSr9UpnCyTWaSEs4nlK2Ve+//746X4DzAHHjvZwvKq8MWwZ6myG+WPutlQtsngCiDue/HLA/CMqILdVmEjEOES0hPQQ+EBHLtVKmShCBh+uFTDPDMoJjRGYbthZsn3ENqSX7yqg+QzAqx6KMakwrIFcIgFh7NgJUrnFtcq1ZjynnG2G22vNcLTimPDjm1n3CsogqRfqUMnaoSjTGN/vL+KAqkIrUWnpA8fl/+7d/Kz/6oz+q/mY/EImZo9g+5JksO6oAsTVuVyh7k7mwGkPmcYZNbLvxAutNyL69e9VaUqu9by1cKp3J5j+fapF2O2adxKWwJU+l0gUWex63uzVcKp2W1EqQypglsuTlrexLuXE0zaV6k0tdn41JYiUQAhguC7GUEgGxttFcan0uBedvHJdK23IpggtYYjcCmkt1N5dCRL54fSEfADXun3ZsGGg7XmBOoAJcP63mUonpGUmHo+IK+MUzPtp2x6waqMQC7IGb3P+RxO9YYjUWxJzmr9CeuFFVftVCc6ne5FLvXZqTxdjqes0lNTEbUyLg3k0Dmku1mEtNzsZUN3djReEnYu1iLCXDwfq1MTJDc6nu5lLpzJL8m//5rlydiebG1rLI+5fn5Jc+f6QiW9hmgAQbM/bsrZxLlS38ceIIMldqtZaIRCVD3xXjAz1u8QSqC/KRwZPNpNV7Xfh0t6B3C0GqVCIh2RXxrZ/skTIyy+v12WbRb7X6b0n102sm2Jf1RL9iloB2oNKH6ieC4xCuV199VQXTywUDH7HAqBRkrCLgsfhTUWUWVPibxYaFhvOGHziv5f1k/DBx8b5Tp06p7Rkez1r4K417771XiTblCGGqGfLZs7KYccvMzSk1jpPJxIrYmlvSEf0MUgAY+wi4TG6G6AcgGcX6wpUDxBlIIfvN+YewrAeq0hgP7C+/M2Z5P79jLWE3TxqkB5EPEoS4zVjkWLFIkn1WLqg048HnQ6AYqwjmCImMX+NasoMhbrK/2LiI9Em/TT/OYuew0VMd1XKTk5MFvT+Nc9+KHrN8NsdSJQas9B7tX/mb5xGh3377bZXt9tRTT6n5xJiTuJHg5qPW9QEbDARu8+LO9cE55FrieuDY/NVf/VVTrIxZ/2I3pmUplRKH1yP+DePqmKyHWDJdIPoZwh/BK2tD9VbD4XRK1rTG7d65U60r3KxAZqtFtVwK+0dzP2B6v9ELqpqxxTjmoXqVtUjsajWXMot+AB7F3FjPvm1l74slM50racl0c8u5LheaS/Ugl/rorCxGHTI7fSPXryaZyF3jmks1j0utHGuC7mVzKeldLoVTDBMcCRe9zKWWsHaehkulxeHxiG98tCwuFY6lCkQ/Q/iLJzPi97ZXLzbOsTrfK9i1e3dLudTs6Q8kcmG1KiWwc7uMHLu1qrE1H03KYiyZ62s+WNy+t5Fg7F6+sSg3F3I8gsTZ3ZsGm7IvzHtm0c9IUOPR7LY19CRcT/SD75YLzaV6j0u9+fGHEgvHJLownevxnkrKUjYjmayOSzWDSzGfqKTzPlEtgKznzOHoy98XmeFscLJDO3Mp7l3N3LNXuVQmFpe5985IJhIV19CADB85LI4y5vu3L8zIlemo+t0YW+9fnpdzk4tyYEv9+3zXApJqzEUZO3buqphLlc0KUPHNfsnlIEvZv0n0M7JxcsHmykBgJBEOq/4tqVhcYguLBY2H64lMIiHRqRsSmbwuycXFAtsCGgUbgSrAd7EGbRqFYvYJy8vNL0ldz45R9SqsoBE0GSB/8Ad/oCYSxBDGGmKIFUy8CD1WWHut8TqyNKjKuuuuu/JVXQb4P3pl8X8ILpADLh6eQ9Bhcuc5tst+aNGvPDC5G73GELqwWGDuwCrRimO3HlI3Jrv3HZQdu/bILUdvlysXPpKJyxeUgEYlnBmMCxa0Xbt2FVgJMB6q7Z3GgsXih+DHfpaTMcGiaVhGsLAZ76Hai31DNGb8MeYYP4CfPMdizbGgcs3ow8bNBd+1mu/Aost2sT295557VAUalgmlqsuUZSbNYRcWJRGOqHk1vrh2PqVXkJUY0PS60TeTXPuXL18ueI6KBb4f37XZNr2c61QyKV6HU5KRqHpw3IyKMIRivOw/+9nPqp6igEQBxj/kq1ZyRSUo1xGJEObxeezYMXUsHnroIZVVx/z3pS99SY3lRlrt0Ptu8ZPLklpYlEw8Icm5BVm8eLms9bjYumFYoLUTXH6f6nViwLCHqdVKtRoulbPTLuRNKQL7VdhRpJNJScZiisvwO4GSRnEp1oFUIinJREIyyu6rPbhU0XG4Dq9pyL4UqTLvX7kFMQTecqG5VO9xqRNHDyvOvWPPAdm6Y48cvPW4XLv0sUxd/URzqUZzKZIgkyl1/8CD363rb45LFb7X43IoQaTXuBTHx+HySjiakHAsIYvReG9zqctXJL0YkSyJPQuLEr48saZCrpzM7/WebyXclvsGj8fdMi6VnJsvEP1A9NIVSc7k7tUqweRsRM5emZXJmahcuhGW0xenlY1vI6BswKOxlWT6wjlmcjaaF/3AQjQpl6YWpRlYWm6fcbi0zrHPFR6U3xpEc6ne41IP331UORqNb90jw5t2yNa9t0rs5iWJz1xRXIpEcTN0XKp+XAoHhE+uz8uVm4ty5caiqmi3Wi7uHA+Jy9lfkDS1cdhftMdfN3Mp+KbX65ME9/HJpMQTqzaQvcalsqm0TH7neYlemZDk7JxEPrks1599UblNrodo0l6TiiUq16oajZDfU5Ak760iLlX2XQcDnoFfCZaWslUtzrYZ0bHCgBCBKrLFGyH6RSYmJQ25isUlfnNGEUUD5kBV/rkqhMxqULwCp/nZ+sU+E7Ev4PcpYl/JfiG+/fAP/7BaRKn2Q1RBLEFM4aaQiZWFm+ew1nv++eeVT7TRU4tsBvPnIbowbngekcgQWYqBGwfGN6KNASatz33uc/L93//9RfuOaRSCxR1vdaopyTTBN52qPQQyFikDVLvFo4ty8rZ9KijC4hXyu+WLTz0ktxw+pCrwip0zFjyjFx9WAlT7VWsXQ7YO2aKMO/apWA9TyB5jj7E0NTWlspesIMsJcQDSw/5ActhPJmSsSCl15zMgoWQ+mVFtxSLvQ/jkWBdbVHN9rHJzFNeMy+lUN4/mYP9ydkmJAWYQlNowHBCfx6WIcNDnkdFBf8PnG2P7xvdBLCWbBdLOccQ+wDyWGg1E2VsOHpLzFy7IpcuXVtcfy5rEnMU4Yu6hhxvkqlbQk/Tb3/62SkJg3FvBWHvmmWdUU3Jl/+h0qvHGMWoU0oieljUvm0yp9XI9IBxbwdl2N7livdxx6B0YEP/wkPiGBsU/OmJL/itFdVzK/trOVpj0o1wDLIkz6rkGXE+G6MdPOB+fgfjXDlyqmBVWX5MtsnKfab920fd5KBQQv68yyyzNpXqPSyVjYXni7gMS9LnF6/PKYNAjP/K9j8qtt2gu1VAu5XKpc2G2hbarJoZLjQ8FTVzKLSMDvcmlDhw6rLLOL68k77G2RePJ3uRS8PBMYZwEF4VMGQkw3DdZwen2VpBw2ywoZx2vRwJ+rwR8Hgn6fS3jUplorKLniwFhC7EPGLMFFZjXZ3OVA3UX/SJRxZmMBClzUj1CnxULsbXPNQL9ffb8pdl2o6CvSCIFYl9wdES8Ky1dyoXmUr3HpfozMfm5L9wlowM+FZfaMhqUf/7Tn5Pbjtyi4lJUtNlBx6Vq51LT89ECoY/e1TfmC+dTn8cpjx/bJtvHgzI+6JOD24bkvkNr27D0Apc6fMstiksZhRB2Tny9wqXik9clS/9WY7wR11gMS2J6/YSe/ZuxR7fGpPpl18bmthUqdxxy77BhOCjjQwHZOBqqmEuVHS1nYFVKsIrZVZRjY2FGsWx0VYadyUgigdqdqir73Yrk/NosqcT8wurkZTe5NEl4UxanlsCpITA0G5A6u32pxN7TCiw1EV5YDAw7TrITmCzwEmbRZfLiOJDJgACIGMiibi21ZpHnQmCSo6S5lmNEmToTOtlBrW5a3QlgISDjwyyiYZlKhgj9+YxjipA2FPTIXQc3yQNHtsod+zaqbAZDxLMT83KWSljb5sYeC65h71pVYDqVUttCiGNssY9W8HkI0Mb3sFZ/klmDWG3uPYfXOcIllaWM4/Xmzlr61lFlyGchTvOwZh6x4FJVSHbxe2+fku2bNucWR8u8tWQJQACuZxYZglaDQW/Tkgw4xjR/Blz/ZBUh4jNuTp48mf+/ZgCCR1bNk48/LpOT1/NBPmulDseG/2OtZB/rcay+9a1vqQwqblTsgIhMVh2JEVwLzIeQMebPcvtV1q/yfP25kaDnGAHPlb85RpCXUlWkbBfBSFWMNTmLmP3DgtKxYsnLdbxe4/KGcKkiQRRHhQkPpc4d6y6VrdZAdrXA8cG233ObcCm3pcK6Vv5SLfhcrOsLnnPQD7v6AK7mUr3HpcYGfPLE8W3yubt3ySNHt8pQcNWNQHOpOnOppSU5/d57sm37DmWnumyZW+3mWda44ZBPxgYDMhDoXS7l8Xrl8SeekMnrk/l1xlod1Ctcqmjz1jK4FFnfW8dC+SWTXjTbxwdUNUQxECNZxJoymlxTSdFoKAuylXhFK7mUKxQs8nxl92CG9Z8VBKs5vpMzYbk+G7HtlV4pqPCzIp1I5ucZhw0/dDRpfoGb+jzuNXNdJfbk9duXfnH7fYXPOZ3itPCrSqC5VO9xqX2bB+RXv3RM/r//4C755c8fka2jQc2lGsil3jv9nuzYtl2GfE7ZOECi4+rrkjbzJ9V9dx/cKA8f3SJHd4023DmhXbkUwvQTTz6pOEs+LtWjXKpYZV85MaNtYwH5qU8dzHMnn8ch/+Szt8hQwL1O67WMejTbKUhpQU6HclLor4JLlb0yI6JQTphTlDPKdorx43GTwegs2iPH7fMV2De5fF5xVOj7TUY0X9R6M7Xc11eQWcmA8nrcNYk8traZps8lQGOtjHHXQCoqBSIF5MawKOT3VjWlJnDmMO2LQejXA+fR8Nu2ez0Typ//+Z+r7BsqqJissOyk0goRD6tFJlgWDR52QQ1j2+xTPYTR+++/X5VLf+c731H2oLUINb0AKuI4T0YlG+eCc0jZN4tFOQ3ZzePEAGOCxcRALWOf/TGTQCr/yIghW8WAYc2JVUGx3k+IfCz8TLx830osO/l++KAzt9YCKlWNPoPM1ZAs9oNMKo45Fa3YU0JwzVWtahJfmd+KZUu2AhxH9p2sInyrybKjutPwDWdeWPNdGgSEkK//xZ/LZz71aTVGLl2+LLt37bIde8wT9QLn8r777ivwlLcCqwmqShGfIdKPPvqoOu+cf+bLUu+tFi6OuWncgD6HQ5yWG+5iCPjcqgcNAVMCFqWuYdX7kyyuFbDaEzjsb9FY5TqtNVhl5lKJVFqScCll2eC2rYgECFJk8vN6A3CvSoUqdawt584syhlIZbNqzqg0SasSIbjVXIrEJcZRnr+0kEtxLDjWJBPAd+HOmktpAM2l2o9LxWkHEY0V8A9mO3NCS7ug3bjUX/751+XJT39GcSmq/uj3Zlc11Atcyont4Bou1S8O32oP81Kgl9uA361EPMSWUuMOQeradDgfpOpbFNkyGhJPi3or15tLxW9OK3cmVV04Piae4SHb97gHB2Tw8AFZOLPq4jNwYK94RuxfXwxUBNDXz2rtiQZnrlKJxFNKoKXat1FcatNIQBZjheIglUrNAlyU4DvCMkIgvLRlcSmfL9efmx7WJHOV2cOaY1zqnkTHpTofmku1YVwqnpCYiX+4HX0y5HPJXCx3P+psIzegduNSX//61+Uzn8lxKdZSfiIE9SKX8m7coLgT7mUK6CMul3hGh8t6/70HN8id+8YkHE8rTmWXTGOAmEGswKUirRwNmiVA18qlyt5LSlqpXiHwFKe6jjLd7JKy6LD2njGD7Bvf4IAqseenx1++v3aB5RZCi2lAs5hnra3RVyxWWPCrrcxy2ewfAU2DCEAo+A5kYxvCZiXNgusBo3S2XKGtkWAfuKDZn3L2hQrNcDQukVhC/eRvu8wcbD8feeQRtWj88R//sZpgqfrjM5hUmfSoBlwvaFyvCr1PPvlEVRGyT2+++Wa+RF3DHgiyp0+fzot3HD+OHYs9v5fT084IyJqBR3W1FX5mkCRgJwozxhD/6OtIlhKTKXYfxUQ/AwjBCJqIiZUAOwTIAgJjPWBk0nLdGJajWNaC2dkZtRCbX2v6o6q5uZGAWJFRZVRYcozJHgKcE/zFmwE+67EnnpCXX31FNoyP5/uFWjNL6w3OTymCxBwIkYJ0Qq6YN41rinMOIW0E6Hs3sGOb9GMp1Sfi8HhkYOe2ikQivtt6gSpgZ0GZSjXHwsgOcKBr167VtA2DS8WTaYklck4FcCnWRETAYkAYDPl94vd61E9rlnU56DOuc9Nxh8dYeyzm7OpyPfiqXUMdNjdsCG3txKUQ+7A+bmWgKr8vWLJ4POJcqS5dD5lMVqKxhLoB4Sd/W6G5VOejU7kUiV/dy6XmCriUFVY3lFajnbjU448/Lq++8rKMj2+Qm9M5LuWvoIdpV3Epl0uC27aoAJX62+OW4LatFXMpsr/XWzNmF2IFmeks6zcXKrO3bFcuFZu6IdGJSWU3n47GVM/p5PxC0fcNHtgrmx59QMbuul02PXK/DB0unYRqB473wW3D4nSsHnes56wVDZwW4mQ8XzWXsplPqGQzzvmA3yMHt4/IaMgrIyGv7N08JONDzb2fg0MhAMKnWs6lXC7FJ+FT5ewLXPz6TFhuzEXUT/62QnOpzofmUu3HpeZmZ9fEpUiqAIhYG5o8j3UUl3rsUVV5yDHNx6UabPXdrlzKFfDLxgdOijMYUNk3roGQbHropDgqiCk4ceUIekqKfiBpsfLPPdc8m9dauVTZdyecLNVLwvYLp4tW/RkBjfI/qcg2XE4JDA/lMqKp5urvz/m5GqAyiGq97JKkspl8gKtSAuIeCKnPUKRxeVmclPFuGF/zfdR30qgIiMXxROHFwd8Of3/RHm1MHp///OflvffeU5V2gMAB9p78X6nzywRN0IGJB8KNeGiu5qoECH0sgmSo0KuNQEaxMmeNnCCMzSol3gSXyLTiXDHxc7O23nnINa31Fg0yVXsOWYiYx1gkrX36GCMsUCzo1fQM5D18L4JxZN6UA76jEeRiPFfSpH69QB/7ATkwcO7CBbn9tmOF+0ylFtmaVPdU2SexkTAfD6oqjb6enKdm2e4ODeUygY/efrtcvnZNhkZH5fLkNdm/TsVqreA6Ya6jorRY30my6BjPTz75pLKbZewyNzG+1wuw1kqyhvftkUZj2cZCwe65ZoFjXStxNbhUwoYoYllerOoPkFFWa1YZ86ePNRELjJXK+KTJhoXnl7FVVTeAKcW1vMFA0V50RT/H6czbtOZtmCwVfZpLVT+/M1bM4G9//6pVthWaS3UmOpVLsXZ3K5c6f+6cHLf0SiFIheDXDgmZ7c6lbj9+m0xOXJGxkWG5PnFFBg4U2uf3FJfy+2RwT2V2ldUgnVnrZpSxea4TuVT8xvSa/6MC0DNk34/LiPXwqAX06rx970Zl54kASK/q89fm8v/Pc+OhXCCRJDYV1C5TjDKDhCh4r9HXD9HPY0k8DPnc6qFRGTLZrMyFV13JmAn5GzG9WAsCzaU6E5pLtR+XOnf+nNx+vJBLcd1tGApIyO8u2QakVWgnLnXb0SNy5fIlGR4aVD+trYh6iUt5x0dl21OPS6OxZBODarbdZy1cquwrio0qhd7muzXr6yo7JqwuV26szDeUSvSz+q+amh9X8hm+kWEZ3L1TBvfskuCWTTXZXWmsolgPxvV6MyLc4Q9snmzxD2YCKgUqA3kdPdaYeCjBNry8la1DmV7DxuJnlG8TfCHwgQWkRnEgtD744IPqmOObblxfBInWyyTKzzc2mQ1Gw2DOAVWfbJvs9VJ9qWhGe/bsWWUvyns4n3hzm0EfP8ZKLQIYWfhkL5ntSMsBPQbZv3o1B2ZbCNTGDSbbJfuxINBHckTALy6sE9tQ9LOC78K44TxTTdDs3qZkOU3dvCGZpay43G557rnn8oSv3iDjDvLEHGfXdxJAvD73uc+p/YJwGueQa4H3V5vk0E6wE5taOVbrkbFWiks1C4o/ORx5q3DzMV22VI4hBKbMSVYVfAbXidfvUw+Pr3l9rXqWS63Tz0Bzqc5EJ3KpakW/juBSKwKfGR6Pu2znk17nUlu3bJGbN6ZkKZtRGeqaSzUe9Fcu57nO5FI2CWJNCsAi6gW8LpWwxXXlN1l6jgTcyvrTvE/VVNEowdDnVa5ZOQetyhOxNOyRSmeLWuOWguZSnQnNpdqLS+FwYuUffr9XhkPethT92jIuNTWlODxxIM2lGo9+m4rATopL9S2XwY4QSBBdUOpHxzeusfb0ed2q/0yzoXrkJFM5G5ulNcafuUz1NrOv62WkM1mJJ9aKsViXVePj/MorryhBsBIg4hmLDuXZZFKvB2yW6PFmBcIfQQkCFGzLsF/FToBgiMYqKH/nOr311lvV8WehImhUrM8f/8+cQ2WnGQgt9FmEdGNFAIkzgi2UOlOCbzcBYzll7udnh3JeUy7wuEZEZB/LBcfH2newGhB0Y/yZ7ROwpyWDy4NFMvN3n+Qs5Trs5pFsIqp4yXa67bbbmr7YQvDefvttcZN8ks6qXpX33HdStuzaWbeAH+vaCy+8oCqcuQa++c1vqj6npazFAFZqCIVcE2Ts0Vy72SS03iDLmWq0PE2hr3ALhWqOMfM7NyzVHFszlxoZ27DG2hPLs2osPGuFCkilclxqyVJJBpgnfDVmx2vUD9h6Wiv+DDtYzaW6G5pLtQmX8njyAjzXXCcIfu3IpeCkzv5+OQeXuuce2bh5s+ZSDQB24pPT4XxPOgSrLWMhVdnU6VxqWPolMZNLiDCAhSq9/poNkm+uz0YknszIlmHvmp5Lyo3KpgJbozWgfdHs4mrFn4GRAb94q+h/qeNSnQPNpdqHS+G0B8rtb95O0Fyqt+JSS0vLSssw4lIMV3r8dUpcylnuoObiR+BA5APpleAxgl8pa6pGQlXneT3q4CdjsTUZXp02eXQ7yN6gp445K52/q7UuK5WVXAxGGTQ3DQTtywHiEzcXlNOaLZNYwGiqqYj8SjUVVToskmwfiyLeoyFqcUfMw48aEYNqJYQ/rlnjOuV8Gs9RrcRxNIQ/MmmweyWz/IknnrAV1HgPgQyrmItYSDn+eqjHYkSlFfMlkzDiH9lN5Xw2YNFA0IQgsThWA0rpuRk2kysWA7KBjKz8Tq5gZjzQ36iRePbZZ/PnjCrfw4cP5xd0RGYqybeOb1TP7dq6VV569VUJ+v0yuLH2Cjvmjb/9279VCQ3GXENPHJIMuDYQtosBD32qOBh3jL9uWP/6+vtUpZia65cLe8S1AlR7sy9UvdDou1IUcClfbg5TiVQQR3drEqiAYUGlkqmw+bSsrRx3jfYBnImsQ7O9CPOR5lLdD82l2ohLdVjiVLtxKY7lru3b1XPcL7344ouqZ04gVHuSieZSa++/t24YULaUcClEDbvM9Y7kUnDlPpHk3IKKwvnHx8Q7Vvk26wFiGlvHBnJ9kpOrwUED3cDLuwnELxG/SUw3gGWrp8pqWB2X6hxoLrU+NJdaH5pL9VZcqr+/T/w+T07LWM7dj3dSXKosxY7m8dzo5Ak8AauVoFU7QAWtvF5Jxk1ZO9hMVVBto9F4KBsMn0cFOhmkqufPij1GNahFqOGmvlxRjkmLzBQalnLjatx0IEbxf2bE43GVtQpOnTqlAgVMfBo5a0IEO3rV3Hnnner8c1NE+T/HkzJ1skIAoh+2VogdBAd4H2JIqUALARpEPl5v+EiTVcy5KyWYlEPYr169qqo6CV7QO9IYs4wBxD7ssfgu7B+l98yZTMAIxlQ5ArPIaQfGFwQCQXq915qBPRfjmeuB48T+mfHGG2/IiRMnytqWRi4gdccdd6jjyUKKWH3//ferQ8M5iS+GJbhrlcDcceyYvPPW2/LgU5+qafFnHL300ktqTjFXurI/zCtWOzU78Npjxwr7OHY6lMV3m4jVcCAEWeP6rhRWLhX0t1fmt+JSfp8kI1Hzk+IqY+xpND/pjQS8JbXu9ImrhkxZzaU6C73GpcgUv3DhQr43jOZS3cGlErGYEmiNMQBPJXnv5P33ay7VAPRbrCi7iUuFtm9Tj3aBYXWO+GcGc7OGtNV5Ghv0SySeUhXcCOQBn1tzqR5Br3EpHZfqTGgu1V7o6+sTZ4fGpcoW/siqamdQxeLx+zu6XLgXoKrj6lTVUIuHv3kRNsBCifjCQs1+snjim8vnYIVz7tw59RoWe64JqrmsMBMA+gtCJrixrebGphuBrRA3/tinIlZxHLH7NARVKyBIlfRNIciAxzVECMIEaeP4Q5CsgpgVBCfsqgMhf5xX9hHSByFknyCDhoBsHQtkLRuiHK/n/YwdMjPs+u3wGvaVG1m2SyNcMpbxWDdXN/KdKOsmi5394vjx/8VsGKhANYitRnmA/JN5/tBDDykBDstWLKmMStJNGzfK3Py8jKyIc5yzbIYs6uVczX8N2fFUrTJWqFRgjlJZw6mUmoseffRRfQrbAHAhrrtqrE86gUvBnbwDIVlaqURUXKqDK1u6FUqk1VyqZ6G5lOZSHc+lNm9WPNZojQCXgvfUCs2lOgPdzqXg8soSmHY0K3EqHZdqP3BOQv763CPruFTnQXMpzaXaHZpLadSLS5Vt9WnttdWuJKuTrFcI4hPc71sRHXRwrTJYrSIrAeILlVXmiyQSiajnIAEITlxIWIMa20e84zUQecYZr8EKyAwEIcRDw6aPbVHi/OCDD3YU4acfBE2vcYLxuOsronOMyumtWE12JIEDrBHtfPf5Dtu2Fc8IpQydPn+GiIfAxucz/xlVe4ZdESCDeb0+fgQ0zP0e2SZjjLGDwIgYyDyAOInAQ1aWAcY2AqDR65B9MayrsD3l9aUq+RAxETzvu+8+aRS4oQ1HEpLOMlb6JRTwqCreTgbXLqL9888/r0RqhFOO9VtvvaWE5R179sjrL7wgI8Orx77fXVu/RMYdY4ykAx5kFjL3MAYQshnTrZw/2A/ONee4lfZQ7QCuZwT9atBRXKoFvQarRTaVlkyKzHqqE70dbWfcCmgu1WCerwK/fXW3hNFcqou4VHZJFqJxZTnH/DsQ8LasjUbTuNSOHfLm66/L3XffnX8P10ct10gncCnjvrWT7gkbgV7gUtwX0MOyU5CORCU5v6D22zs6Io4O4oHtAM2lGoeJmZic+mRWAl6n3HdoXFmy1guaS3UPl1qMpeSv3rgs12bjEvQ55VO3b5NdGzq7R73mUmvBWCRWjb22jkuNlM2lyrqroNrAKHWuFFTgLWeXpI+eJDoYkwcVIkyyBshypMdOJwmXrQSiCzeRtVhbWTNLEfa4mUDMsQMl9cb/0Y+Cm1cWOOs+GLZZk5OT6saW6q8bN26o/e0E0ANidjGW/9vl6JfRwUBFE6uakFXz0yVxuN2qaqSVwI7hhRdeKCn8qay/UEidN8RbxgPXIyTFDoi6lQLbV8gRgQnIlblnJAEK6/4gTPM6xju2DXjSG9aHBE5KHf/XX389b6vUCPAZ84sxya70mcouL8l8OK6aojvrSMhbAdY7I8MK0Z5qUYguVlWMpS27d8u1qSnZunmTOLxe8QxURyqpIL1y5Yr6aSQhkLUD+WaNuD41pcZsKwNEyVRGZhZXe+gOBrwSrFN2bCeCYCJrQTWohUtx/OnpxljodZJrRjqRkMTCYv7vVCwmgZFh6W/xmtMp0FyqcchkspJIpvJ/c91i0VrJfK6qvpWlq6heRFiRtRKaS9UfnGM4t9H/HBFwbjGmeDfnvJu51KYtW2Ti2jXZumWLmrOrbdFRiktxHU5NXW85lyLRN55YnQ+o2O70RLlO5FK4GWTTaXG4nNKvrTfzSMzOyeL5i/m/Y9dvyPAtB8Rpuk/VKA7NpRqHl87ekF/4/dcklcmtkYe2Dsof/JP7JOh1VZbAml2SZVleEQs0l+q2uBQc6o+/e17mIsQfRRUwfOX5C/LjTxyQ8cHOblmhudQq4sm0TM5EVKsLgF3zULB316mBCrhUf7kEy9zgvFyk43GJzc5JfGFB/UzFTD34ehxm0c9APSxOegFUTL377rs19bJiwbLrmYXgw43jel7bEAZEwKmpKbUAIhiitpPtYlSpcd0gDnGjyw1pJ4CFeS5ceJ2ms0sSiSfL38bSskSnZyQ+NyeJ+QWJ3rip5oJWw1qdaQf6qxlN45nzyHiq9uayGCBVkCQzuTKeJxvKCioKEY2x/TQsaMm0KhbA4BwSVMGe1mpnW0+QaWOIfmYksQjsAhAw4hrH4hdwDvibHjSHjtwqE/Oz4hkbFf/IsASCQWULXAmwAaYHJNs1yDJrwNUrV2R0ZEStEfxEeK6mYXwpYR8hbzYcV+ewFBCaZhajBfY1C9GEEgN7FcwLXIPVoFouxbhIJJJqfWG9obebRg7JRQvZXV6WZJE1XKMQmks1DsyZZtHPmE8R8SrZBgLQQiQh4WhCZheikkitvXdoNjSXagSXWrsWd8s6W4pLHTh4UC5duaISBD1er3LVqBeXunLlqoyMjCouxc96cykERYS8eDK17na5ls2iH0hRqb7SnqQX0QouRTXbwoWLErkyIQsXLimxSyOH8KXCJNflbFai1yq7FnsVmks1Dsydv/qHb6lYlIGPri3Kf/rWuYq2kUgmJZlKqXk3nkiq+bvV0FyqvrixkJDZcE70A/xAHPr4WnXrTLuhW7lUOJaSSzcW5erNsCTTpa9LuLJZ9APTCzGJJVt/b9QJXKps4c/oWVUuljIZSUYKgy+paFSyJW56ycRgQia4RWZct6KYB3gt3uC9BKrn6BlRS+YmYg5CnXWckRGKLaTd+OPzzOcIUYXJjwnYGLMo7m+88YacOXMmX+HH6/j/Tji/7KLdfq4nEFiv8yWLsB2fz9myttqCg3PFA+shMplYLOk5wnPYF1y7dk0F1qnoBJzPSue+srK7bUqyGXsTExPrvt9oKl3s//huZGB1gg1OuwOLC5IBuJ4BPRohU8wRRkNwxP+jR4/KhQsX1t0elhiGeMO4OnLkSF7EVUHe2dk1QvOunTtrShxgv6hiBTSwv3pzUebCCZldjMvlqQVJlbj5IShld9l2i7hbDThvzCXVoCoutbS0RujjOi9FulmLkknGWaqrA4uGbdqa5ytYr3oZmks1H5XcLMcSXL+Fr1+MJNqKS5FkpLlUg9BFhd3N5lKzs3NruNTOnbvqxqUQ8MOxhBLiE8m0LEbituKtWfS3fb6H16pmc6lsMiWxqZsFz8VvzkgmkSj6noVoUs5emZUzV2ZlJlz8dV3BpWySz5faINGkE6C5VOMQSWRkPkoMbfU5qvYu34yUvY20umcqnIMRATWX6q64VH+RuHA3uWp3G5eaXojLu59My8TNiFy6EZa3z92QeLJ4jCmdWSoQ/QwkSryn2xGqgEuVVQ7CALOrjiqFbJFgIoIg9gprXp/NKtFv9e8loXCq0y3j7GB4+1sXnFaXnXcC6N9QbQUWwo5xjFmIOAdMPIb9I2OQCfCxxx6zFRU3b96stsGky/uYYOkVyPs2bNig3sMEbAdK45mMH374YWln8LVZOK2TaiX2Ulzja8BNBX1uWmT3y/lmITPbuPI7FXaIffRzpDoToY/njHHC4kmlXb1Aj0gCf+a+fwZUv88y2AmvsQseQtpOnz6t+qVUOl9XA8YED3Ngkr33uivry9jugDwZojDXPv1ieDCmsK8i+EmVMMSL80uVqB3IvDp16pQaU5xDyBXboO8jcxrjj/mH560ZXufOn1e2wcb44H2qL6xlvPD5iNlmKw1IO6QOJKSwypTrfG4xLhtHghWR6F62muTagiBXg2q4VNGAIT0XbTgD84i50kgJf25XQ6t/WwVle+p0rllzHF02BzUCmku1BpXwfO6D7MC87WhRJENzqfoDHoWlvrmiQXOpxnCp8+fP1YVL+YKF1Was0giAAZ998LPo5dpNEck251LZIp+VTaRs7SxnwwkVkDQwNReTw9tHZOOwX7oNjH9nwC+ZaKHrjCsYaNk+dQo0l2osgl6nDAXcshBbFf+YNXdusF8fijlR2T6/3LopWHOp+mN80CtbRvwyORfLn1v41eHtlbfmaWd0E5e6spB7vXGF4iR25WZYDmwbrij+pONSybLGTlmRILN9YbnoLyIU0OvPDnY2lzzXjcIfcLndkjZlmxAM6MbAXL1AMPP9999XEwiTEZkGlfZr4PhSVm8IfajjZ8+eVZV5bI9AKh7XxbbJxIkXNhPkgQMHKtp/hB6j9187g+8+FPKt6fEXLHIzawfb3kqI3S0UthHzsPA0yuNZtIyfCIJUbvI7WRPWLNJyxlgaG74VO1On2y1uv8/2fYyzw4cPF82A2bNnz7qfZZc0wHuxr621ErbyseKXSCyhAlaO/j41Thwt7kHUCECoyKYyN7lmPqFnDb0jAfNHqUAEROrpp59WN4mMOQhbPB5Xcw7nDnsFzr/1/GEjytzzjW98Qz7zmc+oTGeqikk8YF02HqzTbMdqHcLf7Oerr74qo9v2V1TNy/rr97gKLBQg0X7P+ra53QrjWFeDarhUseu52PN2VoI81638wjc4ILH5+XyVHzYn7oAOVhWD5lLNQc7O2y0Jk70fN6aV9PSyW0v7SiRkdAOXoio9HMOqaVn8XrcMBjw9waWGBwKqgiydyapxEvJ7W97Psdu4VDAYqBuXOnC4MBBm7HcxqHt8esubKvDZv07v4dhJXKq/yNzb77I/B1iPWfHJ1EJXCn9gYM8umf/onCytJI65BwcksGVTq3erbaG5VHPAPPlvfvQO+YXfe02SKz3+Dm4dlJ94fH/520AsyLZX3kWjudTMYlwlK8BxhoNe2Twa6HouBTf+oQf3yLffvSYTMzEZ8Lvk0aNbZChQXe/gdka3cKlMcPua/SrlROV2OiTkdyt7UAMuZ78M+LvvHDeCS5V1B4qnazk+xNbgv9PjkYxJgSQg4yhC1Oyca1pdgt1IcBPg9njy39GoAtQobqNADzT6lgEWS/r8UanF86Ojo+seOiYm8+uMRqlMfGynnH4BfBbiX6V48803VZl7J8DrdsqG4aCaeOFKHpezorHpDgbUdZ81TUK+4aGWjm8IOpkp5oo/A4wJGhRXOscZSOMbb+oplU7krLi8lkxJ9qHYZyBCQ/DKCc5bCRbiNc+VaqrcKBCgGgh2dsPksiuL+vvXVFnxO9W8kKO77rqrpLjPNjj/EGyynMnEMicG8GCMkBBi3v4bb76pqk4heZx3KochYiRBGDcPbBfSxrx4zz33rPls5jjgcTnW+Kd7PaXHHIkABKuNgCTibi9nVnGs7Xr0NoxL9dM0vb+g+odxUaxyyNb6spu5lNMpgdFRWcpQMU1y2dqMQ41VaC7VPBDo9/s8ygKwT3LXcSVjE+Erlc6qudcA6223cqloIlXQY9oQAIdDvp7gUoOaS9WVS5ldhFgv33zzjbpxKUd//xprT673UvB4XOLI5N5HgNJV4b1Vt6HZXMrh9Yp7ICQpU19gKtqcKwH3cpLiMtnu5VJOr0dGjxyWbCLJBSMOj7unx+d60FyqeTh5cIN87dcek1MXZiXgdcm9B8eUAFAuXE6nuocyJ2d4Wjy+G8mlqFa+fGN1npuaj0l2eVm2j4e6nksRw3z6zh3S7egWLsX1HE0U8oCQr/S43zAUEJ/bJQmSmvv7ZTCo41LpMrlUWcIfJ73SbHEGkycUFKfHnQvIOBzq92KTLDfE1kar3W59qcW+8kFGyrFjx/J/M5lQgkwpM/aMlCpjxWn01bMDWQ/btxdmFjAhlpPNUiuYmMnqYUyzoBs95NoVho1jtePaPzqihD/sPRH7basAmwgWmEOHDhX9/2I9Rcvpx0O1nxUq4cEi/JWypqmkitRMsC5fvqx+sshrNBaMn9dee03uvffegueZc5hH3nvvvYI5qhhYS4utp2yn3+tV485YH8jkYW4DEDnIGVXLZAAa6ylzIITLzqaYscL4J8nB5fbIxHQ4H9Cgmm8ouNbiyAy2F1iHhPUSOHd2DgWN5FKMAYeDm9Zl9XcpAYHgI/0szOjGKlwz1DGpoJKql6G5VHNRSqQv14EB4Q97T5fD0fJruZFcKhpP2YqBVuFPc6nORrO4lNfb3zAu5fF6JBJN5NsisO563KXXoNw+OEUbUbeOS/k3bVBiH/en3Ju6QsGiXGok5JVoorCP2Eiou6sKcOZx+rs/mbMe0Fyqudg87JfNJ/zVOzAQj15aUoUmJNq0OsbcSC5FtZ8Vs4uJNcKf5lKdjW7gUn1Ot7x/cSaflD4c9Mi2sdI2vmxvIOCRAenu9bgRXKos1sRJr8aOQXmG0+zTU95OE9QyJjRVEaf7tGiYJglr41jGF6XGPBAAgeF3bAfGFIscVXvNBn3XjIWc0mwuULIgdu3apfoCdltWXf7abxOw+JQ675yD6elpVflZCbkqhWw6XVDhzNgs1i+wUgLK9UAFK9u0LvitBNUJyXRG2ZERBOkmGyMqhLleqTQ2Ko8NQHzWq+hF9IUIWS07rMgJOw7bSi0EYsaq4ddORiBjgPmv2DjYunWrfOtb31J2G2DnxkFZCEeUt/v5C9flImJjf7+aXyGIrb4ZaneUamLeSC7Fe8txiyawuLS8lK8QVOe2yqxRje6D5lKdBZWR20aidrO5FEtgPJkWn6e3uBTfORpPK29Xsp/XE5U6Cd3Cpai+jURjcn1yUq5fn8wlbmku1fZcyh0qrzfY7o2D6p5maj6eF/0ObLXvO6TRe9BcqrNgXg96My613JNxqavTUflwYl6d/1t3DMvGoe5JbOgWLnXHvg0yuxCWG9evy8zl8/LqhOZSjeJS/Y32YbeCAYP4QXWfefCoCkGPW7xej3q0ugRbo33w+uuvq3LiUmBMkeFAqSuTkB2ospucnJRWgomT8mfG/okTJ9SE+/LLL6vGp9VanrQ7+K6ttJqDKK2XGUoVJj7UBjgX9JQspxrU5bWfbOOLYWX7aYCMGJrkMp6p/jQ/8NTGD72c40QpPqLx6dOn84JyOyCZyshCJC6JZFoFrebDcXXT3E3YtGmTOlfWa5VGyxCv8+fPF30vVgpUJpd7LbDmPv/882tsQLDOwFbh2WefVeOA+a7UzQxVzk899VT+b9bVZ7/9jAwNhFSz5fvuu0+RM7K3GJ8a9e8t0zAulclIJp1WldWFma0e8fu86uHz2vfI0ug9aC7V2eh2LmWtLOe7Up1OlfpCtHe4FJWPkzMRWYwlZTGaVN8fTtVN6BYu9cy3vimhUFBzqQ7nUplUWjKpVEFgnaqgwztG5cEjW+SBW7fIbbvHu7Lvpkbl0Fyqs4ETXTdzqdGBteLWgLuv5+JSH19bkD954YK8c3FW3vlkRr783DklBHYTuoFLsda+8Ny3ZWR4QHOpBnMpZ6N92M1gYCWTqfwAY5MIfOasgnoHqBSho+fW0rKyGm215aBG5WB8lMpGYGE0LBTpv0eTUrIPrNkqjIV2CIDiuWyATBseZP689NJLquIGC9NuwFI2K8lYrrEwzZfcXq84q7zJqwWMAzUPZDJriJbhjY1wbF6kPv74Y9XctpyMJxdZFsuivqu1WWkyGlOVj4w7JmYIGz/tPN1ZuD/44AOVCVNqDLBPVGtR2dpO1VnR+FrL01giKW5XddYc7YojR44o8n38+PH8c2TtnTx5Uon4xbLnGHuQrLfeekuJ/usBMoYvupHFwxzBPEhWFfYIEPYzZ86ojK5iVc4GDEsGA0888YR6L1axBknfsGGD2jfzTQXZhtwAMH7JLMNSth3m0Faimt4ydedSS0uSjMdXuRQVtj6f9JvmsIZwqWSKD28L+2aNyqG5VGcCq8/5cEyyK1a/g0GveN3dx6UCXrfiUnORuPquuMDwE9ycj8mAvze41KyNTddcOFFQ9dgN0FxKc6l24FKJSLQgecoTDIjDNL/Rz7GeIB6Vhr8R/Pd6CipwNDoDmkt1JmLXrsulL39VUjNz4vD7ZPv3f1YGbznYdVwKm2K2PzUbUTwq6O6TUW9/z8Wlnv/guvrJrTJMkhX3xTPX5YcetI/TdCo0l9Jcyl0mlyorckNQk0aNtSKdzqxRlWkYSYVfIwCpit6clqX0qu+pb3REXL7mWz1qVA+jio8FyFr5RykxDZbN/dFYxMhyIEDOosrCOTAwoH4SvG5HsK+UPCP+UbbNd6bpbzstoJWA6zxhFsKWlyUVj+f63bTAaoES+LfffjsvurJ/iBqME8QM7AkMwYMxhcBSybGn6i+bSecC4xbwWYjTiC9Xr17NN7+1AvEaUsdrGNOQMOs+QAjx82aRN9s/tAOMXicFz60E7boJZLYZ2VXWDBvWSsYZ85Rhe2AG54wMunJhLt3HtoE5AbBtGm9T5bye6GcH3s+1QKYfcw7jjXnHnO385ptvqu+D+MgNAtfLiy++qJo9sx92369c8Dnc1FSb7d1KcO6rtYuuG5eif6qVS2FjbWrMXW8uFZublyWTh7xvaLCt7Jw11ofmUp0H1tDZxZUEKoNPhOMyOtjfEivtRnMpqv4iiZQkYmu5FCJgeDHHpSYmJtRc3I1cKmvDpbI1Ws+3IzSX0lyq1VwK3mQW/dRzsbj4Bkpbn1ULPis8eV2WUquiZWDjuLj83ZUg2e3QXKrzkE0k5MIf/JFk47mKt2wsLhf/6Kty4J/8pPg2beg6LkXVX6A/u25cqpu5VCJV6DoFs4pbnusGaC6luZS3TC5VlvCHaIIlYa2w8yZuZKl1MhwpEP1AfHZOnFs29XzVQieBRZFKFPrzGcIf4xHbTjJlzKIfIJhMNY45W4YmzLy+nEqbVoHgOtcIDy7gN954I5/5Y/w/pdv17FFIJjkPyqw9Lmfdrguq/azVbwBrulYIfyyKkCajByTVTvyk6SyEBzHDOK68xi7zaT30O5hOCwmW0QiX8YcIzVgt1rDZAA1yuamgchUSRfWqAUgiAk27kStAENJq7dlNPf7MoDKXOQlRzIzbb79dVeLRxxMh3+56gsBDooGRHWdXjWxdGxH6SFxgHqOy8Itf/KL6yXgoJqAZ22AuAcwnjCfew+eyTcYjFYRcI9h0IEwzXrkhMIuKjLkHHnhA3SwwNrkJoMKa5tKVJijQcJp9evjhh4tWSLYSyo4cC83lZTVfcXyN88PNGJyoGnQql0rFYgWiH4gvLEpwfExzqQ6C5lKN41L01MwuZXP9jR2Oul0X6ay9JVUqnWnJ+toMLgUXDVu4lKO/Tz3MXIrfu5FL+dxOiSbSa57rRmgu1d1cirYuSSVyLYuj3yEeT3txqeWVXsgFzzVQZE/MLxSIfiB2c1oGdmzXXKqDoLlU47jUxExUzk+GJeR3yW27hutWcRufuK7EvgIsL0vk3CctEf7aKS7VrVxq+3hAPr62mA9HsvLsGK8+abmdoblUd3OpmwtxOT+5oNofUNF7YOtQ3nq8Ei7VVOHPKF02o5G2YdZA1Wq9b856UKMzgHjHImg0LiXoSeAc27z13mcOtg8PD7dVY187PPjggwW+zcAQBVh0qcIh+M+kVuu1E1vpw2bA7XLI6IC/PtdkkW200iaQY4ZQ4vf71QPRmP3h93rsZ67qLyNZo+8EvbZCIbUtBGsyuSB65QToERoY32fPns0TLN7HdWCMi3ZDyO+VxWhc0pml/HgK+ruzIogxY1TvWf32qdIjS66YtTA99RgLXM/f/OY31frK7xB8yDOWm8WEHaw0IOiQcLZN5R3ZerzPDCw8seeA1GPlefPmTbW/BtnHg50kCGxC8WXHxoM5kp5/CJp8h2KVhNh90C/VEA9fffVVNa8yxpljywFzOdWKzzzzjBIf8YdvF8BRqKgzJzGkl5fFvVLdBhdqdbBKcSnLGGksl7JJVtBcquOguVRjuBQiHLbWBrgZC/i8dbkm+1Soore41GDAI4lURiLx3Dzc39cnm0d7h0uNDfklOxtVxwD4PS4ZGejOiiDNpbqXS2WyWUmYqk34ezm5rPoOtw2Xcjhyiaom9DXQaccuLoX1J2JjX5vHJzRWoblUY7jUi2em5H//yzNimAXdumNIfvVLt9Wlt2a/nT06yZ0tsE03oONSjcUTx7ZKLJGVqzO5vn57NoXkwVvak/fVCs2lupdLzUUScvriTP7v63MxFWs9tmesYi5VtvDHQagVLpdTBTTNN2vuBk64dj1o+vr7tOjXYaDXFQFsY1BjeVdp5gvkpFgpe7vDIFIEMO69914V/MeeDwHB+D+uKTLOiRGRHbUe+cKW0Sz6Aaq1oomUBH21izWGpaf5hop9anUvA44fkzrVTFaxBFBVylirBnw/bzAgy0s+dSPX71zN+kcIohKMJrvlNGY2wCKErzvjHbGbTJZ2BVWjg8Gc4JX7uzNtassFGUEEPxHhrN7aqlKsyPdHVENoA5xPY4ywNnJdM7/RV4+xagbXO9lIvJ5xQXUeY/ijjz5aM5bZp9nZWUV8EBKffvrp/P8hArJ9Y06hgTjiH79z82buQVoM9AMwshXpbcg1wzbJmEJMZJvrASGSRAcaRbMdq41zq2ANAqnnVngLx57MqlI9Z5vCpTwetZ8FXKqOGbdWMJeJtYUn41YnUHUUNJcqj0uRUclfDsf6XIrXm0U/wPsRAz11uL9xOXOWnjgzmNfaVvT4ayaX2jgckJGQT3FVt9OhvnOvcCk4/ObRYK6n48o47GZoLtWdXIpqPyu4T20rLrWSsGmu8nP7fdIo9NvcAyM0NlJs1Kg/NJdan0tRtX726oLiMLdsX61OKQYSXf7jX5/Ni37gg8vz8s1T1+QzJ7bVfM58WzdLYPcOiV68kktc7O8TVygkg0dLFxE0Gjou1TjglPCDD+yWWDKTS07zdKdzggHNpbqTS12fW6tfzIQTks4uicvRXxGXKusKoNyYCoFawUXn8bjz1QwERhuZteoJBSWbTErWsFXoE/GNjGg7hQ4CAWYuXipbCHDzk+fK7Q1FeTwXBNU0nSr8WUGWMt+HfltU6BAYicUT+VJ2AiTrZZsv2dibGAGrekBd636/pJNJFaDmWidY3cosdWO/KOMmG4QMCTLHETtmZmZUIIhFAj/zWravsjZtEjepzCK7qxJLDDJVEIrwfKeE/fHHH5d2hmEh0SsgI4ibHX6as23KPQbm13GNUJ1n/LRuw1ytzFh94YUX1JxGAJQKPNZp87awO7YDVQ9mmw5sWtgOPfu4eWO7CJmlMqWYg82fR8UHY5u+BHj9UzWH4FgKly5dUralfC/GdrsQrPWAoFoOgWw4l/L78yJlf4MDR+5AQPWJKOjxNzjYU9d6p0NzqTK4FD3kYnCp5bwAEwx4VcVZJb1t69mTjWuMaq9IPKnEP7VPfk9eCOtmLoVrgB16hUs5Hb0zv2oulYPmUk3mUv394iVWBLdZFnGQsNlALuUdHJBMPC5ZoxKSYPQGbZneSdBcan0udelGRH7pD16T2UhunB/YMiD/v394t4R8xWN3vDaTLeRT8JzJudp7eQKu6z0/9sMy9ewLEr82Je6hQdn42APi9DVO6C9rv3RcquHHN+BtbaJcM6G5VPdxqb6Vh/Vus6+KuFRZwh8b46auHlBVP6YAJhlhS0u54JXD4axrZiOTvH98TIl/VOA43O5c5rpGxwDRj/FCli7BGOzrCHKXQ8zI7qWypphlXScDuz0C9ogO2eVl8Xp9cuDAQRUAJniVSKXF5ymsQjKjv8h15qqj1YgK3DSwCqVaIBqToY6lIdnjRgUV44VAVaOC2YxFPqea8n32mf5x3V5F12kgkEgvP6xOOLckJtQCa+WgGZAW89gkuPrOO++ocYG1p1mIqwTMr4h+xs0bD5Jz6DfAfMs1wXxjraC2S75g/xAtEQ/tCBZzOFWLbJN95jVkWG3fvl39P/0DmOtbacnMZ1styc1JSnChaoW/unMpk6tBmgobKo/6RFw+nzhLjKVqPss/MqxsjDmHVG63olerRvXQXGp9LpXOLovH681zKcS7RCIl/hIuCMVEwXr1pTGuP6y02w2aS2nUC5pLdR+XolLZWvVnrqJuJy7lNPHZtClhnIrAejrVEJcKbt4kGZWsuyROj8fWnUqjfaG51Ppc6t997axMLmTEM0Jbin45N7kov/+3H8kvfb54MtBI0KOqV6hiMUA8a8tI/WyusfXc/KlHpd2guZRGvaC5VPdxqc0jfrk2m7OrNTA+6MtXUVfCpcq6M+UAUBEQi9Un68K8eBJAxPqBB2KNNeBWM7DtSaUklUhIMhqx7/vXADCQEBs1agOiB+POsAbhwrTrI2LFxYsXVWmvNWDdTSC7msyqI0dukw0bNqoGu+tV9JmDVcOhwiwnsqv9PZIVgxUi48roVUa1ViNFP1BOP5piwNKmHRsna+REIewxyQ4yevc1Yhxhb0b1smFvBClh/GJbZhCUWrePvYHxnbgesEuA9PDdzCjWvxDwPPZvr7/+ep4zMDdRGYI1Klaj2ORQgRGJRFQ2ljGnQ7oQUVsJBC2X252vXnWs/A34PljYVXstNopLpdivhUXJJJNK/IvPzSveU1coO+ms4m3wKTtL1IZxqRrmTo0cNJdan0vdeuSobNywUU6ZuFRmnco9JYqv9KwywM2Y29UbwVzNpTTqBc2luotLwZ28Hreq2lHimjP3d1tzKWJFkajiTzzii2HJpFdco+rJpTJpSSdTkoxGmxuX0lyqZmgutT6XWnBtEod/WCKTubYShEM/vrZY8rh63Q752acPFSRTHdk5LE8c677kfTtoLqVRL2gu1V1cajDgkWO7x2TA71b2tVtHA3LLjuGquFRZd6YcCAYRB6aefRHS6bVkh+yweqmqqoH7/EI+QEXQKpZeED/fp0E9E/jMdDwhqRVbSYKI2Ejo7PjqwaCmAqWSccHre8WGjEwhqn3Or/TtAuV8d5/HpbzXU5klRbQ8rtWedL0AysApj56fn1/zvZkrmPjbBaUq/dScQyA+k5U+Ao5er+4X0QKcOHFCeYlfuHBBWQyrXlGZjLo2sQuodV2DhGBPZh6rZCFV2u+0GOgZaA0KGLajr7zyyroWCWawTwiJVAyyDQgX1m8QE37Hqo3sJG50rP70WC3w/0YfxFaAc2V3vuBAfJ9qE0oaxaVS0ajNc7G6Vf0xLhIk4KyIIIySRDQqvmCwYXON+sxUWhLJdL6CKuD31LWSqteguVRpuF0uGR4ZkXPnV7lUKZvP1ffl3EpIYMxZNDa2jUG7oZu4FMF+4/6hHezpexGaS3UPl0Ls49EpXCodW9sSJB2PF1QEdmJcinmNSkYAZ8MqXjvIVA/NpUpj03BAJqVf4rMTeR61eWR9S82ThzbIro1BuXA9rGxBj+zgOu+dNbibuFRa9U7NJQm7XE7NpVoAzaW6h0uNDnjVo1YuVZbwRwCMnkBTU1MNb4hez2ykpXRmbVY6mVappPQ3yNNZVReaesnx+YlwRHyDA7aTHjeYqrH0yiTKsdY3moWg6oRKFCawcppXotb30jHE0jOWSIrXl+tj6Pf7xOsp7ybF6XCoRy8CAkV1FpO6FfSTrDe4vrlZKKdi1W5eREyy2taq4Hg4IllTRipzkF/332o6mHOwOfnOd76jGpwbQhmL8je/+U1VnYcFA4Q+Go0qgg/B4JxW0xOw3qC/H9Wvdp9ZjWjJd+VGhZuYY8eOKQtRfqeyn4xQu+/CMbpy5YrKxKKSEX92MkjbBRwjzmu1Im6juJQdbzJEunoAHmO3PYRtoxqy3qCfmSH6AWwXI7GEDARy9sx2+5imyhErUqdT2XP1Eg8oB5pLlQaWnowxn9eX51I+b3njG0G6V0XpbuFSSZIMTfeM3Jt5AwE9jzQZmksVQnOpFnOpDo9LMY8Zop/6uKUlNdcVm9vYP6PKkaR1+JTmUoXQXKo0/ukXj8qv/Jc3xOFySzadlOHhkPzUp8oTrTYP+9WjF9EtXCpBT3jTPWMmmxWfVydSNRuaSxXCo+NS5Vl9AoJ2BL0anS1QTw/V5TVtEI3ni7y+DuTOHIA3oEiezbaZFFN4yWezeaJFIE2jEPSfoo8WPdkoZ10PlOW2U1ZMo0E2ZcDvlf1798rU9UkJ+H06k68M0I/NamFowLihNMA1+v7776sycbygIWBmUlMO9uzZoyxoq8E999yjMk6soMrPOucsZ5fqb/OnUTawGKZfJJV/rCmMJSr1WEPpKclPhC8SGcKzs/LGSy8ra6FWW/Cwj9h72sGwW64UrGds1wDVj3z/Ujah9957r3qQgfWf//N/VkIpYmCrjw+AA7H/taARXMrpXtuDzGmxH2wm6nGuEP6soN+H3aZVglU0quZCglzYdZkDXRo5aC61fk8qhOX9+/fK9NSkDAT8PSvm9RyXWrkPM4MAud09nUZ7cak777xTIuGIvPXmW5JOpVvOFTSX6lwu5XCvTZp1utydHZeyiSupRC67uFQ2K8l4XL3HEAz1/eRaaC5VGnfsHZX/8gsPyC/84IPyI3cPyn/5hQdl03BjhO1uQldwqaXlNfuhqo6b1B5CozYuFY1E5O233lKahOZS0nVxqbKbUPBlJycnpZ5wu12SSqXzEwSin50lRLVQGd/9/Wsy1a32V9zYxaZuSjaZVK/3jY2KOxSs6jOLZkXZPG9HxniOShCdXSW2lnMscgTMNQpBcMron1CONZVGDl6vV5V+kwVitZGhHJwScDKamOSpPsJWFVCNROUS7y8XXNNUL1GRyk1DJYAIbty4cc3zNIe3g+4v2jpwnqgKIwHhtddeyxOD5WxWnNkl2btjp4Qnp5Qt69YNG+W96Wl56fnnVWbt7kMHFfFvBSB/duA7kH1Yak3iGsLmlGvC7XbL4cOHVQYhx+Kdd95RFX/lgmvRuB5/6qd+Sv3+3e9+V1UBPvzwwy2tAIQDwYVqQSO4lHcgJPEFssZzgr/L5xN3FRmcxcDY5PxbSS6Z4NagkbKxymTU6z0DIXFVMEeaUQGVylX6WUCwSlv1rYXmUqWBZacbLuX19JTFVK9zqWJo/W1976IYlyLhzRGPy56t22UhnhT3hjHZsX27nH7vtOrXwlW7c/cuzaU0l6oYVMEllnNJRMDp8YjLVx2HqSUupQS4SDSXjACX8vvFudIfsZFxKbt+hjzH/um4VCE0lyqNbWMB+Z47t8n0tFeGAo1xBulGdDqXKpbcoMlUG3Kp5WVxOt2yZ98+WYzExO3oU7yJ1i9wKdYIKup1XMrTNXGpsoU/SnmZVOoJFRjyuPMDsN6kgu35hgYlEQ6rGwUWaY+l3x6fHb12Pd9cGTIWu3FT+l1OcZYxefL+rMowXFKEjiBXOlGYac5zdt9N31BWHphGHJ6eni7ZxBLhlEyYYtWjRoNrzkk3EVlU/+3bt7d6NzoKWBuS7WRXIYofNP/HhArhMsgVYAHF7xlrwnJAZghEjiQHFl4yRyoZe7zXjsz1WwLvBhyusqd2jQagb4UsGRZEzDeR6zdkfm5O3nnvvdz846SSJDcGfD6vHNx/QCZnZ+Wv/uqv5Omnn1bVzfiPM3YGBgbktttuU0SfdTgQCKjnIP+sa4wnA9hpMjaxE6XC7sCBA1V/j3A4nM/oY1+Yg60e5+zniy++qLLFGKPG34888oi6Pnhftd7ofE/wuc99TmU0/smf/Il88YtfVGsA4mKzwfew2ppUioZwqf5+8Q8PNZRLeQIBZWNOIIrPo5eo2bWBz47NzSuB2/g7sbAo/f0O2yx6Wy6VXVI3jSSyeNwuSaYKk6N4zva7tUHWXSdBc6nS0FyqB7lUkcrOerrQaNSBSy0tyeInlxWXeveD96XP7RLnwEBexPB5vSoYenXiquZSJmguVeZ4I/l7INTyuBQtHPLiIDbE0ajat3Lu7XLVNbmKPhJZEO2sVXtayGsvLpVMZ9Uj4HV2lcuA5lK9x6WKjd9G9TDVqD4utRiNq7jSu++8A7nK9Sc3zW1wqStXNZfqJi5VdnSYgXL+/HlpBBopvkCm/CUaHi6l03nRz4x0NLau8Jdv0mx6P5n3/qFBScUTOdKFgGjJ2sjvW3//GmuZbhOj6g0qR1566aWSBIvqlDNnzsiRI0dsKwMy6dXz5SGru0tu7BcXFyvO2Ol1sOAVq3SicsnOZx3gwW7NxiqVjcF5MURZbBOxZOD6h6BxQ7AeIHiU6Fsnd7bBnMNNohH89gQDaypxNFoLlRySzcrgwICcOH6cTuecvPxcH45E5NS774pvcFAefPBB9Rx9TckiQrzjhpGxitDHjSN2H5B/7BoMYY9xRX9BrDvI+mOMGn0GqwXj9gtf+IIsLZMZJqoS5q233lQWnnACxi5CH/th3ADwk6psMhPpecg19MILL6htVZsVxXG666671Hel99apU6cUIW32WokIun///pq20bFcirlmhfAW7QNoY+WSSSbXFf4Ul0pwA7oq4FG9ju1iEleI5WVlw+guEvRivrM6KBhVihqN4VK4daTN3NfjUUHGboDmUr3HpQiqU1WDzZ3Bpdw+X9fcH3QLsvQOymRkIBSSO47eJo5gQJyDA6tcKhyWt0+dUvd2tXKp3bvrz6UyS/SrXRav2ylvay7Vm1xqnbgUgp9dT2WqENcT/lQCVjypeiIboE8t3A3xb9nUA7nYvlm5FHOj5lKN41KnL87Ie5dm1e8el0MeOrJZxga6wxZTc6ke5FJ9fer+jT5/5qTNbhK0uwGsEawHcKM7TpwQR5+Is7+vkEu9/ba4PZ62jEulZ+ZkKR4X74YxefuDD3RcqkyUHR2mzJOT13Uo0WtoPaRj8TWiIQH4wOiIeIPFA2QGuFBUlvvKNvhMLjCNUqerTy02VLUw8RQ7rsBcZQL5IBPULPqBZCIpXr+vKaTWqDQkqFBvIs0ErS1iK8fMzExFNoQGOHfleqlTecVCaABCt3vHTsmkU/LSCy/I4PCwbNu2TUZHR4tuIxQKqQXUDk6XSwIr1T46caA9seZaR+AwceBQMCh3njghwbFRNTcAxDPeNzQ0pM49N42c4+PHj68h92fPnpVYNKrmlhvXp9SNwYaNG8RlsQ+qBvOxlMQSOfsfvsWtR4/JhXMfKWtPRD/2BYHPDPaZgIyRBYu/PLYIWCJQqVgNEBtfeeUVRRq5Ofnwww+V+NdMkBVp7llYDbqVS/Xl8wSt/1EGl0pnCkQ/wE0jvWv9vvU5EUEs59Jqb1PGB0F7jcZwKa/XVyD65c5XUvw+e3eLRnEpRgzW5ppL9SaXcnu80h/aKOFYUv7sr5+VfdvHZfv26rkUAXEfyXPwdJ2E2Z6wzC9LOOyQT7Ryzji/d915Z8F9XaVcinGWzi7L1cnriksdumWj+L21c6kbCwlZjOXWKHbt0K23yaULH2suVSW6lUsVRRlLayqdKRD9QDyRklDAVxYnYg5EdDQsP1VCRJV27b2CWrjUfELyoh+g6u+509fkCyd3N0UoyXMpbklNQf96QMelOigu5fXKjpFRVbTy/De/JSNbNtcUl2K8cz9g3jeN9oblFjzHpe66S83/1XKpRsWlpv76GVl4/0P1e5/TIQe/+D1yORHVXKoM9FeSWVVtA9B2BlZ5TmsvnP4+cYdC677XWq2nYIg7ZYCLhzJtLirjoSfH9UGwlwmlFMiqYhKi6oQAMX7F8Vh8zevm5uZUlgt2dHZNausFxkQqkZBkLKYyihOxWMVNeEsFxCk3vuOOO+qyvV4C1Tuc/0pB/zJIbTmgAoosl7yFy2JY0vG4LGeyKmO5L5tVmTLrjQduKIo1fWbeINit54/2BNbRa6qeON8rZMqwajREP+N8czPJOUXUJ1sK0YzMUjOogOMGEuFk7569qhcqZD2bydr2ka0E0cSq6AdY2aYXY3LLrbfK7bffLidPnlTzjtluxMD/n733AJPkrM79T+c8OexszlHaXUmrLCFAIHISOQcLHHAAGxz+9jU22Fxf4Br72tjXBGOMwSb5IhBZCFDOYaVdabWrzWl2cuc4/X/er6dmOs5093Soqn5/z9O7MzUdqit89dZ3znnPpZdeqs4tzaoUAbPvfe97KkNsMWD3eG4yKqPTsYJgkNaLAO8HG9F2VDfjGqHZZNSLWbUUelbaigU9NE4V/XFQ0VeOah08VdKU2y2eQEA93H5/Res+snwtFUVVVBu1VCKRVBWi8XhCYvE4tVQHailUTd3z9Hl5+tS0nJmIiWNwkzx7LtwQLcUKF/1icznFlndNyabTkp6aXggIwpba7SrQwrVqqWR6VlX7XX75FdKHydBUusR2ulZmIon5oJ9a76zImYmw7NzZPC2lKukTaYkn0wXzEtRS+iZn6Vl0HKAaw7l0ElSlsa8WLYXeyKgQVA+vt+C+hDRWS50Zmy7JjZtES4gnn26JlkKCHSpEoaeiMWqpjpyXmp2VmaMnJDo6JulgSC5evVbS41ON0VJMoNItSCzITy7AJSJdFP1DMdJytFQz5qWm9x+cD/qp9U5n5Mx3fyQ7t21rmpaanc3KTCQpoWiuat7IWqrqij9EapHtjp2MUmOzgJ3mGx6U+NS0pOMJsdpt4u7tURO1S1HWAqaOQY6T9bWBCT2cXDMzM4tax6HSBA+cpDjB8ycXUaKMEnWP16tKknFxa6bAQvZcQaAYgUCUKC9inVYN+G7op4WBzqwTnal0RiZDEKRZ8bkd0uVrXFUsjp/9+/er/V/r9it3USkHqqI02wZYPuYfBypDK52RlStGlHhasWJFxfeBncJtt90mb3nLWzhmGAzsZ+9AvySCIckkk2Kx2cTV1aWuN9VUF+Bm8ZprrlHZosXHHQL/EPCrRlaWNNrGOIeK0HpJpkqTW7C6CM5h3XGTgaywSt8Z4xIEEc4xrCd6F/z0pz+VN73pTWVfA2H1H788IpPhXJ/c1f1eeevzNovbaVMZhFovw0cffVSdLxjX660grBX0M4AGqmSzUi1m1lLoXZOMRNQ4h0Ag+gJWY5WHqq3y71n7OpDmayn0tC7WUl6vpyVaCpWG+RUNGI8weZWfXVwPnaCl0ulZmYnmtJTH5RC/x2lYLXV+KqquFyA7d+5PhBPi6xmkljIx2M+B1SslNjYh6XhcJe96BvvFmncMLVdLDQyPyNBQoZZKZTLilvq1VDxZXkvhfV1We8O1FO6bTozOqCAm8Djtsna4KzfZRy2la1RPZb9PUrG4miRVPZU97qp6ZOXG3tJjjVpKn1rqzEw6dwGbKyYYPXtSnC6P7NixV5w2S3O1FKpD0QsybzxCdSicNpZDJ2ipcDwlz5yaVuPsSJ9P1g8vXSyiVy2VDIUlE48XjD/pWExW9PdTS5kY7Ge/1y2xRDI3p2PNWbTm34/rcV4qfmFctcpRyfN5bdtSMyFxDfQ1XEvFkml5+NkLEk3kApbdPqdctmVIHDZjaqmqA38o+cUkFW7wW22v1WxUM+f+8uX5i+HwepS1VEGPv0CAk08tAJlT9913n1x77bXVTUh6PHL69GmZGBtXGQs4WXAca5mhOHkxGDWLctWhmsXCciYrMdDAEqBcg10zkExn5Oi56fnKn6lwXIbSGRnoLqrSXQYY28bGxkouTkuuWzIphw8fVj/jwge7Qwz2iwmvStXAPp9Xxicnyn4GMmeQeYXsGjSOxbJqfdyJvq4z7p4yN4Q1nP+aRUw+EDgQX/EyFc2VxhYch8eOHVPjHuySKj0PjZ7LYbNaVHYrMlJf+cpXVlxfvC/eHw+AYxmiqxLfe/CETEdyQT9wZjIqP99/Rl6xL/d6DZxr8J7H+7VKYEH74Lwrtw9qwdRaSk1Y1Z7x5nDYJZ3JlPT4YyBPv1pqbHyiQEuhx19rtFRpBjK11NJggurMWGg+AQ6ZqwgE9gTchtRS0IblcLq9Ep48V/YzqKXMgXJIGF6wKauHxbTUdChadXJKtVrKYbdW1FjN0FJnJ8LzQT/1/GRaRqcisrK/cEKMWkqfKBcDb+125eiDnEZVRUGPv8KqDaIfLWUfPSmnDh8Wu6dLtf9YuXaT7Fw3oAL1oJlaqtgSFlBLLU04lpLvPXBcUnNa9NCZGbk0NiC711e2xdT1vFQFLeVze2RyrLSaj1rKZEnp7uXNJ7Z6XsrRHSgtYUcFot/XFC311PEJ5ZyggYTDZ09Pya51/YbUUlUH/pB1sHnzZjWgmG2yarkZ7shuRwY0rBnYCL414HhE2TAujPk+1ZWAHzEe8LU+d/acbNu+XWXPab7r2sWsWTRDdGPCDRlB+F5mZTJYaPcHLkxHpb+rcX0ZcbGpJysNfcs0kDWC98EFB8clSuHRPFm7sGioCq+SrCu3GjuQKaIFBrVML/yO/QvrB3zfBx980FRVQqQxKHsep0NSyQVbTrBYVhXGThx/yBa68sorC44rLSHB73VJJJ4qmGTtDXjUsYl+Fhh7a8lKrNSwXOPsZLTAZz5nh1U6CYebGFhHtBJoH2ig5WawUktV0FJul8o6ROc2VCWYNVPYTFrq7Llzsn37tvkqkpZoKatFpDEu6R2lpWA1WGypOxmKSbffZUgt1ecvDFgmEwnVe7LH75Jjz1BLkWVci1xOlQWfj7vYqr1GLdXrd0swmiio/Bvu9aqxsxlaKn+iarFl1FImnMj1uCSNoEQ2KzYbtZSetdTevT2ybsMGefipo9I3vFIGutyydnAheY7zUvrjwMlJFfTLl1OPPTcuu9b2qaRYo2kpe1GCAfp1o+cflnNeiuhtXqrvkouV1Wf8/IVc4nw2KytedIPY3K6maKmZSFIryi5YZlQtVXXgD2zbtk1FU1/1qlfVs36mRPncujgR3w5wPN59991VCSwNn88n6Uxa7A57yYmyZs0a1agUge1GB+rsTmeJrzF89JfzOU888YTs3r1bzAz6uFRabrc1Zh9B2BQLoVrBpKdm4YH3Qxk5lmFQRqUePKWRAQIrT6fPK8lILpihLmp2h7pY4XjWGilXqgTFsnqqRB944AElCCuVv5sdiADNqx77xYzZrxBT+F65DFFL2TEO4NhE83D8bWhoSHmDw/IAk9/azQb8zq+77rrcMdvnV8E/bD9kE7uddpVJiGMZPQVwUwGLv0YQ8DhkMpSYF1nYTV2e+i0hGgm0T6O+J7VUKbmeAUvbghL9aCloGoe9tVoKn4eKhnycztzYVy+doKWKE6jml2Ny2GJMLXXJxgF54ti4ShZBhfCa1QGJBKeppZoI3Eu0exmlOUyYoOF2OZTtFapkcWq4HA4VQFmullo33J2rtM3MisdlF6/L0TQthfujZDpbVdVhq6GWasGEK7WUYbRUb3dA1vS7ZOvG/pZqKa06NB8XtdSSJNACI1vGtjk9KzanzZBayr9qRMJnz6svgr5ujsF+mZqZoZZqIp2gpZo1L7XhnW+U4NOHlSWtd+UK8a5e2TQt5XLY5m0+NdB+xqhaqqbAH8oY0YyWED2AwQS+trh41XLgY1ApV0KPwQSBGAwYGGhgY4ULZiPAoIUm2cjAwYUVfSnyM+RrBdaPGNzMavGpgRtjZKrn47TbGpZVhewnBMMggmoBxwca18736cuzzMDxhYf2vAMHDijxhBJ2XJQQ8EUgGA2VxycmpKevV3lh5wflKgl8ZKYgGwb7vlqQMYjjDxfRaixIGhdoy2WbtjvIhiqi/KxJ9DVwwcfchCLLhnFlkWMZxwAm7HETiewkbQzat2+f+l8LKuM6r1nMqMq/ol5Q6DuBbCyA47pRvPTS1fKfdx6d69ok4rRZ5YW7V4oewDZpVHCAWoroCaNpKVTJ57QUJjcKqw1rpVO0FBI2wrFkSSCgkoWhEbTUmkG/jPR51URcaGZKLOI1p5ZCcNaqAy2VTksiHJn/PRVPiNtfXR9Xo4FJcTwaqaVwrnUX9ShvlpZCwtbJC7nzSvvsoZ7l9ZRvFNRSxKwYTUuhN7KqDJuzHaaWWpoVvV45ej40/zuuyl1ep7gcVsNqKVdvjzi7u1TrqompKUHzK7NpKQRv8Oj2OtueBNNJWqoZ81KYQ++5eEdLtNSOtb3y6JExdb+JcRLzz1tW9RhWS9V05OPNsYMI0QsrV65UWQK46FQLqqu0C2AxmPyBrSIEGy5MiKYjy0BNMi0TZHM4nE4V+FmOuELz6EOHDsmuXbvE7MC2qS+vBw0u1muGuhoyAYJ9CjFdz3aMRqMquwTHCI49ZOWVA+u5YcMGtb/yRRiW4wKPYwtCH5V+OI6Xop5qP1wM4f+MYxtCrtmEowkZn47IZDAq49NhSaaWf+4sh3LfGcG/TgPHKm4KcFOKY6HcGIRj6/HHH1c/wxZkMfB67dEoNq7okvfftE1uuGhEXnDxSvnAS3fIUE/tfU6aAbRPowJ/1FJEbxhJS2ES2+lwqEo/aqnqCHidBUEHTPIN9/kNr6XwPXxuhwRnzKelovGETIWiMhOOyVQwIqkGnDvLIVncryWblWSs+vHCLBhBSyFZa+NIjwz2eGWoxysbV/ao4L8eoJYiZsZQWspqUToKlX7UUtWxZWW37Fy7MKb7PQ554Z5VhtdSmKO0OZ0yPTNjOi31wKEL8q8/OyT/dedz8uXbD8np8YWgWzugljKOlhro8sjVO1bIppXdKuB37c4RCRQlwxtJS9WkAvHmBw8eVDtKb32mMPAkkmlJZzLqQuZ2oqLDfHZupBSUBCNDBheyatB6py11oYJ4w+P8+fMyPj6uBiU03G03yGRAVkO7s38roZpDz2VBLXcd8XpkruLmFRVkmORp1PeG6Kl3Ih9CBSK9msw7ZLKg6bd6XTgsqXBUrBDavT0qiKuNp2gIi+N4ZGSk4nvV4lsN0GgcjV9x04DXwu6hliawtYIxOBpPFthfzIRi0t/rb1hlQa1ofROXWmYWcBwh2xTHp3YsIUsK9jNXXXXVoq/F83DDsNjzcHORnwXY6G2JQF+9wb56bkCqAeco7HYaGfjTs5ZCNiLGC2W/OWfVQcwPtZS+UGMrHhZLQ7RUf7dXegJuyc7iBnn579lOLTUZistUOCEep11V/WEi1ExaCglTscTChBiusqFIXHq7fG0bj+FSUc0ys2B0LYVAX73BPmqpBmqpDCxjLcrphVqqM6CWEt2di+lMrkVMI7TUFVuHZM+GftXrz+uyN2x+ox1aKnzshESOnRRHT7f07r3IdFrq2GhIHjo8Nv97Mj0rP3z4pLznRVuVg1g7oJYylpYKeJx1B/v0pqVqUoQIrKCU96mnnpJLL71U9AI2ajiWKKgswQR0d8Dbtslm0jpwoXvooYeqDvyhlBgnTLXWTiiDB7gB1At6vXnAOYgsZYBV9KFBcAMurLA5KtNiY1mEQiHp7u6uW2DhOKqF6LnzEjp2UiLRqIxOjIvd75O+bVtUAAB2HrBdwDGG463S/kUWCz4boqmqm85MZn6shh0DBF0zQU+UkvWYs9u01nEcZGZn1eQXvgtsl1yLWC9VAgkgxf2NzJYUovZ1KiWPPvqYRGNRueLKK+Xo0aNKLGFcRLYUmg4vZcWBcRQ2lIuB98w//uDBfvvtt8uePXtq6mvRSJKpjKqMQO9PnDp9Aa+4GpjdDjsFbLv169c35P30rKWSiaSaqNLIpDPi8rh1e80hjYNaSj+k4nGJTwdV4A+Z2O7ebjVx3AgtVZvXi/601OGz0/LE0QmJRSMyPTEmfQGnXLl9pboJxs2/GbQUklhL1mNOE9nryGbGBFsC98hZUZq8nh5gcKnIvzZoy8yEtq8fefRRiUWjSjd1kpaCjowHQ7mJSYtF3F2Bhow7namlEso6TwNBQJfXSy3VAVBL6YcL01E5cHJS3R867VbZvX5AuUk1ovcXHkbWUud/+ks58bVvSzSVlPFYVPybN8qW97/TVFrq/FRUMOWTPw2E4N90OFlXkjGs15fbaqBztNSsPProIyow13FaKpmS2PSMZLGfLRbx4B6uinOk2Vqqpts/RPQvv/xyeeCBB0RPYFK32E4OJ2Yi2fzyYdJ+4IONi1K1/SfD4XBVF6hiMNDnl8W3C5zoGET1BgZ4LegHkHARjsVLgi56YLkZGDgWahFYuAAg6Lf/0DMSikRkw+o1sra3X1YGumXv3r0q8AdLBfwfiVS2IECwupp9DxF21113zWd0Ab/fryoMm0mlgFo9CRg4nmAZCuvQSCwpU8FoQTVhtRTvJ9X4vkZxXLHKPBiS0NlzEjpzThLBYMOzjGbTGXXsLJZVj8+MTs/I7T/6kfT7/XL5xbvF5XSqfQ/7mAcffFBOnjyp9v9i4LjCWIpMwMWALUP+jQkE/Atf+EI18aL5r7cSTIROBCPqpg4ceOop+dkv7pR0pnFjNTQPBGqj+kLqVUvhWCq+GdGy1on5oZbSB5lUWuJTMzkRNZcdHJucltkGjmlG1VLxZFoF/Y4eekpi0bCsWL1OXD0rxeYfVDf5ZtFSlbZpPdsa9y3BSEziiZTEkykJR+N13R87vZ6Cz0dA2ulxN+QYio1PyOQzz8rk089K9MJ447XU7KzSJ4u9L/4Wicblp7f/XPr6B+Syy68Ql8vdMVpKjTMzwXm9ifW4645flGiC5dBRWqpIN2kJesT8UEvpg3A8JU8en5i/P0TA57GjYypZtNO1VGomKCe+/h15ZmJMIqmUrAl0S9+FSek6N2YqLeV12guCfhoel70uHRGLxVW7GFivxxPJuizYm6mlIrGEXJgMyehkSM2ftUtLRWMIvv1c+gcG5fLLr+woLTWL+fDJqVzQD1rqwAG58/Y7SjRBO7RUzcoLFoN6E1gI8pXDzHZupBBkD6C8HWXDS/mqY7Ct9+ZZD1UPeg38lctQBplZ/QksWAssJwuk1syqNOwSxi6oTOsVg4O548hikczcsQrh09PTozKHFgsuj46OVuwDkL9uOA9wQcB7amMhfONR6dpM3C6Hanybj8eN3gG13+RDvBSP4bC6qnVcx0XR7XapPgawrnC5GmO3kwyFJT45JbPJlMwiS3pyWpLBhYbfywHfMT4TlMjYuEQnJiV8YVzSifL7LhGJyN133yUX7bpI+fKr1841+L7sssvUcfC6171uyc9E5h1ARtZi4BjM91mHcMM2xusbNZlTC7iByz8kQsGgyrRW1Q0NAppHaxrdKPSopQo2ZMFiaqlOgVqq/WTKXacxkazDSeNWa6lwLCkTY+fFardL38DwnJ4XicRT5tJSzlItlVtW+zUWAb/iIRxJVDVrKZtNVYC5fD5x+X3iDvjVhNVyiY9PSvjUGcnE4koTR86cldiFcWkEKkErFpN4OKy0UiwUqpjIgkk87O+LLrpYhodXqG0Wm0tm7AQtpYJSecfETCgoiWRCJZ81CmopaqlOgVqq/UyF46pSPh8EAYOx5l6/jaClYmMTciESEpvFIoPenIW4xWaTxNi4qbTUjrU90ut3qvZDmqTau7FfAp7aE8DLBfmSSM7WiZaKxJMyHY4rC1okP89E4kozt1pLIbFMaamL57TUnL7qHC2VLNBSwVBQksmEpBt4rNerpeoK/GFn6olc36/S5fVYmRDjAg9qHJ8oH17KlrOeiUxMqk9NTYke0ONEbMUMZXW5LazQwQWpnd8BjYWXYzWDzKpqLyZ47tNHDkt3V7c47A45euqkjI6Pq4uCzZu7qEFUIasFvSQrCbdTp05V9FlHWfvx48flnnvukUcffVSuvvrqAitbvD+WN7vkHZV9vd0+8XtcKuDX5Xern+shU64339yjnmMz1/C3cT0iEfgrJhFqTOAvHY9LKhpbWICM+KnpknMGmUwHnzogK1eMSE9etlO2jvML22Xfvn3qOLzzzjvLNtzGcYbtWK4vHc6HdiRGFFeTXrx7j4xeON+w98d2vPfee5sS+NOblqp042E2CxKyONRS7aXiOFoUCFJVJeip3EFa6viRQxKY01LnTh2XqfEL6v66y+s0lZbCMdDt94rX7VQBP7/XrX5uZHJsvetlc9jFZrc37Hofgx6uYlk9YGKquMoqEY2W1VIHDh6UkZUr5ycmQT3nl1G1VPFEyt6Ld8vo6IW6NHc5qKWopToNaqn2Yq+gLYqXR+JpmY7UngxjZC11eHxUuj1ecdhscjI4LePRiKpQ8qwaMZWWQh+/N163Ua7ZMSy7N/TLSy9dLdfuGK7rvbI611JwxypdtuDE1iotdfDAQVlZpKWyHaSlLEWfuefii+X86GjD3n85WqrmOtdrrrlGRV4RXUUgRA9gAwe8HglFF6pBcIPksDeuxw8xBrg4oQHomTNnVEn5jh07ShrdIgsLF6NqewJq4OJ07NgxVaYMcKyhOW2rQUnzmjVrRG8g0I5J+PxJhpz/tXV+e50aC8lUKFfl5nM7ZP2KbhW4bwfLyQJZu3atylTavn37os9D9SmaNe/avVvS0zPinbOYHZualBMzU7LDnfsdY+kjjzyiGoJrGS7FmWBnz56tOMg/9thj6rUYn8td5HBR3Lp1q2zatEmaDY4Bb51NcIvFGnq15oNsd/30bS0jYLKNs3orfW9YB2WUUAQQQdjXHodT1haPB3VWNWvHNo5HiHUcT/li6sCBA2pM1RNOh00cNqvKcANen0/1QApOTYpvJNeftR4wXsFb/plnnpHnnntObYtGolct5XK7JJFn2exAlQkDfx0HtVT7sLvdYgnnJmE0bDgP5yZflI1OHH3Nc3+HhvJ53G3rXdtKLbVn90UyNBWXh1wedbmdmZqQTPCs9HqGTKel1PXdtXwtheOjuP8yjhU9OJhUpEFaqpJNJewsUd2Qr6XcHp86HvPBFuoULYXxBb24oTMB7LWQ9DM5PSUrPOUnd6uhY7WUxyMJuLrM3RM7nE41yUs6C2qp9jHU7ZFjLrtEEwv31D0+l3T7cmMx7hv/9D8elW/fe0L9fvnmAfnn37xK+hrQA1DvWmr3ZZfJzG/axPX5ryIKJ5OxqEytXyWuXdtMp6Uwn3TJpsK54Hr3D9rQNMJFzmjUoqU83lItJZ2kpZxO5UqiWXv6vD517Z+cmZEVS/Q0bLaWqlmB9Pf3qyaLCKq84Q1vED0FHXoDXhV0sHbISUgqs2rVKpVtgAa1qP7bvXv3vJfwuXPnKmaoLIUWLMTJd/jw4bbsAnhtL+V33A5wzvl9HlXijUweBGlcTsf8uTg6FZ0P+gFYM526EJQNIwsZIa1CEz64iCODBOIDP2O9VRZ93v/IUCoWY8g+QUAZAWYcZ3g/PC9/3IF9AQZmHHuqr9zggDgDAUlFItLrdIrD71N/RwNleKjjwlXc5Pb8+fPqOShhR2l8OfA5eP/FjmmcA6tXrxYjgeQNTFahp482UdUbqL0Rc7Nw+v0SnypsSu30+5oq/i1zQXJUNcNaAyLo7W97u2SQdZVnxQHbiOWAHqjom4LPwXG+ceNGdYwjk6vS2IPnwbO9nNhrJjj2+7t9EoomlN0wEg3e9+531GWJpqGd+7A6QeNxnMN9fX0NXW+9aillQYJKZExWUUt1PNRSrcditYhvoE+S4Yi62cYNJK4tmr5An7b8HjVwUIjEExLwLr9HiBG01Nohh/R3uWU6nBC3c5X0BVzUUosAHY5jRAv+Ka3egH4yjcLd1yuRs4VV+u7+BdumZvZKLNBSb3+HpFTvmrz1qLPK0qhaytPdLcloVAX/oAXe8Z53L8uCjFrKSy1FFNRSrQf3g5dvGZbjF4ISS6bF73bIuqHA/Pj/zz86JN+5Lxf0A48enZA//LeH5Yu/fW1HaKmBqy8X/+YNEj1xRhw9XeLftD6npbq6OC9VBhQU5Xrc5eZbsGndDUjOauS8WTBS2O6qXqeI5WopuLsVaClXZ2kpb3+vJHAPl0qrhKq3v/e9YrW1X0vVlXp0ww03yK9+9StdTVbNl80y4EfyjoedO3eqknZYqj3vec9TF0pkBS6nnF57bwRs2jGg4ORvh2dxNVgXyVAOlSk3D7XJZx0CCaIK+xEDKYQOso/wu1Yarv2PCk9UeyLjCQ+8FoMuLHCQ8RQOh9VyLbiG5yJQBwGHRrb5F0ub26Ue5cgP+kH03X///bJixYqK2VIayPBaKtsF64vvayTwnXsC3nlb2Jyls34SOpxdAbVemuUnJmZdPQt2m8vB4fVIMhorqPjA++OYVD7riYTs3btXjWMQ9lmXU9KJXE9EW4OyiiGkEGyGyIKQAxBdlYDgg1XJclAV+2hwXmuzYits0RozkYmEDgTccd6/7GUvk1tvvVVpnmagZy1V1j+ddCTUUm3Y5kic6gqU/Vtx9RZIl1mmdy1ls9nlwsSknDt7VsbHxiQYCovPW52WgmMEHuWglipEBfq8bjURg0o6vVX7eYYG1QRRfHxClfq5+/vEu6IxlVt2p1PSqVRBYpTD5VLnV6mWcokrm1V9fLA+cA6opz+1kbWUGnfmkmSXC7UUtRQpOr84L9VyHHarbFlZPsH8roOjBcEJ9P+755kLYjQtZUml5cj/u01OPfOsTFsysvMVL5WulSNVaSn34IB6lINaqhDlDOByzfc81Fu1nx9uW9mshOO5uVWf2ykBr6stWgrLcF+SlWyu1Y7VoFpqdlays1kVvKsFbBf0cdSblqprdvD5z3++fOxjH6vrAwlpx8X0kksuUcE/BOsQIT99+vSyK6BQYbVUL8FGg4tNsxvhttJr3dYmayqIGQTscDxASC1m2YrMKTw3FoupANqBAwdV5gkySXAsTU5OCZIurr32WmU3AxEAwaYJuErg/fA+xWiBagT8yv29HIsFgrUsEaPSLivYqgRgT7d6LAZ69SUjETXhhoCew+ddUihCMKDiIxWLK9EBCyb7XMAYnvqafbHml4/nO8pYcTQCCLlqQCX1Ug2+FztGY2Pjqtk4RKvd65HAmlViraFReaOYmJiYP79hJ/HLX/5SPv7xjzfls6iliJGgltIH5a4f7Zp7WI6WeuzxJ8Xpdqvq567eXhm9MC7DQ/3UUk2iERMvzTqefSuG1GMxYqNjEjl9VgUHPSuGxbtyxdJaCjrN55ufsFITUHO6oqyWsljE2STd0SotFT17PldBCS3l90n35o3KKrjVUEsRUh5qKX3Q43Oo1smzecG/QIWEIt1qqclJ+cXf/oNIMCK9Hq+4LBY5+O/fkF2/9g5qqSah1+ILVZTic6vHYiRTaeXOBpwOu3o0Q0s55lrTGFVLTT3xlEw+fkBZ4bqHBmTFC69X81NG1lJ1B/4OHjyooo+oSCFE73R3d6uKv/vuu08FARtl09nqwR9lzfguRmSoF3Z8yYLWHSv6GpNZWisokYYXNTImqrnYQYjhgQBaKBQu8STHxQ1CHpad+L+a3o/wRi/uM4nJMGSyoAlytUE/HA94nWZlW4ye+l50Ggj6xSan5n/PJHPNw11VWHEimOf0FVq/QlxpJf56YzlNlJPTMxK7MD7/ezoak9DJM9K9aXmV2fWAHrE4z/F9oHFgFw3N0wyopYjRoJZqPx6nQ0JFFX6N6AXXSi2FqsULk9OyceOmElsifBdqKZJP7PwFmTrwzPzvyemgckTwr106gVNNQOX1hDGzlopPTErkzLn539PhiMwcOSp9O3N9m1oJtRQhlaGWaj+/9bLtcueBUbFkFyr+/uC1uwylpSKnzkh/2iJrV6/Lf4IMJdKqGpBaihQH/SJ5DmzpDOalYMW5dMC7k7RU6MgxmXz0yfnf42MTcu6Ou2TNK28SI2upuqIWqHJBtPWOO+6o60OJ+UAfkujomIROn5Xo2HhBKbCe0KzyUPJ+/Pjxss/B3zPJlKQTyfnvgWXBYFBVcsFaER7aAN7CrQSl1Fo2hdGALdPm1b3S3+WR3oBbNqzoVj+36ziAPSYq9GoBZevIcilGs4pYs2aNqiatx7IV2VtPPvmkEn217GMkXyCIWAkc58u1tiX1kQyHq1pWDaFQSFkL61FcAVgew0ak1nMKoJdVMekYrE7bcx3Rzsuf//znKlGk0f39NKilSDHoUx1PJCUaS6j/lWWbkbVUOi2ZVHr+e1BLLR+73aYyerVMXZ/Hpfq4GUlLQTvbrJWtc6ilSD7hU2dKl50sXSadrqWmgyXLEPzDPXo7oJYi7QJ9uMamo3J2IiQTM1GZzS/rMqCWisRTqj0KglPaMs5LLY9LNvbLf//xC+XN162X1121Vr7wwWvkzdcVJmTrXkulUmXds2BPCKilSD7oEV7Nsk7XUlHlLpEH7EwvjMtsqr5tpRctVXcN5o033qg+/G1ve1u9b0FMAoJjweMn50+GVDhXsRFYu1pX3sdahhUaY6JcdmpqoRIn/7tEJyYlk8jZaSrvYq9bjp04oQIsCKAg8IPBDkGaVk/KIaiEi7hR8boc4h1szwRVMSj/xsWguHpvMTKZtDjK2ADl9wGBBUM1wTYcO3jgHEHQ79FHH5Xrrruu5nOmXCByYX0zuvMgb+ZNXgwVdbNZNTHqdjra/r3zxwet5wrGmNjEpOoRqNkkVAKZVBhrcHOHSlJUguoVnBewu0VyxP79+2sSghX70CxiBYxjeznZXNUAjQOt00yopUjB5E40vjBuZHLjmtfjavtYVrOWgn1vMCSz6bT6XfUtsdvk6LFj1FINAP3H7J7aek7oSUtlZzPKsqYYZ15/Wmqp9oDM4lyfu6wKzkJPtXv8KUkmtVrUspnDR8W3amRJ+6WO0VIVrPEtlsp53tRSxGwgyHdqLCipdG7ciEhKoom0rB4MtH0sq1VLIdB36PSkBKPJ+Z516/rccubUcWqpBrBrbY/81dsvFaNqKftgv3h6e3N+7wj2Yc7HZpXu7QvOU9RS7QEJN6qIJDsrVrtd9clr+/hTMG2dFSseWZFkLKbWz7rInGInaSkr5uewr/Ln+dW5ZTP0vFTdPoUvetGL5Gc/+5lus5FJ60iGwiURcFRr4KE30OAWXrmg3LGbCIXng37qOWiwe/BpNVjALlELsqC8HoGdCHp3tYhoNKoePT3lmxST2tAaJddStYn+e/Bht84FJFQ/EFwo8wIX2D+o2Fuq/2MqlZq/OMCHeteuXXVZx6ICddWqVWX/hnVAr0Gzk8EEUDgqiWRakumMROPJAiuDdlHQdw8TV3MPjJnhs+dVpl65Hp4HDhxQtsT4Gb1Ir7zyStm3b59ufeXzwQ0LMpEgsqrF3T9305KHZ6B/UfEEj3M8mjUG4/oAjQOt00yopYgG7A+LdQnGNjyMpqWS0dh80E97ztFnD5fVUsjKhLZpFdRS+tBSvd1+FcDEKI+M9S6fpyCJilqq9UCDxOIJ5W6BpINkKiXJOrPBG4lnOK9Pi80qFgSILSLR86My/th+lWyaT6dqKS+2U5Fu8o4Mi2WRJCpqKWI2wrHkfNBPI55Mq4fRtNSZ8dB80A/ge937GOelzEg9WgrP3PSON4p//Tqxud2qF9nG975VXAMLFUHUUq1nNjMr8VBY0smkcj1JxeIquNZunI6FwJVNsuKQrPofvfvikVJ3gE7VUt07t+V0U56e6r14R+VEdYNoqbor/q6//nq5cOGCmljetq313vFEP1Sy9dSj3ScufseOHZs/gTTP3PweXCVUsJvDhJXWzLQVYMC67LLLWvZ5ncDmzZtVv8ft27dX9XwE61wu55L991AdislFTG4hI6YYNGTO78mH5yMjpb+/v+bvgB5+yGgpB3y3YR1hdmBTUHy7lEilxTsLO9X2ZVc5A341ziChoCBrCGSzkgyGxNPfp46rxx57TI1HmBDHcYlAsFHB8YyssGqxu92qn198fFJdNxx+n7h6e5YUcti2Dz74oOrfuljlaz3AHmJ8fFxV4TYTaimyQPlEOj3m1y2ppfKCfkt9EWS813PtqxdqKX1oKbfLJd3+xSu1qKVaCwJ+JcsyGXHOuVO0C/+61aqnXwT2S0WrgWz6yNlz0r15I7WU1yN9u7ZJ9PwFZZXu7O4S9+DiYyu1FDGjZXrZ5Tq0+1xKS4XjpYkXiaKgpgbnpTpTS/kG+6X/1xZ34KOWai0I+BWDNlJZ9+yiwaNmg7YAGB4TyaQK+JVbb6fH0/FaytXXI6tf9RKZPnBIZpMp8a4aka5tmwyvpeo+8tAwFBNWP/3pT8v+HV88nZnV5UWWNBZHOYsVq1VN5uoNnIQQVWDjxo0lvr/lSpwxQFe64W3ljTCEYDmbSVI/mGzEIFpt5TICedXuA1iywg622LoDnwVRl2/ZCrGOYGCt3HvvvbJly4KdQz54P4zTZkP1jcrMqgkq7eau0u7LVphIbxUYH9zdXdI1sqLs35FZhX6h99xzj6qEQQPfyy+/XPV6MDIIdtc6NuJ64V+9UllEu/t6l3w9Kq7R2xLbDduv0e4D0DbQOM0+h6iliEalm4T8KijjaKnSdV7MdppaythQSxkPXDOTqYyqgtGqiitppna7+2B86Nq0XoavuaKkog2rjOpiaqkcdq9Xujaul+4tG8UzNEAtNQfmo3C84/6BmBu0FSkGOaAuZ931Dm3TUq68Ch0NB6qeqaVMCbWU8VAB+0xGJTzOa6UKmqndURGMGx63U7oDvuIcKgXm1ailcrj6emX4+qtk5MbrpXv7ZlNoqWXNJtx0003yk5/8pKxd0XQoKqFITNmvxfKsE0ltaH3A9IzN5RL/qpXzGQwWu00Cq1cpP2M9gwCOJrYQ0EEW+MnR83LyzBlVynzo8GE5cvQ5yZYRkBp43fnz51XwaHJysmnriqou0hwuvvhiVW21FFoQD1af1R5faMCK/n0PP/ywqpDGsfLUU0+pTK7iCwiOOfj8V4vqv2KzKU/4SpkhZqvGVn2j4gmJxOISjSckHImpAKDTXnpjhEo/a7u91DWwLkUB48efelK+95Mfq6yqG264QdnDmglY4J44caKpnwGrCZxPsLp9/PHHGy6wXvziF0sroJZqPobQUlaruiHTwPCF/n66Gcdq0FInTp+Wk6dOqevaM88eksNHjkhmkeprainjQy1lHDAWTgajMjYdlomZiIxOhJRVernkAz31icb9pd1XqMH3HzokP7jrLmqpZWB2LRVLpOT0+IycmwzJ6fGgTIfjLVkfM2IELQU7u5F+/7x2gpX0yoGA2HWYRLWUlopNnpML505LKpmU488dlpPHnpN+X+X5NWop40MtZbAEqnhcErGY+h92mQgA2hz2mgpJ2kFx5eFjjz8u3731VmopE2upZV0BX/7yl8sdd9xRUKmCEyAcLRRU8URKUin9+WobYYIbwVM88LOehRYs7Xq2bJLeLZulZ9PG8lWAOqSrq0sF7A4ePKis9bbt2CGrt26WR595Wnbt3SO7r7xSbE5nxf4zCOD4fD71Mzy5NbuGRoJgENbvoosuVpNoej4OjAhsxhB8WQpkWCCQVs66sxKnT59Wx87evXuVoEfQEK9HUKRciTia5VbLUpMx8XjcdBV/uI6gkjwfBAAddpt48ybMMYHe5fXoRmBhPXzDg2J15oJ/uLEbC87IlddeqzKE9LKejWTDhg1qbL3zzjub9hk4l2CngOB6fgXtcsE5iwbKr3jFK6QVUEs1D2W1m0hKFMkCsbj6Wc/XUIfdLgGfRz38Xo/qgWZILbV9u6xcv04e2b9fdl10sVx86SXKUYFayrxQSxmHSDxZ0O8KI+JEMKq0kzMvSUm5FricutEoWI++ndvng3+z2VmZloxcc+MLqKWWgZm1FCr9xmYiBQUYM5G4CgaS6lHze2fOycRTz6hH6PRZXbZ00fB7nLJxZY9sWtkrG0Z6xLNIEreetdRFu3bINXs2y4XjB+WafXvk5c+/Qrq8jrZqqdFDh+XB79wqqyMJmXr6kLJhJo2DWso4ZFKpkr54CACi+MWRl9CNIJvL59WVlnJ5vfPBP8xLnb9wQa648kpqKRNrqWWVZO3cuVOGh4flF7/4hRJboJKFAiZr6VJYPYlkSlVOauR+TorHvXhvsXaiBjObPga0asHxe/z4cbXuWqZrT2+v3HDjC+f7uCHzBr0sV65cqSwZi8EybXktFVuLBWyOHDmiBg3csMTjMdmzZ69ax9z5lSobOCL10+gLMW6QnnnmGXXMoAku0OwbK1VvYn/Dgx2vrXZ9tMzAchcIswX9gGZHVQy2mcflFPdcYE0vwiofiMDAyhF1oxyJRmXbxRcrWxezguMdD3idNxPs6z179jT0PaFpRkZGWtYfk1qq2ckCC1pK/ZwU1atVr+hx/KpLS/X0yPOef8O8loL9CbWUudGTloJlUbXVsp2mpfLvLwts1Gez4nDYxT7noqDHscjmdsngpXuUvWc0FpOdvV3UUg3ArFoqlcmUdV1DH3CjBIP0QHR0TOITC60rEpPTYrFYxb+qfCsDPZBLkBXDa6mBvl555UteqAstlZyYkslnj8hFmzZLNpWW6KmzuDmX3otac453CnrSUpyXql07Yps53C6xz91r6lFLoYWUx+9X6xqJRGTrtm3UUibXUvblfrFXvvKVctttt80LLEsFK59Ky0ktDdb1m1llVHAMozIFPdfy0cSV9hxUekH0oOIO/tuL9x/LKKuF/PeoBrwO3u5jY2NK1Pn9flWdUHxRQfCvlotwO4nGUzIejKkAZpfXKb0BtyHWe7ngeEIlV6UJo3L778L4pLgD/RJNh5QdSV/X0tUelbYlPr9S7z8jk2t4nqm4HYxwbCG7CvYtl112mXQCuGE0ynilAU0DbdOqdaaWah75QT+NzCyzkxsNtVTzj2MkBGICGxXuTkftfVQ7RUsdOHpOfvVcUjJil4Eul9x0yWrp8S+uxztNS6Gyr/xy42gpJFPtf/JJaikdowctBdv/Ws4BUp7ETKkjTRIuNToO/BkRvWup8UeekISjsDVF9Pyo9OwqbWGiRw6fnZHvP3RK4smM7NsyIC/es9IQ690OLXVudFyStoDYZ9Kq1+Tqwa6yPSc7WUsV22Uacl7KYuG8VIdoqWWrnle96lVy6623zgcncjYhhfFEiC5XGa9bsghl9qv+hw7jAVGDrClkpi/F5s2blXBC7zRkY5UDZb0QSU8//XRN6/HEE0+oPnCweLjmmmuUuDI6CPo9d3ZapsMJCUaTcno8LBemy1tT6IFG2b8hQxwVmZXEFQT6xMREiR3y6Pik2OfKohHkH5+JLrlOlS4AyNypthehkcC1pfgmXk82VNWAfQMbllpvwIwKbhYfeeQRMdI14Xvf+57SNq2EWqo5VEhFa9KndS7UUs0N+qGFAqq08DP6piMIqFfaqaXOTUbkrieOS2o2d3s7EUrI9x44sWTiZKdpKb/HVdLvqsevH3v0aqCW0jd60VIOm00CnsIKf4fdKr6iZWRxyo4NFgZPO05LlZ2fNMZ1A0G/P/rKw3LngfPywLNj8o8/eFq+cXfjrVDNoKUisYQcPzsuVntuXiqRysiJ0RnlpLAYnaalMG9XHPxzuFzUUjpmdwfPSy37iv2CF7xAEomE3H333fPL0GvJ53GJy+lQ9mtdPmPdTOgBVxlfVOecjR1pHBBXsNsbHBys2oIBWVZ4PvzXL1y4UPB3t9utMlqqDdzhon7vvfeqUno0BMX756PZ7RQvM8L5hEq/YnmAwJ8e+yshEANRshjw3F+KU6dOyZkzZ2TdunUVn5O/7zCYQ1zffc89snfvpXMVbTlQJZlaYrIKwaPi9T558qQ6nsyI6pXncatgH64vuM4UJ5roHfh/Y793Crh5xc1G8U2FXoGWQQYttE0roZZqDrCtq2YZWR7UUs2jXJAvrqr/qKWKtdQPf/pLWbf1onkthU0UjqdlKpxYdBt3mpZCAtVgr196Ah7p8rllsMdnuEAItZS+0ZOW6g14ZKDbK11el/T63bKiN1C1DTDJ4R0cKNkU3qHKlWbEnFrKt3pVyWt8a1YZYl7qew+eUoGr2Wyury341r3HqaXKzEv98s575KI9ewvmpZBAlUguPn/RaVpK9crzeFSwz+50qp+1JH6jQC3VOVpq2YE/h8Mhb3jDG+Qb3/hGwUmAyVgEAN0uhyEuBnoDE1Met1NsNqt6IIBqtAluI4DjV/NQrzVQhF4CGCwhtMpRzaTMgQMHlCArFlYaWDcEfHM+9Rb1eXgYAfQKKUaH81QKCGZYWSwGbiThd56fQYUMunMnT0kiFJZkLKaWYX8uNuYhAILsKoj7Bx54QAn8q666WnxlRPlSIyeOQdhGPvnkk/Lcc8/JPffco9YBNiFmRbu+IPBXz7nbbiCKcQGvFWQvIwMvFImrClE9TvoudpzieDcC0DLQNK0eZ6mlmgP2o8vpVDeveGDccBjkGmokqKWah4GG+mVpqfOnTksyHJFkLF63lrr08ivF7fbVbOvXiVoKgQ+f2ykBr/ESqJajpaCdUsmkJBNJSafS1FIdoqVwrPfOBbor2X+Syrh6uyWwbrU4An5xBHwSWLtK3H25HmGkc7SUZ2hA+nbvEkd3l9j9PglsWi9dW4zRrz6WTKugXz7p9GzZ+SrDaqmDB+XU04ckfPqMxCan6tZSV151lfh8pfNSSzkkd6KWUvOzDoc4cK/ZQfNSmdlZCUXjMhOOSSyepJYygJZqSI3+m9/8ZvnWt76lys1J48DkFKpb8GCGenPo7u5eVuPjvr6+sgIN1WHImFnsnEilUuqCvVRWF97f7XapB44DowTS0dOvGL9Hn4kAAwMDiwosZEChITKq+TTxDOuMNX39EpmYlIOPPyEHHn5EVg6UZkTmA1F1+vRp9Vm4yMI+Aw24Pe7S7QIf9WIrpnI3CFdffbVq1AwLhWuvvVa2bzeGz36n8stf/lIdA7WA6s+pYEzZ58aTaQlFExKOLV7BoCfym9TrGYzJ3/72t5WmaQfUUs0BVfIet0s9jJI4YzSopZoHevoVA21gKi01MCiRqWk5uP9JOfDoY7JycKguLbV1VbfSTvmbZs2gT3r9i1ezUUt1hpbC8RaPxVTAL5NOqwAgHkaBWqo6qKWag6u7S7o3rJXuDevE1dPdpE/pbIygpTwrhmToystk+JorpGvThop9zvTGvs2FczSI/+/Z0LfkXIthtNRzz8lAIi1Tzx6R/XfdK4/++GcyaHfWpaW6fe75nr8aPrdDnGX0aD7UUp0yLzUrE9MRicSSEkukZCYSV4npRsHSofNSDRnpnve85ykrrx/84AeNeDuiE5LTMzL+0GNy4d4HZebZ5yQ755dvNrQ+APUAAVVuIhHCCx7ClTzXAcTVYg2ZjU5fwC1DPd75qjW/2yFrh7pEj6ASZLFMOAy88EfH87DPkcm0bmSlzKbSMjQ4KJs2bJBNGzZKNpaQ2Qo2jtPT08pqY9euXer3qampgmx0WC15XA5xOmzi9zilr8tb9cReb2+vjIyMSKtJpjISxMU+mlC9h0h1Aks7BqoFoqrYVz+eSC9r7GolOLeMsK7QMNAy119/fVs+n1rKnCSDIZncf0DGH3lCQsdOUEuVgVqqMlqFuwYmqbxut5hGS61cJdl0Zl5Lbd64Ef6mdWkpTEy94boNsm1Vt6we8MllmwfkZZet0b2WUr0b40mJJ5KSWcLindSvpRDwK+5BkElnDJOpTi1VHdRS5oTzUktDLVWZl1yySt583Qax23J64OJ1vfKR114kZtFSK/1dkg5HZLCvTzasXi0b16yRxNnzkqmQ3LKYlkIf1A0reqTb51K6aqDbI2uGunSvpTDXgG0Di3xqqeZpKSSiF89LRRMpVQVoBLIdOi/VkNRnRExvueUW+Zd/+Rd5zWte04i3JG1E2aCEwjJ6z4MYQdWyxMSUpKNR6d97sen6Tx05ckQF6uoBZeybNm0qWAZ/dVguxOPxRS+QqNAKBoNl/zYdjqt+eKj0wQV3Zb9fWb4aCXz3FX0+Ge71qvtsI/dTQBYUBBYuEseOHZOLLrpIkrD3LPPcLCarymSRQJxBjOA9kFmD98sHk3mwoTEKwUhCxmeiBcu6fC7p79JfT1d1gZ+bRIQNQzvX70UvepFs27pVYjNBtU5YH5fft6g9RCXxD3cSI4wK8PdHg3m9Aw3z/ve/v21ZYNRS5gLnbToSlfGHH5/3a8TEVToWl96d28RM6FVLIWkiEk2ofeFw2FTPcaPZvOG7w+7fPRf809v1dblaKgV7z0SpmlLJhnVoKbhNvHBPaR8ivYJjNBxNqCrF3K5NituJdhku3e3rbGZW0kmsq1VsLmfbtdSWrdtkYiaieg9BQ/f4PYveK1XSUliut21dDmqp6qCWMhecl2q/lpp5+lkZu+8hmU0kxbt2lYzc+Dyx6TQBqRL47m+/YZO89Xkblb2nw2DzaktpqfCJU5LA/i26zs0mk2Ir0knVaCkkoq8aCIhRSKXSEkskJZKAhXduGeZQYe+st+s79G0mlVbrZW2zmxu01NZNmyV0+qwKEkPb+YYGxbpIr8LioJ9G1iATUyc7dF6qYZ5H73vf++QTn/iEnDhxQtatW9eotyUtJBWLS3xqWg1G8TPnSi4c0VNnpXfXDjVAmQUIpM2bN9f9egwauOACDNq4eCIyD/GEiyh8tRfzVEapfjHhWFLOTix4dodiSTk1FpL1K4xpq6H6E4r+gZ/92bNnlXVCMRBGsC/I97y3VbggWitYyXV1dSk/dog17PsdO3aIUcEkS3HQTwsGwmYLPWP0AoJr8bxG0yrQ5q2+mrLR2G02iU3PzFf9YAyITafF29dbcZ1g9YbJwXwQSC+24dAr58+fV03i9Qyqs3/+85/Ll770pbauB7WU8ckg2zQaUxoqdgo2PIVaKnZuVLq3bqp4rTAietRSCdgi51nPoEIdvSh6u7xiRPQ2cdEoLVVpcqFSMoyZtBSyo7WgXz6w9FY2/3mVnu0mgwm10Qvz2sXmcolveLBtNm82m13GpsMqQVLTpfh9qC9QMdEQQcFMsdOdxTjnFrVU9VBLGZ9kMCjhU2dlNp2WdDDEeal2zUsdPyXnfvbLhd+PnpAzidtl7c2vFCOC64N1rurPTFrK4fOVNoW2WisGaM2kpaADEsnCoB+IxFNqDsXrXtzytNX3iNGp6fl9hflET29P23QIXMeCJ08q5w0tUByMx6V7w/qK+g5B4WiRs6ealzJIMP18h85LNWzvrFq1Sl7+8peryCQxDpgUj05MSvD8qMQmJudv6FR1TJlovhnsPrWMT2Q+ud3on1j/jTWEFEQUHpj02rp1q8qYicVi6ud6BvFgtLR3lyqfpv1PU0HCArzOse9wQSiXWZWPHb03vYUTiJ7+vkUnQeCbj7ES74djz6igGXYlMGGlJxLRwgAlxrZUon398dLJVMk4qjK/Fukz41LZ/84CcdXl118GWyVgh4ybFD3z+c9/Xl7xileUvcFqJdRSBr7pTCQlFo1KIhKd10/qXC+TGEkt1XwtlYCtXxGpdGY+UEB0oqXQe9NTqIc8vd0doaW0Y3Gh2m+B9NwkjF6Ijo0XjFuZREISM+WrQ1oBrLyKz2VUcSDgXwmb3S72/Hs+Cya7qaUaCbUUWQ64F5o5ckzG9x+Q4LGTKugH0FqD81Lt0VKhI0cLL1DZrERRHRQ3Tq/5TtBS7sF+1X9xHotFerdvWTTJ0CxaKpudVcNDuUI0JP3piRh6eOatKAKByfBCgnqrScVi80E/DYy3KdzLVgBJaX6Pq2BeCkmVnJfSt5ZqaFj2wx/+sPzf//t/Ve8yon9wAxcZn5A0JsGLMnwc3UW92CwWcfZ0i1VH2af1gAubFuhD5lOjq1Phl40L9OrVq5d8LuwUyl1kK9bHGWOO37DgYnX55ZfL7bffrvZNdC5ghOw32GQU7ytlv9XXozKevQP94h8ZFkfe5BWOA63pcj6RSMQQDWUXw26vfOnQk40agvzlrJ0q9Q5qBQODA3J+tFDAV4PP45SBHp/0dXvVAxlsRgFJFstJsNDAvgzHEjI5E5GpUFRN4jeCUCgk//zP/6w0jB6gljIWOC6hLZBZDSu8fBw9RZX6FhFHV2BRCxUjYAwtVQH9XKJMSV1aqqdb6SilqYYGxO7uDC2l6aVyk1UWnWkpbQI+n8USllqhpUbr0FIOp0PcXo+4PG5xezxiNUiGeqO11NhMVI6dn5aTF2ZKHCXqhVqKLOu4zGRk+tARSc4E1UR0/v2bzVvUCoPzUq3TUpXGSB1do8xIPVqqZ8smGbh0j/RdtFOGrrhU3P19HaGlYD9eKaatu3mpMoUcmTL6qlUM9PfL+QsXyvxl8SRJv9elHBYGe/wy2Ovv2HmpiWBMTl2YkTPjoYYVPzRLS1kb3UwZvtLttsoi1ZFB5UmFCXAE/jxrVuUmpywWcQ30ycAVlxgmkl9t0+NGfh9M+qEkf/369VVNmj322GPKk7uYHn+p6Or2OlUpNmku8MBHM2JYZUxMTMjhw4fl6NGjaj/lHzf5oEQfGev5tlSjo6PqWIAog4+0BoQa9vsll1xi6F2JXir93Z6y4qrbp7OMsTLnOHzt20XfwIBEYAOYB8ahStaxJc+zWg01DuM8QEZhIwhG4soWLZnOqKx+9PZpRPAPmgXZsY1qnrxcqKWMhQr4Veh3oLTUutVigUU6Jqp6e6Vv9y5DncNG1VJul6Ns9bSR+w2bW0vZxe7qLC2F6zmyprXhQ/sfh6hHZ4mW5Sow22lXPDjQryqsC9YH96uOpSefVF+dDtZSaCcxOhVRNmjBaFKOnptuyIQVtRRZDuj3OpssH4S2wWWnr2deS3FeqoXzUru25xKm8sbLrm2by/aNI+3XUg6fV1y93QX7x+xaCvNPLqdTXA5riSbw6cjms+K8VBsDrwMrVkisyAnLYrOJvcjVrByavWenaqnRqYhqfRRNpFWrrpOjM4u6TrRbSzVUsWOnf/SjH5U//MM/lA9+8IMVByRiDDwrV0j/HuNPUBVbICDjBVSaqKsXlOPDRqEann76adm3b1/ZAITHZZd1w91qIEH/D7/bKYM9xuxJY0R27dolzzzzTN0iCFlZOMaQBDE1NTU/Ds7MzCixdu2115rinEKADxNTCMakZ7PitFtVA2UEBfUCtrPT7Zbk3DmvLXO42teDED76FpdTTZih8hBizx3wt61PTjOBvSd6fzVCuKCRdLms9Fg8KQ5/aRC6lgDGZz/7WfnMZz6jm/OSWsrA4OatKKHKMzwkPZs26Ob46hQthezTnoBHIrGkWkenw64qp0lroJaqDo/bIQ6HVeKJlLrOIRjocTnamqBUjKrKHOiT6IXxhWXol1xc0dxC3Ogx6LKJw25V/f0cttz5rqftpkstNZuVqXBhcx6M4JOhmKzsD9T9vtRSpKEoXWFR125NO7mHB6X7miuopVqspdxDg7L2da+U8Ycek9l4XHxrV8vAlZfVsjfJMqCWqg6Hwy69Aa9E40lJZWaVloLmt+lNS3V3SWx6ZmGZ1SpO9GdsEy6PR+z9faoXJPr7WZ1O5WLWzmCkUbTUTKQwYIqr1nQkLsNOvy61VMPPhJtvvlmVTv7Hf/xHo9+aNBib07Ew2awaTOQskGDn6fT7xDvQZypxVQyOU2Q4NYLx8XGViYMsgmpAuT6yeCrhcztU8G/jSK8M9fpMvR/0BppgY0ITmXL1oO0rHAs4JjRv5v3796tGsmbal2juC/vJFX1+6evy6irop4F+Lm6fTwX7EAR0+9sbZEPT9W3bt4u3t0f8A/3q/3ZmzTeTRx99VN1INoQKsYXlhhy++tWvqj4J0C56glrKOORPlKjxHVm2Vqu6abK7nOLym/sarmcthWAf+k70dfuULY2Z94PeoJaqHrvNJn6vW7p8HvF5XLoMXqGntX/lCnH39ap+1oGVI22dGIKW2rF9mwz1BmTlQLfhrKbapaUQXK5lebVQS5Hl4vD7ClvKZGZV+xO71yOewX7p3rDe1NdwPWsp76oRWfval8v6t9wsgwi+mjAooFeopaoH2glaCgFAJKPrKeinAWcLb3+fuAJ+cXcFxNff11bLcWip7bt2Svf6tdK7dbP639bGBHmjaKlsJS1V3/RxS7RUw48yVLf8+Z//uXz84x9XUVWjgJ2XiickFgxJLBSSdBv7FrQKTH77BvrVyY2fbS6n+IYGxT80KO6eblNWoABNNOL4bIS/r+ahfs0110hXV5ccPHiwpAlvPvjb8PBwQz6XNIdt27bJoUOH6nptIBBQIg1+6WisDXBM4Gcz37DoGUxOIfBndzrbvg+QydOocUfv/unQAzgXGgF2GzL6i3HB9qdOcIMNrfKxj31Md/0NjKylUGEbnZ5Rj1SRfYgZwZjiQhXvnGaC7Ynb71OVvE6Pp+1jTrOgliJLQS1lLmAd5uoKiBMuBW1O9KKWqg+b1aKcZYrp8tQ/0Uct1RwtlU6lJBGPq0c7e0C1CgSTerZunu+DbPf7pGfbJtW3zLdypO1jTrOgliJLQS1lLmx2uzi9XnHgHrHN8+3UUvXby7qdpXNHfo9Dt1qqKUfa2972NmVn9uUvf1mMQjqRkFQ8Lln0asnMSjIa64jgHypNkGkQWDEsvoGBqvpMGR3VpH6uJ08jJ+RUU92eHtm5c6eK1CPYU5y5hcEVNpIbNmxo2OeSxtPf3y+Tk5OqgXI9ILCrBXdPnDihjjV4tBPi9/slHA43dEPg+ILFLDz8cbyhKXC7eeSRR+Tiiy9u7Pja5RHnXEY/hu6A1122j1e1QKMgMPnWt75V9IgRtVQqFlf6CTa2eCTCkY4I/iHoh+Cfx+NW+0yPFTuNhlqKLAW1FGkW1FL1a6l1Q93KWUbr0TPS55cuX/2BP2qpxoNAH+YMcJ1VyempVEcE/5CE3rNlo/Tv3im92zaLvUHJg3qGWoosBbUUaRbUUvVrqVUDXeKdS6SClhrq8UrAq18t1RR/MUQo/+Iv/kI+8pGPyDvf+c6GZfw3k1SiNMiXTiRVhQgxF+vWrVOT4wCBnUZE1IvLffv6+qS3t1f5q+NiPTAwoD7rnnvuMZ3do5mPk3Pnzsnq1avrfo/jx4+rIEwjAyDE+DSiJxYmBGDPACsX0N3drfpuoXE3ejUg8ACbWc1qtpUg6QHnDxIgGkUqPSvBWFK8Hpf0OmzLHkNh5/tXf/VX8r//9//WXbWfkbVUMl7YOwjATaGdfTVJc6CWItUeJ9RSpBlQS9VOMpWRqUhShnp8asKKWkqfQOOXW4ZKEWIuqKVItccJtRRpBtRS9c9LoeWR2yDzUpZsI/Z0GVBRdfnll8sb3/hG+eM//mPRO9GZ4Fwj4UJ7OFg1EXOBQ/6BBx6QSy+9VE6fPi0bN25c9nsePny4opUjgoywAEUz0R07dizqoU70w5NPPikrVqyQwcHBul5/5MgRSSQSqikzIRoI/sMWuFaBgOphHJP4H4IAgT1UDiOxoBzIDkbgeWpqSq644oqW7QCMhUhy2L59e8Pe89RYWG5/4rSkM7lr9OVbBmXvxoFlvef//J//U77zne/Igw8+qOvqLKNpqfDkVFkt5e3pbts6keZALUWqgVqKNANqqdo5ci4o37n3mCTT6J0m8oLdK+XaHctrPUEt1RzisVh5W3G3u0mfSNoFtRSpBmop0gyopTpnXqppaUNY4b/927+VV73qVfLe975X9z3NYHGZKbJltOU3GCYNFTjxZFoymVmx223iakCUvBbwWTg+UY2CSfRGVP2hTBqVXQjwFbNq1SoV9MPnMOhnHCKRiKrUrBXsZzSPRcUng36k+JjCWFHLeAdb0Mcff1xV8sFGGFV91YA+glu2bJHbb7+9xNYYNw/IHCw3Xi2np99DDz0ka9euVe/dsPdNpuX2x09LenYhmPTQ4TEZ7PbIqv76kihgiQqBddttt+k66GdELQWXBFin5+Nws9qvGeC8jiZSks7MKgtct3P51Ru1QC1FqoFaijQaaqnaiSbS8u17jkkqM6t+h6K6Y/9ZWdnnlQ3Dgbr2A7VU88C8RHG7CVb7NQfVLiGalGQ6o3SU3+2gliK6g1qKNOOY4rxU58xLNXXG64YbbpAXvehFqkGh3nF63GJ3OVXjIExmONxu2nw2SVxNBmMyFYpJMJqQyWBUgpFSa7BWgYlxVGYtF0zOBwLlb5wg3M+ePVt35RhpD9ifuCAuVomj3ZTh/zNnzsj999+vgh+o7EQFKCEaqC5++OGHF62E03p5aCAxAcfT1VdfLfv27as66JfP1q1b1bGpHaf33XefsgKtNF7VA94Xx/5VV13V0KAfmAonC8QVQGzjwkxpNnS1/Pmf/7m8+MUvluc973liBIykpVw+r9JP0FFoWO70esROm8+Gg3FibDoi4zNRmQ7H5cJ0ROmqdkEtRSpBLUUaCbVUfYzNxOeDfvla6sxE5fucpaCWah52h6Mg0Ge323VrS290LXXyQlBOjQVldCoiJ0Zn5PxkY/uw1wK1FKkEtRRpJNRSnTcv1XSj8E996lOye/du+Y3f+A3Zu3ev6BVMUjk9HvUgzQNR8kSq0Lc+Ek+J1+0Uh731glazzFsusHREVc4ll1xS8jdUFuJzGmEpSloHjounnnpKBTO0QN/Y2JgcPXpUjRfaDRiCHnguqjlhqaj3CiLSHpBg8PznP79kOY4fBAS1Sh0t8Kf9fu211y7rZh9WoJr9JgKAOI7rsRqtBHqO3H333erYR5Vho/G4Sr87NpHHWZ98eeyxx+SrX/2q7N+/X4yEkbQUgn94kOYRS6QklizUUqFYUvwelzgd1FJEP1BLkUZCLVUfPre9rJbyuevTbdRSzUUloTscTdHVZIFQNKm0Uz4Tobj0Bjyq+q/VcF6KVIJaijQSaqnOm5dq+hUNVS8f/vCH5Td/8zeVhywnxTubzOxsxeUOac1kVTPaWq5evVp+/OMfSzQaVe+/cuVKtRxWkQgIXnTRRWJW8H0nZqLzwjngcUp/t7elNhnNAFV7CJhg3Oru7paZmRm1XxHgYNYlqZVNmzapqrgrr7xSnRto4ouxAefPnj17mmYDjGB1/rgEoQfrYYxZywXviQpCfCdPk5Jmenwu2bG6R54+PS1WiwiSrPxuu8xm0nJ6LCgjfX55+umD6jsu1csQQU9okd///d83XEUutRTJpzjbcGH5rDippQwJxlP0Zp2dcxKAzkDVB7UUIQtQS9XHQJdbLtvUL488NzGvpQJuu6oU339sXHas6ZVDzzxNLUU6imSRnapGas72sxVwXqrx2xNuYkiQw0yUx+WQgC/nRGJkOC9FGgm1VOPmpdxOm3LhQZL91lXQUvqcl7Jkm3G1KQJfHL2J/uzP/kxuueWWZn8c0TGJZFomgtGS5UO9frHbWlMpBfvG48ePz/dfQ3AH1gqNqPqDLR+s/DB5g0mb8+fPq2MfFh1mBUG/mUhhT6cev1v6usxRPYvxC5aLPT097V4VogOys7MSOnlGklPTgiu+d3hIPMODVd1QjI+Pq8AbEmDwfFRutSKbF587MTGhek+OjIwo2+FG9J9EpSJ6+g0NDUkzgUx57lxQzk9FJZ5MyXCPR2xQWyLiddll9MQhFThFdTWsTSvxhS98QT75yU/KgQMH6rJNbTfUUkQD5wFsqYpZORAQR4vswKilGgt0YyZdWMUJqzezVHxQS5F8ZlXrh6jqU2oRi3T5XNLldVFLNVlLPXVySk5cCMtUKC5DPe55LdXjc0pm8ji1FOkowrGkHB+dKVm+dVVfy9wTqKUaSygSl0i8sIrT53FKwOsWM0AtRYqv64lQWNLJ3DGPFhsOj4daqgXzUmcmIzIZiku31yHWuXnAbq9TkhPHdKmlWhL4A9///vflPe95jxw6dEhVQZHOBVk4EFoa3X63+NzOln0+gnEI5GDCupGBP4DTCZPhl19+uXQKJ0dnJF3UNwJB3LXD3WImMomkJIJB9bMzEBC729XuVSJtIHj8pCQmpgqW+deuEs+gvq9rGJsQbPzZz36mgtjoGbic7EckOWAMHR4ellaBvhsQWMVMnzksV1xxubJLqDT2Ivi5bds2+cpXviKvfOUrxahQSxGN6XCsIOkGyTYBT+uuS9RSjSUej+f8Yoqtc93mmKzSyKRSkorkEgAdXo/YnK3T/0Q/jM9EVKuHfPoCHgl49a2tzaClDp2ektPjYSmeAHJHT8tV1FKkw0AS1djMQlL6yn6/GotaBbVUY7kwFZLZIlcMm9Uqg71+MROpUFgip86on70rR8TZ09XuVSJtIB4MSTpRWIDh8vtU8E/PmEFLHT4zLWcnSrWUPXxKl1qqZc2oXvWqV6mGhR/60Ida9ZFEp3T53DLU65O+Lq+q9Gtl0E+bXHHPTaSgPxXKchsFBiy8HxqmdgrlxmiDuymUkI7FZOb4CYlPTKpH8PiJ+Ykr0lkkUOlXvGyydJne0MQUmgd3dXXJ1FRh8LIWnn766Vzl4MCApOIJJThblENUFo/Xq9ZpzZo1FZ/ze7/3e0qDGDnoB6iliEaP3yMr+wMy1OOTVQNdLQ36AWqpxmIy2VSWdCIp4fOjkgiG1CN8/oKkEfAkHUe0KOgHiqs0OkVLDQwOSiKZkkQq3RotVWGw8bg91FKk4xju9cmWVb2yfrhbtq7ua2nQD1BLNRZUkJcsM5nASkxOybmf3ykzh46ox7lf3CWxC+PtXi3SBoqDfiBVZllHaKmBQeWIAz0FV4lmYzGYlmpZ4A987nOfkx/84AcqY510NnabTXmnt8res9iS0+XKTZAdO3asYdV+GldddZWcOZPLwOkEun2l2eiw+jQCuMGOXhiXmaPHJXTilKRj5SegomPjJZn40QsXWrSWpJr9iAnFVCwus0VWaY2mbDaSwe4oYCtTb09BCDPcpI4MD0tsJijJaFQSkaj6GTaozQTV4Rpeu8iI1yKrfFY1cRwKheSHP/yh+r+Y733ve+pv//RP/yRmgFqKaDjsNtW/hFrK+MDWsxij2MRXq6Xi09NSnBobK5NMQ8yvpcoFnywdqKWGV4xIMByTaDwp0VhC/VxcrdJoRnoX1nkg4JI963pk36Y+yc5mqKVIR+Jy2MXvcYrT3hp7z3w4L9VYYOtZzTK9XoNPXAjKY8+NyVMnJiSU55CWz9RTz+TuuTE3NfeY2n+g5etLKu9HWPen8/p2N40yuqlc8NvsWmpoxQqZDkclEktIOJaQmVBU9dBrJsO9C/acfrdd1gx4ZcOQX6LxhAR1OC/V0qjLypUr5bOf/az8xm/8hkzj5o+QNvDMM8+I358r90dvKpTaNhKc4B6dl1c3EvTlQMWBx2VXD1Rz6t2qRyN85pxEz51XdgmJ6RmZPvxc2ezzchMgs+kmX8hJ1eIqNj0jselpiQeDEpmYVJNWzcIzNFhmmb5tPouB1bGW/FALEFawLUBvwkS4sL8YbkCSsZg0E4/TLuuGu6XHbZMBt1XsVovYLCLB6WlZOTSsLB4wtuOY0IQWBCE0x9/93d+pbDAzQC1F9AC1VGOxz/Xzs6AHrNWqfi4XDDSylsoW2cJXWkbMr6XQz68Yo9w7NFJLYZIqH2SpxxLNrXzs8jrl0s2DsrrfK1tGAuJx2sRhs6qJqpWrVlFLEdJCqKUai9ftlG6/R/VoxAM/e1zGCPzBhvnw2RmZCMVVX/uHnr0g4TLV8Zky+ioT13+VV8f03ENSdDSq5kXikYgKADYLh8dd1TLTa6loqZaKNllLBTxO2bNxUAa73DLc41E6ymq1yMRUUHoHVuhOS7X8jvLd7363/Nd//Zey/Py3f/u3Vn+8+TIz0xlJZzIqsu9w2MXWhgo6owHvX6s1t526u7vl3LlzDfUDhoDDANRJ+L1O9TAS2UxGEhOTRQuzEh+fFP/qlQWL7R6PJJOFF227wS6qZgUTU5m5hsYamLSyu5xq8rTReEeGxWKzKctPvD+Cfq4eY/WzxPiH5Bt4qudz4sQJCQaDcvHFF89bIWM8C4fD6jU2m02uu+469bdydlSzLZjA9bkdYk3aVZ8o5LNNTk2Jz+uVgd5eiSYT8sADD6j1ht3yq1/9avnwhz8se/bskXe9611iJqilGqylMhnJIJnDYhGHnVqqGqilGg8CfUYJ9tWjpWwuZ0kiFZaRztNScAuBrkClG/LTEQj0uhxiJJqmpZqcpQ56/W7x2C2SmZ1VlZaTk5NKSw0ODkk0EqGWIjXDean6oJZqPHDCwMNIpDOzcnqiKKk2m1X9WLev7i1Y7urvk3QksuCggH7Q/YXPIe0hU6bKDwFAaPtmuBo4vV6xWKw5y0+LRZwet9jrCKIZXUuVs/bMZJpv99njd6n1iiXTav9OT02J1+uVQE+/rEzHdaWlWn53iQ3yxS9+UXbv3i3f+ta35I1vfGOrV8E0pFJpSaYWbqAxceVxuxj8W4INGzbIc889J5s3b1a/4+REFD4QCNS9L3CTNjY2JkeOHFECDpna1aDd8BnN3sYMVOqjkc2W3nB7hwZlNpmct6+yuVziW9G65rGkMrOZ8nZUCEKhmXejwbnqHR5UD6Ny2WWXKeGEB8aqjRs3ylNPPaVElDYWYYxEw3mILXivF4OJwGJrT6utNRY52joi0+vg00/LtVdfrX7fsWOH+huyv2644Qa57bbblJ3C/v37TTfGUks1jlQ6rfSUBvr0ul3UUktBLUVq1VKe3h6ZTaXnA0xWh0M8fZys6lQthWBfucq/TtJSVoulZMKqZUm8+Vrq4EG59tpr1e/UUqQeOC9lDi2F4QhVK6S1VOpJVs76uW/3TkmHw5KYyPVFc3QFpP+S3U1fR7I0lRJ3MGeCxPFGA63h9HrUo6O1lNVScq7Y26ClDj1zUK646hpdailLtiVdpEv5zne+I7fccos88cQTqgyS1AZ2WyQaL987z83s2aW4++671Q0OTroDBw7Izp07azoBJyYm5J577pEVK1ao3/Havr4+NVBV8z4qKy6VUhOM6vVWqzidTtNNTusZ7IPpw0clU2RPGFi/VlzdXWWfP6uq/rJi5b7SDcloTBJl/LP9gwNNyVI3G7FYTN0YQpzAag79SZ999lnV+3T16tUVXwfv+ngoPN/7EkE/dyAglhbcLCbjcbn/7nsknUnLpXsvUeudtVrk1LlzykJh/fr1amxFRtWXvvQlufnmm8WsUEs1oDdZGTs7ZBG6WYlkCC2VSiZVhi3ATaLT46GW0ruWmqv6szYpA5rUDrVUe7QUknbDkfh84QaCrAGfuyXnRTKVmstGz8ill16q1huTlqdOnqCWIjXBeSnja6npcFzCcz3lXA6b9Hd7m5L0QSrvgwefHZVwLFXQCnn3+n4Z6vGWfX4abTeyWbEH/NRSOgH3JKkyVqwezJFQ7zZPS6UzEozEFrSUzSpdPo9Krmo2kVhCfnHXfWpubPfcvJTDJjI5ekZXWqptgT+AwN/hw4fljjvuUJMsZPkCCxdoj8e42ZONAiXW6C+C7WR3u0uskx5++GFlx4kTE4OLlmVVC/fdd59cPVeQMiEjAABuGElEQVRpUisI+qHsNx81YWWw0uz5BrazWcF8v2ahahQwWRg6cUrSkShWXlXxeQb7271apI6+NPkWVe6ugDg6qM9mu0D2GkQOhGwrJ3ChG2KRqGxev17tf6vdJvc++KDs27dP3G63Gtdf8IIXyLZt2+QLX/iCmB1qqcYH/nAtg4NCp5PTUoncxILbpc5zPWkp3GCni+wJkYTgMuD4n8u0zyoLY6MlrVBLGR9qqfaBLHXVtsNiURnqLdVSsbiaUMNkED723nvuoZbivFTNcF5qcRBQT6Uz6jxz2G0lAbV2a6lQNKECf/m4nHYZ6vGJEe9NMa9jdTqUQ5ORSKQy8uTxcZmOJMVmtcimkW5ZO1h/5SdpX4+/fLtPJ+YmnCzMaZ2WyhVDtVJLBcNRWbVuo8qHd9qt8vjD9+tOS7W1kQSaGqK08y//8i/l4x//eDtXxXDgQIZogDd/PjY7A6iYiI6Mjc9XoiSCIfH29xX4HSPqjpNwufugXor3myYK1aSPgbJBMLhOzERV4A+gP0e3vzWZqo3A5nBIz2YM0rRcNSo41jw93SrwB7EP6zCj9UgyKpicboeQxSTVL3/5S/F071a2Cg8++KBcfvnl89YPf/7nfy6jo6Pyox/9SDoBaqn6UUFrq7XEmgU3DJ0OqrIi45MLWioUVraM6PmlFy1V3EdDW2Y0LYV1xkSBpkWgTTBRYJTvQC1lfKil2gcsqpxWe9u0lNt9UU5LPUAtxXmp+uC81OJzPuG8ShTkUvnRhytvzq7dWiqWyLkm5JNIpg2npZLBkJz/+Z25Sjg4D2zdJANXXGqY74BKy31bhpXtp0oCM8h6kwWwz1xeb+5eBPNSNlvLWqF0Ou3WUsO9fl3PS7U1pdTv98u3v/1t+exnP6s8T0ltwNIzP2PI4bCrLKJOJzETnJ+o0ohjWR75E33VFr0q+5NTp+afD4E2MzNT1zpWuowb6QKP7TAZjM0H/UA0kZJovFQ86h1sdyNte1KIypJ2uVSVH4N+tRGNRpXVE6wIAMY0ZJvCakEvwObhu9/9rhJTuDk+efKk0g9nz56VRx99VNnjaOIKWgKaAhaYPp/xMlXrgVpqecDSM79a3WG3F0zIdCrx4IKVr0YiqC8tVQkjXc9VdnAsVrD9UEEHZwijQS1lbKil6odayvhQSy0PzkuVJxZPFlg3gmgioSstVa6nH2SU0bTU6C/uzrk4zRF69jn1MBqwJzTStidlEiFwL+l0MuhXI9RSzaPtZRFo0Pj5z39e3vnOd6oy902bNjX0/RFtT0aikkVzcqdDHF7z9P5Q2ZkeF6uVqswAzwe+55OTkzIwMFB1E2VMNsND/fTp00qgYaK5u7u7rn1ndzgkWST6sMxIQGemM6WVi8lUWnwelrMTc5FOJNWk9yyuJQ67uLu7lcVkM0CmH84tG4RjkxsTo88urAiefvppOXTokBrTMM719vaKRwd2eWjujP6nWBdkSmG9MPYiABgMBlXQTwMBS2gJaIqLLrpIOolma6lMZlaC0YSy0XA6bOL3OM2lpdzUUsVky2qpWV1pKYfTqYJmxcuMBrKCi1F98Az4XQhZDNjdReMJyc5mlb7xelxN6yPVSvtcailz0HQtNTsrsXhK6XyHzSpul8NcWorzUiVgX5csy0ua1oOWCnhdEksUtqDp9rnFSMymUpIKhgoXWkRiF8aka1vt1qmE6BkkCMYR45idVS3TXH5f04KMKPJIpNLKitzZ5MRYaikTB/7AW9/6Vrn//vvl9a9/vWpM26gsfQR7ouOT8zfU6PmWSiTE7vOpzBZkxphBbJnhOzTackhNmBQsKzzUBwcHVRUJBNbKlStV01D47i7GmjVr5Pz588o/HVlWy+lnp/Xzgy0ppB9ueo1WqVTpsCuXNUZaB8a9VDiixj07rETcxhLuegTjSWwyVxEHMsmURCenxDfY3/DxF8JqMgjLt9zvPrdDunzNs3yDWHQ4HLJ79271O6rqEFDr729vr8tIJKKq+dAQGWMvxmwEAdH3AhmuaJAM3/T850NDvPvd71aaohNplpbCRNX5qfD8RAUqu9EPBP0nHA6b9KB5tgnGfWqpQqwOe6mWsutMS83189Oq4/C70ZKoKsHjsb1AQ6USSZXlhnsI3FuQ5SeQhPP60yPBKRyJS5e/8Um5KvE3r/+nbW5soJailmrbvNTsrEwHY/OBIKT/hlENls31fUOghVrKfEArF+f2FCd1tltLuRx2ZVMXjiXU8ehx2cXrNlbikepBjW1QsLEthuvzZzbiybScuBCSVGZWhnu8Mtjd/qRiowN9E8sLcmM+Gb97e7obrnHCsaQcPx+cv24NdLllpN9PLTVoPC1lyVZbT95kIM5f9KIXyfDwsHzjG99Y1sVLIxGOSDIULllu6wqozD/lweuqLmsdNyu4qFqsud56vCHX92AYHp/ATsstsFrF199XcNOOw/6hhx6Sc+fOyc6dO9VkN7L8luLo0aMqsyocDsuNN94org4XE+FoQlWBaOBUGuzxq4wQ0nowSRs+e76gesDT3yfOgF83uwPBg8xsRiwQ4zZjjKXJcET1tyrG298rtgZWZGBcOj8ZKnbXk96ARzwuR1PsFJ555hm59NJL1e/INoWIwU0kgoHtAje/x48fl6uuukppgSeffFJV+KHxfbnjBdfmN73pTTI2Nia33357W9fdjFoqGInLTKSwQj2/Vy2y/0b6A1VrKbwOk1vUUvpGJc9NTC1U/lmt4u3rLUikopZqDEhKxCMft9/fkPOX1A6uKbGitgFOn1ccOtL8OPcwnmLYxXFiBC0VTyTL9pIKeAv7XTXEPje+EGDUgDZoRpIltZT5aIaWisaSEokvBKM1YuilpmzGrTLSV72W4ryUMYDmnQlHlX2jNj4FvJ6CMY9aqjFMH3hGJh/dP5+dbnU4ZNUrXiwOf2e0ftAbqCK9/fHTkkhl1C6BpLps86BsWJFrz6EHMsmkJGeCKjbgQuDMAP34krGYJKOlLVk8XV0lxS7LnbN7+uREQVsnsHYoID3+xhcWUEs1F92UGMHKCz15rrjiCvnLv/xL9Vg2ZaxzFHM3crjIQjQhC3AxEsmUemggKws9YYxwk9WJJNMZsfj8C4E/m01SsJTJe46270ZGRlRvK9gjVNPAeOPGjeo5aNDZqWjnDS4GbqddbDaLuqBD0CI7jEG/9va3LLYMQ6UaBK8exivcqMbQ1XwOrBNsnvSwbvXR2PVG9nu5VJxkKtOUwB+CfrDExDkNq8/x8XG5/vrrW74/VAPsdEYi8Zg8+dRTKvMVFp6wsEEgEuu4WAXiX/zFX6jqQDRT7uSgX7O0VDmbouJrLiauvEsco/FkSk38zq+rwy4eaindgvHIismSPC2Vyc5SSzVSS2Uyc5O3VnG63TlbevSsRV8QBv3aRioWL+lvibYR2C960Cs4ZhJ5Y2ktiaztpcL6NXi1K+U0q3t+EdNqKc3aFMmpSJiiljKIlsJhkoUN7qyqjllK73NeylhMh2MSiqZU5R/AJHoma5GB7oVgFOel6gdjXvTUGUlMTosj4Jeh510j8dELKujXtXWjcnoj7eGZ01NqDiO3n3LLHj86LuuHq0twaDbJYEgmnzqoWoIBm8ctA3suUseOvmmNlsL9fXHQD0QTaenxm1dLzUJLzWYlEjGXltJN4A9go37/+99XGxZVWG9+85uX9X42l1Mkr8GrIpcaWVOWTn7QT+tP4LDPNjQ7kTSOXAasBV2O85aV9qpBrygE/cC6detUf6vt27cv+t6xWEwuXLigKlE6lVQqXbA9MTkFexI9XMA7nXL9LZXSwkMH+yd/0n8+KzuZUokUegaWqaggz58IhKUIrPAaSSWLH+1msdEkEgk5fPiwElarVq1S41qrz+P49IycOXxEjhw/pvpa7Lv+OpWxBsGHSbrnPe95i64TMrH//u//Xu69915lkUMar6XcDruEJLnopOpS5hHQUsXnP/rBIpHKYTCb605hFpXZJVqqNKGOWqo+UOEHex4N2JQi+Ectpc+ei3oiWXRfivEX2tzp1M8EQznQHzZWVDyuWh00OMhd6Rxq1rmlBy0Fu2PY/h0+ckR80FL79ilXGmop/Wip3PGfKgzU5k2IV5NoxXkp46H1zsufQC9X+UwtVR/j9z0sMwcP5eY5slnxrlklIy++oel9XcnSxJMZNcblg/MgncmKw97+eanpQ0fmg34gE4tL6Pgp6d6yUfQMktBQ9VcwL2WzNbzHX6VijmYVeehBS8WTKTlz9qwcOXJEvF6f7LvsMvG43abQUrqbbUEE9etf/7oSV+jtc+WVV9b9XnaXS1xdAUloHrgWi9iKKl+Wyqgtbr6bL7zsTckbJMtFKyUvWlryPBxfiMIjkIebI3j4z8zMLNoYGX0h9RS5bzUY8IqDqLnqv6UrZ0nzwZiXLir9R4BKD+JXy0SudozVExBS3v4+SYRCyvYOmWBuWEY3WIxg8gsN1tE/bX6Zzdq0Pgu4viIzHBMarRZWEHdPPPKITJ85J709PXL53kskGovJfXf8QjxDg6oH6mWXXbboezzwwAPya7/2a0pk7dq1q2XrbgQaqaXcLoey9JgOx8tOTuHQQX+QxcA1ovxy/Z//nctcGUL+kjLjBLVUfUk6+UE/bRkeRuv3bEagmzJzfSM1tBYRetVS2ay+g5XaPXeXzyPReEJdQzB55HE33vUB74f7tXTeOYZlzTq32q2l9u/fr/rJ9PX2ypVXXKHssh568EHV0xD3rNRS+tBScDnweZwSieWSoHAWJ9IL99Q4cpbWUpyXMhqaxedSy6ilaicxMZUL+oG56yKq/yInz4h//Zo63pE0kr6AS85MROZ/x1HvdduVrbEu7NLLWIKno0VFQzrEirmhri5JRKPzc7Aun7fh+gMabbjXK6NTC9vE5bBJf1fjbT71oKWeeGK/TAdD0tvXK5dfntNS993/oHjcTnGaQEvp8u7yFa94hfz1X/+1vOpVr1LNlWGvWC+qJ4PXo8o1Z7OzKiNy/m9VWLZUqsCgBY9+wYlZXFngKpMFi32PkmI04kylUsrHH9F8nKi4YSwHbqDybyQ7jUpVHTppFdrxOLsCkk4k50ULfMq9Q/rINsH5hkfxsWKURvbwTEd/q2aDwB8EMawxsG28LmfTtpGahOtqrc8+EgdgmwCBtW3DRpHhEbX89Nmzcv7CBdmza5f0rlsjdo9nyX6r0Aif/OQnlWYgzdVSOC79HqearE0k0zIexM1GVlWjDvb4lsz+s1rK/90o538n4nDYSyqLsKwYaqkGaimdV5p1Cg63S/VM1oJ/OMbRc1HPWspSYYzVG0hmCvgWv743AgS9EKxVVroI+tlsTZtIareW2rVz5/x966nTp+Xc2bNyySWXqITWpSoAqKVaq6WQyAcrT5y+qCxIhmLqXIaWgvXjklqK81KGA4lzF6YjhcsCpWMgtVTtpOHGU255pPxy0lq2rOqRqXBCTo/n9ofLaZNrdqzQxW5QusDtkkxeCxpg93rFCFjtNvF0BZr+OcO9PvE47creE+2d+gLuhrs06EVLbdu+A1FVtfz06dNy/tw52bN3r/R2BZZ0ejSCltJl4A/83u/9nhw7dkxe/vKXq1LJvr6+5d0k2SxiFasSxtX0ctPAgY2gUXGPP/Yx0y84Md0Wp6SRRZfN/V7uZMWxAFuF/Oy9xarWUPILb1+/Tm7+20Fl+xxjTDiYHdUzb7BfsukeNYGIyjSLjibWUTkUixf1pdG5NVU7cDsd6mE2jh8/LmfOnJGLL75YCTtU48eiMTn47CEVGNq3d6963lKNtScnJ5U2ePvb3y6/+7u/26K1NyaN1lK2uV6ua925IGC5rOVKk704pjHRVailWCmuV6CHnM4Fq3T8Xk4jUUvVTqUq/KXGPtIalDbx+5SOwj0jgid6qPbLT0JMJgu1VLmgfKdTacwym5ZCQiqsPg8cOKAmztCXTrHEMUst1cZ5KUsuCOitVUtxXspwYB8P91okPFfpiSS6cn0cqaVqx9nXU9bqy9Vf//lJGgfGtSu3DctF69OSTs9KwOtoWtCoHnq2bZHJJw/OJ92hx59/HStFi+nyudTD9PNSyZTEEkk5eDCnpfZdfrl6nsUkWsqS1XGpDiYbXv/618v09LT85Cc/UXaM7VwXeBJjAMMEFsAFHE0vHTabBLxGaKpOikHmXn6/Ppy4yA4t9uTFTRUadF5zzTWm3YgYCpDhrE1yVMoSxbbIr5zN2Z82b8IB6xM7NyrpSFTsPq94RoZ5rhmAdCaTC74rq4CF4DuqhDLoHSW5sZTjpvnBOYzxE+Pqpk2b5pcnEwn5xW0/lPWrVsnw4KBa5gz4xTuU+7kcyMi66aab1KTLt7/9bVNO6plZS2FcgFU6Ar32fC2VQu9kaimjQi1VpKVUwChXnVHJIQQT9ejzl9+zw4FIaxPXK3T4qCSnpsXZ2yOBLRt5/TUAsIRNzwX5bA6HqmSbt6lSPc1zWdLUUh2spZJJufuuu1Ql2ooVKxZ6hi4ynlBLmW9eaiIUV/3kYMU22O3hmGBAqKWKetemM+p/zCNox3kxwWefkwt33T8f/Ou7bLf0XbK7afsI6zMZiqv+dW6nTVVA8fqrfxKT0xI9P6p+RksR90AuOJxJJCU5E1SJ8tDGje6TR4ylpX7xq7tk/YYNygkQOO128XpcptBSuk4PxIaDr/oLXvACede73iX/+Z//2TaLzVzW4MLBMjoVmc/cAfh5pN/Pgd9AwN6zGFQAPvfccyWBP1zQK/UoMgM4ppVPdF7/PqfHMz/BkA8CfTgfNPucZp6TWK+JR5+Q2NnRhabNq0ekb+/FTTvXVPATN1MOe02fgWyhZCyutqG64fa4ddFbrx0g4JfIy0bPVYw41LGTmwjV9aXH1OC8TYYj6ni1OR3i8DR/cgB9VDFR0t/fr36Hfzsy08H1L71JrKm0ZNNpsblc4vD7Kr4PjqN3vvOdEo/H5Wtf+5quxZWe0JOWUkkAc/tNaanJiITytFQompSVA9RSRoJaagEc07hxzO/DhOqssq4TDseClrJam66lzvzgZxJ69rl5LdW1Y6usfOkLm6el5jSzZk1Z/euyEoklJJ2ZVckB6InVqe0VEBxOxhb6Nmt9IZWNpepjx2tgu4DWh2sB+j5Duzj9vrZqqauuvlq1ulDJmxhPFtFH1FLmm5d67ty0jAfj8515EQTcvrqX81IGglpqARzTwUhM6QANv8dV1iWoa+sm8a5aIcmZkLqHdDTR/hDrdfjstAr8aQx0uWXTSE/TzrV0LCazqbQ40MOthvte3OcnQiHJpNKqb7Er4O/YoFZ8fEKmDhxa+H1sQnq2bxHP8KDYXE7x6KQtTqdqqaTSUrNqX+Acbuu81LVXi9XuyM2T2WwqKdksWkrXFX8aY2Njct1116lo6v/5P/+n7SImlkjJmfFQyfKRPr+6QSXGAMcVevwhY2/Lli0q6AcOHz6sfi9XDnz27FnV2LOdWX7NAFnn+ZnnGp5AoK3nW3xsXMbuf6Rk+dA1V4irv7fxlYWTU5KKzPXHs1rFNzQgtioy8NVrg0F10dLA6z3dXRW3n6oI0IKnOr9Q1EoMAdCS/jMW8Xqa0wyYVAeOt/DYuJqo0kAPXE9PT/2VLZnMQg8d++LBcvifj4+Pq/ETjZsxIV7LZ/3O7/yO3H777XL33XeXJGcQY2qp02NltFS/X1khEWNALVXZEUHD7Xa19XwLnzglp75zW8nydW9+rXhX5fqsNjSRLJmat4i1zH3/aibIVSZ9MKoq2TTQ+6q321fRCi9XXZlr4WC2AGEsHC7p+6i0ZQe3HNAD0D2hs+ckO+dqMe9WMJCbRKoVVb2ZSqn3ResEu2txFyFqqfaiNy2F5KkDJyZKlm9b1Su9Ad53GQVqqQVgtxfNaw2i0dfV/KDAYkxHEvLMqcmS5TvX9kuXt7H3LUoP7T8g0dNn1e9Wl1MGr7hMnFUENvHayNi4SmbXQNDQPzhQMSld3c9nZsVqWzxxxIiMP/KEpIr6QcLWc+iKS9u2TiR3zIXPni+cl/L7lqWl0rG4SpLD/QDmuBYrwjjaYfNShii7GBwcVJYKsFkcGRmR/+//+//auj6wVihH2sQVYWYExxUejzzySIGIqCQo1q9fr8p+Dx48KLFYzFC2nyrrJxxRN5bI9nb5vGLPC14WTyzkL29nz5l0LFF+eTwujQ69ogpKC/pp3z0yNiGBlSuWFJnIpsoP+mmvzyRT6ga+GNg2JePxAosvp9s8N2dlR0j955iYnmQ0WiCuQCoaE5c/oJpE15Odml8ljIlep6vyBDdsqPCoBzRL/u///m+57777DCGu9IjetFQ6U35MyJ/0J/qno7TUXFBrdhbjnkVV2ORXXeVX+hW/rp2TVelQ4YSHRioUbvxnpTPzQT+ALRJPJMVTRfATlr/F5z/uuZLJtOoRXPJZqbSk8twF7A57U+1S9YAB8nVNj3JNyAv6qWWhsLh7ulVlRc3vF4up+4X8ewR3oHLlO7VUe9GblsK4WXY5tZSh6DQtFY0nJJXO2VV7XE5x5rWNqTTXCo1ls1n0d64VXQ8albClBf3AbCIp4w8/JiMvuH7pealEsiDoB3D/n04klNNPMal4QhKRBZ3o8LjF5fWKWcifq9Aong8hrQfB2JJ5qXBEZuvUUvGZkKqQnX+vWEy8/f3KxrUcnaalDJMaiYvbj3/8Y/nUpz4lX/jCF9q6LvBOL7/cEHFUUgRsmXryKl7yJyyK8Xg8cskllyjLQiMRD4XUxR7BKAyw8WCo4CazUjZEu60qnd3ls5qqyXaqFYikYrCtKgVFi55ZYWm2vNViXtBPu8nXLJzMQDkffr2Xv3cCFQP82dmqJrsxeZtEkHuuh1WxkM71HGq8kP785z8vn/70p5UGWLduXcPfv5PQlZZyVtBSTmNdX0nnaKlEIqm+F2IvuXEx97sGrKzL0e6KEPdQ+exZ92B9WbWLgR6e9QerKmmpUnANyg/6aYHAZlyD2kU53YTKetJeKk0Yolpiaeu1sMSmZyQZic67JuTfj2nP0/o6NhJqKXNqKZ/bUdNyom86QUuFowkVRMvdT+bsvdHPL7/SvxyVNFarqHROeV2N3/7JqZmSZRk4KpVpVVSt5iq3XNlW5wX9QCoWV1bjZsHdn+vnl4+rzDKik3mpJbQUjtnohXEJnz2n3NrUOIJ+2HlBP/W8dEYF/xrN5w06L2WYwB/YvXu3fP/735ff//3fl//4j/9o23rA63Wot7AP0UC3V9ycrDLFjfVSVkGPP/54gSDTO1rlWTGpRGHFWXFZP3r8tXuyytndJT27thUs67l4hzgCjbc5stjqD36qiZgym6rcBE3lCQP9T1bhwgpP7KVAFUT+eYVAoLOMLz9pPjj3w+dGJXjyjKTDsRLRjywoq82+eFYmbshSaVXJkZwLAFYMiDe4GuGrX/2q/MEf/IHcdtttSgMQ82gpp90mw0VaapBayrCYXkshsaHMuIdxMX8bFE9MwTqm3VrKPTQow8/PqwawWGT4hdc3ZeJjOd8V91flXo6xohj0Yi5HdclaxtBSDrdbbHnWQ9CUZnKHMBKZZFJCp87I9NETyh2kVEtZxbZIArByERmfVNWCsKJCf8D49Ezl/vHUUrpHL1oK80+bRroLbkPXDXWJn4E/Q2J2LYVWIOkycx5ILNVwOx2qx28+6PHXbi2FwB/OrXzWD3eJt4wjwXJBv7MSQQRL8yqCvDbMuZTZVpjvq3b+CUETs2ipwIa14lkxNP+7e3BAujetb/LakXJA88TiCYkiiO10lRynquf5Elpq5thJiY1PSGI6KNHzFyR8+mzF47jR9wRfNfC8lLHSQ9Bw8frr5bvf/a685jWvUX6sb3zjG9uyHl1el/hcDknNNZ8vvjgRY2VWaTZMyKrC75VQ1VrJpOzdu1f0iMraCYVV9RgGTrffX5WFH747Svrx+moaw7eSwMb14lkxLOloTOw+j9jLWBQ0AldXIHczn3eBcPdW16xZbetAQNmpan37XH5f2W2o1+rKakBPTPQgwNi7atUqWb16ddnn4fu7Xc75iZF2C/VOBdlPmKjSjulMIiEWiClHrhcfjjlvX29FCwRtUrt4ggtWbNkKoqyRx/E3v/lN+c3f/E259dZbVT8VYkIt5XOJ1+1Qdj8OO7WUkTGVlsL6xWLz/UsRbKlGE6nnOp1zfedy2el66TvXd+keCWzeKMmZoDh7upuSQAUcDrvSoNmiZKBqdAC2VU/AKzNhbPus6uvX5XeXdRGodN0ygt6oRUu5PB7JzgX7jPDdzKqlZo6dmM9Cz8RFTUxlbQhUW1TioG94aFH9Ayu14sS/dDwhjgp2aoslZNUKtZT5tRSSpnp8LomnMsqZqlyyBDEGptJS6bRExyeUqxJax3j7+8TmXrpZC757l8+jqgCxLew2W1kd0A5G+nzSF3BLHBbkTntFJ7hGzH9Fzp6X2UQiFxzJZqVn57aq7rOtc9s6NjWtrjuqN3BvT9mgYaX304t2bYiWgrbctlm6t2w0zJybGZmdSybPx9bfJ5nxXI9aNS81PLjo/knMBGW2aExUVutIZJw7Twrev4GFB980+LyU4QJ/4MYbb1QbHuIKJ/qrX/3qtqwHLkB6uQiV7bExmxW73So2Dm6LAm/fY8eOqf9DoZD09VXOgIaHulenntcQRrGZ4PyNJf6PzcyIt7dHbE6nylYtziQuFll6tRCyez3q0Uwgkvwjw5KKIHiXFZvLpTzOqwXbztvTvWQvH3yO3eEosFCAENPrti9ugouSdoisQGBpu9VGTFJhAhHBbASvMNGCYDYFWw19aIoynbKptPiGBpUQwk3YUvtoMYs29FIq6K+E47hBCQPf+9735L3vfa98+9vfVtd8Yl4tpefkKQS5Z7OzSkcZ4Sa4nZhJSyXQD3Vu7NR+d/t86jgorvrL7/E3r6V0kjhVjKMroB7NBME6j8ctKdiXZ7V7JVtNVX8DPf6ltRQqrOy5IOP8Mpt+ktb0pqWQvZ+M4bjOqoRAp9fLQGKVJIOhEuup2VRaulaOqPsri6pUXWIfLZJ17vL5CvsrqUrPxtwTUEt1jpbC2ImHXt1HkFyMCuZ6eop3EmbSUuHzo2qsVL/PzqrfA6tWquM039qzXMsklUil0zZKCPY1K+CngQDpiuuvkgiqmVJpFdioxZ4d1X2B4aEltRR0lN3tUoko88vg3GQAp6aatVQD7uPQz3E6FJP07Kw6Znv9nrZb0BqFfIeUeSxW8a1eqWwoq5qXqmQDms2q4DaC3Vrwzxnwi921dKJBp2gpS9bAXcL/3//7f/KOd7xD2Su87nWva/fq6ALszlA0IfHEQlChy+cWdxNK0M3E3XffLddee60abJA9sn379pLnJBIJ1cBz37594vc3J1N6OWRSaYmVKXd3+ryqkW8iHFbCGxc93PDbYSFA2oLW1wMPZQ9kz1Vg6Z1gMKiCO7ixbcXEJrZPZGKyIHsHwT9vb68htle7iU9NS2x8smS5f/VI2ebelQIfsGQoxud1q32AY1m7qWjUPkGz5He+8528trcIaqkKFrfxXE9LDZ/HpdtJCL1gBi2F6068qN8JcLhcaiIFdscI/mG4g40nMtFJO7XUbK4y05oLuBpBG7RDSyExMB9MvsOpwgjbq93EJiYlOjpWsrxr3Rpx+KqbdMf9VxR6Nh+LRfxDA+o+QGmpuQrjRiW3UUu1FmqpCknJU9OSCi9cUz39fWpugphbS6UTCQmfPV+yHG5Kru4upbERCMB39Lgcyi2AtNHKPpULzuP6A61rBG3Qai0Fi9qzE6GCojIEgId7/YbYXu0G99S4hyrG43ZWvf9S0ZgEj58srejcslEl/uX6/WVUIiC1VCGGTl9GsO8///M/1QQh/iciiVS6IOgHgpF45R4CRLFjxw65//771WCBMvGDBw/KiRMnCrYOft+1a5cuxZVikeuNsl4MBMTX36cqABn0ay9adaWaSNRB/59q6erqUpmFrapmSMXjJSX7yHozQj9EPVBuQgqZ6bVkP6Faw1WU9QeBph2z+B+VF406hr/+9a/Lu971Lvmv//ovJvS0CGqpUpCJnB/0A5FYQtkPEpNrqUVQ1osup3g9bvG43Qz66WB/2FDlgn5ABkmgaoeWSpeZaMHESMXMaVKAE2NV0aEFpw57La4gToea7J4Hk91oJzAX5FNaCoHrBgX9qKVaD7VUKehnmR/00wLpvI/rBC1V+XqM8Q7JdN0Br3T5PQz6tRnsDwT7nB6PmpuilipPJJ4qab+bSGVU6y+yNOXcfSDba3HUcXg94hsZnu8NCKv1wJpV824f6r4ALW2opUowfGoF7BSQ0XbzzTercvf3ve990smk0+UHnkwGfdtavjqGob+/XwYHB1Wp+NDQkOzcuVOOHDkiqVRKZXQDWC4gswrPqwaItSeeeEJ5r0ejUZWNcumll6r/mwEGPGTwFjfjbVSJMyEtp9Icu3EL1VsKLKj8q0YkemFMjQv43bdice/0ciALE3Z22OzQWc26IfjSl74kH/rQh1TW9Itf/OKmfAYpD7VUaaVrOZBEZbWywsvMWgrjo6rAKUqYg/URIcakvGbKVhRZJB+byylda1dL+FzOtg4WbIGVIzVrKafXo1oIqMq+BiZMFUMt1T6opQopbjOS3/vNCLbM7cIMWgrJDiWtZiwWcfh8Tfk8QppNxeknzktVBQJ8brdTEgkEUBGbsIjbVXt1qVY1jAQSJGFRS1WHKUJBN910k/zgBz9QE4Z///d/L51MpZ6DevEeVqXkmVllk6Q3l9mRkRHlFa0BQQV7BWRUXbhwQYmktWvXyiOPPFLVuj/wwAMqCwsCa+vWrapB8/j4eNPWX1kldHerQB88kjFJpRr5UlgTg1IuaI1ju1xzalI5M6p7/Vrp3bxButauUjdh9ZCr7GucnWcxuHZ/+MMfVtdyBv3aA7XU0ppJd1oqQy3VaJRDAvr5oRofvR1tNvU7ezwSowJniWK0Y5tUByarezdvlP4dW6VnwzoVDKyH+co+ainTQi21QKX7Nb2MPdBScHjAg/NSjUVV9a0YEoffp9p02N1uCYysaFj/UkJajbdM6yxUsem1t6oeQXsEtIvx+zzKPaXeeyvVKqmJrml/b8J5KUP3+CvmwQcflJe//OXy/ve/Xz75yU8apky5kWB3ouFofsNcn8epyunbDTLl44nkvLDKRfn1VU5+7tw5lVG1adMmWblypVrXdDqtqklPnTold9xxh0QiESXqkSW1GAj0PfbYYypL6+TJk7Jhw4amZVURYlbQIyAeCqsMadijuru7dHPDSJYPxtg/+ZM/kS9+8Yvywx/+UK644gpu1jZDLZU7LsPRuKTzKv/QK9mjg9640FKxeKGW8rippQghlUmnUpKMRJWWwmS8C8HsCsmixHhQS+kPaqnccRm5MCaZxELVFyo1cC/XbpA4NRmMSGbOwh2T970Br24SvADnpQjRF7FESiZDMVXE4rLbpb/bW9bCkhiTrInnpUwV+AOHDh2Sl770pfK85z1P7TCtHL6TwC5NJNOqF43dbhWnTjJrYnH0x5ktifqjX4qeOH36tHz5y19W1X0ve9nLlMVCPB6X7373u3LJJZdIT0+P/OIXv5DXv/71HXl8EUJII4BlzS233CJ33nmn/OQnP1GV0UQfUEstZIJDS9l0lNEZjZXRUnabskvRE9RShBDSfKil9Au11JyWisYkm8m1HLC79ZEEPTETKUiUBx6XQ7r9HtET1FKEENJ8UiaflzJd4E/LjkHl34oVK+Rb3/qWjpvedhaRaKxkmRX2lDU0R28V09PT8stf/lJGR0dVld7FF1+sGiu/6lWvUoG/J598UiYmJuS6664TO20HCSGkJsLhsLzhDW9QY+yPfvQjdb0m+oJaSp+EI2W0lNWiLFP0BrUUIYQ0D2op/UMtpU9GJ4IlXU5RuTPQo795Q2opQghpHuEOmJcyZeAPBINBufnmm1Vw5vvf/76sXr263avU8URj8RL/dJtq8qmPzK9ikFGPJsihUEjZfcLi85WvfKWyJkWzZfTug/0nAn+7du0Sj0dfGWKktaDBbBoNrLNzDa0ZECakYvYqkijQvP6///u/paur/ZY/pDzUUvojEi2jpWxWZfepR6ilSC3AXjeRTAkOcZfTrptKW0L0BrWUcaCW0h9jU2Fl15ePy2GX3i6v6BFqKVILqGaNJlJqXgqVrE4HtRQhnaylTBv4AwjM/NZv/ZaK2t52223KppG0D/S8Q4+/fDBRVW9Tz1aRSCRUhR/Kf3fs2KEq/sDjjz+uqkkh5vE3nEoYMLZs2dLuVSYtJpNOSzwYKljm8vvF7qQVbCvBOZhJpSWbnRWbzS5WA0wYJlJpiUQTMpvNisNmk4BP/2Picnj00UeVuIKN8j//8z/TLtkAUEvpizS0VLxQS3k9+h83qKVINRNVU8FowTLYriEASFqrpTDJDF2CBE29jy0gnkzJTBhaalacdpv0BDxq3c0KtZTxoJbSF2iLMxVauN5YLCL9XT5lna5nqKXIUiRTGbkwFS6oaB3o9qoAIGmxlspkcr2VbTb10DuTobgcHw1KKj0rAa9TNq/sVprKrDzaQfNSpg78AXy9T3/60/KJT3xCvva1r8mrX/3qdq9SR4MbSUxaoW2yDQOggW7KELiEVz8qAPHz8PCwdHd3qwEDwUB8t82bN8vAwEC7V5W0mFgwJLPpdMEyi9Uq3p5u7osayM5mVdAO2w6VtTW9NpuVeCgsmVRqfpnL5xOHTqtgtEnO6VCspHKnN+Cp+fsbgVtvvVXe8Y53yJ//+Z/LRz7yEVN+R7NCLaUvkKWeQW8aS65XMrUUMQPTwagki3ou2awW6deh9ZqewSTTbGZWJT/Vo6USiWRBJYzT6RCHjl0sMMk5PhMpsewb7PGZUmdQSxkXain93YchAROjhBtuPTbOSxHjMzYdkXgyXXJNHOkPtG2djAh0UDqTFYfdqtpT1aylYrGC+UGn2y12p776secTiiXlyWMTBcu8brvs2TBALWUCTB/40/jOd74j7373u+VjH/sYJxzJssFpMz4+LjMzM7J27Vpx6ngQJ80nOjMj2UyhXQhSB329uepQsjSpeEJS8fjctssF7WqxS8XrE5HCiR+AfYBAoh4JRxMSgw1HEbCZgUA303j5mc98Rj7+8Y/LV77yFWXDTYwJtRRpJNRSJJ+JmYhkirQU5loGezlZVS2JYEg9tI3nHegTu6v6BKhUOi3JZKkuQQ9RvQbRgpG4hGOFVdBgqNenEiPMArWUeaCWIo2EWorkc34iJKkyWmr1IBPSq2U8GJOx6VxFMIJ+awYD4nVXXw2G9j9JbV4rD4/fr9t5qROjQTk7ESnpfXrJpkHxuPSb/FUr2Q6dlzLPHlyC17/+9bJu3Tp57Wtfq/q2feELX2BPNlI3uPkdHBxUD0IQoEpnCicdjGAzqRdQpTcf9ANZkUQ4Ip7urqonmmClUHb57Kxu7Z4qfTV9Tq3VRywWk1tuuUXuvPNO+cUvfiH79u1r9yqRZUAtRRoJtRTJB/38igN/7PFXPel4fCHoB7JZiY5PSmBkuOqJJjgvlEPZfuo08FcZo61vZailzAW1FGkk1FIkH/TzKw78oX8lqQ4kEmlBP03/nBoLyeZVPVXPKWH+qVLQyaLjcaScAjSc9FuEWAfPS+lzNrRJYMc+/PDDcuzYMbn++utVI0dCCFkuTq9XrHnVaRabVVWskepQlnU1BPPKUck3Xc8WeK4yPSAxyWm1mkNhnTp1Sl1rjx8/Lg899FBHiSszQy1FCGkGfq+rINAH27WAz82NXSXpoj7qCvSYKbKiXwxLBf1Rq81VK/GW6VuEiU/YxJoBailzQi1FCGkGPeiN7FjQUg6bVfoCHm7sKoklSjUTgn+wFa+WSvNPenVOAIPdHimWTd0+Z8GxZGROdfi8lH5nRJvEihUr5I477pC9e/eqnX3PPfe0e5UIIQYHF3F3wK8q1NSjq0vXASe9UWmiyWKpfhvaXU6xFTXkRfBVr3YKAHaePQGPmujEBKfbaZduv37ttGoB19bLL79cLrnkEnXNxbWXmAdqKUJIo0FwCdfEvm6f9HV7pa/Lq9uKfT1SKQGqFh0Ea8zibY4ef3rWJXa7TQZ6fGpyCroKdlw4dvS8ztVCLWVuqKUIIY0GCcTocbuiz68ew31+Q/WvbDc2W3ntUMs2xJxUflGA1uNPz/NSsPO8aP2A9Pic4nXZZUWvV7av6aWWMgkd0+OvGHztz33uc/JHf/RH8jd/8zfy27/924Y6qKPnL0hiYlINKL41q8TuZRYHIcS443E8FJZsni2CzekQl9db8/tk0mn1PjabnXarbQD74B/+4R/kT/7kT+RTn/qU/NZv/Zahrq2ks7RUMhyWdDSmbsSc3V0lyQOEEGIUoH0iF8ZkNs9FweHziqfGftMY12FThf+RxMZEttZDLdVZGF1LRWJJiSVTKnkjUFS5TQghRiIzm5Xj56clmV6Yl+rxu2Skz1+7lspkFrSUiXoOGwVqqQU6NvCXn0n3pje9SW644QbV989nAHu+0NHjEjxybN5wFxNWQ1fvE3uNk+SEEKIXcClKJxKqvwz6I2IC3kg3vUQkHA7LBz7wAfnVr34l3/rWt+Saa67hZukQjKil4lPTEp+cWliACas1qxj8I4QYOviXDEfU/1aHQxxeD7WUwaCW6lyMqKWmwzGZDi/0acet28r+Lgb/CCGGJTM7K1OhhKQzszlHJp+TWspgUEsVot9a0xZx7bXXyqOPPirnzp2TK6+8Up599lnRM7iRCz53fO6XrHpgWfgE+xUSQowLgnwOt1ucXo/YnRRXRgPXzquuukpdS3FNZdCvszCclkKVcX7QL7dQEtMz7VolQghZNkgGdXUFxN3TLU6fOewuOwlqqc7GiFoqP+iXWyYSjCbatk6EELJcYHk+0O2RFX0+Ve1HLWUsqKVK6fjAHxgeHpaf/exn8vKXv1z1JPrGN74heiWbmc0pqoKFWZlNpdq1SoQQQjoYXDNx7XzFK16hrqW4ppLOw0haSmbLm13k2w0TQgghrYJaihhNS1XyDYNVMCGEENJqqKXK0/FWn8Xceuut8t73vlfZLHz2s58Vj0d/vfNG731Q0pGISJ7Y6tm5TXyrV7ZztQghhHQQsVhMPvzhD8s3v/lN+bd/+zd59atf3e5VIjrBCFoqeOq0zCYLk6Y8g/3i6upq2zoRQgjpLKiliJG11JnxGUnl9cIC/V1e1euPEEIIaQXUUovDwF8ZTpw4IW9+85slHo+rCc2tW7eKnkhHYzLx2H5JR6Lqd/+6NdK1dRNLkAnREal0RtLptPrZYbeLnY3OqwZVN2rboRmyzSY2u715O4rUxaFDh9REhNfrlf/6r/+SdevWcUsSQ2mpTColkXOj844Jru4ucff3UUsRoiPiiZREE7lz1Od2istJPVAts+idrLSUiM1mFZvN1sQ9ReqBWooYXUvhfvfCVFhScKUSkS6vS3oD7C1KiJ5IxeKSjsXUzw6vV+xuBuarBRXMsURaWRs7HTZxOqhD9Qa11NLQ6rMMmMC888475UUvepHs27dP/uM//kP0hN3rkaFrrpAVN1wjIy+8Xrq3beZEFSE6IpVKSyyeUDdDeETnfibVBf3isZikk0lJp1KSjMfVz0Q/4JoI+6GbbrpJXSsZ9CNG1FI2h0MCa1ZJ17o10r1hnXgG+qmlCNER0XhSJoJRiSVS6jE+E1GBQLI0s7NZicXiSo+m0mmJJ5LqZ6IfqKWIGbSUw26TlQNdsnqwW9YO9UhfF3uLEqInUtGYxCYm1f94RMcnVCCQLE1mdlYmgzGlR6FDZ8JxicU5L6UnqKWqgxV/S3Dbbbcpi4WXvOQl8k//9E/S1QYLKGQXaEEDu80mVisbtROiZ8KRmMq0Lm4S7PO627ZORiGZSKhKnGLcPh8n5dtMMBiU3/qt35Kf/OQn8uUvf1le+cpXtnuViEHQg5bCmIyJb4zMTju0FHPfCNEzo5MhSc9VkeRPMg/1+tu2TkYhgeSpMglnXo+bWqrNUEsRo89LZeYcbVBFbKGWIkTXhM+PymyRHrA5HeIbGmzbOhmFcDShAn7FDPRwXqrdUEvVBmc9lgATm/v375cLFy7I3r175f7775dWlxYHwzGJxBLqEYxEJVN0E0yMD0T06OioRNC7kRiebH4DTm1ZpQ7opHhDcYvoEFz7cA3EtRDXRAb9iJG0lMrYnImoTM1gOC4TM5Gyk+LE2FBLma9qrRhqqeqglNIn1FLE6PNS8XBYkrGYesQiEZnNUEuZDWopc5Etp6VmOZ9crw5V24/TVW2FWqp2GPirgpGREfnxj38sH/zgB+XGG2+Uv/7rv5ZMi0QOyorzK4fwYySeaMlnk9YQDoflrrvuUkE/VNJAVBNjg8rcYmzs8VcV5apwLBZWORdk2mYy6tGKCVD0B8I1D9c+XANxLcQ1kRAjaalwJFFw84ZTJxihzY2ZoJYyH+4y/fzY46864DJRDLXUAtRSxKi0U0ul4vHCe49sVrVkIOaBWsp8lOvnZ3PThaqqbWcv1VJw3+PUVKEzIZJpOS+lb2j1WSOPPvqovO1tb5P+/n7593//d9m0aZM0k5lwtCTTAFPgPV2+pn4uaS6pVEomJiaUZ//MzIxs375dBfzQsJuT6sYHFz70+NMsqux2m3hcTk66VLntUrD7nLORgbJyud1iLRNM7TQwRsAKNX8Sz+lyNe24eu655+Sd73ynTE5Oyte//nW59NJLm/I5pPNotZZChV+xWwJOm8HeQFM/lzQXailzg8THqWBU4smcHvC4HNIb8FBLVamlEsnUfEAA453b5aLF8ZyWSiQWevRAQ7maqNGppYhZtFQsHC5bKeRtg+UoaRzUUuYG52xsclrSc0F6u8cjnr4eaqlqtl02K6FoQhJzOtRqsUh3wF02yb/TwH11NBaf9zlDQNTXRDt5aqnlwcBfHUSjUfnjP/5j1ePob//2b+WWW25p2gEejsbn+/vlZ3F2+T1N+TzSfE6ePCmnT5+W8fFx2bZtmwr2MQvXnGiZL9y/taMqX7NZ1TuC2y9HojjTdq6/hsPplEaCz/jCF74gf/AHfyDve9/75G/+5m/E4+E1hxhXS82EYpJIzSUTzGG3WaWvm0lURoVaqnOglqofzUUEYyu1VI54PFFWSzmdDmkk1FLEbFoqEY0uJGbmObW4/ey7alSopToHaqnltYzA9sM8PLVUjnAkVuBMCBwOuyp2aCTUUo2Bgb9l8LOf/Uze8573qCoITJKuWLFCmtLjL7Iw2QsZ5/d51IQVaQ3Y9pp/PaqOljPYJxIJ5Ul8ww03yMMPPywXXXSRuFlqT2o4FmHBgcy8QCAgDkdjJyn09l2fffZZ9X337Nkjdnup5Vc5kN2OCZxmEwqF1DXg+c9/vvT19UkriMdiJctUpnoDx5Dz58+rSYPHH39cTSK8+MUvbth7E9IuLYWsRFQOaTcouIz3BryqGpu0bkxXlQJzAQhqKdIuqKU6W0vFYqXWhBiP3GXs0OqFWoqYdV4qEYkUBM7dPh9dWVoIdFQ6nhCL1SK2Zbq+cF6KLOtY5LxUR2upYDhasgyBUZ+X81J6hIG/ZQILNHis//SnP5V//Md/lLe85S0NzwLQvHMByopRRktaAwJ+sWBwvimuzWEXdyBQ1z5GRt6DDz4oV199tbhcLmX1+fOf/1xuvvnmqoMapPOYnp6Ww4cPq5stHHcI+OF4QUAsGAzKZZddJn4dZVqiVyUmPOq1m0kmk/LMM88oAYNqWJ/PJ4888ohcf/31ZZ+P5x06dEhtH4yV2Eb4+aqrrlpSiOGzFqtkQ4AVY3wsFlPviX532Ob4H/sAnw2B1arMr2ZW/OF9//M//1N+53d+R17ykpfI5z73Oent7V32+xKiFy0F2/QkMtWzIk4HtBQTqFqFmixEf+q58Qvb3umub8KKWorUA7UUtVQrKv6opUgnzEtpVX84b+DMQlpDJpGQmeeOy2wqpX53+P3StWFtXfuAWorUA7UUtZRGKBIr0VIOtDdqQBIVtVTjYeCvQXznO9+R3/zN35Rrr71W/vmf/7kpWVak9USnZ+ar/TQcHre4vN6a3+tXv/qVXHfddUokI7ARj8dlw4YN0t3d3cA1Nh+YrE1nMurGBZWunVBej6AesvBOnDihLnyVKt5gGTs6OqqyO9u1XRAEQwAM64f1xc84tq+55hpxOp0qEIiqVhz3uMlAlSKW5wfXVC+aREIOHDignoeelwhwapw5c0b5end1deUSIeZeAxD03LJlS8F7Hj9+XC5cuKDeE+81MDCgtg9+RxUh1hHPxwNBPQ08J1/AYF2RNYXgINYL3xHrhZ/xOpzT6KuxVDNjBNCwjssFwcpUcqEvDUC133L3PQK1uH7de++96vqFZARC2gG1lDlBtbKWQKVhd9jrSlqgllqee4WqtuwQqyJqKWqpyolfuYlzDVT7UUsRs0AtZU6mnj0imaKKZc/QoPhGhmt+L2qp+phNpyUdjSkdZfd5qaXy4LxUZ81LpdMZiSKpM299fZ7l95LmvFRzYOCvgaBnG6olkGX1D//wD/LWt761Iy4GZgWDZmRyqmS51W4Xb3dXzTeZmFRH1RIG5qeeekouv/zyBq6tOUGl63QoqhUJqCySnoDHdOcVqsjOnTunLtwQBqhy83o8snLVKvXzUuPOkSNH1PG6e/du9Xz8fOzYMXXhhEBAIgICzMvdbmfPnlXBPVzQtffCOkN0IAC4evVqFZyDgME6YZnX61XCJv9nBO40UQDRooK6drvs2LGjYvUrziE88NxqLU5RZXL06FGVAQvwus2bNxcEFRvRO2cxgYPALIKWCIQ26jNV9WcDrIdxnHz961+fr/LDdQtilJB2Qi1lLjDOxKOlNsUYN12e2uxgqKXqAwG/RN5kAq4dzgYkjRhBS2GCZBW11DzUUoVaCk00bMtMKqSWInqEWspcYJyZ2H+gZLnD75PuTRtqei9qqfpIRaIyfeiIZOeKAhwBv/Rs3WS6qteyWsrtllWrV3Neag5qqRzKESszOz9PSy2lXxj4awL//d//raonENj5p3/6J1m7dm0zPoa0AAT+irMm7C5nXU2ske2B4AeOBy0I2ArPZyMzPh1WFX/5eN1O8Xsb14ejHUAsICCESjYcEwiWrVy5UgXE+nt7VfN02NEBp88r9ioCXXif/fv3q/9xEcZxhveEJQM+a2ZmRj0PQbkbb7yxqvXEsY/jFq/HhRxBoY0bN5pusrCZjI2NKStSVPdif+hl2yGACzughx56iFV+RJdQS5mHWKRMHwi7TZyu2q/l1FL1bP/IvM2qBnSFo47tbxQt1dPbJ+FoXJNSEvC6xelY2lafWkqfUEsRUh/UUuZh4sDTkp1r/6Ph6u2RwNrVNb8XtVTtjD9xQGaLnHe8K1eIf9WImFVL9QYCEh0bF8F8nMUi3sF+cVThfEYtpU+opToXBv6aBCpMPvrRj8o3v/lN+cQnPqEqKhjkMR7pRFLi4fDCAlzwuruqbmINa0OUvaPfGfb/Aw88IFdeeaVa/sQTT6hlqEBqVRNWI4Gg09hU3rafAxM3qPrTEwimPfbYY6pEf2hoSAXH8kEgDhlTJ0+eVD+jqm14eFhV4uVXr+Fv8WCo5P3dAX/DGqejz+QVV1xRVXUfKtVwfGI99RKwMuqxjH2PCkwN2J+ixyAqgFHpODg42DJxj8q+//E//oe86U1vkk9/+tMcf4huoZYyB+gHlEwU2RR73FXbwVBLLbPiEoG/IqApXIv0uDWylsrMziq3iGJ6/F5V3dUIqKVaD7UUIfVBLWUOEtMzEjpxav53i80mPVs2ic1VnW06tVT9oMpv7NH9JcudPd3Ss6VQq5hmXgrtXM6cK0kcQ6DTVqX70lJQS7UeaqnOhIG/JvOLX/xCPvCBD0hPT4984QtfkL1794rewIAfiiZUma7NahG/1616qZGFCasMekFYUO1Xm2/xXXfdpbyc0bvsoosuUoEUBP7yJ+Hh7TwxMaGCMfl+0J0OLkqo+Cu2qfa4HBLw1WYP1jQr2EhETp06JVNTU3LVVVepYwPVXaiyg6Xl+vXrxeVyycGDB1XFF6wwK1lZgnQyJUlU+xXh9HhUpWkjrBuwLljX4u+CrC78HdYwOB7hEY5jdzHwunQiIZlUWn139L80m91Fs8ZcbG8cC9hu999/v0oCwPkPz/Wl7F3rBTcB73//+9V+/pd/+Rd5wQte0JTPIaQztVRWwkpLZcRmtYrP66KWyt8+mVmleZSWstnFYq0+mYRaqvGBP0za1FNxaQQtlUilVbVfMeg94nY6dKmlcK+hejCib5DDwWSrKqCWIsR8WgrjIXpw4v9ce4fcvRLJgf5yyVBIxGIVd2+3WGsIwFBLLe+4HH9sv2TnbA3zeywG1tVecWkELQVrU1XtV4Snv0+cgdrdz1qhpaCjVEsUi0Vsc+1kyOJQS3UGDPy1AFR0/PVf/7X87d/+rRJbf/mXf6n6bekBDJCTM1GVHauB8bG/20eR1QCeeeYZta+RQfPII4+owN8rXvEK1fg1H1T+3HPPPerCh34kJEc8mZJgeGHyxmq1SF+Xt+yxiSy2J598Uk0sIogCW0pUWuIYf/rpp5V4wOsQVEEmVLn3wGsvvvjikv2jWRZAeECAaO+FXnEjIyOqSW4lGw18/po1axYVVvOfn0pLoswEndPrFXuFySq8v2ZHC3FTTuDg+0ME4rvj+2nfHd8Ff4MIRIYXrB0grKrtBxgPhSUdX9g/mLDy9vYw+FcnqA5G9l2j+gFqQPD/xV/8hXz+85+XP/iDP5A//dM/VRWHhBgJvWupqWCsREtVul6R2qCWakDFZf612mJR1X4Wk2qpJLLUI6WBPyQ2uirYfSotNXf+Yru0Ukth32AfzWOxiNvr5YRVnVBLEWJcLZVIJEvarLjdLo6HDYBaannEJ6cl+Nyx+d+tTqf07dxaNvhqBi2VjsUlMnqhZLl3cEAcPm/lcziVs6N1OWwt1VKpZDKXYJiHy4T9rFsFtZS5YOCvhSCjAZafTz31lHzqU5+Sd77znW2fEEqm0jIdipUsD3hd4nGz+qwRIKB37bXXqp+PHDkid999t9x0003KO7v4Av7www+rUntk4XQauFDDdgC2p7jga6TTGUmmMygSEJfToYJ/eC5sS/B8iCVc0BHI2Lp163zg9PDhw8paEX/btm3bvJUiLmKrVq0qKwLwvrAcwHlZ/HeINogPPCCsmoESS+GIylbSwAQUrD7Lra/KikwkCgJ/yODPfy6yv370ox/JZZddNn9cIdCMcQjfc8+ePXUJImQHRScmS5ajJyEClaQ+MEZAyBfvE+wr2HVArFe7v7CPvvrVr8of/uEfqopjWHzu3LmTu4YYGn1qqYzMhEu1FPrRokqdLB9qqeVpKZUFrSouF7KgzaylgpGYcjKZ/1yrVbr9nvJaCjbr0WhB4E8F3vLGlWZpKe2zi7E7neKgC0jdUEsRYjwtlcnMqnG1GFT9VROoIEtDLbXMeSlUXAZDYrFZxdXbK1a7zdRaKnJ+VDJ5Vv1Wp0P8I+VbwEBznRkLqXk74LTbZNVgoMD9pGlaCnNoeQluGhg34KJA6oNayjww8NdiMCh9+9vflt///d+XtWvXyuc+97m22ixUCvxhssprgsAfLpjlLpbVgolz9KXBjbmym3A6xWZfutcaLmAI4uGijPe4+uqr5/+Gih5c3LD/MRlfDP6GizgydsyedYZKJGwfeI+jGhLC58SJE0ok5QeztMAoLt5Yjv+RzQTRtJjY0WxCjIRmnwlbNEw6ORbJckTQD9svHxzv+fZdEJpo5AsrWRyPOL6QhYYMK+8yAnTITo9NTZcsd3g84vI3x6qyk8FYhhuGo0ePqp6AyBxcytbzt3/7t9V4g6zeN7zhDYY7FwgxkpaayatO1/B7nKZIolqulkIlZCSWUJN6VvRK9jjFUU3lFrXUklBLVR4jYomUOvbQxsDjclY8fhPRaGHFHSbV7HZx5WmkZmkpBGMTsdL7MExUOXRgxWo2qKUI0a+WwvkJm0+zBv6WraUysxKMxFWABYnQSNR3Vqhiz4daammopcqDOdBEMCSzqZRYoYu6uyo6O52dCEkkVnj++j0OGekPNF9LqTnbRMlyaLn8voWkMVBLGQ/jX0ENBi70b3zjG+VlL3uZsllAQOgd73iHfPzjH29LoMdht6lJmNkiS4VKVjhG41vf+pYa7BFQys9iQxAJlTN4LJ45kphvaKtVV7ms7iUz4iCw4vG4mqBHZst9992nLnBaMAul7Shbx8UPPb3yQTAQVYF4DkrczQjK+CHgYW2KCzWsB3DDgf0B//FyYDsi46oW4W/EQIcKMFdpw1gc9CtehsAqMjpxHD7xxBNKWMEfXcsyWw5WW86+odiOxWaSsUNvYL/OzMyonxcTsGfPnpWPfexj8rWvfU0+9KEPKVvPZvUMJKRdGEVLVTMh0wlaKhyJz28b/I9eiF0+q9iW6CdNLbU41FKLjxHVJjDmuyyUW9ZMLaUm0KBVi8YOaCzSeKilCNGvlqo0v9LuSkS9aKnpcEz1kwb4HwlnvV0esS9xvaCWWhxqqcooB4Se6qyAE8lSLRVPtkhLVZjzM8vYoTeopYwHK/50UD77J3/yJ6rkGb2XPvrRj5b1cW4myBpCL4w0PKjhD+1zq0kso4NsEpTHY2BC+b3W6BUNrWGZh9J69NurNCmOyrNnDj49X46vBTlQTeWo0G+tOBMCDXZRkYYL27lz59TFB2IP+/jMmTOqcS0yXIrBuqIB8759+0wxaa96SU5OqgAfmgujtN/sFY2tAJYGxYE31bsnL3CImwnYTuCYa3TGUyaVkvhMcH4dYPEJq0/S2HEM1wlkxcEyBONXObCPP/3pT6vqvpe//OXyyU9+UjZv3sxdQToCPWgplYkdjeeq2qClvC5qKdVT5IQ8eeCgbN22TQYGFrSUz+MSt2vpwAy11ALUUs0hHim0WNcCb+48/d1MLaWq/mBRNaelaPPZeKilCDGGlsIcCOZHENhSLSxUmw/jT94vd17q2PET8sSTB2XLVmipgXkthXm7apJcqKUWoJZqDqcuzBQE+oDbaZM1Q92t0VJFVX+0+Ww81FLGhYE/nYCKsI985CPK7hBNln/t137NFJYGlUAg7F//9V9VEGzdunWq6g32jj/84Q9VjzuIHnhjQ9SgEW7xxRqC0FlF7wsE7xBgQwANz4foQeNY2N/hPSC2ICbRFFezhdSCGJFwRHbt3Kn6bU1MTqrlKHeHl/d809lAQFasWCEPPfywWqYJU3wXTLqjZB2fif2KJrZaNgq+2+rVq5VveCXwOlT+3XDDDWJkUPl4//33z+/ravYbqd/WAIHpVt4gaecFju1K1g+keiCIUdmHcSscDqubO4wllfYpnv+lL31JVfnheZ/5zGdUJS0hnQi1lP60FMaxzdt2yuTEhJrsykpWXbucdrt4PS5laYgEqZEVI/Lwww9RS1WAWqp5wOYTdp/5uHw+lXDTUi01d+4Y0a1Cb1BLEVI/1FL601KhcFg2bt4hE5MTMjU3LwUt5Xbaxed1i93uED+01MgKeYTzUhWhlmoe8URaTo/j2M39DimzerBLHaOtglqqsVBLmQcG/nQEBqrvfve78kd/9Efq5//xP/6HvO1tb6s6AFjcE00PGQGxWExN6OAiC0GDQBeEDiwHRkdHVck3AmvIcrr22mvVcyGK8Hy8FvaPEF4QRQBiCMuxDO+BgCGypPBzJcGFCjOUk0Mc4fWwUUC1GX5HABKiC59bPLGuWX0iqJGP2+ORTCYt4ZkZmZ6ZUY18B4eGZEOeEMT7Hjp0SH1n7A9UuC3Vk6sYrN+9994r1113nRgZ7N/HH39cdl28W2X3wA7N5XKq6lKyfHCcaJnqyFA3Q1ZkJ4FJcVQDIzEA+xIJBbABRkLBYlm2EGJf//rX5ROf+IQaY/7X//pf8trXvlY34z8h7YJaSn9aKjRXCamBUarL71GOE9PBkMzMTCstNTw0JJs3bph/HrVUqZbas3u32s4Y6xE05TW/MeA4hosBsHG7Gg5qKUL0p6UwNYVedHrADPNS06GY0k0auOXr7fJKKpWRC5MzEpyZkbGxC7JqxZBs3cJ5qcW01O7de+a1FHpIUks1hmQ6I+FYcr7PudMELnKdBLWUeWHgT4dAiKA/EyZ0cRGqRmglkmk1saIElsUiXf7W2nUiMwnCB8IIF1T8D+GDDCmUc0MQYf2R7a2Jod7eXvVd0bsNQgeCB9m1WgYTwMUYlWIrV65Uv2siCeB5sKTAZJGWNaX9De+N1+H1+ExMquPzUYGH9cPnIRinvW8lVBZXEnYTuYomzeIzHg6XPNfl9aoGso3yGkfVD7LKGuF73S6wT9C0d3xiUi659LKCY9jvXbpXIiFmFVUYuzBmIriH6l8E+6o5HxDww/Xhr/7qr9T49Gd/9mfy9re/nY2rCWmAlkqnM5JIJucnVFxO15J96BqJmbVULJFUE1bQqB6XU23fYCRW8lxog6X61XSqlkKWPyoF8o9hWHsz4YN0ItRShOhTS0XiSRmbiqqevjarRYb7/C2t+jGrlsL2jMaSkkrjvS3KMh1h1bHp0nmpvi5vw/pMm01LTU5OlWgpt9tFLUU6EmqpzoCBPwMILUzwQij86Z/+adkJXkykTAULLWowodLX5WtaltWPf/xjJVa0bCYIqa6uLmXRBHGD/9sFhBcEFawUAGwT+vv71YQ5xByEC0Qflm3cuLHm90dGcCJWOlnlgP3nMr43MsDOnj2rBCPsJPbs2VP2eclUWjKzWbFbreJw6C+LBkL76NGjahtD8G7evEXsztLt4nI61IOQTgBZpM8++6wanzB2wpYTN571XA8AbrxxPTCzJTQhrdRSmMCJxQutk4HX07zASidrqRTeJ1a6vREUXI42qFZLYQIIPYSgk1tp6VivllKW+Hn9ezVQ9cfrAOkUqKUI0beWSqYycnosWLAMCT9rhrua5vbTyVoqkUzJVKh0Xgp9phEYbLaWmo4kJJZIi9dll25f+7ZzLVrK7S69/0bVH7UU6RSopToPBv4MQH6FBy7CH/rQh+SWW25RggbE4sn5kup8uv3uhmX6lLuIwqYA63P11Vd3VN822CrGI5GS5U5kj9XRpPaZZ55Rog/bENlgEH6VJqFCkbjEEjkrIuBxOVRT51YLWGTSweYCFw1MnuX/DeIQwhX2CSCdyUi0zOQeA3/E7OB8OHbsmJw/f17d6G3durXmmwqca1/84hfl7/7u79SNq1bhx5sTQhqrpVKptCTnrP7ygTV1oyrQiulkLQVtEI7GS5Z73a66tGstWiqO6sP0gnaBQwb2s561FLQnKhOKwbWgHu1JiFGgliLEOFpqJhKXiZnSQNSKPr943c25VnWylkL138RM6bxUt9+j5omaqaUOn5mSMxMLn716wC+bV/aInrUUbOcraSkE/wgxK9RSnQ0DfwYCF7Jbb71VPv3pT6uS+1//9V+X3/3d35X+gSEJRUsDKz0BT812nxgQvvKVr8iOHTtURox2MYUFAXzNtYsmelKhhx18yfHoJIGFbZKMxSSTTs8vQ281WH1Wqgq45557lP0DxFOxgELT6HXr1i35uaj0g7d7MfB2b4WtK4Q+xCBEFfoV4oFgxlIBCGwvTO5pPSg10Iiaff6IWUFmJ4J+uNlYyrql0uv//u//Xv7lX/5Fdu7cKR/96EflNa95jS4rUwgxg5YaGh6WZLI08OdGT9oazztqqeq2UTSeUJNWGrBV9S9SYdkILYX9j4S5Yjzu1ti6LkdLJROJEi3ldLlom05MC7UUIcbSUl29AzI2XehEBVb2B8Ttqi2wQi1V3TaaCccknlyYl8K8EKw+m6mlZiIJeey5sZLll24elC6vS9daKpFIlmgpJH+xBQ0xK9RShIE/A4IL1b333iuf+cxn5Ec/+pG8+S1vkfe87wNy0cW7559jt1lV4K8eeypkwTz00ENKEGDCGRd/2CccP35cWRIA+HvDi7xT+4pgHyDwhwxsi9Wqsq2X2hbInoLVAzKStIbS27Ztq/ozUemHir9iunxucS+S0QVhjuAtrBoQuMW+K7blKAdsz9DoGkFeHBMQhQgGQ1TVSs5CLSmZuV6JsPKys9kvMSnoHwAuuuiiml/72GOPqeq+b3zjG/Kyl71MPvKRj6gG84SQ5mqpt7zlLfKBX/+NAjsj2EC6XfX1/aCWqm4fIPAHbQArMFT6NVtLobIT1ljFuFwOcSwyYaQXLZVK5iassJ2gPZkMQswKtRQhBpyXevNb5M3vfJ9s33nx/HNcDpusHAhQSzVxHyDwBycDJDCh0q/ZWur8ZESeOT1VsnzHmj4Z7vXqXksh0U/TUqj0o5YiZoVaigDWMxsQXKAwEYwHekZ99rOflVe+7MWyZ89eueX9vy6vu/l1dQX9cDG9/fbbVaAPF+BLL71UXVhRtQKwDH9DJlCnBvw0tAkXqcFeCdsS+wsCBRlFtVr1IZhbjsUy1CFsfvnLX6qqTOw3+JvDCgPrgqpO+N6XA0LoJz/5iezatUsuvvjiqgTZYuD7osKPELODmxlkvqIBerXgfPzOd74j//iP/yiPP/64vOtd75L9+/cra1BCSOu01ItfdKPs3btXfv3Xf0NuvvlmcXsqZ0xXglqqtn1Qq63ncrVUpYxuq8UYWspVps8fIWaDWooQ42qpN77mZXLx7j3ynve9X177uptlqK/2oB+1VG37QNl61mDtuVwt5alQvVlpud60lNutv36EhDQaaimiwYo/k4Ay93/7t3+Tz33ucxIOh5Xdwgc+8IGaLeYmJibkxIkTKvMH9p7wTNeaJW/fvl0NHhBi5cQbMmVwIcZFG9k78NQmhaLlgQceUNsS9n/1ZCiFowmJ5llUed1O8S9ipwA7Vlg4wP6geF3uu+++RUXWXXfdNW/1AEEGcYbjAq/dvXu3eDyljZEJ6QQwDj733HPqvCgeC3HjhAxGBP6WsgxB03RYeeKB13zwgx+U97znPXWNDYSQ5UMt1RlaChV/qPzTQLY3+g5XglqKkMZDLUWIOaGW6gwtdfT8jJy8EJr/fd1QQDas6K74fGopQhoPtRSpBgb+TAYyaX7605+qypGf/exn8upXv1oFAG+88caafatRio/y//Pnz6uA3urVq+cbN5e1vsxkVGAItgFjY2OqwTKWYyIclqEMFOVAsADCBwFazeIAwVWfz1d2H8HDHDareA62pdVml2efPSzpVFKGh4dKbBmwzVFxhPfH++3bt6/scYIA7qOPPiqveMUryu5T7Evsf+ByudS64jjAfr3//vtVNdLIyEhNxxQhRiUej6vMSJxX3d3dKrBXT3IDzj1UVn/hC1+Q733ve/LiF79Yfvu3f1tuuukm9hYgRCdQS5lfS9ntDjl8+Fll9zQ0NEgtRUgLoJYipHOgljK/lkpmLHLwmWfFmk3LmlUrqKUIaQHUUqRWGPgzMahI+eIXvyj/+q//qi7et9xyi7z3ve9teLAGlYGwA0VlGQQC+gBCFKDKDIFC+AqjGrCefldmB1V0k5OTapvhgaAdHtiO2J4QXwi6IZiLgBuCqxBoCKLCJuH06dPKjiwfvP5Xv/qVeg4CExBlWIb9pDUyRpUSLBYQzC3naY7nodoT64YqUHx28d/x+vXr1zd5CxHSXg4cOKDOU5yH6CXg9/vreh/0JPjyl7+sxmScixiLMSbXYglKCGk91FL6h1qKEH1DLUVIZ0MtpX+opQjRN9RSpF4Y+OsAECj6/ve/rypM7rjjDnnlK18p73vf++QlL3nJsj2yAQJEsI1EkAiZXQgy4oEgFQJPqEgrtpoki4PtiO2JxsfIwCoHsrJg0zk0NFQ2IwvBOQTsYLeB98F+QsAPDy0AiP2D6iVU96FKMz8IiEwuNHrGg7atpBPBeYLKaVTj1QPOK/QkQPLFbbfdJi984QtVBfarXvWqhoy9hJDWQS1lPKilCGk/1FKEEA1qKeNBLUVI+6GWIsuBgb8OA5V5X/rSl+Tf//3fVVDoHe94h7z73e9WPdsaXX4My09UueBzLrvsMuUhTho38MPPGQE9bGcIMs1SFb+fOXNmPriHACGCg6ggzO9Hhr9jP+F98L/Wz48QkgPn1Z133qnOK5xD1bJ//37Vc/VrX/uaqhR817veJb/2a78mGzZs4KYlxARQS5kDailCmg+1FCGkHNRS5oBaipDmQy1FlgMDfx08cPziF79Qk9Pf+c53VFUeAoBvfvOby1qBIngHezpkaaFCrJYKMLwWveRQTQabPFhQ4n/NGpQ0BuwblH+jQg82oNy2hDTmZubIkSPKPhc9UzFulbPHPXv2rHzzm9+Ur3zlK6oX4Otf/3o1pr7gBS/guUiISaGWMh/UUoQ0HmopQkglqKXMB7UUIY2HWorUCwN/RPl5f/vb31ZVgHfffbfqxffSl75Urr76almxYoWy7MQDPd1QtQfryOKebwCVLbCHRIUZHvg5v8IM74HgISrMtP5xFQ/MOUtKVCKyUpAQ0m6RNTo6qsbKp59+Wo2LYGZmRtknIyj40EMPyXXXXaeq+97whjeoMZAQ0jlQSxFCSGWopQgh1FKEEFI/1FKkHhj4I2WrVr7xjW/II488Ii9+8YtV5Qp6Ug0ODlbcWlo/OQT2MPmFB4J7mt3k/AFnsajeVuh9pb2uErCu3Lp1q6oOJIQQPQB7XfTrQ6U0+v/Bxvgtb3mLvPGNb5SVK1e2e/UIITqAWooQQipDLUUIoZYihJD6oZYi1cLAH1nUdx1BwO9+97uqmgUVgK997WvlNa95jWzevJlbjhDSEcDq89Zbb1Vj4X333SeXX365Ggvf9KY3sW8fIWRRqKUIIYRaihBSP9RShBBCLUXqg4E/UnX2+ve//3018f3zn/9c9ZC76aab1AM9rHp6erglCSGmAHbGsPBERd9Pf/pTOXXqlNx4440q2Ic+f+X6oBJCyFJQSxFCOgVqKUJIM6CWIoR0CtRSpBEw8EdqJhwOy5133qkmxPFAzyvYchJCiBlIp9OyY8eO+eSG5z3vebQcJoQ0FGopQoiZoZYihDQbailCiJmhliKNgIE/smxGR0clEolwSxJCTIHP55Ph4eF2rwYhpIOgliKEmAlqKUJIq6GWIoSYCWop0ggY+COEEEIIIYQQQgghhBBCCCHEBFjbvQKEEEIIIYQQQgghhBBCCCGEkOXDwB8hhBBCCCGEEEIIIYQQQgghJoCBP0IIIYQQQgghhBBCCCGEEEJMAAN/hBBCCCGEEEIIIYQQQgghhJgABv4IIYQQQgghhBBCCCGEEEIIMQEM/BFCCCGEEEIIIYQQQgghhBBiAhj4I4QQQgghhBBCCCGEEEIIIcQEMPBHCCGEEEIIIYQQQgghhBBCiAlg4I8QQgghhBBCCCGEEEIIIYQQE8DAHyGEEEIIIYQQQgghhBBCCCEmgIE/QgghhBBCCCGEEEIIIYQQQkwAA3+EEEIIIYQQQgghhBBCCCGEmAAG/gghhBBCCCGEEEIIIYQQQggxAQz8EUIIIYQQQgghhBBCCCGEEGIC7O1egU4nHo9LMpls92oQQggxGU6nU9xud7tXg5CmQy1FCCGkGVBLkU6BWooQQkgzoJZqLwz8tVlc+ftGJBObbudqEEIIMSErVqyQY8eOMfhHTA21FCGEkGZBLUU6AWopQgghzYJaqr0w8NdGUOmHoN+at/2L2F1esVhyyy0Wi1itCz8DK5ZJ3t/nnovlFuvCz9rftfdSr7MU/13m31+9vujvVrGI5L++ZF0KP6vk/a2V10V7vWXuA5Z6f/XelnLPlerWxVLFumg/F6136fsX7o/57zr/XZZ4vbVwec3rIlWsS7n3yj9W8t6/+O9q++V9l/znlrxefa/ccrxk/nPz9uWi6yLl16XwuC2zDyWr/s/OZvHP3M+zIvhd+xn/43n5y7Sf516D37XnyuysZLPZ+Z9zz8sW/bzwd/XZuTcu//nZ4s/Ca+Zeki1d1+LPL3n/gtfkfa+lPl97/2zhe87/rN5rfsXmX7Pw91nJysL2Krst89er6Lvk1kVbx3Kfj5+LP79ov81vg0qfn/f+8+8lZd9/YXuV//yF/VHuGMtW+C6ln1+4L7X3lArbcuE1pZ+/8P3VsvxtlL/v5/5e/F2i2Vl53/ln1XWGVX/EzFBLUUsBailqKWopailqKUKopfI1AeelOC/FeSnOS3FeivNSnQ4DfzrA6vSI1bkQ+CsfDFsIai0V+CsO5pUGwyyLB/6KX7+MwF/59y/6XksE/qp97rLXpYrAX/57lb7/0oG/St+rGeuyVOCvILi6WLCuYuCvzLpUG/irsC4NC/zlB+7qCfwVBHKKg1VVBv7y379ssGyJwF/e5y8WxKzl85cOvBX9PT/wtFTgrei7FAS2avj8soG/aj5/0cBbPYG/os8v+11q/3yxzL2/ZVbEkhf4s5T5u/b5WJYf1M7mzoeslqGhls79nJ37ee7tCOkUqKVy24FailqKWopailqKWooQainOS1XShZyX4rwU56U4L8V5qc5BKyIjhBBCCCGEEEIIIYQQQgghhBgYBv4IIYQQQgghhBBCCCGEEEIIMQEM/BFCCCGEEEIIIYQQQgghhBBiAhj4I4QQQgghhBBCCCGEEEIIIcQEMPBHCCGEEEIIIYQQQgghhBBCiAlg4I8QQgghhBBCCCGEEEIIIYQQE8DAHyGEEEIIIYQQQgghhBBCCCEmgIE/QgghhBBCCCGEEEIIIYQQQkwAA3+EEEIIIYQQQgghhBBCCCGEmAAG/gghhBBCCCGEEEIIIYQQQggxAQz8EUIIIYQQQgghhBBCCCGEEGICGPgjhBBCCCGEEEIIIYQQQgghxAQw8EcIIYQQQgghhBBCCCGEEEKICWDgjxBCCCGEEEIIIYQQQgghhBATwMAfIYQQQgghhBBCCCGEEEIIISaAgT9CCCGEEEIIIYQQQgghhBBCTAADf4QQQgghhBBCCCGEEEIIIYSYAHu7V4CIzCZjMmuxiMWS2xpZi0WycyFZy9xCtUwWlmXznmuxLvwM8t/LarGINe/n3OtFrNaFZfnPVf+LRST/9UXrgvdbeK8y728t/HvJ+6u3t1T1/uq9LeWeK9Wti6WKddF+Llrv0vfPX573Xee/yxKvtxYur3ldpIp1KfdeeZ+T//7Ff1fbL++75D+35PXqe+WW4yXzn5u3LxddFym/LoXHbZl9OHcWZGez+Gfu51kR/K79jP/xvPxl2s9zr8Hv2nNldlay2bmzS3s9fi/4eeHv6rNzb1z+87PFn4XXzL0kW7quxZ9f8v4Fr8n7Xkt9vvb+2cL3nP9Zvdf8is2/ZuHvs5KVhe1Vdlvmr1fRd8mti7aO5T4fPxd/ftF+m98GlT4/7/3n30vKvv/C9ir/+Qv7o9wxlq3wXUo/v3Bfau8pFbblwmtKP3/h+6tl+dsof9/P/b34u0S11xLSIVBL5bYDtRS1FLUUtRS1FLUUIdRSnJeqpAs5L8V5Kc5LcV6K81KdgyU7P0tJWk08HpcNGzbI+fPnufEJIYQ0lBUrVsixY8fE7XZzyxLTQi1FCCGkWVBLkU6AWooQQkizoJZqLwz86UBkJZNJMQvBYFDWrFkjp06dkq6urnavjq7htuL24vGlD8x6LjqdTgb9SEdALdW5mHX8bhbcXtxePLZqg1qKdArUUp0LtQG3F48vfWDWc5Faqr3Q6rPNoBLDjNUYGKTMNFA1E24rbi8eX/qA5yIhxoRainD8rg1uL26vZsFjixBjQi1FOH7XBrcXt1ez4LFFGslcRxBCCCGEEEIIIYQQQgghhBBCiJFh4I8QQgghhBBCCCGEEEIIIYQQE8DAH2koLpdLPvaxj6n/CbcVj632wXOR24oQYkw4fnNb8djSBzwXua0IIcaE4ze3FY8tfcBzkduKtBdLNpvNtnkdCCGEEEIIIYQQQgghhBBCCCHLhBV/hBBCCCGEEEIIIYQQQgghhJgABv4IIYQQQgghhBBCCCGEEEIIMQEM/BFCCCGEEEIIIYQQQgghhBBiAhj4Iw0hGo3Ky1/+ctm+fbtcdNFF8g//8A/zf3vuuedk3759snnzZvmN3/gNYVtJkQ9+8IMyNDSktks+3FaVue2222Tbtm2yZcsW+eIXv8gzt4jXve510tvbK294wxvmlz344IOya9cude59/OMf5zab49SpU/L85z9fdu7cKbt375Zvfetb3F6EkLZDLVUb1FK1Qy21ONRS1UMtRQjRI9RStUEtVTvUUotDLVU91FKkJWQJaQCRSCT7y1/+Uv0cDoez27dvzx4+fFj9fvPNN2e///3vq59f+9rXzv/cydx9993Zhx9+OHvZZZcVLOe2Kk8qlcpu2bIle/r06WwwGMxu3rw5OzEx0ZJ9ZRTuuOOO7Pe+973s61//+vll+/btyz7xxBNq++HnJ598sq3rqBfOnj2bfeyxx9TPo6Oj2VWrVqlxi9uLENJOqKVqg1qqNqilloZaqnqopQgheoRaqjaopWqDWmppqKWqh1qKtAJW/JGG4PV65YYbblA/+3w+VZV17tw5Vd13333/f3t3G1pl+ccB/DedlGVlZrQ1qaykMkJdPqQFpZRmNaKQBkY5El8EPUDQi8qwMIpAKyIKKcrKGFTYg2UqvfOFMYxEwkioJM1e+BTYg5Z6/lwXzb+bO27HnT109vnAcefhPufc/rzP/O7+7bquDXHrrbfmx+69995YtWrVgK/6tddeG+ecc077JrxaFdE6cq2uri7OOOOMPLp07dq1A/44Otb06dNzbVrt3LkzDh06lEe0VVdXx9y5c332/lVbWxvjx4/P19PI2xEjRsTu3bvVC+hTslRpZKnSyFKdk6W6TpYC+iNZqjSyVGlkqc7JUl0nS9EbNP7okeHKmzdvjvr6+tizZ08+qV5VVZUfGzVqVPzyyy+q3gG1Ki41sVLTr5XjqHNq1jUbN26MI0eOxK5duxxjQL8hS50cWao4uaB0atY1shTQH8lSJ0eWKk4uKJ2adY0sRU+p7rFXpiJdffXVcfDgwePuX7duXZx//vlx4MCBaGxsjCVLluSRf2mO9fZam4ADvVbtdbT24UC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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:23:26\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.4484 (1.0686), Variance: 2855.0186, Range: [-127.2646, 142.1201]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1987, sigma²=63.8202, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-------------|----------|---------|----------|--------|\n", - "| OLS_wendland | -- | 101102 | 317.965 | 55.0531 | -32.2659 | 0.37s |\n", - "| OLS_sphere | 500 | 11.1792 | 3.3435 | 1.9492 | 0.9963 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1318.79 | 36.3151 | 26.6953 | 0.5661 | 61.96s |\n", - "| DeepKriging_wendland* | -- | 1153.61 | 33.9648 | 23.9072 | 0.6204 | 31.68s |\n", - "| DeepKriging_mrts | 200 | 532.661 | 23.0795 | 17.5822 | 0.8247 | 30.25s |\n", - "| DeepKriging_sphere | 50 | 11.1602 | 3.3407 | 2.2061 | 0.9963 | 50.03s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.8518 | 3.1388 | 2.0622 | 0.9968 | 37.95s |\n", - "| UniversalKriging | 200 | 10.4095 | 3.2264 | 1.7436 | 0.9966 | 1.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:27:36\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -1.1763 (1.0857), Variance: 2946.8972, Range: [-130.7595, 140.0390]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1239, sigma²=50.4573, nugget=0.0235\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3337.41 | 57.7703 | 33.564 | -0.1665 | 0.47s |\n", - "| OLS_sphere | 500 | 22.0142 | 4.6919 | 2.0381 | 0.9923 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1078.11 | 32.8347 | 24.3861 | 0.6232 | 61.10s |\n", - "| DeepKriging_wendland* | -- | 931.104 | 30.514 | 21.191 | 0.6746 | 24.79s |\n", - "| DeepKriging_mrts | 200 | 251.119 | 15.8467 | 12.6953 | 0.9122 | 20.55s |\n", - "| DeepKriging_sphere | 50 | 24.0302 | 4.9021 | 3.081 | 0.9916 | 33.09s |\n", - "| DeepKriging_sphere_Huber | 50 | 20.7168 | 4.5516 | 2.4246 | 0.9928 | 36.97s |\n", - "| UniversalKriging | 200 | 20.4233 | 4.5192 | 2.084 | 0.9929 | 1.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:31:12\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.1042 (1.0698), Variance: 2861.3010, Range: [-129.8552, 142.9909]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1533, sigma²=45.4046, nugget=0.0007\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3487.41 | 59.0543 | 36.2751 | -0.1226 | 0.41s |\n", - "| OLS_sphere | 500 | 12.6441 | 3.5559 | 1.8492 | 0.9959 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1072.59 | 32.7504 | 22.5598 | 0.6547 | 66.63s |\n", - "| DeepKriging_wendland* | -- | 1054.08 | 32.4666 | 21.9476 | 0.6607 | 25.36s |\n", - "| DeepKriging_mrts | 200 | 569.355 | 23.8612 | 18.6841 | 0.8167 | 17.98s |\n", - "| DeepKriging_sphere | 50 | 12.386 | 3.5194 | 2.47 | 0.996 | 42.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.207 | 3.0343 | 1.8244 | 0.997 | 39.11s |\n", - "| UniversalKriging | 200 | 10.8777 | 3.2981 | 1.7094 | 0.9965 | 4.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:35:11\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.3079 (1.0760), Variance: 2894.2856, Range: [-129.0918, 143.0201]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2195, sigma²=53.3476, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 13139.7 | 114.628 | 38.7125 | -3.4616 | 0.42s |\n", - "| OLS_sphere | 500 | 10.1919 | 3.1925 | 1.8906 | 0.9965 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1025.33 | 32.0208 | 22.8709 | 0.6518 | 59.73s |\n", - "| DeepKriging_wendland* | -- | 922.279 | 30.369 | 19.9023 | 0.6868 | 30.34s |\n", - "| DeepKriging_mrts | 200 | 23.5958 | 4.8576 | 2.8127 | 0.992 | 57.07s |\n", - "| DeepKriging_sphere | 50 | 11.0329 | 3.3216 | 1.993 | 0.9963 | 47.99s |\n", - "| DeepKriging_sphere_Huber | 50 | 12.7776 | 3.5746 | 2.254 | 0.9957 | 40.21s |\n", - "| UniversalKriging | 200 | 12.1184 | 3.4811 | 1.7096 | 0.9959 | 3.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:39:53\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.6375 (1.0454), Variance: 2732.1628, Range: [-130.8851, 141.4872]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1357, sigma²=44.8683, nugget=0.5081\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 3735.43 | 61.1182 | 37.2159 | -0.3682 | 0.40s |\n", - "| OLS_sphere | 500 | 13.9524 | 3.7353 | 1.8793 | 0.9949 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1002.29 | 31.6589 | 22.6845 | 0.6329 | 63.26s |\n", - "| DeepKriging_wendland* | -- | 990.474 | 31.4718 | 21.7705 | 0.6372 | 24.01s |\n", - "| DeepKriging_mrts | 200 | 381.19 | 19.5241 | 14.4757 | 0.8604 | 20.84s |\n", - "| DeepKriging_sphere | 50 | 11.241 | 3.3528 | 2.0888 | 0.9959 | 38.97s |\n", - "| DeepKriging_sphere_Huber | 50 | 11.4098 | 3.3778 | 2.2415 | 0.9958 | 39.33s |\n", - "| UniversalKriging | 200 | 13.5225 | 3.6773 | 1.7172 | 0.995 | 2.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:43:49\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 2.0680 (1.0589), Variance: 2803.1404, Range: [-130.2135, 141.7366]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1153, sigma²=43.1260, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 5356.4 | 73.1874 | 40.0126 | -0.7202 | 0.48s |\n", - "| OLS_sphere | 500 | 19.7122 | 4.4398 | 2.0518 | 0.9937 | 0.08s |\n", - "| DeepKriging_wendland | -- | 1469.42 | 38.333 | 26.3592 | 0.5281 | 61.77s |\n", - "| DeepKriging_wendland* | -- | 1383.84 | 37.2 | 25.3526 | 0.5556 | 24.78s |\n", - "| DeepKriging_mrts | 200 | 423.035 | 20.5678 | 15.4952 | 0.8641 | 23.67s |\n", - "| DeepKriging_sphere | 50 | 15.276 | 3.9085 | 2.1063 | 0.9951 | 41.04s |\n", - "| DeepKriging_sphere_Huber | 50 | 18.5242 | 4.304 | 2.5018 | 0.9941 | 35.11s |\n", - "| UniversalKriging | 200 | 15.7824 | 3.9727 | 1.7527 | 0.9949 | 3.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:47:50\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.2976 (1.0545), Variance: 2779.6921, Range: [-128.7603, 138.8580]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1401, sigma²=40.3753, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 2300.32 | 47.9616 | 37.9281 | 0.2893 | -1.23s |\n", - "| OLS_sphere | 500 | 20.9 | 4.5717 | 2.141 | 0.9935 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1440.54 | 37.9545 | 27.925 | 0.5549 | 63.29s |\n", - "| DeepKriging_wendland* | -- | 1342.61 | 36.6416 | 25.2665 | 0.5852 | 39.14s |\n", - "| DeepKriging_mrts | 200 | 367.245 | 19.1637 | 15.2037 | 0.8865 | 22.06s |\n", - "| DeepKriging_sphere | 50 | 28.8142 | 5.3679 | 2.3241 | 0.9911 | 41.49s |\n", - "| DeepKriging_sphere_Huber | 50 | 965.543 | 31.0732 | 23.5792 | 0.7017 | 18.51s |\n", - "| UniversalKriging | 200 | 21.3065 | 4.6159 | 1.9701 | 0.9934 | 4.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:51:54\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 0.8383 (1.0792), Variance: 2911.6101, Range: [-130.0600, 143.2504]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0870, sigma²=43.5500, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1805.92 | 42.4961 | 31.928 | 0.3179 | 0.42s |\n", - "| OLS_sphere | 500 | 29.8994 | 5.468 | 2.2306 | 0.9887 | 0.06s |\n", - "| DeepKriging_wendland | -- | 1156.93 | 34.0137 | 24.1589 | 0.563 | 61.08s |\n", - "| DeepKriging_wendland* | -- | 1126.15 | 33.5581 | 22.6448 | 0.5747 | 25.69s |\n", - "| DeepKriging_mrts | 200 | 26.8774 | 5.1843 | 2.8232 | 0.9898 | 59.59s |\n", - "| DeepKriging_sphere | 50 | 19.1665 | 4.378 | 1.9049 | 0.9928 | 55.46s |\n", - "| DeepKriging_sphere_Huber | 50 | 684.36 | 26.1603 | 18.8873 | 0.7415 | 18.43s |\n", - "| UniversalKriging | 200 | 22.0416 | 4.6948 | 2.1214 | 0.9917 | 3.62s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 17:56:33\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: -0.5837 (1.0550), Variance: 2782.7305, Range: [-130.2035, 142.9471]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.1289, sigma²=44.6854, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 1965.74 | 44.3367 | 33.4779 | 0.3489 | 0.47s |\n", - "| OLS_sphere | 500 | 19.2035 | 4.3822 | 2.3444 | 0.9936 | 0.07s |\n", - "| DeepKriging_wendland | -- | 1157.88 | 34.0277 | 24.516 | 0.6165 | 61.70s |\n", - "| DeepKriging_wendland* | -- | 1218.09 | 34.9012 | 24.8853 | 0.5965 | 22.79s |\n", - "| DeepKriging_mrts | 200 | 33.9568 | 5.8272 | 3.1721 | 0.9888 | 59.18s |\n", - "| DeepKriging_sphere | 50 | 22.2188 | 4.7137 | 2.8524 | 0.9926 | 39.75s |\n", - "| DeepKriging_sphere_Huber | 50 | 20.8017 | 4.5609 | 2.5053 | 0.9931 | 43.23s |\n", - "| UniversalKriging | 200 | 15.6468 | 3.9556 | 1.8464 | 0.9948 | 4.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-10 18:01:22\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 500, 'DeepKriging_mrts': 200, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 200}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|-------------------|-------------|------------|------------|\n", - "| OLS_wendland | 13847.09±29257.80 | 86.59±79.69 | 37.97±6.18 | -3.59±9.62 |\n", - "| OLS_sphere | 16.73±6.44 | 4.01±0.79 | 1.99±0.22 | 0.99±0.00 |\n", - "| DeepKriging_wendland | 1199.18±159.30 | 34.55±2.27 | 24.69±1.72 | 0.60±0.04 |\n", - "| DeepKriging_wendland* | 1134.48±153.06 | 33.61±2.27 | 23.07±1.77 | 0.62±0.04 |\n", - "| DeepKriging_mrts | 261.83±211.44 | 14.10±7.95 | 10.51±6.53 | 0.91±0.07 |\n", - "| DeepKriging_sphere | 16.27±6.60 | 3.95±0.81 | 2.30±0.37 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 176.14±330.47 | 8.66±10.05 | 6.01±7.69 | 0.94±0.11 |\n", - "| UniversalKriging | 14.80±5.01 | 3.79±0.68 | 1.80±0.21 | 0.99±0.00 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 3.79±0.68)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_egg64.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_egg64.ipynb deleted file mode 100644 index ec896b1..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_egg64.ipynb +++ /dev/null @@ -1,1493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "64eaed54", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "import gc\n", - "\n", - "# Simulate data\n", - "num_sample = 2500\n", - "seed = 1\n", - "\n", - "rng = np.random.default_rng(seed)\n", - "\n", - "# Generate spherical coordinates uniformly\n", - "phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - "theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - "lat_rad = np.pi/2 - theta\n", - "lon_rad = phi - np.pi\n", - "\n", - "lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - "lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - "\n", - "# Convert to Cartesian coordinates for distance calculation\n", - "x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - "y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - "z_cart = np.sin(lat_rad)\n", - "\n", - "# Eggholder Mean Term\n", - "x = 64.0 * x_cart\n", - "y = 64.0 * y_cart\n", - "\n", - "term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - "term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - "mean_term = (term1 + term2).astype(np.float32)\n", - "\n", - "coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - "\n", - "# Covariance matrix with exponential covariance function\n", - "dist_matrix = np.arccos(np.clip(coords @ coords.T, -1.0, 1.0))\n", - "cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - "\n", - "# Add jitter for numerical stability\n", - "jitter = 1e-3\n", - "cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - "\n", - "# Cholesky decomposition with error handling\n", - "try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - "except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - "\n", - "# Generate Gaussian process: y = mean + L @ z\n", - "z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - "z_value = mean_term + (L @ z_std).astype(np.float32)\n", - "\n", - "print(f\"z mean: {np.mean(z_value):.4f} ({np.std(z_value) / np.sqrt(num_sample):.4f}), Variance: {np.var(z_value, ddof=0):.4f}, Range: [{np.min(z_value):.4f}, {np.max(z_value):.4f}]\")\n", - "\n", - "# Plot using Robinson projection\n", - "fig = plt.figure(figsize=(14, 8))\n", - "ax = plt.subplot(1, 1, 1, projection=ccrs.Robinson())\n", - "\n", - "ax.set_global()\n", - "ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - "ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - "ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - "ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - "vmin, vmax = np.min(z_value), np.max(z_value)\n", - "sc = ax.scatter(lon_deg, lat_deg, c=z_value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - "ax.set_title(f'Simulated Data (n={num_sample}, seed={seed})', fontsize=12, pad=5)\n", - "\n", - "# Add colorbar\n", - "cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', pad=0.05, fraction=0.046)\n", - "cbar.set_label('z value', fontsize=10)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('simulated_data_robinson.png', dpi=300, bbox_inches='tight')\n", - "plt.show()\n", - "\n", - "# Clean up\n", - "del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - "gc.collect()" - ] - }, - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def create_config(input_dim, categorical_dim, loss, epochs,\n", - " hidden_layers, activation, dropout_rate, optimizer, batch_size, patience):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=hidden_layers,\n", - " activation=activation,\n", - " dropout_rate=dropout_rate,\n", - " output_type='continuous',\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=patience,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def create_config_original(input_dim, categorical_dim, loss, epochs, batch_size):\n", - " return SimpleNamespace(\n", - " input_dim=input_dim,\n", - " num_categorical_features=categorical_dim,\n", - " hidden_layers=[100, 100, 100],\n", - " activation='elu',\n", - " dropout_rate=0.0,\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " metrics=['mse', 'mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = create_config_original(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = create_config(\n", - " input_dim=X_train.shape[1],\n", - " categorical_dim=X_train_cat.shape[1],\n", - " loss=loss,\n", - " epochs=epochs,\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=0.3,\n", - " optimizer=optimizer,\n", - " batch_size=batch_size,\n", - " patience=40\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 50" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data(num_sample=num_sample, seed=seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K100_mrts.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K100_mrts.ipynb deleted file mode 100644 index 220c245..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K100_mrts.ipynb +++ /dev/null @@ -1,5316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-04 10:22:57.789202: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770171777.803132 1743691 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770171777.807106 1743691 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770171777.818238 1743691 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770171777.818268 1743691 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770171777.818270 1743691 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770171777.818271 1743691 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "# from spherical_deepkriging.models.universal_kriging import UniversalKriging" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def print_config(name_model, config):\n", - " \"\"\"Print all configuration parameters.\"\"\"\n", - " print(f\"\\n{'='*70}\")\n", - " print(f\"📋 Model Config for {name_model}\")\n", - " print(f\"{'='*70}\")\n", - " \n", - " # Get all attributes from config\n", - " config_dict = vars(config)\n", - " \n", - " # Print each parameter\n", - " for key, value in sorted(config_dict.items()):\n", - " # Format the value for better readability\n", - " # Check basic types FIRST (important!)\n", - " if value is None:\n", - " value_str = 'None'\n", - " elif isinstance(value, bool): # Must check bool before int!\n", - " value_str = str(value)\n", - " elif isinstance(value, (int, float)):\n", - " value_str = str(value)\n", - " elif isinstance(value, str):\n", - " value_str = f\"'{value}'\"\n", - " elif isinstance(value, (list, tuple)):\n", - " value_str = str(value)\n", - " elif hasattr(value, '__name__'): # For functions/classes\n", - " value_str = value.__name__\n", - " else: # For complex objects (optimizer, loss, etc.)\n", - " class_name = value.__class__.__name__\n", - " value_str = f\"{class_name}(...)\"\n", - " \n", - " # Truncate very long strings\n", - " if len(value_str) > 60:\n", - " value_str = value_str[:57] + '...'\n", - " \n", - " print(f\" {key:20s}: {value_str}\")\n", - " \n", - " print(f\"{'='*70}\\n\")\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " elif name_model == \"DeepKriging_wendland_new\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland_new\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "HP_DR = [0, 0.2, 0.5]\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "fixed_order = 100\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "db81c91f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0\n", - "# Progress: 1/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770171806.132962 1743691 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770171808.034471 1743956 service.cc:152] XLA service 0x7540ac00ed00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770171808.034543 1743956 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770171808.277113 1743956 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770171809.490571 1743956 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7540f455d3f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7540f45937f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3984.44 | 63.1225 | 47.8825 | 0.4006 | 53.32s |\n", - "| DK_W_new | 3982.14 | 63.1042 | 46.607 | 0.401 | 52.09s |\n", - "| DK_mrts | 146.615 | 12.1085 | 7.096 | 0.9779 | 89.59s |\n", - "| DK_S | 88.7811 | 9.4224 | 5.1582 | 0.9866 | 35.56s |\n", - "| DK_S_H | 78.8645 | 8.8806 | 5.1928 | 0.9881 | 37.21s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:28:03\n", - "\n", - "✅ Completed DR=0, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3575.27 | 59.7936 | 45.8032 | 0.4349 | 62.45s |\n", - "| DK_W_new | 3942.92 | 62.7926 | 46.4877 | 0.3768 | 50.22s |\n", - "| DK_mrts | 2284.69 | 47.7984 | 36.8638 | 0.6389 | 22.28s |\n", - "| DK_S | 98.6176 | 9.9306 | 5.7334 | 0.9844 | 35.98s |\n", - "| DK_S_H | 176.972 | 13.3031 | 6.8136 | 0.972 | 32.55s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:31:43\n", - "\n", - "✅ Completed DR=0, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3032.35 | 55.0668 | 39.7057 | 0.4342 | 62.51s |\n", - "| DK_W_new | 2831.99 | 53.2164 | 37.2979 | 0.4715 | 76.61s |\n", - "| DK_mrts | 1930.34 | 43.9356 | 33.2512 | 0.6398 | 26.06s |\n", - "| DK_S | 65.7809 | 8.1105 | 4.8912 | 0.9877 | 37.61s |\n", - "| DK_S_H | 71.5519 | 8.4588 | 4.8209 | 0.9866 | 37.08s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:36:02\n", - "\n", - "✅ Completed DR=0, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 4220.26 | 64.9635 | 48.25 | 0.4106 | 65.96s |\n", - "| DK_W_new | 4106.32 | 64.0806 | 45.6577 | 0.4265 | 61.50s |\n", - "| DK_mrts | 191.069 | 13.8228 | 7.7568 | 0.9733 | 85.54s |\n", - "| DK_S | 62.8552 | 7.9281 | 4.8412 | 0.9912 | 41.56s |\n", - "| DK_S_H | 167.13 | 12.9279 | 5.3444 | 0.9767 | 34.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:41:13\n", - "\n", - "✅ Completed DR=0, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3604.68 | 60.039 | 45.1012 | 0.4072 | 65.51s |\n", - "| DK_W_new | 3576.71 | 59.8056 | 43.9007 | 0.4118 | 63.00s |\n", - "| DK_mrts | 169.728 | 13.028 | 8.1162 | 0.9721 | 54.09s |\n", - "| DK_S | 118.161 | 10.8702 | 6.2539 | 0.9806 | 33.55s |\n", - "| DK_S_H | 116.399 | 10.7888 | 5.6632 | 0.9809 | 35.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:45:50\n", - "\n", - "✅ Completed DR=0, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3665.2 | 60.5409 | 45.7153 | 0.4388 | 63.50s |\n", - "| DK_W_new | 3687.12 | 60.7216 | 44.6809 | 0.4354 | 45.54s |\n", - "| DK_mrts | 165.672 | 12.8714 | 7.7589 | 0.9746 | 76.04s |\n", - "| DK_S | 42.377 | 6.5098 | 4.1085 | 0.9935 | 36.53s |\n", - "| DK_S_H | 72.6729 | 8.5248 | 4.9999 | 0.9889 | 33.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:50:33\n", - "\n", - "✅ Completed DR=0, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 4314.99 | 65.6886 | 49.3791 | 0.3414 | 63.79s |\n", - "| DK_W_new | 4222.21 | 64.9785 | 48.0796 | 0.3555 | 59.18s |\n", - "| DK_mrts | 250.638 | 15.8316 | 8.4064 | 0.9617 | 67.10s |\n", - "| DK_S | 104.852 | 10.2397 | 6.3306 | 0.984 | 33.32s |\n", - "| DK_S_H | 97.8868 | 9.8938 | 5.4606 | 0.9851 | 36.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 10:55:23\n", - "\n", - "✅ Completed DR=0, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3045.27 | 55.184 | 41.8459 | 0.5334 | 67.22s |\n", - "| DK_W_new | 2845.86 | 53.3466 | 36.7278 | 0.5639 | 87.81s |\n", - "| DK_mrts | 405.44 | 20.1355 | 12.5858 | 0.9379 | 45.64s |\n", - "| DK_S | 59.3513 | 7.704 | 4.5757 | 0.9909 | 40.71s |\n", - "| DK_S_H | 63.6894 | 7.9806 | 4.8782 | 0.9902 | 51.70s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:00:49\n", - "\n", - "✅ Completed DR=0, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3974.23 | 63.0415 | 45.8506 | 0.4371 | 60.16s |\n", - "| DK_W_new | 3654.18 | 60.4499 | 42.3813 | 0.4824 | 51.67s |\n", - "| DK_mrts | 2310.36 | 48.0662 | 37.5105 | 0.6727 | 18.00s |\n", - "| DK_S | 72.1908 | 8.4965 | 4.4448 | 0.9898 | 29.94s |\n", - "| DK_S_H | 104.617 | 10.2282 | 5.5383 | 0.9852 | 29.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:04:35\n", - "\n", - "✅ Completed DR=0, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3041.54 | 55.1502 | 40.3839 | 0.4783 | 51.66s |\n", - "| DK_W_new | 3108.64 | 55.7552 | 39.965 | 0.4668 | 64.26s |\n", - "| DK_mrts | 114.182 | 10.6856 | 7.4792 | 0.9804 | 58.40s |\n", - "| DK_S | 57.9272 | 7.611 | 5.3086 | 0.9901 | 34.36s |\n", - "| DK_S_H | 48.9359 | 6.9954 | 4.3868 | 0.9916 | 35.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:09:17\n", - "\n", - "✅ Completed DR=0, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|-------------|-------------|---------------|\n", - "| DK_W | 3645.82±459.29 | 60.26±3.83 | 44.99±3.15 | 0.4316±0.0477 |\n", - "| DK_W_new | 3595.81±481.46 | 59.83±4.09 | 43.18±3.78 | 0.4392±0.0570 |\n", - "| DK_mrts | 796.87±910.40 | 23.83±15.14 | 16.68±12.69 | 0.8729±0.1463 |\n", - "| DK_S | 77.09±23.02 | 8.68±1.31 | 5.16±0.71 | 0.9879±0.0038 |\n", - "| DK_S_H | 99.87±40.81 | 9.80±1.97 | 5.31±0.62 | 0.9845±0.0059 |\n", - "\n", - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0.2\n", - "# Progress: 2/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3916.22 | 62.5797 | 47.6986 | 0.4109 | 54.37s |\n", - "| DK_W_new | 4068.93 | 63.7881 | 47.4314 | 0.3879 | 37.63s |\n", - "| DK_mrts | 2770.42 | 52.6347 | 43.1949 | 0.5833 | 21.64s |\n", - "| DK_S | 126.395 | 11.2426 | 6.816 | 0.981 | 28.70s |\n", - "| DK_S_H | 101.29 | 10.0643 | 5.5921 | 0.9848 | 38.84s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:12:59\n", - "\n", - "✅ Completed DR=0.2, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3625.37 | 60.2111 | 45.8557 | 0.427 | 49.21s |\n", - "| DK_W_new | 3811.57 | 61.7379 | 45.6657 | 0.3975 | 37.73s |\n", - "| DK_mrts | 2081.87 | 45.6275 | 36.5368 | 0.6709 | 21.54s |\n", - "| DK_S | 116.714 | 10.8034 | 6.3731 | 0.9816 | 30.19s |\n", - "| DK_S_H | 146.333 | 12.0968 | 6.6772 | 0.9769 | 32.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:16:33\n", - "\n", - "✅ Completed DR=0.2, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3082.15 | 55.5171 | 40.5428 | 0.4249 | 63.84s |\n", - "| DK_W_new | 3154.81 | 56.1677 | 40.008 | 0.4113 | 29.70s |\n", - "| DK_mrts | 1138.48 | 33.7413 | 25.9246 | 0.7876 | 30.43s |\n", - "| DK_S | 60.63 | 7.7865 | 4.912 | 0.9887 | 41.43s |\n", - "| DK_S_H | 65.7687 | 8.1098 | 4.5054 | 0.9877 | 69.33s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:21:13\n", - "\n", - "✅ Completed DR=0.2, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 4238.8 | 65.1061 | 47.9478 | 0.408 | 70.88s |\n", - "| DK_W_new | 4128.15 | 64.2507 | 46.1238 | 0.4234 | 55.72s |\n", - "| DK_mrts | 3547.51 | 59.561 | 46.7651 | 0.5045 | 22.32s |\n", - "| DK_S | 71.5413 | 8.4582 | 5.1017 | 0.99 | 48.71s |\n", - "| DK_S_H | 73.3159 | 8.5625 | 4.8413 | 0.9898 | 55.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:26:16\n", - "\n", - "✅ Completed DR=0.2, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3725.18 | 61.0342 | 45.5391 | 0.3874 | 62.79s |\n", - "| DK_W_new | 3510.22 | 59.2471 | 43.1384 | 0.4228 | 48.36s |\n", - "| DK_mrts | 2469.01 | 49.6892 | 39.4229 | 0.594 | 24.44s |\n", - "| DK_S | 106.544 | 10.322 | 6.0287 | 0.9825 | 41.15s |\n", - "| DK_S_H | 100.145 | 10.0072 | 5.4413 | 0.9835 | 50.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:30:55\n", - "\n", - "✅ Completed DR=0.2, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3665.18 | 60.5407 | 45.6848 | 0.4388 | 62.63s |\n", - "| DK_W_new | 3590.4 | 59.9199 | 43.649 | 0.4502 | 55.10s |\n", - "| DK_mrts | 1961.55 | 44.2894 | 35.4674 | 0.6996 | 25.80s |\n", - "| DK_S | 63.6179 | 7.9761 | 4.8685 | 0.9903 | 43.77s |\n", - "| DK_S_H | 56.2506 | 7.5 | 4.8813 | 0.9914 | 35.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:35:30\n", - "\n", - "✅ Completed DR=0.2, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 4375.6 | 66.1484 | 49.6845 | 0.3321 | 47.83s |\n", - "| DK_W_new | 4404.02 | 66.3628 | 48.3825 | 0.3278 | 43.00s |\n", - "| DK_mrts | 2664.26 | 51.6165 | 41.6403 | 0.5933 | 19.57s |\n", - "| DK_S | 107.361 | 10.3615 | 6.371 | 0.9836 | 30.46s |\n", - "| DK_S_H | 97.6057 | 9.8796 | 5.7177 | 0.9851 | 37.54s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:39:26\n", - "\n", - "✅ Completed DR=0.2, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3073.12 | 55.4358 | 42.4659 | 0.5291 | 52.27s |\n", - "| DK_W_new | 2963.89 | 54.4416 | 37.8846 | 0.5458 | 63.38s |\n", - "| DK_mrts | 1817.66 | 42.634 | 30.9447 | 0.7215 | 28.44s |\n", - "| DK_S | 4786 | 69.1809 | 54.3746 | 0.2666 | 14.09s |\n", - "| DK_S_H | 71.849 | 8.4764 | 5.2154 | 0.989 | 48.70s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:43:51\n", - "\n", - "✅ Completed DR=0.2, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3971.98 | 63.0237 | 46.2326 | 0.4374 | 48.17s |\n", - "| DK_W_new | 3735.03 | 61.1149 | 43.3597 | 0.4709 | 38.63s |\n", - "| DK_mrts | 3424.06 | 58.5155 | 46.1831 | 0.515 | 14.92s |\n", - "| DK_S | 80.2713 | 8.9594 | 5.1596 | 0.9886 | 26.46s |\n", - "| DK_S_H | 74.7022 | 8.643 | 4.8927 | 0.9894 | 34.35s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:47:37\n", - "\n", - "✅ Completed DR=0.2, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3107.18 | 55.7421 | 40.2381 | 0.467 | 49.82s |\n", - "| DK_W_new | 3107.8 | 55.7476 | 39.8506 | 0.4669 | 56.87s |\n", - "| DK_mrts | 2516.02 | 50.16 | 39.0792 | 0.5684 | 18.64s |\n", - "| DK_S | 57.9445 | 7.6121 | 5.2601 | 0.9901 | 35.69s |\n", - "| DK_S_H | 56.8248 | 7.5382 | 4.9116 | 0.9903 | 41.77s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:52:07\n", - "\n", - "✅ Completed DR=0.2, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0.2\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|-------------|-------------|---------------|\n", - "| DK_W | 3678.08±446.79 | 60.53±3.71 | 45.19±2.99 | 0.4263±0.0484 |\n", - "| DK_W_new | 3647.48±451.55 | 60.28±3.75 | 43.55±3.29 | 0.4305±0.0553 |\n", - "| DK_mrts | 2439.08±692.78 | 48.85±7.28 | 38.52±6.23 | 0.6238±0.0877 |\n", - "| DK_S | 557.70±1409.63 | 15.27±18.01 | 10.53±14.63 | 0.9143±0.2159 |\n", - "| DK_S_H | 84.41±26.11 | 9.09±1.35 | 5.27±0.59 | 0.9868±0.0041 |\n", - "\n", - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0.5\n", - "# Progress: 3/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3958.4 | 62.9158 | 48.5464 | 0.4046 | 53.70s |\n", - "| DK_W_new | 3865.75 | 62.1752 | 45.5165 | 0.4185 | 43.96s |\n", - "| DK_mrts | 2078.8 | 45.5939 | 36.3291 | 0.6873 | 26.53s |\n", - "| DK_S | 131.326 | 11.4598 | 7.1776 | 0.9802 | 34.83s |\n", - "| DK_S_H | 166.775 | 12.9141 | 7.358 | 0.9749 | 41.17s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 11:56:35\n", - "\n", - "✅ Completed DR=0.5, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3607.63 | 60.0635 | 46.0774 | 0.4298 | 51.08s |\n", - "| DK_W_new | 3985.43 | 63.1303 | 47.1528 | 0.3701 | 33.46s |\n", - "| DK_mrts | 2322.91 | 48.1966 | 39.0795 | 0.6328 | 22.89s |\n", - "| DK_S | 117.611 | 10.8449 | 7.1237 | 0.9814 | 38.66s |\n", - "| DK_S_H | 131.667 | 11.4746 | 7.0336 | 0.9792 | 41.69s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:00:55\n", - "\n", - "✅ Completed DR=0.5, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3027.7 | 55.0245 | 40.0487 | 0.435 | 67.18s |\n", - "| DK_W_new | 2852.73 | 53.411 | 37.4015 | 0.4677 | 76.50s |\n", - "| DK_mrts | 1715.63 | 41.4202 | 32.5065 | 0.6799 | 41.09s |\n", - "| DK_S | 91.747 | 9.5785 | 6.5517 | 0.9829 | 47.97s |\n", - "| DK_S_H | 104.16 | 10.2059 | 6.7614 | 0.9806 | 44.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:06:46\n", - "\n", - "✅ Completed DR=0.5, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4211.41 | 64.8953 | 47.6166 | 0.4118 | 70.51s |\n", - "| DK_W_new | 4150.16 | 64.4218 | 46.2657 | 0.4204 | 65.92s |\n", - "| DK_mrts | 2344.82 | 48.4234 | 37.9348 | 0.6725 | 32.55s |\n", - "| DK_S | 107.392 | 10.363 | 6.5144 | 0.985 | 56.58s |\n", - "| DK_S_H | 150.198 | 12.2555 | 6.9589 | 0.979 | 46.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:12:35\n", - "\n", - "✅ Completed DR=0.5, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3610.05 | 60.0837 | 45.0852 | 0.4064 | 75.04s |\n", - "| DK_W_new | 3574.24 | 59.7849 | 43.9944 | 0.4122 | 67.49s |\n", - "| DK_mrts | 2478.32 | 49.7827 | 40.0555 | 0.5925 | 29.97s |\n", - "| DK_S | 129.151 | 11.3645 | 7.2238 | 0.9788 | 47.29s |\n", - "| DK_S_H | 144.49 | 12.0204 | 7.4527 | 0.9762 | 47.48s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:18:21\n", - "\n", - "✅ Completed DR=0.5, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|---------|\n", - "| DK_W | 3737.82 | 61.1377 | 46.493 | 0.4277 | 77.74s |\n", - "| DK_W_new | 3510.77 | 59.2518 | 42.869 | 0.4624 | 115.87s |\n", - "| DK_mrts | 1678.1 | 40.9647 | 31.6245 | 0.743 | 36.98s |\n", - "| DK_S | 69.6429 | 8.3452 | 5.622 | 0.9893 | 47.51s |\n", - "| DK_S_H | 65.5086 | 8.0937 | 5.7389 | 0.99 | 67.38s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:25:29\n", - "\n", - "✅ Completed DR=0.5, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4372.42 | 66.1243 | 49.5967 | 0.3326 | 76.68s |\n", - "| DK_W_new | 4221.96 | 64.9766 | 47.7363 | 0.3556 | 92.06s |\n", - "| DK_mrts | 2559.89 | 50.5954 | 40.6307 | 0.6093 | 33.64s |\n", - "| DK_S | 141.843 | 11.9098 | 7.2642 | 0.9783 | 41.14s |\n", - "| DK_S_H | 165.615 | 12.8692 | 7.6633 | 0.9747 | 47.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:31:44\n", - "\n", - "✅ Completed DR=0.5, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 2989.58 | 54.6771 | 42.3704 | 0.5419 | 62.58s |\n", - "| DK_W_new | 2984.36 | 54.6293 | 38.0669 | 0.5427 | 67.26s |\n", - "| DK_mrts | 2501.9 | 50.019 | 39.6404 | 0.6166 | 25.69s |\n", - "| DK_S | 141.637 | 11.9011 | 7.6845 | 0.9783 | 34.64s |\n", - "| DK_S_H | 123.559 | 11.1157 | 7.148 | 0.9811 | 57.89s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:37:19\n", - "\n", - "✅ Completed DR=0.5, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 4078.16 | 63.8605 | 47.6953 | 0.4223 | 81.12s |\n", - "| DK_W_new | 3876.86 | 62.2644 | 44.7107 | 0.4509 | 47.06s |\n", - "| DK_mrts | 3125.89 | 55.9097 | 43.8343 | 0.5572 | 27.42s |\n", - "| DK_S | 95.9225 | 9.794 | 6.9328 | 0.9864 | 54.50s |\n", - "| DK_S_H | 114.617 | 10.706 | 6.6509 | 0.9838 | 56.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:43:15\n", - "\n", - "✅ Completed DR=0.5, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 100\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|-----------|---------|---------|--------|--------|\n", - "| DK_W | 3102.08 | 55.6963 | 40.3564 | 0.4679 | 80.83s |\n", - "| DK_W_new | 3065.56 | 55.3675 | 39.5202 | 0.4742 | 84.32s |\n", - "| DK_mrts | 2589.81 | 50.8902 | 40.1017 | 0.5558 | 25.17s |\n", - "| DK_S | 93.7959 | 9.6848 | 6.5709 | 0.9839 | 49.17s |\n", - "| DK_S_H | 103.993 | 10.1977 | 6.9055 | 0.9822 | 62.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 12:49:49\n", - "\n", - "✅ Completed DR=0.5, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0.5\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|------------|------------|---------------|\n", - "| DK_W | 3669.52±473.97 | 60.45±3.95 | 45.39±3.20 | 0.4280±0.0500 |\n", - "| DK_W_new | 3608.78±470.77 | 59.94±3.98 | 43.32±3.57 | 0.4375±0.0516 |\n", - "| DK_mrts | 2339.61±408.68 | 48.18±4.28 | 38.17±3.56 | 0.6347±0.0574 |\n", - "| DK_S | 112.01±22.99 | 10.52±1.11 | 6.87±0.55 | 0.9825±0.0035 |\n", - "| DK_S_H | 127.06±29.89 | 11.19±1.40 | 6.97±0.51 | 0.9802±0.0043 |\n", - "\n", - "\n", - "################################################################################\n", - "# ALL EXPERIMENTS COMPLETED\n", - "################################################################################\n", - "\n" - ] - } - ], - "source": [ - "# Initialize results storage for different dropout rates\n", - "all_dr_results = {}\n", - "\n", - "for dr_idx, dropout_rate in enumerate(HP_DR):\n", - " print(f\"\\n{'#'*80}\")\n", - " print(f\"# DROPOUT RATE: {dropout_rate}\")\n", - " print(f\"# Progress: {dr_idx+1}/{len(HP_DR)}\")\n", - " print(f\"{'#'*80}\\n\")\n", - " \n", - " # Initialize experiment results for this dropout rate\n", - " experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]\n", - " }\n", - " \n", - " for repeat in range(repeat_experiment):\n", - " current_seed = seed + repeat\n", - " \n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 DR={dropout_rate} | Repeat {repeat+1}/{repeat_experiment} | Seed={current_seed}\")\n", - " print(f\"{'='*80}\")\n", - " \n", - " # Generate data\n", - " dataframe = simulate_data(num_sample=num_sample, seed=current_seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - "\n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " Phi_sphere = max_Phi_sphere[:, :fixed_order].astype(np_f32)\n", - "\n", - " # Compute max MRTS_Euclidean\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - " Phi_mrts = max_Phi_mrts[:, :fixed_order].astype(np_f32)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - " \n", - " \n", - " # Store metrics for this repeat\n", - " Record = {}\n", - " \n", - " # 1. DeepKriging_wendland (DK_W) - fixed dropout=0.5\n", - " print(f\"\\n--- Training DK_W (Wendland basis, DR=0.5) ---\")\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DK_W\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " \n", - " # 2. DeepKriging_wendland_new (DK_W_new) - variable dropout\n", - " print(f\"\\n--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\")\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland_new\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DK_W_new\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - " # 3. DeepKriging_mrts - variable dropout\n", - " print(f\"\\n--- Training DK_mrts (Euclidean basis, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", dropout_rate, *parts\n", - " )\n", - " Record[\"DK_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # 4. DeepKriging_sphere (DK_S) - variable dropout\n", - " print(f\"\\n--- Training DK_S (Sphere basis, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", dropout_rate, *parts\n", - " )\n", - " Record[\"DK_S\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # 5. DeepKriging_sphere_Huber (DK_S_H) - variable dropout\n", - " print(f\"\\n--- Training DK_S_H (Sphere basis + Huber, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), dropout_rate, *parts\n", - " )\n", - " Record[\"DK_S_H\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # Print current repeat results\n", - " result_table = []\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " result_table.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{Record[model_name]['MSPE']:.4f}\",\n", - " \"RMSE\": f\"{Record[model_name]['RMSE']:.4f}\",\n", - " \"MAE\": f\"{Record[model_name]['MAE']:.4f}\",\n", - " \"R2\": f\"{Record[model_name]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model_name]['Time']:.2f}s\"\n", - " })\n", - " \n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - " \n", - " # Save results\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " experiment_results[model_name][\"MSPE\"].append(Record[model_name][\"MSPE\"])\n", - " experiment_results[model_name][\"RMSE\"].append(Record[model_name][\"RMSE\"])\n", - " experiment_results[model_name][\"MAE\"].append(Record[model_name][\"MAE\"])\n", - " experiment_results[model_name][\"R2\"].append(Record[model_name][\"R2\"])\n", - " \n", - " # Cleanup\n", - " del Phi_wendland, Phi_sphere, max_Phi_sphere, dataframe\n", - " del location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - " \n", - " save_notebook()\n", - " print(f\"\\n✅ Completed DR={dropout_rate}, Repeat {repeat+1}/{repeat_experiment}\")\n", - " \n", - " # Store results for this dropout rate\n", - " all_dr_results[dropout_rate] = experiment_results\n", - " \n", - " # Print summary for this dropout rate\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"📊 Summary for Dropout Rate = {dropout_rate}\")\n", - " print(f\"{'='*80}\")\n", - " \n", - " avg_results = []\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " metrics = experiment_results[model_name]\n", - " avg_results.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.4f}±{np.std(metrics['R2']):.4f}\",\n", - " })\n", - " \n", - " df_avg = pd.DataFrame(avg_results)\n", - " print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - " print()\n", - "\n", - "print(f\"\\n{'#'*80}\")\n", - "print(\"# ALL EXPERIMENTS COMPLETED\")\n", - "print(f\"{'#'*80}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "48702f5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 FINAL SUMMARY: All Dropout Rates\n", - "================================================================================\n", - "\n", - "| DR | Model | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|------|----------|-------------------|-------------------|------------------|-----------------|\n", - "| 0 | DK_W | 3645.82±459.29 | 60.26±3.83 | 44.99±3.15 | 0.4316±0.0477 |\n", - "| 0 | DK_W_new | 3595.81±481.46 | 59.83±4.09 | 43.18±3.78 | 0.4392±0.0570 |\n", - "| 0 | DK_mrts | 796.87±910.40 | 23.83±15.14 | 16.68±12.69 | 0.8729±0.1463 |\n", - "| 0 | DK_S | 77.09±23.02 | 8.68±1.31 | 5.16±0.71 | 0.9879±0.0038 |\n", - "| 0 | DK_S_H | 99.87±40.81 | 9.80±1.97 | 5.31±0.62 | 0.9845±0.0059 |\n", - "| 0.2 | DK_W | 3678.08±446.79 | 60.53±3.71 | 45.19±2.99 | 0.4263±0.0484 |\n", - "| 0.2 | DK_W_new | 3647.48±451.55 | 60.28±3.75 | 43.55±3.29 | 0.4305±0.0553 |\n", - "| 0.2 | DK_mrts | 2439.08±692.78 | 48.85±7.28 | 38.52±6.23 | 0.6238±0.0877 |\n", - "| 0.2 | DK_S | 557.70±1409.63 | 15.27±18.01 | 10.53±14.63 | 0.9143±0.2159 |\n", - "| 0.2 | DK_S_H | 84.41±26.11 | 9.09±1.35 | 5.27±0.59 | 0.9868±0.0041 |\n", - "| 0.5 | DK_W | 3669.52±473.97 | 60.45±3.95 | 45.39±3.20 | 0.4280±0.0500 |\n", - "| 0.5 | DK_W_new | 3608.78±470.77 | 59.94±3.98 | 43.32±3.57 | 0.4375±0.0516 |\n", - "| 0.5 | DK_mrts | 2339.61±408.68 | 48.18±4.28 | 38.17±3.56 | 0.6347±0.0574 |\n", - "| 0.5 | DK_S | 112.01±22.99 | 10.52±1.11 | 6.87±0.55 | 0.9825±0.0035 |\n", - "| 0.5 | DK_S_H | 127.06±29.89 | 11.19±1.40 | 6.97±0.51 | 0.9802±0.0043 |\n", - "\n", - "\n", - "================================================================================\n", - "🏆 BEST MODELS (by RMSE)\n", - "================================================================================\n", - "\n", - "| Dropout Rate | Best Model | RMSE | R2 |\n", - "|----------------|--------------|------------|---------------|\n", - "| 0 | DK_S | 8.68±1.31 | 0.9879±0.0038 |\n", - "| 0.2 | DK_S_H | 9.09±1.35 | 0.9868±0.0041 |\n", - "| 0.5 | DK_S | 10.52±1.11 | 0.9825±0.0035 |\n", - "\n", - "\n", - "================================================================================\n", - "📈 MODEL COMPARISON (averaged across all dropout rates)\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|-----------------|-------------|-------------|---------------|\n", - "| DK_W | 3664.48±460.35 | 60.41±3.83 | 45.19±3.12 | 0.4286±0.0488 |\n", - "| DK_W_new | 3617.36±468.60 | 60.01±3.95 | 43.35±3.55 | 0.4357±0.0548 |\n", - "| DK_mrts | 1858.52±1028.17 | 40.28±15.35 | 31.12±13.23 | 0.7105±0.1550 |\n", - "| DK_S | 248.93±842.96 | 11.49±10.81 | 7.52±8.75 | 0.9615±0.1291 |\n", - "| DK_S_H | 103.78±37.30 | 10.02±1.82 | 5.85±0.98 | 0.9838±0.0056 |\n", - "\n", - "\n", - "================================================================================\n", - "✅ ALL ANALYSIS COMPLETED\n", - "================================================================================\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 FINAL SUMMARY: All Dropout Rates\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "# Create comprehensive summary table\n", - "summary_data = []\n", - "\n", - "for dr in HP_DR:\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " metrics = all_dr_results[dr][model_name]\n", - " \n", - " summary_data.append({\n", - " \"DR\": f\"{dr:.1f}\",\n", - " \"Model\": model_name,\n", - " \"MSPE (mean±std)\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(metrics['R2']):.4f}±{np.std(metrics['R2']):.4f}\",\n", - " })\n", - "\n", - "df_summary = pd.DataFrame(summary_data)\n", - "print(df_summary.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "# Find best model for each metric\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"🏆 BEST MODELS (by RMSE)\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "best_models = []\n", - "for dr in HP_DR:\n", - " best_rmse = float('inf')\n", - " best_model = None\n", - " \n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " mean_rmse = np.mean(all_dr_results[dr][model_name]['RMSE'])\n", - " if mean_rmse < best_rmse:\n", - " best_rmse = mean_rmse\n", - " best_model = model_name\n", - " \n", - " best_models.append({\n", - " \"Dropout Rate\": f\"{dr:.1f}\",\n", - " \"Best Model\": best_model,\n", - " \"RMSE\": f\"{best_rmse:.2f}±{np.std(all_dr_results[dr][best_model]['RMSE']):.2f}\",\n", - " \"R2\": f\"{np.mean(all_dr_results[dr][best_model]['R2']):.4f}±{np.std(all_dr_results[dr][best_model]['R2']):.4f}\"\n", - " })\n", - "\n", - "df_best = pd.DataFrame(best_models)\n", - "print(df_best.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "# Model comparison across all dropout rates\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📈 MODEL COMPARISON (averaged across all dropout rates)\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "model_comparison = []\n", - "for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " all_mspe = []\n", - " all_rmse = []\n", - " all_mae = []\n", - " all_r2 = []\n", - " \n", - " for dr in HP_DR:\n", - " all_mspe.extend(all_dr_results[dr][model_name]['MSPE'])\n", - " all_rmse.extend(all_dr_results[dr][model_name]['RMSE'])\n", - " all_mae.extend(all_dr_results[dr][model_name]['MAE'])\n", - " all_r2.extend(all_dr_results[dr][model_name]['R2'])\n", - " \n", - " model_comparison.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{np.mean(all_mspe):.2f}±{np.std(all_mspe):.2f}\",\n", - " \"RMSE\": f\"{np.mean(all_rmse):.2f}±{np.std(all_rmse):.2f}\",\n", - " \"MAE\": f\"{np.mean(all_mae):.2f}±{np.std(all_mae):.2f}\",\n", - " \"R2\": f\"{np.mean(all_r2):.4f}±{np.std(all_r2):.4f}\"\n", - " })\n", - "\n", - "df_comparison = pd.DataFrame(model_comparison)\n", - "print(df_comparison.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"✅ ALL ANALYSIS COMPLETED\")\n", - "print(\"=\"*80)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K500_mrts.ipynb b/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K500_mrts.ipynb deleted file mode 100644 index cb9f661..0000000 --- a/notebook/test/simulation_spherical_GP_eggholder_without_outliers_fixed_K500_mrts.ipynb +++ /dev/null @@ -1,5316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-03 22:43:20.020924: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770129800.036486 62829 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770129800.040819 62829 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770129800.052868 62829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770129800.052891 62829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770129800.052893 62829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770129800.052894 62829 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "# from spherical_deepkriging.models.universal_kriging import UniversalKriging" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data(num_sample, seed):\n", - " \"\"\"\n", - " Experiment I : Stationary Gaussian processes + Eggholder Mean Function.\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " # Convert to Cartesian coordinates for distance calculation\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - "\n", - " # Eggholder Mean Term\n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " \n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " coords = np.column_stack([x_cart, y_cart, z_cart]).astype(np.float32)\n", - " \n", - " # Covariance matrix with exponential covariance function\n", - " dist_matrix = np.sqrt(((coords[:, None, :] - coords[None, :, :]) ** 2).sum(axis=2))\n", - " cov_matrix = np.exp(-dist_matrix / 0.5).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability (same as old function)\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition with error handling (same as old function)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process: y = mean + L @ z\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " y = mean_term + (L @ z_std).astype(np.float32)\n", - " \n", - " z = y\n", - " \n", - " print(f\"\\nSimulate Data | z mean: {np.mean(y):.4f} ({np.std(y) / np.sqrt(num_sample):.4f}), Variance: {np.var(y, ddof=0):.4f}, Range: [{np.min(y):.4f}, {np.max(y):.4f}]\")\n", - " \n", - " del phi, theta, lat_rad, lon_rad, x_cart, y_cart, z_cart, coords, dist_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def print_config(name_model, config):\n", - " \"\"\"Print all configuration parameters.\"\"\"\n", - " print(f\"\\n{'='*70}\")\n", - " print(f\"📋 Model Config for {name_model}\")\n", - " print(f\"{'='*70}\")\n", - " \n", - " # Get all attributes from config\n", - " config_dict = vars(config)\n", - " \n", - " # Print each parameter\n", - " for key, value in sorted(config_dict.items()):\n", - " # Format the value for better readability\n", - " # Check basic types FIRST (important!)\n", - " if value is None:\n", - " value_str = 'None'\n", - " elif isinstance(value, bool): # Must check bool before int!\n", - " value_str = str(value)\n", - " elif isinstance(value, (int, float)):\n", - " value_str = str(value)\n", - " elif isinstance(value, str):\n", - " value_str = f\"'{value}'\"\n", - " elif isinstance(value, (list, tuple)):\n", - " value_str = str(value)\n", - " elif hasattr(value, '__name__'): # For functions/classes\n", - " value_str = value.__name__\n", - " else: # For complex objects (optimizer, loss, etc.)\n", - " class_name = value.__class__.__name__\n", - " value_str = f\"{class_name}(...)\"\n", - " \n", - " # Truncate very long strings\n", - " if len(value_str) > 60:\n", - " value_str = value_str[:57] + '...'\n", - " \n", - " print(f\" {key:20s}: {value_str}\")\n", - " \n", - " print(f\"{'='*70}\\n\")\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " elif name_model == \"DeepKriging_wendland_new\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " print_config(name_model, config)\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland_new\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "HP_DR = [0, 0.2, 0.5]\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "fixed_order = 500\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "db81c91f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0\n", - "# Progress: 1/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770129823.099668 62829 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770129825.095596 63084 service.cc:152] XLA service 0x757420010fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770129825.095665 63084 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770129825.364718 63084 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770129826.537381 63084 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x75747c68a560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x757474405990> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4018.68 | 63.3931 | 48.5649 | 0.3955 | 50.16s |\n", - "| DK_W_new | 4016.53 | 63.3761 | 46.9956 | 0.3958 | 55.83s |\n", - "| DK_mrts | 126.379 | 11.2418 | 6.9376 | 0.981 | 59.83s |\n", - "| DK_S | 192.668 | 13.8805 | 7.863 | 0.971 | 53.48s |\n", - "| DK_S_H | 213.517 | 14.6122 | 8.5615 | 0.9679 | 45.13s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 22:48:16\n", - "\n", - "✅ Completed DR=0, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3677.92 | 60.6458 | 46.1976 | 0.4187 | 50.70s |\n", - "| DK_W_new | 4036.65 | 63.5346 | 47.2979 | 0.362 | 29.82s |\n", - "| DK_mrts | 171.906 | 13.1113 | 7.9779 | 0.9728 | 66.18s |\n", - "| DK_S | 198.518 | 14.0896 | 7.7847 | 0.9686 | 38.68s |\n", - "| DK_S_H | 338.914 | 18.4096 | 10.5051 | 0.9464 | 28.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 22:52:08\n", - "\n", - "✅ Completed DR=0, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3358.74 | 57.9546 | 41.8564 | 0.3732 | 53.42s |\n", - "| DK_W_new | 2837.66 | 53.2697 | 36.8702 | 0.4705 | 78.80s |\n", - "| DK_mrts | 105.521 | 10.2723 | 6.4717 | 0.9803 | 60.18s |\n", - "| DK_S | 121.165 | 11.0075 | 7.2587 | 0.9774 | 44.12s |\n", - "| DK_S_H | 122.411 | 11.0639 | 7.0148 | 0.9772 | 39.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 22:57:06\n", - "\n", - "✅ Completed DR=0, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4194.91 | 64.7681 | 48.233 | 0.4141 | 64.34s |\n", - "| DK_W_new | 4193.99 | 64.761 | 45.9359 | 0.4142 | 66.62s |\n", - "| DK_mrts | 129.929 | 11.3986 | 7.3974 | 0.9819 | 45.42s |\n", - "| DK_S | 140.02 | 11.833 | 7.2014 | 0.9804 | 39.79s |\n", - "| DK_S_H | 257.508 | 16.047 | 8.9455 | 0.964 | 33.00s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:01:38\n", - "\n", - "✅ Completed DR=0, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3583.26 | 59.8604 | 44.4845 | 0.4108 | 62.78s |\n", - "| DK_W_new | 3588.98 | 59.9081 | 44.2721 | 0.4098 | 59.53s |\n", - "| DK_mrts | 138.444 | 11.7662 | 7.4475 | 0.9772 | 45.48s |\n", - "| DK_S | 146.439 | 12.1012 | 7.2485 | 0.9759 | 41.26s |\n", - "| DK_S_H | 227.604 | 15.0866 | 9.0127 | 0.9626 | 34.92s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:06:09\n", - "\n", - "✅ Completed DR=0, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3693.45 | 60.7737 | 46.0351 | 0.4345 | 62.39s |\n", - "| DK_W_new | 3595.45 | 59.9621 | 44.3031 | 0.4495 | 49.38s |\n", - "| DK_mrts | 153.503 | 12.3896 | 7.9065 | 0.9765 | 42.12s |\n", - "| DK_S | 168.532 | 12.982 | 7.3047 | 0.9742 | 43.84s |\n", - "| DK_S_H | 221.976 | 14.8988 | 8.4377 | 0.966 | 27.59s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:10:20\n", - "\n", - "✅ Completed DR=0, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4411.97 | 66.4227 | 49.251 | 0.3266 | 59.85s |\n", - "| DK_W_new | 4267.17 | 65.3236 | 48.4327 | 0.3487 | 70.45s |\n", - "| DK_mrts | 219.56 | 14.8176 | 9.1825 | 0.9665 | 44.16s |\n", - "| DK_S | 241.069 | 15.5264 | 9.3699 | 0.9632 | 39.62s |\n", - "| DK_S_H | 274.939 | 16.5813 | 9.1884 | 0.958 | 28.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:14:54\n", - "\n", - "✅ Completed DR=0, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3153.95 | 56.16 | 43.2885 | 0.5167 | 48.24s |\n", - "| DK_W_new | 2955.2 | 54.3618 | 37.1925 | 0.5472 | 62.42s |\n", - "| DK_mrts | 229.628 | 15.1535 | 8.1037 | 0.9648 | 50.37s |\n", - "| DK_S | 159.334 | 12.6228 | 6.4259 | 0.9756 | 28.36s |\n", - "| DK_S_H | 156.88 | 12.5252 | 7.9358 | 0.976 | 30.98s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:19:08\n", - "\n", - "✅ Completed DR=0, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4054.46 | 63.6746 | 46.9675 | 0.4257 | 46.09s |\n", - "| DK_W_new | 3725.39 | 61.0359 | 43.3243 | 0.4723 | 43.32s |\n", - "| DK_mrts | 167.559 | 12.9445 | 7.6888 | 0.9763 | 39.08s |\n", - "| DK_S | 158.116 | 12.5744 | 7.3853 | 0.9776 | 43.29s |\n", - "| DK_S_H | 213.617 | 14.6156 | 8.4485 | 0.9697 | 42.41s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:23:21\n", - "\n", - "✅ Completed DR=0, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3057.4 | 55.2938 | 39.7248 | 0.4756 | 61.70s |\n", - "| DK_W_new | 3222.34 | 56.7656 | 40.4187 | 0.4473 | 52.94s |\n", - "| DK_mrts | 113.889 | 10.6719 | 6.6466 | 0.9805 | 75.00s |\n", - "| DK_S | 175.399 | 13.2438 | 8.1516 | 0.9699 | 44.90s |\n", - "| DK_S_H | 309.424 | 17.5905 | 9.7961 | 0.9469 | 31.67s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:28:26\n", - "\n", - "✅ Completed DR=0, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|------------|------------|---------------|\n", - "| DK_W | 3720.47±426.21 | 60.89±3.51 | 45.46±2.94 | 0.4191±0.0493 |\n", - "| DK_W_new | 3643.93±479.40 | 60.23±4.04 | 43.50±3.89 | 0.4317±0.0556 |\n", - "| DK_mrts | 155.63±40.05 | 12.38±1.56 | 7.58±0.75 | 0.9758±0.0057 |\n", - "| DK_S | 170.13±32.41 | 12.99±1.22 | 7.60±0.74 | 0.9734±0.0049 |\n", - "| DK_S_H | 233.68±61.92 | 15.14±2.09 | 8.78±0.91 | 0.9635±0.0100 |\n", - "\n", - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0.2\n", - "# Progress: 2/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4028.17 | 63.4679 | 48.7245 | 0.3941 | 59.24s |\n", - "| DK_W_new | 4027.13 | 63.4597 | 47.5294 | 0.3942 | 46.95s |\n", - "| DK_mrts | 191.615 | 13.8425 | 9.2343 | 0.9712 | 51.69s |\n", - "| DK_S | 247.106 | 15.7196 | 8.9021 | 0.9628 | 35.65s |\n", - "| DK_S_H | 269.038 | 16.4024 | 9.5859 | 0.9595 | 35.77s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:32:53\n", - "\n", - "✅ Completed DR=0.2, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3667.92 | 60.5633 | 46.0211 | 0.4202 | 61.62s |\n", - "| DK_W_new | 3776.78 | 61.4555 | 45.6886 | 0.403 | 49.78s |\n", - "| DK_mrts | 191.432 | 13.8359 | 8.201 | 0.9697 | 65.13s |\n", - "| DK_S | 262.354 | 16.1974 | 9.3787 | 0.9585 | 35.85s |\n", - "| DK_S_H | 278.339 | 16.6835 | 9.3833 | 0.956 | 35.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:37:43\n", - "\n", - "✅ Completed DR=0.2, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3076.85 | 55.4694 | 40.5131 | 0.4259 | 64.54s |\n", - "| DK_W_new | 2965.71 | 54.4583 | 37.7007 | 0.4466 | 50.41s |\n", - "| DK_mrts | 185.417 | 13.6168 | 8.528 | 0.9654 | 64.45s |\n", - "| DK_S | 140.172 | 11.8394 | 7.4325 | 0.9738 | 36.37s |\n", - "| DK_S_H | 190.149 | 13.7895 | 8.6851 | 0.9645 | 36.23s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:42:46\n", - "\n", - "✅ Completed DR=0.2, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4176.99 | 64.6297 | 47.679 | 0.4166 | 59.98s |\n", - "| DK_W_new | 4170.29 | 64.5778 | 45.8571 | 0.4175 | 51.79s |\n", - "| DK_mrts | 164.159 | 12.8125 | 8.6782 | 0.9771 | 47.58s |\n", - "| DK_S | 185.586 | 13.623 | 7.9231 | 0.9741 | 30.75s |\n", - "| DK_S_H | 183.723 | 13.5544 | 8.0371 | 0.9743 | 48.97s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:47:34\n", - "\n", - "✅ Completed DR=0.2, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3506.39 | 59.2148 | 44.364 | 0.4234 | 69.58s |\n", - "| DK_W_new | 3387.06 | 58.1985 | 42.7168 | 0.443 | 87.17s |\n", - "| DK_mrts | 211.573 | 14.5456 | 9.7224 | 0.9652 | 71.20s |\n", - "| DK_S | 227.302 | 15.0765 | 9.2019 | 0.9626 | 42.77s |\n", - "| DK_S_H | 216.58 | 14.7167 | 9.3536 | 0.9644 | 42.20s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:53:40\n", - "\n", - "✅ Completed DR=0.2, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3662.64 | 60.5198 | 45.038 | 0.4392 | 71.45s |\n", - "| DK_W_new | 3517.02 | 59.3045 | 43.0578 | 0.4615 | 73.43s |\n", - "| DK_mrts | 489.919 | 22.1341 | 16.2211 | 0.925 | 28.15s |\n", - "| DK_S | 254.393 | 15.9497 | 8.8037 | 0.961 | 39.61s |\n", - "| DK_S_H | 222.153 | 14.9048 | 8.3917 | 0.966 | 42.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-03 23:58:49\n", - "\n", - "✅ Completed DR=0.2, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4302.44 | 65.593 | 48.7069 | 0.3433 | 60.17s |\n", - "| DK_W_new | 4261.52 | 65.2803 | 47.7977 | 0.3495 | 70.56s |\n", - "| DK_mrts | 295.07 | 17.1776 | 11.2294 | 0.955 | 60.50s |\n", - "| DK_S | 304.268 | 17.4433 | 10.4778 | 0.9536 | 38.68s |\n", - "| DK_S_H | 316.204 | 17.7821 | 10.4888 | 0.9517 | 40.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:04:19\n", - "\n", - "✅ Completed DR=0.2, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|---------|\n", - "| DK_W | 3042.34 | 55.1574 | 42.4819 | 0.5338 | 76.19s |\n", - "| DK_W_new | 2899.03 | 53.8427 | 37.0281 | 0.5558 | 106.18s |\n", - "| DK_mrts | 207.873 | 14.4178 | 8.7711 | 0.9681 | 79.64s |\n", - "| DK_S | 164.097 | 12.81 | 8.3699 | 0.9749 | 37.75s |\n", - "| DK_S_H | 170.705 | 13.0654 | 8.453 | 0.9738 | 40.03s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:10:57\n", - "\n", - "✅ Completed DR=0.2, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4039.98 | 63.5608 | 47.2622 | 0.4277 | 77.38s |\n", - "| DK_W_new | 3728.86 | 61.0644 | 42.582 | 0.4718 | 53.16s |\n", - "| DK_mrts | 219.827 | 14.8265 | 10.4383 | 0.9689 | 47.44s |\n", - "| DK_S | 167.484 | 12.9415 | 7.6326 | 0.9763 | 46.70s |\n", - "| DK_S_H | 203.832 | 14.277 | 8.8048 | 0.9711 | 42.44s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:16:29\n", - "\n", - "✅ Completed DR=0.2, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.2 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.2) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.2\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3256.98 | 57.0699 | 41.7771 | 0.4413 | 79.31s |\n", - "| DK_W_new | 3156.2 | 56.1801 | 40.2103 | 0.4586 | 76.45s |\n", - "| DK_mrts | 189.198 | 13.7549 | 7.9326 | 0.9675 | 89.02s |\n", - "| DK_S | 132.141 | 11.4952 | 7.7753 | 0.9773 | 51.05s |\n", - "| DK_S_H | 149.132 | 12.212 | 8.1725 | 0.9744 | 38.57s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:23:12\n", - "\n", - "✅ Completed DR=0.2, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0.2\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|------------|------------|---------------|\n", - "| DK_W | 3676.07±431.86 | 60.52±3.58 | 45.26±2.78 | 0.4266±0.0447 |\n", - "| DK_W_new | 3588.96±462.04 | 59.78±3.88 | 43.02±3.62 | 0.4402±0.0524 |\n", - "| DK_mrts | 234.61±91.36 | 15.10±2.59 | 9.90±2.32 | 0.9633±0.0138 |\n", - "| DK_S | 208.49±55.41 | 14.31±1.93 | 8.59±0.90 | 0.9675±0.0082 |\n", - "| DK_S_H | 219.99±50.00 | 14.74±1.66 | 8.94±0.72 | 0.9656±0.0076 |\n", - "\n", - "\n", - "################################################################################\n", - "# DROPOUT RATE: 0.5\n", - "# Progress: 3/3\n", - "################################################################################\n", - "\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 1/10 | Seed=1\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.9206 (1.5864), Variance: 6291.5864, Range: [-217.0852, 204.6014]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3922.21 | 62.6275 | 48.0224 | 0.41 | 82.25s |\n", - "| DK_W_new | 4017.56 | 63.3843 | 47.015 | 0.3957 | 52.10s |\n", - "| DK_mrts | 276.14 | 16.6175 | 12.3278 | 0.9585 | 60.28s |\n", - "| DK_S | 249.312 | 15.7896 | 9.1662 | 0.9625 | 46.59s |\n", - "| DK_S_H | 236.347 | 15.3736 | 9.4945 | 0.9644 | 47.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:29:09\n", - "\n", - "✅ Completed DR=0.5, Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 2/10 | Seed=2\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.5604 (1.5780), Variance: 6225.3340, Range: [-207.2291, 204.4075]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3548.12 | 59.5661 | 45.4579 | 0.4392 | 77.60s |\n", - "| DK_W_new | 3879.73 | 62.2875 | 46.4362 | 0.3868 | 49.37s |\n", - "| DK_mrts | 272.914 | 16.5201 | 12.329 | 0.9569 | 53.90s |\n", - "| DK_S | 224.282 | 14.976 | 9.2402 | 0.9645 | 38.92s |\n", - "| DK_S_H | 293.885 | 17.1431 | 10.1671 | 0.9535 | 41.87s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:34:38\n", - "\n", - "✅ Completed DR=0.5, Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 3/10 | Seed=3\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.9580 (1.5850), Variance: 6280.9121, Range: [-211.3044, 207.7005]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3270.23 | 57.1859 | 41.3839 | 0.3898 | 77.92s |\n", - "| DK_W_new | 3131.45 | 55.9594 | 39.402 | 0.4157 | 51.60s |\n", - "| DK_mrts | 203.57 | 14.2678 | 10.0392 | 0.962 | 88.91s |\n", - "| DK_S | 130.625 | 11.4291 | 7.036 | 0.9756 | 45.29s |\n", - "| DK_S_H | 161.34 | 12.702 | 8.5448 | 0.9699 | 54.46s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:41:05\n", - "\n", - "✅ Completed DR=0.5, Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 4/10 | Seed=4\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4982 (1.5934), Variance: 6347.6392, Range: [-215.1728, 203.1806]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|---------|\n", - "| DK_W | 4233.22 | 65.0632 | 48.1308 | 0.4088 | 80.13s |\n", - "| DK_W_new | 4094.85 | 63.991 | 44.8237 | 0.4281 | 105.20s |\n", - "| DK_mrts | 339.763 | 18.4327 | 13.369 | 0.9525 | 57.60s |\n", - "| DK_S | 182.585 | 13.5124 | 9.1409 | 0.9745 | 39.52s |\n", - "| DK_S_H | 193.163 | 13.8983 | 9.1839 | 0.973 | 56.07s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:48:00\n", - "\n", - "✅ Completed DR=0.5, Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 5/10 | Seed=5\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.4363 (1.6030), Variance: 6424.0918, Range: [-200.3989, 204.7173]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|---------|\n", - "| DK_W | 3635.91 | 60.2985 | 45.1617 | 0.4021 | 74.88s |\n", - "| DK_W_new | 3611.8 | 60.0983 | 44.0563 | 0.4061 | 63.37s |\n", - "| DK_mrts | 266.918 | 16.3376 | 10.4393 | 0.9561 | 102.80s |\n", - "| DK_S | 216.586 | 14.7169 | 9.2291 | 0.9644 | 56.53s |\n", - "| DK_S_H | 231.67 | 15.2207 | 9.9496 | 0.9619 | 42.74s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 00:54:56\n", - "\n", - "✅ Completed DR=0.5, Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 6/10 | Seed=6\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 15.2323 (1.6054), Variance: 6443.1089, Range: [-208.5709, 204.0599]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|---------|\n", - "| DK_W | 3565.94 | 59.7155 | 44.9447 | 0.454 | 80.52s |\n", - "| DK_W_new | 3482.71 | 59.0145 | 42.6988 | 0.4667 | 114.08s |\n", - "| DK_mrts | 418.551 | 20.4585 | 15.5919 | 0.9359 | 52.18s |\n", - "| DK_S | 212.253 | 14.5689 | 8.6799 | 0.9675 | 50.19s |\n", - "| DK_S_H | 245.837 | 15.6792 | 9.0287 | 0.9624 | 44.82s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 01:01:56\n", - "\n", - "✅ Completed DR=0.5, Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 7/10 | Seed=7\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 13.4518 (1.6090), Variance: 6472.4946, Range: [-209.0064, 205.4675]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 4377.07 | 66.1594 | 49.7033 | 0.3319 | 75.97s |\n", - "| DK_W_new | 4255.45 | 65.2338 | 47.7477 | 0.3505 | 87.71s |\n", - "| DK_mrts | 322.224 | 17.9506 | 12.0998 | 0.9508 | 80.39s |\n", - "| DK_S | 230.349 | 15.1772 | 9.4824 | 0.9648 | 53.65s |\n", - "| DK_S_H | 284.734 | 16.8741 | 10.688 | 0.9565 | 44.64s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 01:09:04\n", - "\n", - "✅ Completed DR=0.5, Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 8/10 | Seed=8\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 10.6661 (1.6226), Variance: 6581.6841, Range: [-211.8653, 204.7726]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|---------|\n", - "| DK_W | 3033.42 | 55.0765 | 41.6252 | 0.5352 | 81.60s |\n", - "| DK_W_new | 2928 | 54.111 | 37.7335 | 0.5513 | 101.30s |\n", - "| DK_mrts | 200.498 | 14.1597 | 9.5404 | 0.9693 | 76.67s |\n", - "| DK_S | 187.921 | 13.7084 | 8.8516 | 0.9712 | 41.21s |\n", - "| DK_S_H | 209.149 | 14.462 | 9.6698 | 0.968 | 49.79s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 01:16:18\n", - "\n", - "✅ Completed DR=0.5, Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 9/10 | Seed=9\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 11.8324 (1.6281), Variance: 6626.7510, Range: [-214.5135, 205.5588]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3972.26 | 63.0259 | 46.4951 | 0.4373 | 78.15s |\n", - "| DK_W_new | 3905.75 | 62.496 | 45.2982 | 0.4468 | 44.42s |\n", - "| DK_mrts | 595.577 | 24.4045 | 19.9036 | 0.9156 | 40.68s |\n", - "| DK_S | 196.873 | 14.0311 | 9.9795 | 0.9721 | 38.43s |\n", - "| DK_S_H | 188.131 | 13.7161 | 9.0668 | 0.9734 | 41.34s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 01:21:54\n", - "\n", - "✅ Completed DR=0.5, Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 DR=0.5 | Repeat 10/10 | Seed=10\n", - "================================================================================\n", - "\n", - "Simulate Data | z mean: 12.7194 (1.5889), Variance: 6311.7573, Range: [-209.6805, 206.5175]\n", - "\n", - "--- Training DK_W (Wendland basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_units : 100\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mse', 'mae']\n", - " num_hidden_layers : 3\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : None\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_wendland_new\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 1830\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_mrts (Euclidean basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_mrts\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S (Sphere basis, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : 'mse'\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "--- Training DK_S_H (Sphere basis + Huber, DR=0.5) ---\n", - "\n", - "======================================================================\n", - "📋 Model Config for DeepKriging_sphere_Huber\n", - "======================================================================\n", - " activation : 'relu'\n", - " batch_size : 256\n", - " dropout_rate : 0.5\n", - " epochs : 500\n", - " hidden_layers : [1024, 512, 256, 128, 64]\n", - " input_dim : 500\n", - " loss : Huber(...)\n", - " metrics : ['mae']\n", - " optimizer : Adam(...)\n", - " output_type : 'continuous'\n", - " patience : 40\n", - " verbose : 0\n", - "======================================================================\n", - "\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 | Time |\n", - "|----------|----------|---------|---------|--------|--------|\n", - "| DK_W | 3065.4 | 55.3661 | 40.3644 | 0.4742 | 81.68s |\n", - "| DK_W_new | 2953.68 | 54.3478 | 39.0013 | 0.4934 | 96.23s |\n", - "| DK_mrts | 202.754 | 14.2392 | 9.8928 | 0.9652 | 79.48s |\n", - "| DK_S | 155.348 | 12.4639 | 8.9382 | 0.9734 | 48.60s |\n", - "| DK_S_H | 168.93 | 12.9973 | 8.8716 | 0.971 | 61.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-04 01:29:30\n", - "\n", - "✅ Completed DR=0.5, Repeat 10/10\n", - "\n", - "================================================================================\n", - "📊 Summary for Dropout Rate = 0.5\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|------------|------------|---------------|\n", - "| DK_W | 3662.38±439.20 | 60.41±3.63 | 45.13±2.99 | 0.4282±0.0515 |\n", - "| DK_W_new | 3626.10±460.06 | 60.09±3.87 | 43.42±3.39 | 0.4341±0.0551 |\n", - "| DK_mrts | 309.89±115.57 | 17.34±3.04 | 12.55±3.02 | 0.9523±0.0150 |\n", - "| DK_S | 198.61±34.08 | 14.04±1.25 | 8.97±0.73 | 0.9691±0.0046 |\n", - "| DK_S_H | 221.32±43.13 | 14.81±1.44 | 9.47±0.62 | 0.9654±0.0065 |\n", - "\n", - "\n", - "################################################################################\n", - "# ALL EXPERIMENTS COMPLETED\n", - "################################################################################\n", - "\n" - ] - } - ], - "source": [ - "# Initialize results storage for different dropout rates\n", - "all_dr_results = {}\n", - "\n", - "for dr_idx, dropout_rate in enumerate(HP_DR):\n", - " print(f\"\\n{'#'*80}\")\n", - " print(f\"# DROPOUT RATE: {dropout_rate}\")\n", - " print(f\"# Progress: {dr_idx+1}/{len(HP_DR)}\")\n", - " print(f\"{'#'*80}\\n\")\n", - " \n", - " # Initialize experiment results for this dropout rate\n", - " experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]\n", - " }\n", - " \n", - " for repeat in range(repeat_experiment):\n", - " current_seed = seed + repeat\n", - " \n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 DR={dropout_rate} | Repeat {repeat+1}/{repeat_experiment} | Seed={current_seed}\")\n", - " print(f\"{'='*80}\")\n", - " \n", - " # Generate data\n", - " dataframe = simulate_data(num_sample=num_sample, seed=current_seed)\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - " \n", - "\n", - " # Compute max MRTS_Sphere\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - " Phi_sphere = max_Phi_sphere[:, :fixed_order].astype(np_f32)\n", - "\n", - " # Compute max MRTS_Euclidean\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - " Phi_mrts = max_Phi_mrts[:, :fixed_order].astype(np_f32)\n", - " \n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - " \n", - " \n", - " # Store metrics for this repeat\n", - " Record = {}\n", - " \n", - " # 1. DeepKriging_wendland (DK_W) - fixed dropout=0.5\n", - " print(f\"\\n--- Training DK_W (Wendland basis, DR=0.5) ---\")\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DK_W\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " \n", - " # 2. DeepKriging_wendland_new (DK_W_new) - variable dropout\n", - " print(f\"\\n--- Training DK_W_new (Wendland basis + ModelConfig, DR=0.5) ---\")\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland_new\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DK_W_new\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - " # 3. DeepKriging_mrts - variable dropout\n", - " print(f\"\\n--- Training DK_mrts (Euclidean basis, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", dropout_rate, *parts\n", - " )\n", - " Record[\"DK_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # 4. DeepKriging_sphere (DK_S) - variable dropout\n", - " print(f\"\\n--- Training DK_S (Sphere basis, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", dropout_rate, *parts\n", - " )\n", - " Record[\"DK_S\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # 5. DeepKriging_sphere_Huber (DK_S_H) - variable dropout\n", - " print(f\"\\n--- Training DK_S_H (Sphere basis + Huber, DR={dropout_rate}) ---\")\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, current_seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), dropout_rate, *parts\n", - " )\n", - " Record[\"DK_S_H\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \n", - " \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"]\n", - " }\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - " \n", - " # Print current repeat results\n", - " result_table = []\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " result_table.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{Record[model_name]['MSPE']:.4f}\",\n", - " \"RMSE\": f\"{Record[model_name]['RMSE']:.4f}\",\n", - " \"MAE\": f\"{Record[model_name]['MAE']:.4f}\",\n", - " \"R2\": f\"{Record[model_name]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model_name]['Time']:.2f}s\"\n", - " })\n", - " \n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - " \n", - " # Save results\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " experiment_results[model_name][\"MSPE\"].append(Record[model_name][\"MSPE\"])\n", - " experiment_results[model_name][\"RMSE\"].append(Record[model_name][\"RMSE\"])\n", - " experiment_results[model_name][\"MAE\"].append(Record[model_name][\"MAE\"])\n", - " experiment_results[model_name][\"R2\"].append(Record[model_name][\"R2\"])\n", - " \n", - " # Cleanup\n", - " del Phi_wendland, Phi_sphere, max_Phi_sphere, dataframe\n", - " del location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - " \n", - " save_notebook()\n", - " print(f\"\\n✅ Completed DR={dropout_rate}, Repeat {repeat+1}/{repeat_experiment}\")\n", - " \n", - " # Store results for this dropout rate\n", - " all_dr_results[dropout_rate] = experiment_results\n", - " \n", - " # Print summary for this dropout rate\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"📊 Summary for Dropout Rate = {dropout_rate}\")\n", - " print(f\"{'='*80}\")\n", - " \n", - " avg_results = []\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " metrics = experiment_results[model_name]\n", - " avg_results.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.4f}±{np.std(metrics['R2']):.4f}\",\n", - " })\n", - " \n", - " df_avg = pd.DataFrame(avg_results)\n", - " print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - " print()\n", - "\n", - "print(f\"\\n{'#'*80}\")\n", - "print(\"# ALL EXPERIMENTS COMPLETED\")\n", - "print(f\"{'#'*80}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "48702f5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 FINAL SUMMARY: All Dropout Rates\n", - "================================================================================\n", - "\n", - "| DR | Model | MSPE (mean±std) | RMSE (mean±std) | MAE (mean±std) | R2 (mean±std) |\n", - "|------|----------|-------------------|-------------------|------------------|-----------------|\n", - "| 0 | DK_W | 3720.47±426.21 | 60.89±3.51 | 45.46±2.94 | 0.4191±0.0493 |\n", - "| 0 | DK_W_new | 3643.93±479.40 | 60.23±4.04 | 43.50±3.89 | 0.4317±0.0556 |\n", - "| 0 | DK_mrts | 155.63±40.05 | 12.38±1.56 | 7.58±0.75 | 0.9758±0.0057 |\n", - "| 0 | DK_S | 170.13±32.41 | 12.99±1.22 | 7.60±0.74 | 0.9734±0.0049 |\n", - "| 0 | DK_S_H | 233.68±61.92 | 15.14±2.09 | 8.78±0.91 | 0.9635±0.0100 |\n", - "| 0.2 | DK_W | 3676.07±431.86 | 60.52±3.58 | 45.26±2.78 | 0.4266±0.0447 |\n", - "| 0.2 | DK_W_new | 3588.96±462.04 | 59.78±3.88 | 43.02±3.62 | 0.4402±0.0524 |\n", - "| 0.2 | DK_mrts | 234.61±91.36 | 15.10±2.59 | 9.90±2.32 | 0.9633±0.0138 |\n", - "| 0.2 | DK_S | 208.49±55.41 | 14.31±1.93 | 8.59±0.90 | 0.9675±0.0082 |\n", - "| 0.2 | DK_S_H | 219.99±50.00 | 14.74±1.66 | 8.94±0.72 | 0.9656±0.0076 |\n", - "| 0.5 | DK_W | 3662.38±439.20 | 60.41±3.63 | 45.13±2.99 | 0.4282±0.0515 |\n", - "| 0.5 | DK_W_new | 3626.10±460.06 | 60.09±3.87 | 43.42±3.39 | 0.4341±0.0551 |\n", - "| 0.5 | DK_mrts | 309.89±115.57 | 17.34±3.04 | 12.55±3.02 | 0.9523±0.0150 |\n", - "| 0.5 | DK_S | 198.61±34.08 | 14.04±1.25 | 8.97±0.73 | 0.9691±0.0046 |\n", - "| 0.5 | DK_S_H | 221.32±43.13 | 14.81±1.44 | 9.47±0.62 | 0.9654±0.0065 |\n", - "\n", - "\n", - "================================================================================\n", - "🏆 BEST MODELS (by RMSE)\n", - "================================================================================\n", - "\n", - "| Dropout Rate | Best Model | RMSE | R2 |\n", - "|----------------|--------------|------------|---------------|\n", - "| 0 | DK_mrts | 12.38±1.56 | 0.9758±0.0057 |\n", - "| 0.2 | DK_S | 14.31±1.93 | 0.9675±0.0082 |\n", - "| 0.5 | DK_S | 14.04±1.25 | 0.9691±0.0046 |\n", - "\n", - "\n", - "================================================================================\n", - "📈 MODEL COMPARISON (averaged across all dropout rates)\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|----------|----------------|------------|------------|---------------|\n", - "| DK_W | 3686.31±433.17 | 60.61±3.58 | 45.28±2.91 | 0.4246±0.0488 |\n", - "| DK_W_new | 3619.67±467.80 | 60.03±3.94 | 43.31±3.65 | 0.4353±0.0545 |\n", - "| DK_mrts | 233.38±108.33 | 14.94±3.20 | 10.01±3.03 | 0.9638±0.0155 |\n", - "| DK_S | 192.41±45.00 | 13.78±1.61 | 8.39±0.98 | 0.9700±0.0066 |\n", - "| DK_S_H | 224.99±52.63 | 14.90±1.76 | 9.06±0.82 | 0.9648±0.0082 |\n", - "\n", - "\n", - "================================================================================\n", - "✅ ALL ANALYSIS COMPLETED\n", - "================================================================================\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 FINAL SUMMARY: All Dropout Rates\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "# Create comprehensive summary table\n", - "summary_data = []\n", - "\n", - "for dr in HP_DR:\n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " metrics = all_dr_results[dr][model_name]\n", - " \n", - " summary_data.append({\n", - " \"DR\": f\"{dr:.1f}\",\n", - " \"Model\": model_name,\n", - " \"MSPE (mean±std)\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE (mean±std)\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE (mean±std)\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2 (mean±std)\": f\"{np.mean(metrics['R2']):.4f}±{np.std(metrics['R2']):.4f}\",\n", - " })\n", - "\n", - "df_summary = pd.DataFrame(summary_data)\n", - "print(df_summary.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "# Find best model for each metric\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"🏆 BEST MODELS (by RMSE)\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "best_models = []\n", - "for dr in HP_DR:\n", - " best_rmse = float('inf')\n", - " best_model = None\n", - " \n", - " for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " mean_rmse = np.mean(all_dr_results[dr][model_name]['RMSE'])\n", - " if mean_rmse < best_rmse:\n", - " best_rmse = mean_rmse\n", - " best_model = model_name\n", - " \n", - " best_models.append({\n", - " \"Dropout Rate\": f\"{dr:.1f}\",\n", - " \"Best Model\": best_model,\n", - " \"RMSE\": f\"{best_rmse:.2f}±{np.std(all_dr_results[dr][best_model]['RMSE']):.2f}\",\n", - " \"R2\": f\"{np.mean(all_dr_results[dr][best_model]['R2']):.4f}±{np.std(all_dr_results[dr][best_model]['R2']):.4f}\"\n", - " })\n", - "\n", - "df_best = pd.DataFrame(best_models)\n", - "print(df_best.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "# Model comparison across all dropout rates\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📈 MODEL COMPARISON (averaged across all dropout rates)\")\n", - "print(\"=\"*80 + \"\\n\")\n", - "\n", - "model_comparison = []\n", - "for model_name in [\"DK_W\", \"DK_W_new\", \"DK_mrts\", \"DK_S\", \"DK_S_H\"]:\n", - " all_mspe = []\n", - " all_rmse = []\n", - " all_mae = []\n", - " all_r2 = []\n", - " \n", - " for dr in HP_DR:\n", - " all_mspe.extend(all_dr_results[dr][model_name]['MSPE'])\n", - " all_rmse.extend(all_dr_results[dr][model_name]['RMSE'])\n", - " all_mae.extend(all_dr_results[dr][model_name]['MAE'])\n", - " all_r2.extend(all_dr_results[dr][model_name]['R2'])\n", - " \n", - " model_comparison.append({\n", - " \"Model\": model_name,\n", - " \"MSPE\": f\"{np.mean(all_mspe):.2f}±{np.std(all_mspe):.2f}\",\n", - " \"RMSE\": f\"{np.mean(all_rmse):.2f}±{np.std(all_rmse):.2f}\",\n", - " \"MAE\": f\"{np.mean(all_mae):.2f}±{np.std(all_mae):.2f}\",\n", - " \"R2\": f\"{np.mean(all_r2):.4f}±{np.std(all_r2):.4f}\"\n", - " })\n", - "\n", - "df_comparison = pd.DataFrame(model_comparison)\n", - "print(df_comparison.to_markdown(index=False, tablefmt=\"github\"))\n", - "print()\n", - "\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"✅ ALL ANALYSIS COMPLETED\")\n", - "print(\"=\"*80)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_nonGP_eggholder_without_outliers_all_model.ipynb b/notebook/test/simulation_spherical_nonGP_eggholder_without_outliers_all_model.ipynb deleted file mode 100644 index 37ef457..0000000 --- a/notebook/test/simulation_spherical_nonGP_eggholder_without_outliers_all_model.ipynb +++ /dev/null @@ -1,2087 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-11 11:00:20.603773: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770778820.617072 3225363 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770778820.620976 3225363 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770778820.631481 3225363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770778820.631504 3225363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770778820.631506 3225363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770778820.631508 3225363 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_nonstationary(num_sample, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-stationary Gaussian process with coordinate warp and scaling function.\n", - " \n", - " Parameters:\n", - " -----------\n", - " scenario : str\n", - " 'E1' for c=0.1, 'E2' for c=1.0\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Parameters\n", - " p = q = 3.5\n", - " c = 0.1 if scenario == 'E1' else 1.0\n", - " sigma_y_sq = 1.0 # variance parameter\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample) # longitude [0, 2π]\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample)) # colatitude [0, π]\n", - " \n", - " # Apply coordinate warp (Option A)\n", - " phi_warped = 2 * np.pi * (phi / (2 * np.pi)) ** p\n", - " theta_warped = np.pi * (theta / np.pi) ** q\n", - " \n", - " # Convert to latitude for scaling function\n", - " lat_rad = np.pi/2 - theta # latitude [-π/2, π/2]\n", - " lon_rad = phi - np.pi # longitude [-π, π]\n", - " \n", - " # Scaling function S(s) = 1 + 4(1 - |λ|/(π/2))\n", - " # At poles (|λ|=π/2): S=1, At equator (λ=0): S=5\n", - " S_values = 1 + 4 * (1 - np.abs(lat_rad) / (np.pi/2))\n", - " \n", - " # Convert warped coordinates to Cartesian for distance calculation\n", - " x_warp = np.cos(np.pi/2 - theta_warped) * np.cos(phi_warped - np.pi)\n", - " y_warp = np.cos(np.pi/2 - theta_warped) * np.sin(phi_warped - np.pi)\n", - " z_warp = np.sin(np.pi/2 - theta_warped)\n", - " coords_warp = np.column_stack([x_warp, y_warp, z_warp]).astype(np.float32)\n", - " \n", - " # Compute great-circle distance in warped space\n", - " dist_matrix = np.arccos(np.clip(coords_warp @ coords_warp.T, -1.0, 1.0))\n", - " \n", - " # Non-stationary covariance: C_E(s,u;c) = σ²S(s)S(u)exp(-γ(g(s),g(u))/c)\n", - " S_matrix = np.outer(S_values, S_values).astype(np.float32)\n", - " cov_matrix = (sigma_y_sq * S_matrix * np.exp(-dist_matrix / c)).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " gp_component = (L @ z_std).astype(np.float32)\n", - " \n", - " # Eggholder Mean Term (using original coordinates)\n", - " lat_rad_orig = np.pi/2 - theta\n", - " lon_rad_orig = phi - np.pi\n", - " x_cart = np.cos(lat_rad_orig) * np.cos(lon_rad_orig)\n", - " y_cart = np.cos(lat_rad_orig) * np.sin(lon_rad_orig)\n", - " \n", - " x = 128.0 * x_cart\n", - " y = 128.0 * y_cart\n", - " term1 = -(y + 47.0) * np.sin(np.sqrt(np.abs(x + y/2.0 + 47.0)))\n", - " term2 = -x * np.sin(np.sqrt(np.abs(x - (y + 47.0))))\n", - " mean_term = (term1 + term2).astype(np.float32)\n", - " \n", - " # Final response\n", - " z = mean_term + gp_component\n", - " \n", - " # Convert to degrees for output\n", - " lat_deg = np.rad2deg(lat_rad_orig).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad_orig).astype(np.float32)\n", - " \n", - " print(f\"\\n=== Scenario {scenario} (c={c}) ===\")\n", - " print(f\"GP component - mean: {np.mean(gp_component):.4f}, var: {np.var(gp_component, ddof=0):.4f}\")\n", - " print(f\"Final z - mean: {np.mean(z):.4f}, var: {np.var(z, ddof=0):.4f}, range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " print(f\"Scaling S(s) - range: [{np.min(S_values):.4f}, {np.max(S_values):.4f}]\")\n", - " \n", - " del coords_warp, dist_matrix, S_matrix, cov_matrix, L, z_std, gp_component\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: 0.6077, var: 12.0369\n", - "Final z - mean: 13.7370, var: 6313.8218, range: [-229.9894, 209.3307]\n", - "Scaling S(s) - range: [1.0901, 4.9996]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3135.6184 3424.6047 \n", - " 100 2160.7847 2289.2241 \n", - " 200 1525.7430 1309.3751 \n", - " 500 469.3387 348.3210 \n", - " 700 336.5965 263.9327 *\n", - " 1000 474.2920 357.1104 \n", - " 1400 3897.1702 4512.3901 \n", - " Best order: 700\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770778852.206417 3225363 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770778854.132553 3225623 service.cc:152] XLA service 0x765b44006990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770778854.132620 3225623 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770778854.509943 3225623 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770778856.899081 3225623 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x765b802013f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x765b7046aa70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 197.1543 218.8760 *\n", - " 100 2533.1184 2956.2014 \n", - " 200 2736.2930 3066.9966 \n", - " 500 283.7029 164.6398 \n", - " 700 276.0620 197.5200 \n", - " 1000 394.4260 557.1135 \n", - " 1400 1092.5385 1606.0070 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 41.9204 55.9710 *\n", - " 100 68.8114 106.2083 \n", - " 200 84.2070 141.1559 \n", - " 500 278.0100 249.3620 \n", - " 700 334.0734 207.3535 \n", - " 1000 796.7697 611.3984 \n", - " 1400 2164.2166 1862.9034 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 5.3615 74.4921 *\n", - " 100 6.0579 92.4947 \n", - " 200 6.5067 154.1395 \n", - " 500 11.4035 247.0692 \n", - " 700 14.6354 313.6265 \n", - " 1000 22.0251 659.1088 \n", - " 1400 39.3404 1967.2969 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.0139, sigma²=7110.1332, nugget=0.0021\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4770, sigma²=3135.2923, nugget=0.0017\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.2803, sigma²=1622.2018, nugget=0.0015\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0679, sigma²=214.4398, nugget=0.0030\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0276, sigma²=0.0230, nugget=108.0164\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=183.5861, sigma²=0.0003, nugget=62.5863\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=3.5863, sigma²=0.0001, nugget=29.6851\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 186.3414 180.3362 \n", - " 100 186.2437 184.6806 *\n", - " 200 191.6129 175.9610 \n", - " 500 254.3433 186.0278 \n", - " 700 336.5785 263.9172 \n", - " 1000 474.2939 357.1081 \n", - " 1400 3897.1060 4512.6757 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4770, sigma²=3135.2923, nugget=0.0017\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8380.52 | 91.5452 | 61.2982 | -0.2534 | 0.43s |\n", - "| OLS_sphere | 700 | 263.933 | 16.246 | 9.2089 | 0.9605 | 0.10s |\n", - "| DeepKriging_wendland | -- | 3979.34 | 63.082 | 48.6681 | 0.4049 | 59.02s |\n", - "| DeepKriging_wendland* | -- | 3819.04 | 61.7984 | 45.928 | 0.4288 | 17.15s |\n", - "| DeepKriging_mrts | 50 | 2767.86 | 52.6104 | 41.2005 | 0.586 | 28.03s |\n", - "| DeepKriging_sphere | 50 | 97.3217 | 9.8652 | 6.0933 | 0.9854 | 40.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 110.949 | 10.5332 | 5.3422 | 0.9834 | 46.24s |\n", - "| UniversalKriging | 100 | 184.681 | 13.5897 | 6.9314 | 0.9724 | 3.39s |\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:17:39\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -1.1395, var: 14.6009\n", - "Final z - mean: 11.3295, var: 6260.6660, range: [-210.3709, 203.4722]\n", - "Scaling S(s) - range: [1.0410, 4.9975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4762, sigma²=2884.9278, nugget=0.0013\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 12541.2 | 111.987 | 61.085 | -0.9632 | 0.37s |\n", - "| OLS_sphere | 700 | 282.405 | 16.8049 | 8.2185 | 0.9558 | 0.11s |\n", - "| DeepKriging_wendland | -- | 3692.51 | 60.766 | 46.1105 | 0.422 | 52.14s |\n", - "| DeepKriging_wendland* | -- | 3914.36 | 62.5648 | 46.2001 | 0.3873 | 19.82s |\n", - "| DeepKriging_mrts | 50 | 2317.64 | 48.1419 | 38.4396 | 0.6372 | 23.10s |\n", - "| DeepKriging_sphere | 50 | 93.3714 | 9.6629 | 5.3442 | 0.9854 | 32.66s |\n", - "| DeepKriging_sphere_Huber | 50 | 132.949 | 11.5304 | 7.1043 | 0.9792 | 31.96s |\n", - "| UniversalKriging | 100 | 198.323 | 14.0827 | 7.6702 | 0.969 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:20:57\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -0.4183, var: 10.7042\n", - "Final z - mean: 11.1820, var: 6345.4043, range: [-208.2559, 206.9017]\n", - "Scaling S(s) - range: [1.0339, 4.9992]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4915, sigma²=3266.2988, nugget=0.0014\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 8407.32 | 91.6914 | 60.3411 | -0.1816 | 0.40s |\n", - "| OLS_sphere | 700 | 233.999 | 15.297 | 8.2483 | 0.9671 | 0.09s |\n", - "| DeepKriging_wendland | -- | 4233.51 | 65.0654 | 48.1131 | 0.405 | 47.22s |\n", - "| DeepKriging_wendland* | -- | 3669.77 | 60.5787 | 42.3334 | 0.4842 | 25.52s |\n", - "| DeepKriging_mrts | 50 | 1550.22 | 39.3728 | 29.6058 | 0.7821 | 28.68s |\n", - "| DeepKriging_sphere | 50 | 66.7468 | 8.1699 | 5.374 | 0.9906 | 29.38s |\n", - "| DeepKriging_sphere_Huber | 50 | 62.7409 | 7.9209 | 4.9676 | 0.9912 | 30.63s |\n", - "| UniversalKriging | 100 | 173.58 | 13.175 | 6.881 | 0.9756 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:24:19\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -0.1514, var: 8.0998\n", - "Final z - mean: 13.3185, var: 6451.9526, range: [-207.6994, 205.9593]\n", - "Scaling S(s) - range: [1.1287, 4.9990]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4955, sigma²=3282.1844, nugget=0.0014\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 6517.98 | 80.734 | 59.6518 | 0.009 | 0.41s |\n", - "| OLS_sphere | 700 | 220.178 | 14.8384 | 8.9332 | 0.9665 | 0.07s |\n", - "| DeepKriging_wendland | -- | 4421.35 | 66.4932 | 50.0943 | 0.3278 | 45.85s |\n", - "| DeepKriging_wendland* | -- | 4162.44 | 64.517 | 46.6383 | 0.3671 | 25.02s |\n", - "| DeepKriging_mrts | 50 | 3121.9 | 55.8739 | 44.2132 | 0.5253 | 22.25s |\n", - "| DeepKriging_sphere | 50 | 109.779 | 10.4775 | 6.6472 | 0.9833 | 30.39s |\n", - "| DeepKriging_sphere_Huber | 50 | 119.14 | 10.9151 | 6.4212 | 0.9819 | 30.86s |\n", - "| UniversalKriging | 100 | 264.978 | 16.2781 | 8.6 | 0.9597 | 3.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:27:38\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -1.4651, var: 18.8173\n", - "Final z - mean: 12.8368, var: 6515.7700, range: [-209.4294, 211.6545]\n", - "Scaling S(s) - range: [1.0703, 4.9989]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4730, sigma²=3184.8004, nugget=0.0022\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 28246 | 168.065 | 66.7043 | -3.3172 | 0.43s |\n", - "| OLS_sphere | 700 | 337.305 | 18.3659 | 9.9671 | 0.9484 | 0.06s |\n", - "| DeepKriging_wendland | -- | 3491.39 | 59.088 | 45.3002 | 0.4664 | 49.46s |\n", - "| DeepKriging_wendland* | -- | 3114.66 | 55.8091 | 41.0292 | 0.5239 | 26.55s |\n", - "| DeepKriging_mrts | 50 | 2030.1 | 45.0567 | 33.7989 | 0.6897 | 26.12s |\n", - "| DeepKriging_sphere | 50 | 118.107 | 10.8677 | 7.5134 | 0.9819 | 30.73s |\n", - "| DeepKriging_sphere_Huber | 50 | 102.872 | 10.1426 | 6.2965 | 0.9843 | 37.75s |\n", - "| UniversalKriging | 100 | 319.36 | 17.8706 | 9.0206 | 0.9512 | 3.54s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:31:17\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -0.6319, var: 14.7856\n", - "Final z - mean: 13.5592, var: 6690.9556, range: [-212.2876, 208.5751]\n", - "Scaling S(s) - range: [1.0556, 4.9977]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4976, sigma²=3213.7599, nugget=0.0062\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|------------|----------|---------|---------|--------|\n", - "| OLS_wendland | -- | 25253.5 | 158.913 | 69.9532 | -2.7205 | 0.38s |\n", - "| OLS_sphere | 700 | 250.976 | 15.8422 | 8.8315 | 0.963 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3727.01 | 61.0493 | 45.4535 | 0.4509 | 55.05s |\n", - "| DeepKriging_wendland* | -- | 3410.32 | 58.398 | 43.2301 | 0.4976 | 21.66s |\n", - "| DeepKriging_mrts | 50 | 2104.02 | 45.8696 | 37.5749 | 0.69 | 22.17s |\n", - "| DeepKriging_sphere | 50 | 36.1736 | 6.0144 | 4.3735 | 0.9947 | 32.72s |\n", - "| DeepKriging_sphere_Huber | 50 | 64.2229 | 8.0139 | 4.8672 | 0.9905 | 31.29s |\n", - "| UniversalKriging | 100 | 116.641 | 10.8 | 6.4312 | 0.9828 | 3.65s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:34:51\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: 1.2364, var: 12.5505\n", - "Final z - mean: 16.1148, var: 6776.4199, range: [-219.3592, 209.5675]\n", - "Scaling S(s) - range: [1.0507, 4.9999]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4434, sigma²=3012.8191, nugget=0.0044\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|---------|--------|\n", - "| OLS_wendland | -- | 9319.01 | 96.535 | 60.9026 | -0.3997 | 0.36s |\n", - "| OLS_sphere | 700 | 188.626 | 13.7341 | 8.3251 | 0.9717 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3975.42 | 63.0509 | 47.1492 | 0.4029 | 47.54s |\n", - "| DeepKriging_wendland* | -- | 3192.86 | 56.5054 | 39.8635 | 0.5204 | 29.91s |\n", - "| DeepKriging_mrts | 50 | 2179.62 | 46.6864 | 36.4979 | 0.6726 | 24.44s |\n", - "| DeepKriging_sphere | 50 | 47.7445 | 6.9097 | 4.8274 | 0.9928 | 32.62s |\n", - "| DeepKriging_sphere_Huber | 50 | 55.798 | 7.4698 | 5.0237 | 0.9916 | 34.27s |\n", - "| UniversalKriging | 100 | 173.633 | 13.177 | 7.0213 | 0.9739 | 3.81s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:38:35\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: 0.0539, var: 11.1731\n", - "Final z - mean: 11.3417, var: 6381.8970, range: [-195.7368, 208.4041]\n", - "Scaling S(s) - range: [1.0894, 4.9991]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4863, sigma²=2920.2059, nugget=0.0013\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4548.95 | 67.4459 | 52.8474 | 0.2377 | 0.39s |\n", - "| OLS_sphere | 700 | 305.718 | 17.4848 | 10.0782 | 0.9488 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3193.69 | 56.5127 | 40.7849 | 0.4648 | 53.78s |\n", - "| DeepKriging_wendland* | -- | 3340.59 | 57.7979 | 42.0923 | 0.4402 | 18.23s |\n", - "| DeepKriging_mrts | 50 | 2830.16 | 53.1992 | 41.8021 | 0.5257 | 21.03s |\n", - "| DeepKriging_sphere | 50 | 75.6736 | 8.6991 | 5.3075 | 0.9873 | 43.33s |\n", - "| DeepKriging_sphere_Huber | 50 | 94.2558 | 9.7085 | 5.6604 | 0.9842 | 39.40s |\n", - "| UniversalKriging | 100 | 246.308 | 15.6942 | 8.4048 | 0.9587 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:42:28\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -0.2158, var: 9.8141\n", - "Final z - mean: 13.5499, var: 6763.3384, range: [-224.8059, 208.3613]\n", - "Scaling S(s) - range: [1.0284, 4.9998]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4651, sigma²=3208.3365, nugget=0.0032\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4745.1 | 68.8847 | 55.0412 | 0.1751 | 0.41s |\n", - "| OLS_sphere | 700 | 273.881 | 16.5494 | 9.5378 | 0.9524 | 0.08s |\n", - "| DeepKriging_wendland | -- | 3459.95 | 58.8214 | 44.4389 | 0.3985 | 49.75s |\n", - "| DeepKriging_wendland* | -- | 3494.64 | 59.1155 | 43.3718 | 0.3925 | 29.03s |\n", - "| DeepKriging_mrts | 50 | 2764.71 | 52.5805 | 42.19 | 0.5194 | 18.74s |\n", - "| DeepKriging_sphere | 50 | 85.8267 | 9.2643 | 5.7184 | 0.9851 | 31.32s |\n", - "| DeepKriging_sphere_Huber | 50 | 76.0007 | 8.7178 | 5.2916 | 0.9868 | 29.87s |\n", - "| UniversalKriging | 100 | 166.178 | 12.891 | 6.878 | 0.9711 | 3.31s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:46:06\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "GP component - mean: -0.6177, var: 11.4833\n", - "Final z - mean: 14.2761, var: 6229.4360, range: [-210.6101, 204.1307]\n", - "Scaling S(s) - range: [1.0732, 4.9996]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4807, sigma²=3013.7279, nugget=0.0048\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|-----------|---------|---------|--------|--------|\n", - "| OLS_wendland | -- | 4562.59 | 67.5469 | 53.0056 | 0.2194 | 0.42s |\n", - "| OLS_sphere | 700 | 164.338 | 12.8194 | 7.8906 | 0.9719 | 0.09s |\n", - "| DeepKriging_wendland | -- | 3506.11 | 59.2124 | 44.9455 | 0.4002 | 50.09s |\n", - "| DeepKriging_wendland* | -- | 3368.7 | 58.0405 | 42.2956 | 0.4237 | 20.21s |\n", - "| DeepKriging_mrts | 50 | 3890.74 | 62.3758 | 49.5698 | 0.3344 | 18.78s |\n", - "| DeepKriging_sphere | 50 | 84.0013 | 9.1652 | 6.7293 | 0.9856 | 31.29s |\n", - "| DeepKriging_sphere_Huber | 50 | 73.5542 | 8.5764 | 5.4955 | 0.9874 | 30.72s |\n", - "| UniversalKriging | 100 | 174.107 | 13.195 | 7.0031 | 0.9702 | 3.09s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 11:49:40\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_nonstationary(num_sample=num_sample, seed=seed, scenario='E1')\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 700, 'DeepKriging_mrts': 50, 'DeepKriging_sphere': 50, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|------------------|--------------|------------|------------|\n", - "| OLS_wendland | 11252.21±8130.20 | 100.33±34.43 | 60.08±5.22 | -0.72±1.21 |\n", - "| OLS_sphere | 252.14±49.75 | 15.80±1.60 | 8.92±0.73 | 0.96±0.01 |\n", - "| DeepKriging_wendland | 3768.03±361.73 | 61.31±2.93 | 46.11±2.47 | 0.41±0.04 |\n", - "| DeepKriging_wendland* | 3548.74±317.95 | 59.51±2.65 | 43.30±2.16 | 0.45±0.05 |\n", - "| DeepKriging_mrts | 2555.70±628.99 | 50.18±6.16 | 39.49±5.32 | 0.60±0.12 |\n", - "| DeepKriging_sphere | 81.47±24.50 | 8.91±1.45 | 5.79±0.91 | 0.99±0.00 |\n", - "| DeepKriging_sphere_Huber | 89.25±25.21 | 9.35±1.33 | 5.65±0.70 | 0.99±0.00 |\n", - "| UniversalKriging | 201.78±55.66 | 14.08±1.91 | 7.48±0.84 | 0.97±0.01 |\n", - "\n", - "🏆 Best Model: DeepKriging_sphere (RMSE: 8.91±1.45)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_nonGP_without_outliers_all_model.ipynb b/notebook/test/simulation_spherical_nonGP_without_outliers_all_model.ipynb deleted file mode 100644 index c9b54f4..0000000 --- a/notebook/test/simulation_spherical_nonGP_without_outliers_all_model.ipynb +++ /dev/null @@ -1,2061 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-11 17:02:52.748071: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770800572.764956 276655 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770800572.769859 276655 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770800572.783446 276655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770800572.783505 276655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770800572.783506 276655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770800572.783507 276655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_nonstationary(num_sample, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-stationary Gaussian process with coordinate warp and scaling function.\n", - " \n", - " Parameters:\n", - " -----------\n", - " scenario : str\n", - " 'E1' for c=0.1, 'E2' for c=1.0\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Parameters\n", - " p = q = 3.5\n", - " c = 0.1 if scenario == 'E1' else 1.0\n", - " sigma_y_sq = 1.0 # variance parameter\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample) # longitude [0, 2π]\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample)) # colatitude [0, π]\n", - " \n", - " # Apply coordinate warp (Option A)\n", - " phi_warped = 2 * np.pi * (phi / (2 * np.pi)) ** p\n", - " theta_warped = np.pi * (theta / np.pi) ** q\n", - " \n", - " # Convert to latitude for output\n", - " lat_rad = np.pi/2 - theta # latitude [-π/2, π/2]\n", - " lon_rad = phi - np.pi # longitude [-π, π]\n", - " \n", - " # Scaling function S(s) = 1 + 4(1 - |λ|/(π/2))\n", - " # At poles (|λ|=π/2): S=1, At equator (λ=0): S=5\n", - " S_values = 1 + 4 * (1 - np.abs(lat_rad) / (np.pi/2))\n", - " \n", - " # Convert warped coordinates to Cartesian for distance calculation\n", - " x_warp = np.cos(np.pi/2 - theta_warped) * np.cos(phi_warped - np.pi)\n", - " y_warp = np.cos(np.pi/2 - theta_warped) * np.sin(phi_warped - np.pi)\n", - " z_warp = np.sin(np.pi/2 - theta_warped)\n", - " coords_warp = np.column_stack([x_warp, y_warp, z_warp]).astype(np.float32)\n", - " \n", - " # Compute great-circle distance in warped space\n", - " dist_matrix = np.arccos(np.clip(coords_warp @ coords_warp.T, -1.0, 1.0))\n", - " \n", - " # Non-stationary covariance: C_E(s,u;c) = σ²S(s)S(u)exp(-γ(g(s),g(u))/c)\n", - " S_matrix = np.outer(S_values, S_values).astype(np.float32)\n", - " cov_matrix = (sigma_y_sq * S_matrix * np.exp(-dist_matrix / c)).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process (no mean term)\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " z = (L @ z_std).astype(np.float32)\n", - " \n", - " # Convert to degrees for output\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " print(f\"\\n=== Scenario {scenario} (c={c}) ===\")\n", - " print(f\"z - mean: {np.mean(z):.4f}, var: {np.var(z, ddof=0):.4f}, range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " print(f\"Scaling S(s) - range: [{np.min(S_values):.4f}, {np.max(S_values):.4f}]\")\n", - " \n", - " del coords_warp, dist_matrix, S_matrix, cov_matrix, L, z_std\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: 0.6077, var: 12.0369, range: [-17.4552, 11.8090]\n", - "Scaling S(s) - range: [1.0901, 4.9996]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 6.4255 5.5597 \n", - " 100 5.3943 5.1840 \n", - " 200 4.6159 4.1083 *\n", - " 500 4.6915 4.0532 \n", - " 700 5.0791 4.1412 \n", - " 1000 9.9142 9.5314 \n", - " 1400 148.6124 76.2892 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770800603.028611 276655 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770800605.679190 276932 service.cc:152] XLA service 0x585747bada40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770800605.679264 276932 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770800604.540000 276932 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770800607.599024 276932 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d3fcc4de560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x7d3fa837f2e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 4.2370 3.9398 *\n", - " 100 4.7207 4.9316 \n", - " 200 5.1231 4.9764 \n", - " 500 6.3746 6.8593 \n", - " 700 6.6964 7.7274 \n", - " 1000 4.9451 4.9391 \n", - " 1400 4.5594 5.3145 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 8.3311 7.9902 \n", - " 100 3.5335 3.3735 \n", - " 200 3.3734 3.3320 *\n", - " 500 3.7034 3.4539 \n", - " 700 3.9225 4.1592 \n", - " 1000 4.3473 4.5115 \n", - " 1400 5.3460 6.8270 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 2.3126 8.1447 \n", - " 100 2.3301 8.4593 \n", - " 200 1.0632 3.2029 *\n", - " 500 1.1850 3.6076 \n", - " 700 1.1321 4.3744 \n", - " 1000 1.2572 4.7949 \n", - " 1400 1.5832 7.6380 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0799, sigma²=6.0944, nugget=0.0003\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0645, sigma²=5.2406, nugget=0.0005\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0422, sigma²=3.9231, nugget=0.0001\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=4.0605, sigma²=0.0000, nugget=2.0802\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.1906, sigma²=0.0000, nugget=1.5461\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.8445, sigma²=0.0000, nugget=1.0030\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.4884, sigma²=0.0000, nugget=0.5506\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3.1493 2.9251 \n", - " 100 3.1485 2.8897 *\n", - " 200 3.5070 2.9243 \n", - " 500 4.6915 4.0532 \n", - " 700 5.0791 4.1412 \n", - " 1000 9.9142 9.5313 \n", - " 1400 148.6378 76.2920 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0645, sigma²=5.2406, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 10.4064 | 3.2259 | 2.3806 | 0.0861 | 0.43s |\n", - "| OLS_sphere | 200 | 4.1083 | 2.0269 | 1.5298 | 0.6392 | 0.02s |\n", - "| DeepKriging_wendland | -- | 6.255 | 2.501 | 1.9785 | 0.4507 | 69.65s |\n", - "| DeepKriging_wendland* | -- | 5.6869 | 2.3847 | 1.8635 | 0.5006 | 34.65s |\n", - "| DeepKriging_mrts | 50 | 3.8077 | 1.9513 | 1.434 | 0.6656 | 34.54s |\n", - "| DeepKriging_sphere | 200 | 3.3525 | 1.831 | 1.2838 | 0.7056 | 35.65s |\n", - "| DeepKriging_sphere_Huber | 200 | 3.3003 | 1.8167 | 1.2757 | 0.7102 | 33.81s |\n", - "| UniversalKriging | 100 | 2.8897 | 1.6999 | 1.2389 | 0.7462 | 5.58s |\n" - ] - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:19:04\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -1.1395, var: 14.6009, range: [-13.4857, 14.9034]\n", - "Scaling S(s) - range: [1.0410, 4.9975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0553, sigma²=5.3725, nugget=0.0287\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|----------|--------|\n", - "| OLS_wendland | -- | 834.592 | 28.8893 | 4.2755 | -53.4089 | 0.46s |\n", - "| OLS_sphere | 200 | 5.0533 | 2.2479 | 1.6367 | 0.6706 | 0.02s |\n", - "| DeepKriging_wendland | -- | 10.8657 | 3.2963 | 2.3704 | 0.2916 | 70.78s |\n", - "| DeepKriging_wendland* | -- | 10.6823 | 3.2684 | 2.3424 | 0.3036 | 21.76s |\n", - "| DeepKriging_mrts | 50 | 4.6601 | 2.1587 | 1.5578 | 0.6962 | 39.48s |\n", - "| DeepKriging_sphere | 200 | 3.8791 | 1.9696 | 1.4106 | 0.7471 | 32.17s |\n", - "| DeepKriging_sphere_Huber | 200 | 11.8081 | 3.4363 | 2.7331 | 0.2302 | 15.32s |\n", - "| UniversalKriging | 100 | 3.3712 | 1.8361 | 1.3471 | 0.7802 | 3.27s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:22:43\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -0.4183, var: 10.7042, range: [-11.1974, 12.7710]\n", - "Scaling S(s) - range: [1.0339, 4.9992]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0570, sigma²=5.2626, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 8.444 | 2.9059 | 2.0784 | 0.1553 | 0.42s |\n", - "| OLS_sphere | 200 | 4.6792 | 2.1632 | 1.5965 | 0.5319 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.2176 | 2.6866 | 1.8466 | 0.278 | 70.60s |\n", - "| DeepKriging_wendland* | -- | 7.4828 | 2.7355 | 1.9261 | 0.2515 | 19.89s |\n", - "| DeepKriging_mrts | 50 | 5.7048 | 2.3885 | 1.7083 | 0.4293 | 28.15s |\n", - "| DeepKriging_sphere | 200 | 7.9413 | 2.818 | 2.0814 | 0.2056 | 17.54s |\n", - "| DeepKriging_sphere_Huber | 200 | 7.8615 | 2.8038 | 2.0081 | 0.2136 | 16.42s |\n", - "| UniversalKriging | 100 | 3.3243 | 1.8233 | 1.3306 | 0.6675 | 3.44s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:25:56\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -0.1514, var: 8.0998, range: [-15.2766, 11.0782]\n", - "Scaling S(s) - range: [1.1287, 4.9990]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0532, sigma²=4.7136, nugget=0.0273\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 8.8707 | 2.9784 | 2.1582 | -0.1225 | 0.37s |\n", - "| OLS_sphere | 200 | 5.8433 | 2.4173 | 1.7913 | 0.2606 | 0.02s |\n", - "| DeepKriging_wendland | -- | 5.7468 | 2.3973 | 1.7507 | 0.2728 | 70.57s |\n", - "| DeepKriging_wendland* | -- | 6.106 | 2.471 | 1.8221 | 0.2273 | 27.85s |\n", - "| DeepKriging_mrts | 50 | 4.9388 | 2.2223 | 1.6001 | 0.375 | 35.54s |\n", - "| DeepKriging_sphere | 200 | 3.231 | 1.7975 | 1.2527 | 0.5911 | 35.95s |\n", - "| DeepKriging_sphere_Huber | 200 | 6.5922 | 2.5675 | 1.979 | 0.1658 | 16.91s |\n", - "| UniversalKriging | 100 | 3.4633 | 1.861 | 1.3299 | 0.5617 | 3.22s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:29:44\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -1.4651, var: 18.8173, range: [-11.1008, 16.2213]\n", - "Scaling S(s) - range: [1.0703, 4.9989]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0729, sigma²=6.3682, nugget=0.0339\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 68.4365 | 8.2726 | 3.5218 | -2.0647 | 0.41s |\n", - "| OLS_sphere | 200 | 5.6758 | 2.3824 | 1.7731 | 0.7458 | 0.02s |\n", - "| DeepKriging_wendland | -- | 9.7343 | 3.12 | 2.3331 | 0.5641 | 71.64s |\n", - "| DeepKriging_wendland* | -- | 19.7533 | 4.4445 | 3.6419 | 0.1154 | 18.98s |\n", - "| DeepKriging_mrts | 50 | 5.1116 | 2.2609 | 1.6512 | 0.7711 | 39.80s |\n", - "| DeepKriging_sphere | 200 | 14.5325 | 3.8122 | 3.1873 | 0.3492 | 15.96s |\n", - "| DeepKriging_sphere_Huber | 200 | 14.0021 | 3.7419 | 3.0902 | 0.373 | 18.26s |\n", - "| UniversalKriging | 100 | 3.0902 | 1.7579 | 1.2566 | 0.8616 | 1.40s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:33:13\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -0.6319, var: 14.7856, range: [-12.2477, 10.0232]\n", - "Scaling S(s) - range: [1.0556, 4.9977]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0474, sigma²=4.4961, nugget=0.0524\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 17.9262 | 4.2339 | 2.8574 | -0.0605 | 0.37s |\n", - "| OLS_sphere | 200 | 4.5938 | 2.1433 | 1.5926 | 0.7282 | 0.02s |\n", - "| DeepKriging_wendland | -- | 8.2539 | 2.873 | 2.3117 | 0.5117 | 74.71s |\n", - "| DeepKriging_wendland* | -- | 7.7649 | 2.7866 | 2.1711 | 0.5406 | 36.02s |\n", - "| DeepKriging_mrts | 50 | 4.3086 | 2.0757 | 1.5189 | 0.7451 | 43.22s |\n", - "| DeepKriging_sphere | 200 | 3.5668 | 1.8886 | 1.3211 | 0.789 | 34.90s |\n", - "| DeepKriging_sphere_Huber | 200 | 3.6118 | 1.9005 | 1.3002 | 0.7863 | 37.78s |\n", - "| UniversalKriging | 100 | 3.1464 | 1.7738 | 1.2707 | 0.8139 | 3.15s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:37:49\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: 1.2364, var: 12.5505, range: [-13.4585, 16.2034]\n", - "Scaling S(s) - range: [1.0507, 4.9999]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0550, sigma²=4.9356, nugget=0.0003\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 11.3825 | 3.3738 | 2.4346 | 0.1325 | 0.39s |\n", - "| OLS_sphere | 200 | 4.962 | 2.2276 | 1.498 | 0.6218 | 0.02s |\n", - "| DeepKriging_wendland | -- | 8.7096 | 2.9512 | 2.1628 | 0.3362 | 73.70s |\n", - "| DeepKriging_wendland* | -- | 8.4954 | 2.9147 | 2.1127 | 0.3525 | 30.90s |\n", - "| DeepKriging_mrts | 50 | 4.8759 | 2.2081 | 1.4822 | 0.6284 | 42.75s |\n", - "| DeepKriging_sphere | 200 | 10.8247 | 3.2901 | 2.5726 | 0.175 | 15.04s |\n", - "| DeepKriging_sphere_Huber | 200 | 10.678 | 3.2677 | 2.5511 | 0.1862 | 15.56s |\n", - "| UniversalKriging | 100 | 3.2293 | 1.797 | 1.2742 | 0.7539 | 4.05s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:41:42\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: 0.0539, var: 11.1731, range: [-15.8676, 10.8588]\n", - "Scaling S(s) - range: [1.0894, 4.9991]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0542, sigma²=5.0082, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 12.706 | 3.5645 | 2.411 | -0.14 | 0.38s |\n", - "| OLS_sphere | 200 | 5.398 | 2.3233 | 1.7167 | 0.5157 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.3564 | 2.7123 | 2.1103 | 0.34 | 71.93s |\n", - "| DeepKriging_wendland* | -- | 7.3371 | 2.7087 | 2.0948 | 0.3417 | 33.32s |\n", - "| DeepKriging_mrts | 50 | 5.1711 | 2.274 | 1.5776 | 0.536 | 51.00s |\n", - "| DeepKriging_sphere | 200 | 4.0775 | 2.0193 | 1.4452 | 0.6342 | 34.26s |\n", - "| DeepKriging_sphere_Huber | 200 | 4.0921 | 2.0229 | 1.4294 | 0.6328 | 37.40s |\n", - "| UniversalKriging | 100 | 3.8016 | 1.9498 | 1.4025 | 0.6589 | 4.47s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:46:24\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -0.2158, var: 9.8141, range: [-10.7529, 11.9437]\n", - "Scaling S(s) - range: [1.0284, 4.9998]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0569, sigma²=5.4622, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 7.3875 | 2.718 | 1.9912 | 0.1928 | 0.38s |\n", - "| OLS_sphere | 200 | 5.4264 | 2.3295 | 1.721 | 0.4071 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.035 | 2.6524 | 1.9243 | 0.2313 | 70.94s |\n", - "| DeepKriging_wendland* | -- | 7.2183 | 2.6867 | 1.9428 | 0.2113 | 22.43s |\n", - "| DeepKriging_mrts | 50 | 5.224 | 2.2856 | 1.6922 | 0.4292 | 28.19s |\n", - "| DeepKriging_sphere | 200 | 3.99 | 1.9975 | 1.4128 | 0.564 | 30.88s |\n", - "| DeepKriging_sphere_Huber | 200 | 4.026 | 2.0065 | 1.435 | 0.5601 | 32.09s |\n", - "| UniversalKriging | 100 | 3.7473 | 1.9358 | 1.3815 | 0.5906 | 3.94s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:50:28\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "z - mean: -0.6177, var: 11.4833, range: [-12.1898, 11.9859]\n", - "Scaling S(s) - range: [1.0732, 4.9996]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0516, sigma²=5.0552, nugget=0.0008\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 10.0262 | 3.1664 | 2.3059 | 0.077 | 0.54s |\n", - "| OLS_sphere | 200 | 4.3857 | 2.0942 | 1.5474 | 0.5962 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.3361 | 2.7085 | 2.037 | 0.3246 | 71.49s |\n", - "| DeepKriging_wendland* | -- | 7.898 | 2.8103 | 2.1333 | 0.2729 | 30.72s |\n", - "| DeepKriging_mrts | 50 | 4.9335 | 2.2211 | 1.6381 | 0.5458 | 33.76s |\n", - "| DeepKriging_sphere | 200 | 7.6372 | 2.7635 | 2.1034 | 0.2969 | 15.95s |\n", - "| DeepKriging_sphere_Huber | 200 | 7.7989 | 2.7927 | 2.1377 | 0.282 | 17.27s |\n", - "| UniversalKriging | 100 | 3.4167 | 1.8484 | 1.351 | 0.6855 | 4.36s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 17:54:22\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_nonstationary(num_sample=num_sample, seed=seed, scenario='E1')\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 200, 'DeepKriging_mrts': 50, 'DeepKriging_sphere': 200, 'DeepKriging_sphere_Huber': 200, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|--------------|-----------|-----------|-------------|\n", - "| OLS_wendland | 99.02±245.81 | 6.33±7.68 | 2.64±0.69 | -5.52±15.98 |\n", - "| OLS_sphere | 5.01±0.54 | 2.24±0.12 | 1.64±0.10 | 0.57±0.14 |\n", - "| DeepKriging_wendland | 7.85±1.49 | 2.79±0.26 | 2.08±0.20 | 0.36±0.11 |\n", - "| DeepKriging_wendland* | 8.84±3.86 | 2.92±0.56 | 2.21±0.50 | 0.31±0.12 |\n", - "| DeepKriging_mrts | 4.87±0.50 | 2.20±0.11 | 1.59±0.09 | 0.58±0.13 |\n", - "| DeepKriging_sphere | 6.30±3.67 | 2.42±0.67 | 1.81±0.63 | 0.51±0.22 |\n", - "| DeepKriging_sphere_Huber | 7.38±3.58 | 2.64±0.66 | 1.99±0.61 | 0.41±0.22 |\n", - "| UniversalKriging | 3.35±0.27 | 1.83±0.07 | 1.32±0.05 | 0.71±0.09 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 1.83±0.07)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/test/simulation_spherical_nonGP_without_outliers_all_model_add_x.ipynb b/notebook/test/simulation_spherical_nonGP_without_outliers_all_model_add_x.ipynb deleted file mode 100644 index 0619b64..0000000 --- a/notebook/test/simulation_spherical_nonGP_without_outliers_all_model_add_x.ipynb +++ /dev/null @@ -1,2101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb75f2ac", - "metadata": { - "papermill": { - "duration": 0.013139, - "end_time": "2026-01-08T18:26:22.324767", - "exception": false, - "start_time": "2026-01-08T18:26:22.311628", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8e1c49d", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.349146Z", - "iopub.status.busy": "2026-01-08T18:26:22.348181Z", - "iopub.status.idle": "2026-01-08T18:26:22.371989Z", - "shell.execute_reply": "2026-01-08T18:26:22.369366Z" - }, - "papermill": { - "duration": 0.039537, - "end_time": "2026-01-08T18:26:22.376192", - "exception": false, - "start_time": "2026-01-08T18:26:22.336655", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46af0f5c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:22.399728Z", - "iopub.status.busy": "2026-01-08T18:26:22.398927Z", - "iopub.status.idle": "2026-01-08T18:26:25.172611Z", - "shell.execute_reply": "2026-01-08T18:26:25.170256Z" - }, - "papermill": { - "duration": 2.787959, - "end_time": "2026-01-08T18:26:25.176438", - "exception": false, - "start_time": "2026-01-08T18:26:22.388479", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-11 18:10:06.599543: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1770804606.613856 703331 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1770804606.617944 703331 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1770804606.629224 703331 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770804606.629257 703331 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770804606.629258 703331 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1770804606.629260 703331 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "/home/user/miniconda3/envs/air-pollution/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import multiprocessing as mp\n", - "if mp.get_start_method(allow_none=True) is None:\n", - " mp.set_start_method('spawn')\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=UserWarning)\n", - "warnings.filterwarnings('ignore', message='.*input_shape.*')\n", - "warnings.filterwarnings('ignore', message='.*structure of.*inputs.*')\n", - "\n", - "import os, time, gc\n", - "from types import SimpleNamespace\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time as time_module\n", - "from scipy.stats import t\n", - "from scipy.special import kv, gamma\n", - "\n", - "import jax, jax.numpy as jnp\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.keras import mixed_precision\n", - "from tensorflow.keras.optimizers import Adam\n", - "from tensorflow.keras.losses import Huber\n", - "from tensorflow.keras import backend as K\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", - "from sklearn.linear_model import LinearRegression, Ridge\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "import optuna\n", - "import plotly.io as pio\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as mcolors\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature" - ] - }, - { - "cell_type": "markdown", - "id": "3642d69c", - "metadata": { - "papermill": { - "duration": 0.0043, - "end_time": "2026-01-08T18:26:25.189157", - "exception": false, - "start_time": "2026-01-08T18:26:25.184857", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Environment setting" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9244ac2c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:25.199342Z", - "iopub.status.busy": "2026-01-08T18:26:25.198211Z", - "iopub.status.idle": "2026-01-08T18:26:26.647373Z", - "shell.execute_reply": "2026-01-08T18:26:26.645122Z" - }, - "papermill": { - "duration": 1.458379, - "end_time": "2026-01-08T18:26:26.651196", - "exception": false, - "start_time": "2026-01-08T18:26:25.192817", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np_f32 = np.float32\n", - "jnp_f32 = jnp.float32\n", - "dtype_basis = np.float32\n", - "\n", - "jax.config.update(\"jax_enable_x64\", False)\n", - "\n", - "pio.renderers.default = \"notebook\"\n", - "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - "os.environ.update({\"TF_CPP_MIN_LOG_LEVEL\": \"2\"})\n", - "optuna.logging.set_verbosity(optuna.logging.WARNING)\n", - "\n", - "os.environ.setdefault(\"OMP_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"MKL_NUM_THREADS\", \"12\")\n", - "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"12\")\n", - "\n", - "def init_hardware(dtype: str = \"float32\"):\n", - " gpus = tf.config.list_physical_devices(\"GPU\")\n", - " for g in gpus:\n", - " tf.config.experimental.set_memory_growth(g, True)\n", - " strategy = (tf.distribute.MirroredStrategy() if len(gpus) > 1 else tf.distribute.get_strategy())\n", - " mixed_precision.set_global_policy(dtype)\n", - " return strategy\n", - "\n", - "strategy = init_hardware(dtype=\"float32\")" - ] - }, - { - "cell_type": "markdown", - "id": "631dc3ef", - "metadata": { - "papermill": { - "duration": 0.011565, - "end_time": "2026-01-08T18:26:26.675666", - "exception": false, - "start_time": "2026-01-08T18:26:26.664101", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Auto save notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8082d639", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.692892Z", - "iopub.status.busy": "2026-01-08T18:26:26.692003Z", - "iopub.status.idle": "2026-01-08T18:26:26.706355Z", - "shell.execute_reply": "2026-01-08T18:26:26.702852Z" - }, - "papermill": { - "duration": 0.026905, - "end_time": "2026-01-08T18:26:26.710566", - "exception": false, - "start_time": "2026-01-08T18:26:26.683661", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import display, Javascript\n", - "\n", - "def save_notebook():\n", - " display(Javascript('IPython.notebook.save_checkpoint()'))\n", - " current_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n", - " print(f\"💾 Notebook saved at {current_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9399eb89", - "metadata": { - "papermill": { - "duration": 0.006688, - "end_time": "2026-01-08T18:26:26.727016", - "exception": false, - "start_time": "2026-01-08T18:26:26.720328", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Our function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8391a63c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:26.739697Z", - "iopub.status.busy": "2026-01-08T18:26:26.738844Z", - "iopub.status.idle": "2026-01-08T18:26:40.997851Z", - "shell.execute_reply": "2026-01-08T18:26:40.995902Z" - }, - "papermill": { - "duration": 14.269816, - "end_time": "2026-01-08T18:26:41.001847", - "exception": false, - "start_time": "2026-01-08T18:26:26.732031", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "R callback write-console: Registered S3 methods overwritten by 'RcppEigen':\n", - " method from \n", - " predict.fastLm RcppArmadillo\n", - " print.fastLm RcppArmadillo\n", - " summary.fastLm RcppArmadillo\n", - " print.summary.fastLm RcppArmadillo\n", - " \n" - ] - } - ], - "source": [ - "from spherical_deepkriging.basis_functions.wendland.wenland import wendland\n", - "from spherical_deepkriging.basis_functions.mrts.mrts import mrts0\n", - "\n", - "from spherical_deepkriging.models.deep_kriging import DeepKrigingTrainer, DeepKrigingDefaultTrainer\n", - "from spherical_deepkriging.configs import DeepKrigingModelConfig, DeepKrigingDefaultConfig\n", - "from spherical_deepkriging.models.universal_kriging import UniversalKriging\n", - "\n", - "from rpy2.robjects.conversion import localconverter\n", - "from spherical_deepkriging.basis_functions.mrts_sphere.sphere import mrts_sphere, numpy2ri_converter" - ] - }, - { - "cell_type": "markdown", - "id": "f7d37f3e", - "metadata": { - "papermill": { - "duration": 0.005258, - "end_time": "2026-01-08T18:26:41.013048", - "exception": false, - "start_time": "2026-01-08T18:26:41.007790", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Simulation Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "076fe31f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.025568Z", - "iopub.status.busy": "2026-01-08T18:26:41.024647Z", - "iopub.status.idle": "2026-01-08T18:26:41.047376Z", - "shell.execute_reply": "2026-01-08T18:26:41.044784Z" - }, - "papermill": { - "duration": 0.031351, - "end_time": "2026-01-08T18:26:41.050961", - "exception": false, - "start_time": "2026-01-08T18:26:41.019610", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def simulate_data_nonstationary(num_sample, seed, scenario='E1'):\n", - " \"\"\"\n", - " Non-stationary Gaussian process with coordinate warp and scaling function.\n", - " Mean term: Cartesian coordinates (x, y, z)\n", - " \n", - " Parameters:\n", - " -----------\n", - " scenario : str\n", - " 'E1' for c=0.1, 'E2' for c=1.0\n", - " \"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " \n", - " # Parameters\n", - " p = q = 3.5\n", - " c = 0.1 if scenario == 'E1' else 1.0\n", - " sigma_y_sq = 1.0\n", - " \n", - " # Generate spherical coordinates uniformly\n", - " phi = rng.uniform(0, 2 * np.pi, num_sample)\n", - " theta = np.arccos(rng.uniform(-1, 1, num_sample))\n", - " \n", - " # Apply coordinate warp (Option A)\n", - " phi_warped = 2 * np.pi * (phi / (2 * np.pi)) ** p\n", - " theta_warped = np.pi * (theta / np.pi) ** q\n", - " \n", - " # Convert to latitude/longitude for output\n", - " lat_rad = np.pi/2 - theta\n", - " lon_rad = phi - np.pi\n", - " \n", - " # Cartesian coordinates as mean term\n", - " x_cart = np.cos(lat_rad) * np.cos(lon_rad)\n", - " y_cart = np.cos(lat_rad) * np.sin(lon_rad)\n", - " z_cart = np.sin(lat_rad)\n", - " \n", - " # Mean term: you can choose one or combination\n", - " # Option A: use x coordinate only\n", - " mean_term = x_cart.astype(np.float32)\n", - " \n", - " # Option B: use combination (e.g., x + y + z)\n", - " # mean_term = (x_cart + y_cart + z_cart).astype(np.float32)\n", - " \n", - " # Option C: scaled version\n", - " # mean_term = (10.0 * x_cart).astype(np.float32)\n", - " \n", - " # Scaling function S(s) = 1 + 4(1 - |λ|/(π/2))\n", - " S_values = 1 + 4 * (1 - np.abs(lat_rad) / (np.pi/2))\n", - " \n", - " # Convert warped coordinates to Cartesian for distance calculation\n", - " x_warp = np.cos(np.pi/2 - theta_warped) * np.cos(phi_warped - np.pi)\n", - " y_warp = np.cos(np.pi/2 - theta_warped) * np.sin(phi_warped - np.pi)\n", - " z_warp = np.sin(np.pi/2 - theta_warped)\n", - " coords_warp = np.column_stack([x_warp, y_warp, z_warp]).astype(np.float32)\n", - " \n", - " # Compute great-circle distance in warped space\n", - " dist_matrix = np.arccos(np.clip(coords_warp @ coords_warp.T, -1.0, 1.0))\n", - " \n", - " # Non-stationary covariance\n", - " S_matrix = np.outer(S_values, S_values).astype(np.float32)\n", - " cov_matrix = (sigma_y_sq * S_matrix * np.exp(-dist_matrix / c)).astype(np.float32)\n", - " \n", - " # Add jitter for numerical stability\n", - " jitter = 1e-3\n", - " cov_matrix += np.float32(jitter) * np.eye(num_sample, dtype=np.float32)\n", - " \n", - " # Cholesky decomposition\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " cov_matrix += np.float32(1e-2) * np.eye(num_sample, dtype=np.float32)\n", - " try:\n", - " L = np.linalg.cholesky(cov_matrix)\n", - " except np.linalg.LinAlgError:\n", - " eigenvals, eigenvecs = np.linalg.eigh(cov_matrix)\n", - " eigenvals = np.maximum(eigenvals, 1e-6)\n", - " L = eigenvecs @ np.diag(np.sqrt(eigenvals))\n", - " \n", - " # Generate Gaussian process\n", - " z_std = rng.standard_normal(num_sample).astype(np.float32)\n", - " gp_component = (L @ z_std).astype(np.float32)\n", - " \n", - " # Final response: mean + GP\n", - " z = mean_term + gp_component\n", - " \n", - " # Convert to degrees for output\n", - " lat_deg = np.rad2deg(lat_rad).astype(np.float32)\n", - " lon_deg = np.rad2deg(lon_rad).astype(np.float32)\n", - " \n", - " print(f\"\\n=== Scenario {scenario} (c={c}) ===\")\n", - " print(f\"Mean term - mean: {np.mean(mean_term):.4f}, range: [{np.min(mean_term):.4f}, {np.max(mean_term):.4f}]\")\n", - " print(f\"GP component - mean: {np.mean(gp_component):.4f}, var: {np.var(gp_component, ddof=0):.4f}\")\n", - " print(f\"Final z - mean: {np.mean(z):.4f}, var: {np.var(z, ddof=0):.4f}, range: [{np.min(z):.4f}, {np.max(z):.4f}]\")\n", - " print(f\"Scaling S(s) - range: [{np.min(S_values):.4f}, {np.max(S_values):.4f}]\")\n", - " \n", - " del coords_warp, dist_matrix, S_matrix, cov_matrix, L, z_std, gp_component\n", - " gc.collect()\n", - " \n", - " return pd.DataFrame({\n", - " \"longitude\": lon_deg,\n", - " \"latitude\": lat_deg,\n", - " \"z\": z,\n", - " })" - ] - }, - { - "cell_type": "markdown", - "id": "57a9ba52", - "metadata": { - "papermill": { - "duration": 0.003384, - "end_time": "2026-01-08T18:26:41.060958", - "exception": false, - "start_time": "2026-01-08T18:26:41.057574", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Helper" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b3d5a807", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.069616Z", - "iopub.status.busy": "2026-01-08T18:26:41.068797Z", - "iopub.status.idle": "2026-01-08T18:26:41.107756Z", - "shell.execute_reply": "2026-01-08T18:26:41.105757Z" - }, - "papermill": { - "duration": 0.047555, - "end_time": "2026-01-08T18:26:41.111544", - "exception": false, - "start_time": "2026-01-08T18:26:41.063989", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def data_preprocessing(data_frame):\n", - " data = data_frame.copy()\n", - "\n", - " numeric_cols = [\"longitude\", \"latitude\", \"z\"]\n", - " data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\n", - " data.dropna(subset=numeric_cols, inplace=True)\n", - "\n", - " lon, lat = data[\"longitude\"].to_numpy(), data[\"latitude\"].to_numpy()\n", - "\n", - " norm_lon = (lon - lon.min()) / (lon.max() - lon.min())\n", - " norm_lat = (lat - lat.min()) / (lat.max() - lat.min())\n", - "\n", - " location_data = np.column_stack([lat, lon]).astype(\"float32\")\n", - " location_data_norm = np.column_stack([norm_lon, norm_lat]).astype(\"float32\")\n", - " y_combined = data['z'].to_numpy().astype(\"float32\")[:, None]\n", - "\n", - " # Handle\n", - " categorical_data = None\n", - "\n", - " return location_data, location_data_norm, categorical_data, y_combined\n", - "\n", - "\n", - "def precompute_wendland(location, num_basis):\n", - " parts = []\n", - " for nb in num_basis:\n", - " grid = np.column_stack(np.meshgrid(\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " np.linspace(0, 1, int(np.sqrt(nb)), dtype=np_f32),\n", - " )).reshape(-1, 2).astype(np_f32)\n", - "\n", - " theta = np_f32(2.5)/np_f32(np.sqrt(nb))\n", - " parts.append(\n", - " wendland(location, grid, theta=theta, k=2)\n", - " )\n", - "\n", - " # Clean up the memory\n", - " del grid\n", - " gc.collect()\n", - "\n", - " return np.hstack(parts).astype(dtype_basis, copy=False)\n", - "\n", - "\n", - "def precompute_max_mrts(distance_type, location_data, knot_num, order_max, knot=None):\n", - " if knot is None:\n", - " idx_knot = np.random.choice(location_data.shape[0], knot_num, replace=False)\n", - " knot = location_data[idx_knot].astype(np_f32)\n", - " else:\n", - " idx_knot = None\n", - "\n", - " if distance_type == \"sphere\":\n", - " with localconverter(numpy2ri_converter):\n", - " res_r = mrts_sphere(knot, order_max, location_data.astype(np_f32))\n", - " res_dict = dict(zip(res_r.names(), res_r))\n", - " phi = np.asarray(res_dict[\"mrts\"], dtype=dtype_basis)\n", - " else:\n", - " phi = np.asarray(\n", - " mrts0(jnp.asarray(knot, dtype=jnp_f32), k=order_max, \n", - " x=jnp.asarray(location_data, dtype=jnp_f32)), dtype=dtype_basis\n", - " )\n", - "\n", - " return phi, idx_knot, knot\n", - "\n", - "\n", - "def prepare_data(categorical_data, basis, y_combined, seed, split_ratio=(0.8, 0.1, 0.1)):\n", - " idx_all = np.arange(basis.shape[0])\n", - " train_ratio, val_ratio, test_ratio = split_ratio\n", - " \n", - " train_val_x1, test_x1 = train_test_split(\n", - " idx_all, train_size=train_ratio+val_ratio, random_state=seed)\n", - " train_x1, val_x1 = train_test_split(\n", - " train_val_x1, train_size=train_ratio/(train_ratio+val_ratio), random_state=seed)\n", - " \n", - " X_train_cont, X_val_cont, X_test_cont = (\n", - " basis[train_x1], basis[val_x1], basis[test_x1])\n", - " y_train, y_val, y_test = (\n", - " y_combined[train_x1], y_combined[val_x1], y_combined[test_x1])\n", - " \n", - " def flatten(targets):\n", - " return targets.reshape(-1).astype(np_f32, copy=False)\n", - " y_train_flat, y_val_flat, y_test_flat = map(flatten, [y_train, y_val, y_test])\n", - "\n", - " def flatten(covariates):\n", - " return covariates.reshape(-1, basis.shape[1]).astype(np_f32)\n", - " X_train_cont_flat, X_val_cont_flat, X_test_cont_flat = map(flatten, [X_train_cont, X_val_cont, X_test_cont])\n", - " \n", - " # Handle categorical features\n", - " if categorical_data is None:\n", - " zero_vector = lambda n: np.zeros((n, 0), dtype=np_f32)\n", - " X_train_cat, X_val_cat, X_test_cat = map(zero_vector, [len(X_train_cont_flat), len(X_val_cont_flat), len(X_test_cont_flat)])\n", - " else:\n", - " cat_train = categorical_data.reshape(-1, 1)[train_x1]\n", - " cat_val = categorical_data.reshape(-1, 1)[val_x1]\n", - " cat_test = categorical_data.reshape(-1, 1)[test_x1]\n", - " \n", - " OHE = OneHotEncoder(sparse_output=False, dtype=np_f32)\n", - " X_train_cat = OHE.fit_transform(cat_train).astype(np_f32)\n", - " X_val_cat = OHE.transform(cat_val).astype(np_f32)\n", - " X_test_cat = OHE.transform(cat_test).astype(np_f32)\n", - " \n", - " return (X_train_cont_flat, X_train_cat, y_train_flat,\n", - " X_val_cont_flat, X_val_cat, y_val_flat,\n", - " X_test_cont_flat, X_test_cat, y_test_flat)\n", - "\n", - "\n", - "def train_eval(name_model, epochs, batch_size, loss, dropout_rate,\n", - " X_train, X_train_cat, y_train,\n", - " X_val, X_val_cat, y_val,\n", - " X_test, X_test_cat, y_test):\n", - "\n", - " if name_model in [\"OLS_wendland\", \"OLS_sphere\"]:\n", - " t0 = time.time()\n", - " model = LinearRegression().fit(X_train, y_train)\n", - " \n", - " val_loss = float(mean_squared_error(y_val, model.predict(X_val)))\n", - " y_pred = model.predict(X_test).astype(np_f32).reshape(-1)\n", - " training_time = time.time() - t0\n", - " \n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " return metrics, model\n", - " \n", - " elif name_model == \"DeepKriging_wendland\":\n", - " config = DeepKrigingDefaultConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " optimizer=Adam(learning_rate=1e-3),\n", - " loss=loss,\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " elif name_model in [\"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\"]:\n", - " optimizer = Adam(learning_rate=5e-3)\n", - " config = DeepKrigingModelConfig(\n", - " input_dim=X_train.shape[1],\n", - " output_type='continuous',\n", - " hidden_layers=[1024, 512, 256, 128, 64],\n", - " activation='relu',\n", - " dropout_rate=dropout_rate,\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=['mae'],\n", - " epochs=epochs,\n", - " batch_size=batch_size,\n", - " patience=40,\n", - " verbose=0\n", - " )\n", - "\n", - " t0 = time.time()\n", - " with strategy.scope():\n", - " if name_model == \"DeepKriging_wendland\":\n", - " model = DeepKrigingDefaultTrainer(config)\n", - " elif name_model == \"DeepKriging_wendland*\":\n", - " model = DeepKrigingTrainer(config)\n", - " else:\n", - " model = DeepKrigingTrainer(config)\n", - " model.model.compile(optimizer=config.optimizer, loss=config.loss, metrics=config.metrics)\n", - "\n", - " checkpoint_path = f\"best_{name_model}_{time.time_ns()}.weights.h5\"\n", - " if name_model == \"DeepKriging_wendland\":\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0)\n", - " ]\n", - " else:\n", - " callbacks = [\n", - " tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_path, monitor=\"val_loss\", mode=\"min\",\n", - " save_best_only=True, save_weights_only=True, verbose=0),\n", - " tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss', patience=config.patience, restore_best_weights=True, verbose=0),\n", - " tf.keras.callbacks.ReduceLROnPlateau(\n", - " monitor='val_loss', factor=0.5, patience=max(5, config.patience // 2), min_lr=1e-6, verbose=0)\n", - " ]\n", - "\n", - " train_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_train, X_train_cat), y_train\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " val_dataset = tf.data.Dataset.from_tensor_slices((\n", - " (X_val, X_val_cat), y_val\n", - " )).batch(config.batch_size).prefetch(tf.data.AUTOTUNE)\n", - "\n", - " history = model.model.fit(\n", - " train_dataset,\n", - " validation_data=val_dataset,\n", - " epochs=epochs,\n", - " callbacks=callbacks,\n", - " verbose=0\n", - " )\n", - "\n", - " if os.path.exists(checkpoint_path):\n", - " model.model.load_weights(checkpoint_path)\n", - " os.remove(checkpoint_path)\n", - " \n", - " val_loss = float(np.min(history.history[\"val_loss\"]))\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1).astype(np_f32)\n", - " training_time = time.time() - t0\n", - "\n", - " metrics = {\n", - " \"Model\": name_model,\n", - " \"Val_loss\": float(val_loss),\n", - " \"MSPE\": float(mean_squared_error(y_test, y_pred)),\n", - " \"RMSE\": float(np.sqrt(float(mean_squared_error(y_test, y_pred)))),\n", - " \"MAE\": float(mean_absolute_error(y_test, y_pred)),\n", - " \"R2\": float(r2_score(y_test, y_pred)),\n", - " \"Time\": float(training_time),\n", - " }\n", - " \n", - " del train_dataset, val_dataset\n", - " gc.collect()\n", - " \n", - " return metrics, model\n", - "\n", - "\n", - "def cleanup_tf_session():\n", - " K.clear_session()\n", - " gc.collect()\n", - " try:\n", - " tf.keras.backend.clear_session()\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1573eecc", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_robinson(ax, longitude, latitude, value, vmin, vmax, title):\n", - " \"\"\"Plot data on Robinson projection\"\"\"\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - "\n", - " sc = ax.scatter(longitude, latitude, c=value, \n", - " cmap=mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256), \n", - " s=10, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n", - "\n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " return sc\n", - "\n", - "\n", - "def create_subplot_robinson(fig, position, locations, values, vmin, vmax, title, plot_type='prediction', cbar_label=None):\n", - " \"\"\"Create subplot with Robinson projection\"\"\"\n", - " ax = fig.add_subplot(*position, projection=ccrs.Robinson())\n", - " \n", - " # Choose colormap based on plot type\n", - " if plot_type == 'residual':\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"blue-white-red\", [\"#2166AC\", \"#F7F7F7\", \"#B2182B\"], N=256)\n", - " else:\n", - " cmap = mcolors.LinearSegmentedColormap.from_list(\"teal-yellow-red\", [\"#00AAAA\", \"#FFFFBB\", \"#FF3333\"], N=256)\n", - " \n", - " # Set global view\n", - " ax.set_global()\n", - " ax.add_feature(cfeature.LAND, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.OCEAN, facecolor=\"white\", alpha=0.3)\n", - " ax.add_feature(cfeature.COASTLINE, linewidth=0.3, alpha=0.5)\n", - " ax.add_feature(cfeature.BORDERS, linewidth=0.2, alpha=0.5)\n", - " \n", - " # Plot scatter\n", - " sc = ax.scatter(locations['longitude'], locations['latitude'], c=values, \n", - " cmap=cmap, s=10, transform=ccrs.PlateCarree(), \n", - " vmin=vmin, vmax=vmax)\n", - " \n", - " ax.set_title(title, fontsize=10, pad=3)\n", - " \n", - " # Add colorbar\n", - " cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', fraction=0.046, pad=0.04, shrink=0.8)\n", - " \n", - " if cbar_label is None:\n", - " cbar_label = \"Residual\" if plot_type == 'residual' else \"Prediction Value\"\n", - " \n", - " # Increased fontsize to match old version\n", - " cbar.set_label(cbar_label, fontsize=10)\n", - " cbar.ax.tick_params(labelsize=7)\n", - " \n", - " return ax, sc\n", - "\n", - "\n", - "def visualize_comparison(dataframe, models_dict, basis_dict, y_combined, seed, model_list=None, experiment_info=None):\n", - " \"\"\"Visualize model predictions and residuals using Robinson projection\"\"\"\n", - " if model_list is None:\n", - " model_list = ['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging']\n", - " \n", - " idx_all = np.arange(len(y_combined))\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " y_test = y_combined[test_idx].reshape(-1)\n", - " test_locations = dataframe.iloc[test_idx]\n", - " \n", - " predictions = {}\n", - " for model_name in model_list:\n", - " if model_name not in models_dict or models_dict[model_name] is None:\n", - " continue\n", - " \n", - " model = models_dict[model_name]\n", - " X_test = basis_dict[model_name][test_idx]\n", - " \n", - " if \"DeepKriging\" in model_name:\n", - " X_test_cat = np.zeros((len(X_test), 0), dtype=np.float32)\n", - " y_pred = model.model.predict([X_test, X_test_cat], verbose=0).reshape(-1)\n", - " elif model_name == \"UniversalKriging\":\n", - " coords_test = dataframe[['longitude', 'latitude']].iloc[test_idx].values.astype(np.float32)\n", - " y_pred = model.predict(coords_test, X_test, return_centered=False)\n", - " else:\n", - " y_pred = model.predict(X_test).reshape(-1)\n", - " \n", - " mse = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(mse)\n", - " order = models_dict.get(f\"{model_name}_order\", \"\")\n", - " \n", - " predictions[model_name] = {\n", - " 'values': y_pred,\n", - " 'rmse': rmse,\n", - " 'order': order\n", - " }\n", - " \n", - " # Calculate global min/max for consistent color scale\n", - " all_values = [dataframe['z'].values] + [p['values'] for p in predictions.values()]\n", - " all_values_concat = np.concatenate(all_values)\n", - " vmin = np.percentile(all_values_concat, 2)\n", - " vmax = np.percentile(all_values_concat, 98)\n", - " \n", - " # Figure 1: Predictions comparison\n", - " fig1 = plt.figure(figsize=(16, 14))\n", - " \n", - " noise_info = \"\"\n", - " if experiment_info:\n", - " noise_info = f\"Noise={experiment_info.get('noise', 'None')}\"\n", - " if experiment_info.get('noise_var'):\n", - " noise_info += f\", Var={experiment_info['noise_var']}\"\n", - " \n", - " # Plot real data\n", - " create_subplot_robinson(\n", - " fig1, (2, 2, 1), dataframe, dataframe['z'], vmin, vmax,\n", - " f'Real Data (n={len(dataframe)})',\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Plot predictions\n", - " subplot_positions = [(2, 2, 2), (2, 2, 3), (2, 2, 4)]\n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " pred = predictions[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig1, subplot_positions[i], test_locations, pred['values'], vmin, vmax,\n", - " f\"{display_name} (order={pred['order']}) | Test n={len(test_idx)} | RMSE={pred['rmse']:.4f}\",\n", - " plot_type='prediction'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " \"Prediction Comparison: Real Data vs. Models Predict\\n\"\n", - " f\"Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect, as in the old version\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig1)\n", - " \n", - "\n", - " # Figure 2: Residuals comparison\n", - " fig2 = plt.figure(figsize=(18, 6))\n", - " \n", - " residuals_map = {k: (y_test - predictions[k]['values']) \n", - " for k in model_list if k in predictions}\n", - " vmax_abs = max(np.max(np.abs(r)) for r in residuals_map.values())\n", - " \n", - " for i, model_name in enumerate(model_list):\n", - " if model_name in predictions:\n", - " residuals = residuals_map[model_name]\n", - " display_name = model_name.replace('DeepKriging_sphere', 'DK_S').replace('_Huber', '_H').replace('UniversalKriging', 'UK')\n", - " \n", - " create_subplot_robinson(\n", - " fig2, (1, 3, i+1), test_locations, residuals, -vmax_abs, vmax_abs,\n", - " f\"{display_name} Residuals (order={predictions[model_name]['order']})\",\n", - " plot_type='residual'\n", - " )\n", - " \n", - " # Modified title fontsize and layout to match old version\n", - " plt.suptitle(\n", - " f\"Residuals Comparison | Stationary Gaussian processes + Eggholder (without noise and outliers)\",\n", - " fontsize=20, fontweight='bold', y=0.84\n", - " )\n", - " # Reverted to tight_layout with rect\n", - " plt.tight_layout(rect=[0, 0.02, 1, 0.94])\n", - " plt.show()\n", - " plt.close(fig2)\n", - " \n", - " return predictions, test_idx" - ] - }, - { - "cell_type": "markdown", - "id": "ca651900", - "metadata": { - "papermill": { - "duration": 0.00388, - "end_time": "2026-01-08T18:26:41.176752", - "exception": false, - "start_time": "2026-01-08T18:26:41.172872", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Experiment Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ce2f573", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.185147Z", - "iopub.status.busy": "2026-01-08T18:26:41.184593Z", - "iopub.status.idle": "2026-01-08T18:26:41.193431Z", - "shell.execute_reply": "2026-01-08T18:26:41.191964Z" - }, - "papermill": { - "duration": 0.017038, - "end_time": "2026-01-08T18:26:41.197209", - "exception": false, - "start_time": "2026-01-08T18:26:41.180171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Model Setup\n", - "seed = 1\n", - "epochs = 500\n", - "batch_size = 256\n", - "num_sample = 2500\n", - "huber_delta = 1.345\n", - "\n", - "# Basis Setup\n", - "num_basis = [10**2, 19**2, 37**2]\n", - "knot_num = 1400\n", - "order_max = 1400\n", - "\n", - "base_orders = [50, 100, 200, 500, 700, 1000, 1400]\n", - "\n", - "# Repeat \n", - "repeat_experiment = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fed92e3c", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:26:41.210477Z", - "iopub.status.busy": "2026-01-08T18:26:41.210040Z", - "iopub.status.idle": "2026-01-08T18:56:08.725619Z", - "shell.execute_reply": "2026-01-08T18:56:08.723300Z" - }, - "papermill": { - "duration": 1767.525735, - "end_time": "2026-01-08T18:56:08.729624", - "exception": false, - "start_time": "2026-01-08T18:26:41.203889", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "🏃 Repeat 1/10, Seed=1\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: 0.0014, range: [-0.9992, 0.9991]\n", - "GP component - mean: 0.6077, var: 12.0369\n", - "Final z - mean: 0.6091, var: 11.6575, range: [-16.6370, 11.3082]\n", - "Scaling S(s) - range: [1.0901, 4.9996]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tuning order parameter for OLS_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 6.4640 5.6286 \n", - " 100 5.2732 5.0434 \n", - " 200 4.4902 4.2973 *\n", - " 500 4.4924 3.7102 \n", - " 700 5.6757 4.0041 \n", - " 1000 8.9212 13.3222 \n", - " 1400 252.8254 224.7422 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_mrts\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1770804636.789176 703331 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 17712 MB memory: -> device: 0, name: NVIDIA RTX 4000 Ada Generation, pci bus id: 0000:70:00.0, compute capability: 8.9\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1770804639.252643 703600 service.cc:152] XLA service 0x71c8a800c420 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1770804639.252716 703600 service.cc:160] StreamExecutor device (0): NVIDIA RTX 4000 Ada Generation, Compute Capability 8.9\n", - "I0000 00:00:1770804639.629352 703600 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", - "I0000 00:00:1770804642.200930 703600 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x71c8ec1e5240> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x71c8e427a440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 4.3039 3.9821 \n", - " 100 4.5530 4.4799 \n", - " 200 5.4458 4.7329 \n", - " 500 3.9043 4.3717 *\n", - " 700 7.4485 6.3755 \n", - " 1000 4.4593 4.9653 \n", - " 1400 5.5520 5.0878 \n", - " Best order: 500\n", - "\n", - "Tuning order parameter for DeepKriging_sphere\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 8.5902 8.3770 \n", - " 100 3.6086 3.1647 \n", - " 200 3.5748 3.1056 *\n", - " 500 3.7548 3.6754 \n", - " 700 4.0582 3.5367 \n", - " 1000 7.3259 6.1420 \n", - " 1400 7.8275 6.5373 \n", - " Best order: 200\n", - "\n", - "Tuning order parameter for DeepKriging_sphere_Huber\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 1.0509 3.1460 *\n", - " 100 1.0514 3.2116 \n", - " 200 1.1004 3.2703 \n", - " 500 1.1657 3.8507 \n", - " 700 1.1962 3.6144 \n", - " 1000 2.0174 6.2994 \n", - " 1400 1.7244 5.5849 \n", - " Best order: 50\n", - "\n", - "Tuning order parameter for UniversalKriging\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0791, sigma²=6.0534, nugget=0.0004\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0643, sigma²=5.2243, nugget=0.0004\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0421, sigma²=3.9175, nugget=0.0001\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=6.2667, sigma²=0.0000, nugget=2.1092\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=1.2555, sigma²=0.0000, nugget=1.6592\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.7632, sigma²=0.0000, nugget=1.1071\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.5500, sigma²=0.0000, nugget=0.5657\n", - " Order Val Loss Test MSE \n", - " ----------------------------------\n", - " 50 3.1489 2.9477 \n", - " 100 3.1236 2.8841 *\n", - " 200 3.4370 3.0674 \n", - " 500 4.4924 3.7102 \n", - " 700 5.6757 4.0041 \n", - " 1000 8.9213 13.3222 \n", - " 1400 252.8372 224.7365 \n", - " Best order: 100\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0643, sigma²=5.2243, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 11.4264 | 3.3803 | 2.4556 | -0.0311 | 0.39s |\n", - "| OLS_sphere | 200 | 4.2973 | 2.073 | 1.5373 | 0.6122 | 0.02s |\n", - "| DeepKriging_wendland | -- | 6.8884 | 2.6246 | 2.1053 | 0.3784 | 60.96s |\n", - "| DeepKriging_wendland* | -- | 6.2175 | 2.4935 | 1.9636 | 0.4389 | 31.38s |\n", - "| DeepKriging_mrts | 500 | 5.8665 | 2.4221 | 1.8968 | 0.4706 | 22.35s |\n", - "| DeepKriging_sphere | 200 | 3.3719 | 1.8363 | 1.3232 | 0.6957 | 31.19s |\n", - "| DeepKriging_sphere_Huber | 50 | 3.3236 | 1.8231 | 1.3032 | 0.7001 | 31.04s |\n", - "| UniversalKriging | 100 | 2.8841 | 1.6983 | 1.2429 | 0.7397 | 2.84s |\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:25:19\n", - "\n", - "✅ Completed Repeat 1/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 2/10, Seed=2\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0134, range: [-0.9990, 0.9995]\n", - "GP component - mean: -1.1395, var: 14.6009\n", - "Final z - mean: -1.1529, var: 14.9815, range: [-14.2788, 14.4549]\n", - "Scaling S(s) - range: [1.0410, 4.9975]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0556, sigma²=5.3834, nugget=0.0292\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|----------|---------|--------|----------|--------|\n", - "| OLS_wendland | -- | 913.812 | 30.2293 | 4.4315 | -54.8827 | 0.40s |\n", - "| OLS_sphere | 200 | 4.6473 | 2.1557 | 1.5313 | 0.7158 | 0.02s |\n", - "| DeepKriging_wendland | -- | 11.0471 | 3.3237 | 2.3831 | 0.3244 | 65.05s |\n", - "| DeepKriging_wendland* | -- | 11.7006 | 3.4206 | 2.499 | 0.2845 | 20.55s |\n", - "| DeepKriging_mrts | 500 | 9.125 | 3.0208 | 2.3122 | 0.442 | 19.74s |\n", - "| DeepKriging_sphere | 200 | 3.6499 | 1.9105 | 1.3529 | 0.7768 | 31.53s |\n", - "| DeepKriging_sphere_Huber | 50 | 11.5328 | 3.396 | 2.5694 | 0.2947 | 14.89s |\n", - "| UniversalKriging | 100 | 3.3444 | 1.8288 | 1.3439 | 0.7955 | 3.28s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:28:31\n", - "\n", - "✅ Completed Repeat 2/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 3/10, Seed=4\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0031, range: [-0.9989, 0.9999]\n", - "GP component - mean: -0.4183, var: 10.7042\n", - "Final z - mean: -0.4215, var: 11.3067, range: [-12.0557, 13.4864]\n", - "Scaling S(s) - range: [1.0339, 4.9992]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0561, sigma²=5.2020, nugget=0.0002\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 8.8274 | 2.9711 | 2.1245 | 0.1815 | 0.42s |\n", - "| OLS_sphere | 200 | 4.4905 | 2.1191 | 1.5897 | 0.5836 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.2927 | 2.7005 | 1.9008 | 0.3238 | 61.46s |\n", - "| DeepKriging_wendland* | -- | 7.8729 | 2.8059 | 1.9793 | 0.27 | 27.81s |\n", - "| DeepKriging_mrts | 500 | 4.3907 | 2.0954 | 1.5552 | 0.5929 | 36.53s |\n", - "| DeepKriging_sphere | 200 | 3.6192 | 1.9024 | 1.3466 | 0.6644 | 29.60s |\n", - "| DeepKriging_sphere_Huber | 50 | 3.5807 | 1.8923 | 1.3721 | 0.668 | 34.21s |\n", - "| UniversalKriging | 100 | 3.3494 | 1.8301 | 1.3389 | 0.6894 | 3.75s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:32:19\n", - "\n", - "✅ Completed Repeat 3/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 4/10, Seed=7\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0066, range: [-0.9993, 0.9997]\n", - "GP component - mean: -0.1514, var: 8.0998\n", - "Final z - mean: -0.1580, var: 8.4328, range: [-15.4726, 11.3854]\n", - "Scaling S(s) - range: [1.1287, 4.9990]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0537, sigma²=4.7369, nugget=0.0281\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 8.9661 | 2.9943 | 2.2052 | -0.0823 | 0.41s |\n", - "| OLS_sphere | 200 | 5.6947 | 2.3863 | 1.7619 | 0.3126 | 0.02s |\n", - "| DeepKriging_wendland | -- | 6.3683 | 2.5235 | 1.8815 | 0.2313 | 61.09s |\n", - "| DeepKriging_wendland* | -- | 6.5197 | 2.5534 | 1.8632 | 0.213 | 29.03s |\n", - "| DeepKriging_mrts | 500 | 5.8493 | 2.4185 | 1.7818 | 0.294 | 21.28s |\n", - "| DeepKriging_sphere | 200 | 3.1463 | 1.7738 | 1.2182 | 0.6202 | 27.59s |\n", - "| DeepKriging_sphere_Huber | 50 | 3.179 | 1.783 | 1.2735 | 0.6163 | 24.46s |\n", - "| UniversalKriging | 100 | 3.4628 | 1.8609 | 1.3353 | 0.582 | 2.93s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:35:49\n", - "\n", - "✅ Completed Repeat 4/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 5/10, Seed=11\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: 0.0033, range: [-0.9996, 0.9985]\n", - "GP component - mean: -1.4651, var: 18.8173\n", - "Final z - mean: -1.4618, var: 19.3594, range: [-11.7413, 17.0244]\n", - "Scaling S(s) - range: [1.0703, 4.9989]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0742, sigma²=6.4381, nugget=0.0395\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 64.5331 | 8.0333 | 3.4752 | -1.8126 | 0.48s |\n", - "| OLS_sphere | 200 | 5.4371 | 2.3318 | 1.7114 | 0.763 | 0.02s |\n", - "| DeepKriging_wendland | -- | 10.1881 | 3.1919 | 2.385 | 0.556 | 53.03s |\n", - "| DeepKriging_wendland* | -- | 19.1463 | 4.3757 | 3.5183 | 0.1655 | 16.31s |\n", - "| DeepKriging_mrts | 500 | 6.6265 | 2.5742 | 2.0081 | 0.7112 | 18.94s |\n", - "| DeepKriging_sphere | 200 | 3.5388 | 1.8812 | 1.3377 | 0.8458 | 25.68s |\n", - "| DeepKriging_sphere_Huber | 50 | 16.306 | 4.0381 | 3.3309 | 0.2893 | 15.61s |\n", - "| UniversalKriging | 100 | 3.0813 | 1.7553 | 1.2544 | 0.8657 | 3.01s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:38:45\n", - "\n", - "✅ Completed Repeat 5/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 6/10, Seed=16\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0074, range: [-0.9996, 0.9988]\n", - "GP component - mean: -0.6319, var: 14.7856\n", - "Final z - mean: -0.6393, var: 15.9816, range: [-12.5389, 10.6917]\n", - "Scaling S(s) - range: [1.0556, 4.9977]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0477, sigma²=4.5126, nugget=0.0520\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 17.1053 | 4.1359 | 2.8408 | 0.0731 | 0.47s |\n", - "| OLS_sphere | 200 | 4.6367 | 2.1533 | 1.581 | 0.7487 | 0.02s |\n", - "| DeepKriging_wendland | -- | 8.2237 | 2.8677 | 2.2832 | 0.5544 | 78.11s |\n", - "| DeepKriging_wendland* | -- | 13.3634 | 3.6556 | 2.8844 | 0.2758 | 17.53s |\n", - "| DeepKriging_mrts | 500 | 5.1815 | 2.2763 | 1.7312 | 0.7192 | 21.03s |\n", - "| DeepKriging_sphere | 200 | 12.2941 | 3.5063 | 2.8381 | 0.3338 | 17.20s |\n", - "| DeepKriging_sphere_Huber | 50 | 3.1677 | 1.7798 | 1.2637 | 0.8283 | 34.41s |\n", - "| UniversalKriging | 100 | 3.1655 | 1.7792 | 1.275 | 0.8285 | 3.37s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:42:23\n", - "\n", - "✅ Completed Repeat 6/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 7/10, Seed=22\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: 0.0105, range: [-0.9999, 0.9994]\n", - "GP component - mean: 1.2364, var: 12.5505\n", - "Final z - mean: 1.2469, var: 12.2511, range: [-12.7319, 15.9160]\n", - "Scaling S(s) - range: [1.0507, 4.9999]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0550, sigma²=4.9402, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 11.4289 | 3.3807 | 2.4084 | 0.1191 | 0.41s |\n", - "| OLS_sphere | 200 | 5.056 | 2.2486 | 1.4822 | 0.6103 | 0.02s |\n", - "| DeepKriging_wendland | -- | 8.1453 | 2.854 | 2.0809 | 0.3722 | 73.44s |\n", - "| DeepKriging_wendland* | -- | 7.7602 | 2.7857 | 2.0088 | 0.4019 | 40.10s |\n", - "| DeepKriging_mrts | 500 | 8.3802 | 2.8948 | 2.2569 | 0.3541 | 21.75s |\n", - "| DeepKriging_sphere | 200 | 3.4665 | 1.8618 | 1.3324 | 0.7328 | 31.38s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.9098 | 3.148 | 2.4525 | 0.2362 | 15.70s |\n", - "| UniversalKriging | 100 | 3.2372 | 1.7992 | 1.2801 | 0.7505 | 4.32s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:46:21\n", - "\n", - "✅ Completed Repeat 7/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 8/10, Seed=29\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: 0.0065, range: [-0.9999, 0.9993]\n", - "GP component - mean: 0.0539, var: 11.1731\n", - "Final z - mean: 0.0604, var: 11.6084, range: [-15.1643, 11.1069]\n", - "Scaling S(s) - range: [1.0894, 4.9991]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0547, sigma²=5.0334, nugget=0.0005\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|---------|--------|--------|---------|--------|\n", - "| OLS_wendland | -- | 12.7821 | 3.5752 | 2.4082 | -0.1008 | 0.41s |\n", - "| OLS_sphere | 200 | 5.6222 | 2.3711 | 1.7306 | 0.5158 | 0.02s |\n", - "| DeepKriging_wendland | -- | 6.9841 | 2.6427 | 2.0637 | 0.3985 | 66.37s |\n", - "| DeepKriging_wendland* | -- | 7.1561 | 2.6751 | 2.0649 | 0.3837 | 29.58s |\n", - "| DeepKriging_mrts | 500 | 5.773 | 2.4027 | 1.8438 | 0.5028 | 28.54s |\n", - "| DeepKriging_sphere | 200 | 4.1425 | 2.0353 | 1.4544 | 0.6432 | 37.95s |\n", - "| DeepKriging_sphere_Huber | 50 | 9.1591 | 3.0264 | 2.3539 | 0.2112 | 17.85s |\n", - "| UniversalKriging | 100 | 3.7983 | 1.9489 | 1.3986 | 0.6729 | 4.39s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:50:18\n", - "\n", - "✅ Completed Repeat 8/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 9/10, Seed=37\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0025, range: [-0.9999, 0.9998]\n", - "GP component - mean: -0.2158, var: 9.8141\n", - "Final z - mean: -0.2183, var: 10.2273, range: [-10.6177, 12.8116]\n", - "Scaling S(s) - range: [1.0284, 4.9998]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0572, sigma²=5.4813, nugget=0.0004\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 7.522 | 2.7426 | 2.0244 | 0.201 | 0.43s |\n", - "| OLS_sphere | 200 | 5.3431 | 2.3115 | 1.7054 | 0.4325 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.2577 | 2.694 | 1.9686 | 0.2291 | 67.42s |\n", - "| DeepKriging_wendland* | -- | 6.9597 | 2.6381 | 1.9689 | 0.2607 | 21.58s |\n", - "| DeepKriging_mrts | 500 | 7.1421 | 2.6725 | 2.0003 | 0.2414 | 24.65s |\n", - "| DeepKriging_sphere | 200 | 7.425 | 2.7249 | 2.1093 | 0.2113 | 18.43s |\n", - "| DeepKriging_sphere_Huber | 50 | 3.8947 | 1.9735 | 1.3722 | 0.5863 | 36.61s |\n", - "| UniversalKriging | 100 | 3.7296 | 1.9312 | 1.3823 | 0.6038 | 3.56s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:54:06\n", - "\n", - "✅ Completed Repeat 9/10\n", - "\n", - "================================================================================\n", - "🏃 Repeat 10/10, Seed=46\n", - "================================================================================\n", - "\n", - "=== Scenario E1 (c=0.1) ===\n", - "Mean term - mean: -0.0212, range: [-0.9997, 0.9998]\n", - "GP component - mean: -0.6177, var: 11.4833\n", - "Final z - mean: -0.6390, var: 11.7803, range: [-12.9245, 12.9613]\n", - "Scaling S(s) - range: [1.0732, 4.9996]\n", - "Covariance function: exponential (Matérn ν=0.5)\n", - "Fitted parameters: nu=0.5000, rho=0.0517, sigma²=5.0656, nugget=0.0008\n", - "\n", - "| Model | Param | MSPE | RMSE | MAE | R2 | Time |\n", - "|--------------------------|---------|--------|--------|--------|--------|--------|\n", - "| OLS_wendland | -- | 9.783 | 3.1278 | 2.2899 | 0.1047 | 0.39s |\n", - "| OLS_sphere | 200 | 4.5632 | 2.1362 | 1.5847 | 0.5824 | 0.02s |\n", - "| DeepKriging_wendland | -- | 7.1863 | 2.6807 | 2.0372 | 0.3423 | 74.82s |\n", - "| DeepKriging_wendland* | -- | 6.7746 | 2.6028 | 1.9709 | 0.38 | 30.80s |\n", - "| DeepKriging_mrts | 500 | 4.7782 | 2.1859 | 1.5417 | 0.5627 | 52.71s |\n", - "| DeepKriging_sphere | 200 | 3.6087 | 1.8997 | 1.3086 | 0.6697 | 35.24s |\n", - "| DeepKriging_sphere_Huber | 50 | 4.1942 | 2.048 | 1.3984 | 0.6161 | 39.76s |\n", - "| UniversalKriging | 100 | 3.439 | 1.8545 | 1.3549 | 0.6853 | 5.02s |\n" - ] - }, - { - "data": { - "application/javascript": "IPython.notebook.save_checkpoint()", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "💾 Notebook saved at 2026-02-11 18:59:00\n", - "\n", - "✅ Completed Repeat 10/10\n" - ] - } - ], - "source": [ - "best_orders = {}\n", - "\n", - "experiment_results = {\n", - " model: {\"MSPE\": [], \"RMSE\": [], \"MAE\": [], \"R2\": []}\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]\n", - "}\n", - "\n", - "\n", - "for repeat in range(repeat_experiment):\n", - " seed = seed + repeat\n", - "\n", - " print(f\"\\n{'='*80}\")\n", - " print(f\"🏃 Repeat {repeat+1}/{repeat_experiment}, Seed={seed}\")\n", - " print(f\"{'='*80}\")\n", - "\n", - " dataframe = simulate_data_nonstationary(num_sample=num_sample, seed=seed, scenario='E1')\n", - " location_data, location_data_norm, categorical_data, y_combined = data_preprocessing(dataframe)\n", - "\n", - " # Compute max MRTS_Sphere, MRTS_Euclidean\n", - " max_Phi_sphere, idx_knot, knot = precompute_max_mrts(\"sphere\", location_data, knot_num, order_max, knot=None)\n", - " max_Phi_sphere = max_Phi_sphere.astype(dtype_basis, copy=False)\n", - "\n", - " max_Phi_mrts, idx_knot_mrts, knot_mrts = precompute_max_mrts(\"mrts\", location_data, knot_num, order_max, knot=location_data[idx_knot])\n", - " max_Phi_mrts = max_Phi_mrts.astype(dtype_basis, copy=False)\n", - "\n", - " # Compute Wendland basis\n", - " Phi_wendland = precompute_wendland(location_data_norm, num_basis)\n", - "\n", - "\n", - " if repeat == 0:\n", - " # Tuning order parameter for OLS_sphere\n", - " Best_val_OLS_S = float('inf')\n", - " Best_order_OLS_S = None\n", - " Results_order_OLS_S = []\n", - " \n", - " print(\"\\nTuning order parameter for OLS_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metrics, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " \n", - " Results_order_OLS_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_OLS_S:\n", - " Best_val_OLS_S = metrics[\"Val_loss\"]\n", - " Best_order_OLS_S = order\n", - " \n", - " del Phi_sphere, parts, model\n", - " gc.collect()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_OLS_S:\n", - " marker = \" *\" if res['order'] == Best_order_OLS_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_OLS_S}\")\n", - " best_orders['OLS_sphere'] = Best_order_OLS_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_mrts\n", - " Best_val_DK_mrts = float('inf')\n", - " Best_order_DK_mrts = None\n", - " Results_order_DK_mrts = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_mrts\")\n", - " for order in base_orders:\n", - " Phi_mrts = max_Phi_mrts[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_mrts.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_mrts:\n", - " Best_val_DK_mrts = metrics[\"Val_loss\"]\n", - " Best_order_DK_mrts = order\n", - " \n", - " del Phi_mrts, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_mrts:\n", - " marker = \" *\" if res['order'] == Best_order_DK_mrts else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_mrts}\")\n", - " best_orders['DeepKriging_mrts'] = Best_order_DK_mrts\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere\n", - " Best_val_DK_S = float('inf')\n", - " Best_order_DK_S = None\n", - " Results_order_DK_S = []\n", - "\n", - " print(\"\\nTuning order parameter for DeepKriging_sphere\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S:\n", - " Best_val_DK_S = metrics[\"Val_loss\"]\n", - " Best_order_DK_S = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S}\")\n", - " best_orders['DeepKriging_sphere'] = Best_order_DK_S\n", - "\n", - "\n", - " # Tuning order parameter for DeepKriging_sphere_Huber\n", - " Best_val_DK_S_H = float('inf')\n", - " Best_order_DK_S_H = None\n", - " Results_order_DK_S_H = []\n", - " \n", - " print(\"\\nTuning order parameter for DeepKriging_sphere_Huber\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metrics, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " \n", - " # Store results\n", - " Results_order_DK_S_H.append({'order': order, 'val_loss': metrics[\"Val_loss\"], 'mspe': metrics[\"MSPE\"]})\n", - " \n", - " if metrics[\"Val_loss\"] < Best_val_DK_S_H:\n", - " Best_val_DK_S_H = metrics[\"Val_loss\"]\n", - " Best_order_DK_S_H = order\n", - " \n", - " del Phi_sphere, parts\n", - " cleanup_tf_session()\n", - "\n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_DK_S_H:\n", - " marker = \" *\" if res['order'] == Best_order_DK_S_H else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_DK_S_H}\")\n", - " best_orders['DeepKriging_sphere_Huber'] = Best_order_DK_S_H\n", - "\n", - "\n", - " # Tuning order parameter for UniversalKriging\n", - " Best_val_UK = float('inf')\n", - " Best_order_UK = None\n", - " Results_order_UK = []\n", - " \n", - " print(\"\\nTuning order parameter for UniversalKriging\")\n", - " for order in base_orders:\n", - " Phi_sphere = max_Phi_sphere[:, :order].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, val_idx = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - " \n", - " # Prepare coordinates, basis and y\n", - " coords_train, coords_val, coords_test = location_data[train_idx], location_data[val_idx], location_data[test_idx]\n", - " phi_train, phi_val, phi_test = Phi_sphere[train_idx], Phi_sphere[val_idx], Phi_sphere[test_idx]\n", - " y_train, y_val, y_test = y_combined[train_idx].flatten(), y_combined[val_idx].flatten(), y_combined[test_idx].flatten()\n", - " \n", - " # Train UniversalKriging\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - " \n", - " # Predict on validation set\n", - " y_pred_val = uk_model.predict(coords_val, phi_val, return_centered=True)\n", - " val_loss = mean_squared_error(y_val - uk_model.y_mean, y_pred_val)\n", - " \n", - " # Predict on test set\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - " test_mse = mean_squared_error(y_test, y_pred_test)\n", - " \n", - " # Store results\n", - " Results_order_UK.append({'order': order, 'val_loss': val_loss, 'mspe': test_mse})\n", - " \n", - " if val_loss < Best_val_UK:\n", - " Best_val_UK = val_loss\n", - " Best_order_UK = order\n", - " \n", - " # Cleanup\n", - " uk_model.cleanup()\n", - " del uk_model, Phi_sphere, coords_train, coords_val, coords_test\n", - " del phi_train, phi_val, phi_test, y_train, y_val, y_test\n", - " gc.collect()\n", - " \n", - " # Print results table\n", - " print(f\" {'Order':<10} {'Val Loss':<12} {'Test MSE':<12}\")\n", - " print(f\" {'-'*34}\")\n", - " for res in Results_order_UK:\n", - " marker = \" *\" if res['order'] == Best_order_UK else \"\"\n", - " print(f\" {res['order']:<10} {res['val_loss']:<12.4f} {res['mspe']:<12.4f}{marker}\")\n", - " print(f\" Best order: {Best_order_UK}\")\n", - " best_orders['UniversalKriging'] = Best_order_UK\n", - " gc.collect()\n", - "\n", - "\n", - " # Repeat experiment\n", - " Record = {}\n", - "\n", - " # OLS_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_wendland\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # OLS_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['OLS_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " metric, model = train_eval(\n", - " \"OLS_sphere\", None, None, None, None, *parts\n", - " )\n", - " Record[\"OLS_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['OLS_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland\", epochs, batch_size, \"mse\", 0.5, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_wendland*\n", - " parts = prepare_data(categorical_data, Phi_wendland, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_wendland*\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_wendland*\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": \"--\", \"model\": model if repeat == 0 else None\n", - " }\n", - "\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_mrts\n", - " Phi_mrts = max_Phi_mrts[:, :best_orders['DeepKriging_mrts']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_mrts, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_mrts\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_mrts\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_mrts'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_mrts, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere\", epochs, batch_size, \"mse\", 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # DeepKriging_sphere_Huber\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']].astype(np_f32)\n", - " parts = prepare_data(categorical_data, Phi_sphere, y_combined, seed)\n", - " with strategy.scope():\n", - " metric, model = train_eval(\n", - " \"DeepKriging_sphere_Huber\", epochs, batch_size, Huber(delta=huber_delta), 0.01, *parts\n", - " )\n", - " Record[\"DeepKriging_sphere_Huber\"] = {\n", - " \"MSPE\": metric[\"MSPE\"], \"RMSE\": metric[\"RMSE\"], \"MAE\": metric[\"MAE\"], \"R2\": metric[\"R2\"],\n", - " \"Time\": metric[\"Time\"], \"Param\": best_orders['DeepKriging_sphere_Huber'], \"model\": model if repeat == 0 else None\n", - " }\n", - " if repeat != 0:\n", - " del model\n", - " cleanup_tf_session()\n", - " del Phi_sphere, parts\n", - " gc.collect()\n", - "\n", - "\n", - " # UniversalKriging\n", - " t0 = time.time()\n", - " Phi_sphere = max_Phi_sphere[:, :best_orders['UniversalKriging']].astype(np_f32)\n", - " \n", - " idx_all = np.arange(Phi_sphere.shape[0])\n", - " train_val_idx, test_idx = train_test_split(idx_all, train_size=0.9, random_state=seed)\n", - " train_idx, _ = train_test_split(train_val_idx, train_size=8/9, random_state=seed)\n", - "\n", - " # Prepare train/test data\n", - " coords_train, coords_test = location_data[train_idx], location_data[test_idx]\n", - " phi_train, phi_test = Phi_sphere[train_idx], Phi_sphere[test_idx]\n", - " y_train, y_test = y_combined[train_idx].flatten(), y_combined[test_idx].flatten()\n", - "\n", - " # Train model\n", - " uk_model = UniversalKriging(num_neighbors=30, cov_function='exponential')\n", - " uk_model.fit(coords_train, phi_train, y_train, center_y=True)\n", - "\n", - " y_pred_test = uk_model.predict(coords_test, phi_test, return_centered=False)\n", - "\n", - " Record[\"UniversalKriging\"] = {\n", - " \"MSPE\": mean_squared_error(y_test, y_pred_test),\n", - " \"RMSE\": np.sqrt(mean_squared_error(y_test, y_pred_test)),\n", - " \"MAE\": mean_absolute_error(y_test, y_pred_test),\n", - " \"R2\": r2_score(y_test, y_pred_test),\n", - " \"Time\": time.time() - t0,\n", - " \"Param\": best_orders['UniversalKriging'],\n", - " \"model\": uk_model if repeat == 0 else None\n", - " }\n", - "\n", - " # Cleanup\n", - " if repeat != 0:\n", - " uk_model.cleanup()\n", - " del uk_model\n", - " del Phi_sphere, coords_train, coords_test, phi_train, phi_test, y_train, y_test\n", - " gc.collect()\n", - "\n", - "\n", - " # Model performance comparison\n", - " result_table = []\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " result_table.append({\n", - " \"Model\": model, \"Param\": Record[model][\"Param\"],\n", - " \"MSPE\": f\"{Record[model]['MSPE']:.4f}\", \"RMSE\": f\"{Record[model]['RMSE']:.4f}\", \"MAE\": f\"{Record[model]['MAE']:.4f}\", \"R2\": f\"{Record[model]['R2']:.4f}\",\n", - " \"Time\": f\"{Record[model]['Time']:.2f}s\"\n", - " })\n", - "\n", - " df_res = pd.DataFrame(result_table)\n", - " print(\"\\n\", df_res.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - " # Visualized model predict and residual\n", - " if repeat == 0:\n", - " experiment_info = {'noise': 'None', 'noise_var': None}\n", - " \n", - " visualized_model = {\n", - " \"DeepKriging_sphere\": Record[\"DeepKriging_sphere\"][\"model\"],\n", - " \"DeepKriging_sphere_order\": Record[\"DeepKriging_sphere\"][\"Param\"],\n", - " \"DeepKriging_sphere_Huber\": Record[\"DeepKriging_sphere_Huber\"][\"model\"],\n", - " \"DeepKriging_sphere_Huber_order\": Record[\"DeepKriging_sphere_Huber\"][\"Param\"],\n", - " \"UniversalKriging\": Record[\"UniversalKriging\"][\"model\"],\n", - " \"UniversalKriging_order\": Record[\"UniversalKriging\"][\"Param\"]\n", - " }\n", - " \n", - " visualized_basis = {\n", - " \"DeepKriging_sphere\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere']],\n", - " \"DeepKriging_sphere_Huber\": max_Phi_sphere[:, :best_orders['DeepKriging_sphere_Huber']],\n", - " \"UniversalKriging\": max_Phi_sphere[:, :best_orders['UniversalKriging']]\n", - " }\n", - " \n", - " predictions, test_idx = visualize_comparison(\n", - " dataframe, visualized_model, visualized_basis, y_combined, seed,\n", - " model_list=['DeepKriging_sphere', 'DeepKriging_sphere_Huber', 'UniversalKriging'],\n", - " experiment_info=experiment_info\n", - " )\n", - " cleanup_tf_session()\n", - "\n", - " # Save all results\n", - " for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " experiment_results[model][\"MSPE\"].append(Record[model][\"MSPE\"])\n", - " experiment_results[model][\"RMSE\"].append(Record[model][\"RMSE\"])\n", - " experiment_results[model][\"MAE\"].append(Record[model][\"MAE\"])\n", - " experiment_results[model][\"R2\"].append(Record[model][\"R2\"])\n", - "\n", - " # Clean up\n", - " del Phi_wendland, max_Phi_sphere, dataframe, location_data, location_data_norm\n", - " cleanup_tf_session()\n", - " gc.collect()\n", - "\n", - " save_notebook()\n", - " print(f\"\\n✅ Completed Repeat {repeat+1}/{repeat_experiment}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f230fcba", - "metadata": { - "papermill": { - "duration": 0.018429, - "end_time": "2026-01-08T18:56:08.794172", - "exception": false, - "start_time": "2026-01-08T18:56:08.775743", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Summary of All Experiments" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "db4949b8", - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-08T18:56:08.832409Z", - "iopub.status.busy": "2026-01-08T18:56:08.831489Z", - "iopub.status.idle": "2026-01-08T18:56:08.861214Z", - "shell.execute_reply": "2026-01-08T18:56:08.858396Z" - }, - "papermill": { - "duration": 0.053277, - "end_time": "2026-01-08T18:56:08.865004", - "exception": false, - "start_time": "2026-01-08T18:56:08.811727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "📊 Summary of repeat experiments\n", - "================================================================================\n", - "Selected Best Orders: {'OLS_sphere': 200, 'DeepKriging_mrts': 500, 'DeepKriging_sphere': 200, 'DeepKriging_sphere_Huber': 50, 'UniversalKriging': 100}\n", - "================================================================================\n", - "\n", - "| Model | MSPE | RMSE | MAE | R2 |\n", - "|--------------------------|---------------|-----------|-----------|-------------|\n", - "| OLS_wendland | 106.62±269.55 | 6.46±8.06 | 2.67±0.71 | -5.62±16.43 |\n", - "| OLS_sphere | 4.98±0.49 | 2.23±0.11 | 1.62±0.09 | 0.59±0.13 |\n", - "| DeepKriging_wendland | 7.96±1.44 | 2.81±0.25 | 2.11±0.17 | 0.37±0.11 |\n", - "| DeepKriging_wendland* | 9.35±3.97 | 3.00±0.59 | 2.27±0.51 | 0.31±0.08 |\n", - "| DeepKriging_mrts | 6.31±1.45 | 2.50±0.28 | 1.89±0.25 | 0.49±0.15 |\n", - "| DeepKriging_sphere | 4.83±2.75 | 2.13±0.53 | 1.56±0.49 | 0.62±0.19 |\n", - "| DeepKriging_sphere_Huber | 6.82±4.38 | 2.49±0.79 | 1.87±0.70 | 0.50±0.21 |\n", - "| UniversalKriging | 3.35±0.26 | 1.83±0.07 | 1.32±0.05 | 0.72±0.09 |\n", - "\n", - "🏆 Best Model: UniversalKriging (RMSE: 1.83±0.07)\n" - ] - } - ], - "source": [ - "print(\"\\n\" + \"=\"*80)\n", - "print(\"📊 Summary of repeat experiments\")\n", - "print(\"=\"*80)\n", - "print(f\"Selected Best Orders: {best_orders}\")\n", - "print(\"=\"*80)\n", - "\n", - "avg_results = []\n", - "for model in [\"OLS_wendland\", \"OLS_sphere\", \"DeepKriging_wendland\", \"DeepKriging_wendland*\", \"DeepKriging_mrts\", \"DeepKriging_sphere\", \"DeepKriging_sphere_Huber\", \"UniversalKriging\"]:\n", - " metrics = experiment_results[model]\n", - " \n", - " avg_results.append({\n", - " \"Model\": model,\n", - " \"MSPE\": f\"{np.mean(metrics['MSPE']):.2f}±{np.std(metrics['MSPE']):.2f}\",\n", - " \"RMSE\": f\"{np.mean(metrics['RMSE']):.2f}±{np.std(metrics['RMSE']):.2f}\",\n", - " \"MAE\": f\"{np.mean(metrics['MAE']):.2f}±{np.std(metrics['MAE']):.2f}\",\n", - " \"R2\": f\"{np.mean(metrics['R2']):.2f}±{np.std(metrics['R2']):.2f}\",\n", - " })\n", - "\n", - "df_avg = pd.DataFrame(avg_results)\n", - "print(\"\\n\", df_avg.to_markdown(index=False, tablefmt=\"github\"), sep=\"\")\n", - "\n", - "if avg_results:\n", - " best_model = min(avg_results, key=lambda x: float(x[\"RMSE\"].split(\"±\")[0]))\n", - " print(f\"\\n🏆 Best Model: {best_model['Model']} (RMSE: {best_model['RMSE']})\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "air-pollution", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 1790.816365, - "end_time": "2026-01-08T18:56:12.520226", - "environment_variables": {}, - "exception": null, - "input_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "output_path": "/home/wywrocks/MLspatial/notebook/results/simulation/testtt/simulation_case3_eggholder_outliers5.ipynb", - "parameters": {}, - "start_time": "2026-01-08T18:26:21.703861", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyproject.toml b/pyproject.toml index f915487..42eab74 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,10 @@ readme = "README.md" requires-python = ">=3.10" license = {text = "LICENSE"} authors = [ - {name = "Your Name", email = "your.email@example.com"}, + {name = "Wen-Ting Wang", email = "egpivo@gmail.com"}, + {name = "Hao-Yun Huang", email = "hhuscout@gms.ndhu.edu.tw"}, + {name = "Ping-Hsun Chiang", email = "andrew501228@gmail.com"}, + {name = "Wu-Wei Ying", email = "wuweiying1011@gms.ndhu.edu.tw"}, ] classifiers = [ "Development Status :: 3 - Alpha", diff --git a/setup.py b/setup.py index e57b546..96f6d25 100644 --- a/setup.py +++ b/setup.py @@ -40,6 +40,16 @@ def build_extension(self, ext: Extension) -> None: f"-DPython3_EXECUTABLE={python_exe}", ] + # Ensure CMake can locate pybind11Config.cmake in isolated build envs. + try: + import pybind11 # type: ignore + + pybind11_dir = Path(pybind11.get_cmake_dir()).resolve() + cmake_args.append(f"-Dpybind11_DIR={pybind11_dir}") + except Exception: + # Let CMake try its default discovery path if pybind11 is unavailable. + pass + subprocess.check_call( ["cmake", "-S", ext.sourcedir, "-B", str(build_temp), *cmake_args] ) @@ -100,4 +110,3 @@ def _load_project_metadata() -> dict: cmdclass={"build_ext": CMakeBuild}, zip_safe=False, ) - diff --git a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/CMakeLists.txt b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/CMakeLists.txt index 499cb3f..09eaa0a 100644 --- a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/CMakeLists.txt +++ b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/CMakeLists.txt @@ -73,4 +73,3 @@ endif() set_target_properties(spherical_basis PROPERTIES OUTPUT_NAME "spherical_basis" ) - diff --git a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/__init__.py b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/__init__.py index c73bcdd..59bc2d4 100644 --- a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/__init__.py +++ b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/__init__.py @@ -36,4 +36,3 @@ "Please build the extensions using 'make build-cpp'.", ImportWarning, ) - diff --git a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/setup.py b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/setup.py index b7e702f..a98839f 100644 --- a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/setup.py +++ b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/setup.py @@ -26,4 +26,3 @@ def build_extension(self, ext): include_package_data=True, zip_safe=False, ) - diff --git a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/spherical_basis.cpp b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/spherical_basis.cpp index b9317f9..81b76c0 100644 --- a/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/spherical_basis.cpp +++ b/spherical_deepkriging/basis_functions/mrts_sphere/cpp_extensions/spherical_basis.cpp @@ -476,4 +476,3 @@ PYBIND11_MODULE(spherical_basis, m) { "Compute top k largest eigenvalues and eigenvectors using Spectra (matches RSpectra behavior)", py::arg("M"), py::arg("k")); } - diff --git a/spherical_deepkriging/basis_functions/mrts_sphere/fn_cpp.R b/spherical_deepkriging/basis_functions/mrts_sphere/fn_cpp.R index 26af8a2..7136a32 100644 --- a/spherical_deepkriging/basis_functions/mrts_sphere/fn_cpp.R +++ b/spherical_deepkriging/basis_functions/mrts_sphere/fn_cpp.R @@ -65,13 +65,13 @@ SEXP eigenMapMatMult(const Eigen::Map A, Eigen::Map None: """Clear the cache for K matrix, Konev, and eigenpairs.""" global _cache _cache.clear() - diff --git a/spherical_deepkriging/configs.py b/spherical_deepkriging/configs.py index 3014e9e..910af59 100644 --- a/spherical_deepkriging/configs.py +++ b/spherical_deepkriging/configs.py @@ -14,7 +14,7 @@ class DeepKrigingModelConfig: metrics: Optional[List[str]] = None epochs: int = 10 batch_size: int = 32 - # for EarlyStopping; None = no early stopping + # for EarlyStopping; None = no early stopping patience: Optional[int] = None verbose: int = 1 diff --git a/spherical_deepkriging/models/deep_kriging.py b/spherical_deepkriging/models/deep_kriging.py index ef886f1..f3af100 100644 --- a/spherical_deepkriging/models/deep_kriging.py +++ b/spherical_deepkriging/models/deep_kriging.py @@ -3,8 +3,10 @@ import numpy as np import tensorflow as tf -from spherical_deepkriging.configs import DeepKrigingDefaultConfig, DeepKrigingModelConfig - +from spherical_deepkriging.configs import ( + DeepKrigingDefaultConfig, + DeepKrigingModelConfig, +) class DeepKrigingTrainer: @@ -15,29 +17,35 @@ def __init__(self, config: DeepKrigingModelConfig) -> None: def _build_model(self) -> tf.keras.Sequential: model = tf.keras.Sequential() - model.add(tf.keras.layers.Dense( - self.config.hidden_layers[0], - activation=None, - use_bias=False, - kernel_initializer='he_normal', - input_shape=(self.config.input_dim,) - )) + model.add( + tf.keras.layers.Dense( + self.config.hidden_layers[0], + activation=None, + use_bias=False, + kernel_initializer="he_normal", + input_shape=(self.config.input_dim,), + ) + ) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Activation(self.config.activation)) model.add(tf.keras.layers.Dropout(self.config.dropout_rate)) for units in self.config.hidden_layers[1:]: - model.add(tf.keras.layers.Dense( - units, - activation=None, - use_bias=False, - kernel_initializer='he_normal' - )) + model.add( + tf.keras.layers.Dense( + units, + activation=None, + use_bias=False, + kernel_initializer="he_normal", + ) + ) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Activation(self.config.activation)) model.add(tf.keras.layers.Dropout(self.config.dropout_rate)) - output_activation = ("linear" if self.config.output_type == "continuous" else "sigmoid") + output_activation = ( + "linear" if self.config.output_type == "continuous" else "sigmoid" + ) model.add(tf.keras.layers.Dense(1, activation=output_activation)) return model @@ -61,9 +69,9 @@ def train( return self.model.fit( train_features, train_labels, - validation_data=(valid_features, valid_labels) - if valid_features is not None - else None, + validation_data=( + (valid_features, valid_labels) if valid_features is not None else None + ), epochs=self.config.epochs, batch_size=self.config.batch_size, verbose=self.config.verbose, @@ -86,13 +94,15 @@ def _build_model(self) -> tf.keras.Sequential: act = self.config.activation for i in range(n): - model.add(tf.keras.layers.Dense( - units, - activation=None, - use_bias=False, - kernel_initializer="he_normal", - input_shape=(self.config.input_dim,) if i == 0 else None, - )) + model.add( + tf.keras.layers.Dense( + units, + activation=None, + use_bias=False, + kernel_initializer="he_normal", + input_shape=(self.config.input_dim,) if i == 0 else None, + ) + ) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Activation(act)) if i < n - 1: @@ -119,7 +129,9 @@ def train( return self.model.fit( train_features, train_labels, - validation_data=(valid_features, valid_labels) if valid_features is not None else None, + validation_data=( + (valid_features, valid_labels) if valid_features is not None else None + ), epochs=self.config.epochs, batch_size=self.config.batch_size, verbose=self.config.verbose, diff --git a/spherical_deepkriging/models/universal_kriging.py b/spherical_deepkriging/models/universal_kriging.py index c75c9b6..4844da0 100644 --- a/spherical_deepkriging/models/universal_kriging.py +++ b/spherical_deepkriging/models/universal_kriging.py @@ -1,9 +1,9 @@ +import gc import logging + +import gpboost as gpb import numpy as np import pandas as pd -import gpboost as gpb -import gc -import warnings logger = logging.getLogger(__name__) @@ -12,6 +12,7 @@ class UniversalKriging: """ UniversalKriging module using GPBoost for spherical spatial data. """ + def __init__(self, num_neighbors, cov_function): """ Initialize Universal Kriging model. @@ -23,14 +24,14 @@ def __init__(self, num_neighbors, cov_function): self.has_covariates = False self.y_mean = 0.0 self.nu_was_refitted = False - + @staticmethod def coords_to_radians(coords): """ Convert (lat, lon) from degrees to radians. """ return np.deg2rad(coords).astype(np.float32) - + @staticmethod def compute_spherical_distance_matrix(coords_rad): """ @@ -38,49 +39,49 @@ def compute_spherical_distance_matrix(coords_rad): """ lat_rad = coords_rad[:, 0] lon_rad = coords_rad[:, 1] - + # Compute cosine of angular distance - cos_theta = (np.sin(lat_rad[:, None]) * np.sin(lat_rad[None, :]) + - np.cos(lat_rad[:, None]) * np.cos(lat_rad[None, :]) * - np.cos(lon_rad[:, None] - lon_rad[None, :])) - + cos_theta = np.sin(lat_rad[:, None]) * np.sin(lat_rad[None, :]) + np.cos( + lat_rad[:, None] + ) * np.cos(lat_rad[None, :]) * np.cos(lon_rad[:, None] - lon_rad[None, :]) + # Compute angular distance (clip to handle numerical errors) theta_mat = np.arccos(np.clip(cos_theta, -1.0, 1.0)).astype(np.float32) return theta_mat - + @staticmethod def extract_gp_params(gp_model): """ Extract parameters from GPBoost model. """ cov_pars = gp_model.get_cov_pars() - + # Extract nu (don't enforce limit here, just extract) - if 'Matern_nu' in cov_pars.columns: - nu = float(cov_pars['Matern_nu'].values[0]) + if "Matern_nu" in cov_pars.columns: + nu = float(cov_pars["Matern_nu"].values[0]) else: nu = 0.5 - - rho = float(cov_pars['GP_range'].values[0]) - sigma2 = float(cov_pars['GP_var'].values[0]) - nugget = float(cov_pars['Error_term'].values[0]) + + rho = float(cov_pars["GP_range"].values[0]) + sigma2 = float(cov_pars["GP_var"].values[0]) + nugget = float(cov_pars["Error_term"].values[0]) return nu, rho, sigma2, nugget - + def _get_gpboost_cov_params(self): """ Convert user-friendly covariance function name to GPBoost parameters. """ - if self.cov_function == 'exponential': - return 'matern', 0.5, False - elif self.cov_function == 'gaussian': - return 'gaussian', None, False + if self.cov_function == "exponential": + return "matern", 0.5, False + elif self.cov_function == "gaussian": + return "gaussian", None, False def fit(self, coords_train, phi_train, y_train, center_y=True): """ Fit the Universal Kriging model with robust error handling. """ y_train = y_train.reshape(-1).astype(np.float32) - + # Center y if requested if center_y: self.y_mean = np.mean(y_train) @@ -88,27 +89,29 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): else: self.y_mean = 0.0 y_train_centered = y_train - + # Convert coordinates to radians coords_rad = self.coords_to_radians(coords_train) - + # Store some information self._coords_rad = coords_rad - self._phi_train = phi_train.astype(np.float32) if phi_train is not None else None + self._phi_train = ( + phi_train.astype(np.float32) if phi_train is not None else None + ) self._y_train = y_train_centered - + # Determine number of features n_features = 0 if phi_train is None else phi_train.shape[1] - + # Prepare fit parameters fit_params = { # "std_dev": True, "trace": False } - + # Get covariance function parameters cov_name, cov_shape, estimate_shape = self._get_gpboost_cov_params() - + # Case 1: Using a fixed covariance function (exponential, gaussian, etc.) if not estimate_shape: try: @@ -121,7 +124,7 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): cov_fct_shape=cov_shape, likelihood="gaussian", gp_approx="vecchia", - num_neighbors=self.num_neighbors + num_neighbors=self.num_neighbors, ) else: # Gaussian or other function without shape parameter @@ -130,31 +133,37 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): cov_function=cov_name, likelihood="gaussian", gp_approx="vecchia", - num_neighbors=self.num_neighbors + num_neighbors=self.num_neighbors, ) - + # Fit model if phi_train is None: self.has_covariates = False self.gp_model.fit(y=y_train_centered, X=None, params=fit_params) else: self.has_covariates = True - self.gp_model.fit(y=y_train_centered, X=self._phi_train, params=fit_params) - + self.gp_model.fit( + y=y_train_centered, X=self._phi_train, params=fit_params + ) + # Extract parameters - nu_final, rho_final, sigma2_final, nugget_final = self.extract_gp_params(self.gp_model) - + nu_final, rho_final, sigma2_final, nugget_final = ( + self.extract_gp_params(self.gp_model) + ) + self.nu_was_refitted = False nu_est = nu_final - + except Exception as e: - raise RuntimeError(f"Model fitting failed with {self.cov_function} covariance: {str(e)}") - + raise RuntimeError( + f"Model fitting failed with {self.cov_function} covariance: {str(e)}" + ) + # Case 2: Estimate shape parameter (matern_auto) else: nu_est = None estimation_succeeded = False - + try: # Create GP model with nu estimation gp_model_estimate = gpb.GPModel( @@ -162,30 +171,34 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): cov_function="matern_estimate_shape", likelihood="gaussian", gp_approx="vecchia", - num_neighbors=self.num_neighbors + num_neighbors=self.num_neighbors, ) - + # Fit model if phi_train is None: self.has_covariates = False gp_model_estimate.fit(y=y_train_centered, X=None, params=fit_params) else: self.has_covariates = True - gp_model_estimate.fit(y=y_train_centered, X=self._phi_train, params=fit_params) - + gp_model_estimate.fit( + y=y_train_centered, X=self._phi_train, params=fit_params + ) + # Extract estimated parameters - nu_est, rho_est, sigma2_est, nugget_est = self.extract_gp_params(gp_model_estimate) - + nu_est, rho_est, sigma2_est, nugget_est = self.extract_gp_params( + gp_model_estimate + ) + # Check if parameters are valid if np.isnan(nu_est) or np.isnan(rho_est) or np.isnan(sigma2_est): raise ValueError("NaN detected in estimated parameters") - + estimation_succeeded = True - - except Exception as e: + + except Exception: nu_est = None estimation_succeeded = False - + # Re-fit if nu > 0.5 or estimation failed if not estimation_succeeded or (nu_est is not None and nu_est > 0.5): try: @@ -196,23 +209,27 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): cov_fct_shape=0.5, likelihood="gaussian", gp_approx="vecchia", - num_neighbors=self.num_neighbors + num_neighbors=self.num_neighbors, ) - + # Fit model again if phi_train is None: self.gp_model.fit(y=y_train_centered, X=None, params=fit_params) else: - self.gp_model.fit(y=y_train_centered, X=self._phi_train, params=fit_params) - + self.gp_model.fit( + y=y_train_centered, X=self._phi_train, params=fit_params + ) + # Extract new parameters - nu_final, rho_final, sigma2_final, nugget_final = self.extract_gp_params(self.gp_model) - + nu_final, rho_final, sigma2_final, nugget_final = ( + self.extract_gp_params(self.gp_model) + ) + self.nu_was_refitted = True - + except Exception as e: raise RuntimeError(f"Model fitting failed: {str(e)}") - + else: # Use the original model self.gp_model = gp_model_estimate @@ -220,14 +237,14 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): rho_final = rho_est sigma2_final = sigma2_est nugget_final = nugget_est - + self.nu_was_refitted = False - + # Clean up temporary model if refitted if self.nu_was_refitted and estimation_succeeded: del gp_model_estimate gc.collect() - + # Extract beta coefficients beta = None if n_features > 0: @@ -240,28 +257,28 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): beta = coefs.reshape(-1, 1) except Exception: beta = np.zeros((n_features, 1)) - + # Store parameters self.params = { - 'nu': nu_final, - 'rho_rad': rho_final, - 'sigma2': sigma2_final, - 'nugget': nugget_final, - 'beta': beta, - 'nu_initial_estimate': nu_est if nu_est is not None else nu_final, - 'was_refitted': self.nu_was_refitted, - 'cov_function': self.cov_function + "nu": nu_final, + "rho_rad": rho_final, + "sigma2": sigma2_final, + "nugget": nugget_final, + "beta": beta, + "nu_initial_estimate": nu_est if nu_est is not None else nu_final, + "was_refitted": self.nu_was_refitted, + "cov_function": self.cov_function, } - + # Log fitted parameters cov_info = f"Covariance function: {self.cov_function}" - if self.cov_function == 'gaussian': + if self.cov_function == "gaussian": logger.info("%s (Gaussian/RBF)", cov_info) - elif self.cov_function == 'exponential': + elif self.cov_function == "exponential": logger.info("%s (Matérn ν=0.5)", cov_info) else: logger.info("%s", cov_info) - + logger.info( "Fitted parameters: nu=%.4f, rho=%.4f, sigma²=%.4f, nugget=%.4f", nu_final, @@ -269,31 +286,35 @@ def fit(self, coords_train, phi_train, y_train, center_y=True): sigma2_final, nugget_final, ) - + return self - + def predict(self, coords_new, phi_new=None, return_centered=True): """ Predict at new locations. """ # Convert coordinates to radians coords_rad = self.coords_to_radians(coords_new) - + # Make predictions if self.has_covariates: phi_new = phi_new.astype(np.float32) - pred = self.gp_model.predict(X_pred=phi_new, gp_coords_pred=coords_rad, predict_var=False) + pred = self.gp_model.predict( + X_pred=phi_new, gp_coords_pred=coords_rad, predict_var=False + ) else: - pred = self.gp_model.predict(X_pred=None, gp_coords_pred=coords_rad, predict_var=False) - + pred = self.gp_model.predict( + X_pred=None, gp_coords_pred=coords_rad, predict_var=False + ) + predictions = pred["mu"] - + # Add back mean if requested if not return_centered: predictions = predictions + self.y_mean - + return predictions - + def get_coef(self): """ Get fitted coefficients. @@ -303,19 +324,19 @@ def get_coef(self): coefs = raw_coefs.to_numpy().flatten() else: coefs = np.array(raw_coefs).flatten() - + return coefs - + def decompose_prediction(self, coords_new, phi_new): """ Decompose prediction into fixed and random effects. """ # Get total prediction y_pred_total = self.predict(coords_new, phi_new, return_centered=True) - + # Get coefficients coefs = self.get_coef() - + # Calculate fixed effect n_features = phi_new.shape[1] if len(coefs) == n_features: @@ -323,13 +344,15 @@ def decompose_prediction(self, coords_new, phi_new): elif len(coefs) == n_features + 1: y_pred_fixed = phi_new @ coefs[:-1] + coefs[-1] else: - raise ValueError(f"Coefficient shape mismatch. Expected {n_features} or {n_features+1}, got {len(coefs)}") - + raise ValueError( + f"Coefficient shape mismatch. Expected {n_features} or {n_features+1}, got {len(coefs)}" + ) + # Calculate random effect y_pred_random = y_pred_total - y_pred_fixed - + return y_pred_total, y_pred_fixed, y_pred_random - + def cleanup(self): """Release memory.""" if self.gp_model is not None: diff --git a/tests/conftest.py b/tests/conftest.py index cb45dc4..6572fcd 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,10 +3,8 @@ import sys from pathlib import Path - # Ensure repo root is on sys.path so `import spherical_deepkriging` works # even when you don't `pip install -e .` first. ROOT = Path(__file__).resolve().parents[1] if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) - diff --git a/tests/test_basis_utils_and_wendland.py b/tests/test_basis_utils_and_wendland.py new file mode 100644 index 0000000..5d16475 --- /dev/null +++ b/tests/test_basis_utils_and_wendland.py @@ -0,0 +1,99 @@ +import numpy as np +import pytest + +from spherical_deepkriging.basis_functions.utils import create_knots_grid +from spherical_deepkriging.basis_functions.wendland.wenland import ( + wendland, + wendland_core, +) + + +def test_create_knots_grid_returns_cartesian_grid(): + grid = create_knots_grid(resolution=3, start=-1.0, end=1.0) + + expected = np.array( + [ + [-1.0, -1.0], + [0.0, -1.0], + [1.0, -1.0], + [-1.0, 0.0], + [0.0, 0.0], + [1.0, 0.0], + [-1.0, 1.0], + [0.0, 1.0], + [1.0, 1.0], + ] + ) + + assert np.allclose(grid, expected) + + +@pytest.mark.parametrize( + ("kwargs", "message"), + [ + ({"resolution": 0}, "resolution must be a positive integer"), + ({"resolution": 2, "start": 1.0, "end": 1.0}, "start must be less than end"), + ], +) +def test_create_knots_grid_validates_inputs(kwargs, message): + with pytest.raises(ValueError, match=message): + create_knots_grid(**kwargs) + + +@pytest.mark.parametrize("k", [0, 1, 2]) +def test_wendland_matches_single_knot_core(k): + points = np.array([[0.0, 0.0], [0.5, 0.0], [1.5, 0.0]]) + knot = np.array([0.0, 0.0]) + + combined = wendland(points, knots=np.array([knot]), theta=1.0, k=k) + core = wendland_core(points, knot=knot, theta=1.0, k=k) + + assert combined.shape == (3, 1) + assert np.allclose(combined[:, 0], core) + assert core[-1] == 0.0 + + +@pytest.mark.parametrize( + ("points", "knot", "theta", "k", "message"), + [ + (np.array([0.0, 1.0]), np.array([0.0]), 1.0, 0, "Points must be a 2D array"), + ( + np.array([[0.0], [1.0]]), + np.array([[0.0]]), + 1.0, + 0, + "Knot must be a 1D array", + ), + ( + np.array([[0.0, 0.0, 0.0]]), + np.array([0.0, 0.0, 0.0]), + 1.0, + 0, + "Only 1D and 2D spaces are supported", + ), + ( + np.array([[0.0, 0.0]]), + np.array([0.0]), + 1.0, + 0, + "Points and knot dimensions must match", + ), + ( + np.array([[0.0, 0.0]]), + np.array([0.0, 0.0]), + 0.0, + 0, + "Theta must be a positive value", + ), + ( + np.array([[0.0, 0.0]]), + np.array([0.0, 0.0]), + 1.0, + 3, + "Unsupported value of k", + ), + ], +) +def test_wendland_core_validates_inputs(points, knot, theta, k, message): + with pytest.raises(ValueError, match=message): + wendland_core(points, knot=knot, theta=theta, k=k) diff --git a/tests/test_basis_visualization.py b/tests/test_basis_visualization.py new file mode 100644 index 0000000..1c30752 --- /dev/null +++ b/tests/test_basis_visualization.py @@ -0,0 +1,125 @@ +import numpy as np +import pytest + +jax = pytest.importorskip("jax") + +from spherical_deepkriging.basis_functions.mrts import visualization as mrts_vis +from spherical_deepkriging.basis_functions.wendland import visualization as w_vis + + +class FakePlotlyFigure: + def __init__(self): + self.traces = [] + self.layout_updates = [] + self.x_updates = [] + self.y_updates = [] + self.show_called = False + + def add_trace(self, trace, row=None, col=None): + self.traces.append((trace, row, col)) + + def update_layout(self, **kwargs): + self.layout_updates.append(kwargs) + + def update_xaxes(self, **kwargs): + self.x_updates.append(kwargs) + + def update_yaxes(self, **kwargs): + self.y_updates.append(kwargs) + + def show(self): + self.show_called = True + + +def test_mrts_plot_helpers_run_without_display(monkeypatch): + monkeypatch.setattr(mrts_vis.plt, "show", lambda: None) + monkeypatch.setattr(mrts_vis.plt, "tight_layout", lambda *args, **kwargs: None) + + r = np.linspace(0.0, 1.0, 10) + basis_1d = np.column_stack([r, r**2, r**3]) + mrts_vis.plot_1d_basis_functions(basis_1d, r, num_basis=3) + + basis_2d = np.arange(16, dtype=float).reshape(4, 4) + mrts_vis.plot_2d_basis_functions( + basis_2d, grid_size=2, num_basis=4, start=0.0, end=1.0 + ) + + grid_points = np.array( + [[x, y, z] for x in range(2) for y in range(2) for z in range(2)], + dtype=float, + ) + basis_3d = np.arange(16, dtype=float).reshape(8, 2) + mrts_vis.plot_3d_basis_functions( + basis_3d, grid_points=grid_points, grid_size=2, num_basis=2 + ) + + +def test_plot_mrts_basis_functions_dispatches_by_dimension(monkeypatch): + calls = [] + + monkeypatch.setattr( + mrts_vis, + "plot_1d_basis_functions", + lambda basis_values, r, num_basis: calls.append( + ("1d", basis_values.shape, r.shape, num_basis) + ), + ) + monkeypatch.setattr( + mrts_vis, + "plot_2d_basis_functions", + lambda basis_values, grid_size, num_basis, start, end: calls.append( + ("2d", basis_values.shape, grid_size, num_basis, start, end) + ), + ) + monkeypatch.setattr( + mrts_vis, + "plot_3d_basis_functions", + lambda basis_values, grid_points, grid_size, num_basis: calls.append( + ("3d", basis_values.shape, grid_points.shape, grid_size, num_basis) + ), + ) + + def fake_mrts0(knot, k, x=None): + rows = len(x) if x is not None else len(knot) + return np.ones((rows, k), dtype=float) + + monkeypatch.setattr(mrts_vis, "mrts0", fake_mrts0) + + mrts_vis.plot_mrts_basis_functions(num_basis=3, resolution=4, ndims=1) + mrts_vis.plot_mrts_basis_functions(num_basis=4, resolution=3, ndims=2) + mrts_vis.plot_mrts_basis_functions(num_basis=10, resolution=2, ndims=3) + + assert [call[0] for call in calls] == ["1d", "2d", "3d"] + + with pytest.raises(ValueError, match="Invalid k"): + mrts_vis.plot_mrts_basis_functions(num_basis=1, ndims=1) + + with pytest.raises(ValueError, match="Unsupported ndims"): + mrts_vis.plot_mrts_basis_functions(num_basis=6, ndims=4) + + +def test_wendland_visualization_builds_expected_traces(monkeypatch): + figures = [] + + monkeypatch.setattr( + w_vis.go, + "Figure", + lambda: figures.append(FakePlotlyFigure()) or figures[-1], + ) + monkeypatch.setattr( + w_vis, + "make_subplots", + lambda **kwargs: figures.append(FakePlotlyFigure()) or figures[-1], + ) + + knots_2d = np.array([[0.25, 0.25], [0.75, 0.75]]) + w_vis.visualize_2d_basis_functions(knots_2d, theta=0.5, k=1, num_basis_to_show=2) + fig2d = figures[0] + assert len(fig2d.traces) == 2 + assert fig2d.show_called is True + + knots_1d = np.array([[0.0, 0.0], [0.5, 0.0]]) + w_vis.visualize_1d_basis_functions(knots_1d, theta=1.0, k=0, num_basis_to_show=2) + fig1d = figures[1] + assert len(fig1d.traces) == 2 + assert fig1d.show_called is True diff --git a/tests/test_configs.py b/tests/test_configs.py index 84c6b76..fbe0b62 100644 --- a/tests/test_configs.py +++ b/tests/test_configs.py @@ -1,6 +1,3 @@ -import pytest - - def test_deepkriging_model_config_continuous_defaults(): from spherical_deepkriging.configs import DeepKrigingModelConfig @@ -31,4 +28,3 @@ def test_deepkriging_default_config_discrete_defaults(): cfg = DeepKrigingDefaultConfig(input_dim=4, output_type="discrete") assert cfg.loss == "binary_crossentropy" assert cfg.metrics == ["accuracy"] - diff --git a/tests/test_deep_kriging_shapes.py b/tests/test_deep_kriging_shapes.py index 2ae70ef..f1d5831 100644 --- a/tests/test_deep_kriging_shapes.py +++ b/tests/test_deep_kriging_shapes.py @@ -1,8 +1,6 @@ import numpy as np - import pytest - tf = pytest.importorskip("tensorflow") @@ -38,7 +36,8 @@ def test_deep_kriging_default_dropout_count(): ) trainer = DeepKrigingDefaultTrainer(cfg) - dropout_layers = [l for l in trainer.model.layers if isinstance(l, tf.keras.layers.Dropout)] + dropout_layers = [ + l for l in trainer.model.layers if isinstance(l, tf.keras.layers.Dropout) + ] # In deep_kriging.py: dropout is added only for i < n-1 assert len(dropout_layers) == n - 1 - diff --git a/tests/test_deep_kriging_train.py b/tests/test_deep_kriging_train.py new file mode 100644 index 0000000..36ddb8d --- /dev/null +++ b/tests/test_deep_kriging_train.py @@ -0,0 +1,111 @@ +from types import SimpleNamespace + +import numpy as np +import pytest + +from spherical_deepkriging.configs import ( + DeepKrigingDefaultConfig, + DeepKrigingModelConfig, +) + +tf = pytest.importorskip("tensorflow") + +from spherical_deepkriging.models.deep_kriging import ( # noqa: E402 + DeepKrigingDefaultTrainer, + DeepKrigingTrainer, +) + + +def test_deep_kriging_discrete_output_uses_sigmoid(): + trainer = DeepKrigingTrainer( + DeepKrigingModelConfig( + input_dim=3, + hidden_layers=[4, 2], + output_type="discrete", + dropout_rate=0.1, + ) + ) + + assert trainer.model.layers[-1].activation.__name__ == "sigmoid" + + +def test_deep_kriging_train_passes_validation_and_callbacks(monkeypatch, tmp_path): + trainer = DeepKrigingTrainer( + DeepKrigingModelConfig( + input_dim=2, + hidden_layers=[4], + output_type="continuous", + epochs=2, + batch_size=4, + verbose=0, + ) + ) + + compile_kwargs = {} + fit_kwargs = {} + + def fake_compile(**kwargs): + compile_kwargs.update(kwargs) + + def fake_fit(*args, **kwargs): + fit_kwargs.update(kwargs) + return SimpleNamespace(history={"loss": [1.0]}) + + monkeypatch.setattr(trainer.model, "compile", fake_compile) + monkeypatch.setattr(trainer.model, "fit", fake_fit) + + train_x = np.ones((6, 2), dtype=np.float32) + train_y = np.ones((6, 1), dtype=np.float32) + valid_x = np.zeros((3, 2), dtype=np.float32) + valid_y = np.zeros((3, 1), dtype=np.float32) + + history = trainer.train( + train_x, + train_y, + valid_features=valid_x, + valid_labels=valid_y, + log_dir=str(tmp_path), + ) + + assert history.history == {"loss": [1.0]} + assert compile_kwargs == { + "optimizer": trainer.config.optimizer, + "loss": trainer.config.loss, + "metrics": trainer.config.metrics, + } + assert fit_kwargs["validation_data"] == (valid_x, valid_y) + assert fit_kwargs["epochs"] == 2 + assert fit_kwargs["batch_size"] == 4 + assert len(fit_kwargs["callbacks"]) == 1 + assert isinstance(fit_kwargs["callbacks"][0], tf.keras.callbacks.TensorBoard) + + +def test_default_trainer_discrete_output_and_optional_arguments(monkeypatch): + trainer = DeepKrigingDefaultTrainer( + DeepKrigingDefaultConfig( + input_dim=2, + hidden_units=4, + num_hidden_layers=2, + output_type="discrete", + epochs=1, + batch_size=2, + verbose=0, + ) + ) + + fit_kwargs = {} + monkeypatch.setattr(trainer.model, "compile", lambda **kwargs: None) + monkeypatch.setattr( + trainer.model, + "fit", + lambda *args, **kwargs: fit_kwargs.update(kwargs) + or SimpleNamespace(history={}), + ) + + train_x = np.zeros((2, 2), dtype=np.float32) + train_y = np.zeros((2, 1), dtype=np.float32) + trainer.train(train_x, train_y) + + assert trainer.model.layers[-1].activation.__name__ == "sigmoid" + assert fit_kwargs["validation_data"] is None + assert fit_kwargs["callbacks"] == [] diff --git a/tests/test_logger.py b/tests/test_logger.py new file mode 100644 index 0000000..e9d885d --- /dev/null +++ b/tests/test_logger.py @@ -0,0 +1,32 @@ +import logging + +from spherical_deepkriging.logger import setup_logger + + +def test_setup_logger_creates_handler_and_supports_set_level(): + logger_name = "spherical_deepkriging.tests.logger.unique" + logger = logging.getLogger(logger_name) + logger.handlers.clear() + + configured = setup_logger(logger_name, level=logging.WARNING) + + assert configured.name == logger_name + assert configured.level == logging.WARNING + assert len(configured.handlers) == 1 + + configured.set_level(logging.ERROR) + + assert configured.level == logging.ERROR + assert configured.handlers[0].level == logging.ERROR + + +def test_setup_logger_reuses_existing_logger_handlers(): + logger_name = "spherical_deepkriging.tests.logger.reuse" + logger = logging.getLogger(logger_name) + logger.handlers.clear() + logger.addHandler(logging.NullHandler()) + + reused = setup_logger(logger_name) + + assert reused is logger + assert len(reused.handlers) == 1 diff --git a/tests/test_mrts_modules.py b/tests/test_mrts_modules.py new file mode 100644 index 0000000..b1809a7 --- /dev/null +++ b/tests/test_mrts_modules.py @@ -0,0 +1,68 @@ +import numpy as np +import pytest + +jnp = pytest.importorskip("jax.numpy") + +from spherical_deepkriging.basis_functions.mrts.mrts import mrts0 +from spherical_deepkriging.basis_functions.mrts.utils import ( + build_extended_matrix, + compute_h, + dist, + predict_rabf, +) + + +def test_dist_and_compute_h_cover_supported_dimensions(): + a = jnp.array([[0.0], [3.0]]) + b = jnp.array([[0.0], [4.0]]) + distances = np.array(dist(a, b)) + + assert np.allclose(distances, np.array([[0.0, 4.0], [3.0, 1.0]])) + + d = jnp.array([[0.0, 2.0]], dtype=jnp.float32) + assert np.allclose(np.array(compute_h(d, 1)), np.array([[0.0, 8.0 / 12.0]])) + expected_2d = np.array([[0.0, (1.0 / 8.0) * 4.0 * np.log(2.0 + 1e-8)]]) + assert np.allclose(np.array(compute_h(d, 2)), expected_2d) + assert np.allclose(np.array(compute_h(d, 3)), np.array([[0.0, -0.25]])) + + +def test_build_extended_matrix_and_predict_rabf_cover_both_branches(): + xu = jnp.array([[0.0], [1.0], [2.0]], dtype=jnp.float32) + aha, uz = build_extended_matrix(xu, k=3, ndims=1, slice_size=1) + + assert aha.shape == (3, 3) + assert uz.shape == (5, 3) + assert np.all(np.isfinite(np.array(aha))) + assert np.all(np.isfinite(np.array(uz))) + + _, obj, k = mrts0(xu, k=3) + predicted = predict_rabf( + obj, newx=jnp.array([[0.5], [1.5]], dtype=jnp.float32), k=k + ) + assert predicted.shape == (2, 3) + + _, obj_small, k_small = mrts0(xu, k=2) + predicted_small = predict_rabf( + obj_small, newx=jnp.array([[0.5], [1.5]], dtype=jnp.float32), k=k_small + ) + assert predicted_small.shape == (2, 2) + assert np.allclose(np.array(predicted_small[:, 0]), 1.0) + + returned_obj = predict_rabf(obj, newx=None, k=k) + assert set(returned_obj) == set(obj) + assert np.allclose(np.array(returned_obj["Xu"]), np.array(obj["Xu"])) + + +def test_mrts0_returns_tuple_or_prediction_and_validates_k(): + knot = jnp.array([[0.0], [1.0], [2.0]], dtype=jnp.float32) + + xu, obj, used_k = mrts0(knot, k=3) + assert used_k == 3 + assert xu.shape == knot.shape + assert set(obj) == {"S", "UZ", "Xu", "BBBH", "ndims"} + + prediction = mrts0(knot, k=3, x=jnp.array([[0.25], [1.25]], dtype=jnp.float32)) + assert prediction.shape == (2, 3) + + with pytest.raises(ValueError, match="Invalid k"): + mrts0(knot, k=1) diff --git a/tests/test_sphere_cpp_unit.py b/tests/test_sphere_cpp_unit.py new file mode 100644 index 0000000..33bab1c --- /dev/null +++ b/tests/test_sphere_cpp_unit.py @@ -0,0 +1,94 @@ +import numpy as np +import pytest + +import spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp as sphere_cpp + + +def test_sphere_cpp_validates_inputs_and_unavailable_extensions(monkeypatch): + monkeypatch.setattr(sphere_cpp, "CPP_EXTENSIONS_AVAILABLE", False) + with pytest.raises(ImportError, match=r"C\+\+ extensions not available"): + sphere_cpp.mrts_sphere(np.array([[1.0, 2.0]]), k=1) + + monkeypatch.setattr(sphere_cpp, "CPP_EXTENSIONS_AVAILABLE", True) + + with pytest.raises(ValueError, match="knot must be a 2D array"): + sphere_cpp.mrts_sphere(np.array([1.0, 2.0]), k=1) + + with pytest.raises(ValueError, match="knot must have at least one point"): + sphere_cpp.mrts_sphere(np.empty((0, 2)), k=1) + + with pytest.raises(ValueError, match="k must be at least 1"): + sphere_cpp.mrts_sphere(np.array([[1.0, 2.0]]), k=0) + + with pytest.raises(ValueError, match="cannot be greater than the number of knots"): + sphere_cpp.mrts_sphere(np.array([[1.0, 2.0]]), k=2) + + with pytest.raises(ValueError, match="X must be a 2D array"): + sphere_cpp.mrts_sphere( + np.array([[1.0, 2.0], [3.0, 4.0]]), k=1, X=np.array([1.0, 2.0]) + ) + + +def test_sphere_cpp_builds_cache_and_reuses_precomputed_values(monkeypatch): + sphere_cpp.clear_cache() + monkeypatch.setattr(sphere_cpp, "CPP_EXTENSIONS_AVAILABLE", True) + + calls = {"cpp_K": 0, "cpp_Kmatrix": 0} + + def fake_cpp_K(lat, lon, n): + calls["cpp_K"] += 1 + return np.array( + [[2.0, 1.0, 0.5], [1.0, 2.0, 0.5], [0.5, 0.5, 2.0]], dtype=float + ) + + def fake_get_eigen_top_k(matrix, k): + return np.array([0.0, 2.0, 1.0], dtype=float), np.eye(3, dtype=float) + + def fake_cpp_Kmatrix(k, knot, X, Konev, eiKvecmval, n, N): + calls["cpp_Kmatrix"] += 1 + assert np.allclose(Konev, np.array([1.16666667, 1.16666667, 1.0])) + assert eiKvecmval.shape == (3, 2) + assert np.allclose(eiKvecmval[:, 0], 0.0) + return np.full((N, k), 7.0) + + monkeypatch.setattr(sphere_cpp, "cpp_K", fake_cpp_K, raising=False) + monkeypatch.setattr(sphere_cpp, "getEigenTopK", fake_get_eigen_top_k, raising=False) + monkeypatch.setattr(sphere_cpp, "cpp_Kmatrix", fake_cpp_Kmatrix, raising=False) + + knot = np.array([[10.0, 20.0], [11.0, 21.0], [12.0, 22.0]], dtype=float) + out1 = sphere_cpp.mrts_sphere(knot, k=3) + assert out1["mrts"].shape == (3, 3) + assert len(sphere_cpp._cache) == 1 + + monkeypatch.setattr( + sphere_cpp, + "cpp_K", + lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("cache miss")), + raising=False, + ) + out2 = sphere_cpp.mrts_sphere(knot, k=3, X=np.array([[13.0, 23.0]], dtype=float)) + assert out2["mrts"].shape == (1, 3) + assert calls["cpp_K"] == 1 + assert calls["cpp_Kmatrix"] == 2 + + +def test_sphere_cpp_raises_on_non_finite_kernel_and_can_clear_cache(monkeypatch): + sphere_cpp.clear_cache() + monkeypatch.setattr(sphere_cpp, "CPP_EXTENSIONS_AVAILABLE", True) + monkeypatch.setattr( + sphere_cpp, + "cpp_K", + lambda lat, lon, n: np.array([[1.0, np.nan], [np.nan, 1.0]], dtype=float), + raising=False, + ) + + with pytest.raises(FloatingPointError, match="K contains non-finite values"): + sphere_cpp.mrts_sphere(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=float), k=2) + + sphere_cpp._cache[(b"x", 1)] = { + "K": np.eye(1), + "Konev": np.ones(1), + "eiKvecmval": np.ones((1, 1)), + } + sphere_cpp.clear_cache() + assert sphere_cpp._cache == {} diff --git a/tests/test_sphere_wrapper_and_cpp_init.py b/tests/test_sphere_wrapper_and_cpp_init.py new file mode 100644 index 0000000..c9d027f --- /dev/null +++ b/tests/test_sphere_wrapper_and_cpp_init.py @@ -0,0 +1,187 @@ +import builtins +import importlib +import importlib.util +import sys +import types +import warnings # pragma: no cover +from pathlib import Path + +import numpy as np +import pytest +import setuptools +from setuptools import Distribution + + +class FakeConverter: + def __add__(self, other): + return ("combined", other) + + +def test_named_values_and_cpp_path(monkeypatch): + import spherical_deepkriging.basis_functions.mrts_sphere.sphere as sphere + + captured = {} + + def fake_cpp(knot, k, X=None): + captured["knot_dtype"] = knot.dtype + captured["X_dtype"] = None if X is None else X.dtype + return {"mrts": np.ones((2, k))} + + monkeypatch.setattr(sphere, "CPP_EXTENSIONS_AVAILABLE", True) + monkeypatch.setattr(sphere, "_mrts_sphere_cpp", fake_cpp) + + result = sphere.mrts_sphere( + np.array([[1, 2], [3, 4]], dtype=np.float32), + k=2, + X=np.array([[5, 6], [7, 8]], dtype=np.float32), + ) + + assert result.names() == ["mrts"] + assert "mrts" in result + assert list(result.keys()) == ["mrts"] + assert list(result)[0].shape == (2, 2) + assert captured["knot_dtype"] == np.float64 + assert captured["X_dtype"] == np.float64 + + +def test_sphere_r_fallback_and_import_error(monkeypatch): + import spherical_deepkriging.basis_functions.mrts_sphere.sphere as sphere + + monkeypatch.setattr(sphere, "CPP_EXTENSIONS_AVAILABLE", False) + monkeypatch.setattr(sphere, "_RPY2_AVAILABLE", True) + calls = {} + + def fake_r(knot, k, x): + calls["args"] = (knot, k, x) + return {"mrts": knot} + + monkeypatch.setattr(sphere, "_mrts_sphere_r", fake_r) + + knot = np.array([[1.0, 2.0], [3.0, 4.0]]) + sphere.mrts_sphere(knot, k=2) + assert np.array_equal(calls["args"][2], knot) + + monkeypatch.setattr(sphere, "_RPY2_AVAILABLE", False) + monkeypatch.setattr(sphere, "_mrts_sphere_r", None) + with pytest.raises( + ImportError, + match=r"Neither C\+\+ extensions nor rpy2/R implementation are available", + ): + sphere.mrts_sphere(knot, k=2) + + +def test_sphere_import_time_rpy2_initialization(monkeypatch): + fake_ro = types.ModuleType("rpy2.robjects") + commands = [] + fake_ro.globalenv = {"mrts_sphere": lambda knot, k, x: {"mrts": x}} + fake_ro.r = lambda cmd: commands.append(cmd) + fake_ro.default_converter = FakeConverter() + fake_numpy2ri = types.SimpleNamespace(converter="numpy-converter") + fake_ro.numpy2ri = fake_numpy2ri + + fake_rpy2 = types.ModuleType("rpy2") + monkeypatch.setitem(sys.modules, "rpy2", fake_rpy2) + monkeypatch.setitem(sys.modules, "rpy2.robjects", fake_ro) + monkeypatch.setattr("subprocess.check_output", lambda args: b"/fake/R/home\n") + + fake_cpp = types.ModuleType( + "spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp" + ) + fake_cpp.CPP_EXTENSIONS_AVAILABLE = False + fake_cpp.mrts_sphere = lambda **kwargs: None + monkeypatch.setitem( + sys.modules, + "spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp", + fake_cpp, + ) + sys.modules.pop("spherical_deepkriging.basis_functions.mrts_sphere.sphere", None) + + module = importlib.import_module( + "spherical_deepkriging.basis_functions.mrts_sphere.sphere" + ) + module = importlib.reload(module) + + assert module._RPY2_AVAILABLE is True + assert module.numpy2ri_converter == ("combined", "numpy-converter") + assert module._mrts_sphere_r is fake_ro.globalenv["mrts_sphere"] + assert any("source(" in command for command in commands) + + +def test_cpp_extensions_init_with_and_without_binary(monkeypatch): + module_name = "spherical_deepkriging.basis_functions.mrts_sphere.cpp_extensions" + sys.modules.pop(module_name, None) + fake_binary = types.ModuleType(f"{module_name}.spherical_basis") + for name in [ + "cpp_exp", + "cpp_fk", + "cpp_K", + "cpp_Kf", + "cpp_Kmatrix", + "getEigen", + "getEigenTopK", + ]: + setattr(fake_binary, name, object()) + monkeypatch.setitem(sys.modules, f"{module_name}.spherical_basis", fake_binary) + + module = importlib.import_module(module_name) + module = importlib.reload(module) + assert "cpp_K" in module.__all__ + + sys.modules.pop(module_name, None) + sys.modules.pop(f"{module_name}.spherical_basis", None) + real_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name.endswith("spherical_basis"): + raise ImportError("forced missing binary") + return real_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + module = importlib.import_module(module_name) + module = importlib.reload(module) + assert module.__all__ == [] + assert not hasattr(module, "cpp_K") + + +def test_cpp_setup_executes_and_build_class_issues_cmake_commands( + monkeypatch, tmp_path +): + setup_path = ( + Path(__file__).resolve().parents[1] + / "spherical_deepkriging" + / "basis_functions" + / "mrts_sphere" + / "cpp_extensions" + / "setup.py" + ) + captured = {} + monkeypatch.setattr( + setuptools, "setup", lambda **kwargs: captured.setdefault("kwargs", kwargs) + ) + monkeypatch.setattr(setuptools, "find_packages", lambda: ["pkg"]) + + spec = importlib.util.spec_from_file_location("tmp_cpp_setup", setup_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + assert captured["kwargs"]["name"] == "spherical_basis" + assert captured["kwargs"]["include_package_data"] is True + + commands = [] + monkeypatch.setattr( + module.os, + "makedirs", + lambda path, exist_ok: commands.append(("mkdir", path, exist_ok)), + ) + monkeypatch.setattr(module.os, "system", lambda cmd: commands.append(cmd) or 0) + + dist = Distribution() + cmd = module.CMakeBuild(dist) + cmd.build_temp = str(tmp_path / "build") + cmd.debug = False + cmd.get_ext_fullpath = lambda name: str(tmp_path / f"{name}.so") + ext = types.SimpleNamespace(name="demo") + cmd.build_extension(ext) + + assert any("cmake -S . -B" in entry for entry in commands if isinstance(entry, str)) + assert any("cmake --build" in entry for entry in commands if isinstance(entry, str)) diff --git a/tests/test_spherical_cpp.py b/tests/test_spherical_cpp.py index 6461f19..0abb42c 100644 --- a/tests/test_spherical_cpp.py +++ b/tests/test_spherical_cpp.py @@ -181,26 +181,20 @@ def test_import_error_when_extensions_unavailable(self): """Test that ImportError is raised when C++ extensions are unavailable.""" from unittest.mock import patch - import spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp as ( - spherical_module, - ) + import spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp as spherical_module # Patch the CPP_EXTENSIONS_AVAILABLE flag directly with patch.object(spherical_module, "CPP_EXTENSIONS_AVAILABLE", False): knot = np.array([[45.0, -120.0], [46.0, -121.0]]) # Escape the + in "C++" for regex - with pytest.raises( - ImportError, match="C\\+\\+ extensions not available" - ): + with pytest.raises(ImportError, match="C\\+\\+ extensions not available"): spherical_module.mrts_sphere(knot, k=2) def test_floating_point_error_for_non_finite_k(self): """Test that FloatingPointError is raised when K matrix contains non-finite values.""" from unittest.mock import MagicMock, patch - import spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp as ( - spherical_module, - ) + import spherical_deepkriging.basis_functions.mrts_sphere.sphere_cpp as spherical_module knot = np.array([[45.0, -120.0], [46.0, -121.0]]) # Create a K matrix with NaN @@ -212,26 +206,28 @@ def test_floating_point_error_for_non_finite_k(self): ) mock_cpp_Kmatrix = MagicMock(return_value=np.ones((2, 2))) - with patch.object( - spherical_module, "CPP_EXTENSIONS_AVAILABLE", True - ), patch.object( - spherical_module, - "cpp_K", - lambda lat, lon, n: K_with_nan, - create=True, - ), patch.object( - spherical_module, - "getEigenTopK", - mock_getEigenTopK, - create=True, - ), patch.object( - spherical_module, - "cpp_Kmatrix", - mock_cpp_Kmatrix, - create=True, + with ( + patch.object(spherical_module, "CPP_EXTENSIONS_AVAILABLE", True), + patch.object( + spherical_module, + "cpp_K", + lambda lat, lon, n: K_with_nan, + create=True, + ), + patch.object( + spherical_module, + "getEigenTopK", + mock_getEigenTopK, + create=True, + ), + patch.object( + spherical_module, + "cpp_Kmatrix", + mock_cpp_Kmatrix, + create=True, + ), ): with pytest.raises( FloatingPointError, match="K contains non-finite values" ): spherical_module.mrts_sphere(knot, k=2) - diff --git a/tests/test_spherical_cpp_setup.py b/tests/test_spherical_cpp_setup.py index 8c3ccaa..aac1685 100644 --- a/tests/test_spherical_cpp_setup.py +++ b/tests/test_spherical_cpp_setup.py @@ -50,4 +50,3 @@ def test_setup_module_structure(self): assert "setup(" in content assert "name=" in content assert "spherical_basis" in content - diff --git a/tests/test_universal_kriging_coords.py b/tests/test_universal_kriging_coords.py index a7613b9..26ee35c 100644 --- a/tests/test_universal_kriging_coords.py +++ b/tests/test_universal_kriging_coords.py @@ -1,8 +1,6 @@ import numpy as np - import pytest - pytest.importorskip("gpboost") @@ -14,4 +12,3 @@ def test_universal_kriging_coords_to_radians_dtype_and_shape(): assert out.shape == coords.shape assert out.dtype == np.float32 - diff --git a/tests/test_universal_kriging_unit.py b/tests/test_universal_kriging_unit.py new file mode 100644 index 0000000..6989d0d --- /dev/null +++ b/tests/test_universal_kriging_unit.py @@ -0,0 +1,236 @@ +import importlib +import sys +import types + +import numpy as np +import pandas as pd +import pytest + + +class FakeGPModel: + instances = [] + next_cov_pars = [] + next_predictions = [] + next_coefs = [] + fit_error = None + + def __init__(self, **kwargs): + self.kwargs = kwargs + self.fit_calls = [] + FakeGPModel.instances.append(self) + + def fit(self, y, X, params): + if FakeGPModel.fit_error is not None: + raise FakeGPModel.fit_error + self.fit_calls.append({"y": y, "X": X, "params": params}) + + def get_cov_pars(self): + return FakeGPModel.next_cov_pars.pop(0) + + def predict(self, X_pred, gp_coords_pred, predict_var): + self.predict_call = { + "X_pred": X_pred, + "gp_coords_pred": gp_coords_pred, + "predict_var": predict_var, + } + return {"mu": FakeGPModel.next_predictions.pop(0)} + + def get_coef(self): + coef = FakeGPModel.next_coefs.pop(0) + if isinstance(coef, Exception): + raise coef + return coef + + +def load_universal_module(monkeypatch): + FakeGPModel.instances = [] + FakeGPModel.next_cov_pars = [] + FakeGPModel.next_predictions = [] + FakeGPModel.next_coefs = [] + FakeGPModel.fit_error = None + + fake_gpboost = types.ModuleType("gpboost") + fake_gpboost.GPModel = FakeGPModel + monkeypatch.setitem(sys.modules, "gpboost", fake_gpboost) + sys.modules.pop("spherical_deepkriging.models.universal_kriging", None) + + module = importlib.import_module("spherical_deepkriging.models.universal_kriging") + return importlib.reload(module) + + +def make_cov_pars(nu=0.5, rho=1.5, sigma2=2.0, nugget=0.1, include_nu=True): + data = { + "GP_range": [rho], + "GP_var": [sigma2], + "Error_term": [nugget], + } + if include_nu: + data["Matern_nu"] = [nu] + return pd.DataFrame(data) + + +def test_static_helpers_cover_distance_and_parameter_extraction(monkeypatch): + module = load_universal_module(monkeypatch) + uk = module.UniversalKriging + + coords = np.array([[0.0, 0.0], [0.0, 90.0]], dtype=np.float32) + coords_rad = uk.coords_to_radians(coords) + distances = uk.compute_spherical_distance_matrix(coords_rad) + + assert coords_rad.dtype == np.float32 + assert distances.shape == (2, 2) + assert np.isclose(distances[0, 1], np.pi / 2, atol=1e-5) + assert uk.extract_gp_params( + types.SimpleNamespace(get_cov_pars=lambda: make_cov_pars(include_nu=False)) + ) == (0.5, 1.5, 2.0, 0.1) + + +def test_fit_with_fixed_covariance_and_covariates(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.next_cov_pars = [make_cov_pars(nu=0.5)] + FakeGPModel.next_coefs = [pd.Series([1.0, -2.0])] + model = module.UniversalKriging(num_neighbors=4, cov_function="exponential") + + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + phi = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32) + y = np.array([[2.0], [4.0]], dtype=np.float32) + + fitted = model.fit(coords, phi, y, center_y=True) + + assert fitted is model + assert model.has_covariates is True + assert model.nu_was_refitted is False + assert np.isclose(model.y_mean, 3.0) + assert model.params["cov_function"] == "exponential" + assert model.params["beta"].shape == (2, 1) + assert FakeGPModel.instances[0].kwargs["cov_function"] == "matern" + assert FakeGPModel.instances[0].fit_calls[0]["X"].dtype == np.float32 + + +def test_fit_uses_zero_beta_when_get_coef_fails(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.next_cov_pars = [make_cov_pars()] + FakeGPModel.next_coefs = [RuntimeError("coef failure")] + model = module.UniversalKriging(num_neighbors=2, cov_function="gaussian") + + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + phi = np.array([[1.0], [3.0]], dtype=np.float32) + y = np.array([[2.0], [4.0]], dtype=np.float32) + + model.fit(coords, phi, y) + + assert np.array_equal(model.params["beta"], np.zeros((1, 1))) + assert FakeGPModel.instances[0].kwargs["cov_function"] == "gaussian" + assert "cov_fct_shape" not in FakeGPModel.instances[0].kwargs + + +def test_fit_wraps_fixed_covariance_errors(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.fit_error = ValueError("boom") + model = module.UniversalKriging(num_neighbors=2, cov_function="gaussian") + + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + y = np.array([[1.0], [2.0]], dtype=np.float32) + + with pytest.raises( + RuntimeError, match="Model fitting failed with gaussian covariance: boom" + ): + model.fit(coords, None, y) + + +def test_fit_refits_when_estimated_nu_is_too_large(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.next_cov_pars = [ + make_cov_pars(nu=0.9), + make_cov_pars(nu=0.5, rho=3.0), + ] + FakeGPModel.next_coefs = [np.array([0.25])] + + monkeypatch.setattr( + module.UniversalKriging, + "_get_gpboost_cov_params", + lambda self: ("matern_estimate_shape", None, True), + ) + + model = module.UniversalKriging(num_neighbors=3, cov_function="matern_auto") + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + y = np.array([[1.0], [2.0]], dtype=np.float32) + + model.fit(coords, None, y) + + assert model.nu_was_refitted is True + assert model.params["was_refitted"] is True + assert model.params["nu_initial_estimate"] == 0.9 + assert model.params["rho_rad"] == 3.0 + assert len(FakeGPModel.instances) == 2 + + +def test_fit_keeps_estimated_model_when_nu_is_valid(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.next_cov_pars = [make_cov_pars(nu=0.4, rho=2.2)] + FakeGPModel.next_coefs = [np.array([1.0])] + + monkeypatch.setattr( + module.UniversalKriging, + "_get_gpboost_cov_params", + lambda self: ("matern_estimate_shape", None, True), + ) + + model = module.UniversalKriging(num_neighbors=3, cov_function="matern_auto") + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + phi = np.array([[1.0], [2.0]], dtype=np.float32) + y = np.array([[1.0], [2.0]], dtype=np.float32) + + model.fit(coords, phi, y, center_y=False) + + assert model.nu_was_refitted is False + assert model.params["nu"] == 0.4 + assert model.y_mean == 0.0 + assert len(FakeGPModel.instances) == 1 + + +def test_predict_get_coef_decompose_and_cleanup(monkeypatch): + module = load_universal_module(monkeypatch) + FakeGPModel.next_cov_pars = [make_cov_pars()] + FakeGPModel.next_coefs = [ + np.array([2.0, 3.0]), + np.array([2.0, 3.0]), + np.array([2.0, 3.0]), + np.array([2.0, 3.0, 4.0]), + np.array([1.0]), + ] + FakeGPModel.next_predictions = [ + np.array([10.0, 20.0], dtype=np.float32), + np.array([10.0, 20.0], dtype=np.float32), + np.array([10.0, 20.0], dtype=np.float32), + np.array([5.0, 6.0], dtype=np.float32), + np.array([7.0, 8.0], dtype=np.float32), + ] + + model = module.UniversalKriging(num_neighbors=2, cov_function="gaussian") + coords = np.array([[10.0, 20.0], [11.0, 21.0]], dtype=np.float32) + phi = np.array([[1.0, 0.0], [0.0, 1.0]], dtype=np.float32) + y = np.array([[2.0], [4.0]], dtype=np.float32) + model.fit(coords, phi, y) + + pred_centered = model.predict(coords, phi_new=phi, return_centered=True) + pred_uncentered = model.predict(coords, phi_new=phi, return_centered=False) + coef = model.get_coef() + total, fixed, random = model.decompose_prediction(coords, phi) + + assert np.array_equal(pred_centered, np.array([10.0, 20.0], dtype=np.float32)) + assert np.array_equal(pred_uncentered, np.array([13.0, 23.0], dtype=np.float32)) + assert np.array_equal(coef, np.array([2.0, 3.0])) + assert np.array_equal(fixed, np.array([2.0, 3.0])) + assert np.array_equal(random, total - fixed) + + total_i, fixed_i, random_i = model.decompose_prediction(coords, phi) + assert np.array_equal(fixed_i, np.array([6.0, 7.0])) + assert np.array_equal(random_i, total_i - fixed_i) + + with pytest.raises(ValueError, match="Coefficient shape mismatch"): + model.decompose_prediction(coords, phi) + + model.cleanup() + assert model.gp_model is None + assert model.params is None